Serum Receptor Activator of Nuclear Factor-Kappa B Ligand Levels In Chinese Patients With Ankylosing Spondylarthritis: A Meta-Analysis

DOI: https://doi.org/10.21203/rs.3.rs-774465/v1

Abstract

Objective: We aimed to determine the association between serum receptor activator of nuclear factor-kappa B ligand (sRANKL) levels and ankylosing spondylarthritis (AS) in Chinese populations.

Methods: The PubMed, Cochrane Library, Embase, Chinese Biomedical Database, Web of Science, China National Knowledge Infrastructure, VIP, and Wan Fang databases were searched for studies conducted before October 1, 2020 without language restrictions. STATA version 12.0 and Revman version 5.3 were used to analyze the data. The standard mean differences (SMDs) and corresponding 95% confidence intervals (95%CIs) were calculated.

Results: Twelve clinical case–control studies, including 585 AS patients and 423 healthy controls, were included. The combined SMD for sRANKL suggested that the sRANKL level was significantly higher in the Chinese AS patients than in the healthy controls (SMD: 3.27, 95%CI: 2.11-4.43, P<0.00001). RANKL-related factor osteoprotegerin OPG (SMD: 0.86, 95%CI: 0.09-1.64, P<0.03) and RANKL/OPG (SMD=1.05, 95%CI=0.64-1.46, P<0.00001). Subgroup analysis indicated that the patients from North and South China had higher sRANKL levels than the controls; the patients sRANKL levels from South China were higher in the subgroup with Bath Ankylosing Spondylitis Functional Index (BASFI) >4. In terms of duration, ≤8years AS patients had higher sRANKL levels than the controls. Other subgroup analyses were conducted by region, language, source of control, age, and Bath Ankylosing Spondylitis Disease Activity Index (BASDAI). In these subgroups, the sRANKL levels were significantly higher in the AS patients than in the controls. BASFI and BASDAI were sources of heterogeneity.

Conclusions: The sRANKL levels are higher in the Chinese AS patients, especially among the patients from South China. sRANKL levels may be positively correlated with the pathogenesis of AS among the Chinese patients. 

Introduction

Ankylosing Spondylarthritis (AS) is a type of inflammatory arthritis, which belongs to the spondyloarthritis family that includes reactive arthritis and psoriatic arthritis [1]. There are a large number of AS patients worldwide. The prevalence of AS in China is approximately 0.3%, which is 4 million out of the 1.4 billion population of China. As such, AS will cause a serious economic burden on families and society [2]. Meanwhile, an in-depth understanding of AS pathogenesis may address the problems with delayed diagnosis of AS and insufficient therapeutic strategy for the disease [3]. Features of AS include the anatomical distribution of the affected joints, types of joint damage, extraarticular manifestations, and gender distribution and eyes, intestine, and skin effects [4] [5]. Recent studies have suggested that cytokines, including leptin, adiponectin, and resistin, may play important roles in the process of AS [6]. When inflammation occurs, new bone formation leads to bone sclerosis, which can lead to AS; reports have indicated that osteopenia and osteoporosis both occur in AS [7]. Receptor activator of NF-kappa B ligand RANKL was first found on the surface of osteoblasts and involve in different stages of bone metabolism [8, 9]. It is a transmembrane protein that belongs to the TNF superfamily [10], which consists of 316 amino acids [11], and is mainly expressed in the bone surface and lymphoid tissue [12]. RANKL and its receptor RANKL play an important role in bone metabolism and the immune system [13]. RANKL adherence to the bone surface is necessary to promote osteoclast differentiation, activation and survival and accelerates the progress of osteoclast biology [14, 15]. However, osteoclast overactivation, leads to bone resorption and has been observed in a variety of bone diseases, such as bone metastasis and osteoporosis; likewise, RANKL is necessary for osteoclast differentiation and immune regulation [16]. MRI results have indicated that bone inflammation and osteitis are associated with the presence of RANKL [17]. Targeted deletion of RANKL in bone cells prevents osteoclast formation [18] .

