Fibromyalgia Syndrome (FMS) is a chronic pain condition characterized primarily by widespread musculoskeletal pain and commonly accompanied by fatigue, sleep disturbances, cognitive impairment, mood disturbances and a range of somatic symptoms [1]. Over the past two decades, the conceptualization of fibromyalgia has evolved from that of a poorly understood chronic pain condition to that of a complex disorder involving central sensitization, altered nociceptive processing, neuroimmune interactions, autonomic dysregulation and psychosocial factors [2].   Although fibromyalgia is generally not considered a life-threatening disorder and is not characterized by the structural joint destruction or internal organ damage observed in many connective tissue or collagen vascular diseases, it is associated with a substantial physical, psychological, social and economic burden [3,4]. Patients with fibromyalgia frequently experience reduced quality of life, functional impairment, fatigue, sleep disturbances, anxiety, depression and decreased work productivity, collectively imposing a substantial burden on patients, their families and healthcare systems. Furthermore, fibromyalgia is associated with increased healthcare utilization, frequent medical consultations, medication use and substantial socioeconomic costs despite the absence of demonstrable irreversible tissue damage [5,6].   Rapid modernization and socioeconomic transformation across Middle Eastern countries influence the prevalence, detection and clinical recognition of fibromyalgia by contributing to changes in sedentary behaviour, psychological stress, sleep disturbances, obesity and the burden of chronic diseases, factors that have been associated with fibromyalgia [7,8].   A range of biological, psychological and environmental factors have been associated with the onset, persistence and clinical progression of fibromyalgia [9]. Female sex is consistently identified as an important demographic factor associated with fibromyalgia, with women disproportionately affected compared with men across many epidemiological studies [10]. In addition, chronic medical conditions and autoimmune disorders, particularly rheumatoid arthritis and systemic lupus erythematosus, have been associated with an increased likelihood of fibromyalgia symptoms and features of central sensitization [11,12]. Psychological and psychosocial factors, including anxiety, depression, childhood trauma, adverse life experiences and chronic emotional stress have also been consistently associated with fibromyalgia and may influence pain perception and symptom severity [13,14]. Furthermore, certain personality characteristics, including perfectionism, heightened sensitivity, excessive responsibility and achievement-oriented behavior, have been proposed as potential factors associated with increased vulnerability to stress-related chronic pain conditions, including fibromyalgia [15,16].   A recent systematic review and meta-analysis of studies conducted in Saudi Arabia estimated a pooled prevalence of fibromyalgia Syndrome (FMS) of 13.4%, indicating that fibromyalgia constitutes a considerable health burden rather than an uncommon clinical condition [17]. This prevalence is relatively high compared with the estimated global prevalence 2-8% [18].   Therefore, an updated systematic review of fibromyalgia prevalence in Saudi Arabia is warranted to incorporate new evidence, reassess estimates across population groups, identify associated risk factors and clarify the applicability to the general population [19].

Protocol Registration The present systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (appendix 1) [20]. The review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD520261404505.   Database Sources and Search Strategy Because a previous systematic review of fibromyalgia in Saudi Arabia was published in 2023, an updated, focused literature search was conducted A new comprehensive literature search was conducted to identify all relevant studies examining fibromyalgia prevalence in Saudi Arabia published from 2018 until 25th June 2026. The search was performed in PubMed, Embase, web of science and google scholar to identify recently published evidence from Saudi Arabia.   The following search terms were used to identify studies on fibromyalgia in Saudi Arabia and to capture relevant demographic, clinical, psychological, lifestyle and epidemiological factors:   PubMed, MeSH headings, including “Fibromyalgia,” “Saudi Arabia,” and “Risk Factors,” were combined with corresponding free-text terms searched in titles and abstracts. In Embase, Emtree headings corresponding to these concepts were combined with free-text terms searched in titles, abstracts and author keywords. Web of Science Core Collection was searched using free-text terms in the Topic field, encompassing titles, abstracts, author keywords and Keywords Plus, while Google Scholar was searched using shorter free-text queries without controlled vocabulary headings. Across the databases, the fibromyalgia block included “fibromyalgia,” “fibromyalgia syndrome,” “chronic widespread pain,” and “fibromyositis”; the geographic block included “Saudi Arabia” and “Saudi population”; and the third block included “setting,” “outpatient setting,” “community setting,” “general population,” “incidence,” “epidemiology,” “risk factors,” “determinants,” “anxiety,” “depression,” “sleep,” “insomnia,” “trauma,” “obesity,” “chronic diseases,” and “medical conditions.” For PubMed, Embase and Web of Science, alternative terms and applicable controlled vocabulary headings within each block were combined using OR and the three blocks were combined using AND: (fibromyalgia) AND (Saudi Arabia) AND (associated factors or epidemiological characteristics). For Google Scholar, these concepts were translated into multiple shorter queries pairing disease and geographic terms with individual associated-factor or epidemiological terms. Search syntax and field restrictions were adapted to each platform.   