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INTRODUCTION: Epigenetic aging, a marker of biological aging measured by DNA methylation, may be affected by behaviors, including sleep and physical activity. However, investigations of physical activity and sleep with epigenetic aging among pediatric populations are scant and have not accounted for correlated behaviors. METHODS: The study population included 472 Mexico City adolescents (52% female). Blood collection and 7-day wrist actigraphy (Actigraph GTX-BT) occurred during a follow-up visit when participants were 14.5 (2.09) years. Leukocyte DNA methylation was measured with the Infinium MethylationEPIC array after bisulfite conversion, and 9 epigenetic clocks were calculated. Sleep vs wake time was identified through a pruned dynamic programing algorithm, and physical activity was processed with Chandler cut-offs. Kmeans clustering was used to select actigraphy-assessed physical activity and sleep behavior clusters. Linear regression analyses were used to evaluate adjusted associations between the clusters and epigenetic aging. RESULTS: There were 3 unique clusters: "Short sleep/high sedentary behavior", "Adequate sleep duration and late timing/low moderate or vigorous physical activity (MVPA)", and "Adequate sleep duration/high MVPA". Compared to the "Adequate duration/high MVPA", adolescents with "Adequate duration and late sleep timing/low MVPA" had more accelerated aging for the GrimAge clock (ß = 0.63;95% CI 0.07, 1.19). In pubertal-stratified analyses, more mature adolescents in the "Adequate duration and late sleep timing/low MVPA group" had accelerated epigenetic aging. In contrast, females in the "Short sleep/high sedentary" group had decelerated epigenetic aging for the Wu pediatric clock. CONCLUSIONS: Associations between behavior clusters and epigenetic aging varied by pubertal status and sex. Contrary results in the Wu clock suggest the need for future research on pediatric-specific clocks.

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BACKGROUND: Sedentary behavior is a modifiable risk factor for cardiometabolic health; however, the assessment of total sedentary time may not capture youth's highly active and interrupted activity patterns. This study examined the associations between sedentary activity patterns and cardiometabolic risk factors among Mexican youth, who have a disproportionate burden of metabolic diseases, using a repeated measure design out of a longitudinal data. METHODS: 570 subjects in the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) birth cohort, who were followed up to three-time points during adolescence, were included. Bout duration, and frequency and percentages of waking time spent in specific intensities of activity, were quantified using ActiGraph wGT3X-BT wrist accelerometers. Self-reported questionnaires were used to query the usual duration of different sedentary behaviors. Outcomes were fasting lipid profile, markers for glucose homeostasis, anthropometry, and blood pressure. Associations were modeled using linear mixed-effects models, and isotemporal substitution approach was additionally used to assess the effect of replacing objectively assessed sedentary activity with other activity intensities, adjusting for potential confounders. RESULTS: Each hour of self-reported screen-based time was positively associated with diastolic blood pressure (mm Hg) [ß = 0.30, 95% confidence interval (95% CI) = 0.10, 0.51], and an hour of other sedentary time was associated with log serum glucose (mg/dL) [ß = 0.01, 95% CI = 0.004, 0.017]. Substitution models showed that replacing 5% of sedentary time with moderate to vigorous physical activity (MVPA) was associated with lower waist circumference (cm) [ß = - 1.35, 95% CI = - 1.91, - 0.79] and log serum triglycerides (mg/dL) [ß = - 0.11, 95% CI = - 0.18, - 0.03]. Substituting one uninterrupted sedentary bout with light activity was associated with lower insulin (µIU/mL) [ß = - 0.06, 95% CI = - 0.10, - 0.02]. CONCLUSIONS: Sedentary time was associated with cardiometabolic risk factors in Mexican youth in a context-specific manner. Replacing sedentary time with higher intensities was associated with improvements in some cardiometabolic markers.

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BACKGROUND: Alterations in body composition (BC) during adolescence relates to future metabolic risk, yet underlying mechanisms remain unclear. OBJECTIVES: To assess the association between the metabolome with changes in adiposity (body mass index [BMI], waist circumference [WC], triceps skinfold [TS], fat percentage [BF%]) and muscle mass (MM). METHODS: In Mexican adolescents (n = 352), untargeted serum metabolomics was profiled at baseline. and data were reduced by pairing hierarchical clustering with confirmatory factor analysis, yielding 30 clusters with 51 singleton metabolites. At the baseline and follow-up visits (1.6-3.5 years apart), anthropometry was collected to identify associations between baseline metabolite clusters and change in BC (∆) using seemingly unrelated and linear regression. RESULTS: Between visits, MM increased in boys and adiposity increased in girls. Sex differences were observed between metabolite clusters and changes in BC. In boys, aromatic amino acids (AAA), branched chain amino acids (BCAA) and fatty acid oxidation metabolites were associated with increases in ∆BMI, and ∆BF%. Phospholipids were associated with decreases in ∆TS and ∆MM. Negative associations were observed for ∆MM in boys with a cluster including AAA and BCAA, whereas positive associations were found for a cluster containing tryptophan metabolites. Few associations were observed between metabolites and BC change in girls, with one cluster comprising methionine, proline and lipids associated with decreases in ∆BMI, ∆WC and ∆MM. CONCLUSION: Sex-specific associations between the metabolome and change in BC were observed, highlighting metabolic pathways underlying adolescent physical growth.

