Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.

Systematic review and meta-analysis of the genetics of peripheral arterial disease

Background

Peripheral artery disease (PAD) impacts more than 200 million people worldwide. The understanding of the genetics of the disease and its clinical implications continue to evolve. This systematic review provides a comprehensive summary of all DNA variants that have been studied in association with the diagnosis and progression of PAD, with a meta-analysis of the ones replicated in the literature.

Methods

A systematic review of all studies examining DNA variants associated with the diagnosis and progression of PAD was performed. Candidate gene and genome-wide association studies (GWAS) were included. A meta-analysis of 13 variants derived from earlier smaller candidate gene studies of the diagnosis of PAD was performed. The literature on the progression of PAD was limited, and a meta-analysis was not feasible because of the heterogeneity in the criteria used to characterize it.

Results

A total of 231 DNA variants in 112 papers were studied for the association with the diagnosis of PAD. There were significant variations in the definition of PAD and the selection of controls in the various studies. GWAS have established 19 variants associated with the diagnosis of PAD that were replicated in several large patient cohorts. Only variants in intercellular adhesion molecule-1 (rs5498), IL-6 (rs1800795), and hepatic lipase (rs2070895) showed significant association with the diagnosis of PAD. However, these variants were not noted in the published GWAS.

Conclusions

Genetic research in the diagnosis of PAD has significant heterogeneity, but recent GWAS have demonstrated variants consistently associated with the disease. More research focusing on the progression of PAD is needed to identify patients at risk of adverse events and develop strategies that would improve their outcomes.

Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations

Polygenic risk scores (PRS) have improved in predictive performance supporting their use in clinical practice. Reduced predictive performance of PRS in diverse populations can exacerbate existing health disparities. The NHGRI-funded eMERGE Network is returning a PRS-based genome-informed risk assessment to 25,000 diverse adults and children. We assessed PRS performance, medical actionability, and potential clinical utility for 23 conditions. Standardized metrics were considered in the selection process with additional consideration given to strength of evidence in African and Hispanic populations. Ten conditions were selected with a range of high-risk thresholds: atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity, and type 2 diabetes. We developed a pipeline for clinical PRS implementation, used genetic ancestry to calibrate PRS mean and variance, created a framework for regulatory compliance, and developed a PRS clinical report. eMERGE's experience informs the infrastructure needed to implement PRS-based implementation in diverse clinical settings.

Quantifying factors that affect polygenic risk score performance across diverse ancestries and age groups for body mass index

Polygenic risk scores (PRS) have led to enthusiasm for precision medicine. However, it is well documented that PRS do not generalize across groups differing in ancestry or sample characteristics e.g., age. Quantifying performance of PRS across different groups of study participants, using genome-wide association study (GWAS) summary statistics from multiple ancestry groups and sample sizes, and using different linkage disequilibrium (LD) reference panels may clarify which factors are limiting PRS transferability. To evaluate these factors in the PRS generation process, we generated body mass index (BMI) PRS (PRSBMI) in the Electronic Medical Records and Genomics (eMERGE) network (N=75,661). Analyses were conducted in two ancestry groups (European and African) and three age ranges (adult, teenagers, and children). For PRSBMI calculations, we evaluated five LD reference panels and three sets of GWAS summary statistics of varying sample size and ancestry. PRSBMI performance increased for both African and European ancestry individuals using cross-ancestry GWAS summary statistics compared to European-only summary statistics (6.3% and 3.7% relative R2 increase, respectively, pAfrican=0.038, pEuropean=6.26x10-4). The effects of LD reference panels were more pronounced in African ancestry study datasets. PRSBMI performance degraded in children; R2 was less than half of teenagers or adults. The effect of GWAS summary statistics sample size was small when modeled with the other factors. Additionally, the potential of using a PRS generated for one trait to predict risk for comorbid diseases is not well understood especially in the context of cross-ancestry analyses - we explored clinical comorbidities from the electronic health record associated with PRSBMI and identified significant associations with type 2 diabetes and coronary atherosclerosis. In summary, this study quantifies the effects that ancestry, GWAS summary statistic sample size, and LD reference panel have on PRS performance, especially in cross-ancestry and age-specific analyses.

Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure

Heart failure is a leading cause of cardiovascular morbidity and mortality. However, the contribution of common genetic variation to heart failure risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We report a multi-ancestry genome-wide association study meta-analysis of all-cause heart failure including up to 115,150 cases and 1,550,331 controls of diverse genetic ancestry, identifying 47 risk loci. We also perform multivariate genome-wide association studies that integrate heart failure with related cardiac magnetic resonance imaging endophenotypes, identifying 61 risk loci. Gene-prioritization analyses including colocalization and transcriptome-wide association studies identify known and previously unreported candidate cardiomyopathy genes and cellular processes, which we validate in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provide convergent evidence for the roles of BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization identifies 9 circulating proteins associated with heart failure or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate common genetic variation in the pathogenesis of heart failure, and identify circulating proteins that may represent cardiomyopathy treatment targets.

Clinical Applications Measuring Arterial Stiffness: An Expert Consensus for the Application of Cardio-Ankle Vascular Index

BACKGROUND

The purpose of this document is to provide clinicians with guidance, using expert consensus, to help summarize evidence and offer practical recommendations.

METHODS

Expert Consensus Documents are intended to provide guidance for clinicians in areas in which there are no clinical practice guidelines, especially for new and evolving tests such as arterial stiffness measurements, until any formal guidelines are released.

RESULTS

This expert consensus document is intended as a source of information for decision-making and to guide clinician-patient discussions in various clinical scenarios.

CONCLUSIONS

The goal is to help clinicians and patients make a more informed decision together.

A unified framework identifies new links between plasma lipids and diseases from electronic medical records across large-scale cohorts

Plasma lipids are known heritable risk factors for cardiovascular disease, but increasing evidence also supports shared genetics with diseases of other organ systems. We devised a comprehensive three-phase framework to identify new lipid-associated genes and study the relationships among lipids, genotypes, gene expression and hundreds of complex human diseases from the Electronic Medical Records and Genomics (347 traits) and the UK Biobank (549 traits). Aside from 67 new lipid-associated genes with strong replication, we found evidence for pleiotropic SNPs/genes between lipids and diseases across the phenome. These include discordant pleiotropy in the HLA region between lipids and multiple sclerosis and putative causal paths between triglycerides and gout, among several others. Our findings give insights into the genetic basis of the relationship between plasma lipids and diseases on a phenome-wide scale and can provide context for future prevention and treatment strategies.

Neutral, Negative, or Negligible? Changes in Patient Perceptions of Disease Risk Following Receipt of a Negative Genomic Screening Result

Most individuals who undergo genomic screening will receive negative results or results not sufficient to warrant a clinical response. Even though a majority of individuals receive negative results, little is known about how negative results may impact individuals' perception of disease risk. Changes in risk perception (specifically reductions in perceived risk) may affect both probands and their family members if inaccurate information is communicated to family members. We surveyed patients who received negative results as part of their participation in a genomic screening study and assessed their perceptions of disease risk following receipt of results. Participants had either hyperlipidemia or colon polyps (or both) and received their negative genomic screening results by mail. Of 1712 total individuals recruited, 1442 completed the survey (84.2% completion rate). Approximately one quarter of individuals believed their risk for heart disease to be lower and approximately one third of individuals believed their risk for colon cancer to be lower after receiving and evaluating their negative genomic screening result. 78% of those who believed their risk for one or both diseases had declined had already shared or intended to share their result with family members. Our study suggests patients may interpret a negative genomic screening result as implying a reduction in their overall disease risk.

What guidance does HIPAA offer to providers considering familial risk notification and cascade genetic testing?

BACKGROUND

It is unclear how the Health Insurance Portability and Accountability Act (HIPAA) should be interpreted in the context of sharing of genomic information between family members.

METHODS

The authors analyzed the HIPAA Privacy Rule, reviewed the literature and constructed a clinical scenario to inform how HIPAA can be interpreted for multiple forms of patient- and provider-mediated genetic risk notification.

RESULTS

Under HIPAA, healthcare providers can lawfully notify relatives to recommend genetic risk assessment using multiple approaches, including supporting the patient telling their own relatives, contacting relatives directly with the patient's authorization, or contacting a relative's provider directly.

CONCLUSIONS

Multiple forms of patient- or provider-mediated contact of relatives are already legally permissible under HIPAA, are consistent with ethical obligations of care to patients and their families, and could result in improved population health through identification of clinically actionable disease risk. Unanswered questions remain about implementation and impacts of provider-mediated programs.

