Reclassification of cardiovascular risk using integrated clinical and molecular biosignatures: Design of and rationale for the Measurement to Understand the Reclassification of Disease of Cabarrus and Kannapolis (MURDOCK) Horizon 1 Cardiovascular Disease Study

A Bayesian genomic selection approach incorporating prior feature ordering and population structures with application to coronary artery disease

Coronary artery disease is one of the most common types of cardiovascular disease. Death from coronary heart disease is influenced by genetic factors in both women and men. In this article, we propose a novel Bayesian variable selection framework for the identification of important genetic variants associated with coronary artery disease disease status. Instead of treating each feature independently as in conventional Bayesian variable selection methods, we propose an innovative prior for the inclusion probabilities of genetic variants that accounts for their ordering structure. We assume that neighboring variants are more likely to be selected together as they tend to be highly correlated and have similar biological functions. Additionally, we propose to group participating subjects based on underlying population structure and fit separate regressions, so that the regression coefficients can better reflect different disease risks in different population groups. Our approach borrows strength across regression models through an innovative prior inspired by the Markov random fields. The proposed framework can improve variable selection and prediction performances as demonstrated in the simulation studies. We also apply the proposed framework to the CATHeterization GENetics data with binary Coronary artery disease disease status.

Novel plasma biomarkers improve discrimination of metabolic health independent of weight

We sought to determine if novel plasma biomarkers improve traditionally defined metabolic health (MH) in predicting risk of cardiovascular disease (CVD) events irrespective of weight. Poor MH was defined in CATHGEN biorepository participants (n > 9300), a follow-up cohort (> 5600 days) comprising participants undergoing evaluation for possible ischemic heart disease. Lipoprotein subparticles, lipoprotein-insulin resistance (LP-IR), and GlycA were measured using NMR spectroscopy (n = 8385), while acylcarnitines and amino acids were measured using flow-injection, tandem mass spectrometry (n = 3592). Multivariable Cox proportional hazards models determined association of poor MH and plasma biomarkers with time-to-all-cause mortality or incident myocardial infarction. Low-density lipoprotein particle size and high-density lipoprotein, small and medium particle size (HMSP), GlycA, LP-IR, short-chain dicarboxylacylcarnitines (SCDA), and branched-chain amino acid plasma biomarkers were independently associated with CVD events after adjustment for traditionally defined MH in the overall cohort (p = 3.3 × 10-4-3.6 × 10-123), as well as within most of the individual BMI categories (p = 8.1 × 10-3-1.4 × 10-49). LP-IR, GlycA, HMSP, and SCDA improved metrics of model fit analyses beyond that of traditionally defined MH. We found that LP-IR, GlycA, HMSP, and SCDA improve traditionally defined MH models in prediction of adverse CVD events irrespective of BMI.

Preliminary evidence of effects of potassium chloride on a metabolomic path to diabetes and cardiovascular disease

INTRODUCTION

Low potassium intake can affect cardiovascular disease (CVD) risk and cardiometabolic risk factors.

OBJECTIVE

We hypothesize that potassium chloride (KCl) supplementation can improve cardiovascular risk metabolomic profile.

METHODS

In this secondary analysis of a pilot randomized clinical trial (RCT) of 26 participants with prediabetes randomized to KCl or placebo, we performed targeted mass-spectrometry-based metabolomic profiling on baseline and 12-week (end-of-study) plasma samples. Principal component analysis (PCA) was used to reduce the many correlated metabolites into fewer, independent factors that retain most of the information in the original data.

RESULTS

Those taking KCl had significant reductions (corresponding to lower cardiovascular risk) in the branched-chain amino acids (BCAA) factor (P = 0.004) and in valine levels (P = 0.02); and non-significant reductions in short-chain acylcarnitines (SCA) factor (P = 0.11).

CONCLUSIONS

KCl supplementation may improve circulating BCAA levels, which may reflect improvements in overall cardiometabolic risk profile.

CLINICAL TRIALS REGISTRY

Clinicaltrials.gov identifier: NCT02236598; https://clinicaltrials.gov/ct2/show/NCT02236598.

Harnessing technology and molecular analysis to understand the development of cardiovascular diseases in Asia: a prospective cohort study (SingHEART)

Background

Cardiovascular disease (CVD) imposes much mortality and morbidity worldwide. The use of “deep learning”, advancements in genomics, metabolomics, proteomics and devices like wearables have the potential to unearth new insights in the field of cardiology. Currently, in Asia, there are no studies that combine the use of conventional clinical information with these advanced technologies. We aim to harness these new technologies to understand the development of cardiovascular disease in Asia.

Methods

Singapore is a multi-ethnic country in Asia with well-represented diverse ethnicities including Chinese, Malays and Indians. The SingHEART study is the first technology driven multi-ethnic prospective population-based study of healthy Asians. Healthy male and female subjects aged 21–69 years old without any prior cardiovascular disease or diabetes mellitus will be recruited from the general population. All subjects are consented to undergo a detailed on-line questionnaire, basic blood investigations, resting and continuous electrocardiogram and blood pressure monitoring, activity and sleep tracking, calcium score, cardiac magnetic resonance imaging, whole genome sequencing and lipidomic analysis. Outcomes studied will include mortality and cause of mortality, myocardial infarction, stroke, malignancy, heart failure, and the development of co-morbidities.

Discussion

An initial target of 2500 patients has been set. From October 2015 to May 2017, an initial 683 subjects have been recruited and have completed the initial work-up the SingHEART project is the first contemporary population-based study in Asia that will include whole genome sequencing and deep phenotyping: including advanced imaging and wearable data, to better understand the development of cardiovascular disease across different ethnic groups in Asia.

High-Density Lipoprotein Particle Subfractions in Heart Failure With Preserved or Reduced Ejection Fraction

BACKGROUND

Circulating high-density lipoprotein particle (HDL-P) subfractions impact atherogenesis, inflammation, and endothelial function, all of which are implicated in the pathobiology of heart failure (HF).

