Introduction
Fibromyalgia (FM)
2The abbreviations used are:
FM
fibromyalgia
ACR
American College of Rheumatology
ANA
antinuclear antibody
ANOVA
analysis of variance
ATR
attenuated total reflectance
BDI
Beck depression index
BMI
body mass index
CCP
cyclic citrullinated protein
CRP
C-reactive protein
ESR
erythrocyte sedimentation rate
FIQR
fibromyalgia impact questionnaire revised version
FT
Fourier transform
IR
infrared spectroscopy
ICD
interclass distances
MPI
McGill pain index
NIR
near infrared
OA
osteoarthritis
PC
principal component
PCA
principal component analysis
PDA
photodiode array detection
PLSR
partial least-squares regression
RA
rheumatoid arthritis
RF
rheumatoid factor
SECV
S.E. of cross-validation
SIMCA
soft independent modeling of class analogy
SLE
systemic lupus erythematosus
SLEDAI
systemic lupus erythematosus disease activity index
SSS
symptom severity scale
uHPLC
ultra-high performance liquid chromatography
WPI
widespread pain index
3D
three-dimensional
DSDNA
double-stranded DNA.
is a member of a class of disorders called “central sensitivity syndromes” (
1Editorial review: an update on central sensitivity syndromes and the issues of nosology and psychobiology.
2- Arnold L.M.
- Clauw D.J.
- McCarberg B.H.
- FibroCollaborative
Improving the recognition and diagnosis of fibromyalgia.
,
3- Smith H.S.
- Harris R.
- Clauw D.
Fibromyalgia: an afferent processing disorder leading to a complex pain generalized syndrome.
4- Harte S.E.
- Harris R.E.
- Clauw D.J.
The neurobiology of central sensitization.
) or “overlapping chronic pain conditions” (
5- Maixner W.
- Fillingim R.B.
- Williams D.A.
- Smith S.B.
- Slade G.D.
Overlapping chronic pain conditions: implications for diagnosis and classification.
), all of which present significant diagnostic and therapeutic challenges to medicine. FM remains undiagnosed in as many as 3 of 4 people with the condition, with an average of 5 years between the time of onset of symptoms to diagnosis, resulting in delayed and potentially suboptimal treatment (
6Central sensitivity syndromes: a new paradigm and group nosology for fibromyalgia and overlapping conditions, and the related issue of disease versus illness.
). FM appears to result from variable combinations of sensitization of the central threat response system, dysregulation of neuroendocrine function, and abnormal nociceptive processing. The syndrome manifests clinically as widespread pain and tenderness in reproducible anatomic locations accompanied by associated sleep disturbances and a variety of comorbid conditions (
2- Arnold L.M.
- Clauw D.J.
- McCarberg B.H.
- FibroCollaborative
Improving the recognition and diagnosis of fibromyalgia.
,
3- Smith H.S.
- Harris R.
- Clauw D.
Fibromyalgia: an afferent processing disorder leading to a complex pain generalized syndrome.
,
7Dadabhoy, D., and Clauw, D. J., (2008) Musculoskeletal signs and symptoms: the fibromyalgia syndrome. in Primer on the Rheumatic Diseases, 13th Ed. (Klippel, J. H., ed) pp. 87–93, Springer, New York
8The pathogenesis of chronic pain and fatigue syndromes, with special reference to fibromyalgia.
,
9A comparative evaluation of the 2011 and 2016 criteria for fibromyalgia.
10- Wolfe F.
- Clauw D.J.
- Fitzcharles M.A.
- Goldenberg D.L.
- Katz R.S.
- Mease P.
- Russell A.S.
- Russell I.J.
- Winfield J.B.
- Yunus M.B.
The American College of Rheumatology preliminary diagnostic criteria for fibromyalgia and measurement of symptom severity.
).
Fibromyalgia is the most common cause of chronic widespread pain in the United States (
3- Smith H.S.
- Harris R.
- Clauw D.
Fibromyalgia: an afferent processing disorder leading to a complex pain generalized syndrome.
,
7Dadabhoy, D., and Clauw, D. J., (2008) Musculoskeletal signs and symptoms: the fibromyalgia syndrome. in Primer on the Rheumatic Diseases, 13th Ed. (Klippel, J. H., ed) pp. 87–93, Springer, New York
,
8The pathogenesis of chronic pain and fatigue syndromes, with special reference to fibromyalgia.
), and females are 4–9 times more likely to be diagnosed with FM than are men (
7Dadabhoy, D., and Clauw, D. J., (2008) Musculoskeletal signs and symptoms: the fibromyalgia syndrome. in Primer on the Rheumatic Diseases, 13th Ed. (Klippel, J. H., ed) pp. 87–93, Springer, New York
,
8The pathogenesis of chronic pain and fatigue syndromes, with special reference to fibromyalgia.
). Current evidence suggests that FM belongs to a much larger continuum of chronic pain syndromes, which includes chronic fatigue syndrome, irritable bowel syndrome and other functional gastrointestinal syndromes, temporomandibular syndrome, migraine, and interstitial cystitis/bladder pain syndrome, among others, all with considerable overlap (
1Editorial review: an update on central sensitivity syndromes and the issues of nosology and psychobiology.
,
3- Smith H.S.
- Harris R.
- Clauw D.
Fibromyalgia: an afferent processing disorder leading to a complex pain generalized syndrome.
,
7Dadabhoy, D., and Clauw, D. J., (2008) Musculoskeletal signs and symptoms: the fibromyalgia syndrome. in Primer on the Rheumatic Diseases, 13th Ed. (Klippel, J. H., ed) pp. 87–93, Springer, New York
,
8The pathogenesis of chronic pain and fatigue syndromes, with special reference to fibromyalgia.
