Loading
Life sciences · Journal article
Obesity Reviews · October 12, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Journal article.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
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.