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Background <p>Previous studies on psychological frailty in older adults have typically examined individual components or assumed population homogeneity, often overlooking latent heterogeneity and complex symptom interrelations. This study aimed to identify latent profiles of psychological frailty and to integrate computational simulation with network analysis to explore patterns of symptom associations.</p> Methods <p>This cross-sectional study included 4,735 adults aged 65 years or older. Psychological distress, cognitive function, physical vulnerability, and memory decline were assessed using established measures from the China Health and Retirement Longitudinal Study. Latent profile analysis was conducted in Mplus to identify subgroups, followed by Gaussian graphical model network analysis in R to examine symptom interrelations within each profile. Additionally, an in silico simulation approach using the NodeIdentifyR algorithm was applied to estimate changes in overall network burden under hypothetical symptom perturbations.</p> Results <p>Three latent profiles were identified: Relatively Healthy (57.8%), Moderate Distress (32.8%), and Severe Cognitive Deterioration (9.4%). Network structures differed significantly between the Moderate Distress and Severe Cognitive Deterioration profiles. The former showed higher centrality of orientation-related symptoms, whereas the latter was characterized by greater centrality of affective symptoms. In simulation analyses, hypothetical increases in powerlessness, depression, attentional problems, and functional limitations were associated with the largest estimated increases in overall network burden, whereas symptom-response patterns differed across latent profiles.</p> Conclusion <p>This cross-sectional study identified distinct latent profiles of psychological frailty and demonstrated differences in symptom network organization across profiles. The simulation findings indicate that certain symptoms may be associated with greater model-based changes in network activation. These results provide an exploratory framework for understanding heterogeneity in psychological frailty, but require validation in longitudinal and intervention studies.</p>

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Latent profiles and network dynamics of psychological frailty in older adults: a computational simulation study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Yangjuan Bao,
  • Zhili Jiang,
  • Fangjun Wang,
  • Min Fang,
  • Zhilong Wu,
  • Ziwen Chen

摘要

Background

Previous studies on psychological frailty in older adults have typically examined individual components or assumed population homogeneity, often overlooking latent heterogeneity and complex symptom interrelations. This study aimed to identify latent profiles of psychological frailty and to integrate computational simulation with network analysis to explore patterns of symptom associations.

Methods

This cross-sectional study included 4,735 adults aged 65 years or older. Psychological distress, cognitive function, physical vulnerability, and memory decline were assessed using established measures from the China Health and Retirement Longitudinal Study. Latent profile analysis was conducted in Mplus to identify subgroups, followed by Gaussian graphical model network analysis in R to examine symptom interrelations within each profile. Additionally, an in silico simulation approach using the NodeIdentifyR algorithm was applied to estimate changes in overall network burden under hypothetical symptom perturbations.

Results

Three latent profiles were identified: Relatively Healthy (57.8%), Moderate Distress (32.8%), and Severe Cognitive Deterioration (9.4%). Network structures differed significantly between the Moderate Distress and Severe Cognitive Deterioration profiles. The former showed higher centrality of orientation-related symptoms, whereas the latter was characterized by greater centrality of affective symptoms. In simulation analyses, hypothetical increases in powerlessness, depression, attentional problems, and functional limitations were associated with the largest estimated increases in overall network burden, whereas symptom-response patterns differed across latent profiles.

Conclusion

This cross-sectional study identified distinct latent profiles of psychological frailty and demonstrated differences in symptom network organization across profiles. The simulation findings indicate that certain symptoms may be associated with greater model-based changes in network activation. These results provide an exploratory framework for understanding heterogeneity in psychological frailty, but require validation in longitudinal and intervention studies.