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An insight into the association of sclerostin and disease activity in rheumatoid arthritis: A cross-sectional study
*Corresponding author: Sarama Saha, Department of Biochemistry, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India. saramasaha@yahoo.co.in
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Received: ,
Accepted: ,
How to cite this article: Varshney T, Singh PK, Saha S, Pai VS. An insight into the association of sclerostin and disease activity in rheumatoid arthritis: A cross-sectional study. J Lab Physicians. 2025;17:317-21. doi: 10.25259/JLP_110_2025
Abstract
Objectives:
Rheumatoid arthritis (RA) causes joint swelling and deformities, impacting quality of life. Wnt pathway dysregulation is linked to RA. Sclerostin (SOST), released from osteocytes, inhibits bone formation by interfering with Wnt signalling pathway and Dickkopf-1 (DKK-1) is a secreted protein which is also a inhibitor of the pathway. Receptor activator of nuclear factor kappa-beta (RANKL) induces osteoclast genesis and is opposed by osteoprotegerin (OPG). To investigate the role of SOST, DKK-1, OPG, and RANKL in the pathogenesis of RA cases in the Indian population, an observational study was conducted at All India Institute of Medical Sciences, Rishikesh.
Materials and Methods:
In this observational study, 38 RA cases and 38 controls were recruited. Serum levels of SOST, DKK-1, OPG, RANKL, and Beclin-1 were measured using enzyme-linked immunosorbent assay kits. Messenger RNA (mRNA) quantification was done by quantitative real-time polymerase chain reaction. An independent t-test/Mann–Whitney U-test was used to compare cases and controls. Spearman’s correlation coefficient was calculated to observe the association.
Statistical analysis:
Statistical analysis was performed using Statistical Package for the Social Sciences (SPSS) version 23. Mann Whitney U test/independent t-test was used for group comparison. Correlations were done using Spearman’s correlation test. Receiver operating characteristic (ROC) curve analysis used for predictive value of biomarkers.
Results:
The findings revealed that SOST expression was significantly higher in RA patients compared to controls and correlated with increased SOST mRNA expression. Moreover, there was a positive correlation of SOST with RANKL and disease activity score-28, and a negative correlation of SOST with beclin-1 and OPG. The ROC curve analysis revealed predictive value of SOST for detecting RA cases, with an optimal cutoff value of 24.70, sensitivity of 90.2%, and specificity of 64.4%.
Conclusions:
The study concludes that SOST might play an important role in RA severity and disease activity. Diagnosis accuracy might be improved by combining SOST assessment with additional markers (RANKL, OPG, DKK-1, or Beclin-1). Understanding the interplay of these markers could lead to novel therapeutic interventions for RA.
Keywords
Disease activity score 28
Osteoprotegerin
Receptor activator of nuclear factor kappa-beta
Rheumatoid arthritis
Sclerostin
INTRODUCTION
Rheumatoid arthritis (RA) is a chronic disease characterized by joint swelling and deformities, and progression of the disease can lead to physical disability affecting quality of life.[1] Wnt pathway regulates cell proliferation, differentiation, and fate specification during embryogenesis and tissue homeostasis, and its dysregulation is associated with various diseases, including RA.[2] Earlier studies reported that Beclin-1 plays an important role in the pathogenesis of RA.[3,4] Sclerostin (SOST) is highly expressed by osteocytes and decreases osteoblastic bone formation by inhibiting the canonical Wnt/beta-catenin signaling pathway, therefore, helps in regulating bone homeostasis. Hence, the prevention of deformities could be possible by targeting the Wnt signaling pathway, as it plays a crucial role in bone homeostasis.[5] Hence, early diagnosis by identifying specific biomarkers associated with disease activity might enhance RA management and could improve patient outcomes and quality of life. Receptor activator of nuclear factor kappa-beta (RANKL), secreted by activated T cells, expressed by osteoblasts, osteocytes, and immune cells, may induce osteoclastogenesis, and RANKL action is opposed by osteoprotegerin (OPG).[6] Dickkopf-related protein 1 (DKK-1) is a secreted protein that also plays a crucial role in embryonic development by inhibiting the Wnt signaling pathway, and its role in the etiology and progression of various autoimmune diseases, including RA, has been documented.[7]
Literature review reveals that most of the studies related to SOST and RA were conducted outside India. Since SOST expression is differentially associated with race Costa et al., and the majority of previous research has focused only on its protein (translational) level,[8] hence, the study was designed to investigate the expression of SOST both at transcriptional and translational levels. In addition, the roles of SOST, DKK-1, OPG, and RANKL in RA were investigated specifically in the Indian population.
