TY - JOUR T1 - Interaction of methylation-related genetic variants with circulating fatty acids on plasma lipids: a meta-analysis of 7 studies and methylation analysis of 3 studies in the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium. JF - Am J Clin Nutr Y1 - 2016 A1 - Ma, Yiyi A1 - Follis, Jack L A1 - Smith, Caren E A1 - Tanaka, Toshiko A1 - Manichaikul, Ani W A1 - Chu, Audrey Y A1 - Samieri, Cecilia A1 - Zhou, Xia A1 - Guan, Weihua A1 - Wang, Lu A1 - Biggs, Mary L A1 - Chen, Yii-der I A1 - Hernandez, Dena G A1 - Borecki, Ingrid A1 - Chasman, Daniel I A1 - Rich, Stephen S A1 - Ferrucci, Luigi A1 - Irvin, Marguerite Ryan A1 - Aslibekyan, Stella A1 - Zhi, Degui A1 - Tiwari, Hemant K A1 - Claas, Steven A A1 - Sha, Jin A1 - Kabagambe, Edmond K A1 - Lai, Chao-Qiang A1 - Parnell, Laurence D A1 - Lee, Yu-Chi A1 - Amouyel, Philippe A1 - Lambert, Jean-Charles A1 - Psaty, Bruce M A1 - King, Irena B A1 - Mozaffarian, Dariush A1 - McKnight, Barbara A1 - Bandinelli, Stefania A1 - Tsai, Michael Y A1 - Ridker, Paul M A1 - Ding, Jingzhong A1 - Mstat, Kurt Lohmant A1 - Liu, Yongmei A1 - Sotoodehnia, Nona A1 - Barberger-Gateau, Pascale A1 - Steffen, Lyn M A1 - Siscovick, David S A1 - Absher, Devin A1 - Arnett, Donna K A1 - Ordovas, Jose M A1 - Lemaitre, Rozenn N KW - Apolipoproteins E KW - ATP Binding Cassette Transporter 1 KW - Cholesterol, HDL KW - Cohort Studies KW - Diet KW - DNA Methylation KW - Eicosapentaenoic Acid KW - Epigenesis, Genetic KW - Fatty Acids KW - Gene Expression Regulation KW - Humans KW - Lipids KW - Polymorphism, Single Nucleotide KW - Promoter Regions, Genetic KW - Triglycerides AB -

BACKGROUND: DNA methylation is influenced by diet and single nucleotide polymorphisms (SNPs), and methylation modulates gene expression.

OBJECTIVE: We aimed to explore whether the gene-by-diet interactions on blood lipids act through DNA methylation.

DESIGN: We selected 7 SNPs on the basis of predicted relations in fatty acids, methylation, and lipids. We conducted a meta-analysis and a methylation and mediation analysis with the use of data from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium and the ENCODE (Encyclopedia of DNA Elements) consortium.

RESULTS: On the basis of the meta-analysis of 7 cohorts in the CHARGE consortium, higher plasma HDL cholesterol was associated with fewer C alleles at ATP-binding cassette subfamily A member 1 (ABCA1) rs2246293 (β = -0.6 mg/dL, P = 0.015) and higher circulating eicosapentaenoic acid (EPA) (β = 3.87 mg/dL, P = 5.62 × 10(21)). The difference in HDL cholesterol associated with higher circulating EPA was dependent on genotypes at rs2246293, and it was greater for each additional C allele (β = 1.69 mg/dL, P = 0.006). In the GOLDN (Genetics of Lipid Lowering Drugs and Diet Network) study, higher ABCA1 promoter cg14019050 methylation was associated with more C alleles at rs2246293 (β = 8.84%, P = 3.51 × 10(18)) and lower circulating EPA (β = -1.46%, P = 0.009), and the mean difference in methylation of cg14019050 that was associated with higher EPA was smaller with each additional C allele of rs2246293 (β = -2.83%, P = 0.007). Higher ABCA1 cg14019050 methylation was correlated with lower ABCA1 expression (r = -0.61, P = 0.009) in the ENCODE consortium and lower plasma HDL cholesterol in the GOLDN study (r = -0.12, P = 0.0002). An additional mediation analysis was meta-analyzed across the GOLDN study, Cardiovascular Health Study, and the Multi-Ethnic Study of Atherosclerosis. Compared with the model without the adjustment of cg14019050 methylation, the model with such adjustment provided smaller estimates of the mean plasma HDL cholesterol concentration in association with both the rs2246293 C allele and EPA and a smaller difference by rs2246293 genotypes in the EPA-associated HDL cholesterol. However, the differences between 2 nested models were NS (P > 0.05).

