Loading
Life sciences · Journal article
Journal of Diabetes Investigation · October 9, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Journal article.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
BACKGROUND: Cardiovascular-liver-metabolic multimorbidity (CLMM), defined as the co-occurrence of at least two of cardiovascular disease, type 2 diabetes, and metabolic dysfunction-associated steatotic liver disease, is an increasing health burden. We evaluated the cholesterol, high-density lipoprotein, and glucose (CHG) index and its obesity- and inflammation-derived variants for predicting incident CLMM. METHODS: This prospective cohort included 6,534 adults aged ≥45 years from the China Health and Retirement Longitudinal Study. CHG, eight obesity-derived indices, and CHG-CRP were assessed, with the triglyceride-glucose index and TG/HDL-C ratio as comparators. Associations with incident CLMM were evaluated using logistic regression, restricted cubic splines, and threshold analysis. Predictive performance was compared using receiver operating characteristic analysis, the integrated discrimination improvement (IDI), and the net reclassification improvement (NRI), with additional joint-effect, interaction, subgroup, and sensitivity analyses. RESULTS: At the 2015 follow-up, 406 participants (6.2%) met the criteria for CLMM. Higher CHG was linearly associated with incident CLMM (highest vs lowest quartile: OR 1.64, 95% confidence interval [CI] 1.22-2.22). Obesity-derived variants showed stronger associations, particularly CHG-BMI (OR 3.13, 95% CI 2.21-4.43), which also had the highest discrimination (AUC 0.643, 95% CI 0.616-0.669) and continuous NRI (34.08%). Participants with both high CHG and high BMI had the greatest CLMM risk (OR 2.59, 95% CI 1.94-3.51). Results were robust in sensitivity analyses. CONCLUSIONS: The CHG index and its obesity-derived variants, particularly CHG-BMI, are accessible and inexpensive candidate metabolic markers for predicting new-onset CLMM. CHG-BMI showed the highest apparent discrimination among the evaluated indices, although its AUC was modest. External population validation is needed.