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Background <p>Cardiometabolic multimorbidity and late-life depression are increasingly prevalent among rural older adults in China, imposing a compounded health burden. Although social support is recognized as a protective factor, the specific linkages between its dimensions and individual depressive symptoms in this population remain unclear. Network analysis provides a novel approach to explore these nuanced relationships. This study employed network analysis to examine the detailed associations between multidimensional social support and specific depressive symptoms in rural older adults with cardiometabolic multimorbidity.</p> Methods <p>A cross-sectional survey was conducted among 1,486 rural adults aged ≥ 60 years with cardiometabolic multimorbidity in Shandong Province, China. Depressive symptoms were assessed using the Patient Health Questionnaire-9, and social support was measured with the Social Support Rating Scale. A regularized partial correlation network was constructed to visualize symptom-level interactions. Centrality and bridge centrality indices were calculated to identify core and bridging nodes.</p> Results <p>The strongest connections were within depressive symptoms, notably between anhedonia and sad mood (edge weight = 0.55), and anhedonia and fatigue (0.33). Subjective and objective support were closely linked (0.45). The strongest (negative) bridge connection emerged between subjective support and sad mood (-0.05). Sad mood, fatigue, and anhedonia were the most central symptoms, while subjective support and sad mood were key bridge nodes. The network showed high stability.</p> Conclusion <p>Interventions targeting central depressive symptoms—sad mood, anhedonia, and fatigue—may effectively alleviate depression in this population. Given the modest strength of bridge connections within the network, interventions focusing on bridge nodes such as subjective support should be implemented in conjunction with those addressing core depressive symptoms, rather than in isolation. These findings underscore the necessity for integrated psychosocial and medical care within rural health services.</p>

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The association between social support and depressive symptoms in rural older adults with cardiometabolic multimorbidity: a network analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月12日

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  • Zhiwei Wang,
  • Shengjia Xu,
  • Xueqing Song,
  • Lan Gao,
  • Siya Meng,
  • Xiaorong Luan,
  • Wenzhe Qin

摘要

Background

Cardiometabolic multimorbidity and late-life depression are increasingly prevalent among rural older adults in China, imposing a compounded health burden. Although social support is recognized as a protective factor, the specific linkages between its dimensions and individual depressive symptoms in this population remain unclear. Network analysis provides a novel approach to explore these nuanced relationships. This study employed network analysis to examine the detailed associations between multidimensional social support and specific depressive symptoms in rural older adults with cardiometabolic multimorbidity.

Methods

A cross-sectional survey was conducted among 1,486 rural adults aged ≥ 60 years with cardiometabolic multimorbidity in Shandong Province, China. Depressive symptoms were assessed using the Patient Health Questionnaire-9, and social support was measured with the Social Support Rating Scale. A regularized partial correlation network was constructed to visualize symptom-level interactions. Centrality and bridge centrality indices were calculated to identify core and bridging nodes.

Results

The strongest connections were within depressive symptoms, notably between anhedonia and sad mood (edge weight = 0.55), and anhedonia and fatigue (0.33). Subjective and objective support were closely linked (0.45). The strongest (negative) bridge connection emerged between subjective support and sad mood (-0.05). Sad mood, fatigue, and anhedonia were the most central symptoms, while subjective support and sad mood were key bridge nodes. The network showed high stability.

Conclusion

Interventions targeting central depressive symptoms—sad mood, anhedonia, and fatigue—may effectively alleviate depression in this population. Given the modest strength of bridge connections within the network, interventions focusing on bridge nodes such as subjective support should be implemented in conjunction with those addressing core depressive symptoms, rather than in isolation. These findings underscore the necessity for integrated psychosocial and medical care within rural health services.