80 results on '"Symen Ligthart"'
Search Results
2. Author Correction: Genetic analysis of over half a million people characterises C-reactive protein loci
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Saredo Said, Raha Pazoki, Ville Karhunen, Urmo Võsa, Symen Ligthart, Barbara Bodinier, Fotios Koskeridis, Paul Welsh, Behrooz Z. Alizadeh, Daniel I. Chasman, Naveed Sattar, Marc Chadeau-Hyam, Evangelos Evangelou, Marjo-Riitta Jarvelin, Paul Elliott, Ioanna Tzoulaki, and Abbas Dehghan
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Science - Published
- 2022
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3. An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis
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Jun Liu, Elena Carnero-Montoro, Jenny van Dongen, Samantha Lent, Ivana Nedeljkovic, Symen Ligthart, Pei-Chien Tsai, Tiphaine C. Martin, Pooja R. Mandaviya, Rick Jansen, Marjolein J. Peters, Liesbeth Duijts, Vincent W. V. Jaddoe, Henning Tiemeier, Janine F. Felix, Gonneke Willemsen, Eco J. C. de Geus, Audrey Y. Chu, Daniel Levy, Shih-Jen Hwang, Jan Bressler, Rahul Gondalia, Elias L. Salfati, Christian Herder, Bertha A. Hidalgo, Toshiko Tanaka, Ann Zenobia Moore, Rozenn N. Lemaitre, Min A Jhun, Jennifer A. Smith, Nona Sotoodehnia, Stefania Bandinelli, Luigi Ferrucci, Donna K. Arnett, Harald Grallert, Themistocles L. Assimes, Lifang Hou, Andrea Baccarelli, Eric A. Whitsel, Ko Willems van Dijk, Najaf Amin, André G. Uitterlinden, Eric J. G. Sijbrands, Oscar H. Franco, Abbas Dehghan, Tim D. Spector, Josée Dupuis, Marie-France Hivert, Jerome I. Rotter, James B. Meigs, James S. Pankow, Joyce B. J. van Meurs, Aaron Isaacs, Dorret I. Boomsma, Jordana T. Bell, Ayşe Demirkan, and Cornelia M. van Duijn
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Science - Abstract
Our understanding of the functional link between differential DNA methylation and type 2 diabetes and obesity remains limited. Here the authors present a blood-based EWAS of fasting glucose and insulin among 4808 non-diabetic Europeans and identify nine CpGs not previously implicated in glucose, insulin homeostasis and diabetes.
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- 2019
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4. Lifetime risk to progress from pre-diabetes to type 2 diabetes among women and men: comparison between American Diabetes Association and World Health Organization diagnostic criteria
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Maarten J G Leening, M Arfan Ikram, Abbas Dehghan, Maryam Kavousi, Mandy van Hoek, Eric J G Sijbrands, Aloysius G Lieverse, Thijs T W van Herpt, and Symen Ligthart
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Diseases of the endocrine glands. Clinical endocrinology ,RC648-665 - Abstract
Introduction Pre-diabetes, a status conferring high risk of overt diabetes, is defined differently by the American Diabetes Association (ADA) and the WHO. We investigated the impact of applying definitions of pre-diabetes on lifetime risk of diabetes in women and men from the general population.Research design and methods We used data from 8844 women without diabetes and men aged ≥45 years from the prospective population-based Rotterdam Study in the Netherlands. In both gender groups, we calculated pre-diabetes prevalence according to ADA and WHO criteria and estimated the 10-year and lifetime risk to progress to overt diabetes with adjustment for competing risk of death.Results Out of 8844 individuals, pre-diabetes was identified in 3492 individuals (prevalence 40%, 95% CI 38% to 41%) according to ADA and 1382 individuals (prevalence 16%, 95% CI 15% to 16%) according to WHO criteria. In both women and men and each age category, ADA prevalence estimates doubled WHO-defined pre-diabetes. For women and men aged 45 years having ADA-defined pre-diabetes, the 10-year risk of diabetes was 14.2% (95% CI 6.0% to 22.5%) and 9.2% (95% CI 3.4% to 15.0%) compared with 23.2% (95% CI 6.8% to 39.6%) and 24.6% (95% CI 8.4% to 40.8%) in women and men with WHO-defined pre-diabetes. At age 45 years, the remaining lifetime risk to progress to overt diabetes was 57.5% (95% CI 51.8% to 63.2%) vs 80.2% (95% CI 74.1% to 86.3%) in women and 46.1% (95% CI 40.8% to 51.4%) vs 68.4% (95% CI 58.3% to 78.5%) in men with pre-diabetes according to ADA and WHO definitions, respectively.Conclusion Prevalence of pre-diabetes differed considerably in both women and men when applying ADA and WHO pre-diabetes definitions. Women with pre-diabetes had higher lifetime risk to progress to diabetes. The lifetime risk of diabetes was lower in women and men with ADA-defined pre-diabetes as compared with WHO. Improvement of pre-diabetes definition considering appropriate sex-specific and age-specific glycemic thresholds may lead to better identification of individuals at high risk of diabetes.
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- 2020
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5. Maternal plasma folate impacts differential DNA methylation in an epigenome-wide meta-analysis of newborns
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Bonnie R. Joubert, Herman T. den Dekker, Janine F. Felix, Jon Bohlin, Symen Ligthart, Emma Beckett, Henning Tiemeier, Joyce B. van Meurs, Andre G. Uitterlinden, Albert Hofman, Siri E. Håberg, Sarah E. Reese, Marjolein J. Peters, Bettina Kulle Andreassen, Eric A. P. Steegers, Roy M. Nilsen, Stein E. Vollset, Øivind Midttun, Per M. Ueland, Oscar H. Franco, Abbas Dehghan, Johan C. de Jongste, Michael C. Wu, Tianyuan Wang, Shyamal D. Peddada, Vincent W. V. Jaddoe, Wenche Nystad, Liesbeth Duijts, and Stephanie J. London
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Science - Abstract
Folic acid is routinely recommended for women trying to conceive to ensure proper fetal development. Here, the authors perform a large epigenomics study to examine which fetal epigenetic changes are associated with varied maternal plasma folate levels.
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- 2016
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6. Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study.
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Paul S de Vries, Maria Sabater-Lleal, Daniel I Chasman, Stella Trompet, Tarunveer S Ahluwalia, Alexander Teumer, Marcus E Kleber, Ming-Huei Chen, Jie Jin Wang, John R Attia, Riccardo E Marioni, Maristella Steri, Lu-Chen Weng, Rene Pool, Vera Grossmann, Jennifer A Brody, Cristina Venturini, Toshiko Tanaka, Lynda M Rose, Christopher Oldmeadow, Johanna Mazur, Saonli Basu, Mattias Frånberg, Qiong Yang, Symen Ligthart, Jouke J Hottenga, Ann Rumley, Antonella Mulas, Anton J M de Craen, Anne Grotevendt, Kent D Taylor, Graciela E Delgado, Annette Kifley, Lorna M Lopez, Tina L Berentzen, Massimo Mangino, Stefania Bandinelli, Alanna C Morrison, Anders Hamsten, Geoffrey Tofler, Moniek P M de Maat, Harmen H M Draisma, Gordon D Lowe, Magdalena Zoledziewska, Naveed Sattar, Karl J Lackner, Uwe Völker, Barbara McKnight, Jie Huang, Elizabeth G Holliday, Mark A McEvoy, John M Starr, Pirro G Hysi, Dena G Hernandez, Weihua Guan, Fernando Rivadeneira, Wendy L McArdle, P Eline Slagboom, Tanja Zeller, Bruce M Psaty, André G Uitterlinden, Eco J C de Geus, David J Stott, Harald Binder, Albert Hofman, Oscar H Franco, Jerome I Rotter, Luigi Ferrucci, Tim D Spector, Ian J Deary, Winfried März, Andreas Greinacher, Philipp S Wild, Francesco Cucca, Dorret I Boomsma, Hugh Watkins, Weihong Tang, Paul M Ridker, Jan W Jukema, Rodney J Scott, Paul Mitchell, Torben Hansen, Christopher J O'Donnell, Nicholas L Smith, David P Strachan, and Abbas Dehghan
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Medicine ,Science - Abstract
An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.
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- 2017
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7. Obesity and Life Expectancy with and without Diabetes in Adults Aged 55 Years and Older in the Netherlands: A Prospective Cohort Study.
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Klodian Dhana, Jana Nano, Symen Ligthart, Anna Peeters, Albert Hofman, Wilma Nusselder, Abbas Dehghan, and Oscar H Franco
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Medicine - Abstract
Overweight and obesity are associated with increased risk of type 2 diabetes. Limited evidence exists regarding the effect of excess weight on years lived with and without diabetes. We aimed to determine the association of overweight and obesity with the number of years lived with and without diabetes in a middle-aged and elderly population.The study included 6,499 individuals (3,656 women) aged 55 y and older from the population-based Rotterdam Study. We developed a multistate life table to calculate life expectancy for individuals who were normal weight, overweight, and obese and the difference in years lived with and without diabetes. For life table calculations, we used prevalence, incidence rate, and hazard ratios (HRs) for three transitions (healthy to diabetes, healthy to death, and diabetes to death), stratifying by body mass index (BMI) at baseline and adjusting for confounders. During a median follow-up of 11.1 y, we observed 697 incident diabetes events and 2,192 overall deaths. Obesity was associated with an increased risk of developing diabetes (HR: 2.13 [p < 0.001] for men and 3.54 [p < 0.001] for women). Overweight and obesity were not associated with mortality in men and women with or without diabetes. Total life expectancy remained unaffected by overweight and obesity. Nevertheless, men with obesity aged 55 y and older lived 2.8 (95% CI -6.1 to -0.1) fewer y without diabetes than normal weight individuals, whereas, for women, the difference between obese and normal weight counterparts was 4.7 (95% CI -9.0 to -0.6) y. Men and women with obesity lived 2.8 (95% CI 0.6 to 6.2) and 5.3 (95% CI 1.6 to 9.3) y longer with diabetes, respectively, compared to their normal weight counterparts. Since the implications of these findings could be limited to middle-aged and older white European populations, our results need confirmation in other populations.Obesity in the middle aged and elderly is associated with a reduction in the number of years lived free of diabetes and an increase in the number of years lived with diabetes. Those extra years lived with morbidity might place a high toll on individuals and health care systems.
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- 2016
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8. Discovery of Genetic Variation on Chromosome 5q22 Associated with Mortality in Heart Failure.
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J Gustav Smith, Janine F Felix, Alanna C Morrison, Andreas Kalogeropoulos, Stella Trompet, Jemma B Wilk, Olof Gidlöf, Xinchen Wang, Michael Morley, Michael Mendelson, Roby Joehanes, Symen Ligthart, Xiaoyin Shan, Joshua C Bis, Ying A Wang, Marketa Sjögren, Julius Ngwa, Jeffrey Brandimarto, David J Stott, David Aguilar, Kenneth M Rice, Howard D Sesso, Serkalem Demissie, Brendan M Buckley, Kent D Taylor, Ian Ford, Chen Yao, Chunyu Liu, CHARGE-SCD consortium, EchoGen consortium, QT-IGC consortium, CHARGE-QRS consortium, Nona Sotoodehnia, Pim van der Harst, Bruno H Ch Stricker, Stephen B Kritchevsky, Yongmei Liu, J Michael Gaziano, Albert Hofman, Christine S Moravec, André G Uitterlinden, Manolis Kellis, Joyce B van Meurs, Kenneth B Margulies, Abbas Dehghan, Daniel Levy, Björn Olde, Bruce M Psaty, L Adrienne Cupples, J Wouter Jukema, Luc Djousse, Oscar H Franco, Eric Boerwinkle, Laurie A Boyer, Christopher Newton-Cheh, Javed Butler, Ramachandran S Vasan, Thomas P Cappola, and Nicholas L Smith
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Genetics ,QH426-470 - Abstract
Failure of the human heart to maintain sufficient output of blood for the demands of the body, heart failure, is a common condition with high mortality even with modern therapeutic alternatives. To identify molecular determinants of mortality in patients with new-onset heart failure, we performed a meta-analysis of genome-wide association studies and follow-up genotyping in independent populations. We identified and replicated an association for a genetic variant on chromosome 5q22 with 36% increased risk of death in subjects with heart failure (rs9885413, P = 2.7x10-9). We provide evidence from reporter gene assays, computational predictions and epigenomic marks that this polymorphism increases activity of an enhancer region active in multiple human tissues. The polymorphism was further reproducibly associated with a DNA methylation signature in whole blood (P = 4.5x10-40) that also associated with allergic sensitization and expression in blood of the cytokine TSLP (P = 1.1x10-4). Knockdown of the transcription factor predicted to bind the enhancer region (NHLH1) in a human cell line (HEK293) expressing NHLH1 resulted in lower TSLP expression. In addition, we observed evidence of recent positive selection acting on the risk allele in populations of African descent. Our findings provide novel genetic leads to factors that influence mortality in patients with heart failure.
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- 2016
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9. Pleiotropy among common genetic loci identified for cardiometabolic disorders and C-reactive protein.
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Symen Ligthart, Paul S de Vries, André G Uitterlinden, Albert Hofman, CHARGE Inflammation working group, Oscar H Franco, Daniel I Chasman, and Abbas Dehghan
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Medicine ,Science - Abstract
Pleiotropic genetic variants have independent effects on different phenotypes. C-reactive protein (CRP) is associated with several cardiometabolic phenotypes. Shared genetic backgrounds may partially underlie these associations. We conducted a genome-wide analysis to identify the shared genetic background of inflammation and cardiometabolic phenotypes using published genome-wide association studies (GWAS). We also evaluated whether the pleiotropic effects of such loci were biological or mediated in nature. First, we examined whether 283 common variants identified for 10 cardiometabolic phenotypes in GWAS are associated with CRP level. Second, we tested whether 18 variants identified for serum CRP are associated with 10 cardiometabolic phenotypes. We used a Bonferroni corrected p-value of 1.1×10-04 (0.05/463) as a threshold of significance. We evaluated the independent pleiotropic effect on both phenotypes using individual level data from the Women Genome Health Study. Evaluating the genetic overlap between inflammation and cardiometabolic phenotypes, we found 13 pleiotropic regions. Additional analyses showed that 6 regions (APOC1, HNF1A, IL6R, PPP1R3B, HNF4A and IL1F10) appeared to have a pleiotropic effect on CRP independent of the effects on the cardiometabolic phenotypes. These included loci where individuals carrying the risk allele for CRP encounter higher lipid levels and risk of type 2 diabetes. In addition, 5 regions (GCKR, PABPC4, BCL7B, FTO and TMEM18) had an effect on CRP largely mediated through the cardiometabolic phenotypes. In conclusion, our results show genetic pleiotropy among inflammation and cardiometabolic phenotypes. In addition to reverse causation, our data suggests that pleiotropic genetic variants partially underlie the association between CRP and cardiometabolic phenotypes.
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- 2015
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10. Vitamin D and C-Reactive Protein: A Mendelian Randomization Study.
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Marte C Liefaard, Symen Ligthart, Anna Vitezova, Albert Hofman, André G Uitterlinden, Jessica C Kiefte-de Jong, Oscar H Franco, M Carola Zillikens, and Abbas Dehghan
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Medicine ,Science - Abstract
Vitamin D deficiency is widely prevalent and has been associated with many diseases. It has been suggested that vitamin D has effects on the immune system and inhibits inflammation. The aim of our study was to investigate whether vitamin D has an inhibitory effect on systemic inflammation by assessing the association between serum levels of vitamin D and C-reactive protein. We studied the association between serum 25-hydroxyvitamin D and C-reactive protein through linear regression in 9,649 participants of the Rotterdam Study, an observational, prospective population-based cohort study. We used genetic variants related to vitamin D and CRP to compute a genetic risk score and perform bi-directional Mendelian randomization analysis. In linear regression adjusted for age, sex, cohort and other confounders, natural log-transformed CRP decreased with 0.06 (95% CI: -0.08, -0.03) unit per standard deviation increase in 25-hydroxyvitamin D. Bi-directional Mendelian randomization analyses showed no association between the vitamin D genetic risk score and lnCRP (Beta per SD = -0.018; p = 0.082) or the CRP genetic risk score and 25-hydroxyvitamin D (Beta per SD = 0.001; p = 0.998). In conclusion, higher levels of Vitamin D are associated with lower levels of C-reactive protein. In this study we did not find evidence for this to be the result of a causal relationship.
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- 2015
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11. Thyroid Function and the Risk of Prediabetes and Type 2 Diabetes
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Oscar H Roa Dueñas, Anna C Van der Burgh, Till Ittermann, Symen Ligthart, M Arfan Ikram, Robin Peeters, Layal Chaker, Epidemiology, and Internal Medicine
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Endocrinology, Diabetes and Metabolism ,Biochemistry (medical) ,Clinical Biochemistry ,Thyrotropin ,Biochemistry ,Hyperthyroidism ,Prediabetic State ,Thyroxine ,Endocrinology ,Diabetes Mellitus, Type 2 ,Hypothyroidism ,SDG 3 - Good Health and Well-being ,Humans ,Prospective Studies - Abstract
Context Thyroid hormones are important regulators of glucose metabolism, and studies investigating the association between thyroid function and type 2 diabetes incidence have shown conflicting results. Objective We aimed to combine the evidence from prospective studies addressing the association between thyroid function and type 2 diabetes risk. Methods We systematically searched in Embase, Medline (Ovid), Web of Science, Cochrane, and Google Scholar for prospective studies assessing the association of thyroid function and incident type 2 diabetes. Data extraction was performed using a standardized protocol by 2 independent reviewers. We assessed study quality using the Newcastle-Ottawa Scale and pooled hazard ratios (HRs) and 95% CI using random-effects models. Results From the 4574 publications identified, 7 met our inclusion criteria and were included in the qualitative synthesis. Six publications were included in the meta-analysis. Studies assessed hypothyroidism (6 studies), hyperthyroidism (5 studies), thyrotropin (TSH) in the reference range (4 studies), and free thyroxine (FT4) in the reference range (3 studies) in relation to incident type 2 diabetes. The pooled HR for the risk of type 2 diabetes was 1.26 (95% CI, 1.05-1.52) for hypothyroidism, 1.16 (95% CI, 0.90-1.49) for hyperthyroidism, 1.06 (95% CI, 0.96-1.17) for TSH in the reference range, and 0.95 (95% CI, 0.91-0.98) for FT4 in the reference range. Conclusion Current evidence suggests an increased type 2 diabetes risk in people with hypothyroidism and lower FT4 levels in the reference range. Further population-based studies are needed to address this association given the limited evidence.
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- 2022
12. American Heart Association’s Life’s Simple 7: Lifestyle Recommendations, Polygenic Risk, and Lifetime Risk of Coronary Heart Disease
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Natalie R. Hasbani, Symen Ligthart, Michael R. Brown, Adam S. Heath, Allison Bebo, Kellan E. Ashley, Eric Boerwinkle, Alanna C. Morrison, Aaron R. Folsom, David Aguilar, Paul S. de Vries, and Intensive Care
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Cohort Studies ,SDG 3 - Good Health and Well-being ,Cardiovascular Diseases ,Risk Factors ,Physiology (medical) ,Humans ,Coronary Disease ,Genetic Predisposition to Disease ,American Heart Association ,Cardiology and Cardiovascular Medicine ,Life Style ,United States - Abstract
Background: Understanding the effect of lifestyle and genetic risk on the lifetime risk of coronary heart disease (CHD) is important to improving public health initiatives. Our objective was to quantify remaining lifetime risk and years free of CHD according to polygenic risk and the American Heart Association’s Life’s Simple 7 (LS7) guidelines in a population-based cohort study. Methods: Our analysis included data from participants of the ARIC (Atherosclerosis Risk in Communities) study: 8372 White and 2314 Black participants; 45 years of age and older; and free of CHD at baseline examination. A polygenic risk score (PRS) comprised more than 6 million genetic variants was categorized into low (80th percentile). An overall LS7 score was calculated at baseline and categorized into “poor,” “intermediate,” and “ideal” cardiovascular health. Lifetime risk and CHD-free years were computed according to polygenic risk and LS7 categories. Results: The overall remaining lifetime risk was 27%, ranging from 16.6% in individuals with an ideal LS7 score to 43.1% for individuals with a poor LS7 score. The association of PRS with lifetime risk differed according to ancestry. In White participants, remaining lifetime risk ranged from 19.8% to 39.3% according to increasing PRS categories. Individuals with a high PRS and poor LS7 had a remaining lifetime risk of 67.1% and 15.9 fewer CHD-free years than did those with intermediate polygenic risk and LS7 scores. In the high-PRS group, ideal LS7 was associated with 20.2 more CHD-free years compared with poor LS7. In Black participants, remaining lifetime risk ranged from 19.1% to 28.6% according to increasing PRS category. Similar lifetime risk estimates were observed for individuals of poor LS7 regardless of PRS category. In the high-PRS group, an ideal LS7 score was associated with only 4.5 more CHD-free years compared with a poor LS7 score. Conclusions: Ideal adherence to LS7 recommendations was associated with lower lifetime risk of CHD for all individuals, especially in those with high genetic susceptibility. In Black participants, adherence to LS7 guidelines contributed to lifetime risk of CHD more so than current PRSs. Improved PRSs are needed to properly evaluate genetic susceptibility for CHD in diverse populations.
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- 2022
13. Meta-analysis of epigenome-wide association studies of carotid intima-media thickness
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Shih-Jen Hwang, Jordana T. Bell, Olli T. Raitakari, Mikko Hurme, Joanna M. Wardlaw, W. David Hill, Joshua C. Bis, Traci M. Bartz, Anton J.M. Roks, John M. Starr, Wolfgang Koenig, M. Arfan Ikram, Eliana Portilla-Fernandez, Alexander Teumer, Annette Peters, Mika Kähönen, Ian J. Deary, Maryam Kavousi, Nona Sotoodehnia, Joachim Thiery, Jennifer A. Brody, Melanie Waldenberger, Ulf Schminke, Abbas Dehghan, Hans J. Grabe, Roby Joehanes, Symen Ligthart, Daniel Levy, Bruce M. Psaty, A.H. Jan Danser, Wolfgang Rathmann, Henry Völzke, Andrew Wong, Mohsen Ghanbari, Jochen Seissler, Terho Lehtimäki, Ken K. Ong, Jane Maddock, Rory P. Wilson, Christopher J. O'Donnell, Cornelia Then, Christine Meisinger, Pashupati P. Mishra, Sahar Ghasemi, Marcus Dörr, Portilla-Fernández, Eliana [0000-0003-4105-8586], Ong, Kenneth [0000-0003-4689-7530], Apollo - University of Cambridge Repository, Tampere University, Department of Clinical Chemistry, Clinical Medicine, Department of Clinical Physiology and Nuclear Medicine, BioMediTech, Epidemiology, and Internal Medicine
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0301 basic medicine ,Oncology ,medicine.medical_specialty ,Epidemiology ,Differentially methylated regions ,Aryl hydrocarbon receptor repressor ,Coronary Artery Disease ,030204 cardiovascular system & hematology ,Carotid Intima-Media Thickness ,Coronary artery disease ,03 medical and health sciences ,Epigenome ,0302 clinical medicine ,SDG 3 - Good Health and Well-being ,Risk Factors ,Internal medicine ,Mendelian randomization ,Medicine ,Vascular outcomes ,Humans ,ddc:610 ,cardiovascular diseases ,Stroke ,Cardiovascular risk factors ,Epigenome-wide association studies ,DNA methylation ,business.industry ,medicine.disease ,humanities ,3142 Public health care science, environmental and occupational health ,ddc ,030104 developmental biology ,Cross-Sectional Studies ,CpG site ,Intima-media thickness ,cardiovascular system ,Common carotid intima-media thickness ,business ,Meta-Analysis - Abstract
Funder: Nederlandse Organisatie voor Wetenschappelijk Onderzoek; doi: http://dx.doi.org/10.13039/501100003246, Funder: ZonMw; doi: http://dx.doi.org/10.13039/501100001826, Funder: Research Institute for Diseases in the Elderly, Funder: Ministerie van Onderwijs, Cultuur en Wetenschap; doi: http://dx.doi.org/10.13039/501100003245, Funder: Health Promotion Administration, Ministry of Health and Welfare; doi: http://dx.doi.org/10.13039/100013227, Funder: Municipality of Rotterdam, Common carotid intima-media thickness (cIMT) is an index of subclinical atherosclerosis that is associated with ischemic stroke and coronary artery disease (CAD). We undertook a cross-sectional epigenome-wide association study (EWAS) of measures of cIMT in 6400 individuals. Mendelian randomization analysis was applied to investigate the potential causal role of DNA methylation in the link between atherosclerotic cardiovascular risk factors and cIMT or clinical cardiovascular disease. The CpG site cg05575921 was associated with cIMT (beta = -0.0264, p value = 3.5 × 10-8) in the discovery panel and was replicated in replication panel (beta = -0.07, p value = 0.005). This CpG is located at chr5:81649347 in the intron 3 of the aryl hydrocarbon receptor repressor gene (AHRR). Our results indicate that DNA methylation at cg05575921 might be in the pathway between smoking, cIMT and stroke. Moreover, in a region-based analysis, 34 differentially methylated regions (DMRs) were identified of which a DMR upstream of ALOX12 showed the strongest association with cIMT (p value = 1.4 × 10-13). In conclusion, our study suggests that DNA methylation may play a role in the link between cardiovascular risk factors, cIMT and clinical cardiovascular disease.
