# Impact of Concomitant Fibromyalgia on Patient-Reported Outcomes and Inflammatory Markers in Psoriatic Arthritis: A Multinational Cross-Sectional Study

Avin Maroof, Nelly Ziadé, Noura Abbas, Laure Gossec, Lina El Kibbi, Bassel Elzorkany, Nizar Abdulateef Jassim, Faiq Gorial, Asal Adnan, Nada Al Shamaa, et al.

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## ► To cite this version:

Avin Maroof, Nelly Ziadé, Noura Abbas, Laure Gossec, Lina El Kibbi, et al. Impact of Concomitant Fibromyalgia on Patient-Reported Outcomes and Inflammatory Markers in Psoriatic Arthritis: A Multinational Cross-Sectional Study. Rheumatology International, 2025, 46 (1), pp.6. 10.1007/s00296-025-06054-4. hal-05441402

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# Impact of Concomitant Fibromyalgia on Patient-Reported Outcomes and Inflammatory Markers in Psoriatic Arthritis: A Multinational Cross-Sectional Study

## Abstract

**Background:** Fibromyalgia (FM) is frequently observed in patients with psoriatic arthritis (PsA) and can contribute to reduced quality of life (QoL) and potentially unnecessary treatment escalation. Cultural factors may influence the presentation and impact of FM.

**Objectives:** To determine the prevalence of FM in patients with PsA using the Fibromyalgia Rapid Screening Tool (FiRST), identify associated clinical factors, and assess agreement between FiRST results and physician-reported FM based on clinical opinion.

**Methods:** post-hoc, cross-sectional study of the multicounty TACTIC dataset across 13 Arab countries. FM was defined by a FiRST ≥5. We compared Disease activity (DAPSA), Physician Global Assessment (PhGA), and validated patient-reported outcomes (PROs), and fitted a multivariable logistic model to identify predictors. Agreement between FiRST and physician-reported FM was assessed using the Intraclass Correlation Coefficient (ICC), diagnostic discrimination of FiRST against physician reported FM was evaluated by ROC/AUC.

**Results:** Among 536 PsA patients, 19.0% screened positive for FM (FiRST ≥ 5). In multivariable analysis, higher BMI, dyslipidemia, non-oligoarticular pattern, lower ESR, higher DAPSA, and higher PHQ-4 were independently associated with FM. Agreement between FiRST and physician-reported FM status was moderate (ICC 0.59; 95% CI 0.51–0.66), and FiRST discrimination for physician diagnosis was excellent (AUC=0.833, 95% CI 0.793-0.873).

**Conclusion:** Approximately 1 in 5 patients with PsA in Arab countries screened positive for FM. FM was associated with higher disease activity and PRO burden despite lower ESR. Findings support routine FM screening in PsA, particularly when PROs are high but inflammatory markers are low, to ensure assessment and holistic management.
**Keywords:** Arthritis, Psoriatic, Fibromyalgia, Comorbidity, Patient-Reported Outcomes, Cross-Sectional Studies, Arab World.

# 1. Introduction

Psoriatic arthritis (PsA) is a chronic inflammatory autoimmune disease affecting the skin and joints, with a global prevalence of up to 1%, requiring comprehensive, treatment to target. Management strategies, as recommended by both the European Alliance of Associations for Rheumatology (EULAR) and the American College of Rheumatology/National Psoriasis Foundation (ACR/NPF) guidelines [1, 2]. Patients with PsA frequently experience comorbidities, notably chronic widespread pain, which is not always attributable to inflammatory activity. A substantial proportion of this pain may stem from fibromyalgia (FM), a central sensitization disorder characterized by musculoskeletal pain, fatigue, sleep disturbance, and cognitive dysfunction [3, 4]. FM affects approximately 2–5% of the general population [5], but its prevalence is notably higher among PsA patients, ranging from 22% in Saudi Arabia [5] to 40% in European cohorts [7, 8], depending on diagnostic criteria and study population [9].

The coexistence of FM and PsA poses significant diagnostic and therapeutic challenges [10, 11]. While PsA-related pain is typically nociceptive or inflammatory, FM involves nociceptive pain, characterized by altered pain processing, which may amplify symptom burden irrespective of disease activity [3, 12]. This overlap complicates clinical assessment, potentially leading to misinterpretation of disease activity, unnecessary treatment intensification, and increased healthcare costs [4, 13]. Moreover, recent real-world evidence confirms that concomitant FM is independently associated with worse quality of life, functional impairment, and higher scores on patient-reported outcomes (PROs) in PsA [14, 15].

The Fibromyalgia Rapid Screening Tool (FiRST) is a brief, validated questionnaire designed to screen for FM in patients with rheumatic diseases [16]. Its use may improve early identification of FM and aid in differentiating inflammatory from non-inflammatory pain.
Cultural and contextual factors may influence FM expression and PsA disease perception. A recent study by Abbas et al. (2024) found better concordance between patient and physician global assessments in Arab PsA patients than previously reported elsewhere, suggesting potential regional differences in symptom reporting or communication [13]. These may relate to healthcare access, sociocultural norms around pain expression, or the burden of comorbid conditions with overlapping symptoms [5, 10, 18]. To our knowledge, no previous study using a validated FM screening tool has explored the associations of FM in Arab PsA populations, addressing a significant evidence gap and informing context-appropriate management strategies.

This study aimed to estimate the prevalence of FM in patients with PsA across Arab countries using the FiRST tool, identify associated demographic and clinical factors, and assess agreement between FiRST results and physician-reported FM based on clinical opinion.

## 2. Methods

This study is a cross-sectional, post-hoc analysis of data derived from the TACTIC study (TranslAte, Culturally adapt, and validate the PsAID in ArabIC) [13]. TACTIC was a multinational, multicenter observational study conducted across 13 Arab countries under the Arab League of Associations for Rheumatology (ArLAR) research group (ARCH). Its primary aim was to evaluate patient-perceived impact of PsA, identify factors associated with high disease activity, and explore concordance between patient and physician assessments of PsA activity in this regional context [13].

### 2.1. Study Population

Eligible participants were adults (≥18 years) diagnosed with PsA by a rheumatologist according to the Classification Criteria for Psoriatic Arthritis (CASPAR) [14, 20, 21]. Patients were included if complete demographic, clinical, and FM screening data (FiRST questionnaire and physician assessment) were available. Exclusion criteria comprised: (1) missing or incomplete data on key study variables; (2) overlap with other rheumatic or autoimmune disorders; (3) current severe psychiatric or neurological disease could confound pain assessment; and (4) withdrawal consent.
## 2.2. Data Source and Collection

Data were extracted from the TACTIC database, which was collected prospectively during routine clinical consultations. The dataset included:

• Sociodemographic data: age, sex, marital status, education level, occupation, and body mass index (BMI).

