Quantifying the Contributions of Environmental Factors to Prostate Cancer and Detecting Risk-Related Diet Metrics and Racial Disparities.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2023-04-27 eCollection Date: 2023-01-01 DOI:10.1177/11769351231168006
Wensheng Zhang, Kun Zhang
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Abstract

The relevance of nongenetic factors to prostate cancer (PCa) has been elusive. We aimed to quantify the contributions of environmental factors to PCa and identify risk-related diet metrics and relevant racial disparities. We performed a unique analysis of the Diet History Questionnaire data of 41 830 European Americans (EAs) and 1282 African Americans (AAs) in the PLCO project. The independent variables in the regression models consisted of age at trial entry, race, family history of prostate cancer (PCa-fh), diabetes history, body mass index (BMI), lifestyle (smoking and coffee consumption), marital status, and a specific nutrient/food factor (X). P < .05 and a 95% confidence interval excluding zero were adopted as the criteria for determining a significant difference (effect). We established a priority ranking among PCa risk-related genetic and environmental factors according to the deviances explained by them in the multivariate Cox-PH regression analysis: age > PCa-fh > diabetes ⩾ race > lifestyle ⩾marital-status ⩾BMI > X. We confirmed previous studies showing that (1) high protein and saturated fat levels in diet were related to increased PCa risk, (2) high-level supplementary selenium intake was harmful rather than beneficial for preventing PCa, and (3) supplementary vitamin B6 was beneficial for preventing benign PCa. We obtained the following novel findings: high-level organ meat intake was an independent predictor for increased aggressive PCa risk; supplementary iron, copper and magnesium increased benign PCa risk; and the AA diet was "healthy" in terms of the relatively lower protein and fat levels and was "unhealthy" in that it more commonly contained organ meat. In conclusion, we established a priority ranking among the contributing factors for PCa and identified several risk-related diet metrics and the racial disparities. Our findings suggested some new approaches to prevent PCa such as restriction of organ meat intake and supplementary microminerals.

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量化环境因素对前列腺癌的影响,检测与风险相关的饮食指标和种族差异。
非遗传因素与前列腺癌(PCa)的相关性一直难以捉摸。我们旨在量化环境因素对 PCa 的影响,并确定与风险相关的饮食指标和相关的种族差异。我们对 PLCO 项目中 41 830 名欧洲裔美国人(EA)和 1282 名非洲裔美国人(AA)的饮食史问卷数据进行了独特的分析。回归模型中的自变量包括参加试验时的年龄、种族、前列腺癌家族史(PCa-fh)、糖尿病史、体重指数(BMI)、生活方式(吸烟和喝咖啡)、婚姻状况以及特定的营养素/食物因子(X)。我们证实了之前的研究显示:(1)饮食中的高蛋白和饱和脂肪水平与 PCa 风险增加有关;(2)高水平的补充硒摄入对预防 PCa 有害而无益;(3)补充维生素 B6 对预防良性 PCa 有益。我们得出了以下新发现:高水平的内脏肉类摄入量是增加侵袭性 PCa 风险的独立预测因素;补充铁、铜和镁会增加良性 PCa 风险;AA 饮食的 "健康 "之处在于其蛋白质和脂肪水平相对较低,而 "不健康 "之处在于其更常含有内脏肉类。总之,我们对导致 PCa 的因素进行了优先排序,并确定了几种与风险相关的饮食指标和种族差异。我们的研究结果提出了一些预防 PCa 的新方法,如限制内脏肉类的摄入和补充微量元素。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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