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- 2021
14. Septic Shock: A Genomewide Association Study and Polygenic Risk Score Analysis
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Matthew A. Brown, Nicholas G. Martin, Joseph E. Powell, Jeremy Cohen, Dorrilyn Rajbhandari, Andrew Rhodes, Scott D. Gordon, Jason Meyer, Qiang Li, Elizabeth Peach, Shannon D’Urso, Gabriel Cuellar-Partida, Colin McArthur, Symen Ligthart, Simon Finfer, Balasubramanian Venkatesh, David M. Evans, John Myburgh, Erika De Guzman, Sarah E. Medland, Antje Blumenthal, Epidemiology, and Internal Medicine
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0301 basic medicine ,Multifactorial Inheritance ,medicine.medical_specialty ,Population ,Context (language use) ,Hematocrit ,Gastroenterology ,Sepsis ,03 medical and health sciences ,0302 clinical medicine ,Risk Factors ,Internal medicine ,Genotype ,medicine ,Genetic predisposition ,Humans ,education ,Genetics (clinical) ,Randomized Controlled Trials as Topic ,Genetic association ,education.field_of_study ,medicine.diagnostic_test ,business.industry ,Septic shock ,Obstetrics and Gynecology ,medicine.disease ,Shock, Septic ,030104 developmental biology ,Pediatrics, Perinatology and Child Health ,business ,030217 neurology & neurosurgery ,Genome-Wide Association Study - Abstract
Previous genetic association studies have failed to identify loci robustly associated with sepsis, and there have been no published genetic association studies or polygenic risk score analyses of patients with septic shock, despite evidence suggesting genetic factors may be involved. We systematically collected genotype and clinical outcome data in the context of a randomized controlled trial from patients with septic shock to enrich the presence of disease-associated genetic variants. We performed genomewide association studies of susceptibility and mortality in septic shock using 493 patients with septic shock and 2442 population controls, and polygenic risk score analysis to assess genetic overlap between septic shock risk/mortality with clinically relevant traits. One variant, rs9489328, located in AL589740.1 noncoding RNA, was significantly associated with septic shock (p = 1.05 × 10–10); however, it is likely a false-positive. We were unable to replicate variants previously reported to be associated (p < 1.00 × 10–6 in previous scans) with susceptibility to and mortality from sepsis. Polygenic risk scores for hematocrit and granulocyte count were negatively associated with 28-day mortality (p = 3.04 × 10–3; p = 2.29 × 10–3), and scores for C-reactive protein levels were positively associated with susceptibility to septic shock (p = 1.44 × 10–3). Results suggest that common variants of large effect do not influence septic shock susceptibility, mortality and resolution; however, genetic predispositions to clinically relevant traits are significantly associated with increased susceptibility and mortality in septic individuals.
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- 2020
15. Genetic susceptibility, obesity and lifetime risk of type 2 diabetes
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Alanna C. Morrison, James S. Pankow, M. Arfan Ikram, Abbas Dehghan, Maarten J.G. Leening, Symen Ligthart, Natalie R Hasbani, Eric J.G. Sijbrands, Paul S. de Vries, Maryam Kavousi, André G. Uitterlinden, Elizabeth Selvin, Eric Boerwinkle, Fariba Ahmadizar, Thijs T. W. van Herpt, Intensive Care, Epidemiology, Internal Medicine, and Cardiology
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Male ,Risk ,Multifactorial Inheritance ,Endocrinology, Diabetes and Metabolism ,Population ,Type 2 diabetes ,Article ,White People ,Rotterdam Study ,Endocrinology ,SDG 3 - Good Health and Well-being ,Diabetes mellitus ,Internal Medicine ,medicine ,Genetic predisposition ,Humans ,Genetic Predisposition to Disease ,Obesity ,education ,Aric study ,Life Style ,Aged ,education.field_of_study ,business.industry ,Genetic Variation ,Middle Aged ,medicine.disease ,Diabetes Mellitus, Type 2 ,Lifetime risk ,Female ,business ,Demography - Abstract
Aims: Both lifestyle factors and genetic background contribute to the development of type 2 diabetes. Estimation of the lifetime risk of diabetes based on genetic information has not been presented, and the extent to which a normal body weight can offset a high lifetime genetic risk is unknown. Methods: We used data from 15,671 diabetes-free participants of European ancestry aged 45 years and older from the prospective population-based ARIC study and Rotterdam Study (RS). We quantified the remaining lifetime risk of diabetes stratified by genetic risk and quantified the effect of normal weight in terms of relative and lifetime risks in low, intermediate and high genetic risk. Results: At age 45 years, the lifetime risk of type 2 diabetes in ARIC in the low, intermediate and high genetic risk category was 33.2%, 41.3% and 47.2%, and in RS 22.8%, 30.6% and 35.5% respectively. The absolute lifetime risk for individuals with normal weight compared to individuals with obesity was 24% lower in ARIC and 8.6% lower in RS in the low genetic risk group, 36.3% lower in ARIC and 31.3% lower in RS in the intermediate genetic risk group, and 25.0% lower in ARIC and 29.4% lower in RS in the high genetic risk group. Conclusions: Genetic variants for type 2 diabetes have value in estimating the lifetime risk of type 2 diabetes. Normal weight mitigates partly the deleterious effect of high genetic risk.
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- 2021
16. The trans-ancestral genomic architecture of glycemic traits
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Albertine J. Oldehinkel, Wieland Kiess, Xueling Sim, Norihiro Kato, Philippe Froguel, Astrid van Hylckama Vlieg, Josée Dupuis, Nanette R. Lee, Symen Ligthart, Harry Campbell, Marta E. Alarcón-Riquelme, P. Eline Slagboom, Massimo Mangino, Tian Xie, Niek Verweij, James B. Meigs, Chaolong Wang, Michael Y. Tsai, Erik Ingelsson, Colin N. A. Palmer, Erik B. van den Akker, Fumihiko Matsuda, Rainer Rauramaa, Yi-Cheng Chang, Lars Lind, Stefan R. Bornstein, Mandy Vogel, Sven Bergmann, Ya X. Wang, Ching-Ti Liu, Annette Schürmann, Michael Boehnke, David J. Porteous, Kazuya Setoh, Qibin Qi, Ayse Demirkan, Francesco Cucca, Allan Linneberg, Claire J. Steves, Jun Liu, Leslie A. Lange, Noël P. Burtt, Diana Kuh, Cassandra N. Spracklen, Ken K. Ong, Charumathi Sabanayagam, Jost B. Jonas, Ele Ferrannini, Lawrence J. Beilin, Qing Duan, Blair H. Smith, Isobel D. Stewart, Alexander P. Reiner, Simon P. Mooijaart, Tim D. Spector, Paul W. Franks, E. Shyong Tai, Mark I. McCarthy, Anna L. Gloyn, D.I. Boomsma, Dennis Raven, Nicholas J. Timpson, Rona J. Strawbridge, George Dedoussis, Susan Redline, Jaeyoung Hong, Harald Grallert, Jagadish Vangipurapu, Rico Rueedi, Diane M. Becker, Marian Beekman, Claudia P. Cabrera, Johannes Waage, Jin Fang Chai, Yii-Der Ida Chen, Graciela E. Delgado, Thibaud S. Boutin, Yang Hai, Yoriko Heianza, Wei Zhao, Andres Metspalu, Tien Yin Wong, Mila Desi Anasanti, Inger Njølstad, Hans Bisgaard, Valeriya Lyssenko, Denis Rybin, Wanqing Wen, Torben Hansen, James F. Wilson, Sameline Grimsgaard, Annette Peters, Michele K. Evans, Damia Noce, Sarah C. Nelson, May E. Montasser, Nan Wang, Geltrude Mingrone, Gudny Eiriksdottir, Nicholas J. Wareham, Fouad Kandeel, Linda S. Adair, Kelvin Lam, Jaana Lindström, Eco J. C. de Geus, Debbie A Lawlor, Sara M. Willems, Xu Lin, Harold Snieder, Matt J. Neville, Naveed Sattar, Chelsea K. Raulerson, Paul M. Ridker, Jer-Yuarn Wu, Weihua Zhang, H. Janaka de Silva, Jana V. van Vliet-Ostaptchouk, Elena Tremoli, Toru Nabika, Jing Hua Zhao, Vilmundur Gudnason, Tao Huang, Robert C. Kaplan, Sohee Han, Mohammad Hadi Zafarmand, Aaron Leong, Yen-Feng Chiu, Kumaraswamy Naidu Chitrala, Ivana Kolcic, Franco Giulianini, Tao Wang, Lu Qi, Stephan J. L. Bakker, Laura J Corbin, Zoltán Kutalik, Bruna Gigante, Willa A. Hsueh, Peter J. van der Most, Tin Louie, Yujie Wang, Stella Trompet, Fernando Rivideneira, Yasumasa Ohyagi, Lynne E. Wagenknecht, Jerry L. Nadler, Michael Stumvoll, Mark O. Goodarzi, Sahoko Ichihara, Jeffrey R. O'Connell, Tomohiro Katsuya, Giorgio Pistis, Alice Stanton, Sirkka Keinänen-Kiukaanniemi, Momoko Horikoshi, Honglan Li, Tanja G. M. Vrijkotte, Caroline Hayward, Karen L. Mohlke, Carola Marzi, Girish N. Nadkarni, Laura J. Rasmussen-Torvik, Alain G. Bertoni, Andrew R. Wood, Annique Claringbould, Mi Yeong Hwang, Hugh Watkins, Heikki A. Koistinen, Mattias Frånberg, Jani Heikkinen, Elizabeth Selvin, Donald W. Bowden, Abbas Dehghan, Christian Fuchsberger, Audrey Y. Chu, Kent D. Taylor, Katherine A. Kentistou, Johanna Kuusisto, Jingyi Tan, Huaixing Li, Eric Boerwinkle, Catharina A. Hartman, Archie Campbell, Kari E. North, Oluf Pedersen, Sölve Elmståhl, Emil V. R. Appel, Chang-Hsun Hsieh, Dennis O. Mook-Kanamori, Rob M. van Dam, Pontiano Kaleebu, Corri Black, Jennifer A. Brody, Bengt Sennblad, Shaofeng Huo, M. Larissa Avilés-Santa, Ruth J. F. Loos, Patricia B. Munroe, Chien-Hsiun Chen, Liang Sun, Zorayr Arzumanyan, Rebecca Rohde, Yasuharu Tabara, Albert V. Smith, Betina H. Thuesen, Niels Grarup, Jorgen Engmann, Tatijana Zemunik, M. Arfan Ikram, Marit E. Jørgensen, Christian Herder, Ching-Yu Cheng, Serena Sanna, Damiano Baldassarre, Tarunveer S. Ahluwalia, Mark J. Caulfield, Anne Ndungu, Carl D. Langefeld, Lisa R. Yanek, Luigi Ferrucci, Ananda R. Wickremasinghe, Raymond Noordam, Trevor A. Mori, Tom Wilsgaard, Mika Kivimäki, Rita R. Kalyani, Alan B. Zonderman, Veronique Vitart, Patricia A. Peyser, Shuiqing Lai, Richa Saxena, Li-Ching Chang, Karin Leander, Wei Huang, Peter Vollenweider, Tanya M. Teslovich, Ying Wu, Shufa Du, Brian E. Cade, Patrik K. E. Magnusson, John C. Chambers, Stephen C. J. Parker, Tamar Sofer, Winfried März, Sharon L.R. Kardia, Peter K. Joshi, Neil R. Robertson, Anny H. Xiang, Fumihiko Takeuchi, N. Amin, Jouke-Jan Hottenga, Carol A. Wang, Stefan Gustafsson, Jung Ho Gong, Penny Gordon-Larsen, Yu-Tang Gao, Abhishek Nag, Gonneke Willemsen, Michael A. Province, Aliki-Eleni Farmaki, Segun Fatumo, Antje Körner, Pim van der Harst, Marie Loh, Kei Hang Katie Chan, Gonçalo R. Abecasis, Nicholette D. Palmer, Simin Liu, Ishminder K. Kooner, Javier Gayán, Arne Astrup, Laura J. Scott, Erwin P. Bottinger, Andrew Wong, Inga Prokopenko, Ping An, Markku Laakso, Matthias Blüher, Susan R. Heckbert, Thomas A. Buchanan, Tatsuaki Matsubara, Andrew P. Morris, Brian H. Chen, Kristi Läll, Teresa Tusie, Timo A. Lakka, Jie Yao, Michael Preuss, Teemu Kuulasmaa, Carlos Lorenzo, Stephen S. Rich, Marie Lauzon, Laura M. Raffield, Pankow S. James, Takahisa Kawaguchi, Kathleen A. Ryan, Wei Zheng, Igor Rudan, Thomas Sparsø, Hugoline G. de Haan, Sandosh Padmanabhan, Richard M. Watanabe, Alicia Huerta-Chagoya, Anette P. Gjesing, Andrew A. Hicks, Richard N. Bergman, Mitsuhiro Yokota, Heather M. Stringham, Bruce M. Psaty, Jian'an Luan, Anuj Goel, Eleanor Wheeler, Masahiro Nakatochi, Young-Jin Kim, Xiao-Ou Shu, Mickaël Canouil, Robert A. Scott, Marika Kaakinen, Mari Nelis, Adolfo Correa, Jaspal S. Kooner, Michiya Igase, Anubha Mahajan, Peter E. H. Schwarz, Craig E. Pennell, Claudia Schurmann, Xiaoran Chai, Ji Chen, Lori L. Bonnycastle, Peter S. Sever, Thorkild I. A. Sørensen, André G. Uitterlinden, Ilja M. Nolte, Gaëlle Marenne, Timothy M. Frayling, Bong-Jo Kim, Kerrin S. Small, Cecilia M. Lindgren, Bernhard O. Böhm, Shih-Yi Lin, Katharina E. Schraut, Cornelia M. van Duijn, Sanghoon Moon, Mark Walker, Chiea Chuen Khor, Ruifang Li-Gao, Qiuyin Cai, Neil Schneiderman, Ko Willems van Dijk, Ozren Polasek, W. Craig Johnson, Dermot F. Reilly, Inês Barroso, Anke Tönjes, Manjinder S. Sandhu, Wen B. Wei, Jose C. Florez, Lorraine Southam, Leif Groop, Lawrence F. Bielak, Peter Kovacs, Jianjun Liu, Jouko Saramies, Helen R. Warren, Man Li, Daniel I. Chasman, Eleftheria Zeggini, Xiaoshuai Zhang, Loic Yengo, Shi Jinxiu, Jirong Long, Xiuqing Guo, Meena Kumari, Leslie J. Raffel, Jill M. Norris, Henrik Vestergaard, Jing He, Peter P. Pramstaller, Diana van Heemst, Kevin Sandow, Marjo-Ritta Jarvelin, Carlos A. Aguilar-Salinas, Peitao Wu, Hortensia Moreno-Macías, Jerome I. Rotter, Kathryn Roll, Frits R. Rosendaal, Bernardo L. Horta, Heming Wang, Fernando Pires Hartwig, Richard A. Jensen, Matti Uusitupa, Rozenn N. Lemaitre, Paul R. H. J. Timmers, Timo Saaristo, Jaakko Tuomilehto, Reedik Mägi, Debashree Ray, J. Wouter Jukema, Claudia Langenberg, Marcus E. Kleber, Francis S. Collins, Klaus Bønnelykke, Lenore J. Launer, Arushi Varshney, Anders Hamsten, European Commission, Génétique, génomique fonctionnelle et biotechnologies (UMR 1078) (GGB), Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO)-Université de Brest (UBO)-EFS-Institut National de la Santé et de la Recherche Médicale (INSERM), Sanger Institute, Wellcome Trust, Marenne, Gaëlle [0000-0002-4363-7170], Varshney, Arushi [0000-0001-9177-9707], Corbin, Laura J [0000-0002-4032-9500], Parker, Stephen CJ [0000-0001-8122-0117], Langenberg, Claudia [0000-0002-5017-7344], Wheeler, Eleanor [0000-0002-8616-6444], Morris, Andrew P [0000-0002-6805-6014], Barroso, Inês [0000-0001-5800-4520], Apollo - University of Cambridge Repository, Lifelines Cohort Study, Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC), de Haan, H.G., van den Akker, E., van der Most, P.J., de Geus, EJC, van Dam, R.M., van Heemst, D., van Hylckama Vlieg, A., van Willems van Dijk, K., de Silva, H.J., van der Harst, P., van Duijn, C., Centre of Excellence in Complex Disease Genetics, HUS Abdominal Center, Institute for Molecular Medicine Finland, Leif Groop Research Group, HUS Internal Medicine and Rehabilitation, Department of Medicine, Department of Biochemistry and Developmental Biology, Helsinki University Hospital Area, University of Helsinki, Clinicum, Department of Public Health, Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, Physiology, AMS - Musculoskeletal Health, AMS - Tissue Function & Regeneration, APH - Mental Health, Nutrition and Health, APH - Methodology, Epidemiology and Data Science, ACS - Atherosclerosis & ischemic syndromes, APH - Aging & Later Life, ARD - Amsterdam Reproduction and Development, Public and occupational health, Epidemiology, Internal Medicine, Life Course Epidemiology (LCE), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Groningen Institute for Organ Transplantation (GIOT), Groningen Kidney Center (GKC), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Center for Liver, Digestive and Metabolic Diseases (CLDM), and Cardiovascular Centre (CVC)
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Blood Glucose ,Disease risk ,Multifactorial Inheritance ,Glycated Hemoglobin A ,[SDV]Life Sciences [q-bio] ,Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC) ,LOCI ,Genome-wide association study ,Type 2 diabetes ,VARIANTS ,GLUCOSE ,Epigenesis, Genetic ,chemistry.chemical_compound ,0302 clinical medicine ,Mechanisms ,WIDE ASSOCIATION ,genetics ,Gene-expression ,HEMOGLOBIN ,ComputingMilieux_MISCELLANEOUS ,11 Medical and Health Sciences ,GENE-EXPRESSION ,Genetics ,Genetics & Heredity ,0303 health sciences ,INSULIN-RESISTANCE ,Genome ,Loci ,1184 Genetics, developmental biology, physiology ,Variants ,ALSPAC ,Physical Chromosome Mapping ,Life Sciences & Biomedicine ,Human ,Quantitative Trait Loci ,Wide association study ,Biology ,Quantitative trait locus ,Article ,White People ,diseases ,MECHANISMS ,Quantitative Trait ,03 medical and health sciences ,Insulin resistance ,Quantitative Trait, Heritable ,SDG 3 - Good Health and Well-being ,Genetic ,Lifelines Cohort Study ,Diabetes mellitus ,medicine ,Humans ,Hemoglobin ,Heritable ,METAANALYSIS ,Alleles ,030304 developmental biology ,Genetic association ,Glycemic ,Glycated Hemoglobin ,Science & Technology ,Genome, Human ,Whites ,Gene Expression Profiling ,DISEASE RISK ,Settore MED/13 - ENDOCRINOLOGIA ,Insulin-resistance ,06 Biological Sciences ,medicine.disease ,Glucose ,chemistry ,Blood Glucose/genetics ,European Continental Ancestry Group/genetics ,Genome-Wide Association Study ,Glycated Hemoglobin A/metabolism ,Multifactorial Inheritance/genetics ,Quantitative Trait Loci/genetics ,Glycated hemoglobin ,030217 neurology & neurosurgery ,Meta analysis ,Epigenesis ,Developmental Biology - Abstract
Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.
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- 2021
17. Blood
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Paul S. de Vries, Michael Laffan, Symen Ligthart, Weihong Tang, Myriam Fornage, Moniek P.M. de Maat, Barbara McKnight, Dipender Gill, Nathan Pankratz, Cavin K. Ward-Caviness, Eric Boerwinkle, Alisa S. Wolberg, Abbas Dehghan, Nicholas L. Smith, Jillian Maners, Stéphanie Debette, Alanna C. Morrison, Martin Dichgans, Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Hematology, and Epidemiology
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Male ,0301 basic medicine ,LOCI ,Genome-wide association study ,030204 cardiovascular system & hematology ,Fibrinogen ,Biochemistry ,Thrombosis and Hemostasis ,0302 clinical medicine ,SPLICE VARIANT ,Risk Factors ,1102 Cardiorespiratory Medicine and Haematology ,Hematology ,Anticoagulant ,GENETIC-VARIATION ,Mendelian Randomization Analysis ,ASSOCIATION ,Venous Thromboembolism ,3. Good health ,Coagulation ,Cardiology ,Female ,Life Sciences & Biomedicine ,medicine.drug ,medicine.medical_specialty ,medicine.drug_class ,CHARGE Inflammation Working Group ,Immunology ,COAGULATION ,ATHEROSCLEROSIS RISK ,INVENT Consortium ,GENOTYPE IMPUTATION ,03 medical and health sciences ,INFLAMMATION ,MEGASTROKE consortium of the International Stroke Genetics Consortium (ISGC) ,Internal medicine ,Mendelian randomization ,medicine ,Humans ,METAANALYSIS ,Ischemic Stroke ,Genetic association ,Science & Technology ,business.industry ,Genetic Variation ,1103 Clinical Sciences ,Cell Biology ,030104 developmental biology ,CHAIN ,1114 Paediatrics and Reproductive Medicine ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,business ,Genome-Wide Association Study - Abstract
Fibrinogen is a key component of the coagulation cascade, and variation in its circulating levels may contribute to thrombotic diseases, such as venous thromboembolism (VTE) and ischemic stroke. Gamma prime (γ′) fibrinogen is an isoform of fibrinogen that has anticoagulant properties. We applied 2-sample Mendelian randomization (MR) to estimate the causal effect of total circulating fibrinogen and its isoform, γ′ fibrinogen, on risk of VTE and ischemic stroke subtypes using summary statistics from genome-wide association studies. Genetic instruments for γ′ fibrinogen and total fibrinogen were selected, and the inverse-variance weighted MR approach was used to estimate causal effects in the main analysis, complemented by sensitivity analyses that are more robust to the inclusion of pleiotropic variants, including MR-Egger, weighted median MR, and weighted mode MR. The main inverse-variance weighted MR estimates based on a combination of 16 genetic instruments for γ′ fibrinogen and 75 genetic instruments for total fibrinogen indicated a protective effect of higher γ′ fibrinogen and higher total fibrinogen on VTE risk. There was also a protective effect of higher γ′ fibrinogen levels on cardioembolic and large artery stroke risk. Effect estimates were consistent across sensitivity analyses. Our results provide evidence to support effects of genetically determined γ′ fibrinogen on VTE and ischemic stroke risk. Further research is needed to explore mechanisms underlying these effects and their clinical applications.
