Ralph B D'Agostino

Boston Biomedical Research Institute, Boston, Massachusetts, United States

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Publications (877)7741.79 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
  • Diabetes care 06/2015; 38(6):e84-5. DOI:10.2337/dc15-0157 · 8.57 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; 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; 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
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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
  • Steven Y. Hua, Siyan Xu, Ralph B. D'Agostino
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    ABSTRACT: Bioequivalence of two drugs is usually demonstrated by rejecting two one-sided null hypotheses using the two one-sided tests for pharmacokinetic parameters: area under the concentration-time curve (AUC) and maximum concentration (Cmax). By virtue of the intersection–union test, there is no need for multiplicity adjustment in testing the two one-sided null hypotheses within each parameter. However, the decision rule for bioequivalence often requires equivalence to be achieved simultaneously on both parameters that contain four one-sided null hypotheses together; without adjusting for multiplicity, the family wise error rate (FWER) could fail to be controlled at the nominal type-I error rate α. The multiplicity issue for bioequivalence in this regard is scarcely discussed in the literature. To address this issue, we propose two approaches including a closed test procedure that controls FWER for the simultaneous AUC and Cmax bioequivalence and requires no adjustment of the type-I error, and an alpha-adaptive sequential testing (AAST) that controls FWER by pre-specifying the significance level on AUC (α1) and obtaining it for Cmax (α2) adaptively after testing of AUC. While both methods control FWER, the closed test requires testing of eight intersection null hypotheses each at α, and AAST is at times accomplished through a slight deduction in α1 and no deduction in α2 relative to α. The latter considers equivalence reached in AUC a higher importance than that in Cmax. Illustrated with published data, the two approaches, although operate differently, can lead to the same substantive conclusion and are better than a traditional method like Bonferroni adjustment. Copyright © 2014 John Wiley & Sons, Ltd.
    Statistics in Medicine 01/2015; 34(2). DOI:10.1002/sim.6247 · 2.04 Impact Factor
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    ABSTRACT: Analyses using conventional statistical methodologies have yielded conflicting results as to whether low-density lipoprotein cholesterol (LDL-C) or non-high-density lipoprotein cholesterol (non-HDL-C) or apolipoprotein B (apoB) is the best marker of the apoB-associated risk of coronary heart disease. The aim of this study was to determine the additional value of apoB beyond LDL-C or non-HDL-C as a predictor of coronary heart disease. For each patient from the Framingham Offspring Cohort aged 40-75 years (n = 2966), we calculated the extent to which the observed apoB differed from the expected apoB based on their LDL-C or non-HDL-C. We added this difference to a Cox model predicting new onset coronary heart disease over a maximum of 20 years adjusting for standard risk factors plus LDL-C or non-HDL. The difference between observed and expected apoB over LDL-C or non-HDL-C was highly prognostic of future coronary heart disease events: adjusted hazard ratios 1.26 (95% confidence interval: 1.15, 1.37) and 1.20 (1.11, 1.29), respectively, for each standard deviation increase beyond expected apoB levels. When this difference between observed and expected apoB was added to standard coronary heart disease prediction models including LDL-C or non-HDL-C, prediction improved significantly (likelihood ratio test p-values <0.0001) and discrimination c-statistics increased from 0.72 to 0.73. The corresponding relative integrated discrimination improvements were 11% and 8%, respectively. apoB improves risk assessment of future coronary heart disease events over and beyond LDL-C or non-HDL-C, which is consistent with coronary risk being more closely related to the number of atherogenic apoB particles than to the mass of cholesterol within them. © The European Society of Cardiology 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
    European Journal of Preventive Cardiology 01/2015; DOI:10.1177/2047487315569411 · 2.68 Impact Factor
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    ABSTRACT: -Many young adults with moderate hyperlipidemia do not meet statin treatment criteria under the new AHA/ACC cholesterol guidelines as they focus on 10-year cardiovascular risk. We evaluated the association between years of exposure to hypercholesterolemia in early adulthood and future coronary heart disease (CHD) risk. -We examined Framingham Offspring Cohort data to identify adults without incident cardiovascular disease to age 55 (n=1478), and explored the association between moderate hyperlipidemia (non-high-density lipoprotein cholesterol [HDL-C] ≥160 mg/dL) duration in early adulthood and subsequent CHD. At median 15-year follow-up, CHD rates were significantly elevated among adults with prolonged hyperlipidemia exposure by age 55: 4.4% for those with no exposure, 8.1% for 1-10 years, and 16.5% for those with 11-20 years exposure (p<0.001); this association persisted after adjustment for other cardiac risk factors including non-HDL-C at age 55 (HR 1.39, 95% CI 1.05-1.85 per decade of hyperlipidemia). Overall, 85% of young adults with prolonged hyperlipidemia would not have been recommended for statin therapy at age 40, under current national guidelines. However, among those not considered statin therapy candidates at age 55, there remained a significant association between cumulative exposure to hyperlipidemia in young adulthood and subsequent CHD risk (adjusted HR 1.67, 95% CI 1.06-2.64). -Cumulative exposure to hyperlipidemia in young adulthood increases subsequent risk of CHD in a dose-dependent fashion. Adults with prolonged exposure to even moderate elevations in non-HDL-C have elevated risk for future CHD and may benefit from more aggressive primary prevention.
