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The Pennsylvania State University

The Graduate School

The Intercollege Graduate Degree Program in Integrative Biomedical Physiology

ASSOCIATION OF VARIABILITY IN CARDIOVASCULAR RESPONSIVENESS TO

REFLEX ACTIVATION OF THE AUTONOMIC NERVOUS SYSTEM WITH

GENETICS OF PERIPHERAL SENSORY RECEPTORS

A Dissertation in

Integrative and Biomedical Physiology

Nathan M. Garvin

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

August 2017

The dissertation of Nathan M. Garvin was reviewed and approved* by the following:

James Anthony Pawelczyk Associate Professor of Integrative and Biomedical Physiology, and Kinesiology Dissertation Advisor Chair of Committee

David Nathan Proctor Professor of Integrative and Biomedical Physiology, and Kinesiology

Gail Doreen Thomas Professor of Integrative and Biomedical Physiology, and Medicine

John E Hayes Associate Professor of Food Science

Donna Hope Korzick Professor of Integrative and Biomedical Physiology, and Kinesiology Chair of the Intercollege Graduate Program in Integrative and Biomedical Physiology

*Signatures are on file in the Graduate School.

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Abstract

Cardiovascular disease accounts for approximately 1 in 3 deaths in the United States and high blood pressure (BP) is one of the risk factors implicated. Given its importance, extensive research has been done on the regulation of BP and BP responsiveness to stress. “Stress reactivity” has long been used as a metric to assess and predict morbidity and mortality of cardiovascular diseases. For example, exaggerated exercise and noxious cold water-induced BP changes may represent a risk factor for death from cardiovascular causes, act as a predictor for hypertension, and be related to heart disease and cardiovascular events.

The source(s) of the variation in cardiovascular reactivity are probably multifaceted and poorly understood. This dissertation explores the thesis that genetic variation accounts for, at least in part, population differences in autonomic responses to stress.

The general experimental design was two-fold: 1) the normal range of BP responses to post-exercise circulatory arrest (PECA) following static handgrip (SHG), a common laboratory exercise technique, was rigorously defined; and 2) these norms were used to selectively recruit groups with high and low responses to investigate the mechanisms accounting for the disparate BP responses between groups and to relate these differences to functional single-nucleotide polymorphisms (SNPs) in genes encoding sensory receptors.

The first study (Chapter 3) aimed to define the normal range of BP responses to 2-min

PECA following 3-min SHG at 30% of maximum voluntary contraction. Testing involved instrumentation for continuous measurement of BP (finger photoplethysmography) and heart rate

(ECG) during PECA. Regression modeling of the BP change from control vs. the grip force was used to identify individuals with BP residuals at the highest and lowest response quartile. Average mean arterial pressure change from control was 29±12 mmHg during PECA after 3 min of SHG.

Grip force during SHG accounted for ~37% of the variability in BP response. High and low response groups were defined as having a BP residual of +6 and –6 mmHg, respectively.

The second study (Chapters 4-9) was designed to compare these explicitly defined cohorts with stressors that evoke autonomic responses and to test for associations of responsiveness to

SNPs in the genes coding for several sensory afferent receptors. Thus, the aim of the study was to associate a BP phenotype with sensory afferent genotype.

Volunteers recruited from each group performed up to 6 protocols in a single ~4 hour visit to the lab. Subjects were instrumented for the continuous measurement of BP (finger photoplethysmography), heart rate (ECG), blood flow (duplex Doppler ultrasound), and muscle sympathetic nerve activity (peroneal microneurography) and performed exercise (SHG and rhythmic handgrip (RHG); both followed by PECA), caloric stimulations of the hand (cold, hot, and thermoneutral water immersion), and hypercapnic, hyperoxic rebreathing. In general, there was an inter-stressor response specificity to stressors primarily sensed in the periphery (hand and forearm); that is, subjects with a high BP response during the initial PECA protocol, on average, had a high response to SHG, RHG, PECA, cold immersion, and hot immersion. Hypercapnic rebreathing, which is primarily transduced by central chemoreceptors, did not show any dichotomy between high and low response groups, suggesting that metabo/chemo-sensation during PECA after SHG is not related to chemosensation during hypercapnia.

A chi-square test of independence was performed on the response group (high vs. low) and trait allele frequency (trait allele carriers vs. non-trait allele carriers). In addition, gene scores for multiple SNPs of interest were created by coding the trait allele in all individuals. Genetic results indicated that the frequency of trait allele carriers for the TRPV1 gene SNP rs8065080 may be present in different frequencies in the low and high response groups (P=0.055). The non-

v carriers:carriers ratio of the trait allele was 13:12 and 9:24 for the low and high response groups, respectively. However, BP residuals, and BP and heart rate response during SHG and RHG with

PECA, cold and hot immersion, and rebreathing could not be directly associated with any combination of SNPs via gene score analysis.

The results of this dissertation can be summarized in three key findings: 1) the BP response to PECA following SHG is variable, normally distributed, and can be used to define groups with distinctly different BP responses; 2) subject BP responsiveness classification is maintained over several, but not all, types of stresses against homeostasis (SHG, RHG, PECA, and cold and hot water immersion, but not hypercapnia); and 3) the trait allele for the missense (isoleucine→valine)

TRPV1 gene SNP rs8065080, that codes for an ion channel previously known to be expressed in afferent nerves, may appear with disproportionate frequency among high and low response groups and thus may partially explain the BP response variability between them. An exaggerated BP response to stress is of interest for cardiovascular health and disease. The genetics of peripheral sensory receptors may be one component of the variation of the normal human BP responsiveness to stress.

TABLE OF CONTENTS

LIST OF FIGURES ...... x LIST OF TABLES ...... xix LIST OF ABBREVIATIONS ...... xxiii ACKNOWLEDGEMENTS ...... xxvi Dissertation Committee ...... xxvi Special Thanks ...... xxvi Funding ...... xxvi CHAPTER 1: INTRODUCTION ...... 1 Background and Significance ...... 1 Specific Aims ...... 3 Delimitations and Assumptions ...... 5 CHAPTER 2: REVIEW OF LITERATURE ...... 7 Cardiovascular and Autonomic Responsiveness ...... 7 Static Exercise with a Focus on Handgrip ...... 9 Rhythmic Handgrip Exercise ...... 13 The Exercise Pressor Reflex ...... 16 Cold Pressor Test ...... 26 Hot Immersion Test ...... 29 Hyperoxic/Hypercapnic Rebreathing...... 31 Cell Membrane Ion Channels ...... 34 Acid Sensing Ion Channels ...... 34 Purinergic Receptors ...... 38 The Transient Receptor Potential Receptors ...... 42 Selected Genes ...... 47 ASIC1- Acid Sensing Ion Channel Subunit 1...... 49 ASIC2- Acid Sensing Ion Channel Subunit 2...... 50 P2RX4- Purinergic Receptor P2X Member 4 ...... 51 P2RX5- Purinergic Receptor P2X Member 5 ...... 52 P2RX7- Purinergic Receptor P2X Member 7 ...... 53 TRPA1- Transient Receptor Potential Cation Channel Subfamily A Member 1 . 54 TRPV1- Transient Receptor Potential Cation Channel Subfamily V Member 1 . 55 TRPV3- Transient Receptor Potential Cation Channel Subfamily V Member 3 . 57 TRPV4- Transient Receptor Potential Cation Channel Subfamily V Member 4 . 58

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CHAPTER 3: POPULATION BLOOD PRESSURE RESPONSIVENESS - DEFINING LOW AND HIGH RESPONSE GROUPS TO STATIC HANDGRIP EXERCISE ...... 60 Introduction ...... 60 Methods...... 62 Systematic Review ...... 62 Validation Study ...... 63 Standardization of Static Handgrip Exercise ...... 64 Procedure ...... 65 Data Processing ...... 67 Population Characteristics and Statistical Analysis ...... 67 Results ...... 69 Discussion ...... 80 Systematic Review ...... 80 Considerations from the Validation Study ...... 82 Best practices for Standardization ...... 85 Limitations ...... 88 Summary ...... 89 CHAPTER 4: OVERALL STUDY DESIGN TO EXAMINE AUTONOMIC REFLEX RESPONSES ...... 90 Introduction ...... 90 Study Design and Familiarization ...... 91 Subject Preparation and Instrumentation ...... 94 General Procedure and Randomization ...... 97 Data Processing and Reporting ...... 98 Subject Characteristics ...... 100 Statistics ...... 102 CHAPTER 5: STATIC HANDGRIP EXERCISE IN HIGH AND LOW RESPONSE GROUPS ...... 103 Introduction ...... 103 Methods...... 104 Statistics ...... 104 Results ...... 105 Discussion ...... 113 Limitations ...... 116 CHAPTER 6: RHYTHMIC HANDGRIP EXERCISE IN HIGH AND LOW RESPONSE GROUPS ...... 117

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Introduction ...... 117 Methods...... 118 Statistics ...... 119 Results ...... 120 Discussion ...... 135 Limitations ...... 138 CHAPTER 7: CALORIC STIMULATION OF THE HAND IN HIGH AND LOW RESPONSE GROUPS ...... 140 Introduction ...... 140 Methods...... 141 Statistics ...... 141 Results ...... 142 Discussion ...... 151 Limitations ...... 153 CHAPTER 8: HYPERCAPNIC, HYPEROXIC REBREATHING IN HIGH AND LOW RESPONSE GROUPS ...... 155 Introduction ...... 155 Methods...... 156 Statistics ...... 157 Results ...... 158 Discussion ...... 170 Limitations ...... 172 CHAPTER 9: GENETIC ASSOCIATIONS WITH BLOOD PRESSURE RESPONSIVENESS ...... 174 Introduction ...... 174 SNP Selection Criteria ...... 175 SNPs Selected ...... 176 Methods...... 179 Statistical Analyses ...... 179 Duplicate DNA Samples ...... 183 Results ...... 184 Duplicate DNA Samples ...... 199 Discussion ...... 200 Limitations ...... 204 CHAPTER 10: SUMMARY, CONCLUSIONS, AND FUTURE DIRECTIONS...... 206 Summary ...... 206

Functional implications ...... 211 Conclusions ...... 214 Strengths and Limitations ...... 215 Future Directions ...... 221 APPENDIX A: SUPPLEMENTARY DATA ...... 226 Supplementary Data - Minute Averages ...... 226 APPENDIX B: INFORMED CONSENTS ...... 249 REFERENCES ...... 268

LIST OF FIGURES Figure 2.1: Schematic summarizing the mechanisms involved in mediating the autonomic adjustments to exercise. Neural signals originating from higher brain centers (i.e., central command), chemically sensitive receptors in the carotid and aortic bodies (i.e., arterial chemoreflex), stretch receptors in the carotid and aortic arteries (i.e., arterial baroreflex), mechanically and metabolically sensitive afferents from skeletal muscle (i.e., exercise pressor reflex), mechanically sensitive stretch receptors in the cardiopulmonary region (i.e., cardiopulmonary baroreflex) and metabolically sensitive afferents from respiratory muscles (i.e., respiratory metaboreflex) are processed within brain cardiovascular control areas that influence efferent sympathetic and parasympathetic nerve activity. The alterations in autonomic outflow elicited by these inputs during exercise evoke changes in cardiac and vascular function, as well as release of catecholamines from the adrenal medulla. Source: Fisher et al.72 ...... 17 Figure 2.2: Discharge patterns of 4 thin fiber muscle afferents that responded to static contraction. Contraction period depicted by black bar. A: group III fiber (conduction velocity 17.8 m/s) discharged vigorously at onset of contraction, but then its firing rate decreased even though muscle continued to contract. B: group III fiber (conduction velocity 9.6 m/s) discharged vigorously at start of contraction, adapted and then fired again during contraction. C: group IV fiber (conduction velocity 1.3m/s) started to fire 10s after the onset of contraction and then gradually increased its firing rate during contraction period. Note that firing of fiber slowed even though muscle continued to contract. D: group IV fiber (conduction velocity 1.1 m/s) fired irregularly 4 s after onset of static contraction. Source: Kaufman et al.12 ...... 18 Figure 2.3: Schematic depicting a muscle afferent neuron synapsing on a cell in the dorsal horn of the spinal cord. Also shown are some of the mediators that are thought to be important for dorsal horn transmission of the muscle pressor reflex, including the compounds that are the subject of this review, i.e., nitric oxide (NO) and acetylcholine. Current evidence indicates that activation of muscle afferent neurons releases the excitatory amino acids (EAA), glutamate, and aspartate. The EAA in turn excite dorsal horn cells via N-methyl- D-aspartic acid (NMDA) and non-NMDA receptors. The muscle afferent neuron also releases the neuropeptide substance P (SP), which enhances the excitation of dorsal horn cells by the EAA via the activation of neurokinin-1 (NK-1) receptors. On the other hand, this schematic also illustrates that there are several receptor systems that, when activated, can reduce the efficacy of the muscle afferent fiber to dorsal horn cell transmission, and thus attenuate the pressor reflex. Thus far there is only evidence for opiate receptors exerting a presynaptic action with respect to this reflex. Source: L.Britt Wilson79 ...... 21 Figure 2.4: Simplified schematic illustrating putative neural areas involved in the control of parasympathetic and sympathetic outflow. The activity of efferent autonomic outflow is determined by complex interactions within and between central neural circuits, peripheral afferent inputs to the nucleus tractus solitarii (NTS) and other neural areas (not shown) as well as circulating factors. The integrated response of all of these ascending and descending neural signals ultimately determines parasympathetic and sympathetic outflow. See text for additional details. CVLM, caudal ventrolateral medulla; CVO, circumventricular organs; IML, intermediolateral cell column; NA, nucleus ambiguus; RVLM, rostral ventrolateral medulla. Source: Fisher et al.72 ...... 23

Figure 2.5: The exercise pressor reflex arc. Skeletal muscle contraction excites group III and group IV sensory afferent neurons through several putative stimuli. Stretch or the application of pressure to skeletal muscle distorts the receptive field of mechanically sensitive receptors located predominately on group III afferent neurons. These mechanoreceptors have yet to be identified but are likely mechanogated cation, potassium, or calcium channels. Metabolites released during muscle contraction such as potassium, lactic acid, bradykinin, analogs of ATP, by-products of arachidonic acid metabolism, and diprotonated phosphate excite metabolically sensitive receptors located predominately on group IV afferent neurons. Pharmacological agonists for acid sensing ion channels (ASICs), bradykinin receptors, transient potential vanilloid receptor 1 (TRPV1), purinergic receptors, and cannabanoid receptors have been demonstrated to evoke group IV afferent activity. However, there are likely other potential metaboreceptors that have yet to be identified. Group III and group IV afferent sensory neurons transmit impulses, via the spinal cord, to the cardiovascular control centers located within the medullary region of the brain stem, specifically the nucleus tractus solitarius, rostral ventrolateral medulla, and caudal ventrolateral medulla, as well as other secondary nuclei. Central processing of this input promotes increases in sympathetic nerve activity and withdrawal of parasympathetic nerve activity. Thus heart rate, arterial blood pressure, and vascular resistance are reflexively increased upon the onset of muscle contraction. Source Murphy et al.85...... 25 Figure 2.6: Schematic diagrams of important phenotypes in Asic3 knockouts. Source: Lin et al.136 ...... 35 Figure 2.7: ASICs and nociception in primary and secondary sensory neurons. Different ASIC channel subtypes are expressed in primary sensory neurons (Dorsal Root Ganglia and TriGeminal ganglia), and in neurons of the dorsal spinal cord, which receive sensory- nociceptive information from the periphery. The picture shows a rat DRG neuron in primary culture. Activation of ASIC channels containing ASIC1 and/or ASIC3 has a direct impact on the sensory neuron's activity, by generating sufficient depolarization to reach the threshold for action potential (AP) triggering, or to sensitize neurons to other stimuli. The dorsal spinal cord contains a neuronal network composed of secondary sensory neurons and excitatory and inhibitory interneurons (shown in gray). These spinal neurons express homomeric ASIC1a and heteromeric ASIC1a/ASIC2 channels, which are involved in opioid-dependant (ASIC1a and ASIC1a/ASIC2b) and opioid-independent (ASIC1a/ASIC2a) analgesia. Note that the different elements are not drawn to scale. Source: Deval and Lingueglia137...... 37

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Figure 2.8: Hypothetical schematic of the roles of purine nucleotides and nucleosides in pain pathways. At sensory nerve terminals in the periphery, P2X3 and P2X2/3 receptors have been identified as the principal P2X purinoceptors present, although recent studies have also shown expression of P2Y1 and possibly P2Y2 receptors on a subpopulation of P2X3 receptor-immunopositive fibers. Other known P2X purinoceptor subtypes (1–7) are also expressed at low levels in dorsal root ganglia. Although less potent than ATP, adenosine (AD) also appears to act on sensory terminals, probably directly via P1(A2) purinoceptors; however, it also acts synergistically (broken line) to potentiate P2X2/3 receptor activation, which also may be true for 5-hydroxytryptamine, capsaicin, and protons. At synapses in sensory pathways in the CNS, ATP appears to act postsynaptically via P2X2, P2X4, and/or P2X6 purinoceptor subtypes, perhaps as heteromultimers, and after breakdown to adenosine, it acts as a prejunctional inhibitor of transmission via P1(A2) purinoceptors. P2X3 receptors on the central projections of primary afferent neurons in lamina II of the dorsal horn mediate facilitation of glutamate and probably also ATP release. Sources of ATP acting on P2X3 and P2X2/3 receptors on sensory terminals include sympathetic nerves, endothelial, Merkel, and tumor cells. Modified from Burnstock and Wood (1996) and reproduced with permission of Elsevier. Source: Burnstock148...... 40 Figure 2.9: Phylogenetic tree of the TRP superfamily. Source: Pendersen et al.169 ...... 43 Figure 2.10: Classification of thermosensing nociceptive TRP channels in mammalian sensory neurons. The upper row of individual boxes denotes chemical/mechanical activators of marked TRP channels. The lower panel depicts the magnitude of channel activity upon activation by temperature of the independent TRP channels shown. Source: Mickle et al.170 ...... 44 Figure 2.11: The Karyotype view of ASIC1. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=ASIC1191 ...... 49 Figure 2.12: The Karyotype view of ASIC2. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=ASIC2192 ...... 50 Figure 2.13: The Karyotype view of P2RX4. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=P2RX4193 ...... 51 Figure 2.14: The Karyotype view of P2RX5. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=P2RX5194 ...... 52 Figure 2.15: The Karyotype view of P2RX7. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=P2RX7195 ...... 53 Figure 2.16: The Karyotype view of TRPA1. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=TRPA1196 ...... 54 Figure 2.17: The Karyotype view of TRPV1. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=TRPV1197 ...... 55

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Figure 2.18: The Karyotype view of TRPV3. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=TRPV3198...... 57 Figure 2.19: The Karyotype view of TRPV4. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=TRPV4199 ...... 58 Figure 3.1: Systematic Literature Review. The process of elimination for articles to be included in the prediction of population norms via literature review. 152 articles were initially identified that included static exercise and post-exercise circulatory arrest; only four met all criteria for inclusion...... 62 Figure 3.2: Literature Review Consolidation of Results. Distribution of ∆MAP during PECA responses for 4 studies- (n=46 total). The observations were re-sampled with replacement to bootstrap a theoretical population distribution with a mean and standard deviation reflecting the study pool. Dashed curve is the overlaid normal distribution. Gray bar and dashed vertical line represent the 25th – 75th percentile range and 50th percentile, respectively. The curve did not differ from normality...... 70 Figure 3.3: Blood Pressure and Heart Rate Absolute Value Distributions. Distributions of resting blood pressure and heart rate, as well as, the absolute responses to three min of SHG and two min of PECA. Dashed curve is the overlaid normal distribution. Gray bars and dashed vertical lines represent the 25th – 75th percentile ranges and 50th percentiles, respectively. All curves did not differ from normality...... 71 Figure 3.4: Blood Pressure and Heart Rate Changes from Control Distributions. Distributions of heart rate and blood pressure in response to three min of SHG and two min of PECA. Values are changes from rest (∆). Gray bars and dashed vertical lines represent the 25th – 75th percentile ranges and 50th percentiles, respectively. All curves did not differ from normality...... 72 Figure 3.5: Absolute and Relative Heart Rate and Blood Pressure Responses Over Time. Absolute heart rate response (A) change in heart rate from the average of 5 min of rest (B) absolute blood pressure response (C), and the change in blood pressure (D) over time. In C and D, SBP is listed on top, DBP on bottom, and MAP in the middle. Statistical comparisons for HR and MAP measured for a change from the average of 5 min of rest. *=P<0.00625 (Bonferroni adjusted P-value of 0.05 adjusted for eight comparisons). Error bars represent standard deviation...... 74 Figure 3.6: Correlation between Relative Grip Force, Grip Variation, and Blood Pressure Response. Panel A shows that there is no correlation between the average % MVC that each subject achieved during the three min of SHG and the ∆MAP during the second min of PECA; i.e. there was no relation between grip maintenance and the MAP response. Panel B shows the significant positive correlation between SHG variation (kp) and the ∆MAP during the second min of PECA...... 76 Figure 3.7: Correlation between Average Grip Force and Blood Pressure Response. Panel A shows the significant correlation between the average SHG (kp) that each subject achieved during the 3 min and the ∆MAP during the second min of PECA. Absolute force accounted for approximately 37% of the variability in blood pressure response. Dashed lines represent the 25th – 75th percentile range of the normal distribution created by this sample of n=101. Panel B shows the individual blood pressure residuals sorted in ascending order- range: (-21) – 29 mmHg...... 78

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Figure 3.8: Blood Pressure Residuals Distribution. The distribution of the residuals from the regression of ∆MAP during the second min of PECA on average SHG force (kp) that each subject achieved during the three min. Gray bars and dashed vertical line represent the 25th – 75th percentile ranges and 50th percentiles, respectively. The curve did not differ from normality...... 79 Figure 5.1: Changes in BP from control in high vs. low during SHG and PECA. ∆=high and O=low, respectively. *=high vs. low within time point (P<0.05). Letters indicate differences between time points: a=E3 vs. P2; b=E3 vs. Rec3; c=P2 vs. Rec3 (P<0.05). The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction. .. 106 Figure 5.2: Group mean changes in HR during the SHG and PECA. ∆=high and O=low response groups, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction...... 107 Figure 5.3 Changes in calf blood flow and resistance during SHG and PECA. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction...... 108 Figure 5.4: Changes in MSNA during SHG and PECA. ∆=high and O=low, respectively. *=high vs. low (P<0.05). There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction...... 110 Figure 5.5: Sympathetic transduction during SHG and PECA. Hashed=high and solid=low response groups, respectively. Units are ∆Resistance (mmHg*min*100g/ml) / ∆Total MSNA (AU/min). There was no difference between groups. N=6 and n=9 for the low and high response groups, respectively...... 111 Figure 5.6: Group average pain ratings (0-10) during SHG and PECA. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction...... 112 Figure 6.1: Group changes in BP from during RHG and PECA. ∆=high and O=low, respectively. *=high vs. low within time point (P<0.05). Letters indicate significance within low or high between time points: a=E14 vs. P2; b=E14 vs. Rec3; c=P2 vs. Rec3 (P<0.05). No low subject in the low response group completed E15. ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction. Sample sizes (n) are shown in the included table for each min of exercise and PECA. They are the same for SBP, MAP, and DBP...... 121

Figure 6.2: Average group changes in HR from control RHG and PECA. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. No subject in the low response group completed E15. ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction. Sample sizes (n) are shown in the included table for each min of exercise and PECA...... 122 Figure 6.3: The changes in calf blood flow and resistance from control in high vs. low during the RHG and PECA protocol. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the main effect of responder group high vs. low. Time is the main effect of time in 1 min bins. H/L*Time is the interaction. Sample sizes (n) are shown in the included table for each exercise, PECA and recovery...... 123 Figure 6.4: Changes in MSNA during RHG and PECA. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. No subject in the low response group completed E15. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction. Sample sizes (n) are shown in the included table for each min of exercise and PECA. The n is the same for each MSNA variable. 125 Figure 6.5: The changes in MSNA from control in high vs. low during RHG plotted as percent of total exercise time. ∆=high and O=low, respectively...... 127 Figure 6.6: Sympathetic transduction during RHG and PECA. Hashed=high and solid=low response groups, respectively. Units are ∆Resistance (mmHg*min*100g/ml) / ∆Total MSNA (AU/min). There was no difference between groups. N=7 and n=10 for the low and high response groups, respectively...... 128 Figure 6.7: Average group brachial blood flow responses during RHG. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. No low subject went to E15. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction. Sample sizes (n) are shown in the included table for each min of rest and exercise...... 129 Figure 6.8: The changes from control in brachial blood flow in high vs. low during the RHG and PECA protocol. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. No subject in the low response group completed E15. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction. Sample sizes (n) are shown in the included table for each min of exercise. 130 Figure 6.9: Mean group pain ratings (0-10) RHG with progressive BF restrictions. ∆=high and O=low response groups, respectively. There was no significant difference between the high and low response groups. No subject in the low response group completed E15. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction. Sample sizes (n) are shown in the included table for each min of rest and exercise...... 131

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Figure 6.10: Average group Ratings of Perceived Exertion (RPE- 6-20) during RHG. ∆=high and O=low response groups, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction. Sample sizes (n) are shown in the included table for each min of rest and exercise...... 132 Figure 6.11: Average ratings of pain and perceived exertion at the end of RHG. Solid=low and hashed=high response groups, respectively. There were no significant differences between groups...... 133 Figure 6.12: The duration of RHG exercise performed before 90-100% cuff occlusion of BF in the high and low response groups. Cuff was initially inflated after 3 min of free flow exercise to 36 mmHg and inflated at a rate of 3 mmHg every 15 seconds thereafter for each group. Hashed=high and solid=low, respectively. * indicates a significant increase in time relative to the low response group...... 134 Figure 7.1: Average group changes in BP during caloric stimulation of the hand. ∆=high and O=low response groups, respectively. *=high vs. low within time point (P<0.05). Letters indicate significance within low or high between time points: a=C1/H1 vs. C3/H3; b=C1/H1 vs. Rec3; c=C3/H3 vs. Rec3 (P<0.05). The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction...... 143 Figure 7.2: Changes in HR from control during caloric stimulation of the hand. ∆=high and O=low response groups, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the group main. Time is the time main effect. H/L*Time is the interaction of group x time...... 144 Figure 7.3: The changes in calf blood flow and resistance from control in high vs. low during water immersion. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the main effect of responder group high vs. low. Time is the main effect of time in 1 min bins. H/L*Time is the interaction...... 146 Figure 7.4: (Continued on following page.) Average group changes in MSNA during caloric stimulation. ∆=high and O=low, respectively. *=high vs. low within time point (P<0.05). Letters indicate group differences between time points: a=C1/H1 vs. C3/H3; b=C1/H1 vs. Rec3; c=C3/H3 vs. Rec3 (P<0.05). The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction...... 147 Figure 7.5: Sympathetic transduction during caloric stimulation of the hand. Hashed=high and Solid=low response groups, respectively. Units are ∆Resistance (mmHg*min*100g/ml) / ∆Total MSNA (AU/min). There was no difference between groups. CPT: n=10 and n=10 for the low and high response groups, respectively. Hot immersion: n=9 and n=9 for the low and high response groups, respectively...... 149 Figure 7.6: The pain response (0-10) in high vs. low during water immersion. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction...... 150

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Figure 8.1: Mean changes in BP from control during the rebreathing protocol. ∆=high and O=low, response groups, respectively. There was no significant difference between the high and low response groups. ANOVA results for main effects and interactions are also shown. H/L is the main effect of group. Time is the time main effect. H/L*Time is the group x time interaction...... 159 Figure 8.2: The changes in HR from control in high vs. low during the rebreathing protocol. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the main effect of response group high vs. low. H/L*Time is the group x time interaction...... 160 Figure 8.3: Changes in calf blood flow and resistance in high vs. low during hypercapnic rebreathing. ∆=high and O=low, respectively. There were no significant differences between or within any groups...... 161 Figure 8.4: Changes in MSNA during the rebreathing protocol. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction...... 163 Figure 8.5: Sympathetic transduction during caloric stimulation of the hand. Red=high and Blue=low response groups, respectively. Units are ∆Resistance (mmHg*min*100g/ml) / ∆Total MSNA (AU/min). There was no difference between groups. N=7 and n=11 for the low and high response groups, respectively...... 164 Figure 8.6: Changes in MSNA during hypercapnic rebreathing. ∆=high and O=low response groups, respectively. *=high vs. low within time point (P<0.05). Letters indicate significance within low or high between time points: a=Reb1 vs. Reb5; b=Reb1 vs. Rec3; c=Reb 5 vs. Rec3 (P<0.05). ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction...... 166 Figure 8.7: The relation between PetCO2 and MAP during 5 min rebreathing. ∆=high and O=low response groups, respectively. Regression analysis for the high and low response groups are shown to the upper left and lower right, respectively. There was no significant difference in slope or y-intercept between groups...... 168 Frequency ...... 189 Figure 9.1: The distribution of GS responses in the GS sum model for primary SNPs of interest...... 189 Figure 9.3: GS average for the SNPs of interest. BPR was plotted against GS average. The equation, R2 value and P-value are parameters of the regression line (black dashed line). P indicates the significance of the slope of the regression line relative to 0. Significance=P<0.05...... 190 Figure 9.2: GSsum for the SNPs of interest. BPR was plotted against GS sum. The equation, R2 value, and P-value are parameters of the regression line (black dashed line). P indicates the significance of the slope of the regression line relative to 0. Significance=P<0.05. 190

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Figure 10.1: Sympathetic transduction in high and low response groups across protocols. Each subject is plotted individually with data points from rest-SHG-PECA, rest-CPT, and rest- hot immersion resulting in 7 data points each. The average slope of the lines for the high and low response groups were compared via t-test. Low response n=5 and high response n=8. There was no difference in the average slopes between the high and low response groups (P=0.416)...... 208

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LIST OF TABLES Table 3.1: Subject characteristics n=101. BMI-body mass index, SD-standard deviation, *-self- reported data...... 64 Table 3.2: Included Publication Details. Details of the four publications that meet all inclusion criteria. Three of the four publication were published by the same first and last author. BP tech- BP measurement technique, fina- finger photoplethysmography, Ex mode- exercise mode, SHG- static handgrip, % MVC- percent maximum voluntary contraction, MinEx- min of exercise, MinPECA- min of post-exercise circulatory arrest, n/r- not reported, N- total number of volunteers in the control group...... 69 Table 3.3: Differences between distributions determined using paired t-tests with Bonferroni correction for multiple comparisons where necessary (rest vs SHG, rest vs. PECA, and SHG vs. PECA). R=the average of 5 min rest. E3=the third min of SHG. P2=the second minute of PECA. Significance=P<0.01...... 73 Table 3.4: Rating of Perceived Pain. Perceived pain on a scale from 1-10 was asked twice during rest and once per min during the remainder of the protocol. Pain levels that were significantly higher compared to rest are highlighted in grey- P<0.00625 (Bonferroni adjusted P-value of 0.05 adjusted for eight comparisons). Exer#, SHG exercise during min 1, 2, and 3 respectively. PECA#, Post-exercise circularity arrest min 1 and 2 respectively. Rec#, recovery min 1, 2, and 3, respectively. Data reported as mean (SD). 75 Table 4.1: Baseline subject characteristics for the full study. M=male, F=female, R=right, L=Left, Drink/Wk=drinks/week, HTN=hypertension, HR=heart rate, BP=blood pressure, SBP=systolic blood pressure, MAP (mean arterial pressure, and DBP=diastolic pressure. If given a range, the drink/Wk was calculated as the largest number. BP is the average of three brachial BPs after 15-min of rest and before any protocols began. MAP was calculated as (2/3)(DBP)+(1/3)(SBP). *Self-reported...... 101 Table 5.1: Force produced during the maximal voluntary contraction and the average handgrip force during SHG. AVG (kp)=average force in kiloponds during 3 min of squeezing. AVG%=average % MVC maintained during SHG. P2MAP Predict=the predicted change in MAP from control during the second min of PECA calculated from grip force. The P- value is based on a two-tailed, unpaired t-test...... 105 Table 6.1: Handgrip performance during RHG. Data are shown as mean (SE). Max=maximum, AVG=average, and kp=kiloponds of handgrip force. Significance=P<0.05 ...... 120 Table 8.1: Hypercapnic cardiovascular, autonomic and ventilator responses. Regressions were performed with the categorical predictor for “group” to compare the high and low response groups. The y-intercept and Slope columns indicate the difference in each parameter between the high and low response groups. P<0.05 indicates a significant difference between groups...... 169 Table 9.1 shows the assay information for the Human, TaqMan® SNP Genotyping Assays supplied by ThermoFisher Scientific. Chrom.=the chromosome number on which the gene and SNP are located...... 178

Table 9.2: Outcome variables included in bivariate correlations for SNP vs. cardiovascular parameters. SHG=Static handgrip. RHG=Rhythmic handgrip. CPT=Cold Pressor Test. Hot=Hot Immersion Test. E=Exercise. P=Post-exercise Circulatory Arrest. C=CPT. H=Hot. Reb=Rebrethe. #=corresponding minute of perturbation. DBP=diastolic blood pressure. SBP=systolic blood pressure. MAP=mean arterial blood pressure. HR=heart rate. ∆=change from control. RHG “LastEx” corresponds the BP or HR for each individual during the last minute they exercised. A correlation analysis was therefore run for each SNP against 56 different outcome variables...... 181 Table 9.3: Sample sizes available for gene*BPR analysis...... 184 Table 9.4: SNP details used for genetic analysis. The trait allele was coded with “1” during all analyses. MAF=minor allele frequency. Grey indicates a trait allele that was identified as the major allele. *indicates SNP not included in subsequent analysis due to being outside of HWE. All data are based on the European (EUR) population267...... 185 Table 9.5: Bivariate correlations from BPR and SNP. P-value indicates a 2-tailed bivariate correlation test. Significance=P<0.05...... 186 Table 9.6: Regression results from BPR being regressed onto SNP genotype. P-value indicates the slope of the regression line. Significance=P<0.05...... 187 Table 9.7: This table shows the gene scores for the three gene families included in this research: ASIC, P2RX, and TRP. (#) is representative of the number of SNPs included in the score for each gene family. GSSum and GSAvg are gene score sum, and gene score average, respectively. The BP residual for each subject was regressed onto the gene score for each gene family, and the resultant R2 value is displayed...... 191 Table 9.8A: This table shows the significant correlations between the SNPs in the rows and the cardiovascular variable at a given protocol time in the columns. Significant correlations are highlighted in grey. * P<0.05 (2-tailed). **P<0.01 (2-tailed)...... 193 Table 9.8B: This table shows the significant correlations between the SNPs in the rows and the cardiovascular variable at a given protocol time in the columns. Significant correlations are highlighted in grey. *P<0.05 (2-tailed). **P<0.01 (2-tailed)...... 194 Table 9.9: Linear regressions with TRPV1, P2RX4, and P2RX5 SNPs as the a priori genes of interest. For SNPs that were used for multiple regression comparisons (MC), Bonferroni adjustments were performed (family-wise P<0.05). Significant associations are highlighted in grey...... 195 Table 9.10: This table shows the gene scores for the primary SNPs of interest (TRPV1,P2X4, and P2X5) and the full group of GS. Regressions were run against the listed dependent variable. “Sum” indicates the additive model of gene scores and “avg” indicates the average scores of each gene mean...... 196 Table 9.11A: Frequency analysis of trait carriers for the ASIC1, ASIC2, P2RX4, P2RX5, and P2RX7 SNPs. Trait carriers were defined as the sum of heterozygotes and homozygotes for the trait allele. P<0.05 in shaded cells...... 197 Table 9.11B: Frequency analysis of trait carriers for the TRPV1, TRPV3, and TRPV4 SNPs. Trait carriers were defined as the sum of heterozygotes and homozygotes for the trait allele. P<0.05 in shaded cells. Interesting findings that were not statistically significant are bolded...... 198

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Table 9.12: (A) Overall repeatability of DNA analysis in 16 (~9%) subjects. (B) Overall repeatability of DNA analysis accounting for 2 subjects who had a high number of undetermined SNP genotypes. “Same call”, “Different call”, and “different genotype assigned” describe the relation between the initial and repeat analysis. The “initial- indeterminate” and “repeat- indeterminate” refer to observations within the initial and repeat analysis only...... 199 Table A.1: SHG and PECA blood pressure and heart rate. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. E3; b=R1 vs. P2; c=E3 vs. P2; d=E3 vs. Rec3; e=P2 vs. Rec3 (P<0.05)...... 227 Table A.2: SHG and PECA blood flow and resistance. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R3 vs. E3; b=R3 vs. P2; c=E3 vs. P2; d=E3 vs. Rec3; e=P2 vs. Rec3 (P<0.05)...... 228 Table A.3: SHG and PECA MSNA. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. E3; b=R1 vs. P2; c=E3 vs. P2; d=E3 vs. Rec3; e=P2 vs. Rec3 (P<0.05)...... 229 Table A.4: RHG and PECA blood pressure and heart rate. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. E14; b=R1 vs. P2; c=E14 vs. P2; d=E14 vs. Rec3; e=P2 vs. Rec3 (P<0.05)...... 230 Table A.5: RHG and PECA blood flow and resistance. Grey shading indicates a significant difference between low and high at a given time point (p<0.05). Letters indicate significance within low or high between time points: a=R3 vs. E15; b=R3 vs. P2; c=E15 vs. P2; d=E15 vs. Rec3; e=P2 vs. Rec3 (p<0.05)...... 231 Table A.6: RHG and PECA MSNA. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. E14; b=R1 vs. P2; c=E14 vs. P2; d=E14 vs. Rec3; e=P2 vs. Rec3 (P<0.05)...... 232 Table A.7: CPT blood pressure and heart rate. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. C3; b=R1 vs. Rec3; c=C3 vs. Rec3 (P<0.05)...... 233 Table A.8: CPT blood flow and resistance. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R3 vs. C3; b=R3 vs. Rec3; c=C3 vs. Rec3 (P<0.05)...... 234 Table A.9: CPT MSNA. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. C3; b=R1 vs. Rec3; c=C3 vs. Rec3 (P<0.05)...... 235 Table A.10: Hot water immersion blood pressure and heart rate. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. H3; b=R1 vs. Rec3; c=H3 vs. Rec3 (P<0.05)...... 236

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Table A.11: Hot water immersion blood flow and resistance. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R3 vs. H3; b=R3 vs. Rec3; c=H3 vs. Rec3 (P<0.05)...... 237 Table A.12: Hot water immersion MSNA. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. H3; b=R1 vs. Rec3; c=H3 vs. Rec3 (P<0.05)...... 238 Table A.13: Neutral water immersion blood pressure and heart rate. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. N3; b=R1 vs. Rec3; c=N3 vs. Rec3 (P<0.05)...... 239 Table A.14: Neutral water immersion blood flow and resistance. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R3 vs. N3; b=R3 vs. Rec3; c=N3 vs. Rec3 (P<0.05)...... 240 Table A.15: Neutral water immersion MSNA. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. N3; b=R1 vs. Rec3; c=N3 vs. Rec3 (P<0.05)...... 241 Table A.16: Neutral water immersion chance in blood pressure and heart rate from control. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=N1 vs. N3; b=N1 vs. Rec3; c=N3 vs. Rec3 (P<0.05)...... 242 Table A.17: Neutral water immersion change in blood flow and resistance from control. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=N3 vs. Rec3 (P<0.05)...... 243 Table A.18: Neutral water immersion chance in MSNA from control. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=N1 vs. N3; b=N1 vs. Rec3; c=N3 vs. Rec3 (P<0.05)...... 244 Table A.19: Rebreathing blood pressure and heart rate. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. Reb5; b=R1 vs. Rec3; c=Reb5 vs. Rec3 (P<0.05)...... 245 Table A.20: Rebreathing blood flow and resistance. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R3 vs. Reb5; b=R3 vs. Rec3; c=Reb5 vs. Rec3 (P<0.05)...... 246 Table A.21: Rebreathing MSNA. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. Reb5; b=R1 vs. Rec3; c=Reb5 vs. Rec3 (P<0.05)...... 247 Table A.22: Rebreathing respiratory parameters. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. Reb5 (P<0.05)...... 248

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LIST OF ABBREVIATIONS ANOVA ...... Analysis of Variance AMPA ...... α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic Acid ASIC ...... Acid Sensing Ion Channel ATP ...... Adenosine Triphosphate AU ...... Arbitrary Units

BF ...... Blood Flow BMI ...... Body Mass Index BP ...... Blood Pressure BPR ...... Blood Pressure Residual

C ...... Cold Ca2+ ...... Calcium CC ...... Central Command CO2 ...... Carbon Dioxide CNS ...... Central Nervous System CPT ...... Cold Pressor Test CVD ...... Cardiovascular Disease CVLM ...... Caudal Ventrolateral Medulla °C ...... Degrees Centigrade

DBP ...... Diastolic Blood Pressure DEG ...... Degenerin DNA ...... Deoxyribonucleic Acid DRG ...... Dorsal Root Ganglia

E ...... Exercise ENaC ...... Epithelial Sodium Channel EPR ...... Exercise Pressor Reflex

GS ...... Gene Score GSavg ...... Gene Score Average GSsum ...... Gene Score Sum

H ...... Hot HCVR ...... Hypercapnic Ventilatory Response HR ...... Heart Rate HWE ...... Hardy-Weinberg Equilibrium kp...... Kilopond kg...... Kilogram

LC ...... Locus Coeruleus LTF ...... Lateral Tegmental Field

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MAF ...... Minor Allele Frequency MAP ...... Mean Arterial Pressure mRNA ...... Messenger Ribonucleic Acid MSNA ...... Muscle Sympathetic Nerve Activity MVC ...... Maximum Voluntary Contraction n...... Sample Size N2 ...... Nitrogen NA ...... Nucleus Ambiguous Neu ...... Neutral NG ...... Nodose Ganglia NMDA ...... N-methyl-D-aspartate NO ...... Nitric Oxide NTS ...... Nucleus Tractus Solitarii

O2 ...... Oxygen

P ...... Post-exercise Circulatory Arrest P2X ...... Purinergic type-2X PAG...... Periaqueductal Grey PCO2 ...... Partial Pressure of Carbon Dioxide PCR ...... Polymerase Chain Reaction PECA ...... Post-exercise Circulatory Arrest PetCO2 ...... Partial Pressure of End-Tidal Carbon Dioxide PetO2 ...... Partial Pressure of End-Tidal Oxygen PO2 ...... Partial Pressure of Oxygen PWV ...... Pulse Wave Velocity PNS ...... Peripheral Nervous System PPADS ...... Pyridoxalphosphate-6-azophenyl-2',4'-disulfonic acid tetrasodium salt

Q̇ ...... Cardiac Output

R ...... Rest Reb ...... Rebreathe Rec ...... Recovery RHG ...... Rhythmic Handgrip RNA ...... Ribonucleic Acid ROS ...... Reactive Oxygen Species RR ...... Respiratory Rate RTN...... Retrotrapezoid Nucleus RVLM ...... Rostral Ventrolateral Medulla

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SBP ...... Systolic Blood Pressure SC ...... Spinal Cord SD ...... Standard Deviation SE ...... Standard Error SG ...... Sympathetic Ganglia SHG...... Static Handgrip SNA...... Sympathetic Nerve Activity SNP ...... Single Nucleotide Polymorphism SSNA ...... Skin Sympathetic Nerve Activity SV ...... Stroke Volume

TG ...... Trigeminal Ganglia TPR ...... Total Peripheral Resistance TRP ...... Transient Receptor Potential

VC ...... Vital Capacity VOP...... Venous Occlusion Plethsymography VT ...... Tidal Volume V̇ E ...... Minute Ventilation

∆ ...... Change from Control χ2 ...... Chi-Sqaured

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ACKNOWLEDGEMENTS

Dissertation Committee

Dissertation Committee Chair and Thesis Advisor ...... James Pawelczyk, PhD Committee Member-Regular ...... David Proctor, PhD Committee Member- Regular ...... Gail Thomas, PhD Committee Member- Outside Field, Outside Unit ...... John Hayes, PhD

Special Thanks

Expert Guidance for Genetic Analysis ...... David Vandenbergh, PhD Expert Guidance for Genetic Analysis ...... Amanda Griffin DNA Analysis by the PSU Genomic Core Facility ...... Ashley Price Laboratory Support ...... Cory Arnold Doppler Ultrasound Flow Technician ...... Dani Kim, PhD Doppler Ultrasound Flow Technician ...... Yasina Somani, MS

Funding

Mini grant sponsored by the College of Health and Human Development in conjunction with the Huck Institutes of the Life Sciences- made possible by the Raymond and Erin Schultz Dean’s Chair and J. Lloyd Huck endowments.

This dissertation is the culmination of years of work and countless hours spent in Noll

Lab, much of which could not or would not have been done without the support of the above named people as well as a whole network of family and friends who provided the feedback, ideas, motivation, and love necessary to complete this work. Here at PSU, I first want to thank my committee members and especially Dr. Jim Pawelczyk for the guidance, advice, and direction along the way- you all have helped me to become a more critical thinker and have consistently challenged me to do more than I thought I could. To Dr. Pawelczyk, you have not only functioned as my academic advisor, but you have also naturally and willingly become one of my key “life advisors” as well, and for that I am forever grateful. I want to thank the entire herd of PSU undergraduate students who helped me over the years- the quality of which are unrivaled. Thank you to Dani Kim and Yasina Somani for the selfless volunteering of your time

xxvii and effort to help collect data for this research. To the other residents of Noll lab, such as Cyndi

Flanagan and the other staff of the Clinical Research Center, and Randy McCullough and Denny

Ripka, your willingness to help at any hint of need always astounded me and you truly help this entire building function on a daily basis. To Cory Arnold, who could simultaneously do the jobs that would normally take three others people, and who provided an incredible amount of time and energy to the final stages of this research, I am hugely thankful, for without your dedication I would doubtless still be collecting data to this day.

A special thanks to David Vandenbergh, Hobart (Bo) Cleveland, Amanda Griffin, Ashley

Price, and Kerry Hair for the advice, guidance, and analysis you all provided for the genetic information collected during this research. Being thrust so far out of my element, your timely feedback and statistical advice were truly paramount for the successful completion of this study.

I want to say thank you to my siblings and parents (both my wife’s and my own) for the unwavering love and support throughout the years despite my inability to ever explain the answer to the question: “What are you actually doing in graduate school?” I also want to thank my church family, especially here in Penns Valley, for providing a safe escape from the rigors, toils, and challenges of graduate school while helping me to maintain perspective on what matters most.

Finally, my words cannot express the magnitude of gratitude I have for the support from my wife, Bobbie Garvin, who has quite literally been by my side since day-1 of graduate school.

The impact of having the unconditional love and support of another, day-in and day-out, cannot be over exaggerated, and to you, Bobbie, I am truly grateful for everything you have done.

