Donna K Arnett

University of Alabama at Birmingham, Birmingham, Alabama, United States

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Publications (476)2407.77 Total impact

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    ABSTRACT: Carotid intima-media thickness (cIMT) is a subclinical measure of atherosclerosis with mounting evidence that higher cIMT confers an increased risk of cardiovascular disease. The ryanodine receptor 3 gene (RYR3) has previously been linked to increased cIMT; however, the causal variants have not yet been localized. Therefore, we sequenced 339 480 bp encompassing 104 exons and 2 kb flanking region of the RYR3 gene in 96 HIV-positive white men from the extremes of the distribution of common cIMT from the Fat Redistribution and Metabolic Changes in HIV infection study (FRAM). We identified 2710 confirmed variants (2414 single-nucleotide polymorphisms (SNPs) and 296 insertion/deletions (indels)), with a mean count of 736 SNPs (ranging from 528 to 1032) and 170 indels (ranging from 128 to 214) distributed in each individual. There were 39 variants in the exons and 15 of these were non-synonymous, of which with only 4 were common variants and the remaining 11 were rare variants, one was a novel SNP. We confirmed that the common variant rs2229116 was significantly associated with cIMT in this design (P<7.9 × 10(-9)), and observed seven other significantly associated SNPs (P<10(-8)). These variants including the private non-synonymous SNPs need to be followed up in a larger sample size and also tested with clinical atherosclerotic outcomes.Journal of Human Genetics advance online publication, 11 December 2014; doi:10.1038/jhg.2014.104.
    Journal of human genetics. 12/2014;
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    ABSTRACT: Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple-even distinct-traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10(-8)) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10(-7)) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes. Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
    The American Journal of Human Genetics 12/2014; · 11.20 Impact Factor
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    ABSTRACT: Although apolipoprotein E (APOE) variants are associated with age-related diseases, the underlying mechanism is unknown and DNA methylation may be a potential one. With methylation data, measured by the Infinium Human Methylation 450 array, from 993 participants (age ranging from 18 to 87 years) in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, and from Encyclopedia of DNA Elements (ENCODE) consortium, combined with published methylation datasets, we described the methylation pattern of 13 CpG sites within APOE locus, their correlations with gene expression across cell types, and their relationships with age, plasma lipids, and sequence variants. Based on methylation levels and the genetic regions, we categorized the 13 APOE CpG sites into three groups: Group 1 showed hypermethylation (> 50%) and were located in the promoter region, Group 2 exhibited hypomethylation (< 50%) and were located in the first two exons and introns, and Group 3 showed hypermethylation (> 50%) and were located in the exon 4. APOE methylation was negatively correlated with gene expression (minimum r = -0.66, P = 0.004). APOE methylation was significantly associated with age (minimum P = 2.06E-08) and plasma total cholesterol (minimum P = 3.53E-03). Finally, APOE methylation patterns differed across APOE ε variants (minimum P = 3.51E-05) and the promoter variant rs405509 (minimum P = 0.01), which further showed a significant interaction with age (P = 0.03). These findings suggest that methylation may be a potential mechanistic explanation for APOE functions related to aging and call for further molecular mechanistic studies. © 2014 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.
    Aging cell 12/2014; · 7.55 Impact Factor
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    ABSTRACT: Very long-chain saturated fatty acids (VLSFA) are saturated fatty acids with 20 or more carbons. In contrast to the more abundant saturated fatty acids, such as palmitic acid, there is growing evidence that circulating VLSFA may have beneficial biological properties. Whether genetic factors influence circulating levels of VLSFA is not known. We investigated the association of common genetic variation with plasma phospholipid/erythrocyte levels of three VLSFA by performing genome-wide association studies in seven population-based cohorts comprising 10,129 subjects of European ancestry. We observed associations of circulating VLSFA concentrations with common variants in two genes, SPTLC3 (serine palmitoyl-transferase, long-chain base subunit 3), a gene involved in the rate-limiting step of de novo sphingolipid synthesis, and CERS4 (ceramide synthase 4). The SPTLC3 variant at rs680379 was associated with higher 20:0 (arachidic acid, p = 5.81x10-13). The CERS4 variant at rs2100944 was associated with higher levels of 20:0 (p = 2.65x10-40) and in analyses that adjusted for 20:0, with lower levels of 22:0 (behenic acid, p = 4.22x10-26) and 24:0 (lignoceric acid, p = 3.20x10-21). These novel associations highlight the inter-relationship of circulating VLSFA and sphingolipid synthesis.
