Ralph B D'Agostino

Duke University, Durham, North Carolina, United States

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Publications (885)7926.09 Total impact

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    ABSTRACT: Inflammatory cytokines in the colonic microenvironment have been shown to increase with advance colorectal cancer disease state. However, the contribution of inflammatory cytokines to pre-malignant disease, such as the formation of adenomas, is unclear. Using the Milliplex® MAP Human Cytokine/ Chemokine Magnetic Bead Panel Immunoassay, serum cytokine and chemokine profiles were assayed among participants without an adenoma (n = 97) and those with an adenoma (n = 97) enrolled in the NCI-funded Insulin Resistance Atherosclerosis Colon Study. The concentrations of interleukin-10 (IL-10), IL-1β, IL-6, IL-17A, IL-2, IL-4, IL-7, IL-12(p70), interferon-γ (IFN-γ), macrophage chemoattractant protein-1 (MCP-1), regulated on activation, normal T cell expressed and secreted (RANTES), tumor necrosis factor-alpha (TNF-α), vascular endothelial growth factor (VEGF), granulocyte macrophage colony-stimulating factor (GM-CSF), and macrophage inflammatory protein-1β (MIP-1β) were determined. Multiple logistic regression analyses were used to evaluate the association between adenoma prevalence and cytokine levels. The presence of colorectal adenomas was not associated with significant increases in the systemic levels of proinflammatory (TNF-α, IL-6, IL-1β) or T-cell polarizing (IL-12, IL-2, IL-10, IL-4, IL-17, IFN-γ) cytokines. Furthermore, MCP-1 and RANTES levels were equivalent in the serum of study participants with and without adenomas. These findings suggest colorectal adenoma prevalence may not be associated with significant alterations in systemic inflammation.
    BMC Cancer 12/2015; 15(1):1115. DOI:10.1186/s12885-015-1115-2 · 3.32 Impact Factor
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    ABSTRACT: The 2013 American College of Cardiology/American Heart Association (ACC/AHA) guidelines for cholesterol management defined new eligibility criteria for statin therapy. However, it is unclear whether this approach improves identification of adults at higher risk of cardiovascular events. To determine whether the ACC/AHA guidelines improve identification of individuals who develop incident cardiovascular disease (CVD) and/or have coronary artery calcification (CAC) compared with the National Cholesterol Education Program's 2004 Updated Third Report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (ATP III) guidelines. Longitudinal community-based cohort study, with participants for this investigation drawn from the offspring and third-generation cohorts of the Framingham Heart Study. Participants underwent multidetector computed tomography for CAC between 2002 and 2005 and were followed up for a median of 9.4 years for incident CVD. Statin eligibility was determined based on Framingham risk factors and low-density lipoprotein thresholds for ATP III, whereas the pooled cohort calculator was used for ACC/AHA. The primary outcome was incident CVD (myocardial infarction, death due to coronary heart disease [CHD], or ischemic stroke). Secondary outcomes were CHD and CAC (as measured by the Agatston score). Among 2435 statin-naive participants (mean age, 51.3 [SD, 8.6] years; 56% female), 39% (941/2435) were statin eligible by ACC/AHA compared with 14% (348/2435) by ATP III (P < .001). There were 74 incident CVD events (40 nonfatal myocardial infarctions, 31 nonfatal ischemic strokes, and 3 fatal CHD events). Participants who were statin eligible by ACC/AHA had increased hazard ratios for incident CVD compared with those eligible by ATP III: 6.8 (95% CI, 3.8-11.9) vs 3.1 (95% CI, 1.9-5.0), respectively (P<.001). Similar results were seen for CVD in participants with intermediate Framingham Risk Scores and for CHD. Participants who were newly statin eligible (n = 593 [24%]) had an incident CVD rate of 5.7%, yielding a number needed to treat of 39 to 58. Participants with CAC were more likely to be statin eligible by ACC/AHA than by ATP III: CAC score >0 (n = 1015): 63% vs 23%; CAC score >100 (n = 376): 80% vs 32%; and CAC score >300 (n = 186): 85% vs 34% (all P < .001). A CAC score of 0 identified a low-risk group among ACC/AHA statin-eligible participants (306/941 [33%]) with a CVD rate of 1.6%. In this community-based primary prevention cohort, the ACC/AHA guidelines for determining statin eligibility, compared with the ATP III, were associated with greater accuracy and efficiency in identifying increased risk of incident CVD and subclinical coronary artery disease, particularly in intermediate-risk participants.
