DrRahaPazoki

Schmidt AF, Holmes MV, Preiss D, Swerdlow D, Denaxas S, Fatemifar G, Faraway R, Finan C, Lumbers T, Henry A, Valentine D, Fairhurst-Hunter Z, Hartwig FP, Horta BL, Hypponen E, Power C, Moldovan M, van Iperen E, Hovingh K, Demuth I, Norman K, Steinhagen-Thiessen E, Demuth J, Bertram L, Lill CM, Coassin S, Willeit J, Kiechl S, Willeit K, Mason D, Wright J, Morris R, Wanamethee G, Whincup P, Ben-Shlomo Y, McLachlan S, Price JF, Kivimaki M, Welch C, Sanchez-Galvez A, Marques-Vidal P, Nicolaides A, Panayiotou AG, Onland-Moret NC, van der Schouw YT, Matullo G, Fiorito G, Guarrera S, Sacerdote C, Wareham NJ, Langenberg C, Scott RA, Luan J, Bobak M, Malyutina S, Pajak A, Kubinova R, Tamosiunas A, Pikhart H, Grarup N, Pedersen O, Hansen T, Linneberg A, Jess T, Cooper J, Humphries SE, Brilliant M, Kitchner T, Hakonarson H, Carrell DS, McCarty CA, Lester KH, Larson EB, Crosslin DR, Andrade MD, Roden DM, Denny JC, Carty C, Hancock S, Attia J, Holliday E, Scott R, Schofield P, ODonnell M, Yusuf S, Chong M, Pare G, van der Harst P, Said MA, Eppinga RN, Verweij N, Snieder H, Christen T, Mook-Kanamori DO, Gustafsson S, Lind L, Ingelsson E, Pazoki R, Franco O, Hofman A, Uitterlinden A, Dehghan A, Teumer A, Baumeister S, Dörr M, Lerch MM, Völker U, Völzke H, Ward J, Pell JP, Meade T, Christophersen IE, Maitland-van der Zee AH, Baranova EV, Young R, Ford I, Campbell A, Padmanabhan S, Bots ML, Grobbee DE, Froguel P, Thuillier D, Roussel R, Bonnefond A, Cariou B, Smart M, Bao Y, Kumari M, Mahajan A, Hopewell JC, Seshadri S, Dale C, Costa RPE, Ridker PM, Chasman DI, Reiner AP, Ritchie MD, Lange LA, Cornish AJ, Dobbins SE, Hemminki K, Kinnersley B, Sanson M, Labreche K, Simon M, Bondy M, Law P, Speedy H, Allan J, Li N, Went M, Weinhold N, Morgan G, Sonneveld P, Nilsson B, Goldschmidt H, Hemminki K, Sud A, Engert A, Hansson M, Hemingway H, Asselbergs FW, Patel RS, Keating BJ, Sattar N, Houlston R, Casas JP, Hingorani ADet al., 2018, Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in <i>PCSK9</i>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>We characterised the phenotypic consequence of genetic variation at the <jats:italic>PCSK9</jats:italic> locus and compared findings with recent trials of pharmacological inhibitors of PCSK9.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Published and individual participant level data (300,000+ participants) were combined to construct a weighted <jats:italic>PCSK9</jats:italic> gene-centric score (GS). Fourteen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The <jats:italic>PCSK9</jats:italic> GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95%CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95%CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95%CI 0.57; 1.22) for the GS, compared to 0.85 (95%CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95%CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer’s disease – outcomes for which large-scale trial data were unavailable.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Genetic variation at the <jats:italic>PCSK9</jats:italic> locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid

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Pazoki R, Dehghan A, Evangelou E, Warren H, Gao H, Caulfield M, Elliott P, Tzoulaki Iet al., 2018, Genetic predisposition to high blood pressure and lifestyle factors. Associations with midlife blood pressure levels and cardiovascular events, Circulation, Vol: 137, Pages: 653-661, ISSN: 0009-7322

Background:High blood pressure (BP) is a major risk factor for cardiovascular diseases (CVDs), the leading cause of mortality worldwide. Both heritable and lifestyle risk factors contribute to elevated BP levels. We aimed to investigate the extent to which lifestyle factors could offset the effect of an adverse BP genetic profile and its effect on CVD risk.Methods:We constructed a genetic risk score for high BP by using 314 published BP loci in 277 005 individuals without previous CVD from the UK Biobank study, a prospective cohort of individuals aged 40 to 69 years, with a median of 6.11 years of follow-up. We scored participants according to their lifestyle factors including body mass index, healthy diet, sedentary lifestyle, alcohol consumption, smoking, and urinary sodium excretion levels measured at recruitment. We examined the association between tertiles of genetic risk and tertiles of lifestyle score with BP levels and incident CVD by using linear regression and Cox regression models, respectively.Results:Healthy lifestyle score was strongly associated with BP (P<10–320) for systolic and diastolic BP and CVD events regardless of the underlying BP genetic risk. Participants with a favorable in comparison with an unfavorable lifestyle (bottom versus top tertile lifestyle score) had 4.9, 4.3, and 4.1 mm Hg lower systolic BP in low, middle, and high genetic risk groups, respectively (P for interaction=0.0006). Similarly, favorable in comparison with unfavorable lifestyle showed 30%, 33%, and 31% lower risk of CVD among participants in low, middle, and high genetic risk groups, respectively (P for interaction=0.99).Conclusions:Our data further support population-wide efforts to lower BP in the population via lifestyle modification. The advantages and disadvantages of disclosing genetic predisposition to high BP for risk stratification needs careful evaluation.

