Sleep influences on cardio-metabolic health in Indigenous populations

The built environment is more predictive of cardiometabolic health than other aspects of lifestyle in two rapidly transitioning Indigenous populations

Background

Many subsistence-level and Indigenous societies around the world are rapidly experiencing urbanization, nutrition transition, and integration into market-economies, resulting in marked increases in cardiometabolic diseases. Determining the most potent and generalized drivers of changing health is essential for identifying vulnerable communities and creating effective policies to combat increased chronic disease risk across socio-environmental contexts. However, comparative tests of how different lifestyle features affect the health of populations undergoing lifestyle transitions remain rare, and require comparable, integrated anthropological and health data collected in diverse contexts.

Methods

We developed nine scales to quantify different facets of lifestyle (e.g., urban infrastructure, market-integration, acculturation) in two Indigenous, transitioning subsistence populations currently undergoing rapid change in very different ecological and sociopolitical contexts: Turkana pastoralists of northwest Kenya (n = 3,692) and Orang Asli mixed subsistence groups of Peninsular Malaysia (n = 688). We tested the extent to which these lifestyle scales predicted 16 measures of cardiometabolic health and compared the generalizability of each scale across the two populations. We used factor analysis to decompose comprehensive lifestyle data into salient axes without supervision, sensitivity analyses to understand which components of the multidimensional scales were most important, and sex-stratified analyses to understand how facets of lifestyle variation differentially impacted cardiometabolic health among males and females.

Findings

Cardiometabolic health was best predicted by measures that quantified urban infrastructure and market-derived material wealth compared to metrics encompassing diet, mobility, or acculturation, and these results were highly consistent across both populations and sexes. Factor analysis results were also highly consistent between the Turkana and Orang Asli and revealed that lifestyle variation decomposes into two distinct axes-the built environment and diet-which change at different paces and have different relationships with health.

Interpretation

Our analysis of comparable data from Indigenous peoples in East Africa and Southeast Asia revealed a surprising amount of generalizability: in both contexts, measures of local infrastructure and built environment are consistently more predictive of cardiometabolic health than other facets of lifestyle that are seemingly more proximate to health, such as diet. We hypothesize that this is because the built environment impacts unmeasured proximate drivers like physical activity, increased stress, and broader access to market goods, and serves as a proxy for the duration of time that communities have been market-integrated.

Joint Association of Sleep Onset Time and Sleep Duration With Cardiometabolic Health Outcome

BACKGROUND

The association of sleep onset time and duration with cardiometabolic health is not well characterized.

METHODS AND RESULTS

This study included 6696 adults aged 20 to 80 years from the NHANES (National Health and Nutrition Examination Study) 2015 to 2018. Participants were categorized into 9 groups according to the cross-tabulation of sleep onset time (<22:00 [early], 22:00-23:59 [optimal], and ≥24:00 [late]) and duration (<7 hours [insufficient], 7-8 hours [sufficient], and ≥9 hours [excessive]), with optimal sleep onset time and sufficient duration as the reference. The primary outcomes included hypertension, hypertriglyceridemia, low high-density lipoprotein cholesterol, hyperglycemia, central obesity, and metabolic syndrome. Inappropriate sleep onset time and sleep duration were associated with increased odds of hypertension, hypertriglyceridemia, and metabolic syndrome, especially among participants aged 40 to 59 years. Compared with men reporting optimal onset and sufficient duration, men reporting optimal onset with excessive duration (odds ratio [OR]: 2.01 [95% CI, 1.12-3.58]) and late onset with insufficient duration (OR, 1.74 [95% CI, 1.13-2.68]) had higher odds of metabolic syndrome. Compared with women reporting optimal onset and sufficient duration, women reporting optimal onset and insufficient duration (OR, 1.61 [95% CI, 1.11-2.32]) and early onset and excessive duration (OR, 2.16 [95% CI, 1.30-3.57]) had higher odds of hypertension, and women reporting late onset and excessive duration (OR, 5.64 [95% CI, 1.28-6.77]) were at the highest odds of hypertriglyceridemia.

CONCLUSIONS

Late sleep onset as well as insufficient or excessive sleep duration are associated with adverse cardiometabolic outcomes, particularly in participants aged 40 to 59 years.

Association between indigenous status and Body Mass Index (BMI) in Australian adults: Does sleep duration affect the relationship?

Background

Overweight/obesity is a well-defined risk factor for a variety of chronic cardiovascular and metabolic diseases. Sleep duration has been associated with overweight/obesity and other cardio metabolic and neurocognitive problems. Notably, overweight/obesity and many of the associated comorbidities are prevalent in Indigenous Australians. Generally, sleep duration has been associated with BMI for Australian adults but information about Australian Indigenous adults’ sleep is scant. A recent report established that sleep is a weak predictor of obesity for Indigenous Australian adults.

Aim

To determine whether sleep remains a predictor of obesity when physical activity, diet and smoking status are accounted for; and to determine whether sleep duration plays a mediating role in the relationship between Indigenous status and BMI.

Methods

Statistical analyses of 5,886 Australian adults: 5236 non-Indigenous and 650 Indigenous people aged over 18 years who participated in the Australian Health Survey 2011–2013. Demographic and lifestyle characteristics were described by χ² and t-tests. ANOVA was used to determine the variables that significantly predicted BMI and sleep duration. Stepwise regression analyses were performed to determine the strongest significant predictors of BMI. Sleep duration was self-reported; BMI was calculated from measurement.

