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Modeling and predicting mood instability in a longitudinal cohort of bipolar disorder

Nature Mental Health volume 3, pages 1267–1275 (2025)Cite this article

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Abstract

Increasing evidence suggests that bipolar disorders are associated with mood instability even outside the context of mood episodes. Here we use data from the Prechter Longitudinal Study of Bipolar Disorder to identify subgroups of individuals with bipolar disorders based on mood instability, identify biopsychosocial predictors of mood instability and determine whether mood instability predicts future outcomes. In a total of 481 participants, mood was assessed every 2 months (Patient Health Questionnaire and Altman Self-Rating Mania Scale) over 5 years, and clinical and functioning outcomes were assessed in year 6. Low, moderate and high mood instability classes were identified. Neuroticism, sleep quality, childhood emotional neglect and physical abuse, stimulant abuse, hypomania age of onset and number of depressive episodes were the most influential predictors of mood instability. Being in the high instability class (based on mood from years 1 to 5) predicted greater suicidal ideation and functional impairment in year 6. In summary, we show that mood instability represents a core phenotype of bipolar disorder with distinct predictors and long-term implications. Routine assessment may improve personalization in bipolar disorder treatment and research.

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Fig. 1: Diagram of study aims and statistical analysis.
Fig. 2: STROBE diagram.
Fig. 3: LPA on intraindividual mood dynamics.
Fig. 4: Optimized RF variables of importance plot.

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Data availability

Data collected at the University of Michigan require a fully executed Data Use Agreement to be shared outside of the institution. Longitudinal and outcomes data used in the present study, along with data dictionaries, are available subject to review of the proposed analyses and acceptance of a Data Use Agreement. Enquiries can be addressed at https://medresearch.umich.edu/labs-departments/centers/prechter-program/research/longitudinal-study-bipolar-disorder/data-requests-repositories.

Code availability

All Mplus and R codes are provided on the Open Science Framework webpage: https://osf.io/3ms7b/.

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Acknowledgements

With gratitude, we acknowledge the Prechter Bipolar Longitudinal Research participants and thank the research team of the University of Michigan Prechter Bipolar Research Program for their contributions in the collection and stewardship of the data used in this publication. We acknowledge the funding for this study, including the Prechter Family Charitable Fund (to M.G.M.) and the Richard Tam Foundation (to M.G.M.). S.H.S. has grant-funded support for the present paper from a Brain and Behavior Foundation Young Investigator Award (number 30719; S.H.S.), a National Institute of Mental Health Loan Repayment Award (L30MH127613; S.H.S.) and a National Institute of Mental Health K23 Career Development Award (K23MH131601; S.H.S.). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the paper.

Author information

Authors and Affiliations

  1. Department of Psychology, University of Michigan, Ann Arbor, MI, USA

    Audrey R. Stromberg & Sarah H. Sperry

  2. Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA

    Audrey R. Stromberg, Anastasia K. Yocum, Melvin G. McInnis, Ivy F. Tso & Sarah H. Sperry

  3. Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA

    Ivy F. Tso

Authors
  1. Audrey R. Stromberg
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  2. Anastasia K. Yocum
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Contributions

A.R.S.: methodology, formal analysis and writing (original draft). A.K.Y.: methodology, software, validation, formal analysis, data curation, writing (original draft) and visualization. M.G.M.: conceptualization, resources, writing (review and editing), supervision and funding acquisition. I.F.T.: conceptualization, writing (review and editing) and supervision. S.H.S.: conceptualization, methodology, validation, formal analysis, data curation, writing (original draft), visualization, supervision, project administration and funding acquisition. A.R.S., A.K.Y. and S.H.S. all accessed the data, verified the data and replicated all results.

Corresponding author

Correspondence to Sarah H. Sperry.

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Competing interests

A.R.S., A.K.Y., M.G.M. and I.F.T. have no competing interests to disclose. S.H.S. serves as an ad hoc consultant for Boehringer Ingelheim International GmbH.

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Nature Mental Health thanks Jess G. Fiedorowicz, Pedro Vieira Da Silva Magalhães and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Tables 1–8, Figs. 1 and 2, Appendices 1–4, and Supplementary and Appendix References.

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Stromberg, A.R., Yocum, A.K., McInnis, M.G. et al. Modeling and predicting mood instability in a longitudinal cohort of bipolar disorder. Nat. Mental Health 3, 1267–1275 (2025). https://doi.org/10.1038/s44220-025-00506-3

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