CONCLUSION: We obtained little evidence that the gene-by-fatty acid interactions on blood lipids act through DNA methylation.

VL - 103 IS - 2 U1 - http://www.ncbi.nlm.nih.gov/pubmed/26791180?dopt=Abstract ER - TY - JOUR T1 - DNA Methylation Analysis Identifies Loci for Blood Pressure Regulation. JF - Am J Hum Genet Y1 - 2017 A1 - Richard, Melissa A A1 - Huan, Tianxiao A1 - Ligthart, Symen A1 - Gondalia, Rahul A1 - Jhun, Min A A1 - Brody, Jennifer A A1 - Irvin, Marguerite R A1 - Marioni, Riccardo A1 - Shen, Jincheng A1 - Tsai, Pei-Chien A1 - Montasser, May E A1 - Jia, Yucheng A1 - Syme, Catriona A1 - Salfati, Elias L A1 - Boerwinkle, Eric A1 - Guan, Weihua A1 - Mosley, Thomas H A1 - Bressler, Jan A1 - Morrison, Alanna C A1 - Liu, Chunyu A1 - Mendelson, Michael M A1 - Uitterlinden, André G A1 - van Meurs, Joyce B A1 - Franco, Oscar H A1 - Zhang, Guosheng A1 - Li, Yun A1 - Stewart, James D A1 - Bis, Joshua C A1 - Psaty, Bruce M A1 - Chen, Yii-Der Ida A1 - Kardia, Sharon L R A1 - Zhao, Wei A1 - Turner, Stephen T A1 - Absher, Devin A1 - Aslibekyan, Stella A1 - Starr, John M A1 - McRae, Allan F A1 - Hou, Lifang A1 - Just, Allan C A1 - Schwartz, Joel D A1 - Vokonas, Pantel S A1 - Menni, Cristina A1 - Spector, Tim D A1 - Shuldiner, Alan A1 - Damcott, Coleen M A1 - Rotter, Jerome I A1 - Palmas, Walter A1 - Liu, Yongmei A1 - Paus, Tomáš A1 - Horvath, Steve A1 - O'Connell, Jeffrey R A1 - Guo, Xiuqing A1 - Pausova, Zdenka A1 - Assimes, Themistocles L A1 - Sotoodehnia, Nona A1 - Smith, Jennifer A A1 - Arnett, Donna K A1 - Deary, Ian J A1 - Baccarelli, Andrea A A1 - Bell, Jordana T A1 - Whitsel, Eric A1 - Dehghan, Abbas A1 - Levy, Daniel A1 - Fornage, Myriam KW - Aged KW - Blood Pressure KW - CpG Islands KW - Cross-Sectional Studies KW - DNA Methylation KW - Epigenesis, Genetic KW - Genetic Variation KW - Genome-Wide Association Study KW - Humans KW - Mendelian Randomization Analysis KW - Middle Aged KW - Nerve Tissue Proteins KW - Quantitative Trait Loci KW - Tetraspanins AB -