Recently, several studies have shown that serum RANKL (sRANKL) levels are correlated with the AS disease activity and are significantly elevated in those patients. However, other studies have found no clear link between RANKL and AS in Asians [1921]. The relationship between sRANKL and AS among Chinese populatios is still unclear. Thus, we performed this meta-analysis to find the link between sRANKL level and AS in Chinese populations and to determine which diagnosis and treatment of AS are more convenient and effective.

Materials And Methods

2.1 Literature search

We searched the following electronic databases without any language restrictions: PubMed, Cochrane library, Embase, Chinese Biomedical Database, Web of Science, Chinese National Knowledge Infrastructure, VIP, and Wan Fang. The search strategy was highly sensitive and performed in combination with the following keywords and MeSH terms: “Ankylosing Spondylarthritis” or “Ankylosing Spondylarthritides” or “AS” and “RANKL” or “OPGL Protein” or “Osteoclast Differentiation Factor” or “Osteoprotegerin Ligand” or “TRANCE Protein.”

2.2 Selection criteria

The selection criteria were as follows: (1) only case–control studies in the population to explore the relationship between sRANKL and AS, (2) patients who meet the modified New York criteria or ASAS [22] [23], (3) articles should be associated with sRANKL concentration, and (4) sufficient and original data. Studies that did not meet the selection criteria were excluded. If one author published different studies about the same topic, the most recently published or the study with the largest sample size was selected.

2.3 Data extraction

From the selected articles, two researchers (Feifei Ni and Xiaoxiao Peng) independently extracted and recorded the required information. Disagreements over data or included studies were agreed upon through discussion of all items. The recorded information included surname of initial authors, region, language, publication years, age, duration, Bath Ankylosing Spondylitis Functional Index (BASFI), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), sRANKL detection method, and sRANKL and OPG levels in the cases and the controls.

2.4 Quality of the study

Two observers (Feifei Ni and Xiaoxiao Peng) used the Newcastle–Ottawa Scale (NOS) to assess the quality of the included studies [24]. The NOS involves three aspects: (1) subject selection: 0–4; (2) comparability of subject: 0–2; and (3) clinical outcome: 0–3. The NOS scores range from 0 to 9 with two levels of included studies: low quality (0–6) and high quality (7–9). When the two researchers disagreed or discrepancies on the NOS score of the study were present, a third reviewer intervened.

The relationship between sRANKL levels and AS susceptibility was assessed using the standardized mean differences (SMDs) and 95% confidence interval (95%CI). Cochran’s Q-statistic (P < 0.05 was considered significant) and I2 tests were used to quantify heterogeneity among studies [25]. The random effects model was used when heterogeneity was significant (P < 0.05 or I2 test exhibited > 50%), whereas SMDs were pooled based on the fixed-effects model [26]. When heterogeneity was significant, subgroup analysis was performed to find the potential reasons for the difference in sRANKL levels between AS and the control. In addition, sensitivity analysis was used to assess if a single study had an impact on the whole assessment. The impact of publication bias was analyzed by Egger’s test (P < 0.05 was considered significant), which can be used to evaluate the funnel plot asymmetry that reveals potential publication bias [27] [28]. The data were analyzed using the software Review Manager 5.3 and STATA version 12.0.

2.5. Statistical analysis


See Figures 1 and 2

Results

3.1 Inclusion criteria

We selected 499 potentially relevant articles from eight databases. After deleting duplicates, 348 records remained. By skimming the titles and abstracts, we excluded 258 papers due to at least one of following reasons: (1) articles were comments, letters, reviews, or editorials; (2) not related to research topics, and (3) not human research. Full-text articles from the remaining 90 articles were reviewed again and another 60 trials were excluded (30 were not case–control,18 were not relevant to RANKL, and 12 were not relevant to AS), leaving 30 studies to the next select step. After studies not related to the Chinese population and those lacking data integrity were removed, 12 studies were chosen [2940](Fig. 1).

