{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T17:36:49Z","timestamp":1792517809879,"version":"4.5.0"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Nutr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Chronic prostatitis\/chronic pelvic pain syndrome (CP\/CPPS) has a multifactorial etiology where diet is considered an important factor. This study aimed to develop a predictive model for CP\/CPPS symptom severity by analyzing food frequency questionnaire (FFQ) data with machine learning techniques, providing a basis for personalized nutritional interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study included 313 patients with CP\/CPPS. We used principal component analysis (PCA) to extract dietary patterns from FFQ data and applied LASSO regression to select key predictors of symptom severity. Subsequently, six machine learning models (logistic regression, random forest, XGBoost, support vector machine, K-nearest neighbors, and multilayer perceptron) were trained and compared. Model performance was evaluated using ROC curves, decision curve analysis (DCA), and calibration plots. SHapley Additive exPlanations (SHAP) were used to interpret the optimal model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>PCA identified two major dietary patterns: a \u201cRed Meat and Processed Food\u201d dietary pattern (PC1) and a \u201cDairy-rich\u201d pattern (PC2). LASSO regression selected key predictors, among which the \u201cRed Meat and Processed Food\u201d dietary pattern demonstrated the strongest positive association with CP\/CPPS symptom severity. Among the models, while support vector machine (SVM) and logistic regression showed high AUC values, the XGBoost model demonstrated the best overall performance across a balance of metrics including accuracy, precision, recall, and F1-score, and was selected as the final model (AUC = 0.883). SHAP analysis identified the Red Meat and Processed Food dietary pattern as the most important feature associated with symptom severity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study successfully developed a machine learning model based on dietary patterns that effectively predicts CP\/CPPS symptom severity. The model underscores the significant association between nutrition and disease management and, with its strong predictive performance and interpretability, offers a novel tool for precision nutrition in CP\/CPPS.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnut.2025.1660430","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T16:31:06Z","timestamp":1768840266000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Dietary patterns and chronic prostatitis: a symptom severity prediction model based on nutritional clustering and machine learning"],"prefix":"10.3389","volume":"12","author":[{"given":"Zhen","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Urology, Hefei Second People\u2019s Hospital, Hefei","place":["Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Urology, Hefei Second People\u2019s Hospital, Hefei","place":["Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Urology, Hefei Second People\u2019s Hospital, Hefei","place":["Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongle","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Urology, Hefei Second People\u2019s Hospital, Hefei","place":["Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhua","family":"Xi","sequence":"additional","affiliation":[{"name":"Department of Urology, Hefei Second People\u2019s Hospital, Hefei","place":["Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanbin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Urology, Hefei Second People\u2019s Hospital, Hefei","place":["Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1007\/s40266-021-00890-2","article-title":"The NIH Chronic Prostatitis Symptom Index (NIH-CPSI): a symptom index for chronic prostatitis\/chronic pelvic pain syndrome.","volume":"63","author":"Shoskes","year":"2004","journal-title":"Urology."},{"key":"B2","doi-asserted-by":"publisher","first-page":"2532","DOI":"10.3389\/fimmu.2017.00898","article-title":"Chronic prostatitis\/chronic pelvic pain syndrome.","volume":"172","author":"Pontari","year":"2004","journal-title":"J Urol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s00005-021-00628-3","article-title":"Prostatitis.","volume":"38","author":"Nickel","year":"2011","journal-title":"Urol Clin North Am."},{"key":"B4","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1038\/pcan.2015.57","article-title":"The etiology and epidemiology of chronic prostatitis\/chronic pelvic pain syndrome.","volume":"63","author":"Shoskes","year":"2004","journal-title":"Urology."},{"key":"B5","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1038\/pcan.2014.18","article-title":"The role of diet in chronic prostatitis\/chronic pelvic pain syndrome.","volume":"7","author":"Shoskes","year":"2006","journal-title":"Curr Urol Rep."},{"key":"B6","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1002\/ijc.21112","article-title":"Diet and prostate cancer: a review of the evidence.","volume":"62","author":"Song","year":"2010","journal-title":"Nutr Cancer."},{"key":"B7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/nu8100626","article-title":"Dietary patterns and prostate cancer risk: a review of the evidence.","volume":"25","author":"Yang","year":"2015","journal-title":"Curr Opin Urol."