The trillions of microbes in our gut have been increasingly found to play important roles in human health, from aiding in the metabolism and absorption of nutrients, to facilitating healthy immune development and function 1. Despite the immense progress made in gut microbiome research, there is still no clear consensus of what constitutes a ‘healthy’ gut microbiota. Nor is there a simple test that can be applied in a clinical setting to monitor the changing influence of an individual’s gut microbiome on health. The development of new technologies and large reductions in cost have made it feasible to greatly expand the generation of large, multi-omic data in the form of personal dense, dynamic, data (PD3) clouds for thousands of individuals. In our cohort, these data consisted of microbiomes, coupled with human genomes, metabolomes, proteomes, and clinical labs, as well as data from wearable devices, health questionnaires and coaching interactions, a subset of which was used for this paper. These data can be leveraged to provide deeper insights into biological function across systems, including the gut microbiome and its relationship with host physiology.
Our work started with the hypothesis that it might be possible to predict gut microbial α-diversity, a measure of ecological community structure, from analytes measured in a simple blood draw 2. In particular, we focused on circulating plasma metabolites, given the important role the gut microbiome plays in digestion, metabolism, and nutrient absorption. We hypothesized that the metabolic capacity of the gut microbiome may be more stable and consistent than the individual microbes identified through commonly used sequencing methods. We found that not only were we able to predict gut microbiome diversity from the human blood metabolome, but we could do it using a subset of only 40 metabolites. Nearly ⅓ of the metabolites identified in our analysis were previously shown to be synthesized by the gut microbiota 3. Several of these microbial metabolites were known to exert biological effects on various organs (kidneys, heart, liver, etc.) 4, 5. Our metabolite model performed far better at predicting microbiome diversity than a wide panel of clinical laboratory tests and blood proteins. The metabolite model further generated robust predictions of gut microbiome diversity across disease states and showed consistent results in a separate validation cohort.
Similar to prior work, lower gut α-diversity in our study was associated with diarrhea and abdominal pain, while higher diversity was associated with constipation. These associations were reflected in concentrations of the identified metabolites. Several metabolites identified to be positively associated with gut microbial diversity in our analysis have been previously shown to exert potentially toxic effects in the host (i.e. p-cresol sulfate and trimethylamine N-oxide (TMAO)) 5. Collectively, these findings challenge the belief that higher gut diversity is always better for health. Instead, our results support the existence of a ‘Goldilocks Zone’ for gut microbial α-diversity, above which higher α-diversity may be associated with worse health outcomes.
Gastrointestinal infections continue to be a major health concern, particularly in the elderly, where they may result in severe complications and even death. Clostridium difficile alone is estimated to infect ~500,000 patients and account for ~15,000 deaths annually in the U.S. 6. One of the primary risk factors for recurrent Clostridium difficile infection is low gut microbial α-diversity, often brought on by antibiotics or other medication use 7, 8. A reliable clinical blood test for gut microbial diversity would allow us to screen individuals at higher risk for recurrent infections. Then, appropriate preventive measures could be taken as needed.
Although promising, our findings still require further validation for ultimate translation to the clinic. We are only beginning to understand the complex interconnections between host metabolism, gut microbial
composition, and human health. We believe a key to understanding what constitutes a healthy microbiome for any individual will be found primarily through its reflections in the blood. Future clinical blood tests that provide insight into the state of the gut microbiome carry with them new possibilities for effectively monitoring wellness and targeting individuals at high risk for disease.
This post was a collaborative effort by Tomasz Wilmanski, Noa Rappaport, Sean Gibbons, and Nathan Price, and thanks to Allison Kudla for designing the images.
1. Dominguez-Bello, M.G., Godoy-Vitorino, F., Knight, R. & Blaser, M.J. Role of the microbiome in human development. Gut 68, 1108-1114 (2019).
2. Wilmanski*, T., Rappaport*, N., Earls J.C., Magis, A.T., Manor, O., Lovejoy, J., Omenn, G.S., Hood, L., Gibbons, S., Price, N. Blood metabolome predicts gut microbiome α-diversity in humans, Nature Biotechnology (2019), doi:10.1038/s41587-019-0233-9.
3. Wikoff, W.R. et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc Natl Acad Sci U S A 106, 3698-3703 (2009).
4. Koh, A. et al. Microbially Produced Imidazole Propionate Impairs Insulin Signaling through mTORC1. Cell 175, 947-961 e917 (2018).
5. Koeth, R.A. et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med 19, 576-585 (2013).
6. Lessa, F.C., Winston, L.G., McDonald, L.C. & Emerging Infections Program, C.d.S.T. Burden of Clostridium difficile infection in the United States. N Engl J Med 372, 2369-2370 (2015).
7. Chang, J.Y. et al. Decreased diversity of the fecal Microbiome in recurrent Clostridium difficile-associated diarrhea. J Infect Dis 197, 435-438 (2008).
8. Owens, R.C., Jr., Donskey, C.J., Gaynes, R.P., Loo, V.G. & Muto, C.A. Antimicrobial-associated risk factors for Clostridium difficile infection. Clin Infect Dis 46 Suppl 1, S19-31 (2008).
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