Keywords
traumatic brain injury - gut microbiome - neurological outcomes - pilot study - feasibility
Introduction
Traumatic brain injury (TBI) poses a major global health challenge, contributing significantly
to morbidity, mortality, and long-term disability. The complex pathophysiology of
TBI involves a cascade of events, beginning with primary mechanical insult and progressing
to secondary injury mechanisms like neuroinflammation, oxidative stress, and excitotoxicity.[1] Recent research has highlighted the potential role of the gut microbiome, the dynamic
community of microorganisms within the gastrointestinal tract, in modulating systemic
inflammation and influencing neurological outcomes following TBI.[2]
[3]
[4]
[5]
The gut–brain axis, a bidirectional communication network intricately linking neural,
endocrine, and immune pathways, is crucial for maintaining physiological homeostasis.[2] Disruptions to this axis, particularly following TBI, can precipitate gut microbiome
dysbiosis, characterized by alterations in microbial diversity and composition. Recent
research suggests that the gut microbiota modulates the inflammatory response through
the regulation of peripheral immune cell infiltration after TBI.[6] Consequently, the resulting inflammatory milieu can exacerbate secondary brain injury[7] and impede neurological recovery.[8]
Clinical and preclinical studies implicate gut dysbiosis as a consequence of TBI and
an amplifier of brain damage.[5] However, these studies have primarily focused on characterizing microbial changes
without thoroughly exploring their functional implications or associations with specific
clinical outcomes. This represents a critical knowledge gap, as understanding the
functional consequences of gut dysbiosis is essential for developing targeted therapeutic
interventions.
While probiotic interventions have shown promise in other neurological conditions,[9] their efficacy in TBI patients remains largely unexplored. Although pilot studies
have demonstrated the feasibility of such interventions, further research is needed
to determine optimal probiotic strains, dosages, and treatment protocols for TBI.[10]
[11]
[12]
Despite the growing body of evidence implicating the gut microbiome in TBI pathophysiology,
significant knowledge gaps persist. First, the longitudinal dynamics of gut microbiome
alterations and their association with specific clinical outcomes, such as neurological
recovery, in-hospital complications, and mortality, are not fully understood. Second,
the functional mechanisms through which gut dysbiosis influences neuroinflammation
and neurological function require further elucidation. Third, the potential of targeted
interventions, such as fecal microbiota transplantation or pre-/probiotics, to modulate
the gut microbiome and improve clinical outcomes in TBI patients necessitates further
investigation.
Therefore, this pilot and feasibility study aims to address these critical knowledge
gaps by prospectively characterizing the temporal alterations of gut microbiome diversity
and composition on admission and at 1-week post-injury and exploring their potential
impact on clinical outcomes, and we seek to elucidate the potential impact of severe
TBI (sTBI)-associated gut microbiome dysbiosis on in-hospital clinical outcomes and
neurological function recovery at 3 months post-injury. This study aims to answer
the overarching research question: Does the patient's microbiota predict any clinical
outcome following brain injury? Ultimately, this research will provide valuable insights
into the role of the gut microbiome in TBI pathophysiology and inform the development
of future therapeutic strategies.
Materials and Methods
Study Design
This is a prospective, observational longitudinal pilot feasibility study designed
to assess the feasibility of conducting a larger scale trial. The study evaluates
the feasibility of recruiting TBI patients, collecting and analyzing microbiome samples,
and monitoring patient outcomes. The pilot study informs the design of a future randomized
controlled trial by identifying potential barriers to implementation.
Participants
Inclusion Criteria
Inclusion criteria: adults (18–40 years) with sTBI (Glasgow Coma Scale [GCS] ≤ 8)
admitted to the neurosurgery department at All India Institute of Medical Sciences,
New Delhi, within 24 hours of injury.
