Explore the vast range of titles available.

BackgroundRecent observational research has documented that dermatomyositis (DM) has lower gut microbial diversity and a distinct taxonomic composition[1]. However, the underlying causality remains unclear.ObjectivesTo explore the potential causal associations between gut microbial genera and DM, a two-sample Mendelian randomization (MR) analysis was conducted.MethodsWe selected genetic variants associated with gut microbiota traits (n = 14,306) [2]and DM (n=213,353) from genome-wide association studies (GWAS). Two-sample MR analysis was conducted to identify potential causal gut microbial genera for DM using the inverse-variance weighted (IVW) method, weighted-median, and MR-Egger regression. The sensitivity analysis was also performed to verify the robustness of the primary results of the MR analysis. Cochran’s Q statistics were used to quantify the heterogeneity of instrumental variables[3].ResultsThree bacterial genera were associated with the risk of DM in the IVW method. Ruminococcaceae genus(id.11360) was positively associated with DM [odds ratio(OR):2.44, 95%CI:1.17-5.07], as well as for Sutterella genus (id.2896, OR:3.39, 95%CI:1.30-8.84). In addition, results indicated a negative association for the Anaerotruncus genus (id.2054, OR:0.31, 95%CI:0.11-0.88) with DM (Table 1). Moreover, the sensitive analysis excluded the influence of heterogeneity and horizontal pleiotropy. Leave-one-out analysis also suggested the results were robust.ConclusionThis study demonstrates that the Ruminococcaceae, Sutterella, and Anaerotruncus are causally related to DM, providing novel insights into the gut microbiota-mediated development mechanism of DM to access more therapeutic methods.References[1]Bae, S.S., et al., Altered Gut Microbiome in Patients With Dermatomyositis. ACR Open Rheumatol, 2022. 4(8): p. 658-670.[2]Kurilshikov, A., et al., Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet, 2021. 53(2): p. 156-165.[3]Davey Smith, G. and G. Hemani, Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet, 2014. 23(R1): p. R89-98.Table 1.Mendelian Randomization results for association between gut microbiota and DM. DM, dermatomyositis.DMN of SNPsCochran’s Q statistic(p-value)Odds ration95% confidence intervalp-valueAnaerotruncus genusinverse variance weighted138.083(0.697)0.314(0.112,0.881)0.027MR-Egger139.067(0.705)0.075(0.003,1.526)0.120weighted median130.278(0.069,1.118)0.075Ruminococcaceae genusinverse variance weighted2213.54(0.809)2.439(1.173,5.070)0.025MR-Egger2211.34(0.879)8.456(1.222,58.477)0.038weighted median222.801(1.056,7.428)0.1122Sutterella genusinverse variance weighted126.922(0.805)3.392(1.301,8.838)0.012MR-Egger126.427(0.778)14.542(0.225,936.652)0.236weighted median123.244(0.851,12.363)0.071Acknowledgements:NIL.Disclosure of InterestsNone Declared.

