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Start Over Author "Pilling, Luke C." Publisher escholarship, university of california
5 results on '"Pilling, Luke C."'

1. Blood Leukocyte DNA Methylation Predicts Risk of Future Myocardial Infarction and Coronary Heart Disease

Author

Agha, Golareh, Mendelson, Michael M, Ward-Caviness, Cavin K, Joehanes, Roby, Huan, TianXiao, Gondalia, Rahul, Salfati, Elias, Brody, Jennifer A, Fiorito, Giovanni, Bressler, Jan, Chen, Brian H, Ligthart, Symen, Guarrera, Simonetta, Colicino, Elena, Just, Allan C, Wahl, Simone, Gieger, Christian, Vandiver, Amy R, Tanaka, Toshiko, Hernandez, Dena G, Pilling, Luke C, Singleton, Andrew B, Sacerdote, Carlotta, Krogh, Vittorio, Panico, Salvatore, Tumino, Rosario, Li, Yun, Zhang, Guosheng, Stewart, James D, Floyd, James S, Wiggins, Kerri L, Rotter, Jerome I, Multhaup, Michael, Bakulski, Kelly, Horvath, Steven, Tsao, Philip S, Absher, Devin M, Vokonas, Pantel, Hirschhorn, Joel, Fallin, M Daniele, Liu, Chunyu, Bandinelli, Stefania, Boerwinkle, Eric, Dehghan, Abbas, Schwartz, Joel D, Psaty, Bruce M, Feinberg, Andrew P, Hou, Lifang, Ferrucci, Luigi, Sotoodehnia, Nona, Matullo, Giuseppe, Peters, Annette, Fornage, Myriam, Assimes, Themistocles L, Whitsel, Eric A, Levy, Daniel, and Baccarelli, Andrea A

Subjects
Human Genome, Heart Disease, Clinical Research, Prevention, Heart Disease - Coronary Heart Disease, Atherosclerosis, Cardiovascular, Genetics, 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, coronary artery disease, coronary heart disease, epigenetics, genomics, gene expression regulation, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Public Health and Health Services, Cardiovascular System & Hematology
Abstract

BackgroundDNA 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.MethodsNine 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.ResultsAmong 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

Published
2019

2. DNA methylation-based measures of biological age: meta-analysis predicting time to death

Author

Chen, Brian H, Marioni, Riccardo E, Colicino, Elena, Peters, Marjolein J, Ward-Caviness, Cavin K, Tsai, Pei-Chien, Roetker, Nicholas S, Just, Allan C, Demerath, Ellen W, Guan, Weihua, Bressler, Jan, Fornage, Myriam, Studenski, Stephanie, Vandiver, Amy R, Moore, Ann Zenobia, Tanaka, Toshiko, Kiel, Douglas P, Liang, Liming, Vokonas, Pantel, Schwartz, Joel, Lunetta, Kathryn L, Murabito, Joanne M, Bandinelli, Stefania, Hernandez, Dena G, Melzer, David, Nalls, Michael, Pilling, Luke C, Price, Timothy R, Singleton, Andrew B, Gieger, Christian, Holle, Rolf, Kretschmer, Anja, Kronenberg, Florian, Kunze, Sonja, Linseisen, Jakob, Meisinger, Christine, Rathmann, Wolfgang, Waldenberger, Melanie, Visscher, Peter M, Shah, Sonia, Wray, Naomi R, McRae, Allan F, Franco, Oscar H, Hofman, Albert, Uitterlinden, André G, Absher, Devin, Assimes, Themistocles, Levine, Morgan E, Lu, Ake T, Tsao, Philip S, Hou, Lifang, Manson, JoAnn E, Carty, Cara L, LaCroix, Andrea Z, Reiner, Alexander P, Spector, Tim D, Feinberg, Andrew P, Levy, Daniel, Baccarelli, Andrea, van Meurs, Joyce, Bell, Jordana T, Peters, Annette, Deary, Ian J, Pankow, James S, Ferrucci, Luigi, and Horvath, Steve

Subjects
Biological Sciences, Genetics, Clinical Research, Prevention, Good Health and Well Being, Aging, DNA Methylation, Epigenesis, Genetic, Female, Humans, Logistic Models, Male, Mortality, Racial Groups, Risk Factors, Survival Analysis, T-Lymphocyte Subsets, all-cause mortality, lifespan, epigenetics, epigenetic clock, DNA methylation, mortality, Biochemistry and cell biology, Clinical sciences
Abstract

