Review
Decoding the Depression Obesity Axis: Genetics Inflammation and Artificial Intelligence Applications
Ana-Maria Dumitrescu1*, Nicoleta Loredana Hilițanu2, Lucia Corina Dima Cozma3, Laurian-Lucian Frâncu1, Luiza-Simona Pohaci-Antonesei4, Andrei Ionuț Cucu5, Roxana Florentina Gavril1, Liviu Ciprian Gavril1, Ioan Gotcă6, Kolluru Sumanth7, Cătălin Alexandru Pohaci-Antonesei4
1Department of Morpho-Functional Sciences I, Grigore T. Popa, University of Medicine and Pharmacy, Iași, Romania
2Department of Preventive Medicine and Interdisciplinarity, Grigore T. Popa, University of Medicine and Pharmacy, Iaşi, Romania
3Department of Medicals I, Grigore T. Popa, University of Medicine and Pharmacy, Iași, Romania
4Department of Morpho-Functional Sciences II, Grigore T. Popa, University of Medicine and Pharmacy, Iași, Romania
5Faculty of Medicine and Biological Sciences, Stefan cel Mare University of Suceava, Romania
6Centrul de Sănătate Mintală Dr. Ghelerter
7Masters in Management, Coventry University, London, UK
*Corresponding Author Ana-Maria Dumitrescu, Department of Morpho-Functional Sciences I, Grigore T. Popa University of Medicine and Pharmacy, Iași, Romania
Received Date:
2026-05-01
Accepted Date:
2026-05-14
Published Date:
2026-05-29
Abstract
Depression and obesity are among the most prevalent and disabling health conditions worldwide, imposing a substantial burden on healthcare systems and society. Increasing evidence suggests a bidirectional relationship between these disorders, supported by common biological, genetic, metabolic, inflammatory, and psychosocial mechanisms. Obesity contributes significantly to the development of numerous chronic diseases, including cardiovascular disease, type 2 diabetes mellitus, metabolic syndrome, non-alcoholic fatty liver disease, sleep disorders, and neurodegenerative conditions, all of which may further exacerbate depressive symptomatology. Recent advances in artificial intelligence (AI) have created new opportunities for identifying, predicting, and treating obesity-related depression through machine learning, deep learning, digital phenotyping, and precision medicine approaches. This narrative review examines the complex relationship between depression and obesity, discusses the major diseases associated with obesity, and explores the current and future role of AI in clinical decision-making, risk prediction, genetic profiling, and personalized therapeutic interventions
