To explore the link between daytime napping and the risk of major adverse cardiovascular events (MACE) and mortality in individuals with type 2 diabetes.

This prospective study included 21,129 participants with diabetes from the UK Biobank, all of whom were free of MACE and cancer at baseline. Data on habitual daytime napping and sleep duration were collected via a baseline questionnaire. Cox proportional hazards regression models were employed to assess the relationship between daytime napping and MACE, all-cause mortality, and cardiovascular disease (CVD) specific mortality. MACE was defined as a composite of myocardial infarction, heart failure, and stroke.

Over an average 11.9-year follow-up, 5,611 MACE cases, 3,854 all-cause deaths, and 1,839 CVD deaths were identified. Compared to those who never/rarely napped, the multivariable-adjusted hazard ratios for usually napping were: 1.39 (1.08, 1.65) for MACE; 1.44 (1.01, 1.92) for myocardial infarction; 1.33 (1.07, 1.64) for heart failure; 1.57 (1.06, 2.33) for stroke; 1.28 (1.01, 1.60) for all-cause mortality; 1.33 (0.97, 1.94) for CVD mortality.

Frequent daytime napping is significantly associated with an increased risk of MACE and mortality among individuals with diabetes, particularly those who have extended sleep durations of more than 10 h and severe diabetes.

Sleep is a potential risk factor for metabolic syndrome. We investigated the associations of various sleep characteristics with the status and incidence of metabolic syndrome in middle-aged Koreans.

Using data from a community-based Korean Medicine Daejeon Citizen Cohort study on participants aged 30-50years, cross-sectional (n = 1984) and longitudinal (n = 1216, median follow-up: 2.1years) analyses were performed. To study the association of metabolic syndrome and five components with various sleep characteristics, measured using the Pittsburgh Sleep Quality Index, we used Poisson and logistic regression and Cox proportional hazard regression analyses, adjusting for covariates.

Of 1984 participants, 66%, 19%, and 15% belonged to the non-metabolic syndrome, pre-metabolic syndrome, and metabolic syndrome groups, respectively. After covariate adjustments, the pre-metabolic syndrome group was associated with late mid-sleep time (≥5:00; prevalence ratios 1.61, 95% confidence interval 1.01-2.54) and late bedtime (≥2:00; prevalence ratios 1.55, 95% confidence interval 1.03-2.34), and the metabolic syndrome group was associated with long sleep latency (prevalence ratios 1.33, 95% confidence interval 1.03-1.73), poor sleep quality (prevalence ratios 1.38, 95% confidence interval 1.07-1.78), and early wake time (<6:00; prevalence ratios 1.29, 95% confidence interval 1.01-1.63). Longitudinal analysis of participants without metabolic syndrome at baseline indicated a significant increase in metabolic syndrome risk associated with very short sleep duration (<6 hours; hazard ratio 1.72, 95% confidence interval 1.06-2.79), long sleep latency (>30 minutes; hazard ratio 1.86, 95% confidence interval 1.1-3.12), and early wake time (<6:00 o'clock; hazard ratio 1.73, 95% confidence interval 1.01-2.97).

Sleep characteristics, such as short duration, long latency, and early wake time, were associated with an increased risk of metabolic syndrome in middle-aged adults.

In this cross-sectional study, we aim to investigate the interactions between obesity, siesta behavior, and the genetic propensity for siesta in a Mediterranean population, in whom siesta is deeply rooted.

We applied a previously generated Siesta-Polygenic Score (PGS) in the ONTIME study (n = 1278). Siesta and other Mediterranean lifestyle behaviors were characterized using questionnaires. We further determined obesity grade. Secondarily, we measured weight loss during treatment as well as long-term weight-loss maintenance. Logistic regression analyses were performed to address our aim.

A total of 42.4% of the population usually took siesta. A significant genetic influence on siesta propensity was found, with a higher genetic predisposition linked to taking siesta more frequently (odds ratio [OR] = 1.17, 95% CI: 1.03-1.32; p = 0.015). Participants with a higher genetic propensity for siesta showed poorer dietary habits (p < 0.05). Among individuals with a high genetic propensity for siesta, we found that those who usually take siesta have lower odds of having obesity (p = 0.038) compared with those who do not. Similarly, in exploratory analysis, among individuals with a high genetic propensity for siesta, we found that those who usually take siesta have higher odds of weight-loss success (p = 0.007) compared with those who do not.    CONCLUSIONS: Considering the ongoing debate regarding whether siesta is beneficial or detrimental, our findings suggest that individual genetic predisposition to siesta might influence the association between siesta and health.

Obesity is a global health issue influenced primarily by genetic variants and environmental factors. This study aimed to examine the relationship between genetic and lifestyle factors and their interaction with obesity risk among university students.

A total of 658 students from the same university participated in this study, including 531 females (mean age (SD): 21.6 (3.9) years) and 127 males (21.9 (4.6) years). Among them, 550 were classified as normal weight or underweight (456 females and 94 males), while 108 were identified as overweight or obese (75 females and 33 males). All the participants underwent anthropometric and genetic screening and completed lifestyle and sleep quality questionnaires.

