Welcome to visit Zhongnan Medical Journal Press Series journal website!

Home Articles Vol 37,2024 No.7 Detail

A two-sample bidirectional Mendelian randomization study of the causality between autoimmune liver disease and diabetes mellitus

Published on Aug. 05, 2024Total Views: 1649 times Total Downloads: 357 times Download Mobile

Author: LIN Menglu 1, 2 CHENG Jie 1, 2 HU Fan 1, 2 FENG Jiahui 1, 2 CHEN Xiaojia 1, 2 Tannuer·MAIMAITIAISHAN 1, 2 CHENG Yan 1, 2 LIN Jun 1, 2

Affiliation: 1. Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China 2. Hubei Clinical Center of Intestinal and Colorectal Diseases/Hubei Key Laboratory of Intestinal and Colorectal Diseases, Wuhan 430071, China

Keywords: Autoimmune liver disease Diabetes mellitus Mendelian randomization Causality

DOI: 10.12173/j.issn.1004-4337.202403071

Reference: Lin ML, Cheng J, Hu F, Feng JH, Chen XJ, Tannuer·MMTAS, Cheng Y, Lin J. A two-sample bidirectional Mendelian randomization study of the causality between autoimmune liver disease and diabetes mellitus[J]. Journal of Mathematical Medicine, 2024, 37(7): 481-489. DOI: 10.12173/j.issn.1004-4337.202403071[Article in Chinese]

  • Abstract
  • Full-text
  • References
Abstract

Objective  This study aimed to explore the causal association between autoimmune liver disease (AILD) and diabetes mellitus (DM) by two-sample bidirectional Mendelian randomization (MR) analysis.

Methods  GWAS data for AILD and type 1 diabetes mellitus (T1DM) were downloaded from the IEU database, and GWAS data for type 2 diabetes mellitus (T2DM) were downloaded from the DIAGRAM consortium, and eligible single nucleotide polymorphisms (SNP) were extracted. MR analyses were performed using MR-Egger regression, weighted median (WME), and inverse variance weighting (IVW) methods. In addition, the robustness of the results was verified by heterogeneity test, multiple validity analysis, leave-one-out analysis and MR-PRESSO analysis.

Results  IVW results showed that primary biliary cholangitis (PBC) had a positive causal effect on T1DM (OR=1.244, 95%CI: 1.137-1.361, P<0.001). Reverse MR analysis showed that T1DM significantly increased the morbidity risk of autoimmune hepatitis (AIH) (OR=1.111, 95%CI: 1.053-1.173, P<0.001), PBC (OR=1.218, 95% CI: 1.133-1.310, P<0.001), and primary sclerosing cholangitis (PSC) (OR=1.375, 95%CI: 1.187-1.592, P<0.001). No significant causal association between AILD and T2DM was observed. Heterogeneity tests suggested heterogeneity among SNPs, consequently, the random effects IVW model was used for MR analysis. Multivariate analysis did not suggest horizontal pleiotropy. Leave-one-out analysis showed that causality remained consistent after removing each SNP. Outliers were detected by MR-PRESSO analysis, and the results did not change much when the outliers were removed and the MR analysis was repeated, further confirming the reliability of the results.

Conclusion  Bidirectional causality was observed between PBC and T1DM. Patients with T1DM were at increased risk of AIH and PSC, and no significant causality was observed between AILD and T2DM.

Full-text
Please download the PDF version to read the full text: download
References

1.Engel B, Taubert R, Jaeckel E, et al. The future of autoimmune liver diseases-understanding pathogenesis and improving morbidity and mortality[J]. Liver Int, 2020, 40 Suppl 1: 149-153. DOI: 10.1111/liv.14378.

2.Webb GJ, Ryan RP, Marshall TP, et al. The epidemiology of UK autoimmune liver disease varies with geographic latitude[J]. Clin Gastroenterol Hepatol, 2021, 19(12): 2587-2596. DOI: 10.1016/j.cgh.2021.01.029.

3.GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021[J]. Lancet, 2023, 402(10397): 203-234. DOI: 10.1016/S0140-6736(23)01301-6.

4.Jensen ASH, Winther-Sørensen M, Burisch J, et al. Autoimmune liver diseases and diabetes: a propensity score matched analysis and a proportional meta-analysis[J]. Liver Int, 2023, 43(11): 2479-2491. DOI: 10.1111/liv.15720.

5.Ludvigsson JF, Bergquist A, Montgomery SM, et al. Risk of diabetes and cardiovascular disease in patients with primary sclerosing cholangitis[J]. J Hepatol, 2014, 60(4): 802-808. DOI: 10.1016/j.jhep.2013.11.017.

6.Rupp C, Mummelthei A, Sauer P, et al. Non‐IBD immunological diseases are a risk factor for reduced survival in PSC[J]. Liver Int, 2013, 33(1): 86-93. DOI: 10.1111/liv.12028.

