Parasympathetic nervous function and prevalence of prediabetes/diabetes mellitus: a cross-sectional study: WASEDA'S Health Study

doi:10.21203/rs.3.rs-3330777/v1

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Parasympathetic nervous function and prevalence of prediabetes/diabetes mellitus: a cross-sectional study: WASEDA'S Health Study

https://doi.org/10.21203/rs.3.rs-3330777/v1

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Purpose:Limited data are available on the relationship of parasympathetic nervous function with the prevalence of prediabetes and diabetes in men and women. We conducted a cross-sectional study to investigate the relationship between diving reflex - markers of parasympathetic nervous function - with the prevalence of prediabetes and diabetes among men and women in WASEDA'S Health Study.

Methods: Participants were 199 men and 75 women who completed a medical examination, maximal exercise test, and diving reflex test. The participants were divided into tertiles based on the diving reflex indexes. The diving reflex indexes were the peak value of the R–R interval during the test (R–Rmax) and the relative difference between the baseline and peak response due to the test (R–Rchange). Odds ratios and 95% confidence intervals for the prevalence of prediabetes and diabetes were obtained using logistic regression models while adjusting for sex, age, body fat percentage, family history of diabetes, smoking status, and drinking status, sleeping hours, energy intake, and peak oxygen uptake.

Results: Forty-one participants had prediabetes (n=24) or diabetes (n=17). Using the lowest diving reflex indexes as reference, we calculated odds ratios and 95% confidence intervals for the outcomes if interests. We found inverse relationships between R-Rmax and prediabetes and diabetes (P for trend = 0.104) as well as R-Rchange and diabetes (P for trend = 0.023).

Conclusions: In this cross-sectional analysis, the data suggest diving reflex indexes, especially R-Rchange, may be related to the prevalence of diabetes.

diving reflex

exercise test

prediction

observational study

Autonomic function plays a vital role in maintaining the life activities of human beings. Among others, a decline in parasympathetic nerve function leads to the development of cardiovascular disease, diabetes, and all-cause mortality [1, 2]. Therefore, the assessment of parasympathetic nerve function can provide critical information on our health management and disease prediction.

Heart rate recovery (HRR) after exercise, one of the assessment indicators for parasympathetic nerve function, is a strong predictor of all-cause mortality [1, 3]. For example, it is reported that people with HRR of lower than 12 beats per minute during the first minute after peak exercise have a higher mortality rate than people with HRR of 12 beats per minute and over [4]. However, accurate HRR assessments require the implementation of a graded exercise test [5, 6], which makes it difficult to assess HRR at health management and clinical sites. Heart rate variability (HRV) is another assessment indicator for parasympathetic nerve function, reported to be associated with the incidence of diabetes, hypertension, and dyslipidemia [7–9]. Nonetheless, accurate HRV assessments require subjects to be at rest for at least 20 minutes [10], making it no less difficult than HRR to assess HRV at health management and clinical sites [11]. In order to use the results of parasympathetic nerve function assessment, we need to establish an assessment indicator that makes it easy to measure parasympathetic nerve function.

The diving reflex test is an easy way to assess parasympathetic nerve function, which has long been in clinical use to assess the parasympathetic nerve function of patients with autonomic neuropathy [12–14]. In this method, subjects develop bradycardia by cooling their face for a specified length of time [15, 16]. It is shown to reflect parasympathetic nerve function because bradycardia caused by cooling their face declines by the administration of atropine, and those with congenital autonomic neuropathy never develop bradycardia [17]. As this test only requires cooling the subject’s face, which can be completed within 5 minutes including preparation, it can measure a large group in a short time with little burden on both subjects and examiners. Unlike HRR and HRV, however, there seems to be no previous study that reports the relationship between diving bradycardia and disease. In order to use the diving reflex test for health management and disease prediction, we need to assess the relationship between diving bradycardia and disease.

