Precision Nutrition in Diabesity: Integrating Genomics, Lipidomics, and Microbiome Data for Personalized Intervention

Nalongo Bina K.

Faculty of Medicine Kampala International University Uganda

                                                                      ABSTRACT
Obesity and type 2 diabetes increasingly co-occur as “diabesity,” yet individuals with similar BMI and lifestyle can show strikingly different metabolic risk and treatment responses. This heterogeneity reflects complex interactions among genetic variants, lipid metabolism and gut microbiota, superimposed on diet and environment. Precision nutrition seeks to harness this variability by using multi-layer omics data to design individualized dietary interventions that optimize weight, glycemic control and cardiometabolic risk rather than relying on one-size-fits-all guidelines. Large nutrigenetic and microbiome-informed nutrition trials demonstrate that inter-individual variation in postprandial glycemia and lipemia can be partially predicted from genomic, clinical and microbiome features, and that diets tailored using these predictors can improve glycemic profiles beyond standard advice. Parallel advances in lipidomics and metabolomics have identified lipid signatures that better capture diabesity risk than traditional lipids and may serve as targets and readouts for tailored diets. Integrative multiomics frameworks and machine learning now provide tools to combine genomics, lipidomics and microbiome data into clinically usable models. This review summarizes the genomic, lipidomic and microbiome foundations of precision nutrition in diabesity, outlines emerging multiomics integration strategies and discusses how these can be translated into personalized interventions. We highlight current limitations in evidence, equity, data integration and implementation, and propose research priorities for moving from proofof-concept algorithms to scalable precision nutrition services in obesity related diabetes care.

Keywords: diabesity; precision nutrition; nutrigenomics; lipidomics; gut microbiome.

CITE AS: Nalongo Bina K. (2026). Precision Nutrition in Diabesity: Integrating Genomics, Lipidomics, and Microbiome Data for Personalized Intervention. Research Output Journal of Public Health and Medicine 6(1):36-42. https://doi.org/10.59298/ROJPHM/2026/613642