3.2 Features of the studies

Twelve studies, including 585 AS patients and 423 control, were included in accordance with the selection criteria. The basic features of the studies are shown in Fig. 2. sRANKL levels in all the 12 studies were tested via enzyme-linked immunosorbent assay. The methodological quality assessment using NOS is shown in Fig. 2.

3.3 Meta-analysis in AS

Significant heterogeneity was found in the 12 studies (P < 0.00001, 𝐼2 = 97%), and random-effects model showed that sRANKL levels in AS patients were statistically different compared with those in controls (SMD = 3.27, 95%CI = 2.11–4.43, P < 0.00001) (Fig. 3). Subgroups, including language, source of control, age, and BASDI, were analyzed. In terms of these subgroups, the sRANKL levels of AS patients were significantly higher than those of controls (Figs. 4 and 5).

Subgroup analysis by region was divided into patients from North and South China because of the geographical differences in China’s population distribution. The AS patients in the two groups had obviously higher sRANKL levels than controls, but the patients from south had higher sRANKL levels (SMD = 3.55, 95% CI = 2.18–4.92, P < 0.00001) than the patients from North (SMD = 2.62, 95% CI = 0.34–4.9, P < 0.02). Further subgroup analysis indicated that BASFI > 4 (SMD = 3.14, 95% CI = 2.69–3.58, P < 0.00001) and duration ≤ 8years (SMD = 2.02, 95% CI = 1.03–3.02, P < 0.0001) had a positive correlation among AS patients, but BASFI ≤ 4 (SMD = 2.2, 95% CI=-0.9-5.3, P = 0.16) and duration > 8 (SMD = 11.9, 95% CI= -10.33-34.13, P = 0.29) did not (Figs. 4 and 5). sRANKL-related factor serum OPG levels (SMD = 0.86, 95% CI = 0.09–1.64, P = 0.03) (Fig. 6) in AS patients were lower than those in controls, and the ratio of RANKL/OPG (SMD = 1.05,95% CI = 0.64–1.46, P < 0.00001) (Fig. 6) in AS patients was higher than that in controls. All the results show that high sRANKL expression is an important risk factor for the occurrence of AS in the Chinese population.

3.4 Sensitivity analysis and publication bias

The results of the sensitivity analysis indicated that none of the studies had an effect on the overall estimate of the association between RANKL levels and AS risk. Thus, the data presented in our meta-analysis were relatively stable and credible (Fig. 7). The graphical funnel plots of the 12 included studies showed symmetry, and Egger’s test showed no publication bias (P = 0.056) (Fig. 8).

