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  • 1
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    German Medical Science GMS Publishing House; Düsseldorf
    In:  Mainz//2011; 56. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 6. Jahrestagung der Deutschen Gesellschaft für Epidemiologie (DGEpi); 20110926-20110929; Mainz; DOC11gmds385 /20110920/
    Publication Date: 2011-09-20
    Keywords: ddc: 610
    Language: English
    Type: conferenceObject
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  • 2
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    German Medical Science GMS Publishing House; Düsseldorf
    In:  Mainz//2011; 56. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 6. Jahrestagung der Deutschen Gesellschaft für Epidemiologie (DGEpi); 20110926-20110929; Mainz; DOC11gmds292 /20110920/
    Publication Date: 2011-09-20
    Keywords: Metabolomics ; Reliabilität ; Biomarker ; ddc: 610
    Language: German
    Type: conferenceObject
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  • 3
    Abstract: Metabolomic approaches in prospective cohorts may offer a unique snapshot into early metabolic perturbations that are associated with a higher risk of cardiovascular diseases (CVD) in healthy people. We investigated the association of 105 serum metabolites, including acylcarnitines, amino acids, phospholipids and hexose, with risk of myocardial infarction (MI) and ischemic stroke in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (27,548 adults) and Heidelberg (25,540 adults) cohorts. Using case-cohort designs, we measured metabolites among individuals who were free of CVD and diabetes at blood draw but developed MI (n = 204 and n = 228) or stroke (n = 147 and n = 121) during follow-up (mean, 7.8 and 7.3 years) and among randomly drawn subcohorts (n = 2214 and n = 770). We used Cox regression analysis and combined results using meta-analysis. Independent of classical CVD risk factors, ten metabolites were associated with risk of MI in both cohorts, including sphingomyelins, diacyl-phosphatidylcholines and acyl-alkyl-phosphatidylcholines with pooled relative risks in the range of 1.21-1.40 per one standard deviation increase in metabolite concentrations. The metabolites showed positive correlations with total- and LDL-cholesterol (r ranged from 0.13 to 0.57). When additionally adjusting for total-, LDL- and HDL-cholesterol, triglycerides and C-reactive protein, acyl-alkyl-phosphatidylcholine C36:3 and diacyl-phosphatidylcholines C38:3 and C40:4 remained associated with risk of MI. When added to classical CVD risk models these metabolites further improved CVD prediction (c-statistics increased from 0.8365 to 0.8384 in EPIC-Potsdam and from 0.8344 to 0.8378 in EPIC-Heidelberg). None of the metabolites was consistently associated with stroke risk. Alterations in sphingomyelin and phosphatidylcholine metabolism, and particularly metabolites of the arachidonic acid pathway are independently associated with risk of MI in healthy adults.
    Type of Publication: Journal article published
    PubMed ID: 29181692
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  • 4
    Abstract: BACKGROUND: The application of metabolomics in prospective cohort studies is statistically challenging. Given the importance of appropriate statistical methods for selection of disease-associated metabolites in highly correlated complex data, we combined random survival forest (RSF) with an automated backward elimination procedure that addresses such issues. METHODS: Our RSF approach was illustrated with data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, with concentrations of 127 serum metabolites as exposure variables and time to development of type 2 diabetes mellitus (T2D) as outcome variable. Out of this data set, Cox regression with a stepwise selection method was recently published. Replication of methodical comparison (RSF and Cox regression) was conducted in two independent cohorts. Finally, the R-code for implementing the metabolite selection procedure into the RSF-syntax is provided. RESULTS: The application of the RSF approach in EPIC-Potsdam resulted in the identification of 16 incident T2D-associated metabolites which slightly improved prediction of T2D when used in addition to traditional T2D risk factors and also when used together with classical biomarkers. The identified metabolites partly agreed with previous findings using Cox regression, though RSF selected a higher number of highly correlated metabolites. CONCLUSIONS: The RSF method appeared to be a promising approach for identification of disease-associated variables in complex data with time to event as outcome. The demonstrated RSF approach provides comparable findings as the generally used Cox regression, but also addresses the problem of multicollinearity and is suitable for high-dimensional data.