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Pazoki R, 2018, Methods for Polygenic Traits, GENETIC EPIDEMIOLOGY: METHODS AND PROTOCOLS, Editors: Evangelou, Publisher: HUMANA PRESS INC, Pages: 145-156, ISBN: 978-1-4939-7867-0

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Ashar FN, Mitchell RN, Albert CM, Newton-Cheh C, Brody JA, Muller-Nurasyid M, Moes A, Meitinger T, Mak A, Huikuri H, Junttila MJ, Goyette P, Pulit SL, Pazoki R, Tanck MW, Blom MT, Zhao X, Hauvlinna AS, Jabbari R, Glinge C, Tragante V, Escher SA, Chakravarti A, Ehret G, Coresh J, Li M, Prineas RJ, Franco OH, Kwok P-Y, Lumley T, Dumas F, McKnight B, Rotter JI, Lemaitre RN, Heckbert SR, ODonnell CJ, Hwang S-J, Tardif J-C, Kortelainen M-L, VanDenburgh M, Uitterlinden AG, Hofman A, Stricker BHC, de Bakker PIW, Franks PW, Jansson J-H, Asselbergs FW, Halushka MK, Maleszewski JJ, Tfelt-Hansen J, Engstrom T, Salomaa V, Virmani R, Kolodgie F, Wilde AAM, Tan HL, Bezzina CR, Eijgelsheim M, Rioux JD, Jouven X, Kaab S, Psaty BM, Siscovick DS, Arking D, Sotoodehnia Net al., 2017, A Comprehensive Evaluation of the Genetic Architecture of Sudden Cardiac Arrest

<jats:title>ABSTRACT</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Sudden cardiac arrest (SCA) accounts for 10% of adult mortality in Western populations. While several risk factors are observationally associated with SCA, the genetic architecture of SCA in the general population remains unknown. Furthermore, understanding which risk factors are causal may help target prevention strategies.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We carried out a large genome-wide association study (GWAS) for SCA (n=3,939 cases, 25,989 non-cases) to examine common variation genome-wide and in candidate arrhythmia genes. We also exploited Mendelian randomization methods using cross-trait multi-variant genetic risk score associations (GRSA) to assess causal relationships of 18 risk factors with SCA.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>No variants were associated with SCA at genome-wide significance, nor were common variants in candidate arrhythmia genes associated with SCA at nominal significance. Using cross-trait GRSA, we established genetic correlation between SCA and (1) coronary artery disease (CAD) and traditional CAD risk factors (blood pressure, lipids, and diabetes), (2) height and BMI, and (3) electrical instability traits (QT and atrial fibrillation), suggesting etiologic roles for these traits in SCA risk.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Our findings show that a comprehensive approach to the genetic architecture of SCA can shed light on the determinants of a complex life-threatening condition with multiple influencing factors in the general population. The results of this genetic analysis, both positive and negative findings, have implications for evaluating the genetic architecture of patients with a family his

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Pazoki R, Dehghan A, Evangelou E, Warren H, Gao H, Caulfield M, Elliott P, Tzoulaki Iet al., 2017, Genetic Predisposition to High Blood Pressure and Lifestyle: Associations With Midlife Blood Pressure Levels and Cardiovascular Health Outcomes, Scientific Sessions of the American-Heart-Association / Resuscitation Science Symposium, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322

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Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, Ntritsos G, Dimou N, Cabrera CP, Karaman I, Ng FL, Evangelou M, Witkowska K, Tzanis E, Hellwege JN, Giri A, Velez Edwards DR, Sun YV, Cho K, Gaziano JM, Wilson PWF, Tsao PS, Kovesdy CP, Esko T, Mägi R, Milani L, Almgren P, Boutin T, Debette S, Ding J, Giulianini F, Holliday EG, Jackson AU, Li-Gao R, Lin W-Y, Luan J, Mangino M, Oldmeadow C, Prins B, Qian Y, Sargurupremraj M, Shah N, Surendran P, Thériault S, Verweij N, Willems SM, Zhao J-H, Amouyel P, Connell J, de Mutsert R, Doney ASF, Farrall M, Menni C, Morris AD, Noordam R, Paré G, Poulter NR, Shields DC, Stanton A, Thom S, Abecasis G, Amin N, Arking DE, Ayers KL, Barbieri CM, Batini C, Bis JC, Blake T, Bochud M, Boehnke M, Boerwinkle E, Boomsma DI, Bottinger EP, Braund PS, Brumat M, Campbell A, Campbell H, Chakravarti A, Chambers JC, Chauhan G, Ciullo M, Cocca M, Collins F, Cordell HJ, Davies G, de Borst MH, de Geus EJ, Deary IJ, Deelen J, Del Greco FM, Demirkale CY, Dörr M, Ehret GB, Elosua R, Enroth S, Erzurumluoglu AM, Ferreira T, Frånberg M, Franco OH, Gandin I, Gasparini P, Giedraitis V, Gieger C, Girotto G, Goel A, Gow AJ, Gudnason V, Guo X, Gyllensten U, Hamsten A, Harris TB, Harris SE, Hartman CA, Havulinna AS, Hicks AA, Hofer E, Hofman A, Hottenga J-J, Huffman JE, Hwang S-J, Ingelsson E, James A, Jansen R, Jarvelin M-R, Joehanes R, Johansson Å, Johnson AD, Joshi PK, Jousilahti P, Jukema JW, Jula A, Kähönen M, Kathiresan S, Keavney BD, Khaw K-T, Knekt P, Knight J, Kolcic I, Kooner JS, Koskinen S, Kristiansson K, Kutalik Z, Laan M, Larson M, Launer LJ, Lehne BC, Lehtimäki T, Levy D, Liewald DCM, Lin L, Lind L, Lindgren CM, Liu Y, Loos RJF, Lopez LM, Lu L, Lyytikäinen L-P, Mahajan A, Mamasoula C, Marrugat J, Marten J, Milaneschi Y, Morgan A, Morris AP, Morrison AC, Munson PJ, Nalls MA, Nandakumar P, Nelson CP, Newton-Cheh C, Niiranen T, Nolte IM, Nutile T, Oldehinkel AJ, Oostra BA, O'Reilly PF, Org E, Padmanabhan S, Palmas W, Palotie A Pet al., 2017, Genetic analysis of over one million people identifies 535 novel loci for blood pressure

<jats:title>Abstract</jats:title><jats:p>High blood pressure is the foremost heritable global risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits to date (systolic, diastolic, pulse pressure) in over one million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also reveal shared loci influencing lifestyle exposures. Our findings offer the potential for a precision medicine strategy for future cardiovascular disease prevention.</jats:p>