The polygenic risk score, based on seven genetic variants (ADCY3 rs11676272, CLOCK rs1801260, GPR61 rs41279738, FTO rs1421085, RP11-775H9.2 rs1296328, SLC22A3 rs9364554, and TFAP2B rs734597), explained 8.3% (p < 0.0001) of the variance in body mass index (BMI). On the other hand, lifestyle factors-such as meal frequency, frequency of overeating, nut consumption as a snack, eating without hunger, frequency of antibiotic use in the past year, symptoms of dysbiosis, years of physical activity, sleep duration, bedtime, ground coffee consumption frequency, and evening coffee consumption time-accounted for 7.8% (p < 0.0001) of the variance in BMI. The model based on gene-environment interactions contributed 15% (p < 0.0001) to BMI variance.

This study revealed that individuals with a higher genetic predisposition, as defined by the seven polymorphic loci, are more susceptible to becoming overweight or obese under certain lifestyle conditions.

Napping is a common habit in many countries. Nevertheless, studies about the chronic effects of napping on obesity are contradictory, and the molecular link between napping and metabolic alterations has yet to be studied. We aim to identify molecular mechanisms in adipose tissue (AT) that may connect napping and abdominal obesity.

In this cross-sectional study, we extracted the RNA repeatedly across 24 hours from cultured AT explants and performed RNA sequencing. Circadian rhythms were analyzed using six consecutive time points across 24 hours. We also assessed global gene expression in each group (nappers vs. non-nappers).

With napping, there was an 88% decrease in the number of rhythmic genes compared to that in non-nappers, a reduction in rhythm amplitudes of 29%, and significant phase changes from a coherent unimodal acrophase in non-nappers, towards a scattered and bimodal acrophase in nappers. Those genes that lost rhythmicity with napping were mainly involved in pathways of glucose and lipid metabolism, and of the circadian clock. Additionally, we found differential global gene expression between nappers and non-nappers with 34 genes down- and 32 genes upregulated in nappers. The top upregulated gene (IER3) and top down-regulated pseudogene (VDAC2P2) in nappers have been previously shown to be involved in inflammation.

These new findings have implications for our understanding of napping's relationship with obesity and metabolic disorders.

Intrinsic capacity (IC), a multidimensional construct encompassing mental and physical capacities, has been established in the aging framework by the World Health Organization. However, the detailed relationship between IC and Chinese sleep patterns (nighttime sleep and post-lunch naps) remains inadequately elucidated.

Participants in this study were individuals aged ≥45 years residing in China, included in the China Health and Retirement Longitudinal Study (CHARLS). We analyzed 4 years of CHARLS data from the first wave (May 2011-March 2012) to the second wave (July 2015-January 2016). Data from these waves were utilized for longitudinal analysis. Self-reported data included nighttime sleep and nap duration, along with other baseline characteristics. The IC evaluation involved physical examinations and blood tests. Initially, linear regression was used to assess the relationship between total sleep duration, nighttime sleep duration, nap duration, and IC change between the two waves that were determined by marginal effects (ME) and their corresponding 95% confidence intervals (CIs). Regression splines were employed to explore potential nonlinear associations. Subgroup and sensitivity analyses were conducted to investigate the heterogeneity of IC change under specific conditions and the robustness of our results. Mediation analysis was performed to identify potential factors mediating the relationship between sleep patterns and IC change.

Both excessive (>10 h) (total, ME: -1.12; 95% CI: -1.61, -0.64; nighttime, ME: -1.44; 95% CI: -2.29, -0.59) and insufficient (<6 h) sleep duration (total, ME: -0.43; 95% CI: -0.68, -0.18; nighttime, ME: -0.50; 95% CI: -0.73, -0.27) negatively impacted IC change. Moderate naps (≤60 min) mitigated the decline in IC change (ME: 0.28; 95% CI: 0.07, 0.49). IC values decreased at the slowest rate when nap time constituted one-seventh of total sleep time. The onset of dyslipidemia partially mediated the association between naps (≤60 min) and IC change (P =  0.02).

These findings suggest that maintaining a healthy sleep pattern of 6-8 h of nighttime or total sleep, along with a post-lunch nap of ≤60 min, helps preserve optimal IC or delay its decline. This is particularly beneficial for cognitive, psychological, and locomotion performance among middle-aged and older adults.

To review the evidence on the relationship between daytime napping and obesity.

There is concern that napping may be harmful to metabolic health. Prospective studies have shown long time daytime napping (> 1 h) is associated with increased diabetes risk which may be partly associated with obesity. Evidence from numerous cross-sectional studies and meta-analyses of cross-sectional studies have shown that long time napping (> 1 h) but not short time napping is associated with increased risk of obesity, and this is seen worldwide. Inference regarding the nature of association from cross-sectional studies is limited; it is suggested the association is bidirectional. Prospective studies on the association between daytime napping and obesity are few and results unclear. Large longitudinal studies integrating daytime napping duration and night-time sleep behaviour and detailed information on lifestyle influences is needed to help elucidate further the associations of long time napping with obesity.

The aim of the study is to estimate the prevalence and associated factors of insomnia among older adults in the Philippines.

In all, 5206 cross-sectional nationally representative data from older adults (≥ 60 years) of the 2018 Longitudinal Study on Ageing and Health in the Philippines (LSAHP) was analysed. Napping frequency and duration were assessed by self-report.

