Abstract

   Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is an
   unexplained chronic, debilitating illness characterized by fatigue,
   sleep disturbances, cognitive dysfunction, orthostatic intolerance and
   gastrointestinal problems. Using ultra performance liquid
   chromatography-tandem mass spectrometry (UPLC-MS/MS), we analyzed the
   plasma proteomes of 39 ME/CFS patients and 41 healthy controls.
   Logistic regression models, with both linear and quadratic terms of the
   protein levels as independent variables, revealed a significant
   association between ME/CFS and the immunoglobulin heavy variable (IGHV)
   region 3-23/30. Stratifying the ME/CFS group based on self-reported
   irritable bowel syndrome (sr-IBS) status revealed a significant
   quadratic effect of immunoglobulin lambda constant region 7 on its
   association with ME/CFS with sr-IBS whilst IGHV3-23/30 and
   immunoglobulin kappa variable region 3–11 were significantly associated
   with ME/CFS without sr-IBS. In addition, we were able to predict ME/CFS
   status with a high degree of accuracy (AUC = 0.774–0.838) using a panel
   of proteins selected by 3 different machine learning algorithms: Lasso,
   Random Forests, and XGBoost. These algorithms also identified proteomic
   profiles that predicted the status of ME/CFS patients with sr-IBS (AUC
   = 0.806–0.846) and ME/CFS without sr-IBS (AUC = 0.754–0.780). Our
   findings are consistent with a significant association of ME/CFS with
   immune dysregulation and highlight the potential use of the plasma
   proteome as a source of biomarkers for disease.

Introduction

   Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a
   debilitating disease of unknown cause that affects up to 2.5 million
   people in the USA alone [[52]1]. The disease is defined by persistent
   fatigue lasting longer than six months, post-exertional malaise,
   unrefreshing sleep, and either cognitive dysfunction or orthostatic
   intolerance [[53]1]. These symptoms are often accompanied by others
   that may include chronic pain, influenza-like symptoms, and
   gastro-intestinal disturbances [[54]2]. Whilst there is no known cause
   of ME/CFS, multiple studies have reported immune, metabolic, and
   neurological disturbances. There are no approved diagnostic tests
   [[55]3, [56]4].

   Mass spectrometry analysis of plasma has identified disturbances in
   energy, amino acid, and lipid metabolism [[57]5–[58]10]. Proteomic
   reports are limited to two studies of cerebrospinal fluid [[59]11,
   [60]12], one study of saliva [[61]13], and one study of platelet
   mitochondria [[62]14]. Baraniuk et al. (2005) [[63]11] found that
   innate immune and amyloidogenic proteins were detected more frequently
   in the cerebrospinal fluid of patients compared to controls, and that
   the presence of one or more of a subset of proteins (α-1-macroglobulin,
   amyloid precursor-like protein 1, keratin 16, orosomucoid 2, and
   pigment epithelium-derived factor) predicted ME/CFS status with 80%
   accuracy. Another study of cerebrospinal fluid by Schutzer et al (2011)
   [[64]12] showed differing proteomic profiles between ME/CFS and
   post-treatment Lyme disease patients that included enrichment in the
   ME/CFS group of proteins involved in the complement cascade as well as
   pathways related to CDK5 signaling and dopamine signaling. Ciergia et
   al (2013) [[65]13] studied the saliva of monozygotic twins discordant
   for ME/CFS and found 13 differentially expressed proteins related to
   inflammation and metabolism. Another study of the platelet
   mitochondrial proteome in the same pair of twins identified
   upregulation of aconitate hydratase, ATP synthase subunit beta, and
   malate dehydrogenase that was replicated in saliva of a larger cohort
   of 45 subjects with ME/CFS [[66]14].

   Here we report a ME/CFS-related plasma proteome analysis using
   untargeted ultra-performance liquid chromatography-tandem mass
   spectrometry (UPLC-MS/MS). We identified differing profiles between
   ME/CFS patient, as well as ME/CFS subgroups, based on their
   self-reported irritable bowel syndrome (sr-IBS) status, and controls.
   In addition, we identify a set of proteins that may predict ME/CFS
   status.

Materials and methods

Study population

   50 ME/CFS cases and 50 controls were collected in ME/CFS clinics in New
   York, NY; Salt Lake City, UT; Sierra, NV; and Miami, FL [[67]15]. All
   ME/CFS cases met the 1994 CDC Fukuda [[68]16] and/or Canadian consensus
   criteria for ME/CFS [[69]17]. Controls were frequency-matched to cases
   on age, sex, race/ethnicity, geographic/clinical site, and season of
   sampling [[70]18]. All ME/CFS cases completed standardized screening
   and assessment instruments including medical history and symptom rating
   scales as well as a physical examination. ME/CFS cases were excluded if
   they met any exclusion criteria from the 1994 CDC Fukuda and/or
   Canadian consensus criteria for ME/CFS such as having chronic
   infections, rheumatic and chronic inflammatory diseases, neurological
   disorders, psychiatric conditions, or were taking any immunomodulatory
   medication. Normal controls underwent the same screening process as
   ME/CFS subjects and were excluded if they reported ME/CFS or other
   conditions deemed by the recruiting physician to be inconsistent with a
   healthy control population. Potential normal controls were also
   excluded if they had a history of substance abuse, psychiatric illness,
   antibiotics in the prior three months, immunomodulatory medications in
   the prior year, and clinically significant findings on physical exam or
   screening laboratory tests [[71]18]. Self-reported IBS was not part of
   the exclusion criteria of controls. Full selection criteria for both
   ME/CFS cases and healthy controls can be found in [72]S1 Appendix.

   Every participant provided informed written consent in accordance with
   protocols of the study. The study was approved by the Institutional
   Review Board at Columbia University Irving Medical Center.

Plasma collection

   Blood samples were collected into BD VacutainerTM Cell Preparation
   Tubes (CPT) with sodium citrate anticoagulant between June and October
   2014, and centrifuged to pellet red blood cells. The plasma samples
   were shipped to Columbia University at 4°C. After aliquoting, samples
   were stored at -80°C until they were thawed for proteomic analyses. All
   samples were analyzed within four years of collection.