Exclusion Criteria
Exclusion criteria were age less than 18 years, polytrauma, pregnancy, transfer from
outside hospitals, severe malnutrition, infection, drug use, alcohol abuse, preoperative
liver and kidney dysfunction, history of previous gastrointestinal surgery, history
of gastrointestinal diseases, history of immune-related diseases or being given immunotherapy,
history of severe systemic diseases, tumors, and death within 48 hours of injury.
Recruitment Methods
Patients were recruited from the emergency department and neurosurgical intensive
care unit (ICU). Informed consent were obtained from legally authorized representatives
due to the unconscious state of many participants.
Sample Size
Given the funding constraints and the exploratory nature of the study, the final sample
size for the pilot study was adjusted to 10 sTBI patients, including sampling at two
time points, thereby, a total of 20 samples are included in the study. This decision
is aligned with previous pilot studies of similar nature and allows for a preliminary
exploration of feasibility, acceptability, and potential effects of an intervention
while keeping participant burden and resource constraints low, particularly when aiming
to gather initial data on key variables and identify potential issues with the study
design before scaling up to a larger sample size in a full-scale trial.
Gut Microbiome Analysis
Gut microbiome analysis was performed using whole metagenome sequencing.
Sample Preparation
Stool samples were collected using Invitek Molecular Stool Collection Modules (Cat.
No. 1038111300, Invitek Molecular GmbH). Approximately two to three spoons of stool
were collected into the 8 mL stabilizing solution within the collection tube. Following
collection, samples were gently mixed with the stabilizing solution for 15 seconds
and sealed before being shipped at room temperature to the processing unit.
DNA Extraction
DNA was extracted from stool samples using the QIAamp Fast DNA Stool Mini Kit (QIAGEN)
following the manufacturer's protocol. The extraction protocol involves lysis and
separation of impurities using InhibitEX Buffer (QIAGEN) and purification of DNA using
QIAamp mini spin columns. Eluted DNA was collected, and the quantity and quality were
assessed using Qubit 2.0 DNA HS Assay (ThermoFisher) and NanoDrop (Roche).
Sequencing
Whole metagenome sequencing was performed on all samples using long-read sequencing
technology. DNA libraries were prepared with the Ligation Sequencing Kit (Oxford Nanopore
Technologies [ONT]), loaded onto R10.4.1 PromethION flow cells, and sequenced on the
ONT PromethION 2 Integrated (P2i) device. Basecalling and demultiplexing of sequence
reads were performed with Guppy v4.2.2 and MinKNOW GUI v6.2.14 (PromethION). Raw sequencing
reads were stored in FastQ format.
Upstream Metagenomics Analysis
The upstream analysis involved quality control (QC) and quality improvement measures,
including host (human) sequence removal. This was followed by alignment of quality-processed
reads to a reference database of microbial genomes. The percentage of normalized abundances
of all identified microorganisms was quantified. Raw sequencing data underwent quality
checks using NanoStat[13] and removal of short and sub-par quality reads. Reads suitable for further analysis
were mapped to the human reference genome GRCh38[14] using Bowtie2[15] to remove host (human) sequences. Kraken 2 was used for rapid, accurate, and sensitive
microbial classification and quantification of species.[16] A custom database built on the Reference Sequence (RefSeq) collection was used as
the reference database. The result were raw abundance profiles of prokaryotes (bacteria,
archaea), eukaryotes (protozoa, metazoa), and viruses, stratified across all taxonomic
levels.
Downstream Metagenomic Analysis
Data filtering and normalization were performed to remove low-quality or uninformative
features from raw abundance data. Features with exceedingly small counts (<5 reads)
and in very few samples (<10% prevalence) were filtered out, followed by a low variance
filter using variances measured by interquartile range. Normalization was performed
using the trimmed mean of M-values method.[17] Taxonomic composition of communities across baseline and follow-up visits was visualized
for direct quantitative comparison of abundances. Percentage bar plots were created
for comparing groups at various taxonomic levels. Alpha diversity was characterized
using Chao1 index (richness-based measure) and Shannon index (richness and evenness).