Estimates of biological age based on DNA methylation patterns, often referred to as "epigenetic age", "DNAm age", have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2x10-9), independent of chronological age, even after adjusting for additional risk factors (p

Published
2016

3. The transcriptional landscape of age in human peripheral blood.

Author

Peters, Marjolein J, Joehanes, Roby, Pilling, Luke C, Schurmann, Claudia, Conneely, Karen N, Powell, Joseph, Reinmaa, Eva, Sutphin, George L, Zhernakova, Alexandra, Schramm, Katharina, Wilson, Yana A, Kobes, Sayuko, Tukiainen, Taru, NABEC/UKBEC Consortium, Ramos, Yolande F, Göring, Harald HH, Fornage, Myriam, Liu, Yongmei, Gharib, Sina A, Stranger, Barbara E, De Jager, Philip L, Aviv, Abraham, Levy, Daniel, Murabito, Joanne M, Munson, Peter J, Huan, Tianxiao, Hofman, Albert, Uitterlinden, André G, Rivadeneira, Fernando, van Rooij, Jeroen, Stolk, Lisette, Broer, Linda, Verbiest, Michael MPJ, Jhamai, Mila, Arp, Pascal, Metspalu, Andres, Tserel, Liina, Milani, Lili, Samani, Nilesh J, Peterson, Pärt, Kasela, Silva, Codd, Veryan, Peters, Annette, Ward-Caviness, Cavin K, Herder, Christian, Waldenberger, Melanie, Roden, Michael, Singmann, Paula, Zeilinger, Sonja, Illig, Thomas, Homuth, Georg, Grabe, Hans-Jörgen, Völzke, Henry, Steil, Leif, Kocher, Thomas, Murray, Anna, Melzer, David, Yaghootkar, Hanieh, Bandinelli, Stefania, Moses, Eric K, Kent, Jack W, Curran, Joanne E, Johnson, Matthew P, Williams-Blangero, Sarah, Westra, Harm-Jan, McRae, Allan F, Smith, Jennifer A, Kardia, Sharon LR, Hovatta, Iiris, Perola, Markus, Ripatti, Samuli, Salomaa, Veikko, Henders, Anjali K, Martin, Nicholas G, Smith, Alicia K, Mehta, Divya, Binder, Elisabeth B, Nylocks, K Maria, Kennedy, Elizabeth M, Klengel, Torsten, Ding, Jingzhong, Suchy-Dicey, Astrid M, Enquobahrie, Daniel A, Brody, Jennifer, Rotter, Jerome I, Chen, Yii-Der I, Houwing-Duistermaat, Jeanine, Kloppenburg, Margreet, Slagboom, P Eline, Helmer, Quinta, den Hollander, Wouter, Bean, Shannon, Raj, Towfique, Bakhshi, Noman, Wang, Qiao Ping, Oyston, Lisa J, Psaty, Bruce M, Tracy, Russell P, Montgomery, Grant W, and Turner, Stephen T

Subjects
NABEC/UKBEC Consortium, Humans, Gene Expression Profiling, DNA Methylation, Aging, European Continental Ancestry Group, Transcriptome, Biomarkers
Abstract

Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the 'transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.

Published
2015

4. A meta-analysis of gene expression signatures of blood pressure and hypertension.

Author

Huan, Tianxiao, Esko, Tõnu, Peters, Marjolein J, Pilling, Luke C, Schramm, Katharina, Schurmann, Claudia, Chen, Brian H, Liu, Chunyu, Joehanes, Roby, Johnson, Andrew D, Yao, Chen, Ying, Sai-Xia, Courchesne, Paul, Milani, Lili, Raghavachari, Nalini, Wang, Richard, Liu, Poching, Reinmaa, Eva, Dehghan, Abbas, Hofman, Albert, Uitterlinden, André G, Hernandez, Dena G, Bandinelli, Stefania, Singleton, Andrew, Melzer, David, Metspalu, Andres, Carstensen, Maren, Grallert, Harald, Herder, Christian, Meitinger, Thomas, Peters, Annette, Roden, Michael, Waldenberger, Melanie, Dörr, Marcus, Felix, Stephan B, Zeller, Tanja, International Consortium for Blood Pressure GWAS (ICBP), Vasan, Ramachandran, O'Donnell, Christopher J, Munson, Peter J, Yang, Xia, Prokisch, Holger, Völker, Uwe, van Meurs, Joyce BJ, Ferrucci, Luigi, and Levy, Daniel

