Diabetes Care 1

Elodie Lespagnol,1 Olivia Bocock,2,3 In Amateur Athletes With Type 1 Joris Heyman,4 François-Xavier Gamelin,1 Serge Berthoin,1 Bruno Pereira,5 Diabetes, a 9-Day Period of Cycling Julien Boissiere,` 1 Martine Duclos,2,3 and at Moderate-to-Vigorous Intensity Elsa Heyman1 Unexpectedly Increased the Time Spent in a State of Hyperglycemia, Which Was Associated With Impairment in Heart Rate Variability https://doi.org/10.2337/dc19-1928

OBJECTIVE In type 1 diabetes, autonomic dysfunction may occur early as a decrease in heart rate variability (HRV). In populations without diabetes, the positive effects of exercise training on HRV are well-documented. However, exercise in individuals 1ULR 7369 - URePSSS - Unite´ de Recherche with type 1 diabetes, particularly if strenuous and prolonged, can lead to sharp Pluridisciplinaire Sport Sante´ Societ´ e,´ Universite´ glycemic variations, which can negatively impact HRV. This study explores the de Lille, Universited´ ’Artois, Universite´ du Littoral ˆ ’ impact of a 9-day cycling tour on HRV in this population, with a focus on exercise- Cote d Opale, Lille, France 2Unite´ de Nutrition Humaine, INRA, UMR 1019, induced glycemic excursions. UNH, CRNH Auvergne, Clermont Universite,´ Uni- RISK METABOLIC AND CARDIOVASCULAR versited´ ’Auvergne, Clermont-Ferrand, France RESEARCH DESIGN AND METHODS 3Service de Medecinedu´ Sport et des Explorations Twenty amateur athletes with uncomplicated type 1 diabetes cycled 1,500 km. HRV and Fonctionnelles, CHU Clermont-Ferrand, Clermont- glycemic variability were measured by heart rate and continuous glucose monitoring. Ferrand, France 4CNRS, UMR 6118: Transferts d’eau et de matiere` Linear mixed models were used to test the effects of exercise on HRV, with concomitant dans les milieux het´ erog´ enes` complexes – glycemic excursions and subject characteristics considered as covariates. Geosciences,´ UniversiteRennes1,Rennes,´ France RESULTS 5Unite´ de Biostatistiques (DRCI), CHU Clermont- Nighttime HRV tended to decrease with the daily distance traveled. The more time Ferrand, Clermont-Ferrand, France the subjects spent in hyperglycemia, the lower the parasympathetic tone was. This Corresponding author: Elsa Heyman, elsa.heyman @univ-lille.fr result is striking given that hyperglycemic excursions progressively increased through- Received 27 September 2019 and accepted 19 out the 9 days of the tour, and to a greater degree on the days a longer distance was June 2020 traveled, while time spent in hypoglycemia surprisingly decreased. This phenomenon This article contains supplementary material on- occurred despite no changes in insulin administration and a decrease in carbohydrate line at https://doi.org/10.2337/figshare.12517397. intake from snacks. M.D. and E.H. share the responsibility for this study on behalf of the Physical Activity Group CONCLUSIONS from Societ´ e´ Francophone du Diabete.` In sports enthusiasts with type 1 diabetes, multiday prolonged exercise at moderate- © 2020 by the American Diabetes Association. to-vigorous intensity worsened hyperglycemia, with hyperglycemia negatively asso- Readers may use this article as long as the work is ciated with parasympathetic cardiac tone. Considering the putative deleterious properly cited, the use is educational and not for profit, and the work is not altered. More infor- consequences on cardiac risks, future work should focus on understanding and mation is available at https://www.diabetesjournals managing exercise-induced hyperglycemia. .org/content/license. Diabetes Care Publish Ahead of Print, published online July 30, 2020 2 Exercise and Diabetic Autonomic Dysfunction Diabetes Care

In type 1 diabetes, cardiac autonomic blood glucose values, registered over a (Polar H7) to assess HRV at night plus neuropathy results from dysfunction of regular 5-day period, was associated with during time spent at different exercise sympathetic and/or parasympathetic ner- impaired HRV in adults with type 1 di- intensities during cycling (12). vous system activity and is associated with abetes (9). However, the literature offers Helped by the onsite dietitian, riders an increased risk of ventricular arrhythmia no data about cardiac autonomic activity reported their self-estimation of carbo- and cardiovascular morbidity and mortal- changes accompanying exercise-induced hydrates consumed at every meal (break- ity (1). Long before the appearance of glycemic fluctuations,eventhoughexercise- fast, lunch, and dinner). The day prior to autonomic neuropathy clinical signs, sub- induced hypoglycemic episodes may ap- the start of the tour, riders were inter- tle cardiac autonomic dysfunction can pear long (24 h) after the exercise session. viewed by the dietitian to assess their manifest as a decrease in heart rate var- Theaimof thisobservationalstudywas ability to accurately count carbohydrates. iability (HRV) and its components (2). A to explore, in riders with uncomplicated Those who were less accustomed to this large body of literature describes an al- type 1 diabetes, the impact of a 9-day practice benefited from a closer follow-up tered parasympathetic tone in individuals cycling tour on HRV, taking into consid- by the dietitian throughout the tour. with uncomplicated type 1 diabetes com- eration concomitant exercise-induced gly- Riders also reported the exact times and pared with healthy control subjects, result- cemic excursions and their influencing types of snacks consumed. For better ing in relative sympathetic overactivity (2). factors (i.e., diet and insulin). standardization, they were encouraged In an 11-year follow-up study of 83 sub- to use the gels, bars, and recovery drinks jects with type 1 diabetes, Makimattila¨ RESEARCH DESIGN AND METHODS provided by the staff. Among the 13 in- et al. (2) showed that chronic hypergly- Subjects dividuals treated with