The microbiome is the ecological community of commensal, symbiotic, and pathogenic microorganisms that share our body space with a composition that differs according to body location. Given the relationship to the external environment, the gut functions as an immune organ in conjunction with the gut microbiome (GM), which influences the development and maintenance of the immune system and, subsequently, inflammation.1,2 Gut microbes protect against transiently invading pathogens by providing tonic stimulation to the innate immune system via toll-like receptor signaling that increases intestinal motility, reinforces epithelial integrity, and produces metabolites.3,4 While alterations in the diversity of the human GM have recently been implicated in a number of disease states, relatively little is still known in the context of traumatic injury.5–8
Trauma can influence the gut in a number of ways whether by directly injuring the gut, interrupting the brain-gut axis, or systemically via global hypoperfusion or inflammation. The impact on the gut following traumatic injury in turn alters the GM and causes dysbiosis, or an imbalance or altered distribution of gut microbes.9 Rapid dysbiosis has also been demonstrated in critical illness, with worsening during prolonged hospitalization, and has also been attributed to septic complications.10,11
Although reductions in GM diversity have been linked to increased mortality in critically ill patients, less defined is the impact of trauma on the intestinal microbial community. There is a distinct dysbiosis in trauma patients likely related to this population's susceptibility to complications such as multiple organ failure (MOF), hospital-acquired infection, and the systemic inflammatory response.12,13 Several early clinical studies in small patient populations have demonstrated phylogenetic changes among gut microbial populations following traumatic and burn injuries, yet these studies have lacked power to include clinically relevant outcomes.14–17 Preclinical data from various injury models including polytrauma, burn injury, traumatic brain injury, and spinal cord injury also support the concept that traumatic injury alters the GM, which impacts outcomes.16,18–23 Larger clinical studies are needed to address this gap and better understand microbial changes that occur in the gut following injury and their relationship to outcomes.
To this end, we conducted a prospective, observational cohort study of severely injured patients. The aims of this study were to characterize differences in gut microbial communities at admission in trauma patients and to begin to elucidate the potential impact on clinical outcomes. We hypothesized that the diversity and composition of the GM would be differentially altered depending on clinical course of the patient. Specifically, we examined whether patients who experienced infectious complications, lengthy hospital and intensive care unit (ICU) stays, or mortality had distinct GM characteristics upon admission to the emergency department.
PATIENTS AND METHODS
Approval was obtained from the University of Texas Health San Antonio Institutional Review Board to conduct this study. Adult patients (age, ≥18 years; N = 67) sustaining a severe injury from blunt or penetrating trauma admitted to University Hospital (UH), a level 1 trauma center in San Antonio, Texas, were enrolled prospectively from 2015 to 2016. Enrollment criteria included age of 18 years or greater, an estimated Injury Severity Score (ISS) of greater than 15, ground transport to UH from the scene, and admission to the UH Surgical Intensive Care Unit. Exclusion criteria included prisoners, age less than 18 years, pregnancy, and patients transferred from outside hospitals. Patients were initially enrolled under a waiver of consent on admission to the UH Emergency Department. Consent to participate and continue the study was obtained from the patient or a legal authorized representative as soon as possible following admission. Healthy volunteers (n = 13) were also enrolled for comparison purposes but not included in any statistical analysis included herein.
This is a follow-up study to a previous study by our group, and both collection and data processing were performed in a similar manner.9 Fecal specimens were collected on admission to the UH Emergency Department (Day 0) by rectal swab (Copan Diagnostics, Murrieta, CA) on routine trauma evaluation. All fecal samples were stored at −80°C within 20 minutes of sampled collection for DNA isolation at a later time. Extensive demographic, injury, clinical, and outcome data were prospectively collected on all patients. Of note, only 3 of the 67 patients enrolled received antibiotics before the rectal examination performed on secondary survey.