). Estimates suggest that at least 2% of the adult population in the United States may be affected by FM, with an overall annual impact considering work absenteeism, lost productivity, and health care rivaling the costs of rheumatoid arthritis (
11- Silverman S.
- Dukes E.M.
- Johnston S.S.
- Brandenburg N.A.
- Sadosky A.
- Huse D.M.
The economic burden of fibromyalgia: comparative analysis with rheumatoid arthritis.
,
12- Annemans L.
- Wessely S.
- Spaepen E.
- Caekelbergh K.
- Caubère J.P.
- Le Lay K.
- Taïeb C.
Health economic consequences related to the diagnosis of fibromyalgia syndrome.
13- White L.A.
- Birnbaum H.G.
- Kaltenboeck A.
- Tang J.
- Mallett D.
- Robinson R.L.
Employees with fibromyalgia: medical comorbidity, healthcare costs, and work loss.
).
The diagnosis of FM has evolved from relying primarily on evidence for multiple painful tender points as promulgated in the 1990 American College of Rheumatology (ACR) criteria (
14- Wolfe F.
- Smythe H.A.
- Yunus M.B.
- Bennett R.M.
- Bombardier C.
- Goldenberg D.L.
- Tugwell P.
- Campbell S.M.
- Abeles M.
- Clark P.
The American College of Rheumatology 1990 Criteria for the Classification of Fibromyalgia. Report of the Multicenter Criteria Committee.
) to one that is more globally encompassing and based on a combination of pain plus a multitude of other symptoms. The 2010 ACR criteria included symptom severity criteria coupled with pain and other somatic symptoms (
10- Wolfe F.
- Clauw D.J.
- Fitzcharles M.A.
- Goldenberg D.L.
- Katz R.S.
- Mease P.
- Russell A.S.
- Russell I.J.
- Winfield J.B.
- Yunus M.B.
The American College of Rheumatology preliminary diagnostic criteria for fibromyalgia and measurement of symptom severity.
), and in 2016, the FM criteria were further refined to add additional distinctions to help avoid misclassifying patients with regional myofascial pain syndromes as having FM (
9A comparative evaluation of the 2011 and 2016 criteria for fibromyalgia.
). Fibromyalgia is currently diagnosed on the basis of the following findings: 1) presence of generalized pain, defined as pain in at least four of the five regions of the body; 2) symptoms present at a similar level for at least 3 months; 3) widespread pain index (WPI) ≥ 7 and symptom severity scale (SSS) score ≥ 5 or WPI of 4–6 and SSS score ≥ 9; and 4) the diagnosis of FM is valid irrespective of other diagnoses. Some studies have shown that a diagnosis alone improves FM patients’ health satisfaction, with fewer symptoms reported over the long term and reductions in the cost of medical resource utilization by patients (
12- Annemans L.
- Wessely S.
- Spaepen E.
- Caekelbergh K.
- Caubère J.P.
- Le Lay K.
- Taïeb C.
Health economic consequences related to the diagnosis of fibromyalgia syndrome.
,
13- White L.A.
- Birnbaum H.G.
- Kaltenboeck A.
- Tang J.
- Mallett D.
- Robinson R.L.
Employees with fibromyalgia: medical comorbidity, healthcare costs, and work loss.
).
Unfortunately, no reliable diagnostic test for FM exists. Such a test would be a significant step toward earlier diagnosis of and intervention for this condition, helping to improve patient outcomes, contain health care and/or legal costs, and potentially provide clues to the etiopathogenesis of the syndrome. Our group has recently reported the first metabolomics approach in two central sensitivity syndromes, interstitial cystitis (
15- Rubio-Diaz D.E.
- Pozza M.E.
- Dimitrakov J.
- Gilleran J.P.
- Giusti M.M.
- Stella J.L.
- Rodriguez-Saona L.E.
- Buffington C.A.
A candidate serum biomarker for bladder pain syndrome/interstitial cystitis.
) and FM (
16- Hackshaw K.V.
- Rodriguez-Saona L.
- Plans M.
- Bell L.N.
- Buffington C.A.T.
A bloodspot-based diagnostic test for fibromyalgia syndrome and related disorders.
). We isolated a low-molecular weight fraction of human blood using centrifugal ultrafiltration through a semipermeable membrane (filtrate), leaving high molecular solutes in the retentate (
16- Hackshaw K.V.
- Rodriguez-Saona L.
- Plans M.
- Bell L.N.
- Buffington C.A.T.
A bloodspot-based diagnostic test for fibromyalgia syndrome and related disorders.
). The low-molecular weight fraction was analyzed using IR microspectroscopy, which provided a unique spectral pattern based on functional groups (
e.g. methyl, carbonyl, indole, etc.) in serum samples that vibrated in predictable ways after absorbing IR light. Pattern recognition analysis of the spectra allowed us to discriminate FM patients from those with rheumatoid arthritis (RA) or osteoarthritis (OA) that appeared to be metabolically similar (
16- Hackshaw K.V.
- Rodriguez-Saona L.
- Plans M.
- Bell L.N.
- Buffington C.A.T.
A bloodspot-based diagnostic test for fibromyalgia syndrome and related disorders.
). The approach did not conclusively identify the metabolites responsible for the diagnostic spectral differentiation, although changes in tryptophan catabolism seemed to be involved (
16- Hackshaw K.V.
- Rodriguez-Saona L.
- Plans M.
- Bell L.N.
- Buffington C.A.T.
A bloodspot-based diagnostic test for fibromyalgia syndrome and related disorders.
