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1. Applying the causal roadmap to longitudinal national Danish registry data: a case study of second-line diabetes medication and dementia

Author

Nance, Nerissa, Mertens, Andrew, Gerds, Thomas, Wang, Zeyi, Torp-Pedersen, Christian, van der Laan, Mark, Kvist, Kajsa, Lange, Theis, Zareini, Bochra, and Petersen, Maya

Subjects
Statistics - Applications
Abstract

The causal roadmap is a formal framework for causal and statistical inference that supports clear specification of the causal question, interpretable and transparent statement of required causal assumptions, robust inference, and optimal precision. The roadmap is thus particularly well-suited to evaluating longitudinal causal effects using large scale registries; however, application of the roadmap to registry data also introduces particular challenges. In this paper we provide a detailed case study of the longitudinal causal roadmap applied to the Danish National Registry to evaluate the comparative effectiveness of second-line diabetes drugs on dementia risk. Specifically, we evaluate the difference in counterfactual five-year cumulative risk of dementia if a target population of adults with type 2 diabetes had initiated and remained on GLP-1 receptor agonists (a second-line diabetes drug) compared to a range of active comparator protocols. Time-dependent confounding is accounted for through use of the iterated conditional expectation representation of the longitudinal g-formula as a statistical estimand. Statistical estimation uses longitudinal targeted maximum likelihood, incorporating machine learning. We provide practical guidance on the implementation of the roadmap using registry data, and highlight how rare exposures and outcomes over long-term follow up can raise challenges for flexible and robust estimators, even in the context of the large sample sizes provided by the registry. We demonstrate how simulations can be used to help address these challenges by supporting careful estimator pre-specification. We find a protective effect of GLP-1RAs compared to some but not all other second-line treatments.

Published
2023

3. A Causal Roadmap for Hybrid Randomized and Real-World Data Designs: Case Study of Semaglutide and Cardiovascular Outcomes

Author

Dang, Lauren E, Fong, Edwin, Tarp, Jens Magelund, Clemmensen, Kim Katrine Bjerring, Ravn, Henrik, Kvist, Kajsa, Buse, John B, van der Laan, Mark, and Petersen, Maya

Subjects
Statistics - Applications
Abstract

Introduction: Increasing interest in real-world evidence has fueled the development of study designs incorporating real-world data (RWD). Using the Causal Roadmap, we specify three designs to evaluate the difference in risk of major adverse cardiovascular events (MACE) with oral semaglutide versus standard-of-care: 1) the actual sequence of non-inferiority and superiority randomized controlled trials (RCTs), 2) a single RCT, and 3) a hybrid randomized-external data study. Methods: The hybrid design considers integration of the PIONEER 6 RCT with RWD controls using the experiment-selector cross-validated targeted maximum likelihood estimator. We evaluate 95% confidence interval coverage, power, and average patient-time during which participants would be precluded from receiving a glucagon-like peptide-1 receptor agonist (GLP1-RA) for each design using simulations. Finally, we estimate the effect of oral semaglutide on MACE for the hybrid PIONEER 6-RWD analysis. Results: In simulations, Designs 1 and 2 performed similarly. The tradeoff between decreased coverage and patient-time without the possibility of a GLP1-RA for Designs 1 and 3 depended on the simulated bias. In real data analysis using Design 3, external controls were integrated in 84% of cross-validation folds, resulting in an estimated risk difference of -1.53%-points (95% CI -2.75%-points to -0.30%-points). Conclusions: The Causal Roadmap helps investigators to minimize potential bias in studies using RWD and to quantify tradeoffs between study designs. The simulation results help to interpret the level of evidence provided by the real data analysis in support of the superiority of oral semaglutide versus standard-of-care for cardiovascular risk reduction., Comment: 75 pages, 6 figures (5 main text, 1 supplementary), 9 tables (2 main text, 7 supplementary)

