Does restrictive anorexia nervosa impact brain aging? A machine learning approach to estimate age based on brain structure.
Authors
Affiliations (12)
Affiliations (12)
- Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Jena, Germany. Electronic address: [email protected].
- Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Jena, Germany; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
- Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Jena, Germany.
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; Department of Neurology, Jena University Hospital, Jena, Germany; German Center for Mental Health (DZPG), Germany.
- Centre for Medical Imaging Computer, University College London, London, UK; Dementia Research Centre, University College London, London, UK.
- Eating Disorders Clinical and Research Program, Massachusetts General Hospital, Boston, USA; Department of Psychiatry, Harvard Medical School, Boston, USA; Mass General Brigham Multidisciplinary Eating Disorder Research Collaborative, Massachusetts General Hospital, Boston, USA.
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Mass General Brigham Multidisciplinary Eating Disorder Research Collaborative, Massachusetts General Hospital, Boston, USA; Harvard Medical School, Boston, USA.
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Psychiatry, Harvard Medical School, Boston, USA.
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Psychiatry, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, USA.
- Mass General Brigham Multidisciplinary Eating Disorder Research Collaborative, Massachusetts General Hospital, Boston, USA; Harvard Medical School, Boston, USA; Neuroendocrine Unit, Massachusetts General Hospital, Boston, USA.
- Harvard Medical School, Boston, USA; Neuroendocrine Unit, Massachusetts General Hospital, Boston, USA.
- Mass General Brigham Multidisciplinary Eating Disorder Research Collaborative, Massachusetts General Hospital, Boston, USA; Neuroendocrine Unit, Massachusetts General Hospital, Boston, USA; Division of Pediatric Endocrinology, University of Virginia, Charlottesville, USA; Department of Pediatrics, University of Virginia, Charlottesville, USA.
Abstract
Anorexia nervosa (AN), a severe eating disorder marked by extreme weight loss and malnutrition, leads to significant alterations in brain structure. This study used machine learning (ML) to estimate brain age from structural MRI scans and investigated brain-predicted age difference (brain-PAD) as a potential biomarker in AN. Structural MRI scans were collected from female participants aged 10-40 years across two institutions (Boston, USA, and Jena, Germany), including acute AN (acAN; n=113), weight-restored AN (wrAN; n=35), and age-matched healthy controls (HC; n=90). The ML model was trained on 3487 healthy female participants (ages 5-45 years) from ten datasets, using 377 neuroanatomical features extracted from T1-weighted MRI scans. The model achieved strong performance with a mean absolute error (MAE) of 1.93 years and a correlation of r = 0.88 in HCs. In acAN patients, brain age was overestimated by an average of +2.25 years, suggesting advanced brain aging. In contrast, wrAN participants showed significantly lower brain-PAD than acAN (+0.26 years, p=0.0026) and did not differ from HC (p=0.98), suggesting normalization of brain age estimates following weight restoration. A significant group-by-age interaction effect on predicted brain age (p<0.001) indicated that brain age deviations were most pronounced in younger acAN participants. Brain-PAD in acAN was significantly negatively associated with BMI (r = -0.291, p<sub>fdr</sub> = 0.005), but not in wrAN or HC groups. Importantly, no significant associations were found between brain-PAD and clinical symptom severity. These findings suggest that acute AN is linked to advanced brain aging during the acute stage, and that may partially normalize following weight recovery.