CHAPTER 1: INTRODUCTION

Background and Significance

Cardiovascular disease (CVD) accounts for approximately 1 in 3 deaths in the United

States1 and high blood pressure (BP) is one of the main health factors implicated. Hypertension is the most common condition seen in primary care2 and is a major risk factor for CVD3. Given its importance, extensive research has been done on the regulation of BP.

The regulation of BP on a heartbeat-by-heartbeat basis is maintained by peripheral receptors in the carotid arteries and aortic arch that sends feedback to the central nervous system

(CNS) in a system called the arterial baroreflex4, 5. The regulation of BP during exercise is also by the arterial baroreflex; however, important regulation also comes centrally - via central command

(CC)6, 7 and peripherally via the exercise pressor reflex (EPR)8, 9, 10, 11.

Important to the EPR, are unmyelinated and thinly myelinated afferents innervating the interstitial environment that mediate much of the sensory feedback from the periphery12. These interstitial afferents have numerous membrane receptors which act as ion channels to modulate action potential firing, thus encoding feedback to the CNS. Stimuli for these receptors may be polymodal- they may be thermal, chemical, mechanical, or some combination13 and interaction among the channels is significant14, 15.

The response to activation of these receptors and the activation of their corresponding afferent nerves can be probed in vivo in conscious awake humans using a variety of stimuli that can be applied externally (hot or cold water immersion) or manipulated internally (metabolic state of the muscle and circulating carbon dioxide (CO2) levels). However, even under tightly controlled conditions, the BP response to these stimuli reveals substantial inter-individual variability. For

2 example, the change in mean arterial pressure (MAP) from control during post-exercise circulatory arrest (PECA) after three min of static handgrip exercise (SHG) at 30% of maximum voluntary contraction (MVC) in an individual may range from >40 mmHg to <10 mmHg16 while the response to the cold pressor test (CPT) has been observed for decades to vary from “normal” to “abnormal” based on BP response range17.

Simplified here, the broad question is, “Why is there such a range in BP responsiveness during SHG in otherwise healthy individuals?” Said another way, “What is the mechanism that drives the heterogeneity in BP responsiveness to sympathetic stimulation?” This dissertation is designed to evaluate the thesis that such variability can be explained, to a substantial extent, by variations in the genes that encode sensory receptors.

Although the measurement of resting BP is extremely common and healthy BP levels/goals have been thoroughly studied2, 18 and BP responsiveness to stress has been of interest for decades17, 19, 20 the normal range of BP responsiveness in healthy people to PECA following SHG has not been rigorously defined. Additionally, it is not known if the responsiveness of BP to PECA following SHG, parallels a similar BP change during other modalities of activating the sympathetic nervous system. Finally, the extent to which genetics of peripheral afferent receptors play a role in the BP response to sympathetic activation is also a relatively novel topic of study and is subsequently poorly understood. The purpose of this investigation was to first: characterize the distribution of BP responses to SHG and PECA in a young and healthy population; second: to exploit this rigorously defined distribution to recruit, and more thoroughly characterize, the cardiovascular responsiveness in subjects with the highest and lowest BP responses; and third: to associate single nucleotide polymorphisms (SNP) in the genes for several peripheral afferent receptors with BP responsiveness.

Specific Aims

1. To develop population standards for a young and healthy population for the BP

responses to SHG and PECA.

Hypothesis: There will be a normal distribution of BP responses to SHG and PECA

in the young health population.

2. To suggest a set of best-practice guidelines for researchers interested in using this

common paradigm.

Hypothesis: The extensive heterogeneity in protocols for measuring SHG and

PECA will make it difficult to compare literature results between studies.

3. To use the population standard to define high and low response groups to SHG with

PECA.

Hypothesis: The natural range of BP responses to SHG and PECA will allow for

delineation between high and low BP response groups in an otherwise healthy

population.

4. In high and low response groups, to describe how the cardiovascular response to SHG

with PECA relates to the cardiovascular and autonomic responses to a diverse set of

autonomic stimuli.

Hypothesis: The high response group to SHG with PECA will maintain inter-

stressor response specificity.

5. To associate SNPs in the genes for several peripheral afferent receptors with

responsiveness to autonomic stimuli.

Hypothesis: Gene scores defined by trait alleles will correlate to BP responsiveness

and explain a statistically significant portion part of the variability in BP

responsiveness between individuals.

Delimitations and Assumptions

This research was designed to study a healthy, young-adult population only. The aim of this research of creating norms for the healthy, young-adult population in combination with the novel approach of identifying dichotomous BP response groups to associate with genotype, necessarily prevents extrapolation of results to other populations such as the aged or to any number of disease states. To maintain the accuracy of results and to stay within monetary confines, the complexity of the study was limited to recording only non-invasive or minimally invasive cardiovascular and autonomic variables. Similarly, the measurement of additional variables such and cardiac stoke velocity and ventricular diameter had potential to add value to the data collected, but the potential added value was out-weighted by other limitations. For example, because of the combination of the added monetary expense to attain an experienced technician for measuring stroke velocity, along with the risk of lowering the quality of the other data collected due to the complexity of adding another measurement, it was decide to limit data collection to the variables outlined in upcoming chapters.

This research was not designed to make any conclusions on what is a “healthy” BP response and what is an “unhealthy” BP response. This research only outlines the normal distribution of responses and then further investigates some of the specific/potential differences among individuals that fall in the normal distribution. Potential for generalizability to other populations and disease states is discussed in Chapter 10.

The delimitations for the included population are expressed explicitly by the inclusion and exclusion criteria defined at the outset of the study. The combined inclusion criteria for the studies included: aged 18-45; non-smoking; fluent English speaker; mentally competent and willing to give informed consent; able to complete a handgrip exercise test; currently healthy

6 with no known cardiovascular risk factors; willing to take a pregnancy test; and willing to give a buccal swab DNA sample for indefinite storage. The combined exclusion criteria for the studies included: pregnancy; women taking hormonal contraceptives; smoked in the last 30 days; taking

BP medication, prescription pain medication, or any other drug that affects BP; anyone classified as an adult unable to give consent; history of or the need to use medication for asthma in the last year; have resting hypertension or tachycardia; have BMI ≥30; or are training for an exercise competition or have trained for competition in the last 30 days.

Several assumptions were made about the research subjects, the data collected, and the readers of this dissertation. The integrity of the subjects was assumed and the answers supplied by the subjects were assumed to be truthful. Blood samples were not tested for levels of alcohol, caffeine, or other medications so the required abstinence of caffeine and alcohol before the study and the required reporting of all medications was assumed to be truthful. No invasive measures of BP nor blood flow were attained. It was assumed that the accuracy of the non-invasive continuous measurements were accurate and values were confirmed by two methods when possible, however, we cannot know that values completely reflected actual physiological values.

Assumptions for the reader include a basic understanding of cardiovascular physiology and an agreement that, in general, a lifetime goal of “normal” BP is better for cardiovascular health than living with hypertension. The data presented in this report does not, however, assume that responses defined as high and low are directly related to cardiovascular health or future prognosis for disease, only that BP responsiveness has been associated with a number of health- related factors that are outline in Chapter 2 and discussed further in Chapter 10.

CHAPTER 2: REVIEW OF LITERATURE

Cardiovascular and Autonomic Responsiveness

Cardiovascular parameters and especially the response of the cardiovascular system to stresses like pain and exercise have long been used to predict the likelihood of developing cardiovascular-related diseases, the severity of those diseases, and their prognosis. For example, the magnitude of exercise-induced BP (independent of resting BP) has been considered a risk factor for death from cardiovascular and non-cardiovascular causes20 and has been used as an early predictor for hypertension21. Similarly, the early rise in BP during exercise helps predict cardiovascular mortality in healthy middle-aged men22.

The response to the CPT has also been used as a predictor. For example, a study that involved a CPT and a 45 year follow-up demonstrated that hyperreactivity to the CPT while young was associated with future hypertension19. Similar studies related: CPT BP response to hypertension17, ambulatory BP and essential hypertension23, exercise BP response and hypertrophic cardiomyopathy 24, CPT BP response and aortic stiffness25, CPT BP response and coronary heart disease26, exercise BP response and cardiovascular events/hypertension27, and exercise BP response and mortality in adults suspected for coronary artery disease28. Clearly, an abnormally high BP response to autonomic stimuli is an intriguing prognostic of future disease.

Indeed, noninvasive cardiac stress testing is a key diagnostic and prognostic tool for cardiovascular healthcare professionals29. In a review focused toward training athletes, but relevant to the population as a whole, Palatini states, “One of the potential risks related to an exaggerated BP response to stress testing is that in the long run, repetitive BP peaks triggered by physical activity may determine chronic target organ damage or favour the occurrence of an acute cardiovascular event”30.

The prevention, prediction, and treatment of cardiovascular conditions (of which BP responsiveness is one factor) is and will remain an important topic for human health. According to the American Heart Association, CVD, although declining, still accounts for ~1:3 deaths in the

United States, that is 800,937 deaths in 2013, 2,200+ every day, and 1 every 40 seconds1. The financial burden on the country as a whole is also significant. The estimated costs of CVD is $316.6 billion every year1. Additionally, cardiovascular-associated health behaviors (e.g., smoking, exercise, and obesity) and health factors (e.g., genetics, high BP, and metabolic syndrome) remain an important consideration for the health of many individuals1. From 2009-2012, 32.6% of adults

20 and older had hypertension and from 2003-2013 the death rate attributable to high BP increased by 8.2%1. It is with the importance of BP responsiveness in mind that we evaluate the potential mechanistic differences between high and low BP response groups in responsiveness to autonomic stimuli.

Static Exercise with a Focus on Handgrip

Static handgrip exercise, sometimes erroneously referred to as, “isometric handgrip,” is routinely utilized to interrogate neural mechanisms mediating cardiovascular exercise responses12,

31, 32. The pattern of BP response is predictable: both systolic (SBP) and diastolic (DBP) blood pressure rise in proportion to exercise duration and intensity33. Arresting blood flow (BF) to the contracting muscle magnifies the response34, 35, and partially preserves it after exercise34, 36. Even when a relatively small proportion of total muscle mass is engaged, static exercise generally leads to robust increases in BP; particularly when intramuscular pressure is sufficient to impede BF37.

Artificially restricting or arresting BF artificially magnifies the effect, and doing so after exercise maintains, at least in part, the exercise response. Accordingly, PECA has been used for nearly 80 years to investigate how the skeletal muscle interstitial environment can modulate BP34.

SHG at 30% MVC elicits an increase in BP, heart rate (HR), and muscle sympathetic nerve activity (MSNA), and PECA maintains the BP and MSNA increase from control, while the HR returns to or falls below baseline38. This evidence suggests that CC mediates the increase in HR during SHG, as HR returns to baseline during PECA while a portion of the BP and MSNA response to static exercise are a result of the EPR. Additionally, although BP and MSNA increase during involuntary bicep exercise at 20% MVC, HR does not - again pointing to the importance of CC in the tachycardia response38. To further test the influence of CC on the response to isometric exercise, Goodwin et al. used vibration of the biceps tendon to activate spindle reflexes in the biceps brachii during forearm flexion and extension and thus modulated the “amount” of CC needed to maintain a given static contraction tension6. They showed that BP, HR, and pulmonary minute ventilation (V̇ E) all increased or decreased in proportion to CC at a given tension. Similarly, static exercise in partially curarized humans (with ~50% decrease in handgrip strength) relative to

10 control, showed an increase in the MAP, HR, and Q̇ to SHG that was attributed to CC39. An experimental model using decerebrate cats walking or running normally compared to fictive locomotion suggests a similar role for CC and further illuminates the possibility that the brainstem is a region where CC originates or is modified7. Respiration and BP increased in both intact moving cats, and paralyzed cats with locomotory activity in the motor nerves to the legs despite the absence of muscular contraction or change of metabolic rate suggesting a feed-forward cardio-respiratory response from CC7.

The MSNA response to SHG plus PECA may be different to the arms and legs. Wallin et al showed that in supine subjects, 2 min of SHG at 30% MVC with simultaneous MSNA recordings increased approximately equally to the arm and leg (radial and peroneal nerve recordings, respectively)36. However, during PECA following the SHG, MSNA level stayed the same in the leg, while radial MSNA increased, suggesting that EPR activation from the forearm may cause unequal neural outflow to the arms and legs36.

Muscle fiber type may play a role in the reflex response to static exercise as isometric stimulation in primarily glycolytic vs. oxidative muscles of the same animal showed an attenuated response from the oxidative muscle40. The response to muscle stretch was the same, however, indicating the importance of metabolic byproducts to the reflex response to muscle stimulation40.

Similarly, static exercise at various % MVC levels in the arm (handgrip) and the legs (dorsiflexion and plantar flexion) resulted in MSNA increases in the order of magnitude of handgrip > dorsiflexion > plantar flexion41. The corresponding fiber types were from wrist- equal type I and

II fibers, to legs- increasing number of type I fibers thus indicating that more fast-twitch fibers may influence the MSNA response41. Additionally, it was found that MSNA response correlated with the contraction force in all muscle groups, highlighting the importance of force to the reflex

11 response41. The magnitude of the skin sympathetic nerve activity (SSNA) response to static exercise is related to relative effort and not muscle mass as shown by SHG and static knee extension at the same relative intensity producing a similar SSNA response42.

The majority of the BP response to static exercise is a result of an increase in cardiac output

(Q̇ ) via mobilization of central blood volume and elevated stroke volume (SV) and not total peripheral resistance (TPR) or leg resistance43. However, during PECA, MAP and MSNA remain elevated while Q̇ decreased to (or below) baseline, indicating that the control of Q̇ is independent of the EPR feedback from the periphery or is modified by other inhibiting stimuli like an enhanced parasympathetic activity43. Central venous pressure does not change during sustained static exercise44. During SHG, the arterial baroreflex control of MSNA changes over time; baroreflex disinhibition may be one the mechanisms for the increase in BP and MSNA during SHG45.

The response to SHG can also be observed and modulated in numerous ways. For example, changing exercise from one to multiple limbs is not just an additive process- adding exhausting

(50% MVC) SHG to low-level calf exercise can decrease BF to the active calf muscle via a decrease in conductance due to an increase in MSNA46.

One way to alter the response is by exercise training. For example, forearm endurance training (6 weeks) decreased the MSNA response during SHG at 33% of MVC and during PECA, while it did not significantly change the HR or BP response47. These authors concluded that endurance training decreased chemoreflex stimulation and therefore MSNA as well47.

Interestingly, detraining may have a similar effect. With exercise intensities set to 30% and 70%

MVC measured before bedrest (14 day- 6° head-down), bedrest did not change the MSNA and BP response to SHG and decreased the response to PECA48. However, it significantly reduced the

MSNA and BP response to static calf exercise and PECA48. This somewhat surprising results

12 suggests that bedrest reduced sympathetic activation from static leg exercise. The authors suggest a possible mechanism for this response to be an altered central modulation due to immobilization or a change in muscle afferent sensitivity in atrophied legs48.

The EPR is highly dependent on afferent feedback from the exercising muscle49. This feedback can be probed, modulated, stimulated and inhibited by techniques such as passive stretch50, carbon monoxide inhalation51, graded reductions in perfusion52, 53 and changes in the concentration of exogenous and endogenous ligands that interact with a number of neuronal receptors. Some examples of these ligands are: lactic acid54, cyclooxygenase products55, 56, and adenosine triphosphate (ATP)57. These ligands can bind and activate a number of ion channels embedded in the cell membrane of the afferent nerves such as: the transient receptor potential

(TRP)V1 receptor58, 59, the TRPA1 receptor60, the acid-sensing ion channel (ASIC)58, the thromboxane receptor61, the peripheral µ-opioid receptor62, and the adenosine receptor63.

Rhythmic Handgrip Exercise

Rhythmic handgrip exercise (RHG), a form of dynamic exercise, elicits changes in HR,

BP, electromyography, and post-exercise hyperemia that are qualitatively similar to SHG, but with important differences. All variables increased progressively with RHG exercise intensity64 and

HR and MAP were directly related to EMG (the index of CC used) 64. MSNA increased during moderate RHG (and was maintained during PECA) but not during mild RHG suggesting a threshold of activation of the EPR and the importance of the active skeletal muscle environment in disinhibition or frank activation of sympathetic discharge65.

To better characterize the local intramuscular partial pressure of oxygen (PO2), pH, and partial pressure of carbon dioxide (PCO2) environment during RHG, the PO2, pH, and PCO2 were measured from venous blood via blood sampling and intramuscularly via fiber optic sensor66.

Values of pH and PCO2 were similar at rest and during low level exercise, but differed when

66 exercise was greater than 30% MVC . During 15% MVC and greater exercise, venous PO2 declined from 40 to 21 Torr while intramuscular PO2 declined from 24 to 8 Torr at 15% MVC, and to essentially 0 Torr at 30% MVC and above66. In the muscle, pH declined when intramuscular

66 PO2 reached 10 Torr and continued to fall thereafter .

The dimensions of the brachial artery, the primary conduit artery in the upper arm, affects the BF to the exercising muscle during RHG. During RHG of ~9.5% MVC with a duty ratio of 1:1 sec, brachial artery diameter increases from rest but not during contractions with a duty ratio of

1:2 sec67. However this dilation did not occur until 2 min of exercise with the first 30 seconds of

RHG causing a brachial constriction. The authors suggest that metabolic products from the muscle in the circulatory system thus likely caused the brachial dilation67.

RHG has also been used to evaluate the effect of training on cardiovascular responsiveness.

Similar to what was observed with SHG, the response to forearm training was an increase in threshold for reflex engagement (induced with RHG exercise in a forearm positive pressure apparatus to progressively reduce transmural and perfusion pressure), a decrease in metabolite concentration, and lower BP during exercise68.

The BP response to dynamic exercise in general is different than that of static exercise11, 49. Generally, during dynamic exercise there is a large increase in HR, SV, and Q̇ , a modest increase in SBP and MAP, and a decrease in DBP that facilitates an increase in blood supply to the exercising muscle. To contrast, static exercise, with large and continuous increases in intramuscular pressure, decreases BF to exercising muscle and elicits a larger increase in BP11,

49. These interactions are exemplified by a statement from Kaufman and Hayes49 that “…the intensity of dynamic exercise is proportional to the increase in Q̇ and oxygen consumption, whereas the intensity of static exercise is proportional to the increase in mean arterial pressure”.

Similarly, at a similar time-tension index, with a variable tension, dynamic (rhythmic) muscle contraction causes a larger cardiovascular response than static contraction in cats and when the peak tension is kept the same, the cardiovascular responses become similar69.

The BP response to RHG may restore BF. During RHG at 40% MVC, progressive reductions in limb perfusion produced by positive external pressure were ameliorated by increasing perfusion pressure during low levels of positive external pressure; however, flow was not restored at the highest external pressure (+50mmHg)70. These findings suggests that the early increase in BP aids in maintaining perfusion, while a “clearly engaged”70 EPR may cause active vasoconstriction in the exercising muscle70. Using a different method to decrease perfusion and a lower % MVC, it was demonstrated that restoration of BF during RHG with hypoperfusion was

15 not because of the changes in BP and was likely in response to local vasodilation and increased collateral flow71.

The Exercise Pressor Reflex

During exercise, the autonomic nervous system plays a key role in regulating BP and mediating cardiovascular adjustments that are sufficient to meet the metabolic demands of active muscle72. BP is regulated, in part, by three coordinating systems; regulation occurs centrally via

CC6, 7, and peripherally via feedback from arterial baroreflexes4, 5 and the EPR8, 9, 10, 11. Shown in

Figure 2.1, these three mechanisms (along with arterial chemoreflex, cardiopulmonary baroreceptors, and the respiratory metaboreflex) function in concert to maintain an appropriate BP for a given exercise72. The purpose of this section is to report more explicitly on the EPR, with focus on the metabolic component of the EPR and its reflex pathway.

Figure 2.1: Schematic summarizing the mechanisms involved in mediating the autonomic adjustments to exercise. Neural signals originating from higher brain centers (i.e., central command), chemically sensitive receptors in the carotid and aortic bodies (i.e., arterial chemoreflex), stretch receptors in the carotid and aortic arteries (i.e., arterial baroreflex), mechanically and metabolically sensitive afferents from skeletal muscle (i.e., exercise pressor reflex), mechanically sensitive stretch receptors in the cardiopulmonary region (i.e., cardiopulmonary baroreflex) and metabolically sensitive afferents from respiratory muscles (i.e., respiratory metaboreflex) are processed within brain cardiovascular control areas that influence efferent sympathetic and parasympathetic nerve activity. The alterations in autonomic outflow elicited by these inputs during exercise evoke changes in cardiac and vascular function, as well as release of catecholamines from the adrenal medulla. Source: Fisher et al.72

The EPR is a powerful BP raising reflex that is activated by two components: the muscle metaboreflex (which depends on and buildup of metabolic byproducts during muscle contraction) and the muscle mechanoreflex (which depends on muscle stretch/mechanical deformation of the muscle)12. As demonstrated in Figure 2.2, these two components are both mediated by thinly- myelinated group III and unmyelinated group IV muscle afferent nerves, respectively12. These nerves mediate feedback to the CNS reflecting metabolic and tensile states, respectively, of exercising skeletal muscle10, 11, 49, 73.

Figure 2.2: Discharge patterns of 4 thin fiber muscle afferents that responded to static contraction. Contraction period depicted by black bar. A: group III fiber (conduction velocity 17.8 m/s) discharged vigorously at onset of contraction, but then its firing rate decreased even though muscle continued to contract. B: group III fiber (conduction velocity 9.6 m/s) discharged vigorously at start of contraction, adapted and then fired again during contraction. C: group IV fiber (conduction velocity 1.3m/s) started to fire 10s after the onset of contraction and then gradually increased its firing rate during contraction period. Note that firing of fiber slowed even though muscle continued to contract. D: group IV fiber (conduction velocity 1.1 m/s) fired irregularly 4 s after onset of static contraction. Source: Kaufman et al.12

The EPR neural pathway can be described in several parts: the afferent limb, spinal cord

(SC), brainstem, and efferent limb11. As mentioned, the afferent limb involves the activation of group III and group IV afferent nerves12. Group III nerves are predominantly mechanically (stretch and pressure) sensitive74 while group IV fibers and are predominately metabosensitive12. The contribution of the feedback modality, i.e., metaboreflex vs. mechanoreflex, remains poorly defined11. That said, the magnitude of total feedback (i.e., mechanical plus metabolic sensing), rather than the individual modality of feedback to the CNS, may be the important determinant of the reflex response75.

Given that the group IV afferent activation during exercise is generally in response to metabolic byproducts of muscle activation, substantial research has gone into determining the endogenous chemical stimuli that activate this reflex. Lactic acid, hydrogen ions, bradykinin, potassium ions, arachidonic acid, adenosine, analogs of adenosine triphosphate (ATP), diprotonated phosphate, and prostaglandins have all been identified as putative metabolites that can activate skeletal muscle afferents11. Many of these metabolites function by interaction within protein receptors on the cell membrane of the afferent neurons. Several receptor families including, but not limited to, the transient receptor potential vanilloid type-1 (TRPV1), the purinergic type

2X (P2X) channels, and the acid-sensing ion channel type 3 (ASIC3) have been implicated in afferent activation 14, 73. For example, arterial injection of pyridoxal phosphate-6-azophenyl-2',4'- disulfonic acid (PPADS)76, a P2 receptor antagonist, or α,β-methylene ATP, a P2X receptor agonist77, decreases and increases the EPR, respectively. Additionally, the intra-arterial injection of capsaicin, the prototypical TRPV1 receptor agonist, into exercising muscle increases the BP in dog, cats, and rats11.

Group III and group IV neurons generally enter the dorsal horn of the SC where they synapse with ascending and interneurons11. There is evidence for release of neuropeptides including substance P78; however, the increase in concentration may be from activation of either primary afferent nerves or from second order neurons in the SC11. Other receptors may modulate the EPR at the level of the SC including, N-methyl-D-aspartate (NMDA) receptors, non-MNDA receptors, P2X receptors, and bradykinin receptors among others11, 79. Figure 2.3 shows a schematic of the current understanding and some questions that remain about the sensory afferents and their SC synapses.

Figure 2.3: Schematic depicting a muscle afferent neuron synapsing on a cell in the dorsal horn of the spinal cord. Also shown are some of the mediators that are thought to be important for dorsal horn transmission of the muscle pressor reflex, including the compounds that are the subject of this review, i.e., nitric oxide (NO) and acetylcholine. Current evidence indicates that activation of muscle afferent neurons releases the excitatory amino acids (EAA), glutamate, and aspartate. The EAA in turn excite dorsal horn cells via N-methyl-D-aspartic acid (NMDA) and non-NMDA receptors. The muscle afferent neuron also releases the neuropeptide substance P (SP), which enhances the excitation of dorsal horn cells by the EAA via the activation of neurokinin-1 (NK-1) receptors. On the other hand, this schematic also illustrates that there are several receptor systems that, when activated, can reduce the efficacy of the muscle afferent fiber to dorsal horn cell transmission, and thus attenuate the pressor reflex. Thus far there is only evidence for opiate receptors exerting a presynaptic action with respect to this reflex. Source: L.Britt Wilson79

In their review of the EPR, Smith et al. summarize the brainstem areas important for the functions the EPR11. Skeletal muscle activity can modify the activity of the nucleus tractus solitarii

(NTS), a potential central site for integration of the EPR11, 72. Additionally, activity in the rostral ventrolateral medulla (RVLM), the caudal ventrolateral medulla (CVLM), the lateral tegmental field (LTF), the ventromedial region of the periaqueductal grey (PAG), and the nucleus ambiguous

(NA) changes during exercise11, 72. The neurotransmitters and neuromodulators in this network are still under investigation. Putative agonists and receptors include, but are not limited to, nitric oxide

(NO), glutamate, serotonin, and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors11.

The afferent signals converge at the NTS, and the integrated output from the brainstem, determines the overall parasympathetic and sympathetic outflow72. An exercise induced reflex response is manifest by increases in sympathetic nerve activity (SNA) originating in the RVLM and transmitted via the intermediolateral cell columns of the SC and paravertebral sympathetic chain ganglia11, 72. Concomitantly, parasympathetic nerve activity decreases (though cardiac vagal motor neurons originating in the NA) 11, 72. Together, these responses from the RVLM and NA results in an increase in vasomotor activity and HR11, 72. A figure summarizing the reflex pathway and neural areas involved is shown in Figure 2.4.

Figure 2.4: Simplified schematic illustrating putative neural areas involved in the control of parasympathetic and sympathetic outflow. The activity of efferent autonomic outflow is determined by complex interactions within and between central neural circuits, peripheral afferent inputs to the nucleus tractus solitarii (NTS) and other neural areas (not shown) as well as circulating factors. The integrated response of all of these ascending and descending neural signals ultimately determines parasympathetic and sympathetic outflow. See text for additional details. CVLM, caudal ventrolateral medulla; CVO, circumventricular organs; IML, intermediolateral cell column; NA, nucleus ambiguus; RVLM, rostral ventrolateral medulla. Source: Fisher et al.72

The question remains whether the muscle metaboreflex is a flow- or BP-raising reflex.

O’Leary suggests that indeed, the metaboreflex does partially restore BF with the exception of several conditions like strong static contraction with high tissue pressure, or in heart failure patients with limited Q̇ 80, 81, 82, 83. Joyner, however, discusses the evidence for EPR-mediated changes in

SNA to the exercising muscles as a limiting factor for the functional relevance of the EPR as a flow-raising reflex80, 81, 82, 83. The discord may arise from species differences, as O’Leary’s work has utilized dogs, while Joyner’s research focuses on humans. Thus, the BF response may depend on species (different relative ability to increases Q̇ , different splanchnic blood volume manipulation ability) as well muscles involved (large vs. small mass), exercise intensity and mode

(high/low, static/dynamic), and relative flow restriction80, 81, 82, 83. Although still debated, preponderance of evidence is consistent with a functional role for the EPR in maintaining/partially restoring BF during exercise, especially in exercise that is dynamic, not Q̇ limited, and submaximal yet supra-threshold for the EPR. Indeed, evidence from our lab suggests that during low-intensity

RHG that is dynamic, not Q̇ limited, and reaches EPR threshold via external cuff occlusion, individuals with a larger EPR may maintain perfusion longer (as evidenced by a longer time to complete BF occlusion) relative to individuals with a smaller EPR response84.

In summary, the EPR is a powerful reflex initiated in exercising skeletal muscle that functions to increase activity of the sympathetic nervous system and decrease the parasympathetic nervous system. The EPR arc is summarized by Murphy et al.85 and is shown in

Figure 2.5.

Cold Pressor Test

Since at least the early 1900s the CPT has been used in many iterations; for example, as a test of “vasomotor tonus”17, a predictor for mortality and coronary heart disease26, and as predictor for hypertension19. One of the earliest studies to employ the CPT with a large cohort of individuals was published in 1936 by Hines and Drown 17. They studied 571 individuals with 1-min CPT in

4⁰C and attributed the pronounced BP response primarily to widespread vasoconstriction with little change in HR or Q̇ 17. While the rise in BP is primarily a result of vasoconstriction, there may also be a modest increase in Q̇ with increased HR86, 87.

The response to the CPT does not appear to be altered with the surface area exposed to cold. Performing a CPT with 2 vs 1 hand indicates that the stimulus area during the CPT increased the MSNA response, however the BP response was not changed with stimulus area88. However, the CPT response is dose-dependent relative to the magnitude of the CPT stimulus89 and requires functional innervation of the sensory receptors in the skin. Lower body anesthesia (laminectomy after injury) during foot immersion blocked the pressor response while the BP response to hand immersion was unchanged90. To investigate the role of the cortex on the CPT, 15 subjects underwent deep anesthesia (suppression of the cortex but not the subcortex) and the CPT was decreased by 26%, indicating that although there is some cortical modulation, the reflex is largely regulated by the subcortical hypothalamus and medulla91. The CPT reflex arc is initiated by pain and temperature afferent fibers that enter the dorsal roots, travel rostral via the lateral spinothalamic, anterior spinothalamic, and spinotectal tracts synapsing in the thalamus, somesthetic (somatosensory) cortex, medulla, and tectum86.

The CPT (typically administered in water in the range from 0-5°C) must be considered not only a response to a change in temperature but also with pain. In an early study, subjects were

27 asked to report their subjective feeling during the CPT- the responses followed a general pattern of feeling of cold, increased cold, pain, increased pain, leveled off pain, decreased pain, and then cold alone92. BP followed the same pattern of rise, plateau, and fall as that of the pain response and was directly proportional to pain with various water temperatures93. Thus, two distinct sensations (cold and pain) can be perceived indicating that the CPT test has essentially two parts; the first from the cold per se, and the second from the pain with responses mediated subcortically, and a combination of subcortically and cortically, respectively86. The response to pain vs cold may be somewhat heterogeneous as the response associated with the CPT pain included a positive change in Q̇ and TPR, while the non-pain related response was limited to effects on TPR94.

The CPT response is variable within the population. Distinct high and low response groups were identified in otherwise normotensive and healthy college age men. The higher response was associated with a high pain response, a steeper rise in TPR, an increase in HR and a relatively elevated baseline TPR87, 94.

MSNA increases during the CPT and the increase correlates with the change in MAP95, 96,

97. HR also increases during the first 30 sec of exposure, but returns to control after two min.

Similarly, the CPT increases low frequency SBP fluctuations reflecting an increase in sympathetic vasomotor control98. The CPT is proposed to be independent of the baroreflex96 but evidence also suggests that baroreceptor reflex sensitivity has a negative relation with intensity of experienced pain elicited by the CPT demonstrating a pain-inhibiting effect of baroreceptor activity99.

In the brain, the CPT is associated with activation of the bilateral middle and superior frontal gyrus, anterior cingulate cortex and thalamus, left insula, right inferior frontal gyrus, and left inferior temporal gyrus100. The activity in the PAG was positively associated with pain

28 threshold, and negatively associated with perceived pain indicating a key role for the PAG in inhibiting pain100.

As a highly provocative autonomic stimulus, the CPT has also been used in a number of settings to initiate cardiovascular responses with prognostic value. For example, the CPT BP response has been positively correlated with the 5-year change in pulse wave velocity (PWV) the gold standard index of aortic stiffness25, negatively correlated with fitness level101, and was unchanged during spaceflight96. The pain response is inversely correlated with the serotonin 5-

HT1A receptor in the brain29.

Hot Immersion Test

The immersion of the hand into hot water is far less common than the CPT, but it is an effective autonomic stimulus. Streff et al. published one of the first paired comparisons for the

CPT to the hot immersion test102 in which they discuss the possible advantages of hot immersion relative to the CPT. As discussed above, the CPT involves both cold and pain sensation, and it is difficult to parse cardiovascular reactivity between the interacting effect of the two stimuli.

Because hot immersion stimulates elicits a similar level of pain and a lower cardiovascular response, hot immersion may be a more appropriate stimulus to investigate models of pain as it decreases the confounding variable of BP-related reflexes102.

In another study that used the CPT (7±0.3°C) and hot water immersion (47.5±0.5°C) for 5 minutes, researchers found that the BP and HR responses to immersion were not related to the pain responses103. They found that both hot and cold produced a stable elevation of BP with a greater response from the CPT103. Interestingly, they found that ~1/3 of subjects were “pain adaptive”

(steady decline in pain rating after ~1 min) while the remaining subjects were pain “non-adaptive”

(approximate plateau in pain around 1.5 min with no decline). Some subjects were adaptive to both types of pain while most were not103, suggesting that neural mechanisms elicited by these two caloric stimuli are functionally distinct.

Generally speaking, the response to the CPT has been considered to be greater than the response to hot. Using either water immersion103 or a cutaneous thermode 104, it appears there is less of a dichotomy between “pain-sensitive” and “pain-tolerant” responses to hot stimuli.

Moreover, in a study investigating the pain response to 4 types of noxious stimuli (contact heat, electric shock, ischemic exercise, and the CPT), it was shown the response to heat (contact thermode 45.5 – 50° C) was generally less than to cold (5°C- 5 min), and that cold and ischemic

30 pain (20 ischemic repetitions of 50% MVC handgrip followed by 15 min total occlusion) are most similar105.

Hot pain stimulation of the hand (46-49°C thermode) caused activation of a number of brain areas including the insula, inferior frontal gyrus, cingulate gyrus, secondary somatosensory cortex, cerebellum, and medial frontal gyrus106.

Hyperoxic/Hypercapnic Rebreathing

The ventilatory chemoreflex can be elicited by hyperoxic/hypercapnic rebreathing.

Introduced in 1967, this relatively simple method plots a progressive increase in the partial

107 pressure of end-tidal carbon dioxide (PetCO2) and the associated change of V̇ E and assumes a

107 linear relation been PetCO2, arterial PCO2, and PCO2 of the medullary chemoreceptor . The procedure as outlined by Read107, is described briefly below, however, many iterations and variations of this original procedure have been used since its original inception. The technique involves a deep expiration followed by rebreathing from a bag filled with a gas mixture of CO2 and oxygen (O2). Rebreathing from the bag is then continued to an end point of either time (often

~5 min) or a given PetCO2 (often PetCO2 60-70 mmHg) while breath-by-breath data is collection for respiratory rate (RR), tidal volume (VT), V̇ E, PetCO2, and partial pressure of end-tidal oxygen

(PetO2).

This technique (and modified versions of it) have been used to measure the hypercapnic ventilatory response (HCVR) to investigate a number of biological topics including, but not limited to: exercise training108, 109, detraining109, untrained vs. specific track and field event participants110,

111, 112 113 114 adults vs children , chronic low-level CO2 exposure , short-term microgravity , bed rest deconditioning115, aging112, body morphology (height/weight/lung size) 112, gender 112, 116 and similarities in twins 112, 117. Hypercapnia can occur, and thus the threshold and magnitude of the

HCVR may be important in diseases such as COPD118, 119, sleep apnea120, and congenital central hypoventilation syndrome121, 122.

Inspiration of CO2 elicits a robust increase in V̇ E that is mediated centrally by medullary chemoreceptors and to a lesser extent, the carotid and aortic bodies. A wide range of ventilatory

112, 123 responses to CO2 have been reported . McGurk et al. proposed that in non-athletic, healthy

32 individuals, a low response is <1.5 L/min/mmHg, intermediate response is 1.5-3.1 L/min/mmHg, and high response is >3.1 L/min/mmHg with a mean response of 2.3±0.8 L/min/mmHg112. The response to CO2 may also depend on the concomitant O2 environment. BP increases to a similar absolute level during hyperoxic, hypercapnic and hypoxic, hypercapnic rebreathing to a PetCO2 of

~50-55 mmHg; however, during hypoxic, hypercapnic rebreathing the Q̇ , HR, SV, systemic vascular conductance, and MSNA either increase to a greater absolute level, or begin to increase

124 at a lower PetCO2 indicating an enhanced sensitivity to changes in CO2 .

For mild to moderate, rhythmic, large muscle, steady state exercise, hyperpnoea appears to be mediated, at least in part, by muscle chemoreception, suggesting an interaction between the

HCVR and the EPR. Intrathecal fentanyl (a µ-opiate receptor agonist) injections, which decrease the signal from the type III and IV afferents, caused sustained hypoventilation, decreased BP, and

HR125. Employing a small muscle mass exercise paradigm (2 min SHG followed by 2 min of

PECA), Lykidis et al. showed that V̇ E increased during SHG with and without hypercapnia and remained elevated during PECA with hypercapnia and not during normoxia, suggesting a central interaction between the hypercapnic chemoreflex and the muscle metaboreflex126, 127. The change in HR and BP between the normal and hypercapnic conditions were not different, however127.

Another report showed that hypercapnia with concomitant muscle metaboreflex activation, did cause a change in MAP, HR, Q̇ , and SV that was greater than muscle metaboreflex alone128.

The response to hypercapnia is not the same for all people and a heightened sensitivity to

129, 130 CO2 is a known correlate with panic disorder . The pathophysiology of panic disorder is not understood, however it is often associated with shortness of breath and feelings of suffocation and

Klein suggests “false suffocation alarms” may be triggered at the respiratory control centers129, 131, 132. The amygdala and functional ASIC channels are both important for fear

33 behaviors133. ASIC channels are expressed in the amygdala and are important for sensing local

133 CO2/pH and modulating the fear response. . Additionally, two SNPs in the gene coding for

ASIC1 (rs685012 and rs10875995) were associated with panic disorder and amygdala

129 structure/function . Therefore, the sensitivity to CO2 and ASIC genotype in the high vs. low response groups is of interest.

Cell Membrane Ion Channels

A number of membrane ion channels are located in group III and IV afferents with activation patterns consistent with sensory receptors. For genetic analysis, 3 families of proteins were chosen for evaluation; the Acid-Sensing Ion Channel (ASIC) family, the purinergic receptor

(P) family, and the Transient Receptor Potential (TRP) family.

Acid Sensing Ion Channels

The ASIC family of receptors is one member of a larger superfamily - the Degenerin/ epithelial sodium channel (DEG/ENaC) family134. ENaC receptors are important in modulating the amount of Na+ in extracellular fluid and therefore have a central role in ECF volume and BP regulation134.

ASICs are also sensors for decreases in extracellular pH135. They have 2 transmembrane domains with 1 extracellular loop; both the N- and C-terminal domains are located intracellularly136, and function on a protein level as homo or heterotrimeric with homologous subunit of ASIC1-3136, 137. Local acidification and therefore activation of the ASICs can occur during normal physiological and pathophysiological situations such as vigorous synaptic transmission, local ischemia, energy consumption, and inflammation136, 138.

ASIC expression occurs in many locations in the CNS and PNS136, 137. ASIC3- the main

ASIC receptor of interest in these studies- is expressed in regions important to autonomic cardiovascular regulation such as the brain stem, dorsal root ganglion (DRG), skin, and vasculature136, 137.

ASIC knockout models have been widely used to infer function by evaluating the phenotypic consequences of removing the ASIC gene/protein. In their comprehensive review, Lin et al.136 summarized much of what is known about knockouts in ASIC1a, ASIC2, and ASIC3.

Figure 2.6 shows their summary for ASIC3, noting especially the potential role of ASIC3 in knockouts with reduced muscle fatigue, reduced mechanical noxious pinch sensitivity, decreased hyperalgesia to acid injection, abnormal cardiac autonomic regulation, and increased susceptibility to cardiac ischemia- all of which could be theoretically associated with the EPR.

Figure 2.6: Schematic diagrams of important phenotypes in Asic3 knockouts. Source: Lin et al.136

ASICs have a well-established role in nociception136, 137, 138, 139, 140. They have an impact in both peripheral (importantly ASIC3 and ASIC1) and central (importantly ASIC1a) neurons and function in detection, modulation, and sensitization of the pain message137. Amiloride, a K+ sparing diuretic, is a non-specific ASIC blocker and can block the pain response to acidic solutions (direct infusion of acidic solutions (pH > or=6.0) into human skin and transdermal iontophoresis of protons)141, 142. Furthermore, non-steroidal anti-inflammatory drugs (NSAID), which are also non-selective ASIC inhibitors, can decrease the pain response to acid142. The involvement of the TRPV1 channel is also implicated in these responses, however its role is likely limited and may be reserved for nociception under extreme acidic conditions137, 141, 142.

Finally, a role for ASICs in pain sensation in humans has been suggested in studies demonstrating that MitTx, a small peptide purified from the venom of the Texas coral snake

Micrurus tener tener, activates somatosensory neurons and elicits extreme pain via ASIC1 and

ASIC2a activation137, 143.

ASIC3 is highly sensitive to changes in pH (threshold ~pH 7.2) and generates a biphasic current upon activation: an initial transient that is enough to stimulate DRG action potentials and a sustained component that is maintained as long as acidosis remains which is thought to contribute to the lasting sensitization of pain from other stimuli137. Figure 2.7 shows some of the proposed receptors, their depolarization patterns, and the pathway involved in somatosensory pain transmission.

Figure 2.7: ASICs and nociception in primary and secondary sensory neurons. Different ASIC channel subtypes are expressed in primary sensory neurons (Dorsal Root Ganglia and TriGeminal ganglia), and in neurons of the dorsal spinal cord, which receive sensory-nociceptive information from the periphery. The picture shows a rat DRG neuron in primary culture. Activation of ASIC channels containing ASIC1 and/or ASIC3 has a direct impact on the sensory neuron's activity, by generating sufficient depolarization to reach the threshold for action potential (AP) triggering, or to sensitize neurons to other stimuli. The dorsal spinal cord contains a neuronal network composed of secondary sensory neurons and excitatory and inhibitory interneurons (shown in gray). These spinal neurons express homomeric ASIC1a and heteromeric ASIC1a/ASIC2 channels, which are involved in opioid-dependant (ASIC1a and ASIC1a/ASIC2b) and opioid-independent (ASIC1a/ASIC2a) analgesia. Note that the different elements are not drawn to scale. Source: Deval and Lingueglia137.

A number of investigations have explored the potential role ASICs in exercise-associated conditions such as ischemia, muscle fatigue, and the EPR139, 144, 145, 146. The injection of specific and non-specific ASIC blockers demonstrated the role of ASICs on the metaboreflex portion of the EPR to acid injection, static contraction, and to PECA in decerebrate cats with significantly attenuated responses when the ASICs were blocked144, 145. This said, afferent receptors do not generally work in isolation. DRG neurons respond to combinations of protons, ATP, and lactate to a greater extent in combination than alone, from which an interaction of ASIC, P2X, and

TRPV1 channels may be inferred14. Similarly, muscle ischemia is accompanied by release of

ATP, which binds the P2X5 receptor, which is in-turn complexed with ASIC3- this interaction sensitizes ASIC3 to the ischemic pH change thereby allowing the sensation of ischemia to be transduced146. Finally, Pollak et al. demonstrated that the sensation of fatigue was evoked by injection of physiological concentrations of combinations of muscle metabolites (protons, lactate and ATP) in resting muscle, but not when these metabolites were injected singly15. This study revealed dose-dependent responses where low levels of metabolites produced the sensation of fatigue and higher levels of metabolites produced the sensation of pain that were attributed to the co-activation of ASIC, P2X, and TRPV1 channels, thus indicating the importance of multiple metabolites to the activation of these nerves which corresponds well with the normal physiological changes to the extracellular environment that occur during exercise15.

Purinergic Receptors

Purinergic signaling is ubiquitous; purinoceptor family of receptors depends on extracellular ATP, UTP and their metabolites as signaling molecules147. ATP functions as a transmitter in the PNS and CNS and receptors for ATP (purines and pyrimidines) are widely expressed on nerve and non-neuronal cells148, 149. ATP is released from (e.g. exocytosis/connexin

39 hemichannels, or other membrane channels), and leaked though, healthy (and unhealthy) non- excitable and excitable cells as a normal physiological mechanism in response to, for example, mechanical deformation (shear/stretch/osmotic swelling) and hypoxia147, 148.

The purinoceptors can be subdivided into P1 (adenosine binding) and P2 (ATP/ADP binding) receptors while the P2 receptors can be further subdivided into P2X (ligand-gated ion channels) and P2Y (G protein-coupled receptors)147, 148, 149, 150, 151. There are 7 P2X receptors

(P2X1-7) with intracellular n- and c- termini and 2 transmembrane spanning regions (associated with gating and the ion pore, respectively) and the proteins form a trimer that can form from homo-or-heteromultimers148.

In a 2016 review148, Burnstock describes his “unifying purinergic hypothesis”. He suggests that the release of ATP is one important factor of the mechanism for the sensation of several types of pain. For example, ATP released from sympathetic nerve terminals is involved in sympathetic pain, ATP released from vascular endothelial cells are associated with migraine, angina, and ischemia, and ATP released from tumor cells can cause cancer pain148. Nociceptive fibers of the

DRG commonly express P2X3 receptors and contribute to the pain response152; indeed, P2X3 and

P2X2/3 are the predominant P2 receptors for the initial sensation of nociception148. Figure 2.8 outlines the proposed pathway and receptors involved in pain transmission from skin cells, endothelial cells, sympathetic nerves, and cancer cells by Burnstock148.

P2X receptors are also implicated in the EPR. The concentration of ATP is linearly related to the BP response to static muscle contraction and ATP may be is released from mechanosensitive channels153. ATP stimulated a pressor response at low doses and enhanced the response to muscle stretch in decerebrate cats57. Blocking P2X receptors with PPADS decreased the pressor response to electrically-stimulated contraction and stimulation of P2X receptors with α,β-methylene augmented the response154. Microdialyzing α,β-methylene into the dorsal horn and static contraction both increase the release of glutamate in the dorsal horn and then blocking the P2X receptors with PPADS decreases both the pressor response and glutamate release to static contraction suggesting the activation of the afferent nerves containing P2X function by releasing glutamate155.

The importance of the purinergic signaling in the human EPR has also been demonstrated.

