Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals

Abstract

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 ...

Genes Encoding Microbial Acyl Coenzyme A Binding Protein/Diazepam-Binding Inhibitor Orthologs Are Rare in the Human Gut Microbiome and Show No Links to Obesity

“…To evaluate a possible link between microbial ACBP/DBI ortholog genes and obesity, we performed a meta-analysis of correlations between species-level abundances and BMI as a read-out using 1,899 gut samples from healthy individuals curated within the curatedMetagenomicData (35) effort (Figure 3, Table 2). We found Table 3), with species such as Flavonifractor plautii, Coprococcus comes and Blautia hydrogenotrophica associated with increased BMI, in line with previous reports (37,38). We also found species associated with decreased BMI, which included Oscillibacter sp 57_20, Alistipes shahii and Odoribacter splanchnicus, as previously described (39).…”

Dietary Influences on the Microbiota–Gut–Brain Axis

“…Although many previously reported dietary studies in human cohorts had timeframes of weeks or months [74], with changes in a limited number of species within the gut microbiota [75,76] or failure to demonstrate any significant diet-induced changes in the gut microbiota [77], more recent compelling data reveal changes in the gut microbiota composition resulting from short-term dietary changes [78]. In one of the most deeply phenotyped studies reported to date using metagenomic sequencing, there were significant associations between gut microbes and specific nutrients and food groups, driven particularly by healthy and diverse plant-based foods [79]. Furthermore, overall microbiome composition was predictive for multiple cardio-metabolic blood markers, suggesting the potential for future stratification of the gut microbiota as a predictor of future health and illness prior to the development of clinically manifesting disease [79].…”

“…In one of the most deeply phenotyped studies reported to date using metagenomic sequencing, there were significant associations between gut microbes and specific nutrients and food groups, driven particularly by healthy and diverse plant-based foods [79]. Furthermore, overall microbiome composition was predictive for multiple cardio-metabolic blood markers, suggesting the potential for future stratification of the gut microbiota as a predictor of future health and illness prior to the development of clinically manifesting disease [79].…”

Correcting for Background Noise Improves Phenotype Prediction from Human Gut Microbiome Data

“…For example, confounding covariates were found to be pervasive 26 in one of the largest metagenomic datasets available, the American Gut Project (AGP) 27 . Confounding can also arise when datasets from different studies are combined together to augment the power to detect associations 8,28 , a practice that is becoming increasingly common [29][30][31][32][33] and is potentially a powerful means to validate results in a discovery dataset with held out datasets 1,34,35 . For example, Gibbons et al 36 found that combining datasets to detect members of the microbiome that were associated with colorectal cancer resulted in false positive detection of differentially abundant taxa due to the different case-control ratio in different combined studies.…”

“…For example, confounding covariates were found to be pervasive 26 in one of the largest metagenomic datasets available, the American Gut Project (AGP) 27 . Confounding can also arise when datasets from different studies are combined together to augment the power to detect associations 8,28 , a practice that is becoming increasingly common 29–33 and is potentially a powerful means to validate results in a discovery dataset with held out datasets 1,34,35 . For example, Gibbons et al .…”