Enrique Almanza-Aguilera, Miriam Martínez-Huélamo, Yamilé López-Hernández, Daniel Guiñón-Fort, Anna Guadall, Meryl Cruz, Aurora Perez-Cornago, Agnetha L Rostgaard-Hansen, Anne Tjønneland, Christina C Dahm, Verena Katzke, Matthias B Schulze, Giovanna Masala, Claudia Agnoli, Rosario Tumino, Fulvio Ricceri, Cristina Lasheras, Marta Crous-Bou, Maria-Jose Sánchez, Amaia Aizpurua-Atxega, Marcela Guevara, Kostas K Tsilidis, Anastasia Chrysovalantou Chatziioannou, Elisabete Weiderpass, Ruth C Travis, David S Wishart, Cristina Andrés-Lacueva, Raul Zamora-Ros
{"title":"Prediagnostic Plasma Nutrimetabolomics and Prostate Cancer Risk: A Nested Case-Control Analysis Within the EPIC Study.","authors":"Enrique Almanza-Aguilera, Miriam Martínez-Huélamo, Yamilé López-Hernández, Daniel Guiñón-Fort, Anna Guadall, Meryl Cruz, Aurora Perez-Cornago, Agnetha L Rostgaard-Hansen, Anne Tjønneland, Christina C Dahm, Verena Katzke, Matthias B Schulze, Giovanna Masala, Claudia Agnoli, Rosario Tumino, Fulvio Ricceri, Cristina Lasheras, Marta Crous-Bou, Maria-Jose Sánchez, Amaia Aizpurua-Atxega, Marcela Guevara, Kostas K Tsilidis, Anastasia Chrysovalantou Chatziioannou, Elisabete Weiderpass, Ruth C Travis, David S Wishart, Cristina Andrés-Lacueva, Raul Zamora-Ros","doi":"10.3390/cancers16234116","DOIUrl":null,"url":null,"abstract":"<p><p><b>Background and Objective</b>: Nutrimetabolomics may reveal novel insights into early metabolic alterations and the role of dietary exposures on prostate cancer (PCa) risk. We aimed to prospectively investigate the associations between plasma metabolite concentrations and PCa risk, including clinically relevant tumor subtypes. <b>Methods</b>: We used a targeted and large-scale metabolomics approach to analyze plasma samples of 851 matched PCa case-control pairs from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Associations between metabolite concentrations and PCa risk were estimated by multivariate conditional logistic regression analysis. False discovery rate (FDR) was used to control for multiple testing correction. <b>Results</b>: Thirty-one metabolites (predominately derivatives of food intake and microbial metabolism) were associated with overall PCa risk and its clinical subtypes (<i>p</i> < 0.05), but none of the associations exceeded the FDR threshold. The strongest positive and negative associations were for dimethylglycine (OR = 2.13; 95% CI 1.16-3.91) with advanced PCa risk (n = 157) and indole-3-lactic acid (OR = 0.28; 95% CI 0.09-0.87) with fatal PCa risk (n = 57), respectively; however, these associations did not survive correction for multiple testing. <b>Conclusions</b>: The results from the current nutrimetabolomics study suggest that apart from early metabolic deregulations, some biomarkers of food intake might be related to PCa risk, especially advanced and fatal PCa. Further independent and larger studies are needed to validate our results.</p>","PeriodicalId":9681,"journal":{"name":"Cancers","volume":"16 23","pages":""},"PeriodicalIF":4.5000,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancers","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/cancers16234116","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background and Objective: Nutrimetabolomics may reveal novel insights into early metabolic alterations and the role of dietary exposures on prostate cancer (PCa) risk. We aimed to prospectively investigate the associations between plasma metabolite concentrations and PCa risk, including clinically relevant tumor subtypes. Methods: We used a targeted and large-scale metabolomics approach to analyze plasma samples of 851 matched PCa case-control pairs from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Associations between metabolite concentrations and PCa risk were estimated by multivariate conditional logistic regression analysis. False discovery rate (FDR) was used to control for multiple testing correction. Results: Thirty-one metabolites (predominately derivatives of food intake and microbial metabolism) were associated with overall PCa risk and its clinical subtypes (p < 0.05), but none of the associations exceeded the FDR threshold. The strongest positive and negative associations were for dimethylglycine (OR = 2.13; 95% CI 1.16-3.91) with advanced PCa risk (n = 157) and indole-3-lactic acid (OR = 0.28; 95% CI 0.09-0.87) with fatal PCa risk (n = 57), respectively; however, these associations did not survive correction for multiple testing. Conclusions: The results from the current nutrimetabolomics study suggest that apart from early metabolic deregulations, some biomarkers of food intake might be related to PCa risk, especially advanced and fatal PCa. Further independent and larger studies are needed to validate our results.
期刊介绍:
Cancers (ISSN 2072-6694) is an international, peer-reviewed open access journal on oncology. It publishes reviews, regular research papers and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.