Gut Microbiome Analysis
Microbial DNA was isolated from all fecal samples using the QIAGEN QIAamp DNA Stool Mini Kit (QIAGEN, Hilden, Germany). DNA was quantified using the Thermo Scientific NanoDrop 1000 Spectrophotometer. Extracted genomic DNA was then used to amplify the V1-V2 variable region of the 16S rRNA genes with custom-designed primers (F27/R355) using Polymerase Chain Reaction. The forward Bosshard sequence was AGAGTTTGATCMTGGCTCAG (27F), and the reverse Bosshard sequence was GCTGCCTCCCGTAGGAGT (355R) with the amplicon size of V1-V2 about 340 bp (355-27). Libraries for all samples were prepared and sequenced by paired-end sequencing (2 × 300 bp) using the Illumina MiSeq platform.
Subsequently, raw data were processed through the software package Quantitative Insights Into Microbial Ecology (QIIME).30 A mean of 164,813 pair-end raw reads (median of 165,738 pair-end raw reads) per sample were generated with read length of 301 bps. Raw sequences were quality trimmed by removing reads shorter than 200 bases, resulting in a median quality score of 36 for forward reads and 30 for reverse reads. The operational taxonomic units (OTUs) were clustered based on at 97% similarity. Taxonomic classifications were made using the QIIME-formatted Greengenes (gg_13_8) 16S rRNA gene database according to standard phylogenetic methods. The OTU table was further filtered by removing OTUs found in only one sample. Rarefaction was performed to a depth of 28,000 base pairs, which allowed inclusion of all samples.
β-Diversity or the interpopulation diversity (the microbial diversity between patients at each time point) was estimated by constructing principal coordinate analysis plots for the following β-diversity measures: weighted and unweighted UniFrac distances, Bray-Curtis, and Jaccard Indices using QIIME. These β-diversity measures plot the three largest variances of the whole data set against each other in three axes, to allow clustering of different samples/groups as a way to measure similarity/dissimilarity between groups. Mortality was the primary outcome. Because of the available sample size, secondary outcomes and risk factor data were not analyzed in a continuous fashion but rather categorized according to the following criteria: body mass index (BMI)—underweight (<18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), and obese (>30 kg/m2); hospital length of stay (LOS)—low (0–7 days), medium (8–14 days), high (15–30 days), profound (>30 days), and died; intensive care unit LOS—low (0–3 days), medium (4–7 days), high (8–14 days), profound (>14 days), and died; ventilator days—low (0–3 days), medium (4–7 days), and high (>7 days); presence of documented infection (pneumonia, urinary tract infection, bacteremia, wound infection, etc. as defined by positive cultures); and acute respiratory distress syndrome (ARDS; as documented and confirmed by PaO2/FiO2 ratio of ≤300). Statistical analysis of these measures was performed with a permutational analysis of variance (PERMANOVA) for overall significance, with post hoc pairwise PERMANOVAs run to assess differences across groups.
α-Diversity or the intrapopulation diversity (microbial diversity within individual patients) was estimated by calculating the number of observed OTUs (richness), evenness of OTU abundance, and diversity using the Faith_PD and Shannon Diversity Indices. Two-way analysis of variance with Tukey multiple comparisons was used to perform statistical analyses of α-diversity. For all parameters comparing survivors and nonsurvivors (e.g., demographics and phylogeny), D'Agostino and Pearson normality tests were performed. Data that were normally distributed are presented as mean ± SEM, while non-normally distributed data are presented as median ± interquartile range. Significance was performed with t tests or Mann-Whitney tests with Welch correction as needed (when variance was not equal). α < 0.05 was considered significant for all analyses. QIIME, STAMP, and GraphPad Prism were used for the visualization and the statistics of the comparative metagenomics data sets.
Patient characteristics shown in Table 1 reveal that the patients (N = 67) were predominantly male with a mean age of 45 years and an average ISS of 21. The majority of the patients suffered blunt trauma with only 22% sustaining penetrating injuries. The mean ± SD BMI was 27.0 ± 0.7 kg/m2. The average transport time from the scene of injury to the emergency department was 29 minutes and was significantly faster in patients who died versus those who survived. Patients who died were older (p = 0.04) and more severely injured as indicated by a greater ISS (p = 0.0001) but not shock index. There was no difference in sex or BMI in patients who lived or died.
