Networks of episodic and persistent/recurrent depressive symptoms in older adults: Results from the survey of health, ageing, and retirement in Europe.

Authors

M W Stratmann, J Petter, L Kriston, B C Schneider, H-H König, A Hajek

Published in

International psychogeriatrics. Pages 100266. Sep 05, 2026. Epub Sep 05, 2026.

Abstract

The network theory of psychopathology proposes that dense symptom networks foster persistent symptoms. Studies testing this hypothesis in late-life depression (LLD) are lacking. This study examined whether, and how, cross-sectional networks of episodic and persistent/recurrent depressive symptoms in older adults differ.
Data of adults aged 60 + were drawn from the Survey of Health, Ageing and Retirement in Europe (SHARE). Depressive symptoms were assessed using the EURO-D scale. Participants were classified as episodic or persistent/recurrent based on their EURO-D score at wave 5 (2013) and wave 6 (2015) (EURO-D ≥4). For each group, separate cross-sectional symptom networks at wave 5 were estimated as Ising models and corrected for selection on the sum-score (Berkson's bias). The permutation-based Network Comparison Test was used to compare global strength and network structure.
Of 8519 participants, 3746 (44.0%) participants showed episodic depressive symptoms, while 4773 (56.0%) showed persistent/recurrent depressive symptoms. Neither global network strength nor network structure differed statistically significantly between groups (p = .17 and p = .60, respectively). In both networks, lack of interest, depressive mood, and suicidality showed highest strength and expected influence. Findings were consistent across sensitivity analyses.
Our results showed no difference in global strength and network structure between episodic and persistent/recurrent depressive symptoms in older adults, complementing mixed evidence from younger and middle-aged adults. Causal symptom interactions may play a smaller role in the persistence/recurrence of depressive symptoms in later life. Future studies should examine longitudinal networks, subtypes of LLD, and consider external factors.

PMID:
42701078
Bibliographic data and abstract were imported from PubMed on 06 Sep 2026.

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