Predicting Coronary Artery Disease Risk Research Articles

Impact of C-reactive protein levels on lipoprotein(a)-associated aortic stenosis incidence and progression.

Elevated lipoprotein(a) [Lp(a)] levels are associated with the risk of coronary artery disease (CAD) and calcific aortic valve stenosis (CAVS). Observational studies revealed that Lp(a) and C-reactive protein (CRP) levels, a biomarker of systemic inflammation, may jointly predict CAD risk. Whether Lp(a) and CRP levels also jointly predict CAVS incidence and progression is unknown. We investigated the association of Lp(a) with CAVS according to CRP levels in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk study (n = 18 226, 406 incident cases) and the UK Biobank (n = 438 260, 4582 incident cases), as well as in the ASTRONOMER study (n = 220), which assessed the haemodynamic progression rate of pre-existing mild-to-moderate aortic stenosis. In EPIC-Norfolk, in comparison to individuals with low Lp(a) levels (<50 mg/dL) and low CRP levels (<2.0 mg/L), those with elevated Lp(a) (>50 mg/dL) and low CRP levels (<2.0 mg/L) and those with elevated Lp(a) (>50 mg/dL) and elevated CRP levels (>2.0 mg/L) had a higher CAVS risk [hazard ratio (HR) = 1.86 (95% confidence intervals, 1.30-2.67) and 2.08 (1.44-2.99), respectively]. A comparable predictive value of Lp(a) in patients with vs. without elevated CRP levels was also noted in the UK Biobank. In ASTRONOMER, CAVS progression was comparable in patients with elevated Lp(a) levels with or without elevated CRP levels. Lp(a) predicts the incidence and possibly progression of CAVS regardless of plasma CRP levels. Lowering Lp(a) levels may warrant further investigation in the prevention and treatment of CAVS, regardless of systemic inflammation.

Evaluating the use of novel atherogenicity indices and insulin resistance surrogate markers in predicting the risk of coronary artery disease: a case‒control investigation with comparison to traditional biomarkers

BackgroundDue to the contribution of coronary artery disease (CAD) to serious cardiovascular events, determining biomarkers that could robustly predict its risk would be of utmost importance. Thus, this research was designed to assess the value of traditional cardio-metabolic indices, and more novel atherogenicity indices and insulin resistance surrogate markers in the identification of individuals at risk of CAD.MethodsA case‒control survey was conducted, in which 3085 individuals were enrolled. Their clinical and biochemical data were gathered at baseline. The investigated indices included the atherogenic index of plasma (AIP), triglyceride-glucose (TyG) index, TyG-body mass index (TyG-BMI), lipoprotein combine index (LCI), cholesterol index (CHOLINDEX), Castelli’s risk indices-I, II (CRI-I, CRI-II), and metabolic score for insulin resistance (METS − IR). To examine the relationship between these variables and CAD risk, multiple regression analyses adjusted for potential confounders were conducted.ResultsOverall, 774 angiographically confirmed CAD patients (mean age = 54 years) were compared with 3085 controls (mean age = 51 years). Higher triglyceride, total cholesterol and fasting blood sugar levels and lower HDL-C levels were related to an elevated risk of CAD (P-for-trend < 0.001), while the direct association between increased serum LDL-C concentrations and a greater risk of CAD only became apparent when excluding those with diabetes, and statin users. Among novel indices, greater values of the majority of these markers, including AIP, CRI-I, and -II, CHOLINDEX, LCI, and TyG-index, in comparison to the lower values, significantly elevated CAD risk (P-for-trend < 0.001).ConclusionAccording to the current findings, novel atherogenicity indices and insulin resistance surrogate markers, in particular, AIP, CRI-I and II, CHOLINDEX, LCI, and TyG-index, may be useful in predicting CAD risk.

Development of Machine Learning Tools for Predicting Coronary Artery Disease in the Chinese Population.