Largely, the evenness, Faith_PD, and Shannon indices of α-diversity did not reveal differences in most all of the different demographic and outcome variables. It was shown that OTUs greatly increased in aging populations (Fig. 1), which is unlikely related to trauma, as this has been shown previously in healthy individuals.24 However, we saw an increase in the OTUs from fecal swabs of patients who survived (n = 59) compared with those who died (n = 8; p = 0.049).
Significance levels for all β-diversity values are shown in Table 2, and the principle components analysis (PCA) plots for all of these variables are shown in Figures 2 and 3, as well as Supplemental Digital Content, Supplemental Figures 1 to 5 (http://links.lww.com/TA/B557). As previously reported, ISS significantly influenced β-diversity, whereas the mechanism of injury (i.e., blunt vs. penetrating trauma) did not.9 Bray-Curtis dissimilarity was significantly clustered based on BMI (p = 0.025; Fig. 2) and sex (p = 0.0005; Supplemental Digital Content, Supplemental Fig. 1, http://links.lww.com/TA/B557) but not smoking (p = 0.298).
Figure 2 also reveals that many other clinically important endpoints and risk factors were significantly clustered for different β-diversity measures. For example, the weighted UniFrac (taking into account the relative number of different species) but not the unweighted UniFrac was significantly clustered based on overall hospital LOS (p = 0.007). Moreover, every single β-diversity measure analyzed revealed substantial clustering based on the ICU LOS (Fig. 2; Supplemental Digital Content, Supplemental Fig. 2, http://links.lww.com/TA/B557). Interestingly, in general, more severe outcomes tended to cluster in space on PCA plots more closely to those of healthy controls than outcomes generally considered to be preferred. For example, in the case of ICU LOS (Fig. 2), death (black squares) and profound ICU LOS over 14 days (blue squares) cluster more closely to healthy controls (orange squares) than lower LOS (Fig. 2; Supplemental Digital Content, Supplemental Fig. 3, http://links.lww.com/TA/B557); however, the healthy controls were not included in the statistical analysis.
In terms of pulmonary function, the most relevant β-diversity measure was the Jaccard similarity index (Fig. 2; Supplemental Digital Content, Supplemental Fig. 4, http://links.lww.com/TA/B557). For this measure, patients who developed ARDS at some point in their hospital stay were clustered in a significantly different manner on the PCA than those that did not experience ARDS. Moreover, the number of days on a ventilator was also clustered based on whether patients spent 0 to 3, 4 to 7, 8 to 13, or more than 14 days on a ventilator (Fig. 2; Supplemental Digital Content, Supplemental Fig. 4, http://links.lww.com/TA/B557). Surprisingly, only one of the four β-diversity measures (unweighted UniFrac) was able to distinguish patients based on the presence of infection, perhaps owing to the mixed bag of infectious complications included (Supplemental Digital Content, Supplemental Fig. 5, http://links.lww.com/TA/B557).
Figure 3 shows the PCA plots for all for β-diversity measures separating patients who died versus those that survived. Every single measure examined was successful in identifying differences in admission microbiome between patients who died and those that did not (p ≤ 0.05). It was surprising that all measures found such significant differences in β-diversity given the small sample size of the group that died (n = 8) and that the mean time to death was 8 days. Moreover, the GM from patients who died tended to cluster more closely to the GM from healthy volunteer samples, rather than surviving patients. The statistical analysis, however, only included data from trauma patients and not healthy volunteers, so it is not known whether this difference was statistically significant.
