Abstract P030: Complementary Variable Selection Methods Highlight Joint Contribution Of Cystatin C And Apolipoprotein B For Cardiovascular Risk Prediction

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Joshua Elliott ◽  
Matthew Whitaker ◽  
Barbara Bodinier ◽  
Paul Elliott ◽  
Ioanna Tzoulaki ◽  
...  
Keyword(s):  
Variable Selection ◽  
Risk Prediction ◽  
Cystatin C ◽  
Random Forests ◽  
Apolipoprotein B ◽  
Selection Methods ◽  
Cvd Risk ◽  
Polygenic Risk ◽  
Stability Selection

Introduction: Variable selection methods can provide an unbiased means of identifying informative predictors but have rarely been applied to CVD risk prediction. Hypothesis: Additional variables beyond those in pooled cohort equations may improve CVD risk prediction. Methods: Use of two complementary variable selection methods (LASSO stability selection, parametric, and survival random forests, non-parametric) to identify jointly informative sets of predictors for CVD risk and rank them in order of predictive accuracy. We used a prospective cohort (UK Biobank) of 304,839 participants aged 40-69 years at enrollment (2006—2010) without prior CVD, with follow-up to March 2017. Variables comprised those in pooled cohort equations with additional biochemistry and hematology data and polygenic risk scores for CVD. Outcomes were CVD hospitalization, procedure/operation or mortality. Data were sex-stratified and divided into independent variable selection (40%), training (30%) and test (30%) sets. Variable selection via penalized (LASSO) Cox regression with stability analysis. Variables ranked according to mean change in C statistic after variable permutation in survival random forests. Results: Mean age 55.9 years; 10,267 CVD events (6,277 men [59.0%]), median 8.1 years follow-up. The Figure summarizes results from LASSO stability selection. Jointly informative predictors for both men and women were cystatin C, apolipoprotein B, family history of coronary artery disease and polygenic risk score in addition to age, systolic blood pressure, antihypertensive use and current smoking used in pooled cohort equations. Other than variables already included in pooled cohort equations, cystatin C and apolipoprotein B ranked highest in random forests for men and for women. Conclusions: Use of two complementary data-driven variable selection methods identified variables more highly selected for CVD prediction beyond those included in pooled cohort equations.

Abstract 36: The Role of Sleep as a Cardiovascular Health Metric: Does It Improve Cardiovascular Disease Risk Prediction? Results From The Multi-Ethnic Study of Atherosclerosis

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Nour Makarem ◽  
Cecilia Castro-Diehl ◽  
Marie-Pierre St-Onge ◽  
Susan Redline ◽  
Steven Shea ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Sleep Duration ◽  
Risk Prediction ◽  
Cardiovascular Health ◽  
Cvd Risk ◽  
Sleep Study ◽  
Lower Odds ◽  
Wrist Actigraphy ◽  
Incident Cases

Background: The AHA Life’s Simple 7 (LS7) is a measure of cardiovascular health (CVH). Sufficient and healthy sleep has been linked to higher LS7 scores and lower cardiovascular disease (CVD) risk, but sleep has not been included as a CVH metric. Hypothesis: A CVH score that includes the LS7 plus sleep metrics would be more strongly associated with CVD outcomes than the LS7 score. Methods: Participants were n=1920 diverse adults (mean age: 69.5 y) in the MESA Sleep Study who completed 7 days of wrist actigraphy, overnight in-home polysomnography, and sleep questionnaires. Logistic regression and Cox proportional hazards models were used to compare the LS7 score and 4 new CVH scores that incorporate aspects of sleep in relation to CVD prevalence and incidence (Table). There were 95 prevalent CVD events at the Sleep Exam and 93 incident cases during a mean follow up of 4.4y. Results: The mean LS7 score was 7.3, and the means of the alternate CVH scores ranged from 7.4 to 7.8. Overall, 63% of participants slept <7h, 10% had sleep efficiency <85%, 14% and 36% reported excess daytime sleepiness and insomnia, respectively, 47% had obstructive sleep apnea, and 39% and 25% had high night-to-night variability in sleep duration and sleep onset timing. The LS7 score was not significantly associated with CVD prevalence or incidence (Table). Those in the highest vs. lowest tertile of CVH score 1, that included sleep duration, and CVH score 2, that included sleep characteristics linked to CVD in the literature, had lower odds of prevalent CVD. Those in the highest vs. lowest tertile of CVH scores 3 and 4, which included sleep characteristics linked to cardiovascular risk in MESA, had lower odds of prevalent CVD and lower risk of developing CVD. Conclusions: CVH scores that include sleep were more strongly associated with CVD prevalence and incidence than the traditional LS7 score. The incorporation of sleep as a metric of CVH, akin to other health behaviors, may improve CVD risk prediction. Findings warrant confirmation in larger samples and over longer follow-up.