BackgroundThere is growing evidence supporting an association between gut microbiota and the risk of systemic sclerosis (SSc)[1]. However, the causal relationship between gut microbiota and SSc remains ambiguous.ObjectivesA two-sample Mendelian randomization (MR) analysis was performed to reveal the causal relationship between gut microbiota and SSc.MethodsWe obtained data on intestinal flora from the MR-base website [2] and 218,499 samples for systemic sclerosis from the IEU database for a two-sample MR analysis. Inverse variance weighting, MR-Egger, and weighted median were used to examine the causal relationship between gut flora and SSc. Then a series of sensitivity analyses were performed to verify the robustness of the results. Finally, the bacteria which appeared causality with SSc were subjected to reverse MR analysis to assess the possibility of reverse causality.ResultsInverse variance-weighted estimates indicated that genus Collinsella (odds ratio(OR) = 8.982, 95% confidence interval(CI): 1.571-51.366，P = 0.014), phylum Actinobacteria (OR = 5.746, 95% CI: 1.247-26.491，P = 0.025), genus Ruminococcaceae (OR = 5.500, 95% CI: 1.477-20.477，P = 0.011) had a risk effect on SSc (Table 1). According to the results of the reverse MR analysis, SSc had no significant causal effect on intestinal flora. No significant heterogeneity or horizontal pleiotropy of instrumental variables was found.ConclusionThe present study revealed a causal relationship between three gut microflora genera and SSc, providing new insights into the mechanisms of gut microbiota-mediated scleroderma development.References[1]Lemos, M.P.C., et al., Dysbiosis and Gut Microbiota Modulation in Systemic Sclerosis. J Clin Rheumatol, 2022. 28(2): p. e568-e573.[2]Kurilshikov, A. and C. Medina-Gomez, Large-scale association analyses identify host factors influencing human gut microbiome composition. 2021. 53(2): p. 156-165.Table 1.Mendelian randomization analysis for the association between gut microbiota and SScSScN of SNPsCochran’s Q statistic(p-value)Odds ratio95% CIp-valueGenus CollinsellaInverse variance weighted93.853 (0.870)8.982(1.571，51.366)0.014MR Egger93.545 (0.8301)1.515(0.002，1029.541)0.904Weighted median914.103(1.466，135.692)0.022Phylum ActinobacteriaInverse variance weighted1517.720 (0.220)5.746(1.247,26.491)0.025MR Egger1516.880 (0.205)79.147(0.110, 57116.960)0.216Weighted median152.109(0.275,16.168)0.448Genus RuminococcaceaeInverse variance weighted126.317 (0.851)5.500(1.477,20.477)0.011MR Egger126.183 (0.800)2.552(0.034,191.160)0.680Weighted median125.698(1.009,32.173)0.052SSc, systemic sclerosis; MR, Mendelian randomization; CI, confidence intervalAcknowledgements:NIL.Disclosure of InterestsNone Declared.

BackgroundAccumulating inflammatory leukocytes in articular tissues is the hallmark feature of rheumatoid arthritis (RA). However, no relevant study indicates whether there is insidious causation between leukocyte count (LC) and the risk of RA. Traditional observational studies were susceptible to bias from confounding and reverse causation. It was necessary to estimate an exact correlation between LC and the risk of RA.ObjectivesThis study aimed at evaluating the correlation between LC and the risk of RA by performing two-sample Mendelian Randomization(MR).MethodsThis study selected single nucleotide polymorphism (SNPs) associated(p<5e-8) with exposure (LC) from a genome-wide association study (GWAS), which included 171846 European ancestry participants and wiped off confounding factors. The association estimates of these SNPs with the risk of RA were obtained from another GWAS, including 14361 RA cases and 42923 controls. Upon SNPs were compared with SNPs related to the risk of RA and performed harmonization analysis to wipe off the irrelevant or inconsistent site SNPs. Ultimately, the remaining SNPs were selected as instrument variables(IVs). Then, we utilized two-sample Mendelian randomization (MR) to evaluate whether LC was causally associated with the risk of RA. Furthermore, Cochran’s Q test, MR pleiotropy residual sum and outlier (MR-presso), and leave-one-out analysis were performed as a sensitivity test.ResultsA total of 70 independent SNPs were selected as IVs in the MR analysis of total WBC. Higher leukocyte counts were associated with a higher risk of RA (OR=1.025,95% CI: 0.998-1.053,p-value=0.038) by the IVW method (Figure 1). The MR-Egger regression test did not reveal any evidence of directional pleiotropy (intercept = -0.0002, stand error (SE) = 0.008, p = 0.981). In addition, leave-one-out