A paucimorphic variant in the HMG-CoA reductase gene is associated with lipid-lowering response to statin treatment in diabetes: a GoDARTS study

Genetic Polymorphisms in the HMGCR Gene and Associations with Cognitive Decline in Parkinson's Disease Patients

Parkinson's disease (PD) is a common neurodegenerative disease characterized by motor and non-motor symptoms including cognitive impairment and dementia. The etiopathogenesis of PD, as well as its protective and susceptibility factors, are still elusive. 3-Hydroxy-3-methyglutaryl coenzyme A reductase (HMGCR) is an enzyme regulating cholesterol synthesis. Single-nucleotide polymorphisms (SNPs) in the gene coding HMGCR have recently been correlated with the risk of Alzheimer's disease. Alternative splicing of exon 13 of the HMGCR transcript and its strongly associated HMGCR haplotype 7 (H7: rs17244841, rs3846662, rs17238540) may downregulate protein activity and cholesterol synthesis, with lower low-density lipoprotein cholesterol (LDL) levels associated with PD that may affect cognitive abilities. We genotyped three SNPs in the H7 HMGCR gene in 306 PD patients divided into three groups-without cognitive decline, with mild cognitive impairment (MCI), and with PD dementia-and in 242 healthy participants. A correlation between the rs17238540 genotype and PD susceptibility as well as a minor association between rs3846662 and cognitive status in PD patients was observed; however, the two-sided analysis of these groups did not reveal any significance. We observed a statistically significant elevated high-density lipoprotein cholesterol (HDL) plasma level in the minor allele carriers of rs17238540 and rs17244841 among PD patients. This study should be replicated in a larger population.

Genetic Determinants of Response to Statins in Cardiovascular Diseases

Despite extensive efforts to identify patients with cardiovascular disease (CVD) who could most benefit from the treatment approach, patients vary in their benefit from therapy and propensity for adverse drug events. Genetic variability in individual responses to drugs (pharmacogenetics) is considered an essential determinant in responding to a drug. Thus, understanding these pharmacogenomic relationships has led to a substantial focus on mechanisms of disease and drug response. In turn, understanding the genomic and molecular bases of variables that might be involved in drug response is the main step in personalized medicine. There is a growing body of data evaluating drug-gene interactions in recent years, some of which have led to FDA recommendations and detection of markers to predict drug responses (e.g., genetic variant in VKORC1 and CYP2C9 genes for prediction of drug response in warfarin treatment). Also, statins are widely prescribed drugs for the prevention of CVD. Atorvastatin, fluvastatin, rosuvastatin, simvastatin, and lovastatin are the most common statins used to manage dyslipidemia. This review provides an overview of the current knowledge on the pharmacogenetics of statins, which are being used to treat cardiovascular diseases.

A gut feeling of statin

Statins, known as HMG-CoA reductase inhibitors, are widely utilized to reduce blood cholesterol levels and possess pleiotropic effects, including the influence on inflammation and macrophage proliferation. Despite their significant impact in diminishing the incidence of cardiovascular events and mortality, individual responses to statin therapy vary considerably. Understanding this variability is essential for optimizing treatment outcomes and minimizing adverse effects. The gut microbiota, a complex ecosystem of microorganisms within the gastrointestinal tract, plays a critical role in human health and disease. Emerging evidence has linked the gut microbiota to drug metabolism and response, with the potential to modulate the efficacy of statin therapy and its side effects. This review provides a comprehensive overview of the interaction between the gut microbiota and statins. It discusses how the gut microbiota can influence the therapeutic effects and side effects of statins and examines the mechanisms by which the gut microbiota affect statin response and cardiovascular diseases.

Practical Guide to Honest Causal Forests for Identifying Heterogeneous Treatment Effects

"Heterogeneous treatment effects" is a term which refers to conditional average treatment effects (i.e., CATEs) that vary across population subgroups. Epidemiologists are often interested in estimating such effects because they can help detect populations that may particularly benefit from or be harmed by a treatment. However, standard regression approaches for estimating heterogeneous effects are limited by preexisting hypotheses, test a single effect modifier at a time, and are subject to the multiple-comparisons problem. In this article, we aim to offer a practical guide to honest causal forests, an ensemble tree-based learning method which can discover as well as estimate heterogeneous treatment effects using a data-driven approach. We discuss the fundamentals of tree-based methods, describe how honest causal forests can identify and estimate heterogeneous effects, and demonstrate an implementation of this method using simulated data. Our implementation highlights the steps required to simulate data sets, build honest causal forests, and assess model performance across a variety of simulation scenarios. Overall, this paper is intended for epidemiologists and other population health researchers who lack an extensive background in machine learning yet are interested in utilizing an emerging method for identifying and estimating heterogeneous treatment effects.

HMGCR gene polymorphism is associated with residual cholesterol risk in premature triple-vessel disease patients treated with moderate-intensity statins

BACKGROUND

To investigate the association of HMGCR and NPC1L1 gene polymorphisms with residual cholesterol risk (RCR) in patients with premature triple-vessel disease (PTVD).

METHODS

Three SNPs within HMGCR including rs12916, rs2303151, and rs4629571, and four SNPs within NPC1L1 including rs11763759, rs4720470, rs2072183, and rs2073547 were genotyped. RCR was defined as achieved low-density lipoprotein cholesterol (LDL-C) concentrations after statins higher than 1.8 mmol/L (70 mg/dL).

RESULTS

Finally, a total of 609 PTVD patients treated with moderate-intensity statins were included who were divided into two groups: non-RCR group (n = 88) and RCR group (n = 521) according to LDL-C concentrations. Multivariate logistic regression showed the homozygotes for the minor allele of rs12916 within HMGCR gene (CC) were associated with a 2.08 times higher risk of RCR in recessive model [odds ratio (OR): 2.08, 95% confidence interval (CI): 1.16-3.75]. In codominant model, the individuals homozygous for the minor allele of rs12916 (CC) were associated with a 2.26 times higher risk of RCR (OR: 2.26, 95% CI: 1.16-4.43) while the heterozygous individuals (CT) were not, compared with the individuals homozygous for the major allele of rs12916 (TT). There was no significant association between the SNPs within NPC1L1 gene and RCR in various models.