MATERIALS AND METHODS
This observational study was conducted in the Department of Biochemistry in collaboration with the Rheumatology Department at All India Institute of Medical Sciences (AIIMS), Rishikesh. The study period extended from January 1, 2020, to December 15, 2021, following the approval of the institutional ethical clearance letter No. AIIMS/IEC/19/933, dated July 19, 2019.
Sample size
The sample size was determined using G Power software (version 3.1). A total of 76 study participants (38 RA cases and 38 healthy controls) were included, ensuring a balanced allocation ratio of 1:1. The calculations were based on an effect size of 0.6, a significance level (a error) of 0.05, and a statistical power of 0.8. Study participants were carefully recruited, and informed consent was obtained from all individuals before enrollment. Participants were recruited consecutively from the Rheumatology Department.
Inclusion criteria and exclusion criteria
The study included adult participants aged 18 years and above, who were diagnosed with RA based on the established criteria of the American College of Rheumatology/European League Against Rheumatism.[9] Individuals with comorbidities such as diabetes, malignancies, or skeletal disorders were excluded to ensure a homogenous study population and to eliminate potential confounding variables that might affect the interpretation of the results.
Methodology
The study’s design and methodology have been described in detail in a previous publication.[4] In brief, venous blood samples were collected by a certified phlebotomist and collected in red-top and ethylenediaminetetraacetic acid (EDTA) vials. After centrifugation, serum was separated and stored at −80°C until further analysis to preserve the integrity of the samples. Serum levels of SOST, DKK-1, OPG, and RANKL were quantified using enzyme-linked immunosorbent assay kits as per the manufacturer’s instructions (Boster Bio, USA). The concentrations of these markers, measured in pg/mL, provided critical data for understanding the molecular interactions and pathways involved in RA pathology.
In addition to protein analysis, molecular investigations were performed to assess gene expression. RNA was isolated using trizol reagent (Invitrogen, USA), followed by complementary DNA synthesis using reagents from Applied Biosystems (USA). Messenger RNA (mRNA) quantification by quantitative real-time polymerase chain reaction (BioRad, USA) was carried out as described in a previous study.[10] The primer sequences utilized for this analysis are listed in Table 1.
| Gene | Primer sequence | |
|---|---|---|
| SOST | Forward | 5’- ACACAGCCTTCCGTGTAGTG -3’ |
| Reverse | 5’- GGTTCATGGTCTTGTTGTTCTCC -3’ | |
| B-actin | Forward | 5’- GCA TGG GTC AGA AGG ATT CCT A-3’ |
| Reverse | 5’- TGT AGA AGG TGT GGT GCC AGA T-3’ | |
SOST: Sclerostin
Statistical analysis
Data analysis was performed using the Statistical Package for the Social Sciences (version 23). Group comparisons for continuous variables were carried out using the Mann–Whitney U-test or the independent t-test, depending on the distribution of the data. Correlations between biomarkers, including SOST, DKK-1, OPG, RANKL, and Beclin-1, were assessed using Spearman’s correlation coefficient. This approach facilitated the exploration of potential relationships and interactions among these key markers in the context of RA.
The predictive value of the biomarkers was evaluated using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) was calculated to quantify the different biomarkers’ discriminatory ability.
RESULTS
Demographic and biochemical parameters for all 76 study participants (38 RA cases and 38 controls) are presented in Table 2.