Since autonomic neuropathy is often developed as a complication of diabetes [18], autonomic function can possibly be associated with diabetes. In fact, it has been reported that HRV indicates a strong relationship with diabetic autonomic neuropathy [19]. Furthermore, the report shows that low HRR can be an independent risk factor of the development of type 2 diabetes, inferring the mechanism of how a declined parasympathetic nerve function causes the development of diabetes [20]. As these two mechanisms—autonomic neuropathy as a complication of diabetes and the development of diabetes as a result of declined parasympathetic nerve function—are confirmed, diving bradycardia that reflects parasympathetic nerve function can also be associated with diabetes. However, there seems to be no previous study that discusses the relationship between diving bradycardia and diabetes. Since the diving reflex test is an easy way to assess parasympathetic nerve function, discussing the possible relationship between diving bradycardia and diabetes can bring new opportunities into the parasympathetic nerve function assessment at health management and clinical sites. Therefore, in this study, we conducted a cross-sectional study on middle-aged and older men and women to determine the relationship between diving bradycardia and pre-diabetes/diabetes prevalence.

Participants

This is a cross-sectional study using the baseline survey data in Waseda Alumni’s Sports, Exercise, Daily Activity, Sedentariness and Health Study (WASEDA’S Health Study). WASEDA’S Health Study is a cohort study conducted with Waseda University’s alumni and their spouses aged 40 or older to examine the relationship between their health outcomes and their habits of sports, exercise, physical activities, and sedentary behavior. Waseda University is a Japanese private university located in the Tokyo and Saitama area. We have published the overview and objectives of the study through its alumni association to invite participants. WASEDA’S Health Study consists of four cohorts (Cohort A-D). For participants in Cohort A, a survey about their physical activities and health outcomes was conducted through the online self-administered questionnaire. For participants in Cohort B, measurements of the amount of their physical activities and sedentary behavior were conducted using an accelerometer, in addition to the online self-administered questionnaire. For participants in Cohort C, several medical examinations were conducted, in addition to the Cohort B’s survey items. For participants in Cohort D, physical fitness tests and more detailed medical examinations were conducted, in addition to the Cohort B’s survey items. The participants of Cohort D were chosen as the participants of this study. The participants of this study are 349 alumni who joined the Cohort D survey of WASEDA’S Health Study, as well as the diving reflex test and HRR test as the baseline survey during the period from March 2015 to November 30th, 2018. For accurate analysis, we set some exclusion criteria. First, we excluded the following participants from the analysis data set: those who did not receive a dual-energy X-ray absorptiometry (DXA) test or were unable to get precise results of the test due to metal objects inside or outside their bodies (n = 24), those who had breakfast on the morning of the test (n = 3), and those who fell into the exclusion criteria for the energy intake calculation in brief-type self-administered diet history questionnaire (BDHQ; n = 1). Additionally, we also excluded the participants when their answers to the self-administered questionnaire were incomplete (n = 38), their peak oxygen uptake did not reach the endpoint at a graded exercise test (n = 46), or their blood test results were incomplete (n = 6). Some excluded participants met two or more of these conditions. As a result, 274 alumni were enrolled as the participants of this study. Before the baseline survey, these participants received an explanation of this study and signed a letter of intent to join it.

Medical examination

We conducted a survey on the participants’ sex (male or female), age (continuous variables), exercise habits (yes or no), family history of diabetes (yes or no), drinking habits (less than once a week, 1 − 2 times a week, 3 − 4 times a week, 5 times or more a week), and smoking habits (current smoker, former smoker, never smoker) by using a self-administered questionnaire. Sleeping time were surveyed using Question 4 from the Japanese version of the Pittsburgh Sleep Quality Index. Energy intake was calculated using BDHQ. A stadiometer and total body composition analyzer (MC-980A, TANITA corporation, Japan) were used to measure their height and weight. Body weight was measured with participants removing their shoes and wearing light clothing. Based on the results, body mass index (BMI) was calculated. The participants were instructed to fast since the night before. We collected the participants’ venous blood from their forearm veins after making sure that they did not have breakfast. The survey items used for this study include the items concerned with fat metabolism [total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides], glucose metabolism (fasting blood glucose, HbA1c), liver function [glutamic oxaloacetic transaminase (GOT), glutamate pyruvate transaminase (GPT), gamma-glutamyl transpeptidase (γ-GTP)], and catecholamine (adrenalin, noradrenalin). When the adrenaline level exceeded the lower detection limit, we used 0.01 ng/mL for analysis. As for blood pressure at rest, we had the participants sit on a chair and measured their systolic blood pressure and diastolic blood pressure more than two times each using an automated sphygmomanometer (HEM-7122, OMRON Colin Co., Ltd., Japan), and used the mean of the results. Total body fat amount was also measured using DXA (Delphi A/Horizon A, Hologic Inc., Marlborough, MA, USA).