CONCLUSIONS

We first reported that the variant homozygous CC of rs12916 within HMGCR gene may incur a significantly higher risk of RCR in PTVD patients treated with statins, providing new insights into early individualized guidance of precise lipid-lowering treatment.

A Possible Role for HMG-CoA Reductase Inhibitors and Its Association with HMGCR Genetic Variation in Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease characterised by both motor- and non-motor symptoms, including cognitive impairment. The aetiopathogenesis of PD, as well as its protective and susceptibility factors, are still elusive. Neuroprotective effects of 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase inhibitors-statins-via both cholesterol-dependent and independent mechanisms have been shown in animal and cell culture models. However, the available data provide conflicting results on the role of statin treatment in PD patients. Moreover, cholesterol is a vital component for brain functions and may be considered as protective against PD. We present possible statin effects on PD under the hypothesis that they may depend on the HMG-CoA reductase gene (HMGCR) variability, such as haplotype 7, which was shown to affect cholesterol synthesis and statin treatment outcome, diminishing possible neuroprotection associated with HMG-CoA reductase inhibitors administration. Statins are among the most prescribed groups of drugs. Thus, it seems important to review the available data in the context of their possible neuroprotective effects in PD, and the HMG-CoA reductase gene's genetic variability.

Pharmacogenetics in Cardiovascular Medicine

Considerable interindividual variability in response to cardiovascular pharmacotherapy exists with drug responses varying from being efficacious to inadequate to induce severe adverse events. Fueled by advancements and multidisciplinary collaboration across disciplines such as genetics, bioinformatics, and basic research, the vision of personalized medicine, rather than a one-size-fits-all approach, may be within reach. Pharmacogenetics offers the potential to optimize the benefit-risk profile of drugs by tailoring diagnostic and treatment strategies according to the individual patient. To date, a multitude of studies has tried to delineate the effects of gene-drug interactions for drugs commonly used to treat cardiovascular-related disease. The focus of this review is on how genetic variability may modify drug responsiveness and patient outcomes following therapy with commonly used cardiovascular drugs including clopidogrel, warfarin, statins, and β-blockers. Also included are examples of how genetic studies can be used to guide drug discovery and examples of how genetic information may be deployed in clinical decision making.

Orally Administered Baker's Yeast β-Glucan Promotes Glucose and Lipid Homeostasis in the Livers of Obesity and Diabetes Model Mice

Baker's yeast glucan (BYG) has been reported to be an anti-diabetic agent. In the work described herein, further study on the effect of orally administered BYG on glucose and lipid homeostasis in the livers of ob/ob mice was performed. It was found that BYG decreased the blood glucose and the hepatic glucose and lipid disorders. Western blotting analysis revealed that BYG up-regulated p-AKT and p-AMPK, and down-regulated p-Acc in the liver. Furthermore, RNA-Seq analysis indicated that BYG down-regulated genes responsible for gluconeogenesis (G6pase and Got1), fatty acid biosynthesis (Acly, Acc, Fas, etc.), glycerolipid synthesis (Gpam and Lipin1/2), and cholesterol synthesis (Hmgcr, Fdps, etc.). Additionally, BYG decreased glucose transporters SGLT1 and GLUT2, fat emulsification, and adipogenic genes/proteins in the intestine to decrease glucose and lipid absorption. All these findings demonstrated that BYG is beneficial for regulating glucose and lipid homeostasis in diabetic mice, and thus has potential applications in anti-diabetic foods or drugs.

Statin-induced expression change of INSIG1 in lymphoblastoid cell lines correlates with plasma triglyceride statin response in a sex-specific manner

Statins are widely prescribed to lower plasma low-density lipoprotein (LDL) cholesterol levels. They also modestly reduce plasma triglyceride (TG), an independent cardiovascular disease risk factor, in most people. The mechanism and inter-individual variability of TG statin response is poorly understood. We measured statin-induced gene expression changes in lymphoblastoid cell lines derived from 150 participants of a simvastatin clinical trial and identified 23 genes (false discovery rate, FDR=15%) with expression changes correlated with plasma TG response. The correlation of insulin-induced gene 1 (INSIG1) expression changes with TG response (rho=0.32, q=0.11) was driven by men (interaction P=0.0055). rs73161338 was associated with INSIG1 expression changes (P=5.4 × 10-5) and TG response in two statin clinical trials (P=0.0048), predominantly in men. A combined model including INSIG1 expression level and splicing changes accounted for 29.5% of plasma TG statin response variance in men (P=5.6 × 10-6). Our results suggest that INSIG1 variation may contribute to statin-induced changes in plasma TG in a sex-specific manner.

Pharmacogenetic Foundations of Therapeutic Efficacy and Adverse Events of Statins

Background: In the era of precision medicine, more attention is paid to the search for predictive markers of treatment efficacy and tolerability. Statins are one of the classes of drugs that could benefit from this approach because of their wide use and their incidence of adverse events. Methods: Literature from PubMed databases and bibliography from retrieved publications have been analyzed according to terms such as statins, pharmacogenetics, epigenetics, toxicity and drug–drug interaction, among others. The search was performed until 1 October 2016 for articles published in English language. Results: Several technical and methodological approaches have been adopted, including candidate gene and next generation sequencing (NGS) analyses, the latter being more robust and reliable. Among genes identified as possible predictive factors associated with statins toxicity, cytochrome P450 isoforms, transmembrane transporters and mitochondrial enzymes are the best characterized. Finally, the solute carrier organic anion transporter family member 1B1 (SLCO1B1) transporter seems to be the best target for future studies. Moreover, drug–drug interactions need to be considered for the best approach to personalized treatment. Conclusions: Pharmacogenetics of statins includes several possible genes and their polymorphisms, but muscular toxicities seem better related to SLCO1B1 variant alleles. Their analysis in the general population of patients taking statins could improve treatment adherence and efficacy; however, the cost–efficacy ratio should be carefully evaluated.

