Ralph B D'Agostino

Duke University, Durham, North Carolina, United States

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Publications (833)7689.51 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 · 3.36 Impact Factor
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    ABSTRACT: Myocardial injury because of oxidative stress manifesting through reductions in left ventricular ejection fraction (LVEF) may occur after the administration of anthracycline-based chemotherapy (A-bC). We hypothesized that bilirubin, an effective endogenous antioxidant, may attenuate the reduction in LVEF that sometimes occurs after receipt of A-bC. We identified 751 consecutively treated patients with cancer who underwent a pre-A-bC LVEF measurement, exhibited a serum total bilirubin level <2 mg/dl, and then received a post-A-bC LVEF assessment because of symptomatology associated with heart failure. Analysis of variance, Tukey's Studentized range test, and chi-square tests were used to evaluate an association between bilirubin and LVEF changes. The LVEF decreased by 10.7 ± 13.7%, 8.9 ± 11.8%, and 7.7 ± 11.5% in group 1 (bilirubin at baseline ≤0.5 mg/dl), group 2 (bilirubin 0.6 to 0.8 mg/dl), and group 3 (bilirubin 0.9 to 1.9 mg/dl), respectively. More group 1 patients experienced >15% decrease in LVEF compared with those in group 3 (p = 0.039). After adjusting for age, coronary artery disease/myocardial infarction, diabetes mellitus, hematocrit, and the use of cardioactive medications, higher precancer treatment bilirubin levels and lesser total anthracycline doses were associated with LVEF preservation (p = 0.047 and 0.011, respectively). In patients treated with anthracyclines who subsequently develop symptoms associated with heart failure, pre-anthracycline treatment serum bilirubin levels inversely correlate with subsequent deterioration in post-cancer treatment LVEF. In conclusion, these results suggest that increased levels of circulating serum total bilirubin, an intrinsic antioxidant, may facilitate preservation of LVEF in patients receiving A-bC for cancer.
    The American journal of cardiology 10/2015; DOI:10.1016/j.amjcard.2015.08.042 · 3.28 Impact Factor
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    ABSTRACT: Objective: To determine whether duration and degree of weight gain is differentially associated with diabetes risk in younger versus middle-aged black and white adults. Research design and methods: We combined data from three cohort studies: Atherosclerosis Risk in Communities (ARIC), Coronary Artery Risk Development in Young Adults (CARDIA), and the Framingham Heart Study. A total of 17,404 participants (56% women; 21% black) were stratified by baseline age (younger: ≥30 and <45 years; middle aged: ≥45 and <60 years) and examined for incident diabetes (median follow-up 9 years). Duration and degree of gain in BMI was calculated as "BMI-years" above one's baseline BMI. Results: Diabetes incidence per 1,000 person-years in the younger and middle-aged groups were 7.2 (95% CI 5.7, 8.7) and 24.4 (22.0, 26.8) in blacks, respectively; and 3.4 (2.8, 4.0) and 10.5 (9.9, 11.2) in whites, respectively. After adjusting for sex, baseline BMI and other cardiometabolic factors, and age and race interaction terms, gains in BMI-years were associated with higher risk of diabetes in the younger compared with middle-aged groups: hazard ratios for 1-unit increase in log BMI-years in younger vs. middle-aged blacks were 1.18 (P = 0.02) and 1.02 (P = 0.39), respectively (P for interaction by age-group = 0.047); and in whites were 1.35 (P < 0.001) and 1.11 (P < 0.001), respectively (P for interaction by age-group = 0.008). Conclusions: Although middle-aged adults have higher rates of diabetes, younger adults are at greater relative risk of developing diabetes for a given level of duration and degree of weight gain.
    Diabetes care 09/2015; DOI:10.2337/dc14-2770 · 8.42 Impact Factor
  • Michael J Pencina · Ralph B D'Agostino
    JAMA The Journal of the American Medical Association 09/2015; 314(10):1063-1064. DOI:10.1001/jama.2015.11082 · 35.29 Impact Factor
  • Lawson Wulsin · Paul Horn · Joseph Massaro · Ralph D'Agostino
    The Journal of Clinical Endocrinology and Metabolism 09/2015; 100(9):L70. DOI:10.1210/jc.2015-2600 · 6.21 Impact Factor