    Journal of lipid research. 11/2014;
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    ABSTRACT: DWS, defined using posterior wall thickness (PWT) measurements from standard echocardiographic images (DWS = [PWT(systole)-PWT(diastole)]/PWT(systole)), has been proposed as a marker of LV diastolic stiffness. However, the equation for DWS is closely related to systolic radial strain, and whether DWS is associated with abnormal cardiac mechanics (reduced systolic strains and diastolic tissue velocities) is unknown. We sought to determine the relationship between diastolic wall strain (DWS), a proposed marker of left ventricular (LV) diastolic stiffness, and cardiac mechanics.
    Cardiovascular ultrasound. 10/2014; 12(1):40.
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    ABSTRACT: Little is known about the interplay between n-3 fatty acids and genetic variants for diabetes-related traits at the genome-wide level. The present study aimed to examine variance contributions of genotype by environment (GxE) interactions for different erythrocyte n-3 fatty acids and genetic variants for diabetes-related traits at the genome-wide level in a non-Hispanic white population living in the U.S.A. (n = 820). A tool for Genome-wide Complex Trait Analysis (GCTA) was used to estimate the genome-wide GxE variance contribution of four diabetes-related traits: HOMA-Insulin Resistance (HOMA-IR), fasting plasma insulin, glucose and adiponectin. A GxE genome-wide association study (GWAS) was conducted to further elucidate the GCTA results. Replication was conducted in the participants of the Boston Puerto Rican Health Study (BPRHS) without diabetes (n = 716).
    BMC Genomics 09/2014; 15(1):781. · 4.40 Impact Factor
  • Circulation 09/2014; · 15.20 Impact Factor
  • Journal of the American College of Cardiology 09/2014; · 14.09 Impact Factor
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    ABSTRACT: The aim of this study was to investigate whether there is a genotype-by-treatment interaction in patients experiencing stroke and treated with one of three antihypertensive drugs, that is chlorthalidone, amlodipine, or lisinopril.
    Pharmacogenetics and Genomics 08/2014; · 3.61 Impact Factor
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    ABSTRACT: Hypertension is the most common chronic condition seen by physicians in ambulatory care and a condition for which life-long medications are commonly prescribed. There is evidence for genetic factors influencing blood pressure variation in populations and response to medications. This review summarizes recent genetic discoveries that surround blood pressure, hypertension, and antihypertensive drug response from genome-wide association studies, while highlighting ancestry-specific findings and any potential implication for drug therapy targets. Genome-wide association studies have identified several novel loci for inter-individual variation of blood pressure and hypertension risk in the general population. Evidence from pharmacogenetic studies suggests that genes influence the blood pressure response to antihypertensive drugs, although results are somewhat inconsistent across studies. There is still much work that remains to be done to identify genes both for efficacy and adverse events of antihypertensive medications.
    Current Hypertension Reports 08/2014; 16(8):461. · 3.90 Impact Factor
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    ABSTRACT: Sunlight exposure has been shown to alter DNA methylation patterns across several human cell-types, including T-lymphocytes. Since epigenetic changes establish gene expression profiles, changes in DNA methylation induced by sunlight exposure warrant investigation. The purpose of this study was to assess the effects of sunlight exposure on CD4+ T-cell methylation patterns on an epigenome-wide scale in a North American population of European origin (n = 991). In addition, we investigated the genetic contribution to epigenetic variation (methylQTL). We used linear regression to test the associations between methylation scores at 461 281 cytosine-phosphate-guanine (CpG) sites and sunlight exposure, followed by a genome-wide association analysis (methylQTL) to test for associations between methylation at the top CpG locus and common genetic variants, assuming an additive genetic model. We observed an epigenome-wide significant association between sunlight exposure and methylation status at cg26930596 (p = 9.2 × 10−8), a CpG site located in protein kinase C zeta (PRKCZ), a gene previously shown to be entrained by light. MethylQTL analysis resulted in significant associations between cg26930596 and two intergenic single nucleotide polymorphisms on chromosome 3, rs4574216 (p = 1.5 × 10−10) and rs4405858 (p = 1.9 × 10−9). These common genetic variants reside downstream of WWTR1, a transcriptional co-activator of PRKCZ. Associations observed in the North American population, however, did not replicate in an independent Mediterranean cohort. Our preliminary results support the role of sunlight exposure in epigenetic processes, and lay the groundwork for future studies of the molecular link between sunlight and physiologic processes such as tumorigenesis and metabolism.