Results

The study revealed two main findings: (i) short sleep duration was an independent predictor of obesity (adjusted-R² = 0.056, p <0.0001); and (ii) controlling for sleep duration and other possible confounders, Indigenous status was a significant predictor of BMI overweight/obesity. Sleep duration played a weak, partial mediator role in this relationship. Increased BMI was associated with lower socioeconomic status and level of disadvantage of household locality for non-remote Indigenous and non-Indigenous people.

Conclusion

Indigenous status strongly predicted increased BMI. The effect was not mediated by the socioeconomic indicators but was partially mediated by sleep duration.

Late bedtime and body mass index gain in indigenous Australian children in the longitudinal study of indigenous children

AIM

To explore sleep patterns in indigenous Australian children and assess the role of sleep timing in longitudinal changes in body mass index (BMI).

METHODS

Latent profile analysis was conducted with the Australian Longitudinal Study of Indigenous Children (LSIC) cohort data (wave 5), to determine distinct patterns of bed and wake timing, taking account of weekday sleep duration, weekday and weekend bedtimes, and weekday wake times. Multilevel models with a random intercept were used to investigate the role of baseline sleep pattern in predicting longitudinal changes in BMI.

RESULTS

Baseline data for 1258 children (50.7% males), mean age 6.32 ± 1.52 years, indicated the presence of five classes of sleep patterns: early/long sleepers (4.5%), normative sleepers (25.5%), late sleepers (49.9%), consistent late sleepers (11.1%) and early risers (9%). Late sleeping was significantly associated with longitudinal gains in BMI. Compared with early sleepers, consistent late sleepers experienced 1.03 unit gain in BMI at follow-up (95% CI: 0.001-2.05, P = .05).

CONCLUSION

This study underscores the importance of looking beyond sleep duration and highlights the positive outcomes of early bedtimes in children. As sleep timing is modifiable, this offers the opportunity for improvement in sleep and protecting against future weight gain in indigenous children.

Is sleep duration associated with overweight/obesity in Indigenous Australian adults?

Background

Associations between high BMI and sleep duration and chronic illness are recognised. Short sleep is an accepted predictor of high BMI for children, including Indigenous Australian children. Short sleep has also been associated with high BMI in Australian adults, although not specifically in Indigenous Australian adults. This study aims to determine whether the relationship between sleep duration and BMI observed in non-Indigenous adults holds for Indigenous adults.

Methods

Data collected from 5204 non-Indigenous and 646 Indigenous participants aged over 18 years in a nationally representative Australian Health Survey 2011–2013 were analysed. Sleep duration was self-reported as the time between going to bed and time waking up; BMI was derived from measurement and categorised into normal weight (BMI = 18.5–24.9) and overweight/obese (BMI ≥ 25). Logistic regression was performed for the non-Indigenous and Indigenous groups separately to examine the association between sleep duration and BMI in each group.

Results

Proportionally more Indigenous people were classified as overweight/obese than non-Indigenous (χ² = 21.81, p < 0.001). Short sleep was reported by similar proportions in both groups (Indigenous 15% vs non-Indigenous 17%) whereas long sleep of > 9 h was reported by proportionally more Indigenous than non-Indigenous people (41% vs 26%). Without accounting for possible confounders, the association between sleep duration and BMI for the Indigenous group was not significant but a possible dose-response relationship was evident, with the odds of overweight/obesity being greatest for those who typically slept < 7 h (OR = 1.77, 95% CI 0.38–3.94) and < 6 h (OR = 1.55, 95%CI = 0.58–4.14). The same model for the non-Indigenous group was significant, with the odds of overweight/obesity being greatest for those who typically slept < 6 h (OR = 1.67, 95%CI 1.25–2.25). The risk of overweight/obesity diminished for both groups with sleep > 7 h. Accounting for a range of socioeconomic and personal confounders attenuated the strength of these relationships marginally.

Conclusion

Adding to reports relating sleep duration and BMI for Australian adults, this study provides evidence for an inverse relationship in non-Indigenous adults and suggests a similar trend for Indigenous adults. This trend was non-significant but is consistent with previous results for Indigenous children.

Sleep Disorders in Individuals With High Risk for Diabetes in Indian Population

Background:

Sleep restores physiology and neurochemical components of our body and is essential for physical and mental health. Sleep disorders (SDs) are associated with insulin resistance and metabolic disorders. The association between SDs and diabetes needs to be understood in the Indian population.

Purpose:

The purpose was to investigate the association between SD and diabetes in the Indian population.

Methods:

As a part of nationwide Niyantrita Madhumeha Bharata Abhiyaan-2017 (NMB-2017), a cross-sectional study was conducted and data was collected from seven zones of India, after screening through the Indian Diabetes Risk Score (IDRS). The sleep quality was assessed on a scale of 1 to 4 (very good = 1, very bad = 4). The time taken to fall asleep (sleep latency) was assessed on a scale of 0 to 5 (“0” = nil and “5” = >1.5 h). Stress was assessed by the perceived stress scale.

Results:

Bad sleep quality was positively (odds ratio 1.055, CI [1.001, 1.113], and P < .01) associated with self-reported known diabetes. Increased time taken to fall in sleep (sleep latency) was associated significantly with IDRS high risk (odds ratio 1.085, CI [1.008, 1.168], and P = .01), with an average sleep latency /time takes to fall in sleep (maximum range 5 [>1.5 h], mode 2 [10 to 30 min]) minutes. Moderate stress was significantly associated with bad sleep quality (odds ratio 1.659).

Conclusion:

A positive association of bad sleep quality and stress with diabetes, and an increased sleep latency in the IDRS high-risk population point to the role of modifiable risk factors. Behavioral modification and stress reduction by using yoga may be beneficial in the better management of diabetes.