Association of Sleep Duration and Variability With Body Mass Index: Sleep Measurements in a Large US Population of Wearable Sensor Users.
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ABSTRACT:
SUBMITTER: Jaiswal SJ
PROVIDER: S-EPMC7490746 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVE:Sleep duration is associated with obesity and cardiometabolic disease. It is unclear, though, how these relationship differs across age groups. METHODS:Data from 2007 to 2008 National Health and Nutrition Examination Survey (NHANES) were used, including respondents aged 16+ with complete data (N?=?5,607). Sleep duration and age were evaluated by self-report, and body mass index (BMI) was assessed objectively. Sleep duration was evaluated continuously and categorically [very short (?4 h), short (5-6 h), and long (?9 h) versus average (7-8 h)]. Age was also evaluated continuously and categorically [adolescent (16-17 years), young adult (18-29 years), early middle age (30-49 years), late middle age (50-64 years), and older adult (?65 years)]. RESULTS:There was a significant interaction with age for both continuous (Pinteraction ?=?0.014) and categorical (Pinteraction ?=?0.035) sleep duration. A pseudo-linear relationship was seen among the youngest respondents, with the highest BMI associated with the shortest sleepers and the lowest BMI associated with the longest sleepers. This relationship became U-shaped in middle-age, and less of a relationship was seen among the oldest respondents. CONCLUSIONS:These findings may provide insights for clinical recommendations and could help to guide mechanistic research regarding the sleep-obesity relationship.
| S-EPMC4700549 | biostudies-literature
Project description:BackgroundAdolescents who receive an adequate amount of sleep benefit from a positive health status. Previous studies have documented several health consequences connected with obesity as well as short sleep duration among adolescents. Poor sleep quality with obesity and uncontrolled diet can lead to chronic diseases in the future. This study aimed to examine the link between eating habits, sleep duration, and body mass index (BMI) among King Saud University (KSU) students.MethodsThe study was cross-sectional and conducted from February to May 2021 on 311 recruited students (male and female) of KSU premises. Pittsburgh Sleep Quality Index questionnaire was used to describe sleep duration linked with a dietary pattern that included fruit and vegetable intake. The questionnaire consists of two sections of 15 and 10 questions each. The questionnaire was created using the Google Forms tool and distributed through social media platforms like Twitter and WhatsApp. The obtained data was transferred into excel to perform the statistical analysis.ResultsThe mean total of students who participated in this study was 21.45 ± 23.11. Female students (72.3%) were actively involved in this study. About 30.2% of students were found to be overweight and obese. Around 67.8% of students had insufficient sleep, 32.2% had adequate sleep, and over 70% of students fell asleep within 30 min of going to bed. A total of 71.7% of students showed good sleep quality, whereas 28.3% reported poor sleep quality. BMI was categorized into four groups: 17.7% of individuals were underweight, 52.1% were of normal weight threshold, 20.6% were overweight, and 9.6% were obese. On a regular basis, 12.5% of students consume vegetables and 6.4% fruits daily. The results of this study show that only 8% of students eat breakfast, whereas 62.1% eat lunch, and 29.9% eat dinner.ConclusionThis study concludes that short sleep duration was associated with obesity among KSU students. This association was also found between sleep duration and dietary factors, specifically in the consumption of fruits and vegetables in terms of eating behaviour.