Discussion

In this study, we evaluated the sRANKL levels in Chinese AS patients from 12 articles through meta-analysis, and we investigated the probable relationship between sRANKL level and AS. Our results suggested that RANKL may play a key role in the pathogenesis of Chinese AS patients. AS is a chronic, progressive systemic rheumatism that affects the sacroiliac joints, central axis bones, peripheral joints, and other extra-articular organs [41]. A recent study suggests that AS pathogenesis involves bone resorption and formation [42]. Although many studies have evaluated the correlation between sRANKL and AS in the Chinese population, the results are controversial [4345]. Therefore, we conducted this study to investigate the association between sRANKL and AS. In the process of AS peripheral joint ossification, the coupling imbalance between osteoblasts and osteoclasts is a condition that cannot be neglected in ossification [46]. RANKL produced by osteocytes is an important source of osteoclast formation and bone reconstruction [47]. The RANKL/RANK /OPG pathway controls osteoclast activity and formation and plays an important role in the AS process [48]. Denosumab is a monoclonal antibody against RANKL that prevents osteoclast formation and has been used as a first-line treatment for osteoporosis. Therefore, lowering the level of RANKL in serum may be beneficial to the progression of AS [49]. OPG in bone formation is a kind of protective factor conducive to the growth of osteoblasts, normal osteoblasts, and osteoclasts in the body is in a dynamic balance with RANKL; both are not excessively activated but otherwise may lead to bone disease [50]. The ratio of RANKL and OPG is closely related to osteoclast formation and maturity. In the present study, AS patients’ OPG level (SMD = 0.86, 95%CI = 0.09–1.64, P < 0.03) in peripheral blood was significantly lower than that of healthy controls, but the sRANKL level (SMD = 3.27, 95%CI = 2.11–4.43, P < 0.00001) was significantly higher in the former than the latter. As such, osteoblast activity is restrained. In-depth studies have shown that the RANKL/OPG ratio determines the direction of bone change. As the ratio decreases, bone loss decreases [51], In this study, RANKL/OPG (SMD = 1.05, 95%CI = 0.64–1.46, P < 0.00001) was higher in Chinese AS patients than controls. This result suggests that excessive activation of osteoclasts increases inflammation as confirmed in animal models. The OPG gene knockout mice had insufficient osteoblast production and decreased bone mass, presenting severe osteoporosis with a high incidence of bone fracture [52]. The RANKL knockout mice developed severe osteosclerosis, and only a small number of osteoclasts were observed in the bone tissue of those mice [53, 54]. In addition, RANKL-positive osteocytes were elevated in animal models of inflammation, such as periodontitis and spinal injuries [55]. Osteoblasts also produce RANKL and activated T cells to regulate adaptive immunity [11, 56]. Some studies have found that RANKL is expressed on the surface of T cells and lymphocytes and regulates lymph node formation and T cell and dendritic cell communication. Overactivation of the immune system may contribute to the disease process of AS [57]. RANKL-expressed T cells can affect osteoclast formation, which explains bone loss in chronic inflammatory disease [15]. Recently, CD4 + T and CD8 + T cells have been confirmed to participate in the pathogenesis of AS, although many problems remain to be solved; furthermore, RANKL overexpression in T cells in the RANKL knock out mouse context can restore osteoclast production and a partial return to the normal bone marrow cavity [58]. Meanwhile, in RANKL deficiency mutant mice, the lack of osteoclast leads to severe osteoporosis and failure of tooth and lymph node formation [59]. Thus, bone loss due to inflammation may arise from the complex interactions of bone cells, T and B cells, and signaling pathways, such as the RANKL/RANK/OPG pathway [60]. This phenomenon may explain the roles of systemic activation of T cells and RANKL production through T cells as important mediators of bone loss in vivo [61]. In autoimmune diseases, local inflammation of the bone caused by infection, or in arthritis, T cells are usually activated first, and RANKL are overexpressed, resulting in bone loss [62].

Considering that other related factors may have a connection with high levels of sRANKL and AS pathogenesis, a stratified analysis based on region, language, source of control, age, duration, BASDAI, and BASFI, was conducted. BASDAI and BASFI represent the activity of the disease [63]. In the current study, sRANKL in the disease group of BASDAI > 4 and BASFI > 4 was significantly higher than that in the control group. sRANKL in the subgroup with duration < 8 years was significantly higher than that in the control group and may be attributed to the abnormal activation of the immune system and inflammatory cytokine in the early stages of AS [64]. All these findings suggest that BASDAI > 4, BASFI > 4, and duration affect sRANKL expression. We also found that subgroup BASDAI > 4 and BASFI > 4 are sources of heterogeneity. In terms of region, the relationship was significant among the Chinese population, especially among the patients from South. This result may be attributed to the differences in China’s vast territory and geographical distribution of people, as well as different factors, such as living environment and genetics.

There are several limitations of this meta-analysis. First, the small sample size in the 12 studies may affect the results. Second, articles that provide only median and range or upper quartiles and lower quartiles were excluded because if we force the conversion of these data, the result of the transformation is not accurate. even if a method of transformation had been reported by Hozo et al. [21, 65, 66].Third, in the 12 studies, information on factors affecting sRANKL, such as HLA-B27, body mass index, and sex, were not detailed enough. Therefore, we cannot safely further analyze the relationship between serum RANKL and AS given that the sex ratio between male and female may have an impact on the reliability of our study.

