- Open Access
Obesity indicators as mediators of association between daytime napping and type 2 diabetes mellitus: the Guangzhou Biobank Cohort Study
BMC Public Health volume 22, Article number: 56 (2022)
To examine the mediating effect of obesity indicators on the association between daytime napping and type 2 diabetes mellitus (T2DM) qualitatively and quantitatively using baseline data from the Guangzhou Biobank Cohort Study.
Twenty-nine thousand three hundred fifty-five participants aged 50+ years were included in this cross-sectional study. Mediation analysis was used to assess the mediating effect of body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) on the association between daytime napping and T2DM after adjustment for sex, age, education, occupation, smoking status, alcohol use and physical activity.
The mean (standard deviation) age of participants was 61.5 ( 7.1) years. The prevalence of T2DM and daytime napping was 12.5% and 65.2%, respectively. After adjustment for potential confounders, WC, WHR and WHtR showed partial mediating effects on the association between daytime napping and T2DM, with the proportion (95% confidence interval) of mediation effect being 10.17% (8.14–14.43%), 14.91% (11.95–21.24%) and 9.36% (7.49–13.29%), respectively. No mediating effect of BMI or HC on the association between daytime napping and T2DM was found.
Our results showed significant mediating effects of WC, WHR and WHtR on the association between daytime napping and T2DM, suggesting that waist circumference management could be important in daytime nappers.
Type 2 Diabetes Mellitus (T2DM) is a major public health concerns worldwide . It was estimated that the number of patients will increase to 700 million by 2045 . Of this, more than 75% will occur in developing countries [2, 3]. Daytime napping is a common habit in China and other Asian countries. A national study showed that more than one-third of Chinese adults had daytime napping . Daytime napping is highly recommended and traditionally considered as a healthy lifestyle for compensating for inadequate nocturnal sleep, reducing sleepiness and improving performance and alertness [5,6,7]. However, the health effects related to daytime napping remain controversial [8,9,10]. One of our previous studies showed that frequent daytime napping was associated with a higher risk of diabetes , which was also supported by a number of prospective studies [4, 12,13,14,15,16,17,18,19] and Mendelian randomization . However, the underlying mechanism is still unclear.
One of the possible explanations lies in obesity. Previous studies including Mendelian randomization have shown that daytime napping was likely causally associated with both general and central obesity [21,22,23,24]. Moreover, obesity has been suggested as a major causal factor of T2DM [25, 26]. Several epidemiological studies describing the association between daytime napping and diabetes have suggested a potential mediating role of obesity, but without quantifying nor examining it explicitly [4, 15, 17,18,19, 27]. To our knowledge, the potential mediating effect of obesity on the association between daytime napping and diabetes has not been reported in the literature. Hence, we examined the mediation effects of obesity indicators, including body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR), on the association between daytime napping and T2DM using data from the Guangzhou Biobank Cohort Study (GBCS).
The Guangzhou Biobank Cohort Study (GBCS) is a three-way collaborative prospective cohort study between the Guangzhou Twelfth People’s Hospital and the Universities of Hong Kong and Birmingham. Details of the GBCS have been described previously . Briefly, all participants were recruited from a government-oriented welfare organization, the Guangzhou Health and Happiness Association for the Respectable Elders (GHHARE) from 2003 to 2008. Membership is open to permanent residents of Guangzhou aged 50 years or above for a nominal fee of 4 CNY (≈ 50 US cents) per month. Information of demographic characteristics, lifestyle, and personal medical history were collected at recruitment by face-to-face interview by trained nurses using a computer-assisted questionnaire. Anthropometric measurements were also measured by trained nurses using the standard protocol. Physical activity was categorized into inactive, minimally active, and active based on the short version of the International Physical Activity Questionnaire (IPAQ), which has been validated by us previously . Fasting blood samples are obtained from all participants after overnight fasted, and glucose was determined automatically in the hospital laboratory. Ethical approval of the study was granted from the Guangzhou Medical Ethics Committee of the Chinese Medical Association, and informed consent was obtained from all participants before participation. The study was performed in accordance with the Declaration of Helsinki.
The exposure in this study was self-reported daytime napping, collected through a questionnaire. All participants were asked the following question: “Do you have a nap during the daytime, especially after lunch?”, if the answer is “Yes”, then further asking about the napping frequency within a week . Participants were classified as non-nappers if they answered “no”, and nappers if “yes”. After accounting for daytime napping frequency, participants were further classified as non-nappers and habitual nappers (i.e., those with napping > 3 times/week) in the sensitivity analysis.