• Clinical data: disease duration, PsA phenotype (polyarthritis, oligoarthritis, monoarthritis, axial disease), extra-musculoskeletal manifestations (enthesitis, dactylitis, uveitis, inflammatory bowel disease), treatment regimen, HLA-B27 status, radiographic erosions, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), family history of spondyloarthritis, skin and nail psoriasis, and comorbid conditions (smoking, hypertension, diabetes, dyslipidemia, cardiovascular disease, cancer, osteoporosis, chronic infections, peptic ulcer disease, and diagnosed depression).

• Patient-reported outcome measures (PROs) using validated Arabic and English questionnaires:

• Disease Activity in Psoriatic Arthritis (DAPSA): includes tender and swollen joint counts, CRP levels, and patient assessments of disease activity and pain (0–10 scale) [7, 20].

• Health Assessment Questionnaire (HAQ): assesses physical functioning [25].

• Patient Global Assessments (PGA): 11-point numerical rating scales for pain (PGA-Pain), PsA activity (PGA-PsA), and psoriasis activity (PGA-Psoriasis) [8].

• Patient Health Questionnaire-4 (PHQ-4): a brief screening tool for symptoms of anxiety and depression [11].

Through harmonized data collection forms and training provided in the TACTIC protocol, data integrity and standardization were ensured across all participating centers.

## 2.3. Fibromyalgia Assessment

FM was assessed using the FiRST, a validated self-administered questionnaire comprising six binary (yes/no) items. A score of ≥5 out of 6 indicates FM [20]. The FiRST has demonstrated validity in patients with psoriatic arthritis and other rheumatic conditions [23]. Furthermore, a
physician-reported FM status was collected independently (simple yes/no question) based on
their clinical opinion [4].

## 2.4. Reporting guideline:

This study was conducted and reported in accordance to the STORBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines. The completed STORBE checklist is provided in Appendix 1. <https://www.equator-network.org/reporting-guidelines/strobe/>

## 2.5. Statistical Analysis

Descriptive statistics were used to summarize the study population. Continuous variables were expressed as means and standard deviations (SD), while categorical variables were reported as frequencies and percentages. The prevalence of FM was calculated based on the proportion of patients screening positive on the FiRST (score ≥5).

Group comparisons between patients with and without FM were conducted using the Mann-Whitney U test for non-normally distributed continuous variables and Chi-square tests for categorical variables.

Bivariate analyses examined associations between FM status and demographic, clinical, and patient-reported outcome variables. Variables showing significant associations (p < 0.05) were subsequently considered for inclusion in a multivariate logistic regression model to identify independent predictors of FM. A forward stepwise (likelihood ratio) logistic regression was employed for variable selection. All variables from Tables 1, 2 and 3 were initially considered. However, PHQ depression and anxiety scores were excluded due to their demonstrated collinearity with the total PHQ score.

Similarly, while the Psoriatic Arthritis Impact of Disease (PsAID) score showed a significant association in bivariate analysis, it was not retained in the final multivariate model by the stepwise selection process, as its independent contribution did not significantly improve model fit, accounting for other strong predictors already included, such as DAPSA and PHQ scores.
134 The final model included BMI, PsA pattern (oligoarthritis vs others), dyslipidemia, ESR,
135 DAPSA score, and PHQ-4 score.

136 This logistic regression model's overall fit and predictive performance were assessed using Cox
137 and Snell R-squared, Nagelkerke R-squared, Hosmer-Lemeshow test, and classification
138 accuracy.

139 The agreement between FiRST-based and physician-reported FM status (based on clinical
140 opinion) was evaluated using the Intraclass Correlation Coefficient (ICC) with 95% confidence
141 intervals. Missing data were handled using multiple imputation by chained equations (MICE) via
142 the wholly conditional specification method in SPSS to minimize bias and preserve statistical
143 power. All statistical analyses were conducted using IBM SPSS Statistics, version 27.0 (IBM
144 Corp., Armonk, NY, USA), with two-sided p values <0.05 considered statistically significant.

2.6. Receiver Operating Characteristic (ROC) Curve Analysis

146 A receiver operating characteristic (ROC) curve analysis was conducted to evaluate the
147 diagnostic performance of the FiRST score in differentiating between patients with and without
148 fibromyalgia, as determined by the physician's diagnosis. The analysis assessed the tool's ability
149 to discriminate true positives from false positives across a range of threshold values.

150 The Area Under the Curve (AUC) was calculated to quantify overall discriminative capacity,
151 with values interpreted as follows: 0.5 = no discrimination, 0.7-0.8 = acceptable, 0.8-0.9 =
152 excellent, and ≥0.9 = outstanding discrimination.

153 To identify the optimal FiRST cutoff point, the Youden Index (sensitivity + specificity - 1) was
154 applied. This approach aims to maximize sensitivity and specificity simultaneously, providing a
155 balanced threshold for clinical use. Sensitivity, specificity, positive predictive value (PPV), and
156 negative predictive value (NPV) were also reported for the optimal cutoff.

157 All analyses were performed using standard ROC methodology within IBM SPSS, version 27.0
158 (IBM Corp., Armonk, NY, USA).

159 Preliminary results of this study were presented as an abstract at the ACR Convergence 2024.
160

## 2.7. Ethical Approval

The parent TACTIC study obtained full ethics approval from the coordinating center (Hôtel-Dieu de France Hospital, Saint-Joseph University, Beirut, Lebanon; March 2022) and from the Institutional Review Boards of all 13 participating Arab countries prior to patient recruitment and data collection. These approvals covered consent procedures, local regulatory compliance, and creation of the anonymised multinational database used in the present work. All participants provided written consent at enrollment, and the study was conducted in accordance with the Declaration of Helsinki.

The current study is a post-hoc ancillary analysis that used fully anonymised TACTIC data and did not involve new data collection, direct patient contact, or access to identifiable information. Therefore, additional ethics approvals from all 13 sites were not required, consistent with international guidance on the secondary use of anonymised datasets.

The ancillary analysis protocol was independently approved by the University of Kurdistan Hewlêr IRB (RE0016; January 28, 2024).

175

## 176 Results

Of the 554 patients initially recruited from the TACTIC study database, 17 were excluded due to incomplete demographic information, and one was excluded for not completing the FiRST questionnaire, resulting in a final sample of 536 patients included in the analysis. These patients met all predefined inclusion criteria: adult patients (≥18 years) diagnosed with psoriatic arthritis (PsA) based on CASPAR criteria, literate, and capable of completing self-administered questionnaires. The mean age of the analyzed cohort was 45.5 years (range 36–55), with 219 (40.9%) males and 317 (59.1%) females.

## 184 3.1. Proportion of FM:

Among the 536 included patients, 102 (19.0%) screened positive for fibromyalgia based on a FiRST score ≥5.
3.2. Associated Factors:

3.2.1. In bivariate analysis, several variables were associated with FM according to a positive FiRST questionnaire, including female sex (p<0.001), higher BMI (p=0.001), a lower level of education (p=0.005), dyslipidemia (p=0.010), polyarthritis (p=0.001), absence of oligoarthritis (p<0.001), higher proportion of dactylitis (p=0.049), gout (p=0.036), higher ESR (p=0.032), and worse PROS (all p<0.001, Tables 1, 2, and 3).