). Another metabolomics approach has involved LC/quadrupole–TOF/MS with multivariate statistical analysis aimed at discriminating FM (
n = 22) patients and controls (
n = 21) from blood plasma analysis (
17- Caboni P.
- Liori B.
- Kumar A.
- Santoru M.L.
- Asthana S.
- Pieroni E.
- Fais A.
- Era B.
- Cacace E.
- Ruggiero V.
- Atzori L.
Metabolomics analysis and modeling suggest a lysophosphocholines-PAF receptor interaction in fibromyalgia.
). According to the investigators, lysophosphocholine dominated the metabolite profile, suggesting that there may be additional potential biomarkers for FM diagnosis (
17- Caboni P.
- Liori B.
- Kumar A.
- Santoru M.L.
- Asthana S.
- Pieroni E.
- Fais A.
- Era B.
- Cacace E.
- Ruggiero V.
- Atzori L.
Metabolomics analysis and modeling suggest a lysophosphocholines-PAF receptor interaction in fibromyalgia.
). Recently, Malatji
et al. (
18- Malatji B.G.
- Meyer H.
- Mason S.
- Engelke U.F.H.
- Wevers R.A.
- van Reenen M.
- Reinecke C.J.
A diagnostic biomarker profile for fibromyalgia syndrome based on an NMR metabolomics study of selected patients and controls.
) used NMR metabolomics to identify a diagnostic biomarker profile for FM from urine, identifying metabolites associated with indicators of pain and fatigue symptoms (succinic acid, taurine, and creatine) and perturbations in the gut microbiome (hippuric, 2-hydroxyisobutyric, and lactic acids).
Vibrational spectroscopy technology currently is being developed for routine clinical use in many areas of medicine (
19- Eikje N.S.
- Aizawa K.
- Ozaki Y.
Vibrational spectroscopy for molecular characterisation and diagnosis of benign, premalignant and malignant skin tumours.
), including cancer (
20Romeo, M. J., Dukor, R. K., and Diem, M., (2008) Introduction to spectral imaging, and applications to diagnosis of lymph nodes. In Handbook of Vibrational Spectroscopy (Chalmers, J. M., and Griffiths, P. R., eds) pp. 1–25, Wiley, Chichester, UK
,
21- Kendall C.
- Isabelle M.
- Bazant-Hegemark F.
- Hutchings J.
- Orr L.
- Babrah J.
- Baker R.
- Stone N.
Vibrational spectroscopy: a clinical tool for cancer diagnostics.
22- Osterberg E.C.
- Laudano M.A.
- Li P.S.
Clinical and investigative applications of Raman spectroscopy in urology and andrology.
), urology (
23- Carvalho C.S.
- Martin A.A.
- Santo A.M.E.
- Andrade L.E.C.
- Pinheiro M.M.
- Cardoso M.A.G.
- Raniero L.
A rheumatoid arthritis study using Raman spectroscopy.
), and rheumatology (
24- Lechowicz L.
- Chrapek M.
- Gaweda J.
- Urbaniak M.
- Konieczna I.
Use of Fourier-transform infrared spectroscopy in the diagnosis of rheumatoid arthritis: a pilot study.
,
25Clustering and classification of analytical data.
). Vibrational (mid-IR and Raman) spectroscopy fingerprinting capabilities offer rapid, high-throughput, and nondestructive analysis of a wide range of sample types producing a characteristic chemical “fingerprint” with a unique signature profile. Raman spectroscopy offers an attractive fingerprinting technique because of the little or no sample preparation requirement, noncontact and nondestructive capabilities, and weak Raman response of water, allowing measurement in aqueous solutions and on substances enclosed in transparent containers, such as bags or vials, without opening them (
26- Santos M.I.
- Gerbino E.
- Tymczyszyn E.
- Gomez-Zavaglia A.
Applications of infrared and Raman spectroscopies to probiotic investigation.
). However, a major hurdle in biological samples has been the interference of fluorescence. Traditional Raman handheld units are equipped with visible laser excitation wavelengths, such as 532 and 785 nm, to increase the Raman scattering signal. Unfortunately, these shorter excitation wavelengths increase fluorescence background, which obscure the Raman signal. To reduce the fluorescence limitation, Raman spectrometers can be equipped with longer excitation wavelength (NIR, 830–1064 nm), but this results in diminished Raman signal intensity. To address this signal limitation, a new generation of semiconductor detectors (indium gallium arsenide array) recently became available (
27Raman spectroscopy of minerals and mineral pigments in archaeometry.
). In addition, the surface-enhanced Raman spectroscopy phenomenon enhances (10
6–10
7) due to the large electromagnetic field induced by localized surface plasmon resonance by using metallic nanostructures (typically gold and silver) that are in proximity of the metal surface. Such refinements result in lower limits of detection of surface-enhanced Raman spectroscopy in the ppb or single molecule level (
28- Roggo Y.
- Chalus P.
- Maurer L.
- Lema-Martinez C.
- Edmond A.
- Jent N.
A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies.
).
Advances in instrumentation and pattern recognition techniques are making this technology ideal for rapid screening and analysis of biological samples, permitting the separation of spectra into discrete clusters that permit classification of individuals based on subtle physiological differences. Pattern recognition solves the class-membership problem (
29- Sjöström M.
- Wold S.
- Lindberg W.
- Persson J.Å.
- Martens H.
A multivariate calibration problem in analytical chemistry solved by partial least-squares models in latent variables.
) by using unsupervised and supervised methods. In unsupervised techniques, there is no information available prior to analysis in regard to group structure of the samples (
30- Aletaha D.
