Recent recognition that the intestinal microbiome plays potential roles in the pathogenesis of multiple common diseases has led to a growing interest in personalized microbiome analysis among clinical investigators and patients. Permissibility of direct access testing has allowed the emergence of commercial companies offering microbiome analysis to patients seeking to gain a better understanding of their symptoms and disease conditions. In turn, physicians are often asked to help with interpretation of such tests or even requested by their patients to order them. Therefore, physicians need to have a basic understanding of the current state of microbiome science. This review examines how the perspective of microbial ecology, which is fundamental to understanding the microbiome, updates the classical version of the germ theory of disease. We provide the essential vocabulary of microbiome science and describe its current limitations. We look forward to the future when microbiome diagnostics may live up to its potential of becoming integral to clinical care that will become increasingly individualized, and microbiome analysis may become incorporated into that future paradigm. However, we caution patients and providers that the current microbiome tests, given the state of knowledge and technology, do not provide much value in clinical decisions. Considerable research remains to be carried out to make this objective a reality.
The advances in high-throughput DNA sequencing technology and computational methodology over recent years have allowed investigations of complex microbial communities without reliance on laboratory culture techniques. The term ‘microbiome’, which is typically used to refer to these communities, has become ubiquitous in the scientific literature and the lay press. Most of the research work on the microbiome as it relates to human health and disease has been focused on intestinal microbes, which is reasonable given that the intestine has the greatest density of microbes in the human body. DNA from fecal samples is typically sequenced, and these sequences are classified against taxonomic databases to provide proxies for the microbiota present. Importantly, though, this characterization of the microbiome predominantly represents the luminal content near the end of the digestive tract and thus is not representative of mucosal microbiota or specific sections of the intestinal tract, e.g., the small intestine or cecum.
Investigations carried out over the past one and a half decades have demonstrated that the intestinal microbial communities or the ‘intestinal microbiota’ participate in many aspects of human physiology, including the development of the immune system, energy metabolism, and even the activity of the nervous system . Microbiota appears to have important roles in obesity, inflammatory bowel disease, autoimmunity, neurodevelopmental and neurodegenerative disorders, pathogenesis of many cancers, and even responsiveness to cancer immunotherapy. Therefore, there has been intense interest in the possibility of targeting the intestinal microbiota therapeutically, as demonstrated by the remarkable success of fecal microbiota transplantation (FMT) in the treatment of Clostridium difficile infections [2,3,4]. Current uses of FMT and the mechanisms by which they effectively treat various pathologies have been recently reviewed [5, 6], and additional information is available from the American Gastrological Association (https://www.gastro.org/research-and-awards/registries-and-studies/fecal-microbiota-transplantation-fmt-national-registry), but further discussion is beyond the scope of the current review.
Based on the many roles the microbiome plays in host health, it is reasonable to believe that analysis of the microbiome may have diagnostic value, including recent interest in screening for drug efficacy and evaluating the effect of therapeutic compounds on the microbiota [7, 8]. Indeed, there are commercial laboratories that market directly to patients and suggest that they can provide insights into disease pathogenesis based on a microbiome profile. Many physicians, and gastroenterologists in particular, are now routinely encountering patients bringing in test results from such laboratories or requesting their physicians to assist with interpretation or ordering of the tests, perhaps to enable the potential for insurance coverage. Clearly, in order to be able to communicate effectively about these complex topics, it is imperative that physicians understand the basics of the microbiome science and the limitations of current microbiome-based diagnostics. In this review, we will briefly examine the history of the modern ideas and common misconceptions about the microbiome, cover the essential vocabulary, and review several specific examples.
History of Microbiota-Associated Disease Paradigms
The development of methods to culture microorganisms in the late nineteenth century, begun by the work of Louis Pasteur in 1864 , led to the creation of the field of bacteriology. Publication of Koch’s postulates in 1882 definitively revealed the bacterial cause of tuberculosis and established the basic paradigm for making causal associations between certain pathogenic bacterial species and specific diseases . This was followed by rapid adaptation of the germ theory of disease , arguably one of the most transformative and successful achievements in the history of medicine.