3.2.2. In the multivariable analysis, after considering confounding factors through forward stepwise (likelihood ratio) logistic regression, the following were independently associated with FM: increasing BMI (odds ratio [OR] for one-point BMI 1.07, p=0.014), absence of oligoarthritis (OR 0.38, p=0.001), dyslipidemia (OR 2.29, p=0.031), lower ESR (OR 0.99, p=0.029), worse DAPSA (OR 1.05, p<0.001) and higher PHQ (OR 1.45, <0.001). The stepwise process included variables from Table 1,2 and 3. PHQ depression and anxiety scores were excluded due to collinearity with the total PHQ score.

The PsAID score, despite its significance in bivariate analysis, was not retained in the final model by the stepwise selection.

The final logistic regression model contained the six most statistically significant predictive variables (χ2 test = 147.0, p<0.001), indicating the ability to distinguish between patients with ongoing vs. resolved chronic pain. The model explained 28% (Cox and Snell R-squared) to 45% (Nagelkerke R squared) of the variance in pain status and correctly classified 69.5% of the cases. The Hosmer-Lemeshow test was insignificant (p=0.658), indicating good model fit.

3.3. Concordance between FiRST and Physician Diagnosis

Physicians' diagnosis of FM showed moderate agreement with the FiRST score, with physicians reporting FM less frequently than identified by the FiRST. Of those with a positive FiRST score, 58 patients (56.9%) were diagnosed with FM by a physician, 27 patients (26.5%) were not diagnosed, and 17 patients (16.7%) were labeled as suspicious. This difference was statistically significant (p < 0.001, Table 4). The ICC between FiRST and physician diagnosis was 0.59 (95% CI 0.51 to 0.66), indicating moderate agreement.

3.4. ROC Curve Analysis:
218 The ROC curve analysis demonstrated an AUC of 0.833 (95% CI 0.793 to 0.873) (Figure ROC),
219 indicating good discrimination between patients with and without FM, as identified by the FiRST
220 score.

221
222
## 3. Discussion
223
Addressing a significant gap in the literature and providing valuable insights for this region, this
224
multinational study revealed a 19% proportion of FM in Arab patients with PsA, identifying FM
225
as a significant comorbidity within this population. While our findings suggest it ranked as the
226
third most frequent comorbidity in our specific study cohort, after hypertension and obesity, it is
227
essential to note that the relative frequency of comorbidities can vary across different studies
228
and populations. FM is, however, recognized as a prevalent comorbidity in PsA patients [18].
229
FM is observed in 10-30% of patients with rheumatic diseases (due to shared mechanisms and
230
risk factors) compared to 1-2% in the healthy population [11]. While this aligns with the
231
established link between FM and PsA [17], the prevalence varied across studies. Elsawy et al.
232
reported a higher 38.3%, potentially due to single-center recruitment and differing diagnostic
233
criteria [3, 27]. In comparison, Mease et al. found 11.1% in an extensive US registry, with
234
20.6% reporting widespread pain, highlighting the broader spectrum of pain in PsA [11].

235 Despite these variations, a consistent theme emerged: FM is significantly associated with worse
236 PsA outcomes [9, 25]. Numerous studies emphasize the importance of identifying FM in PsA
237 patients to prevent misinterpretation of disease activity, optimize treatment strategies, and
238 improve overall quality of life. FM can amplify PsA symptoms, leading to unnecessary
239 escalation of immunosuppressive treatments. Our study, alongside Elsawy et al. and Mease et
240 al., confirms that FM negatively affects PROs, including pain, fatigue, and quality of life [3, 11,
241 24]. Elsawy et al. observed unchanged inflammatory markers (CRP/SJC) in FM-PsA, while our
242 study found lower ESR, suggesting a shift toward central sensitization [11, 27]. This
243 observation is further supported by recent biomarker-based evidence showing that PsA patients
244 with comorbid fibromyalgia demonstrate significantly lower CRP levels and reduced
245 inflammatory biomarker activity compared with PsA-only patients [32]. This reinforces the
246 importance of considering non-inflammatory pain mechanisms in PsA management [4, 25].
247 Composite measures and treatment targets in PsA should account for these factors [20, 21].
248 Recognizing FM allows for targeted interventions, including pain management and
249 psychological support, reducing healthcare costs and enhancing physician-patient
250 communication.

251 Our study further identified associations between FM and metabolic factors (higher BMI,
252 dyslipidemia), psychological distress (PHQ), and absence of oligoarthritis. These findings align
253 with Mease et al., which linked FM to female sex, higher BMI, and comorbidities, highlighting
254 the complex interplay of factors contributing to FM in PsA [11, 13]. The consistency of these
255 findings across diverse populations strengthens their validity.

256 Additionally, Ulus et al. reported a 34% prevalence of FM, highlighting the profound impact of
257 FM on fatigue in PsA [24]. Their observation of significantly elevated fatigue scores in FM-
258 PsA, alongside increased pain and disease activity, aligns with our findings and reinforces the
259 importance of recognizing comorbid FM. The 2023 EULAR recommendations highlighted the
260 importance of accurate disease activity evaluation to guide therapeutic decisions in PsA [1]. The
261 current study extends this evidence by underscoring that concomitant FM may distort these
262 assessments, potentially leading to overtreatment or misclassification in routine care.

263 A particularly crucial finding of this study is the moderate agreement observed between
264 physician-reported FM status and FM identified using the FiRST questionnaire (ICC 0.59).
265 Despite strong concordance between patient and physician evaluations of PsA disease activity
266 observed in our previous study [18], the current finding suggests diagnostic challenges and
267 significant variability in FM identification in clinical practice. Several factors may contribute to
268 this observation. Firstly, the physician's assessment of FM was based on subjective opinion
269 rather than on standardized diagnostic or classification criteria (e.g., ACR 2010 criteria) [4].
270 Secondly, while validated, the FiRST questionnaire [17] is a screening tool and may not fully
271 capture the complexity of FM. These challenges indicate a critical need for standardized
272 approaches to FM assessment in PsA, including validated screening tools like FiRST. The
273 clinical implications are substantial. Discrepancies between self-reported FiRST screening and
274 physician-diagnosed FM demonstrate the need for standardized screening approaches for FM.
275 The FiRST questionnaire offers a valuable tool for FM detection, as shown in previous studies
276 on rheumatic diseases [17, 26]. Clinicians should remain vigilant for FM, especially when
277 patient-reported outcomes diverge from objective markers, to ensure accurate disease assessment
278 and tailored treatment for PsA patients, potentially involving multidisciplinary care.

279
280 Moreover, as emphasized by Mease et al. and supported by our findings, FM's confounding
281 influence on disease activity assessment necessitates considering FM as a key contextual factor
282 [4, 25]. Integrating FM assessment into routine PsA care can prevent misinterpretation of disease
283 activity and reduce unnecessary treatment escalation. These findings are consistent with
284 population-based evidence demonstrating that PsA patients with coexisting FM patients have
285 more complex treatment courses, higher rates of biologic switching, and significantly shorter
286 biologic survival, underscoring the clinical consequences of misinterpreting nociplastic pain as
287 inflammatory activity [31].