- Neogi T.
- Silman A.J.
- Funovits J.
- Felson D.T.
- Bingham 3rd, C.O.
- Birnbaum N.S.
- Burmester G.R.
- Bykerk V.P.
- Cohen M.D.
- Combe B.
- Costenbader K.H.
- Dougados M.
- Emery P.
- Ferraccioli G.
- et al.
2010 rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative.
). These techniques usually are applied to discover sample groupings within data, reveal any abnormal spectra in a data set, and determine the natural variation among the samples. Principal component analysis (PCA) is used to reduce large data sets into a smaller number of orthogonal variables called principal components (PCs), which carry the major variance of the original variables. Thus, PCA reduces the dimensionality of data sets while simultaneously retaining the information present in the data (
29- Sjöström M.
- Wold S.
- Lindberg W.
- Persson J.Å.
- Martens H.
A multivariate calibration problem in analytical chemistry solved by partial least-squares models in latent variables.
). Supervised classification uses a group of samples as a training set, in which the categories of each sample are known prior to analysis. Training set performance is then evaluated by comparing the predictions made by the model with the true categories of the samples used for validation (
31- Yu C.
- Gershwin M.E.
- Chang C.
Diagnostic criteria for systemic lupus erythematosus: a critical review.
). Supervised classification methods include K-nearest neighbors, soft independent modeling of class analogy (SIMCA), linear discriminant analysis, partial least-squares discriminant analysis. Among the most common of the supervised techniques is SIMCA, which allows for the visualization of clustering patterns among samples (
31- Yu C.
- Gershwin M.E.
- Chang C.
Diagnostic criteria for systemic lupus erythematosus: a critical review.
). In SIMCA, a PCA is performed on each class in the data set, and a sufficient number of principal components are retained to account for most of the variation within each class (
32- Bennett R.M.
- Friend R.
- Jones K.D.
- Ward R.
- Han B.K.
- Ross R.L.
The revised fibromyalgia impact questionnaire (FIQR): validation and psychometric properties.
). An important feature of SIMCA is that it only assigns an unknown sample to the class for which it has a high probability. If the residual variance of a sample exceeds the upper limit for every modeled class in the data set, the sample is not assigned to any of the classes represented in the data set.
The objectives of the current study were to develop simple, rapid, sensitive, and robust methods for the diagnosis of FM based on the highly characteristic mid-IR and Raman “fingerprint” from dried blood spots of peripheral blood samples obtained by finger-stick combined with supervised pattern recognition techniques. In addition, we evaluated the identification capabilities of LC-MS/MS to assist in a metabolomics approach for biomarker elucidation and to provide information to better understand the etiology and pathogenesis of FM.
Discussion
This study assessed the feasibility of vibrational spectroscopy to differentiate individuals with FM from those with several other rheumatic conditions, including RA, SLE, and OA. In addition, we wanted to determine whether various degrees of severity of FM were biochemically distinguishable from each other using a novel means of rapid detection. Advantages of such a methodology, if developed and honed to reproducibility, would be a capability for identifying specific treatment subsets for FM as well as identifying new targets as differentiated from each other metabolically by spectroscopy (UV-visible, MS, and vibrational). The results of the study found a unique Raman spectral signature that clustered all subjects into classes (FM, RA, and SLE) with no misclassifications. The discriminating power was dominated by vibrations of the backbone in proteins and nucleic acids, and also indicated mineral differences in blood as biomarker. In addition to Raman (differentiating FM from SLE and RA), HPLC-PDA-MS/MS also distinguished between disease groups with certain metabolites existing in significantly different proportions, which resulted in discriminating UV-visible chromatograms. Furthermore, preliminary studies showed the capability of Raman to differentiate between severe and mild FM based on FIQR disease assessments. Two recent studies have identified the value of subcategorizing patients with FM based on similar symptoms, with the rationale being that such groupings could provide a means toward more individualized clinical evaluation and intervention (
40- Vincent A.
- Hoskin T.L.
- Whipple M.O.
- Clauw D.J.
- Barton D.L.
- Benzo R.P.
- Williams D.A.
OMERACT-based fibromyalgia symptom subgroups: an exploratory cluster analysis.
,
41- Segura-Jiménez V.
- Soriano-Maldonado A.
- Álvarez Gallardo -I.C.
- Estévez-López F.
- Carbonell-Baeza A.
- Delgado-Fernández M.
Subgroups of fibromyalgia patients using the 1990 American College of Rheumatology criteria and the modified 2010 preliminary diagnostic criteria: the al-Ándalus project.
). Therefore, FM appears to be characterized by distinctive subsets, which further studies may distinguish as biochemically distinct.
Through the use of uHPLC-PDA-MS/MS, differences in the metabolic profiles of serum samples from patients with FM, RA, OA, and SLE were observed in terms of very polar and less polar compounds, which may be correlated to the Raman and IR findings, which differentiated disease groups based on aromatic amino acid groups, glycans, collagen, and mineral content of samples. Differences in early eluting metabolites were observed from mass spectral data. Generally, samples from FM and OA patients appeared to share more metabolic similarities when compared with those from RA or SLE groups; this was observed in both the more polar mass spectral data (
Table 5) and the later eluting components of the UV-visible chromatograms (
Fig. 8). This was in partial contrast to previous findings in which RA and OA groups were metabolically similar and distinctive from the FM group (
16- Hackshaw K.V.
- Rodriguez-Saona L.
- Plans M.
- Bell L.N.
- Buffington C.A.T.
A bloodspot-based diagnostic test for fibromyalgia syndrome and related disorders.