Notably, Elie Metchnikoff, the father of cellular immunology and one of Louis Pasteur’s successors at the Pasteur Institute, focused some of his later investigations on the role of the indigenous intestinal microbes in human disease . In fact, he established the science of gerontology and proposed (with an ironic nod to Galen’s miasma theory) that aging results from toxic effects of intestinal microbes on the human body . In Metchnikoff’s view, the host immune system was engaged in an adversarial struggle with its microbial inhabitants. Various remedies were developed in the early twentieth century to counter these harms emanating from the gut microbes, ranging from Kellogg’s advocacy of bran as a dietary means to promote laxation  to Lane’s development of colectomy for treatment of chronic constipation and systemic illnesses hypothetically linked to it . Metchnikoff himself was undoubtedly inspired by the reports of the remarkable (and unsubstantiated) longevity of peasants in the remote mountain villages in the Balkan States and the southern periphery of the Russian Empire, where fermented milk products were a major traditional dietary staple . Building on the work of Grigorov , he suggested that certain microbes, such as Lactobacillus species found in yogurt, could counteract the putrefactive intestinal microbes, and have beneficial effects on health, such as delaying senility. Thus, Elie Metchnikoff became the father of probiotics, although that term was not coined until decades later .
It is fair to say that the notion of microbes being classifiable as strictly “bad” or “good” continues to be pervasive in the minds of the lay public, physicians, and even microbiome scientists. However, while it is indisputable that virulent pathogens are bad, the concept of “good” microbes is more problematic, as every life form follows Darwinian rules of survival and is ultimately selfish. Even in the case of potential pathogens, many, including C. difficile , may colonize a host as non-harmful commensals. In some way, the attempts to identify “good” microbes by isolating and cultivating individual species in the laboratory is a reflection of the enormous success of the germ theory of disease and the power of Koch’s postulates for pathogens, although the concept of “good microbes” is very much anthropocentric.
It is critical to appreciate that the majority of indigenous intestinal microbes do not exist in nature as monocultures of individual species, but are members of complex microbial communities inhabiting different ecological niches within the host . The functionality of these microbial communities cannot be easily reduced to a simple summation of potential beneficial and harmful activities that may be elicited in a laboratory. The community functionality is dependent on the specific microbial strains present, their density, all the various relationships between them (e.g., mutually beneficial, competitive, etc.) , and multiple host-specific factors that may include diet, activity of the immune system, and others . Therefore, predicting the outcomes of different assemblages of microbiota may not be possible, even if we knew all the variables in detail, something that remains a distant hope.
For most of the twentieth century, investigators studying the activity of gut microbes were limited by the cultivable microbiology methods and measurements of selected microbial metabolites. This has resulted in the “great plate count anomaly” [22, 23], an issue of fewer than 1% of bacteria being cultivable in the laboratory setting. Despite these technological limitations, multiple functions of gut microbes beneficial to the host were recognized, including being a potential source of certain vitamins (e.g., vitamin K, many B vitamins), energy harvest via short chain fatty acids from digestion of complex polysaccharides, and providing colonization resistance against pathogens [1, 24, 25]. Nevertheless, the study of pathogens dominated medical microbiology, and clinicians generally gave little thought to the potential bystander effects of antibiotic treatments on indigenous microbes [26, 27]. In fact, extreme measures including universal decolonization of critical care patients have been suggested , although more recent evidence suggests a beneficial role for some commensal skin microbiota [29, 30].
The rapid advances in DNA sequencing technologies over the past two decades [31, 32] have revolutionized the study of complex microbial communities and facilitated the creation of the field of metagenomics . This has opened up an entirely new frontier of investigations and a re-evaluation of the roles intestinal microbiota play in health and disease. Metagenomics-based studies entail the sequencing of the total DNA of all microorganisms (i.e., bacteria, archaea, fungi, and protozoa—the ‘metagenome’; Table 1) in an environment, enabling characterization of both the composition and potential functional traits of entire microbial communities in an environment without a need for laboratory cultivation [34,35,36]. Such studies yield enormous amounts of data requiring sophisticated computational tools for analyses. However, the clinician should be cautious not to mistake the amount of data and mathematical calculations for definitive insights. We are still very much in the infancy of the microbiome science, and our current technologies might be comparable to Antonie van Leeuwenhoek’s single-lens microscopes, which were able to visualize the microbial world for the first time, in the field of microscopy. Nevertheless, the clinician today should have a working knowledge of the microbial ecology concepts and toolbox to complement classical medical microbiology taught in medical schools.
