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Two-sample MR analyses with data from large genetic studies were conducted to replicate identified associations in phenome-wide MR analyses. Multivariable MR analyses were performed to investigate mediation effects of hormone-related biomarkers in observed associations with diseases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Phenome-wide MR analyses examined associations of genetically predicted SHBG, testosterone and oestradiol levels with 1211 disease outcomes, and identified 28 and 13 distinct phenotypes associated with genetically predicted SHBG and testosterone, respectively; 22 out of 28 associations for SHBG and 10 out of 13 associations for testosterone were replicated in two-sample MR analyses. Higher genetically predicted SHBG levels were associated with a reduced risk of hypertension, type 2 diabetes, diabetic complications, coronary atherosclerotic outcomes, gout and benign and malignant neoplasm of uterus, but an increased risk of varicose veins and fracture (mainly in females). Higher genetically predicted testosterone levels were associated with a lower risk of type 2 diabetes, coronary atherosclerotic outcomes, gout and coeliac disease mainly in males, but an increased risk of cholelithiasis in females.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings suggest that sex hormones may causally affect risk of several health outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ije\/dyac036","type":"journal-article","created":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T07:09:25Z","timestamp":1644476965000},"page":"1931-1942","source":"Crossref","is-referenced-by-count":58,"title":["Genetically predicted sex hormone levels and health outcomes: phenome-wide Mendelian randomization investigation"],"prefix":"10.1093","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5055-5627","authenticated-orcid":false,"given":"Shuai","family":"Yuan","sequence":"first","affiliation":[{"name":"Department of Big Data in Health Science, Center of Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou, China"},{"name":"Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet , Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Big Data in Health Science, Center of Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Big Data in Health Science, Center of Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Big Data in Health Science, Center of Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Big Data in Health Science, Center of Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Big Data in Health Science, Center of Clinical Big Data and Analytics of 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