). Some of the metabolites that most distinguished FM from the other disease groups included heme, cysteine-GSH disulfide, and NAD
+ (
16- Hackshaw K.V.
- Rodriguez-Saona L.
- Plans M.
- Bell L.N.
- Buffington C.A.T.
A bloodspot-based diagnostic test for fibromyalgia syndrome and related disorders.
). However, these larger metabolites were not found in significant proportions under the conditions of this study. Metabolites differentiating between disease groups were small compounds with
m/
z+ < 300.
Less polar components, eluting later during the uHPLC runs, were also found to differentiate between the disease groups. UV-visible absorbance of 280 nm was common to many of these separated peaks, which is typical of compounds composed of aromatic rings. Previous works have found some of the distinguishing metabolites of FM patients to be related to tryptophan metabolism and catabolism (
16- Hackshaw K.V.
- Rodriguez-Saona L.
- Plans M.
- Bell L.N.
- Buffington C.A.T.
A bloodspot-based diagnostic test for fibromyalgia syndrome and related disorders.
,
42- Schwarz M.J.
- Offenbaecher M.
- Neumeister A.
- Ewert T.
- Willeit M.
- Praschak-Rieder N.
- Zach J.
- Zacherl M.
- Lossau K.
- Weisser R.
- Stucki G.
- Ackenheil M.
Evidence for an altered tryptophan metabolism in fibromyalgia.
). Due to its aromatic moiety, tryptophan is one of the few amino acids capable of absorbance of 280 nm and is actually responsible for UV light absorbance by many proteins. Perhaps the differences in the UV chromatograms among the disease groups may be partially a result of these amino acids metabolites or derivatives. These findings suggest that the combination of HPLC-PDA-MS/MS analytical methods may be useful in the diagnosis of FM from different classes of “central sensitivity syndromes.”
In our current study, we recorded the medications that patients were on at the time of their blood spot analysis for all disease conditions. There was no obvious medication signal/effect that could be discerned by spectroscopy or MS/MS among the current cohort; however, the effect of medications on these analyses was beyond the scope of this current study. It would require medication-free control populations with similar demographic (age, BMI, sex, etc.) and clinical features (FIQR, BDI, MPI) to be compared with a correspondingly matched population on a specific medication(s) to determine what medication effects might have on these results. It would be of interest to determine potential static and prospective changes over time elicited by various medication groups (tricyclics, serotonin, norepinephrine reuptake inhibitors, etc.).
There is a wide range of opinions on FM among physicians. Many physicians lack the necessary training to accurately diagnose this condition. As a result, patients with poorly explained symptoms are often lumped into the FM category, although their clinical features might fit other diagnoses better. Examples might include generalized anxiety disorder, somatoform disorder, and restless leg syndromes, among many others. Some physicians do try to adhere to the updated classification criteria for diagnosis of FM (
1Editorial review: an update on central sensitivity syndromes and the issues of nosology and psychobiology.
,
9A comparative evaluation of the 2011 and 2016 criteria for fibromyalgia.
,
10- Wolfe F.
- Clauw D.J.
- Fitzcharles M.A.
- Goldenberg D.L.
- Katz R.S.
- Mease P.
- Russell A.S.
- Russell I.J.
- Winfield J.B.
- Yunus M.B.
The American College of Rheumatology preliminary diagnostic criteria for fibromyalgia and measurement of symptom severity.
,
14- Wolfe F.
- Smythe H.A.
- Yunus M.B.
- Bennett R.M.
- Bombardier C.
- Goldenberg D.L.
- Tugwell P.
- Campbell S.M.
- Abeles M.
- Clark P.
The American College of Rheumatology 1990 Criteria for the Classification of Fibromyalgia. Report of the Multicenter Criteria Committee.
), whereas still another segment of the physician population displays a level of skepticism about the disease that dissuades many patients from even seeking a diagnosis due to concern that their symptoms will be thought to be entirely “psychological.” Despite adoption of the newly revised criteria by many, it is increasingly recognized that use of these criteria for diagnosis is fraught with error due to a significant level of subjectivity in the survey elements (WPI and SSS) (
9A comparative evaluation of the 2011 and 2016 criteria for fibromyalgia.
).
For physicians, a diagnosis of FM often provides an explanation for difficult to understand symptoms. For patients, a diagnosis of FM may offer them some confirmation that symptoms are real and not psychological. The development of new criteria for FM has helped somewhat with uniformity of clinical diagnosis in published reports. Unfortunately, for the majority of patients with FM, the updated diagnostic criteria still fail with regard to providing an objective measure confirmatory of disease, which is what many FM patients are seeking. Consequently, despite revised criteria, there remains no gold standard for defining or diagnosing FM. Results from the 2012 United States National Health Interview Survey revealed that most patients who received a diagnosis of FM from a health professional did not satisfy published FM criteria (
43- Walitt B.
- Nahin R.L.
- Katz R.S.
- Bergman M.J.
- Wolfe F.
The prevalence and characteristics of fibromyalgia in the 2012 national health interview survey.
). In addition, use of the updated diagnostic criteria has not translated into health care cost savings. We surmise that the reason for this is that true cost savings (such as avoiding unnecessary testing like magnetic resonance imaging, computed tomography scan, repeated blood testing, etc.) will only occur once we have discovered a reproducible biomarker that is widely accepted among practitioners and patients. Hughes
et al. (
44- Hughes G.
- Martinez C.
- Myon E.
- Taïeb C.
- Wessely S.
The impact of a diagnosis of fibromyalgia on health care resource use by primary care patients in the UK: an observational study based on clinical practice.