Features of the Microbiome
A critical concept in interpreting microbiome data is that the communities characterized are approximations of the actual composition of the microbiota and reflect merely a snapshot at a single time point. As such, these datasets are subject to the same biases and limitations related to sampling methods, storage conditions, and technical considerations as environmental studies . Nevertheless, there are currently three primary features of the microbial community that are typically evaluated: diversity, composition, and function.
Microbial diversity is described as either alpha diversity or beta diversity . Alpha diversity is analogous to the diversity (e.g., different individuals) within a household unit, while beta diversity refers to the differences in diversity between households, neighborhoods, etc. For example, an apartment building will have greater alpha diversity than a single-family home due to a greater number of individuals. However, beta diversity could be used to measure differences between the composition of households in a farming community and an urban area, which will differ in population density as well as numbers of different occupations held by residents.
Microbial alpha diversity is traditionally measured using three indices, although many more exist: (1) richness, or the number of species present; (2) the Simpson index , a measure of concentration of individuals of the same species; and (3) the Shannon–Weaver index , a measure of the distribution (evenness) of all species . Richness estimates in microbial studies have been adapted and modified from mark–release–recapture statistics used in macroecology for estimating the size of animal populations. Microbial communities are very diverse relative to typical examples from macroecology and feature a high degree of over-dispersion (many taxa present in low abundance) , therefore adjusted indices are used to estimate microbial richness in microbiome data . These include the Chao1 index  and abundance-based coverage estimate  measures, which consider the number of singleton (occurring once) and doubleton (occurring twice) taxa or all taxa that occur fewer than 10 times, respectively. However, data processing steps to remove likely sequence errors may impede the accuracy of these adjusted richness estimates by removing low-abundance sequence reads, and sequencing errors may still complicate true quantitative estimates of diversity.
Some of the most commonly used calculations for beta diversity are the Bray–Curtis distance , which accounts for differences in abundances of taxa between two communities; the Jaccard index , which is analogous to a qualitative Bray–Curtis distance that is naïve to relative abundances; and the UniFrac distance , which is a measure of differences in the relatedness of two communities. Since very few bacteria are cultivable, taxa are typically defined by a sequence similarity cutoff between 95 and 99% , although advanced bioinformatics methods which discriminate at 100% sequence similarity are becoming standard. Therefore, sequences that are less similar than this cutoff are treated as separate taxa, and information related to the amount of sequence divergence and, consequently, species relatedness are lost using more traditional indices. By considering taxa presence and abundances (Jaccard or Bray–Curtis), one can evaluate the extent to which the composition of the community is different, while incorporating relatedness (UniFrac) allows an assessment of the evolutionary divergence between two communities. The primary difference between these measures stems from the idea that related species, although taxonomically distinct, are more likely to perform similar functions .
