Causal Relationship Between Systemic Lupus Erythematosus and Osteoporosis: A Mendelian Randomization Analysis

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Causal Relationship Between Systemic Lupus Erythematosus and Osteoporosis: A Mendelian Randomization Analysis

https://doi.org/10.21203/rs.3.rs-3228370/v1

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Background

Systemic Lupus Erythematosus (SLE) and Osteoporosis are two prevalent medical conditions. Previous studies have suggested a possible correlation between SLE and osteoporosis, though the underpinning causal relationship remains largely unknown. The current study aimed to elucidate the causal association between SLE and osteoporosis by employing a Mendelian randomization (MR) approach.

Methods

We performed two-sample MR analysis using the inverse variance-weighted (IVW), weighted median, and MR-Egger methods on publicly available summary statistics datasets using a SLE genome-wide association study (GWAS) as an exposure and osteoporosis GWASs in people with East Asia ancestry as outcomes. The pleiotropy and heterogeneity were examined using a variety of techniques, including the MR-Egger intercept, the MR-PRESSO approach, and the Cochran’s Q test.

Results

We selected 74 single-nucleotide polymorphisms from a SLE GWAS as instrumental variables for osteoporosis. The IVW (p < 0.05) and weighted median (p < 0.05) method results support a causal association between SLE and osteoporosis. MR-Egger intercept (p = 0.3474) and MR-PRESSO global test (p = 0.3285) did not suggest evidence of horizontal or directional pleiotropy. Cochran’s Q test (p = 0.3027) showed that there was no heterogeneity between IVs.

Conclusion

This MR analysis provides insight into the potential causal relationship between SLE and osteoporosis, emphasizing the potential contribution of SLE-associated genetic variants to osteoporosis risk.

Mendelian randomization

Systemic lupus erythematosus

Osteoporosis

Causal relationship

Systemic lupus erythematosus (SLE) is a complex autoimmune disorder characterized by the production of autoantibodies and multi-organ involvement[1]. Patients with SLE present with varying clinical manifestations, including musculoskeletal, renal, cardiovascular, and neuropsychiatric symptoms. The prevalence of SLE varies globally, with higher rates reported in African Americans, Hispanics, and Asians than in Caucasians[2–4]. Over the years, the management of SLE has improved significantly, increasing the life expectancy of affected individuals[5, 6]. However, SLE patients still face numerous challenges, such as the increased risk of developing secondary complications like osteoporosis.

Osteoporosis, a skeletal disorder characterized by compromised bone strength and an increased risk of fractures, is a major public health concern worldwide[7]. The association between SLE and osteoporosis has been studied extensively and there is evidence suggesting an increased risk of osteoporosis in patients with SLE[8, 9]. Recently, Orsolini et al.[10] reported that osteoporosis and fractures are frequently found in patients with SLE and Lai et al.[11] showed that patients with SLE had a higher incidence of osteoporosis. Possible factors contributing to low bone mineral density in SLE patients include chronic inflammation, glucocorticoid therapy, and the presence of various cytokines produced by immune cells that interfere with bone homeostasis. Lifestyle factors and low vitamin D levels in SLE patients may further contribute to the risk of osteoporosis[12, 13]. Nevertheless, the causal relationship between these two conditions remains largely unknown, and understanding this connection is crucial for effective prevention and management strategies.

Mendelian randomization (MR) is a sophisticated statistical technique that exploits genetic variants as instrumental variables (IVs) to make causal inferences from observational data[14]. By using genetic instruments, MR analysis reduces the potential for confounding and reverse causation, making it a valuable approach for assessing causal relationships between medical conditions and risk factors[15].

Considering the complex interplay between SLE and osteoporosis and the numerous modifiable risk factors involved, assessing the causal relationship between these two medical conditions is vital for better understanding and management of patients. This study examined this puzzle by conducting an MR analysis to investigate the causal relationship between SLE and osteoporosis.

Study Design

A two-sample MR study design was used to investigate the causal relationship between SLE as the exposure and osteoporosis as the outcome. This research design is based on the IV method, which uses genetic variants as proxies for the exposure of interest to make unbiased inferences about the causal effects on the outcome. The flowchart was presented in Fig. 1.