Clinical assessments

   Clinical symptoms and baseline health status were assessed on the day
   of physical examination and biological sample collection from both
   cases and control subjects using the following surveys: the Short Form
   36 Health Survey (SF-36), the Multidimensional Fatigue Inventory (MFI),
   DePaul Symptom Questionnaire (DSQ) [[73]19] and Pittsburgh Sleep
   Quality Index (PSQI) [[74]20]. The SF-36 includes the following
   subject-reported evaluations about current health status: physical and
   social functioning, physical and emotional limitations, vitality, pain,
   general health perceptions, and mental health change [[75]21]. The MFI
   comprises a 20-item self-reported questionnaire focused on general,
   physical and mental fatigue, activity, and motivation [[76]22].
   Cognitive function was tested based on the DSQ questionnaire data and
   was scored using a standard cognitive disturbance definition as well as
   a modified definition based on a subset of questionnaire variables.
   Sleeping disturbances linked to ME/CFS were tested and scored based on
   DSQ and PSQI questionnaire items. Each instrument was transformed into
   a 0–100 scale to facilitate combination and comparison, wherein a score
   of 100 is equivalent to maximum disability or severity and a score of
   zero is equivalent to no disability or disturbance.

   Self-reported IBS was determined based on answers from the medical
   history form. Subjects were asked if they had received a previous IBS
   diagnosis or had a history of IBS. Those that answered “YES” were
   considered positive. An IBS diagnosis was not made at the time of
   recruitment. 24 of the 50 ME/CFS patients (48%) and one of the 50
   control subjects (2%) reported sr-IBS.

Proteomics analysis

   Denaturation/reduction of the sample was performed in 8 M urea/10 mM
   dithiothreitol/50 mM NH4HCO3 (pH 8.0) for over 60 minutes at 52°C. The
   solution was stored at room temperature in 20 mM iodoacetamide at the
   dark for 60 minutes. The urea was diluted to a concentration of 1 M
   with 50 mM NH[4]HCO[3] and then digested with trypsin (1:50 ratio) at
   37°C with shaking for 16 hrs. After tryptic digestion, the peptide
   mixture was desalted with C18 micro spin column (C18, Harvard
   Apparatus, Holliston, MA). The column was washed with 200 μL of 100%
   acetonitrile and equilibrated with 200 μL of loading buffer (0.1%
   formic acid). Peptides were loaded onto the column, washed with a
   loading buffer, and eluted with 200 μL of 70% acetonitrile/ 0.1% formic
   acid. All steps for loading, washing, and elution were carried out with
   benchtop centrifugation (300 x g for 2 minutes). The fraction was
   collected with a 1.5 mL tube and dried on a speed-vac.

   2 μg peptides were taken from each sample and mixed with appropriate
   iRT standard peptides HRM Calibration kit, Biognosys, Schlieren,
   Switzerland). Desalted peptides were analyzed on a Q-Exactive HF
   (Thermo Scientific) attached to an Ultimate 300 nano UPLC system
   (Thermo Scientific). The column (30 cm × 75 μm) was packed in-house
   with Reprosil 3 μm, 100 Å pore size C18 beads (Maisch GmbH HPLC).
   Peptides were eluted with a 50 min gradient from 2% to 32% ACN (50min)
   and to 90% ACN over 5 min in 0.1% formic acid. The method consisted of
   a full MS1 scan at a resolution of 120 K from m/z 400 to m/z 1000, with
   AGC set to 1E6 (maximum injection time of 60 ms), followed by 15 m/z
   windows acquired at a resolution of 30K with AGC set to 1e6 (maximum
   injection time of  ms); HCD: MS2 activation (collision energy: 27).

   All the DIA data were also analyzed by Spectronaut Pulsar X (Biognosys
   AG, Switzerland, version: 13.10.191212.43655) against the human plasma
   spectra library. Calibration was set to nonlinear iRT calibration with
   precision iRT enabled. Identification was performed using a 0.01
   q-value cutoff at both the precursor and protein level. The 0.01
   q-value corresponds to a false discovery rate of 1% or an estimated 1%
   of false identifications amongst the accepted identifications. For
   quantification, the interference correction function was enabled, and
   the top 3 peptide precursors were summed for protein quantification. We
   measured relative protein abundance comprehensively, and the effect of
   the variability potentially generated by the sample preparation and the
   liquid chromatography–mass spectrometry (LC-MS) performance was
   minimized using the Local Regression Normalization algorithm described
   by Callister et al. (2006) [[77]23]. Filtered values may be due to
   protein levels below the detection threshold of the instrument, or to
   exclusion from the analysis because of a peptide or protein
   identification confidence q-value higher than 0.01.

   Samples from 50 ME/CFS cases and 50 controls were run in two batches of
   20 samples (9 ME/CFS cases, 11 controls) and 80 samples (39 ME/CFS
   cases, 41 controls). The 20 samples in the first batch were randomly
   selected. The cases and controls were frequency-matched on the same
   matching variables as the total study population. 3444 unique peptides
   matched 257 annotated proteins in the 20 subject sample set. 5308
   unique peptides that matched to 279 annotated proteins in the 80
   subject sample set. 207 annotated proteins were found in both sample
   sets. Differences in the number of proteins identified between the two
   groups may reflect patient heterogeneity or batch effects, particularly
   amongst low abundance proteins.

Statistical analyses

   For each protein analyte, non-detectable values were replaced with 50%
   of its smallest available value. Protein levels were then
   log-transformed with base 10 and rescaled by the control standard
   deviation.

   To test the ME/CFS association and to assess the predictive capacity of
   each individual protein analyte, we used logistic regression models
   adjusted for body mass index (BMI), sr-IBS, antidepressant medication
   use, and all matching variables (age, sex, race/ethnicity,
   geographic/clinical site, and season of sampling). Prentice and Pyke
   (1979) [[78]24] showed that the odds ratio estimators and their
   asymptotic variance matrices can be obtained by applying the logistic
   regression model to the case-control study as if the data had been
   obtained in a prospective study. Two separate models were fitted: one
   with only the linear term (the transformed values) of the protein
   levels, and one with both linear and quadratic terms (square of the
   transformed values) of the protein levels as independent variables.
   Likelihood-ratio tests were used to compare the goodness-of-fit between
   the two nested models. These were gate-keeping hypothesis tests that
   would serve to allow further exploration of specific protein compounds.
   We used the Hochberg step-up procedure [[79]25] to correct for the
   multiple tests over the annotated proteins controlling the family-wise
   error rate (FWER) at the level of 0.05. If, for any protein analyte,
   the model with quadratic term fit the data significantly better than
   the model with only the linear term through the step-up procedure,
   adjusted odds ratios (aORs), together with their 95% confidence
   intervals (95% CI), were calculated comparing ME/CFS risk of various
   protein levels to that of the reference level at which the ME/CFS risk
   was at the lowest.

   In earlier work with this cohort, sr-IBS was identified as the
   strongest driving factor in the separation of topological networks
   based on fecal microbiome and plasma metabolic pathways through an
   unsupervised and data-driven algorithm (Ayasdi, Menlo Park, California)
   in Nagy-Szakal et al [[80]9, [81]26]. It was in this context that we
   tested, in a stratified analysis, the hypothesis that ME/CFS patients
   with sr-IBS have altered proteomic profiles.