| Parameters | Study participants (Males and Females) |
P-value | |
|---|---|---|---|
| Cases (38) (Male=5; Females=33) |
Controls (38) (Male=7; Females=31) |
||
| Age (years) | 45.00 (24.25) | 38.00 (7.00) | 0.025 |
| SOST (pg/mL) | 36.15 (29.26) | 20.27 (15.17) | <0.001 |
| DKK-1 (pg/mL) | 1253.39±753.16 | 1438.89±588.82 | 0.211 |
| OPG (pg/mL) | 39.81 (22.05) | 42.17 (21.04) | 0.555 |
| RANKL (pg/mL) | 73.36 (57.58) | 71.71 (77.11) | 0.290 |
| RANKL/OPG ratio | 1.84 | 1.70 | - |
SOST: Sclerostin, DKK-1: Dickkopf-1, OPG: Osteoprotegerin, RANKL: Receptor activator of nuclear factor kappa-beta, Data are represented as median (IQR) and mean ± Standard deviation
Our data revealed that serum SOST levels were significantly elevated in RA patients compared to the control group. The median serum SOST level in the RA group was 36.15 pg/mL (interquartile range [IQR]: 29.26), which was significantly higher than the controls at 20.27 pg/mL (IQR: 15.17) (P < 0.001). These findings were further supported by SOST mRNA expression levels, as depicted in Table 2 and Figure 1. In addition, SOST exhibited significant correlations with key clinical and molecular markers. A strong positive correlation was observed between SOST and RANKL (r = 0.804, P < 0.001) as well as with disease activity score-28 (DAS-28) (r = 0.449, P < 0.001), indicating its potential role in RA disease activity and bone remodeling processes. Conversely, SOST demonstrated significant negative correlations with Beclin-1 (r = −0.342, P = 0.002) and OPG (r = −0.423, P < 0.001), suggesting an inverse relationship with autophagy-related and bone-protective mechanisms. Furthermore, the correlation analysis revealed that RANKL and OPG were negatively correlated among the total study participants (r = −0.547, P < 0.001) [Table 3].
| Parameters | Rho (Spearman’s correlation coefficient) | P-value |
|---|---|---|
| SOST | ||
| DKK-1 | 0.046 | 0.682 |
| Beclin1 | −0.342** | 0.002 |
| OPG | −0.423** | <0.001 |
| RANKL | 0.804** | <0.001 |
| DAS | 0.449** | <0.001 |
| RANKL | ||
| OPG | −0.547** | <0.001 |
- Graphical representation of SOST expression levels in healthy controls and RA patients. *P < 0.001. RA: Rheumatoid arthritis. SOST: Sclerostin.
The ROC curve of the study participants is presented in Figure 2. The ROC curve showed that SOST created a significantly very high (*P < 0.001) AUC 0.786 with a 95% confidence interval, 0.681–0.890. The optimal predictive value of SOST is 24.70 for detecting RA cases with a sensitivity of 90.2% and specificity of 64.4%.
- ROC curve for the prediction of rheumatoid arthritis by SOST. ROC: Receiver operating characteristic, SOST: Sclerostin. DKK-1: Dickkopf-1, OPG: Osteoprotegerin, RANKL: Receptor activator of nuclear factor kappa-beta; TNFRSF: Tumor necrosis factor receptor superfamily
Using a cut-off value of 24.7 pg/mL for SOST, 92.1% of RA patients could be identified. In comparison, anti-cyclic citrullinated peptide (anti-CCP) (cut-off: 20 U/mL) detected 78.9% of cases, while rheumatoid factor (RF) identified only 68.4%.
DISCUSSION
RA is a chronic, progressive, and disabling autoimmune disease that affects multiple systems in the body. Developing a highly sensitive and specific diagnostic marker would significantly improve early identification and enhance patient outcomes. This study mainly aimed to investigate the role of SOST, DKK-1, OPG, and RANKL in the pathogenesis of RA and its relation to disease activity.
The study findings conducted by El-Bakry et al.[11] and Hussein and Aboukhamis[12] demonstrated increased expression of SOST in the serum of RA patients, which was significantly correlated to RA disease activity at the early stage, which is consistent with our study, indicating that SOST could be used to assess disease activity status of RA patients.[11,12]
Some studies reported that SOST concentration may not be associated with the advanced stage of the disease radiologically.[11] This discrepancy might be due to the difference in the stage of disease and the mode of assessment of disease activity. In our study, participants were from the early stage of the disease, and disease activity was assessed by DAS.
Moreover, in our study, the highest AUC of the ROC curve analysis suggests that SOST could serve as a diagnostic marker with high sensitivity (90.2%) and specificity, which could contribute to understanding of its potential clinical utility. It indicates that SOST exhibits better sensitivity and specificity compared to anti-CCP antibodies (68.3%) and RF-immunoglobulin M antibodies (88.3%) documented in previous studies.[12,13] Consequently, SOST represents a sensitive and specific biomarker for early detection of RA. However, robustness and generalizability of the results would be improved by a more extensive multicentric study with a larger sample size.
