ObjectiveTo investigate the association between the stress-induced hyperglycemia ratio (SHR) and all-cause, cardiovascular, and diabetes-related mortality in patients with advanced cardiovascular-kidney-metabolic (CKM) syndrome, and to evaluate the value of SHR as an independent prognostic marker. MethodsThis retrospective cohort study used data from the 1999–2018 U.S. National Health and Nutrition Examination Survey (NHANES). A total of 2 135 patients with advanced CKM (stages 3 and 4) were included. Kaplan-Meier analysis and multivariable Cox regression models were applied to assess the relationship between SHR and mortality outcomes. Restricted cubic spline (RCS) analysis was employed to explore potential non-linear associations. Subgroup analyses were conducted to identify possible effect modifiers. ResultsOver a mean follow-up of 248 months, 674 all-cause, 198 cardiovascular, and 31 diabetes-related deaths occurred. Elevated SHR was significantly associated with diabetes-related mortality (HR=3.48, P<0.001) in a dose-response manner. SHR exhibited a U-shaped relationship with both all-cause and cardiovascular mortality (non-linearity P<0.001), indicating increased risk at both low and high SHR levels. Subgroup analyses revealed that sex, BMI, and hyperlipidemia significantly modified the association between SHR and diabetes-related death. ConclusionSHR is an independent predictor of mortality risk in patients with advanced CKM syndrome, particularly for diabetes-related death. These findings support the integration of SHR into risk stratification of high-risk CKM populations and provide a basis for metabolic stress-targeted interventions.
The National Health and Nutrition Examination Survey (NHANES) in the United States is a research program designed to assess the health and nutritional status of adults and children in the country. Due to its adoption of a complex sampling design, it is imperative to correctly apply the weights provided by NHANES to ensure the accuracy and external validity of research results. This article first introduces the relevant concepts of the NHANES database and its weights, followed by a practical demonstration of weight selection, calculation, and implementation in the R programming language. It further discusses the significance of using weights in NHANES data and systematically showcases how to perform weighted analysis based on R, providing a valuable reference for relevant researchers.