288
289 The moderate agreement observed between physician-identified and FiRST-screened FM should
290 not be interpreted as undermining the validity of the screening tool but as reflecting conceptual
291 and methodological differences in assessment. The FiRST primarily identifies nociplastic pain
292 symptoms through patient self-report, whereas physician diagnosis integrates broader clinical
293 judgment, comorbidities, and disease context. Previous studies have also reported partial
294 concordance between symptom-based and clinician-based assessments of fibromyalgia in
295 inflammatory rheumatic diseases, suggesting that these tools capture complementary dimensions
296 of pain rather than identical constructs [17].

297
298 In addition to standardized clinical and questionnaire-based evaluations, musculoskeletal
299 ultrasound (MSUS) may be a valuable adjunct in distinguishing between inflammatory and
300 nociplastic pain components in PsA. Ultrasound can objectively detect synovitis, tenosynovitis,
301 and enthesitis, which are hallmarks of inflammatory activity, whereas these features are typically
302 absent in FM [28, 29]. Incorporating MSUS in equivocal cases may improve diagnostic accuracy
303 and prevent overestimating disease activity. Work suggests that although FM may present with
304 enthesal tenderness, ultrasound rarely reveals Doppler-positive inflammatory lesions, further
305 supporting its role in differentiation [29]. Ultrasound also offers important practical advantages;
306 it is quick, patient-friendly, and radiation-free, and has been shown to facilitate simple and
307 accurate detection of musculoskeletal abnormalities in routine care [30].
308
309 The current study adds to previous Rheumatology International publications by providing the
310 first multinational evaluation of FM prevalence and its clinical correlates among patients with
311 PsA in 13 Arab countries. Previous RHEI publication from the TACTIC-program characterized
312 patient impact and physician-patient discordance; the present study extends this work in
313 Rheumatology International, exploring patient-reported impact and physician-patient
314 discordance in PsA [13, 18], and by evaluating agreement between FiRST-based and clinician-
315 reported FM, this evidence quantifies the influence of concomitant FM on disease-activity
316 indices, quality of life, and symptom interpretation. This study also uniquely compares FiRST
317 screening with physician-identified FM and identifies predictors of discordance, offering
318 pragmatic insights that align with recent EULAR (2024), ACR/NPF (2019), and GRAPPA
319 (2022) recommendations. These findings position FM as a critical contextual factor in PsA
320 assessment and inform regionally relevant strategies for more precise, patient-centered disease
321 evaluation. This expands the journal's existing evidence base with regionally specific, clinically
322 actionable insights.
323

324
325 It is essential to acknowledge the limitations of our study. The cross-sectional design limits
326 causal inferences, and reliance on the FiRST may not capture all FM to prevent unnecessary
327 treatment escalation. A comprehensive approach to managing PsA and FM should integrate
328 inflammatory and non-inflammatory mechanisms, ultimately improving patient outcomes and
329 quality of life.
330

330
cases. Furthermore, the assessment of physician-reported FM was based on individual clinical opinion rather than formal diagnostic criteria, which may influence the observed agreement with the FiRST tool. Although treatment data were available in the TACTIC registry, these variables were not analyzed in the present work, as the primary objective was to evaluate the prevalence and clinical correlates of FM independently of therapeutic exposure. Future studies incorporating treatment profiles with imaging and patient-reported outcomes would help clarify these interactions. Longitudinal research is also warranted to assess the long-term impact of FM on PsA and develop targeted interventions to optimize multimodal care strategies for PsA with coexisting FM [26].

In conclusion, this multinational study confirms FM as a significant comorbidity in Arab patients with PsA, with an observed prevalence of 19%. Our findings emphasize the importance of recognizing FM in PsA assessment and management, given its association with more severe disease activity and worse PROs. The discrepancies between self-reported (FiRST) and physician-diagnosed FM highlight the need for standardized screening tools in clinical practice, such as the FiRST questionnaire. Clinicians must remain vigilant for FM, especially when patient-reported symptoms diverge from objective markers, to ensure accurate disease assessment, tailored treatment strategies (including multidisciplinary care approaches), and to prevent unnecessary treatment escalation. A comprehensive approach to managing PsA and FM should integrate inflammatory and non-inflammatory mechanisms, ultimately improving patient outcomes and quality of life.
363 **Declarations**

364 **Funding:**

365 This study received no specific grant from any funding agency in the public, commercial, or not-
366 for-profit sectors.

367 **Conflicts of interest:**

368 The authors declare that they have no conflicts of interest relevant to this article.

369 **Ethics approval and consent to participate:**

370 The TACTIC study received ethics approval from the *Ethics Committee of Hôtel-Dieu de France*
371 *Hospital, Saint-Joseph University (Beirut, Lebanon)* in March 2022, and from the *Kurdistan*
372 *Board of Medical Specialties Research Protocol Ethics Committee (Erbil, Iraq; March 2022)*.
373 The secondary analysis was approved by the *University of Kurdistan Hewlêr IRB (RE0016;*
374 *January 28, 2024).*

375 All participants provided written informed consent.

376 **Consent for publication:**

377 It is not applicable (no identifiable participant data is included).

378 **Availability of data and materials:**

379 In accordance with the *Rheumatology International* data-sharing principles, anonymized data
380 supporting the findings of this study are available from the corresponding author upon
381 reasonable request, subject to ArLAR/ARCH data-sharing regulations.

382 **Authors' contributions:**

383 AM conceived the study, designed the analysis, curated data, interpreted results, drafted the
384 manuscript, and coordinated revisions. NS, and LG supervised the study design, analysis plan,
385 and manuscript development. NZ, NA, LG, LEK, BE, NAJ, FIG, AA, NAS, CH, SH, SS, HH,
386 LAJ, BKM, AABo, LAy, BR, KA, and IH contributed to patient recruitment, data verification,
387 interpretation of findings, and critical manuscript revision. All authors meet the ICMJE criteria
388 for authorship, approved the final version, and accept full responsibility for the integrity and
389 accuracy of all aspects of the work.

390 Acknowledgements:

391 The authors thank all participating rheumatologists and patients involved in the TACTIC study.

392 Prof. Nick Clement and Prof Deiary Kader, United Kingdom, for their assistance with statistical
393 analysis and interpretation.

394
395 Note on prior abstract publication:
396 Preliminary findings of this work were presented at the ACR Convergence Meeting 2024
397 (American College of Rheumatology) as an abstract titled "Frequency of Fibromyalgia Using
398 Validated Measures in a Sample of Patients with Psoriatic Arthritis and Impact on Disease: A
399 Cross-Sectional Multinational Study." Abstract number 1229, poster session B, date November
400 17, 2024, 10:30 AM-12:30 PM. Available at: https://acrabSTRACTS.org/ABSTRACT/fREQUENCY-OF-
401 fibromyalgia-using-validated-measures-in-a-sample-of-patients-with-psoriatic-arthritis-and-
402 IMPACT-ON-DISEASE-A-CROSS-SECTIONAL-MULTINATIONAL-STUDY/

403
404 AI and language assistance disclosure:
405 Language improvement was performed using Grammarly under the author's supervision. No
406 generative AI tools were used to draft or edit this manuscript.