OBJECTIVES

The authors sought to identify key differences in plasma HDL-P subfractions between patients with HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF) to determine their prognostic utility.

METHODS

Patients with HFrEF (n = 782), HFpEF (n = 1,004), and no HF (n = 4,742) were identified in the CATHGEN (Catheterization Genetics) biorepository of sequential patients undergoing cardiac catheterization. Nuclear magnetic resonance-based lipoprotein profiling was performed on frozen fasting plasma obtained at catheterization. The authors used multivariable analysis of covariance to compare high-density lipoprotein particle (HDL-P) subfractions across groups, and Cox proportional hazards modeling to determine associations between HDL-P subfractions and time to death or major adverse cardiac events.

RESULTS

Mean HDL-P size was greater in HFrEF than HFpEF, both of which were greater than in no HF (all 2-way p < 0.0001). By contrast, concentrations of small HDL-P and total HDL-P were lesser in HFrEF than HFpEF, which were both lesser than no HF (all 2-way p ≤ 0.0002). In both HFrEF and HFpEF, total HDL-P and small HDL-P were inversely associated with time to adverse events. These findings persisted after adjustment for 14 clinical covariates (including high-density lipoprotein cholesterol content, coronary artery disease, and the inflammatory biomarker GlycA), and in sensitivity analyses featuring alternate left ventricular ejection fraction definitions, or stricter inclusion criteria with diastolic dysfunction or left ventricular end-diastolic pressure thresholds.

CONCLUSIONS

In the largest analysis of HDL-P subfractions in HF to date, derangements in HDL-P subfractions were identified that were more severe in HFrEF than HFpEF and were independently associated with adverse outcomes. These data may help refine risk assessment and provide new insights into the complex interaction of HDL and HF pathophysiology.

The missing indicator approach for censored covariates subject to limit of detection in logistic regression models

PURPOSE

In several biomedical studies, one or more exposures of interest may be subject to nonrandom missingness because of the failure of the measurement assay at levels below its limit of detection. This issue is commonly encountered in studies of the metabolome using tandem mass spectrometry-based technologies. Owing to a large number of metabolites measured in these studies, preserving statistical power is of utmost interest. In this article, we evaluate the small sample properties of the missing indicator approach in logistic and conditional logistic regression models.

METHODS

For nested case-control or matched case control study designs, we evaluate the bias, power, and type I error associated with the missing indicator method using simulation. We compare the missing indicator approach to complete case analysis and several imputation approaches.

RESULTS

We show that under a variety of settings, the missing indicator approach outperforms complete case analysis and other imputation approaches with regard to bias, mean squared error, and power.

CONCLUSIONS

For nested case-control and matched study designs of modest sample sizes, the missing indicator model minimizes loss of information and thus provides an attractive alternative to the oft-used complete case analysis and other imputation approaches.

Peripheral blood metabolite profiles associated with new onset atrial fibrillation

BACKGROUND

Peripheral blood metabolite profiles have yielded mechanistic insights into various cardiovascular disease states. We hypothesized that peripheral blood metabolite profiles would be associated with new onset atrial fibrillation (AF).

METHODS AND RESULTS

The study population comprised 1892 patients without AF at baseline, who, as part the MURDOCK Cardiovascular Disease Study molecular profiling cohort (n = 2023), had previously had determination of levels of 69 metabolites from frozen, fasting plasma specimens obtained during coronary angiography. We used Cox proportional hazards models to examine the association of 13 uncorrelated metabolite factors created from these data using principal components analysis (PCA) with new occurrences of AF during a median follow up of 2.8 (0.1-4.9) years. A total of 233 patients developed new AF (12.3%) during follow up. Patients with new onset AF were older (median 67 vs. 60 years); more often white (82 vs. 71%) and male (68 vs. 60%), and had more comorbidities than those who did not develop AF. After adjustment, PCA factor 1 (medium chain acylcarnitines; hazard ratio [HR]: 1.11 [1.01-1.22]), factor 2 (short chain dicarboxylacylcarnitines; HR: 1.21 [1.09-1.34]) and factor 5 (long chain acylcarnitines; HR: 1.19 [1.06-1.34]) were associated with new onset AF.

CONCLUSION

Metabolite profiles were associated with new onset AF among patients referred for coronary angiography. Validation of these observations in broader patient populations may provide better mechanistic insight into the development of AF, and may provide new opportunities for prevention and treatment.

Cardiovascular Metabolomics

Disturbances in cardiac metabolism underlie most cardiovascular diseases. Metabolomics, one of the newer omics technologies, has emerged as a powerful tool for defining changes in both global and cardiac-specific metabolism that occur across a spectrum of cardiovascular disease states. Findings from metabolomics studies have contributed to better understanding of the metabolic changes that occur in heart failure and ischemic heart disease and have identified new cardiovascular disease biomarkers. As technologies advance, the metabolomics field continues to evolve rapidly. In this review, we will discuss the current state of metabolomics technologies, including consideration of various metabolomics platforms and elements of study design; the emerging utility of stable isotopes for metabolic flux studies; and the use of metabolomics to better understand specific cardiovascular diseases, with an emphasis on recent advances in the field.

Atherogenic Lipoprotein Determinants of Cardiovascular Disease and Residual Risk Among Individuals With Low Low-Density Lipoprotein Cholesterol

Background

Levels of LDL (low‐density lipoprotein) cholesterol in the population are declining, and increasing attention is being focused on residual lipid‐related pathways of atherosclerotic cardiovascular disease risk beyond LDL cholesterol. Among individuals with low (<130 mg/dL) LDL cholesterol, we undertook detailed profiling of circulating atherogenic lipoproteins in relation to incident cardiovascular disease in 2 populations.