The main outcome in this study was T2DM, which was defined as fasting plasma glucose ≥7.0 mmol/L, and/or self-reported diabetes diagnosed by a physician, and/or having anti-diabetic therapy [11, 30]. 3,655 (12.5%) participants had T2DM. Impaired fasting glucose (IFG) was defined as fasting plasma glucose ≥5.6 mmol/L and < 7.0 mmol/L in non-diabetic participants (N = 25,700) [11, 30]. Fasting plasma glucose was included as a continuous outcome variable in participants without physician-diagnosed diabetes.
Anthropometric measurements were performed by trained nurses using standard protocols in the morning before breakfast, including weight, standing height and WC, with light clothing and no shoes. BMI was calculated as weight (kg) divided by height squared (m2). WC was measured horizontally around the smallest circumference between the ribs and iliac crest, or at the navel, if no natural waistline was present. WHR was calculated by dividing waist circumference (cm) by hip circumference (cm), and the WHtR was calculated by dividing waist circumference (cm) by height (cm).
Continuous variables were presented as means ± standard deviations (SD) or medians (25th percentile, 75th percentile). Categorical variables were presented as percentage. Pearson chi-square test and one-way analysis of variance or Kruskal-Wallis test were used to assess baseline characteristics according to daytime napping status at baseline. The multivariable linear regression was used to obtain the regression coefficients (β) and 95% confidence intervals (CI) for obesity indicators and fasting plasma glucose related to daytime napping. Potential confounders (i.e., factors associated with both daytime napping and T2DM in univariate analysis or reported in the literature) including sex, age, education, occupation, smoking status, alcohol use and physical activity, were considered in the full adjustment model. Mediation analysis was performed using the medeff package in Stata. Calculation of the underlying theoretical results is based on the counterfactual framework of potential outcomes [31, 32]. Briefly, the main conceptual models for the simple mediation analysis included exposure T (daytime napping), mediator M (obesity indicators), and outcome Y (T2DM). To enable comparison of the effect sizes of the different obesity indicators, each obesity indicator was transformed into Z-score before mediation analysis. All statistical analyses were 2-sided, and the P < 0.05 could be identified as statistical significance. Stata, version 16/MP (Stata Corp. LP, College Station, TX, USA) was used to perform all statistical processes.
Of 30,430 participants at baseline, we excluded participants with missing information on daytime napping (N = 306), obesity indicators (N = 144), diagnosis of T2DM (N = 218) and other potential confounders (N = 407), leaving 29,355 participants included in the analysis on T2DM. Moreover, for the mediation analysis involving fasting plasma glucose, we further excluded 2,337 participants with self-reported diagnosed diabetes and/or having anti-diabetic therapy to minimizing the impact of anti-diabetic therapy on fasting plasma glucose level, leaving 27,018 participants included in the analysis on fasting plasma glucose. The participants were aged from 50 to 96 years, with the mean age being 62 years.
Table 1 shows that, compared to the non-nappers, nappers had a higher proportion of men, were older, had higher education, more current smokers and current alcohol users, and were more physically active. Moreover, they also had higher levels of WC, WHR, WHtR and fasting plasma glucose than non-nappers (all P < 0.05). The means (SD) of fasting plasma glucose were 5.8 (1.7) mmol/L for all participants, 5.7 (1.6) mmol/L for non-nappers and 5.8 (1.7) mmol/L for nappers. No significant differences in BMI and HC were found between nappers and non-nappers. Nappers also had higher prevalence of T2DM and IFG than non-nappers (13.4 and 30.7% versus 10.7 and 27.7%, respectively, both P < 0.001).
Table 2 shows that after adjusting for all the potential confounding factors, including sex, age, education, occupation, smoking status, alcohol use and physical activity, nappers showed higher levels of WC, WHR, WHtR, and fasting plasma glucose compared with the no-nappers, with regression coefficient β (95%CI) being 0.461 (0.250–0.672) cm, 0.004 (0.003-0.006), 0.003 (0.001–0.004) and 0.073 (0.041–0.105) mmol/L, respectively. However, no association of daytime napping with BMI and HC was found. Similar associations were found for analyses by habitual napping status (Supplementary Table 1).
