Ralph B D'Agostino

Wake Forest School of Medicine, Winston-Salem, North Carolina, United States

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Publications (768)6728.31 Total impact

  • [show abstract] [hide abstract]
    ABSTRACT: It is unclear to what extent the incremental predictive performance of a novel biomarker is impacted by the method used to control for standard predictors. We investigated whether adding a biomarker to a model with a published risk score overestimates its incremental performance as compared to adding it to a multivariable model with individual predictors (or a composite risk score estimated from the sample of interest) and to a null model. We used 1000 simulated datasets (with a range of risk factor distributions and event rates) to compare these methods, using the continuous net reclassification index (NRI), the integrated discrimination index (IDI), and change in the C-statistic as discrimination metrics. The new biomarker was added to the following: null model, model including a published risk score, model including a composite risk score estimated from the sample of interest, and multivariable model with individual predictors. We observed a gradient in the incremental performance of the biomarker, with the null model resulting in the highest predictive performance of the biomarker and the model using individual predictors resulting in the lowest (mean increases in C-statistic between models without and with the biomarker: 0.261, 0.085, 0.030, and 0.031; NRI: 0.767, 0.621, 0.513, and 0.530; IDI: 0.153, 0.093, 0.053 and 0.057, respectively). These findings were supported by the Framingham Study data predicting atrial fibrillation using novel biomarkers. We recommend that authors report the effect of a new biomarker after controlling for standard predictors modeled as individual variables. Copyright © 2014 John Wiley & Sons, Ltd.
    Statistics in Medicine 04/2014; · 2.04 Impact Factor
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    ABSTRACT: To estimate temporal changes in the prevalence of diabetic ketoacidosis (DKA) at diagnosis of type 1 or type 2 diabetes in youth and to explore factors associated with its occurrence. Five centers identified incident cases of diabetes among youth aged 0 to 19 years starting in 2002. DKA presence was defined as a bicarbonate level <15 mmol/L and/or a pH <7.25 (venous) or <7.30 (arterial or capillary) or mention of DKA in the medical records. We assessed trends in the prevalence of DKA over 3 time periods (2002-2003, 2004-2005, and 2008-2010). Logistic regression was used to determine factors associated with DKA. In youth with type 1 diabetes (n = 5615), the prevalence of DKA was high and stable over time (30.2% in 2002-2003, 29.1% in 2004-2005, and 31.1% in 2008-2010; P for trend = .42). Higher prevalence was associated with younger age at diagnosis (P < .0001), minority race/ethnicity (P = .019), income (P = .019), and lack of private health insurance (P = 008). Among youth with type 2 diabetes (n = 1425), DKA prevalence decreased from 11.7% in 2002-2003 to 5.7% in 2008-2010 (P for trend = .005). Higher prevalence was associated with younger age at diagnosis (P = .001), minority race/ethnicity (P = .013), and male gender (P = .001). The frequency of DKA in youth with type 1 diabetes, although stable, remains high, indicating a persistent need for increased awareness of signs and symptoms of diabetes and better access to health care. In youth with type 2 diabetes, DKA at onset is less common and is decreasing over time.
    PEDIATRICS 03/2014; · 4.47 Impact Factor
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    ABSTRACT: The value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of first cardiovascular events is uncertain. To determine whether adding information on HbA1c values to conventional cardiovascular risk factors is associated with improvement in prediction of cardiovascular disease (CVD) risk. Analysis of individual-participant data available from 73 prospective studies involving 294,998 participants without a known history of diabetes mellitus or CVD at the baseline assessment. Measures of risk discrimination for CVD outcomes (eg, C-index) and reclassification (eg, net reclassification improvement) of participants across predicted 10-year risk categories of low (<5%), intermediate (5% to <7.5%), and high (≥7.5%) risk. During a median follow-up of 9.9 (interquartile range, 7.6-13.2) years, 20,840 incident fatal and nonfatal CVD outcomes (13,237 coronary heart disease and 7603 stroke outcomes) were recorded. In analyses adjusted for several conventional cardiovascular risk factors, there was an approximately J-shaped association between HbA1c values and CVD risk. The association between HbA1c values and CVD risk changed only slightly after adjustment for total cholesterol and triglyceride concentrations or estimated glomerular filtration rate, but this association attenuated somewhat after adjustment for concentrations of high-density lipoprotein cholesterol and C-reactive protein. The C-index for a CVD risk prediction model containing conventional cardiovascular risk factors alone was 0.7434 (95% CI, 0.7350 to 0.7517). The addition of information on HbA1c was associated with a C-index change of 0.0018 (0.0003 to 0.0033) and a net reclassification improvement of 0.42 (-0.63 to 1.48) for the categories of predicted 10-year CVD risk. The improvement provided by HbA1c assessment in prediction of CVD risk was equal to or better than estimated improvements for measurement of fasting, random, or postload plasma glucose levels. In a study of individuals without known CVD or diabetes, additional assessment of HbA1c values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk.