The prevalence of regular nappers was 35.7%, low or moderate napping (1-59 min) was 10.5% and long napping (≥ 60 min) duration was 25.2%. In the final adjusted model, older age was not significantly associated with low or moderate napping duration but older age was positively associated with long napping duration. High wealth status, physical activity and late insomnia were positively associated with low or moderate napping duration. High wealth status, urban residence, daily activity limitations, and physical activity were positively associated, and currently working status, poor self-rated health status and current alcohol use were negatively associated with long napping duration.

One in four older adults reported long napping duration. Sociodemographic, health status and behaviour and sleep parameters were associated with low or moderate and/or long napping duration.

Gastric cancer is a major cause of morbidity and mortality in Japan and worldwide. Emerging literature has suggested unfavorable health outcomes associated with daytime napping. Herein, we aimed to investigate the association between daytime napping and the risk of gastric cancer among Japanese people.

This prospective cohort study included 49,037 participants, aged 40-79 years, from the Japan Collaborative Cohort Study (JACC Study). Participants with positive cancer history and those who reported night or rotational shift work were excluded. Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) of incident gastric cancer among daytime nappers.

Within 650,040 person-years (median = 13.7 years) of follow-up, 1,164 participants developed gastric cancer. Daytime napping was associated with the increased risk of gastric cancer in the multivariable-adjusted model: HR (95% CI) = 1.14 (1.01, 1.29). The excess risk did not significantly differ across sexes, age groups (<65 and ≥65 years), and employment status (employed and unemployed) (p-interactions > 0.40). However, sleep duration modified this effect: HRs (95% CIs) = 1.66 (1.23, 2.23) in sleep duration ≤6 h/night versus 1.06 (0.93, 1.21) in sleep duration >6 h/night (p-interaction = 0.006).

Daytime napping was associated with increased gastric cancer risk, especially among those who reported short sleep duration.

Previous observational studies have suggested an association between sleep disturbance and metabolic syndrome (MetS). However, it remains unclear whether this association is causal. This study aims to investigate the causal effects of sleep-related traits on MetS using Mendelian randomization (MR).

Single-nucleotide polymorphisms strongly associated with daytime napping, insomnia, chronotype, short sleep, and long sleep were selected as genetic instruments from the corresponding genome-wide association studies (GWAS). Summary-level data for MetS were obtained from two independent GWAS datasets. Univariable and multivariable MR analyses were conducted to investigate and verify the causal effects of sleep traits on MetS.

The univariable MR analysis demonstrated that genetically predicted daytime napping and insomnia were associated with increased risk of MetS in both discovery dataset (OR daytime napping = 1.630, 95% CI 1.273, 2.086; OR insomnia = 1.155, 95% CI 1.108, 1.204) and replication dataset (OR daytime napping = 1.325, 95% CI 1.131, 1.551; OR insomnia = 1.072, 95% CI 1.046, 1.099). For components, daytime napping was positively associated with triglycerides (beta = 0.383, 95% CI 0.160, 0.607) and waist circumference (beta = 0.383, 95% CI 0.184, 0.583). Insomnia was positively associated with hypertension (OR = 1.101, 95% CI 1.042, 1.162) and waist circumference (beta = 0.067, 95% CI 0.031, 0.104). The multivariable MR analysis indicated that the adverse effect of daytime napping and insomnia on MetS persisted after adjusting for BMI, smoking, drinking, and another sleep trait.

Our study supported daytime napping and insomnia were potential causal factors for MetS characterized by central obesity, hypertension, or elevated triglycerides.

Sleep and circadian rhythm disruptions are common in patients with mood disorders. The intricate relationship between these disruptions and mood has been investigated, but their causal dynamics remain unknown.

We analysed data from 139 patients (76 female, mean age = 23.5 ± 3.64 years) with mood disorders who participated in a prospective observational study in South Korea. The patients wore wearable devices to monitor sleep and engaged in smartphone-delivered ecological momentary assessment of mood symptoms. Using a mathematical model, we estimated their daily circadian phase based on sleep data. Subsequently, we obtained daily time series for sleep/circadian phase estimates and mood symptoms spanning >40,000 days. We analysed the causal relationship between the time series using transfer entropy, a non-linear causal inference method.

The transfer entropy analysis suggested causality from circadian phase disturbance to mood symptoms in both patients with MDD (n = 45) and BD type I (n = 35), as 66.7% and 85.7% of the patients with a large dataset (>600 days) showed causality, but not in patients with BD type II (n = 59). Surprisingly, no causal relationship was suggested between sleep phase disturbances and mood symptoms.

Our findings suggest that in patients with mood disorders, circadian phase disturbances directly precede mood symptoms. This underscores the potential of targeting circadian rhythms in digital medicine, such as sleep or light exposure interventions, to restore circadian phase and thereby manage mood disorders effectively.

Institute for Basic Science, the Human Frontiers Science Program Organization, the National Research Foundation of Korea, and the Ministry of Health & Welfare of South Korea.

The obesity rate of adolescents is gradually increasing, which seriously affects their mental health, and sleep plays an important role in adolescent obesity.

To investigate the relationship between sleep rhythm and obesity among adolescents and further explores the interactive effect of sleep rhythm and gender on adolescent obesity, providing a theoretical basis for developing interventions for adolescent obesity.