Table 3 shows that the associations of daytime napping with T2DM and IFG were partly mediated by increments in WC, WHR and WHtR after similar adjustment. However, no evidence for the mediating role of BMI and HC was found. The proportions of mediation through WC, WHR and WHtR was 10.17% (95% CI, 8.14–14.43%), 14.91% (95% CI, 11.95–21.24%) and 9.36% (95% CI, 7.49–13.29%), respectively, for the association between daytime napping and T2DM; and 17.59% (95% CI, 11.23–42.69%), 19.82% (95% CI, 12.72–47.67%) and 15.81% (95% CI, 10.11–38.30%), through WC, WHR and WHtR, respectively, for the association between daytime napping and IFG.
Table 4 shows the individual indirect effects of daytime napping on fasting plasma through WC (β = 0.0106, P < 0.05), WHR (β = 0.0133, P < 0.05) and WHtR (β = 0.0095, P < 0.05) individually was significant after similar adjustment. The proportion of the total effect of daytime napping mediated by WC was 14.57% (95% CI, 10.11–25.40%), by WHR was 18.22% (95%CI, 12.62–31.78%) and by WHtR was 13.12% (95% CI, 9.10–22.89%). No significant mediation through BMI and HC was found. Sensitivity analyses on non-nappers and habitual nappers showed consistent mediation results (Supplementary Table 2 and 3).
Our study showed that daytime napping was associated with T2DM and IFG through waist circumference-related obesity indicators, with the mediation effect from 9 to 20%. However, there was no evidence for the mediation through BMI or HC. Our results were generally consistent with previous studies showing napping was associated with higher risks of obesity  and diabetes  and added to the literature by quantifying the mediation effects through obesity.
In our study, waist circumference-related obesity indicators, rather than BMI nor HC, significantly mediated the association between daytime napping and T2DM/fasting plasma glucose, which has not been reported previously. A possible explanation is related to the body’s regional fat distribution, especially visceral fat accumulation. BMI is widely used as the measure of general obesity, but cannot distinguish lean and fat components of body composition. Measures of abdominal obesity (waist -related obesity indicators such as WC, WHR and WHtR) have been shown to have higher predictive ability than BMI along in predicting T2DM risk in older people , probably due to the adverse effects related to the excessive visceral fat accumulation . Older adults may have a higher percentage of visceral fat accumulation than younger people because of the aging-related redistribution of fat mass to a more central deposition [36, 37], and become more vulnerable to T2DM .
The present study also supported the positive association between daytime napping and obesity (especially abdominal obesity) [33, 39, 40]. Compared with non-napping, daytime napping was associated with a greater risk of obesity in the Hispanic Community Health Study/Study of Latinos Sueño Ancillar Study . The Study of Osteoporotic Fractures also showed a dose-response association between a longer duration of daytime napping and a higher risk of obesity , which was also supported by the China Health and Retirement Longitudinal Study .
Some possible biological mechanisms have been proposed for the positive association of daytime napping with obesity and further development of T2DM. First, napping could increase adiposity through circadian rhythm-related neuroendocrine dysfunction and behavior-related changes [41, 42], which may further lead to a higher risk of T2DM . Moreover, the sympathetic nervous system might be activated after daytime napping and subsequently increase the cortisol level , which may promote eating behavior and fat deposition and lead to weight gain . The fat deposition tended to occur in the abdominal area through fat redistribution from peripheral to central depots . In addition, daytime napping is directly associated with a longer duration bedtime, which may also decrease energy expenditure and cause fat deposition . Furthermore, higher level of inflammatory biomarkers may have also played a role in the association of daytime napping with obesity and T2DM [48,49,50].
There were some limitations in this study. First, the causal pathway from napping to obesity and then to diabetes could not be confirmed in this study. However, previous prospective cohort studies and Mendelian randomization have reported consistently positive associations of daytime napping with T2DM [4, 12,13,14,15,16,17,18,19, 24] and obesity [21,22,23,24, 27]. These results support the causal link being inferred. Moreover, of these studies mentioned above, some proposed the possible mediation via obesity but without examining the mediation effects explicitly [4, 15, 17,18,19, 27]. Our study adds to the previous literature by investigating and quantifying the mediation role of napping. Moreover, reverse causation (i.e., patients with diabetes tended to have daytime napping) cannot be ruled out, although daytime napping is a long-accepted culture in China, which might have been formed since young age. Notably, previous prospective cohort studies and Mendelian randomization have shown a positive association from daytime napping to obesity, but not vice versa [21,22,23,24], supporting the causal direction from napping to obesity. Second, assessment of daytime napping was self-reported and thus misclassification error was possible. Further studies using more accurate and detailed assessment of napping are needed. Third, as participants of this study were volunteers of older people in southern China, sample representativeness might also be a concern. However, within age-group, our participants had similar prevalence of T2DM and IFG to the nationally representative samples of Chinese . Finally, residual confounding cannot be completely ruled out, although we have adjusted for many potential confounders reported in the literature to minimize the confounding effect.