Coronary artery disease (CAD) is one of the major cardiovascular diseases and the leading cause of death globally. Blood lipid profile is associated with CAD early risk. Therefore, we aim to establish machine learning models utilizing blood lipid profile to predict CAD risk. In this study, 193 non-CAD controls and 2001 newly-diagnosed CAD patients (1647 CAD patients who received lipid-lowering therapy and 354 who did not) were recruited. Clinical data and the result of routine blood lipids tests were collected. Moreover, low-density lipoprotein cholesterol (LDL-C) subfractions (LDLC-1 to LDLC-7) were classified and quantified using the Lipoprint system. Six predictive models (k-nearest neighbor classifier (KNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost)) were established and evaluated by the confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), recall (sensitivity), accuracy, precision, and F1 score. The selected features were analyzed and ranked. While predicting the CAD development risk of the CAD patients without lipid-lowering therapy in the test set, all models obtained AUC values above 0.94, and the accuracy, precision, recall, and F1 score were above 0.84, 0.85, 0.92, and 0.88, respectively. While predicting the CAD development risk of all CAD patients in the test set, all models obtained AUC values above 0.91, and the accuracy, precision, recall, and F1 score were above 0.87, 0.94, 0.87, and 0.92, respectively. Importantly, small dense LDL-C (sdLDL-C) and LDLC-4 play pivotal roles in predicting CAD risk. In the present study, machine learning tools combining both clinical data and blood lipid profile showed excellent overall predictive power. It suggests that machine learning tools are suitable for predicting the risk of CAD development in the near future.

Abstract 10476: Impact of C-Reactive Protein Levels on Lipoprotein(a)-Associated Coronary Artery Disease and Aortic Stenosis Risk in Apparently Healthy Individuals

Introduction: Elevated Lipoprotein(a) (Lp[a]) levels are strongly and linearly associated with coronary artery disease (CAD) and calcific aortic valve stenosis (CAVS). Observational studies in primary and secondary prevention settings revealed that Lp(a) and other CAD risk factors such as C-reactive protein (CRP) levels, a biomarker of systemic inflammation, may jointly predict CAD risk. Whether Lp(a) and CRP levels also jointly predict CAVS risk is unknown. Methods: We investigated the long-term associations of Lp(a) with CAD and CAVS according to CRP levels in the European Prospective Investigation Into Cancer and Nutrition (EPIC)-Norfolk study. Lp(a) and CRP levels were measured in 18,459 participants who were followed for 20 years. During the follow-up, 3915 and 418 participants had incident CAD and CAVS, respectively. Results: As presented in Figure 1, in comparison to individuals with low Lp(a) levels (&lt;30 mg/dL) and low CRP levels (&lt;2.0 mg/dL), those with elevated Lp(a) (&gt;30 mg/dL) and low CRP levels (&lt;2.0 mg/dL) and those with elevated Lp(a) (&gt;30 mg/dL) and elevated CRP levels (&gt;2.0 mg/dL) had a higher CAD risk (hazard ratio [HR] = 1.38 [95% CI, 1.28-1.49] and 1.93 [95% CI, 1.74-2.13] , respectively), and a higher CAVS risk (HR = 1.46 [95% CI, 1.08-1.98] and 1.87 [95% CI, 1.40-2.49], respectively). Conclusions: Results of this study confirm the joint association of high Lp(a) and CRP levels on incident CAD and extend these joint associations to valvular diseases such as CAVS. Lowering Lp(a) levels may warrant further investigation in the prevention of CAD and CAVS, especially in the context of systemic inflammation.

FACTS REVEALED; TOTAL/HIGH DENSITY LIPOPROTEIN CHOLESTEROL VALUES AND THEIR RATIO AMONG MACRO-VASCULAR COMPLICATIONS OF DIABETES