Alpha diversity analyses were performed using the phyloseq package.[18]
[19] Differential abundance (DA) analysis was performed to identify significantly altered
microbial abundances.[20] To ensure robustness, DA analysis was performed with five different DA tools: univariate
analysis (T-test ANOVA),[21] MetagenomeSeq,[22]
[23] EdgeR,[24] DeSeq2,[25] and LEfSe.[26] Microbial species with adjusted p-value <0.05 were considered significant, and consensus was determined by agreement
across three or more DA tools.
Clinical Data Collection
Clinical data were collected to characterize the study population and assess relevant
clinical parameters. Inflammatory markers, including total leukocyte count, procalcitonin
(PCT), interleukin-6 (IL-6), and C-reactive protein (CRP), were measured at baseline
(within 24 hours of injury) and at 1-week post-injury. Neuroimaging data were obtained
from plain CT (computed tomography) head scans performed at admission and at 2 weeks
post-injury to monitor primary lesions and secondary injury progression. Demographic
and clinical data were also collected, including patient age, sex, body mass index
(BMI), GCS score, history of antibiotic or probiotic use, dietary habits, eating behaviors,
and administration of antibiotics and proton pump inhibitors during the hospital stay.
Outcome Measures
Clinical outcomes were assessed to evaluate the impact of TBI and associated gut microbiome
alterations. These outcomes include in-hospital and 3-month mortality rates, gastrointestinal
complications such as gastroparesis and feeding intolerance, septic complications
including multiple organ failure, hospital-acquired infection, and systemic inflammatory
response, and prolonged hospital and ICU stays. Neurological functional recovery is
defined using the Glasgow Outcome Scale Extended (GOSE), with a GOSE score of <8 at
3 months post-injury indicating poor functional recovery.
Feasibility Assessment
Feasibility was assessed based on several indicators to determine the practicality
and viability of conducting a larger study. Primary feasibility outcomes include the
recruitment rate, defined as the number of eligible patients enrolled per month, the
retention rate, defined as the percentage of participants completing follow-up, and
compliance with sample collection protocols for fecal samples. Secondary clinical
outcomes were also used to assess feasibility, including changes in gut microbiome
composition from baseline to day 7 and levels of inflammatory biomarkers (IL-6, CRP,
and PCT) at baseline and day 7.
Results
Feasibility of the Study
The feasibility of conducting gut microbiome analysis in sTBI patients was assessed
over 2 months (January–February 2025). A total of 10 patients were recruited; however,
sampling was successfully completed for only seven patients. Several challenges were
encountered at multiple stages of the study, including patient recruitment, sample
collection, transportation, and QC.
Challenges in Sample Collection
Patient recruitment presented significant challenges, with only 10 patients meeting
the inclusion criteria out of a larger screened population. This highlights the narrow
age range (18–40 years) and stringent exclusion criteria, including the exclusion
of polytrauma, pre-existing comorbidities, and patients transferred from outside hospitals,
as major limiting factors.
Furthermore, fecal sample collection proved to be particularly difficult. Out of the
10 recruited patients, samples were successfully collected from only 7. Reasons for
failed sample collection included: empty bowel at the time of collection, patients
not passing stool within the required timeframe, and the frequent need for urgent
surgical intervention within 24 hours of injury, precluding sample collection. Intubation
and the overall clinical instability of sTBI patients also contributed to the complexity
of sample acquisition.
Quality Control Issues
Of the seven samples collected, significant QC issues were encountered. Two samples,
obtained after protocol-mandated enema administration, were deemed unsuitable due
to their watery consistency and insufficient DNA yield. Additionally, for two other
patients, one of the two scheduled samples (either baseline or 7-day) did not meet
laboratory QC standards due to insufficient sample volume. This resulted in only three
patients having both time points samples that were suitable for analysis. The frequent
need for surgical intervention before samples could be gathered resulted in many baseline
samples being missed.
