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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(s) -
Schmidt Julie A.,
Fensom Georgina K.,
Rinaldi Sabina,
Scalbert Augustin,
Appleby Paul N.,
Achaintre David,
Gicquiau Audrey,
Gunter Marc J.,
Ferrari Pietro,
Kaaks Rudolf,
Kühn Tilman,
Boeing Heiner,
Trichopoulou Antonia,
Karakatsani Anna,
Peppa Eleni,
Palli Domenico,
Sieri Sabina,
Tumino Rosario,
BuenodeMesquita Bas,
Agudo Antonio,
Sánchez MariaJose,
Chirlaque MaríaDolores,
Ardanaz Eva,
Larrañaga Nerea,
PerezCornago Aurora,
Assi Nada,
Riboli Elio,
Tsilidis Konstantinos K.,
Key Timothy J.,
Travis Ruth C.
Publication year - 2019
Publication title -
international journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.475
H-Index - 234
eISSN - 1097-0215
pISSN - 0020-7136
DOI - 10.1002/ijc.32314
Subject(s) - metabolite , prostate cancer , medicine , prostate , metabolomics , cancer , oncology , european prospective investigation into cancer and nutrition , prospective cohort study , metabolome , confidence interval , endocrinology , biology , bioinformatics
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 (Absolute IDQ 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 (OR 1SD ) 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 (OR 1SD = 0.77, 95% confidence interval 0.66–0.89), acylcarnitines C18:1 and C18:2, glutamate, ornithine and taurine (OR 1SD = 0.72, 0.57–0.90), or lysophosphatidylcholines (OR 1SD = 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 (OR 1SD = 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.