continuous sub- cemia (high HbA1c) was a strong predictor Twenty-three riders agreed to participate cutaneous insulin infusion (CSII), 8 made of a lower HRV. Chronic hyperglycemia in this investigation,traveling the 1,456 km their insulin pump data available for the might be attenuated by interventions such that separates Brussels and Geneva over study analyses. Every morning of the as exercise training (3), which has indeed 10 days (mHealth Grand Tour, 3–12 Sep- tour, just before breakfast, blood pres- been suggested as a way to improve HRV tember 2015), including a recovery day sure and body composition (bioelectric in type 1 diabetes (4,5). (day 4) (Supplementary Table 1). The in- impedance) were noted. Data from the However, aerobic exercise, particularly clusion criteria were age $18 years, a heart rate monitor, CGMs, carbohydrate when prolonged, intense, and/or unusual, history of type 1 diabetes for .1year,an intake, capillary blood glucose, and symp- may also trigger glycemic variability (6). HbA1c (dating back no more than 3 months) tomatic (awareness) episodes of hypogly- Hypoglycemic episodes are common due ,9% (75 mmol z mol21), and to have cemia were gathered via Bluetooth on to the increased muscle glucose disposal already experienced a 1-day ride .160 km smartphones and thereafter downloaded associated with high peripheral insulin as well as rides of 100 km on consecutive withspecificsoftwareforfurtheranalyses. concentrations, while nondecreased insu- days. All participants were free from overt lin levels in the portal vein prevent glucose micro- and macrovascular complications, HRV Analysis release from the liver. Transient hypergly- except one who suffered from arterio- The HRV analysis was performed with cemic episodes may also occur, for exam- pathy; thus, the latter was excluded from Kubios HRV software in accordance with ple, during early recovery from intense the analyses. Written informed consent the Task Force of the European Society of exercise performed in a postabsorptive was obtained, and data collection was Pacing and Electrophysiology (13). HRV state. Notably, it is not only sedentary or granted approval by Commission Natio- was analyzed during a standardized calm inactive patients who are prone to these nale de l’Informatique et des Libertes´ (sleeping) period between midnight and exercise-induced glycemic fluctuations but (MMS/TDG/ALU/AE151191). Usual phys- 4:00 A.M. throughout the 9 days of cycling. also the increasing number of sports en- ical activity was assessed using the short We analyzed time domain parameters thusiasts with type 1 diabetes engaging in version of the International Physical Activ- (SD of normal to normal R-R [SDNN]), outdoor ultra-endurance events. ity Questionnaire. Additionally, 10 riders had (percentage of differences .50 ms be- Interestingly, outside the context of undergone an incremental maximal ex- tween successive NN intervals [pNN50]), exercise, it has been suggested that acute ercise test (VO2max) as part of indepen- and the root mean square of differences glycemic excursions impair cardiac auto- dent medical monitoring of athletes. of successive NN intervals [RMSSD]) as nomic activity. Thus, Nguyen et al. (7) Whether participants suffered from hy- well as frequency domains of HRV by the provided pilot data in six subjects with poglycemia unawareness was also re- Fast Fourier Transform (high frequency – type 1 diabetes, showing that periods of ported. According to VO2max or training [HF], 0.15 0.40 Hz, and low frequency naturally occurring hyperglycemia (mea- status,theparticipantswererecreationally [LF], 0.04–0.15 Hz). suredoveronenight)wereassociatedwith trained or trained cyclists (10,11). an impaired global HRV and parasympa- Glycemic Variability Analysis thetic tone, compared with the nonhyper- The Cycling Tour Glycemic excursions and variability were glycemic periods. Besides, Koivikko et al. Throughout the 10 days of the tour, calculated from CGM recordings over (8)showedareductioninHRVandpara- subjects wore a CGM (Dexcom G4 Plat- several specific periods: 1) from midnight sympathetic tone in response to hyper- inum) (three or more calibrations per day to 4:00 A.M., concomitant with the period insulinemic-hypoglycemic clamp as by capillary finger-stick measurement of HRV analysis; 2) the day before, from compared with euglycemic clamp in sub- with TAPcheck glucometer) to evaluate the beginning of breakfast to 2 h post- jects with type 1 diabetes. Additionally, glycemic excursions and variability. They dinner; 3) over periods of 24 h; 4) during a greater glycemic variability toward low concomitantly wore a heart rate monitor the cycling periods excluding the lunch care.diabetesjournals.org Lespagnol and Associates 3

break; and 5) during early and late recovery with glycemic variability and excursions as and mean blood pressure decreased sig- (2 and 6 h following the cycling periods). dependent variables, we tested the ef- nificantly (main time effect e 20.43 and Glycemic excursions considered were the fects of exercise characteristics and sub- e 20.39, respectively, P , 0.05). Anthro- percentage of time spent in range (between ject characteristics, respectively, with pometric, demographic, and diabetes- 70 and 180 mg z dL21 [3.9 and 10 mmol z time and, for the 24-h periods, circadian related variables were not significantly L21]), below range (level 1 hypoglyce- effects as covariates. Subsequently, we related to blood pressure throughout mia, ,70 mg z dL21 [,3.9 mmol z L21]; tested the effects of carbohydrates the tour. level 2 hypoglycemia, ,54 mg z dL21 (grams) ingested (seventh set of mod- 2 [,3.0 mmol z L 1]), and above range els) and the effects of carbohydrates HRV During the Tour (level 1 hyperglycemia, .180 mg z dL21; ingested and insulin administered (n 5 The results of HRV are presented in level 2 hyperglycemia, .250 