Methods and Results

We performed proton nuclear magnetic resonance spectroscopy to quantify concentrations of LDL and VLDL (very low‐density lipoprotein) particle subclasses in 11 984 JUPITER trial participants (NCT00239681). Adjusted Cox models examined cardiovascular disease risk associated with lipoprotein measures according to treatment allocation. Risk (adjusted hazard ratio [95%CI] per SD increment) among placebo‐allocated participants was associated with total LDL particles (1.19 [1.02, 1.38]) and total VLDL particles (1.21 [1.04, 1.41]), as well as apolipoprotein B, non–high‐density lipoprotein cholesterol, and triglycerides, but not LDL‐c. Rosuvastatin reduced LDL measures but had variable effects on triglyceride and VLDL measures. On‐statin levels of the smallest VLDL particle subclass were associated with a 68% per‐SD (adjusted hazard ratio 1.68 [1.28, 2.22]) increase in residual risk—this risk was related to VLDL cholesterol and not triglyceride or larger VLDL particles. There was evidence that residual risk prediction during statin therapy could be significantly improved through the inclusion of key VLDL measures (Harrell C‐index 0.780 versus 0.712; P<0.0001). In an independent, prospective cohort of 4721 individuals referred for cardiac catheterization (CATHGEN), similar patterns of lipoprotein‐related risk were observed.

Conclusions

Atherogenic lipoprotein particle concentrations were associated with cardiovascular disease risk when LDL cholesterol was low. VLDL lipoproteins, particularly the smallest remnant subclass, may represent unused targets for risk prediction and potential therapeutic intervention for reducing residual risk.

Clinical Trial Registration

URL: http://www.clinicaltrials.gov. Unique identifier: NCT00239681.

A Novel Protein Glycan-Derived Inflammation Biomarker Independently Predicts Cardiovascular Disease and Modifies the Association of HDL Subclasses with Mortality

BACKGROUND

Evidence suggests that systemic inflammation may adversely impact HDL function. In this study we sought to evaluate the independent and incremental predictive performance of GlycA-a novel serum inflammatory biomarker that is an aggregate measure of enzymatically glycosylated acute phase proteins-and HDL subclasses on adverse events in a retrospective observational study of a secondary prevention population and to understand a priori defined potential interactions between GlycA and HDL subclasses.

METHODS

GlycA and HDL subclasses were measured using proton nuclear magnetic resonance spectroscopy in 7617 individuals in the CATHGEN (CATHeterization GENetics) cardiac catheterization biorepository.

RESULTS

GlycA was associated with presence [odds ratio (OR) 1.07 (1.02-1.13), P = 0.01] and extent [OR 1.08 (1.03, 1.12) P < 0.0005] of coronary artery disease and with all-cause mortality [hazard ratio (HR) 1.34 (1.29-1.39), P < 0.0001], cardiovascular mortality [1.37 (1.30-1.45), P < 0.0001] and noncardiovascular mortality [1.46 (1.39-1.54) P < 0.0001] in models adjusted for 10 cardiovascular risk factors. GlycA and smaller HDL subclasses had independent but opposite effects on mortality risk prediction, with smaller HDL subclasses being protective [HR 0.69 (0.66-0.72), P < 0.0001]. There was an interaction between GlycA and smaller HDL subclasses-increasing GlycA concentrations attenuated the inverse association of smaller HDL subclasses with mortality. Adding GlycA and smaller HDL subclasses into the GRACE (Global Registry of Acute Coronary Events) and Framingham Heart Study Risk Scores improved mortality risk prediction, discrimination and reclassification.

CONCLUSIONS

These findings highlight the interaction of systemic inflammation and HDL with clinical outcomes and may increase precision for clinical risk assessment in secondary prevention populations.

Metabolomic Profiling Identifies Novel Circulating Biomarkers of Mitochondrial Dysfunction Differentially Elevated in Heart Failure With Preserved Versus Reduced Ejection Fraction: Evidence for Shared Metabolic Impairments in Clinical Heart Failure

BACKGROUND

Metabolic impairment is an important contributor to heart failure (HF) pathogenesis and progression. Dysregulated metabolic pathways remain poorly characterized in patients with HF and preserved ejection fraction (HFpEF). We sought to determine metabolic abnormalities in HFpEF and identify pathways differentially altered in HFpEF versus HF with reduced ejection fraction (HFrEF).

METHODS AND RESULTS

We identified HFpEF cases, HFrEF controls, and no-HF controls from the CATHGEN study of sequential patients undergoing cardiac catheterization. HFpEF cases (N=282) were defined by left ventricular ejection fraction (LVEF) ≥45%, diastolic dysfunction grade ≥1, and history of HF; HFrEF controls (N=279) were defined similarly, except for having LVEF <45%. No-HF controls (N=191) had LVEF ≥45%, normal diastolic function, and no HF diagnosis. Targeted mass spectrometry and enzymatic assays were used to quantify 63 metabolites in fasting plasma. Principal components analysis reduced the 63 metabolites to uncorrelated factors, which were compared across groups using ANCOVA. In basic and fully adjusted models, long-chain acylcarnitine factor levels differed significantly across groups (P<0.0001) and were greater in HFrEF than HFpEF (P=0.0004), both of which were greater than no-HF controls. We confirmed these findings in sensitivity analyses using stricter inclusion criteria, alternative LVEF thresholds, and adjustment for insulin resistance.

CONCLUSIONS

We identified novel circulating metabolites reflecting impaired or dysregulated fatty acid oxidation that are independently associated with HF and differentially elevated in HFpEF and HFrEF. These results elucidate a specific metabolic pathway in HF and suggest a shared metabolic mechanism in HF along the LVEF spectrum.

Association of standard clinical and laboratory variables with red blood cell distribution width

BACKGROUND

Red blood cell distribution width (RDW) strongly predicts clinical outcomes among patients with coronary disease and heart failure. The factors underpinning this association are unknown.

METHODS

In 6,447 individuals enrolled in the Measurement to Understand the Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) Study who had undergone coronary angiography between 2001 and 2007, we used Cox proportional hazards modeling to examine the adjusted association between RDW and death, and death or myocardial infarction (MI). Multiple linear regression using the R(2) model selection method was then used to identify clinical factors associated with variation in RDW.