Our study firstly identified and quantified the mediating role of increased WC, WHR and WHtR in the association of daytime napping with T2DM. The findings provided new insights in understanding the possible mechanisms involved in the association between daytime napping and T2DM.
Availability of data and materials
Due to privacy or ethical restrictions, the data that support the findings will be made available on requests from the Guangzhou Biobank Cohort Study Data Access Committee (kh.ukh@atadscbg). The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Zimmet PZ, Magliano DJ, Herman WH, Shaw JE. Diabetes: a 21st century challenge. Lancet Diabetes Endocrinol. 2014;2(1):56–64.
Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the international diabetes federation diabetes atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157:107843.
Standl E, Khunti K, Hansen TB, Schnell O. The global epidemics of diabetes in the 21st century: current situation and perspectives. Eur J Prev Cardiol. 2019;26(2_Suppl):7–14.
Wang H, Chen L, Shen D, Cao Y, Zhang X, Xie K, et al. Association of daytime napping in relation to risk of diabetes: evidence from a prospective study in Zhejiang, China. Nutr Metab (Lond). 2021;18(1):18.
Leong RLF, Yu N, Ong JL, Ng ASC, Jamaluddin SA, Cousins JN, et al. Memory performance following napping in habitual and non-habitual nappers. Sleep. 2021;44(6):zsaa277.
Faraut B, Andrillon T, Vecchierini MF, Leger D. Napping: a public health issue. From epidemiological to laboratory studies. Sleep Med Rev. 2017;35:85–100.
Ru T, Chen Q, You J, Zhou G. Effects of a short midday nap on habitual nappers' alertness, mood and mental performance across cognitive domains. J Sleep Res. 2019;28(3):e12638.
Zhang Z, Xiao X, Ma W, Li J. Napping in older adults: a review of current literature. Curr Sleep Med Rep. 2020;6(3):129–35.
Wang C, Bangdiwala SI, Rangarajan S, Lear SA, AlHabib KF, Mohan V, et al. Association of estimated sleep duration and naps with mortality and cardiovascular events: a study of 116 632 people from 21 countries. Eur Heart J. 2019;40(20):1620–9.
Mantua J, Spencer RMC. Exploring the nap paradox: are mid-day sleep bouts a friend or foe? Sleep Med. 2017;37:88–97.
Lam KB, Jiang CQ, Thomas GN, Arora T, Zhang WS, Taheri S, et al. Napping is associated with increased risk of type 2 diabetes: the Guangzhou biobank cohort study. Sleep. 2010;33(3):402–7.
Han X, Liu B, Wang J, Pan A, Li Y, Hu H, et al. Long sleep duration and afternoon napping are associated with higher risk of incident diabetes in middle-aged and older Chinese: the Dongfeng-Tongji cohort study. Ann Med. 2016;48(4):216–23.
Liu R, Li Y, Mao Z, Liu X, Zhang H, Yang K, et al. Gender-specific independent and combined dose-response association of napping and night sleep duration with type 2 diabetes mellitus in rural Chinese adults: the RuralDiab study. Sleep Med. 2018;45:106–13.
Chen GC, Liu MM, Chen LH, Xu JY, Hidayat K, Li FR, et al. Daytime napping and risk of type 2 diabetes: a meta-analysis of prospective studies. Sleep Breath. 2018;22(3):815–24.
Xu Q, Song Y, Hollenbeck A, Blair A, Schatzkin A, Chen H. Day napping and short night sleeping are associated with higher risk of diabetes in older adults. Diabetes Care. 2010;33(1):78–83.
Kowall B, Lehnich AT, Strucksberg KH, Führer D, Erbel R, Jankovic N, et al. Associations among sleep disturbances, nocturnal sleep duration, daytime napping, and incident prediabetes and type 2 diabetes: the Heinz Nixdorf recall study. Sleep Med. 2016;21:35–41.
Leng Y, Cappuccio FP, Surtees PG, Luben R, Brayne C, Khaw KT. Daytime napping, sleep duration and increased 8-year risk of type 2 diabetes in a British population. Nutr Metab Cardiovasc Dis. 2016;26(11):996–1003.