   To examine the utility of the proteomics assay as a biomarker tool for
   ME/CFS, we applied three machine learning algorithms: Lasso (least
   absolute shrinkage and selection operator) [[82]27], Random Forests
   [[83]28] and XGboost [[84]29]. We fitted the linear terms of the levels
   of all protein analytes, excluding the ones with undetectable/filtered
   values in more than 50% of the subjects (29 proteins), as predictors in
   the three classifiers. We excluded these protein analytes in this
   analysis since they do not have enough power to serve as potential
   biomarkers. We then measured the importance for each predictor in the
   classifiers. Lasso regularizes the least squares by adding a penalty
   term in which the L1 norm of the parameter vector is no greater than a
   given value, and increasing the penalty drives more coefficients of
   unimportant predictors to absolute zero. Therefore, measure of
   importance can be represented as the number of iterations in which the
   predictor’s parameter estimate in the best fitting model is nonzero.
   Random Forests measures the mean decrease in accuracy when values of
   the predictor are randomly permuted. For unimportant predictors, the
   permutation should have little to no effect on model accuracy, while
   permuting values of important predictors should significantly decrease
   it. For XGBoost, a measure of ‘gain’ can be calculated to indicate the
   relative contribution of the corresponding predictor to the model
   calculated by taking each predictor’s contribution for each tree in the
   model. We then selected the protein analytes that were ranked in the
   top 20 in all three importance measurements and fitted them in the
   classifiers again, except that here we used the logistic regression
   instead of Lasso. Since only the linear terms of the protein levels
   were fitted as predictors, these classifiers did not identify
   biomarkers associated with ME/CFS with quadratic effects. The
   predictive performance was evaluated in random resampling
   cross-validation (CV) with 1000 iterations. In each iteration, the
   sample set was randomly divided into an 80% training set and a 20% test
   set. We generated Receiver Operating Characteristic (ROC) curves by
   evaluating model sensitivity and specificity at different probability
   thresholds, and then calculated the Area under the Receiver Operating
   Characteristic curve (AUROC), a performance measure that ranges from
   0.5 (fair coin toss) to 1 (perfect prediction).

   Data analyses were conducted using Matlab (R2013a, The Mathworks Inc.,
   MA) and R (version 3.5.1). All p-values were 2-tailed.

Results

Study population

   The study included plasma samples from 50 ME/CFS cases and 50 healthy
   controls recruited at four sites across the United States. The subset
   of 39 ME/CFS cases and 41 controls were used in the data analysis to
   eliminate potential batch effects. The robustness of findings was
   verified through sensitivity analysis in the total study population
   with the adjustment of the batches. Characteristics of these two nested
   sample sets are demonstrated in [85]Table 1.

Table 1. Characteristics of the study cohort.

   Demographics ME/CFS (n = 50) Control (n = 50) ME/CFS (n = 39) Control
   (n = 41)
   Sex Female 41 41 30 32
   Male 9 9 9 9
   Age Mean (SEM) 51.08 (11.19) 51.32 (11.46) 52.06 (10.87) 51.43 (11.89)
   Race White 49 48 39 39
   Asian 1 1 1 1
   Other 0 1 0 1
   Ethnicity Not Hispanic or Latino 46 45 37 37
   Hispanic or Latino 4 5 2 4
   Site Miami, FL 10 9 6 7
   New York, NY 14 14 12 12
   Salt Lake City, UT 14 15 11 12
   Sierra, NV 12 12 10 10
   Season Summer 27 26 22 22
   Fall 23 24 17 19
   sr-IBS Yes 24 1 18 1
   No 26 49 21 40
   BMI Overweight (>25) 28 22 24 18
   Normal (< 25) 22 28 15 23
   Disease Duration < 3 years 4 N/A 2 N/A
   >3 years 46 N/A 37 N/A
   SF-36 Mean (SD) Physical Functioning 40.5 (26.71) 96.1 (6.95) 40.13
   (27.08) 95.85 (7.41)
   Physical Limitations 8 (22.27) 97 (14.85) 9.62 (24.75) 98.78 (5.45)
   Emotional Limitations 53.33 (47.62) 96 (15.99) 54.7 (48.66) 98.37
   (7.27)
   Energy/Fatigue 15.6 (17.46) 74.77 (15.58) 16.03 (18.25) 73.25 (14.43)
   Emotional Well-being 63.44 (19.94) 81.6 (15.25) 65.44 (20.05) 81.27
   (11.95)
   Social Functioning 32.75 (25.24) 93.25 (13.88) 32.37 (26.39) 93.6
   (13.15)
   Pain 46 (27.17) 91.8 (10.06) 44.04 (28.75) 90.79 (10.3)
   General Health 26.38 (15.33) 83.35 (13.88) 26.63 (16.96) 82.26 (14.07)
   [86]Open in a new tab

   The demographics and characteristics of the whole study cohort (n =
   100) as well as the subset used for the analysis (n = 80) are shown.
   ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome, sr-IBS:
   self- reported irritable bowel syndrome, BMI: body mass index, SEM:
   standard error of mean, SF-36: Short form 36 health survey; score on
   0–100 scale with 0 = poor and 100 = excellent.

ME/CFS and ME/CFS subgroups are associated with an altered proteomic profile

   [87]S1 Table shows the sample mean and the standard error of the mean
   (SEM) of levels of each protein within ME/CFS cases, ME/CFS cases with
   sr-IBS, ME/CFS cases without sr-IBS and controls. [88]S2 Table shows
   associations between levels of individual protein analytes and ME/CFS
   outcomes and includes aORs, 95% CIs and corresponding p-values from the
   logistic regression model in which only the linear term of the protein
   levels was fitted as an independent variable. The p-values of the
   likelihood ratio tests that compare the goodness-of-fit of the model
   with both linear and quadratic terms of the protein levels to that of
   the model with only the linear term are also presented in [89]S2 Table.