mg z dL21; 8 subjects on CSII [eighth set of models]) 16 individuals because 4 riders did not and hyperglycemia .300 mg z dL21 [14] on glycemic variability and excursions, wear their heart rate monitor correctly at [10, 13.9, and 16.7 mmol z L21]) levels considering the time effect and, when night. The results of mixed models for the (15). Glycemic variability was assessed significant in the 5th set of models, the association of time, subject and exercise through coefficient of variation (%CV) exercise characteristics. characteristics, and glycemic excursions, (15), SD, mean amplitude of glycemic A P value ,0.05 was considered sta- with temporal and frequency HRV do- excursions (MAGE), continuous overlap- tistically significant. All results are ex- mains displaying significant main effects, ping net glycemic action 1&2 (CONGA pressed as the mean estimation “e.” A are presented in Table 2. 1&2), and average daily risk ratio (ADRR) particular focus was also given to the Parasympathetic tone parameters indexes (16). difference magnitude in addition to in- (i.e., HF 910.4 6 1,440.2 ms2, pNN50 17.2 ferential statistical tests expressed using 6 20.2%, RMSSD 43.5 6 32.5 ms, mean Statistics P values. over the 9 days) and sympathetic-vagal All statistical analyses were performed balance (i.e., LF-to-HF ratio 4.1 6 2.2, with SPSS software (version 19;IBM). The RESULTS mean over the 9 days) did not signifi- quantitative data are described as the Technical problems were encountered in cantly change with time throughout the mean 6 SD. Normality was checked with the collection of nighttime beat-to-beat tour and were not altered by the char- the Shapiro-Wilk test. A logarithm trans- heart rate and/or interstitial glucose acteristics of the exercise performed formation was applied to data with a (e.g., disconnection of sensor, synchro- each day. However, global HRV (as re- non-Gaussian distribution. In all of the nization problems) for three subjects. flected by SDNN, 101.5 6 39.0 ms, over following models, covariates were added Thus, 20 subjects were included in the the 9 days) tended to decrease with the as fixed effects and subjects as random final analyses. Their characteristics are number of kilometers traveled the day effects to consider between- and within- displayedinTable1. before, without the influence of exercise participant variability. Carbohydrate data were processed for intensity or time. In the 1st set of models, HRV param- 19 of the 20 riders because one of them The 12 men had a higher sympathetic- eters were studied as dependent varia- incorrectly completed the food question- vagal balance than the four women. Age bles in linear mixed models with time naire. Among these 19 riders, 15 were associated with decreased parasympa- (i.e., days 1–10, except for day 4, which considered as having mastered advanced thetic tone. Aerobic fitness (VO2max) was was a recovery resting day) as covariates. carbohydrate counting including appro- positively associated with global HRV and In a 2nd set of models, we then succes- priate carbohydrate gram estimation. parasympathetic tone. sively analyzed the effects of subjects and The other four participants benefited Global HRV was not linked with gly- exercise characteristics during the tour, from closer assistance from the dietitian cemic excursions. A decrease in sympa- with time kept as a covariate. In a 3rd set with counting carbohydrates at every thetic-vagal balance and an increase in of models, the effects of time as well as meal throughout the tour. Exercise in- parasympathetic tone during the night glycemic variability and excursions were tensities and exact duration of the cy- were associated with longer time spent tested as covariates, in addition to ex- cling periods (Supplementary Table 1) alert with low glucose values during the ercise characteristics if significant in the were obtained only for 46 (i.e., 25.6%) previous day and the 6-h postexercise 2nd set of models. full days over the 180 (i.e., 9 days for recovery period. A decrease in parasym- In a 4th set of models, glycemic var- 20 riders) days of cycling analyzed be- pathetic tone during the night was as- iability and excursions were studied as cause of problems with transient dis- sociated with a longer time spent in a dependent variables in mixed models or connection between the belt and heart state of level 2 hyperglycemia during the multinomial and binary logistic regres- rate watch monitor. Riders spent a large concomitant night, as well as in a state 2 sionswithtime(i.e.,days1–10,exceptfor part of the cycling period at moderate with hyperglycemia .300 mg z dL 1 day 4) and circadian (i.e., night vs. day (i.e., 160.0 6 38.3 min per day) and during the previous day. periods, only for analyses over the peri- vigorous (i.e., 155.1 6 27.0 min per day) ods of 24 h) effects as covariates. Mul- intensities. Morning body mass, percent Glycemic Excursions and Variability tinomial or binary logistic regressions of fat mass, muscular mass, and hydra- Change in time spent in states of hypo-, were specifically used to assess the per- tion did not change over the 9-day normo-, and hyperglycemia throughout centage of time spent in hypo- and hy- period. While systolic blood pressure thetourispresentedinFig.1(forthe perglycemia (see details in the legend of remained unchanged throughout the 24-h periods) and Supplementary Fig. 1 Table 3). In the 5th and 6th sets of models, tour, morning diastolic blood pressure (for cycling and early and late recovery 4 Exercise and Diabetic Autonomic Dysfunction Diabetes Care

