Allostatic Load, Senescence, and Aging Among Japanese Elderly

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Rachael Elizabeth Leahy, M.Sc.

Graduate Program in Anthropology

The Ohio State University

2014

Dissertation Committee:

Dr. Douglas E. Crews, Advisor

Dr. Jeffrey Cohen

Dr. Randy Nelson

Rachael Elizabeth Leahy

2014

Abstract

Senescence varies substantially within and among populations. Data examined here extend knowledge on modern human variation by analyzing elders from Nagasaki

Prefecture, Japan, enhance our understanding of relationships between senescence and human biology, and provide more information concerning the use of allostatic load (AL) as a measure of senescent decline among a non-Western population. Developing a valid method for assessing physiological variation due to senescence will benefit those studying health outcomes and survival of elders. It also will aid in focusing healthcare funds and interventions by targeting those most likely to experience unwanted outcomes.

Understanding how Japan’s elders are surviving and adapting to old age, life-long stress, and developing dysfunction with increasing age provides a model of how others may slow senescence in other settings.

Background: 96 elderly residents of Sakiyama City, Nagasaki Prefecture (ages

55-89) and 27 elderly residents of Hizen-Oshima, Nagasaki Prefecture, Japan (ages 51-

82) were sampled for components of allostatic load (AL) and other aspects of physical and physiological variation. Surveys were conducted by local health care nursing staff and members of a joint American-Japanese research team during participants’ yearly physical examinations. Japan was selected as the study site because Japanese men and

ii women rank among the longest-lived people in the world and the population is relatively genetically homogenous.

Methods: AL is a summary measure of physiological activity across multiple regulatory systems pertinent to disease risks. AL incorporates data on ten components: systolic and diastolic blood pressure, high density lipoproteins, total cholesterol, glycosylated hemoglobin, dihydroepiandrosterone-sulfate, cortisol, noradrenaline, adrenaline, and waist:hip ratio. It is calculated by summing the number of components for which an individual’s values are in the highest risk quartile. Two alternate measures of AL were calculated for comparison. The first used decile as opposed to quartile cut- points. The second was constructed using principal components analysis (ALPC1).

Multivariate regressions were used to analyze associations between AL, controlling for age and sex, and physiological variables in each sample.

Results: AL was higher among men than women and was poorly associated with age. Multivariate models of AL, sex, and age predict GTP, creatinine, white blood cell count, percent body fat, weight, dopamine, red blood cell count, GPT, and blood glucose variably by location. Associations between AL and physiological variables change when quartile vs. decile cut-points were used to construct the measure. ALPC1 showed significant associations in the Sakiyama sample with GPT, GTP, white blood cell count, creatine, dopamine, hematocrit, hemoglobin, red blood cell count, uric acid, and the self maintenance score of the Tokyo Metropolitan Institute of Gerontology Index of

Competency.

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Conclusions: The poor association between AL and age suggests AL may be assessing underlying senescence better than age alone. Higher AL indicates men in this sample experienced greater cumulative physiological and physical stress over their lives as compared to women in this Japanese setting. AL is significantly associated with immune, liver, and renal function, and aspects of frailty. These results provide additional support for suggesting AL measures physiological dysregulation and senescent decline across multiple somatic systems. Results from ALPC1 indicate sex is an important component in AL.

Dedication

Dedicated to my family:

Nancy and Glenn Leahy, for realizing that Dr. Shelly was more than a childhood fantasy

and for supporting my dream every step of the way

Sarah Leahy Dore,

for always being my role-model and my inspiration

Acknowledgements

This dissertation could not have been completed without assistance from many people. First, I thank my advisor, Dr. Douglas E. Crews, for his direction, support, and encouragement from my first year at The Ohio State University through the end of the dissertation process. Dr. Crews has spent countless hours reading and re-reading this manuscript and many others; for his tireless efforts to make me into a better scientist, I thank him gratefully. I would also like to thank the team of researchers in Japan who, with my advisor, carefully collected these data: Yosuke Kusano, Kiyoshi Aoyagi,

Takahiro Maeda, Aiko Iwamoto, and especially Dr. Yoshiaki Sone. My other committee members, from my candidacy examination through to the dissertation, have also been helpful: Dr. Clark Larsen, Dr. Randy Nelson, and Dr. Jeffrey Cohen. I also thank my research assistants for their support and hardwork: Katie Duff and Jenny Ahern. I would also like to give special thanks to the residents of and participants from Sakiyama and

Hizen-Oshima without whom this study would not have been possible.

I would like to thank my family, Nancy and Glenn Leahy, and Sarah and Chris

Dore for the tremendous support they have given me throughout graduate school. Finally,

I would like to thank Kraig Frederickson for his love and and unwavering support of all my endeavors.

Vita

2007...... A.B. Anthropology, Bowdoin College

2009...... M.Sc. Anthropologie Biologique, Préhistoire, et

Paléoanthropologie, Université Bordeaux I: Science et

Technologies

2010-2014 ...... Susan L. Huntington Dean’s Distinguished Fellow, The

Ohio State University

2012-2013 ...... Graduate Teaching Associate, The Ohio State University

Publications

Andre A., R. Leahy, and S. Rottier. 2013. Cremated human remains deposited in two phases: Evidence from the necropolis of the Tuileries Site (Lyon, France: 2nd century AD). International Journal of Osteoarchaeology. http://onlinelibrary.wiley.com/doi/10.1002/oa.2317/abstract.

Leahy R. 2012. Diagnoses sexuelles primaires et secondaires [Primary and secondary sexual diagnoses]. In Rottier S, Piette J, Mordant C, eds. Archéologie funéraire du Bronze final dans les vallées de l'Yonne et de la haute Seine. Les nécropoles de Barbey, Barbuise et La Saulsotte. Dijon, Editions Universitaire de Dijon.

Leahy R. and D.E. Crews. 2012. Physiological dysregulation and somatic decline among elders: Modeling, applying, and re-interpreting allostatic load. Collegium Antropologicum 36(1):11-22.

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Publications

Deguilloux M.F., S. Rottier, R. Leahy, and M.H. Pemonge. 2012. European neolithization and ancient DNA: an assessment. Evolutionary Anthropology 21:24-37.

Deguilloux M.F., S. Ricaud, R. Leahy, and M.H. Pemonge. 2011. Analysis of ancient human DNA and primers contamination: One step backward one step forward. Forensic Science International 210(1-3):102-109.

Fields of Study

Major Field: Anthropology

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Table of Contents

Abstract ...... ii Dedication ...... v Acknowledgements ...... vi Vita ...... vii Chapter 1: Introduction ...... 1 1.1 Introduction ...... 1 1.2 Theories of Senescence ...... 3 1.2.1 Non-evolutionary theories of senescence ...... 4 1.2.2 Evolutionary theories of senescence ...... 8 1.3 Studying Senescence ...... 13 1.3.1 Demographic Transition ...... 13 1.4 Stress, Stressors, and Stress Responses ...... 16 1.4.1 Defining Stress ...... 16 1.4.2 Stressors ...... 19 1.4.3 Measuring Stress Responses ...... 20 1.4.4 Neuroendocrinology of Stress ...... 24 1.4.5 Stress and anthropology...... 26 1.5 Project Objectives and Hypotheses ...... 28 1.5.1 Project Objectives ...... 28 1.5.2 Project Hypotheses ...... 30 Chapter 2: Materials and Methods ...... 32 2.1 Research Design ...... 32 2.2 Study Population ...... 32

2.3 Study Sample...... 36 2.3.1 Sakiyama ...... 36 2.3.2 Hizen-Oshima ...... 36 2.4 Data Collection ...... 37 2.4.1 Procedures ...... 37 2.4.2 Allostatic Load ...... 38 2.4.3 Principal Component Analysis ...... 40 2.4.4 Data Analyses ...... 43 Chapter 3: Physiological and Sociocultural Variables...... 45 3.1 Introduction ...... 45 3.2 Physiological Components of Allostatic Load ...... 45 3.2.1 Adrenaline ...... 45 3.2.2 Cortisol ...... 48 3.2.3 Systolic and Diastolic Blood Pressure ...... 49 3.2.4 Dehydroepiandrosterone-sulfate ...... 52 3.2.5 Glycated Hemoglobin ...... 54 3.2.6 Noradrenaline ...... 55 3.2.7 Plasma Lipids: Total Cholesterol and High Density Lipoproteins ...... 56 3.2.8 Waist Hip Ratio ...... 58 3.3 Dependent Physiological and Sociocultural Variables ...... 58 3.3.1 Dopamine...... 58 3.3.2 Plasma Lipids: Low Density Lipoprotein and Triglycerides ...... 59 3.3.3 Renal Function: Creatine and Creatinine...... 61 3.3.4 Liver Function ...... 63 3.3.5 Blood ...... 65 3.3.6 Immune Function ...... 69 3.3.7 Frailty...... 70 3.3.8 Activities of Daily Living - The Tokyo Metropolitan Institute of Gerontology Index of Competence ...... 72

Chapter 4: Results I - Sakiyama Participant Demographics, Physiological Characteristics, Allostatic Load, and Sociocultural Variables ...... 74 4.1 Introduction ...... 74 4.2 Descriptive Statistics ...... 75 4.3 Allostatic Load ...... 78 4.4 Sex ...... 83 4.5 Age ...... 85 Chapter 5: Results II - Associations of Allostatic Load with Physiological and Sociocultural Variables Among Elderly Sakiyama Residents ...... 87 5.1 Introduction ...... 87 5.2 Quartile Cut-Point Allostatic Load Associations ...... 87 5.2.1 Bivariate Associations ...... 87 5.2.2 Multivariate Association: Allostatic Load and Age ...... 89 5.2.3 Multivariate Association: Allostatic Load and Sex ...... 92 5.2.4 Multivariate Association: Allostatic Load, Age, and Sex ...... 95 5.3 Decile Cut-Point Allostatic Load Associations ...... 98 5.3.1 Bivariate Associations ...... 98 5.3.2 Multivariate Associations: Allostatic Load and Age ...... 100 5.3.3 Multivariate Associations: Allostatic Load and Sex ...... 103 5.3.4 Multivariate Associations: Allostatic Load, Age, and Sex ...... 106 Chapter 6: Results III - Hizen-Oshima Participant Demographics, Physiological Characteristics, Allostatic Load, and Sociocultural Variables...... 110 6.1 Introduction ...... 110 6.2 Descriptive Statistics ...... 110 6.3 Allostatic Load ...... 113 6.4 Sex ...... 118 6.5 Age ...... 120 Chapter 7: Results IV: Associations of Allostatic Load with Physiological and Sociocultural Variables among Elderly Hizen-Oshima Residents ...... 122 7.1 Introduction ...... 122 7.2 Allostatic Load Associations (Quartile Cut-Offs) ...... 122 xi

7.2.1 Bivariate Associations ...... 122 7.2.2 Multivariate associations: AL and Age ...... 124 7.2.3 Multivariate associations: AL and Sex ...... 127 7.2.4 Multivariate associations: AL, Age, and Sex ...... 129 7.3 Allostatic Load Associations (Decile Cut-Offs) ...... 132 7.3.1 Bivariate Associations ...... 132 7.3.2 Multivariate associations: AL and Age ...... 134 7.3.3 Multivariate associations: AL and Sex ...... 136 7.3.4 Multivariate associations: AL, Age, and Sex ...... 138 Chapter 8: Results V: Amalgamating Data Sets: Hizen-Oshima/Sakiyama ...... 142 Participant Demographics,Physiological Characteristics, Allostatic Load,...... 142 and Sociocultural Variables ...... 142 8.1 Introduction ...... 142 8.2 Descriptive Statistics ...... 142 8.3 Allostatic Load-Raw Values ...... 145 8.4 Allostatic Load - Standardized Values ...... 149 8.5 Sex ...... 154 8.6 Age ...... 156 Chapter 9: Results VI: Associations of Allostatic Load Combined Data Sets with Physiological Variables among Elderly Hizen-Oshima and Sakiyama Residents ...... 159 9.1 Introduction ...... 159 9.2 Allostatic Load Associations (Quartile Cut-Offs: Raw Data) ...... 159 9.2.1 Bivariate Associations ...... 159 9.2.2 Multivariate Associations: AL and Age ...... 161 9.2.3 Multivariate Associations: AL and Sex ...... 164 9.2.4 Multivariate Associations: AL, Age, and Sex ...... 168 9.3 Allostatic Load Associations (Decile Cut-Offs: Raw Data) ...... 171 9.3.1 Bivariate Associations ...... 171 9.3.2 Multivariate Associations: AL and Age ...... 173 9.3.3 Multivariate Associations: AL and Sex ...... 175 xii

9.3.4 Multivariate Associations: AL, Age, and Sex ...... 179 9.4 Allostatic Load Associations (Quartile Cut-Offs: Standardized z-scores) ...... 182 9.4.1 Bivariate Associations ...... 182 9.4.2 Multivariate Associations: AL and Age ...... 184 9.4.3 Multivariate Associations: AL and Sex ...... 187 9.4.4 Multivariate Associations: AL, Age, and Sex ...... 190 9.5 Allostatic Load Associations (Decile Cut-Offs: Standardized z-scores) ...... 194 9.5.1 Bivariate Associations ...... 194 9.5.2 Multivariate Associations: AL and Age ...... 196 9.5.3 Multivariate Associations: AL and Sex ...... 199 9.5.4 Multivariate Associations: AL, Age, and Sex ...... 203 Chapter 10: Principal Components Analysis ...... 207 10.1 Introduction ...... 207 10.2 Model 1: PCA of Allostatic Load Variables ...... 207 10.3 Model 2: PCA of Allostatic Load Variables, Age, and Sex ...... 211 10.4 Model 3: PCA of Allostatic Load Variables and Location ...... 215 Chapter 11: Discussion and Conclusions ...... 217 11.1 Introduction ...... 217 11.2 Limitations of study ...... 218 11.3 Results ...... 219 11.3.1 Age...... 219 11.3.2 Sex ...... 221 11.3.3 Human Variation ...... 224 11.3.4 Allostatic load and physiological variation ...... 225 11.3.5 Principal Components Analysis...... 233 11.4 Conclusions ...... 234 References ...... 237

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List of Tables

Table 4.1 Common acronyms of physiological and sociocultural variables ...... 74

Table 4.2: Descriptive statistics and ranges for independent variables of elderly Sakiyama residents (N=96) ...... 76 Table 4.3: Descriptive statistics and ranges for dependent variables of elderly Sakiyama residents (N=96) ...... 77 Table 4.4 Established reference ranges for independent and dependent variables compared to values from Sakiyama sample ...... 78 Table 4.5 Cut-points for estimating AL of elderly Sakiyama residents...... 79 Table 4.6 Allostatic load estimates derived using quartile cut-offs for elderly residents of Sakiyama by sex and age group divided at the median ...... 80 Table 4.7 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs and median age division for elderly residents of Sakiyama residents ...... 80 Table 4.8 Allostatic load estimates derived using quartile cut-offs for elderly residents of Sakiyama by sex and age group divided at age 70 ...... 81 Table 4.9 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs and age division at 70 for elderly residents of Sakiyama residents ...... 81 Table 4.10 Allostatic load estimates derived using decile cut-offs for elderly residents of Sakiyama by sex and age group divided at the median ...... 82 Table 4.11 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs and median age division for elderly residents of Sakiyama residents ...... 82 Table 4.12 Allostatic load estimates derived using decile cut-offs for elderly residents of Sakiyama by sex and age group divided at 70 ...... 82 xiv

Table 4.13 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs and age division at 70 for elderly residents of Sakiyama residents ...... 83 Table 4.14 Comparisons of independent variable means by sex...... 84 Table 4.15 Comparisons of dependent variable means by sex...... 84 Table 4.16 Frequency of Sakiyama sample men and women with an age division at 70 85 Table 4.17 p-values from independent t-test comparing means of Tokyo Metropolitan Institute of Gerontology Index of Competence scores and sub-scores between age cohorts divided at median age and age 70 ...... 86 Table 5.1 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly residents of Sakiyama ...... 89

Table 5.2 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly residents of Sakiyama ...... 91 Table 5.3 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly residents of Sakiyama ...... 94 Table 5.4 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between age and sex, and dependent variables among elderly residents of Sakiyama ...... 95 Table 5.5 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly residents of Sakiyama ...... 96 Table 5.6 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions between age and sex (age*sex), and dependent variables among elderly residents of Sakiyama ...... 98

Table 5.7 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly residents of Sakiyama ...... 100 Table 5.8 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly residents of Sakiyama ...... 102 Table 5.9 Significant multivariate associations between allostatic load (independent variable), controlling for age and interactions between AL and age (AL*age), and dependent variables among elderly residents of Sakiyama ...... 103 Table 5.10 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly residents of Sakiyama ...... 104 xv

Table 5.11 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex), and dependent variables among elderly residents of Sakiyama ...... 105

Table 5.12 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly residents of Sakiyama ...... 107 Table 5.13 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly residents of Sakiyama ...... 109 Table 6.1: Descriptive statistics and ranges for independent variables of elderly Hizen- Oshima residents (N=27). Parentheses indicate descriptive statistics and ranges for independent variables calculated without outliers...... 112 Table 6.2: Descriptive statistics and ranges for dependent variables of elderly Hizen- Oshima residents (N=27). Parentheses indicate descriptive statistics and ranges for independent variables calculated without outliers...... 112 Table 6.3 Established reference ranges for independent and dependent variables compared to values from Hizen-Oshima sample ...... 113 Table 6.4 Cut-points for estimating AL of elderly Hizen-Oshima residents...... 114 Table 6.5 Allostatic load estimates derived using quartile cut-offs for elderly residents of Hizen-Oshima by sex and age group divided at the median ...... 115 Table 6.6 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs and median age division for elderly residents of Hizen- Oshima residents ...... 115 Table 6.7 Allostatic load estimates derived using quartile cut-offs for elderly residents of Hizen-Oshima by sex and age group divided at age 70 ...... 116 Table 6.8 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs and age division at 70 for elderly residents of Hizen- Oshima residents ...... 116 Table 6.9 Allostatic load estimates derived using percentile cut-offs for elderly residents of Hizen-Oshima by sex and age group divided at the median ...... 117 Table 6.10 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs and median age division for elderly residents of Hizen- Oshima residents ...... 117

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Table 6.11 Allostatic load estimates derived using percentile cut-offs for elderly residents of Hizen-Oshima by sex and age group divided at 70 ...... 118 Table 6.12 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs and age division at 70 for elderly residents of Hizen-Oshima residents ...... 118 Table 6.13 t-test comparisons of independent variable means by sex among elderly residents of Hizen-Oshima ...... 119 Table 6.14 t-test comparisons of dependent variable means by sex among elderly residents of Hizen-Oshima ...... 120 Table 6.15 Frequency of elderly residents of Hizen-Oshima divided by an age division at 70 ...... 121 Table 7.1 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly Hizen-Oshima residents ...... 124 Table 7.2 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly residents of Hizen-Oshima ...... 126 Table 7.3 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly residents of Hizen-Oshima ...... 128 Table 7.4 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex), and dependent variables among elderly residents of Hizen-Oshima ...... 129 Table 7.5 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly Hizen-Oshima residents ...... 130 Table 7.6 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly Hizen-Oshima residents ...... 132 Table 7.7 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly Hizen-Oshima residents ...... 133 Table 7.8 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly residents of Hizen-Oshima ...... 135

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Table 7.9 Association between allostatic load (independent variable), controlling for age and interactions between AL and age, and dependent variables among elderly residents of Hizen-Oshima ...... 136 Table 7.10 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly residents of Hizen-Oshima ...... 137 Table 7.11 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex) and dependent variables among elderly residents of Hizen-Oshima ...... 138 Table 7.12 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly Hizen-Oshima residents ...... 139 Table 7.13 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly Hizen-Oshima residents ...... 141 Table 8.1: Descriptive statistics and ranges for independent variables of elderly Hizen- Oshima/Sakiyama residents (N=123)...... 144 Table 8.2: Descriptive statistics and ranges for dependent variables of elderly Hizen- Oshima/Sakiyama residents (N=123). Parentheses indicate descriptive statistics and ranges for independent variables calculated without outliers...... 144 Table 8.3 Established reference ranges for independent and dependent variables compared to values from the Hizen-Oshima/Sakiyama sample ...... 145 Table 8.4 Cut-points for estimating AL of elderly Hizen-Oshima/Sakiyama residents. .146 Table 8.5 Allostatic load estimates derived using quartile cut-offs for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at the median ...... 147 Table 8.6 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs and median age division for elderly residents of Hizen- Oshima/Sakiyama residents ...... 147 Table 8.7 Allostatic load estimates derived using quartile cut-offs for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at age 70 ...... 147 Table 8.8 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs and age division at 70 for elderly residents of Hizen- Oshima/Sakiyama residents ...... 147 Table 8.9 Allostatic load estimates derived using decile cut-offs for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at the median ...... 148 xviii

Table 8.10 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs and median age division for elderly residents of Hizen- Oshima/Sakiyama residents ...... 149 Table 8.11 Allostatic load estimates derived using decile cut-offs for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at 70 ...... 149 Table 8.12 p-values resulting from independent t-tests comparing allostatic load estimates derived using percentile cut-offs and age division at 70 for elderly residents of Hizen- Oshima/Sakiyama residents ...... 149 Table 8.13 Cut-points for estimating AL of elderly Hizen-Oshima/Sakiyama residents estimated using z-scores...... 150 Table 8.14 Allostatic load estimates derived using quartile cut-offs estimated from z- scores for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at the median ...... 151 Table 8.15 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs estimated from z-scores and median age division for elderly residents of Hizen-Oshima/Sakiyama residents ...... 151 Table 8.16 Allostatic load estimates derived using quartile cut-offs for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at age 70 ...... 152 Table 8.17 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs estimated from z-scores and age division at 70 for elderly residents of Hizen-Oshima/Sakiyama residents ...... 152 Table 8.18 Allostatic load estimates derived using decile cut-offs estimated from z-scores for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at the median ...... 153 Table 8.19 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs estimated from z-scores and median age division for elderly residents of Hizen-Oshima/Sakiyama residents ...... 153 Table 8.20 Allostatic load estimates derived using decile cut-offs estimated from z- scores for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at 70 ...... 154 Table 8.21 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs estimated from z-scores and age division at 70 for elderly residents of Hizen-Oshima/Sakiyama residents ...... 154 Table 8.22 Comparisons of independent variable means by sex among elderly residents of Hizen-Oshima/Sakiyama ...... 155 xix

Table 8.23 Comparisons of dependent variable means by sex among elderly residents of Hizen-Oshima/Sakiyama ...... 155 Table 8.24 Frequency of elderly residents of Hizen-Oshima/Sakiyama divided by an age division at 70 ...... 156 Table 8.25 p-values from independent t-test comparing means of independent variables divided at median age among elderly residents of Hizen-Oshima/Sakiyama ...... 157 Table 8.26 p-values from independent t-test comparing means of independent variables divided at age 70 among elderly residents of Hizen-Oshima/Sakiyama ...... 157 Table 8.27 p-values from independent t-test comparing means of dependent variables divided at median age among elderly residents of Hizen-Oshima/Sakiyama ...... 158 Table 8.28 p-values from independent t-test comparing means of dependent variables divided at age 70 among elderly residents of Hizen-Oshima/Sakiyama ...... 158 Table 9.1 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using raw data and quartile cut-points) ...... 160 Table 9.2 Significant multivariate associations between allostatic load (independent variable), controlling for participants’ town of origin, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using raw data and quartile cut-points) ...... 161 Table 9.3 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly residents of Sakiyama/Hizen- Oshima (SHO sample, AL constructed using raw data and quartile cut-points) ...... 163 Table 9.4 Significant multivariate associations between allostatic load (independent variable), controlling for age and interactions between AL and age, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut-points) ...... 164 Table 9.5 Significant multivariate associations between allostatic load (independent variable), controlling for age and town, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut-points) ...... 164 Table 9.6 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly residents of Sakiyama/Hizen- Oshima (SHO sample, AL constructed using raw data and quartile cut-points) ...... 166 Table 9.7 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex), and xx dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut-points) ...... 167 Table 9.8 Significant multivariate associations between allostatic load (independent variable), controlling for sex and town, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut-points) ...... 167 Table 9.9 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut- points) ...... 169 Table 9.10 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut-points) ...... 170 Table 9.11 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and town, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut-points) ...... 171 Table 9.12 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using raw data and decile cut-points) ...... 172 Table 9.13 Significant multivariate associations between allostatic load (independent variable), controlling for participants’ town of origin, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using raw data and decile cut-points) ...... 173 Table 9.14 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly residents of Sakiyama/Hizen- Oshima (SHO sample, AL constructed using raw data and decile cut-points) ...... 174 Table 9.15 Significant multivariate associations between allostatic load (independent variable), controlling for age and interactions between AL and age (AL*age), and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut-points) ...... 175 Table 9.16 Significant multivariate associations between allostatic load (independent variable), controlling for age and town, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut- points) ...... 175 xxi

Table 9.17 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly residents of Sakiyama/Hizen- Oshima (SHO sample, AL constructed using raw data and decile cut-points) ...... 177 Table 9.18 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex), and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut-points) ...... 178 Table 9.19 Significant multivariate associations between allostatic load (independent variable), controlling for sex, interactions between AL and sex (AL*sex), and town, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut-points) ...... 178 Table 9.20 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut- points) ...... 180 Table 9.21 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut-points) ...... 181 Table 9.22 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and town, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut-points) ...... 182 Table 9.23 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points) ...... 183 Table 9.24 Significant multivariate associations between allostatic load (independent variable), controlling for participants’ town of origin, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points) ...... 184 Table 9.25 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points) ...... 186 Table 9.26 Significant multivariate associations between allostatic load (independent variable), controlling for age and town, and dependent variables among elderly residents

xxii of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using z-scores and quartile cut-points) ...... 187 Table 9.27 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points) ...... 189 Table 9.28 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex), and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points) ...... 190 Table 9.29 Significant multivariate associations between allostatic load (independent variable), controlling for sex and town, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points) ...... 190 Table 9.30 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly Sakiyama/Hizen- Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points) ...... 192 Table 9.31 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points) ...... 193 Table 9.32 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and town, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points) ...... 194 Table 9.33 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points) ...... 195 Table 9.34 Significant multivariate associations between allostatic load (independent variable), controlling for participants’ town of origin, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points) ...... 196 Table 9.35 Multivariate associations between allostatic load (independent variable), controlling for age, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points) ...... 198

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Table 9.36 Multivariate associations between allostatic load (independent variable), controlling for age and town, and dependent variables among elderly Sakiyama/Hizen- Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points) ...... 199 Table 9.37 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points) ...... 201 Table 9.38 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex), and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points) ...... 202 Table 9.39 Significant multivariate associations between allostatic load (independent variable), controlling for sex and town, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points) ...... 202 Table 9.40 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly Sakiyama/Hizen- Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points) 204 Table 9.41 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points) ...... 205 Table 9.42 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and town, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points) ...... 206 Table 10.1 Results determining applicability of PCA ...... 208 Table 10.2 Significant PCA component eigenvalues and percent total variance explained ...... 208 Table 10.3 Variable loading on significant components (significant variables in each component in bold) ...... 210

Table 10.4: Bivariate associations between ALPC1 (independent variable) and dependent variables among elderly Sakiyama/Hizen-Oshima residents ...... 211 Table 10.5 Results determining applicability of PCA ...... 211

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Table 10.6 Significant PCA component eigenvalues and percent total variance explained ...... 212 Table 10.7 Variable loading on significant components ...... 214 Table 10.8 Significant PCA component eigenvalues and percent total variance explained ...... 215 Table 10.9 Variable loading on significant components (significant variables in each component in bold) ...... 216 Table 11.1 Comparing AL between sexes...... 223 Table 11.2 Comparisons of variable means by sex among elderly residents of Sakiyama/Hizen-Oshima...... 224 Table 11.3 Significant associations between AL and dependent physiological variables ...... 228

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List of Figures

Figure 1: Compression of mortality: “Squaring” of survival curves with rising life expectancy ...... 15

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Chapter 1: Introduction

1.1 Introduction

Prior to the recent past, few individuals survived past their fourth decade of life

(Crews 2003; Caspari and Lee 2004). However, over the past hundred years, medical and public health advances, control of and vaccination against infectious diseases, and improvements in standards of living have drastically reduced global mortality rates among children and older adults (Manton 1986; Olshansky and Carnes 1994; Crews

1997; Olshansky et al. 1998; Crews 2007b), resulting in individuals surviving into old age at unprecedented rates. Today, both the number and proportion of elders are increasing in populations around the world (Crews 2003; Karasik 2005). Life expectancy estimates, defined as the average life span of a general population (Karasik 2005), have reached an astonishing 77.9 and 82.7 years for Americans and Japanese respectively

(Shrestha and Heisler 2011).

Elderhood is an evolutionarily modern development and describes the life history phase from full maturity (i.e., the end of puberty) to death (Crews 2007). Occurring after cessation of reproduction, elderhood lies largely beyond the influence of natural selection

(Bogin and Smith 1996; Bribiescas 2010). Instead, elderhood is heavily influenced by genetic predispositions and sociocultural factors (Finch 1990; Barinaga 1991; Karasik et

1 al. 2005). During elderhood, individuals experience senescence, described as an increased accumulation of metabolic byproducts and a decreased probability of reproduction and survival (Williams 1957; Kirkwood 1977; Austad 1992; Arking 1998;

Cristofalo 1999; Crews 2007). Senescence is age-independent, individualistic, cumulative, progressive, multifactorial, and deleterious (Arking 2006; Crews 2007). It affects every physiological system in the body and results in declining function expressed as increased proneness to falls, infections, chronic degenerative diseases, and death.

Treating illnesses, assisting with activities of daily living (ADL) and instrumental activities of daily living (IADL) (Lawton and Brody 1969; Katz 1983), and caring for elders imposes heavy pecuniary burdens on spouses, relatives, and society at large. As more adults survive into elderhood and improved health care options become available, the financial burden associated with geriatric care continues to escalate (Darton et al.

2003; Pearson et al. 2012).

Despite its theoretical significance as a recently developed life history stage, the practical financial ramifications of better understanding senescence, and the importance of describing modern human variation in senescence as part of evolutionary studies, elderhood is as yet poorly understood and little researched by biological anthropologists.

This project applies an innovative assessment of senescent decline, allostatic load (AL), in a sample of Japanese elders. This measure has potential to identify sociocultural factors influencing senescence and clarify causal pathways leading from decreased physiological functionality to morbidity and ultimately mortality, but has previously been validated primarily among Western samples (Leahy and Crews 2012). Specific objectives

2 of this project are to identify associations among culture, lifeways, and senescence, to expand understanding of modern human variation in senescence, and to validate the use of AL as a measure of senescent decline in a non-Western sample.

1.2 Theories of Senescence

Philosophers and scientists have been long engaged in trying to understand why most species senesce and die. As early as the thirteenth-century, Roger Bacon, an English philosopher, posited that humans were immortal but that injurious behaviors, such as increasingly decadent and unhealthy lifestyles, resulted in decreased life spans (ca. 1247 reviewed in Burke 1998). Today, most scientists accept senescence and death as inevitable for species in which germ line and soma segregate (Kirkwood 1982; Heininger

2012). However, understanding senescence in terms of evolutionary and life history theory has led to much debate because senescence is, in essence, a theoretical paradox. If natural selection works to eliminate detrimental traits from a population, how can deleterious traits associated with senescence be evolutionarily favored? Organisms which maintain full function until their death should theoretically have greater fitness than those that do not. Williams (1957) observed that natural selection should operate against factors which compromise homeostasis, but that senescence is essentially ubiquitous. Therefore, according to traditional evolutionary theory, senescence should not exist at all (Borkan

1982, Pfeiffer 1990).

Late 19th and early 20th century theorists neatly sidestepped this paradoxical dilemma by concentrating on purely physiological theories explaining senescence as the unavoidable result of cellular wear-and-tear, the gradual accumulation of toxins, or

3 radiological damage (Weismann 1882-1889, Metchnikoff 1907, Pearl 1928, Wynne-

Edwards 1962). Today, non-evolutionary theories purporting to explain why organisms age, such as programmed death (Weismann 1882-1889) and rate-of-living (Rubner 1908), have been disproved. Those which persist, i.e. physiological models, explicate how organisms age as opposed to why organisms age. They are mechanistic (proximate) explanations of physiological decline and do not provide a unified theory of senescence

(Rose 1991; Austad 1992, Austad 1997, Crews 2003).

Continued research into aging and senescence has led scientists to suggest that these processes are not, in fact, a product of natural selection, but rather a result of evolutionary neglect (Olshansky 2011). From this vantage point, senescence is seen as a by-product of the weakening force of natural selection exerted on an individual after their period of reproductive effort. Senescence is believed to exist, therefore, because there is virtually no selection against degenerative changes in old age or because genes which increase early reproductive success are favored even when they result in senescent decline later in life (Williams 1957; Bell 1984). Although there are hundreds of mechanisms to describe how senescence progresses, there are but few evolutionary theories of senescence which explain why organisms senesce (reviewed in Crews and Ice

2012). Although at first glance the relationship between senescence and evolution seems paradoxical, theories which adhere to the tenets of Darwinian evolution ultimately explain senescence more completely than any non-evolutionary theory which preceded it.

1.2.1 Non-evolutionary theories of senescence

In the 19th century, August Weismann, a German evolutionary biologist presented a series of talks on senescence, among which he proposed that senescence was a precursor to programmed death, a biological system designed to limit population size and/or “accelerate the turnover of generations, thereby aiding the adaptation of organisms to changing environments” (Weismann 1882-1889 reviewed in Kirkwood and Austad

2000:233). Weismann (1882-1889) argued that through death ‘worn out’ individuals could be continually replaced with younger ones. He believed this strategy of group selection would be beneficial to the species because old individuals unavoidably accumulated debilitating bodily injuries through continued exposure to environmental stressors and could no longer contribute to reproduction as effectively as the younger individuals with whom they were replaced (Olshansky et al. 1998). Weismann’s theory of group selection and programmed death was later adopted by Wynne-Edwards (1962) who suggested that senescence was selectively advantageous to the group, although not to the individual, because it eliminated unproductive members (Borkan et al. 1982).

Several flaws in these theories have been revealed by research in senescence and aging. For example, it has been demonstrated that for most species senescence does not contribute significantly to mortality in the wild (Kirkwood and Austad 2000). Because the majority of organisms die before they reach the senescent life history stage, natural selection has a limited opportunity to exert a direct influence over processes of senescence (Kirkwood and Austad 2000). In addition, Weismann equated senescence with mechanical wear-and-tear, but failed to propose a mechanism through which his programmed death theory could operate. Since the conception of this theory in the 19th

5 century, researchers have failed to discover a cellular, systemic, or organismal “death- mechanism” which would support it (Williams 1957). Finally, programmed death and group selection theories have been rejected because they assume natural selection acts directly on senescence, when in fact it does not, contradicting tenets of traditional evolutionary theory (Borkan 1982:182). Although Weismann (1882-1889) was wrong about the mechanism, his insights into the evolutionary biology of senescence formed the basis for Williams’ (1957) models of antagonistic pleiotropy and alleles with late-life effort.

The Grandmother Hypothesis (Williams 1957; Hawkes 2003) built on the concept of group selection, positing that at some point during human evolution, “it may have become advantageous for a woman of forty-five or fifty to stop dividing her declining faculties between the care of extant offspring and the production of new ones” (Williams

1957:407). It was then suggested that post-menopausal women could contribute knowledge, support, and skills to other group members thereby enhancing group fitness

(Hawkes 2003). The evolutionary value of this post-reproductive survival was hypothesized to have resulted in positive selection for longevity and deceleration of senescence (Hawkes 2003). The Grandmother Hypothesis, like previous versions of programmed death and group selection theories, has not been supported by research on senescence in modern populations. For example, among Hadza women there was no evidence of increased fitness in the form of more grandchildren among post-menopausal women as compared to still-fertile women of the same age (Hawkes et al. 1997).

The final non-evolutionary theory of senescence is known as rate-of-living theory.

Originally proposed by Max Rubner (1908) and developed by Raymond Pearl (1928), this theory built on the idea that physiological adaptations to specific ecological niches were associated with predetermined life spans (Borkan et al. 1982). The relationship between adaptations to an ecological niche and longevity was originally thought to be mediated by body weight. However, since experimental data from animal models did not fit this model, energy expenditure was proposed as an alternate mediator (Borkan et al.

1982). Early research suggested, “. . . lifetime energy expenditure per unit body weight was similar for the cow, horse, guinea pig, cat, and dog. . . ” (Rubner 1908:404). From this observation, Rubner posited rate-of-living theory, hypothesizing:

. . . all mammal species expend approximately the same total amount of energy per gram during their lifetimes and, therefore, that animals with rapid metabolic rates spend their allotted energy rapidly and die young, while animals with slower rates of expenditure live longer (Rubner 1908 translated in Austad 1992:4).

Aging and senescence research has revealed evidence making rate-of-living theory an untenable overarching explanation for senescence. According to this theory, birds should be shorter lived than similarly-sized mammals. In general, however, the reverse is true with birds living nearly twice as long as their mammal counterparts

(Austad 1992). Even if only considered applicable to mammals, rate-of-living is flawed.

According to the theory, marsupials should outlive placental mammals, as the former have metabolic rates approximately 70-80 percent of the latter (Austad 1992). However, observations of marsupial and placental animals have revealed the opposite pattern is true; placental mammals tend to survive appreciably longer than marsupials. Rate-of-

7 living’s final coup de grâce arrived when it was discovered that “human life span is much greater than would be expected based on metabolic rate” (Borkan et al. 1982:183).

Today, rate-of-living theory persists as a mechanistic explanation of biochemical breakdown in cells and the functioning of other metabolic molecules (Austad 1992,

Austad 1997). However, as a unifying theory of senescence, it has been entirely replaced by evolutionary paradigms.

1.2.2 Evolutionary theories of senescence

Once senescence was conceptualized as an evolutionary by-product, as opposed to a direct result of selection, evolutionary theories emerged and replaced the untenable and evidentially unsupported non-evolutionary theories (Olshanskyet al. 1998, Olshansky

2011). Peter Medawar (1952) was among the first to propose senescence existed because natural selection exerted a weaker influence on older, as opposed to younger, organisms.

He noted that because death from environmental accidents was inevitable, fewer individuals would survive to progressively later ages, regardless of the role of senescence. Therefore, by default, young individuals would have a greater opportunity to reproduce and contribute to succeeding generations (Martin et al. 1996:25). Interpreting senescence as a product of decreasing pressure from natural selection has resulted in four major theories of senescence grounded in Darwinian evolution: mutation accumulation theory (Medawar 1952), antagonistic pleiotropy (Williams 1957, Hamilton 1966), the

“thrifty genotype” hypothesis (Neel 1962, 1982, 1999), and disposable soma theory

(Weismann 1891, Kirkwood 1977).

Mutation accumulation and antagonistic pleiotropy theories take as their starting point post-reproductive individuals’ decreased susceptibility to forces of natural selection

(Medawar 1952). Both suggest latent problems in DNA replication and cell functioning will adversely affect organisms over time, although mechanisms proposed by each theory differs. Mutation accumulation theory posits genetic drift and accumulation of DNA mutations throughout life lead to the loss of late-acting beneficial genes or the appearance of late-acting harmful genes (Medawar 1952). Because the force of selection is diminished among older organisms, it cannot oppose the accumulation of germ-line mutations with late-acting deleterious effects (Borkan et al. 1982, Kirkwood and Austad

2000). Mutation accumulation theory partially explains the considerable amount of variability seen among aging individuals; because deleterious alleles implicated in senescence remain virtually unaffected by selection, considerable heterogeneity among late-acting alleles and their multifactorial outcomes is anticipated (Kirkwood and Austad

2000).

Similar to mutation accumulation theory, antagonistic pleiotropy envisages senescence as a product of evolutionary neglect. However, unlike mutation accumulation theory, which attributes no positive role to late-acting deleterious alleles, antagonistic pleiotropy posits that some genes implicated in detrimental senescent processes may be maintained in the gene pool by natural selection because they confer a benefit at early ages (Williams 1957, Crews and Gerber 1994, Kirkwood and Austad 2000). Williams

(1957:410) further hypothesized that “[s]election of a gene that confers an advantage at one age and a disadvantage at another will depend not only on the magnitudes of the

9 effects themselves, but also on the times of the effects.” Therefore, a gene or protein product which enhanced reproductive capabilities would be maximized by natural selection even if this same gene or protein product contributed to senescence at older ages

(Williams 1957:410, Crews 1997:86). This theory has come to be labeled “antagonistic pleiotropy.” Subsequent research on senescence and aging in Drosophila melanogaster and Schizosaccharomyces pombe has provided evidence in support of the theory of antagonist pleiotropy (Rose and Charlesworth 1980; Curtsinger et al. 1994; Avelar et al.

2013). It is important also to note that in an evolutionary framework, mutation accumulation and antagonistic pleiotropy are not mutually exclusive; both theories may act synergistically to explain senescent processes.

The thrifty genotype hypothesis was first proposed by J.V. Neel (1962, 1982,

1999) to explain why certain populations were plagued by high proportions of individuals with diabetes. He suggested that some individuals possess better ability to turn food into energy and to store that energy as fat. He further postulated that this ability was the result of genes enhancing metabolic extraction of energy or promoting more efficient storage of energy as adipose tissue (Neel 1962, 1982, 1999). Carriers of such genes were labeled as having a “thrifty genotype,” and later this was termed a “thrifty phenotype” (Barker

1992, Barker 1997). In times of food scarcity or starvation, individuals with these thrifty genotypes may have been able to out-compete those with “non-thrifty” phenotypes (Neel

1962, 1982, 1999).

Today, due primarily to the ready availability of fats, sugar, and calories in the modern diet, individuals possessing thrifty genotypes may actually be at increased risk

10 for certain chronic degenerative diseases associated with senescence (Crews and Gerber

1994). For example, in resource-scarce environments, individuals with a thrifty genotype resulting in greater insulin response may have been favored due to their exceptional ability to process food (when it was available) quickly and store it efficiently. However, in today’s environment, these individuals are not favored. Instead, they are subject to repeated exposure to hyperinsulinemia, peripheral muscle resistance to insulin, increased adipose tissue, and obesity – four classic hallmarks of non-insulin dependent diabetes mellitus (Crews and Gerber 1994).