Comorbidity Characterization Among eMERGE Institutions: A Pilot Evaluation with the Johns Hopkins Adjusted Clinical Groups® System

Electronic health records (EHR) are valuable to define phenotype selection algorithms used to identify cohorts ofpatients for sequencing or genome wide association studies (GWAS). To date, the electronic medical records and genomics (eMERGE) network institutions have developed and applied such algorithms to identify cohorts with associated DNA samples used to discover new genetic associations. For complex diseases, there are benefits to stratifying cohorts using comorbidities in order to identify their genetic determinants. The objective of this study was to: (a) characterize comorbidities in a range of phenotype-selected cohorts using the Johns Hopkins Adjusted Clinical Groups® (ACG®) System, (b) assess the frequency of important comorbidities in three commonly studied GWAS phenotypes, and (c) compare the comorbidity characterization of cases and controls. Our analysis demonstrates a framework to characterize comorbidities using the ACG system and identified differences in mean chronic condition count among GWAS cases and controls. Thus, we believe there is great potential to use the ACG system to characterize comorbidities among genetic cohorts selected based on EHR phenotypes.

Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network

The link between cardiovascular diseases and neurological disorders has been widely observed in the aging population. Disease prevention and treatment rely on understanding the potential genetic nexus of multiple diseases in these categories. In this study, we were interested in detecting pleiotropy, or the phenomenon in which a genetic variant influences more than one phenotype. Marker-phenotype association approaches can be grouped into univariate, bivariate, and multivariate categories based on the number of phenotypes considered at one time. Here we applied one statistical method per category followed by an eQTL colocalization analysis to identify potential pleiotropic variants that contribute to the link between cardiovascular and neurological diseases. We performed our analyses on ~530,000 common SNPs coupled with 65 electronic health record (EHR)-based phenotypes in 43,870 unrelated European adults from the Electronic Medical Records and Genomics (eMERGE) network. There were 31 variants identified by all three methods that showed significant associations across late onset cardiac- and neurologic- diseases. We further investigated functional implications of gene expression on the detected "lead SNPs" via colocalization analysis, providing a deeper understanding of the discovered associations. In summary, we present the framework and landscape for detecting potential pleiotropy using univariate, bivariate, multivariate, and colocalization methods. Further exploration of these potentially pleiotropic genetic variants will work toward understanding disease causing mechanisms across cardiovascular and neurological diseases and may assist in considering disease prevention as well as drug repositioning in future research.

Relationship of circulating spexin with markers of cardiovascular disease: a pilot study in adolescents with obesity

PURPOSE

Spexin, a novel peptide, has potential implications in obesity, satiety and energy homeostasis. The current study examined the relationship of spexin with various biomarkers of cardiovascular disease and endothelial function in adolescents with obesity.

METHODS

Nineteen adolescents with obesity (age, 15.8 ± 1.7 years) were studied. Spexin, leptin and various cardiovascular disease biomarkers were measured. Endothelial function was assessed by high-resolution Doppler ultrasonography of the right brachial artery.

RESULTS

Spexin concentration (median [interquartile range] 0.38 ng/mL [0.29-0.59 ng/mL]) was inversely correlated (r = -0.50, P = 0.03) with leptin. When participants were clustered into two groups ('high spexin and low leptin' vs. 'low spexin and high leptin'), the odds of having 'low spexin and high leptin' in participants with higher hs-CRP (≥ 3 mg/L) were 12.25 times (95 per cent CI -1 to139, P = 0.026) higher than those of participants with lower hs-CRP (<3 mg/L). Spexin levels, however, were not associated with measures of endothelial function.

CONCLUSIONS

The inverse association between spexin and leptin and the presence of higher concentrations of hs-CRP in adolescents with obesity in the setting of 'low spexin and high leptin' suggest a potential role for spexin in the regulation of satiety and certain cardiovascular risk factors in children with obesity.

Abstract 489: A Data Driven Approach to Identify Subtypes of Abdominal Aortic Aneurysm With Distinct Clinical and Genetic Characteristics

Background: Abdominal aortic aneurysm (AAA) is a complex and heterogeneous disorder. We hypothesized that dense phenomapping using electronic health records (EHR) can identify subtypes of AAA with distinct clinical and genetic characteristics.