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eururo.2025.07.017","article-title":"Anti-inflammatory and antioxidant properties of fruits and vegetables.","volume":"7","author":"Parra","year":"2016","journal-title":"Food Funct."},{"key":"B9","doi-asserted-by":"publisher","first-page":"463","DOI":"10.3390\/nu13020496","article-title":"Dietary patterns and prostate cancer: a review of the evidence.","volume":"19","author":"Davis","year":"2016","journal-title":"Curr Opin Clin Nutr Metab Care."},{"key":"B10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.numecd.2018.10.011","article-title":"Nutritional epidemiology: issues and challenges.","volume":"41","author":"Willett","year":"2012","journal-title":"Int J Epidemiol."},{"key":"B11","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1056\/NEJMoa1200303","article-title":"Primary prevention of cardiovascular disease with a Mediterranean diet.","volume":"368","author":"Estruch","year":"2013","journal-title":"N Engl J Med."},{"key":"B12","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1093\/advances\/nmaa183","article-title":"Machine learning: a review of applications in medical diagnosis.","volume":"40","author":"Kotsiantis","year":"2007","journal-title":"Artif Intell Med."},{"key":"B13","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1186\/s12911-023-02262-9","article-title":"Machine learning in nutritional epidemiology.","volume":"22","author":"Seldin","year":"2019","journal-title":"Curr Opin Clin Nutr Metab Care."},{"key":"B14","volume-title":"Principal Component Analysis.","author":"Jolliffe","year":"2002"},{"key":"B15","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso.","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J R Stat Soc Series B."},{"key":"B16","doi-asserted-by":"crossref","DOI":"10.1002\/9781118548387","volume-title":"Applied Logistic Regression.","author":"Hosmer","year":"2013"},{"key":"B17","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests.","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn."},{"key":"B18","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: a gradient boosting machine.","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann Stat."},{"key":"B19","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"XGBoost: a scalable tree boosting system.","author":"Chen","year":"2016","journal-title":"Proc 22nd ACM SIGKDD Int Conf Knowl Discov Data Min."},{"key":"B20","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks.","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach Learn."},{"key":"B21","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification.","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans Inf Theory."},{"key":"B22","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The perceptron: a probabilistic model for information storage and organization in the brain.","volume":"65","author":"Rosenblatt","year":"1958","journal-title":"Psychol Rev."},{"key":"B23","doi-asserted-by":"publisher","first-page":"4765","DOI":"10.48550\/arXiv.1705.07874","article-title":"A unified approach to interpreting model predictions.","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv Neural Inf Process Syst."},{"key":"B24","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.eururo.2004.07.030","article-title":"Diet and chronic inflammation: a review.","volume":"11","author":"Pirozzi","year":"2018","journal-title":"J Inflamm Res."},{"key":"B25","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1016\/j.jacc.2006.03.052","article-title":"The effects of diet on inflammation: emphasis on the metabolic syndrome.","volume":"48","author":"Giugliano","year":"2006","journal-title":"J Am Coll Cardiol."},{"key":"B26","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/s40120-023-00505-5","article-title":"Machine learning in medicine: a practical guide.","volume":"19","author":"Sidey-Gibbons","year":"2019","journal-title":"BMC Med Res Methodol."},{"key":"B27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21037\/atm.2019.03.29","article-title":"A review of interpretable machine learning in healthcare.","volume":"4","author":"Ghassemi","year":"2021","journal-title":"NPJ Digit Med."},{"key":"B28","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1001\/jama.282.3.236","article-title":"NIH consensus definition and classification of prostatitis.","volume":"282","author":"Krieger","year":"1999","journal-title":"JAMA."},{"key":"B29","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: a novel method for evaluating prediction models.","volume":"26","author":"Vickers","year":"2006","journal-title":"Med Decis Making."},{"key":"B30","unstructured":"Chronic pelvic pain guideline\n          \n          2023"}],"container-title":["Frontiers in Nutrition"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnut.2025.1660430\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T16:31:07Z","timestamp":1768840267000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnut.2025.1660430\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,19]]},"references-count":30,"alternative-id":["10.3389\/fnut.2025.1660430"],"URL":"https:\/\/doi.org\/10.3389\/fnut.2025.1660430","relation":{},"ISSN":["2296-861X"],"issn-type":[{"value":"2296-861X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,19]]},"article-number":"1660430"}}