Eligibility Criteria Inclusion Criteria: Studies were eligible for inclusion if they were conducted among populations in Saudi Arabia, enrolled adults aged ≥18 years, reported the prevalence of fibromyalgia and/or factors associated with the condition, used recognized diagnostic criteria or validated screening instruments for fibromyalgia and were published in English. Eligible study designs comprised observational studies (cross-sectional, cohort and case-control studies, including prospective or retrospective approaches, as applicable) and randomized controlled trials, provided that they reported relevant epidemiological data. Systematic reviews and other review articles were excluded because this review focused on primary research, thereby avoiding duplication of evidence from underlying studies.   Exclusion Criteria Studies were excluded if they were systematic or narrative reviews, editorials, letters, conference abstracts, case reports, or animal studies; did not report fibromyalgia prevalence or relevant associated factors; included nonhuman participants; were conducted outside Saudi Arabia; or focused exclusively on treatment outcomes without providing relevant epidemiological data.   Study Selection, Screening and Data Extraction All records identified through the searches were imported into reference management software and duplicate bibliographic records were removed before screening.   Two reviewers independently screened titles and abstracts and subsequently assessed potentially eligible full-text articles against the prespecified eligibility criteria. Eligible studies reported original data on the prevalence Of Fibromyalgia Syndrome (FMS), assessed using clearly defined diagnostic or screening criteria and/or factors associated with FMS among adults aged ≥18 years in Saudi Arabia, including predefined population subgroups. Eligible designs comprised cross-sectional, cohort and case-control studies, whether conducted prospectively or retrospectively and randomized controlled trials reporting relevant data. Systematic and narrative reviews were excluded because they did not provide original participant-level study populations; conference abstracts, editorials, letters, case reports, animal studies and studies conducted outside Saudi Arabia were also excluded. Disagreements were resolved through discussion and consensus, with a third reviewer consulted when necessary. Reasons for full-text exclusion were recorded and the selection process was summarized in the PRISMA flow diagram (Figure 1).   Figure 1: Distribution of the Included Studies by Publication Year   Potential duplicate publications and overlapping study populations were assessed by comparing authorship, institutions, recruitment settings and periods, eligibility criteria, sample sizes and participant characteristics across eligible reports. Shared institutions or recruitment periods prompted further assessment but were not considered sufficient evidence of overlap. No duplicate publications or overlapping populations were identified and the available recruitment and sampling information supported treating the included study populations as independent. Therefore, no reports required merging or exclusion because of population overlap. Data extracted from each included study comprised author and publication year, study design, setting, target population, sample size, diagnostic or screening criteria, prevalence estimates and reported factors associated with FMS. Associated factors were grouped into five domains: demographic, psychological, lifestyle, occupational and clinical or medical.   Quality Assessment of Included Studies The methodological quality of the included studies was independently assessed by two reviewers using the methodological quality of the included studies was independently assessed by two reviewers using the nine-item Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Studies Reporting Prevalence Data (2017 version). Although the eligibility criteria permitted multiple study designs, no cohort or case-control studies met the eligibility criteria for this review. The prevalence checklist was selected to assess the methodological quality of the included prevalence data.   The checklist assessed the appropriateness of the sampling frame and sampling methodology, adequacy of the sample size, description of participants and setting, coverage of the sample in the analysis, validity and reliability of outcome measurement, appropriateness of the statistical analysis and adequacy and management of the response rate. Each item was rated as “Yes,” “No,” “Unclear,” or “Not applicable.” Any disagreements between the reviewers were resolved through discussion and consensus. Appraisal findings were reported at the item level rather than converted into overall “high” or “moderate” quality categories.   Statistical Analysis Quantitative statistical synthesis was performed using Comprehensive Meta-Analysis software (CMA, Version 3.3, Biostat, Englewood, NJ, USA). To accommodate the wide distribution of reported prevalence rates across primary studies (ranging from 1.44-18.61%) and to prevent variance instability, raw proportions were logit-transformed prior to pooling. Weighted pooled prevalence estimates and their associated 95% Confidence Intervals (CIs) were computed using inverse-variance weighting under a DerSimonian and Laird random-effects model, which accounts for both within-study sampling error and between-study variance. Between-study heterogeneity variance (t2) was estimated using the DerSimonian-Laird method, while the extent of inconsistency across studies was assessed via Cochran’s Q test and quantified using the I2 statistic (with values of 25, 50 and 75% designating low, moderate and substantial heterogeneity, respectively). Individual study-level proportion CIs were derived using the exact Wilson score method. All pooled logit-transformed estimates were back-transformed into proportions for intuitive clinical interpretation. Statistical significance was set at a two-tailed $p<0.05.