Graded exercise test

We measured the participant peak oxygen uptake (VO₂peak) using a cycle ergometer (Ergomedic 828E, MONARK, Sweden). After recording an electrocardiogram (Stress Test System ML-9000 and MXL-1000, Fukuda Denshi Co., Ltd., Japan) for three minutes at rest, the participant started exercise (males started from 30W, females started from 15W), adding an extra 15W every minute until they were exhausted. During the measurement, they were wearing a mask for respiratory gas analysis to measure the level of oxygen and carbon dioxide in the exhaled gas using a respiratory gas analyzer (AE310S and AE100i, Minato Medical Science Co., Ltd., Japan) and breath-by-breath method. They were also wearing a cuff for a sphygmomanometer on their left upper arm to measure their blood pressure using an automated sphygmomanometer for the graded exercise tests (Tango M2, Suntech Medical Instruments, USA). The heart rate and blood pressure were used for safety control before and during the exercise. The endpoint of the graded exercise test was when the participant peak oxygen uptake reached the plateau or until their heart rate reached approximately 90% of the predicted maximum heart rate by age [21]. However, the test was canceled in the following situations: when the Borg’s rating of perceived exertion reached 18 or above, when the participant expressed an inability to continue exercise, or when the systolic blood pressure reached 250 mm Hg. After the exercise, the participants had a one-minute recovery time and two-minute resting in the sitting position to conclude the test. Then, the highest value of the average oxygen uptake at intervals of 30 seconds during exercise was defined as VO₂peak.

Measurement of parasympathetic nerve function

A diving reflex test was conducted to evaluate the parasympathetic nerve function. The diving reflex test was performed in all participants in the sitting position with their face immersed in cold water (2 − 5°C). Electrocardiograms were recorded at a sampling rate of 1000 Hz through an analog/digital converter (Powerlab 4/25, ML845, ADInstruments, Bio Research Center, Japan) for 30 seconds at rest, with the face being immersed in water, and after the face being immersed in water. Based on the recorded electrocardiograms, beat-to-beat R-R intervals were measured using the software (Chart ver. 8, AD Instruments, Bio Research Center, Japan). Rate of change from the average R-R interval for 30 seconds at rest to the longest R-R interval (R-Rchange) was used as an index of diving bradycardia, as well as the longest R-R interval (R-Rmax).

After the termination of the graded exercise test, a one-min period was utilized as a cool-down with pedaling at no loading followed by a 2-min rest on a cycle ergometer. This period was defined as the recovery period. Peak heart rate was determined as the highest rate during the graded exercise test. HRR was calculated as the change from peak heart rate to the rate one, two and three minutes of recovery period (HRR1, HRR2, and HRR3, respectively) as stated in the previous studies.

Determination of pre-diabetes and diabetes

We defined the participants who answered affirmative to diabetes in the self-administered questionnaire conducted during the baseline survey as diabetics. Also, in accordance with the Japan Diabetes Society guidelines, the participants with fasting blood glucose level of 126 mg/dL (7 mmol/mol) or above in the baseline survey, or with HbA1c of 6.5% (48 mmol/mol) or above were also defined as diabetics [22]. The participants with fasting blood glucose level of 110 mg/dL (6.1 mmol/L) or above [22], or with HbA1c of 6.0% (42 mmol/mol) or above [23] were defined as pre-diabetics.

Ethics

This study has been approved by the research ethics committee of Waseda University (approval number: 2014-G002, 2018-G001) and reported in accordance with the reporting guidelines of cohort studies issued by Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).