    JAMA The Journal of the American Medical Association 03/2014; 311(12):1225-33. · 29.98 Impact Factor
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    ABSTRACT: PurposeThe purpose of this study was to evaluate perceptions of the role of midlevel providers among pediatric type 1 diabetes patients.Methods The study population was a convenience sample of 82 youth with type 1 diabetes who were enrolled in the SEARCH for Diabetes in Youth Study, Carolina site, and attended either baseline (n = 22) or follow-up (n = 60) visits between May 25, 2012, and October 3, 2012. Self-administered surveys queried participants' understanding of providers' roles and perceived employment at clinics and whether participants had seen providers since diagnosis. Midlevel providers of interest included dietitians, nurse practitioners, physician assistants, and medical social workers. Mean proportions for each provider were compared to dietitians (referent) via a t test. Fisher exact tests were used to determine associations between survey responses.ResultsBaseline participants reported seeing a dietitian since diagnosis more often than they reported seeing an nurse practitioner, physician assistant, or medical social worker. Baseline and follow-up participants both reported understanding the role of dietitians significantly more than the role of other providers. Dietitians were reported by all participants to be employed at clinics more frequently than physician assistants or medical social workers. Seeing the provider was associated with patients' self-reported understanding of providers and their employment at diabetes care clinics.Conclusions The survey population reported a high understanding of dietitian roles. However, the roles of other midlevel providers were not as well understood by youth with type 1 diabetes and their parents, which could represent a missed opportunity for care.
    The Diabetes Educator 03/2014; · 1.94 Impact Factor
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    ABSTRACT: Background The 2013 guidelines of the American College of Cardiology and the American Heart Association (ACC-AHA) for the treatment of cholesterol expand the indications for statin therapy for the prevention of cardiovascular disease. Methods Using data from the National Health and Nutrition Examination Surveys of 2005 to 2010, we estimated the number, and summarized the risk-factor profile, of persons for whom statin therapy would be recommended (i.e., eligible persons) under the new ACC-AHA guidelines, as compared with the guidelines of the Third Adult Treatment Panel (ATP III) of the National Cholesterol Education Program, and extrapolated the results to a population of 115.4 million U.S. adults between the ages of 40 and 75 years. Results As compared with the ATP-III guidelines, the new guidelines would increase the number of U.S. adults receiving or eligible for statin therapy from 43.2 million (37.5%) to 56.0 million (48.6%). Most of this increase in numbers (10.4 million of 12.8 million) would occur among adults without cardiovascular disease. Among adults between the ages of 60 and 75 years without cardiovascular disease who are not receiving statin therapy, the percentage who would be eligible for such therapy would increase from 30.4% to 87.4% among men and from 21.2% to 53.6% among women. This effect would be driven largely by an increased number of adults who would be classified solely on the basis of their 10-year risk of a cardiovascular event. Those who would be newly eligible for statin therapy include more men than women and persons with a higher blood pressure but a markedly lower level of low-density lipoprotein cholesterol. As compared with the ATP-III guidelines, the new guidelines would recommend statin therapy for more adults who would be expected to have future cardiovascular events (higher sensitivity) but would also include many adults who would not have future events (lower specificity). Conclusions The new ACC-AHA guidelines for the management of cholesterol would increase the number of adults who would be eligible for statin therapy by 12.8 million, with the increase seen mostly among older adults without cardiovascular disease. (Funded by the Duke Clinical Research Institute and others.).
