Leveraging OGTT derived metabolic features to detect Binge-eating disorder in individuals with high weight: a “seek out” machine learning approach

Binge eating disorder (BED) carries a 6 times higher risk for obesity and accounts for roughly 30% of type 2 diabetes cases. Timely identification of early glycemic disturbances and comprehensive treatment can impact on the likelihood of associated metabolic complications and the overall outcome. In...

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Bibliographic Details
Published in:Translational psychiatry 2025-02, Vol.15 (1), p.57-6, Article 57
Main Authors: Rania, Marianna, Procopio, Anna, Zaffino, Paolo, Carbone, Elvira Anna, Fiorentino, Teresa Vanessa, Andreozzi, Francesco, Segura-Garcia, Cristina, Cosentino, Carlo, Arturi, Franco
Format: Article
Language:English
Subjects:
692/53/2421
692/699/476
Adult
Algorithms
Behavioral Sciences
Binge-Eating Disorder - complications
Binge-Eating Disorder - diagnosis
Binge-Eating Disorder - pathology
Biological Psychology
Eating disorders
Female
Glucose
Glucose Tolerance Test
Humans
Machine Learning
Male
Medicine
Medicine & Public Health
Metabolic Diseases - complications
Metabolic Diseases - diagnosis
Metabolic Diseases - physiopathology
Metabolism
Middle Aged
Multidisciplinary practices
Neurosciences
Obesity - complications
Obesity - physiopathology
Pharmacotherapy
Psychiatry
Risk factors
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