   Models with only the linear term (transformed values of the protein
   levels) failed to identify any protein significantly associated with
   ME/CFS after the FWER correction with the step-up procedure. For
   certain protein analytes, the residual plots of the linear logistic
   regression models followed a U-shape pattern ([90]S1 Fig). Therefore,
   we applied the logistic regression models that included both the linear
   and the quadratic terms of each protein analyte as independent
   variables. For immunoglobulin heavy variable (IGHV) 3-23/30, the model
   with both linear and quadratic terms (square of the transformed values
   of the protein levels) of the protein levels fit the data significantly
   better than the model with only its linear term (χ^2 = 17.57, df = 1,
   p-value = 2.77E-5). We use the term IGHV3-23/30 because our assay did
   not distinguish between IGHV3-23 and IGHV3-30. The multiple comparisons
   of the likelihood-ratio tests over all annotated protein analytes were
   adjusted using the FWER correction. For IGHV3-23/30, we rejected the
   null hypothesis of a linear logistic relation between ME/CFS and the
   analyte in favor of a quadratic relation. Conducting this preliminary
   gate-keeping test of the quadratic term ensured that the overall type I
   (false positive) error was controlled at the level of 0.05;
   accordingly, further examination of this protein analyte using the
   model with the quadratic term of its levels was justified under the
   closed-testing principle. The quadratic term can be viewed as an
   interaction term, hence the aOR depends on the protein level used as a
   reference. [91]Fig 1A displays, for all ME/CFS subjects versus
   controls, the plot of aORs with 95% CI for the reference level at
   51,000 based on the fitted logistic model with quadratic term.
   Specifically ([92]Table 2), increasing the protein levels from 51,000
   to 100,000 was associated with an increased risk of ME/CFS with an aOR
   of 4.44 (95% CI: 1.29–15.29, p-value = 0.018), while decreasing the
   protein level from 51,000 to 25,000 yielded an aOR of 5.65 (95% CI:
   1.18–27.04, p-value = 0.030). In a sensitivity analysis that also
   included the 20-sample test set, the model with quadratic term
   continued to out-perform the model with only the linear term (χ^2 =
   14.58, df = 1, p-value = 1.34E-4).

Fig 1. Quadratic effect of immunoglobulin proteins with ME/CFS and ME/CFS
subgroups.

   [93]Fig 1
   [94]Open in a new tab

   Two separate models were fitted: one with only the linear term of the
   protein levels, and one with both linear and quadratic terms of the
   protein levels as independent variables. In both models we adjusted for
   BMI, sr-IBS, antidepressant medication use, age, sex, race/ethnicity,
   geographic/clinical site and season of sampling. Likelihood-ratio tests
   were used to compare the goodness-of-fit between the two nested models.
   The Hochberg step-up procedure was applied to correct for the multiple
   tests over the annotated proteins, controlling the family-wise error
   rate (FWER) at the level of 0.05. For the protein analytes associated
   with ME/CFS with significant quadratic effect, adjusted odds ratios
   (aORs), together with their 95% confidence intervals (95% CI), were
   calculated comparing ME/CFS risk of various protein levels to that of
   the reference level at which the ME/CFS risk was at the lowest. (A) All
   ME/CFS cases versus controls, (B) ME/CFS cases with sr-IBS versus
   controls, (C & D) ME/CFS cases without sr-IBS versus controls. ME/CFS:
   myalgic encephalomyelitis/chronic fatigue syndrome, a.u.: arbitrary
   units, sr-IBS: self-reported irritable bowel syndrome, IGHV:
   immunoglobulin heavy variable, IGLC: immunoglobulin lambda constant,
   IGKV: immunoglobulin kappa variable.

Table 2. Quadratic relationship of immunoglobulin proteins with ME/CFS and
ME/CFS subgroups.

   Group Protein Reference Level Comparison aOR 95% CI Protein p-value
   All ME/CFS IGHV3-23/30 51,000 decreased to 25,000 5.646 1.179 27.035
   0.0303
   increased to 100,000 4.439 1.289 15.286 0.0182
   ME/CFS with sr-IBS IGLC7 3,326 decreased to 1,500 3.851 1.115 13.303
   0.033
   increased to 7,000 3.257 1.216 8.722 0.019
   ME/CFS without sr-IBS IGKV3(D)-11 544,370 decreased to 171,000 59.492
   1.062 3332.100 0.047
   increased to 1,100,000 4.527 1.138 18.001 0.032
   IGHV3-23/30 51,000 decreased to 25,000 6.582 1.244 34.816 0.027
   increased to 100,000 4.545 1.284 16.086 0.019
   [95]Open in a new tab

   Reference levels based on relative intensity are shown for each protein
   as well as the aOR, 95% CI and p-value when increasing and decreasing
   from this point for all ME/CFS patients, ME/CFS patients with sr-IBS
   and ME/CFS patients without sr-IBS, when compared to the control group.
   ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome, sr-IBS:
   self-reported irritable bowel syndrome, BMI: body mass index, IGHV:
   immunoglobulin heavy variable; IGLC: immunoglobulin lambda constant;
   IGKV: immunoglobulin kappa variable, aOR: adjusted odds ratio, CI:
   confidence interval.

   A quadratic effect was also found in female subjects, comprising 30
   ME/CFS subjects and 32 controls in our 80-subject sample set ([96]S3
   Table). IGHV 3-23/30 was associated with ME/CFS with a significant
   quadratic effect (χ^2 = 16.27, df = 1, p-value = 5.50E-5) after
   controlling the FWER at the level of 0.05. We did not have sufficient
   statistical power in male subjects (9 ME/CFS patients and 9 controls)
   for a similar analysis.

   Previous work on the same cohort of patients showed that sr-IBS was one
   of the strongest drivers of separation among ME/CFS cases in terms of
   both the fecal microbiome and the plasma metabolome [[97]9, [98]26].
   For this reason, we split the ME/CFS group into two subgroups based on
   their sr-IBS status, and conducted the same analysis using the binary
   outcome of sr-IBS subgroups vs. controls ([99]S4 Table). Subjects were
   considered to have sr-IBS if they had reported a previous IBS diagnosis
   on the medical history form at the time of recruitment.

ME/CFS cases with sr-IBS versus controls

   When we compared ME/CFS cases with sr-IBS to controls, the
   immunoglobulin lambda constant (IGLC) 7 had a significant quadratic
   effect ([100]Fig 1B) on its association with the outcome (χ^2 = 19.44,
   df = 1, p-value = 1.04E-5). A protein level of 7,000 resulted in an aOR
   of 3.26 (95% CI: 1.22–8.72, p-value = 0.019) compared to a reference
   level of 3,326 and decreasing the protein levels from the reference
   level to 1,500 yielded an aOR of 3.85 (95% CI: 1.12–13.30, p-value =
   0.033).

ME/CFS cases without sr-IBS versus controls

   When we compared ME/CFS patients without sr-IBS to controls, we found
   disturbances with quadratic effects in immunoglobulin kappa variable
   (IGKV) 3(D)-11 (χ^2 = 14.87, df = 1, p-value = 1.15E-4) and
   immunoglobulin heavy variable (IGHV) 3-23/30 (χ^2 = 17.31, df = 1,
   p-value = 3.18E-5) ([101]Fig 1C and 1D). We use the term IGKV3(D)-11
   because our assay did not distinguish between IGKV3-11 and IGKV3(D)-11.
   Increasing the levels of IGKV3(D)-11 from the reference level of
   544,370 to 1,100,000 yielded an aOR of 4.53 (95% CI: 1.14–18.00,
   p-value = 0.032), while decreasing from the reference level to 171,000
   resulted in an aOR of 59.49 (95% CI: 1.06–3,332.10, p-value = 0.047).
   For IGHV3-23/30, altering its levels from 51,000 to 100,000 and from
   51,000 to 25,000 resulted in aORs of 4.54 (95% CI: 1.28–16.09, p-value
   = 0.019) and 6.58 (95% CI: 1.24–34.82, p-value = 0.027), respectively.