Research data source Tianjin Mental Health Promotion Program for Students. From April to June 2022, this study selected 14201 students from 13 middle schools in a certain district of Tianjin as the research subject using the convenient cluster sampling method. Among these students, 13374 accepted and completed the survey, with an effective rate of 94.2%.The demographic data and basic information of adolescents, such as height and weight, were collected through a general situation questionnaire. The sleep rhythm of adolescents was evaluated using the reduced version of the morningness-eveningness questionnaire.

A total of 13374 participants (6629 females, accounting for 49.56%; the average age is 15.21 ± 1.433 years) were analyzed. Among them, the survey showed that 2942 adolescent were obesity, accounting for 22% and 2104 adolescent were overweight, accounting for 15.7%. Among them, 1692 male adolescents are obese, with an obesity rate of 25.1%, higher than 18.9% of female adolescents. There is a statistically significant difference between the three groups (χ2 = 231.522, P < 0.000). The obesity group has the smallest age (14.94 ± 1.442 years), and there is a statistical difference in age among the three groups (F = 69.996, P < 0.000).Obesity rates are higher among individuals who are not-only-child, have residential experience within six months, have family economic poverty, and have evening-type sleep (P < 0.05). Logistic regression analysis shows a correlation between sleep rhythm and adolescent obesity. Evening-type sleep rhythm can increase the risk of obesity in male adolescents [1.250 (1.067-1.468)], but the effect on female obesity is not remarkable. Further logistic regression analysis in the overall population demonstrates that the interaction between evening-type sleep rhythm and the male gender poses a risk of adolescent obesity [1.122 (1.043-1.208)].

Among adolescents, the incidence of obesity in males is higher than in females. Evening-type sleep rhythm plays an important role in male obesity but has no significant effect on female obesity. Progressive analysis suggests an interactive effect of sleep rhythm and gender on adolescent obesity, and the combination of evening-type sleep and the male gender promotes the development of adolescent obesity. In formulating precautions against adolescent obesity, obesity in male adolescents with evening-type sleep should be a critical concern.

To examine the association of sleep duration and timing with BMI among adults. Also, to identify obesogenic and unhealthy behaviors (e.g.diet/sleep quality, physical activity, screen time, smoking) associated with short sleep duration and late bedtime.

Participants (n=755) were part of exploratory, population-based research, with data collection in a virtual environment.

For purposes of characterizing the population we considered short sleepers<7h/night, and the population bedtime median was used to stratify participants into early and late sleepers (before and after 23:08). Student's t-test and chi-square test were performed to assess differences in characteristics between groups. Linear regression analyses were conducted to determine the association of sleep duration, bedtime, and wake-up time with BMI. Quantile regression was estimated for the 25th, 50th, and 75th quantiles to identify the distributional correlations between BMI and sleep variables. Restricted cubic splines were also used to study the shape of the association between sleep-BMI. Analyses were adjusted for potential confounding variables.

BMI decreased by 0.40Kg/m2 for each additional hour of sleep duration [95%CI=-0.68,-0.12,p=0.005] and increased by 0.37 kg/m2 for each additional hour of bedtime [95%CI=0.12,0.61,p=0.003]. The association between bedtime and BMI remained even after adjustment for sleep duration. These effects were higher and stronger with higher BMI values (p75th). Wake-up time did not show statistically significant associations.

Because we found that beyond sleep duration, bedtime was significantly associated with BMI, our data reflect the pertinence of assessing sleep timing patterns in disentangling sleep-obesity association. Insights into the characteristics, obesogenic and unhealthy behaviors related to short and late sleep may support specific strategies to prevent and treat excess body adiposity and other negative health outcomes.

Obstructive sleep apnea (OSA) and poor sleep quality are highly prevalent in children with obesity, but their individual associations with health-related quality of life (HRQOL) are unknown in this population. The primary objective was to describe the independent association of OSA and sleep quality with HRQOL in children with obesity.

This was a cross-sectional study of children with obesity at 2 tertiary care centers. Sleep quality and HRQOL were measured with the Pittsburgh Sleep Quality Index and Pediatric Quality of Life Inventory questionnaires, respectively. Multivariable regression models were created to evaluate associations between OSA and sleep quality with HRQOL.

There were 98 children (median age 15.0 years, median body mass index z-score 3.8, 44% females). Among the study population, 49/98 (50%) children reported poor sleep quality, 41/98 (42%) children had OSA, and 52/98 (53%) children reported impaired HRQOL. Self-reported poor sleep quality was independently associated with reduced HRQOL, whereas the presence of OSA was not. Children with poor sleep quality had a reduced Pediatric Quality of Life Inventory score by 8.8 compared to children with good sleep quality (95% confidence interval, 2.6-14.9; P = .006), when adjusting for age, sex, body mass index z-score, attention-deficit/hyperactivity disorder, mood/anxiety disorder, and study site.

In the current study of children with obesity, we found that HRQOL was more strongly associated with the self-reported experience of sleep than the presence of OSA. Clinicians should assess and optimize sleep quality as part of the evaluation for OSA in children with obesity.

Xiao L, Voutsas G, Ryan CM, Katz SL, Narang I. The association between sleep quality and obstructive sleep apnea with health-related quality of life in children with obesity. J Clin Sleep Med. 2023;19(11):1877-1883.