BP and MSNA responses were measured during SHG (total exercise response), PECA (isolated metaboreflex portion of the EPR), and passive muscle stretch (isolated mechanoreflex portion of the EPR) with and without (control- saline injection) local infusion of pyridoxine hydrochloride

156 (vitamin B6) . Pyridoxine hydrochloride is converted to pyridoxal-5-phospahate, a P2 antagonist151, 157, 158, in vivo159, 160. Vitamin B6 administration attenuated the BP and MSNA responses to EPR activation, suggesting a functional role for P2 receptors in the human EPR156.

Finally, the EPR may be dysregulated in some disease states11, 85, 161, 162, 163, 164, 165, 166, 167, 168. For example, evidence suggests that the metaboreflex is attenuated and the mechanoreflex is accentuated in patients with congestive heart failure163 and that P2X receptors may be upregulated after myocardial infarction167.

The Transient Receptor Potential Receptors

The TRP family of ion channels contains 25+ cation channels and can be divided into 7 subfamilies: TRPC, TRPV, TRPM, TRPP, TRPML, TRPA, and TRPN (Canonical, Vanilloid

Melastatin, Polycystin, Mucolipin, Ankyrin, and NOMPC, respectively)13, 169, 170. Figure 2.9 shows the TRP family of receptors169. This dissertation focuses on the TRPV and TRPA subfamily for their roles in peripheral sensation, especially hot and cold pain13, 169, and their potential roles in the

EPR14, 59, 171.

Functional TRP channels are tetramers and each subunit has 6 transmembrane segments with a hydrophilic loop between the 5th and 6th transmembrane segments170, 172. Each of the hydrophilic loops end up in close proximity in the assembled tetramer to form the ionic conduction pore170. There are also often a series of ankyrin repeat domains (paired alpha-helices) that may function in protein-protein binding170. The N- and C- termini, both located intracellularly, are the most variable sites between channels and thus are most important in distinguishing channel properties and type170. TRP channels in general are only weakly voltage sensitive, however, sensitization to voltage changes may occur with stimulation by other modalities170. Most TRP channels are homotetramers, however the formation of heterotetramers may occur170.

Figure 2.9: Phylogenetic tree of the TRP superfamily. Source: Pendersen et al.169

Sensory TRP channels have been implicated as the key transducers of nociception and

TRPV, TRPM, and TRPA all likely play a role170. These TPR channels make up the majority of molecular detectors/transducers and have various degrees of stimulus specificity170. In their excellent and in-depth chapter on TRP channels and nociception, Mickle et al. supply a summarized figure (shown in Figure 2.10) of the various activators (caloric, chemical, and mechanical) for the major TRP channels. For reference, this dissertation investigates functional roles of SNPs in several TRP channels: TRPV1/3 and noxious heat (48°C), TPRA1 and noxious cold (3°C), and TRPV4 and mechanical force170.

Figure 2.10: Classification of thermosensing nociceptive TRP channels in mammalian sensory neurons. The upper row of individual boxes denotes chemical/mechanical activators of marked TRP channels. The lower panel depicts the magnitude of channel activity upon activation by temperature of the independent TRP channels shown. Source: Mickle et al.170

Although many of the TRP channels have been studied extensively, the genes for TRPV and TRPA are the TRP channels of interest in this dissertation and constitute the focus of the remainder of this review. The TRPV1, TRPV2, TRPV3, and TRPV4 channels make up the

TPRV subfamily with the TRPV1 the most extensively studied and well characterized of the group170. The TRPV1 receptor is expressed in neurons of the PNS and SNS, primarily in the

DRG and the nodose, sympathetic, and trigeminal ganglia (NG, SG, and TG) making up group

III and group IV fibers170. In the CNS the TPRV1 receptor is located in the SC and some specific areas in the brain (e.g. brain stem, NTS, and NA) while non-neuronal expression is limited but may include, the bladder, lungs, and blood vessels170. Activation of the TRPV1 channel by ligand binding can occur intra- or extra-cellularly depending mostly on the hydrophilic vs lipophilic nature of the ligand170. The pore region of the TRPV1 channel is critical to its intrinsic heat sensitivity, however the precise mechanism for opening in response to noxious heat is still unknown170, 173, 174. Once open, the TRPV1 receptor is a nonselective cation channel primarily associated with calcium movement and depolarization (calcium: sodium selectivity ratio of

~10)170, 175, 176, but may include the release of neuropeptides such as calcitonin gene-related peptide and substance P, which, with sustained TPRV1 opening and neuropeptide release, can lead to vasodilation and pro-inflammatory pathway activation. In a positive feedback loop scenario, this can lead to the increased expression of TRPV1 which can further increase the signal170. Modulation of TRPV1 sensitivity can occur via local pH changes; normal activation temperatures of >42°C decrease to ~35-37°C with tissue acidification between pH ~7.0-6.5 thus causing substantial activation from normal body temperature under conditions of, for example, tissue injury and inflammation170. This could have important effects in the TRPV1 mediated

46 sensory response to exercise, as both the local environmental temperature increases and the pH drops, suggesting a role for TPRV1 in afferent sensory feedback from muscle.

TRPV1 is implicated in pain responses; for example, capsazepine (a competitive TRPV1 inhibitor) decreases the pain response in rodents while TRPV1 knockouts have a decreased response to inflammatory pain170. Moreover, capsaicin creams have been used to alleviate joint pain by the method of TPRV1 receptor desensitization170, 177, 178.

TRPV2 has thus far been primarily studied in rodents and is sequentially similar to

TPRV1; however, it does not appear to contribute significantly to nociception170. TRPV3 can be found in DRG nerves, SC, and brain (among others), can be activated by warm temperatures (32-

40°C), and can form homo- or heterotetrameric (with TRPV1) channels170. Its role in nociception is controversial because of interspecies differences in tissue expression and mode of activation170.

Finally, the TRPV4 channel, involved in Ca2+ signaling and ubiquitously expressed, was one of the first identified TRP channels and functions as a detector of osmotic changes, pressure, shear stress, and energy homeostasis in neurons and in muscles170, 178, 179, 180.

The TRPA1 channel, the final thermosensitive TPR channel to be discussed, is named for the large number of Ankyrin repeats on the N-terminus (~18 compared to 4-6 in TRPC and TRPV) and was not cloned until 2003170, 181. These domains are posited to function in trafficking to, or insertion in, the membrane and probably play a role in mechanotransduction, although their specific function is far from understood170. TRPA1 expression is similar to TRPV1 and can be found in the DRG, TG and NG and in peripheral nerves is primarily found on unmyelinated and lightly myelinated group III and IV nerves170, 181, 182, 183. Coexpression of TRPA1 and TRPV1 is common in sensory nerves, but depends on the tissues involved. For example, only ~10% skin

47 neurons coexpress both receptors. In the CNS, TRPA1 is expressed on the superficial laminae of the SC causing the release of glutamate184. TRPA1 is also expressed in the ANS where it can cause vasodilation (in the skin of mice) and dose-dependent decreases in BP with IV injection of cinnamaldehyde (a TRPA1 agonist), suggesting a role in autonomic reflexes and neurovascular responses170, 185

Numerous modulators, regulators, and agonists, both endogenous and exogenous, affect

TRPA1 channels, and can be broadly categorized as electrophilic (positively charged) and non- electrophilic170. Electrophilic modulators act on the cysteine rich N-terminus causing activation via an as-yet undetermined mechanism170. Some examples of electrophilic modulators are mustard oil, wasabi, and reactive oxygen species-ROS170. Human (but not rodent) TRPA1 is sensitive to decreased pH and noxious cold and the response is localized to the transmembrane domains and pore loop of the channel170, 186, 187, 188. TRPA1 is also important in, but not required for, mechanotransduction in, for example, the skin and gastrointestinal tract170.

Selected Genes

The previous section focused primarily on relevant receptors and their roles in humans and other species, with specific focus on protein/amino acid- based structures, without quantification of protein structure, gene expression (protein synthesis or messenger ribonucleic acid, mRNA), or tissue content of functional proteins. Germane to this dissertation is additional consideration of the genetic variation when functional sensory receptors are expressed.

Genes, the DNA unit of heredity, are contained on chromosomes and are comprised of segments of exons (encoded nucleotide sequences) and introns (not encoded nucleotide sequences). Proteins are produced from peptide bonds between individual amino acids via transcription (DNA→RNA) and translation (RNA→proteins). The sequence of the gene therefore

48 defines the primary sequence of the protein, which helps defines the shape and function of the folded protein, e.g., the shape and function of an ion channel protein. A single nucleotide polmorphism is a change in a single DNA nucleotide (A- adenine, C-cytosine, G-guanine, and T- thymine) which necessarily changes the overall sequence of the DNA. This change may

(nonsynonymous SNP), or may not (synonymous SNP), cause a change in the amino acid sequence of the corresponding protein. A missense mutation is a type of nonsynonymous mutation that codes from an alternative amino acid while a nonsense mutation codes for a stop codon.

Various alleles (alternative forms of the same gene) and haplotypes (combinations of alleles) may affect channel function, and ultimately BP/HR responsiveness to autonomic stimuli.

Therefore, details of the genes involved in coding for the ion channel receptors are also of interest.

Outlined below are the genes that contain SNPs included in the studies incorporated in this dissertation. Each gene encodes an ion channel. Basic information, chromosome location, and some gene product information for each gene is referenced verbatim from the “GeneCards®: The

Human Gene Database” 189. The “Karyotype View in the Ensembl genome browser displays the set of chromosomes for a species, including the centromere and banding pattern as they would appear using light microscopy. Dark bands indicate heterochromatin, light bands indicate homochromatin”190 and the red marker indicates the location of the gene.

ASIC1- Acid Sensing Ion Channel Subunit 1

Figure 2.11: The Karyotype view of ASIC1. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=ASIC1191

GeneCards®:ASIC1191 This gene encodes a member of the acid-sensing ion channel (ASIC) family of proteins, which are part of the degenerin/epithelial sodium channel (DEG/ENaC) superfamily. Members of the ASIC family are sensitive to amiloride and function in neurotransmission. The encoded proteins function in learning, pain transduction, touch sensation, and development of memory and fear. Alternatively spliced transcript variants have been described.

Isoform 2 and isoform 3 function as proton-gated sodium channels; they are activated by a drop of the extracellular pH and then become rapidly desensitized. The channel generates a biphasic current with a fast inactivating and a slow sustained phase. Has high selectivity for sodium ions and can also transport lithium ions with high efficiency. Isoform 2 can also transport potassium, but with lower efficiency. It is nearly impermeable to the larger rubidium and cesium ions. Isoform 3 can also transport calcium ions. Mediates glutamate- independent Ca2+ entry into neurons upon acidosis. This Ca2+ overloading is toxic for cortical neurons and may be in part responsible for ischemic brain injury. Heteromeric channel assembly seems to modulate channel properties. Functions as a postsynaptic proton receptor that influences intracellular Ca2+ concentration and calmodulin-dependent protein kinase II phosphorylation and thereby the density of dendritic spines. Modulates activity in the circuits underlying innate fear.

Acid-sensing ion channels (ASIC) are members of the ENaC/Deg (epithelial amiloride- sensitive Na+ channel and degenerin) family of ion channels and are widely expressed in neurons of the central and peripheral nervous system. Potentiated by Ca2+, Mg2+, Ba2+ and multivalent cations. Inhibited by anti-inflammatory drugs like salicylic acid (By similarity). Potentiated by FMRFamide-related neuropeptides. PH dependence may be regulated by serine proteases.

Size: 528 amino acids Molecular mass: 59909 Da Quaternary structure: Homotrimer or heterotrimer with other ASIC proteins (by similarity).

ASIC2- Acid Sensing Ion Channel Subunit 2

Figure 2.12: The Karyotype view of ASIC2. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=ASIC2192

GeneCards®: ASIC2192 This gene encodes a member of the degenerin/epithelial sodium channel (DEG/ENaC) superfamily. The members of this family are amiloride-sensitive sodium channels that contain intracellular N and C termini, 2 hydrophobic transmembrane regions, and a large extracellular loop, which has many cysteine residues with conserved spacing. The member encoded by this gene may play a role in neurotransmission. In addition, a heteromeric association between this member and acid-sensing (proton-gated) ion channel 3 has been observed to co-assemble into proton-gated channels sensitive to gadolinium. Alternative splicing has been observed at this locus and two variants, encoding distinct isoforms, have been identified.

Cation channel with high affinity for sodium, which is gated by extracellular protons and inhibited by the diuretic amiloride. Also permeable for Li+ and K+. Generates a biphasic current with a fast inactivating and a slow sustained phase. Heteromeric channel assembly seems to modulate.

Size: 512 amino acids Molecular mass: 57709 Da Quaternary structure: Homotrimer or heterotrimer with other ASIC proteins (by similarity).

P2RX4- Purinergic Receptor P2X Member 4

Figure 2.13: The Karyotype view of P2RX4. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=P2RX4193

GeneCards®: P2RX4193 The product of this gene belongs to the family of purinoceptors for ATP. This receptor functions as a ligand-gated ion channel with high calcium permeability. The main pharmacological distinction between the members of the purinoceptor family is the relative sensitivity to the antagonists suramin and PPADS. The product of this gene has the lowest sensitivity for these antagonists. Multiple alternatively spliced transcript variants, some protein-coding and some not protein-coding, have been found for this gene.

Size: 388 amino acids Molecular mass: 43369 Da Quaternary structure: Functional P2XRs are organized as homomeric and heteromeric trimers.

P2RX5- Purinergic Receptor P2X Member 5

Figure 2.14: The Karyotype view of P2RX5. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=P2RX5194

GeneCards®: P2RX5194 The product of this gene belongs to the family of purinoceptors for ATP. This receptor functions as a ligand-gated ion channel. Alternative splicing results in multiple transcript variants. Read-through transcription also exists between this gene and the neighboring downstream gene, TAX1BP3 (Tax1 binding protein 3).

Size: 422 amino acids Molecular mass: 47205 Da Quaternary structure: Functional P2XRs are organized as homomeric and heteromeric trimers.

P2RX7- Purinergic Receptor P2X Member 7

Figure 2.15: The Karyotype view of P2RX7. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=P2RX7195

GeneCards®: P2RX7195 The product of this gene belongs to the family of purinoceptors for ATP. This receptor functions as a ligand-gated ion channel and is responsible for ATP-dependent lysis of macrophages through the formation of membrane pores permeable to large molecules. Activation of this nuclear receptor by ATP in the cytoplasm may be a mechanism by which cellular activity can be coupled to changes in gene expression. Multiple alternatively spliced variants have been identified, most of which fit nonsense-mediated decay (NMD) criteria.

P2X receptors are members of the ligand-gated ion channel family that open in response to extracellular ATP. Each receptor is made up of a trimer of subunits (P2X1-7) all of which share the common structure of two transmembrane domains. Responsible for ATP- dependent lysis of macrophages through the formation of membrane pores permeable to large molecules. Could function in both fast synaptic transmission and the ATP-mediated lysis of antigen-presenting cells.

Size: 595 amino acids Molecular mass: 68585 Da Quaternary structure: Functional P2XRs are organized as homomeric and heteromeric trimers

TRPA1- Transient Receptor Potential Cation Channel Subfamily A Member 1

Figure 2.16: The Karyotype view of TRPA1. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=TRPA1196

GeneCards®: TRPA1196 The structure of the protein encoded by this gene is highly related to both the protein ankyrin and transmembrane proteins. The specific function of this protein has not yet been determined; however, studies indicate the function may involve a role in signal transduction and growth control.

Receptor-activated non-selective cation channel involved in detection of pain and possibly also in cold perception and inner ear function. Has a central role in the pain response to endogenous inflammatory mediators and to a diverse array of volatile irritants, such as mustard oil, cinnamaldehyde, garlic and acrolein, an irritant from tears gas and [vehicle] exhaust fumes. Is also activated by menthol (in vitro). Acts also as a ionotropic cannabinoid receptor by being activated by delta(9)-tetrahydrocannabinol (THC), the psychoactive component of marijuana. May be a component for the mechanosensitive transduction channel of hair cells in inner ear, thereby participating in the perception of sounds. Probably operated by a phosphatidylinositol second messenger system (by similarity).

The ankyrin-like receptor family (TRPA1) is a member of the transient receptor potential (TRP) superfamily of ion channels. TRPA1 is classed as a thermoTRP: a TRP channel which is gated by temperature and functions as a temperature transducer.

Size: 1119 amino acids Molecular mass: 127501 Da Quaternary structure: Homotetramer

TRPV1- Transient Receptor Potential Cation Channel Subfamily V Member 1

Figure 2.17: The Karyotype view of TRPV1. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=TRPV1197

GeneCards®: TRPV1197 Capsaicin, the main pungent ingredient in hot chili peppers, elicits a sensation of burning pain by selectively activating sensory neurons that convey information about noxious stimuli to the central nervous system. The protein encoded by this gene is a receptor for capsaicin and is a non-selective cation channel that is structurally related to members of the TRP family of ion channels. This receptor is also activated by increases in temperature in the noxious range, suggesting that it functions as a transducer of painful thermal stimuli in vivo. Four transcript variants encoding the same protein, but with different 5' UTR sequence, have been described for this gene.

Ligand-activated non-selective calcium permeant cation channel involved in detection of noxious chemical and thermal stimuli. Seems to mediate proton influx and may be involved in intracellular acidosis in nociceptive neurons. Involved in mediation of inflammatory pain and hyperalgesia. Sensitized by a phosphatidylinositol second messenger system activated by receptor tyrosine kinases, which involves PKC isozymes and PCL. Activation by vanilloids, like capsaicin, and temperatures higher than 42°C, exhibits a time- and Ca2+- dependent outward rectification, followed by a long-lasting refractory state. Mild extracellular acidic pH (6.5) potentiates channel activation by noxious heat and vanilloids, whereas acidic conditions (pH<6) directly activate the channel. Can be activated by endogenous compounds, including 12-hydroperoxytetraenoic acid and bradykinin. Acts as ionotropic endocannabinoid receptor with central neuromodulatory effects. Triggers a form of long-term depression (TRPV1-LTD) mediated by the endocannabinoid anandamine in the hippocampus and nucleus accumbens by affecting AMPA receptors endocytosis.

Channel activity is activated via the interaction with PIRT and phosphatidylinositol 4,5- bisphosphate (PIP2). Both PIRT and PIP2 are required to activate channel activity (by similarity). The channel is sensitized by ATP binding. Repeated stimulation with capsaicin gives rise to progressively smaller responses, due to desensitization. This desensitization is triggered by the influx of calcium ions and is inhibited by elevated ATP levels. Ca(2+) and CALM displace ATP from its binding site and trigger a conformation change that leads to a closed, desensitized channel. Intracellular PIP2 inhibits desensitization. The double- knot toxin (DkTx) from the Chinese earth tiger tarantula activates the channel and traps it in an open conformation (by similarity).

Vanilloids are a group of compounds, structurally related to capsaicin, thought to exert their actions via vanilloid receptors. The vanilloid receptor family (TRPV) is a member of the transient receptor potential (TRP) superfamily of ion channels, and have six members (TRPV1-6). Responses evoked by low pH and heat, and capsaicin can be antagonized by capsazepine.

Size: 839 amino acids Molecular mass: 94956 Da Quaternary structure: Interacts with PIRT (by similarity). Homotetramer (by similarity). Interacts with TRPV3 and may also form a heteromeric channel with TRPV3.

TRPV3- Transient Receptor Potential Cation Channel Subfamily V Member 3

GeneCards®: TRPV3198 This gene product belongs to a family of nonselective cation channels that function in a variety of processes, including temperature sensation and vasoregulation. The thermosensitive members of this family are expressed in subsets of sensory neurons that terminate in the skin, and are activated at distinct physiological temperatures. This channel is activated at temperatures between 22 and 40°C. This gene lies in close proximity to another family member gene on chromosome 17, and the two encoded proteins are thought to associate with each other to form heteromeric channels. Multiple transcript variants encoding different isoforms have been found for this gene.

Putative receptor-activated non-selective calcium permeant cation channel. It is activated by innocuous (warm) temperatures and shows an increased response at noxious temperatures greater than 39°C. Activation exhibits an outward rectification. May associate with TRPV1 and may modulate its activity. Is a negative regulator of hair growth and cycling: TRPV3-coupled signaling suppresses keratinocyte proliferation in hair follicles and induces apoptosis and premature hair follicle regression (catagen).

Size: 790 amino acids Molecular mass: 90636 Da Quaternary structure: May form a heteromeric channel with TRPV1. Interacts with TRPV1.

TRPV4- Transient Receptor Potential Cation Channel Subfamily V Member 4

Figure 2.19: The Karyotype view of TRPV4. Dark bands indicate heterochromatin, light bands indicate homochromatin, and the red marker indicates the location of the gene. Source: http://www.genecards.org/cgi-bin/carddisp.pl?gene=TRPV4199

GeneCards®: TRPV4199 This gene encodes a member of the OSM9-like transient receptor potential channel (OTRPC) subfamily in the transient receptor potential (TRP) superfamily of ion channels. The encoded protein is a Ca2+-permeable, nonselective cation channel that is thought to be involved in the regulation of systemic osmotic pressure. Mutations in this gene are the cause of spondylometaphyseal and metatropic dysplasia and hereditary motor and sensory neuropathy type IIC. Multiple transcript variants encoding different isoforms have been found for this gene.

Non-selective calcium permeant cation channel involved in osmotic sensitivity and mechanosensitivity. Activation by exposure to hypotonicity within the physiological range exhibits an outward rectification (PubMed:18826956, PubMed:18695040). Also activated by heat, low pH, citrate and phorbol esters (PubMed:18826956, PubMed:18695040). Increase of intracellular Ca2+ potentiates currents. Channel activity seems to be regulated by a calmodulin-dependent mechanism with a negative feedback mechanism (PubMed:12724311, PubMed:18826956). Promotes cell-cell junction formation in skin keratinocytes and plays an important role in the formation and/or maintenance of functional intercellular barriers (By similarity). Acts as a regulator of intracellular Ca2+ in synoviocytes (PubMed:19759329). Plays an obligatory role as a molecular component in the nonselective cation channel activation induced by 4-alpha-phorbol 12,13-didecanoate and hypotonic stimulation in synoviocytes and also regulates production of IL-8 (PubMed:19759329). Together with PKD2, forms mechano- and thermosensitive channels in cilium (PubMed:18695040). Negatively regulates expression of PPARGC1A, UCP1, oxidative metabolism and respiration in adipocytes (By similarity). Regulates expression of chemokines and cytokines related to proinflammatory pathway in adipocytes (By similarity). Together with AQP5, controls regulatory volume decrease in salivary epithelial cells (By similarity).

Size: 871 amino acids Molecular mass: 98281 Da Quaternary structure: Homotetramer (probable). Self-associates in a isoform- specific manner Isoforms 1/A and 5/D but not isoform 2/B, 4/C and 6/E can oligomerize.

CHAPTER 3: POPULATION BLOOD PRESSURE RESPONSIVENESS - DEFINING LOW AND HIGH RESPONSE GROUPS TO STATIC HANDGRIP EXERCISE

Introduction

SHG is routinely utilized to investigate neural mechanisms mediating exercise cardiovascular responses12, 31, 32. The pattern of BP response is predictable: SBP and DBP rise in proportion to exercise duration and intensity33. Arresting BF to the contracting muscle magnifies the response35, and partially preserves it after exercise34, 36. Even when a relatively small proportion of total muscle mass is engaged, static exercise generally leads to robust increases in BP; particularly when intramuscular pressure is sufficient to impede BF37. Restricting or arresting BF artificially magnifies the effect, and doing so after exercise maintains, at least in part, the exercise response. Accordingly, PECA has been used for nearly 80 years to investigate how the skeletal muscle interstitial environment can modify BP34.

BP control is regulated acutely during exercise by three interacting neural mechanisms:

CC, the arterial baroreflex, and the EPR11. The EPR is a powerful BP raising reflex that is activated by two components: the muscle metaboreflex and the muscle mechanoreflex, which activate the sympathetic nervous system by responding to increases in interstitial byproducts of muscle metabolism and mechanical deformation, respectively12. PECA isolates the metabolic component of the EPR from the mechanical component and CC.

While the pattern of SHG and PECA BP responses are generally predictable, the magnitude of responses between individuals and populations vary considerably. Cardiovascular responses to static exercise have been reported to differ in a number of conditions, including hypertension200,

201, 202, prehypertension 203, 204, 205, peripheral arterial disease161, 162, dilated cardiomyopathy168,

61 heart failure206, 207, McArdle’s disease208, 209, type two diabetes210, chronic kidney disease211, 212, end stage renal disease213, and chronic obstructive pulmonary disease214. Thus, population norms for responses to SHG with PECA could have prognostic and diagnostic utility if the “normal” response is known.

Normative data should be obtained with standardized procedures that can be easily reproduced in many laboratories, consistent with the National Institutes of Health initiatives to improve rigor and reproducibility in biomedical research215. With this backdrop in mind, we performed this study with two purposes: 1) to develop population standards for the BP responses to SHG with PECA and 2) to suggest a set of best-practice guidelines for researchers interested in using this common paradigm. We used two complementary approaches: 1) construction of a hypothetical distribution of responses based on analysis of published literature; and 2) experimental validation of these findings. We hypothesize that MAP response to SHG with PECA will be normally distributed in a young, healthy population.

Methods

Systematic Review

To identify similar paradigms of SHG exercise followed by PECA, we conducted a systematic review using PubMed with the search terms “exercise pressor reflex”,

“metaboreflex”, “ergoreflex”, “reflex”,

“handgrip”, “static”, “isometric”, “exercise”,

“post-exercise circulatory arrest”, “post- exercise circulatory occlusion”, and “post- exercise ischemia”216 Results were filtered for “species- human” and no review articles Figure 3.1: Systematic Literature Review. The process of elimination for articles to be included or case studies were included. We identified in the prediction of population norms via literature review. 152 articles were initially 152 studies (publication dates 1985 – 2015) identified that included static exercise and post- exercise circulatory arrest; only four met all that quantified both the pressor response to criteria for inclusion. handgrip exercise and the response during PECA in healthy humans aged 11-44 years. Although a considerably larger number of studies are available using static exercise without PECA, we included only studies using SHG with PECA.

Figure 3.1 depicts the filters we employed to develop a homogenous data set. From the initial pool of 152 studies we included exercise mode (static), muscles engaged (wrist flexors), exercise intensity (30-33% MVC), BP measured continuously from a peripheral artery (finger, wrist or antecubital fossa), duration of exercise (three min), time of onset of PECA (no more than

10 sec prior to cessation of exercise), duration of occlusion (at least two min), and method of data presentation (absolute values vs. changes from baseline).

For each study, the results from the control group were used for bootstrapping. A data- informed hypothetical distribution was generated based on the mean and standard deviation (SD) of the control group responses during the PECA phase from each publication (Minitab 17). With the export variables set to the means and SDs from the respective publications, normal distributions were generated for BP responses from each study. These values were aggregated to yield 46 estimates of the original data, weighted according to the sample size of each study. This pool of

46 observations was then re-sampled with replacement to bootstrap a theoretical population distribution with a mean and SD approximating the original estimates (Figure 3.2, SPSS 24). The population mean and distribution was then validated experimentally.

Validation Study

The bootstrapped distribution should reflect the population as whole. To test this hypothesis, a large sample of subjects was recruited from the local population to participate in a validation study. Having provided written, informed consent, a total of 152 adults were screened.

Subjects selected to participate were recreationally active but were excluded if they were training for competition. Additional exclusion criteria included: pregnancy (according to pregnancy test on the day of testing); oral contraceptive use; self-report of smoking in the last 30 days; use of BP medication or any other drug that may affect BP regulation; history of asthma, asthma symptoms or lung disease other than asthma; history of hypertension, resting tachycardia, or a body mass index (BMI) of ≥30 kg/m2. The final sample consisted of 101 subjects (65 men and 36 women).

Characteristics of the group are presented in Table 3.1.

Range Mean (SD) Age* (years) 18 − 42 22.4 (4.4) Height (m) 1.48 − 1.89 1.72 (0.10) Weight (kg) 44.4 − 98.8 70.1 (12.1) BMI (kg/m2) 18.5 − 29.7 23.6 (2.7) Gender* Women 36 Men 65 Handedness* Right 91 Left 10 Ethnicity* African American 4 American Indian 0 Asian/Pacific Islander 16 Caucasian 69 Hispanic/Latino 2 >1 reported 9 Not reported 1 Table 3.1: Subject characteristics n=101. BMI- body mass index, SD-standard deviation, *-self- reported data.

Standardization of Static Handgrip Exercise

All tests were conducted in the morning to minimize any effect of circadian rhythms on resting BP. All subjects were at least 30 days free from smoking, 24 hr free from heavy exercise,

12 hr free from caffeine and alcohol, and fasted overnight. Subjects assumed a semi-recumbent posture (30° head-up and 20° legs-up) in a quiet, dim, temperature controlled (22°C) room for at least 15 min prior to the start of testing. MVC was determined as the best out of three attempts using the dominant hand with at least one min between each attempt (Smedley Hand

Dynamometer; Stoelting). Subjects were then instrumented for HR (ECG) and BP (Omron HEM-

705CP and Finometer Midi FMS). A 10 cm wide pneumatic cuff was wrapped around the upper arm on the dominant side for PECA (Hokanson: AG101 Cuff Inflator Air Source, E20 Rapid Cuff

Inflator).

After at least 15 min rest during instrumentation and 5 min of quiet rest following instrumentation, BP was measured in triplicate (1 min between each measurement) via oscillometry from the brachial artery (Omron). Continuous BP measurement was then initiated

(Finometer, TNO, Netherlands). The placement and tension of the finger cuff was adjusted until the optoplethysmographic and oscillometric MAP were in close agreement.

Procedure

The general outline for the continuous data collection was as follows: 5 min of quiet rest,

3 min of SHG at 30% MVC, inflation of the pneumatic occlusion cuff 5 sec before exercise termination, 2 min of PECA, and 3 min of recovery.

The following steps were included to ensure data quality and reproducibility:

1. Handgrip dynamometer calibration: The handgrip dynamometer voltage output was

linearly related to force applied without hysteresis, confirmed by the device’s integrated

analog scale and voltage output in response to known weights.

2. Position of arm: From the semi-recumbent position the arm was abducted 90° away from

the midline at the shoulder and placed on a level table horizontal with the floor and level

with the mid-axillary line. The brachial and antebrachial regions were supported with

padding. The handgrip dynamometer was secured vertically on the table and therefore the

wrist was slightly pronated to hold the device. Grip size was adjusted so that force was

applied by the proximal palm and the pads of the middle phalanges. The thumb was

opposed. Subjects rested their hand near the device (without holding it) during the resting

data collection. With 20-30 seconds before the initiation of exercise, subjects were

instructed to rest their hand loosely around the dynamometer in preparation for SHG.

3. Subject instruction and feedback. Before data collection began we reviewed each stage of

the protocol with subjects. They were instructed to remain still; to relax all muscles (except

those for gripping) during the entire duration of the protocol; and to breathe regularly,

avoiding Valsalva maneuvers.

4. Force feedback as a percent of maximum was displayed on a screen with the target force

set to 30% MVC. During the three min of exercise the subjects were verbally motivated as

necessary to maintain 30% MVC. Subjects were encouraged to maintain their grip and not

“re-grip” the device once the exercise started to minimize any effect of the muscle pump.

Exercise continued for exactly three min. The trial was rejected if the subject failed to

maintain at least 27% MVC averaged for three min or adjusted their grip multiple times.

5. Rest time prior to experiment. The handgrip protocol was initiated after at least 15 min of

rest and five min of complete quiescence. Continuous BP measurements commenced 5 min

before the start of exercise.

6. The brachial occlusion cuff was inflated five sec prior to cessation of exercise. The inflation

pressure was 250 mmHg or at least 50 mmHg greater than the peak exercise SBP to ensure

complete occlusion for the full two min. Subjects released the handgrip device and rested

their hand near the dynamometer, but all other movement was discouraged.

7. During PECA, subjects were reminded to lay still and breathe regularly, avoiding talking

or Valsalva maneuvers.

8. Using a visual analog scale, subjects provided a rating of perceived pain during the

protocol, with zero representing no pain whatsoever and ten being unbearable. Perceived

pain was recorded twice during the 5 min of rest and then once per min during the

remainder of the protocol.

Data Processing

BP, force, and ECG data were sampled at 1 kHz and recorded for offline analysis

(ADInstruments PowerLab 16/30 V.5.5.6 and LabChart 7; Castle Hill, Australia). All exercise and

PECA data were binned in 1 min averages. SBP, DBP, and MAP were extracted beat-by-beat from the maximum, minimum, and time integral, respectively, of the Finometer trace. Changes from rest (∆) are presented relative to the average of the 5 min prior to exercise.

Population Characteristics and Statistical Analysis

Bootstrapped distributions from the systematic review and actual distributions from the validation study were described with quartiles, median, skewness, and kurtosis and data sets were then evaluated for normality using the Shapiro-Wilk test (SPSS 24). Differences between distributions were determined using paired t-tests with Bonferroni correction for multiple comparisons where necessary (rest vs SHG, rest vs. PECA, and SHG vs. PECA). Quartiles were based on the properties of the normal curve fit to each distribution.

Temporal comparisons were made relative to five minutes of rest (control) using paired t- tests with Bonferroni correction for multiple comparisons. Significant changes were therefore defined as P<0.00625.

Potential predictors for the cardiovascular responses to SHG and PECA were evaluated using Principal Components Analysis (Minitab 17). This was done by entering subject characteristics (age, sex, race, BMI, etc.), subject handgrip performance (average % MVC, average kilopond (kp) force, etc.) and cardiovascular responses (MAP during SHG and PECA) for analysis.

Using Principal Components Analysis and Cluster Analysis, the main subject characteristics and handgrip performance parameters that related to cardiovascular responses were identified.

Subsequently, to improve generalizability of the results, the parameters that clustered together were entered into a stepwise regression (Minitab 17) to identify the main predictors for the BP response to PECA following SHG at 30% MVC. The model was optimized to a minimal set of predictors.

Results

A total of four studies217, 218, 219, 220 met all protocol criteria for the systematic review. Table

3.2 summarizes the methods used in each study. An insufficient number of studies provided data from both SHG and PECA phases of the protocol; therefore, responses based on the literature data could be summarized for PECA only.

1st BP Ex % Min N Age Year Posture MinEX Author Mode Tech MVC PECA (male) (SE) 13 11.83 2010 Dipla220 fina SHG n/r 30 3 3 (13) (0.22) 11 25.1 2012 Dipla219 fina SHG n/r 30 3 3 (11) (0.5) 13 24.8 2012 Dipla217 fina SHG seated 30 3 3 (13) (1.4) 9 25 2009 Ogoh218 fina SHG semi-r 30 3 3 (n/r) (1) Table 3.2: Included Publication Details. Details of the four publications that meet all inclusion criteria. Three of the four publication were published by the same first and last author. BP tech- BP measurement technique, fina- finger photoplethysmography, Ex mode- exercise mode, SHG- static handgrip, % MVC- percent maximum voluntary contraction, MinEx- min of exercise, MinPECA- min of post-exercise circulatory arrest, n/r- not reported, N- total number of volunteers in the control group.

Figure 3.2 shows resampled distributions (method explained under “systematic review” section) of data points for the final min of PECA with descriptive statistics. The change in MAP during 3 min of PECA after 3 min of SHG at 30% MVC was a mean increase of 20 mmHg (95% confidence interval 18-23 mmHg).

Figure 3.2: Literature Review Consolidation of Results. Distribution of ∆MAP during PECA responses for 4 studies- (n=46 total). The observations were re-sampled with replacement to bootstrap a theoretical population distribution with a mean and standard deviation reflecting the study pool. Dashed curve is the overlaid normal distribution. Gray bar and dashed vertical line represent the 25th – 75th percentile range and 50th percentile, respectively. The curve did not differ from normality.

Our validation study of similarly young, healthy, active individuals revealed that the typical response was greater than predicted. Figure 3.3 summarizes responses at rest, the third min of

SHG, and the second min of PECA for MAP and HR. All data were normally distributed. Figure

3.4 depicts the same data as changes from baseline. Again, responses were normally distributed.

Figure 3.3: Blood Pressure and Heart Rate Absolute Value Distributions. Distributions of resting blood pressure and heart rate, as well as, the absolute responses to three min of SHG and two min of PECA. Dashed curve is the overlaid normal distribution. Gray bars and dashed vertical lines represent the 25th – 75th percentile ranges and 50th percentiles, respectively. All curves did not differ from normality.

Figure 3.4: Blood Pressure and Heart Rate Changes from Control Distributions. Distributions of heart rate and blood pressure in response to three min of SHG and two min of PECA. Values are changes from rest (∆). Gray bars and dashed vertical lines represent the 25th – 75th percentile ranges and 50th percentiles, respectively. All curves did not differ from normality.

Differences between distributions as determined by paired t-tests with Bonferroni correction are shown in Table 3.3. Each comparison between rest, exercise, and PECA were highly significant.

Comparison MAP P-value ∆MAP P-value HR P-value ∆HR P-value R vs. E3 <0.001 - <0.001 - R vs. P2 <0.001 - <0.001 - E3 vs. P2 <0.001 <0.001 <0.001 <0.001 Table 3.3: Differences between distributions determined using paired t-tests with Bonferroni correction for multiple comparisons where necessary (rest vs SHG, rest vs. PECA, and SHG vs. PECA). R=the average of 5 min rest. E3=the third min of SHG. P2=the second minute of PECA. Significance=P<0.01.

Temporal changes for MAP and HR are shown in Figure 3.5. HR (panel A) was significantly increased throughout exercise with an average increase of 21±11 BPM for the third min. Average HR remain significantly elevated (4±7 BPM) during PECA. Notably, % of subjects had a HR lower than baseline during PECA. Average HR returned to baseline early during recovery and was significantly less than baseline during the third min of recovery (-2±4). Panels

C and D display the results for SBP, DBP, and MAP. Average resting BP was 120±12 mmHg,

61±8 mmHg, and 79±9 mmHg, respectively. All BP variables were significantly increased compared to rest for the entire duration of the protocol. The largest average BP increase occurred during the third min of SHG (40±18 mmHg, 28±10 mmHg, and 34±12 mmHg, respectively). The

BP elevation during the second min of PECA was approximately 85% of the third min of exercise

(35±16 mmHg, 23±10 mmHg, and 29±12 mmHg, respectively).

Figure 3.5: Absolute and Relative Heart Rate and Blood Pressure Responses Over Time. Absolute heart rate response (A) change in heart rate from the average of 5 min of rest (B) absolute blood pressure response (C), and the change in blood pressure (D) over time. In C and D, SBP is listed on top, DBP on bottom, and MAP in the middle. Statistical comparisons for HR and MAP measured for a change from the average of 5 min of rest. *=P<0.00625 (Bonferroni adjusted P-value of 0.05 adjusted for eight comparisons). Error bars represent standard deviation.

Ratings of perceived pain are shown in Table 3.4. Pain ratings were normally distributed, and were significantly greater than rest throughout SHG, PECA, and the first min of recovery.

Notably, average ratings were highest during PECA.

Rest Exer1 Exer2 Exer3 PECA1 PECA2 Rec1 Rec2 Rec3 Pain 0(0) 1(0.9) 3(1.4) 4(2.0) 5(1.8) 5(2.1) 1(1.4) 0(0.9) 0(0.6) MAP 79(9) 87(12) 100(14) 113(16) 105(14) 108(15) 88(11) 84(11) (84(11) HR 61(9) 72(11) 77(12) 83(13) 66(10) 66(11) 62(8) 61(8) 60(9) Table 3.4: Rating of Perceived Pain. Perceived pain on a scale from 1-10 was asked twice during rest and once per min during the remainder of the protocol. Pain levels that were significantly higher compared to rest are highlighted in grey- P<0.00625 (Bonferroni adjusted P-value of 0.05 adjusted for eight comparisons). Exer#, SHG exercise during min 1, 2, and 3 respectively. PECA#, Post-exercise circularity arrest min 1 and 2 respectively. Rec#, recovery min 1, 2, and 3, respectively. Data reported as mean (SD).

The relative and absolute exercise intensities for the 3 min of SHG was 29.1% MVC and

12.8 kp, respectively. Trials were excluded if average % MVC was <27% for the 3 min. Figure

3.6A shows that there was no correlation between average % MVC achieved during the exercise and the ∆MAP from baseline during the second min of PECA (R2=0.01, p=0.332). To assure that subjects were maintaining their grip, we related the variation in force during SHG to BP during

PECA. Figure 3.6B illustrates a positive correlation between the average SD of SHG force during the 3 min of gripping and the ∆MAP from baseline during the second min of PECA (R2=0.055, p=0.018.

Figure 3.6: Correlation between Relative Grip Force, Grip Variation, and Blood Pressure Response. Panel A shows that there is no correlation between the average % MVC that each subject achieved during the three min of SHG and the ∆MAP during the second min of PECA; i.e. there was no relation between grip maintenance and the MAP response. Panel B shows the significant positive correlation between SHG variation (kp) and the ∆MAP during the second min of PECA.

To gain insight about the variable response in BP response to SHG we used Principal

Components Analysis and Cluster analysis to generate a loading plot identifying uncorrelated variables. Factors that related to physical size (i.e., grip-force, BMI, height, weight, and male sex) clustered together. While other factors were uncorrelated (i.e., handedness, age, grip variance during squeezing, and ethnicity). Stepwise regression indicated that absolute exercise intensity

(average force in kp) during the 3 min of SHG was the sole significant predictor of the BP response

(R2=0.37), as shown in Figure 3.7. Attempts at adding in a number of other predictors (for example, sex, BMI, and height) did not substantially improve the model. Finally, in Figure 3.8 the BP residuals (BPR) are plotted as a histogram. The properties of the normal curve define 25% and

75% quartiles as a BPR of -6 and +6 mmHg, respectively, thus defining the low and high response groups to SHG and PECA.

Figure 3.7: Correlation between Average Grip Force and Blood Pressure Response. Panel A shows the significant correlation between the average SHG (kp) that each subject achieved during the 3 min and the ∆MAP during the second min of PECA. Absolute force accounted for approximately 37% of the variability in blood pressure response. Dashed lines represent the 25th – 75th percentile range of the normal distribution created by this sample of n=101. Panel B shows the individual blood pressure residuals sorted in ascending order- range: (-21) – 29 mmHg.

Discussion

This study was conceived to describe the expected range of BP response to SHG and PECA in a young, healthy population. Using two complementary approaches – systematic review and experimental validation with a large cohort of individuals – we show that the BP and HR responses to SHG at 30% MVC and PECA are normally distributed. Literature values and experimental findings were similar. Quantizing the data in quartiles, the middle 50% of the population had an increase in MAP of 25 – 43 mmHg during the third min of SHG, and MAP remained 22 – 38 mmHg above resting during the second min of PECA.

Here we review the opportunities and challenges afforded by each approach, and offer a series of recommendations to improve rigor and reproducibility of data collected using this common paradigm.

Systematic Review

A summary of available literature is limited by inconsistency in the ways that a small exercise paradigm are implemented and the manner in which the data are reported. Of the 152 original articles investigating the response to exercise with PECA (Figure 3.1), more than half were excluded because the relative contraction percent was dissimilar. Observations in our lab and published research are in general agreement that relative exercise intensity (i.e., % MVC maintained during exercise) is positively correlated to the BP response33. Therefore, any accurate literature comparisons must be done with protocols that utilize the same relative exercise intensity goal.

Modes of exercise (e.g. static/isometric, dynamic, intermittent static, and artificial ischemia- via cuff occlusion) affect the interstitial environment of the exercising muscle

81 differently. Feedback from the periphery via afferent nerves (via group III and group IV fibers) is highly dependent on muscle metabolic state49. Therefore, results published from protocols using modes of exercise other than static are probably not comparable. This exclusion criterion eliminated 97 (63%) of publications. Further exclusion of studies without continuous BP measurement removed another 66 publications. Of the remaining 31 publications, 20 were excluded because of differences in exercise duration. Since exercise BP is positively correlated with duration of SHG,33 valid comparisons can only be made between studies with similar exercise duration.

The final limiter for the remaining 11 studies reflected data reporting practices.

Cardiovascular responses are depicted in various combinations of absolute values and changes from baseline at different time points in the protocol; rarely do publications give all data points as both absolute values and changes from baseline. We included studies only when changes from control were reported.

From the remaining four studies (table 3.1), our reconstructed distribution of MAP responses to PECA after 3 min of SHG was somewhat lower than what we subsequently measured experimentally (∆20 mmHg predicted vs. ∆29 mmHg observed). This discrepancy may be for any of the following reasons: First, data points may not have been independent when studies were reported from the same lab. Second, the control group of one study consisted of healthy boys (~12 yr old)220. Inclusion was justified because a similar study by the same group217 concluded that there was no difference in the MAP response to PECA between boys and men. However, Turely221

(using a similar but not identical protocol) has shown that the results may differ between men and boys with the latter having a smaller percent increase in SBP. Third, three of the studies217, 219, 220 averaged the entire three min of PECA rather than using one min bins. Although averaging the

82 entire PECA period would likely lower the final results slightly, we observed no difference between the first min and second min of PECA. Finally, posture of the subjects was not reported in two studies. We did not anticipate large changes in the MAP response to PECA during sitting vs. supine222, vs. semi-recumbent, however modulating perfusion pressure to the exercising muscle by changing the hydrostatic gradient can affect muscle fatigue during exercise223. It is plausible that sitting with the forearm below heart level217 increased the perfusion pressure compared to a supine or semi-recumbent posture. In the former case it would be likely that the BP responses would be attenuated due to the “gravity assisted” increase in perfusion.

Considerations from the Validation Study

Systematic review failed to identify sufficient number of similar studies from which the responses during SHG could be aggregated. We examined several methodological considerations that might affect reproducibility. Subjects were observed carefully to assure that they maintained their grip and target force throughout the exercise period. Figure 3.6 demonstrates that subjects consistently maintained their relative exercise intensity. There was no relation (p=0.332) between

% MVC achieved (range: >27% and <31%) during the 3 min of SHG and the ∆MAP during the second min of PECA. On the other hand, increasing SHG variability was modestly but significantly related to the BP response, suggesting that subjects who struggled to maintain their grip had greater engagement of the EPR, not less as one would expect if the grip was intermittently released. That said, the protocol was well-tolerated overall; only seven subjects could not complete the three min. Data from these subjects was excluded to ensure that success was not a determining factor in BP response.

Muscle mass activation is an important factor in the BP response to static exercise224, 225.

Although the mass of the wrist flexors was not directly measured in this study, cluster analysis revealed that functional measures (i.e. maximal handgrip force) and scaling variables related to muscle mass (i.e., height, weight, body weight) were similarly associated with the magnitude of the pressor response. Average SHG force (kp) for the three min (the only significant predictor identified during stepwise regression) explained a modest but statistically significant proportion

(approximately 37%) of the MAP pressor response during the second min of PECA (Figure 3.7).

The absolute force of contraction had other ramifications for evaluation of these experiments. For example, absolute handgrip force explained all of the gender difference in the pressor response. With this covariate, there was no statistical difference in the pressor response between men and women.

