CETP genotype and concentrations of HDL and lipoprotein subclasses in African-American men

High-Density Lipoprotein Subclasses and Their Role in the Prevention and Treatment of Cardiovascular Disease: A Narrative Review

The association between high-density lipoprotein cholesterol (HDL-C) and cardiovascular disease (CVD) is controversial. HDL-C is one content type of high-density lipoprotein (HDL). HDL consists of diverse proteins and lipids and can be classified into different subclasses based on size, shape, charge, and density, and can change dynamically in disease states. Therefore, HDL-C levels alone cannot represent HDLs' cardioprotective role. In this review, we summarized the methods for separating HDL subclasses, the studies on the association between HDL subclasses and cardiovascular risk (CVR), and the impact of lipid-modifying medications and nonpharmacological approaches (exercise training, dietary omega fatty acids, and low-density lipoprotein apheresis) on HDL subclasses. As HDL is a natural nanoplatform, recombinant HDLs (rHDLs) have been used as a delivery system in vivo by loading small interfering RNA, drugs, contrast agents, etc. Therefore, we further reviewed the HDL subclasses used in rHDLs and their advantages and disadvantages. This review would provide recommendations and guidance for future studies on HDL subclasses' cardioprotective roles.

CETP gene polymorphisms and haplotypes are explanatory variables for HDL cholesterol level in sickle cell disease

Variations in lipid profile have been observed in sickle cell disease (SCD) and understanding their relationship with disease severity is crucial. This study aimed to investigate the association of polymorphisms of the CETP gene and laboratory markers of disease severity with lipid profile in a pediatric population with SCD. Biochemical and anthropometric analyses and CETP and alpha-thalassemia genotyping were performed. The study included 133 children and adolescents with sickle cell anemia (SCA) or hemoglobin SC disease (SCC), in steady-state. The SCA and no hydroxyurea (no HU) groups had higher values of ApoB, total cholesterol, low-density lipoprotein cholesterol (LDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C) compared to the SCC and HU groups. However, there were no significant differences in ApoA1 and HDL-C levels between the groups based on genotype. Furthermore, the groups with altered levels of ApoA1, HDL-C, and the triglyceride/HDL ratio exhibited lower hemoglobin (Hb) levels and higher white blood cell counts. Hb level was associated to HDL-C levels. Analysis of CETP gene variants showed that the minor alleles of rs3764261 (C>A), rs247616 (C>T), and rs183130 (C>T), as well as the TTA haplotype, are explanatory variables for HDL-C levels. These findings suggested that dyslipidemia in SCD, specifically related to HDL-C levels, may be influenced by individual genetic background. Additionally, further investigation is needed to determine if clinical manifestations are impacted by CETP gene variants.

Bayesian LASSO for population stratification correction in rare haplotype association studies

Population stratification (PS) is one major source of confounding in both single nucleotide polymorphism (SNP) and haplotype association studies. To address PS, principal component regression (PCR) and linear mixed model (LMM) are the current standards for SNP associations, which are also commonly borrowed for haplotype studies. However, the underfitting and overfitting problems introduced by PCR and LMM, respectively, have yet to be addressed. Furthermore, there have been only a few theoretical approaches proposed to address PS specifically for haplotypes. In this paper, we propose a new method under the Bayesian LASSO framework, QBLstrat, to account for PS in identifying rare and common haplotypes associated with a continuous trait of interest. QBLstrat utilizes a large number of principal components (PCs) with appropriate priors to sufficiently correct for PS, while shrinking the estimates of unassociated haplotypes and PCs. We compare the performance of QBLstrat with the Bayesian counterparts of PCR and LMM and a current method, haplo.stats. Extensive simulation studies and real data analyses show that QBLstrat is superior in controlling false positives while maintaining competitive power for identifying true positives under PS.

Cholesteryl Ester Transfer Protein (CETP) Variations in Relation to Lipid Profiles and Cardiovascular Diseases: An Update

Lipid metabolism plays an essential role in the pathogenesis of cardiovascular and metabolic diseases. Cholesteryl ester transfer protein (CETP) is a crucial glycoprotein involved in lipid metabolism by transferring cholesteryl esters (CE) and triglycerides (TG) between plasma lipoproteins. CETP activity results in reduced HDL-C and increased VLDL- and LDL-C concentrations, thus increasing the risk of cardiovascular and metabolic diseases. In this review, we discuss the structure of CETP and its mechanism of action. Furthermore, we focus on recent experiments on animal CETP-expressing models, deciphering the regulation and functions of CETP in various genetic backgrounds and interaction with different external factors. Finally, we discuss recent publications revealing the association of CETP single nucleotide polymorphisms (SNPs) with the risk of cardiovascular and metabolic diseases, lifestyle factors, diet and therapeutic interventions. While CETP SNPs can be used as effective diagnostic markers, diet, lifestyle, gender and ethnic specificity should also be considered for effective treatment.

Population stratification correction using Bayesian shrinkage priors for genetic association studies

INTRODUCTION

Population stratification (PS) is a major source of confounding in population-based genetic association studies of quantitative traits. Principal component regression (PCR) and linear mixed model (LMM) are two commonly used approaches to account for PS in association studies. Previous studies have shown that LMM can be interpreted as including all principal components (PCs) as random-effect covariates. However, including all PCs in LMM may dilute the influence of relevant PCs in some scenarios, while including only a few preselected PCs in PCR may fail to fully capture the genetic diversity.

MATERIALS AND METHODS

To address these shortcomings, we introduce Bayestrat-a method to detect associated variants with PS correction under the Bayesian LASSO framework. To adjust for PS, Bayestrat accommodates a large number of PCs and utilizes appropriate shrinkage priors to shrink the effects of nonassociated PCs.

RESULTS

Simulation results show that Bayestrat consistently controls type I error rates and achieves higher power compared to its non-shrinkage counterparts, especially when the number of PCs included in the model is large. As a demonstration of the utility of Bayestrat, we apply it to the Multi-Ethnic Study of Atherosclerosis (MESA). Variants and genes associated with serum triglyceride or HDL cholesterol are identified in our analyses.

DISCUSSION

The automatic and self-selection features of Bayestrat make it particularly suited in situations with complex underlying PS scenarios, where it is unknown a priori which PCs are potential confounders, yet the number that needs to be considered could be large in order to fully account for PS.