Influence of the metabolic and inflammatory profile in patients with frozen shoulder - systematic review and meta-analysis

Abstract

Background: Frozen Shoulder (FS), also known as adhesive shoulder capsulitis, is characterized by a fibrotic inflammatory process of unknown origin, with the most prominent symptoms being pain, stiffness, and reduced joint mobility.

Methods: The systematic review and meta-analysis presented herein provide insights into the pathogenesis of this condition, as well as common metabolic biomarkers potentially implicated in FS, such as glycated hemoglobin (HbA1c), and inflammatory biomarkers, including interleukins (IL-1, IL-6) and tumor necrosis factor alpha (TNF-α). Dyslipidemia and hormonal factors, such as thyroid dysfunctions, are also examined.

Results: A total of 7,499 individuals were included in the meta-analysis, and one additional study collected 28,416 blood samples from individuals with FS from biobanks. The meta-analysis of metabolic variables showed that HbA1c was the most significantly elevated marker in FS, with a standardized mean difference (SMD) of μ^ = 0.3970 (95% CI: 0.0998 to 0.6943), indicating a moderate effect. Glucose showed a mean difference of -0.28 (95% CI: -0.60 to 0.05), which was not statistically significant, suggesting that short-term fluctuations in glucose levels may not be as relevant as long-term metabolic control. Cholesterol had a standardized difference of 0.278 (95% CI: 0.171 to 0.385), being significantly higher in FS. For triglycerides, the SMD was μ^ = 1.0318 (95% CI: -1.0027 to 3.0664), indicating high heterogeneity and preventing a clear conclusion. Hypothyroidism was also evaluated, with a total SMD of 0.067, a total variance of 0.0021, and a 95% confidence interval of -0.024 to 0.158, confirming no association between FS and thyroid function. Regarding inflammatory biomarkers, IL-1β was the most predominant, showing significantly higher levels in FS, with an SMD of μ^ = 2.2671 (95% CI: 0.5750 to 3.9591). TNF-α had a mean difference of μ^ = 0.7814 (95% CI: 0.1013 to 1.4615), reflecting a significant difference from zero (z = 2.2520, p = 0.0243). Finally, IL-6 did not show a significant association, with an SMD of μ^ = 1.6721 (95% CI: -0.9368 to 4.2810).

Conclusion: This meta-analysis highlights the role of metabolic dysfunction and chronic inflammation in the pathogenesis of FS. HbA1c and cholesterol were the most associated metabolic biomarkers, while IL-1β and TNF-α showed a strong link to inflammation and fibrosis. The heterogeneity in triglycerides and IL-6 underscores the need for studies with standardized methodologies and subgroup analyses. Future research should focus on biomarker progression, patient stratification, and new therapeutic strategies targeting metabolic and immune modulation, considering FS within a broader metabolic-inflammatory framework to improve its classification and treatment.

Keywords: Biomarkers; Cytokines; Frozen shoulder; Inflammation; Interleukins; Metabolism.

Fig. 1

Flow chart outlining the process for the selection and exclusion of the studies following the PRISMA statement

Fig. 2

Forest Plot of metabolic biomarkers Standardized Mean Difference (SMD): The x-axis represents the standardized mean difference, which quantifies the size and direction of the effect for each biomarker. Positive values indicate higher levels in the study group compared to controls, while negative values indicate lower levels. Biomarkers Analyzed: Five key metabolic biomarkers are presented, ordered by the magnitude of their effect size Confidence Intervals: The horizontal lines extending from each point represent the 95% confidence intervals. When these intervals do not cross the vertical zero line, the result is considered statistically significant (p < 0.05)

Fig. 3

Forest Plot of inflammatory biomarkers Horizontal Axis (X): Represents the SMD, indicating the effect size. A vertical line at zero serves as a reference for the absence of effect. If the line of a study (or estimate) crosses this point, the effect is considered not statistically significant.. Vertical Axis (Y): Lists the different biomarkers included in the analysis (e.g., Interleukin 1 Beta, Tumor Necrosis Factor Alpha, Interleukin 6). Each horizontal line corresponds to a biomarker or study within the meta-analysis.Points and Horizontal Lines: Each point indicates the point estimate (SMD) for that biomarker, the horizontal lines extending from each point show the 95% confidence interval (95% CI),the range of the error (minimum and maximum) allows for the assessment of the precision of each estimate: wider intervals suggest greater uncertainty or heterogeneity between studies