All phylogenetic differences found associated with mortality are shown in Figure 4 and in Supplemental Digital Content, Supplemental Figures 6 to 9 (http://links.lww.com/TA/B557). Patients who survived had GM compositions with a significantly lower relative abundance of the Firmicutes phylum (p = 0.0049). These patients had a tendency to contain more Proteobacteria; however, the difference did not reach statistical significance (p = 0.24). Largely, the difference in Firmicutes can be attributed to members of the order Clostridiales (p = 0.0024) and the family Ruminococcaceae (p = 0.0017, Supplemental Digital Content, Supplemental Fig. 6, http://links.lww.com/TA/B557). Increases in the genus Prevotella (p = 0.024) and Corynebacterium (p = 0.055) were also seen in patients who survived versus those who died (Supplemental Digital Content, Supplemental Figs. 7 and 8, http://links.lww.com/TA/B557). Lastly, we were surprised to see that there was an increase in the gut microbial composition of traditional probiotic bacteria in patients who died versus those who survived. Specifically, Eubacterium biforme, Ruminococcus flavefaciens, Akkermansia muciniphila, and Oxalobacter formigenes (p = 0.037, 0.010, 0.0004, and <0.0001, respectively) were all increased in the admission fecal swabs of patients who died (Fig. 4B).
From an evolutionary standpoint, one can imagine that the ecological community of commensal, symbiotic, and pathogenic microorganisms that share our body space has a great influence on homeostasis. What has become apparent in the past decade is that the GM can greatly influence pathogenic states as well. Once thought to be more influential in metabolic syndromes such as obesity and diabetes, the GM has been implicated in a wide variety of conditions, both acute and chronic. Herein, we describe that admission rectal swabs of severely injured patients can categorize the GM based on a number of clinically important outcomes to include hospital and ICU LOS, ventilator days, and mortality. Moreover, to our knowledge, this is the first report to show that specific bacteria are altered upon admission in trauma in patients that succumbed to their injury.
The importance of the microbiome has been demonstrated in many conditions (e.g., surgery); however, the specific role of the microbiome in trauma patients has been limited to temporal changes post-injury.25 Our previous study on this data set also suggested that changes may be linked to perfusion, as the amounts of blood products administered impacted the bacterial composition.9 That study used healthy volunteers as a comparison, which was limited by the fact that healthy volunteers may be demographically different than trauma patients. While we show the data for healthy controls here on PCA plots, our statistical analyses only included patient admission samples. The average prehospital transport time for these patients was less than 30 minutes, and rectal swabs occurred within 30 minutes of admission. Thus, the differences observed have occurred within 1 hour of traumatic injury. While this does not address the presumably massive effects of clinical care (drugs, fluids, etc.) on the GM, this highlights the potential for the admission microbiome to hold powerful prognostic value on endpoints such as mortality and LOS. Indeed, the GM has been described as one of the key tenets that will facilitate precision medicine in becoming a reality.26
One challenge in microbiome research is the rapidly evolving bioinformatics used in the interpretation of the data. It is very likely that realizing the diagnostic value of different β-diversity values will require a deep understanding of the nuances between these values (i.e., UniFrac vs. Bray-Curtis). The data presented herein show that the Jaccard index was very efficient at categorizing patients based on how long the needed a ventilator and whether they developed ARDS. This reveals the possibility that, if a patient presents with an injury pattern that causes concern over pulmonary function, targeted methods to probe the Jaccard index may be warranted. The nuanced differences between these variables are also worth considering. For example, while Bray-Curtis and Jaccard indices are used to quantify compositional dissimilarity, Jaccard is based on metric space. Similarly, while UniFrac is a distance metric comparing communities, weighted UniFrac is considered more quantitative because of the fact that it takes into account abundances of various species.27
To further emphasize the importance of the bioinformatic processing, setting variables such as the sampling depth (rarefaction) or OTU similarity used can drastically influence findings. Efforts should be made to, at minimum, report how this processing is done, if not to standardize how data are treated within the trauma field. Moreover, while we did not use false discovery rate methods, we do report 21 unique bacterial reads in the phylogenic results (to include related microbes such as Clostridia and Clostridiales) which would, mathematically, lead to 1.05 false discoveries. This type of statistical check will become very important when larger multicenter data sets are available. Still, we believe that the wealth of information that comes from analyzing the microbiome holds untapped valuable information.
