Authors: Andrew Brooks (Vanderbilt Genetics Institute, TN, USA)
Take a look behind the scenes of a recent Future Microbiology review, entitled ‘How could ethnicity-associated microbiomes contribute to personalized therapies?’, as we ask the authors about the factors that influence the microbiome composition, how these could be manipulated and the future of this field.
What inspired you to write this piece?
This work was motivated by two key factors: 1) that health disparities among many common diseases are a serious societal cost that is under-addressed in current medical research (> US $250 billion / year in 2015 in the USA alone – Harvard Business Review), and 2) a wealth of new public genetic data will allow health disparities to be better understood if the importance of this issue is more widely considered across basic science and translational clinical research.
What are ethnicity-associated microbiomes and what do we currently know?
Evidence suggests that ethnicity-associated microbiome composition is a subtle, but still relevant explanatory factor in total microbiome structure across individuals. Many factors like diet, environment and genetics each play their own roles in microbiome assembly, and each may also vary across ethnic groups leading to ethnicity-specific signatures in microbiome composition. It appears to be a subset of microbial taxa that reproducibly associate with ethnicity, but these signals provide novel biomarkers that could be targeted in future microbiome medical interventions. It has not yet been investigated if metagenomic composition (microbial genetic capacity) also associates with ethnicity.
How can these subtle differences and similarities be utilized for personalized treatment?
These subtle signals could be utilized in two key ways: 1) as direct microbial targets that could be supplemented with prebiotics or probiotics, included in future Fecal Microbiome Transplants (FMT), eliminated by targeted phage treatment cocktails, or targeted directly in some other way, or 2) indirectly as biomarkers for other physiological queues or environmental exposures. What the second approach suggests is that survey questionnaires can be biased by question structure, the examiner, and study participant trust of the medical system. Free and direct measures of microbiome composition at public health clinics could predict, for example, higher smoking in a population than participants may inherently admit to health professionals, and spawn a public-health anti-smoking campaign.
Direct microbiome measures could also suggest where childhood nutrition may lack complex carbohydrates (vegetables) to support diverse and healthy microbiomes, which could be addressed with supplemental nutrition programs and school lunch nutrition programs. If the correct biomarkers can be reliably and efficiently measured, they likely will offer more reliable health indicators than many public questionnaires and surveys.
For what diseases could disparity-associated microbiomes provide insights for?
The human gut microbiome has been linked to many diseases to varying degrees, from gastrointestinal disorders like IBD and Crohn’s Disease etc. to chronic conditions like diabetes and heart disease, as well as neurological disorders like Alzheimer’s. For this reason, it could be suspected that any disease with microbiome associations and that presents as a health disparity may benefit from further investigation, but unfortunately ethnically stratified case-control disease studies are nearly non-existent in current microbiome literature.
What is the issue with this research? Are there any social considerations?
A major concern with ethnicity-specific microbiome composition is the challenge in characterizing the factors that shape microbiomes across ethnicities. Much of the body of current research suggests that diet is consistently the most influential factor in microbiome assembly, but social transmission, lifestyle, diet, genetics, age, sex and a myriad of other factors can play a part. There is likely a much larger sociocultural and environmental influence on ethnicity-specific microbiomes than any fundamental biological or ancestral variation given trends in the field, but this also means ethnicity-specific variation may also be stratified within an ethnicity by geographic locality, or overlap other ethnicities living in similar conditions. Further investigation, more controlled studies, and increased diversity in participant recruitment will be important to disentangle which factors are important, and the underlying mechanisms of why? (like our ongoing diet controlled, multi-ethnic clinical trial – The Vanderbilt Microbiome Initiative).
What more needs to be done in this area? What do you predict for the next 10–15 years? What work will you focus on?
Initially the field would benefit most by increasing representative participant recruitment, so that studies better reflect the larger population and results are more generalizable. New analytical methods and creative ways of asking questions can help uncover signals in existing data, but a dearth of representative studies that sample multi-ethnic and societally disadvantaged populations limit such approaches. Lack of ethnic, sex, age and other representation for the sake of experimental controls is not only unethical toward disadvantaged populations, it is an affront to the core principle of Justice in the Belmont report that guides all human subjects research.
In 10–15 years I hope that creative approaches to address health disparities are less necessary because these populations are adequately represented in the pool of research studies. To get there, steps need to be made to demonstrate the utility of targeting biological systems directly to treat health disparities, instead of solely focusing on societal structure and inequalities that have less direct influence and may be important to people’s self-identity.
In the future I am beginning a postdoctoral position at Stanford (CA, USA) under Carlos Bustamante and Michael Snyder, where I will be focusing on connecting physiological systems through multi’omics approaches to understand how different parts of the body predict each other, and how external stimuli such as changes in diet or ethnically-biased exposures lead to cascades of change throughout these systems.
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