Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2026 Jun 5;30(6):e70308. doi: 10.1002/ejp.70308

Relationship Between Central Sensitization and Genetic Polymorphisms—A Case–Control Study in Fibromyalgia

Nicolas Macian 1,✉, Christian Dualé 1,2, Marine Delay 1, Vincent Leray 1, Bruno Pereira 3, Yoan Renaud 4, Yvonne Gloor 5, Jules Desmeules 5, Gisèle Pickering 1,2
PMCID: PMC13238813  PMID: 42246686

ABSTRACT

Background

Fibromyalgia is a syndrome characterised by widespread chronic pain, which is believed to be mediated by a state of central sensitisation (CS). Based on the hypothesis that CS itself could have genetic determinants, our aim was to further explore this pathway.

Methods

This study included 201 women with fibromyalgia and 40 healthy women matched on age and menopausal status. The nociceptive flexion reflex (NFR) threshold was used as a surrogate marker of CS. Genotyping of 60 candidate‐genes likely to influence the pathogenesis of fibromyalgia or chronic pain was explored in all subjects.

Results

The NFR threshold was lower in patients (n = 141, median and [interquartile range]: 34 mA [24–46]) than in controls (n = 35, 58 mA [44–77], p = 0.0003), but none of the tested polymorphism genotype was significant. An association was found between fibromyalgia and three polymorphisms related to TNF, TACR1, and IL10 genes, either by testing the simple allelic frequency or an additive model. When the analysis was adjusted on NFR threshold, only the association with TACR1 was still significant, along with other associations with polymorphisms related to FAAH, HTR1A, and MYT1L. Associations were however non significant when a Bonferroni's correction was applied.

Conclusions

This study confirmed that CS is a major feature of fibromyalgia. Although several polymorphisms stand out in the analysis, no significant relationship between CS and 60 selected candidate‐genes genotype could be highlighted. This information may help however to guide further research to explore this important pathway in larger well phenotyped cohorts.

Significance Statement

Sixty selected fibromyalgia and pain candidate‐genes have no significant association with the nociceptive flexion reflex, a surrogate marker of central sensitization in 201 fibromyalgia women. But the study may help however to guide further research to explore this important pathway in larger well phenotyped cohorts.

Trial Registration

ClinicalTrials.gov identifier: NCT04624581

Keywords: case–control, fibromyalgia, genetic, nociception flexion reflex, pain

1. Introduction

The fibromyalgia syndrome (FMS) is a chronic pain condition with a worldwide prevalence of 2% to 4% (Berwick et al. 2022). It associates chronic widespread pain (CWP) with sleep disturbances, fatigue, impaired cognitive and physical function, psychological distress, and other comorbidities (Giorgi et al. 2022; Nicholas et al. 2019). CWP is defined by the IASP as nociplastic pain, that is, “pain that arises from altered nociception despite no clear evidence of actual or threatened tissue damage causing the activation of peripheral nociceptors or evidence for disease or lesion of the somatosensory system causing the pain” (Kosek et al. 2016; Raja et al. 2020). Presentations of FMS are heterogeneous and classifications according to different symptomatic, biological, psychological or sociological features, do not reach consensus (Yim et al. 2017). Moreover, the underlying mechanisms of CWP are still not clear, although a pivotal role for central sensitisation (CS), an “amplification of neural signalling within the central nervous system that elicits pain hypersensitivity” is underlined (Woolf 2011). Abnormal cortical processing, increased activity in the pain matrix, alterations of the inhibitory pain modulation and molecular changes in pain pathways have been described in FMS (Gyorfi et al. 2022).

Quantification of CS seems to be possible in humans by exploring the nociceptive flexion reflex (NFR), a spinal polysynaptic withdrawal reflex (Skljarevski and Ramadan 2002) and a validated measurement of spinal nociceptive excitability (Leone et al. 2025). The NFR threshold, that is, the lowest noxious stimulation intensity required to trigger a reflex motor response (Leroux et al. 1995), is decreased in patients with chronic musculoskeletal pain. It has been suggested as a good surrogate of CS, as shown in 5 of the 9 included studies of a recent systematic review, despite methodological disparities (Lim et al. 2011).

A genetic predisposition is also highly suspected in FMS. A review of seven cohort studies of twins reported heritability rates from 27% to 71% for CWP (Kerr and Burri 2017), and familial aggregation in FMS patients (Hackshaw 2021). Many candidate‐gene association studies have been conducted in the last three decades and these have identified more than 50 genes associated with FMS, including monoamines, catechol‐O‐methyl‐transferase (COMT), and neurotransmitters involved in pain (Kerr and Burri 2017; D'Agnelli et al. 2019; Janssen et al. 2021). Genome‐wide association studies (GWAS) have also been conducted in CWP/FMS, but none of the significantly associated single nucleotide polymorphisms (SNPs) provided a definite determinant of the hypothetic pathophysiology (Docampo et al. 2014; Rahman et al. 2021).

As CS appears as a major feature of CWP, our hypothesis is that it could be a mediator of the effect of the genotype on the FMS phenotype. In line with this, in FMS patients, the NFR threshold has been reported to be influenced by a variant of COMT, a gene often cited in the pathogenesis of FMS (Desmeules et al. 2014), and by a variant possibly linked to the synthesis of a neuronal trafficking protein (Gloor et al. 2022). Then, the objective of the current study was to further explore the mediating role of CS in the genome‐related pathogenesis of FMS, this by testing a more refined set of candidate genes. With the same methods as in the two above‐mentioned studies, we used the NFR threshold as the surrogate marker of CS.

2. Materials and Methods

2.1. Study Design

This prospective case–control study was conducted according to the guidelines of the Declaration of Helsinki, was approved by a research Ethics committee (CPP Ile‐de‐France II, Paris, France) on 31/08/2020 (20.06.30.58854 RIPH2 HPS) and registered on ClinicalTrials.gov (NCT04624581). The study took place at the Clinical Investigation Centre of the University Hospital of Clermont‐Ferrand (France).

2.2. Study Population

All participants were women, 18 to 65 years old, and gave their informed consent prior to the inclusion. The FMS cases were defined using the 2016 American College of Rheumatology criteria of FMS (Wolfe et al. 2016). The healthy volunteers were matched on age (±5 years) and menopausal status with FMS patients and must be free from any chronic or current pain or analgesic medication.

Exclusion criteria for all participant were: childbearing age with no effective contraceptive, pregnancy or breastfeeding; concomitant involvement in another interventional trial; antibiotics intake in the last 3 months, any serious disease that could interfere with the study. FMS patient with long‐term drug treatment could be included but had to accept to be free of any analgesics medication the day of the visit for the exploration.

2.3. Central Sensitization Test

Two objective parameters have been measured to assess CS in FMS cases and healthy volunteers: the NFR threshold (primary parameter), and the temporal summation (TS). The Central Sensitization Inventory (CSI) Part A questionnaire‐based assessment of CS was also obtained.

NFR was explored as previously described (Desmeules et al. 2003): we used a Nicolet Viking Quest 2 CH device (Nicolet Biomedical, Madison, WI, USA). The participant was lying in bed in a supine position, in order to obtain muscular relaxation. Electromyographic responses were recorded using a pair of surface electrodes (single differential), placed over the biceps femoris muscle of the dominant side, 10 cm above the popliteal fossa, with a reference electrode attached over the anterolateral aspect of the ipsilateral tibia bone plateau. Repeated electrocutaneous stimulations were applied over the retro‐malleolar track of the ipsilateral sural nerve using a pair of electrodes, and delivered with a single rectangular impulse (0.5 ms) and a 6–10 s interstimulus interval, by a constant current stimulator at intensities ranging from 1 to 100 mA. The NFR was identified as a multiphasic signal appearing with a latency between 90 and 250 ms, and was considered to be present when the corrected computed surface was 0.5 mV.ms−1 (positive response). The participant was instructed that the sensation intensity could randomly increase, decrease, or stay the same, with stimulus repetition occurring independently of her reply. Following every electrical stimulation of the sural nerve, the participant was asked to rate her pain on a numerical rating scale (NRS) from 0 (no pain at all) to 10 (worst pain imaginable) with a positively painful response defined by a cut‐off value of 4. A successful NFR threshold was then defined as the intensity of current (in mA) that induced 50% of positive responses to a series of 30 to 40 stimulations, by fitting the percentage of positive responses to Hill's equation. We can specify that increasing the range does increase the number of possible stimulations, but not this of painful ones, as this was an ascending method with an up‐and‐down adjustment made only around the first level of painful intensities, so it was generally possible to estimate the threshold after 10 painful stimulations or less, whatever the threshold. Evoked fear or anxiety was avoided by the choice made in our protocol not to increase intensity and we stopped the test when the patient felt the electrical stimulation as too painful, even before the NFR had been evoked.

There is no gold standard to assess NFR with sometimes heterogeneity in the results (Amiri et al. 2021), our choice was to follow a methodology already used (Desmeules et al. 2003), not only because it succeeded to identify a greater CS in FMS (which is our working hypothesis), but also it identified interactions between genetic variants and CS in FMS (Gloor et al. 2022). The investigators in charge of explorations (V.L. and N.M.) have been trained to the methodology in Geneva University Hospital. The measurement of the NFR threshold could not be obtained for all individuals, because the electrical stimulation was too painful (“early pain failure”) or because no multiphasic signal appeared even after strong stimulations or once the cut‐off level of 100 mA stimulation was reached (“excessive threshold failure”). In both situations, stop values were recorded.

TS was measured by using a size 6.45 (180‐g) Von Frey hair applied to the volar side of the non‐dominant forearm. The hair was applied once, and the participant rated her pain as described previously, then the hair was applied 10 times at a rate of 1 Hz and pain was rated during this series. This exploration was repeated 5 times. TS was calculated as the ratio of pain elicited by the series of 10 stimuli and by the initial single stimulus; the higher the ratio, the higher TS (Latremoliere and Woolf 2009).

