Individuals with cardiovascular-kidney-metabolic (CKM) syndrome face substantially elevated risks of both all-cause and cardiovascular mortality, highlighting the need for improved risk stratification tools. A study published in Cardiovascular Diabetology assessed whether estimated glucose disposal rate (eGDR) and a body shape index (ABSI) jointly relate to mortality risk in this population.

The analysis used National Health and Nutrition Examination Survey (NHANES) data from 1999 to 2018, including 18,186 adults with stage 0-4 CKM syndrome. Associations between eGDR, ABSI, and mortality were evaluated using Cox proportional hazards models, Kaplan-Meier survival curves, and subgroup analyses. Incremental prognostic value was assessed using integrated discrimination improvement (IDI) and net reclassification index (NRI). Six machine learning algorithms, including extreme gradient boosting (XGBoost), were applied to develop mortality prediction models.

During follow-up, 2,536 all-cause deaths and 790 cardiovascular deaths were documented. After multivariable adjustment, both low eGDR and high ABSI were independently associated with higher mortality risk. Individuals with both low eGDR and high ABSI had the greatest risk, with hazard ratios of 2.79 (95% CI 2.30 to 3.38) for all-cause mortality and 4.53 (95% CI 2.96 to 6.92) for cardiovascular mortality. Among the machine learning models, XGBoost showed the highest performance, with areas under the curve of 0.877 for all-cause mortality and 0.850 for cardiovascular mortality.

These findings suggest that combined assessment of eGDR and ABSI may improve risk stratification for mortality among individuals with CKM syndrome.