Objectives: There is key concept that raised mortality among diabetics with stroke, peripheral vascular disease and particularly coronary artery diseases may be owing to dyslipidaemia and its prior detection is needed for timely prevention. Total /HDL cholesterol values and its ratio can be predictive tool among macro-vascular complications of diabetes. Methodology: A retrospective observational study was conducted in Alamgir Medical Services, airport road Bahawalpur; during a period of 6 months from 30-06-2021 to 30-12-2021.A quantitative data of 100 diabetics with macro-vascular complications were analysed. 52 patients with coronary artery disease (CAD) were identified as group A, 30 subjects had cerebro-vascular accident (CVA) and labelled as group B while 18 patients with peripheral vascular disease (PVD) as group C. Values of lipoproteins and main outcome measure of TC: HDLC ratio were compared between the three groups accordingly. Results: In patients with CAD, the mean (SD) concentration of total cholesterol (TC) was 220.08(27.9) mg/dl, HDL – C was 34.8 (6.2) mg/dl while TC: HDLC ratio was &gt; 6 and in high risk range (normal ratio should be &lt; 5). Both lipoproteins (TC and HDL and their ratio) were observed within normal range in group B (CVA) as mean SD 180.68(23.4) mg/dl and 35.09(6.9) mg/dl respectively, and in group C (PVD) as 160.20(19) mg/dl and 40.6(5.4) mg/dl respectively. Conclusion: Diabetics having CAD have raised TC: HDL C ratio and there is advantage to use this ratio for predicting CAD risk.

Assessment of practical applicability and clinical relevance of a commonly used LDL-C polygenic score in patients with severe hypercholesterolemia

Background and aimsLow-density lipoprotein cholesterol (LDL-C) levels vary in patients with familial hypercholesterolemia (FH) and can be explained by a single deleterious genetic variant or by the aggregate effect of multiple, common small-effect variants that can be captured in a polygenic score (PS). We set out to investigate the contribution of a previously published PS to the inter-individual LDL-C variation and coronary artery disease (CAD) risk in patients with a clinical FH phenotype. MethodsFirst, in a cohort of 628 patients referred for genetic FH testing, we evaluated the distribution of a PS for LDL-C comprising 12 genetic variants. Next, we determined its association with coronary artery disease (CAD) risk using UK Biobank data. ResultsThe mean PS was higher in 533 FH-variant-negative patients (FH/M-) compared with 95 FH-variant carriers (1.02 vs 0.94, p < 0.001). 39% of all patients had a PS equal to the top 20% from a population-based reference cohort and these patients were less likely to carry an FH variant (OR 0.22, 95% CI 0.10–0.48) compared with patients in the lowest 20%. In UK Biobank data, the PS explained 7.4% of variance in LDL-C levels and was associated with incident CAD. Addition of PS to a prediction model using age and sex and LDL-C did not increase the c-statistic for predicting CAD risk. ConclusionsThis 12-variant PS was higher in FH/M- patients and associated with incident CAD in UK Biobank data. However, the PS did not improve predictive accuracy when added to the readily available characteristics age, sex and LDL-C, suggesting limited discriminative value for CAD.

Genetics of coronary artery disease in the post-GWAS era.

During the past decade, genome-wide association studies (GWAS) have transformed our understanding of many heritable traits. Three recent large-scale GWAS meta-analyses now further markedly expand the knowledge on coronary artery disease (CAD) genetics in doubling the number of loci with genome-wide significant signals. Here, we review the unprecedented discoveries of CAD GWAS on low-frequency variants, underrepresented populations, sex differences and integrated polygenic risk. We present the milestones of CAD GWAS and post-GWAS studies from 2007 to 2021, and the trend in identification of variants with smaller odds ratio by year due to the increasing sample size. We compile the 321 CAD loci discovered thus far and classify candidate genes as well as distinct functional pathways on the road to indepth biological investigation and identification of novel treatment targets. We draw attention to systems genetics in integrating these loci into gene regulatory networks within and across tissues. We review the traits, biomarkers and diseases scrutinized by Mendelian randomization studies for CAD. Finally, we discuss the potentials and concerns of polygenic scores in predicting CAD risk in patient care as well as future directions of GWAS and post-GWAS studies in the field of precision medicine.

Validation and comparison of 28 risk prediction models for coronary artery disease.