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- 2020
18. Trans-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation
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Anubha Mahajan, Cassandra N Spracklen, Weihua Zhang, Maggie CY Ng, Lauren E Petty, Hidetoshi Kitajima, Grace Z Yu, Sina Rüeger, Leo Speidel, Young Jin Kim, Momoko Horikoshi, Josep M Mercader, Daniel Taliun, Sanghoon Moon, Soo-Heon Kwak, Neil R Robertson, Nigel W Rayner, Marie Loh, Bong-Jo Kim, Joshua Chiou, Irene Miguel-Escalada, Pietro della Briotta Parolo, Kuang Lin, Fiona Bragg, Michael H Preuss, Fumihiko Takeuchi, Jana Nano, Xiuqing Guo, Amel Lamri, Masahiro Nakatochi, Robert A Scott, Jung-Jin Lee, Alicia Huerta-Chagoya, Mariaelisa Graff, Jin-Fang Chai, Esteban J Parra, Jie Yao, Lawrence F Bielak, Yasuharu Tabara, Yang Hai, Valgerdur Steinthorsdottir, James P Cook, Mart Kals, Niels Grarup, Ellen M Schmidt, Ian Pan, Tamar Sofer, Matthias Wuttke, Chloe Sarnowski, Christian Gieger, Darryl Nousome, Stella Trompet, Jirong Long, Meng Sun, Lin Tong, Wei-Min Chen, Meraj Ahmad, Raymond Noordam, Victor JY Lim, Claudia HT Tam, Yoonjung Yoonie Joo, Chien-Hsiun Chen, Laura M Raffield, Cécile Lecoeur, Nisa M Maruthur, Bram Peter Prins, Aude Nicolas, Lisa R Yanek, Guanjie Chen, Richard A Jensen, Salman Tajuddin, Edmond Kabagambe, Ping An, Anny H Xiang, Hyeok Sun Choi, Brian E Cade, Jingyi Tan, Fernando Abaitua, Linda S Adair, Adebowale Adeyemo, Carlos A Aguilar-Salinas, Masato Akiyama, Sonia S Anand, Alain Bertoni, Zheng Bian, Jette Bork-Jensen, Ivan Brandslund, Jennifer A Brody, Chad M Brummett, Thomas A Buchanan, Mickaël Canouil, Juliana CN Chan, Li-Ching Chang, Miao-Li Chee, Ji Chen, Shyh-Huei Chen, Yuan-Tsong Chen, Zhengming Chen, Lee-Ming Chuang, Mary Cushman, Swapan K Das, H. Janaka de Silva, George Dedoussis, Latchezar Dimitrov, Ayo P Doumatey, Shufa Du, Qing Duan, Kai-Uwe Eckardt, Leslie S Emery, Daniel S Evans, Michele K Evans, Krista Fischer, James S Floyd, Ian Ford, Myriam Fornage, Oscar H Franco, Timothy M Frayling, Barry I Freedman, Christian Fuchsberger, Pauline Genter, Hertzel C Gerstein, Vilmantas Giedraitis, Clicerio González-Villalpando, Maria Elena González-Villalpando, Mark O Goodarzi, Penny Gordon-Larsen, David Gorkin, Myron Gross, Yu Guo, Sophie Hackinger, Sohee Han, Andrew T Hattersley, Christian Herder, Annie-Green Howard, Willa Hsueh, Mengna Huang, Wei Huang, Yi-Jen Hung, Mi Yeong Hwang, Chii-Min Hwu, Sahoko Ichihara, Mohammad Arfan Ikram, Martin Ingelsson, Md. Tariqul Islam, Masato Isono, Hye-Mi Jang, Farzana Jasmine, Guozhi Jiang, Jost B Jonas, Marit E Jørgensen, Torben Jørgensen, Yoichiro Kamatani, Fouad R Kandeel, Anuradhani Kasturiratne, Tomohiro Katsuya, Varinderpal Kaur, Takahisa Kawaguchi, Jacob M Keaton, Abel N Kho, Chiea-Chuen Khor, Muhammad G Kibriya, Duk-Hwan Kim, Katsuhiko Kohara, Jennifer Kriebel, Florian Kronenberg, Johanna Kuusisto, Kristi Läll, Leslie A Lange, Myung-Shik Lee, Nanette R Lee, Aaron Leong, Liming Li, Yun Li, Ruifang Li-Gao, Symen Ligthart, Cecilia M Lindgren, Allan Linneberg, Ching-Ti Liu, Jianjun Liu, Adam E Locke, Tin Louie, Jian’an Luan, Andrea O Luk, Xi Luo, Jun Lv, Valeriya Lyssenko, Vasiliki Mamakou, K Radha Mani, Thomas Meitinger, Andres Metspalu, Andrew D Morris, Girish N. Nadkarni, Jerry L Nadler, Michael A Nalls, Uma Nayak, Ioanna Ntalla, Yukinori Okada, Lorena Orozco, Sanjay R Patel, Mark A Pereira, Annette Peters, Fraser J Pirie, Bianca Porneala, Gauri Prasad, Sebastian Preissl, Laura J Rasmussen-Torvik, Alexander P Reiner, Michael Roden, Rebecca Rohde, Katheryn Roll, Charumathi Sabanayagam, Maike Sander, Kevin Sandow, Naveed Sattar, Sebastian Schönherr, Claudia Schurmann, Mohammad Shahriar, Jinxiu Shi, Dong Mun Shin, Daniel Shriner, Jennifer A Smith, Wing Yee So, Alena Stančáková, Adrienne M Stilp, Konstantin Strauch, Ken Suzuki, Atsushi Takahashi, Kent D Taylor, Barbara Thorand, Gudmar Thorleifsson, Unnur Thorsteinsdottir, Brian Tomlinson, Jason M Torres, Fuu-Jen Tsai, Jaakko Tuomilehto, Teresa Tusie-Luna, Miriam S Udler, Adan Valladares-Salgado, Rob M van Dam, Jan B van Klinken, Rohit Varma, Marijana Vujkovic, Niels Wacher-Rodarte, Ellie Wheeler, Eric A Whitsel, Ananda R Wickremasinghe, Konstantin Willems van Dijk, Daniel R Witte, Chittaranjan S Yajnik, Ken Yamamoto, Toshimasa Yamauchi, Loïc Yengo, Kyungheon Yoon, Canqing Yu, Jian-Min Yuan, Salim Yusuf, Liang Zhang, Wei Zheng, null FinnGen, Leslie J Raffel, Michiya Igase, Eli Ipp, Susan Redline, Yoon Shin Cho, Lars Lind, Michael A Province, Craig L Hanis, Patricia A Peyser, Erik Ingelsson, Alan B Zonderman, Bruce M Psaty, Ya-Xing Wang, Charles N Rotimi, Diane M Becker, Fumihiko Matsuda, Yongmei Liu, Eleftheria Zeggini, Mitsuhiro Yokota, Stephen S Rich, Charles Kooperberg, James S Pankow, James C Engert, Yii-Der Ida Chen, Philippe Froguel, James G Wilson, Wayne HH Sheu, Sharon LR Kardia, Jer-Yuarn Wu, M Geoffrey Hayes, Ronald CW Ma, Tien-Yin Wong, Leif Groop, Dennis O Mook-Kanamori, Giriraj R Chandak, Francis S Collins, Dwaipayan Bharadwaj, Guillaume Paré, Michèle M Sale, Habibul Ahsan, Ayesha A Motala, Xiao-Ou Shu, Kyong-Soo Park, J Wouter Jukema, Miguel Cruz, Roberta McKean-Cowdin, Harald Grallert, Ching-Yu Cheng, Erwin P Bottinger, Abbas Dehghan, E-Shyong Tai, Josee Dupuis, Norihiro Kato, Markku Laakso, Anna Köttgen, Woon-Puay Koh, Colin NA Palmer, Simin Liu, Goncalo Abecasis, Jaspal S Kooner, Ruth JF Loos, Kari E North, Christopher A Haiman, Jose C Florez, Danish Saleheen, Torben Hansen, Oluf Pedersen, Reedik Mägi, Claudia Langenberg, Nicholas J Wareham, Shiro Maeda, Takashi Kadowaki, Juyoung Lee, Iona Y Millwood, Robin G Walters, Kari Stefansson, Simon R Myers, Jorge Ferrer, Kyle J Gaulton, James B Meigs, Karen L Mohlke, Anna L Gloyn, Donald W Bowden, Jennifer E Below, John C Chambers, Xueling Sim, Michael Boehnke, Jerome I Rotter, Mark I McCarthy, and Andrew P Morris
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0303 health sciences ,Transferability ,Translation (biology) ,Type 2 diabetes ,Biology ,medicine.disease ,03 medical and health sciences ,0302 clinical medicine ,Evolutionary biology ,Global health ,medicine ,Genetic risk ,Gene ,030217 neurology & neurosurgery ,030304 developmental biology ,Genetic association - Abstract
We assembled an ancestrally diverse collection of genome-wide association studies of type 2 diabetes (T2D) in 180,834 cases and 1,159,055 controls (48.9% non-European descent). We identified 277 loci at genome-wide significance (p-8), including 237 attaining a more stringent trans-ancestry threshold (p-9), which were delineated to 338 distinct association signals. Trans-ancestry meta-regression offered substantial enhancements to fine-mapping, with 58.6% of associations more precisely localised due to population diversity, and 54.4% of signals resolved to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying foundations for functional investigations. Trans-ancestry genetic risk scores enhanced transferability across diverse populations, providing a step towards more effective clinical translation to improve global health.
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- 2020
19. The Trans-Ancestral Genomic Architecture of Glycaemic Traits
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Hugoline G. de Haan, Andrew A. Hicks, Achilleas Pitsilides, Fernando Pires Hartwig, Richard A. Jensen, Matti Uusitupa, Anders Hamsten, Dennis O. Mook-Kanamori, Zorayr Arzumanyan, Betina H. Thuesen, Karin Leander, Fernando Rivideneira, Lynne E. Wagenknecht, Andrew R. Wood, Annique Claringbould, Ele Ferranni, Sölve Elmståhl, Eleanor Wheeler, Sharon L.R. Kardia, Richa Saxena, Tatijana Zemunik, Cassandra N. Spracklen, Ken K. Ong, Xiao-Ou Shu, Johannes Waage, Blair H. Smith, Rozenn N. Lemaitre, Torben Hansen, Peter K. Joshi, Lisa R. Yanek, Neil R. Robertson, Sven Bergmann, Mila Desi Anasanti, Inger Njølstad, Ananda R. Wickremasinghe, Xu Lin, Harold Snieder, Wanqing Wen, Veronique Vitart, Paul R. H. J. Timmers, Timo Saaristo, James F. Wilson, Tian Xie, Tao Huang, Rainer Rauramaa, Kei Hang, Rebecca Rohde, Li-Ching Chang, Jing Hua Zhao, Kazuya Setoh, Yasuharu Tabara, Michael Stumvoll, Mark O. Goodarzi, Igor Rudan, James B. Meigs, Jaakko Tuomilehto, Richard M. Watanabe, Ruth J. F. Loos, Reedik Mägi, Jouke-Jan Hottenga, Ozren Polasek, Michael Y. Tsai, Donald W. Bowden, Diana Kuh, Erik B. van den Akker, Yii-Der Ida Chen, Daniel I. Chasman, Weihua Zhang, Nicholette D. Palmer, Marcus E. Kleber, Anny H. Xiang, Chang-Hsun Hsieh, Alan B. Zonderman, Stefan Gustafsson, Timo A. Lakka, Brian H. Chen, Dermot F. Reilly, Francis S. Collins, Oluf Pedersen, Corri Black, Yang Hai, Zoltán Kutalik, Yoriko Heianza, Willa A. Hsueh, Vilmundur Gudnason, Robert C. Kaplan, Jun Liu, Michael A. Province, Aliki-Eleni Farmaki, Stephen S. Rich, Jian'an Luan, Erik Ingelsson, Marie Loh, Michael Preuss, Lars Lind, Stefan R. Bornstein, Mandy Vogel, Colin N. A. Palmer, Fumihiko Matsuda, Takahisa Kawaguchi, Sohee Han, Ching-Ti Liu, Young-Jin Kim, L. Southam, Sara M. Willems, Mickaël Canouil, Robert A. Scott, Marika Kaakinen, Stephan J. L. Bakker, Momoko Horikoshi, Tarunveer S. Ahluwalia, Annette Schürmann, Graciela E. Delgado, Thibaud S. Boutin, Thomas Sparsø, Sandosh Padmanabhan, Fouad Kandeel, Eco J. C. de Geus, Anubha Mahajan, Claudia Schurmann, Klaus Bønnelykke, Leslie A. Lange, Qing Duan, Rona J. Strawbridge, Dennis Raven, Gonçalo R. Abecasis, Mitsuhiro Yokota, Jani Heikkinen, Elizabeth Selvin, Audrey Y. Chu, Anke Tönjes, Marta E. Alarcón-Riquelme, Hans Bisgaard, P. Eline Slagboom, Eric Boerwinkle, Massimo Mangino, Catharina A. Hartman, Geltrude Mingrone, Lenore J. Launer, Michael Boehnke, Emil V. R. Appel, Niels Grarup, Arushi Varshney, Archie Campbell, Kari E. North, W. Craig Johnson, Inês Barroso, Ya X. Wang, Carola Marzi, Anuj Goel, Eleftheria Zeggini, Lu Qi, Yasumasa Ohyagi, Tien Yin Wong, Tanja G. M. Vrijkotte, Gudny Eiriksdottir, Harald Grallert, Ishminder K. Kooner, Trevor A. Mori, Jagadish Vangipurapu, Laura J Corbin, Tomohiro Katsuya, Wen B. Wei, Segun Fatumo, Debashree Ray, Annette Peters, Lori L. Bonnycastle, Ilja M. Nolte, M. Arfan Ikram, Manjinder S. Sandhu, Marit E. Jørgensen, Christian Herder, Damia Noce, Sarah C. Nelson, Chien-Hsiun Chen, Heather M. Stringham, Yong-Bing Xiang, Bruce M. Psaty, Alain G. Bertoni, Gaëlle Marenne, Timothy M. Frayling, Jose C. Florez, Penny Gordon-Larsen, Yu-Tang Gao, Abhishek Nag, Damiano Baldassarre, J. Wouter Jukema, Wei Huang, Yi-Cheng Chang, Albertine J. Oldehinkel, Xiaoshuai Zhang, Yujie Wang, Shaofeng Huo, Xueling Sim, Norihiro Kato, Bernhard O. Böhm, Lorraine Southam, Mari Nelis, Gonneke Willemsen, Laura J. Rasmussen-Torvik, Philippe Froguel, Charumathi Sabanayagam, Leif Groop, Loic Yengo, Shi Jinxiu, Adolfo Correa, Serena Sanna, Arne Astrup, Teemu Kuulasmaa, Symen Ligthart, Shih-Yi Lin, David J. Porteous, Harry Campbell, Peter Vollenweider, Mark J. Caulfield, Kristi Läll, Anne Ndungu, Carl D. Langefeld, Tanya M. Teslovich, Heikki A. Koistinen, Ying Wu, Mattias Frånberg, D.I. Boomsma, Lawrence F. Bielak, Diana van Heemst, Peter Kovacs, Markku Laakso, Leslie J. Raffel, Katharina E. Schraut, Noël P. Burtt, Michiya Igase, Craig E. Pennell, Claudia Langenberg, Huaixing Li, Teresa Tusie, Laura M. Raffield, Jorgen Engmann, Stephen C. J. Parker, Michele K. Evans, Chaolong Wang, Rico Rueedi, Jianjun Liu, Pankow S. James, Hortensia Moreno-Macías, Fumihiko Takeuchi, Cornelia M. van Duijn, Sanghoon Moon, Susan R. Heckbert, Thomas A. Buchanan, Ko Willems van Dijk, Toru Nabika, May E. Montasser, Caroline Hayward, Jie Yao, Aaron Leong, Antje Körner, Jouko Saramies, Jost B. Jonas, Pim van der Harst, Naveed Sattar, Helen R. Warren, Alice Stanton, Yen-Feng Chiu, Kumaraswamy Naidu Chitrala, Mi Yeong Hwang, Jin Fang Chai, Alicia Huerta-Chagoya, Anette P. Gjesing, Ching-Yu Cheng, Debbie A Lawlor, Simin Liu, Man Li, Ivana Kolcic, Erwin P. Bottinger, Andrew Wong, Stella Trompet, Heming Wang, Jirong Long, Xiuqing Guo, Jeffrey R. O'Connell, Meena Kumari, Sirkka Keinänen-Kiukaanniemi, Rita R. Kalyani, Bengt Sennblad, Mohammad Hadi Zafarmand, Kent D. Taylor, Katherine A. Kentistou, Carol A. Wang, Shuiqing Lai, Patricia B. Munroe, Patricia A. Peyser, Lawrence J. Beilin, Niek Verweij, Inga Prokopenko, Brian E. Cade, Patrik K. E. Magnusson, John C. Chambers, Tamar Sofer, Ping An, Matthias Blüher, Isobel D. Stewart, Alexander P. Reiner, Anna L. Gloyn, Simon P. Mooijaart, Tim D. Spector, Paul W. Franks, Wei Zhao, Andres Metspalu, Wieland Kiess, Kathleen A. Ryan, Astrid van Hylckama Vlieg, Jaana Lindström, Wei Zheng, E. Shyong Tai, Josée Dupuis, Nanette R. Lee, Laura J. Scott, Nicholas J. Timpson, George Dedoussis, Mark I. McCarthy, Tatsuaki Matsubara, Carlos Lorenzo, Denis Rybin, Luigi Ferruci, Chelsea K. Raulerson, Mika Kivimäki, Paul M. Ridker, Jer-Yuarn Wu, Shufa Du, Jaeyoung Hong, Linda S. Adair, Tin Louie, Valeriya Lyssenko, Susan Redline, Kelvin Lam, Qibin Qi, H. Janaka de Silva, Jana V. van Vliet-Ostaptchouk, Peter J. van der Most, Sahoko Ichihara, Nicholas J. Wareham, Ayse Demirkan, Francesco Cucca, Allan Linneberg, Rob M. van Dam, Claire J. Steves, Liang Sun, Albert V. Smith, Raymond Noordam, Tom Wilsgaard, Winfried März, Jung Ho Gong, Matt J. Neville, Jerry L. Nadler, Giorgio Pistis, Karen L. Mohlke, Bruna Gigante, Jennifer A. Brody, Andrew P. Morris, Marie Lauzon, Peter E. H. Schwarz, Bernardo L. Horta, Xiaoran Chai, Ji Chen, Peter S. Sever, Thorkild I. A. Sørensen, André G. Uitterlinden, Javier Gayán, Elena Tremoli, Girish N. Nadkarni, Najaf Amin, Hugh Watkins, Johanna Kuusisto, Jingyi Tan, Sameline Grimsgaard, Bong-Jo Kim, Kerrin S. Small, Jill M. Norris, Cecilia M. Lindgren, Richard N. Bergman, Mark Walker, Henrik Vestergaard, Larissa Aviles-Santa, Jing He, Masahiro Nakatochi, Peter P. Pramstaller, Chiea Chuen Khor, Ruifang Li-Gao, Qiuyin Cai, Neil Schneiderman, Kevin Sandow, Jaspal S. Kooner, Carlos A. Aguilar-Salinas, Peitao Wu, Jerome I. Rotter, Kathryn Roll, Frits R. Rosendaal, Diane M. Becker, Marian Beekman, Claudia P. Cabrera, Nan Wang, Franco Giulianini, Tao Wang, Honglan Li, Abbas Dehghan, Christian Fuchsberger, and Pontiano Kaleebu
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0303 health sciences ,medicine.medical_specialty ,business.industry ,Type 2 diabetes ,medicine.disease ,Fasting insulin ,Fasting glucose ,03 medical and health sciences ,0302 clinical medicine ,Endocrinology ,Internal medicine ,Diabetes mellitus ,medicine ,Genomic architecture ,business ,Glycated haemoglobin ,030217 neurology & neurosurgery ,030304 developmental biology ,Genetic association - Abstract
Glycaemic traits are used to diagnose and monitor type 2 diabetes, and cardiometabolic health. To date, most genetic studies of glycaemic traits have focused on individuals of European ancestry. Here, we aggregated genome-wide association studies in up to 281,416 individuals without diabetes (30% non-European ancestry) with fasting glucose, 2h-glucose post-challenge, glycated haemoglobin, and fasting insulin data. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P-8), 80% with no significant evidence of between-ancestry heterogeneity. Analyses restricted to European ancestry individuals with equivalent sample size would have led to 24 fewer new loci. Compared to single-ancestry, equivalent sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase understanding of diabetes pathophysiology by use of trans-ancestry studies for improved power and resolution.
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- 2020
20. Lifetime risk to progress from pre-diabetes to type 2 diabetes among women and men: Comparison between American Diabetes Association and World Health Organization diagnostic criteria
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Symen Ligthart, Abbas Dehghan, Thijs T. W. van Herpt, Eric J.G. Sijbrands, Maryam Kavousi, Aloysius G Lieverse, Mandy van Hoek, Maarten J.G. Leening, M. Arfan Ikram, Epidemiology, Internal Medicine, Cardiology, Neurology, and Radiology & Nuclear Medicine
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Research design ,pre-diabetic state ,Male ,diagnosis ,Endocrinology, Diabetes and Metabolism ,Population ,Type 2 diabetes ,World Health Organization ,lcsh:Diseases of the endocrine glands. Clinical endocrinology ,Prediabetic State ,Rotterdam Study ,SDG 3 - Good Health and Well-being ,Diabetes mellitus ,medicine ,Humans ,Prospective Studies ,education ,Glycemic ,Netherlands ,education.field_of_study ,lcsh:RC648-665 ,business.industry ,risk assessment ,Glucose Tolerance Test ,Middle Aged ,medicine.disease ,United States ,type 2 ,Diabetes Mellitus, Type 2 ,diabetes mellitus ,Lifetime risk ,Female ,Clinical care/Education/Nutrition ,business ,Risk assessment ,Demography - Abstract
IntroductionPre-diabetes, a status conferring high risk of overt diabetes, is defined differently by the American Diabetes Association (ADA) and the WHO. We investigated the impact of applying definitions of pre-diabetes on lifetime risk of diabetes in women and men from the general population.Research design and methodsWe used data from 8844 women without diabetes and men aged ≥45 years from the prospective population-based Rotterdam Study in the Netherlands. In both gender groups, we calculated pre-diabetes prevalence according to ADA and WHO criteria and estimated the 10-year and lifetime risk to progress to overt diabetes with adjustment for competing risk of death.ResultsOut of 8844 individuals, pre-diabetes was identified in 3492 individuals (prevalence 40%, 95% CI 38% to 41%) according to ADA and 1382 individuals (prevalence 16%, 95% CI 15% to 16%) according to WHO criteria. In both women and men and each age category, ADA prevalence estimates doubled WHO-defined pre-diabetes. For women and men aged 45 years having ADA-defined pre-diabetes, the 10-year risk of diabetes was 14.2% (95% CI 6.0% to 22.5%) and 9.2% (95% CI 3.4% to 15.0%) compared with 23.2% (95% CI 6.8% to 39.6%) and 24.6% (95% CI 8.4% to 40.8%) in women and men with WHO-defined pre-diabetes. At age 45 years, the remaining lifetime risk to progress to overt diabetes was 57.5% (95% CI 51.8% to 63.2%) vs 80.2% (95% CI 74.1% to 86.3%) in women and 46.1% (95% CI 40.8% to 51.4%) vs 68.4% (95% CI 58.3% to 78.5%) in men with pre-diabetes according to ADA and WHO definitions, respectively.ConclusionPrevalence of pre-diabetes differed considerably in both women and men when applying ADA and WHO pre-diabetes definitions. Women with pre-diabetes had higher lifetime risk to progress to diabetes. The lifetime risk of diabetes was lower in women and men with ADA-defined pre-diabetes as compared with WHO. Improvement of pre-diabetes definition considering appropriate sex-specific and age-specific glycemic thresholds may lead to better identification of individuals at high risk of diabetes.
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- 2020
21. Age at natural menopause and risk of type 2 diabetes: a prospective cohort study
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Eralda Asllanaj, Joop S.E. Laven, Taulant Muka, Maryam Kavousi, Oscar H. Franco, Najada Stringa, M. Arfan Ikram, Symen Ligthart, Loes Jaspers, Jelena Milic, Naim Avazverdi, Abbas Dehghan, Epidemiology, and Obstetrics & Gynecology
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Adult ,Risk ,medicine.medical_specialty ,Endocrinology, Diabetes and Metabolism ,Population ,030209 endocrinology & metabolism ,Type 2 diabetes ,Article ,03 medical and health sciences ,Rotterdam Study ,0302 clinical medicine ,SDG 3 - Good Health and Well-being ,Diabetes mellitus ,Internal medicine ,Internal Medicine ,medicine ,Humans ,Women ,Prospective Studies ,education ,Prospective cohort study ,Gynecology ,education.field_of_study ,030219 obstetrics & reproductive medicine ,business.industry ,Incidence ,Diabetes ,Confounding ,Age Factors ,Middle Aged ,medicine.disease ,Obesity ,Early menopause ,Postmenopause ,Menopause ,Diabetes Mellitus, Type 2 ,Female ,business - Abstract
Aims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators. Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study. Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, 55 years [reference]). Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits. HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens. Results During a median follow-up of 9.2 years, we identified 348 individuals with incident type 2 diabetes. After adjustment for confounders, HRs for type 2 diabetes were 3.7 (95% CI 1.8, 7.5), 2.4 (95% CI 1.3, 4.3) and 1.60 (95% CI 1.0, 2.8) for women with premature, early and normal menopause, respectively, relative to those with late menopause (p trend
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- 2017
22. Gamma-glutamyltransferase levels, prediabetes and type 2 diabetes: a Mendelian randomization study
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Harry L.A. Janssen, Albert Hofman, Taulant Muka, Oscar H. Franco, Abbas Dehghan, Jana Nano, Symen Ligthart, Sarwa Darwish Murad, and Epidemiology
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0301 basic medicine ,Blood Glucose ,Male ,medicine.medical_specialty ,Epidemiology ,Population ,Type 2 diabetes ,digestive system ,Polymorphism, Single Nucleotide ,Prediabetic State ,03 medical and health sciences ,Rotterdam Study ,0302 clinical medicine ,SDG 3 - Good Health and Well-being ,Risk Factors ,Diabetes mellitus ,Internal medicine ,Mendelian randomization ,Medicine ,Humans ,030212 general & internal medicine ,Prediabetes ,Prospective Studies ,education ,Aged ,Netherlands ,Proportional Hazards Models ,education.field_of_study ,business.industry ,Hazard ratio ,Confounding ,General Medicine ,gamma-Glutamyltransferase ,Mendelian Randomization Analysis ,Middle Aged ,medicine.disease ,digestive system diseases ,030104 developmental biology ,Diabetes Mellitus, Type 2 ,Female ,business ,Genome-Wide Association Study - Abstract
Background High levels of serum gamma-glutamyltransferase (GGT) are associated with increased risk of prediabetes and type 2 diabetes in observational studies. It is unclear whether this relationship is causal, arises from residual confounding or is a consequence of reverse causation. Methods We used data from a prospective population-based cohort study, compromising 8611 individuals without diabetes at baseline. Cox proportional hazard models were used to study the association between serum GGT levels and incident prediabetes and diabetes. A Mendelian randomization (MR) study was performed using a genetic risk score consisting of 26 GGT-related variants, based on a genome-wide association study (GWAS) on liver enzymes. Association with diabetes and glycaemic traits were investigated within the Rotterdam Study and large-scale GWAS. Results During follow-up, 1125 cases of prediabetes (mean follow-up 5.7 years) and 811 cases of type 2 diabetes (6.9 years) were ascertained. The predicted hazard ratios per standard deviation (SD) change in GGT levels in the multivariable model were 1.10 for prediabetes [95% confidence interval (CI): 1.02-1.19] and 1.19 for type 2 diabetes (95% CI: 1.10-1.30). The genetic risk score associated with increased GGT levels (beta per SD log GGT = 0.41, 95% CI: 0.35-0.47), explaining 3.5% of the observed variation in GGT. MR analysis did not provide evidence for a causal role of GGT, with a causal relative risk for prediabetes and type 2 diabetes per SD of log GGT of 0.97 (95% CI: 0.91-1.04) and 0.96 (95% CI: 0.89-1.04), respectively. Multiple instrumental analysis using genetic associations with type 2 diabetes and glycaemic traits from previous GWA studies detected no causal effect of GGT. Conclusions MR analyses did not support a causal role of GGT on the risk of prediabetes or diabetes. The association of GGT with diabetes in observational studies is likely to be driven by reverse causation or confounding bias. As such, therapeutics targeted at lowering GGT levels are unlikely to be effective in preventing diabetes.
