Skip to main content
Download PDF

Prevalence of metabolic syndrome in ankylosing spondylitis: a multi national meta-analysis study

Diabetology & Metabolic Syndrome volume 17, Article number: 158 (2025) Cite this article

Abstract

Background

Ankylosing spondylitis (AS) is a chronic inflammatory disease associated with an increased risk of metabolic syndrome (MetS), a cluster of cardiometabolic risk factors. However, the prevalence of MetS in AS remains uncertain. This meta-analysis estimates the global prevalence of MetS in AS patients and identifies factors contributing to its variability.

Methods

A systematic search of PubMed, Scopus, Embase, and Web of Science was conducted for studies published up to January 18, 2024. A random-effects model was used to estimate pooled prevalence, while meta-regression and subgroup analyses explored sources of heterogeneity.

Results

Seventeen studies meeting the eligibility criteria were included. The pooled prevalence of MetS in AS patients was 15.5% (95% confidence interval [CI]: 10.9–20.8%). The highest prevalence was reported in Africa (37.0%) and the lowest in Asia (8.0%). Variability in AS diagnostic criteria influenced prevalence estimates, with the highest MetS rates found in studies using the Assessment of SpondyloArthritis International Society (ASAS) criteria (37.0%). Meta-regression identified significant associations between MetS prevalence and older age (β = 0.04, P < 0.01), higher body mass index (β = 0.09, P < 0.01), triglyceride levels (β = 0.01, P < 0.01), waist circumference (β = 0.03, P < 0.01), diastolic blood pressure (β = 0.04, P = 0.02) and disease activity, measured by the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) (β = 0.03, P = 0.02). Erythrocyte sedimentation rate was significantly correlated with MetS prevalence (β = 0.01, P = 0.04), while C-reactive protein was not (β = -0.01, P = 0.12).

Conclusion

MetS is a prevalent comorbidity in AS, significantly influenced by inflammation, obesity, and disease activity. Given its strong association with cardiovascular risk, routine metabolic screening should be incorporated into AS management. Clinicians should adopt an integrated approach that includes lifestyle modifications, targeted therapies, and careful cardiovascular risk assessment to mitigate long-term complications. Standardized diagnostic criteria for MetS in AS are needed to improve risk stratification and patient outcomes.

Introduction

Ankylosing spondylitis (AS) is a chronic inflammatory disorder primarily affecting the axial skeleton, characterized by pain, stiffness, and progressive spinal fusion [1,2,3]. AS is classified under the spondyloarthropathies, a group of diseases that also includes psoriatic arthritis, inflammatory bowel disease-related arthritis, and reactive arthritis [4,5,6]. While the primary clinical focus of AS remains its musculoskeletal manifestations, emerging evidence has suggested that patients with AS are at increased risk for comorbid conditions, including metabolic syndrome (MetS), which can significantly impact overall health and quality of life [7,8,9,10].

MetS, a cluster of interrelated risk factors such as abdominal obesity, dyslipidemia, hyperglycemia, and hypertension, is associated with an increased risk of cardiovascular disease, type 2 diabetes, and stroke [11,12,13]. The relationship between AS and MetS is complex and bidirectional, with inflammation, an essential feature of AS, potentially influencing the development of MetS components [14,15,16,17]. Furthermore, the sedentary lifestyle, prevalent in patients with AS due to pain and disability, may exacerbate the risk for metabolic disturbances [17, 18].

Recent studies have indicated a higher prevalence of MetS in individuals with AS compared to the general population [7, 8, 19]. However, the exact prevalence of MetS in this cohort remains unclear, with different studies reporting varying estimates due to differences in population characteristics, diagnostic criteria, and study methodologies [14, 20, 21]. This variability underscores the need for a comprehensive assessment of the prevalence of MetS in AS patients to better understand the burden of these comorbidities and guide clinical management.

The purpose of this meta-analysis is to synthesize the available data on the prevalence of MetS in patients with ankylosing spondylitis. By pooling results from multiple studies, this analysis aims to provide a more precise estimate of MetS prevalence in this population and explore potential factors that may contribute to its occurrence.

Methods

This meta-analysis was executed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [22]. The study protocol was registered on the International Prospective Register of Systematic Reviews (Registration ID: CRD 42025640096). The main objective of this analysis was to calculate the global prevalence of MetS in individuals diagnosed with AS by synthesizing data from multiple studies published in peer-reviewed journals.

Search strategy

A comprehensive and systematic search was performed across databases including PubMed, Scopus, Embase, and Web of Science, covering studies published up to January 18, 2024. No restrictions were placed on the language of publication. Keywords used in the search included “Ankylosing Spondylitis,” “Spondylitis Ankylosing,” “Bechterew’s disease,” “Axial Spondyloarthritis,” “Metabolic Syndrome,” “Metabolic Syndrome X,” and “Syndrome X.” In addition, relevant articles were identified by reviewing the reference lists of key studies. The detailed search formula for each database is provided in Table S1.

Study selection and eligibility criteria

For this meta-analysis, studies that evaluated the prevalence of MetS in patients with AS, or studies that reported the raw number of patients with MetS and without MetS in AS patients within their respective settings, were considered eligible for inclusion. We excluded case reports, review articles, editorials, and studies that did not provide adequate data for comprehensive analysis.

Two independent reviewers screened the titles and abstracts of all identified studies. Full-text assessments were performed to verify the eligibility of the studies. Disagreements between the reviewers were resolved through discussion or consultation with a third reviewer.

Quality assessment

The studies included in the meta-analysis were independently assessed for quality by two reviewers using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist [23,24,25]. Any differences in their assessments were resolved by involving a third reviewer to ensure a final consensus.

Data extraction

Data were independently extracted from each study using a standardized extraction form. The extracted data included study characteristics (author, year, country), demographic details (age, gender), diagnostic criteria for AS and MetS, sample size, and prevalence estimates for MetS and its individual components. If necessary, the corresponding authors were contacted to obtain any missing data or to clarify ambiguities in the reported results.

Statistical analyses

All statistical analyses were conducted using STATA software (version 18.0) to pool prevalence estimates. A random-effects model was used to account for the anticipated heterogeneity between studies. Prevalence rates with 95% confidence intervals (CIs) were reported as percentages.

The degree of heterogeneity was assessed using I² statistics, with values above 50% indicating substantial heterogeneity. Sensitivity analyses were performed to assess the impact of each study by sequentially removing individual studies.

To examine publication bias, funnel plots were visually inspected. Egger’s and Begg’s tests were used to assess publication bias, with a p-value of less than 0.1 considered indicative of significant bias.

Meta-regression analyses were conducted to explore potential factors contributing to the heterogeneity observed across studies, including variations in study year and sample size.

Results

Study selection

A total of 658 records were identified through database searches, including PubMed (n = 52), Scopus (n = 189), Embase (n = 252), and Web of Science (n = 165). After removing 299 duplicate records, 359 unique records remained for screening. During the screening process, 273 records were excluded. The remaining 86 reports were assessed for eligibility. Ultimately, 17 studies met the inclusion criteria and were included in the final review (Fig. 1).