Risk prediction models (RPMs) for coronary artery disease (CAD), using variables to calculate CAD risk, are potentially valuable tools in prevention strategies. However, their use in the clinical practice is limited by a lack of poor model description, external validation, and head-to-head comparisons. CAD RPMs were identified through Tufts PACE CPM Registry and a systematic PubMed search. Every RPM was externally validated in the three cohorts (the UK Biobank, LifeLines, and PREVEND studies) for the primary endpoint myocardial infarction (MI) and secondary endpoint CAD, consisting of MI, percutaneous coronary intervention, and coronary artery bypass grafting. Model discrimination (C-index), calibration (intercept and regression slope), and accuracy (Brier score) were assessed and compared head-to-head between RPMs. Linear regression analysis was performed to evaluate predictive factors to estimate calibration ability of an RPM. Eleven articles containing 28 CAD RPMs were included. No single best-performing RPM could be identified across all cohorts and outcomes. Most RPMs yielded fair discrimination ability: mean C-index of RPMs was 0.706 ± 0.049, 0.778 ± 0.097, and 0.729 ± 0.074 (P < 0.01) for prediction of MI in UK Biobank, LifeLines, and PREVEND, respectively. Endpoint incidence in the original development cohorts was identified as a significant predictor for external validation performance. Performance of CAD RPMs was comparable upon validation in three large cohorts, based on which no specific RPM can be recommended for predicting CAD risk.

The Atherogenic Index of Plasma: A Powerful and Reliable Predictor for Coronary Artery Disease in Patients With Type 2 Diabetes.

We evaluated the predictive power of the atherogenic index of plasma (AIP) for coronary artery disease (CAD) in patients with type 2 diabetes mellitus (T2DM). A total of 3278 patients who underwent coronary angiography were consecutively enrolled, including 2052 patients with CAD and 1226 patients with T2DM but without CAD. Patients in the CAD group had higher levels of triglyceride (TG), total cholesterol, low-density lipoprotein cholesterol, AIP and a lower level of high-density lipoprotein cholesterol (HDL-C). In correlation analyses, AIP correlated positively with body mass index, log (homeostasis model assessment of insulin resistance), TG, remnant lipoprotein cholesterol, non-HDL-C, but negatively with age and HDL-C. Multivariate logistic regression analyses demonstrated that AIP was an independent risk factor for CAD in diabetic patients and was validated by multiple models. Furthermore, the ORs for CAD risk were raised with increasing AIP quartiles; ORs of AIP quartiles Q2-Q4 compared with Q1 were 1.56, 1.70, and 2.22, respectively (Ps < .001), which suggested AIP was the lipid parameter that most strongly associated with incident CAD. In conclusion, AIP is a powerful and reliable biomarker for predicting CAD risk beyond individual lipid profiles in patients with T2DM.

Low-Density Lipoprotein Cholesterol 4: The Notable Risk Factor of Coronary Artery Disease Development

Background: Coronary artery disease (CAD) is the leading cause of death worldwide, which has a long asymptomatic period of atherosclerosis. Thus, it is crucial to develop efficient strategies or biomarkers to assess the risk of CAD in asymptomatic individuals.Methods: A total of 356 consecutive CAD patients and 164 non-CAD controls diagnosed using coronary angiography were recruited. Blood lipids, other baseline characteristics, and clinical information were investigated in this study. In addition, low-density lipoprotein cholesterol (LDL-C) subfractions were classified and quantified using the Lipoprint system. Based on these data, we performed comprehensive analyses to investigate the risk factors for CAD development and to predict CAD risk.Results: Triglyceride, LDLC-3, LDLC-4, LDLC-5, LDLC-6, and total small and dense LDL-C were significantly higher in the CAD patients than those in the controls, whereas LDLC-1 and high-density lipoprotein cholesterol (HDL-C) had significantly lower levels in the CAD patients. Logistic regression analysis identified male [odds ratio (OR) = 2.875, P < 0.001], older age (OR = 1.018, P = 0.025), BMI (OR = 1.157, P < 0.001), smoking (OR = 4.554, P < 0.001), drinking (OR = 2.128, P < 0.016), hypertension (OR = 4.453, P < 0.001), and diabetes mellitus (OR = 8.776, P < 0.001) as clinical risk factors for CAD development. Among blood lipids, LDLC-3 (OR = 1.565, P < 0.001), LDLC-4 (OR = 3.566, P < 0.001), and LDLC-5 (OR = 6.866, P < 0.001) were identified as risk factors. To predict CAD risk, six machine learning models were constructed. The XGboost model showed the highest AUC score (0.945121), which could distinguish CAD patients from the controls with a high accuracy. LDLC-4 played the most important role in model construction.Conclusions: The established models showed good performance for CAD risk prediction, which can help screen high-risk CAD patients in asymptomatic population, so that further examination and prevention treatment might be taken before any sudden or serious event.

