ArticleEuropean archives of psychiatry and clinical neuroscience2026
Triglyceride-glucose waist-to-height ratio improves risk stratification for depressive symptoms: evidence from NHANES.
Article in European archives of psychiatry and clinical neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe triglyceride-glucose (TyG) index is linked to metabolic dysfunction that may contribute to depressive symptoms, but its value for population risk stratification is unclear. We aimed to examine TyG-related indices and develop an internally validated prediction model for depressive symptoms in U.S. adults.
methodsData were obtained from the National Health and Nutrition Examination Survey (NHANES). Survey-weighted logistic regression was used to evaluate associations between TyG-related indices, including the TyG-waist-to-height ratio (TyG-WHtR), and depressive symptoms. Predictors were selected using least absolute shrinkage and selection operator (LASSO) regression and entered into a multivariable logistic regression model to construct a nomogram. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) and internally validated using bootstrap resampling and leave-one-out cross-validation.
resultsAmong 3,870 adults, higher TyG-related indices were associated with greater odds of depressive symptoms, with TyG-WHtR showing the strongest association [highest versus lowest tertile: odds ratio (OR) = 2.37, 95% Confidence Intervals (CI) : 1.59-3.52]. Poverty income ratio, smoking status, and TyG-WHtR were retained in the final parsimonious model, which demonstrated acceptable discrimination (AUC = 0.715) and stable internal performance after bootstrap correction (optimism-corrected AUC = 0.711).
conclusionTyG-WHtR was independently associated with depressive symptoms and improved population-level risk stratification in U.S. adults. The internally validated model requires external validation before any clinical implementation.
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