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Blood metabolome predicts gut microbiome α-diversity in humans

Nature Biotechnology volume 37, pages 1217–1228 (2019)Cite this article

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Abstract

Depleted gut microbiome α-diversity is associated with several human diseases, but the extent to which this is reflected in the host molecular phenotype is poorly understood. We attempted to predict gut microbiome α-diversity from ~1,000 blood analytes (laboratory tests, proteomics and metabolomics) in a cohort enrolled in a consumer wellness program (N = 399). Although 77 standard clinical laboratory tests and 263 plasma proteins could not accurately predict gut α-diversity, we found that 45% of the variance in α-diversity was explained by a subset of 40 plasma metabolites (13 of the 40 of microbial origin). The prediction capacity of these 40 metabolites was confirmed in a separate validation cohort (N = 540) and across disease states, showing that our findings are robust. Several of the metabolite biomarkers that are reported here are linked with cardiovascular disease, diabetes and kidney function. Associations between host metabolites and gut microbiome α-diversity were modified in those with extreme obesity (body mass index ≥ 35), suggesting metabolic perturbation. The ability of the blood metabolome to predict gut microbiome α-diversity could pave the way to the development of clinical tests for monitoring gut microbial health.

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Fig. 1: The plasma metabolome can predict Shannon diversity.
Fig. 2: Correlation of the eleven strongest metabolite predictors with the ten most abundant microbial genera.
Fig. 3: Shannon diversity in clinical laboratory tests and the host metabolome.
Fig. 4: The gut microbiome–host metabolome in severe obesity.
Fig. 5: Gut microbiome–host metabolome relationship in a validation cohort.

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Data availability

The model summary statistics for all metabolites, proteins and clinical laboratory results analyzed are available to download in Supplementary Tables 1–3. Qualified researchers can access the full deidentified dataset for research purposes. Requests should be sent to nathan.price@systemsbiology.org.

Code availability

The packages and code used in this study are available at https://github.com/PriceLab/ShannonMets.

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Acknowledgements

We thank C. Diener, A. Zimmer and M. Robinson for helpful discussions. This research was supported by the M.J. Murdock Charitable Trust (L.H. and N.D.P.), Arivale and a generous gift from C. Ellison. S.M.G. was supported by a Washington Research Foundation Distinguished Investigator Award and by start-up funds from the Institute for Systems Biology.

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Author notes

  1. Andrew T. Magis

    Present address: Institute for Systems Biology, Seattle, WA, USA

  2. These authors contributed equally: Tomasz Wilmanski, Noa Rappaport.

Authors and Affiliations

  1. Institute for Systems Biology, Seattle, WA, USA

    Tomasz Wilmanski, Noa Rappaport, John C. Earls, Jennifer Lovejoy, Leroy Hood, Sean M. Gibbons & Nathan D. Price

  2. Arivale, Seattle, WA, USA

    Andrew T. Magis, Ohad Manor & Jennifer Lovejoy

  3. Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Gilbert S. Omenn

  4. eScience Institute, University of Washington, Seattle, WA, USA

    Sean M. Gibbons

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Contributions

T.W., N.R., S.M.G., L.H. and N.D.P. conceived of the study. T.W., N.R., J.C.E., G.S.O., S.M.G., L.H. and N.D.P. participated in study design. T.W., N.R., A.M. and O.T.M. processed the data and performed computational analysis. A.T.M., O.T.M. and J.L. managed the logistics of data collection and integration. T.W., N.R., S.M.G. and N.D.P. were the primary writers of the paper, with contributions from all authors. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Leroy Hood, Sean M. Gibbons or Nathan D. Price.

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Competing interests

At the time this research was conducted, L.H. and N.D.P. were co-founders of Arivale (where these data come from) and held stock in the company; N.D.P. was on the Arivale board of directors; L.H. was chair of, and G.S.O. a member of, Arivale’s scientific advisory board; A.T.M., O.M. and J.L. were employees of Arivale and had stock options in the company, as did G.S.O. and J.C.E. Arivale’s program is now closed, so there are no longer any competing interests. The authors declare no other current competing interests.

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Integrated supplementary information

Supplementary Figure 1 Investigating collinearity among the identified predictors of Shannon diversity and β-diversity in the discovery cohort.

