The gut microbiome is shaped by diet and influences host metabolism, but these links are complex and can be unique to each individual. We performed deep metagenomic sequencing of >1,100 gut microbiomes from individuals with detailed long-term diet information, as well as hundreds of fasting and same-meal postprandial cardiometabolic blood marker measurements. We found strong associations between microbes and specific nutrients, foods, food groups, and general dietary indices, driven especially by the presence and diversity of healthy and plant-based foods. Microbial biomarkers of obesity were reproducible across cohorts, and blood markers of cardiovascular disease and impaired glucose tolerance were more strongly associated with microbiome structure. While some microbes such as Prevotella copri and Blastocystis spp., were indicators of reduced postprandial glucose metabolism, several species were more directly predictive for postprandial triglycerides and C-peptide. The panel of intestinal species associated with healthy dietary habits overlapped with those associated with favourable cardiometabolic and postprandial markers, indicating our large-scale resource can potentially stratify the gut microbiome into generalizable health levels among individuals without clinically manifest disease. Fig. 1: The PREDICT 1 study associates gut microbiome structure with habitual diet and blood cardiometabolic markers. (A)The PREDICT 1 study assessed the gut microbiome of 1,098 volunteers from the UK and US via metagenomic sequencing of stool samples. Phenotypic data obtained through in-person assessment, blood/biospecimen collection, and the return of validated study questionnaires queried a range of relevant host/environmental factors including (1) personal characteristics, such as age, BMI, and estimated visceral fat; (2) habitual dietary intake using semi-quantitative food frequency questionnaires (FFQs);(3) fasting; and (4) postprandial cardiometabolic blood and inflammatory markers, total lipid and lipoprotein concentrations, lipoprotein particle sizes, apolipoproteins, derived metabolic risk scores, glycaemic-mediated metabolites, and metabolites related to fatty acid metabolism. (B) Overall microbiome alpha diversity, estimated as the total number of confidently identified microbial species in a given sample (richness), was correlated with HDL-D (positive) and estimated hepatic steatosis (negative). Up to ten strongest absolute Spearman correlations are reported for each category with q<0.05. Top species based on Shannon diversity are reported in Supplementary Fig. 1A and all correlations are in Supplementary Table 1. Microbial diversity and composition are linked with diet and fasting and postprandial biomarkersWe first leveraged a unique subpopulation of our study comprised of 480 twins to disentangle the confounding effects of shared genetics from other factors on microbiome composition. Our data confirmed that host genetics influences microbiome composition only to a small extent 18 , as intra-twin pair microbiome ...

Metagenomic assembly enables new organism discovery from microbial communities, but it can only capture few abundant organisms from most metagenomes. Here we present MetaPhlAn 4, which integrates information from metagenome assemblies and microbial isolate genomes for more comprehensive metagenomic taxonomic profiling. From a curated collection of 1.01 M prokaryotic reference and metagenome-assembled genomes, we define unique marker genes for 26,970 species-level genome bins, 4,992 of them taxonomically unidentified at the species level. MetaPhlAn 4 explains ~20% more reads in most international human gut microbiomes and >40% in less-characterized environments such as the rumen microbiome and proves more accurate than available alternatives on synthetic evaluations while also reliably quantifying organisms with no cultured isolates. Application of the method to >24,500 metagenomes highlights previously undetected species to be strong biomarkers for host conditions and lifestyles in human and mouse microbiomes and shows that even previously uncharacterized species can be genetically profiled at the resolution of single microbial strains.

Metagenomic assembly enables novel organism discovery from microbial communities, but from most metagenomes it can only capture few abundant organisms. Here, we present a method - MetaPhlAn 4 - to integrate information from both metagenome assemblies and microbial isolate genomes for improved and more comprehensive metagenomic taxonomic profiling. From a curated collection of 1.01M prokaryotic reference and metagenome-assembled genomes, we defined unique marker genes for 26,970 species-level genome bins, 4,992 of them taxonomically unidentified at the species level. MetaPhlAn 4 explains ~20% more reads in most international human gut microbiomes and >40% in less-characterized environments such as the rumen microbiome, and proved more accurate than available alternatives on synthetic evaluations while also reliably quantifying organisms with no cultured isolates. Application of the method to >24,500 metagenomes highlighted previously undetected species to be strong biomarkers for host conditions and lifestyles in human and mice microbiomes, and showed that even previously uncharacterized species can be genetically profiled at the resolution of single microbial strains. MetaPhlAn 4 thus integrates the novelty of metagenomic assemblies with the sensitivity and fidelity of reference-based analyses, providing efficient metagenomic profiling of uncharacterized species and enabling deeper and more comprehensive microbiome biomarker detection.

Background Chronic inflammation, which can be modulated by diet, is linked to high white blood cell counts and correlates with higher cardiometabolic risk and risk of more severe infections, as in the case of COVID-19. Methods Here, we assessed the association between white blood cell profile (lymphocytes, basophils, eosinophils, neutrophils, monocytes and total white blood cells) as markers of chronic inflammation, habitual diet and gut microbiome composition (determined by sequencing of the 16S RNA) in 986 healthy individuals from the PREDICT-1 nutritional intervention study. We then investigated whether the gut microbiome mediates part of the benefits of vegetable intake on lymphocyte counts. Results Higher levels of white blood cells, lymphocytes and basophils were all significantly correlated with lower habitual intake of vegetables, with vegetable intake explaining between 3.59 and 6.58% of variation in white blood cells after adjusting for covariates and multiple testing using false discovery rate (q < 0.1). No such association was seen with fruit intake. A mediation analysis found that 20.00% of the effect of vegetable intake on lymphocyte counts was mediated by one bacterial genus, Collinsella, known to increase with the intake of processed foods and previously associated with fatty liver disease. We further correlated white blood cells to other inflammatory markers including IL6 and GlycA, fasting and post-prandial glucose levels and found a significant relationship between inflammation and diet. Conclusion A habitual diet high in vegetables, but not fruits, is linked to a lower inflammatory profile for white blood cells, and a fifth of the effect is mediated by the genus Collinsella. Trial registration The ClinicalTrials.gov registration identifier is NCT03479866.

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