Genetic Variants in RANK and OPG Could Influence Disease Severity and Bone Remodeling in Patients with Early Arthritis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Determination of Anti-Citrullinated Cyclic Peptide Antibodies (ACPAs)
2.3. Serum RANKL, OPG and IL-6 Measurements
2.4. Genotyping
2.5. Variables
2.6. Statistical Analysis
Multivariate Analyses
3. Results
3.1. Screening of SNPs in Bone Metabolism Genes Related to Relevant Clinical Parameters in RA
3.2. SNPs in the OPG Gene Are Associated with Serum OPG Levels and Predict Disease Activity after Two Years of Follow-Up
3.3. The SNP rs4355801, Located near the TNFRSF11B Gene, Is Associated with Good Prognosis and Higher OPG Levels during Follow-Up
3.4. Combined Genotypic Model of SNPs Related to TNFRSF11B Gene Shows Significant Association with Bone Mass Gain and Serum OPG Levels during Follow-Up
3.5. The CC-rs1805034 Genotype of the RANK Gene Is Associated with Low BMD Values and High IL-6 Levels at the Baseline Visit
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Smolen, J.S.; Aletaha, D.; McInnes, I.B. Rheumatoid Arthritis. Lancet 2016, 388, 2023–2038. [Google Scholar] [CrossRef] [PubMed]
- McInnes, I.B.; Schett, G. Pathogenetic Insights from the Treatment of Rheumatoid Arthritis. Lancet 2017, 389, 2328–2337. [Google Scholar] [CrossRef]
- Barturen, G.; Beretta, L.; Cervera, R.; Van Vollenhoven, R.; Alarcón-Riquelme, M.E. Moving towards a Molecular Taxonomy of Autoimmune Rheumatic Diseases. Nat. Rev. Rheumatol. 2018, 14, 75–93. [Google Scholar] [CrossRef]
- Arron, J.R.; Choi, Y. Bone versus Immune System. Nature 2000, 408, 535–536. [Google Scholar] [CrossRef]
- Auréal, M.; Machuca-Gayet, I.; Coury, F. Rheumatoid Arthritis in the View of Osteoimmunology. Biomolecules 2020, 11, 48. [Google Scholar] [CrossRef]
- David, J.-P. Osteoimmunology: A View from the Bone. Adv. Immunol. 2007, 95, 149–165. [Google Scholar] [CrossRef] [PubMed]
- Danks, L.; Takayanagi, H. Immunology and Bone. J. Biochem. 2013, 154, 29–39. [Google Scholar] [CrossRef] [PubMed]
- Arboleya, L.; Castañeda, S. Osteoimmunology: The Study of the Relationship between the Immune System and Bone Tissue. Reumatol. Clin. 2013, 9, 303–315. [Google Scholar] [CrossRef]
- Okamoto, K.; Nakashima, T.; Shinohara, M.; Negishi-Koga, T.; Komatsu, N.; Terashima, A.; Sawa, S.; Nitta, T.; Takayanagi, H. Osteoimmunology: The Conceptual Framework Unifying the Immune and Skeletal Systems. Physiol. Rev. 2017, 97, 1295–1349. [Google Scholar] [CrossRef]
- Llorente, I.; García-Castañeda, N.; Valero, C.; González-Álvaro, I.; Castañeda, S. Osteoporosis in Rheumatoid Arthritis: Dangerous Liaisons. Front. Med. 2020, 7, 601618. [Google Scholar] [CrossRef]
- Yang, M.; Zhu, L. Osteoimmunology: The Crosstalk between T Cells, B Cells, and Osteoclasts in Rheumatoid Arthritis. Int. J. Mol. Sci. 2024, 25, 2688. [Google Scholar] [CrossRef] [PubMed]
- Tsukasaki, M.; Takayanagi, H. Osteoimmunology: Evolving Concepts in Bone-Immune Interactions in Health and Disease. Nat. Rev. Immunol. 2019, 19, 626–642. [Google Scholar] [CrossRef] [PubMed]
- Noh, J.-Y.; Yang, Y.; Jung, H. Molecular Mechanisms and Emerging Therapeutics for Osteoporosis. Int. J. Mol. Sci. 2020, 21, 7623. [Google Scholar] [CrossRef] [PubMed]