Fig. 1
figure 1

Study selection process

Full size image

Study characteristics

A total of 17 studies were included in this meta-analysis, covering a diverse range of geographic regions, study designs, and diagnostic criteria (Table 1). The sample sizes of the included studies varied from 24 to 5,905 participants, with total of 9,746 individuals diagnosed with AS. The studies were conducted across multiple countries, including Turkey [26,27,28,29], Romania [30], Italy [19, 31], Morocco [32], Egypt [33], Norway [34], Taiwan [35, 36], China [8, 37], Brazil [7], Greece [38], and Thailand [39].

In terms of diagnostic criteria, most studies employed the Modified New York Criteria (MNYC) for AS classification, while some utilized the International classification of diseases, 9th 9evision, clinical modification (ICD-9-CM) classification. The diagnosis of MetS was based on different criteria, including national cholesterol education program adult treatment panel III (NCEP-ATP III), international diabetes federation (IDF)/ joint consensus (JC), Chinese diabetes society (CDS), world health organization (WHO), European group for the study of insulin resistance (EGIR), and the harmonized definition, contributing to some variability in prevalence estimates. The detailed definition of each criterion is presented in table S2.

The methodological quality of included studies was assessed using the JBI Critical Appraisal Checklist, with all studies meeting the inclusion criteria for methodological rigor. The quality of included studies is presented in table S3.

Table 1 Characteristics of included studies
Full size table

Results of meta-analysis

Total prevalence of MetS in AS

The meta-analysis included 17 studies assessing the prevalence of MetS in patients with AS (Fig. 2). The pooled prevalence of MetS in AS patients was 15.5% (95% CI: 10.9–20.8%), with substantial heterogeneity across studies (I² = 96.39%). The prediction interval analysis determined that the prevalence of MetS in patients with AS ranges from 0.3 to 44.6% (Fig. 2.A). A sensitivity analysis was conducted by systematically omitting each study to evaluate the robustness of the findings. The results demonstrated that the pooled prevalence remained consistent, with no significant difference observed after the removal of any individual study (Fig. 2.B). The Galbraith analysis was also conducted in order to assess the influence of individual studies and identify any outliers that could disproportionately affect the meta-analysis results (Fig. 2.C).

Analyses based on sex revealed that the prevalence of MetS in men with AS was 26.5% (95% CI: 21.1–32.3%), with low heterogeneity (I² = 28.58%) (Fig. 2C), while in women, the prevalence was 23.3% (95% CI: 9.1–40.9%), with high heterogeneity (I² = 73.98%) (Fig. 2D).

Fig. 2
figure 2

A: Forest plot of the pooled prevalence of metabolic syndrome (MetS) in patients with ankylosing spondylitis. Overall prevalence of MetS in AS patients. B: Sensitivity analysis showing the impact of omitting each individual study on the pooled prevalence estimate. C: Galbraith plot showing outlier studies with disproportionate influence on the meta-analysis results

Full size image

Sex-based comparison of MetS in AS patients

The analysis indicated no significant difference between men and women in the prevalence of MetS among patients with AS (Fig. 3). The pooled odds ratio (OR) for the association between sex and MetS was 1.16 (95% CI: 0.30–4.44, P = 0.78). Substantial heterogeneity was observed across studies (I² = 65.72%). The prediction interval analysis revealed that the OR for sex differences in MetS in AS patients ranges from 0.05 to 26.72 (Fig. 3.A), highlighting the variability between studies.

A sensitivity analysis was conducted by systematically omitting each study to assess the robustness of the findings. The pooled OR remained stable, with no significant change after the removal of any individual study (Fig. 3.B). The Galbraith analysis was performed to assess the influence of individual studies and identify any outliers that might disproportionately affect the results. The study by Akaltun, 2021, was identified as an outlier (Fig. 3.C).

The contour-enhanced funnel plot did not suggest significant publication bias, which was further confirmed by the Egger’s test (p = 0.61) and Begg’s test (P = 0.80) (Fig. 3.D). The trim-and-fill analysis did not impute any studies (Fig. 3.E).

Fig. 3
figure 3

Analysis of Sex-Based Differences in MetS Prevalence Among AS Patients. A: Forest plot of the pooled result. B: Sensitivity analysis plot. C: Galbraith plot. D: Contour-enhanced funnel plot. E: Trim-and-fill analysis

Full size image

Results of subgroup analysis

Subgroup analyses were conducted to explore sources of heterogeneity in the prevalence of MetS among patients with AS. Regarding geographical distribution, the highest prevalence of MetS was observed in Africa (37.0%; 95% CI: 23.0–53.0%), though this estimate was based on a single study. In contrast, Asia reported the lowest prevalence (8.0%; 95% CI: 5.0–13.0%), based on five studies. In Europe, the pooled prevalence was 16.0% (95% CI: 11.0%–21.0. A single study from South America estimated the prevalence at 27.0% (95% CI: 16.0–38.0%) (Table 2).

The prevalence of MetS also differed based on the AS definition criteria. Studies using the axial spondyloarthritis classification criteria (ASAS) criteria reported the highest prevalence (37.0%; 95% CI: 23.0–53.0%), whereas those employing the ICD-9-CM classification showed the lowest prevalence (3.0%; 95% CI: 2.0–3.0%). The Modified New York criteria, the most frequently used classification, yielded a prevalence of 16.0% (95% CI: 12.0–22.0%) (Table 2).

In terms of MetS diagnostic criteria, studies employing the NCEP-ATP III definition reported a prevalence of 20.0% (95% CI: 12.0–31.0%), while those using the IDF criteria estimated a prevalence of 19.0% (95% CI: 12.0–28.0%). Harmonized definition and CDS criteria yielded an estimated prevalence of 13.0% (95% CI: 4.0–25.0%) and 19.0% (95% CI: 12.0–27.0%) respectively (Table 2).

In terms of sex-based differences, the pooled prevalence of MetS in men with AS was 26.5% (95% CI: 21.1–32.3%). In women, the prevalence of MetS was 23.3% (95% CI: 9.1–40.9%)(Table 2).

The prevalence of MetS in AS patients also varied across age groups. In the subgroup of patients aged 34–39.9 years, the prevalence was 9.0% (95% CI: 5.0–14.0%). In patients aged 40–44.9 years, the prevalence increased to 26.0% (95% CI: 20.0–32.0%), and for those over 45 years, the prevalence was 30.0% (95% CI: 9.0–57.0%) (Table 2).

Table 2 Results of subgroup analysis
Full size table

Results of meta-regression analysis

Meta-regression analysis was performed to investigate potential sources of heterogeneity in the prevalence of MetS among patients with AS. Several variables were found to be significantly associated with MetS prevalence (P < 0.05) (Table 3).