The validation study confirmed that the BP and HR responses to SHG exercise and PECA are normally distributed; highly variable between individuals, even under precisely controlled conditions; and dependent on absolute exercise intensity as defined by handgrip force. Using our sample of n=101, we plotted the distribution of residuals from the linear regression of ∆MAP during PECA vs handgrip force (Figure 8). Analysis of residuals yielded a SD of 9 mmHg, and

25, 50 and 75% quartiles of -6, 0, and 6mmHg, respectively. In other words, 50% of young healthy people fall within ±6 mmHg of the force-corrected response.

These findings emphasize the need to account for absolute exercise intensity when characterizing population norms for SHG. For example, consider two subjects; both performing

SHG at 30% MVC: One generates high handgrip force and robust responses to SHG and PECA while the other generates much lower force and the identical BP response. Whereas the BP response for the stronger subject may be considered typical in the context of the population, the

84 response of the weaker subject would be characterized as disproportionately large relative to their absolute exercise intensity.

The dependence of cardiovascular responses on absolute exercise intensity, even with a small muscle mass, raises the intriguing notion whether the SHG paradigm should be re- conceptualized in terms of absolute exercise intensity. Such a deviation from decades-old practice is probably not warranted for two reasons: First, an absolute force sufficient to engage the EPR is likely to produce wide disparities in time to fatigue, leading to premature subject dropout. Second,

Principal Components Analysis failed to identify any orthogonal predictor beyond absolute handgrip force; simple linear regression of the average handgrip force was sufficient to model this portion of the variance.

The source of the remaining variation remains unexplained. It is possible that differences in receptors on muscle sensory afferents, central determinants of sympathetic activation, neural- vascular transduction, and/or vascular smooth muscle responsiveness could be partially responsible for the activation of the EPR in normal subjects. Exercise training or disease would likely modify these responses via any combination of these mechanisms. All are testable hypotheses worthy of further investigation.

Taken together, the results indicate that a systematic review of SHG with PECA is a challenging proposition at best; reproducibility of responses is seriously affected by differences in mode, intensity of exercise, data timing, recording, and reporting, and differences in samples from the population. Further considerations for scientifically rigorous and reproducible protocols are enumerated below.

Best practices for Standardization

Systematic review highlighted the fact that among hundreds of published studies using

SHG and PECA in some variation and thousands using some static exercise alone or in combination, there is considerable dissimilarity in protocols. Data can be more easily replicated and compared across studies for both clinical application and cardiovascular research if the paradigm is better standardized. When designing the protocol for the study, we considered exercise mode, duration, and intensity as well as a number of methodological details. We offer the following recommendations for nine specific issues, and provide our rationale to standardize SHG protocols:

1. Mode: Static Exercise. Static exercise is well tolerated by most people, and generally

produces a robust increase in BP. Tissue pressures increases in proportion to force and

partially or fully occludes blood to the exercising muscle. It is important that the static

exercise is maintained during the entire duration of the protocol as decreasing the force

even temporarily would to allow an, albeit brief, restoration of BF and the potential release

of some sequestered metabolites.

2. Intensity: 30% Maximum Voluntary Contraction. We chose a relative intensity of 30% of

maximum rather than an absolute force to elicit a more homogenous duration of exercise

commensurate with each subject’s ability. 30% of maximum is a level that would be

challenging and produce substantial metabolite accumulation, but most subjects could

complete to three min. We utilized a calibrated mechanical device with electronic output

that is widely available, with a grip that could be adjusted to fit each person’s hand.

Maximum was considered the best of three attempts. 22.8% of participants achieved their

maximum on the first attempt, 36.6% on the second attempt, and 40.6% on the third

attempt.

3. Exercise duration: 3 min. We selected a temporal stopping point of three min rather than a

more subjective point “to fatigue.” At an exercise intensity of 30% MVC, this period of

time elicits a robust pressor response. Defined time points keep the test relatively easy to

administer, and creates consistent time points for comparison across subjects.

4. Muscle engagement: wrist flexors. We selected a small muscle mass for both practical and

scientific reasons; i.e., that the test could be administered easily and that Q̇ would not limit

the BP response. In a young, healthy population this type of activity increases Q̇ less than

1 L/min with little increase in systemic vascular resistance39.

5. Duration of PECA: 2 min. PECA isolates the metabolic component of the EPR without

CC. However, the metabolic state can be altered by the time of circulatory arrest just prior

to and following the cessation of exercise. The MAP response during PECA after 3 min of

SHG at 30% MVC does not continue to increase from 2-3225, 226 and 3-4225 min of PECA.

Since ratings of perceived pain were highest during PECA, and the BP had plateaued, we

recommend stopping PECA at the end of two min, which should provide ample time for

measurements to characterize responses to PECA with minimal subject discomfort.

6. Recovery from Exercise: Greater than three min. Three min of recovery was insufficient

for HR and BP to return to baseline. We cannot say what duration of time is long enough

for BP to return to baseline from the current study.

7. Method of Blood Pressure Measurement: Continuous. We recommend continuous BP

measurement to better characterize the temporal response to exercise. Methods for

continuous BP measurement usually sample at the wrist (tonometry or direct measurement)

or finger (photo-plethysmographic clamping; Penàz method). This approach has

advantages and disadvantages. BP may be highly variable but beat-to-beat recording

provides continuous feedback that can be integrated over any time span. However,

peripheral BPs are typically affected by pulse wave amplification227, yielding higher

systolic and lower diastolic BPs than more central arteries. Accordingly, we based our

norms on MAP, which is largely insensitive to pulse wave amplification228, 229.

Available non-invasive, plethysmographic or tonometric technology to record

peripheral BP requires consistent application of sensors to assure that data are valid. Subtle

variations in anatomy, sensor tightness and sensor position may have considerable impact

on the absolute values of BP measured. If direct measurements are unavailable, we

recommend alignment of non-invasive MAP measurements with well-standardized

brachial measurements employing Korotkoff method (auscultation or oscillometry) to

assure accuracy.

8. Data Reporting: Both Absolute values and changes from baseline. The number of studies

that could be included in the systematic review was limited by differences in data reporting.

Even when protocols matched our criteria, some studies had to be excluded (n=2) because

changes from control were not provided. Both absolute values and changes from control

should be given in publications whenever possible to facilitate careful examination of the

responses.

9. Characterizing responses: Use BP residuals to correct for differences in absolute exercise

intensity (handgrip force). A cluster of scaling factors related to body size relate to the BP

responses to static exercise. Absolute force is an easy-to-measure proxy of this effect. For

purposes of identifying high and low cardiovascular responses to exercise, we were able to

utilize residual analysis of the BP response during PECA by employing a simple linear

regression equation, where ∆MAP (mmHg)=2.0+2.14 * (handgrip force in kp).

Limitations

The results from this study can only be applied to a young healthy population as described above. Attempts should not be made to extrapolate these “normal” responses to other populations or to other modes, intensities, or durations of exercise. Although step-wise regression did not identify sex as a significant predictor for the BP response PECA, we cannot say that men and women are not different in their method of BP raising, only that sex was not a predictor to the BP response once grip force was accounted for. Discussion of this topic is further expanded in the study limitations in Chapter 10.

Summary

Three min of SHG at 30% MVC and two min of PECA elicited robust pressor responses that were normally distributed in a healthy population. Clearly, cardiovascular responses depend on the mass and metabolic capacity of exercising skeletal muscle. We therefore emphasize that this study, and the normative data presented here, provide a reference point to the healthy, active, population for handgrip exercise only. The SHG paradigm we describe, which is near-maximal for most people, is sufficient to generate a metabolic state that activates the metabolic component of the EPR. While absolute force accounts for a proportion of the inter-individual variation in BP responses, the majority of these differences are unexplained, affords opportunities for future investigation.

CHAPTER 4: OVERALL STUDY DESIGN TO EXAMINE AUTONOMIC REFLEX RESPONSES

Introduction

Chapters 5-8 outline a series of individual protocols that were collected during a single ~4 hour visit to the lab. These protocols were completed with participants who qualified to join either the high or low response group as defined in Chapter 3. The groups were defined as the highest and lowest ~25% of BP response during PECA relative to the force produced during exercise (i.e. the outer quartiles of responses) while the middle ~50% of responses were not tested. The subjects were blinded to which group they belonged. For the remaining chapters, the groups are referred to as the high response group and the low response group, respectively. To avoid population stratification during genetic analysis and to maintain adequate sample size, only European-

American subjects were recruited for these studies.

Study Design and Familiarization

This study was designed to activate the sympathetic nervous system using three different stress paradigms: exercise, caloric stimulation, and hypercapnia. The exercise protocols were subdivided into static and rhythmic handgrip, the caloric protocols were subdivided into hot immersion and the CPT using the hand, and hyperoxic hypercapnia was induced via rebreathing.

Protocols were chosen to activate sensory receptor populations encoded by genes included in the genetic analysis.

From the outset, it was understood that any significant association between a genetic variation (outlined in Chapter 9) and a cardiovascular or autonomic variable would likely be small, therefore we used intentionally dichotomous response groups to give the greatest chance of detecting differences. The SHG protocol was outlined in Chapter 3 where the merits of the protocol were thoroughly described. Briefly, the SHG protocol is near-maximal for most people, is sufficient to generate a metabolic state that activates the metabolic component of the EPR, and is relatively simple to complete in a research/clinical setting.

The RHG protocol involved repetitive low-level (10% of MVC) contractions of a handgrip dynamometer at 30/min and a duty cycle of 50%. Rhythm was maintained with a metronome and force-feedback was displayed on a screen. Brachial blood flow velocity was monitored with Doppler ultrasound to estimate blood flow to the wrist flexors. After a stabilization period, flow was systematically decreased by gradually inflating (3 mmHg/15 seconds) a pneumatic cuff placed on the upper arm. This protocol was chosen because intensity is sub-threshold for the EPR52. However, activation of the EPR occurs during graded reductions of flow, which allows for the investigation of cardiovascular adaptations that occur before and after EPR threshold has been engaged, i.e., the onset of any changes in MSNA, BP, etc.52.

With 5 seconds left before the conclusion of exercise in both the SHG and RHG protocols, the cuff was inflated to supra-systolic pressure to arrest circulation for 2 min - referred to as PECA. This maneuver maintains the metabolic component of the EPR, without the mechanical component of the EPR. Our pilot data and published literature indicates that 2 min of PECA is sufficient to complete cardiovascular measurements. Longer time periods are not necessary; the MAP response during PECA after 3 min of SHG at 30% MVC does not continue to increase from 2-3 min225, 226 and 3-4 min225 of PECA. Given that the previous information, the data from the screenings (Chapter 3) showing that ratings of perceived pain were highest during

PECA, and the plateau of BP, PECA was released at the end of two min.

For caloric stimulation, 3 min of immersion was used. During the CPT, 3 min is sufficient to achieve a maximum response87, and our pilot testing indicate that the same was true for hot water immersion. Initial immersion temperatures were set precisely to 3.0-3.5 °C, and

47.5-48.0 °C, as a difference in only 2 °C can influence the reported pain and tolerance time during the CPT230. Heat and cold were chosen to because they activate (semi)disparate afferent ion channel receptors, for example, TRPV1 activation with heat and TRPA1 activation with cold170.

The rebreathing test was chosen to activate the sympathetic nervous system via central chemoreceptors112 and was therefore less dependent on feedback from the hand/arm .

Additionally, heightened sensitivity to CO2 is an established biological substrate of panic disorder and two of the ASIC SNPs selected for analysis in Chapter 9 have been associated with panic disorder and amygdala structure/function129. The basic protocol was to rebreathe a gas mixture of 8% CO2 and 92% O2 after a period of quiet breathing. Rebreathing continued for 5 minutes or until a PetCO2 of 70 mmHg was reached.

After the screening protocol outlined in Chapter 3 was completed, subjects were given the opportunity to familiarize themselves with the all protocols during their initial visit. Subjects experienced, in part, all of the protocols that they would perform if they were recruited into either of the two subject groups. Familiarization was performed to reduce novelty, unpredictability, and uncontrollability (subjects were reminded they could stop any protocol at any time if they were to uncomfortable), three of the main components that determine the response to stress231. Therefore, familiarization allowed for practice of the protocols to increase subject success and decrease intra- subject variability in performance and simultaneously helped to decrease the emotional/cognitive response to stress and optimize recording of the biological (autonomic) responses231. They practiced the squeezing pattern for RHG (Chapter 6), experienced the hot immersion and the CPT for at least 60 seconds (Chapter 7), and practiced breathing through the apparatus for the rebreathing protocol (Chapter 8).

Methods for each protocol is, unless otherwise noted in the individual chapters, were identical for the studies presented in Chapters 5-8 and are described below.

Subject Preparation and Instrumentation

All tests were conducted in the morning to minimize any effect of circadian rhythms on resting BP. All subjects were at least 30 days free from smoking, 24 hr free from heavy exercise,

12 hr free from caffeine and alcohol, and fasted overnight. All women subjects were tested on days

2-6 of their menstrual cycle (with the exception of 1 subject who was tested on day 8 due to a scheduling conflict) due to a potential menstrual cycle influence on baseline MSNA232, 233.

Subjects assumed a semi-recumbent posture (30° head-up and 20° legs-up) in a quiet, dim, temperature controlled (22°C) room for at least 15 min prior to the start of testing. Maximum handgrip was determined as the best of three attempts using the dominant hand with at least one min between each attempt (Smedley Hand Dynamometer- Stoelting). Vital capacity (VC) was determined using a metabolic cart (PARVO Medics, TrueMax 2400, Metabolic Measurement

System, Salt Lake City, Utah, USA and Dell Optiplex GX110) with a maximum inhalation followed by maximum exhalation. The largest of three attempts was considered the maximum.

Subjects were instrumented for HR (3-lead ECG), BP (Omron HEM-705CP and Finometer

Midi FMS), and venous occlusion plethysmography (VOP) of the calf. Briefly, VOP equipment was placed to measure the calf BF ipsilateral to the dominant hand. A 17 cm wide thigh cuff

(Hokanson CC17) was placed just above the knee for venous occlusion at 45 mmHg. A 7 cm wide ankle cuff (Hokanson TMC17) was placed around the ankle for full occlusion at 250 mmHg. The circumference of the calf was measured after the subject was supine for >10 min and a strain gauge size was selected that was 1-3 cm smaller than the measured circumference to ensure a close fit and continual contact with the skin (Hokanson Hg Strain Gauge). Changes in calf circumference were monitored using a plethysmograph (EC6 Plethysmograph, Hokanson). A 10 cm wide

95 pneumatic cuff was wrapped around the dominant upper arm for PECA (Hokanson: AG101 Cuff

Inflator Air Source, E20 Rapid Cuff Inflator).

After initial instrumentation, the subject was briefed on the procedure for recording

MSNA234, 235. External stimulation was used to identify the location of the peroneal nerve where low threshold muscle twitches could be elicited by weak electrical stimulation. Multiunit postganglionic recordings of MSNA were made using 200 µm diameter tungsten microelectrodes of 2-3 MΩ impedance (Frederick Haer Co.) inserted percutaneously into muscle nerve fascicles of the peroneal nerve. The recording electrode was placed in the peroneal nerve at the fibular head or the popliteal fossa, and the reference electrode was placed subcutaneously 2-3 cm from the recording electrode.

Adjustments of the recording electrode were made until sites were found in which clear spontaneously occurring sympathetic bursts were recorded. The nerve signal was amplified (total amplification 40,000-80,000x) and band pass filtered (high pass of 0.3-0.7 kHz; low pass of 2-3 kHz) to generate a filtered neurogram. The filtered neurogram was full-wave rectified, and filtered with a resistance-capacitance circuit with 0.1 sec time constant. Criteria for adequate MSNA recording and discrimination from skin activity included at least 3 of the 4 following criteria: 1) pulse synchrony; 2) facilitation during the hypotensive phase of the Valsalva maneuver and suppression during the hypertensive overshoot following release; 3) increases in response to breath holding, and 4) insensitivity to emotional stimuli (loud noises or stressful mental arithmetic).

Furthermore, muscle afferent activity could be identified by stretching the muscle innervated by the fascicle being recorded (tapping or muscle stretch), but not by stroking the skin. Neural activity was monitored on a storage oscilloscope (Kikusui) and a loudspeaker during the experiment.

Establishing an acceptable recording required ~5-60 min. Attempt to establish a recording were terminated at 60 min.

Once an MSNA recording was established or the attempt was terminated the subject was allowed to rest quietly for 10 min. BP was then measured in triplicate (1 min between each measurement) via oscillometry from the brachial artery (Omron) and the accuracy of continuous

BP measurement (Finometer, TNO, Netherlands) was then confirmed. If necessary the placement and tension of the finger cuff was adjusted until the optoplethysmographic and oscillometric MAPs were in close agreement.

General Procedure and Randomization

The details for each of the protocols included in subsequent chapters, however, the general outline for each protocol was similar. The continuous data collection was as follows: 3-5 min of quiet rest in the case of an exercising forearm, 3-15 min perturbation, and 3 min of recovery. Other than the forearm during handgrip exercise, subjects were instructed to remain completely still and relaxed at all times.

The order of the protocol was pseudorandomized. That is, the overall order of the protocols was initially randomized, then refined so that “like protocols” (caloric stimulation of the hand or

SHG/RHG) were not proximal. Additionally, the protocol order was partially determined by the availability of the technicians; i.e., Doppler ultrasound measurements were performed by a guest technician and therefore the RHG protocol that required Doppler ultrasound was performed whenever the technician was available. Of the 6 protocols performed, SHG was completed on average 3rd (3.2 SE±0.3), RHG- 4th (3.5 SE±0.2), CPT- 2nd (2.3 SE±0.2), hot immersion- 3rd

(2.5 SE±0.03) thermos-neutral- 6th (5.7 SE±0.2), and rebreathing- 3rd (3.0 SE±0.2). Neutral was a control test with immersion of a hand in thermoneutral water, was only performed with a subset of subjects, and was most often completed at the end of the study (time permitting). To allow adequate time for recovery, 15 min of quiet rest was provided between perturbations. Subjects rated their perceived pain on a scale from 0-10 during rest and once per min during the perturbation and during recovery in all protocols except rebreathing. All of the same steps to ensure data quality outlined in Chapter 3 were maintained during the full study.

Data Processing and Reporting

BP, force, ECG, MSNA, and BF velocity data were sampled at 1 kHz and recorded for offline analysis (ADInstruments PowerLab 16/30 V.5.5.6 and LabChart 7; Castle Hill, Australia).

Limb BF was measured using VOP (EC6 Plethysmograph Hokanson) and recorded (Hokanson

NIVP3 version 5.27c) for offline analysis. Each slope was inspected individually and 3 slopes were recorded and averaged for each determination when possible. SBP, DBP, and MAP were extracted beat-by-beat from the maximum, minimum, and time integral, respectively, of the

Finometer trace. All exercise and PECA data were binned in 1-min averages. Limb (calf) resistance was calculated as the quotient of minute average MAP and limb BF.

MSNA was partitioned into 1-min segments and analyzed beat-by-beat for all complete cardiac cycles contained in the segment (LabVIEW Instruments, Microsoft Excel). Burst height was normalized for each protocol by averaging the height of the three tallest peaks during rest and setting that equal to arbitrary unit (AU) of 100. Each burst was visually inspected by the same person for each subject for each protocol. MSNA was reported as burst frequency (burst/min), burst incidence (burst/100 heart beats), burst height (AU/burst), and total activity (AU/min).

Changes from rest (∆) are presented relative to the average of the 3-min prior to perturbation.

In Chapters 5-8, the quotient of change from control in calf resistance and change from control in total MSNA was plotted as a metric of sympathetic transduction. The metric was therefore limited to subjects for which measures of both resistance and MSNA where available.

The corresponding N for each are listed in their respective figure captions. In Chapter 10, in a subset of subjects, scatter plots of calf resistance by total MSNA were plotted for the high and low response groups during rest and perturbation. These plots included individual subjects grouped for

SHG, PECA, and caloric stimulation of the hand. The average slope of the lines for the high and

99 low response groups were considered the group sympathetic transduction and compared via t-test.

The comparison was limited to subjects for which calf resistance and MSNA was available for all

3 protocols (low response n=5 and high response n=8).

The study was powered statistically for the primary outcome variable: BP changes from control. The finger cuff was adjusted to match MAP to the brachial MAP determined by brachial oscillometry. Precise matching was not possible for every subject. Absolute BP data is provided in the Appendix.

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Subject Characteristics

Table 4.1 presents the descriptors for the low and high response groups (total of 31 subjects). Gender, age, handedness, drinks/week, and diagnosis of either or both parents with hypertension, were all self-reported. Height, weight, HR and brachial BP were all measured the morning of the study. HR was recorded from the oscillometric device. All subsequent HR data are calculated from the DBP period. There were no significant group differences in any of the baseline characteristics.

101

Low High P-value n 14 17

Gender*- M:F 9:5 12:5 Gender*- M=1 0.64 0.71 0.720 Age* (years) 22.9 (0.7) 21.6 (0.7) 0.228 Height (m) 1.72 (0.03) 1.73 (.02) 0.762 Weight (kg) 68.9 (4.0) 71.9 (2.9) 0.537 BMI (kg/m2) 23.1 (0.7) 24.0 (0.6) 0.373

Handedness*- R:L 13:1 16:1 Handedness*- R=1 0.93 (0.1) 0.94 (0.1) 0.892 Caffeine Drinks/Wk* 6.8 (2.1) 5.6 (1.2) 0.621 Alcohol Drinks/Wk* 6.1(1.6) 9.1 (2.4) 0.350

1 parent HTN*- Y:N 7:7 7:10 1 parent HTN*- Y=1 0.50 0.41 0.637

2 parents HTN*- Y:N 2:12 1:17 2 parents HTN*- Y=1 0.14 0.06 0.448 HR (BPM) 63 (2) 59 (2) 0.256

Brachial BP SBP (mmHg) 114 (3) 120 (2) 0.118 MAP (mmHg) 83 (2) 86 (2) 0.224 DBP (mmHg) 67 (2) 70 (1) 0.170 Table 4.1: Baseline subject characteristics for the full study. M=male, F=female, R=right, L=Left, Drink/Wk=drinks/week, HTN=hypertension, HR=heart rate, BP=blood pressure, SBP=systolic blood pressure, MAP (mean arterial pressure, and DBP=diastolic pressure. If given a range, the drink/Wk was calculated as the largest number. BP is the average of three brachial BPs after 15-min of rest and before any protocols began. MAP was calculated as (2/3)(DBP)+(1/3)(SBP). *Self-reported.

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Statistics

T-tests were used to compare subject characteristics between high and low response groups (2-tailed, unpaired) and any comparisons between protocols (2-tailed, paired).

For each protocol (Chapters 5-8), a 2-way (group x time) repeated-measures analysis of variance (ANOVA) was run for each variable for absolute data (rest-perturbation-recovery) and for changes from control (perturbation-recovery). All ANOVAs were run using SigmaPlot 13.0.

Data were entered into columns coded with 1) subject ID, 2) group (low response group=1 and high response group=2), 3) time in 1 min bins, and 4) the corresponding variables measured during each of the bins (for example DBP or change in HR). Therefore, each subject had a row with columns that represented ID, group, time, and the data point at the time. If either the equal variance test or the normality test failed, the test was still continued.

Tukey post-hoc tests were used to compare groups within individual time points, and between time points within individual groups if there was a significant interaction. Data were reported as mean±standard error (SE). A P-value of P<0.05 was considered significant for all tests. In the event of multiple comparisons, the Bonferroni correction was applied to apportion the family-wise P-value. Analysis methods and corresponding statistics for the DNA data are outlined specifically in Chapter 9.

103

CHAPTER 5: STATIC HANDGRIP EXERCISE IN HIGH AND LOW RESPONSE GROUPS

Introduction

The SHG protocol used in Chapter 3 was repeated during this study but added measurements of limb BF and MSNA. Along with the measures of BP and HR as completed in

Chapter 3, the addition of limb BF and MSNA allow for a more through characterization of the response to 3 min of 30% MVC SHG followed by PECA. The addition of limb BF is critical for calculating the vascular resistance of the calf using Ohm’s Law (pressure=flow/resistance). In this study, a 1 min average of MAP was divided by flow to the lower leg to calculate resistance.

Measuring pressure, flow, and resistance allow for a clearer understanding of the cardiovascular responses to SHG and PECA.

The addition of MSNA is also beneficial in two important ways. First, microneurography entails a direct recording of sympathetic nerve activity, not a metric of activity such as epinephrine spill-over or heart rate variability. It is thus a powerful technique for measurement efferent activity changes with perturbation. A second reason this technique is beneficial is because the location of recording (peroneal nerve). Recoding MSNA burst activity to the lower leg and simultaneous blood flow in the contralateral lower leg, allows for an accurate assessment of sympathetic transduction as the change in resistance for a given change in MSNA.

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Methods

All methods are outlined in Chapters 3 and 4. The protocol was the same as that outlined in Chapter 3 except that the time of resting data collection was 3-min duration rather than 5.

Statistics

Statistics were run as outlined in Chapter 4. The 2-way repeated measures ANOVA factors for analysis were response group (high and low) and protocol time (8 1-min bins for

SHG, PECA, and recovery for the change from control data and 11 1-min bins covering rest,

SHG, PECA, and recovery for the absolute data).

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Results

Results are summarized in Table 5.1. The groups were well matched with respect to force.

There was no difference between groups in MVC, the actual average grip force during the 3 min of SHG, or the %MVC maintained. Based on the analysis presented in Chapter 3 that related BP during PECA to handgrip force, the predicted MAP during PECA between groups was not different; however, as expected, the actual change in MAP during P2 was less in the low response group (Figure 5.1).

MVC (kp) AVG (kp) AVG (%) P2MAP Predict (mmHg) Low 42.2 (3.9) 12.3 (1.1) 29.1 (0.2) 28 (2) High 43.6 (2.8) 12.8 (0.8) 29.3 (0.2) 29 (2) P-value 0.800 0.755 0.651 0.755 Table 5.1: Force produced during the maximal voluntary contraction and the average handgrip force during SHG. AVG (kp)=average force in kiloponds during 3 min of squeezing. AVG%=average % MVC maintained during SHG. P2MAP Predict=the predicted change in MAP from control during the second min of PECA calculated from grip force. The P-value is based on a two-tailed, unpaired t-test.

The BP changes from control during SHG and PECA are shown Figure 5.1. ANOVA indicated significant group x time interactions for MAP and DBP. MAP and DBP in the high response group were significantly higher during E3, P1, and P2 compared to the low response groups. In the low response group, but not the high response group, MAP and DBP during P2 (i.e., when the metabolic component of the EPR was isolated) was significantly lower than E3.

The absolute BP response during SHG can be found in Appendix Table A.1. There were significant group x time interactions for MAP and DBP. There were no differences in resting BP.

The only significant difference between groups was DBP during P1 and P2.

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SBP (mmHg) SBP

E2 E3 P1 P2

-10 E1

Rec1 Rec2 Rec3 50 Time (min) * * * Low- a,b,c High- b,c 30

10 ANOVA H/L Time H/L*Time DBP 0.253 <0.001 <0.001 MAP (mmHg) MAP SBP 0.591 <0.001 0.089

Δ MAP 0.387 <0.001 0.001

E2 E3 P1 P2 -10 E1 ΔDBP 0.016 <0.001 0.037

ΔSBP 0.194 <0.001 0.206

Rec1 Rec2 Rec3 ΔMAP 0.026 <0.001 0.044 50 Time (min)

* * * Low- a,b,c 30 High- b,c

DBP (mmHg) DBP

P1 E2 E3 P2

-10 E1

Rec1 Rec2 Rec3 Time (min) Figure 5.1: Changes in BP from control in high vs. low during SHG and PECA. ∆=high and O=low, respectively. *=high vs. low within time point (P<0.05). Letters indicate differences between time points: a=E3 vs. P2; b=E3 vs. Rec3; c=P2 vs. Rec3 (P<0.05). The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction.

107

HR changes from control are shown in Figure 5.2. There was a time main effect neither a group main effect nor a group x time interaction. Absolute HR responses are shown in Appendix

Table A.1. Once again, there was a significant time main effect, but no group main effect or group x time interaction. To summarize, HR increased during SHG and returned to baseline during PECA but there was no significant difference between groups.

10 ANOVA H/L Time H/L*Time HR 0.079 <0.001 0.555 ∆HR (bpm) ∆HR 0 ΔHR 0.090 <0.001 0.844

-10

Time (min) Figure 5.2: Group mean changes in HR during the SHG and PECA. ∆=high and O=low response groups, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction.

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Limb BF and limb vascular resistance changes are shown in Figure 5.3. There was a time effect but neither a group main effect nor a group x time interaction. Similar results were obtained when absolute calf blood flow and vascular resistance were considered (Table A.2). To summarize, calf blood flow decreased and calf vascular resistance increased during PECA, but there was no difference between groups.

1.00 50

40 0.50 30 0.00 20 -0.50

Resistance 10 ∆

∆Flow (ml/min*100g) ∆Flow -1.00 0 (mmHg*min*100g/ml) E3 P2 Rec3 E3 P2 Rec3 Time (min) Time (min)

ANOVA H/L Time H/L*Time Flow 0.152 0.020 0.637 Resis. 0.366 <0.001 0.513 ΔFlow 0.683 0.015 0.491 ΔResis. 0.822 0.013 0.328

Figure 5.3 Changes in calf blood flow and resistance during SHG and PECA. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction.

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Figure 5.4 portrays the changes from control in MSNA represented in frequency

(burst/min), incidence (burst/100 beats), amplitude, and total activity. There was a time main effect but neither a group main effect nor a group x time interaction. Similar results were obtained when absolute values were considered (Table A.3). To summarize, MSNA increased during SHG and

PECA but there was no difference between groups.

110

40 40

30 30

20 20

(burst/min) 10

∆Frequency 10 ∆Incidence

0 (burst/100 heart beats) heart (burst/100

40 4000

30 3000

20 2000

∆Height

(AU/min) (AU/burst)

10 1000 ∆Total Activity ∆Total

0 0

Time (min) Time (min)

ANOVA H/L Time H/L*Time Freq. 0.520 <0.001 0.284 Incid. 0.837 <0.001 0.694 Height 0.861 <0.001 0.799 Total 0.338 <0.001 0.813 ΔFreq. 0.742 <0.001 0.154 ΔIncid. 0.587 <0.001 0.578 ΔHeight 0.672 <0.001 0.635 ΔTotal 0.858 <0.001 0.515 Figure 5.4: Changes in MSNA during SHG and PECA. ∆=high and O=low, respectively. *=high vs. low (P<0.05). There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction.

111

Sympathetic transduction was plotted for the high and low response groups during E3 and P2 in Figure 5.5. There was no difference in the sympathetic transduction between the high and low response groups during either E3 or P2 (P=0.354 and P=0.772, respectively).

SHG PECA 30 30

20 20

10 10

0 0 ∆Resistance/∆Total MSNA ∆Resistance/∆Total ∆Resistance/∆Total MSNA ∆Resistance/∆Total Low High Low High

Figure 5.5: Sympathetic transduction during SHG and PECA. Hashed=high and solid=low response groups, respectively. Units are ∆Resistance (mmHg*min*100g/ml) / ∆Total MSNA (AU/min). There was no difference between groups. N=6 and n=9 for the low and high response groups, respectively.

112

Ratings of perceived pain are shown in Figure 5.6. There was a time main effect but neither a group effect nor or a group x time interaction.

10) -

5 ANOVA H/L Time H/L*Time

Pain 0.888 <0.001 0.985 Pain (0 Pain

E1 E2 E3 P1 P2

R1 R2

Rec3 Rec1 Rec2 Time (min)

Figure 5.6: Group average pain ratings (0-10) during SHG and PECA. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction.

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Discussion

There were no differences in BP during rest. As planned, the high response group had larger increases in MAP and DBP during SHG and PECA despite gripping with the same force and the same % MVC. Thus, the high response group demonstrated a larger EPR and a BP during

PECA that was not different from E3, suggesting that the metabolic component of the EPR had a larger influence on the BP response to SHG in the high response group. Previous research has shown that HR rises along with SV during SHG exercise which results in an increase in Q̇ of about

~1 L/min43. Although SV was not measured during this study, it is plausible that an augmented

SV response in the high response group could function to maintain a higher Q̇ and thus higher

MAP. Similarly, although TPR was not measured in the study, it is also possible that TPR was higher in the high response groups thus maintaining BP at a higher level relative to the low response group. However, muscle vascular resistance (as measured by CVR) did not differ between groups so any potential differences in TPR would result from differences in circulations other than the muscle circulation. It is also possible that a combination of small increases Q̇ and

TPR function to maintain BP during PECA. Studies that reported Q̇ and TPR during PECA show that SV returns to baseline levels during PECA, and there is either no increase in TPR despite the maintenance of a higher BP43 or a small increase in TPR during PECA39. The conclusions suggest that although BP is consistently maintained during PECA, the mechanism of the maintenance is attributed, albeit inconsistently, to vasoconstriction. The variable results may be a function of normal physiological variation of Q̇ and TPR, exercise intensity and duration, and the mass of muscle engaged during exercise.

Again, there was no difference between high and low response groups in calf resistance. If the group BP difference resulted from TPR, than the muscles in the lower legs were not highly

114 influential and differences in other vascular beds must have been present. For example, SHG and

PECA each increase (~59% and 41%, respectively) renal cortical vascular resistance236 and PECA may cause heterogeneous MSNA responses to the upper and lower limbs36. Similarly, there was no difference in sympathetic transduction between the high and low response groups suggesting that vascular response to a given MSNA level was not a causal reason for the differences between groups. There were also no differences in the pain response to SHG, suggesting that anxiety associated with pain was not the reason for the larger BP change in the high response group.

During exercise and PECA both BP and HR increase during exercise, while BP remains elevated during PECA and HR returns toward or to baseline9, 49. The increase in HR during exercise is due to the combined effects of a parasympathetic withdrawal and a sympathetic activation237, 238. However, during PECA, HR returns to resting levels and therefore has limited effect on Q̇ , and subsequently BP239, 240, 241, 242. Consequently, HR has been considered to have little role in the EPR243, 244. The results of this study are consistent with current literature as HR returned toward resting levels during PECA and there was no difference between high and low response groups, and highlights the fact that control of HR and BP are distinctly different.

Using exercising dogs, O’Leary238 showed that the muscle metaboreflex control of HR, i.e. a fall in HR during PECA, was primarily a function of an increase in parasympathetic activity to the heart after exercise, rather than a decrease in sympathetic activity. Blocking the beta- adrenergically mediated effects of the sympathetic nervous system (atenolol or propranolol) decreased BP and HR when the EPR was activated during exercise, while blocking the parasympathetic cholinergic influences (atropine) did not change the exercise response. However, muscarinic antagonism did prevent the drop in HR that typically occurs during PECA, thus indicating the parasympathetic role in HR recovery during PECA. This rise in parasympathetic

115 activity thus “obscures the effects of the sustained increase in sympathetic activity” 238. Similar observations has been reported in both dogs and humans241, 242, 245.

Arterial baroreflexes attenuate the EPR246, 247, 248 and has an augmented sensitivity during

PECA249 and may therefore be partially responsible for the increases in parasympathetic activity along with the sudden drop in CC238, 241, 242, 250. Thus, the rise in parasympathetic activity and subsequent interference with sympathetic activity may be due in part to a sudden loss of CC and resultant baroreflex modulation of the EPR241.

There does not appear to be a difference in the sympathetic response to SHG (no MSNA or limb resistance group main effect or interaction), consistent with the notion that the difference between high and low response groups involves an attenuated parasympathetic response, rather than an augmented sympathetic response.

116

Limitations

The results from this study can only be applied to a young healthy population. Attempts should not be made to extrapolate these responses to other populations or to other modes, intensities, or durations of exercise. Only subjects who were previously identified as having a high or low response during PECA after SHG were recruited and therefore these data should not be used to estimate the mean response of the population overall.

117

CHAPTER 6: RHYTHMIC HANDGRIP EXERCISE IN HIGH AND LOW RESPONSE GROUPS

Introduction

The EPR is not constitutively active and therefore a certain level of exercise is required to

53 activate the reflex . When O2 delivery to active skeletal muscles falls below the level required for metabolism the EPR is engaged; thus, the EPR can be initiated when blood flow is experimentally restricted, even with an exercise intensity that was previously sub-threshold52, 53. This protocol was designed so that subjects would initially perform RHG at a level below the threshold of activation for the EPR. Then, by progressively occluding BF with inflation of a pneumatic cuff proximal to the exercising muscles, perfusion could be reduced in a graded fashion to experimentally activate the EPR and determine the threshold and sensitivity of the reflex.

To evaluate the effectiveness of the EPR as a functional reflex for maintaining nutritive BF to the exercising muscle, the duration of exercise for the high and low response groups was evaluated. Unlike the SHG experiment, this protocol was designed to terminate at fatigue to test the hypothesis the duration of exercise was related to the function of the EPR.

118

Methods

The protocol emulates the experiment originally described by Wyss et al.53 and later modified for use with humans52. Subjects performed RHG while velocity of blood flow in the brachial artery of the exercising arm was measured with duplex Doppler ultrasound (HDI 5000,

Philips; Bothell, Washington). Exercise began at 10% MVC at 30 contractions/min with a duty cycle (contract:relax) of 50%. An audible metronome helped subjects maintain rhythm and a computer monitor provided force feedback to the subject. Verbal encouragement and feedback was provided throughout exercise.

Prior to the start of exercise, 3 min of resting data was collected and then exercise was initiated with a verbal countdown. Exercise was performed with without any BF restriction for the first 3 min to establish a steady-state BF response. After 3 min, perfusion was systematically decreased by inflating the arm cuff positioned proximal to the Doppler probe and the antecubital fossa. The cuff was initially inflated to 36 mmHg and then pressure was continuously increased at a rate of 3 mmHg/15 seconds. Exercise continued until approximately BF was occluded 90%-

100% or until the subject’s rating of perceived exertion (RPE, Borg 6-20251) reached 19-20. RPE was assessed every 3 min at the start of the protocol and once per min near the end of exercise.

Five seconds before exercise ceased, the occlusion cuff on the exercising arm was inflated to 250 mmHg to maintain muscle metaboreflex activation for 2 min (PECA). After 2 min, the cuff pressure was released and recovery data was collected for a final 3 min. Calf blood flow was measured with VOP in triplicate each time. It was measured at rest, once every 3 min during exercise, once during the final min of exercise (even if it did not end on a 3 min interval), once during the second min of PECA, and once during final min of recovery. Similar to the SHG protocol above, data were averaged into 1-min bins for statistical analysis.

119

Brachial artery diameter and BF velocity of the brachial artery were recorded for post hoc analysis as described previously252 (HDI 5000, Philips; Bothell, Washington). The brachial artery was insonated at a constant angle of 60° with the sample volume adjusted to cover the width of the artery for recording BF velocity. Velocity measurements were sampled in real time (1000 Hz) using a data acquisition system (ADInstruments PowerLab 16/30 V.5.5.6 and LabChart 7; Castle

Hill, Australia). Nominally, high-resolution diameter measurements (6 MHz probe) were taken from 15-second recordings performed immediately before baseline rest. Images were recorded directly to computer using a video capture device and software (Dazzle Video Creator, Pinnacle,

Mountain View, California). Brachial artery diameter was determined as the average across the cardiac cycle using edge-detection software (Brachial Analyzer Software, Medical Imaging

Applications; Coralville, IA). When analysis equipment was not available (n=13), diameter was measured directly by an average of 3 still images using the measurement tool on the HDI 5000.

BF was calculated by multiplying the cross-sectional area of the brachial artery with mean blood velocity:

Brachial BF=π (diameter/2)2 × velocity × 60.

Statistics

Statistics were run as outlined in Chapter 4. The 2-way repeated measures ANOVA factors for analysis were response group (high and low) and protocol time (19 1-min bins for

RHG, PECA, and recovery for the change from control data and 22 1-min bins covering rest,

RHG, PECA, and recovery for the absolute data).

120

Results

Handgrip performance during the protocol is shown in Table 6.1. There was no difference in the maximum handgrip force, the average handgrip force during the exercise, or the average handgrip % MVC achieved during the test (two-tailed t-test, significance P<0.05).

MVC (kp) AVG (kp) AVG (%) Low 42.2 (3.9) 2.3 (0.2) 5.0 (0.1) High 43.6 (2.8) 2.1 (0.1) 4.9 (0.1) P-value 0.800 0.493 0.150 Table 6.1: Handgrip performance during RHG. Data are shown as mean (SE). Max=maximum, AVG=average, and kp=kiloponds of handgrip force. Significance=P<0.05

BP changes from control during RHG and PECA are shown in Figure 6.1. There was a significant time x group interaction for SBP, DBP, and MAP. The initial rise in SBP, MAP, and

DBP are not different between the high and low response groups. However the low vs. high MAP and DBP began to diverge starting at E11 and SBP at E14. SBP, MAP, and DBP remained different between groups through P2.

Within the high response group, the SBP and DBP differences between E14 and P2 are significant. They are not different in the low response group. P2 BP is significantly higher than

Rec3 for both the high and low response groups.

Absolute values for BP responses can be found in Appendix Table A.4. SBP, DBP, and

MAP were all higher in the high response group during the last third of the exercise protocol and during PECA. There was also a significant group difference between during Rec1. This difference was not obvious when expressed as changes from control because of a non-significant but consistent difference in resting BP.

121

50 Low- b,c * 40 High- a,b,c * * ANOVA H/L Time H/L*Time 30 DBP 0.024 <0.001 <0.001 SBP 0.066 <0.001 <0.001 20 MAP 0.020 <0.001 <0.001 ΔDBP 0.065 <0.001 <0.001

SBP (mmHg) (mmHg) SBP ΔSBP 0.120 <0.001 <0.001

Δ 10 ΔMAP 0.054 <0.001 <0.001

0 Figure 6.1: Group changes in BP

E1 E2 E3 E4 E5 E6 E7 E8 E9 P1 P2

E11 E12 E13 E14 E15

E10 from during RHG and PECA.

Rec2 Rec3 Rec1 ∆=high and O=low, respectively. 50 Time (min) *=high vs. low within time point (P<0.05). Letters indicate 40 Low- b,c High- b,c * * * * * * significance within low or high between time points: a=E14 vs. 30 P2; b=E14 vs. Rec3; c=P2 vs. Rec3 (P<0.05). No low subject in 20 the low response group completed E15. ANOVA results for main MAP (mmHg) MAP 10 Δ effects and interactions are also shown. H/L is the group main

0 effect. Time is the time main

E1 E2 E3 E4 E5 E6 E7 E8 E9 P1 P2

E11 E12 E13 E14 E15

E10 effect. H/L*Time is the group x

Rec2 Rec3 Rec1 time interaction. Sample sizes (n) 50 Time (min) are shown in the included table for each min of exercise and PECA. 40 Low- b,c They are the same for SBP, MAP, High- a,b,c and DBP. 30 * * * * * *

20 DBP (mmHg) DBP

Δ 10

E1 E2 E3 E4 E5 E6 E7 E8 E9 P1 P2

E10 E11 E12 E13 E14 E15

Rec1 Rec2 Rec3 Time (min)

Stage E1 E2 E3 E4 E5 E6 E7 E8 R9 E10 E11 E12 E13 E14 E15 P1 P2 Low n 14 14 14 14 14 14 14 14 14 14 12 11 6 4 0 14 14 High n 14 14 14 14 14 14 14 14 14 14 14 14 10 5 4 13 13

122

HR changes from control are shown in Figure 6.2. There was a time main effect but neither a group main effect nor an interaction of group x time. The absolute HR data, are shown in

Appendix Table A.4. Once again, there was a time main effect but neither a group main effect nor an interaction of group x time. To summarize, while HR increased progressively during exercise, there was no difference between groups.

10 ANOVA H/L Time H/L*Time HR 0.113 <0.001 0.922

5 ΔHR 0.253 <0.001 0.980 ∆HR (bpm) ∆HR 0

-5

E1 E2 E3 E4 E5 E6 E7 E8 E9 P1 P2

E10 E11 E12 E13 E14 E15

Rec1 Rec2 Rec3 Time (min)

Stage E1 E2 E3 E4 E5 E6 E7 E8 R9 E10 E11 E12 E13 E14 E15 P1 P2 Low n 14 14 14 14 14 14 14 14 14 14 12 11 6 4 0 14 14 High n 14 14 14 14 14 14 14 14 14 14 14 14 10 5 4 13 13

123

Calf BF and calf vascular resistance changes from control are shown in Figure 6.3. Flow was measured once every 3 min and during the last min of exercise. Therefore, flows measured during E10, E11, or E12 were binned with E12 and those measured during E13, E14, and E15 were binned with E15. There was a main effect of time but not a group main effect or a group x time interaction for flow and resistance. The absolute data is shown in appendix Table A.5.

Significant time and group main effects were noted but there was not a significant group x interaction. To summarize, calf blood flow increased during exercise without a change in vascular resistance. There was no difference between groups for calf blood flow, but calf resistance lower in the low response group.

1.00 60

0.50 40

0.00 20 ∆Flow ∆Flow

-0.50 ∆Resistance

(ml/min*100g) 0 (mmHg*min*100g/ml)

-1.00 -20

E3 E6 E9 P2

E12 E15

Rec3 Rec3 Time (min) Time (min)

Stage E3 E6 R9 E12 E15 P2 Rec3 ANOVA H/L Time H/L*Time Low n 13 14 13 14 7 13 12 Flow 0.007 <0.001 0.986 High n 13 13 12 11 5 12 13 Resis. <0.001 <0.001 0.197 ΔFlow 0.611 <0.001 0.980 ΔResis. 0.254 <0.001 0.134

Figure 6.3: The changes in calf blood flow and resistance from control in high vs. low during the RHG and PECA protocol. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the main effect of responder group high vs. low. Time is the main effect of time in 1 min bins. H/L*Time is the interaction. Sample sizes (n) are shown in the included table for each exercise, PECA and recovery.

124

MSNA results from RHG and PECA are shown in Figure 6.4. There was a time main effect with a robust increase in all MSNA variables, but neither a group main effect nor a group x time interaction. Absolute data for MSNA are presented in Appendix Table A.6 with identical findings.

Therefore, MSNA increased during exercise and PECA but there was no statistically significant difference between groups.

125

Stage E1 E2 E3 E4 E5 E6 E7 E8 R9 E10 E11 E12 E13 E14 E15 P1 P2 Low n 8 8 7 8 8 7 8 6 8 8 5 6 3 2 0 8 8 High n 9 10 10 10 9 10 10 10 10 9 10 10 7 2 1 10 10

ANOVA H/L Time H/L*Time Freq. 0.966 <0.001 0.830 Incid. 0.593 <0.001 0.908 Height 0.784 <0.001 0.996 Total 0.779 <0.001 0.956 ΔFreq. 0.422 <0.001 0.945 ΔIncid. 0.282 <0.001 0.956 ΔHeight 0.559 <0.001 0.993 ΔTotal 0.320 <0.001 0.969 Figure 6.4: Changes in MSNA during RHG and PECA. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. No subject in the low response group completed E15. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction. Sample sizes (n) are shown in the included table for each min of exercise and PECA. The n is the same for each MSNA variable.