The CSI test Part A (Mayer et al. 2012) assesses the frequency of health‐related symptoms associated with CS syndromes. The participant was asked to rate each question of the 25‐item survey on a 5‐point scale (0 = “never”, 4 = “always”) with a maximum sum of 100. Higher CSI scores represent greater self‐reported symptomatology. A cut‐off score of 40 or greater has been shown as acceptable in psychometrics for identifying patients with CS syndromes (Neblett et al. 2013; Salaffi et al. 2022).

2.4. Genomics

A 4 mL blood sample was collected in an EDTA tube for every individual. The tubes were aliquoted in 1 mL of whole blood and stored frozen at −80°C until processing by the Laboratory for Clinical Pharmacology and Toxicology of the Geneva University Hospital (HUG). DNA purification and concentration of 241 samples (201 FMS patients and 40 controls) were performed using the QIAsymphony DSP DNA kit (QIAGEN) on a QIAsymphony SP/AS. Quantification was performed with the Invitrogen BR dsDNA Assay kit on a Qubit 4 Fluorometer (both from Thermo Fischer Scientific, Waltham, Massachusetts, United States of America).

A total of 60 SNPs of 48 candidate genes (Table 1) were selected from the literature (44 SNPs) and from HUG previous study (Gloor et al. 2022). SNP selection was based on the strategy outlined by Belfer et al. (Belfer et al. 2004), taking into account: (i) demonstrated clinical association, (ii) clinical study replication, (iii) predicted polymorphism abundance in the target population, and (iv) SNP functionality (Tunbridge et al. 2019).

TABLE 1.

List of the genes and SNPs tested in the current study.

Gene/region Chr. SNP Encoded protein Function (pain/FMS) Previously reported association References
ADRB2 5 rs1042713; rs1042714; rs1820074 Adrenergic rec, type β2 ? CWP, FMS impact, FMS‐associated CS (?) (Diatchenko et al. 2006; Vargas‐Alarcón et al. 2009; Hocking et al. 2010; George et al. 2016)
APOL3 22 rs132615 Apolipoprotein L3 ? FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
APT2C1 3 rs10490825 Ca2+ ATPase, type 2C, member 1 ? CWP (GWAS) (Rahman et al. 2021)
BDNF 11 rs6265; rs11030104 Brain‐derived neurotrophic factor Fibre regen FMS (Xiao et al. 2011; Park et al. 2018; da Silveira Alves et al. 2020)
CHRM2 7 rs7800170 Acetylcholine rec, type M2 ? TMD (Smith et al. 2011)
COL20A1 20 rs9680065 Collagen, type XX, α1 ? FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
COMT 22 rs6269; rs4633; rs4818; rs4680 Catechol‐O‐methyltransferase S.sp/Desc antinoc Pain sensitivity (exp); FMS; FMS‐associated pain sensitivity/psychological distress (Diatchenko et al. 2004; Martínez‐Jauand et al. 2012; Smith et al. 2014; Nielsen et al. 2016; Tunbridge et al. 2019; Ho et al. 2019)
DRD2 11 rs1800497; rs6277 Dopamine rec, type 3 S.sp/Desc antinoc Response to brain stimulation (Jääskeläinen et al. 2014; Tunbridge et al. 2019)
DRD3 3 rs6280 Dopamine rec, type 4 S.sp/Desc antinoc FMS, FMS‐altered descending controls, FMS‐associated hyperalgesia (Potvin et al. 2009; Tunbridge et al. 2019)
ESR1 6 rs3020377 Oestrogen rec S.sp/Desc antinoc (COMT epistasis) TMD (Smith et al. 2014)
FAAH 1 rs324420 Fatty acid amide hydrolase S.sp/Desc antinoc Pain cold sensitivity, postperative pain (Cajanus et al. 2016)
FOXP3 23 rs2232365; rs3761548 Forkhead box P3 Immune response regul Rheumatoid arthritis (Lee et al. 2015; Paradowska‐Gorycka et al. 2014; Jiang et al. 2016; Kwon et al. 2017; Hashemi et al. 2018)
GCH1 14 rs10483639; rs3783641; rs8007267 GTP cyclohydrolase 1 (NO synthesis) Sp sensit Pain sensitivity; various chronic pain conditions; FMS (Kim et al. 2013; Lötsch et al. 2007; Smith et al. 2014; Tegeder et al. 2006, 2008)
GRIA4 11 rs10895837 Glutamate ionotropic rec, AMPA type, s/u 4 Sp synaptic transmission FMS (Smith et al. 2012)
HAP1 17 rs4796604 Huntingtin associated protein 1 Neuronal architecture FMS‐associated CS (GWAS) (Gloor et al. 2022)
HTR1A 5 rs6295 Serotonin rec, type 1A S.sp/Desc antinoc Response to brain stimulation; interaction with OPRM1 (Malaguti et al. 2011)
HTR1B 6 rs6296 Serotonin rec, type 1B S.sp/Desc antinoc Interaction with HTR1A (?) (Tour et al. 2017)
HTR2A 13 rs9316233; rs6313 Serotonin rec, type 2A S.sp/Desc antinoc Pain sensitivity; FMS (Tander et al. 2008; Smith et al. 2011; Nicholl et al. 2011; Louca Jounger et al. 2021)
HTR3A 11 rs1062613 Serotonin rec, type 3A S.sp/Desc antinoc TMD pain intentity; pain catastrophizing (Louca Jounger et al. 2021)
HTR3B 11 rs1176744 Serotonin rec, type 3B S.sp/Desc antinoc FMS (Horjales‐Araujo et al. 2013; Louca Jounger et al. 2021)
HTR4 5 rs1820074 5‐Hydroxytryptamine rec 4/β2‐adrenergic rec S.sp/Desc antinoc FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
IL10 1 rs1800896 Interleukin, type 10 Immune response TMD (Smith and Humphries 2009; Assis et al. 2014; Moudi et al. 2018; Vilkeviciute et al. 2021)
IL17 6 rs2275913 Interleukin, type 17 Inflammation None (Jiang et al. 2015; Dai et al. 2016)
IL6 7 rs1800795 Interleukin, type 6 Periph sensit None (Knisely et al. 2019)
IL8 4 rs4073 Interleukin, type 8 Immune response None (Smith and Humphries 2009)
ILR6 1 rs2228145 Interleukin rec type 6 Periph sensit None (Marinou et al. 2010)
ITGA1 5 rs6450076 Integrin alpha‐1 Immune response None (Gloor et al. 2022)
KANK1 9 rs3739592 KN motif and ankyrin repeat domain‐containing protein 1 Nerve growth regul FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
KCNK17 6 rs10807205 Potassium channel, subfamily K, member 17 ? Migraine (GWAS); FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
LINC00351/LINC00375 13 rs2497410 Long intergenic non‐protein coding RNA 351 & 375 ? FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
LOC101926964 1 rs10889198 Unknown ? FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
LUZP2/ANO3 11 rs4923288 Leucine zipper protein 2 / Anoctamin 3 ? FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
MAOA 23 rs909525; rs979606 Monoamine‐oxydase, type A S.sp/Desc antinoc FMS (Pinsonneault et al. 2006; Xu et al. 2017)
MTSS1/MIR4662B 8 rs13270557 Metastasis suppressor protein 1 / μRNA n°4662B ? FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
MYTL1 2 rs11127292 Myelin transcription factor 1 like ? FMS (GWAS) (Docampo et al. 2014)
NPY2R 4 rs6827846 Neuropeptide Y rec, type 2 ? FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
OPRM1 6 rs1799971 Opioid rec, type μ1 Central antinoc Pain sensitivity (exp); interaction with HTR1A; FMS (Fillingim et al. 2005; Solak et al. 2014; Tour et al. 2017)
OPRPN 4 rs6832923 Opiorphin prepropeptide Central antinoc BMS; FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
P2RX7 12 rs7958311 P2X purinoceptor type 7 Periph transd Chronic pain (Gloor et al. 2022)

PSAT1 /

LOC101927450

9 rs10117274 Phosphoserine aminotransferase ? FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
RNF123 3 rs1491985 E3 ubiquitin‐protein ligase RNF123 ? CWP (GWAS) (Rahman et al. 2021)
SCML4 6 rs3734754 SCM polycomb group protein like 4 ? FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
SLC6A4 17 rs2129785; rs11867581 Solute carrier, family 6, member 4 (sodium‐dependent serotonin transporter) S.sp/Desc antinoc Migraine, TMD, FMS, FMS‐associated anxiety (Vinkhuyzen et al. 2011)
SYNPR 3 rs1562859 Synaptoporin ? FMS‐associated CS (GWAS, ns) (Gloor et al. 2022)
TACR1 2 rs11688000 Tachykinin rec, type 1 Sp sensit OA pain, FMS (Warner et al. 2017)
TNF 3 rs1800629 Tumour necrosis factor, α type Periph sensit FMS (Smith and Humphries 2009; Zhang et al. 2014; Knisely et al. 2019)
TPH2 12 rs4570625 Tryptophan hydroxylase 2 (serotonin biosynthesis) S.sp/Desc antinoc IBS‐associated fatigue (Han et al. 2018; Pratelli and Pasqualetti 2019)
TSPO 22 rs6971 Translocator protein (neurosteroid biosynthesis) Inflammation FMS, FMS‐associated pain severity (Kosek et al. 2016; Fanton et al. 2022)
Open in a new tab

Note: For each gene or region listed in alphabetic order are shown, from left to right: the chromosome where the gene or region is located (“Chr”); the single nucleotide polymorphism (SNP) that has been tested; the name of the protein encoded by the gene, if known (“rec” stands for “receptor”); the function of the protein within either the pathophysiology of pain or of fibromyalgia syndrome (FMS), if known; the type of previously reported association between the gene or region, and conditions close to fibromyalgia, if any; and the literature references.