Age was significantly associated with the prevalence of MetS (β = 0.04, P < 0.01), suggesting that older individuals with AS were more likely to have MetS (Fig. 4.A). Similarly, BMI showed a significant positive association with MetS prevalence (β = 0.09, P < 0.01), indicating that higher BMI values correlated with an increased likelihood of MetS (Fig. 4.B and Table 3).

WC was also a strong predictor of MetS prevalence (β = 0.03, P < 0.01) (Fig. 4.C). Additionally, inflammatory markers played a role, as erythrocyte sedimentation rate (ESR) demonstrated a significant association with MetS prevalence (β = 0.01, P = 0.04) (Fig. 4.D and Table 3).

Among metabolic parameters, triglyceride (TG) levels were significantly associated with MetS prevalence (β = 0.01, P < 0.01) (Fig. 4.F). Total cholesterol (TC) was also a significant factor (β = 0.01, P = 0.05) (Fig. 4.G and Table 3).

Regarding clinical measures, diastolic blood pressure (DBP) was significantly associated with MetS prevalence (β = 0.04, P = 0.02) (Fig. 4.K). Furthermore, disease activity, as measured by the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), was also a significant factor (β = 0.03, P = 0.02), indicating that higher disease activity scores correlated with an increased prevalence of MetS (Fig. 4.L and Table 3). TNF inhibitor consumption was another factor which had a significant positive effect on the prevalence of MetS in patients with AS (β = 0.91, P < 0.01) (Fig. 4.M and Table 3).

Table 3 Results of meta-regression analysis
Full size table
Fig. 4
figure 4

Bubble plots of the meta-regression analysis of different variables in the prevalence of metabolic syndrome (MetS) in patients with ankylosing spondylitis. Age (A), Body Mass Index (BMI) (B), Waist Circumference (WC) (C), Erythrocyte Sedimentation Rate (ESR) (D), C-Reactive Protein (CRP) (E), Triglycerides (TG) (F), Total Cholesterol (TC) (G), High-Density Lipoprotein (HDL) (H), Low-Density Lipoprotein (LDL) (I), Systolic Blood Pressure (SBP) (J), Diastolic Blood Pressure (DBP) (K), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) (L), Duration of the Disease (M), Tumor necrosis factor alpha (TNF-a) inhibitor consumption (N)

Full size image

Discussion

Our meta-analysis provides a comprehensive assessment of the global prevalence of MetS in patients with AS, revealing an overall pooled prevalence of 15.5%. This finding highlights a substantial burden of MetS in AS patients. The presence of MetS in AS is particularly concerning, as it compounds cardiovascular risk and exacerbates disease morbidity [40].

The relationship between AS and MetS is likely bidirectional, with chronic inflammation playing a crucial role in metabolic dysregulation [1]. Systemic inflammation, a hallmark of AS, has been implicated in insulin resistance, lipid abnormalities, and endothelial dysfunction, all of which contribute to MetS development [19, 41, 42]. Furthermore, sedentary behavior due to pain and disability in AS patients may aggravate metabolic disturbances [43, 44].

Previous studies have reported a higher MetS prevalence in AS patients [7, 8, 19, 45]. The genetic interplay between MetS and AS is substantial, primarily driven by shared immune-mediated pathways. For instance, Shi et al. identified a significant genetic overlap between these conditions, highlighting common genes involved in metabolic regulation, inflammation, and autophagy [46]. Their findings reinforce a causal link between MetS and AS, independent of potential confounders.

Our meta-regression analysis revealed a significant association between BMI and TG levels with MetS prevalence, underscoring the role of obesity and dyslipidemia in this comorbidity. Consistent with our findings, Liu et al. [8] identified BMI as an independent risk factor for MetS in AS patients (OR = 5.165; 95% CI = 1.935–13.787; P = 0.001). They also reported a higher prevalence of overweight/obesity in AS patients with MetS (78.3% vs. 31.9%; P < 0.001), aligning with previous studies [37, 47]. These findings highlight the critical role of BMI management in reducing MetS risk in AS patients.

The underlying mechanisms have been well-documented, with adipose tissue secreting proinflammatory cytokines (e.g., TNF-α) and adipokines (e.g., leptin, adiponectin), which disrupt metabolic homeostasis [21, 48,49,50]. Furthermore, AS patients often experience reduced physical activity due to chronic pain and disability, making them more susceptible to weight gain and increased BMI, further exacerbating metabolic disturbances [43, 51].

Also, TG levels have been implicated in the metabolic disturbances associated with AS. Maia et al. reported that AS patients undergoing anti-TNF-α therapy exhibited significantly higher TG levels than controls, with a markedly elevated TG-to-HDL ratio (p = 0.002), underscoring its predictive value for MetS and cardiovascular risk [7]. Similarly, Azarpazhooh et al. identified the TG/HDL-C ratio as a key marker of MetS, insulin resistance, and atherosclerosis severity. Their findings suggest that an increased TG/HDL-C ratio substantially raises the risk of MetS, highlighting its clinical relevance in AS and other chronic inflammatory conditions [52].

Furthermore, in our study age was found to be a significant predictor, with older AS patients demonstrating a higher prevalence of MetS. According to Dominguez and Barbagallo, aging is a major contributor to the increasing prevalence of MetS, with older individuals showing higher rates due to factors such as central obesity, insulin resistance, sarcopenic obesity, and systemic inflammation. Their review highlights that MetS prevalence rises from 18.3% in individuals aged 20–39 years to 46.7% in those over 60 years, emphasizing the impact of aging on metabolic health [53].

Similarly, Goodpaster et al. conducted a study on 3,035 individuals aged 70 to 79, demonstrating that aging is directly associated with lower HDL cholesterol levels, elevated blood glucose, and increased blood pressure—key metabolic abnormalities contributing to MetS [54]. These findings underscore the role of aging as a critical risk factor for MetS, particularly in individuals with ankylosing spondylitis, where its predictive value warrants further investigation in clinical assessments. Also, Papadakis et al., reported a significantly higher prevalence of MetS in AS patients (34.9% vs. 19.0%, p < 0.05) [38]. The study highlighted a strong age-dependent effect, with MetS prevalence reaching 50.0% in AS patients over 41 years, compared to 23.8% in controls (p = 0.017), whereas no significant difference was observed in younger patients (p = 0.401). These findings suggest that AS patients face an elevated risk of MetS and cardiovascular complications, particularly with increasing age, emphasizing the interplay between systemic inflammation and metabolic disturbances in AS.

Additionally, inflammatory markers revealed distinct associations with MetS prevalence. While ESR, a marker of chronic inflammation, was positively correlated with MetS, CRP, an acute-phase reactant, did not show a significant association. This indicates that persistent systemic inflammation, rather than acute inflammatory episodes, may play a more substantial role in metabolic disturbances among AS patients [55, 56].