    Chronobiology International. 07/2014;
  • Circulation 07/2014; · 15.20 Impact Factor
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    ABSTRACT: -Genetic research regarding blood lipids has largely focused on DNA sequence variation; few studies have explored epigenetic effects. Genome-wide surveys of DNA methylation may uncover epigenetic factors influencing lipid metabolism.
    Circulation 06/2014; · 15.20 Impact Factor
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    ABSTRACT: Background and Aims Eggs are a ubiquitous and important source of dietary cholesterol and nutrients, yet their relationship to coronary heart disease (CHD) remains unclear. While some data have suggested a positive association between egg consumption and CHD, especially among diabetic subjects, limited data exist on the influence of egg consumption on subclinical disease. Thus, we sought to examine whether egg consumption is associated with calcified atherosclerotic plaques in the coronary arteries. Methods In a cross-sectional design, we studied 1848 participants of the NHLBI Family Heart Study without known CHD. Egg consumption was assessed by a semi-quantitative food frequency questionnaire and coronary-artery calcium (CAC) was measured by cardiac CT. We defined prevalent CAC using an Agatston score of at least 100 and fitted generalized estimating equations to calculate prevalence odds ratios of CAC. Results Mean age was 56.5 years and 41% were male. Median consumption of eggs was 1/week. There was no association between frequency of egg consumption and prevalent CAC. Odds ratios (95% CI) for CAC were 1.0 (reference), 0.95 (0.66-1.38), 0.94 (0.63-1.40), and 0.90 (0.57-1.42) for egg consumption of almost never, 1-3 times per month, once per week, and 2+ times per week, respectively (p for trend 0.66), adjusting for age, sex, BMI, smoking, alcohol, physical activity, income, field center, total calories, and bacon. Additional control for hypertension and diabetes mellitus, or restricting the analysis to subjects with diabetes mellitus or fasting glucose >126 mg/dL did not alter the findings. Conclusions These data do not provide evidence for an association between egg consumption and prevalent CAC in adult men and women.
    e-SPEN Journal. 06/2014;
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    ABSTRACT: Combining multiple genetic variants related to obesity into a genetic risk score (GRS) might improve identification of individuals at risk of developing obesity. Moreover, characterizing gene-diet interactions is a research challenge to establish dietary recommendations to individuals with higher predisposition to obesity. Our objective was to analyze the association between an obesity GRS and body mass index (BMI) in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) population, focusing on gene-diet interactions with total fat and saturated fatty acid (SFA) intake, and to replicate findings in the Multi-Ethnic Study of Atherosclerosis (MESA) population. Cross-sectional analyses included 783 white US participants from GOLDN and 2,035 from MESA. Dietary intakes were estimated with validated food frequency questionnaires. Height and weight were measured. A weighted GRS was calculated on the basis of 63 obesity-associated variants. Multiple linear regression models adjusted by potential confounders were used to examine gene-diet interactions between dietary intake (total fat and SFA) and the obesity GRS in determining BMI. Significant interactions were found between total fat intake and the obesity GRS using these variables as continuous for BMI (P for interaction=0.010, 0.046, and 0.002 in GOLDN, MESA, and meta-analysis, respectively). These association terms were stronger when assessing interactions between SFA intake and GRS for BMI (P for interaction=0.005, 0.018, and <0.001 in GOLDN, MESA, and meta-analysis, respectively). SFA intake interacts with an obesity GRS in modulating BMI in two US populations. Although determining the causal direction requires further investigation, these findings suggest that potential dietary recommendations to reduce BMI effectively in populations with high obesity GRS would be to reduce total fat intake mainly by limiting SFAs.
