Maria Elena Lacruz1, Alexander Kluttig2, Daniel Tiller2, Daniel Medenwald2, Ina Giegling2, Dan Rujescu2, Cornelia Prehn2, Jerzy Adamski2, Stefan Frantz2, Karin Halina Greiser2, Rebecca Thwing Emeny2, Gabi Kastenmüller2, Johannes Haerting2. 1. From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany (J.A.); German Center for Diabetes Research (DZD), Neuherberg (J.A.); Division of Cancer Epidemiology, German Cancer Research Centre, Heidelberg (K.H.G.); Laboratory of Immunology, Wadsworth Center, New York State Department of Health, Albany (R.T.E.); and Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (G.K.). elena.lacruz@uk-halle.de. 2. From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany (J.A.); German Center for Diabetes Research (DZD), Neuherberg (J.A.); Division of Cancer Epidemiology, German Cancer Research Centre, Heidelberg (K.H.G.); Laboratory of Immunology, Wadsworth Center, New York State Department of Health, Albany (R.T.E.); and Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (G.K.).
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.
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.
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Authors: Eline H van Roekel; Laura Trijsburg; Nada Assi; Marion Carayol; David Achaintre; Neil Murphy; Sabina Rinaldi; Julie A Schmidt; Magdalena Stepien; Rudolf Kaaks; Tilman Kühn; Heiner Boeing; Khalid Iqbal; Domenico Palli; Vittorio Krogh; Rosario Tumino; Fulvio Ricceri; Salvatore Panico; Petra H Peeters; Bas Bueno-de-Mesquita; Eva Ardanaz; Leila Lujan-Barroso; J Ramón Quirós; José M Huerta; Elena Molina-Portillo; Miren Dorronsoro; Konstantinos K Tsilidis; Elio Riboli; Agnetha Linn Rostgaard-Hansen; Anne Tjønneland; Kim Overvad; Elisabete Weiderpass; Marie-Christine Boutron-Ruault; Gianluca Severi; Antonia Trichopoulou; Anna Karakatsani; Anastasia Kotanidou; Anders Håkansson; Johan Malm; Matty P Weijenberg; Marc J Gunter; Mazda Jenab; Mattias Johansson; Ruth C Travis; Augustin Scalbert; Pietro Ferrari Journal: Nutrients Date: 2018-05-22 Impact factor: 5.717
Authors: Khalid Iqbal; Stefan Dietrich; Clemens Wittenbecher; Jan Krumsiek; Tilman Kühn; Maria Elena Lacruz; Alexander Kluttig; Cornelia Prehn; Jerzy Adamski; Martin von Bergen; Rudolf Kaaks; Matthias B Schulze; Heiner Boeing; Anna Floegel Journal: Int J Epidemiol Date: 2018-12-01 Impact factor: 7.196
Authors: Eline H van Roekel; Martijn J L Bours; Linda van Delden; Stéphanie O Breukink; Michèl Aquarius; Eric T P Keulen; Audrey Gicquiau; Vivian Viallon; Sabina Rinaldi; Paolo Vineis; Ilja C W Arts; Marc J Gunter; Michael F Leitzmann; Augustin Scalbert; Matty P Weijenberg Journal: Sci Rep Date: 2021-07-02 Impact factor: 4.379