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Risk factors for prostate cancer: An umbrella review of prospective observational studies and mendelian randomization analyses.

Authors :
Cui, Huijie
Zhang, Wenqiang
Zhang, Li
Qu, Yang
Xu, Zhengxing
Tan, Zhixin
Yan, Peijing
Tang, Mingshuang
Yang, Chao
Wang, Yutong
Chen, Lin
Xiao, Chenghan
Zou, Yanqiu
Liu, Yunjie
Zhang, Ling
Yang, Yanfang
Yao, Yuqin
Li, Jiayuan
Liu, Zhenmi
Yang, Chunxia
Source :
PLoS Medicine; 3/15/2024, Vol. 21 Issue 3, p1-33, 33p
Publication Year :
2024

Abstract

Background: The incidence of prostate cancer is increasing in older males globally. Age, ethnicity, and family history are identified as the well-known risk factors for prostate cancer, but few modifiable factors have been firmly established. The objective of this study was to identify and evaluate various factors modifying the risk of prostate cancer reported in meta-analyses of prospective observational studies and mendelian randomization (MR) analyses. Methods and findings: We searched PubMed, Embase, and Web of Science from the inception to January 10, 2022, updated on September 9, 2023, to identify meta-analyses and MR studies on prostate cancer. Eligibility criteria for meta-analyses were (1) meta-analyses including prospective observational studies or studies that declared outcome-free at baseline; (2) evaluating the factors of any category associated with prostate cancer incidence; and (3) providing effect estimates for further data synthesis. Similar criteria were applied to MR studies. Meta-analysis was repeated using the random-effects inverse-variance model with DerSimonian—Laird method. Quality assessment was then conducted for included meta-analyses using AMSTAR-2 tool and for MR studies using STROBE-MR and assumption evaluation. Subsequent evidence grading criteria for significant associations in meta-analyses contained sample size, P values and 95% confidence intervals, 95% prediction intervals, heterogeneity, and publication bias, assigning 4 evidence grades (convincing, highly suggestive, suggestive, or weak). Significant associations in MR studies were graded as robust, probable, suggestive, or insufficient considering P values and concordance of effect directions. Finally, 92 selected from 411 meta-analyses and 64 selected from 118 MR studies were included after excluding the overlapping and outdated studies which were published earlier and contained fewer participants or fewer instrument variables for the same exposure. In total, 123 observational associations (45 significant and 78 null) and 145 causal associations (55 significant and 90 null) were categorized into lifestyle; diet and nutrition; anthropometric indices; biomarkers; clinical variables, diseases, and treatments; and environmental factors. Concerning evidence grading on significant associations, there were 5 highly suggestive, 36 suggestive, and 4 weak associations in meta-analyses, and 10 robust, 24 probable, 4 suggestive, and 17 insufficient causal associations in MR studies. Twenty-six overlapping factors between meta-analyses and MR studies were identified, with consistent significant effects found for physical activity (PA) (occupational PA in meta: OR = 0.87, 95% CI: 0.80, 0.94; accelerator-measured PA in MR: OR = 0.49, 95% CI: 0.33, 0.72), height (meta: OR = 1.09, 95% CI: 1.06, 1.12; MR: OR = 1.07, 95% CI: 1.01, 1.15, for aggressive prostate cancer), and smoking (current smoking in meta: OR = 0.74, 95% CI: 0.68, 0.80; smoking initiation in MR: OR = 0.91, 95% CI: 0.86, 0.97). Methodological limitation is that the evidence grading criteria could be expanded by considering more indices. Conclusions: In this large-scale study, we summarized the associations of various factors with prostate cancer risk and provided comparisons between observational associations by meta-analysis and genetically estimated causality by MR analyses. In the absence of convincing overlapping evidence based on the existing literature, no robust associations were identified, but some effects were observed for height, physical activity, and smoking. Huijie Cui and team identify and evaluate various risk factors of prostate cancer reported in meta-analyses of prospective observational studies and Mendelian randomization analyses. Author summary: Why was this study done?: The incidence of prostate cancer is increasing with the growing trend of aging globally. Effective preventions and interventions for prostate cancer require better understandings of its etiology. The well-known risk factors for prostate cancer are age, ethnicity, and family history, but few modifiable factors have been firmly established. What did the researchers do and find?: Our study extensively collected, evaluated, and compared the current observational and genetic evidence for various factors modifying the risk of prostate cancer based on meta-analyses and Mendelian randomization (MR) studies. Totally 123 observational associations (45 significant and 78 null) from 92 meta-analyses and 145 causal associations (55 significant and 90 null) from 64 MR studies were identified and categorized into lifestyle; diet and nutrition; anthropometric indices; biomarkers; clinical variables, diseases, and treatments; and environmental factors. Concerning evidence grading on significant associations, there were 5 highly suggestive, 36 suggestive, and 4 weak associations in meta-analyses, and 10 robust, 24 probable, 4 suggestive, and 17 insufficient causal associations in MR studies. Consistent significant associations between meta-analysis and MR studies were found for physical activity, height, and smoking, which however were not robust. What do these findings mean?: Most included cohort studies were conducted in developed western countries, and hence the findings in this study are limited for mainly European descendants. The comparison between observational associations by meta-analysis and genetically estimated causality by MR analyses does not provide robust evidence due to the lack of overlapping associations and high-quality evidence, especially in MR studies. Evidence grading criteria for meta-analyses could be further improved by adding more indices such as magnitude of effect size and different levels of sample size. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15491277
Volume :
21
Issue :
3
Database :
Complementary Index
Journal :
PLoS Medicine
Publication Type :
Academic Journal
Accession number :
176070092
Full Text :
https://doi.org/10.1371/journal.pmed.1004362