The association between triglyceride-glucose index and its combination with post-stroke depression: NHANES 2005-2018.

Abstract

Background: Growing evidence indicates a link between insulin resistance and post-stroke depression (PSD). This study employed the triglyceride glucose (TyG) index as a measure of insulin resistance to investigate its relationship with PSD.

Methods: This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (2005-2018). PSD was assessed using data from patient health questionnaires, while the TyG index was calculated based on fasting venous blood glucose and fasting triglyceride levels. The formula used for the TyG index is ln[triglycerides (mg/dL) × fasting blood glucose (mg/dL)/2]. Participants were categorized into four groups according to the TyG index quartiles. A weighted multivariable logistic regression model was applied to examine the relationship between the TyG index and PSD.

Results: A total of 1217 patients were included in the study, of which 232 were diagnosed with PSD. The TyG index was divided into quartiles (Q1-Q4) for analysis. After adjusting for potential confounders, we found a significant positive association between the highest quartile of the TyG index (Q4: ≥9.33) and PSD (OR = 2.51, 95% CI: 1.04-6.07, p = 0.041). This suggests that in the U.S. adult stroke population, individuals with higher TyG indices are more likely to experience depressive symptoms. Subgroup analysis further confirmed a stable and independent positive association between the TyG index and PSD (all trend p > 0.05).

Conclusion: In this large cross-sectional study, our results suggest that among US adults who have experienced a stroke, those with higher TyG index levels are more likely to exhibit depressive symptoms. This provides a novel approach for the clinical prevention of PSD. Patients with higher TyG indices in the stroke population may require closer psychological health monitoring and timely intervention. Additionally, since the TyG index is calculated using only fasting blood glucose and triglyceride levels, it can help identify high-risk PSD patients, particularly in regions with limited healthcare resources.