Published
2023

4. A Causal Roadmap for Generating High-Quality Real-World Evidence

Author

Dang, Lauren E, Gruber, Susan, Lee, Hana, Dahabreh, Issa, Stuart, Elizabeth A, Williamson, Brian D, Wyss, Richard, Díaz, Iván, Ghosh, Debashis, Kıcıman, Emre, Alemayehu, Demissie, Hoffman, Katherine L, Vossen, Carla Y, Huml, Raymond A, Ravn, Henrik, Kvist, Kajsa, Pratley, Richard, Shih, Mei-Chiung, Pennello, Gene, Martin, David, Waddy, Salina P, Barr, Charles E, Akacha, Mouna, Buse, John B, van der Laan, Mark, and Petersen, Maya

Subjects
Statistics - Methodology
Abstract

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized controlled trials with outcomes assessed using RWD to fully observational studies. Yet many RWE study proposals lack sufficient detail to evaluate adequacy, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to pre-specify analytic study designs; it addresses a wide range of guidance within a single framework. By requiring transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on pre-specified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers, with companion papers demonstrating application of the Causal Roadmap for specific use cases., Comment: 51 pages, 2 figures, 4 tables

Published
2023

5. Targeted Maximum Likelihood Based Estimation for Longitudinal Mediation Analysis

Author

Wang, Zeyi, van der Laan, Lars, Petersen, Maya, Gerds, Thomas, Kvist, Kajsa, and van der Laan, Mark

Subjects
Statistics - Methodology, 62G05 (Primary) 62G20, 62G35 (Secondary)
Abstract

Causal mediation analysis with random interventions has become an area of significant interest for understanding time-varying effects with longitudinal and survival outcomes. To tackle causal and statistical challenges due to the complex longitudinal data structure with time-varying confounders, competing risks, and informative censoring, there exists a general desire to combine machine learning techniques and semiparametric theory. In this manuscript, we focus on targeted maximum likelihood estimation (TMLE) of longitudinal natural direct and indirect effects defined with random interventions. The proposed estimators are multiply robust, locally efficient, and directly estimate and update the conditional densities that factorize data likelihoods. We utilize the highly adaptive lasso (HAL) and projection representations to derive new estimators (HAL-EIC) of the efficient influence curves of longitudinal mediation problems and propose a fast one-step TMLE algorithm using HAL-EIC while preserving the asymptotic properties. The proposed method can be generalized for other longitudinal causal parameters that are smooth functions of data likelihoods, and thereby provides a novel and flexible statistical toolbox.

Published
2023

6. Beyond the Cox Hazard Ratio: A Targeted Learning Approach to Survival Analysis in a Cardiovascular Outcome Trial Application

Author

Chen, David, Petersen, Maya L, Rytgaard, Helene Charlotte, Grøn, Randi, Lange, Theis, Rasmussen, Søren, Pratley, Richard E, Marso, Steven P, Kvist, Kajsa, Buse, John, and van der Laan, Mark J

Subjects
Prevention, Causal roadmap, LEADER, Semiparametric efficiency, TMLE, Statistics
Abstract

The Hazard Ratio (HR) is a well-established treatment effect measure in randomized trials involving right-censored time-to-events, and the Cardiovascular Outcome Trials (CVOTs) conducted since the FDA’s 2008 guidance have indeed largely evaluated excess risk by estimating a Cox HR. On the other hand, the limitations of the Cox model and of the HR as a causal estimand are well known, and the FDA’s updated 2020 CVOT guidance invites us to reassess this default approach to survival analyses. We highlight the shortcomings of Cox HR-based analyses and present an alternative following the causal roadmap—moving in a principled way from a counterfactual causal question to identifying a statistical estimand, and finally to targeted estimation in a large statistical model. We show in simulations the robustness of Targeted Maximum Likelihood Estimation (TMLE) to informative censoring and model misspecification and demonstrate a targeted learning analogue of the original Cox HR-based analysis of the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results (LEADER) trial. We discuss the potential reliability, interpretability, and efficiency gains to be had by updating our survival methods to incorporate the recent decades of advancements in formal causal frameworks and efficient nonparametricestimation.