    New England Journal of Medicine 03/2014; · 51.66 Impact Factor
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    ABSTRACT: Background. The incidence of Clostridium difficile infection (CDI) has risen dramatically during the last decade. Although patients respond well to medical therapy such as vancomycin, 20-30% of patients treated suffer a recurrence of CDI. Methods. We developed a simple/practical scoring rule (logistic regression model) for recurrent CDI using data from two large Phase 3 clinical trials (www.clinicaltrials.gov: study NCT00314951 and study NCT00468728). 77 baseline CDI factors were classified: demographics, co-morbidity, medications, vital signs, laboratory tests, severity and symptoms. Predictors with the highest discrimination in each class (using Receiver Operating Characteristics Curve) were selected. For the final model, stepwise selection was performed. Discrimination, calibration and internal validation were used to assess the model. Results. The final model with a simple scoring rule was developed. It includes four independent risk factors which are readily available when the patient makes initial contact: age (<75 vs. ≥75years), number of unformed bowel movements during previous 24 hours (<10 vs. ≥10), serum creatinine levels (<1.2 mg/dL, ≥1.2 mg/dL) and prior episode of CDI (yes vs. no). In addition, the model includes choice of treatment (vancomycin or fidaxomicin). Conclusions. The prediction model for recurrence may be useful for treatment decision.
    Clinical Infectious Diseases 03/2014; · 9.37 Impact Factor
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    ABSTRACT: Although colonoscopy is the accepted standard for detection of colorectal adenomas and cancers, many adenomas and some cancers are missed. To avoid interval colorectal cancer, the adenoma miss rate of colonoscopy needs to be reduced by improvement of colonoscopy technique and imaging capability. We aimed to compare the adenoma miss rates of full-spectrum endoscopy colonoscopy with those of standard forward-viewing colonoscopy. We did an international, multicentre, randomised trial at three sites in Israel, one site in the Netherlands, and two sites in the USA between Feb 1, 2012, and March 31, 2013. Patients aged 18-70 years referred for colorectal cancer screening, polyp surveillance, or diagnostic assessment underwent same-day, back-to-back tandem colonoscopy with standard forward-viewing colonoscope and the full-spectrum endoscopy colonoscope. The patients were randomly assigned (1:1), via computer-generated randomisation with block size of 20, to which procedure was done first. The endoscopist was masked to group allocation until immediately before the start of colonoscopy examinations; patients were not masked. The primary endpoint was adenoma miss rates. We did per-protocol analyses. This trial is registered with ClinicalTrials.gov, number NCT01549535. 197 participants were enrolled. 185 participants were included in the per-protocol analyses: 88 (48%) were randomly assigned to receive standard forward-viewing colonoscopy first, and 97 (52%) to receive full-spectrum endoscopy colonoscopy first. By per-lesion analysis, the adenoma miss rate was significantly lower in patients in the full-spectrum endoscopy group than in those in the standard forward-viewing procedure group: five (7%) of 67 vs 20 (41%) of 49 adenomas were missed (p<0·0001). Standard forward-viewing colonoscopy missed 20 adenomas in 15 patients; of those, three (15%) were advanced adenomas. Full-spectrum endoscopy missed five adenomas in five patients in whom an adenoma had already been detected with first-pass standard forward-viewing colonoscopy; none of these missed adenomas were advanced. One patient was admitted to hospital for colitis detected at colonoscopy, whereas five minor adverse events were reported including vomiting, diarrhoea, cystitis, gastroenteritis, and bleeding. Full-spectrum endoscopy represents a technology advancement for colonoscopy and could improve the efficacy of colorectal cancer screening and surveillance. EndoChoice.
    The Lancet Oncology 02/2014; · 25.12 Impact Factor
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    ABSTRACT: In their Strategic Impact Goal Statement, the American Heart Association focused on primordial prevention of cardiovascular risk factors by defining metrics for ideal cardiovascular health (ICH). The prevalence of ICH among youth with type 1 diabetes is unknown. Youth with type 1 diabetes face an increased risk of cardiovascular disease (CVD) as they age. The purpose of this report was to examine the prevalence of ICH in a population of youth with type 1 diabetes and to examine the association of ICH with measures of cardiovascular structure and function. This report is based on SEARCH CVD an ancillary study to the SEARCH for Diabetes in Youth. A total of 190 adolescents with type 1 diabetes had complete data on all of the ICH metrics at baseline and had measures of arterial stiffness [pulse wave velocity (PWV), brachial distensibility (BrachD), and augmentation index (AIx)] and carotid intima-media thickness completed at a follow-up visit [on average 5 yr after baseline (interquartile range 4-5)]. No subjects met the ICH criteria for all 7 metrics. Meeting an increasing number of ICH metrics was significantly associated with lower arterial stiffness [lower PWV of the trunk (β = -0.02 ±0.01; p = 0.004) and AIx (β = -2.2 ±0.66; p = 0.001), and increased BrachD (β = 0.14 ±0.07; p = 0.04)]. Increasing number of ICH metrics was significantly associated with decreased arterial stiffness, but prevalence of ICH in this population was low. Youth with type 1 diabetes could benefit from improvements in their cardiovascular health.