) showed that initial diagnosis of FM leads to modest decreases in health care costs for 1–2 years post-diagnosis, but those initial savings dissipated and health care costs and utilization escalated subsequently well beyond prediagnosis levels. The authors theorized that the reason why costs rose is because patients remained in pain post-diagnosis, and possibly because in FM there is a lack of “effective treatment” (
44- Hughes G.
- Martinez C.
- Myon E.
- Taïeb C.
- Wessely S.
The impact of a diagnosis of fibromyalgia on health care resource use by primary care patients in the UK: an observational study based on clinical practice.
). Alternatively, we surmise that many patients feel that the “diagnosis” is still subjective, and satisfaction may never be achieved for the great majority until we have a widely accepted gold standard or biomarker. Unfortunately, current Food and Drug Administration–approved therapies for FM have not been able to show superior efficacy over mindfulness techniques or health coaching (
45- Hackshaw K.V.
- Plans-Pujolras M.
- Rodriguez-Saona L.E.
- Moore M.A.
- Jackson E.K.
- Sforzo G.A.
- Buffington C.A.T.
A pilot study of health and wellness coaching for fibromyalgia.
,
46- Robinson R.
- Kroenke K.
- Williams D.
- Chen Y.
- Peng X.
- Faries D.
- McCarberg B.
- Wohlreich M.
- Mease P.
Longitudinal observation of treatment patterns and outcomes for patients with fibromyalgia.
).
Thus, our studies have great importance both for development of a reproducible biomarker and for identifying potential new therapeutic targets for treatment. With advances in the methodologies described here and subsequent identification of differentiating metabolites, techniques for treatment of FM and related disorders may be advanced.
Materials and methods
Patients
All studies involving human subjects were approved by the Ohio State University Institutional Review Board and abide by the Declaration of Helsinki principles. Following institutional review board approval (2015H0312), blood samples were obtained from patients with FM (
n = 50), RA (
n = 29), SLE (
n = 23), and OA (
n = 19) at the Ohio State University Rheumatology clinics located at Care Point East. Criteria for diagnosis of FM included the following: age 18–80 years with history of FM and meeting current revised ACR criteria (
9A comparative evaluation of the 2011 and 2016 criteria for fibromyalgia.
,
10- Wolfe F.
- Clauw D.J.
- Fitzcharles M.A.
- Goldenberg D.L.
- Katz R.S.
- Mease P.
- Russell A.S.
- Russell I.J.
- Winfield J.B.
- Yunus M.B.
The American College of Rheumatology preliminary diagnostic criteria for fibromyalgia and measurement of symptom severity.
). We also required that no physical trauma or infection prior to the onset of FM could be identified as the primary initiating factor in their FM. The diagnoses of RA and SLE were based on ACR criteria for each disorder (
47The McGill pain questionnaire: major properties and scoring methods.
,
48- Stewart A.L.
- Hays R.D.
- Ware Jr., J.E.
The MOS short-form general health survey: reliability and validity in a patient population.
). Patients with FM, RA, SLE, and OA were screened using current or history of exclusionary medical and psychiatric diagnoses (
e.g. cancer or connective tissue disorder, multiple sclerosis, congestive heart failure, diabetes, bipolar disorder, melancholic depression). In addition, samples were collected only from patients who had been under the care of the clinician investigator (K. V. H.) for at least 6 months. This additional requirement was applied to provide a further safeguard for the accuracy of the diagnosis of patients to ensure that we had obtained samples from patients whose disorder had been correctly diagnosed.
Questionnaires
Self-reported symptoms were obtained from all subjects. The FIQR is a 10-item self-rating instrument that measures physical functioning, work status, depression, anxiety, sleep, pain, stiffness, fatigue, and well-being. It is the most frequently used assessment tool for gauging overall impact of FM on quality of life (
49- Gladman D.D.
- Ibañez D.
- Urowitz D.M.
Systemic lupus erythematosus disease activity index 2000.
).
The McGill pain questionnaire is a reliable and valid instrument for indicating both descriptive aspects of pain and pain intensity. This tool is used to assess the level of generalized pain severity for subjects. This instrument uses sensory, affective, and evaluative word descriptors to measure the patient's subjective pain experience. Scores are obtained for three classes of verbal descriptors as well as an overall measure of current pain, with higher scores indicating greater pain intensity. This tool has been shown to be effective in both acute and chronic pain populations (
50- De Maesschalck R.
- Candolfi A.
- Massart D.L.
- Heuerding S.
Decision criteria for soft independent modelling of class analogy applied to near infrared data.
).
The Beck Depression Inventory is a 21-item self-administered questionnaire with established reliability and validity (
51- Beck A.T.
- Epstein N.
- Brown G.
- Steer R.A.
An inventory for measuring clinical anxiety: psychometrical properties.
,
52- Beck A.T.
- Ward C.H.
- Mendelson M.
- Mock J.
- Erbaugh J.
Inventory for measuring depression.
). It is widely used to measure depression in patient populations. It can be used to quantify the psychological/behavioral dimension of FM impact. For each item on the inventory, subjects pick one of four statements to describe how they have been feeling in the past week; higher scores indicate greater depression. The BDI generates a total score (range 0–63) and two subscores (cognitive/affective, range 0–42; somatic, range 0–21).
The most commonly used study of lupus activity is called the SLEDAI (
49- Gladman D.D.
- Ibañez D.
- Urowitz D.M.
Systemic lupus erythematosus disease activity index 2000.
). It is a list of 24 items, including 16 clinical items and 8 items of laboratory results. These items are scored based on their presence or absence within the 10 days prior to the blood draw obtained for diagnosis of SLE. Other assessments of disease include the erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), rheumatoid factor (RF), cyclic citrullinated protein (CCP), and anti-nuclear antibody (ANA).
