Effects of aerobic and resistance exercise on glycosylated hemoglobin (HbA1c) concentrations in non-diabetic Taiwanese individuals based on the waist-hip ratio

Abstract

Background

Glycosylated hemoglobin (HbA1c) reflects the average blood sugar over the past eight to twelve weeks. Several demographic and lifestyle factors are known to affect HbA1c levels. We evaluated the association of HbA1c with aerobic and resistance exercise in non-diabetic Taiwanese adults based on the waist-hip ratio (WHR).

Methods

We conducted this study based on TWB data collected from 90,958 individuals between 2008 and 2019. We estimated the Beta (β) coefficient and 95% confidence intervals (CI) for HbA1c using multivariate regression models.

Results

Based on the multivariate analysis, lower HbA1c levels were associated with both resistance exercise (β-coefficient = -0.027, 95% CI -0.037 to -0.017) and aerobic exercise (β-coefficient = 0.018, 95% CI, -0.023 to -0.013). Higher HbA1c levels were associated with abnormal WHR compared to normal WHR (β-coefficient = 0.091, 95% CI, 0.086 to 0.096). We detected an interaction between exercise and WHR (p for interaction = 0.0181). To determine the magnitude of the interaction, we performed additional analyses (with the reference group being ’abnormal WHR with no exercise’) and observed substantial decreases in HbA1c regardless of the WHR and exercise category. However, the largest reduction occurred in the ’normal WHR and resistance exercise’ group (β = -0.121, 95% CI, -0.132 to -0.109).

Conclusions

We found that normal resistance exercise, coupled with a normal WHR was significantly associated with lower HbA1c levels among non-diabetic individuals in Taiwan.

Introduction

Hemoglobin A1c, formed by the nonenzymatic glycosylation of hemoglobin is essential for monitoring the management of chronic diabetes [1]. It is the main fraction among the various glycated hemoglobins and appears to be affected by iron deficiency and vitamin B12 deficiency [2]. Its measurement is essential for long-term glucose control, where a concentration of less than 7% is considered the target for good control in most cases with diabetes [3]. According to the 2009 International Expert Committee Report on the Role of the A1C Assay in the Diagnosis of Diabetes, HbA1c is considered a more stable biological index than fasting plasma glucose (FPG) and oral glucose tolerance test (OGTT) [4, 5].

Associations have been reported between HbA1c levels and cardiovascular diseases in both diabetic and non-diabetic patients [6–9]. Individuals with elevated HbA1c concentrations are more likely to develop complications related to diabetes such as coronary lesions [10]. Harvey and colleagues observed a significant decrease in major adverse cardiovascular events (MACE) among individuals with diabetes who had HbA1c levels below 7% [11]. Mortality associated with diabetes mellitus was reportedly 15.3% higher among patients who had HbA1c levels above 8% than in those with levels below 6% [12].

Besides glucose, demographic factors including age, sex, body mass index, and other variables have been investigated for HbA1c variability [13]. Results pointed to BMI and sex-specific differences in HbA1c variability among the study participants. In epidemiological studies, WHR and BMI were positively related to HbA1c among cases with prediabetes [14] and type 2 diabetes [15]. On the contrary, negative correlations have also been reported [16]. According to a previous study investigating anthropometric indices, WHR remains a superior index to predict T2D [17]. As noted above, WHR and BMI are both positively correlated with HbA1c, a screening tool to detect Type 2 diabetes. However, to our knowledge, numerous research works in Taiwan have focused on BMI.

Exercise and HbA1c are essential for glycemic control in people with diabetes. In prior studies, resistance training was shown to significantly lower HbA1c levels in people with diabetes mellitus [18–20]. The results of meta-analyses indicated that both resistance and aerobic exercise significantly improved HbA1c levels among diabetic individuals [21]. Another systematic review and meta-analysis suggested that high- but not low-intensity resistance exercise was more beneficial for improving HbA1c levels in patients with diabetes mellitus (i.e., 0.61% vs. 0.23% reduction) [22]. HbA1c levels were substantially affected by resistance exercise compared to aerobic exercise [23]. Most of the studies describing correlations between changes in HbA1c and sociodemographic factors have focused on individuals with diabetes mellitus. In light of this, we evaluated the association of HbA1c with aerobic and resistance exercise in non-diabetic Taiwanese adults based on WHR.