Research in American Samoa may provide support for the thrifty genotype hypothesis. Although inhabitants of American Samoa today have a cosmopolitan lifestyle

(Crews 2007), the cultural and environmental modifications that altered their traditional way of life are relatively recent phenomenon, essentially arising as the islands were used as a center for Allied naval forces during World War II. Changes brought to the islands included substantial alteration of subsistence practices, declines in physical activity (as a result of non-labor intensive occupations), and dependence on nonlocal resources (Crews

2007). The high number of diabetes cases on American Samoa may be associated with rapid modernization and changing subsistence strategies (Crews 2007). Thrifty genotypes predisposed to food scarcity may explain the prevalence of diabetes among the islanders now that non-traditional foods have replaced traditional ones and an unlimited supply of calories is readily available (see also: Bindon and Baker 1985;Black et al. 2011).

Increased insulin production as a means to maximize food storage potential is the classic example of a thrifty genotype/phenotype (Neel 1962, 1982, 1999). However, any

11 resource that was relatively scarce or very abundant during our evolutionary history may also be associated with an associated ‘thrifty genotype/phenotype.’ As long as the resource in question was relatively scarce or abundant, individuals with these thrifty genotypes ought to have been flourished. If carriers of these genotypes still enjoy any reproductive advantage over their “non-thrifty” counterparts, antagonistic pleiotropy suggests natural selection will maintain them regardless of deleterious senescent effects that manifest later in life (Crews and Gerber 1994:162).

A final Darwinian explanation for why organisms senesce is the disposable soma theory. Rejecting his own programmed death/group selection hypothesis in his later years, Weismann (1891) contributed to the development of the more enduring disposable soma theory when he suggested that organisms must balance demands for their energy and resources between reproductive and somatic maintenance needs. Disposable soma theory focuses on the importance of maintaining the germ line across generations even to the detriment of the soma (Kirkwood 2002:739). It predicts that enough energy and resources will be allocated to maintain the soma effectively and in good repair through normal expectation of life in the wild (Kirkwood 1977, 1990, 1996, 2002). Disposable soma theory differs from previous theories of senescence (mutation accumulation, antagonistic pleiotropy, and thrifty genotypes) in that it does not implicate gene action in later life but instead focuses on energy allocation (Kirkwood 2002:740).

Disposable soma theory further suggests that if a species’ extrinsic mortality rate is high (from predation, accident, etc.), average life expectancy will be short and little selection will favor maintenance of the soma past this survival period. Instead, resources

12 will be directed towards reproduction to maximize fitness (Kirkwood 2002). Organisms with higher average life expectancies, on the other hand, devote more energy to somatic maintenance, although maintaining a high level of reproductive fitness remains a necessity (Kirkwood 2002:739). Laboratory experiments using Drosophila melanogaster and Caenorhabditis elegans have provided evidence in support of disposable soma theory

(Zwaan et al.1995, Sgró and Partridge 1999, Kirkwood and Austad 2000). Field research on opossums has provided additional support for the disposable soma theory (Kirkwood

2002). Mainland opossums, subject to significant predation, have been observed to have a significantly shorter expected lifespan as compared to opossums living on an island and subject to little predation (Austad 1992).

1.3 Studying Senescence

Although many theories have been posited to explain senescence at the genomic level (Vijq 2007), more research is necessary to understand how decreased cell functionality and loss of cells translates to increased morbidity and mortality among elders. Furthermore, additional data are needed to determine how aspects of modernization, urbanization, and “Westernization” influences affect long-term health and lifespans of local human populations.

1.3.1 Demographic Transition

In early human populations, illness and accidents ensured that relatively few individuals lived past middle age (Borkan et al. 1982:182). It is only in recent history that sufficient numbers of people are surviving long enough to make research questions concerning the elderly practicable and of widespread interest. The remarkable increases

13 seen in life expectancies, especially in economically developed nations, are attributable to decreased mortality rates among children and older adults (Manton 1986; Olshansky and

Carnes 1994; Crews 1997; Olshansky et al. 1998; Crews 2007). Controlling, preventing, and vaccinating against infectious diseases has been the primary factor contributing to declining mortality rates. In fact, the majority of the gain in life expectancy at birth in the twentieth century can be traced to reductions in morbidity and mortality caused by infectious diseases among children (Olshansky and Carnes 1994). This rapid and sustained decline in mortality has resulted in significant increases in both the number and proportions of elders in populations around the world (Crews 1997).

In recent years, a new trend has emerged; in addition to mortality rates declining among young individuals, they are also declining among older individuals (Crews 2007).

Some authors suggest that we are experiencing a new mortality transition, typified by declines in mortality and gains in life expectancy among elderly individuals (Olshansky and Carnes 1994). This assertion is supported by the observation that, “[t]he 1968-82 period was unique in that the mortality reductions were concentrated at ages 60 and older, primarily reflecting delays in mortality from vascular diseases” (Olshansky et al.

1990:634). Improvements in lifestyle and medical care as well as the biocultural evolution of highly constructed and protective niches have contributed not only to this reduction in mortality among the elderly, but also to the phenomenon known as compression of mortality (Fries 1980; Myers and Manton 1984; Manton et al. 1991;

Crews and Bogin 2010).

Compression of mortality suggests that as larger segments of the population are born with higher life expectancies at birth, a higher percentage of individuals will survive to older ages. At a certain age (as of yet unknown), it has been suggested that, “intrinsic biological processes limiting life span” (Manton et al.1991:604), i.e. senescence, must manifest, resulting in a probability of survival near zero in a short age range (Manton et al. 1991). Theoretically, as life expectancy at birth increases and the onset of senescence is delayed (through changes in lifestyle, behavior, medical advances, etc.), survival curves, indicating the percentage of a population surviving at a specific age, will become progressively more “square.” This increasing “squareness” is compression of mortality

(Olshansky et al. 1990, Manton et al. 1991) (Fig. 1). Unfortunately, perfect compression of mortality has not been observed, nor is it expected to be observed in the foreseeable future.

Figure 1: Compression of mortality: “Squaring” of survival curves with rising life expectancy (Figure from: Westendorp 2006:405S).

However, decreases in infectious diseases, improvements in healthcare and access to resources, and less demanding living and working environments have results in greater survival at all ages and an increasing numbers and percents of elders within populations.

This growth of elderly populations has resulted in subsequent increases in chronic degenerative conditions (CDCs) (Crews and Gerber 1994; Gerber and Crews 1999).

CDCs are defined as, “. . . conditions that lead to progressive deterioration in one or more clinical, metabolic, or physiological traits . . . [they] are commonly diagnosed when a specific metabolic and/or physiological parameter falls above or below a particular critical value” (Crews and Gerber 1994:155). Common examples include: osteoporosis, osteoarthritis, sarcopenia, artherosclerosis, coronary heart disease, cerebrovascular disease, cancer, and diabetes (Crews and Gerber 1994).

1.4 Stress, Stressors, and Stress Responses

1.4.1 Defining Stress

Stress is a widely studied but poorly defined somatic phenomenon. Everyone in our culture understands what it means to feel “stressed out.” However, explaining what stress means is challenging. It is therefore not surprising that a plethora of definitions of

“stress” pervade the literature (Johnson et al. 1992; Dressler 1996; McEwen 1999;

McEwen and Seeman 1999;Schulin et al.2004; Clark et al. 2007). Different definitions and interpretations of stress suggest poor construct validity or perhaps disagreement but do not render the concept ambiguous or meaningless. Use of varying definitions makes comparing results across studies difficult but not impossible. This problem has been compounded as different research teams have used a multiplicity of methods employed 16 for measuring stress. It is not yet resolved if self-perceived stress better indicates stress than physiological responses. Or does each indicate a different aspect of somatic stress?

Questionnaires completed by participants may better measure some aspects of stress, while assessing hormone levels may reveal other aspects of physiological arousal.

Unraveling these complications in stress research is important for biological anthropologists. A codified definition of and standardized methods for measuring stress are needed to identify cultural differences in chronic and acute stressors, context-specific coping mechanisms, and how cultural contexts and lifeways mediate relationships among stressors, disease, and adaptation.

Stress has been used to refer to three different components of the stress process:

1) input or stress stimuli, 2) processing systems (physiological and psychological), and 3) output or stress response (e.g., rise in blood pressure) (Levine and Ursine 1991). In today’s literature, definitions of stress range from sublime to ridiculous, among the vague for example: “the stress of modernizing society” (Bindon 1997:147). Because they provide no information, such concepts are irreproducible and therefore incomparable.

Some equate stress with other poorly defined concepts, for example “cultural change”

(Cassel et al. 1960; also reviewed in Dressler 1996), which also yield non-reproducible results. An improved, but still vague and broad, definition considers stress as any outside pressure brought to bear on an individual (Dressler 1996). This could include, for example, work-related pressure such as increasing responsibilities or managerial duties

(Schnorpfeil et al. 2003).

Previous definitions tried to equate the concept of stress with a “thing” – such as a pressure or a change. Definitions approaching the ‘sublime’ forego this approach and identify stress as a continuous process rather than as an object. Proponents of this viewpoint suggest stress is the ongoing response process through which organisms respond to risk factors (stressors) while utilizing resources to resist deleterious outcomes

(stress responses) to the best of their abilities (Dressler 1996). Walter Cannon (1930) was among the first to identify stress as a process when he recognized the role of the sympathoadrenalmedullary (SAM) system in coordinating “fight or flight” responses

(Johnson et al. 1992). Hans Selye (1936) built on this concept and defined four stages of stress reaction which characterized the “General Adaptation Syndrome”:

1) an initial “alarm reaction,” characterized by an immediate sympathoadrenomedullary discharge; 2) a “stage of resistance,” characterized by activation of the hypothalamic-pituitary-adrenal (HPA) axis; 3) a stage of adrenal hypertrophy, gastrointestinal ulceration, and thymic and lymphoid shrinkage, which he called the “General Adaptation Syndrome”; and 4) a final stage of exhaustion and death (see Johnson et al. 1992:116).

Modern theory conceptualizes stress as an integrated process involving integrated stress responses from the neuroendocrine system and other physiological mechanisms to cope with psychosocial, environmental, and physical stressors which challenge somatic homeostasis (Johnson et al. 1992; McEwen 1999; Schulkin et al. 2004). Whereas Selye’s

General Adaptation Syndrome concentrated on “non-specific responses of the body to any demand imposed upon it” (Goldstein 2003:69), revisions view types and intensities of stressors as well as an organism’s past experiences as invoking differential responses from the neuroendocrine system (Goldstein 2003:69). Phrased more succinctly, stress is

18 the generalized physiological responses occurring as organisms react to environmental stimuli (stressors) (McEwen and Stellar 1993:2094; see also Sterling and Eyer 1980;

Leahy and Crews 2012; Schulkin et al. 2004).

1.4.2 Stressors

Stressors include any chemical or biological agent, environmental conditions, or external stimulus which causes stress to an organism; they are in effect any perturbation in the outside world which disrupts homeostasis as well as the anticipation (rational or otherwise) of physical and emotional stressors (Grossman 1987; Sapolsky 1992).

External stimulus includes psychosocial stressors such as food or housing insecurity, depression, commuting, mental stress, or life changes, etc. (Goodman et al. 1988; James et al. 1989). Although environmental, physiological, and psychosocial stressors may be considered separately, physiological responses to them may be similar. In addition, responses to a stressor may be complicated by interactions with other stressors (Goodman et al. 1988) or attenuated by previous conditioning (Selye 1976).

Universal stressors are difficult to identify as effects of a potential stressor are significantly mediated by individuals’ interpretations of it. Smaller but repetitive disturbances or daily hassles may be perceived as chronic, long-term stressors, whereas major life events may be interpreted as more acute stressors. In either case, any stimulus perceived as a “threat” may incur a significant stress response (Maderthaner 1987). In contrast, if the same stimulus is not perceived as a threat by a different individual, then it may not incur a physiological reaction. In addition, people can adopt a variety of coping mechanisms, learning to perceive previously “threatening” stimulus as benign and

19 thereby mitigating the stress response (Maderthaner 1987). Stressors often differ according to life experiences and cultural contexts (Schell 1997; Dressler and Bindon

1997). They can also change over time as individuals’ particular experiences and cultural milieus change (Schell 1997; Dressler and Bindon 1997).

1.4.3 Measuring Stress Responses

Three primary approaches to measuring effects of stressors can be identified: environmental, psychological, and biological. The environmental approach, couching stress in terms of change, quantifies the concept as the, “number and magnitude of key life events experienced by a person in a given time period” (Clark 2007:18). The Holmes and Rahe Stress Scale (alternately known as the Social Readjustment Rating Scale), developed to determine the relationship between stressful events and illness, is an example of an early operationalization of the environmental approach (Holmes and Rahe

1967; Rahe and Arthur 1970, 1978). Measures utilizing this approach weight the impact and importance of different stressors or stimuli according to the adaptive response presumed necessary to react to them (Clark 2007). Such a measure is highly subjective, dependent on the population in question, and difficult to replicate because of the infinite number of factors it could assess. For example, how does one quantify and standardize a measure of stress using the environmental approach where stress is synonymous with

“cultural change?” Such a measure could consider changing subsistence patterns, but it could also incorporate factors such as living arrangements, access to public transportation, or standard minimum wage. An added difficulty appears when attempting to differentiate between stressors and stress. Are changes causing stress or are they a

20 result of stressors? Or, to make the measure more confusing, could cultural change be both a cause of stress and a result of stressors? Of course, the answer is yes. In addition, what constitutes “key life events” may differ significantly from population to population further rendering comparisons difficult.

The second approach to measuring stress is known as the psychological approach

(Cohn and Rubinstein 1954; Clarke 1994; Clark et al. 2007; Reynolds and Wagner 2008).

This approach emphasizes the importance of how life events are perceived and evaluated by ego. Higher levels of self-reported stress are significantly associated with future negative health outcomes (Foster et al. 2008). The psychological approach to stress improves upon the environmental approach by recognizing that individuals’ reactions to stressors vary depending on their lifestyles, genetic predispositions, health, and exposures to other biosocial factors. Measures of self-perceived stress remain difficult to standardize for cross-cultural research because some stressors are not applicable to all cultures. In Japan, declining observation of the tradition of filial piety (hyo) is a significant stressor among elders (Palmore and Maeda 1985). In the United States, with no tradition of filial piety, no reasonable researcher would pose a question concerning filial piety on a self-perceived stress questionnaire.

Perhaps the best approach currently available for measuring stress follows a physiological and biomedical approach. Building on definitions of stress emphasizing somatic reactions to stimuli, measures using this approach evaluate changes in physiological systems occurring as our somas respond to stressors (McEwen and Stellar

1993; Seeman et al. 1997; Schulkinet al. 2004; Strahler et al. 2010). Proposed

21 physiological approaches to measuring stress focus either on associations between stress and an individual biomarker (e.g., cortisol, see Goldman et al. 2005; Glover 2006;

Varadhan et al. 2008; Strahler et al. 2010) or the association between stress and an index measure encompassing several biomarkers (e.g., allostatic load, see Seeman et al. 1997;

Karlamangla et al. 2002; Karlamangla et al. 2006; Maselko et al. 2007).

Cortisol, a glucocorticoid heavily implicated in physiological reactions to chronic stress and control of inflammation (Griffin and Ojeda 2000), is used often as a biomarker of stress (e.g., Goldman et al. 2005; Glover 2006; Strahler et al. 2010). High and low levels of cortisol (i.e. values in the upper and lower quartiles of a population’s distribution of cortisol values) have both been associated with a variety of stressors (e.g.,

Glover 2006; Hellhammer et al. 2006). Low cortisol is particularly associated with symptoms of post-traumatic stress syndrome (PTSD) (Glover 2006). Variations in individual biomarkers across individuals are difficult to interpret as they may be attributable to multiple differences in stress responses or to multiple other factors. When samples were collected relative to circadian rhythm, when stressors were experienced, individual baseline values, exposures to biosocial factors, along with lifestyle and behavioral habits such as substance abuse all may influence comparisons (Glover 2006).

For these reasons, an increasing tendency is to measure physiological stress responses with an index measure incorporating multiple biomarkers.

Allostatic Load: Allostatic load (AL) is currently the most viable measure of stress, when stress is understood as the physiological process of coping with any “threat, real or implied, to the psychological or physiological integrity of an individual”

(McEwen 1999:108). Allostatic load measures cumulative dysregulation across multiple physiological systems associated with somatic stress responses (McEwen and Stellar

1993; Seeman et al. 1997). By measuring physiological dysregulation, AL improves on the environmental approach by removing possible confounding of stressors with stress.

AL differs from psychological approaches by removing participants’ subjective interpretations of stress from analyses. Finally, AL consistently outperforms measures of individual biomarkers when assessing stress (Seeman et al. 2001 and 2004; Goldman et al. 2005; Glover et al. 2006; Maselko et al. 2007; see also Leahy and Crews 2012 for recent review).

Despite being the best measure of stress currently available to researchers, AL is not a perfect assessment. Although future research may solve some problems, some difficulties with AL are inherent to its constructions, making them a challenge to overcome. A primary problem with AL is that it remains unclear whether or not AL measures cumulative physiological dysregulation as a result of somatic responses to chronic stressors over a lifetime or is more a measure of responses to recent, acute stressors (see Leahy and Crews 2012). More importantly for cross-cultural and comparative research, there is no consensus concerning which biomarkers should be included or how AL should be calculated. To date, inclusion of multiple different biomarkers has not appreciably altered associations of AL with stress or other outcomes

(Seplaki et al. 2005). Likewise, using different methods to calculate AL has produced similar results (Seplaki et al. 2005). Although comparable results are reached using different biomarkers and calculations, researchers eventually must reach a consensus

23 concerning biomarkers included in and how to calculate AL. Conversely, if they choose to use alternate biomarkers and calculations, then they should justify their choices.

Measures of AL are based on distributions of biomarkers within a sample from a population being studied. For this reason, AL scores are not directly comparable across populations. Stress responses are inherently individualistic and influenced by genes, ancestry, lifestyle choices, and developmental experiences (McEwen and Stellar 1993).

Individuals from a specific population will share more common experiences and will also be more similarly adapted to their local ecological niches. For these reasons, baseline

“average” values of certain biomarkers may vary significantly across different populations. Using Western clinical definitions of low or high biomarker values would fail to take this bio-culturally derived heterogeneity into consideration, rendering AL an artificial construct based on Western norms. For the moment, it is unclear whether or not a variation of AL can be formulated that will render it easily comparable among different populations. However, using a variety of statistical methods, such comparisons are possible.

1.4.4 Neuroendocrinology of Stress

Stress responses induced by stressors exert significant influence on multiple regulatory systems. Exposure to stressors affects hormonal control of metabolism

(Weissman 1990; Wenk 1998) reproduction (Moberg 1991; Rivier and Rivest 1991,

1995), growth (Stratakis et al. 1995) and immunity (Peterson et al. 1991; Sheridan et al.

1998). Understanding the stress response is, therefore, of interest to a wide variety of researchers and medical practitioners.

In 1939, Hans Selye reported marked changes in the sizes of endocrine tissues subsequent to exposure to a stressor. Since then, his observations have been confirmed repeatedly (Matteri et al. 2000) and it has become clear that the stress response itself is controlled by responses to stressors from two neuroendocrine axes; the hypothalamic- pituitary-adrenal system (HPA) and the sympathetic-adrenal-medullary system (SAM).

Both systems influence numerous physiological functions in a stimulus-response manner to internal and external environments (Berne and Levy 1993). Both also function in tandem to coordinate the stress response.

Stressors are first perceived by the amygdala, the area of the brain that interprets sounds and images. The amygdala exerts neural control over both the HPA and SAM, acting to stimulate or inhibit hormone secretion in response to both external and internal stimuli arising from visual, auditory, olfactory, gustatory, tactile, pressure sensation, pain, fright, injury, sexual excitement, and stress (Berne and Levy 1993). After being stimulated, the amygdala signals the hypothalamus that in turn controls the autonomic nervous system (ANS), comprising both the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). The ANS controls involuntary bodily functions, including: breathing, blood pressure, heartbeat, and dilation or constriction of bronchioles in the lungs. In contrast, the PNS down-regulates the SNS to return the soma to its pre- stressor state.

Activation of the SNS by the hypothalamus in response to a stressor stimulates production and release of the catecholamines epinephrine (adrenaline) and norepinephrine (noradrenaline) from the adrenal glands. Circulating epinephrine and

25 norepinephrine promote faster breathing, increased blood pressure, higher heart rate, and vasodilation – reactions often associated with the “fight or flight” response. Epinephrine further triggers release of blood sugar (glucose) and fats from temporary storage sites to supply energy to all parts of the body.

As the initial surge of epinephrine subsides, the hypothalamus activates the HPA axis, stimulating production of glucocorticoids (e.g., cortisol). The HPA axis regulates continued function of the SNS. If the stressor continues to be a threat, the HPA releases corticotropin-releasing hormone (CRH). Taken up by the pituitary, CRH triggers release of adrenocorticotropic hormone (ACTH) that travels to the adrenal glands and prompts release of cortisol. Cortisol prompts the SNS to continue physiological stress responses.

When the stressor is removed, cortisol levels fall and the PNS down-regulates stress responses.

1.4.5 Stress and anthropology

Stress is an inherently personal experience (McEwen and Stellar 1993). Therefore stress research may not seem, at first glance, to fall within the purview of anthropology.

However, underlying physiological reactions to stress are generalizable and can, if studied and interpreted carefully, provide anthropologists with important insight into human variation, diversity, and adaptability.

Evaluating physiological responses to stress using a biological approach allows anthropologists to identify biological diversity among human populations. Using a biological approach in conjunction with a psychosocial approach, anthropologists may determine also what constitutes a stressor in different cultures and groups. Comparing

26 physiological reactions to similar environmental/psychosocial stimuli also permits anthropologists to characterize cross-cultural heterogeneity in physiological responses to similar factors. Such comparisons lend themselves to identifying socio-cultural adaptations mitigating effects of specific stressors and evaluating their effectiveness.

Supplementing cross-sectional analyses with a historical perspective and longitudinal follow-ups, anthropologists further nuance their interpretations. Taking population histories into account, we garner a better understanding of how historical circumstances generate specific configurations of stress, adaptation, and disease and how different populations adapted to their local ecologically and socio-culturally specific niches (Dressler 1996). Longitudinal data are crucial for establishing causality when assessing relationships linking biosocial factors, stressors, physiological stress responses, and morbid/mortal outcomes. Clarifying relationships today will aid in transforming AL into an useful clinical adjunct. Research already indicates that lowering AL decreases risk of mortality, even among older adults (Karlamangla et al. 2006). Such results suggest AL facilitates assessing patients’ current health status, evaluating their current predisposition to specific outcomes, and monitoring progressive senescent decline.

Conceptualizing stress as a process alleviates difficulties encountered when attempting to equate it with an object. Stress is best thought of as the generalized physiological patterns occurring in reaction to multiple environmental stimuli (McEwen and Stellar 1993). Measuring stress, as the multiplicity of definitions suggests, also is inherently difficult. Environmental and psychosocial approaches are difficult to interpret, subjective, and tricky to compare cross-culturally. A biological approach, specifically

27 allostatic load, likewise is difficult to compare cross-culturally, but it is reproducible and not open to subjective interpretations. The best way to assess stress is combining psychosocial and biological approaches within the same study. Psychosocial approaches clarify participants’ perceived levels of stress (a significant indicator of AL (McEwen

1999)) and identify culturally-specific stressors, differences in lifestyle and behavioral choices, and heterogeneity in socio-cultural adaptations that mitigate or increase stress.

At the same time, the biological approach provides irrefutable data concerning physiological functioning across systems and associations between stressors and physiological dysregulation over time. Anthropologists can use cross-cultural studies of stress to answer a number of cross-cultural questions. These include: What constitutes stressors among different populations? What biologically and socially adaptive factors mitigate or enhance effects of stressors? How are relationships among stress, disease, and adaptation modified by socio-cultural and historical contexts?

1.5 Project Objectives and Hypotheses

1.5.1 Project Objectives

Senescence varies substantially within and among populations (Crews 1993;

Arking 1998). Most studies on senescence are from Western, mostly US and European, samples (Stewart 2006; McEwen and Tucker 2011; Duru et al. 2012; Leahy and Crews

2012; Westerlund et al. 2012). Among these samples, sociocultural factors influencing senescence include socioeconomic status (SES), occupation, and exposure to adversity during adolescence (e.g., Schnorpfeil et al. 2003; Seeman et al. 2008; Westerlund et al.

2012). However, outside these sampled Western regions, little is known about the range

28 of modern human variation in and the relative importance of different sociocultural factors on senescence.

One objective of this project is to extend knowledge on modern human variation in senescence by analyzing elders from Nagasaki Prefecture, Japan. Collecting data on the physiology of senescence from a Japanese sample generates cross-cultural data on variation in senescence in a non-Western context. Increasing knowledge of current modern human variation provides insight into the evolutionary trajectory and biology of specific populations. To date, few baseline data concerning AL and frailty among elderly

Japanese are available (see: Kusano et al. 2007; Crews et al. 2012; Iwamoto et al. 2012).

This project not only increases our understanding of modern human variation among elderly individuals, it also creates a baseline data set against which to compare future changes. These data will also allow our research group to continue to assess change and variation during senescence among Japanese elders, another important component of understanding overall human variation and life history (Bogin 2006).

Another project objective is to improve understanding of relationships between senescence and human biology. Because senescence is an age-independent, individualistic, cumulative, progressive, multifactorial, and deleterious process (Arking

2006; Crews 2007), it may vary significantly among populations worldwide. Among

Western samples, the relative roles of genes and culture are difficult to differentiate because most populations express high genetic heterogeneity. In contrast, Japan’s population is relatively homogeneous (Crews et al. 2012). Therefore, intra-populational

29 variation in morbidity and senescence should be largely attributable to sociocultural, as opposed to genetic, differences.

Athird project objective is to validate use of AL as an assessments of senescence in a non-Western population by evaluating how well these measures are significantly associated with physiological differences and morbidity in a Japanese sample. Previous research applying AL to evaluate senescence in Japan has been limited (Kusano et al.

2007; Crews et al. 2012; Iwamoto et al. 2012). These studies, conducted by the PI and colleagues, were completed on Hizen-Oshima (a less traditional-living sample), the island of Ikutsuki-shima (a more traditional-living sample), and in Sakiyama (a more traditional-living sample) (see Section 2.2). Results suggest AL scores among Japanese participants associate significantly with physiological biomarkers indicative of frailty, including serum levels of dopamine, uric acid, white blood cell counts (Kusano et al.

2007; Crews et al. 2012), and diet (Iwamoto et al. 2012).

1.5.2 Project Hypotheses

To complete the proposed objectives, means of independent and dependent variables are compared to ranges of “normal” variability for United States samples to investigate variation. In addition, hypotheses establishing the conceptual and operational integrity of the AL paradigm in a non-Western population are postulated.

H1) Age will correlate poorly with AL.

H2) Older men and women will demonstrate higher AL than their younger counterparts.

H3) Men and women in the same age groups will have comparable AL.

H3) Regardless of age and sex, AL will associate significantly with biomarkers indicative of future morbidity.

H4) Alternate constructions of AL will not significantly change relationships between

AL and biomarkers indicative of future morbidity.

Chapter 2: Materials and Methods

2.1 Research Design

Data presented here were collected during the first wave of a projected longitudinal study to follow Japanese elders residing in urban and rural settings of

Nagasaki Prefecture over the next decade. To contextualize this analysis, data were obtained first from a pilot study of 27 elderly Japanese aged 55 to 83 living on Hizen-

Oshima Island July-September 2005 (Crews et al. 2012). Additional data from a sample of 96 rural residents of the Goto Islands were obtained during fieldwork in 2010. Both the individual and combined samples from the island settings are examined and compared here.

Associations of AL with age, sex, and place of residence are assessed. By continuing to investigate associations of AL with biomarkers predictive of morbidity, we observe how AL influences multiples areas of physiological stress responses, morbidity, and senescent declines. We propose that sex and lifestyles buffer individuals from stressors, thereby limiting their AL and reducing other risks.

2.2 Study Population

Japan is an ideal location for studying influences of lifestyle on AL. Foremost, is

Japan’s notable longevity; Japanese men and women rank among the longest-lived

32 people worldwide with average life expectancies of 80.6 and 87.4 years, respectively

(CIA Factbook 2013).

In addition to being long-lived, Japan’s population is also relatively genetically homogenous (Hewtink and Oostra 2002; Burgess 2004, 2010; Weaver 2009). Japan was settled by multiple migration streams from mainland Asia. Ryukuyans (Okinawans) are believed to have migrated from China and Austronesia around 35,000 BCE and Ainu around 15,000 (Hammer et al. 2006). Modern Ainu account for less than 6% of the total

Japanese population and live primarily in Hokkaido (Weaver 2009). Okinawans, live primarily on the southern Ryukyi Islands that compose Okinawa Prefecture (Weaver

2009). Because five times as many Okinawans live to be 100 as in the rest of Japan (Nat.

Geo. 1993) and because there are 34.7 centenarians per 100,000 inhabitants, the highest ratio in the world (Santrock 2002), Okinawans have been the object of much longevity research (e.g., Nomura et al. 1988; Miyagi et al. 2002; Nagahama et al. 2004; Tanaka et al. 2006). However, Okinawans are not representative of the majority Japanese population.

The majority of modern Japanese (98%) people are putatively descended from interbreeding of the Jomon Era people (15,000-500 BCE), composed of Ryukuyans and later migrants from the mainland (Hammer et al. 2006; Weaver 2009). Subsequent to the

Jomon Era, the population experienced significant changes as a result of political and religious turmoil, conquests, and ecological disasters. However, over the last 100 generations, Japan’s population has remained genetically isolated, with most population growth occurring as a result of natural increase as opposed to gene flow (Hewtink and

Oostra 2002). Because of internal population growth, Japan today shows little genetic heterogeneity (Hewtink and Oostra 2002) and exhibits a relatively high kinship coefficient compared to European or African populations. Collecting data in an area with a high degree of genetic homogeneity suggests intra-populational variation in senescence should be largely attributable to sociocultural, as opposed to genetic, differences.

Socioculturally, Japan is complex. Following a period of homogenous “cultural nationalism” immediately after World War II (Burgess 2010), Japan’s multiculturalism has since increased dramatically. The most dramatic example of this multiculturalism is seen when comparing modern urban and rural dwellers. Urban Japan has adopted multiple aspects of a “Western” lifestyle, including consumption of a “Westernized” diet, higher in fats and sugars, lower in seafood and legumes (Cwiertka 2006). Urban dwellers also tend to live with their nuclear families (parents and children), a more “Western” lifestyle as opposed to the more traditional extended families (grandparents, parents, children, and possibly other relatives)found earlier in Japan and more often in rural areas today (Maeda 1983). Because of urban living arrangements in these areas, elders are increasingly relying on state-run nursing homes and geriatric centers for care and are less able to rely on family to provide long-term support and assistance.

Rural-dwellers more consistently follow “traditional” lifestyles. “Traditional” here is used to denote, “belief[s] or behavior[s] passed down within a group or society with symbolic meaning or special significance with origins in the past” (Green

1997:800). Occupations and economic activities often center on fishing and agriculture

(Iwamoto in review). In addition, residents of more rural areas are able to maintain diets

34 based on locally produced foods and local styles of food preparation, eschewing the more processed and “Westernized” diet adopted by their urban counterparts (Cwiertka 2006).

Finally, rural-dwellers tend to keep actively participating in ritual and religious activities, festivals, and ceremonies, and retain more traditional concepts of social interactions and responsibilities (Iwamoto in review). This is especially apparent in living arrangements where extended families are the norm and filial piety (hyo) remains of utmost importance

(Palmore 1975, 1985). Living with younger family members, rural-dwelling elders are less likely to require assistance from state-run nursing homes for geriatric care.

Finally, Japan’s elderly population has increased steadily throughout the 20th and early 21st centuries secondary to declining birth rates and improved survival at all ages, particularly among those 55 years and older (MIC 2005). Today, almost 20% of Japan’s population (i.e., 25.6 million elders among 127 million Japanese) is aged 65 years or older (MIC 2005) and this percent is expected to increase to 25% by 2020. Life expectancy in Japan is about 85.6 years among women and 78.6 among men (MIC 2005).

However, with over 25,000 centenarians in Japan, it is clear that many individuals live past these life expectancies (MIC 2005). Increasing numbers of elders lead to increased demand for healthcare, housing, and pensions (Iwamoto et al. 2013) as well as increasing the range of age-related phenotypic variation.

Developing a valid method for assessing physiological variation due to senescence will benefit those studying health outcomes and survival of elders. It also will aid in focusing healthcare funds and interventions by targeting those most likely to experience unwanted outcomes. Understanding how Japan’s elders are surviving and

35 adapting to old age, life-long stress, and developing dysfunction with increasing age provides a model of how others may slow senescence in other settings.

2.3 Study Sample

2.3.1 Sakiyama

Participants in this study were recruited through local community and religious leaders and surveyed in 2011. These 96 persons included 28 men and 26 women aged 55-

69 years and 20 men and 22 women aged 70-89 years. Individuals were asked to complete a food intake survey, an oral interview, a brief self-administered diet history questionnaire, a 12-hour overnight urine collection, and a venapuncture when completing their annual physical examinations in October 2011. Procedures and goals were explained and written consent obtained before data collection. This research was approved by the

Internal Review Boards at both The Ohio State University and Osaka City University.

Each paricipant received a small honorarium (U.S. equivalent of $50) upon completing their survey and physical exams.Privacy has been protected by assigning each participant a numeric code and disassocating data from individuals’ names after survey.

2.3.2 Hizen-Oshima

At the time of survey (2005), Hizen-Oshima had a total population of 5,792 (Crews et al. 2012; Ministry of Internal Affairs and Communications 2005). Ofthese, 2,917 were aged 50+ years, 1,523 were aged 65+years, 733 were aged 75+ and 60 were over 90 years ofage (Crews et al. 2012; Ministry of Internal Affairs and Communications 2005). In the past, most residents practiced some form of traditional agriculture and fishing for

36 sustenance. However, in the 19th and 20th centuries, Hizen-Oshima become an important source for coal production, stimulating major changes in demography and subsistence strategies. Large villages developed facing Nagasaki on the west side of the island as hundreds of workers and their families migrated to the area from both other parts of

Hizen-Oshima and the main island. After abandonment of the coal mines in the mid-20th century, the port was converted to ship-building uses. The ship-building industry continues to provide wage labor and economic stability to the west coast of Hizen-

Oshima today. The east coast of Hizen-Oshima, less affected by both coal mining and the ship-building industry, remained more traditional. However, residents of both sides of the island have adopted some non-local foods and customs and moved away from traditional agriculture and fishing as their sole means of sustenance. This process was accelerated during the late 20th century by construction of a bridge linking northwest Hizen-Oshima to the main island.

2.4 Data Collection

2.4.1 Procedures

Physical examinations including blood draws and 12-hour overnight urine collections were completed 13-14 November 2011 in Sakiyama City. During their physical examinations, all participants were asked about their current and past activity levels using an Index of Competence questionnaire developed by the Tokyo Metropolitan

Institute of Gerontology (TMIG-IC) for use in Japan that is similar to Activities of Daily

Living Scales, originally developed by Katz and colleagues (1983). The TMIG-IC index totals 13 questions about physical and social competency divided into 3 domains:

Instrumental Self-Maintenance – 5, Intellectual Activity – 4, Social Role – 4. Positive answers (ability to complete a task) are scored as 1; negative answers (inability to complete) are scored as 0 (see Supplemental Table 2 for the full questionnaires). Scores are summed similar to how AL is determined, except that higher scores indicate better function and less disability.

When participants were recruited details and significance of the project, as well as the voluntary nature of participation were explained at the point of contact and all study protocols were incorporated into annual physical examination. Thus, as part of this study, each participant was provided additional testing of their blood chemistries, lipids and other medically important assessments. As hospital records, data generated by this project respected individual privacy and autonomy of participants to the highest degree possible, while also allowing clinical data to be available for use in treatment and diagnoses. Prior to being measured or completing any study protocols, participants provided informed consent according to guidelines established at the Graduate School of

Human Life Science, Department of Interdisciplinary Studies for Advanced Aged

Society, Osaka City University.

2.4.2Allostatic Load

AL summarizes levels of physiological activity across multiple regulatory systems pertinent to disease risks. Data on ten components are obtained. (1) Systolic and (2) diastolic blood pressure (SBP and DBP) indicate cardiovascular activity (Seeman et al.

2001; Seeman et al. 2004). Low levels of (3) serum high density lipoproteins (HDL) and high (4) total cholesterol indicate increased risk atherosclerosis (Seeman et al. 2001).

Serum levels of (5) glycosylated hemoglobin are an integrated measure of glucose metabolism over the previous 30-90 days (Dunn et al. 1979). (6) Serum dihydroepiandrosterone-sulfate (DHEA-S) and (7) cortisol are a functional hypothalamic- pituitary-adrenal (HPA) axis antagonist and a stress-responsive hormone that provides an integrated measure of 12-hour HPA axis activity, respectively (Svec and Lopez 1989;

Seeman et al. 2004). (8) Noradrenaline and (9) adrenaline are integrated indices of 12- hour sympathetic nervous system activity (SNS) (Seeman et al. 2004). (10) Waist:hip ratio assesses chronic metabolism activity and adipose tissue deposition, both influenced by increased glucocorticoid activity (Bjorntorp 1987).

SBP and DBP were measured as the average of the second and third of three seated blood pressure readings, following the Hypertension and Detection Follow-Up

Program protocol (HDFP 1978). Waist:hip ratio is calculated based on waist circumference (at narrowest point between the ribs and iliac crest) and hip circumference

(at maximal buttocks) (Lohman et al. 1988). HDL cholesterol, total cholesterol, glycosylated hemoglobin, DHEA-S, fibrinogen, IL-6, CRP, albumin, and creatinine were assayed from blood samples at the Nagasaki University School of Medicine. Cortisol, adrenaline, noradrenaline were assayed from 12-hour overnight urine samples (collected the evening before physical examinations and submitted by participants at their exams), also at the Nafasaki University School of Medicine. Collecting urine samples overnight minimizes potential confounding effects of physical activity, daytime activities, and stressors experienced during the day as much of this time participants likely are at home/ in bed (Seeman et al. 2004).

AL is calculatedby summing the number of components for which an individual’s values are in the highest risk quartile. This is the top quartile for all parameters except

HDL cholesterol and DHEA-S for which the lowest quartile represents highest risk

(Seeman et al. 1997; Seeman et al. 2004). Alternately, AL is calculated by counting top and bottom quartiles as at-risk, using deciles instead of quartiles, or incorporating markers of inflammation and renal function (Karlamangla et al. 2002; Seeman et al.

2004; Karlamangla et al. 2006; Stewart 2006; Leahy and Crews 2012).Because past research suggests AL and frailty may differ significantly between men and women

(Glover et al. 2006; Crews 2007; Maselko et al. 2007; Westerlund et al. 2012), analyses will be stratified by sex unless it is statistically determined that sexes can be pooled.

2.4.3 Principal Component Analysis

Common to AL studies is the concern that there may be differential impacts with respect to health outcomes based on components used to construct AL (e.g., Seeman et al. 2010; Booth et al. 2013). AL typically is measured using a count of biomarkers that exceed varying thresholds considered indicative of stress, or that are within, the top quartile for a given sample (e.g., see Crimmins et al. 2010). However, artificial dichotomization can be problematic and lead to unwanted effects such as loss of information, reduced effect size, and lower statistical power in bivariate analyses. These may lead to spurious, but statistically significant findings. Such aggregated data also fails to include nonlinear effects (MacCallum et al. 2002).

An alternative approach to measuring AL is principal components analysis

(PCA). Seeman et al. (2010) and Booth et al. (2013) developed models of AL using,

40 confirmatory factor analysis (CFA), another multisystem construct technique. Both CFA and PCA have potential advantages when modeling AL. Variables may be treated as continuous, thus minimizing information loss caused by dichotomizing. Such methods allow us to test the plausibility that AL is a general summary variable and the specific contributions made by individual biomarkers. This is of particular importance as published research suggests that although AL is predictive of outcomes, specific components may still exert unique effects on health (see Juster et al. 2010; Leahy and

Crews 2011).

Seeman et al. (2010) presented a final CFA model with five first-order factors and a second-order AL latent construct that provided moderately good fit to their data. Their first order factors included heart rate variability (mean heart rate and variability in high- and low- frequency bands), blood pressure, inflammation (fibrinogen, C-reactive protein, and interleukin-6), metabolism (WHR, HDL, LDL, triglycerides, and fasting glucose and insulin), salivary cortisol, and norepinephrine. Seeman et al. (2010) suggest the applicability of a CFA AL model in young adults. Booth et al. (2013) replicated the model suggested by Seeman et al. (2010) among a non-medicated and a medicated sample of older adults from the Lothian Birth Cohort Study. First-order factors and the latent second-order AL term identified by the previous study were significant among the non-medicated sample. However, the second-order model failed to attain significance in the medicate group, suggesting reliable measurements of AL may be complicated by the presence of medications (Booth et al. 2013).

CFA and PCA differ in their underlying assumptions, but usually show similar results. CFA is used preferentially in situations where an investigator has a set of hypotheses that form the conceptual basis for the factor analysis. In contrast, PCA is utilized when there are no guiding hypotheses, rather underlying factors are explored without a priori assumptions. In factor analysis, components are conceptualized from the outset as “real world” conditions such as depression or anxiety. In PCA, components begin as geometrical abstractions and relationships to “real world” constructs are identified through analyses. Another difference between the two approaches has to do with how variance is analyzed. In PCA, all observed variance is analyzed, while in factor analysis only shared variances are analyzed. Here, PCA was used instead of CFA because at this stage, exploratory approaches, rather than confirmatory hypothesis testing, still have a valid role to play in AL research.

PCA is a multivariate statistical technique used to reduce variables in a data set into a smaller number of ‘dimensions’. From an initial set of n correlated variables, PCA creates uncorrelated indices or components, where each component is a linear weighted combination of the initial variables (Vyas and Kumaranayake 2006). The uncorrelated property of the components is highlighted by the fact that they are orthogonal to each other (i.e., the cross product between any two is zero; Manly 1994).