Sample preparation
Blood was collected from subjects and applied to blood spot cards, which were then dried and transported to the Rodriguez-Saona spectroscopy laboratory for analysis. Variation in blood spot size was minimized by collecting samples on cards (Whatman 903 Protein Saver Snap Apart Card, GE Healthcare) with preprinted circles as guides to standardize the volume of blood applied; when applied to its border, each circle contains ∼50 μl of blood. Upon arrival to the laboratory, 3-mm samples were punched from the card, extracted with 1 ml of ammonium acetate buffer (1% in water), and mixed by sonication (Sonic Dismembrator model 100, Fisher), and the supernatant was transferred to Amicon® Ultra centrifugal filter devices (30 K) and centrifuged (model 5415, Eppendorf, Westbury, NY) at 14,000 ×
g for 15 min at 4 °C. Centrifugal membrane filter devices were used to remove large nominal molecular mass (<30-kDa) blood components that interfered with resolving targeted biomarker compounds (
15- Rubio-Diaz D.E.
- Pozza M.E.
- Dimitrakov J.
- Gilleran J.P.
- Giusti M.M.
- Stella J.L.
- Rodriguez-Saona L.E.
- Buffington C.A.
A candidate serum biomarker for bladder pain syndrome/interstitial cystitis.
). Overall, the membrane filters removed proteins and isolated water-soluble molecules, such as sugars, amino acids, peptides, and lipids. Two small-volume (2-μl) drops of the blood filtrate fluid from all patients were deposited onto SpectRIM
TM Raman IR slides (Tienta Sciences, Inc., Indianapolis, IN) and were allowed to dry at room temperature (∼30 min), and Raman spectra were collected from each drop. Raman spectra were collected on the edge of the dried sample because it deposited more material providing reproducible signatures from samples. Esmonde-White
et al. (
53- Esmonde-White K.A.
- Mandair G.S.
- Raaii F.
- Jacobson J.A.
- Miller B.S.
- Urquhart A.G.
- Roessler B.J.
- Morris M.D.
Raman spectroscopy of synovial fluid as a tool for diagnosing osteoarthritis.
), studying the molecular changes associated with osteoarthritis by Raman spectroscopy, reported that proteins in specimens tend to accumulate on the edge of drops, whereas smaller and more soluble components precipitate in the center during drop deposition. In the case of IR spectroscopy, 5-μl aliquots of the filtrate were diluted in methanol (50 μl), mixed, and placed onto the ATR well for data collection.
Vibrational spectroscopy of samples
FT-IR spectra were collected using a 5500 portable system equipped with a heated five-reflection ZnSe ATR crystal. The optical bench includes a Michelson interferometer with a mechanical bearing moving mirror, a potassium bromide beam splitter, and a deuterated triglycine sulfate detector operating at room temperature. To enhance the signal-to-noise ratio, 64 scans were co-added and signal-averaged. The ATR cell was warmed to 40 ± 1 °C so that all remaining solvent was evaporated before measurement.
Raman reflectance spectra were recorded using an NRS-4100 dispersive laser Raman microscope (Jasco Inc., Easton, MD), equipped with a with a motorized x-y stage; ×5, ×20, and ×100 NIR objectives; and an indium gallium arsenide detector, which works at a temperature of −70 °C. Spectra were collected from 2920 to 500 cm−1 using a resolution of 1 cm−1. Laser wavelength was at 1064 nm, exposure was 60 s, and accumulation was 2. The grating, slit, and attenuator settings were 150 liters/mm, 200 × 800 μm, and 25%, respectively. Measurements were performed using a ×20 NIR objective. Background reflectance spectra were recorded between samples to minimize effects of the environment on the sample spectrum.
Multivariate analysis
Spectral differences (IR and Raman spectra and UV-visible chromatograms from HPLC-PDA detection) between samples from subjects with FM and those with RA, SLE, or OA were evaluated using multivariate statistical techniques to resolve spectral information of interest, to cluster the samples according to the presence of the health condition (class), and to correlate symptom severity with spectral information. SIMCA and PLSR were carried out using Pirouette pattern recognition software (Pirouette® version 4.5, Infometrix Inc., Woodville, WA) as described previously (
15- Rubio-Diaz D.E.
- Pozza M.E.
- Dimitrakov J.
- Gilleran J.P.
- Giusti M.M.
- Stella J.L.
- Rodriguez-Saona L.E.
- Buffington C.A.
A candidate serum biomarker for bladder pain syndrome/interstitial cystitis.
). For all multivariate analysis, the only transformation applied to the data during the development were mean centering followed by a second derivative (35-point window). Second derivative transformation of the spectra allowed for further extraction of useful information and reduced spectral noise.
SIMCA, a supervised pattern recognition data-analysis method that uses the variance-covariance matrix, was used to reduce the dimensionality of the multivariate data sets by determining the principal components that best explained the systematic variation (
29- Sjöström M.
- Wold S.
- Lindberg W.
- Persson J.Å.
- Martens H.
A multivariate calibration problem in analytical chemistry solved by partial least-squares models in latent variables.