- Nakashima, T.; Takayanagi, H. Osteoimmunology: Crosstalk between the Immune and Bone Systems. J. Clin. Immunol. 2009, 29, 555–567. [Google Scholar] [CrossRef]
- Takegahara, N.; Kim, H.; Choi, Y. RANKL Biology. Bone 2022, 159, 116353. [Google Scholar] [CrossRef]
- Gupta, N.; Kanwar, N.; Arora, A.; Khatri, K.; Kanwal, A. The Interplay of Rheumatoid Arthritis and Osteoporosis: Exploring the Pathogenesis and Pharmacological Approaches. Clin. Rheumatol. 2024, 43, 1421–1433. [Google Scholar] [CrossRef] [PubMed]
- Okada, Y.; Wu, D.; Trynka, G.; Raj, T.; Terao, C.; Ikari, K.; Kochi, Y.; Ohmura, K.; Suzuki, A.; Yoshida, S.; et al. Genetics of Rheumatoid Arthritis Contributes to Biology and Drug Discovery. Nature 2014, 506, 376–381. [Google Scholar] [CrossRef]
- Wielińska, J.; Kolossa, K.; Świerkot, J.; Dratwa, M.; Iwaszko, M.; Bugaj, B.; Wysoczańska, B.; Chaszczewska-Markowska, M.; Jeka, S.; Bogunia-Kubik, K. Polymorphisms within the RANK and RANKL Encoding Genes in Patients with Rheumatoid Arthritis: Association with Disease Progression and Effectiveness of the Biological Treatment. Arch. Immunol. Ther. Exp. 2020, 68, 24. [Google Scholar] [CrossRef]
- Sakthiswary, R.; Uma Veshaaliini, R.; Chin, K.Y.; Das, S.; Sirasanagandla, S.R. Pathomechanisms of bone loss in rheumatoid arthritis. Front. Med. 2022, 9, 962969. [Google Scholar] [CrossRef]
- Zhou, A.; Wu, B.; Yu, H.; Tang, Y.; Liu, J.; Jia, Y.; Yang, X.; Xiang, L. Current Understanding of Osteoimmunology in Certain Osteoimmune Diseases. Front. Cell Dev. Biol. 2021, 9, 698068. [Google Scholar] [CrossRef]
- Mohamed, R.H.; Mohamed, R.H.; El-Shahawy, E.E. Relationship Between RANK and RANKL Gene Polymorphisms with Osteoporosis in Rheumatoid Arthritis Patients. Genet. Test. Mol. Biomark. 2016, 20, 249–254. [Google Scholar] [CrossRef]
- Assmann, G.; Koenig, J.; Pfreundschuh, M.; Epplen, J.T.; Kekow, J.; Roemer, K.; Wieczorek, S. Genetic variations in genes encoding RANK, RANKL, and OPG in rheumatoid arthritis: A case-control study. J. Rheumatol. 2010, 37, 900–904. [Google Scholar] [CrossRef] [PubMed]
- Arnett, F.C.; Edworthy, S.M.; Bloch, D.A.; McShane, D.J.; Fries, J.F.; Cooper, N.S.; Healey, L.A.; Kaplan, S.R.; Liang, M.H.; Luthra, H.S. The American Rheumatism Association 1987 Revised Criteria for the Classification of Rheumatoid Arthritis. Arthritis Rheum. 1988, 31, 315–324. [Google Scholar] [CrossRef] [PubMed]
- Aletaha, D.; Neogi, T.; Silman, A.J.; Funovits, J.; Felson, D.T.; Bingham, C.O.; Birnbaum, N.S.; Burmester, G.R.; Bykerk, V.P.; Cohen, M.D.; et al. 2010 Rheumatoid Arthritis Classification Criteria: An American College of Rheumatology/European League Against Rheumatism Collaborative Initiative. Ann. Rheum. Dis. 2010, 69, 1580–1588. [Google Scholar] [CrossRef]
- Verpoort, K.N.; van Dongen, H.; Allaart, C.F.; Toes, R.E.M.; Breedveld, F.C.; Huizinga, T.W.J. Undifferentiated Arthritis—Disease Course Assessed in Several Inception Cohorts. Clin. Exp. Rheumatol. 2004, 22, S12–S17. [Google Scholar]