scholarly journals Risk Prediction Using Polygenic Risk Scores for Prevention of Stroke and Other Cardiovascular Diseases

Stroke ◽  
2021 ◽  
Author(s):  
Gad Abraham ◽  
Loes Rutten-Jacobs ◽  
Michael Inouye
Keyword(s):  
Risk Prediction ◽  
Risk Scores ◽  
Future Research ◽  
Cvd Risk ◽  
Polygenic Risk ◽  
Monogenic Disorders ◽  
Current State ◽  
Common Genetic Variants ◽  
Artery Disease

Early prediction of risk of cardiovascular disease (CVD), including stroke, is a cornerstone of disease prevention. Clinical risk scores have been widely used for predicting CVD risk from known risk factors. Most CVDs have a substantial genetic component, which also has been confirmed for stroke in recent gene discovery efforts. However, the role of genetics in prediction of risk of CVD, including stroke, has been limited to testing for highly penetrant monogenic disorders. In contrast, the importance of polygenic variation, the aggregated effect of many common genetic variants across the genome with individually small effects, has become more apparent in the last 5 to 10 years, and powerful polygenic risk scores for CVD have been developed. Here we review the current state of the field of polygenic risk scores for CVD including stroke, and their potential to improve CVD risk prediction. We present findings and lessons from diseases such as coronary artery disease as these will likely be useful to inform future research in stroke polygenic risk prediction.

scholarly journals Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses

PLoS Medicine ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. e1003498
Author(s):  
Luanluan Sun ◽  
Lisa Pennells ◽  
Stephen Kaptoge ◽  
Christopher P. Nelson ◽  
Scott C. Ritchie ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Scores ◽  
Cvd Risk ◽  
Polygenic Risk ◽  
History Of ◽  
Predicted Risk ◽  
Conventional Risk Factors

Background Polygenic risk scores (PRSs) can stratify populations into cardiovascular disease (CVD) risk groups. We aimed to quantify the potential advantage of adding information on PRSs to conventional risk factors in the primary prevention of CVD. Methods and findings Using data from UK Biobank on 306,654 individuals without a history of CVD and not on lipid-lowering treatments (mean age [SD]: 56.0 [8.0] years; females: 57%; median follow-up: 8.1 years), we calculated measures of risk discrimination and reclassification upon addition of PRSs to risk factors in a conventional risk prediction model (i.e., age, sex, systolic blood pressure, smoking status, history of diabetes, and total and high-density lipoprotein cholesterol). We then modelled the implications of initiating guideline-recommended statin therapy in a primary care setting using incidence rates from 2.1 million individuals from the Clinical Practice Research Datalink. The C-index, a measure of risk discrimination, was 0.710 (95% CI 0.703–0.717) for a CVD prediction model containing conventional risk predictors alone. Addition of information on PRSs increased the C-index by 0.012 (95% CI 0.009–0.015), and resulted in continuous net reclassification improvements of about 10% and 12% in cases and non-cases, respectively. If a PRS were assessed in the entire UK primary care population aged 40–75 years, assuming that statin therapy would be initiated in accordance with the UK National Institute for Health and Care Excellence guidelines (i.e., for persons with a predicted risk of ≥10% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), then it could help prevent 1 additional CVD event for approximately every 5,750 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5% to <10%) 10-year CVD risk could help prevent 1 additional CVD event for approximately every 340 individuals screened. Such a targeted strategy could help prevent 7% more CVD events than conventional risk prediction alone. Potential gains afforded by assessment of PRSs on top of conventional risk factors would be about 1.5-fold greater than those provided by assessment of C-reactive protein, a plasma biomarker included in some risk prediction guidelines. Potential limitations of this study include its restriction to European ancestry participants and a lack of health economic evaluation. Conclusions Our results suggest that addition of PRSs to conventional risk factors can modestly enhance prediction of first-onset CVD and could translate into population health benefits if used at scale.

