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Background <p>To explore the associations between clustering of cardiometabolic risk factors, physical activity (PA), and digital exclusion with depressive symptoms trajectories, and to assess whether PA and digital exclusion mediate these associations.</p> Methods <p>This study included 8999 participants from the Health and Retirement Study (HRS, <i>n</i> = 4380) and the English Longitudinal Study of Ageing (ELSA, <i>n</i> = 4519). Cardiometabolic risk factors included waist circumference, high-density lipoprotein (HDL) cholesterol, systolic and diastolic blood pressure, type 2 diabetes mellitus, glycated hemoglobin (HbA1c), and C-reactive protein (CRP). Depressive symptoms, PA, and digital exclusion were assessed via self-report. Statistical analyses used multinomial logistic regression and the Karlson-Holm-Breen (KHB) method.</p> Results <p>We identified three clustering of cardiometabolic risk factors and four depressive symptoms trajectories. Compared to participants classified as healthy obesity, those in the obesity-hypertension group had an OR of 1.26 (95% CI: 1.09–1.46) for the moderately severe depressive symptom trajectory, while those in the complex cardiometabolic group had an OR of 1.31 (95% CI: 1.08–1.58), 1.79 (95% CI: 1.45–2.23), or 2.19 (95% CI: 1.66–2.90) for the moderate, moderately severe, or severe depressive symptom trajectory, respectively. Compared to those with PA insufficiency and digital exclusion, among PA sufficient participants, the OR of being in the moderate, moderately severe, or severe depressive symptom trajectory was 0.74 (95% CI: 0.65–0.84), 0.57 (95% CI: 0.50–0.66), or 0.35 (95% CI: 0.20–0.61), respectively; among digitally engaged participants, the OR of being in the moderately severe or severe trajectory was 0.63 (95% CI: 0.53–0.75) or 0.42 (95% CI: 0.33–0.54), respectively. Compared with the healthy obesity group, those with sufficient PA or digital inclusion in obesity-hypertension and complex cardiometabolic groups had lower odds of depressive symptoms trajectories. Mediation analysis showed that PA and digital inclusion accounted for 12.5% and 6.67% of the association between clustering of cardiometabolic risk factors and the severe depressive symptom trajectory, respectively.</p> Conclusions <p>The obesity-hypertension was only associated with increased odds of the moderately severe depressive symptom trajectory, while the complex cardiometabolic pattern was associated with increased odds of depressive symptoms trajectories; in contrast, sufficient PA and digital inclusion were associated with reduced odds.</p>

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Clustering of cardiometabolic risk factors, physical activity, digital exclusion, and depressive symptoms trajectories in adults aged 50 years and older: findings from two prospective cohort studies

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  • 发表日期：

    2025年12月28日

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  • Xiaosheng Dong,
  • Jiaqiang Xiao,
  • Xiangren Yi,
  • Meng Ding,
  • Mingyang Bai,
  • Xinmeng Guo,
  • Xiao Hou,
  • Chengchao Zhou

摘要

Background

To explore the associations between clustering of cardiometabolic risk factors, physical activity (PA), and digital exclusion with depressive symptoms trajectories, and to assess whether PA and digital exclusion mediate these associations.

Methods

This study included 8999 participants from the Health and Retirement Study (HRS, n = 4380) and the English Longitudinal Study of Ageing (ELSA, n = 4519). Cardiometabolic risk factors included waist circumference, high-density lipoprotein (HDL) cholesterol, systolic and diastolic blood pressure, type 2 diabetes mellitus, glycated hemoglobin (HbA1c), and C-reactive protein (CRP). Depressive symptoms, PA, and digital exclusion were assessed via self-report. Statistical analyses used multinomial logistic regression and the Karlson-Holm-Breen (KHB) method.

Results

We identified three clustering of cardiometabolic risk factors and four depressive symptoms trajectories. Compared to participants classified as healthy obesity, those in the obesity-hypertension group had an OR of 1.26 (95% CI: 1.09–1.46) for the moderately severe depressive symptom trajectory, while those in the complex cardiometabolic group had an OR of 1.31 (95% CI: 1.08–1.58), 1.79 (95% CI: 1.45–2.23), or 2.19 (95% CI: 1.66–2.90) for the moderate, moderately severe, or severe depressive symptom trajectory, respectively. Compared to those with PA insufficiency and digital exclusion, among PA sufficient participants, the OR of being in the moderate, moderately severe, or severe depressive symptom trajectory was 0.74 (95% CI: 0.65–0.84), 0.57 (95% CI: 0.50–0.66), or 0.35 (95% CI: 0.20–0.61), respectively; among digitally engaged participants, the OR of being in the moderately severe or severe trajectory was 0.63 (95% CI: 0.53–0.75) or 0.42 (95% CI: 0.33–0.54), respectively. Compared with the healthy obesity group, those with sufficient PA or digital inclusion in obesity-hypertension and complex cardiometabolic groups had lower odds of depressive symptoms trajectories. Mediation analysis showed that PA and digital inclusion accounted for 12.5% and 6.67% of the association between clustering of cardiometabolic risk factors and the severe depressive symptom trajectory, respectively.

Conclusions

The obesity-hypertension was only associated with increased odds of the moderately severe depressive symptom trajectory, while the complex cardiometabolic pattern was associated with increased odds of depressive symptoms trajectories; in contrast, sufficient PA and digital inclusion were associated with reduced odds.