Despite our small sample size (n = 8 patients who died), we found robust differences in patients who died after trauma versus those who survived. Interestingly, all β-diversity variables were clustered differently depending on survival, and those who died tended to cluster more closely with healthy individuals. Moreover, we found a variety of bacterial populations that were different in the patients who died, including down to the species level. Surprisingly, four of the species that were identified to be enriched in patients who died (E. biforme, R. flavefaciens, A. muciniphila, and O. formigenes) are commercially available as probiotics. Probiotic species have been proposed to prevent mortality in, for example, necrotizing enterocolitis.28 While the implication of these findings is unclear, we propose that there is possibly a compensatory response in the patients who died, which was ultimately unsuccessful. Our data also suggest the possibility that an early alteration in gut microbial communities after traumatic injury may convey a protective benefit to patients and play an integral part in survival and outcome.
There are several limitations of the current study worth noting. This study represents a single-center study and is representative of the population in the South Texas and urban San Antonio area. Given the substantial variability in the GM of healthy individuals, especially in regards to diet, larger-multicenter trials interrogating the microbiome are warranted. Also, the findings herein are purely associative, and causation between the GM and outcomes cannot be inferred. In addition, the use of antibiotics most certainly affects the GM, which represents a confounder that will likely occur in all trauma trials. Only three patients received antibiotics in the prehospital setting en route to the hospital, which drastically alter the GM.29 Similarly, only one time point was examined. However, these admission samples were largely taken before administration of antibiotics or resuscitation products, and thus, the iatrogenic influences on the GM were not examined herein. Lastly, the patient cohort was not large enough to identify significant differences in several α-diversity parameters that may ultimately prove informative.
In conclusion, to our knowledge the current report represents the largest report on the GM of trauma patients to date. Mortality due to traumatic injury is associated with a GM that includes fewer unique organisms but does contain a higher number of several different commercially available probiotic species. Moreover, all β-diversity parameters probed were able to categorize patients according to whether or not they succumbed to their injury. While there were some of these diversity calculations that also were able to cluster other important outcomes, the specific associations are not as robust as the ones between mortality and all β-diversity parameters. Taken together, the GM holds great promise for diagnostic and therapeutic targets in traumatic injury, and further clinical and preclinical studies are needed.11
D.M.B. contributed in the data and statistical analysis and bioinformatics, data interpretation, critical revision, generation of figures, and article drafting. T.R.J. contributed in the literature search and data collection. Z.L. contributed in the metagenomic sequencing and data analysis. S.S. contributed in the data collection, sample preparation, laboratory analysis, and data generation. M.D. contributed in the data collection and patient enrollment, study coordinator for patient enrollment, and study completion. R.B.J. contributed in the data collection and patient enrollment, study coordinator for patient enrollment, and study completion. C.Z. contributed in the data collection and patient enrollment, literature review, and critical revision. E.S. contributed in the data interpretation and critical revision. R.M.S. contributed in the critical revision and mentorship. M.G.S contributed in the data interpretation, critical revision, and mentorship. D.H.J. contributed in the data interpretation, critical revision, and mentorship. B.J.E. contributed in the critical revision and mentorship. S.E.N. contributed in the study design and idea, literature search, data collection and patient enrollment, data generation, data analysis, data interpretation, article drafting and critical revision, and project oversight.
The authors would like to thank the following individuals for their support: Basil A. Pruitt, Jr., Dawn Garcia, and Korri S. Weldon for 16S sequencing sample processing and data generation.
This work was funded by the National Center for Advancing Translational Sciences, National Institutes of Health, through grant KL2 TR001118.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Department of the Army and the Department of Defense. Support was also received by the University of Texas Health San Antonio Military Health Institute and the Bob Kelso Endowment awarded to the University of Texas Health San Antonio Department of Surgery.
For all authors, no conflicts are declared.
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JEFFREY A. CLARIDGE, M.D., M.Sc. (Cleveland, Ohio): Good afternoon. Thanks, AAST, for allowing me to present this great paper; and as evidence of yesterday's presidential address, it is all about the poo. So, no offense to Dr. Croce, my mentor in the past, but, you know, this has taken the death poo to a new level, so, I'll stop with the poo jokes for a minute.
I do want to say the authors present an observational study of severely injured patients to characterize differences in the gut microbiome communities in trauma patients.