Assessment of the proteomics assay as a potential diagnostic tool for ME/CFS

   After excluding the proteins with more than 50% undetectable/filtered
   values, we fitted the remaining 250 protein analytes as predictors in
   three different classifiers: Lasso, Random Forests, and XGBoost.
   Protein analytes that were ranked in top 20 in importance measurements
   in all three classifiers ([102]Table 3) were fitted as predictors in
   the same classifiers again, except that here we used the logistic
   regression model instead of Lasso. [103]Fig 2A, [104]Fig 2B and
   [105]Fig 2C show the ROC curves and the AUROC values from these three
   classifiers with the trimmed set of protein analytes as predictors
   differentiating all ME/CFS, ME/CFS with sr-IBS, and ME/CFS without
   sr-IBS, respectively, from the controls. All of them performed
   significantly better than a fair coin toss ([106]S5 Table). Notably,
   XGBoost with six protein analytes ([107]Table 3) distinguished ME/CFS
   with sr-IBS from the controls with a high degree of accuracy with a
   cross-validated AUROC value of 0.846 (95% CI: 0.703–0.927).

Table 3. Potential plasma protein biomarkers for ME/CFS.

   Gene Name Uniprot ID Direction Lasso Random Forest XGBoost
   Percentage[108]^1 Rank[109]^4 Mean Decrease in accuracy[110]^2
   Rank[111]^4 Gain[112]^3 Rank[113]^4
   All ME/CFS
   CAMP [114]P49913 Increased 22.80% 1 0.1284 4 0.0652 2
   LRG1 [115]P02750 Decreased 9.90% 9 0.1302 3 0.0327 4
   IGF1 [116]P05019 Decreased 3.90% 19 0.1320 2 0.0318 6
   GSN [117]P06396 Decreased 3.70% 20 0.0743 9 0.0281 8
   IGFALS [118]P35858 Decreased 11.60% 7 0.0988 7 0.0292 7
   IGLV1-47 [119]P01700 Decreased 14.10% 2 0.0639 14 0.0319 5
   FCRL3 [120]Q96P31 Decreased 4.70% 17 0.0545 20 0.0127 17
   CRTAC1 [121]Q9NQ79 Decreased 13.30% 3 0.2653 1 0.1225 1
   ME/CFS with sr-IBS
   CAMP [122]P49913 Increased 30.10% 1 0.1772 2 0.0852 2
   SERPINA3 [123]P01011 Decreased 4.20% 16 0.0731 7 0.0249 6
   IGF1 [124]P05019 Decreased 11.00% 6 0.1768 3 0.1132 1
   ITIH2 [125]P19823 Decreased 13.60% 4 0.1870 1 0.0529 4
   IGHV1-18 A0A0C4DH31 Decreased 19.30% 3 0.0535 17 0.0157 14
   CRTAC1 [126]Q9NQ79 Decreased 4.80% 13 0.0922 4 0.0577 3
   ME/CFS without sr-IBS
   PON3 [127]Q15166 Increased 7.20% 3 0.0601 19 0.0571 2
   KNG1 [128]P01042 Increased 3.70% 13 0.0674 17 0.0122 20
   LRG1 [129]P02750 Decreased 5.50% 6 0.0960 8 0.0400 4
   IGLC7 [130]A0M8Q6 Decreased 8.40% 2 0.0664 18 0.0196 14
   CRTAC1 [131]Q9NQ79 Decreased 3.90% 12 0.1031 6 0.0740 1
   [132]Open in a new tab

   Proteins with more than 50% undetectable/filtere values were excluded.
   All 250 protein analytes were fitted as predictors in 3 different
   classifiers: Lasso, Random Forests, and XGBoost. Table shows the
   proteins that were ranked in the top 20 of importance measurements for
   all ME/CFS patients, ME/CFS patients with sr-IBS and ME/CFS patients
   without sr-IBS. Direction is measured relative to controls. ME/CFS:
   myalgic encephalomyelitis/chronic fatigue syndrome, sr-IBS:
   self-reported irritable bowel syndrome, CAMP: cathelicidin
   antimicrobial protein, LRG1: Leucin-rich glycoprotein 1, IGF1:
   insulin-like growth factor 1, IGFALS: Insulin-like growth
   factor-binding protein complex acid labile subunit, IGLV1-47:
   immunoglobulin lambda variable region 1–47, FCRL3: Fc receptor-like
   protein 3, SERPINA3: Alpha-1-antichymotrypsin, ITIH2:
   Inter-alpha-trypsin inhibitor heavy chain H2, IGHV1-18: immunoglobulin
   heavy variable region 1–18, PON3: Serum paraoxonase/lactonase 3, KNG1:
   Kininogen 1, IGLC7: immunoglobulin lambda constant region 7.

   ^1Percentage: Lasso regularizes the least squares by adding a penalty
   term in which the L1 norm of the parameter vector is no greater than a
   given value, and increasing the penalty drives more coefficients of
   unimportant predictors to absolute zero. Therefore, measure of
   importance can be represented as the percentage of iterations (out of
   1,000 random resampling cross-validation iterations) in which the
   predictor’s parameter estimate in the best fitting model is nonzero.

   ^2Mean Decrease in Accuracy: Random Forests measures the mean decrease
   in accuracy when values of the predictor are randomly permuted. For
   unimportant predictors, the permutation should have little to no effect
   on model accuracy, while permuting values of important predictors
   should significantly decrease it.

   ^3Gain: XGBoost measures the importance of predictors in ‘Gain’ to
   indicate the relative contribution of the corresponding predictor to
   the model calculated by taking each predictor’s contribution for each
   tree in the model.

   ^4Rank: We selected the protein analytes that were ranked in the top 20
   in all three importance measurements.

Fig 2. Diagnostic performance (AUROC) of ME/CFS and ME/CFS subgroup plasma
proteomes.