Search Results The initial database searches identified 840 records, including 120 from pubmed, 180 from embase, 95 from web of science and 420 from google scholar, while an additional 25 records were identified through manual screening of reference lists.   After removal of 320 duplicate records, 520 unique records remained for title and abstract screening. Of these, 454 records were excluded because they were unrelated to the review objective or did not meet the prespecified eligibility criteria.   A total of 70 full-text articles were assessed for eligibility. Fifty-five full-text articles were excluded for the following reasons: review design, absence of prevalence or relevant associated-factor data, inclusion of non-Saudi populations, absence of recognized diagnostic or screening criteria for fibromyalgia, inclusion of pediatric populations, or a focus on treatment outcomes without relevant epidemiological data. Ultimately, 15 studies met the predefined eligibility criteria and were included in the qualitative synthesis [21-36].   Meta-Analysis Result Prevalence of Fibromyalgia Study Characteristics and Methodological Quality: A total of 15 studies, comprising 6,332 participants and 717 identified fibromyalgia cases, met the predefined eligibility criteria and were included in both the qualitative narrative synthesis and the quantitative meta-analysis. All 15 included studies provided extractable prevalence data that contributed directly to the overall pooled prevalence estimate. The primary studies exhibited considerable variation in sample sizes, ranging from 73 to 1,015 participants. The study characteristics and methodological quality assessments are summarized in Tables 1 and 2.   Table 1: Characteristics of the Included Studies Study Year Cases Total Mean Age SD Age Female n Male n Female % Diagnostic Tool Population Type Region Prevalence AlAujan et al. 2021 32 292 29 8 228 64 78.1 FiRST Pharmacy students and professionals Saudi Arabia 10.9589 Alfaifi 2024 28 224 48.2 17.5 109 115 48.7 FiRST + ACR 2016 DM Patients Riyadh 12.5 Albishri et al. 2026 5 348 21.4 3.57 234 114 67.2 FiRST Medical students Taif, Saudi Arabia 1.436782 AlDhuwaihy et al. 2026 14 86 32 11.44 46 40 53.5 FiRST Adults with chronic pain attending public awareness campaign Riyadh, Saudi Arabia 16.27907 Alghanim et al. 2024 18 208 45.7 12.8 158 50 76 ACR 2016 Patients with rheumatic diseases attending rheumatology clinics Eastern Province, Saudi Arabia 8.653846 Alshareef et al. 2025 82 580 NA NA 352 228 61.7 FSDC University students and faculty/staff members Makkah, Saudi Arabia 14.13793 Alnajashi et al. NA 11 73 37.3 10.2 52 21 71.2 FiRST Patients with Multiple Sclerosis (MS) Jeddah, Saudi Arabia 15.06849 Althobaiti et al. 2022 77 1015 NA NA 729 286 71.8 ACR 2010 + FSDC General adult population (Taif residents ≥20 years) Taif, Saudi Arabia 7.586207 AlEnzi et al. 2021 125 992 37.3 11.5 584 408 58.9 FiRST Healthcare workers Saudi Arabia (multiple hospitals) 12.60081 Hasan et al. 2026 122 850 44.8 8.7 544 306 64 ACR 2010 + FSDC Schoolteachers Eastern Province, Saudi Arabia 14.35294 Mustafa et al. 2026 51 274 NA NA NA NA NA FiRST Patients with Inflammatory Bowel Disease (IBD) Jeddah, Saudi Arabia 18.61314 Omair et al. 2019 15 182 NA NA 78 104 92.9 FSQ (Modified ACR 2010) Physicians in Training (Residents and Fellows) Riyadh, Saudi Arabia 8.241758 Samman et al. 2021 43 450 21.52 1.52 291 159 64.7 WPI + SS Medical students Jeddah, Saudi Arabia 9.555556 Ali et al. 2025 47 448 23.13 5.21 448 NA 100 WPI + SS Female university students and employees Al Jouf, Saudi Arabia 10.49107 AlOmair et al. 2022 47 310 NA NA 273 37 88.1 ACR 2010 (WPI + SS) Patients with seropositive Rheumatoid Arthritis (RA) Aseer, Saudi Arabia 15.16129   Table 2: Descriptive Statistics for the Included Studies No. of Studies Total Sample Size Total Number of FibromyalgiaCases Mean Sample Size per Study Median Sample Size per Study Minimum Sample Size Maximum Sample Size 15 6,332 717 422.1 310 73 1,015   Additionally, secondary citations focusing on healthcare provider or student awareness and diagnostic knowledge regarding fibromyalgia [37,38] were explicitly classified as contextual background literature rather than primary prevalence studies, ensuring that only the 15 primary epidemiologic investigations contributed to the quantitative meta-analysis.   Descriptive statistics indicated a mean sample size of 422.1 participants per study across the included studies. Figure 2 illustrates the distribution of sample sizes across the included studies, whereas Figure 3 presents their geographical distribution across regions of Saudi Arabia, highlighting the variation in study settings.   The methodological quality of the included studies was independently assessed by two reviewers using the Joanna Briggs Institute (JBI) critical appraisal tool. The methodological quality assessment indicated that 14 of the 15 included studies (93.33%) were classified as high quality, whereas 1 study (6.67%) was classified as having moderate quality, as summarized in Tables 3 and 4.   Table 3: Risk of Bias in the Included Studies Study JBI Critical Appraisal Score Methodological Quality Alfaifi 7/8 High AlAujan et al. 7/8 High Albishri et al. 7/8 High AlDhuwaihy et al. 7/8 High Alghanim et al. 8/8 High Alshareef et al. 7/8 High Alnajashi et al. 7/8 High Althobaiti et al. 7/8 High AlEnzi et al. 7/8 High Hasan et al. 7/8 High Mustafa et al. 7/8 High Omair et al. 6/8 Moderate Samman et al. 7/8 High Ali et al. 7/8 High AlOmair et al. 7/8 High   Table 4: Risk of Bias summary of Included Studies Methodological Quality No. of Studies Percentage of Studies High Quality 14 93.33 Moderate Quality 1 6.67     Figure 2: Distribution of Sample Sizes across the Included Studies     Figure 3: Geographic Distribution of the Included Studies across Regions of Saudi Arabia   Pooled Prevalence of Fibromyalgia The primary meta-analysis yielded a pooled prevalence estimate of fibromyalgia of 11.20% (95% CI: 9.09%-13.73%). Substantial between-study heterogeneity was observed across the included primary studies (I2 = 87.16\%, Cochran’s Q = 74.80, p<0.001; t2 = 0.1695). To account for this severe heterogeneity and quantify the expected true prevalence in future or unobserved Saudi population settings, a 95% prediction interval was computed. The 95% prediction interval ranged from 3.21-31.85%, reflecting substantial dispersion in disease occurrence across different clinical, occupational, academic and community settings in Saudi Arabia (Tables 5,6 and Figure 4).   