What is the clinical evidence from this study?

Study design: Review. Population: Depression and obesity. Intervention: Artificial intelligence.

What does this research mean for the field?

Depression and obesity share a complex bidirectional relationship driven by common biological and psychosocial mechanisms, which can be increasingly identified, predicted, and treated using artificial intelligence applications. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The review aims to investigate the interconnectedness of depression and obesity, and the impact of AI on their management.

June 16, 20260 citationsOpen Access

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Decoding the Depression Obesity Axis: Genetics Inflammation and Artificial Intelligence Applications

DADumitrescu Ana-MariaGrigore T. Popa University of Medicine and Pharmacy HLHilițanu Nicoleta LoredanaGrigore T. Popa University of Medicine and Pharmacy CDCozma Lucia Corina DimaGrigore T. Popa University of Medicine and Pharmacy

Key Result

This narrative review highlights the bidirectional relationship between depression and obesity and explores the potential of artificial intelligence in clinical decision-making and personalized therapy.

Key Points

The review aims to investigate the interconnectedness of depression and obesity, and the impact of AI on their management.
Narrative review analyzing existing literature on depression, obesity, and associated diseases.
Discussion of AI methodologies including machine learning and deep learning in understanding mental and physical health.
Exploration of genetic profiling and personalized medicine in treatment approaches.
Depression and obesity exhibit a bidirectional relationship, influenced by genetic and inflammatory factors.
Obesity is linked with several chronic diseases, worsening depressive symptoms through metabolic pathways.
AI technologies offer significant potential for enhancing diagnosis, treatment prediction, and personalized interventions in obesity-related depression.

PICO

Population

Depression and obesity

Exposure / Comparator

Artificial intelligence

Abstract

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.

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Decoding the Depression Obesity Axis: Genetics Inflammation and Artificial Intelligence Applications

Key Result

Key Points

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Abstract

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