Genome-wide association studies have identified hundreds of genetic variants associated with blood pressure (BP), but sequence variation accounts for a small fraction of the phenotypic variance. Epigenetic changes may alter the expression of genes involved in BP regulation and explain part of the missing heritability. We therefore conducted a two-stage meta-analysis of the cross-sectional associations of systolic and diastolic BP with blood-derived genome-wide DNA methylation measured on the Infinium HumanMethylation450 BeadChip in 17,010 individuals of European, African American, and Hispanic ancestry. Of 31 discovery-stage cytosine-phosphate-guanine (CpG) dinucleotides, 13 replicated after Bonferroni correction (discovery: N = 9,828, p < 1.0 × 10-7; replication: N = 7,182, p < 1.6 × 10-3). The replicated methylation sites are heritable (h2 > 30%) and independent of known BP genetic variants, explaining an additional 1.4% and 2.0% of the interindividual variation in systolic and diastolic BP, respectively. Bidirectional Mendelian randomization among up to 4,513 individuals of European ancestry from 4 cohorts suggested that methylation at cg08035323 (TAF1B-YWHAQ) influences BP, while BP influences methylation at cg00533891 (ZMIZ1), cg00574958 (CPT1A), and cg02711608 (SLC1A5). Gene expression analyses further identified six genes (TSPAN2, SLC7A11, UNC93B1, CPT1A, PTMS, and LPCAT3) with evidence of triangular associations between methylation, gene expression, and BP. Additional integrative Mendelian randomization analyses of gene expression and DNA methylation suggested that the expression of TSPAN2 is a putative mediator of association between DNA methylation at cg23999170 and BP. These findings suggest that heritable DNA methylation plays a role in regulating BP independently of previously known genetic variants.

VL - 101 IS - 6 ER - TY - JOUR T1 - Blood Leukocyte DNA Methylation Predicts Risk of Future Myocardial Infarction and Coronary Heart Disease. JF - Circulation Y1 - 2019 A1 - Agha, Golareh A1 - Mendelson, Michael M A1 - Ward-Caviness, Cavin K A1 - Joehanes, Roby A1 - Huan, Tianxiao A1 - Gondalia, Rahul A1 - Salfati, Elias A1 - Brody, Jennifer A A1 - Fiorito, Giovanni A1 - Bressler, Jan A1 - Chen, Brian H A1 - Ligthart, Symen A1 - Guarrera, Simonetta A1 - Colicino, Elena A1 - Just, Allan C A1 - Wahl, Simone A1 - Gieger, Christian A1 - Vandiver, Amy R A1 - Tanaka, Toshiko A1 - Hernandez, Dena G A1 - Pilling, Luke C A1 - Singleton, Andrew B A1 - Sacerdote, Carlotta A1 - Krogh, Vittorio A1 - Panico, Salvatore A1 - Tumino, Rosario A1 - Li, Yun A1 - Zhang, Guosheng A1 - Stewart, James D A1 - Floyd, James S A1 - Wiggins, Kerri L A1 - Rotter, Jerome I A1 - Multhaup, Michael A1 - Bakulski, Kelly A1 - Horvath, Steven A1 - Tsao, Philip S A1 - Absher, Devin M A1 - Vokonas, Pantel A1 - Hirschhorn, Joel A1 - Fallin, M Daniele A1 - Liu, Chunyu A1 - Bandinelli, Stefania A1 - Boerwinkle, Eric A1 - Dehghan, Abbas A1 - Schwartz, Joel D A1 - Psaty, Bruce M A1 - Feinberg, Andrew P A1 - Hou, Lifang A1 - Ferrucci, Luigi A1 - Sotoodehnia, Nona A1 - Matullo, Giuseppe A1 - Peters, Annette A1 - Fornage, Myriam A1 - Assimes, Themistocles L A1 - Whitsel, Eric A A1 - Levy, Daniel A1 - Baccarelli, Andrea A KW - Adult KW - Aged KW - Cohort Studies KW - Coronary Disease KW - CpG Islands KW - DNA Methylation KW - Europe KW - Female KW - Genome-Wide Association Study KW - Humans KW - Incidence KW - Leukocytes KW - Male KW - Middle Aged KW - Myocardial Infarction KW - Population Groups KW - Prognosis KW - Prospective Studies KW - Risk KW - United States AB -

BACKGROUND: DNA methylation is implicated in coronary heart disease (CHD), but current evidence is based on small, cross-sectional studies. We examined blood DNA methylation in relation to incident CHD across multiple prospective cohorts.