407
408 Reporting Transparency
409 The authors confirm adherence to EQUATOR Network standards, specifically the STORBE
410 statement for observational studies.

411
412 Availability of data and materials:
413 Due to ethical restrictions, the anonymized datasets generated and analyzed during the current
414 study are not publicly available, but they are available from the corresponding author upon
415 reasonable request and with permission from the Arab League of Associations for Rheumatology
416 (ArLAR) Research Committee. Data sharing will be considered per institutional ethics policies
417 and Rheumatology International's open data principles.

418

419 References

420

421

422

423

1. Gossec L, Kerschbaumer A, Ramiro S, et al. EULAR recommendations for the management of psoriatic arthritis with pharmacological therapies: 2023 update. *Ann Rheum Dis*. 2024;83(6):706-19. doi:10.1136/ard-2024-225531

424

425

426

2. Singh JA, Guyatt G, Ogdie A, et al. 2018 American College of Rheumatology/National Psoriasis Foundation guideline for the treatment of psoriatic arthritis. *Arthritis Rheumatol*. 2019;71(1):5-32. doi:10.1002/art.40726

427

428

3. Clauw DJ. Fibromyalgia: a clinical review. *JAMA*. 2014;311(15):1547-55. doi:10.1001/jama.2014.3266

429

430

431

4. Wolfe F, Clauw DJ, Fitzcharles MA, Goldenberg DL, Häuser W, Katz RL, et al. 2016 revisions to the 2010/2011 fibromyalgia diagnostic criteria. *Semin Arthritis Rheum*. 2016;46(3):319-29. doi:10.1016/j.searthrit.2016.08.012

432

433

434

5. Bawazir Y. Prevalence of fibromyalgia syndrome in Saudi Arabia: a systematic review and meta-analysis. *BMC Musculoskelet Disord*. 2023;24(1):692. doi.org/10.1186/s12891-023-06821-z

435

436

437

6. Kılıç E, Kılıç G, Tekeoğlu İ, Sargın B, Kasman SA, Alkan H, et al. Regional variations in psoriatic arthritis: Insights from a nationwide multicenter analysis in Türkiye. *Arch Rheumatol*. 2024;39(3):339-49. doi:10.46497/archrheumatol.2024.10591

438

439

440

441

7. Wang CTM, Kwan YH, Fong W, Xiong SQ, Leung YY. Factors associated with patient- physician discordance in a prospective cohort of patients with psoriatic arthritis: An Asian perspective. *Int J Rheum Dis*. 2019;22(7):1209-15. doi:10.1111/1756-185X.13568

442

443

444

8. Mülkoğlu C, Ayhan FF. The impact of coexisting fibromyalgia syndrome on disease activity in patients with psoriatic arthritis and rheumatoid arthritis: A cross-sectional study. *Mod Rheumatol*. 2020;30(6):1044-51. doi:10.1080/14397595.2020.1823069

445

446

447

9. Ulutatar F, Unal- Ulutatar C, Duruoz MT. Fibromyalgia in patients with psoriatic arthritis: Relationship with enthesopathy, sleep, fatigue and quality of life. *Int J Rheum Dis*. 2020;24(2):183-8. doi:10.1111/1756-185X.1396
10. Magrey MN, Antonelli M, James N, Khan MA. High Frequency of Fibromyalgia in Patients with Psoriatic Arthritis: A Pilot Study. *Arthritis. 2013;2013:762921*. doi:10.1155/2013/762921

11. Mease P, Reed G, Ogdie A, Pappas DA, Kremer JM. Prevalence of Fibromyalgia and Widespread Pain in Psoriatic Arthritis: Association With Disease Severity Assessment in a Large US Registry. *Arthritis Care Res*. 2024;76(9):1313-21. doi:10.1002/acr.25358

12. Littlejohn GO. Fibromyalgia and psoriatic arthritis: Partners together. *Int J Rheum Dis*. 2021;24(2):141-3. doi:10.1111/1756-185X.14029

13. Ziadé N, Abbas N, Hmamouchi I, El Kibbi L, Maroof A, Elzorkany B, et al. Is the patient-perceived impact of psoriatic arthritis a global concept? An international study in 13 Arab countries (TACTIC study). *Rheumatol Int*. 2024;44(5):885-99. doi:10.1007/s00296-024-05552-1

14. Taylor W, Gladman D, Helliwell P, Marchesoni A, Mease P, Mielants H. Classification criteria for psoriatic arthritis: development of new criteria from a large international study. *Arthritis Rheum*. 2006;54(8):2665-73. doi:10.1002/art.2197

15. Brikman S, Furer V, Wollman J, Borok S, Matz H, Polachek A, et al. The Effect of the Presence of Fibromyalgia on Common Clinical Disease Activity Indices in Patients with Psoriatic Arthritis: A Cross-sectional Study. *J Rheumatol*. 2016;43(9):1749-54. doi:10.3899/jrheum. 151491

16. Navarini L, Currado D, Pilato A, et al. Association between patient acceptable symptom state (PASS) and fibromyalgia in psoriatic arthritis: a real-world study. *Clin Exp Rheumatol*. 2024;42(5):945-53. doi:10.55563/cer/fmpa2024

17. Fan A, Tournadre A, Pereira B, Tatar Z, Couderc M, Malochet-Guinamand S, et al. Performance of Fibromyalgia Rapid Screening Tool (FiRST) to detect fibromyalgia syndrome in rheumatic diseases. *Rheumatology (Oxford)*. 2016;55(10):1746-50. doi:10.1093/rheumatology/kew244

18. Abbas N, Hmamouchi I, Kibbi LE, Maroof A, Elzorkany B, Abdulateef N, et al. Discordance between patient and physician global assessments of psoriatic arthritis activity: an observational study in 13 Arab countries. *Rheumatol Int*. 2025;45(4). doi:10.1007/s00296-025-05819-1
478

479

19. Coates LC, Corp N, van der Windt D, et al. GRAPPA treatment recommendations: 2021 update. *J Rheumatol*. 2022;49(1):1–14. doi:10.3899/jrheum.21133

480

481

482

483

20. Sumpton D, Kelly A, Tunnicliffe DJ, Craig JC, Hassett G, Chessman D, et al. Patients’ perspectives and experience of psoriasis and psoriatic arthritis:a systematic review and thematic synthesis of qualitative studies. *Arthritis Care Res (Hoboken)*. 2020;72(5):711–22. doi:10.1002/acr.23896

484

485

486

21. Leung YY, Thumboo J. Fibromyalgia as a contextual factor influencing disease activity measurements in spondyloarthritis and psoriatic arthritis. *J Rheumatol*. 201;43(11):1953–5. doi:10.3899/jrheum.16115