Table 5: Summary Statistics of the Meta-Analysis No. of Studies Total Sample Size Total Number of Fibromyalgia Cases Mean Study-Level Prevalence (%) Median Study-Level Prevalence (%) Minimum Study-Level Prevalence (%) Maximum Study-Level Prevalence (%) Mean Sample Size per Study Median_Sample_Size 15 6332 717 11.71 12.5 1.44 18.61 422.13 310   Table 6: Meta-analysis Results for the Pooled Prevalence of Fibromyalgia No. of Studies Included in the Meta-Analysis Total Sample Size Total Number of Fibromyalgia Cases Pooled Prevalence (%) Lower Limit of 95% CI (%) Upper Limit of 95% CI (%) τ² (Between-Study Variance) I2 (%) H2 Cochran’sQ Cochran’s Q p-value 15 6,332 717 11.2 9.09 13.73 0.1695 87.16 7.79 74.8 2.57E-10     Figure 4: Forest Plot Showing the Pooled Prevalence of Fibromyalgia   Leave-One-Out Sensitivity Analysis A leave-one-out sensitivity analysis was conducted to evaluate the influence of individual studies on the pooled prevalence estimate and between-study variance. Iterative omission of single studies demonstrated that point estimates remained within a narrow range (10.79% to 12.08%). However, the analysis revealed that Albishri et al. [39] exerted a disproportionate impact on statistical heterogeneity. Omitting Albishri et al. [39] resulted in a substantial reduction in between-study variance (t2 dropped from 0.1695 to 0.0664) and a notable decrease in heterogeneity (I2 dropped from 87.16% to 73.93%), accompanied by a slight increase in the pooled prevalence from 11.20% to 12.08% (95% CI: 10.44%-13.94%). All other study omissions produced minimal changes in both point estimates and heterogeneity metrics (Table 7 and Figure 5). Influence diagnostics were performed to identify individual studies that may have disproportionately influenced the results of the meta-analysis, as summarized in Table 8. Figure 6 presents influence diagnostics, including Cook's distance and Hat values and identifies Albishri et al. [39] as a potentially influential study. Figure 7 presents the Baujat Plot, which illustrates the relative contributions of individual studies to overall heterogeneity and the influence of each study on the pooled effect estimate.   Table 7: Results of the Leave-One-Out Sensitivity Analysis Study Removed Publication Year Pooled Prevalence Estimate (%) Lower Limit of 95% CI (%) Upper Limit of 95% CI (%) τ² (Between-Study Variance) I² (%) Cochran’s Q Cochran’s Q p-value AlAujan et al. 2021 11.16 8.85 13.99 0.1995 89.05 74.46 1.2E-10 Alfaifi 2024 11.06 8.77 13.85 0.198 89.06 74.76 1.06E-10 Albishri et al. 2026 12.08 10.44 13.94 0.0664 73.93 49.87 3.14E-06 AlDhuwaihy et al. 2026 10.93 8.76 13.55 0.1794 88.32 73.34 1.94E-10 Alghanim et al. 2024 11.36 9.09 14.11 0.1838 88.47 72.51 2.77E-10 Alshareef et al. 2025 10.94 8.69 13.68 0.1923 87.89 72.11 3.27E-10 Alnajashi et al. 2025 10.99 8.79 13.65 0.1852 88.69 74.17 1.36E-10 Althobaiti et al. 2022 11.59 9.38 14.22 0.1583 85.65 53.76 6.67E-07 AlEnzi et al. 2021 11.03 8.72 13.85 0.2015 87.71 74.45 1.2E-10 Hasan et al. 2026 10.92 8.68 13.66 0.1907 87.18 69.7 9.11E-10 Mustafa et al. 2026 10.79 8.8 13.17 0.1439 85.13 63.12 1.44E-08 Omair et al. 2019 11.39 9.14 14.11 0.1794 88.27 72.29 3.03E-10 Samman et al. 2021 11.29 8.97 14.11 0.194 88.58 72 3.42E-10 Ali et al. 2025 11.2 8.87 14.04 0.1997 88.81 73.71 1.66E-10 AlOmair et al. 2022 10.9 8.7 13.58 0.1843 88.03 71.79 3.75E-10   Table 8: Influence Diagnostics for the Individual Studies Study Publication Year Cook’s Distance Hat Value DFFITS Covariance Ratio τ² After Study Deletion AlAujan et al. 2021 0.001 0.0688 0.0294 1.2301 0.1995 Alfaifi 2024 0.0144 0.067 0.1127 1.2203 0.198 Albishri et al. 2026 0.5257 0.0378 -0.8526 0.501 0.0664 AlDhuwaihy et al. 2026 0.0536 0.0553 0.2272 1.11 0.1794 Alghanim et al. 2024 0.0182 0.0611 -0.131 1.1398 0.1838 Alshareef et al. 2025 0.0487 0.0767 0.2082 1.202 0.1923 Alnajashi et al. 2025 0.0333 0.0509 0.1774 1.1354 0.1852 Althobaiti et al. 2022 0.1041 0.0767 -0.333 1.0246 0.1583 AlEnzi et al. 2021 0.0208 0.0788 0.133 1.252 0.2015 Hasan et al. 2026 0.0555 0.0786 0.2228 1.1959 0.1907 Mustafa et al. 2026 0.1221 0.0727 0.3752 0.944 0.1439 Omair et al. 2018 0.0262 0.0582 -0.1586 1.1131 0.1794 Samman et al. 2021 0.0059 0.0721 -0.0721 1.2053 0.194 Ali et al. 2025 0 0.0729 -0.0008 1.236 0.1997 AlOmair et al. 2022 0.0637 0.0724 0.2433 1.155 0.1843     Figure 5: Leave-One-Out Sensitivity Analysis of the Pooled Prevalence of Fibromyalgia     Figure 6: Influence Plot for Identifying Influential Studies     Figure 7: Baujat Plot for Identifiying Influential Studies   Subgroup Analysis A subgroup analysis by region was conducted to explore geographic variation in the prevalence of fibromyalgia among the included studies. As shown in Table 9 and Figure 8, considerable descriptive variation in prevalence estimates was observed across the eight geographic regions. Among the regions represented by two or more studies, the pooled prevalence was highest in Jeddah at 13.69% (95% CI: 9.44-19.45), followed by Saudi Arabia at 12.23% (95% CI: 10.55-14.14) and the Eastern Province at 12.05% (95% CI: 8.41-16.96). The pooled prevalence was 11.61% (95% CI: 8.82-15.13) in Riyadh and was lowest in Taif at 3.55% (95% CI: 1.06-11.22). For regions represented by a single study, the observed prevalence was 14.14% (95% CI: 11.40-17.24) in Makkah, 10.49% (95% CI: 7.81-13.71) in Al Jouf and 15.16% (95% CI: 11.36-19.65) in Aseer. These single-study estimates should be interpreted as study-specific prevalence estimates rather than pooled regional estimates.   Substantial within-region heterogeneity was observed in several multi-study subgroups, particularly in Taif (I² = 92.74%, p = 0.0002), Jeddah (I² = 83.46%, p = 0.0024) and the Eastern Province (I² = 78.36%, p = 0.0316). Moderate heterogeneity was observed in Riyadh (I² = 49.03%, p = 0.1406), whereas no heterogeneity was detected in the Saudi Arabia subgroup (I² = 0%, p = 0.4520). Despite the descriptive differences in prevalence across geographic regions, the between-region subgroup test did not indicate a statistically significant difference in fibromyalgia prevalence (QM = 10.365, df = 7, p = 0.1688), as shown in Table 10. Therefore, the observed variation in prevalence estimates across regions was not statistically significant at the 0.05 level. Overall, these findings suggest that geographic region did not significantly explain the variation in fibromyalgia prevalence among the included studies, although the substantial within-region heterogeneity observed in several subgroups warrants consideration when interpreting the regional estimates.   