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OBJECTIVE/BACKGROUND: Self-reported sleep difficulties, such as insomnia symptoms, have been reported among adolescents. Yet, studies of their prevalence and correlates are scarce among Latin Americans. This study sought (1) to describe associations between sociodemographic and lifestyle factors with self-reported sleep difficulties and (2) to examine associations between self-reported sleep difficulties and actigraphy-based sleep. PARTICIPANTS: Participants included 477 Mexican adolescents from the ELEMENT cohort. METHODS: Over 7 days, self-reported sleep measures (hard time falling asleep, overall sleep difficulties, and specific types of sleep difficulties) were obtained from daily sleep diaries. Actigraphy-based sleep measures (duration, i.e. sleep onset to morning wake, midpoint, and fragmentation) were concurrently assessed using a wrist actigraph. RESULTS: Mean (SD) age was 15.9 (2.2) years, and 53.5% were females. Mean (SD) sleep duration was 8.5 (1.2) h/night. Half reported a hard time falling asleep at least 3 days, and 25% had sleep difficulties at least 3 days over 7 days. The 3 types of sleep difficulties commonly reported among the entire cohort were insomnia/restlessness (29%), environmental (27%), and mental/emotional difficulties (19%). Female sex, smoking behavior, and socioeconomic indicators were among the most consistent factors associated with sleep difficulties. Subjective sleep difficulties were associated with shorter sleep duration (ß = -20.8 [-35.3, -6.2] min), while subjective hard time falling asleep was associated with longer sleep duration (ß = 11.3 [4.6, 27.2] min). CONCLUSION: A high proportion of Mexican adolescents in the sample reported sleep difficulties. Findings demonstrate the importance of obtaining subjective and objective sleep measures for a more comprehensive assessment of adolescent sleep.

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STUDY OBJECTIVES: Sleep deprivation and low sleep quality are widespread among adolescents, and associate with obesity risk. Plausible mediators include diet and physical activity. Another potential interrelated pathway, as yet unexplored in adolescents, could involve epigenetic modification of metabolism genes. METHODS: In a cohort of 351 Mexico City adolescents (47% male; mean [SD] age = 14 [2] years), 7-day actigraphy was used to assess average sleep duration, sleep fragmentation, and movement index. DNA isolated from blood leukocytes was bisulfite-converted, amplified, and pyrosequenced at four candidate regions. Linear mixed models evaluated sex-stratified associations between sleep characteristics (split into quartiles [Q]) and DNA methylation of each region, adjusted for potential confounders. RESULTS: Mean sleep duration was 8.5 [0.8] hours for boys and 8.7 [1] hours for girls. There were sex-specific associations between sleep duration and LINE-1 (long interspersed nuclear element) methylation. Boys with longer sleep duration (Q4) had lower LINE-1 methylation than boys in the 3rd quartile reference category, while girls with both longer and shorter sleep duration had higher LINE-1 methylation compared to Q3. Longer sleep duration was associated with higher H19 methylation among girls (comparing highest to third quartile, -0.9% [-2.2, 0.5]; p, trend = 0.047). Sleep fragmentation was inversely associated with peroxisome proliferator-activated receptor alpha (PPARA) methylation among girls (comparing highest to lowest fragmentation quartile, 0.9% [0.1 to 1.8]). Girls also showed an inverse association between sleep fragmentation and hydroxysteroid (11-beta) dehydrogenase 2 (HSD11B2; Q4 to Q1, 0.6% [-1.2%, 0%]). CONCLUSIONS: Sleep duration and fragmentation in adolescents show sex-specific associations with leukocyte DNA methylation patterns of metabolism genes.

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OBJECTIVE: To assess whether adiposity measures differed according to joint categories of sleep duration and sleep variability in a sample of Mexican adolescents. STUDY DESIGN: A sample of 528 Mexico City adolescents aged 9-17 years wore wrist actigraphs for 6-7 days. Average sleep duration was categorized as age-specific sufficient or insufficient. Sleep variability, the standard deviation of sleep duration, was split at the median into stable versus variable. Adiposity measures-body mass index (BMI)-for-age Z score (BMIz), triceps skinfolds, waist circumference, and percent body fat-were collected by trained assistants. We regressed adiposity measures on combined sleep duration and variability categories. Log binomial models were used to estimate prevalence ratios and 95% CI for obesity (>2 BMIz) by joint categories of sleep duration and variability, adjusting for sex, age, and maternal education. RESULTS: Approximately 40% of the adolescents had insufficient sleep and 13% were obese. Relative to sufficient-stable sleepers, adolescents with insufficient-stable sleep had higher adiposity across all 4 measures (eg, adjusted difference in BMIz was 0.68; 95% CI, 0.35-1.00) and higher obesity prevalence (prevalence ratio, 2.54; 95% CI, 1.36-4.75). Insufficient-variable sleepers had slightly higher BMIz than sufficient-stable sleepers (adjusted difference, 0.30; 95% CI, 0.00-0.59). CONCLUSIONS: Adolescents with consistently insufficient sleep could be at greater risk for obesity. The finding that insufficient-variable sleepers had only slightly higher adiposity suggests that opportunities for "catch-up" sleep may be protective.

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