487

488

489

22. Perrot S, Bouhassira D, Fermanian J; CEDR (Cercle d'Etude de la Douleur en Rhumatologie). Development and validation of the Fibromyalgia Rapid Screening Tool (FiRST). *Pain*. 2010;150(2):250–6. doi:10.1016/j.pain.2010.03.034

490

491

492

23. AlAujan SS, Almalag HM, Assiri GA, Alodaibi FA, Omair MA. Fibromyalgia Rapid Screening Tool (FiRST): Arabic Translation and Cross-Cultural Adaptation and Validation. *Healthcare (Basel)*. 2023;11(7):961. doi:10.3390/healthcare11070961

493

494

495

24. Ulus Y, Akyol Y, Bilgici A, Kuru O. The impact of the presence of fibromyalgia on fatigue in patients with psoriatic arthritis: comparison with controls. *Adv Rheumatol*. 2019;60(1). doi:10.1186/s42358-019-0104-6

496

497

498

25. Husted JA, Thavaneswaran A, Chandran V, Gladman DD. Incremental Effects of Comorbidity on Quality of Life in Patients with Psoriatic Arthritis. *J Rheumatol*. 2013;40(8):1349–56. doi:10.3899/jrheum.121500

499

500

501

502

26. Coates LC, Orbai AM, Azevedo VF, Cappelleri JC, Steinberg K, Lippe R, et al. Results of a global, patient-based survey assessing the impact of psoriatic arthritis discussed in the context of the Psoriatic Arthritis Impact of Disease (PsAID) questionnaire. *Health Qual Life Outcomes*. 2020;18(1). doi:10.1186/s12955-020-0142

503

504

505

27. Elsawy NA, Helal AH, Abd ElHamid HA, Abdel-Fattah YH. Fibromyalgia in patients with psoriatic arthritis: Impact on disease activity indices, fatigue and health-related quality of life. *Int J Rheum Dis*. 2021;24(2):189–96. doi:10.1111/1756-185X.13987

506

507

28. D'Agostino MA, Terslev L, Wakefield R, Østergaard M, Balint P, Naredo E, Iagnocco A, Backhaus M, Grassi W, Emery P. Novel algorithms for the pragmatic use of ultrasound
508 in the management of patients with rheumatoid arthritis: from diagnosis to remission. *Ann Rheum Dis.* 2016;75(11):1902-8. doi:10.1136/annrheumdis-2016-209646

510 29. Bibas N, Pignon C, Lopez-Medina C, Gandjbakhch F, Fautrel B, Gossec L. Ultrasonography for the assessment of enthesitis in psoriatic arthritis: systematic review with meta-analysis. *Rheumatology (Oxford)*. 2025;64(5):2377-84. doi:10.1093/rheumatology/keae705

514 30. Mısırlı S, Yalçınkaya B, Çolak AF, Kara M. Isolated Extensor Carpi Ulnaris Tenosynovitis: 'Ultra'simple Diagnosis With Ultrasound. *Am J Phys Med Rehabil.* 2025;104(4):e64-e65. doi:10.1097/PHM.0000000000002621.

517 31. Elizur Y, Amital M, Ben-Shabat N, et al. The association between psoriasis, psoriatic arthritis, and fibromyalgia syndrome: Effects on treatment-A population-based study. *Medicina*. 2025;61(1):1809. doi:10.3390/medicina61101809

520 32. Paroli M, Gioia C, Accapezzato D, Caccavale R. Demographic characteristics and inflammatory biomarker profile in psoriatic arthritis patients with comorbid fibromyalgia: A cross-sectional study. *Medicina*. 2025;61(6):1050. doi:10.3390/medicina61061050.

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527 **Table 1.** Patient demographics, family history of psoriasis, and comorbidities according to presence of fibromyalgia.

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<table><thead><tr><th>Variable</th><th>Group</th><th>No FM (n=434)</th><th>FM (n=102)</th><th>Total (n=536)</th><th>Odds Ratio / Difference (95% CI)</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="2">Gender</td><td>Male</td><td>240 (55.3%)</td><td>25 (24.5%)</td><td>265 (49.4%)</td><td rowspan="2">0.40 (0.25 to 0.66)</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Female</td><td>194 (44.7%)</td><td>77 (75.5%)</td><td>271 (50.6%)</td></tr><tr><td rowspan="2">Marital Status</td><td>No</td><td>87 (20.0%)</td><td>18 (17.6%)</td><td>105 (19.6%)</td><td rowspan="2">1.17 (0.67 to 2.05)</td><td rowspan="2">0.583</td></tr><tr><td>Yes</td><td>347 (80.0%)</td><td>84 (82.4%)</td><td>431 (80.4%)</td></tr><tr><td>Age (years)</td><td>Mean (SD)</td><td>45.5 (13.3)</td><td>46.2 (12.0)</td><td>45.6 (13.1)</td><td>0.7 (-2.1 to 3.5)</td><td>0.626</td></tr></tbody></table>
<table><tbody><tr><td rowspan="5">BMI (kg/m²)</td><td>Mean (SD)</td><td>28.3 (5.3)</td><td>30.4 (7.0)</td><td>28.7 (5.7)</td><td>2.1 (0.9 to 3.3)</td><td>0.001</td></tr><tr><td rowspan="4">Occupation</td><td>White Collar</td><td>177 (40.8%)</td><td>33 (32.4%)</td><td>210 (39.2%)</td><td>-</td><td>0.373</td></tr><tr><td>Blue Collar</td><td>63 (14.5%)</td><td>16 (15.7%)</td><td>79 (14.7%)</td><td>-</td><td>-</td></tr><tr><td>Retired</td><td>38 (8.8%)</td><td>8 (7.8%)</td><td>46 (8.6%)</td><td>-</td><td>-</td></tr><tr><td>Unemployed</td><td>156 (35.9%)</td><td>45 (44.1%)</td><td>201 (37.5%)</td><td>-</td><td>-</td></tr><tr><td rowspan="5">Education</td><td>Elementary</td><td>68 (15.7%)</td><td>30 (29.4%)</td><td>98 (18.3%)</td><td>-</td><td>0.005</td></tr><tr><td>Secondary</td><td>141 (32.5%)</td><td>21 (20.6%)</td><td>162 (30.2%)</td><td>-</td><td>-</td></tr><tr><td>University</td><td>215 (49.5%)</td><td>48 (47.1%)</td><td>263 (49.1%)</td><td>-</td><td>-</td></tr><tr><td>Other</td><td>10 (2.3%)</td><td>3 (2.9%)</td><td>13 (2.4%)</td><td>-</td><td>-</td></tr><tr><td>Family History of PsA</td><td>Positive</td><td>64 (14.7%)</td><td>24 (23.5%)</td><td>88 (16.4%)</td><td>-</td><td>0.78</td></tr><tr><td rowspan="4">Family History of Psoriasis</td><td>Unknown</td><td>59 (13.6%)</td><td>10 (9.8%)</td><td>69 (12.9%)</td><td>-</td><td>0.426</td></tr><tr><td>Positive</td><td>180 (41.5%)</td><td>49 (48.0%)</td><td>229 (42.7%)</td><td>-</td><td>-</td></tr><tr><td>Negative</td><td>209 (48.2%)</td><td>42 (41.2%)</td><td>251 (46.8%)</td><td>-</td><td>-</td></tr><tr><td>Hypertension</td><td>Yes</td><td>121 (27.9%)</td><td>36 (35.3%)</td><td>157 (29.3%)</td><td>1.41 (0.89 to 2.23)</td><td>0.139</td></tr><tr><td rowspan="2">Diabetes</td><td rowspan="2">Yes</td><td>65 (15.0%)</td><td>19 (18.6%)</td><td>84 (15.7%)</td><td>1.30 (0.74 to 2.28)</td><td>0.361</td></tr><tr><td>61 (14.1%)</td><td>25 (24.5%)</td><td>86 (16.0%)</td><td>1.99 (1.17 to 3.36)</td><td>0.01</td></tr><tr><td rowspan="2">Dyslipidemia</td><td rowspan="2">Yes</td><td>83 (19.1%)</td><td>16 (15.7%)</td><td>99 (18.5%)</td><td>0.79 (0.44 to 1.41)</td><td>0.421</td></tr><tr><td>24 (5.5%)</td><td>9 (8.8%)</td><td>33 (6.2%)</td><td>1.65 (0.74 to 3.67)</td><td>0.287</td></tr><tr><td rowspan="2">Chronic Infection</td><td rowspan="2">Yes</td><td>7 (1.6%)</td><td>4 (3.9%)</td><td>11 (2.1%)</td><td>2.49 (0.72 to 8.67)</td><td>0.234</td></tr><tr><td>1 (0.2%)</td><td>0 (0%)</td><td>1 (0.2%)</td><td>-</td><td>0.999</td></tr><tr><td rowspan="2">COPD</td><td rowspan="2">Yes</td><td>0 (0%)</td><td>2 (2.0%)</td><td>2 (0.4%)</td><td>-</td><td>0.036</td></tr><tr><td>25 (5.8%)</td><td>9 (8.8%)</td><td>34 (6.3%)</td><td>1.58 (0.72 to 3.50)</td><td>0.253</td></tr><tr><td rowspan="2">Osteoporosis</td><td rowspan="2">Yes</td><td>21 (4.8%)</td><td>2 (2.0%)</td><td>23 (4.3%)</td><td>0.39 (0.09 to 1.71)</td><td>0.279</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><tr><td>Gastric Ulcer</td><td>Yes</td><td>14 (3.2%)</td><td>2 (2.0%)</td><td>16 (3.0%)</td><td>0.60 (0.13 to 2.68)</td><td>0.748</td></tr></table>

