Almanza-Aguilera Enrique, Martínez-Huélamo Miriam, López-Hernández Yamilé, Guiñón-Fort Daniel, Guadall Anna, Cruz Meryl, Perez-Cornago Aurora, Rostgaard-Hansen Agnetha L, Tjønneland Anne, Dahm Christina C, Katzke Verena, Schulze Matthias B, Masala Giovanna, Agnoli Claudia, Tumino Rosario, Ricceri Fulvio, Lasheras Cristina, Crous-Bou Marta, Sánchez Maria-Jose, Aizpurua-Atxega Amaia, Guevara Marcela, Tsilidis Kostas K, Chatziioannou Anastasia Chrysovalantou, Weiderpass Elisabete, Travis Ruth C, Wishart David S, Andrés-Lacueva Cristina, Zamora-Ros Raul
Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), 08908 Barcelona, Spain.
Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain.
Cancers (Basel). 2024 Dec 8;16(23):4116. doi: 10.3390/cancers16234116.
: 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. : 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. : Thirty-one metabolites (predominately derivatives of food intake and microbial metabolism) were associated with overall PCa risk and its clinical subtypes ( < 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. : 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.
营养代谢组学可能会揭示早期代谢改变以及饮食暴露对前列腺癌(PCa)风险的作用的新见解。我们旨在前瞻性地研究血浆代谢物浓度与PCa风险之间的关联,包括临床相关的肿瘤亚型。:我们采用了靶向和大规模代谢组学方法,对来自欧洲癌症与营养前瞻性调查(EPIC)队列的851对匹配的PCa病例对照样本的血浆样本进行分析。通过多变量条件逻辑回归分析估计代谢物浓度与PCa风险之间的关联。错误发现率(FDR)用于控制多重检验校正。:31种代谢物(主要是食物摄入和微生物代谢的衍生物)与总体PCa风险及其临床亚型相关(<0.05),但没有一种关联超过FDR阈值。最强的正相关和负相关分别是二甲基甘氨酸(OR = 2.13;95%CI 1.16 - 3.91)与晚期PCa风险(n = 157)以及吲哚 - 3 - 乳酸(OR = 0.28;95%CI 0.09 - 0.87)与致命PCa风险(n = 57);然而,这些关联在多重检验校正后并不显著。:当前营养代谢组学研究的结果表明,除了早期代谢失调外,一些食物摄入的生物标志物可能与PCa风险有关,特别是晚期和致命性PCa。需要进一步的独立且更大规模的研究来验证我们的结果。