A Study by de Vries MK et al. found that ESR performed better than CRP in assessing disease activity and predicting treatment response in AS patients receiving anti-TNF therapy. Among 155 patients, ESR showed the strongest correlation with BASDAI (β = 0.49) compared to CRP (β = 0.43). Elevated baseline CRP levels had a high predictive value (81%) for treatment response. These findings suggest that ESR may be a more reliable marker for monitoring disease activity, while CRP could help predict response to therapy [57].

Furthermore, our findings suggest that the BASDAI, which is commonly used to assess disease activity in AS, is significantly associated with MetS prevalence. Higher BASDAI scores indicate increased disease activity and inflammation, which could contribute to metabolic disturbances [58,59,60]. This suggests that patients with more severe AS may have a heightened risk of MetS, emphasizing the need for integrated management strategies that address both inflammatory and metabolic components of the disease. Recent studies indicate a significant correlation between BASDAI scores and various health parameters, including disability indices and inflammatory markers, highlighting its relevance in clinical practice [61, 62]. Consistent with our findings, the study by Papadakis et al. [38] also reported that AS patients with MetS were older (p = 0.005) and exhibited greater disease activity (BASDAI: 5.1 vs. 3.7, p = 0.043).

Moreover, DBP was significantly associated with MetS prevalence, whereas SBP did not exhibit a notable effect. Elevated blood pressure, including both systolic and diastolic, is a recognized component of MetS [63, 64]. A study involving 5,809 Koreans without baseline MetS found that elevated DBP levels were significantly associated with an increased hazard ratio (HR) for MetS. Specifically, DBP levels of 71–74 mm Hg and 75–79 mm Hg were associated with HRs of 1.31 and 1.51, respectively, compared to the reference group with DBP < 70 mm Hg [65]. This highlights the potential importance of DBP as a more sensitive cardiovascular risk indicator in AS patients with MetS, warranting further investigation into its pathophysiological relevance. This relationship persists even after adjusting for various covariates, indicating a robust association between DBP and MetS prevalence.

Our subgroup analyses revealed substantial regional differences in MetS prevalence among AS patients, with the highest prevalence observed in Africa (37.0%) and the lowest in Asia (8.0%). However, this high prevalence in the African region must be interpreted with caution, as it is based on a single study conducted in Egypt, which may not be representative of the broader African population [33]. The limited data raises the potential for selection bias. Supporting this, obesity rates in Africa differ widely across the region. In countries like South Africa, prevalence can reach as high as 54% among specific groups, whereas in nations such as Ethiopia and Madagascar, rates are considerably lower [66,67,68]. This discrepancy suggests that the elevated MetS rate reported in the Egyptian study may reflect unique sample characteristics rather than a regional trend. Additionally, variability in healthcare infrastructure and accessibility—particularly in the availability of routine metabolic screening—may contribute to differences in detection and diagnosis of MetS across regions [69, 70]. Genetic diversity may also play a role; for instance, the distribution of HLA-B27 subtypes varies significantly across ethnic groups and may influence the interplay between inflammatory and metabolic pathways in AS [71,72,73]. Future multi-center studies in African populations are essential to provide a more accurate regional estimate and explore potential environmental and genetic factors unique to this population.

Additionally, the discrepancies in AS criteria contributed to observed heterogeneity. Among the diagnostic criteria used across studies for AS diagnosis, those employing the ASAS classification reported the highest MetS prevalence, whereas studies relying on ICD-9-CM criteria documented the lowest prevalence. Similarly, variations in MetS prevalence were evident across different diagnostic definitions. Each set of criteria varies in terms of the number and type of metabolic components required, as well as the threshold values applied. For example, the NCEP-ATP III criteria define MetS as the presence of any three out of five metabolic abnormalities, which may result in broader classification and a higher reported prevalence. In contrast, the JC criteria apply stricter standards, and produced a substantially lower prevalence in our analysis [74,75,76]. For instance, a key distinction between ATP-III and JC is the emphasis on central obesity. While JC mandates waist circumference with ethnicity-specific cutoffs, ATP-III adopts a flexible, risk-based approach, diagnosing MetS if any three of five risk factors are present. This makes ATP-III more inclusive, particularly for individuals with metabolic dysfunction but lower waist circumference. Moreover, ATP-III exhibits greater sensitivity in detecting cardiovascular risk and insulin resistance, as it does not strictly depend on central obesity. Studies indicate that ATP-III identifies more high-risk individuals than JC, especially in non-obese populations with dyslipidemia and hypertension [75, 77, 78]. These inconsistencies underscore the critical need for standardized diagnostic criteria to improve the accuracy and comparability of MetS prevalence estimates in AS patients. Selecting a uniform diagnostic approach will support better risk stratification, improve patient monitoring, and facilitate more comparable research outcomes across diverse populations of AS patients. Based on these observations, we recommend that future studies consider adjusting diagnostic criteria according to population-specific characteristics such as ethnicity, regional obesity patterns, and metabolic profiles. Such tailored thresholds could improve diagnostic accuracy, enhance inter-study comparability, and provide more meaningful estimates of MetS prevalence in diverse AS populations [75, 79].

Given the substantial prevalence of MetS in AS patients, early screening and management of metabolic risk factors should be integrated into AS treatment strategies. Lifestyle interventions, including regular physical activity and dietary modifications, should be prioritized to mitigate cardiovascular risk. Moreover, the long-term metabolic impact of biologic therapies, particularly TNF inhibitors warrants further investigation [7, 8, 14].

Limitations and future directions

Despite the robust methodology employed, our meta-analysis has several limitations. First, significant heterogeneity was observed among studies, likely due to differences in study populations, diagnostic criteria, and geographic distribution. Although we addressed this through extensive subgroup and meta-regression analyses, residual confounding factors may remain.

Second, the cross-sectional nature of the included studies precludes any conclusions regarding causality between ankylosing spondylitis and metabolic syndrome. Longitudinal studies are needed to elucidate these temporal relationships.

Third, while we attempted to evaluate the impact of biological therapies—particularly TNF inhibitors—on the prevalence of MetS through meta-regression, only a limited number of studies reported relevant data. As such, these results should be interpreted cautiously. Future research should prioritize collecting and reporting data on biologic treatment regimens, especially TNF inhibitors, to better understand their potential role in modifying metabolic risk among AS patients.

Fourth, we conducted publication bias assessment for the sex-based subgroup analysis, incorporating Egger’s test, Begg’s test, funnel plots, and trim-and-fill methods. Although we did not find significant publication bias, it cannot be entirely ruled out due to the relatively small number of included studies. More well-designed and transparently reported studies are needed to enhance the validity of future meta-analyses and allow for a more robust assessment of potential publication bias.

Fifth, while we explored sex-based differences in MetS prevalence and found a higher rate in males compared to females, the difference was not statistically significant. However, the limited number of gender-stratified studies remains a notable gap, and we recommend that future research further investigate sex-based differences in MetS risk among AS patients, including potential roles of sex hormones and age-matched subgroup effects.