Materials and methods

Participants, data source, and extraction

We obtained data from the TWB data source, which is the first large-scale biological database in Taiwan. Its purpose is to collect genetic, environmental, clinical, and lifestyle data and track the health of at least 200,000 adults for at least ten years. Additionally, this data source provides scholars and experts with important information about the causes and mechanisms of common diseases in Taiwan, which can lead to the improvement of health treatment policies and prevention strategies. Subjects in the biobank were between 30 and 70 years old and did not have a history of cancer. These participants had signed informed consents at various centers during assessment visits (between 2008 and 2019). Demographic and clinical data were available for 132,720 subjects in the biobank. We excluded those with diabetes mellitus (n = 12,711) and those with missing or incomplete information (n = 29,051). Our final analysis models included data from 90,958 subjects. We received ethical approval from the Institution Review Board of Chung Shan Medical University (CS1-21197).

Outcome, exposure, and covariates

The primary outcome was HbA1c and WHR and exercise were the exposure variables. During recruitment, participants in TWB completed a questionnaire indicating how often, how long, and what kind of exercise they engaged in. Exercise was categorized as aerobic, resistance, and no exercise. Resistance exercise studied consisted of weight training, ball games, or a combination of both. On the other hand, aerobic physical activities included brisk walking, jogging, Taijiquan, rope jumping, gymnastics, yoga, Gigong, Chinese martial arts, swimming, hiking, biking, basketball, table tennis, soccer, badminton, tennis, golf, aerobic dance, ballroom dance, and hula hooping. Regular exercisers were those who participated in either aerobic or resistance exercise at least three times a week, lasting at least thirty minutes per session. The WHRs were categorized as normal or abnormal based on cut-off points: 0.92 for men and 0.88 for women as defined by the Health Promotion Administration, Ministry of Health and Welfare in Taiwan. It was measured as waist circumference (in cm) divided by hip circumference (in cm). HbA1c concentrations were measured using the automated XN-9000 hematology analyzer (Sysmex, Kakogawa, Japan).

Statistical analysis

Differences in baseline characteristics stratified by normal WHR and abnormal WHR were tested using the χ2 test (for categorical variables, which were presented as absolute numbers [n] and percentages [%]) and student’s t-test (for continuous variables, which were explained as mean and standard error). We used multiple regression models to calculate β- coefficients of HBA1c with their 95% CIs. Baseline characteristics included exercise, HbA1c, WHR, sex, age, smoking, alcohol intake, hypertension, hyperlipidemia, and genetic variants. Data analyses were done by use of Plink version 1.9 and SAS 9.4 software (SAS Institute, Cary, NC, USA).

Results

Table 1 shows the baseline characteristics of subjects with normal (n = 66,096, 72.67%) and abnormal (n = 24,862, 27.33%) WHR. The mean (±SE) age was 46.45 ± 0.041 years for those who had normal WHR and 51.63 ± 0.068 years for those with abnormal WHR. Effects of the study variables on HBA1c concentrations are shown in Table 2. In the multivariate model, lower HBA1c levels were associated with resistance exercise (β-coefficient = -0.027, 95% CI, -0.037 to -0.017; p<0.001) and aerobic exercise (β-coefficient = 0.018, 95% CI, -0.23 to -0.013; p<0.001). Higher HBA1c was associated with aging (β-coefficient = 0.009, 95% CI, 0.009 to 0.009; p<0.001), abnormal WHR (β-coefficient = 0.091, 95% CI, 0.086 to 0.096; p<0.001), current smoking (β-coefficient = 0.012, 95% CI, 0.005 to 0.020; p = 0.001), hypertension (β-coefficient = 0.046, 95% CI, 0.038 to 0.053; p = p<0.001), and hyperlipidemia (β-coefficient = 0.064, 95% CI, 0.055 to 0.074; p<0.001), and male sex (β-coefficient = 0.040, 95% CI, 0.036 to 0.045; p<0.001). We detected an interaction between exercise and WHR (p = 0.0181). In the multiple linear regression model stratified by WHR (Table 3), lower HbA1c was associated with aerobic exercise combined with either normal or abnormal WHR (β-coefficient = -0.019, 95% CI -0.024 to -0.013) and β-coefficient = -0.017, 95% CI -0.027 to -0.008, respectively (p<0.05). The β-coefficient was -0.028 (95% CI -0.039 to -0.017; p<0.001) for resistance exercise, coupled with normal WHR and -0.023 (95% CI, -0.046 to 0.000; p = 0.053) for resistance exercise, coupled with abnormal WHR. In the regression model with abnormal WHR and no exercise representing the reference group (Table 4), all combinations of either normal or abnormal WHR with exercise type (aerobic, resistance, or no exercise) were associated with decreased HBA1c levels. However, among the various subgroups, the lowest HBA1c levels were found in the ‘normal WHR and resistance exercise’ group (β-coefficient = -0.121, 95% CI, -0.132 to -0.109; p<0.001).