RESULTS

Median follow-up was 4.2 (interquartile range 2.3-5.9) years, and the median RDW was 13.5% (interquartile range 12.9%-14.3%, clinical laboratory reference range 11.5%-14.5%). Red blood cell distribution width was independently associated with death (adjusted hazard ratio 1.13 per 1% increase in RDW, 95% CI 1.09-1.17), and death or MI (adjusted hazard ratio 1.12, 95% CI 1.08-1.16). Twenty-seven clinical characteristics and laboratory measures were assessed in the multivariable linear regression model; a final model containing 18 variables explained only 21% of the variation in RDW.

CONCLUSIONS

Although strongly associated with death and death or MI, only one-fifth of the variation in RDW was explained by routinely assessed clinical characteristics and laboratory measures. Understanding the latent factors that explain variation in RDW may provide insight into its strong association with risk and identify novel targets to mitigate that risk.

Association of Plasma Small-Molecule Intermediate Metabolites With Age and Body Mass Index Across Six Diverse Study Populations

BACKGROUND

Older age and obesity are associated with metabolic dysregulation; the mechanism by which these factors impact metabolism across the lifespan is important, but relatively unknown. We evaluated a panel of amino acids (AAs) and acylcarnitines (ACs) to identify effects of age and adiposity (body mass index) on circulating small-molecule metabolites in a meta-analysis of six diverse study populations.

METHODS

Targeted metabolic profiling was performed in six independent studies, representing 739 subjects with a broad range of age, body mass index, health states, and ethnic origin. Principal components analysis was performed on log-normalized values for AAs and ACs separately, generating one AC factor and two AA factors for each study. A common AC factor consisted primarily of acetylcarnitine, medium-chain AC, and several long-chain AC. AA Factor 1 consisted primarily of large neutral AAs. Glycine was its own factor.

RESULTS

Metabolic profiling and factor analysis identified clusters of related metabolites of lipid and AA metabolism that were consistently associated with age and body mass in a series of studies with a broad range of age, body mass index, and health status. An inverse association of glycine with body mass index and male gender supports its role as a marker of favorable metabolic health.

CONCLUSIONS

An important focus of future investigations should be to determine whether these clusters of metabolic intermediates are possible early predictors of health outcomes associated with body mass; are involved with accelerated aging; are involved in the causative pathway of aging; and how modification of these metabolic pathways impact the biology of aging.

High-density lipoprotein subclass measurements improve mortality risk prediction, discrimination and reclassification in a cardiac catheterization cohort

BACKGROUND AND AIMS

Recent failures of HDL cholesterol (HDL-C)-raising therapies to prevent cardiovascular disease (CVD) events have tempered the interest in the role of HDL-C in clinical risk assessment. Emerging data suggest that the atheroprotective properties of HDL depend on specific HDL particle characteristics not reflected by HDL-C. The purpose of this study was to determine the association of HDL particle concentration (HDL-P) and HDL subclasses with mortality in a high-risk cardiovascular population and to examine the clinical utility of these parameters in mortality risk discrimination and reclassification models.

METHODS

Using nuclear magnetic resonance spectroscopy, we measured HDL-P and HDL subclasses in 3972 individuals enrolled in the CATHGEN coronary catheterization biorepository; tested for association with all-cause mortality in robust clinical models; and examined the utility of HDL subclasses in incremental mortality risk discrimination and reclassification.

RESULTS

Over an average follow-up of eight years, 29.6% of the individuals died. In a multivariable model adjusted for ten CVD risk factors, HDL-P [HR, 0.71 (0.67-0.76), p = 1.3e-24] had a stronger inverse association with mortality than did HDL-C [HR 0.93 (0.87-0.99), p = 0.02]. Larger HDL size conferred greater risk and the sum of medium- and small-size HDL particles (MS-HDL-P) conferred less risk. Furthermore, the strong inverse relation of HDL-P levels with mortality was accounted for entirely by MS-HDL-P; HDL-C was not associated with mortality after adjustment for MS-HDL-P. Addition of MS-HDL-P to the GRACE Risk Score significantly improved risk discrimination and risk reclassification.

CONCLUSION

HDL-P and smaller HDL subclasses were independent markers of residual mortality risk and incremental to HDL-C in a high-risk CVD population. These measures should be considered in risk stratification and future development of HDL-targeted therapies in high-risk populations.

SLCO1B1 genetic variants, long-term low-density lipoprotein cholesterol levels and clinical events in patients following cardiac catheterization

AIM

SLCO1B1 variants are associated with intermediate outcomes that may increase risk of death/myocardial infarction (MI) in statin-treated patients.

PATIENTS & METHODS

In high-risk Caucasians undergoing cardiac catheterization, we tested the association between rs4149056/625T>C and rs2306283/492A>G with low-density lipoprotein cholesterol (LDL-c) over 3 years (n = 1402) and death/MI over 6 years (n = 2994), accounting for statin use or type during follow-up.

RESULTS

Carriers of the rs4149056 C allele had 6.2 ± 1.7 mg/dl higher LDL-c per C allele (p < 0.001) but were not at higher risk for death/MI (p = 0.9). We found no associations between rs2306283 and LDL-c or death/MI (p > 0.6).

CONCLUSION

Functional SLCO1B1 variants are not associated with death/MI in patients commonly treated with statins, despite higher LDL-c in carriers of the rs4149056 C allele.

