Jiantao Ma1, Casey M Rebholz2, Kim V E Braun2, Lindsay M Reynolds3, Stella Aslibekyan4, Rui Xia5, Niranjan G Biligowda4, Tianxiao Huan6, Chunyu Liu6,7, Michael M Mendelson8,9, Roby Joehanes6, Emily A Hu2, Mara Z Vitolins3, Alexis C Wood9, Kurt Lohman10, Carolina Ochoa-Rosales11,12, Joyce van Meurs13, Andre Uitterlinden13, Yongmei Liu3, Mohamed A Elhadad14,15,16, Margit Heier17, Melanie Waldenberger14,15,16, Annette Peters14,15,16, Elena Colicino18, Eric A Whitsel19,20, Antoine Baldassari19, Sina A Gharib21, Nona Sotoodehnia21, Jennifer A Brody21, Colleen M Sitlani21, Toshiko Tanaka22, W David Hill23,24, Janie Corley23,24, Ian J Deary23,24, Yan Zhang25, Ben Schöttker25,26, Hermann Brenner25,26, Maura E Walker27, Shumao Ye28, Steve Nguyen29, Jim Pankow29, Ellen W Demerath29, Yinan Zheng30, Lifang Hou30, Liming Liang31,32, Alice H Lichtenstein28, Frank B Hu31,33, Myriam Fornage5, Trudy Voortman11, Daniel Levy6. 1. Nutrition Epidemiology & Data Science, Friedman School of Nutrition Science and Policy (J.M.), USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA. 2. Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD (C.M.R., E.A.H.). 3. Department of Epidemiology & Prevention (L.M.R., M.Z.V., Y.L.), Wake Forest School of Medicine, Winston-Salem, NC. 4. Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL (S.A., N.G.B.). 5. Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX (R.X., M.F.). 6. Population Sciences Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD & the Framingham Heart Study, Framingham, MA (J.M., T.H., C.L., M.M.M., R.J., D.L.). 7. Department of Biostatistics, Boston University, Boston, MA (C.L.). 8. Department of Cardiology, Boston Children's Hospital, Harvard Medical School, Boston, MA (M.M.M.). 9. USDA/ARS Children's Nutrition Rsrch Center, Baylor College of Medicine, Houston, TX (A.C.W.). 10. Department of Biostatistics (K.L.), Wake Forest School of Medicine, Winston-Salem, NC. 11. Department of Epidemiology (K.V.E.B., C.O.-R., T.V.). 12. Centro de Vida Saludable de la Universidad de Concepción, Concepción, Bío Bío, Chile (C.O.-R.). 13. Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (J.v.M., A.U.). 14. Institute of Epidemiology (M.A.E., M.H., M.W., A.P.), Helmholtz Zentrum München, German Center for Environmental Health, Neuherberg, Germany. 15. Research Unit of Molecular Epidemiology (M.A.E., M.W., A.P.), Helmholtz Zentrum München, German Center for Environmental Health, Neuherberg, Germany. 16. DZHK (German Centre for Cardiovascular Rsrch), partner site Munich Heart Alliance (M.A.E., M.W., A.P.). 17. KORA Study Centre, University Hospital of Augsburg, Augsburg, Germany (M.H.). 18. Department of Environmental Health Sciences, Columbia University, New York City, NY (E.C.). 19. Department of Epidemiology, Gillings School of Global Public Health (E.A.W., A.B.), School of Medicine, University of North Carolina, Chapel Hill, NC. 20. Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill, NC. 21. The Cardiovascular Health Research Unit, University of Washington, Seattle, WA (S.A.G., N.S., J.A.B., C.M.S.). 22. Longitudinal Study Section, Nat Institute of Aging, NIH, Bethesda, MD (T.T.). 23. Lothian Birth Cohorts (W.D.H., J.C., I.J.D.), University of Edinburgh, Edinburgh, United Kingdom. 24. Department of Psychology (W.D.H., J.C., I.J.D.), University of Edinburgh, Edinburgh, United Kingdom. 25. Division of Clinical Epidemiology & Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany (Y. Zhang, B.S., H.B.). 26. Network Aging Research (NAR), University of Heidelberg, Germany (B.S., H.B.). 27. Section of Preventive Medicine & Epidemiology, Boston University School of Medicine, MA (M.E.W.). 28. Cardiovascular Nutrition Laboratory (S.Y., A.H.L.), USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA. 29. Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN (S.N., J.P., E.W.D.). 30. Center for Population Epigenetics, Robert H. Lurie Comprehensive Cancer Center & Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (Y. Zheng, L.H.). 31. Department of Epidemiology (L.L., F.B.H.), Harvard T.H. Chan School of Public Health, Boston, MA. 32. Department of Biostatistics (L.L.), Harvard T.H. Chan School of Public Health, Boston, MA. 33. Department of Nutrition (F.B.H.), Harvard T.H. Chan School of Public Health, Boston, MA.