Abbreviations: antinoc, antinociception; BMS, burning mouth syndrome; CS, central sensitisation; CWP, chronic widespread pain; desc, descending; exp, experimental; FMS, fibromyalgia syndrome; GWAS, genome‐wide association study; IBS, irritable bowel syndrome; ns, non‐significant; OA, osteoarthritis; periph, peripheral; regen, regeneration; regul, regulation; S.sp, supraspinal; s/u, subunit; sensit, sensitization; Sp, spinal; TMD, temporomandibular disorder; transd, transduction.

The cut‐off for minor allele frequency (MAF) was > 0.1. All genetic coordinates refer to the Ensembl GRCh37 reference assembly. After selecting the 60 targets SNPs, a custom TaqMan OpenArray Genotyping array has been prepared by Life Technologies (Thermo Fischer Scientific, Waltham, Massachusetts, United States of America). Genotyping was performed at the iGE3 genomic platform of the University of Geneva (UniGE) using an Open Array AccuFill System and a QuantStudio 12 K Flex real‐time PCR system from Life Technologies.

2.5. FMS Pain and Impact Status

FMS pain and impact status assessments were done in FMS patients only. CWP, as a recall of the 2 past weeks, was assessed by the Brief Pain Inventory (BPI), a 11‐item questionnaire. The response to each item on a 11‐point NRS and two scores ranging from 0 to 10 are calculated, one for pain severity (i.e., overall averaged intensity), and one for pain interference (i.e., overall averaged impact of pain on different physical and psychosocial aspects of daily life Cleeland and Ryan 1994). Another aspect of the impact of FMS was assessed using the Fibromyalgia Impact Questionnaire (FIQ); an average impact of FMS is defined with FIQ over > 50, and a severe impact with FIC over 70 (Bennett 2009).

2.6. Sample Size Calculation and Statistical Analyses

The sample size was estimated firstly in order to detect differences between patients and controls for the NFR threshold. We based our calculation on the study of Desmeules et al. that compared a mean NFR threshold of 22.7 mA in the 85 FMS cases to 33 mA in the 40 matched controls (Desmeules et al. 2003). With α = 5% and 1–β = 95%, we estimated that 100 cases and 20 controls were enough to identify a difference between cases and controls. However, by simulating effect size on this basis, we estimated that, with 200 fibromyalgia patients and 40 healthy volunteers, an effect size greater than 0.5 can be highlighted with a two‐sided type I error of 5% and a statistical power greater than 80%. This sample size is also sufficient to detect differences in allelic frequency between groups, assuming odds ratios greater than 2 and the same statistical assumptions parameters. The sample size calculation was performed on Stata.

Continuous data were expressed as mean and standard deviation or as quartiles, depending on their distribution. The assumption of normality (Gaussian distribution) was assessed using the Shapiro–Wilk test. In order to study the genetic signature of FMS, we used a stepwise approach from the most simple test of association between the genotype (gene by gene) and the main phenotype (case/control, i.e., FMS yes/no), to analysis of the mediation by central sensitisation on this association, if any. To do so, we used two different ways, (i) by studying the relationship between the genotype and the without and with adjustment on the NFR threshold, and (ii) by studying the relationship between the genotype and the NFR threshold, here considered as an “intermediate” phenotype (mediator). A mediating effect would be suggested by either a stronger significance obtained after adjustment or a direct relationship. Comparisons between groups (patients vs. controls) were performed using the chi‐square test or the Fisher's exact test for categorical variables, and Student's t‐test or Mann–Whitney U test for continuous variables. Allelic frequency comparisons were conducted using the chi‐square test or the Fisher's exact test, as well as logistic regression and Wright's fixation index analysis. A complementary analysis was conducted according to an additive genetic model, in which the dependent variable was no longer the minor allele frequency but a score attributed to each observation, with 0 for the genotype ‘AA’, 1 for ‘Aa’, and 2 for ‘aa’. The score was then tested with the phenotype (case/control) as the sole explanatory variable (unadjusted logistic regression), and with the NFR threshold as a covariate (adjusted logistic regression). Results were presented as odds ratios (OR) with their 95% confidence intervals. The additive score was also tested with the NFR threshold as the sole explanatory variable within the sample of FMS cases, by using a linear regression. A linear regression was also used to test the correlations between MAFs, of the study (study controls vs. expected European population and cases vs. controls); the significance of the relationship was tested by an ANOVA and the effect size by the coefficient of determination R 2. Statistical analyses were performed using Stata software (version 15, StataCorp, College Station, TX, USA) and XLStat (Addinsoft, Paris, F). A two‐sided type I error rate of 5% was considered statistically significant. The p‐values were directly interpreted without and with a Bonferroni's correction for multiple comparisons.

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to containing information that could compromise the privacy of research participants.

3. Results

3.1. Sample Description

Participants were included from November 2020 to January 2022. The flow chart of the study is shown in Figure 1. Patients and controls were matched based on age, BMI, and menopause status. All FMS patients were positive for the ACR 2016 criteria (Table 2). They also reported high pain severity and interference scores (BPI), with an important impact of FMS as evaluated by the FIQ, some patients being severely affected (FIQ > 70).

FIGURE 1.

FIGURE 1

Open in a new tab

STROBE flow chart of the study.

TABLE 2.

General characteristics of the participants.

FMS cases (n = 201) Controls (n = 40) p
Age (years) 50 [43–56] 52 [47–55] 0.441
BMI (kg.m−2) 25 [21–29] 24 [22–28] 0.376
BMI classes
Normal 97 (48.3) 23 (57.5) 0.611
Overweighed 65 (32.3) 11 (27.5)
Obese 39 (19.4) 6 (15.0)
Menopause (not) 104 (53.3) 19 (47.5) 0.201
Substituted menopause 7 (3.6) 4 (10.0) NC
Not substituted menopause 84 (43.1) 17 (42.5) NC
ACR 2016 criteria
WPI (0‐to‐19 scale) 14 [11–16] NA
SSS part A (0‐to‐9 scale) 8 [7–8]
SSS part B (0‐to‐3 scale) 3 [2–3]
SSS total (0‐to‐12 scale) 10 [9–11]
FS (0‐to‐21 scale) 23 [21–26]
Other FMS pain and impact status
Time since first diagnosis (months) 61 [34–144] NA
BPI severity (0‐to‐10 scale) 6 [5–6.8]
BPI interference (0‐to‐10 scale) 6.3 [4.7–7.4]
BPI sleep interference (0‐to‐10 sub‐scale) 8 [6–9]
FIQ total score (0‐to‐100) 62 [54–73]
Open in a new tab

Note: The numerical values are expressed as median and [first quartile–third quartile]; the categorical values are expressed as headcount and (percentage of the related sample).

Abbreviations: ACR, American College of Rheumatology; BMI, body mass index; BPI, brief pain inventory; CS, component summary (quality of life); CSI, central sensitization inventory; FIQ, fibromyalgia impact questionnaire (total score); FMS, fibromyalgia syndrome; FS, fibromyalgia score; NA, not applicable; NC, not calculated; SSS, symptom severity score; WPI, widespread pain index.

3.2. CS Testing

Results of CS are described in Table 3. NFR threshold was obtained in 70.1% of FMS cases and 87.5% controls. Compared to controls, FMS patients had a greater CS, lower threshold values (p = 0.0003), a higher pain score when NFR threshold was reached (p = 0.002), a higher TS ratio (p < 0.0001), and a higher CSI total score (p < 0.0001).

TABLE 3.

Parameters of central sensitisation.

FMS cases (n = 201) Controls (n = 40) p
NFR threshold (primary parameter)
Failure for early pain 27 (13.4) 3 (7.5) 0.073
Failure for excessive threshold 33 (16.4) 2 (5.0)
Successful measurement 141 (70.1) 35 (87.5)
Threshold value (mA) 34 [24–46] 58 [44–77] 0.0003
Pain at threshold (scale 0‐to‐10) 6 [4–7] 4 [3–6] 0.002
Secondary parameters
Temporal summation ratio 1.8 [1.3–3.1] 0 [0–2] < 0.0001
CSI (scale 0‐to‐100) 62 [53–70] 16 [13–24] < 0.0001
Open in a new tab

Note: The numerical values are expressed as median and [First quartile–Third quartile]; the categorical values are expressed as headcount and (percentage of the related sample).

Abbreviations: CSI, central sensitization inventory; FMS, fibromyalgia syndrome; NFR, nociceptive flexion reflex.

3.3. Genetic Association Analyses With Disease

All samples and SNPs were above the call rate (CR) threshold (> 98%) and respected the Hardy–Weinberg equilibrium (p‐value threshold < 1.10−3 (0.000833 = 0.05/60)). The mean CR (SNP and sample) was 99.84%. The MAF observed in the sample of controls was well correlated with that expected in the European population (as reported by https://www.ensembl.org/index.html), with an R2 of 0.794 (p < 0.0001) (Figure 2). The MAF observed in the sample of cases was also well correlated with that of the sample of controls, with an R 2 of 0.819 (p < 0.0001) (Figure 2).

FIGURE 2.

FIGURE 2

Open in a new tab

Scatterplots showing the correlations between minor allele frequencies (MAF) for the 60 single nucleotide polymorphisms (SNPs) of interest.

Overall results of analyses of the FMS phenotype are available in Table S1.

When the FMS phenotype was analysed according to allelic frequencies, a significant relationship was found for the SNPs rs1800629, rs11688000, and rs1800896, respectively related to the TNF, TACR1, and IL10 genes (Table 4). When, instead of testing the allelic frequencies, a genetic additive model was used, results were similar (respective p‐values: 0.034, 0.029 and 0.041; see Table S1). When, instead of testing the allelic frequencies, the genotype was used, a significant association was also found for TNF and TACR1, but no longer for IL10 (Table 5).

TABLE 4.

Genetic associations (comparison of allelic frequencies).

