@article {6951, title = {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.}, journal = {Am J Clin Nutr}, volume = {103}, year = {2016}, month = {2016 Feb}, pages = {567-78}, abstract = {

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 {\texttimes} 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 {\texttimes} 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.

}, keywords = {Apolipoproteins E, ATP Binding Cassette Transporter 1, Cholesterol, HDL, Cohort Studies, Diet, DNA Methylation, Eicosapentaenoic Acid, Epigenesis, Genetic, Fatty Acids, Gene Expression Regulation, Humans, Lipids, Polymorphism, Single Nucleotide, Promoter Regions, Genetic, Triglycerides}, issn = {1938-3207}, doi = {10.3945/ajcn.115.112987}, author = {Ma, Yiyi and Follis, Jack L and Smith, Caren E and Tanaka, Toshiko and Manichaikul, Ani W and Chu, Audrey Y and Samieri, Cecilia and Zhou, Xia and Guan, Weihua and Wang, Lu and Biggs, Mary L and Chen, Yii-der I and Hernandez, Dena G and Borecki, Ingrid and Chasman, Daniel I and Rich, Stephen S and Ferrucci, Luigi and Irvin, Marguerite Ryan and Aslibekyan, Stella and Zhi, Degui and Tiwari, Hemant K and Claas, Steven A and Sha, Jin and Kabagambe, Edmond K and Lai, Chao-Qiang and Parnell, Laurence D and Lee, Yu-Chi and Amouyel, Philippe and Lambert, Jean-Charles and Psaty, Bruce M and King, Irena B and Mozaffarian, Dariush and McKnight, Barbara and Bandinelli, Stefania and Tsai, Michael Y and Ridker, Paul M and Ding, Jingzhong and Mstat, Kurt Lohmant and Liu, Yongmei and Sotoodehnia, Nona and Barberger-Gateau, Pascale and Steffen, Lyn M and Siscovick, David S and Absher, Devin and Arnett, Donna K and Ordovas, Jose M and Lemaitre, Rozenn N} } @article {7583, title = {DNA Methylation Analysis Identifies Loci for Blood Pressure Regulation.}, journal = {Am J Hum Genet}, volume = {101}, year = {2017}, month = {2017 Dec 07}, pages = {888-902}, abstract = {

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~{\texttimes} 10-7; replication: N = 7,182, p~<~1.6~{\texttimes} 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.

}, keywords = {Aged, Blood Pressure, CpG Islands, Cross-Sectional Studies, DNA Methylation, Epigenesis, Genetic, Genetic Variation, Genome-Wide Association Study, Humans, Mendelian Randomization Analysis, Middle Aged, Nerve Tissue Proteins, Quantitative Trait Loci, Tetraspanins}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2017.09.028}, author = {Richard, Melissa A and Huan, Tianxiao and Ligthart, Symen and Gondalia, Rahul and Jhun, Min A and Brody, Jennifer A and Irvin, Marguerite R and Marioni, Riccardo and Shen, Jincheng and Tsai, Pei-Chien and Montasser, May E and Jia, Yucheng and Syme, Catriona and Salfati, Elias L and Boerwinkle, Eric and Guan, Weihua and Mosley, Thomas H and Bressler, Jan and Morrison, Alanna C and Liu, Chunyu and Mendelson, Michael M and Uitterlinden, Andr{\'e} G and van Meurs, Joyce B and Franco, Oscar H and Zhang, Guosheng and Li, Yun and Stewart, James D and Bis, Joshua C and Psaty, Bruce M and Chen, Yii-Der Ida and Kardia, Sharon L R and Zhao, Wei and Turner, Stephen T and Absher, Devin and Aslibekyan, Stella and Starr, John M and McRae, Allan F and Hou, Lifang and Just, Allan C and Schwartz, Joel D and Vokonas, Pantel S and Menni, Cristina and Spector, Tim D and Shuldiner, Alan and Damcott, Coleen M and Rotter, Jerome I and Palmas, Walter and Liu, Yongmei and Paus, Tom{\'a}{\v s} and Horvath, Steve and O{\textquoteright}Connell, Jeffrey R and Guo, Xiuqing and Pausova, Zdenka and Assimes, Themistocles L and Sotoodehnia, Nona and Smith, Jennifer A and Arnett, Donna K and Deary, Ian J and Baccarelli, Andrea A and Bell, Jordana T and Whitsel, Eric and Dehghan, Abbas and Levy, Daniel and Fornage, Myriam} } @article {8507, title = {Blood Leukocyte DNA Methylation Predicts Risk of Future Myocardial Infarction and Coronary Heart Disease.}, journal = {Circulation}, volume = {140}, year = {2019}, month = {2019 08 20}, pages = {645-657}, abstract = {

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.

