Combined triglyceride–glucose and frailty index (TyGFI) and risk of endometrial cancer in U.S. women aged ≥45: NHANES 2011–2018 analysis integrating data engineering and machine learning with logistic modeling

Endometrial cancer (EC) incidence is closely linked to metabolic and hormonal factors. The TyGFI, a composite indicator integrating the triglyceride–glucose index and frailty index, may capture combined risk dimensions relevant to EC etiology and prediction. This study aimed to evaluate the association between TyGFI and EC prevalence among U.S. women aged 45 years and older, and to explore its predictive utility using machine-learning approaches. Data were drawn from the National Health and Nutrition Examination Survey 2011–2018 cycles. The exposure was TyGFI, and the outcome was EC status ascertained from self-reported cancer history and standardized questionnaires. From an initial 39,156 participants, we excluded males, individuals aged under 45 years, those missing TyGFI components or EC data, and extreme TyGFI values, yielding a final cohort of 2,837 women. We performed exploratory feature selection and built six predictive models using machine-learning algorithms to identify key predictors and evaluate TyGFI’s contribution to model performance. Survey-weighted multivariable logistic regression estimated the association between TyGFI and EC prevalence with sequential adjustment for covariates. In a weighted sample representing 30,489,082 U.S. women aged 45 years and older, higher TyGFI was significantly associated with EC prevalence. After full adjustment, each unit increase in TyGFI corresponded to a 57% increase in the odds of EC (odds ratio 1.570; 95% confidence interval 1.033–2.370; p = 0.0322), with a significant dose-response trend across quartiles (p for trend = 0.0257). Among six machine-learning models, CatBoost achieved the highest predictive performance, with an area under the curve of 0.999. SHAP analysis identified TyGFI as the most influential predictor, followed by age and serum albumin. In this nationally representative sample of middle-aged and older U.S. women, TyGFI was significantly associated with EC prevalence and emerged as the dominant predictor in machine-learning models. These findings suggest that TyGFI may enhance risk stratification for EC beyond established reproductive and metabolic factors, though prospective studies are warranted to validate its clinical utility.