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Changes in Gut and Plasma Microbiome following Exercise Challenge in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)


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Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disease characterized by intense and debilitating fatigue not due to physical activity that has persisted for at least 6 months, post-exertional malaise, unrefreshing sleep, and accompanied by a number of secondary symptoms, including sore throat, memory and concentration impairment, headache, and muscle/joint pain. In patients with post-exertional malaise, significant worsening of symptoms occurs following physical exertion and exercise challenge serves as a useful method for identifying biomarkers for exertion intolerance. Evidence suggests that intestinal dysbiosis and systemic responses to gut microorganisms may play a role in the symptomology of ME/CFS. As such, we hypothesized that post-exertion worsening of ME/CFS symptoms could be due to increased bacterial translocation from the intestine into the systemic circulation. To test this hypothesis, we collected symptom reports and blood and stool samples from ten clinically characterized ME/CFS patients and ten matched healthy controls before and 15 minutes, 48 hours, and 72 hours after a maximal exercise challenge. Microbiomes of blood and stool samples were examined. Stool sample microbiomes differed between ME/CFS patients and healthy controls in the abundance of several major bacterial phyla. Following maximal exercise challenge, there was an increase in relative abundance of 6 of the 9 major bacterial phyla/genera in ME/CFS patients from baseline to 72 hours post-exercise compared to only 2 of the 9 phyla/genera in controls (p = 0.005). There was also a significant difference in clearance of specific bacterial phyla from blood following exercise with high levels of bacterial sequences maintained at 72 hours post-exercise in ME/CFS patients versus clearance in the controls. These results provide evidence for a systemic effect of an altered gut microbiome in ME/CFS patients compared to controls. Upon exercise challenge, there were significant changes in the abundance of major bacterial phyla in the gut in ME/CFS patients not observed in healthy controls. In addition, compared to controls clearance of bacteria from the blood was delayed in ME/CFS patients following exercise. These findings suggest a role for an altered gut microbiome and increased bacterial translocation following exercise in ME/CFS patients that may account for the profound post-exertional malaise experienced by ME/CFS patients.
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Changes in Gut and Plasma Microbiome
following Exercise Challenge in Myalgic
Encephalomyelitis/Chronic Fatigue Syndrome
Sanjay K. Shukla
*, Dane Cook
, Jacob Meyer
, Suzanne D. Vernon
, Thao Le
Derek Clevidence
, Charles E. Robertson
, Steven J. Schrodi
, Steven Yale
, Daniel
N. Frank
1Marshfield Clinic Research Foundation, Marshfield, WI, United States of America, 2William S. Middleton
Memorial Veterans Hospital, Madison, WI, United States of America, 3University of Wisconsin, Madison, WI,
United States of America, 4Bateman Horne Center of Excellence, Salt Lake City, UT, United States of
America, 5University of Colorado Denver Anschutz Medical Campus, Aurora, CO, United States of America,
6Marshfield Clinic, Marshfield, WI, United States of America
¤a Current address: Meriter Medical Group, Monoa, WI, United States of America
¤b Current address: North Florida Regional Medical Center, Gainseville, FL, United States of America
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disease characterized
by intense and debilitating fatigue not due to physical activity that has persisted for at least 6
months, post-exertional malaise, unrefreshing sleep, and accompanied by a number of sec-
ondary symptoms, including sore throat, memory and concentration impairment, headache,
and muscle/joint pain. In patients with post-exertional malaise, significant worsening of
symptoms occurs following physical exertion and exercise challenge serves as a useful
method for identifying biomarkers for exertion intolerance. Evidence suggests that intestinal
dysbiosis and systemic responses to gut microorganisms may play a role in the symptomol-
ogy of ME/CFS. As such, we hypothesized that post-exertion worsening of ME/CFS symp-
toms could be due to increased bacterial translocation from the intestine into the systemic
circulation. To test this hypothesis, we collected symptom reports and blood and stool sam-
ples from ten clinically characterized ME/CFS patients and ten matched healthy controls
before and 15 minutes, 48 hours, and 72 hours after a maximal exercise challenge. Micro-
biomes of blood and stool samples were examined. Stool sample microbiomes differed
between ME/CFS patients and healthy controls in the abundance of several major bacterial
phyla. Following maximal exercise challenge, there was an increase in relative abundance
of 6 of the 9 major bacterial phyla/genera in ME/CFS patients from baseline to 72 hours
post-exercise compared to only 2 of the 9 phyla/genera in controls (p = 0.005). There was
also a significant difference in clearance of specific bacterial phyla from blood following
exercise with high levels of bacterial sequences maintained at 72 hours post-exercise in
ME/CFS patients versus clearance in the controls. These results provide evidence for a sys-
temic effect of an altered gut microbiome in ME/CFS patients compared to controls. Upon
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 1/15
Citation: Shukla SK, Cook D, Meyer J, Vernon SD,
Le T, Clevidence D, et al. (2015) Changes in Gut and
Plasma Microbiome following Exercise Challengein
Myalgic Encephalomyelitis/Chronic Fatigue
Syndrome (ME/CFS). PLoS ONE 10(12): e0145453.
Editor: Francesco Cappello, University of Palermo,
Received: July 13, 2015
Accepted: December 3, 2015
Published: December 18, 2015
Copyright: © 2015 Shukla et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
Data Availability Statement: All sequences and
quality scores were deposited into the NCBI Short
Read Archive under project accession number
PRJNA302040. Additional data are available from the
PI Sanjay K. Shukla, Marshfield Clinic Research
Foundation, upon request for researchers who meet
the criteria for access to confidential data: [CONTACT
INFORMATION: Sanjay K. Shukla, PhD, Research
Scientist, Marshfield Clinic Research Foundation].
Funding: This study was funded from a grant from
The CFIDS Association of America to SKS and
donors to the Marshfield Clinic Research Foundation.
exercise challenge, there were significant changes in the abundance of major bacterial
phyla in the gut in ME/CFS patients not observed in healthy controls. In addition, compared
to controls clearance of bacteria from the blood was delayed in ME/CFS patients following
exercise. These findings suggest a role for an altered gut microbiome and increased bacte-
rial translocation following exercise in ME/CFS patients that may account for the profound
post-exertional malaise experienced by ME/CFS patients.
The Centers for Disease Control estimates that between 836,000 and 2.5 million people in the
U.S. suffer from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) [1], resulting
in substantial disability [2] and significant socio-economic impact [3,4]. ME/CFS is a complex
disorder that has been associated with neuro-inflammatory and oxidative processes [5]. One
current model of disease suggests that a trigger event (e.g. infection) results in a chronic inflam-
matory state characterized by increased proinflammatory cytokine production, increased reac-
tive oxygen and nitrogen species, altered intracellular signaling, increased intestinal
permeability and systemic activation of innate immune receptors, altered glutaminergic and
dopaminergic neurotransmission, mitochondrial dysfunction, and aberrant autoimmune
responses [611]. These pathogenic processes appear to be self-sustaining and self-amplifying
and account for the characteristic symptoms of ME/CFS and other systemic disorders charac-
terized by central and peripheral fatigue [5,9]. Despite recent advances in understanding the
biochemical underpinnings of disease, diagnosis continues to rely on clinical presentation [12].
Guidelines established in 1994 by Fukuda et al as part of the International Chronic Fatigue
Syndrome Study Group define CFS as clinically evaluated, unexplained, persistent fatigue that
is not alleviated by rest that occurs in conjunction with four or more characteristic symptoms,
including impaired memory or concentration, sore throat, tender lymph nodes, muscle or joint
pain in the absence of redness or swelling, headaches, unrefreshing sleep, and post-exertional
malaise (PEM) lasting more than 24 hours, persisting or recurring over 6 or more consecutive
months of illness [13]. However, not all patients experience the same symptoms indicating the
existence of ME/CFS subgroups. For example, subgroups based on the presence or absence of
gastrointestinal symptoms [14] and post-exertional malaise [15] has been described.