They also sought to identify changes in gut bacterial composition across time in these patients, and most importantly, began to elucidate the potential impact on clinical outcomes. The key element in this study is that the authors investigated the majority of patients' gut flora within the first hour of major trauma.
They hypothesized that the diversity and composition of the gut microbiota would be differentially altered depending on the clinical course of the patients.
In other words, could the content of someone's stool be an early marker of outcomes after traumatic injury?
Specifically, they evaluated whether patients who experienced infectious complications as well, lengthy hospital and ICU stays, and mortality had distinct gut microbiota characteristics upon admission to the emergency department.
This is a follow-up study that they nicely showed us today, and the results demonstrated robust differences in patients who died after trauma versus those who survived.
Interestingly, all beta diversity variables were clustered differently depending on survival. And those who died tended to cluster more closely with healthy individuals. I thought this very interesting.
Moreover, they found a variety of bacterial populations that were different in the patients that died, including down to the species level. I do have some questions and concerns that I'm sure you can clarify pretty easy.
How did you deal with patients who did die with your other outcome comparisons? Were they excluded; and it's very likely, because it's very important to evaluate other outcomes excluding patients who die, or were they in those comparisons?
You chose to create strata for continuous data, such as BMI, ventilator days, and length of stay. I'm a little unclear why you chose to do this – if you could expand on that.
And then perhaps it's ignorance on my part, due to the huge explosion in the analytic methodology involved in evaluating the microbiome and bioinformatics processing that occurs now, what is the significance of different results being obtained in different ways to determine beta diversity?
In other words, is it really about the test that's used? In other words, why did you get different results in beta diversity between weighted and unweighted, UniFrac distances, Bray-Curtis, and the Jaccard indices? For you, this might be an easy question to answer; for me, I was very confused.
Is a rectal sample shortly after trauma a reliable way to evaluate the microbiome?
And you also stated that patients who died had shorter travel times, and is that perhaps just why you saw them more equal to healthy subjects?
And again, I'd like to thank you for your work, and I look forward to more Adventures of Poo.
NICHOLAS NAMIAS, M.D., M.B.A. (Miami, Florida): Very exciting paper. I have two questions.
One, do you think that the patients had different microbiomes before trauma and that predicted the outcome, or did their microbiome change because of the trauma?
And two, what about the other microbiomes, not just the gut? Do you think there's anything to looking at skin microbiomes, different parts of the body's skin microbiome, or oral microbiome?
Very interesting work. Thank you.
HASAN B. ALAM, M.D. (Ann Arbor, Michigan): Excellent work, and I think it's a fascinating area, and we're just scratching the surface. Although very interesting, I find the data a little confusing as well.
If I look at the patients who survived and who died, they all had changes, but there was a fair amount of overlap.
So if you're exploring it as a therapeutic or diagnostic tool, and if you have one data point, not the entire spectrum but one data point, it can easily fall into either the survivor or the non-survivor plots.
IAN BROWN, M.D. (Sacramento, California): Just to tail off of that last question, I think the next step for the work sort of depends on whether you see these findings as being correlative or causative with respect to death.
And I was just wondering what your opinion in that area might be and how you plan to move it forward based on that?
How would you propose we sort through all of this complexity of scattered data to focus on the individual patients, because eventually the clinical decisions have to be made on a patient-by-patient basis.
In other words, how would you translate these data into actionable information?
SUSANNAH E. NICHOLSON, M.D., M.S. (San Antonio, Texas): Thank you, Dr. Claridge, and to those with the questions; and I agree, it is all in the poo. So, I will try to address, I missed one of the questions, but I will try to address as many of them as I can.
So, the first question, how did, with patients that did die, addressing the exclusion, those that may have been excluded, these were actually all of the patients that died in the patient population.
The, we enrolled, there were a few patients from our original dataset that didn't have admission samples taken, but none of those patients were the ones that died.
In terms of what is the significance of looking at the different tests and the different indices, each beta diversity index – and I'm not a bioinformatics person, but each beta diversity index represents a varying level of similarity or some of them encompass dissimilarity and similarity, some of them just look at dissimilarity, so I think in the context, the strongest relationship actually was with mortality because there was an association across all four indices, so it wasn’t just one, whereas some of the other outcomes, it was maybe one out of four indices or two out of four. I just showed representative plots for those other outcomes.