Impact of New Genomic Technologies on Understanding Adverse Drug Reactions

It is well established that variations in genes can alter the pharmacokinetic and pharmacodynamic profile of a drug and immunological responses to it. Early advances in pharmacogenetics were made with traditional genetic techniques such as functional cloning of genes using knowledge gained from purified proteins, and candidate gene analysis. Over the past decade, techniques for analysing the human genome have accelerated greatly as knowledge and technological capabilities have grown. These techniques were initially focussed on understanding genetic factors of disease, but increasingly they are helping to clarify the genetic basis of variable drug responses and adverse drug reactions (ADRs). We examine genetic methods that have been applied to the understanding of ADRs, review the current state of knowledge of genetic factors that influence ADR development, and discuss how the application of genome-wide association studies and next-generation sequencing approaches is supporting and extending existing knowledge of pharmacogenetic processes leading to ADRs. Such approaches have identified single genes that are major contributing genetic risk factors for an ADR, (such as flucloxacillin and drug-induced liver disease), making pre-treatment testing a possibility. They have contributed to the identification of multiple genetic determinants of a single ADR, some involving both pharmacologic and immunological processes (such as phenytoin and severe cutaneous adverse reactions). They have indicated that rare genetic variants, often not previously reported, are likely to have more influence on the phenotype than common variants that have been traditionally tested for. The problem of genotype/phenotype discordance affecting the interpretation of pharmacogenetic screening and the future of genome-based testing applied to ADRs are also discussed.

Prediction of atorvastatin plasmatic concentrations in healthy volunteers using integrated pharmacogenetics sequencing

AIM

To use variants found by next-generation sequencing to predict atorvastatin plasmatic concentration profiles (AUC) in healthy volunteers.

SUBJECTS & METHODS

A total of 60 healthy Mexican volunteers were enrolled in this study. We used variants with a predicted functional effect across 20 genes involved in atorvastatin metabolism to construct a regression model using a support vector approach with a radial basis function kernel to predict AUC refining it afterwards in order to explain a greater extent of the variance.

RESULTS

The final support vector regression model using 60 variants (including six novel variants) explained 94.52% of the variance in atorvastatin AUC.

CONCLUSION

An integrated analysis of several genes known to intervene in the different steps of metabolism is required to predict atorvastatin's AUC.

Pharmacogenomics of statins: understanding susceptibility to adverse effects

Statins are a cornerstone of the pharmacologic treatment and prevention of atherosclerotic cardiovascular disease. Atherosclerotic disease is a predominant cause of mortality and morbidity worldwide. Statins are among the most commonly prescribed classes of medications, and their prescribing indications and target patient populations have been significantly expanded in the official guidelines recently published by the American and European expert panels. Adverse effects of statin pharmacotherapy, however, result in significant cost and morbidity and can lead to nonadherence and discontinuation of therapy. Statin-associated muscle symptoms occur in ~10% of patients on statins and constitute the most commonly reported adverse effect associated with statin pharmacotherapy. Substantial clinical and nonclinical research effort has been dedicated to determining whether genetics can provide meaningful insight regarding an individual patient’s risk of statin adverse effects. This contemporary review of the relevant clinical research on polymorphisms in several key genes that affect statin pharmacokinetics (eg, transporters and metabolizing enzymes), statin efficacy (eg, drug targets and pathways), and end-organ toxicity (eg, myopathy pathways) highlights several promising pharmacogenomic candidates. However, SLCO1B1 521C is currently the only clinically relevant pharmacogenetic test regarding statin toxicity, and its relevance is limited to simvastatin myopathy.

RP1-13D10.2 Is a Novel Modulator of Statin-Induced Changes in Cholesterol

BACKGROUND

Numerous genetic contributors to cardiovascular disease risk have been identified through genome-wide association studies; however, identifying the molecular mechanism underlying these associations is not straightforward. The Justification for the Use of Statins in Primary Prevention: An Intervention Trial Evaluating Rosuvastatin (JUPITER) trial of rosuvastatin users identified a sub-genome-wide association of rs6924995, a single-nucleotide polymorphism ≈10 kb downstream of myosin regulatory light chain interacting protein (MYLIP, aka IDOL and inducible degrader of low-density lipoprotein receptor [LDLR]), with LDL cholesterol statin response. Interestingly, although this signal was initially attributed to MYLIP, rs6924995 lies within RP1-13D10.2, an uncharacterized long noncoding RNA.

METHODS AND RESULTS

Using simvastatin and sham incubated lymphoblastoid cell lines from participants of the Cholesterol and Pharmacogenetics (CAP) simvastatin clinical trial, we found that statin-induced change in RP1-13D10.2 levels differed between cell lines from the tails of the white and black low-density lipoprotein cholesterol response distributions, whereas no difference in MYLIP was observed. RP1-13D10.2 overexpression in Huh7 and HepG2 increased LDLR transcript levels, increased LDL uptake, and decreased media levels of apolipoprotein B. In addition, we found a trend of slight differences in the effects of RP1-13D10.2 overexpression on LDLR transcript levels between hepatoma cells transfected with the rs6924995 A versus G allele and a suggestion of an association between rs6924995 and RP1-10D13.2 expression levels in the CAP lymphoblastoid cell lines. Finally, RP1-13D10.2 expression levels seem to be sterol regulated, consistent with its potential role as a novel lipid regulator.

CONCLUSIONS

RP1-13D10.2 is a long noncoding RNA that regulates LDLR and may contribute to low-density lipoprotein cholesterol response to statin treatment. These findings highlight the potential role of noncoding RNAs as determinants of interindividual variation in drug response.

Seventeen years of statin pharmacogenetics: a systematic review

AIM

We evaluated the evidence of pharmacogenetic associations with statins in a systematic review.

METHODS

Two separate outcomes were considered of interest: modification of low-density lipoprotein cholesterol (LDL-C) response and modification of risk for cardiovascular events.

RESULTS

In candidate gene studies, 141 loci were claimed to be associated with LDL-C response. Only 5% of these associations were positively replicated. In addition, six genome-wide association studies of LDL-C response identified common SNPs in APOE, LPA, SLCO1B1, SORT1 and ABCG2 at genome-wide significance. None of the investigated SNPs consistently affected the risk reduction for cardiovascular events.

CONCLUSION

Only five genetic loci were consistently associated with LDL-C response. However, as effect sizes are modest, there is no evidence for the value of genetic testing in clinical practice.