Metabolomic Quantitative Trait Loci (mQTL) Mapping Implicates the Ubiquitin Proteasome System in Cardiovascular Disease Pathogenesis

Levels of certain circulating short-chain dicarboxylacylcarnitine (SCDA), long-chain dicarboxylacylcarnitine (LCDA) and medium chain acylcarnitine (MCA) metabolites are heritable and predict cardiovascular disease (CVD) events. Little is known about the biological pathways that influence levels of most of these metabolites. Here, we analyzed genetics, epigenetics, and transcriptomics with metabolomics in samples from a large CVD cohort to identify novel genetic markers for CVD and to better understand the role of metabolites in CVD pathogenesis. Using genomewide association in the CATHGEN cohort (N = 1490), we observed associations of several metabolites with genetic loci. Our strongest findings were for SCDA metabolite levels with variants in genes that regulate components of endoplasmic reticulum (ER) stress (USP3, HERC1, STIM1, SEL1L, FBXO25, SUGT1) These findings were validated in a second cohort of CATHGEN subjects (N = 2022, combined p = 8.4x10^-6–2.3x10^-10). Importantly, variants in these genes independently predicted CVD events. Association of genomewide methylation profiles with SCDA metabolites identified two ER stress genes as differentially methylated (BRSK2 and HOOK2). Expression quantitative trait loci (eQTL) pathway analyses driven by gene variants and SCDA metabolites corroborated perturbations in ER stress and highlighted the ubiquitin proteasome system (UPS) arm. Moreover, culture of human kidney cells in the presence of levels of fatty acids found in individuals with cardiometabolic disease, induced accumulation of SCDA metabolites in parallel with increases in the ER stress marker BiP. Thus, our integrative strategy implicates the UPS arm of the ER stress pathway in CVD pathogenesis, and identifies novel genetic loci associated with CVD event risk.

Cardiovascular disease is a strongly heritable trait. Despite application of the latest genomic technologies, the genetic architecture of disease risk remains poorly defined, and mechanisms underlying this susceptibility are incompletely understood. In this study, we performed genome-wide mapping of heart disease-related metabolites measured in the blood as the genetic traits of interest (instead of the disease itself), in a large cohort of 3512 patients at risk of heart disease from the CATHGEN study. Our goal was to discover new cardiovascular disease genes and thereby mechanisms of disease pathogenesis by understanding the genes that regulate levels of these metabolites. These analyses identified novel genetic variants associated with metabolite levels and with cardiovascular disease itself. Importantly, by utilizing an unbiased systems-based approach integrating genetics, gene expression, epigenetics and metabolomics, we uncovered a novel pathway of heart disease pathogenesis, that of endoplasmic reticulum (ER) stress, represented by elevated levels of circulating short-chain dicarboxylacylcarnitine (SCDA) metabolites.

Association of Roadway Proximity with Fasting Plasma Glucose and Metabolic Risk Factors for Cardiovascular Disease in a Cross-Sectional Study of Cardiac Catheterization Patients

BACKGROUND

The relationship between traffic-related air pollution (TRAP) and risk factors for cardiovascular disease needs to be better understood in order to address the adverse impact of air pollution on human health.

OBJECTIVE

We examined associations between roadway proximity and traffic exposure zones, as markers of TRAP exposure, and metabolic biomarkers for cardiovascular disease risk in a cohort of patients undergoing cardiac catheterization.

METHODS

We performed a cross-sectional study of 2,124 individuals residing in North Carolina (USA). Roadway proximity was assessed via distance to primary and secondary roadways, and we used residence in traffic exposure zones (TEZs) as a proxy for TRAP. Two categories of metabolic outcomes were studied: measures associated with glucose control, and measures associated with lipid metabolism. Statistical models were adjusted for race, sex, smoking, body mass index, and socioeconomic status (SES).

RESULTS

An interquartile-range (990 m) decrease in distance to roadways was associated with higher fasting plasma glucose (β = 2.17 mg/dL; 95% CI: -0.24, 4.59), and the association appeared to be limited to women (β = 5.16 mg/dL; 95% CI: 1.48, 8.84 compared with β = 0.14 mg/dL; 95% CI: -3.04, 3.33 in men). Residence in TEZ 5 (high-speed traffic) and TEZ 6 (stop-and-go traffic), the two traffic zones assumed to have the highest levels of TRAP, was positively associated with high-density lipoprotein cholesterol (HDL-C; β = 8.36; 95% CI: -0.15, 16.9 and β = 5.98; 95% CI: -3.96, 15.9, for TEZ 5 and 6, respectively).

CONCLUSION

Proxy measures of TRAP exposure were associated with intermediate metabolic traits associated with cardiovascular disease, including fasting plasma glucose and possibly HDL-C.

A Guide for a Cardiovascular Genomics Biorepository: the CATHGEN Experience

The CATHeterization GENetics (CATHGEN) biorepository was assembled in four phases. First, project start-up began in 2000. Second, between 2001 and 2010, we collected clinical data and biological samples from 9334 individuals undergoing cardiac catheterization. Samples were matched at the individual level to clinical data collected at the time of catheterization and stored in the Duke Databank for Cardiovascular Diseases (DDCD). Clinical data included the following: subject demographics (birth date, race, gender, etc.); cardiometabolic history including symptoms; coronary anatomy and cardiac function at catheterization; and fasting chemistry data. Third, as part of the DDCD regular follow-up protocol, yearly evaluations included interim information: vital status (verified via National Death Index search and supplemented by Social Security Death Index search), myocardial infarction (MI), stroke, rehospitalization, coronary revascularization procedures, medication use, and lifestyle habits including smoking. Fourth, samples were used to generate molecular data. CATHGEN offers the opportunity to discover biomarkers and explore mechanisms of cardiovascular disease.

Gene Expression Profiles Link Respiratory Viral Infection, Platelet Response to Aspirin, and Acute Myocardial Infarction

Background

Influenza infection is associated with myocardial infarction (MI), suggesting that respiratory viral infection may induce biologic pathways that contribute to MI. We tested the hypotheses that 1) a validated blood gene expression signature of respiratory viral infection (viral GES) was associated with MI and 2) respiratory viral exposure changes levels of a validated platelet gene expression signature (platelet GES) of platelet function in response to aspirin that is associated with MI.

