Interpretation of STROBE-MR: a statement for strengthening the reporting of observational studies in epidemiology using Mendelian randomization_Chinese Journal of Evidence-Based Medicine

Authors：

LIU Ming ^1,2 , GAO Ya ^1,2,3 , YANG Kelu ⁴ , XU Jianguo ^1,2 , ZHENG Qingyong ^1,5 , ZHAO Liang ^1,5 , GE Long ^6,7 , LI Lun ⁸ ,  ZHANG Junhua ⁹ , TIAN Jinhui ^1,2

1. Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, P. R. China;
2. Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou 730000, P. R. China;
3. Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton L8S4L8, Canada;
4. Department of Public Health and Primary Care, Academic Centre for Nursing and Midwifery, KU Leuven-University of Leuven, Leuven B-3000, Belgium;
5. School of Nursing, Lanzhou University, Lanzhou 730000, P. R. China;
6. Evidence-Based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou 730000, P. R. China;
7. Department of Social Science and Health Management, School of Public Health, Lanzhou University, Lanzhou 730000, P. R. China;
8. Department of Breast Cancer, The Second Xiangya Hospital, Central South University, Changsha 410083, P. R. China;
9. Evidence-based Medicine Center of Tianjin University of Traditional Chinese Medicine, Tianjin 300193, P. R. China;

Corresponding author：

ZHANG Junhua, Email: tianjh@lzu.edu.cn

Keywords：

Mendelian randomization; STROBE-MR; Statement; Interpretation

DOI：

10.7507/1672-2531.202112103

Video：

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Mendelian randomization (MR) studies use genetic variants as instrumental variables to explore the effects of exposures on health outcomes. STROBE-MR (strengthening the reporting of observational studies in epidemiology using Mendelian randomization) assists authors in reporting their MR studies clearly and transparently, and helpfully to improve the quality of MR. This paper interpreted the STROBE-MR, aiming to help Chinese scholars better understand, disseminate, and apply it.

Citation： LIU Ming, GAO Ya, YANG Kelu, XU Jianguo, ZHENG Qingyong, ZHAO Liang, GE Long, LI Lun, ZHANG Junhua, TIAN Jinhui. Interpretation of STROBE-MR: a statement for strengthening the reporting of observational studies in epidemiology using Mendelian randomization. Chinese Journal of Evidence-Based Medicine, 2022, 22(8): 978-987. doi: 10.7507/1672-2531.202112103 Copy