In particular, post-exertional malaise has emerged as a distinguishing feature of ME/CFS. It
is described as significant worsening of several symptoms following physical and mental exer-
tion. Post-exertional malaise has been associated with abnormal neurovascular regulation [16]
and altered immune and metabolic response to aerobic exercise [1719]. Variability in symp-
tom constellation and severity over time make ME/CFS patients a heterogeneous population
and several studies have failed to detect differences between patients and controls at baseline
[17,18,20]. As such, exercise challenge in those suffering from PEM may serve as a useful tool
for exacerbating symptoms in a controlled setting to attempt to calibrate symptom constella-
tion and severity across the patient group and to uncover potential differences between patients
and controls that might not be apparent at rest. Characterization of the gut microbiome in
patients with ME/CFS has demonstrated significant alterations compared to healthy controls
[21,22]. Additionally, systemic antibody responses to enteric microorganisms have been shown
to occur in patients with ME/CFS, suggesting that increased intestinal permeability and bacte-
rial translocation across the intestinal barrier may result in further inflammation and contrib-
ute to ME/CFS symptoms [9,23,24]. IgA antibody responses to enteric bacteria in ME/CFS
Microbiome after Exercise Challenge in ME/CFS
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The funding agency has no role in the study design or
in the preparation of the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
patients have to date been associated with higher serum IL-1, TNFα, and neopterin levels, auto-
immune responses to serotonin, and increased symptoms of irritable bowel syndrome [25,26].
We hypothesized that the ecology of the gut microbiota of ME/CFS patients would differ from
that of matched healthy controls and that these differences would be associated with increased
bacterial translocation from the gut to the circulatory system following exercise challenge with
corresponding worsening of symptoms (i.e, pain, fatigue, and mood). The results presented
here add further to the previous findings suggesting that ME/CFS patients have an altered gut
microbiome and further suggest that increased bacterial translocation following exercise pro-
vides a potential explanation for the profound post-exertional malaise experienced by some
ME/CFS patients.
Study Subjects
We enrolled 10 ME/CFS patients and 10 healthy controls from the Madison and Marshfield,
WI areas from 122 potential subjects responding to study advertisements in local clinics. We
were able to reach 104 subjects by phone for screening, 45 of whom did not qualify. By phone,
13 potential patients and 46 potential controls were identified. We enrolled 10 ME/CFS
patients and 10 healthy controls from this group for the microbiome study. All patients met
the ME/CFS case definition criteria established by Fukuda et al in 1994 [13]. The control group
was comprised of healthy people who had no persistent complaints of fatigue. Patients and
controls were matched based on age, gender, BMI, and self-reported general activity patterns.
Subject age ranged between 20 and 60 years. Each subject gave written consent in an IRB
approved consent form to participate in the study. This study was approved by Marshfield
Clinic and University of WisconsinMadison Institutional Review Boards.
Inclusion and Exclusion Criteria
Standard medical history was reviewed and a physical exam was conducted for each subject to
rule out any major illnesses other than ME/CFS. Routine blood and urine chemistry tests were
also used to screen for exclusionary medical conditions or other conditions that may explain
the patientssymptoms. Exclusionary conditions included untreated hypothyroidism, sleep dis-
orders, side effects of medication, relapsing of past medical issues (e.g. Lyme disease, hepatitis
B or C), major psychiatric issues, including major depressive disorder with psychotic or melan-
cholic features, alcohol or other substance abuse within two years of the onset of ME/CFS, and
severe obesity as defined by a body mass index (BMI) of greater than 40 kg/m
. Potential par-
ticipants were also excluded if they had: 1) current use of immunomodulatory medications,
stool softeners, laxatives, anti-diarrheal agents, antibiotics, or probiotics, 2) current use of opi-
oids, 3) a history of cardiovascular disease or uncontrolled hypertension, or 4) current fatigue
sufficient to interfere with or preclude exercise testing. Patients were asked to confirm the
absence of exclusionary medications on the day of testing and to list other current medication
use. Although we did not explicitly exclude for low-dose antidepressant use, gastrointestinal
disease, or smoking, which could influence gut flora, none of the participants were currently
taking this class of medication, reported any gastrointestinal disorder, or were smokers. Fur-
ther, none of the participants were diagnosed with depression.
After completion of informed consent, study subjects underwent four non-consecutive days
of testing at the Exercise Psychology laboratory at the University of WisconsinMadison. Day
1 included a clinical interview and screening blood draw. Day 2 occurred approximately one
week later and involved pre-exercise symptom assessment, stool sample collection, maximal
exercise test, post-exercise symptom assessment, and blood draws. Expired gases, heart rate,
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and ratings of perceived exertion and leg muscle pain were collected during exercise and recov-
ery. Participants returned to the laboratory at 48 and 72 hours after exercise to complete self-
report symptom questionnaires and provide follow-up stool and blood samples.
Exercise Test
Participants performed a maximal exercise test on an electronically braked cycle ergometer
(Sensormedics, Loma Linda, CA) following the protocol previously described by Cook et al
[27]. Seat height was adjusted prior to exercise to the desired fit of the participant. Resistance
was software-controlled (Sensormedics, Loma Linda, CA) via an interface cable from a com-
puter to the ergometer. Participants were seated on the stationary bicycle for 1 minute of quiet
rest to acquaint themselves to breathing through the mouthpiece and the overall setup. Exercise
began with a 3-minute warm-up at 25 Watts (W). Participants were instructed to maintain a
pedal rate between 60 and 70 revolutions per minute (RPM) throughout the test. After the
warm-up period the work rate was increased by 5 W every 20 seconds (15 W/min) until voli-
tional exhaustion. The exercise test ended when either, 1) the participant could no longer keep
up the pedal rate of 6070 RPMs, 2) the participant stopped pedaling, or 3) some other factor
caused us to stop the test (e.g. testing became unsafe for the participant, equipment malfunc-
tion). Peak effort was determined based on meeting at least two of the following criteria: 1)
respiratory exchange ratio 1.1, 2) achievement of 85% of age-predicted maximum heart rate,
3) rating of perceived exertion (RPE) 17, and 4) a change in VO
of <200 ml with an
increase in work. Following peak effort, the workload was reduced to 20 W and the participant
underwent a three-minute active recovery phase maintaining the pedal rate of 6070 RPM.
After the three-minute recovery period, the participants were provided water ad libitum. Par-
ticipants were guided to a reclined chair for continued recovery. Blood was drawn at 15 min-
utes post peak effort and immediately followed by questionnaire completion.
Symptom Assessment
On day 1, participants completed the Centers for Disease Control Symptom Inventory [28]to
document the presence, frequency, and severity of the symptoms most commonly reported by
ME/CFS patients. On day 1 and all subsequent days, participants completed the 1) McGill Pain
Questionnaireshort-form (MPQ) [29], 2) Profile of Mood States (POMS) [30], 3) Fatigue
Visual Analog Scale (FVAS) [31], and the 4) Multidimensional Fatigue Inventory (MFI) [32]
both before and immediately, 48-hrs, and 72-hrs post exercise.
Sample Collection
A total of 80 blood samples (four per subject) were collected in PAXgene blood DNA collection
tubes (Qiagen) and 60 stool samples (three per subject) were collected separately in single use,
disposable, Protocult collection hats followed by self-transference to a 100 ml sterile capped
container ( Blood samples were collected at baseline (pre-exercise) and 15 minutes,
48 hours, and 72 hours post-exercise. Stool samples were collected at baseline (pre-exercise)
and 48 and 72 hours post-exercise. After collection, stool samples were immediately stored in a
freezer at -20°C or on dry ice before being transferred to an additional freezer for long-term
storage at -80°C. Frozen samples were sent for laboratory processing on dry ice.