Weights for each principal component are given by eigenvectors of the correlation matrix when data are unstandardized. The variance explained by each principal component is given by the eigenvalue of the corresponding eigenvector (Bolch and

Huang 1974). Components are ordered so that the first component explains the largest

42 possible amount of variation in the original data, subject to the constraint that the sum of the squared weights is equal to one. The second component is completely uncorrelated with the first and explains additional but less variation, subject to the same constraint.

Subsequent components follow the same pattern, with each capturing an additional dimension of the data while explaining smaller and smaller proportions of the variation observed among the original variables (Vyas and Kumaranayake 2006). The higher the correlation among original variables in the data, the fewer components required to capture the common information (Bolch and Huang 1974). Generally, components with an eigenvector over one are retained as significant.

2.4.4 Data Analyses

H1) Age will correlate poorly with AL.

 Linear regression will be utilized to assess the association between AL and age.

Low coefficients of determination (R2) indicate a poor association between age

and AL.

H2) Older men and women will demonstrate higher AL than their younger counterparts.

 t-tests will be used to test differences between age cohorts’ average AL, dividing

samples at both the median age and at age 70.

H3) Men and women in the same age groups will have comparable AL.

 t-tests will be used to test differences between sexes’ average AL.

H3) Regardless of age and sex, AL will associate significantly with biomarkers indicative of future morbidity.

 Multiple regression is used to examine independent associations of AL and

physiological variables, controlling for age, sex, and age by sex.

H4) Alternate constructions of AL will not significantly change relationships between

AL and biomarkers indicative of future morbidity.

 AL will be constructed using components’ quartile cut-points, decile cut-points,

and PCA. Associations between AL and physiological variables will be compared

to assess whether relationships are dependent on AL construction or are static.

Chapter 3: Physiological and Sociocultural Variables

3.1 Introduction

Descriptions of physiological and sociocultural variables are reviewed chapter to elucidate their importance in later analyses. When available, healthy ranges based upon current clinically accepted evidence are included. However, it should be noted that

“normal” ranges are contingent upon the source utilized as different laboratories often present slightly different values as representative. Ranges, then, should be understood as approximations. Unless otherwise stated, ranges presented reflect medical standards in the United States. These values will later help to situate data from Hizen-Oshima and

Sakiyama in the context of modern human physiological variation. Primary mediators and secondary outcomes later combined to assess allostatic load are reported first. These are followed by the study’s dependent variables organized by functional systems (e.g., liver enzymes, blood factors)

3.2 Physiological Components of Allostatic Load

3.2.1 Adrenaline

Adrenaline was discovered in 1921 by Otto Loewi. Although Loewi called the substance “accelerance,” it was renamed adrenaline (synonym: epinephrine) in 1936 (von

Bohlen et al. 2006). Many important advances concerning our knowledge of this hormone, including its role in stress, are attributed to Walter Cannon (1914).

Adrenaline is a hormone released from the adrenal medulla (the inner part of the adrenal gland) into the bloodstream that acts directly on both α- and β-adrenergic receptors (Pollard 2007; “Epinephrine” 2011). With noradrenaline and dopamine, adrenaline is among a group of hormones known as catecholamines, all synthesized from the amino acid tyrosine (Pollard 2007). It is among the most potent hormones in the body, capable of producing a wide variety of effects on multiple organs and tissues

(Goldstein 2008). Adrenaline plays a major role in the body’s physiological response to stress, is under control of the sympathetic nerves, and is the most important indirect pathway of sympathetic activation (Pollard 2007; Cannon 1932).

In the bloodstream, adrenaline promotes increased heart rate, force of contraction, and cardiac output (Pollard 2007). It constricts blood vessels in the skin as well as induces formation of platelet plugs in blood vessel walls; both actions serve to minimize blood loss from physical trauma (Pollard 2007; Goldstein 2008). In conjunction with constricting blood vessels near the skin’s surface, adrenaline acts to dilate blood vessels a few millimeters below the surface. Dilation redistributes blood toward coronary arteries, skeletal muscles, and the brain in preparation for a “fight or flight” response (Pollard

2007; Goldstein 2008). Adrenaline can also constrict blood vessels in the gut and kidneys, promoting a continuous steady flow to vital organs (Goldstein 2008).

In addition to modulating blood flow and platelet formation, adrenaline promotes the role of glucagon in converting glycogen to glucose (glycogenolysis) in the liver and stimulates lipolytic activity in adipose tissues (lipolysis), increasing levels of plasma free fatty acids. Both glucose and plasma free fatty acids provide readily accessible energy

46 sources for the body to utilize. Adrenaline increases serum levels of low- and high- density lipoprotein cholesterol (Pollard 2007; Hall 2010) while instigating bronchodilation, promoting increased oxygen uptake. All changes modulated by adrenaline prepare the body for immediate physical and mental activity, contributing to the “fight or flight” response (Pollard 2007).

Chronic and repeated acute exposures to adrenaline contribute to a variety of long-term physiological declines. For example, acceleration of artherosclerosis resulting in increased risk of cardiovascular disease and sustained increases in blood pressure often culminating in hypertension (Pollard 2007). Chronic adrenaline elevation may produce long-term elevations of lipids by promoting synthesis of very low density lipoproteins from free fatty acids, which are metabolized to atherogenic low-density lipoproteins

(LDL). Finally, adrenaline directly influences myocardial infarction and cerebrovascular accident by affecting platelets, clotting time, and precipitating cardiac arrhythmias

(Pollard 2007).

Adrenaline alters numerous immune system aspects, possibly mediating susceptibilities to infectious diseases and cancer. For example, individuals showing large increases in adrenal activity when responding to stressors also show significant immune declines (Pollard 2007). Currently, it is unclear how adrenaline influences immune system or disease susceptibility. One possibility is adrenaline alters lymphocyte migration from lymphoid organs such as the spleen (Pollard 2007). Normal serum adrenaline ranges from 0-900 ug/L (Catecholamines 2013).

3.2.2 Cortisol

Cortisol, a glucocorticoid, plays a primary role in stress responses as well as significant roles in glucose, protein, and lipid metabolism (Sherwood 2013). Production and release of cortisol is controlled by a neurological cascade originating in the hypothalamus either in response to normal diurnal rhythms or exposure to a stressor. The hypothalamus releases corticotrophin-releasing hormone, signaling the pituitary to produce adrenocorticotropic hormone (ACTH). ACTH signals the adrenal cortex to produce cortisol, modulating a wide variety of somatic functions (Sherwood 2013). The cortisol-producing axis is known as the hypothalamic-pituitary-adrenal (HPA) axis.

Like adrenaline, cortisol promotes increased serum glucose and free fatty acids in response to stressors, providing extra energy in the event of a “fight or flight” situation.

In addition, cortisol promotes protein catabolism to free amino acids for additional energy if additional energy is needed during stressful situations. Although neither adrenaline nor cortisol play significant roles in regulating fuel metabolism under normal conditions, both are influential during the stress response (Sherwood 2013). Cortisol suppresses the digestive and reproductive systems and growth processes during periods of stress (Mayo Clinic 2013), insuring that only energy needed to react to the stressor is expended.

Cortisol plays an important role in inflammatory and immune responses, especially during reactions to stressors. In the event of tissue injury, inflammation serves a protective role, but an exaggerated inflammatory response can be harmful (Sherwood

2013). Cortisol exerts an anti-inflammatory effect by blocking production of

48 inflammatory chemical mediators, suppressing migration of neutrophils to the injury, interfering with phagocytic activity, and inhibiting proliferation of fibroblasts in wound repair (Sherwood 2013). Cortisol inhibits immune responses by interfering with antibody production in lymphocytes (Sherwood 2013). Cyclically, lymphocytes have been shown to secrete ACTH which can stimulate production of cortisol. Interactions between the neuroendocrine system, specifically the HPA axis, and the immune system are a relatively new area of research.

Constant exposure to high cortisol levels produces detrimental physiological changes. Among these risks are impaired cognition, decreased thyroid function, and accumulation of abdominal fat, which itself has implications for cardiovascular health. In extreme cases, individuals may develop Cushing’s Syndrome, characterized by dangerously high cortisol levels. Cushing’s Syndrome is associated with rapid weight gain, hyperhydrosis, and hypercalcemia, along with various psychological and endocrine problems. Low cortisol may also be indicative of a maladaptive stress response as research indicates that it may be associated with symptoms of post-traumatic stress syndrome (PTSD) (Glover 2006).

3.2.3 Systolic and Diastolic Blood Pressure

Blood pressure assesses the force exerted by blood against blood vessel walls during systole and diastole. This force depends on the volume of blood within the vessel as well as the distensibility of vessel walls. All vessels are comprised of an endothelium, or lining of flat endothelial cells contiguous with those lining the heart, smooth muscle, and connective tissue (Sherwood 2013). Arterial connective tissue comprises both

49 collagen, providing tensile strength, and elastin fibers, providing arterial wall elasticity

(Sherwood 2013). Tensile strength helps arteries withstand high pressure blood ejections while elasticity allows expansion to accommodate increased amounts of blood during and immediately following systole.

During systole, a higher volume of blood enters the arteries from the heart than can flow into arterioles because the latter exert greater resistance than the former

(Sherwood 2013). To contain the excess volume of blood temporarily, arterial walls expand during systole and then “deflate” during diastole. Pressure exerted on blood in the arteries during deflation continues to push blood into the arterioles even during diastole, ensuring continued blood flow to organs even when the heart is not actively pumping blood into the system (Sherwood 2013).

Systolic blood pressure (SBP) is used to measure the maximum pressure exerted in arteries when blood is ejected into them from the heart during systole and averages 120 mm Hg in adults over 18 years of age. Diastolic blood pressure (DBP) measures the minimum pressure within the arteries when blood is draining off into arterioles or other vessels during diastole and averages 80 mm Hg among adults over 18 years of age.

Blood pressure actively responds to stressors and challenges over both short and long periods of time and is impacted by release of adrenaline into circulation. Sustained increases in blood pressure, such as those associated with exposure to chronic and/or repeated acute stressors, may eventually lead to hypertension (high blood pressure;

Pollard 2007). Hypertension may cause significant, asymptomatic damage to major organs, leading to life-threatening complications (Mayo Clinic 2011). For example, high

50 blood pressure can damage the endothelial cells lining arteries, eventually leading to artherosclerosis and possibly blocking blood flow to the heart, kidneys, brain and limbs.

Artherosclerosis can cause angina, heart attack, heart and kidney failure, stroke, peripheral arterial diseases, eye damage, and aneurysms (Mayo Clinic 2011). In addition to damaging arteries, hypertension can also cause damage to the heart. High blood pressure has been implicated in coronary artery disease, arrhythmia, thickening or stiffening of the left ventricle (left ventricular hypertrophy), heart attacks, and eventually heart failure.

In the brain, hypertension has been associated with temporary disruptions of blood leading to transient ischemic attacks, stroke, dementia, and mild cognitive impairment (Mayo Clinic 2011). High blood pressure can also cause a variety of kidney diseases (nephropathy), including: kidney failure, kidney scarring, and kidney artery aneurysm. Finally, high blood pressure may damage delicate blood vessels which supply blood to the eyes. This type of damage can result in fluid build-up under the retina resulting in distorted vision or scarring that impairs vision and/or nerve damage leading to bleeding within the eye or vision loss (Mayo Clinic 2011).

Low blood pressure or hypotension (generally considered to by <90 mm Hg systolic/ < 60 mm Hg diastolic among adults over 18 years of age), may result from myriad conditions, including: pregnancy, low heart rate, heart valve problems, heart attack, dehydration, blood less, severe infection or allergic reaction, or improper nutrients. It can also be caused by endocrine imbalances, such as an under- or over-active thyroid, adrenal insufficiency (Addison’s disease), hypoglycemia, and diabetes. Like

51 hypertension, hypotension may have severe consequences such as dizziness, fainting, nausea, fatigue, and depression. In addition, hypotension often signals a more serious underlying problem (Mayo Clinic 2011).

3.2.4 Dehydroepiandrosterone-sulfate

Dehydroepiandrosterone and its sulfated analog, dehydroepiandrosterone-sulfate

(DHEAs),are steroid hormones primarily produced in the adrenal cortex, with lesser quantities produced by the gonads and brain. DHEAs is the most abundant steroid in the human body, with concentrations 250-500 times higher than DHEA and 100-500 times higher than testosterone (Salimetrics 2008). After production, DHEAs circulates in the blood to peripheral tissues where it is enzymatically desulfated to DHEA. Most DHEA serves as precursors to a variety of estrogenic and androgenic compounds, but a small portion may be reconverted to the sulfate form (Krobath 1999). Because of the ease with which DHEA can be sulfated or unsulfated, DHEA and DHEAs are often discussed together as DHEA(s).

Circulating levels of DHEA(s) peak around age 20-30 and then decline as individuals age (Salimetrics 2008). By the age of 70-80, DHEA(s) levels are only 20-

30% of peak levels (Krobath 1999). Because of the coincidence between the natural decline of DHEA(S) levels with age and the onset of diseases associated with the aging process, a great deal of research has been directed toward examining what both hormones do in the body. Low levels of DHEA(s) have been associated with emotional stress and a variety of morbid conditions: rheumatic disease (Imrich 2002), cardiovascular disease

(Thijs 2003), immune system disorders (Chen and Parker 2004), and osteoporosis

(Dharia and Parker 2004). In addition, elevated levels of DHEA(s) have been associated with obesity (Rosmond 2006), type II diabetes (Rosmond 2006), post-traumatic stress disorder (Yehuda et al. 2006) and prolonged exposure to physical stress (Krobath 1999).

Campbell suggests DHEA(s) plays a significant role in neurological function, with minor effects on immune function and growth (2006).

The molecular mechanism through which DHEA(s) exerts its influence is relatively unknown, leading some to suggest it may only be a precursor molecule to an active metabolite (Widstrom and Dillon 2004). For example, while DHEA(s) sometimes acts as a partial agonist of androgen receptions, its affinity is so low that it is unlikely to be of any significance under normal circumstances (Chen et al. 2005; Gao et al. 2005).

However, other research indicates DHEA acts as a full agonist of ERβ, activating the receptor to the same degree as estradiol (Chen et al. 2005), suggesting DHEA may be an important and potentially major endogenous estrogen.

In addition to acting as an important precursor molecule and antagonizing an important estrogen-receptor, DHEA(s) plays an important, if still unclear, role in regulating somatic stress responses. Some studies indicate DHEA(s) has an antagonistic action to that of cortisol; as circulating DHEAs levels increase following removal of a stressor, there is a concomitant decrease in circulating cortisol levels (Boudarene et al.

2002). Researchers have suggested that this inverse relationship between circulating cortisol and DHEA(s) may be related to competition for synthesis and release by the adrenal gland (Boudarene et al. 2002). Other studies have shown concomitant increases in both cortisol and DHEA(s) among primates, suggesting that DHEA(s) plays an active

53 role in the stress response as opposed to an active role in down-regulating the stress response (Maninger et al. 2010).

3.2.5 Glycated Hemoglobin

Glucose, a simple monosaccharide found in plants, is the human body’s primary source of energy. Absorbed directly into the bloodstream during digestion, glucose is preferentially used over other dietary monosaccharides (fructose or galactose) for the production of ATP because it has a lower tendency to react (glycate) non-specifically with proteins’ amino groups. Such non-enzymatic glycation is undesirable as it alters the shapes of enzymes, reducing their activity. For example, glycation of proteins and lipids may be responsible for several long-term complications of diabetes, such as blindness, renal failure, and peripheral neuropathy (Koenig et al. 1976; Cerami American Diabetes

Association 2010).

Hemoglobin is among the structures to which glucose is able to bind. Glycation of hemoglobin is an irreversible result of hemoglobin’s exposure to plasma glucose. As glycemia increases, such as in prediabetes or diabetes mellitus, the fraction of glycated hemoglobin increases concomitantly. Glycated hemoglobin (HbA1c) concentrations indicate average glucose concentration red blood cells (erythrocytes) were exposed to over their lifespans. Average erythrocyte lifespan in the human body is approximately

100-120. Because of the stable relationship between plasma glucose and percent glycated hemoglobin in the blood, the latter reflects the average glucose concentration in the plasma over the preceding 3-4 months.

An HbA1c of 5.6% or less is considered normal. Higher levels may indicate the onset (5.7-6.4%) or presence of (6.5% or higher) diabetes. In addition, HbA1c is a marker for cardiovascular risk, coronary heart disease, and stroke (de Vegt 1999; Meigs et al.

2002; Blake et al. 2004; Khaw et al. 2004; Pradhan et al. 2007; Stout et al. 2007; Brewer et al. 2008; Gerstein et al. 2008; Levitan et al. 2008; Selvin et al. 2010)

3.2.6 Noradrenaline

Noradrenaline (synonym: norepinephrine) is the direct precursor of adrenaline.

Prior to 1940, researchers believed noradrenaline’s primary or sole function was its role as an intermediary substrate in the synthesis of adrenaline (von Bohlen et al. 2006).

However, in the 1940s evidence began to accumulate that noradrenaline plays a significant role as a neurotransmitter, specifically affecting the central nervous (von

Bohlen et al. 2006).

As a catecholamine, noradrenaline mediates the sympathetic nervous system.

Most noradrenaline is released from synaptic nerve endings in the sympathetic nervous system and primarily affects nearby target cells. A small amount of noradrenaline secreted from the adrenal medulla may enter the bloodstream. However, serum levels must reach relatively high concentrations before exerting hormonal effects (Goldstein

2008; Hall 2010). When individuals are not undergoing a stress response, noradrenaline modulates alertness, rest cycles, attention, and memory. In conjunction with acetylcholine

(another neurotransmitter),it also plays a significant role in regulating heart rate

(Goldstein 2008; Hall 2010).

During stress responses, production and secretion of noradrenaline increases

(Weinshilboum et al. 1971; LeBlanc and Ducharme 2007) Because noradrenaline is nearly identical to adrenaline in molecular structure, when the sympathetic nervous system responds to stressors, the former exerts similar effects on the soma; it increases heart rate and blood pressure, promotes glycogenolysis and lipolysis, and mediates relaxation of bronchial smooth muscle to open air passages to the lungs (Hall 2010). As is the case for adrenaline, these physiological responses prepare an individual for a “fight or flight” response. Normal noradrenaline ranges from 0-600 ug/L among adults greater than 18 years of age (Catecholamines 2013).

3.2.7 Plasma Lipids: Total Cholesterol and High Density Lipoproteins

Cholesterol is a sterol, or modified steroid, produced primarily in the liver.

Cholesterol is classified as a lipid because it helps to establish cell membrane permeability and fluidity and plays a role in the metabolism of fat soluble substances, including vitamins A, D, E, and K. In addition, cholesterol is the precursor in biosynthesis of many steroid hormones (including cortisol), bile, and vitamin D and also acts in intracellular transport, cell signaling, nerve fiber insulation, and nerve conduction.

Cholesterol is transported in the blood by lipoproteins, or a molecule containing both a lipid (e.g., cholesterol, triglycerides) and a protein. There are three types of lipoproteins; high density lipoproteins (HDL) are discussed here, while (very) low density lipoproteins ((V)LDL) and triglycerides are discussed in Section 3.3.2.

Total cholesterol is defined as the sum of HDL, LDL, and VLDL. The American

Heart Association and the 1987 report of the National Cholesterol Education Program

56 suggest that total cholesterol less than 200 mg/dL is healthy, from 200-239 mg/dL is borderline high, and that any amount over 240 mg/dL is high. High cholesterol, specifically a high LDL to HDL ratio, results in hypercholesterolemia. This condition is strongly associated with artherosclerosis, which can result in myocardial infarction, peripheral vascular disease, and stroke.

HDL is the smallest of the lipoproteins and contains the highest proportion of protein to cholesterol. HDL’s primary function is transporting cholesterol from cells elsewhere in the body back to the liver in a process known as reverse cholesterol transport (Jia et al. 2006). Once in the liver, HDL molecules are broken down to be used again or expelled from the body as waste. HDL is often referred to as good cholesterol because it does not contribute to the harmful buildup of cholesterol in the body associated with low density and very low density lipoproteins. In addition, numerous epidemiologic studies have demonstrated an inverse relationship between HDL and the incidence and prevalence of coronary heart disease (Wilson et al. 1988; Gordon et al. 1989; Goldbourt et al. 1997; Ballantyne et al. 2003). This effect has been primarily attributed to HDL’s role in reverse cholesterol transport (Fielding and Fielding 2003).

The American Heart Association suggest that among men and women, HDL less than 40 mg/dL or 50 mg/dL respectively are considered risk factors for cardiovascular disease. HDL greater than 60 mg/dL is considered protective against heart disease for both sexes.

3.2.8 Waist Hip Ratio

Waist hip ratio (WHR) assesses the amount of adipose tissue distributed around an individual’s midsection compared to their buttocks. Stress, specifically increases cortisol production, and is associated with increased adipose deposition. Excessive adiposity leads to detrimental hormonal and metabolic changes, contributing to heart disease and other health problems. WHR predict mortality better than either waist circumference or BMI (Price et al. 2006; Mørkedal et al. 2011). WHR is preferred over measures of BMI and body fat percentage because it takes into account differences in body structure. The World Health Organization equates WHR over 0.90 for men and 0.85 for women with abdominal obesity. Over these levels, individuals are at higher risk for diabetes, cardiovascular disorder and, among women, ovarian cancers (Dobbelsteyn et al.

2001).

3.3 Dependent Physiological and Sociocultural Variables

3.3.1 Dopamine

Dopamine is a catecholamine with multiple functions. In the central nervous system it acts as a neurotransmitter. In the periphery, including the gut, adrenal gland, and kidneys, it acts as an autocrine/paracrine hormone, produced in, released from, and acting locally on the same type of cells (Goldstein 2008). Dopamine may affect luteinizing hormone (LH), a glycoprotein hormone which exerts significant control over the gonads. Among women, LH induces ovulation and formation of the corpus luteum as well as stimulates steroid secretion in the ovaries. Among men, LH stimulates steroid secretion in the testes. Whether dopamine stimulates or inhibits LH release is currently

58 unclear and some suggest its regulatory effects on LH are minimal (Griffin and Ojeda

2000). Dopamine modulates thyroid function by inhibiting thyroid-stimulating hormone secretion (Griffin and Ojeda 2000). Dopamine can also affect growth hormone (GH), either stimulating or inhibiting release depending on the targeted receptor (Griffin and

Ojeda 2000). GH itself significantly impacts growth and metabolism, affecting not only statute but also blood amino acid concentration, blood urea nitrogen, DNA, RNA and protein synthesis, blood glucose levels, fat oxidation, growth and calcification of cartilage, and possibly the onset of diabetes mellitus (Griffin and Ojeda 2000).

Dopamine plays a major role in reward-motivated behaviors and is amplified by a variety of addictive drugs, including cocaine, amphetamines, and methamphetamines.

Dopamine also is implicated in several diseases of the nervous system such as

Parkinson’s disease. Finally, accumulating evidence suggests that maladaptive dopamine secretion and/or uptake may influence schizophrenia, attention deficit hyperactivity disorder (ADHD), and restless leg syndrome (RLS) (Griffin and Ojeda 2000). Normal amounts of urine dopamine collected over 24 hours range between 65-400 micrograms

(MedlinePlus 2011).

3.3.2 Plasma Lipids: Low Density Lipoprotein and Triglycerides

Low density lipoproteins are among the smallest lipoproteins found in the human body. These molecules, similar to the HDL discussed previously, aid in the transportation of lipids, such as cholesterol and triglycerides. In contrast to HDL, LDL primarily carry cholesterol from the liver to cells elsewhere in the body. If more cholesterol is transported by LDLs from the liver to peripheral cells than is needed, harmful build-up

59 leading to artherosclerosis can result. If severe enough, artherosclerosis can result in heart attack, stroke, and peripheral vascular disease. In addition, LDL can be a risk factor for cardiovascular disease when they are trapped in the endothelium and become oxidized.

Current research indicates that the concentration and size of LDL particles more strongly associates with plaque formation than does concentration of cholesterol carried by LDL. Some individuals suffer from a hereditary form of high LDL known as familial hypercholesterolemia. The American Heart Association, NIH, and NCEP provide guidelines for fasting LDL-Cholesterol levels. As of 2004 guidelines pertaining to adults were: <70 mg/dL no risk for LDL-related cardiovascular disease, 70-129 mg/dL low risk,

130-159 mg/dL borderline high risk, >160 mg/dL high risk (NIH 2001; American Heart

Association 2007; Cholesterol Levels 2009)

Triglycerides are the chemical form in which most fat exists in the body. They originate either as the end product of digesting and breaking down fats in food or as a result of synthesis from other energy sources in the body such as carbohydrates.

Triglycerides are primarily present in blood plasma. However, if more triglycerides are present than can be utilized, they are stored as fat in adipocytes for later energy use.

Hormone-sensitive lipase and desnutrin are key enzymes that mediate the rate of lipolysis in adipose tissues. These enzymes are regulated by insulin and catecholamines, especially adrenaline and noradrenaline.

High triglycerides are associated with increased risk of cardiovascular disease, especially among individuals with high LDL and low HDL cholesterol. High triglycerides also potentially exacerbate other conditions, including hypertension and

60 diabetes. The National Cholesterol Education Program sets guidelines for triglyceride levels among adults: <150 mg/dL normal, 150-199 borderline high, 200-499 mg/dL high, and >500 mg/dL very high.

3.3.3 Renal Function: Creatine and Creatinine

3.3.3.1 Creatine

Creatine is a nitrogenous organic acid. It helps supply energy to cells by increasing formation of ATP. Creatine is naturally synthesized in the kidney and liver from the amino acids arginine, glycine, and methionine. Although creatine can be transported in the blood to all cells in the body, it is primarily concentrated (95%) in skeletal muscles (“Creatine” 2013). Creatine is primarily derived from animal protein, although it can also be synthesized after consumption of oral supplements or plant-based proteins rich in arginine, glycine, and methionine (Burke et al. 2003). Because of its prevalence, it is worth noting that creatine supplementation (often increasing intake to 2-

3 times that of a normal diet) is popular among athletes. It is believed that creatine encourages production of muscle mass as well as improves short, high-intensity efforts such as sprinting (“Creatine” 2013). Over-supplementation may be associated with kidney damage, altered liver function, or asthmatic symptoms (Poortmans and Francaux

2000).

3.3.3.2 Creatinine

Each day, 1-2% of muscle creatine is converted to creatinine (creatine to creatine phosphate to creatinine; Taylor 1989). Creatinine may subsequently be cleared from muscle tissue or the blood stream through skeletal muscle itself or by the kidney. Serum

61 creatinine is an important indicator of renal health because it is excreted unchanged by the kidneys (Taylor 1989). Men tend to have higher levels of creatinine than women because the former have greater skeletal muscle mass (Taylor 1989). Because its concentration is directly correlated to that of creatine, increased dietary intake of creatine in any form increases creatinine excretion (Talyor 1989). Normal levels of creatinine in the blood range between 19-311 mg/dL (PAML). Elderly individuals tend to have lower creatinine levels. Higher creatinine levels may indicate kidney impairment, indicating possible hypertension or diabetes mellitus.

3.3.3.3 Uric Acid

Uric acid forms when purines, the building blocks of DNA, are metabolized by the body. It may act as an antioxidant and measured to assess oxidative stress in addition to renal function (Becker 1993; Glantzounis et al. 2005). It is excreted in urine after being filtered from blood by the kidneys. Healthy levels of uric acid in human blood plasma are: 3.4-7.2 mg/dL for men and 2.4-6.1 mg/dL for women (“Harmonisation of

Reference Intervals” 2013). Another source indicates adult men have mean serum urate values of 6.8 mg/dL, premenopausal women have mean serum urate values of 6 mg/dL, and that values for women increase after menopause and approximate those of men

(Burns et al. 2012). Throughout adulthood, concentrations rise steadily and can vary with height, blood pressure, body weight, renal function, and alcohol intake (Burns et al.

2012). Higher than normal levels of uric acid (hyperuricemia) may be caused by a wide variety of conditions: acidosis, alcoholism, chemotherapy, diabetes, excessive exercise, gout, hypoparathyroidism, lead poisoning, leukemia, kidney disease, nephrolithiasis, a

62 purine-rich diet, renal failure, and toxemia of pregnancy. High uric acid levels may lead to and occur secondary to gout. Lower than normal serum uric acid may result from

Fanconi syndrome, a low-purine diet, Wilson’s disease, or the Syndrome of inappropriate antidiuretic hormone secretion.

3.3.4 Liver Function

3.3.4.1 Glutamic Oxaloacetic Transaminase

Glutamic oxaloacetic transaminase (GOT), also known as aspartate aminotransferase (AST), catalyzes the reversible transfer of an amino group from aspartate to α-ketoglutarate to form glutamate and oxaloacetate. Glutamate is an important mediator of excitatory signals in the central nervous systems and is involved in most aspects of normal brain function including cognition, memory and learning.

Oxaloacetate is a metabolic intermediate which plays an important role in gluconeogenesis, the urea cycle, glyoxylate cycle, amino acid synthesis, fatty acid synthesis, and the citric acid cycle (Nelson and Cox 2005).

GOT is normally present in low quantities in serum and in various body tissues, especially the heart and liver. Because GOT is concentrated in the liver and is responsive to damage of hepatic cells, serum levels are often considered as a marker of liver function. However, GOT’s responsiveness to a wide variety of conditions make it a better indicator of general well-being and health than a specific indicator of liver function. For example, serum concentrations increase in response to tissue injury, particularly in the event of myocardial infarction, damage to hepatic cells, or the onset of some muscle diseases such as progressive muscular dystrophy. Amount of GOT in blood is directly

63 correlated to the extent of tissue damage. After severe damage, GOT levels rise in 6 to 10 hours and remain high for about 4 days. In addition to tissue damage, high levels of GOT may also be attributed to necrosis, medication, high doses of vitamin A, kidney or lung damage, mononucleosis, and some types of cancer.

Normal values for serum GOT range between 14-20 units per liter (U/L) for adult men and 10-36 U/L for women (Fischbach and Dunning 2009). Values above this range are a non-specific indicator of poor health (Richards 2012).

3.3.4.2 Glutamic Pyruvic Transaminase

Glutamic pyruvic transaminase (GPT), also known as alanine aminotransferase

(ALT), is found primarily in the liver and at smaller concentrations in plasma. GPT plays an important role in catalyzing the alanine cycle, metabolizing alanine to glucose. Higher levels of GPT are released into the blood stream following liver injury, making GPT an indicator of liver status. However, caution must be used when drawing conclusions based on measurements of this enzyme. For example, individuals with viral hepatitis A may initially display high GPT levels which subsequently subside and those with hepatitis C may only ever demonstrate a slight increase in GPT. In addition, GPT may be released into the blood stream after muscle damage or be elevated despite normal liver function.

GPT values typically range between 10-45 U/L for adult men and 10-34 U/L for adult women (Fischbach and Dunning 2009).

3.3.4.3 Gamma Glutamyl Transpeptidase

Gamma glutamyl transpeptisdase (GTP), also known as phosphoenolpyruvatecarboxykinase, regulates the metabolic pathway resulting in

64 gluconeogenesis. Gluconeogenesis is the process by which glucose is generated from non-carbohydrate carbon substrates (e.g., pyruvate, lactate, some amino and fatty acids) and through which the body prevents hypoglycemia. GTP is located primarily in the liver and is generally up-regulated by release of glucagon from the pancreas and down- regulated by the presence of insulin in the blood. However, GTP activity also increases in direct response to cortisol secretion, implicating the enzyme in somatic stress reactions.

GTP levels may be elevated in cases of liver disease, alcoholism, bile duct obstruction, cholangitis, drug abuse, and, in some cases, excessive magnesium ingestion.

Decreased levels are associated with hypothyroidism, hypothalamic malfunction, and low magnesium levels. Normal adult ranges are between 9-48 U/L (Mayo Clinic 2012).

3.3.5 Blood

3.3.5.1 Hemoglobin

Hemoglobin (Hb) in the blood binds with oxygen molecules that are then transported from the lungs to other parts of the body. Once delivered, the oxygen helps burn nutrients necessary to sustaining metabolism. Hb is known as a metalloprotein because it contains iron as a metal ion cofactor (Maton et al. 1993). In addition to carrying oxygen, Hb to a more limited extent can also bind with carbon dioxide and nitric acid. Hb molecules are catabolized when RBCs reach their useful limits due to aging or defect. The iron ion cofactor is recycled while degradation of the rest of the molecule results in minor production of carbon monoxide and bilirubin (Kikuchi et al. 2005).

Hb deficiencies can be caused by too few hemoglobin molecules (anemia), iron deficiency, decreased capacity of hemoglobin to bind with oxygen, loss of blood,

65 nutritional deficiency, bone marrow problems, chemotherapy, kidney failure, or abnormal hemoglobin structure (e.g., sickle cell anemia) (Hemoglobin 2009). High hemoglobin levels may be related to high altitude, smoking, dehydration, tumors, macrocytic anemia, or increased number/size of red blood cells associated with congenital heart disease, pulmonary fibrosis, or other heart related defects (Hemoglobin 2009). Genetic disorders, known as porphyrias, also disrupt hemoglobin synthesis. Among adults, normal Hb levels range between 13.8 to 18.0 g/dL (8.56 to 11.17 mmol/L) for men and 12.1 to 15.1 g/dL

(7.51 to 9.37 mmol/L) for women (Handin et al. 2003).

3.3.5.2 Hematocrit

Hematocrit (Ht) is a blood test that measures the percentage of the volume of whole blood that is made up of red blood cells. The measurement, expressed as a percentage, depends on the number and size of red blood cells in the sample (Hematocrit

2012). Low Ht may be a result of anemia, bleeding, RBC apoptosis, leukemia, malnutrition, over hydration and specific nutritional deficiencies: iron, folate, and vitamins B12 and B6. High Ht is related to congenital heart disease, corpulmonale

(failure of the right side of the heart), dehydration, erythrocytosis (excessive RBC production), hypoxia, pulmonary fibrosis, and polycythemia (a specific bone marrow disease) (Hematocrit 2012). Normal Ht levels are between 40.7 - 50.3% for adult men and 36.1 - 44.3% for adult women (Hematocrit 2012).

3.3.5.3 Mean Corpuscular Hemoglobin

Mean corpuscular hemoglobin (MCH) assesses the average mass of hemoglobin per red blood cell (Perkins 2009; Vajpayee et al. 2011). MCH, in conjunction with mean

66 corpuscular volume (MCV), can be used to determine if an anemia is hypo-, normo-, or hyperchromic (Ryan 2010). MCH and MCV must be considered together since cell volume directly impacts the content of hemoglobin present per cell (Ryan 2010). MCH is not measured directly, but instead calculated using hemoglobin concentration (Hb) and red blood cell count (RBC) as follows: MCH = Hb (in g/L)/RBC (in millions/µL).

Average adult MCH is between MCH: 27-33 picograms /cell (Vajpayee et al. 2011).

3.3.5.4 Mean Corpuscular Hemoglobin Concentration

Mean Corpuscular Hemoglobin Concentration (MCHC) assesses the average weight of hemoglobin per unit volume of red blood cells or, in other words, the ratio of hemoglobin mass to the volume of red bloods cells (Perkins 2009). Elevated MCHC can be indicative of spherocytosis, homozygous sickle cell anemia, or hemoglobin C disease

(Perkins 2009). MCHC is calculated from hemoglobin concentration (Hb) and hematocrit

(Ht) as: MCHC = Hb (in g/dL)/Hct (in L/L). Average adult MCHC is 33-36 g/dL

(Vajpayee et al. 2011).

3.3.5.5 Mean Corpuscular Volume

Mean corpuscular volume (MCV) is the average volume of red blood cells in a specimen. MCV is determined by average red cell size; low MCV indicates microcytic

(small average RBC size), normal MCV indicates normocytic (normal average RBC size), and high MCV indicates macrocytic (large average RBC size) (Vajpayee et al.

2011). MCV, in conjunction with MCH, is utilized to help classify types of anemia based on red cell morphology (Ryan 2010). Among adults, the reference range for MCV is 80-

96 fL/red cell (Vajpayee et al. 2011).

3.3.5.6 Red Blood Cell Count

The primary function of red blood cells (RBC, erythrocytes) is to contain and transport hemoglobin molecules to deliver oxygen from the lungs to other parts of the body via the blood. Secondary functions include inducing various signaling mechanisms to increase blood flow to vessels and tissues that are under-oxygenated (Dieson et al.

2008; Wan et al. 2008). The cells’ color is due to the iron ion cofactor of hemoglobin. In humans, RBC develop in bone marrow, are released into the blood stream, circulate for

100-120 days, and then are broken down and recycled by macrophages. Approximately

2.4 million new red blood cells are produced per second and there are approximately 20-

30 trillion in the bloodstream at a given time (Sackman 1995). RBC comprise approximately one quarter of the total human body cell number and are significantly more common than other blood components (e.g., white blood cells and platelets).

A red blood cell count assesses the number of RBC present per microliter of blood. High RBC can be caused by cigarette smoking, congenital heart disease, corpulmonale, dehydration, kidney tumors, hypoxia, pulmonary fibrosis, and polycythemia vera. Low RBC can be due to anemia, bone marrow failure, hemolysis

(RBC destruction), hemorrhage, leukemia, malnutrition, multiple myeloma, over hydration, pregnancy, and specific nutritional deficiencies including iron, copper, folate, and vitamins B12 and B6 (RBC Count 2012). Among males, a normal RBC is between

4.7 to 6.1 million cells per microliter (cells/mcL). Among females, it is between 4.2 to

5.4 million cells/mcL.

3.3.6 Immune Function

3.3.6.1 White Blood Cells

White blood cells (WBC, leukocytes) are an important part of the immune system.

Produced in bone marrow, they are found throughout the body, including blood and the lymphatic system (Maton et al. 1997). Each performing a different function, there are five major types of leukocytes in the human body: basophils, eosinophils, lymphocytes, monocytes, and neutrophils (LaFleur-Brooks 2008). Basophils release histamine during inflammatory responses. Eosinophils target parasitic intrusions and help to regulate allergic reactions. Lymphocytes release antibodies, target virus- and cancer-infected cells, and help regulate the immune system after exposure to infection. Monocytes act as microphages, and neutrophils protect against bacteria and fungi. Together, the different types of WBC fight infection as well as protect against foreign material in the body.

The number of WBC (including all five types) in the blood often indicates disease. A low WBC count (leucopenia) may indicate bone marrow deficiency or failure, collagen-vascular diseases, diseases of the liver or spleen, or exposure to radiation. In contrast, a high number of WBC (leukocytosis) may be due to anemia, bone marrow tumors, infectious diseases, inflammatory disease (such as rheumatoid arthritis or allergies), leukemia, severe emotional or physical stress, or burns or other tissue damage

(WBC Count 2011). A healthy adult WBC range is considered to be approximately

4,100-10,900 WBC/mL (WBC Count 2011).

3.3.6.2 Platelets

Platelets (thrombocytes) are small prokaryotic cells which are produced in bone marrow and which circulate in blood. They produce growth factors related to cell growth, proliferation, and differentiation and are also crucial to the clotting process. Blood platelet counts can be affected by a variety of diseases or be used to identify the cause of excess bleeding. A low platelet count (thrombocytopenia) may indicate chemotherapy, disseminated intravascular coagulation. hemolytic anemia, hypersplenism, idiopathic thrombocytopenic purpura, leukemia, blood transfusion, prosthetic heart valve, thombotic thrombocytopenic purpura, celiac disease, or vitamin K deficiency. On the other hand, a high platelet count (thrombocytosis) may be due to (non-hemolytic) anemia, chronic myelogenous leukemia, polycythemia vera, primary thrombocythemia, or recent spleen removal (Platelet count 2011). Normal platelet counts range between 150,00-400,000 platelets per microliter (mcL).

3.3.7 Frailty

Frailty is associated with loss of function and reserve capacity as well as increased susceptibility to illness, falls, disability, institutionalization, and death (Fried et al. 2001). A multiplicity of definitions of frailty exist, including notions as varied as dependence on others, risk of adverse health outcomes, loss of physiological reserves, experiencing ‘uncoupling’ from the environment, suffering from co-morbidity, having complex medical and/or psychosocial problems, having atypical disease presentations, benefiting from specialized geriatric programs, and experiencing accelerated aging

(Rockwood et al. 2000). Frailty has also been defined as being synonymous with

70 disability, co-morbidity, or advanced old age (Fried et al. 2001). Today, the most widely accepted hypothesis proposes that frailty is a “biologic syndrome of decreased reserve and resistance to stressors, resulting from cumulative declines across multiple physiologic systems, and causing vulnerability to adverse outcomes” (Fried et al.

2001:M146).

Similar to allostatic load, frailty cannot be defined by dysregulation in a single physiological system, but is instead the result of functional decline across several systems

(Fried et al. 2004). Recent research suggests “frailty is related, nonlinearly, to the number of abnormal physiological systems, independent of specific system abnormalities, number of chronic diseases, and chronological age” (Fried et al. 2009). Altered functioning of one or several physiological systems could impair one or several phenotypic components. Synergistic interactions among altered systems could likewise be expressed as a variety of phenotypic outcomes, suggesting that frailty phenotypes may be highly individualistic even if underlying etiologies are similar.

Proposed markers of the ‘frailty phenotype’ include wasting (both loss of muscle mass and strength, and weight loss), loss of endurance, decreased balance and mobility, slower performance and relative inactivity, and potentially, decreases in cognitive function (Fried et al. 2001, Fried et al. 2004, Fried et al. 2005). Other authors have proposed difficulty in completing ADLs/IADLs, continence, mobility, and unintentional weight loss as indicators of frailty (Rockwood et al. 1999, Crews 2007). A “Frailty

Index”, comprising 70 deficits observable during clinical examination, has also been proposed to identify frail individuals (Rockwood et al. 2007). In this sample, walking

71 speed, weight, and percent body fat may indicate individuals’ frailty, although collection of more data are necessary to test the relationship between AL and a complete index measure of frailty.

3.3.8 Activities of Daily Living - The Tokyo Metropolitan Institute of Gerontology Index of Competence

Activities of daily living (ADLs) were developed to study results of treatment and prognosis in the elderly and chronically ill (Katz et al. 1963). Assessing individuals’ ability to complete a variety of different tasks independently, such as bathing, dressing, going to the toilet, continence, and feeding, helps physicians screen for progressive functional loss and disability (Katz et al. 1963, Fried et al. 2004:255, Crews 2007:1029).