| S-EPMC9918940 | biostudies-literature
Project description:Study objectivesMounting evidence indicates that sleep disturbance contributes to the increased risk for cardiometabolic diseases. Obesity and underweight are also closely linked to cardiometabolic risk. Thus, the objective of this study was to examine the association between sleep duration, quality, and body mass index (BMI) categories.MethodsUsing data from a cohort of 107,718 Korean individuals (63,421 men and 44,297 women), we conducted cross-sectional analysis with sex subgroup analysis. Sleep duration was classified into 3 groups-short (< 7 hours), normal (7-9 hours) and long sleep (> 9 hours)-and Pittsburgh Sleep Quality Index (PSQI) score was used to divide sleep quality into 2 groups-poor (PSQI > 5) and good sleep (PSQI ≤ 5). Compared to normal sleep and good sleep quality, adjusted odds ratios of short and long sleep and poor sleep for BMI categories were calculated. BMI categories included underweight (BMI < 18.5 kg/m2), overweight (BMI 23 to < 25 kg/m2), obesity (BMI 25 to < 30 kg/m2) and severe obesity (BMI ≥ 30 kg/m2).ResultsShort sleep duration had the dose-dependent relationship with obesity categories from overweight to severe obesity, and inverse relationship with underweight (adjusted odds ratios [95% confidence intervals] for underweight, overweight, obesity, and severe obesity versus normal weight; 0.88 [0.82-0.94], 1.15 [1.11-1.20], 1.31 [1.26-1.37], 1.70 [1.54-1.85]). Poor sleep quality was significantly associated with severe obesity in male subgroup (1.16 [1.05-1.27]) and with obesity (1.18 [1.10-1.25]) and severe obesity in female subgroup (1.66 [1.40-1.98]).ConclusionsShort sleep duration and poor sleep quality was more positively associated with obesity across BMI than underweight.
| S-EPMC6086945 | biostudies-literature
Project description:BackgroundAlthough associations between cumulative risk, sleep, and overweight/obesity have been demonstrated, few studies have examined relationships between these constructs longitudinally across childhood. This study investigated how cumulative risk and sleep duration are related to current and later child overweight/obesity in families across the United States sampled for high sociodemographic risk.MethodsWe conducted secondary analyses on 3690 families with recorded child height and weight within the Fragile Families and Child Well-Being Study. A cumulative risk composite (using nine variables indicating household/environmental, family, and sociodemographic risk) was calculated for each participant from ages 3-9 years. Path analyses were used to investigate associations between cumulative risk, parent-reported child sleep duration, and z-scored child body mass index (BMI) percentile at ages 3 through 9.ResultsHigher cumulative risk experienced at age 5 was associated with shorter sleep duration at year 9, b = - 0.35, p = .01, 95% CI [- 0.57, - 0.11]. At 5 years, longer sleep duration was associated with lower BMI, b = - 0.03, p = .03, 95% CI [- 0.06, - 0.01]. Higher cumulative risk at 9 years, b = - 0.34, p = .02, 95% CI [- 0.57, - 0.10], was concurrently associated with shorter sleep duration. Findings additionally differed by child sex, such that only male children showed an association between sleep duration and BMI.ConclusionsResults partially supported hypothesized associations between child sleep duration, cumulative risk, and BMI emerging across childhood within a large, primarily low socioeconomic status sample. Findings suggest that reducing cumulative risk for families experiencing low income may support longer child sleep duration. Additionally, child sleep duration and BMI are concurrently related in early childhood for male children.
| S-EPMC9447344 | biostudies-literature
Project description:BackgroundThis study aimed at investigating the association of sleep duration with body mass index (BMI) by gender among adult residents in rural Hanzhong of Shaanxi province, Northwest China.MethodsA two-level stratified random cluster sampling method was used to select adult residents between the ages of 18 and 80 years. All information including sociodemographic characteristics and lifestyles was collected by face-to-face interview with a structured questionnaire. According to standard methods, trained staff were responsible for anthropometric measurements using calibrated instruments in an empty room. By gender, both ordinary least square regression (OLS) and quantile regression (QR) were used to analyze the relationship between sleep time and BMI controlling for other confounders. The restricted cubic splines with five knots were further used to express the potentially non-linear association between sleep time and BMI.ResultsA total of 3,017 eligible participants were included in the study. After controlling for confounding factors including sociodemographic characteristics and lifestyles, OLS regression did not indicate any significant association of sleep duration with BMI among men and women. Among men, it was clear that there is an inverse U-shaped relationship between sleep time and BMI beyond the 66.0th percentile (BMI ≥24). Among women, quantile regression presented a significant U-shaped relationship between BMI and sleep duration. According to the restricted cubic splines, the women who sleep for approximately 9 h had the lowest BMI, and when sleep duration approached approximately 7 h among men, their BMI would be the highest.ConclusionsThe U-shaped and inverse U-shaped relationships between sleep duration and BMI were clearly observed for women and men, respectively, in our study. The identification of potentially relevant modifiable risk factors may provide better preventive approaches to obesity.