fi Table 1—Anthropometric, demographic, and physical activity characteristics of signi cantly associated with the concom- the riders itant insulin basal rate throughout the tour. Sex, n male/female 16/4 Age (years) 37.9 6 10.5 (19.0–54.0) BMI (kg z m22) 23.8 6 2.6 (18.3–30.8) CONCLUSIONS Fat mass (%) 17.6 6 5.7 (7.4–36.9) Our study of 20 riders with type 1 di- Waist-to-hip circumference ratio 0.9 6 0.1 (0.7–1.0) abetes highlighted, for the first time, that Diabetes duration (years) 19.6 6 7.7 (5.0–35.0) the repetition of long-duration exercise 21 bouts at moderate-to-vigorous intensity HbA1c (mmol z mol ) 54.1 6 9.1 (42.1–74.9) HbA (%) 7.1 6 0.8 (6.0–9.0) (n 5 19) over 9 days may trigger hyperglycemic 1c excursions, which were negatively asso- n habit to wear a CGM/no habit 12/8 Brands of CGM used N 5 4 from Medtronic, N 5 8 from Dexcom ciated with parasympathetic tone, while CSII/MDI 13/7 time spent in a state of hypoglycemia ICR (grams/unit of insulin) 11.2 6 4.9 (n 5 15) decreased. The type of statistical model used was chosen to ensure that the Other drugs n 5 1 calcium antagonists observed relationships (e.g., between n 5 2 thyroid drugs hyperglycemia, a covariate, and para- VO (mL z min21 z kg21) 53.1 6 7.9 (38.6–67.4) (n 5 10) 2max sympathetic tone, the dependent end IPAQ score (MET min z week21) 7,559.0 6 5,104.4 (2,187.0–25,194.0) (n 5 19) point) were not due to simultaneous Mean km/year traveled (cycling) changes in time or in exercise character- in daily life 6,339 6 3,518 (500–15,000) istics but appeared for any given value of Data are means 6 SD (minimum–maximum) unless otherwise indicated. The number of subjects is thesetwooutcomes,whichwereaddedas indicated for outcomes with some lacking data. ICR, insulin-to-carbohydrate ratio; IPAQ, International Physical Activity Questionnaire; MDI, multiple daily insulin injections. covariates in the model. While demographic characteristics such as age (17) and sex (2) are well- periods). Glycemic variability is reported in content ingested via daytime snacks known predictors of sympathetic-vagal Supplementary Fig. 2. Factors influencing (e210.29,P , 0.001)but didnot change balance, as confirmed in our study, the glycemic outcomes are presented in Table 3. thecarbohydratecontentofthenighttime impact of exercise training on cardiac Hypoglycemia unawareness (subjec- snacks or the three meals (Fig. 1C). In this autonomicfunctionintype1diabetesis tively reported) did not influence hypo- context of sustained repeated exercise, documented less frequently. Interest- glycemic excursions. A higher HbA1c level neither the carbohydrates from meals nor ingly, in agreement with a recent study was associated with a longer time spent the carbohydrates from nighttime or day- (18), we found that VO2max,asare- above range but lower glycemic variabil- time snacks (which decreased with time) flection of regular aerobic training level, ity. Neither mode of insulin therapy nor were significantly associated with glyce- was positively associated with cardiac the habit of using CGM influenced gly- mic excursions throughout the tour (sev- autonomic function (parasympathetic cemic outcomes. enth set of models). In the sample of tone in our study). Notably, while the number of hypo- subjects providing insulin pump data, However, nighttime beat-to-beat heart glycemic episodes of whichsubjects were bolus and basal rates were not signif- rate recordings throughout the tour re- aware did not change during the tour (0– icantly changed throughout the tour vealed that unusual multiday sustained 1 episode/day among the riders over the (eighth set of models) (Fig. 1D). Notably, moderate-to-vigorous exercise, without 9 days), the percentage of time spent in comparison of insulin doses used appropriate recovery, may conversely below range (hypoglycemia levels 1 and during normal daily life (data obtained trigger impairment of HRV in type 1 di- 2) was decreased in riders (time effect), from six of the eight subjects during a abetes. The longer the distance ridden as measured over all the periods studied. usual week before the tour) with those during the day, the lower the global HRV However, this decrease was at the ex- administered during the tour, no signif- tendedtobeduring the subsequent night. pense of glycemic variability and hyper- icant difference appeared (paired t test) This phenomenon could reflect a state of glycemic excursions (as measured over (insulin basal rate [mean 6 SD] 14.9 6 overreaching as already observed in rec- all the periods studied), which worsened 7.5 vs. 20.9 6 10.0 units z day21, bolus reationally trained runners without dia- throughout the tour (time effect) and 18.8 6 10.7 vs. 23.9 6 13.0 units z day21 betes (19), who displayed a significant weremore frequent onthe days a greater throughout the 9 days of the tour vs. reduction in indexes of HRV (including distance was ridden (for hyperglycemia during 1 week of daily life, respectively). SDNN) up to 24 h after an ultramarathon .300 mg z dL21, MAGE and SD), without Nevertheless, during the tour, the larger (64 km distance, 1,572 m accumulative a significant effect of exercise intensity. the daily amount of insulin bolus (either altitude change). It should, however, be This was accompanied by a decrease in in units or in units z kg21) was, the greater noted that studies on the effect of over- time in range. Throughout the tour, the extent of time spent below range reaching on HRV in healthy athletes re- subjects experienced less time in range (level 2 hypoglycemia, e increase of 8.87 main few and far between, sometimes and more time above range during the or 0.11, respectively, P , 0.05) experienced with the finding of nosignificant change in daytime compared with the nighttime. byriders from breakfast to 2 h postdinner. HRV (20,21). Throughout the tour, the riders progres- Glycemic outcomes during the night (be- In our study, it is worth noting that some sively decreased the daily carbohydrate tween midnight and 4:00 A.M.)werenot cardiac autonomic function parameters Lespagnol and Associates 5 Table 2—Results of mixed models for influence of subject and exercise