ADLs and Instrumental Activities of Daily Living (IADLs) (Johnson and Wolinsky

1994) predict hospitalization, mortality, and remaining life span among the aged and disabled (Crews 2007). They represent a set of conditions which does not usually cause death but rather limits functionality and quality of life. Inability to complete one or more

ADLs can limit individuals’ capacity to care for themselves. ADL and IADL losses occur more frequently as individuals age and often result from chronic morbidity (Crews

1997).Unfortunately, causes for and meanings of inabilities to complete ADLs have not been sufficiently clarified using current research paradigms. However, their multifactorial etiology is obvious from their complex nature (Crews 1997).

In this sample, Activities of Daily Living are assessed using the Tokyo

Metropolitan Institute of Gerontology Index of Competence (TMIG-IC). The TMIG-IC index totals 13 questions about physical and social competency divided into 3 domains:

Instrumental Self-Maintenance, Intellectual Activity, and Social Role. Positive answers

(ability to complete a task) are scored as 1; negative answers (inability to complete) are scored as 0. Scores are then summed similar to how AL is determined, except that higher

TMIG-IC scores indicate better function and less disability.

Chapter 4: Results I - Sakiyama Participant Demographics, Physiological Characteristics, Allostatic Load, and Sociocultural Variables

4.1 Introduction

Descriptions of demographics, physiological characteristics, allostatic load, and sociocultural variables of Sakiyama participants are presented in this chapter. Differences in variables are presented between age groups (divided at the median and between old/oldest of the old) and sex. Common acronyms are listed in Table 4.1.

Acronym Name Acronym Name AD Adrenaline (μg/L) MCH Mean corpuscular hemoglobin (pg/cell) Mean corpuscular hemoglobin ADL Activities of Daily Living Total (Intel+SelfM+Soc) MCHC concentration (g/dL) Cort Cortisol (μg/L) MCV Mean corpuscular volume (fL/red cell)

UCreatinine Urinary creatinine (mg/dL) NAD Noradrenaline (μg/L)

Creatine Creatine (mg/dL) PBodyFat Percent Body Fat (%)

DBP Diastolic blood pressure (mmHg) Platelet Platelet count

DHEAs Dehydroepiandrosterone-sulfate (μg/dL) RBC Red blood cell count (count/mL)

DOP Dopamine (μg/L) SBP Systolic blood pressure (mmHg)

GOT Glutamic oxaloacetic transaminase (U/L) SelfM Instrumental self-maintenance

GPT Glutamic pyruvic transaminase (U/L) Soc Social Role

GTP Phosphoenolpyruvatecarboxykinase (U/L) TCho Total cholesterol (mg/dL)

Hemoglobin Hemoglobin content (g/dL) TG Triglycerides (mg/dL)

HbA1c Glycosylated hemoglobin (%) Uric Uric acid (mg/dL)

HDL High density lipoproteins (mg/dL) Walk 8 Second walking speed

Hematocrit Hematocrit value (%) WBC White blood cell count (count per mL)

Intel Intellectual Activity Weight Weight (Kg)

LDL Low density lipoproteins (mg/dL) WHR Waist:Hip Ratio Table 4.1 Common acronyms of physiological and sociocultural variables

4.2 Descriptive Statistics

A total of 96 Japanese elders participated in this study. Their ages ranged from 55 to 88 years (std. dev.=8.6 years) with mean and median ages of 67.9 and 66.5 years respectively. Means, standard deviations, and ranges for all independent and dependent variables are presented in Tables 4.2 and 4.3 respectively. Slightly elevated blood pressure (147.3/86.9 mmHg, sd 23.9/12.6) is observed in these Japanese elders compared to United States (136/74 mmHg), but not European standards (150/85 mmHg at ages 65-

69) (see Wolf-Maier et al. 2003 and Table 4.4). Dopamine levels (668.61 ug/L, sd

523.72) are significantly higher than standard reference ranges reported in the United

States (30-163 ug/L) A relatively lean body habitus (waist:hip ratio 0.85, sd 0.07) also is observed as compared to European men aged 55 to 64 years old (range 0.89-1.01), but not women (0.79 to 0.82) (see Molarius et al. 1999). Similarly, GOT levels (23.9 U/L, sd

7.2) are higher than expected as compared to American men (14-20 U/L), but not women

(10-36 U/L). Average total cholesterol (Tcho 208.81mg/dl, sd 35.4) is rather high compared to international standards wherein less than 200mg/dl is considered desirable.

However, high-density lipoprotein cholesterol (HDL 65 mg/dl, sd 16.6) is above the

50mg/dl determined as the international standard for healthy HDL-cholesterol, resulting in a ratio of total cholesterol to HDL-cholesterol of 3.2 mg/dl which is below the level determined as low risk (3.8-4.0 mg/dl) by international standards. Creatine levels are borderline high (0.7 mg/dL, sd 0.136), falling at the highest end of reference ranges given for men and women (0.17-0.70 mg/dL). The average red blood cell count (462.7

75 count/mL, sd 44.7) is low as compared to American men (470-610 count/mL), but not as compared to American women (420-540).

Variable Mean Std Dev Min Max AD 10.25 10.21 0.5 58.9 Cort 22.97 17.91 5 99.5 DBP 86.9 12.6 65 125 DHEAs 112.1 66.9 23 384 HbA1c 5.26 0.63 4.5 8.3 HDL 65.0 16.6 38 143 NAD 99.8 57.04 20.9 318.2 SBP 147.3 23.9 87 226 Tcho 208.8 35.4 127 302 WHR 0.85 0.07 0.7 1.03 Table 4.2: Descriptive statistics and ranges for independent variables of elderly Sakiyama residents (N=96)

Variable Mean Std. Deviation Minimum Maximum Creatine 0.700 0.136 .46 1.08 DOP 668.61 523.72 119.3 3722.3 GOT/AST 23.9 7.2 13 68 GPT/ALT 20.4 9.8 7 77 GTP 30.3 25.1 9 164 Hematocrit 43.4 4.0 36 52 Hemoglobin 14.2 1.4 12 17 LDL 116.1 34.7 48 203 MCH 30.7 1.3 28 35 MCHC 32.7 0.8 30 34 MCV 94.0 3.7 84 102 PBodyFat 27.34 7.84 9.7 46.9 Platelet 22.7 5.6 8 41 RBC 462.7 44.7 372 592 TG 101.7 51.5 35 285 Ucreatinine 72.454 34.986 19.43 204.75 Uric 5.40 1.26 2.4 9.5 Walking speed 6.79 1.58 4.5 13.5 WBC 5609.4 1380.1 2900 9200 Weight 56.74 10.13 38.7 88.7 ADL 12.0 1.3 9 13 Intel 3.3 0.9 0 4 SelfM 4.9 0.4 3 5 Soc 3.8 0.4 2 4 Table 4.3: Descriptive statistics and ranges for dependent variables of elderly Sakiyama residents (N=96)

Reference Ranges Variable Men Women Sakiyama Average Low High Low High AD (μg/L) 0 900 0 900 10.23 Cort (μg/L) 3 23 3 23 22.97 UCreatinine (mg/dL) 19 311 19 311 72.454 Creatine (mg/dL) 0.17 0.7 0.17 0.7 0.7 DBP (mmHg) 60 79 60 79 86.9 DHEAs (μg/dL) 28 310 26 200 112.1 DOP (ug/L) 30 163 30 163 668.61 GOT (U/L) 14 20 10 36 23.9 GPT (U/L) 10 45 10 34 20.4 GTP (U/L) 9 48 9 48 30.3 Hemoglobin (g/dL) 13.8 18 12.1 15.1 14.2 HbA1c (%) <5.4 >5.5 <5.4 >5.5 5.26 HDL (mg/dL) <40 >60 <50 >60 65 Hematocrit (%) 40.7 50.3 36.1 44.3 43.4 LDL (mg/dL) <70 >160 <70 >160 116.1 MCH (pg/cell) 27 33 27 33 30.7 MCHC (g/dL) 33 36 33 36 32.7 MCV (fL/red cell) 80 96 80 96 94 NAD (μg/L) 0 600 0 600 99.8 PBodyFat (%) 17.9 31.1 24 38.2 27.34 Platelet 15 40 15 40 22.7 RBC (count/mL) 470 610 420 540 462.7 SBP (mmHg) 90 119 90 119 147.3 Tcho (mg/dL) 200 240 200 240 208.8 G (mg/dL) <150 >200 <150 >200 101.7 Uric (mg/dL) 3.4 7.2 2.4 6.1 5.4 WBC (count per mL) 4100 10900 4100 10900 5609.4 WHR 0.89 1.01 0.79 0.82 0.85 Table 4.4 Established reference ranges for independent and dependent variables compared to values from Sakiyama sample

4.3 Allostatic Load

Upper bounds for quartile and decile cut-points for all 10 variables used to calculate AL are listed in Table 4.5. Using independent variable quartiles to calculate AL, the overall average estimate for AL in this sample is 2.60 (sd = 1.51), ranging from 1-6

(Tables 4.6 and 4.7). 78

Quartiles: Although there appears to be a slight tendency for estimated AL to be higher among those of younger age, the difference is statistically insignificant (p=0.50) when the sample is divided into age classes based upon the median age of 66.5 years.

However, age explains only about 0.5% of the variation in AL. Using regression, no significant linear (p=0.50) or quadratic (p=0.77) association of AL with age was observed. Women showed slightly lower AL on average than did men across the total sample and both age groups. None of these differences were statistically significant at p<0.05. Younger men (3.0, sd=1.4) had marginally higher AL than older men (2.7, sd=1.3; p=0.44). Younger and older women had similar average AL (younger 2.4, sd=1.7, older 2.7, sd=1.6; p=0. 88). Only one female participant (ID#61043) is reclassified from the “young” to the “old” group when the mean (67.9 years) is used to divide the sample by age instead of the median (66.5 years). This change does not significantly change any results and data are not presented.

Percentile Variable 0.10 0.25 0.50 0.75 0.90 AD 2.30 3.90 6.05 12.90 21.05 Cort 5.40 10.10 18.80 29.10 47.85 DBP 71.7 77.3 85.0 96.0 101.6 DHEAs 46.1 63.0 100.0 154.3 210.3 HbA1c 4.80 4.90 5.10 5.30 5.76 HDL 47.0 53.3 63.5 71.8 80.0 NAD 35.5 59.2 85.4 124.8 187.6 SBP 116.4 134.0 146.0 163.5 175.6 Tcho 163.0 185.5 206.0 238.3 256.0 WHR 0.76 0.80 0.84 0.90 0.95 Table 4.5 Cut-points for estimating AL of elderly Sakiyama residents. Upper quartile or 90th percentile is used for all variables except HDL and DHEAs for which the lower quartile or 10th percentile is used.

Total Men Women Ages AL Std Dev AL Std Dev AL Std Dev All 2.6 1.5 2.8 1.3 2.4 1.6 55-66.5 2.7 1.6 3.0 1.4 2.4 1.7 66.6-88 2.5 1.5 2.7 1.3 2.4 1.6 Table 4.6 Allostatic load estimates derived using quartile cut-offs for elderly residents of Sakiyama by sex and age group divided at the median

Group 1 Group 2 p-value Quartile cut-off All men All women 0.178 All 55-66.5 All 66.6-88 0.502 Men 55-66.5 Women 55-66.5 0.251 Men 66.6-88 Women 66.6-88 0.494 Men 55-66.5 Men 66.6-88 0.435 Women 55-66.5 Women 66.6-88 0.877 Table 4.7 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs and median age division for elderly residents of Sakiyama residents

When the sample is separated into higher and lower age classes using age 70 as the dividing point (Tables 4.8 and 4.9), the tendency for estimated AL to be higher among those of younger age (p=0.27) remains. Women presented slightly lower AL on average than did men across both age groups. None of these differences fell below the standard statistical significance of p=0.05. Younger men(2.9, sd=1.4) and women (2.6, sd=1.7) had higher AL than older men (2.6, sd=1.3) and women (2.2, sd=1.6), respectively. However, neither comparison between age groups was significant (men: p=.46, women: p=0.42).

Total Men Women Ages AL Std Dev AL Std Dev AL Std Dev 55-70 2.7 1.5 2.9 1.4 2.6 1.7 71-88 2.4 1.5 2.6 1.3 2.2 1.6 Table 4.8 Allostatic load estimates derived using quartile cut-offs for elderly residents of Sakiyama by sex and age group divided at age 70

Group 1 Group 2 p-value Quartile cut-off All 55-70 All 71-88 0.274 Men 55-70 Women 55-70 0.345 Men 71-88 Women 71-88 0.337 Men 55-70 Men 71-88 0.457 Women 55-70 Women 71-88 0.424 Table 4.9 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs and age division at 70 for elderly residents of Sakiyama residents

Deciles: Using decile cut-offs, the overall average estimate for AL in this sample is 0.93 (sd 1.03) and ranges from 0-6 (Tables 4.10 and 4.11). Also seen when quartiles were used to calculate AL, men have higher AL on average as compared to women. This remains true when the younger and older groups are examined separately (p=0.110, p=0.780 respectively). Comparing the sexes together and comparing age cohorts based on the median age, AL is slightly higher among the younger group (p=0.626). However, age explains only about 0.1% of the variation in AL. Using regression, no significant linear

(p=0.726) or quadratic (p=0.561) association of AL with age was observed. Younger men had marginally higher AL as compared to older men (p=0.365). On the contrary, younger women had slightly lower AL as compared to younger men (p=0.527).

Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev All 0.9 1.0 1.1 1.2 0.8 0.8 55-66.5 1.0 1.2 1.2 1.4 0.7 0.8 66.6-88 0.9 0.9 0.9 1.0 0.8 0.8 Table 4.10 Allostatic load estimates derived using decile cut-offs for elderly residents of Sakiyama by sex and age group divided at the median

Group 1 Group 2 p-value Decile cut-off All male All female 0.141 All 55-66.5 All 66.6-88 0.626 Male 55-66.5 Female 55-66.5 0.110 Male 66.6-88 Female 66.6-88 0.780 Male 55-66.5 Male 66.6-88 0.365 Female 55-66.5 Female 66.6-88 0.527 Table 4.11 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs and median age division for elderly residents of Sakiyama residents

When the sample is separated into higher and lower age classes at age 70 (Tables

4.12 and 4.13), slightly higher AL among the younger group as opposed to the older is still observed (p=0.519). Marginally lower AL was observed among women as compared to men across both age groups (younger: p=0.192, older: p=0.500). Younger men and women demonstrated higher AL than older men (p=0.543) and women (p=0.810) respectively.

Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev 55-70 1.0 1.1 1.2 1.4 0.8 0.7 71-88 0.8 0.9 1.0 1.0 0.7 0.9 Table 4.12 Allostatic load estimates derived using decile cut-offs for elderly residents of Sakiyama by sex and age group divided at 70 82

Group 1 Group 2 p-value Percentile cut-off All 55-70 All 71-88 0.519 Male 55-70 Female 55-70 0.192 Male 71-88 Female 71-88 0.500 Male 55-66.5 Male 66.6-88 0.543 Female 55-66.5 Female 66.6-88 0.810 Table 4.13 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs and age division at 70 for elderly residents of Sakiyama residents

4.4 Sex

There are equal numbers of men (n=48) and women (n=48) in the Sakiyama sample (n=96, X2=0, p=1.0). Women demonstrated significantly lower SBP, DBP, WHR,

DHEA-s, and cortisol and significantly higher HDL-cholesterol as compared to men

(Table 4.14). Women also were observed to have significantly lower liver enzymes

(GOT, GPT, GTP), creatine, uric acid, creatine, blood measures (MCHC, RBC, WBC,

HT, HB, and MCH) and significantly higher body fat and platelet counts as compared to men (Table 4.15). Women scored significantly higher on the Self Maintenance section of the Tokyo Metropolitan Institute of Gerontology Index of Competence (ADL) than did men (p=0.014), although this did not significantly increase their overall score.

Variable Mean(Women) Mean(Men) p AD 11.51 8.98 0.227 Cort 19.27 26.66 0.043 DBP 82.8 92.2 0.001 DHEAs 70.1 154 <0.001 HbA1c1 5.22 5.3 0.519 HDL 68.9 61.1 0.022 NAD 91.82 107.78 0.172 SBP 138.3 155.3 0 TCho 213.7 203.9 0.18 WHR 0.811 0.872 0.005 Table 4.14 Comparisons of independent variable means by sex.

Variable Mean (Women) Mean (Men) p ADL 12.1 11.9 0.379 Creatine 0.6 0.8 <0.001 DOP 574.17 763.06 0.077 GOT 22.5 25.4 0.043 GPT 18 22.7 0.018 GTP 22.4 38.3 0.002 Hematocrit 40.6 46.3 <0.001 Hemoglobin 13.2 15.2 <0.001 Intel 3.3 3.4 0.913 LDL 118.4 113.9 0.532 MCH 30.3 31.1 0.003 MCHC 32.5 32.9 0.010 MCV 93.4 94.6 0.107 PBodyFat 29.14 25.55 0.024 Platelet 23.8 21.5 0.044 RBC 434.7 490.6 <0.001 SelfM 5 4.8 0.014 Soc 3.8 3.8 0.650 TG 95.1 108.2 0.216 UCreatinine 60.32 84.6 0.001 Uric 4.8 6 <0.001 Walk 7 6.6 0.289 WBC 5206.3 6422 0.003 Table 4.15 Comparisons of dependent variable means by sex.

4.5 Age

Of 96 individuals in the sample, 58 were between 55-70 years of age and 38 were

71 years old or older. There were significantly more young participants in this sample than old participants when “old” participants are delineated as age 71 or older (n=96,

X2=4.167, p=0.041). However, differences between observed and expected number of female and male participants grouped by an age division at 70 were statistically nonsignificant (n=96, X2=0 p=1) (Table 4.16).

Age group Total Men Women 55-70 58 29 29 71-88 38 19 19 Table 4.16 Frequency of Sakiyama sample men and women with an age division at 70

Because there were no statistically significant differences between the means of independent variables divided at the median age or at age 70, these tables are not presented. Among the dependent variables, the younger cohort scored significantly lower on the Tokyo Metropolitan Institute of Gerontology Index of Competence (ADL) as compared to the older cohort (groups divided at median age, p=0.007; at age 70, p=0.047) (Table 4.17). The difference derived primarily from statistically significant differences in scores on the Intellectual Activity sub-section, wherein the older group outscored the younger group (groups divided at median age, p=0.011; at age 70, p=0.052). The difference in overall ADL score can also be attributed to the older cohort’s

85 higher score on the Index of Competence Self-Maintenance subsection (groups divided at median age, p=0.143; at age 70, p=0.027). Because there were no other statistically significant differences between the means of dependent variables divided at the median age or at age 70, these figures are not presented.

Median Age 70 Variable Young Old P Young Old P ADL 11.7 12.3 0.007 11.8 12.3 0.047 Intel 3.1 3.6 0.011 3.2 3.6 0.052 SelfM 4.8 4.9 0.143 4.8 5 0.027 Soc 3.7 3.8 0.364 3.8 3.8 0.892 Table 4.17 p-values from independent t-test comparing means of Tokyo Metropolitan Institute of Gerontology Index of Competence scores and sub-scores between age cohorts divided at median age and age 70

Chapter 5: Results II - Associations of Allostatic Load with Physiological and Sociocultural Variables Among Elderly Sakiyama Residents

5.1 Introduction

Associations of sociocultural and physiological measures with allostatic load examined with linear regression are reported in this chapter. All possible bivariate associations are examined, as are all possible multivariate associations controlling for age, sex, and age by sex. Associations were analyzed using allostatic load scores constructed using both quartile and decile cut-offs.

5.2 Quartile Cut-Point Allostatic Load Associations

5.2.1 Bivariate Associations

Among the Sakiyama sample, allostatic load associated significantly with several dependent variables: creatine (p=0.002), creatinine (p=0.002), gamma glutamyl transpeptisdase (GTP) (p=0.009), and white blood cell count (p=0.025) (Table 5.1).

Because creatinine is derived from conversion of creatine (creatine to creatine phosphate to creatinine; Taylor 1989), it is unsurprising that if one correlates significantly with AL, the other does as well. Creatine, synthesized in the kidney and liver but found primarily in skeletal muscles, helps supply energy to cells by increasing formation of ATP. Both are indicators of renal health. Among the Sakiyama sample, a single unit increase in AL associates with a 0.004 mg/dL increase in creatine and a 7.228 mg/dL increase in creatinine, indicating that higher AL may be associated with lower renal function.

A unit increase in AL also results in a 4.416 U/L increase in GTP, a liver enzyme implicated in gluconeogenesis and regulated by the combined actions of glucagon, insulin, and cortisol. The association between AL and GTP may be indicative of changing liver function and/or of changing levels of those hormones controlling its release over time. On average, white blood cells increase by 209.0 cells/mL per unit increase in AL. WBC play important roles in immune responses: releasing histamines, attacking parasites, bacteria, and fungi, and regulating allergic reactions.

Dependent Regression Variable CoE SE Pearson correlation, p R2 Creatine 0.004 0.009 0.002 0.696 DOP 45.007 35.442 0.207 0.017 GOT/AST 0.584 0.486 0.233 0.015 GPT/ALT 1.25 0.656 0.06 0.037 GTP 4.416 1.652 0.009 0.071 Hematocrit 0.333 0.273 0.225 0.016 Hemoglobin 0.123 0.094 0.194 0.018 LDL 4.036 2.333 0.087 0.031 MCH 0.143 0.088 0.107 0.027 MCHC 0.034 0.057 0.552 0.004 MCV 0.24 0.253 0.183 0.019 PBodyFat 0.958 0.526 0.072 0.034 Platelet -0.251 0.379 0.51 0.005 RBC 1.995 0.3045 0.514 0.005 TG 1.534 3.51 0.663 0.002 Ucreatinine 7.228 2.268 0.002 0.097 Uric 0.081 0.086 0.345 0.009 Walking speed 0.012 0.108 0.910 0 WBC 208.594 91.703 0.025 0.052 Weight 1.161 0.681 0.092 0.03 ADL 0.021 0.087 0.807 0.001 Intel 0.097 0.062 0.124 0.025 SelfM -0.054 0.028 0.055 0.039 Soc -0.022 0.03 0.477 0.005 Table 5.1 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly residents of Sakiyama

5.2.2 Multivariate Association: Allostatic Load and Age

When controlling for age, the significant associations between AL and creatinine

(p=0.002), GTP (p=0.010), and white blood cell count (p=0.020) presented in Section

5.2.1 are still observed. These results indicate that AL is an important predictor of changes in these variables even when taking advancing age into consideration. However,

89 when controlling for age, the significant association between AL and creatine is no longer observed. Instead, increasing age correlates significantly with creatine, suggesting that age may be a better predictor of creatine than AL. Age also associates significantly with the following dependent variables: dopamine (p=0.03), glutamic pyruvic transaminase

(GPT, p=0.005), low-density lipoproteins (p=0.036), walking speed (p<0.001), and the

Intellectual Activity subsection of the Index of Competence (p=0.023; Table

5.2).Potential combined effects of AL and age were explored by including the interaction term AL*age in the model. Because the interaction term did not approach statistical significance in any model, data are not presented.

Dependent Independent Regression CoE SE Pearson correlation, p R2 Creatine AL 0.005 0.009 0.584 0.049 Age 0.003 0.002 0.035 DOP AL 50.396 34.822 0.151 0.066 Age 13.416 6.088 0.030 GOT/AST AL 0.61 0.489 0.215 0.021 Age 0.065 0.085 0.447 GPT/ALT AL 1.123 0.634 0.080 0.115 Age -0.318 0.111 0.005 GTP AL 4.402 1.665 0.010 0.071 Age -0.034 0.291 0.907 Hematocrit AL 0.307 0.272 0.262 0.034 Age -0.064 0.048 0.184 Hemoglobin AL 0.114 0.094 0.229 0.039 Age -0.023 0.016 0.160 LDL AL 3.693 2.297 0.111 0.076 Age -0.854 0.401 0.036 MCH AL 0.148 0.088 0.096 0.036 Age 0.014 0.015 0.378 MCHC AL 0.032 0.057 0.579 0.006 Age -0.005 0.01 0.614 MCV AL 0.361 0.253 0.158 0.034 Age 0.053 0.044 0.235 PBodyFat AL 0.978 0.529 0.068 0.037 Age 0.051 0.092 0.581 Platelet AL -0.296 0.376 0.434 0.034 Age -0.111 0.066 0.094 RBC AL 1.607 3.015 0.595 0.039 Age -0.967 0.527 0.070 TG AL 1.504 3.537 0.672 0.002 Age -0.074 0.618 0.904 Ucreatinine AL 7.122 2.281 0.002 0.102 Age -0.263 0.399 0.512 Uric AL 0.089 0.086 0.300 0.028 Age 0.02 0.015 0.190 Walking speed AL 0.052 0.091 0.566 0.299 Age 0.100 0.016 <0.001 WBC AL 217.226 91.539 0.020 Age 21.489 16.003 0.183 Weight AL 1.047 0.666 0.119 0.088 Age -0.283 0.116 0.017 ADL AL 0.01 0.086 0.907 0.036 Age -0.028 0.015 0.067 Intel AL 0.087 0.061 0.158 0.078 Age -0.025 0.011 0.023 SelfM AL -0.053 0.028 0.062 0.042 Age 0.003 0.005 0.553 Soc AL -0.024 0.03 0.431 0.018 Age -0.006 0.005 0.268 Table 5.2 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly residents of Sakiyama 91

5.2.3 Multivariate Association: Allostatic Load and Sex

When controlling for sex, the significant associations between AL and creatinine

(p=0.005), GTP (p=0.021) and white blood cell count (p=0.054) presented in Section

4.2.1 are still observed (Table 5.3). However, sex is also significant in these correlations, suggesting that both AL and sex may modulate creatinine and GTP levels as well as white blood cell counts. When controlling for sex, as was also observed when controlling for age, the significant association between AL and creatine is no longer present

(p=0.533). Instead, sex correlates significantly with creatine (p<0.001), suggesting that sex, like age, is an important factor in differences observed in creatine among this sample. In addition to previously observed associations, AL correlates significantly with percent body fat when controlling for sex. This is less surprising when one notes that sex also correlates significantly with both percent body fat (p=0.010) and weight (p<0.001), reflecting known sex differences in both body fat distribution and weight. Per unit increase in AL, body fat increases by 1.15%. Increased body fat deposition has been related to chronically increased levels of cortisol and may reflect long term changes in body habitus. Sex also associates significantly with the following dependent variables:

GPT (p=0.031), blood analyses (HT p<0.001, HB p<0.001, MCH p=0.005, platelets p=0.053, RBC p<0.001, MCHC p=0.012), uric acid (p<0.001), weight (p<0.001), and the

Self-Maintenance sub-section of the Index of Competence (p=0.024).

In two instances, controlling for effects of interaction between AL and sex renders correlations between sex and dependent variables statistically insignificant (Table 5.4).

Statistically significant correlations between the interaction term and GTP (p=0.016)and

WBC (p=0.008) suggest an interplay between sex and AL contributes to explaining variation observed in these variables.

Dependent Independent Regression CoE SE Pearson correlation, p R2 Creatine AL -0.004 0.007 0.533 0.419 Sex 0.176 0.022 <0.001 DOP AL 37.012 35.478 0.300 0.044 Sex 173.47 106.671 0.107 GOT/AST AL 0.456 0.484 0.349 0.052 Sex 2.768 1.456 0.060 GPT/ALT AL 1.053 0.650 0.108 0.084 Sex 4.269 1.954 0.031 GTP AL 3.759 1.603 0.021 0.150 Sex 14.246 4.821 0.004 Hematocrit AL 0.069 0.194 0.723 0.518 Sex 5.732 0.582 <0.001 Hemoglobin AL 0.028 0.064 0.660 0.560 Sex 2.061 0.193 <0.001 LDL AL 4.324 2.359 0.070 0.039 Sex -6.260 7.093 0.380 MCH AL 0.108 0.085 0.207 0.109 Sex 0.746 0.256 0.005 MCHC AL 0.014 0.055 0.801 0.070 Sex 0.427 0.167 0.012 MCV AL 0.288 0.254 0.260 0.041 Sex 1.115 0.764 0.148 PBodyFat AL 1.145 0.515 0.029 0.101 Sex -4.060 1.548 0.010 Platelet AL -0.148 0.377 0.695 0.044 Sex -2.226 1.134 0.053 RBC AL -0.592 2.409 0.806 0.395 Sex 56.143 7.244 <0.001 TG AL 0.950 3.536 0.789 0.017 Sex 12.667 10.631 0.236 Ucreatinine AL 6.228 2.178 0.005 0.193 Sex 21.682 6.549 0.001 Uric AL 0.029 0.078 0.713 0.213 Sex 1.144 0.233 <0.001 Walking speed AL 0.029 0.109 0.794 0.013 Sex -0.356 0.327 0.280 WBC AL 174.790 89.590 0.054 0.122 Sex 733.421 269.366 0.008 Weight AL 0.646 0.574 0.264 0.264 Sex 11.170 1.726 <0.001 ADL AL 0.032 0.087 0.712 0.010 Sex -0.243 0.263 0.359 Intel AL 0.098 0.063 0.126 0.025 Sex -0.020 0.191 0.917 SelfM AL -0.045 0.028 0.103 0.090 Sex -0.189 0.083 0.024 Soc AL -0.020 0.031 0.515 0.000 Sex -0.033 0.093 0.721 Table 5.3 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly residents of Sakiyama 94

Dependent Independent Regression CoE SE Pearson correlation, p R2 GTP AL 0.633 2.018 0.755 .202 Sex -6.383 9.652 0.510 AL*Sex 7.798 3.187 0.016 WBC AL -17.405 111.991 0.877 .187 Sex -534.762 535.589 0.321 AL*Sex 479.383 176.870 0.008 Table 5.4 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between age and sex, and dependent variables among elderly residents of Sakiyama

5.2.4 Multivariate Association: Allostatic Load, Age, and Sex

Multivariate associations controlling for age and sex reiterate correlations observed in previous sections. AL remains a significant predictor of GTP (p=0.023), percent body fat (p=0.026), creatinine (p=0.006), and white blood cell count (p=0.043).

Sex, but not age, also significantly associates with these variables in the multivariate models (GTP p=0.004; percent body fat p=0.01; creatinine p=0.001; and white blood cell count p=0.009), indicating that AL is a robust predictor of these dependent variables, even after partialing out the effects of sex. Similar associations to those observed in

Sections 4.2.2 and 4.2.3 between age, sex, and dependent variables are also observed

(Table 5.5). An interaction term exploring combined effects of age and sex was a statistically significant predictor of LDL (p=0.018) and walking speed (p=0.022) (Table

5.6). In neither case, however, was AL also a significant predictor of the dependent variable. For GTP and WBC, interactions between AL and sex identified previously remain significant (GTP p=0.018; WBC p=0.009; Table 5.6), rendering associations

95 between AL and the dependent variables statistically insignificant (GTP p=0.758; WBC p=0.953).

Dependent Independent Regression CoE SE Pearson correlation, p R2 Creatine AL -0.003 0.007 0.651 0.456 Age 0.003 0.001 0.013 Sex 0.174 0.021 <0.001 DOP AL 42.636 34.893 0.225 0.090 Age 13.076 6.043 0.033 Sex 165.406 .104.686 0.118 GOT/AST AL 0.482 0.487 0.325 0.057 Age 0.060 0.084 0.481 Sex 2.732 1.461 0.065 GPT/ALT AL 0.913 0.625 0.148 0.167 Age -0.327 0.108 0.003 Sex 4.471 1.875 0.019 GTP AL 3.732 1.616 0.023 0.151 Age -0.063 0.280 0.821 Sex 14.285 4.849 0.004 Hematocrit AL 0.036 0.190 0.849 0.544 Age -0.076 0.033 0.024 Sex 5.778 0.570 <0.001 Hemoglobin AL 0.016 0.062 0.794 0.589 Age -0.028 0.011 0.012 Sex 2.078 0.187 <0.001 LDL AL 3.962 2.324 0.092 0.083 Age -0.842 0.402 0.039 Sex -5.741 6.972 0.412 MCH AL 0.114 0.086 0.188 0.115 Age 0.012 0.015 0.417 Sex 0.739 0.257 0.005 MCHC AL 0.011 0.056 0.838 0.073 Age -0.006 0.010 0.542 Sex 0.431 0.167 0.012 Continued

Table 5.5 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly residents of Sakiyama 96

Table 5.5 continued

Dependent Independent Regression CoE SE Pearson correlation, p R2 MCV AL 0.310 0.255 0.226 0.054 Age 0.051 0.044 0.253 Sex 1.084 0.764 0.159 PBodyFat AL 1.170 0.518 0.026 0.105 Age 0.060 0.090 0.507 Sex -4.097 1.554 0.010 Platelet AL -0.194 0.375 0.606 0.072 Age -0.107 0.065 0.103 Sex -2.160 1.124 0.058 RBC AL -1.058 2.340 0.652 0.439 Age -1.083 0.405 0.009 Sex 56.811 7.020 <0.001 TG AL 0.907 3.564 0.800 0.017 Age -0.101 0.617 0.871 Sex 12.729 10.694 0.237 Ucreatinine AL 6.096 2.188 0.006 0.198 Age -0.308 0.379 0.419 Sex 21.871 6.565 0.001 Uric AL 0.036 0.077 0.643 0.228 Age 0.017 0.013 0.197 Sex 1.134 0.232 <0.001 Walking speed AL 0.072 0.091 0.432 0.317 Age 0.101 0.016 <0.001 Sex -0.418 0.274 0.131 WBC AL 183.394 89.520 0.043 0.138 Age 20.007 15.504 0.200 Sex 721.083 268.578 0.009 Weight AL 0.514 0.549 0.351 0.399 Age -0.307 0.095 0.002 Sex 11.360 1.646 <0.001 ADL AL 0.021 0.087 0.812 0.044 Age -0.027 0.015 0.072 Sex -0.226 0.260 0.387 Continued

Table 5.5 continued

Dependent Independent Regression CoE SE Pearson correlation, p R2 Intel AL 0.087 0.062 0.163 0.078 Age -0.025 0.011 0.024 Sex -0.005 0.187 0.980 SelfM AL -0.044 0.028 0.116 0.094 Age 0.003 0.005 0.491 Sex -0.191 0.083 0.024 Soc AL -0.023 0.031 0.465 0.020 Age -0.006 0.005 0.276 Sex -0.030 0.093 0.750

Dependent Independent Regression CoE SE Pearson correlation, p R2 LDL AL 4.120 2.267 .072 0.137 Age 0.215 0.589 0.716 Sex 122.742 53.858 0.025 Age*Sex -1.895 0.788 0.018 Walking Speed AL 0.078 0.089 0.383 0.355 Age 0.142 0.023 <0.001 Sex 4.488 2.121 0.037 Age*Sex -0.072 0.031 0.022 Table 5.6 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions between age and sex (age*sex), and dependent variables among elderly residents of Sakiyama

5.3 Decile Cut-Point Allostatic Load Associations

5.3.1 Bivariate Associations

Using decile cut-points, AL associated significantly with several dependent variables: creatinine (p=0.042), GTP (p=0.020),WBC (p=0.058), weight (p=0.019), percent body fat (p=0.017), and walking speed (p=0.034) (Table 5.7). Whereas a single unit increase in AL associated with a 7.23 mg/dL increase in creatinine using quartile cut- 98 points, it is associated with a 7.01 mg/dL increase in creatinine when using decile cut- points. This similarity suggests AL is a robust predictor of creatinine, regardless of whether quartile or decile-cut points are used in its calculation. Similarly, an unit increase in AL results in a 5.712 U/L increase in GTP when using decile cut-points, compared to

4.416 U/L when using quartile cut-points. The association between AL and GTP also appears to be robust and may be an important indicator of changing liver function over time. Also comparable to the previous analysis, an unit increase in AL correlates to a

258.2 cell/mL increase in WBC. Results demonstrate the possibility of a strong correlation between allostatic load and immune system function.

In addition to associations detected in bivariate analyses utilizing quartile cut- points, this analysis reveals correlations between AL and percent body fat, weight, and walking speed. Associations with body habitus are expected as WHR is included in the measure of AL. Still, an unit increase in AL associates with an 1.8% increase in body fat, a 2.328kg weight gain, and a 0.34 second increase in time used to walk 8 meters (walking speed). As mentioned previously, body fat deposition and cortisol levels are related and may reflect long term changes in body habitus. Being predictive of weight and walking speed indicates AL is predicts frailty, a syndrome characterized by, “. . . decreased reserve and resistance to stressors, resulting from cumulative declines across multiple physiologic systems, and causing vulnerability to adverse outcomes” (Fried et al.

2001:M146).

Dependent Regression CoE SE Pearson correlation, p R2 Creatine 0.101 0.013 0.456 0.006 DOP 92.433 51.126 0.074 0.034 GOT/AST 0.279 0.712 0.697 0.002 GPT/ALT 1.06 0.967 0.276 0.013 GTP 5.712 2.423 0.020 0.056 Hematocrit 0.513 0.396 0.198 0.018 Hemoglobin 0.219 0.137 0.111 0.027 LDL 2.713 3.437 0.432 0.007 MCH 0.053 0.129 0.685 0.002 MCHC 0.109 0.082 0.187 0.018 MCV -0.159 0.372 0.670 0.002 PBodyFat 1.831 0.755 0.017 0.059 Platelet 0.131 0.553 0.813 0.001 RBC 6.377 4.391 0.150 0.022 TG 4.7 5.088 0.358 0.009 Ucreatinine 7.01 3.398 0.042 0.043 Uric 0.123 0.125 0.326 0.01 Walking speed 0.329 0.153 0.034 0.047 WBC 258.227 134.43 0.058 0.038 Weight 2.328 0.977 0.019 0.057 ADL 0.029 0.126 0.821 0.001 Intel 0.072 0.092 0.433 0.007 SelfM -0.057 0.041 0.165 0.02 Soc 0.014 0.044 0.759 0.001 Table 5.7 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly residents of Sakiyama

5.3.2 Multivariate Associations: Allostatic Load and Age

Controlling for age, significant associations between AL and creatinine (p=0.045),

GTP (p=0.022), white blood cell count (p=0.052), percent body weight (p=0.017), weight

(p=0.021), and walking speed (p=0.006) presented in Section 4.3.1 are still observed.

100

Results indicate that AL predicts variability in these factors, even after controlling for the effects of age.

When controlling for age (p=0.031), AL retains its associations with dopamine

(p=0.058), although its significance is now borderline. Dopamine may partly regulate luteinizing and growth hormones, but it definitely plays a major role in reward-motivated behaviors, motor and cognitive performance, and disease (Griffin and Ojeda 2000). As with walking speed, this limited association suggests that AL may be indicative of increasing frailty as evidenced by poor coordination. An unit increase in AL is associated with a 96.4 ug/L increase in dopamine. That a borderline association remains after age effects are partialed out suggests dopamine levels may be related to physiological declines, frailty, and AL irrespective of age. A term to control for potential interactions between AL and age was significant in predictions of dopamine (p=0.033) and walking speed (p=0.003). For dopamine, including the interaction term reduced the significance of age in the model (from p=0.031 to p=0.733), suggesting interplay between AL and age is more important than age itself when predicting this variable (Table 5.9).

Age, but not AL, associates significantly with creatine (p=0.035), low-density lipoproteins (p=0.031), and the Intellectual Activity subsection of the Index of

Competence (p=0.023; Table 5.8).

101

Dependent Independent Regression CoE SE Pearson correlation, p R2 Creatine AL 0.011 0.013 0.403 0.053 Age 0.003 0.002 0.035 DOP AL 96.424 50.153 0.058 0.081 Age 13.218 6.026 0.031 GOT/AST AL 0.297 0.715 0.679 0.007 Age 0.059 0.086 0.493 GPT/ALT AL 0.961 0.931 0.305 0.096 Age -0.327 0.112 0.004 GTP AL 5.693 2.437 0.022 0.056 Age -0.063 0.293 0.829 Hematocrit AL 0.494 0.395 0.214 0.037 Age -0.065 0.047 0.172 Hemoglobin AL 0.212 0.136 0.122 0.048 Age -0.024 0.016 0.149 LDL AL 2.444 3.372 0.47 0.055 Age -0.888 0.405 0.031 MCH AL 0.056 0.13 0.666 0.008 Age 0.012 0.016 0.441 MCHC AL 0.107 0.082 0.195 0.021 Age -0.005 0.01 0.617 MCV AL -0.144 0.372 0.699 0.014 Age 0.048 0.045 0.286 PBodyFat AL 1.846 0.758 0.017 0.062 Age 0.047 0.091 0.605 Platelet AL 0.099 0.548 0.858 0.028 Age -0.107 0.066 0.106 RBC AL 6.087 4.339 0.164 0.056 Age -0.96 0.521 0.069 TG AL 4.678 5.119 0.363 0.009 Age -0.072 0.615 0.906 Ucreatinine AL 6.913 3.407 0.045 0.05 Age -0.32 0.409 0.437 Uric AL 0.129 0.124 0.303 0.028 Age 0.019 0.015 0.201 Walking Speed AL 0.36 0.127 0.006 0.353 Age 0.101 0.015 <0.001 Continued

Table 5.8 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly residents of Sakiyama

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Table 5.8 continued

Dependent Independent Regression CoE SE Pearson correlation, p R2 WBC AL 264.259 134.139 0.052 0.053 Age 19.974 16.117 0.218 Weight AL 2.242 0.951 0.021 0.117 Age -0.286 0.114 0.014 ADL AL 0.02 0.124 0.872 0.036 Age -0.028 0.015 0.066 Intel AL 0.065 0.09 0.473 0.063 Age -0.026 0.011 0.02 SelfM AL -0.056 0.041 0.174 0.025 Age 0.003 0.005 0.502 Soc AL 0.012 0.044 0.788 0.013 Age -0.006 0.005 0.298

Dependent Independent Regression CoE SE Pearson correlation, p R2 DOP AL -776.206 407.211 0.060 0.125 Age 2.632 7.680 0.733 AL*age 13.023 6.033 0.033 Walking Speed AL -2.673 1.009 0.009 0.411 Age 0.064 0.019 0.001 AL*age 0.045 0.015 0.003 Table 5.9 Significant multivariate associations between allostatic load (independent variable), controlling for age and interactions between AL and age (AL*age), and dependent variables among elderly residents of Sakiyama

5.3.3 Multivariate Associations: Allostatic Load and Sex

Controlling for sex, significant associations between AL and GTP (p=0.051), percent body fat (p=0.005), and walking speed (p=0.021), as presented in Section 5.3.1, are still observed (Table 5.10). Sex is a significant covariate with GTP and percent body fat while an AL by sex interaction term is a significant covariate with walking speed

(Table 5.11), suggesting that both AL and sex modulate variation in these dependent

103 variables. The association between AL and weight is attenuated (p=0.068) because sex now explains a significant amount of variation (p<0.001). Controlling for sex, the significant association between AL and dopamine, as well as between AL and white blood cell count, are no longer observed. Instead, sex significantly predicts WBC

(p=0.008). Sex, but not AL, associates significantly with creatine (p<0.001), GPT

(p=0.031), GOT (p=0.048), blood cytology and volumes (HT p<0.001, Hb p<0.001,

MCH p=0.003, platelets p=0.038, RBC p<0.001, MCHC p=0.016), uric acid (p<0.001), and the Self-Maintenance sub-section of the Index of Competence (p=0.022).

