Xiong-Fei Pan1, Jae Jeong Yang1, Xiao-Ou Shu1, Steven C Moore2, Nicholette D Palmer3, Marta Guasch-Ferré4, David M Herrington5, Sei Harada6, Heather Eliassen4, Thomas J Wang7,8, Robert E Gerszten9, Demetrius Albanes2, Ioanna Tzoulaki10,11,12,13, Ibrahim Karaman10,11,12, Paul Elliott10,11,12, Huilian Zhu14, Lynne E Wagenknecht15, Wei Zheng1, Hui Cai1, Qiuyin Cai1, Charles E Matthews2, Cristina Menni16, Katie A Meyer17, Loren P Lipworth1, Jennifer Ose18,19, Myriam Fornage20, Cornelia M Ulrich18,19, Danxia Yu1. 1. Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA. 2. Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA. 3. Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA. 4. Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA. 5. Section on Cardiology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA. 6. Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan. 7. Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. 8. Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA. 9. Broad Institute of Harvard and Massachusetts Institute of Technology and Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA. 10. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. 11. MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom. 12. Dementia Research Institute, Imperial College London, London, United Kingdom. 13. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece. 14. Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China. 15. Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA. 16. Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom. 17. Department of Nutrition and Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA. 18. Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA. 19. Huntsman Cancer Institute, Salt Lake City, UT, USA. 20. Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center, Houston, TX, USA.
Abstract
BACKGROUND: Choline is an essential nutrient; however, the associations of choline and its related metabolites with cardiometabolic risk remain unclear. OBJECTIVE: We examined the associations of circulating choline, betaine, carnitine, and dimethylglycine (DMG) with cardiometabolic biomarkers and their potential dietary and nondietary determinants. METHODS: The cross-sectional analyses included 32,853 participants from 17 studies, who were free of cancer, cardiovascular diseases, chronic kidney diseases, and inflammatory bowel disease. In each study, metabolites and biomarkers were log-transformed and standardized by means and SDs, and linear regression coefficients (β) and 95% CIs were estimated with adjustments for potential confounders. Study-specific results were combined by random-effects meta-analyses. A false discovery rate <0.05 was considered significant. RESULTS: We observed moderate positive associations of circulating choline, carnitine, and DMG with creatinine [β (95% CI): 0.136 (0.084, 0.188), 0.106 (0.045, 0.168), and 0.128 (0.087, 0.169), respectively, for each SD increase in biomarkers on the log scale], carnitine with triglycerides (β = 0.076; 95% CI: 0.042, 0.109), homocysteine (β = 0.064; 95% CI: 0.033, 0.095), and LDL cholesterol (β = 0.055; 95% CI: 0.013, 0.096), DMG with homocysteine (β = 0.068; 95% CI: 0.023, 0.114), insulin (β = 0.068; 95% CI: 0.043, 0.093), and IL-6 (β = 0.060; 95% CI: 0.027, 0.094), but moderate inverse associations of betaine with triglycerides (β = -0.146; 95% CI: -0.188, -0.104), insulin (β = -0.106; 95% CI: -0.130, -0.082), homocysteine (β = -0.097; 95% CI: -0.149, -0.045), and total cholesterol (β = -0.074; 95% CI: -0.102, -0.047). In the whole pooled population, no dietary factor was associated with circulating choline; red meat intake was associated with circulating carnitine [β = 0.092 (0.042, 0.142) for a 1 serving/d increase], whereas plant protein was associated with circulating betaine [β = 0.249 (0.110, 0.388) for a 5% energy increase]. Demographics, lifestyle, and metabolic disease history showed differential associations with these metabolites. CONCLUSIONS: Circulating choline, carnitine, and DMG were associated with unfavorable cardiometabolic risk profiles, whereas circulating betaine was associated with a favorable cardiometabolic risk profile. Future prospective studies are needed to examine the associations of these metabolites with incident cardiovascular events.
BACKGROUND: Choline is an essential nutrient; however, the associations of choline and its related metabolites with cardiometabolic risk remain unclear. OBJECTIVE: We examined the associations of circulating choline, betaine, carnitine, and dimethylglycine (DMG) with cardiometabolic biomarkers and their potential dietary and nondietary determinants. METHODS: The cross-sectional analyses included 32,853 participants from 17 studies, who were free of cancer, cardiovascular diseases, chronic kidney diseases, and inflammatory bowel disease. In each study, metabolites and biomarkers were log-transformed and standardized by means and SDs, and linear regression coefficients (β) and 95% CIs were estimated with adjustments for potential confounders. Study-specific results were combined by random-effects meta-analyses. A false discovery rate <0.05 was considered significant. RESULTS: We observed moderate positive associations of circulating choline, carnitine, and DMG with creatinine [β (95% CI): 0.136 (0.084, 0.188), 0.106 (0.045, 0.168), and 0.128 (0.087, 0.169), respectively, for each SD increase in biomarkers on the log scale], carnitine with triglycerides (β = 0.076; 95% CI: 0.042, 0.109), homocysteine (β = 0.064; 95% CI: 0.033, 0.095), and LDL cholesterol (β = 0.055; 95% CI: 0.013, 0.096), DMG with homocysteine (β = 0.068; 95% CI: 0.023, 0.114), insulin (β = 0.068; 95% CI: 0.043, 0.093), and IL-6 (β = 0.060; 95% CI: 0.027, 0.094), but moderate inverse associations of betaine with triglycerides (β = -0.146; 95% CI: -0.188, -0.104), insulin (β = -0.106; 95% CI: -0.130, -0.082), homocysteine (β = -0.097; 95% CI: -0.149, -0.045), and total cholesterol (β = -0.074; 95% CI: -0.102, -0.047). In the whole pooled population, no dietary factor was associated with circulating choline; red meat intake was associated with circulating carnitine [β = 0.092 (0.042, 0.142) for a 1 serving/d increase], whereas plant protein was associated with circulating betaine [β = 0.249 (0.110, 0.388) for a 5% energy increase]. Demographics, lifestyle, and metabolic disease history showed differential associations with these metabolites. CONCLUSIONS: Circulating choline, carnitine, and DMG were associated with unfavorable cardiometabolic risk profiles, whereas circulating betaine was associated with a favorable cardiometabolic risk profile. Future prospective studies are needed to examine the associations of these metabolites with incident cardiovascular events.
Authors: L E Wagenknecht; E J Mayer; M Rewers; S Haffner; J Selby; G M Borok; L Henkin; G Howard; P J Savage; M F Saad Journal: Ann Epidemiol Date: 1995-11 Impact factor: 3.797
Authors: Diane E Bild; David A Bluemke; Gregory L Burke; Robert Detrano; Ana V Diez Roux; Aaron R Folsom; Philip Greenland; David R Jacob; Richard Kronmal; Kiang Liu; Jennifer Clark Nelson; Daniel O'Leary; Mohammed F Saad; Steven Shea; Moyses Szklo; Russell P Tracy Journal: Am J Epidemiol Date: 2002-11-01 Impact factor: 4.897
Authors: Gabriele Giacomo Schiattarella; Anna Sannino; Evelina Toscano; Giuseppe Giugliano; Giuseppe Gargiulo; Anna Franzone; Bruno Trimarco; Giovanni Esposito; Cinzia Perrino Journal: Eur Heart J Date: 2017-10-14 Impact factor: 29.983