Toward “Obesityomics”: Domain‐Specific Omics in Precision Obesity Medicine with a Brief AI Perspective

BACKGROUND: Obesity's global rise has outpaced the effectiveness of "one-size-fits-all" prevention and treatment strategies. The promise of precision medicine lies in addressing the etiological and biological diversity of obesity and transforming it into actionable endotypes. OBJECTIVES: In a narrative review, we analyze "obesityomics," looking at the contributions each omics layer makes to understanding and managing obesity. The areas we evaluate are genomics (heritability, polygenic risk, and monogenic forms), epigenomics (development and environment imprinting), transcriptomics (tissue-specific programs of adipose, liver, muscle, and hypothalamus), proteomics (signaling networks and adipokines), metabolomics and lipidomics (fuel partitioning, lipotoxicity, and metabolic inflection points), and microbiome science (metabolites, host-microbe interactions, and microbial ecology). Our domain-by-domain (single-omics) approach demonstrates how disease taxonomy, causal pathways, and biomarkers can be refined and targeted therapeutically. DISCUSSION: We argue that "obesityomics" is a pragmatic approach to aligning omics modalities to the right questions at the right time, not a wholesale merger of datasets. Artificial intelligence (AI)-including machine learning and deep learning-should be used in conjunction with quality control, feature selection, modest data integration, and obesity intervention prediction. Among the key challenges in managing sensitive multilayer data are harmonization, standardization, reproducibility, equitable representation, and governance. The translation priorities include prospective studies related to patient-centered outcomes, biomarker panels that are clinic-ready, interoperability standards, and cost-effectiveness evaluations. CONCLUSION: The review maps concrete steps to assist people with obesity in making better choices by identifying what each omics domain excels at and introducing a brief AI perspective.

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Publication Details

Journal
Obesity Reviews
Published
2026-10-12
DOI
https://doi.org/10.1111/obr.70226
Primary Topic
Adipokines, Inflammation, and Metabolic Diseases
Type
article
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article

Toward “Obesityomics”: Domain‐Specific Omics in Precision Obesity Medicine with a Brief AI Perspective

Zeyar Aung, Abdulrahim A. Sajini, Mira Mousa, Selena E. Richards et al.
Obesity Reviews
Adipokines, Inflammation, and Metabolic Diseases
article

Toward “Obesityomics”: Domain‐Specific Omics in Precision Obesity Medicine with a Brief AI Perspective

Zeyar Aung, Abdulrahim A. Sajini, Mira Mousa, Selena E. Richards, Dorret Irene Boomsma, Mohammed Al-Hadad, Wael M. Osman, Alia Al Tikriti, Fatima Sulaiman, Sara G. I. Suliman, Maithah Ebraheim AlSamahi, Nadia Abu Samra, Fadwa Alhantoobi, Haneen Abufarajallah
article en

Abstract

BACKGROUND: Obesity's global rise has outpaced the effectiveness of "one-size-fits-all" prevention and treatment strategies. The promise of precision medicine lies in addressing the etiological and biological diversity of obesity and transforming it into actionable endotypes. OBJECTIVES: In a narrative review, we analyze "obesityomics," looking at the contributions each omics layer makes to understanding and managing obesity. The areas we evaluate are genomics (heritability, polygenic risk, and monogenic forms), epigenomics (development and environment imprinting), transcriptomics (tissue-specific programs of adipose, liver, muscle, and hypothalamus), proteomics (signaling networks and adipokines), metabolomics and lipidomics (fuel partitioning, lipotoxicity, and metabolic inflection points), and microbiome science (metabolites, host-microbe interactions, and microbial ecology). Our domain-by-domain (single-omics) approach demonstrates how disease taxonomy, causal pathways, and biomarkers can be refined and targeted therapeutically. DISCUSSION: We argue that "obesityomics" is a pragmatic approach to aligning omics modalities to the right questions at the right time, not a wholesale merger of datasets. Artificial intelligence (AI)-including machine learning and deep learning-should be used in conjunction with quality control, feature selection, modest data integration, and obesity intervention prediction. Among the key challenges in managing sensitive multilayer data are harmonization, standardization, reproducibility, equitable representation, and governance. The translation priorities include prospective studies related to patient-centered outcomes, biomarker panels that are clinic-ready, interoperability standards, and cost-effectiveness evaluations. CONCLUSION: The review maps concrete steps to assist people with obesity in making better choices by identifying what each omics domain excels at and introducing a brief AI perspective.

Obesity Reviews
Khalifa University of Science and Technology (AE), American University of Sharjah (AE), Healthpoint (AE), Imperial College London Diabetes Centre (AE), Cognitive Research (United States) (US)
Openalex Percentile: Top 12%
Adipokines, Inflammation, and Metabolic Diseases
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