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Davies G, Lam M, Harris SE, Trampush JW, Luciano M, Hill WD, Hagenaars SP, Ritchie SJ, Marioni RE, Fawns-Ritchie C, Liewald DCM, Okely JA, Ahola-Olli AV, Barnes CLK, Bertram L, Bis JC, Burdick KE, Christoforou A, DeRosse P, Djurovic S, Espeseth T, Giakoumaki S, Giddaluru S, Gustavson DE, Hayward C, Hofer E, Ikram MA, Karlsson R, Knowles E, Lahti J, Leber M, Li S, Mather KA, Melle I, Morris D, Oldmeadow C, Palviainen T, Payton A, Pazoki R, Petrovic K, Reynolds CA, Sargurupremraj M, Scholz M, Smith JA, Smith AV, Terzikhan N, Thalamuthu A, Trompet S, Lee SJVD, Ware EB, Windham BG, Wright MJ, Yang J, Yu J, Ames D, Amin N, Amouyel P, Andreassen OA, Armstrong NJ, Assareh AA, Attia JR, Attix D, Avramopoulos D, Bennett DA, Böhmer AC, Boyle PA, Brodaty H, Campbell H, Cannon TD, Cirulli ET, Congdon E, Conley ED, Corley J, Cox SR, Dale AM, Dehghan A, Dick D, Dickinson D, Eriksson JG, Evangelou E, Faul JD, Ford I, Freimer NA, Gao H, Giegling I, Gillespie NA, Gordon SD, Gottesman RF, Griswold ME, Gudnason V, Harris TB, Hartmann AM, Hatzimanolis A, Heiss G, Holliday EG, Joshi PK, Kähönen M, Kardia SLR, Karlsson I, Kleineidam L, Knopman DS, Kochan NA, Konte B, Kwok JB, Hellard SL, Lee T, Lehtimäki T, Li S-C, Liu T, Koini M, London E, Longstreth WT, Lopez OL, Loukola A, Luck T, Lundervold AJ, Lundquist A, Lyytikäinen L-P, Martin NG, Montgomery GW, Murray AD, Need AC, Noordam R, Nyberg L, Ollier W, Papenberg G, Pattie A, Polasek O, Poldrack RA, Psaty BM, Reppermund S, Riedel-Heller SG, Rose RJ, Rotter JI, Roussos P, Rovio SP, Saba Y, Sabb FW, Sachdev PS, Satizabal C, Schmid M, Scott RJ, Scult MA, Simino J, Slagboom PE, Smyrnis N, Soumaré A, Stefanis NC, Stott DJ, Straub RE, Sundet K, Taylor AM, Taylor KD, Tzoulaki I, Tzourio C, Uitterlinden A, Vitart V, Voineskos AN, Kaprio J, Wagner M, Wagner H, Weinhold L, Wen KH, Widen E, Yang Q, Zhao W, Adams HHH, Arking DE, Bilder RM, Bitsios P, Boerwinkle E, Chiba-Falek O, Corvin A, Jager PLD, Debette S, Donohoe G, Elliott P, Fitzpatrick AL Get al., 2017, Ninety-nine independent genetic loci influencing general cognitive function include genes associated with brain health and structure (N = 280,360)

<jats:p>General cognitive function is a prominent human trait associated with many important life outcomes<jats:sup>1,2</jats:sup>, including longevity<jats:sup>3</jats:sup>. The substantial heritability of general cognitive function is known to be polygenic, but it has had little explication in terms of the contributing genetic variants<jats:sup>4,5,6</jats:sup>. Here, we combined cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N=280,360; age range = 16 to 102). We found 9,714 genome-wide significant SNPs (<jats:italic>P</jats:italic>&lt;5 x 10<jats:sup>−8</jats:sup>) in 99 independent loci. Most showed clear evidence of functional importance. Among many novel genes associated with general cognitive function were<jats:italic>SGCZ</jats:italic>,<jats:italic>ATXN1</jats:italic>,<jats:italic>MAPT</jats:italic>,<jats:italic>AUTS2</jats:italic>, and<jats:italic>P2RY6</jats:italic>. Within the novel genetic loci were variants associated with neurodegenerative disorders, neurodevelopmental disorders, physical and psychiatric illnesses, brain structure, and BMI. Gene-based analyses found 536 genes significantly associated with general cognitive function; many were highly expressed in the brain, and associated with neurogenesis and dendrite gene sets. Genetic association results predicted up to 4% of general cognitive function variance in independent samples. There was significant genetic overlap between general cognitive function and information processing speed, as well as many health variables including longevity.</jats:p>

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Visser AE, Pazoki R, Pulit SL, van Rheenen W, Raaphorst J, van der Kooi AJ, Ricano-Ponce I, Wijmenga C, Otten HG, Veldink JH, van den Berg LHet al., 2017, No association between gluten sensitivity and amyotrophic lateral sclerosis, JOURNAL OF NEUROLOGY, Vol: 264, Pages: 694-700, ISSN: 0340-5354

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Schmidt AF, Swerdlow DI, Holmes MV, Patel RS, Fairhurst-Hunter Z, Lyall DM, Hartwig FP, Horta BL, Hypponen E, Power C, Moldovan M, van Iperen E, Hovingh GK, Demuth I, Norman K, Steinhagen-Thiessen E, Demuth J, Bertram L, Liu T, Coassin S, Willeit J, Kiechl S, Willeit K, Mason D, Wright J, Morris R, Wanamethee G, Whincup P, Ben-Shlomo Y, McLachlan S, Price JF, Kivimaki M, Welch C, Sanchez-Galvez A, Marques-Vidal P, Nicolaides A, Panayiotou AG, Onland-Moret NC, van der Schouw YT, Matullo G, Fiorito G, Guarrera S, Sacerdote C, Wareham NJ, Langenberg C, Scott R, Luan J, Bobak M, Malyutina SA, Pajak A, Kubinova R, Tamosiunas A, Pikhart H, Husemoen LLN, Grarup N, Pedersen O, Hansen T, Linneberg A, Simonsen KS, Cooper J, Humphries SE, Brilliant M, Kitchner T, Hakonarson H, Carrell DS, McCarty CA, Kirchner HL, Larson EB, Crosslin DR, de Andrade M, Roden DM, Denny JC, Carty C, Hancock S, Attia J, Holliday E, Donnell MO, Yusuf S, Chong M, Pare G, van der Harst P, Said MA, Eppinga RN, Verweij N, Snieder H, Christen T, Mook-Kanamori DO, Gustafsson S, Lind L, Ingelsson E, Pazoki R, Franco O, Hofman A, Uitterlinden A, Dehghan A, Teumer A, Baumeister S, Doerr M, Lerch MM, Voelker U, Voelzke H, Ward J, Pell JP, Smith DJ, Meade T, Maitland-van der Zee AH, Baranova EV, Young R, Ford I, Campbell A, Padmanabhan S, Bots ML, Grobbee DE, Froguel P, Thuillier D, Balkau B, Bonnefond A, Cariou B, Smart M, Bao Y, Kumari M, Mahajan A, Ridker PM, Chasman DI, Reiner AP, Lange LA, Ritchie MD, Asselbergs FW, Casas J-P, Keating BJ, Preiss D, Hingorani AD, Sattar Net al., 2016, PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study, Lancet Diabetes and Endocrinology, Vol: 5, Pages: 97-105, ISSN: 2213-8587