Genetic Contribution of Variants near SORT1 and APOE on LDL Cholesterol Independent of Obesity in Children

Objective

To assess potential effects of variants in six lipid modulating genes (SORT1, HMGCR, MLXIPL, FADS2, APOE and MAFB) on early development of dyslipidemia independent of the degree of obesity in children, we investigated their association with total (TC), low density lipoprotein (LDL-C), high density lipoprotein (HDL-C) cholesterol and triglyceride (TG) levels in 594 children. Furthermore, we evaluated the expression profile of the candidate genes during human adipocyte differentiation.

Results

Expression of selected genes increased 10¹ to >10⁴ fold during human adipocyte differentiation, suggesting a potential link with adipogenesis. In genetic association studies adjusted for age, BMI SDS and sex, we identified significant associations for rs599839 near SORT1 with TC and LDL-C and for rs4420638 near APOE with TC and LDL-C. We performed Bayesian modelling of the combined lipid phenotype of HDL-C, LDL-C and TG to identify potentially causal polygenic effects on this multi-dimensional phenotype and considering obesity, age and sex as a-priori modulating factors. This analysis confirmed that rs599839 and rs4420638 affect LDL-C.

Conclusion

We show that lipid modulating genes are dynamically regulated during adipogenesis and that variants near SORT1 and APOE influence lipid levels independent of obesity in children. Bayesian modelling suggests causal effects of these variants.

Cardiovascular pharmacogenomics: current status and future directions

Drugs are widely used and highly effective in the treatment of heart disease. Nevertheless, in some instances, even drugs effective in a population display lack of efficacy or adverse drug reactions in individual patients, often in an apparently unpredictable fashion. This review summarizes the genomic factors now known to influence variability in responses to widely used cardiovascular drugs such as clopidogrel, warfarin, heparin and statins. Genomic approaches being used to discover new pathways in common cardiovascular diseases and thus potential new targets for drug development are described. Finally, the way in which this new information is likely to be used in an electronic medical record environment is discussed.

Recent developments in genome and exome-wide analyses of plasma lipids

PURPOSE OF REVIEW

Genome-wide association scans (GWAS) have identified over 100 human loci associated with variation in lipids. The identification of novel genes and variants that affect lipid levels is made possible by next-generation sequencing, rare variant discovery and analytic advances. The current status of the genetic basis of lipid traits will be presented.

RECENT FINDINGS

Expansion of GWAS sample sizes for lipid traits has not substantially increased the proportion of trait variance explained by common genetic variants (less than 15% of trait variation captured). Although GWAS has discovered novel loci and pathways with putative biological function and impact on cardiovascular disease risk, discovery of the genes in these loci remains challenging. Exome sequencing promises to identify genes with protein-coding variants with a large impact on lipids, as shown for LDL-cholesterol levels associated with novel (PNPLA5) and known (LDLR, PCSK9, APOB) genes.

SUMMARY

Current results have increased our understanding of the genetic architecture of lipids, expanding the range of effect and frequency for variants identified for lipid traits. Identification of novel lipid-associated gene variants, even if small in effect or rare in the population, could provide important novel drug targets and biological pathways for dyslipidemia.

Pharmacogenomics of statin therapy: any new insights in efficacy or safety?

PURPOSE OF REVIEW

To examine the current evidence concerning the effects of genetic variation on statin-related low-density lipoprotein cholesterol reductions, clinical efficacy, and adverse events and the relevance for patient care.

RECENT FINDINGS

Recent years have seen the emergence of large-scale genetic experiments, including genome-wide association studies and candidate gene studies, exploring the impact of common genetic variation on patient response to statins. These studies have built on previous smaller scale evidence, providing improved statistical power and enhanced ability to explore the genome. Current evidence suggests that common genetic variants do not alter low-density lipoprotein cholesterol response by more than a few percent, or materially alter the effect of statin on vascular risk reduction, and therefore that patients benefit from statins independent of common genetic variation. However, knowledge of SLCO1B1 genotypes is believed to have clinical utility for predicting myopathy risk and ensuring that statins are prescribed as safely as possible. Furthermore, new hypothesis-generating studies, such as those associating GATM with myopathy risk, offer potential insights for the future.

SUMMARY

Common genetic variation does not appear to be an important determinant of statin response, with the exception of SLCO1B1 and risk of myopathy. Future studies will help to determine the impact of low-frequency and rare genetic variation on statin response.

Resistance and intolerance to statins

BACKGROUND AND AIMS

Many patients treated with statins are considered statin-resistant because they fail to achieve adequate reduction of low density lipoprotein cholesterol (LDL-C) levels. Some patients are statin-intolerant because they are unable to tolerate statin therapy at all or to tolerate a full therapeutic statin dose because of adverse effects, particularly myopathy and increased activity of liver enzymes.

RESULTS

The resistance to statins has been associated with polymorphisms in the 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMG-CoA-R), P-glycoprotein (Pg-P/ABCB1), breast cancer resistance protein (BCRP/ABCG2), multidrug resistance-associated proteins (MRP1/ABCC1 and MRP2/ABCC2), organic anion transporting polypeptides (OATP), RHOA, Nieman-Pick C1-like1 protein (NPC1L1), farnesoid X receptor (FXR), cholesterol 7alpha-hydroxylase (CYP7A1), Apolipoprotein E (ApoE), proprotein convertase subtilisin/kexin type 9 (PCSK9), low density lipoprotein receptor (LDLR), lipoprotein (a) (LPA), cholesteryl ester transfer protein (CETP), and tumor necrosis factor α (TNF-α) genes. However, currently, there is still not enough evidence to advocate pharmacogenetic testing before initiating statin therapy. Patients with inflammatory states and HIV infection also have diminished LDL-C lowering as a response to statin treatment. Pseudo-resistance due to nonadherence or non-persistence in real-life circumstances is probably the main cause of insufficient LDL-C response to statin treatment.

CONCLUSIONS

If a patient is really statin-resistant or statin-intolerant, several other treatment possibilities are nowadays available: ezetimibe alone or in combination with bile acid sequestrants, and possibly in the near future mipomersen, lomitapide, or monoclonal antibodies against PCSK9.

Prediction of LDL cholesterol response to statin using transcriptomic and genetic variation

Background

Statins are widely prescribed for lowering LDL-cholesterol (LDLC) levels and risk of cardiovascular disease. There is, however, substantial inter-individual variation in the magnitude of statin-induced LDLC reduction. To date, analysis of individual DNA sequence variants has explained only a small proportion of this variability. The present study was aimed at assessing whether transcriptomic analyses could be used to identify additional genetic contributions to inter-individual differences in statin efficacy.

