Low Insulin Sensitivity Score Accelerates Aging and Raises Death Risk in Heart-Kidney-Metabolic Disease

Population aging and the rising burden of cardiovascular-kidney-metabolic (CKM) syndrome are converging public health crises. CKM syndrome—formally defined by the American Heart Association in 2023—encompasses overlapping cardiovascular, renal, and metabolic dysfunction and affects nearly 90% of U.S. adults across its risk spectrum. Patients in advanced CKM stages face life expectancy reductions of 10–20 years. Understanding what drives accelerated aging and excess mortality in this population is urgently needed.

This study investigated whether the estimated glucose disposal rate (eGDR)—a validated, inexpensive proxy for insulin resistance derived from waist circumference, hypertension status, and HbA1c—predicts biological aging acceleration and mortality in CKM stages 1–4. Using NHANES 2005–2010 data (n=4,826), researchers assessed two aging acceleration measures: Biological Age Acceleration (BioAgeAccel, based on eight clinical biomarkers via the Klemera-Doubal method) and Phenotypic Age Acceleration (PhenoAgeAccel, based on nine clinical indicators spanning metabolic, inflammatory, and immune domains). Mortality was tracked via death certificate linkage, capturing 831 all-cause and 208 cardiovascular deaths.

In fully adjusted Cox proportional hazards models, each 1-unit increase in eGDR was associated with a 10% lower all-cause mortality risk (HR=0.90, 95% CI 0.86–0.93) and a 13% lower cardiovascular mortality risk (HR=0.87, 95% CI 0.81–0.93). Survey-weighted logistic regression revealed that higher eGDR was independently associated with lower odds of both BioAgeAccel (OR=0.85) and PhenoAgeAccel (OR=0.78). Dose-response analyses confirmed these relationships were largely linear. Findings were replicated in the China Health and Retirement Longitudinal Study (CHARLS), strengthening generalizability across ethnicities and healthcare systems.

Causal mediation analysis was a methodological highlight: PhenoAgeAccel mediated 23.53% of eGDR's total effect on all-cause mortality and 15.73% on cardiovascular mortality. This means insulin resistance accelerates aging biology, and that accelerated aging is a meaningful—though partial—pathway through which poor insulin sensitivity raises death risk. Nine machine learning models were compared for mortality prediction; the K-Nearest Neighbors algorithm achieved the highest AUC of 0.926. A clinical nomogram was also developed to facilitate bedside risk estimation.

The study has important caveats. Its observational cross-sectional design for the aging acceleration analyses precludes causal inference. eGDR, while practical, is an estimate and may not fully capture insulin resistance compared to gold-standard hyperinsulinemic-euglycemic clamp testing. Residual confounding from unmeasured lifestyle or genetic factors cannot be excluded. The NHANES sample spans only adults with available fasting subsample data, which may limit representativeness. Nevertheless, the dual-cohort design and robust multivariable adjustment lend meaningful credibility to the findings.