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Int J Cancer. 2019 Apr 5. doi: 10.1002/ijc.32314. [Epub ahead of print]

Patterns in metabolite profile are associated with risk of more aggressive prostate cancer: A prospective study of 3,057 matched case-control sets from EPIC.

Author information

1
Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
2
International Agency for Research on Cancer, Lyon, France.
3
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
4
Department of Epidemiology, German Institute of Human Nutrition (DIfE) Potsdam-Rehbrücke, Nuthetal, Germany.
5
Hellenic Health Foundation, Athens, Greece.
6
2nd Pulmonary Medicine Department, School of Medicine, National and Kapodistrian University of Athens, "ATTIKON" University Hospital, Haidari, Greece.
7
Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy.
8
Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy.
9
Cancer Registry and Histopathology Department, "Civic - M.P.Arezzo" Hospital, Azienda Sanitaria Provinciale Di Ragusa (ASP), Ragusa, Italy.
10
Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
11
Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, The Netherlands.
12
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
13
Department of Social & Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
14
Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.
15
CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain.
16
Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria ibs.GRANADA, Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain.
17
Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia, Spain.
18
Department of Health and Social Sciences, Murcia University, Murcia, Spain.
19
Navarra Public Health Institute, Pamplona, Spain.
20
IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.
21
Basque Regional Health Department, Public Health Division of Gipuzkoa-BIODONOSTIA, San Sebastian, Spain.
22
Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.

Abstract

Metabolomics may reveal novel insights into the etiology of prostate cancer, for which few risk factors are established. We investigated the association between patterns in baseline plasma metabolite profile and subsequent prostate cancer risk, using data from 3,057 matched case-control sets from the European Prospective Investigation into Cancer and Nutrition (EPIC). We measured 119 metabolite concentrations in plasma samples, collected on average 9.4 years before diagnosis, by mass spectrometry (AbsoluteIDQ p180 Kit, Biocrates Life Sciences AG). Metabolite patterns were identified using treelet transform, a statistical method for identification of groups of correlated metabolites. Associations of metabolite patterns with prostate cancer risk (OR1SD ) were estimated by conditional logistic regression. Supplementary analyses were conducted for metabolite patterns derived using principal component analysis and for individual metabolites. Men with metabolite profiles characterized by higher concentrations of either phosphatidylcholines or hydroxysphingomyelins (OR1SD  = 0.77, 95% confidence interval 0.66-0.89), acylcarnitines C18:1 and C18:2, glutamate, ornithine and taurine (OR1SD  = 0.72, 0.57-0.90), or lysophosphatidylcholines (OR1SD  = 0.81, 0.69-0.95) had lower risk of advanced stage prostate cancer at diagnosis, with no evidence of heterogeneity by follow-up time. Similar associations were observed for the two former patterns with aggressive disease risk (the more aggressive subset of advanced stage), while the latter pattern was inversely related to risk of prostate cancer death (OR1SD  = 0.77, 0.61-0.96). No associations were observed for prostate cancer overall or less aggressive tumor subtypes. In conclusion, metabolite patterns may be related to lower risk of more aggressive prostate tumors and prostate cancer death, and might be relevant to etiology of advanced stage prostate cancer.

KEYWORDS:

epidemiology; metabolomics; prostate cancer risk; treelet transform

PMID:
30951192
DOI:
10.1002/ijc.32314
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