It has been suggested that higher microbial community alpha diversity correlates with greater stability and resilience to challenges (e.g., a course of antibiotics) . However, it is unknown what the optimal intestinal microbial alpha diversity should be in humans, and the absolute values of the various indices will vary depending on the sequencing methods used (described below). Fecal samples from human communities living an ancestral lifestyle display higher bacterial richness than samples from individuals living in industrialized Western countries . Loss of microbial richness in the Western populations has been hypothesized to be due to loss of nutrient complexity in the diet , widespread use of antibiotics , and greater levels of hygiene . However, it is noteworthy that the host imposes severe constraints on the intestinal diversity by virtue of the unique ecological niches being offered and the vigilance of the immune system. The microbial residents of the human intestinal tract are highly adapted to the human host and, while the intestinal bacterial richness of ~ 1000 taxa may appear to be great [20, 50, 54], it is far lower than microbial diversities found in samples from soil or ocean environments . In fact, some immunodeficiency disorders may be associated with greater microbial alpha diversity in the intestine relative to healthy controls, likely due to loss of constraints enforced by the mucosal immune system [56, 57]. It is also important to note that most current studies focus on metrics measuring bacterial and archaeal composition, due to methodological challenges of sequencing, because other domains of life are technologically more challenging. However, the presence of parasites, including helminthes and protozoa, which is likely much more common in ancestral-type human communities, is associated with greater bacterial alpha diversity [58, 59]. On the other hand, it is likely that extremely low alpha diversity values (e.g., Shannon index < 2) that are seen in patients receiving multiple courses of antibiotics (e.g., in cases of recurrent C. difficile infections, intensive chemotherapy, or complications of advanced liver disease) are suggestive of disease. Alpha diversity metrics also tend to be somewhat lower in populations of patients with common conditions being intensely investigated, such as obesity and inflammatory bowel disease [60, 61], although it remains unclear whether they can be diagnostically informative by themselves.
Composition of the Microbiome
A healthy intestinal microbial community is comprised predominantly of members of the phyla Firmicutes and Bacteroidetes [50, 62], representing broad divisions of metabolically diverse species. There is considerable variability among human individuals with respect to the specific species and strains comprising the microbial communities, yet intra-individual, temporal differences in microbiome composition tend to be less dramatic than inter-individual differences . However, the ‘core microbiome’, which is defined by key functional attributes of intestinal microbial communities is much more conserved among healthy humans , at least in a geographically limited dataset. The wide variety of stable assemblages in a healthy intestinal microbiome is a result of a high degree of functional redundancy—multiple taxa can perform the same function . Nevertheless, the majority of the individual species comprising the microbiome are present in very low abundances, making resolution of a core microbiome among healthy individuals difficult [20, 50, 64], if one even exists.
Three stable assemblages, or ‘enterotypes’, were proposed as geographically ubiquitous, robust compositions to which the human microbiome could be stratified . These compositions were affiliated with greater abundances of either Bacteroides, Prevotella, or Ruminococcus genera. However, an accumulation of data subsequently revealed that, while enterotypes provided an attractive framework to understand the diversity of the human microbiome, there is actually much greater plasticity, even within the same individual, and the various assemblages of a healthy microbiome exist on a much broader continuum [66, 67].
Characterizing microbiome composition using the currently available sequencing technologies reflects a tradeoff between taxonomic resolution (e.g., species identification), the extent to which an entire community can be characterized, and cost . Amplicon sequencing is the most commonly used method and is a cost-effective way to sequence nearly the entire microbial community within a particular domain, although exhaustive characterization is not possible due to unavoidable primer biases [69, 70]. Amplicon sequencing entails the amplification and sequencing of a small portion of a taxonomic marker gene (e.g., 16S ribosomal RNA or an internal transcribed spacer region), and the inexpensive generation of tens of thousands of reads per sample reveals low abundance taxa and broad features of the microbiome that may play important roles in host health. However, since only a short sequence read is used for taxonomic identification, species-level classification is generally less accurate, and some taxa might only be classified at low resolution . In contrast, shotgun sequencing is untargeted and, since it is not limited to a single, short DNA target for taxonomic identification, species-level identification is possible. Since sequencing is not constrained to only one gene, however, much more data from highly abundant microorganisms from all domains are sequenced, meaning lower-abundance taxa are missed and obtaining representative community characterization is more expensive. As a result of tradeoffs between methodologies, it is often difficult to completely understand the relationships between the distributions of specific groups of microorganisms and their roles in health outcomes.