Dependent Independent Regression CoE SE Pearson correlation, p R2 Creatine AL -0.003 0.01 0.794 0.416 Sex 0.175 0.022 <0.001 DOP AL 80.45 51.344 0.121 0.058 Sex 163.751 106.103 0.126 GOT/AST AL 0.064 0.709 0.929 0.043 Sex 2.938 1.466 0.048 GPT/ALT AL 0.732 0.958 0.447 0.064 Sex 4.480 1.979 0.026 GTP AL 4.661 2.356 0.051 0.137 Sex 14.356 4.869 0.004 Hematocrit AL 0.094 0.282 0.740 0.518 Sex 5.731 0.584 <0.001 Hemoglobin AL 0.069 0.093 0.459 0.562 Sex 2.051 0.193 <0.001 LDL AL 3.110 3.485 0.375 0.013 Sex -5.430 7.202 0.453 MCH AL -0.005 0.125 0.965 0.093 Sex 0.793 0.259 0.003 MCHC AL 0.079 0.080 0.331 0.078 Sex 0.409 0.166 0.016 Continued

Table 5.10 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly residents of Sakiyama

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Table 5.10 continued

MCV AL -0.255 0.372 0.495 0.032 Sex 1.315 0.769 0.091 PBodyFat AL 2.143 0.737 0.005 0.132 Sex -4.253 1.524 0.006 Platelet AL 0.305 0.549 0.580 0.046 sex -2.383 1.135 0.038 RBC AL 2.340 3.505 0.506 0.398 sex 55.164 7.243 <0.001 TG AL 3.832 5.141 0.458 0.022 sex 11.865 10.623 0.267 Ucreatinine AL 5.356 3.265 0.104 0.146 sex 22.603 6.746 0.001 Uric AL 0.039 0.113 0.729 0.213 sex 1.144 0.234 <0.001 Walking speed AL 0.363 0.154 0.021 0.067 sex -0.457 0.319 0.155 WBC AL 203.890 131.551 0.125 0.109 sex 742.534 271.849 0.008 Weight AL 1.526 0.828 0.068 0.346 sex 10.963 1.710 <0.001 ADL AL 0.046 0.128 0.717 0.010 sex -0.244 0.264 0.358 Intel AL 0.072 0.093 0.440 0.007 sex -0.002 0.193 0.993 SelfM AL -0.043 0.040 0.290 0.074 sex -0.195 0.084 0.022 Soc AL 0.017 0.045 0.706 0.004 sex -0.047 0.093 0.615

Dependent Independent Regression CoE SE Pearson correlation, p R2 Walking Speed AL 0.832 0.285 0.004 0.104 sex 0.107 0.427 0.804 AL*sex -0.656 0.337 0.055 Table 5.11 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex), and dependent variables among elderly residents of Sakiyama

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5.3.4 Multivariate Associations: Allostatic Load, Age, and Sex

Controlling for age and sex, AL remains a significant predictor of GTP (p=0.054), percent body fat (p=0.004), and walking speed (p=0.002), but not creatinine (p=0.268), white blood cell count (p=0.113), or weight (p=0.112) (Table 5.12).Additionally controlling for interaction between AL and age (p=0.024), AL significantly predicts dopamine (0.041), suggesting an important relationship among these variables.

Controlling for age, sex, and an interaction between age and sex (p=0.034), AL is a significant predictor of walking speed (Table 5.13). However, when also controlling for previously identified significant associations between AL*age (p=0.016) and AL*sex

(p=0.143), both AL (p=0.092) and the interaction between age and sex (p=0.054) become only borderline predictors of walking speed.

Sex, but not age, also significantly associates with GTP (p=0.004) and percent body fat (p=0.006), whereas both sex and age correlate with walking speed (sex p=0.053, age p<0.001). Similar associations to those observed in Sections 4.3.2 and 4.3.3 between age, sex, and dependent variables also are observed (Table 5.12). In addition, interaction between age and sex, but not AL, is a significant predictor of LDL (p=0.026; Table 5.13).

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Dependent Independent Regression CoE SE Pearson correlation, p R2 Creatine AL -0.002 0.010 0.869 0.455 Age 0.003 0.001 0.012 Sex 0.173 0.021 <0.001 DOP AL 84.871 50.403 0.096 0.103 Age 12.936 5.988 0.033 Sex 156.710 104.122 0.136 GOT/AST AL 0.082 0.712 0.909 0.047 Age 0.054 0.085 0.526 Sex 2.909 1.471 0.051 GPT/ALT AL 0.617 0.918 0.503 0.152 Age -0.336 0.109 0.003 Sex 4.662 1.896 0.016 GTP AL 4.631 2.370 0.054 0.137 Age -0.089 0.282 0.752 Sex 14.404 4.895 0.004 Hematocrit AL 0.068 0.276 0.806 0.544 Age -0.076 0.033 0.023 Sex 5.772 0.571 <0.001 Hemoglobin AL 0.060 0.091 0.511 0.591 Age -0.027 0.011 0.012 Sex 2.066 0.187 <0.001 LDL AL 2.809 3.421 0.414 0.060 Age -0.879 0.406 0.033 Sex -4.952 7.067 0.485 MCH AL -0.002 0.126 0.989 0.098 Age 0.011 0.015 0.479 sex 0.788 0.260 0.003 MCHC AL 0.077 0.081 0.345 0.082 age -0.006 0.010 0.555 sex 0.412 0.167 0.015 MCV AL -0.239 0.372 0.522 0.043 age 0.046 0.044 0.305 sex 1.290 0.769 0.097 Continued

Table 5.12 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly residents of Sakiyama

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Table 5.12 continued

PBodyFat AL 2.161 0.740 0.004 0.135 age 0.055 0.088 0.533 sex -4.283 1.530 0.006 Platelet AL 0.270 0.545 0.622 0.071 age -0.103 0.065 0.115 sex -2.327 1.126 0.042 RBC AL 1.978 3.402 0.562 0.440 age -1.060 0.404 0.010 sex 55.742 7.027 <0.001 TG AL 3.800 5.172 0.464 0.022 age -0.094 0.615 0.879 sex 11.916 10.685 0.268 Ucreatinine AL 5.232 3.270 0.113 0.154 age -0.361 0.388 0.355 sex 22.800 6.755 0.001 Uric AL 0.045 0.113 0.690 0.227 age 0.017 0.013 0.203 sex 1.135 0.233 <0.001 Walking speed AL 0.398 0.127 0.002 0.379 age 0.102 0.015 <0.001 sex -0.513 0.262 0.053 WBC AL 210.266 131.357 0.113 0.123 age 18.655 15.606 0.235 sex 732.380 271.358 0.008 Weight AL 210.266 131.357 0.112 0.123 age 18.655 15.606 0.235 sex 732.380 271.358 0.008 ADL AL 0.037 0.126 0.770 0.044 age -0.027 0.015 0.070 sex -0.229 0.260 0.382 Intel AL 0.064 0.091 0.487 0.063 age -0.026 0.011 0.020 sex 0.012 0.188 0.949 Continued

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Table 5.12 continued

SelfM AL -0.042 0.041 0.306 0.080 age 0.004 0.005 0.447 Sex -0.197 0.084 0.021 Soc AL 0.015 0.045 0.737 0.015 Age -0.006 0.005 0.307 Sex -0.044 0.093 0.638

Dependent Independent Regression CoE SE Pearson correlation, p R2 Dopamine AL -837.794 404.716 0.041 0.152 Age 1.728 7.621 0.821 Sex 173.481 102.045 0.093 AL*age 13.752 5.987 0.024 LDL AL 2.189 3.359 0.516 0.110 Age 0.125 0.597 0.834 Sex 118.060 54.974 0.034 Age*Sex -1.810 0.803 0.026 Walking Speed AL 0.376 0.125 0.003 Age 0.138 0.022 <0.001 Sex 3.851 2.041 0.062 Age*Sex -0.064 0.030 0.034 Table 5.13 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly residents of Sakiyama

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Chapter 6: Results III - Hizen-Oshima Participant Demographics, Physiological Characteristics, Allostatic Load, and Sociocultural Variables

6.1 Introduction

Descriptions of demographics, physiological characteristics, allostatic load, and sociocultural variables of Hizen-Oshima participants are presented in this chapter.

Differences in variables are presented between age groups (divided at the median and between old/oldest of the old) and sex.

6.2 Descriptive Statistics

A total of 27 Japanese elders participated in this study. Their ages ranged from 51 to 82 years (std. dev.=7.4 years) with mean and median ages of 70.9 and 73 years respectively. Means, standard deviations, and ranges for all independent and dependent variables are presented in tables 6.1 and 6.2 respectively. One value for adrenaline concentration (examination number 7186) was recorded as “less than 1”. Calculations of the mean and standard deviation of adrenaline concentration when a value of 0 and a value of 1 were substituted for this description did not significantly differ. This description has therefore been replaced with a value of 0 for all statistic calculations.

Outliers were identified using boxplots and means, standard deviations, and ranges for affected independent and dependent variables are presented in parentheses in tables 6.1 and 6.2.

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Slightly elevated blood pressure (142.0/80.7 mmHg, sd 16.4/10.5) is observed in this sample of Japanese elders compared to United States (136/74 mmHg), but not

European standards (150/85 mmHg at ages 65-69) (see Wolf-Maier et al. 2003 and Table

6.3). Dopamine levels in this sample (747.0 ug/L, sd 752.0) are significantly higher than standard reference ranges reported in the United States (30-163 ug/L) A relatively lean body habitus (waist:hip ratio 0.86, sd 0.07) also is observed in this sample compared to

European men aged 55 to 64 years old (range 0.89-1.01), but not women (0.79 to 0.82)

(see Molarius et al. 1999). Similarly, GOT levels in this sample (25.6 U/L, sd 7.0) are high than expected as compared to American men (14-20 U/L), but not women (10-36

U/L). Blood sugar among the Hizen-Oshima sample (116.8 mg/dL, sd 18.7) is slightly high as compared to American standards (79.2-110 mg/dL). However, normal blood sugar levels vary widely with many individuals attaining up to 140 mg/dL immediately after eating. DHEA-s measurements among this sample are particularly high (668.6

μg/dL, sd 366.6) as compared to American reference values for both men and women

(28-310 μg/dL and 26-200 μg/dL respectively). Creatine levels among the Hizen-Oshima sample (0.741 mg/dL, sd 0.177) are borderline high as compared to American reference values (0.17-0.70 mg/dL). Finally, the average amount of the liver enzyme glutamic pyruvic transaminase (GPT) is slightly low among sample participants (8 U/L, sd 42) as opposed to the standard range for American men or women (10-45 U/L and 10-34 U/L respectively).

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Variable Mean Std Dev Min Max AD 6.5 (4.7) 6.4 (3.0) 0 (0) 31 (13) Cort 15.5 7.8 14.5 22.6 DBP 80.7 10.5 60 94 DHEAs 668.6 (633.5) 366.6 (324.5) 89 (89) 1580 (1410) HbA1c 5.40 (5.3) 0.3 (0.2) 4.9 (4.9) 6.2 (5.8) HDL 55.4 15.1 32 84 NAD 76.6 44.2 23 166 SBP 142 16.4 106 172 Tcho 210.3 42.9 142 315 WHR 0.857 0.065 0.74 0.97 Table 6.1 Descriptive statistics and ranges for independent variables of elderly Hizen- Oshima residents (N=27). Parentheses indicate descriptive statistics and ranges for independent variables calculated without outliers.

Variable Mean Std Dev Min Max BS 116.8 (114.6) 18.7 (15.0) 94 (94) 174 (148) Creatine 0.741 0.177 0.46 1.06 DOP 747 752 139 3490 GOT 25.6 7 13 43 GPT 8 42 20 8.8 GTP 33.1 (25.3) 33.6 (12.2) 10 (10) 181 (52) Hemoglobin 13.55 (13.7) 1.47 (1.0) 10.1 (11.9) 17.0 (15.3) Hematocrit 41.1 (41.1) 3.8 (3.0) 32.1 (34.0) 49.7 (47.2) RBC 450.4 59 329 641 TG 157.3 (150.5) 71.9 (63.9) 64 (64) 334 (288) Uric 5.1 1.2 3.1 7.2 WBC 5834.1 1516.2 3500 9700 Weight 54.4 10.41 34.3 81.6 ADL 11.3 (11.6) 1.5 (1.1) 7 (9) 13 (13) Intellect 3 1.1 0 4 SelfM 4.9 0.3 4 5 Social 3.4 (3.6) 1.0 (0.7) 1 (2) 4 (4) Table 6.2 Descriptive statistics and ranges for dependent variables of elderly Hizen- Oshima residents (N=27). Parentheses indicate descriptive statistics and ranges for independent variables calculated without outliers. 112

Reference Ranges Variable Men Women Hizen -Oshima Average Low High Low High AD (μg/L) 0 900 0 900 6.5 Blood Sugar (mg/dL) 79.2 110 79.2 110 116.8 Cort (μg/L) 3 23 3 23 15.5 Creatine (mg/dL) 0.17 0.7 0.17 0.7 0.741 DBP (mmHg) 60 79 60 79 80.7 DHEAs (μg/dL) 28 310 26 200 668.6 DOP (ug/L) 30 163 30 163 747 (752) GOT (U/L) 14 20 10 36 25.6 GPT (U/L) 10 45 10 34 8 GTP (U/L) 9 48 9 48 33.1 Hemoglobin (g/dL) 13.8 18 12.1 15.1 13.6 HbA1c (%) <5.4 >5.5 <5.4 >5.5 5.4 HDL (mg/dL) <40 >60 <50 >60 55.4 Hematocrit (%) 40.7 50.3 36.1 44.3 41.1 NAD (μg/L) 0 600 0 600 76.6 RBC (count/mL) 470 610 420 540 450.4 SBP (mmHg) 90 119 90 119 142 Tcho (mg/dL) 200 240 200 240 210.3 TG (mg/dL) <150 >200 <150 >200 157.3 Uric (mg/dL) 3.4 7.2 2.4 6.1 5.1 WBC (count per mL) 4100 10900 4100 10900 5834.1 WHR 0.89 1.01 0.79 0.82 0.857 Table 6.3 Established reference ranges for independent and dependent variables compared to values from Hizen-Oshima sample

6.3 Allostatic Load

Upper and lower bounds for quartile and decile cut-points for all 10 variables used to calculate AL are listed in Table 6.4. Using independent variable quartiles to calculate AL, the overall average estimate for AL in this sample is 3.1 (sd =1.6), ranging from 1-7 (Table 6.5).

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Quartiles: There is a slight tendency for estimated AL to be higher among those of younger age (p=0.378) when the sample is divided into age classes based upon the median age of 73 years. However, age explains only about 0.5% of the variation in AL.

Using regression, no significant linear (p=0.737) or quadratic (p=0.833) association of

AL with age was observed. Women showed slightly lower AL on average than did men across the total sample (p=0.088) and both age groups (younger cohort: p=0.028, older cohort: p=0.569). Younger men (4.8, sd=1.7) and women (2.8, sd=1.0) had higher AL than older men (3.0, sd=1.7) and women (2.5, sd=1.5), respectively. However, neither comparison was significant (men: p=0.147, women: p=0.705). Young men had the highest AL in these comparisons (4.8, sd=1.7), while older women had the lowest (2.5, sd=1.5).

Percentile Variable 0.1 0.25 0.5 0.75 0.9 AD 1 3 5 8 15.4 Cort 4.5 8.6 14.5 22.9 25.7 DBP 68 70 80 90 92 DHEAs 267.6 398 585 864 1314 HbA1c 5.1 5.2 5.3 5.5 5.8 HDL 35.2 44 50 70 75.6 NAD 28.4 44 64 111 153 SBP 116.8 134 140 156 164.4 Tcho 158 177 203 246 265.6 WHR 0.77 0.8 0.87 0.9 0.95 Table 6.4 Cut-points for estimating AL of elderly Hizen-Oshima residents. Upper quartile or 90th percentile is used for all variables except HDL and DHEAs for which the lower quartile or 10th percentile is used.

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Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev All 3.1 1.6 3.7 1.8 2.6 1.3 51-72 3.4 1.6 4.8 1.7 2.8 1.0 73-82 2.9 1.6 3.0 1.7 2.5 1.5 Table 6.5 Allostatic load estimates derived using quartile cut-offs for elderly residents of Hizen-Oshima by sex and age group divided at the median

Group 1 Group 2 p-value Quartile cut-off All male All female 0.088 All 51-72 All 73-82 0.378 Male 51-72 Female 51-72 0.028 Male 73-82 Female 73-82 0.569 Male 51-72 Male 73-82 0.147 Female 51-72 Female 73-82 0.705 Table 6.6 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs and median age division for elderly residents of Hizen- Oshima residents

When the sample is separated into older and younger age classes using age 70 as the dividing point (Tables 6.7 and 6.8), two individuals are re-classified from the younger and into the older group. The tendency for estimated AL to be higher among those of younger age both for the total sample (p=0.642) and across both age groups remains

(males: p=0.124, females: p=0.705). Women had lower AL on average than did men across both age groups. The difference between younger men and women’s average AL fell below the standard statistical difference of 0.05 (p=0.022), mirroring the statistically significant difference observed between younger men and women observed when age classes were divided using the median age. Younger men(5.5, sd=2.1) and women (2.8,

115 sd=1.0) had higher AL than older men (3.3, sd=1.9) and women (2.5, sd=1.5), respectively. However, neither comparison between age groups was statistically significant (men: p=0.124, women: p=0.705).

Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev 51-70 3.3 1.6 5.5 2.1 2.8 1.0 71-82 3.0 1.9 3.3 1.9 2.5 1.5 Table 6.7 Allostatic load estimates derived using quartile cut-offs for elderly residents of Hizen-Oshima by sex and age group divided at age 70

Group 1 Group 2 p-value Quartile cut-off All 51-70 All 71-82 0.642 Male 51-70 Female 51-70 0.022 Male 71-82 Female 71-82 0.349 Male 51-70 Male 71-82 0.124 Female 51-70 Female 71-82 0.705 Table 6.8 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs and age division at 70 for elderly residents of Hizen- Oshima residents

Deciles: Using decile cut-offs, the overall average estimate for AL in this sample is 0.9 (sd = 1.1) and ranges from 0-3 (Tables 6.9 and 6.10). Estimated AL is slightly lower among younger residents of Hizen-Oshima (p=0.665) when the sample is divided into higher and lower age classes based upon the median age of 73 years. However, age explains no variation in AL (R2=0.00). Using regression, no significant linear (p=0.992) or quadratic (p=0.716) association of AL with age is observed. Women showed slightly higher average AL than men (p=0.498). However, among younger participants (ages 51-

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72), men and women had the same AL (p=1.0). The difference observed in the total sample can, therefore, be attributed primarily if not solely to the difference in AL between older men (0.7, sd=1.2) and older women (1.3, sd=1.2) (p=0.583). Younger men had marginally higher AL as compared to older men (p=0.911). On the contrary, older women had higher AL than younger women (p=0.380).

Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev All 0.9 1.1 0.7 1.1 1.0 1.1 51-72 0.8 1.0 0.8 1.0 0.8 1.0 73-82 0.9 1.2 0.7 1.2 1.3 1.2 Table 6.9 Allostatic load estimates derived using percentile cut-offs for elderly residents of Hizen-Oshima by sex and age group divided at the median

Group 1 Group 2 p-value Decile cut-off All male All female 0.498 All 51-72 All 73-82 0.665 Male 51-72 Female 51-72 1.000 Male 73-82 Female 73-82 0.583 Male 51-72 Male 73-82 0.911 Female 51-72 Female 73-82 0.380 Table 6.10 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs and median age division for elderly residents of Hizen- Oshima residents

When the sample is separated into older and younger age classes using age 70 as the dividing point (Tables 6.11 and 6.12), higher average AL among those of younger age is again observed (p=0.850). Younger women had slightly lower AL as compared to younger men (p=0.779), whereas older women had higher AL as compared to older men

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(0.281). Younger men had higher AL than older men (p=0.681), whereas older women had higher AL than younger women (p=0.380). Older women had the highest observed

AL in this comparison (1.3, sd=1.2) while older men had the lowest (0.6, sd=1.1).

Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev 51-70 0.8 1.0 1.0 1.4 0.8 1.0 71-82 0.9 1.1 0.6 1.1 1.3 1.2 Table 6.11 Allostatic load estimates derived using percentile cut-offs for elderly residents of Hizen-Oshima by sex and age group divided at 70

Group 1 Group 2 p-value Decile cut-off All 51-70 All 71-82 0.850 Male 51-70 Female 51-70 0.779 Male 71-82 Female 71-82 0.281 Male 51-70 Male 71-82 0.681 Female 51-70 Female 71-82 0.380 Table 6.12 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs and age division at 70 for elderly residents of Hizen-Oshima residents

6.4 Sex

The sample from Hizen-Oshima comprises 16 women, 10 men, and one individual for whom sex was not recorded (X2=1.385, p=0.239). Among the independent variables used to calculate AL, women demonstrated significantly lower WHR,DHEA-s, and cortisol as well as significantly higher HDL-cholesterol as compared to men in this sample (Table 6.13). Among the dependent variables, women were also observed to have significantly lower average weight, creatine, uric acid, and blood sugar as compared to

118 men. They also demonstrated a significantly higher average amount of the liver enzyme

GOT (Table 6.14).

Variable Mean(Women) Mean(Men) P AD 5.6 8.1 0.359 Cort 13.1 19.3 0.048 DBP 83 77.2 0.182 DHEAs 480.7 977.9 <0.001 HbA1c1 5.4 5.4 0.922 HDL 60.3 48.7 0.037 NAD 69.6 83.6 0.442 SBP 144.8 135.6 0.165 TCho 212.6 202.5 0.569 WHR 0.83 0.89 0.022 Table 6.13 t-test comparisons of independent variable means by sex among elderly residents of Hizen-Oshima

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Variable Mean (Women) Mean (Men) p ADL 11.44 11 0.492 BS 110.75 127 0.031 Creatine 0.6519 0.86 0.001 DOP 853.94 548.2 0.331 GOT 28.19 21.5 0.017 GPT 21.88 17.3 0.209 GTP 32.63 33.9 0.929 Hemoglobin 13.325 13.74 0.490 Hematocrit 40.65 41.33 0.663 SelfM 4.94 4.8 0.367 Intel 3.06 2.9 0.729 RBC 443.25 457.3 0.568 Social 3.44 3.3 0.736 TG 147.94 159.9 0.675 Uric 4.513 5.92 0.003 WBC 5687.5 5682 0.992 Weight 48.981 61.92 0.001 Table 6.14 t-test comparisons of dependent variable means by sex among elderly residents of Hizen-Oshima

6.5 Age

Participant ages ranged from 51 to 82 years (std. dev.=7.4 years) with mean and median ages of 70.9 and 73 years respectively. Of the 27 individuals in the sample, 10 were between 52-70 years of age and 17 were 71 years old or older. There was no statistically significant difference between the number of younger and older individuals in this sample (X2=1.815, p=0.178). Likewise, when examined by both age and sex, no significant differences in the sample were observed (younger group: X2=3.6, p=0.058; older group: X2=.000, p=1.000; Table 6.15).

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Age group Total Men Women 52-70 10 2 8 71-82 17 8 8 Table 6.15 Frequency of elderly residents of Hizen-Oshima divided by an age division at 70

There were no statistically significant differences between the means of independent variables divided at the median age or at age 70. Because there were no statistically significant differences between the means of dependent variables divided at the median age or at age 70, these data are not reviewed.

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Chapter 7: Results IV: Associations of Allostatic Load with Physiological and Sociocultural Variables among Elderly Hizen-Oshima Residents

7.1Introduction

Associations of sociocultural and physiological measures with allostatic load examined with linear regressions are reported in this chapter. All possible bivariate associations are examined, as are all possible multivariate associations controlling for age, sex, and age by sex. Associations were analyzed using allostatic load scores constructed using both quartile and decile cut-points.

Given that this data set derives from an exploratory pilot study and given the small sample size, associations based on an alpha value of 0.10 are reported in addition to those observed at or below the 0.05 level. This slightly higher alpha value reveals additional possible important associations between AL and dependent variables.

7.2 Allostatic Load Associations (Quartile Cut-Offs)

7.2.1 Bivariate Associations

Among the Hizen-Oshima sample, allostatic load is strongly predictive of weight,

RBC, dopamine, GPT, WBC, hemoglobin, and uric acid (Table 7.1) . For weight, a single unit increase in AL is associated with a 4.6kg increase in weight (p<0.001), explaining about 48.5% of total variance. Similarly, RBC increase by 15.4 cells/mL for every one unit increase in AL (p=0.033), explaining 16.9% of variation in RBC. Red blood cells transport hemoglobin molecules throughout the body and also act as signaling

122 mechanisms to increase blood flow to under-oxygenated vessels and tissues (Dieson et al.

2008; Wan et al. 2008). No individual in this sample exceeds the range for normal RBC.

Thus no correlation between RBC and a specific etiology or the presence of a morbid condition can be assumed. An unit increase in AL also associates with a 184.4 ug/L increase in dopamine (p=0.046), a 1.9 U/L increase in GPT (p=0.089), a 349.3 cell/mL increase in white blood cells (p=0.062), a 0.40 g/dL increase in hemoglobin content

(p=0.052), and 0.3 mg/dL increase in uric acid (p=0.073). These variables represent a variety of somatic functions (neurotransmitter, liver function, immune function, renal function) which alter during senescence and are associated with frailty.

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Dep Regression CoE SE Pearson correlation, p R2 BS -2.5 2.3 0.297 0.043 Creatine 0 0 0.251 0.052 DOP 184.4 87.9 0.046 0.15 GOT -0.3 0.9 0.744 0.004 GPT 1.9 1 0.089 0.112 GTP 4.6 4.2 0.277 0.047 Hb 0.4 0.2 0.052 0.143 HT 0.9 0.4 0.061 0.133 RBC 15.4 6.8 0.033 0.169 TG 0 9.1 0.999 0 Uric 0.3 0.1 0.073 0.123 WBC 349.3 179.1 0.062 0.132 Weight 4.6 0.9 <0.001 0.485 ADL 0.1 0.2 0.583 0.012 Intel 0.1 0.1 0.458 0.022 SelfM 0 0 0.375 0.032 Soc 0 0.1 0.782 0.003 Table 7.1 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly Hizen-Oshima residents

7.2.2 Multivariate associations: AL and Age

When controlling for age, significant associations between AL and weight

(p<0.001), white blood cell count (p=0.054), red blood cell count (p=0.040), and dopamine (p=0.050) presented in Section 7.2.1 are still observed (Table 7.2). Results indicate that AL is an important predictor of differences in these variables independent of advancing age. In addition, controlling for age also reveals significant associations between AL and hemoglobin (p=0.060), hematocrit (p=0.070), and uric acid (p=0.081).

An one unit increase in AL corresponds to a 0.3 g/dL increase in hemoglobin and a 0.9% increase in hematocrit. Uric acid concentration increases by 0.3 mg/dL per unit increase

124 in AL. When controlling for age, the significant association between AL and GPT is no longer observed. Age alone associates with the dependent variable GOT (p=0.071).

Interaction between AL and age does not explain a significant amount of variation observed in any dependent variable.

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Dep Ind Regression CoE SE Pearson correlation, p R2 Weight AL 4.6 1.0 <0.001 0.486 age 0.0 0.2 0.845 WBC AL 362.7 178.7 0.054 0.174 age 42.2 38.0 0.278 RBC AL 15.0 6.9 0.040 0.190 age -1.2 1.5 0.430 Hb AL 0.3 0.2 0.060 0.156 age 0.0 0.0 0.556 HT AL 0.9 0.5 0.070 0.140 age 0.0 0.1 0.677 TG AL 0.3 9.3 0.971 0.011 age 1.0 2.0 0.608 GOT AL -0.4 0.8 0.640 0.133 age -0.3 0.2 0.071 GPT AL 1.8 1.0 0.101 0.175 age -0.3 0.2 0.188 GTP AL 4.6 4.3 0.290 0.047 age -0.1 0.9 0.955 Creatine AL 0.0 0.0 0.246 0.062 age 0.0 0.0 0.617 Uric AL 0.3 0.2 0.081 0.124 age 0.0 0.0 0.875 BS AL -2.3 2.3 0.326 0.067 age 0.4 0.5 0.446 DOP AL 185.1 89.9 0.050 0.150 age 2.2 19.1 0.910 ADL AL 0.1 0.2 0.585 0.013 age 0.0 0.0 0.888 SelfM AL 0.0 0.0 0.407 0.052 age 0.0 0.0 0.484 Intel AL 0.1 0.1 0.477 0.025 age 0.0 0.0 0.809 Soc AL 0.0 0.1 0.823 0.025 age 0.0 0.0 0.465 Table 7.2 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly residents of Hizen-Oshima

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7.2.3 Multivariate associations: AL and Sex

When controlling for sex, significant associations between AL and weight

(p=0.001), red blood cell count (p=0.065), GPT (p=0.012), and dopamine (p=0.013) presented in Section 7.2.1 are still observed (Table 7.3). Sex is significant for all these measures except red blood cell count, suggesting that both AL and sex explain significant amounts of variation in weight, GPT, and dopamine, while AL independently explains red blood count variation. The interaction between AL and age (p=0.020) supersedes the predictive ability of age (p=0.222) alone with respect to dopamine when both terms are included in the model. Including the interaction term, the association between AL and dopamine is strengthened (Table 7.4). Controlling for sex, AL is also predictive of blood glucose (p=0.043), Explaining more than a third (31.7%) of the variation in this variable, an unit increase in AL results in a 4.8 mg/dL decrease in blood glucose. Blood sugar below 70 mg/dL (hypoglycemia) can result in various symptoms including confusion, abnormal behavior, inability to complete routine tasks, double or blurred visions, heart palpitations, shakiness, anxiety, sweating, and hunger (Hypoglycemia 2012). When

HbA1c, a direct indictor of blood glucose, is removed from AL, association between AL and blood glucose remains (p=0.059). The persistent association indicates changes in other components of AL are significant predictors of variation in blood glucose even when direct assessment of this variable is not included in the AL model. Controlling for both sex and potential interaction between sex and AL, the association between AL and the Self Maintenance sub-scale of the TMIG-IC becomes borderline statistically significant (p=0.060); both sex (p<0.001) and the interaction term (p<0.001) are also

127 significant in this model (Table 7.4). The interaction term alone is borderline association with GOT (p=0.091), while sex alone is significantly associated with creatine (p=0.002), and uric acid (p=0.009).

Dep Ind Regression CoE SE Pearson correlation, p R2 Weight AL 3.5 0.9 0.001 0.632 sex 9.1 2.8 0.004 WBC AL 279.3 179.8 0.134 0.095 sex -305.8 566.0 0.594 RBC AL 15.0 7.8 0.065 0.152 sex -2.1 24.5 0.931 Hb AL 0.3 0.2 0.126 0.117 sex 0.1 0.6 0.891 HT AL 0.8 0.5 0.126 0.106 sex -0.2 1.6 0.910 TG AL -6.0 9.7 0.541 0.024 sex 18.4 30.4 0.551 GOT AL 0.5 0.9 0.613 0.226 sex -7.2 2.8 0.018 GPT AL 2.9 1.1 0.012 0.293 sex -7.7 3.4 0.031 GTP AL 5.4 4.7 0.266 0.054 sex -4.5 14.9 0.764 Creatine AL 0.0 0.0 0.861 0.355 sex 0.2 0.1 0.002 Uric AL 0.1 0.1 0.413 0.338 sex 1.3 0.5 0.009 BS AL -4.8 2.2 0.043 0.317 sex 21.4 7.0 0.006 DOP AL 248.8 92.9 0.013 0.268 sex -573.1 292.4 0.062 Continued

Table 7.3 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly residents of Hizen-Oshima

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Table 7.3 continued

Dep Ind Regression CoE SE Pearson correlation, p R2 ADL AL 0.1 0.2 0.572 0.034 sex -0.6 0.7 0.408 SelfM AL 0.1 0.0 0.212 0.108 sex -0.2 0.1 0.164 Intel AL 0.1 0.2 0.484 0.026 sex -0.3 0.5 0.575 Soc AL 0.0 0.1 0.744 0.010 sex -0.1 0.4 0.842

Dep Ind Regression CoE SE Pearson correlation, p R2 Dopamine AL 484.911 126.208 0.001 0.430 sex 736.228 585.908 0.222 AL*sex -422.499 168.809 0.020 GOT AL 2.153 1.284 0.108 0.322 sex 2.221 5.961 0.713 AL*sex -3.033 1.718 0.091 SelfM AL -0.100 0.050 0.060 0.498 sex -1.064 0.234 <0.001 AL*sex 0.279 0.068 <0.001 Table 7.4 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex), and dependent variables among elderly residents of Hizen-Oshima

7.2.4 Multivariate associations: AL, Age, and Sex

Multivariate associations controlling for age and sex reiterate correlations observed in previous sections. AL remains a significant predictor of weight (p=0.001),

GPT (p=0.021), dopamine (p=0.011), and blood sugar (p=0.053) (Tables 7.5). Except for

GPT, sex but not age significantly associates with these variables in multivariate models

(weight p=0.003; blood sugar p=0.009; dopamine p=0.049). Controlling for significant

129 effects of interactions between AL and sex highlights the association between AL and dopamine (p=0.001) and reveal a significant relationship between AL and Self

Maintenance score (p=0.066; Table 7.6). When HbA1c is not included in the AL model, the association between AL and blood glucose becomes borderline (p=0.071).

Multivariate results suggest AL is a robust predictor of multiple dependent variables, even after partialing out the effects of age and sex. Similar associations to those observed in Sections 7.2.2 and 7.2.3 between age, sex, and dependent variables are also observed.

Dep Ind Regression CoE SE Pearson correlation, p R2 Weight AL 3.4 0.9 0.001 0.643 age -0.2 0.2 0.412 sex 9.9 0.3 0.003 WBC AL 315.1 185.4 0.393 0.125 age 32.9 37.8 0.393 sex -463.2 597.0 0.446 RBC AL 13.5 8.0 0.106 0.180 age -1.4 1.6 0.392 sex 4.7 25.8 0.857 Hb AL 0.3 0.2 0.191 0.145 age 0.0 0.0 0.401 sex 0.3 0.6 0.700 HT AL 0.7 0.5 0.177 0.121 age -0.1 0.1 0.552 sex 0.1 1.7 0.941 TG AL -6.1 10.1 0.555 0.024 age -0.1 2.1 0.974 sex 18.7 32.6 0.571 Continued

Table 7.5 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly Hizen-Oshima residents

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Table 7.5 continued

Dep Ind Regression CoE SE Pearson correlation, p R2 GOT AL 0.2 0.9 0.835 0.285 age -0.2 0.2 0.191 sex -6.0 2.9 0.050 GPT AL 2.8 1.1 0.021 0.304 age -0.1 0.2 0.553 sex -7.0 3.6 0.061 GTP AL 5.5 5.0 0.279 0.054 age 0.1 1.0 0.924 sex -4.9 16.0 0.758 Creatine AL 0.0 0.0 0.743 0.370 age 0.0 0.0 0.476 sex 0.2 0.1 0.002 Uric AL 0.1 0.1 0.587 0.380 age 0.0 0.0 0.234 sex 1.5 0.5 0.005 BS AL -4.8 2.3 0.053 0.317 age 0.0 0.5 0.991 sex 21.4 7.5 0.009 DOP AL 265.1 96.1 0.011 0.287 age 15.1 19.6 0.450 sex -645.3 309.6 0.049 ADL AL 0.1 0.2 0.562 0.035 age 0.0 0.0 0.849 sex -0.6 0.7 0.407 SelfM AL 0.1 0.0 0.261 0.113 age 0.0 0.0 0.735 sex -0.2 0.1 0.228 Intel AL 0.1 0.2 0.537 0.029 age 0.0 0.0 0.817 sex -0.2 0.5 0.650 Soc AL 0.0 0.1 0.868 0.030 age 0.0 0.0 0.504 sex -0.2 0.5 0.696

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Dep Ind Regression CoE SE Pearson correlation, p R2 Hb AL 0.171 0.199 0.397 0.258 age -0.001 0.043 0.989 sex 12.420 6.833 0.083 Age*sex -0.167 0.094 0.088 Hematocrit AL 0.425 0.504 0.408 0.285 age 0.042 0.110 0.706 sex 38.059 17.359 0.040 Age*sex -0.522 0.238 0.040 Intel AL 0.189 0.164 0.261 0.168 age -0.038 0.036 0.306 sex -10.752 5.637 0.070 Age*sex 0.145 0.077 0.075 Dopamine AL 492.064 128.422 0.001 0.441 age 11.421 17.839 0.529 sex 652.535 608.142 0.295 AL*sex -413.108 171.747 0.025 GOT AL 1.980 1.246 0.127 0.395 age -0.276 0.173 0.126 sex 4.242 5.901 0.480 AL*sex -3.260 1.667 0.064 SelfM AL -0.100 0.052 0.066 0.499 age -0.001 0.007 0.921 sex -1.059 0.246 <0.001 AL*sex 0.279 0.069 0.001 Table 7.6 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly Hizen-Oshima residents

7.3 Allostatic Load Associations (Decile Cut-Offs)

7.3.1 Bivariate Associations

Using decile cut-points, AL associated significantly with dopamine (p=0.002) and

GPT (p=0.041) (Table 7.7). Whereas a single unit increase in AL associated with a 184.4 mg/dL increase in dopamine using quartile cut-points, it is associated with a 396.8 mg/dL

132 increase in dopamine when using decile cut-points. The difference in magnitude suggests that minor variation in independent variables can have dramatic effect on dependent variables. The robust correlation also indicates AL is an important predictor of this neurotransmitter. Similarly, an unit increase in AL results in a 3.264 U/L increase in GPT when using decile cut-points, compared to 1.9 U/L when using quartile cut-points, suggesting AL is an important predictor of liver function during senescence.

Variable Regression CoE SE Pearson correlation, p R2 BS -2.576 3.470 0.465 0.022 Creatine -0.042 0.032 0.199 0.065 DOP 396.835 116.928 0.002 0.315 GOT -0.186 1.314 0.888 0.001 GPT 3.264 1.513 0.041 0.157 GTP -4.202 6.257 0.508 0.108 Hb -0.010 0.276 0.972 0.000 HT 0.091 0.715 0.900 0.001 RBC 6.176 11.018 0.580 0.012 TG 6.030 13.467 0.658 0.008 Uric -0.140 0.230 0.547 0.015 WBC 2.254 285.125 0.994 0.000 Weight 2.685 1.882 0.166 0.075 ADL 0.249 0.286 0.392 0.029 Intel 0.175 0.209 0.410 0.027 SelfM -0.015 0.060 0.804 0.003 Soc 0.089 0.182 0.627 0.010 Table 7.7 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly Hizen-Oshima residents

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7.3.2 Multivariate associations: AL and Age

Controlling for age, significant associations between AL and dopamine (p=0.003) and GPT (p=0.036) presented in Section 7.3.1 remain (Table 7.8). Results indicate that

AL explains a significant amount of variation in these variables, even when taking age into consideration. Age, but not AL, associates significantly with GOT (p=0.076).

Controlling for an interaction between age and AL, the significant association between

AL and dopamine is no longer observed (Table 7.9).

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Dep Ind Regression CoE SE Pearson correlation, p R2 BS AL -2.581 3.491 0.467 0.049 age 0.420 0.501 0.410 Creatine AL -0.042 0.033 0.207 0.072 age 0.002 0.005 0.669 DOP AL 396.843 119.337 0.003 0.315 age -0.590 17.129 0.973 GOT AL -0.182 1.254 0.886 0.126 age -0.334 0.180 0.076 GPT AL 3.269 1.474 0.036 0.232 age -0.324 0.212 0.139 GTP AL -4.200 6.384 0.517 0.018 age -0.116 0.916 0.900 Hb AL -0.010 0.279 0.973 0.019 age -0.027 0.040 0.504 HT AL 0.092 0.726 0.901 0.012 age -0.054 0.104 0.611 RBC AL 6.196 11.069 0.581 0.043 age -1.393 1.589 0.389 TG AL 6.016 13.668 0.664 0.019 age 1.019 1.962 0.608 Uric AL -0.140 0.234 0.555 0.018 age -0.009 0.034 0.792 WBC AL 1.742 286.216 0.995 0.033 age 36.973 41.082 0.377 Weight AL 2.685 1.921 0.175 0.076 age -0.026 0.276 0.926 ADL AL 0.249 0.292 0.402 0.030 age 0.004 0.042 0.918 Intel AL 0.175 0.213 0.418 0.031 age -0.009 0.031 0.769 SelfM AL -0.015 0.061 0.807 0.026 age -0.007 0.009 0.455 Soc AL 0.089 0.183 0.631 0.033 age 0.020 0.026 0.454 Table 7.8 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly residents of Hizen-Oshima

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Dep Ind Regression CoE SE Pearson correlation, p R2 Dop AL 999.162 1819.023 0.588 0.319 age 4.792 23.825 0.842 AL*age -8.366 25.209 0.743 Table 7.9 Association between allostatic load (independent variable), controlling for age and interactions between AL and age, and dependent variables among elderly residents of Hizen-Oshima

7.3.3 Multivariate associations: AL and Sex

Controlling for sex, significant associations between AL and dopamine (p=0.003) and GPT (p=0.067) presented in Section 7.3.1 remain (Table 7.10). Sex explains a significant amount of variation in neither variable. However, when accounting for sex differences in weight (p<0.001), AL becomes a significant predictor with an unit increase associated with a 3.9k weight gain. Together, sex and AL explain 54.7% of the variation in weight among the Hizen-Oshima sample. Sex, but not allostatic load, also associates significantly with: blood sugar (p=0.041), creatine (p=0.002), GOT (p=0.017), and uric acid (p=0.004). Interaction between AL and sex (p=0.053) and sex (0.042), but not AL, associate with the Self Maintenance sub-section of the TMIG-IC (7.11).