Table 9: Subgroup Analysis of Fibromyalgia Prevalence by Region Region Studies Cases Total Prevalence Lower_95CI Upper_95CI I2 Q P_heterogeneity Prevalence (95% CI) Saudi Arabia 2 157 1284 12.22741 10.5455 14.13519 0 0.56574 0.451957 12.23 (10.55-14.14) Riyadh 3 57 492 11.60953 8.822473 15.13092 0.490304 3.92391 0.140583 11.61 (8.82-15.13) Taif 2 82 1363 3.545148 1.057899 11.21724 0.927367 13.76794 0.000207 3.55 (1.06-11.22) Eastern Province 2 140 1058 12.04599 8.411096 16.96084 0.783634 4.621806 0.031568 12.05 (8.41-16.96) Makkah 1 82 580 14.13793 11.40481 17.24221 14.14 (11.40-17.24) Jeddah 3 105 797 13.69426 9.443582 19.44735 0.834636 12.09451 0.002364 13.69 (9.44-19.45) Al Jouf 1 47 448 10.49107 7.810814 13.70607 10.49 (7.81-13.71) Aseer 1 47 310 15.16129 11.35751 19.64639 15.16 (11.36-19.65)     Figure 8: Forest Plot of Fibromyalgia Prevalence by Region     Figure 9: Distribution of Diagnostic Tools Used Across Among the Included Studies   To evaluate clinical sources of heterogeneity, an additional prespecified subgroup analysis was performed based on participant population setting (Table 10 and Figure 10). The pooled prevalence of fibromyalgia varied substantially across population settings. Clinical and comorbid patient cohorts exhibited the highest pooled prevalence at 15.21% (95% CI: 12.84%-17.92%), reflecting high secondary fibromyalgia overlap in patients with chronic inflammatory, autoimmune, or pain conditions. Occupational and academic cohorts (schoolteachers, healthcare workers and university students) demonstrated a moderate pooled prevalence of 11.45% (95% CI: 9.32%-13.98%). In contrast, general community-based adult samples demonstrated the lowest pooled prevalence estimate at 7.59% (95% CI: 6.08%-9.42%). The between-subgroup test demonstrated a statistically significant difference across population settings (QM = 18.42, df = 2, p<0.001), indicating that population setting represents a primary driver of the observed between-study heterogeneity.     Figure 10: Distribution of the Included Studies by Population Type   Table 10: Subgroup Differences in Fibromyalgia Prevalence by Region Subgroup Variable QM Degrees of Freedom (df) p-Value Region 10.365 7 0.1688   Publication Bias Potential small-study effects and publication bias were formally evaluated using Egger’s linear regression test and Duval and Tweedie’s Trim-and-Fill method (Table 11 and Figure 11,12). Egger’s regression test revealed statistically significant funnel plot asymmetry (Z = -2.36, p = 0.018), indicating the presence of potential small-study effects or reporting bias. Although Duval and Tweedie’s Trim-and-Fill method imputed zero missing studies and left the pooled prevalence estimate unchanged at 11.20% (95% CI: 9.09%-13.73%), this result does not definitively rule out publication bias. Given the substantial statistical and clinical heterogeneity across included studies (I² = 87.16%), funnel plot asymmetry may also reflect methodological variations, differences in diagnostic instruments and sample size discrepancies rather than selective publication alone.   Table 11: Results of the Publication Bias Assessment No. of Studies Egger’s Z Statistic Egger’s p-Value Number of Imputed Studies Original Pooled Prevalence (%) Original Lower Limit of 95% CI (%) Original Upper Limit of 95% CI (%) Adjusted Pooled Prevalence (%) Adjusted Lower Limit of 95% CI (%) Adjusted Upper Limit of 95% CI (%) 15 -2.3603 0.01826 0 11.2 9.09 13.73 11.2 9.09 13.73     Figure 11: Funnel Plot for Assessment Potential Publication Bias     Figure 12: Funnel Plot after Trim-and-Fill Adjustment   Determinants and Risk Factors Associated with Fibromyalgia The systematic review evaluated various demographic, clinical, psychological and lifestyle variables associated with fibromyalgia across Saudi cohorts. Because all included primary investigations utilized observational cross-sectional designs, these variables are categorized as associated factors and correlates rather than causal risk factors or temporal determinants. Furthermore, reported effect estimates (such as female sex unadjusted OR = 4.32, 95% CI: 2.11-8.84; anxiety AOR = 8.035, 95% CI: 3.42-18.88; vitamin D deficiency OR = 3.12, 95% CI: 1.45-6.72; and severe sleep disturbance OR = 12.99, 95% CI: 4.15-40.67) derive from distinct study populations and multivariable models. They are presented strictly for qualitative narrative synthesis rather than direct quantitative comparison or meta-analytic pooling.   Psychosocial and lifestyle factors were also associated with the prevalence of fibromyalgia. Mental health factors, including anxiety, depression and a history of psychiatric illness, were significantly associated with fibromyalgia, with one study reporting an Adjusted Odds Ratio (AOR) of 8.035 for anxiety symptoms. Sleep disturbances, including frequent awakening, sleep apnea and poor sleep quality, were consistently reported as factors associated with fibromyalgia. From an occupational and lifestyle perspective, long working hours (41-45 hours per week), shift work, physical inactivity and a family history of fibromyalgia were identified as factors associated with fibromyalgia. Conversely, variables such as nationality and certain laboratory measures (e.g., HbA1c) generally showed no statistically significant associations with fibromyalgia, suggesting that the factors most consistently associated with fibromyalgia in the included studies were sex, chronic health conditions and psychosocial factors.   Common Variables and Determinants of Fibromyalgia To establish a comprehensive framework of factors associated with Fibromyalgia Syndrome (FMS) within the Saudi population, synthesized findings were categorized into five distinct domains: Demographic, psychosocial, clinical/comorbidities, nutritional/medication and lifestyle/occupational correlates (Table 12). In the psychosocial sphere, affective distress exerted a profound influence on disease likelihood. Severe anxiety symptoms conferred the highest adjusted odds ratio within mental health parameters (AOR = 8.035), closely followed by a documented history of psychiatric illness (OR = 7.42).   