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Background <p>Late-life depression (LLD) poses a significant health risk among the elderly, with widowhood as a prominent contributing factor. However, the mechanisms that render some widowed individuals susceptible to depression while others remain resilient remain poorly understood.</p> Methods <p>In this five-year longitudinal study, we followed 203 cognitively healthy, widowed elderly individuals (mean age: 65.2 years, 100 women). The median follow-up time was 4.8 years. Brain structural networks were constructed via diffusion tensor imaging and analyzed using graph theory metrics. Logistic regression and Cox proportional hazards models were employed to assess the predictive role of structural network attributes in depression onset. Moderation models further examined the influence of psychosocial factors on depression risk.</p> Results <p>During our follow-up, 22 participants developed LLD (mean age: 65.6 years, 12 women). Altered brain structural network properties, alongside key psychosocial factors, were observed in those at risk of developing depression prior to symptom emergence. Logistic and Cox regression models revealed that decreased rich-club connections, reduced nodal efficiency in the left hippocampus (HIP.L), and lower network modularity significantly predicted depression onset. Additionally, these network alterations correlated with greater depression severity at follow-up. Moderation analyses indicated that weekly exercise frequency and time spent with children notably mitigated the effects of network disruptions on depression severity.</p> Conclusions <p>Among cognitively healthy widowed elders, diminished rich-club connections, modularity, and HIP.L nodal efficiency are strong predictors of future depression risk. Furthermore, low physical activity and limited family interaction may amplify susceptibility within this high-risk group, suggesting that targeted early interventions could reduce depression risk in this vulnerable population.</p> Clinical trial number <p>Not applicable.</p>

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Long-term risk of late-life depression in widowed elderly: a five-year follow-up study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月19日

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  • Yang Li,
  • Hu Xu,
  • Jiale Wu,
  • Yi Ding,
  • Yunqian Zhu,
  • Yang Wang,
  • Xingbing Chen,
  • Hui Su

摘要

Background

Late-life depression (LLD) poses a significant health risk among the elderly, with widowhood as a prominent contributing factor. However, the mechanisms that render some widowed individuals susceptible to depression while others remain resilient remain poorly understood.

Methods

In this five-year longitudinal study, we followed 203 cognitively healthy, widowed elderly individuals (mean age: 65.2 years, 100 women). The median follow-up time was 4.8 years. Brain structural networks were constructed via diffusion tensor imaging and analyzed using graph theory metrics. Logistic regression and Cox proportional hazards models were employed to assess the predictive role of structural network attributes in depression onset. Moderation models further examined the influence of psychosocial factors on depression risk.

Results

During our follow-up, 22 participants developed LLD (mean age: 65.6 years, 12 women). Altered brain structural network properties, alongside key psychosocial factors, were observed in those at risk of developing depression prior to symptom emergence. Logistic and Cox regression models revealed that decreased rich-club connections, reduced nodal efficiency in the left hippocampus (HIP.L), and lower network modularity significantly predicted depression onset. Additionally, these network alterations correlated with greater depression severity at follow-up. Moderation analyses indicated that weekly exercise frequency and time spent with children notably mitigated the effects of network disruptions on depression severity.

Conclusions

Among cognitively healthy widowed elders, diminished rich-club connections, modularity, and HIP.L nodal efficiency are strong predictors of future depression risk. Furthermore, low physical activity and limited family interaction may amplify susceptibility within this high-risk group, suggesting that targeted early interventions could reduce depression risk in this vulnerable population.

Clinical trial number

Not applicable.