Our study showed no significant correlation between SOST and DKK-1 (r = 0.046, P = 0.682) and serum DKK-1 levels were higher in controls (1438.89 ± 588.82 pg/mL) as compared to cases (1253.39 ± 753.16 pg/mL) while study conducted in Turkey by Aydemir et al.[14] concluded only positive correlation (r = 0.382, P = 0.003) between SOST and DKK-1 which was in discordance with our study results (r = 0.046, P = 0.682) which might be due to recruitment of RA patients from different disease stages.[14]
An ESPOIR cohort by Seror et al. found decreased expression of SOST and higher levels of DKK-1 in the synovium of early RA patients.[15] The observed disparities in findings could potentially stem from variations in the sample populations examined. These distinctions might arise from differences in sample sizes, demographics, disease severity, or other factors impacting serum SOST and DKK-1 levels.[15] Our study showed decreased serum levels of OPG in RA cases as compared to controls and increased serum levels of RANKL in cases as compared to controls, which is a similar finding in a study conducted by Fadda et al., suggesting a role of OPG/RANKL in the pathogenesis of RA.[16] A significant correlation between OPG and RANKL and higher OPG/RANKL ratio was also found in the present study.
The study conducted by Quaresma et al. in which 33 patients with RA, 32 with spondyloarthritis, and 18 with osteoarthritis were recruited found no statistically significant difference in the RANKL and OPG levels among the groups.[17] The discrepancy of the findings from our study could be due to differences in the type of sample subjected to investigation. In our study, we used serum samples.
In RA, autophagy has been shown to play a role in the pathogenesis of the disease, potentially affecting the inflammatory process and bone erosion characteristic of RA. SOST levels have also been studied in RA for their potential correlation with disease progression and severity. While the direct relationship between Beclin-1 and SOST has not been extensively characterized, the negative association between SOST and Beclin-1 suggests that the regulation of autophagy by Beclin-1 could intersect with the bone metabolism processes regulated by SOST.
The study is limited by its relatively small sample size and was conducted at a single center, which may affect the generalizability of the findings. The study focused on early-stage RA patients, which may not fully represent the disease’s spectrum. The results may potentially be impacted by variations in the study population, including variations in disease severity, demography, and other factors.
To improve the findings of the study, applicability to various communities and ethnic groups, the study must include a larger sample size and should be carried out across multiple centers to increase the generalizability of the findings to different ethnicities. The study should include long-term follow-ups to assess the progression of RA and the stability of biomarkers such as SOST, Beclin-1, DKK1, OPG, and RANKL over time. Combining SOST assessment with other biomarkers, such as anti-CCP antibodies and RF-IgM, can improve diagnostic accuracy and offer a more complete picture of disease activity. Study of these biomarkers in both early and advanced stages of rheumatoid arthritis helps clarify their roles throughout the disease’s progression. By implementing these recommendations, future research can better understand the complex interplay of these biomarkers in RA and pave the way for improved diagnostic and therapeutic strategies.
CONCLUSIONS
This study revealed that SOST has a high predictive value for detecting RA cases, with an optimal cutoff value of 24.70, sensitivity of 90.2%, and specificity of 64.4%. Combining all findings, the SOST assessment with other markers may enhance diagnostic accuracy. So, Understanding the complex interactions of RANKL, OPGN, DKK-1, or Beclin-1 in rheumatoid arthritis may pave the way for new therapeutic strategies and improved diagnostic approaches.
Acknowledgment:
Current affiliation of PKS is Department of Biochemistry, Maharishi Markandeshwar Medical College & Hospital, Solan, Himachal Pradesh, India.
Authors’ contribution:
TV, PKS: Wrote manuscript, collected samples, and performed tests; VSP: Helped in selection of patients and literature review; SS: Involved in conceptualization, data collection, statistics, and editing of the manuscript. All authors have read and approved the manuscript.
Ethical approval:
The research/study was approved by the Institutional Review Board at AIIMS Rishikesh, approval number AIIMS/IEC/19/933, dated 19th July 2019.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript, and no images were manipulated using AI.
Financial support and sponsorship: Intramural fund provided by AIIMS Rishikesh (sanctioned letter-IM/RC161/2018/24, dated September 20, 2019).
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