METHODS: Nine population-based cohorts from the United States and Europe profiled epigenome-wide blood leukocyte DNA methylation using the Illumina Infinium 450k microarray, and prospectively ascertained CHD events including coronary insufficiency/unstable angina, recognized myocardial infarction, coronary revascularization, and coronary death. Cohorts conducted race-specific analyses adjusted for age, sex, smoking, education, body mass index, blood cell type proportions, and technical variables. We conducted fixed-effect meta-analyses across cohorts.

RESULTS: Among 11 461 individuals (mean age 64 years, 67% women, 35% African American) free of CHD at baseline, 1895 developed CHD during a mean follow-up of 11.2 years. Methylation levels at 52 CpG (cytosine-phosphate-guanine) sites were associated with incident CHD or myocardial infarction (false discovery rate<0.05). These CpGs map to genes with key roles in calcium regulation (ATP2B2, CASR, GUCA1B, HPCAL1), and genes identified in genome- and epigenome-wide studies of serum calcium (CASR), serum calcium-related risk of CHD (CASR), coronary artery calcified plaque (PTPRN2), and kidney function (CDH23, HPCAL1), among others. Mendelian randomization analyses supported a causal effect of DNA methylation on incident CHD; these CpGs map to active regulatory regions proximal to long non-coding RNA transcripts.

CONCLUSION: Methylation of blood-derived DNA is associated with risk of future CHD across diverse populations and may serve as an informative tool for gaining further insight on the development of CHD.

VL - 140 IS - 8 ER - TY - JOUR T1 - Epigenetic Age and the Risk of Incident Atrial Fibrillation. JF - Circulation Y1 - 2021 A1 - Roberts, Jason D A1 - Vittinghoff, Eric A1 - Lu, Ake T A1 - Alonso, Alvaro A1 - Wang, Biqi A1 - Sitlani, Colleen M A1 - Mohammadi-Shemirani, Pedrum A1 - Fornage, Myriam A1 - Kornej, Jelena A1 - Brody, Jennifer A A1 - Arking, Dan E A1 - Lin, Honghuang A1 - Heckbert, Susan R A1 - Prokic, Ivana A1 - Ghanbari, Mohsen A1 - Skanes, Allan C A1 - Bartz, Traci M A1 - Perez, Marco V A1 - Taylor, Kent D A1 - Lubitz, Steven A A1 - Ellinor, Patrick T A1 - Lunetta, Kathryn L A1 - Pankow, James S A1 - Paré, Guillaume A1 - Sotoodehnia, Nona A1 - Benjamin, Emelia J A1 - Horvath, Steve A1 - Marcus, Gregory M KW - Aged KW - Aging KW - Atrial Fibrillation KW - DNA Methylation KW - Epigenesis, Genetic KW - Epigenomics KW - Female KW - Follow-Up Studies KW - Humans KW - Incidence KW - Male KW - Mendelian Randomization Analysis KW - Middle Aged KW - Models, Cardiovascular KW - Models, Genetic AB -

BACKGROUND: The most prominent risk factor for atrial fibrillation (AF) is chronological age; however, underlying mechanisms are unexplained. Algorithms using epigenetic modifications to the human genome effectively predict chronological age. Chronological and epigenetic predicted ages may diverge in a phenomenon referred to as epigenetic age acceleration (EAA), which may reflect accelerated biological aging. We sought to evaluate for associations between epigenetic age measures and incident AF.

METHODS: Measures for 4 epigenetic clocks (Horvath, Hannum, DNA methylation [DNAm] PhenoAge, and DNAm GrimAge) and an epigenetic predictor of PAI-1 (plasminogen activator inhibitor-1) levels (ie, DNAm PAI-1) were determined for study participants from 3 population-based cohort studies. Cox models evaluated for associations with incident AF and results were combined via random-effects meta-analyses. Two-sample summary-level Mendelian randomization analyses evaluated for associations between genetic instruments of the EAA measures and AF.

