Estimated glucose disposal rate outperforms thirteen other insulin resistance indices in predicting new-onset cardiometabolic multimorbidity: a longitudinal study from the china health and retirement longitudinal study (CHARLS) and cross-sectional replication from the national health and nutrition examination survey (NHANES).

backgroundThe co-occurrence of multiple cardiometabolic conditions has been mechanistically linked to impaired insulin signaling, yet the relative utility of surrogate insulin resistance (IR) markers in stratifying cardiometabolic multimorbidity (CMM) risk has not been systematically established. This study aimed to systematically evaluate and compare the associations of 14 IR indices with CMM incidence in a longitudinal Chinese cohort, with external replication in a U.S. nationally representative sample.

methodsThis study included 8,522 participants from the China Health and Retirement Longitudinal Study (CHARLS) without CMM at baseline (2011). CMM was defined as the concurrent presence of at least two of the following three cardiometabolic conditions: type 2 diabetes, heart disease, and stroke. All 14 IR indices were evaluated both at baseline and as cumulative time-weighted averages derived from repeated measurements in 2011 and 2015. Associations between IR indices and CMM risk were examined using multivariable logistic regression, with dose-response relationships characterized through restricted cubic spline (RCS) modeling. Discriminatory capacity was quantified via receiver operating characteristic (ROC) curve analysis, complemented by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics. To verify the robustness of primary findings, Cox proportional hazards regression and pre-defined subgroup analyses were performed as supplementary sensitivity analyses. External cross-sectional replication was performed in the National Health and Nutrition Examination Survey (NHANES; 1999-2018).

resultsDuring follow-up through 2020, 591 CHARLS participants (6.9%) developed incident CMM. After comprehensive covariate adjustment, each of the 14 surrogate IR indices demonstrated statistically significant and independent associations with incident CMM risk. eGDR and SPISE demonstrated significant inverse associations, indicating that higher values reflect greater insulin sensitivity and lower CMM risk (cumulative eGDR per SD: OR 0.51, 95% CI 0.41-0.65; cumulative SPISE per SD: OR 0.64, 95% CI 0.54-0.75), whereas the remaining 12 indices showed significant positive associations. RCS analyses revealed predominantly linear dose-response relationships across most indices, except for TyHGB and TyG-AIP, which exhibited significant non-linearity. eGDR achieved the highest discriminatory performance at both baseline (AUC: 0.716) and cumulative (AUC: 0.727) assessments, significantly outperforming TyG (all P < 0.001), and yielded the greatest NRI and IDI improvements among all 14 indices. These findings were consistently replicated in the NHANES cross-sectional validation: eGDR and SPISE again demonstrated the strongest inverse associations (eGDR per SD: OR 0.598, 95% CI 0.499-0.717; SPISE per SD: OR 0.704, 95% CI 0.614-0.807), and eGDR achieved the highest AUC (0.756) and the greatest NRI and IDI improvements among all indices.

conclusionsAcross both the Chinese and U.S. study populations, statistically significant and independent associations with CMM were consistently observed for all 14 evaluated IR indices. Among these, eGDR consistently demonstrated the most superior discriminatory performance and risk reclassification capability across longitudinal and cross-sectional settings, single time-point and cumulative assessments, and ethnically distinct populations, supporting its adoption as a preferred, clinically accessible marker for CMM risk stratification. The convergent findings across two large, nationally representative populations of distinct ethnic backgrounds further underscore the cross-population generalizability of eGDR and its potential translational utility in diverse clinical settings.