Changes in sleep duration and sleep difficulties from adolescence to young adulthood and the risk of obesity: Bidirectional evidence in the GINIplus and LISA studies

Accelerometry-assessed sleep clusters and obesity in adolescents and young adults: a longitudinal analysis in GINIplus/LISA birth cohorts

BACKGROUND

Some studies have revealed various sleep patterns in adolescents and adults using multidimensional objective sleep parameters. However, it remains unknown whether these patterns are consistent from adolescence to young adulthood and how they relate to long-term obesity.

METHODS

Seven-day accelerometry was conducted in German Infant Study on the influence of Nutrition Intervention PLUS environmental and genetic influences on allergy development (GINIplus) and Influence of Lifestyle factors on the development of the Immune System and Allergies in East and West Germany (LISA) birth cohorts during the 15-year and 20-year follow-ups, respectively. Five sleep clusters were identified by k-means cluster analysis using 12 sleep characteristics at each follow-up. Adjusted linear and logistic regression models using generalized estimating equations were examined. Further, the interaction effects with time of follow-ups and polygenic risk scores (PRS) for body mass index (BMI) were tested.

RESULTS

Five sleep clusters were classified consistently in both adolescence (n = 1347, aged 14.3-16.4 years) and young adulthood (n = 1262, aged 19.5-22.4 years). Adolescents in the "good sleep", "delayed sleep phase", and "fragmented sleep" clusters displayed greater stability transitioning into young adulthood, while those in the "sleep irregularity and variability", and "prolonged sleep latency" clusters showed lower stability (n = 636). Compared to the "good sleep" cluster, the "prolonged sleep latency" cluster exhibited associations with higher BMI [β = 0.56, 95% confidence interval (CI) = (0.06, 1.05)] and increased odds of overweight/obesity [Odds ratio = 1.55, 95% CI = (1.02, 2.34)]. No significant PRS-sleep cluster interaction was found for BMI or overweight/obesity. Among males only, the "delayed sleep phase", "sleep irregularity and variability" and "fragmented sleep" clusters showed stronger associations with overweight/obesity as age increased.

CONCLUSION

Adolescents and young adults shared five consistent sleep patterns, with the "prolonged sleep latency" pattern linked to higher BMI and overweight/obesity.

High Body Roundness Index Is Associated With Unhealthy Sleep Patterns: Insights From NHANES (2007-2014)

BACKGROUND

Substantial evidence suggests an association between obesity and sleep. However, research investigating sleep patterns in relation to novel anthropometric indices is limited. Therefore, we conducted a cross-sectional analysis of data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2014 to examine the relationship between the body roundness index (BRI) and unhealthy sleep patterns.

OBJECTIVE

This study aimed to investigate the association between the BRI and unhealthy sleep patterns among US adults.

METHODS

Data were sourced from NHANES (2007-2014), including respondents aged 20 years and older. Participants were categorized into two groups based on the healthiness of their sleep patterns. The data were weighted, and multiple potential covariates were included in the analysis to provide national estimates and account for the comprehensive sampling design. A multivariable weighted logistic regression model was used, employing restricted cubic spline (RCS) curves to examine potential associations, and subgroup analyses were conducted to determine the stability of the results. Receiver operating characteristic (ROC) analysis was used to compare the diagnostic performance of BRI and body mass index (BMI) in identifying unhealthy sleep patterns.

RESULTS

In the fully adjusted multivariable logistic regression model, the prevalence odds ratio (POR) for the association between BRI and unhealthy sleep patterns was 1.09, with a 95% confidence interval (CI) of 1.07-1.10. The RCS analysis found that the nonlinear association between BRI and unhealthy sleep patterns was not significant. Subgroup and sensitivity analyses indicated a consistently positive association between high BRI and unhealthy sleep patterns across most subgroups. ROC diagnostic tests showed that BRI's effectiveness in diagnosing unhealthy sleep patterns was comparable to that of BMI, and it was not inferior to BMI in assessing certain components of sleep patterns.

CONCLUSION

High BRI is positively associated with unhealthy sleep patterns significantly, indicating that BRI could be a promising metric for evaluating sleep health.