scholarly journals An assessment of the potential miscalibration of cardiovascular disease risk predictions caused by a secular trend in cardiovascular disease in England

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Alexander Pate ◽  
Tjeerd van Staa ◽  
Richard Emsley
Keyword(s):  
Cardiovascular Disease ◽  
Risk Prediction ◽  
Structural Model ◽  
Validation Cohort ◽  
Secular Trend ◽  
Marginal Structural Model ◽  
Cvd Risk ◽  
Model Framework ◽  
Statin Use

Abstract Background A downwards secular trend in the incidence of cardiovascular disease (CVD) in England was identified through previous work and the literature. Risk prediction models for primary prevention of CVD do not model this secular trend, this could result in over prediction of risk for individuals in the present day. We evaluate the effects of modelling this secular trend, and also assess whether it is driven by an increase in statin use during follow up. Methods We derived a cohort of patients (1998–2015) eligible for cardiovascular risk prediction from the Clinical Practice Research Datalink with linked hospitalisation and mortality records (N = 3,855,660). Patients were split into development and validation cohort based on their cohort entry date (before/after 2010). The calibration of a CVD risk prediction model developed in the development cohort was tested in the validation cohort. The calibration was also assessed after modelling the secular trend. Finally, the presence of the secular trend was evaluated under a marginal structural model framework, where the effect of statin treatment during follow up is adjusted for. Results Substantial over prediction of risks in the validation cohort was found when not modelling the secular trend. This miscalibration could be minimised if one was to explicitly model the secular trend. The reduction in risk in the validation cohort when introducing the secular trend was 35.68 and 33.24% in the female and male cohorts respectively. Under the marginal structural model framework, the reductions were 33.31 and 32.67% respectively, indicating increasing statin use during follow up is not the only the cause of the secular trend. Conclusions Inclusion of the secular trend into the model substantially changed the CVD risk predictions. Models that are being used in clinical practice in the UK do not model secular trend and may thus overestimate the risks, possibly leading to patients being treated unnecessarily. Wider discussion around the modelling of secular trends in a risk prediction framework is needed.

Abstract P070: Long-term Cumulative Blood Pressure Improves CVD Risk Prediction Algorithms

Circulation ◽  
2016 ◽  
Vol 133 (suppl_1) ◽  
Author(s):  
Norrina B Allen ◽  
Hongyan Ning ◽  
Donald Lloyd-Jones
Keyword(s):  
Blood Pressure ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Estimation ◽  
Incidence Rates ◽  
Cvd Risk ◽  
Prediction Algorithms ◽  
C Statistic

Background: Published risk prediction algorithms only include current BP; however, long-term BP patterns are associated with atherosclerotic (AS)CVD incidence. We tested whether the long-term (5- and 10-year) cumulative blood pressure improves 10 year ASCVD prediction. Methods: This study used the Lifetime Risk Pooling Project (LRPP) including the Framingham, CARDIA and ARIC cohorts. Participants with 15- and 20-year follow-up (5- and 10- years prior to risk calculation and 10 year follow-up), no history of prior CVD, and between the ages of 45 and 65 at the time of risk estimation were included. We calculated 10 year ASCVD risk using the 2013 ACC/AHA 10-year ASCVD Pooled Cohort Equations. Study-specific coefficients were calculated. Differences in the C-statistic, the category-free net reclassification index (NRI) and improved discrimination index (IDI) were examined between the model with baseline as compared to the model with cumulative SBP. Analyses were stratified by gender. Results: Among 11,475 individuals (42.4% male and 12.7% African American), those in the highest tertile of cumulative SBP were older, more likely to be male, and had a higher burden of other CVD risk factors. Overall, 1,487 (13%) participants experienced a CVD event (mean follow-up time was 12 years). ASCVD incidence rates increased with higher tertiles of cumulative SBP from 4 events per 1,000 person-years in the lowest tertile to 8 and 18 in the second and third tertiles, respectively. No significant improvements were seen in the C-statistic when including 5- or 10-year cumulative SBP (see table). However, the replacement cumulative SBP resulted in significant improvements in model reclassification of NRI and IDI with greater improvements for the 10- than 5-year cumulative measure. Conclusions: Measures of cumulative BP can improve the ability of CVD risk prediction models to correctly classify individuals. Additional studies on the inclusion of these measures in future risk prediction algorithms are warranted.