Data Sources and Genetic Instruments

We obtained summary-level genome-wide association study (GWAS) data for SLE and Osteoporosis from publicly available sources. In choosing the exposure and outcome GWAS datasets, we comprehensively considered factors such as population origin, larger sample sizes, more extensive single-nucleotide polymorphism (SNP) coverage, balanced gender composition, and data publication date. For SLE, we used the largest available GWAS, which included 12653 samples and 5691661 SNPs from East Asian(https://gwas.mrcieu.ac.uk/), with the ID number of “ebi-a-GCST90011866”.For Osteoporosis, we used GWAS data with 212453 samples and 8885805 SNPs from East Asian (https://gwas.mrcieu.ac.uk/), with the ID number of “bbj-a-137”.

The genetic instruments (or SNPs) were selected based on their strong association with SLE and osteoporosis at a genome-wide significance threshold (p-value < 5 × 10^− 6). Only independent SNPs were retained, after accounting for linkage disequilibrium (r² < 0.001) and clumping (windows size = 10,000 kb) to minimize potential bias due to pleiotropy. The F statistic is calculated as F = R² (n-2)/[(1-R²)], R² = 2×(1-eaf)×eaf×beta². The F statistic of IV should be greater than 10. In this formula, eaf refers to the effect allele frequency, beta refers to the estimated effect of SNP, and n refers to the sample size of the GWAS. In addition, we remove the SNP directly associated with the outcome(such as bone mineral density, vitamin D, calcium, et al.) by searching on the PhenenoScanner website (https://www.phenoscanner.medschl.cam.ac.uk/).

Data Harmonization

The selected SNP data were harmonized between the SLE and osteoporosis datasets through extraction and alignment of effect estimates and standard errors. SNPs were checked for strand alignment, and any palindromic SNPs with intermediate allele frequencies were excluded to avoid any potential ambiguity.

Mendelian Randomization Analysis

We used two-sample MR analysis to assess the causal effect of SLE on osteoporosis using the selected SNPs as IVs. For an MR study to provide accurate and reliable estimates, three key assumptions need to be satisfied:

1.Relevance Assumption: The genetic variants used as instruments (SNPs) should be strongly associated with the exposure of interest.
2.Independence Assumption: The genetic variants should be independent of any confounders - variables that are related to both the exposure and the outcome.
3.Exclusion-Restriction Assumption: The genetic variants only affect the outcome through their influence on the exposure, and not through other pathways. This is also known as the absence of pleiotropic effects where the genetic variants (SNPs) cannot directly affect the outcome and do not share any causal pathways with the outcome.[16].

We examined different MR methods, including inverse-variance weighted (IVW), weighted median, and MR Egger, each catering to different assumptions and methodological strengths[17]. IVW was our method of choice, while MR-Egger and weighted median were used to improve the IVW estimates, to verify the robustness of our findings[18].

Heterogeneity among the genetic instruments was assessed using Cochran’s Q statistic with the corresponding p-value, and potential pleiotropy was evaluated with the MR-Egger intercept test[19, 20]. The presence of outliers or influential SNPs was appraised through leave-one-out analyses. All analyses were performed using the TwoSampleMR (version 0.5.6) package in R (version 4.3)[21].

Ethical Considerations

As this study used publicly available summary-level genome-wide association study data, no additional ethics approval was required.

After removing the IVs with linkage disequilibrium and the SNP directly associated with osteoporosis, 74 SNPs were obtained in this study (Details showed in supplementary file). The F-statistic for the SNP exceeds 10.

In the IVW analysis, there was an evidence to support a significant causal association between SLE and risk of osteoporosis (OR = 1.0434, SE = 0.0113, p = 0.0002). At the same time, the analysis of weighted median support the above conclusion (OR = 1.0372, SE = 0.0162, p = 0.0239). The MR-Egger approach (OR = 1.0155, SE = 0.0309, p = 0.6211), is aims to provide an estimate of the causal effect while allowing for possible pleiotropy, which is when genetic variants affect the outcome through multiple pathways, not only the one you are interested in. The results were shown in Table 2 and Fig. 2. A p-value of > 0.05 from MR Egger suggests that the causal relationship is not significant after taking into consideration potential pleiotropic effects. In the subsequent sensitivity analysis, the MR Egger intercept (intercept = 0.0064, SE = 0.0068, p = 0.3474), MR-PRESSO global test (p = 0.3285) did not suggest evidence of horizontal or directional pleiotropy. The above analysis revealed that pleiotropy was unlikely to be biasing the results. Meanwhile, Cochran’s Q test for heterogeneity showed that there was no heterogeneity between IVs(Cochran’s Q = 78.72, p = 0.3027) (Table 3).