Background: Statin treatment and variants in the gene encoding HMG-CoA reductase are associated with reductionsin both the concentration of LDL cholesterol and the risk of coronary heart disease, but also with modesthyperglycaemia, increased bodyweight, and modestly increased risk of type 2 diabetes, which in no way off sets theirsubstantial benefi ts. We sought to investigate the associations of LDL cholesterol-lowering PCSK9 variants with type 2diabetes and related biomarkers to gauge the likely eff ects of PCSK9 inhibitors on diabetes risk.Methods: In this mendelian randomisation study, we used data from cohort studies, randomised controlled trials,case control studies, and genetic consortia to estimate associations of PCSK9 genetic variants with LDL cholesterol,fasting blood glucose, HbA1c, fasting insulin, bodyweight, waist-to-hip ratio, BMI, and risk of type 2 diabetes, usinga standardised analysis plan, meta-analyses, and weighted gene-centric scores.Findings: Data were available for more than 550 000 individuals and 51 623 cases of type 2 diabetes. Combined analysesof four independent PCSK9 variants (rs11583680, rs11591147, rs2479409, and rs11206510) scaled to 1 mmol/L lowerLDL cholesterol showed associations with increased fasting glucose (0·09 mmol/L, 95% CI 0·02 to 0·15), bodyweight(1·03 kg, 0·24 to 1·82), waist-to-hip ratio (0·006, 0·003 to 0·010), and an odds ratio for type diabetes of 1·29 (1·11 to 1·50).Based on the collected data, we did not identify associations with HbA1c (0·03%, –0·01 to 0·08), fasting insulin (0·00%,–0·06 to 0·07), and BMI (0·11 kg/m², –0·09 to 0·30).Interpretation: PCSK9 variants associated with lower LDL cholesterol were also associated with circulating higherfasting glucose concentration, bodyweight, and waist-to-hip ratio, and an increased risk of type 2 diab

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Iotchkova V, Huang J, Morris JA, Jain D, Barbieri C, Walter K, Min JL, Chen L, Astle W, Cocca M, Deelen P, Elding H, Farmaki A-E, Franklin CS, Franberg M, Gaunt TR, Hofman A, Jiang T, Kleber ME, Lachance G, Luan J, Malerba G, Matchan A, Mead D, Memari Y, Ntalla I, Panoutsopoulou K, Pazoki R, Perry JRB, Rivadeneira F, Sabater-Lleal M, Sennblad B, Shin S-Y, Southam L, Traglia M, van Dijk F, van Leeuwen EM, Zaza G, Zhang W, Amin N, Butterworth A, Chambers JC, Dedoussis G, Dehghan A, Franco OH, Franke L, Frontini M, Gambaro G, Gasparini P, Hamsten A, Issacs A, Kooner JS, Kooperberg C, Langenberg C, Marz W, Scott RA, Swertz MA, Toniolo D, Uitterlinden AG, van Duijn CM, Watkins H, Zeggini E, Maurano MT, Timpson NJ, Reiner AP, Auer PL, Soranzo Net al., 2016, Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps, Nature Genetics, Vol: 48, Pages: 1303-1312, ISSN: 1061-4036

Large-scale whole genome sequence datasets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole genome sequence data from the UK10K and the 1000 Genomes Projects into 35,981 study participants of European ancestry, followed by association analysis with twenty quantitative cardiometabolic and hematologic traits. We describe 17 novel associations, including six rare (minor allele frequency [MAF]<1%) or low frequency variants (1%<MAF<5%) with platelet count (PLT), red cell indices (MCH, MCV) and high-density lipoprotein (HDL) cholesterol. Applying fine-mapping analysis to 233 known and novel loci associated with the twenty traits, we resolve associations of 59 loci to credible sets of 20 or less variants, and describe trait enrichments within regions of predicted regulatory function. These findings augment understanding of the allelic architecture of risk factors for cardiometabolic and hematologic diseases, and provide additional functional insights with the identification of potentially novel biological targets.

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Polfus LM, Khajuria RK, Schick UM, Pankratz N, Pazoki R, Brody JA, Chen MH, Auer PL, Floyd JS, Huang J, Lange L, van Rooij FJA, Gibbs RA, Metcalf G, Muzny D, Veeraraghavan N, Walter K, Chen L, Yanek L, Becker LC, Peloso GM, Wakabayashi A, Kals M, Metspalu A, Esko T, Fox K, Wallace R, Franceschini N, Matijevic N, Rice KM, Bartz TM, Lyytikäinen LP, Kähönen M, Lehtimäki T, Raitakari OT, Li-Gao R, Mook-Kanamori DO, Lettre G, van Duijn CM, Franco OH, Rich SS, Rivadeneira F, Hofman A, Uitterlinden AG, Wilson JG, Psaty BM, Soranzo N, Dehghan A, Boerwinkle E, Zhang X, Johnson AD, O'Donnell CJ, Johnsen JM, Reiner AP, Ganesh SK, Sankaran VGet al., 2016, Erratum: Whole-Exome Sequencing Identifies Loci Associated with Blood Cell Traits and Reveals a Role for Alternative GFI1B Splice Variants in Human Hematopoiesis (American Journal of Human Genetics (2016) 99(2) (481–488)(S0002929716302208)(10.1016/j.ajhg.2016.06.016)), American Journal of Human Genetics, Vol: 99, Pages: 785-785, ISSN: 0002-9297

© 2016 American Society of Human Genetics (The American Journal of Human Genetics 99, 481–488; August 4, 2016) In the originally published version of this paper, Nora Franceschini's surname was misspelled. It has now been corrected online. The authors apologize for the error.