Methods

A previously defined viral GES was projected into blood RNA data from 594 patients undergoing elective cardiac catheterization and used to classify patients as having evidence of viral infection or not and tested for association with acute MI using logistic regression. A previously defined platelet GES was projected into blood RNA data from 81 healthy subjects before and after exposure to four respiratory viruses: Respiratory Syncytial Virus (RSV) (n=20), Human Rhinovirus (HRV) (n=20), Influenza A virus subtype H1N1 (H1N1) (n=24), Influenza A Virus subtype H3N2 (H3N2) (n=17). We tested for the change in platelet GES with viral exposure using linear mixed-effects regression and by symptom status.

Results

In the catheterization cohort, 32 patients had evidence of viral infection based upon the viral GES, of which 25% (8/32) had MI versus 12.2% (69/567) among those without evidence of viral infection (OR 2.3; CI [1.03-5.5], p=0.04). In the infection cohorts, only H1N1 exposure increased platelet GES over time (time course p-value = 1e-04).

Conclusions

A viral GES of non-specific, respiratory viral infection was associated with acute MI; 18% of the top 49 genes in the viral GES are involved with hemostasis and/or platelet aggregation. Separately, H1N1 exposure, but not exposure to other respiratory viruses, increased a platelet GES previously shown to be associated with MI. Together, these results highlight specific genes and pathways that link viral infection, platelet activation, and MI especially in the case of H1N1 influenza infection.

Simultaneous consideration of multiple candidate protein biomarkers for long-term risk for cardiovascular events

BACKGROUND

Although individual protein biomarkers are associated with cardiovascular risk, rarely have multiple proteins been considered simultaneously to identify which set of proteins best predicts risk.

METHODS AND RESULTS

In a nested case-control study of 273 death/myocardial infarction (MI) cases and 273 age- (within 10 years), sex-, and race-matched and event-free controls from among 2023 consecutive patients (median follow-up 2.5 years) with suspected coronary disease, plasma levels of 53 previously reported biomarkers of cardiovascular risk were determined in a core laboratory. Three penalized logistic regression models were fit using the elastic net to identify panels of proteins independently associated with death/MI: proteins alone (Model 1); proteins in a model constrained to retain clinical variables (Model 2); and proteins and clinical variables available for selection (Model 3). Model 1 identified 6 biomarkers strongly associated with death/MI: intercellular adhesion molecule-1, matrix metalloproteinase-3, N-terminal pro-B-type natriuretic peptide, interleukin-6, soluble CD40 ligand, and insulin-like growth factor binding protein-2. In Model 2, only soluble CD40 ligand remained strongly associated with death/MI when all clinical risk predictors were retained. Model 3 identified a set of 6 biomarkers (intercellular adhesion molecule-1, matrix metalloproteinase-3, N-terminal pro-B-type natriuretic peptide, interleukin-6, soluble CD40 ligand, and insulin-like growth factor binding protein-2) and 5 clinical variables (age, red-cell distribution width, diabetes mellitus, hemoglobin, and New York Heart Association class) strongly associated with death/MI.

CONCLUSIONS

Simultaneously assessing the association between multiple putative protein biomarkers of cardiovascular risk and clinical outcomes is useful in identifying relevant biomarker panels for further assessment.

HLA-DQA1 and PLCG2 Are Candidate Risk Loci for Childhood-Onset Steroid-Sensitive Nephrotic Syndrome

Steroid-sensitive nephrotic syndrome (SSNS) accounts for >80% of cases of nephrotic syndrome in childhood. However, the etiology and pathogenesis of SSNS remain obscure. Hypothesizing that coding variation may underlie SSNS risk, we conducted an exome array association study of SSNS. We enrolled a discovery set of 363 persons (214 South Asian children with SSNS and 149 controls) and genotyped them using the Illumina HumanExome Beadchip. Four common single nucleotide polymorphisms (SNPs) in HLA-DQA1 and HLA-DQB1 (rs1129740, rs9273349, rs1071630, and rs1140343) were significantly associated with SSNS at or near the Bonferroni-adjusted P value for the number of single variants that were tested (odds ratio, 2.11; 95% confidence interval, 1.56 to 2.86; P=1.68×10(-6) (Fisher exact test). Two of these SNPs-the missense variants C34Y (rs1129740) and F41S (rs1071630) in HLA-DQA1-were replicated in an independent cohort of children of white European ancestry with SSNS (100 cases and ≤589 controls; P=1.42×10(-17)). In the rare variant gene set-based analysis, the best signal was found in PLCG2 (P=7.825×10(-5)). In conclusion, this exome array study identified HLA-DQA1 and PLCG2 missense coding variants as candidate loci for SSNS. The finding of a MHC class II locus underlying SSNS risk suggests a major role for immune response in the pathogenesis of SSNS.

Validation of the association between a branched chain amino acid metabolite profile and extremes of coronary artery disease in patients referred for cardiac catheterization

OBJECTIVE

To validate independent associations between branched-chain amino acids (BCAA) and other metabolites with coronary artery disease (CAD).

METHODS

We conducted mass-spectrometry-based profiling of 63 metabolites in fasting plasma from 1983 sequential patients undergoing cardiac catheterization. Significant CAD was defined as CADindex ≥ 32 (at least one vessel with ≥ 95% stenosis; N = 995) and no CAD as CADindex ≤ 23 and no previous cardiac events (N = 610). Individuals (N = 378) with CAD severity between these extremes were excluded. Principal components analysis (PCA) reduced large numbers of correlated metabolites into uncorrelated factors. Association between metabolite factors and significant CAD vs. no CAD was tested using logistic regression; and between metabolite factors and severity of CAD was tested using linear regression.

RESULTS

Of twelve PCA-derived metabolite factors, two were associated with CAD in multivariable models: factor 10, composed of BCAA (adjusted odds ratio, OR, 1.20; 95% CI 1.05-1.35, p = 0.005) and factor 7, composed of short-chain acylcarnitines, which include byproducts of BCAA metabolism (adjusted OR 1.30; 95% CI 1.14-1.48, p = 0.001). After adjustment for glycated albumin (marker of insulin resistance [IR]) both factors 7 (p = 0.0001) and 10 (p = 0.004) remained associated with CAD. Severity of CAD as a continuous variable (including patients with non-obstructive disease) was associated with metabolite factors 2, 3, 6, 7, 8 and 9; only factors 7 and 10 were associated in multivariable models.