Table 1

Causal efect between SLE and osteoporosis by diferent MR analysis methods
Exposure	Outcome	method	nSNP	OR	95% CI		SE	p value
SLE	Osteoporosis	Inverse variance weighted	74	1.0434	1.0206	1.0669	0.0113	0.0002
SLE	Osteoporosis	Weighted median	74	1.0372	1.0039	1.0716	0.0162	0.0239
SLE	Osteoporosis	MR Egger	74	1.0155	0.9558	1.0789	0.0309	0.6211
SLE, Systemic lupus erythematosus; SNP, Single nucleotide polymorphism; SE, Standard Error; MR, Mendelian randomization.

Table 2

Heterogeneity test of MR
Heterogeneity test	Q	df	Q_P Value
MR egger	77.76	72	0.3005
IVW	78.72	73	0.3027
MR, Mendelian randomization; IVW, inverse variance-weighted.

The “leave-one-out” sensitivity indicated that there was no single SNP to influence the causal effect between SLE and osteoporosis (Fig. 3). Furthermore, the funnel plot was symmetry, indicating no pleiotropy (Fig. 4).

Therefore, the MR analysis results support a potential causal association between SLE and osteoporosis.

To our knowledge, this is the first study that has used the MR approach to investigate the causal relationship between SLE and osteoporosis. Our results provide valuable insights into the association between these two complex diseases and may have significant implications for their prevention and treatment strategies.

First, it is essential to recognize that both SLE and osteoporosis are multifactorial diseases influenced by a multitude of genetic and environmental factors. Consequently, it is challenging to establish a clear causal relationship using conventional observational studies, which can be impacted by confounding and reverse causation. MR analysis offers a robust alternative by using genetic instruments as proxies for the exposure of interest, thereby minimizing biases and providing a more reliable assessment of causality[16].

The results of our MR analysis provide evidence for a potential causal relationship between SLE and an increased risk of osteoporosis. This supports the notion that the autoimmune-mediated inflammation characteristic of SLE may have a direct impact on bone metabolism and remodeling processes, potentially leading to the development of osteoporosis. Several possible biological mechanisms may underlie this causative relationship. One possible explanation for the link between SLE and osteoporosis is that chronic inflammation, a hallmark of SLE, can lead to bone loss[22]. Inflammation can activate bone-resorbing cytokines, which stimulate the activity of osteoclasts (cells that break down bone tissue) and inhibit the activity of osteoblasts (cells that build new bone tissue). This results in a net loss of bone mass over time, leading to a higher risk of osteoporosis. Several studies have reported that individuals with SLE have higher inflammatory cytokine levels than healthy controls, supporting this theory[22, 23]. Another potential mechanism is the use of corticosteroids in the management of SLE[24]. Corticosteroids are frequently used to control inflammation in SLE patients, but long-term use of these drugs increases the risk of osteoporosis[25]. Corticosteroids can interfere with the normal balance of bone remodeling, leading to decreased bone formation and increased bone resorption[26]. Moreover, renal involvement in SLE might contribute to secondary hyperparathyroidism, increased osteoclastic bone resorption, and reduced synthesis of 1,25(OH)2D, ultimately leading to a reduction in bone mass[27–31]. Lastly, limitations in physical activity and sunlight exposure among SLE patients could lead to reduced bone mineral density. These are all important mechanisms underlying the link between SLE and osteoporosis[32, 33]. However, more research is needed to elucidate the complex interactions among these factors and to develop more effective strategies for managing and preventing osteoporosis in SLE patients.