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Polfus LM, Khajuria RK, Schick UM, Pankratz N, Pazoki R, Brody JA, Chen M-H, Auer PL, Floyd JS, Huang J, Lange L, van Rooij FJA, Gibbs RA, Metcalf G, Muzny D, Veeraraghavan N, Walter K, Chen L, Yanek L, Becker LC, Peloso GM, Wakabayashi A, Kals M, Metspalu A, Esko T, Fox K, Wallace R, Franceschini N, Matijevic N, Rice KM, Bartz TM, Lyytikainen L-P, Kahonen M, Lehtimaki T, Raitakari OT, Li-Gao R, Mook-Kanamori DO, Lettre G, van Duijn CM, Franco OH, Rich SS, Rivadeneira F, Hofman A, Uitterlinden AG, Wilson JG, Psaty BM, Soranzo N, Dehghan A, Boerwinkle E, Zhang X, Johnson AD, O'Donnell CJ, Johnsen JM, Reiner AP, Ganesh SK, Sankaran VGet al., 2016, Whole-Exome Sequencing Identifies Loci Associated with Blood Cell Traits and Reveals a Role for Alternative <i>GFI1B</i> Splice Variants in Human Hematopoiesis, AMERICAN JOURNAL OF HUMAN GENETICS, Vol: 99, Pages: 481-488, ISSN: 0002-9297

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• Citations: 30

Pankratz N, Schick UM, Zhou Y, Zhou W, Ahluwalia TS, Allende ML, Auer PL, Bork-Jensen J, Brody JA, Chen M-H, Clavo V, Eicher JD, Grarup N, Hagedorn EJ, Hu B, Hunker K, Johnson AD, Leusink M, Lu Y, Lyytikainen L-P, Manichaikul A, Marioni RE, Nalls MA, Pazoki R, Smith AV, van Rooij FJA, Yang M-L, Zhang X, Zhang Y, Asselbergs FW, Boerwinkle E, Borecki IB, Bottinger EP, Cushman M, de Bakker PIW, Deary IJ, Dong L, Feitosa MF, Floyd JS, Franceschini N, Franco OH, Garcia ME, Grove ML, Gudnason V, Hansen T, Harris TB, Hofman A, Jackson RD, Jia J, Kahonen M, Launer LJ, Lehtimaki T, Liewald DC, Linneberg A, Liu Y, Loos RJF, Nguyen VM, Numans ME, Pedersen O, Psaty BM, Raitakari OT, Rich SS, Rivadeneira F, Di Sant AMR, Rotter JI, Starr JM, Taylor KD, Thuesen BH, Tracy RP, Uitterlinden AG, Wang J, Wang J, Dehghan A, Huo Y, Cupples LA, Wilson JG, Proia RL, Zon LI, O'Donnell CJ, Reiner AP, Ganesh SKet al., 2016, Meta-analysis of rare and common exome chip variants identifies S1PR4 and other loci influencing blood cell traits, NATURE GENETICS, Vol: 48, Pages: 867-+, ISSN: 1061-4036

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Tajuddin SM, Schick UM, Eicher JD, Chami N, Giri A, Brody JA, Hill WD, Kacprowski T, Li J, Lyytikäinen LP, Manichaikul A, Mihailov E, O'Donoghue ML, Pankratz N, Pazoki R, Polfus LM, Smith AV, Schurmann C, Vacchi-Suzzi C, Waterworth DM, Evangelou E, Yanek LR, Burt A, Chen MH, van Rooij FJ, Floyd JS, Greinacher A, Harris TB, Highland HM, Lange LA, Liu Y, Mägi R, Nalls MA, Mathias RA, Nickerson DA, Nikus K, Starr JM, Tardif JC, Tzoulaki I, Velez Edwards DR, Wallentin L, Bartz TM, Becker LC, Denny JC, Raffield LM, Rioux JD, Friedrich N, Fornage M, Gao H, Hirschhorn JN, Liewald DC, Rich SS, Uitterlinden A, Bastarache L, Becker DM, Boerwinkle E, de Denus S, Bottinger EP, Hayward C, Hofman A, Homuth G, Lange E, Launer LJ, Lehtimäki T, Lu Y, Metspalu A, O'Donnell CJ, Quarells RC, Richard M, Torstenson ES, Taylor KD, Vergnaud AC, Zonderman AB, Crosslin DR, Deary IJ, Dörr M, Elliott P, Evans MK, Gudnason V, Kähönen M, Psaty BM, Rotter JI, Slater AJ, Dehghan A, White HD, Ganesh SK, Loos RJ, Esko T, Faraday N, Wilson JG, Cushman M, Johnson AD, Edwards TL, Zakai NA, Lettre G, Reiner AP, Auer PLet al., 2016, Large-scale exome-wide association analysis identifies loci for white blood cell traits and pleiotropy with immune-mediated diseases, American Journal of Human Genetics, Vol: 99, Pages: 22-39, ISSN: 1537-6605

White blood cells play diverse roles in innate and adaptive immunity. Genetic association analyses of phenotypic variation in circulating white blood cell (WBC) counts from large samples of otherwise healthy individuals can provide insights into genes and biologic pathways involved in production, differentiation, or clearance of particular WBC lineages (myeloid, lymphoid) and also potentially inform the genetic basis of autoimmune, allergic, and blood diseases. We performed an exome array-based meta-analysis of total WBC and subtype counts (neutrophils, monocytes, lymphocytes, basophils, and eosinophils) in a multi-ancestry discovery and replication sample of ∼157,622 individuals from 25 studies. We identified 16 common variants (8 of which were coding variants) associated with one or more WBC traits, the majority of which are pleiotropically associated with autoimmune diseases. Based on functional annotation, these loci included genes encoding surface markers of myeloid, lymphoid, or hematopoietic stem cell differentiation (CD69, CD33, CD87), transcription factors regulating lineage specification during hematopoiesis (ASXL1, IRF8, IKZF1, JMJD1C, ETS2-PSMG1), and molecules involved in neutrophil clearance/apoptosis (C10orf54, LTA), adhesion (TNXB), or centrosome and microtubule structure/function (KIF9, TUBD1). Together with recent reports of somatic ASXL1 mutations among individuals with idiopathic cytopenias or clonal hematopoiesis of undetermined significance, the identification of a common regulatory 3' UTR variant of ASXL1 suggests that both germline and somatic ASXL1 mutations contribute to lower blood counts in otherwise asymptomatic individuals. These association results shed light on genetic mechanisms that regulate circulating WBC counts and suggest a prominent shared genetic architecture with inflammatory and autoimmune diseases.

