Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals
Francesco Asnicar, Sarah E Berry, Ana M Valdes, Long H Nguyen, Gianmarco Piccinno, David A Drew, Emily Leeming, Rachel Gibson, Caroline Le Roy, Haya Al Khatib, Lucy Francis, Mohsen Mazidi, Olatz Mompeo, Mireia Valles-Colomer, Adrian Tett, Francesco Beghini, Léonard Dubois, Davide Bazzani, Andrew Maltez Thomas, Chloe Mirzayi, Asya Khleborodova, Sehyun Oh, Rachel Hine, Christopher Bonnett, Joan Capdevila, Serge Danzanvilliers, Francesca Giordano, Ludwig Geistlinger, Levi Waldron, Richard Davies, George Hadjigeorgiou, Jonathan Wolf, José M Ordovás, Christopher Gardner, Paul W Franks, Andrew T Chan, Curtis Huttenhower, Tim D Spector, Nicola Segata, Francesco Asnicar, Sarah E Berry, Ana M Valdes, Long H Nguyen, Gianmarco Piccinno, David A Drew, Emily Leeming, Rachel Gibson, Caroline Le Roy, Haya Al Khatib, Lucy Francis, Mohsen Mazidi, Olatz Mompeo, Mireia Valles-Colomer, Adrian Tett, Francesco Beghini, Léonard Dubois, Davide Bazzani, Andrew Maltez Thomas, Chloe Mirzayi, Asya Khleborodova, Sehyun Oh, Rachel Hine, Christopher Bonnett, Joan Capdevila, Serge Danzanvilliers, Francesca Giordano, Ludwig Geistlinger, Levi Waldron, Richard Davies, George Hadjigeorgiou, Jonathan Wolf, José M Ordovás, Christopher Gardner, Paul W Franks, Andrew T Chan, Curtis Huttenhower, Tim D Spector, Nicola Segata
Abstract
The gut microbiome is shaped by diet and influences host metabolism; however, these links are complex and can be unique to each individual. We performed deep metagenomic sequencing of 1,203 gut microbiomes from 1,098 individuals enrolled in the Personalised Responses to Dietary Composition Trial (PREDICT 1) study, whose detailed long-term diet information, as well as hundreds of fasting and same-meal postprandial cardiometabolic blood marker measurements were available. We found many significant associations between microbes and specific nutrients, foods, food groups and general dietary indices, which were driven especially by the presence and diversity of healthy and plant-based foods. Microbial biomarkers of obesity were reproducible across external publicly available cohorts and in agreement with circulating blood metabolites that are indicators of cardiovascular disease risk. While some microbes, such as Prevotella copri and Blastocystis spp., were indicators of favorable postprandial glucose metabolism, overall microbiome composition was predictive for a large panel of cardiometabolic blood markers including fasting and postprandial glycemic, lipemic and inflammatory indices. The panel of intestinal species associated with healthy dietary habits overlapped with those associated with favorable cardiometabolic and postprandial markers, indicating that our large-scale resource can potentially stratify the gut microbiome into generalizable health levels in individuals without clinically manifest disease.
Conflict of interest statement
Conflict of interest statement
TD Spector, SE Berry, AM Valdes, F Asnicar, PW Franks, C Huttenhower, and N Segata, are consultants to Zoe Global Ltd (“Zoe”). J Wolf, G Hadjigeorgiou, R Davies, J Capdevila, C Bonnett, R Hine, L Francis, F Giordano, and S Danzanvilliers are or have been employees of Zoe. Other authors have no conflict of interest to declare.
Figures
![Extended Data Fig. 1. Alpha diversity linked…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0007.jpg)
![Extended Data Fig. 2. Species-level correlation with…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0008.jpg)
![Extended Data Fig. 3. Top foods, food…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0009.jpg)
![Extended Data Fig. 4. Performance for random…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0010.jpg)
![Extended Data Fig. 5. Distributions of BMI…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0011.jpg)
![Extended Data Fig. 6. Pairwise partial Spearman…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0012.jpg)
![Extended Data Fig. 7. Species-level correlations with…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0013.jpg)
Extended Data Fig. 8. Pairwise partial Spearman…
Extended Data Fig. 8. Pairwise partial Spearman correlations between bacterial gene families and pathway abundances…
Extended Data Fig. 9. Concordance of Random…
Extended Data Fig. 9. Concordance of Random Forest scores with species-level partial correlations.
Volcano plots…
Extended Data Fig. 10. Prevotella copri and/or…
Extended Data Fig. 10. Prevotella copri and/or Blastocystis presence are indicators of a more favourable…
Fig. 1:. The PREDICT 1 study associates…
Fig. 1:. The PREDICT 1 study associates gut microbiome structure with habitual diet and blood…
Fig. 2:. Food quality, regardless of source,…
Fig. 2:. Food quality, regardless of source, is linked to overall and feature-level composition of…
Fig. 3:. Random forest machine learning models…
Fig. 3:. Random forest machine learning models trained on microbial or functional profiles are capable…
Fig. 4:. Fasting and postprandial cardiometabolic responses…
Fig. 4:. Fasting and postprandial cardiometabolic responses to standardized test meals associated with the microbiome.
Fig. 5:. Species-level segregation into healthy and…
Fig. 5:. Species-level segregation into healthy and unhealthy microbial signatures of fasting and postprandial cardiometabolic…
Fig. 6:. The panel of 30 species…
Fig. 6:. The panel of 30 species showing the strongest overall correlations with a selection…
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- Clinical Trial
- Research Support, N.I.H., Extramural
- Research Support, Non-U.S. Gov't
- Adult
- Biomarkers / metabolism
- Blastocystis / genetics
- Blood Glucose / metabolism
- Child
- Diet / adverse effects
- Fasting / metabolism
- Feeding Behavior
- Female
- Food Microbiology
- Gastrointestinal Microbiome / genetics*
- Glucose / genetics
- High-Throughput Nucleotide Sequencing
- Humans
- Male
- Metagenome / genetics*
- Microbiota / genetics*
- Middle Aged
- Obesity / genetics
- Obesity / metabolism
- Obesity / microbiology*
- Postprandial Period / genetics
- Prevotella / genetics
- Prevotella / isolation & purification
- Biomarkers
- Blood Glucose
- Glucose
- Prevotella copri
- Full Text Sources
- Other Literature Sources
- Medical
- Miscellaneous
![Extended Data Fig. 8. Pairwise partial Spearman…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0014.jpg)
![Extended Data Fig. 9. Concordance of Random…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0015.jpg)
![Extended Data Fig. 10. Prevotella copri and/or…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0016.jpg)
![Fig. 1:. The PREDICT 1 study associates…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0001.jpg)
![Fig. 2:. Food quality, regardless of source,…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0002.jpg)
![Fig. 3:. Random forest machine learning models…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0003.jpg)
![Fig. 4:. Fasting and postprandial cardiometabolic responses…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0004.jpg)
![Fig. 5:. Species-level segregation into healthy and…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0005.jpg)
![Fig. 6:. The panel of 30 species…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8353542/bin/nihms-1711077-f0006.jpg)
Source: PubMed