Despite extensive research on the individual effects of breakfast and sleep on health outcomes, there has been limited investigation into their combined effects. We aimed to evaluate the relationship between breakfast-eating behavior and sleep timing on cardiovascular disease (CVD) risk factors. A total of 16,121 participants (6744 men and 9377 women) aged 19 years or older were selected from the Korea National Health and Nutrition Examination Surveys (2016-2018, 2021). We classified participants into four groups: early sleep + regular breakfast eaters (group 1), late sleep + regular breakfast eaters (group 2), early sleep + infrequent breakfast eaters (group 3), and late sleep + infrequent breakfast eaters (group 4). In men, group 4 had a lower prevalence of obesity than group 1 (OR 0.78, 95%CI 0.62-0.97), and groups 2, 3, and 4 had a higher prevalence of metabolic syndrome (MetS) than group 1 (OR 1.43, 1.62, and 1.47, respectively). In women, group 4 had a lower prevalence of dyslipidemia than group 1 (OR 0.59, 95%CI 0.44-0.80), and group 2 had a higher prevalence of MetS than group 1 (OR 1.24, 95%CI 1.03-1.50). The combination of skipping breakfast and late sleep timing was associated with the higher prevalence of MetS particularly in men. Moreover, the relationship between breakfast and sleep timing on CVD risk factors differed by sex and age group.

To examine cross-sectional and longitudinal associations of individual sleep domains and multidimensional sleep health with current overweight or obesity and 5-year weight change in adults.

We estimated sleep regularity, quality, timing, onset latency, sleep interruptions, duration, and napping using validated questionnaires. We calculated multidimensional sleep health using a composite score (total number of "good" sleep health indicators) and sleep phenotypes derived from latent class analysis. Logistic regression was used to examine associations between sleep and overweight or obesity. Multinomial regression was used to examine associations between sleep and weight change (gain, loss, or maintenance) over a median of 1.66 years.

The sample included 1016 participants with a median age of 52 (IQR = 37-65), who primarily identified as female (78%), White (79%), and college-educated (74%). We identified 3 phenotypes: good, moderate, and poor sleep. More regularity of sleep, sleep quality, and shorter sleep onset latency were associated with 37%, 38%, and 45% lower odds of overweight or obesity, respectively. The addition of each good sleep health dimension was associated with 16% lower adjusted odds of having overweight or obesity. The adjusted odds of overweight or obesity were similar between sleep phenotypes. Sleep, individual or multidimensional sleep health, was not associated with weight change.

Multidimensional sleep health showed cross-sectional, but not longitudinal, associations with overweight or obesity. Future research should advance our understanding of how to assess multidimensional sleep health to understand the relationship between all aspects of sleep health and weight over time.

Sleep duration is associated with BMI and waist circumference. However, less is known about whether sleep duration affects different measurements of obesity differently.

To investigate the association between sleep duration and different measures of obesity.

In this cross-sectional analysis 1309, Danish, older adults (55% men) completed at least 3 days of wearing a combined accelerometer and heart rate-monitor for assessing sleep duration (hours/night) within self-reported usual bedtime. Participants underwent anthropometry and ultrasonography to assess BMI, waist circumference, visceral fat, subcutaneous fat, and fat percentage. Linear regression analyses examined the associations between sleep duration and obesity-related outcomes.

Sleep duration was inversely associated with all obesity-related outcomes, except visceral-/subcutaneous-fat-ratio. After multivariate adjustment the magnitude of associations became stronger and statistically significant for all outcomes except visceral-/subcutaneous-fat-ratio, and subcutaneous fat in women. The associations with BMI and waist circumference demonstrated the strongest associations, when comparing standardized regression coefficients.

Shorter sleep duration were associated with higher obesity across all outcomes except visceral-/subcutaneous-fat-ratio. No specifically salient associations with local or central obesity were observed. Results suggest that poor sleep duration and obesity correlate, however, further research is needed to conclude on beneficial effects of sleep duration regarding health and weight loss.

The aim of this study was to determine the association between siestas/no siestas and obesity, considering siesta duration (long: >30 minutes, short: ≤30 minutes), and test whether siesta traits and/or lifestyle factors mediate the association of siestas with obesity and metabolic syndrome (MetS).

This was a cross-sectional study of 3275 adults from a Mediterranean population (the Obesity, Nutrigenetics, TIming, and MEditerranean [ONTIME] study) who had the opportunity of taking siestas because it is culturally embedded.

Thirty-five percent of participants usually took siestas (16% long siestas). Compared with the no-siesta group, long siestas were associated with higher values of BMI, waist circumference, fasting glucose, systolic blood pressure, and diastolic blood pressure, as well as with a higher prevalence of MetS (41%; p = 0.015). In contrast, the probability of having elevated SBP was lower in the short-siesta group (21%; p = 0.044) than in the no-siesta group. Smoking a higher number of cigarettes per day mediated the association of long siestas with higher BMI (by 12%, percentage of association mediated by smoking; p < 0.05). Similarly, delays in nighttime sleep and eating schedules and higher energy intake at lunch (the meal preceding siestas) mediated the association between higher BMI and long siestas by 8%, 4%, and 5% (all p < 0.05). Napping in bed (vs. sofa/armchair) showed a trend to mediate the association between long siestas and higher SBP (by 6%; p = 0.055).