1.	Smith GD, Ebrahim S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease. Int J Epidemiol, 2003, 32(1): 1-22.
2.	Gray R, Wheatley K. How to avoid bias when comparing bone marrow transplantation with chemotherapy. Bon Mar Trans, 1991, 7(Suppl 3): 9-12.
3.	Davey SG. Capitalising on Mendelian randomization to assess the effects of treatments, 2006. Available at: https://www.jameslindlibrary.org/articles/capitalising-on-mendelian-randomization-to-assess-theeffects-of-treatments/.
4.	Smith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol, 2004, 33(1): 30-42.
5.	Xu J, Li M, Gao Y, et al. Using Mendelian randomization as the cornerstone for causal inference in epidemiology. Environ Sci Pollut Res Int, 2022, 29(4): 5827-5839.
6.	Niu M, Gao Y, Yang M, et al. The quality and clinical applicability of recommendations in anxiety disorders guidelines: A systematic review of seventeen guidelines from seven countries. J Affect Disord, 2021, 295: 1301-1309.
7.	Gao Y, Liu M, Shi S, et al. Prespecification of subgroup analyses and examination of treatment-subgroup interactions in cancer individual participant data meta-analyses are suboptimal. J Clin Epidemiol, 2021, 138: 156-167.
8.	Gao Y, Shi S, Li M, et al. Statistical analyses and quality of individual participant data network meta-analyses were suboptimal: a cross-sectional study. BMC Med, 2020, 18(1): 120.
9.	Boef AG, Dekkers OM, le Cessie S. Mendelian randomization studies: a review of the approaches used and the quality of reporting. Int J Epidemiol, 2015, 44(2): 496-511.
10.	Lor GCY, Risch HA, Fung WT, et al. Reporting and guidelines for mendelian randomization analysis: A systematic review of oncological studies. Cancer Epidemiol, 2019, 62: 101577.
11.	Diemer EW, Labrecque JA, Neumann A, et al. Mendelian randomisation approaches to the study of prenatal exposures: a systematic review. Paediatr Perinat Epidemiol, 2021, 35(1): 130-142.
12.	Davey SG, Davies NM, Dimou N, et al. STROBE-MR: guidelines for strengthening the reporting of Mendelian randomization studies. Peer J, 2019. Available at: https://doi.org/10.7287/peerj.preprints.27857v1.
13.	Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR Statement. JAMA, 2021, 326(16): 1614-1621.
14.	Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ, 2021, 375: n2233.
15.	Yuan S, Bruzelius M, Damrauer SM, et al. Cardiometabolic, lifestyle, and nutritional factors in relation to varicose veins: a Mendelian randomization study. J Am Heart Assoc, 2021, 10(21): e022286.
16.	Millard LAC, Davies NM, Tilling K, et al. Searching for the causal effects of body mass index in over 300 000 participants in UK Biobank, using Mendelian randomization. PLoS Genet, 2019, 15(2): e1007951.
17.	Zhao JV, Luo S, Schooling CM. Sex-specific Mendelian randomization study of genetically predicted insulin and cardiovascular events in the UK Biobank. Commun Biol, 2019, 2: 332.
18.	Burgess S, Seaman S, Lawlor DA, et al. Missing data methods in Mendelian randomization studies with multiple instruments. Am J Epidemiol, 2011, 174(9): 1069-1076.
19.	Liyanage UE, Ong JS, An J, et al. Mendelian randomization study for genetically predicted polyunsaturated fatty acids levels on overall cancer risk and mortality. Cancer Epidemiol Biomarkers Prev, 2019, 28(6): 1015-1023.
20.	Trajanoska K, Morris JA, Oei L, et al. Assessment of the genetic and clinical determinants of fracture risk: genome wide association and mendelian randomisation study. BMJ, 2018, 362: k3225.
21.	Vimaleswaran KS, Berry DJ, Lu C, et al. Causal relationship between obesity and vitamin D status: bi-directional Mendelian randomization analysis of multiple cohorts. PLoS Med, 2013, 10(2): e1001383.
22.	Mountjoy E, Davies NM, Plotnikov D, et al. Education and myopia: assessing the direction of causality by mendelian randomisation. BMJ, 2018, 361: k2022.
23.	Wootton RE, Lawn RB, Millard LAC, et al. Evaluation of the causal effects between subjective wellbeing and cardiometabolic health: mendelian randomisation study. BMJ, 2018, 362: k3788.
24.	Tillmann T, Vaucher J, Okbay A, et al. Education and coronary heart disease: mendelian randomisation study. BMJ, 2017, 358: j3542.
25.	McInnes MDF, Moher D, Thombs BD, et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA Statement. JAMA, 2018, 319(4): 388-396.