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Eicher JD, Chami N, Kacprowski T, Nomura A, Chen MH, Yanek LR, Tajuddin SM, Schick UM, Slater AJ, Pankratz N, Polfus L, Schurmann C, Giri A, Brody JA, Lange LA, Manichaikul A, Hill WD, Pazoki R, Elliot P, Evangelou E, Tzoulaki I, Gao H, Vergnaud AC, Mathias RA, Becker DM, Becker LC, Burt A, Crosslin DR, Lyytikäinen LP, Nikus K, Hernesniemi J, Kähönen M, Raitoharju E, Mononen N, Raitakari OT, Lehtimäki T, Cushman M, Zakai NA, Nickerson DA, Raffield LM, Quarells R, Willer CJ, Peloso GM, Abecasis GR, Liu DJ, Global Lipids Genetics Consortium, Deloukas P, Samani NJ, Schunkert H, Erdmann J, CARDIoGRAM Exome Consortium, Myocardial Infarction Genetics Consortium, Fornage M, Richard M, Tardif JC, Rioux JD, Dube MP, de Denus S, Lu Y, Bottinger EP, Loos RJ, Smith AV, Harris TB, Launer LJ, Gudnason V, Velez Edwards DR, Torstenson ES, Liu Y, Tracy RP, Rotter JI, Rich SS, Highland HM, Boerwinkle E, Li J, Lange E, Wilson JG, Mihailov E, Mägi R, Hirschhorn J, Metspalu A, Esko T, Vacchi-Suzzi C, Nalls MA, Zonderman AB, Evans MK, Engström G, Orho-Melander M, Melander O, O'Donoghue ML, Waterworth DM, Wallentin L, White HD, Floyd JS, Bartz TM, Rice KM, Psaty BM, Starr JM, Liewald DC, Hayward C, Deary IJ, Greinacher A, Völker U, Thiele T, Völzke H, van Rooij FJ, Uitterlinden AG, Franco OH, Dehghan A, Edwards TL, Ganesh SK, Kathiresan S, Faraday N, Auer PL, Reiner AP, Lettre G, Johnson ADet al., 2016, Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals, American Journal of Human Genetics, Vol: 99, Pages: 40-55, ISSN: 1537-6605

Platelet production, maintenance, and clearance are tightly controlled processes indicative of platelets' important roles in hemostasis and thrombosis. Platelets are common targets for primary and secondary prevention of several conditions. They are monitored clinically by complete blood counts, specifically with measurements of platelet count (PLT) and mean platelet volume (MPV). Identifying genetic effects on PLT and MPV can provide mechanistic insights into platelet biology and their role in disease. Therefore, we formed the Blood Cell Consortium (BCX) to perform a large-scale meta-analysis of Exomechip association results for PLT and MPV in 157,293 and 57,617 individuals, respectively. Using the low-frequency/rare coding variant-enriched Exomechip genotyping array, we sought to identify genetic variants associated with PLT and MPV. In addition to confirming 47 known PLT and 20 known MPV associations, we identified 32 PLT and 18 MPV associations not previously observed in the literature across the allele frequency spectrum, including rare large effect (FCER1A), low-frequency (IQGAP2, MAP1A, LY75), and common (ZMIZ2, SMG6, PEAR1, ARFGAP3/PACSIN2) variants. Several variants associated with PLT/MPV (PEAR1, MRVI1, PTGES3) were also associated with platelet reactivity. In concurrent BCX analyses, there was overlap of platelet-associated variants with red (MAP1A, TMPRSS6, ZMIZ2) and white (PEAR1, ZMIZ2, LY75) blood cell traits, suggesting common regulatory pathways with shared genetic architecture among these hematopoietic lineages. Our large-scale Exomechip analyses identified previously undocumented associations with platelet traits and further indicate that several complex quantitative hematological, lipid, and cardiovascular traits share genetic factors.

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Chami N, Chen MH, Slater AJ, Eicher JD, Evangelou E, Tajuddin SM, Love-Gregory L, Kacprowski T, Schick UM, Nomura A, Giri A, Lessard S, Brody JA, Schurmann C, Pankratz N, Yanek LR, Manichaikul A, Pazoki R, Mihailov E, Hill WD, Raffield LM, Burt A, Bartz TM, Becker DM, Becker LC, Boerwinkle E, Bork-Jensen J, Bottinger EP, O'Donoghue ML, Crosslin DR, de Denus S, Dubé MP, Elliott P, Engström G, Evans MK, Floyd JS, Fornage M, Gao H, Greinacher A, Gudnason V, Hansen T, Harris TB, Hayward C, Hernesniemi J, Highland HM, Hirschhorn JN, Hofman A, Irvin MR, Kähönen M, Lange E, Launer LJ, Lehtimäki T, Li J, Liewald DC, Linneberg A, Liu Y, Lu Y, Lyytikäinen LP, Mägi R, Mathias RA, Melander O, Metspalu A, Mononen N, Nalls MA, Nickerson DA, Nikus K, O'Donnell CJ, Orho-Melander M, Pedersen O, Petersmann A, Polfus L, Psaty BM, Raitakari OT, Raitoharju E, Richard M, Rice KM, Rivadeneira F, Rotter JI, Schmidt F, Smith AV, Starr JM, Taylor KD, Teumer A, Thuesen BH, Torstenson ES, Tracy RP, Tzoulaki I, Zakai NA, Vacchi-Suzzi C, van Duijn CM, van Rooij FJ, Cushman M, Deary IJ, Velez Edwards DR, Vergnaud AC, Wallentin L, Waterworth DM, White HD, Wilson JG, Zonderman AB, Kathiresan S, Grarup N, Esko T, Loos RJ, Lange LA, Faraday N, Abumrad NA, Edwards TL, Ganesh SK, Auer PL, Johnson AD, Reiner AP, Lettre Get al., 2016, Exome genotyping identifies pleiotropic variants associated with red blood cell traits, American Journal of Human Genetics, Vol: 99, Pages: 8-21, ISSN: 1537-6605