characteristics, glycemic variability (during day or night), and excursions on HRV Global HRV, Parasympathetic tone, Sympathetic-vagal 2 Dependent variables (midnight–4:00 A.M. HRV) SDNN (ms) pNN50 (%) RMSSD (ms)† HF (ms )† balance, LF–to–HF ratio 2nd set of models Effect of subject characteristics (no effect of HbA1c, diabetes duration, mode of insulin therapy, BMI, % fat mass, or riders’ regular physical activity (subjectively assessed by the IPAQ) Sex NS NS NS NS e 22.68; P 5 0.08 Age NS NS e 20.01; P 5 0.08 e 20.03; P , 0.05 NS Aerobic fitness (VO2max )e13.63; P , 0.01 e 11.76; P , 0.05 e 10.03; P , 0.01 e 10.05; P , 0.01 NS Effect of exercise characteristics (no effect of altitude changes, duration of cycling, or exercise intensity) Kilometers traveled e 20.32; P 5 0.08 NS NS NS NS 3rd set of models Effects of glycemic excursions and variability measured by period During the concomitant night (midnight–4:00 A.M.) % time .250 mg z dL21† NS e 24.76; P 5 0.06 e 20.09; P , 0.05 e 20.21; P , 0.05 e 10.84; P , 0.05 Throughout the day before (from beginning of breakfast to 2 h postdinner) 2 % time ,70 mg z dL 1† NS NS NS NS e 21.14; P , 0.05 % time .300 mg z dL21† NS NS e 20.13; P , 0.05 e 20.28; P , 0.05 NS During the cycling period, the day before SD NS NS e 10.007; P , 0.05 NS NS During the 6-h postexercise period, the day before % time ,54 mg z dL21† NS e 14.78; P , 0.05 NS NS NS % time ,70 mg z dL21† NS e 13.58; P , 0.05 e 10.09; P , 0.06 e 10.24; P , 0.05 NS N 5 16. For e data, 1, increase; 2, decrease. In the 1st set of models, no significant effect of time was detected. In the 2nd set of models, the subject characteristics analyzed were 1) anthropometric (% fat mass) and demographic (age, sex) characteristics, 2) disease and treatment (HbA1c, diabetes duration, mode of insulin therapy, habit of wearing a CGM), and 3) physical activity and fitness (International Physical Activity Questionnaire [IPAQ] score, VO2max ) characteristics. In the 2nd set of models, the exercise characteristics analyzed were kilometers daily traveled, cumulative altitude change, and cycling duration, combined in a 1st model.Then,the effectofpercentageof timespentinmoderate-andvigorous-intensityactivitywastestedwiththeexercisecharacteristic(s)keptas(a)covariate(s) ifsignificantintheprecedingmodel.Inthe 3rdsetof models,glycemicvariabilityandexcursionsduringthe cyclingperiodandduringthe 6-hrecoveryperiodwereobtainedfor42(i.e.,26.2%)and85(i.e.,53.1%)days,respectively,overthe160daysof analysis,sincethese periods were determined based on heart rate data from morning and afternoon, or afternoon only, respectively (compared with problems of transient disconnection between the belt and heart rate watch monitor). No significant effects were detected for time spent in range (70–180 mg z dL21), above range level 1 (.180 mg z dL21), or most of the glycemic variability indexes (%CV, ADRR, MAGE, CONGA 1&2). The kilometers traveled was added as a covariate for all models with SDNN as the dependent variable. SD, SD of glycemia. †Data values are log transformed; glycemic and HRV data from the last (i.e., the 10th) night were not used because several riders decided to remove their collection devices before that night. care.diabetesjournals.org xrieadDaei uooi Dysfunction Autonomic Diabetic and Exercise 6

Table 3—Results of mixed models and logistic regression for parameters influencing glycemic excursions and variability over periods of 24 h (8:00 A.M.–8:00 P.M. and 8:00 P.M.–8:00 A.M.) Hypoglycemic Euglycemia: excursions Hyperglycemic excursions Glycemic variability % time % time ,54 % time ,70 between 70 % time % time %time 2 2 2 2 2 2 mg z dL 1* mg z dL 1* and 180 mg z dL 1 .180 mg z dL 1* .250 mg z dL 1* .300 mg z dL 1* MAGE ADRR CONGA 1 CONGA 2 SD Effect of time throughout the tour e 20.21; e 20.10; e 21.67; e 10.33; e 10.16; e 10.18; e 12.54; e 10.57; e 11.82; e 11.97; e 11.07; (4th set of models) P , 0.001 P 5 0.06 P , 0.001 P , 0.001 P , 0.01 P , 0.001 P , 0.01 P , 0.05 P , 0.001 P , 0.001 P , 0.01 Circadian effects throughout the tour NS NS e 27.50; e 11.21; e 11.59; e 11.26; NS NS NS NS e 111.24; (4th set of models) P , 0.01 P , 0.01 P , 0.001 P , 0.001 P , 0.001 Effects of exercise characteristics (5th NS NS NS NS e 10.03; e 10.62; NS NS NS e 10.26; set of models): kilometers (no P , 0.05 P 5 0.06 P 5 0.06 effect of altitude change, cycling duration or exercise intensity) Effects of subject characteristics (6th set of models) Anthropometric (no effect of age or sex) % fat mass NS NS NS NS NS e 10.07; NS NS NS NS NS P 5 0.09 Disease and treatment (no effect of mode of treatment nor habit to use a CGM) HbA1c NS e 20.03; NS e 10.04; NS e 10.04; NS NS e 20.71; e 20.51; NS P 5 0.08 P , 0.05 P , 0.05 P , 0.01 P 5 0.06 Diabetes duration NS e 10.06; NS NS NS NS NS NS NS NS NS P 5 0.08 Forthe circadianeffect, nightwaschosenasthe reference. Forthe modeoftreatment effect, CSII waschosenasthe reference. ForhabitwithCGMuse, the factthatthe riderwas notfamiliarwiththe wearof aCGM was chosenasareference.Glycemicdatafromthelast(i.e.,the10th)nightwerenotusedbecauseseveralridersdecidedtoremovetheirCGMbeforethatnight.Inthe5thsetofmodels,theexercisecharacteristicsanalyzed were kilometers daily traveled, cumulative altitude change, and cycling duration, combined in a 1st model. Then, the effect of % time spent in moderate- and vigorous-intensity activity was tested with the exercise characteristic(s) kept as (a) covariate(s) if significant in the preceding model. In the 6th set of models, the subject characteristics analyzed were 1) anthropometric (% fat mass) and demographic (age, sex) characteristics, 2) disease and treatment (HbA1c, diabetes duration, mode of insulin therapy, habit of wearing a CGM), and 3) physical activity and fitness (International Physical Activity Questionnaire [IPAQ] score, VO2max ) characteristics. Neither daily physical