Results

Using expression array data from immortalized lymphoblastoid cell lines derived from 372 participants of the Cholesterol and Pharmacogenetics clinical trial, we identify 100 signature genes differentiating high versus low statin responders. A radial-basis support vector machine prediction model of these signature genes explains 12.3% of the variance in statin-mediated LDLC change. Addition of SNPs either associated with expression levels of the signature genes (eQTLs) or previously reported to be associated with statin response in genome-wide association studies results in a combined model that predicts 15.0% of the variance. Notably, a model of the signature gene associated eQTLs alone explains up to 17.2% of the variance in the tails of a separate subset of the Cholesterol and Pharmacogenetics population. Furthermore, using a support vector machine classification model, we classify the most extreme 15% of high and low responders with high accuracy.

Conclusions

These results demonstrate that transcriptomic information can explain a substantial proportion of the variance in LDLC response to statin treatment, and suggest that this may provide a framework for identifying novel pathways that influence cholesterol metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0460-9) contains supplementary material, which is available to authorized users.

The pharmacogenomics of statins

The statin class of cholesterol-lowering drugs have been used for decades to successfully lower plasma cholesterol concentrations and cardiovascular risk. Adverse effects of statins are generally considered mild, but increase with age of patients and polypharmacy. One aspect of statin therapy that is still difficult for prescribers to predict is the individual's response to statin therapy. Recent advances in the field of pharmacogenomics have indicated variants of candidate genes that affect statin efficacy and safety. In this review, a number of candidates that affect statin pharmacokinetics and pharmacodynamics are discussed. Some of these candidates, in particular those involved in import and efflux of statins, have now been linked to increased risk of side effects. Furthermore, pharmacogenomic studies continue to reveal new players that are involved in the fine-tuning of the complex regulation of cholesterol homeostasis and response to statins.

Robust association of the LPA locus with low-density lipoprotein cholesterol lowering response to statin treatment in a meta-analysis of 30 467 individuals from both randomized control trials and observational studies and association with coronary artery disease outcome during statin treatment

OBJECTIVES

The LPA single-nucleotide polymorphism rs10455872 has been associated with low-density lipoprotein cholesterol (LDLc) lowering response to statins in several randomized control trials (RCTs) and is a known coronary artery disease (CAD) marker. However, it is unclear what residual risk of CAD this marker may have during statin treatment.

METHODS

Using electronic medical records linked to the GoDARTS genotyped population, we identified over 8000 patients on statins in Tayside, Scotland.

RESULTS

We replicated the findings of the RCTs, with the G allele of rs10455872 being associated with a 0.10 mmol/l per allele poorer reduction in LDLc in response to statin treatment, and conducted a meta-analysis with previously published RCTs (P = 1.46 × 10, n = 30 467). We showed an association between rs10455872 and CAD in statin-treated individuals and have replicated this finding in the Utrecht Cardiovascular Pharmacogenetics study (combined odds ratio 1.41, 95% confidence interval 1.17-1.68, P = 4.5 × 10, n = 8822) suggesting that statin treatment does not abrogate this well-established genetic risk for CAD. Furthermore, in a Cox proportional hazards model with LDLc measured time dependently, we demonstrated that the relationship between CAD and rs10455872 was independent of LDLc during statin treatment.

CONCLUSION

Individuals with the G allele of rs10455872, which represents approximately one in seven patients, have a higher risk of CAD than the majority of the population even after treatment with statins; and therefore represent a vulnerable group requiring an alternative medication in addition to statin treatment.

Expanding role of pharmacogenomics in the management of cardiovascular disorders

Cardiovascular disease is a leading cause of death worldwide. Many pharmacologic therapies are available that aim to reduce the risk of cardiovascular disease but there is significant inter-individual variation in drug response, including both efficacy and toxicity. Pharmacogenetics aims to personalize medication choice and dosage to ensure that maximum clinical benefit is achieved whilst side effects are minimized. Over the past decade, our knowledge of pharmacogenetics in cardiovascular therapies has increased significantly. The anticoagulant warfarin represents the most advanced application of pharmacogenetics in cardiovascular medicine. Prospective randomized clinical trials are currently underway utilizing dosing algorithms that incorporate genetic polymorphisms in cytochrome P450 (CYP)2C9 and vitamin k epoxide reductase (VKORC1) to determine warfarin dosages. Polymorphisms in CYP2C9 and VKORC1 account for approximately 40 % of the variance in warfarin dose. There is currently significant controversy with regards to pharmacogenetic testing in anti-platelet therapy. Inhibition of platelet aggregation by aspirin in vitro has been associated with polymorphisms in the cyclo-oxygenase (COX)-1 gene. However, COX-1 polymorphisms did not affect clinical outcomes in patients prescribed aspirin therapy. Similarly, CYP2C19 polymorphisms have been associated with clopidogrel resistance in vitro, and have shown an association with stent thrombosis, but not with other cardiovascular outcomes in a consistent manner. Response to statins has been associated with polymorphisms in the cholesterol ester transfer protein (CETP), apolipoprotein E (APOE), 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, calmin (CLMN) and apolipoprotein-CI (APOC1) genes. Although these genes contribute to the variation in lipid levels during statin therapy, their effects on cardiovascular outcomes requires further investigation. Polymorphisms in the solute carrier organic anion transporter 1B1 (SLCO1B1) gene is associated with increased statin exposure and simvastatin-induced myopathy. Angiotensin-converting enzyme (ACE) inhibitors and β-adrenoceptor antagonists (β-blockers) are medications that are important in the management of hypertension and heart failure. Insertion and deletion polymorphisms in the ACE gene are associated with elevated and reduced serum levels of ACE, respectively. No significant association was reported between the polymorphism and blood pressure reduction in patients treated with perindopril. However, a pharmacogenetic score incorporating single nucleotide polymorphisms (SNPs) in the bradykinin type 1 receptor gene and angiotensin-II type I receptor gene predicted those most likely to benefit and suffer harm from perindopril therapy. Pharmacogenetic studies into β-blocker therapy have focused on variations in the β1-adrenoceptor gene and CYP2D6, but results have been inconsistent. Pharmacogenetic testing for ACE inhibitor and β-blocker therapy is not currently used in clinical practice. Despite extensive research, no pharmacogenetic tests are currently in clinical practice for cardiovascular medicines. Much of the research remains in the discovery phase, with researchers struggling to demonstrate clinical utility and validity. This is a problem seen in many areas of therapeutics and is because of many factors, including poor study design, inadequate sample sizes, lack of replication, and heterogeneity amongst patient populations and phenotypes. In order to progress pharmacogenetics in cardiovascular therapies, researchers need to utilize next-generation sequencing technologies, develop clear phenotype definitions and engage in multi-center collaborations, not only to obtain larger sample sizes but to replicate associations and confirm results across different ethnic groups.

