{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T20:34:21Z","timestamp":1793046861791,"version":"4.5.0"},"reference-count":59,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Women's Health"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>While highly prevalent, risk factors for incident polycystic ovary syndrome (PCOS) are poorly delineated. Using a population-based cohort, we sought to identify predictors of incident PCOS diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>\n                      A matched case\u2013control analysis was completed utilizing patients enrolled in Kaiser Permanente Washington from 2006 to 2019. Inclusion criteria included female sex, age 16\u201340 years, and \u22653 years of prior enrollment with \u22651 health care encounter. PCOS cases were identified using International Classification of Diseases codes. For each incident case (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      = 2,491), 5 patients without PCOS (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      = 12,455) were matched based on birth year and enrollment status. Potential risk factors preceding diagnosis included family history of PCOS, premature menarche, parity, race, weight gain, obesity, valproate use, metabolic syndrome, epilepsy, prediabetes, and types 1 and 2 diabetes. Potential risk factors for incident PCOS diagnosis were assessed with univariate and multivariable conditional logistic regressions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Mean age of PCOS cases was 26.9 years (SD 6.8). PCOS cases, compared with non-PCOS, were more frequently nulliparous (70.9% versus 62.4%) and in the 3 years prior to index date were more likely to have obesity (53.8% versus 20.7%), metabolic syndrome (14.5% versus 4.3%), prediabetes (7.4% versus 1.6%), and type 2 diabetes (4.1% versus 1.7%) (\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      &lt; 0.001 for all comparisons). In multivariable models, factors associated with higher risk for incident PCOS included the following: obesity (compared with nonobese) Class I\u2013II (body\u2013mass index [BMI], 30\u201340 kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ; odds ratio [OR], 3.8; 95% confidence interval [CI], 3.4\u20134.2), Class III (BMI &gt; 40 kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ; OR, 7.5, 95% CI, 6.5\u20138.7), weight gain (compared with weight loss or maintenance) of 1\u201310% (OR, 1.7, 95% CI, 1.3\u20132.1), 10\u201320% (OR, 1.9; 95% CI, 1.5\u20132.4), and &gt;20% (OR, 2.6; 95% CI, 1.9\u20133.6), prediabetes (OR, 2.7; 95% CI, 2.1\u20133.4), and metabolic syndrome (OR, 1.8: 95% CI, 1.5\u20132.1).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Excess weight gain, obesity, and metabolic dysfunction may play a key role in the ensuing phenotypic expression of PCOS. Treatment and prevention strategies targeted at preventing weight gain in early reproductive years may help reduce the risk of this syndrome.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1089\/jwh.2023.0741","type":"journal-article","created":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T07:41:53Z","timestamp":1711957313000},"page":"879-886","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Risk Factors for Incident Polycystic Ovary Syndrome Diagnosis"],"prefix":"10.1177","volume":"33","author":[{"given":"Jacob P.","family":"Christ","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology, School of Medicine, University of Washington, Seattle, Washington, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Onchee","family":"Yu","sequence":"additional","affiliation":[{"name":"Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brooke","family":"Barton","sequence":"additional","affiliation":[{"name":"School of Medicine, University of Washington, Seattle, Washington, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renate","family":"Schulze-Rath","sequence":"additional","affiliation":[{"name":"IEG &amp; BI, Bayer AG, Berlin, Germany."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jane","family":"Grafton","sequence":"additional","affiliation":[{"name":"Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Cronkite","sequence":"additional","affiliation":[{"name":"Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Covey","sequence":"additional","affiliation":[{"name":"Kaiser 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