Additionally, the relatively small number of included studies and the lack of representation from certain regions may limit the generalizability of our findings. We recommend that future studies include more diverse populations and standardized diagnostic criteria to improve global comparability and risk stratification.

Conclusion

This meta-analysis highlights a significant prevalence of MetS in patients with AS. The findings suggest that metabolic abnormalities in AS are influenced by chronic inflammation, obesity, disease activity, and demographic factors. Notably, older age, higher BMI, elevated triglycerides, and increased disease activity (BASDAI) were significantly associated with MetS prevalence, emphasizing the need for proactive metabolic monitoring in AS management.

From a clinical perspective, routine metabolic screening can be incorporated into AS care to identify high-risk patients early. Given the interplay between systemic inflammation and MetS, integrating lifestyle interventions with pharmacologic management may provide dual benefits in controlling both AS symptoms and metabolic complications. Rheumatologists should work closely with cardiologists and endocrinologists to optimize cardiovascular risk reduction strategies for AS patients.

To enhance consistency in diagnosis and improve comparability across studies, we recommend adopting standardized or population-adjusted diagnostic criteria for MetS in AS patients. While criteria such as the JC framework offer clinically practical approaches that account for ethnic variation in metabolic thresholds, it is essential that future studies further refine these definitions based on regional, anthropometric, and metabolic characteristics. Tailoring thresholds in this way can improve diagnostic accuracy and better reflect true metabolic risk in diverse AS populations. Additionally, ongoing research should continue to explore the long-term effects of biologic therapies and the impact of personalized lifestyle interventions in reducing MetS prevalence and improving outcomes for AS patients.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

AS:

Ankylosing spondylitis

MetS:

Metabolic syndrome

BASDAI:

Bath Ankylosing Spondylitis Disease Activity Index

BMI:

Body mass index

CI:

Confidence interval

ESR:

Erythrocyte sedimentation rate

IDF:

International Diabetes Federation

JC:

Joint Consensus

NCEP-ATP III:

National Cholesterol Education Program Adult Treatment Panel III

TG:

Triglycerides

TNF:

Tumor necrosis factor

References

  1. Wenker KJ, Quint JM. Ankylosing spondylitis, StatPearls, StatPearls publishing copyright © 2025. StatPearls Publishing LLC., Treasure Island (FL); 2025.

  2. Agrawal P, Tote S, Sapkale B. Diagnosis Treat Ankyl Spondylitis Cureus. 2024;16(1):e52559.

  3. Shim MR. Efficacy of TNF inhibitors in advanced ankylosing spondylitis with total spinal fusion: case report and review of literature. Open Access Rheumatol. 2019;11:173–7.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Fragoulis GE, Liava C, Daoussis D, Akriviadis E, Garyfallos A, Dimitroulas T. Inflammatory bowel diseases and spondyloarthropathies: from pathogenesis to treatment. World J Gastroenterol. 2019;25(18):2162–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Sen R, Goyal A, Hurley JA. Seronegative spondyloarthropathy, StatPearls, StatPearls publishing copyright © 2025. StatPearls Publishing LLC., Treasure Island (FL); 2025.

  6. Coates LC, Schett G, Wang C, Weiss PF. Unmet needs in spondyloarthritis: pathogenesis, clinical trial design, and nonpharmacologic therapy. J Rheumatol. 2024;51(12):1254–8.

    Article  CAS  PubMed  Google Scholar 

  7. Maia DG, Augusto KL, Bezerra MC, Rodrigues CEM. Metabolic syndrome in patients with ankylosing spondylitis receiving anti-TNFα therapy: association with predictors of cardiovascular risk. Clin Rheumatol. 2017;36(10):2371–6.

    Article  PubMed  Google Scholar 

  8. Liu M, Huang Y, Huang Z, Huang Q, Guo X, Wang Y, Deng W, Huang Z, Li T. Prevalence of metabolic syndrome and its associated factors in Chinese patients with ankylosing spondylitis. Diabetes Metab Syndr Obes. 2019;12:477–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zhu W, He X, Cheng K, Zhang L, Chen D, Wang X, Qiu G, Cao X, Weng X. Ankylosing spondylitis: etiology, pathogenesis, and treatments. Bone Res. 2019;7:22.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Habibi A, Letafatkar N, Sattari N, Nobakht S, Rafat Z, Soltani Moghadam S, Mirdamadi A, Javid M, Jamilian P, Hassanipour S, Keivanlou M-H, Amini-Salehi E. Modulation of inflammatory markers in type 2 diabetes mellitus through gut microbiome-targeted interventions: an umbrella review on meta-analyses. Clin Nutr ESPEN. 2025;65:93–104.

    Article  PubMed  Google Scholar 

  11. Dhondge RH, Agrawal S, Patil R, Kadu A, Kothari M. A comprehensive review of metabolic syndrome and its role in cardiovascular disease and type 2 diabetes mellitus: mechanisms, risk factors, and management. Cureus. 2024;16(8):e67428.

    PubMed  PubMed Central  Google Scholar 

  12. Rus M, Crisan S, Andronie-Cioara FL, Indries M, Marian P, Pobirci OL. A.I. Ardelean, prevalence and risk factors of metabolic syndrome: A prospective study on cardiovascular health. Med (Kaunas) 59(10) (2023).

  13. Wu Z, Luo S, Cai D, Lin W, Hu X, Zhou T, Zhang X, Feng Y, Luo J. The causal relationship between metabolic syndrome and its components and cardiovascular disease: A Mendelian randomization study. Diabetes Res Clin Pract. 2024;211:111679.

    Article  CAS  PubMed  Google Scholar 

  14. Petcharat C, Srinonprasert V, Chiowchanwisawakit P. Association between syndesmophyte and metabolic syndrome in patients with psoriatic arthritis or ankylosing spondylitis: a cross-sectional study. BMC Musculoskelet Disord. 2021;22(1):367.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hashemi SM, Kheirandish M, Rafati S, Ghazalgoo A, Amini-Salehi E, Keivanlou M-H, Abbaszadeh S, Saberian P, Rahimi A. The association between neutrophil and lymphocyte to high-density lipoprotein cholesterol ratio and metabolic syndrome among Iranian population, finding from Bandare Kong cohort study. Lipids Health Dis. 2024;23(1):393.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Mahapatro A, Bawna F, Kumar V, Daryagasht AA, Gupta S, Raghuma N, Moghdam SS, Kolla A, Mahapatra SS, Sattari N, Amini-Salehi E, Nayak SS. Anti-inflammatory effects of probiotics and synbiotics on patients with non-alcoholic fatty liver disease: an umbrella study on meta-analyses. Clin Nutr ESPEN. 2023;57:475–86.

    Article  PubMed  Google Scholar 

  17. Pishgahi A, Abolhasan R, Danaii S, Amanifar B, Soltani-Zangbar MS, Zamani M, Kamrani A, Ghorbani F, Mehdizadeh A, Kafil HS, Jadidi-Niaragh F, Yousefi B, Hajialiloo M, Yousefi M. Immunological and oxidative stress biomarkers in ankylosing spondylitis patients with or without metabolic syndrome. Cytokine. 2020;128:155002.