RESULTS: Among 5600 participants (mean age, 65.5 years; female, 60.1%; Black, 50.7%), there were 905 incident AF cases during a mean follow-up of 12.9 years. Unadjusted analyses revealed all 4 epigenetic clocks and the DNAm PAI-1 predictor were associated with statistically significant higher hazards of incident AF, though the magnitudes of their point estimates were smaller relative to the associations observed for chronological age. The pooled EAA estimates for each epigenetic measure, with the exception of Horvath EAA, were associated with incident AF in models adjusted for chronological age, race, sex, and smoking variables. After multivariable adjustment for additional known AF risk factors that could also potentially function as mediators, pooled EAA measures for 2 clocks remained statistically significant. Five-year increases in EAA measures for DNAm GrimAge and DNAm PhenoAge were associated with 19% (adjusted hazard ratio [HR], 1.19 [95% CI, 1.09-1.31]; <0.01) and 15% (adjusted HR, 1.15 [95% CI, 1.05-1.25]; <0.01) higher hazards of incident AF, respectively. Mendelian randomization analyses for the 5 EAA measures did not reveal statistically significant associations with AF.

CONCLUSIONS: Our study identified adjusted associations between EAA measures and incident AF, suggesting that biological aging plays an important role independent of chronological age, though a potential underlying causal relationship remains unclear. These aging processes may be modifiable and not constrained by the immutable factor of time.

VL - 144 IS - 24 ER - TY - JOUR T1 - Epigenome-wide association study of serum urate reveals insights into urate co-regulation and the SLC2A9 locus. JF - Nat Commun Y1 - 2021 A1 - Tin, Adrienne A1 - Schlosser, Pascal A1 - Matias-Garcia, Pamela R A1 - Thio, Chris H L A1 - Joehanes, Roby A1 - Liu, Hongbo A1 - Yu, Zhi A1 - Weihs, Antoine A1 - Hoppmann, Anselm A1 - Grundner-Culemann, Franziska A1 - Min, Josine L A1 - Kuhns, Victoria L Halperin A1 - Adeyemo, Adebowale A A1 - Agyemang, Charles A1 - Arnlöv, Johan A1 - Aziz, Nasir A A1 - Baccarelli, Andrea A1 - Bochud, Murielle A1 - Brenner, Hermann A1 - Bressler, Jan A1 - Breteler, Monique M B A1 - Carmeli, Cristian A1 - Chaker, Layal A1 - Coresh, Josef A1 - Corre, Tanguy A1 - Correa, Adolfo A1 - Cox, Simon R A1 - Delgado, Graciela E A1 - Eckardt, Kai-Uwe A1 - Ekici, Arif B A1 - Endlich, Karlhans A1 - Floyd, James S A1 - Fraszczyk, Eliza A1 - Gao, Xu A1 - Gào, Xīn A1 - Gelber, Allan C A1 - Ghanbari, Mohsen A1 - Ghasemi, Sahar A1 - Gieger, Christian A1 - Greenland, Philip A1 - Grove, Megan L A1 - Harris, Sarah E A1 - Hemani, Gibran A1 - Henneman, Peter A1 - Herder, Christian A1 - Horvath, Steve A1 - Hou, Lifang A1 - Hurme, Mikko A A1 - Hwang, Shih-Jen A1 - Kardia, Sharon L R A1 - Kasela, Silva A1 - Kleber, Marcus E A1 - Koenig, Wolfgang A1 - Kooner, Jaspal S A1 - Kronenberg, Florian A1 - Kuhnel, Brigitte A1 - Ladd-Acosta, Christine A1 - Lehtimäki, Terho A1 - Lind, Lars A1 - Liu, Dan A1 - Lloyd-Jones, Donald M A1 - Lorkowski, Stefan A1 - Lu, Ake T A1 - Marioni, Riccardo E A1 - März, Winfried A1 - McCartney, Daniel L A1 - Meeks, Karlijn A C A1 - Milani, Lili A1 - Mishra, Pashupati P A1 - Nauck, Matthias A1 - Nowak, Christoph A1 - Peters, Annette A1 - Prokisch, Holger A1 - Psaty, Bruce M A1 - Raitakari, Olli T A1 - Ratliff, Scott M A1 - Reiner, Alex P A1 - Schöttker, Ben A1 - Schwartz, Joel A1 - Sedaghat, Sanaz A1 - Smith, Jennifer A A1 - Sotoodehnia, Nona A1 - Stocker, Hannah R A1 - Stringhini, Silvia A1 - Sundström, Johan A1 - Swenson, Brenton R A1 - van Meurs, Joyce B J A1 - van Vliet-Ostaptchouk, Jana V A1 - Venema, Andrea A1 - Völker, Uwe A1 - Winkelmann, Juliane A1 - Wolffenbuttel, Bruce H R A1 - Zhao, Wei A1 - Zheng, Yinan A1 - Loh, Marie A1 - Snieder, Harold A1 - Waldenberger, Melanie A1 - Levy, Daniel A1 - Akilesh, Shreeram A1 - Woodward, Owen M A1 - Susztak, Katalin A1 - Teumer, Alexander A1 - Köttgen, Anna KW - Amino Acid Transport System y+ KW - Cohort Studies KW - CpG Islands KW - DNA Methylation KW - Epigenome KW - Female KW - Genetic Predisposition to Disease KW - Genome-Wide Association Study KW - Glucose Transport Proteins, Facilitative KW - Gout KW - Humans KW - Male KW - Uric Acid AB -