But the other thing along with that is that there, in particular, for instance, with some of the pulmonary outcomes, including ARDS and ventilator days, there may be something within the bioinformatics with that.
For that, for those two outcomes, the Jaccard index was actually highly associative with those two, and so some of them, it would be, I think, worth looking into the bioinformatics part to see if there's particular indices that are more related to some of those outcomes.
The question about is a rectal sample a reliable way to evaluate the microbiome, there's really no, and when you're looking at trauma patients, there's really no great way to do this.
You know a lot of times in colorectal studies, the samples are obtained from a colonoscopy. You know, that's not something we can do baseline or do in that acute setting.
So this is really the best method that we have, was a rectal swab on exam, so, and to tag on with one of the questions that was subsequently asked, really there's been very little done in this area.
So in terms of skin microbiome, the oral microbiome, I think that it's wide open to look at other areas on the body that may potentially correlate with the gut microbiome itself.
But the gut microbiome has been shown in a vast array of literature to be associated with a number of other disease processes, and so this was why we chose to go with the gut microbiome.
In terms of travel time and whether this might represent a healthy microbiome, and that also goes along with a subsequent question, there's no way to know.
You know, we can't get a sample out in the general population and know whether or not that patient is going to be in a trauma. I think this opens the door for – obviously diet heavily influences the microbiome.
I think this opens the door for multicenter studies that take place in different geographic locations.
Additionally, I think there are some other control populations that, moving forward, I plan to incorporate in future studies.
But that is correct, there is a possibility that this could represent their baseline microbiome. However, in our original study, we did see that there was very different microbiome in the healthy volunteers that had provided samples for us.
So again, I think a much larger study is needed to really hash that out.
To address the question of do I think that patients, if they had, if patients that survived versus those that died, looking at those plots, and there is some overlap, again, I think this is where larger studies are needed to really look at that granularity and to be able to better say what is a particular microbiome that might be associated with some of those findings.
However, even in this small patient population, we were finding statistical significance between those patients that were associated with those outcomes.
And then, how do we translate that data to action? I think this is also an area where, you know, it's hard to, right now if you measure a stool sample, the sequencing is not up to speed in terms of, you know, the direct diagnostic capacity, but there is the potential that there could be, you know, a related biomarker within the blood that's easily measured that reflects those changes in the stool.
And so, I think that that is one way that, you know, as we do more work in this area and are able to potentially conduct studies with larger patient populations we can better define that.
And then in terms of if this was correlative versus causal, this is not a causal, you know, this study does not address causality. This is purely associative at this point.
I have some studies in progress looking at, trying to get more at the mechanism behind some of the changes that we see in the microbiome, and that more preclinical work is needed to really get at causality in this case.
Thank you to the AAST for the privilege of the podium.Follow up:
A couple of the unanswered questions came down to sample size. For example, patients that died were included when examining other outcomes. The same can be said for including variables like BMI as categorical variables- we just currently didn’t have enough patients to analyze these as continuous variables. This definitely highlights the need for larger, multi-center studies, which can also start to address other questions like baseline microbiome status, inclusion of other symbiotic flora, etc.
We also wanted to expand on the discussion of the different beta diversity measures. These measures a different in nuanced ways as to whether they take into account phylogeny (specific species) or their abundance. The Jaccard index, for example, does not take into account phylogeny or abundance, but rather simply looks at whether there are shared features. On the other side of the coin, the weighted UNIFRAC measure does take into account which species are more abundant. Since there is a significant difference with hospital length of stay, perhaps there is a highly abundant species that is altered (up or down) after trauma that is driving this similarity.
These nuances make the question of “how to make this information actionable” difficult. We envision the ultimate solution would involve several of these metrics combined with biomarkers in some type of algorithm to inform which patients are at risk. However, to initiate those types of analyses would undoubtedly need larger sample sizes from multi-center institutions.