Assessing the Risk of Prostate Cancer with Nutritional and Environmental Factors: A Cross-Sectional Study from National Health and Nutrition Examination Survey 2001-2010.
Screening for prostate cancer (PCa) using prostate-specific antigen (PSA) is common. This study aimed to identify potential nutritional variables associated with PSA levels and develop a PCa risk prediction model. A total of 5,725 participants from the 2001-2010 National Health and Nutrition Examina...
| Publicado en: | Nutrition & Cancer Vol. 75; no. 5; pp. 1361 - 1373 |
|---|---|
| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
2023
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=171930612&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171930612 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01635581 7MS jtl: Nutrition & Cancer issn: 01635581 maglogo: N pubinfo: dt: 2023 vid: 75 iid: 5 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 171930612 162974560 171930612 171930612 10.1080/01635581.2023.2197687 171930612 ppf: 1361 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Assessing the Risk of Prostate Cancer with Nutritional and Environmental Factors: A Cross-Sectional Study from National Health and Nutrition Examination Survey 2001-2010. aug: au: Shuai Li Xiaopeng Hu affil: Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China sug: subj: Prostatic Neoplasms Risk Factors Nutrition Environmental Health Risk Assessment Prostate-Specific Antigen Cancer Screening Prediction Models Tumor Markers, Biological Diagnostic Use Human Male Cross Sectional Studies Surveys Machine Learning Algorithms Erythrocytes Lipoproteins, LDL Cholesterol Lipoproteins, HDL Cholesterol Questionnaires Scales Logistic Regression Male ab: Screening for prostate cancer (PCa) using prostate-specific antigen (PSA) is common. This study aimed to identify potential nutritional variables associated with PSA levels and develop a PCa risk prediction model. A total of 5,725 participants from the 2001-2010 National Health and Nutrition Examination Survey (NHANES) database were recruited and divided into discovery and validation sets. Three machine learning (ML) algorithms were performed in discovery set to select features associated with a high-risk PSA level. Age, red blood cell (RBC), blood cholesterol, blood lead, and prognostic nutritional index (PNI) were overlapping important contributors among the three ML models. Furthermore, the relationship between the selected variables and the high-risk PSA level was verified using weighted logistic regression models and restricted cubic spline (RCS) curves. After adjustment, age and blood lead were significantly positively associated with high-risk PSA, while PNI was significantly negatively associated with high-risk PSA. A nomogram including age, RBC, blood cholesterol, and PNI was constructed for predicting the PCa risk according to PSA levels, and the area under the receiver operating characteristic curve (AUROC) of discovery and validation sets was 0.755 and 0.756, respectively. The constructed nomogram may be used for targeted PSA testing and cancer screening in community healthcare. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|