Elevated serum urate levels, a complex trait and major risk factor for incident gout, are correlated with cardiometabolic traits via incompletely understood mechanisms. DNA methylation in whole blood captures genetic and environmental influences and is assessed in transethnic meta-analysis of epigenome-wide association studies (EWAS) of serum urate (discovery, n = 12,474, replication, n = 5522). The 100 replicated, epigenome-wide significant (p < 1.1E-7) CpGs explain 11.6% of the serum urate variance. At SLC2A9, the serum urate locus with the largest effect in genome-wide association studies (GWAS), five CpGs are associated with SLC2A9 gene expression. Four CpGs at SLC2A9 have significant causal effects on serum urate levels and/or gout, and two of these partly mediate the effects of urate-associated GWAS variants. In other genes, including SLC7A11 and PHGDH, 17 urate-associated CpGs are associated with conditions defining metabolic syndrome, suggesting that these CpGs may represent a blood DNA methylation signature of cardiometabolic risk factors. This study demonstrates that EWAS can provide new insights into GWAS loci and the correlation of serum urate with other complex traits.

VL - 12 IS - 1 ER - TY - JOUR T1 - Meta-analyses identify DNA methylation associated with kidney function and damage. JF - Nat Commun Y1 - 2021 A1 - Schlosser, Pascal A1 - Tin, Adrienne A1 - Matias-Garcia, Pamela R A1 - Thio, Chris H L A1 - Joehanes, Roby A1 - Liu, Hongbo A1 - Weihs, Antoine A1 - Yu, Zhi A1 - Hoppmann, Anselm A1 - Grundner-Culemann, Franziska A1 - Min, Josine L A1 - Adeyemo, Adebowale A A1 - Agyemang, Charles A1 - Arnlöv, Johan A1 - Aziz, Nasir A A1 - Baccarelli, Andrea A1 - Bochud, Murielle A1 - Brenner, Hermann A1 - Breteler, Monique M B A1 - Carmeli, Cristian A1 - Chaker, Layal A1 - Chambers, John C A1 - Cole, Shelley A A1 - Coresh, Josef A1 - Corre, Tanguy A1 - Correa, Adolfo A1 - Cox, Simon R A1 - de Klein, Niek A1 - Delgado, Graciela E A1 - Domingo-Relloso, Arce A1 - Eckardt, Kai-Uwe A1 - Ekici, Arif B A1 - Endlich, Karlhans A1 - Evans, Kathryn L A1 - Floyd, James S A1 - Fornage, Myriam A1 - Franke, Lude A1 - Fraszczyk, Eliza A1 - Gao, Xu A1 - Gào, Xīn A1 - Ghanbari, Mohsen A1 - Ghasemi, Sahar A1 - Gieger, Christian A1 - Greenland, Philip A1 - Grove, Megan L A1 - Harris, Sarah E A1 - Hemani, Gibran A1 - Henneman, Peter A1 - Herder, Christian A1 - Horvath, Steve A1 - Hou, Lifang A1 - Hurme, Mikko A A1 - Hwang, Shih-Jen A1 - Jarvelin, Marjo-Riitta A1 - Kardia, Sharon L R A1 - Kasela, Silva A1 - Kleber, Marcus E A1 - Koenig, Wolfgang A1 - Kooner, Jaspal S A1 - Kramer, Holly A1 - Kronenberg, Florian A1 - Kuhnel, Brigitte A1 - Lehtimäki, Terho A1 - Lind, Lars A1 - Liu, Dan A1 - Liu, Yongmei A1 - Lloyd-Jones, Donald M A1 - Lohman, Kurt A1 - Lorkowski, Stefan A1 - Lu, Ake T A1 - Marioni, Riccardo E A1 - März, Winfried A1 - McCartney, Daniel L A1 - Meeks, Karlijn A C A1 - Milani, Lili A1 - Mishra, Pashupati P A1 - Nauck, Matthias A1 - Navas-Acien, Ana A1 - Nowak, Christoph A1 - Peters, Annette A1 - Prokisch, Holger A1 - Psaty, Bruce M A1 - Raitakari, Olli T A1 - Ratliff, Scott M A1 - Reiner, Alex P A1 - Rosas, Sylvia E A1 - Schöttker, Ben A1 - Schwartz, Joel A1 - Sedaghat, Sanaz A1 - Smith, Jennifer A A1 - Sotoodehnia, Nona A1 - Stocker, Hannah R A1 - Stringhini, Silvia A1 - Sundström, Johan A1 - Swenson, Brenton R A1 - Tellez-Plaza, Maria A1 - van Meurs, Joyce B J A1 - van Vliet-Ostaptchouk, Jana V A1 - Venema, Andrea A1 - Verweij, Niek A1 - Walker, Rosie M A1 - Wielscher, Matthias A1 - Winkelmann, Juliane A1 - Wolffenbuttel, Bruce H R A1 - Zhao, Wei A1 - Zheng, Yinan A1 - Loh, Marie A1 - Snieder, Harold A1 - Levy, Daniel A1 - Waldenberger, Melanie A1 - Susztak, Katalin A1 - Köttgen, Anna A1 - Teumer, Alexander KW - Adult KW - Aged KW - CpG Islands KW - DNA Methylation KW - Female KW - Glomerular Filtration Rate KW - Humans KW - Interferon Regulatory Factors KW - Kidney KW - Kidney Function Tests KW - LIM Domain Proteins KW - Male KW - Membrane Proteins KW - Middle Aged KW - Renal Insufficiency, Chronic KW - Transcription Factors AB -

Chronic kidney disease is a major public health burden. Elevated urinary albumin-to-creatinine ratio is a measure of kidney damage, and used to diagnose and stage chronic kidney disease. To extend the knowledge on regulatory mechanisms related to kidney function and disease, we conducted a blood-based epigenome-wide association study for estimated glomerular filtration rate (n = 33,605) and urinary albumin-to-creatinine ratio (n = 15,068) and detected 69 and seven CpG sites where DNA methylation was associated with the respective trait. The majority of these findings showed directionally consistent associations with the respective clinical outcomes chronic kidney disease and moderately increased albuminuria. Associations of DNA methylation with kidney function, such as CpGs at JAZF1, PELI1 and CHD2 were validated in kidney tissue. Methylation at PHRF1, LDB2, CSRNP1 and IRF5 indicated causal effects on kidney function. Enrichment analyses revealed pathways related to hemostasis and blood cell migration for estimated glomerular filtration rate, and immune cell activation and response for urinary albumin-to-creatinineratio-associated CpGs.