Despite the above limitations, this is the first meta-analysis about the association between sRANKL levels and Chinese AS patients.

In conclusion, our study indicated that sRANKL levels in Chinese AS patients especially the patients with AS in the south, were obviously higher than those in the healthy control. sRANKL level may have a positive correlation with the pathogenesis of Chinese AS patients and serve as a promising biomarker for the severity of AS in Chinese populations. The results of our study may ultimately contribute to the development of new treatments for bone damage to Chinese AS as this field has not been thoroughly studied. Further large sample size and intensive study in Chinese population are needed.

Conclusions subheading : This finding suggests that sRANKL have a positive correlation with the pathogenesis of Chinese AS patients and may potentially serve as a biomarker for the severity of AS in Chinese populations.

Declarations

Acknowledgments

We would like to acknowledge the reviewers for their helpful comments on this paper.

Funding

This work was supported by National Natural Science Foundation of China(81971829).

Consent for publication

Not applicable

Conflicts of interest

The authors have no conflicts of interests to declare.

Ethical approval

As this article is a systematic review and meta-analysis, ethical approval is not needed.

Data availability

The authors confirm that the data supporting the findings of this study are available within the article.

Authors’ contributions

Feifei Ni designed the study, analyzed most of the data, and wrote manuscript. Yanchao Zhang、Yi Peng 、Xiaoxiao Peng and Jianjun Li proofread the manuscript.

References

1. Dougados, M. and D. Baeten, Spondyloarthritis. Lancet, 2011. 377(9783): p. 2127-2137.

2. Zhao, J., et al., Prevalence of ankylosing spondylitis in a Chinese population: a systematic review and meta-analysis. Rheumatol Int, 2020. 40(6): p. 859-872.

3. Smith, J.A., Update on ankylosing spondylitis: current concepts in pathogenesis. Curr Allergy Asthma Rep, 2015. 15(1): p. 489.

4. Liu, W., et al., Elevated serum levels of IL-6 and IL-17 may associate with the development of ankylosing spondylitis. Int J Clin Exp Med, 2015. 8(10): p. 17362-17376.

5. Wu, Y., et al., Risk Factors of Renal Involvement Based on Different Manifestations in Patients with Ankylosing Spondylitis. Kidney Blood Press. Res., 2018. 43(2): p. 367-377.

6. Maksymowych, W.P., et al., Serum matrix metalloproteinase 3 is an independent predictor of structural damage progression in patients with ankylosing spondylitis. Arthritis Rheum, 2007. 56(6): p. 1846-53.

7. Stupphann, D., et al., Intracellular and surface RANKL are differentially regulated in patients with ankylosing spondylitis. Rheumatol. Int., 2008. 28(10): p. 987-993.

8. Lacey, D.L., et al., Osteoprotegerin ligand is a cytokine that regulates osteoclast differentiation and activation. Cell, 1998. 93(2): p. 165-176.

9. Yasuda, H., et al., Identity of osteoclastogenesis inhibitory factor (OCIF) and osteoprotegerin (OPG): a mechanism by which OPG/OCIF inhibits osteoclastogenesis in vitro. Endocrinology, 1998. 139(3): p. 1329-1337.

10. Ma, Q., et al., Mature osteoclast-derived apoptotic bodies promote osteogenic differentiation via RANKL-mediated reverse signaling. J. Biol. Chem., 2019. 294(29): p. 11240-11247.

11. Anderson, D.M., et al., A homologue of the TNF receptor and its ligand enhance T-cell growth and dendritic-cell function. Nature, 1997. 390(6656): p. 175-179.

12. Amin, N., et al., Probiotics and bone disorders: the role of RANKL/RANK/OPG pathway. Aging Clin Exp Res, 2019.

13. Nagy, V. and J.M. Penninger, The RANKL-RANK Story. Gerontology, 2015. 61(6): p. 534-542.

14. Hofbauer, L.C. and A.E. Heufelder, Role of receptor activator of nuclear factor-kappaB ligand and osteoprotegerin in bone cell biology. J. Mol. Med., 2001. 79(5-6): p. 243-253.