   [133]Fig 2
   [134]Open in a new tab

   Three machine learning algorithms were used to examine the utility of
   the proteomics assay as a biomarker tool for ME/CFS: Lasso (least
   absolute shrinkage and selection operator), Random Forests, and
   XGboost. We fitted all protein analytes, excluding the ones with more
   than 50% undetectable/filtered values, as predictors in the three
   classifiers and measured the importance for each predictor in the
   classifiers. The protein analytes that were ranked in the top 20 in all
   three importance measurements were fitted in the classifiers again
   (Trimmed set), except that here we used the logistic regression model
   instead of Lasso. The predictive performance was evaluated in random
   resampling cross-validation (CV) with 1,000 iterations from which we
   calculated the Area under the Receiver Operating Characteristic curve
   (AUROC) values and generated Receiver Operating Characteristic (ROC)
   curves for (A) all ME/CFS cases, (B) ME/CFS cases with sr-IBS and (C)
   ME/CFS cases without sr-IBS. ME/CFS: myalgic encephalomyelitis/chronic
   fatigue syndrome, sr-IBS: self-reported irritable bowel syndrome.

Discussion

   We pursued proteomic analyses of plasma from subjects with ME/CFS with
   the objectives of obtaining insight into the pathogenesis of ME/CFS and
   finding biomarkers for the disease. Using models with only linear
   terms, we did not identify significant differences between cases and
   controls following FWER correction. However, using a model that
   included both linear and quadratic terms we found a significant
   association between ME/CFS and IGHV3-23/30. The association with
   quadratic effect revealed both a positive and negative correlation
   relationship between levels of IGHV3-23/30 and ME/CFS. Whilst 12
   patients had extremely high levels (>100,000) of IGHV3-23/30, only 3
   patients had extremely low levels (<25,000) of this protein. IGHV3-23
   is one of the most commonly used heavy variable regions in the human
   immunoglobulin (Ig) repertoire [[135]30]. Its usage has been linked
   with non-Hodgkin lymphomas (NHL) such as chronic lymphoid leukemia
   (CLL) [[136]31–[137]34], mantle cell lymphoma (MCL) [[138]35], splenic
   marginal zone lymphoma (MZL) [[139]36, [140]37], Waldenström’s
   macroglobulinemia [[141]38], and follicular lymphoma (FL) [[142]39,
   [143]40]. Disease progression in these B-cell malignancies is driven by
   chronic stimulation from either microbial or auto-antigens.
   Interruption of B-cell signaling through use of kinase inhibitors has
   been shown to have therapeutic benefit in some patients with MZL, CLL,
   FL, and MCL [[144]41, [145]42]. Increased IGHV3-23 usage is also
   reported in anti-myelin associated glycoprotein neuropathy and in
   monoclonal gammopathy of undetermined significance [[146]43], a
   disorder that may progress to malignant lymphoproliferative disease
   [[147]44]. We speculate that, at least in a subset of ME/CFS subjects,
   an increased level of IGHV3-23 may be due to antigen driven clonal
   expansion, and that these patients might benefit from identification of
   the antigen driving B-cell receptor signaling or kinase inhibitors that
   interrupt signaling. In this context, we also note that ME/CFS has been
   linked with an increased risk of developing MZL and other NHL
   [[148]45].

   Due to the high prevalence of sr-IBS in ME/CFS patients and to the fact
   that previous studies showed it was one of the strongest drivers in the
   separation of fecal metagenomics and plasma metabolomics within the
   patient group [[149]9, [150]26], we further stratified the ME/CFS group
   based on sr-IBS status. Similar to all ME/CFS cases, IGHV3-23 was
   associated with ME/CFS without sr-IBS with a quadratic effect. In
   addition, IGKV3(D)-11 was also significantly associated with ME/CFS
   without sr-IBS. This protein showed the same pattern in ME/CFS subjects
   as a whole, but did not reach significance. IGKV3-11, like IGHV3-23, is
   one of the most commonly used light chain variable regions.
   Anti-hemagglutinin antibodies following influenza infection
   predominantly represent this light chain variable region [[151]46].
   IGKV3-11 is repeatedly paired with IGHV3-30 in protective antibodies to
   CMV [[152]47, [153]48] as well as the 23F polysaccharide of
   Streptococcus pneumoniae [[154]49–[155]51]. However, since mass
   spectrometry does not provide us with information in terms of
   heavy/light chain pairings, it is unclear whether this is relevant in
   our cohort. ME/CFS patients with sr-IBS, on the other hand, presented
   with a different proteomics profile. Only IGLC7 was significantly
   associated with disease in ME/CFS patients with sr-IBS. Unlike the
   variable region which is involved in antigen recognition, the constant
   region is thought to have more of a regulatory role, although it may
   have an impact on the variable region’s structure and function
   [[156]52]. The link between sr-IBS and this specific lambda constant
   region is currently unclear. However, our results show that, as was
   found with metabolites and the microbiome [[157]9, [158]26], sr-IBS
   also influences the proteomic profile. B cell dysregulation has
   previously been reported in ME/CFS [[159]53]. Bradley et al. (2013)
   [[160]54] found greater numbers of naïve and transitional B cells in
   patients whilst Brenu et al. (2014) [[161]55] found increased numbers
   of memory B cells. Guenther et al (2015) [[162]56] found a decrease in
   IgG3 and 4, but an increase in IgG2 and IgM. We do not see any overall
   changes to Ig isotypes. Although ME/CFS patients with sr-IBS had
   decreased IgG1 (aOR = 0.361, p-value = 0.037) and increased IGHA2 (aOR
   = 2.852, p-value = 0.046) levels, these findings were not significant
   after adjustment for multiple testing.

   We also employed a data-driven approach to identify plasma protein
   signatures to assess their utility as biomarkers of ME/CFS. Using the
   top 20 ranked proteins identified by Lasso, Random Forests, and
   XGBoost, we found a panel of eight plasma proteins that yielded high
   AUC values of up to 0.838. These proteins were cathelicidin
   antimicrobial peptide (CAMP), Ig lambda variable region 1–47
   (IGLV1-47), Fc receptor-like protein 3 (FCRL3), leucin-rich
   glycoprotein 1 (LRG1), gelsolin (GSN), cartilage acidic protein 1
   (CRTAC1), insulin-like growth factor 1 (IGF1), and IGF-binding protein
   acid labile subunit (IGFALS). IGHV3-23/30 was not identified as a
   top-ranked biomarker because it is associated with ME/CFS only with a
   quadratic effect. While Random Forests and XGBoost are both tree models
   and can potentially reveal non-linear relationships between predictors
   and the outcome, Lasso is parametric and cannot identify quadratic
   relationships if only the linear terms of protein analytes were used as
   predictors. Since we selected the panel of protein analytes that were
   highly ranked in all three classifiers, the proteins that were
   significantly associated with ME/CFS quadratically would be filtered
   out by this strategy. We are not aware of other serum or plasma
   proteomic studies that led to proposed biomarker sets. However,
   Baraniuk et al (2005) [[163]11] used a logistic model to predict ME/CFS
   status based on the cerebrospinal fluid proteome that predicted ME/CFS
   status with 80% accuracy based on alpha-1-macroglobulin, amyloid
   precursor-like protein 1, keratin 16, orosomucoid 2, and pigment
   epithelium-derived factor.