sensitivity analysis showed similar findings, which further emphasized the effectiveness and stability of the causation. However, in the Cochrane’s Q test, it turned out [Cochrane’s Q statistic= 69, p-value=3.472e-20], suggesting heterogeneity exists. For this reason, the processed steps were rechecked, and finally got, the conclusion that heterogeneity is produced from sysmaticness.ConclusionThis study indicated the insidious causation between LC and the risk of RA. Further studies are warranted to determine how related pathways may contribute to the unnormal LC to promote the comprehension of the pathology of the happening of RA and access more therapeutic methods.References[1] Astle, W.J., et al., The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell, 2016. 167(5): p. 1415-1429.e19.[2] Ha, E. and S.C. Bae, Large-scale meta-analysis across East Asian and European populations updated genetic architecture and variant-driven biology of rheumatoid arthritis, identifying 11 novel susceptibility loci. 2021. 80(5): p. 558-565.Figure 1.(A) Forest plot derived from the analyses of different Mendelian Randomization methods. (B) Scatter plot showing causal effect estimates. (C) Funnel plot for Mendelian Randomization analyses.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

BackgroundGut microbiota (GM) abundance is associated with giant cell arteritis, but their causality remains unclear[1].ObjectivesIn order to comprehensively investigate the causal relationship between gut microbiota abundance and giant cell arteritis risk and to identify specific beneficial or pathogenic microbe taxa, we performed a two-sample Mendelian randomized (MR) analysis.MethodsMR analysis was conducted on genome-wide association study (GWAS) summary statistics of gut microbiota abundance and giant cell arteritis. Specifically, the microbiome GWAS in 14,306 individuals from the MiBioGen consortium was used as exposure[2], while the GWAS (n = 213,604) for giant cell arteritis was used as the outcome. At the suggestive significance level (p = 1×10-5), SNPs associated with bacteria abundance were utilized as instrumental variables. Inverse variance weighted (IVW) was the primary method for analyzing causality, and Cochran’s Q statistic was used to quantify the heterogeneity of instrumental variables.ResultsInverse variance weighted estimates suggested that genus Eubacterium rectale group (odds ratio = 0.38, 95% confidence interval: 0.16-0.93, P = 0.03), class bacteroidia (odds ratio = 0.44, 95% confidence interval: 0.21-0.93, P = 0.03), order Bacteroidales (odds ratio = 0.44, 95% confidence interval: 0.21-0.93, P = 0.03), family Lachnospiraceae (odds ratio = 0.32, 95% confidence interval: 0.16-0.63, P = 0.001) and genus Lactobacillus (odds ratio = 0.6, 95% confidence interval: 0.37-0.97, P = 0.04) had a protective effect on Giant cell arteritis. In addition, the relative abundance of class Betaproteobacteria (odds ratio = 2.53, 95% confidence interval: 1.03-6.19, P = 0.04) and relative abundance of genus Ruminococcaceae (odds ratio = 1.7, 95% confidence interval: 1.04-2.8, P = 0.04) were negative significant correlated with Giant cell arteritis risk (Figure 1). Further sensitivity analyses validated the robustness of the MR results.ConclusionThe study suggested that gut microbiota abundance was causally associated with the risk of giant cell arteritis, which may provide novel insights into the prevention and diagnosis of giant cell arteritis.References[1]Tariq, S. & Clifford, A. H. An update on the microbiome in vasculitis. Current opinion in rheumatology 33, 15-23, doi:10.1097/bor.0000000000000758 (2021).[2]Kurilshikov, A. & Medina-Gomez, C. Large-scale association analyses identify host factors influencing human gut microbiome composition. 53, 156-165, doi:10.1038/s41588-020-00763-1 (2021).Figure 1.Mendelian randomization estimates for the associations between GCA and genus Eubacterium rectale group, class Betaproteobacteria, class bacteroidia, genus Ruminococcaceae, order Bacteroidales, family Lachnospiraceae, genus Lactobacillus. GCA, giant cell arteritis.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

