Protocol for a Pilot Study to Implement a Systems-Level Precision Nutrition Approach Using Multi-omics and Machine Learning to Predict Cardiometabolic Risk in a Young Adult Population of Malaysia

Vimaleswaran, K. S. ORCID: https://orcid.org/0000-0002-8485-8930 (2026) Protocol for a Pilot Study to Implement a Systems-Level Precision Nutrition Approach Using Multi-omics and Machine Learning to Predict Cardiometabolic Risk in a Young Adult Population of Malaysia. Journal of Diabetology. ISSN 2543-3288 (In Press)

Abstract/Summary

Background: Malaysia still relies on single-dimensional approaches that fail to capture the exact causes of cardiometabolic disease (CMD) risk. Objectives: The Malaysian Obesity Nutrigenetics Environment Study aims to narrow down this gap by integrating multi-omics, clinical, biochemical, body composition, environment, and lifestyle data using machine learning techniques to strengthen population-level precision nutrition strategies for obesity and CMD prevention. Materials and Methods: Metabolically healthy Malaysian young adults (aged 18–25 years) will be systematically recruited from the multi-ethnic population of the Kinta District, Perak. Individuals with a minimum of two generations of Malaysian residency will be recruited to ensure genetic and cultural representativeness. The sociodemographic profile, dietary intake, physical activity, environmental factors, and lifestyle behaviors will be collected at baseline using pretested and validated questionnaires. Nutritional anthropometry alongside clinical and biochemical measurements will be systematically performed to establish the phenotypic profile. Nutrigenetics, gut microbiome, and metabolomics will be carried out to establish the multi-omics profile. All data will be integrated using big data analytics. Finally, a next-generation artificial intelligence (AI) predictive model will be developed to map gene–environment–microbiome–metabolome interactions and generate personalized obesity risk stratification tools for the Malaysian population. Conclusion: This is the first comprehensive, integrative obesity research initiative to combine nutrigenetics, gut microbiome, metabolomics, clinical profiling, and lifestyle determinants within a unified big data and AI-driven framework that offers a precision obesity risk prediction model uniquely tailored to Malaysia’s multi-ethnic population and delivers locally actionable solutions, thus establishing a blueprint for next-generation obesity research worldwide.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/131418
Refereed	Yes
Divisions	Interdisciplinary Research Centres (IDRCs) > Institute for Food, Nutrition and Health (IFNH) Life Sciences > School of Chemistry, Food and Pharmacy > Department of Food and Nutritional Sciences > Human Nutrition Research Group
Publisher	Diabetes In Asia Study Group (DASG)
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