Nutritional status and height, weight and BMI centiles of school-aged children and adolescents of 6–18-years from Kinshasa (DRC)

<p><b>Background:</b> The last study to establish centiles of main anthropometric measurements in Kinshasa was conducted over 60 years ago, which questions its current adequacy to describe or monitor growth in this population.</p> <p><b>Aim:</b> To assess the nutritional status of school-aged children and adolescents and to estimate centile curves of height, weight and body mass index (BMI).</p> <p><b>Subjects and methods:</b> A representative sample of 7541 school-aged children and adolescents (48% boys) aged 6–18 years was measured between 2010–2013. Smooth centiles of height, weight and BMI-for-age were estimated with the LMS method and compared with the WHO 2007 reference. Nutritional status was assessed by comparing measurements of height and BMI against the appropriate WHO cut-offs.</p> <p><b>Results:</b> Compared to the WHO reference, percentiles of height and BMI were generally lower. This difference was larger in boys than in girls and increased as they approached adolescence. The prevalence of short stature (< –2 SD) and thinness (< –2 SD) was higher in boys (9.8% and 12%) than in girls (3.4% and 6.1%), but the prevalence of overweight (> 1 SD) was higher in girls (8.6%) than in boys (4.5%).</p> <p><b>Conclusion:</b> Children from Kinshasa fall below WHO centile references. This study established up-to-date centile curves for height, weight and BMI by age in children and adolescents. These reference curves describe the current status of these anthropometric markers and can be used as a basis for comparison in future studies.</p

Interrelationships among skeletal age, growth status and motor performances in female athletes 10–15 years

Motor performances of youth are related to growth and maturity status, among other factors. To estimate the contribution of skeletal maturity status per se to the motor performances of female athletes aged 10–15 years and the mediation effects of growth status on the relationships. Skeletal age (TW3 RUS SA), body size, proportions, estimated fat-free mass (FFM), motor performances, training history and participation motivation were assessed in 80 non-skeletally mature female participants in several sports. Hierarchical and regression-based statistical mediation analyses were used. SA per se explained a maximum of 1.8% and 5.8% of the variance in motor performances of athletes aged 10–12 and 13–15 years, respectively, over and above that explained by covariates. Body size, proportions, and hours per week of training and participation motivation explained, respectively, a maximum of 40.7%, 18.8%, and 22.6% of the variance in performances. Mediation analysis indicated specific indirect effects of SA through stature and body mass, alone or in conjunction with FFM on performances. SA per se accounted for small and non-significant amounts of variance in several motor performances of female youth athletes; rather, SA influenced performances indirectly through effects on stature, body mass and estimated FFM.</p

Average maximum oxygen uptake in birth weight quarters.

<p>Q1–Q4: Birth weight quarter 1–4 (Q1 = lowest quarter).</p

Regression Coefficients for Birth Weight in the Individual Analysis.

<p>Results were obtained by use of a three-level random intercept, random slope (for time) model.</p><p>Model 1: birth weight, gestational age, time point and sex.</p><p>Model 2: model 1+zygosity-chorionicity, smoking, physical activity, height and weight (the latter not included in the V02 peak models).</p><p>Model 3: model 2+parental BMI.</p

Regression Coefficients for Birth Weight: Mean of the Twin Pair and Difference within the Twin Pair.

<p>Results were obtained by use of a three-level random intercept, random slope (for time) model.</p><p>Model: birth weight, gestational age, time point, sex, zygosity-chorionicity, smoking, physical activity, height, weight (not included in the V02 peak models) and parental BMI.</p

Twin Characteristics at the Start of the Study.

*<p>For description purposes, smoking was analyzed for the final measuring moment instead of at the start of the study.</p><p>Results are given as mean ± SD, median [interquartile range] or as n (%).</p><p>MZ: monozygotic, DZ: dizygotic,</p>**<p>Comparison MZ and DZ twins <0.05 (t-test).</p

Regression Coefficients for Δ Birth Weight in MZ and DZ twins (Pair Wise, Stratified Analysis).

<p>Results were obtained by use of a two-level random intercept, random slope (for time) model.</p><p>Model 1: Δ birth weight and time point.</p><p>Model 2: model 1+chorionicity, Δ physical activity, Δ height and Δ weight (the latter not included in the V02 peak models).</p><p>MZ: Monozygotic, DZ: Dizygotic.</p

Meta‐analysis of genome‐wide DNA methylation and integrative omics of age in human skeletal muscle

Background: Knowledge of age-related DNA methylation changes in skeletal muscle is limited, yet this tissue is severely affected by ageing in humans. Methods: We conducted a large-scale epigenome-wide association study meta-analysis of age in human skeletal muscle from 10 studies (total n = 908 muscle methylomes from men and women aged 18–89 years old). We explored the genomic context of age-related DNA methylation changes in chromatin states, CpG islands, and transcription factor binding sites and performed gene set enrichment analysis. We then integrated the DNA methylation data with known transcriptomic and proteomic age-related changes in skeletal muscle. Finally, we updated our recently developed muscle epigenetic clock (https://bioconductor.org/packages/release/bioc/html/MEAT.html). Results: We identified 6710 differentially methylated regions at a stringent false discovery rate <0.005, spanning 6367 unique genes, many of which related to skeletal muscle structure and development. We found a strong increase in DNA methylation at Polycomb target genes and bivalent chromatin domains and a concomitant decrease in DNA methylation at enhancers. Most differentially methylated genes were not altered at the mRNA or protein level, but they were nonetheless strongly enriched for genes showing age-related differential mRNA and protein expression. After adding a substantial number of samples from five datasets (+371), the updated version of the muscle clock (MEAT 2.0, total n = 1053 samples) performed similarly to the original version of the muscle clock (median of 4.4 vs. 4.6 years in age prediction error), suggesting that the original version of the muscle clock was very accurate. Conclusions: We provide here the most comprehensive picture of DNA methylation ageing in human skeletal muscle and reveal widespread alterations of genes involved in skeletal muscle structure, development, and differentiation. We have made our results available as an open-access, user-friendly, web-based tool called MetaMeth (https://sarah-voisin.shinyapps.io/MetaMeth/)