activity (International Physical Activity Questionnaire score) nor VO2 max significantly influenced glycemic variability and excursions. When we focused our analysis on glycemic outcomes measured in the periods between breakfast and 2 h postdinner, the effects of time during the tour (days) were comparable with those presented here, i.e., for the 24-h periods. The negative effect of kilometers traveled on glycemic variability was also found when we focused specifically on subsequent early (2-h) and late (6-h) postexercise recovery periods (early recovery: MAGE, e 12.32, P , 0.01; late recovery: SD, e 10.41, P 5 0.09; MAGE, e 12.03, P , 0.01). In addition, cumulative altitude change increased glycemic variability during late postexercise recovery periods (%CV, P , 0.05, e 10.01; SD, e 10.02, P , 0.05; MAGE, e 10.07, P , 0.01). There was no significant result for %CV in other models except for the circadian effect in the 4th set of models (P , 0.05, e 13.25). % time in vigorous intensity tended to decrease the time with levels ,70 mg z dL21 during subsequent late recovery period (e 20.04, P 5 0.08) and to increase the time in range during cycling as well as subsequent early and late recovery periods (e 10.44, P 5 0.09; e 10.97, P , 0.05; and e 10.67, P , 0.05, respectively). The percentage time in moderate intensity also decreased the time with levels ,70 mg z dL21 during subsequent late recovery periods (e 20.08, P , 0.05). *For binary and multinomial logistic regressions, a positive or a negative e represents an increase or a decrease, respectively, of the probability of being in the following categories: ,54, ,70, .180, .250, .300 mg z dL21. The multinomial or binary logistic regressions were specifically used to assess % time in states of hypo- and hyperglycemia. Ordinal categories 1, 2, and 3 (i.e., no time in the target glucose range and time below the median

2 2 Care Diabetes and above the median in the target glucose range, respectively) were derived from time ,70, .180, or .250 mg z dL 1 (3.9, 9.9, or 13.9 mmol z L 1). Additionally, categories 0 and 1 (i.e., no time in the target glucose 2 2 range or some time in the target glucose range, respectively) were designed to reflect time ,54 or .300 mg z dL 1 (3.0 or 16.7 mmol z L 1). care.diabetesjournals.org Lespagnol and Associates 7

Figure 1—Percentage of time spent in states of hypo-, normo- and hyperglycemia in relation to insulin administration and carbohydrate intake. A: 21 Between 8:00 A.M. and 8:00 P.M. B: Between 8:00 P.M. and 8:00 A.M. the next day. N 5 20. Black bars, percentage of time spent with levels ,70 mg z dL ; hatchbars,between70and180mgzdL21;whitebars,.180mgz dL21.TheeffectsoftimeontheseglycemicoutcomesaredisplayedinTable3.SD values varied between 3.02% and 16.12%, 9.18% and 28.07%, and 11.78% and 30.17% for daytime data and between 3.81% and 20.36%, 16.44% and 31.30%, and 12.52% and 29.26% for nighttime data for time spent in a state of hypoglycemia, euglycemia, and hyperglycemia, respectively. Glycemic data from the last (i.e., the 10th) night were not used in analyses because several riders decided to remove their CGM before that night. C: N 5 19. Day, from breakfast to 2 h postdinner; night, from 2 h postdinner to breakfast the next day. Black bars, snacks during the tour; white bars, three meals of the day. Carbohydrates from meals of day 10 were not taken into account because most of the riders did not correctly report their intake of the dinner following the end of the tour. The effects of time on carbohydrate ingestion are indicated in RESULTS. SD values varied between 15.0 and 28.4, 0.6 and 4.6, and 10.5 8 Exercise and Diabetic Autonomic Dysfunction Diabetes Care

were actually linked with glycemic hyperglycemia observed during the tour. case report in an athlete with type 1 excursions. Although reducing insulin administration diabetes, showing progressive increase Thus, a longer time spent at low and/or increasing carbohydrate intake in markers of inflammation (CRP) and glucose levels (corresponding to both are commonly recommended for avoid- muscle damage (creatine kinase), which level 1 and 2 hypoglycemia) (15) during ing hypoglycemic episodes around phys- are two factors of insulin resistance late recovery (i.e., the 6-h period post- ical exercise in type 1 diabetes (6), these (30,31), while changes in cortisol, an exercise) was associated with increased measures may not be needed in athletes activator of hepatic gluconeogenesis, parasympathetic activity during the sub- for whom insulin doses and diet are did not exactly follow trends of hyper- sequent night. To the best of our knowl- already well adjusted to their usual in- glycemic excursions (32). Future studies edge, the only studies that have explored tensive exercise training. Accordingly, in on larger sample sizes will be needed to the link between hypoglycemia and car- our work, the cyclists did not increase ascertain the cause of the observed diac autonomic balance specifically in the carbohydrate intake throughout the persistent hyperglycemia. Data from a context of physical exercise (only one tour and presumably did not change study using nonrepeated sustained ex- session) have considered either the their usual insulin dose (as verified ercise, i.e., a single marathon, suggest changes occurring during the 60 min among the individuals using an insulin that persistence of free fatty acid oxida- before the hypoglycemic episode (22) or pump). Thus, while insulin and diet tion long after exercise may suppress the changes occurring concomitant with might not be the direct cause of exer- carbohydrate oxidation (33). Addition- the hypoglycemic period (23,24) and cise-induced hyperglycemia worsening, ally, in subjects without diabetes, the showed either a decrease or an increase the characteristics of the multiday ex- exercise-induced increase in