Red blood cell (RBC) traits are important heritable clinical biomarkers and modifiers of disease severity. To identify coding genetic variants associated with these traits, we conducted meta-analyses of seven RBC phenotypes in 130,273 multi-ethnic individuals from studies genotyped on an exome array. After conditional analyses and replication in 27,480 independent individuals, we identified 16 new RBC variants. We found low-frequency missense variants in MAP1A (rs55707100, minor allele frequency [MAF] = 3.3%, p = 2 × 10(-10) for hemoglobin [HGB]) and HNF4A (rs1800961, MAF = 2.4%, p < 3 × 10(-8) for hematocrit [HCT] and HGB). In African Americans, we identified a nonsense variant in CD36 associated with higher RBC distribution width (rs3211938, MAF = 8.7%, p = 7 × 10(-11)) and showed that it is associated with lower CD36 expression and strong allelic imbalance in ex vivo differentiated human erythroblasts. We also identified a rare missense variant in ALAS2 (rs201062903, MAF = 0.2%) associated with lower mean corpuscular volume and mean corpuscular hemoglobin (p < 8 × 10(-9)). Mendelian mutations in ALAS2 are a cause of sideroblastic anemia and erythropoietic protoporphyria. Gene-based testing highlighted three rare missense variants in PKLR, a gene mutated in Mendelian non-spherocytic hemolytic anemia, associated with HGB and HCT (SKAT p < 8 × 10(-7)). These rare, low-frequency, and common RBC variants showed pleiotropy, being also associated with platelet, white blood cell, and lipid traits. Our association results and functional annotation suggest the involvement of new genes in human erythropoiesis. We also confirm that rare and low-frequency variants play a role in the architecture of complex human traits, although their phenotypic effect is generally smaller than originally anticipated.

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Chaker L, Falla A, van der Lee SJ, Muka T, Imo D, Jaspers L, Colpani V, Mendis S, Chowdhury R, Bramer WM, Pazoki R, Franco OHet al., 2015, The global impact of non-communicable diseases on macro-economic productivity: a systematic review, EUROPEAN JOURNAL OF EPIDEMIOLOGY, Vol: 30, Pages: 357-395, ISSN: 0393-2990

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• Citations: 83

Muka T, Imo D, Jaspers L, Colpani V, Chaker L, van der Lee SJ, Mendis S, Chowdhury R, Bramer WM, Falla A, Pazoki R, Franco OHet al., 2015, The global impact of non-communicable diseases on healthcare spending and national income: a systematic review, EUROPEAN JOURNAL OF EPIDEMIOLOGY, Vol: 30, Pages: 251-277, ISSN: 0393-2990

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• Citations: 193

Jaspers L, Colpani V, Chaker L, van der Lee SJ, Muka T, Imo D, Mendis S, Chowdhury R, Bramer WM, Falla A, Pazoki R, Franco OHet al., 2015, The global impact of non-communicable diseases on households and impoverishment: a systematic review, EUROPEAN JOURNAL OF EPIDEMIOLOGY, Vol: 30, Pages: 163-188, ISSN: 0393-2990

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• Citations: 92

Sedaghat S, Pazoki R, Uitterlinden AG, Hofman A, Stricker BHC, Ikram MA, Franco OH, Dehghan Aet al., 2014, Association of Uric Acid Genetic Risk Score With Blood Pressure The Rotterdam Study, HYPERTENSION, Vol: 64, Pages: 1061-+, ISSN: 0194-911X

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• Citations: 29

Pazoki R, Tanck MWT, Wilde AAM, Bezzina CRet al., 2013, Complex Inheritance for Susceptibility to Sudden Cardiac Death, CURRENT PHARMACEUTICAL DESIGN, Vol: 19, Pages: 6864-6872, ISSN: 1381-6128

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• Citations: 3

Pazoki R, de Jong JSSG, Marsman RF, Bruinsma N, Dekker LRC, Wilde AAM, Bezzina CR, Tanck MWTet al., 2013, SNPs Identified as Modulators of ECG Traits in the General Population Do Not Markedly Affect ECG Traits during Acute Myocardial Infarction nor Ventricular Fibrillation Risk in This Condition, PLOS ONE, Vol: 8, ISSN: 1932-6203

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• Citations: 11

Eghbali SS, Amirinejad R, Obeidi N, Mosadeghzadeh S, Vahdat K, Azizi F, Pazoki R, Sanjdideh Z, Amiri Z, Nabipour I, Zandi Ket al., 2012, Oncogenic human papillomavirus genital infection in southern Iranian women: population-based study versus clinic-based data, VIROLOGY JOURNAL, Vol: 9

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• Citations: 17

Arking DE, Junttila MJ, Goyette P, Huertas-Vazquez A, Eijgelsheim M, Blom MT, Newton-Cheh C, Reinier K, Teodorescu C, Uy-Evanado A, Carter-Monroe N, Kaikkonen KS, Kortelainen M-L, Boucher G, Lagace C, Moes A, Zhao X, Kolodgie F, Rivadeneira F, Hofman A, Witteman JCM, Uitterlinden AG, Marsman RF, Pazoki R, Bardai A, Koster RW, Dehghan A, Hwang S-J, Bhatnagar P, Post W, Hilton G, Prineas RJ, Li M, Koettgen A, Ehret G, Boerwinkle E, Coresh J, Kao WHL, Psaty BM, Tomaselli GF, Sotoodehnia N, Siscovick DS, Burke GL, Marban E, Spooner PM, Cupples LA, Jui J, Gunson K, Kesaniemi YA, Wilde AAM, Tardif J-C, O'Donnell CJ, Bezzina CR, Virmani R, Stricker BHC, Tan HL, Albert CM, Chakravarti A, Rioux JD, Huikuri HV, Chugh SSet al., 2011, Identification of a Sudden Cardiac Death Susceptibility Locus at 2q24.2 through Genome-Wide Association in European Ancestry Individuals, PLOS GENETICS, Vol: 7, ISSN: 1553-7390

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• Citations: 98

Pazoki R, Wilde AAM, Bezzina CR, 2010, Genetic Basis of Ventricular Arrhythmias., Curr Cardiovasc Risk Rep, Vol: 4, Pages: 454-460, ISSN: 1932-9520

Sudden cardiac death (SCD) is a leading cause of total and cardiovascular mortality, and ventricular fibrillation is the underlying arrhythmia in the majority of cases. In the young, where the incidence of SCD is low, a great proportion of SCDs occur in the context of inherited disorders such as cardiomyopathy or primary electrical disease, where a monogenic hereditary component is a strong determinant of risk. Marked advancement has been made over the past 15 years in the understanding of the genetic basis of the primary electrical disorders, and this has had an enormous impact on the management of these patients. At older ages, the great majority of SCDs occur in the context of acute myocardial ischemia and infarction. Although epidemiologic studies have shown that heritable factors also determine risk in these cases, inheritance is likely complex and multifactorial, and progress in understanding the genetic and molecular mechanisms that determine susceptibility to these arrhythmias, affecting a greater proportion of the population, has been very limited. We review the most recent insights gained into the genetic basis of both the monogenic and the more complex ventricular arrhythmias.