Fig. 4

Forest Plot of SMD in triglyceride levels between patients with FS and healthy controls Forest plot of SMD in triglyceride levels between patients with frozen shoulder and healthy controls for each included study. Blue dots represent individual studies with horizontal lines showing 95% confidence intervals. The size of each dot is proportional to the study's weight in the meta-analysis. The red diamond at the bottom represents the combined overall effect using a random effects model. The vertical dashed line at zero indicates no difference between groups. The plot shows considerable heterogeneity, with Salek et al. [27] showing a large positive effect, while Korean studies show effects closer to zero

Fig. 5

Subgroup analysis of SMD in triglyceride levels by country Individual studies are represented by blue dots with horizontal lines showing 95% confidence intervals. Green diamonds represent subgroup effects (e.g., for Korean studies), while the red diamond shows the combined overall effect. The plot reveals substantial differences between the Dhakar study [27], which shows a large positive effect, and Korean studies [33, 39], which show effects close to zero. This geographical variation suggests that regional factors, such as genetics, diet, or diagnostic criteria, may influence the relationship between frozen shoulder and triglyceride levels

Fig. 6

Meta-regression of SMD by publication year This meta-regression plot illustrates the relationship between publication year and effect size (SMD). Each blue circle represents a study, with the circle size proportional to the study's weight in the meta-analysis. The red dashed line shows the regression trend. The plot reveals a decreasing trend in effect size over time, suggesting that more recent studies tend to report smaller differences in triglyceride levels between patients with frozen shoulder and controls. However, this relationship is not statistically significant (p = 0.880), likely due to the limited number of studies included in the analysis

Fig. 7

Meta-regression of SMD by total sample size Each blue circle represents a study, with the size proportional to the study's weight in the meta-analysis. The red dashed line indicates the regression trend. The plot suggests a slight negative association between sample size and effect size, with larger studies reporting smaller differences in triglyceride levels. However, this relationship is not statistically significant (p = 0.663), indicating that sample size alone does not explain the observed heterogeneity among studies

Fig. 8

Forest plot of SMD in IL-6 levels between patients with frozen shoulder and controls for each included study Blue dots represent individual studies with horizontal lines showing 95% confidence intervals. The size of each dot is proportional to the study's weight in the meta-analysis. The red diamond at the bottom represents the combined overall effect using a random effects model. The vertical dashed line at zero indicates no difference between groups. The plot shows considerable heterogeneity (I² = 96.17%), with Lho et al. [32] showing a large positive effect (SMD = 4.53), while Kabbabe et al. [28] shows a much smaller effect (SMD = 0.29)

Fig. 9

Subgroup analysis of IL-6 SMD by tissue type This subgroup analysis shows substantial differences between studies using synovial biopsy (Kabbabe et al., 2010) [28], which reported a small positive effect, and those using capsular/bursa tissue (Lho et al., 2013 [32]; Yano et al., 2020 [38]), which reported larger effects. This tissue-specific variation suggests that the type of tissue analyzed may significantly influence the measured IL-6 levels

Fig. 10

This forest plot for subgroup analysis shows the SMD in IL-6 levels grouped by country Individual studies are represented by blue dots with horizontal lines showing 95% confidence intervals. Green diamonds represent subgroup effects, while the red diamond shows the combined overall effect. The plot reveals substantial geographical variations, with the Korean study [32] (Lho et al., 2013) showing the largest effect size (SMD = 4.53), followed by the UK study [38] (Yano et al., 2020, SMD = 1.67), and the Australian study [28] (Kabbabe et al., 2010, SMD = 0.29) showing the smallest effect. This geographical variation suggests that regional factors, such as genetics, environmental influences, or differences in clinical protocols, may significantly contribute to the observed heterogeneity in IL-6 levels

Fig. 11

Meta-regression of IL-6 SMD by publication year. This meta-regression analysis shows the relationship between publication year and effect size (SMD) for IL-6 levels. The regression trend line (dashed red line) suggests no significant linear relationship between publication year and effect size (p = 0.452). This indicates that temporal factors alone do not account for the observed heterogeneity

Fig. 12

Meta-regression of IL-6 SMD by total sample size. This meta-regression analysis examines the relationship between total sample size and effect size (SMD) for IL-6 levels. A negative trend is observed, suggesting that studies with larger samples report smaller effects. However, this relationship is not statistically significant (p = 0.378), indicating that sample size alone does not fully explain the observed heterogeneity