Physical exercise associated with improved BMD independently of sex and vitamin D levels in young adults

PURPOSE: Young men and women accrue the majority of their bone mass in their teens and twenties, where their bone mass peaks (PBM), yet little is known about the roles of physical exercise, vitamin D levels and bone mineral density (BMD) near PBM. METHODS: To comparatively examine the effect of physical exercise and two vitamin D levels (insufficient s-25[OH]D <50 nmol/L and sufficient s-25[OH]D >80 nmol/L) on the BMD measured at the femoral neck, total hip (bilaterally) and the lumbar spine (L2–L4) in male and female participants approaching PBM. RESULTS: The insufficient s-25[OH]D group, median age 21.6 (19.8–22.8) years, and BMI 24.2 ± 5.0 kg/m(2) had BMD 0.10 (0.03, 0.17) g/cm(2) (p = 0.008) lower at all DXA-scan sites compared to the sufficient s-25[OH]D group, median age 19.5 (19.0–22.3) years, and BMI of 22.6 ± 1.8 kg/m(2). Exercise was positively associated with the BMD at all DXA-scan sites (p(trend) = 0.0001) and with equal benefit; there was no interaction between exercise and the DXA-scan site (p = 0.09). The male participants did not have a systematically higher BMD than the female participants for all scan sites; only for hips total and femoral neck bilaterally, while it was equal at the lumbar spine. CONCLUSION: The BMD in young healthy adults is associated with physical exercise, independent of sex and s-25[OH]D status. A sufficient s-25[OH]D status was systematically associated with a higher BMD for all levels of exercise. For both sexes and vitamin D levels exercise was equally positively associated with BMD

Determinants of vitamin D status in young adults:influence of lifestyle, sociodemographic and anthropometric factors

Abstract Background Very few studies have investigated the determinants of circulating 25-hydroxyvitamin D (25[OH]D) in young adults (18–25 years old) using a set of variables that include lifestyle, sociodemographic, and anthropometric data. Our aim was to investigate the association between these variables and vitamin D status in a sample of untreated young adults. Methods A total of 738 young adults were enrolled in a (June cross-sectional study 2012 to May 2014) and were recruited from educational institutions in the Copenhagen area. For multivariate logistic regression subjects was categorized based on 25[OH]D in serum into; vitamin D sufficiency (S-25[OH]D > 50 nmol/L), vitamin D insufficiency (25 nmol/L ≤ S-25[OH]D ≤ 50 nmol/L), vitamin D deficiency (S-25[OH]D < 25 nmol/L). Information on lifestyle factors and education was obtained by self-reported questionnaires. Results 700 subjects with a valid measurement of S-25[OH]D and a completed questionnaire was analysed. 238 had vitamin D insufficiency, 135 had vitamin D deficiency of which 13 had severe vitamin D deficiency (S-25[OH]D < 12.5 nmol/L). The relative risk (RR) for vitamin D deficiency was highest for men 2.09 (1.52, 2.87); obese subjects 2.00 (1.27, 3.15); smokers 1.33 (1.02, 1.73); subjects who exercised 0-½ hours a week 1.88 (1.21, 2.94); and subjects who consumed fast food once a week 1.59 (1.05, 2.43). The relative risk was significantly lower for subjects who were studying for a Bachelor’s degree (0.40 (0.23, 0.68). For vitamin D insufficiency, the highest RR was again for men 1.31 (1.06, 1.61); obese subjects 1.57 (1.17, 2.11); and subjects who exercised 0-½ hours a week 1.51 (1.11, 2.06). Conclusion In this study of young adults, vitamin D deficiency was highly prevalent. Modifiable factors such as smoking, maintenance of normal BMI, and physical activity are all potential targets for interventional trials to determine the causal order; such knowledge would be useful in improving S-25[OH]D in young adults. The small group with severe vitamin D deficiency warrants increased attention

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

Background Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. Methods We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18–94 years of age) and applied the models to the test sets including 648 patients with SZ (18–66 years of age), 185 patients with BD (18–64 years of age), and 990 HC subjects (17–68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. Results Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen’s d = −0.29) and patients with BD (Cohen’s d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy–based models showed larger group differences than the models based on other DTI-derived metrics. Conclusions Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners

Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study

BACKGROUND: Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. METHODS: We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. RESULTS: Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics. CONCLUSIONS: Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners