Rheumatoid arthritis (RA) is a multifaceted autoimmune disease that is marked by a complex molecular profile influenced by an array of factors, including genetic, epigenetic, and environmental elements. Despite significant advancements in research, the precise etiology of RA remains elusive, presenting challenges in developing innovative therapeutic markers. This study takes an integrated multi-omics approach to uncover novel therapeutic markers for RA. By analyzing both transcriptomics and epigenomics datasets, we identified common gene candidates that span these two omics levels in patients diagnosed with RA. Remarkably, we discovered eighteen multi-evidence genes (MEGs) that are prevalent across transcriptomics and epigenomics, twelve of which have not been previously linked directly to RA. The bioinformatics analyses of the twelve novel MEGs revealed they are part of tightly interconnected protein–protein interaction networks directly related to RA-associated KEGG pathways and gene ontology terms. Furthermore, these novel MEGs exhibited direct interactions with miRNAs linked to RA, underscoring their critical role in the disease’s pathogenicity. Overall, this comprehensive bioinformatics approach opens avenues for identifying new candidate markers for RA, empowering researchers to validate these markers efficiently through experimental studies. By advancing our understanding of RA, we can pave the way for more effective therapies and improved patient outcomes.

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.