Subjects
International Consortium for Blood Pressure GWAS, Humans, Hypertension, Genetic Predisposition to Disease, Gene Expression Regulation, Blood Pressure, Genotype, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Transcriptome, Cardiovascular, Aging, Genetics, Prevention, Human Genome, Biotechnology, 2.1 Biological and endogenous factors, Developmental Biology
Abstract

Genome-wide association studies (GWAS) have uncovered numerous genetic variants (SNPs) that are associated with blood pressure (BP). Genetic variants may lead to BP changes by acting on intermediate molecular phenotypes such as coded protein sequence or gene expression, which in turn affect BP variability. Therefore, characterizing genes whose expression is associated with BP may reveal cellular processes involved in BP regulation and uncover how transcripts mediate genetic and environmental effects on BP variability. A meta-analysis of results from six studies of global gene expression profiles of BP and hypertension in whole blood was performed in 7017 individuals who were not receiving antihypertensive drug treatment. We identified 34 genes that were differentially expressed in relation to BP (Bonferroni-corrected p

Published
2015

5. A Genome-Wide Association Study of Depressive Symptoms

Author

Hek, Karin, Demirkan, Ayse, Lahti, Jari, Terracciano, Antonio, Teumer, Alexander, Cornelis, Marilyn C, Amin, Najaf, Bakshis, Erin, Baumert, Jens, Ding, Jingzhong, Liu, Yongmei, Marciante, Kristin, Meirelles, Osorio, Nalls, Michael A, Sun, Yan V, Vogelzangs, Nicole, Yu, Lei, Bandinelli, Stefania, Benjamin, Emelia J, Bennett, David A, Boomsma, Dorret, Cannas, Alessandra, Coker, Laura H, de Geus, Eco, De Jager, Philip L, Diez-Roux, Ana V, Purcell, Shaun, Hu, Frank B, Rimm, Eric B, Hunter, David J, Jensen, Majken K, Curhan, Gary, Rice, Kenneth, Penman, Alan D, Rotter, Jerome I, Sotoodehnia, Nona, Emeny, Rebecca, Eriksson, Johan G, Evans, Denis A, Ferrucci, Luigi, Fornage, Myriam, Gudnason, Vilmundur, Hofman, Albert, Illig, Thomas, Kardia, Sharon, Kelly-Hayes, Margaret, Koenen, Karestan, Kraft, Peter, Kuningas, Maris, Massaro, Joseph M, Melzer, David, Mulas, Antonella, Mulder, Cornelis L, Murray, Anna, Oostra, Ben A, Palotie, Aarno, Penninx, Brenda, Petersmann, Astrid, Pilling, Luke C, Psaty, Bruce, Rawal, Rajesh, Reiman, Eric M, Schulz, Andrea, Shulman, Joshua M, Singleton, Andrew B, Smith, Albert V, Sutin, Angelina R, Uitterlinden, André G, Völzke, Henry, Widen, Elisabeth, Yaffe, Kristine, Zonderman, Alan B, Cucca, Francesco, Harris, Tamara, Ladwig, Karl-Heinz, Llewellyn, David J, Räikkönen, Katri, Tanaka, Toshiko, van Duijn, Cornelia M, Grabe, Hans J, Launer, Lenore J, Lunetta, Kathryn L, Mosley, Thomas H, Newman, Anne B, Tiemeier, Henning, and Murabito, Joanne

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Mental Illness, Clinical Research, Brain Disorders, Depression, Human Genome, Mental Health, 2.1 Biological and endogenous factors, Aged, Aged, 80 and over, Chromosomes, Human, Pair 5, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Center for Epidemiologic Studies Depression Scale, CHARGE consortium, depression, depressive symptoms, genetics, genome-wide association study, meta-analysis, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological sciences, Biomedical and clinical sciences, Psychology
Abstract

BackgroundDepression is a heritable trait that exists on a continuum of varying severity and duration. Yet, the search for genetic variants associated with depression has had few successes. We exploit the entire continuum of depression to find common variants for depressive symptoms.MethodsIn this genome-wide association study, we combined the results of 17 population-based studies assessing depressive symptoms with the Center for Epidemiological Studies Depression Scale. Replication of the independent top hits (p

Published
2013

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