{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:29:35Z","timestamp":1790314175592,"version":"4.1.0"},"reference-count":34,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2023,4,16]],"date-time":"2023-04-16T00:00:00Z","timestamp":1681603200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Prostate"],"published-print":{"date-parts":[[2023,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate prostate cancer risk assessment will enable identification of men who are at increased risk of the disease. Using the UK Biobank population\u2010based cohort, we developed and validated a simple model comprising age, family history, and a polygenic risk score (PRS) to predict 5\u2010year risk of prostate cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Eligible participants were unaffected Caucasian men aged 40\u201369 years at their baseline assessment who had genotyping data available and had completed 6 or more weeks of follow\u2010up. Family history was the number of affected first\u2010degree relatives: 0, 1, or 2+. We used 264 single\u2010nucleotide polymorphisms (SNPs) of a previously developed 269\u2010SNP PRS and population standardized the PRS to have a mean of 1. Age was categorized into 10\u2010year groups: 40\u201349, 50\u201359, and 60\u201369. In a 70% training data set, we used Cox regression with age as the time axis to model family history, PRS, and age group. The model estimates were used with prostate cancer incidences to derive 5\u2010year risks of prostate cancer. Using 5 years of follow\u2010up in a 30% testing data set, the model was tested in terms of its association per quintile of risk, discrimination, and calibration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the 198 334 eligible participants, 8996 (4.5%) were diagnosed with incident prostate cancer during follow\u2010up and had a mean age of 67.9 (SD\u2009=\u20095.8) years at diagnosis. The best\u2010fitting model included the PRS, family history, 10\u2010year age group, interactions between age and PRS, and age and family history. In the 30% testing data set with follow\u2010up limited to 5 years, the hazard ratio\u00a0per SD of 5\u2010year risk was 3.058 (95% confidence interval [CI], 2.720\u20133.438) and the Harrell's C\u2010index was 0.811 (95% CI, 0.800\u20130.821). Overall, there were 1088 observed and 1159.1 expected prostate cancers, a standardized incidence ratio of 0.939 (95% CI, 0.885\u20130.996).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Men at increased risk of prostate cancer could benefit from informed discussions around the risks and benefits of available options for screening for prostate cancer. Although the model was developed in Caucasian men, it can be used with ethnicity\u2010specific polygenic risk and incidence rates for other populations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pros.24537","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T01:19:35Z","timestamp":1681694375000},"page":"962-969","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Development and validation of a simple prostate cancer risk prediction model based on age, family history, and polygenic risk"],"prefix":"10.1002","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2448-2548","authenticated-orcid":false,"given":"Gillian S.","family":"Dite","sequence":"first","affiliation":[{"name":"Genetic Technologies Limited  Fitzroy Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5397-3493","authenticated-orcid":false,"given":"Erika","family":"Spaeth","sequence":"additional","affiliation":[{"name":"Phenogen Sciences Inc  Charlotte North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7881-1952","authenticated-orcid":false,"given":"Nicholas M.","family":"Murphy","sequence":"additional","affiliation":[{"name":"Genetic Technologies Limited  Fitzroy Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8635-5237","authenticated-orcid":false,"given":"Richard","family":"Allman","sequence":"additional","affiliation":[{"name":"Genetic Technologies Limited  Fitzroy Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,4,16]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.14740\/wjon1191"},{"key":"e_1_2_9_3_1","unstructured":"National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology: Prostate Cancer Early Detection. Version 1. Published 2022. Accessed 9 May 9 2022.https:\/\/www.nccn.org\/guidelines\/guidelines-detail?category=2&id=1460"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2018.3712"},{"key":"e_1_2_9_5_1","unstructured":"National Health Service. PSA Testing: Prostate Cancer. Accessed January 31 2022.https:\/\/www.nhs.uk\/conditions\/prostate-cancer\/psa-testing\/"},{"key":"e_1_2_9_6_1","unstructured":"Public Health England. Prostate Cancer Risk Management Programme: Overview. Published 2016. 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Data Providers and Dates of Data Availability. Published 2021. Accessed December 13 2021.https:\/\/biobank.ctsu.ox.ac.uk\/showcase\/exinfo.cgi?src=Data_providers_and_dates"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0214311"},{"key":"e_1_2_9_21_1","unstructured":"R Core Team. R: A Language and Environment for Statistical Computing. [computer program]. 2020. 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