Strategies implemented to address feasibility challenges: to address these challenges,
several corrective measures were implemented. Regular meetings and discussions were
held with clinical nursing staff and laboratory personnel to improve coordination
and ensure adherence to the sampling protocol. Standard operating procedures (SOPs)
for sample collection were revised and customized to accommodate the unique challenges
associated with sTBI patients, including the need for rapid surgical intervention
and the frequent use of enemas. The laboratory was instructed to attempt DNA extraction
from all samples, regardless of initial quantity, and to only reject samples if the
DNA yield was insufficient or if contamination was evident. To improve sample collection
efficiency, residents were trained to collect stool samples during routine per rectal
(PR) examinations, effectively scooping stool from the patient to ensure collection.
Feasibility Outcomes
Recruitment rate: 70% of eligible patients consented to participate.
Sample collection compliance: 7 out of 10 recruited patients provided samples, but
only 5 patients had complete baseline and follow-up samples that passed QC.
Data completeness: 85% of patients had complete clinical data; however, microbiome
analysis was only possible for a subset due to sample quality constraints.
Despite these challenges, the study demonstrated the feasibility of recruiting and
sampling sTBI patients for microbiome analysis. The lessons learned from this pilot
study will inform protocol adjustments in future trials to enhance sample collection
efficiency and minimize losses due to QC failures. The implementation of modified
SOPs and improved coordination among clinical and laboratory teams has already resulted
in better adherence to study protocols and will be instrumental in scaling up the
study in a larger cohort.
Preliminary Findings
The metagenomic analysis revealed key shifts in microbiome composition and abundance
between the 24-hour within injury and 7th-day post-injury samples. Notably, a general
negative shift (dysbiosis) was observed 7 days post-injury.
Clinical and laboratory findings: inflammatory markers were markedly elevated at baseline,
with CRP levels ranging from 54 to 202 mg/L and IL-6 levels from 38.8 to 139.5 pg/mL,
indicating a strong systemic inflammatory response. These markers showed a declining
trend in two patients by day 7, whereas one patient showed a paradoxical increase
in CRP despite radiological improvement. Nutritional assessments revealed that all
patients were either overweight or obese (BMI range: 26.8–39.3), with hypoalbuminemia
and low prealbumin levels at baseline. Enteral feeding was initiated early via Ryle's
or nasogastric tubes and continued for 9 to 18 days. All patients tolerated feeding
well, without signs of gastroparesis or septic complications, and were managed with
antibiotics and proton pump inhibitors. Favorable outcomes were observed in all cases,
with no mortality reported ([Fig. 1]).
Fig. 1 Relative abundance of gut microbial species before and after severe traumatic brain
injury.
The bar plot illustrates the relative abundance of various gut microbial species before
injury (pre, baseline) and 7 days after sTBI (post, STBI +7D), revealing significant
shifts indicative of post-injury gut dysbiosis. Beneficial commensal gut microbiome
species, such as Prevotella copri, Phocaeicola plebeius, and Prevotella hominis, were reduced in post-injury samples, while pathogenic species like Klebsiella pneumoniae and opportunistic pathogens such as Bacteroides fragilis increased. Additionally, the abundance of phylum Actinobacteria (which includes Bifidobacterium probiotics) and Firmicutes (which includes Lactobacillus probiotics) was reduced, potentially impacting gut health and immune function. Notably, Prevotella copri increased post-injury, possibly due to an inflammatory response or microbial adaptation
to systemic changes following TBI. In contrast, Faecalibacterium prausnitzii, a key anti-inflammatory bacterium, decreases, potentially contributing to immune
dysregulation. Meanwhile, Bacteroides thetaiotaomicron and Phocaeicola vulgatus exhibit an upward trend, while Escherichia coli remains present with minor fluctuations. These findings suggest that sTBI significantly
alters gut microbial composition, which may influence gut–brain interactions, systemic
inflammation, and recovery outcomes. Understanding these microbiome shifts is crucial
for identifying post-TBI complications and developing potential therapeutic strategies.