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    ABSTRACT: Investigate the relationship of G-tube placement timing on post-operative outcomes. 908 patients underwent resection of head and neck upper aerodigestive tract tumors between 2007 and 2013. Patient charts were retrospectively screened for patient demographics, pre-operative nutrition variables, co-morbid conditions, Tumor-Node-Metastasis staging, surgical treatment type, and timing of G-tube placement. Exclusionary criteria included death within the first three months of the resection and resections performed solely for nodal disease. Post-surgical outcomes, including wound and medical complications, hospital re-admissions, length of inpatient hospital stay (LOS), intensive care unit (ICU) time. 793 surgeries were included: 8% of patients had G-tubes pre-operatively and 25% had G-tubes placed post-operatively. Patients with G-tubes (pre-operative or post-operative) were more likely to have complications and prolonged hospital care as compared to those without G-tubes (p < 0.001). Patients with pre-operative G-tubes had shortened length of stay (p = 0.007), less weight loss (p = 0.03), and fewer wound care needs (p < 0.0001), when compared to those that received G-tubes post-operatively. Those with G-tubes placed post-operatively had worse outcomes in all categories, except pre-operative BMI. Though having enteral access in the form of a G-tube at any point suggests a more high risk patient, having a G-tube placed in the pre-operative period may protect against poor post-operative outcomes. Post-operative outcomes can be predicted based on patient characteristics available to the physician in the pre-operative period. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Surgical Oncology 08/2015; DOI:10.1016/j.suronc.2015.08.005 · 3.27 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 · 35.29 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 · 17.81 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 · 35.29 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 · 35.29 Impact Factor
  • Diabetes care 06/2015; 38(6):e84-5. DOI:10.2337/dc15-0157 · 8.42 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 IFN-β and multiple IFN-α 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, IFN-α2a and IFN-β, 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 IFN-β in resistant HNSCC cells. When added exogenously, IFN-β was significantly more effective at protecting HNSCC cells from VSV oncolysis than was IFN-α2a. In contrast, normal keratinocytes and endothelial cells were protected equivalently by both IFN subtypes. Differential responsiveness of tumor cells to IFN-α and -β was further supported by the finding that autocrine IFN-β but not IFN-α promoted survival of HNSCC cells during persistent VSV infection. Therefore, IFN-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 IFN-α2a may enhance selectivity of oncolytic VSV therapy for HNSCC by inhibiting VSV replication in normal cells without a corresponding inhibition in cancer cells.
    Journal of Virology 05/2015; 89(15). DOI:10.1128/JVI.00757-15 · 4.44 Impact Factor
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    ABSTRACT: Unlabelled: Influenza virus can cause life-threatening infections in neonates and young infants. Although vaccination is a major countermeasure against influenza, current vaccines are not approved for use in infants less than 6 months of age, in part due to the weak immune response following vaccination. Thus, there is a strong need to develop new vaccines with improved efficacy for this vulnerable population. To address this issue, we established a neonatal African green monkey (AGM) nonhuman primate model that could be used to identify effective influenza vaccine approaches for use in young infants. We assessed the ability of flagellin, a Toll-like receptor 5 (TLR5) agonist, to serve as an effective adjuvant in this at-risk population. Four- to 6-day-old AGMs were primed and boosted with inactivated PR8 influenza virus (IPR8) adjuvanted with either wild-type flagellin or inactive flagellin with a mutation at position 229 (m229), the latter of which is incapable of signaling through TLR5. Increased IgG responses were observed following a boost, as well as at early times after challenge, in infants vaccinated with flagellin-adjuvanted IPR8. Inclusion of flagellin during vaccination also resulted in a significantly increased number of influenza virus-specific T cells following challenge compared to the number in infants vaccinated with the m229 adjuvant. Finally, following challenge infants vaccinated with IPR8 plus flagellin exhibited a reduced pathology in the lungs compared to that in infants that received IPR8 plus m229. This study provides the first evidence of flagellin-mediated enhancement of vaccine responses in nonhuman primate neonates. Importance: Young infants are particularly susceptible to severe disease as a result of influenza virus infection. Compounding this is the lack of effective vaccines for use in this vulnerable population. Here we describe a vaccine approach that results in improved immune responses and protection in young infants. Incorporation of flagellin during vaccination resulted in increased antibody and T cell responses together with reduced disease following virus infection. These results suggest that flagellin may serve as an effective adjuvant for vaccines targeted to this vulnerable population.
    Journal of Virology 05/2015; 89(14):JVI.00549-15. DOI:10.1128/JVI.00549-15 · 4.44 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 · 8.41 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 · 2.57 Impact Factor