- Wagner, D.; Fahrleitner-Pammer, A. Levels of Osteoprotegerin (OPG) and Receptor Activator for Nuclear Factor Kappa B Ligand (RANKL) in Serum: Are They of Any Help? Wien. Med. Wochenschr. 2010, 160, 452–457. [Google Scholar] [CrossRef]
- González-Álvaro, I.; Ortiz, A.M.; Alvaro-Gracia, J.M.; Castañeda, S.; Díaz-Sánchez, B.; Carvajal, I.; García-Vadillo, J.A.; Humbría, A.; López-Bote, J.P.; Patiño, E.; et al. Interleukin 15 Levels in Serum May Predict a Severe Disease Course in Patients with Early Arthritis. PLoS ONE 2011, 6, e29492. [Google Scholar] [CrossRef] [PubMed]
- Prevoo, M.L.; van ’t Hof, M.A.; Kuper, H.H.; van Leeuwen, M.A.; van de Putte, L.B.; van Riel, P.L. Modified Disease Activity Scores That Include Twenty-Eight-Joint Counts. Development and Validation in a Prospective Longitudinal Study of Patients with Rheumatoid Arthritis. Arthritis Rheum. 1995, 38, 44–48. [Google Scholar] [CrossRef]
- Castrejón, I.; Carmona, L.; Ortiz, A.M.; Belmonte, M.A.; Martínez-López, J.A.; González-Álvaro, I. Development and Validation of a New Disease Activity Index as a Numerical Sum of Four Variables in Patients with Early Arthritis. Arthritis Care Res. 2013, 65, 518–525. [Google Scholar] [CrossRef]
- Gonzalez-Alvaro, I.; Castrejon, I.; Ortiz, A.M.; Toledano, E.; Castañeda, S.; Garcia-Vadillo, A.; Carmona, L.; EMECAR Study Group. Cut-Offs and Response Criteria for the Hospital Universitario La Princesa Index (HUPI) and Their Comparison to Widely-Used Indices of Disease Activity in Rheumatoid Arthritis. PLoS ONE 2016, 11, e0161727. [Google Scholar] [CrossRef]
- Castañeda, S.; González-Alvaro, I.; Rodríguez-Salvanés, F.; Quintana, M.L.; Laffon, A.; García-Vadillo, J.A. Reproducibility of Metacarpophalangeal Bone Mass Measurements Obtained by Dual-Energy X-Ray Absorptiometry in Healthy Volunteers and Patients with Early Arthritis. J. Clin. Densitom. 2007, 10, 298–305. [Google Scholar] [CrossRef] [PubMed]
- Llorente, I.; Merino, L.; Escolano, E.; Quintanilla, D.M.; García-Vadillo, J.A.; González-Álvaro, I.; Castañeda, S. Reproducibility of Metacarpal Bone Mineral Density Measurements Obtained by Dual-Energy X-Ray Absorptiometry in Healthy Volunteers and Patients With Early Arthritis. J. Clin. Densitom. 2020, 23, 678–684. [Google Scholar] [CrossRef] [PubMed]
- Llorente, I.; Merino, L.; Ortiz, A.M.; Escolano, E.; González-Ortega, S.; García-Vicuña, R.; García-Vadillo, J.A.; Castañeda, S.; González-Álvaro, I. Anti-Citrullinated Protein Antibodies Are Associated with Decreased Bone Mineral Density: Baseline Data from a Register of Early Arthritis Patients. Rheumatol. Int. 2017, 37, 799–806. [Google Scholar] [CrossRef]
- Ziolkowska, M.; Kurowska, M.; Radzikowska, A.; Luszczykiewicz, G.; Wiland, P.; Dziewczopolski, W.; Filipowicz-Sosnowska, A.; Pazdur, J.; Szechinski, J.; Kowalczewski, J.; et al. High Levels of Osteoprotegerin and Soluble Receptor Activator of Nuclear Factor Kappa B Ligand in Serum of Rheumatoid Arthritis Patients and Their Normalization after Anti-Tumor Necrosis Factor Alpha Treatment. Arthritis Rheum. 2002, 46, 1744–1753. [Google Scholar] [CrossRef] [PubMed]
- Richards, J.B.; Rivadeneira, F.; Inouye, M.; Pastinen, T.M.; Soranzo, N.; Wilson, S.G.; Andrew, T.; Falchi, M.; Gwilliam, R.; Ahmadi, K.R.; et al. Bone Mineral Density, Osteoporosis, and Osteoporotic Fractures: A Genome-Wide Association Study. Lancet 2008, 371, 1505–1512. [Google Scholar] [CrossRef]