}, keywords = {Adult, Aged, Cohort Studies, Coronary Disease, CpG Islands, DNA Methylation, Europe, Female, Genome-Wide Association Study, Humans, Incidence, Leukocytes, Male, Middle Aged, Myocardial Infarction, Population Groups, Prognosis, Prospective Studies, Risk, United States}, issn = {1524-4539}, doi = {10.1161/CIRCULATIONAHA.118.039357}, author = {Agha, Golareh and Mendelson, Michael M and Ward-Caviness, Cavin K and Joehanes, Roby and Huan, Tianxiao and Gondalia, Rahul and Salfati, Elias and Brody, Jennifer A and Fiorito, Giovanni and Bressler, Jan and Chen, Brian H and Ligthart, Symen and Guarrera, Simonetta and Colicino, Elena and Just, Allan C and Wahl, Simone and Gieger, Christian and Vandiver, Amy R and Tanaka, Toshiko and Hernandez, Dena G and Pilling, Luke C and Singleton, Andrew B and Sacerdote, Carlotta and Krogh, Vittorio and Panico, Salvatore and Tumino, Rosario and Li, Yun and Zhang, Guosheng and Stewart, James D and Floyd, James S and Wiggins, Kerri L and Rotter, Jerome I and Multhaup, Michael and Bakulski, Kelly and Horvath, Steven and Tsao, Philip S and Absher, Devin M and Vokonas, Pantel and Hirschhorn, Joel and Fallin, M Daniele and Liu, Chunyu and Bandinelli, Stefania and Boerwinkle, Eric and Dehghan, Abbas and Schwartz, Joel D and Psaty, Bruce M and Feinberg, Andrew P and Hou, Lifang and Ferrucci, Luigi and Sotoodehnia, Nona and Matullo, Giuseppe and Peters, Annette and Fornage, Myriam and Assimes, Themistocles L and Whitsel, Eric A and Levy, Daniel and Baccarelli, Andrea A} } @article {9001, title = {Epigenetic Age and the Risk of Incident Atrial Fibrillation.}, journal = {Circulation}, volume = {144}, year = {2021}, month = {2021 12 14}, pages = {1899-1911}, abstract = {

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.

}, keywords = {Aged, Aging, Atrial Fibrillation, DNA Methylation, Epigenesis, Genetic, Epigenomics, Female, Follow-Up Studies, Humans, Incidence, Male, Mendelian Randomization Analysis, Middle Aged, Models, Cardiovascular, Models, Genetic}, issn = {1524-4539}, doi = {10.1161/CIRCULATIONAHA.121.056456}, author = {Roberts, Jason D and Vittinghoff, Eric and Lu, Ake T and Alonso, Alvaro and Wang, Biqi and Sitlani, Colleen M and Mohammadi-Shemirani, Pedrum and Fornage, Myriam and Kornej, Jelena and Brody, Jennifer A and Arking, Dan E and Lin, Honghuang and Heckbert, Susan R and Prokic, Ivana and Ghanbari, Mohsen and Skanes, Allan C and Bartz, Traci M and Perez, Marco V and Taylor, Kent D and Lubitz, Steven A and Ellinor, Patrick T and Lunetta, Kathryn L and Pankow, James S and Par{\'e}, Guillaume and Sotoodehnia, Nona and Benjamin, Emelia J and Horvath, Steve and Marcus, Gregory M} } @article {9006, title = {Epigenome-wide association study of serum urate reveals insights into urate co-regulation and the SLC2A9 locus.}, journal = {Nat Commun}, volume = {12}, year = {2021}, month = {2021 12 09}, pages = {7173}, abstract = {

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.

