Obesity Biomarkers: Exploring Factors, Ramification, Machine Learning, and AI-Unveiling Insights in Health Research

Abstract

Biomarkers play a pivotal role in the detection and management of diseases, including obesity—a growing global health crisis with complex biological underpinnings. The multifaceted nature of obesity, coupled with socioeconomic disparities, underscores the urgent need for precise diagnostic and therapeutic approaches. Recent advances in biosciences, including next-generation sequencing, multi-omics analysis, high-resolution imaging, and smart sensors, have revolutionized data generation. However, effectively leveraging these data-rich technologies to identify and validate obesity-related biomarkers remains a significant challenge. This review bridges this gap by highlighting the potential of machine learning (ML) in obesity research. Specifically, it explores how ML techniques can process complex data sets to enhance the discovery and validation of biomarkers. Additionally, it examines the integration of advanced technologies for understanding obesity mechanisms, assessing risk factors, and optimizing treatment strategies. A detailed discussion is provided on the applications of ML in multi-omics analysis and high-throughput data integration for actionable insights. The academic value of this review lies in synthesizing the latest technological and analytical innovations in obesity research. By providing a comprehensive overview, it aims to guide future studies and foster the development of targeted, data-driven strategies in obesity management.

Keywords

biomarker / data mining / knowledge discovery in databases (KDD) / obesity / omic biomarker / oxidative stress biomarker

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