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1.  Genetic variants influencing circulating lipid levels and risk of coronary artery disease 
Objectives
Genetic studies might provide new insights into the biological mechanisms underlying lipid metabolism and risk of CAD. We therefore conducted a genome-wide association study to identify novel genetic determinants of LDL-c, HDL-c and triglycerides.
Methods and results
We combined genome-wide association data from eight studies, comprising up to 17,723 participants with information on circulating lipid concentrations. We did independent replication studies in up to 37,774 participants from eight populations and also in a population of Indian Asian descent. We also assessed the association between SNPs at lipid loci and risk of CAD in up to 9,633 cases and 38,684 controls.
We identified four novel genetic loci that showed reproducible associations with lipids (P values 1.6 × 10−8 to 3.1 × 10−10). These include a potentially functional SNP in the SLC39A8 gene for HDL-c, a SNP near the MYLIP/GMPR and PPP1R3B genes for LDL-c and at the AFF1 gene for triglycerides. SNPs showing strong statistical association with one or more lipid traits at the CELSR2, APOB, APOE-C1-C4-C2 cluster, LPL, ZNF259-APOA5-A4-C3-A1 cluster and TRIB1 loci were also associated with CAD risk (P values 1.1 × 10−3 to 1.2 × 10−9).
Conclusions
We have identified four novel loci associated with circulating lipids. We also show that in addition to those that are largely associated with LDL-c, genetic loci mainly associated with circulating triglycerides and HDL-c are also associated with risk of CAD. These findings potentially provide new insights into the biological mechanisms underlying lipid metabolism and CAD risk.
doi:10.1161/ATVBAHA.109.201020
PMCID: PMC3891568  PMID: 20864672
lipids; lipoproteins; genetics; epidemiology
2.  Determinants of Residual Risk in Secondary Prevention Patients Treated with High-Versus Low-Dose Statin Therapy: The Treating to New Targets (TNT) Study 
Circulation  2012;125(16):1979-1987.
Background
Cardiovascular events occur among statin-treated patients, albeit at lower rates. Risk factors for this “residual risk” have not been studied comprehensively. We aimed to identify determinants of this risk above and beyond lipid-related risk factors.
Methods and Results
9,251 coronary patients with LDL cholesterol<130 mg/dL randomized to double-blind atorvastatin 10 or 80 mg/day in the Treating to New Targets (TNT) study had complete on-treatment 1-year lipid data. Median follow-up was 4.9 years. The primary endpoint was major cardiovascular events (n=729): coronary death, non-fatal myocardial infarction, resuscitation after cardiac arrest, or fatal or non-fatal stroke. Multivariable determinants of increased risk were older age (adjusted hazard ratio 1.13 per 1-SD [8.8 years], 95% CI 1.04–1.23), increased body-mass index (BMI) (1.09, 1.02–1.17 per 4.5 kg/m2), male gender (1.33, 1.07–1.65), hypertension (1.38, 1.17–1.63), diabetes (1.33, 1.11–1.60), baseline apolipoprotein B (1.19, 1.11–1.28 per 19 mg/dL) and blood urea nitrogen (1.10, 1.03–1.17 per 4.9 mg/dL), in addition to current smoking, prior cardiovascular disease, and calcium channel blocker use. Determinants of decreased risk were high-dose statin (0.82, 0.70–0.94), aspirin use (0.67, 0.56–0.81), and baseline apolipoprotein A-I (0.91, 0.84–0.99 per 25 mg/dL). On-treatment 1-year lipids or apolipoproteins were not additionally associated with risk in multivariable models. Known baseline variables performed moderately well in discriminating future cases from non-cases (Harrell’s c-index=0.679).
Conclusions
Determinants of residual risk in statin-treated secondary prevention patients included lipid-related and non-lipid factors such as baseline apolipoproteins, increased BMI, smoking, hypertension, and diabetes. A multi-faceted prevention approach should be underscored to address this risk.