BackgroundOsteoarthritis (OA) is increasingly recognized as a disease with a significant inflammatory component[1]. Signs of inflammation such as synovitis can be identified in OA patients using magnetic resonance imaging (MRI) and ultrasonographic imaging. The white blood cell count in synovial tissue has been considered useful in the assessment and diagnosis of arthritis. However, the causal relationship between the white blood cell count and OA has yet to be well.ObjectivesTo investigate associations between white blood cell count and OA.MethodsMendelian randomized analysis (MR) was performed using white blood cell count as exposure data and OA as outcome data. This study included 173,480 individuals of European ancestry for exposure traits from three extensive UK studies[2]. The random effects inverse variance weighting (IVW) method was used for the primary analysis, and the weighted median and MR-Egger methods were supplemented for analysis. Then, we performed Cochran’s Q test, MR pleiotropy residual sum and outlier (MR-PRESSO), to test for heterogeneity and horizontal multiplicity. The sensitivity analysis was also performed to verify the robustness of the primary results of the MR analysis.ResultsIVW method showed that knee or hip (OR=0.942, 95% CI:0.895-0.990, P =0.019), knee (OR=0.944, 95% CI:0.886-1.007, P=0.041) and hip (OR=0.929, 95% CI:0.868-0.995, P=0.035)had a protective effect on white blood cell count. After removing the confounding factors, we identified a significant causal association between white blood cell count and OA by IVW using residual single nucleotide polymorphisms (157 remaining in knee, 159 remaining in hip, and 159 remaining in knee or hip). P values of IVW were less than 0.05, indicating a significant causal relationship. Further sensitivity analyses validated the robustness of the MR results.(Figure 1)ConclusionThere was a significant causal relationship between white blood cell count and OA. This is meaningful for the prevention and treatment of OA in the future.References[1]McCabe PS, Parkes MJ, Maricar N, Hutchinson CE, Freemont A, O’Neill TW, et al. Brief Report: Synovial Fluid White Blood Cell Count in Knee Osteoarthritis: Association With Structural Findings and Treatment Response. Arthritis & rheumatology (Hoboken, NJ). 2017;69(1):103-7.[2]Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, et al. The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell. 2016;167(5):1415-29.e19.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

BackgroundAnkylosing spondylitis (AS) is a chronic inflammatory immune response disease mainly presenting with fibrosis of the sacroiliac joint and calcification [1]. Interleukin-10 (IL-10) is a factor that suppresses Th17 cell activity and induced Treg cells to achieve immunosuppression. Observational studies show that IL-10 may be one of the important contributing sources of important inflammatory cytokines for AS [2], but the causal relationship between them is not clear.ObjectivesThe objective was to investigate the casual association between AS and IL-10.MethodsThe GWAS summary statistics of IL-10 and AS (Ncase = 968, Ncontrol = 336,191) were downloaded from the IEU GWAS database in the study. After a series of screens, SNPs strongly associated with exposure were included as instrumental variables(IVs). The results are from using robust analyses with three two-sample different assumptions (Inverse variance weighting (IVW), Weighted median (WM) and MR-Egger). Meanwhile, we used MR-Egger intercept test, Cochran’sQ test and leave-one-out sensitivity analysis for assessment to avoid the biasing effects of potential genetic variation, horizontal pleiotropy and heterogeneity. MR-PRESSO outlier test was used to identify and eliminate horizontal pleiotropy.ResultsOur result showed that the level of IL-10 has a causal relationship with the risk of developing AS (IVW: OR=0.9992, 95% confidence interval (CI) 0.9985-0.9999, P = 0.03, Figure 1a, 1b). Increasing the IL-10 level can reduce the risk of AS disease. MR-Egger intercept test (MR-Egger intercept = -1.5705×10-5, P = 0.5158, Figure 1b), Cochran’s Q test (IVW Q = 4.3071, Q_pval = 0.8284), leave-one-out sensitivity analysis (Figure 1c) and the funnel plot (Figure 1d) suggest almost no bias in the study, indicating that the MR was robust.ConclusionOur results indicate a protective role for IL-10 in AS, suggesting that elevated IL-10 levels may be able to alleviate the inflammatory response in AS, providing new insights into AS treatment.References[1]Braga, M.; Lara-Armi, F. F.; Neves, J. S. F., et al., Influence of IL10 (rs1800896) Polymorphism and TNF-α, IL-10, IL-17A, and IL-17F Serum Levels in Ankylosing Spondylitis. Front Immunol 2021, 12, 653611.doi:10.3389/fimmu.2021.653611[2]Wu, X., Innate Lymphocytes in Inflammatory Arthritis. Front Immunol 2020, 11, 565275.doi:10.3389/fimmu.2020.565275Acknowledgements:NIL.Disclosure of InterestsNone Declared.

