Yiqing Wang1, Wei Sha2,3, Huijun Wang4, Annie Green Howard5,6, Matthew C B Tsilimigras1,5,7, Jiguo Zhang4, Chang Su4, Zhihong Wang4, Bing Zhang4, Anthony A Fodor2, Penny Gordon-Larsen8,9. 1. Department of Nutrition, Gillings School of Global Public Health & School of Medicine, University of North Carolina at Chapel Hill (UNC-Chapel Hill), Chapel Hill, NC, USA. 2. Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA. 3. Department of Cancer Biostatistics, Levine Cancer Institute, Atrium Health, Charlotte, NC, USA. 4. Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China. 5. Carolina Population Center, UNC-Chapel Hill, Chapel Hill, NC, USA. 6. Department of Biostatistics, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, NC, USA. 7. Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, NC, USA. 8. Department of Nutrition, Gillings School of Global Public Health & School of Medicine, University of North Carolina at Chapel Hill (UNC-Chapel Hill), Chapel Hill, NC, USA. pglarsen@unc.edu. 9. Carolina Population Center, UNC-Chapel Hill, Chapel Hill, NC, USA. pglarsen@unc.edu.
Abstract
INTRODUCTION: Urbanization is associated with major changes in environmental and lifestyle exposures that may influence metabolic signatures. OBJECTIVES: We investigated cross-sectional urban and rural differences in plasma metabolome analyzed by liquid chromatography/mass spectrometry platform in 500 Chinese adults aged 25-68 years from two neighboring southern Chinese provinces. METHODS: We first examined the overall metabolome differences by urban and rural residential location, using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) and random forest classification. We then tested the association between urbanization status and individual metabolites using a linear regression adjusting for age, sex, and province and conducted pathway analysis (Fisher's exact test) to identify metabolic pathways differed by urbanization status. RESULTS: We observed distinct overall metabolome by urbanization status in OPLS-DA and random forest classification. Using linear regression, out of a total of 1108 unique metabolite features identified in this sample, we found that 266 metabolites were differed by urbanization status (positive false discovery rate-adjusted p-value, q-value < 0.05). For example, the following metabolites were positively associated with urbanization status: caffeine metabolites from xanthine metabolism, hazardous pollutants like 4-hydroxychlorothalonil and perfluorooctanesulfonate, and metabolites implicated in cardiometabolic diseases, such as branched-chain amino acids. In pathway analysis, we found that xanthine metabolism pathways differed by urbanization status (q-value = 1.64E-04). CONCLUSION: We detected profound differences in host metabolites by urbanization status. Urban residents were characterized by metabolites signaling caffeine metabolism and toxic pollutants and metabolites on known pathways to cardiometabolic disease risks, compared to their rural counterparts. Our findings highlight the importance of considering urbanization in metabolomics analysis.
INTRODUCTION: Urbanization is associated with major changes in environmental and lifestyle exposures that may influence metabolic signatures. OBJECTIVES: We investigated cross-sectional urban and rural differences in plasma metabolome analyzed by liquid chromatography/mass spectrometry platform in 500 Chinese adults aged 25-68 years from two neighboring southern Chinese provinces. METHODS: We first examined the overall metabolome differences by urban and rural residential location, using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) and random forest classification. We then tested the association between urbanization status and individual metabolites using a linear regression adjusting for age, sex, and province and conducted pathway analysis (Fisher's exact test) to identify metabolic pathways differed by urbanization status. RESULTS: We observed distinct overall metabolome by urbanization status in OPLS-DA and random forest classification. Using linear regression, out of a total of 1108 unique metabolite features identified in this sample, we found that 266 metabolites were differed by urbanization status (positive false discovery rate-adjusted p-value, q-value < 0.05). For example, the following metabolites were positively associated with urbanization status: caffeine metabolites from xanthine metabolism, hazardous pollutants like 4-hydroxychlorothalonil and perfluorooctanesulfonate, and metabolites implicated in cardiometabolic diseases, such as branched-chain amino acids. In pathway analysis, we found that xanthine metabolism pathways differed by urbanization status (q-value = 1.64E-04). CONCLUSION: We detected profound differences in host metabolites by urbanization status. Urban residents were characterized by metabolites signaling caffeine metabolism and toxic pollutants and metabolites on known pathways to cardiometabolic disease risks, compared to their rural counterparts. Our findings highlight the importance of considering urbanization in metabolomics analysis.
Authors: Thomas J Wang; Martin G Larson; Ramachandran S Vasan; Susan Cheng; Eugene P Rhee; Elizabeth McCabe; Gregory D Lewis; Caroline S Fox; Paul F Jacques; Céline Fernandez; Christopher J O'Donnell; Stephen A Carr; Vamsi K Mootha; Jose C Florez; Amanda Souza; Olle Melander; Clary B Clish; Robert E Gerszten Journal: Nat Med Date: 2011-03-20 Impact factor: 53.440
Authors: Cristina Menni; Delyth Graham; Gabi Kastenmüller; Nora H J Alharbi; Safaa Md Alsanosi; Martin McBride; Massimo Mangino; Philip Titcombe; So-Youn Shin; Maria Psatha; Thomas Geisendorfer; Anja Huber; Annette Peters; Rui Wang-Sattler; Tao Xu; Mary Julia Brosnan; Jeff Trimmer; Christian Reichel; Robert P Mohney; Nicole Soranzo; Mark H Edwards; Cyrus Cooper; Alistair C Church; Karsten Suhre; Christian Gieger; Anna F Dominiczak; Tim D Spector; Sandosh Padmanabhan; Ana M Valdes Journal: Hypertension Date: 2015-06-01 Impact factor: 10.190