Clinical Trial Registration Information
http://clinicaltrials.gov; Unique identifier: NCT00327691
doi:10.1161/CIRCULATIONAHA.111.088591
PMCID: PMC3338158  PMID: 22461416
apolipoproteins; risk factors; risk prediction; secondary prevention
3.  On-Treatment Non-HDL Cholesterol, Apolipoprotein B, Triglycerides, and Lipid Ratios in Relation to Residual Vascular Risk after Treatment with Potent Statin Therapy: The JUPITER Trial 
Objectives
To determine whether residual risk after high-dose statin therapy for primary prevention individuals with low LDL cholesterol (LDL-C) is related to on-treatment apolipoprotein B, non-HDL cholesterol (non-HDL-C), or lipid ratios, and how they compare with on-treatment LDL cholesterol (LDL-C).
Background
Guidelines focus on LDL-C as the primary target of therapy, yet residual risk for cardiovascular disease (CVD) among statin-treated individuals remains high and not fully explained.
Methods
Participants in the randomized placebo-controlled JUPITER trial were adults without diabetes or CVD, with baseline LDL-C<130 mg/dL, high-sensitivity C-reactive protein ≥2 mg/L, and triglycerides <500 mg/dL. Individuals allocated to rosuvastatin 20 mg daily with baseline and on-treatment lipids and lipoproteins were examined in relation to the primary endpoint of incident CVD (non-fatal myocardial infarction or stroke, hospitalization for unstable angina, arterial revascularization, or cardiovascular death).
Results
Using separate multivariate Cox models, statistically significant associations of a similar magnitude with residual risk of CVD were found for on-treatment LDL-C, non-HDL-C, apolipoprotein B, total/HDL-C, LDL-C/HDL-C, and apolipoprotein B/A-1. The respective adjusted standardized hazard ratios (95% confidence intervals) for each of these measures were 1.31 (1.09–1.56), 1.25 (1.04–1.50), 1.27 (1.06–1.53), 1.22 (1.03–1.44), 1.29 (1.09–1.52), and 1.27 (1.09–1.49). The overall residual risk and the risk associated with these measures decreased among participants achieving on-treatment LDL-C ≤70 mg/dL, on-treatment non-HDL-C ≤100 mg/dL, or on-treatment apolipoprotein B ≤80 mg/dL. By contrast, on-treatment triglycerides showed no association with CVD.
Conclusions
In this primary prevention trial of non-diabetic individuals with low LDL-C, on-treatment LDL-C was as valuable as non-HDL-C, apolipoprotein B, or ratios in predicting residual risk.
doi:10.1016/j.jacc.2011.12.035
PMCID: PMC3338194  PMID: 22516441
apolipoproteins; lipids; lipoproteins; primary prevention; trials
4.  Validation of a model to investigate the effects of modifying cardiovascular disease (CVD) risk factors on the burden of CVD: the rotterdam ischemic heart disease and stroke computer simulation (RISC) model 
BMC Medicine  2012;10:158.
Background
We developed a Monte Carlo Markov model designed to investigate the effects of modifying cardiovascular disease (CVD) risk factors on the burden of CVD. Internal, predictive, and external validity of the model have not yet been established.
Methods
The Rotterdam Ischemic Heart Disease and Stroke Computer Simulation (RISC) model was developed using data covering 5 years of follow-up from the Rotterdam Study. To prove 1) internal and 2) predictive validity, the incidences of coronary heart disease (CHD), stroke, CVD death, and non-CVD death simulated by the model over a 13-year period were compared with those recorded for 3,478 participants in the Rotterdam Study with at least 13 years of follow-up. 3) External validity was verified using 10 years of follow-up data from the European Prospective Investigation of Cancer (EPIC)-Norfolk study of 25,492 participants, for whom CVD and non-CVD mortality was compared.
Results
At year 5, the observed incidences (with simulated incidences in brackets) of CHD, stroke, and CVD and non-CVD mortality for the 3,478 Rotterdam Study participants were 5.30% (4.68%), 3.60% (3.23%), 4.70% (4.80%), and 7.50% (7.96%), respectively. At year 13, these percentages were 10.60% (10.91%), 9.90% (9.13%), 14.20% (15.12%), and 24.30% (23.42%). After recalibrating the model for the EPIC-Norfolk population, the 10-year observed (simulated) incidences of CVD and non-CVD mortality were 3.70% (4.95%) and 6.50% (6.29%). All observed incidences fell well within the 95% credibility intervals of the simulated incidences.