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- 2017
23. Novel inflammatory markers for incident pre-diabetes and type 2 diabetes: the Rotterdam Study
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Oscar H. Franco, Abbas Dehghan, Symen Ligthart, Albert Hofman, Mohsen Ghanbari, Maryam Kavousi, Adela Brahimaj, Mohammad Arfan Ikram, Epidemiology, Neurology, and Radiology & Nuclear Medicine
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0301 basic medicine ,Oncology ,Male ,endocrine system diseases ,Epidemiology ,medicine.medical_treatment ,Inflammatory markers ,Type 2 diabetes ,Comorbidity ,GLUCOSE ,Cohort Studies ,Rotterdam Study ,0302 clinical medicine ,Risk Factors ,Prospective Studies ,Novel ,Public, Environmental & Occupational Health ,Netherlands ,RISK ,INSULIN-RESISTANCE ,Incidence ,C-REACTIVE PROTEIN ,1117 Public Health And Health Services ,Interleukin 18 ,Female ,Pre-diabetes ,Life Sciences & Biomedicine ,medicine.medical_specialty ,030209 endocrinology & metabolism ,Prediabetic State ,03 medical and health sciences ,Immune system ,EN-RAGE ,SDG 3 - Good Health and Well-being ,Internal medicine ,medicine ,Diabetes Mellitus ,Humans ,Survival analysis ,Aged ,Proportional Hazards Models ,Inflammation ,INTERLEUKIN-13 ,Science & Technology ,RECEPTOR ,Proportional hazards model ,business.industry ,Insulin ,IL17 ,nutritional and metabolic diseases ,Phase-specific ,medicine.disease ,030104 developmental biology ,Endocrinology ,Diabetes Mellitus, Type 2 ,T-CELLS ,Insulin therapy ,IL13 ,business ,BETA-CELL DYSFUNCTION ,Biomarkers ,RESPONSES - Abstract
The immune response involved in each phase of type 2 diabetes (T2D) development might be different. We aimed to identify novel inflammatory markers that predict progression from normoglycemia to pre-diabetes, incident T2D and insulin therapy. We used plasma levels of 26 inflammatory markers in 971 subjects from the Rotterdam Study. Among them 17 are novel and 9 previously studied. Cox regression models were built to perform survival analysis. Main Outcome Measures: During a follow-up of up to 14.7 years (between April 1, 1997, and Jan 1, 2012) 139 cases of pre-diabetes, 110 cases of T2D and 26 cases of insulin initiation were identified. In age and sex adjusted Cox models, IL13 (HR = 0.78), EN-RAGE (1.30), CFH (1.24), IL18 (1.22) and CRP (1.32) were associated with incident pre-diabetes. IL13 (0.62), IL17 (0.75), EN-RAGE (1.25), complement 3 (1.44), IL18 (1.35), TNFRII (1.27), IL1ra (1.24) and CRP (1.64) were associated with incident T2D. In multivariate models, IL13 (0.77), EN-RAGE (1.23) and CRP (1.26) remained associated with pre-diabetes. IL13 (0.67), IL17 (0.76) and CRP (1.32) remained associated with T2D. IL13 (0.55) was the only marker associated with initiation of insulin therapy in diabetics. Various inflammatory markers are associated with progression from normoglycemia to pre-diabetes (IL13, EN-RAGE, CRP), T2D (IL13, IL17, CRP) or insulin therapy start (IL13). Among them, EN-RAGE is a novel inflammatory marker for pre-diabetes, IL17 for incident T2D and IL13 for pre-diabetes, incident T2D and insulin therapy start. Electronic supplementary material The online version of this article (doi:10.1007/s10654-017-0236-0) contains supplementary material, which is available to authorized users.
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- 2017
24. Serum magnesium and the risk of prediabetes: a population-based cohort study
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Symen Ligthart, Oscar H. Franco, Robert Zietse, Bruno H. Stricker, Albert Hofman, Ewout J. Hoorn, Abbas Dehghan, Brenda C.T. Kieboom, Jeroen H. F. de Baaij, Steef Kurstjens, Epidemiology, and Internal Medicine
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Male ,Epidemiology ,Endocrinology, Diabetes and Metabolism ,030204 cardiovascular system & hematology ,Cohort Studies ,0302 clinical medicine ,Risk Factors ,1114 Paediatrics And Reproductive Medicine ,Magnesium ,Prediabetes ,Cation Transport Proteins ,Diabetes ,Population-based cohort ,Middle Aged ,3. Good health ,1117 Public Health And Health Services ,Female ,Sodium-Potassium-Exchanging ATPase ,medicine.medical_specialty ,Diabetes risk ,Urinary system ,TRPM Cation Channels ,chemistry.chemical_element ,030209 endocrinology & metabolism ,Polymorphism, Single Nucleotide ,Article ,Prediabetic State ,03 medical and health sciences ,Endocrinology & Metabolism ,Insulin resistance ,Cyclins ,Internal medicine ,Diabetes mellitus ,Internal Medicine ,medicine ,Humans ,Aged ,business.industry ,Mediation ,1103 Clinical Sciences ,Magnesium regulating genes ,Impaired fasting glucose ,medicine.disease ,Single nucleotide polymorphism ,Renal disorders Radboud Institute for Molecular Life Sciences [Radboudumc 11] ,Endocrinology ,chemistry ,Claudins ,business - Abstract
Aims/hypothesis Previous studies have found an association between serum magnesium and incident diabetes; however, this association may be due to reverse causation, whereby diabetes may induce urinary magnesium loss. In contrast, in prediabetes (defined as impaired fasting glucose), serum glucose levels are below the threshold for urinary magnesium wasting and, hence, unlikely to influence serum magnesium levels. Thus, to study the directionality of the association between serum magnesium levels and diabetes, we investigated its association with prediabetes. We also investigated whether magnesium-regulating genes influence diabetes risk through serum magnesium levels. Additionally, we quantified the effect of insulin resistance in the association between serum magnesium levels and diabetes risk. Methods Within the population-based Rotterdam Study, we used Cox models, adjusted for age, sex, lifestyle factors, comorbidities, kidney function, serum levels of electrolytes and diuretic use, to study the association between serum magnesium and prediabetes/diabetes. In addition, we performed two mediation analyses: (1) to study if common genetic variation in eight magnesium-regulating genes influence diabetes risk through serum magnesium levels; and (2) to quantify the proportion of the effect of serum magnesium levels on diabetes that is mediated through insulin resistance (quantified by HOMA-IR). Results A total of 8555 participants (mean age, 64.7 years; median follow-up, 5.7 years) with normal glucose levels (mean ± SD: 5.46 ± 0.58 mmol/l) at baseline were included. A 0.1 mmol/l decrease in serum magnesium level was associated with an increase in diabetes risk (HR 1.18 [95% CI 1.04, 1.33]), confirming findings from previous studies. Of interest, a similar association was found between serum magnesium levels and prediabetes risk (HR 1.12 [95% CI 1.01, 1.25]). Genetic variation in CLDN19, CNNM2, FXYD2, SLC41A2, and TRPM6 significantly influenced diabetes risk (p
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- 2017
25. An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis
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Jennifer A. Smith, Andrea A. Baccarelli, Jordana T. Bell, James B. Meigs, James S. Pankow, Jun Liu, Ayse Demirkan, Marjolein J. Peters, Jerome I. Rotter, Elias Salfati, Bertha Hidalgo, Audrey Y. Chu, André G. Uitterlinden, Luigi Ferrucci, Pooja R. Mandaviya, Nona Sotoodehnia, Eric A. Whitsel, Liesbeth Duijts, Marie-France Hivert, Elena Carnero-Montoro, Eric J.G. Sijbrands, Ann Zenobia Moore, Janine F. Felix, Jenny van Dongen, Najaf Amin, Tim D. Spector, Oscar H. Franco, Harald Grallert, Tiphaine Martin, Ivana Nedeljkovic, Josée Dupuis, Aaron Isaacs, Themistocles L. Assimes, Pei-Chien Tsai, Cornelia M. van Duijn, Lifang Hou, Daniel Levy, Ko Willems van Dijk, Rahul Gondalia, Samantha Lent, Abbas Dehghan, Rozenn N. Lemaitre, Jan Bressler, Christian Herder, Min A. Jhun, Symen Ligthart, Dorret I. Boomsma, Gonneke Willemsen, Toshiko Tanaka, Rick Jansen, Joyce B. J. van Meurs, Shih-Jen Hwang, Vincent W. V. Jaddoe, Donna K. Arnett, Eco J. C. de Geus, Henning Tiemeier, Stefania Bandinelli, Pediatric surgery, Epidemiology and Data Science, Biological Psychology, APH - Mental Health, APH - Personalized Medicine, APH - Methodology, Epidemiology, Internal Medicine, Pediatrics, Erasmus MC other, Child and Adolescent Psychiatry / Psychology, RS: CARIM - R1 - Thrombosis and haemostasis, RS: FHML MaCSBio, Biochemie, and RS: Carim - B01 Blood proteins & engineering
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0301 basic medicine ,Epigenomics ,Male ,Netherlands Twin Register (NTR) ,medicine.medical_treatment ,General Physics and Astronomy ,02 engineering and technology ,Type 2 diabetes ,UP-REGULATION ,Epigenesis, Genetic ,Glucose homeostasis ,Homeostasis ,Insulin ,lcsh:Science ,Genetics ,Aged, 80 and over ,Multidisciplinary ,Middle Aged ,021001 nanoscience & nanotechnology ,3. Good health ,Multidisciplinary Sciences ,DNA methylation ,Science & Technology - Other Topics ,Epigenetics ,Female ,0210 nano-technology ,WAIST CIRCUMFERENCE ,Metabolic Networks and Pathways ,Adult ,EXPRESSION ,Science ,610 Medicine & health ,Biology ,Polymorphism, Single Nucleotide ,Article ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,Young Adult ,TYPE-2 ,SDG 3 - Good Health and Well-being ,360 Social problems & social services ,Diabetes mellitus ,medicine ,GLYCEMIC TRAITS ,Humans ,Computer Simulation ,Obesity ,Transcriptomics ,Aged ,EPIGENOME-WIDE ASSOCIATION ,Science & Technology ,Gene Expression Profiling ,DIABETES-MELLITUS ,General Chemistry ,DNA Methylation ,medicine.disease ,GENE ,IRS2 ,BODY-MASS INDEX ,030104 developmental biology ,Glucose ,Diabetes Mellitus, Type 2 ,Gene Expression Regulation ,lcsh:Q ,CpG Islands ,Genome-Wide Association Study ,FASTING GLUCOSE - Abstract
Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D., Our understanding of the functional link between differential DNA methylation and type 2 diabetes and obesity remains limited. Here the authors present a blood-based EWAS of fasting glucose and insulin among 4808 non-diabetic Europeans and identify nine CpGs not previously implicated in glucose, insulin homeostasis and diabetes.
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- 2019
26. Blood Leukocyte DNA Methylation Predicts Risk of Future Myocardial Infarction and Coronary Heart Disease A Longitudinal Study of 11 461 Participants From Population-Based Cohorts
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Symen Ligthart, Luigi Ferrucci, Rosario Tumino, Kerri L. Wiggins, Bruce M. Psaty, Dena G. Hernandez, Philip S. Tsao, Kelly M. Bakulski, Roby Joehanes, Tianxiao Huan, Steven Horvath, Luke C. Pilling, Eric Boerwinkle, Simone Wahl, Annette Peters, Vittorio Krogh, Devin Absher, Myriam Fornage, M. Daniele Fallin, Cavin K. Ward-Caviness, Elena Colicino, Nona Sotoodehnia, Michael M. Mendelson, Lifang Hou, Chunyu Liu, Pantel S. Vokonas, Carlotta Sacerdote, Abbas Dehghan, Andrew P. Feinberg, Jerome I. Rotter, Yun Li, Themistocles L. Assimes, Jennifer A. Brody, Rahul Gondalia, Andrea A. Baccarelli, Christian Gieger, Guosheng Zhang, Jan Bressler, Salvatore Panico, Stefania Bandinelli, Amy R. Vandiver, James S. Floyd, Michael L. Multhaup, Brian H. Chen, Elias Salfati, Giuseppe Matullo, James D. Stewart, Toshiko Tanaka, Joel Schwartz, Daniel Levy, Simonetta Guarrera, Allan C. Just, Andrew B. Singleton, Golareh Agha, Joel N. Hirschhorn, Eric A. Whitsel, Giovanni Fiorito, and Epidemiology
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Regulation of gene expression ,0303 health sciences ,medicine.medical_specialty ,business.industry ,Genomics ,030204 cardiovascular system & hematology ,medicine.disease ,Coronary heart disease ,Coronary artery disease ,03 medical and health sciences ,0302 clinical medicine ,Coronary Artery Disease ,Coronary Heart Disease ,Epigenetics ,Gene Expression Regulation ,Physiology (medical) ,Internal medicine ,DNA methylation ,medicine ,Cardiology ,Myocardial infarction ,Cardiology and Cardiovascular Medicine ,business ,030304 developmental biology - 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 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.
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- 2019
27. Commentary: CRP and schizophrenia: cause, consequence or confounding?
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Symen Ligthart and Epidemiology
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medicine.medical_specialty ,Epidemiology ,Schizophrenia (object-oriented programming) ,MEDLINE ,Lipoproteins, VLDL ,Polymorphism, Single Nucleotide ,Mendelian Randomization ,Leucine ,Risk Factors ,Humans ,Medicine ,Genetic Predisposition to Disease ,Psychiatry ,Triglycerides ,Inflammation ,Random allocation ,business.industry ,Confounding ,Bayes Theorem ,General Medicine ,Mendelian Randomization Analysis ,Lipids ,C-Reactive Protein ,Phenotype ,Schizophrenia ,business ,Biomarkers - Abstract
Blood immunoreactive biomarkers, such as C-reactive protein (CRP), and metabolic abnormalities have been associated with schizophrenia. Studies comprehensively and bidirectionally probing possible causal links between such blood constituents and liability to schizophrenia are lacking.To disentangle putative causal links between CRP blood levels and schizophrenia in both directions, we conducted multiple univariable Mendelian-randomization (MR) analyses, ranging from fixed-effect to inverse variance-weighted (IVW), weighted-median, MR Egger and generalized summary-data-based Mendelian-randomization (GSMR) models. To prioritize metabolic risk factors for schizophrenia, a novel multivariable approach was applied: multivariable Mendelian-randomization-Bayesian model averaging (MR-BMA).All forward univariable MR analyses consistently showed that CRP has a protective effect on schizophrenia, whereas reverse MR analyses consistently suggested absent causal effects of schizophrenia liability on CRP blood levels. Using MR-BMA, as the top protective factors for schizophrenia we prioritized leucine and as the prime risk-factor triglycerides in medium very-low-density lipoprotein (VLDL). The five best-performing MR-BMA models provided one additional risk factor: triglycerides in large VLDL; and two additional protective factors: citrate and lactate.Our results add to a growing body of literature hinting at metabolic changes-in particular of triglycerides-independently of medication status in schizophrenia. We also highlight the absent effects of genetic liability to schizophrenia on CRP levels.
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- 2019
28. Blood Leukocyte DNA Methylation Predicts Risk of Future Myocardial Infarction and Coronary Heart Disease
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Golareh, Agha, Michael M, Mendelson, Cavin K, Ward-Caviness, Roby, Joehanes, TianXiao, Huan, Rahul, Gondalia, Elias, Salfati, Jennifer A, Brody, Giovanni, Fiorito, Jan, Bressler, Brian H, Chen, Symen, Ligthart, Simonetta, Guarrera, Elena, Colicino, Allan C, Just, Simone, Wahl, Christian, Gieger, Amy R, Vandiver, Toshiko, Tanaka, Dena G, Hernandez, Luke C, Pilling, Andrew B, Singleton, Carlotta, Sacerdote, Vittorio, Krogh, Salvatore, Panico, Rosario, Tumino, Yun, Li, Guosheng, Zhang, James D, Stewart, James S, Floyd, Kerri L, Wiggins, Jerome I, Rotter, Michael, Multhaup, Kelly, Bakulski, Steven, Horvath, Philip S, Tsao, Devin M, Absher, Pantel, Vokonas, Joel, Hirschhorn, M Daniele, Fallin, Chunyu, Liu, Stefania, Bandinelli, Eric, Boerwinkle, Abbas, Dehghan, Joel D, Schwartz, Bruce M, Psaty, Andrew P, Feinberg, Lifang, Hou, Luigi, Ferrucci, Nona, Sotoodehnia, Giuseppe, Matullo, Annette, Peters, Myriam, Fornage, Themistocles L, Assimes, Eric A, Whitsel, Daniel, Levy, and Andrea A, Baccarelli
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Risk ,Adult ,Male ,Clinical Sciences ,Myocardial Infarction ,Coronary Disease ,Cardiorespiratory Medicine and Haematology ,Cardiovascular ,Article ,Cohort Studies ,Population Groups ,Clinical Research ,Leukocytes ,Genetics ,genomics ,Humans ,Prospective Studies ,coronary heart disease ,Heart Disease - Coronary Heart Disease ,Aged ,epigenetics ,Incidence ,Prevention ,Human Genome ,gene expression regulation ,DNA Methylation ,Middle Aged ,Prognosis ,Atherosclerosis ,United States ,Europe ,Heart Disease ,Cardiovascular System & Hematology ,coronary artery disease ,Public Health and Health Services ,CpG Islands ,Female ,Genome-Wide Association Study - 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
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- 2019
29. Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders
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Symen Ligthart, Ahmad Vaez, Urmo Võsa, Maria G. Stathopoulou, Paul S. de Vries, Bram P. Prins, Peter J. Van der Most, Toshiko Tanaka, Elnaz Naderi, Lynda M. Rose, Ying Wu, Robert Karlsson, Maja Barbalic, Honghuang Lin, René Pool, Gu Zhu, Aurélien Macé, Carlo Sidore, Stella Trompet, Massimo Mangino, Maria Sabater-Lleal, John P. Kemp, Ali Abbasi, Tim Kacprowski, Niek Verweij, Albert V. Smith, Tao Huang, Carola Marzi, Mary F. Feitosa, Kurt K. Lohman, Marcus E. Kleber, Yuri Milaneschi, Christian Mueller, Mahmudul Huq, Efthymia Vlachopoulou, Leo-Pekka Lyytikäinen, Christopher Oldmeadow, Joris Deelen, Markus Perola, Jing Hua Zhao, Bjarke Feenstra, Marzyeh Amini, Jari Lahti, Katharina E. Schraut, Myriam Fornage, Bhoom Suktitipat, Wei-Min Chen, Xiaohui Li, Teresa Nutile, Giovanni Malerba, Jian’an Luan, Tom Bak, Nicholas Schork, Fabiola Del Greco M., Elisabeth Thiering, Anubha Mahajan, Riccardo E. Marioni, Evelin Mihailov, Joel Eriksson, Ayse Bilge Ozel, Weihua Zhang, Maria Nethander, Yu-Ching Cheng, Stella Aslibekyan, Wei Ang, Ilaria Gandin, Loïc Yengo, Laura Portas, Charles Kooperberg, Edith Hofer, Kumar B. Rajan, Claudia Schurmann, Wouter den Hollander, Tarunveer S. Ahluwalia, Jing Zhao, Harmen H.M. Draisma, Ian Ford, Nicholas Timpson, Alexander Teumer, Hongyan Huang, Simone Wahl, YongMei Liu, Jie Huang, Hae-Won Uh, Frank Geller, Peter K. Joshi, Lisa R. Yanek, Elisabetta Trabetti, Benjamin Lehne, Diego Vozzi, Marie Verbanck, Ginevra Biino, Yasaman Saba, Ingrid Meulenbelt, Jeff R. O’Connell, Markku Laakso, Franco Giulianini, Patrik K.E. Magnusson, Christie M. Ballantyne, Jouke Jan Hottenga, Grant W. Montgomery, Fernando Rivadineira, Rico Rueedi, Maristella Steri, Karl-Heinz Herzig, David J. Stott, Cristina Menni, Mattias Frånberg, Beate St. Pourcain, Stephan B. Felix, Tune H. Pers, Stephan J.L. Bakker, Peter Kraft, Annette Peters, Dhananjay Vaidya, Graciela Delgado, Johannes H. Smit, Vera Großmann, Juha Sinisalo, Ilkka Seppälä, Stephen R. Williams, Elizabeth G. Holliday, Matthijs Moed, Claudia Langenberg, Katri Räikkönen, Jingzhong Ding, Harry Campbell, Michele M. Sale, Yii-Der I. Chen, Alan L. James, Daniela Ruggiero, Nicole Soranzo, Catharina A. Hartman, Erin N. Smith, Gerald S. Berenson, Christian Fuchsberger, Dena Hernandez, Carla M.T. Tiesler, Vilmantas Giedraitis, David Liewald, Krista Fischer, Dan Mellström, Anders Larsson, Yunmei Wang, William R. Scott, Matthias Lorentzon, John Beilby, Kathleen A. Ryan, Craig E. Pennell, Dragana Vuckovic, Beverly Balkau, Maria Pina Concas, Reinhold Schmidt, Carlos F. Mendes de Leon, Erwin P. Bottinger, Margreet Kloppenburg, Lavinia Paternoster, Michael Boehnke, A.W. Musk, Gonneke Willemsen, David M. Evans, Pamela A.F. Madden, Mika Kähönen, Zoltán Kutalik, Magdalena Zoledziewska, Ville Karhunen, Stephen B. Kritchevsky, Naveed Sattar, Genevieve Lachance, Robert Clarke, Tamara B. Harris, Olli T. Raitakari, John R. Attia, Diana van Heemst, Eero Kajantie, Rossella Sorice, Giovanni Gambaro, Robert A. Scott, Andrew A. Hicks, Luigi Ferrucci, Marie Standl, Cecilia M. Lindgren, John M. Starr, Magnus Karlsson, Lars Lind, Jun Z. Li, John C. Chambers, Trevor A. Mori, Eco J.C.N. de Geus, Andrew C. Heath, Nicholas G. Martin, Juha Auvinen, Brendan M. Buckley, Anton J.M. de Craen, Melanie Waldenberger, Konstantin Strauch, Thomas Meitinger, Rodney J. Scott, Mark McEvoy, Marian Beekman, Cristina Bombieri, Paul M. Ridker, Karen L. Mohlke, Nancy L. Pedersen, Alanna C. Morrison, Dorret I. Boomsma, John B. Whitfield, David P. Strachan, Albert Hofman, Peter Vollenweider, Francesco Cucca, Marjo-Riitta Jarvelin, J. Wouter Jukema, Tim D. Spector, Anders Hamsten, Tanja Zeller, André G. Uitterlinden, Matthias Nauck, Vilmundur Gudnason, Lu Qi, Harald Grallert, Ingrid B. Borecki, Jerome I. Rotter, Winfried März, Philipp S. Wild, Marja-Liisa Lokki, Michael Boyle, Veikko Salomaa, Mads Melbye, Johan G. Eriksson, James F. Wilson, Brenda W.J.H. Penninx, Diane M. Becker, Bradford B. Worrall, Greg Gibson, Ronald M. Krauss, Marina Ciullo, Gianluigi Zaza, Nicholas J. Wareham, Albertine J. Oldehinkel, Lyle J. Palmer, Sarah S. Murray, Peter P. Pramstaller, Stefania Bandinelli, Joachim Heinrich, Erik Ingelsson, Ian J. Deary, Reedik Mägi, Liesbeth Vandenput, Pim van der Harst, Karl C. Desch, Jaspal S. Kooner, Claes Ohlsson, Caroline Hayward, Terho Lehtimäki, Alan R. Shuldiner, Donna K. Arnett, Lawrence J. Beilin, Antonietta Robino, Philippe Froguel, Mario Pirastu, Tine Jess, Wolfgang Koenig, Ruth J.F. Loos, Denis A. Evans, Helena Schmidt, George Davey Smith, P. Eline Slagboom, Gudny Eiriksdottir, Andrew P. Morris, Bruce M. Psaty, Russell P. Tracy, Ilja M. Nolte, Eric Boerwinkle, Sophie Visvikis-Siest, Alex P. Reiner, Myron Gross, Joshua C. Bis, Lude Franke, Oscar H. Franco, Emelia J. Benjamin, Daniel I. Chasman, Josée Dupuis, Harold Snieder, Abbas Dehghan, Behrooz Z. Alizadeh, H. Marike Boezen, Gerjan Navis, Marianne Rots, Morris Swertz, Bruce H.R. Wolffenbuttel, Cisca Wijmenga, Emelia Benjamin, Tarunveer Singh Ahluwalia, James Meigs, Russell Tracy, Josh Bis, Nathan Pankratz, Alex Rainer, James G. Wilson, Josee Dupuis, Bram Prins, Urmo Vaso, Maria Stathopoulou, Terho Lehtimaki, Yalda Jamshidi, Sophie Siest, Andre G. Uitterlinden, Mohammadreza Abdollahi, Renate Schnabel, Ursula M. Schick, Aldi Kraja, Yi-Hsiang Hsu, Daniel S. Tylee, Alyson Zwicker, Rudolf Uher, George Davey-Smith, Andrew Hicks, Cornelia M. van Duijn, Cavin Ward-Caviness, J. Rotter, Ken Rice, Leslie Lange, Eco de Geus, Kari Matti Makela, David Stacey, Johan Eriksson, Tim M. Frayling, Eline P. Slagboom, Erasmus University Medical Center [Rotterdam] (Erasmus MC), University Medical Center Groningen [Groningen] (UMCG), University of Isfahan, University of Tartu, Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire (IGE-PCV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), The University of Texas Health Science Center at Houston (UTHealth), National Institute on Aging [Bethesda, USA] (NIA), National