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      Higher plasma levels of lysophosphatidylcholine 18:0 are related to a lower risk of common cancers in a prospective metabolomics study


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          First metabolomics studies have indicated that metabolic fingerprints from accessible tissues might be useful to better understand the etiological links between metabolism and cancer. However, there is still a lack of prospective metabolomics studies on pre-diagnostic metabolic alterations and cancer risk.


          Associations between pre-diagnostic levels of 120 circulating metabolites (acylcarnitines, amino acids, biogenic amines, phosphatidylcholines, sphingolipids, and hexoses) and the risks of breast, prostate, and colorectal cancer were evaluated by Cox regression analyses using data of a prospective case-cohort study including 835 incident cancer cases.


          The median follow-up duration was 8.3 years among non-cases and 6.5 years among incident cases of cancer. Higher levels of lysophosphatidylcholines (lysoPCs), and especially lysoPC a C18:0, were consistently related to lower risks of breast, prostate, and colorectal cancer, independent of background factors. In contrast, higher levels of phosphatidylcholine PC ae C30:0 were associated with increased cancer risk. There was no heterogeneity in the observed associations by lag time between blood draw and cancer diagnosis.


          Changes in blood lipid composition precede the diagnosis of common malignancies by several years. Considering the consistency of the present results across three cancer types the observed alterations point to a global metabolic shift in phosphatidylcholine metabolism that may drive tumorigenesis.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12916-016-0552-3) contains supplementary material, which is available to authorized users.

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          Most cited references32

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          ROBPCA: A New Approach to Robust Principal Component Analysis

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            Batch effect removal methods for microarray gene expression data integration: a survey.

            Genomic data integration is a key goal to be achieved towards large-scale genomic data analysis. This process is very challenging due to the diverse sources of information resulting from genomics experiments. In this work, we review methods designed to combine genomic data recorded from microarray gene expression (MAGE) experiments. It has been acknowledged that the main source of variation between different MAGE datasets is due to the so-called 'batch effects'. The methods reviewed here perform data integration by removing (or more precisely attempting to remove) the unwanted variation associated with batch effects. They are presented in a unified framework together with a wide range of evaluation tools, which are mandatory in assessing the efficiency and the quality of the data integration process. We provide a systematic description of the MAGE data integration methodology together with some basic recommendation to help the users in choosing the appropriate tools to integrate MAGE data for large-scale analysis; and also how to evaluate them from different perspectives in order to quantify their efficiency. All genomic data used in this study for illustration purposes were retrieved from InSilicoDB http://insilico.ulb.ac.be.
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              Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study.

              Studying gene-lifestyle interaction may help to identify lifestyle factors that modify genetic susceptibility and uncover genetic loci exerting important subgroup effects. Adequately powered studies with prospective, unbiased, standardised assessment of key behavioural factors for gene-lifestyle studies are lacking. This case-cohort study aims to investigate how genetic and potentially modifiable lifestyle and behavioural factors, particularly diet and physical activity, interact in their influence on the risk of developing type 2 diabetes. Incident cases of type 2 diabetes occurring in European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts between 1991 and 2007 from eight of the ten EPIC countries were ascertained and verified. Prentice-weighted Cox regression and random-effects meta-analyses were used to investigate differences in diabetes incidence by age and sex. A total of 12,403 verified incident cases of type 2 diabetes occurred during 3.99 million person-years of follow-up of 340,234 EPIC participants eligible for InterAct. We defined a centre-stratified subcohort of 16,154 individuals for comparative analyses. Individuals with incident diabetes who were randomly selected into the subcohort (n = 778) were included as cases in the analyses. All prevalent diabetes cases were excluded from the study. InterAct cases were followed-up for an average of 6.9 years; 49.7% were men. Mean baseline age and age at diagnosis were 55.6 and 62.5 years, mean BMI and waist circumference values were 29.4 kg/m(2) and 102.7 cm in men, and 30.1 kg/m(2) and 92.8 cm in women, respectively. Risk of type 2 diabetes increased linearly with age, with an overall HR of 1.56 (95% CI 1.48-1.64) for a 10 year age difference, adjusted for sex. A male excess in the risk of incident diabetes was consistently observed across all countries, with a pooled HR of 1.51 (95% CI 1.39-1.64), adjusted for age. InterAct is a large, well-powered, prospective study that will inform our understanding of the interplay between genes and lifestyle factors on the risk of type 2 diabetes development.

                Author and article information

                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                28 January 2016
                28 January 2016
                : 14
                [ ]Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120 Heidelberg, Germany
                [ ]Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114, D-14558 Nuthetal, Germany
                [ ]Department of Metabolomics, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, D-04318 Leipzig, Germany
                [ ]Department of Proteomics, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, D-04318 Leipzig, Germany
                [ ]University of Aalborg, Fredrik Bajers Vej 7H, 9220 Aalborg East, Denmark
                © Kühn et al. 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funded by: FundRef http://dx.doi.org/10.13039/501100001656, Helmholtz Association;
                Award ID: n/a
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung;
                Award ID: 01ER0809
                Award Recipient :
                Research Article
                Custom metadata
                © The Author(s) 2016

                metabolomics,epidemiology,breast cancer,prostate cancer,colorectal cancer
                metabolomics, epidemiology, breast cancer, prostate cancer, colorectal cancer


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