L-shaped relationship between stress hyperglycemia ratio and cardiovascular disease risk in middle-aged and older adults: Insight from the China Health and Retirement Longitudinal Study. - Zixi Zhang; Jiabao Zhou; Cancan Wang; Kang Wang; Yongguo Dai; Qiuzhen Lin; Yichao Xiao; Qiming Liu (PLoS ONE)
Background The stress hyperglycemia ratio (SHR) has emerged as a potential marker for predicting cardiovascular disease (CVD) and mortality. However, its relationship with CVD and all-cause mortality in middle-aged and older Chinese populations remains unclear. Methods This study analyzed data from the China Health and Retirement Longitudinal Study (CHARLS), including individuals aged 45 years and older. Cross-sectional analysis assessed the associations between SHR and CVD incidence, whereas longitudinal Cox regression models evaluated the relationships between SHR, CVD risk, and all-cause mortality. Restricted cubic spline analyses were employed to explore potential non-linear relationships. The predictive performance of SHR was compared with that of fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c). Results The cross-sectional analysis identified an inverse association between the SHR and CVD incidence. Longitudinal analysis indicated that SHR was independently associated with an increased risk of CVD, with a significant L-shaped relationship (non-linear P = 0.001). Threshold effect analysis identified 0.985 as the inflection point for SHR, with hazard ratios (HRs) increasing sharply below this level (HR: 0.32, 95% CI: 0.17-0.61, P = 0.001). However, no significant non-linear relationship was observed between SHR and all-cause mortality (non-linear P = 0.942). FBG, HbA1c, and SHR provided similar predictive value for all-cause mortality (area under the curve: 0.526 vs. 0.535 vs. 0.513), without significant incremental predictive value. Conclusions The SHR is an independent predictor of CVD risk in middle-aged and older Chinese adults, with an L-shaped relationship. Future large-scale, multicenter studies are needed to validate these findings.