Chapter 7: Pharmacogenomics

There is great variation in drug-response phenotypes, and a “one size fits all” paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.

Individualized risk for statin-induced myopathy: current knowledge, emerging challenges and potential solutions

Skeletal muscle toxicity is the primary adverse effect of statins. In this review, we summarize current knowledge regarding the genetic and nongenetic determinants of risk for statin induced myopathy. Many genetic factors were initially identified through candidate gene association studies limited to pharmacokinetic (PK) targets. Through genome-wide association studies, it has become clear that SLCO1B1 is among the strongest PK predictors of myopathy risk. Genome-wide association studies have also expanded our understanding of pharmacodynamic candidate genes, including RYR2. It is anticipated that deep resequencing efforts will define new loci with rare variants that also contribute, and sophisticated computational approaches will be needed to characterize gene-gene and gene-environment interactions. Beyond environment, race is a critical covariate, and its influence is only partly explained by geographic differences in the frequency of known pharmacodynamic and PK variants. As such, admixture analyses will be essential for a full understanding of statin-induced myopathy.

Impact of common genetic variation on response to simvastatin therapy among 18 705 participants in the Heart Protection Study

Aims

Statins reduce LDL cholesterol (LDL-C) and the risk of vascular events, but it remains uncertain whether there is clinically relevant genetic variation in their efficacy. This study of 18 705 individuals aims to identify genetic variants related to the lipid response to simvastatin and assess their impact on vascular risk response.

Methods and results

A genome-wide study of the LDL-C and apolipoprotein B (ApoB) response to 40 mg simvastatin daily was performed in 3895 participants in the Heart Protection Study, and the nine strongest associations were tested in 14 810 additional participants. Selected candidate genes were also tested in up to 18 705 individuals. There was 90% power to detect differences of 2.5% in LDL-C response (e.g. 42.5 vs. 40% reduction) in the genome-wide study and of 1% in the candidate gene study. None of the associations from the genome-wide study was replicated, and nor were significant associations found for 26 of 36 candidates tested. Novel lipid response associations with variants in LPA, CELSR2/PSRC1/SORT1, and ABCC2 were found, as well as confirmatory evidence for published associations in LPA, APOE, and SLCO1B1. The largest and most significant effects were with LPA and APOE, but were only 2–3% per allele. Reductions in the risk of major vascular events during 5 years of statin therapy among 18 705 high-risk patients did not differ significantly across genotypes associated with the lipid response.

Conclusions

Common genetic variants do not appear to alter the lipid response to statin therapy by more than a few per cent, and there were similar large reductions in vascular risk with simvastatin irrespective of genotypes associated with the lipid response to simvastatin. Consequently, their value for informing clinical decisions related to maximizing statin efficacy appears to be limited.

[Clinical and health economic challenges of personalized medicine]

Healthcare systems across the globe are currently challenged by aging populations, increases in chronic diseases and the difficult task of managing a healthcare budget. In this health economic climate, personalized medicine promises not only an improvement in healthcare delivery but also the possibility of more cost-effective therapies. It is important to remember, however, that personalized medicine has the potential to both increase and decrease costs. Each targeted therapy must be evaluated individually. However, standard clinical trial design is not suitable for personalized therapies. Therefore, both scientists and regulatory authorities will need to accept innovative study designs in order to validate personalized therapies. Hence correct economic evaluations are difficult to carry out due to lack of clear clinical evidence, longitudinal accounting and experience with patient/clinician behavior in the context of personalized medicine. In terms of reimbursement, payers, pharmaceutical companies and companion diagnostic manufacturers will also need to explore creative risk-sharing concepts. Germany is no exception to the challenges that face personalized medicine and for personalized medicine to really become the future of medicine many health economic challenges first need to be overcome. The health economic implications of personalized medicine remain unclear but it is certain that the expansion of targeted therapies in current healthcare systems will create a host of challenges.

Genetic determinants of response to cardiovascular drugs

PURPOSE OF REVIEW

To survey genetic variation contributing to variable responsiveness and toxicity to important cardiovascular drugs and highlight recent developments in the field of cardiovascular pharmacogenomics and personalized medicine.

RECENT FINDINGS

Previously recognized pharmacogenomic associations with drug efficacy have been further validated (e.g. with clopidogrel and warfarin) and shown to influence clinically important outcomes. The clinical significance of variants modulating toxicity (e.g. SLCO1B1 with simvastatin) has also been confirmed. The genetic contribution to variable efficacy and toxicity of other important classes of cardiovascular drugs, such as beta-blockers, is becoming increasingly recognized. Prospective trials testing whether the use of genomic information improves clinical care are underway. Guidance based on the most well-established pharmacogenomic findings has appeared in prescribing labeling and is in the early stages of being implemented into routine clinical care.

SUMMARY

Clinically validated gene variants that modulate responsiveness to cardiovascular drugs continue to be discovered and validated. Early steps are underway to translate these discoveries into clinical care.

Statin dose in Asians: is pharmacogenetics relevant?

Historically the efficacy and safety of statins has mostly been studied in western populations. Few such studies have been carried out in Asians until recent years. These studies revealed interesting similarities and differences for statin use between Asians and Caucasians. One clinically important question subsequently raised is whether Asians need lower statin doses compared with Caucasians. Many practicing physicians believe that statin doses are lower in Asians because of the generally lower bodyweight and BMI. Whether this belief is based on sound scientific evidence needs to be reviewed. Furthermore, since the decision of optimal dose is based on both efficacy and safety, both of which may be impacted by genetic factors, one may ask whether pharmacogenetics plays a role in the dose difference, if such a difference exists. There is a clinical need to critically and comprehensively article the literature to answer these questions, and summarize future directions of research in the field. This article serves the above purpose.

