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 proof
of-concept algorithms to scalable precision nutrition services in obesity-related diabetes care.