   CAMP is an antimicrobial peptide that has both pro- and
   anti-inflammatory effects and direct bactericidal activity, as well as
   anti-viral and anti-fungal properties [[164]57]. Serum levels are
   increased during both bacterial and viral infections [[165]58,
   [166]59], and in autoimmune diseases such as psoriasis, systemic lupus
   erythematosus, and rheumatoid arthritis [[167]60]. The increase
   observed in our cohort is consistent with inflammation caused either by
   infection or autoimmunity. FCRL3 is a transmembrane protein
   preferentially expressed on B lymphocytes. It has been shown to inhibit
   B cell receptor mediated signaling [[168]61] and abrogate plasma cell
   differentiation and antibody production [[169]62]. IGLV1-47 and FCRL3
   are both associated with B cells and are decreased in the ME/CFS
   patients supporting the idea that there may be dysregulation of the B
   cell response. LRG1 is a pleiotropic secreted pro-inflammatory
   glycoprotein whose overexpression has been linked to a number of
   autoimmune diseases such as rheumatoid arthritis and Crohn disease
   [[170]63], as well as neurodegenerative diseases such as Parkinson
   disease [[171]64]. It is expressed during differentiation of
   granulocytes [[172]65] and is present in neutrophil granules [[173]66].
   Abnormalities reported in neutrophil function in ME/CFS patients
   include increased apoptosis, characterized by increased annexin V
   binding and increased expression of the death receptor tumor necrosis
   factor 1 [[174]67], and decreased capacity to produce reactive oxygen
   species following Escherichia coli phagocytosis [[175]68]. Lower LRG1
   levels observed in this cohort may reflect neutrophil dysfunction. GSN
   is a multifunctional protein that has actin binding activities. It is
   thought to have anti-inflammatory effects and to protect against
   oxidative stress by scavenging actin released from damaged cells and
   tissues [[176]69]. Lower serum levels are observed in inflammatory
   states including acute injury [[177]70], sepsis [[178]71], and
   rheumatoid arthritis [[179]72]. Decreased plasma levels in ME/CFS
   patients may reflect a pro-inflammatory environment. IGF1 is an
   important growth factor that has many roles in growth development and
   homeostasis. IGF1 decreases in response to pro-inflammatory cytokines,
   such as tumor necrosis factor-α, interleukin (IL) 1β, and IL-6; low
   circulating levels of IGF1 are associated with infection, trauma, and
   aging [[180]73, [181]74]. IGFALS binds IGF1 in serum thereby increasing
   its half-life. Levels of both IGFALS and IGF1 are decreased in the
   plasma of our ME/CFS patients. IGF1 deficiency has previously been
   linked to ME/CFS [[182]75] although data has been conflicting [[183]76,
   [184]77]. Levels of CRTAC1 were also decreased. It is an extracellular
   matrix protein secreted by chondrocytes [[185]78]. Our data-driven
   approach identified the protein analytes that were most powerful in
   predicting ME/CFS status in our cohort. Whilst pathway enrichment
   analysis on so few proteins failed to identify a significant pathway
   that encompasses the selected analytes, many of the proteins have a
   role in immune and inflammatory responses, which have shown to be
   dysregulated in ME/CFS.

   Using Lasso, Random Forests, and XGBoost, we also identified a panel of
   proteins that predicted both ME/CFS with and without sr-IBS with high
   accuracy. The predictors for the ME/CFS without sr-IBS included LRG1
   and CRTAC1 as well as IGLC7, KNG1, and PON3. Levels of KNG1 were
   increased in ME/CFS without sr-IBS patients with an aOR of 4.116
   (p-value = 0.031). KING1 is an acute phase protein and part of the
   kallikrein-kinin system, also known as the contact system [[186]79].
   Its cleavage product, bradykinin, is an inflammatory mediator leading
   to vasodilation, increase of vascular permeabilization, smooth muscle
   contraction, fever, and release of nitric oxide [[187]80]. Increased
   KNG1 levels may be a sign of increased inflammation. Elevated levels of
   proteins involved with acute phase signaling have been found in
   fibromyalgia [[188]81] and increased KNG1 was found associated with
   pain in women with chronic widespread pain [[189]82]. However, in our
   study, we did not find a significant association between KNG1 levels
   and fibromyalgia when we compared ME/CFS patients with fibromyalgia (n
   = 20) and ME/CFS without fibromyalgia (n = 19) using the Wilcoxon
   rank-sum test (z-score = 0.126, p-value = 0.899). PON3, like PON1, is
   thought to be associated with high-density lipoproteins (HDL) and to
   protect low-density lipoproteins (LDL) from oxidation [[190]83,
   [191]84]. Infection and inflammation leads to increased LDL oxidation
   [[192]85] and higher LDL oxidation has previously been shown to be
   associated with ME/CFS [[193]76, [194]86, [195]87] Increased levels of
   PON3 may be a mechanism to compensate for increased oxidative stress
   caused by increased inflammatory mediators in the blood.

   Predictors in ME/CFS patients with sr-IBS that were also identified in
   the whole ME/CFS cohort included IGF1, CRTAC1, and CAMP along with
   SERPINA3, ITIH2, and IGHV1-18. The presence of immunoglobulin proteins
   among the predictors is consistent with the involvement of B cells in
   ME/CFS pathogenesis. SERPINA3 and ITIH2 are protease inhibitors
   involved in inflammation. Their levels were slightly decreased in our
   ME/CFS with sr-IBS cohort. Increased serum and cerebrospinal fluid
   levels of the acute phase protein SERPINA3 are associated with
   Alzheimer disease [[196]88]; however, it is also thought to limit
   inflammation by controlling superoxide generation and inhibiting
   proteases, such as elastase and chymase, with cathepsin G thought to be
   its primary target [[197]89]. Levels of CAMP were particularly elevated
   in sr-IBS patients with an aOR = 4.335 (p-value = 0.011). Levels of
   CAMP are increased in the mucosa of inflammatory bowel disease (IBD)
   patients [[198]90, [199]91]. We are not aware of similar studies in
   sr-IBS. Polymeric immunoglobulin receptor (PIGR) was detected more
   often in ME/CFS subjects compared to controls (56.4% versus 26.8%
   respectively, chi squared p-value = 0.007). However, we do not consider
   PIGR as a biomarker for ME/CFS because it was not detected >50% of
   subjects. PIGR transports IgA to the lumen at mucosal surfaces. Its
   expression in the gut is regulated by exposure to bacterial and viral
   products from both the microbiota and pathogens [[200]92]. We speculate
   that microbial dysbiosis [[201]26, [202]93] leads to increased
   inflammation and increased gut permeability, resulting in higher plasma
   levels of CAMP and PIGR.