Clinical utility of pharmacogenetic biomarkers in cardiovascular therapeutics: a challenge for clinical implementation

In the past decade, significant strides have been made in the area of cardiovascular pharmacogenomic research, with the discovery of associations between certain genotypes and drug-response phenotypes. While the motivations for personalized and predictive medicine are promising for patient care and support a model of health system efficiency, the implementation of pharmacogenomics for cardiovascular therapeutics on a population scale faces substantial challenges. The greatest obstacle to clinical implementation of cardiovascular pharmacogenetics may be the lack of both reproducibility and agreement about the validity and utility of the findings. In this review, we present the scientific evidence in the literature for diagnostic variants for the US FDA-labeled cardiovascular therapies, namely CYP2C19 and clopidogrel, CYP2C9/VKORC1 and warfarin, and CYP2D6/ADRB1 and β-blockers. We also discuss the effect of HMGCR/LDLR in decreasing the effectiveness of low-density lipoprotein cholesterol with statin therapy, the SLCO1B1 genotype and simvastatin myotoxicity, and ADRB1/ADD1 for antihypertensive response.

Association between diet and polymorphisms in individuals with statin-controlled dyslipidaemia grouped according to oxidative stress biomarkers

The objective of this study was to investigate whether differences in diet and in single-nucleotide polymorphisms (SNPs) found in paraoxonase-1 (PON-1), 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR), cholesterol ester transfer protein (CETP) and apolipoprotein E (APOE) genes, are associated with oxidative stress biomarkers and consequently with susceptibility of low-density cholesterol (LDL) to oxidation. A multivariate approach was applied to a group of 55 patients according to three biomarkers: plasma antioxidant activity, malondialdehyde and oxidized LDL (oxLDL) concentrations. Individuals classified in Cluster III showed the worst prognoses in terms of antioxidant activity and oxidative status. Individuals classified in Cluster I presented the lowest oxidative status, while individuals grouped in Cluster II presented the highest levels of antioxidant activity. No difference in nutrient intake was observed among the clusters. Significantly higher γ- and δ-tocopherol concentrations were observed in those individuals with the highest levels of antioxidant activity. No single linear regression was statistically significant, suggesting that mutant alleles of the SNPs selected did not contribute to the differences observed in oxidative stress response. Although not statistically significant, the p value of the APO E coefficient for oxLDL response was 0.096, indicating that patients who carry the TT allele of the APO E gene tend to present lower plasma oxLDL concentrations. Therefore, the differences in oxidative stress levels observed in this study could not be attributed to diet or to the variant alleles of PON-1, CETP, HMGCR or APO E. This data supports the influence of γ-tocopherol and δ-tocopherol on antioxidant activity, and highlights the need for further studies investigating APO E alleles and LDL oxidation.

Genome-wide association study of genetic determinants of LDL-c response to atorvastatin therapy: importance of Lp(a)

We carried out a genome-wide association study (GWAS) of LDL-c response to statin using data from participants in the Collaborative Atorvastatin Diabetes Study (CARDS; n = 1,156), the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT; n = 895), and the observational phase of ASCOT (n = 651), all of whom were prescribed atorvastatin 10 mg. Following genome-wide imputation, we combined data from the three studies in a meta-analysis. We found associations of LDL-c response to atorvastatin that reached genome-wide significance at rs10455872 (P = 6.13 × 10(-9)) within the LPA gene and at two single nucleotide polymorphisms (SNP) within the APOE region (rs445925; P = 2.22 × 10(-16) and rs4420638; P = 1.01 × 10(-11)) that are proxies for the ε2 and ε4 variants, respectively, in APOE. The novel association with the LPA SNP was replicated in the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) trial (P = 0.009). Using CARDS data, we further showed that atorvastatin therapy did not alter lipoprotein(a) [Lp(a)] and that Lp(a) levels accounted for all of the associations of SNPs in the LPA gene and the apparent LDL-c response levels. However, statin therapy had a similar effect in reducing cardiovascular disease (CVD) in patients in the top quartile for serum Lp(a) levels (HR = 0.60) compared with those in the lower three quartiles (HR = 0.66; P = 0.8 for interaction). The data emphasize that high Lp(a) levels affect the measurement of LDL-c and the clinical estimation of LDL-c response. Therefore, an apparently lower LDL-c response to statin therapy may indicate a need for measurement of Lp(a). However, statin therapy seems beneficial even in those with high Lp(a).

Cardiovascular pharmacogenomics

Patients vary in their responses to drug therapy, and some of that variability is genetically determined. This review outlines general approaches used to identify genetic variation that influences drug response. Examples from specific therapeutic areas are presented, such as cholesterol management, arrhythmias, heart failure, hypertension, warfarin anticoagulation, and antiplatelet agents. A brief view of potential pathways to implementation is presented.

Bridging the efficacy-effectiveness gap: a regulator's perspective on addressing variability of drug response

Drug regulatory agencies should ensure that the benefits of drugs outweigh their risks, but licensed medicines sometimes do not perform as expected in everyday clinical practice. Failure may relate to lower than anticipated efficacy or a higher than anticipated incidence or severity of adverse effects. Here we show that the problem of benefit-risk is to a considerable degree a problem of variability in drug response. We describe biological and behavioural sources of variability and how these contribute to the long-known efficacy-effectiveness gap. In this context, efficacy describes how a drug performs under conditions of clinical trials, whereas effectiveness describes how it performs under conditions of everyday clinical practice. We argue that a broad range of pre- and post-licensing technologies will need to be harnessed to bridge the efficacy-effectiveness gap. Successful approaches will not be limited to the current notion of pharmacogenomics-based personalized medicines, but will also entail the wider use of electronic health-care tools to improve drug prescribing and patient adherence.

