Loading
Life sciences · Journal article
BMC Public Health · September 17, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Journal article.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
Whether long-term ambient air pollution is associated with incident dynapenic abdominal obesity (DAO), and whether climate conditions modify these associations across cardiovascular-kidney-metabolic (CKM) stages, remains unclear. This cohort study included 7,919 adults aged ≥ 45 years from the China Health and Retirement Longitudinal Study. Missing covariates were handled using 20 chained-equation-imputed datasets. Separate single-pollutant logistic models were fitted under minimal, primary confounder, and full prognostic adjustment, with the primary model governing inference. Principal component analysis assessed pollution-mixture effects, and 36 pollutant-climate interactions were tested with Benjamini-Hochberg correction. Nine ML algorithms were compared using repeated nested stratified cross-validation, and SHAP was used to interpret the final model overall and across CKM stages 0–3. During follow-up, 184 participants developed DAO. In the primary model, SO 2 was associated with incident DAO (OR per interquartile-range increase, 1.61; 95% CI, 1.20–2.15; q = 0.016) and was the only single-pollutant association retained after correction across 12 tests. The first pollution-mixture principal component explained 71.0% of standardized exposure variance and was positively associated with DAO (OR per SD increase, 1.10; 95% CI, 1.01–1.19). SO 4 2− -dryness and NH 4 + -dryness interactions remained significant after correction across 36 tests (both q = 0.049). XGBoost achieved the highest mean cross-validated PR-AUC (0.131), although sensitivity and PPV were limited. SHAP consistently identified O 3, SO 2, and CO as the leading pollutant contributors across CKM stages. SO 2 showed the most robust association with incident DAO. Pollution-mixture and dryness-related findings suggest additional combined environmental effects, while SHAP indicated a largely conserved pollutant-attribution hierarchy across CKM stages. These findings require validation in larger prospective cohorts.