Statistics analysis

First, we classified the participants into tertiles based on R-Rchange, which is considered to be an index of parasympathetic nerve function, and compared the physical characteristics of R-Rchange tertiles. To describe the data, continuous variables were expressed as mean ± standard deviation or median (interquartile range), and categorical variables were expressed on a percentage basis. In order to examine the relationship between parasympathetic nerve function and pre-diabetes/diabetes prevalence, we entered with or without diabetes into the logistic regression models as a dependent variable, while entering R-Rchange (tertile), R-Rmax (tertile), HRR1 (tertile), HRR2 (tertile), and HRR3 (tertile) as a dependent variable with age. Based on the first tertile, we calculated the age-adjusted odds ratio, age- and sex-adjusted odds ratio, and 95% confidence interval (95% CI) of the second and third tertiles. Then, we calculated the multivariable-adjusted odds ratio and 95% CI when entering the factors considered to be related to pre-diabetes/diabetes prevalence— body fat percentage (continuous variables), family history of diabetes (yes or no), smoking status (current smoker, former smoker, never smoker), drinking status (less than 1 time/week, 1 or 2 times/week, 3 or 4 times/week, more than 5 times/week), sleeping hours (continuous variables), energy intake (continuous variables), and peak oxygen uptake (continuous variables)—into the logistic regression model. In addition, we conducted a linearity test, where we entered the parasympathetic nerve function category (tertile) into the logistic regression model as a continuous variable to evaluate whether a linear relationship existed between parasympathetic nerve function and pre-diabetes/diabetes prevalence.

For sensitivity analysis, multivariable-adjusted odds ratio and 95% CI were calculated by entering the factors related with liver function (GOT, GPT, and γ-GTP), fat metabolism (total cholesterol, HDL-c, LDL-c, and triglyceride), and catecholamine (adrenalin and noradrenaline) into the logistic regression model (Appendix Table 1).

Table 1

Characteristics of the study participants according to R-Rchange (tertile)
Characteristic	1st tertile n = 91	2nd tertile n = 91	3rd tertile n = 92
Age, y	61.8 ± 10.3	55.5 ± 9.3	52.7 ± 10.5
Men, n (%)	74 (81)	64 (70)	61 (66)
Body mass index, kg/m²	23.4 ± 2.7	23.0 ± 2.8	23.6 ± 3.9
Body fat percentage, (%)	22.3 ± 5.6	22.9 ± 6.0	22.7 ± 6.3
Median R-Rchange (interquartile range), %	115.1 (108.6–121.4)	144.7 (136.8–156.6)	207.1 (185.8–257.2)
Median R-Rmax (interquartile range), ms	998 (912–1,074)	1,227 (1,120–1,351)	1,784 (1,548–2,185)
HRR1, beats/min	25.3 ± 9.5	27.1 ± 8.9	24.6 ± 7.6
HRR2, beats/min	47.5 ± 11.1	49.5 ± 11.7	47.5 ± 10.9
HRR3, beats/min	57.7 ± 12.6	58.6 ± 11.6	58.4 ± 12.7
Systolic blood pressure, mmHg	139.3 ± 19.7	129.8 ± 17.5	126.7 ± 20.0
Diastolic blood pressure, mmHg	85.2 ± 11.2	81.8 ± 10.3	80.2 ± 11.9
Peak oxygen uptake, mL/kg/min	27.7 ± 5.6	28.8 ± 5.5	29.5 ± 6.3
Energy intake, kcal/day	1,962 ± 535	1,944 ± 497	2,073 ± 592
Sleep time, h	6.3 ± 1.0	6.3 ± 0.9	6.4 ± 1.0
Habitual exercise, n (%)	65 (71)	61 (67)	53 (58)
Current smoker, n (%)	3 (3)	4 (4)	6 (7)
Habitual drinker, n (%)	70 (77)	60 (66)	57 (62)
Fasting plasma glucose, mg/dL	101.3 ± 17.6	94.7 ± 10.5	93.9 ± 8.7
HbA1c, %	5.5 ± 0.5	5.3 ± 0.4	5.4 ± 0.3
Family history of diabetes, n (%)	19 (21)	26 (29)	19 (21)
Prediabetes, n (%)	11 (12)	7 (8)	6 (7)
Diabetes, n (%)	12 (13)	4 (4)	1 (1)
Unless otherwise noted, values are presented as mean ± SD or number (percentage).
HRR1: heart rate recovery after exercise at 1 minute, HRR2: at 2 minutes, HRR3: at 3 minutes.

All statistical analyses were performed using SPSS Statistics version 23 (IBM Corporation, Chicago, IL).