Abstract P370: Risk Factors for Progression of Coronary Artery Calcification in Patients with Chronic Kidney Disease

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
Joshua D Bundy ◽  
Lawrence J Appel ◽  
Matthew Budoff ◽  
Jing Chen ◽  
Alan S Go ◽  
...  
Keyword(s):  
Risk Factors ◽  
Chronic Kidney Disease ◽  
Coronary Artery ◽  
Kidney Disease ◽  
Cystatin C ◽  
Coronary Artery Calcification ◽  
Lipid Lowering ◽  
Cvd Risk Factors ◽  
Cvd Risk

Introduction: Coronary artery calcification (CAC) is prevalent among patients with chronic kidney disease (CKD) and predicts the risk of cardiovascular disease (CVD). Risk factors for the progression of CAC in patients with CKD have not been well studied. Hypothesis: We assessed the hypothesis that several established and novel CVD risk factors are associated with progression of CAC among patients with CKD. Methods: In a random subsample of 1,123 participants from the Chronic Renal Insufficiency Cohort (CRIC) Study, CAC was measured at baseline and the follow-up visit using electron beam computed tomography (CT) or multidetector CT. CAC progression was defined as an increase of Agatston score ≥100 units during follow-up. Multiple logistic regression and mixed-effects regression models were used to assess risk factors for progression of CAC. Results: Over an average of 3-year follow-up, 332 (29.6%) participants developed CAC progression. After adjusting for age, sex, race, clinical site, total cholesterol, HDL-cholesterol, systolic blood pressure, antihypertensive treatment, diabetes, and current smoking in the multivariable models, history of CVD (odds ratio [OR] 1.53, 95% CI 1.09-2.15, p=0.02), lipid-lowering treatment (OR 1.81, 95% CI 1.28-2.55, p<0.001), higher serum phosphate (OR 1.37, 95% CI 1.17-1.61, p<0.001), hemoglobin A1c (OR 1.32, 95% CI 1.10-1.58, p=0.002), and cystatin C (OR 1.24, 95% CI 1.06-1.45, p=0.007), and lower estimated-glomerular filtration rate (eGFR) (OR 1.32, 95% CI 1.10-1.56, p=0.002) were associated with CAC progression. In addition, lower physical activity, lipid-lowering treatment, body-mass index, LDL-cholesterol, lower serum calcium, phosphate, total parathyroid hormone, fibrinogen, interleukin-6, tumor necrosis factor-α, fibroblast growth factor-23, lower eGFR, cystatin C, and 24-hour urine albumin were associated with square root transformed change in CAC score from baseline in multiple-adjusted models. These findings persisted after additional adjustment for baseline CAC score. Conclusions: In conclusion, these data suggest that reduced kidney function, calcium and phosphate metabolic disorders and inflammation, in addition to established CVD risk factors, might play a role in CAC progression among patients with CKD.

scholarly journals Polygenic Risk Scores Predict Hypertension Onset and Cardiovascular Risk

Hypertension ◽  
2021 ◽  
Vol 77 (4) ◽  
pp. 1119-1127 ◽  
Author(s):  
Felix Vaura ◽  
Anni Kauko ◽  
Karri Suvila ◽  
Aki S. Havulinna ◽  
Nina Mars ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Early Onset ◽  
Late Onset ◽  
Risk Scores ◽  
Event Analysis ◽  
Cvd Risk ◽  
Polygenic Risk ◽  
Clinical Risk ◽  
Traditional Risk Factors ◽  
Association Data