Siesta duration is relevant in obesity/MetS. Timing of nighttime sleep and eating, energy intake at lunch, cigarette smoking, and siesta location mediated this association.

Existing evidence concerning the relationship between daytime napping and type 2 diabetes (T2D) is inconsistent, and whether the effects of napping differ by body fat percentage (BFP) and C-reactive protein (CRP) is unclear. We aimed to investigate the association between daytime napping frequency and T2D risk and whether such an association was modified by BFP and CRP.

We included 435 342 participants free of diabetes from the UK Biobank. Participants were categorized as nonnappers, occasional nappers, and frequent nappers based on napping frequency, and BFP/CRP was divided into quartiles. Cox proportional hazards models were used.

During a median follow-up of 9.2 years, 17 592 T2D cases occurred. Higher frequency of daytime napping was significantly associated with an increased risk of T2D. Compared with nonnappers, the adjusted hazard ratios (HRs) for occasional nappers and habitual nappers were 1.28 (95% confidence interval [CI]: 1.24-1.32) and 1.49 (95% CI: 1.41-1.57), respectively. There was a significant additive and multiplicative interaction (relative excess risk due to interaction [RERI] = 0.490, 95% CI 0.307-0.673; p for multiplicative interaction <.001) between napping and BFP, whereby a higher hazard of T2D associated with more frequent napping was greatest among participants in the highest BFP quartile (HR = 4.45, 95% CI: 3.92-5.06). The results for CRP were similar (RERI = 0.266, 95% CI: 0.094-0.439; p for multiplicative interaction <.001).

Higher daytime napping frequency is associated with an increased T2D risk, and such relationships are modified by BFP and CRP. These findings underscore the importance of adiposity and inflammation control to mitigate diabetes risk.

Sleep dysregulation has been linked to gastrointestinal dysfunction and inflammation.

To explore the associations between sleep duration, daytime napping and inflammatory bowel disease (IBD), Crohn's disease (CD) and ulcerative colitis (UC).

Exposure information was obtained from the baseline questionnaire. Sleep duration was coded as continuous and categorical (≤5, 6, 7, 8, ≥9 h/day) variables. Daytime napping was defined as yes (sometimes/usually) and no (never/rarely). Incident IBD cases were defined from primary care and hospital inpatient records. Polygenic risk scores (PRS) for the outcomes were constructed and categorised into low, intermediate and high risk. Hazard ratio (HR) and confidence interval (CI) were estimated using Cox proportional hazard regression.

The analysis included 2604 incident IBD cases (806 CD and 1798 UC) with a median follow-up of 12.0 years. Comparing sleep duration ≤5 with 7 h/day, the HR of IBD, CD and UC was 1.36 (95% CI, 1.17-1.59), 1.53 (95% CI, 1.17-2.00) and 1.29 (95% CI, 1.07-1.56), respectively. Comparing participants with and without daytime napping, the HR of IBD, CD and UC was 1.13 (95% CI, 1.05-1.23), 1.25 (95% CI, 1.08-1.44) and 1.09 (95% CI, 0.90-1.20), respectively. No interaction of sleep duration and daytime napping with PRS was detected.  However, the associations appeared stronger in individuals with high rather than low PRS.

This study reveals positive associations between short sleep duration and daytime napping and IBD risk.

Sleep duration and daytime napping and obesity are related to adiposity; however, it is not clear whether the association between daytime napping and adiposity measures can differ by sleep duration.

To clarify the association between daytime napping and general and abdominal obesity based on sleep duration of participants.

This cross-sectional study was conducted on 1,683 individuals (837 men and 846 women) aged ≥ 35 years. Height, weight and waist circumference (WC) were measured according to the standard protocols. Body mass index (BMI) was calculated. Self-reported sleep duration (in a 24-hour cycle) was recorded. The odds of general and abdominal obesity were compared between nappers and non-nappers, stratified by their sleep duration (≤ 6 h, 6-8 h, ≥ 8 h).

The mean (SD) age of participants was 47.48 ± 9.35 years. Nappers with a short sleep duration (≤ 6 h) had greater BMI and higher risk for overweight/obesity compared with counterpart non-nappers after adjustment for potential confounders (OR = 1.61, 95% CI = 1.07-2.41). In subjects with moderate sleep duration (6-8 h), nappers had a tendency towards higher BMI in comparison with non-nappers (28.04 ± 0.25 vs. 26.93 ± 0.51 kg/m²; p = 0.05), however, no significant difference was observed for the risk of obesity. Daytime napping was not related to the risk of obesity in long sleepers. No significant association was observed for abdominal obesity measures.

Daytime napping is associated with increased risk of overweight/obesity in short sleepers. However, in subjects with longer sleep duration, it is not related to the risk of overweight/obesity.

Research on the relationship between sleep duration and obesity defined using multiple anthropometric and bioelectrical indices in women remains scarce. We aimed to explore the association between sleep duration and body mass index (BMI), waist-hip ratio (WHR), body fat percentage (PBF) and visceral fat area (VFA) among females.

We recruited women for medical examination using multistage cluster sampling. Sleep was assessed using Pittsburgh Sleep Quality Index (PSQI) and sleep duration was categorized into short (<7 h), optimal (7 <9 h) and long sleep (≥ 9 h). Weight and height were measured using a calibrated stadiometer. Waist circumference was manually measured. PBF, and VFA were estimated by bioelectrical impedance analysis. Data on sociodemographic characteristics and lifestyle factors were also collected and included in the logistic regression models to explore the independent association between sleep duration and obesity defined by different indices.