Rather than identifying specific pathogens involved in disease, microbiome research is now focused on characterizing how imbalances in the distribution of typically commensal bacteria, which are typically not harmful and may be beneficial, are associated with disease. When microbial diversity is reduced, an overgrowth of commensal bacteria that have the ability to promote disease, called pathobionts, may occur [21, 72, 73]. Moreover, some pathobionts may even behave as ‘keystone species’  and induce negative effects like inflammation even when they are present at low abundances and could drive the rearrangement of microbial communities to more deleterious assemblages. Ultimately, in severe illness, persistent dysbiosis may occur in which a microbiota detrimental to the host drives the progression of disease by promoting inflammation, immunodeficiency, and antibiotic resistance [74, 75]. Moreover, the role of the microbiota in promoting inflammation and pathogenesis has been recently reviewed . Importantly, alterations in the intestinal microbiome studied to date have primarily focused on bacteria and archaea, and, more recently, fungi. However, inter-domain interactions as well as the roles of viruses and protozoa in promoting pathobiont expansion and dysbiosis require further study. Moreover, characterization of the microbiome by current sequencing-based approaches are generally unable to differentiate live and dead cells, so causative roles of specific taxa are currently only inferred, at best.
Microbial Community Function
Ultimately, clinicians are primarily interested in the functionality of microbiota. However, most of the current microbiome literature reflects primarily compositional data. Functionality is generally inferred based on knowledge about a number of well-characterized species that comprise only a fraction of the total in the current databases [77, 78], which also remain incomplete [79, 80]. This leads investigators to make highly speculative predictions regarding potential functions in the discussion sections of manuscripts that must be understood as hypotheses that still need to be tested. Attempts to infer functionality based on taxonomy are imperfect, and may be considered analogous to trying to infer that a person wearing a hard hat is a construction worker, whereas in reality it could be an architect or a visitor to a construction site, or even an infantry soldier on a battlefield. Rather, these observations should inform further efforts to confirm functionality. Untargeted, shotgun metagenomic sequencing, which is much more resource-intensive relative to 16S ribosomal RNA gene-based taxonomy, provides information as to which functional genes are present, based on the reference database used, thereby representing some fraction of the functional capacity of the microbiome. However, it cannot distinguish which genes are being actively transcribed by which taxa. In addition, even if one had a complete catalogue of all microorganisms and their genes, one would not be able to predict with confidence the functionality of the entire community. Using the earlier construction site metaphor, we cannot assume that, once we have identified a team that includes an architect, carpenters, electricians, and plumbers, we can predict that a house will be built on schedule or that construction has even started. There may be a shortage of building supplies, the carpenters may be unmotivated, we may be missing a construction manager to coordinate the work, and with our limited knowledge we did not know one was needed.
More direct investigations of microbiota functionality are possible using metatranscriptomics, metaproteomics, and metabolomics. Metatranscriptomic sequencing provides an imperfect characterization of which RNAs are empirically synthesized , but does not reflect in vivo function because the degrees of protein expression and function also depend on the rates of translation, post-translational modifications, and protein turnover. Metaproteomics and metabolomics employ analytical chemistry-based approaches to characterize proteins and metabolites that are being produced by the microbiota, but distinguishing human and microbial products using these methods can be difficult [82, 83]. Thus, understanding the functions performed by the intestinal microbiota requires a combination of multiple approaches and strategies, which are all complimentary, as well as technological advances to improve analyte annotation and assay affordability.
Evaluating Microbiome-Based Health Screening
The increasing recognition of the important roles the intestinal microbiota plays in human health, the compelling associations between population-wide changes in the microbiome and the rising prevalence of many diseases in the industrialized countries, and the relatively easy generation of certain microbiome-associated analytic metrics have provided a fertile ground for some biotechnology companies to market personalized microbiome testing, which suggests the potential for novel insights into individual patient symptoms and even actionable therapeutics. Commonly, the provided results summarize the microbiome data in terms of abundances of “good” and “bad” bacteria, often along some health-associated index or continuum, based on the available literature and frequently in relation to some healthy cohort . The reports may provide relative abundances of bacterial species, functions, and metabolites that have been associated in some (mostly exploratory) studies with conditions ranging from bloating and flatulence to inflammatory bowel diseases and metabolic syndrome, based on the literature or proprietary machine-learning-based approaches.
