The symptom network structure of depressive symptoms in late-life: Results from a European population study

Martino Belvederi Murri, Mario Amore, Matteo Respino, George S. Alexopoulos

Research output: Contribution to journalArticle

Abstract

The network theory conceptualizes mental disorders as complex networks of symptoms influencing each other by creating feedback loops, leading to a self-sustained syndromic constellation. Symptoms central to the network have the greatest impact in sustaining the rest of symptoms. This analysis focused on the network structure of depressive symptoms in late-life because of their distinct etiologic factors, clinical presentation, and outcomes. We analyzed cross-sectional data from wave 2 of the 19 country Survey of Health, Ageing, and Retirement in Europe (SHARE) and included non-institutionalized adults aged 65 years or older (mean age 74 years, 59% females) endorsing at least one depressive symptom on the EURO-D scale for depression (N =8,557). We characterized the network structure of depressive symptoms in late-life and used indices of “strength”, “betweenness”, and “closeness” to identify symptoms central to the network. We used a case-dropping bootstrap procedure to assess network stability. Death wishes, depressed mood, loss of interest, and pessimism had the highest values of centrality. Insomnia, fatigue and appetite changes had lower centrality values. The identified network remained stable after dropping 74.5% of the sample. Sex or age did not significantly influence the network structure. In conclusion, death wishes, depressed mood, loss of interest, and pessimism constitute the “backbone” that sustains depressive symptoms in late-life. Symptoms central to the network of depressive symptoms may be used as targets for novel, focused interventions and in studies investigating neurobiological processes central to late-life depression.

Original languageEnglish
JournalMolecular Psychiatry
DOIs
Publication statusAccepted/In press - Jan 1 2018

ASJC Scopus subject areas

  • Molecular Biology
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience

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