Cardiovascular-kidney-metabolic (CKM) syndrome has become a major global health burden, while physical activity, as an important lifestyle intervention, remains unclear regarding its protective effects on disease risk across different CKM stages. We utilized data from the China Health and Retirement Longitudinal Study (CHARLS), including 7,159 participants. According to the quartile distribution of total weekly metabolic equivalents, all participants were divided into four groups: Q1 group (≤ 1732.5 MET-min/week, reference group), Q2 group (1732.6–4158.0 MET-min/week), Q3 group (4158.1–9744.0 MET-min/week), and Q4 group (> 9744.0 MET-min/week). For CKM analysis, participants were categorized into two groups: low-risk CKM (stages 0–2) and high-risk CKM (stages 3–4), with high-risk CKM progression defined as the risk of being in stages 3–4 versus stages 0–2. We employed three progressively adjusted logistic regression models to assess the associations between physical activity metabolic equivalents and outcomes of heart disease, stroke, diabetes, cardiovascular disease (CVD), cardiometabolic disease (CMD), and cardiometabolic multimorbidity (CMM). Dose-response relationships were evaluated using restricted cubic spline functions, and subgroup analyses and receiver operating characteristic (ROC) curve assessments were conducted to evaluate predictive performance. In the fully adjusted model, compared to Q1, Q4 was significantly associated with reduced risks of heart disease (OR = 0.760, 95%CI: 0.631–0.912), stroke (OR = 0.571, 95%CI: 0.376–0.850), diabetes (OR = 0.701, 95%CI: 0.551–0.888), CVD (OR = 0.741, 95%CI: 0.621–0.883), CMD (OR = 0.728, 95%CI: 0.621–0.853), and CMM (OR = 0.529, 95%CI: 0.360–0.762). Similarly, for high-risk CKM progression, the Q4 group demonstrated significant protective effects with a 27.5% risk reduction (OR = 0.725, 95%CI: 0.617–0.850, P < 0.001), while Q2 and Q3 groups showed no significant associations. Dose-response analysis revealed significant linear relationships for all diseases (P overall < 0.05). Subgroup analyses identified age, gender, and residence as significant effect modifiers, with greater health benefits observed among elderly individuals, males, and urban residents. ROC analysis showed good predictive performance with area under the curve (AUC) values exceeding 0.7 for all diseases, with CMM showing the highest predictive effect (AUC: 0.827). Among CKM stage 0–4 populations, high-level physical activity provides significant protective effects against cardiovascular and metabolic diseases. This highlights the important role of physical activity intervention in the prevention of these diseases.
Zhao et al. (2025) studied this question.