BackgroundMetformin is a biguanide oral hypoglycemic agent that is widely used as a first-line drug treatment for type II diabetes. In addition, metformin is also used in the treatment of certain autoimmune diseases, such as systemic lupus erythematosus (SLE). It plays a key role in the pathogenesis of SLE through pathways such as inhibiting type 1 IFN, regulating T effector cells and proinflammatory cytokines[1]. However, the causal relationship of the metformin-related targets on the risk of SLE is still unclear.ObjectivesThe purpose of this study was to assess the causal effect of metformin targets (AMPK, MCI, MG3, GDF 15 and GLP 1/ GCG) on the risk of SLE using Mendelian randomization (MR).MethodsGenetic variants including downstream and 100 kb upstream of encoding five genes were selected as candidate tool variants. The SNP was further screened as in low linkage disequilibrium (r2 < 0.3) and associated with HbA 1 c (P ≤ 0.05). The Genome-wide association study (GWAS) of HbA 1 c comes from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) (n = 88,355)[2]. The SLE data (ncase = 5,201, ncontrol = 9,066) are pooled from publicly available GWAS. Two-sample MR was performed using inverse variance weighted (IVW), MR-Egger, and weighted median (WM) to obtain causal estimates of metformin-related targets and risk of SLE. In addition, sensitivity analysis, outlier (MR-PRESSO) and leave-one-out analysis were conducted to test the heterogeneity and level pleiotropy of the results.ResultsGenetically predicted increase in HbA1c instrumented by GDF 15 variants was associated with increased risk of SLE (GDF 15[p = 0.028]). However, genetically predicted AMPK, MG3, MCI, and GLP 1/ GCG were not associated with SLE risk (AMPK [p = 0.122], MG3 [p = 0.105], MCI [p = 0.762], GLP 1/ GCG [p = 0.361]) (Figure 1). No outlier between GDF 15 and the risk of SLE was identified via the MR-PRESSO test. The leave-one-out analysis verified the robustness of the outcomes.ConclusionThis study suggests that the GDF 15 gene may be a plausible mechanism of action to reduce the risk of SLE and provides critical evidence to guide future clinical trials of metformin.References[1]Kim JW, Choe JY, Park SH. Metformin and its therapeutic applications in autoimmune inflammatory rheumatic disease. Korean J Intern Med 2022; 37(1):13-26.[2]Wheeler E, Leong A, Liu CT, Hivert MF, Strawbridge RJ, Podmore C, et al. Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis. PLoS Med 2017; 14(9):e1002383.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

BackgroundAnkylosing spondylitis (AS) is an axial rheumatic and spinal disease based on autoimmune and genetic susceptibility that affects the sacroiliac joints and the spine[1]. Interleukin 17 (IL-17) as an inflammatory cytokine positively correlated with disease activity in AS patients[2]. However, the causal relationship between the IL-17 family and its receptors and AS is inconclusive.ObjectivesThe aim of this study was to discover the casual association between the IL-17 family members with their receptors and AS.MethodsTo clarify the causal relationship between the IL-17 family members (and their receptors) and AS, we used two-sample Mendelian randomization (MR). We selected single-nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) which included IL-17 family members and their receptors (N = 3301) and AS (Ncase = 968, Ncontrol = 336,191). Confounding factors were excluded, heterozygosity and lateral polymorphisms were examined and corrected.ResultsWe found that IL-17RB level was significantly causally associated with AS (IVW OR=1.0007, 95%CI 1.0002~1.0011, P= 0.0020, Figure 1a). Sensitivity analysis also confirmed the absence of horizontal polymorphism (MR-Egger intercept = 6.5989×10-5, P = 0.5158, Figure 1b) and heterogeneity (IVW Q = 10.7388, Q_pval = 0.5514) that