While the premise behind individual microbiome testing is to inform personalized diagnosis and therapies [85, 86], the current body of knowledge is not sufficient to allow for meaningful diagnoses, given the high degree of inter-individual variability in the microbiome [20, 87] and the severe limitations of commonly used analytic methods. Microbiome-based indices have been identified that can distinguish, for example, individuals with Crohn’s disease , and this index has been reported to be stable across multiple patient cohorts [89, 90]. However, a separate group found a different consortium of bacteria that was predictive of Crohn’s disease among several international datasets . Thus, while some microbiome signatures may effectively stratify patients, a consistent, precise microbiome signature for disease diagnosis has yet to be identified, as has also been the case with obesity . Importantly, the goal of current microbiome research is to develop precision-medicine, personalized approaches for disease diagnosis and treatment. However, a caveat to this research is that inter-individual variability is too great to generalize a microbiome-based solution. Therefore, one of the ongoing benefits of crowd-sourced microbiome data is the expansion of current microbiome datasets to better capture this variability and to resolve clear signatures and a causative relationship between the microbiome and the host .
While microbiome data are not diagnostic at this time, irrespective of what a company puts in their marketing material, data from commercial kits do provide an individual with a snapshot of their microbiome in relation to other individuals from different backgrounds and environments. Some features, such as the abundances of certain genera that may contain true pathogens, might be potentially informative of risk for illnesses, but current sequencing-based technologies used to describe the entire microbiome lack the resolution to distinguish pathogenic from related commensal bacterial strains or to identify genes encoding toxins [84, 94]. The obvious risk of applying such technologies to diagnosis of infectious disease is to increase unnecessary confirmatory testing, provoke unjustified patient anxiety, and compel patients to embark on useless or harmful therapies. Diagnosis of infectious disease should be based solely on highly specific, targeted diagnostic assays that have been thoroughly validated.
The use of machine-learning-based approaches to identify “good” or “bad” species is also problematic due to overfitting of models and the use of irrelevant taxa to predict a particular disease state . Descriptions of bacterial functions and metabolites from these tests may also be of anecdotal interest, but causative relationships between microbial functions and the progression of disease have yet to be concretely established in humans.
Current sequencing technologies have advanced to the point that relatively rapid and information-rich characterization of the intestinal microbiota is possible. The microbiota is recognized to be integral to many aspects of human physiology and has been likened to an organ in the body . Therefore, it is reasonable to look toward the possibility of leveraging metagenomic and multi-omics tools to develop diagnostic markers that will inform therapeutic interventions. However, sequencing technologies have vastly outpaced the analytical tools necessary to properly integrate and contextualize an exponentially greater volume of data than was available even a decade ago. As a result, false-positive findings with seeming statistical significance from small cohorts are frequently represented as deterministic diagnostic indices in the literature, and correlative studies are often misinterpreted as mechanistic evidence, based on loosely linked data regarding microbial physiology.
The field of microbiome research is currently approaching a transition from infancy to toddlerhood. Our means of explorations are changing from crawling to walking, but still require much more growth to acquire fine motor and cognitive skills to make sense of the world around us. Same-day sequencing technologies are now emerging as potentially powerful diagnostic tools, but a true understanding of how these data should be interpreted clinically cannot be rushed by commercial ingenuity. There is still ample work to be carried out in animal models and clinical trials to truly elucidate and validate the roles that commensal species, specific pathobionts, and community structures play in the progression of various pathologies. Nevertheless, the development and optimization of microbiota-based diagnostics and therapeutics offer a promising and powerful new avenue in personalized patient care that should be pursued cautiously, but optimistically.