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Variable Ind Regression CoE SE Pearson correlation, p R2 BS AL -1.779 3.363 0.602 0.190 sex 15.716 7.257 0.041 Creatine AL -0.023 0.027 0.400 0.374 sex 0.201 0.058 0.002 DOP AL 404.114 120.827 0.003 0.354 sex -184.503 260.724 0.486 GOT AL -0.623 1.234 0.619 0.225 sex -6.874 2.663 0.017 GPT AL 3.021 1.574 0.067 0.194 sex -3.669 3.397 0.291 GTP AL -4.317 6.671 0.524 0.018 sex -0.020 14.396 0.999 Hb AL 0.072 0.282 0.801 0.023 sex 0.437 0.609 0.481 HT AL 0.288 0.734 0.698 0.015 sex 0.766 1.583 0.633 RBC AL 8.822 11.455 0.449 0.039 sex 16.697 24.718 0.506 TG AL 11.021 13.279 0.415 0.036 sex 15.269 28.654 0.599 Uric AL -0.023 0.201 0.911 0.318 sex 1.401 0.434 0.004 WBC AL 123.701 260.380 0.639 0.010 sex 31.610 561.857 0.956 Weight AL 3.991 1.376 0.008 0.547 sex 14.136 2.970 <0.001 ADL AL 0.285 0.293 0.342 0.058 sex -0.352 0.633 0.584 Intel AL 0.203 0.218 0.362 0.041 sex -0.102 0.470 0.831 SelfM AL -0.021 0.063 0.736 0.049 sex -0.144 0.135 0.297 Soc AL 0.103 0.192 0.595 0.017 sex -0.107 0.414 0.799 Table 7.10 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly residents of Hizen-Oshima

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Variable Ind Regression CoE SE Pearson correlation, p R2 SelfM AL -0.111 0.073 0.143 0.201 sex -0.346 0.160 0.042 AL*sex 0.250 0.122 0.053 Table 7.11 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex) and dependent variables among elderly residents of Hizen-Oshima

7.3.4 Multivariate associations: AL, Age, and Sex

When controlling for age and sex, AL remains a significant predictor of dopamine

(p=0.004), GPT (p=0.055), and weight (p=0.004) (Table 7.12). Neither sex nor age is significantly correlated with dopamine or GPT in the multivariate model. However, both are significantly associated with weight (age p=0.082, sex p<0.001). In the multivariate model, sex but not allostatic load or age, associates with blood sugar (p=0.064), creatine

(p=0.002), GOT (p=0.038), and uric acid (p=0.002). Age associates independently with no dependent variable. Interactions between age and sex associate significantly with hemoglobin, hematocrit, and the total and Intellectual Activity sub-section scores of the

TMIG-IC (Table 7.13).

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Dep Ind Regression CoE SE Pearson correlation, p R2 BS AL -1.889 3.431 0.587 0.198 age 0.230 0.506 0.653 sex 14.851 7.626 0.064 Creatine AL -0.022 0.027 0.432 0.385 age -0.002 0.004 0.543 sex 0.211 0.061 0.002 DOP AL 404.631 123.839 0.004 0.354 age -1.085 18.249 0.953 sex -180.427 275.232 0.519 GOT AL -0.503 1.212 0.682 0.289 age -0.250 0.179 0.175 sex -5.935 2.693 0.038 GPT AL 3.161 1.557 0.055 0.250 age -0.293 0.229 0.215 sex -2.568 3.460 0.466 GTP AL -4.266 6.836 0.539 0.019 age -0.107 1.007 0.917 sex 0.380 15.194 0.980 Hb AL 0.095 0.281 0.739 0.079 age -0.048 0.041 0.258 sex 0.617 0.624 0.334 HT AL 0.336 0.738 0.653 0.052 age -0.101 0.109 0.361 sex 1.147 1.639 0.492 RBC AL 9.842 11.326 0.394 0.105 age -2.140 1.669 0.213 sex 24.733 25.172 0.337 TG AL 10.978 13.610 0.429 0.036 age 0.092 2.006 0.964 sex 14.924 30.249 0.627 Uric AL -0.004 0.198 0.985 0.371 age -0.040 0.029 0.186 sex 1.550 0.440 0.002 Continued

Table 7.12 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly Hizen-Oshima residents

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Table 7.12 continued

Dep Ind Regression CoE SE Pearson correlation, p R2 WBC AL 115.352 265.687 0.668 0.019 age 17.508 39.152 0.659 sex -34.141 590.491 0.954 Weight AL 4.160 1.315 0.004 0.607 age -0.354 0.194 0.082 sex 15.464 2.922 <0.001 ADL AL 0.285 0.301 0.354 0.058 age 0.000 0.044 0.998 sex -0.352 0.669 0.604 Intel AL 0.210 0.222 0.355 0.050 age -0.015 0.033 0.655 sex -0.046 0.494 0.927 SelfM AL -0.019 0.064 0.770 0.063 age -0.005 0.009 0.572 sex -0.124 0.141 0.391 Soc AL 0.094 0.194 0.635 0.039 age 0.020 0.029 0.490 sex -0.182 0.432 0.677

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Dep Ind Regression CoE SE Pearson correlation, p R2 ADL AL 0.406 0.296 0.184 0.180 age -0.043 0.049 0.386 sex -13.357 7.416 0.086 age*sex 0.181 0.103 0.093 Hb AL -0.034 0.270 0.901 0.232 age -0.002 0.045 0.961 sex 14.431 6.776 0.045 age*sex -0.193 0.094 0.053 Hematocrit AL -0.053 0.686 0.939 0.261 age 0.037 0.114 0.745 sex 42.874 17.205 0.021 age*sex -0.582 0.239 0.024 Intel AL 0.307 0.216 0.170 0.193 age -0.049 0.036 0.182 sex -10.431 5.413 0.068 age*sex 0.145 0.075 0.068 Table 7.13 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly Hizen-Oshima residents

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Chapter 8: Results V: Amalgamating Data Sets: Hizen-Oshima/Sakiyama Participant Demographics, Physiological Characteristics, Allostatic Load, and Sociocultural Variables

8.1 Introduction

This and the next chapter explore associations in the combined Hizen-Oshima and

Sakiyama data. Given proximity, both are semi-rural settings on islands located in the

South China Sea, and similarities between them in lifeways and sociocultural attributes, along with the same protocols being applied in each, combing samples seems appropriate.

Across Japan there is relative social and genetic homogeneity, suggesting most relationships among independent and dependent variables probably are relatively constant across the population. Additionally, although there is a wealth of cultural diversity related to socioeconomic and geographic variation, both Hizen-Oshima and

Sakiyama are relatively small, rural, and economically agrarian towns. Such cultural similarities should partly control for any differences in AL–associated observations related to different lifestyles.

This chapter summarizes demographics and physiological characteristics compared to the combined Hizen-Oshima/Sakiyama data. Variables are examined by age groups (divided at the median and at age 70) and by sex. AL is determined using two methods for combining data.

8.2 Descriptive Statistics

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The total sample includes 123 Japanese elders. Their ages range from 51 to 88 years (sd 8.5 years) with mean and median ages of 68.5 and 69 years, respectively.

Means, standard deviations, and ranges for all study independent and dependent variables are presented in tables 8.1 and 8.2 respectively. Slightly elevated blood pressure

(146.2/85.6 mmHg, sd 22.5/12.4) is observed in this sample of Japanese elders compared to United States (136/74 mmHg), but not compared with European standards (150/85 mmHg at ages 65-69) (see Wolf-Maier et al. 2003 and Table 8.3). Dopamine levels

(685.9 ug/L, sd 578.8) are noticeably higher than standard reference ranges reported in the United States (30-163 ug/L) A relatively lean body habitus (WHR 0.85, sd 0.07) also is observed compared to European men aged 55 to 64 years old (range 0.89-1.01), but not women (0.79 to 0.82) (see Molarius et al. 1999). Similarly, GOT levels (24.3 U/L, sd 7.1) are higher than expected based upon American standards for men (14-20 U/L), but not women (10-36 U/L). Average total cholesterol (Tcho 209.1 mg/dl, sd 37.0) is slightly high compared to international standards wherein less than 200mg/dl is considered desirable. However, high-density lipoprotein cholesterol (HDL 62.9 mg/dl, sd 16.7) in this sample is well above the 50mg/dl international standard for healthy HDL-cholesterol, resulting in a ratio of total cholesterol to HDL-cholesterol of 3.3 mg/dl which is below the level determined as low risk (3.8-4.0 mg/dl) by international standards. Creatine levels are borderline high (0.7 mg/dL, sd 0.146), falling at the highest end of reference ranges given for men and women (0.17-0.70 mg/dL). Average red blood cell count (460.0 count/mL, sd 48.2) is low compared to American men (470-610 count/mL), but not compared to American women (420-540 count/mL).

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Variable Mean Std Dev Min Max AD 9.43 9.61 0.0 58.9 Cort 21.33 16.50 2.8 99.5 DBP 85.6 12.4 60 125 DHEAs 234.2 292.6 23 1580 HbA1c 5.29 0.57 4.5 8.3 HDL 62.9 16.7 32 143 NAD 94.7 55.2 21 318 SBP 146.2 22.5 87 226 Tcho 209.1 37.0 127 315 WHR 0.853 0.068 0.70 1.03 Table 8.1: Descriptive statistics and ranges for independent variables of elderly Hizen- Oshima/Sakiyama residents (N=123). Parentheses indicate descriptive statistics and ranges for independent variables calculated without outliers.

Variable Mean Std Dev Min Max Creatine .709 .146 .46 1.08 Dop 685.9 578.8 119 3722 GOT 24.3 7.1 13 68 GPT 20.3 9.6 7 77 GTP 31.0 27.1 9 181 Hematocrit 42.92 4.08 32.1 51.7 Hemoglobin 14.07 1.43 10.1 17.4 RBC 460.0 48.2 329 641 TG 113.9 60.8 35 334 Uric 5.33 1.26 2.4 9.5 WBC 5658.7 1407.7 2900 9700 Weight 56.22 10.20 34.3 88.7 ADL 11.9 1.4 7 13 SelfM 4.9 0.4 3 5 Intel 3.3 1.0 0 4 Soc 3.7 0.6 1 4 Table 8.2: Descriptive statistics and ranges for dependent variables of elderly Hizen- Oshima/Sakiyama residents (N=123). Parentheses indicate descriptive statistics and ranges for independent variables calculated without outliers.

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Reference Ranges Hizen -Oshima/ Variable Men Women Sakiyama Averages Low High Low High AD (μg/L) 0 900 0 900 9.43 Cort (μg/L) 3 23 3 23 21.33 Creatine (mg/dL) 0.17 0.7 0.17 0.7 0.709 DBP (mmHg) 60 79 60 79 85.6 DHEAs (μg/dL) 28 310 26 200 234.2 DOP (ug/L) 30 163 30 163 685.9 GOT (U/L) 14 20 10 36 24.3 GPT (U/L) 10 45 10 34 20.3 GTP (U/L) 9 48 9 48 31 Hemoglobin (g/dL) 13.8 18 12.1 15.1 14.07 HbA1c (%) <5.4 >5.5 <5.4 >5.5 5.29 HDL (mg/dL) <40 >60 <50 >60 62.9 Hematocrit (%) 40.7 50.3 36.1 44.3 42.92 RBC (count/mL) 470 610 420 540 460 SBP (mmHg) 90 119 90 119 146.2 Tcho (mg/dL) 200 240 200 240 209.1 TG (mg/dL) <150 >200 <150 >200 113.9 Uric (mg/dL) 3.4 7.2 2.4 6.1 5.33 WBC (count per mL) 4100 10900 4100 10900 5658.7 WHR 0.89 1.01 0.79 0.82 0.853 Table 8.3 Established reference ranges for independent and dependent variables compared to values from the Hizen-Oshima/Sakiyama sample

8.3 Allostatic Load-Raw Values

The first calculation of the combined data assumes all participants represent the same population. Therefore, upper and lower bounds for quartile and decile cut-points for the 10 variables comprising AL are calculated directly from observed distributions (i.e., values were not standardized (See section 8.4). Percentiles used to determine cut-points are presented in Table 8.4. Using quartiles when calculating AL, average AL is estimated as 2.37 (sd =1.47), ranging from 0-7 (Table 8.5).

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Quartiles: There is a slight tendency for AL to be higher among those of younger age (Tables 8.5 and 8.6) when age classes are based upon the median age of 69 years.

However, age explains only about 0.7% of the variation in AL. Using regression, no significant linear (p=0.374) or quadratic (p=0.662) association of AL with age is observed. Women (2.2, sd=1.6) showed slightly lower AL on average than did men (2.6, sd=1.3) across the total sample (p=0.137) and in among the younger cohort (p=0.050).

Younger men (2.9, sd=1.3) had higher AL than older men (2.2, sd=1.3; p=0.048).

However, younger women (2.2, sd=1.6) showed the same AL as older women (2.2, sd=1.6; p=0.98). Young men experienced the highest AL in all comparisons, while older women demonstrated the lowest.

When the sample is separated into older and younger age classes using age 70 as the dividing point (Tables 8.7 and 8.8), two individuals are re-classified from the younger and into the older group. Observations made dividing the sample at the median age remain.

Percentile Variable 10 25 50 75 90 AD 2.1 3.7 6.0 12.2 19.7 Cort 5.4 9.9 16.6 25.3 44.0 DBP 70.0 76.0 85.0 94.0 100.0 DHEAs 52.00 67.0 123.0 227.0 696.2 HbA1c 4.8 5.0 5.2 5.4 5.8 HDL 44.4 51.0 61.0 71.0 80.0 NAD 33.5 52.0 80.7 122.0 171.6 SBP 117.4 134.0 145.0 162.0 171.2 Tcho 163.0 185.0 204.0 239.0 256.0 WHR 0.76 0.80 0.85 0.90 0.95 Table 8.4 Cut-points for estimating AL of elderly Hizen-Oshima/Sakiyama residents. Upper quartile or 90th percentile is used for all variables except HDL and DHEAs for which the lower quartile or 10th percentile is used. 146

Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev All 2.4 1.5 2.6 1.3 2.2 1.6 51-69 2.5 1.5 2.9 1.3 2.2 1.6 70-88 2.2 1.5 2.2 1.3 2.2 1.6 Table 8.5 Allostatic load estimates derived using quartile cut-offs for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at the median

Group 1 Group 2 p-value Quartile cut-off All male All female 0.137 All 51-69 All 70-88 0.269 Male 51-69 Female 51-69 0.050 Male 70-88 Female 70-88 0.902 Male 51-69 Male 70-88 0.048 Female 51-69 Female 70-88 0.980 Table 8.6 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs and median age division for elderly residents of Hizen- Oshima/Sakiyama residents

Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev 51-70 2.5 1.4 2.9 1.3 2.2 1.5 71-88 2.2 1.5 2.2 1.3 2.2 1.7 Table 8.7 Allostatic load estimates derived using quartile cut-offs for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at age 70

Group 1 Group 2 p-value Quartile cut-off All 51-70 All 71-88 0.293 Male 51-70 Female 51-70 0.051 Male 71-88 Female 71-88 0.859 Male 51-70 Male 71-88 0.062 Female 51-70 Female 71-88 0.919 Table 8.8 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs and age division at 70 for elderly residents of Hizen- Oshima/Sakiyama residents

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Deciles: Using decile cut-points, the overall average estimate for AL in this sample is 0.9 (sd = 1.0) and ranges from 0-5 (Tables 8.9 and 8.10). AL is slightly higher among younger participants (p=0.259) when the sample is divided into higher and lower age classes based upon the median age of 69 years. However, age explains only 1.7% of the variation in AL. Using regression, no significant linear (p=0.151) or quadratic

(p=0.339) association of AL with age was observed. Women (0.8, sd=0.9) had slightly lower average AL as compared to men (1.1, sd=1.1; p=0.158) and across both age groups

(younger cohort p=0.207, older cohort p=0.489). Younger men (1.2, sd=1.3) and younger women (0.9, sd=0.9) had lower AL than older men (0.9, sd=0.9) or older women (0.7, sd= 0.8) respectively. However, neither comparison approached statistical significance at p<0.05 (men p=0.299; women p=0.582). Younger men had the highest AL in this comparison (1.2, sd=1.3), whereas older women had the lowest (0.7, sd=0.8).

When the sample is separated into older and younger age classes using age 70 as the dividing point (Tables 8.11 and 8.12), observations made dividing the sample at the median age remain.

Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev All 0.9 1.0 1.1 1.1 0.8 0.9 51-69 1.0 1.1 1.2 1.3 0.9 0.9 70-88 0.8 0.9 0.9 0.9 0.7 0.8 Table 8.9 Allostatic load estimates derived using decile cut-offs for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at the median

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Group 1 Group 2 p-value Decile cut-off All male All female 0.158 All 51-69 All 70-88 0.259 Male 51-69 Female 51-69 0.207 Male 70-88 Female 70-88 0.489 Male 51-69 Male 70-88 0.299 Female 51-69 Female 70-88 0.582 Table 8.10 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs and median age division for elderly residents of Hizen- Oshima/Sakiyama residents

Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev 51-70 1.0 0.1 1.2 1.3 0.9 0.9 71-82 0.8 0.9 0.9 0.9 0.7 0.9 Table 8.11 Allostatic load estimates derived using decile cut-offs for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at 70

Group 1 Group 2 p-value Decile cut-off All 51-70 All 71-88 0.232 Male 51-70 Female 51-70 0.204 Male 71-88 Female 71-88 0.454 Male 51-70 Male 71-88 0.303 Female 51-70 Female 71-88 0.462 Table 8.12 p-values resulting from independent t-tests comparing allostatic load estimates derived using percentile cut-offs and age division at 70 for elderly residents of Hizen- Oshima/Sakiyama residents

8.4 Allostatic Load - Standardized Values

The second calculation of a joint AL score for these two data sets assumed that although both samples came from the same underlying population, differences between the two villages may significantly impact the relationship between the raw values of each variable and their mean for each group. Therefore, values for independent variables were standardized as z-scores respective to the mean of each variable with each sub-sample.

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Percentiles were calculated after combining z-scores from Hizen-Oshima and Sakiyama into one data set (8.13). Using this techniques, the overall average estimate for AL in this sample is 2.6 (sd=1.5), ranging from 0-7 (Table 8.14).

Percentile Variable 10 25 50 75 90 zAD -0.778 -0.621 -0.392 0.231 1.072 -1.003 -0.729 -0.171 0.504 1.295 zCort zDBP -1.154 -0.867 -0.071 0.803 1.103 zDHEAs -0.999 -0.734 -0.180 0.579 1.474 zHbA1c -0.733 -0.574 -0.255 0.222 1.114 zHDL -1.082 -0.721 -0.180 0.542 1.131 -1.104 -0.719 -0.268 0.574 1.543 zNAD zSBP -1.318 -0.558 -0.056 0.696 1.232 zTcho -1.270 -0.672 -0.136 0.834 1.331 zWHR -1.337 -0.782 -0.098 0.673 1.452 Table 8.13 Cut-points for estimating AL of elderly Hizen-Oshima/Sakiyama residents estimated using z-scores. Upper quartile or 90th percentile is used for all variables except HDL and DHEAs for which the lower quartile or 10th percentile is used.

Quartiles: AL is higher among those of younger age (2.8, sd=1.5) as compared to older age (2.4, sd=1.5; p=0.166) when the sample is divided into age classes based upon the median age of 69 years (Table 8.14; Table 8.15). However, age explains only about

0.6% of the variation in AL. Using regression, no significant linear (p=0.390) or quadratic (p=0.408) association of AL with age was observed. Women (2.4, sd=1.6) showed slightly lower AL on average than did men (2.7, sd=1.4) across the total sample

(p=0.166) and both age groups (younger cohort: p=0.213, older ages: p=0.617). Younger men (3.0, sd=1.4) had higher AL than older men (2.4, sd=1.3; p=0.114) and younger

150 women (2.5, sd=1.6) similarly had higher AL than older women (2.3, sd=1.6; p=0.462).

Young men had the highest AL in these comparisons, while older women had the lowest.

Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev All 2.6 1.5 2.7 1.4 2.4 1.6 51-69 2.8 1.5 3.0 1.4 2.5 1.6 70-88 2.4 1.5 2.4 1.3 2.3 1.6 Table 8.14 Allostatic load estimates derived using quartile cut-offs estimated from z- scores for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at the median

Group 1 Group 2 p-value Quartile cut-off All male All female 0.220 All 51-69 All 70-88 0.166 Male 51-69 Female 51-69 0.213 Male 70-88 Female 70-88 0.617 Male 51-69 Male 70-88 0.114 Female 51-69 Female 70-88 0.462 Table 8.15 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs estimated from z-scores and median age division for elderly residents of Hizen-Oshima/Sakiyama residents

When the sample is separated into older and younger age classes using age 70 as the dividing point (Tables 8.16 and 8.17), the tendency for estimated AL to be higher among those of younger age for the total sample (p=0.216) and across both age groups

(men p=0.150; women p= 0.474) remains. Younger women (2.5, sd=1.5) have lower AL as compared to younger men (3.0, sd=1.4; p=0.211). This trend is repeated among the older cohort wherein older women (2.2, sd=1.7) have lower AL than older men (2.4,

151 sd=1.3; p=0.150). Younger men again have the highest AL in this comparison while older women have the lowest AL.

Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev 51-70 2.7 1.5 3.0 1.4 2.5 1.5 71-82 2.4 1.5 2.4 1.3 2.2 1.7 Table 8.16 Allostatic load estimates derived using quartile cut-offs for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at age 70

Group 1 Group 2 p-value Quartile cut-off All 51-70 All 71-88 0.216 Male 51-70 Female 51-70 0.211 Male 71-88 Female 71-88 0.589 Male 51-70 Male 71-88 0.150 Female 51-70 Female 71-88 0.474 Table 8.17 p-values resulting from independent t-tests comparing allostatic load estimates derived using quartile cut-offs estimated from z-scores and age division at 70 for elderly residents of Hizen-Oshima/Sakiyama residents

Deciles: Using decile cut-points, the overall average estimate for AL in this sample is 1.1 (sd = 1.1) and ranges from 0-6 (Tables 8.18 and 8.19). AL is slightly higher among younger residents of the combined sample (p=0.615) when the sample is divided into higher and lower age classes based upon the median age of 69 years. However, age explains only 0.2% of the variation in AL. Using regression, no significant linear

(p=0.610) or quadratic (p=0.615) association of AL with age was observed. Women (1.0, sd=1.0) had slightly lower average AL as compared to men (1.3, sd=1.3) across the total sample (p=0.211) and in the younger cohort (women 0.9, sd=0.9; men 1.5, sd=1.4; p=0.053). Among the older cohort, men (1.0, sd=1.1) had marginally higher AL than 152 women (1.1, sd=1.1; p=0.734). Younger men had higher AL than older men (p=0.165), whereas younger women had lower AL than older women (p=0.373). Younger men had the highest AL in this comparison; younger women had the lowest AL.

Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev All 1.1 1.13 1.3 1.3 1.0 1.0 51-69 1.2 1.2 1.5 1.4 0.9 0.9 70-88 1.1 1.1 1.0 1.1 1.1 1.1 Table 8.18 Allostatic load estimates derived using decile cut-offs estimated from z-scores for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at the median

Group 1 Group 2 p-value Decile cut-off All male All female 0.211 All 51-69 All 70-88 0.615 Male 51-69 Female 51-69 0.053 Male 70-88 Female 70-88 0.734 Male 51-69 Male 70-88 0.165 Female 51-69 Female 70-88 0.373 Table 8.19 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs estimated from z-scores and median age division for elderly residents of Hizen-Oshima/Sakiyama residents

When the sample is separated into older and younger age classes using age 70 as the dividing point (Tables 8.20 and 8.21), higher average AL among those of younger age is again observed (p=0.677). Younger women had lower AL as compared to younger men

(p=0.053). This trend was reversed among the older cohort where women again had slightly higher AL as compared to men (p=0.719). Younger men had higher AL than older men (p=0.183), and older women had slightly higher AL than younger women 153

(p=0.388). Younger men had the highest observed AL in this comparison (1.5, sd=1.4) and younger women had the lowest AL (0.9, sd=0.8).

Total Male Female Ages AL Std Dev AL Std Dev AL Std Dev 51-70 1.2 1.1 1.5 1.4 0.9 0.8 71-82 1.1 1.1 1.0 1.1 1.2 1.2 Table 8.20 Allostatic load estimates derived using decile cut-offs estimated from z-scores for elderly residents of Hizen-Oshima/Sakiyama by sex and age group divided at 70

Group 1 Group 2 p-value Decile cut-off All 51-70 All 71-88 0.677 Male 51-70 Female 51-70 0.053 Male 71-88 Female 71-88 0.719 Male 51-70 Male 71-88 0.183 Female 51-70 Female 71-88 0.388 Table 8.21 p-values resulting from independent t-tests comparing allostatic load estimates derived using decile cut-offs estimated from z-scores and age division at 70 for elderly residents of Hizen-Oshima/Sakiyama residents

8.5 Sex

The combined Hizen-Oshima/Sakiyama sample comprises 64 women and 58 men

(X2=.295, p=.587). Among the independent variables used to calculate AL, women demonstrated significantly lower systolic blood pressure (SBP), diastolic blood pressure

(DBP), waist:hip ratio (WHR), dehydroepiandostrerone-sulfate (DHEA-s), and cortisol as well as significantly higher HDL-cholesterol as compared to men (Table 8.22). Among the dependent variables, women were observed to have significantly lower liver enzymes

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(GTP), creatine, uric acid, blood measures (hematocrit, hemoglobin, red blood cell count, white blood cell count), and weight as compared to men (Table 8.23). Women scored significantly higher on the Self Maintenance section of the Tokyo Metropolitan Institute of Gerontology Index of Competence (ADL) than did men, although this did not result in significant differences in overall scores.

Variable Mean(Women) Mean(Men) P AD 10.04 8.8 0.491 Cort 17.72 25.4 0.012 DBP 82.86 88.6 0.010 DHEAs 172.77 296.05 0.023 HbA1c1 5.261 5.319 0.581 HDL 17.7 14.7 0.010 NAD 0.23 0.28 0.603 SBP 139.92 152.79 0.001 TCho 213.42 203.69 0.147 WHR 0.816 0.893 <0.001 Table 8.22 Comparisons of independent variable means by sex among elderly residents of Hizen-Oshima/Sakiyama

Variable Mean(Women) Mean(Men) P Creatine 0.62 0.8 <0.001 Dop 644.11 726.01 0.439 GOT 23.89 24.74 0.515 GPT 18.98 21.79 0.106 GTP 25.43 27.73 0.010 Hematocrit 40.58 45.452 <0.001 Hb 13.21 14.99 <0.001 RBC 436.84 484.86 <0.001 TG 108.34 117.12 0.415 Uric 4.75 5.97 <0.001 WBC 5326.56 5955.52 0.010 Weight 50.51 62.37 <0.001 ADL 11.94 11.72 0.388 Intel 3.27 3.28 0.954 SelfM 4.97 4.78 0.007 Soc 3.7 3.67 0.785 Table 8.23 Comparisons of dependent variable means by sex among elderly residents of Hizen-Oshima/Sakiyama 155

8.6 Age

Participant ages ranged from 51 to 88 years (sd=8.5 years) with mean and median ages of 68.5 and 69 years respectively. Of the 123 individuals in the combined sample, 68 were between 51-70 years of age and 55 were 71 years old or older. There was no statistically significant difference between the number of younger and older individuals in this sample (X2=1.374, p=0.241). Likewise, when examined by both age and sex, no significant differences in the sample were observed (younger group: X2=0.529, p=0.467; older group: X2=.000, p=1.000; Table 8.24).

Age group Total Men Women 52-70 68 31 37 71-88 55 27 27 Table 8.24 Frequency of elderly residents of Hizen-Oshima/Sakiyama divided by an age division at 70

Diastolic blood pressure was significantly lower among the younger cohort when the sample was divided at both the median (Table 8.25) and age 70 (Table 8.26).

Similarly, total cholesterol was higher in the younger cohort across both age divisions.

Among the independent variables, GPT, hematocrit, hemoglobin, red blood cell count, and weight are all higher among the younger cohort when the sample was divided at both the median (Table 8.27) and age 70 (Table 8.28). In addition, when the sample was divided at the median, the younger cohort scored significantly higher on the Intellectual

Activity section of the Tokyo Metropolitan Institute of Gerontology Index of

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Competence (ADL), although this did not result in significant differences in overall scores.

Variable 51-69 70-88 p AD 10.12 11.22 0.408 Cort 22.76 19.78 0.319 DBP 88.8 82.03 0.002 DHEAs 200.73 270.54 0.187 HbA1c1 5.28 5.30 0.808 HDL 65.48 60.07 0.073 NAD 0.23 0.27 0.642 SBP 143.39 149.19 0.155 TCho 215.81 201.88 0.036 WHR 0.85 0.86 0.352 8.25 p-values from independent t-test comparing means of independent variables divided at median age among elderly residents of Hizen-Oshima/Sakiyama

Variable 51-70 71-88 p AD 10.42 8.21 0.208 Cort 22.45 19.94 0.403 DBP 87.94 82.6 0.017 DHEAs 195.65 281.91 0.104 HbA1c1 5.34 5.24 0.402 HDL 65.44 59.73 0.059 NAD 0.24 0.27 0.638 SBP 142.76 150.38 0.062 TCho 215.29 201.51 0.039 WHR 0.85 0.86 0.253 8.26 p-values from independent t-test comparing means of independent variables divided at age 70 among elderly residents of Hizen-Oshima/Sakiyama

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Variable 51-69 70-88 p Creatine 0.69 0.73 0.168 Dop 642.35 733.07 0.387 GOT 24.73 23.85 0.494 GPT 23.17 17.17 <0.001 GTP 32 29.81 0.656 Hematocrit 43.79 41.97 0.013 Hb 14.36 13.75 0.017 RBC 469.47 449.66 0.022 TG 58.46 63.66 0.601 Uric 5.35 5.32 0.913 WBC 5513.75 5815.93 0.236 Weight 58.46 53.8 0.011 ADL 12.02 11.66 0.148 Intel 3.47 3.07 0.022 SelfM 4.89 4.86 0.716 Soc 3.66 3.73 0.516 8.27 p-values from independent t-test comparing means of dependent variables divided at median age among elderly residents of Hizen-Oshima/Sakiyama

Variable 51-70 71-88 p Creatine 0.69 0.73 0.111 Dop 625.11 761.00 0.197 GOT 24.50 24.07 0.743 GPT 22.76 17.24 0.001 GTP 31.19 30.65 0.913 Hematocrit 43.57 42.11 0.047 Hb 14.30 13.78 0.048 RBC 467.68 450.44 0.048 TG 109.16 119.75 0.339 Uric 5.29 5.39 0.639 WBC 5545.29 5798.91 0.323 Weight 58.10 53.90 0.023 ADL 11.94 11.73 0.386 Intel 3.41 3.11 0.088 SelfM 4.85 4.91 0.437 Soc 3.68 3.71 0.772 Table 8.28 p-values from independent t-test comparing means of dependent variables divided at age 70 among elderly residents of Hizen-Oshima/Sakiyama

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Chapter 9: Results VI: Associations of Allostatic Load Combined Data Sets with Physiological Variables among Elderly Hizen-Oshima and Sakiyama Residents

9.1 Introduction

Results of linear regression analyses of sociocultural and physiological measures and AL are examined next. For this analysis, all possible bivariate associations, along with all possible multivariate associations while controlling for age, sex, or age by sex are examined. Differences attributable to participants’ town of origin (Sakiyama or Hizen-

Oshima) are considered in each section to control for unidentified sociocultural and/or physiological inconsistencies between Sakiyama and Hizen-Oshima. Associations are investigated using AL scores constructed using both raw data and standardized z-scores.

9.2 Allostatic Load Associations (Quartile Cut-Offs: Raw Data)

9.2.1 Bivariate Associations

In the combined Sakiyama/Hizen-Oshima sample, AL is significantly predictive of GPT, GTP, WBC, weight, and the Intellectual Activity sub-section of the Tokyo

Metropolitan Index of Gerontology (Table 9.1). An unit increase in AL associates with

1.59 U/L and 4.33 U/L increases in the liver enzymes GPT (p=0.006) and GTP (p=0.009) respectively. AL explains about 6.0% of variation observed in this sample for both enzymes (GPT 6.0%, GTP 5.6%). A single unit increase in AL is associated with a 2.2k increase in weight (p<0.001), explaining about 9.7% of the variance in this variable. 159

White blood cell count increases 244.564 cells/mL per unit increase in AL (p=0.004).

Finally, each unit increase of AL is associated with a 0.137 point increase in Intellectual

Activity score. This increase was not sufficient enough, however, to result in a significant association between AL and the overall ADL score.

Participants’ town of origin (Sakiyama or Hizen-Oshima) was significant in several analyses; hematocrit (p=0.012), hemoglobin (p=0.046), triglycerides (p<0.001), and the total (p=0.037), and Social Role sub-section scores of the TMIG-IC (p=0.007;

Table 9.2). However, associations with town did not impact observed bivariate associations between AL and dependent variables.

Variable Regression CoE SE Pearson correlation, p R2 Creatine 0.009 0.009 0.335 0.008 Dop 63.453 35.246 0.074 0.026 GOT 0.135 0.441 0.760 0.001 GPT 1.588 0.571 0.006 0.060 GTP 4.333 1.623 0.009 0.056 Hematocrit 0.282 0.250 0.263 0.010 Hb 0.097 0.088 0.273 0.010 RBC 1.327 2.972 0.656 0.002 TG 2.673 3.745 0.477 0.004 Uric 0.097 0.077 0.211 0.013 WBC 244.564 83.974 0.004 0.066 Weight 2.151 0.598 <0.001 0.097 ADL 0.106 0.083 0.204 0.013 Intel 0.137 0.059 0.022 0.043 SelfM -0.043 0.023 0.077 0.026 Soc 0.012 0.038 0.750 0.001 Table 9.1 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using raw data and quartile cut-points)

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Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL 0.196 0.247 0.429 0.062 town 2.244 0.876 0.012 Hb AL 0.073 0.088 0.406 0.042 town 0.625 0.310 0.046 TG AL 4.885 3.490 0.164 0.158 town -57.997 12.370 <0.001 ADL AL 0.083 0.083 0.320 0.048 town 0.617 0.293 0.037 Soc AL -0.002 0.037 0.962 0.06 town 0.364 0.132 0.007 Table 9.2 Significant multivariate associations between allostatic load (independent variable), controlling for participants’ town of origin, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using raw data and quartile cut-points)

9.2.2 Multivariate Associations: AL and Age

When controlling for age, significant associations between AL and GPT

(p=0.051), GTP (p=0.009), WBC (p=0.003), and weight (p=0.001) as presented in bivariate results remain (Table 9.3). Results indicate that some aspect of AL is either results from or is predictive of changes in these variables beyond the effect of age. In addition, controlling for age reveals a significant association between AL and dopamine that was not obvious in bivariate analyses. When controlling for age, a single unit increase in AL results in a 68.97 ug/L increase in dopamine. After controlling for age, the association between AL and Intellectual Activity decreases (p=0.084). In contrast, the

Self-Maintenance sub-section of the Tokyo Metropolitan Index of Gerontology associates significantly with both AL (p=0.032) and age (p=0.025) in multivariate analyses. An unit increase in AL results in a 0.126 point increase in Self-Maintenance score, whereas a year

161 increase in age associates with a -0.023 point decrease. RBC decrease 1.108 cell/mL per year increase in age (p=0.032), a change not strongly associated with AL (p=0.782).

Similarly, creatine increases 0.003 mg/dL per year increase in age (p=0.025), but associates poorly with AL (p=0.247). Interaction between AL and age, but not AL, is borderline significantly associated with GOT (p=0.055; Table 9.4). Town associated significantly with hematocrit (p=0.021), TG (p<0.001), and Social Role (p=0.008), but did not change significance of associations observed among AL, age, and dependent variables (Table 9.5).

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Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.010 0.009 0.247 0.048 Age 0.003 0.002 0.025 Dop AL 68.973 34.959 0.051 0.056 Age 11.888 6.094 0.053 GOT AL 0.138 0.444 0.756 0.001 Age 0.007 0.077 0.933 GPT AL 1.446 0.553 0.010 0.132 Age -0.305 0.096 0.002 GTP AL 4.328 1.635 0.009 0.056 Age -0.010 0.285 0.972 Hematocrit AL -0.246 0.249 0.325 0.036 Age -0.077 0.043 0.078 Hb AL 0.084 0.087 0.339 0.037 Age -0.028 0.015 0.067 RBC AL 0.813 2.937 0.782 0.039 Age -1.108 0.512 0.032 TG AL 2.922 3.763 0.439 0.010 Age 0.535 0.656 0.416 Uric AL 0.103 0.077 0.186 0.020 Age 0.013 0.013 0.339 WBC AL 256.931 83.442 0.003 0.091 Age 26.636 14.545 0.070 Weight AL 2.043 0.590 0.001 0.134 Age -0.234 0.103 0.025 ADL AL 0.094 0.083 0.256 0.038 Age -0.025 0.014 0.080 Intel AL -0.042 0.024 0.084 0.026 Age 0.001 0.004 0.754 SelfM AL 0.126 0.058 0.032 0.082 Age -0.023 0.010 0.025 Soc AL 0.010 0.038 0.787 0.004 Age -0.004 0.007 0.571 9.3 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly residents of Sakiyama/Hizen- Oshima(SHO sample, AL constructed using raw data and quartile cut-points)

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Dep Ind Regression CoE SE Pearson correlation, p R2 GOT AL 6.837 3.478 0.052 0.032 Age 0.240 0.142 0.095 AL*age -0.980 0.050 0.055 Table 9.4 Significant multivariate associations between allostatic load (independent variable), controlling for age and interactions between AL and age, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut-points)

Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL 0.174 0.246 0.482 0.078 age -0.063 0.043 0.144 town 2.065 0.880 0.021 TG AL 4.937 3.510 0.162 0.159 age 0.147 0.613 0.81 town -57.582 12.539 <0.001 Soc AL -0.002 0.038 0.952 0.061 age -0.001 0.007 0.837 town 0.36 0.134 0.008 Table 9.5 Significant multivariate associations between allostatic load (independent variable), controlling for age and town, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut-points)

9.2.3 Multivariate Associations: AL and Sex

When controlling for sex, significant associations between AL and GPT

(p=0.010), GTP (p=0.019), WBC (p=0.017), weight (p=0.002), and Intellectual Activity

(p=0.026) presented in Section 8.2.1 are still observed (Table 9.6). Sex also is predictive of GTP (p=0.023), WBC (p=0.023), and weight (p<0.001), but not GPT (p=0.198) or

Intellectual Activity (p=.805). Associations between AL and GPT, GTP, and weight are attenuated when the interaction between AL and sex is included in calculations. Instead, the interaction term is significant (GPT p=0.027; GTP p=0.017; weight p=0.002), 164 suggesting a strong association between AL and sex mediates variation observed among these dependent variables (Table 9.7). Sex alone associates with: hematocrit (p<0.001),

Hb (p<0.001), RBC (p<0.001), uric acid (p<0.001), creatine (p<0.001), and Self

Maintenance (p=0.013). Town associated significantly with creatine (p=0.039), hematocrit (p<0.001), hemoglobin (p<0.001), TG (p<0.001), and the total (p=0.019) and

Social Role sub-section scores (p=0.004) of the TMIG-IC, but did not change significance of associations observed among AL, sex, and dependent variables (Table

9.8).

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Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL -0.001 0.007 0.877 0.378 Sex 0.177 0.021 <0.001 Dop AL 59.256 35.980 0.102 0.027 Sex 58.374 105.608 0.581 GOT AL 0.088 0.450 0.845 0.004 Sex 0.816 1.321 0.538 GPT AL 1.523 0.579 0.010 0.075 Sex 2.204 1.701 0.198 GTP AL 3.858 1.624 0.019 0.096 Sex 10.984 4.766 0.023 Hematocrit AL 0.043 0.206 0.836 0.358 Sex 4.860 0.605 <0.001 Hb AL 0.008 0.070 0.905 0.387 Sex 1.772 0.207 <0.001 RBC AL -1.118 2.629 0.671 0.250 Sex 48.462 7.717 <0.001 TG AL 1.268 3.704 0.733 0.007 Sex 8.274 10.872 0.448 Uric AL 0.037 0.069 0.589 0.239 Sex 1.209 0.203 <0.001 WBC AL 196.382 81.489 0.017 0.097 Sex 550.973 239.184 0.023 Weight AL 1.602 0.501 0.002 0.391 Sex 11.220 1.470 <0.001 ADL AL 0.112 0.084 0.187 0.021 Sex -0.258 0.248 0.300 Intel AL 0.136 0.060 0.026 0.041 Sex -0.044 0.177 0.805 SelfM AL -0.036 0.024 0.137 0.076 Sex -0.179 0.071 0.013 Soc AL 0.012 0.039 0.759 0.001 Sex -0.035 0.114 0.756 Table 9.6 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly residents of Sakiyama/Hizen- Oshima(SHO sample, AL constructed using raw data and quartile cut-points)

166

Dep Ind Regression CoE SE Pearson correlation, p R2 GPT AL 0.504 0.729 0.491 0.113 Sex -4.115 3.280 0.212 AL*sex 2.617 1.169 0.027 GTP AL 0.778 2.037 0.703 0.139 Sex -8.108 9.161 0.378 AL*sex 7.908 3.264 0.017 Weight AL 0.372 0.617 0.548 0.440 Sex 3.598 2.777 0.198 AL*sex 3.157 0.989 0.002 Table 9.7 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex), and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut-points)

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.001 0.007 0.883 0.400 Sex 0.181 0.021 <0.001 Town -0.053 0.025 0.039 Hematocrit AL -0.038 0.203 0.851 0.397 Sex 4.737 0.590 <0.001 Town 1.993 0.722 0.007 Hb AL -0.013 0.070 0.851 0.409 Sex 1.739 0.204 <0.001 Town 0.534 0.250 0.035 TG AL 3.464 3.489 0.323 0.144 Sex 11.601 10.163 0.256 Town -54.126 12.431 <0.001 ADL AL 0.083 0.084 0.323 0.065 Sex -0.301 0.244 0.219 Town 0.709 0.298 0.019 Soc AL -0.004 0.038 0.915 0.069 Sex -0.060 0.111 0.591 Town 0.395 0.135 0.004 Table 9.8 Significant multivariate associations between allostatic load (independent variable), controlling for sex and town, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut-points) 167

9.2.4 Multivariate Associations: AL, Age, and Sex

Multivariate associations controlling for age and sex reiterate correlations observed in previous sections. AL remains a significant predictor of weight (p=0.003),

GPT (p=0.018), GTP (p=0.021), WBC (p=0.012), and Intellectual Activity (p=0.042).

