Measuring pre-diagnostic blood metabolites may help identify novel risk factors for prostate cancer. Using data from 4387 matched case-control pairs from the European Prospective Investigation into Cancer and Nutrition (EPIC) study, we investigated the associations of 148 individual metabolites and three previously defined metabolite patterns with prostate cancer risk. Metabolites were measured by liquid chromatography-mass spectrometry. Multivariable-adjusted conditional logistic regression was used to estimate the odds ratio per standard deviation increase in log metabolite concentration and metabolite patterns (OR1SD) for prostate cancer overall, and for advanced, high-grade, aggressive. We corrected for multiple testing using the Benjamini-Hochberg method. Overall, there were no associations between specific metabolites or metabolite patterns and overall, aggressive, or high-grade prostate cancer that passed the multiple testing threshold (padj <0.05). Six phosphatidylcholines (PCs) were inversely associated with advanced prostate cancer diagnosed at or within 10 years of blood collection. metabolite patterns 1 (64 PCs and three hydroxysphingomyelins) and 2 (two acylcarnitines, glutamate, ornithine, and taurine) were also inversely associated with advanced prostate cancer; when stratified by follow-up time, these associations were observed for diagnoses at or within 10 years of recruitment (OR1SD 0.80, 95% CI 0.66-0.96 and 0.76, 0.59-0.97, respectively) but were weaker after longer follow-up (0.95, 0.82-1.10 and 0.85, 0.67-1.06). Pattern 3 (8 lyso PCs) was associated with prostate cancer death (0.82, 0.68-0.98). Our results suggest that the plasma metabolite profile changes in response to the presence of prostate cancer up to a decade before detection of advanced-stage disease.
International journal of cancer. 2024 Oct 08 [Epub ahead of print]
Zoe S Grenville, Urwah Noor, Sabina Rinaldi, Marc J Gunter, Pietro Ferrari, Claudia Agnoli, Pilar Amiano, Alberto Catalano, María Dolores Chirlaque, Sofia Christakoudi, Marcela Guevara, Matthias Johansson, Rudolf Kaaks, Verena Katzke, Giovanna Masala, Anja Olsen, Keren Papier, Maria-Jose Sánchez, Matthias B Schulze, Anne Tjønneland, Tammy Y N Tong, Rosario Tumino, Elisabete Weiderpass, Raul Zamora-Ros, Timothy J Key, Karl Smith-Byrne, Julie A Schmidt, Ruth C Travis
Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK., Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France., Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumouri, Milan, Italy., CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain., Centre for Biostatistics, Epidemiology, and Public Health, Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy., Department of Epidemiology and Biostatistics, White City Campus, Imperial College, London, UK., Instituto de Salud Pública y Laboral de Navarra, Pamplona, Spain., Department of Cancer Epidemiology, German Cancer research Center (DKFZ), Heidelberg, Germany., Clinical Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy., The Danish Cancer Institute, Copenhagen, Denmark., Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain., Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany., Hyblean Association for Epidemiology Research, AIRE ONLUS, Ragusa, Italy., International Agency for Research on Cancer, World Health Organization, Lyon, France., Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain.