The identification of new biomarkers for the early detection of increased risk of mortality remains an unmet need. There has been significant progress in establishing the epidemiology of novel blood biomarkers for all-cause and cause-specific mortality, such as serum neurofilament light chain,^1,2 N-terminal pro-brain natriuretic peptide,³ retinol,⁴ alpha tocopherol,⁵ and beta carotene.⁶ Despite this progress, simple, inexpensive, and reliable indicators are still essential for predicting mortality in clinical practice. At present, routine laboratory tests are widely used in primary care and hospitals, and further use of routine clinical biomarkers is expected. For example, several new biomarkers, such as the prognostic nutritional index, ratio of neutrophil to lymphocyte, and ratio of platelet to lymphocyte, were derived from routine laboratory tests. These simple integrated biomarkers showed significant associations with all-cause and cause-specific mortality in the general population.^7-9 Therefore, there is still substantial interest in the use of routine biomarkers to identify persons who are at risk for mortality.

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.

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.