biased the causality. The leave-one-out sensitivity test showed that no single SNP strongly affected the causal relationship between IL-17RB and AS (Figure 1c). Furthermore, funnel plot symmetry suggests that our study fits the IV hypothesis(Figure 1d). However, our results show no significant causal relationship between AS and other IL-17 family members and their receptors.ConclusionOur results show that IL-17RB is a protective factor for AS. We offer a new insight into the drug targets of AS.References[1]El Maghraoui, A., Extra-articular manifestations of ankylosing spondylitis: prevalence, characteristics and therapeutic implications. Eur J Intern Med 2011, 22 (6), 554-60. doi:10.1016/j.ejim.2011.06.006[2]Zhang, X.; Yuan, Y.; Pan, Z.; Ma, Y.; Wu, M.; Yang, J.; Han, R.; Chen, M.; Hu, X.; Liu, R.; Sam, N. B.; Xu, S.; Pan, F., Elevated circulating IL-17 level is associated with inflammatory arthritis and disease activity: A meta-analysis. Clin Chim Acta 2019, 496, 76-83. doi:10.1016/j.cca.2019.06.026Acknowledgements:NIL.Disclosure of InterestsNone Declared.

BackgroundAdult-onset Still’s disease (AOSD) is a characteristic non-familial, multi-genic systemic auto-inflammatory disorder, characterized by fever and joint pain [1]. Whether physical activities (PAs)/ leisure sedentary behaviors (LSBs) directly affect AOSD susceptibility is inconclusive.ObjectivesThis study aimed to investigate the causal effects of PAs and LSBs on AOSD.MethodsWe performed a two-sample Mendelian randomization (MR) analysis to investigate the causality of 5 PAs and 4 LSBs with AOSD, of which PAs include accelerometer-base physical activity measurement (average acceleration) (n=91,084), moderate to vigorous physical activity levels (MVPA) (n=377,234), vigorous physical activity (VPA) (n=377,234), moderate physical activity 10+ minutes (n=440,266), vigorous physical exercise 10+ minutes (n=440,512) [2], and LSBs include the length of mobile phone use (n=456,972), time spent driving (n=310,555), time spent using computer (n=360,895) and time spent watching television (TV) (n=437,887). Genome-wide significant genetic instruments (p < 5 × 10–8) for PAs and LSBs were extracted from European-descent genome-wide association studies (GWASs). And to satisfy the assumption that single-nucleotide polymorphisms (SNPs) were not related to outcomes, one SNP (rs2857693) was removed. The PLINK algorithm (r2 ＜0.001) was used to clump SNPs at linkage disequilibrium (LD). AOSD data was acquired from the UK Biobank database (1,486 cases, 359,708 control samples, from Neale lab). Inverse variance weighted (IVW) was selected as the primary method for MR analysis. The MR-Egger and weighted median were considered secondarily. Moreover, sensitivity analyses were implemented with Cochran’s Q test, MR-Egger intercept test, leave-one-out analysis, and the funnel plot to evaluate for heterogeneity and pleiotropy.ResultsOnly spent driving time was positively correlated with AOSD in LSBs (odds ratio (OR) =1.02, 95% confidence interval (CI):0.001-0.319, p=0.0004). The Cochran’s Q statistic with the IVW method revealed no heterogeneity (p=0.43), and no pleiotropy was observed (p = 0.618). However, the IVW results of PAs were not statistically significant (p＞0.05), so PAs were not associated with AOSD (Figure 1).Figure 1.Mendelian randomization results for gene-level causality between physical activities (PAs), leisure sedentary behaviors (LSBs) and adult-onset Still’s disease (AOSD) were evaluated by the odds ratio (OR) values of IVW, MR Egger, and Weighted median. Funnel plot (A), scatter plot (B), leave-one-out analysis (C), and forest plot (D) assessed that time spent driving increases the risk of AOSD. MR, mendelian randomization; IVW, inverse-variance weighted method; MR-Egger, Mendelian randomization-Egger; SNP, single nucleotide polymorphisms; CI, confidence interval.ConclusionOur findings support the hypothesis that time spent driving increases the risk of AOSD, suggesting leisurely sedentary behaviors may increase the probability of AOSD.References[1] Gerfaud-Valentin M, Jamilloux Y, Iwaz J, Sève P. Adult-onset Still’s disease. Autoimmun Rev. 2014 Jul;13(7):708-22. doi: 10.1016/j.autrev.2014.01.058. Epub 2014 Mar 19. PMID: 24657513.