- Xu, S.; Wang, Y.; Lu, J.; Xu, J. Osteoprotegerin and RANKL in the Pathogenesis of Rheumatoid Arthritis-Induced Osteoporosis. Rheumatol. Int. 2012, 32, 3397–3403. [Google Scholar] [CrossRef]
- Yang, H.; Liu, W.; Zhou, X.; Rui, H.; Zhang, H.; Liu, R. The Association between RANK, RANKL and OPG Gene Polymorphisms and the Risk of Rheumatoid Arthritis: A Case-Controlled Study and Meta-Analysis. Biosci. Rep. 2019, 39, BSR20182356. [Google Scholar] [CrossRef]
- Wang, P.; Li, S.; Liu, L.-N.; Lv, T.-T.; Li, X.-M.; Li, X.-P.; Pan, H.-F. Circulating Osteoprotegerin Levels Are Elevated in Rheumatoid Arthritis: A Systematic Review and Meta-Analysis. Clin. Rheumatol. 2017, 36, 2193–2200. [Google Scholar] [CrossRef]
- Lacey, D.L.; Timms, E.; Tan, H.L.; Kelley, M.J.; Dunstan, C.R.; Burgess, T.; Elliott, R.; Colombero, A.; Elliott, G.; Scully, S.; et al. Osteoprotegerin Ligand Is a Cytokine That Regulates Osteoclast Differentiation and Activation. Cell 1998, 93, 165–176. [Google Scholar] [CrossRef]
- Xu, J.; Yu, L.; Liu, F.; Wan, L.; Deng, Z. The Effect of Cytokines on Osteoblasts and Osteoclasts in Bone Remodeling in Osteoporosis: A Review. Front. Immunol. 2023, 14, 1222129. [Google Scholar] [CrossRef]
Number of EA Patients | N = 268 |
---|---|
Disease onset (years), median [IQR] | 55.8 [46.2–67.8] |
Females/males, n (% of females) | 208/60 (77.6) |
Population ethnicity, n (%) | |
Western Europe (Spanish) | 260 (97) |
South America (Latin/Hispanic) | 6 (2.2) |
Eastern Europe (Slavic) | 2 (0.7) |
Smoking habit, n (%) | |
Non-smoker | 136 (50.8) |
Previous smoker | 66 (24.6) |
Current smoker | 66 (24.6) |
ACPA-positive, n (%) | 119 (44.9) |
RF-positive, n (%) | 135 (50.4) |
Diagnosis, n (%) | |
RA | 187 (69.8) |
UA | 81 (30.2) |
DAS28 baseline, median [IQR] | 4.46 [3.35–5.55] |
HUPI baseline, median [IQR] | 7 [4.5–9.5] |
HAQ baseline, median [IQR] | 0.875 [0.5–1.6] |
BMD at lumbar spine (g/cm2), median [IQR] | 0.933 [0.836–1.041] |
BMD at femoral head (g/cm2), median [IQR] | 0.739 [0.642–0.832] |
BMD at total femur (g/cm2), median [IQR] | 0.878 [0.801–0.973] |
BMD at MCP joints (g/cm2), median [IQR] | 0.265 [0.233–0.296] |
BMI (kg/m2), median [IQR] | 26.15 [23.43–29.42] |
Age menopause (years), median [IQR] | 50 [46–52] |
Remission (DAS28 < 2.6) | Low Disease Activity (DAS28: 2.6 to 3.2) | Moderate Disease Activity (DAS28 > 3.2 to 5.1) | High Disease Activity (DAS28 > 5.1) | p | |
---|---|---|---|---|---|
Number of EA patients | 99 | 39 | 73 | 10 | |
Females/males, n (% of males) | 68/31 (31.31%) | 31/8 (20.51%) | 60/13 (17.80%) | 10/0 (0%) | 0.047 |
Disease onset (years), median [IQR] | 52.3 [38.8–67.9] | 61 [52–70.5] | 55.9 [49.8–67.5] | 55.3 [51.4–62.7] | n.s. |
Smoking habit, n (%) | |||||
Non-smoker | 46 | 22 | 39 | 4 | n.s. |
Former smoker | 26 | 9 | 18 | 2 | |
Active smoker | 27 | 8 | 16 | 3 | |
ACPA-positive, n (%) | 46 | 18 | 35 | 3 | n.s. |
RF-positive, n (%) | 53 | 19 | 37 | 3 | n.s. |
Diagnosis, n (%) | |||||
RA | 64 | 30 | 57 | 7 | n.s. |
UA | 35 | 9 | 16 | 3 |