Publication Stats

84k Citations
7,689.51 Total Impact Points


  • 2015
    • Duke University
      Durham, North Carolina, United States
  • 2014–2015
    • Boston Biomedical Research Institute
      Boston, Massachusetts, United States
    • Comprehensive Cancer Centers of Nevada
      Las Vegas, Nevada, 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 Public Health Sciences
      • • School of Medicine
      Winston-Salem, North Carolina, United States
  • 2013
    • University of Lausanne
      Lausanne, Vaud, Switzerland
    • Furman University
      • Department of Health Sciences
      Гринвилл, South Carolina, United States
    • Colorado Department of Public Health and Environment
      Denver, Colorado, United States
  • 1982–2013
    • Boston Medical Center
      Boston, Massachusetts, United States
  • 1989–2012
    • University of Massachusetts Boston
      • Clinical Epidemiology Research and Training Unit
      Boston, Massachusetts, 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
    • University of California, Berkeley
      • School of Public Health
      Berkeley, CA, United States
  • 1992–2010
    • Tufts Medical Center
      • • Department of Radiology
      • • Department of Medicine
      Boston, Massachusetts, United States
    • Mass College of Liberal Arts
      Boston, Massachusetts, United States
    • Tufts University
      Бостон, Georgia, 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
  • 2004–2007
    • University of Toronto
      • Institute for Clinical Evaluative Sciences
      Toronto, Ontario, Canada
    • The Harvard Drug Group
      Ливония, Michigan, United States
    • 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
  • 2002–2007
    • University of Texas at San Antonio
      San Antonio, Texas, United States
  • 2006
    • Universität Potsdam
      Potsdam, Brandenburg, Germany
    • University of Washington Seattle
      Seattle, Washington, United States
    • University of North Carolina at Wilmington
      Wilmington, North Carolina, United States
    • Medical University of South Carolina
      Charleston, South Carolina, United States
  • 2002–2006
    • National Institutes of Health
      Maryland, United States
  • 2005
    • Uppsala University
      • Department of Public Health and Caring Sciences
      Uppsala, Uppsala, Sweden
  • 1999–2005
    • Beth Israel Deaconess Medical Center
      • Department of Medicine
      Boston, MA, United States
    • University of Cambridge
      Cambridge, England, United Kingdom
  • 2001–2004
    • University of Missouri
      • Department of Family and Community Medicine
      Columbia, MO, United States
  • 2000–2003
    • University of Texas Health Science Center at San Antonio
      • • Division of Clinical Epidemiology
      • • Division of Hospital Medicine
      San Antonio, TX, United States
    • 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 Illinois, Urbana-Champaign
      Urbana, Illinois, United States
    • University of Florida
      Gainesville, Florida, United States
  • 1988–1994
    • Beverly Hospital, Boston MA
      Beverly, Massachusetts, United States