Clinical Nutrition · Feb 23, 2026
Multi-omics analysis of dynamic profiles in response to various nutrient loads provides novel insights into obesity
By Fu Q, Dai H, Wang J et al.
BACKGROUND& AIMS: Obesity is a global health issue driven by improper nutrient intake and metabolic dysregulation. The complexity of dietary components and the dynamic nature of postprandial metabolism limit our understanding of how different nutrient loads associated with obesity. This study aims to characterize the dynamic metabolic responses to nutrient intake using multi-omics approaches, assess the influence of dietary habits and gut microbiota, and evaluate the acute obesity-risk signature (AORS) associated with different macronutrients. METHODS: We conducted a mixed meal tolerance test (MMTT) in 147 non-diabetic individuals (54 controls, 38 overweight, 55 obese). Blood samples were collected at multiple time points for untargeted metabolomics, lipidomics, proteomics, and hormone assays. Gut microbiota was profiled via metagenomic sequencing. A separate single macronutrient tolerance test (SMNTT) involving glucose, whey protein, butter, and olive oil was performed in 24 healthy volunteers to compare acute metabolic responses and derive an AORS based on postprandial multi-omics data. RESULTS: Postprandial multi-omic analytes showed stronger associations with obesity indicators than fasting measures. Distinct temporal changes in metabolites, lipids, and proteins were observed across different BMI groups, with enrichment in pathways such as bile acid biosynthesis, triglyceride metabolism, and complement activation. Dietary habits and gut microbiota significantly influenced postprandial metabolic profiles, with specific metabolites and proteins mediating their effects on obesity. In SMNTT, glucose load exhibited the lowest AORS among isocaloric macronutrients (0.1082 ± 0.1917 %). Gut microbiota composition further modulated metabolic responses, with olive oil showing divergent AORS between Bacteroides- and Prevotella-dominated enterotypes (p = 0.043). CONCLUSION: Postprandial multi-omics provides superior insights into obesity pathophysiology compared to fasting measurements. Our findings reveal that dietary habits and gut microbiota significantly influence postprandial metabolism and obesity risk, and demonstrate that different macronutrients confer distinct AORS values, which are further modified by an individual's gut microbiota composition. This underscores the potential for personalized nutritional strategies based on dynamic metabolic responses and microbial ecology.