Donna K Arnett

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

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Publications (467)2342.35 Total impact

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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.
  • 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: 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
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    ABSTRACT: Objective To determine the influence of regular physical activity on stable warfarin dose and risk of major hemorrhage in patients on chronic anticoagulation therapy. DesignRegular physical activity (maintained over > 80% of visits) was ascertained by self-report at initiation of warfarin therapy (target international normalized ratio [INR] = 2–3) in 1272 patients, with changes documented at monthly anticoagulation clinic visits in a population-based prospective cohort. Multi-variable linear regression and survival analysis, respectively, were used to assess influence on warfarin and risk of hemorrhage. SettingOutpatient anticoagulation clinic Participants1272 anticoagulated patients Measurement and Main ResultsThere were 683 (53.7%) patients who were regularly physically active (≥ 30 min ≥ 3 times/week). Physically active patients required warfarin doses that were 6.9% higher (p=0.006) than in physically inactive patients after controlling for sociodemographic factors, vitamin K intake, clinical factors, and genetic variations.The overall incidence of major hemorrhagic events was 7.6/100 person-years (p-yrs, 95% confidence interval [CI] 6.4–8.9) in our population. The incidence was lower for physically active patients (5.6/100 p-yrs, 95% CI 4.2–7.2) than in inactive patients (10.3/100 p-yrs, 95% CI 8.2–12.9, p=0.0004). Active patients had a 38% lower risk of hemorrhage (hazard ratio 0.62, 95% CI 0.42–0.98, p=0.03) compared with inactive patients. Conclusions Regular physical activity is associated with higher warfarin dose requirements and lower risk of hemorrhage. The influence of physical activity on drug response needs to be further explored, and the mechanisms through which it exerts these effects need to be elucidated.
    Pharmacotherapy 02/2014; · 2.31 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. 01/2014;
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    ABSTRACT: Comorbidities are common in heart failure (HF), and the number of comorbidities has been associated with poor outcomes in HF patients. However, little is known about the effect of multiple comorbidities on cardiac mechanics, which could impact the pathogenesis of HF. We sought to determine the relationship between comorbidity burden and adverse cardiac mechanics. We performed speckle-tracking analysis on echocardiograms from the HyperGEN study (n=2150). Global longitudinal, circumferential, and radial strain, and early diastolic (e') tissue velocities were measured. We evaluated the association between comorbidity number and cardiac mechanics using linear mixed effects models to account for relatedness among subjects. The mean age was 51±14 years, 58% were female, and 47% were African American. Dyslipidemia and hypertension were the most common comorbidities (61% and 58%, respectively). After adjusting for left ventricular (LV) mass index, ejection fraction, and several potential confounders, the number of comorbidities remained associated with all indices of cardiac mechanics except global circumferential strain (eg, β=-0.32 [95% CI -0.44, -0.20] per 1-unit increase in number of comorbidities for global longitudinal strain; β=-0.16 [95% CI -0.20, -0.11] for e' velocity; P≤0.0001 for both comparisons). Results were similar after excluding participants with abnormal LV geometry (P<0.05 for all comparisons). Higher comorbidity burden is associated with worse cardiac mechanics, even in the presence of normal LV geometry. The deleterious effect of multiple comorbidities on cardiac mechanics may explain both the high comorbidity burden and adverse outcomes in patients who ultimately develop HF.
    Journal of the American Heart Association. 01/2014; 3(3):e000631.
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    ABSTRACT: Increased postprandial lipid (PPL) response to dietary fat intake is a heritable risk factor for cardiovascular disease (CVD). Variability in postprandial lipids results from the complex interplay of dietary and genetic factors. We hypothesized that detailed lipid profiles (eg, sterols and fatty acids) may help elucidate specific genetic and dietary pathways contributing to the PPL response.
    PLoS ONE 01/2014; 9(6):e99509. · 3.53 Impact Factor
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    ABSTRACT: Lipoprotein subclass concentrations are modifiable markers of cardiovascular disease risk. Fenofibrate is known to show beneficial effects on lipoprotein subclasses, but little is known about the role of genetics in mediating the responses of lipoprotein subclasses to fenofibrate. A recent genomewide association study (GWAS) associated several single nucleotide polymorphisms (SNPs) with lipoprotein measures, and validated these associations in two independent populations. We used this information to construct genetic risk scores (GRSs) for fasting lipoprotein measures at baseline (pre-fenofibrate), and aimed to examine whether these GRSs also associated with the responses of lipoproteins to fenofibrate. Fourteen lipoprotein subclass measures were assayed in 817 men and women before and after a three week fenofibrate trial. We set significance at a Bonferroni corrected alpha <0.05 (p < 0.004). Twelve subclass measures changed with fenofibrate administration (each p = 0.003 to <0.0001). Mixed linear models which controlled for age, sex, body mass index (BMI), smoking status, pedigree and study-center, revealed that GRSs were associated with eight baseline lipoprotein measures (p < 0.004), however no GRS was associated with fenofibrate response. These results suggest that the mechanisms for changes in lipoprotein subclass concentrations with fenofibrate treatment are not mediated by the genetic risk for fasting levels.
    Biology. 01/2014; 3(3):536-550.
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    ABSTRACT: -Adult height has been hypothesized to be inversely associated with coronary heart disease but studies have produced conflicting results. We sought to examine the relationship between adult height and the prevalence of coronary artery calcium (CAC), a direct measure of subclinical atherosclerosis and surrogate marker of CHD. Method and Results-We evaluated the relationship between adult height and CAC in 2,703 participants from the NHLBI Family Heart Study who underwent cardiac computed tomography. We used generalized estimating equations to calculate the prevalence odds ratios for the presence of CAC (CAC>0) across sex-specific quartiles of height. The mean age of the sample was 54.8 years and 60.2% were female. There was an inverse association between adult height and CAC. After adjusting for age, race, field center, waist circumference, smoking, alcohol, physical activity, systolic blood pressure, antihypertensive medications, diabetes, diabetic medications, LDL cholesterol, HDL cholesterol, lipid-lowering medications, and income, individuals in the tallest quartile had 30% lower odds of having prevalent CAC. The odds ratios (95% CI) for the presence of CAC across consecutive sex-specific quartiles of height were 1.0 (reference), 1.15 (0.86-1.53), 0.95(0.73-1.22), and 0.70 (0.53-0.93), p for trend <0.01. There was no evidence of effect modification for the relationship between adult height and CAC by age or socioeconomic status. -The results of our study suggest an inverse, independent association between adult height and CAC.
    Circulation Cardiovascular Imaging 12/2013; · 5.80 Impact Factor

Publication Stats

10k Citations
2,342.35 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 California, San Diego
      • Department of Family and Preventive Medicine
      San Diego, CA, United States
    • University of Massachusetts Medical School
      • Department of Quantitative Health Sciences
      Worcester, MA, 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
    • Korea University
      Sŏul, Seoul, South Korea
    • University of Houston
      Houston, Texas, United States
  • 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
      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
    • Pennsylvania State University
      University Park, Maryland, United States
    • Lexington Medical Center
      West Columbia, South Carolina, United States
  • 2003–2005
    • Boston University
      • Section of Preventive Medicine and Epidemiology
      Boston, MA, 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