Institutes of Health [Bethesda] (NIH), Brigham and Women's Hospital [Boston], University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet [Stockholm], University of Split, Boston University School of Medicine (BUSM), Boston University [Boston] (BU), Process & Energy Laboratory, Delft University of Technology (TU Delft), Grand Lyon : communauté urbaine de Lyon, Interuniversity Cardiology Institute Netherlands, Department of Twin Research and Genetic Epidemiology, King's College London, London, Huazhong University of Science and Technology [Wuhan] (HUST), Division of Statistical Genomics, Washington University School of Medicine, Department of Psychiatry, VU University Medical Center [Amsterdam], Institut fuer Theoretische Physik (Institut fuer Theoretische Physik), Universität Heidelberg [Heidelberg] = Heidelberg University, Molecular Epidemiology, MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Mahidol University [Bangkok], Northwest A and F University, Laboratoire d'Optimisation des Systèmes Industriels (LOSI), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Institute of Genetics and Biophysics, CNR, Naples, Università degli studi di Verona = University of Verona (UNIVR), Department of Molecular Medicine [Scripps Research Institute], The Scripps Research Institute [La Jolla, San Diego], Department of Physics, Indian Institute of Technology Kanpur (IIT Kanpur), Deptartment of Medical Biochemistry and Microbiology, Uppsala University, Department of Electrical and Computer Engineering [Waterloo] (ECE), University of Waterloo [Waterloo], University of Maryland School of Medicine, University of Maryland System, Institut National de l'Environnement Industriel et des Risques (INERIS), Institute of Pop. Genetics, CNR, Sassari, Interfaculty Institute for Genetics and Functional Genomics, Universität Greifswald - University of Greifswald, IT University of Copenhagen (ITU), Robertson Centre for Biostatistics, University of Glasgow, Centre for Causal Analyses in Translational Epidemiology, University of Bristol [Bristol]-Medical Research Council, King‘s College London, Jinan University [Guangzhou], Institute of Oceanology [China], School Medicine, University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE)-Pennsylvania Commonwealth System of Higher Education (PCSHE), General Internal Medicine, Johns Hopkins School of Medicine, Johns Hopkins University School of Medicine [Baltimore], Shardna life science Pula Cagliari, Section Molecular Epidemiology, Leiden University Medical Center (LUMC), Department of Medicine, University of Eastern Finland-Kuopio University Hospital, Medstar Research Institute, Department of Cardiology, Ernst-Moritz-Arndt University, Center for Biological Sequence Analysis [Lyngby], Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Department of Internal Medicine, University of Groningen and University Medical Center Groningen, Department of Epidemiology, Harvard School of Public Health, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Metacohorts Consortium, INEOS Technologies (SWITZERLAND), MRC Epidemiology Unit, University of Cambridge [UK] (CAM)-Institute of Metabolic Science, University of Edinburgh, School of Population Health [Crawley, Western Australia], The University of Western Australia (UWA), Institute of Genetics and Biophysics, National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), The Scripps Translational Science Institute and Scripps Health, Tulane Center for Cardiovascular Health, Tulane University Health Sciences Center, Centre for Population Health Sciences, Genomic Research Laboratory, Service of Infectious Disease, Hôpitaux Universitaires de Genève (HUG), Infectious diseases division, Department of internal medicine, Washington University in Saint Louis (WUSTL), Luleå University of Technology (LUT), Recherche en épidémiologie et biostatistique, Université Paris-Sud - Paris 11 (UP11)-Institut National de la Santé et de la Recherche Médicale (INSERM), Austrian Institute of Technology [Vienna] (AIT), Icahn School of Medicine at Mount Sinai [New York] (MSSM), Department of Rheumatology and Clinical Epidemiology, Leiden University Medical Center (LUMC), Department of Rheumatology and Clinical Epidemiology [Leiden University Medical Center] (LUMC), Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden-Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden, Department of Biostatistics and Center for Statistical Genetics, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, University of Virginia, Tampere University Hospital, Department of Medical Genetics, Université de Lausanne = University of Lausanne (UNIL), Department of Pathological Biochemistry, Royal Infirmary, Oxford University, University of Oxford, University of Newcastle [Callaghan, Australia] (UoN), Department of neurology, Institute of Metabolic Science, MRC, The Wellcome Trust Centre for Human Genetics [Oxford], Uppsala Universitet [Uppsala], QIMR Berghofer Medical Research Institute, Institute of Genetic Epidemiology [Neuherberg, Germany], Institute of Human Genetics, Helmholtz Zentrum München = German Research Center for Environmental Health, Schizophrenia Research Institute [Sydney], Department of Genetics, University of North Carolina System (UNC)-University of North Carolina System (UNC), Vrije Universiteit Brussel (VUB), Population Health Sciences and Education, St George's University of London, Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), Institute of Health Sciences and Biocenter Oulu, University of Oulu, Medizinische Klinik und Poliklinik, Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University (JGU), Institute of Clinical Chemistry and Laboratory Medicine, Icelandic Heart Association, Heart Preventive Clinic and Research Institute, Departments of Epidemiology and Nutrition, Institute of Epidemiology [Neuherberg] (EPI), Medical University Graz, Transplantation Laboratory [Helsinki], Haartman Institute [Helsinki], Faculty of Medecine [Helsinki], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Faculty of Medecine [Helsinki], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Department of Chronic Disease Prevention, National Institute for Health and Welfare [Helsinki], Dept. of Epidemiology Research, Statens Serum Institut [Copenhagen], CLinical Psychology, Genetics and Pathology, Geriatric Rehabilitation Unit, Azienda Sanitaria Firenze, Center For Narcolepsy, Stanford University, Centre for Bone and Arthritis Research, University of Gothenburg (GU)-Institute of Medicine, MRC Human Gentics Unit, Inst Genet and Mol Med, Western General Hospital, Edinburgh, University of Maryland School of Medicine [Baltimore, MD, USA], Génétique des maladies multifactorielles (GMM), Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), Department of Physics [Stockholm], Stockholm University, University of Bristol [Bristol], Universiteit Leiden, Department of Epidemiology, University of Washington, University of Washington [Seattle], Department of Epidemiology [Rotterdam], University of Groningen [Groningen], Dutch Initiative on Crohn and Colitis (ICC), Icelandic Heart Association [Kopavogur, Iceland] (IHA), Department of Physiology and Biophysics [Jackson, MS, USA], University of Southern Mississippi (USM), Human Genetics Branch, National Institutes of Health [Bethesda] (NIH)-National Institute of Mental Health (NIMH), Faculty of Medicine and Life Sciences [Tampere], University of Tampere [Finland], German Center for Cardiovascular Research (DZHK), Berlin Institute of Health (BIH), MRC Centre for Neuropsychiatric Genetics and Genomics, Medical Research Council-Cardiff University, Department of Social Medicine, School of Medicine [Los Angeles], University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), Department of Medicine [Aurora, CO, USA], University of Colorado [Denver], Institute for Molecular Medicine Finland [Helsinki] (FIMM), Helsinki Institute of Life Science (HiLIFE), Mathematical Institute [Oxford] (MI), Institute of Psychiatry, Psychology & Neuroscience, King's College London, LifeLines Cohort Study, CHARGE Inflammation Working Group, Ligthart, S., Vaez, A., Vosa, U., Stathopoulou, M. G., de Vries, P. S., Prins, B. P., Van der Most, P. J., Tanaka, T., Naderi, E., Rose, L. M., Wu, Y., Karlsson, R., Barbalic, M., Lin, H., Pool, R., Zhu, G., Mace, A., Sidore, C., Trompet, S., Mangino, M., Sabater-Lleal, M., Kemp, J. P., Abbasi, A., Kacprowski, T., Verweij, N., Smith, A. V., Huang, T., Marzi, C., Feitosa, M. F., Lohman, K. K., Kleber, M. E., Milaneschi, Y., Mueller, C., Huq, M., Vlachopoulou, E., Lyytikainen, L. -P., Oldmeadow, C., Deelen, J., Perola, M., Zhao, J. H., Feenstra, B., Alizadeh, B. Z., Boezen, H. M., Franke, L., van der Harst, P., Navis, G., Rots, M., Snieder, H., Swertz, M., Wolffenbuttel, B. H. R., Wijmenga, C., Amini, M., Benjamin, E., Chasman, D. I., Dehghan, A., Ahluwalia, T. S., Meigs, J., Tracy, R., Bis, J., Eiriksdottir, G., Pankratz, N., Gross, M., Rainer, A., Wilson, J. G., Psaty, B. M., Dupuis, J., Prins, B., Vaso, U., Stathopoulou, M., Lehtimaki, T., Koenig, W., Jamshidi, Y., Siest, S., Uitterlinden, A. G., Abdollahi, M., Schnabel, R., Schick, U. M., Nolte, I. M., Kraja, A., Hsu, Y. -H., Tylee, D. S., Zwicker, A., Uher, R., Davey-Smith, G., Morrison, A. C., Hicks, A., van Duijn, C. M., Ward-Caviness, C., Boerwinkle, E., Rotter, J., Rice, K., Lange, L., de Geus, E., Morris, A. P., Makela, K. M., Stacey, D., Eriksson, J., Frayling, T. M., Slagboom, E. P., Lahti, J., Schraut, K. E., Fornage, M., Suktitipat, B., Chen, W. -M., Li, X., Nutile, T., Malerba, G., Luan, J., Bak, T., Schork, N., Del Greco, M. F., Thiering, E., Mahajan, A., Marioni, R. E., Mihailov, E., Ozel, A. B., Zhang, W., Nethander, M., Cheng, Y. -C., Aslibekyan, S., Ang, W., Gandin, I., Yengo, L., Portas, L., Kooperberg, C., Hofer, E., Rajan, K. B., Schurmann, C., den Hollander, W., Zhao, J., Draisma, H. H. M., Ford, I., Timpson, N., Teumer, A., Huang, H., Wahl, S., Liu, Y., Huang, J., Uh, H. -W., Geller, F., Joshi, P. K., Yanek, L. R., Trabetti, E., Lehne, B., Vozzi, D., Verbanck, M., Biino, G., Saba, Y., Meulenbelt, I., O'Connell, J. R., Laakso, M., Giulianini, F., Magnusson, P. K. E., Ballantyne, C. M., Hottenga, J. J., Montgomery, G. W., Rivadineira, F., Rueedi, R., Steri, M., Herzig, K. -H., Stott, D. J., Menni, C., Franberg, M., S, t. Pourcain B., Felix, S. B., Pers, T. H., Bakker, S. J. L., Kraft, P., Peters, A., Vaidya, D., Delgado, G., Smit, J. H., Grossmann, V., Sinisalo, J., Seppala, I., Williams, S. R., Holliday, E. G., Moed, M., Langenberg, C., Raikkonen, K., Ding, J., Campbell, H., Sale, M. M., Chen, Y. -D. I., James, A. L., Ruggiero, D., Soranzo, N., Hartman, C. A., Smith, E. N., Berenson, G. S., Fuchsberger, C., Hernandez, D., Tiesler, C. M. T., Giedraitis, V., Liewald, D., Fischer, K., Mellstrom, D., Larsson, A., Wang, Y., Scott, W. R., Lorentzon, M., Beilby, J., Ryan, K. A., Pennell, C. E., Vuckovic, D., Balkau, B., Concas, M. P., Schmidt, R., Mendes de Leon, C. F., Bottinger, E. P., Kloppenburg, M., Paternoster, L., Boehnke, M., Musk, A. W., Willemsen, G., Evans, D. M., Madden, P. A. F., Kahonen, M., Kutalik, Z., Zoledziewska, M., Karhunen, V., Kritchevsky, S. B., Sattar, N., Lachance, G., Clarke, R., Harris, T. B., Raitakari, O. T., Attia, J. R., van Heemst, D., Kajantie, E., Sorice, R., Gambaro, G., Scott, R. A., Hicks, A. A., Ferrucci, L., Standl, M., Lindgren, C. M., Starr, J. M., Karlsson, M., Lind, L., Li, J. Z., Chambers, J. C., Mori, T. A., de Geus, E. J. C. N., Heath, A. C., Martin, N. G., Auvinen, J., Buckley, B. M., de Craen, A. J. M., Waldenberger, M., Strauch, K., Meitinger, T., Scott, R. J., Mcevoy, M., Beekman, M., Bombieri, C., Ridker, P. M., Mohlke, K. L., Pedersen, N. L., Boomsma, D. I., Whitfield, J. B., Strachan, D. P., Hofman, A., Vollenweider, P., Cucca, F., Jarvelin, M. -R., Jukema, J. W., Spector, T. D., Hamsten, A., Zeller, T., Nauck, M., Gudnason, V., Qi, L., Grallert, H., Borecki, I. B., Rotter, J. I., Marz, W., Wild, P. S., Lokki, M. -L., Boyle, M., Salomaa, V., Melbye, M., Eriksson, J. G., Wilson, J. F., Penninx, B. W. J. H., Becker, D. M., Worrall, B. B., Gibson, G., Krauss, R. M., Ciullo, M., Zaza, G., Wareham, N. J., Oldehinkel, A. J., Palmer, L. J., Murray, S. S., Pramstaller, P. P., Bandinelli, S., Heinrich, J., Ingelsson, E., Deary, I. J., Magi, R., Vandenput, L., Desch, K. C., Kooner, J. S., Ohlsson, C., Hayward, C., Shuldiner, A. R., Arnett, D. K., Beilin, L. J., Robino, A., Froguel, P., Pirastu, M., Jess, T., Loos, R. J. F., Evans, D. A., Schmidt, H., Slagboom, P. E., Tracy, R. P., Visvikis-Siest, S., Reiner, A. P., Bis, J. C., Franco, O. H., Benjamin, E. J., AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, Graduate School, Epidemiology, Internal Medicine, Groningen Institute for Organ Transplantation (GIOT), Lifestyle Medicine (LM), Groningen Kidney Center (GKC), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Cardiovascular Centre (CVC), Life Course Epidemiology (LCE), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Stem Cell Aging Leukemia and Lymphoma (SALL), Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), VU University medical center, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, APH - Methodology, APH - Digital Health, Biological Psychology, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, Universität Heidelberg [Heidelberg], University of Verona (UNIVR), Department of Molecular and Experimental Medicine, The Scripps Research Institute, The Scripps Research Institute, Université Grenoble Alpes - UFR Sciences de l'Homme et de la Société (UGA UFR SHS), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), IT University of Copenhagen, Technical University of Denmark [Lyngby] (DTU), Consiglio Nazionale delle Ricerche (CNR), University of Virginia [Charlottesville], Université de Lausanne (UNIL), University of Oxford [Oxford], University of Newcastle [Australia] (UoN), Centre d'économie industrielle i3 (CERNA i3), Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Helmholtz-Zentrum München (HZM), Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA (UMR_7583)), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Universitätsmedizin der Johannes-Gutenberg Universität Mainz, University of Helsinki-University of Helsinki-Faculty of Medecine [Helsinki], University of Helsinki-University of Helsinki, Cardiff University-Medical Research Council, University of California-University of California, and DE CARVALHO, Philippe
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0301 basic medicine ,Male ,Netherlands Twin Register (NTR) ,Bipolar Disorder ,LD SCORE REGRESSION ,[SDV]Life Sciences [q-bio] ,Genome-wide association study ,[SDV.GEN] Life Sciences [q-bio]/Genetics ,Body Mass Index ,inflammatory disorder ,80 and over ,WIDE ASSOCIATION ,EPIDEMIOLOGY ,ta318 ,International HapMap Project ,Child ,Genetics (clinical) ,2. Zero hunger ,Genetics ,Genetics & Heredity ,Aged, 80 and over ,[SDV.MHEP] Life Sciences [q-bio]/Human health and pathology ,C-reactive proteingenome-wide association studyinflammationMendelian randomizationinflammatory disordersDEPICTcoronary artery diseaseschizophreniasystem biology ,system biology ,DEPICT ,Mendelian Randomization Analysis ,11 Medical And Health Sciences ,Middle Aged ,C-reactive protein ,coronary artery disease ,genome-wide association study ,inflammation ,inflammatory disorders ,Mendelian randomization ,schizophrenia ,Adolescent ,Adult ,Aged ,Biomarkers ,C-Reactive Protein ,Female ,Genetic Loci ,Genome-Wide Association Study ,Humans ,Inflammation ,Liver ,Metabolic Networks and Pathways ,Schizophrenia ,Young Adult ,3. Good health ,[SDV] Life Sciences [q-bio] ,Medical genetics ,Biomarker (medicine) ,Life Sciences & Biomedicine ,Human ,medicine.medical_specialty ,CHARGE Inflammation Working Group ,Biology ,IMMUNITY ,ta3111 ,Article ,03 medical and health sciences ,SDG 3 - Good Health and Well-being ,medicine ,CORONARY-HEART-DISEASE ,Mendelian Randomization Analysi ,1000 Genomes Project ,METAANALYSIS ,Genetic association ,[SDV.GEN]Life Sciences [q-bio]/Genetics ,Science & Technology ,ta1184 ,Metabolic Networks and Pathway ,Biomarker ,INSTRUMENTS ,06 Biological Sciences ,030104 developmental biology ,[SDV.MHEP]Life Sciences [q-bio]/Human health and pathology ,LifeLines Cohort Study - Abstract
International audience; C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10-8). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.Copyright © 2018 American Society of Human Genetics. All rights reserved.
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- 2018
30. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps
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George Dedoussis, M. Arfan Ikram, Marit E. Jørgensen, Christian Herder, Gudmar Thorleifsson, Oscar H. Franco, Xueling Sim, Philippe Froguel, Symen Ligthart, Ellen M. Schmidt, Jason M. Torres, Karen L. Mohlke, Markku Laakso, Barbara Thorand, Erwin P. Bottinger, Matthias Thurner, Torben Hansen, Jerome I. Rotter, Weihua Zhang, Ching-Ti Liu, Michael Boehnke, Kristi Läll, Konstantin Strauch, Kent D. Taylor, Andrew D. Morris, Daniel R. Witte, Anna L. Gloyn, Jonathan Marchini, Andres Metspalu, Florian Kronenberg, Anubha Mahajan, Claudia Schurmann, Jennifer Kriebel, James S. Pankow, Matthias Wuttke, Valeriya Lyssenko, Jose C. Florez, Josée Dupuis, Patricia A. Peyser, Leif Groop, John C. Chambers, Torben Jørgensen, Timothy M. Frayling, Lawrence F. Bielak, Nicholas J. Wareham, Thomas Meitinger, Alena Stančáková, Vasiliki Mamakou, Cramer Christensen, Michael Preuss, Bram P. Prins, Niels Grarup, Amanda J. Bennett, Vilmantas Giedraitis, Jian'an Luan, Young-Jin Kim, Mickaël Canouil, Robert A. Scott, Kari Stefansson, Jana Nano, Donald W. Bowden, Oluf Pedersen, Cécile Lecoeur, Anna Köttgen, Jette Bork-Jensen, Francis S. Collins, Unnur Thorsteinsdottir, Naveed Sattar, Stella Trompet, Martin Ingelsson, Maggie C.Y. Ng, Jaakko Tuomilehto, Reedik Mägi, James B. Meigs, Lauren E. Petty, J. Wouter Jukema, Andrew T. Hattersley, Claudia Langenberg, Harald Grallert, Christian Gieger, Chad M. Brummett, Ian Ford, Andrew P. Morris, James P. Cook, Vibe Nylander, Sebastian Schönherr, Daniel Taliun, Xiuqing Guo, Valgerdur Steinthorsdottir, Cecilia M. Lindgren, Jennifer E. Below, Eleftheria Zeggini, Anthony Payne, Loic Yengo, Sharon L.R. Kardia, Neil R. Robertson, Abbas Dehghan, Ivan Brandslund, Erik Ingelsson, Colin N. A. Palmer, Ruth J. F. Loos, Girish N. Nadkarni, Gonçalo R. Abecasis, Adam E. Locke, Johanna Kuusisto, Lars Lind, Krista Fischer, Annette Peters, Kai-Uwe Ec Kardt, Ioanna Ntalla, Chloé Sarnowski, Mark I. McCarthy, N. William Rayner, Allan Linneberg, Sophie Hackinger, Centre of Excellence in Complex Disease Genetics, Institute for Molecular Medicine Finland, University of Helsinki, HUS Abdominal Center, Mahajan, Anubha [0000-0001-5585-3420], Morris, Andrew P [0000-0002-6805-6014], Boehnke, Michael [0000-0002-6442-7754], McCarthy, Mark I [0000-0002-4393-0510], Apollo - University of Cambridge Repository, Lee Kong Chian School of Medicine (LKCMedicine), and Epidemiology
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0301 basic medicine ,Male ,Linkage disequilibrium ,endocrine system diseases ,Genome-wide association study ,DISEASE ,Linkage Disequilibrium ,Body Mass Index ,Epigenesis, Genetic ,0302 clinical medicine ,Gene Frequency ,High-Throughput Screening Assays/methods ,European Continental Ancestry Group/genetics ,Genetics & Heredity ,0303 health sciences ,Chromosome Mapping ,11 Medical And Health Sciences ,Functional Genomics ,3. Good health ,Genetic Loci/genetics ,Medical genetics ,Female ,Islets of Langerhans/metabolism ,Life Sciences & Biomedicine ,TRAITS ,EXPRESSION ,medicine.medical_specialty ,SUSCEPTIBILITY LOCI ,Genomics ,030209 endocrinology & metabolism ,PHENOTYPES ,Computational biology ,Biology ,Polymorphism, Single Nucleotide ,White People ,Article ,GENETIC ARCHITECTURE ,Islets of Langerhans ,03 medical and health sciences ,Sex Factors ,SDG 3 - Good Health and Well-being ,Meta-Analysis as Topic ,SCORE ,Genetics ,medicine ,Humans ,Medicine [Science] ,Genetic Predisposition to Disease ,Allele ,GENOME-WIDE ASSOCIATION ,Genotyping ,Allele frequency ,METAANALYSIS ,030304 developmental biology ,Science & Technology ,IDENTIFICATION ,Genome, Human ,Haplotype ,06 Biological Sciences ,Human genetics ,Genetic architecture ,High-Throughput Screening Assays ,Minor allele frequency ,030104 developmental biology ,Diabetes Mellitus, Type 2 ,Diabetes Mellitus, Type 2/epidemiology ,Sample size determination ,Genetic Loci ,Case-Control Studies ,Genome, Human/genetics ,Genome-wide Association Studies ,3111 Biomedicine ,Chromosome Mapping/methods ,030217 neurology & neurosurgery ,Imputation (genetics) ,Developmental Biology ,Genome-Wide Association Study - Abstract
We aggregated genome-wide genotyping data from 32 European-descent GWAS (74,124 T2D cases, 824,006 controls) imputed to high-density reference panels of >30,000 sequenced haplotypes. Analysis of ˜27M variants (˜21M with minor allele frequency [MAF]p−8; MAF 0.02%-50%; odds ratio [OR] 1.04-8.05), 135 not previously-implicated in T2D-predisposition. Conditional analyses revealed 160 additional distinct association signals (p−5) within the identified loci. The combined set of 403 T2D-risk signals includes 56 low-frequency (0.5%≤MAF2. Forty-one of the signals displayed effect-size heterogeneity between BMI-unadjusted and adjusted analyses. Increased sample size and improved imputation led to substantially more precise localisation of causal variants than previously attained: at 51 signals, the lead variant after fine-mapping accounted for >80% posterior probability of association (PPA) and at 18 of these, PPA exceeded 99%. Integration with islet regulatory annotations enriched for T2D association further reduced median credible set size (from 42 variants to 32) and extended the number of index variants with PPA>80% to 73. Although most signals mapped to regulatory sequence, we identified 18 genes as human validated therapeutic targets through coding variants that are causal for disease. Genome wide chip heritability accounted for 18% of T2D-risk, and individuals in the 2.5% extremes of a polygenic risk score generated from the GWAS data differed >9-fold in risk. Our observations highlight how increases in sample size and variant diversity deliver enhanced discovery and single-variant resolution of causal T2D-risk alleles, and the consequent impact on mechanistic insights and clinical translation.