Molecular Methods
Total genomic DNA was extracted from each clinical sample using the QIAamp DNA Blood
Mini Kit, Qiagen, Valencia, CA for blood samples and QIAamp DNA Stool Mini Kit (Qiagen)
Microbiome after Exercise Challenge in ME/CFS
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for stool samples. All DNA samples were quantified using the Qubit DSDNA HS assay kit (Life
Technologies, Grand Island, NY) and stored at -80°C until use. All barcoded primers were
designed using Roches User manual for the 454 sequencing platform and synthesized by Inte-
grated DNA Technologies (Ames, IA). DNA Samples were amplified using a mixture of bar-
coded 27F forward primers in a 4:1:1:1 ratio (27f-YM:27f-Bif:27f-Bor:27f-Chl) and reverse
primer 534R [33]. The 40 μl PCR mixture consisted of 20 pmol forward and reverse primers,
40 μg bovine serum albumin, 20 μl Hot Start mixture, and 2 μl template DNA. The PCR was
done in a PE9700 thermocycler (Applied Biosystems, Waltham, MA) with 25 cycles for the
stool samples and 40 cycles for the blood samples. Cycling conditions were as follows: initial
denaturation at 95°C for 15 min followed by the number of cycles described above (25 or 40) of
denaturation at 94°C for 30 seconds, annealing at 60°C for 30 seconds, and elongation at 72°C
for 40 seconds, with a final elongation time of 10 minutes. Each amplicon was normalized to
20 ng/μl using the SequalPrep Normalization Kit (Life Technologies, Grand Island, NY).
Amplicons were sequenced using the 454 GS FLX+ system using Titanium chemistry (Roche,
Branford, CT).
DNA Sequence Analysis
Polishing and bioinformatic analysis of 16S sequences was performed as previously described
[3437]. In brief, all pyrosequences were screened for nucleotide quality and sequences with
bases at 5and 3ends with mean Q <20 over a 10 nucleotide window, ambiguous bases with
any N residues, or shorter than minimum length (<300 nucleotides) were discarded. Chimera
screening was performed using the ChimeraSlayer tool, which requires that sequences be previ-
ously aligned with NAST-iEr (Hass BJ 2011). Putative chimeras and other sequences that could
not be aligned by NAST-iEr were removed from subsequent analyses. Genus-level taxonomic
calls were produced by the RDP Classifier [38], which performs naïve Bayesian taxonomic clas-
sification versus a training set. Operational taxonomic units (OTUs) were produced by cluster-
ing sequences with identical taxonomic assignments. Relative abundances of OTUs were
calculated for each subject by dividing the sequence counts observed for each OTU by the total
number of high-quality bacterial sequences generated for the subject. Goods coverage index
was calculated for each sample using the Explicet software package [39].
Statistical Analysis
Independent samples t-tests were used to determine group differences at baseline and during
exercise. A two group (ME/CFS and control) X 3 time (immediately post, 48-hrs post, and
72-hrs post) doubly-multivariate repeated measures MANOVA was used to analyze symptom
changes (i.e. post-exertional malaise (PEM)) from baseline. The overall MANOVA compared
the groups on a linear combination of symptom variables. This analysis was used to determine
the presence of PEM in our ME/CFS participants and is similar to the approach taken previ-
ously by our lab [18]. In brief, we chose variables a priori to be entered into the model. Variable
selection was based on three conditions: 1) the variables were not highly related, 2) they dis-
criminated between patients and controls, and 3) they were responsive to exercise. These
choices were made to avoid issues of multicollinearity and artificial inflation of variance
explained by the model. With this model we are able to determine whether symptoms are
changing over time (i.e. time main effect), whether they differ between ME/CFS and control
patients (i.e. group main effect), and whether the changes in symptoms over time differ
between the groups (i.e. group X time interaction). In addition to this omnibus test for PEM,
we also calculated effect sizes (d) [40] to illustrate the magnitude of each symptom change
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post-exercise. These analyses were conducted using SPSS for Windows (Version 22.0, Chicago,
We hypothesized that changes in relative abundances of bacterial OTUs in blood samples
from baseline to post-exercise challenge would differ between ME/CFS patients and healthy
controls. To test this hypothesis for each taxonomic group, we calculated the difference
between the pre-exercise OTU relative abundances and the average of the 48 hour and 72 hour
post-exercise counts for each individual. The change in sequence read counts was compared
between patients at ten taxonomic groups using four different statistical tests: (i) a T-test using
10,000 permutations to obtain P-values to evaluate the significance of the mean differences in
count change between patients and controls; (ii) to evaluate the difference in variances (of
change in sequence reads) between patients and controls, an F-test was performed, also with
10,000 permutations for the P-values and to compare distribution differences between patients
and controls, both (iii) a nonparametric Kolmogorov-Smirnov test was performed (no permu-
tations) and a (iv) Mann-Whitney U test with 10,000 permutations. These analyses were per-
formed using the XLISP-STAT programming language for the permutation routines and
Mathematica for the calculation of the Kolmogorov-Smirnov test.
Phenotypic Characteristics
The phenotypic characteristics and fatigue measurements of patients and controls at baseline
are presented in Table 1. Age, weight, and height were similar for patients and healthy controls
and 80% of subjects were female in both groups. The patient group had greater fatigue, less
vigor, and scored more poorly on mood symptoms than controls at baseline (p<0.05). Particu-
larly germane to this study, only 3 of the 10 patients and 2 of the 10 controls reported gastroin-
testinal symptoms and all participants reported mild severity and symptoms present a little of
the time.All subjects completed a maximal exercise test as described above with results pre-
sented in Table 2. Results of maximal exercise testing were similar between the two groups,
except that average heart rate was significantly lower in the ME/CFS case group than in the
healthy control group.
Post-exertion Malaise
Self-reported symptoms and effect sizes for selected symptom changes pre- to post-exercise for
ME/CFS patients and controls are presented in Table 3. For a complete detailed analysis of all
symptoms pre- and post-exercise challenge please see Meyer et al [18]. For the doubly-multi-
variate repeated measures MANOVA, we compared symptom changes for fatigue (Fatigue
VAS), pain (MPQ total) and confusion (POMS Confusion subscale) at three points post-exer-
cise (immediate, 48-hrs, and 72-hrs). Results indicated a significant main effect of Time
[WilksΛ= 0.408; F(6,68) = 6.416; p = 0.000] and a significant Group x Time interaction
[WilksΛ= 0.611; F(6,68) = 3.163; p = 0.009], showing that symptoms were changing from
pre- to post-exercise and that these changes were different between ME/CFS and control
patients. Effect size calculations showed that ME/CFS patients had large changes in their symp-
toms of pain, fatigue, and confusion at various times post-exercise compared to controls.
Microbiome Characteristics
A total of 406,880 high quality sequencing reads were generated with a median of 2,160 reads
per specimen. The sequencing coverage at the genus level as estimated by Goods coverage
mean index was 95% for sequence libraries from all individual blood and stool samples,
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indicating that each sequence dataset adequately captured the underlying biodiversity in the
sample from which it was generated. The blood samples, as expected, yielded a lower number
of bacterial sequences (n = 111,492; 1,394 per sample) than the stool samples (n = 295,388;
4,923 reads per sample).
Table 2. Maximal Exercise Test Results for ME/CFS Patients (n = 10) and Health Controls (n = 10).
Results at Peak Exercise ME/CFS Patients, mean (±SD) Controls, mean (±SD)
(ml/kg/min) 28.6 (±9.0) 28.2 (±9.6)
Respiratory Exchange Ratio 1.19 (±0.10) 1.20 (±0.11)
Rating of Perceived Exertion 18.2 (±2.0) 16.4 (±2.7)
Leg Muscle Pain 5.6 (±3.3) 4.2 (±1.6)
Heart Rate (beats per minute) 159.0 (±16.5)
178.8 (±6.8)
Time to Fatigue (min) 11.7 (±2.8) 13.1 (±3.4)
ME/CFS, myalgic encephalomyelitis/chronic fatigue syndrome; SD, standard deviation
Signicantly different from control (p<0.05)
Table 1. Baseline Characteristics of ME/CFS Patients (n = 10) and Healthy Controls (n = 10).