Table 12: Matrix of Common Variables and Determinants of Fibromyalgia across Included Studies Factor Domain Specific Determinant/Variable Commonality and Included Study Context Statistical Measure/Association Summary Demographic Factors Female Sex Reported consistently across datasets [32,34,46,47] Strongly associated; reported Odds Ratio (OR = 4.32). Advancing Age (≥25 years) Highlighted across population and clinical cohorts (notably 31-39 and ≥40 age groups) Statistically significant positive correlation with FM occurrence. Non-Significant: Nationality Evaluated across multi-center studies No statistically significant association observed. Psychosocial Factors Anxiety Symptoms Identified in student, academic and community datasets [46,47] Primary predictor; Adjusted Odds Ratio (AOR = 8.035). History of Psychiatric Illness Synthesized across community and clinical samples Marked predictive strength; reported (OR = 7.42). Depression and Academic Stress Documented in medical students, healthcare staff and community cohorts Significant positive association with disease severity and prevalence. Clinical and Comorbidities Autoimmune and Rheumatic Diseases Concomitant in RA, SLE and general rheumatic clinics [41] High secondary FM overlap and central pain sensitization. Diabetes Mellitus and Metabolic Disease Primary care and specialized diabetic clinics in Riyadh [34] Statistically significant association with diabetes duration and complications. Neurological and GI Comorbidities Multiple Sclerosis [42] and Inflammatory Bowel Disease [40] Substantial clinical co-occurrence and symptom burden. Nutritional and Medication Factors Vitamin D Deficiency Evaluated in female student and clinical review datasets [36] Statistically significant association; reported (OR = 3.12). Chronic Pain Medication and 5-ASA Use Recorded among IBD and chronic pain patients [40] Positive association with chronic analgesics and anti-inflammatory use. Non-Significant: Glycemic Control (HbA1c) Assessed in diabetic patient subgroups Generally showed no statistically significant association. Lifestyle and Occupational Factors Severe Sleep Disturbances and Apnea Frequent awakenings and sleep deprivation across shift workers, students and clinical cohorts Highest overall risk effect size; reported (OR = 12.99). Long Working Hours and Shift Work Schoolteachers [43], physicians in training [45] and healthcare workers [44] Strong occupational association (41-45 hrs/week; OR = 4.86). Physical Inactivity and Family History Identified in female cohorts and general community reviews Statistically significant predisposing lifestyle factors.   Summary of Associated Factors and Determinants of Fibromyalgia A granular evaluation of the 15 primary studies demonstrates how fibromyalgia risk profiles vary systematically across distinct clinical, occupational, academic and community settings in Saudi Arabia (Table 13):   Table 13: Study-Specific Summary of Associated Factors and Determinants of Fibromyalgia Study (Author and Year) Target Population and Setting Sample Size (N) Specific Factors Associated with Fibromyalgia Statistical Findings and Association Summary AlAujan et al. [33] Pharmacy Professionals and Students (Saudi Arabia) 292 Academic/occupational stress, female sex, long work/study hours Higher prevalence observed in high-stress healthcare education settings. Alfaifi [34] Diabetic Patients (Primary and Specialized Clinics, Riyadh) 224 Female sex, duration of diabetes, poor glycemic control, chronic pain conditions Statistically significant co-occurrence with chronic diabetic complications. Albishri et al. [39] Medical Students (Taif) 348 Academic distress, sleep quality impairment, female sex Identified lower prevalence (1.44%) but maintained association with academic stress. Aldhuwaihy et al. [35] Adults attending chronic pain public awareness campaign (Riyadh) 86 Pre-existing chronic pain, female sex, lack of somatic pain awareness High awareness-campaign prevalence (16.28%) associated with chronic pain history. Alghanim et al. [41] Patients with Rheumatic Diseases (Eastern Province) 208 Concomitant inflammatory joint diseases (RA, SLE), central sensitization, female sex Significant fibromyalgia overlap in clinical rheumatology cohorts. Alshareef et al. [46] University Students and Faculty/Staff (Makkah) 580 Female sex, severe anxiety symptoms, poor sleep quality, sedentary lifestyle High odds ratios reported for anxiety symptoms and severe sleep disruption. Alnajashi et al. [42] Patients with Multiple Sclerosis (Jeddah) 73 Overlap with MS, neuro-inflammatory impairment, chronic fatigue, functional disability Substantial disease overlap (15.07%) driven by neurological symptom burden. Althobaiti et al. [32] General Community Adult Population (Taif) 1,015 Female sex (OR = 4.32), advancing age (≥25 years), psychiatric comorbidities Strong population-level association with female sex and psychological distress. AlEnzi et al. [44] Healthcare Workers during COVID-19 (Multiple Hospitals) 992 High occupational stress, prolonged shift work, severe sleep deprivation Occupational burnout and shift work strongly correlated with FM features. Hasan et al. [43] Schoolteachers (Eastern Province) 850 Work-related stress, repetitive physical strain, long working hours (41-45 hrs/week; OR = 4.86) High prevalence (14.35%) associated with occupational strain. Mustafa et al. [40] Patients with Inflammatory Bowel Disease (IBD, Jeddah) 274 Concomitant GI inflammatory conditions, chronic pain medication / 5-ASA use Highest overall clinical prevalence (18.61%) linked to gut-brain interactions. Omair et al. [45] Physicians in Training / Residents and Fellows (Riyadh) 182 Long working hours, residency training stress, sleep deprivation, female sex Significant workload-related exhaustion and chronic pain features. Samman et al. [47] Medical Students (Jeddah) 450 Female sex, intense academic workload, psychological anxiety, sleep disruption Consistently identified female sex and mental stress as primary predictors. Ali et al. [36] Female University Students and Staff (Al Jouf) 448 Vitamin D deficiency (OR = 3.12), physical inactivity, chronic emotional stress High correlation with nutritional deficiency and lifestyle factors among females. AlOmair et al. Patients with Rheumatoid Arthritis (Aseer) 310 Concomitant RA disease activity, joint inflammation, secondary central sensitization High secondary FM prevalence (15.16%) among seropositive RA patients.   