Identification and functional characterization in vivo of a novel splice variant of LDLR in rhesus macaques

In the course of developing a low-density lipoprotein receptor (LDLR) gene therapy treatment for homozygous familial hypercholesterolemia (HoFH), we planned to examine the efficacy in a nonhuman primate model, the rhesus macaque heterozygous for an LDL receptor mutation fed a high-fat diet. Unexpectedly, our initial cDNA sequencing studies led to the identification of a heretofore unidentified splicing isoform of the rhesus LDLR gene. Compared with the publicly available GenBank reference sequence of rhesus LDLR, the novel isoform contains a 21 bp in frame insertion. This sequence coincides with part of exon 5 and creates a site for the restriction enzyme MscI. Using this site as a marker for the 21 bp in-frame insertion, we conducted a restriction enzyme screen to examine for the prevalence of the novel isoform in rhesus liver tissue cDNA and its homolog in human liver tissue cDNA. We found that the novel isoform is the predominant LDLR cDNA found in rhesus liver and the sole LDLR cDNA found in human liver. Finally, we compared the in vivo functionality of the novel and previously identified rhesus LDLR splicing isoforms in a mouse model of HoFH.

Clinical implications of pharmacogenetic variation on the effects of statins

The last decade has seen an increase in the trend of HMG-CoA reductase inhibitor (statin) usage in the Western world, which does not come as a surprise noting that the latest American Heart Association heart and stroke statistics indicate an alarming prevalence of 80  million Americans (one in three) with one or more forms of diagnosed cardiovascular disease (CVD). Meta-analysis of several large-scale, randomized clinical trials has demonstrated statins to be efficacious in significantly reducing CVD-associated mortality in both primary and secondary prevention. Despite their proven efficacy, statins have also gained attention with respect to adverse drug reactions (ADRs) of muscle myopathy, derangements in hepatic function and even ADRs classified as psychiatric in nature. The depletion of cholesterol within the myocyte cell wall and/or the depletion of key intermediates within the cholesterol synthesis pathway are hypothesized as possible mechanisms of statin-associated ADRs. However, pharmacogenetic variability may also be a risk factor for ADRs and can include, for example, enzymes, transporters, cell membrane receptors, intracellular receptors or components of ion channels that contribute to the pharmacokinetics or pharmacodynamics of response to a particular drug. The cytochrome P450 (CYP) enzymatic pathways that comprise the polymorphic genes, CYP2D6, CYP3A4 and CYP3A5, and also a hepatic transporter, solute carrier organic anion transporter (SLCO1B1), which is a single nucleotide polymorphism discovered to be associated with statin-induced myopathy through a genome-wide association study, are discussed with respect to their effect on altering the pharmacokinetic profile of statin metabolism. Variants of the Apolipoprotein E (APO-E) gene, polymorphisms in the cholesteryl ester transfer protein (CETP) gene, the HMG-CoA reductase gene and other proteins are discussed with respect to altering the pharmacodynamic profile of statins. Pharmacogenetics and its application in medicine to individualize drug therapy has been previously shown to be clinically and economically beneficial through quality-adjusted life-year assessment. Therefore, polymorphisms affecting the pharmacokinetic and pharmacodynamic profiles of statins, which are widely used in therapy, with their potential application in the personalized prescribing of statin therapy, need further research. In this review, we update the recent literature with respect to genetic polymorphisms that may influence the pharmacokinetics and pharmacodynamics of statin therapy, and consider the relevance of these findings to the efficacy of treatment, prevention of ADRs and what this may mean for patient tolerance and compliance.

Common nonsynonymous substitutions in SLCO1B1 predispose to statin intolerance in routinely treated individuals with type 2 diabetes: a go-DARTS study

SLCO1B1 gene variants are associated with severe statin-induced myopathy. We examined whether these variants are also associated with general statin intolerance in a large population of patients with type 2 diabetes receiving statins as part of routine clinical care. A total of 4,196 individuals were genotyped for rs4149056 (Val174Ala) and rs2306283 (Asp130Asn). Intolerance was defined by serum biochemistry and also by discontinuation, switching, or reduction in dose of the prescribed statin drug. Ala174 was associated with higher intolerance (odds ratio = 2.05, P = 0.043), whereas Asp130 was associated with lower intolerance (odds ratio = 0.71, P = 0.026). Ala174 was associated with a lower low-density lipoprotein cholesterol (LDLc) response to statins (P = 0.01) whereas 130D was associated with a greater LDLc response to statins (P = 0.048), as previously reported; however, this association was no longer present when data for statin-intolerant individuals were removed from the analysis. This study suggests that common genetic variants selected for an extreme phenotype of statin-induced myopathy also predispose to more common milder statin intolerance and may, for this reason, impact lipid-lowering efficacy.

Functional promoter polymorphisms govern differential expression of HMG-CoA reductase gene in mouse models of essential hypertension

3-Hydroxy-3-methylglutaryl-coenzyme A [HMG-CoA] reductase gene (Hmgcr) is a susceptibility gene for essential hypertension. Sequencing of the Hmgcr locus in genetically hypertensive BPH (blood pressure high), genetically hypotensive BPL (blood pressure low) and genetically normotensive BPN (blood pressure normal) mice yielded a number of single nucleotide polymorphisms (SNPs). BPH/BPL/BPN Hmgcr promoter-luciferase reporter constructs were generated and transfected into liver HepG2, ovarian CHO, kidney HEK-293 and neuronal N2A cells for functional characterization of the promoter SNPs. The BPH-Hmgcr promoter showed significantly less activity than the BPL-Hmgcr promoter under basal as well as nicotine/cholesterol-treated conditions. This finding was consistent with lower endogenous Hmgcr expression in liver and lower plasma cholesterol in BPH mice. Transfection experiments using 5′-promoter deletion constructs (strategically made to assess the functional significance of each promoter SNP) and computational analysis predicted lower binding affinities of transcription factors c-Fos, n-Myc and Max with the BPH-promoter as compared to the BPL-promoter. Corroboratively, the BPH promoter-luciferase reporter construct co-transfected with expression plasmids of these transcription factors displayed less pronounced augmentation of luciferase activity than the BPL construct, particularly at lower amounts of transcription factor plasmids. Electrophoretic mobility shift assays also showed diminished interactions of the BPH promoter with HepG2 nuclear proteins. Taken together, this study provides mechanistic basis for the differential Hmgcr expression in these mouse models of human essential hypertension and have implications for better understanding the role of this gene in regulation of blood pressure.