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

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

• Fengjiao Liang,
• Xiaoqian Shan,
• Xiang Chen &
• Banghua Yang 

BMC Psychiatry volume 25, Article number: 243 (2025) Cite this article

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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.

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Introduction

Post-stroke depression (PSD) is one of the most prevalent complications following a stroke, marked by high incidence, mortality, and disability rates. A meta-analysis by Hackett et al., which included 51 studies, revealed that the comorbidity rate of PSD is approximately 33% [1]. Jianglong Guo summarized several representative studies and found that the incidence of PSD ranges from 11 to 41% [2]. Overall, about one-third of stroke patients experience depression. In recent years, the number of PSD patients has been gradually increasing due to the sharp rise in the number of stroke patients [1,2,3]. The occurrence of PSD significantly impacts the daily life and rehabilitation of stroke patients, increasing the burden on their families. Researches indicate that the development of PSD is closely related to various factors, including social factors and individual characteristics [4, 5].

Insulin resistance (IR) refers to the reduced sensitivity of insulin target cells to insulin concentration. It is not only associated with the occurrence of various metabolic-related diseases but also closely linked to the development of cardiovascular and cerebrovascular diseases [6, 7]. A longitudinal analysis of the Netherlands Study of Depression and Anxiety (NESDA) cohort by researchers from Stanford University revealed that individuals with insulin resistance exhibited a two-fold increased risk of developing major depressive disorder (MDD) compared to their insulin-sensitive counterparts. Notably, even among participants without baseline depressive symptoms, those with insulin resistance demonstrated a substantially elevated MDD incidence during follow-up [8]. Fernandes BS and colleagues have proposed that insulin resistance may serve as a biomarker for acute depressive states, with their meta-analysis demonstrating significant associations between IR and depression severity scores [9]. Studies have shown that for each unit increase in the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), the risk of PSD in stroke patients increases accordingly [10, 11]. This confirms the positive correlation between insulin resistance and the risk of PSD, suggesting that insulin resistance is an independent risk factor for PSD. The HOMA-IR is useful for the early prediction and assessment of PSD. However, the calculation of HOMA-IR requires fasting serum insulin, which is not a routine biochemical indicator and is thus limited in clinical application (it is not practical in clinical practice).

The triglyceride-glucose index (TyG index) is also an effective means of assessing insulin resistance [12]. Compared to the HOMA-IR, the TyG index offers distinct advantages in clinical applicability. Its calculation does not require fasting insulin measurements, relying solely on triglycerides and fasting glucose levels, thereby simplifying implementation and reducing costs in routine practice [13]. Furthermore, the TyG index exhibits high sensitivity and specificity for identifying insulin resistance, outperforming HOMA-IR in detecting individuals with reduced insulin sensitivity across diverse populations [14]. Critically, the TyG index demonstrates superior predictive performance for metabolic abnormalities compared to HOMA-IR, with consistent efficacy observed in key subgroups such as older adults and individuals with obesity [15]. To date, no studies have directly indicated a correlation between the TyG index and the risk of PSD. Therefore, our research team used a cross-sectional design based on data from the National Health and Nutrition Examination Survey (NHANES) to evaluate whether there is an association between the TyG index and the risk of PSD. The aim of this study is to provide a potential new method for clinically identifying PSD, which could be used for the early detection of high-risk groups, thereby guiding targeted interventions to mitigate the burden of PSD and improve patient outcomes.

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