    Article  CAS  PubMed  Google Scholar 

  18. Chomiuk T, Niezgoda N, Mamcarz A, Śliż D. Physical activity in metabolic syndrome. Front Physiol. 2024;15:1365761.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Malesci D, Niglio A, Mennillo GA, Buono R, Valentini G, La Montagna G. High prevalence of metabolic syndrome in patients with ankylosing spondylitis. Clin Rheumatol. 2007;26(5):710–4.

    Article  PubMed  Google Scholar 

  20. Cioffi G, Viapiana O, Tarantini L, Orsolini G, Idolazzi L, Sonographer FO, Dalbeni A, Gatti D, Fassio A, Rossini M, Giollo A. Clinical profile and outcome of patients with chronic inflammatory arthritis and metabolic syndrome. Intern Emerg Med. 2021;16(4):863–74.

    Article  PubMed  Google Scholar 

  21. Medina G, Vera-Lastra O, Peralta-Amaro AL, Jiménez-Arellano MP, Saavedra MA, Cruz-Domínguez MP, Jara LJ. Metabolic syndrome, autoimmunity and rheumatic diseases. Pharmacol Res. 2018;133:277–88.

    Article  CAS  PubMed  Google Scholar 

  22. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, Shekelle P, Stewart LA. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4(1):1.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Institute TJB, Checklist for Cohort Studies. 2017. https://jbi.global/sites/default/files/2019-05/JBI_Critical_Appraisal-Checklist_for_Cohort_Studies2017_0.pdf

  24. Institute TJB, Ckecklist for Case Control Studies. 2017. https://jbi.global/sites/default/files/2019-05/JBI_Critical_Appraisal-Checklist_for_Case_Control_Studies2017_0.pdf

  25. Institute JB. Checklist for Analytical Cross Sectional Studies, 2017. https://jbi.global/sites/default/files/2019-05/JBI_Critical_Appraisal-Checklist_for_Analytical_Cross_Sectional_Studies2017_0.pdf

  26. Akaltun MS, Altindag Ö, Turan N, Aydeniz A, Gürsoy S, Gür A. Relationship between metabolic syndrome and vitamin D level in patients with ankylosing spondylitis. CUKUROVA Med J. 2021;46(2):772–9.

    Article  Google Scholar 

  27. Batmaz I, Karakoc M, Sariyildiz MA, Yazici S, Tahtasiz M, Atilgan Z, Cevik R, Nas K. Metabolic syndrome in patients with ankylosing spondylitis. J Endocrinol METABOLISM. 2011;1(5):215–9.

    Google Scholar 

  28. Kesikburun B, Ekşioğlu E, Çakcı A. Metabolic syndrome in rheumatoid arthritis and ankylosing spondylitis. Ankara Med J. 2018;18(2):198–206.

    Google Scholar 

  29. Sari I, Kebapcilar L, Taylan A, Bilgir O, Kozaci DL, Yildiz Y, Yuksel A, Gunay N, Akkoc N. Fetuin-A and interleukin-18 levels in ankylosing spondylitis. Int J Rheum Dis. 2010;13(1):75–81.

    Article  PubMed  Google Scholar 

  30. Amaricai E, Catan L, Popa D, Dragoi M, Nemes D. Metabolic syndrome in patients with autoimmune inflammatory rheumatisms. Biomed Res. 2018;29(11):2416–20.

    Article  CAS  Google Scholar 

  31. Chimenti MS, Fonti GL, Conigliaro P, Sunzini F, Scrivo R, Navarini L, Triggianese P, Peluso G, Scolieri P, Caccavale R, Diamanti AP, De Martino E, Salemi S, Birra D, Altobelli A, Paroli M, Bruzzese V, Lagana B, Gremese E, Conti F, Afeltra A, Perricone R. One-year effectiveness, retention rate, and safety of Secukinumab in ankylosing spondylitis and psoriatic arthritis: a real-life multicenter study, expert Opin. Biol Ther. 2020;20(7):813–21.

    CAS  Google Scholar 

  32. Hassani Sbai SE, Rostom S, Amine B, Bahiri R. Prevalence of metabolic syndrome in patients with ankylosing spondylitis. J Orthop Rheumatol. 2019;6(1):5.

    Google Scholar 

  33. Gado SE, Elwan SA, El Sharkawy AM, El-Banna HS, Evaluation of metabolic syndrome in egyptian patients with radiographic and non-radiographic axial spondyloarthritis (Cross Sectional Study). Rheumatology. 2022;30(2):28–34.

  34. Halvorsen S, Vollestad NK, Provan SA, Semb AG, Van der Heijde D, Hagen KB, Dagfinrud H. Cardiorespiratory fitness and cardiovascular risk in patients with ankylosing spondylitis: A Cross-Sectional comparative study. Arthritis Care Res. 2013;65(6):969–76.

  35. Lai YF, Lin TY, Chien WC, Sun CA, Chung CH, Chen YH, Chen JT, Chen CL. Uveitis as a risk factor for developing acute myocardial infarction in ankylosing spondylitis: A National Population-Based longitudinal cohort study. Front Immunol 12 (2021).

  36. Lin TY, Lai YF, Chien WC, Chen YH, Sun CA, Chung CH, Chen JT, Chen CL. Association between endophthalmitis and the incidence of acute coronary syndrome in patients with ankylosing spondylitis: A nationwide, Population-Based cohort study. Front Immunol 13 (2022).

  37. Mok CC, Ko GT, Ho LY, Yu KL, Chan PT, To CH. Prevalence of atherosclerotic risk factors and the metabolic syndrome in patients with chronic inflammatory arthritis. Arthritis Care Res (Hoboken). 2011;63(2):195–202.

  38. Papadakis JA, Sidiropoulos PI, Karvounaris SA, Vrentzos GE, Spanakis EK, Ganotakis ES, Kritikos HD, Mikhailidis DP, Boumpas DT. High prevalence of metabolic syndrome and cardiovascular risk factors in men with ankylosing spondylitis on anti-TNFalpha treatment: correlation with disease activity. Clin Exp Rheumatol. 2009;27(2):292–8.

  39. Petcharat C, Srinonprasert V, Chiowchanwisawakit P. Association between syndesmophyte and metabolic syndrome in patients with psoriatic arthritis or ankylosing spondylitis: a cross-sectional study. BMC Musculoskelet Disord 22(1) (2021).

  40. Genre F, López-Mejías R, Miranda-Filloy JA, Ubilla B, Mijares V, Carnero-López B, Gómez-Acebo I, Dierssen-Sotos T, Remuzgo-Martínez S, Blanco R, Pina T, González-Juanatey C, Llorca J. González-Gay, Anti-TNF-α therapy reduces endothelial cell activation in non-diabetic ankylosing spondylitis patients. Rheumatol Int. 2015;35(12):2069–78.