β Coeff. | [95% CI] | p-Value | ||
---|---|---|---|---|
Treatment | ||||
No treatment | Ref. | |||
MTX | −0.875 | −1.036 | −0.714 | 0.000 |
LEF | −0.721 | −0.941 | −0.501 | 0.000 |
AMs | −0.585 | −0.820 | −0.349 | 0.000 |
Anti-TNFα | −0.996 | −1.395 | −0.598 | 0.000 |
SSZ | −0.769 | −1.235 | −0.302 | 0.001 |
Diagnosis | ||||
RA | Ref. | |||
UA | −0.781 | −1.057 | −0.541 | 0.000 |
TNFRSF11B (rs10505346) | ||||
CC | Ref. | |||
CA | 0.103 | −0.153 | 0.360 | 0.429 |
AA | 0.597 | 0.027 | 1.167 | 0.040 |
β Coeff. | [95% CI] | p-Value | ||
---|---|---|---|---|
Age at disease onset | ||||
<45 years | Ref. | |||
45–65 years | 1.145 | 0.303 | 1.987 | 0.008 |
>65 years | 0.808 | −0.089 | 1.706 | 0.078 |
Diagnosis | ||||
RA | Ref. | |||
UA | −0.418 | −1.156 | 0.320 | 0.267 |
ACPAs | ||||
Negative | Ref. | |||
Positive | −0.173 | −0.822 | 0.476 | 0.602 |
TNFRSF11B (rs3134058) | ||||
GG | Ref. | |||
GA | 0.247 | −0.419 | 0.913 | 0.467 |
AA | 0.862 | 0.049 | 1.674 | 0.038 |
β Coeff. | [95% CI] | p-Value | ||
---|---|---|---|---|
Gender | ||||
Male | Ref. | |||
Female | 0.990 | 0.331 | 1.649 | 0.003 |
Age at disease onset | ||||
<45 years | Ref. | |||
45–65 years | 1.354 | 0.608 | 2.099 | 0.000 |
>65 years | 1.125 | 0.332 | 1.918 | 0.005 |
Diagnosis | ||||
RA | Ref. | |||
UA | −0.498 | −1.165 | 0.170 | 0.144 |
ACPAs | ||||
Negative | Ref. | |||
Positive | −0.376 | −0.969 | 0.218 | 0.215 |
TNFRSF11B (rs4355801) | ||||
AA | Ref. | |||
AG | −0.569 | −1.169 | 0.032 | 0.063 |
GG | −0.961 | −1.729 | −0.193 | 0.014 |
Β-Coeff. | [95% CI] | p-Value | ||
---|---|---|---|---|
Age at disease onset (years) and Gender | ||||
<45 Male | Ref. | |||
<45 Female | −0.040 | −0.075 | −0.005 | 0.023 |
45–65 Male | −0.019 | −0.057 | 0.018 | 0.314 |
45–65 Female | −0.065 | −0.097 | −0.032 | 0.000 |
>65 Male | −0.060 | −0.095 | −0.025 | 0.001 |
>65 Female | −0.122 | −0.155 | −0.089 | 0.000 |
BMI | 0.003 | 0.002 | 0.004 | 0.000 |
TNFRSF11A (rs1805034) | ||||
AA | Ref. | |||
AG | −0.012 | −0.023 | −0.001 | 0.032 |
GG | −0.017 | −0.030 | −0.003 | 0.018 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Triguero-Martínez, A.; Pardines, M.; Montes, N.; Ortiz, A.M.; de la Iglesia-Cedeira, A.; Valero-Martínez, C.; Martín, J.; González-Álvaro, I.; Castañeda, S.; Lamana, A. Genetic Variants in RANK and OPG Could Influence Disease Severity and Bone Remodeling in Patients with Early Arthritis. Life 2024, 14, 1109. https://doi.org/10.3390/life14091109
Triguero-Martínez A, Pardines M, Montes N, Ortiz AM, de la Iglesia-Cedeira A, Valero-Martínez C, Martín J, González-Álvaro I, Castañeda S, Lamana A. Genetic Variants in RANK and OPG Could Influence Disease Severity and Bone Remodeling in Patients with Early Arthritis. Life. 2024; 14(9):1109. https://doi.org/10.3390/life14091109
Chicago/Turabian StyleTriguero-Martínez, Ana, Marisa Pardines, Nuria Montes, Ana María Ortiz, Alba de la Iglesia-Cedeira, Cristina Valero-Martínez, Javier Martín, Isidoro González-Álvaro, Santos Castañeda, and Amalia Lamana. 2024. "Genetic Variants in RANK and OPG Could Influence Disease Severity and Bone Remodeling in Patients with Early Arthritis" Life 14, no. 9: 1109. https://doi.org/10.3390/life14091109