Gut Microbiota Community Shift with Severity of Coronary Artery Disease

Gut microbiota community shift with coronary artery disease (CAD) has been reported in several limited cohorts during the past several years. However, whether the enriched or decreased microbiota taxa with CAD can be reproducible deserves further investigation and validation. In this study, 78 human subjects were recruited. Of these, 19 were diagnosed without stenosis in coronary artery (control group, referred to herein as Ctrl), 14 with stenosis less than 50% (LT50), and 45 with stenosis greater than 50% (GT50). Fecal samples were collected and DNA was extracted to perform 16S ribosomal RNA (rRNA) gene sequencing. The operational taxonomic units (OTUs) were analyzed to identify taxa specific to different groups; next, multivariate logistic regression was employed to test whether the defined taxa could independently predict CAD risk. We found that Deltaproteobacteria, Fusobacterium , Bilophila , Actinomyces , and Clostridium XIX were enriched in Ctrl; Prevotellaceae, Parabacteriodes , and Butyricicoccus were enriched in LT50; and Roseburia and Butyricimonas were enriched in GT50. Further analysis revealed that increased populations of Deltaproteobacteria, Fusobacterium , Bilophila , and Desulfovibrionaceae were associated with a 0.26-fold, 0.21-fold, 0.18-fold, and 0.26-fold decreased risk of CAD, respectively ( p < 0.05), and an increased Prevotellaceae population was associated with a 5.63-fold increased risk of CAD ( p < 0.01). A combination of the 20 microbial taxa achieved an area under the receiver operating characteristic (ROC) curve of higher than 0.88 for all discriminations between LT50 vs Ctrl, GT50 vs Ctrl, LT50 + GT50 vs Ctrl, and GT50 vs Ctrl + LT50. However, the microbial taxa previously reported as enriched in CAD patients or healthy controls could not be observed in our cohort except for Bacteroides . In conclusion, CAD patients showed a different microbial taxa signature than the healthy controls. However, the non-reproducibility of the microbiota taxa enriched in CAD across different cohorts limits the use of this signature in early diagnosis and prevention. Only decreased Bacteroides abundance was found to be a reliable marker to indicate CAD progression.

Associations of the serum metabolite profile with a healthy Nordic diet and risk of coronary artery disease

A healthy Nordic diet (HND) rich in wholegrain cereals, berries, vegetables, and fish, has been associated with a lower risk of cardiovascular disease, but the molecular links remain unclear. Here, we present the application of nontargeted metabolic profiling based on liquid chromatography with tandem mass spectrometry (LC-MS/MS) to identify metabolites that would potentially reflect the adherence to HND and their relationship with the risk of coronary artery disease (CAD). From a Finnish population-based prospective cohort (Kuopio Ischaemic Heart Disease Risk Factor Study; KIHD), we collected 364 baseline serum samples in 4 groups: 1) 94 participants with high adherence to HND who developed CAD during the follow-up of 20.4±7.6 years (cases), 2) 88 participants with high adherence who did not develop CAD during follow-up (controls), 3) 93 CAD cases with low adherence, and 4) 89 controls with low adherence. Indolepropionic acid, proline betaine, vitamin E derivatives, and medium-chain acylcarnitines were associated with adherence to HND after adjustments for age, waist-to-hip ratio (WHR), physical activity, and total cholesterol. These metabolites also correlated negatively with blood lipid profiles, BMI, insulin, inflammation marker high-sensitivity C reactive protein (hsCRP), smoking, and alcohol consumption, as well as positively with physical activity. Predictors of CAD risk included several lipid molecules, which also indicated lower adherence to HND. But, only the associations with the plasmalogens PC(O-16:0/18:2) and PC(O-16:1/18:2) remained significant after adjusting for age, smoking, systolic blood pressure, LDL cholesterol, and WHR. These plasmalogens did not correlate with any investigated risk factors of CAD at baseline, which may highlight their potential as novel predictors of CAD risk. Interestingly, the metabolic profile predicting CAD risk differed based on the adherence to HND. Also, HND adherence was more distinct within CAD cases than controls, which may emphasize the interaction between HND adherence and CAD risk. The association between higher adherence to HND and a lower risk of CAD likely involves a complex interaction of various endogenous, plant-, and microbial-derived metabolites.

