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Gui-Ming Zhu

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Open access Sep 2026

Association between exposure to indoor PM2.5 constituents and mortality risk among chinese older adults: a nationwide prospective cohort study

With global population aging, investigating the health impacts of PM 2.5 and its chemical components in older adults has become increasingly important. Due to age-related limitations in mobility and activity patterns, older adults spend the majority of their time indoors. The chemical composition of PM 2.5 is complex, and its various components may adversely affect the health of older adults by inducing systemic inflammatory responses. This study examines the association between indoor PM 2.5 and its components and all-cause mortality, and explores the potential intermediate pathway of systemic inflammatory factors. We analyzed 12,607 elderly participants (mean age 86.81 ± 11.34) followed from 2008 to 2018. Over a median follow-up of 4.14 years, 8,643 deaths were recorded. Indoor concentrations of PM 2.5 , black carbon (BC), organic matter (OM), sulfate (SO 4 2− ), ammonium (NH 4 + ), and nitrate (NO 3 − ) were estimated using high-resolution ambient data and infiltration factors. Time-varying Cox proportional hazards models were used to assess the associations between PM 2.5 and its major components and all-cause mortality. Dose-response relationships were characterized using restricted cubic splines (RCS), and sensitivity analysis with a one-year lag exposure window was also conducted. Additionally, fixed-effects models were used to explore the relationship between PM 2.5 and the neutrophil-to-lymphocyte ratio. Time-varying Cox models identified PM 2.5 , BC, OM, and SO 4 2− as significantly associated with increased all-cause mortality. Specifically, a 1 µg/m³ increase in PM 2.5 was associated with a 0.3% higher risk ( HR  = 1.003, 95% CI : 1.001–1.005). Regarding the constituents, each 1 µg/m³ increase in BC corresponded to a 7.2% higher risk ( HR  = 1.072, 95% CI : 1.035–1.111); the HRs for OM and SO 4 2− were 1.009 (95% CI : 1.002–1.016) and 1.010 (95% CI : 1.001–1.019), respectively. These associations remained robust in sensitivity analyses after excluding participants with hypertension or diabetes. Furthermore, the mortality risk was more pronounced among rural residents, and the concentration–risk relationships exhibited a nonlinear pattern. Notably, significant associations were observed between PM 2.5 and the neutrophil-to-lymphocyte ratio. This study underscores the need to prioritize control of BC, OM, and SO 4 2− in indoor environments. Stricter emission controls and component-specific standards are needed to reduce health risks.

Yan-Chao Wen, Wan-Ying Liu, Jin Wang et al. · 0 citations
Sep 2026

Improving 10-year cardiovascular disease risk prediction using automated machine learning.

AIMS To develop a cardiovascular disease (CVD) risk prediction model with improved accuracy and interpretability by integrating diverse risk factors and applying Automated Machine Learning (AutoML), thereby enhancing clinical utility over conventional models. METHODS This is a prospective cohort study. Data were obtained from the Multi-Ethnic Study of Atherosclerosis (MESA), including baseline and fifth follow-up visits, comprising 4713 participants. Exercise and dietary data were harmonized via Metabolic Equivalent of Task (MET) and Healthy Eating Index-2015 (HEI-2015), respectively. Predictor selection was performed using the Boruta algorithm alongside Random Forest (RF) error rate cross-validation. Logistic regression, four traditional machine learning algorithms, and H2O AutoML were each applied for model training and evaluation. Finally, the best-performing model was further interpreted using SHapley Additive exPlanations (SHAP). RESULTS A total of 21 predictors were selected, including age, sex, and Total Cholesterol (TC). Among the evaluated models, H2O AutoML outperformed other methods with an accuracy of 0.864, specificity of 0.892, precision of 0.610, F1 score of 0.670, and a Youden index of 0.635, achieving the highest AUC of 0.882 (0.846-0.918). SHAP analysis revealed the relative importance of predictors, with age, TC and Digit Symbol Score (DSS) ranking highest. CONCLUSIONS This study developed an AutoML-based CVD risk prediction model with superior discrimination and calibration, providing clinicians a practical tool for risk stratification. By enabling personalized prevention and early identification of high-risk individuals, this model has the potential to reduce CVD burden at the population level. Notably, DSS exhibited high importance and may represent a candidate risk marker.

Si-Min He, Ju-Ping Wang, Le Zhao et al. · 0 citations

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