A Patient in the Physician Office
One of the primary tasks of a physician is to be a patient educator. This mission is often complicated by direct-to-consumer advertising of drugs by the pharmaceutical industry. Such advertising has been blamed to be one of the causes for rising health care costs, a reason for inappropriate prescribing and drug over-utilization, and a source of misinformation. A number of arguments have also been advanced for direct-to-consumer advertising, including patient empowerment, expansion of information sources, encouragement of contact and dialogue with the physician, and reduction in under-diagnosis. Similar arguments for and against have been voiced with regard to direct access testing, which enables patients to obtain diagnostic services without the benefit of a complete pre-testing evaluation or objective post-testing counseling. Unfortunately, such laboratory businesses also commonly offer testing panels that are based on preliminary and speculative scientific information, while simply catering to a patient’s need to understand their illness because they are not satisfied with the level of information provided to them otherwise.
The physician mission as a patient educator is handicapped by the realities of typical clinical practice, which encourage short visits, simple diagnoses, and quick solutions. Therefore, it is not surprising that patients often feel objectified by the medical industry and search for alternatives. The current permissive regulatory environment with regard to direct access testing has enabled the rapid growth of commercial laboratories that exploit the potential of microbiome science and sell products directly to patients, promising answers they cannot find in their physician offices. Microbiome testing is rapidly being incorporated into the toolbox of alternative medicine, which itself has become a formidable industrial complex.
The availability of commercial microbiome diagnostics to patients and their appearance in the physician offices today is greatly complicated by gaps in the education of physicians as much as of their patients. Many physicians are simply not up-to-date on the basics of the microbiome science, which includes familiarity with the principles of microbial ecology and understanding of the limitations of the current technologies and the shortcomings of the original germ theory of disease. Most physicians and other health care providers still retain the simplistic view of microbes as either “good” or “bad”, and have themselves succumbed to the seductive marketing of various unsubstantiated treatments, such as various probiotics products that scientifically or legally cannot explicitly claim to treat, mitigate, or prevent disease, but nevertheless promise vaguely defined health benefits. Moreover, a recent study among Chinese volunteers revealed a greater effect of geography on microbiome variation than that due to dysbiotic signatures of a particular pathology, highlighting the need for careful interpretation of these data and the need for future, larger-scale studies to further parse signatures from disease versus those from environmental influences .
Fortunately, many patients that bring in their volumes of microbiome testing results are highly intelligent and obviously have an affinity for data. Most retain a healthy degree of skepticism about anything being sold to them. In fact, some of these patients may have a more sophisticated understanding of community networks and big data, which are increasingly encountered in our everyday world. They may quickly grasp the current challenges in microbiome science. In addition, recent high profile discussions about potential inappropriate harvesting of big data by social media companies can resonate with them. Patients may understand that, while their individual data have little diagnostic value on their own, they may be of value to the testing service company that is trying to develop proprietary diagnostic algorithms based on data collected from tens of thousands of patients. However, proprietary algorithms are not subject to peer review, and generating large datasets from a geographically similar population are unlikely to result in highly generalizable findings.
Physicians should question the ethical boundaries challenged by some of the biotechnology companies engaged in microbiome diagnostics. If such companies are engaged in accumulating big data for the purpose of developing next-generation diagnostic tests, they should obtain informed consent from the patients providing them with samples. On the other hand, the microbiome may indeed hold some important keys to the pathogenesis of common diseases, and targeting the microbiome with diet and therapeutics may lead to the development of new strategies for disease prevention and treatment. Given the great deal of research that needs to be carried out to achieve these objectives, public engagement is absolutely essential. The physician can steer the patient to participate in research projects that are open about their goals and do not promise diagnostic answers to individual participants. Some of such projects may even be crowd-funded, like the American Gut Project —an academic rather than commercial endeavor—and allow the opportunity for the empowerment that patients seek through contributing to a common purpose of alleviating suffering of many. The physicians may even team up with their patients in such initiatives and learn alongside them about this exciting new frontier in medicine. Such collaboration might actually build greater levels of trust between the patient and the physician, which is essential to a healthy, therapeutic relationship.
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The authors declare that they have no conflict of interest.
Christopher Staley and Alexander Khoruts share senior authorship.
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Staley, C., Kaiser, T. & Khoruts, A. Clinician Guide to Microbiome Testing. Dig Dis Sci 63, 3167–3177 (2018). https://doi.org/10.1007/s10620-018-5299-6
- Next-generation sequencing