Conclusions
We have confirmed the internal, predictive, and external validity of the RISC model. These findings provide a basis for analyzing the effects of modifying cardiovascular disease risk factors on the burden of CVD with the RISC model.
doi:10.1186/1741-7015-10-158
PMCID: PMC3566939  PMID: 23217019
Cardiovascular disease prevention; Simulation modeling; Model validation
5.  R1: The relationship between plasma Angiopoietin-like protein 4 (Angptl4) levels, ANGPTL4 genotype and coronary heart disease risk 
Objective
To investigate the relationship between Angiopoietin-like protein 4 (Angptl4) levels, CHD biomarkers and ANGPTL4 variants.
Methods and Results
Plasma Angptl4 was quantified in 666 subjects of the Northwick Park Heart Study II using a validated ELISA. Seven ANGPTL4 SNPs were genotyped and CHD biomarkers assessed in the whole cohort (n=2775). Weighted mean (±SD) plasma Angptl4 levels were 10.0(±11.0) ng/ml. Plasma Angptl4 concentration correlated positively with age (r=0.15, P<0.001), body fat mass (r=0.19, P=0.003) but negatively with plasma HDL-cholesterol (r=−0.13, P=0.01). No correlation with triglycerides was observed. T266M was independently associated with plasma Angptl4 levels (P<0.001), but not associated with triglycerides or with CHD risk in the meta-analysis of five studies (4,061 cases/15,395 controls). E40K showed no independent association with plasma Angptl4 levels. In HEK293 and Huh7 cells compared to wild-type, E40K and T266M showed significantly altered synthesis and secretion, respectively.
Conclusions
These data suggest that circulating Angptl4 levels do not influence triglyceride levels or CHD risk since (1) Angptl4 levels were not correlated with triglycerides, (2) T266M, although associated with Angptl4 levels, showed no association with plasma triglycerides (3) Triglyceride-lowering E40K did not influence Angptl4 levels. These results provide new insights into the role of Angptl4 in triglyceride metabolism.
doi:10.1161/ATVBAHA.110.212209
PMCID: PMC3319296  PMID: 20829508
Angplt4; E40K; T266M; cardiovascular disease; LPL
6.  Large-scale association analyses identifies 13 new susceptibility loci for coronary artery disease 
Schunkert, Heribert | König, Inke R. | Kathiresan, Sekar | Reilly, Muredach P. | Assimes, Themistocles L. | Holm, Hilma | Preuss, Michael | Stewart, Alexandre F. R. | Barbalic, Maja | Gieger, Christian | Absher, Devin | Aherrahrou, Zouhair | Allayee, Hooman | Altshuler, David | Anand, Sonia S. | Andersen, Karl | Anderson, Jeffrey L. | Ardissino, Diego | Ball, Stephen G. | Balmforth, Anthony J. | Barnes, Timothy A. | Becker, Diane M. | Becker, Lewis C. | Berger, Klaus | Bis, Joshua C. | Boekholdt, S. Matthijs | Boerwinkle, Eric | Braund, Peter S. | Brown, Morris J. | Burnett, Mary Susan | Buysschaert, Ian | Carlquist, Cardiogenics, John F. | Chen, Li | Cichon, Sven | Codd, Veryan | Davies, Robert W. | Dedoussis, George | Dehghan, Abbas | Demissie, Serkalem | Devaney, Joseph M. | Do, Ron | Doering, Angela | Eifert, Sandra | El Mokhtari, Nour Eddine | Ellis, Stephen G. | Elosua, Roberto | Engert, James C. | Epstein, Stephen E. | Faire, Ulf de | Fischer, Marcus | Folsom, Aaron R. | Freyer, Jennifer | Gigante, Bruna | Girelli, Domenico | Gretarsdottir, Solveig | Gudnason, Vilmundur | Gulcher, Jeffrey R. | Halperin, Eran | Hammond, Naomi | Hazen, Stanley L. | Hofman, Albert | Horne, Benjamin D. | Illig, Thomas | Iribarren, Carlos | Jones, Gregory T. | Jukema, J.Wouter | Kaiser, Michael A. | Kaplan, Lee M. | Kastelein, John J.P. | Khaw, Kay-Tee | Knowles, Joshua W. | Kolovou, Genovefa | Kong, Augustine | Laaksonen, Reijo | Lambrechts, Diether | Leander, Karin | Lettre, Guillaume | Li, Mingyao | Lieb, Wolfgang | Linsel-Nitschke, Patrick | Loley, Christina | Lotery, Andrew J. | Mannucci, Pier M. | Maouche, Seraya | Martinelli, Nicola | McKeown, Pascal P. | Meisinger, Christa | Meitinger, Thomas | Melander, Olle | Merlini, Pier Angelica | Mooser, Vincent | Morgan, Thomas | Mühleisen, Thomas W. | Muhlestein, Joseph B. | Münzel, Thomas | Musunuru, Kiran | Nahrstaedt, Janja | Nelson, Christopher P. | Nöthen, Markus M. | Olivieri, Oliviero | Patel, Riyaz S. | Patterson, Chris C. | Peters, Annette | Peyvandi, Flora | Qu, Liming | Quyyumi, Arshed A. | Rader, Daniel J. | Rallidis, Loukianos S. | Rice, Catherine | Rosendaal, Frits R. | Rubin, Diana | Salomaa, Veikko | Sampietro, M. Lourdes | Sandhu, Manj S. | Schadt, Eric | Schäfer, Arne | Schillert, Arne | Schreiber, Stefan | Schrezenmeir, Jürgen | Schwartz, Stephen M. | Siscovick, David S. | Sivananthan, Mohan | Sivapalaratnam, Suthesh | Smith, Albert | Smith, Tamara B. | Snoep, Jaapjan D. | Soranzo, Nicole | Spertus, John A. | Stark, Klaus | Stirrups, Kathy | Stoll, Monika | Tang, W. H. Wilson | Tennstedt, Stephanie | Thorgeirsson, Gudmundur | Thorleifsson, Gudmar | Tomaszewski, Maciej | Uitterlinden, Andre G. | van Rij, Andre M. | Voight, Benjamin F. | Wareham, Nick J. | Wells, George A. | Wichmann, H.-Erich | Wild, Philipp S. | Willenborg, Christina | Witteman, Jaqueline C. M. | Wright, Benjamin J. | Ye, Shu | Zeller, Tanja | Ziegler, Andreas | Cambien, Francois | Goodall, Alison H. | Cupples, L. Adrienne | Quertermous, Thomas | März, Winfried | Hengstenberg, Christian | Blankenberg, Stefan | Ouwehand, Willem H. | Hall, Alistair S. | Deloukas, Panos | Thompson, John R. | Stefansson, Kari | Roberts, Robert | Thorsteinsdottir, Unnur | O’Donnell, Christopher J. | McPherson, Ruth | Erdmann, Jeanette | Samani, Nilesh J.
Nature genetics  2011;43(4):333-338.
We performed a meta-analysis of 14 genome-wide association studies of coronary artery disease (CAD) comprising 22,233 cases and 64,762 controls of European descent, followed by genotyping of top association signals in 60,738 additional individuals. This genomic analysis identified 13 novel loci harboring one or more SNPs that were associated with CAD at P<5×10−8 and confirmed the association of 10 of 12 previously reported CAD loci. The 13 novel loci displayed risk allele frequencies ranging from 0.13 to 0.91 and were associated with a 6 to 17 percent increase in the risk of CAD per allele. Notably, only three of the novel loci displayed significant association with traditional CAD risk factors, while the majority lie in gene regions not previously implicated in the pathogenesis of CAD. Finally, five of the novel CAD risk loci appear to have pleiotropic effects, showing strong association with various other human diseases or traits.