[2] Klimentidis YC, Raichlen DA, Bea J, Garcia DO, Wineinger NE, Mandarino LJ, Alexander GE, Chen Z, Going SB. Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE. Int J Obes (Lond). 2018 Jun;42(6):1161-1176. doi: 10.1038/s41366-018-0120-3. Epub 2018 Jun 13. PMID: 29899525; PMCID: PMC6195860.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

BackgroundSystemic sclerosis (SSc) is a systemic autoimmune disease characterized by microvascular damage, dysregulation of innate and adaptive immunity, and fibrosis in many organs[1]. Observational studies have suggested associations between SSc and cardiovascular diseases (CVD)[2]. However, this association is easily disturbed by confusion and reverse causality. In the study, we used Mendelian randomization (MR) to conduct a study on bidirectional causality to examine the relationship between SSc and CVD.ObjectivesThe study aims to evaluate the connection between SSc and CVD, and further to provide comprehensive CVD assessment and treatment for SSc patients.MethodsSummary-level statistical data for SSc were derived from a large meta-analysis of GWAS, including 55,114 cases and 482,295 controls. The summary data for 12 CVD were retrieved from genome-wide association studies (GWAS). In this study, we conducted MR analysis using the random-effects inverse-variance weighted (IVW) method as the primary method. The weighted median approach can yield consistent causal estimates, assessing these genetic variants’ horizontal pleiotropy and heterogeneity using the MR-Egger intercept test and Cochran’s Q test. In addition, we utilized the leave-one-out analysis to detect the robustness and consistency of the results.ResultsAtrial fibrillation(AF) increased the risk of SSc (IVW:OR = 1.428, 95% CI = 1.101-1.854, p = 0.007). Meanwhile, MR-Egger and weighted median pointed toward a similar direction of effect (weighted median: OR = 1.374; 95% CI=0.987-1.912; p = 0.063; MR-Egger: OR = 1.471, 95% CI=0.905-2.391, p=0.140). In the reverse MR, the results of IVW demonstrated that SSc was negatively correlated with the risk of hypertension after removing abnormal single nucleotide polymorphisms(SNPs) (IVW: OR: 0.996,95%CI: 0.993–1.000, p = 0.036). The heterogeneity test showed no significant heterogeneity among selected instrumental variables(IVs) (Q_p value >0.05) except for miocardial infarction(MI)(MR Egger: Q_p value=0.09974) and AF(MR Egger: Q_pval=0.00010; IVE: Q_pval=0.00021). Moreover, no significant evidence of horizontal pleiotropy was observed for IVs. There was no causal genetic relationship between SSc and other CVDs, such as coronary heart disease, heart failure, and pulmonary embolism.ConclusionWe verified that SSc could cause pathological hypertension processes. Furthermore, SSc may be causally impacted by AF. The main mechanism of this causal relationship may be conduction system ischemia and left ventricular systolic failure.References[1]Zhao, M., Wu, J., Wu, H., Sawalha, A. H. & Lu, Q. Clinical Treatment Options in Scleroderma: Recommendations and Comprehensive Review. Clin Rev Allergy Immunol 62, 273-291, doi:10.1007/s12016-020-08831-4 (2022).[2]Ngian, G. S. et al. Prevalence of coronary heart disease and cardiovascular risk factors in a national cross-sectional cohort study of systemic sclerosis. Ann Rheum Dis 71, 1980-1983, doi:10.1136/annrheumdis-2011-201176 (2012).Acknowledgements:NIL.Disclosure of InterestsNone Declared.