A total of 7,763 women with a mean age of 42.6 ± 13.5 years were included. The percentage of women reporting short and long sleep was 10.3 and 13.4% respectively. The mean BMI, WHR, PBF and VFA were 23.07 ± 3.30 kg/m², 0.78 ± 0.06, 32.23 ± 6.08% and 91.64 ± 35.97cm², respectively. Short sleep was independently associated with 35% (95% CI: 1.05-1.75) increased odds of general obesity (BMI ≥ 28 kg/cm²), and long sleep was associated with 18% (95% CI: 1.01-1.37) increased odds of visceral obesity (VFA > 100 cm²). No association was observed between sleep deprivation or excessive sleep and high WHR or high PBF.

In women, short sleep was associated with an increased odds of general obesity, whereas long sleep was associated with an increased odds of visceral obesity. Longitudinal observations are needed to confirm this cross-sectional relationship.

Blood pressure variability (BPV) has been reported to be a predictor of cardiovascular and some cognitive diseases. However, the association between napping and BPV remains unknown. This study aimed to explore the association between napping and BPV.

A cross-sectional study including 105 university students was conducted. Participants' 24 h ambulatory blood pressure monitoring (24 h ABPM) were measured, and napping behaviors were investigated. BPV were measured by the 24 h ABPM, included standard deviation (SD), coefficient of variation (CV), and average real variability (ARV).

Among the participants, 61.9% reported daytime napping. We found that nap duration was significantly associated with daytime CV of diastolic blood pressure (DBP) (r = 0.250, P = 0.010), nighttime CV of systolic blood pressure (SBP) (r = 0.217, P = 0.026), 24 h WCV of DBP (r = 0.238, P = 0.014), 24 h ARV of SBP (r = 0.246, P = 0.011) and 24 h ARV of DBP (r = 0.291, P = 0.003). Compared with the no napping group, 24 h WCV of DBP, daytime CV of DBP, and daytime SD of DBP were significantly higher in participants with napping duration >60 min. With multiple regression analysis we found that nap duration was an independent predictor for 24 h ARV of SBP (β = 0.859, 95% CI, 0.101-1.616, P = 0.027) and 24 h ARV of DBP (β = 0.674, 95% CI, 0.173-1.175, P = 0.009).

Napping durations are associated with BPV among university students. Especially those with napping durations >60 min had a significantly higher BPV than those non-nappers.

Accumulating evidence implies that sleep disturbance is involved in metabolic disorders.

We comprehensively evaluated the association between various dimensions of sleep behaviors and the risk for metabolic dysfunction-associated fatty liver disease (MAFLD).

In this cross-sectional study of 5011 participants with self-reported sleep behaviors and radiologically diagnosed MAFLD, a comprehensive healthy sleep score was generated to evaluate the associations between sleep behaviors and MAFLD risk using multivariate logistic regression adjusting for demographics, lifestyles, medication, and metabolic comorbidities. Furthermore, mediation analysis was utilized to assess the extent to which obesity explains the effect of sleep quality on MAFLD risk.

Late bedtime, snoring, and daytime napping for over 30 minutes significantly associated with an increased risk of MAFLD, with odds ratios (OR) of 1.37 (95% CI 1.10, 1.70), 1.59 (95% CI 1.33, 1.91), and 1.17 (95% CI 1.02, 1.35), respectively, after full adjustments including obesity. Participants with disturbance in nighttime sleep and prolonged daytime napping showed the highest risk for MAFLD (OR 2.38, 95% CI 1.73, 3.27). Each additional increase of healthy sleep score was associated with a 16% reduction in MAFLD risk. Further stratified analysis revealed that people with a sedentary lifestyle and central obesity experienced more prominent adverse effects from poor sleep quality than others. Moreover, obesity accounted for only 20.77% of the total effect of sleep quality on MAFLD risk.

Sleep behaviors, both cumulatively and individually, are associated with MAFLD risk. Public health awareness and strategies should be encouraged to curb MAFLD.

The study aimed to explore the association between sleep duration, insomnia symptoms and the components of metabolic syndrome (MetS) among middle-aged and older adults. A cross-sectional study was conducted in five community health centers and physical check-up centers of two comprehensive hospitals in Guangdong. We recruited 1252 participants (658 female), aged 40-96 years and with a body mass index (BMI) of 16.26-35.56 kg/m2. MetS was assessed based on the guidelines of the International Diabetes Federation. Self-reported sleep duration was evaluated by a simplified questionnaire. Compared with the participants who slept 6-8 h/day, those who slept shorter (<6 h/day) or longer (>8 h/day) periods of time with or without insomnia symptoms had significantly increased odds ratios (ORs) of high blood pressure (except for the SBP in model 2) and high triglycerides (TGs) in all models (p < 0.05), whereas the participants who slept longer (>8 h/day) or shorter (<6 h/day) periods of time with insomnia symptoms had significantly increased ORs of low HDL-C in all models (p < 0.05), but non-significant in those without insomnia symptoms. BMI is significant for insomnia symptoms but not for sleep duration. Our study indicated that the association of sleep duration with MetS components was partially associated with insomnia symptoms. These findings have significant implications to explore the appropriate sleep duration for adults.