Gene CHR SNP A1 A2 MAF Frequency OR SE p
EU FMS cases Controls
TNF 3 rs1800629 A G 13.4% 11.9% 21.3% 0.502 (0.272–0.929) 0.314 0.032
TACR1 2 rs11688000 G A 37.3% 36.6% 50.0% 0.576 (0.356–0.934) 0.246 0.032
IL10 1 rs1800896 C T 45.3% 44.8% 32.5% 1.684 (1.014–2.797) 0.259 0.048
Open in a new tab

Note: For each association found significant (p < 0.05), are shown, from left to right: the related gene and chromosome code (CHR), the single nucleotide polymorphism (SNP) identifier, the minor (A1) and the major (A2) alleles, the expected minor allele frequency (MAF) for European (EU) databases, the observed MAF in the study samples, the odds ratio (OR) along with its 95% confidence interval limits, the standard error (SE), and the p‐value for the association test.

TABLE 5.

Relationships between genotype, FMS and controls.

Minor/minor Minor/major Major/major p
rs1800629 (TNF) AA AG GG
FMS (n = 201) 5 (2.5) 38 (18.9) 158 (78.6) 0.030 a
Controls (n = 40) 1 (2.5) 15 (37.5) 24 (60.0)
rs11688000 (TACR1) GG GA AA
FMS (n = 201) 30 (14.9) 87 (43.3) 84 (41.8) 0.028 b
Controls (n = 40) 8 (20.0) 24 (60.0) 8 (20.0)
rs1800896 (IL10) CC CT TT
FMS (n = 201) 38 (18.9) 104 (51.7) 59 (29.4) 0.122
Controls (n = 40) 4 (10.0) 18 (45.0) 18 (45.0)
Open in a new tab

Note: For each single nucleotide polymorphism (SNP) for which a significant association with the clinical condition (FMS = fibromyalgia syndrome, or healthy matched controls) was found in the non‐adjusted analyses, this contingency table shows the respective distribution of the genotypes. Each genotype is the possible combination of the two alleles of the SNP, the minor and the major one. The statistical test was either the Fisher's exact test or the chi‐square test.

a

A post hoc analysis merged the ‘AA’ and the ‘AG’ genotypes, and the proportion of ‘GG’ genotypes was significantly superior in FMS patients than in controls (p = 0.012).

b

A post hoc analysis merged the ‘GG’ and the ‘GA’ genotypes, and the proportion of ‘AA’ genotypes was significantly superior in FMS patients than in controls (p = 0.012).

When the analyses were adjusted on the NFR threshold as a covariate, a significant relationship was found for four SNPs. These were: the above mentioned rs11688000 (TACR1, OR = 0.502 [0.257–0.978]; p = 0.043); rs6295 (HTR1A, OR = 0.471 [0.244–0.910]; p = 0.025); rs11127292 (MYT1L, OR = 0.338 [0.122–0.936]; p = 0.037); and rs324420 (FAAH, OR = 2.81 [1.007–7.887]; p = 0.048). However, when the NFR threshold was compared between the different allelic categories of those four SNPs, no significant association was found (Figure 3). Similarly, when the genetic additive model was used to explain the NFR threshold within the sample of FMS patients, no relationship was found with any of the tested SNPs (Table S2).

FIGURE 3.

FIGURE 3

Open in a new tab

Box plots showing the distribution of the threshold of the nociceptive flexion reflex (NFR) depending on the genotype, for each of the four single nucleotide polymorphisms (SNP) for which a significant association has been found between the phenotype and the allelic frequency, after adjustment on the NFR threshold. No significant difference has been found between genotypes for any of the SNP studied.

3.4. Other Associations

We failed to determine the NFR threshold in some participants and as concomitant medications (e.g., analgesics, antidepressants or antiepileptics for pain) could contribute to this result, we performed an analysis of interaction and did not find any bias (Table S3).

4. Discussion

This study confirms central sensitisation in well‐defined cases of FMS, using NFR as an indirect proxy of CS and as a covariate in our genetic association study. Indeed, questionnaires (including BPI and FIQ) showed that the included FMS patients were substantially affected by their condition.

Our patients sample appears to display increased CS, as shown by the lower values of NFR threshold. Considering the NFR threshold a marker of spinal cord hyperexcitability (Lim et al. 2011) and that the threshold of the RIII reflex seems to be a reliable readout of spinal changes (Leone et al. 2025), our results (34 [24–46] vs. 58 [44–77] mA p = 0.0003) align with a recent meta‐analysis reporting—despite heterogeneity—a mean difference of −3.16 mA (95% CI limits: −6.82 and 0.50 mA) in NFR threshold between FMS patients and controls (Amiri et al. 2021). Most other case–control studies reported similar lower NFR thresholds in FMS (Gloor et al. 2022; Van Oosterwijck et al. 2025), alike in other chronic pain conditions (Mattoo et al. 2018).

Other findings highlight hyperexcitability of nociceptive systems, as previously reported (Gyorfi et al. 2022; Latremoliere and Woolf 2009). Pain intensity during NFR determination was higher in FMS patients, confirming previous studies (Desmeules et al. 2003, 2014). The higher TS ratio and CSI scores also supported increased CS (Neblett et al. 2013) even if for CSI the reliability of the results in FMS patients should be taken with caution because many items of the CSI overlap with those of the diagnostic criteria for FMS (Leone et al. 2025).

Genetic analyses of the selected candidate genes identified significant associations between the FMS phenotype and the allelic frequencies of rs1800629, rs11688000 and rs1800896, all previously described in pain or FMS comorbidities.

rs1800629 belongs to the TNF gene which encodes for tumour necrosis factor, a pro‐inflammatory chemical messenger produced by the immune system. In our observation, the MAF (A is the minor allele) was lower in cases (OR = 0.502), but it was the GG genotype that we found to be more frequent in FMS cases vs. controls. This is discrepant with previous reports, as an association study in somatoform disorder reported that GG appeared as protective towards pain, while being a carrier of the A allele might be a risk factor for developing somatoform disorder associated with pain (Harms et al. 2013). Furthermore, other studies reported higher serum concentrations of TNF in FMS patients (O'Mahony et al. 2021), and showed that the AA genotype was associated with a higher serum concentration of TNF (Wilson et al. 1997). However, we could not validate this underlying hypothesis, not having conducted assays on cytokines serum concentrations. Of note, rs1800629 had primarily been studied in migraine and rheumatoid arthritis, conditions which can be concomitant with FMS (O'Rielly et al. 2009; Schürks et al. 2009), and the AA genotype of rs1800629 has been found associated with sleep disorders and fatigue in cancer patients (Aouizerat et al. 2009). It remains possible that other SNPs than rs1800629 in connection with TNF are modulated in patients with FMS, given its influence on neural networks for the modulation of pain signalling (Baral et al. 2019). Finally, results could be explained by a type‐I error inflation with a loss of significance after adjustment on the NFR threshold.

rs11688000 belongs to TACR1 which encodes for a postsynaptic tachykinin receptor involved in CS (possibly in inflammatory conditions), and some polymorphisms of TACR1 may be involved in inter‐individual variability of pain (Storm et al. 2013). In our observation, the MAF (G is the minor allele) was lower in cases (OR = 0.576), but it was the GA genotype that we found to be more frequent in FMS cases. In a study in osteoarthritis (OA), the G allele at rs11688000 was associated with a decreased risk of symptomatic OA compared to asymptomatic OA cases, but the mechanism of pain is different in OA from FM (Warner et al. 2017). A study conducted in a smaller sample of Jewish patients failed to identify a significant association between FMS and polymorphisms of TACR1 (Ablin et al. 2009).

rs1800896 belongs to a gene encoding for interleukin 10, a cytokine implicated in the regulation of substance P, the natural agonist of the above mentioned tachykinin receptor TACR1 (Wallace et al. 2001). In our observation, the MAF (C is the minor allele) was higher in cases (OR = 1.684), but no genotype was found to be differently frequent in FMS cases. Testing this SNP was motivated by a recent systematic review reporting higher circulating concentrations of IL‐10 in FMS patients (O'Mahony et al. 2021) and rs1800896 has also been explored in other chronic pain conditions such as ankylosing spondylitis (Braga et al. 2021) or in irritable bowel syndrome (Zhu et al. 2019). The complexity in the genomics of inflammatory processes lies in the pro−/anti‐inflammatory balance, which is much more complex than the function of a sole protein (Peck et al. 2020). This could also explain our discrepant observations between the TNF‐ and IL10‐related SNPs. Studies on cytokines and their implications in pain expression of FMS are still needed.

Concerning the analyses we conducted with the NFR threshold as a covariate, the results have to be interpreted cautiously, as no genotype within any of these 4 SNPs was shown to influence the NFR threshold. A study in healthy volunteers previously explored the links between genotypic variations of rs6295 (HTR1A) and thermal perception levels as well as NFR thresholds, but like in our study, the authors did not observe any difference regarding NFR thresholds (Lindstedt et al. 2012). Results of rs11688000 (TACR1) can be interpreted as a non‐interaction of CS on the association with FMS, while those for rs324420 (FAAH), rs6295 (HTR1A) and rs11127292 (MYT1L) seem to be rather more a random effect than a true interaction of CS.

While we selected and studied a number of SNPs we considered to be of interest in the study of FMS, we did not identify significant associations. This applies to rs4796604 (HAP1), which has been identified as potentially associated with NFR threshold (Gloor et al. 2022), and recently a mechanistic study suggests that HAP1 may regulate Cav1.2 surface expression, which can lead to reduced neuronal excitability, BDNF secretion, inflammatory responses, and ultimately influences neuropathic pain progression (Pan et al. 2023). In our observation, we did not find significant association for the SNP related to HAP1 gene. Furthermore, our exploratory methods were strictly the same as in this previous study, although a difference was in the diagnosis criteria of FMS (ACR 2016 vs. 1990), and more concomitant medications that could influence NFR—even though we did not observe any influence of these treatments in our study (data not presented).

