Comparison analysis of the triglyceride-glucose index, triglyceride-glucose and frailty index in cardiovascular disease and mortality association among patients with rheumatoid arthritis

Cardiovascular disease (CVD) is a major cause of excess mortality among patients with rheumatoid arthritis (RA). Conventional risk assessment may not fully capture the metabolic abnormalities and physiological vulnerability associated with RA. We investigated whether the triglyceride-glucose-frailty index (TyG-FI), which combines a surrogate marker of insulin resistance with accumulated health deficits, was associated with prevalent CVD and cardiovascular mortality among adults with RA. We analyzed 1090 adults with self-reported RA from 10 cycles of the National Health and Nutrition Examination Survey conducted between 1999 and 2018. TyG-FI was calculated as the product of the triglyceride-glucose index and a 49-item deficit-accumulation frailty index. Multivariable logistic regression was used to examine the association with prevalent CVD, whereas Cox proportional hazards regression was used to examine cardiovascular mortality. Restricted cubic splines, subgroup analyses, sensitivity analyses, and discrimination analyses were conducted as secondary analyses. In the fully adjusted models, each 1-unit higher TyG-FI was associated with higher odds of prevalent CVD (odds ratio, 4.50; 95% confidence interval, 3.20-6.33) and a higher hazard of cardiovascular mortality (hazard ratio, 1.77; 95% confidence interval, 1.16-2.70). The reported areas under the receiver operating characteristic curve were 0.80 for prevalent CVD and 0.81 for cardiovascular mortality in models incorporating TyG-FI, compared with 0.73 and 0.80, respectively, in models incorporating TyG. An exploratory subgroup analysis suggested a stronger association between TyG-FI and prevalent CVD among participants aged ≤65 years (P for interaction = .044). Higher TyG-FI was associated with prevalent CVD and cardiovascular mortality among US adults with self-reported RA. TyG-FI may capture complementary metabolic and frailty-related information; however, its incremental predictive value, calibration, clinical utility, and external validity require further evaluation before clinical implementation.