A) Heatmap showing the strength of correlation between each metabolite-metabolite pair. B) Histogram of all calculated Pearson r values for the 1560 metabolite-metabolite comparisons in the discovery cohort (N=399). Only six comparisons yielded a Pearson r value |r|>0.80. C) Metabolites showing significant association with inter-individual gut microbial variations (Benjamini-Hochberg FDR<0.05). Highlighted in red are the 4 metabolites (out of 11) identified to be the top predictors of Shannon diversity in our analysis. PERMANOVA was performed on the OTU-level Bray-Curtis dissimilarity matrix, including Shannon diversity as a covariate. D) β-dispersion across obesity subtypes. Dissimilarity between individuals’ microbiomes is significantly different across obesity (ANOVA P-Value=0.005). Post-hoc comparisons between BMI groups demonstrated that Obese II/III, but not Obese I individuals, were significantly more dispersed relative to normal weight individuals (Tukey HSD test P<0.05, two-sided). Sample sizes for individual BMI groups are as follows: normal weight n=143, overweight n=134, obese I n=66, obese II/III n=57. Boxplots represent the interquartile range (25th to 75th percentile, IQR), with the middle line demarking the median. Whiskers span 1.5*IQR. Points beyond this range are individually shown. E) Blood metabolome capacity to predict Shannon diversity across increasing time lag between stool and blood samples. Participants in the discovery cohort were stratified into tertiles based on the number of days between when the stool and blood samples were collected. LASSO using the 40 metabolites identified in MEP_L_fig1Fig. 1 was used to evaluate out-of-sample performance in predicting Shannon diversity across 5-fold CV. Tertiles 1 and 2 demonstrated similar performance to results reported on the whole cohort, while tertile 3 (with the largest time lag) demonstrated a decrease in performance. Importantly, limiting the allowed time gap between when samples were collected (Tertiles 1 & 2) did not result in improved performance relative to the model fitted on the entire cohort. Comparison between the whole cohort and Tertile 3 performance was performed using a two-sided nonparametric Mann-Whitney U-Test. Values are shown as mean +/- the Standard Error of the Mean.

Supplementary Figure 2 Variable relationships of sex steroids and bile acids with Shannon diversity.

A) Shannon diversity is not significantly different across sex (male n=111, female n=288), assessed using an OLS model adjusted for age and BMI. B) 5α-androstan-3β-17α blood concentration is higher in men than women, modelled using OLS regression and adjusting for age and BMI (P-Value=3.79e-13). Boxplots represent the interquartile range (25th to 75th percentile, IQR), with the middle line demarking the median. Whiskers span 1.5*IQR. Points beyond this range are individually shown. C) 5α-androstan-3β-17α is positively associated with Shannon diversity in both males and females. D&E) Secondary bile acids retained by LASSO in the prediction model show opposite association with Shannon diversity. Lines shown are y~x regression lines for the whole cohort (black) or for males (blue) and females (red) separately. The shaded region corresponds to the 95% confidence intervals for the slope of the line. Abbreviations: 5α-androstan-3β-17α: 5α-androstan-3β-17α-diol disulfate.

Supplementary Figure 3 Spearman correlations of each of the top metabolites with microbiome genera.

Spearman correlations of each of the 11 metabolites retained by all 10 LASSO models (rows) with microbiome genera (columns), in the discovery cohort (N=399), correcting for multiple hypothesis testing (Benjamini-Hochberg FDR<0.05). Only genera with at least one significant correlation value are displayed. Top color row labels the phylum for each genus. Left color column labels the sign of the mean β-coefficient for that metabolite across the 10 LASSO models generated to predict Shannon diversity (blue - negative, red - positive). The top bar graph represents the fractional abundance of each genus, with bars colored by phylum.

Supplementary Figure 4 Comparison of different omics platforms in predicting Shannon diversity.

