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<p>Obesity affects over 650 million people globally and drives metabolic disorders such as type 2 diabetes (T2DM) and cardiovascular diseases (CVD), which cause 17.9 million deaths annually. Multi-omics approaches spanning genomics, transcriptomics, proteomics, and metabolomics combined with artificial intelligence (AI) offer powerful tools to unravel obesity-related disease mechanisms and improve prediction models. Recent studies show that applying machine learning to high dimensional omics data can identify biomarkers and improve prediction accuracy by 5 to 15% over the 85 to 90% baseline achieved in similar early detection tasks. Deep learning models capture complex patterns in heterogeneous data but face challenges in harmonizing diverse omics types. This review critically evaluates AI-driven multi-omics integration strategies, compares their strengths, limitations, and optimal use cases, and outlines key considerations such as data standardization, privacy, and regulatory compliance for clinical translation in obesity research.</p>

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Artificial Intelligence and Multi-Omics Integration in Obesity: A Review of Computational Models for Predicting Metabolic Comorbidities

  • 期刊论文

  • 发表日期：

    2025年9月23日

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  • Ankur Pan Saikia,
  • Ananya Kalita

摘要

Obesity affects over 650 million people globally and drives metabolic disorders such as type 2 diabetes (T2DM) and cardiovascular diseases (CVD), which cause 17.9 million deaths annually. Multi-omics approaches spanning genomics, transcriptomics, proteomics, and metabolomics combined with artificial intelligence (AI) offer powerful tools to unravel obesity-related disease mechanisms and improve prediction models. Recent studies show that applying machine learning to high dimensional omics data can identify biomarkers and improve prediction accuracy by 5 to 15% over the 85 to 90% baseline achieved in similar early detection tasks. Deep learning models capture complex patterns in heterogeneous data but face challenges in harmonizing diverse omics types. This review critically evaluates AI-driven multi-omics integration strategies, compares their strengths, limitations, and optimal use cases, and outlines key considerations such as data standardization, privacy, and regulatory compliance for clinical translation in obesity research.