Alpha diversity analysis revealed that species richness (number of species) increased
post-hospitalization, while species evenness (distribution of species abundances)
decreased ([Fig. 2A, B]). Although not statistically significant, notable differences in diversity indices
were observed between baseline and 7th-day post-injury samples. Specifically, species
richness increased by the 7th day, as indicated by the Chao1 index ([Fig. 2A]), suggesting an expansion in microbial diversity. In contrast, species evenness
declined within a week post-injury, as reflected in the Shannon index ([Fig. 2B]), implying that while more species were present, their distribution was uneven.
This indicates that while more species were present, their distribution became more
uneven, likely due to the dominance of specific taxa post-injury. These findings align
with the observed changes in relative abundance, where some microbial species proliferated
while others diminished. The underlying factors driving these shifts, including clinical
parameters, medications, and dietary influences, will be further explored upon the
study's completion. Understanding these microbial alterations may provide insights
into gut dysbiosis following TBI and its potential implications for patient recovery
and systemic inflammation.
Fig. 2 Changes in alpha diversity of gut microbiota following severe traumatic brain injury.
(A) Chao1 index showing an increase in species richness on the 7th day post-injury compared
with baseline. (B) Shannon index indicating a decrease in species evenness post-injury.
Beta diversity analysis revealed differences in microbial composition, abundance,
and diversity between the baseline and 7th day post-injury groups ([Fig. 3]). The Non-metric Multidimensional Scaling plot based on Bray–Curtis dissimilarity
illustrates the spatial distribution of microbial communities across the two time
points. While some variation in microbial composition is apparent, the small sample
size limits the ability to draw definitive conclusions. PERMANOVA analysis yielded
a nonsignificant p-value (p = 0.4), indicating that the observed differences are not statistically significant.
The R-squared value (0.21899) suggests that only a small proportion of the variance
in microbial composition is attributable to the time point differences. These findings
indicate that although minor shifts in microbial communities may occur post-injury,
the overall composition remains relatively stable within the observed period.
Fig. 3 Species Beta diversity NMDS adonis bray. NMDS, Non-metric Multidimensional Scaling.
DA analysis identified significant changes in microbial composition between baseline
(within 24 hours post-injury) and the 7th day post-injury ([Table 1]). Several species exhibited notable shifts in abundance, with a Log2 fold change (log2FC) of at least ± 2, p-values <0.01, and relatively low false discovery rates. Among the significantly increased
species, Bacteroides thetaiotaomicron and Bacteroides fragilis were notable. While Bacteroides thetaiotaomicron is considered beneficial for gut homeostasis, Bacteroides fragilis is an opportunistic pathogen known to translocate into the bloodstream, posing risks
for posttraumatic infections. Additionally, species from the Klebsiella genus (Klebsiella variicola, Klebsiella pneumoniae, and Klebsiella quasipneumoniae) were significantly elevated, raising concerns as these bacteria are associated with
hospital-acquired infections and antibiotic resistance. Conversely, beneficial species
such as Limosilactobacillus mucosae, known for its role in gut barrier integrity and immune modulation, were significantly
reduced, suggesting a net negative shift in gut microbial composition. The depletion
of Clostridium sartagoforme and Eubacterium, which are associated with maintaining gut homeostasis, further indicates dysbiosis
following injury.
