Brain aging in bipolar disorder using a neuroimaging and machine learning-derived metric: Findings from the ENIGMA BD Working Group.

Clinical Snapshot

Key Findings

• P Value: Not explicitly reported, but effects appear statistically significant based on SE ratios

• Effect Size: Age × diagnosis interaction +0.05 years per year, AED monotherapy +3.20 years, lithium protective effect -4.48 to -5.01 years vs AED

• Primary Outcome: Brain-predicted age difference (brain-PAD) showing accelerated brain aging in BD

• Nnt Or Sensitivity: BD-I subtype associated with +1.50 years advanced brain age compared to BD-II/NOS

• Confidence Interval: Standard errors: 0.02 for interaction, 0.78-0.92 for medication effects

Clinical Application

Requires advanced neuroimaging and machine learning infrastructure, currently research-based rather than clinical routine Relevant to Australian practice where lithium is PBS-subsidised first-line mood stabiliser, supports current RACGP guidelines favouring lithium, may inform TGA considerations for neuroprotective claims Adults with bipolar disorder across all subtypes, particularly relevant for medication selection and monitoring

Abstract

BACKGROUND: Bipolar disorder (BD) is associated with clinical and biological markers of premature aging. In this largest study of brain age in BD to date, with 2919 participants, we compared brain-predicted age difference (brain-PAD) in individuals with BD and healthy comparison (HC) participants. Brain-PAD is a machine learning-estimated metric that quantifies the difference between an individual's predicted brain age and their chronological age, a potential clinical bio-signature of premature brain aging. Within individuals with BD, we also examined how medication and clinical characteristics were related to brain-PAD. METHODS: Age was predicted from 77 MRI measures of regional subcortical and lateral ventricle volumes, cortical thickness, and surface area for 1342 BD and 1577 HC adult participants, aged 18-75 yrs. old (μ = 37.2; SD = 12.3), from the curated ENIGMA Bipolar Disorder working group (ENIGMA-BD) and leveraging an ENIGMA machine learning model previously trained and validated using independent samples. Chronological age was subtracted from predicted age to produce an individual-level estimate known as brain-PAD. Linear mixed models (adjusting for sex and age as fixed effects and site as a random effect) were used to examine group differences and clinical associations. RESULTS: BD was associated with higher brain-PAD, compared to HC, primarily among older patients, as demonstrated by a significant age by diagnosis interaction (+0.05 [SE: 0.02] years). Individuals with BD on antiepileptic (AED) medications only (+3.20 [SE: 0.78] years) or on both AED and second-generation antipsychotics (SGA) (+3.74 [SE: 0.89] years) demonstrated greater brain-PAD compared to individuals who were not on any of the examined medications. Those taking lithium, whether alone or with AED and SGA independently, showed no difference in brain-PAD compared to individuals not taking any of the examined medications. However, individuals who were taking lithium showed lower brain-PAD compared to those on AED (-4.48 [SE: 0.84] years) or AED and SGA (-5.01 [SE:0.92] years). Individuals with a BD I subtype diagnosis had a higher brain-PAD (+1.50 [SE:0.55] years) compared to those with BDII or subtypes that are not otherwise specified (NOS). CONCLUSIONS: Results from this study suggest compounding effects of BD diagnosis and older age on brain-PAD, an ML-derived summary metric of structural alterations. Within BD, brain-PAD was differentially related to medication use, consistent with prior findings from ENIGMA-BD. Notably, AED use was generally related to more advanced brain age. Lithium use, alone or in combination with other medications, was not associated with advanced brain age, suggesting a possible neuroprotective effect of lithium. Brain-PAD as an ML-derived summary metric of structural alterations of the brain may provide clinical utility in assessing long-term holistic brain health to monitor the effectiveness of lifestyle modifications or treatments over time. LIMITATIONS: The cross-sectional nature of the study design and the limited granularity of the clinical data limit interpretation. Longitudinal studies with detailed chronicity data, medications and clinical measures overtime will improve brain-PAD modeling in BD.

References

1. 1.Ng, H. X., Abé, C., Alda, M., Alonso-Lana, S., Anmella, G., Bauer, J., Borgers, T., Brosch, K., Busatto, G. F., Bøen, E., Canales-Rodríguez, E. J., Cannon, D. M., Chaim-Avancini, T. M., Cole, J. H., Dannlowski, U., De La Cruz, A., Einenkel, K., Elvsåshagen, T., Favre, P., ... Eyler, L. T. (2026). Brain aging in bipolar disorder using a neuroimaging and machine learning-derived metric: Findings from the ENIGMA BD Working Group. Journal of Affective Disorders. https://doi.org/10.1016/j.jad.2026.121234