Sleep duration and timing are prospectively linked with insulin resistance during late adolescence

The optimal measurement period of actigraphy for circadian rhythm in relation to adiposity: A retrospective case-control study

BACKGROUND

This study focused on the relationship between adiposity and Rest-Activity Rhythms (RAR), utilizing both parametric cosine-based models and non-parametric algorithms. The emphasis was on the impact of varying measurement periods (7-28 days) on this relationship.

METHODS

We retrieved actigraphy data from two datasets, encompassing a diverse cohort recruited from an obesity outpatient clinic and a workplace health promotion program. Participants were required to wear a research-grade wrist actigraphy device continuously for a minimum of four weeks. The final dataset included 115 individuals (mean age 40.7 ± 9.5 years, 51 % female). We employed both parametric and non-parametric methods to quantify RAR using six standard variables. Additionally, the study evaluated the correlations between three key adiposity indices - Body Mass Index (BMI), Visceral Adipose Tissue (VAT) area, and Body Fat Percentage (BF%) - and circadian rhythm indicators, controlling for factors like physical activity, age, and gender.

RESULTS

The obesity group displayed a significantly lower relative amplitude (RA) as per non-parametric algorithm findings, with a decreased amplitude noted in the parametric algorithm analysis, in comparison to the overweight and control groups. The relationship between circadian rhythm indicators and adiposity metrics over 7- to 28-day periods was examined. A notable negative correlation was observed between RA and both BMI and VAT, while correlation coefficients between adiposity indicators and non-parametric circadian parameters increased with extended durations of actigraphy data. Specifically, RA over a 28-day period was significantly correlated with BF%, a trend not seen in the 7-day measurement (p = 0.094) in multivariate linear regression. The strength of the correlation between BF% and 28-day RA was more pronounced than that in the 7-day period (p = 0.044). However, replacing RA with amplitude as per parametric cosinor fitting yielded no significant correlations for any of the measurement periods.

CONCLUSION

The study concludes that a 28-day measurement period more effectively captures the link between disrupted circadian rhythms and adiposity. Non-parametric algorithms, in particular, were more effective in characterizing disrupted circadian rhythms, especially when extending the measurement period beyond the standard 7 days.

Circadian desynchrony and glucose metabolism

The circadian timing system controls glucose metabolism in a time-of-day dependent manner. In mammals, the circadian timing system consists of the main central clock in the bilateral suprachiasmatic nucleus (SCN) of the anterior hypothalamus and subordinate clocks in peripheral tissues. The oscillations produced by these different clocks with a period of approximately 24-h are generated by the transcriptional-translational feedback loops of a set of core clock genes. Glucose homeostasis is one of the daily rhythms controlled by this circadian timing system. The central pacemaker in the SCN controls glucose homeostasis through its neural projections to hypothalamic hubs that are in control of feeding behavior and energy metabolism. Using hormones such as adrenal glucocorticoids and melatonin and the autonomic nervous system, the SCN modulates critical processes such as glucose production and insulin sensitivity. Peripheral clocks in tissues, such as the liver, muscle, and adipose tissue serve to enhance and sustain these SCN signals. In the optimal situation all these clocks are synchronized and aligned with behavior and the environmental light/dark cycle. A negative impact on glucose metabolism becomes apparent when the internal timing system becomes disturbed, also known as circadian desynchrony or circadian misalignment. Circadian desynchrony may occur at several levels, as the mistiming of light exposure or sleep will especially affect the central clock, whereas mistiming of food intake or physical activity will especially involve the peripheral clocks. In this review, we will summarize the literature investigating the impact of circadian desynchrony on glucose metabolism and how it may result in the development of insulin resistance. In addition, we will discuss potential strategies aimed at reinstating circadian synchrony to improve insulin sensitivity and contribute to the prevention of type 2 diabetes.