126

Because of the variable exercise end-times during the RHG protocol and the resultant unequal sample sizes, the MSNA results for change from control for frequency, incidence, and height were re-binned (7 bins total) and plotted by percent of total exercise time. No rest, PECA, or recovery data are included. The plots shown in Figure 6.5 show nearly superimposable responses in the high and low response groups, providing giving further evidence that there were no group differences in MSNA.

127

0 MSNA ∆Frequency MSNA -10 30 0 20 40 60 80 100 % of Total Exercise Time 20

0 MSNA ∆Incidence MSNA -10 30 0 20 40 60 80 100 % of Total Exercise Time 20

0 MSNA ∆Height MSNA

-10 0 20 40 60 80 100 % of Total Exercise Time Figure 6.5: The changes in MSNA from control in high vs. low during RHG plotted as percent of total exercise time. ∆=high and O=low, respectively.

128

Sympathetic transduction was plotted for the high and low response groups during the last minute of RHG and P2 in Figure 6.6. There was no difference in the sympathetic transduction between the high and low response groups during either the last minute of exercise or P2 (P=0.597 and P=0.520, respectively).

RHG PECA 30 30

20 20

10 10

0 0 ∆Resistance/∆Total MSNA ∆Resistance/∆Total ∆Resistance/∆Total MSNA ∆Resistance/∆Total Low High Low High

Figure 6.6: Sympathetic transduction during RHG and PECA. Hashed=high and solid=low response groups, respectively. Units are ∆Resistance (mmHg*min*100g/ml) / ∆Total MSNA (AU/min). There was no difference between groups. N=7 and n=10 for the low and high response groups, respectively.

129

Absolute values and changes in brachial BF during RHG are shown in Figure 6.7 and 6.8, respectively. There was main effect of time but not a group main effect or a group x time. Thus, flow was reduced during RHG as expected, but there was no difference between groups.

150

100

50 (ml/min)

Brachial Blood Flow Flow Blood Brachial 0

E1 E2 E3 E4 E5 E6 E7 E8 E9

R1 R2 R3

E10 E11 E12 E13 E14 E15 Time (min)

Stage R1 R2 R3 E1 E2 E3 E4 E5 E6 E7 E8 R9 E10 E11 E12 E13 E14 E15 Low n 8 9 9 9 9 9 9 9 9 9 9 9 9 7 6 3 2 0 High n 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 6 3 2

ANOVA H/L Time H/L*Time Flow 0.768 <0.001 1.000

Figure 6.7: Average group brachial blood flow responses during RHG. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. No low subject went to E15. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction. Sample sizes (n) are shown in the included table for each min of rest and exercise.

130

100

0 (ml/min) -50

∆Brachial Blood Flow Flow Blood ∆Brachial -100

E1 E2 E3 E4 E5 E6 E7 E8 E9

E10 E11 E12 E13 E14 E15 Time (min)

Stage E1 E2 E3 E4 E5 E6 E7 E8 R9 E10 E11 E12 E13 E14 E15 Low n 9 9 9 9 9 9 9 9 9 9 7 6 3 2 0 High n 9 9 9 9 9 9 9 9 9 9 9 9 6 3 2

ANOVA H/L Time H/L*Time ΔFlow 0.481 <0.001 0.975

Figure 6.8: The changes from control in brachial blood flow in high vs. low during the RHG and PECA protocol. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. No subject in the low response group completed E15. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction. Sample sizes (n) are shown in the included table for each min of exercise.

131

Pain and RPE responses to RHG and PECA are shown in Figures 6.9 and 6.10, respectively. For pain, there was a significant main effect of time but not a group main effect or a group x time interaction. Analysis of the RPE revealed significant main effect of time but not a group main effect or a group x time interaction. Overall, the high and low response groups perceived the protocol similarly.

10) - 6

(0 Pain 2

E1 E2 E3 E4 E5 E6 E7 E8 E9 P1 P2

E10 E12 E13 E14 E15

E11

Rec2 Rec1 Rec3 Time (min)

Stage E1 E2 E3 E4 E5 E6 E7 E8 R9 E10 E11 E12 E13 E14 E15 P1 P2 Low n 14 14 14 14 14 14 13 14 14 13 12 10 6 4 0 14 14 High n 14 14 14 14 14 14 14 14 14 14 13 13 10 5 3 13 13

ANOVA H/L Time H/L*Time Pain 0.927 <0.001 0.833

Figure 6.9: Mean group pain ratings (0-10) RHG with progressive BF restrictions. ∆=high and O=low response groups, respectively. There was no significant difference between the high and low response groups. No subject in the low response group completed E15. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction. Sample sizes (n) are shown in the included table for each min of rest and exercise.

132

20) 10 -

RPE (6 RPE 0

E1 E2 E3 E4 E5 E6 E7 E8 E9

E12 E10 E11 E13 E14 E15 Time (min)

Stage R E3 E6 R9 E12 E13 E14 E15 Low n 8 14 14 14 10 4 4 0 High n 5 13 14 14 13 8 3 4

ANOVA H/L Time H/L*Time RPE 0.050 <0.001 0.125

133

End-exercise ratings of pain and RPE are summarized Figure 6.11. The pain rating was relatively modest (5±0.6 and 6±0.7, respectively P=0.696) while RPE was consistent with heavy exercise and somewhat unexpected considering the modest HR response (16±0.6 and 15±0.7, respectively P=0.167). There were no differences between groups.

20) - 15

10) and RPE10)(6 and -

5 Pain (0 Pain 0 Pain RPE

Figure 6.11: Average ratings of pain and perceived exertion at the end of RHG. Solid=low and hashed=high response groups, respectively. There were no significant differences between groups.

134

A serendipitous finding was a significant group difference in total exercise time. Only 1 of 31 subjects reported an end-exercise RPE of 19 and no subjects reported 20; therefore, the end of exercise was always BF related. In fact, the end-exercise BF was almost identical between the groups (5±3 vs. 5±4 mL/min, respectively P=0.982). As shown in Figure 6.12, the high response group exercised significantly longer than the low response group (12.4±0.4 vs 13.5±0.3 min, p=0.025).

15 *

5 Minutes of Exerciseof Minutes 0 Low High

Figure 6.12: The duration of RHG exercise performed before 90-100% cuff occlusion of BF in the high and low response groups. Cuff was initially inflated after 3 min of free flow exercise to 36 mmHg and inflated at a rate of 3 mmHg every 15 seconds thereafter for each group. Hashed=high and solid=low, respectively. * indicates a significant increase in time relative to the low response group.

135

Discussion

Although there was no difference in the resting BP or HR between the low and the high response groups, the high response group had a greater increase in BP during exercise. Although

BP recovered somewhat during PECA, the group difference remained. For comparison during

SHG, the maximum change in MAP was 31±4 mmHg and 39±4 mmHg in the low and high response groups, respectively while during RHG (at E14) it was 21±2 mmHg and 33±2 mmHg in the low and high response groups, respectively. Similarly, the increase in HR during SHG was

21±3 BPM and 24±3 BPM in the low and high response groups, respectively and during RHG change in HR was 13±1 BPM and 14±2 BPM for the low and high response groups, respectively.

Plotting all of the cardiovascular data as a percent-time of total exercise or comparing the response groups via the last min of completed exercise was considered because of the decreasing sample size toward the end of exercise. However, the sample size was large enough to maintain statistical significance between the high and low response groups through the end of exercise and alternative analysis would not fundamentally change the results of the study with the high response group remaining high. For example, when the last min of exercise (rather than E14) was compared to ensure that all subjects were included in the analysis, MAP at end exercise was lower in the low response group relative to the high response group (19±2 mmHg vs. 28±3 mmHg, respectively,

P=0.018). Similarly, when data was plotted as a percent of total exercise time the final of 7 equal bins (~85-100% of total time) the MAP was lower in the low response group relative to the high response group (17±1 mmHg vs. 27±2 mmHg, respectively, P<0.001). Therefore, in the three methods of data analysis, the low response group was always lower and the MAP difference between the high and low responders was ~12 mmHg (time domain), ~9 mmHg (final min exercise), and ~10 mmHg (% of total exercise time).

136

Absolute calf vascular resistance was higher and flow was lower in the high response group

(however changes from control were not different). It is unclear why this is the case. Although the

ANOVA indicates that absolute resting BP is still not significantly different between low and high response groups at rest before RHG, during the SHG protocol the 3 min averages of resting

SBP/DBP were nearly identical: 138/68 mmHg and 138/69 mmHg (MAP=89 vs 89 mmHg) for the low and high response groups, respectively. While during the RHG, 3 min averages were

130/65 mmHg and 138/71mmHg (MAP=84 vs. 91 mmHg) respectively. This means that it is plausible that slight (nonsignificant) changes in MAP in the high vs low response groups at rest was a result of an decrease in TPR in the low response group that was manifested by an increase in calf BF and therefore a decrease in the calculated calf resistance. It appears that the difference in resistance between the high and low response groups was not from a greater resistance in the high response group, but from lower resistance in the low response group. It is therefore possible, that the larger change in BP in the high response group was partially attributed to a greater resistance relative to the low response group. Excluding the thermoneutral protocol (which was only performed on a subset of individuals), the RHG protocol was, on average, performed last.

Therefore an order effect cannot be excluded as no other protocol had a main effect of group (high greater than low) for the absolute calf resistance results.

There were no differences in BF perfusing the wrist flexors as measured by Doppler ultrasound. In general, the BF by the end of the free-flow period may have been slightly underestimated, as this level of exercise has been shown to elicit some brachial artery dilation67 and diameter was measured prior to the start of exercise. However, any underestimation should have been similar between groups. We cannot rule out additional contributions from collateral flow. However, the occlusion cuff would likely affect collateral blood similarly to brachial blood

137 flow. There were also no differences in the pain or RPE responses between groups indicating that groups perceived the work similarly. This is evidence that central command was likely similar between groups. Finally, there was no difference in sympathetic transduction between the high and low response groups suggesting that vascular response to a given MSNA level was not a causal reason for the differences between groups.

The different end-exercise times illuminates the function of the EPR as a flow-raising reflex. As discussed in Chapter 2, controversy remains regarding the role of the reflex80, 81. The high response group continued exercise ~1 min longer despite similar changes in MSNA and calf vascular resistance. Assuming that the leg hemodynamic conditions are transferable to the forearm, the increase in exercise time is likely due to the greater perfusion pressure in the high response group. Although some assumptions must be made about the relative effectiveness of the cuff occlusion in each group (similar arm sizes and similar hemodynamic conditions distal to the cuff),

Table 6.3 shows the theoretical maintenance of perfusion pressure (SBP-cuff pressure) that could have contributed to some maintenance of BF late in the RHG protocol.

RHG Min SBP Low Response SBP High Response Cuff Pressure group group 13 161±5 mmHg 169±6 mmHg 156 mmHg 14 163±8 mmHg 177±5 mmHg 168 mmHg 15 - 179±6 mmHg 180 mmHg Table 6.3: The relation between SBP and cuff pressure in the high and low response groups.

138

Limitations

Only subjects who were previously identified as having a high or low response during

PECA after SHG were recruited and therefore these data should not be used to estimate the mean response of the population overall. While these groups had higher and lower BP responses, respectively, to RHG and PECA, the groups were sorted a priori only by their response to PECA after SHG. Since the data are not grouped to show the highest responses to RHG, one can only say that high responses to PECA are, on average, associated with higher responses to RHG as well. Alternative sorting would need to be done in order to better characterize the highest responses to RHG. The results from this study can only be applied to a young healthy population as described above. Attempts should not be made to extrapolate these responses to other populations or to other modes, intensities, or durations of exercise.

Although data is presented on the overall exercise duration, we caution that this protocol was not designed with exercise duration as an outcome variable. Time was recorded to the minute, and the study should be repeated with greater fidelity in time recordings. Thus, the differences in exercise time should be considered interesting, observation that deserves further study.

Substantial venous filling occurs during the graded occlusion, with some subjects developing petechiae. Thus, venous congestion may have affected microcirculatory flow independent of the change in perfusion pressure. A mode of BF restriction that was more specific to the arterial side of the circulation could yield different results. For example, using a brachial artery balloon to impede BF, authors were surprised to find that RHG (10% or 20%

MVC with 1 s contraction and 2 s relaxation) “did not evoke a marked pressor response”71.

Results were explained by the potential restoration of distal perfusion via collateral flow around

139 the elbow as this model of occlusion only decreased flow through the artery in which the balloon was placed71.

140

CHAPTER 7: CALORIC STIMULATION OF THE HAND IN HIGH AND LOW RESPONSE GROUPS

Introduction

The water immersion protocols were designed to stimulate the cold and hot temperature and pain sensitive receptors in the hand. As described in Chapter 4, 3 min of immersion was used for both hot and cold water, sufficient time to elicit the maximum BP responses87. The CPT elicits large BP and MSNA responses with only small or no changes in HR96. The cold/noxious sensation may involve pathways to the vasomotor centers that regulate MSNA and thus can be used to test the efferent signaling pathway96.

Noxious temperatures were chosen to probe the function of (semi)distinct afferent ion channel receptors and pathways that are distinct from commonly conceived “thermoreceptors,” i.e., TRPV1 activation with hot water and TRPA1 activation in cold water170. From the outset, it was understood that any significant association between a genetic variation (outlined in Chapter

9) and a cardiovascular or autonomic variable would likely be modest, therefore we used intentionally dichotomous response groups to give the greatest chance of detecting differences.

Protocols were chosen to elicit near-maximal stimulation while maintaining subject safety.

141

Methods

After 3 min resting data collection, the subject’s dominant hand was manually lowered up to the wrist into cold (3.0-3.5°C), hot (47.5-48°C), or thermoneutral (35-36°C) water. The subject was instructed to lay still without assisting the experimenter move their arm into or out of the water. Subjects were instructed to keep their arm/hand/fingers still and relaxed when immersed.

Advection was maximized by aerating the water around the hand.

After 3 min after the arm was lifted out of the water, dried, covered, and placed alongside or across the abdomen of the subject. Three min of recovery data were recorded. Beat-by-beat data were recorded for 9 consecutive min and calf blood flow (measured using VOP) was recorded in triplicate in the last min of each 3-min period (rest, water, and recovery). The CPT and hot immersion trials were randomly assigned, and at least 15 min was allowed before trials.

Statistics

Statistics were run as outlined in Chapter 4. The 2-way repeated measures ANOVA was set up independently for the CPT and hot immersion. The factors for analysis were response group (high and low) and protocol time (6 1-min bins for immersion and recovery for the change from control data and 9 1-min bins covering rest, immersion, and recovery for the absolute data).

142

Results

BP changes from control are shown in Figure 7.1. BP rose during both the hot immersion test and the CPT. The rise in BP was greater in the high response group relative to the low response group for both the hot immersion test and the CPT. In the high response group, BP during C1 was significantly lower than C3. Also in the high response group, BP during H1 was significantly lower than H3. However, in the low response group, BP for H1 is not differnet than H3. C3 was significantly higher than Rec3 and H3 was significantly higher than Rec3. To summarize, there was a significant time x group interaction for DBP and MAP but not SBP. There was, however, a main effect of time for SBP for both the CPT and hot immersion.

The absolute BP responses to the CPT and hot immersion are shown in Tables A.7 and

A.10 respectively. There were no differences in resting BP between response groups before the start of the CPT but there was a main effect of BP during hot as the high response group had a higher resting DBP and MAP before the start of hot. The difference between groups was maintained throughout hot water immersion and throughout recovery. Within high and low response groups for both the CPT and hot immersion, C3 was higher than R1 and Rec3 and H3 was higher than R1 and Rec3 for MAP and DBP.

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COLD HOT

40 40 30 30 20 20 10 10

SBP (mmHg) SBP 0

C3 C2 -10 C1

-10

Rec1 Rec2 Rec3

40 * Time * (min)Low- a,c 40 Time (min) 30 High-a,b,c 30 * * Low-c 20 20 High-a,b,c

10 10 MAP (mmHg) MAP

0 (mmHg) MAP 0

Δ Δ

C2 C3

-10 C1 -10

Rec3 Rec1 Rec2 40 Time (min) 40 Time (min) 30 * * Low- a,c 30 High-a,b,c 20 20 * * Low-c High-a,b,c 10 10

DBP (mmHg) DBP 0

C3 C2 -10 C1

-10

Rec1 Rec2 Rec3 Time (min) Time (min) ANOVA H/L Time H/L*Time ANOVA H/L Time H/L*Time DBP 0.050 <0.001 <0.001 DBP 0.003 <0.001 0.001 SBP 0.127 <0.001 0.132 SBP 0.041 <0.001 0.073 MAP 0.034 <0.001 <0.001 MAP 0.004 <0.001 0.003 ΔDBP 0.049 <0.001 0.003 ΔDBP 0.134 <0.001 0.002 Cold Hot ΔSBP 0.241 <0.001 0.113 ΔSBP 0.182 <0.001 0.097 ΔMAP 0.037 <0.001 0.010 ΔMAP 0.117 <0.001 0.007 HR 0.036 <0.001 0.148 HR 0.146 <0.001 0.924 ΔHR 0.164 <0.001 0.129 ΔHR 0.461 <0.001 0.898

Figure 7.1: Average group changes in BP during caloric stimulation of the hand. ∆=high and O=low response groups, respectively. *=high vs. low within time point (P<0.05). Letters indicate significance within low or high between time points: a=C1/H1 vs. C3/H3; b=C1/H1 vs. Rec3; c=C3/H3 vs. Rec3 (P<0.05). The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction.

144

The HR changes from control during the CPT and hot immersion are shown in Figure 7.2.

There was a main effect of time but not a group main effect or an interaction of group x time. The absolute data for HR during the CPT are shown in appendix Table A.7. ANOVA revealed a main effect of time and response group but not an interaction of response group and time. The absolute data for HR during hot are shown in appendix Table A.10. There was a main effect of time but neither a group main effect nor an interaction of response x time. Therefore, HR decreased during caloric stimulation but there was no group difference for the change in HR during either the CPT or hot immersion. Overall, HR was lower in the high response group.

COLD HOT 25 25

15 15

5 5

(bpm) HR

∆HR (bpm) ∆HR Δ

-5 -5

C1 C2 C3

H1 H2 H3

Rec2 Rec3

Rec1

Rec1 Rec2 Rec3 Time (min) Time (min) ANOVA H/L Time H/L*Time ANOVA H/L Time H/L*Time Cold HR 0.036 <0.001 0.148 Hot HR 0.146 <0.001 0.924 ΔHR 0.164 <0.001 0.129 ΔHR 0.461 <0.001 0.898

Figure 7.2: Changes in HR from control during caloric stimulation of the hand. ∆=high and O=low response groups, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the group main. Time is the time main effect. H/L*Time is the interaction of group x time.

145

Calf hemodynamic changes from control for the CPT and hot water immersion are shown in Figure 7.3. ANOVA revealed no main effects for calf blood flow and calf resistance. The absolute data for calf BF and vascular resistance are shown in Appendix Tables A.8 and A.11.

There was a main effect of time only for calf resistance during hot immersion and there were no other main effects for flow or resistance during either caloric condition. To summarize, there was no difference between groups in the hemodynamic responses to either hot or cold caloric stimulation.

146

COLD HOT 1.00 1.00

0.50 0.50

0.00

0.00 ∆Flow ∆Flow

(ml/min*100g) -0.50 -0.50 ∆Flow (ml/min*100g) ∆Flow -1.00 -1.00

60 C3 Rec3 60 H3 Rec3 )

40 40

20 20

Resistance ∆Resistance

∆ 0 0

mmHg*min*100g/min

( (mmHg*min*100g/min) -20 -20 C3 Rec3 H3 Rec3

ANOVA H/L Time H/L*Time ANOVA H/L Time H/L*Time Flow 0.234 0.216 0.375 Flow 0.451 0.472 0.402 Resis. 0.663 0.088 0.619 Resis. 0.300 0.012 0.169 Cold Hot ΔFlow 0.700 0.063 0.178 ΔFlow 0.363 0.818 0.333 ΔResis. 0.792 0.193 0.443 ΔResis. 0.426 0.057 0.132

Figure 7.3: The changes in calf blood flow and resistance from control in high vs. low during water immersion. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the main effect of responder group high vs. low. Time is the main effect of time in 1 min bins. H/L*Time is the interaction.

147

MSNA changes to the CPT and hot immersion are shown in Figure 7.4. During the CPT, the change from control for burst frequency and incidence were higher in the high response group at C1, but no other points. There was a significant interaction for the change in burst frequency and incidence during the CPT but not for burst height or total activity, and there was not a significant group x time interaction for any variable during hot immersion. For hot immersion the ANOVA revealed a significant main effect of time but not group or an interaction of response group x time. Therefore, both groups responded with a similar increase in MSNA during heat stimulation.

Absolute MSNA responses to the CPT and hot immersion are shown in Tables A.9 and

A.12, respectively. Both stimuli elicited significant changes in MSNA over time. There was no group main effect or any interaction between response x time with heat stimulation. During the

CPT there was a higher absolute MSNA frequency during the first minute of hand immersion, and the only significant group x time interaction was for MSNA frequency.

Figure 7.4: (Continued on following page.) Average group changes in MSNA during caloric stimulation. ∆=high and O=low, respectively. *=high vs. low within time point (P<0.05). Letters indicate group differences between time points: a=C1/H1 vs. C3/H3; b=C1/H1 vs. Rec3; c=C3/H3 vs. Rec3 (P<0.05). The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction.

ANOVA H/L Time H/L*Time ANOVA H/L Time H/L*Time Freq. 0.525 <0.001 0.049 Freq. 0.788 <0.001 0.449 Incid. 0.186 <0.001 0.055 Incid. 0.802 <0.001 0.286 Height 0.076 <0.001 0.265 Height 0.862 <0.001 0.062 Total 0.211 <0.001 0.162 Total 0.756 <0.001 0.187 Cold Hot ΔFreq. 0.679 <0.001 0.007 ΔFreq. 0.144 <0.001 0.894 ΔIncid. 0.511 <0.001 0.033 ΔIncid. 0.055 0.002 0.947 ΔHeight 0.137 <0.001 0.777 ΔHeight 0.053 <0.001 0.323 ΔTotal 0.182 <0.001 0.283 ΔTotal 0.158 <0.001 0.340

148

45 COLD 45 HOT

35 35 * Low- a,c 25 High-b,c 25

15 15

(burst/min)

(burst/min) ∆Frequency ∆Frequency 5 5

-5 -5

H1 H2 H3

C1 C2 C3

Rec1 Rec2 Rec3

Rec2 Rec1 Rec3 45 Time (min) 45 Time (min) * Low- a,c 35 High-c 35 25 25 15 15 5

∆Incidence 5 ∆Incidence

-5

H2 H3

-5 H1

C1 C2 C3

(burst/100 heart beats) heart(burst/100

Rec1 Rec2 Rec3

(burst/100 heart beats) heart(burst/100

Rec2 Rec1 Rec3 Time (min) 45 Time (min) 45 35 35 25 25 15

15 ∆Height

∆Height (AU/burst)

(AU/burst) 5 5

-5

H2 H3

-5 H1

C1 C2 C3

Rec1 Rec2 Rec3

Rec1 Rec2 Rec3 3000 Time (min) 3000 Time (min) 2500 2500 2000 2000 1500 1500

1000 1000 (AU/min)

500 ∆Total Activity ∆Total (AU/min) 500 0

∆Total Activity ∆Total 0

H2 H3

-500 H1

C2 C3

-500 C1

Rec1 Rec2 Rec3

Rec1 Rec2 Rec3 Time (min) Time (min)

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Sympathetic transduction was plotted for the high and low response groups during caloric stimulation of the hand in Figure 7.5. There was no difference in the sympathetic transduction between the high and low response groups during either the C3 or H3 (P=0.156 and P=0.311, respectively). Negative values were calculated when either a negative change in resistance or

MSNA was recorded during the perturbation.

Cold Hot 200 200

100 100

0 0

-100 -100

-200 -200 ∆Resistance/∆Total MSNA ∆Resistance/∆Total ∆Resistance/∆Total MSNA ∆Resistance/∆Total Low High Low High

Figure 7.5: Sympathetic transduction during caloric stimulation of the hand. Hashed=high and Solid=low response groups, respectively. Units are ∆Resistance (mmHg*min*100g/ml) / ∆Total MSNA (AU/min). There was no difference between groups. CPT: n=10 and n=10 for the low and high response groups, respectively. Hot immersion: n=9 and n=9 for the low and high response groups, respectively.

150

Pain responses to the CPT and hot immersion are shown in Figure 7.6. There was a main effect of time but not a main effect of group or an interaction of response x time. Therefore, there was no difference between groups in their sensation of pain.

10 10

COLD HOT

10) 10)

- - 5 5

(0 Pain Pain (0 Pain

0 0

R1 R2 C1 C2 C3

H1 R2 H2 H3

Rec1 Rec2 Rec3

Rec1 Rec2 Rec3 Time (min) Time (min)

ANOVA H/L Time H/L*Time ANOVA H/L Time H/L*Time Cold Pain 0.779 <0.001 0.365 Hot Pain 0.386 <0.001 0.771

Figure 7.6: The pain response (0-10) in high vs. low during water immersion. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction.

Thermoneutral water was used as a control. The absolute responses and changes from baseline are shown in Appendix Tables A.13 - A18. There were no significant interactions for any variable (i.e., BP, HR, MSNA, flow, resistance, or pain) and there were no differences between groups at any specific time point.

151

Discussion

The cardiovascular responses to caloric stimulation near maximal thermal tolerance were relatively similar overall with a slightly greater response, in general, to the CPT. These results tend to agree with published literature (summarized in Chapter 2). For example, it is well established that there is a large heterogeneity in BP responsiveness to the CPT17. Importantly, the high response group displayed a higher BP response to both the CPT and hot immersion, thus indicating the transitive nature of the BP response to SHG and caloric stimulation.

The change in MAP during the CPT may be explained, at least in part, by a larger initial increase in MSNA response during C1. There was no difference in the change in calf vascular resistance between groups and the pain responses were also not different. Similarly, there was no difference in sympathetic transduction between the high and low response groups. Taken together, the results suggest that the MSNA response and the muscle vascular resistance did not play a large role in determining the BP difference between high and low response groups during immersions of different temperatures. Since the resistance to other vascular beds is not known, it is plausible that they could have played a part in the larger BP response from the high response group.

As shown in Figure 2.10 the TRP family of receptors responds to wide range of thermal stimuli. Of particular interest the caloric stimuli protocol used in this research are the TRPA1 and

TRPV1 receptors. These channels many function in the sensation of noxious cold and noxious hot, respectively. The TRPA1 channel, has a large number of Ankyrin repeats on the N-terminus (~18 compared to 4-6 in TRPC and TRPV) 170, 181. These domains are posited to function in trafficking to, or insertion in, the membrane and probably play a role in mechanotransduction, although their specific function is far from understood170. This potential role in mechanotransduction also links the TRPA1 receptor with a potential role in the response to exercise. Additionally, some examples

152 of other TRPA1 modulators are reactive oxygen species-ROS and decreased pH with the response localized to the transmembrane domains and pore loop of the channel170, 186, 187, 188. Therefore

TRPA1 could once again be linked to exercise as a local decrease in pH is common with exercise.

TRPA1 is found in the DRG, TG, and NG and in peripheral nerves is primarily found on unmyelinated and lightly myelinated group III and IV nerves and may be coexpressed with

TRPV1170, 181, 182, 183.

As shown in Figure 2.10, TRPV1 not only responds to noxious temperatures but may also respond to various metabolites produced during exercise. Additionally, sensitization of TRPV1 on afferent nerves likely occurs with concomitant activation of others channel during exercise (e.g.

ASIC and P2X channels)14. Therefore TPRA1 and TRPV1 may be linked to both the response to caloric stimulation and exercise possibly explaining, at least in part, the transitive nature of the BP response to SHG and caloric stimulation.

In summary, an individual who had a high response to PECA following SHG, may in general, be considered a high BP responder overall. A similarity had been demonstrated in the BP response between exercise protocols and caloric stimulation, both of which elicit a significant pain response. This response likely relates to activation of thinly myelinated group III and unmyelinated group IV afferent neurons. Protein receptors embedded in the membrane of the afferent nerves likely mediated the response. Receptors like TRPA1 and TRPV1 respond to the local metabolic, mechanical, and thermal environment, and are often coexpressed. Their activation may be a key mediator to the similarity in responsiveness between stressors.

153

Limitations

Only subjects who were previously identified as having a high or low response during

PECA were recruited and therefore these data should not be used to estimate the mean response of the population overall. While these groups had higher and lower BP responses, respectively, to caloric stimulation, the groups were sorted a priori only by their response to PECA. Since the data are not grouped to show, for example, the highest responses to the CPT, one can only say that high responses to PECA are, on average, associated with higher responses to the CPT as well.

Alternative sorting would need to be done in order to better characterize the highest responses to the CPT (or hot immersion).

It cannot be determined, specifically, where the sensations from the caloric stimulation were being sensed, e.g., in the skin vs. in the interstitial space of the muscles of the hand, and therefore from where reflex responses were primarily initiated. The assumption was that the skin was the first to undergo a temperature change and as the hand remained in the water, the temperatures subsequently affected the local environment of the muscles of the hand as well. No surface, subcutaneous, or intramuscular temperatures were measured. Similarly, no specific protocol was used to bring each subject’s hand to the same temperature before immersion- i.e. pre-

CPT/hot immersion into neutral water not was used as a control immediately before the noxious immersions. However, subjects were given at least 15 min between each protocol and the CPT/hot immersions were never done back-to-back, so it was assumed that each subject would have a skin temperature that was thermoneutral before each immersion. Verbal questioning of the subject confirmed that subjects did not perceive a difference in skin temperature prior to the next protocol.

Despite these controls, some habituation of the stimulus cannot be completely ruled out.

154

During the 3 min of water immersion, the water temperature did change by ~1.0-1.5°C.

During the CPT, the water warmed slightly, and during the hot immersion, the water cooled slightly. Conductive heat transfer is the most likely reason for these changes; no mechanism to continually heat/cool the water during the immersion was included in order to minimize electrical interference with the neural recordings. The water temperature was checked immediately before and after immersion to confirm the temperatures. Given the magnitude of the stimuli, it is difficult to attribute physiological importance to this small change in temperature. While it is likely that habituation played some role in the small but apparent decline in the reflex responses during the course of caloric stimulation, a change in temperature cannot be completely dismissed.

155

CHAPTER 8: HYPERCAPNIC, HYPEROXIC REBREATHING IN HIGH AND LOW RESPONSE GROUPS

Introduction

The response to hypercapnia (with hyperoxia) was used to activate reflex autonomic and cardiovascular adjustments253. MAP, Q̇ , HR, SV, MSNA, and systemic vascular conductance increase during hyperoxic hypercapnia and the responses are generally greater during hypoxic hypercapnia124. In this study we focused hyperoxic hypercapnia based on the established biological

129 correlation of heightened sensitivity to CO2 and panic disorder . The ASIC family of receptors may be involved in CO2 sensing and two of the ASIC SNPs selected for analysis (described in

Chapter 9) have been associated with panic disorder and amygdala structure/function129. The goal of the protocol was to test the effect of increasing circulating CO2 without simultaneous low O2.

Similarly, the aim was to use a stimulus that was primarily sensed in the central nervous system and to limit sensation from the periphery (i.e. the peripheral chemoreceptors). As the peripheral chemoreceptors are primarily responsible for sensing hypoxia124, hyperoxic rebreathing was chosen rather than hypoxic rebreathing.

156

Methods

During instrumentation and before MSNA was established, VC was measured in triplicate.

The largest-measured VC was then used to calculate the volume of gas to fill a 15 L rebreathing bag. The bag was prefilled with VC+1 L of a premixed gas containing 92% O2 and 8% CO2.

The rebreathing equipment and the protocol were modified from Read 107. For resting data collection, the subject breathed through a mouth piece connected to a two-way valve that allowed inhalation of room air followed by exhalation into the expired circuit of a metabolic cart (PARVO

Medics, TrueMax 2400, Metabolic Measurement System, Salt Lake City, Utah, USA and Dell

Optiplex GX110). Air samples were drawn continuously from a cannula inserted in the mouthpiece to attain breath-by-breath data. Volume of exhaled gas was measured with an in-line pneumotachograph. The expired arm of the open-circuit system could be switched for rebreathing with a 3-way valve, which allowed the rebreathing bag to be prefilled without disturbing a subject’s breathing. Subjects laid at quiet rest while breath-by-breath respiratory and beat-by beat cardiovascular data were collected for 5 min. VOP was measured in triplicate during the final min of rest. To account for any dead space effects and the novelty of breathing through the mouthpiece, the first two (of five) minutes of data was not used and therefore 3 min of resting are reported.

After 5 min of rest, the data collection was paused, the mouth piece was removed, and a mouth piece without a respiratory valve was connected to the expired portion of the circuit.

Subjects then completed a forced expiration to residual volume and then inserted the mouth piece.

Once the subject had obtained a good mouth seal, the 3-way valve was switched to open the rebreathing bag. The subject was instructed to take 3 rapid deep breaths to ensure rapid mixing.

After the three deep breaths, the subject was instructed to return to normal resting breathing and to breathe at whatever rate and depth felt normal. The duration of the rebreathing was 5 min or

157

until the PetCO2 reached a level of 70 mmHg, after which the mouth piece was immediately removed and the subject breathed room air once again.

Other than allowing the researcher to remove the mouth piece, the subject was instructed to remain still and quiet while 3-min of recovery cardiovascular (but not respiratory data) was collected. After the conclusion of the recovery period, the subject was asked to rate their highest perceived stress during rebreathing on a scale from 0 (no stress) to 10 (severely distressed).

Statistics

Time domain statistics were conducted as outlined in Chapter 4; i.e., a 2-way repeated measures ANOVA considering response group and time as main effects. Factors included group

(high vs. low response group) and time (11 1-min bins for absolute data for rest, rebreathing, and recovery and 8 1-min bins for changes from control for rebreathing and recovery). A full model was specified. Furthermore, since CO2 is the stimulus to central chemoreceptors, rebreathing data were analyzed by linear regression with PetCO2 as the independent variable. To synchronize respiratory data that were collected breath-by-breath with cardiovascular data that were collected beat-by-beat, data were aligned in the time domain using 1-min bins of the breath-by-breath and beat-by-beat data, respectively. Cardiovascular responses (BP, HR, and MSNA) to increasing

PetCO2 during min 1-5 of the rebreathing protocol were analyzed with univariate regression

(Minitab 14.0, State College, PA). A categorical predictor was used to discriminate the high and low response groups.

158

Results

Rebreathing was tolerated well by most subjects. Three trials were terminated before 5 min of rebreathing was completed: one trial because the PetCO2 reached 70 mmHg, one trial because the volume of air in the breathing bag was not sufficient to allow a complete inspiratory effort, and one trial because of subject request. The post-test report of maximum stress was not different between the high and low response groups (4.4±0.6 vs. 5.5±0.6, respectively, P=0.237).

Data are presented as both the responses over time and the response to a given level of

PetCO2. The BP responses to rebreathing over time are shown in Figure 8.1. BP responses were similar between the high and low response groups. ANOVA revealed a main effect of time but neither a main effect of group nor an interaction of group x time. The absolute BP data are shown in Appendix Table A.19 and once again, ANOVA revealed a main effect of time but neither a main effect of group nor an interaction of group x time. Therefore, there was no difference in the

BP response between the groups.

159

SBP (mmHg) SBP Δ

-5

Rec1 Rec2 Rec3

Reb1 Reb2 Reb3 Reb4 Reb5 25 Time (min)

ANOVA H/L Time H/L*Time 15 DBP 0.180 <0.001 0.867 SBP 0.619 <0.001 0.738 MAP 0.275 <0.001 0.910 5 ΔDBP 0.419 <0.001 0.878

ΔSBP 0.869 <0.001 0.443 MAP (mmHg) MAP

Δ ΔMAP 0.558 <0.001 0.842

-5

Rec1 Rec2 Rec3

Reb1 Reb2 Reb3 Reb4 Reb5 Time (min) 25

DBP (mmHg) DBP Δ

-5

Rec1 Rec2 Rec3

Reb1 Reb2 Reb3 Reb4 Reb5 Time (min)

160

The HR response to rebreathing is shown in Figure 8.2. ANOVA revealed a main effect of time but not a main effect of group or an interaction of group x time. The absolute HR data, shown in appendix Table A.19, revealed a main effect of time and group but not an interaction of group x time indicating an overall lower HR in the high response group. Therefore, initial HR was lower in the high response group but there was no difference in the HR response to rebreathing between the groups.

10 ANOVA H/L Time H/L*Time HR (bpm) HR 5 HR 0.019 <0.001 0.697 Δ ΔHR 0.468 <0.001 0.587

Rec1 Rec2 Rec3

Reb1 Reb2 Reb3 Reb4 Reb5 Time (min) Figure 8.2: The changes in HR from control in high vs. low during the rebreathing protocol. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. The ANOVA results for main effects and interactions are also shown. H/L is the main effect of response group high vs. low. H/L*Time is the group x time interaction.

161

Calf BF and resistance changes are shown in Figure 8.3. ANOVA revealed no main effect of time or group. The absolute data for calf blood flow and resistance are shown in appendix Table

A.20. There was a main effect of time for resistance but not a main effect of time for flow. There were no main effects of group or interactions of group x time. Therefore, over time the high response group demonstrated greater vasoconstriction during rebreathing with preserved BF for the absolute but not the change from control data.

1.00 50

0.50 35

0.00 20

-0.50 ∆Resistance 5 ∆Flow ∆Flow (ml/min*100g) -1.00 (mmHg*min*100g/min) -10 Reb5 Rec3 Reb5 Rec3

H/L Time H/L*Time Flow 0.673 0.244 0.584 Resis. 0.545 0.035 0.437 ΔFlow 0.218 0.174 0.921 ΔResis. 0.825 0.071 0.301

Figure 8.3: Changes in calf blood flow and resistance in high vs. low during hypercapnic rebreathing. ∆=high and O=low, respectively. There were no significant differences between or within any groups.

162

MSNA responses to rebreathing are shown in Figure 8.4. ANOVA revealed a main effect of time but neither a main effect of group nor an interaction of group x time. The absolute data shown in appendix Table A.21 showed a similar pattern. Therefore, while hypercapnia elicited sympathoexcitation, there was no statistical differences between high and low response groups for

MSNA.

163

25 25

15 15

5 5

(burst/min)

∆Incidence ∆Frequency

-5 -5

(burst/100 heart beats) heart(burst/100

Rec1 Rec2 Rec3

Reb3 Reb1 Reb2 Reb4 Reb5

Rec1 Rec2 Rec3

Reb5 Reb1 Reb2 Reb3 Reb4 25 Time (min) 2000 Time (min) 1500 15 1000 500

∆Height 5 (AU/burst) 0

-5 -500

∆Total Activity (AU/min) Activity ∆Total

Rec1 Rec2 Rec3

Reb1 Reb2 Reb3 Reb4 Reb5

Rec1 Rec2 Rec3

Reb1 Reb2 Reb3 Reb4 Reb5 Time (min) Time (min)

H/L Time H/L*Time Freq. 0.858 <0.001 0.972 Incid. 0.859 <0.001 0.967 Height 0.742 <0.001 0.977 Total 0.637 <0.001 0.989 ΔFreq. 0.615 <0.001 0.905 ΔIncid. 0.397 0.004 0.935 ΔHeight 0.343 <0.001 0.992 ΔTotal 0.457 <0.001 0.987

Figure 8.4: Changes in MSNA during the rebreathing protocol. ∆=high and O=low, respectively. There was no significant difference between the high and low response groups. ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction.

164

Sympathetic transduction was plotted for the high and low response groups during rebreathing in Figure 8.5. There was no difference in the sympathetic transduction between the high and low response groups (P=0.736). Negative values were calculated when either a negative change in resistance or MSNA was recorded during the perturbation.

Reb 200

100

-100

-200 ∆Resistance/∆Total MSNA ∆Resistance/∆Total Low High

Figure 8.5: Sympathetic transduction during caloric stimulation of the hand. Red=high and Blue=low response groups, respectively. Units are ∆Resistance (mmHg*min*100g/ml) / ∆Total MSNA (AU/min). There was no difference between groups. N=7 and n=11 for the low and high response groups, respectively.

165

Changes in respiratory variables during rebreathing are shown in Figure 8.6. ANOVA revealed a main effect of time for PetCO2 and V̇ E and a significant interaction for RR and VT.

However there were no group differences. The absolute respiratory data are shown in Appendix

Table A.22. ANOVA revealed a main effect of time for PetCO2, RR, VT, and V̇ E but neither a main effect of group nor an interaction of group x time.

166

10 30 Low- a High- a 7 20

(mmHg)

2 4 10

(BPM) ∆RR

1 ∆ETCO

-2

Reb1 Reb2 Reb3 Reb4 Reb5

Reb4 Reb1 Reb2 Reb3 Reb5 40 Time (min) 2.0 Time (min)

30 1.5

20 1.0

∆TV (L) ∆TV Low- a

∆VE (L/min) ∆VE 10 0.5 High- a

0 0.0

Reb4 Reb1 Reb2 Reb3 Reb5

Reb1 Reb2 Reb3 Reb4 Reb5 Time (min) Time (min) ANOVA H/L Time H/L*Time PetCO2 0.105 <0.001 0.630 RR 0.867 <0.001 0.373 VT 0.256 <0.001 0.146 V̇ E 0.407 <0.001 0.965 ΔPetCO2 0.517 <0.001 0.109 ΔRR 0.689 <0.001 0.042 ΔVT 0.503 <0.001 0.009 ΔV̇ E 0.555 <0.001 0.976 Figure 8.6: Changes in MSNA during hypercapnic rebreathing. ∆=high and O=low response groups, respectively. *=high vs. low within time point (P<0.05). Letters indicate significance within low or high between time points: a=Reb1 vs. Reb5; b=Reb1 vs. Rec3; c=Reb 5 vs. Rec3 (P<0.05). ANOVA results for main effects and interactions are also shown. H/L is the group main effect. Time is the time main effect. H/L*Time is the group x time interaction.

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The stimulus-response relations between PetCO2 and the cardiovascular and autonomic data are shown in Figure 8.7 and Table 8.1. As shown in Figure 8.7, MAP was highly correlated with PetCO2 (overall model P=<0.001). There was no group difference in the y-intercept (y- intercept difference of -10.5 mmHg, P=0.353) and no difference in the slopes (0.142 mmHg/mmHg, P=0.454) indicating that there were group differences in neither MAP at any

PetCO2 nor was there a difference in the MAP response to an increase in PetCO2. Similarly, the

V̇ E response to increasing PetCO2 also was not different between high and low response groups

(Table 8.1). The HCVR slopes for the V̇ E were 2.17 L/min/mmHg and 2.36 L/min/mmHg for the high and low response groups, respectively. This is within the normal range of 1.5-3.1

L/min/mmHg and very close to the reported mean of 2.3±0.8 L/min/mmHg outlined by McGurk

112 et al. . Thus, neither cardiovascular nor respiratory sensitivity to CO2 was different between the groups.

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110 y = 0.99x + 40.60 105 R² = 0.97

100

90 y = 1.13x + 30.08 85 MAP (mmHg) MAP R² = 0.95

70 40 50 60 70

PetCO2 (mmHg)

Figure 8.7: The relation between PetCO2 and MAP during 5 min rebreathing. ∆=high and O=low response groups, respectively. Regression analysis for the high and low response groups are shown to the upper left and lower right, respectively. There was no significant difference in slope or y-intercept between groups.

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Independent Dependent Variable y-intercept P-value Slope P-value Variable RR (breath/min) -15.47 0.113 0.268 0.107 V̇ E (L/min) -10.1 0.583 0.187 0.549 VT (L/breath) 0.951 0.150 -0.017 0.130 DBP (mmHg) -6.8 0.563 0.067 0.735 SBP (mmHg) -11.7 0.345 0.181 0.386 P CO et 2 MAP (mmHg) -10.5 0.353 0.142 0.454 (mmHg) HR (beat/min) 12.5 0.576 -0.081 0.830 Frequency (burst/min) 16.7 0.238 -0.248 0.296 Incidence (burst/100 beats) 12.1 0.590 -0.179 0.639 Height (AU) -8.3 0.455 0.194 0.314 Total Activity (AU) 175 0.759 1.360 0.888 ∆RR (breath/min) -5.56 0.077 0.298 0.082 ∆V̇ E (L/min) -4.97 0.349 0.250 0.386 ∆VT (L/breath) 0.23 0.216 -0.015 0.143 ∆DBP (mmHg) -1.75 0.608 0.051 0.785 ∆SBP (mmHg) -1.92 0.618 0.138 0.517 ∆P CO et 2 ∆MAP (mmHg) -2.37 0.458 0.107 0.536 (mmHg) ∆HR (beat/min) -0.31 0.962 -0.067 0.852 ∆Frequency (burst/min) 2.04 0.657 -0.048 0.848 ∆Incidence (burst/100 beats) 3.52 0.635 -0.014 0.972 ∆Height (AU) -2.15 0.547 0.289 0.167 ∆Total Activity (AU) -76 0.706 12.900 0.263

Table 8.1: Hypercapnic cardiovascular, autonomic and ventilator responses. Regressions were performed with the categorical predictor for “group” to compare the high and low response groups. The y-intercept and Slope columns indicate the difference in each parameter between the high and low response groups. P<0.05 indicates a significant difference between groups.

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Discussion

Hypercapnia is a large stimulus to the central chemoreceptors. The cardiovascular and respiratory responses in both the high and low response groups were substantial, with increases in

124 VT, RR, V̇ E, BP, HR, and MSNA which is agreement with published research . There were not, however, any temporal differences between the high and low BP response groups. Similarly, there was no difference in sympathetic transduction between the high and low response group. When plotting the cardiovascular and respiratory data as a stimulus-response relation with PetCO2 as the independent variable, the results remained the same.

Whereas the reflex response to caloric stimulation of the hand (noxious thermal pain) and

SHG and RHG exercise (exercise induced pain and metabolite build-up) are substantially mediated by the peripheral nervous system, specifically by the afferent receptors located in the hand and arm, the response to hyperoxic hypercapnia is primarily mediated by the central chemoreceptor network distributed in the medulla124, 254, 255, 256. The central chemoreceptors do not appear to function as a single locus of neurons, but are reported to be a combination of sites within the medulla256. Glutamatergic neurons in the retrotrapezoid nucleus (RTN), serotonergic neurons in the medullary raphe, and noradrenergic neurons in the locus ceruleus (LC), have all been implicated in central chemoreception257, 258, 259, 260. These locations are close to cerebral blood

256 vessels and focal ablation of these areas decreased CO2 responsiveness . There does appear to be neural pathways critical to sensory feedback from the periphery and areas critical to chemosensation; for example, there are NTS neurons that relay peripheral information to the

RTN258. As a corollary, muscle afferent feedback to the CNS is obligatory to exercise hyperpnea, at least for moderate intensity rhythmic, large muscle, steady-state exercise125. If the EPR is the cause of the greater BP response in the high response group, it is plausible that exercise-induced

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changes in respiration may be different between groups independent of central CO2 sensitivity.