    Circulation 01/2015; 131(5). DOI:10.1161/CIRCULATIONAHA.114.012477 · 14.95 Impact Factor
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    ABSTRACT: Metabolomic studies of increased fat oxidation showed increase in circulating acylcarnitines C2, C8, C10, and C12 and decrease in C3, C4, and C5. We hypothesize that urinary F2-isoprostanes reflect intensity of fatty acid oxidation and are associated with circulating C2, C8, C10, and C12 directly and with C3, C4, and C5 inversely. Four urinary F2-isoprostane isomers and serum acylcarnitines are quantified using LC-MS/MS within the Insulin Resistance Atherosclerosis Study nondiabetic cohort (n = 682). Cross-sectional associations between fasting urinary F2-isoprostanes (summarized as a composite index) and the selected acylcarnitines are examined using generalized linear models. F2-isoprostane index is associated with C2 and C12 directly and with C5 inversely: the adjusted beta coefficients are 0.109, 0.072, and -0.094, respectively (P < 0.05). For these acylcarnitines and for F2-isoprostanes, the adjusted odds ratios (ORs) of incident diabetes are calculated from logistic regression models: the ORs (95% CI) are 0.77 (0.60-0.97), 0.79 (0.62-1.01), 1.18 (0.92-1.53), and 0.51 (0.35-0.76) for C2, C12, C5, and F2-isoprostanes, respectively. The direction of the associations between urinary F2-isoprostanes and three acylcarnitines (C2, C5, and C12) supports our hypothesis. The inverse associations of C2 and C12 and with incident diabetes are consistent with the suggested protective role of efficient fat oxidation.
    Oxidative Medicine and Cellular Longevity 01/2015; 2015:729191. DOI:10.1155/2015/729191 · 3.36 Impact Factor
  • Karol M. Pencina, Michael J. Pencina, Ralph B. D'Agostino
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    ABSTRACT: The net reclassification improvement (NRI) has become a popular measure of incremental usefulness of markers added to risk prediction models. However, the expected magnitude of the three-category NRI is not well understood, leading researchers to rely on statistical significance. In this paper, we describe a slight modification to the original definition of the NRI, which weighs each reclassification by the number of categories by which a given individual is reclassified. This modification resolves some recent criticisms of the three-category NRI and at the same time has a minimal impact on its magnitude. Then we show that using this modified definition, the event and nonevent NRIs have simple interpretations as sums of changes in sensitivities and specificities calculated at the risk thresholds. We exploit this relationship to arrive at closed-form solutions for the NRI under normality within the event and nonevent subgroups. We observe that the size of the intermediate risk category and the event rate have limited impact on the magnitude of the NRI. As expected, the NRI increases with the strength of the added marker, and this relationship appears fairly proportional for markers with non-weak net effect size (above 0.25). Furthermore, we conclude that using the NRI as a metric, it is harder to improve models that already perform well. Copyright © 2014 John Wiley & Sons, Ltd.
    Statistics in Medicine 12/2014; 33(28). DOI:10.1002/sim.6286 · 2.04 Impact Factor
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    ABSTRACT: The SEARCH for Diabetes in Youth (SEARCH) study was initiated in 2000, with funding from the Centers for Disease Control and Prevention and support from the National Institute of Diabetes and Digestive and Kidney Diseases, to address major knowledge gaps in the understanding of childhood diabetes. SEARCH is being conducted at five sites across the U.S. and represents the largest, most diverse study of diabetes among U.S. youth. An active registry of youth diagnosed with diabetes at age <20 years allows the assessment of prevalence (in 2001 and 2009), annual incidence (since 2002), and trends by age, race/ethnicity, sex, and diabetes type. Prevalence increased significantly from 2001 to 2009 for both type 1 and type 2 diabetes in most age, sex, and race/ethnic groups. SEARCH has also established a longitudinal cohort to assess the natural history and risk factors for acute and chronic diabetes-related complications as well as the quality of care and quality of life of persons with diabetes from diagnosis into young adulthood. Many youth with diabetes, particularly those from low-resourced racial/ethnic minority populations, are not meeting recommended guidelines for diabetes care. Markers of micro- and macrovascular complications are evident in youth with either diabetes type, highlighting the seriousness of diabetes in this contemporary cohort. This review summarizes the study methods, describes key registry and cohort findings and their clinical and public health implications, and discusses future directions. © 2014 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.
