Mishra SR, Chung HF, Waller M, Mishra GD. Duration of oestrogen exposure during reproductive years, age at menarche and age at menopause, and risk of cardiovascular disease events, all-cause and cardiovascular mortality: a systematic review and meta-analysis. BJOG. 2020. https://doi.org/10.1111/1471-0528.16524.

Article  PubMed  PubMed Central  Google Scholar 

Anagnostis P, Bosdou JK, Vaitsi K, Goulis DG, Lambrinoudaki I. Estrogen and bones after menopause: a reappraisal of data and future perspectives. Hormones (Athens). 2020. https://doi.org/10.1007/s42000-020-00218-6.

Article  Google Scholar 

Anagnostis P, Siolos P, Gkekas NK, et al. Association between age at menopause and fracture risk: a systematic review and meta-analysis. Endocrine. 2019;63(2):213–24. https://doi.org/10.1007/s12020-018-1746-6.

CAS  Article  PubMed  Google Scholar 

Menarche, menopause, and breast cancer risk: individual participant meta-analysis, including 118,964 women with breast cancer from 117 epidemiological studies. Lancet Oncol. 2012;13(11):1141–51. https://doi.org/10.1016/s1470-2045(12)70425-4

Yarmolinsky J, Relton CL, Lophatananon A, et al. Appraising the role of previously reported risk factors in epithelial ovarian cancer risk: a Mendelian randomization analysis. PLoS Med. 2019;16(8): e1002893. https://doi.org/10.1371/journal.pmed.1002893.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Wu Y, Sun W, Liu H, Zhang D. Age at menopause and risk of developing endometrial cancer: a meta-analysis. Biomed Res Int. 2019;2019:8584130. https://doi.org/10.1155/2019/8584130.

Article  PubMed  PubMed Central  Google Scholar 

Pinkerton JV. Hormone therapy for postmenopausal women. N Engl J Med. 2020;382(5):446–55. https://doi.org/10.1056/NEJMcp1714787.

Article  PubMed  Google Scholar 

Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey SG. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8):1133–63. https://doi.org/10.1002/sim.3034.

Article  PubMed  Google Scholar 

Day FR, Ruth KS, Thompson DJ, et al. Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair. Nat Genet. 2015;47(11):1294–303. https://doi.org/10.1038/ng.3412.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Neumeyer S, Banbury BL, Arndt V, et al. Mendelian randomisation study of age at menarche and age at menopause and the risk of colorectal cancer. Br J Cancer. 2018;118(12):1639–47. https://doi.org/10.1038/s41416-018-0108-8.

CAS  Article  PubMed  PubMed Central  Google Scholar 

van der Plaat DA, Pereira M, Pesce G, et al. Age at menopause and lung function: a Mendelian randomisation study. Eur Respir J. 2019. https://doi.org/10.1183/13993003.02421-2018.

Article  PubMed  PubMed Central  Google Scholar 

Millard LAC, Davies NM, Tilling K, Gaunt TR, Davey SG. 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. https://doi.org/10.1371/journal.pgen.1007951.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Millard LAC, Munafo MR, Tilling K, Wootton RE, Davey SG. MR-pheWAS with stratification and interaction: searching for the causal effects of smoking heaviness identified an effect on facial aging. PLoS Genet. 2019;15(10): e1008353. https://doi.org/10.1371/journal.pgen.1008353.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Magnus MC, Guyatt AL, Lawn RB, et al. Identifying potential causal effects of age at menarche: a Mendelian randomization phenome-wide association study. BMC Med. 2020;18(1):71. https://doi.org/10.1186/s12916-020-01515-y.

Article  PubMed  PubMed Central  Google Scholar 

Allen NE, Sudlow C, Peakman T, Collins R. UK biobank data: come and get it. Sci Transl Med. 2014;6(224):224ed4. https://doi.org/10.1126/scitranslmed.3008601.

Article  PubMed  Google Scholar 

Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3): e1001779. https://doi.org/10.1371/journal.pmed.1001779.