    JAMA The Journal of the American Medical Association 07/2015; 314(2):134-41. DOI:10.1001/jama.2015.7515 · 30.39 Impact Factor
  • David C Goff · Ralph B D'Agostino · Michael Pencina · Donald M Lloyd-Jones
    Annals of internal medicine 07/2015; 163(1):68. DOI:10.7326/L15-5105 · 16.10 Impact Factor
  • Allan D Sniderman · Ralph B D'Agostino · Michael J Pencina
    JAMA The Journal of the American Medical Association 07/2015; 314(1):25-26. DOI:10.1001/jama.2015.6177 · 30.39 Impact Factor
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    ABSTRACT: The prevalence of cardiometabolic multimorbidity is increasing. To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689 300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128 843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499 808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates. A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). All-cause mortality and estimated reductions in life expectancy. In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity.
    JAMA The Journal of the American Medical Association 07/2015; 314(1):52-60. DOI:10.1001/jama.2015.7008 · 30.39 Impact Factor
  • Diabetes care 06/2015; 38(6):e84-5. DOI:10.2337/dc15-0157 · 8.57 Impact Factor
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    ABSTRACT: Oncolytic viruses (OV) preferentially kill cancer cells due in part to defects in their antiviral responses upon exposure to type I interferons (IFNs). However, IFN responsiveness of some tumor cells confers resistance to OV treatment. The human type I IFNs include one IFNβ and multiple IFNα subtypes that share the same receptor but are capable of differentially inducing biological responses. The role of individual IFN subtypes in promoting tumor cell resistance to OV is addressed here. Two human IFNs which have been produced for clinical use, IFNα2a and IFNβ, were compared for activity in protecting human head and neck squamous cell carcinoma (HNSCC) lines from oncolysis by vesicular stomatitis virus (VSV). Susceptibility of HNSCC lines to killing by VSV varied. VSV infection induced increased production of IFNβ in resistant HNSCC cells. When added exogenously, IFNβ was significantly more effective at protecting HNSCC cells from VSV oncolysis than was IFNα2a. In contrast, normal keratinocytes and endothelial cells were equivalently protected by both IFN subtypes. Differential responsiveness of tumor cells to IFNs α and β was further supported by the finding that autocrine IFNβ but not IFNα promoted survival of HNSCC cells during persistent VSV infection. Therefore, IFNs α and β differentially affect VSV oncolysis, justifying the evaluation and comparison of IFN subtypes for use in combination with VSV therapy. Pairing VSV with IFNα2a may enhance selectivity of oncolytic VSV therapy for HNSCC by inhibiting VSV replication in normal cells without a corresponding inhibition in cancer cells. There has been a great deal of progress in the development of oncolytic viruses. However, a major problem is that individual cancers vary in their sensitivity to oncolytic viruses. In many cases this is due to differences in their production and response to interferons (IFNs). The experiments described here compared the responses of head and neck squamous cell carcinoma cell lines to two IFN subtypes, IFNα2a and IFNβ, in protection from oncolytic vesicular stomatitis virus. We found that IFNα2a was significantly less protective for cancer cells than was IFNβ, whereas normal cells were equivalently protected by both IFNs. These results suggest that from a therapeutic standpoint, selectivity for cancer versus normal cells may be enhanced by pairing VSV with IFNα2a. Copyright © 2015, American Society for Microbiology. All Rights Reserved.