Other associations we did not confirm must also be discussed, including the one with rs3783641, though reported as associated with decreased pain sensitivity in FMS (Kim et al. 2013), and related to GCH1, a gene involved both in CS and in dopamine synthesis. This also applies to rs1799971 (OPRM1), previously described as associated with alterations of fMRI brain connectivity during pressure pain test in FMS (Ellerbrock et al. 2021). More noticeably, SNPs related to COMT had been reported to be linked to the risk of developing FMS (Estévez‐López et al. 2018; Matsuda et al. 2010) or to correlate with severe pain intensity in sensitised FMS patients (Gerra et al. 2024)—although results similar to ours have also been published (Lee et al. 2015). COMT has probably been the most studied gene in association studies of FMS, with mostly positive results, but meta‐analyses have generally either minimised the size of such association, or even rejected it (Tammimäki and Männistö 2012; Zhang et al. 2014; Lee et al. 2015). In addition, COMT variants have been suspected in the pathogenesis of pain, (e.g., experimental, back pain, migraine…). Furthermore, the natural frequency of the favouring genotype is quite high, and COMT might be rather involved in gene–gene or gene–environment interactions, which are however rarely studied. Finally, we failed to replicate the associations found for two SNPs by a GWAS of CWP patients, but the related genes (RNF123 and APT2C1) have no known involvement in pain or inflammation.

Discrepancies from other studies may stem from FMS heterogeneity as some phenotypes may be present involving biopsychosocial factors, psychological disturbances, and autonomic dysfunction that are difficult to address by cross‐sectional studies. Furthermore, if there is some genetic influence on both pain and its biopsychosocial determinants, this is unlikely to be monogenic, gene–gene, or polygenic risk scores. Moreover, gene–environment interactions are hardly addressed by gene‐association studies, even by GWAS.

Technical limitations include failed NFR measurements in some participants, although the team which conducted explorations had been trained for this purpose. Concomitant medications (e.g., antidepressants, analgesics, or anxiolytics) may have contributed, though our analysis found no bias. Stress, sleep quality, physical activity, or cardiac variability were not assessed (Sandrini et al. 2005). Failure rate was quite higher than we expected, considering the previous reports, although such rate had not been clearly reported in most studies (Amiri et al. 2021). However, some works reported 7/160 patients (4.4%) (Desmeules et al. 2014) or 8/15 patients (53%) (Umeda et al. 2013) with missing values.

In conclusion, we confirmed that spinal nociceptive excitability using NFR is higher in FMS patients and although several polymorphisms stand out in the analysis, no relationship between CS and the 60 selected SNPs could be highlighted. Larger cohort studies conducted in better phenotyped FMS patients, combined with either candidate gene association studies or GWAS, are still needed to complete our knowledge.

Author Contributions

This study was designed by N.M., G.P., B.P., V.L., C.D., J.D. and Y.G. The experiments were performed by N.M., V.L., G.P. and C.D. Genetic assays were performed by Y.G. and N.M. The data were analysed by B.P., Y.G., Y.R., C.D., M.D. and N.M. and the results were critically examined by all authors. N.M. had a primary role in preparing the manuscript, which was edited by G.P., J.D., Y.G. and C.D. All authors have approved the final version of the manuscript and agree to be accountable for all aspects of the work.

Funding

This work was supported by APICIL foundation.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1: Overall results of the analyses of the association between the genotype and the phenotype (FMS = fibromyalgia syndrome, or healthy matched controls) for each of the 60 studied candidate genes. For each analysis are shown, from left to right: the single nucleotide polymorphism (SNP) identifier, the related gene, the minor and the major alleles, the expected minor allele frequency (MAF) for European databases (as reported by https://www.ensembl.org/index.html), the observed MAF in the study samples, and the results of the simple comparison of allelic frequencies, the simple logistic regression in which a genetic additive model was used to express the genotype, and the same logistic regression with the NFR threshold taken as a covariate (adjusted analysis). To help to compare the results between each step, these are expressed as an odds ratio (OR) along with its 95% confidence interval limits, and the p‐value for the chi‐square test (conducted on the contingency table for the comparison of allelic frequencies, and on the Wald chi‐square test for the logistic regressions).

EJP-30-0-s002.xlsx (18.1KB, xlsx)

Table S2: Overall results of the analyses of the association between the genotype (expressed as a score from the genetic additive model) and the NFR threshold within the sample of fibromyalgia syndrome (FMS) patients. For each analysis are shown, from left to right: the single nucleotide polymorphism (SNP) identifier, the related gene, the beta coefficient of the simple linear regression along with its 95% confidence interval limits, and the p‐value from the ANOVA that tested the significance of the model.

EJP-30-0-s001.xlsx (12.8KB, xlsx)

Table S3: Overall results of the analyses of the association between the NFR measurement type (failure for early pain, successful measurement, failure for excessive threshold) and the long‐term concomitant medications (not taken on the day of the visit) within the sample of fibromyalgia syndrome (FMS) patients.

EJP-30-0-s003.docx (12.7KB, docx)

Acknowledgements

We thank the patients for their participation, the APICIL foundation for their financial support, the University Hospital of Clermont‐Ferrand for their administrative support, the University Hospital of Geneva for their support for the genetic analysis, and the PIC/CIC Inserm 1405 team for their support for the investigations.