Perhaps concomitant EPR activation would have augmented the respiratory responses to hypercapnia and/or elicited differences between the low and high response groups, but these remain, for the time being, opportunities for future study.

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Limitations

Several methodological limitations to this investigation must be considered. First, the

261 respiratory pump elicits changes in Q̇ proportionate to increases in V̇ E ; therefore, the observed cardiovascular changes could occur with rapid deep breathing. Similarly, voluntary hyperventilation alone does increase HR (~18 BPM), but it does not change Q̇ , SV, MAP, or

MSNA, suggesting that increasing V̇ E per se, was not a large influence on the cardiovascular response to rebreathing124.

Second, we used PetCO2 as an index for PaCO2 and medullary interstitial PCO2.

Normally, the diffusion gradient for CO2 is from tissue, to blood, to alveoli, and then expiration.

Therefore, measurement of PetCO2 will necessarily underestimate the venous and tissue CO2 content. However, rebreathing CO2 from PetCO2 of ~40-70 mmHg over a 5 min duration creates a condition where inspired PCO2 is a function of PetCO2 which leads to equilibration between

107, 124 venous, arterial, and medullary interstitial PCO2 . Therefore, although we did not have a direct measure of medullary interstitial PCO2, it was assumed that the PetCO2 reflected the PCO2 that was acting as the central chemoreflex stimulus.

Finally, our response groups were based on the cardiovascular response to PECA. The utility of this factor to investigate ventilator chemosensation is a reasonable point of debate.

Alternative sorting could be performed to test for possible differences in rebreathing response, however, that is outside the scope of the current research.

To summarize, differences in EPR responsiveness did not, in our hands, generalize to central chemoreception. We caution that the results from this study should only be applied to a young healthy population. Attempts should not be made to extrapolate these responses to other

173 populations or different protocols (e.g., those that include hypoxia). Repeating this study under different oxygen conditions would be a worthwhile consideration for future study.

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CHAPTER 9: GENETIC ASSOCIATIONS WITH BLOOD PRESSURE RESPONSIVENESS

Introduction

Genetic influences on individual variation in BP regulation is a novel topic that is gaining interest. BP and its response to stress have important implications for cardiovascular health and disease19, 20, 22. As more SNPs are evaluated for functional associations and genetic analysis becomes more readily available to both researchers and the public, the association between SNPs and BP responsiveness is of interest for understanding the variability of responsiveness to the EPR.

There is already substantial evidence of a genetic role in a number of cardiovascular parameters. Studies comparing identical twins have yielded intriguing findings. The similarity of

MSNA recorded from identical twins gives evidence that sympathetic outflow may be partially influenced by genetics262. Similarly, another study showed that genetics explained up to 45% of the individual variation in baroreflex sensitivity263. Finally, cardiovascular stress reactivity to the

CPT yielded heritability estimates of 0.21 – 0.55 among studies of identical twins in which BP and

HR were assessed and a number of genes were revealed that may account for part for part of the heritability of stress reactivity264.

Such mutations have also been associated with health status. For example, hypertension has been the subject of fairly extensive gene association studies with a number of rare mutations and SNPs associated with the disease265. Pain sensitivity is associated with a number of SNPs, and specifically, with SNPs for genes that code for protein ion channels in peripheral afferent nerves266.

With this background in mind, it was the goal of this portion of the study to associate genetic variation in afferent (chemical, thermal and mechanical) sensory receptors with cardiovascular responsiveness.

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The functional significance of an individual SNP may be large, small or non-existent. The likelihood of discovering a SNP association with BP or other autonomic responses that is so large that the individual SNP alone accounts for a majority of the variability in BP is highly unlikely.

However, available evidence supports the notion that a SNP or group of SNPs that accounts for some proportion of the variability is plausible. For this reason, we selected SNPs for this study

(outlined below) with known associations with autonomic responses. The challenge was to discriminate SNPs with modest but physiologically important effects from truly non-functional

SNPs. We employed a “gene score” approach (outlined below) to group genes/SNPs that were of greatest interest into aggregate scores. This approach was advantageous in that it allowed for the possibility of summation of small effects into a single larger effect that could be related to BP responsiveness.

SNP Selection Criteria

Our design considered BP variation in normal people initially, and then subsequent genetic analysis was performed. To constrain the genomic analysis of autonomic tests to a relatively small cohort of individuals, we used a small-scale genomic analysis that primarily included SNPs with the following features:

1) a minor allele frequency (MAF) of at least 20% in the general population

2) known associations to autonomic disorders

3) SNPs in the ASIC3, P2RX4, P2RX5, and TRPV1 genes because they encode

receptors on sensory afferents that respond alone or in combination to tissue

metabolites.

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The 20% MAF was selected to limit the total number of subjects who would need to be screened, with a reasonable probability of identifying homozygous individuals for laboratory study. Assuming at least 100 subjects with both DNA and BP data, and assuming Hardy-Weinberg

Equilibrium (HWE), one would expect ~4 homozygotes of the rare allele.

The 26 SNPs selected for study targeted known associations previously reported in the literature or based on theoretical mechanisms. Genes were selected that encoded afferent sensory receptors in the muscle interstitial space and skin. From a methodological perspective, one could focus on SNPs in a single gene that codes for a single protein. For example, the gene for TRPV1 has >1000 SNPs identified (dbSNP, filtered: homo sapiens267) and other membrane proteins of interest would likely have a similar magnitude of SNPs in their genetic sequence. However, the study was insufficiently powered to evaluate a large number of genes with many SNPs in one or more genes, and certainly not a considerable portion of a complex system. The 25 SNPs included were selected from 9 different genes located on three different chromosomes, see Table 9.1.

SNPs Selected

 ASIC: SNPs selected for the ASIC family were from the ASIC1 and ASIC2 genes.

Although the inclusion of SNPs of ASIC3 was sought, only 4 SNPs were identified by

dbSNP267 when filtering for homo sapiens and a MAF of 0.20, none of the initial ASIC3

candidates had reported associations with autonomic responses.

 P2RX: SNPs selected for the P2RX family were from the P2RX4, P2RX5, and P2RX7

genes.

 TRP: SNPs selected for the TRP family were from the TRPV1, TRPV3, TRPV4, and

TRPA1 genes. TRPV1 is likely the most studied and understood of the genes included in

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this study. As it is implicated in the EPR and temperature sensation, SNPs from TRPV1

were the most selected for this study13, 59, 170.

Details for reference of the SNPs included for the above mentioned genes are outlined in Table 9.1.

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Gene Cytogenetic SNP RS# Chrom. SNP Type Assay ID Symbol Band rs10875995 ASIC1 12 12q13.12 Intron C__30879540_10 rs685012 ASIC1 12 12q13.12 5' putative promoter C____582136_10 rs1354492 ASIC2 17 17q11.2 Intron C___7472703_10 rs25644 P2RX4 12 12q24.31 Missense Mutation C____641312_1_ rs149245 P2RX5 17 17p13.2 Intron C___3211364_20 rs222775 P2RX5 17 17p13.2 Intron C___3211349_20 rs591874 P2RX7 12 12q24.31 Intron C__26388055_10 rs920829 TRPA1 8 821.11 Missense Mutation C__26087429_10 rs11655540 TRPV1 17 17p13.2 Intron C___3269503_10 rs150846 TRPV1 17 17p13.2 Intron C___3269525_10 rs150854 TRPV1 17 17p13.2 Intron C___2952709_10 rs150908 TRPV1 17 1713.2 Intron C___1093673_10 rs161365 TRPV1 17 17p13.2 Intron C___3269512_20 rs222741 TRPV1 17 17p13.2 Intron C___1093709_10 rs222747 TRPV1 17 1713.2 Missense Mutation C___1093688_20 rs224498 TRPV1 17 17p13.2 Intron C___3269554_10 rs224546 TRPV1 17 1713.2 Intron C__11679673_20 rs4790522 TRPV1 17 17p13.2 UTR 3 C___3269499_10 rs8065080 TRPV1 17 1713.2 Missense Mutation C__11679656_10 rs7217270 TRPV3 17 17p13.2 Intron C__30577301_10 rs10735104 TRPV4 12 12q24.11 Intron C__30924786_10 rs1861809 TRPV4 12 1224.11 Intron C___2879077_10 rs3742035 TRPV4 12 1224.11 Intron C___2879131_10 rs3742037 TRPV4 12 12q24.11 Silent Mutation C__25624159_10 rs6606743 TRPV4 12 1224.11 Intron C___2879055_10 Table 9.1 shows the assay information for the Human, TaqMan® SNP Genotyping Assays supplied by ThermoFisher Scientific. Chrom.=the chromosome number on which the gene and SNP are located.

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Methods

Buccal mucosa cells were collected under the guidance of the Penn State Genomics Core

Facility (Huck Institute The Pennsylvania State University, 407 Chandlee, University Park, PA

16802) using a protocol adapted from Freeman et al268. Subjects were given one cotton swab at a time for a total of 10 swabs. To optimize yield, samples were collected in the morning before subjects had eaten or brushed their teeth. Each swab was rubbed inside the mouth on the cheeks and gums for 20 seconds per swab. After 20 seconds the swab was placed by the subjects in to a vial of extraction buffer. Vials were kept at room temperature and delivered to the Genomics Core

Facility for DNA extraction and storage. For storage, the purified DNA was re-suspended in Tris

Ethylenediaminetetraacetic acid and frozen at -80° C.

After samples were collected from the large cohort, frozen DNA samples were thawed and

SNP analysis was completed using TaqMan® OpenArray® plates after amplification via Real-

Time Polymerase Chain Reaction (PCR). The open-array was custom designed for the study by

Life Technologies, ThermoFisher Scientific in a format pre-loaded with 32 assays and the analysis of 96 samples (subjects) per plate269. Only 26 SNPs were included on the plate due to technical limitations set forth by ThermoFisher Scientific. Table 9.4 details the breakdown of SNPs analyzed including gene, encoded protein, rs number, and MAF.

Statistical Analyses

All allele frequencies were first evaluated for HWE. Any SNPs that did not fall within

HWE were removed from subsequent analysis.

The primary statistical analysis was performed in two phases. The initial round of analysis was performed using all available subjects (N=101) as explained in Chapter 3.

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Each SNP was assigned a dummy code for trait/risk allele and the code was based on the literature to the extent possible (limitations of this technique are discussed in the limitations section). This code was employed in all subsequent analyses. The trait allele was coded 1 for individual subject data and the non-trait allele was coded 0, thus yielding a possible score of 0, 1, or 2 for each subjects’ genotype for each SNP. For this phase of analysis, an additive model was assumed for each genotype, a dominant model was never assumed, and non-allelic genes were not considered. The trait allele was identified as the minor allele for all but 1 SNP

Each individual subject’s data was sorted and aligned according to their BPR (their BP response to PECA accounting for grip force) and their respective genotypes for each of the selected

SNPs. Once aligned a bivariate correlation (IBM SPSS Statistics 24) was run comparing the SNPs and the BPR as an initial probe of the data. After the initial probe, a linear regression (IBM SPSS

Statistics 24) was performed on each of the individual SNPs compared to the BPRs.

Given the proposed interaction between P2RX4, P2RX5, and TRPV1 a decision was made a priori to create gene scores (GS) involving all analyzed SNPs in these 3 genes initially (14 of the 24 SNPs) and compare them individually to the BPR270. Because of the disproportionate number of TRPV1 SNPs included in the analysis, a GS sum (GSsum) and a GS average (GSavg) was created. With 1 P2RX4 SNP, 2 P2RX5 SNPs, and 11 TRPV1 SNPs, 14 genotypes were coded and combined to create the GS, with a theoretical range from 0 (no trait alleles) to 28 (all trait alleles). The GSavg was an average of the GS of all SNPs for a given gene (GSP2XR4/1, GSP2X5/2, and GSTRPV1/11) weighted for the unequal number of SNPs analyzed for each gene. This procedure produced a score from 0-2 (no trait allele- all trait alleles). After this comparison, a GSsum analysis for all peripheral SNPs (all 24 SNPs) was analyzed with BPR thus giving a possible range of 0-48

(no trait alleles-all trait alleles). Finally, although investigating the interaction of the gene products

181 was the primary goal, a sub-score for the three gene families (ASIC, P2RX, and TRP) were also made individually. This enabled a secondary check for the possible influence of SNPs from a single gene family rather than the interaction of multiple gene families.

The second phase of analysis was performed similar to the first, except limiting the participants to those who completed all autonomic tests and expanding the outcome variables from the BP responses to the initial SHG screening test to the BP and HR responses during all protocols.

Bivariate correlations (IBM SPSS Statistics 24) were run: SNPs vs SHG and PECA, SNPs vs RHG and PECA, SNPs vs CPT, SNPs vs hot immersion, and SNPs vs rebreathing. Not every possible combination was included in the analysis. Table 9.2 shows which outcome variables were included in the bivariate correlations for each SNP.

Protocol Bivariate Correlation Outcome Variables E3SBP, E3DBP, E3MAP, E3HR E3∆SBP, E3∆DBP, E3∆MAP, E∆3HR SHG P2SBP, P2DBP, P2MAP, P2HR P2∆SBP, P2∆DBP, P2∆MAP, P23HR LastExSBP, LastExDBP, LastExMAP, LastExHR LastEx∆SBP, LastEx∆DBP, LastEx∆MAP, LastEx∆3HR RHG P2SBP, P2DBP, P2MAP, P2HR P2∆SBP, P2∆DBP, P2∆MAP, P23HR C2SBP, C2DBP, C2MAP, C1HR CPT C2∆SBP, C2∆DBP, C2∆MAP, C1∆3HR H2SBP, H2DBP, H2MAP, H1HR Hot H2∆SBP, H2∆DBP, H2∆MAP, H1∆3HR Reb5SBP, Reb5DBP, Reb5MAP, Reb5HR Rebreathe Reb5∆SBP, Rec5∆DBP, Reb5∆MAP, Reb5∆3HR Table 9.2: Outcome variables included in bivariate correlations for SNP vs. cardiovascular parameters. SHG=Static handgrip. RHG=Rhythmic handgrip. CPT=Cold Pressor Test. Hot=Hot Immersion Test. E=Exercise. P=Post-exercise Circulatory Arrest. C=CPT. H=Hot. Reb=Rebrethe. #=corresponding minute of perturbation. DBP=diastolic blood pressure. SBP=systolic blood pressure. MAP=mean arterial blood pressure. HR=heart rate. ∆=change from control. RHG “LastEx” corresponds the BP or HR for each individual during the last minute they exercised. A correlation analysis was therefore run for each SNP against 56 different outcome variables.

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Bivariate correlations were once again used as initial probe of the data. Significant correlations in the P2RX4, P2RX5, and TRPV1 (the defined genes of interest) were identified and those variables were then analyzed using linear regression. This method minimizes the problem of repeated measures while probing the genes of greatest interest. Regression comparisons were

Bonferroni corrected for multiple comparisons (the number of comparisons and family-wise adjusted P-value and shown in Table 9.9).

Once again, GSs were created for the P2RX4, P2RX5, and TRPV1 and they were compared to change in MAP during each protocol. Because of the disproportionate number of TRPV1 SNPs included in the analysis, both a GSsum and a GSavg were created. The SNPs included in the GS analysis were those with significant correlations identified during the initial data probe; i.e., 1

P2RX4 SNP, 1 P2RX5 SNP, and 6 TRPV1 SNPs for a total of 8 SNPs combined to create the GS.

The GSsum was created with a possible range of 0-16 (no trait alleles- all trait alleles). The GSavg was made by averaging the GS of all SNPs for a given gene (GSP2X4/1, GSP2X5/1, and GSTRPV1/6) and then averaging those together thus factoring in the unequal number of SNPs analyzed for each gene. After this comparison, GSsum for all peripheral SNPs was analyzed.

To test whether the frequency of the presence of the trait alleles was different between the high and low response groups, a χ2 test (response group x trait) was performed (Microsoft Excel

2013). This test represents a dominant model of analysis. A χ2 test between all three allele groups

(homozygous non-trait allele, heterozygous, and homozygous trait allele) was considered; however, the sample size was insufficient for this type of analysis. Instead, a trait allele carrier group was summing heterozygotes and homozygotes of the trait allele carriers which was then compared against the homozygous non-trait allele. A P-value of P< 0.05 was considered significant for all tests.

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Duplicate DNA Samples

To test repeatability of SNP allele identification, 16 of 177 were randomly selected for repeat analysis.

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Results

Although 26 SNPs were initially selected for analysis, 1 SNP was removed due to an incorrect selection and faulty assay. A second SNP was removed from further analysis due to falling outside of HWE (no homozygotes for the minor allele were identified for this SNP) leaving

24 SNPs that were analyzed. The breakdown of sample sizes for analysis are shown in Table 9.3, indicating that 101/177 subjects were included in the initial analysis.

DNA Samples n Full Group 177 European Ancestry Only 136 Performed SHG screening 101

Table 9.3: Sample sizes available for gene*BPR analysis.

Genetic information used in the analysis is shown in Table 9.4. The European (EUR) MAF and major/minor designation were used in all cases (dbSNP267). As shown in Table 9.5, there were no significant correlations for any SNP-BPR pair during the initial probe of the data. Although no significant correlations were identified, a linear regression was performed on each SNP-BPR group as a secondary check. There were no slopes different from 0 for all regressions; results shown in

Table 9.6.

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Gene Major Minor Trait SNP RS# MAF Abbreviation Allele Allele Allele 685012 T C 0.34 C ASIC1 10875995 T C 0.33 C 1354492 ASIC2 G A 0.44 A 25644 P2RX4 A G 0.13 G 149245 A G 0.38 G P2RX5 222775 C G 0.47 G 591874 P2XR7 A C 0.26 C 920829 TRPA1 G A 0.13 G 150846 C T 0.38 T 150854 A C 0.43 C 150908 G A 0.45 A 161365 C T 0.28 T 222741 T C 0.27 C 222747 TRPV1 C G 0.25 G 224498 T G 0.40 G 224546 T C 0.46 C 4790522 C A 0.43 A 8065080 T C 0.41 C 11655540 T G 0.30 G 7217270 TRPV3 G A 0.45 A 1861809 C T 0.48 T 3742035 A G 0.30 G 3742037* TRPV4 C T 0.14 C 6606743 A G 0.50 G 10735104 T C 0.48 C Table 9.4: SNP details used for genetic analysis. The trait allele was coded with “1” during all analyses. MAF=minor allele frequency. Grey indicates a trait allele that was identified as the major allele. *indicates SNP not included in subsequent analysis due to being outside of HWE. All data are based on the European (EUR) population267.

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SNP RS# Bivariate Correlation P-value n 25644 -0.044 0.668 99 149245 0.080 0.429 99 150846 -0.073 0.469 100 150854 0.052 0.613 98 150908 -0.063 0.535 100 161365 -0.083 0.414 99 222741 -0.084 0.407 100 222747 0.024 0.816 99 222775 0.117 0.248 99 224498 0.046 0.649 100 224546 0.052 0.608 100 591874 0.083 0.412 100 685012 -0.018 0.862 100 920829 -0.008 0.939 98 1354492 -0.134 0.184 100 1861809 0.053 0.598 101 3742035 -0.060 0.550 100 4790522 0.028 0.781 101 6606743 -0.010 0.923 97 7217270 0.026 0.793 101 8065080 0.169 0.093 100 10735104 0.059 0.561 99 10875995 -0.036 0.725 98 11655540 -0.011 0.912 101 Table 9.5: Bivariate correlations from BPR and SNP. P-value indicates a 2-tailed bivariate correlation test. Significance=P<0.05.

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SNP RS# R R2 Std. Error of the Estimate P-value 25644 0.044 0.002 11.153 0.668 149245 0.080 0.006 11.243 0.429 150846 0.073 0.005 11.239 0.469 150854 0.052 0.003 10.971 0.613 150908 0.063 0.004 11.256 0.535 161365 0.083 0.007 11.273 0.414 222741 0.084 0.007 11.237 0.407 222747 0.024 0.001 11.198 0.816 222775 0.117 0.014 11.227 0.248 224498 0.046 0.002 11.265 0.649 224546 0.052 0.003 11.263 0.608 591874 0.083 0.007 11.177 0.412 685012 0.018 0.000 11.153 0.862 920829 0.008 0.000 11.260 0.939 1354492 0.134 0.018 11.175 0.184 1861809 0.053 0.003 11.206 0.598 3742035 0.060 0.004 11.258 0.550 4790522 0.028 0.001 11.217 0.781 6606743 0.010 0.000 11.425 0.923 7217270 0.026 0.001 11.218 0.793 8065080 0.169 0.029 10.949 0.093 10735104 0.059 0.004 11.305 0.561 10875995 0.036 0.001 11.029 0.725 11655540 0.011 0.000 11.221 0.912 Table 9.6: Regression results from BPR being regressed onto SNP genotype. P-value indicates the slope of the regression line. Significance=P<0.05.

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Histogram properties were tested with IBM SPSS Statistics 24. As shown in Figure 9.1, the GSsum frequency was normally distributed with a mean score of 9.6±0.3. The GSavg was non- normal (P=0.001) with a slight right skew (0.825) and the GSsum for all SNPs, not only the primary SNPs of interest, was normally distributed (P=0.143). For high and low response groups only, the frequency distribution of GSsum was not normally distributed (P=0.048), the GSavg is normal (P=0.051), and GSsum for all SNPs is normal (P=0.554). As the GS analysis is completed using linear regression, the cases on non-normality are not of great concern as the predictor (x- axis) values in linear regression do not depend on a normal (Gaussian) distribution.

The results for the BPR compared to GSsum and GSavg for the main SNPs of interest are shown in Figures 9.2 and 9.3 respectively. Neither the GSsum nor the GSavg yielded a slope different from zero and therefore there was not a statistically significant relation between the BPR and the GS for the SNPs of interest. The GSsum was also calculated for every SNP compared to

BPR, and once again no significant relation was identified (y=0.075x+0.656, R2=0.001, P=0.807).

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Statistic Mean 9.6±0.3 Median 10.0 Variance 8.3 Std. Deviation 2.9 Minimum 2 Maximum 17 Range 15

Frequency Normality P=0.192

Figure 9.1: The distribution of GS responses in the GS sum model for primary SNPs of interest.

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GS Sum 50 y = 0.1786x + 0.3117 R² = 0.0021 P = 0.643 30

(mmHg) -10

-30

Blood Pressure Residual Residual Pressure Blood 0 5 10 15 20 Gene Score

Figure 9.2: GSsum for the SNPs of interest. BPR was plotted against GS sum. The equation, R2 value, and P-value are parameters of the regression line (black dashed line). P indicates the significance of the slope of the regression line relative to 0. Significance=P<0.05.

GS Average 50 y = 1.8714x + 0.9507 R² = 0.0023 30 P = 0.637

(mmHg) -10

-30

Blood Pressure Residual Residual Pressure Blood 0.0 0.5 1.0 1.5 2.0 Gene Score

Figure 9.3: GS average for the SNPs of interest. BPR was plotted against GS average. The equation, R2 value and P-value are parameters of the regression line (black dashed line). P indicates the significance of the slope of the regression line relative to 0. Significance=P<0.05.

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For reference, a gene sub-score was also created from the full cohort of subjects for each of the gene families separately; i.e., ASIC, P2RX, and TRP. The results of these scores are shown in Table 9.7. The R2 values for regression of the BP residual onto the GS are also given in

Table 9.7. As shown, virtually none of the variability in BP responsiveness to PECA after SHG is accounted for via the gene scores created from the individual gene families.

ASIC (3) P2RX (4) TRP (17) GSSum 2.1 2.4 13.8 GSAvg 0.7 0.6 0.8 R2 0.008 0.009 0.001

Table 9.7: This table shows the gene scores for the three gene families included in this research: ASIC, P2RX, and TRP. (#) is representative of the number of SNPs included in the score for each gene family. GSSum and GSAvg are gene score sum, and gene score average, respectively. The BP residual for each subject was regressed onto the gene score for each gene family, and the resultant R2 value is displayed.

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Functional Associations The next step in DNA analysis was performed on the 30 subjects who completed all protocols. A separate analysis was run for each protocol: SHG and PECA, RHG and PECA, CPT, hot water immersion, and rebreathing, respectively. An abridged correlation table is shown in

Tables 9.8A and 9.8B in which only the significant correlations are displayed. There were no significant correlations for the RHG/PECA protocol, there was 1 significant SNP for SHG, 5 for

CPT, 5 for hot, and 5 for rebreathing.

Linear regression between the GS and BP was performed on those SNPs demonstrating a significant correlation during the initial review. Table 9.9 shows that although 8 of the 24 SNPs showed significance individually, if the Bonferroni correction for multiple comparisons was necessary, none of the SNPs remained significant. The SNPs, rs25644(P2RX4), rs150908(TRPV1), and rs222775(P2RX5) were 3 SNPs identified by the correlation analysis to be significantly related to only one variable outlined in Table 9.2. As such, they were only used in a single regression and therefore were not subjected to the Bonferroni correction and are considered significant at the 0.05 level. All other SNPs were found to be significantly correlated to >1 variable from Table 9.2 and were therefore subject to Bonferroni correction. The number of comparisons and family-wise adjusted P-values are shown in Table 9.9.

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30% E3DBP E3SBP E3MAP E3∆DBP E3∆SBP E3∆MAP E3HR E3∆HR Corr. -0.325 -0.289 -0.361 -0.247 -0.205 -0.248 -0.283 -0.114 rs1354492 P-value 0.113 0.162 0.076 0.233 0.326 0.231 0.171 0.588 P2DBP P2SBP P2MAP P2∆DBP P2∆SBP P2∆MAP P2HR P2∆HR Corr. -0.226 -0.214 -0.274 -0.094 -0.078 -0.098 -.495* -0.326 rs1354492 P-value 0.277 0.303 0.184 0.653 0.712 0.64 0.012 0.112 N 25 25 25 25 25 25 25 25 Cold C2DBP C2SBP C2MAP C2∆DBP C2∆SBP C2∆MAP C1HR C1∆HR Corr. 0.153 0.11 0.164 .386* 0.341 .407* -0.071 0.316 rs224498 P-value 0.429 0.57 0.395 0.039 0.07 0.028 0.721 0.101 Corr. -0.341 -0.248 -.372* -0.341 -0.354 -0.364 -0.303 -0.235 rs1354492 P-value 0.071 0.195 0.047 0.07 0.06 0.052 0.117 0.229 Corr. 0.323 0.34 0.249 0.23 .438* 0.261 -0.065 0.039 rs3742035 P-value 0.087 0.071 0.192 0.23 0.018 0.172 0.743 0.843 Corr. 0.03 -0.036 0.085 0.238 -0.088 0.202 0.11 .413* rs10735104 P-value 0.879 0.852 0.66 0.214 0.649 0.293 0.579 0.029 N 29 29 29 29 29 29 28 28

Table 9.8A: This table shows the significant correlations between the SNPs in the rows and the cardiovascular variable at a given protocol time in the columns. Significant correlations are highlighted in grey. * P<0.05 (2- tailed). **P<0.01 (2-tailed).

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Hot H2DBP H2SBP H2MAP H2∆DBP H2∆SBP H2∆MAP H1HR H1∆HR Corr. -0.267 -0.238 -0.254 -.502** -.577** -.519** -.430* -.578** rs11655540 P-value 0.161 0.214 0.183 0.006 0.001 0.004 0.02 0.001 Corr. -0.215 -0.205 -0.209 -.378* -.443* -.397* -0.334 -.474** rs161365 P-value 0.263 0.286 0.276 0.043 0.016 0.033 0.076 0.009 Corr. .388* .442* .410* .460* .525** .499** .381* .443* rs4790522 P-value 0.038 0.016 0.027 0.012 0.003 0.006 0.042 0.016 Corr. -.410* -.403* -.385* -0.254 -0.253 -0.261 0.017 0.132 rs7217270 P-value 0.027 0.03 0.039 0.184 0.185 0.172 0.93 0.495 N 29 29 29 29 29 29 29 29 Corr. -.420* -0.248 -0.369 -0.247 -0.197 -0.244 -0.052 -0.119 rs25644 P-value 0.026 0.203 0.054 0.206 0.315 0.211 0.794 0.547 Corr. 0.306 0.287 0.3 0.345 .461* .397* .484** .435* rs224546 P-value 0.114 0.139 0.121 0.072 0.014 0.036 0.009 0.021 N 28 28 28 28 28 28 28 28 Rebreathe Reb5DBP Reb5SBP Reb5MAP Reb5∆DBP Reb5∆SBP Reb5∆MAP Reb5HR Reb5∆HR rs150908 Corr. 0.164 0.293 0.254 -0.207 -.455* -0.239 0.042 -0.2 P-value 0.396 0.123 0.183 0.281 0.013 0.211 0.83 0.298 rs222775 Corr. -0.057 -0.197 -0.089 0.084 -0.044 0.08 0.12 .383* P-value 0.769 0.306 0.646 0.666 0.82 0.682 0.535 0.04 rs1861809 Corr. 0.323 0.228 .408* 0.204 0.035 0.16 -0.094 -0.031 P-value 0.088 0.234 0.028 0.288 0.857 0.408 0.629 0.875 rs7217270 Corr. -0.332 -.466* -0.358 0.000 -0.084 -0.023 -0.036 0.011 P-value 0.078 0.011 0.057 0.998 0.665 0.908 0.854 0.953 rs10735104 Corr. 0.307 0.301 .390* 0.232 0.056 0.193 -0.062 0.049 P-value 0.105 0.113 0.036 0.226 0.774 0.316 0.75 0.799 N 29 29 29 29 29 29 29 29

Table 9.8B: This table shows the significant correlations between the SNPs in the rows and the cardiovascular variable at a given protocol time in the columns. Significant correlations are highlighted in grey. *P<0.05 (2- tailed). **P<0.01 (2-tailed).

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Dependent Adjusted Adjusted Gene RS R R2 P-value MC Variable R2 P-value P2RX4 25644 H2DBP 0.420 0.177 0.145 0.026 no 0.05 P2RX5 222775 Reb5∆HR 0.383 0.147 0.115 0.040 no 0.05 150908 Reb5∆SBP 0.455 0.207 0.177 0.013 no 0.05 H1∆HR 0.474 0.225 0.196 0.009 H2∆DBP 0.378 0.143 0.111 0.043 161365 yes-4 6.250E-06 H2∆MAP 0.397 0.157 0.126 0.033 H2∆SBP 0.443 0.197 0.167 0.016 C2∆DBP 0.386 0.149 0.117 0.039 224498 yes-2 0.003 C2∆MAP 0.407 0.166 0.135 0.028 H2∆SBP 0.461 0.212 0.182 0.014 H2∆MAP 0.397 0.158 0.126 0.036 224546 yes-4 6.250E-06 H1HR 0.484 0.234 0.205 0.009 H1∆HR 0.435 0.189 0.158 0.021 H1∆HR 0.443 0.196 0.167 0.016 TRPV1 H1HR 0.381 0.145 0.113 0.042 H2∆DBP 0.460 0.211 0.182 0.012 H2∆MAP 0.499 0.249 0.221 0.006 4790522 yes-8 3.906E-11 H2∆SBP 0.525 0.276 0.249 0.003 H2DBP 0.388 0.150 0.119 0.038 H2MAP 0.410 0.168 0.137 0.027 H2SBP 0.442 0.195 0.165 0.016 H1∆HR 0.578 0.334 0.310 0.001 H1HR 0.430 0.184 0.154 0.020 11655540 H2∆DBP 0.502 0.252 0.224 0.006 yes-5 3.125E-07 H2∆MAP 0.519 0.269 0.242 0.004 H2∆SBP 0.577 0.333 0.308 0.001

Table 9.9: Linear regressions with TRPV1, P2RX4, and P2RX5 SNPs as the a priori genes of interest. For SNPs that were used for multiple regression comparisons (MC), Bonferroni adjustments were performed (family-wise P<0.05). Significant associations are highlighted in grey.

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Similar to the initial GS results, the GS for the primary SNPs of interest during the full study protocols, as shown in Table 9.10, no dependent variable was significantly associated with

GS. Also shown in Table 9.9 are the GSsum results for all SNPs: similarly, none were significant.

Independent Variable Dependent Variable R R2 Adjusted R2 P-value C2∆MAP 0.061 0.004 -0.033 0.755 H2∆MAP 0.203 0.041 0.006 0.291 Primary GSSum 30%P2∆MAP 0.079 0.006 -0.037 0.706 10%P2∆MAP 0.052 0.003 -0.037 0.795 Reb5∆MAP 0.069 0.005 -0.032 0.722 C2∆MAP 0.080 0.006 -0.030 0.680 H2∆MAP 0.087 0.008 -0.029 0.652 Primary GSAvg 30%P2∆MAP 0.155 0.024 -0.019 0.461 10%P2∆MAP 0.150 0.022 -0.017 0.456 Reb5∆MAP 0.134 0.018 -0.018 0.488 C2∆MAP 0.108 0.012 -0.025 0.576 H2∆MAP 0.066 0.004 -0.033 0.734 Full GSSum 30%P2∆MAP 0.003 0.000 -0.043 0.988 10%P2∆MAP 0.060 0.004 -0.036 0.766 Reb5∆MAP 0.147 0.022 -0.015 0.446

Table 9.10: This table shows the gene scores for the primary SNPs of interest (TRPV1,P2X4, and P2X5) and the full group of GS. Regressions were run against the listed dependent variable. “Sum” indicates the additive model of gene scores and “avg” indicates the average scores of each gene mean.

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The frequency analyses of trait vs. non-trait carriers are shown in Table 9.11A and Table

9.11B. Any comparison that included <5 members was considered invalid. Although no significant difference was identified, the missense SNP for TRPV1 (rs8065080) approached significance (P=0.055) and the TRPV3 receptor SNP (rs7217270) was significant but invalidated due to low frequency.

Homozygous Homozygous Trait Allele Gene RS# Group Heterozygous P-value Non-trait Allele Trait Allele Carriers 685012 low 12 9 4 13 0.847 685012 high 15 16 2 18 ASIC1 10875995 low 11 9 4 13 0.938 10875995 high 15 15 2 17 1354492 low 6 11 8 19 ASIC2 0.983 1354492 high 8 20 5 25 25644 low 18 6 1 7 P2RX4 0.232 25644 high 28 5 0 5 149245 low 13 8 4 12 0.621 149245 high 15 14 4 18 P2RX5 222775 low 11 10 4 14 0.407 222775 high 11 16 6 22 591874 low 12 11 1 12 P2XR7 0.658 591874 high 15 16 3 19

Table 9.11A: Frequency analysis of trait carriers for the ASIC1, ASIC2, P2RX4, P2RX5, and P2RX7 SNPs. Trait carriers were defined as the sum of heterozygotes and homozygotes for the trait allele. P<0.05 in shaded cells.

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Homozygous Homozygous Trait Allele Gene RS# Group Heterozygous P-value Non-trait Allele Trait Allele Carriers 920829 low 1 7 17 24 TRPA1 0.726 920829 high 2 6 25 31 150846 low 7 15 3 18 0.258 150846 high 14 11 8 19 150854 low 4 14 6 20 0.198 150854 high 2 19 12 31 150908 low 8 14 3 17 0.471 150908 high 14 16 4 20 161365 low 7 17 1 18 0.072 161365 high 17 14 2 16 222741 low 12 11 2 13 0.079 222741 high 24 5 5 10 222747 low 12 11 0 11 TRPV1 0.620 222747 high 20 12 2 14 224498 low 7 18 0 18 0.951 224498 high 9 20 4 24 224546 low 9 14 2 16 0.832 224546 high 11 14 8 22 4790522 low 9 14 2 16 0.955 4790522 high 12 15 7 22 8065080 low 13 9 3 12 0.055 8065080 high 9 18 6 24 11655540 low 8 14 3 17 0.792 11655540 high 12 19 3 22 7217270 low 9 9 7 16 TRPV3 0.026 7217270 high 4 25 5 30 1861809 low 5 12 8 20 0.412 1861809 high 10 17 7 24 3742035 low 14 9 2 11 0.367 3742035 high 15 17 2 19 TRPV4 6606743 low 9 11 5 16 0.647 6606743 high 10 17 6 23 10735104 low 8 13 4 17 0.831 10735104 high 10 17 7 24

Table 9.11B: Frequency analysis of trait carriers for the TRPV1, TRPV3, and TRPV4 SNPs. Trait carriers were defined as the sum of heterozygotes and homozygotes for the trait allele. P<0.05 in shaded cells. Interesting findings that were not statistically significant are bolded.

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Duplicate DNA Samples

DNA analysis was repeated for 16 individuals: 4 were chosen because of a relatively high frequency of unclassified SNPs in the initial analysis, and 12 were randomly selected. Results from the repeat analysis are shown in Table 9.12. Less than 1% of SNP determinations for a genotype were different from the initial DNA analysis. The buccal swab was repeated for one subject with

11 initial indeterminate variants. There were 0 indeterminate variants in the repeat analysis, likely indicating a poor DNA sample collection initially; therefore, the repeat analysis was used for this subject. All other data from the initial analysis was considered valid and used for analysis. One subject had 0 indeterminate variants in the initial analysis and 12 indeterminate variants in the repeat analysis, likely indicating a procedural problem with the repeat analysis for this subject.

These two subjects were included in the Table 9.12 (A) and excluded (B).

A: n % B: n % Subject 16 Subject 14 SNP 26 SNP 26 Total (Subject*SNP) 416 Total (Subject*SNP) 364 Same Call 373 89.7 Same Call 345 94.8 Different Call 43 10.3 Different Call 19 5.2 Initial- Indeterminate 22 5.3 Initial- Indeterminate 11 3.0 Repeat- Indeterminate 17 4.1 Repeat- Indeterminate 5 1.4 Different Genotype Assigned 4 1.0 Different Genotype Assigned 3 0.8 Table 9.12: (A) Overall repeatability of DNA analysis in 16 (~9%) subjects. (B) Overall repeatability of DNA analysis accounting for 2 subjects who had a high number of undetermined SNP genotypes. “Same call”, “Different call”, and “different genotype assigned” describe the relation between the initial and repeat analysis. The “initial- indeterminate” and “repeat- indeterminate” refer to observations within the initial and repeat analysis only.

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Discussion

GS analysis of the selected SNPs failed to reveal convincing evidence that the selected

SNPs accounted for in inter-individual variations in BP responses. That said, a number of the correlation and regression data are intriguing. Significant relations between rs25644(P2X4) and the absolute DBP response to hot water immersion, rs150908(TRPV1) and the change in SBP during rebreathing, and rs222775(P2RX5) and the change in HR during rebreathing were all statistically significant.

Any SNP that was correlated with more than one cardiovascular data point was no longer considered significant due to multiple comparisons. Therefore, somewhat counterintuitively, but inherent in the nature of genetic statics with many comparisons, SNPs that correlated to multiple cardiovascular parameters across multiple protocols were the least likely to be considered significant as they were compared multiple times. While those that correlated to only a single data point, were the only SNPs to be considered significant.

Correlation and regression are related in cases with only a single independent and single dependent variable in each comparison. A correlation pivot table was inspected as an initial probe of the data, however, because of the discontinuous, non-normal independent variable of gene score, regression. This leaves a circular-conundrum for analyses considering multiple comparisons, because the regressions were based on bivariate correlation of all possible combinations that was not corrected for multiple comparisons.

The primary goal, however, of using the selected regressions was to give the best chance of identifying related genes with important SNPs via GSs. The primary genes of interest were

ASIC3, TRPV1, P2X4, and P2X5 because of their proposed interaction in sensing the skeletal muscle interstitial environment14, 15. By creating GSs rather than comparing individual SNPs,

201 multiple comparison were limited and a small effect of an individual SNP might be more easily observed if grouped with other SNPs with similar small effects, that may function in an additive manner. The results of these GSs, for both the main genes of interest and the whole set of genes, in both the initial population distribution study, and all other protocols, were not statistically significant. Similarly, the individual gene family scores also did not account for the variation in

BP response. Therefore, although the regression data is intriguing, the conservative interpretation of the primary GS outcomes is that there is no significant effect on reflex activation of the cardiovascular system by the interaction of genetic SNPs for TRPV1, P2X4, and P2X5 in the few

SNPs selected for this analysis.

Genome-wide association studies have identified hundreds of cardiovascular-related genetic variants; however, individual variants have limited predictive ability as they explain only a small fraction of the risk variation270. Using information from multiple variants in the form of a

GS, however, has become a method for examining the cumulative predictive ability of multiple genetic variations on disease outcome and phenotype270, 271. Although SNP selection for building a GS based on SNPs identified as significant in repeated genome-wide association studies is the gold standard, it is not uncommon to create GSs based on biologically plausible candidate gene association studies270. This is especially true if applicable genome-wide association studies with replication are not yet done for the topic of interest. As the current study involved the use of BP residuals based on PECA after SHG, genome-wide association studies were not available.

Therefore, the literature was used to create a list of biologically plausible candidate SNPs for analysis. As described above, however, this approach was not successful in the current study.

Certain SNPs may, however, be unequally represented between the high and low response groups. Of the 101 subjects with European ancestry who both completed the SHG screening

202 protocol and gave a DNA sample, there were 25 subjects in the low response group and 34 in the high response group (42 were in the middle 50% and were not included). 14/25 subjects with low response and 17/34 subjects completed all protocols. While this sample size was adequately powered for comparison of cardiovascular variables, it limited the utility of contingency analysis.

At best, the χ2 results raise the intriguing notion that the trait-allele TPRV1 SNP rs8065080 is disproportionately represented in the high response group (P=0.055).

The TRPV1 SNP rs8065080 is missense SNP also called Ile585Val for the amino acid substitution of isoleucine for valine. This SNP may play a role in hereditary pain sensitivity266, 272,

273. Interestingly, this TRPV1 SNP has been associated with cold hypoalgesia273 and longer cold withdrawal times (and not heat pain intensity)272. Binder et al. suggested a complex role for

TPRV1 with multiple interactions occurring within the PNS and CNS including possible coexpression with TRPA1273. In an association with asthma, rs8065080 was related to the risk of wheezing and its gene product was later shown to modulate channel activity as measured by Ca2+ uptake in response heat and capsaicin in isolated cells (transfected HeLa cells bathed in isotonic solution of 140 NaCl, 5 KCl, 1.2 CaCl2, 0.5 MgCl2, 5 glucose, 10 HEPES and fura-2 based fluorescence imaging274).

To the contrary, studies on cold and pain sensitivity275, smoking/cough276, and cellular response to stimulation in whole-cell patch clamping and Ca2+ imaging (HEK293 cell in

DMEM/F12 medium with 10% bovine serum and fura-2 based fluorescence imaging172 and modified Eagle’s medium with non-essential amino acids, 10% fetal calf serum and glutamine277) all included rs8065080 and none found significance for the SNP. One possible reason for the lack of consistent effects may be that both isoleucine and valine are hydrophobic and therefore their exchange may not cause significant protein conformation alterations, which leads to only small, if

203 any, phenotypic changes273, 277. Nevertheless, the trend observed in this study is sufficiently interesting the future study is warranted.

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Limitations

A limitation to the analysis was the subjective assignment of the trait alleles. SNPs were generally identified in and chosen from published literature in which the SNPs were identified as significant. To the extent possible, SNPs were then assigned a trait allele based on that literature.

The limitation to this approach comes with inconsistent, unclear, or missing trait allele assignments in the publications, thus leading to the possibility of incorrect trait allele assignment.

The “EUR” (European- as defined in dbSNP) population was used to identify the MAF and to check for HWE. For the genetic analysis, it was assumed that the genetic background of each subject was aligned with that of the EUR population because of their self-reported Caucasian race, however, no further analysis was done to confirm European ancestry. Only Caucasians were included in the genetic analysis, as Caucasians were the largest individual cohort of individuals available and only a single race is included to avoid population stratification of results (i.e. difference populations can have different MAFs for a given SNP thus possibly altering results based on race rather than on measured outcomes).

Another limitation to the current approach was the attempt to connect a genotype directly with a phenotype with little to no ability to measure the physiological/mechanistic link between the two. That is, there are many possible confounding variables that occur between genotype and protein production at the peripheral afferent nerves. For example, gene expression, mRNA production, protein synthesis, and post translational modification of the proteins may all differ between tissues and between people. That said, this novel approach of linking BP responsiveness to genotype was never designed to be a complete mechanistic explanation of all possible differences between individuals. It was, however, designed as an attempt at a possible starting

205 point for a deeper/clearer understating of the normal BP variation among healthy individuals and how the genes of the peripheral afferents may play a role in this variation.

Finally, although a substantial number of subjects were genotyped, the overall sample size of the study may still be considered quite small. It is not uncommon for genomic studies to included thousands of subjects. For example, a genome-wide association study may attempt to show association between a genetic variant and a trait, but include thousands of observations increasing its power to observe a potential difference. Once again, however, this study was never meant to be a genome-wide association study, but an initial attempt to identify significant SNPs using a highly selective process for SNP selection, rather than a full-genome approach. The strengths of this approach include responsible use of available resources, as specifically choosing a number of SNPs that have the highest chance of showing significance, had the opportunity to be the most cost-effective manner to identify important SNPs. Additionally, choosing a limited number of SNPs has the advantage of decreasing statistical multiple comparisons.

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CHAPTER 10: SUMMARY, CONCLUSIONS, AND FUTURE DIRECTIONS

Summary

The key finding from Chapters 5-8 is that, compared to the low response group, the high response group had a larger BP response to SHG followed by PECA, RHG followed by PECA, and caloric stimulation of the hand. Therefore, responses were consistent when using several different stimuli that activate the autonomic nervous system.

Central to this dissertation is the thesis that between group differences could be attributed, at least in part, to differences in sensory receptor function. Alternative hypotheses include differences in central integration of these reflex pathways, differences in efferent sympathetic activity and its distribution, and/or differences in the coupling of the vascular response to changes in sympathetic activity (sympathetic transduction). I consider these possibilities below.

Because BP was higher during both SHG and RHG with PECA, the high response group had a greater EPR relative to the low response group. The persistence of a larger BP during

PECA specifically suggests that either an enhanced metaboreflex portion of the EPR is functioning; the stimulus is greater for this group; and/or that the high response group may maintain BP during PECA because of a shift in sympatho-vagal balance favoring sympathetic efferent activity.