    Pediatric Diabetes 01/2014; · 2.08 Impact Factor
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    ABSTRACT: Abstract BACKGROUND: Atherosclerotic renal-artery stenosis is a common problem in the elderly. Despite two randomized trials that did not show a benefit of renal-artery stenting with respect to kidney function, the usefulness of stenting for the prevention of major adverse renal and cardiovascular events is uncertain. METHODS: We randomly assigned 947 participants who had atherosclerotic renal-artery stenosis and either systolic hypertension while taking two or more antihypertensive drugs or chronic kidney disease to medical therapy plus renal-artery stenting or medical therapy alone. Participants were followed for the occurrence of adverse cardiovascular and renal events (a composite end point of death from cardiovascular or renal causes, myocardial infarction, stroke, hospitalization for congestive heart failure, progressive renal insufficiency, or the need for renal-replacement therapy). RESULTS: Over a median follow-up period of 43 months (interquartile range, 31 to 55), the rate of the primary composite end point did not differ significantly between participants who underwent stenting in addition to receiving medical therapy and those who received medical therapy alone (35.1% and 35.8%, respectively; hazard ratio with stenting, 0.94; 95% confidence interval [CI], 0.76 to 1.17; P=0.58). There were also no significant differences between the treatment groups in the rates of the individual components of the primary end point or in all-cause mortality. During follow-up, there was a consistent modest difference in systolic blood pressure favoring the stent group (-2.3 mm Hg; 95% CI, -4.4 to -0.2; P=0.03). CONCLUSIONS: Renal-artery stenting did not confer a significant benefit with respect to the prevention of clinical events when added to comprehensive, multifactorial medical therapy in people with atherosclerotic renal-artery stenosis and hypertension or chronic kidney disease. (Funded by the National Heart, Lung and Blood Institute and others; ClinicalTrials.gov number, NCT00081731.).
    New England Journal of Medicine 01/2014; 2014 Jan 2;370(1):13-22.. · 51.66 Impact Factor
  • Pharmacoepidemiology and Drug Safety 01/2014; 23(1):109-10. · 2.90 Impact Factor
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    ABSTRACT: When comparing prediction models, it is essential to estimate the magnitude of change in performance rather than rely solely on statistical significance. In this paper we investigate measures that estimate change in classification performance, assuming 2-group classification based on a single risk threshold. We study the value of a new biomarker when added to a baseline risk prediction model. First, simulated data are used to investigate the change in sensitivity and specificity (ΔSe and ΔSp). Second, the influence of ΔSe and ΔSp on the net reclassification improvement (NRI; sum of ΔSe and ΔSp) and on decision-analytic measures (net benefit or relative utility) is studied. We assume normal distributions for the predictors and assume correctly specified models such that the extended model has a dominating receiver operating characteristic curve relative to the baseline model. Remarkably, we observe that even when a strong marker is added it is possible that either sensitivity (for thresholds below the event rate) or specificity (for thresholds above the event rate) decreases. In these cases, decision-analytic measures provide more modest support for improved classification than NRI, even though all measures confirm that adding the marker improved classification accuracy. Our results underscore the necessity of reporting ΔSe and ΔSp separately. When a single summary is desired, decision-analytic measures allow for a simple incorporation of the misclassification costs.
