Characterization of metabolic responses to healthy diets and the association with blood pressure: application to the Optimal Macronutrient Intake Trial for Heart Health (OmniHeart), a Randomized Control Study

Background: Inter-individual variation in the response to diet is common but the underlying mechanism for such variation is unclear.

Objective: The objective of this study was to use a metabolic profiling approach to identify a panel of urinary metabolites representing individuals demonstrating typical (homogeneous) metabolic responses to healthy diets, and subsequently to define the association of these metabolites with improvement of risk factors for cardiovascular diseases (CVD).

Design: 24-h urine samples from 158 participants, with pre-hypertension and stage 1 hypertension collected at baseline and following the consumption of a carbohydrate-rich, a protein-rich and a monounsaturated fat-rich healthy diet (6-weeks per diet) in a randomized, crossover study, were analyzed by proton (1H) nuclear magnetic resonance (NMR) spectroscopy. Urinary metabolite profiles were interrogated to identify typical and variable responses to each diet. We quantified the differences in absolute excretion of metabolites distinguishing between dietary comparisons within the typical response groups and established their associations with CVD risk factors using linear regression.

Results: Globally all three diets induced a similar pattern of change in the urinary metabolic profiles for the majority of participants (60.1%). Diet-dependent metabolic variation was not significantly associated with total cholesterol or low density lipoprotein cholesterol levels. However, blood pressure (BP) was found to be significantly associated with six urinary metabolites reflecting: dietary intake (proline-betaine [inverse], carnitine [direct]); gut microbial co-metabolites (hippurate [direct], 4-cresyl sulfate [inverse], phenylacetylglutamine [inverse]), and tryptophan metabolism (N-methyl-2-pyridone-5-carboxamide [inverse]). A dampened clinical response was observed in some individuals with variable metabolic responses, which could be attributed to non-adherence to diet (up to 25.3%), variation in gut microbiome activity (7.6%) or a combination of both (7.0%).

Conclusion: These data indicate inter-individual variations in BP in response to dietary change and highlight the potential influence of the gut microbiome in mediating this relationship. This approach provides a framework for stratification of individuals undergoing dietary management.