15. Kong, Y.Y., et al., OPGL is a key regulator of osteoclastogenesis, lymphocyte development and lymph-node organogenesis. Nature, 1999. 397(6717): p. 315-323.

16. Nakashima, T., M. Hayashi, and H. Takayanagi, New insights into osteoclastogenic signaling mechanisms. Trends Endocrinol. Metab., 2012. 23(11): p. 582-590.

17. Jones, R.M., J.G. Mulle, and R. Pacifici, Osteomicrobiology: The influence of gut microbiota on bone in health and disease. Bone, 2018. 115: p. 59-67.

18. de Vries, T.J. and C. Huesa, The Osteocyte as a Novel Key Player in Understanding Periodontitis Through its Expression of RANKL and Sclerostin: a Review. Curr Osteoporos Rep, 2019. 17(3): p. 116-121.

19. Du, W., et al., MiR-495 targeting dvl-2 represses the inflammatory response of ankylosing spondylitis. Am J Transl Res, 2019. 11(5): p. 2742-2753.

20. Dhir, V., R. Srivastava, and A. Aggarwal, Circulating Levels of Soluble Receptor Activator of NF- κ B Ligand and Matrix Metalloproteinase 3 (and Their Antagonists) in Asian Indian Patients with Ankylosing Spondylitis. Int J Rheumatol, 2013. 2013: p. 814350.

21. Taylan, A., et al., Biomarkers and cytokines of bone turnover: extensive evaluation in a cohort of patients with ankylosing spondylitis. BMC Musculoskelet Disord, 2012. 13: p. 191.

22. van der Linden, S., H.A. Valkenburg, and A. Cats, Evaluation of diagnostic criteria for ankylosing spondylitis. A proposal for modification of the New York criteria. Arthritis Rheum., 1984. 27(4): p. 361-368.

23. Sieper, J., et al., The Assessment of SpondyloArthritis international Society (ASAS) handbook: a guide to assess spondyloarthritis. Ann Rheum Dis, 2009. 68 Suppl 2: p. ii1-44.

24. Stang, A., Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur. J. Epidemiol., 2010. 25(9): p. 603-605.

25. Zintzaras, E. and J.P. Ioannidis, Heterogeneity testing in meta-analysis of genome searches. Genet. Epidemiol., 2005. 28(2): p. 123-137.

26. Higgins, J.P. and S.G. Thompson, Quantifying heterogeneity in a meta-analysis. Stat Med, 2002. 21(11): p. 1539-1558.

27. Song, F. and S. Gilbody, Bias in meta-analysis detected by a simple, graphical test. Increase in studies of publication bias coincided with increasing use of meta-analysis. Bmj, 1998. 316(7129): p. 471.

28. Peters, J.L., et al., Comparison of two methods to detect publication bias in meta-analysis. Jama, 2006. 295(6): p. 676-680.

29. AN Xiao-bei, et al. Analysis of related factor osteoporosis in ankylosing spondylitis .J. CHINESE JOU RNAL OF RHEUMATOLOGY.2010 14(9): p. 620-623.

30. Chen, C.H., et al., Soluble receptor activator of nuclear factor-kappaB ligand (RANKL) and osteoprotegerin in ankylosing spondylitis: OPG is associated with poor physical mobility and reflects systemic inflammation. Clin. Rheumatol., 2010. 29(10): p. 1155-1161.

31. Hou, C., L. Luan, and C. Ren, Oxidized low-density lipoprotein promotes osteoclast differentiation from CD68 positive mononuclear cells by regulating HMGB1 release. Biochem. Biophys. Res. Commun., 2018. 495(1): p. 1356-1362.

32. LI Xiu-juan, et al, The study on the expression of interieukin17 and receptor activator of nuclear factors κB-ligand in serum of patients with Ankylosing spondylitis . J Chinese Journal of Rheumatology. 2013. 17(11): p. 769-771.

