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

HX Ng; C Abé; M Alda; S Alonso-Lana; G Anmella; J Bauer; T Borgers; K Brosch; GF Busatto; E Bøen; EJ Canales-Rodríguez; DM Cannon; TM Chaim-Avancini; JH Cole; U Dannlowski; A De La Cruz; K Einenkel; T Elvsåshagen; P Favre; L Fisch; K Flinkenflügel; S Foley; JM Fullerton; J Goltermann; D Grotegerd; O Gruber; BCM Haarman; T Hahn; T Hajek; LKM Han; C Henry; D Hidalgo-Mazzei; J Houenou; FM Howells; M Ingvar; N Jahanshad; E Jiménez; A Karuk; T Kircher; L Klahn; A Kraus; A Krug; B Krämer; RT Kuplicki; B Lafer; M Landén; R Machado-Vieira; UF Malt; FM Martyn; C McDonald; G McPhilemy; S Meinert; T Meller; PB Mitchell; B Mwangi; L Nabulsi; I Nenadić; N Opel; MCG Otaduy; BJ Overs; M Polosan; E Pomarol-Clotet; A Pouchon; J Radua; L Rauer; J Repple; G Roberts; E Rodríguez-Cano; PGP Rosa; R Salvador; S Sarró; TD Satterthwaite; J Savitz; L Schmaal; PR Schofield; M Serpa; K Sim; JC Soares; N Topolski; MG Soeiro-de-Souza; DJ Stein; F Stein; AN Sutherland; HS Temmingh; SI Thomopoulos; A Uhlmann; E Vieta; E Vilajosana; DH Wolf; MJ Wu; GB Zunta-Soares; MV Zanetti; W van den Brink; PM Thompson; OA Andreassen; CRK Ching; LT Eyler

Journal article

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

HX Ng, C Abé, M Alda, S Alonso-Lana, G Anmella, J Bauer, T Borgers, K Brosch, GF Busatto, E Bøen, EJ Canales-Rodríguez, DM Cannon, TM Chaim-Avancini, JH Cole, U Dannlowski, A De La Cruz, K Einenkel, T Elvsåshagen, P Favre, L Fisch Show all

Journal of Affective Disorders | Published : 2026

DOI: 10.1016/j.jad.2026.121234

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 a..

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