[Figure omitted. See PDF.]

[Figure omitted. See PDF.]

[Figure omitted. See PDF.]

[Figure omitted. See PDF.]

Discussion

Our analysis of TWB data indicated that compared with no exercise, aerobic and resistance exercises are associated with decreased levels of HbA1c in adults with no history of diabetes in Taiwan. However, reductions in HbA1c levels were greater in participants who performed resistance exercises (β-coefficient = -0.027 vs. -0.018). The same trend had also been noted among individuals with diabetes [23, 24] even though according to Yang and his team, it is yet to be proven whether resistance exercise differs from aerobic exercise regarding their effect on cardiovascular risk markers [21].

Additionally, our findings also indicated that normal compared to abnormal WHR raised HbA1c levels. It is important to note that the association of HbA1c with anthropometric parameters was also examined in populations without diabetes [25]. The results indicated that the degree of association varied. We detected an interaction between exercise and WHR in relation to HbA1c (p = 0.0181). Our stratified analysis (with ’abnormal WHR and no exercise’ as the reference group) showed that HbA1c levels decreased regardless of the subgroup. However, the greatest reductions occurred in the ‘normal WHR and resistance exercise’ group. In this group, the reduction in HbA1c was 0.121%. These results suggest that both WHR and exercise may be essential for regulating glycated hemoglobin. As far as we know, this is the first study to examine resistance and aerobic exercise in tandem with WHR among Taiwanese adults without diabetes. These findings add to our understanding of the factors controlling HbA1c concentrations, although further studies would help clarify their mechanisms.

In the general model, we also found that HbA1c was positively associated with age, abnormal compared to normal WHR, current compared to nonsmoking, and male compared to the female sex. Similar results have been previously reported among those with diabetes and prediabetes [26, 27]. In one of these studies [27], current smokers had higher HbA1c levels than non-smokers, by 0.08% (0.9 mmol/mol) and this was linked to oxidative stress. However, Nakagami and colleagues published contrary results for Japanese populations [28]. The WHR has been used to a lesser extent compared to other anthropometric parameters [15]. In line with previous findings [29, 30], we also found higher HbA1c values in participants with hypertension and hyperlipidemia.

We acknowledge a few limitations. First, exercise data were collected from self-reported questionnaires within the biobank; hence recall bias might have possibly occurred. Next, resistance exercise intensities have been investigated for glycemic biomarkers in patients with type 2 diabetes, where patients who performed high-intensity resistance exercise showed lower HbA1c levels than those who performed low-intensity resistance exercise [22]. However, in the current study, information was not available on the exercise intensity or volume.

Conclusions

For the very first time, we have provided evidence that normal WHR together with resistance exercise may be associated with greater HbA1c reduction among non-diabetic individuals in Taiwan. Resistance exercise and WHR may be essential for strategies aimed at managing or improving HbA1c.

Citation: Chou Y-H, Cheng Y-Y, Nfor ON, Chen P-H, Chen C, Chen H-L, et al. (2022) Effects of aerobic and resistance exercise on glycosylated hemoglobin (HbA1c) concentrations in non-diabetic Taiwanese individuals based on the waist-hip ratio. PLoS ONE 17(5): e0267387. https://doi.org/10.1371/journal.pone.0267387

About the Authors:

Ying-Hsiang Chou

Roles: Conceptualization, Methodology, Writing – original draft

Affiliations Department of Radiation Oncology, Chung Shan Medical University Hospital, Taichung, Taiwan, School of Medicine, Chung Shan Medical University, Taichung, Taiwan, School of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan

Yung-Yin Cheng

Roles: Conceptualization, Methodology, Writing – original draft

Affiliations School of Medicine, Chung Shan Medical University, Taichung, Taiwan, School of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan, Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan

Oswald Ndi Nfor

Roles: Conceptualization, Formal analysis, Methodology, Writing – review & editing

Affiliation: Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung, Taiwan

Pei-Hsin Chen

Roles: Data curation, Formal analysis, Writing – review & editing

Affiliation: Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung, Taiwan

Che‐Hong Chen

Roles: Conceptualization, Methodology, Writing – review & editing

Affiliation: Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, United States of America