The normal SHG exercise-induced increase in MAP is thought to be primarily a function of an increase in Q̇ (via increased SV and HR) with little change in systemic vascular resistance43, 278, 279, 280, 281. The data presented herein are consistent with this concept. Neither the change in calf resistance nor the increase in MSNA was different between groups, suggesting

207 that sympathetic transduction (the vascular response to a given change in sympathetic activity) did not differ between groups. Indeed, the change in calf resistance over the change in total

MSNA was plotted for each protocol (Chapters 5-8) and there was no difference between the high and low response groups during any perturbation. Show in Figure 10.1 are scatter plots of calf resistance by total MSNA for a subset of subjects. Data were pooled across perturbations in an attempt to better understand the sympathetic transduction between groups. These plots included individual subjects grouped for rest-SHG-PECA, rest-CPT, and rest-hot immersion thus giving 7 data point to plot for each subject. The average slope of the lines for the high and low response groups were considered the group sympathetic transduction and compared via t-test.

The comparison was limited to subjects for which calf resistance and MSNA was available for all 3 protocols (low response n=5 and high response n=8). There was no difference in the slope of the line relating the change in resistance to the change in total MSNA between the high and low response groups (P=0.416) giving further evidence that the difference in BP response is not likely from a difference in sympathetic transduction. As discussed in Chapter 5, the resistance in other vasculatures was not determined, however, and could theoretically play a role in the overall

BP differences between the high and low response groups during SHG.

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Low Response High Response 250 250

200 200

150 150

100 100

∆Resistance ∆Resistance ∆Resistance ∆Resistance

50 50

(mmHg*min*100/ml) (mmHg*min*100/ml) 0 0 0 3000 6000 9000 0 3000 6000 9000 ∆MSNA (AU/min) ∆MSNA (AU/min)

Figure 10.1: Sympathetic transduction in high and low response groups across protocols. Each subject is plotted individually with data points from rest-SHG-PECA, rest-CPT, and rest-hot immersion resulting in 7 data points each. The average slope of the lines for the high and low response groups were compared via t-test. Low response n=5 and high response n=8. There was no difference in the average slopes between the high and low response groups (P=0.416).

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The maintenance of a higher BP during PECA in the high response group is evidence that an enhanced metaboreflex portion of the EPR is functioning. The maintenance BP must come from, by definition, a higher Q̇ , TPR, or combination of the two. Both SV and HR typically return to baseline during PECA39, 43. Although TPR does not generally increase substantially during the handgrip exercise, it may be higher during PECA39. The calf resistance that was measured in this study was not different between groups, however, TPR was not known. Along with the current data, repeating these protocols with direct measures of SV and intra-arterial pressure for accurate calculations of Q̇ and TPR would be beneficial to test for differences between high and low response groups to help explain the different group responses.

It does not seem to be the case that the pain stimuli from the various protocols is greater for the high response group. During a CPT, the pain response is positively related to the peak

MAP response87. During this study, however, for each protocol the perceived pain, exertion, and stress were not higher for the high response group despite the higher BP. This is strong evidence that the BP response differences were not from subject perception. Given the importance of peripheral receptors for pain sensation and the fact that there was no difference in the pain responses between groups during substantially different protocols; it is also likely that there is not a substantial difference in sensory receptor function between groups. However, using the current protocols there is no way to parse the difference in sensations from noxious vs. non- noxious exercise ischemia/fatigue, cold, and hot which could be different between groups.

Finally, if there was a difference in sensory receptor function between response groups, it is also possible that the central integration of signals were different leading to similar pain responses but heterogeneous BP responses.

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As discussed in the individual chapters, the lack of a consistent difference in MSNA response between the high and low response groups suggested that there is no difference in efferent sympathetic activity between the groups at least to the vasculature of the lower limb.

The efferent response to other vasculatures, however, is unknown.

The water immersion tests demonstrate that there is some degree of inter-stressor response consistency between the exercise and water immersion paradigms; that is the group with a higher BP response to PECA also had a higher BP response to both forms of caloric stimulation. Additionally, MSNA was higher in the high response group during the first min of the CPT, suggesting a possible increased sympathetic responsiveness to cold/pain. By the third minute of immersion, however, there was no difference in MSNA in the high and low response groups and there was no change in the relation between calf resistance and MSNA. Although the activation of brain areas determined with functional magnetic resonance imaging were not precisely the same between noxious cold100, noxious hot106, post-exercise ischemic pain282, and muscle pain (hypertonic saline injection)282, the results of this research suggest a common integration of these pain (and other sensory) signals that manifested as a higher BP response in the high response group.

During reflex activation of the autonomic nervous system, the high response group responded with a consistently higher BP response. Given the lack of convincing genomic evidence of functional genetic differences between groups, a lack of evidence for a difference in perception (no differences in pain), and a lack of evidence for differences in efferent sympathetic activity or sympathetic transduction, it is possible the difference between group is partly from differences in central integration and/or efferent sympathetic activity distribution.

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Functional implications

As described earlier, there is substantial evidence that BP responsiveness may be considered an important predictor of cardiovascular risk. In one study, a higher BP responsiveness as a young adult was associated with increased risk of hypertension later in

(mid)life; i.e., those who exhibited high BP reactivity to psychological stress, but appeared otherwise healthy, were more likely to develop hypertension later in life283. They suggested several possible explanations for the reported association: that frequent BP changes lead to vascular damage that impairs compliance, that hyperreactivity is from a general hyperadrenergic state leading to risk for hypertension, or that hyperreactivity is from symptoms of endothelial dysfunction and the inability to counteract sympathetic vasoconstrictor stimuli283. The current research adds to these hypotheses with the general observation that the high response group may also present with an exaggerated EPR. Indeed, we observed that there were high and low BP responses in groups completing the same protocols. Moreover, we attempted to find a possible mechanistic link for the higher BP responses. Although the links between genetic variation and

BP were weak, this novel method dividing healthy individuals into high and low response groups based on a population response is promising. If there was a positive association between the high response group and hypertension, the larger pressor response to exercise, PECA, and caloric stimulation of the hand could all also fit in the aforementioned disease etiology proposed by

Matthews (damage, hyperadrenergic, or endothelial dysfunction)283. However a long-term follow-up would be needed to evaluate any links between our findings and future cardiovascular disease.

The response to increasing PetCO2 was not different between the high and low response groups. This suggests that feedback from the peripheral afferents, but not central

212 chemoreceptors, is an important discriminating factor between the high and low BP response.

However, it cannot be determined if a key difference in responsiveness is from differences in sensory receptor function or if central integration/efferent sympathetic activity distribution is different between group. The pain responses were similar to SHG, RHG, PECA, the CPT, and hot immersion while a subjective rating of “stress level” (not pain) was assessed for the hypercapnic rebreathing (the rebreathing protocol did not elicit pain). In general, the results from the hyperoxic hypercapnic rebreathing protocol suggest that BP responsiveness testing using hypercapnia as the stimulus is not an effective way to evaluate individual differences.

The results from the genotypic analysis were not definitive; however, they suggested a possible role for the TRPV1 SNP rs8065080. This missense SNP has shown a moderate, but not conclusive, biological role in the literature. As described in Chapter 9, rs8065080 causes the amino acid substitution of isoleucine for valine. Publications that included this SNP suggest that it may play a role in hereditary pain sensitivity266, 272, 273, cold sensation272, 273, and asthma related wheezing and Ca2+ uptake in isolated cells274. However, as mentioned, the importance of the role of this SNP is uncertain as, other studies on cold and pain sensitivity275, smoking/cough276, and cellular response to stimulation in whole-cell patch clamping and Ca2+ imaging172, 277 all found no significance for the SNP. Insufficient data are available to conduct a systematic review; nevertheless, one possible reason for the lack of consistent effects may be that isoleucine and valine are functionally and morphologically similar, and therefore their exchange may not cause significant protein conformation alterations which leads to only small, if any, phenotypic changes273.

The frequencies of the non-carriers:carries of the trait allele in the subject cohort was

13:12 (52%) and 9:24 (27%) for the low and high response group, respectively, suggesting that

213 the frequency of the alleles coding for the amino acid valine may be associated with a higher BP response in an otherwise healthy population. If a direct link between the higher BP responses in the high response group and rs8065080 were to be postulated, it would be that this version of the

TRPV1 protein, may affect the afferent feedback to the CNS, by modulating the responsiveness of the peripheral receptors thereby modulating the BP reflex response. Alternatively, the SNP could potentially affect the overall expression of the gene/mRNA/protein, thereby having its modulating affect in that manner (which has been shown in other SNPs, but not yet in rs8065080172). Definitive testing of these hypotheses, however, must be performed before any conclusions can be proposed.

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Conclusions

The cardiovascular response to PECA after SHG at 30% MVC is normally distributed and is highly variable among individuals. While absolute force accounts for a portion of the inter- individual variation in BP responses, the majority of the variation is still unexplained. There is large heterogeneity in the research methods employed using SHG and PECA and, as such, a reliable comparison between published studies was nearly impossible. A method has been outlined for more reliable data collection and data analysis (accounting for grip force) to accurately assess the reflex response to PECA. Although the SHG and PECA technique is commonly used, a consistent protocol would be an advantageous way to evaluate the future literature and create a reliable standard for normal (and abnormal) responses. The range of typical responses was described by a normal curve from which high and low response group were specifically characterized with a BP residual during PECA of +6 (+25%) or -6 mmHg (-25%) during PECA.

In general, an individual in the high response group to PECA following SHG, may be considered a high BP responder overall. The similarity in BP response between exercise protocols and water immersion protocols, both of which elicit a significant pain response, likely relates to activation of thinly myelinated group III and unmyelinated group IV afferent neurons. Protein receptors embedded in the membrane of the afferent nerves likely mediated the response.

Receptors like ASIC3, P2RX4, P2RX5, TRPA1, and TRPV1 respond to the local metabolic, mechanical, and thermal environment, are often coexpressed on neurons and sensitize each other in an in vivo excitatory environment. Their activation may be the key mediator to the similarity in responsiveness between stressors. The relatively few number of SNPs analyzed in this research did identify one possible candidate TRPV1 SNP that may be present more often in the high response group.

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Strengths and Limitations

A primary strength of this study was the subject sample size used to plot the normal distribution of responses. From the literature review (Chapter 3), 9 studies were identified that used SHG at 30-33% MVC and PECA for 2-3 minutes. They averaged 13 subjects per control group, with a maximum of 15 in a single study and a total of 104 subjects among all 9 publications.

Although groups of ~12 may be enough to identify differences between experimental conditions, they are not likely large enough to create an accurate representation of the population responses as a whole. Our study of 101 relatively young, healthy adults, may be the most accurate description of the range of BP responses to 2 min of PECA following SHG at 30% MVC available for this population. The study was powered to measure MAP and given the sample size and variation measured, the study was powered to measure group differences as small as ~3 mmHg (alpha at

5% and power >80%).

The method of identification of high and low response group was another strength to this research. A subject in the high response group was not just a subject with a high BP response overall, but a high BP residual when average handgrip force during 3 min of SHG was included as a covariate. Therefore, it was theoretically possible for two subjects with the same absolute BP response to be characterized differently, as high and low response group, because of substantially different handgrip forces. The results from the exercise test for both SHG and RHG confirm the importance of recruiting in this manner to normalize groups, as the grip forces and % MVC maintained during each test were not different between the high and low response groups, but the

BP difference was preserved.

The genomic portion of this study was primarily limited to SNPs that were already shown to have known functional associations with genes that appear with a relatively high MAF in the

216

Caucasian population. This is both a strength and a limitation. Given the limited number of subjects and limited number of SNPs that that were financially viable for this study, it was advantageous to be highly selective of the included SNPs. This approach gave the greatest likelihood of discovering functional SNPs to be included in gene scores. However, as discussed in

Chapter 9, the gold standard of SNP selection is from genome-wide association studies that have been repeatedly validated. This type of study, however, was not available for BP residuals during

PECA after SHG.

Although a relatively thorough health history form was filled out by each subject, a limitation of this study was that there were some subject characteristics and/or health history parameters that are still unknown. For example, their subjective sleep quality, either long term, or the night before the study, was not assessed. Additionally, high dietary sodium or phosphate may play a role in the EPR284. Dietary consumption was not evaluated. A post hoc dietary recall form was considered; however, one review suggests that studies consistently show that the longer the delay between consumption and recall, the lower the accuracy of the report285; therefore this analysis was not included because of the likely lack of reliability.

Another limitation of the study was that the analysis was not completely blinded. Each subject was assigned a study ID that was unrelated to group (high or low response), thus providing a level of anonymity. However, a blinding technique was never explicitly assigned for the analysis of the cardiovascular and autonomic data. The same researcher who did the initial high vs. low response group assignment for each subject and recruiting also did the data analysis. Similarly, throughout the study, subjects were given similar verbal instructions, but a prepared script was not used. This approach assured a degree of consistency between subjects while simultaneous

217 attempting to minimize any trepidation that might otherwise affect blood pressure (e.g., ‘white coat hypertension’).

The results from this study can only be applied to a young healthy population as described in the previous chapters. Attempts should not be made to extrapolate these “normal” responses to other populations or to other modes, intensities, or durations of exercise (Chapters 3, 5, and 6), to other types/temperatures of caloric stimulation (Chapter 7), or to genetic backgrounds other than

European (Chapter 9). Appropriate methods to apply/generalize these results, however, are outlined below in the future directions section.

The cardiovascular control mechanisms that function in response to stress between men and women may be different286. For example, parasympathetic modulation and sympathetic modulation may dominate in women and men, respectively286. Although step-wise regression did not identify sex as a significant predictor for the BP response to PECA (Chapter 3), it cannot be determined that men and women are not different in the physiology of their BP response, only that sex was not a predictor to the BP response. A more thorough characterization of the cardiovascular changes that occur during these protocols (discussed below in the future direction) could be used to identify sex-related differences if they exist. Similarly, autonomic responses may vary over the course of the menstrual cycle287, 288. Although women were tested at a consistent point in their menstrual cycle when hormonal influences on autonomic function are minimal, results could have been different at other time points.

The protocols from Chapters 5-8 were completed using only subjects who were previously identified as having a high or low response and therefore this data should not be used to estimate the mean response of the population overall. While these groups had higher and lower BP responses, respectively, to caloric stimulation of the hand, the groups were sorted a priori only by

218 their response to PECA. Therefore, this data (for example) is not grouped to show the highest responses to the CPT; only that, on average, high responses to PECA are, on average, associated with higher responses to the CPT as well. Alternative sorting would need to be done in order to better characterize the highest responses to the CPT.

It cannot be determined, specifically, where the sensations from the caloric stimulation were being sensed, e.g., in the skin vs. in the interstitial space of the muscles of the hand, and therefore from where the reflex response was primarily initiated. The assumption was that the skin was the first to sense the water temperatures and as the hand remained in the water, the temperatures subsequently affected the local environment of the muscles of the hand as well. No surface, subcutaneous, or intramuscular temperatures were measured. Similarly, no specific protocol was used to bring each subject’s hand to the same temperature before immersion- i.e. pre-

CPT/hot immersion into neutral water was not done as a control. However, each subject was given at least 15 min between each protocol and the CPT/hot water immersions were never done back- to-back, so it was assumed that each subject would have a skin temperature that was thermoneutral before each immersion. Verbal questioning of the subject confirmed that subjects did not perceive a difference in skin temperature prior to the next protocol. Despite these controls, some habituation of the stimulus cannot be completely ruled out.

During the 3 min of water immersion, the water temperature did change by ~1.0-1.5°C.

During the CPT, the water warmed slightly, and during the hot test, the water cooled slightly.

Conductive heat transfer is the most likely reason for these changes; no mechanism to continually heat/cool the water during the immersion was included in order to minimize electrical interference with the neural recordings. The water temperature was checked immediately before and after immersion to confirm the temperatures. Given the magnitude of the stimuli, it is difficult to

219 attribute physiological importance to this small change in temperature. Nevertheless, a change in temperature cannot be completely dismissed as a reason for the small apparent decline in the reflex responses.

Muscle fiber type may play a role in the cardiovascular responses to exercise40, and therefore it is plausible that characterizing high and low BP responses grouped the subjects via a transitive association with fiber type of the wrist flexors. However, a prevalence of Type I fibers is associated with lower metabolic activity and fatigue resistance in addition to a lower pressor response40, 41. This would suggest that those with the lowest fatigue would have the least activation of the EPR. The current results are not consistent with this hypothesis based on two lines of evidence: first, the high response group exercised for a longer duration with a higher EPR response; and second, there was no difference in the end-exercise RPE between the high and low response groups.

Another uncontrolled factor during the protocols that involved PECA was pain induced by the mechanical pressure of cuff inflation, independent of the metabolic state of the arm. Every attempt was made to minimize pinching from the cuff and the inflation procedure was identical between subjects, and therefore the pain from cuff occlusion, albeit minimal, should have been similar between the high and low response groups.

As mentioned previously, the order of the protocols was pseudo-randomized to separate the caloric stimulation trials and therefore an order effect cannot be ruled out completely.

Similarly, subjects’ bladders may have filled during the experiment to the point that bladder pressure influenced the baseline BP during the later protocols. However, a new resting baseline was taken before each protocol and therefore within each individual protocol, it was expected

220 that the BP changes from control would remain an accurate measure of reflex activation.

Comparisons between protocols, however, should consider the potential order effect.

The limitations of the genetic analyses are described thoroughly in Chapter 9. Briefly, however, a limitation to the genetic analysis was the assignment of the trait alleles. To the extent possible, SNPs were assigned a trait allele based on the literature. The limitation to this approach comes with inconsistent, unclear, or missing trait allele assignments in the published literature, thus leading to the possibility of incorrect trait allele assignments, which would decrease the accuracy of associations and gene scores. Only subjects of European descent were included in the genetic analysis, as they were the largest cohort of individuals available and only a single race was is included to avoid population stratification of results (i.e. difference populations can have different MAFs for a given SNP thus possibly altering results based on race rather than on measured outcomes). This regional/racial variation may represent a future opportunity to explore geographic and racial differences in autonomic responsiveness.

Another limitation to the current approach lies in the expressome itself. There are many steps between genotype, protein production at the peripheral afferent nerves, and receptor activation. For example, mRNA production, protein synthesis, and post-translational modification of the proteins may all differ between tissues and between people; none of which were assessed in the current study. That said, this novel approach of linking BP responsiveness to genotype was never designed to be a complete mechanistic explanation of all possible differences between individuals. Rather, it should be considered a starting point for a more thorough understanding of the normal BP variation among healthy individuals and how the genes of the peripheral afferents may play a role in this variation.

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Future Directions

This research raises the intriguing possibility that the EPR has functional effects in vivo.

Said another way, this approach may be able to address the question: for a small muscle-mass exercise like RHG, can the EPR enhance performance by increasing time to fatigue? The results of this study suggest that, to at least some extent, the answer is yes; however, these results should be replicated in a study specially designed to evaluate this question more thoroughly. In the current research, exercise was terminated on the nearest minute. For a study dedicated to testing the functionality of the EPR using graded occlusion, a more accurate assessment of exercise time should be performed. One approach to determine endpoint could be stopping exercise at the objectively defined complete occlusion of BF through the brachial artery using Doppler ultrasound, thus removing the subjective feelings of fatigue. A corollary to this approach would be to determine if time to fatigue in the low response group could be prolonged experimentally by activating the sympathetic nervous system for example, by a simultaneous CPT on the contralateral hand or either foot. An increase in BP in the low response group that does increase exercise duration would be strong evidence of a functional EPR.

Anecdotally, the temporal responses to a given stimulus seems to be heterogeneous among individuals. For example, the BP of an individual at the onset of SHG may shift quickly to a higher level and then continue to increase thereafter, or, BP may have a long delay before increasing. The association of differences in BP timing response and the overall BP and EPR reflex response is intriguing, especially as the rapid onset of BP/MSNA has been associated with hypertension289.

Future directions also involve other methods of data collection and/or analysis that could be employed to enhance the understanding of the results of this research. Primarily, an accurate measure of Q̇ distribution (and ideally an intra-arterial BP measurement) would be beneficial to

222 more thoroughly describe the differences in the cardiovascular responses between the high and low response group. The current findings have shown that high and low response group have different BPs; however, the regional and local cardiovascular mechanisms responsible for the differences in pressure remain to be determined.

Different approaches for analysis of the current data may also be attempted. For example, change-point analysis to assess the threshold and gain of the EPR during the RHG protocol, baroreflex sensitivity using beat-by-beat HR and BP data, and HR variability at rest. During the initial screening study, Chapter 3, 5 min of resting BP was recorded which is sufficient to measure

HR variability. Each of these approaches could be used to evaluate the high and low response groups.

Average SHG force (kp) for the 3 min protocol was the only significant predictor identified during stepwise regression for the BP responses; i.e., there was no difference in response between sexes. That said, a future analysis could separate the high and low response groups into men and women to analyze for possible sex differences. There have been sex differences reported in the reflex response to SHG, however it is not clear if the differences were primarily due to a difference in grip force or if it was related to other factors such as elevated estrogen levels 286, 290.

As mentioned in the limitations sections, the normal range of BP and HR responses

(Chapter 3), the responses from the high and low response group (Chapter 5-8), and the genetic associations (Chapter 9), should only be considered applicable to the populations studied; i.e. young and healthy adults of mostly European descent (Chapter 3) and only European descent

(Chapters 5-9). That said, the question remains: “Are these protocols or results applicable to other populations to predict future health and disease?” The most direct way to test the hypothesis would be a long-term follow-up study involving the same subjects. For example, a study that used three

223 methods of sympatho-excitation concluded that young adults who had a large BP responses were at risk for hypertension after a 13 year follow-up283. The aforementioned research involved >4000 subjects. A limitation to a long term follow-up in the current research, therefore, is the relatively small sample size. However, an advantage would be the rigorous characterization method of high vs. low response group that is novel to the current literature. A relatively simple, yet plausible method for this long-term follow-up could be to poll subjects once every ~5 years for the next 20-

25 years to ascertain the number of hypertension diagnoses since the original research.

A second approach would be to repeat the protocols (rather than the subjects) but with a different subject population. For example, it could be advantageous to repeat the study with a group of a different genetic background, or similarly, from a different base population like post- menopausal women, or individuals already diagnosed with a disease like hypertension. As mentioned, the “normal curve” of responses defined in the study only apply to this population.

Therefore, the results from the current research could indicate that the BP response of, for example, a 65 year old man, may be low, normal, or high relative to a young healthy person, but this research cannot say whether it is normal for a man of his age. Repeating these protocols in these other populations would help to solve that problem. Similarly, PWV has been generally accepted as an index of aortic stiffness. It was assumed that the arterial stiffness was similar in the young, healthy population of subjects included in this research; however, a measure of PWV could be used to evaluate the veracity of this assumption and to better characterize BP responses, especially in older groups.

Another limitation of the current research was the inability to know conclusively that the local environment of the exercising muscle was the same between high and low response groups.

It was assumed that the same exercise intensity and force production on average between the

224 groups would also mean the same local metabolite changes, oxygen use, pH changes, and etc.

However the intramuscular environment was not measured. In order to activate the peripheral receptors independent of voluntary exercise it is possible to experimentally simulate the metabolic environment of the exercising muscle in an otherwise resting human15. The infusion of protons, lactate, and ATP under the fascia of skeletal muscle can cause the sensation of fatigue and/or pain depending on the concentration and pH of the metabolites15. Doing an infusion of these metabolites into the skeletal muscle of the high and low response group would be a way to investigate more specifically the group III and IV afferent receptors independent of the exercise response per se.

Additionally, infusion into a limb that is temporarily fully-occluded would also take away the effect of differences in local BF if they existed. A limitation to this approach, however, would be that the aforementioned research15 only involved a small muscle (abductor pollicis brevis) and focused on sensation (fatigue and pain) and not on cardiovascular responses. It is not known if the amount of protons, lactate, and ATP needed to cause fatigue and pain would be sufficient to also

+ increase BP. Furthermore, near-infrared spectroscopy could be employed to investigate the O2/H dynamics of the exercising muscles. For example, it was assumed that the same exercise technique and same cuff occlusion protocols caused the same intramuscular environment between groups.

However, if the low response group had a substantially different predominant skeletal muscle fiber-type, microcirculation, and/or an inherent or diet-induced (for example, inorganic nitrate and nitrite291) difference in mitochondrial efficiency, it is plausible that the metabolic environments, and therefore the activation patterns of the group III and IV afferents would be different independent of any genetic variability in receptors.

Immersing the hand into water first causes sensations from the skin afferents and then possibly from the muscle afferents in the hand. One method that could potentially be used to

225 experimentally increase heat in the muscle independent of both exercise and heating the skin would be ultrasonic deep-heating. In one example of the effects of therapeutic ultrasound, at 3 cm deep, muscle temperature was increased 5°C292. Heating is time- and intensity- dependent and could theoretically be used to activate thermoreceptors with various intramuscular temperatures. Using this technique, it may be possible to isolate muscle hot thermoreceptors in the high and low response group (although some skin heating is still reported292). A limitation of this technique is that the area of muscle insonated is typically small and would therefore be unlikely to elicit systemic cardiovascular changes. To overcome this limitation, the area to be heated could be increased, or alternatively, direct microneurographic recording of thermal afferents could be attempted in order to record the firing pattern caused by activation of thermal receptors in the high and low response group.

The MSNA and respiratory data were not included in the DNA analysis. The high and low response groups were defined by BP and therefore the genetic data was primarily compared to BP.

That said, a future direction may be to compare the MSNA/respiratory data to allele frequencies or GSs. Finally, for the genetic association analyses, it may be beneficial to further increase the number of participants recruited for DNA samples and SHG with PECA screenings to increase the number of samples and power of the χ2 analysis.

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APPENDIX A: SUPPLEMENTARY DATA

Supplementary Data - Minute Averages

Data represented as changes from control are discussed and graphed primarily in the previous chapter throughout the document. Below is a number of tables that are the data represented as absolute values and in some case, change from control. Details for each table are explain the caption.

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DBP 30% R1 R2 R3 E1 E2 E3 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 68 68 68 73 83 93 83 85 70 68 67 a, b, c, d, e N 12 12 12 12 12 12 12 12 12 12 12 SE 3 3 3 3 4 4 4 4 3 3 3 High Avg. 68 68 70 75 89 101 92 95 75 72 71 a, b, d, e N 13 13 13 13 13 13 13 13 13 13 13 SE 2 1 2 2 3 4 3 3 2 2 2 SBP 30% R1 R2 R3 E1 E2 E3 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 138 137 139 146 157 172 162 164 147 144 143 N 12 12 12 12 12 12 12 12 12 12 12 SE 5 5 5 5 6 8 7 6 6 5 5 High Avg. 137 137 139 144 160 180 172 173 151 146 146 N 13 13 13 13 13 13 13 13 13 13 13 SE 3 2 3 3 4 6 4 5 3 3 3 MAP 30% R1 R2 R3 E1 E2 E3 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 89 89 89 95 107 120 110 111 93 91 90 a, b, c, d, e N 12 12 12 12 12 12 12 12 12 12 12 SE 4 4 4 4 5 6 5 5 4 4 4 High Avg. 89 88 90 96 112 128 119 122 99 95 94 a, b, d, e N 13 13 13 13 13 13 13 13 13 13 13 SE 2 1 2 2 3 4 3 4 3 3 2 HR 30% R1 R2 R3 E1 E2 E3 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 67 67 71 77 82 89 70 70 67 65 64 N 12 12 12 12 12 12 12 12 12 12 12 SE 2 2 3 3 3 4 3 3 3 3 2 High Avg. 59 60 62 71 76 84 68 68 62 59 58 N 13 13 13 13 13 13 13 13 13 13 13 SE 2 2 2 2 3 4 3 3 3 2 2

Table A.1: SHG and PECA blood pressure and heart rate. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. E3; b=R1 vs. P2; c=E3 vs. P2; d=E3 vs. Rec3; e=P2 vs. Rec3 (P<0.05).

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Flow 30% R3 E3 P2 Rec3 Significance Low Avg. 1.7 1.8 1.3 1.6 N 11 10 9 11 SE 0.2 0.4 0.2 0.2 High Avg. 1.4 1.6 1.3 1.2 N 13 13 13 12 SE 0.1 0.1 0.1 0.1 Resistance 30% R3 E3 P2 Rec3 Significance Low Avg. 59.6 94.4 102.4 69.1 N 11 10 9 11 SE 6.5 20.7 16.7 9.8 High Avg. 69.5 88.4 102.5 84.1 N 13 13 13 12 SE 5.7 8.4 11.8 5.2

Table A.2: SHG and PECA blood flow and resistance. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R3 vs. E3; b=R3 vs. P2; c=E3 vs. P2; d=E3 vs. Rec3; e=P2 vs. Rec3 (P<0.05).

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Frequency 30% R1 R2 R3 E1 E2 E3 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 19 17 15 21 33 49 40 41 25 24 22 N 10 10 10 10 9 8 10 10 10 10 10 SE 3 2 2 3 4 7 3 4 3 4 3 High Avg. 18 15 14 23 35 45 36 37 25 25 22 N 10 10 10 10 10 10 10 10 10 10 10 SE 3 3 3 4 3 3 3 4 3 3 3 Incidence 30% R1 R2 R3 E1 E2 E3 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 29 26 21 27 40 52 57 57 38 38 35 N 10 10 10 10 9 8 10 10 10 10 10 SE 4 3 3 4 4 5 3 4 6 6 5 High Avg. 31 25 22 30 46 53 54 55 41 42 39 N 10 10 10 10 10 10 10 10 10 10 10 SE 4 4 5 5 4 3 4 5 4 5 5 Height 30% R1 R2 R3 E1 E2 E3 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 15 15 12 16 29 45 47 48 26 25 21 N 10 10 10 10 9 8 10 10 10 10 10 SE 2 2 2 2 3 5 5 6 5 4 3 High Avg. 15 12 10 18 36 45 43 45 26 23 20 N 10 10 10 10 10 10 10 10 10 10 10 SE 2 2 2 2 4 6 3 5 3 2 2 Total Activity 30% R1 R2 R3 E1 E2 E3 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 1009 941 829 1209 2376 4234 3231 3463 1672 1551 1340 N 10 10 10 10 9 8 10 10 10 10 10 SE 98 108 110 153 328 643 317 531 301 266 209 High Avg. 895 761 620 1298 2704 3829 2856 2938 1564 1337 1134 N 10 10 10 10 10 10 10 10 10 10 10 SE 91 138 123 163 282 488 194 294 165 114 127

Table A.3: SHG and PECA MSNA. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. E3; b=R1 vs. P2; c=E3 vs. P2; d=E3 vs. Rec3; e=P2 vs. Rec3 (P<0.05).

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DBP 10% R1 R2 R3 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 64 64 66 67 68 69 69 69 70 71 72 74 76 75 78 86 87 76 76 66 66 68 a, b, d, e N 13 14 14 14 14 14 14 14 14 14 14 14 14 12 11 6 4 14 14 14 14 14 SE 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 High Avg. 71 70 71 71 74 75 76 77 77 78 80 82 84 87 88 92 97 97 89 89 74 71 73 a, b, c, d, e N 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 10 5 4 13 13 13 12 12 SE 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 4 3 6 3 3 3 3 3 SBP 10% R1 R2 R3 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 128 129 133 135 137 138 139 139 138 139 141 144 146 147 149 161 163 149 148 137 136 139 a, b, d, e N 13 14 14 14 14 14 14 14 14 14 14 14 14 12 11 6 4 14 14 14 14 14 SE 3 3 3 4 4 4 4 5 4 4 5 5 5 5 6 5 8 5 4 4 3 3 High Avg. 137 137 140 140 142 145 147 147 148 149 151 153 157 161 163 169 177 179 167 165 149 143 147 a, b, d, e N 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 10 5 4 13 13 13 12 12 SE 3 3 3 4 4 4 4 4 4 4 4 4 5 4 5 6 5 6 5 5 4 4 4 MAP 10% R1 R2 R3 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 83 83 85 87 89 89 90 90 91 92 93 95 98 97 100 109 112 99 99 87 87 89 a, b, d, e N 13 14 14 14 14 14 14 14 14 14 14 14 14 12 11 6 4 14 14 14 14 14 SE 2 2 2 3 3 3 3 3 3 3 3 3 3 3 4 3 4 3 3 3 2 3 High Avg. 91 90 92 92 95 97 98 99 99 100 102 104 107 111 113 117 125 127 115 115 98 94 96 a, b, d, e N 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 10 5 4 13 13 13 12 12 SE 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 5 3 5 4 4 3 3 4 HR 10% R1 R2 R3 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 66 66 68 70 69 71 71 71 72 72 73 74 74 76 78 83 86 71 69 66 64 64 N 13 14 14 14 14 14 14 14 14 14 14 14 14 12 11 6 4 14 14 14 14 14 SE 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 2 2 2 2 2 High Avg. 60 60 62 65 64 66 66 66 67 66 67 69 70 72 74 76 78 79 69 67 64 58 59 N 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 10 5 4 13 13 13 12 12 SE 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 3 2 1 2

Table A.4: RHG and PECA blood pressure and heart rate. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. E14; b=R1 vs. P2; c=E14 vs. P2; d=E14 vs. Rec3; e=P2 vs. Rec3 (P<0.05).

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Flow 10% R3 E3 E6 E9 E12 E15 P2 Rec3 Significance Low Avg. 2.0 2.1 2.0 2.1 2.3 2.0 2.0 1.8 N 13 13 13 12 11 5 12 13 SE 0.2 0.3 0.2 0.3 0.3 0.3 0.3 0.2 High Avg. 1.3 1.4 1.4 1.5 1.7 1.5 1.3 1.2 N 14 13 14 13 14 7 13 12 SE 0.1 0.1 0.1 0.1 0.2 0.2 0.1 0.1 Resistance 10% R3 E3 E6 E9 E12 E15 P2 Rec3 Significance Low Avg. 48.2 51.8 51.2 54.1 50.4 70.3 61.0 54.9 N 13 13 13 12 11 5 12 13 SE 5.4 6.0 5.8 6.4 6.0 8.5 7.6 6.0 High Avg. 73.1 73.1 75.4 75.7 81.4 107.5 96.1 84.4 N 14 13 14 13 14 7 13 12 SE 5.1 4.3 4.6 6.1 11.1 15.4 8.9 6.6

Table A.5: RHG and PECA blood flow and resistance. Grey shading indicates a significant difference between low and high at a given time point (p<0.05). Letters indicate significance within low or high between time points: a=R3 vs. E15; b=R3 vs. P2; c=E15 vs. P2; d=E15 vs. Rec3; e=P2 vs. Rec3 (p<0.05).

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Frequency 10% R1 R2 R3 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 17 15 12 14 17 16 17 16 18 22 23 26 26 31 32 43 52 36 35 30 27 26 N 8 8 8 8 8 7 8 8 7 8 6 8 8 5 6 3 2 8 8 8 7 7 SE 2 2 2 3 2 3 3 3 2 4 4 3 3 6 4 7 6 3 5 4 4 5 High Avg. 18 19 14 17 15 17 17 15 19 20 23 22 27 29 33 31 27 22 34 34 25 24 22 N 10 10 10 9 10 10 10 9 10 10 10 10 9 10 10 7 2 1 10 10 9 9 9 SE 4 4 3 4 2 4 4 2 3 3 4 4 4 4 5 3 8 3 3 3 2 3 Incidence 10% R1 R2 R3 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 25 21 17 19 24 20 23 21 23 28 29 33 33 38 40 47 56 49 50 45 42 40 N 8 8 8 8 8 7 8 8 7 8 6 8 8 5 6 3 2 8 8 8 7 7 SE 3 3 2 3 3 3 3 3 3 4 5 3 4 6 5 6 5 3 6 6 5 6 High Avg. 28 30 22 24 23 24 25 23 27 30 33 32 38 40 45 40 33 27 50 53 40 42 38 N 10 10 10 9 10 10 10 9 10 10 10 10 9 10 10 7 2 1 10 10 9 9 9 SE 5 5 3 5 4 5 5 4 4 5 5 5 5 6 6 5 9 3 4 5 3 5 Height 10% R1 R2 R3 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 12 10 9 10 13 11 12 11 12 16 19 22 25 26 30 31 34 40 37 28 27 23 N 8 8 8 8 8 7 8 8 7 8 6 8 8 5 6 3 2 8 8 8 7 7 SE 1 1 1 1 1 1 1 2 1 2 3 3 4 4 6 4 3 7 7 5 4 4 High Avg. 14 15 12 11 13 12 14 12 14 17 20 20 25 28 33 29 17 21 36 40 27 26 24 N 10 10 10 9 10 10 10 9 10 10 10 10 9 10 10 7 2 1 10 10 9 9 9 SE 2 2 2 2 2 2 2 3 2 3 4 4 5 5 6 7 3 4 5 4 3 5 Total Activity 10% R1 R2 R3 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 P1 P2 Rec1 Rec2 Rec3 Significance Low Avg. 836 712 629 733 867 812 842 847 910 1170 1470 1658 1842 2058 2369 2802 3131 2802 2586 1805 1700 1446 N 8 8 8 8 8 7 8 8 7 8 6 8 8 5 6 3 2 8 8 8 7 7 SE 98 100 73 122 97 132 119 130 101 156 259 200 265 306 394 357 338 360 435 297 271 279 High Avg. 843 894 753 776 829 824 909 789 976 1106 1305 1399 1706 2002 2314 2202 1402 1648 2404 2484 1598 1489 1363 N 10 10 10 9 10 10 10 9 10 10 10 10 9 10 10 7 2 1 10 10 9 9 9 SE 164 158 133 178 137 168 164 151 155 192 256 245 304 321 371 460 289 181 237 210 140 227

Table A.6: RHG and PECA MSNA. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. E14; b=R1 vs. P2; c=E14 vs. P2; d=E14 vs. Rec3; e=P2 vs. Rec3 (P<0.05).

233

DBP Cold R1 R2 R3 C1 C2 C3 Rec1 Rec2 Rec3 Significance Low Avg. 65 66 66 74 85 84 77 72 70 a, c N 14 14 14 13 14 14 14 14 14 SE 2 2 2 2 3 3 3 3 3 High Avg. 69 69 71 82 98 95 85 76 74 a, c N 15 15 15 15 15 15 15 14 14 SE 2 2 2 2 3 3 3 3 3 SBP Cold R1 R2 R3 C1 C2 C3 Rec1 Rec2 Rec3 Significance Low Avg. 130 130 134 146 159 158 148 140 139 N 14 14 14 13 14 14 14 14 14 SE 2 3 3 3 4 4 4 4 3 High Avg. 136 136 138 151 173 170 158 147 144 N 15 15 15 15 15 15 15 14 14 SE 4 4 4 5 5 5 5 5 5 MBP Cold R1 R2 R3 C1 C2 C3 Rec1 Rec2 Rec3 Significance Low Avg. 84 85 86 95 108 108 99 93 91 a, b, c N 14 14 14 13 14 14 14 14 14 SE 2 2 2 3 3 3 3 3 3 High Avg. 89 89 91 104 123 121 110 100 96 a, b, c N 15 15 15 15 15 15 15 14 14 SE 2 3 3 3 4 4 3 3 3 HR Cold R1 R2 R3 C1 C2 C3 Rec1 Rec2 Rec3 Significance Low Avg. 67 67 69 77 72 68 66 65 65 N 14 14 14 13 14 14 14 14 14 SE 2 2 3 3 2 2 2 2 2 High Avg. 59 59 61 71 68 64 59 57 56 N 15 15 15 15 15 15 15 14 14 SE 2 2 2 3 3 3 3 3 2

Table A.7: CPT blood pressure and heart rate. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. C3; b=R1 vs. Rec3; c=C3 vs. Rec3 (P<0.05).

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Flow Cold R3 C3 Rec3 Significance Low Avg. 1.9 2.0 2.0 N 13 14 14 SE 0.3 0.3 0.3 High Avg. 1.7 1.9 1.4 N 15 15 14 SE 0.2 0.2 0.1 Resistance Cold R3 C3 Rec3 Significance Low Avg. 57.6 83.9 56.4 N 13 14 14 SE 8.5 26.1 6.8 High Avg. 63.6 79.3 73.9 N 15 15 14 SE 6.1 13.9 5.9

Table A.8: CPT blood flow and resistance. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R3 vs. C3; b=R3 vs. Rec3; c=C3 vs. Rec3 (P<0.05).

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Frequency Cold R1 R2 R3 C1 C2 C3 Rec1 Rec2 Rec3 Significance Low Avg. 16 14 10 24 40 35 32 24 19 a,c N 11 11 11 11 11 11 11 11 11 SE 3 2 2 3 3 3 2 3 3 High Avg. 16 17 14 34 42 35 31 25 20 a,c N 10 10 10 10 10 10 10 9 9 SE 3 2 2 4 5 4 3 2 3 Incidence Cold R1 R2 R3 C1 C2 C3 Rec1 Rec2 Rec3 Significance Low Avg. 25 21 15 31 55 51 48 37 29 N 11 11 11 11 11 11 11 11 11 SE 4 3 2 4 3 4 4 4 4 High Avg. 25 27 23 46 59 53 51 45 37 N 10 10 10 10 10 10 10 9 9 SE 4 3 3 5 5 5 4 4 6 Height Cold R1 R2 R3 C1 C2 C3 Rec1 Rec2 Rec3 Significance Low Avg. 13 12 9 19 37 34 29 20 14 N 11 11 11 11 11 11 11 11 11 SE 2 2 1 2 2 2 2 2 2 High Avg. 13 15 13 32 46 43 35 25 20 N 10 10 10 10 10 10 10 9 9 SE 2 2 1 5 7 7 4 3 3 Total Activity Cold R1 R2 R3 C1 C2 C3 Rec1 Rec2 Rec3 Significance Low Avg. 846 802 601 1491 2717 2336 1888 1270 953 N 11 11 11 11 11 11 11 11 11 SE 137 117 94 192 220 165 142 140 138 High Avg. 804 884 718 2276 3167 2758 2075 1365 1095 N 10 10 10 10 10 10 10 9 9 SE 139 137 71 348 410 351 231 137 119

Table A.9: CPT MSNA. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. C3; b=R1 vs. Rec3; c=C3 vs. Rec3 (P<0.05).

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DBP Hot R1 R2 R3 H1 H2 H3 Rec1 Rec2 Rec3 Significance Low Avg. 60 61 62 64 68 68 63 62 62 a, c N 13 14 14 14 14 14 14 14 14 SE 3 2 3 3 3 4 3 2 2 High Avg. 71 70 71 78 84 84 73 70 70 a, c N 15 15 15 15 15 15 15 15 15 SE 2 2 2 3 4 4 2 2 2 SBP Hot R1 R2 R3 H1 H2 H3 Rec1 Rec2 Rec3 Significance Low Avg. 126 128 131 138 141 141 132 127 127 N 13 14 14 14 14 14 14 14 14 SE 3 3 3 4 5 5 4 3 3 High Avg. 135 135 139 148 158 158 142 134 135 N 15 15 15 15 15 15 15 15 15 SE 2 3 3 5 5 6 3 3 3 MBP Hot R1 R2 R3 H1 H2 H3 Rec1 Rec2 Rec3 Significance Low Avg. 79 81 82 86 89 90 83 81 81 a, c N 13 14 14 14 14 14 14 14 14 SE 2 2 3 4 4 4 3 2 3 High Avg. 90 89 91 99 107 107 94 90 91 a, c N 15 15 15 15 15 15 15 15 15 SE 2 2 2 4 5 5 2 2 2 HR Hot R1 R2 R3 H1 H2 H3 Rec1 Rec2 Rec3 Significance Low Avg. 64 66 66 76 73 73 68 66 66 N 13 14 14 14 14 14 14 14 14 SE 3 2 3 3 3 3 2 2 2 High Avg. 60 60 60 73 70 68 63 62 60 N 15 15 15 15 15 15 15 15 15 SE 2 2 2 4 4 3 2 2 2

Table A.10: Hot water immersion blood pressure and heart rate. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. H3; b=R1 vs. Rec3; c=H3 vs. Rec3 (P<0.05).

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Flow Hot R3 H3 Rec3 Significance Low Avg. 1.9 1.9 1.9 N 13 14 14 SE 0.2 0.2 0.3 High Avg. 1.6 1.8 1.6 N 14 15 15 SE 0.1 0.2 0.1 Resistance Hot R3 H3 Rec3 Significance Low Avg. 50.7 58.7 56.7 N 13 14 14 SE 6.1 7.6 8.5 High Avg. 63.1 73.2 59.3 N 14 15 15 SE 5.3 9.2 4.3

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Frequency Hot R1 R2 R3 H1 H2 H3 Rec1 Rec2 Rec3 Significance Low Avg. 19 20 16 22 26 27 22 19 18 N 10 9 10 10 10 10 10 10 10 SE 2 2 2 3 5 5 3 3 2 High Avg. 14 14 12 25 29 28 20 21 18 N 10 10 10 9 10 10 10 10 9 SE 3 2 2 6 4 4 3 3 3 Incidence Hot R1 R2 R3 H1 H2 H3 Rec1 Rec2 Rec3 Significance Low Avg. 28 29 24 27 35 36 30 28 25 N 10 9 10 10 10 10 10 10 10 SE 3 3 3 4 6 6 3 3 2 High Avg. 24 23 21 33 39 39 31 34 31 N 10 10 10 9 10 10 10 10 9 SE 4 3 4 5 3 3 4 5 4 Height Hot R1 R2 R3 H1 H2 H3 Rec1 Rec2 Rec3 Significance Low Avg. 15 16 14 15 20 22 17 16 14 N 10 9 10 10 10 10 10 10 10 SE 2 2 2 3 5 5 3 2 2 High Avg. 12 11 11 21 26 27 16 17 15 N 10 10 10 9 10 10 10 10 9 SE 2 1 2 5 4 5 2 2 2 Total Activity Hot R1 R2 R3 H1 H2 H3 Rec1 Rec2 Rec3 Significance Low Avg. 1008 1060 929 1155 1523 1703 1220 1073 996 N 10 9 10 10 10 10 10 10 10 SE 117 103 99 218 384 447 222 176 124 High Avg. 697 662 639 1491 1857 1876 974 995 889 N 10 10 10 9 10 10 10 10 9 SE 113 79 95 307 281 336 136 135 120

Table A.12: Hot water immersion MSNA. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. H3; b=R1 vs. Rec3; c=H3 vs. Rec3 (P<0.05).

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DBP Neu R1 R2 R3 N1 N2 N3 Rec1 Rec2 Rec3 Significance Low Avg. 69 70 70 70 71 70 70 70 70 N 5 5 5 5 5 5 5 5 5 SE 4 4 5 4 4 4 4 3 4 High Avg. 71 71 71 71 72 72 70 73 72 N 8 8 8 8 8 8 8 8 8 SE 2 1 2 1 1 2 1 1 1 SBP Neu R1 R2 R3 N1 N2 N3 Rec1 Rec2 Rec3 Significance Low Avg. 141 144 143 143 145 144 145 143 142 N 5 5 5 5 5 5 5 5 5 SE 6 6 7 6 5 5 6 5 6 High Avg. 143 144 146 147 147 148 146 145 146 N 8 8 8 8 8 8 8 8 8 SE 3 3 3 3 3 3 3 4 3 MBP Neu R1 R2 R3 N1 N2 N3 Rec1 Rec2 Rec3 Significance Low Avg. 91 92 91 91 92 92 92 91 92 N 5 5 5 5 5 5 5 5 5 SE 4 4 5 4 4 4 4 3 4 High Avg. 92 92 92 91 93 93 91 93 93 N 8 8 8 8 8 8 8 8 8 SE 2 2 2 2 2 2 2 2 2 HR Neu R1 R2 R3 N1 N2 N3 Rec1 Rec2 Rec3 Significance Low Avg. 75 74 72 72 71 73 73 73 74 N 5 5 5 5 5 5 5 5 5 SE 4 3 3 3 3 4 3 3 4 High Avg. 60 59 59 59 58 59 62 59 63 N 8 8 8 8 8 8 8 8 8 SE 3 3 3 2 2 2 3 2 2

Table A.13: Neutral water immersion blood pressure and heart rate. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. N3; b=R1 vs. Rec3; c=N3 vs. Rec3 (P<0.05).