Characteristics of the study participants

The total number of participants was 274, of which 199 were men and 75 were women. The average age of men (standard deviation) was 58.4 (11.0) years ranging from 40 to 87 years of age and 52.2 (8.5) years for women ranging from 40 to 74 years of age. Among the participants, 17 had diabetes and 24 had pre-diabetes (in total, 41 had either diabetes or pre-diabetes). Table 1 shows the physical characteristics and lifestyle habits of the participants classified into tertiles based on R-Rchange. A higher R-Rchange results in a higher R-Rmax and VO₂peak, and conversely a lower age, systolic blood pressure, diastolic blood pressure and fasting blood glucose level. In addition, the ratios of men as well as those with habitual exercise and drinking decrease for those with higher R-Rchange. No trends concerning other factors were observed. The characteristics of the participants according to pre-diabetes and diabetes status, sex, R-Rmax, HRR1, HRR2, and HRR3 are shown in Appendix tables 3–8 respectively. Table 2 shows age-adjusted odds ratios, age- and sex-adjusted odds ratios, and multivariable-adjusted odds ratios, adjusted for potential confounding factors, as well as 95% CI of pre-diabetes/diabetes by parasympathetic nerve adjustment function. The multivariable-adjusted odds ratios (95% CI) of pre-diabetes/diabetes in the second and third R-Rchange tertiles calculated based on the first tertile were 0.49 (0.19–1.22) and 0.31 (0.11–0.89) respectively, indicating a significant negative relationship (P for trend = 0.023). As for R-Rmax and HRR3, the odds ratios of pre-diabetes/diabetes in the second and third tertiles showed lower point estimates in that order compared with the first tertile, however, no significant relationship was observed (P for trend = 0.104 and 0.250).

Table 2

Multivariable-adjusted odds ratios for prevalence of prediabetes and diabetes by parasympathetic nervous regulation indices
Variable	Participants	Number of cases (per 1000 persons)	Age-adjusted odds ratios	Age, sex-adjusted odds ratios	Multivariable-adjusted odds ratios
R-Rchange 1st tertile 2nd tertile 3rd tertile P for trend	91 91 92	253 121 76	1.00 (reference) 0.57 (0.25–1.29) 0.37 (0.15–0.96) 0.034	1.00 (reference) 0.57 (0.25–1.31) 0.37 (0.15–0.96) 0.034	1.00 (reference) 0.49 (0.19–1.22) 0.31 (0.11–0.89) 0.023
R-Rmax 1st tertile 2nd tertile 3rd tertile P for trend	92 90 92	217 156 76	1.00 (reference) 0.86 (0.39–1.90) 0.48 (0.18–1.25) 0.146	1.00 (reference) 0.83 (0.38–1.84) 0.45 (0.17–1.18) 0.112	1.00 (reference) 0.68 (0.28–1.65) 0.41 (0.14–1.22) 0.104
HRR1 1st tertile 2nd tertile 3rd tertile P for trend	100 92 82	180 163 98	1.00 (reference) 1.21 (0.54–2.70) 0.80 (0.31–2.07) 0.720	1.00 (reference) 1.33 (0.59-3.00) 0.84 (0.32–2.19) 0.826	1.00 (reference) 1.37 (0.55–3.41) 0.91 (0.31–2.68) 0.975
HRR2 1st tertile 2nd tertile 3rd tertile P for trend	92 97 85	207 144 94	1.00 (reference) 0.77 (0.35–1.70) 0.68 (0.26–1.76) 0.396	1.00 (reference) 0.82 (0.37–1.82) 0.77 (0.29–2.04) 0.566	1.00 (reference) 0.71 (0.29–1.71) 0.89 (0.30–2.65) 0.712
HRR3 1st tertile 2nd tertile 3rd tertile P for trend	95 93 86	221 151 70	1.00 (reference) 0.77 (0.36–1.68) 0.43 (0.16–1.19) 0.107	1.00 (reference) 0.84 (0.38–1.84) 0.48 (0.17–1.36) 0.181	1.00 (reference) 0.72 (0.29–1.79) 0.53 (0.17–1.63) 0.250
Adjusted for age (y), sex (men, women), body fat percentage (%), family history of diabetes (yes, no), smoking status (current smoker, former smoker, never smoker), drinking status (less than 1 time/week, 1 or 2 times/week, 3 or 4 times/week, more than 5 times/weeks), sleep time (hour), energy intake (kcal/day), peak oxygen uptake (mL/kg/min).
HRR1: heart rate recovery after exercise at 1 minute, HRR2: at 2 minutes, HRR3: at 3 minutes.