Among these associations, age also associates significantly with GPT (p=0.002) and

Intellectual Activity (p=0.023), while sex associates significantly with GTP (p=0.023) and WBC (p=0.029). Both sex and age associate significantly with weight (age p=0.001; sex p<0.001). Similar associations to those observed in Sections 9.2.2 and 9.2.3 between age, sex, town, and dependent variables are also observed (Table 9.9 and 9.11). The interaction between age and sex associates significantly with creatine (p=0.048; Table

9.10).

168

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.000 0.007 0.958 0.400 age 0.003 0.001 0.039 sex 0.175 0.021 <0.001 Dop AL 65.951 35.789 0.068 0.055 age 11.568 6.190 0.064 sex 45.706 104.739 0.663 GOT AL 0.089 0.454 0.844 0.004 age 0.002 0.079 0.982 sex 0.814 1.329 0.542 GPT AL 1.343 0.561 0.018 0.150 age -0.311 0.097 0.002 sex 2.545 1.641 0.124 GTP AL 3.831 1.639 0.021 0.096 age -0.046 0.283 0.871 sex 11.034 4.796 0.023 Hematocrit AL -0.015 0.201 0.939 0.400 age -0.100 0.035 0.005 sex 4.969 0.588 <0.001 Hb AL -0.013 0.068 0.851 0.433 age -0.037 0.012 0.002 sex 1.812 0.200 <0.001 RBC AL -1.902 2.554 0.458 0.305 age -1.355 0.442 0.003 sex 49.946 7.474 <0.001 TG AL 1.448 3.735 0.699 0.008 age 0.312 0.646 0.631 sex 7.932 10.930 0.469 Uric AL 0.042 0.070 0.548 0.242 age 0.008 0.012 0.520 sex 1.200 0.204 <0.001 WBC AL 208.250 81.519 0.012 0.113 age 20.506 14.100 0.149 sex 528.517 238.572 0.029 Continued

Table 9.9 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut- points)

169

Table 9.9 continued

Dep Ind Regression CoE SE Pearson correlation, p R2 Weight AL 1.436 0.482 0.003 0.447 age -0.287 0.083 0.001 sex 11.535 1.410 <0.001 ADL AL 0.097 0.084 0.252 0.047 age -0.026 0.015 0.076 sex -0.229 0.246 0.353 Intel AL 0.122 0.060 0.042 0.083 age -0.024 0.010 0.023 sex -0.018 0.174 0.919 SelfM AL -0.035 0.024 0.152 0.078 age 0.002 0.004 0.668 sex -0.181 0.071 0.012 Soc AL 0.010 0.039 0.806 0.004 age -0.004 0.007 0.549 sex -0.031 0.114 0.787

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.001 0.007 0.839 0.420 Age 8.59E-5 0.002 0.961 Sex -0.157 0.167 0.350 Age*sex 0.005 0.002 0.048 Table 9.10 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut-points)

170

Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL -0.081 0.198 0.685 0.430 Age -0.089 0.034 0.010 Sex 4.849 0.578 <0.001 Town 1.761 0.710 0.015 TG AL 3.456 3.516 0.328 0.144 Age -0.016 0.608 -0.002 Sex 11.621 10.235 0.258 Town -54.168 12.583 <0.001 ADL AL 0.073 0.083 0.386 0.084 Age -0.022 0.014 0.128 Sex -0.274 0.243 0.262 Town 0.652 0.299 0.031 Soc AL -0.005 0.038 0.899 0.069 Age -0.002 0.007 0.798 Sex -0.058 0.112 0.606 Town 0.391 0.137 0.005 Table 9.11 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and town, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and quartile cut-points)

9.3 Allostatic Load Associations (Decile Cut-Offs: Raw Data)

9.3.1 Bivariate Associations

Using decile cut-points, AL associates significantly with weight (p=0.001) and the Self Maintenance sub-section of the Tokyo Metropolitan Index of Competence

(p=0.018) (Table 9.3.1). A single unit increase in AL correlates with a 2.96k increase in weight and explains 8.2% of variation observed for this variable. In contrast, an unit increase in AL associates with a .086 point decrease in Self-Maintenance score, explaining 4.6% of the variation in this variable. Significant associations between AL and

171

GPT, GTP, and WBC observed when AL was constructed using quartile cut-points are no longer observed.

Participants’ town of origin (Sakiyama or Hizen-Oshima) was significant in several analyses; hematocrit (p=0.012), hemoglobin (p=0.050), triglycerides (p<0.001), and the total (p=0.038), and Social Role sub-section scores of the TMIG-IC (p=0.010;

Table 9.13). Associations with town did not impact observed bivariate associations between AL and dependent variables, except for triglycerides for which a borderline association is observed when town is taken into consideration (p=0.071).

Variable Regression CoE SE Pearson correlation, p R2 Creatine 0.008 0.013 0.548 0.003 Dop 15.933 53.215 0.765 0.001 GOT -0.368 0.656 0.576 0.003 GPT 1.174 0.872 0.181 0.015 GTP 2.647 2.477 0.287 0.009 Hematocrit 0.437 0.373 0.244 0.011 Hb 0.162 0.131 0.219 0.012 RBC 4.192 4.417 0.344 0.007 TG 5.562 5.572 0.320 0.008 Uric 0.144 0.115 0.113 0.213 WBC 184.475 128.388 0.153 0.017 Weight 2.963 0.899 0.001 0.082 ADL 0.146 0.124 0.240 0.011 Intel 0.157 0.089 0.079 0.025 SelfM -0.086 0.036 0.018 0.046 Soc 0.075 0.056 0.186 0.014 Table 9.12 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using raw data and decile cut-points)

172

Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL 0.291 0.370 0.433 0.062 town 2.230 0.879 0.012 Hb AL 0.121 0.131 0.355 0.044 town 0.615 0.311 0.050 TG AL 9.436 5.189 0.071 0.168 town -59.151 12.340 <0.001 ADL AL 0.106 0.124 0.393 0.046 town 0.617 0.294 0.038 Soc AL 0.052 0.056 0.350 0.067 town 0.344 0.132 0.010 Table 9.13 Significant multivariate associations between allostatic load (independent variable), controlling for participants’ town of origin, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using raw data and decile cut-points)

9.3.2 Multivariate Associations: AL and Age

When controlling for age, significant associations between AL and weight

(p=0.003) and Self Maintenance are observed (Table 9.14). Age is also a significant predictor of weight (p=0.036), which a year increase in age associating with a 0.221k decrease in weight when controlling for AL. A moderate association between AL and triglycerides (p=0.065) is observed when controlling for age and the interaction between age and AL (p=0.047). Age, but neither AL nor the relevant interaction term, associates significantly with creatine (p=0.025), dopamine (p=0.070), GPT (p=0.002), RBC

(p=0.039), and Intellectual Activity (p=0.029). Town associated significantly with hematocrit (p=0.021), TG (p<0.001), and Social Role (p=0.012), but did not change significance of associations observed among AL, age, and dependent variables (Table

9.16).

173

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.012 0.013 0.368 0.044 Age 0.004 0.002 0.025 Dop AL 28.580 53.163 0.592 0.028 Age 11.351 6.217 0.070 GOT AL -0.370 0.665 0.579 0.003 Age -0.001 0.078 0.990 GPT AL 0.825 0.849 0.333 0.090 Age -0.313 0.099 0.002 GTP AL 2.612 2.509 0.300 0.009 Age -0.031 0.293 0.915 Hematocrit AL 0.353 0.373 0.346 0.035 Age -0.075 0.044 0.087 Hb AL 0.131 0.131 0.317 0.038 Age -0.027 0.015 0.076 RBC AL 2.995 4.394 0.497 0.042 Age -1.074 0.514 0.039 TG AL 6.218 5.624 0.271 0.015 Age 0.588 0.658 0.373 Uric AL 0.159 0.116 0.172 0.021 Age 0.014 0.014 0.306 WBC AL 213.744 128.403 0.099 0.041 Age 26.269 15.016 0.083 Weight AL 2.716 0.894 0.003 0.115 Age -0.221 0.104 0.036 ADL AL 0.119 0.124 0.341 0.035 Age -0.025 0.015 0.088 Intel AL 0.132 0.088 0.137 0.063 Age -0.023 0.010 0.029 SelfM AL -0.085 0.036 0.020 0.046 Age 0.001 0.004 0.881 Soc AL 0.072 0.057 0.211 0.016 Age -0.003 0.007 0.671 Table 9.14 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly residents of Sakiyama/Hizen- Oshima(SHO sample, AL constructed using raw data and decile cut-points)

174

Dep Ind Regression CoE SE Pearson correlation, p R2 TG AL -87.853 47.097 0.065 0.047 Age -0.615 0.883 0.488 AL*age 1.406 0.699 0.047 Table 9.15 Significant multivariate associations between allostatic load (independent variable), controlling for age and interactions between AL and age (AL*age), and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut-points)

Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL 0.232 0.370 0.531 0.078 age -0.062 0.043 0.153 town 2.066 0.883 0.021 TG AL 9.640 5.240 0.068 0.168 age 0.217 0.612 0.723 town -58.577 12.490 <0.001 Soc AL 0.052 0.056 0.360 0.067 age -0.001 0.007 0.919 town 0.342 0.134 0.012 Table 9.16 Significant multivariate associations between allostatic load (independent variable), controlling for age and town, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut-points)

9.3.3 Multivariate Associations: AL and Sex

Controlling for sex, significant associations between AL and weight (p=0.004) and Self Maintenance (p=0.037) are observed (Table 9.17). Sex also explains a significant amount of variation in both variables (weight p<0.001; SelfM p<0.001).

Together, AL and sex explain 38.5% of the total variation in weight and 9.3% of the variation in Self Maintenance scores among this combined sample of elderly

Sakiyama/Hizen-Oshima residents. Sex, but not AL, also associates significantly with 175 creatine (p<0.001), GTP (p=0.015), hematocrit (p<0.001), hemoglobin (p<0.001), RBC

(p<0.001), uric acid (p<0.001), and WBC (p<0.001). An interaction term assessing combined effects of AL and sex was significantly associated with creatine (p=0.054) and hemoglobin (p=0.038); AL was significant in neither of these analyses (Table 9.18).Town associated significantly with creatine (p=0.042), hematocrit (p<0.001), hemoglobin

(p=0.029), TG (p<0.001), and the total (p=0.019) and Social Role sub-section scores

(p=0.007) of the TMIG-IC, but did not change significance of associations observed among AL, sex, interactions terms, and dependent variables (Table 9.19).

176

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL -0.004 0.011 0.728 0.379 Sex 0.178 0.021 <0.001 Dop AL 10.543 53.909 0.845 0.005 Sex 79.218 106.694 0.459 GOT AL -0.432 0.666 0.518 0.007 Sex 0.961 1.318 0.467 GPT AL 1.010 0.878 0.252 0.032 Sex 2.551 1.738 0.145 GTP AL 1.863 2.457 0.450 0.058 Sex 12.041 4.862 0.015 Hematocrit AL 0.120 0.305 0.696 0.359 Sex 4.846 0.604 <0.001 Hb AL 0.046 0.104 0.660 0.388 Sex 1.764 0.207 <0.001 RBC AL 1.061 3.898 0.786 0.249 Sex 47.748 7.714 <0.001 TG AL 4.958 5.472 0.367 0.012 Sex 7.513 10.830 0.489 Uric AL 0.065 0.102 0.528 0.240 Sex 1.207 0.202 <0.001 WBC AL 143.158 122.959 0.247 0.064 Sex 592.471 243.355 0.016 Weight AL 2.222 0.746 0.004 0.385 Sex 11.290 1.476 <0.001 ADL AL 0.162 0.125 0.197 0.020 Sex -0.255 0.248 0.306 Intel AL 0.159 0.090 0.080 0.260 Sex -0.030 0.178 0.865 SelfM AL -0.075 0.035 0.037 0.093 Sex -0.174 0.070 0.014 Soc AL 0.078 0.057 0.175 0.016 Sex -0.051 0.113 0.655 Table 9.17 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly residents of Sakiyama/Hizen- Oshima(SHO sample, AL constructed using raw data and decile cut-points)

177

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.021 0.017 0.200 0.398 sex 0.215 0.028 <0.001 AL*sex -0.042 0.021 0.054 Hb AL -0.221 0.164 0.180 0.410 sex 1.367 0.278 <0.001 AL*sex 0.442 0.210 0.038 Table 9.18 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex), and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut-points)

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.026 0.017 0.12 0.421 sex 0.22 0.028 <0.001 AL*sex -0.043 0.021 0.042 town -0.054 0.025 0.032 Hematocrit AL -0.003 0.300 0.993 0.397 sex 4.724 0.590 <0.001 town 1.975 0.722 0.007 Hb AL -0.265 0.162 0.106 0.433 sex 1.319 0.274 <0.001 AL*sex 0.458 0.207 0.029 town 0.542 0.246 0.030 TG AL 8.390 5.138 0.105 0.156 sex 10.933 10.083 0.280 town -55.350 12.352 <0.001 ADL AL 0.118 0.124 0.343 0.065 sex -0.299 0.244 0.223 town 0.709 0.298 0.019 Soc AL 0.055 0.056 0.332 0.076 sex -0.074 0.110 0.505 town 0.374 0.135 0.007 Table 9.19 Significant multivariate associations between allostatic load (independent variable), controlling for sex, interactions between AL and sex (AL*sex), and town, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut-points)

178

9.3.4 Multivariate Associations: AL, Age, and Sex

When controlling for age and sex, AL remains a significant predictor of weight

(p=0.010) and Self Maintenance (p=0.043) (Table 9.20). Age and sex also associate significantly with weight (age p=0.001; p<0.001); the full model explains 43.8% of variation observed in this variable. Sex, but not age, associates significantly with Self

Maintenance (p=0.014). The model explains 9.4% of the variance observed in Self

Maintenance scores. Including the previously identified significant term controlling for interaction between AL and sex, a marginally significant association between AL and triglycerides was observed (p=0.066; Table 9.21). In the multivariate model, age associates independently with GPT (p=0.001) and Intellectual Activity (p=0.024) while sex associates independently with GTP (p=0.015), uric acid (p<0.001), and WBC

(p=0.021). Both sex and age, but not AL, associate with creatine (age p=0.041; sex p<0.001), hematocrit (age p=0.005; sex p<0.001), Hb (age p=0.003; sex p<0.001), and

RBC (age p=0.003; sex p<0.001). Similar associations to those observed in Sections 9.2.2 and 9.2.3 between age, sex, interactions, town, and dependent variables are also observed

(Table 9.22).

179

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL -0.001 0.011 0.951 0.400 age 0.003 0.001 0.041 sex 0.175 0.021 <0.001 Dop AL 23.480 54.005 0.665 0.030 age 10.809 6.304 0.089 sex 66.568 106.092 0.532 GOT AL -0.440 0.675 0.515 0.007 age -0.007 0.079 0.929 sex 0.969 1.326 0.466 GPT AL 0.622 0.853 0.467 0.112 age -0.325 0.100 0.001 sex 2.931 1.676 0.083 GTP AL 1.762 2.491 0.481 0.059 age -0.084 0.291 0.774 sex 12.139 4.893 0.015 Hematocrit AL 0.000 0.299 1.000 0.400 age -0.100 0.035 0.005 sex 4.963 0.588 <0.001 Hb AL 0.002 0.102 0.981 0.433 age -0.036 0.012 0.003 sex 1.806 0.200 <0.001 RBC AL -0.531 3.811 0.889 0.302 age -1.330 0.445 0.003 sex 49.305 7.487 <0.001 TG AL 5.406 5.542 0.331 0.015 age 0.375 0.647 0.564 sex 7.075 10.887 0.517 Uric AL 0.075 0.104 0.473 0.243 age 0.008 0.012 0.495 sex 1.197 0.203 <0.001 WBC AL 166.639 123.739 0.181 0.078 age 19.618 14.444 0.177 sex 569.512 243.084 0.021 Continued

Table 9.20 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut-points)

180

Table 9.20 continued

Dep Ind Regression CoE SE Pearson correlation, p R2 Weight AL 1.886 0.723 0.010 0.438 Age -0.281 0.084 0.001 Sex 11.619 1.421 <0.001 ADL AL 0.132 0.125 0.295 0.045 Age -0.026 0.015 0.083 Sex -0.225 0.246 0.363 Intel AL 0.130 0.089 0.147 0.067 Age -0.024 0.010 0.024 Sex -0.002 0.176 0.989 SelfM AL -0.073 0.036 0.043 0.094 Age 0.001 0.004 0.771 Sex -0.175 0.070 0.014 Soc AL 0.074 0.058 0.202 0.018 Age -0.003 0.007 0.656 Sex -0.047 0.114 0.68

Dep Ind Regression CoE SE Pearson correlation, p R2 Hb AL -0.261 0.158 0.102 0.454 Age -0.036 0.012 0.002 Sex 1.414 0.269 <0.001 AL*sex 0.436 0.203 0.034 TG AL -85.452 46.011 0.066 0.047 Age -0.785 0.865 0.366 Sex 7.439 10.755 0.490 AL*sex 1.358 0.683 0.049 Table 9.21 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut-points)

181

Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL -0.096 0.296 0.745 0.430 age -0.090 0.034 0.010 sex 4.843 0.577 <0.001 town 1.754 0.710 0.015 TG AL 12.755 8.211 0.123 0.159 age 0.050 0.607 0.935 sex 17.263 13.880 0.216 AL*sex -7.103 10.466 0.499 town -55.541 12.532 <0.001 ADL AL 0.096 0.124 0.444 0.083 age -0.022 0.014 0.135 sex -0.270 0.243 0.269 town 0.656 0.299 0.030 Soc AL 0.054 0.057 0.346 0.076 age -0.001 0.007 0.896 sex -0.072 0.111 0.515 town 0.372 0.137 0.008 Table 9.22 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and town, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using raw data and decile cut-points)

9.4 Allostatic Load Associations (Quartile Cut-Offs: Standardized z-scores)

9.4.1 Bivariate Associations

Among the combined SHO sample, AL is strongly predictive of dopamine

(p=0.009), WBC (p=0.002), and weight (p=0.002) (Table 9.23). An unit increase in AL associates with a 90.235 ug/L increase in dopamine, a 255.614 cell/mL in WBC, and a

1.8416k increase in weight. These variables represent a variety of somatic functions which are reportedly altered during senescence and associated with frailty. Town was significant in several analyses; hematocrit (p=0.004), hemoglobin (p=0.019),

182 triglycerides (p<0.001), and the total (p=0.025), and Social Role sub-section scores of the

TMIG-IC (p=0.010; Table 9.24). However, associations with town did not impact observed bivariate associations between AL and dependent variables.

Variable Regression CoE SE Pearson correlation, p R2 Creatine 0.010 0.009 0.279 0.010 Dop 90.235 33.997 0.009 0.055 GOT 0.164 0.431 0.704 0.001 GPT 1.001 0.570 0.081 0.025 GTP 2.797 1.615 0.086 0.024 Hematocrit 0.248 0.246 0.314 0.008 Hb 0.108 0.086 0.212 0.013 RBC 2.916 2.900 0.317 0.008 TG 5.230 3.644 0.154 0.017 Uric 0.084 0.076 0.267 0.010 WBC 255.614 81.825 0.002 0.075 Weight 1.846 0.593 0.002 0.074 ADL -0.001 0.082 0.993 0.000 Intel 0.082 0.059 0.164 0.016 SelfM -0.042 0.024 0.082 0.025 Soc -0.041 0.037 0.268 0.010 Table 9.23 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points)

183

Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL 0.355 0.241 0.143 0.073 town 2.534 0.873 0.004 Hb AL 0.139 0.065 0.106 0.058 town 0.736 0.309 0.019 TG AL 2.954 3.443 0.393 0.150 town -54.025 12.465 <0.001 ADL AL 0.028 0.081 0.735 0.041 town 0.671 0.295 0.025 Soc AL -0.026 0.037 0.470 0.064 town 0.349 0.132 0.010 Table 9.24 Significant multivariate associations between allostatic load (independent variable), controlling for participants’ town of origin, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points)

9.4.2 Multivariate Associations: AL and Age

When controlling for age, significant associations between AL and weight

(p=0.004), dopamine (p=0.005), and WBC (p=0.001) presented in Section 9.4.1 are still observed (Table 9.4.2). In the multivariate model, age is also significantly predictive of weight (p=0.024) and dopamine (p=0.043), but less so for WBC (p=0.067). Results indicate that AL is an important predictor of these variables independent of advancing age. Age, but not AL, associates with dependent variables creatine (p=0.025), GPT

(p=0.002), RBC (p=0.035), and Intellectual Activity score (p=0.023). Town associated significantly with hematocrit (p=0.009), hemoglobin (p=0.036), TG (p<0.001), and total

(p=0.046) and Social Role sub-section scores (p=0.012) of the TMIG-IC, but did not change significance of associations observed among AL, age, and dependent variables

184

(Table 9.26). Interactions between AL and age did not associate significantly with any dependent variable; data are omitted.

185

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.011 0.009 0.202 0.051 age 0.004 0.002 0.025 Dop AL 95.620 33.662 0.005 0.087 age 12.247 5.992 0.043 GOT AL 0.167 0.435 0.701 0.001 age 0.007 0.077 0.929 GPT AL 0.863 0.551 0.120 0.101 age -0.313 0.098 0.002 GTP AL 2.783 1.627 0.090 0.024 age -0.032 0.290 0.912 Hematocrit AL 0.214 0.244 0.382 0.034 age -0.078 0.043 0.077 Hb AL 0.095 0.085 0.266 0.040 age -0.028 0.015 0.068 RBC AL 2.438 2.868 0.397 0.044 age -1.086 0.511 0.035 TG AL 5.481 3.659 0.137 0.023 age 0.570 0.651 0.383 Uric AL 0.090 0.076 0.238 0.017 age 0.013 0.013 0.347 WBC AL 267.370 81.270 0.001 0.100 age 26.737 14.467 0.067 Weight AL 1.741 0.585 0.004 0.113 age -0.238 0.104 0.024 ADL AL -0.013 0.081 0.878 0.028 age -0.027 0.014 0.065 Intel AL 0.072 0.058 0.218 0.058 age -0.024 0.010 0.023 SelfM AL -0.041 0.024 0.088 0.026 age 0.001 0.004 0.749 Soc AL -0.043 0.037 0.248 0.014 age -0.005 0.007 0.495 Table 9.25 Multivariate associations between allostatic load (independent variable) controlling for age and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points)

186

Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL 0.321 0.242 0.187 0.088 age -0.059 0.043 0.170 town 2.338 0.881 0.009 Hb AL 0.125 0.085 0.085 0.075 age -0.023 0.015 0.135 town 0.661 0.311 0.036 TG AL 3.041 3.475 0.383 0.150 age 0.149 0.618 0.810 town -53.532 12.680 <0.001 ADL AL 0.015 0.081 0.857 0.060 age -0.022 0.014 0.128 town 0.598 0.297 0.046 Soc AL -0.028 0.037 0.457 0.065 age -0.002 0.007 0.780 town 0.343 0.135 0.012 Table 9.26 Significant multivariate associations between allostatic load (independent variable), controlling for age and town, and dependent variables among elderly residents of Sakiyama/Hizen-Oshima (SHO sample, AL constructed using z-scores and quartile cut-points)

9.4.3 Multivariate Associations: AL and Sex

Reminiscent of the results seen in Section 9.4.2, but when controlling for sex instead of age, significant associations between AL and weight (p=0.006), dopamine

(p=0.014), and WBC (p=0.012) are still observed (Table 9.27). In the multivariate model, sex also is significantly predictive of weight (p<0.001) and WBC (p=0.020), but not of dopamine (p=0.612). Results indicate that AL is an important predictor of these variables independent of sex. Sex, but not AL, associates with dependent variables creatine (p<0.001), GTP (p=0.017), hematocrit (p<0.001), Hb (p<0.001), RBC (p<0.001), uric acid (p<0.001), and Self Maintenance score (p=0.011).Interaction between AL and

187 sex is significant only to predictions of weight (p=0.023) (Table 9.28). Town associated significantly with hematocrit (p=0.009), hemoglobin (p=0.036), TG (p<0.001), and the total (p=0.046) and Social Role sub-section scores (p=0.006) of the TMIG-IC, but did not change significance of associations observed among AL, sex, and dependent variables

(Table 9.29).

188

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.001 0.007 0.925 0.378 sex 0.177 0.021 <0.001 Dop AL 87.119 34.831 0.014 0.055 sex 52.849 103.799 0.612 GOT AL 0.122 0.442 0.783 0.004 sex 0.810 1.317 0.540 GPT AL 0.960 0.579 0.100 0.044 sex 2.489 1.724 0.152 GTP AL 2.400 1.617 0.140 0.071 sex 11.715 4.819 0.017 Hematocrit AL 0.045 0.202 0.824 0.358 sex 4.862 0.603 <0.001 Hb AL 0.033 0.069 0.638 0.388 sex 1.765 0.206 <0.001 RBC AL 0.861 2.583 0.739 0.249 sex 47.731 7.697 <0.001 TG AL 3.535 3.625 0.331 0.013 sex 7.598 10.802 0.483 Uric AL 0.034 0.068 0.621 0.239 sex 1.212 0.202 <0.001 WBC AL 203.523 79.805 0.012 0.102 sex 561.077 237.821 0.020 Weight AL 1.380 0.497 0.006 0.379 sex 11.396 1.480 <0.001 ADL AL -0.003 0.083 0.970 0.006 sex -0.212 0.249 0.395 Intel AL 0.078 0.060 0.197 0.014 sex -0.016 0.179 0.930 SelfM AL -0.037 0.024 0.122 0.078 sex -0.181 0.070 0.011 Soc AL -0.044 0.038 0.245 0.012 sex -0.016 0.113 0.888 Table 9.27 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points)

189

Dep Ind Regression CoE SE Pearson correlation, p R2 Weight AL 0.454 0.631 0.474 0.406 sex 5.449 2.957 0.680 AL*sex 2.296 0.994 0.023 Table 9.28 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex), and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points)

Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL 0.321 0.242 0.187 0.088 sex -0.059 0.043 0.170 town 2.338 0.881 0.009 Hb AL 0.125 0.085 0.144 0.075 sex -0.023 0.015 0.135 town 0.661 0.311 0.036 TG AL 3.041 3.475 0.383 0.150 sex 0.149 0.618 0.810 town -53.532 12.680 <0.001 ADL AL 0.015 0.081 0.857 0.060 sex -0.022 0.014 0.128 town 0.598 0.297 0.046 Soc AL -0.029 0.037 0.431 0.073 sex -0.050 0.111 0.650 town 0.378 0.135 0.006 Table 9.29 Significant multivariate associations between allostatic load (independent variable), controlling for sex and town, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points)

9.4.4 Multivariate Associations: AL, Age, and Sex

Multivariate associations controlling for age and sex reiterate correlations observed in previous sections. AL remains a significant predictor of dopamine (p=0.008),

190

WBC (p=0.008) and weight (p=0.012) (Table 9.30). In addition to AL, age associates with dopamine (p=0.049), sex associates with WBC (p=0.025), and both age and sex associate with weight (age p=0.001; sex p<0.001). Results indicate that AL is a robust predictor of these dependent variables, even after partialing out effects of age and sex. A term controlling for interaction between AL and sex associates significantly with weight

(p=0.032), highlighting the synergistic effects of these independent variables (Table

9.31). Similar associations to those observed in Section 9.4.2 and 9.4.3 between age, sex, town, and dependent variables are also observed (Table 9.32).In addition, interaction between age and sex significantly associates with creatine (p=0.047; Table 9.31).

191

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.002 0.007 0.758 0.401 age 0.003 0.001 0.037 sex 0.174 0.021 <0.001 Dop AL 94.079 34.585 0.008 0.085 age 12.104 6.091 0.049 sex 40.054 102.738 0.697 GOT AL 0.124 0.446 0.782 0.004 age 0.002 0.079 0.976 sex 0.807 1.325 0.543 GPT AL 0.775 0.559 0.168 0.123 age -0.321 0.099 0.001 sex 2.828 1.662 0.091 GTP AL 2.360 1.632 0.151 0.071 age -0.070 0.287 0.807 sex 11.790 4.848 0.017 Hematocrit AL -0.012 0.197 0.950 0.400 age -0.100 0.035 0.005 sex 4.967 0.586 <0.001 Hb AL 0.012 0.067 0.862 0.433 age -0.036 0.012 0.003 sex 1.803 0.200 <0.001 RBC AL 0.102 2.514 0.968 0.302 age -1.320 0.443 0.003 sex 49.126 7.469 <0.001 TG AL 3.739 3.654 0.308 0.016 age 0.353 0.644 0.584 sex 7.224 10.855 0.507 Uric AL 0.038 0.068 0.579 0.241 age 0.008 0.012 0.522 sex 1.204 0.203 <0.001 WBC AL 215.452 79.823 0.008 0.119 age 20.743 14.059 0.143 sex 539.150 237.120 0.025 Continued

Table 9.30 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly Sakiyama/Hizen- Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points)

192

Table 9.30 continued

Dep Ind Regression CoE SE Pearson correlation, p R2 Weight AL 1.213 0.478 0.012 0.436 age -0.290 0.084 0.001 sex 11.703 1.419 <0.001 ADL AL -0.019 0.083 0.816 0.036 age -0.028 0.015 0.057 sex -0.183 0.247 0.460 Intel AL 0.064 0.059 0.285 0.059 age -0.025 0.010 0.019 sex 0.010 0.176 0.953 SelfM AL -0.036 0.024 0.136 0.079 age 0.002 0.004 0.673 sex -0.183 0.071 0.011 Soc AL -0.047 0.038 0.219 0.017 age -0.005 0.007 0.452 sex -0.011 0.113 0.926

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.003 0.007 0.688 0.421 age 0.000 0.002 0.951 sex -0.158 0.167 0.346 Age*Sex 0.005 0.002 0.047 Weight AL 0.383 0.606 0.529 0.458 age -0.278 0.083 0.001 sex 6.312 2.848 0.029 AL*Sex 2.077 0.956 0.032 Table 9.31 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points)

193

Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL 0.065 0.196 0.742 0.430 age -0.087 0.034 0.013 sex 4.794 0.578 <0.001 town 1.761 0.713 0.015 TG AL 1.482 3.478 0.671 0.138 age -0.033 0.612 0.957 sex 12.320 10.278 0.233 town -51.668 12.675 <0.001 ADL AL 0.011 0.083 0.895 0.078 age -0.023 0.015 0.118 sex -0.251 0.244 0.306 town 0.692 0.301 0.023 Soc AL -0.031 0.038 0.412 0.074 age -0.002 0.007 0.729 sex -0.047 0.111 0.673 town 0.371 0.137 0.008 Table 9.32 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and town, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and quartile cut-points)

9.5 Allostatic Load Associations (Decile Cut-Offs: Standardized z-scores)

9.5.1 Bivariate Associations

Among the combined SHO sample and mirroring results seen when using quartile, as opposed to decile cut-points, AL is strongly predictive of dopamine

(p=0.012), WBC (p=0.047), and weight (p=0.003) (Table 9.33).

An unit increase in AL associates with a 115.648 ug/L increase in dopamine, a 224.843 cell/mL in WBC, and a 2.375k increase in weight. These variables represent a variety of somatic functions which are reportedly altered during senescence and associated with the construct of frailty.

194

Town was significant in several analyses; hematocrit (p=0.008), hemoglobin

(p=0.033), triglycerides (p<0.001), and the total (p=0.026), and Social Role sub-section scores of the TMIG-IC (p=0.006; Table 9.34). However, associations with town did not impact observed bivariate associations between AL and dependent variables.

Variable Regression CoE SE Pearson correlation, p R2 Creatine 0.000 0.012 0.990 0.000 Dop 115.648 45.536 0.012 0.051 GOT 0.289 0.576 0.617 0.002 GPT 1.416 0.760 0.065 0.028 GTP 3.303 2.164 0.130 0.019 Hematocrit 0.390 0.328 0.236 0.012 Hb 0.138 0.115 0.231 0.012 RBC 4.362 3.872 0.262 0.010 TG 7.739 4.861 0.114 0.021 Uric 0.091 0.101 0.371 0.007 WBC 224.843 111.815 0.047 0.032 Weight 2.375 0.795 0.003 0.069 ADL 0.075 0.109 0.493 0.004 Intel 0.086 0.079 0.276 0.010 SelfM -0.058 0.032 0.070 0.027 Soc 0.047 0.050 0.246 0.007 Table 9.33 Bivariate associations between allostatic load (independent variable) and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points)

195

Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL 0.394 0.319 0.220 0.069 town 2.343 0.865 0.008 Hb AL 0.139 0.113 0.220 0.049 town 0.661 0.306 0.033 TG AL 7.642 4.508 0.093 0.165 town -55.559 12.211 <0.001 ADL AL 0.076 0.107 0.479 0.045 town 0.657 0.291 0.026 Soc AL 0.048 0.048 0.326 0.068 town 0.364 0.131 0.006 Table 9.34 Significant multivariate associations between allostatic load (independent variable), controlling for participants’ town of origin, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points)

9.5.2 Multivariate Associations: AL and Age

When controlling for age, significant associations between AL and weight

(p=0.004), dopamine (p=0.009), and WBC (p=0.038) presented in Section 9.5.1 are still observed (Table 9.35). However, when interaction between AL and age is controlled

(p=0.058), the relationship between AL and dopamine is attenuated (p=0.113), indicating this interaction is important to predicting dopamine levels. In the multivariate model, age is also significantly predictive of weight (p=0.018) and dopamine (p=0.055), but less so for WBC (p=0.101). Results indicate that AL is an important predictor of changes in these variables independent of advancing age. Additional borderline associations are observed between AL and GPT (p=0.077) and Self Maintenance score (p=0.074). Age, but not AL, associates with dependent variables creatine (p=0.025), GPT (p=0.032), RBC

(p=0.033), and Intellectual Activity score (p=0.020). Town associated significantly with

196 hematocrit (p=0.015), TG (p<0.001), and the total (p=0.046) and Social Role sub-section scores (p=0.007) of the TMIG-IC, but did not change significance of associations observed among AL, age, and dependent variables (Table 9.36). Interactions between AL and age associated marginally with dopamine and no other dependent variables; data are omitted.

197

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.001 0.012 0.909 0.038 age 0.003 0.002 0.032 Dop AL 119.706 45.073 0.009 0.080 age 11.655 6.004 0.055 GOT AL 0.292 0.579 0.616 0.002 age 0.006 0.077 0.934 GPT AL 1.306 0.733 0.077 0.107 age -0.317 0.098 0.001 GTP AL 3.285 2.175 0.134 0.019 age -0.051 0.290 0.861 Hematocrit AL 0.363 0.325 0.266 0.038 age -0.078 0.043 0.073 Hb AL 0.128 0.114 0.262 0.040 age -0.029 0.015 0.062 RBC AL 3.980 3.819 0.299 0.047 age -1.095 0.509 0.033 TG AL 7.928 4.872 0.106 0.026 age 0.543 0.649 0.405 Uric AL 0.095 0.101 0.351 0.013 age 0.012 0.013 0.373 WBC AL 233.358 111.144 0.038 0.054 age 24.454 14.806 0.101 Weight AL 2.289 0.781 0.004 0.111 age -0.248 0.104 0.018 ADL AL 0.066 0.108 0.543 0.031 age -0.026 0.014 0.070 Intel AL 0.078 0.077 0.317 0.054 age -0.024 0.010 0.020 SelfM AL -0.057 0.032 0.074 0.028 age 0.002 0.004 0.710 Soc AL 0.046 0.050 0.361 0.010 age -0.004 0.007 0.583 Table 9.35 Multivariate associations between allostatic load (independent variable), controlling for age, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points)

198

Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL 0.372 0.318 0.245 0.085 age -0.063 0.043 0.146 town 2.155 0.870 0.015 TG AL 7.693 4.531 0.092 0.165 age 0.143 0.610 0.815 town -55.131 12.395 <0.001 ADL AL 0.069 0.107 0.523 0.063 age -0.022 0.014 0.128 town 0.591 0.293 0.046 Soc AL 0.047 0.048 0.332 0.068 age -0.001 0.007 0.875 town 0.361 0.133 0.007 Table 9.36 Multivariate associations between allostatic load (independent variable), controlling for age and town, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points)

9.5.3 Multivariate Associations: AL and Sex

Reminiscent of the results seen in Section 9.5.2 but when controlling for sex instead of age, significant associations between AL and weight (p=0.007), dopamine

(p=0.015), and WBC (p=0.065) are still observed (Table 9.37). In the multivariate model, sex is also significantly predictive of weight (p<0.001) and WBC (p=0.018), and the interaction between AL and sex is marginally predictive of dopamine (p=0.051; Table

9.38). Results indicate that AL is an important predictor of changes in weight and WBC independent of sex, while the interaction between the two independent variables must be taken considered when predicting dopamine. Sex, but not AL, associates with dependent variables creatine (p<0.001), GTP (p=0.016), hematocrit (p<0.001), Hb (p<0.001), RBC

(p<0.001), uric acid (p<0.001), and Self Maintenance score (p=0.012).Interaction

199 between AL and sex associates significantly with creatine (p=0.039) and GTP (p=0.033).

Town associated significantly with hematocrit (p<0.001), hemoglobin (p<0.001), TG

(p<0.001), and the total (p=0.012) and Social Role sub-section scores (p=0.004) of the

TMIG-IC, but did not change significance of associations observed among AL, sex, and dependent variables (Table 9.39).

200

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL -0.009 0.009 0.345 0.383 sex 0.179 0.021 <0.001 Dop AL 113.288 46.102 0.015 0.053 sex 52.423 103.931 0.615 GOT AL 0.251 0.584 0.669 0.005 sex 0.786 1.317 0.552 GPT AL 1.287 0.765 0.095 0.044 sex 2.474 1.724 0.154 GTP AL 2.700 2.144 0.210 0.066 sex 11.813 4.834 0.016 Hematocrit AL 0.145 0.267 0.589 0.359 sex 4.839 0.602 <0.001 Hb AL 0.049 0.091 0.593 0.388 sex 1.763 0.206 <0.001 RBC AL 1.962 3.412 0.566 0.251 sex 47.508 7.693 <0.001 TG AL 7.542 4.763 0.116 0.026 sex 6.814 10.737 0.527 Uric AL 0.029 0.090 0.746 0.238 sex 1.216 0.202 <0.001 WBC AL 198.958 106.836 0.065 0.080 sex 577.178 240.849 0.018 Weight AL 1.800 0.657 0.007 0.378 sex 11.388 1.482 <0.001 ADL AL 0.088 0.110 0.424 0.012 sex -0.236 0.248 0.343 Intel AL 0.087 0.080 0.275 0.010 sex -0.012 0.179 0.945 SelfM AL -0.048 0.031 0.124 0.078 sex -0.180 0.070 0.012 Soc AL -0.049 0.050 0.327 0.009 sex -0.044 0.113 0.701 Table 9.37 Multivariate associations between allostatic load (independent variable) controlling for sex and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points)

201

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.015 0.014 0.316 0.405 sex 0.223 0.029 <0.001 AL*sex -0.039 0.019 0.039 Dop AL 223.337 72.051 0.002 0.083 sex 257.684 146.244 0.081 AL*sex -183.327 92.996 0.051 GTP AL -2.886 3.340 0.389 0.101 sex 1.394 6.780 0.838 AL*sex 9.306 4.311 0.033 Table 9.38 Significant multivariate associations between allostatic load (independent variable), controlling for sex and interactions between AL and sex (AL*sex), and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points)

Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL 0.158 0.260 0.545 0.399 sex 4.682 0.589 <0.001 town 1.981 0.713 0.006 Hb AL 0.052 0.090 0.561 0.411 sex 1.721 0.204 <0.001 town 0.529 0.247 0.034 TG AL 7.204 4.454 0.108 0.156 sex 10.936 10.086 0.280 town -51.990 12.219 <0.001 ADL AL 0.093 0.108 0.388 0.064 sex -0.296 0.244 0.227 town 0.756 0.295 0.012 Soc AL 0.052 0.049 0.287 0.077 sex -0.075 0.110 0.498 town 0.396 0.133 0.004 Table 9.39 Significant multivariate associations between allostatic load (independent variable), controlling for sex and town, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points)

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9.5.4 Multivariate Associations: AL, Age, and Sex

Multivariate associations controlling for age and sex reiterate correlations observed in previous sections. AL remains a significant predictor of dopamine (p=0.011),

WBC (p=0.056) and weight (p=0.008) (Table 9.40). In addition to AL, age associates with dopamine (p=0.067), sex associates with WBC (p=0.022), and both age and sex associate with weight (age p<0.001; sex p<0.001). However, when previously identified significant interactions are controlled for in the model, the significance of dopamine is attenuated (p=0.243) (Table 9.41). Results indicate that AL is a robust predictor of these dependent variables, even after partialing out effects of age and sex. Similar associations to those observed in Section 9.5.2 and 9.5.3 between age, sex, town, and interaction terms and dependent variables are also observed (Table 9.42).