Abstract P457: Age Dependence of Genetic Risk Scores in Relation to Coronary Artery Disease

Background: Coronary artery disease (CAD) is an age-related multi-factorial disease with a high public health burden. Genetic loci for CAD have been discovered and collectively predict CAD risk independent of traditional risk factors. It remains unclear whether age modifies the genetic predisposition to CAD. Methods: A total of 173,315 Caucasian subjects from the UK Biobank were included in this study, including 26,472 CAD patients with age of onset ranging from 32 to 78 years old, and 146,843 control subjects &gt; 60 years old without CAD. CAD diagnosis was extracted from in-patient Hospital Episode Statistics (HES) and National Death Registry based on ICD-10 and OPCS-4 codes. CAD genetic risk score (GRS) was calculated based on the 161 known CAD loci. Sub-GRSs for body mass index (BMI, 16 loci), blood pressure (BP, 26 loci) and lipids (17 loci) were constructed to examine the genetic risk for CAD from certain intermediate trait. The CAD GRSs among onset age groups were compared to the controls using linear regression models, controlling for sex, BMI, creatinine, c-reactive protein, high density lipoprotein, low density lipoprotein, triglyceride, diabetes, heart failure history, hypertension and smoking status. The sub-GRSs were compared using the same models, excluding sub-GRS specific intermediate traits. Interactions of age groups and GRSs were tested using logistic regression models for CAD status, controlling for the same variables described above. Association analyses of each GRS were performed within each category of CAD age at onset comparing to controls. Results: Compared to the controls, the mean CAD GRS in CAD patients with age of onset ≤ 50 years was 0.58 standard deviations (SD) higher (95% CI 0.53-0.63), followed by CAD age of onset between 50 and 60 years (0.50 SD higher, 95% CI 0.48-0.52), between 60 and 70 years (0.35 SD higher, 95% CI 0.33-0.37), and &gt; 70 years (0.25 SD higher, 95% CI 0.21-0.29). The sub-GRSs showed the similar trend. Age of onset stratified analysis revealed a significant CAD GRS-age interaction (p&lt;0.001) on CAD risk where genetic effect is stronger among those early onset CAD patients - the odds ratios (OR) per CAD GRS SD among subjects with CAD onset age ≤ 50, 50 to 60, 60 to 70, &gt; 70 years were 1.80 (95% CI 1.71-1.89), 1.66 (95% CI 1.62-1.70), 1.44 (1.41-1.47), 1.29 (95%CI 1.24-1.35), respectively. Among the three sub-GRSs, significant GRS-age interactions were observed for BP sub-GRS and lipids sub-GRS but not BMI sub-GRS. Conclusion: The observed gene-age interaction indicates that the genetic susceptibility for CAD decreases for older subjects. The contribution of the CAD GRS to CAD risk is almost 64% lower in those developing CAD at age &gt; 70 years compared to those ≤ 50 years. Future considerations and applications of CAD genetic risk in precision medicine needs to include age of onset to accurately evaluate the genetic contribution.

Pleiotropic effects of HDL subfractions and HDL-associated enzymes on protection against coronary artery disease.