doi:10.1038/ng.784
PMCID: PMC3119261  PMID: 21378990
7.  Prospective study of insulin-like growth factor-I, insulin-like growth factor-binding protein 3, genetic variants in the IGF1 and IGFBP3 genes and risk of coronary artery disease 
Although experimental studies have suggested that insulin-like growth factor I (IGF-I) and its binding protein IGFBP-3 might have a role in the aetiology of coronary artery disease (CAD), the relevance of circulating IGFs and their binding proteins in the development of CAD in human populations is unclear. We conducted a nested case-control study, with a mean follow-up of six years, within the EPIC-Norfolk cohort to assess the association between circulating levels of IGF-I and IGFBP-3 and risk of CAD in up to 1,013 cases and 2,055 controls matched for age, sex and study enrolment date. After adjustment for cardiovascular risk factors, we found no association between circulating levels of IGF-I or IGFBP-3 and risk of CAD (odds ratio: 0.98 (95% Cl 0.90-1.06) per 1 SD increase in circulating IGF-I; odds ratio: 1.02 (95% Cl 0.94-1.12) for IGFBP-3). We examined associations between tagging single nucleotide polymorphisms (tSNPs) at the IGF1 and IGFBP3 loci and circulating IGF-I and IGFBP-3 levels in up to 1,133 cases and 2,223 controls and identified three tSNPs (rs1520220, rs3730204, rs2132571) that showed independent association with either circulating IGF-I or IGFBP-3 levels. In an assessment of 31 SNPs spanning the IGF1 or IGFBP3 loci, none were associated with risk of CAD in a meta-analysis that included EPIC-Norfolk and eight additional studies comprising up to 9,319 cases and 19,964 controls. Our results indicate that IGF-I and IGFBP-3 are unlikely to be importantly involved in the aetiology of CAD in human populations.
PMCID: PMC3166154  PMID: 21915365
Epidemiology; Genetics of cardiovascular disease; Risk factors; IGF1; IGFBP3
8.  The hypertriglyceridemic-waist phenotype and the risk of coronary artery disease: results from the EPIC-Norfolk Prospective Population Study 
Background
Screening for increased waist circumference and hypertriglyceridemia (the hypertriglyceridemic-waist phenotype) has been proposed as an inexpensive approach to identify patients with excess intra-abdominal adiposity and associated metabolic abnormalities. We examined the relationship between the hypertriglyceridemic-waist phenotype to the risk of coronary artery disease in apparently healthy individuals.
Methods
A total of 21 787 participants aged 45–79 years were followed for a mean of 9.8 (standard deviation 1.7) years. Coronary artery disease developed in 2109 of them during follow-up. The hypertriglyceridemic-waist phenotype was defined as a waist circumference of 90 cm or more and a triglyceride level of 2.0 mmol/L or more in men, and a waist circumference of 85 cm or more and a triglyceride level of 1.5 mmol/L or more in women.
Results
Compared with participants who had a waist circumference and triglyceride level below the threshold, those with the hypertriglyceridemic-waist phenotype had higher blood pressure indices, higher levels of apolipoprotein B and C-reactive protein, lower levels of high-density lipoprotein cholesterol and apolipoprotein A-I, and smaller low-density lipoprotein particles. Among men, those with the hypertriglyceridemic-waist phenotype had an unadjusted hazard ratio for future coronary artery disease of 2.40 (95% confidence interval [CI] 2.02–2.87) compared with men who did not have the phenotype. Women with the phenotype had an unadjusted hazard ratio of 3.84 (95% CI 3.20–4.62) compared with women who did not have the phenotype.
Interpretation
Among participants from a European cohort representative of a contemporary Western population, the hypertriglyceridemic-waist phenotype was associated with a deteriorated cardiometabolic risk profile and an increased risk for coronary artery disease.
doi:10.1503/cmaj.091276
PMCID: PMC2942915  PMID: 20643837
9.  Separating the mechanism-based and off-target actions of CETP-inhibitors using CETP gene polymorphisms 
Circulation  2009;121(1):52-62.
Background:
Cholesteryl ester transfer protein (CETP) inhibitors raise HDL-cholesterol but torcetrapib, the first-in-class inhibitor tested in a large outcome trial caused unexpected blood pressure elevation and increased cardiovascular events. Whether the hypertensive effect resulted from CETP-inhibition or an off-target action of torcetrapib has been debated. We hypothesised that common single nucleotide polymorphisms (SNPs) in the CETP-gene could help distinguish mechanism-based from off-target actions of CETP-inhibitors to inform on the validity of CETP as a therapeutic target.