  41. Fahed G, Aoun L, Bou Zerdan M, Allam S, Bou Zerdan M, Bouferraa Y, Assi HI. Metabolic Syndrome: Updates on Pathophysiology and Management in 2021, International journal of molecular sciences 23(2) (2022).

  42. Estefes-Duarte JA, Espinosa-Sánchez A, Pérez-Hernández N, Ortiz MI. E. Fernández-Martínez, Mechanisms of Bioactive Lipids to Modulate Master Regulators of Lipid Homeostasis and Inflammation in Metabolic Syndrome, Current pharmaceutical biotechnology (2024).

  43. Jeong J, Yu J. Prevalence and influencing factors of metabolic syndrome among persons with physical disabilities. Asian Nurs Res. 2018;12(1):50–5.

  44. Coulter EH, McDonald MT, Cameron S, Siebert S, Paul L. Physical activity and sedentary behaviour and their associations with clinical measures in axial spondyloarthritis. Rheumatol Int. 2020;40(3):375–81.

    Article  PubMed  Google Scholar 

  45. Sidiropoulos PI, Karvounaris SA, Boumpas DT. Metabolic syndrome in rheumatic diseases: epidemiology, pathophysiology, and clinical implications. Arthritis Res Therapy. 2008;10(3):207.

    Article  Google Scholar 

  46. Shi Y, Guan S, Liu X, Zhai H, Zhang Y, Liu J, Yang W, Wang Z. Genetic commonalities between metabolic syndrome and rheumatic diseases through disease interactome modules. J Cell Mol Med. 2025;29(1):e70329.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Haroon M, Gallagher P, Heffernan E, FitzGerald O. High prevalence of metabolic syndrome and of insulin resistance in psoriatic arthritis is associated with the severity of underlying disease. J Rheumatol. 2014;41(7):1357–65.

    Article  PubMed  Google Scholar 

  48. Francisco V, Ruiz-Fernández C, Pino J, Mera A, González-Gay MA, Gómez R, Lago F, Mobasheri A, Gualillo O. Adipokines: linking metabolic syndrome, the immune system, and arthritic diseases. Biochem Pharmacol. 2019;165:196–206.

    Article  CAS  PubMed  Google Scholar 

  49. Clemente-Suárez VJ, Redondo-Flórez L, Beltrán-Velasco AI, Martín-Rodríguez A, Martínez-Guardado I, Navarro-Jiménez E, Laborde-Cárdenas CC. J.F. Tornero-Aguilera, The Role of Adipokines in Health and Disease, Biomedicines 11(5) (2023).

  50. Yadav A, Kataria MA, Saini V, Yadav A. Role of leptin and adiponectin in insulin resistance. Clin Chim Acta. 2013;417:80–4.

    Article  CAS  PubMed  Google Scholar 

  51. Dong HJ, Larsson B, Rivano Fischer M, Gerdle B. Facing obesity in pain rehabilitation clinics: profiles of physical activity in patients with chronic pain and obesity-A study from the Swedish quality registry for pain rehabilitation (SQRP). PLoS ONE. 2020;15(9):e0239818.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Azarpazhooh MR, Najafi F, Darbandi M, Kiarasi S, Oduyemi T, Spence JD. Triglyceride/High-Density lipoprotein cholesterol ratio: A clue to metabolic syndrome, insulin resistance, and severe atherosclerosis. Lipids. 2021;56(4):405–12.

    Article  CAS  PubMed  Google Scholar 

  53. Dominguez LJ, Barbagallo M. The biology of the metabolic syndrome and aging. Curr Opin Clin Nutr Metab Care. 2016;19(1):5–11.

    Article  CAS  PubMed  Google Scholar 

  54. Goodpaster BH, Krishnaswami S, Harris TB, Katsiaras A, Kritchevsky SB, Simonsick EM, Nevitt M, Holvoet P, Newman AB. Obesity, regional body fat distribution, and the metabolic syndrome in older men and women. Arch Intern Med. 2005;165(7):777–83.

    Article  PubMed  Google Scholar 

  55. Harrison M. Erythrocyte sedimentation rate and C-reactive protein. Australian Prescriber. 2015;38(3):93–4.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Litao MK, Kamat D. Erythrocyte sedimentation rate and C-reactive protein: how best to use them in clinical practice. Pediatr Ann. 2014;43(10):417–20.

    Article  PubMed  Google Scholar 

  57. de Vries MK, van Eijk IC, van der Horst-Bruinsma IE, Peters MJ, Nurmohamed MT, Dijkmans BA, Hazenberg BP, Wolbink GJ. Erythrocyte sedimentation rate, C-reactive protein level, and serum amyloid a protein for patient selection and monitoring of anti-tumor necrosis factor treatment in ankylosing spondylitis. Arthritis Rheum. 2009;61(11):1484–90.

    Article  PubMed  Google Scholar 

  58. Machado P, van der Heijde D. How to measure disease activity in axial spondyloarthritis? Curr Opin Rheumatol. 2011;23(4):339–45.

    Article  PubMed  Google Scholar 

  59. Ferraz-Amaro I, Rueda-Gotor J, Genre F, Corrales A, Blanco R, Portilla V, González Mazón I, Llorca J, Expósito R, Vicente EF, Quevedo-Abeledo JC, Rodríguez-Lozano C, Ortega-Castro R, Ladehesa-Pineda ML, Fernández-Carballido C, Martínez-Vidal MP, Castro-Corredor D, Anino-Fernández J, García ML, Vivar E, Galíndez-Agirregoikoa D, Peiteado C, Plasencia-Rodríguez E, Montes Perez C, Fernández Díaz S, Castañeda M. González-Gay, potential relation of cardiovascular risk factors to disease activity in patients with axial spondyloarthritis, therapeutic advances in musculoskeletal disease 13 (2021) 1759720x211033755.

  60. Chen YH, Huang WN, Chen YM, Lai KL, Hsieh TY, Hung WT, Lin CT, Tseng CW, Tang KT, Chou YY, Wu YD, Huang CY, Hsieh CW, Chen YJ, Liao YW, Chen HH. The BASDAI Cut-Off for disease activity corresponding to the ASDAS scores in a Taiwanese cohort of ankylosing spondylitis. Front Med. 2022;9:856654.

    Article  Google Scholar 

  61. Van der Zee-Neuen A, Bogensberger K, Fuchs J, Wildburger S, Ritter M, AB0937 improvements in disease activity and functional impairment after multimodal SPA therapy in ankylosing spondylitis patients. Ann Rheum Dis. 2024;83:1778.

  62. Aytekin E, Ozgonenel L, Coskun H, Dede BT, Demir SE. Use of the Oswestry Disability Index in ankylosing spondylitis, Revista da Associacao Medica Brasileira (1992) 69(12) (2023) e20230927.