1. Smith GD, Ebrahim S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease. Int J Epidemiol, 2003, 32(1): 1-22.
2. Gray R, Wheatley K. How to avoid bias when comparing bone marrow transplantation with chemotherapy. Bon Mar Trans, 1991, 7(Suppl 3): 9-12.
3. Davey SG. Capitalising on Mendelian randomization to assess the effects of treatments, 2006. Available at: https://www.jameslindlibrary.org/articles/capitalising-on-mendelian-randomization-to-assess-theeffects-of-treatments/.
4. Smith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol, 2004, 33(1): 30-42.
5. Xu J, Li M, Gao Y, et al. Using Mendelian randomization as the cornerstone for causal inference in epidemiology. Environ Sci Pollut Res Int, 2022, 29(4): 5827-5839.
6. Niu M, Gao Y, Yang M, et al. The quality and clinical applicability of recommendations in anxiety disorders guidelines: A systematic review of seventeen guidelines from seven countries. J Affect Disord, 2021, 295: 1301-1309.
7. Gao Y, Liu M, Shi S, et al. Prespecification of subgroup analyses and examination of treatment-subgroup interactions in cancer individual participant data meta-analyses are suboptimal. J Clin Epidemiol, 2021, 138: 156-167.
8. Gao Y, Shi S, Li M, et al. Statistical analyses and quality of individual participant data network meta-analyses were suboptimal: a cross-sectional study. BMC Med, 2020, 18(1): 120.
9. Boef AG, Dekkers OM, le Cessie S. Mendelian randomization studies: a review of the approaches used and the quality of reporting. Int J Epidemiol, 2015, 44(2): 496-511.
10. Lor GCY, Risch HA, Fung WT, et al. Reporting and guidelines for mendelian randomization analysis: A systematic review of oncological studies. Cancer Epidemiol, 2019, 62: 101577.
11. Diemer EW, Labrecque JA, Neumann A, et al. Mendelian randomisation approaches to the study of prenatal exposures: a systematic review. Paediatr Perinat Epidemiol, 2021, 35(1): 130-142.
12. Davey SG, Davies NM, Dimou N, et al. STROBE-MR: guidelines for strengthening the reporting of Mendelian randomization studies. Peer J, 2019. Available at: https://doi.org/10.7287/peerj.preprints.27857v1.
13. Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR Statement. JAMA, 2021, 326(16): 1614-1621.
14. Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ, 2021, 375: n2233.
15. Yuan S, Bruzelius M, Damrauer SM, et al. Cardiometabolic, lifestyle, and nutritional factors in relation to varicose veins: a Mendelian randomization study. J Am Heart Assoc, 2021, 10(21): e022286.
16. Millard LAC, Davies NM, Tilling K, et al. Searching for the causal effects of body mass index in over 300 000 participants in UK Biobank, using Mendelian randomization. PLoS Genet, 2019, 15(2): e1007951.
17. Zhao JV, Luo S, Schooling CM. Sex-specific Mendelian randomization study of genetically predicted insulin and cardiovascular events in the UK Biobank. Commun Biol, 2019, 2: 332.
18. Burgess S, Seaman S, Lawlor DA, et al. Missing data methods in Mendelian randomization studies with multiple instruments. Am J Epidemiol, 2011, 174(9): 1069-1076.
19. Liyanage UE, Ong JS, An J, et al. Mendelian randomization study for genetically predicted polyunsaturated fatty acids levels on overall cancer risk and mortality. Cancer Epidemiol Biomarkers Prev, 2019, 28(6): 1015-1023.
20. Trajanoska K, Morris JA, Oei L, et al. Assessment of the genetic and clinical determinants of fracture risk: genome wide association and mendelian randomisation study. BMJ, 2018, 362: k3225.
21. Vimaleswaran KS, Berry DJ, Lu C, et al. Causal relationship between obesity and vitamin D status: bi-directional Mendelian randomization analysis of multiple cohorts. PLoS Med, 2013, 10(2): e1001383.
22. Mountjoy E, Davies NM, Plotnikov D, et al. Education and myopia: assessing the direction of causality by mendelian randomisation. BMJ, 2018, 361: k2022.
23. Wootton RE, Lawn RB, Millard LAC, et al. Evaluation of the causal effects between subjective wellbeing and cardiometabolic health: mendelian randomisation study. BMJ, 2018, 362: k3788.
24. Tillmann T, Vaucher J, Okbay A, et al. Education and coronary heart disease: mendelian randomisation study. BMJ, 2017, 358: j3542.
25. McInnes MDF, Moher D, Thombs BD, et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA Statement. JAMA, 2018, 319(4): 388-396.

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