Mechanisms and risk factors of metabolic syndrome in children and adolescents

Metabolic syndrome (MetS) is a complex disorder characterized by abdominal obesity, elevated blood pressure, hyperlipidemia, and elevated fasting blood glucose levels. The diagnostic criteria for MetS in adults are well-established, but there is currently no consensus on the definition in children and adolescents. The etiology of MetS is believed to involve a complex interplay between genetic predisposition and environmental factors. While genetic predisposition explains only a small part of MetS pathogenesis, modifiable environmental risk factors play a significant role. Factors such as maternal weight during pregnancy, children's lifestyle, sedentariness, high-fat diet, fructose and branched-chain amino acid consumption, vitamin D deficiency, and sleep disturbances contribute to the development of MetS. Early identification and treatment of MetS in children and adolescents is crucial to prevent the development of chronic diseases later in life. In this review we discuss the latest research on factors contributing to the pathogenesis of MetS in children, focusing on non-modifiable and modifiable risk factors, including genetics, dysbiosis and chronic low-grade inflammation.

Association between late bedtime and obesity in children and adolescents: a meta-analysis

Background

Short sleep duration has been related to obesity in children and adolescents. However, it remains unknown whether late bedtime is also associated with obesity and whether the association is independent of sleep duration. A meta-analysis was performed to address this issue.

Methods

In order to accomplish the aim of the meta-analysis, a comprehensive search was conducted on databases including PubMed, Embase, and Web of Science to identify observational studies. The cutoff to determine late bedtime in children in this meta-analysis was consistent with the value used among the included original studies. As for obesity, it was typically defined as a body mass index (BMI) > 95th percentile of age and sex specified reference standards or the International Obesity Task Force defined age- and gender-specific cut-off of BMI. The Cochrane Q test was employed to evaluate heterogeneity among the included studies, while the I2 statistic was estimated. Random-effects models were utilized to merge the results, considering the potential impact of heterogeneity.

Results

Tweleve observational studies with 57,728 participants were included. Among them, 6,815 (11.8%) were obese. Pooled results showed that late bedtime reported by the participants or their caregivers was associated with obesity (odds ratio [OR]: 1.27, 95% confidence interval [CI]: 1.16-1.39, p < 0.001; I2 = 0%). Subgroup analysis showed consistent results in studies with (OR: 1.33, 95% CI: 1.04-1.70, p = 0.02) and without adjustment of sleep duration (OR: 1.27, 95% CI: 1.14-1.41, p < 0.001). Further subgroup analysis also showed that the association was not significantly affected by study location, design, age of the participants, or diagnostic methods for obesity (p for subgroup difference all >0.05).

Conclusion

Late bedtime is associated with obesity in children and adolescents, which may be independent of sleep duration.

Accelerometry-assessed sleep clusters and cardiometabolic risk factors in adolescents

OBJECTIVE

This study aimed to identify sleep clusters based on objective multidimensional sleep characteristics and test their associations with adolescent cardiometabolic health.

METHODS

The authors included 1090 participants aged 14.3 to 16.4 years (mean = 15.2 years) who wore 7-day accelerometers during the 15-year follow-up of the German Infant Study on the influence of Nutrition Intervention PLUS environmental and genetic influences on allergy development (GINIplus) and the Influence of Lifestyle factors on the development of the Immune System and Allergies in East and West Germany (LISA) birth cohorts. K-means cluster analysis was performed across 12 sleep characteristics reflecting sleep quantity, quality, schedule, variability, and regularity. Cardiometabolic risk factors included fat mass index (FMI), blood pressure, triglycerides, high-density lipoprotein cholesterol, high-sensitivity C-reactive protein, and insulin resistance (n = 505). Linear and logistic regression models were examined.

RESULTS

Five sleep clusters were identified: good sleep (n = 337); delayed sleep phase (n = 244); sleep irregularity and variability (n = 108); fragmented sleep (n = 313); and prolonged sleep latency (n = 88). The "prolonged sleep latency" cluster was associated with increased sex-scaled FMI (β = 0.39, 95% CI: 0.15-0.62) compared with the "good sleep" cluster. The "sleep irregularity and variability" cluster was associated with increased odds of high triglycerides only in male individuals (odds ratio: 9.50, 95% CI: 3.22-28.07), but this finding was not confirmed in linear models.

CONCLUSIONS

The prolonged sleep latency cluster was associated with higher FMI in adolescents, whereas the sleep irregularity and variability cluster was specifically linked to elevated triglycerides (≥1.7 mmol/L) in male individuals.