   Whilst our exploratory study has identified a plasma protein
   biosignature using machine-learning algorithms that can predict ME/CFS
   status adequately, the clinical utility of these results remains to be
   shown. Validation of the panel of proteins in larger cohorts is needed
   to determine whether it would make a reliable biomarker. In addition,
   the specificity of the biosignature would need to be assessed to see if
   it could successfully distinguish ME/CFS cases, as well as ME/CFS cases
   with or without sr-IBS, from those with other fatiguing illnesses such
   as fibromyalgia and Gulf War Illness. Disease-specific specific
   biomarkers could provide an objective measure to aid in diagnosis of
   this heterogeneous disease. Previous proteomic studies in cerebrospinal
   fluid and saliva have identified protein signatures with predictive
   accuracies comparable to ours [[203]11, [204]14], however, none have
   led to the development clinical biomarkers. In fact, no molecular
   biomarkers have been validated for ME/CFS diagnosis or prognosis
   [[205]4], highlighting the challenges associated with this complex
   disease. Our work, whilst exploratory in nature, shows that the plasma
   proteome is a viable and untapped source of potential biomarkers in
   ME/CFS, and can provide insight into disease pathophysiology. In
   addition, we support previous results that ME/CFS patients with sr-IBS
   may constitute a subgroup with a distinct molecular profile [[206]9,
   [207]26] and that considering subtypes of ME/CFS can lead to greater
   predictive accuracy in biomarker studies.

   Our study is limited by small sample size, and the robustness of our
   findings needs to be verified in larger cohorts. Additionally, IBS
   status determination and stratification could be improved by an
   independent diagnosis at the time of participant recruitment.
   Nonetheless, our results comport with other work in ME/CFS that has
   found evidence in ME/CFS of immune dysregulation, B cell dysfunction,
   chronic inflammation, oxidative stress, and autoimmunity.

Supporting information

   S1 Appendix. Study inclusion and exclusion criteria for ME/CFS patients
   and healthy controls.

   (PDF)
   [208]Click here for additional data file.^ (87.8KB, pdf)
   S1 Fig. Residual plots of IGKV3D-20 and IGHV3-23/30.

   Residual plots of the logistic regression model with only the linear
   term of protein levels as the independent variable for immunoglobulin
   kappa variable (IGKV) 3D-20 and immunoglobulin heavy variable (IGHV)
   3-23/30.

   (TIF)
   [209]Click here for additional data file.^ (513.1KB, tif)
   S1 Table. Mean levels of proteins detected in all ME/CFS cases, ME/CFS
   with sr-IBS and ME/CFS without sr-IBS.

   ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome, IBS:
   irritable bowel syndrome, SEM: standard error of mean.

   (PDF)
   [210]Click here for additional data file.^ (166.8KB, pdf)
   S2 Table. Statistical analysis of protein association with ME/CFS.

   The aOR, 95% confidence levels and p-values from the logistic
   regression model in which only the linear term of the protein levels
   was fitted as an independent variable are shown. Quadratic effect
   p-value corresponds to the likelihood ratio tests comparing the
   goodness-of-fit of the model with both linear and quadratic terms of
   the protein levels to the goodness-of-fit of the model with only the
   linear term. aOR: adjusted odds ratio, CI: confidence interval.
   ^1Quadratic effect p-value: crude p-value of the likelihood ratio test
   comparing the goodness-of-fit between the logistic regression model
   with both linear and quadratic terms of the protein level and the model
   with only the linear term. Hochberg step-up procedure was applied to
   correct for the multiple tests over the annotated proteins controlling
   the family-wise error rate (FWER) at the level of 0.05.

   (PDF)
   [211]Click here for additional data file.^ (81.1KB, pdf)
   S3 Table. Mean levels of proteins detected in all female ME/CFS cases.

   aOR, 95% confidence levels and p-values from the logistic regression
   model in which only the linear term of the protein levels was fitted as
   an independent variable are shown in females only. Quadratic effect
   p-value corresponds to the likelihood ratio tests that compare the
   goodness-of-fit of the model with both linear and quadratic terms of
   the protein levels to that of the model with only the linear term in
   females only. ME/CFS: myalgic encephalomyelitis/chronic fatigue
   syndrome, SEM: standard error of mean, aOR: adjusted odds ratio, CI:
   confidence interval. ^1Quadratic effect p-value: crude p-value of the
   likelihood ratio test comparing the goodness-of-fit between the
   logistic regression model with both linear and quadratic terms of the
   protein level and the model with only the linear term. Hochberg step-up
   procedure was applied to correct for the multiple tests over the
   annotated proteins controlling the family-wise error rate (FWER) at the
   level of 0.05.

   (PDF)
   [212]Click here for additional data file.^ (107.4KB, pdf)
   S4 Table. Statistical analysis of individual protein associations with
   ME/CFS with sr-IBS and ME/CFS without sr-IBS.

   aOR, 95% confidence levels and p-values from the logistic regression
   model in which only the linear term of the protein levels was fitted as
   an independent variable are shown. Quadratic effect p-value corresponds
   to the likelihood ratio tests that compare the goodness-of-fit of the
   model with both linear and quadratic terms of the protein levels to
   that of the model with only the linear term. ME/CFS: myalgic
   encephalomyelitis/chronic fatigue syndrome, sr-IBS: self-reported
   irritable bowel syndrome, aOR: adjusted odds ratio, CI: confidence
   interval. ^1Quadratic effect p-value: crude p-value of the likelihood
   ratio test comparing the goodness-of-fit between the logistic
   regression model with both linear and quadratic terms of the protein
   level and the model with only the linear term. Hochberg step-up
   procedure was applied to correct for the multiple tests over the
   annotated proteins controlling the family-wise error rate (FWER) at the
   level of 0.05.

   (PDF)
   [213]Click here for additional data file.^ (101.7KB, pdf)
   S5 Table. Assessment of predictive power of the classifiers
   Lasso/Logistic regression, Random Forests, and XGBoost for all ME/CFS
   patients, ME/CFS patients with sr-IBS, and ME/CFS patients without
   sr-IBS, when compared to the control group.

   ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome, sr-IBS:
   self-reported irritable bowel syndrome, AUC: area under the curve, CI:
   confidence interval.

   (PDF)
   [214]Click here for additional data file.^ (36KB, pdf)

Acknowledgments