Characteristics ME/CFS Patients, mean (±SD) Controls, mean (±SD)
Age (yrs) 48.6 (±10.5) 46.5 (±13.0)
Height (in) 66.6 (±3.7) 64.4 (±4.1)
Weight (lbs) 150.7 (±30.3) 144.3 (±18.3)
BMI (kg/m
)23.9 (±4.3) 24.6 (±3.3)
Multidimensional Fatigue Inventory (MFI) Scores
General 14.6 (±1.1)
10.3 (±1.9)
Physical 15.4 (±4.0)
6.8 (±2.7)
Reduced Activity 14.0 (±5.3)
7.6 (±3.4)
Reduced Motivation 9.4 (±2.3)
6.3 (±2.9)
Mental 14.5 (±3.4)
7.6 (±3.4)
Prole of Mood States (POMS) Scores
Tension 7.5 (±4.6) 4.4 (±3.5)
Depression 7.5 (±10.5) 5.2 (±9.1)
Anger 4.8 (±7.2) 3.4 (±4.7)
Vigor 8.6 (±3.4)
18.9 (±4.9)
Fatigue 14.5 (±4.8)
4.8 (±6.0)
Confusion 9.1 (±3.6)
4.7 (±4.6)
Total Mood Disturbance 134.8 (±25.6)
103.6 (±28.7)
McGill Pain Questionnaire 6.8 (±5.8) 2.5 (±5.2)
CDC Symptom Inventory: Abdominal Symptoms
Diarrhea 0.6 (±1.0) 0.3 (±0.7)
Stomach/Abdominal Pain 1.1 (±2.1) 0.0
CDC Symptom Inventory: Neurocognitive Symptoms
Memory Problems 4.0 (±3.0) 0.2 (±0.7)
Concentration Problems 4.7 (±2.8) 0.5 (±1.6)
ME/CFS, myalgic encephalomyelitis/chronic fatigue syndrome; SD, standard deviation.
Signicantly different from control (p<0.05)
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Average relative abundances of the bacterial taxa observed in all blood and stool samples
collected from patients and controls are shown in Table 4 with pooling for all time points. In
blood samples, there was a lower relative abundance of Bacteroidetes and higher relative abun-
dance of Firmicutes observed in ME/CFS patients than in healthy controls. In contrast, in the
stool samples there was a higher relative abundance of Bacteroidetes and lower abundance of
Firmicutes observed in ME/CFS patients compared to healthy controls. The relative abundance
of Actinobacteria in the gut was significantly lower in ME/CFS patients than healthy controls
by a Kolmogorov-Smirnov test (p = 0.007). However, differences were not found to be statisti-
cally significant for other bacterial taxa in the blood or stool samples using a non-parametric
test to account for both prevalence and abundance [41].
Microbiome Response to Maximal Exercise
Changes in average relative abundances of bacterial taxa in stool samples were observed follow-
ing maximal exercise testing and these changes were different in ME/CFS patients and healthy
controls. The average relative abundance of 7 out of 9 major taxa increased in the stool in
patients from baseline to 72 hours post-exercise compared to an increase in only 2 of the 9
major phyla/genera in healthy controls (p = 0.005) (Fig 1). In contrast to the ME/CFS patients,
the relative abundance of most major phyla decreased at 72 h in the stool samples from healthy
Table 3. Symptom Responses to Maximal Exercise for ME/CFS Patients (n = 10) and Healthy Controls (n = 10).
Group Means (±SD) Effect sizes (d) for change
Measure Group Pre-Ex Post-Ex 48-hr 72-hr Post-Ex 48-hr 72-hr
Fatigue VAS ME/CFS 50.4 (23.3) 67.1 (15.0) 59.5 (23.7) 57.3 (20.0) 0.25 0.91 0.96
Control 12.3 (12.7) 25.0 (14.5) 7.9 (8.5) 4.1 (6.3) - - -
MPQ Total ME/CFS 6.9 (5.8) 10.2 (6.9) 7.8 (6.1) 5.4 (4.5) 1.23 0.44 -0.31
Control 0.8 (1.1) 1.4 (1.1) 0.6 (0.8) 0.2 (0.4) - - -
POMS Confusion ME/CFS 9.1 (3.6) 12.6 (3.9) 11.3 (4.0) 11.1 (4.9) 1.26 0.85 1.16
Control 4.1 (3.3) 3.3 (3.1) 3.7 (2.7) 2.4 (2.0) - - -
ME/CFS, myalgic encephalomyelitis/chronic fatigue syndrome; SD, standard deviation; VAS, visual analog scale; MPQ, McGill Pain Questionnaire;
POMS, Prole of Mood States; Ex, exercise.
Table 4. Relative Abundance of Bacterial Phyla in Blood and Stool Samples from ME/CFS Patients and Health Controls.
Blood Samples
Stool Samples
Phyla ME/CFS Patients Controls ME/CFS Patients Controls
Actinobacteria 10.52% 10.80% 0.58%
Bacteroidetes 17.84% 22.38% 27.71% 22.43%
Firmicutes 9.52% 7.89% 58.40% 65.29%
Other 7.97% 8.55% 9.71% 9.11%
Proteobacteria 54.14% 50.38% 3.59% 2.12%
ME/CFS, myalgic encephalomyelitis/chronic fatigue syndrome.
Mean relative abundance for 10 case and 10 control samples collected at baseline and 15 min, 48 hours, and 72 hours post-exercise challenge.
Mean relative abundance for 10 case and 10 control samples collected at baseline and 48 and 72 hours post-exercise challenge.
Signicantly different from control (p<0.05)
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 8/15
controls suggesting that the bacterial load in ME/CFS patients is preferentially enhanced dur-
ing post-exertional malaise.
Given the high relative abundance of Firmicutes in stool samples, we further assessed the
potential for translocation of organisms from this phylum into the bloodstream after exercise
challenge. Differential changes in the relative abundance of Firmicutes/Bacilli organisms were
observed in blood and stool samples over time (Fig 2). Of note is the significant increase in the
relative abundance of Bacilli in blood samples collected from ME/CFS patients at the 48 hour
time point. We also observed rapid changes in the relative abundances of the Clostridium XIVa
and IV clusters, belonging to the phylum Firmicutes, in blood samples collected 15 min after
maximal exercise from ME/CFS patients, but not healthy controls (Fig 3). We speculate that
these bacteria may have translocated into the blood stream from the gut after the maximal
exercise challenge. This phenomenon appears to be specific to particular taxa and more promi-
nent in patients than controls.
Neuroinflammation and oxidative dysregulation have been shown to occur in patients with
ME/CFS [5]. Although, differential intestinal microbiome characteristics have been described
Fig 1. Relative abundance of the major bacterial taxa in stool samples collected at baseline (0 hr) and 72 hr after exercise challenge in ME/CFS
patients and healthy controls.
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 9/15
for ME/CFS patients and healthy controls [21] and systemic antibody responses to enteric bac-
teria have been associated with increased inflammation, worsening fatigue, and gastrointestinal
symptoms [25,26], the potential for transient changes in bacterial colonization in the gut and/
or bloodstream to modulate symptomology has not been evaluated in ME/CFS patients. The
evidence of altered intestinal microbiota and bacterial translocation into the bloodstream fol-
lowing exercise challenge in patients with ME/CFS is consistent with previous findings and
provides novel evidence of systemic bacterial signal and exercise induced bacterial transloca-
tionone potential explanation for the worsening of symptoms seen in patients when they
attempt to become more physically active.
Fig 2. Changes in the relative abundance of Firmicutes/Bacilli in blood and stool samples before (0 hr) and after maximal exercise.