Methods & Results: AAA cases (n=1108) and controls (n=4694) were identified from the Mayo vascular disease biorepository and had available high-density genotyping data. 132 Candidate genetic variants (p < 10E-5) associated with atherosclerotic cardiovascular disease (ASCVD) or AAA were selected from GWAS catalog. Using model-based Gaussian-mixture cluster analysis of 46 clinical features from 6 data domains, 24 data elements, 204 variables (including age, sex, vital signs, laboratory data, medication use, family history), spanning over 20 years of observational time in the EHR, we identified two subtypes of AAA: subtype 1(S1, n=421) with faster AAA growth rate (baseline size adjusted mean difference, 95% CI: 0.013, 0.001 to 0.020 cm / year faster, p = 0.03), higher all-cause mortality (age & sex adjusted hazard ratio, 95% CI: 1.36, 1.09 - 1.70, p < 0.01) than subtype 2 (S2, n=687). As compared with the controls, thyroid disorder / rheumatoid or osteoarthritis / infectious conditions were uniquely associated with increased odds ratio for S1; ASCVD / hyperlipidemia / hypertension were uniquely associated with increased odds ratio for S2. After adjustment for all group-specific diseases, genetic variants in CDKN2B-AS1, NOA1-REST, ERG, ZNF335-MMP9 and SMAD3 were associated with S1 and variants in MMP12, DAB2IP, LHFPL2, LPA, FSTL5 and SORT1 were associated with S2 (all FDR p < 0.05). After adjustment for disease comorbidities and genetic variants differently associated with S1 and S2, the increased risk for all-cause death of S1 than S2 and the difference in AAA expansion rate in subgroups attenuated (Both p-values > 0.09)

Conclusions: We identified two subtypes of AAA with different rates of aneurysm expansion and all-cause mortality, which were associated with subtype-specific disease comorbidities and genetic markers, suggesting the potential of leveraging EHR to facilitate individualized medicine in patients with AAA.

Influence of body fatness distribution and total lean mass on aortic stiffness in nonobese individuals

BACKGROUND

Subjects with normal body mass index but high body fat percentage have higher cardiovascular risk than subjects with normal weight and low fat mass. However, the association of fat distribution and lean mass with carotid-femoral pulse wave velocity (cfPWV) among nonobese apparently healthy individuals has never been assessed.

METHODS

In 136 nonobese volunteers (mean age = 45±9 years; 57% women) without manifest cardiovascular disease, cfPWV was measured by applanation tonometry. Fat and lean mass were measured by dual-energy x-ray absorptiometry.

RESULTS

In univariate analysis, total fat (r = 0.17; P < 0.01), trunk fat (r = 0.27; P < 0.01), and trunk/total fat ratio (r = 0.32; P < 0.01) were correlated with cfPWV. After adjustment for age and mean arterial pressure, only central fat distribution (trunk/total fat ratio) was significantly associated with cfPWV. In the fully adjustment model, there was a significant interaction between fat distribution and lean mass. When the study sample was grouped by fat distribution and total lean mass medians, subjects with central fat distribution and low lean mass (group 4) had higher log-transformed cfPWV than the noncentral fat/low lean mass group (group 2) (0.89, 95% confidence interval (CI) = 0.86-0.92 vs. 0.85, 95% CI = 0.83-0.87; P < 0.01) or the noncentral fat/high lean mass group (group 1) (0.89, 95% CI = 0.86-0.92 vs. 0.84, 95% CI = 0.81-0.87; P < 0.01) after adjustments. Aortic stiffness increased from group 1 to group 4 (P for linear trend < 0.001).

CONCLUSIONS

Among normal weight individuals without manifest cardiovascular disease, the combination of central fat distribution and low lean mass is associated with higher cfPWV. These factors are more closely related to cfPWV than total fat mass.

Associations between inflammation and cognitive function in African Americans and European Americans

OBJECTIVES

To examine associations between specific inflammatory biomarkers and cognitive function in African Americans (AAs) and European Americans (EAs) with prevalent vascular risk factors.

DESIGN

Cross-sectional analysis using generalized estimating equations to account for familial clustering; standardized β-coefficients, adjusted for age, sex, and education are reported.

