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Age matters: a narrative review and machine learning analysis on shared and separate multidimensional risk domains for early and late onset suicidal behavior

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Abstract

There is considerable heterogeneity among late-life suicide attempters who can present stark differences in their suicidal trajectories. This work provides a narrative review of sources of heterogeneity of suicide risk in late-life depression and describes a quantitative study of the relative importance of multidimensional risk domains, in discriminating suicide attempters, split into early- and late-onset cases, from depressed non-attempters. The sample comprised 382 depressed middle-aged and older adults (aged 50 years or older, mean age = 63.5 years). Penalized binomial logistic regression and Random Forest models were fit using cross-validation in 100 versions of a training dataset of 83 variables, grouped into seven domains, to distinguish early- and late-onset suicide attempters from depressed non-attempters, and evaluated on testing datasets. Variable and domain importances were defined based on the frequency of each variable in the final models. Variables from the behavioral control and planning domain had high importance in differentiating both early-onset and late-onset attempters from depressed non-attempers. Early-life history as well as mood/anxiety/emotion regulation were important in distinguishing the early-onset group from depressed non-attempters, whereas social dynamics/interactions and cognition/decision-making were important in distinguishing the late-onset group from depressed non-attempters. This study underscores the importance of examining multiple risk factors for suicide together, and the advantages of considering known sources of heterogeneity in populations at risk in the development of more comprehensive and personalized suicide risk assessment tools and guidelines.

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Fig. 1: Model accuracy and AUC of early-onset and late-onset LASSO and Random Forest models on testing datasets, for 100 imputed versions of the dataset.
Fig. 2: Domain and variable importance in LASSO models of early-onset suicide attempt.
Fig. 3: Domain and variable importance in LASSO models of late-onset suicide attempt.

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Funding

This work was supported by the National Institute of Mental Health, USA (KSz, grant number R01MH085651).

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Author notes

  1. These authors contributed equally: Hanga Galfalvy, Elizabeth Campbell.

  2. These authors jointly supervised this work: Anna Szücs, Katalin Szanto

Authors and Affiliations

  1. Department of Psychiatry, Columbia University, New York, NY, USA

    Hanga Galfalvy

  2. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA

    Elizabeth Campbell, Meghan T. Wong, Swathi Gujral & Katalin Szanto

  3. Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Anna Szücs

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  1. Hanga Galfalvy
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  2. Elizabeth Campbell
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  3. Meghan T. Wong
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  4. Swathi Gujral
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  5. Anna Szücs
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  6. Katalin Szanto
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Contributions

Conceptualization: KSz, HG., AS, Methodology: KSz, HG., AS, EC. Formal analysis: HG., EC. Investigation: KSz. Resources: KSz. Data Curation: EC. Writing—Original Draft: HG., EC, MTW, AS, SG, KSz. Writing—Review and Editing: HG., EC, MTW, AS, SG, KSz. Visualization: HG., EC, Project administration: MTW, KSz. Funding acquisition: KSz. Final approval of the version to be published: all authors.

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Correspondence to Anna Szücs.

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Galfalvy, H., Campbell, E., Wong, M.T. et al. Age matters: a narrative review and machine learning analysis on shared and separate multidimensional risk domains for early and late onset suicidal behavior. Neuropsychopharmacol. (2026). https://doi.org/10.1038/s41386-026-02336-y

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