Ralph B D'Agostino

Duke University, Durham, North Carolina, United States

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Publications (812)7626.58 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: Aims: In the dual antiplatelet therapy (DAPT) study, continued thienopyridine beyond 12 months after drug-eluting stent placement was associated with increased mortality compared with placebo. We sought to evaluate factors related to mortality in randomized patients receiving either drug-eluting or bare metal stents in the DAPT study. Methods and results: Patients were enrolled after coronary stenting, given thienopyridine and aspirin for 12 months, randomly assigned to continued thienopyridine or placebo for an additional 18 months (while taking aspirin), and subsequently treated with aspirin alone for another 3 months. A blinded independent adjudication committee evaluated deaths. Among 11 648 randomized patients, rates of all-cause mortality rates were 1.9 vs. 1.5% (continued thienopyridine vs. placebo, P = 0.07), cardiovascular mortality, 1.0 vs. 1.0% (P = 0.97), and non-cardiovascular mortality, 0.9 vs. 0.5% (P = 0.01) over the randomized period (Months 12-30). Rates of fatal bleeding were 0.2 vs. 0.1% (P = 0.81), and deaths related to any prior bleeding were 0.3 vs. 0.2% (P = 0.36), Months 12-33). Cancer incidence did not differ (2.0 vs. 1.6%, P = 0.12). Cancer-related deaths occurred in 0.6 vs. 0.3% (P = 0.02) and were rarely related to bleeding (0.1 vs. 0, P = 0.25). After excluding those occurring in patients with cancer diagnosed before enrolment, rates were 0.4 vs. 0.3% (P = 0.16). Conclusion: Bleeding accounted for a minority of deaths among patients treated with continued thienopyridine. Cancer-related death in association with thienopyridine therapy was mainly not related to bleeding and may be a chance finding. Caution is warranted when considering extended thienopyridine in patients with advanced cancer. Trial registration: clinicaltrials.gov Identifier: NCT00977938.
    European Heart Journal 11/2015; DOI:10.1093/eurheartj/ehv614 · 15.20 Impact Factor
  • Allan D Sniderman · Ralph B D'Agostino · Michael J Pencina ·

    JAMA The Journal of the American Medical Association 11/2015; 314(17):1875-1876. DOI:10.1001/jama.2015.12221 · 35.29 Impact Factor
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    ABSTRACT: Objectives To develop a quantitative tool for identifying outpatients most likely to require life support with mechanical ventilation within 5 years.DesignRetrospective cohort study.SettingFramingham Heart Study (FHS) 1991 to 2009 and Intermountain Healthcare clinics 2008 to 2013.ParticipantsFHS participants (n = 3,666; mean age 74; 58% female) in a derivation cohort and Intermountain Healthcare outpatients aged 65 and older (n = 88,302; mean age 73, 57% female) in an external validation cohort.MeasurementsInformation on demographic characteristics and comorbidities collected during FHS examinations to derive a 5-year risk score for receiving mechanical ventilation in an intensive care unit, with external validation using administrative data from outpatients seen at Intermountain Healthcare. A sensitivity analysis investigating model performance for a composite outcome of mechanical ventilation or death was performed.ResultsEighty (2%) FHS participants were mechanically ventilated within 5 years after a FHS examination. Age, sex, diabetes mellitus, hypertension, atrial fibrillation, alcohol use, chronic pulmonary disease, and hospitalization within the prior year predicted need for mechanical ventilation within 5 years (c-statistic = 0.74, 95% confidence interval (CI) = 0.68–0.80). One thousand seven hundred twenty-five (2%) Intermountain Healthcare outpatients underwent mechanical ventilation. The validation model c-statistic was 0.67 (95% CI = 0.66–0.68). Approximately 1% of individuals identified as low risk and 5% to 12% identified as high risk required mechanical ventilation within 5 years. Sensitivity analysis demonstrated a c-statistic of 0.75 (95% CI = 0.75–0.75) for risk prediction of a composite outcome of mechanical ventilation or death.ConclusionA simple risk score using clinical examination data or administrative data may be used to predict 5-year risk of mechanical ventilation or death. Further study is necessary to determine whether use of a risk score enhances advance care planning or improves quality of care of older adults.
    Journal of the American Geriatrics Society 10/2015; 63(10). DOI:10.1111/jgs.13673 · 4.57 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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  • David J Hunter · Ralph B D'Agostino ·

    New England Journal of Medicine 08/2015; 373(8):691-3. DOI:10.1056/NEJMp1508144 · 55.87 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; 24(3). 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
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    Diabetes care 06/2015; 38(6):e84-5. DOI:10.2337/dc15-0157 · 8.42 Impact Factor

Publication Stats

87k Citations
7,626.58 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
  • 1998-2015
    • 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
      • • Center for Anxiety and Related Disorders
      • • Department of Medicine
      Boston, Massachusetts, United States
  • 2000-2014
    • Wake Forest University
      • • Department of Biostatistical Sciences
      • • Department of Public Health Sciences
      • • School of Medicine
      Winston-Salem, North Carolina, United States
    • Massachusetts General Hospital
      • Cardiovascular Disease Prevention Center
      Boston, MA, United States
  • 2013
    • University of Lausanne
      Lausanne, Vaud, Switzerland
  • 1982-2013
    • Boston Medical Center
      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
    • National Eye Institute
      Maryland, United States
    • Harvard Medical School
      • Department of Medicine
      Boston, Massachusetts, United States
  • 1989-2008
    • University of Massachusetts Boston
      • Clinical Epidemiology Research and Training Unit
      Boston, Massachusetts, United States
  • 2007
    • University of Toronto
      • Institute for Clinical Evaluative Sciences
      Toronto, Ontario, Canada
  • 2004-2007
    • University of Texas at San Antonio
      San Antonio, Texas, United States
    • 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
  • 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
  • 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
      San Antonio, TX, 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
  • 1990-1994
    • Beverly Hospital, Boston MA
      Beverly, Massachusetts, United States