{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T01:16:42Z","timestamp":1777943802466,"version":"3.51.4"},"reference-count":32,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"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":["J Int Med Res"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To analyze pelvic floor dysfunction heterogeneity by identifying distinct clinical subtypes using unsupervised machine learning in a nationally representative cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This cross-sectional study analyzed 7291 women from the National Health and Nutrition Examination Survey (NHANES 2005\u20132012). K-means clustering was applied to pelvic floor dysfunction\u2013positive women to identify latent subtypes based on physiological features. Supervised machine learning models were subsequently developed to predict subtype membership, with SHapley Additive exPlanation analysis used to identify key predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Two distinct subtypes emerged. Phenotype 1 (\u201cmetabolic-inflammatory\u201d) was characterized by severe central obesity (mean body mass index, 36.1\u2009kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ), diabetes, and hypertension. Conversely, Phenotype 2 (\u201cmetabolically-healthy\u201d) revealed pelvic floor dysfunction symptoms but maintained a healthy metabolic profile indistinguishable from the controls. A neural network model accurately differentiated these groups (area under the curve\u2009=\u20090.848), identifying waist circumference as the primary predictive factor.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Pelvic floor dysfunction is not a single disorder but includes a distinct \u201cmetabolic-inflammatory\u201d phenotype strongly associated with systemic metabolic disease. This data-driven classification challenges traditional paradigms, suggesting \u201csubtype-specific\u201d strategies for precision management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/03000605261438949","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T05:16:58Z","timestamp":1777439818000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A metabolic-inflammatory phenotype of pelvic floor dysfunction: A machine learning\u2013based cross-sectional study in a nationally representative US population"],"prefix":"10.1177","volume":"54","author":[{"given":"Jingming","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Urology, Huai\u2019an Hospital of Traditional Chinese Medicine, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8079-888X","authenticated-orcid":false,"given":"Duo","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, The First People\u2019s Hospital of Guannan: Lianyungang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4282-6707","authenticated-orcid":false,"given":"Lan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Huai\u2019an 82 hospital, China RongTong Medical Healthcare Group Co. 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A metabolic-inflammatory phenotype of pelvic floor dysfunction: a machine learning-based discovery in a nationally representative U.S. cohort. 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