References

  1. Ablin, J. N. , Bar‐Shira A., Yaron M., and Orr‐Urtreger A.. 2009. “Candidate‐Gene Approach in Fibromyalgia Syndrome: Association Analysis of the Genes Encoding Substance P Receptor, Dopamine Transporter and α1‐Antitrypsin.” [PubMed]
  2. Amiri, M. , Rhudy J., Masani K., and Kumbhare D.. 2021. “Fibromyalgia and Nociceptive Flexion Reflex (NFR) Threshold: A Systematic Review, Meta‐Analysis, and Identification of a Possible Source of Heterogeneity.” Journal of Pain Research 14: 1653–1665. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Aouizerat, B. E. , Dodd M., Lee K., et al. 2009. “Preliminary Evidence of a Genetic Association Between Tumor Necrosis Factor Alpha and the Severity of Sleep Disturbance and Morning Fatigue.” Biological Research for Nursing 11: 27–41. [DOI] [PubMed] [Google Scholar]
  4. Assis, S. , Marques C. R., Silva T. M., et al. 2014. “IL10 Single Nucleotide Polymorphisms are Related to Upregulation of Constitutive IL‐10 Production and Susceptibility to Helicobacter pylori Infection.” Helicobacter 19, no. 3: 168–173. 10.1111/hel.12119. [DOI] [PubMed] [Google Scholar]
  5. Baral, P. , Udit S., and Chiu I. M.. 2019. “Pain and Immunity: Implications for Host Defence.” Nature Reviews. Immunology 19: 433–447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Belfer, I. , Wu T., Kingman A., Krishnaraju R. K., Goldman D., and Max M. B.. 2004. “Candidate Gene Studies of Human Pain Mechanisms: Methods for Optimizing Choice of Polymorphisms and Sample Size.” Anesthesiology 100: 1562–1572. [DOI] [PubMed] [Google Scholar]
  7. Bennett, R. 2009. “The Fibromyalgia Impact Questionnaire (FIQ): A Review of Its Development, Current Version, Operating Characteristics and Uses.” Clinical and Experimental Rheumatology 23: S154–S162. [PubMed] [Google Scholar]
  8. Berwick, R. , Barker C., and Goebel A.. 2022. “The Diagnosis of Fibromyalgia Syndrome.” Clinical Medicine (London, England) 22: 570–574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Braga, M. , Lara‐Armi F. F., Neves J. S. F., et al. 2021. “Influence of IL10 (rs1800896) Polymorphism and TNF‐α, IL‐10, IL‐17A, and IL‐17F Serum Levels in Ankylosing Spondylitis.” Frontiers in Immunology 12: 653611. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cajanus, K. , Holmström E. J., Wessman M., Anttila V., Kaunisto M. A., and Kalso E.. 2016. “Effect of Endocannabinoid Degradation on Pain.” Pain 157, no. 2: 361–369. 10.1097/j.pain.0000000000000398. [DOI] [PubMed] [Google Scholar]
  11. Cleeland, C. S. , and Ryan K. M.. 1994. “Pain Assessment: Global Use of the Brief Pain Inventory.” Annals of the Academy of Medicine, Singapore 23: 129–138. [PubMed] [Google Scholar]
  12. da Silveira Alves, C. F. , Caumo W., Silvestri J. M., et al. 2020. “Pain Catastrophizing is Associated With the Val66Met Polymorphism of the Brain‐Derived Neurotrophic Factor in Fibromyalgia.” Advances in Rheumatology 60, no. 1: 39. 10.1186/s42358-020-00141-9. [DOI] [PubMed] [Google Scholar]
  13. D'Agnelli, S. , Arendt‐Nielsen L., Gerra M. C., et al. 2019. “Fibromyalgia: Genetics and Epigenetics Insights May Provide the Basis for the Development of Diagnostic Biomarkers.” Molecular Pain 15: 1744806918819944. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dai, Z.‐M. , Zhang T.‐S., Lin S., et al. 2016. “Role of IL‐17A rs2275913 and IL‐17F rs763780 Polymorphisms in Risk of Cancer Development: An Updated Meta‐Analysis.” Scientific Reports 6, no. 1: 20439. 10.1038/srep20439. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Desmeules, J. A. , Cedraschi C., Rapiti E., et al. 2003. “Neurophysiologic Evidence for a Central Sensitization in Patients With Fibromyalgia.” Arthritis & Rheumatism 48: 1420–1429. [DOI] [PubMed] [Google Scholar]
  16. Desmeules, J. , Chabert J., Rebsamen M., et al. 2014. “Central Pain Sensitization, COMT Val158Met Polymorphism, and Emotional Factors in Fibromyalgia.” Journal of Pain 15: 129–135. [DOI] [PubMed] [Google Scholar]
  17. Diatchenko, L. , Anderson A. D., Slade G. D., et al. 2006. “Three Major Haplotypes of the β2 Adrenergic Receptor Define Psychological Profile, Blood Pressure, and the Risk for Development of a Common Musculoskeletal Pain Disorder.” American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 141B, no. 5: 449–462. 10.1002/ajmg.b.30324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Diatchenko, L. , Slade G. D., Nackley A. G., et al. 2004. “Genetic Basis for Individual Variations in Pain Perception and the Development of a Chronic Pain Condition.” Human Molecular Genetics 14, no. 1: 135–143. 10.1093/hmg/ddi013. [DOI] [PubMed] [Google Scholar]
  19. Docampo, E. , Escaramís G., Gratacòs M., et al. 2014. “Genome‐Wide Analysis of Single Nucleotide Polymorphisms and Copy Number Variants in Fibromyalgia Suggest a Role for the Central Nervous System.” Pain 155: 1102–1109. [DOI] [PubMed] [Google Scholar]
  20. Ellerbrock, I. , Sandström A., Tour J., et al. 2021. “Polymorphisms of the μ‐Opioid Receptor Gene Influence Cerebral Pain Processing in Fibromyalgia.” European Journal of Pain 25: 398–414. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Estévez‐López, F. , Camiletti‐Moirón D., Aparicio V. A., et al. 2018. “Identification of Candidate Genes Associated With Fibromyalgia Susceptibility in Southern Spanish Women: The al‐Ándalus Project.” Journal of Translational Medicine 16: 43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Fanton, S. , Sandström A., Tour J., et al. 2022. “The Translocator Protein Gene is Associated With Endogenous Pain Modulation and the Balance Between Glutamate and γ‐Aminobutyric Acid in Fibromyalgia and Healthy Subjects: A Multimodal Neuroimaging Study.” Pain 163, no. 2: 274–286. 10.1097/j.pain.0000000000002309. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Fillingim, R. B. , Kaplan L., Staud R., et al. 2005. “The A118G Single Nucleotide Polymorphism of the μ‐Opioid Receptor Gene (OPRM1) is Associated With Pressure Pain Sensitivity in Humans.” Journal of Pain 6, no. 3: 159–167. 10.1016/j.jpain.2004.11.008. [DOI] [PubMed] [Google Scholar]
  24. George, S. Z. , Wu S. S., Wallace M. R., et al. 2016. “Biopsychosocial Influence on Shoulder Pain: Influence of Genetic and Psychological Combinations on Twelve‐Month Postoperative Pain and Disability Outcomes.” Arthritis Care & Research 68, no. 11: 1671–1680. 10.1002/acr.22876. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Gerra, M. C. , Dallabona C., Manfredini M., et al. 2024. “The Polymorphism Val158Met in the COMT Gene: Disrupted Dopamine System in Fibromyalgia Patients?” Pain 165: e184–e189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Giorgi, V. , Sirotti S., Romano M. E., et al. 2022. “Fibromyalgia: One Year in Review 2022.” Clinical and Experimental Rheumatology 40: 1065–1072. [DOI] [PubMed] [Google Scholar]
  27. Gloor, Y. , Matthey A., Sobo K., et al. 2022. “Uncovering a Genetic Polymorphism Located in Huntingtin Associated Protein 1 in Modulation of Central Pain Sensitization Signaling Pathways.” Frontiers in Neuroscience 16: 807773. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Gyorfi, M. , Rupp A., and Abd‐Elsayed A.. 2022. “Fibromyalgia Pathophysiology.” Biomedicine 10: 3070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hackshaw, K. V. 2021. “The Search for Biomarkers in Fibromyalgia.” Diagnostics (Basel) 11: 156. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Han, C. J. , Jarrett M. E., Cain K. C., Jun S., and Heitkemper M. M.. 2018. “Association of Fatigue With TPH2 Genetic Polymorphisms in Women With Irritable Bowel Syndrome.” Biological Research for Nursing 21, no. 1: 72–79. 10.1177/1099800418806055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Harms, K. C. , Kapitza K. P., Pahl L., et al. 2013. “Association of TNF‐α Polymorphism rs1800629 With Multisomatoform Disorder in a Group of German Patients and Healthy Controls: An Explorative Study.” Cytokine 61: 389–393. [DOI] [PubMed] [Google Scholar]
  32. Hashemi, V. , Farrokhi A. S., Tanomand A., et al. 2018. “Polymorphism of Foxp3 Gene Affects the Frequency of Regulatory T Cells and Disease Activity in Patients With Rheumatoid Arthritis in Iranian Population.” Immunology Letters 204: 16–22. 10.1016/j.imlet.2018.10.001. [DOI] [PubMed] [Google Scholar]
  33. Ho, K. W. D. , Wallace M. R., Staud R., and Fillingim R. B.. 2019. “ OPRM1, OPRK1, and COMT Genetic Polymorphisms Associated With Opioid Effects on Experimental Pain: A Randomized, Double‐Blind, Placebo‐Controlled Study.” Pharmacogenomics Journal 20, no. 3: 471–481. 10.1038/s41397-019-0131-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Hocking, L. J. , Smith B. H., Jones G. T., Reid D. M., Strachan D. P., and Macfarlane G. J.. 2010. “Genetic Variation in the Beta2‐Adrenergic Receptor but not Catecholamine‐ O ‐Methyltransferase Predisposes to Chronic Pain: Results From the 1958 British Birth Cohort Study.” Pain 149, no. 1: 143–151. 10.1016/j.pain.2010.01.023. [DOI] [PubMed] [Google Scholar]
  35. Horjales‐Araujo, E. , Demontis D., Lund E. K., et al. 2013. “Polymorphism in Serotonin Receptor 3B Is Associated with Pain Catastrophizing.” PLoS One 8, no. 11: e78889. 10.1371/journal.pone.0078889. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Jääskeläinen, S. K. , Lindholm P., Valmunen T., et al. 2014. “Variation in the Dopamine D2 Receptor Gene Plays a Key Role in Human Pain and its Modulation by Transcranial Magnetic Stimulation.” Pain 155, no. 10: 2180–2187. 10.1016/j.pain.2014.08.029. [DOI] [PubMed] [Google Scholar]
  37. Janssen, L. P. , Medeiros L. F., Souza A. D., and Silva J. D.. 2021. “Fibromyalgia: A Review of Related Polymorphisms and Clinical Relevance.” Anais da Academia Brasileira de Ciências 93: e20210618. [DOI] [PubMed] [Google Scholar]