    Journal of the American Academy of Nutrition and Dietetics 04/2014; · 3.80 Impact Factor
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    ABSTRACT: Epigenetic processes, defined as heritable changes in gene expression that occur without changes to the DNA sequence, have emerged as a promising area of cardiovascular disease research. Epigenetic information transcends that of the genotype alone and provides for an integrated etiologic picture of cardiovascular disease pathogenesis because of the interaction of the epigenome with the environment. Epigenetic biomarkers, which include DNA methylation, histone modifications, and RNA-based mechanisms, are both modifiable and cell-type specific, which makes them not only responsive to the environment, but also an attractive target for drug development. However, the enthusiasm surrounding possible applications of cardiovascular epigenetics currently outpaces available evidence. In this review, the authors synthesize the evidence linking epigenetic changes with cardiovascular disease, emphasizing the gap between the translational potential and the clinical reality of cardiovascular epigenetics.
    Translational research : the journal of laboratory and clinical medicine. 04/2014;
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    ABSTRACT: Lipoprotein subfractions help discriminate cardiometabolic disease risk. Genetic loci validated as associating with lipoprotein measures do not account for a large proportion of the individual variation in lipoprotein measures. We hypothesized that DNA methylation levels across the genome contribute to inter-individual variation in lipoprotein measures. Using data from participants of the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, n= 663 for discovery and n=331 for replication stages respectively), we conducted the first systematic screen of the genome to determine associations between methylation status at ~470,000 cytosine-guanine dinucleotide (CpG) sites in CD4+ T-cells and fourteen lipoprotein subfraction measures. We modeled associations between methylation at each CpG site and each lipoprotein measure separately using linear mixed models, adjusted for age, sex, study site, cell purity, and family structure. We identified two CpGs, both in the carnitine palmitoyltransferase-1A (CPT1A) gene which reached significant levels of association with VLDL and LDL subfraction parameters in both discovery and replication phases (P<1.1x10-7 in the discovery phase; P<.004 in the replication phase; P<1.1*10-12 in the full sample). CPT1A is regulated by the peroxisome proliferator-activated receptor-alpha (PPARα), a ligand for drugs used to reduce cardiovascular disease (CVD). Our associations between methylation in CPT1A and lipoprotein measures highlight the epigenetic role of this gene in metabolic dysfunction.
    The Journal of Lipid Research 04/2014; · 4.39 Impact Factor
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    ABSTRACT: For analysis of the main effects of SNPs, meta-analysis of summary results from individual studies has been shown to provide comparable results as “mega-analysis” that jointly analyzes the pooled participant data from the available studies. This fact revolutionized the genetic analysis of complex traits through large GWAS consortia. Investigations of gene-environment (G×E) interactions are on the rise since they can potentially explain a part of the missing heritability and identify individuals at high risk for disease. However, for analysis of gene-environment interactions, it is not known whether these methods yield comparable results. In this empirical study, we report that the results from both methods were largely consistent for all four tests; the standard 1 degree of freedom (df) test of main effect only, the 1 df test of the main effect (in the presence of interaction effect), the 1 df test of the interaction effect, and the joint 2 df test of main and interaction effects. They provided similar effect size and standard error estimates, leading to comparable P-values. The genomic inflation factors and the number of SNPs with various thresholds were also comparable between the two approaches. Mega-analysis is not always feasible especially in very large and diverse consortia since pooling of raw data may be limited by the terms of the informed consent. Our study illustrates that meta-analysis can be an effective approach also for identifying interactions. To our knowledge, this is the first report investigating meta-versus mega-analyses for interactions.