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Bezzina CR, Pazoki R, Bardai A, Marsman RF, de Jong JSSG, Blom MT, Scicluna BP, Jukema JW, Bindraban NR, Lichtner P, Pfeufer A, Bishopric NH, Roden DM, Meitinger T, Chugh SS, Myerburg RJ, Jouven X, Kaab S, Dekker LRC, Tan HL, Tanck MWT, Wilde AAMet al., 2010, Genome-wide association study identifies a susceptibility locus at 21q21 for ventricular fibrillation in acute myocardial infarction, NATURE GENETICS, Vol: 42, Pages: 688-U64, ISSN: 1061-4036

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• Citations: 127

Nabipour I, Larijani B, Beigi S, Jafari SM, Amiri M, Assadi M, Pazoki R, Amiri Z, Sanjdideh Zet al., 2008, Relationship among insulinlike growth factor I concentrations, bone mineral density, and biochemical markers of bone turnover in postmenopausal women: a population-based study, MENOPAUSE-THE JOURNAL OF THE NORTH AMERICAN MENOPAUSE SOCIETY, Vol: 15, Pages: 934-939, ISSN: 1072-3714

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• Citations: 15

Nabipour I, Vahdat K, Jafari SM, Amiri M, Shafeiae E, Riazi A, Amini A-L, Pazoki R, Sanjdideh Zet al., 2007, Correlation of hyperhomocysteinaemia and Chlamydia pneumoniae IgG seropositivity with coronary artery disease in a general population., Heart Lung Circ, Vol: 16, Pages: 416-422, ISSN: 1443-9506

BACKGROUND: Both Chlamydia pneumoniae infection and hyperhomocysteinaemia have been assumed to increase the atherosclerotic risk independently of each other and independently of the classic risk factors. The correlation between hyperhomocysteinaemia, C. pneumoniae infection and coronary artery disease (CAD) have not been investigated in the general population. METHODS: In an ancillary study to the Persian Gulf Healthy Heart Study, a cohort study of men and women aged >or=25 years, a random sample of 1699 (48.9% males, 51.1% females) subjects were evaluated. Total homocysteine, high sensitivity C-reactive protein (CRP) and IgG antibodies to C. pneumoniae were determined by ELISA. Minnesota coding criteria of a 12-lead resting electrocardiogram was used for evaluation of CAD. RESULTS: A total of 12.4% of the subjects had electrocardiogram-defined (Minnesota-coding criteria) coronary artery disease. Hyperhomocysteinaemia (>14 micromol/l) and IgG seropositivity were found in 50.8% and 37.7%, respectively. Neither of hyperhomocysteinaemia nor C. pneumoniae IgG seropositivity showed a significant association with CAD after adjusting of sex and age. Concurrent elevated CRP level (>8.2mg/l) and C. pneumoniae seropositivity (chronic C. pneumoniae infection) had a significant association with CAD [OR=1.73, CI (1.09-2.75); p=0.01] after adjusting for age, sex, systolic and diastolic blood pressures, BMI, and serum levels of LDL-cholesterol, fasting blood sugar and triglyceride as covariates in a logistic regression model. This odds ratio increased to 2.11, CI (1.18-4.12; p=0.02) when concurrent hyperhomocysteinaemia and chronic C. pneumoniae infection, as a single covariate entity; was adjusted for multiple risk factors in another logistic regression model. CONCLUSION: Concurrent hyperhomocysteinaemia and chronic C. pneumoniae infection, as a single entity, was independently associated with coronary artery disease in the general population. This synergism may have imp

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Pazoki R, Nabipour I, Seyednezami N, Imami SRet al., 2007, Effects of a community-based healthy heart program on increasing healthy women's physical activity: a randomized controlled trial guided by Community-based Participatory Research (CBPR), BMC PUBLIC HEALTH, Vol: 7

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• Citations: 74

Vahdat K, Jafari SM, Pazoki R, Nabipour Iet al., 2007, Concurrent increased high sensitivity C-reactive protein and chronic infections are associated with coronary artery disease: a population-based study., Indian J Med Sci, Vol: 61, Pages: 135-143, ISSN: 0019-5359

BACKGROUND: An elevated serum level of C-reactive protein (CRP) is an independent predictor of coronary artery disease (CAD). Chronic infections have also been implicated in the pathogenesis of CAD. AIMS: To investigate how concomitant chronic infection and CRP related to electrocardiogram-defined CAD in a general population. SETTING AND DESIGN: A population-based cross-sectional study, which was conducted in three Iranian ports in the northern Persian Gulf. MATERIALS AND METHODS: For evaluation of CAD, we used Minnesota coding criteria of a 12-lead resting electrocardiogram in 1,754 subjects, aged 25 years and over, selected by cluster random sampling. Sera were analyzed for IgG antibodies to Chlamydia pneumoniae (C. pneumoniae), Herpes simplex virus type 1 (HSV-1), Helicobacter pylori (H. pylori) and cytomegalovirus (CMV) using ELISA. Measurement of CRP by a high-sensitivity CRP assay was done. STATISTICAL ANALYSIS: Multiple logistic regression analysis was used. RESULTS: None of the infectious agents (CMV, H. pylori, C. pneumoniae and HSV-1) showed a significant association with electrocardiogram-defined CAD after adjusting for sex and age. Elevated CRP levels did not show significant association with electrocardiogram-defined CAD independent of seropositivity to one of the four infectious agents, but concurrent elevated CRP levels (>10.0 mg/L) and anti-C. pneumoniae [OR = 1.68 (CI, 1.24-2.59; P=0.04)], H. pylori [OR = 1.98 (CI, 1.26-3.13; P=0.003)], CMV [OR = 1.66 (CI, 1.10-2.49; P=0.01)] or HSV-1 [OR=1.79 (CI, 1.18-2.72; P=0.006)] IgG antibodies were associated with prevalence of electrocardiogram-defined CAD in the general population, after adjustment for multiple risk factors, including age, sex and the components of the metabolic syndrome. CONCLUSIONS: Beyond traditional cardiovascular risk factors, concomitant chronic infection and elevated CRP are significantly correlated with electrocardiogram-defined CAD.

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