lactatemia in parasympathetic tone, respectively. ercise, i.e., prolonged and including a has been shown to be enhanced Further studies are needed to confirm significant portion of vigorous intensity, throughout the week following a mara- the increase in nocturnal parasympa- may play a fundamental role. Intense thon (29), and lactate can then serve as thetic tone associated with postexercise exercise (.85% VO2max) to exhaustion is an alternative muscle substrate for spar- hypoglycemic periods and to under- knowntoinduceanincreaseinglycemia ing blood glucose (34). stand its clinical implications. during the early recovery period because Finally, it is noteworthy that in addi- While time spent in a state of hypo- plasma catecholamines and glucagon take tion to the negative link between glyce- glycemia decreased throughout the tour, time (;30 min and 30–50 min, respec- mic excursions and cardiac autonomic we observed a surprisingly significant de- tively) to return to resting concentrations. activity during the 9-day repeated mod- crease in time spent in a state of eugly- In subjects without diabetes, the increase erate-to-vigorous exercise, such activity cemia due to a considerable increase in in glycemia during early recovery following was associated with a progressive de- time spent in a state of hyperglycemia. intense exercise is counteracted by a two- crease in morning diastolic blood pres- While the deleterious impact of chronic fold increase in plasma insulin, whereas sure. This phenomenon has already been hyperglycemia (HbA1c)onHRVand individuals with type 1 diabetes are prone observed in response to long and stren- parasympathetic tone is already well- to transient hyperglycemia unless a bolus uous events in healthy athletes and was documented in youth with type 1 diabe- of insulin is delivered (28). Consistent with suggested to be due to metabolic va- tes (25), this study is the first to show a this theory, the only cyclist in our study sodilatation and/or ineffective transduc- link between cardiac autonomic imbal- who did not experience significant post- tion of sympathetic outflow from arterial ance and acute hyperglycemic periods exercise hyperglycemia received a fre- smooth muscle (35). Therefore, better in the context of physical exercise. To quent insulin correction bolus in the attention should be paid to vulnerabil- our knowledge, only two studies, with hours following exercise. ity to orthostatic challenges after multi- quite controversial results, have attemp- Additionally, when exercise becomes day prolonged exercise in athletes with ted to explore the possible acute impact extremely prolonged, glucose metabo- type 1 diabetes. of hyperglycemia on HRV in individuals lism might be disrupted long after the While our results are particularly rel- with type 1 diabetes but without in- end of exercise, as revealed in a study in evant given the growing popularity of volving concomitant physical exercise and subjects without diabetes (29). Equally long-distance or multiday running or based only on a limited number of gly- of interest, in line with this result, we cycling events, including for persons cemic values (i.e., only one measure taken noticed a positive association of the with type 1 diabetes, the findings should in a fasting state and the other 30 min number of kilometers traveled during be interpreted in the context of the study after a regular meal [26] or one measure the day with time spent in a state of limitations. The nonstandardization of every 30 min during 1 night in only six hyperglycemia as well as with glycemic the resting day and the limited data subjects [7]). variability during the surrounding 24 h. obtained during riders’ daily life prevent As impaired cardiac vagal control is To the best of our knowledge, blood drawing comparisons between control associated with higher cardiac mortality metabolite and hormonal response to resting periods and the multiday cycling (27), it appears to be crucial to elucidate multiday long-distance sports events challenge. We obtained insulin data the factors involved in the worsening of has only been the topic of one single only from a small proportion of the

and 21.5 g for carbohydrates (CHO) from the daytime snacks, the nighttime snacks, and the three meals, respectively. D: N 5 8 treated with an insulin pump. Day, from breakfast to 2 h postdinner; night, from 2 h postdinner to breakfast the next day. White bars, insulin bolus; black bars, basal rates. The effects of time on insulin administration are indicated in RESULTS. SD values varied between 7.7 and 19.3, 0.3 and 2.5, 3.2 and 4.7, and 3.6 and 4.2 units for daytime and nighttime insulin bolus and for daytime and nighttime basal rates, respectively. Data from day 4 are not displayed because they represent a resting day. care.diabetesjournals.org Lespagnol and Associates 9

participants, making generalization diffi- parasympathetic cardiac tone. These re- Complications (DCCT/EDIC) Study Research cult. In addition, the accuracy of CGMs sults are important considering the pu- Group. Intensive diabetes treatment and car- may be reduced during exercise bouts tative consequences on future cardiac diovascular disease in patients with type 1 diabetes. N Engl J Med 2005;353:2643–2653 due to multiple factors, such as subcu- mortality risk. In this context, beyond 2. Makimattila¨ S, Schlenzka A, Mantysaari¨ M, taneous dehydration, temperature var- research on hypoglycemia prevention et al. Predictors of abnormal cardiovascular iations, rapid decreases in glycemia, etc. strategies, future work on understand- autonomic function measured by frequence For addressing these limitations, the ing and managing exercise-induced hy- domain analysis of heart rate variability and conventional tests in patients with type 1 di- CGMs were calibrated at least three perglycemia should be promising. – fi abetes. Diabetes Care 2000;23:1686 1693 times daily by capillary nger-prick test- 3. Tonoli C, Heyman E, Roelands B, et al. Type ing and body hydration was controlled 1 diabetes-associated cognitive decline a meta- every morning. Acknowledgments. The authors thank all analysis and update of the current literature. J Given the observational study de- members of the Orange HealthCare team, par- Diabetes 2014;6:499–513 sign, we cannot draw conclusions about ticularly J. Braive, V.