| S-EPMC9193222 | biostudies-literature
Project description:BackgroundYouth use different forms of screen time (e.g., streaming, gaming) that may be related to body mass index (BMI). Screen time is non-independent from other behaviors, including physical activity and sleep duration. Statistical approaches such as isotemporal substitution or compositional data analysis (CoDA) can model associations between these non-independent behaviors and health outcomes. Few studies have examined different types of screen time, physical activity, and sleep duration simultaneously in relation to BMI.MethodsData were baseline (2017-2018) and one-year follow-up (2018-2019) from the Adolescent Brain Cognitive Development Study, a multi-site study of a nationally representative sample of U.S. youth (N = 10,544, mean [SE] baseline age = 9.9 [0.03] years, 48.9% female, 45.4% non-White). Participants reported daily minutes of screen time (streaming, gaming, socializing), physical activity, and sleep. Sex-stratified models estimated the association between baseline behaviors and follow-up BMI z-score, controlling for demographic characteristics, internalizing symptoms, and BMI z-score at baseline.ResultsIn females, isotemporal substitution models estimated that replacing 30 min of socializing (β [95% CI] = -0.03 [-0.05, -0.002]), streaming (-0.03 [-0.05, -0.01]), or gaming (-0.03 [-0.06, -0.01]) with 30 min of physical activity was associated with a lower follow-up BMI z-score. In males, replacing 30 min of socializing (-0.03 [-0.05, -0.01]), streaming (-0.02 [-0.03, -0.01]), or gaming (-0.02 [-0.03, -0.01]) with 30 min of sleep was associated with a lower follow-up BMI z-score. In males, replacing 30 min of socializing with 30 min of gaming was associated with a lower follow-up BMI z-score (-0.01 [-0.03, -0.0001]). CoDA estimated that in males, a greater proportion of time spent in baseline socializing, relative to the remaining behaviors, was associated with a higher follow-up BMI z-score (0.05 [0.02, 0.08]). In females, no associations between screen time and BMI were observed using CoDA.ConclusionsOne-year longitudinal associations between screen time and BMI may depend on form of screen time, what behavior it replaces (physical activity or sleep), and participant sex. The alternative statistical approaches yielded somewhat different results. Experimental manipulation of screen time and investigation of biopsychosocial mechanisms underlying the observed sex differences will allow for causal inference and can inform interventions.
| S-EPMC10988901 | biostudies-literature
Project description:BackgroundOverweight/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.AimTo 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.MethodsStatistical 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.ResultsThe 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.ConclusionIndigenous status strongly predicted increased BMI. The effect was not mediated by the socioeconomic indicators but was partially mediated by sleep duration.
| S-EPMC8849483 | biostudies-literature
Project description:BackgroundElectrocardiogram (ECG) measured QRS duration has been shown to influence cardiovascular outcomes. However, there is paucity of data on whether ECG QRS duration is influenced by obesity and sex in large populations.MethodsAll ECGs performed by a pathology provider over a 2-year period were included. ECGs with confounding factors and those not in sinus rhythm were excluded from the primary analysis.ResultsOf the 76,220 who met the inclusion criteria, 41,685 (55%) were females. The median age of the study cohort was 61 years (interquartile [IQR] range 48-71 years). The median QRS duration was 86 ms (IQR 80-94 ms). The median BMI was 27.6 kg/m2 (IQR 24.2-31.8 kg/m2). When stratified according to the World Health Organization classification of BMI < 18.50 kg/m2, 18.50-24.99 kg/m2, 25.00-29.99 kg/m2, and ≥ 30.00 kg/m2, the median QRS durations were 82 ms (IQR 76-88 ms), 86 ms (IQR 80-92 ms), 88 ms (IQR 80-94 ms) and 88 ms (IQR 82-94 ms), respectively (p < 0.001 for linear trend). Median QRS duration for females was 84 ms (IQR 78-88 ms); for males, it was 92 ms (IQR 86-98 ms), p < 0.001. Compared to males, females had narrower QRS complexes at similar age and similar BMI. In multiple linear regression analysis, BMI correlated positively with QRS duration (standardized beta 0.095, p < 0.001) independent of age, sex, and heart rate.ConclusionsIn this large cohort there was a positive association between increasing BMI and QRS duration. Females had narrower QRS duration than males at similar age and similar BMI.