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- 2018
31. Dissecting the Association Between Inflammation, Metabolic Dysregulation, and Specific Depressive Symptoms
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Marios K. Georgakis, Symen Ligthart, Nicolas Rost, Darina Czamara, Nils Kappelmann, Gulam Khandaker, Janine Arloth, Elisabeth B. Binder, Khandaker, Golam [0000-0002-4935-9220], Apollo - University of Cambridge Repository, and Epidemiology
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Oncology ,medicine.medical_specialty ,Linkage disequilibrium ,Genome-wide association study ,Patient Health Questionnaire ,Genetic correlation ,Body Mass Index ,03 medical and health sciences ,0302 clinical medicine ,SDG 3 - Good Health and Well-being ,Metabolic Diseases ,Internal medicine ,Mendelian randomization ,medicine ,Humans ,Correlation of Data ,Inflammation ,Depression ,business.industry ,Anhedonia ,Mendelian Randomization Analysis ,Heritability ,030227 psychiatry ,Psychiatry and Mental health ,Regression Analysis ,medicine.symptom ,business ,Body mass index ,Biomarkers ,030217 neurology & neurosurgery ,Genome-Wide Association Study - Abstract
This genetic correlation and 2-sample mendelian randomization study uses large-scale genome-wide association data sources to explore the genetic overlap and associations between inflammatory activity, metabolic dysregulation, and individual depressive symptoms.Importance Observational studies highlight associations of C-reactive protein (CRP), a general marker of inflammation, and interleukin 6 (IL-6), a cytokine-stimulating CRP production, with individual depressive symptoms. However, it is unclear whether inflammatory activity is associated with individual depressive symptoms and to what extent metabolic dysregulation underlies the reported associations. Objective To explore the genetic overlap and associations between inflammatory activity, metabolic dysregulation, and individual depressive symptoms. GWAS Data Sources Genome-wide association study (GWAS) summary data of European individuals, including the following: CRP levels (204402 individuals); 9 individual depressive symptoms (3 of which did not differentiate between underlying diametrically opposite symptoms [eg, insomnia and hypersomnia]) as measured with the Patient Health Questionnaire 9 (up to 117;907 individuals); summary statistics for major depression, including and excluding UK Biobank participants, resulting in sample sizes of 500 199 and up to 230 214 individuals, respectively; insomnia (up to 386533 individuals); body mass index (BMI) (up to 322154 individuals); and height (up to 253280 individuals). Design In this genetic correlation and 2-sample mendelian randomization (MR) study, linkage disequilibrium score (LDSC) regression was applied to infer single-nucleotide variant-based heritability and genetic correlation estimates. Two-sample MR tested potential causal associations of genetic variants associated with CRP levels, IL-6 signaling, and BMI with depressive symptoms. The study dates were November 2019 to April 2020. Results Based on large GWAS data sources, genetic correlation analyses revealed consistent false discovery rate (FDR)-controlled associations (genetic correlation range, 0.152-0.362; FDR P = .006 to P < .001) between CRP levels and depressive symptoms that were similar in size to genetic correlations of BMI with depressive symptoms. Two-sample MR analyses suggested that genetic upregulation of IL-6 signaling was associated with suicidality (estimate [SE], 0.035 [0.010]; FDR plus Bonferroni correction P = .01), a finding that remained stable across statistical models and sensitivity analyses using alternative instrument selection strategies. Mendelian randomization analyses did not consistently show associations of higher CRP levels or IL-6 signaling with other depressive symptoms, but higher BMI was associated with anhedonia, tiredness, changes in appetite, and feelings of inadequacy. Conclusions and Relevance This study reports coheritability between CRP levels and individual depressive symptoms, which may result from the potentially causal association of metabolic dysregulation with anhedonia, tiredness, changes in appetite, and feelings of inadequacy. The study also found that IL-6 signaling is associated with suicidality. These findings may have clinical implications, highlighting the potential of anti-inflammatory approaches, especially IL-6 blockade, as a putative strategy for suicide prevention.Question Do inflammatory pathways share a genetic background with individual depressive symptoms, and do they potentially causally contribute to them? Findings Based on large genome-wide association study data sources, this genetic correlation and 2-sample mendelian randomization study found genetic overlap between a higher C-reactive protein (CRP) level, a broad marker of inflammation, and 9 depressive symptoms; upregulated interleukin-6 signaling, a major stimulator of CRP, emerged as a potential causal risk factor for suicidality. Body mass index, but not interleukin 6 or CRP, was potentially causally associated with 4 other depressive symptoms. Meaning Interleukin 6 overactivity could be associated with suicidality; interleukin-6 blockade may be a novel treatment target that warrants future research.
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- 2021
32. Metabolic syndrome is related to polyneuropathy and impaired peripheral nerve function: a prospective population-based cohort study
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Albert Hofman, Judith Drenthen, Pieter A. van Doorn, Oscar H. Franco, Rens Hanewinckel, Symen Ligthart, M. Arfan Ikram, Abbas Dehghan, Epidemiology, and Neurology
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Male ,medicine.medical_specialty ,Statistics as Topic ,Population ,Neural Conduction ,030209 endocrinology & metabolism ,Type 2 diabetes ,Cohort Studies ,Prediabetic State ,03 medical and health sciences ,Rotterdam Study ,0302 clinical medicine ,Diabetic Neuropathies ,SDG 3 - Good Health and Well-being ,Risk Factors ,Surveys and Questionnaires ,Internal medicine ,Diabetes mellitus ,medicine ,Humans ,Mass Screening ,Longitudinal Studies ,Prospective Studies ,education ,Abdominal obesity ,Aged ,Metabolic Syndrome ,education.field_of_study ,business.industry ,Middle Aged ,Impaired fasting glucose ,medicine.disease ,Psychiatry and Mental health ,Endocrinology ,Population Surveillance ,Female ,Surgery ,Neurology (clinical) ,Metabolic syndrome ,medicine.symptom ,business ,Polyneuropathy ,030217 neurology & neurosurgery - Abstract
Objective Diabetes mellitus is a known risk factor for polyneuropathy, but the role of pre-diabetes and metabolic syndrome remains unclear. We aimed to investigate the role of these factors in a community-dwelling middle-aged and elderly population. Methods 1256 participants of the population-based Rotterdam Study (mean age 70.0, 54.5% females) were screened for polyneuropathy with a questionnaire, neurological examination and nerve conduction studies. Data on type 2 diabetes and components of metabolic syndrome were also collected. Logistic regression was used to investigate associations of diabetes, pre-diabetes and metabolic syndrome and its separate components with polyneuropathy. Linear regression was used to investigate associations with nerve conduction parameters in participants without polyneuropathy. Findings Diabetes was associated with polyneuropathy (OR 3.01, 95% CI 1.60 to 5.65), while impaired fasting glucose was not (OR 1.55, 95% CI 0.70 to 3.44). Metabolic syndrome was associated with polyneuropathy (OR 1.92, 95% CI 1.09 to 3.38), with a stronger association when more components of the syndrome were present. Analysing separate components of metabolic syndrome revealed associations for elevated waist circumference (OR 2.84, 95% CI 1.35 to 5.99) and elevated triglycerides (OR 2.01, 95% CI 1.11 to 3.62). Similar associations were found after excluding participants with diabetes. In participants without polyneuropathy, metabolic syndrome associated with lower sural sensory nerve action potential amplitudes. Conclusions Metabolic syndrome, abdominal obesity and dyslipidaemia, are strongly associated with polyneuropathy, irrespective of the presence of diabetes. Metabolic syndrome also associates with impaired nerve function in people without polyneuropathy. Our study therefore suggests that cardiometabolic disturbances have an impact on peripheral nerve function that extends beyond clinically manifest disease.
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- 2016
33. The role of global and regional DNA methylation and histone modifications in glycemic traits and type 2 diabetes
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John Troup, Abbas Dehghan, Wichor M. Bramer, Rajiv Chowdhury, Oscar H. Franco, Trudy Voortman, Kim V.E. Braun, Taulant Muka, Jana Nano, Saverio Stranges, Symen Ligthart, Chowdhury, Rajiv [0000-0003-4881-5690], Apollo - University of Cambridge Repository, Epidemiology, and Medical Informatics
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0301 basic medicine ,Blood Glucose ,Male ,Candidate gene ,endocrine system diseases ,Endocrinology, Diabetes and Metabolism ,medicine.medical_treatment ,Medicine (miscellaneous) ,Type 2 diabetes ,Bioinformatics ,Epigenesis, Genetic ,Histones ,0302 clinical medicine ,Risk Factors ,Insulin ,Nutrition and Dietetics ,biology ,Acetylation ,Histone ,Phenotype ,DNA methylation ,Epigenetics ,Female ,Cardiology and Cardiovascular Medicine ,Histone modification ,030209 endocrinology & metabolism ,03 medical and health sciences ,Insulin resistance ,SDG 3 - Good Health and Well-being ,Diabetes mellitus ,medicine ,Humans ,Genetic Predisposition to Disease ,Genetic Association Studies ,Global DNA methylation ,nutritional and metabolic diseases ,DNA Methylation ,medicine.disease ,Chromatin Assembly and Disassembly ,030104 developmental biology ,Glucose ,Diabetes Mellitus, Type 2 ,Gene Expression Regulation ,biology.protein ,Gene-Environment Interaction ,Biomarkers - Abstract
Background: New evidence suggests the potential involvement of epigenetic mechanisms in type 2 diabetes (T2D) as a crucial interface between the effects of genetic predisposition and environmental influences.Aim: To systematically review studies investigating the association between epigenetic marks (DNA methylation and histone modifications) with T2D and glycemic traits (glucose and insulin levels, insulin resistance measured by HOMA-IR).Method and Results: Six bibliographic databases (Embase.com, Medline (Ovid), Web-of-Science, PubMed, Cochrane Central and Google Scholar) were screened until 28th August 2015. We included randomized controlled trials, cohort, case-control and cross-sectional studies in humans that examined the association between epigenetic marks (global, candidate or genome-wide methylation of DNA and histone modifications) with T2D, glucose and insulin levels and insulin metabolism.Of the initially identified 3879 references, 53 articles, based on 47 unique studies met our inclusion criteria. Overall, data were available on 10,823 participants, with a total of 3358 T2D cases. There was no consistent evidence for an association between global DNA-methylation with T2D, glucose, insulin and insulin resistance. The studies reported epigenetic regulation of several candidate genes for diabetes susceptibility in blood cells, muscle, adipose tissue and placenta to be related with T2D without any general overlap between them. Histone modifications in relation to T2D were reported only in 3 observational studies.Conclusions and relevance: Current evidence supports an association between epigenetic marks and T2D. However, overall evidence is limited, highlighting the need for further larger-scale and prospective investigations to establish whether epigenetic marks may influence the risk of developing T2D. (C) 2016 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.
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- 2016
34. DNA Methylation in Newborns and Maternal Smoking in Pregnancy
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Anna Bergström, Carrie V. Breton, Marie-France Hivert, Oscar H. Franco, Roy Miodini Nilsen, Leanne K. Küpers, Kelly M. Bakulski, Mariona Pinart, Eva Corpeleijn, Erik Melén, Paul Yousefi, Symen Ligthart, Cilla Söderhäll, Monica Cheng Munthe-Kaas, Hasan Arshad, Donglei Hu, Pieter van der Vlies, Göran Pershagen, Bilal M. Quraishi, Jörg Tost, Ashok Kumar, Inger Kull, Nathanaël Lemonnier, Ahmad Vaez, Albert Hofman, Wilfried Karmaus, Sara E. Benjamin Neelon, Joyce B. J. van Meurs, Susan Ewart, Celeste Eng, Cathrine Hoyo, M. Daniele Fallin, Juha Kere, Andrea A. Baccarelli, Olena Gruzieva, Henning Tiemeier, Allan C. Just, Isabella Annesi-Maesano, Rebecca C Richmond, Andrew P. Feinberg, Gemma C Sharp, Christina A. Markunas, Carlos Ruiz, Charles Auffray, Harold Snieder, Simon Kebede Merid, Nour Baïz, Josep M. Antó, Brenda Eskenazi, Susan K. Murphy, Hongmei Zhang, Fahimeh Falahi, Christine Ladd-Acosta, Martine Vrijheid, Jin Yao, Sarah E. Reese, Marie José Saurel-Coubizolles, Karen Huen, Zdenko Herceg, Tianyuan Wang, Lisa F. Barcellos, Siri E. Håberg, Cheng-Jian Xu, Marjan Kerkhof, Nina Holland, Stephanie J. London, John W. Holloway, Barbara Heude, Hector Hernandez-Vargas, Mariona Bustamante, Marie-Aline Charles, Augusto A. Litonjua, Tom R. Gaunt, Dawn L. DeMeo, Abbas Dehghan, Zongli Xu, Bernard F. Fuemmeler, Caroline L Relton, Jordi Sunyer, Juan R. González, Jie Ren, Marjolein J. Peters, Ulrike Gehring, Sam S. Oh, Jack A. Taylor, Soesma A Jankipersadsing, Wenche Nystad, Matthew W. Gillman, Asa Bradman, Wendy L. McArdle, Vincent W. V. Jaddoe, George Davey Smith, Dirkje S. Postma, Magnus Wickman, Johanna Lepeule, Bonnie R. Joubert, Bert Brunekreef, Stefano Guerra, Liesbeth Duijts, Gerard H. Koppelman, Janine F. Felix, Esteban G. Burchard, Allen J. Wilcox, Michael C. Wu, Lucas A. Salas, Akram Ghantous, Epidemiology, Erasmus MC other, Pediatric Surgery, Pediatrics, Internal Medicine, Gastroenterology & Hepatology, dIRAS RA-2, Risk Assessment, Groningen Research Institute for Asthma and COPD (GRIAC), Reproductive Origins of Adult Health and Disease (ROAHD), Lifestyle Medicine (LM), and Life Course Epidemiology (LCE)
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0301 basic medicine ,AUTISM SPECTRUM DISORDERS ,Bioinformatics ,Epigenesis, Genetic ,Pregnancy ,HYDROCARBON RECEPTOR REPRESSOR ,POSTSYNAPTIC DENSITY ,Genetics(clinical) ,Child ,NEUROPILIN-2 EXPRESSION ,Genetics (clinical) ,Genetics ,Smoking ,Chromosome Mapping ,Methylation ,3. Good health ,Cleft Palate ,CpG site ,Meta-analysis ,Child, Preschool ,DNA methylation ,Female ,medicine.medical_specialty ,Cleft Lip ,European Continental Ancestry Group ,IN-UTERO ,Biology ,Article ,White People ,03 medical and health sciences ,Genetic ,LUNG-FUNCTION DECLINE ,medicine ,Journal Article ,Humans ,BREAST-CANCER ,Epigenetics ,Preschool ,Genetic Association Studies ,Asthma ,PRENATAL EXPOSURE ,Public health ,Infant, Newborn ,LYMPH-NODE METASTASIS ,Infant ,DNA Methylation ,medicine.disease ,Newborn ,030104 developmental biology ,CIGARETTE-SMOKING ,Epigenesis ,Meta-Analysis - Abstract
Epigenetic modifications, including DNA methylation, represent a potential mechanism for environmental impacts on human disease. Maternal smoking in pregnancy remains an important public health problem that impacts child health in a myriad of ways and has potential lifelong consequences. The mechanisms are largely unknown, but epigenetics most likely plays a role. We formed the Pregnancy And Childhood Epigenetics (PACE) consortium and meta-analyzed, across 13 cohorts (n = 6,685), the association between maternal smoking in pregnancy and newborn blood DNA methylation at over 450,000 CpG sites (CpGs) by using the Illumina 450K BeadChip. Over 6,000 CpGs were differentially methylated in relation to maternal smoking at genome-wide statistical significance (false discovery rate, 5%), including 2,965 CpGs corresponding to 2,017 genes not previously related to smoking and methylation in either newborns or adults. Several genes are relevant to diseases that can be caused by maternal smoking (e.g., orofacial clefts and asthma) or adult smoking (e.g., certain cancers). A number of differentially methylated CpGs were associated with gene expression. We observed enrichment in pathways and processes critical to development. In older children (5 cohorts, n = 3,187), 100% of CpGs gave at least nominal levels of significance, far more than expected by chance (p value < 2.2 × 10−16). Results were robust to different normalization methods used across studies and cell type adjustment. In this large scale meta-analysis of methylation data, we identified numerous loci involved in response to maternal smoking in pregnancy with persistence into later childhood and provide insights into mechanisms underlying effects of this important exposure. The BAMSE cohort was supported by The Swedish Research Council, The Swedish Heart-Lung Foundation, Freemason Child House Foundation in Stockholm, MeDALL (Mechanisms of the Development of ALLergy), a collaborative project conducted within the European Union (grant agreement No. 261357), Centre for Allergy Research, Stockholm County Council (ALF), Swedish foundation for strategic research (SSF, RBc08-0027, EpiGene project), the Strategic Research Programme (SFO) in Epidemiology at Karolinska Institutet, The Swedish Research Council Formas and the Swedish Environment Protection Agency.
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- 2016
35. Gait characteristics in older adults with diabetes and impaired fasting glucose: The Rotterdam Study
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Henning Tiemeier, Ana Maksimovic, Albert Hofman, Vincentius J.A. Verlinden, Rens Hanewinckel, Oscar H. Franco, M. Arfan Ikram, Symen Ligthart, Pieter A. van Doorn, Abbas Dehghan, Epidemiology, Neurology, Psychiatry, and Radiology & Nuclear Medicine
- Subjects
Blood Glucose ,Male ,Aging ,medicine.medical_specialty ,Endocrinology, Diabetes and Metabolism ,Population ,Poison control ,030209 endocrinology & metabolism ,Severity of Illness Index ,Cohort Studies ,Prediabetic State ,03 medical and health sciences ,Rotterdam Study ,0302 clinical medicine ,Endocrinology ,Diabetic Neuropathies ,SDG 3 - Good Health and Well-being ,Risk Factors ,Polyneuropathy ,Diabetes mellitus ,Internal medicine ,Internal Medicine ,Humans ,Medicine ,Prediabetes ,education ,Gait ,Gait Disorders, Neurologic ,Aged ,Netherlands ,education.field_of_study ,business.industry ,Diabetes ,Middle Aged ,Cardiovascular disease ,medicine.disease ,Impaired fasting glucose ,Cross-Sectional Studies ,Gait analysis ,Physical therapy ,Female ,business ,human activities ,Biomarkers ,030217 neurology & neurosurgery ,Follow-Up Studies - Abstract
Aims To investigate the association of diabetes mellitus and impaired fasting glucose with gait in the general middle-aged and elderly population. Methods We performed a cross-sectional study on 3019 participants from the population-based Rotterdam Study (aged >45years, 54% women). The presence of diabetes mellitus and impaired fasting glucose was evaluated by measuring serum glucose levels and by documenting anti-diabetic treatment. Participants underwent gait analysis using an electronic walkway. Thirty gait variables were summarized into five independent gait domains for normal walking ( Rhythm , Variability , Phases , Pace and Base of Support ), one for turning ( Turning ) and one for walking heel to toe ( Tandem ), which were averaged into Global Gait . Linear regression analyses were performed to determine the association of diabetes, impaired fasting glucose and continuous glucose levels within the normal range with gait. Results Diabetes mellitus was associated with worse Global Gait (Z-score difference −0.19, 95% confidence interval (CI) −0.30; −0.07), worse Pace (−0.20, 95% CI −0.30; −0.10) and worse Tandem (−0.21, 95% CI −0.33; −0.09), after adjusting for age, sex, height and weight. The association with Tandem remained significant after additional adjustment for cardiovascular risk factors. Impaired fasting glucose and continuous glucose levels within the normal range were not associated with any of the gait domains. Conclusion In our population-based study diabetes mellitus was associated with worse Global Gait , which was mostly reflected in Pace and Tandem . These associations were partly driven by other cardiovascular risk factors, emphasizing the importance of optimal control of cardiovascular risk factor profiles in patients with diabetes.
- Published
- 2016
36. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes
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Ching-Ti Liu, Michael Boehnke, George Dedoussis, Sophie V. Eastwood, Ruth J. F. Loos, Keng-Hung Lin, Denis Rybin, Fredrik Karpe, Martina Müller-Nurasyid, Michael A. Province, Alison D. Murray, Bo Isomaa, Eirini Marouli, Konstantin Strauch, Michael Preuss, Paul M. Ridker, Jette Bork-Jensen, Albert V. Smith, Hugoline G. de Haan, Wayne Huey-Herng Sheu, Barbara Thorand, Wolfgang Rathmann, Lawrence F. Bielak, Peter Kovacs, Marit E. Jørgensen, Jennifer Wessel, Danish Saleheen, Jung-Jin Lee, James B. Meigs, Veikko Salomaa, Alena Stančáková, Tibor V. Varga, Hidetoshi Kitajima, Inês Barroso, Kent D. Taylor, Claudia Langenberg, Yoon Shin Cho, Joanna M. M. Howson, Andrew T. Hattersley, Marie-France Hivert, Markku Laakso, Kai-Uwe Eckardt, Bram P. Prins, Matthias B. Schulze, Andrew D. Morris, Susanne Jäger, Francis S. Collins, Kristi Läll, Xu Lin, Anette Varbo, Benjamin Lehne, Girish N. Nadkarni, Jonathan Marchini, Daniel I. Chasman, Michael Stumvoll, Mark O. Goodarzi, Cécile Lecoeur, Philippe M. Frossard, Noël P. Burtt, Frank Kee, Jasmina Kravic, Alain G. Bertoni, Ivan Brandslund, Najaf Amin, Lenore J. Launer, Oluf Pedersen, Johanna Kuusisto, Line Rode, Eleftheria Zeggini, Yingchang Lu, Markus Perola, Helen R. Warren, André G. Uitterlinden, Hanieh Yaghootkar, Torben Hansen, Harald Grallert, Annemari Käräjämäki, Abbas Dehghan, Gina M. Peloso, Yii-Der Ida Chen, Man Li, Shaofeng Huo, Lars Lind, Karen L. Mohlke, Adrienne Tin, Yang Hai, Renée de Mutsert, Gudmar Thorleifsson, Marie Moitry, Sune F. Nielsen, Sara M. Willems, Matthias Wuttke, Weihua Zhang, Young-Jin Kim, Giovanni Malerba, Richard A. Jensen, Loic Yengo, Mickaël Canouil, Kurt Lohman, Robert A. Scott, Tamara B. Harris, Ruifang Li-Gao, Florian Kronenberg, Anke Tönjes, Bok-Ghee Han, Krista Fischer, Thomas Meitinger, James S. Pankow, Jaakko Tuomilehto, Adam S. Butterworth, Jerome I. Rotter, Olov Rolandsson, Xiuqing Guo, Cramer Christensen, Marie Loh, Elizabeth Selvin, Bong-Jo Kim, Audrey Y. Chu, Reedik Mägi, Josée Dupuis, Anna Köttgen, Jean Ferrières, Jin Li, Robert Sladek, Leslie A. Lange, Niels Grarup, Roberta McKean-Cowdin, Cristen J. Willer, Jose C. Florez, Valgerdur Steinthorsdottir, Karina Meidtner, Annette Peters, Børge G. Nordestgaard, Rajiv Chowdhury, Ioanna Ntalla, Emma Ahlqvist, Leif Groop, Nicholas J. Wareham, Kerrin S. Small, Tiinamaija Tuomi, Cecilia M. Lindgren, Katharine R. Owen, Giovanni Gambaro, Cornelia M. van Duijn, Dennis O. Mook-Kanamori, Kenneth Rice, Erik Ingelsson, Colin N. A. Palmer, Sharon L.R. Kardia, Neil R. Robertson, Dajiang J. Liu, Sebastian Schönherr, Daniel Taliun, Sekar Kathiresan, James G. Wilson, Ping An, Patricia A. Peyser, Matthias Blüher, Frits R. Rosendaal, John C. Chambers, Caroline Hayward, Shoaib Afzal, Fernando Rivadineira, Marielisa Graff, Pranav Yajnik, Vasiliki Mamakou, Juan Fernandez Tajes, Stefan Gustafsson, Heather M. Highland, Vilmantas Giedraitis, Andrew R. Wood, Saima Afaq, Jaspal S. Kooner, Megan L. Grove, Jennifer A. Brody, Andrew P. Morris, James P. Cook, Praveen Surendran, Jennifer Kriebel, Heikki A. Koistinen, Kari Stefansson, Anders Rosengren, Rainer Rauramaa, Satu Männistö, Oscar H. Franco, Yongmei Liu, N. William Rayner, Blair H. Smith, Erwin P. Bottinger, Ayse Demirkan, Allan Linneberg, Jonathan Marten, Huaixing Li, Sung Soo Kim, Sophie Hackinger, Cristina Bombieri, Lia B. Bang, Jun Liu, Asif Rasheed, Tim D. Spector, Paul W. Franks, Mark I. McCarthy, Heiner Boeing, Anne E. Justice, Vilmundur Gudnason, Sohee Han, Unnur Thorsteinsdottir, Panos Deloukas, Naveed Sattar, Eric Boerwinkle, Martin Ingelsson, John Danesh, Vassily Trubetskoy, Marco M Ferrario, Marju Orho-Melander, Wei Gan, Philippe Froguel, Symen Ligthart, Susan R. Heckbert, Jie Yao, Anne Tybjærg-Hansen, Robin Young, Daniel R. Witte, Anubha Mahajan, Peter Almgren, Timothy M. Frayling, Tanya M. Teslovich, Matt Neville, Philippe Amouyel, Wei Zhao, Andres Metspalu, Yao Hu, Olle Melander, Kari Kuulasmaa, Jason Flannick, Torben Jørgensen, Stephen S. Rich, Nicole Soranzo, Bruce M. Psaty, Rohit Varma, Epidemiology, and Internal Medicine
- Subjects
0301 basic medicine ,Male ,Inference ,Genome-wide association study ,Whole Exome Sequencing ,0302 clinical medicine ,type 2 diabetes ,coding variant associations signals ,mechanistic inference ,fine mapping ,Coding region ,Chromosome Mapping/statistics & numerical data ,European Continental Ancestry Group/genetics ,CONFERS SUSCEPTIBILITY ,Exome sequencing ,11 Medical and Health Sciences ,Genetics ,Genetics & Heredity ,0303 health sciences ,MAGIC Consortium ,Chromosome Mapping ,Whole Exome Sequencing/statistics & numerical data ,Identification (information) ,RARE VARIANTS ,LOW-FREQUENCY ,Female ,ExomeBP Consortium ,Life Sciences & Biomedicine ,SUSCEPTIBILITY LOCI ,Posterior probability ,European Continental Ancestry Group ,030209 endocrinology & metabolism ,Context (language use) ,Computational biology ,Biology ,GENOTYPE IMPUTATION ,Article ,White People ,GENETIC ARCHITECTURE ,03 medical and health sciences ,SDG 3 - Good Health and Well-being ,QUALITY-CONTROL ,Exome Sequencing ,Genome-Wide Association Study/statistics & numerical data ,Journal Article ,GIANT Consortium ,Humans ,Genetic Predisposition to Disease ,GENOME-WIDE ASSOCIATION ,Alleles ,Genetic association ,030304 developmental biology ,FATTY LIVER-DISEASE ,Science & Technology ,Genetic Variation ,06 Biological Sciences ,Genetic architecture ,Minor allele frequency ,BODY-MASS INDEX ,030104 developmental biology ,Diabetes Mellitus, Type 2 ,Diabetes Mellitus, Type 2/classification ,030217 neurology & neurosurgery ,Coding (social sciences) ,Genome-Wide Association Study ,Developmental Biology - Abstract
Identification of coding variant associations for complex diseases offers a direct route to biological insight, but is dependent on appropriate inference concerning the causal impact of those variants on disease risk. We aggregated coding variant data for 81,412 type 2 diabetes (T2D) cases and 370,832 controls of diverse ancestry, identifying 40 distinct coding variant association signals (at 38 loci) reaching significance (p−7). Of these, 16 represent novel associations mapping outside known genome-wide association study (GWAS) signals. We make two important observations. First, despite a threefold increase in sample size over previous efforts, only five of the 40 signals are driven by variants with minor allele frequency 1.29. Second, we used GWAS data from 50,160 T2D cases and 465,272 controls of European ancestry to fine-map these associated coding variants in their regional context, with and without additional weighting to account for the global enrichment of complex trait association signals in coding exons. At the 37 signals for which we attempted fine-mapping, we demonstrate convincing support (posterior probability >80% under the “annotation-weighted” model) that coding variants are causal for the association at 16 (including novel signals involving POC5 p.His36Arg, ANKH p.Arg187Gln, WSCD2 p.Thr113Ile, PLCB3 p.Ser778Leu, and PNPLA3 p.Ile148Met). However, at 13 of the 37 loci, the associated coding variants represent “false leads” and naïve analysis could have led to an erroneous inference regarding the effector transcript mediating the signal. Accurate identification of validated targets is dependent on correct specification of the contribution of coding and non-coding mediated mechanisms at associated loci.