Published
2023

7. A Cross-Validated Targeted Maximum Likelihood Estimator for Data-Adaptive Experiment Selection Applied to the Augmentation of RCT Control Arms with External Data

Author

Dang, Lauren Eyler, Tarp, Jens Magelund, Abrahamsen, Trine Julie, Kvist, Kajsa, Buse, John B, Petersen, Maya, and van der Laan, Mark

Subjects
Statistics - Methodology
Abstract

Augmenting the control arm of a randomized controlled trial (RCT) with external data may increase power at the risk of introducing bias. Existing data fusion estimators generally rely on stringent assumptions or may have decreased coverage or power in the presence of bias. Framing the problem as one of data-adaptive experiment selection, potential experiments include the RCT only or the RCT combined with different candidate real-world datasets. To select and analyze the experiment with the optimal bias-variance tradeoff, we develop a novel experiment-selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE). The ES-CVTMLE uses two bias estimates: 1) a function of the difference in conditional mean outcome under control between the RCT and combined experiments and 2) an estimate of the average treatment effect on a negative control outcome (NCO). We define the asymptotic distribution of the ES-CVTMLE under varying magnitudes of bias and construct confidence intervals by Monte Carlo simulation. In simulations involving violations of identification assumptions, the ES-CVTMLE had better coverage than test-then-pool approaches and an NCO-based bias adjustment approach and higher power than one implementation of a Bayesian dynamic borrowing approach. We further demonstrate the ability of the ES-CVTMLE to distinguish biased from unbiased external controls through a re-analysis of the effect of liraglutide on glycemic control from the LEADER trial. The ES-CVTMLE has the potential to improve power while providing relatively robust inference for future hybrid RCT-RWD studies., Comment: 27 pages, 4 figures

Published
2022

8. Case study of semaglutide and cardiovascular outcomes: An application of the Causal Roadmap to a hybrid design for augmenting an RCT control arm with real-world data.

Author

Dang, Lauren, Fong, Edwin, Tarp, Jens, Clemmensen, Kim, Ravn, Henrik, Kvist, Kajsa, Buse, John, van der Laan, Mark, and Petersen, Maya

Subjects
Causal inference, cardiovascular outcomes, diabetes, hybrid study designs, real-world evidence
Abstract

INTRODUCTION: Increasing interest in real-world evidence has fueled the development of study designs incorporating real-world data (RWD). Using the Causal Roadmap, we specify three designs to evaluate the difference in risk of major adverse cardiovascular events (MACE) with oral semaglutide versus standard-of-care: (1) the actual sequence of non-inferiority and superiority randomized controlled trials (RCTs), (2) a single RCT, and (3) a hybrid randomized-external data study. METHODS: The hybrid design considers integration of the PIONEER 6 RCT with RWD controls using the experiment-selector cross-validated targeted maximum likelihood estimator. We evaluate 95% confidence interval coverage, power, and average patient time during which participants would be precluded from receiving a glucagon-like peptide-1 receptor agonist (GLP1-RA) for each design using simulations. Finally, we estimate the effect of oral semaglutide on MACE for the hybrid PIONEER 6-RWD analysis. RESULTS: In simulations, Designs 1 and 2 performed similarly. The tradeoff between decreased coverage and patient time without the possibility of a GLP1-RA for Designs 1 and 3 depended on the simulated bias. In real data analysis using Design 3, external controls were integrated in 84% of cross-validation folds, resulting in an estimated risk difference of -1.53%-points (95% CI -2.75%-points to -0.30%-points). CONCLUSIONS: The Causal Roadmap helps investigators to minimize potential bias in studies using RWD and to quantify tradeoffs between study designs. The simulation results help to interpret the level of evidence provided by the real data analysis in support of the superiority of oral semaglutide versus standard-of-care for cardiovascular risk reduction.