    Medical Decision Making 12/2013; · 2.89 Impact Factor
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    ABSTRACT: Aging process or senescence affects the expression of a wide range of phenotypic traits throughout the life span of organisms. These traits often show modular, synergistic, and even antagonistic relationships, and are also influenced by genomic, developmental, physiological and environmental factors. The cardiovascular system (CVS) in humans represents a major modular system in which the relationships among physiological, anatomical and morphological traits undergo continuous remodeling throughout the life span of an individual. Here we extend the concept of developmental plasticity in order to study the relationships among 14 traits measured on 3,412 individuals from the Framingham Heart Study cohort, relative to age and gender, using exploratory structural equation modeling-a form of systems analysis. Our results reveal differing patterns of association among cardiac traits in younger and older persons in both sexes, indicating that physiological and developmental factors may be channeled differentially in relation to age and gender during the remodeling process. We suggest that systems approaches are necessary in order to understand the coordinated functional relationships among traits of the CVS over the life course of individuals.
    Biogerontology 12/2013; · 3.19 Impact Factor
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    ABSTRACT: Experimental and epidemiological evidence suggest that dysregulation of proteins involved in iron metabolism plays a critical role in cancer. The mechanisms by which cancer cells alter homeostatic iron regulation are just beginning to be understood. Here we demonstrate that iron regulatory protein 2 (IRP2) plays a key role in iron accumulation in breast cancer. Although both IRP1 and IRP2 are over-expressed in breast cancer, the overexpression of IRP2, but not IRP1, is associated with decreased ferritin H and increased transferrin receptor 1 (TfR1). Knock-down of IRP2 in triple negative MDA-MB-231 human breast cancer cells increases ferritin H expression and decreases TfR1 expression, resulting in a decrease in the labile iron pool. Further, IRP2 knockdown reduces growth of MDA-MB-231 cells in the mouse mammary fat pad. Gene expression microarray profiles of breast cancer patients demonstrate that increased IRP2 expression is associated with high grade cancer. Increased IRP2 expression is observed in luminal A, luminal B and basal breast cancer subtypes, but not in breast tumors of the ERBB2 molecular subtype. These results suggest that dysregulation of IRP2 is an early nodal point underlying altered iron metabolism in breast cancer and may contribute to poor outcome of some breast cancer patients.
    Cancer Research 11/2013; · 8.65 Impact Factor
  • Circulation 11/2013; · 15.20 Impact Factor
  • Journal of the American College of Cardiology 11/2013; · 14.09 Impact Factor
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    ABSTRACT: OBJECTIVE To evaluate if presence of cardiovascular (CV) risk factors and their clustering as metabolic syndrome (MetS) is associated with increased arterial stiffness and accelerated progression over time among youth with type 1 diabetes.RESEARCH DESIGN AND METHODS Longitudinal study of 298 youth with type 1 diabetes (age 14.5 years; 46.3% female; duration 4.8 years), with two research visits conducted 5 years apart. CV factors included: waist circumference, blood pressure (BP), fasting lipids (HDL cholesterol, LDL cholesterol [LDL-c], triglycerides), albumin/creatinine ratio, and HbA1c. MetS was based on Adult Treatment Panel III criteria modified for youth. Pulse wave velocity (PWV) in the carotid-femoral segment was measured by tonometry. Mixed models were used to assess the rate of progression in PWV and the association between CV factors and PWV over time.RESULTSPWV increased significantly over time (0.145 m/s/year; P < 0.0001). MetS (P = 0.0035), large waist (P < 0.0001), and elevated BP (P = 0.0003) at baseline were each associated with worse PWV over time. These baseline factors, however, did not significantly influence the rate of progression. Increases in waist circumference (P < 0.0001), LDL-c levels (P = 0.0156), and declining glucose control (HbA1c; P = 0.0419) were independently associated with higher PWV over time.CONCLUSIONS Presence, clustering, and worsening of CV risk factors are associated with increased arterial stiffness over time in youth with type 1 diabetes. Whether improvement in CV risk factors early in life will slow the progression of arterial stiffness and reduce the burden of CV disease in this population requires further study.
