Integration of metabolomics and machine learning for precise management and prevention of cardiometabolic risk in Asians

Rapid changes in dietary patterns have led to a rise in cardiometabolic diseases (CMDs) worldwide, highlighting the urgent need for effective dietary strategies to address the health issues. Compared to Caucasians, Asians are more susceptible to CMDs. Understanding the complex factors driving this increased susceptibility is essential for developing targeted interventions and preventive measures for Asian populations. Metabolomics plays a key role in identifying specific metabolic markers and pathways associated with CMDs, providing insights into disease mechanisms and helping to create individualized risk profiles. However, metabolomics faces several challenges, including difficulties in interpreting results across diverse ethnic groups, limitations in study design, variability in analytical platforms, and inconsistencies in data processing methods. Overcoming these challenges requires the adoption of advanced technologies, standardized approaches, and integration of multi-omics data to maximize the utility of metabolomics in clinical settings. As the volume and complexity of metabolomic data continue to increase, machine learning (ML) algorithms have become essential for effective data integration, interpretation, and knowledge extraction. Advanced ML techniques, such as deep learning and network analysis, can reveal hidden patterns, relationships, and metabolic pathways within large datasets, leading to deeper insights into biological systems and disease processes. By combining metabolomics and ML, we can facilitate early detection, enable personalized interventions, and support the development of targeted nutritional strategies, ultimately improving therapeutic outcomes and reducing the socioeconomic burden of CMDs in this region.