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.
Highlights
Prostate cancer is the second most commonly diagnosed cancer in men worldwide, but few risk factors have been established.[1,2] Using metabolomics, which measures large numbers of small molecules in body fluids reflecting internal and external factors,[3,4,5] may help to identify novel risk factors for prostate cancer
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 found that patterns in plasma metabolite profile were associated with subsequent lower risk of more aggressive tumor subtypes and prostate cancer death
Summary
Prostate cancer is the second most commonly diagnosed cancer in men worldwide, but few risk factors have been established.[1,2] Using metabolomics, which measures large numbers of small molecules in body fluids reflecting internal (the genome, epigenome, transcriptome and proteome) and external factors (diet, lifestyle, environment and gut microbiota),[3,4,5] may help to identify novel risk factors for prostate cancer. Treelet transform combines features from principal component analysis (PCA) and cluster analysis to describe latent patterns in the data, which often are easier to interpret than those of PCA because metabolites irrelevant to the pattern are omitted. Treelet transform has for example been used to successfully describe patterns in serum metabolite profile[12] and to identify patterns in plasma fatty acids which were related to subsequent prostate cancer.[14]
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