SETTING

Community cohort study in Jackson, Mississippi, and Rochester, Minnesota.

PARTICIPANTS

Genetic Epidemiology Network of Arteriopathy (GENOA)-Genetics of Microangiopathic Brain Injury (GMBI) Study participants.

MEASUREMENTS

Associations between inflammation (high-sensitivity C-reactive protein (CRP), interleukin (IL)-6, soluble tumor necrosis factor (TNF) receptor 1 and 2 (sTNFR1, sTNFR2)) and cognitive function (global, processing speed, language, memory, and executive function) were examined in AAs and EAs (N = 1,965; aged 26-95, 64% women, 52% AA, 75% with hypertension).

RESULTS

In AAs, higher sTNFR2 was associated with poorer cognition in all domains (global: -0.11, P = .009; processing speed: -0.11, P < .001; language: -0.08, P = .002; memory: -0.09, P = .008; executive function: -0.07, P = .03); sTNFR1 was associated with slower processing speed (-0.08, P < .001) and poorer executive function (-0.08, P = .008); higher CRP was associated with slower processing speed (-0.04, P = .024), and higher IL6 was associated with poorer executive function (-0.07, P = .02). In EA, only higher sTNFR1 was associated with slower processing speed (-0.05, P = .007). Associations were not found between cognition and sTNFR2, CRP, or IL6 in EA.

CONCLUSION

In a population with high vascular risk, adverse associations between inflammation and cognitive function were especially apparent in AAs, primarily involving markers of TNFα activity.

Clinical Decision Support to Implement CYP2D6 Drug-Gene Interaction

The level of CYP2D6 metabolic activity can be predicted by pharmacogenomic testing, and concomitant use of clinical decision support has the potential to prevent adverse effects from those drugs metabolized by this enzyme. Our initial findings after implementation of clinical decision support alerts integrated in the electronic health records suggest high feasibility, but also identify important challenges.

A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury

OBJECTIVE

To describe a collaborative approach for developing an electronic health record (EHR) phenotyping algorithm for drug-induced liver injury (DILI).

METHODS

We analyzed types and causes of differences in DILI case definitions provided by two institutions-Columbia University and Mayo Clinic; harmonized two EHR phenotyping algorithms; and assessed the performance, measured by sensitivity, specificity, positive predictive value, and negative predictive value, of the resulting algorithm at three institutions except that sensitivity was measured only at Columbia University.

RESULTS

Although these sites had the same case definition, their phenotyping methods differed by selection of liver injury diagnoses, inclusion of drugs cited in DILI cases, laboratory tests assessed, laboratory thresholds for liver injury, exclusion criteria, and approaches to validating phenotypes. We reached consensus on a DILI phenotyping algorithm and implemented it at three institutions. The algorithm was adapted locally to account for differences in populations and data access. Implementations collectively yielded 117 algorithm-selected cases and 23 confirmed true positive cases.

DISCUSSION

Phenotyping for rare conditions benefits significantly from pooling data across institutions. Despite the heterogeneity of EHRs and varied algorithm implementations, we demonstrated the portability of this algorithm across three institutions. The performance of this algorithm for identifying DILI was comparable with other computerized approaches to identify adverse drug events.

CONCLUSIONS

Phenotyping algorithms developed for rare and complex conditions are likely to require adaptive implementation at multiple institutions. Better approaches are also needed to share algorithms. Early agreement on goals, data sources, and validation methods may improve the portability of the algorithms.

Enhancing the power of genetic association studies through the use of silver standard cases derived from electronic medical records

The feasibility of using imperfectly phenotyped "silver standard" samples identified from electronic medical record diagnoses is considered in genetic association studies when these samples might be combined with an existing set of samples phenotyped with a gold standard technique. An analytic expression is derived for the power of a chi-square test of independence using either research-quality case/control samples alone, or augmented with silver standard data. The subset of the parameter space where inclusion of silver standard samples increases statistical power is identified. A case study of dementia subjects identified from electronic medical records from the Electronic Medical Records and Genomics (eMERGE) network, combined with subjects from two studies specifically targeting dementia, verifies these results.

Genetic variants associated with cardiac structure and function: a meta-analysis and replication of genome-wide association data

CONTEXT

Echocardiographic measures of left ventricular (LV) structure and function are heritable phenotypes of cardiovascular disease.