Hsin-Lin Chen

Roles: Conceptualization, Data curation, Writing – review & editing

Affiliation: Department of Radiation Oncology, Chung Shan Medical University Hospital, Taichung, Taiwan

Bo-Jiun Chang

Roles: Conceptualization, Methodology, Writing – review & editing

Affiliation: Department of Radiation Oncology, Chung Shan Medical University Hospital, Taichung, Taiwan

Disline Manli Tantoh

Roles: Conceptualization, Methodology, Writing – review & editing

Affiliations Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan, Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung, Taiwan

Chien-Ning Huang

Roles: Conceptualization, Methodology, Supervision, Writing – review & editing

E-mail: [email protected] (YPL); [email protected] (CNH)

Affiliations Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan, Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan

Yung-Po Liaw

Roles: Conceptualization, Formal analysis, Methodology, Supervision, Writing – review & editing

E-mail: [email protected] (YPL); [email protected] (CNH)

Affiliations Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan, Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung, Taiwan

https://orcid.org/0000-0003-2046-4964

References

1. Sherwani SI, Khan HA, Ekhzaimy A, Masood A, Sakharkar MK. Significance of HbA1c test in diagnosis and prognosis of diabetic patients. Biomarker insights. 2016;11:BMI. S38440. pmid:27398023

2. Nitin S. HbA1c and factors other than diabetes mellitus affecting it. Singapore Med J. 2010;51(8):616–22. pmid:20848057

3. Araya MR, Padilla SG. Feeding behavior pattern and glycosylated hemoglobin in people with type 2 diabetes at the beginning and end of an educational intervention. Endocrinología, Diabetes y Nutrición (English ed). 2020;67(3):155–63.

4. Committee IE. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes care. 2009;32(7):1327–34. pmid:19502545

5. Li A, Li Y, Xiang F, Zhang Y, Yang Q, Hu Z, et al. Optimal cut-off points of BMI WC and WHR for screening of prediabetes and diabetes over 35 years old people. World Journal of Public Health. 2019;4(1):1–9.

6. Greenland P, Alpert JS, Beller GA, Benjamin EJ, Budoff MJ, Fayad ZA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines developed in collaboration with the American Society of Echocardiography, American Society of Nuclear Cardiology, Society of Atherosclerosis Imaging and Prevention, Society for Cardiovascular Angiography and Interventions, Society of Cardiovascular Computed Tomography, and Society for Cardiovascular Magnetic Resonance. Journal of the American College of Cardiology. 2010;56(25):e50–e103. pmid:21144964

7. Anderson TJ, Grégoire J, Hegele RA, Couture P, Mancini GJ, McPherson R, et al. 2012 update of the Canadian Cardiovascular Society guidelines for the diagnosis and treatment of dyslipidemia for the prevention of cardiovascular disease in the adult. Canadian Journal of Cardiology. 2013;29(2):151–67. pmid:23351925

8. Silbernagel G, Grammer TB, Winkelmann BR, Boehm BO, März W. Glycated hemoglobin predicts all-cause, cardiovascular, and cancer mortality in people without a history of diabetes undergoing coronary angiography. Diabetes Care. 2011;34(6):1355–61. pmid:21515847

9. Selvin E, Steffes MW, Zhu H, Matsushita K, Wagenknecht L, Pankow J, et al. Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. New England Journal of Medicine. 2010;362(9):800–11.

10. Ma J, Wang X, Wang Y, Zhao Y, Gao M, Li X. The relationship between glycated hemoglobin and complexity of coronary artery lesions among older patients with diabetes mellitus. PloS one. 2014;9(3):e91972. pmid:24658008

11. White HD, Stewart RAH, Dalby AJ, Stebbins A, Cannon CP, Budaj A, et al. In patients with stable coronary heart disease, low-density lipoprotein-cholesterol levels < 70 mg/dL and glycosylated hemoglobin A1c < 7% are associated with lower major cardiovascular events. American Heart Journal. 2020;225:97–107. pmid:32480059

12. Williams textbook of endocrinology. 12th ed. Philadelphia: Elsevier/Saunders.

13. Mellergård E, Johnsson P, Eek F. Sociodemographic factors associated with HbA1c variability in type 2 diabetes: a prospective exploratory cohort study. BMC Endocrine Disorders. 2020;20(1):102. pmid:32641021

14. Bala M, Meenakshi , Aggarwal S. Correlation of Body Mass Index and Waist/Hip Ratio with Glycated Hemoglobin in Prediabetes. EJIFCC. 2019;30(3):317–24. Epub 2019/11/07. pmid:31695588; PubMed Central PMCID: PMC6803770.