A) Comparison of Precision-Recall curves and B) Receiver Operator Characteristic (ROC) curves for clinical laboratory tests and 11 blood metabolites classifying participants in the bottom quartile of Shannon diversity using 10-fold CV implementation of Random forests. C) Out-of-sample R2 scores from penalized regression models predicting Shannon diversity using each omics platform individually, or in different combinations. Values are presented as mean R2 across 10-fold CV +/- standard error of the mean. Performance of metabolomics alone was compared to performance of metabolomics in combination with clinical labs, proteomics, and clinical labs and proteomics using a two-sided nonparametric Mann-Whitney U test. The P-Values for these comparisons are shown. No multiple hypothesis correction was implemented in this analysis. There was no significant improvement or decrease in performance when all analytes were combined relative to metabolomics data alone.

Supplementary Figure 5 Evaluating the blood metabolome-gut microbiome relationship under different disease conditions.

A&B) Relationship of observed and metabolome predicted Chao1 (A) and PD whole tree (B) diversity with self-reported measures of gastrointestinal (GI) health, as well as antibiotics use, using an OLS regression model adjusted for sex, age, and BMI. Conditions colored in red are significantly associated with both the observed and metabolome predicted metric (P-Value<0.05). C) The relationship of various GI measures with Shannon and metabolome predicted (mShannon) diversity, modelled using OLS regression adjusted for the same covariates as in (A) and (B) as well as antibiotics use (N=311). D) Comparison of mean out-of-sample performance of LASSO in predicting Shannon diversity in the whole cohort (black) and in participants who have not taken antibiotics in the last three months (gray - all 659 metabolites, red - only the 40 metabolites identified in the original analysis). Values are presented as mean +/- the Standard Error of the Mean. E) A scatter plot of metabolome predicted (mShannon) diversity and observed Shannon diversity in participants who have not taken antibiotics in the last three months, generated using only the 40 metabolites originally identified in this study. The mean R2 across the 10 cross validations, Pearson r of observed versus mShannon values, and corresponding P-Value are shown.

Supplementary Figure 6 Relationship of the plasma metabolome and Shannon diversity changes across BMI classes.

A) β-coefficients for each of the 11 metabolites retained by all 10 LASSO models from an OLS regression model with Shannon diversity as the dependent variable and sex and age included as covariates in the discovery cohort. The cohort was stratified based on defined BMI cutoffs and models were fitted independently for each BMI class. B) Scatter plot of PFOS and Shannon diversity for participants whose BMI is less than 25 (normal weight), and equal to or greater than 35 (Obese II/III) in the discovery cohort. C) Comparison of strengths of correlations for 5α-androstan-3β-17α and PFOS with Shannon diversity across obesity in the discovery and validation cohorts. D) Scatter plot of 5α-androstan-3β-17α and Shannon diversity for participants whose BMI is less than 35, and greater than or equal to 35 (Obese II/III) in the validation cohort. Pearson r and P-Values are shown. Lines shown are y~x regression lines, while the shaded region corresponds to the 95% confidence intervals for the slope of the line. Abbreviations: 5α-androstan-3β-17α: 5α-androstan-3β-17α-diol disulfate; PFOS: perfluorooctanosulfic acid.

Supplementary Figure 7 Metabolite-microbiome genera correlations in the validation cohort.

A) Spearman correlation of each of the 11 strongest metabolites identified in the discovery set (rows) with microbiome genera (columns) in the validation cohort, correcting for multiple hypothesis testing (Benjamini-Hochberg FDR<0.05). Only genus-metabolite correlations that were significant in the discovery cohort were considered. The top bar graph represents the fractional abundance of each genus in the validation cohort, with bars colored by phylum. B) The number of significant Spearman correlations of each of the 11 metabolites retained by all 10 LASSO models (rows) with microbiome genera in the discovery and validation cohorts, correcting for multiple hypothesis testing (Benjamini-Hochberg FDR<0.05).

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Supplementary Information

Supplementary Figs. 1–7 and Supplementary Table 4

Reporting Summary

Supplementary Table 1

Metabolites with non-zero β-coefficients in the LASSO models predicting Shannon diversity.

Supplementary Table 2

Association of clinical laboratory results with Shannon diversity in the discovery cohort.

Supplementary Table 3

Association of blood proteomics with Shannon diversity in the discovery cohort.

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Wilmanski, T., Rappaport, N., Earls, J.C. et al. Blood metabolome predicts gut microbiome α-diversity in humans. Nat Biotechnol 37, 1217–1228 (2019). https://doi.org/10.1038/s41587-019-0233-9

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