Fig 3. Relative abundance of (A) Firmicutes/Clostridia/.../LachnoXIVa and (B) Firmicutes/Clostridia/.../LachnoIV in blood samples collected at
baseline and 15 minutes after exercise challenge in ME/CFS patients and healthy controls.
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 10 / 15
Over the last several years our understanding of how alterations in the human microbiome
influence health and disease has increased considerably [42,43]. Murine models suggest that
the gut microbiota could contribute to leanness, obesity, stress, and emotional behavior
through endocrine and neuroendocrine pathways [44] and considerable evidence suggests sim-
ilar effects in humans via effects on host immunity and metabolism [45,46]. The differences in
the relative abundance of bacteria making up the intestinal microbiome noted in the present
study are consistent with those previously reported in the literature [21]. Although no typical
enterotype has been defined to date, reports of dysbiosis in patients with ME/CFS in Norway
and Belgium are consistent with the changes in relative abundance of Firmicutes and Bacteroi-
detes observed here [21].
Perhaps more important than confirmation of intestinal dysbiosis in the context of ME/CFS
is the evidence of temporal changes in microbiome composition following maximal exercise
challenge and the novel finding of bacterial signal in the bloodstream of both ME/CFS patients
and healthy controls occurring concomitantly with symptom exacerbation. This finding is con-
sistent with the systemic response to enteric microorganisms reported by Maes et al in a num-
ber of reports and demonstration of correlation between this systemic response and
pathological ME/CFS processes and symptoms, including higher serum IL-1, TNFα, and neop-
terin levels, autoimmune responses to serotonin, and increased symptoms of irritable bowel
syndrome [9,2326]. In the present study, changes in microbiome and bacterial measures were
accompanied by large changes in fatigue, pain, and confusion in ME/CFS patients. Such exer-
cise-induced changes in the microbiome are consistent with the changes described above and
additional recent reports of altered immune response to exercise in patients with ME/CFS,
while also providing a plausible mechanistic explanation for such alterations [19].
Although intestinal microbiome composition is known to change over time with develop-
ment and aging as well as in response to dietary changes, temporal examination of the human
intestinal microbiome generally spans weeks, months, or even years to assess evidence of an
interventions effect. The notion that exercise might influence gut microbiota composition has
been described in both animal and human models [4751]. The evidence presented here sug-
gests that not only does physical activity influence gut microbiota composition, but that the
temporal effects of such physical activity may manifest differently in healthy and diseased indi-
viduals. These changes are one potential explanation for why acute exercise may make some
individuals with ME/CFS sicker.
Blood is generally considered sterile, although evidence of transient, asymptomatic bacter-
emia has been reported following dental extraction [52] and intestinal insult [53]. In the con-
text of ME/CFS, systemic responses to gut microorganisms suggest that bacterial translocation
across the intestinal barrier may also occur as part of this disease [9,2326]. The notion that
exercise may also result in translocation of bacteria across the intestinal barrier is particularly
interesting, especially in the case of ME/CFS where post-exertional malaise can be a key charac-
teristic of the disease. Following maximal exercise testing, we detected bacterial signal in blood
samples from both ME/CFS patients and healthy controls. Consistent with differences in the
intestinal microbiomes between the two groups, we noted an increased relative abundance of
Firmicutes, particularly those from Clostridium clusters XIVa and IV, in blood samples from
ME/CFS patients at 15 minutes post-exercise challenge. In vitro functional studies will be able
to address this observation better. However, we speculate that some members of Firmicutes
and Bacilli because of their stronger cell walls and inherent ability to survive in harsher envi-
ronmental conditions may have contributed to it surviving longer in bloodstream. Further
investigation of the potential for transient translocation of intestinal microorganisms into the
bloodstream following exercise and how the dysbiosis characteristic of certain disease states,
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 11 / 15
such as ME/CFS, might influence this translocation may provide considerable insight into how
the microbiome influences disease symptoms.
Evidence for altered intestinal permeability in patients with ME/CFS has been mounting [9]
and preliminary studies suggest that treatments designed to modulate the gut microbiota or
enhance intestinal barrier function may be able to improve ME/CFS symptoms [24,5456].
Our ability to detect changes in intestinal microbiome composition over time and to observe
what appears to be transient bacterial translocation from the gut into the bloodstream follow-
ing exercise challenge may provide a protocol for testing future treatments designed to alter
these outcomes and to determine whether this is the mechanism of action for such treatments.
Treatment paradigms that have been tested with some success for other chronic, inflammatory,
non-communicable diseases thought to be related to intestinal dysbiosis include probiotics,
prebiotics, dietary fiber, and fecal microbiotia transplantation [46,57]. Similar trials in ME/CFS
patients may benefit from temporal monitoring for bacterial signal in both the gastrointestinal
tract and bloodstream.
Proper diagnosis of ME/CFS is a very involved and thorough process. One of the strengths
of this study was that clinicians with expertise in diagnosing ME/CFS were engaged in identi-
fying both patients and controls from a cohort of more than 100 possible patients. Identified
patients were included only after examining results of screening blood tests to rule out any
major comorbid conditions that could explain the ME/CFS symptoms and controls were
carefully selected for matching based on age, gender, BMI, and self-reported general activity
patterns. However, given that this was a small pilot study with only 10 patients and 10 con-
trols it was not possible to control for all potential confounders, including the broad age
range of participants, historical use of medications and supplements (e.g. pain killers, antioxi-
dants), and additional medical diagnoses (e.g. depression, gastrointestinal symptoms, aller-
gies). While the careful selection process allowed for high quality case and control
populations, the study sample size was small and as such, many observations failed to rise to
the level of statistical significance. Additionally, the small sample size precluded us from
directly examining the associations between symptoms and changes to the gut and plasma
microbiome. Given these limitations, findings must be interpreted with caution. An increase
in sample size would help us to better assess clinically relevant observations. However, even
this relatively small study points to important temporal differences in intestinal microbiome
composition and transient bacteremia that will inform future larger studies designed to
understand how these differences relate to ME/CFS etiology and/or symptomology. Another
study limitation was in the depth of microbiome sequencing. Our approach did satisfy the
Goods coverage index of >0.95, but additional deep sequencing of the samples already col-
lected would likely increase the statistical power to detect significant changes in the rarer bac-
terial taxa.
We are still a long way from fully understanding how the intestinal microbiota impacts eti-
ology and symptomology in ME/CFS, but the evidence presented here and elsewhere suggests
that changes in gut microbiome are associated with this disease. Here, we present additional
evidence to support the idea that temporal changes in microbial composition in the gut and
translocation of gut microbes into the bloodstream may influence the symptoms of ME/CFS.
Future studies of ME/CFS etiology and treatment approaches should incorporate temporal
microbial analyses to further elucidate this interesting finding.
We are grateful to participants of the study for their time and commitment. We also thank
Rachel Stankowski for her excellent help in preparation and editing of this manuscript.
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 12 / 15
Author Contributions
Conceived and designed the experiments: SKS D. Cook DNF. Performed the experiments: JM
TL CER D. Cook D. Clevidence SY. Analyzed the data: TL CER DNF SJS D. Cook JM SY SDV
SKS. Contributed reagents/materials/analysis tools: DNF. Wrote the paper: SKS SJS D. Cook
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Microbiome after Exercise Challenge in ME/CFS
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... Sixteen studies included participants from clinical populations. Of these, three studies compared data to an apparently healthy population [33,34,36], with the rest comparing exercise groups [35,40,41], waitlist or crossover [37][38][39]42], control groups [43][44][45] or did not include a comparison [30][31][32]. Of the remaining 12 'healthy population' studies, four included apparently healthy sedentary and recreationally active participants [20,[27][28][29]; one study was in a military setting [21], and seven studies assessed athletes only [18,19,[22][23][24][25][26]. ...