33. Luo Hong-yu, Expression and signifinance of serum OPG and sRANKL in patients with Ankylosing spondylitis . Jiangsu Med J . 2011. 37(23): p. 2770-2772.

34. Wei Rong-mei, et al.Correlation of Bone Metabolic Indexes and Levels of RANKL - RANK - OPG System in Ankylosing Spondylitis Patients . J Progres s in Modern Biomedicine. 2013. 13(13): p. 2528-2531,2546.

35. Zhang Zheng , et al. Clinical significance of detection of MIF, IL23, RANKL,OPG and DKK1 in peripheral blood of patients with active ankylosing spondylitis .2018. 40(02): p. 178-182.

36. Zhao Wen-hua,et al. Osteoclast precursors in perip heral blood of patients with

Ankylosing spondylitis. J CHINESE JOURNAL OF RHEUMATOLOGY. 2010. 14(6): p. 373-376.

37. Shen jian, et al. Clinical significance of RANKL, TGF - β1and TGF - β2 in ankylosing spondylitis %J , Shaanxi Medical Journal . 2019. 48(08): p. 981-983.

38. Huang xianqian, et al., Correlation of serum TNF - α, RANKL, OPG and IL - 34 levels with enthesitis in patients with ankylosing spondylitis .J. Zhejiang Medical Journal .2018. 40(22): p. 2454-2458.

39. Zhang peiyi, et al. Expression of chemokine CXCL16 and its receptor CXCR6 can be suppressed by rcombinant human TNF receptor α ⅡIg fusion protein in ankylosing spondylitis %J Journal of Shandong University(Health Sciences) . 2015. 53(12): p. 5156.

40. Zhang, P., et al., TNF Receptor:Fc Fusion Protein Downregulates RANKL/OPG Ratio by Inhibiting CXCL16/CXCR6 in Active Ankylosing Spondylitis. Curr Pharm Biotechnol, 2020.

41. Gouveia, E.B., D. Elmann, and M.S. Morales, Ankylosing spondylitis and uveitis: overview. Rev Bras Reumatol, 2012. 52(5): p. 742-756.

42. Cortes, A., et al., Association study of genes related to bone formation and resorption and the extent of radiographic change in ankylosing spondylitis. Ann. Rheum. Dis., 2015. 74(7): p. 1387-1393.

43. Kim, H.R., et al., Elevated serum levels of soluble receptor activator of nuclear factors-kappaB ligand (sRANKL) and reduced bone mineral density in patients with ankylosing spondylitis (AS). Rheumatology (Oxford), 2006. 45(10): p. 1197-1200.

44. Fan, J., et al., I-BET151 inhibits expression of RANKL, OPG, MMP3 and MMP9 in ankylosing spondylitis and. Exp Ther Med, 2017. 14(5): p. 4602-4606.

45. Ji, W., et al., Beneficial effects of tripterygium glycosides tablet on biomarkers in patients with ankylosing spondylitis. Mol Med Rep, 2015. 12(1): p. 684-690.

46. Holmdahl, R., et al., Collagen induced arthritis as an experimental model for rheumatoid arthritis. Immunogenetics, pathogenesis and autoimmunity. Apmis, 1989. 97(7): p. 575-584.

47. Nakashima, T., et al., Evidence for osteocyte regulation of bone homeostasis through RANKL expression. Nat. Med., 2011. 17(10): p. 1231-1234.

48. Jones, D.H., Y.Y. Kong, and J.M. Penninger, Role of RANKL and RANK in bone loss and arthritis. Ann. Rheum. Dis., 2002. 61 Suppl 2: p. ii32-9.

49. Hinze, A.M. and G.H. Louie, Osteoporosis Management in Ankylosing Spondylitis. Curr Treatm Opt Rheumatol, 2016. 2(4): p. 271-282.

50. Kovács, B., E. Vajda, and E.E. Nagy, Regulatory Effects and Interactions of the Wnt and OPG-RANKL-RANK Signaling at the Bone-Cartilage Interface in Osteoarthritis. Int J Mol Sci, 2019. 20(18).