Abstract
BACKGROUND: DNA methylation patterns associated with habitual diet have not been well studied. METHODS: Diet quality was characterized using a Mediterranean-style diet score and the Alternative Healthy Eating Index score. We conducted ethnicity-specific and trans-ethnic epigenome-wide association analyses for diet quality and leukocyte-derived DNA methylation at over 400 000 CpGs (cytosine-guanine dinucleotides) in 5 population-based cohorts including 6662 European ancestry, 2702 African ancestry, and 360 Hispanic ancestry participants. For diet-associated CpGs identified in epigenome-wide analyses, we conducted Mendelian randomization (MR) analysis to examine their relations to cardiovascular disease risk factors and examined their longitudinal associations with all-cause mortality. RESULTS: We identified 30 CpGs associated with either Mediterranean-style diet score or Alternative Healthy Eating Index, or both, in European ancestry participants. Among these CpGs, 12 CpGs were significantly associated with all-cause mortality (Bonferroni corrected P<1.6×10-3). Hypermethylation of cg18181703 (SOCS3) was associated with higher scores of both Mediterranean-style diet score and Alternative Healthy Eating Index and lower risk for all-cause mortality (P=5.7×10-15). Ten additional diet-associated CpGs were nominally associated with all-cause mortality (P<0.05). MR analysis revealed 8 putatively causal associations for 6 CpGs with 4 cardiovascular disease risk factors (body mass index, triglycerides, high-density lipoprotein cholesterol concentrations, and type 2 diabetes mellitus; Bonferroni corrected MR P<4.5×10-4). For example, hypermethylation of cg11250194 (FADS2) was associated with lower triglyceride concentrations (MR, P=1.5×10-14).and hypermethylation of cg02079413 (SNORA54; NAP1L4) was associated with body mass index (corrected MR, P=1×10-6). CONCLUSIONS: Habitual diet quality was associated with differential peripheral leukocyte DNA methylation levels of 30 CpGs, most of which were also associated with multiple health outcomes, in European ancestry individuals. These findings demonstrate that integrative genomic analysis of dietary information may reveal molecular targets for disease prevention and treatment.
BACKGROUND: DNA methylation patterns associated with habitual diet have not been well studied. METHODS: Diet quality was characterized using a Mediterranean-style diet score and the Alternative Healthy Eating Index score. We conducted ethnicity-specific and trans-ethnic epigenome-wide association analyses for diet quality and leukocyte-derived DNA methylation at over 400 000 CpGs (cytosine-guanine dinucleotides) in 5 population-based cohorts including 6662 European ancestry, 2702 African ancestry, and 360 Hispanic ancestry participants. For diet-associated CpGs identified in epigenome-wide analyses, we conducted Mendelian randomization (MR) analysis to examine their relations to cardiovascular disease risk factors and examined their longitudinal associations with all-cause mortality. RESULTS: We identified 30 CpGs associated with either Mediterranean-style diet score or Alternative Healthy Eating Index, or both, in European ancestry participants. Among these CpGs, 12 CpGs were significantly associated with all-cause mortality (Bonferroni corrected P<1.6×10-3). Hypermethylation of cg18181703 (SOCS3) was associated with higher scores of both Mediterranean-style diet score and Alternative Healthy Eating Index and lower risk for all-cause mortality (P=5.7×10-15). Ten additional diet-associated CpGs were nominally associated with all-cause mortality (P<0.05). MR analysis revealed 8 putatively causal associations for 6 CpGs with 4 cardiovascular disease risk factors (body mass index, triglycerides, high-density lipoprotein cholesterol concentrations, and type 2 diabetes mellitus; Bonferroni corrected MR P<4.5×10-4). For example, hypermethylation of cg11250194 (FADS2) was associated with lower triglyceride concentrations (MR, P=1.5×10-14).and hypermethylation of cg02079413 (SNORA54; NAP1L4) was associated with body mass index (corrected MR, P=1×10-6). CONCLUSIONS: Habitual diet quality was associated with differential peripheral leukocyte DNA methylation levels of 30 CpGs, most of which were also associated with multiple health outcomes, in European ancestry individuals. These findings demonstrate that integrative genomic analysis of dietary information may reveal molecular targets for disease prevention and treatment.
Entities:
Keywords:
DNA methylation; cardiovascular disease; diet; epigenome; triglycerides
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