Table 1
Differential abundance of key microbial species between baseline and 7th day post-injury
|
Species
|
Log2 fold change (log2FC)
|
Log CPM
|
p-Value
|
FDR
|
|
Parabacteroides sp. TM07 1AC
|
7.9613
|
13.191
|
0.0001
|
0.0398
|
|
Bacteroides thetaiotaomicron
|
4.3374
|
15.859
|
0.0002
|
0.0407
|
|
Bacteroides fragilis
|
4.2323
|
15.853
|
0.0014
|
0.0904
|
|
Parabacteroides sp. AF39 10AC
|
4.1422
|
9.1766
|
0.0018
|
0.1049
|
|
Parabacteroides goldsteinii
|
4.0828
|
10.56
|
0.0009
|
0.0687
|
|
Merdimonas faecis
|
3.965
|
9.4597
|
0.0029
|
0.1255
|
|
Enterocloster bolteae
|
3.9483
|
12.232
|
0.004
|
0.1407
|
|
Parabacteroides timonensis
|
3.8156
|
9.5607
|
0.0009
|
0.0687
|
|
Klebsiella variicola
|
3.7552
|
9.166
|
0.0002
|
0.0407
|
|
Klebsiella pneumoniae
|
3.6394
|
13.903
|
0.0005
|
0.0492
|
|
Intestinimonas massiliensis
|
3.5757
|
10.243
|
0.0038
|
0.1407
|
|
Enterococcus avium
|
3.4955
|
8.0538
|
0.0079
|
0.2174
|
|
Parabacteroides sp. ZJ 118
|
3.1084
|
10.318
|
0.0054
|
0.1759
|
|
Klebsiella quasipneumoniae
|
2.7849
|
9.437
|
0.0022
|
0.1086
|
|
Parabacteroides sp. N37
|
2.7817
|
9.5055
|
0.0087
|
0.2265
|
|
Parabacteroides distasonis
|
2.7276
|
14.051
|
0.0079
|
0.2174
|
|
Clostridium sartagoforme
|
−2.6858
|
7.4713
|
0.0094
|
0.2323
|
|
Eubacterium sp. AF22 8LB
|
−2.7838
|
8.2869
|
0.0061
|
0.1874
|
|
Limosilactobacillus mucosae
|
−3.8108
|
9.7525
|
0.0023
|
0.1086
|
|
Novosphingobium sp. c7
|
−3.9955
|
8.0346
|
0.0034
|
0.1376
|
|
Clostridium sp. Sa3CUN1
|
−4.3887
|
8.4158
|
0.0004
|
0.0492
|
Abbreviations: CPM, counts per million; FDR, false discovery rate.
These findings suggest that traumatic injury may disrupt microbial balance, leading
to an increase in potentially pathogenic species and a decrease in beneficial ones,
which could have implications for patient recovery and susceptibility to infections.
Discussion
This pilot study has demonstrated the feasibility challenges associated with conducting
gut microbiome research in sTBI patients, particularly in the context of recruitment
and data collection. The stringent inclusion criteria, designed to minimize confounding
factors, significantly limited patient recruitment, with only 10 patients enrolled
from a larger screened population over 2 months.
Furthermore, fecal sample collection proved to be particularly challenging, with successful
collection from only 7 out of the 10 recruited patients. The urgent nature of surgical
interventions, the frequent occurrence of empty bowels or delayed stool passage, and
the logistical difficulties associated with intubated and critically ill patients
significantly hampered sample acquisition. The QC issues encountered, particularly
with enema-affected samples and insufficient sample volumes, underscore the need for
revised protocols and careful consideration of preanalytical factors.
Regarding data collection protocols and hospital records, this pilot study revealed
that compliance with the planned data collection protocols was feasible, but required
significant adjustments. The availability of needed data from hospital records, such
as GCS scores, gastrointestinal dysfunction details, inflammatory markers, and neuroimaging
findings, was generally satisfactory. However, the definition and scoring of certain
variables, particularly those related to dietary habits and eating behaviors, required
clarification and standardization to ensure consistency across patients.
The corrective measures implemented, including enhanced communication, SOP revisions,
and modified sample collection techniques, represent valuable lessons learned for
future studies. The findings from this pilot study also highlight the need for a more
pragmatic approach to sample collection. The collection of stools during routine PR
examinations, as implemented, may prove to be a better method.