High-density lipoprotein cholesterol (HDL-C) levels are inversely related to the risk of coronary artery disease (CAD). Alterations in HDL-C subclass distribution and HDL-associated enzyme activities may be more important than total HDL levels for the progression of CAD. We intended to investigate the relationship of HDL-C subclass distribution and HDL-associated enzyme activities with CAD. Our study included 101 patients with stable coronary artery disease, and 64 healthy subjects. Serum levels of HDL lipoprotein-associated-phospholipase A2 (HDL-LpPLA2), paraoxonase 1 (PON1), and HDL subfraction distribution were measured. We found increased small HDL (sHDL) subfractions in patients with one-vessel disease (P < 0.001). We also found a reverse correlation between total HDL-C levels and affected vessel number (P < 0.05). Plasma HDL-Lp PLA2 enzyme level was higher in each vessel disease category compared to the control group (P < 0.001). However, PON1 enzyme activity in patients with CAD was not statically significant. Plasma sHDL, HDL-Lp PLA2 enzyme and Lp(a) were significantly different between subjects with CAD and control participants. We demonstrated decreased sHDL particles and a lower cardioprotective HDL-LpPLA, enzyme activity in all patient subgroups compared to controls. Measurement of total HDL-C level only may not be sufficient to predict CAD risk.

Pleiotropic eff ects of HDL subfractions and HDL-associated enzymes on protection against coronary artery disease

Objective High-density lipoprotein cholesterol (HDL-C) levels are inversely related to the risk of coronary artery disease (CAD). Alterations in HDL-C subclass distribution and HDL-associated enzyme activities may be more important than total HDL levels for the progression of CAD. We intended to investigate the relationship of HDL-C subclass distribution and HDL-associated enzyme activities with CAD.Method and results Our study included 101 patients with stable coronary artery disease, and 64 healthy subjects. Serum levels of HDL lipoprotein-associated-phospholipase A2 (HDL-LpPLA2), paraoxonase 1 (PON1), and HDL subfraction distribution were measured. We found increased small HDL (sHDL) subfractions in patients with one-vessel disease (P < 0.001). We also found a reverse correlation between total HDL-C levels and aff ected vessel number (P <0.05). Plasma HDL-Lp PLA2 enzyme level was higher in each vessel disease category compared to the control group (P < 0.001). However, PON1 enzyme activity in patients with CAD was not statically signifi cant. Plasma SHDL, HDL-Lp PLA2 enzyme and Lp(a) were signifi cantly diff erent between subjects with CAD and control participants.Conclusions We demonstrated decreased SHDL particles and a lower cardioprotective HDL-LpPLA2 enzyme activity in all patient subgroups compared to controls. Measurement of total HDL-C level only may not be suffi cient to predict CAD risk.

Do We Need More Research on Platelet Function in South Asian Immigrants?

South Asian Immigrants (SAIs) have high rates of coronary artery disease (CAD) and its risk factors. This is alarming as i) SAIs are the 2nd fastest growing Asian immigrant population in the US; ii) CAD prevalence is 2 times higher than in other immigrant populations; iii) at any risk factor level, SAIs are at a greater risk of CAD; and iv) Type 2 diabetes is a major risk factor and associated co-morbidity in SAIs. Although clinical guidelines for CAD prevention recommend risk management based on Framingham Risk Scores (FRS), available data suggest that FRS underestimate CAD risk in SAIs. This review presents an overview of platelet related research in SAIs, its importance in predicting CAD risk and methods of diagnosis. Further we provide discussion on the importance of platelet function assessment for more appropriate stratification of SAIs based on cardiovascular risk.