CONCLUSIONS

We validated the independent association of metabolites involved in BCAA metabolism with CAD extremes. These metabolites may be reporting on novel mechanisms of CAD pathogenesis that are independent of IR and diabetes.

Aspirin exposure reveals novel genes associated with platelet function and cardiovascular events

OBJECTIVES

The aim of this study was to develop ribonucleic acid (RNA) profiles that could serve as novel biomarkers for the response to aspirin.

BACKGROUND

Aspirin reduces death and myocardial infarction (MI), suggesting that aspirin interacts with biological pathways that may underlie these events.

METHODS

Aspirin was administered, followed by whole-blood RNA microarray profiling, in a discovery cohort of healthy volunteers (HV1) (n = 50) and 2 validation cohorts of healthy volunteers (HV2) (n = 53) and outpatient cardiology patients (OPC) (n = 25). Platelet function was assessed using the platelet function score (PFS) in HV1 and HV2 and the VerifyNow Aspirin Test (Accumetrics, Inc., San Diego, California) in OPC. Bayesian sparse factor analysis identified sets of coexpressed transcripts, which were examined for associations with PFS in HV1 and validated in HV2 and OPC. Proteomic analysis confirmed the association of validated transcripts in platelet proteins. Validated gene sets were tested for association with death or MI in 2 patient cohorts (n = 587 total) from RNA samples collected at cardiac catheterization.

RESULTS

A set of 60 coexpressed genes named the "aspirin response signature" (ARS) was associated with PFS in HV1 (r = -0.31, p = 0.03), HV2 (r = -0.34, Bonferroni p = 0.03), and OPC (p = 0.046). Corresponding proteins for the 17 ARS genes were identified in the platelet proteome, of which 6 were associated with PFS. The ARS was associated with death or MI in both patient cohorts (odds ratio: 1.2 [p = 0.01]; hazard ratio: 1.5 [p = 0.001]), independent of cardiovascular risk factors. Compared with traditional risk factors, reclassification (net reclassification index = 31% to 37%, p ≤ 0.0002) was improved by including the ARS or 1 of its genes, ITGA2B.

CONCLUSIONS

RNA profiles of platelet-specific genes are novel biomarkers for identifying patients who do not respond adequately to aspirin and who are at risk for death or MI.

Branched chain amino acids are novel biomarkers for discrimination of metabolic wellness

OBJECTIVE

To identify novel biomarkers through metabolomic profiles that distinguish metabolically well (MW) from metabolically unwell (MUW) individuals, independent of body mass index (BMI).

MATERIALS/METHODS

This study was conducted as part of the Measurement to Understand the Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) project. Individuals from 3 cohorts were classified as lean (BMI<25kg/m²), overweight (BMI≥25kg/m², BMI<30kg/m²) or obese (BMI≥30kg/m²). Cardiometabolic abnormalities were defined as: (1) impaired fasting glucose (≥100mg/dL and ≤126mg/dL); (2) hypertension; (3) triglycerides ≥150mg/dL; (4) HDL-C <40mg/dL in men, <50mg/dL in women; and (5) insulin resistance (calculated Homeostatic Model Assessment (HOMA-IR) index of >5.13). MW individuals were defined as having <2 cardiometabolic abnormalities and MUW individuals had≥two cardiometabolic abnormalities. Targeted profiling of 55 metabolites used mass-spectroscopy-based methods. Principal components analysis (PCA) was used to reduce the large number of correlated metabolites into clusters of fewer uncorrelated factors.

RESULTS

Of 1872 individuals, 410 were lean, 610 were overweight, and 852 were obese. Of lean individuals, 67% were categorized as MUW, whereas 80% of overweight and 87% of obese individuals were MUW. PCA-derived factors with levels that differed the most between MW and MUW groups were factors 4 (branched chain amino acids [BCAA]) [p<.0001], 8 (various metabolites) [p<.0001], 9 (C4/Ci4, C3, C5 acylcarnitines) [p<.0001] and 10 (amino acids) [p<.0002]. Further, Factor 4, distinguishes MW from MUW individuals independent of BMI.

CONCLUSION

BCAA and related metabolites are promising biomarkers that may aid in understanding cardiometabolic health independent of BMI category.

Metabolomic profiling reveals mitochondrial-derived lipid biomarkers that drive obesity-associated inflammation

Obesity has reached epidemic proportions worldwide. Several animal models of obesity exist, but studies are lacking that compare traditional lard-based high fat diets (HFD) to “Cafeteria diets" (CAF) consisting of nutrient poor human junk food. Our previous work demonstrated the rapid and severe obesogenic and inflammatory consequences of CAF compared to HFD including rapid weight gain, markers of Metabolic Syndrome, multi-tissue lipid accumulation, and dramatic inflammation. To identify potential mediators of CAF-induced obesity and Metabolic Syndrome, we used metabolomic analysis to profile serum, muscle, and white adipose from rats fed CAF, HFD, or standard control diets. Principle component analysis identified elevations in clusters of fatty acids and acylcarnitines. These increases in metabolites were associated with systemic mitochondrial dysfunction that paralleled weight gain, physiologic measures of Metabolic Syndrome, and tissue inflammation in CAF-fed rats. Spearman pairwise correlations between metabolites, physiologic, and histologic findings revealed strong correlations between elevated markers of inflammation in CAF-fed animals, measured as crown like structures in adipose, and specifically the pro-inflammatory saturated fatty acids and oxidation intermediates laurate and lauroyl carnitine. Treatment of bone marrow-derived macrophages with lauroyl carnitine polarized macrophages towards the M1 pro-inflammatory phenotype through downregulation of AMPK and secretion of pro-inflammatory cytokines. Results presented herein demonstrate that compared to a traditional HFD model, the CAF diet provides a robust model for diet-induced human obesity, which models Metabolic Syndrome-related mitochondrial dysfunction in serum, muscle, and adipose, along with pro-inflammatory metabolite alterations. These data also suggest that modifying the availability or metabolism of saturated fatty acids may limit the inflammation associated with obesity leading to Metabolic Syndrome.