    Diabetes care 10/2013; · 7.74 Impact Factor
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    ABSTRACT: Higher left ventricular (LV) mass, wall thickness, and internal dimension are associated with increased heart failure (HF) risk. Whether different LV hypertrophy patterns vary with respect to rates and types of HF incidence is unclear. In this study, 4,768 Framingham Heart Study participants (mean age 50 years, 56% women) were classified into 4 mutually exclusive LV hypertrophy pattern groups (normal, concentric remodeling, concentric hypertrophy, and eccentric hypertrophy) using American Society of Echocardiography-recommended thresholds of echocardiographic LV mass indexed to body surface area and relative wall thickness, and these groups were related to HF incidence. Whether risk for HF types (HF with reduced ejection fraction [<45%] vs preserved ejection fraction [≥45%]) varied by hypertrophy pattern was then evaluated. On follow-up (mean 21 years), 458 participants (9.6%, 250 women) developed new-onset HF. The age- and gender-adjusted 20-year HF incidence increased from 6.96% in the normal left ventricle group to 8.67%, 13.38%, and 15.27% in the concentric remodeling, concentric hypertrophy, and eccentric hypertrophy groups, respectively. After adjustment for co-morbidities and incident myocardial infarction, LV hypertrophy patterns were associated with higher HF incidence relative to the normal left ventricle group (p = 0.0002); eccentric hypertrophy carried the greatest risk (hazard ratio [HR] 1.89, 95% confidence interval [CI] 1.41 to 2.54), followed by concentric hypertrophy (HR 1.40, 95% CI 1.04 to 1.87). Participants with eccentric hypertrophy had a higher propensity for HF with reduced ejection fraction (HR 2.23, 95% CI 1.48 to 3.37), whereas those with concentric hypertrophy were more prone to HF with preserved ejection fraction (HR 1.66, 95% CI 1.09 to 2.51). In conclusion, in this large community-based sample, HF risk varied by LV hypertrophy pattern, with eccentric and concentric hypertrophy predisposing to HF with reduced and preserved ejection fraction, respectively.
    The American journal of cardiology 10/2013; · 3.58 Impact Factor
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    ABSTRACT: In mouse models of prostate cancer, increased epinephrine levels accelerated tumor growth via the beta2-adrenoreceptor/PKA signaling pathway. It is unknown, however, whether men experience increased epinephrine levels sufficient to activate the beta2-adrenoreceptor/PKA pathway in the prostate gland. We measured epinephrine levels in blood samples collected immediately prior to prostate biopsies and measured phosphorylation of S133CREB (PKA site), S112BAD, T202/Y204ERK, and S473 Akt in prostate biopsy tissue samples. Tissue samples and 3 ml of blood were obtained from men (n = 20) recruited from the patients scheduled for prostate biopsies. Epinephrine levels were measured by ELISA. Proteins were extracted from biopsied tissue, and protein phosphorylation was measured by Western blotting with phospho-specific antibodies. Pearson and Spearman's rank correlations were analyzed to assess relationships between blood epinephrine levels and phosphorylation of CREB, BAD, AKT, and ERK. Epinephrine levels above 1 nM were detected in 5 of 20 patients. A strong positive correlation was observed between increased epinephrine levels and CREB phosphorylation. In contrast, no correlation was observed between epinephrine levels and phosphorylation of ERK, BAD, or AKT. Our results suggest that increased blood epinephrine levels activate the beta2-adrenoreceptor/PKA signaling pathway in human prostate glands. These results will inform future studies to examine whether beta2-selective blockers can inhibit activation of the epinephrine/ADRB2/PKA pathway in prostate tumors of men with increased epinephrine levels and explore the use of beta2-selective blockers as adjuvant therapy for prostate cancer.
    International Urology and Nephrology 09/2013; · 1.33 Impact Factor
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    ABSTRACT: Abstract Aim: This study explored the role of glycemic control on cardiac autonomic function, measured by heart rate variability (HRV), in youth with type 1 diabetes. Patients and Methods: A retrospective cohort of 345 youth with type 1 diabetes (mean age, 18.5 years; duration, 10 years) participating in the SEARCH for Diabetes in Youth study were enrolled in the ancillary SEARCH Cardiovascular Disease (CVD) study. Anthropometric, metabolic, and HRV parameters were collected at the current research visit. Glycemic control over time was assessed by the mean glycated hemoglobin (A1c) levels collected over the past 6 years. Multiple linear regression analysis assessed the association between A1c over time and HRV parameters, independent of demographic and CVD risk factors. Participants were categorized into four glycemic control categories based on their mean A1c over time: Group 1, optimal (mean A1c, ≤7.4%); Group 2 (mean A1c, 7.5-8.4%); Group 3 (mean A1c, 8.5-9.4%), and Group 4, poor (mean A1c, ≥9.5%), and a linear trend was explored across these categories. Results: For every 1% increase in the average A1c over 6 years there was a 5% decrease in the SD of the normal RR interval (SDNN) (P=0.02) and 7% decrease in the root mean square successive difference of the RR interval (RMSSD) (P=0.02), independent of demographic and traditional CVD risk factors. A dose-response relationship between worsening glucose control categories and measures of overall reduced HRV was found. Conclusions: Chronic hyperglycemia is the main determinant of early cardiac autonomic dysfunction, manifested as reduced overall HRV and parasympathetic loss, among youth with type 1 diabetes.