Sensitivity analysis

Sensitivity analysis was conducted on R-Rchange and liver function, fat metabolism, and catecholamine in that order using a multivariable-adjusted model shown in Table 2 in order to compare multivariable-adjusted odds ratios. As a result, there was little change in the odds ratios before and after adding those additional factors (Appendix Table 1). We also examined the relationship between the potential confounding factor in the multivariable-adjusted model shown in Table 2 and pre-diabetes/diabetes prevalence, however, there was no clear relationship except for the group with a family history of diabetes when comparing to the group without a family history of diabetes (Appendix Table 2).

We conducted a cross-sectional study among middle-aged men and women to find the relationship between diving bradycardia, a simplified indicator for parasympathetic nerve function, and pre-diabetes/diabetes prevalence. As a result, a significant negative relationship was observed between R-Rchange and pre-diabetes/diabetes prevalence. As for R-Rmax and HRR3, the odds ratios of pre-diabetes/diabetes in the second and third tertiles showed lower point estimates in that order compared with the first tertile, but not significant. This study is the first of its kind conducted to examine the relationship between diving bradycardia and pre-diabetes/diabetes prevalence. It suggests that diving bradycardia, a simplified indicator for parasympathetic nerve function, has a stronger relationship with pre-diabetes/diabetes prevalence than HRR assessments obtained from a maximal graded exercise test.

This study confirms the association between parasympathetic nerve function and pre-diabetes/diabetes prevalence as is the case in two previous studies. The previous studies used HRV and HRR as indicators for parasympathetic nerve function to examine the association with pre-diabetes/diabetes after adjusting for various potential confounding factors [20, 24]. In observational studies, one of the most important confounding factors in assessing the relationship between parasympathetic nerve function and diabetes prevalence is cardiorespiratory fitness. It has been reported that cardiorespiratory fitness indicates a strong relationship with the onset of diabetes [25, 26]. It is also known to be associated with parasympathetic nerve function [27]. Therefore, cardiorespiratory fitness is considered to be an important confounding factor to be adjusted in evaluating the relationship between parasympathetic nerve function and diabetes. However, in the previous studies, only Yu et al. (2016) [20] examined the relationship between parasympathetic nerve function and diabetes by adding cardiorespiratory fitness as a confounding factor, hence, this is the second study that evaluated the relationship between parasympathetic nerve function and diabetes. Moreover, the study by Yu et al. (2016) [20], which added cardiorespiratory fitness as a confounding factor, targeted only men and did not include women. This study targeted middle-aged men and women and revealed the relationship between parasympathetic nerve function and diabetes by adding not only cardiorespiratory fitness but also sex as confounding factors. As a result, a negative association with pre-diabetes/diabetes prevalence was showed in HRR2 and HRR3 even when cardiorespiratory fitness and sex were added as confounding factors, although not statistically significant. Moreover, a statistically significant negative association with pre-diabetes/diabetes prevalence was observed in diving bradycardia (R-Rchange). From these results, it is considered that there is a negative association between parasympathetic nerve function and diabetes even if the confounding factors of cardiorespiratory fitness and sex are removed.

In this study, diving bradycardia was more strongly associated with pre-diabetes/diabetes than HRR. Diving bradycardia is a vagal reflex that results from cooling one’s face and holding their breath [12]. On the other hand, HRR results from an increase in parasympathetic nerve activity, which is caused by sympathetic nerve activity once increased during exercise then reduced by the end of exercise [28]. The effect of sympathetic nerve activity is limited in diving bradycardia, while HRR is more susceptible to sympathetic nerve activity. This may explain the results of this study, which observed a diving reflex indicating a clearer association with pre-diabetes/diabetes than HRR.

In this study, HRR3 showed a negative association with pre-diabetes/diabetes, although not statistically significant, while HRR1 and HRR2 did not. It has been reported that the heart rate decreases immediately after the end of exercise due to rapid activation of parasympathetic nerve activity (fast phase), gradually followed by the decrease in sympathetic nerve activity (slow phase) [28]. It was initially expected that HRR1 and pre-diabetes/diabetes are related because parasympathetic nerve activity has a greater contribution for the first one minute after the end of exercise, which is called “fast phase.” In this study, however, only HRR3 showed an association with pre-diabetes/diabetes. This result differs from the study by Yu et al. (2016) [20] indicating the association between HRR1 and the onset of diabetes. In their study, a treadmill was used for the graded exercise test and recovery time was provided through walking for 30 seconds after exercise. On the other hand, this study uses a cycle ergometer for the graded exercise test and recovery time was provided through 0-watt pedaling for one minute after exercise. These differences in exercise methods and recovery time may have been factors that lead to inconsistent results with the previous study.