  63. Stanciu S, Rusu E, Miricescu D, Radu AC, Axinia B, Vrabie AM, Ionescu R, Jinga M, Sirbu CA. Links between metabolic syndrome and hypertension: the relationship with the current antidiabetic drugs. Metabolites 13(1) (2023).

  64. Franklin SS. Hypertension in the metabolic syndrome. Metab Syndr Relat Disord. 2006;4(4):287–98.

    Article  PubMed  Google Scholar 

  65. Jung JY, Oh CM, Choi JM, Ryoo JH, Chung PW, Hong HP, Park SK. Levels of systolic and diastolic blood pressure and their relation to incident metabolic syndrome. Cardiology. 2019;142(4):224–31.

  66. Ajayi IO, Adebamowo C, Adami HO, Dalal S, Diamond MB, Bajunirwe F, Guwatudde D, Njelekela M, Nankya-Mutyoba J, Chiwanga FS, Volmink J, Kalyesubula R, Laurence C, Reid TG, Dockery D, Hemenway D, Spiegelman D, Holmes MD. Urban-rural and geographic differences in overweight and obesity in four sub-Saharan African adult populations: a multi-country cross-sectional study. BMC Public Health. 2016;16(1):1126.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Sartorius B, Veerman LJ, Manyema M, Chola L, Hofman K. Determinants of obesity and associated population attributability, South Africa: empirical evidence from a National panel survey, 2008–2012. PLoS ONE. 2015;10(6):e0130218.

  68. Amugsi DA, Dimbuene ZT, Mberu B, Muthuri S, Ezeh AC. Prevalence and time trends in overweight and obesity among urban women: an analysis of demographic and health surveys data from 24 African countries, 1991–2014. BMJ Open. 2017;7(10):e017344.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Moucheraud C, McBride K. Variability in health care quality measurement among studies using service provision assessment data from Low- and Middle-Income countries: A systematic review. Am J Trop Med Hyg. 2020;103(3):986–92.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Tessema ZT, Worku MG, Tesema GA, Alamneh TS, Teshale AB, Yeshaw Y, Alem AZ, Ayalew HG, Liyew AM. Determinants of accessing healthcare in Sub-Saharan Africa: a mixed-effect analysis of recent demographic and health surveys from 36 countries. BMJ Open. 2022;12(1):e054397.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Wu X, Wang G, Zhang L, Xu H. Genetics of ankylosing Spondylitis-Focusing on the ethnic difference between East Asia and Europe. Front Genet. 2021;12:671682.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Dashti N, Mahmoudi M, Aslani S, Jamshidi A. HLA-B*27 subtypes and their implications in the pathogenesis of ankylosing spondylitis. Gene. 2018;670:15–21.

    Article  CAS  PubMed  Google Scholar 

  73. Mehra NK, Kanga U. Molecular diversity of the HLA-B27 gene and its association with disease. Mod Rheumatol. 2001;11(4):275–85.

    Article  CAS  PubMed  Google Scholar 

  74. Kassi E, Pervanidou P, Kaltsas G, Chrousos G. Metabolic syndrome: definitions and controversies. BMC Med. 2011;9:48.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SC Jr. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity, Circulation 120(16) (2009) 1640-5.

  76. Curtis JR, Winthrop K, Bohn RL, Suruki R, Siegel S, Stark JL, Xie F, Yun H, Chen L, Deodhar A. The annual diagnostic prevalence of ankylosing spondylitis and axial spondyloarthritis in the united States using medicare and marketscan databases. ACR Open Rheumatol. 2021;3(11):743–52.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Third Report of the National Cholesterol Education Program (NCEP). Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult treatment panel III) final report. Circulation. 2002;106(25):3143–421.

    Article  Google Scholar 

  78. Huang PL. A comprehensive definition for metabolic syndrome. Dis Model Mech. 2009;2(5–6):231–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Lin CC, Liu CS, Li CI, Lin WY, Lai MM, Lin T, Chang PC, Lee YD, Chen CC, Lin CH, Yang CW, Hsiao CY, Chen W, Li TC. The relation of metabolic syndrome according to five definitions to cardiovascular risk factors–a population-based study. BMC Public Health. 2009;9:484.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

None.

Author information

Authors and Affiliations

  1. Division of Hospital Medicine, Department of Internal Medicine, Yale New Heaven Health| Bridgeport Hospital, 267 Grant St, 06611, Bridgeport CT,, USA

    Sandeep Samethadka Nayak

  2. Division of General Medicine, Department of Medicine, Loma Linda University Health, 11234 Anderson St, Loma Linda, CA,, USA

    Kwame Boateng Agyeman

  3. Department of Internal Medicine, Soundview Medical Associates, Hartford Healthcare, Wilton , CT, USA

    Khushbu Viresh Janani

  4. Student Research Committee, Anzali International Campus, Guilan University of Medical Sciences, Rasht, Iran

    Maryam Jafari & Mohammad Amouzadeh Lichahi

  5. School of Medicine, Guilan University of Medical Sciences, Rasht, Iran

    Ehsan Amini-Salehi

  6. Vydehi Institute of Medical Sciences and Research Centre, Nallurahalli Main Road, Whitefield, Bengaluru, Karnataka, India

    Pubali Biswas

  7. Cardiovascular Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran

    Mohammad Hashemi

  8. Department of Medicine, Arnot Ogden Medical Center, Elmira, NY, USA

    Nimra Shafi

  9. Yale New Haven Hospital, 20 York St, New Haven, CT, 06510, USA

    Yasmin Sahli

  10. Ohio Kidney and Hypertension Center, Old Oak Blvd, Ste C111 Middleburg Hts, Cincinnati, OH, 44130, USA

    Narsimha Rao Keetha

Authors
  1. Sandeep Samethadka Nayak
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Kwame Boateng Agyeman
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Khushbu Viresh Janani
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Maryam Jafari
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Mohammad Amouzadeh Lichahi
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Pubali Biswas
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Mohammad Hashemi
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Nimra Shafi
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Yasmin Sahli
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Ehsan Amini-Salehi
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Narsimha Rao Keetha
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

The conceptualization of the research idea was conducted by E.AS, P.B, SS.N, and M.J. The study design, which involved planning the methods to achieve the results, was managed by M.J, M.A.L, and E.AS. Oversight, coordination, and manuscript preparation were supervised by E.AS, N.R.K, and N.S. Data collection and processing, which included conducting experiments and managing patients, along with data analysis and interpretation, were carried out by N.R.K, M.J, M.A.L, M.H, and P.B. All authors made significant contributions to drafting the manuscript.

Corresponding author

Correspondence to Ehsan Amini-Salehi.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Clinical trial number

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nayak, S.S., Agyeman, K.B., Janani, K.V. et al. Prevalence of metabolic syndrome in ankylosing spondylitis: a multi national meta-analysis study. Diabetol Metab Syndr 17, 158 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01720-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01720-w

Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

Contact us