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Flow Neu R3 N3 Rec3 Significance Low Avg. 1.5 1.3 1.5 N 5 5 5 SE 0.1 0.1 0.2 High Avg. 1.4 1.3 1.5 N 8 8 8 SE 0.3 0.2 0.3 Resistance Neu R3 N3 Rec3 Significance Low Avg. 64.0 74.8 66.6 N 5 5 5 SE 6.5 9.0 9.2 High Avg. 74.8 79.5 75.1 N 8 8 8 SE 8.2 9.0 10.7

Table A.14: Neutral water immersion blood flow and resistance. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R3 vs. N3; b=R3 vs. Rec3; c=N3 vs. Rec3 (P<0.05).

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Frequency Neu R1 R2 R3 N1 N2 N3 Rec1 Rec2 Rec3 Significance Low Avg. 21 20 18 21 22 21 21 22 21 N 4 4 4 4 4 4 4 4 4 SE 3 3 3 3 4 3 3 5 2 High Avg. 16 14 11 14 14 14 13 15 10 N 4 4 4 4 4 4 4 4 4 SE 3 2 1 2 3 2 2 2 1 Incidence Neu R1 R2 R3 N1 N2 N3 Rec1 Rec2 Rec3 Significance Low Avg. 29 27 25 30 31 31 29 30 29 N 4 4 4 4 4 4 4 4 4 SE 4 3 4 3 5 5 4 6 3 High Avg. 24 25 19 26 26 24 23 27 17 N 4 4 4 4 4 4 4 4 4 SE 8 6 4 6 7 5 5 4 4 Total MSNA Neu R1 R2 R3 N1 N2 N3 Rec1 Rec2 Rec3 Significance Low Avg. 15 14 13 16 15 16 16 16 16 N 4 4 4 4 4 4 4 4 4 SE 3 2 1 3 3 4 3 3 2 High Avg. 13 13 9 12 12 11 11 13 9 N 4 4 4 4 4 4 4 4 4 SE 5 3 1 2 2 2 2 2 1 Total Activity Neu R1 R2 R3 N1 N2 N3 Rec1 Rec2 Rec3 Significance Low Avg. 1056 1033 906 1116 1049 1116 1172 1146 1117 N 4 4 4 4 4 4 4 4 4 SE 159 169 76 188 210 248 209 191 107 High Avg. 840 716 507 670 674 645 656 752 533 N 4 4 4 4 4 4 4 4 4 SE 165 143 82 75 98 95 56 73 43 Table A.15: Neutral water immersion MSNA. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. N3; b=R1 vs. Rec3; c=N3 vs. Rec3 (P<0.05).

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∆DBP Neu AvgRes N1 N2 N3 Rec1 Rec2 Rec3 Significance Low Avg. 70 0 1 1 1 0 0 N 5 5 5 5 5 5 5 SE 4 0 1 1 1 1 1 High Avg. 71 0 1 1 -1 2 1 N 8 8 8 8 8 8 8 SE 1 1 1 1 1 1 1 ∆SBP Neu AvgRes N1 N2 N3 Rec1 Rec2 Rec3 Significance Low Avg. 143 1 2 2 2 0 0 N 5 5 5 5 5 5 5 SE 6 1 1 1 2 2 2 High Avg. 145 2 3 4 1 1 2 N 8 8 8 8 8 8 8 SE 3 1 1 1 1 1 1 ∆MAP Neu AvgRes N1 N2 N3 Rec1 Rec2 Rec3 Significance Low Avg. 91 0 1 1 1 0 0 N 5 5 5 5 5 5 5 SE 4 0 1 1 1 1 1 High Avg. 92 0 1 1 -1 2 1 N 8 8 8 8 8 8 8 SE 2 1 0 1 1 1 1 ∆HR Neu AvgRes N1 N2 N3 Rec1 Rec2 Rec3 Significance Low Avg. 74 -2 -3 -1 -1 -1 0 N 5 5 5 5 5 5 5 SE 3 1 1 2 1 1 1 High Avg. 59 -1 -2 0 2 0 3 N 8 8 8 8 8 8 8 SE 3 1 1 2 1 2 1

Table A.16: Neutral water immersion chance in blood pressure and heart rate from control. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=N1 vs. N3; b=N1 vs. Rec3; c=N3 vs. Rec3 (P<0.05).

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ΔFlow Neutral N3 R3 Significance Low Avg. -0.2 0.0 N 5 5 SE 0.1 0.1 High Avg. -0.1 0.0 N 8 8 SE 0.1 0.1 ΔResistance Neutral N3 R3 Significance Low Avg. 10.8 2.6 N 5 5 SE 6.4 4.2 High Avg. 4.6 0.2 N 8 8 SE 4.0 6.1

Table A.17: Neutral water immersion change in blood flow and resistance from control. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=N3 vs. Rec3 (P<0.05).

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∆Frequency Neu AvgRes N1 N2 N3 Rec1 Rec2 Rec3 Significance Low Avg. 20 2 2 2 2 3 1 N 4 4 4 4 4 4 4 SE 3 1 2 3 1 2 4 High Avg. 14 1 1 0 0 2 -3 N 4 4 4 4 4 4 4 SE 2 1 2 1 1 0 1 ∆Incidence Neu avg3res N1 N2 N3 Rec1 Rec2 Rec3 Low Avg. 27 3 5 4 2 4 3 N 4 4 4 4 4 4 4 SE 3 1 2 4 2 3 6 High Avg. 23 3 3 1 0 4 -6 N 4 4 4 4 4 4 4 SE 6 1 2 1 4 2 3 ∆Height Neu avg3res N1 N2 N3 Rec1 Rec2 Rec3 Low Avg. 14 2 2 2 2 2 2 N 4 4 4 4 4 4 4 SE 2 1 2 3 1 1 2 High Avg. 12 0 0 0 0 2 -3 N 4 4 4 4 4 4 4 SE 3 1 1 1 2 1 2 ∆Total Activity Neu avg3res N1 N2 N3 Rec1 Rec2 Rec3 Low Avg. 998 118 50 118 174 148 119 N 4 4 4 4 4 4 4 SE 125 101 107 167 87 90 130 High Avg. 688 -18 -14 -43 -32 64 -154 N 4 4 4 4 4 4 4 SE 98 31 45 48 51 48 96

Table A.18: Neutral water immersion chance in MSNA from control. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=N1 vs. N3; b=N1 vs. Rec3; c=N3 vs. Rec3 (P<0.05).

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DBP Rebre R1 R2 R3 Reb1 Reb2 Reb3 Reb4 Reb5 Rec1 Rec2 Rec3 Significance Low Avg. 65 66 65 67 73 74 76 77 64 64 64 N 13 13 13 12 13 13 13 13 13 13 13 SE 2 2 2 3 3 3 3 3 2 3 3 High Avg. 67 69 68 72 76 79 80 81 68 67 68 N 17 17 17 17 17 17 17 16 17 17 17 SE 1 1 1 1 2 1 1 2 1 1 2 SBP Rebre R1 R2 R3 Reb1 Reb2 Reb3 Reb4 Reb5 Rec1 Rec2 Rec3 Significance Low Avg. 130 131 133 134 141 143 147 151 134 138 141 N 13 13 13 12 13 13 13 13 13 13 13 SE 4 3 4 4 3 3 3 3 3 3 3 High Avg. 132 134 136 139 141 148 151 153 137 137 140 N 17 17 17 17 17 17 17 16 17 17 17 SE 3 3 4 3 4 3 2 3 3 3 3 MAP Rebre R1 R2 R3 Reb1 Reb2 Reb3 Reb4 Reb5 Rec1 Rec2 Rec3 Significance Low Avg. 85 85 86 87 95 96 99 101 86 86 86 N 13 13 13 12 13 13 13 13 13 13 13 SE 3 3 3 3 3 3 3 3 3 3 3 High Avg. 87 88 88 93 97 101 103 104 90 88 89 N 17 17 17 17 17 17 17 16 17 17 17 SE 2 2 2 1 3 2 2 2 2 2 2 HR Rebre R1 R2 R3 Reb1 Reb2 Reb3 Reb4 Reb5 Rec1 Rec2 Rec3 Significance Low Avg. 67 68 68 73 72 75 79 81 84 75 72 N 13 13 13 12 13 13 13 13 13 13 13 SE 3 3 3 2 2 3 3 3 3 3 3 High Avg. 58 60 59 67 64 68 72 77 76 66 63 N 17 17 17 17 17 17 17 16 17 17 17 SE 2 2 2 2 3 2 2 3 2 2 2

Table A.19: Rebreathing blood pressure and heart rate. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. Reb5; b=R1 vs. Rec3; c=Reb5 vs. Rec3 (P<0.05).

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Flow Rebre R3 Reb5 Rec3 Significance Low Avg. 1.7 1.6 1.7 N 13 12 12 SE 0.2 0.2 0.2 High Avg. 1.7 1.5 1.6 N 17 17 16 SE 0.2 0.2 0.2 Resis Rebre R3 Reb5 Rec3 Significance Low Avg. 60.5 77.6 67.9 N 13 12 12 SE 7.1 10.6 14.5 High Avg. 66.2 95.5 64.7 N 17 17 16 SE 6.9 17.5 5.1

Table A.20: Rebreathing blood flow and resistance. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R3 vs. Reb5; b=R3 vs. Rec3; c=Reb5 vs. Rec3 (P<0.05).

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Frequency Rebre Res1 Res2 Res3 Reb1 Reb2 Reb3 Reb4 Reb5 Rec1 Rec2 Rec3 Significance Low Avg. 16 14 16 18 19 25 23 23 21 17 19 N 8 8 8 7 8 8 8 7 8 8 8 SE 3 2 3 3 2 3 3 3 5 4 4 High Avg. 16 14 14 16 18 22 23 25 19 17 17 N 11 11 10 9 11 11 11 11 11 11 11 SE 2 3 2 2 3 3 3 4 3 2 3 Incidence Rebre Res1 Res2 Res3 Reb1 Reb2 Reb3 Reb4 Reb5 Rec1 Rec2 Rec3 Low Avg. 23 20 22 25 27 34 31 30 26 24 26 N 8 8 8 7 8 8 8 7 8 8 8 SE 3 3 3 3 2 3 3 4 5 5 5 High Avg. 26 23 23 24 26 31 32 32 23 26 26 N 11 11 10 9 11 11 11 11 11 11 11 SE 4 4 3 2 3 3 3 4 3 3 4 Height Rebre Res1 Res2 Res3 Reb1 Reb2 Reb3 Reb4 Reb5 Rec1 Rec2 Rec3 Low Avg. 12 11 11 13 17 22 22 26 15 12 13 N 8 8 8 7 8 8 8 7 8 8 8 SE 1 2 1 1 2 3 3 7 3 2 2 High Avg. 13 12 11 12 15 21 22 23 11 12 11 N 11 11 10 9 11 11 11 11 11 11 11 SE 2 2 1 2 2 3 3 3 1 1 2 Total Activity Rebre Res1 Res2 Res3 Reb1 Reb2 Reb3 Reb4 Reb5 Rec1 Rec2 Rec3 Low Avg. 763 733 749 935 1203 1543 1614 1872 1190 860 943 N 8 8 8 7 8 8 8 7 8 8 8 SE 101 113 104 122 133 185 218 434 230 174 157 High Avg. 762 735 641 799 1028 1448 1590 1737 921 765 723 N 11 11 10 9 11 11 11 11 11 11 11 SE 118 123 63 78 199 249 262 274 96 87 129

Table A.21: Rebreathing MSNA. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. Reb5; b=R1 vs. Rec3; c=Reb5 vs. Rec3 (P<0.05).

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RR Rebre R1 R2 R3 Reb1 Reb2 Reb3 Reb4 Reb5 Significance Low Avg 13.0 13.7 13.5 13.9 13.7 16.8 18.7 20.7 N 13 13 13 13 13 13 13 13 SE 0.9 1.1 0.8 1.2 1.3 1.3 1.8 1.8 High Avg 13.3 13.5 13.5 15.4 16.1 16.8 18.3 20.2 N 16 15 15 16 16 16 16 15 SE 1.0 1.0 1.1 1.0 1.1 1.2 1.5 1.7 PetCO2 Rebre R1 R2 R3 Reb1 Reb2 Reb3 Reb4 Reb5 Significance Low Avg 40 41 41 52 55 58 61 63 N 13 13 13 13 13 13 13 13 SE 1 1 1 1 1 1 1 1 High Avg 41 41 42 53 57 60 63 65 N 15 15 15 16 16 16 16 15 SE 1 1 1 1 0 0 0 0 PetO2 Rebre R1 R2 R3 Reb1 Reb2 Reb3 Reb4 Reb5 Significance Low Avg 106 106 106 134 178 236 276 291 N 13 13 13 13 13 13 13 13 SE 1 2 2 10 15 19 16 12 High Avg 106 106 106 151 202 269 305 312 N 15 15 15 16 16 16 16 15 SE 2 2 1 17 17 16 12 10 VE Rebre R1 R2 R3 Reb1 Reb2 Reb3 Reb4 Reb5 Significance Low Avg 7.1 7.9 7.3 13.2 19.0 26.0 34.0 39.6 N 13 13 13 13 13 13 13 13 SE 0.5 0.8 0.6 1.4 2.1 3.1 4.0 4.4 High Avg 8.0 8.1 8.1 16.7 21.9 28.5 36.5 43.7 N 15 15 15 16 16 16 16 15 SE 0.4 0.5 0.4 1.2 1.7 2.5 2.8 3.4 VT Rebre R1 R2 R3 Reb1 Reb2 Reb3 Reb4 Reb5 Significance Low Avg 0.6 0.6 0.6 1.1 1.5 1.6 1.8 1.9 N 13 13 13 13 13 13 13 13 SE 0.0 0.1 0.0 0.1 0.2 0.2 0.1 0.2 High Avg 0.7 0.7 0.7 1.2 1.5 1.8 2.1 2.3 N 15 15 15 16 16 16 16 15 SE 0.1 0.1 0.0 0.1 0.1 0.1 0.1 0.2

Table A.22: Rebreathing respiratory parameters. Grey shading indicates a significant difference between low and high at a given time point (P<0.05). Letters indicate significance within low or high between time points: a=R1 vs. Reb5 (P<0.05).

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APPENDIX B: INFORMED CONSENTS CONSENT FOR RESEARCH The Pennsylvania State University

Title of Project: Autonomic Phenotyping of Healthy Men and Women

Principal Investigator: James A Pawelczyk

Address: 107 Noll Laboratory, Pennsylvania State University, University Park, PA 16802

Telephone Number: 814-865-3453

Subject’s Printed Name: ______

We are asking you to be in a research study. This form gives you information about the research. Whether or not you take part is up to you. You can choose not to take part. You can agree to take part and later change your mind. Your decision will not be held against you. Please ask questions about anything that is unclear to you and take your time to make your choice.

1. Why is this research study being done?

This research is being done to find out more about people with different blood pressure responses to the same tests. We will use a variety of blood pressure raising stimuli to record individual responses. We will then compare results from people who have a large response to those with a low response. If you choose to also participate in another study (IRB# STUDY00001782) we may compare your blood pressure responses to your TRPV1 genotype. The TRPV1 channel is in many of your cells and is made of many amino acids that form a certain shape. This channel is partially responsible for increasing blood pressure during challenges like exercise. Among different people there are naturally occurring changes in the instructions for making this channel- that is, differences in the gene for the protein. The knowledge gained from this study will be a valuable addition to understanding individual differences in blood pressure responses.

About 200 people will take part in this research study.

2. What will happen in this research study?

Screening:

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Informed Consent: You will first read and if you chose, sign, the informed consent document (this document) before completing any other parts of the study. An investigator working will be available to answer any questions you have about the research.

Medical History and Contact Information Questionnaire: You will be asked to fill out a form about your medical history (i.e. major injuries, illnesses, etc.). You will also be asked to include contact information. You are free to skip any questions that you would prefer not to answer.

Screening Data: We will collect some initial screening data. Data collected for screening is explained below.

Urine Analysis: Pre-menopausal women will have a urine pregnancy test done prior the study visit.

Familiarization: During screening, you will be shown the lab area and given the opportunity to ask any questions about the study. You will have resting blood pressure and heart rate measured (details below). You will do an initial hand-squeezing exercise- protocol 1 (below). Finally, we will also expose you to the basics of the other protocols (protocols 2-4). We will not collect any data for protocols 2-4, we will just make sure you understand what is going to be done for each protocol. The results from the initial hand squeezing test will help determine if you are invited back for day two of testing.

Finally we would like to know if you would like to be included in another study (IRB# STUDY00001782) in which we collect some mouth cell on several cotton swabs. If you are interested we will show you the (separate) informed consent for that study as well. If you choose to be in both studies we may compare the data collected in this study (blood pressure and etc.) to your genotype measured in the other study.

Testing:

Heart Rate: Electrodes (sticky patches) will be applied to the skin of your upper body to measure your heart’s activity. We may shave 2-3 small areas (~1 inch) on your chest and abdomen (if necessary) for the patches.

Blood Pressure: Pressure will be measured continuously using a Finapres system and periodically with an upper arm cuff. If using an automated blood pressure measurement device, your finger will be placed in a cuff that inflates with air to monitor your blood pressure.

Leg Blood Flow: To measure the blood flow in your leg we will place a cuff on your thigh, just above your knee. We will also place a cuff on your ankle. We will inflate the cuff on your ankle to a high pressure and the cuff on your thigh to a lower pressure. Blood will enter your lower leg but not leave because of the cuffs. Using a monitor on your calf that tells us how much it has changed size, we can measure how much blood has entered your leg.

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Muscle oxygen and/or H+ level: A sticky plastic sensor will be placed over the muscles in your forearm. This sensor will emit light into your forearm muscles. This measures the amount of oxygen and hydrogen ions (H+, a byproduct of metabolism) in your muscle.

Pulse Wave Velocity: Pulse wave velocity is a non-invasive test that allows us to estimate the “stiffness” of your blood vessels. You will lay flat on a bed with a cuff placed around your femoral artery (a blood vessel in your thigh). A researcher then holds a small (pen-sized) sensor over your carotid artery (a blood vessel in your neck). To make a measurement, the cuff placed on your thigh will inflate to a pressure that temporarily prevents blood flow into your leg. The cuff will then deflate over 1-2 minutes. Sensors in the cuff and on your neck measure how fast each pulse of blood travels through your blood vessels. This measurement will be performed 3-4 times.

Nerve Activity: The nerve that we record from is located close to the surface of the skin. It is on the lower leg, just below the knee. To measure nervous system activity, we will first apply a mild external electrical stimulus to this area using a wand-like device. This allows us to find the approximate location of the nerve beneath the skin. After we find the general location of the nerve, a fine wire needle (called a microelectrode) will be inserted through the skin to record activity from the nerve. A second fine wire needle will be inserted about 3 cm away (termed the reference needle). We will measure nervous system activity throughout the experiment. We will measure nervous system activity directed to the muscle. You may be asked to perform Valsalva’s maneuver to check the placement of the electrode. This involves contracting muscles to expel air while not letting any air escape from your lungs.

Breaks: A minimum of 10 minutes will be taken between each protocol as a break before starting the next protocol.

Abstinence from Caffeine and Alcohol: We ask that you abstain from all alcohol, caffeine, energy drinks, large doses of vitamin B, and any over-the-counter substances that contain ephedrine or other stimulants for 12 hours prior to each study.

Exercise: We ask that you abstain from heavy exercise for 24 hours prior to each study. This includes activities like running or lifting weights.

Fasting: We ask that you arrive for each day having not eaten since dinner the night before. We will have a light snack for you if you want it when the study day it over.

Female subjects: You will only be tested during the early follicular phase of your menstrual cycle (days 2-6). We will coordinate scheduling with you based upon your history that you fill out in the screening form. Randomization: The following protocols may be done in any order. They will be semi-randomized with similar tests being placed apart from each other. For example, tests involving hand/arm exercise will not be done back-to-back.

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Protocols:

______Protocol 1: Arm Compression after Continuous Squeezing Exercise. While lying on your back on a bed, we will measure your largest hand-grip force from your dominant hand using a device connected to a computer. We will also cover the upper part of your arm with a cuff connected to a compressor. We will monitor your blood pressure, heart rate, blood flow, pulse wave velocity, blood oxygenation, and nerve activity as outlined above- this will continue for the rest of the time. After at least three minutes of rest, you will begin to exercise at 30% of your maximum. This will be displayed on a computer for you. You will contract and hold at 30% for 3 minutes. With 5 seconds remaining before the end of 3 minutes of exercise, the cuff on your upper arm will be rapidly inflated to a high level- trapping all the current blood in your arm. You will stop exercise and relax and the cuff will remain inflated for 2 more minutes after which it will deflate. You will then remain stationary for 3 more minutes. Before, during, and after the exercise period, you will be asked to judge the difficulty of the exercise on a number scale. You will also be asked to rate pain on a number scale. Duration: approximately 30 minutes.

______Protocol 2: Arm Squeezing Test. The arm squeezing test will consist of you laying on your back on a bed for approximately 15 minutes while you are prepared for the test. We will monitor your blood pressure, heart rate, blood flow, pulse wave velocity, and nerve activity as outlined above- this will continue for the rest of the time. You will have a cuff placed on the upper part of the exercising arm. You will have a custom-built handgrip device fitted to your hand size and placed out to your side. Your maximum hand-grip will be recorded and your exercise goal will be set at 10-15% of your maximum. A blood flow measuring technician will use sound waves to measure the blood flow going into your lower arm. You will then begin to squeeze at 10-15% of your maximum at 30 squeezes per minute. The force you are creating will be displayed on a computer monitor for you to observe during exercise. During this exercise we will slowly pump up the arm cuff. Before, during, and after the exercise period, you will be asked to judge the difficulty of the exercise on a number scale. You will also be asked to rate pain on a number scale. The stopping point is based off of how hard you think you are working and the amount of blood flow measured by the sound waves. Once the stop point is reached (>90% of blood flow occluded or a rating of 19-20 on a 6-20 scale) or whenever you feel you want to stop, the cuff will be rapidly inflated to trap all blood in the arm. You will stop exercise and relax and the cuff will remain inflated for 2 more minutes after which it will deflate. You will then remain stationary for 3 more minutes. Duration: approximately 45 minutes.

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______Protocol 3: Lung Sensitivity Test. While lying on your back we will monitor your blood pressure, heart rate, blood flow, pulse wave velocity, and nerve activity as outlined above- this will continue for the rest of the time. To measure your breathing at rest, you will breathe in room (normal) air and breathe out into a tube. We will measure this air using a computer for a minimum of 3 minutes. To measure your breathing response to a change in air, you will start breathing in and out of a container of air that has oxygen and carbon dioxide. Carbon dioxide stimulates you to breathe more. As carbon dioxide builds-up we will measure your rate and depth of breathing. Your oxygen level will be higher than normal because of the higher level of oxygen in the air you will be breathing. After five minutes or after carbon dioxide builds to a certain amount, we will stop the test. You will begin breathing room air once again. Once breathing room air, we will ask you for your sensation during breathing in the tube. Duration: approximately 30 minutes.

______Protocol 4: Water Temperature Test. While lying on your back we will monitor your blood pressure, heart rate, blood flow, pulse wave velocity, and nerve activity as outlined above- this will continue for the rest of the time. After at least three minutes of rest, we will lower your hand into water up to the wrist for three minutes. Using bubbles, the water will be kept moving around your hand. The temperature of the water will be hot (~47°C/~117°F), warm (~37°C/~98°F), and cold (~3°C/~37°F). Each temperature will be used for three minutes and the order applied will be random (other protocols may be done between temperatures). While your hand is in the water, we will ask you for your pain perception during each minute. After removal from the water, data will be recorded for a final three minutes and that will complete the test. Duration: approximately 60 minutes.

3. What are the risks and possible discomforts from being in this research study?

There is a risk of loss of confidentiality if your information or your identity is obtained by someone other than the investigators, but precautions will be taken to prevent this from happening.

It is not possible to identify all potential risks of these research procedures, but the researchers have taken reasonable safeguards to minimize any known or potential risks.

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 Urine Sample (pregnancy test): There are no known risks associated with the self-collection of one’s urine.

 ECG: There is a minimal risk due to potential allergic reaction to adhesive on the ECG electrodes.

 Blood pressure/Pulse Wave Velocity: There is a risk of temporary discomfort at the sites where cuffs are inflated. The discomfort might be greater the longer the cuffs are inflated. In addition, you may feel a numb, cooling, and/or tingling sensation in your hands/feet while the cuff is inflated. However, these feelings go away quickly after the cuff is deflated. During pulse wave velocity, you may also experience some temporary discomfort while the researcher holds a small sensor over an artery in your neck. However, this feeling will also go away quickly after the sensor is removed.

 Muscle oxygen and/or H+ level: There are no known risks associated with use of the near- infrared device. However, it is possible that the adhesive on the sticky plastic sensor may irritate your skin.

 Microneurography: With regard to the laboratory assessment of nervous system activity, the use of the wand-like device may cause minor discomfort. There may also be mild discomfort when the fine wire needle is inserted through the skin; however, this needle is very small. Brief sensations of pins and needles and/or cramping are likely to be felt during the nerve search. This fine wire needle will be left in place for the duration of the experimental visit (approximately 4 hours). It is also possible that feelings of muscle weakness and/or pins and needles sensations can be felt after completion of the procedure. There is no specific treatment for these sensations, and in the small number of volunteers that have experienced them, they have disappeared spontaneously. It is generally thought that the risks of side effects are minimized when no more than 45 minutes is used to locate the nerve with the fine wire needle; therefore, in this study, this time limit will be strictly enforced. There is also a small risk of infection at the site where the fine wire needle is inserted.

 Valsalva’s Maneuver: Some subjects experience very brief dizziness or lightheadedness during or immediately following forced exhalation (Valsalva maneuver). These symptoms, when present, are very short-lived and are a minimal risk when already laying down.

 Medical Screening: You may feel shy about giving health information. The staff collects the information in a private and professional manner. You may feel shy about being measured. You may request someone of the same sex to conduct the screening.

 Latex: Some gloves and medical materials are made of latex rubber. You will inform us if you are allergic to latex and decline to participate in the study.

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 Squeezing exercise and inflation of the pressure cuff on your arm: Forearm tiredness and muscle “burning” may be present during the 3 minutes of hand squeezing exercise. These feeling should end quickly after the end of the test. You may feel a tightness and/or tingling feeling in your hands and upper arm when the arm cuff is inflated. These sensations are only temporary should last only for the 3 minutes of cuff inflation. Mild soreness in the muscles of the forearm/hand could develop sometime after the exercise test is completed (usually the day following). This soreness, if it occurs, usually goes away in about 2 days.

 Hand in water test: Putting your hand in the hot and cold water may be considered painful. The temperatures used in this study should cause no lasting damage or pain in the hand after 3 minutes in the water. Any pain associated with placing the hand in water should subside quickly once the hand in removed from the water.

 Measurements of blood flow through an artery in your upper arm at rest and during exercise: You may feel minor discomfort (pressure) when the research assistant is pressing the ultrasound sensor against your upper arm. This discomfort, if it occurs, is very mild and stops immediately after the measurement is completed (measurement takes about 15 minutes). There is a small risk that the ultrasound gel will irritate your skin. This irritation/redness, if it occurs, should go away soon after the study is completed. Forearm muscle tiredness is likely. This discomfort goes away quickly after the completion of the exercise. Mild soreness in the muscles of the forearm/hand could develop sometime after the exercise test is completed (usually the day following). This soreness, if it occurs, usually goes away in about 2 days. Finally, there is some risk of the appearance small red dots on the skin (called petechia) due to the high blood pressure in the occluded arm during exercise. If present they may persist for a few days before going away. They should not be associated with any pain.

 Lung Sensitivity Test: During the test you may feel the sensation of “shortness of breath” or feel like it is generally “hard to breathe”. These feelings should subside immediately at the end of the test. The air mixture you are breathing starts high in oxygen and therefore, you will never be short of oxygen during the test. We will continually measure your oxygen and carbon dioxide status each breath during the test and we will end the test at the appropriate time. The build-up of carbon dioxide will stimulate you to breathe more rapidly and deeply. There is little risk of this test at the CO2 levels we will use. About one in 10 people feel a little anxious and develop a slight headache when breathing carbon dioxide. If these feelings occur they should end within minutes after ending the test.

 Leg Blood Flow: During this test, blood flow will be stopped to your foot for up to 2 minutes (usually <1 min) at a time. This may lead to brief tingling in the area that should end quickly.

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The cuff around the upper leg and the stretch measurer around the calf should cause no discomfort. It is possible the tape used will irritate your skin or pull on limb hair.

 Stopping the Test: If you should feel that you cannot continue a test, you may stop the experiment at any time. It will be considered an uncompleted test.

4. What are the possible benefits from being in this research study?

4a. What are the possible benefits to you?

Benefits to you are a measurement of basic health parameters such as height, weight, and blood pressure.

4b. What are the possible benefits to others?

Benefits to society in general relate to the further understanding of the way genes play a role in blood pressure control and how individuals have different blood pressure responses. Eventually, this could help with understanding heart-related diseases.

5. What other options are available instead of being in this research study?

You may decide not to participate in this research.

6. How long will you take part in this research study?

If you agree to take part, it will take you about 5 hours to complete this study. You will be asked to return to the site 2 times. The first visit is a screening (introductory) visit to determine your eligibility (~1 hour). The second visit is the study visit (~4 hours). For each visit, you must avoid all caffeine and alcohol for 12 hours prior to arrival. For each visit you must not do heavy exercise for 24 hours prior to arrival. Scheduling of these visits will depend on your availability.

7. How will your privacy and confidentiality be protected if you decide to take part in this research study?

Efforts will be made to limit the use and sharing of your personal research information to people who have a need to review this information.

 A list that matches your name with your code number (assigned by us) will be kept in a locked file or password protected file physically located in Noll lab or electronically located in the Pawelczyk section of the secure Noll lab computer server system. Only researchers listed on this document will have any access to your research records.

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 Your research records (electronic and physical) will be labeled with your code number and will be kept in a separate locked file or password protected file physically located in Noll lab or electronically located in the Pawelczyk section of the secure Noll lab computer server system. Only researchers listed on this document will have access to your research records.

In the event of any publication or presentation resulting from the research, no personally identifiable information will be shared.

We will do our best to keep your participation in this research study confidential to the extent permitted by law. However, it is possible that other people may find out about your participation in this research study. For example, the following people/groups may check and copy records about this research.  The Office for Human Research Protections in the U. S. Department of Health and Human Services  The Institutional Review Board (a committee that reviews and approves research studies) and  The Office for Research Protections.

Some of these records could contain information that personally identifies you. Reasonable efforts will be made to keep the personal information in your research record private. However, absolute confidentiality cannot be guaranteed.

8. What are the costs of taking part in this research study? What happens if you are injured as a result of taking part in this research study?

In the unlikely event you become injured as a result of your participation in this study, medical care is available. It is the policy of this institution to provide neither financial compensation nor free medical treatment for research-related injury. By signing this document, you are not waiving any rights that you have against The Pennsylvania State University for injury resulting from negligence of the University or its investigators.

9. Will you be paid or receive credit to take part in this research study?

Compensation is based on time spent in the laboratory. The entire study will result in payment of $80 for approximately 4-5 hours of time.

Screening: $5

Placement of the microneurography needle: - $15

Protocol 1: Arm Compression after Continuous Squeezing Exercise. -$15 Protocol 2: Arm Squeezing Test. -$15

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Protocol 3: Lung Sensitivity Test. -$15 Protocol 4: Water Temperature Test. -$15

10. What are your rights if you take part in this research study?

Taking part in this research study is voluntary. . You do not have to be in this research. . If you choose to be in this research, you have the right to stop at any time. . If you decide not to be in this research or if you decide to stop at a later date, there will be no penalty or loss of benefits to which you are entitled.

The person in charge of the research study or the sponsor can remove you from the research study without your approval. Possible reasons for removal include any medical issue or scenario where the researcher/ research-team is uncomfortable with you participating in the protocols described in this document.

During the course of the research you will be provided with any new information that may affect your health, welfare or your decision to continue participating in this research.

11. If you have questions or concerns about this research study, whom should you call?

Please call the head of the research study (principal investigator), James Pawelczyk at 814-865- 3453 if you: . Have questions, complaints or concerns about the research. . Believe you may have been harmed by being in the research study.

You may also contact the Office for Research Protections at (814) 865-1775, [email protected] if you: . Have questions regarding your rights as a person in a research study. . Have concerns or general questions about the research. . You may also call this number if you cannot reach the research team or wish to talk to someone else about any concerns related to the research.

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INFORMED CONSENT TO TAKE PART IN RESEARCH

Signature of Person Obtaining Informed Consent

Your signature below means that you have explained the research to the subject or subject representative and have answered any questions he/she has about the research.

______Signature of person who explained this research Date Time Printed Name (Only approved investigators for this research may explain the research and obtain informed consent.)

Signature of Person Giving Informed Consent Before making the decision about being in this research you should have:  Discussed this research study with an investigator,  Read the information in this form, and  Had the opportunity to ask any questions you may have. Your signature below means that you have received this information, have asked the questions you currently have about the research and those questions have been answered. You will receive a copy of the signed and dated form to keep for future reference.

Signature of Subject

By signing this consent form, you indicate that you voluntarily choose to be in this research and agree to allow your information to be used and shared as described above.

______Signature of Subject Date Time Printed Name

CONSENT FOR RESEARCH The Pennsylvania State University

Title of Project: Genotyping a cohort for gene association studies.

Principal Investigator: James A. Pawelczyk

Address: 107 Noll Laboratory, Pennsylvania State University, University Park, PA 16802

Telephone Number: 814-865-3453

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Subject’s Printed Name: ______

1. Why is this research study being done?

We are asking you to be in this research because we would like to know about your genes related to blood pressure. We will determine what genes you have from a sample you give us and store that sample for future analysis. We will use this sample to find out if you qualify for our future studies.

The purpose of this study is to get a large group of people who all have their genotypes determined. We can then recruit from this large group for other studies. About 300 people will take part in this research study here on Penn State’s campus and the surrounding area.

2. What will happen in this research study?

To participate in this study you must: 1. Read, understand, and then sign this informed consent document.

2. Be eligible by all inclusion and exclusion criteria described on the “screening data” and “medical history” documents.

3. Be willing to give a DNA sample by mouth swab and allow it to be stored indefinitely.

4. Be willing to be contacted for- and likely participate in- future studies by our lab.

5. Allow us share your de-identified data with other labs at Penn State.

6. Be willing to consider volunteering for several protocols designed to raise blood pressure; for example, a “cold pressor test” (where we put your hand in icy water for 3 minutes) and hand squeezing exercise tests. We would not do these in this initial study, but we would in the follow-up studies.

7. Be willing to volunteer for protocols with additional measurements; for example, a technique that measures nerve activity. To do this, we record signals from a nerve located close to the surface of the skin, on the lower leg, just below the knee. A fine wire needle (called a

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microelectrode) will be inserted through the skin to record activity from the nerve. A second fine wire needle will be inserted about 3 cm away (termed the reference needle). We would not do this in this initial study but we would in the follow-up studies.

We are using this study to create a group of people to recruit from for other studies. If you are not willing to be recruited for other studies, we will not include you in this protocol.

If you do meet the above requirements and sign this document, we will go on with the study as follows:

1. You will arrive in the morning without eating anything and without brushing your teeth yet that day.

2. You will lay in bed quietly for 15 minutes. We will then place a cuff on your upper arm and use it measure your blood pressure and heart rate. We will measure your blood pressure 3 times total with a least 1 minute between each measurement. This should take about 20 minutes.

3. You will then stand up and we will measure your height and weight.

4. While still standing we will measure your blood pressure and heart rate once more using the same cuff as explained above.

5. We will explain to you the technique to collect a DNA sample from the inside of your mouth. You will use multiple small swabs to repeatedly wipe the inside of your cheek and then lock your sample in a container. This should take 5-10 minutes.

Please note that this is not a complete analysis of your genetic makeup. We are only currently interested in genes related to blood pressure. However, we will store your sample and may use it to analyze for future genes of interest.

Your total time in lab should be 45-60 minutes.

3. What are the risks and possible discomforts from being in this research study?

There is a risk of loss of confidentiality if your information or your identity is obtained by someone other than the investigators, but precautions will be taken to prevent this from happening.

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The medical risks in this study are minimal.

 Blood pressure: The inflated arm cuff may cause temporary tingling, numbness, and cool or cold feeling in your arm.

 Mouth Swab: A mouth swab has no greater risk above normal daily living.

4. What are the possible benefits from being in this research study?

4a. What are the possible benefits to you?

Benefits to you are a measurement of basic health parameters such as height, weight, and blood pressure.

4b. What are the possible benefits to others?

Benefits to society in general relate to the further understanding of the way genes play a role in blood pressure control. Eventually, this could help with understanding heart-related diseases.

5. What other options are available instead of being in this research study?

You may decide not to participate in this research.

6. How long will you take part in this research study?

If you agree to take part, it will take you about 45-60 minutes to complete this research study. You will not be asked to return to the research site for this study. You may be asked to return for follow-up studies.

7. How will your privacy and confidentiality be protected if you decide to take part in this research study?

We will limit the use and sharing of your personal research information to people who have a need to review this information.

 A list that matches your name with your code number will be kept in a paper file and/or password protected computer file. The paper file will be kept in a locked filing cabinet in a locked room in Noll Lab. Electronically, the file will be secured in a section of a protected computer server located at Penn State.

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 Your research records (electronic and physical) will be labeled with your code number only, and will be kept in a separate locked paper file in Noll Lab. It will be stored electronically in a secure server.

 Your research samples (DNA) will be labeled with your code, the name of our lab, and the date. They will be stored in a secure facility here on campus.

In the event of any publication or presentation resulting from the research, no personally identifiable information will be shared.

We will keep your participation in this research study confidential to the extent permitted by law. However, it is possible that other people may find out about your participation in this research study. For example, the following people/groups may check and copy records about this research.

 The Office for Human Research Protections in the U. S. Department of Health and Human Services

 The Institutional Review Board (a committee that reviews and approves research studies) and

 The Office for Research Protections.

8. What happens if you are injured as a result of taking part in this research study?

10. Will you be paid or receive credit to take part in this research study?

Payment in the form of $3 cash will be given upon completion of the study.

10. Who is paying for this research study?

The primary investigator, Dr. Pawelczyk, is paying with research incentive funds.

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11. What are your rights if you take part in this research study?

Taking part in this research study is voluntary.

. You do not have to be in this research.

. If you choose to be in this research, you have the right to stop at any time.

. If you decide not to be in this research or if you decide to stop at a later date, there will be no penalty or loss of benefits to which you are entitled.

12. If you have questions or concerns about this research study, whom should you call?

Please call the head of the research study (principal investigator), James Pawelczyk at 814-865- 3453 if you:

. Have questions, complaints or concerns about the research. . Believe you may have been harmed by being in the research study.

You may also contact the Office for Research Protections at (814) 865-1775, [email protected] if you:

. Have questions regarding your rights as a person in a research study. . Have concerns or general questions about the research. . You may also call this number if you cannot reach the research team or wish to talk to someone else about any concerns related to the research.

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INFORMED CONSENT TO TAKE PART IN RESEARCH

Signature of Person Obtaining Informed Consent

Your signature below means that you have explained the research to the subject or subject representative and have answered any questions he/she has about the research.

______Signature of person who explained this research Date Time Printed Name (Only approved investigators for this research may explain the research and obtain informed consent.)

Signature of Subject

By signing this consent form, you indicate that you voluntarily choose to be in this research and agree to allow your information to be used and shared as described above.

______Signature of Subject Date Time Printed Name

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Optional part(s) of the study In addition to the main part of the research study, there is another part of the research. You can be in the main part of the research without agreeing to be in this optional part.

Optional Sharing of Genetic Information with Personally Identifiable Information In the main part of this study, we are collecting DNA samples from you. From this DNA we will get genetic information. If you agree, the Pawelczyk Lab may share your genetic information with partner labs with personally identifying information. For this sharing to happen the partner lab must first have an IRB approved protocol specifically including the sharing of information from this study.

These labs may be interested in recruiting you for their studies. If the partner lab expresses interest in recruiting you based on your genotype- our lab would then contact you and ask for permission. If you are interested we will share your contact information with the other lab. If you are NOT interested we will NOT share your contact information.

Optional study details:  It is unlikely that these studies will have a direct benefit to you.  Neither your doctor nor you will receive results of these future research tests, nor will the results be put in your health record.  Sometimes tissue is used for genetic research about diseases that are passed on in families. Even if your sample(s) is used for this kind of research, the results will not be put in your health record.  It is possible that your cells might be used to develop products or tests that could be patented and licensed. There are no plans to provide financial compensation to you should this occur. If you have any questions, you should contact a member of the research team.

Therefore, with your permission given below, we will share your information and sample[s] with our partner labs. We may also contact you for future studies on behalf of our partner labs.

1. Your information and sample[s] may be shared with other investigators/groups with identifying information. ______Yes _____ No

2. We may contact you for recruitment on behalf of our partner labs.

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______Yes _____ No

Signature of Person Obtaining Informed Consent

Your signature below means that you have explained the optional part(s) of the research to the subject or subject representative and have answered any questions he/she has about the research.

______Signature of person who explained this research Date Time Printed Name

Signature of Person Giving Informed Consent

Signature of Subject

By signing below, you indicate that you have read the information written above and have indicated your choices for the optional part(s) of the research study.

______Signature of Subject Date Time Printed Name

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REFERENCES

1. Writing Group, M., et al., Executive Summary: Heart Disease and Stroke Statistics--2016 Update: A Report From the American Heart Association. Circulation, 2016. 133(4): p. 447- 54.

2. James, P.A., et al., 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA, 2014. 311(5): p. 507-20.

3. Alagona, P., Jr. and T.A. Ahmad, Cardiovascular disease risk assessment and prevention: current guidelines and limitations. Med Clin North Am, 2015. 99(4): p. 711-31.

4. Potts, J.T. and J. Li, Interaction between carotid baroreflex and exercise pressor reflex depends on baroreceptor afferent input. Am J Physiol, 1998. 274(5 Pt 2): p. H1841-7.

5. Fadel, P.J., et al., Carotid baroreflex regulation of sympathetic nerve activity during dynamic exercise in humans. Am J Physiol Heart Circ Physiol, 2001. 280(3): p. H1383-90.

6. Goodwin, G.M., Mccloske.Di, and J.H. Mitchell, Cardiovascular and Respiratory Responses to Changes in Central Command during Isometric Exercise at Constant Muscle Tension. Journal of Physiology-London, 1972. 226(1): p. 173-&.

7. Eldridge, F.L., et al., Stimulation by central command of locomotion, respiration and circulation during exercise. Respir Physiol, 1985. 59(3): p. 313-37.

8. Coote, J.H., S.M. Hilton, and J.F. Perez-Gonzalez, The reflex nature of the pressor response to muscular exercise. J Physiol, 1971. 215(3): p. 789-804.

9. McCloskey, D.I. and J.H. Mitchell, Reflex cardiovascular and respiratory responses originating in exercising muscle. J Physiol, 1972. 224(1): p. 173-86.

10. Mitchell, J.H., M.P. Kaufman, and G.A. Iwamoto, The exercise pressor reflex: its cardiovascular effects, afferent mechanisms, and central pathways. Annu Rev Physiol, 1983. 45: p. 229-42.

11. Smith, S.A., J.H. Mitchell, and M.G. Garry, The mammalian exercise pressor reflex in health and disease. Exp Physiol, 2006. 91(1): p. 89-102.

12. Kaufman, M.P., et al., Effects of static muscular contraction on impulse activity of groups III and IV afferents in cats. J Appl Physiol Respir Environ Exerc Physiol, 1983. 55(1 Pt 1): p. 105-12.

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Curriculum Vitae Nathan M. Garvin

Education 2006-2010 B.S. (Science) Exercise and Health Science Major. Alma College, Alma, MI 2010-2017 Ph.D. (Integrative and Biomedical Physiology) Autonomic Neurophysiology Lab. Penn State University, University Park, PA

Appointments Fall 2016 Adjunct Faculty, Department of Health Sciences, Lock Haven University, Lock Haven, PA. Fall 2017 Faculty Instructor, Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ.

Honors and Awards 2006-2010 Trustee Honors Scholarship, Alma College. 2010 University Graduate Fellowship, PSU. 2010 Virginia S. Shirley Memorial Graduate Scholarship, PSU. 2011-2012 Pennsylvania Space Grant Consortium Fellowship Award, PSU. 2015-2016 Harold F. Martin Graduate Assistant Outstanding Teaching Award, PSU.

Courses Taught as Lecturer and/or Teaching Assistant: Fall 2011, ‘12, ‘13 Physiology Laboratory (Biol. 142), PSU Spring 2012, ‘13, ‘14, ’17 Laboratory in Mammalian Physiology (Biol. 473), PSU Summer 2013 Mammalian Anatomy Lecture (Biol. 129), PSU Summer 2014, Spring 2016 Introductory Physiology Lecture (Biol. 141), PSU Fall 2014, Spring 2015 Mammalian Anatomy Laboratory (Biol. 129), PSU Fall 2016 Human Anatomy & Physiology, Lecture & Lab (Hlth. 115), LHU

Pedagogy Courses and Certifications: 2015 OL4500: Graduate Certification In Online Teaching, PSU World Campus 2015 Course in College Teaching- Schreyer Institute for Teaching Excellence, PSU 2017 Penn State Graduate School Teaching Certificate

Journal Publications 2014 Nathan M. Garvin, Benjamin D. Levine, Peter B. Raven, and James A. Pawelczyk. Pneumatic Antishock Garment Inﬂation Activates the Human Sympathetic Nervous System by Abdominal Compression. Experimental Physiology. Volume 99, issue 1, pages 101-110, January 2014.

2015 John E Davis, Dale R Wagner, Nathan M. Garvin, David Moilanen, Jessica Thorington, and Cory Schall. Cognitive and Psychomotor Responses to High- Altitude Exposure in Sea Level and High-Altitude Residents of Ecuador. Journal of Physiological Anthropology. Volume 34, issue 2, 2015.

Professional Societies: 2012-present American Physiological Society 2016-present American College of Sports Medicine