Given the potential causality between SLE and osteoporosis identified here, it is imperative that clinicians and researchers be aware of this connection. Early identification of osteoporosis risk in SLE patients allows healthcare providers to monitor and implement preventive measures against the development of this condition[34, 35]. Preventative measures, such as promoting adequate calcium and vitamin D intake[36], encouraging regular weight-bearing exercise, and considering the use of bone-protecting medications, such as bisphosphonates and denosumab[37], may be crucial in mitigating the increased risk of osteoporosis in SLE patients[38].

Despite the strengths of MR analysis, some limitations should be acknowledged. First, potential pleiotropy, or the influence of genetic variants on multiple traits, may affect the validity of the causal association identified. Second, the extent to which our findings may be generalizable across diverse populations is unclear. As the genetic predisposition to SLE and osteoporosis may vary by race, ethnicity, and genetic background, replication in diverse cohorts is essential to determine the generalizability and robustness of our findings. Future MR studies incorporating more diverse populations are needed to validate these associations.

In conclusion, our MR analysis provides evidence supporting a potential causal relationship between SLE and osteoporosis. Our findings highlight the need for increased awareness regarding the potential risk of osteoporosis among SLE patients and recommend monitoring bone health as a part of routine SLE management. Further studies should refine genetic instruments, explore potential pleiotropic effects, and validate the findings in diverse populations.

Conflict of Interest: DanfengXu and Bing Wu declare that there is no conflict of interest.

Funding: Keqiao Clinical Funding 2022KZ71.

Ethics approval: All data are publicly available and ethical approval was not required.

Data Availability: The GWAS summary datasets for SLE (GWAS ID: ebi-a-GCST9001186) and osteoporosis (GWAS ID: bbj-a-137) are available through the ieu open gwas project (https://gwas.mrcieu.ac.uk/datasets).

Author contributions: Study conception and design: Bing Wu; data analyses: Danfeng Xu; draft preparation: Danfeng Xu; supervision of the study: Bing Wu; All authors reviewed the manuscript.

Acknowledgements: This study was supported by Keqiao Clinical Funding (2022KZ71). We thank textcheck.com for its linguistic assistance during the preparation of this manuscript (http://www.textcheck.com/certificate/pkAKMh).