    Diabetes Technology &amp Therapeutics 09/2013; · 2.21 Impact Factor
  • Diabetes care 09/2013; 36(9):e153-4. · 7.74 Impact Factor

Publication Stats

52k Citations
6,728.31 Total Impact Points

Institutions

  • 1997–2014
    • Wake Forest School of Medicine
      • • Section on Cardiology
      • • Department of Cancer Biology
      • • Department of Biomedical Engineering
      • • Department of Biostatistical Sciences
      • • Division of Public Health Sciences
      • • Section on Infectious Diseases
      Winston-Salem, North Carolina, United States
  • 1987–2014
    • Boston University
      • • Department of Mathematics and Statistics
      • • Endocrinology, Diabetes, and Nutrition
      • • Department of Neurology
      • • Department of Medicine
      • • Division of Mathematics
      • • Section of Preventive Medicine and Epidemiology
      Boston, Massachusetts, United States
  • 2013
    • University of Massachusetts Medical School
      Worcester, Massachusetts, United States
  • 2012–2013
    • Colorado Department of Public Health and Environment
      Denver, Colorado, United States
    • Dana-Farber Cancer Institute
      • Department of Biostatistics and Computational Biology
      Boston, MA, United States
    • McGill University
      Montréal, Quebec, Canada
    • Simmons College
      Boston, Massachusetts, United States
  • 2007–2013
    • Brigham and Women's Hospital
      • Department of Medicine
      Boston, MA, United States
    • University of Toronto
      • Institute for Clinical Evaluative Sciences
      Toronto, Ontario, Canada
  • 2005–2013
    • Karl Jaspers Society of North America
      United States
    • University of Texas Health Science Center at Houston
      • Department of Pediatrics
      Houston, TX, United States
    • Durham University
      Durham, England, United Kingdom
    • University of California, Davis
      • Center for Neuroscience
      Davis, CA, United States
  • 1998–2013
    • National Institutes of Health
      • Branch of Liver Diseases Branch (LDB)
      Maryland, United States
    • University at Buffalo, The State University of New York
      Buffalo, New York, United States
  • 1997–2013
    • National Heart, Lung, and Blood Institute
      • Division of Cardiovascular Sciences (DCVS)
      Maryland, United States
  • 1987–2013
    • Boston Medical Center
      Boston, Massachusetts, United States
  • 2008–2012
    • University of Colorado
      • • Department of Epidemiology
      • • Barbara Davis Center for Childhood Diabetes
      Denver, CO, United States
    • University Medical Center Utrecht
      • Julius Center for Health Sciences and Primary Care
      Utrecht, Provincie Utrecht, Netherlands
    • The University of Manchester
      • Institute of Cardiovascular Sciences
      Manchester, ENG, United Kingdom
  • 2003–2012
    • University of South Carolina
      • Department of Epidemiology & Biostatistics
      Columbia, SC, United States
    • U.S. Department of Veterans Affairs
      Washington, Washington, D.C., United States
  • 1992–2012
    • Tufts Medical Center
      • • Department of Radiology
      • • Department of Medicine
      Boston, Massachusetts, United States
    • Mass College of Liberal Arts
      Boston, Massachusetts, United States
    • Bryant & Stratton College
      Arkansas, United States
    • Massachusetts College of Liberal Arts
      Liberal, Kansas, United States
  • 1990–2012
    • University of Massachusetts Boston
      • Clinical Epidemiology Research and Training Unit
      Boston, Massachusetts, United States
    • New England Research Institutes
      Watertown, Massachusetts, United States
  • 2011
    • Boston Biomedical Research Institute
      Boston, Massachusetts, United States
  • 2010–2011
    • Duke University Medical Center
      • • Duke Cancer Institute
      • • Department of Community and Family Medicine
      Durham, NC, United States
    • University of Chicago
      • Department of Medicine
      Chicago, IL, United States
  • 2007–2011