  38. Jiang, W. , Zheng L., Xu L., et al. 2016. “Association Between FOXP3 Gene Polymorphisms and Risk of Differentiated Thyroid Cancer in Chinese Han Population.” Journal of Clinical Laboratory Analysis 31, no. 5: e22104. 10.1002/jcla.22104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Jiang, Y.‐X. , Li G.‐M., Yi D., and Yu P.‐W.. 2015. “A Meta‐Analysis: The Association Between Interleukin‐17 Pathway Gene Polymorphism and Gastrointestinal Diseases.” Gene 572, no. 2: 243–251. 10.1016/j.gene.2015.07.018. [DOI] [PubMed] [Google Scholar]
  40. Kerr, J. I. , and Burri A.. 2017. “Genetic and Epigenetic Epidemiology of Chronic Widespread Pain.” Journal of Pain Research 10: 2021–2029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Kim, S.‐K. , Kim S.‐H., Nah S.‐S., et al. 2013. “Association of Guanosine Triphosphate Cyclohydrolase 1 Gene Polymorphisms With Fibromyalgia Syndrome in a Korean Population.” Journal of Rheumatology 40: 316–322. [DOI] [PubMed] [Google Scholar]
  42. Knisely, M. R. , Conley Y. P., and Szigethy E.. 2019. “Cytokine Genetic Variants and Health‐Related Quality of Life in Crohn's Disease: An Exploratory Study.” Biological Research for Nursing 21, no. 5: 544–551. 10.1177/1099800419860906. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Kosek, E. , Cohen M., Baron R., et al. 2016. “Do We Need a Third Mechanistic Descriptor for Chronic Pain States?” Pain 157: 1382–1386. [DOI] [PubMed] [Google Scholar]
  44. Kwon, T.‐K. , Chung E. J., Lee N., Roh E. Y., and Song E. Y.. 2017. “Associations of FoxP3 Gene Polymorphisms With Severe Recurrent Respiratory Papillomatosis in Korean Patients.” Journal of Otolaryngology ‐ Head & Neck Surgery 46, no. 1: 21. 10.1186/s40463-017-0197-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Latremoliere, A. , and Woolf C. J.. 2009. “Central Sensitization: A Generator of Pain Hypersensitivity by Central Neural Plasticity.” Journal of Pain 10: 895–926. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Lee, Y. H. , Kim J.‐H., and Song G. G.. 2015. “Association Between the COMT Val158Met Polymorphism and Fibromyalgia Susceptibility and Fibromyalgia Impact Questionnaire Score: A Meta‐Analysis.” Rheumatology International 35: 159–166. [DOI] [PubMed] [Google Scholar]
  47. Leone, C. M. , Lenoir C., and van den Broeke E. N.. 2025. “Assessing Signs of Central Sensitization: A Critical Review of Physiological Measures in Experimentally Induced Secondary Hyperalgesia.” European Journal of Pain 29: e4733. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Leroux, A. , Bélanger M., and Boucher J. P.. 1995. “Pain Effect on Monosynaptic and Polysynaptic Reflex Inhibition.” Archives of Physical Medicine and Rehabilitation 76: 576–582. [DOI] [PubMed] [Google Scholar]
  49. Lim, E. C. W. , Sterling M., Stone A., and Vicenzino B.. 2011. “Central Hyperexcitability as Measured With Nociceptive Flexor Reflex Threshold in Chronic Musculoskeletal Pain: A Systematic Review.” Pain 152: 1811–1820. [DOI] [PubMed] [Google Scholar]
  50. Lindstedt, F. , Karshikoff B., Schalling M., et al. 2012. “Serotonin‐1A Receptor Polymorphism (rs6295) Associated With Thermal Pain Perception.” PLoS One 7: e43221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Lötsch, J. , Belfer I., Kirchhof A., et al. 2007. “Reliable Screening for a Pain‐Protective Haplotype in the GTP Cyclohydrolase 1 Gene (GCH1) Through the Use of 3 or Fewer Single Nucleotide Polymorphisms.” Clinical Chemistry 53, no. 6: 1010–1015. 10.1373/clinchem.2006.082883. [DOI] [PubMed] [Google Scholar]
  52. Louca Jounger, S. , Christidis N., Hedenberg‐Magnusson B., et al. 2021. “Polymorphisms in the HTR2A and HTR3A Genes Contribute to Pain in TMD Myalgia.” Frontiers in Oral Health 2: 647924. 10.3389/froh.2021.647924. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Malaguti, A. , Rossini D., Lucca A., et al. 2011. “Role of COMT, 5‐HT1A, and SERT Genetic Polymorphisms on Antidepressant Response to Transcranial Magnetic Stimulation.” Depression and Anxiety 28, no. 7: 568–573. 10.1002/da.20815. [DOI] [PubMed] [Google Scholar]
  54. Marinou, I. , Walters K., Winfield J., Bax D. E., and Wilson A. G.. 2010. “A Gain of Function Polymorphism in the Interleukin 6 Receptor Influences RA Susceptibility.” Annals of the Rheumatic Diseases 69, no. 6: 1191–1194. 10.1136/ard.2008.100644. [DOI] [PubMed] [Google Scholar]
  55. Martínez‐Jauand, M. , Sitges C., Rodríguez V., et al. 2012. “Pain Sensitivity in Fibromyalgia is Associated With Catechol‐O‐Methyltransferase (COMT) Gene.” European Journal of Pain 17, no. 1: 16–27. 10.1002/j.1532-2149.2012.00153.x. [DOI] [PubMed] [Google Scholar]
  56. Matsuda, J. B. , Barbosa F. R., Morel L. J. F., et al. 2010. “Serotonin Receptor (5‐HT 2A) and Catechol‐O‐Methyltransferase (COMT) Gene Polymorphisms: Triggers of Fibromyalgia?” Revista Brasileira de Reumatologia 50: 141–149. [PubMed] [Google Scholar]
  57. Mattoo, B. , Tanwar S., Bhatia R., Tripathi M., and Bhatia R.. 2018. “CHRONIC TENSION‐TYPE HEADACHE: MUSCLE OVERACTIVITY VERSUS DEFICIENT PAIN CONTROLS.” Asian Journal of Pharmaceutical and Clinical Research 11: 143–146. [Google Scholar]
  58. Mayer, T. G. , Neblett R., Cohen H., et al. 2012. “The Development and Psychometric Validation of the Central Sensitization Inventory.” Pain Practice 12: 276–285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Moudi, B. , Heidari Z., Mahmoudzadeh‐Sagheb H., and Moudi M.. 2018. “Analysis of Interleukin‐10 Gene Polymorphisms in Patients With Chronic Periodontitis and Healthy Controls.” Dental Research Journal 15, no. 1: 71–79. 10.4103/1735-3327.223614. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Neblett, R. , Cohen H., Choi Y., et al. 2013. “The Central Sensitization Inventory (CSI): Establishing Clinically Significant Values for Identifying Central Sensitivity Syndromes in an Outpatient Chronic Pain Sample.” Journal of Pain 14: 438–445. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Nicholas, M. , Vlaeyen J. W. S., Rief W., et al. 2019. “The IASP Classification of Chronic Pain for ICD‐11: Chronic Primary Pain.” Pain 160: 28–37. [DOI] [PubMed] [Google Scholar]
  62. Nicholl, B. I. , Holliday K. L., Macfarlane G. J., et al. 2011. “Association of HTR2A Polymorphisms With Chronic Widespread Pain and the Extent of Musculoskeletal Pain: Results From Two Population‐Based Cohorts.” Arthritis & Rheumatism 63, no. 3: 810–818. 10.1002/art.30185. [DOI] [PubMed] [Google Scholar]
  63. Nielsen, L. M. , Olesen A. E., Sato H., Christrup L. L., and Drewes A. M.. 2016. “Association between Gene Polymorphisms and Pain Sensitivity Assessed in a Multi‐Modal Multi‐Tissue Human Experimental Model – An Explorative Study.” Basic & Clinical Pharmacology & Toxicology 119, no. 4: 360–366. 10.1111/bcpt.12601. [DOI] [PubMed] [Google Scholar]
  64. O'Mahony, L. F. , Srivastava A., Mehta P., and Ciurtin C.. 2021. “Is Fibromyalgia Associated With a Unique Cytokine Profile? A Systematic Review and Meta‐Analysis.” Rheumatology (Oxford, England) 60: 2602–2614. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. O'Rielly, D. D. , Roslin N. M., Beyene J., Pope A., and Rahman P.. 2009. “TNF‐Alpha‐308 G/A Polymorphism and Responsiveness to TNF‐Alpha Blockade Therapy in Moderate to Severe Rheumatoid Arthritis: A Systematic Review and Meta‐Analysis.” Pharmacogenomics Journal 9: 161–167. [DOI] [PubMed] [Google Scholar]
  66. Pan, J. , Zhao Y., Sang R., et al. 2023. “Huntington‐Associated Protein 1 Inhibition Contributes to Neuropathic Pain by Suppressing Cav1.2 Activity and Attenuating Inflammation.” Pain 164: e286–e302. [DOI] [PubMed] [Google Scholar]
  67. Paradowska‐Gorycka, A. , Jurkowska M., Felis‐Giemza A., et al. 2014. “Genetic Polymorphisms of Foxp3 in Patients With Rheumatoid Arthritis.” Journal of Rheumatology 42, no. 2: 170–180. 10.3899/jrheum.131381. [DOI] [PubMed] [Google Scholar]
  68. Park, S. , Lee H. B., Lee J.‐K., et al. 2018. “Associations Between the Brain‐Derived Neurotrophic Factor Val66Met Polymorphisms and Suicide in Patients With Cancer.” Psychiatric Genetics 28, no. 4: 71–72. 10.1097/ypg.0000000000000199. [DOI] [PubMed] [Google Scholar]
  69. Peck, M. M. , Maram R., Mohamed A., et al. 2020. “The Influence of Pro‐Inflammatory Cytokines and Genetic Variants in the Development of Fibromyalgia: A Traditional Review.” Cureus 12: e10276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Pinsonneault, J. K. , Papp A. C., and Sadée W.. 2006. “Allelic mRNA Expression of X‐Linked Monoamine Oxidase a (MAOA) in Human Brain: Dissection of Epigenetic and Genetic Factors.” Human Molecular Genetics 15, no. 17: 2636–2649. 10.1093/hmg/ddl192. [DOI] [PubMed] [Google Scholar]
  71. Potvin, S. , Larouche A., Normand E., et al. 2009. “DRD3 Ser9Gly Polymorphism Is Related to Thermal Pain Perception and Modulation in Chronic Widespread Pain Patients and Healthy Controls.” Journal of Pain 10, no. 9: 969–975. 10.1016/j.jpain.2009.03.013. [DOI] [PubMed] [Google Scholar]
  72. Pratelli, M. , and Pasqualetti M.. 2019. “Serotonergic Neurotransmission Manipulation for the Understanding of Brain Development and Function: Learning From Tph2 Genetic Models.” Biochimie 161: 3–14. 10.1016/j.biochi.2018.11.016. [DOI] [PubMed] [Google Scholar]
  73. Rahman, M. S. , Winsvold B. S., Chavez Chavez S. O., et al. 2021. “Genome‐Wide Association Study Identifies RNF123 Locus as Associated With Chronic Widespread Musculoskeletal Pain.” Annals of the Rheumatic Diseases 80: 1227–1235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Raja, S. N. , Carr D. B., Cohen M., et al. 2020. “The Revised International Association for the Study of Pain Definition of Pain: Concepts, Challenges, and Compromises.” Pain 161: 1976–1982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Salaffi, F. , Farah S., Mariani C., Sarzi‐Puttini P., and Di Carlo M.. 2022. “Validity of the Central Sensitization Inventory Compared With Traditional Measures of Disease Severity in Fibromyalgia.” Pain Practice 22: 702–710. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Sandrini, G. , Serrao M., Rossi P., Romaniello A., Cruccu G., and Willer J. C.. 2005. “The Lower Limb Flexion Reflex in Humans.” Progress in Neurobiology 77: 353–395. [DOI] [PubMed] [Google Scholar]