OBJECTIVE

To identify common genetic variants associated with cardiac structure and function by conducting a meta-analysis of genome-wide association data in 5 population-based cohort studies (stage 1) with replication (stage 2) in 2 other community-based samples.

DESIGN, SETTING, AND PARTICIPANTS

Within each of 5 community-based cohorts comprising the EchoGen consortium (stage 1; n = 12 612 individuals of European ancestry; 55% women, aged 26-95 years; examinations between 1978-2008), we estimated the association between approximately 2.5 million single-nucleotide polymorphisms (SNPs; imputed to the HapMap CEU panel) and echocardiographic traits. In stage 2, SNPs significantly associated with traits in stage 1 were tested for association in 2 other cohorts (n = 4094 people of European ancestry). Using a prespecified P value threshold of 5 x 10(-7) to indicate genome-wide significance, we performed an inverse variance-weighted fixed-effects meta-analysis of genome-wide association data from each cohort.

MAIN OUTCOME MEASURES

Echocardiographic traits: LV mass, internal dimensions, wall thickness, systolic dysfunction, aortic root, and left atrial size.

RESULTS

In stage 1, 16 genetic loci were associated with 5 echocardiographic traits: 1 each with LV internal dimensions and systolic dysfunction, 3 each with LV mass and wall thickness, and 8 with aortic root size. In stage 2, 5 loci replicated (6q22 locus associated with LV diastolic dimensions, explaining <1% of trait variance; 5q23, 12p12, 12q14, and 17p13 associated with aortic root size, explaining 1%-3% of trait variance).

CONCLUSIONS

We identified 5 genetic loci harboring common variants that were associated with variation in LV diastolic dimensions and aortic root size, but such findings explained a very small proportion of variance. Further studies are required to replicate these findings, identify the causal variants at or near these loci, characterize their functional significance, and determine whether they are related to overt cardiovascular disease.

Challenges in translating plasma proteomics from bench to bedside: update from the NHLBI Clinical Proteomics Programs

The emerging scientific field of proteomics encompasses the identification, characterization, and quantification of the protein content or proteome of whole cells, tissues, or body fluids. The potential for proteomic technologies to identify and quantify novel proteins in the plasma that can function as biomarkers of the presence or severity of clinical disease states holds great promise for clinical use. However, there are many challenges in translating plasma proteomics from bench to bedside, and relatively few plasma biomarkers have successfully transitioned from proteomic discovery to routine clinical use. Key barriers to this translation include the need for "orthogonal" biomarkers (i.e., uncorrelated with existing markers), the complexity of the proteome in biological samples, the presence of high abundance proteins such as albumin in biological samples that hinder detection of low abundance proteins, false positive associations that occur with analysis of high dimensional datasets, and the limited understanding of the effects of growth, development, and age on the normal plasma proteome. Strategies to overcome these challenges are discussed.

Adenovirus-mediated gene transfer of macrophage colony stimulating factor to the arterial wall in vivo

Macrophage colony stimulating factor (MCSF) is believed to play a key role in one of the earliest events in atherosclerosis, ie, monocyte to macrophage differentiation in the arterial intima. The aim of this study was to examine the biological effects of vascular wall expression of MCSF. A recombinant adenovirus vector encoding human MCSF (AdMCSF) was generated by standard techniques of homologous recombination in 293 cells. The rabbit carotid artery was transduced with AdMCSF. As negative controls, carotid arteries were transduced with either an adenoviral vector encoding beta-galactosidase, an adenoviral vector encoding apolipoprotein E, or diluent alone. Intima-media thickness ratio was calculated 5 and 21 days after transduction. The cell type present in intimal infiltrates was analyzed by immunohistochemistry. MCSF expression was demonstrated in the vessel wall of AdMCSF-transduced vessels by reverse transcription-polymerase chain reaction and immunofluorescence. In contrast to control vessels, adenovirus-mediated MCSF expression was associated with an intimal cellular infiltrate consisting of smooth muscle cells and small numbers of macrophages. Whereas the intima-media thickness ratio was greater in AdMCSF-transduced vessels at 5 days, this difference was no longer statistically significant at 21 days. These results suggest that MCSF may play a role in recruitment of monocytes and macrophages to the vessel wall and may contribute to smooth muscle cell proliferation and migration.