-D. Dam, O. Graille, M. 4. Shin KO, Moritani T, Woo J, et al. Exercise Montaner-Gomez, and P. Chopineau for their training improves cardiac autonomic nervous causality. However, the current cross- help in providing all the material needed for system activity in type 1 diabetic children. J Phys sectional study was a necessary first step linking, via a connected phone, glycemic data Ther Sci 2014;26:111–115 toward future implementation of inter- with other data (carbohydrate intake, HRV, body 5. Chen SR, Lee YJ, Chiu HW, Jeng C. Impact of ventional randomized control trials. composition, arterial pressure, etc.). Data ex- glycemic control, disease duration, and exercise Based on the current results, these future traction was facilitated by O. Beltoise (Glooko on heart rate variability in children with type 1 Diasend) and by B. Klinkenbijl and S. Guerra from diabetes mellitus. J Formos Med Assoc 2007;106: trials could contribute to reducing hy- ClinInfo teams. The authors are also thankful for 935–942 perglycemia induced by outdoor endur- the crucial logistical support of A. Denton from 6. Riddell MC,GallenIW,SmartCE, et al.Exercise ance sports, instead of focusing only on Hydon Consulting Society, as well as C. Hugues management in type 1 diabetes: a consensus hypoglycemia, and provide observations (diabetes dietician) for collecting diet data statement. Lancet Diabetes Endocrinol 2017;5: throughout the tour, M. Gonnet (Another Brain – of the subsequent impact on HRV. This 377 390 Society), S. Tagougui (Lille University), and P. 7. Nguyen L, Su S, Nguyen HT. Effects of hyper- will make it possible to partition off the Morel (University of the Littoral Opale Coast) for glycemia on variability of RR, QT and corrected possible impact of hyperglycemia from data analysis assistance, J.-F. Gautier (Depart- QT intervals in Type 1 diabetic patients.Conf Proc that of overreaching, the latter having ment of Diabetes and Endocrinology, Lariboisiere IEEE Eng Med Biol Soc 2013;2013:1819–1822 already been reported in athletes with- Hospital, Paris, France) for his contribution to the 8. Koivikko ML, Salmela PI, Airaksinen KE, et al. study design, L. Canipel (Societ´ e´ Française de Effects of sustained insulin-induced hypoglyce- out diabetes. As far as we are aware, our Tel´ em´ edecine)´ for her help with ethics approval, mia on cardiovascular autonomic regulation in study is the first to confirm empirical data P. Fontaine (Endocrinology Unit, Lille University type 1 diabetes. Diabetes 2005;54:744–750 (i.e., based on patients’ narrative and Hospital) and M. Garcia Vigueras for their sci- 9. Jaiswal M, McKeon K, Comment N, et al. onasinglecasereport(36))onthe entific advice, A. Bocock (Melun), A. Denton Association between impaired cardiovascular increased risk of hyperglycemia triggered (Hydon Consulting Society), and B. Heyman autonomic function and hypoglycemia in pa- (University of Rennes) for revising the English, tients with type 1 diabetes. Diabetes Care 2014; by multiday ultra-endurance events in and A. Bertrand (Statistical Methodology and 37:2616–2621 amateur athletes with type 1 diabetes, Computing Service, Universite´ catholique de 10. De Pauw K, Roelands B, Cheung SS, de Geus with concomitant careful consideration Louvain) for checking the statistical analyses. All B, Rietjens G, Meeusen R. Guidelines to classify of exercise intensity and carbohydrate the above mentioned support was supplied at no subject groups in sport-science research. Int J intake. Notably, two other recent reports charge. Sports Physiol Perform 2013;8:111–122 Funding. This study was supported by a dona- 11. Decroix L, De Pauw K, Foster C, Meeusen R. are available (37,38) but on professional tion from Linde Homecare France, which allowed Guidelines to classify female subject groups in cyclists, whose adaptations to elite-stage recruitment of E.L. as a postdoctoral researcher sport-science research. Int J Sports Physiol Per- racesare certainly differentfrom thoseof for 1 year. form 2016;11:204–213 amateur athletes. In addition to offering Duality of Interest. No potential conflicts of 12. American Diabetes Association. Physical ac- interest relevant to this article were reported. widely generalizable data, the unique tivity/exercise and diabetes. Diabetes Care 2004; Author Contributions. E.H. and M.D. conceived 27(Suppl. 1):S58–S62 nature of our study lies above all in the the experiments. E.L. and E.H. performed the experi- 13. Task Force of the European Society of Car- novel way the putative links between ments,collectedandanalyzeddata,andwrotethe diology and the North American Society of Pacing sustained exercise-induced glycemic manuscript. O.B. analyzed insulin data. J.H. created and Electrophysiology. Heart rate variability: excursions and cardiac autonomic ac- algorithms for analyses of glycemic variability. B.P. standards of measurement, physiological in- contributed to statistical analyses. F.-X.G. and S.B. tivity are examined. Further studies are terpretation and clinical use. Circulation 1996; contributed to the HRV analyses. O.B., J.H., B.P., S.B., 93:1043–1065 needed to understand the mechanisms F.-X.G., and M.D. contributed to the performance of 14. Famulla S, Hovelmann¨ U, Fischer A, et al. involved in the hyperglycemic effect the experiments and reviewed the manuscript. 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