... Most clinical populations were characterised by metabolic disorders (n = 11, 60% of clinical studies) [30][31][32]34,[36][37][38]40,41,43,44]. Other clinical population domains included neurological disorder (n = 1, 6%) [45], autoimmune (n = 1, 6%) [42], inflammatory bowel disease (n = 1, 6%) [39], myalgic encephalomyelitis/chronic fatigue syndrome (n = 1, 6%) [33] and elderly populations (n = 1, 6%) [35]. ...
... Details of the exercise interventions are summarised in Table 2. The majority of studies employed aerobic exercise interventions (n = 19) [18][19][20][21][22]24,26,29,31,[33][34][35][36][37][38][40][41][42]44], while eight studies included a combination of resistance and aerobic exercise (labelled as concurrent training) [23,25,[30][31][32]39,43,45] and a single study compared resistance to aerobic training [27]. Three studies were classified as acute with the exercise perturbation consisting of a single effort [19,22,33]. ...
Full-text available
This study systematically reviewed all human longitudinal exercise interventions that reported changes in the gut microbiota; frequency, intensity, duration and type of exercise were assessed to determine the influence of these variables on changes to the gut microbiota in both healthy individuals and clinical populations (PROPERO registration: CRD42022309854). Using PRISMA guidelines, trials analysing gut microbiota change with exercise interventions were included independent of trial randomisation, population, trial duration or analysis technique. Studies were excluded when microbiota abundance was not reported or when exercise was combined with other interventions. Twenty-eight trials were included, of which twelve involved healthy populations only and sixteen involved mixed or clinical-only populations. The findings show that participation in exercise of moderate to high-intensity for 30–90 min ≥3 times per week (or between 150–270 min per week) for ≥8 weeks is likely to produce changes in the gut microbiota. Exercise appears to be effective in modifying the gut microbiota in both clinical and healthy populations. A more robust methodology is needed in future studies to improve the certainty of the evidence.
... Specifically, physical activity has a direct impact on the central nervous system (CNS), modifying the pain experience and cognitive processing [14,17]. On the other hand, it has been shown to positively modulate the gut microbiome (leading to greater diversity and overabundance of beneficial taxa and metabolites) in different settings, including chronic diseases [17,18] thus, representing a potential preventive and therapeutic tool for dysbiosis-related conditions. ...
... As far as we are aware, the bibliography related to these three items (pain, microbiome and lifestyle) is still limited. Despite there being a wide bibliography about the effects of exercise in CWP patients, there is only one article in which exercise is the chosen intervention regardless of microbiota changes [18]. This stresses the need for further research in this very promising field, which could open a new vision and indicate new therapeutic targets for CWP patients. ...
... Otherwise, Torlak et al. manipulated the diet and used different diets with chronic low back pain (CLBP) patients [25]. The other two studies investigated the effect of a maximal exercise challenge in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) [18] and the effect of electroacupuncture in knee osteoarthritis patients [26]. The flowchart is presented in Figure 1 according to the PRISMA guidelines. ...
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Background: Lifestyle interventions have a direct impact on the gut microbiome, changing its composition and functioning. This opens an innovative way for new therapeutic opportunities for chronic widespread patients. Purpose: The goal of the present study was to evaluate a correlation between lifestyle interventions and the gut microbiome in patients with chronic widespread pain (CWP). Methods: The systematic review was conducted until November 2022. Pain and microbiome were the two keywords selected for this revision. The search was conducted in PubMed, Chochrane, PEDro and ScienceDirect, where 3917 papers were obtained. Clinical trials with lifestyle intervention in CWP patients were selected. Furthermore, these papers had to be related with the gut microbiome, excluding articles related to other types of microbiomes. Results: Only six articles were selected under the eligibility criteria. Lifestyle interventions were exercise, electroacupuncture and ingesting a probiotic. Conclusions: Lifestyle intervention could be a suitable choice to improve the gut microbiome. This fact could be extrapolated into a better quality of life and lesser levels of pain. Keywords: gut microbiome; lifestyle; chronic widespread pain
... The chronic, fluctuating nature of ME/CFS suggests that its pathogenesis involves a disruption of homeostasis leading to chronic inflammation and immunological dysfunction. Biomarkers of inflammation and leaky gut syndrome as a possible result of microbiome disturbance and bacterial translocation have been reported (Shukla et al., 2015;Cortes Rivera et al., 2019;Mandarano et al., 2018;Sweetman et al., 2019). According to this theory, an aberrant state of homeostasis is the central cause of ME/CFS (Hatziagelaki et al., 2018;Nacul et al., 2020;Sweetman et al., 2019;Sweetman et al., 2020). ...
... Dysbiosis, an imbalance in the microbiome, seems to be involved in the pathophysiology of ME/CFS. Shukla et al., (2015) studied changes in the gut and plasma microbiome following exercise challenge in pwME/CFS. In exercise challenge, changes occurred in the amount of bacterial phyla in the gut in pwME/CFS that were not observed in healthy controls. ...
... The authors concluded that their results provide evidence for a systemic effect of an altered gut microbiome in ME/CFS patients compared to controls. Nagy-Szakal et al. (2017) partially confirmed the findings of Shukla et al. (2015) in being able to differentiate the faecal metagenomic profiles of patients with ME/CFS from those of people with irritable bowel syndrome. ...
Homeostasis is a fundamental physiological principle that ensures equilibrium, stability and safety of the organism in a continuously changing and potentially life-threatening environment. This article introduces a new theory of the aetiology of ME/CFS that hypothesises that ME/CFS is caused by a breakdown of homeostasis that produces an aberrant state of disequilibrium with endocrinological and immunological changes. The current hypothesis is that ME/CFS involves a changed set point such that homeostasis persistently pushes the organism towards a pathological dysfunctional state because the disequilibrium fails to reset. To use an analogy of a thermostat, if the ‘off switch’ of a thermostat stops working, the house would become warmer and warmer without limit. Here I summarise the evidence in support of the theory. Clinical trials to investigate the role of suggested biological mediators such as thyrotropin-releasing hormone in ME/CFS would appear to be helpful.
... 2. Leaky gut: A leaky gut caused by epithelial barrier dysfunction is thought to be induced by inflammation and in ME/CFS patients may lead to irritable bowel (IB) symptoms, or IB conversely may predispose the development ME/CFS. A leaky gut with increased permeability may support the hypothesis that there is bacterial translocation in systemic circulation [97][98][99][100]. ...
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Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex condition arising in susceptible people, predominantly following viral infection, but also other stressful events. The susceptibility factors discussed here are both genetic and environmental although not well understood. While the dysfunctional physiology in ME/CFS is becoming clearer, understanding has been hampered by different combinations of symptoms in each affected person. A common core set of mainly neurological symptoms forms the modern clinical case definition, in the absence of an accessible molecular diagnostic test. This landscape has prompted interest in whether ME/CFS patients can be classified into a particular phenotype/subtype that might assist better management of their illness and suggest preferred therapeutic options. Currently, the same promising drugs, nutraceuticals, or behavioral therapies available can be beneficial, have no effect, or be detrimental to each individual patient. We have shown that individuals with the same disease profile exhibit unique molecular changes and physiological responses to stress, exercise and even vaccination. Key features of ME/CFS discussed here are the possible mechanisms determining the shift of an immune/inflammatory response from transient to chronic in ME/CFS, and how the brain and CNS manifests the neurological symptoms, likely with activation of its specific immune system and resulting neuroinflammation. The many cases of the post viral ME/CFS-like condition, Long COVID, following SARS-CoV-2 infection, and the intense research interest and investment in understanding this condition, provide exciting opportunities for the development of new therapeutics that will benefit ME/CFS patients.
... The second supportive approach is the theory of Tate and colleagues, who have discussed the role of chronic neuroinflammation and immunological dysfunction in ME/CFS [55,56] and more recently in PASC and ME/CFS [5]. Biomarkers of inflammation and leaky gut syndrome as a possible result of microbiome disturbance and bacterial translocation have been reported [55,[57][58][59]. According to this theory, an aberrant state of homeostasis is the central causal mechanism of ME/CFS [25,26,55,56,60]. ...