203

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL -0.008 0.009 0.395 0.404 age 0.003 0.001 0.042 sex 0.177 0.021 <0.001 Dop AL 117.662 45.704 0.011 0.080 age 11.238 6.087 0.067 sex 41.560 103.063 0.687 GOT AL 0.251 0.587 0.669 0.005 age 0.002 0.078 0.980 sex 0.784 1.324 0.555 GPT AL 1.160 0.735 0.117 0.127 age -0.327 0.098 0.001 sex 2.790 1.658 0.095 GTP AL 2.664 2.155 0.219 0.067 age -0.094 0.287 0.743 sex 11.904 4.860 0.016 Hematocrit AL 0.106 0.260 0.683 0.401 age -0.099 0.035 0.005 sex 4.935 0.586 <0.001 Hb AL 0.035 0.088 0.694 0.434 age -0.036 0.012 0.003 sex 1.798 0.199 <0.001 RBC AL 1.452 3.310 0.662 0.303 age -1.312 0.441 0.004 sex 48.776 7.463 <0.001 TG AL 7.674 4.784 0.111 0.028 age 0.339 0.637 0.595 sex 6.486 10.787 0.549 Uric AL 0.032 0.090 0.723 0.240 age 0.007 0.012 0.546 sex 1.209 0.203 <0.001 WBC AL 206.089 106.684 0.056 0.093 age 18.322 14.209 0.200 sex 559.468 240.573 0.022 Continued

Table 9.40 Multivariate associations between allostatic load (independent variable), controlling for age and sex, and dependent variables among elderly Sakiyama/Hizen- Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points)

204

Table 9.40 continued

Dep Ind Regression CoE SE Pearson correlation, p R2 Weight AL 1.683 0.627 0.008 0.440 age -0.300 0.084 <0.001 sex 11.678 1.450 <0.001 ADL AL 0.078 0.109 0.478 0.040 age -0.027 0.015 0.064 sex -0.210 0.246 0.395 Intel AL 0.077 0.078 0.323 0.058 age -0.025 0.010 0.016 sex 0.012 0.176 0.946 SelfM AL -0.048 0.031 0.132 0.080 age 0.002 0.004 0.619 sex -0.182 0.071 0.011 Soc AL 0.048 0.050 0.344 0.012 age -0.004 0.007 0.563 sex -0.04 0.114 0.727

Dep Ind Regression CoE SE Pearson correlation, p R2 Creatine AL 0.012 0.014 0.391 0.420 age 0.002 0.001 0.079 sex 0.215 0.029 <0.001 AL*sex -0.034 0.019 0.073 Dop AL -489.959 417.583 0.243 0.124 age -0.693 8.559 0.936 sex 217.786 145.783 0.138 AL*sex -130.475 94.429 0.170 AL*age 10.056 5.880 0.090 GTP AL -2.883 3.367 0.394 0.101 age -0.002 0.286 0.994 sex 1.402 6.885 0.839 AL*sex 9.301 4.380 0.036 Table 9.41 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and interactions, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points)

205

Dep Ind Regression CoE SE Pearson correlation, p R2 Hematocrit AL 0.122 0.254 0.632 0.430 age -0.087 0.034 <0.001 sex 4.786 0.577 <0.001 town 1.732 0.704 0.015 TG AL 7.200 4.480 0.111 0.156 age -0.012 0.602 0.984 sex 10.950 10.155 0.283 town -52.024 12.391 <0.001 ADL AL 0.084 0.107 0.434 0.083 age -0.023 0.014 0.121 sex -0.269 0.243 0.270 town 0.692 0.296 0.021 Soc AL 0.051 0.049 0.295 0.078 age -0.001 0.007 0.850 sex -0.073 0.111 0.509 town 0.392 0.135 0.004 Table 9.42 Significant multivariate associations between allostatic load (independent variable), controlling for age, sex, and town, and dependent variables among elderly Sakiyama/Hizen-Oshima residents (SHO sample, AL constructed using z-scores and decile cut-points)

206

Chapter 10: Principal Components Analysis

10.1 Introduction

A variety of models were tested using PCA to assess stability and robustness of factor loadings for Sakiyama, Hizen-Oshima, and the combined sample. In each instance, modeling first includes the ten components assessed as part of AL (Model 1). Next, age and sex are added, based upon significant associations observed previously (Model 2). In the combined sample, location is added to Model 1 to determine its relationship to the sample’s total variance (Model 3). Results are presented by model to facilitate comparison.

10.2 Model 1: PCA of Allostatic Load Variables

The Kaiser-Meyer-Olkin Measure of Sampling Adequacy assesses whether partial correlations among variables are small. Values above 0.6 are preferred, although PCA can be applied successfully in cases where this value is slightly lower (IDRE PCA 2014).

Bartlett’s Test of Sphericity tests the null hypothesis that the correlation matrix is an identity matrix, indicating a factor model is inappropriate. PCA can be applied to data when this test is rejected (e.g., p<0.05) (IDRE PCA 2014). All samples meet minimum criteria used for determining whether PCA can be applied (Table 10.1).

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Kaiser-Meyer-Olkin Measure Bartlett’s Test of Sphericity of Sampling Adequacy (p-value) Sakiyama 0.676 <0.001 Hizen-Oshima 0.623 0.005 Sakiyama/Hizen-Oshima 0.586 <0.001 Table 10.1 Results determining applicability of PCA

For each set of correlated variables, PCA returns an equal number of orthogonally-related (i.e., uncorrelated) components comprised of these variables.

Because the goal of PCA is to reduce the number of variables in a model, generally only those components with an eigenvalue over one are considered significant and retained for consideration (IDRE PCA 2014). When data comprising AL are assessed using PCA, three components are significant in the Sakiyama and Hizen-Oshima samples, while four are significant in the combined sample (Table 10.2).

Component Sakiyama Hizen- Sakiyama/ Oshima Hizen-Oshima Eigenvalue % Variance Eigenvalue % Variance Eigenvalue % Variance 1 2.675 26.749 3.389 33.889 2.153 221.525 2 1.798 17.981 1.556 15.557 1.682 16.820 3 1.256 12.565 1.178 11.780 1.493 14.929 4 ------1.028 10.280 Cumulative 57.295 61.225 63.554 Table 10.2 Significant PCA component eigenvalues and percent total variance explained

Model 1 demonstrates unequal loadings of variables in components across samples (Table 10.3). Variables with a loading score over 0.5 or less than -0.5 are considered significant. Among Sakiyama residents, the first principal component (PC) loads on blood pressure (diastolic and systolic), DHEAs, glycated hemoglobin, and waist

208 hip ratio. Together, these variables could be described as a “body habitus” component;

WHR is a direct indicator of body habitus, while SBP, DBP and HbA1c are indirect indicators. DHEAs, as a cortisol antagonist, may play a role in adipose tissue deposition.

The second PC extracted from the Sakiyama data is a “hormonal” one. It primarily loads on adrenaline, cortisol, and noradrenaline. The third significant PC identified is a lipid component; it loads on both total and high-density lipoprotein cholesterol.

In the Hizen-Oshima sample, the primary component combines body habitus and hormone measures. These include adrenaline, cortisol, DHEAs, blood pressure (diastolic and systolic), glycated hemoglobin, high-density lipoproteins and WHR as significant variables. The next significant PC also loads on blood pressure and noradrenaline.

Reflective of a trend observed in the Sakiyama data, the third lipid component loads primarily on total cholesterol.

In the combined sample, four components, similar to those identified among the

Sakiyama data, are observed. Body habitus measures again load on the first PC: blood pressure, waist hip ratio, and glycated hemoglobin. The second is again a hormonal component loading on adrenaline, cortisol, and noradrenaline. Similarly, the third loads onto DBP and DHEAs, but not SBP. The fourth PC loads heavily on total cholesterol.

From these analyses, three recurring principal components may be tentatively identified:

1. “Body habitus”: DHEAs, HbA1c, DBP, SBP, and WHR

2. “Hormonal”: adrenaline, cortisol, and noradrenaline

3. “Lipid” HDL and total cholesterol.

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Sakiyama H-O S/H-O Component 1 2 3 1 2 3 1 2 3 4 AD 0.217 0.672 -0.006 0.696 0.014 -0.329 0.338 0.623 -0.239 -0.013 Cort 0.403 0.666 0.252 0.764 0.190 -0.180 0.438 0.602 -0.069 0.220 DBP 0.695 -0.184 0.212 -0.617 0.634 0.090 0.618 0.003 0.545 -0.043 DHEAs 0.663 -0.210 0.185 0.748 0.047 0.121 0.105 -0.475 -0.573 0.423 HbA1c 0.530 -0.258 -0.142 0.394 -0.311 0.414 0.511 -0.325 0.029 -0.049 HDL -0.423 0.149 0.656 -0.649 -0.310 -0.063 -0.443 0.411 0.493 0.206 NAD 0.210 -0.772 0.087 0.395 0.565 0.198 0.238 0.584 -0.351 0.222 SBP 0.738 -0.177 0.182 -0.565 0.579 0.219 0.684 -0.133 0.487 -0.048 TCho -0.136 -0.330 0.783 0.007 -0.330 0.817 -0.210 -0.133 0.401 0.807 WHR 0.690 -0.035 -0.092 0.573 0.399 0.292 0.657 -0.233 -0.199 0.227 Table 10.3 Variable loading on significant components (Significant variables in each component in bold)

Using first PC factor scores as weights, an independent variable, ALPC1,may be constructed with a mean of zero and standard deviation of one (Vyas and Kumaranayake

2006). This weighted ALPC1score was used as an independent variable in bivariate regressions. Compared to earlier results using unweighted AL, several associations not identified as significant previously now are. Both AL and ALPC1 associate significantly with GPT, GTP, WBC, and weight (Table 9.1). However, ALPC1 also associates significantly with creatine, dopamine, hematocrit, hemoglobin, red blood cell count, uric acid, and the self-maintenance score of the TMIG-IC (Table 10.4). These results indicate potential benefits of using PCA or other weighting techniques to construct AL. PCA may better identify associations of AL with other physiological variables indicative of current and future morbidity.

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Variable Regression CoE SE Pearson Correlation, p R2 Creatine 0.034 0.013 0.010 0.053 Dop 120.237 51.470 0.021 0.043 GOT 0.656 0.647 0.313 0.008 GPT 2.102 0.847 0.014 0.048 GTP 7.418 2.366 0.002 0.075 Ht 1.208 0.354 0.001 0.088 Hb 0.461 0.123 <0.001 0.104 RBC 10.736 4.272 0.013 0.050 TG 8.968 5.469 0.104 0.022 Uric 0.381 0.109 0.001 0.092 WBC 392.445 122.904 0.002 0.078 Weight 4.691 0.823 <0.001 0.212 ADL -0.034 0.123 0.780 0.001 SelfM -0.091 0.035 0.011 0.052 Intel 0.078 0.089 0.377 0.006 Soc -0.022 0.056 0.690 0.001 Table 10.4 Bivariate associations between ALPC1 (independent variable) and dependent variables among elderly Sakiyama/Hizen-Oshima residents

10.3 Model 2: PCA of Allostatic Load Variables, Age, and Sex

All samples meet minimum standards for determining whether PCA can be applied (Table 10.5). When the study data are assessed using PCA, five components are significant in all samples (Table 10.6)

Kaiser-Meyer-Olkin Measure Bartlett’s Test of Sphericity of Sampling Adequacy (p-value) Sakiyama 0.594 <0.001 Hizen-Oshima 0.568 <0.001 Sakiyama/Hizen-Oshima 0.534 <0.001 Table 10.5 Results determining applicability of PCA

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Component Sakiyama Hizen- Sakiyama/ Oshima Hizen-Oshima Eigenvalue % Variance Eigenvalue % Variance Eigenvalue % Variance 1 3.135 26.124 3.942 32.847 2.504 20.864 2 1.828 15.233 1.645 13.709 1.755 14.623 3 1.477 12.312 1.473 12.271 1.578 13.152 4 1.179 9.822 1.158 9.649 1.188 9.898 5 1.069 8.912 1.070 8.915 1.054 8.785 Cumulative 72.403 77.392 67.322 Table 10.6 Significant PCA component eigenvalues and percent total variance explained

Sex and age contribute significantly to PC across all samples. Sex consistently loads heavily on the first component, while age loads on another component (Table 10.7).

Despite adding age and sex to the model, among Sakiyama residents, the first PC loads on the same variables as did Model 1, with the exception of glycated hemoglobin and addition of sex. Similar to results with Model 1, the second component is hormonal, loading on adrenaline, cortisol, and noradrenaline. The final components load significantly on few variables: the third on total cholesterol and age, the fourth only on glycated hemoglobin, and the final only on systolic blood pressure. The addition of age and sex to Model 2 attenuates the contribution of glycated hemoglobin and high-density lipoproteins to explaining total variance, but little else changes. Consistent loads of physiological variables on PCs indicate the robusticity of AL.

Among the Hizen-Oshima sample, the primary body habitus-hormone component of Model 2 loads significantly on the same variables as in Model 1. Sex also loads significantly on the first PC, as in the Sakiyama sample. The second significant PC loads onto total cholesterol and systolic blood pressure, perhaps suggesting an interactive

212 relationship. The third PC loads on diastolic blood pressure, noradrenaline, and age, the fourth on adrenaline and total cholesterol, and the fifth only on glycated hemoglobin.

In the combined sample, the first PC is body habitus, loading on the same variables as Model 1. Again, glycated hemoglobin is not included when sex is while blood pressure and WHR are added. The second or hormonal component still loads on adrenaline and cortisol. However, after adding age and sex, this component also loads on

DHEAs, but not noradrenaline (as observed in Model 1). The third PC loads on diastolic blood pressure (systolic blood pressure is marginally significant) and noradrenaline. As in

Model 1, the fourth PC loads heavily on total cholesterol, but now additionally on

DHEAs and age. The last component in Model 2 again loads solely on glycated hemoglobin.

From these models, several observations can be made:

 Significant components of Models 1 and 2 are similar, indicating AL is a

robust measure.

 Sex loads significantly on the first component across all samples,

suggesting it has an important role in explaining physiological variability.

 Glycated hemoglobin is less important when sex is included, suggesting

sex may modulate influence of glucose metabolism.

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Sakiyama Component 1 2 3 4 5 AD 0.113 0.640 0.338 -0.441 -0.001 Cort 0.370 0.612 0.428 0.053 -0.023 DBP 0.650 -0.196 0.291 -0.217 0.104 DHEAs 0.724 -0.239 0.193 0.227 -0.139 HbA1c 0.438 -0.211 -0.028 -0.564 0.284 HDL -0.413 0.083 0.382 0.370 0.495 NAD 0.204 0.795 0.018 0.268 0.086 SBP 0.703 -0.092 -0.087 -0.097 0.545 TCho -0.145 -0.418 0.551 0.310 0.343 WHR 0.738 0.014 -0.164 0.172 -0.075 Age 0.019 0.279 -0.749 0.158 0.476 Sex 0.758 -0.061 -0.109 0.443 -0.241 Hizen-Oshima Component 1 2 3 4 5 AD 0.612 -0.203 0.250 -0.593 0.029 Cort 0.736 0.000 0.289 -0.288 -0.071 DBP -0.614 0.488 0.500 -0.019 0.194 DHEAs 0.781 -0.021 0.182 0.315 -0.108 HbA1c 0.336 -0.407 -0.196 -0.171 0.683 HDL -0.673 -0.291 0.156 0.072 -0.320 NAD 0.383 0.244 0.633 -0.067 0.148 SBP -0.541 0.589 0.053 -0.041 0.400 TCho -0.081 -0.595 0.251 0.561 0.382 WHR 0.624 0.371 0.010 0.391 0.198 Age 0.415 0.404 -0.711 -0.008 0.095 Sex 0.681 0.234 0.097 0.340 -0.264 Sakiyama/Hizen-Oshima Component 1 2 3 4 5 AD 0.187 0.565 0.452 0.014 -0.376 Cort 0.374 0.597 0.301 0.164 0.118 DBP 0.517 0.399 -0.532 -0.062 -0.028 DHEAs 0.229 -0.554 0.207 0.506 -0.194 HbA1c 0.423 -0.090 -0.231 -0.106 -0.658 HDL -0.497 0.420 -0.188 -0.076 0.320 NAD 0.186 0.382 0.587 0.057 0.099 SBP 0.634 0.145 -0.426 -0.403 0.060 TCho -0.241 0.125 -0.485 0.525 0.116 WHR 0.760 -0.168 0.032 0.206 0.190 Age 0.174 -0.449 0.326 -0.580 0.267 Sex 0.693 -0.130 -0.003 0.252 0.438 Table 10.7 Variable loading on significant components (significant variables in each component in bold)

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10.4 Model 3: PCA of Allostatic Load Variables and Location

The Sakiyama/Hizen-Oshima sample meet minimum criteria used to determine whether PCA can be applied to data (Kaiser-Meyer-Olkin Measure of Sampling

Adequacy 0.586; Bartlett’s Test of Sphericity p<0.001). When all data are assessed using

PCA, four significant components are observed (Table 10.8).

Component Sakiyama/ Hizen-Oshima Eigenvalue % Variance 1 2.230 20.273 2 2.132 19.378 3 1.568 14.251 4 1.082 9.840 Cumulative 63.742 Table 10.8 Significant PCA component eigenvalues and percent total variance explained

Addition of a variable representing location among the combined sample changes

PCA factor loadings. As opposed to loading as a body habitus component comprised of glycated hemoglobin, blood pressure (systolic and diastolic), and waist hip ratio, the first component loads on hormones (cortisol and DHEAs), diastolic blood pressure, and location (Table 10.9). That location loads significantly on the first PC suggests influences of local factors on observed variation in the combined sample. Although samples are quite similar in lifestyle and culture, some aspects of their lifeways or environments must influence physiological variability. The second PC also differs from Model 1, loading on

DHEAs, glycated hemoglobin, high-density lipoproteins, systolic blood pressure, and

215 waist hip ratio. The third and fourth components load on hormones (noradrenaline and adrenaline) and total cholesterol, respectively.

Sakiyama/Hizen-Oshima Component 1 2 3 4 5 AD 0.450 0.124 0.568 0.568 0.081 Cort 0.533 0.192 0.433 0.433 0.320 DBP 0.530 0.412 -0.426 -0.426 0.093 DHEAs -0.696 0.507 0.246 0.246 0.293 HbA1c 0.048 0.564 -0.206 -0.206 -0.044 HDL 0.202 -0.629 -0.136 -0.136 0.412 NAD 0.340 0.072 0.632 0.632 0.194 SBP 0.430 0.545 -0.451 -0.451 0.085 TCho -0.098 -0.176 -0.363 -0.363 0.785 WHR 0.134 0.684 0.017 0.017 0.038 Town 0.779 -0.373 -0.142 -0.142 -0.211 Table 10.9 Variable loading on significant components (significant variables in each component in bold)

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Chapter 11: Discussion and Conclusions

11.1 Introduction

Patterns of senescence and aging vary both within and across populations, suggesting indices for assessing these patterns developed in one population may not apply equally to another. Here, I examined human variation at older ages among samples from Nagasaki Prefecture, Japan. This research in a non-Western population was undertaken to explore how well allostatic load, a measure of physiological decline, associates with age, sex, physiological variables, and lifeways in an Asian sample. In both samples, women showed lower AL than men (Tables 4.1.2.2 and 6.1.2.2).Similarly, in bivariate analyses, AL associated significantly with white blood cell count in both.

However, local populations show different bivariate associations of AL with creatine,

GTP, creatinine, weight, dopamine, red blood cell count, GPT, hemoglobin, uric acid, and hematocrit (Table 10.3.4). In multivariate analyses, AL and age are significantly predictive of white blood cell count. Except for creatine, associations which varied by location in bivariate observations persist. Multivariate models of AL and sex as well as

AL, sex, and age predict GTP, creatinine, white blood cell count, percent body fat, weight, dopamine, red blood cell count, GPT, and blood glucose variably by location.

After establishing principal components, ALPC1 shows significant associations in the

Sakiyama sample with GPT, GTP, WBC, creatine, dopamine, hematocrit, hemoglobin,

217 red blood cell count, uric acid, and the self-maintenance score of the TMIG-IC (Table

10.4).

11.2 Limitations of study

Of concern is whether this sample is representative of the populations examined.

My goal was to assess AL in a non-urban sample of the Japanese population. The question is whether these samples reflect the villages examined. Volunteers were examined and recruited during their routine, annual physical examinations. The sample is not random. All participants were not only ambulatory, but also capable of collecting an overnight urine sample and bringing it with them to their annual examination. This protocol likely eliminated the most frail and less mobile from our samples. Elsewhere, among European-derived samples, volunteer participants tend to report higher educations, be from higher socioeconomic classes, be more approval motivated, and be more sociable

(Rosenthal and Rosnow 1975; Gravetter and Forzano 2012). Volunteer samples may represent a local area but not a larger population. Here, similar recruitment methods likely limited volunteer bias. Additionally, the same researchers, same incentives, and same data collection methods were utilized in both villages, limiting data and sampling biases.

Because only small numbers were sampled (N=27 Hizen-Oshima; N=96 Sakiyama) for this study, results may not be generalizable beyond these local, non-urban populations.

Sample sizes also affect statistical analyses, particularly multivariate models. These are interpreted cautiously given sample sizes. Additionally, because these are exploratory analyses, corrections (such as Bonferroni) are not applied, as I was looking for associations to provide a baseline for follow-up studies.

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11.3 Results

11.3.1 Age

I expected little association of AL with age. However, I did expect inclusion of age in multivariate models to improve explanatory power. Previous research in another non-Western population also suggested older men show lower Al than younger, and younger women lower AL than older (Crews 2007).

Consistent with expectations, AL and age are poorly correlated in both samples and in the full data set. Age maximally explains 1.7%, and generally only explains about

1%, of variation observed in AL. This likely reflects the narrow age range sampled, but also may support assertions that senescence is age-independent at older ages (Arking

2006; Crews 2007). This finding reinforces the suggestion that senescence does not depend on chronological age. Although often used interchangeably, age refers only to a person or object’s duration or chronological existence. Age does not measure the biological decline, diminished reproductive success, or increased morbidity and mortality that are hallmarks of senescence. All objects age, but only living organisms senesce.

Other results utilizing AL corroborate our findings. For example, Crews (2007) reported age was not correlated significantly with AL among Samoan women, although the association was positive. Among his sample, he observed stress load was not significantly different at older ages, suggesting that all middle-aged Samoans likely share in population-wide elevated risks for chronic degenerative diseases. Similarly, Strahler et al. (2010) concluded that there was no physiological association between AL and age

219 among elderly ballroom dancers. Instead, they hypothesized that physical activity plays an important role in maintaining healthy AL, perhaps more than does younger age.

In contrast, studies utilizing neuroendocrine allostatic load (NAL) report significant correlations between NAL and age (Gersten 2008, Gersten et al. 2010). NAL evaluates catecholamines, cortisol, and DHEA-s as a “physiologically coherent class of markers representative of the neuroendocrine stress response” (Gersten 2008:510). NAL biomarkers represent activity of both the HPA-axis (cortisol and DHEA-s) and SNS-axis

(catecholamines) (Gersten 2008). NAL is scored following the same method outlined in this study, except only hormones are included. In a Taiwanese sample, age and women’s current stress-levels were positively and strongly correlated with NAL (Gersten 2008). In a Costa Rican sample, higher age was associated with NAL (Gersten et al. 2010).

NAL has not been observed to be associated significantly with most long-term stressors. Rather, NAL is only significantly associated with age and current stress-levels in a sample of Taiwanese women and with age and SES in Costa Rican participants. One suggestion is NAL does not represent long-term effects of cumulative stress, but rather reflects more recent stressors.

Despite poor correlation with AL, including age when modeling effects of AL on other measures improves the predictive capacities of these models (Klemera and Doubal

2006). I observed a similar effect in samples from Sakiyama and Hizen-Oshima. Rarely did inclusion of age in models with AL obfuscate associations observed when bivariate associations of AL were assessed. In general, including age improved the amount of

220 variation explained, elucidating the independent predictive capacity of AL after age effects are partialed out.

Based on previous research, I expected younger men to show higher AL than older and older women to show higher AL than younger (Crews 2007). Invariably, the first of these expectations was met; younger men were observed to have higher AL than older men across samples and in the combined data set, regardless of whether AL was constructed using quartile or decile cut-points (Tables 4.6, 4.8, 4.10, 4.12, 6.5, 6.7, 6.9,

6.11, 8.5, 8.7, 8.9, and 8.11). Results suggest older Japanese men have experienced lower cumulative physiological and physical stress loads than have younger men and/or that mortality among individuals with high AL results in lower AL among survivors. With the available cross-sectional data, I was not able to determine which is more likely.

Greater variability was observed among women. In general, younger women had higher AL than older or younger and older women had almost the same AL (Tables 4.6,

4.8, , 6.5, 6.7, 8.5, and 8.7). When decile cut-points were used to construct AL, older women showed higher AL than did younger across all samples (Tables 4.10, 4.12, 6.9,

6.11, 8.9, and 8.11). Considering observations of higher AL among older women when using decile cut-points, results may suggest older women are experiencing higher cumulative physiological and physical stress loads than younger women. However, this difference appears only when extreme levels (upper and lower 10%) of risk factors are used to construct AL.

11.3.2 Sex

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Based upon previous research, I expected to observe differences between men and women in AL. This expectation was not met. Although men had higher Al than women across all samples and age groups, few comparisons reached statistical significance: in the Hizen-Oshima sample across both age divisions when AL was assessed by quartile cut-points (Tables 6.6 and 6.8) and the combined sample across both age divisions with

AL determined by raw values and quartile cut-points (Tables 8.6, and 8.8) and with AL determined by z-scores and decile cut-points (Table 8.19 and 8.21). Rarely do men and women show the same AL or do women have higher AL than men. These examples did not approach statistical significance (Table 11.1).

Averages of multiple variables differed significantly between men and women, sometimes significantly (Tables 4.14, 4.15, 6.13, 6.14, 8.22, and 8.23). To determine independent effects of AL and sex on study variables, multivariate regression was used.

Cortisol, DHEAs, HDL, WHR, creatine, GOT, and uric acid varied significantly between sexes in the Sakiyama, Hizen-Oshima, and/or combined samples (Table 11.2).Observed differences in the combined sample were similar to those observed in the Sakiyama sample, not unsurprising given the Sakiyama sample is over three times the Hizen-

Oshima sample.

Effects of these differences were investigated by including an indicator variable for sex in multivariate analyses of associations between AL and dependent variables.

Numerous variables found to vary significantly between sexes in the Sakiyama sample also varied significantly in the Hizen-Oshima sample:. Most variables observed to vary significantly among the participants from Sakiyama also differ significantly in the

222 combined Sakiyama/Hizen-Oshima data set. This is unsurprising given the larger size of the Sakiyama sample as compared to the Hizen-Oshima data set.

Young Men vs. Old Men vs. Total Men vs. Sample AL cut-point Age group Young Women Old Women Total Women Sakiyama Quartile Median Men Men Men 70 Men Men Decile Median Men Men Men 70 Men Men HO Quartile Median Men** Men Men 70 Men** Men Decile Median Same Female Female 70 Male Female ComboRaw Quartile Median Men* Same Men 70 Men* Same Decile Median Men Men Men 70 Men Men ComboZ Quartile Median Men Men Men 70 Men Men Decile Median Men* Female Men 70 Men* Female Table 11.1 Comparing AL between sexes. Sex noted is that with higher AL. ** denotes statistical difference of p<0.05, * denotes p<0.06

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Variable Sakiyama Hizen-Oshima Combined Cort X X X DBP X X DHEAs X X X HbA1c X HDL X X X SBP X X WHR X X X Creatine X X X GOT X X GPT X GTP X X Hematocrit X X Hb X X MCH X n/a n/a MCHC X n/a n/a PBodyFat X n/a n/a Platelet X n/a n/a RBC X X SelfM X X UCreatinine X n/a n/a Uric X X X WBC X X BS n/a X n/a Table 11.2 Comparisons of variable means by sex among elderly residents of Sakiyama/Hizen-Oshima. Xs denote significant difference between men and women. N/a indicates a variable was not assessed for a particular sample.

11.3.3 Human Variation

To contextualize these data, averages are compared to United States “healthy” or

“normal” values. Averages were often outside these ranges. Observed differences may reflect the small sample of Japanese used for comparison or it may reflect the non-urban location of study samples compared to United States standards. Or, differences may indicate physiological variation between the two populations. In the latter case, differences may be highlighting variable lifeways, diets, environments, and/or cultural adaptations across populations.

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In these samples, blood pressure, dopamine levels, GOT, and creatine were above

United States reference ranges (Tables 4.4 and 6.3). Relatively lean body habitus is observed across samples compared to European men (aged 55-64), but not women. Such differences may be related to different physiological set-points between populations, different responses to environmental stressors, variable patterns of somatic senescence, or sample size.

In Sakiyama, total cholesterol was high compared to international standards, but the ratio of total cholesterol to HDL-cholesterol was below the cut-point for low risk

(National Cholesterol Education Program 1987). In addition, average red blood cell count was low when compared to United States reference values for men, but within the normal range for women (Handin et al. 2003). When analyzed separately by sex, average red blood cell counts in Sakiyama fell within their respective ranges for United States men and women. In Hizen-Oshima, DHEA-s levels were above normal ‘healthy’ ranges for

United States men and women, while GPT levels were borderline low (Krobath 1999;

Fischbach and Dunning 2009). Blood glucose in Hizen-Oshima was elevated compared to American standards. Blood was sampled randomly in Japan and time of last food consumption was not recorded. These differences between Japanese samples and United

States reference values may reflect systemic differences, but more likely they reflect unique properties of the Japanese samples measured.

11.3.4 Allostatic load and physiological variation

Sakiyama and Hizen-Oshima: This study anticipated associations between AL and dopamine, weight, uric acid, and white blood cell count based on previous research.

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Surprisingly, associations between AL and dependent physiological variables differed between the two Japanese samples analyzed. Among participants from Sakiyama, AL associated consistently with GTP, urinary creatinine, white blood cell count, percent body fat, and walking speed (Table 11.3). In my analyses, AL also associates significantly with dopamine, creatine, and body weight. The observed association of WBCs with AL suggests possible connections with degenerative processes of senescence. In late life, changes in percent body fat and slower walking speed are associated with frailty, suggesting a relationship with AL. Additionally, GTP and creatinine, indicators of liver and renal function respectively, are associated with AL. This suggests AL may be sensitive to changes in the functionality of these organs.

In the Hizen-Oshima sample, AL associated significantly with weight, dopamine, red blood cell count, and GPT. AL also was associated with white blood cell count, hemoglobin, uric acid, hematocrit, and blood glucose, but not in all sub-samples. In the

Hizen-Oshima sample, AL is significantly associated (p<0.10) with a neurotransmitter, a liver enzyme, an antioxidant, and an inflammatory marker. These measures represent a variety of somatic functions and are widely reported to be altered during senescence/aging and associated with cognitive function and frailty. An association between AL and uric acid suggests a possible connection of stress with reactive oxygen species. Although these are conjectures from observed associations, they do suggest a need for further exploration of AL and physiological function in older Japanese. No significant associations of AL with the TMIG-IC or its three subscales were observed. At least in this sample, AL may not influence loss of functional ability.

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Sakiyama- Sakiyama- Quartile Decile AL AL, Age AL, Sex AL, Age, AL AL, Age AL, Sex AL, Age, Sex Sex Creatine X GTP X x x x x x x x UCreat X x x x x x WBC X X X X x x PBodyFat x x x x x x Weight x x Walk X X x x Dop x RBC GPT Hb Uric Ht BS Intel SelfM Hizen- Hizen- Oshima- Oshima- Quartile Decile AL AL, Age AL, Sex AL, Age, AL AL, Age AL, Sex AL, Age, Sex Sex Creatine GTP UCreat WBC X x PBodyFat Weight X x x x x x Walk Dop X x x x x X x x RBC X x x x GPT X x X X x x X x Hb X x Uric X x Ht X X BS x X Intel SelfM Continued

Table 11.3 Significant associations between AL and dependent physiological variables

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Table 11.3 continued

Combined Combined -Raw- -Raw- Quartile Decile AL AL, Age AL, Sex AL, Age, AL AL, Age AL, Sex AL, Age, Sex Sex Creatine GTP X x x x UCreat WBC X x x x PBodyFat Weight X x x x x x x x Walk Dop x RBC GPT X x x x Hb Uric Ht BS Intel X x x SelfM x x x x x Combined Combined -z- -z-Decile Quartile AL AL, Age AL, Sex AL, Age, AL AL, Age AL, Sex AL, Age, Sex Sex Creatine GTP UCreat WBC X x x x x x x x PBodyFat Weight x x x x x x x x Walk Dop x x x x x x x x RBC GPT Hb Uric Ht BS Intel SelfM

Table 11.3 Significant associations between AL and dependent physiological variables

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Differences in results between Hizen-Oshima and Sakiyama may relate to small sample sizes. Or, they may indicate as-of-yet unidentified local variation in diet or life style. For example, in both groups AL associates consistently with an enzyme indicative of liver function. However, it was a different enzyme in each sample. AL associates with

GTP among participants from Sakiyama, but with GPT among participants from Hizen-

Oshima. Similarly, AL associates with different measures of body habitus across both groups, percent body fat among denizens of Sakiyama and weight among residents of

Hizen-Oshima.

Different associations between allostatic load and physiological variables found among residents of Hizen-Oshima and Sakiyama suggest results of allostatic load studies are less generalizable than previously thought. Unfortunately, there are no similar data sets from

East Asia with which to compare these results so it is unknown whether these results are unusual or reflective of broader trends in the region.

Allostatic Load Calculated Using Quartile and Decile Cut-Points: Here, as in its original formulation (Seeman et al. 1997), AL is measured first by summing10 biological variables for which an individual falls into the highest risk quartile of each measurement’s distribution. Thus, original sums of AL represent only one-tailed risk and range from 0–10. Recently, several researchers have questioned the validity of using this simple procedure (e.g., Singer and Ryff 1999; Fries et al. 2005; Epel 2009). They argue that physiological reactions to stressful events may lead to either elevated or decreased hormonal or biochemical levels (Fries et al. 2005). For example, both high and low cortisol appear to increase physiological wear-and-tear (Loucks et al. 2008; Crimmins et al. 2009; Gersten et al. 2010). Additionally, symptoms of conditions such as PTSD are 229 linked to low, not high, cortisol levels (Singer and Ryff 1999). Such results suggest future assessments of AL may need to incorporate risks using two--tailed criteria (Fries et al.

2005) or utilize more restricted definitions of “at-risk” (i.e., consider highest risk decile as opposed to highest risk quartile).

To compare different estimates of AL, Seplaki et al.(2005)computed AL for an elderly Taiwanese sample combining from 10 to 16 biomarkers, while using both one- and two-tailed risk categories, and different percentile cut points (<10% and>90% as compared to <25% and >75%). All AL scores were then compared to self-reported health, ADLs, reported mobility, the Centers for Epidemiologic Study of Depression

Scale (CES-D) score, and an assessment of temporal orientation. This study found that alternative methods of constructing AL moderately influenced predictability of morbid outcomes (Seplaki et al. 2005).

In contrast to this previous research, I observed significant differences in associations of AL with dependent variables based upon how I constructed AL in all but one analysis (Table 11.3). For example, using decile cut-points among the Sakiyama sample, AL was associated significantly with weight, walking speed, and dopamine. In contrast, AL constructed using quartile cut-points was not associated significantly with any of these variables. Similarly, significant associations were observed between AL and

WBC, hemoglobin, uric acid, hematocrit, and blood glucose in the Hizen-Oshima sample when AL was constructed using quartile cut-points. None of these associations was significant when AL was constructed using decile cut-points.

230

The most apparent differences between these constructs are observed within the combined data set (raw values). Weight is the only dependent variable consistently associated with AL (Table 11.3). Associations of AL evident using quartile cut-points, including GTP, WBC, GPT, and Intel, are not observed when decile cut-points are used.

In contrast, the least differences between analytic results are observed within the combined sample (z-scores). In these analyses, AL associated significantly with the same dependent variables, WBC, weight, and dopamine, regardless of which cut-points are utilized in its construction.

Bi- and multi-variate models: I anticipated associations between AL and most dependent variables that are strongly associated with sex, except body habitus (e.g., weight and percent body fat) to persist in multi-variate models when effects of age, sex, and age by sex were partialed out. In general, bivariate associations between AL and most dependent variables, including body habitus, persist in multi-variate models.

Exceptions are observed primarily in the Hizen-Oshima sample (quartile cut-points). In these analyses, AL still associated significantly with WBC, hemoglobin, uric acid, and hematocrit after partialing out effects of age. However, after accounting for sex, associations between AL and these variables were no longer significant. In fact, sex explained significantly more variation in these variables than did AL. Additional similar instances are noted. Among Sakiyama participants, AL and creatine associated significantly in bivariate analysis, but not in multivariate analyses controlling for either age or sex.

231

In several analyses, partialing out effects of sex and/or age revealed previously unapparent associations between AL and a dependent variable. The relationship between blood glucose and AL in the Hizen-Oshima sample (quartile cut-points) was nonsignificant until accounting for sex. Likewise, an association between weight and AL in the same sample (decile cut-points) was not significant until effects of sex were partialed out. In two instances, the relationships between AL and dopamine did not approach significance until age was included in the model (Sakiyama decile cut-points and combined sample quartile-cutpoints raw data).

Combining data sets: Two methods for amalgamating samples were explored.

When using raw values, more significant associations of AL with dependent variables were observed. Two subscales of the TMIG-IC (Intellectual Activity and Self

Maintenance) had not been observed in either sample independently. Significant associations with AL differed depending on whether quartile or decile cut-points were used. Significant associations with GTP, WBC, GPT, and sub-sections of the TMIG-IC were observed based upon quartile cut-points. When using decile cut-points, significant associations of AL with weight and Self-Maintenance were observed with both versions of AL.

In contrast to these variable results, after z-scores are used to calculate AL, significant associations are consistent across these two versions of AL. Now, AL associates significantly with WBC, weight, and dopamine and these associations remained consistent when effects of age, sex, and age by sex are included. This suggests

AL calculated from z-scores in amalgamated data sets improves reliability. Future

232 analyses also may wish to explore weighting to account for differences in subsample sizes.

11.3.5 Principal Components Analysis

Artificial dichotomization of variables traditionally used to construct AL may be problematic or lead to unwanted effects. Examples include possible loss of information, reduced effect size, and loss of power in analyses. Using PCA mitigates these problems while allowing explications of specific effects of individual biomarkers.

Among all samples, three significant principal components can be tentatively identified: a “body habitus” component (i.e., DHEAs, HbA1c, DBP, SBP, and WHR), a

“hormonal” component (i.e., adrenaline, cortisol, and noradrenaline), and a “lipid” component (i.e., HDL- and total cholesterol). When sex and age are included as possible exploratory variables, components change little (Tables 10.3 and 10.7). Such results illustrate that AL is a robust model of physiological dysregulation. Interestingly, sex loads significantly into the first component across all samples. As in earlier analyses, sex plays an important role in explaining the total variance among these measures.

Elsewhere, CFA was utilized with a priori variable categories (i.e., inflammation, metabolism, and blood pressure) and significant associations were found in a sample of unmedicated adults, but not in a demographically-matched sample of medicated adults.

Here, using PCA with no a priori categories, variables do not load onto components by their physiological functions. Rather, weightings are more complex, reflecting possible interactions across multiple physiological domains. These results suggest better understanding complexities of physiology using exploratory analyses is necessary to

233 clarify interactions before using a priori frameworks to test hypotheses in future analyses.

11.4 Conclusions

Multiple conclusions follow from these analyses. In a non-urban Japanese setting:

 Allostatic load is poorly associated with age. However, allostatic load

significantly associates with multiple aspects of physiological variability. This

suggests AL may be assessing underlying senescence better than age alone. Still,

accounting for age effects generally improves the explanatory power of AL on

physiological variation.

 The oldest men in these samples demonstrate lower AL than the youngest. Either

older Japanese men experienced lower cumulative physiological and physical

stress loads over their livers than their peers, or earlier mortality among

individuals with high AL in the older cohort results in a group with lower AL

surviving to later older age. The former seems unlikely as the oldest men are

those who survived the later stages of and aftermath of World War II.

 AL is more variable in women: the youngest women generally have higher or the

same AL as older women. Using decile cut-points, older women have higher AL.

Apparently, older women experienced more stressors and greater physiological

stress over their lives than have younger women as yet. This difference appears

only when using extremes of components to determine AL, suggesting deciles

may better assess AL than quartiles.

234

 Men have higher AL than women in all samples and across all age groups. This

suggests men experience greater cumulative physiological and physical stress

over their lives in this Japanese setting.

 Average values for blood pressure, dopamine, waist hip ratio, GOT, GPT, total

and HDL-cholesterol, and creatine are outside ranges for “normal” in the United

States. Differences in blood pressure and lipid profiles between Japan and this

European-derived population have been noted previously. However in this

sample, differences between older Japanese and others are observed for multiple

assessments. This may better inform our knowledge of human variation among

locally adapted modern populations and differences in environments, genes, and

cultural adaptations.

 AL is significantly associated with immune, liver, and renal function, and aspects

of frailty. These results provide additional support for suggesting AL measures

physiological dysregulation and senescent decline across multiple somatic

systems.

 Observed differences in how AL is associated with physiological variability in a

genetically homogenous sample suggest large sample sizes may be needed to

validate results such as are reported here.

 The most consistent assessment of AL based on these results was z-scores. This

suggests a reliable method to create AL from ‘meta’- data sets may be z-scores. In

addition, weighting may be important to account for different sample sizes.

235

 PCA of factors used to calculate AL suggest sex is an important component. In

future analyses, sex should be included when calculating AL or samples should be

of sufficient size to divide by sex before analyzing.

 PCA identified significant components that do not match those proposed for use

with CFA. In this sample, major components identified using PCA varied little

across subsamples, even with addition of age and sex. Future analyses of AL

should not rely solely on preconceived notions of factor interactions. Rather, they

should represent a posteriori results from PCA where from new interactive

components may be determined.

236

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