# The impact of the TyG index and psychosocial factors on depression in elderly non-diabetic patients with atrial fibrillation

## TL;DR

This study finds that cognitive function, social support, sleep quality, and a metabolic biomarker (TyG index) are linked to depression in elderly non-diabetic patients with atrial fibrillation.

## Contribution

The study introduces the TyG index as a novel metabolic predictor of depression in non-diabetic elderly atrial fibrillation patients.

## Key findings

- Lower cognitive function and social support scores were independently associated with depression.
- Poor sleep quality and higher TyG index were also significant predictors of depression.
- The TyG index's effect on depression was more pronounced in patients with low social support.

## Abstract

Atrial fibrillation (AF), a common arrhythmia in the elderly, often causes complications that severely impact quality of life and survival. Depression is common in AF patients and correlates with AF severity. The triglyceride-glucose index (TyG), a novel metabolic biomarker for cardiovascular disease, has also been linked to depression.

This retrospective study enrolled 337 elderly non-diabetic AF patients admitted to the Department of Cardiology at Jiading District Central Hospital from August 2024 to August 2025. Patients were divided into depression and non-depression groups according to a Patient Health Questionnaire-9 (PHQ-9) score≥ 5. Baseline characteristics, clinical biomarkers and emotional assessments were compared between groups. Variables with p<0.1 were entered into logistic regression to identify independent predictors of depression.

No significant differences were observed between the depression (n=86) and non-depression (n=251) groups in demographic or clinical characteristics (age, sex, BMI, smoking, alcohol use, or hypertension; all p> 0.05). However, significant group differences were identified in metabolic markers (total cholesterol, LDL, and urea; p= 0.034, 0.033, and 0.009, respectively) and psychological assessments (Pittsburgh Sleep Quality Index [PSQI], Chinese version of the Mini-Mental State Examination [CMMSE], and Social Support Rating Scale [SSRS]; all p< 0.001). Logistic regression analysis identified four potential predictors of depression: lower CMMSE score (OR = 0.859, 95% CI: 0.779–0.949; p= 0.002), lower SSRS score (OR = 0.808, 95% CI: 0.747–0.874; p< 0.001), poor sleep quality (higher PSQI; OR = 1.392, 95% CI: 1.266–1.531; p< 0.001), and higher TyG index (OR = 2.15, 95% CI: 1.042–4.450; p= 0.038). Exploratory stratified analyses revealed that cognitive function (CMMSE) and sleep quality (PSQI) were not significantly associated with the TyG index (both p>0.05), suggesting their independent contributions to depression. For social support (SSRS), TyG index did not differ between depression and non-depression groups in the high-support subgroup (SSRS> 30), but a significant difference was observed in the low-support subgroup (SSRS 20-30; p = 0.002).

This study identifies cognitive function, social support, sleep quality and the TyG index as potential influencing factors for depression in elderly non-diabetic AF patients. Targeted management of these factors may improve mental health and overall prognosis in this population.