    Diabetes Care 12/2014; 37(12):3336-3344. DOI:10.2337/dc14-0574 · 8.57 Impact Factor
  • Abdul J. Sankoh, Haihong Li, Ralph B. D'Agostino
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    ABSTRACT: The success of a confirmatory clinical trial designed to demonstrate the efficacy of a new treatment is highly dependent on the choice of valid primary efficacy endpoint(s). The optimal clinical and statistical situation for the design of such a trial is one that starts with the selection of a single primary efficacy endpoint that completely characterizes the disease under study, admits the most efficient clinical and statistical evaluation of treatment effect, and provides clear and broad interpretation of drug effect. For diseases with multidimensional presentations, however, the selection of such an endpoint may not be possible, and so drug effectiveness is often characterized by the use of composite or multiple efficacy endpoint(s). The use of a composite endpoint with components that are only slightly correlated but not quite dissimilar in their recognized clinical relevance could lead to a more sensitive statistical test and thus, adequately powered trials with smaller sample size. This note discusses the utility of composite endpoints in clinical trials and some of the common approaches for dealing with multiplicity arising from their use. Copyright © 2014 John Wiley & Sons, Ltd.
    Statistics in Medicine 11/2014; 33(27). DOI:10.1002/sim.6205 · 2.04 Impact Factor
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    ABSTRACT: Future space missions are expected to include increased extravehicular activities (EVAs) during which astronauts are exposed to high-energy space radiation while breathing 100% oxygen. Given that brain irradiation can lead to cognitive impairment, and that oxygen is a potent radiosensitizer, there is a concern that astronauts may be at greater risk of developing cognitive impairment when exposed to space radiation while breathing 100% O2 during an EVA. To address this concern, unanesthetized, unrestrained, young adult male Fischer 344 × Brown Norway rats were allowed to breathe 100% O2 for 30 min prior to, during and 2 h after whole-body irradiation with 0, 1, 3, 5 or 7 Gy doses of 18 MV X rays delivered from a medical linear accelerator at a dose rate of ∼425 mGy/min. Irradiated and unirradiated rats breathing air (∼21% O2) served as controls. Cognitive function was assessed 9 months postirradiation using the perirhinal cortex-dependent novel object recognition task. Cognitive function was not impaired until the rats breathing either air or 100% O2 received a whole-body dose of 7 Gy. However, at all doses cognitive function of the irradiated rats breathing 100% O2 was improved over that of the irradiated rats breathing air. These data suggest that astronauts are not at greater risk of developing cognitive impairment when exposed to space radiation while breathing 100% O2 during an EVA.
    Radiation Research 10/2014; 182(5). DOI:10.1667/RR13643.1 · 2.45 Impact Factor
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    ABSTRACT: Neoadjuvant chemotherapy for breast cancer leads to considerable variability in clinical responses, with only 10 to 20% of cases achieving complete pathologic responses (pCR). Biological and clinical factors that determine the extent of pCR are incompletely understood. Mounting evidence indicates that the patient's immune system contributes to tumor regression and can be modulated by therapies. The cell types most frequently observed with this association are effector tumor infiltrating lymphocytes (TILs), such as cytotoxic T cells, natural killer cells and B cells. We and others have shown that the relative abundance of TILs in breast cancer can be quantified by intratumoral transcript levels of coordinately expressed, immune cell-specific genes. Through expression microarray analysis, we recently discovered three immune gene signatures, or metagenes, that appear to reflect the relative abundance of distinct tumor-infiltrating leukocyte populations. The B/P (B cell/plasma cell), T/NK (T cell/natural killer cell) and M/D (monocyte/dendritic cell) immune metagenes were significantly associated with distant metastasis-free survival of patients with highly proliferative cancer of the basal-like, HER2-enriched and luminal B intrinsic subtypes. Given the histopathological evidence that TIL abundance is predictive of neoadjuvant treatment efficacy, we evaluated the therapy-predictive potential of the prognostic immune metagenes. We hypothesized that pre-chemotherapy immune gene signatures would be significantly predictive of tumor response. In a multi-institutional, meta-cohort analysis of 701 breast cancer patients receiving neoadjuvant chemotherapy, gene expression profiles of tumor biopsies were investigated by logistic regression to determine the existence of therapy-predictive interactions between the immune metagenes, tumor proliferative capacity, and intrinsic subtypes. By univariate analysis, the B/P, T/NK and M/D metagenes were all significantly and positively associated with favorable pathologic responses. In multivariate analyses, proliferative capacity and intrinsic subtype altered the significance of the immune metagenes in different ways, with the M/D and B/P metagenes achieving the greatest overall significance after adjustment for other variables. Gene expression signatures of infiltrating immune cells carry both prognostic and therapy-predictive value that is impacted by tumor proliferative capacity and intrinsic subtype. Anti-tumor functions of plasma B cells and myeloid-derived antigen-presenting cells may explain more variability in pathologic response to neoadjuvant chemotherapy than previously recognized.