Although genetic risk scores have been used to predict hypertension, their utility in the clinical setting remains uncertain. Our study comprised N=218 792 FinnGen participants (mean age 58 years, 56% women) and N=22 624 well-phenotyped FINRISK participants (mean age 50 years, 53% women). We used public genome-wide association data to compute polygenic risk scores (PRSs) for systolic and diastolic blood pressure (BP). Using time-to-event analysis, we then assessed (1) the association of BP PRSs with hypertension and cardiovascular disease (CVD) in FinnGen and (2) the improvement in model discrimination when combining BP PRSs with the validated 4- and 10-year clinical risk scores for hypertension and CVD in FINRISK. In FinnGen, compared with having a 20 to 80 percentile range PRS, a PRS in the highest 2.5% conferred 2.3-fold (95% CI, 2.2–2.4) risk of hypertension and 10.6 years (95% CI, 9.9–11.4) earlier hypertension onset. In subgroup analyses, this risk was only 1.6-fold (95% CI, 1.5–1.7) for late-onset hypertension (age ≥55 years) but 2.8-fold (95% CI, 2.6–2.9) for early-onset hypertension (age <55 years). Elevated systolic BP PRS also conferred 1.3-fold (95% CI, 1.2–1.4) risk of CVD and 2.3 years (95% CI, 1.6–3.1) earlier onset. In FINRISK, systolic and diastolic BP PRSs improved clinical risk prediction of hypertension (but not CVD), increasing the C statistics by 0.7% (95% CI, 0.3–1.1). We demonstrate that genetic information improves hypertension risk prediction. BP PRSs together with traditional risk factors could improve prediction of hypertension and particularly early-onset hypertension, which confers substantial CVD risk.

Association of polymorphisms in ZFHX1B, KCNQ5 and GJD2 with myopia progression and polygenic risk prediction in children

2021 ◽  
pp. bjophthalmol-2020-318708
Author(s):  
Li Jia Chen ◽  
Fen Fen Li ◽  
Shi Yao Lu ◽  
Xiu Juan Zhang ◽  
Ka Wai Kam ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Risk Scores ◽  
Nucleotide Polymorphisms ◽  
Myopia Progression ◽  
Polygenic Risk ◽  
Axial Elongation ◽  
Risk Alleles ◽  
Increased Risk ◽  
Fast Progression

AimsTo assess the association of single-nucleotide polymorphisms (SNPs) with myopia progression for polygenic risk prediction in children.MethodsSix SNPs (ZC3H11B rs4373767, ZFHX1B rs13382811, KCNQ5 rs7744813, MET rs2073560, SNTB1 rs7839488 and GJD2 rs524952) were analysed in 1043 school children, who completed 3-year follow-up, using TaqMan genotyping assays. SNP associations with progression in spherical equivalent (SE) were analysed by logistic regression. Polygenic risk scores (PRS) were applied for computing the sum of the risk alleles of multiple SNPs corresponding to myopia progression, weighted by the effect sizes of corresponding SNPs.ResultsGJD2 rs524952 showed significant association with fast progression (OR=1.32, 95% CI 1.10 to 1.59; p=0.003) and KCNQ5 rs7744813 had nominal association (OR=1.32, 95% CI 1.04 to 1.67; p=0.02). In quantitative traits locus analysis, GJD2 rs524952 and KCNQ5 rs7744813 were associated with progression in SE (β=−0.038 D/year, p=0.008 and β=−0.042 D/year, p=0.02) and axial elongation (β=0.016 mm/year, p=0.01 and β=0.017 mm/year, p=0.027). ZFHX1B rs13382811 also showed nominal association with faster progression in SE (β=−0.041 D/year, p=0.02). PRS analysis showed that children with the highest PRS defined by rs13382811, rs7744813 and rs524952 had a 2.26-fold of increased risk of fast myopia progression (p=4.61×10−5). PRS was also significantly associated with SE progression (R2=1.6%, p=3.15×10−5) and axial elongation (R2=1.2%, p=2.6×10−4).ConclusionsIn this study, multi-tiered evidence suggested SNPs in ZFHX1B, KCNQ5 and GJD2 as risk factors for myopia progression in children. Additional attention and appropriate interventions should be given for myopic children with high-risk PRS as defined by GJD2 rs524952, KCNQ5 rs7744813 and ZFHX1B rs13382811.