). A cross-validation algorithm was then used to determine the number of principal components that yielded the minimum prediction error. In SIMCA, the principal components contain information about influential chemical and/or biological systems that define the classes; by determining the F-statistic, an upper limit for the residual variance (noise) can be calculated for all samples belonging to each class, resulting in a set of probabilities of class membership for each sample. Thus, an unknown sample can only be assigned to the class for which it has a high probability. If the residual variance of a sample exceeds the upper limit for the modeled classes in the data set, it is not assigned to any of the classes; either it is an outlier, or it belongs to a class not represented in the data set (
54Kvalheim, O. M., and Karstang, T. V., (1992) Classification by means of disjoint cross validates principal components models. In Multivariate Pattern Recognition in Chemometrics: Illustrated by Case Studies (Brereton, R. G., ed) pp. 209–249, Elsevier, Amsterdam
). The classification model was developed on a training set (80% of the total number in a class) with known patient diagnosis, and the model performance was evaluated with an external validation set (remaining 20% of samples) of patients that were not used in the training, and their predictions were compared with true categories (model sensitivity).
To distinguish FM activity (flares), signature biomarker band intensities in the spectrum were correlated to the patient’s reported outcomes (FIQR) survey for the development of a quantitative algorithm, PLSR, to determine disease state. PLSR is a bilinear regression based on the extraction of “latent variables” (
29- Sjöström M.
- Wold S.
- Lindberg W.
- Persson J.Å.
- Martens H.
A multivariate calibration problem in analytical chemistry solved by partial least-squares models in latent variables.
). These orthogonal factors (latent variables) explain most of the covariance of the
x (spectra) and
y variables. PLSR reduces the dimensions contained in thousands of IR predictors into a few factors to explain variations in both the dependent variables and the spectral domain. The end result is a linear model able to predict a desired characteristic (disease status) based on a selected set of predictors (IR spectra). PLSR has been particularly successful in developing multivariate calibration models for the spectroscopy field because it reduces the impact of irrelevant
x variations (noise) in the calibration model, resulting in more accurate and reproducible calibration models (
50- De Maesschalck R.
- Candolfi A.
- Massart D.L.
- Heuerding S.
Decision criteria for soft independent modelling of class analogy applied to near infrared data.
,
55Bjorsvik, H. R., and Martens, H., (1982) Data analysis: calibration of NIR instruments by PLS regression. In Handbook of Near-Infrared Analysis (Burns D, ed) pp. 159–180, Dekker, New York
). The performance of the model was evaluated based on the number of latent variables, loading vectors, SECV, coefficient of determination (
R-value), and outlier diagnostics.
Metabolomic analysis by uHPLC-PDA-MS/MS
The untargeted metabolic profiling platform employed for this analysis consisted of uHPLC coupled to a PDA and tandem mass spectrometer (MS/MS) detectors for chemical species extracted from blood samples under alkaline conditions described above. To the dried bloodspot card samples previously extracted from punches, 1 ml of HPLC-MS grade H2O was added, and samples were vortexed for 15 s to homogenize. Aqueous samples were filtered through 0.2-μm nylon syringe filters (Phenomenex, Torrance, CA) into glass HPLC vials. Blood spot extract samples were evaluated by uHPLC-PDA-MS/MS using a uHPLC system (Shimadzu Nexera-i LC-2040C) coupled with tandem MS (Shimadzu LCMS-8040). Samples were evaluated by both direct injection into the system with no column and with the use of a Pinnacle DB C18 column, 1.9-μm particle size, 50 × 2.1-mm length (Restek Corp., Bellefonte, PA). When directly injected (25 μl of sample) into the system, flow consisted of 0.2 ml/min 0.1% formic acid in water. When passed through the column (25 μl of sample), the column was heated to 40 °C, and flow consisted of a binary gradient at 0.2 ml/min. Gradient consisted of solvents A (0.1% formic acid in H2O) and B (acetonitrile at 0% B) for 0–5 min followed by increase of B from 0 to 40% during 5–20 min. Spectral data were collected from 200 to 800 nm.
Conditions for MS/MS by electrospray ionization included the following: 1.5-liter/min nebulizing gas flow, 230 °C desolvation line temperature, 200 °C heat block temperature, and 15-liter/min drying gas flow. m/z data were collected under both positive and negative modes from 25 to 1000 m/z with the first-quadrupole total ion scan with event times of 100 ms. From analysis of preliminary data obtained, ions of interest were monitored under the same ionizing conditions using precursor and product ion scans with collision energy of −35.0 eV using argon gas for a secondary collision event. Data were collected using Lab Solutions software (Shimadzu). uHPLC-PDA-MS was performed on 10 samples (n = 10) from each disease group.
Data imputation and statistical analysis
To assist with data visualization, raw
m/
z intensity values for each metabolite were rescaled by dividing all sample values by the mean value for each individual metabolite as similarly described by Hackshaw
et al. (
16- Hackshaw K.V.
- Rodriguez-Saona L.
- Plans M.
- Bell L.N.
- Buffington C.A.T.
A bloodspot-based diagnostic test for fibromyalgia syndrome and related disorders.
). Each individual determination of
m/
z was then expressed as a ratio relative to this mean value to determine -fold changes in metabolite concentrations. For statistical analyses and data display purposes, any missing values were assumed to be below the limit of detection, and these values were imputed with the compound minimum (minimum value imputation). Statistical analysis of metabolomics data was performed using one-way ANOVA (two-tailed, α = 0.05) and Tukey’s honest significant difference post hoc test (α = 0.05) using Minitab 16 (Minitab Inc., State College, PA). UHPLC-PDA chromatograms (detection from 260 to 450 nm) from samples analyzed with the use of a C18 column were also investigated by multivariate analysis as described above.
Article info
Publication history
Published online: February 15, 2019
Received in revised form:
November 28,
2018
Received:
September 11,
2018
Edited by Norma M. Allewell
Footnotes
The authors declare that they have no conflicts of interest with the contents of this article.
Copyright
© 2019 Hackshaw et al.