    Journal of Virology 05/2015; DOI:10.1128/JVI.00757-15 · 4.65 Impact Factor
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    ABSTRACT: Autoimmune thyroid diseases (AITD) and Type 1 diabetes (T1D) frequently occur in the same individual pointing to a strong shared genetic susceptibility. Indeed, the co-occurrence of T1D and AITD in the same individual is classified as a variant of the autoimmune polyglandular syndrome type 3 (designated APS3v). Our aim was to identify new genes and mechanisms causing the co-occurrence of T1D + AITD (APS3v) in the same individual using a genome-wide approach. For our discovery set we analyzed 346 Caucasian APS3v patients and 727 gender and ethnicity matched healthy controls. Genotyping was performed using the Illumina Human660W-Quad.v1. The replication set included 185 APS3v patients and 340 controls. Association analyses were performed using the PLINK program, and pathway analyses were performed using the MAGENTA software. We identified multiple signals within the HLA region and conditioning studies suggested that a few of them contributed independently to the strong association of the HLA locus with APS3v. Outside the HLA region, variants in GPR103, a gene not suggested by previous studies of APS3v, T1D, or AITD, showed genome-wide significance (p < 5 × 10(-8)). In addition, a locus on 1p13 containing the PTPN22 gene showed genome-wide significant associations. Pathway analysis demonstrated that cell cycle, B-cell development, CD40, and CTLA-4 signaling were the major pathways contributing to the pathogenesis of APS3v. These findings suggest that complex mechanisms involving T-cell and B-cell pathways are involved in the strong genetic association between AITD and T1D. Published by Elsevier Ltd.
    Journal of Autoimmunity 04/2015; 60. DOI:10.1016/j.jaut.2015.03.006 · 7.02 Impact Factor
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    ABSTRACT: Results of the SYMPLICITY HTN-3 (Renal Denervation in Patients With Uncontrolled Hypertension) trial confirmed the safety but not the efficacy of renal denervation for treatment-resistant hypertension at 6 months post procedure. This study sought to analyze the 12-month SYMPLICITY HTN-3 results for the original denervation group, the sham subjects who underwent denervation after the 6-month endpoint (crossover group), and the sham subjects who did not undergo denervation after 6 months (non-crossover group). Eligible subjects were randomized 2:1 to denervation or sham procedure. Subjects were unblinded to their treatment group after the 6-month primary endpoint was ascertained; subjects in the sham group meeting eligibility requirements could undergo denervation. Change in blood pressure (BP) at 12 months post randomization (6 months for crossover subjects) was analyzed. The 12-month follow-up was available for 319 of 361 denervation subjects and 48 of 101 non-crossover subjects; 6-month denervation follow-up was available for 93 of 101 crossover subjects. In denervation subjects, the 12-month office systolic BP (SBP) change was greater than that observed at 6 months (-15.5 ± 24.1 mm Hg vs. -18.9 ± 25.4 mm Hg, respectively; p = 0.025), but the 24-h SBP change was not significantly different at 12 months (p = 0.229). The non-crossover group office SBP decreased by -32.9 ± 28.1 mm Hg at 6 months, but this response regressed to -21.4 ± 19.9 mm Hg (p = 0.01) at 12 months, increasing to 11.5 ± 29.8 mm Hg. These data support no further reduction in office or ambulatory BP after 1-year follow-up. Loss of BP reduction in the non-crossover group may reflect decreased medication adherence or other related factors. (Renal Denervation in Patients With Uncontrolled Hypertension [SYMPLICITY HTN-3]; NCT01418261). Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
    Journal of the American College of Cardiology 04/2015; 65(13):1314-21. DOI:10.1016/j.jacc.2015.01.037 · 15.34 Impact Factor
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    ABSTRACT: Background Arterial stiffness is a useful parameter to predict future cardiovascular disease.Objective We sought to compare arterial stiffness in adolescents and young adults with and without type 1 diabetes (T1D) and explore the risk factors associated with the differences observed.Subjects and methodsCarotid-femoral pulse wave velocity (PWV), augmentation index (AI75), and brachial distensibility (BrachD) were measured in 402 adolescents and young adults with T1D (age 18.8 ± 3.3 yr, T1D duration 9.8 ± 3.8 yr) and 206 non-diabetic controls that were frequency-matched by age, sex, and race/ethnicity in a cross-sectional study. General linear models were used to explore variables associated with an increase in arterial stiffness after adjustment for demographic and metabolic covariates.ResultsT1D status was associated with a higher PWV (5.9 ± 0.05 vs. 5.7 ± 0.1 m/s), AI75 (1.3 ± 0.6 vs. −1.9 ± 0.7%), and lower BrachD (6.2 ± 0.1 vs. 6.5 ± 0.1%Δ/mmHg), all p < 0.05. In multivariate models, age, sex, race, adiposity, blood pressure, lipids, and the presence of microalbuminuria were found to be independent correlates of increased arterial stiffness. After adjustment for these risk factors, T1D status was still significantly associated with arterial stiffness (p < 0.05).Conclusions Peripheral and central subclinical vascular changes are present in adolescents and young adults with T1D compared to controls. Increased cardiovascular risk factors alone do not explain the observed differences in arterial stiffness among cases and controls. Identifying other risk factors associated with increased arterial stiffness in youth with T1D is critical to prevent future vascular complications.