Methods and Results
We compared the effect of CETP SNPs and torcetrapib treatment on lipid fractions, blood pressure and electrolytes in up to 67,687 individuals from genetic studies and 17,911 from randomised trials. CETP SNPs and torcetrapib treatment reduced CETP activity and had directionally concordant effect on eight lipid and lipoprotein traits (total-, LDL- and HDL-cholesterol, HDL2, HDL3, apolipoproteins A-I, -B, and triglycerides), with the genetic effect on HDL-cholesterol (0.13 mmol/L; 95% CI: 0.11, 0.14) being consistent with that expected of a 10 mg dose of torcetrapib (0.13 mmol/L; 0.10, 0.15). In trials, 60mg torcetrapib elevated systolic and diastolic blood pressure by 4.47mmHg (4.10, 4.84) and 2.08mmHg (1.84, 2.31) respectively. However, the effect of CETP SNPs on systolic 0.16mmHg (−0.28, 0.60) and diastolic blood pressure −0.04mmHg (−0.36, 0.28) was null and significantly different from that expected of 10 mg torcetrapib.
Conclusions:
Discordance in the effects of CETP SNPs and torcetrapib treatment on blood pressure despite the concordant effects on lipids indicates the hypertensive action of torcetrapib is unlikely to be due to CETP-inhibition, or shared by chemically dissimilar CETP inhibitors. Genetic studies could find use in drug development programmes as a new source of randomised evidence for drug target validation in man.
doi:10.1161/CIRCULATIONAHA.109.865444
PMCID: PMC2811869  PMID: 20026784
genetics; pharmacology; epidemiology
10.  Both Paraoxonase-1 Genotype and Activity Do Not Predict the Risk of Future Coronary Artery Disease; the EPIC-Norfolk Prospective Population Study 
PLoS ONE  2009;4(8):e6809.
Background
Paraoxonase-1 (PON1) is an antioxidant enzyme, that resides on high-density lipoprotein (HDL). PON1-activity, is heavily influenced by the PON1-Q192R polymorphism. PON1 is considered to protect against atherosclerosis, but it is unclear whether this relation is independent of its carrier, HDL. In order to evaluate the atheroprotective potential of PON1, we assessed the relationships among PON1-genotype, PON1-activity and risk of future coronary artery disease (CAD), in a large prospective case-control study.
Methodology/Principal Findings
Cases (n = 1138) were apparently healthy men and women aged 45–79 years who developed fatal or nonfatal CAD during a mean follow-up of 6 years. Controls (n = 2237) were matched by age, sex and enrollment time. PON1-activity was similar in cases and controls (60.7±45.3 versus 62.6±45.8 U/L, p = 0.3) and correlated with HDL-cholesterol levels (r = 0.16, p<0.0001). The PON1-Q192R polymorphism had a profound impact on PON1-activity, but did not predict CAD risk (Odds Ratio [OR] per R allele 0.98[0.84–1.15], p = 0.8). Using conditional logistic regression, quartiles of PON1-activity showed a modest inverse relation with CAD risk (OR for the highest versus the lowest quartile 0.77[0.63–0.95], p = 0.01; p-trend = 0.06). PON1-activity adjusted for Q192R polymorphism correlated better with HDL-cholesterol (r = 0.26, p<0.0001) and more linearly predicted CAD risk (0.79[0.64–0.98], p = 0.03; p-trend = 0.008). However, these relationships were abolished after adjustment for HDL (particles-cholesterol-size) and apolipoproteinA-I (0.94[0.74–1.18], p-trend = 0.3).
Conclusions/Significance
This study, shows that PON1-activity inversely relates to CAD risk, but not independent of HDL, due to its close association with the HDL-particle. These data strongly suggest that a low PON1-activity is not a causal factor in atherogenesis.
doi:10.1371/journal.pone.0006809
PMCID: PMC2728540  PMID: 19710913

Results 1-10 (10)