Article  PubMed  PubMed Central  Google Scholar 

Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–9. https://doi.org/10.1038/s41586-018-0579-z.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Mitchell R, Hemani G, Dudding T, Corbin LJ, Harrison D, Paternoster L. UK biobank genetic data: MRC-IEU quality control, version 2. 2019. https://data.bris.ac.uk/data/dataset/1ovaau5sxunp2cv8rcy88688v. Accessed 01/03/2019

Ruth KS, Day FR, Hussain J, et al. Genetic insights into biological mechanisms governing human ovarian ageing. Nature. 2021. https://doi.org/10.1038/s41586-021-03779-7.

Article  PubMed  PubMed Central  Google Scholar 

Millard LAC, Davies NM, Gaunt TR, Davey Smith G, Tilling K. Software application profile: PHESANT: a tool for performing automated phenome scans in UK biobank. Int J Epidemiol. 2017. https://doi.org/10.1093/ije/dyx204.

Article  PubMed  PubMed Central  Google Scholar 

Chambers JC, Zhang W, Sehmi J, et al. Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nat Genet. 2011;43(11):1131–8. https://doi.org/10.1038/ng.970.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Pattaro C, Teumer A, Gorski M, et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun. 2016;7:10023. https://doi.org/10.1038/ncomms10023.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Willer CJ, Schmidt EM, Sengupta S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274–83. https://doi.org/10.1038/ng.2797.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Wheeler E, Leong A, Liu CT, 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. https://doi.org/10.1371/journal.pmed.1002383.

Article  PubMed  PubMed Central  Google Scholar 

Estrada K, Styrkarsdottir U, Evangelou E, et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet. 2012;44(5):491–501. https://doi.org/10.1038/ng.2249.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Michailidou K, Lindström S, Dennis J, et al. Association analysis identifies 65 new breast cancer risk loci. Nature. 2017;551(7678):92–4. https://doi.org/10.1038/nature24284.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Shrine N, Guyatt AL, Erzurumluoglu AM, et al. New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries. Nat Genet. 2019;51(3):481–93. https://doi.org/10.1038/s41588-018-0321-7.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Lu Y, Day FR, Gustafsson S, et al. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nat Commun. 2016;7:10495. https://doi.org/10.1038/ncomms10495.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Xue A, Wu Y, Zhu Z, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun. 2018;9(1):2941. https://doi.org/10.1038/s41467-018-04951-w.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Trynka G, Hunt KA, Bockett NA, et al. Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nat Genet. 2011;43(12):1193–201. https://doi.org/10.1038/ng.998.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25. https://doi.org/10.1093/ije/dyv080.

Article  PubMed  PubMed Central  Google Scholar 

Zheng J, Baird D, Borges MC, et al. Recent developments in Mendelian randomization studies. Curr Epidemiol Rep. 2017;4(4):330–45. https://doi.org/10.1007/s40471-017-0128-6.

Article  PubMed  PubMed Central  Google Scholar 

Finkelstein JS, Brockwell SE, Mehta V, et al. Bone mineral density changes during the menopause transition in a multiethnic cohort of women. J Clin Endocrinol Metab. 2008;93(3):861–8. https://doi.org/10.1210/jc.2007-1876.

CAS  Article  PubMed  Google Scholar 

Cumming RG, Klineberg RJ. Breastfeeding and other reproductive factors and the risk of hip fractures in elderly women. Int J Epidemiol. 1993;22(4):684–91. https://doi.org/10.1093/ije/22.4.684.

CAS  Article  PubMed  Google Scholar 

Papaioannou A, Joseph L, Ioannidis G, et al. Risk factors associated with incident clinical vertebral and nonvertebral fractures in postmenopausal women: the Canadian Multicentre Osteoporosis Study (CaMos). Osteoporos Int. 2005;16(5):568–78. https://doi.org/10.1007/s00198-004-1735-x.

Article  PubMed  Google Scholar 

Parazzini F, Tavani A, Ricci E, La Vecchia C. Menstrual and reproductive factors and hip fractures in post menopausal women. Maturitas. 1996;24(3):191–6. https://doi.org/10.1016/s0378-5122(96)82009-3.

CAS  Article  PubMed  Google Scholar