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Depressive symptom complexes of community-dwelling older adults: a latent network model

Molecular Psychiatry volume 27, pages 1075–1082 (2022)Cite this article

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Abstract

Late-life depression has multiple, heterogeneous clinical presentations. The aim of the study was to identify higher-order homogeneous clinical features (symptom complexes), while accounting for their potential causal interactions within the network approach to psychopathology. We analyzed cross-sectional data from community-dwelling adults aged 65–85 years recruited by the European MentDis_ICF65+ study (n = 2623, mean age 74, 49% females). The severity of 33 depressive symptoms was derived from the age-adapted Composite International Diagnostic Interview. Symptom complexes were identified using multiple detection algorithms for symptom networks, and their fit to data was assessed with latent network models (LNMs) in exploratory and confirmatory analyses. Sensitivity analyses included the Partial Correlation Likelihood Test (PCLT) to investigate the data-generating structure. Depressive symptoms were organized by the Walktrap algorithm into eight symptom complexes, namely sadness/hopelessness, anhedonia/lack of energy, anxiety/irritability, self-reproach, disturbed sleep, agitation/increased appetite, concentration/decision making, and thoughts of death. An LNM adequately fit the distribution of individual symptoms’ data in the population. The model suggested the presence of reciprocal interactions between the symptom complexes of sadness and anxiety, concentration and self-reproach and between self-reproach and thoughts of death. Results of the PCLT confirmed that symptom complex data were more likely generated by a network, rather than a latent-variable structure. In conclusion, late-life depressive symptoms are organized into eight interacting symptom complexes. Identification of the symptom complexes of late-life depression may streamline clinical assessment, provide targets for personalization of treatment, and aid the search for biomarkers and for predictors of outcomes of late-life depression.

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Fig. 1: Network of depressive symptoms and complex detection with the Walktrap algorithm.
Fig. 2: Latent network model of depressive symptoms based on the Walktrap complex detection algorithm.
Fig. 3: Diagram of the Walktrap latent network.

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Acknowledgements

The authors would like to thank other collaborators of the Mentdis ICF65+ study: Chiara Da Ronch, Holger Schulz, Maria Dehoust, Susanne Sehner, Anna Suling, Karl Wegscheider, Mike J. Crawford, Yael Hershkovitz, Alan Quirk, Ora Rotenstein, Ana Belén Santos-Olmo, Arieh Shalev, and Jens Strehle. All participants to the study are gratefully acknowledged. We also express our gratitude to all interviewers, dedicated collaborators, and local institutions that made this study possible.

Funding

This research was funded by a grant from the European Commission (Grant No: 223105) within the 7th Framework Research Program of the EU.

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  1. These authors contributed equally: Martino Belvederi Murri, Luigi Grassi.

Authors and Affiliations

  1. Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy

    Martino Belvederi Murri, Luigi Grassi, Rosangela Caruso, Maria Giulia Nanni & Luigi Zerbinati

  2. Department of Medical Psychology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany

    Sylke Andreas & Martin Härter

  3. Institute for Psychology, Universität Klagenfurt, A-9020, Klagenfurt, Austria

    Sylke Andreas

  4. School of Psychology, Personality, Evaluation and Clinical Psychology Department, University Complutense of Madrid, Campus de Somosaguas s/n, 28223, Madrid, Spain

    Berta Ausín & Manuel Muñoz Lopez

  5. Division of Institutional Measures, University Hospitals of Geneva, 1208, Geneva, Switzerland

    Alessandra Canuto & Kerstin Weber

  6. Clinical Psychology & Psychotherapy RG, Department of Psychiatry & Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany

    Hans-Ulrich Wittchen

  7. Department of Psychosocial Prevention, University of Heidelberg, Bergheimer Str. 54, 69115, Heidelberg, Germany

    Jana Volkert

  8. Institute of Psychology, University of Kassel, Holländische Str. 36-38, 34127, Kassel, Germany

    Jana Volkert

  9. Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, USA

    George S. Alexopoulos

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Contributions

MBM, LG, and GA designed the present study, MBM analyzed the data. LG, RC, MGN, LZ, SA, BA, AC, MH, MML, KW, HUW, and JV designed and conducted the MENTDIS study. MBM, GA, and LG wrote the paper with helpful contributions from RC, MGN, LZ, SA, BA, AC, MH, MML, KW, HUW, and JV. All authors contributed to the interpretation of data, provided critical feedback on manuscript drafts, and approved the final draft.

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Correspondence to George S. Alexopoulos.

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Competing interests

GSA served on Advisory Board of Eisai and of Janssen Pharmaceuticals. He also served on the Speakers Bureaus of Allergan, Otsuka, and Takeda-Lundbeck. No other authors report conflicts of interest.

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Belvederi Murri, M., Grassi, L., Caruso, R. et al. Depressive symptom complexes of community-dwelling older adults: a latent network model. Mol Psychiatry 27, 1075–1082 (2022). https://doi.org/10.1038/s41380-021-01310-y

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  • DOI: https://doi.org/10.1038/s41380-021-01310-y

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