}, keywords = {Amino Acid Transport System y+, Cohort Studies, CpG Islands, DNA Methylation, Epigenome, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Glucose Transport Proteins, Facilitative, Gout, Humans, Male, Uric Acid}, issn = {2041-1723}, doi = {10.1038/s41467-021-27198-4}, author = {Tin, Adrienne and Schlosser, Pascal and Matias-Garcia, Pamela R and Thio, Chris H L and Joehanes, Roby and Liu, Hongbo and Yu, Zhi and Weihs, Antoine and Hoppmann, Anselm and Grundner-Culemann, Franziska and Min, Josine L and Kuhns, Victoria L Halperin and Adeyemo, Adebowale A and Agyemang, Charles and Arnl{\"o}v, Johan and Aziz, Nasir A and Baccarelli, Andrea and Bochud, Murielle and Brenner, Hermann and Bressler, Jan and Breteler, Monique M B and Carmeli, Cristian and Chaker, Layal and Coresh, Josef and Corre, Tanguy and Correa, Adolfo and Cox, Simon R and Delgado, Graciela E and Eckardt, Kai-Uwe and Ekici, Arif B and Endlich, Karlhans and Floyd, James S and Fraszczyk, Eliza and Gao, Xu and G{\`a}o, Xin and Gelber, Allan C and Ghanbari, Mohsen and Ghasemi, Sahar and Gieger, Christian and Greenland, Philip and Grove, Megan L and Harris, Sarah E and Hemani, Gibran and Henneman, Peter and Herder, Christian and Horvath, Steve and Hou, Lifang and Hurme, Mikko A and Hwang, Shih-Jen and Kardia, Sharon L R and Kasela, Silva and Kleber, Marcus E and Koenig, Wolfgang and Kooner, Jaspal S and Kronenberg, Florian and Kuhnel, Brigitte and Ladd-Acosta, Christine and Lehtim{\"a}ki, Terho and Lind, Lars and Liu, Dan and Lloyd-Jones, Donald M and Lorkowski, Stefan and Lu, Ake T and Marioni, Riccardo E and M{\"a}rz, Winfried and McCartney, Daniel L and Meeks, Karlijn A C and Milani, Lili and Mishra, Pashupati P and Nauck, Matthias and Nowak, Christoph and Peters, Annette and Prokisch, Holger and Psaty, Bruce M and Raitakari, Olli T and Ratliff, Scott M and Reiner, Alex P and Sch{\"o}ttker, Ben and Schwartz, Joel and Sedaghat, Sanaz and Smith, Jennifer A and Sotoodehnia, Nona and Stocker, Hannah R and Stringhini, Silvia and Sundstr{\"o}m, Johan and Swenson, Brenton R and van Meurs, Joyce B J and van Vliet-Ostaptchouk, Jana V and Venema, Andrea and V{\"o}lker, Uwe and Winkelmann, Juliane and Wolffenbuttel, Bruce H R and Zhao, Wei and Zheng, Yinan and Loh, Marie and Snieder, Harold and Waldenberger, Melanie and Levy, Daniel and Akilesh, Shreeram and Woodward, Owen M and Susztak, Katalin and Teumer, Alexander and K{\"o}ttgen, Anna} } @article {9002, title = {Meta-analyses identify DNA methylation associated with kidney function and damage.}, journal = {Nat Commun}, volume = {12}, year = {2021}, month = {2021 12 09}, pages = {7174}, abstract = {

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.

}, keywords = {Adult, Aged, CpG Islands, DNA Methylation, Female, Glomerular Filtration Rate, Humans, Interferon Regulatory Factors, Kidney, Kidney Function Tests, LIM Domain Proteins, Male, Membrane Proteins, Middle Aged, Renal Insufficiency, Chronic, Transcription Factors}, issn = {2041-1723}, doi = {10.1038/s41467-021-27234-3}, author = {Schlosser, Pascal and Tin, Adrienne and Matias-Garcia, Pamela R and Thio, Chris H L and Joehanes, Roby and Liu, Hongbo and Weihs, Antoine and Yu, Zhi and Hoppmann, Anselm and Grundner-Culemann, Franziska and Min, Josine L and Adeyemo, Adebowale A and Agyemang, Charles and Arnl{\"o}v, Johan and Aziz, Nasir A and Baccarelli, Andrea and Bochud, Murielle and Brenner, Hermann and Breteler, Monique M B and Carmeli, Cristian and Chaker, Layal and Chambers, John C and Cole, Shelley A and Coresh, Josef and Corre, Tanguy and Correa, Adolfo and Cox, Simon R and de Klein, Niek and Delgado, Graciela E and Domingo-Relloso, Arce and Eckardt, Kai-Uwe and Ekici, Arif B and Endlich, Karlhans and Evans, Kathryn L and Floyd, James S and Fornage, Myriam and Franke, Lude and Fraszczyk, Eliza and Gao, Xu and G{\`a}o, Xin and Ghanbari, Mohsen and Ghasemi, Sahar and Gieger, Christian and Greenland, Philip and Grove, Megan L and Harris, Sarah E and Hemani, Gibran and Henneman, Peter and Herder, Christian and Horvath, Steve and Hou, Lifang and Hurme, Mikko A and Hwang, Shih-Jen and Jarvelin, Marjo-Riitta and Kardia, Sharon L R and Kasela, Silva and Kleber, Marcus E and Koenig, Wolfgang and Kooner, Jaspal S and Kramer, Holly and Kronenberg, Florian and Kuhnel, Brigitte and Lehtim{\"a}ki, Terho and Lind, Lars and Liu, Dan and Liu, Yongmei and Lloyd-Jones, Donald M and Lohman, Kurt and Lorkowski, Stefan and Lu, Ake T and Marioni, Riccardo E and M{\"a}rz, Winfried and McCartney, Daniel L and Meeks, Karlijn A C and Milani, Lili and Mishra, Pashupati P and Nauck, Matthias and Navas-Acien, Ana and Nowak, Christoph and Peters, Annette and Prokisch, Holger and Psaty, Bruce M and Raitakari, Olli T and Ratliff, Scott M and Reiner, Alex P and Rosas, Sylvia E and Sch{\"o}ttker, Ben and Schwartz, Joel and Sedaghat, Sanaz and Smith, Jennifer A and Sotoodehnia, Nona and Stocker, Hannah R and Stringhini, Silvia and Sundstr{\"o}m, Johan and Swenson, Brenton R and Tellez-Plaza, Maria and van Meurs, Joyce B J and van Vliet-Ostaptchouk, Jana V and Venema, Andrea and Verweij, Niek and Walker, Rosie M and Wielscher, Matthias and Winkelmann, Juliane and Wolffenbuttel, Bruce H R and Zhao, Wei and Zheng, Yinan and Loh, Marie and Snieder, Harold and Levy, Daniel and Waldenberger, Melanie and Susztak, Katalin and K{\"o}ttgen, Anna and Teumer, Alexander} } @article {9094, title = {Integrative analysis of clinical and epigenetic biomarkers of mortality.}, journal = {Aging Cell}, volume = {21}, year = {2022}, month = {2022 Jun}, pages = {e13608}, abstract = {

