Background: Meat and fish intakes have been associated with various chronic diseases. The use of specific biomarkers may help to assess meat and fish intake and improve subject classification according to the amount and type of meat or fish consumed. Objective: A metabolomic approach was applied to search for biomarkers of meat and fish intake in a dietary intervention study and in free-living subjects from the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Design: In the dietary intervention study, 4 groups of 10 subjects consumed increasing quantities of chicken, red meat, processed meat, and fish over 3 successive weeks. Twenty-four-hour urine samples were collected during each period and analyzed by high-resolution liquid chromatography–mass spectrometry. Signals characteristic of meat or fish intake were replicated in 50 EPIC subjects for whom a 24-h urine sample and 24-h dietary recall were available and who were selected for their exclusive intake or no intake of any of the 4 same foods. Results: A total of 249 mass spectrometric features showed a positive dose-dependent response to meat or fish intake in the intervention study. Eighteen of these features best predicted intake of the 4 food groups in the EPIC urine samples on the basis of partial receiver operator curve analyses with permutation testing (areas under the curve ranging between 0.61 and 1.0). Of these signals, 8 metabolites were identified. Anserine was found to be specific for chicken intake, whereas trimethylamine-N-oxide showed good specificity for fish. Carnosine and 3 acylcarnitines (acetylcarnitine, propionylcarnitine, and 2-methylbutyrylcarnitine) appeared to be more generic indicators of meat and meat and fish intake, respectively. Conclusion: The meat and fish biomarkers identified in this work may be used to study associations between meat and fish intake and disease risk in epidemiologic studies. This trial was registered at clinicaltrials.gov as NCT01684917

Perturbations in levels of amino acids (AA) and their derivatives are observed in hepatocellular carcinoma (HCC). Yet, it is unclear whether these alterations precede or are a consequence of the disease, nor whether they pertain to anatomically related cancers of the intrahepatic bile duct (IHBC), and gallbladder and extrahepatic biliary tract (GBTC). Circulating standard AA, biogenic amines and hexoses were measured (Biocrates AbsoluteIDQ-p180Kit) in a case-control study nested within a large prospective cohort (147 HCC, 43 IHBC and 134 GBTC cases). Liver function and hepatitis status biomarkers were determined separately. Multivariable conditional logistic regression was used to calculate odds ratios and 95% confidence intervals (OR; 95%CI) for log-transformed standardised (mean = 0, SD = 1) serum metabolite levels and relevant ratios in relation to HCC, IHBC or GBTC risk. Fourteen metabolites were significantly associated with HCC risk, of which seven metabolites and four ratios were the strongest predictors in continuous models. Leucine, lysine, glutamine and the ratio of branched chain to aromatic AA (Fischer's ratio) were inversely, while phenylalanine, tyrosine and their ratio, glutamate, glutamate/glutamine ratio, kynurenine and its ratio to tryptophan were positively associated with HCC risk. Confounding by hepatitis status and liver enzyme levels was observed. For the other cancers no significant associations were observed. In conclusion, imbalances of specific AA and biogenic amines may be involved in HCC development

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.

Urinary excretion of 34 dietary polyphenols and their variations according to diet and other lifestyle factors were measured by tandem mass spectrometry in 475 adult participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) cross-sectional study. A single 24-hour urine sample was analysed for each subject from 4 European countries. The highest median levels were observed for phenolic acids such as 4-hydroxyphenylacetic acid (157 μmol/24 h), followed by 3-hydroxyphenylacetic, ferulic, vanillic and homovanillic acids (20–50 μmol/24 h). The lowest concentrations were observed for equol, apigenin and resveratrol (<0.1 μmol/24 h). Urinary polyphenols significantly varied by centre, followed by alcohol intake, sex, educational level, and energy intake. This variability is largely explained by geographical variations in the diet, as suggested by the high correlations (r > 0.5) observed between urinary polyphenols and the intake of their main food sources (e.g., resveratrol and gallic acid ethyl ester with red wine intake; caffeic, protocatechuic and ferulic acids with coffee consumption; and hesperetin and naringenin with citrus fruit intake). The large variations in urinary polyphenols observed are largely determined by food preferences. These polyphenol biomarkers should allow more accurate evaluation of the relationships between polyphenol exposure and the risk of chronic diseases in large epidemiological studies.

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