51. Tobeiha, M., et al., RANKL/RANK/OPG Pathway: A Mechanism Involved in Exercise-Induced Bone Remodeling. Biomed Res Int, 2020. 2020: p. 6910312.

52. Ozaki, Y., et al., Treatment of OPG-deficient mice with WP9QY, a RANKL-binding peptide, recovers alveolar bone loss by suppressing osteoclastogenesis and enhancing osteoblastogenesis. PLoS One, 2017. 12(9): p. e0184904.

53. Hu, Z., et al., Serum from patients with ankylosing spondylitis can increase PPARD, fra-1, MMP7, OPG and RANKL expression in MG63 cells. Clinics (Sao Paulo), 2015. 70(11): p. 738-742.

54. Atkinson, S.M., et al., Anti-RANKL treatment inhibits erosive joint destruction and lowers inflammation but has no effect on bone formation in the delayed-type hypersensitivity arthritis (DTHA) model. Arthritis Res. Ther., 2016. 18: p. 28.

55. Metzger, C.E. and S.A. Narayanan, The Role of Osteocytes in Inflammatory Bone Loss. Front Endocrinol (Lausanne), 2019. 10: p. 285.

56. Wong, B.R., et al., TRANCE is a novel ligand of the tumor necrosis factor receptor family that activates c-Jun N-terminal kinase in T cells. J. Biol. Chem., 1997. 272(40): p. 25190-25194.

57. Varsani, H., et al., Synovial dendritic cells in juvenile idiopathic arthritis (JIA) express receptor activator of NF-kappaB (RANK). Rheumatology (Oxford), 2003. 42(4): p. 583-590.

58. Kim, N., et al., Diverse roles of the tumor necrosis factor family member TRANCE in skeletal physiology revealed by TRANCE deficiency and partial rescue by a lymphocyte-expressed TRANCE transgene. Proc. Natl. Acad. Sci. U.S.A., 2000. 97(20): p. 10905-10910.

59. Dankbar, B., et al., Myostatin is a direct regulator of osteoclast differentiation and its inhibition reduces inflammatory joint destruction in mice. Nat. Med., 2015. 21(9): p. 1085-1090.

60. Di Munno, O. and F. Ferro, The effect of biologic agents on bone homeostasis in chronic inflammatory rheumatic diseases. Clin. Exp. Rheumatol. 37(3): p. 502-507.

61. Hoffmann, D.B., et al., In Vivo siRNA Delivery Using JC Virus-like Particles Decreases the Expression of RANKL in Rats. Mol Ther Nucleic Acids, 2016. 5: p. e298.

62. Wei, C.M., et al., Monocrotaline Suppresses RANKL-Induced Osteoclastogenesis In Vitro and Prevents LPS-Induced Bone Loss In Vivo. Cell. Physiol. Biochem., 2018. 48(2): p. 644-656.

63. Zochling, J., Measures of symptoms and disease status in ankylosing spondylitis: Ankylosing Spondylitis Disease Activity Score (ASDAS), Ankylosing Spondylitis Quality of Life Scale (ASQoL), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), Bath Ankylosing Spondylitis Global Score (BAS-G), Bath Ankylosing Spondylitis Metrology Index (BASMI), Dougados Functional Index (DFI), and Health Assessment Questionnaire for the Spondylarthropathies (HAQ-S). Arthritis Care Res (Hoboken), 2011. 63 Suppl 11: p. S47-58.

64. Watad, A., et al., The Early Phases of Ankylosing Spondylitis: Emerging Insights From Clinical and Basic Science. Front Immunol, 2018. 9: p. 2668.

65. Klingberg, E., et al., Biomarkers of bone metabolism in ankylosing spondylitis in relation to osteoproliferation and osteoporosis. J. Rheumatol., 2014. 41(7): p. 1349-56.

66. Wan, X., et al., Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol, 2014. 14: p. 135.