Clinical and Comorbid Disease Populations: Studies evaluating patients with pre-existing medical conditions identified the highest disease overlap and severity. Mustafa et al. [40] reported the highest overall clinical prevalence (18.61%) among patients with Inflammatory Bowel Disease (IBD), linking FM occurrence to gut-brain axis dysfunction and chronic medication use. Similarly, Aldhuwaihy et al. [35] noted a 16.28% prevalence among adults attending a chronic pain awareness campaign in Riyadh. High prevalence rates driven by joint inflammation and central sensitization were also confirmed in rheumatoid arthritis cohorts by Al-Omair et al. (15.16%) and general rheumatology clinics by Alghanim et al. [41] (8.65%). Additionally, Alnajashi et al. [42] observed a 15.07% rate among Multiple Sclerosis patients, while Alfaifi [34] identified female sex, longer diabetes duration and glycemic complications as key correlates in primary care diabetic patients Occupational and High-Stress Cohorts: Investigations focusing on professional cohorts underlined the severe impact of occupational strain and shift-work fatigue. Hasan et al. [43] observed a 14.35% prevalence among schoolteachers in the Eastern Province, strongly associated with work stress, physical strain and working 41-45 hours per week (OR = 4.86). Among healthcare personnel, AlEnzi et al. [44] identified a 12.60% prevalence during the COVID-19 pandemic linked to sleep deprivation and shift work, while Omair et al. [45] documented significant workload-induced chronic pain among resident physicians (8.24%) Academic and Student Populations: University settings demonstrated consistent patterns of academic stress, sedentary lifestyle and sleep fragmentation. Alshareef et al. [46] reported a 14.14% prevalence among university students and staff in Makkah, identifying anxiety and sleep disruption as major predictors. Studies in medical and pharmacy students by AlAujan et al. [33] (10.96%), Ali et al. [36] (10.49%; emphasizing vitamin D deficiency and physical inactivity) and Samman et al. [47] (9.56%) further corroborated female sex and academic anxiety as primary risk factors. Albishri et al. [39] observed a lower prevalence (1.44%) among medical students in Taif, yet maintained a significant association with sleep and academic distress General Community Population: At the broader population level, Althobaiti et al. [32] conducted the largest community-based study (N = 1,015) in Taif, confirming that female sex (OR = 4.32), advancing age (≥25 years) and pre-existing psychiatric illness serve as the fundamental population-level determinants of fibromyalgia in Saudi Arabia

The overall pooled prevalence estimate of fibromyalgia derived from this meta-analysis was 11.20% (95% CI: 9.09%-13.73%). However, this figure should be interpreted with considerable caution and must not be regarded as a single, uniform national prevalence rate for Saudi Arabia. Rather, it represents a weighted summary average across highly diverse study samples that encompass hospital-based clinical cohorts, university students, healthcare workers and general community populations. This population diversity is directly reflected in the remarkable between-study heterogeneity observed (I2 = 87.16\%). Furthermore, the calculated 95% prediction interval (3.21% to 31.85%) underscores that the expected prevalence in any given future Saudi cohort or specific clinical setting may vary widely depending on underlying population risk profiles, screening instruments and diagnostic criteria. Therefore, epidemiologic evaluations should emphasize setting-specific estimates rather than treating the pooled figure as a generalized nationwide benchmark.   The identification of factors associated with fibromyalgia provides additional insight into the factors that may contribute to the observed prevalence. The analysis indicated that female sex was consistently associated with fibromyalgia across multiple datasets, with one study reporting an Odds Ratio (OR) of 4.32. Furthermore, psychosocial factors were strongly associated with fibromyalgia; notably, anxiety symptoms were associated with an adjusted Odds Ratio (AOR) of 8.035, whereas a history of psychiatric illness was associated with an OR of 7.42. Clinical comorbidities, including autoimmune diseases and diabetes, as well as vitamin D deficiency (OR = 3.12), were also associated with fibromyalgia. Lifestyle and occupational factors, including long working hours (OR = 4.86) and severe sleep disturbances (OR = 12.99), were also associated with increased odds of fibromyalgia. Collectively, these findings suggest that the observed prevalence of fibromyalgia may reflect a complex interplay among sex, chronic health conditions, psychosocial factors and lifestyle characteristics [8,37].   While geographic subgroup analysis revealed no statistically significant differences across Saudi regions, our population-based subgroup analysis identified participant clinical setting as a major driver of between-study heterogeneity. The markedly higher pooled prevalence observed in clinical cohorts (15.21%) compared to academic/occupational cohorts (11.45%) and general community samples (7.59%) aligns with established pathophysiological frameworks of secondary fibromyalgia and central pain sensitization. Patients presenting with pre-existing autoimmune, neuro-inflammatory, or gastrointestinal disorders (such as rheumatoid arthritis, multiple sclerosis and inflammatory bowel disease) experience chronic peripheral nociceptive input and immune activation, which markedly increase vulnerability to central sensitization. Consequently, combining high-risk hospital-based clinical samples with healthy community or student cohorts accounts for much of the statistical heterogeneity (I² = 87.16%) observed in the primary meta-analysis. These findings highlight that future epidemiological evaluations and health resource allocations must differentiate between secondary overlap in clinical populations and baseline community prevalence.a

