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.

FIGURE 1

Types of biomarkers. This figure categorizes biomarkers into diagnostic, susceptibility, safety, monitoring, predictive, prognostic, and pharmacodynamic types. It highlights their roles, such as disease detection, genetic predisposition, treatment response monitoring, and metabolic regulation.

FIGURE 2

Causes of surrogate endpoints failure. (A) In this scenario, the condition affects the purported surrogate endpoint and the actual clinical outcome differently, rendering any association between the two invalid. (B) The hypothesized surrogate endpoint is influenced by the intervention, which partly affects the actual clinical outcome. However, the disease also influences the actual clinical outcome through other processes, making the change in the purported surrogate a poor indicator of the alteration in the actual clinical outcome. (C) The hypothesized surrogate endpoint is impacted by the intervention through mechanisms unrelated to those affecting the actual clinical outcome. Therefore, it is challenging to accurately determine whether the change in the surrogate endpoint represents a change in the actual clinical outcome. (D) All the aforementioned issues are present and accounted for.

FIGURE 3

Exploring multi‐omics biomarkers in obesity research. Insights from genomics, proteomics, and metabolomics. This figure illustrates the progression from a healthy state to obesity‐related diseases, highlighting key metabolic changes. It categorizes obesity biomarkers into genomic, epigenomic, transcriptomic, proteomic, metabolomic, glycomic, and microbiomic levels, emphasizing their role in biomarker identification, disease characterization, and precision prevention strategies.

FIGURE 4

Multi‐omics interrelationships in obesity research. Schematic illustrating the interrelationships between genomics, transcriptomics, proteomics, and metabolomics in obesity research. It shows how genetic variations influence gene expression, which impacts protein production and ultimately alters metabolite levels in obesity. These integrated omics data provide insights into the molecular mechanisms underlying obesity and its biomarker.

FIGURE 5

Key steps in the knowledge discovery in databases (KDD) process: From data selection to knowledge representation. The figure outlines the KDD process, detailing sequential steps including data selection, preprocessing, transformation, data mining, and evaluation, which collectively extract meaningful patterns and transform raw data into actionable knowledge.

FIGURE 6

Integrative data‐driven approach for obesity risk prediction. The schematic represents a multisource data integration framework for obesity risk prediction. Various data sources, including genetic (DNA/RNA), environmental factors, wearable device metrics, dietary information, and social media data, are processed using Group Factor Analysis (GFA). Machine learning models such as Logistic Regression, XGBoost, and Random Forest are applied for predictive modeling. The results section highlights performance metrics (accuracy, sensitivity, specificity) and the predicted obesity risk, demonstrating the effectiveness of this integrative approach.