    Genome Medicine 10/2014; 6(10):80. DOI:10.1186/s13073-014-0080-8 · 4.94 Impact Factor

Publication Stats

82k Citations
7,741.79 Total Impact Points

Institutions

  • 2011–2015
    • Boston Biomedical Research Institute
      Boston, Massachusetts, United States
    • Duke University Medical Center
      • Duke Cancer Institute
      Durham, NC, United States
  • 2000–2015
    • Wake Forest School of Medicine
      • • Department of Biostatistical Sciences
      • • Division of Public Health Sciences
      • • Department of Radiation Oncology
      Winston-Salem, North Carolina, United States
    • Massachusetts General Hospital
      • Cardiovascular Disease Prevention Center
      Boston, MA, United States
  • 1987–2015
    • Boston University
      • • Division of Mathematics
      • • Department of Mathematics and Statistics
      • • Department of Biochemistry
      • • Department of Neurology
      • • Section of Preventive Medicine and Epidemiology
      Boston, Massachusetts, United States
  • 1996–2014
    • Wake Forest University
      • • Department of Biostatistical Sciences
      • • Department of Biochemistry
      • • Department of Hematology and Oncology
      • • Department of Public Health Sciences
      Winston-Salem, North Carolina, United States
  • 2013
    • Furman University
      • Department of Health Sciences
      Гринвилл, South Carolina, United States
    • Colorado Department of Public Health and Environment
      Denver, Colorado, United States
    • University of Lausanne
      Lausanne, Vaud, Switzerland
  • 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
  • 1990–2012
    • 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
    • U.S. Department of Veterans Affairs
      Washington, Washington, D.C., United States
  • 1996–2011
    • University of Washington Seattle
      • • Cardiovascular Health Research Unit (CHRU)
      • • Department of Otolaryngology/Head and Neck Surgery
      Seattle, WA, United States
  • 2004–2010
    • National Heart, Lung, and Blood Institute
      • Division of Cardiovascular Sciences (DCVS)
      Maryland, 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
  • 1995–2010
    • University of Pittsburgh
      • Department of Epidemiology
      Pittsburgh, PA, 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
    • Kaiser Permanente
      Oakland, California, United States
    • University of Colorado
      • Department of Epidemiology
      Denver, CO, United States
  • 2008
    • National Eye Institute
      Maryland, United States
    • Harvard Medical School
      • Department of Medicine
      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
    • University of Cambridge
      Cambridge, England, United Kingdom
  • 2004–2007
    • The Harvard Drug Group
      Ливония, Michigan, United States
  • 2003–2007
    • University of Toronto
      • Institute for Clinical Evaluative Sciences
      Toronto, Ontario, Canada
    • IMIM Hospital del Mar Medical Research Institute
      Barcino, Catalonia, Spain
  • 2000–2007
    • University of Texas at San Antonio
      San Antonio, Texas, United States
  • 2006
    • University of North Carolina at Wilmington
      Wilmington, North Carolina, United States
    • Universität Potsdam
      Potsdam, Brandenburg, Germany
  • 2002–2006
    • National Institutes of Health
      Maryland, United States
    • Royal North Shore Hospital
      Sydney, New South Wales, Australia
  • 1993–2006
    • Medical University of South Carolina
      • Division of Neuroradiology
      Charleston, South Carolina, United States
  • 2005
    • University of Concepción
      Ciudad de Concepcion, Biobío, Chile
    • Uppsala University
      • Department of Public Health and Caring Sciences
      Uppsala, Uppsala, Sweden
    • Durham University
      Durham, England, United Kingdom
  • 2001–2004
    • University of Missouri
      • Department of Family and Community Medicine
      Columbia, MO, United States
  • 1999–2003
    • University of Texas Health Science Center at San Antonio
      • Division of Hospital Medicine
      San Antonio, TX, United States
  • 1990–1999
    • Erasmus Universiteit Rotterdam
      Rotterdam, South Holland, Netherlands
  • 1998
    • University at Buffalo, The State University of New York
      Buffalo, New York, United States
  • 1995–1997
    • 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
    • Southern Illinois University School of Medicine
      • Department of Neurology
      Springfield, Illinois, United States