Two mechanisms are thought to underlie the relationship between parasympathetic nerve function and diabetes. First, diabetes may be associated with a complication of autonomic neuropathy [18]. It has been confirmed that autonomic neuropathy is likely to occur especially in patients with insufficient blood glucose control, and HRV is reduced in diabetic patients [29]. Second, reduced parasympathetic nerve function may cause the development of diabetes [20, 30]. The pancreas is controlled by parasympathetic nerves and releases insulin from β cells to regulate blood glucose [31]. As these two mechanisms—autonomic neuropathy as a complication of diabetes and the development of diabetes as a result of declined parasympathetic nerve function—are confirmed, parasympathetic nerve function is assumed to be associated with diabetes.

As this is a cross-sectional study, the causal relationship between parasympathetic nerve function and diabetes cannot be referred to. However, the values obtained by the diving reflex test are assumed to change more drastically under the influence of both autonomic neuropathy developed in early diabetes and the reduced parasympathetic nerve activity that can cause the development of diabetes. Hence the result is expected to be used as an indicator for predicting future diabetes development with higher accuracy.

This study has several limitations. Firstly, this study is a cross-sectional study and not designed to refer to any causal relationship. Nonetheless, because of the nature of cross-sectional studies, it may be that diving reflex could more accurately predict diabetes that could develop in the near future. Secondly, pre-diabetes and diabetes cannot be analyzed separately due to the small number of pre-diabetes/diabetes participants in this study. However, diabetes is viewed as a continuation of pre-diabetes, and there is no issue in particular problem in analyzing the combined outcome of pre-diabetes and diabetes. Thirdly, since the participants in this study are graduates of a selected university, there is an issue of representativeness. Finally, complications of diabetes were not investigated in this study. If there were patients with diabetic autonomic neuropathy among the participants of this study, it might have influenced the results. Future studies should include investigations into complications of diabetes.

In summary, a clear negative relationship was observed between the prevalence of diabetes and diving bradycardia in middle-aged men and women. Since the diving reflex test is expected to be utilized as an easy way to predict future diabetes, the results of this study are considered to have significant meaning for health care and clinical practice.

ORCiD number of the authors:

(N. Nakamura).

(R. Kawakami). 0000-0001-8211-1553

(H. Tabata). 0000-0001-9396-5247

(K. Tanisawa). 0000-0002-9334-7104

(S. S. Sawada). 0000-0002-7272-0790

(T. Ito). 0000-0003-1970-9696

(Y. Tamura). 0000-0002-1685-7821

(K. Ishii). 0000-0002-8077-4895

(K. Oka). 0000-0001-5571-042X

(K. Suzuki).

(M. Higuchi).

(S. Sakamoto).

The authors have nothing to disclose.

Acknowledgments

The authors thank the study participants and the physicians and staff of WASEDA'S Health Study for assisting with data collection.

Financial Support: This research was supported by the Ministry of Education, Science, Sports and Culture of Japan under grant (Grant No. 18K17939) and Grants-in-Aid for Scientific Research (No. 19H04008) from the Japan Society for the Promotion of Science; MEXT-Supported Program for the Strategic Research Foundation at Private Universities, 2015-2019 from the Ministry of Education, Culture, Sports, Science and Technology (S1511017). This project is a collaborative research with the Institute of Stress Science, Public Health Research Foundation.

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Parasympathetic nervous function and prevalence of prediabetes/diabetes mellitus: a cross-sectional study: WASEDA'S Health Study

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Abstract

Introduction

Participants and methods

Participants

Medical examination

Graded exercise test

Measurement of parasympathetic nerve function

Determination of pre-diabetes and diabetes

Ethics

Statistics analysis

Results

Characteristics of the study participants

Sensitivity analysis

Discussion

Declarations

References

Supplementary Files

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