McWhorter KL, Park YM, Gaston SA, Fang KB, Sandler DP, Jackson CL. Multiple sleep dimensions and type 2 diabetes risk among women in the sister study: differences by race/ethnicity. BMJ Open Diabetes Res Care. 2019;7(1):e000652.
Lin L, Lu C, Chen W, Guo VY. Daytime napping and nighttime sleep duration with incident diabetes mellitus: a cohort study in chinese older adults. Int J Environ Res Public Health. 2021;18(9):5012.
Dashti HS, Daghlas I, Lane JM, Huang YR, Udler MS, Wang HM, et al. Genetic determinants of daytime napping and effects on cardiometabolic health. Nat Commun. 2021;12(1):900.
Dashti HS, Ordovás JM. Genetics of sleep and insights into its relationship with obesity. Annu Rev Nutr. 2021;41:223–52.
Papandreou C, Díaz-López A, Babio N, Martínez-González MA, Bulló M, Corella D, et al. Long Daytime Napping Is Associated with Increased Adiposity and Type 2 Diabetes in an Elderly Population with Metabolic Syndrome. J Clin Med. 2019;8(7):1053.
Chen L, Tian A, Liu Z, Zhang M, Pan X, He C, et al. Frequent daytime napping is detrimental to human health: a phenotype-wide Mendelian randomization study. medRxiv. 2020.01.20.20017723. https://doi.org/10.1101/2020.01.20.20017723%.
Dashti HS, Daghlas I, Lane JM, Huang Y, Udler MS, Wang H, et al. Genetic determinants of daytime napping and effects on cardiometabolic health. Nat Commun. 2021;12(1):900.
Wang T, Zhang R, Ma XJ, Wang SY, He Z, Huang YP, et al. Causal Association of Overall Obesity and Abdominal Obesity with type 2 diabetes: a Mendelian randomization analysis. Obesity. 2018;26(5):934–42.
Yuan S, Larsson SC. An atlas on risk factors for type 2 diabetes: a wide-angled Mendelian randomisation study. Diabetologia. 2020;63(11):2359–71.
Zhou J, Kessler AS, Su D. Association between daytime napping and chronic diseases in China. Am J Health Behav. 2016;40(2):182–93.
Jiang CQ, Thomas GN, Lam TH, Schooling CM, Zhang WS, Lao XQ, et al. Cohort profile: the Guangzhou biobank cohort study, a Guangzhou-Hong Kong-Birmingham collaboration. Int J Epidemiol. 2006;35(4):844–52.
Deng HB, Macfarlane DJ, Thomas GN, Lao XQ, Jiang CQ, Cheng KK, et al. Reliability and validity of the IPAQ-Chinese: the Guangzhou biobank cohort study. Med Sci Sports Exerc. 2008;40(2):303–7.
Assoc AD. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2014;37:S81–90.
Imai K, Keele L, Yamamoto T. Identification, inference and sensitivity analysis for causal mediation effects. Stat Sci. 2010;25(1):51–71.
Hicks R, Tingley D. Causal mediation analysis. Stata J. 2011;11(4):605–19.
Loredo JS, Weng J, Ramos AR, Sotres-Alvarez D, Simonelli G, Talavera GA, et al. Sleep patterns and obesity: Hispanic community health study/study of Latinos Sueño Ancillar study. Chest. 2019;156(2):348–56.
Guo VY, Cao B, Wong CKH, Yu EYT. The association between daytime napping and risk of diabetes: a systematic review and meta-analysis of observational studies. Sleep Med. 2017;37:105–12.
Fan YX, Wang RD, Ding L, Meng ZW, Zhang Q, Shen Y, et al. Waist circumference and its changes are more strongly associated with the risk of type 2 diabetes than body mass index and changes in body weight in Chinese adults. J Nutr. 2020;150(5):1259–65.
Ponti F, Santoro A, Mercatelli D, Gasperini C, Conte M, Martucci M, et al. Aging and imaging assessment of body composition: from fat to facts. Front Endocrinol (Lausanne). 2020;10:861.
Bowman K, Atkins JL, Delgado J, Kos K, Kuchel GA, Ble A, et al. Central adiposity and the overweight risk paradox in aging: follow-up of 130,473 UK biobank participants. Am J Clin Nutr. 2017;106(1):130–5.
Song PG, Li X, Bu YJ, Ding SB, Zhai DS, Wang EH, et al. Temporal trends in normal weight central obesity and its associations with cardiometabolic risk among Chinese adults. Sci Rep. 2019;9:5411.