    Pediatric Diabetes 04/2015; 16(5). DOI:10.1111/pedi.12279 · 2.13 Impact Factor
  • Journal of the American Society of Hypertension 04/2015; 9(4):e53. DOI:10.1016/j.jash.2015.03.120 · 2.68 Impact Factor
  • Lawson R Wulsin · Paul S Horn · Jennifer L Perry · Joe Massaro · Ralph B D'Agostino
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    ABSTRACT: Identifying novel early predictors of metabolic disorders is essential to improving effective primary prevention. To examine the contribution of two measures of autonomic imbalance, resting heart rate (RHR) and heart rate variability (HRV), on the development of five metabolic risk outcomes, and on cardiovascular disease, diabetes, and early mortality. Secondary analysis of prospective data from Offspring Cohort participants (N=1882) in the Framingham Heart Study. Participants at visit 3 (1983-87) with: a) age 18 or older, and b) data on RHR, HRV, and five measures of metabolic risk (blood pressure, fasting glucose, triglycerides, HDL, and body mass index) at three follow-up visits over 12 years. We conducted a backward elimination variable selection procedure on a logistic regression model, using baseline RHR, HRV, age, gender, and smoking status to predict the odds of developing a specific metabolic risk. Measures: 1) hyperglycemia, 2) high blood pressure, 3) high triglycerides, 4) low HDL, and 5) high body mass index over 12 years. Incident diabetes, cardiovascular disease, and early mortality. RHR and HRV, along with gender, age, and smoking were significant predictors of high blood pressure, hyperglycemia, and a diagnosis of diabetes within 12 years. RHR and HRV also predicted the development of cardiovascular disease and early mortality for most of the sample. In this community sample two measures of autonomic imbalance predicted multiple poor metabolic outcomes and mortality, making autonomic imbalance potentially a worthy target for intervention studies to reduce risks for cardiovascular disorders, diabetes, and early death.
    The Journal of Clinical Endocrinology and Metabolism 03/2015; 100(6):jc20144123. DOI:10.1210/jc.2014-4123 · 6.31 Impact Factor
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    ABSTRACT: New American College of Cardiology/American Heart Association (ACC/AHA) cholesterol guidelines emphasize 10-year risk of cardiovascular disease (CVD) to identify adults eligible for statin therapy for primary prevention. Whether these CVD risk thresholds should be individualized by age and sex has not been explored. To determine the potential impact of incorporating age- and sex-specific CVD risk thresholds into current cholesterol guidelines. Using data from the Framingham Offspring Study, we evaluated current treatment recommendations among age- and sex-specific groups in 3685 participants free of CVD. Then, we evaluated how varying age- and sex-specific 10-year CVD risk thresholds for statin treatment affect the sensitivity and specificity for incident 10-year CVD events. Basing statin therapy recommendations on a 10-year fixed risk threshold of 7.5% results in lower statin consideration among women than men (63% vs. 33% <0.0001), yet the vast majority of those aged 66-75 years were recommended for treatment (90.3%). The fixed 7.5% threshold also had relatively low sensitivity for capturing 10-year events in younger women and men (aged 40-55 years). Sensitivity of the recommendations were substantially improved when the treatment threshold was reduced to 5% in those aged 40-55 years (changing sensitivity from 36% to 61% in women, and 49% to 71% in men). Among older adults (aged 66-75) specificity was poor (18% in women, 3% in men), but when the treatment threshold was raised to 10% in women and 15% in men, specificity significantly improved (to 34% in women, 14% in men), with only small to no loss in sensitivity (95% to 87% in women, and 96% at both thresholds in men). Cholesterol treatment recommendations could be improved by utilizing individualized age- and sex-specific CVD risk thresholds. Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