Published
2023

9. A causal roadmap for generating high-quality real-world evidence.

Author

Dang, Lauren, Gruber, Susan, Lee, Hana, Dahabreh, Issa, Stuart, Elizabeth, Williamson, Brian, Wyss, Richard, Díaz, Iván, Ghosh, Debashis, Kıcıman, Emre, Alemayehu, Demissie, Hoffman, Katherine, Vossen, Carla, Huml, Raymond, Ravn, Henrik, Kvist, Kajsa, Pratley, Richard, Shih, Mei-Chiung, Pennello, Gene, Martin, David, Waddy, Salina, Barr, Charles, Akacha, Mouna, Buse, John, van der Laan, Mark, and Petersen, Maya

Subjects
Causal inference, estimands, machine learning, real-world evidence, sensitivity analysis, simulations
Abstract

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.

Published
2023

15. Treatment with glucagon‐like peptide‐1 receptor agonists and incidence of dementia: Data from pooled double‐blind randomized controlled trials and nationwide disease and prescription registers

Author

Nørgaard, Caroline Holm, primary, Friedrich, Sarah, additional, Hansen, Charlotte Thim, additional, Gerds, Thomas, additional, Ballard, Clive, additional, Møller, Daniel Vega, additional, Knudsen, Lotte Bjerre, additional, Kvist, Kajsa, additional, Zinman, Bernard, additional, Holm, Ellen, additional, Torp‐Pedersen, Christian, additional, and Mørch, Lina Steinrud, additional

Published
2022
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17. Association Between Glucagon-Like Peptide 1 Receptor Agonist and Sodium–Glucose Cotransporter 2 Inhibitor Use and COVID-19 Outcomes

Author

Kahkoska, Anna R., primary, Abrahamsen, Trine Julie, primary, Alexander, G. Caleb, primary, Bennett, Tellen D., primary, Chute, Christopher G., primary, Haendel, Melissa A., primary, Klein, Klara R., primary, Mehta, Hemalkumar, primary, Miller, Joshua D., primary, Moffitt, Richard A., primary, Stürmer, Til, primary, Kvist, Kajsa, primary, and Buse, John B., primary

Published
2021
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21. Liraglutide and semaglutide: Pooled post hoc analysis to evaluate risk of dementia in patients with type 2 diabetes

Author

Ballard, Clive, primary, Nørgaard, Caroline Holm, additional, Friedrich, Sarah, additional, Mørch, Lina Steinrud, additional, Gerds, Thomas, additional, Møller, Daniel Vega, additional, Knudsen, Lotte Bjerre, additional, Kvist, Kajsa, additional, Zinman, Bernard, additional, Holm, Ellen, additional, Torp‐Pedersen, Christian, additional, and Hansen, Charlotte Thim, additional

Published
2020
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24. Treatmentwith glucagon-like peptide-1 receptor agonists and incidence of dementia: Data from pooled double-blind randomized controlled trials and nationwide disease and prescription registers.

Author

Nørgaard, Caroline Holm, Friedrich, Sarah, Hansen, Charlotte Thim, Gerds, Thomas, Ballard, Clive, Møller, Daniel Vega, Knudsen, Lotte Bjerre, Kvist, Kajsa, Zinman, Bernard, Holm, Ellen, Torp-Pedersen, Christian, and Mørch, Lina Steinrud

Subjects
GLUCAGON-like peptide-1 receptor, GLUCAGON-like peptide-1 agonists, REPORTING of diseases, TYPE 2 diabetes diagnosis, ALZHEIMER'S disease
Abstract

Introduction: People with type 2 diabetes have increased risk of dementia. Glucagonlike peptide-1 (GLP-1) receptor agonists (RAs) are among the promising therapies for repurposing as a treatment for Alzheimer's disease; a key unanswered question is whether they reduce dementia incidence in people with type 2 diabetes. Methods: We assessed exposure to GLP-1 RAs in patients with type 2 diabetes and subsequent diagnosis of dementia in two large data sources with long-term followup: pooled data from three randomized double-blind placebo-controlled cardiovascular outcome trials (15,820 patients) and a nationwide Danish registry-based cohort (120,054 patients). Results: Dementia rate was lower both in patients randomized to GLP-1 RAs versus placebo (hazard ratio [HR]: 0.47 (95% confidence interval [CI]: 0.25-0.86) and in the nationwide cohort (HR: 0.89; 95% CI: 0.86-0.93 with yearly increased exposure to GLP-1 RAs). Discussion: Treatment with GLP-1 RAs may provide a new opportunity to reduce the incidence of dementia in patients with type 2 diabetes. [ABSTRACT FROM AUTHOR]

Published
2022
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33. Association Between Glucagon-Like Peptide 1 Receptor Agonist and Sodium-Glucose Cotransporter 2 Inhibitor Use and COVID-19 Outcomes.

