High-density lipoprotein (HDL) cholesterol: leveraging practice-based biobank cohorts to characterize clinical and genetic predictors of treatment outcome

Biobanks and Their Clinical Application and Informatics Challenges

Biobanks are one of the most important biomedical research resources and contribute to the development of biomarker detection, molecular diagnosis, translational medicine, and multidisciplinary disease research, as well as studies of interactions between genetic and environmental or lifestyle factors. Aiming for the wide clinical application of biobanks, biobanking efforts have recently switched from a focus on accumulating samples to both formalizing and sustaining collections in light of the rapid progress in the fields of personalized medicine and bioinformatics analysis. With the emergence of novel molecular diagnostic technologies, although the bioinformatics platform of biobanks ensures reliable bioinformatics analysis of patient samples, there are a series of challenges facing biobanks in terms of the overall harmonization of policies, integrated processes, and local informatics solutions across the network. Further, there is a controversy regarding the increased role of ethical boards, governance, and accreditation bodies in ensuring that collected samples have sufficient informatics capabilities to be used in biobanks. In this volume, we present a selection of current issues on the inevitable challenges of the clinical application of biobanks in informatics.

Secondary use of clinical data: the Vanderbilt approach

The last decade has seen an exponential growth in the quantity of clinical data collected nationwide, triggering an increase in opportunities to reuse the data for biomedical research. The Vanderbilt research data warehouse framework consists of identified and de-identified clinical data repositories, fee-for-service custom services, and tools built atop the data layer to assist researchers across the enterprise. Providing resources dedicated to research initiatives benefits not only the research community, but also clinicians, patients and institutional leadership. This work provides a summary of our approach in the secondary use of clinical data for research domain, including a description of key components and a list of lessons learned, designed to assist others assembling similar services and infrastructure.

Pharmacogenetics of statins: achievements, whole-genome analyses and future perspectives

Statins are the most commonly prescribed class of drug worldwide and therapy is highly effective in reducing low-density lipoprotein cholesterol levels and cardiovascular events. However, there is large variability in clinical response to statin treatment. Recent research provides evidence that genetic variation contributes to this variable response to statin treatment. Until recently, pharmacogenetic studies have used mainly candidate gene approaches to investigate these effects. Since candidate gene studies explain only a small part of the observed variation and results have often been inconsistent, genome-wide association (GWA) studies may be a better approach. In this paper the most important candidate gene studies and the first published GWA studies assessing statin response are discussed. Moreover, we describe the PHASE study, an EU-funded GWA study that will investigate the genetic variation responsible for the variation in response to pravastatin in a large randomized clinical trial.

CNR1 genotype influences HDL-cholesterol response to change in dietary fat intake

BACKGROUND

Success in further reducing the burden of cardiovascular disease (CVD) is threatened by the increasing prevalence of obesity-related atherogenic dyslipidemia. HDL-cholesterol (HDL-C) level is inversely correlated with CVD risk; each 1 mg/dl decrease in HDL-C is associated with a 6% reduction in risk. We previously showed that a common CNR1 haplotype, H3 (frequency 20%), is protective against the reduction in HDL-C that typically accompanies weight gain. In the present study, we extend that observation by reporting the effect of CNR1 haplotype on HDL-C response to modification of dietary fat intake in weight maintenance and weight loss.

METHODS

Six haplotype tagging SNPs that cover the CNR1 gene locus were genotyped in 590 adults of varying body mass index (cohort 1 is 411 males with BMI 18.5-30.0 kg/m(2); cohort 2 is 71 females with BMI18.5-30.0 kg/m(2); and cohort 3 is 108 females with BMI 30-39.9 kg/m(2)). Dietary intakes were modified so that fat intake in the "high fat" condition was 15-20% greater than in the "low fat" condition, and lipid profiles were compared between carriers versus noncarriers for each of the five commonly observed CNR1 haplotypes (H1-H5).

RESULTS

In normal to overweight subjects on eucaloric diets, the H3 haplotype was significantly associated with short-term high fat diet induced changes in HDL-C level in females (carriers 5.9 mg/dl>noncarriers, p = 0.007). The H3 haplotype was also significantly associated with HDL-C level after 16 weeks on high fat calorie restricted diet in obese females (carriers 6.8 mg/dl>noncarriers, p = 0.009).

CONCLUSION

Variability within the CNR1 gene locus contributes to gender-related differences in the HDL-cholesterol response to change in dietary fat intake. Functional characterization of this relationship in vitro may offer insights that potentially yield therapeutic guidance targeting dietary macronutrient composition, a direction much needed in the current epidemic of obesity.

CYP4A11 variant is associated with high-density lipoprotein cholesterol in women

The ω-hydroxylase CYP4A11 catalyzes the transformation of epoxyeicosatrienoic acids to omega-hydroxylated-epoxyeicosatrienoic acids, endogenous peroxisome proliferator-activated receptor α (PPARα) agonists. PPARα activation increases high-density lipoprotein-cholesterol (HDL-C). A cytosine-for-thymidine (T8590C) variant of CYP4A11 encodes for a ω-hydroxylase with reduced activity. This study examined the relationship between CYP4A11 T8590C genotype and metabolic parameters in the Framingham Offspring Study and in a clinical practice-based biobank, BioVU. In women in the Framingham Offspring Study, the CYP4A11 8590C allele was associated with reduced HDL-C concentrations (54.2±0.9 mg/dL in CYP4A11 CC or CT genotype women versus 56.7±0.5 mg/dL in TT women, p=0.02), and with an increased prevalence of low HDL-C, defined categorically as ≤50mg/dL [odds ratio 1.39 (95% CI 1.02-1.90), p=0.04]. In the BioVU cohort, the CYP4A11 8590C allele was also associated with low HDL-C in women [odds ratio 1.69 (95% CI 1.03-2.77, p=0.04)]. There was no relationship between genotype and HDL-C in men in either cohort.