    Journal of the American College of Cardiology 02/2015; 65(16). DOI:10.1016/j.jacc.2015.02.025 · 15.34 Impact Factor
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    ABSTRACT: Decreased insulin sensitivity is a cardiovascular risk factor (CVRF) in youth with type 1 diabetes (T1D). Whether baseline insulin sensitivity is independently associated with changes in early arterial stiffness (pulse wave velocity (PWV)) over time in youth with T1D is not known. Two hundred ninety-eight youth with T1D in the SEARCH CVD study had PWV measured~five years apart. Insulin sensitivity and other CVRFs were measured at baseline. The association between baseline insulin sensitivity with PWV over time was explored using linear mixed models. Models were adjusted for baseline age, sex and race, with subsequent adjustment for CVRFs. There was a significant interaction (p=0.0326) between baseline insulin sensitivity and time on PWV, independent of CVRFs, indicating that higher insulin sensitivity levels were associated with lower rate of change in PWV over time. Other significant predictors of PWV change were baseline age [β=0.007 (p=0.03) increase in logPWV/year increase in age] and mean arterial blood pressure (MAP) [β=0.005 (p<0.01) increase in logPWV/mmHg increase in MAP] and smoking status (current vs. never smoker). Lower insulin sensitivity at baseline appears to be an important risk factor for increased arterial stiffness over time in youth with T1D. This identifies a potentially modifiable therapeutic target. Copyright © 2015 Elsevier Inc. All rights reserved.
    Journal of Diabetes and its Complications 02/2015; 29(4). DOI:10.1016/j.jdiacomp.2015.02.004 · 1.93 Impact Factor
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    ABSTRACT: Determine if parental diabetes (DM) is associated with unhealthier cardiovascular disease (CVD) risk profiles in youth with type 2 diabetes (T2D), and whether associations differed by race/ethnicity. Family history was available for 382 youth with T2D from 2001 prevalent and 2002-2005 incident SEARCH for Diabetes in Youth cohorts. Parental DM was evaluated in two ways: two-category-any parent vs. no parent DM (evaluated overall and stratified by race/ethnicity); and four-category-both parents, mother only, father only, or no parent DM (evaluated overall only). Associations with hemoglobin A1c (HbA1c), fasting lipids, blood pressure (BP), and urine albumin:creatinine ratio (ACR) were examined using regression models. Overall, sample characteristics included: 35.9% male, 19.1% non-Hispanic white (NHW), mean T2D duration 26.6±22.2months, mean HbA1c 7.9%±2.5% (62.6±27.8mmol/mol). Unadjusted two-category comparisons showed that youth with parental DM had higher HbA1c, higher DBP, and higher frequency of elevated ACR. Adjusted two-category comparisons showed associations remaining in non-stratified analysis for ACR [OR (95% CI)=2.3 (1.1, 5.0)] and in NHW youth for HbA1c [6.8%±0.4 vs. 8.0±0.4 (51.1±4.8 vs. 63.9±4.2mmol/mol), p=.015], DBP (67.7%±4.5 vs. 76.9±4.4mm Hg, p=.014) and lnTG (4.7±0.3 vs. 5.3±0.3, p=.008). There were no significant findings in the adjusted four-category evaluation. Parental history of diabetes may be associated with unhealthier CVD risk factors in youth with T2D. Copyright © 2015 Elsevier Inc. All rights reserved.