- Published
- 2018
37. DNA Methylation Analysis Identifies Loci for Blood Pressure Regulation
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Marleen M.J. van Greevenbroek, Erik W. van Zwet, Jeroen van Rooij, Wibowo Arindrarto, Tianxiao Huan, Marijn Verkerk, Allan C. Just, Cisca Wijmenga, Coen D.A. Stehouwer, Cornelia M. van Duijn, Coleen M. Damcott, Symen Ligthart, Dorret I. Boomsma, Cristina Menni, Hailiang Mei, Guosheng Zhang, Eric Boerwinkle, Diana van Heemst, Albert Hofman, Casper G. Schalkwijk, Jordana T. Bell, Carla J.H. van der Kallen, John M. Starr, Yucheng Jia, Andrea A. Baccarelli, Jeffrey R. O'Connell, Joyce B. C van Meurs, Steve Horvath, Allan F. McRae, Leonard H. van den Berg, Walter Palmas, Szymon M. Kielbasa, Freerk van Dijk, Stephen Turner, Sharon L.R. Kardia, Donna K. Arnett, Devin Absher, James D. Stewart, Jerome I. Rotter, Jouke J. Hottenga, Rahul Gondalia, Jan Bot, Joris Deelen, René Pool, Tomáš Paus, Yii-Der Ida Chen, Peter A.C. ’t Hoen, Thomas H. Mosley, René Luijk, Zdenka Pausova, Ettje F. Tigchelaar, Bastiaan T. Heijmans, André G. Uitterlinden, P. Mila Jhamai, Eric A. Whitsel, Alanna C. Morrison, Patrick Deelen, Lude Franke, Riccardo E. Marioni, Chunyu Liu, Rick Jansen, Marc Jan Bonder, J. H. Veldink, Xiuqing Guo, Oscar H. Franco, Yongmei Liu, Nona Sotoodehnia, Nico Lakenberg, Joel Schwartz, Daniel Levy, Catriona Syme, May E. Montasser, Irene Nooren, Elias Salfati, Michael M. Mendelson, Abbas Dehghan, Tim D. Spector, Jan Bressler, Jennifer A. Smith, Matthijs Moed, H. Eka D. Suchiman, Martijn Vermaat, Alan R. Shuldiner, Michael M. P. J. Verbiest, A Isaacs, Dasha V. Zhernakova, Jennifer A. Brody, Alexandra Zhernakova, Wei Zhao, Marian Beekman, Ian J. Deary, Maarten van Iterson, Min A. Jhun, Michiel van Galen, Weihua Guan, Jincheng Shen, Stella Aslibekyan, Myriam Fornage, Joyce B. J. van Meurs, Pantel S. Vokonas, Bruce M. Psaty, Melissa A. Richard, Peter Van ‘t Hof, Yun Li, Morris A. Swertz, Themistocles L. Assimes, P. Eline Slagboom, Jenny van Dongen, Pei-Chien Tsai, Lifang Hou, Ruud van der Breggen, Marguerite R. Irvin, Joshua C. Bis, Internal Medicine, Epidemiology, Biological Psychology, APH - Mental Health, APH - Methodology, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, RS: CARIM - R3.01 - Vascular complications of diabetes and the metabolic syndrome, Interne Geneeskunde, MUMC+: HVC Pieken Maastricht Studie (9), and MUMC+: MA Interne Geneeskunde (3)
- Subjects
0301 basic medicine ,Netherlands Twin Register (NTR) ,sequence variation ,Tetraspanins ,Genome-wide association study ,Blood Pressure ,030204 cardiovascular system & hematology ,VARIANTS ,Medical and Health Sciences ,Epigenesis, Genetic ,0302 clinical medicine ,2.1 Biological and endogenous factors ,Aetiology ,Genetics (clinical) ,Genetics ,Genetics & Heredity ,RISK ,DNA methylation ,Methylation ,Biological Sciences ,Middle Aged ,epigenome-wide association study ,CpG site ,HEART ,POPULATIONS ,BIOS Consortium ,Biotechnology ,EXPRESSION ,Quantitative Trait Loci ,Nerve Tissue Proteins ,Quantitative trait locus ,Biology ,Article ,03 medical and health sciences ,Genetic ,SDG 3 - Good Health and Well-being ,Mendelian randomization ,Genetic variation ,Journal Article ,Humans ,Epigenetics ,GENOME-WIDE ASSOCIATION ,COMMON ,METAANALYSIS ,Aged ,HYPERTENSION ,Human Genome ,Genetic Variation ,DNA Methylation ,Mendelian Randomization Analysis ,INDIVIDUALS ,030104 developmental biology ,Cross-Sectional Studies ,gene expression ,CpG Islands ,Epigenesis ,Genome-Wide Association Study - 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× 10-7; replication: N = 7,182, p 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.
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- 2017
38. Incremental predictive value of 152 single nucleotide polymorphisms in the 10-year risk prediction of incident coronary heart disease: the Rotterdam Study
- Author
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Albert Hofman, Maryam Kavousi, Abbas Dehghan, Oscar H. Franco, André G. Uitterlinden, Paul S. de Vries, Symen Ligthart, Epidemiology, and Internal Medicine
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Male ,medicine.medical_specialty ,Epidemiology ,Single-nucleotide polymorphism ,Coronary Artery Disease ,Disease ,030204 cardiovascular system & hematology ,Polymorphism, Single Nucleotide ,Coronary artery disease ,03 medical and health sciences ,Rotterdam Study ,0302 clinical medicine ,Risk Factors ,Internal medicine ,medicine ,Humans ,SNP ,Genetic Predisposition to Disease ,Prospective Studies ,Family history ,10. No inequality ,Aged ,Netherlands ,030304 developmental biology ,0303 health sciences ,business.industry ,Incidence ,General Medicine ,Middle Aged ,medicine.disease ,Coronary heart disease ,Confidence interval ,3. Good health ,Female ,business ,Genome-Wide Association Study - Abstract
textabstractObjective: To examine the incremental predictive value of genetic risk scores of coronary heart disease (CHD) in the 10-year risk prediction of incident CHD. Methods: In 5899 subjects, we used 152 single nucleotide polymorphisms (SNPs) associated with coronary artery disease by the CARDIoGRAMplusC4D consortium to construct three weighted genetic risk scores: (i) GRS gws based on 49 genome-wide significant SNPs; (ii) GRS fdr based on 103 suggestively associated SNPs; and (iii) GRS all based on all 152 SNPs. We examined the changes in discrimination and reclassification of incident CHD when adding the genetic risk scores to models including traditional risk factors. We repeated the analysis for prevalent CHD. Results: The genetic risk scores were associated with incident CHD despite adjustment for traditional risk factors and family history: participants had a 13% higher rate of CHD per standard deviation increase in GRS all . GRS all improved the C-statistic by 0.006 [95% confidence interval (CI): 0.000, 0.013] beyond age and sex, 0.003 (95% CI: -0.001, 0.008) beyond traditional risk factors and 0.003 (95% CI: -0.001, 0.007) beyond traditional risk factors and family history. The genetic risk scores did not improve reclassification. GRS all strongly improved both discrimination and reclassification of prevalent CHD, even beyond traditional risk factors and family history, with a C-statistic improvement of 0.009 (0.003, 0.015). Conclusions: Although the genetic risk scores based on 152 SNPs were associated with incident CHD, they did not improve risk prediction. This discrepancy may be the result of SNP discovery for prevalent rather than incident CHD, since the SNPs do improve prediction for prevalent disease.
- Published
- 2015
39. EN-RAGE
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Sanaz Sedaghat, Symen Ligthart, Abbas Dehghan, Albert Hofman, Oscar H. Franco, M. Arfan Ikram, Epidemiology, and Radiology & Nuclear Medicine
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Male ,medicine.medical_specialty ,Pathology ,Population ,Renal function ,Coronary Disease ,Cohort Studies ,Coronary artery disease ,SDG 3 - Good Health and Well-being ,Risk Factors ,Internal medicine ,medicine ,Humans ,Prospective Studies ,cardiovascular diseases ,education ,Aged ,Netherlands ,Proportional Hazards Models ,Aged, 80 and over ,education.field_of_study ,business.industry ,Incidence ,S100 Proteins ,S100A12 Protein ,Hazard ratio ,Type 2 Diabetes Mellitus ,medicine.disease ,Confidence interval ,Multivariate Analysis ,Biomarker (medicine) ,Female ,Inflammation Mediators ,Cardiology and Cardiovascular Medicine ,business ,Biomarkers ,Cohort study - Abstract
Objective— Inflammation plays a key role in atherosclerosis. We hypothesized that novel inflammatory markers may predict the risk of coronary heart disease (CHD). Approach and Results— We investigated the association of 16 inflammatory biomarkers with the risk of CHD in a random subset of 839 CHD-free individuals in a prospective population-based cohort study. A Bonferroni corrected P value of 3.1×10 −3 was used as a threshold of statistical significance. The mean age at baseline was 72.8 years. During a median follow-up of 10.6 years, 99 cases of incident CHD were observed. Among all inflammatory biomarkers, neutrophil-derived human s100a12 (extracellular newly identified receptor for advanced glycation end-products binding protein [EN-RAGE]) showed the strongest association with the risk of CHD ( P value 2.0×10 −3 ). After multivariable adjustment for established cardiovascular risk factors, each standard deviation increase in the natural log-transformed EN-RAGE was associated with 30% higher risk of incident CHD (hazard ratio, 1.30; 95% confidence interval, 1.06–1.59). Further adjustment for previously studied inflammatory markers did not attenuate the association. Excluding individuals with prevalent type 2 diabetes mellitus, impaired kidney function, or individuals using antihypertensive medication did not change the effect estimates. Cause-specific hazard ratios suggested a stronger association between EN-RAGE and CHD mortality compared with stable CHD. Conclusions— Our results highlight EN-RAGE as an inflammatory marker for future CHD in a general population, beyond traditional CHD risk factors and inflammatory markers.
- Published
- 2014
40. Comparison of HapMap and 1000 genomes reference panels in a large-scale genome-wide association study
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Graciela E. Delgado, Alanna C. Morrison, Jie Huang, Toshiko Tanaka, Karl J. Lackner, Torben Hansen, Magdalena Zoledziewska, Jan W. Jukema, Antonella Mulas, Gordon D.O. Lowe, Philipp S. Wild, Maria Sabater-Lleal, Weihua Guan, David P. Strachan, Johanna Mazur, Paul Mitchell, Stefania Bandinelli, Cristina Venturini, Albert Hofman, Daniel I. Chasman, Paul M. Ridker, Pirro G. Hysi, Fernando Rivadeneira, Kent D. Taylor, P. Eline Slagboom, Massimo Mangino, Jouke J. Hottenga, Vera Grossmann, Maristella Steri, Ian J. Deary, Paul S. de Vries, Ann Rumley, Mark McEvoy, Marcus E. Kleber, Tarunveer S. Ahluwalia, Christopher Oldmeadow, Riccardo E. Marioni, Lynda M. Rose, Harald Binder, Naveed Sattar, Anton J. M. de Craen, René Pool, Francesco Cucca, Saonli Basu, Jie Jin Wang, Oscar H. Franco, Elizabeth G. Holliday, Stella Trompet, Rodney J. Scott, Moniek P.M. de Maat, Winfried März, Annette Kifley, Wendy L. McArdle, Alexander Teumer, Nicholas L. Smith, Eco J. C. de Geus, John M. Starr, Christopher J. O'Donnell, John Attia, Bruce M. Psaty, Mattias Frånberg, Uwe Völker, Tim D. Spector, Harmen H.M. Draisma, Tanja Zeller, Symen Ligthart, Dorret I. Boomsma, Anders Hamsten, Qiong Yang, Barbara McKnight, André G. Uitterlinden, Lu-Chen Weng, Weihong Tang, Geoffrey H. Tofler, Hugh Watkins, Tina L. Berentzen, David J. Stott, Jerome I. Rotter, Anne Grotevendt, Jennifer A. Brody, Lorna M. Lopez, Abbas Dehghan, Luigi Ferrucci, Andreas Greinacher, Dena G. Hernandez, Ming-Huei Chen, Epidemiology, Hematology, Internal Medicine, Biological Psychology, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, APH - Methodology, and Yao, Yong-Gang
- Subjects
Netherlands Twin Register (NTR) ,0301 basic medicine ,Glycobiology ,Social Sciences ,lcsh:Medicine ,Genome-wide association study ,030105 genetics & heredity ,Biochemistry ,Mathematical and Statistical Techniques ,Sociology ,Cell Signaling ,Consortia ,GENETIC-VARIANTS ,Medicine and Health Sciences ,IMPUTATION ,International HapMap Project ,lcsh:Science ,Genetics ,Multidisciplinary ,COMMON VARIANTS ,Genomics ,Multidisciplinary Sciences ,INSIGHTS ,CARDIOVASCULAR-DISEASE ,Physical Sciences ,symbols ,Science & Technology - Other Topics ,Health Services Research ,Genomic Signal Processing ,Statistics (Mathematics) ,Research Article ,Signal Transduction ,Genotyping ,SUSCEPTIBILITY LOCI ,General Science & Technology ,BIOLOGY ,Single-nucleotide polymorphism ,HapMap Project ,Computational biology ,PRESSURE ,Biology ,Research and Analysis Methods ,03 medical and health sciences ,symbols.namesake ,MD Multidisciplinary ,Genome-Wide Association Studies ,Journal Article ,Humans ,Statistical Methods ,1000 Genomes Project ,Molecular Biology Techniques ,Molecular Biology ,METAANALYSIS ,Glycoproteins ,Science & Technology ,lcsh:R ,Human Genome ,CONSORTIUM ,Biology and Life Sciences ,Computational Biology ,Fibrinogen ,Human Genetics ,Cell Biology ,Comparative Genomics ,Genome Analysis ,Health Care ,030104 developmental biology ,Bonferroni correction ,lcsh:Q ,Haplotype estimation ,Mathematics ,Imputation (genetics) ,Meta-Analysis ,Genome-Wide Association Study - Abstract
An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.
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- 2017
41. Serum Levels of Apolipoproteins and Incident Type 2 Diabetes: A Prospective Cohort Study
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M. Arfan Ikram, Abbas Dehghan, Eric J.G. Sijbrands, Maryam Kavousi, Oscar H. Franco, Albert Hofman, Symen Ligthart, Adela Brahimaj, Epidemiology, Neurology, Radiology & Nuclear Medicine, and Internal Medicine
- Subjects
Blood Glucose ,Male ,medicine.medical_specialty ,Endocrinology, Diabetes and Metabolism ,Population ,Blood Pressure ,030209 endocrinology & metabolism ,Type 2 diabetes ,030204 cardiovascular system & hematology ,Body Mass Index ,03 medical and health sciences ,Rotterdam Study ,0302 clinical medicine ,SDG 3 - Good Health and Well-being ,Risk Factors ,Internal medicine ,Diabetes mellitus ,Prevalence ,Internal Medicine ,medicine ,Humans ,Prospective Studies ,Prospective cohort study ,education ,Triglycerides ,Aged ,Proportional Hazards Models ,Aged, 80 and over ,Advanced and Specialized Nursing ,education.field_of_study ,Proportional hazards model ,business.industry ,Incidence ,Cholesterol, HDL ,Hazard ratio ,Cholesterol, LDL ,medicine.disease ,Apolipoproteins ,Cross-Sectional Studies ,Endocrinology ,Diabetes Mellitus, Type 2 ,Cardiovascular Diseases ,Linear Models ,Female ,lipids (amino acids, peptides, and proteins) ,Waist Circumference ,business ,Body mass index ,Follow-Up Studies - Abstract
OBJECTIVE We aimed to investigate the role of serum levels of various apolipoproteins on the risk for type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS We used data from 971 individuals from the prospective population-based Rotterdam Study. We studied the association of HDL cholesterol (HDL-C), apoA1, apoCIII, apoD, and apoE as well as the ratios of apolipoproteins with apoA1 with the risk of T2D. All apolipoproteins, ratios, and HDL-C levels were naturally log-transformed to reach normal distribution. First, their cross-sectional associations with fasting glucose and insulin were investigated by using linear regression. Second, Cox proportional hazard models were used to examine whether apolipoproteins predict the risk for T2D among individuals free of diabetes at baseline. We also studied the apolipoproteins jointly by calculating the apolipoproteinic score from the first step and then performing Cox regression with it. RESULTS During a median follow-up of 13.5 years, diabetes developed in 110 individuals. After adjustment for age, sex, BMI, parental history of diabetes, hypertension, alcohol use, smoking, prevalent cardiovascular disease, and serum lipid–reducing agents, HDL-C (per 1 SD naturally log-transformed hazard ratio 0.74 [95% CI 0.57, 0.97], apoCIII (1.65 [1.42, 1.91]), apoE (1.36 [1.18, 1.55]), apoCIII-to-apoA1 ratio (1.72 [1.51, 1.95]), apoE-to-apoA1 ratio (1.28 [1.13, 1.45]), and apolipoproteinic score (1.60 [1.39, 1.83]) remained significant. Only apoCIII (1.42 [1.03, 1.96]) and apoCIII-to-apoA1 ratio (1.56 [1.04, 2.36]) survived the adjustment for triglycerides in the last model. CONCLUSIONS Serum apoCIII levels as well as apoCIII-to-apoA1 ratio are associated with incident T2D. They are associated independent of known risk factors and stronger than HDL-C levels.
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- 2017
42. ADAMTS13 activity as a novel risk factor for incident type 2 diabetes mellitus: a population-based cohort study
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M. Arfan Ikram, Abbas Dehghan, Oscar H. Franco, Moniek P.M. de Maat, Frank W.G. Leebeek, Thijs T. W. van Herpt, Albert Hofman, Mandy van Hoek, Symen Ligthart, Paul S. de Vries, Eric J.G. Sijbrands, Epidemiology, Internal Medicine, Neurology, Radiology & Nuclear Medicine, and Hematology
- Subjects
0301 basic medicine ,Blood Glucose ,Male ,Epidemiology ,Endocrinology, Diabetes and Metabolism ,Type 2 diabetes ,MICROVASCULAR DYSFUNCTION ,Von Willebrand factor ,030204 cardiovascular system & hematology ,INCREASE ,0302 clinical medicine ,Risk Factors ,hemic and lymphatic diseases ,1114 Paediatrics And Reproductive Medicine ,Medicine ,Insulin ,Prediabetes ,Prospective Studies ,INSULIN-RESISTANCE ,biology ,FACTOR-CLEAVING PROTEASE ,Incidence ,Diabetes ,MEN ,Fasting ,Middle Aged ,ADAMTS13 ,3. Good health ,ISCHEMIC-STROKE ,1117 Public Health And Health Services ,cardiovascular system ,Female ,Life Sciences & Biomedicine ,Cohort study ,circulatory and respiratory physiology ,VonWillebrand factor ,congenital, hereditary, and neonatal diseases and abnormalities ,medicine.medical_specialty ,VON-WILLEBRAND-FACTOR ,ADAMTS13 Protein ,Article ,03 medical and health sciences ,Endocrinology & Metabolism ,SDG 3 - Good Health and Well-being ,Internal medicine ,Diabetes mellitus ,Internal Medicine ,Humans ,Risk factor ,Aged ,Proportional Hazards Models ,Science & Technology ,business.industry ,Type 2 Diabetes Mellitus ,1103 Clinical Sciences ,medicine.disease ,ROTTERDAM ,030104 developmental biology ,Endocrinology ,MYOCARDIAL-INFARCTION ,Diabetes Mellitus, Type 2 ,Immunology ,ENDOTHELIAL DYSFUNCTION ,biology.protein ,business - Abstract
Aims/hypothesis ADAMTS13 is a protease that breaks down von Willebrand factor (VWF) multimers into smaller, less active particles. VWF has been associated with an increased risk of incident type 2 diabetes mellitus. Here, we determine whether ADAMTS13 activity and VWF antigen are associated with incident diabetes. Methods This study included 5176 participants from the Rotterdam Study, a prospective population-based cohort study. Participants were free of diabetes at baseline and followed up for more than 20 years. Cox proportional hazards models were used to examine the association of ADAMTS13 activity and VWF antigen with incident diabetes. Results ADAMTS13 activity was associated with an increased risk of incident diabetes (HR 1.17 [95% CI 1.08, 1.27]) after adjustment for known risk factors and VWF antigen levels. Although ADAMTS13 activity was positively associated with fasting glucose and insulin, the association with incident diabetes did not change when we adjusted for these covariates. ADAMTS13 activity was also associated with incident prediabetes (defined on the basis of both fasting and non-fasting blood glucose) after adjustment for known risk factors (HR 1.11 [95% CI 1.03, 1.19]), while the VWF antigen level was not. VWF antigen was associated with incident diabetes, but this association was attenuated after adjustment for known risk factors. Conclusions/interpretation ADAMTS13 activity appears to be an independent risk factor for incident prediabetes and type 2 diabetes. As the association between ADAMTS13 and diabetes did not appear to be explained by its cleavage of VWF, ADAMTS13 may have an independent role in the development of diabetes. Electronic supplementary material The online version of this article (doi:10.1007/s00125-016-4139-5) contains peer-reviewed but unedited supplementary material, which is available to authorised users.
