hs-CRP/HDL-C can predict the risk of all cause mortality in cardiovascular-kidney-metabolic syndrome stage 1-4 patients

Frontiers in Endocrinology, Journal Year: 2025, Volume and Issue: 16

Published: April 10, 2025

Background The precise function of the hs-CRP/HDL-C ratio in forecasting long-term mortality risk patients with stages 1-4 Cardiovascular-Kidney-Metabolic (CKM) syndrome remains inadequately delineated. This study investigates potential correlation between and individuals CKM 1-4. Methods prospective cohort utilises data from China Health Retirement Longitudinal Study (CHARLS) project, encompassing 6,719 people who satisfied stringent criteria. We developed three Cox proportional hazards regression models to investigate relationship employed Restricted Cubic Spline (RCS) curves for analysis identify any nonlinear correlations. Furthermore, we performed Receiver Operating Characteristic (ROC) curve evaluate predictive performance appropriate cut-off value. To enhance research findings, conducted a stratified influence various sociodemographic factors on this association. Results In 1-4, 10-year incidence all-cause was 14.1%. Upon controlling additional confounding variables, outcomes model distinctly demonstrated statistically significant linear positive association patients. For each quartile increase ratio, probability poor (i.e., mortality) escalated by 15% (Hazard Ratio, HR = 1.15, 95% Confidence Interval, CI: 1.09–1.22, p-value < 0.001). Moreover, integration into baseline prediction model, all pertinent thoroughly adjusted, markedly enhanced model’s capacity, facilitating more assessment Conclusion identified 1 4 syndrome. remarkable discovery not only offers crucial reference enhancing early individualised treatment options but also greatly aids identification prognoses, hence presenting novel perspective improving clinical management pathways these individuals.

Language: Английский

Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0–3 and the development of a machine learning prediction model: a nationwide prospective cohort study

Cardiovascular Diabetology, Journal Year: 2025, Volume and Issue: 24(1)

Published: April 16, 2025

Background The American Heart Association recently introduced the concept of cardiovascular-kidney-metabolic (CKM) syndrome, highlighting increasing importance complex interplay between metabolic, renal, and cardiovascular diseases (CVD). While substantial evidence supports a correlation estimated glucose disposal rate (eGDR) CVD events, its predictive value compared with other insulin resistance (IR) indices, such as triglyceride–glucose (TyG) index, TyG-waist circumference, TyG-body mass TyG-waist-to-height ratio, triglyceride-to-high density lipoprotein cholesterol metabolic score for resistance, remains unclear. Methods This prospective cohort study utilized data from China Health Retirement Longitudinal Study (CHARLS). individuals were categorized into four subgroups based on quartiles eGDR. associations eGDR incident evaluated using multivariate logistic regression analyses restricted cubic spline. Seven machine learning models to assess index events. To model’s performance, we applied receiver operating characteristic (ROC) precision-recall (PR) curves, calibration decision curve analysis. Results A total 4,950 participants (mean age: 73.46 ± 9.93 years), including 50.4% females, enrolled in study. During follow-up 2011 2018, 697 (14.1%) developed CVD, 486 (9.8%) heart disease 263 (5.3%) stroke. outperformed six IR indices predicting demonstrating significant linear relationship all outcomes. Each 1-unit increase was associated 14%, 19% lower risk disease, stroke, respectively, fully adjusted model. incorporation significantly improved prediction performance area under ROC PR curves equal or exceeding 0.90 both training testing sets. Conclusions outperforms stroke CKM syndrome stages 0–3. Its enhances stratification may aid early identification high-risk this population. Further studies are needed validate these findings external cohorts. Graphical abstract

Language: Английский