Increased epicardial adipose tissue in type 1 diabetes is associated with central obesity and metabolic syndrome

Aims The present study evaluated the relationship between metabolic syndrome (MS), body fat composition and epicardial adipose tissue (EAT) in type 1 diabetes. Epicardial adipose tissue is a new independent marker of coronary artery disease (CAD). Methods Forty-five type 1 diabetic women were evaluated (age 36 ± 9 years; body mass index 24.6 ± 4.4 kg/m 2). Metabolic syndrome was defined by the World Health Organization criteria. Body fat composition and EAT were analyzed by dual-energy-X-ray absorptiometry and echocardiogram, respectively. Results Twenty patients (45%) had MS. Patients with MS had greater android (central) fat deposition than patients without MS (41.9 ± 2.0% vs. 33.7 ± 1.8%, p = 0.004). Total body fat and gynoid (peripheric) fat distribution were similar between the groups. Mean EAT was higher in patients with MS (6.15 ± 0.34 mm vs. 4.96 ± 0.25 mm; p = 0.006) and EAT was positively correlated with android (central) fat distribution ( r = 0.44; p = 0.002), however no correlation was found with gynoid (peripheric) fat distribution. Conclusions There was a high incidence of MS in type 1 diabetes related to increased central adiposity, despite the absence of obesity. Metabolic syndrome and central obesity were associated with increased EAT. Thus, young non-obese type 1 diabetic women with central adiposity and/or MS may have increased EAT, what may predict CAD risk.

Both Paraoxonase-1 Genotype and Activity Do Not Predict the Risk of Future Coronary Artery Disease; the EPIC-Norfolk Prospective Population Study

BackgroundParaoxonase-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 FindingsCases (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/SignificanceThis 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.

A Convenient Tool to Profile Patients for Generalized Cardiovascular Disease Risk in Primary Care

The early identification of and intervention in patients with increased risk factors for generalized cardiovascular disease greatly reduces their long-term mortality from hard coronary artery disease (CAD) events and other related co-morbidities. Thus, a number of multivariate risk factor analyses based on large-scale population studies have led to various risk-scoring models aimed at screening for those at high risk for CAD. These assessment systems, by virtue of novel diagnostic markers and better population data, have become increasingly adept at accurately predicting CAD risk in individual patients. Nevertheless, their practical application in the setting of primary care has lagged, because of a reluctance of many primary care physicians to adopt these methods. An effective risk assessment system should not only encompass the risk for hard CAD events but also include risks for related co-morbid conditions, such as stroke and heart failure, and be simple and accurate enough to be efficiently used in primary care clinics. In conclusion, in an attempt to simplify 1 of the more effective risk assessment devices for generalized cardiovascular disease risk that meets all these requirements, the investigators strongly support the model proposed by D'Agostino and colleagues and provide here a commentary on its utility in identifying patients at high risk for cardiovascular disease.

Abstract 3451: Risk for Coronary Artery Disease at the 9p21.3 Locus Is Abolished by a Protective Locus at 8p21.3 Identified by a Genome-Wide Association Study

Background : Coronary artery disease (CAD) is the leading cause of death in the western world. The only common genetic risk locus identified to date by us and others in genome wide association studies (GWAS) resides at 9p21.3. Methods: The Ottawa Heart Genomic Study compared genotypes of 1,542 early onset CAD patients (average 49y) and 1,455 elderly control Caucasians (average 75y). Results: Here, we report two protective haplotypes in the 3′ region of the lipoprotein lipase (LPL) gene at 8p21.3. LPL promotes lipolysis of circulating triglycerides (TG) and increases levels of atheroprotective high density lipoprotein (HDL) cholesterol. Since the 8p21.3 haplotypes are protective, we tested their interaction with the 9p21.3 risk allele. The rs10503669 haplotype associated with elevated TG and HDL levels but did not affect risk from 9p21.3. In contrast, the rs17411031 haplotype was not associated with TG or HDL levels and showed a significant interaction that abolished 9p21.3 risk independent of known risk factors. The interaction was confirmed using data of the Wellcome Trust Case Control Consortium. Excluding carriers of the interacting allele, accuracy of 9p21.3 to predict CAD risk is dramatically improved. Conclusion: Identification of the first modifier gene of the 9p21.3 risk locus for CAD improves CAD risk assessment and provides novel insight into coronary atherosclerosis. This protective 8p21.3 locus may provide a target for novel therapy.