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The purpose of this article is to review the evidence of similar symptomatology of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Post-Acute Sequelae of SARS-CoV-2 infection (PASC). Reanalysis of data from a study by Jason comparing symptom reports by two groups of ME/CFS and PASC shows a notably similar symptomatology. Symptom scores of the PASC group and the ME/CFS group correlated .902 (p <.0001) across items. The hypothesis is presented that ME/CFS and PASC are caused by a chronic state of multisystemic disequilibrium including endocrinological, immunological and/or metabolic changes. The hypothesis holds that a changed set-point persistently pushes the organism towards a pathological dysfunctional state which fails to reset. To use an analogy of a thermostat, if the “off switch” of a thermostat intermittently stops working, for periods the house would become warmer and warmer without limit. The hypothesis draws on recent investigations of the Central Homeostasis Network showing multiple interconnections between the autonomic system, central nervous system and brain stem. The hypothesis helps to explain the shared symptomatology of ME/CFS and PASC and the unpredictable, intermittent and fluctuating pattern of symptoms in ME/CFS and PASC. The current theoretical approach remains speculative and requires in-depth investigation before any definite conclusions can be drawn.
... Giloteaux et al. [59] reported that the fecal bacterial culture of CFS patients was found to reduce the types of intestinal bacteria in CFS patients. Disruption of gut microbiota diversity was also associated with the severity of fatigue symptoms in CFS patients [60]. At the same time, CFS patients have increased sensitivity to intestinal microecological changes [61]. ...
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Fatigue seriously affects people’s work efficiency and quality of life and has become a common health problem in modern societies around the world. The pathophysiology of fatigue is complex and not fully clear. To some degree, interactions between gut microbiota and host may be the cause of fatigue progression. Polyphenols such as tannin, tea polyphenols, curcumin, and soybean isoflavones relieve fatigue significantly. Studies have shown that the gut microbiota is able to convert these active compounds into more active metabolites through intestinal fermentation. However, the mechanism of anti-fatigue polyphenols is currently mainly analyzed from the perspective of antioxidant and anti-inflammatory effects, and changes in gut microbiota are rarely considered. This review focuses on gut microecology and systematically summarizes the latest theoretical and research findings on the interaction of gut microbiota, fatigue, and polyphenols. First, we outline the relationship between gut microbiota and fatigue, including changes in the gut microbiota during fatigue and how they interact with the host. Next, we describe the interactions between the gut microbiota and polyphenols in fatigue treatment (regulation of the gut microbiota by polyphenols and metabolism of polyphenols by the gut microbiota), and how the importance of potential active metabolites (such as urolithin) produced by the decomposition of polyphenols by gut microbiota is emerging. Based on the new perspective of gut microbiota, this review provides interesting insights into the mechanism of polyphenols in fatigue treatment and clarifies the potential of polyphenols as targets for anti-fatigue product development, aiming to provide a useful basis for further research and design.
... SCFA are produced by gut microbiota, such as Bacteroidetes and Lachnospiraceae (14) . It has been suggested that Bacteroidetes are increased from exercise and gut bacteria, and exercise adaptations may play a role (15)(16)(17) . Among SCFA, Butyrate has been shown to be a key modulator of energy metabolism and mitochondrial function by activating PGC-1α gene expression in skeletal muscles and brown adipose tissue (18) . ...
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Previous studies have examined the effects of omega-3 fatty acid intake in supplement form or fish oil capsules but there are few studies based on other foods. Perilla oil is a traditional Japanese seed oil rich in omega-3 fatty acids. This randomised trial aimed to determine the appropriate omega-3 fatty acid dose through consumption of perilla oil, which improves gut function and microbiota in trained athletes, and the amount of fat fuel required to provide energy to athletes involved in high-intensity training to improve athletic performance. Thirty-six female athletes training six times per week were randomly assigned to three groups according to perilla oil intake: 9 g/day (HOI), 3 g/day (LOI), and placebo-supplementation (PLA) groups. The HOI and LOI groups had perilla oil-containing jelly and the PLA group had placebo jelly for 8 weeks. Gut microbiota, constipation score, and urinary biochemical index were measured pre- and post-intervention. The spoilage bacteria, Proteobacteria , significantly decreased ( P = 0.036, d = 0.53), whereas Butyrate -producing bacteria, Lachnospiraceae , significantly increased ( P = 0.007, d = 1.2) in the HOI group. Urinary indoxyl sulphate significantly decreased in the HOI group only ( P = 0.010, d = 0.82). Changes in the constipation score were significantly lower in the HOI group ( P = 0.020) and even lower in the LOI group ( P = 0.073) than in the PLA group; there were significant differences between groups ( P = 0.035). Therefore, perilla oil intake may improve gut function and microbiota in athletes, with higher doses resulting in further improvement.
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, debilitating disorder manifesting as severe fatigue and post-exertional malaise. The etiology of ME/CFS remains elusive. Here, we present a deep metagenomic analysis of stool combined with plasma metabolomics and clinical phenotyping of two ME/CFS cohorts with short-term (<4 years, n = 75) or long-term disease (>10 years, n = 79) compared with healthy controls (n = 79). First, we describe microbial and metabolomic dysbiosis in ME/CFS patients. Short-term patients showed significant microbial dysbiosis, while long-term patients had largely resolved microbial dysbiosis but had metabolic and clinical aberrations. Second, we identified phenotypic, microbial, and metabolic biomarkers specific to patient cohorts. These revealed potential functional mechanisms underlying disease onset and duration, including reduced microbial butyrate biosynthesis and a reduction in plasma butyrate, bile acids, and benzoate. In addition to the insights derived, our data represent an important resource to facilitate mechanistic hypotheses of host-microbiome interactions in ME/CFS.
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The psychosomatic approach to medically unexplained symptoms, myalgic encephalomyelitis and chronic fatigue syndrome (MUS/ME/CFS) is critically reviewed using scientific criteria. Based on the 'Biopsychosocial Model', the psychosomatic theory proposes that patients' dysfunctional beliefs, deconditioning and attentional biases cause or make illness worse, disrupt therapies, and lead to preventable deaths. The evidence reviewed suggests that none of these psychosomatic hypotheses is empirically supported. The lack of robust supportive evidence together with the use of fal-lacious causal assumptions, inappropriate and harmful therapies, broken scientific principles, repeated methodological flaws and an unwillingness to share data all give the appearance of cargo cult science. The psychosomatic approach needs to be replaced by a scientific, biologically grounded approach to MUS/ME/CFS that can be expected to provide patients with appropriate care and treatments. Patients with MUS/ME/CFS and their families have not been treated with the dignity, respect and care that is their human right. Patients with MUS/ME/CFS and their families could consider a class action legal case against the injuring parties.
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease with an unclear etiology and pathogenesis. Both an involvement of the immune system and gut microbiota dysbiosis have been implicated in its pathophysiology. However, potential interactions between adaptive immune responses and the microbiota in ME/CFS have been incompletely characterized. Here, we profiled antibody responses of patients with severe ME/CFS and healthy controls against microbiota and viral antigens represented as a phage-displayed 244,000 variant library. Patients with severe ME/CFS exhibited distinct serum antibody epitope repertoires against flagellins of Lachnospiraceae bacteria. Training machine learning algorithms on this antibody-binding data demonstrated that immune responses against gut microbiota represent a unique layer of information beyond standard blood tests, providing improved molecular diagnostics for ME/CFS. Together, our results point toward an involvement of the microbiota-immune axis in ME/CFS and lay the foundation for comparative studies with inflammatory bowel diseases and illnesses characterized by long-term fatigue symptoms, including post–COVID-19 syndrome.
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Practicing medicine has always been an art dependent upon the judgment of a physician. This art relies on listening, caring, and empathizing for the patient, and on judgment applied to every patient under their own unique circumstances in the backdrop of optimum medical care. Thus, medical treatment and disease management has always been personalized. However, practicing modern medicine is increasingly more complex in the era of genomics and microbiomics, as physicians and their patients have much more information to consider about the disease, and more treatment options are becoming available.