15. Dey SSS. HbA1c and its association with anthropometric parameters and renal profile in type 2 diabetes mellitus patients in Jharkhand. 2020.

16. Baltadjiev A, Vladeva S, Tineshev S, Andreenko E. Correlations between Some Anthropometric Parameters, the Lipid Profile and Glycated Hemoglobin in Bulgarian Women with Type 2 Diabetes Mellitus. 2017.

17. Sekgala MD, Sewpaul R, Opperman M, Mchiza ZJ. Comparison of the Ability of Anthropometric Indices to Predict the Risk of Diabetes Mellitus in South African Males: SANHANES-1. International Journal of Environmental Research and Public Health. 2022;19(6):3224. pmid:35328910

18. Castaneda C, Layne JE, Munoz-Orians L, Gordon PL, Walsmith J, Foldvari M, et al. A randomized controlled trial of resistance exercise training to improve glycemic control in older adults with type 2 diabetes. Diabetes care. 2002;25(12):2335–41. pmid:12453982

19. Balducci S, Zanuso S, Nicolucci A, Fernando F, Cavallo S, Cardelli P, et al. Anti-inflammatory effect of exercise training in subjects with type 2 diabetes and the metabolic syndrome is dependent on exercise modalities and independent of weight loss. Nutrition, Metabolism and Cardiovascular Diseases. 2010;20(8):608–17. pmid:19695853

20. Arora E, Shenoy S, Sandhu J. Effects of resistance training on metabolic profile of adults with type 2 diabetes. Indian Journal of Medical Research. 2009;129(5):515. pmid:19675378

21. Yang Z, Scott CA, Mao C, Tang J, Farmer AJ. Resistance exercise versus aerobic exercise for type 2 diabetes: a systematic review and meta-analysis. Sports medicine. 2014;44(4):487–99. pmid:24297743

22. Liu Y, Ye W, Chen Q, Zhang Y, Kuo C-H, Korivi M. Resistance Exercise Intensity is Correlated with Attenuation of HbA1c and Insulin in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis. International journal of environmental research and public health. 2019;16(1):140. pmid:30621076.

23. Bweir S, Al-Jarrah M, Almalty A-M, Maayah M, Smirnova IV, Novikova L, et al. Resistance exercise training lowers HbA1c more than aerobic training in adults with type 2 diabetes. Diabetology & metabolic syndrome. 2009;1:27–. pmid:20003276.

24. Thomas D, Elliott EJ, Naughton GA. Exercise for type 2 diabetes mellitus. Cochrane database of systematic reviews. 2006;(3). pmid:16855995

25. Skogberg N, Laatikainen T, Lilja E, Lundqvist A, Härkänen T, Koponen P. The association between anthropometric measures and glycated haemoglobin (HbA1c) is different in Russian, Somali and Kurdish origin migrants compared with the general population in Finland: a cross-sectional population-based study. BMC public health. 2019;19(1):1–12. pmid:30606151

26. Dubowitz N, Xue W, Long Q, Ownby JG, Olson DE, Barb D, et al. Aging is associated with increased HbA1c levels, independently of glucose levels and insulin resistance, and also with decreased HbA1c diagnostic specificity. Diabet Med. 2014;31(8):927–35. Epub 2014/04/05. pmid:24698119.

27. Vlassopoulos A, Lean MEJ, Combet E. Influence of smoking and diet on glycated haemoglobin and ’pre-diabetes’ categorisation: a cross-sectional analysis. BMC public health. 2013;13:1013–. pmid:24499114.

28. Nakagami T, Oya J, Kasahara T, Uchigata Y. Effect of hemoglobin levels and sex on HbA1c levels among Japanese Population. Diabetes and Endocrinology (DE). 2017.

29. Tatsumi Y, Morimoto A, Asayama K, Sonoda N, Miyamatsu N, Ohno Y, et al. Fasting blood glucose predicts incidence of hypertension independent of HbA1c levels and insulin resistance in middle-aged Japanese: the Saku study. American journal of hypertension. 2019;32(12):1178–85. pmid:31365923

30. Ghari Arab A, Zahedi M, Kazemi Nejad V, Sanagoo A, Azimi M. Correlation between hemoglobin a1c and serum lipid profile in type 2 diabetic patients referred to the diabetes clinic in gorgan, iran. Journal of Clinical and Basic Research. 2018;2(1):26–31.