    Genetic Epidemiology 04/2014; · 4.02 Impact Factor
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    ABSTRACT: Non-high-density lipoprotein cholesterol(NHDL) is an independent and superior predictor of CVD risk as compared to low-density lipoprotein alone. It represents a spectrum of atherogenic lipid fractions with possibly a distinct genomic signature. We performed genome-wide association studies (GWAS) to identify loci influencing baseline NHDL and its postprandial lipemic (PPL) response. We carried out GWAS in 4,241 participants of European descent. Our discovery cohort included 928 subjects from the Genetics of Lipid-Lowering Drugs and Diet Network Study. Our replication cohorts included 3,313 subjects from the Heredity and Phenotype Intervention Heart Study and Family Heart Study. A linear mixed model using the kinship matrix was used for association tests. The best association signal was found in a tri-genic region at RHOQ-PIGF-CRIPT for baseline NHDL (lead SNP rs6544903, discovery p = 7e-7, MAF = 2 %; validation p = 6e-4 at 0.1 kb upstream neighboring SNP rs3768725, and 5e-4 at 0.7 kb downstream neighboring SNP rs6733143, MAF = 10 %). The lead and neighboring SNPs were not perfect surrogate proxies to each other (D' = 1, r (2) = 0.003) but they seemed to be partially dependent (likelihood ration test p = 0.04). Other suggestive loci (discovery p < 1e-6) included LOC100419812 and LOC100288337 for baseline NHDL, and LOC100420502 and CDH13 for NHDL PPL response that were not replicated (p > 0.01). The current and first GWAS of NHDL yielded an interesting common variant in RHOQ-PIGF-CRIPT influencing baseline NHDL levels. Another common variant in CDH13 for NHDL response to dietary high-fat intake challenge was also suggested. Further validations for both loci from large independent studies, especially interventional studies, are warranted.
    Human Genetics 03/2014; · 4.63 Impact Factor
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    ABSTRACT: Dysregulation in the circadian system induced by variants of clock genes has been associated with type 2 diabetes. Evidence for the role of cryptochromes, core components of the system, in regulating glucose homeostasis is not supported by CRY1 candidate gene association studies for diabetes and insulin resistance in human, suggesting possible dietary influences. The purpose of this study was to test for interactions between a CRY1 polymorphism, rs2287161, and carbohydrate intake on insulin resistance in two independent populations: a Mediterranean (n = 728) and an European origin North American population (n = 820). Linear regression interaction models were performed in two populations to test for gene-diet interactions on fasting insulin and glucose and two insulin-related traits, homeostasis model assessment of insulin resistance (HOMA-IR) and quantitative insulin sensitivity check index (QUICKI). In addition, fixed effects meta-analyses for these interactions were performed. Cohort-specific interaction analyses showed significant interactions between the CRY1 variant and dietary carbohydrates for insulin resistance in both populations (p < 0.05). Findings from the meta-analyses of carbohydrate-single nucleotide polymorphism interactions indicated that an increase in carbohydrate intake (% of energy intake) was associated with a significant increase in HOMA-IR (p = 0.011), fasting insulin (p = 0.007) and a decrease in QUICKI (p = 0.028), only among individuals homozygous for the minor C allele. This novel finding supports the link between the circadian system and glucose metabolism and suggests the importance this CRY1 locus in developing personalized nutrition programs aimed at reducing insulin resistance and diabetes risk.