    Type of Publication: Journal article published
    PubMed ID: 27591264
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  • 5
    Abstract: BACKGROUND: The effects of lifestyle risk factors considered collectively on the human metabolism are to date unknown. We aim to investigate the association of these risk factors with metabolites and their changes during 4 years. METHODS AND RESULTS: One hundred and sixty-three metabolites were measured in serum samples with the AbsoluteIDQ kit p150 (Biocrates) following a targeted metabolomics approach, in a population-based cohort of 1030 individuals, aged 45 to 83 years at baseline. We evaluated associations between metabolite concentrations (28 acylcarnitines, 14 amino acids, 9 lysophosphocholines, 72 phosphocholines, 10 sphingomyelins and sum of hexoses) and 5 lifestyle risk factors (body mass index [BMI], alcohol consumption, smoking, diet, and exercise). Multilevel or simple linear regression modeling adjusted for relevant covariates was used for the evaluation of cross-sectional or longitudinal associations, respectively; multiple testing correction was based on false discovery rate. BMI, alcohol consumption, and smoking were associated with lipid metabolism (reduced lyso- and acyl-alkyl-phosphatidylcholines and increased diacylphosphatidylcholines concentrations). Smoking showed positive associations with acylcarnitines, and BMI correlated inversely with nonessential amino acids. Fewer metabolites showed relative changes that were associated with baseline risk factors: increases in 5 different acyl-alkyl phosphatidylcholines were associated with lower alcohol consumption and BMI and with a healthier diet. Increased levels of tyrosine were associated with BMI. Sex-specific effects of smoking and BMI were found specifically related to acylcarnitine metabolism: in women higher BMI and in men more pack-years were associated with increases in acylcarnitines. CONCLUSIONS: This study showed sex-specific effects of lifestyle risks factors on human metabolism and highlighted their long-term metabolic consequences.
    Type of Publication: Journal article published
    PubMed ID: 27784734
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  • 6
    Publication Date: 2011-09-03
    Description: Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3832838/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3832838/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Suhre, Karsten -- Shin, So-Youn -- Petersen, Ann-Kristin -- Mohney, Robert P -- Meredith, David -- Wagele, Brigitte -- Altmaier, Elisabeth -- CARDIoGRAM -- Deloukas, Panos -- Erdmann, Jeanette -- Grundberg, Elin -- Hammond, Christopher J -- de Angelis, Martin Hrabe -- Kastenmuller, Gabi -- Kottgen, Anna -- Kronenberg, Florian -- Mangino, Massimo -- Meisinger, Christa -- Meitinger, Thomas -- Mewes, Hans-Werner -- Milburn, Michael V -- Prehn, Cornelia -- Raffler, Johannes -- Ried, Janina S -- Romisch-Margl, Werner -- Samani, Nilesh J -- Small, Kerrin S -- Wichmann, H-Erich -- Zhai, Guangju -- Illig, Thomas -- Spector, Tim D -- Adamski, Jerzy -- Soranzo, Nicole -- Gieger, Christian -- 091746/Wellcome Trust/United Kingdom -- 091746/Z/10/Z/Wellcome Trust/United Kingdom -- 1R01HL103931-01/HL/NHLBI NIH HHS/ -- HL087647/HL/NHLBI NIH HHS/ -- MOP172605/Canadian Institutes of Health Research/Canada -- MOP77682/Canadian Institutes of Health Research/Canada -- MOP-82810/Canadian Institutes of Health Research/Canada -- N01-AG-12100/AG/NIA NIH HHS/ -- N01-HC-55015/HC/NHLBI NIH HHS/ -- N01-HC-55016/HC/NHLBI NIH HHS/ -- N01-HC-55018/HC/NHLBI NIH HHS/ -- N01-HC-55019/HC/NHLBI NIH HHS/ -- N01-HC-55020/HC/NHLBI NIH HHS/ -- N01-HC-55021/HC/NHLBI NIH HHS/ -- N01-HC-55022/HC/NHLBI NIH HHS/ -- P01 HL098055/HL/NHLBI NIH HHS/ -- P01HL076491-06/HL/NHLBI NIH HHS/ -- P01HL087018/HL/NHLBI NIH HHS/ -- R01 HL087647/HL/NHLBI NIH HHS/ -- R01 HL087676/HL/NHLBI NIH HHS/ -- R01DK080732/DK/NIDDK NIH HHS/ -- R01HL089650-02/HL/NHLBI NIH HHS/ -- Biotechnology and Biological Sciences Research Council/United Kingdom -- British Heart Foundation/United Kingdom -- Cancer Research UK/United Kingdom -- Intramural NIH HHS/ -- Medical Research Council/United Kingdom -- England -- Nature. 2011 Aug 31;477(7362):54-60. doi: 10.1038/nature10354.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Ingolstadter Landstrasse 1, 85764 Neuherberg, Germany. karsten@suhre.fr〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/21886157" target="_blank"〉PubMed〈/a〉
    Keywords: Adolescent ; Adult ; Aged ; Aged, 80 and over ; *Biomedical Research ; Blood/metabolism ; Child ; Chronic Disease ; Coronary Artery Disease/genetics ; Diabetes Mellitus/genetics ; *Drug Industry ; Female ; Genetic Loci/genetics ; *Genetic Variation ; *Genome-Wide Association Study ; Genotype ; Humans ; Male ; Metabolism/*genetics ; Metabolomics ; Middle Aged ; Pharmacogenetics ; Renal Insufficiency/genetics ; Risk Factors ; Venous Thromboembolism/genetics ; Young Adult
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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