Frequency of Vigorous physical activity and sleep difficulty in adolescents: A multiply-country cross-sectional study

BACKGROUND

Sleep is an essential health behavior, and sleep difficulties are strongly associated with adolescent health, potentially leading to more severe sleep disorders. The beneficial effects of physical activity (PA) in alleviating sleep difficulties have been well-documented. Numerous investigations reveal influence in moderate to high-intensity physical activity (PA) positively influences sleep quality. Despite these findings, a gap in the literature exists, particularly regarding the association between frequency of vigorous-intensity physical activity (VPA) and sleep difficulties.

AIM

This study aims to bridge the knowledge gap by exploring the link between sleep difficulty and frequency of VPA among adolescents. Insights are derived from analyzing data accumulated from the Health Behavior in School-aged Children (HBSC) project.

METHODS

The analysis in this study utilized cross-sectional data from the HBSC (2017/2018). The study sample comprised a total of 171,233 respondents aged 11, 13, and 15 years, with males representing 51.1% of sample. Measurement instruments included a self-administered questionnaire, providing direct insight into sleep difficulty and frequency of VPA levels. Statistical analysis on the associaiton between frequency of VPA and sleep difficulties was conducted using Generalized Linear Models.

RESULTS

50.0% of adolescents reported no sleep difficulties, while 12.3% experienced sleep issues daily. Additionally, 17.1% of adolescents engaged in frequency of VPA on a daily basis, while 6.4% never participated in such activities. daily VPA was associated with fewer sleep difficulties (OR = 1.07 [1.00, 1.15]), 4-6 times a week (OR = 1.08 [1.01, 1.15]), and 2-3 times a week (OR = 1.08 [1.02, 1.16]). However, no significant association was found between sleep difficulties and frequency of VPA in girls. Furthermore, a negative association was observed between sleep difficulties and all frequencies of VPA (p < 0.05) in 11-year-old adolescents. For 13-year-olds, daily VPA was significantly associated with fewer sleep difficulties (OR = 1.10 [1.02, 1.19]), 4-6 times a week (OR = 1.15 [1.07, 1.24]), 2-3 times a week (OR = 1.19 [1.10, 1.27]), and once a week (OR = 1.13 [1.05, 1.22]). However, no significant association was found between sleep difficulties and frequency of VPA in 15-year-old adolescents.

CONCLUSION

More participations in VPA would be an effective approach to reduce sleep difficulties in adolescents. Insights gleaned from this research illustrate a discernible link between sleep difficulty and frequency of VPA, particularly notable in male and 13-year-old participants. It is also imperative to underscore the variability in the connection between sleep difficulty and frequency of VPA, distinctly influenced by factors such as gender and age. Consequently, tailoring sleep intervention methodologies to align with the specific needs dictated by these variables emerges as a pivotal recommendation.

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.

Health behaviours associated with healthy body composition among Aboriginal adolescents in Australia in the 'Next Generation: Youth Well-being study'

This study described the distribution of healthy body composition among Aboriginal adolescents in Australia aged 10-24 years and examined associations with health behaviours and self-rated health. Data were cross-sectional from the 'Next Generation: Youth Well-being study' baseline (N = 1294). We used robust Poisson regression to quantify associations of self-reported health behaviours (physical activity, screen time, sleep, consumption of vegetables, fruit, soft drinks and fast food, and tobacco smoking and alcohol) and self-rated health to healthy body mass index (BMI) and waist/height ratio (WHtR). Overall, 48% of participants had healthy BMI and 64% healthy WHtR, with healthy body composition more common among younger adolescents. Higher physical activity was associated with healthy body composition (5-7 days last week vs none; adjusted prevalence ratio (aPR) healthy BMI 1.31 [95% CI 1.05-1.64], and healthy WHtR 1.30 [1.10-1.54]), as was recommended sleep duration (vs not; aPR healthy BMI 1.56 [1.19-2.05], and healthy WHtR 1.37 [1.13-1.67]). There was a trend for higher proportion of healthy body composition with more frequent fast food consumption. Healthy body composition was also associated with higher self-rated health ('very good/excellent' vs 'poor/fair'; aPR healthy BMI 1.87 [1.45-2.42], and healthy WHtR 1.71 [1.40-2.10]). Culturally appropriate community health interventions with a focus on physical activity and sleep may hold promise for improving body composition among Aboriginal adolescents.