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~{\texttimes}~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~{\texttimes}~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.

}, keywords = {Biomarkers, Cardiovascular Diseases, DNA Methylation, Epigenesis, Genetic, Epigenomics, Humans, Male, Neoplasms}, issn = {1474-9726}, doi = {10.1111/acel.13608}, author = {Huan, Tianxiao and Nguyen, Steve and Colicino, Elena and Ochoa-Rosales, Carolina and Hill, W David and Brody, Jennifer A and Soerensen, Mette and Zhang, Yan and Baldassari, Antoine and Elhadad, Mohamed Ahmed and Toshiko, Tanaka and Zheng, Yinan and Domingo-Relloso, Arce and Lee, Dong Heon and Ma, Jiantao and Yao, Chen and Liu, Chunyu and Hwang, Shih-Jen and Joehanes, Roby and Fornage, Myriam and Bressler, Jan and van Meurs, Joyce B J and Debrabant, Birgit and Mengel-From, Jonas and Hjelmborg, Jacob and Christensen, Kaare and Vokonas, Pantel and Schwartz, Joel and Gahrib, Sina A and Sotoodehnia, Nona and Sitlani, Colleen M and Kunze, Sonja and Gieger, Christian and Peters, Annette and Waldenberger, Melanie and Deary, Ian J and Ferrucci, Luigi and Qu, Yishu and Greenland, Philip and Lloyd-Jones, Donald M and Hou, Lifang and Bandinelli, Stefania and Voortman, Trudy and Hermann, Brenner and Baccarelli, Andrea and Whitsel, Eric and Pankow, James S and Levy, Daniel} } @article {9646, title = {Methylation patterns associated with C-reactive protein in racially and ethnically diverse populations.}, journal = {Epigenetics}, volume = {19}, year = {2024}, month = {2024 Dec}, pages = {2333668}, abstract = {

Systemic low-grade inflammation is a feature of chronic disease. C-reactive protein (CRP) is a common biomarker of inflammation and used as an indicator of disease risk; however, the role of inflammation in disease is not completely understood. Methylation is an epigenetic modification in the DNA which plays a pivotal role in gene expression. In this study we evaluated differential DNA methylation patterns associated with blood CRP level to elucidate biological pathways and genetic regulatory mechanisms to improve the understanding of chronic inflammation. The racially and ethnically diverse participants in this study were included as 50\% White, 41\% Black or African American, 7\% Hispanic or Latino/a, and 2\% Native Hawaiian, Asian American, American Indian, or Alaska Native (total = 13,433) individuals. We replicated 113 CpG sites from 87 unique loci, of which five were novel (, , and cg03282312), across a discovery set of 1,150 CpG sites associated with CRP level ( < 1.2E-7). The downstream pathways affected by DNA methylation included the identification of and CpG-gene transcript pairs which contributed to the innate immune response gene enrichment pathway along with , , and . Gene enrichment analysis also identified the nuclear factor-kappaB transcription pathway. Using two-sample Mendelian randomization (MR) we inferred methylation at three CpG sites as causal for CRP levels using both White and Black or African American MR instrument variables. Overall, we identified novel CpG sites and gene transcripts that could be valuable in understanding the specific cellular processes and pathogenic mechanisms involved in inflammation.

