Background: It remains unclear whether all physical activity (PA) domains (e.g., occupation-related PA [OPA], transportation-related PA [TPA], and leisure-time PA [LTPA]) have equivalent beneficial relationships. We aimed to investigate the associations of OPA, TPA, and LTPA with depressive symptoms in adults. Methods: We included and analyzed 31,221 participants (aged ≥18 years) from the cross-sectional 2007–2018 U.S. National Health and Nutrition Examination Survey (NHANES). The PA domains were assessed by a self-report questionnaire and categorized based on the PA guidelines. Depressive symptoms were measured by the nine-item Patient Health Questionnaire. Results: Participants achieving PA guidelines (≥150 min/week) were 26% (odds ratio [OR] 0.74, 95% confidence interval [CI] 0.68–0.80) and 43% (OR 0.57, 95% CI 0.51–0.62) less likely to have depressive symptoms depending on total PA and LTPA, respectively, while OPA or TPA did not demonstrate lower risks of depressive symptoms. LTPA at levels of 1–149, 150–299, and ≥300 min/week was associated with 31% (OR 0.69, 95% CI 0.60–0.78), 43% (OR 0.57, 95% CI 0.49–0.67), and 51% (OR 0.49, 95% CI 0.43–0.55) lower odds of depressive symptoms, respectively. Conclusion: LTPA, but not OPA or TPA, was associated with a lower risk of depressive symptoms at any amount, suggesting that significant mental health would benefit from increased PA, even at levels below the recommendation.

Testing dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed a new way of testing nonlinear dependence between two continuous variables (X and Y).

High-altitude polycythemia (HAPC) is a chronic high-altitude disease that can lead to an increase in the production of red blood cells in the people who live in the plateau, a hypoxia environment, for a long time. The most frequent symptoms of HAPC include headache, dizziness, breathlessness, sleep disorders, and dilation of veins. Although chronic hypoxia is the main cause of HAPC, the fundamental pathophysiologic process and related molecular mechanisms responsible for its development remain largely unclear yet. This study aimed to explore the related hereditary factors of HAPC in the Chinese Han and Tibetan populations. A total of 140 patients (70 Han and 70 Tibetan) with HAPC and 60 healthy control subjects (30 Han and 30 Tibetan) were recruited for a case-control association study. To explore the genetic basis of HAPC, we investigated the association between HAPC and both phosphatidylinositol-4,5-bisphosphonate 3-kinase, catalytic subunit delta gene (PIK3CD) and collagen type IV α3 chain gene (COL4A3) in Chinese Han and Tibetan populations. Using the unconditional logistic regression analysis and the false discovery rate (FDR) calculation, we found that eight SNPs in PIK3CD and one SNP in COL4A3 were associated with HAPC in the Tibetan population. However, in the Han population, we did not find any significant association. Our study suggested that polymorphisms in the PIK3CD and COL4A3 were correlated with susceptibility to HAPC in the Tibetan population.

Biological age reflects actual ageing and overall health, but current ageing clocks are often complex and difficult to interpret, which limits their clinical application. This study introduces a Gompertz law‐based biological age (GOLD BioAge) model designed to simplify the assessment of ageing. We calculated GOLD BioAge using clinical biomarkers and found significant associations between the difference from chronological age (BioAgeDiff) and the risks of morbidity and mortality in the NHANES and UK Biobank. Using proteomics and metabolomics data, we developed GOLD ProtAge and MetAge, which outperformed the clinical biomarker models in predicting mortality and chronic disease risk in UK Biobank. Benchmark analyses demonstrated that the models outperformed common ageing clocks in predicting mortality across diverse age groups in both the NHANES and UK Biobank cohorts. Additionally, a simplified version called Light BioAge is created, which uses three biomarkers to assess ageing. The Light model reliably captured the mortality risk across three validation cohorts (CHARLS, RuLAS, and CLHLS). It significantly predicted the onset of frailty, stratified frail individuals, and collectively identified individuals at high risk of mortality. In summary, the GOLD BioAge algorithm provides a valuable framework for the assessment of ageing in public health and clinical practice.