Exploring the triglyceride-glucose index's role in depression and cognitive dysfunction: Evidence from NHANES with machine learning support

Chao Ding; Renjie Lu; Zhiyu Kong; Rong Huang

doi:10.1016/j.jad.2025.01.051

Exploring the triglyceride-glucose index's role in depression and cognitive dysfunction: Evidence from NHANES with machine learning support

J Affect Disord. 2025 Jan 11:S0165-0327(25)00063-1. doi: 10.1016/j.jad.2025.01.051. Online ahead of print.

Authors

Chao Ding¹, Renjie Lu¹, Zhiyu Kong², Rong Huang³

Affiliations

¹ Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
² South China University of Technology, Guangzhou, China.
³ Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China. Electronic address: ptn1724@shutcm.edu.cn.

PMID: 39805501
DOI: 10.1016/j.jad.2025.01.051

Abstract

Background: Depression and cognitive impairments are prevalent among older adults, with evidence suggesting potential links to obesity and lipid metabolism disturbances. This study investigates the relationships between the triglyceride-glucose (TyG) index, body mass index (BMI), depression, and cognitive dysfunction in older adults, leveraging data from the NHANES survey and employing machine learning techniques.

Methods: We analysed 1352 participants aged 60-79 from the 2011-2014 NHANES dataset, who underwent cognitive function testing, depression assessments, and TyG index measurements. Multivariate linear regression and subgroup analyses were conducted to examine associations between the TyG index and depression/cognitive impairment. Machine learning models evaluated the importance of predictive factors for depression, while Mendelian randomization (MR) was employed to explore the causal relationship between BMI and depression/cognitive function.

Results: The TyG index was negatively associated with cognitive function scores and positively associated with depression scores in adjusted models (p < 0.001). In fully adjusted subgroup analyses, among obese individuals (BMI ≥ 28), a 100-unit increase in the TyG index was linked to a 3.79-point decrease in depression scores. Machine learning models (Xgboost, AUC = 0.960) identified BMI, TyG-BMI, gender, and comorbidities (e.g., asthma, stroke, emphysema) as key determinants of depression. MR analyses revealed a negative association between BMI and depression risk [OR: 0.9934; 95 % CI (0.9901-0.9968), p = 0.0001] and cognitive dysfunction risk [OR: 0.8514; 95 % CI (0.7929-0.9143), p < 0.05]. No evidence of heterogeneity or pleiotropy was detected.

Limitations: Depression and cognitive impairments were self-reported, potentially introducing bias. The observed associations may be influenced by unmeasured confounders, necessitating further research into the underlying mechanisms.

Conclusions: Our findings reveal associations between the TyG index and psychocognitive health in older adults. While these results highlight lipid metabolism as a potential factor in depression and cognitive dysfunction, further studies are needed to validate these findings and explore underlying mechanisms.

Keywords: BMI; Cognitive function; Depression; NHANES; Older people; Triglyceride-glucose index.