Kiriakidou M, Ching CL. Systemic Lupus Erythematosus. Annals Of Internal Medicine. 2020;172:ITC81-ITC94. DOI 10.7326/aitc202006020
Rees F, Doherty M, Grainge MJ, Lanyon P, Zhang W. The worldwide incidence and prevalence of systemic lupus erythematosus: a systematic review of epidemiological studies. Rheumatology. 2017;56:1945-1961. DOI 10.1093/rheumatology/kex260
Lao C, White D, Rabindranath K, Van Dantzig P, Foxall D, Aporosa A, Lawrenson R. Incidence and prevalence of systemic lupus erythematosus in New Zealand from the national administrative datasets. Lupus. 2023;9612033231182203-9612033231182203. DOI 10.1177/09612033231182203
Stojan G, Petri M. Epidemiology of systemic lupus erythematosus: an update. Current opinion in rheumatology. 2018;30:144-150. DOI 10.1097/bor.0000000000000480
Fava A, Petri M. Systemic lupus erythematosus: Diagnosis and clinical management. Journal Of Autoimmunity. 2019;96:1-13. DOI 10.1016/j.jaut.2018.11.001
Lazar S, Kahlenberg JM. Systemic Lupus Erythematosus: New Diagnostic and Therapeutic Approaches. Annual Review Of Medicine. 2023;74:339-352. DOI 10.1146/annurev-med-043021-032611
Liu J, Curtis EM, Cooper C, Harvey NC. State of the art in osteoporosis risk assessment and treatment. Journal Of Endocrinological Investigation. 2019;42:1149-1164. DOI 10.1007/s40618-019-01041-6
Kim CS, Han K-D, Jung JH, Choi HS, Bae EH, Ma SK, Kim SW. Incidence and risk factors for osteoporotic fractures in patients with systemic lupus erythematosus versus matched controls. Korean Journal Of Internal Medicine. 2021;36:154-+. DOI 10.3904/kjim.2018.378
Tedeschi SK, Kim SC, Guan H, Grossman JM, Costenbader KH. Comparative Fracture Risks Among United States Medicaid Enrollees With and Those Without Systemic Lupus Erythematosus. Arthritis & Rheumatology. 2019;71:1141-1146. DOI 10.1002/art.40818
Orsolini G, Bultink IEM, Adami G, Fassio A, Viapiana O, Giollo A, Gatti D, Rossini M. Bone health, an often forgotten comorbidity in systemic lupus erythematosus: a comment on the new recommendations. Annals Of the Rheumatic Diseases. 2020;79. DOI 10.1136/annrheumdis-2019-215896
Lai S-W, Kuo Y-H, Liao K-F. Bone health in patients with systemic lupus erythematosus. Annals Of the Rheumatic Diseases. 2021;80. DOI 10.1136/annrheumdis-2019-216417
Ciobica ML, Ionescu OP, Sandulescu BA. Osteoporosis and the fracture risk in systemic lupus erythematosus. Romanian Journal Of Military Medicine. 2020;123:341-347
Nakajima T, Doi H, Watanabe R, Murata K, Takase Y, Inaba R, Itaya T, Iwasaki T, Shirakashi M, Tsuji H, Kitagori K, Akizuki S, Nakashima R, Onishi A, Yoshifuji H, Tanaka M, Ito H, Hashimoto M, Ohmura K, Morinobu A. Factors associated with osteoporosis and fractures in patients with systemic lupus erythematosus: Kyoto Lupus Cohort. Modern Rheumatology. 2023.DOI 10.1093/mr/road014
Larsson SC. Mendelian randomization as a tool for causal inference in human nutrition and metabolism. Current opinion in lipidology. 2021;32:1-8. DOI 10.1097/mol.0000000000000721
Emdin CA, Khera AV, Kathiresan S. Mendelian Randomization. Jama-Journal Of the American Medical Association. 2017;318:1925-1926. DOI 10.1001/jama.2017.17219
Burgess S, Scott RA, Timpson NJ, Smith GD, Thompson SG, Epic-Interact C. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. European Journal Of Epidemiology. 2015;30:543-552. DOI 10.1007/s10654-015-0011-z
Bowden J, Del Greco FM, Minelli C, Smith GD, Sheehan NA, Thompson JR. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I-2 statistic. International Journal Of Epidemiology. 2016;45:1961-1974. DOI 10.1093/ije/dyw220
Burgess S, Butterworth A, Thompson SG. Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data. Genetic Epidemiology. 2013;37:658-665. DOI 10.1002/gepi.21758
Davies NM, Holmes MV, Smith GD. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. Bmj-British Medical Journal. 2018;362. DOI 10.1136/bmj.k601
Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature Genetics. 2018;50:693-+. DOI 10.1038/s41588-018-0099-7
Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. International Journal Of Epidemiology. 2017;46:1734-1739. DOI 10.1093/ije/dyx034
Coury F, Peyruchaud O, Machuca-Gayet I. Osteoimmunology of Bone Loss in Inflammatory Rheumatic Diseases. Frontiers In Immunology. 2019;10. DOI 10.3389/fimmu.2019.00679
Bultink IEM. Bone Disease in Connective Tissue Disease/Systemic Lupus Erythematosus. Calcified tissue international. 2018;102:575-591. DOI 10.1007/s00223-017-0322-z