  77. Schürks, M. , Kurth T., Buring J. E., and Zee R. Y. L.. 2009. “A Candidate Gene Association Study of 77 Polymorphisms in Migraine.” Journal of Pain 10: 759–766. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Skljarevski, V. , and Ramadan N. M.. 2002. “The Nociceptive Flexion Reflex in Humans – Review Article.” Pain 96: 3–8. [DOI] [PubMed] [Google Scholar]
  79. Smith, A. J. P. , and Humphries S. E.. 2009. “Cytokine and Cytokine Receptor Gene Polymorphisms and Their Functionality.” Cytokine & Growth Factor Reviews 20, no. 1: 43–59. 10.1016/j.cytogfr.2008.11.006. [DOI] [PubMed] [Google Scholar]
  80. Smith, S. B. , Maixner D. W., Fillingim R. B., et al. 2012. “Large Candidate Gene Association Study Reveals Genetic Risk Factors and Therapeutic Targets for Fibromyalgia.” Arthritis & Rheumatism 64, no. 2: 584–593. 10.1002/art.33338. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Smith, S. B. , Maixner D. W., Greenspan J. D., et al. 2011. “Potential Genetic Risk Factors for Chronic TMD: Genetic Associations From the OPPERA Case Control Study.” Journal of Pain 12, no. 11: T92–T101. 10.1016/j.jpain.2011.08.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Smith, S. B. , Reenilä I., Männistö P. T., et al. 2014. “Epistasis Between Polymorphisms in COMT, ESR1, and GCH1 Influences COMT Enzyme Activity and Pain.” Pain 155, no. 11: 2390–2399. 10.1016/j.pain.2014.09.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Solak, Ö. , Erdoğan M. Ö., Yıldız H., et al. 2014. “Assessment of Opioid Receptor μ1 Gene A118G Polymorphism and its Association With Pain Intensity in Patients With Fibromyalgia.” Rheumatology International 34, no. 9: 1257–1261. 10.1007/s00296-014-2995-1. [DOI] [PubMed] [Google Scholar]
  84. Storm, H. , Støen R., Klepstad P., Skorpen F., Qvigstad E., and Raeder J.. 2013. “Nociceptive Stimuli Responses at Different Levels of General Anaesthesia and Genetic Variability.” Acta Anaesthesiologica Scandinavica 57: 89–99. [DOI] [PubMed] [Google Scholar]
  85. Tammimäki, A. , and Männistö P. T.. 2012. “Catechol‐O‐Methyltransferase Gene Polymorphism and Chronic Human Pain: A Systematic Review and Meta‐Analysis.” Pharmacogenetics and Genomics 22: 673–691. [DOI] [PubMed] [Google Scholar]
  86. Tander, B. , Gunes S., Boke O., et al. 2008. “Polymorphisms of the Serotonin‐2A Receptor and Catechol‐O‐Methyltransferase Genes: A Study on Fibromyalgia Susceptibility.” Rheumatology International 28, no. 7: 685–691. 10.1007/s00296-008-0525-8. [DOI] [PubMed] [Google Scholar]
  87. Tegeder, I. , Adolph J., Schmidt H., Woolf C. J., Geisslinger G., and Lötsch J.. 2008. “Reduced Hyperalgesia in Homozygous Carriers of a GTP Cyclohydrolase 1 Haplotype.” European Journal of Pain 12, no. 8: 1069–1077. 10.1016/j.ejpain.2008.02.004. [DOI] [PubMed] [Google Scholar]
  88. Tegeder, I. , Costigan M., Griffin R. S., et al. 2006. “GTP Cyclohydrolase and Tetrahydrobiopterin Regulate Pain Sensitivity and Persistence.” Nature Medicine 12, no. 11: 1269–1277. 10.1038/nm1490. [DOI] [PubMed] [Google Scholar]
  89. Tour, J. , Löfgren M., Mannerkorpi K., et al. 2017. “Gene‐to‐gene interactions regulate endogenous pain modulation in fibromyalgia patients and healthy controls—antagonistic effects between opioid and serotonin‐related genes.” Pain 158, no. 7: 1194–1203. 10.1097/j.pain.0000000000000896. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Tunbridge, E. M. , Narajos M., Harrison C. H., Beresford C., Cipriani A., and Harrison P. J.. 2019. “Which Dopamine Polymorphisms Are Functional? Systematic Review and Meta‐Analysis of COMT, DAT, DBH, DDC, DRD1‐5, MAOA, MAOB, TH, VMAT1, and VMAT2.” Biological Psychiatry 86: 608–620. [DOI] [PubMed] [Google Scholar]
  91. Umeda, M. , Corbin L. W., and Maluf K. S.. 2013. “Preliminary Investigation of Absent Nociceptive Flexion Reflex Responses Among More Symptomatic Women With Fibromyalgia Syndrome.” Rheumatology International 33: 2365–2372. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Van Oosterwijck, S. , Billens A., Cnockaert E., et al. 2025. “Spinal Hyperexcitability in Patients With Chronic Musculoskeletal Pain or Headache as Evidenced by Alterations in the Nociceptive Withdrawal Reflex: A Systematic Review and Meta‐Analysis.” Pain 166: 1002–1029. [DOI] [PubMed] [Google Scholar]
  93. Vargas‐Alarcón, G. , Fragoso J.‐M., Cruz‐Robles D., et al. 2009. “Association of Adrenergic Receptor Gene Polymorphisms With Different Fibromyalgia Syndrome Domains.” Arthritis & Rheumatism 60, no. 7: 2169–2173. 10.1002/art.24655. [DOI] [PubMed] [Google Scholar]
  94. Vilkeviciute, A. , Cebatoriene D., Kriauciuniene L., Zemaitiene R., and Liutkeviciene R.. 2021. “IL‐9 and IL‐10 Single‐Nucleotide Variants and Serum Levels in Age‐Related Macular Degeneration in the Caucasian Population.” Mediators of Inflammation 2021, no. 1: 6622934. 10.1155/2021/6622934. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Vinkhuyzen, A. A. E. , Dumenil T., Ryan L., et al. 2011. “Identification of Tag Haplotypes for 5HTTLPR for Different Genome‐Wide SNP Platforms.” Molecular Psychiatry 16, no. 11: 1073–1075. 10.1038/mp.2011.68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Wallace, D. J. , Linker‐Israeli M., Hallegua D., Silverman S., Silver D., and Weisman M. H.. 2001. “Cytokines Play an Aetiopathogenetic Role in Fibromyalgia: A Hypothesis and Pilot Study.” Rheumatology (Oxford) 40: 743–749. [DOI] [PubMed] [Google Scholar]
  97. Warner, S. C. , Walsh D. A., Laslett L. L., et al. 2017. “Pain in Knee Osteoarthritis Is Associated With Variation in the Neurokinin 1/Substance P Receptor (TACR1) Gene.” European Journal of Pain 21: 1277–1284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  98. Wilson, A. G. , Symons J. A., McDowell T. L., McDevitt H. O., and Duff G. W.. 1997. “Effects of a Polymorphism in the Human Tumor Necrosis Factor α Promoter on Transcriptional Activation.” Proceedings of the National Academy of Sciences 94: 3195–3199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  99. Wolfe, F. , Clauw D. J., Fitzcharles M.‐A., et al. 2016. “2016 Revisions to the 2010/2011 Fibromyalgia Diagnostic Criteria.” Seminars in Arthritis and Rheumatism 46: 319–329. [DOI] [PubMed] [Google Scholar]
  100. Woolf, C. J. 2011. “Central Sensitization: Implications for the Diagnosis and Treatment of Pain.” Pain 152: S2–S15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Xiao, Y. , Russell I. J., and Liu Y.‐G.. 2011. “A Brain‐Derived Neurotrophic Factor Polymorphism Val66Met Identifies Fibromyalgia Syndrome Subgroup With Higher Body Mass Index and C‐Reactive Protein.” Rheumatology International 32, no. 8: 2479–2485. 10.1007/s00296-011-1990-z. [DOI] [PubMed] [Google Scholar]
  102. Xu, M. K. , Gaysina D., Tsonaka R., et al. 2017. “Monoamine Oxidase A (MAOA) Gene and Personality Traits From Late Adolescence through Early Adulthood: A Latent Variable Investigation.” Frontiers in Psychology 8: 1736. 10.3389/fpsyg.2017.01736. [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Yim, Y.‐R. , Lee K.‐E., Park D.‐J., et al. 2017. “Identifying Fibromyalgia Subgroups Using Cluster Analysis: Relationships With Clinical Variables.” European Journal of Pain 21: 374–384. [DOI] [PubMed] [Google Scholar]
  104. Zhang, L. , Zhu J., Chen Y., and Zhao J.. 2014. “Meta‐Analysis Reveals a Lack of Association Between a Common Catechol‐O‐Methyltransferase (COMT) Polymorphism val158met and Fibromyalgia.” International Journal of Clinical and Experimental Pathology 7: 8489–8497. [PMC free article] [PubMed] [Google Scholar]
  105. Zhu, S. , Wang B., Jia Q., and Duan L.. 2019. “Candidate Single Nucleotide Polymorphisms of Irritable Bowel Syndrome: A Systemic Review and Meta‐Analysis.” BMC Gastroenterology 19: 165. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1: Overall results of the analyses of the association between the genotype and the phenotype (FMS = fibromyalgia syndrome, or healthy matched controls) for each of the 60 studied candidate genes. For each analysis are shown, from left to right: the single nucleotide polymorphism (SNP) identifier, the related gene, the minor and the major alleles, the expected minor allele frequency (MAF) for European databases (as reported by https://www.ensembl.org/index.html), the observed MAF in the study samples, and the results of the simple comparison of allelic frequencies, the simple logistic regression in which a genetic additive model was used to express the genotype, and the same logistic regression with the NFR threshold taken as a covariate (adjusted analysis). To help to compare the results between each step, these are expressed as an odds ratio (OR) along with its 95% confidence interval limits, and the p‐value for the chi‐square test (conducted on the contingency table for the comparison of allelic frequencies, and on the Wald chi‐square test for the logistic regressions).

EJP-30-0-s002.xlsx (18.1KB, xlsx)

Table S2: Overall results of the analyses of the association between the genotype (expressed as a score from the genetic additive model) and the NFR threshold within the sample of fibromyalgia syndrome (FMS) patients. For each analysis are shown, from left to right: the single nucleotide polymorphism (SNP) identifier, the related gene, the beta coefficient of the simple linear regression along with its 95% confidence interval limits, and the p‐value from the ANOVA that tested the significance of the model.

EJP-30-0-s001.xlsx (12.8KB, xlsx)

Table S3: Overall results of the analyses of the association between the NFR measurement type (failure for early pain, successful measurement, failure for excessive threshold) and the long‐term concomitant medications (not taken on the day of the visit) within the sample of fibromyalgia syndrome (FMS) patients.

EJP-30-0-s003.docx (12.7KB, docx)

Articles from European Journal of Pain (London, England) are provided here courtesy of Wiley

RESOURCES