    Journal of Diabetes and its Complications 02/2015; 29(4). DOI:10.1016/j.jdiacomp.2015.02.001 · 1.93 Impact Factor

Publication Stats

89k Citations
7,926.09 Total Impact Points


  • 2015
    • Duke University
      Durham, North Carolina, United States
  • 2011–2015
    • Boston Biomedical Research Institute
      Boston, Massachusetts, United States
  • 1997–2015
    • Wake Forest School of Medicine
      • • Department of Biostatistical Sciences
      • • Division of Public Health Sciences
      • • Department of Radiation Oncology
      Winston-Salem, North Carolina, United States
  • 1981–2015
    • Boston University
      • • Division of Mathematics
      • • Department of Mathematics and Statistics
      • • Section of Preventive Medicine and Epidemiology
      • • Department of Medicine
      Boston, Massachusetts, United States
  • 1996–2014
    • Wake Forest University
      • • Department of Biostatistical Sciences
      • • Department of Biochemistry
      • • Department of Public Health Sciences
      Winston-Salem, North Carolina, United States
  • 2013
    • Furman University
      • Department of Health Sciences
      Гринвилл, South Carolina, United States
    • University of Lausanne
      Lausanne, Vaud, Switzerland
    • Colorado Department of Public Health and Environment
      Denver, Colorado, United States
  • 1982–2013
    • Boston Medical Center
      Boston, Massachusetts, United States
  • 2012
    • University of North Carolina at Chapel Hill
      • Department of Nutrition
      Chapel Hill, NC, United States
  • 1989–2012
    • University of Massachusetts Boston
      • Clinical Epidemiology Research and Training Unit
      Boston, Massachusetts, United States
  • 2006–2011
    • University of Washington Seattle
      • Cardiovascular Health Research Unit (CHRU)
      Seattle, WA, United States
    • Universität Potsdam
      Potsdam, Brandenburg, Germany
    • Medical University of South Carolina
      Charleston, South Carolina, United States
    • University of North Carolina at Wilmington
      Wilmington, North Carolina, United States
  • 2003–2011
    • University of South Carolina
      • Department of Epidemiology & Biostatistics
      Columbia, SC, United States
    • Washington University in St. Louis
      San Luis, Missouri, United States
  • 1995–2010
    • University of Pittsburgh
      • Department of Epidemiology
      Pittsburgh, PA, United States
    • New England Baptist Hospital
      Boston, Massachusetts, United States
    • University of Houston
      Houston, Texas, United States
  • 1993–2010
    • National Heart, Lung, and Blood Institute
      • Division of Cardiovascular Sciences (DCVS)
      Maryland, United States
  • 1992–2010
    • Tufts Medical Center
      • • Department of Radiology
      • • Department of Medicine
      Boston, Massachusetts, United States
    • Tufts University
      Бостон, Georgia, United States
    • Mass College of Liberal Arts
      Boston, Massachusetts, United States
  • 2009
    • University of Colorado
      • Department of Epidemiology
      Denver, CO, United States
  • 2003–2009
    • Kaiser Permanente
      Oakland, California, United States
  • 2008
    • Harvard Medical School
      • Department of Medicine
      Boston, Massachusetts, United States
    • National Eye Institute
      Maryland, United States
  • 1999–2008
    • Beth Israel Deaconess Medical Center
      • • Division of General Medicine and Primary Care
      • • Department of Medicine
      Boston, MA, United States
    • University of Cambridge
      Cambridge, England, United Kingdom
  • 2004–2007
    • The Harvard Drug Group
      Ливония, Michigan, United States
    • University of Toronto
      • Institute for Clinical Evaluative Sciences
      Toronto, Ontario, Canada
    • University of Kuopio
      Kuopio, Northern Savo, Finland
    • Northwestern University
      • Feinberg School of Medicine
      Evanston, Illinois, United States
    • University of Alberta
      • Department of Medicine
      Edmonton, Alberta, Canada
  • 2003–2007
    • IMIM Hospital del Mar Medical Research Institute
      Barcino, Catalonia, Spain
  • 2002–2007
    • University of Texas at San Antonio
      San Antonio, Texas, United States
    • Royal North Shore Hospital
      Sydney, New South Wales, Australia
  • 2002–2006
    • National Institutes of Health
      Maryland, United States
  • 2005
    • Uppsala University
      • Department of Public Health and Caring Sciences
      Uppsala, Uppsala, Sweden
  • 2001–2004
    • University of Missouri
      • Department of Family and Community Medicine
      Columbia, MO, United States
  • 2002–2003
    • University of Texas Health Science Center at San Antonio
      • • Division of Clinical Epidemiology
      • • Division of Hospital Medicine
      San Antonio, TX, United States
  • 2000
    • Massachusetts General Hospital
      • Cardiovascular Disease Prevention Center
      Boston, MA, United States
  • 1990–1999
    • Erasmus Universiteit Rotterdam
      Rotterdam, South Holland, Netherlands
  • 1993–1997
    • University of Maine
      • Department of Psychology
      Orono, MN, United States
  • 1994
    • University of Florida
      Gainesville, Florida, United States
    • University of Illinois, Urbana-Champaign
      Urbana, Illinois, United States
  • 1988–1994
    • Beverly Hospital, Boston MA
      Beverly, Massachusetts, United States