}, keywords = {C-Reactive Protein, CpG Islands, DNA, DNA Methylation, Epigenesis, Genetic, Genome-Wide Association Study, Humans, Inflammation, Intracellular Signaling Peptides and Proteins}, issn = {1559-2308}, doi = {10.1080/15592294.2024.2333668}, author = {Lundin, Jessica I and Peters, Ulrike and Hu, Yao and Ammous, Farah and Avery, Christy L and Benjamin, Emelia J and Bis, Joshua C and Brody, Jennifer A and Carlson, Chris and Cushman, Mary and Gignoux, Chris and Guo, Xiuqing and Haessler, Jeff and Haiman, Chris and Joehanes, Roby and Kasela, Silva and Kenny, Eimear and Lapalainien, Tuuli and Levy, Daniel and Liu, Chunyu and Liu, Yongmei and Loos, Ruth J F and Lu, Ake and Matise, Tara and North, Kari E and Park, Sungshim L and Ratliff, Scott M and Reiner, Alex and Rich, Stephen S and Rotter, Jerome I and Smith, Jennifer A and Sotoodehnia, Nona and Tracy, Russell and Van den Berg, David and Xu, Huichun and Ye, Ting and Zhao, Wei and Raffield, Laura M and Kooperberg, Charles} } @article {9911, title = {Methylome-wide association analyses of lipids and modifying effects of behavioral factors in diverse race and ethnicity participants.}, journal = {Clin Epigenetics}, volume = {17}, year = {2025}, month = {2025 Apr 02}, pages = {54}, abstract = {

Circulating lipid concentrations are clinically associated with cardiometabolic diseases. The phenotypic variance explained by identified genetic variants remains limited, highlighting the importance of searching for additional factors beyond genetic sequence variants. DNA methylation has been linked to lipid concentrations in previous studies, although most of the studies harbored moderate sample sizes and exhibited underrepresentation of non-European ancestry populations. In addition, knowledge of nongenetic factors on lipid profiles is extremely limited. In the Population Architecture Using Genomics and Epidemiology (PAGE) Study, we performed methylome-wide association analysis on 9,561 participants from diverse race and ethnicity backgrounds for HDL-c, LDL-c, TC, and TG levels, and also tested interactions between smoking or alcohol intake and methylation in their association with lipid levels. We identified novel CpG sites at 16 loci (P < 1.18E-7) with successful replication on 3,215 participants. One additional novel locus was identified in the self-reported White participants (P = 4.66E-8). Although no additional CpG sites were identified in the genome-wide interaction analysis, 13 reported CpG sites showed significant heterogeneous association across smoking or alcohol intake strata. By mapping novel and reported CpG sites to genes, we identified enriched pathways directly linked to lipid metabolism as well as ones spanning various biological functions. These findings provide new insights into the regulation of lipid concentrations.

}, keywords = {Adult, Aged, Alcohol Drinking, CpG Islands, DNA Methylation, Ethnicity, Female, Genome-Wide Association Study, Humans, Lipids, Male, Middle Aged, Racial Groups, Smoking, White People}, issn = {1868-7083}, doi = {10.1186/s13148-025-01859-3}, author = {Hu, Yao and Haessler, Jeff and Lundin, Jessica I and Darst, Burcu F and Whitsel, Eric A and Grove, Megan and Guan, Weihua and Xia, Rui and Szeto, Mindy and Raffield, Laura M and Ratliff, Scott and Wang, Yuxuan and Wang, Xuzhi and Fohner, Alison E and Lynch, Megan T and Patel, Yesha M and Lani Park, S and Xu, Huichun and Mitchell, Braxton D and Bis, Joshua C and Sotoodehnia, Nona and Brody, Jennifer A and Psaty, Bruce M and Peloso, Gina M and Tsai, Michael Y and Rich, Stephen S and Rotter, Jerome I and Smith, Jennifer A and Kardia, Sharon L R and Reiner, Alex P and Lange, Leslie and Fornage, Myriam and Pankow, James S and Graff, Mariaelisa and North, Kari E and Kooperberg, Charles and Peters, Ulrike} }