In conclusion, this meta-analysis provides an important estimate of the prevalence of fibromyalgia in the studied population. The findings suggests that fibromyalgia management should extend beyond symptom relief to incorporate assessment and management of relevant psychosocial factors and chronic comorbid conditions identified across the included studies.   Recommendations for Clinical Practice and Public Health:   Implementation of Targeted Screening Protocols: Given the substantial pooled prevalence of 11.2% and the higher odds of fibromyalgia observed among certain demographic groups, healthcare providers may consider targeted screening or case-finding strategies in populations at increased risk. Potentially relevant groups include female patients (with reported ORs up to 4.32), individuals with chronic comorbidities such as autoimmune or rheumatic diseases and individuals with a history of psychiatric illness. Earlier recognition in these groups may facilitate timely clinical evaluation and appropriate management, potentially improving quality of life and reducing the longer-term healthcare burden Holistic and Multidisciplinary Management: Clinical management should extend beyond physical symptoms to address relevant psychosocial and lifestyle factors identified in the literature. The elevated odds associated with anxiety (AOR = 8.035) and sleep apnea (OR = 12.99) suggest that assessment of mental health and sleep-related symptoms should be considered as components of comprehensive treatment plans. Furthermore, clinicians may consider assessing vitamin D status when clinically appropriate, particularly given the reported association between vitamin D deficiency and fibromyalgia (OR = 3.12) Standardization of Diagnostic Criteria: The substantial between-study heterogeneity observed in the meta-analysis (I2 = 87.16%) highlights the importance of consistent and validated diagnostic approaches across studies and clinical settings. Practitioners should use validated diagnostic criteria and tools appropriate to the current clinical guidelines and relevant patient populations, including consideration of characteristic manifestations such as fatigue and widespread pain, to promote consistency in diagnosis and management across regions Workplace and Public Health Initiatives: Public health strategies may be tailored to areas with higher reported pooled prevalence estimates, such as Jeddah (13.86%), while recognizing the uncertainty associated with regional subgroup estimates. Initiatives may also address occupational factors, associated with fibromyalgia, particularly prolonged working hours (41-45 hours per week) and high-stress work environments, while recognizing that these factors have been associated with, rather than established as causal determinants of, the condition   Recommendations for Future Research:   Longitudinal Investigation of Etiological Pathways: Although this review identified strong associations between fibromyalgia and several potential determinants, longitudinal studies are needed to clarify temporal relationships and assess whether these factors contribute causally to the development of fibromyalgia. Future research should examine the temporal relationships between vitamin D deficiency, psychiatric illness and the onset of fibromyalgia to determine whether these factors precede disease development and to identify potential opportunities for prevention or early intervention In-depth Analysis of Regional Differences: Future studies should investigate potential explanations for the observed differences in pooled prevalence estimates across regions, including the lower estimate in Taif (3.52%) compared with Riyadh (11.89%) and Jeddah (13.86%). Research should examine whether the observed regional differences are related to socioeconomic and environmental factors, variations in physical activity, differences in healthcare access, or other population- and setting-specific characteristics Methodological Refinement and Scale: To address the substantial heterogeneity identified across the included studies, future research should prioritize adequately powered, multicenter studies using standardized diagnostic criteria, data-collection procedures and reporting practices. As demonstrated by the Leave-One-Out sensitivity analysis, individual studies including Albishri et al. [39], may substantially influence the pooled prevalence estimate; therefore, larger and more representative datasets are needed to improve the precision and stability of future estimates   Acknowledgement The author acknowledge: Dr. Safa M Alsanosi and. Bayan Hashim Alsharif and Dr. Arwa F. Flemban for the support in the search, study screening, selection and analysis for this systemic review. I would also like to thank Editage (www.editage.com) for English language editing and proofreading.

Supplementary 1: Search Strategy Database Controlled vocabulary and free-text fields Search concepts and Boolean logic Search execution period Publication coverage PubMed MeSH headings, including “Fibromyalgia,” “Saudi Arabia,” and “Risk Factors,” combined with free-text terms in titles and abstracts ([tiab]). Retain only headings confirmed in the executed search. Controlled vocabulary and free-text alternatives within each block combined using OR; the three blocks combined using AND: Block 1 AND Block 2 AND Block 3. 25 May–25 June 2026* 1 January 2018–25 June 2026 Embase Emtree headings corresponding to the three concepts combined with free-text terms in titles, abstracts, and author keywords. Exact headings and syntax should match the platform and executed search. Emtree headings and free-text alternatives within each block combined using OR; the three blocks combined using AND. 25 May–25 June 2026* 1 January 2018–25 June 2026 Web of Science Core Collection Free-text terms in the Topic field (TS), covering titles, abstracts, author keywords, and Keywords Plus. No MeSH or Emtree headings. Alternatives within each block combined using OR; the three blocks combined using AND. 25 May–25 June 2026* 1 January 2018–25 June 2026 Free-text searches without MeSH or Emtree headings; quotation marks used for phrases. Multiple shorter queries combining disease and geographic terms with individual associated-factor or epidemiological terms. 25 May–25 June 2026* 1 January 2018–25 June 2026