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The mammalian intestine harbors a complex microbial ecosystem that influences many aspects of host physiology. Exposure to specific microbes early in development affects host metabolism, immune function, and behavior across the lifespan. Just as the physiology of the developing organism undergoes a period of plasticity, the developing microbial ecosystem is characterized by instability and may also be more sensitive to change. Early life thus presents a window of opportunity for manipulations that produce adaptive changes in microbial composition. Recent insights have revealed that increasing physical activity can increase the abundance of beneficial microbial species. We therefore investigated whether six weeks of wheel running initiated in the juvenile period (postnatal day 24) would produce more robust and stable changes in microbial communities versus exercise initiated in adulthood (postnatal day 70) in male F344 rats. 16S rRNA gene sequencing was used to characterize the microbial composition of juvenile versus adult runners and their sedentary counterparts across multiple time points during exercise and following exercise cessation. Alpha diversity measures revealed that the microbial communities of young runners were less even and diverse, a community structure that reflects volatility and malleability. Juvenile onset exercise altered several phyla and, notably, increased Bacteroidetes and decreased Firmicutes, a configuration associated with leanness. At the genus level of taxonomy, exercise altered more genera in juveniles than in the adults and produced patterns associated with adaptive metabolic consequences. Given the potential of these changes to contribute to a lean phenotype, we examined body composition in juvenile versus adult runners. Interestingly, exercise produced persistent increases in lean body mass in juvenile but not adult runners. Taken together, these results indicate that the impact of exercise on gut microbiota composition as well as body composition may depend on the developmental stage during which exercise is initiated.
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Sedentary lifestyle is linked with poor health, most commonly obesity and associated disorders, the corollary being that exercise offers a preventive strategy. However, the scope of exercise biology extends well beyond energy expenditure and has emerged as a great 'polypill', which is safe, reliable and cost-effective not only in disease prevention but also treatment. Biological mechanisms by which exercise influences homeostasis are becoming clearer and involve multi-organ systemic adaptations. Most of the elements of a modern lifestyle influence the indigenous microbiota but few studies have explored the effect of increased physical activity. While dietary responses to exercise obscure the influence of exercise alone on gut microbiota, professional athletes operating at the extremes of performance provide informative data. We assessed the relationship between extreme levels of exercise, associated dietary habits and gut microbiota composition, and discuss potential mechanisms by which exercise may exert a direct or indirect influence on gut microbiota.
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Many immune-based intestinal disorders, such as ulcerative colitis and Crohn’s disease, as well as other illnesses, may have the intestines as an initial cause or aggravator in the development of diseases, even apparently not correlating directly to the intestine. Diabetes, obesity, multiple sclerosis, depression, and anxiety are examples of other illnesses discussed in the literature. In parallel, importance of the gut microbiota in intestinal homeostasis and immunologic conflict between tolerance towards commensal microorganisms and combat of pathogens is well known. Recent researches show that the immune system, when altered by the gut microbiota, influences the state in which these diseases are presented in the patient directly and indirectly. At the present moment, a considerable number of investigations about this subject have been performed and published. However, due to difficulties on correlating information, several speculations and hypotheses are generated. Thus, the present review aims at bringing together how these interactions work—gut microbiota, immune system, and their influence in the neuroimmune system.
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Cecal microbiota from type 2 diabetic (db/db) and control (db/(+)) mice was obtained following 6 weeks of sedentary or exercise activity. qPCR analysis revealed a main effect of exercise, with greater abundance of select Firmicutes species and lower Bacteroides/Prevotella spp. in both normal and diabetic exercised mice compared with sedentary counterparts. Conversely, Bifidobacterium spp. was greater in exercised normal but not diabetic mice (exercise × diabetes interaction). How exercise influences gut microbiota requires further investigation.
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Many patients with systemic immune-inflammatory and neuro-inflammatory disorders, including depression, rheumatoid arthritis, systemic lupus erythematosus, Sjögren's disease, cancer, cardiovascular disorder, Parkinson's disease, multiple sclerosis, stroke, and chronic fatigue syndrome/myalgic encephalomyelitis, endure pathological levels of fatigue. The aim of this narrative review is to delineate the wide array of pathways that may underpin the incapacitating fatigue occurring in systemic and neuro-inflammatory disorders. A wide array of immune, inflammatory, oxidative and nitrosative stress (O&NS), bioenergetic, and neurophysiological abnormalities are involved in the etiopathology of these disease states and may underpin the incapacitating fatigue that accompanies these disorders. This range of abnormalities comprises: increased levels of pro-inflammatory cytokines, e.g., interleukin-1 (IL-1), IL-6, tumor necrosis factor (TNF) α and interferon (IFN) α; O&NS-induced muscle fatigue; activation of the Toll-Like Receptor Cycle through pathogen-associated (PAMPs) and damage-associated (DAMPs) molecular patterns, including heat shock proteins; altered glutaminergic and dopaminergic neurotransmission; mitochondrial dysfunctions; and O&NS-induced defects in the sodium-potassium pump. Fatigue is also associated with altered activities in specific brain regions and muscle pathology, such as reductions in maximum voluntary muscle force, downregulation of the mitochondrial biogenesis master gene peroxisome proliferator-activated receptor gamma coactivator 1-alpha, a shift to glycolysis and buildup of toxic metabolites within myocytes. As such, both mental and physical fatigue, which frequently accompany immune-inflammatory and neuro-inflammatory disorders, are the consequence of interactions between multiple systemic and central pathways.
Chronic rhinosinusitis (CRS) is a prevalent multifactorial disease process in which bacteria are believed to play a role in the propagation of inflammation. Multiple subtypes of CRS have been described based on clinical and pathologic features, but a detailed examination of the sinus microbiota in patients with CRS and its clinical subtypes has yet to be performed. We sought to examine the resident microbiota of CRS subtypes and determine whether bacterial diversity is a predictor of disease outcomes. Sinus swabs from patients with CRS and healthy subjects collected during endoscopic sinus surgery were analyzed by means of molecular phylogenetic analysis of 16S rDNA pyrosequences. Fifty-six patients with CRS and 26 control subjects were studied. Biodiversity was similar between the CRS and control groups. Among the CRS subtypes examined, only 2 conditions (presence of purulence and comorbid condition of asthma) were associated with significant alterations in microbial community composition. In 27 patients with CRS who were followed postoperatively, those with better outcomes had more diverse bacterial communities present at the time of surgery, along with higher relative abundances of Actinobacteria. Analysis of microbiota in a large cohort reveals that particular CRS phenotypes (asthma and purulence) are characterized by distinct compositions of resident bacterial communities. We found that bacterial diversity and composition are predictors of surgical outcome, promoting the concept of community ecology in patients with CRS. Copyright © 2015 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.
Tremendous progress has been made in characterizing the bidirectional interactions between the central nervous system, the enteric nervous system, and the gastrointestinal tract. A series of provocative preclinical studies have suggested a prominent role for the gut microbiota in these gut-brain interactions. Based on studies using rodents raised in a germ-free environment, the gut microbiota appears to influence the development of emotional behavior, stress- and pain-modulation systems, and brain neurotransmitter systems. Additionally, microbiota perturbations by probiotics and antibiotics exert modulatory effects on some of these measures in adult animals. Current evidence suggests that multiple mechanisms, including endocrine and neurocrine pathways, may be involved in gut microbiota-to-brain signaling and that the brain can in turn alter microbial composition and behavior via the autonomic nervous system. Limited information is available on how these findings may translate to healthy humans or to disease states involving the brain or the gut/brain axis. Future research needs to focus on confirming that the rodent findings are translatable to human physiology and to diseases such as irritable bowel syndrome, autism, anxiety, depression, and Parkinson's disease.