This table presents the demographic characteristics of the study participants, including gender, marital status, age, body mass index (BMI), occupation, education level, family history of psoriatic arthritis and psoriasis, and various comorbidities such as hypertension, diabetes, dyslipidemia, smoking, obesity, cardiovascular events, chronic infections, chronic obstructive pulmonary disease (COPD), gout, depression, osteoporosis, and gastric ulcer, stratified by the presence or absence of fibromyalgia (FM). Data are presented as counts and percentages for categorical variables, and means with standard deviations (SD) for continuous variables. Odds ratios (OR) with 95% confidence intervals (CI) and p-values are provided to show the association between each variable and FM. Percentages were calculated in columns.

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<table><caption>Table 2. Disease characteristics according to the presence of fibromyalgia.</caption><thead><tr><th>Demographic</th><th>Descriptive</th><th>No FM (n=434)</th><th>FM (n=102)</th><th>Total (n=536)</th><th>Odds Ratio / Difference (95% CI)</th><th>p-value</th></tr></thead><tbody><tr><td>Psoriasis Duration (years)</td><td>Mean (SD)</td><td>8.7 (7.3)</td><td>9.0 (7.4)</td><td>8.8 (7.3)</td><td>0.2 (-1.3 to 1.8)</td><td>0.76</td></tr><tr><td>Skin Psoriasis</td><td>Yes</td><td>398 (91.7%)</td><td>93 (91.2%)</td><td>491 (91.6%)</td><td>0.94 (0.44 to 2.01)</td><td>0.862</td></tr><tr><td>Nail Psoriasis</td><td>Yes</td><td>193 (44.5%)</td><td>47 (46.1%)</td><td>240 (44.8%)</td><td>1.07 (0.69 to 1.64)</td><td>0.769</td></tr><tr><td>Polyarthritis</td><td>Yes</td><td>225 (51.8%)</td><td>72 (70.6%)</td><td>297 (55.4%)</td><td>2.23 (1.40 to 3.55)</td><td>0.001</td></tr><tr><td>Oligoarthritis</td><td>Yes</td><td>158 (36.4%)</td><td>18 (17.6%)</td><td>176 (32.8%)</td><td>0.37 (0.22 to 0.65)</td><td>&lt;0.001</td></tr><tr><td>Monoarthritis</td><td>Yes</td><td>20 (4.6%)</td><td>3 (2.9%)</td><td>23 (4.3%)</td><td>0.63 (0.18 to 2.15)</td><td>0.455</td></tr><tr><td>Enthesitis</td><td>Yes</td><td>143 (32.9%)</td><td>40 (39.2%)</td><td>183 (34.1%)</td><td>1.31 (0.84 to 2.05)</td><td>0.23</td></tr><tr><td>Dactylitis</td><td>Yes</td><td>96 (22.1%)</td><td>32 (31.4%)</td><td>128 (23.9%)</td><td>1.61 (1.00 to 2.59)</td><td>0.049</td></tr><tr><td>Axial</td><td>Yes</td><td>142 (32.7%)</td><td>36 (35.3%)</td><td>178 (33.2%)</td><td>1.12 (0.71 to 1.76)</td><td>0.619</td></tr><tr><td>Uveitis</td><td>Yes</td><td>22 (5.1%)</td><td>7 (6.9%)</td><td>29 (5.4%)</td><td>1.38 (0.57 to 3.32)</td><td>0.471</td></tr><tr><td>Crohn's Disease</td><td>Yes</td><td>9 (2.1%)</td><td>1 (1.0%)</td><td>10 (1.9%)</td><td>0.47 (0.06 to 3.73)</td><td>0.696</td></tr><tr><td>Ulcerative Colitis (UC)</td><td>Yes</td><td>8 (1.8%)</td><td>3 (2.9%)</td><td>11 (2.1%)</td><td>1.61 (0.42 to 6.19)</td><td>0.446</td></tr><tr><td rowspan="3">HLAB27</td><td>Negative</td><td>118 (27.2%)</td><td>36 (35.3%)</td><td>154 (28.7%)</td><td>-</td><td>0.109</td></tr><tr><td>Positive</td><td>26 (6.0%)</td><td>9 (8.8%)</td><td>35 (6.5%)</td><td>-</td><td>-</td></tr><tr><td>Unknown</td><td>290 (66.8%)</td><td>57 (55.9%)</td><td>347 (64.7%)</td><td>-</td><td>-</td></tr><tr><td>ESR (mean, SD)</td><td></td><td>29.3 (23.2)</td><td>34.8 (20.7)</td><td>30.3 (22.8)</td><td>5.5 (0.5 to 10.5)</td><td>0.032</td></tr><tr><td>CRP (mg/L; mean, SD)</td><td></td><td>11.7 (25.3)</td><td>9.3 (10.1)</td><td>11.2 (23.9)</td><td>2.4 (-3.0 to 7.8)</td><td>0.386</td></tr></tbody></table>
This table displays disease-related characteristics, including psoriasis duration, the presence of skin and nail psoriasis, the type of inflammatory arthropathy (polyarthritis, oligoarthritis, monoarthritis, enthesitis, dactylitis, axial involvement, uveitis, Crohn's disease, and ulcerative colitis (UC)), HLA-B27 status, and inflammatory markers (erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP)), stratified by the presence or absence of fibromyalgia (FM). Data are presented as counts and percentages for categorical variables and means with standard deviations (SD) for continuous variables. Odds ratios (OR) with 95% confidence intervals (CI) and p-values are provided to show the association between each variable and FM.