Associations between night sleep duration and cardiovascular risk factors in adults have been well documented. However, the associations for daytime napping remain unclear. In this review, six databases were searched for eligible publications to April 8, 2022. A total of 11 articles were identified for umbrella review on the association of daytime napping with diabetes, metabolic syndrome (MetS), cardiovascular disease (CVD), and mortality in adults, 97 for systematic review on the association with CVD and several CVD risk factors. Our umbrella review showed that the associations of daytime napping with diabetes, MetS, CVD, and mortality in most meta-analyses were mainly supported by weak or suggestive evidence. Our systematic review showed that long daytime napping (≥1 h/d) was associated with higher odds of several CVD risk factors, CVD, and mortality, but no significant association was found between short daytime napping and most of the abovementioned outcomes. Our dose-response meta-analyses showed that daytime napping <30 min/d was not significantly associated with higher odds of most CVD risk factors and CVD among young and middle-aged adults. However, among older adults aged >60 years, we observed significant dose-response associations of daytime napping with higher odds of diabetes, dyslipidemia, MetS, and mortality starting from 0 min/d.

Despite cumulative evidence showing obesity is associated with changes in sleep quality and quantity, the study about the relationships between sleep and body composition is scarce, and whether the relationship is causal remains unknown. In this study, we examined whether there are causal associations between sleep and body composition.

First, we estimated genetic correlations between sleep-related phenotypes and body composition using the linkage disequilibrium score regression (LDSC). Mendelian randomization (MR) analysis was then conducted to test 2-way causal relationships on phenotypes with significant genetic associations. Finally, Bayesian colocalization (COLOC) analysis was performed to calculate the posterior probability of causal variation and identify the common genes to verify the results of MR.

For the LDSC analysis, we observed some significant genetic correlations (rG), such as snoring and right leg fat mass (rG = 0.376, P = 7.21 × 10-80). For the MR analysis, we identified some significant causal relationships, such as snoring is the causal risk factor for whole-body fat-free mass (Pweighted median = 1.28 × 10-6, PMR-PRESSO = 1.35 × 10-7), dozing is the causal risk factor for right leg fat mass (Pweighted median = 9.22 × 10-4, PMR-PRESSO = 9.55 × 10-4), and right arm fat mass (Pweighted median = 1.11 × 10-40, PMR-PRESSO = 4.93 × 10-55) is the causal risk factor for snoring. For the COLOC analysis, we identified rs143384 mapping on GDF5 and 6 overlapped single nucleotide polymorphisms (eg, rs1421085, rs11642015) mapping on FTO.

Our study identified the causal relationships between sleep-related phenotypes and body composition. These findings may give insights into the mechanism of sleep disturbances and provide novel therapeutic targets.

We aimed to evaluate the association between sleep duration and obesity and estimate the attributable risk of sleep in the China Health and Retirement Longitudinal Study surveys (CHARLS).

A total of 9061 adults aged 45 years above from CHARLS (2011-2015) were included. The main outcome of this study was incident overweight/obesity specific to general and abdominal obesity. Overweight/obesity was defined as BMI ≥ 23.0 (kg/m2) and BMI≥27.5 (kg/m2), respectively. Abdominal obesity was defined as waist circumference ≥90 cm for men and ≥80 cm for women. Self-reported sleep durations were obtained using a structured questionnaire. We assessed hazard ratios (HRs) and 95% confidence intervals (CIs) as well as the population attributable fraction (PAF) for associations between sleep duration and obesity.

986 and 606 participants were identified as overweight and general obesity respectively, 1253 experienced abdominal obesity events. In multivariable-adjusted models, participants with daytime sleep of 0-30 minutes and more than 30 minutes associated with 30% (HR: 0.70, 95% CI: 0.56-0.87) and 35% (HR: 0.65, 95% CI: 0.55-0.78) decreased incident overweight risk respectively compared to those having no daytime sleep, this association also found among females and middle-aged, not males or elderly. Similarly, 0-30 minutes (HR: 0.68, 95% CI: 0.48-0.96) and more than 30 minutes of daytime sleep (HR: 0.73, 95% CI: 0.59-0.91) were associated with a reduced risk of abdominal obesity, similar results also found among females and middle-aged. Compared with 7-9 hours of nocturnal sleep, people who slept 5-7 hours had a reduced risk of overweight (HR: 0.59, 95% CI=0.47-0.74),but not found in subgroups. The hazard role of long nocturnal sleep for abdominal obesity was only found among elderly (HR: 2.33, 95% CI=1.35-4.04) and males (HR: 2.24, 95% CI=1.17-4.29). Compared with moderate total sleepers (7-9hours/day), participants with short total sleep duration exhibited an elevated risk of overweight (HR: 1.13, 95% CI=1.00-1.28), this also found among middle-aged. The PAF for inadequate total sleep duration (<7 hours/day) was 10.77% for overweight individuals.

Insufficient sleep duration was associated with an elevated risk of overweight only detected among middle-aged not elderly and other subgroups specific by age. The risks for abdominal obesity were increased for males and middle-aged with long nocturnal sleep. Daytime sleep may significantly reduce the risk of overweight and abdominal obesity in female and middle-aged individuals. The link between sleep duration and obesity requires further study.