    • University of Illinois at Chicago
      • • Department of Medicine (Chicago)
      • • Section of Endocrinology, Diabetes and Metabolism
      Chicago, IL, United States
  • 2006–2011
    • Tufts University
      Georgia, United States
    • Harvard University
      • Department of Society, Human Development, and Health
      Boston, MA, United States
  • 2005–2011
    • Rhode Island Hospital
      Providence, Rhode Island, United States
  • 1993–2011
    • University of Washington Seattle
      • • Cardiovascular Health Research Unit (CHRU)
      • • Department of Otolaryngology/Head and Neck Surgery
      Seattle, WA, United States
    • University of Alabama at Birmingham
      • Department of Medicine
      Birmingham, AL, United States
  • 2007–2010
    • Emory University
      • Division of Cardiology
      Atlanta, GA, United States
  • 1996–2010
    • Wake Forest University
      • • Department of Biostatistical Sciences
      • • Department of Public Health Sciences
      Winston-Salem, North Carolina, United States
  • 1995–2010
    • University of Pittsburgh
      • • Department of Epidemiology
      • • Center for Research on Health Care
      Pittsburgh, PA, United States
  • 1992–2010
    • Massachusetts General Hospital
      • • Department of Medicine
      • • Cardiovascular Disease Prevention Center
      Boston, MA, United States
  • 2009
    • The University of Arizona
      • Division of Epidemiology and Biostatistics
      Tucson, AZ, United States
    • Johannes Gutenberg-Universität Mainz
      Mayence, Rheinland-Pfalz, Germany
  • 2005–2009
    • Kaiser Permanente
      Oakland, California, United States
  • 2000–2009
    • University of Texas Health Science Center at San Antonio
      • • Division of Clinical Epidemiology
      • • Division of Hospital Medicine
      San Antonio, TX, United States
  • 2006–2008
    • Partners HealthCare
      Boston, Massachusetts, United States
  • 1999–2008
    • Beth Israel Deaconess Medical Center
      • • Division of General Medicine and Primary Care
      • • Department of Medicine
      Boston, MA, United States
  • 1993–2008
    • University of Pennsylvania
      • Division of Cardiovascular Medicine
      Philadelphia, PA, United States
  • 2003–2007
    • IMIM Hospital del Mar Medical Research Institute
      Barcino, Catalonia, Spain
  • 2004–2006
    • Northwestern University
      • Department of Preventive Medicine
      Evanston, IL, United States
    • Hospital De Clínicas De Porto Alegre
      Pôrto de São Francisco dos Casaes, Rio Grande do Sul, Brazil
    • Countess Of Chester Hospital NHS Foundation Trust
      Chester, England, United Kingdom
    • University of Alberta
      • Department of Medicine
      Edmonton, Alberta, Canada
  • 1993–2006
    • Massachusetts Institute of Technology
      • Laboratory for Computer Science
      Cambridge, Massachusetts, United States
  • 2002–2005
    • Royal North Shore Hospital
      Sydney, New South Wales, Australia
  • 1993–2005
    • Medical University of South Carolina
      • • General Clinical Research Center
      • • Division of Neuroradiology
      Charleston, SC, United States
  • 1988–2005
    • Beverly Hospital, Boston MA
      Beverly, Massachusetts, United States
    • University of Miami Miller School of Medicine
      • Department of Neurology
      Miami, FL, United States
  • 2001–2003
    • Harvard Medical School
      • Department of Medicine
      Boston, Massachusetts, United States
  • 1999–2003
    • National Eye Institute
      Maryland, United States
  • 1996–2002
    • Texas Tech University Health Sciences Center
      • Department of Medicine
      Lubbock, TX, United States
  • 1995–1997
    • University of Maine
      • Department of Psychology
      Orono, MN, United States
  • 1991–1997
    • New England Baptist Hospital
      Boston, Massachusetts, United States
  • 1994
    • University of Illinois, Urbana-Champaign
      Urbana, Illinois, United States
    • University of Florida
      Gainesville, Florida, United States