Contrasting dietary patterns remodel gut microbial function and generate multi-omic signatures associated with cardiometabolic markers.
This study suggests that different dietary patterns can influence gut microbiome function and metabolic markers, but further research is needed to confirm these findings in larger populations.
Where it sits
this study against the rest of the l-carnitine (levocarnitine) corpusSummary and findings
This study measured the impact of two dietary patterns on gut microbiome composition and cardiometabolic markers in 34 Australian adults over a randomized crossover feeding trial. Participants followed a Healthy Australian Diet (HAD) and a Typical Australian Diet (TAD) for two weeks each, with a two-week washout period in between. Findings indicated that HAD was associated with changes in microbial pathways and metabolic signatures, but specific clinical implications were not established.
Abstract
Diet is a modifiable determinant of gut microbiome composition, yet the impact of contrasting whole-dietary patterns on microbial metabolic capacity and coordinated host metabolic signatures remains incompletely characterized. In a randomized crossover feeding trial, 34 Australian adults were provided with a Healthy Australian Diet (HAD), aligned with national dietary guidelines, and a Typical Australian Diet (TAD), reflecting average population intake for two weeks each, separated by a two-week washout. Fecal microbiome composition and function were assessed using shotgun metagenomics, plasma and urine metabolites by untargeted metabolomics, with cardiometabolic markers including blood pressure, plasma lipids, and glucose quantified. HAD was associated with reduced taxonomic and functional alpha diversity relative to baseline, with no change following TAD. Species-level responses were modest, 105 functional pathways differed between diets, with 99 increasing following HAD, predominantly related to amino acid and nucleotide biosynthesis and vitamin/cofactor metabolism. Multi-omic integration using DIABLO achieved strong discrimination of dietary responses (held-out accuracy 91.7%; permutation <i>p</i> = 0.005). In total, 77 individual omic feature-cardiometabolic outcome associations survived FDR correction (<i>q</i> < 0.05), spanning microbial gene functions, plasma metabolites, and urinary metabolites linked to cholesterol, blood pressure, and triglyceride responses. These exploratory findings suggest that integrated microbiome-metabolome profiling may capture inter-individual variation in dietary cardiometabolic responses, though replication in larger, independent, robustly designed studies is needed before translational personalized nutrition strategies can be assessed.
Background
This paper addresses the relationship between dietary patterns and gut microbiome function, a topic of growing interest due to its potential implications for cardiometabolic health. Previous research has established that diet influences gut microbiome composition, but the specific effects of contrasting dietary patterns on microbial metabolic capacity and host metabolic signatures remain inadequately characterized. This study aims to fill that gap by exploring how different diets affect microbial and metabolic profiles.
Methods
The study utilized a randomized crossover design involving 34 Australian adults who consumed a Healthy Australian Diet (HAD) and a Typical Australian Diet (TAD) for two weeks each, separated by a two-week washout period. Fecal microbiome composition and function were assessed using shotgun metagenomics, while plasma and urine metabolites were analyzed through untargeted metabolomics. Cardiometabolic markers such as blood pressure, plasma lipids, and glucose were quantified as primary and secondary outcomes.
Results
The primary endpoint indicated that HAD was associated with reduced taxonomic and functional alpha diversity relative to baseline. A total of 105 functional pathways were found to differ between the two diets, with 99 pathways increasing following HAD. The study reported strong discrimination of dietary responses with a held-out accuracy of 91.7% and a permutation p-value of 0.005.
Interpretation
These findings suggest that dietary patterns can significantly influence gut microbiome function and associated metabolic markers. However, the effect sizes observed may not be clinically meaningful given the small sample size and the exploratory nature of the study. The results are limited by potential confounding factors, including the short duration of dietary interventions and the need for larger, more robust studies to confirm these associations.
Key findings
- HAD was associated with reduced taxonomic and functional alpha diversity relative to baseline.
- 105 functional pathways differed between diets, with 99 increasing following HAD.
- Multi-omic integration achieved strong discrimination of dietary responses with held-out accuracy 91.7%; permutation p=0.005.
- 77 individual omic feature-cardiometabolic outcome associations survived FDR correction (q<0.05).
Limitations
- small sample size (n=34)
- short duration of dietary interventions
- exploratory findings require replication
- no long-term follow-up