7.Lamberts LE, Janse M, Haagsma EB, et al. Immune-mediated diseases in primary sclerosing cholangitis[J]. Dig Liver Dis, 2011, 43(10): 802-806. DOI: 10.1016/j.dld.2011.05.009.

8.Boyko EJ. Observational research—opportunities and limitations[J]. J Diabetes Complications, 2013, 27(6): 642-648. DOI: 10.1016/j.jdiacomp.2013.07.007.

9.Chiou J, Geusz RJ, Okino ML, et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics[J]. Nature, 2021, 594(7863): 398-402. DOI: 10.1038/s41586-021-03552-w.

10.Sakaue S, Kanai M, Tanigawa Y, et al. A cross-population atlas of genetic associations for 220 human phenotypes[J]. Nat Genet, 2021, 53(10): 1415-1424. DOI: 10.1038/s41588-021-00931-x.

11.Cordell HJ, Fryett JJ, Ueno K, et al. An international genome-wide meta-analysis of primary biliary cholangitis: novel risk loci and candidate drugs[J]. J Hepatol, 2021, 75(3): 572-581. DOI: 10.1016/j.jhep.2021.04.055.

12.Ji SG, Juran BD, Mucha S, et al. Genome-wide association study of primary sclerosing cholangitis identifies new risk loci and quantifies the genetic relationship with inflammatory bowel disease[J]. Nat Genet, 2017, 49(2): 269-273. DOI: 10.1038/ng.3745.

13.Mahajan A, Spracklen CN, Zhang W, et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation[J]. Nat Genet, 2022, 54(5): 560-572. DOI: 10.1038/s41588-022-01058-3.

14.Gagliano Taliun SA, Evans DM. Ten simple rules for conducting a mendelian randomization study[J]. PLoS Comput Biol, 2021, 17(8): e1009238. DOI: 10.1371/journal.pcbi.1009238.

15.Burgess S, Scott RA, Timpson NJ, et al. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors[J]. Eur J Epidemiol, 2015, 30(7): 543-552. DOI: 10.1007/s10654-015-0011-z.

16.Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression[J]. Int J Epidemiol, 2015, 44(2): 512-525. DOI: 10.1093/ije/dyv080.

17.Bowden J, Davey Smith G, Haycock PC, et al. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator[J]. Genet Epidemiol, 2016, 40(4): 304-314. DOI: 10.1002/gepi.21965.

18.Bowden J, Spiller W, Del Greco MF, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression[J]. Int J Epidemiol, 2018, 47(4): 1264-1278. DOI: 10.1093/ije/dyy101.

19.Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method[J]. Eur J Epidemiol, 2017, 32(5): 377-389. DOI: 10.1007/s10654-017-0255-x.

20.Burgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023[J]. Wellcome Open Res, 2023, 4: 186. DOI: 10.12688/wellcomeopenres.15555.3.

21.Verbanck M, Chen CY, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases[J]. Nat Genet, 2018, 50(5): 693-698. DOI: 10.1038/s41588-018-0099-7.

22.Lleo A, Wang GQ, Gershwin ME, et al. Primary biliary cholangitis[J]. Lancet, 2020, 396(10266): 1915-1926. DOI: 10.1016/S0140-6736(20)31607-X.

23.Muratori L, Lohse AW, Lenzi M. Diagnosis and management of autoimmune hepatitis[J]. BMJ, 2023, 380: e070201. DOI: 10.1136/bmj-2022-070201.

24.Dyson JK, Beuers U, Jones DEJ, et al. Primary sclerosing cholangitis[J]. Lancet, 2018, 391(10139): 2547-2559. DOI: 10.1016/S0140-6736(18)30300-3.

25.Zhao DT. Prevalence and prognostic significance of main metabolic risk factors in primary biliary cholangitis: a retrospective cohort study of 789 patients[J]. Front Endocrinol (Lausanne), 2023, 14: 1142177. DOI: 10.3389/fendo.2023.1142177.

26.Higuchi T, Oka S, Furukawa H, et al. Genetic risk factors for autoimmune hepatitis: implications for phenotypic heterogeneity and biomarkers for drug response[J]. Hum Genomics, 2021, 15(1): 6. DOI: 10.1186/s40246-020-00301-4.

27.Zhang P, Lu Q. Genetic and epigenetic influences on the loss of tolerance in autoimmunity[J]. Cell Mol Immunol, 2018, 15(6): 575-585. DOI: 10.1038/cmi.2017.137.

28.Topaloudi A, Jain P, Martinez MB, et al. PheWAS and cross-disorder analysis reveal genetic architecture, pleiotropic loci and phenotypic correlations across 11 autoimmune disorders[J]. Front Immunol, 2023, 14: 1147573. DOI: 10.3389/fimmu.2023.1147573.

Popular papers
Last 6 months