The preliminary metagenomic findings indicate that sTBI and subsequent hospitalization
are associated with significant alterations in the gut microbiome. These alterations
include a reduction in beneficial commensal bacteria and an increase in potentially
pathogenic bacteria. The observed dysbiosis, characterized by reduced diversity and
shifts in microbial abundance, may contribute to the pathophysiology of TBI and influence
clinical outcomes. The increase in potential pathogens and a decrease in beneficial
bacteria may contribute to worse outcomes in TBI patients. It is crucial to acknowledge
that the metagenomic findings presented herein are preliminary. A comprehensive bioinformatics
analysis of the complete dataset, contingent upon the collection of all planned samples,
is essential to yield substantial and meaningful results and to formulate definitive
conclusions. Consequently, the interpretation of the current data should be approached
with caution, recognizing its preliminary nature and the limited sample set analyzed.
Notably, Howard et al[27] reported significant changes in microbial diversity early after severe injury in
human patients, indicating that TBI induces rapid and profound shifts in the gut microbial
ecosystem. Furthermore, Burmeister et al found that the gut microbiome composition
could distinguish mortality in trauma patients, suggesting a strong association between
gut dysbiosis and adverse clinical outcomes.[28] In a study focused solely on TBI patients, Mahajan et al characterized the gut microbiome
after TBI, providing further evidence of significant alterations in this population.[4] Similarly, Pyles et al observed that the altered TBI fecal microbiome is stable
and functionally distinct, highlighting the persistent nature of these changes.[29] Urban et al further confirmed these findings, documenting altered fecal microbiome
years after TBI, suggesting long-lasting microbiome changes.[30]
Furthermore, several studies have begun to investigate the gut–brain axis in specific
TBI populations. In premature infants with brain injury, there are altered neuroactive
metabolites, bile acids, and specific genome features associated with brain injury.
These studies provide evidence of the importance of the gut–brain axis in vulnerable
populations. Additionally, Armstrong et al reviewed the link between TBI, abnormal
growth hormone secretion, and gut dysbiosis, suggesting a potential interplay between
these factors.[31] Our preliminary results align with previous research demonstrating gut microbiome
dysbiosis following TBI. However, the small sample size in this pilot study limits
the generalizability of these findings. A larger study is needed to confirm these
observations and to investigate the functional implications of gut microbiome dysbiosis
in TBI.
Implications for a Larger Study
This pilot study provides valuable insights to inform the design of a larger, more
definitive study. To address the feasibility challenges encountered, the following
modifications should be considered. A broader age range and less restrictive exclusion
criteria should be used to improve patient recruitment and achieve a larger sample
size. Alternative sample collection methods, such as rectal swabs or stool collection
bags, should be explored to improve efficiency and mitigate the impact of delayed
stool passage and urgent surgical interventions. The use of enemas should be avoided.
Increased sampling time points may be beneficial to capture the dynamic changes in
the gut microbiome following TBI. Future studies should incorporate functional metagenomics
analyses to investigate the functional consequences of gut microbiome dysbiosis in
TBI. A more comprehensive assessment of clinical variables, including detailed neurological
assessments, inflammatory markers, and medication usage, is needed to better correlate
gut microbiome alterations with clinical outcomes.
A well-designed and adequately powered larger study, incorporating these improvements,
will provide a more comprehensive understanding of the role of the gut microbiome
in TBI and pave the way for potential therapeutic interventions.
Conclusion
This pilot study has demonstrated the feasibility challenges associated with conducting
gut microbiome research in sTBI patients, specifically concerning patient recruitment,
sample collection, and data acquisition. However, the lessons learned and the modifications
implemented will inform the design and implementation of future studies. The preliminary
metagenomic data indicate that sTBI is associated with gut microbiome dysbiosis. A
larger study is warranted to further investigate the association between gut microbiome
composition and clinical outcomes in sTBI, provided that the identified feasibility
challenges are adequately addressed.