VL - 12 IS - 1 ER - TY - JOUR T1 - Integrative analysis of clinical and epigenetic biomarkers of mortality. JF - Aging Cell Y1 - 2022 A1 - Huan, Tianxiao A1 - Nguyen, Steve A1 - Colicino, Elena A1 - Ochoa-Rosales, Carolina A1 - Hill, W David A1 - Brody, Jennifer A A1 - Soerensen, Mette A1 - Zhang, Yan A1 - Baldassari, Antoine A1 - Elhadad, Mohamed Ahmed A1 - Toshiko, Tanaka A1 - Zheng, Yinan A1 - Domingo-Relloso, Arce A1 - Lee, Dong Heon A1 - Ma, Jiantao A1 - Yao, Chen A1 - Liu, Chunyu A1 - Hwang, Shih-Jen A1 - Joehanes, Roby A1 - Fornage, Myriam A1 - Bressler, Jan A1 - van Meurs, Joyce B J A1 - Debrabant, Birgit A1 - Mengel-From, Jonas A1 - Hjelmborg, Jacob A1 - Christensen, Kaare A1 - Vokonas, Pantel A1 - Schwartz, Joel A1 - Gahrib, Sina A A1 - Sotoodehnia, Nona A1 - Sitlani, Colleen M A1 - Kunze, Sonja A1 - Gieger, Christian A1 - Peters, Annette A1 - Waldenberger, Melanie A1 - Deary, Ian J A1 - Ferrucci, Luigi A1 - Qu, Yishu A1 - Greenland, Philip A1 - Lloyd-Jones, Donald M A1 - Hou, Lifang A1 - Bandinelli, Stefania A1 - Voortman, Trudy A1 - Hermann, Brenner A1 - Baccarelli, Andrea A1 - Whitsel, Eric A1 - Pankow, James S A1 - Levy, Daniel KW - Biomarkers KW - Cardiovascular Diseases KW - DNA Methylation KW - Epigenesis, Genetic KW - Epigenomics KW - Humans KW - Male KW - Neoplasms AB -

DNA methylation (DNAm) has been reported to be associated with many diseases and with mortality. We hypothesized that the integration of DNAm with clinical risk factors would improve mortality prediction. We performed an epigenome-wide association study of whole blood DNAm in relation to mortality in 15 cohorts (n = 15,013). During a mean follow-up of 10 years, there were 4314 deaths from all causes including 1235 cardiovascular disease (CVD) deaths and 868 cancer deaths. Ancestry-stratified meta-analysis of all-cause mortality identified 163 CpGs in European ancestry (EA) and 17 in African ancestry (AA) participants at p < 1 × 10 , of which 41 (EA) and 16 (AA) were also associated with CVD death, and 15 (EA) and 9 (AA) with cancer death. We built DNAm-based prediction models for all-cause mortality that predicted mortality risk after adjusting for clinical risk factors. The mortality prediction model trained by integrating DNAm with clinical risk factors showed an improvement in prediction of cancer death with 5% increase in the C-index in a replication cohort, compared with the model including clinical risk factors alone. Mendelian randomization identified 15 putatively causal CpGs in relation to longevity, CVD, or cancer risk. For example, cg06885782 (in KCNQ4) was positively associated with risk for prostate cancer (Beta = 1.2, P  = 4.1 × 10 ) and negatively associated with longevity (Beta = -1.9, P  = 0.02). Pathway analysis revealed that genes associated with mortality-related CpGs are enriched for immune- and cancer-related pathways. We identified replicable DNAm signatures of mortality and demonstrated the potential utility of CpGs as informative biomarkers for prediction of mortality risk.

VL - 21 IS - 6 ER -