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Lifestyle Risk Prediction Model for Prostate Cancer in a Korean Population
PURPOSE: The use of prostate-specific antigen as a biomarker for prostate cancer (PC) has been controversial and is, therefore, not used by many countries in their national health screening programs. The biological characteristics of PC in East Asians including Koreans and Japanese are different fro...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Cancer Association
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6192929/ https://www.ncbi.nlm.nih.gov/pubmed/29268567 http://dx.doi.org/10.4143/crt.2017.484 |
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author | Kim, Sung Han Kim, Sohee Joung, Jae Young Kwon, Whi-An Seo, Ho Kyung Chung, Jinsoo Nam, Byung-Ho Lee, Kang Hyun |
author_facet | Kim, Sung Han Kim, Sohee Joung, Jae Young Kwon, Whi-An Seo, Ho Kyung Chung, Jinsoo Nam, Byung-Ho Lee, Kang Hyun |
author_sort | Kim, Sung Han |
collection | PubMed |
description | PURPOSE: The use of prostate-specific antigen as a biomarker for prostate cancer (PC) has been controversial and is, therefore, not used by many countries in their national health screening programs. The biological characteristics of PC in East Asians including Koreans and Japanese are different from those in the Western populations. Potential lifestyle risk factors for PC were evaluated with the aim of developing a risk prediction model. MATERIALS AND METHODS: A total of 1,179,172 Korean men who were cancer free from 1996 to 1997, had taken a physical examination, and completed a lifestyle questionnaire, were enrolled in our study to predict their risk for PC for the next eight years, using the Cox proportional hazards model. The model’s performance was evaluated using the C-statistic and Hosmer‒Lemeshow type chi-square statistics. RESULTS: The risk prediction model studied age, height, body mass index, glucose levels, family history of cancer, the frequency of meat consumption, alcohol consumption, smoking status, and physical activity, which were all significant risk factors in a univariate analysis. The model performed very well (C statistic, 0.887; 95% confidence interval, 0.879 to 0.895) and estimated an elevated PC risk in patients who did not consume alcohol or smoke, compared to heavy alcohol consumers (hazard ratio [HR], 0.78) and current smokers (HR, 0.73) (p < 0.001). CONCLUSION: This model can be used for identifying Korean and other East Asian men who are at a high risk for developing PC, as well as for cancer screening and developing preventive health strategies. |
format | Online Article Text |
id | pubmed-6192929 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Korean Cancer Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-61929292018-10-24 Lifestyle Risk Prediction Model for Prostate Cancer in a Korean Population Kim, Sung Han Kim, Sohee Joung, Jae Young Kwon, Whi-An Seo, Ho Kyung Chung, Jinsoo Nam, Byung-Ho Lee, Kang Hyun Cancer Res Treat Original Article PURPOSE: The use of prostate-specific antigen as a biomarker for prostate cancer (PC) has been controversial and is, therefore, not used by many countries in their national health screening programs. The biological characteristics of PC in East Asians including Koreans and Japanese are different from those in the Western populations. Potential lifestyle risk factors for PC were evaluated with the aim of developing a risk prediction model. MATERIALS AND METHODS: A total of 1,179,172 Korean men who were cancer free from 1996 to 1997, had taken a physical examination, and completed a lifestyle questionnaire, were enrolled in our study to predict their risk for PC for the next eight years, using the Cox proportional hazards model. The model’s performance was evaluated using the C-statistic and Hosmer‒Lemeshow type chi-square statistics. RESULTS: The risk prediction model studied age, height, body mass index, glucose levels, family history of cancer, the frequency of meat consumption, alcohol consumption, smoking status, and physical activity, which were all significant risk factors in a univariate analysis. The model performed very well (C statistic, 0.887; 95% confidence interval, 0.879 to 0.895) and estimated an elevated PC risk in patients who did not consume alcohol or smoke, compared to heavy alcohol consumers (hazard ratio [HR], 0.78) and current smokers (HR, 0.73) (p < 0.001). CONCLUSION: This model can be used for identifying Korean and other East Asian men who are at a high risk for developing PC, as well as for cancer screening and developing preventive health strategies. Korean Cancer Association 2018-10 2017-12-21 /pmc/articles/PMC6192929/ /pubmed/29268567 http://dx.doi.org/10.4143/crt.2017.484 Text en Copyright © 2018 by the Korean Cancer Association This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Kim, Sung Han Kim, Sohee Joung, Jae Young Kwon, Whi-An Seo, Ho Kyung Chung, Jinsoo Nam, Byung-Ho Lee, Kang Hyun Lifestyle Risk Prediction Model for Prostate Cancer in a Korean Population |
title | Lifestyle Risk Prediction Model for Prostate Cancer in a Korean Population |
title_full | Lifestyle Risk Prediction Model for Prostate Cancer in a Korean Population |
title_fullStr | Lifestyle Risk Prediction Model for Prostate Cancer in a Korean Population |
title_full_unstemmed | Lifestyle Risk Prediction Model for Prostate Cancer in a Korean Population |
title_short | Lifestyle Risk Prediction Model for Prostate Cancer in a Korean Population |
title_sort | lifestyle risk prediction model for prostate cancer in a korean population |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6192929/ https://www.ncbi.nlm.nih.gov/pubmed/29268567 http://dx.doi.org/10.4143/crt.2017.484 |
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