By comparing the methods of Mendelian randomization and randomized controlled trial, this paper focuses on the advantages of Mendelian randomization analysis compared with randomized controlled trial in the field of epidemiology. As an innovative statistical technique, Mendelian randomization has been paid more and more attention in epidemiological research. This method uses genetic variation as an instrumental variable to simulate the randomization process, which helps to solve the confounding and causal inference problems in traditional epidemiological studies. The powerful function of Mendelian randomization analysis was further demonstrated through the example of the application of the Mendelian randomization method to reveal the causal relationship between polycystic ovary syndrome-associated hormones and metabolic dysfunction-associated steatotic liver disease. By mastering the analysis method of Mendelian randomization, researchers can improve their scientific research ability in etiology investigation and disease prevention, and promote the innovation and development of epidemiological research methods.

1.Vandenbroucke JP, von Elm E, Altman DG, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration[J]. Int J Surg, 2014, 12(12): 1500-1524. DOI: 10.1016/j.ijsu.2014.07.014.

2.Hariton E, Locascio JJ. Randomised controlled trials - the gold standard for effectiveness research: Study design: randomised controlled trials[J]. BJOG, 2018, 125(13): 1716. DOI: 10.1111/ 1471-0528.15199.

3.Zuber V, Colijn JM, Klaver C, et al. Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization[J]. Nat Commun, 2020, 11(1): 29. DOI: 10.1038/s41467-019-13870-3.

4.Lovegrove CE, Howles SA, Furniss D, et al. Causal inference in health and disease: a review of the principles and applications of Mendelian randomization[J]. J Bone Miner Res, 2024, 39(11): 1539-1552. DOI: 10.1093/jbmr/zjae136.

5.Schmidt AF, Finan C, Gordillo-Marañón M, et al. Genetic drug target validation using Mendelian randomisation[J]. Nat Commun, 2020, 11(1): 3255. DOI: 10.1038/s41467-020-16969-0.

6.Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR statement[J]. JAMA, 2021, 326(16): 1614-1621. DOI: 10.1001/jama.2021.18236.

7.Sobczyk MK, Zheng J, Davey Smith G, et al. Systematic comparison of Mendelian randomisation studies and randomised controlled trials using electronic databases[J]. BMJ Open, 2023, 13(9): e072087. DOI: 10.1136/bmjopen-2023-072087.

8.Spiga F, Gibson M, Dawson S, et al. Tools for assessing quality and risk of bias in Mendelian randomization studies: a systematic review[J]. Int J Epidemiol, 2023, 52(1): 227-249. DOI: 10.1093/ije/dyac149.

9.Liu W, Yang C, Lei F, et al. Major lipids and lipoprotein levels and risk of blood pressure elevation: a Mendelian Randomisation study[J]. EBioMedicine, 2024, 100: 104964. DOI: 10.1016/j.ebiom.2023.104964.

10.Hoeger KM, Dokras A, Piltonen T. Update on PCOS: consequences, challenges, and guiding treatment[J]. J Clin Endocrinol Metab, 2021, 106(3): e1071-e1083. DOI: 10.1210/clinem/dgaa839.

11.Dewailly D, Robin G, Peigne M, et al. Interactions between androgens, FSH, anti-Mullerian hormone and estradiol during folliculogenesis in the human normal and polycystic ovary[J]. Hum Reprod Update, 2016, 22(6): 709-724. DOI: 10.1093/humupd/dmw027.

12.Zhang HF, Zhang QX, Feng YH, et al. Association of thyroid disease and risk of fatty liver disease: an exposure-wide Mendelian randomization study[J]. Eur Rev Med Pharmacol Sci, 2023, 27(23): 11402-11411. DOI: 10.26355/eurrev_202312_34579.

13.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.

14.Burgess S, Bowden J, Fall T, et al. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants[J]. Epidemiology, 2017, 28(1): 30-42. DOI: 10.1097/EDE.0000000000000559.

15.Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome[J]. Elife, 2018, 7: e34408. DOI: 10.7554/eLife.34408.

16.Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data[J]. Int J Epidemiol, 2017, 46(6): 1734-1739. DOI: 10.1093/ije/dyx034.

17.Larsson SC, Butterworth AS, Burgess S. Mendelian randomization for cardiovascular diseases: principles and applications[J]. Eur Heart J, 2023, 44(47): 4913-4924. DOI: 10.1093/eurheartj/ehad736.

18.Gill D, Georgakis MK, Walker VM, et al. Mendelian randomization for studying the effects of perturbing drug targets[J]. Wellcome Open Res, 2021, 6: 16. DOI: 10.12688/wellcomeopenres.16544.2.

19.Daghlas I, Gill D. Mendelian randomization as a tool to inform drug development using human genetics[J]. Camb Prism Precis Med, 2023, 1: e16. DOI: 10.1017/pcm.2023.5.

20.Zabor EC, Kaizer AM, Hobbs BP. Randomized controlled trials[J]. Chest, 2020, 158(1s): S79-S87. DOI: 10.1016/j.chest.2020. 03.013.

21.Davey Smith G, Holmes MV, Davies NM, et al. Mendel' laws, Mendelian randomization and causal inference in observational data: substantive and nomenclatural issues[J]. Eur J Epidemiol, 2020, 35(2): 99-111. DOI: 10.1007/s10654-020-00622-7.