| S-EPMC8503593 | biostudies-literature
Project description:BackgroundThe clinical recognition of cardiometabolic disorders might be enhanced by anthropometry based on the sagittal abdominal diameter (SAD; also called "abdominal height") or waist circumference rather than on weight. Direct comparisons of body mass index (BMI, weight/height(2)) with SAD/height ratio (SADHtR) or waist circumference/height ratio (WHtR) have not previously been tested in nationally representative populations.MethodsNonpregnant adults without diagnosed diabetes (ages 20-64 years; n = 3071) provided conventional anthropometry and supine SAD (by sliding-beam caliper) in the 2011-2012 US National Health and Nutrition Examination Survey. Population-weighted, logistic models estimated how strongly each anthropometric indicator was associated with 5 cardiometabolic disorders: Dysglycemia (glycated hemoglobin ≥5.7%), HyperNonHDLc (non-high-density-lipoprotein [HDL] cholesterol ≥4.14 mmol/L, or taking anticholesteremic medications), Hypertension (systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg, or taking antihypertensive medications), HyperALT (alanine transaminase ≥p75 [75th percentile, sex-specific]), and HyperGGT (gamma-glutamyltransferase ≥p75 [sex-specific]).ResultsAfter scaling each indicator, adjusted odds ratios (aORs) tended to be highest for SADHtR and lowest for BMI when identifying each disorder except dysglycemia. When SADHtR entered models simultaneously with BMI, the aORs for BMI no longer directly identified any condition, whereas SADHtR identified persons with HyperNonHDLc by aOR 2.78 (95% confidence interval [CI], 1.71-4.51), Hypertension by aOR 2.51 (95% CI, 1.22-5.15), HyperALT by aOR 2.89 (95% CI, 1.56-5.37), and HyperGGT by aOR 5.43 (95% CI, 3.01-9.79). WHtR competed successfully against BMI with regard to Dysglycemia, HyperNonHDLc, and HyperGGT. c-Statistics of SADHtR and WHtR were higher than those of BMI (P <.001) for identifying HyperNonHDLc and HyperGGT.ConclusionsAmong nonelderly adults, SADHtR or WHtR recognized cardiometabolic disorders better than did the BMI.
| S-EPMC5292922 | biostudies-literature
Project description:There is evidence across several species for genetic control of phenotypic variation of complex traits, such that the variance among phenotypes is genotype dependent. Understanding genetic control of variability is important in evolutionary biology, agricultural selection programmes and human medicine, yet for complex traits, no individual genetic variants associated with variance, as opposed to the mean, have been identified. Here we perform a meta-analysis of genome-wide association studies of phenotypic variation using ?170,000 samples on height and body mass index (BMI) in human populations. We report evidence that the single nucleotide polymorphism (SNP) rs7202116 at the FTO gene locus, which is known to be associated with obesity (as measured by mean BMI for each rs7202116 genotype), is also associated with phenotypic variability. We show that the results are not due to scale effects or other artefacts, and find no other experiment-wise significant evidence for effects on variability, either at loci other than FTO for BMI or at any locus for height. The difference in variance for BMI among individuals with opposite homozygous genotypes at the FTO locus is approximately 7%, corresponding to a difference of ?0.5?kilograms in the standard deviation of weight. Our results indicate that genetic variants can be discovered that are associated with variability, and that between-person variability in obesity can partly be explained by the genotype at the FTO locus. The results are consistent with reported FTO by environment interactions for BMI, possibly mediated by DNA methylation. Our BMI results for other SNPs and our height results for all SNPs suggest that most genetic variants, including those that influence mean height or mean BMI, are not associated with phenotypic variance, or that their effects on variability are too small to detect even with samples sizes greater than 100,000.
| S-EPMC3564953 | biostudies-literature