scholarly journals Evaluation of variable selection methods for random forests and omics data sets

2017 ◽  
Vol 20 (2) ◽  
pp. 492-503 ◽  
Author(s):  
Frauke Degenhardt ◽  
Stephan Seifert ◽  
Silke Szymczak
Keyword(s):  
Variable Selection ◽  
Random Forests ◽  
Data Sets ◽  
Omics Data ◽  
Selection Methods

The gender-specific role of prediabetes on 10-year cardiovascular disease incidence: highlights from the ATTICA prospective (2002–2012) study

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Kouvari ◽  
D.B Panagiotakos ◽  
C Chrysohoou ◽  
E.N Georgousopoulou ◽  
C Pitsavos ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Risk Prediction ◽  
Fasting Glucose ◽  
Disease Incidence ◽  
Fasting Blood Glucose ◽  
Total Sample ◽  
Funding Source ◽  
Cvd Risk ◽  
Diabetes Status

Abstract Background/Introduction Prediabetes in terms of impaired fasting glucose (IFG) or impaired glucose tolerance (IGT) are highly discussed for their aggravating effect on cardiac health and their potential inclusion in risk prediction to achieve earlier prevention. Purpose The aim of the present work was to evaluate the association between prediabetes and 10-year first fatal/non fatal cardiovascular disease (CVD) incidence in a sample without prevalent CVD, taking into account the stability of this condition or the transition to type II diabetes. Methods A prospective study was conducted during 2001–2012 studying n=1,514 males and n=1,528 females (aged &gt;18 years old) free of CVD. According to American Diabetes Association Diagnosis, prediabetes in terms of IFG was defined as fasting glucose levels 100–125 mg/dl while type 2 diabetes as fasting blood glucose &gt;125 mg/dl or the use of antidiabetic medication. Ten-year follow-up was performed in n=2,020 participants (n=317 CVD cases); the working sample here was n=1,485 (n=249 CVD cases) (without baseline diabetes and with available data on diabetes status at follow-up). Results Of the 1,485 participants, n=279 had IFG, at baseline. Ten-year CVD incidence was 19.3% in IFG subgroup and 12.3% in normoglycemic subgroup (p&lt;0.001); the IFG-to-normoglycemic CVD incidence ratio in men was 1.22 while in women 1.60. Multi-adjusted analysis revealed that IFG was an independent predictor of CVD within the decade (Hazard ratio (HR)=1.39, 95% Confidence Interval (95% CI) (1.00, 1.95)). Significant interacting effect of gender on the examined association was revealed (p for interaction=0.001); in stratified analysis, IFG was independently associated with increased CVD risk only in women (HR=1.47, 95% CI (1.10, 2.68)). Within the decade, transition to diabetes status was observed in about one out of four participants with prediabetes (25.1%) while the respective rate in normoglycemic participants was 10% (n=191 diabetes cases, in total). Interestingly, sensitivity analysis revealed that when this category (with diabetes onset within the decade) was excluded from the analysis prediabetes retained its independent aggravating –even weaker– effect on 10-year CVD risk in total sample (HR=1.18, 95% CI (1.01, 1.91)) as well as in women (HR=1.25, 95% CI (1.03, 2.97)). Conclusion Here, it was suggested that IFG independently predicted long-term CVD onset, even without transition to a more serious cardiometabolic condition (i.e. diabetes) with more evident outcomes in case of women. Considering the increasing interest for early CVD risk prediction, prediabetes condition in terms of IFG may be a useful predictor towards this perspective. Funding Acknowledgement Type of funding source: Other. Main funding source(s): This work was supported by a research grant from Hellenic Atherosclerosis Society. The ATTICA study is supported by research grants from the Hellenic Cardiology Society [HCS2002] and the Hellenic Atherosclerosis Society [HAS2003].

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