- Published
- 2017
43. Circulating Levels of Interleukin 1-Receptor Antagonist and Risk of Cardiovascular Disease Meta-Analysis of Six Population-Based Cohorts
- Author
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Stefan Blankenberg, David Peretz, Symen Ligthart, Michael Roden, Christa Meisinger, Barbara Thorand, Simone Wahl, Cornelia Huth, Tonia de las Heras Gala, Annette Peters, A. Jula, Veikko Salomaa, Kari Kuulasmaa, Brenda Bongaerts, Abbas Dehghan, M. Arfan Ikram, Olli Saarela, Frank Kee, Julie Sudduth-Klinger, Christian Herder, Astrid Zierer, Arto Pietilä, Tanja Zeller, Maren Carstensen-Kirberg, Wolfgang Koenig, and Epidemiology
- Subjects
Oncology ,medicine.medical_specialty ,Time Factors ,Population ,030209 endocrinology & metabolism ,030204 cardiovascular system & hematology ,Risk Assessment ,03 medical and health sciences ,0302 clinical medicine ,Blood serum ,SDG 3 - Good Health and Well-being ,Risk Factors ,Internal medicine ,Odds Ratio ,Medicine ,Humans ,cardiovascular diseases ,education ,education.field_of_study ,business.industry ,Confounding ,Hazard ratio ,Biomarker ,Cardiovascular Disease ,Cohort Study ,Inflammation ,Interleukin-1 Receptor Antagonist ,Meta-analysis ,Odds ratio ,Prognosis ,Interleukin 1 Receptor Antagonist Protein ,Interleukin 1 receptor antagonist ,Cardiovascular Diseases ,Immunology ,Cardiology and Cardiovascular Medicine ,business ,Biomarkers ,Cohort study - Abstract
Objective— Interleukin (IL)-1β represents a key cytokine in the development of cardiovascular disease (CVD). IL-1β is counter-regulated by IL-1 receptor antagonist (IL-1RA), an endogenous inhibitor. This study aimed to identify population-based studies on circulating IL-1RA and incident CVD in a systematic review, estimate the association between IL-1RA and incident CVD in a meta-analysis, and to test whether the association between IL-1RA and incident CVD is explained by other inflammation-related biomarkers in the MONICA/KORA Augsburg case–cohort study (Multinational Monitoring of Trends and Determinants in Cardiovascular Disease/Cooperative Health Research in the Region of Augsburg). Approach and Results— We performed a systematic literature search and identified 5 cohort studies on IL-1RA and incident CVD in addition to the MONICA/KORA Augsburg case–cohort study for a meta-analysis based on a total of 1855 CVD cases and 18 745 noncases with follow-up times between 5 and 16 years. The pooled standardized hazard ratio (95% confidence interval) for incident CVD was 1.11 (1.06–1.17) after adjustment for age, sex, anthropometric, metabolic, and lifestyle factors ( P 2 =0%; P =0.88). More detailed analyses in the MONICA/KORA study showed that the excess risk for CVD was attenuated by ≥10% after additional separate adjustment for serum levels of high-sensitivity C-reactive protein, IL-6, myeloperoxidase, soluble E-selectin, or soluble intercellular adhesion molecule-1. Conclusions— Serum IL-1RA levels were positively associated with risk of CVD after adjustment for multiple confounders in a meta-analysis of 6 population-based cohorts. This association may at least partially reflect a response to triggers inducing subclinical inflammation, oxidative stress, and endothelial activation.
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- 2017
44. Pleiotropic genes for metabolic syndrome and inflammation
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Inga Prokopenko, Fasil Tekola-Ayele, Jerome I. Rotter, Audrey Y. Chu, Torben Jørgensen, Paul M. Ridker, Yan V. Sun, Daniel I. Chasman, M. Arfan Ikram, Marit E. Jørgensen, Ruth J. F. Loos, Zari Dastani, Martin G. Larson, Andrew D. Johnson, Jennifer A. Smith, Ahmad Vaez, Albert Hofman, Letizia Marullo, Ilja M. Nolte, Abbas Dehghan, Michael A. Province, Leslie A. Lange, Josef Coresh, Christopher J. O'Donnell, Aldi T. Kraja, Eric Boerwinkle, Torben Hansen, Josée Dupuis, Honghuang Lin, Mary F. Feitosa, Renate B. Schnabel, Emelia J. Benjamin, Sarah A. Pendergrass, Weihong Tang, Ingrid B. Borecki, Yaming Shao, W. H. Linda Kao, Alanna C. Morrison, Alexander P. Reiner, Russell P. Tracy, Diane M. Becker, Yi-Hsiang Hsu, Benjamin F. Voight, Dhananjay Vaidya, James S. Pankow, Tuomas O. Kilpeläinen, Kari E. North, Oscar H. Franco, Lisa R. Yanek, Marylyn D. Ritchie, Sharon L.R. Kardia, Rebecca Rohde, James G. Wilson, Mike A. Nalls, Laura J. Rasmussen-Torvik, James B. Meigs, Symen Ligthart, Harold Snieder, Behrooz Z. Alizadeh, Andrew P. Morris, Charles N. Rotimi, Oluf Pedersen, Ronald P. Stolk, Mark Ziegler, André G. Uitterlinden, J. Brent Richards, Santhi K. Ganesh, Hae Kyung Im, Life Course Epidemiology (LCE), Lifestyle Medicine (LM), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Epidemiology, Public Health, Internal Medicine, and Radiology & Nuclear Medicine
- Subjects
Endocrinology, Diabetes and Metabolism ,NF-KAPPA-B ,Inflammatory markers ,Women's Genome Health Study ,Genome-wide association study ,BLOOD-PRESSURE ,Research & Experimental Medicine ,Bioinformatics ,Biochemistry ,Endocrinology ,Cohorts for Heart and ,Pleiotropy ,Genetic Investigation of Anthropometric Traits Consortium ,Gene Regulatory Networks ,Meta-Analyses of Glucose ,Genetics ,Genetics & Heredity ,Pleiotropic associations ,INSULIN-RESISTANCE ,Metabolic Syndrome X ,Genetic Pleiotropy ,Global Lipids Genetics Consortium ,Insulin-related traits Consortium ,Metabolic syndrome ,C-REACTIVE PROTEIN ,Phenotype ,Medicine, Research & Experimental ,DENSITY-LIPOPROTEIN CHOLESTEROL ,CARDIOVASCULAR-DISEASE ,CORONARY-ARTERY-DISEASE ,CIRCULATING ADIPONECTIN ,ADIPOGen Consortium ,Life Sciences & Biomedicine ,Aging Research in Genetic Epidemiology ,Howard University Family Study ,Single-nucleotide polymorphism ,Quantitative trait locus ,Biology ,Article ,Endocrinology & Metabolism ,Quantitative Trait, Heritable ,Meta-Analysis as Topic ,SDG 3 - Good Health and Well-being ,medicine ,SNP ,Humans ,Cross Consortia Pleiotropy Group ,Genetic Predisposition to Disease ,GENOME-WIDE ASSOCIATION ,Molecular Biology ,Global BPgen Consortium ,Inflammation ,Science & Technology ,Computational Biology ,Regulome ,1103 Clinical Sciences ,medicine.disease ,Genetic architecture ,DIABETES SUSCEPTIBILITY LOCI ,Meta-analysis ,Biomarkers ,Genome-Wide Association Study - Abstract
Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular disease. Our study hypothesis is that additional to genes influencing individual MetS risk factors, genetic variants exist that influence MetS and inflammatory markers forming a predisposing MetS genetic network. To test this hypothesis a staged approach was undertaken. (a) We analyzed 17 metabolic and inflammatory traits in more than 85,500 participants from 14 large epidemiological studies within the Cross Consortia Pleiotropy Group. Individuals classified with MetS (NCEP definition), versus those without, showed on average significantly different levels for most inflammatory markers studied. (b) Paired average correlations between 8 metabolic traits and 9 inflammatory markers from the same studies as above, estimated with two methods, and factor analyses on large simulated data, helped in identifying 8 combinations of traits for follow-up in meta-analyses, out of 130,305 possible combinations between metabolic traits and inflammatory markers studied. (c) We performed correlated meta-analyses for 8 metabolic traits and 6 inflammatory markers by using existing GWAS published genetic summary results, with about 2.5 million SNPs from twelve predominantly largest GWAS consortia. These analyses yielded 130 unique SNPs/genes with pleiotropic associations (a SNP/gene associating at least one metabolic trait and one inflammatory marker). Of them twenty-five variants (seven loci newly reported) are proposed as MetS candidates. They map to genes MACF1, KIAA0754, GCKR, GRB14, COBLL1, LOC646736-IRS1, SLC39A8, NELFE, SKIV2L, STK19, TFAP2B, BAZ1B, BCL7B, TBL2, MLXIPL, LPL, TRIB1, ATXN2, HECTD4, PTPN11, ZNF664, PDXDC1, FTO, MC4R and TOMM40. Based on large data evidence, we conclude that inflammation is a feature of MetS and several gene variants show pleiotropic genetic associations across phenotypes and might explain a part of MetS correlated genetic architecture. These findings warrant further functional investigation. (C) 2014 Elsevier Inc. All rights reserved.
- Published
- 2014
45. Thyroid function and risk of type 2 diabetes: a population-based prospective cohort study
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Symen Ligthart, Albert Hofman, Tim I M Korevaar, Abbas Dehghan, Oscar H. Franco, Robin P. Peeters, Layal Chaker, Epidemiology, Internal Medicine, and Erasmus MC other
- Subjects
HYPERTHYROIDISM ,Thyroid function ,medicine.medical_specialty ,Diabetes risk ,Population ,HORMONE REPLACEMENT ,030209 endocrinology & metabolism ,Type 2 diabetes ,METABOLISM ,GRAVES-DISEASE ,03 medical and health sciences ,Medicine, General & Internal ,0302 clinical medicine ,SDG 3 - Good Health and Well-being ,General & Internal Medicine ,Diabetes mellitus ,Internal medicine ,medicine ,030212 general & internal medicine ,Prediabetes ,Prospective cohort study ,education ,Medicine(all) ,INSULIN-RESISTANCE ,education.field_of_study ,Science & Technology ,business.industry ,Diabetes ,Absolute risk reduction ,ASSOCIATION ,11 Medical And Health Sciences ,General Medicine ,MUSCLE ,medicine.disease ,Thyroid hormone ,ADIPOSE-TISSUE ,Endocrinology ,HYPOTHYROIDISM ,FOLLOW-UP ,business ,Life Sciences & Biomedicine ,Research Article - Abstract
Background The association of thyroid function with risk of type 2 diabetes remains elusive. We aimed to investigate the association of thyroid function with incident diabetes and progression from prediabetes to diabetes in a population-based prospective cohort study. Methods We included 8452 participants (mean age 65 years) with thyroid function measurement, defined by thyroid-stimulating hormone (TSH) and free thyroxine (FT4), and longitudinal assessment of diabetes incidence. Cox-models were used to investigate the association of TSH and FT4 with diabetes and progression from prediabetes to diabetes. Multivariable models were adjusted for age, sex, high-density lipoprotein cholesterol, and glucose at baseline, amongst others. Results During a mean follow-up of 7.9 years, 798 diabetes cases occurred. Higher TSH levels were associated with a higher diabetes risk (hazard ratio [HR] 1.13; 95 % confidence interval [CI], 1.08–1.18, per logTSH), even within the reference range of thyroid function (HR 1.24; 95 % CI, 1.06–1.45). Higher FT4 levels were associated with a lower diabetes risk amongst all participants (HR 0.96; 95 % CI, 0.93–0.99, per 1 pmol/L) and in participants within the reference range of thyroid function (HR 0.96; 95 % CI, 0.92–0.99). The risk of progression from prediabetes to diabetes was higher with low-normal thyroid function (HR 1.32; 95 % CI, 1.06–1.64 for TSH and HR 0.91; 95 % CI, 0.86–0.97 for FT4). Absolute risk of developing diabetes type 2 in participants with prediabetes decreased from 35 % to almost 15 % with higher FT4 levels within the normal range. Conclusions Low and low-normal thyroid function are risk factors for incident diabetes, especially in individuals with prediabetes. Future studies should investigate whether screening for and treatment of (subclinical) hypothyroidism is beneficial in subjects at risk of developing diabetes. Electronic supplementary material The online version of this article (doi:10.1186/s12916-016-0693-4) contains supplementary material, which is available to authorized users.
- Published
- 2016
46. Bivariate genome-wide association study identifies novel pleiotropic loci for lipids and inflammation
- Author
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Oscar H. Franco, Symen Ligthart, Abbas Dehghan, Yi-Hsiang Hsu, Ahmad Vaez, Albert Hofman, Behrooz Z. Alizadeh, Ronald P. Stolk, André G. Uitterlinden, Epidemiology, Internal Medicine, Life Course Epidemiology (LCE), Lifestyle Medicine (LM), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), and Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET)
- Subjects
0301 basic medicine ,Multifactorial Inheritance ,Gene Expression ,Genome-wide association study ,DISEASE ,RELEVANCE ,Genetic pleiotropy ,Genetics & Heredity ,2. Zero hunger ,Genetics ,11 Medical And Health Sciences ,Lipids ,3. Good health ,PMI-WG-XCP ,REACTIVE PROTEIN-LEVELS ,lipids (amino acids, peptides, and proteins) ,Life Sciences & Biomedicine ,Research Article ,Biotechnology ,DNA Replication ,Bioinformatics ,In silico ,Quantitative Trait Loci ,Single-nucleotide polymorphism ,Quantitative trait locus ,Biology ,Polymorphism, Single Nucleotide ,C-reactive protein ,03 medical and health sciences ,Genetic Pleiotropy ,STATINS ,Gene ,Genetic Association Studies ,Triglycerides ,SQUALENE SYNTHASE ,Inflammation Working Group of the CHARGE Consortium ,Genetic association ,Inflammation ,08 Information And Computing Sciences ,Science & Technology ,Cholesterol, HDL ,Lipid metabolism ,06 Biological Sciences ,Lipid Metabolism ,BODY-MASS INDEX ,030104 developmental biology ,Biotechnology & Applied Microbiology ,Biomarkers ,LifeLines Cohort Study - Abstract
Background Genome-wide association studies (GWAS) have identified multiple genetic loci for C-reactive protein (CRP) and lipids, of which some overlap. We aimed to identify genetic pleiotropy among CRP and lipids in order to better understand the shared biology of chronic inflammation and lipid metabolism. Results In a bivariate GWAS, we combined summary statistics of published GWAS on CRP (n = 66,185) and lipids, including LDL-cholesterol, HDL-cholesterol, triglycerides, and total cholesterol (n = 100,184), using an empirical weighted linear-combined test statistic. We sought replication for novel CRP associations in an independent sample of 17,743 genotyped individuals, and performed in silico replication of novel lipid variants in 93,982 individuals. Fifty potentially pleiotropic SNPs were identified among CRP and lipids: 21 for LDL-cholesterol and CRP, 20 for HDL-cholesterol and CRP, 21 for triglycerides, and CRP and 20 for total cholesterol and CRP. We identified and significantly replicated three novel SNPs for CRP in or near CTSB/FDFT1 (rs10435719, Preplication: 2.6 × 10−5), STAG1/PCCB (rs7621025, Preplication: 1.4 × 10−3) and FTO (rs1558902, Preplication: 2.7 × 10−5). Seven pleiotropic lipid loci were replicated in the independent set of MetaboChip samples of the Global Lipids Genetics Consortium. Annotating the effect of replicated CRP SNPs to the expression of nearby genes, we observed an effect of rs10435719 on gene expression of FDFT1, and an effect of rs7621025 on PCCB. Conclusions Our large scale combined GWAS analysis identified numerous pleiotropic loci for CRP and lipids providing further insight in the genetic interrelation between lipids and inflammation. In addition, we provide evidence for FDFT1, PCCB and FTO to be associated with CRP levels. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2712-4) contains supplementary material, which is available to authorized users.
- Published
- 2016
47. Discovery of Genetic Variation on Chromosome 5q22 Associated with Mortality in Heart Failure
- Author
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Jemma B. Wilk, Xiaoyin Shan, Christine S. Moravec, Christopher Newton-Cheh, Stella Trompet, Eric Boerwinkle, Javed Butler, Julius S. Ngwa, Laurie A. Boyer, Michael Morley, Nona Sotoodehnia, Nicholas L. Smith, Howard D. Sesso, David J. Stott, David Aguilar, Ying A. Wang, Ian Ford, Kenneth B. Margulies, Kent D. Taylor, André G. Uitterlinden, Jeffrey Brandimarto, Xinchen Wang, Joshua C. Bis, J. Michael Gaziano, Manolis Kellis, Janine F. Felix, Chunyu Liu, Symen Ligthart, Björn Olde, Luc Djoussé, Abbas Dehghan, Serkalem Demissie, Michael M. Mendelson, Roby Joehanes, J. Wouter Jukema, Bruce M. Psaty, Brendan M. Buckley, Marketa Sjögren, Ramachandran S. Vasan, J. Gustav Smith, Stephen B. Kritchevsky, Bruno H. Stricker, Daniel Levy, Andreas P. Kalogeropoulos, Olof Gidlöf, Alanna C. Morrison, Albert Hofman, Kenneth Rice, Oscar H. Franco, Yongmei Liu, L. Adrienne Cupples, Pim van der Harst, Chen Yao, Joyce B. J. van Meurs, Thomas P. Cappola, Cardiovascular Centre (CVC), Massachusetts Institute of Technology. Department of Biology, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Wang, Xinchen, Kellis, Manolis, Boyer, Laurie Ann, Epidemiology, Internal Medicine, and Medical Informatics
- Subjects
0301 basic medicine ,Male ,Cancer Research ,Physiology ,Genome-wide association study ,030204 cardiovascular system & hematology ,VARIANTS ,Bioinformatics ,Biochemistry ,Genome-wide association studies ,0302 clinical medicine ,AFRICAN ANCESTRY ,HUMAN-POPULATIONS ,Basic Helix-Loop-Helix Transcription Factors ,Medicine and Health Sciences ,Genetics (clinical) ,AGING RESEARCH ,RISK ,Hematology ,DNA methylation ,Death rates ,Genomics ,Middle Aged ,Chromatin ,3. Good health ,Body Fluids ,Nucleic acids ,Blood ,PRESERVED EJECTION FRACTION ,Gene Knockdown Techniques ,SURVIVAL ,Chromosomes, Human, Pair 5 ,Female ,Epigenetics ,Anatomy ,DNA modification ,Chromatin modification ,Research Article ,Chromosome biology ,medicine.medical_specialty ,Cell biology ,Genotype ,lcsh:QH426-470 ,Death Rates ,Genetic loci ,Cardiology ,Heart failure ,Biology ,Polymorphism, Single Nucleotide ,Chromosomes ,03 medical and health sciences ,Population Metrics ,Molecular genetics ,Internal medicine ,Genetic variation ,medicine ,Genetics ,Genome-Wide Association Studies ,Humans ,Genetic Predisposition to Disease ,Receptors, Cytokine ,GENOME-WIDE ASSOCIATION ,Enhancer ,Molecular Biology ,Genotyping ,Ecology, Evolution, Behavior and Systematics ,Alleles ,METAANALYSIS ,Genetic association ,Demography ,Heart Failure ,Biology and life sciences ,Population Biology ,Genetic Variation ,Computational Biology ,Human Genetics ,DNA ,medicine.disease ,Genome Analysis ,Black or African American ,lcsh:Genetics ,030104 developmental biology ,HEK293 Cells ,Gene Expression Regulation ,Genetic Loci ,People and Places ,Gene expression ,Genome-Wide Association Study ,EPIDEMIOLOGY CHARGE CONSORTIUM - Abstract
Failure of the human heart to maintain sufficient output of blood for the demands of the body, heart failure, is a common condition with high mortality even with modern therapeutic alternatives. To identify molecular determinants of mortality in patients with new-onset heart failure, we performed a meta-analysis of genome-wide association studies and follow-up genotyping in independent populations. We identified and replicated an association for a genetic variant on chromosome 5q22 with 36% increased risk of death in subjects with heart failure (rs9885413, P = 2.7x10⁻⁹. We provide evidence from reporter gene assays, computational predictions and epigenomic marks that this polymorphism increases activity of an enhancer region active in multiple human tissues. The polymorphism was further reproducibly associated with a DNA methylation signature in whole blood (P = 4.5x10⁻⁴⁰) that also associated with allergic sensitization and expression in blood of the cytokine TSLP (P = 1.1x10⁻⁴). Knockdown of the transcription factor predicted to bind the enhancer region (NHLH1) in a human cell line (HEK293) expressing NHLH1 resulted in lower TSLP expression. In addition, we observed evidence of recent positive selection acting on the risk allele in populations of African descent. Our findings provide novel genetic leads to factors that influence mortality in patients with heart failure., National Heart, Lung, and Blood Institute (HHSN268201100005C), National Heart, Lung, and Blood Institute (HHSN268201100006C), National Heart, Lung, and Blood Institute (HHSN268201100007C), National Heart, Lung, and Blood Institute (HHSN268201100008C), National Heart, Lung, and Blood Institute (HHSN268201100009C), National Heart, Lung, and Blood Institute (HHSN268201100010C), National Heart, Lung, and Blood Institute (HHSN268201100011C), National Heart, Lung, and Blood Institute (HHSN268201100012C), National Heart, Lung, and Blood Institute (N01-HC-55015), National Heart, Lung, and Blood Institute (N01-HC-55016), National Heart, Lung, and Blood Institute (N01-HC-55018), National Heart, Lung, and Blood Institute (N01-HC-55019), National Heart, Lung, and Blood Institute (N01-HC-55020), National Heart, Lung, and Blood Institute (N01-HC-55021), National Heart, Lung, and Blood Institute (N01-HC-55022), National Heart, Lung, and Blood Institute (R01HL087641), National Heart, Lung, and Blood Institute (R01HL59367), National Heart, Lung, and Blood Institute (R01HL086694), National Human Genome Research Institute (U.S.) (U01HG004402), United States. National Institutes of Health (HHSN268200625226C), United States. National Institutes of Health (UL1RR025005), National Heart, Lung, and Blood Institute (HHSN268201200036C), National Heart, Lung, and Blood Institute (N01HC55222), National Heart, Lung, and Blood Institute (HHSN268200800007C), National Heart, Lung, and Blood Institute (N01HC85079), National Heart, Lung, and Blood Institute (N01HC85080), National Heart, Lung, and Blood Institute (N01HC85081), National Heart, Lung, and Blood Institute (N01HC85082), National Heart, Lung, and Blood Institute (N01HC85083), National Heart, Lung, and Blood Institute (N01HC85086), National Heart, Lung, and Blood Institute (U01HL080295), National Science Foundation (U.S.) (R01HL087652), National Heart, Lung, and Blood Institute (R01HL105756), National Heart, Lung, and Blood Institute (R01HL103612), National Heart, Lung, and Blood Institute (R01HL120393), National Institute on Aging (R01AG023629), National Center for Advancing Translational Sciences (U.S.) (UL1TR000124), National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (DK063491), National Heart, Lung, and Blood Institute (N01-HC-25195), National Heart, Lung, and Blood Institute (2K24HL04334), National Heart, Lung, and Blood Institute (R01HL077477), National Heart, Lung, and Blood Institute (R01HL093328), National Heart, Lung, and Blood Institute (NIH R01HL105993), National Institute on Aging (N01AG62101), National Heart, Lung, and Blood Institute (N01AG62103), National Heart, Lung, and Blood Institute (N01AG62106), National Institute on Aging (1R01AG032098-01A1), United States. National Institutes of Health (HHSN268200782096C), National Cancer Institute (U.S.) (CA-34944), National Cancer Institute (U.S.) (CA-40360), National Cancer Institute (U.S.) (CA-097193), National Heart, Lung, and Blood Institute (HL-26490), National Heart, Lung, and Blood Institute (HL-34595)
- Published
- 2016
48. Abstract 32: Novel Genetic Loci for Blood Pressure Regulation Identified by the Analysis of DNA Methylation
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Melissa A Richard, Tianxiao Huan, Symen Ligthart, Abbas Dehghan, Riccardo E Marioni, Jennifer A Brody, Nona Sotoodehnia, Min A Jhun, Sharon L Kardia, Jennifer A Smith, M R Irvin, Donna K Arnett, Stella Aslibekyan, J C Shen, Andrea Baccarelli, Allan C Just, Pei-Chien Tsai, Jordana T Bell, Tim D Spector, Xiuqing Guo, Jerome A Rotter, Walter Palmas, Yongmei Liu, Daniel Levy, and Myriam Fornage
- Subjects
Physiology (medical) ,Cardiology and Cardiovascular Medicine - Abstract
Both the health burden and genetic heritability of blood pressure are well-established, yet only a fraction of blood pressure genes have been identified. Epigenetic changes in or near genes related to blood pressure may explain part of its variance and provide new insights into the biological mechanisms involved in blood pressure regulation. DNA methylation measured on the Infinium HumanMethylation450 BeadChip was tested for association with systolic and diastolic blood pressure in individuals of European (EA) and African ancestry (AA) in the ARIC, CHS, FHS, GENOA, GOLDN, NAS, LBC1936, RS-III, and TwinsUK cohorts (N-total=9,828; N-EA=6,650, N-AA=3,178). In each cohort, linear mixed models were used to estimate associations adjusting for age, sex, blood cell counts, BMI, smoking, and ancestry, as well as surrogate variables and technical covariates to control for batch effects. Effect estimates from all cohorts were combined using inverse variance fixed effects meta-analysis; heterogeneity of effects in race- and sex-stratified analyses was not observed. Thirty-one methylation probes were found to be significantly associated with blood pressure after Bonferroni correction (p Our findings suggest a novel genetic regulation of known blood pressure systems through heritable DNA methylation.
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- 2016
49. Additional file 1: Table S1. of Thyroid function and risk of type 2 diabetes: a population-based prospective cohort study
- Author
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Chaker, Layal, Symen Ligthart, Korevaar, Tim, Hofman, Albert, Franco, Oscar, Peeters, Robin, and Dehghan, Abbas
- Subjects
endocrine system ,endocrine system diseases - Abstract
Sensitivity analyses for association between thyroid function and risk of diabetes. (DOCX 19 kb)
- Published
- 2016
- Full Text
- View/download PDF
50. Additional file 2: Table S2. of Thyroid function and risk of type 2 diabetes: a population-based prospective cohort study
- Author
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Chaker, Layal, Symen Ligthart, Korevaar, Tim, Hofman, Albert, Franco, Oscar, Peeters, Robin, and Dehghan, Abbas
- Abstract
Association between thyroid function in normal range and the risk of incident diabetes in individuals with prediabetes. (DOCX 19 kb)
- Published
- 2016
- Full Text
- View/download PDF
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