    Chronobiology International 02/2014; · 4.35 Impact Factor

Publication Stats

11k Citations
2,407.77 Total Impact Points


  • 2005–2014
    • University of Alabama at Birmingham
      • • Department of Epidemiology
      • • Department of Biostatistics
      Birmingham, Alabama, United States
    • Johns Hopkins Medicine
      • Department of Medicine
      Baltimore, MD, United States
  • 1997–2014
    • University of Minnesota Duluth
      • Laboratory Medicine and Pathology
      Duluth, Minnesota, United States
  • 2013
    • Zhejiang University
      • Department of Food and Nutrition Science
      Hangzhou, Zhejiang Sheng, China
    • Sarawak General Hospital
      Seriki, Sarawak, Malaysia
  • 2009–2013
    • University of Murcia
      • • Faculty of Biology
      • • Departamento de Fisiología
      Murcia, Murcia, Spain
    • University of Massachusetts Medical School
      • Department of Quantitative Health Sciences
      Worcester, MA, United States
    • University of California, San Diego
      • Department of Family and Preventive Medicine
      San Diego, CA, United States
  • 2007–2013
    • University of Texas Health Science Center at Houston
      • • Division of Epidemiology, Human Genetics and Environmental Sciences
      • • School of Public Health
      Houston, TX, United States
    • University of Houston
      Houston, Texas, United States
    • Korea University
      Sŏul, Seoul, South Korea
  • 2012
    • Cincinnati Children's Hospital Medical Center
      Cincinnati, Ohio, United States
    • TNO
      Delft, South Holland, Netherlands
  • 2009–2012
    • United States Department of Agriculture
      Washington, Washington, D.C., United States
  • 2006–2012
    • University of Alabama
      Tuscaloosa, Alabama, United States
    • Universidade Federal do Rio Grande do Sul
      Pôrto de São Francisco dos Casaes, Rio Grande do Sul, Brazil
  • 2000–2012
    • Washington University in St. Louis
      • • Department of Medicine
      • • Division of Biostatistics
      Saint Louis, MO, United States
    • National Heart, Lung, and Blood Institute
      • Division of Cardiovascular Sciences (DCVS)
      Maryland, United States
  • 1998–2012
    • University of Washington Seattle
      • Department of Biostatistics
      Seattle, WA, United States
  • 2011
    • Partners HealthCare
      Boston, Massachusetts, United States
    • University of Pennsylvania
      • Department of Biostatistics and Epidemiology
      Philadelphia, PA, United States
    • Weill Cornell Medical College
      • Division of Hospital Medicine
      New York City, New York, United States
    • Oregon Health and Science University
      • Bone and Mineral Unit
      Portland, OR, United States
    • University of Valencia
      Valenza, Valencia, Spain
  • 2008–2011
    • Brigham and Women's Hospital
      • Division of Aging
      Boston, MA, United States
    • University of Naples Federico II
      Napoli, Campania, Italy
  • 2007–2011
    • Tufts University
      • Nutrition and Genomics Research Laboratory
      Georgia, United States
  • 1994–2011
    • Wake Forest School of Medicine
      • • Department of Biostatistical Sciences
      • • Division of Public Health Sciences
      Winston-Salem, NC, United States
  • 2010
    • University of Toronto
      • Division of Cardiology
      Toronto, Ontario, Canada
  • 1996–2010
    • University of Minnesota Twin Cities
      • • Department of Laboratory Medicine and Pathology
      • • Division of Epidemiology and Community Health
      • • School of Public Health
      Minneapolis, MN, United States
  • 2008–2009
    • Universiteit Utrecht
      • Division of Pharmacoepidemiology and Pharmacotherapy
      Utrecht, Provincie Utrecht, Netherlands
  • 1992–2009
    • University of North Carolina at Chapel Hill
      • • Department of Epidemiology
      • • Department of Biostatistics
      Chapel Hill, NC, United States
  • 2001–2007
    • Cornell University
      • Department of Medicine
      Ithaca, NY, United States
    • Johns Hopkins Bloomberg School of Public Health
      • Department of Epidemiology
      Baltimore, MD, United States
  • 1999–2006
    • University of Utah
      • • Division of Cardiovascular Genetics
      • • Department of Psychiatry
      Salt Lake City, UT, United States
    • University of Wisconsin–Madison
      Madison, Wisconsin, United States
    • Lexington Medical Center
      West Columbia, South Carolina, United States
    • Pennsylvania State University
      University Park, Maryland, United States
  • 2003–2005
    • Boston University
      • Section of Preventive Medicine and Epidemiology
      Boston, MA, United States
    • University of Mississippi Medical Center
      • Department of Medicine
      Jackson, Mississippi, United States
  • 2001–2005
    • New York Presbyterian Hospital
      • Department of Pain Medicine
      New York City, New York, United States
  • 2004
    • University of Massachusetts Boston
      Boston, Massachusetts, United States
  • 2002
    • University of Tampere
      Tammerfors, Province of Western Finland, Finland
  • 1991
    • University of South Florida
      • Department of Cardiology
      Tampa, FL, United States