<table><caption>**Table 3.** Patient-reported outcomes and disease activity measures according to the presence of fibromyalgia.</caption><thead><tr><th rowspan="2">PROs</th><th rowspan="2">Descriptive</th><th colspan="2">Group</th><th rowspan="2">Odds Ratio / Difference (95% CI)</th><th rowspan="2">p-value</th></tr><tr><th>No FM (n=434)</th><th>FM (n=102)</th></tr></thead><tbody><tr><td>PsAID (mean, SD)</td><td></td><td>3.4 (2.1)</td><td>6.2 (1.9)</td><td>2.8 (2.3 to 3.2)</td><td>&lt;0.001</td></tr><tr><td>PHQ (mean, SD)</td><td></td><td>2.7 (2.4)</td><td>6.1 (3.2)</td><td>3.4 (2.9 to 4.0)</td><td>&lt;0.001</td></tr><tr><td>PHQ Anxiety (mean, SD)</td><td></td><td>1.4 (1.4)</td><td>3.0 (1.8)</td><td>1.7 (1.4 to 2.0)</td><td>&lt;0.001</td></tr><tr><td>PHQ Depression (mean, SD)</td><td></td><td>1.3 (1.4)</td><td>3.1 (1.7)</td><td>1.7 (1.4 to 2.1)</td><td>&lt;0.001</td></tr><tr><td>HAQ Adjusted (mean, SD)</td><td></td><td>0.9 (0.8)</td><td>1.7 (0.7)</td><td>0.7 (0.6 to 0.9)</td><td>&lt;0.001</td></tr><tr><td>PhGA PsA Activity (mean, SD)</td><td></td><td>3.6 (2.4)</td><td>5.3 (2.1)</td><td>1.7 (1.1 to 2.2)</td><td>&lt;0.001</td></tr><tr><td>PhGA PSO Activity (mean, SD)</td><td></td><td>2.9 (2.5)</td><td>4.0 (2.7)</td><td>1.1 (0.6 to 1.7)</td><td>&lt;0.001</td></tr><tr><td>PGA PsA Activity (mean, SD)</td><td></td><td>4.4 (2.4)</td><td>6.3 (2.4)</td><td>1.9 (1.4 to 2.4)</td><td>&lt;0.001</td></tr><tr><td>PGA PsO Activity (mean, SD)</td><td></td><td>3.6 (2.6)</td><td>5.5 (3.0)</td><td>1.9 (1.3 to 2.5)</td><td>&lt;0.001</td></tr><tr><td>PGA PsA Pain (mean, SD)</td><td></td><td>4.4 (2.5)</td><td>6.7 (2.2)</td><td>2.3 (1.8 to 2.8)</td><td>&lt;0.001</td></tr><tr><td></td><td></td><td>No FM (n=366)</td><td>FM (n=89)</td><td></td><td></td></tr><tr><td>DAPSA* (mean, SD)</td><td></td><td>16.1 (13.0)</td><td>32.3 (21.0)</td><td>16.1 (12.7 to 19.6)</td><td>&lt;0.001</td></tr></tbody></table>

This table presents the patient-reported outcome measures (PROs), including the Psoriatic Arthritis Impact of Disease (PsAID1) score, Patient Health Questionnaire (PHQ) score (total, anxiety, and depression subscales), Health Assessment Questionnaire (HAQ) adjusted score, Physician Global Assessment (PhGA) for psoriatic arthritis (PsA) and psoriasis (PsO) activity, Patient Global Assessment (PGA) for PsA activity, PsO activity, and PsA pain, and the Disease Activity in Psoriatic Arthritis (DAPSA) score, stratified by the presence or absence of fibromyalgia (FM). Data are presented as means with standard deviations (SD). Odds ratios (OR) with 95% confidence intervals (CI) and p-values are provided to show the association between each variable and FM.

* DAPSA was only available for those who had a C-reactive Protein test available (n=366 (no FM) and n=89 (FM) → n=455).
<table><caption>**Table 4.** Diagnosis of FM according to physician and the FIRST score (using 5 or more as the threshold value).</caption><thead><tr><th rowspan="2">Demographic</th><th rowspan="2">Descriptive</th><th colspan="2">Group</th><th rowspan="2">Odds Ratio<br/>/ Difference<br/>(95% CI)</th><th rowspan="2">p-value*</th></tr><tr><th>No FM<br/>(n=434)</th><th>FM<br/>(n=102)</th></tr></thead><tbody><tr><td rowspan="3">Physician<br/>Diagnosis of<br/>FM<br/>(n, % of group)</td><td>No</td><td>326</td><td>27</td><td></td><td rowspan="3">&lt;0.001</td></tr><tr><td>Yes</td><td>75</td><td>58</td><td></td></tr><tr><td>Suspicious</td><td>33</td><td>17</td><td></td></tr><tr><td rowspan="2">Physician<br/>Diagnosis of<br/>FM<br/>(n, % of group)</td><td>No</td><td>326</td><td>27</td><td>9.34</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Yes</td><td>75</td><td>58</td><td>(5.55 to 15.72)</td></tr></tbody></table>

This table shows the agreement between physician-reported fibromyalgia (FM) status (based on clinical opinion) and FM diagnosed using the Fibromyalgia Rapid Screening Tool (FiRST) score (threshold ≥5). Data are presented as counts and percentages. Odds ratios (OR) with 95% confidence intervals (CI) and p-values (from Chi-Square test) are provided to show the association between the physician diagnosis and the FIRST score result. Additionally, reliability statistics (Cronbach's alpha and intraclass correlation coefficient) are presented. Reliability between the Positive 1st Score and Physician diagnosis of Fibromyalgia was assessed using Cronbach's Alpha, resulting in a value of 0.588.