Fine mapping of a linkage peak with integration of lipid traits identifies novel coronary artery disease genes on chromosome 5

Background

Coronary artery disease (CAD), and one of its intermediate risk factors, dyslipidemia, possess a demonstrable genetic component, although the genetic architecture is incompletely defined. We previously reported a linkage peak on chromosome 5q31-33 for early-onset CAD where the strength of evidence for linkage was increased in families with higher mean low density lipoprotein-cholesterol (LDL-C). Therefore, we sought to fine-map the peak using association mapping of LDL-C as an intermediate disease-related trait to further define the etiology of this linkage peak. The study populations consisted of 1908 individuals from the CATHGEN biorepository of patients undergoing cardiac catheterization; 254 families (N = 827 individuals) from the GENECARD familial study of early-onset CAD; and 162 aorta samples harvested from deceased donors. Linkage disequilibrium-tagged SNPs were selected with an average of one SNP per 20 kb for 126.6-160.2 MB (region of highest linkage) and less dense spacing (one SNP per 50 kb) for the flanking regions (117.7-126.6 and 160.2-167.5 MB) and genotyped on all samples using a custom Illumina array. Association analysis of each SNP with LDL-C was performed using multivariable linear regression (CATHGEN) and the quantitative trait transmission disequilibrium test (QTDT; GENECARD). SNPs associated with the intermediate quantitative trait, LDL-C, were then assessed for association with CAD (i.e., a qualitative phenotype) using linkage and association in the presence of linkage (APL; GENECARD) and logistic regression (CATHGEN and aortas).

Results

We identified four genes with SNPs that showed the strongest and most consistent associations with LDL-C and CAD: EBF1, PPP2R2B, SPOCK1, and PRELID2. The most significant results for association of SNPs with LDL-C were: EBF1, rs6865969, p = 0.01; PPP2R2B, rs2125443, p = 0.005; SPOCK1, rs17600115, p = 0.003; and PRELID2, rs10074645, p = 0.0002). The most significant results for CAD were EBF1, rs6865969, p = 0.007; PPP2R2B, rs7736604, p = 0.0003; SPOCK1, rs17170899, p = 0.004; and PRELID2, rs7713855, p = 0.003.

Conclusion

Using an intermediate disease-related quantitative trait of LDL-C we have identified four novel CAD genes, EBF1, PRELID2, SPOCK1, and PPP2R2B. These four genes should be further examined in future functional studies as candidate susceptibility loci for cardiovascular disease mediated through LDL-cholesterol pathways.

Metabolic profiles predict adverse events after coronary artery bypass grafting

OBJECTIVE

Clinical models incompletely predict the outcomes after coronary artery bypass grafting. Novel molecular technologies can identify biomarkers to improve risk stratification. We examined whether metabolic profiles can predict adverse events in patients undergoing coronary artery bypass grafting.

METHODS

The study population comprised 478 subjects from the CATHGEN biorepository of patients referred for cardiac catheterization who underwent coronary artery bypass grafting after enrollment. Targeted mass spectrometry-based profiling of 69 metabolites was performed in frozen, fasting plasma samples collected before surgery. Principal components analysis and Cox proportional hazards regression modeling were used to assess the relation between the metabolite factor levels and a composite outcome of postcoronary artery bypass grafting myocardial infarction, the need for percutaneous coronary intervention, repeat coronary artery bypass grafting, and death.

RESULTS

During a mean follow-up period of 4.3 ± 2.4 years, 126 subjects (26.4%) experienced an adverse event. Three principal components analysis-derived factors were significantly associated with an adverse outcome on univariate analysis: short-chain dicarboxylacylcarnitines (factor 2, P = .001); ketone-related metabolites (factor 5, P = .02); and short-chain acylcarnitines (factor 6, P = .004). These 3 factors remained independently predictive of an adverse outcome after multivariate adjustment: factor 2 (adjusted hazard ratio, 1.23; 95% confidence interval, 1.10-1.38; P < .001), factor 5 (odds ratio, 1.17; 95% confidence interval, 1.01-1.37; P = .04), and factor 6 (odds ratio, 1.14; 95% confidence interval, 1.02-1.27; P = .03).

CONCLUSIONS

Metabolic profiles are independently associated with adverse outcomes after coronary artery bypass grafting. These profiles might represent novel biomarkers of risk that can augment existing tools for risk stratification of coronary artery bypass grafting patients and might elucidate novel biochemical pathways that mediate risk.

Simplifying informed consent for biorepositories: stakeholder perspectives

PURPOSE

Complex and sometimes controversial information must be conveyed during the consent process for participation in biorepositories, and studies suggest that consent documents in general are growing in length and complexity. As a first step toward creating a simplified biorepository consent form, we gathered data from multiple stakeholders about what information was most important for prospective participants to know when making a decision about taking part in a biorepository.

METHODS

We recruited 52 research participants, 12 researchers, and 20 institutional review board representatives from Durham and Kannapolis, NC. These subjects were asked to read a model biorepository consent form and highlight sentences they deemed most important.

RESULTS

On average, institutional review board representatives identified 72.3% of the sentences as important; researchers selected 53.0%, and participants 40.4% (P = 0.0004). Participants most often selected sentences about the kinds of individual research results that might be offered, privacy risks, and large-scale data sharing. Researchers highlighted sentences about the biorepository's purpose, privacy protections, costs, and participant access to individual results. Institutional review board representatives highlighted sentences about collection of basic personal information, medical record access, and duration of storage.

CONCLUSION

The differing mandates of these three groups can translate into widely divergent opinions about what information is important and appropriate to include a consent form. These differences could frustrate efforts to move simplified forms--for biobanking as well as for other kinds of research--into actual use, despite continued calls for such forms.