Patel SR, Hayes AL, Blackwell T, Evans DS, Ancoli-Israel S, Wing YK, et al. The association between sleep patterns and obesity in older adults. Int J Obes. 2014;38(9):1159–64.
Wang N, Zou J, Fang S, Zhou J. Association between daytime napping and obesity in Chinese middle-aged and older adults. J Glob Health. 2020;10(2):020804.
Qian J, Scheer F. Circadian system and glucose metabolism: implications for physiology and disease. Trends Endocrinol Metab. 2016;27(5):282–93.
Poggiogalle E, Jamshed H, Peterson CM. Circadian regulation of glucose, lipid, and energy metabolism in humans. Metabolism. 2018;84:11–27.
Froy O. Circadian rhythms and obesity in mammals. ISRN Obes. 2012;2012:437198.
Devine JK, Wolf JM. Determinants of cortisol awakening responses to naps and nighttime sleep. Psychoneuroendocrinology. 2016;63:128–34.
Bluher M. Adipose tissue dysfunction contributes to obesity related metabolic diseases. Best Pract Res Clin Endocrinol Metab. 2013;27(2):163–77.
Vekic J, Zeljkovic A, Stefanovic A, Jelic-Ivanovic Z, Spasojevic-Kalimanovska V. Obesity and dyslipidemia. Metabolism. 2019;92:71–81.
Lucassen EA, Rother KI, Cizza G. Interacting epidemics? Sleep curtailment, insulin resistance, and obesity. Ann N Y Acad Sci. 2012;1264:110–34.
Leng Y, Ahmadi-Abhari S, Wainwright NWJ, Cappuccio FP, Surtees PG, Luben R, et al. Daytime napping, sleep duration and serum C reactive protein: a population-based cohort study. BMJ Open. 2014;4(11):e006071.
Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006;444(7121):840–6.
Esser N, Legrand-Poels S, Piette J, Scheen AJ, Paquot N. Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes. Diabetes Res Clin Pract. 2014;105(2):141–50.
Xu Y, Wang L, He J, Bi Y, Li M, Wang T, et al. Prevalence and control of diabetes in Chinese adults. JAMA. 2013;310(9):948–59.
The Guangzhou Biobank Cohort Study investigators include: Guangzhou No. 12 Hospital: WS Zhang, M Cao, T Zhu, B Liu, CQ Jiang (Co-PI); The University of Hong Kong: CM Schooling, SM McGhee, GM Leung, R Fielding, TH Lam (Co-PI); The University of Birmingham: P Adab, GN Thomas, KK Cheng (Co-PI).
This work was funded by the Natural Science Foundation of China (No. 81941019) and the Natural Science Foundation of Guangdong (2018A030313140). Guangzhou Biobank Cohort Study was funded by The University of Hong Kong Foundation for Educational Development and Research (SN/1f/HKUF-DC; C20400.28505200), the Health Medical Research Fund (Grant number: HMRF/13143241) in Hong Kong; Guangzhou Public Health Bureau (201102A211004011) Natural Science Foundation of Guangdong (2018A030313140), and the University of Birmingham, UK.
Ethics approval and consent to participate
Ethical approval of the study was granted from the Guangzhou Medical Ethics Committee of the Chinese Medical Association, and informed consent was obtained from all participants before participation. The study was performed in accordance with the Declaration of Helsinki.
Consent for publication
All authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Regression coefficients for obesity indicators and fasting plasma glucose by daytime napping status in 29,355 participants in the Guangzhou Biobank Cohort Study. Supplementary Table 2. Association between daytime napping with impaired fasting glucose and type 2 diabetes mellitus with mediation by body mass index, waist circumference, hip circumference, waist-to-hip ratio and waist-to-height ratio in 29,355 participants in the Guangzhou Biobank Cohort Study. Supplementary Table 3. Association between daytime napping and fasting plasma glucose with mediation by body mass index, waist circumference, hip circumference, waist-to-hip ratio and waist-to-height ratio in 27,018 participants without type 2 diabetes history in the Guangzhou Biobank Cohort Study.
About this article
Cite this article
Xiao, J.Y., Zhang, W.S., Jiang, C.Q. et al. Obesity indicators as mediators of association between daytime napping and type 2 diabetes mellitus: the Guangzhou Biobank Cohort Study. BMC Public Health 22, 56 (2022). https://doi.org/10.1186/s12889-021-12451-8
- Daytime napping
- Type 2 diabetes mellitus
- Mediation analysis