Cain DW, Cidlowski JA. Immune regulation by glucocorticoids. Nature Reviews Immunology. 2017;17:233-247. DOI 10.1038/nri.2017.1
Quatrini L, Ricci B, Ciancaglini C, Tumino N, Moretta L. Regulation of the Immune System Development by Glucocorticoids and Sex Hormones. Frontiers In Immunology. 2021;12. DOI 10.3389/fimmu.2021.672853
Humphrey MB, Buckley L. Glucocorticoid-Induced Osteoporosis. New England Journal Of Medicine. 2019;380:1378-1379. DOI 10.1056/NEJMc1901239
Adami G, Fassio A, Rossini M, Caimmi C, Giollo A, Orsolini G, Viapiana O, Gatti D. Osteoporosis in Rheumatic Diseases. International journal of molecular sciences. 2019;20. DOI 10.3390/ijms20235867
Bastian HM, Roseman JM, McGwin G, Alarcon GS, Friedman AW, Fessler BJ, Baethge BA, Reveille JD, Grp LS. Systemic lupus erythematosus in three ethnic groups. XII. Risk factors for lupus nephritis after diagnosis. Lupus. 2002;11:152-160. DOI 10.1191/0961203302lu158oa
Wakasugi D, Gono T, Kawaguchi Y, Hara M, Koseki Y, Katsumata Y, Hanaoka M, Yamanaka H. Frequency of Class III and IV Nephritis in Systemic Lupus Erythematosus Without Clinical Renal Involvement: An Analysis of Predictive Measures. Journal Of Rheumatology. 2002;39:79-85. DOI 10.3899/jrheum.110532
Shi Y, Liu T, Yao L, Xing Y, Zhao X, Fu J, Xue X. Chronic vitamin D deficiency induces lung fibrosis through activation of the renin-angiotensin system. Scientific reports. 2017;7. DOI 10.1038/s41598-017-03474-6
Ruiz-Irastorza G, Egurbide MV, Olivares N, Martinez-Berriotxoa A, Aguirre C. Vitamin D deficiency in systemic lupus erythematosus: prevalence, predictors and clinical consequences. Rheumatology. 2008;47:920-923. DOI 10.1093/rheumatology/ken121
Kuhn A, Gensch K, Haust M, Meuth A-M, Boyer F, Dupuy P, Lehmann P, Metze D, Ruzicka T. Photoprotective effects of a broad-spectrum sunscreen in ultraviolet-induced cutaneous lupus erythematosus: A randomized, vehicle-controlled, double-blind study. Journal Of the American Academy Of Dermatology. 2011;64:37-48. DOI 10.1016/j.jaad.2009.12.053
Sharif K, Watad A, Bragazzi NL, Lichtbroun M, Amital H, Shoenfeld Y. Physical activity and autoimmune diseases: Get moving and manage the disease. Autoimmunity Reviews. 2018;17:53-72. DOI 10.1016/j.autrev.2017.11.010
Brance ML, Larroude MS, Zamora NV, Bagur A, Graf CE, Giacoia E, Diehl M, Somma LF, Schneeberger E, Salerni HH, Rey P, Varsavsky M, Gonzalez DC, Massari F, Sanchez JA, Galich AM, Berbotto G, Garcia ML, Zanchetta MB, Martinez Munoz A, Chavero I, Ramirez Stieben LA, Maher MC, Abdala R, Perez B, De La Vega MC, Mansur JL, Brun LR. Argentine Guidelines for the Prevention and Treatment of Glucocorticoid-Induced Osteoporosis in Postmenopausal Women and Men Aged 50 Years and Older. Journal of clinical rheumatology : practical reports on rheumatic & musculoskeletal diseases. 2023. DOI 10.1097/rhu.0000000000001951
Gregson CL, Armstrong DJ, Bowden J, Cooper C, Edwards J, Gittoes NJL, Harvey N, Kanis J, Leyland S, Low R, McCloskey E, Moss K, Parker J, Paskins Z, Poole K, Reid DM, Stone M, Thomson J, Vine N, Compston J. UK clinical guideline for the prevention and treatment of osteoporosis. Archives of osteoporosis. 2022;17. DOI 10.1007/s11657-022-01061-5
Gazitt T, Feld J, Zisman D. Implementation of Calcium and Vitamin D Supplementation in Glucocorticosteroid-Induced Osteoporosis Prevention Guidelines- Insights from Rheumatologists. Rambam Maimonides Medical Journal. 2022;14. DOI 10.5041/rmmj.10497
Gazoni FM, Civile VT, Atallah AN, Santos FC, Trevisani VFM. Intravenous zoledronate for postmenopausal women with osteopenia and osteoporosis: a systematic review and metanalysis. Sao Paulo Medical Journal. 2023;141. DOI 10.1590/1516-3180.2022.0480.r1.27032023
White L. Osteoporosis Prevention, Screening, and Diagnosis: ACOG Recommendations. American Family Physician. 2022;106:587-588

No competing interests reported.

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Causal Relationship Between Systemic Lupus Erythematosus and Osteoporosis: A Mendelian Randomization Analysis

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods

Study Design

Data Sources and Genetic Instruments

Data Harmonization

Mendelian Randomization Analysis

Ethical Considerations

Results

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

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