Predicting long-term outcome in anorexia nervosa: A machine learning analysis of brain structure at different stages of weight recovery

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Abstract

Background Anorexia nervosa (AN) is characterized by sizable, widespread gray matter (GM) reductions in the acutely underweight state. However, evidence for persistent alterations after weight-restoration has been surprisingly scarce despite high relapse rates, frequent transitions to other psychiatric disorders, and generally unfavorable outcome. While most studies investigated brain regions separately (univariate analysis), psychiatric disorders can be conceptualized as brain network disorders characterized by multivariate alterations with only subtle local effects. We tested for persistent multivariate structural brain alterations in weight-restored individuals with a history of AN, investigated their putative biological substrate and relation with 1-year treatment outcome. Methods We trained machine learning models on regional GM measures to classify healthy controls (HC) (N = 289) from individuals at three stages of AN: underweight patients starting intensive treatment (N = 165, used as baseline), patients after partial weight-restoration (N = 115), and former patients after stable and full weight-restoration (N = 89). Alterations after weight-restoration were related to treatment outcome and characterized both anatomically and functionally. Results Patients could be classified from HC when underweight (ROC-AUC = 0.90) but also after partial weight-restoration (ROC-AUC = 0.64). Alterations after partial weight-restoration were more pronounced in patients with worse outcome and were not detected in long-term weight-recovered individuals, i.e. those with favorable outcome. These alterations were more pronounced in regions with greater functional connectivity, not merely explained by body mass index, and even increases in cortical thickness were observed (insula, lateral orbitofrontal, temporal pole). Conclusions Analyzing persistent multivariate brain structural alterations after weight-restoration might help to develop personalized interventions after discharge from inpatient treatment.

Details

OriginalspracheEnglisch
Seiten (von - bis)7827-7836
Seitenumfang10
FachzeitschriftPsychological medicine
Jahrgang53
Ausgabenummer16
PublikationsstatusVeröffentlicht - Dez. 2023
Peer-Review-StatusJa

Externe IDs

Mendeley 4c41bb5b-f904-38f9-be5f-fe777c97a713
ORCID /0000-0002-2864-5578/work/145695546
ORCID /0000-0003-2132-4445/work/145696024
ORCID /0000-0001-8029-8270/work/145696303
ORCID /0000-0002-5112-405X/work/145697981
ORCID /0000-0002-5026-1239/work/145698594
PubMed 37554008

Schlagworte

Ziele für nachhaltige Entwicklung

Schlagwörter

  • Anorexia nervosa, brain structure, machine learning, treatment outcome prediction, Body Mass Index, Magnetic Resonance Imaging, Brain/diagnostic imaging, Humans, Thinness/psychology, Anorexia Nervosa/psychology