Author

Kahkoska, Anna R., Abrahamsen, Trine Julie, Alexander, G. Caleb, Bennett, Tellen D., Chute, Christopher G., Haendel, Melissa A., Klein, Klara R., Mehta, Hemalkumar, Miller, Joshua D., Moffitt, Richard A., Stürmer, Til, Kvist, Kajsa, Buse, John B., Duong, Tim Q., and N3C Consortium

Subjects
SODIUM-glucose cotransporter 2 inhibitors, GLUCAGON-like peptide 1, PEPTIDE receptors, COVID-19, GLUCAGON-like peptide-1 receptor, THROMBOPOIETIN receptors
Abstract

Objective: To determine the respective associations of premorbid glucagon-like peptide-1 receptor agonist (GLP1-RA) and sodium-glucose cotransporter 2 inhibitor (SGLT2i) use, compared with premorbid dipeptidyl peptidase 4 inhibitor (DPP4i) use, with severity of outcomes in the setting of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.Research Design and Methods: We analyzed observational data from SARS-CoV-2-positive adults in the National COVID Cohort Collaborative (N3C), a multicenter, longitudinal U.S. cohort (January 2018-February 2021), with a prescription for GLP1-RA, SGLT2i, or DPP4i within 24 months of positive SARS-CoV-2 PCR test. The primary outcome was 60-day mortality, measured from positive SARS-CoV-2 test date. Secondary outcomes were total mortality during the observation period and emergency room visits, hospitalization, and mechanical ventilation within 14 days. Associations were quantified with odds ratios (ORs) estimated with targeted maximum likelihood estimation using a super learner approach, accounting for baseline characteristics.Results: The study included 12,446 individuals (53.4% female, 62.5% White, mean ± SD age 58.6 ± 13.1 years). The 60-day mortality was 3.11% (387 of 12,446), with 2.06% (138 of 6,692) for GLP1-RA use, 2.32% (85 of 3,665) for SGLT2i use, and 5.67% (199 of 3,511) for DPP4i use. Both GLP1-RA and SGLT2i use were associated with lower 60-day mortality compared with DPP4i use (OR 0.54 [95% CI 0.37-0.80] and 0.66 [0.50-0.86], respectively). Use of both medications was also associated with decreased total mortality, emergency room visits, and hospitalizations.Conclusions: Among SARS-CoV-2-positive adults, premorbid GLP1-RA and SGLT2i use, compared with DPP4i use, was associated with lower odds of mortality and other adverse outcomes, although DPP4i users were older and generally sicker. [ABSTRACT FROM AUTHOR]

Published
2021
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38. Development of a hypoglycaemia risk score to identify high‐risk individuals with advanced type 2 diabetes in DEVOTE.

Author

Heller, Simon, Lingvay, Ildiko, Marso, Steven P., Philis‐Tsimikas, Athena, Pieber, Thomas R., Poulter, Neil R., Pratley, Richard E., Hachmann‐Nielsen, Elise, Kvist, Kajsa, Lange, Martin, Moses, Alan C., Trock Andresen, Marie, and Buse, John B.

Subjects
TYPE 2 diabetes, PATIENT care, TIME perspective, RISK perception
Abstract

Aims: The ability to differentiate patient populations with type 2 diabetes at high risk of severe hypoglycaemia could impact clinical decision making. The aim of this study was to develop a risk score, using patient characteristics, that could differentiate between populations with higher and lower 2‐year risk of severe hypoglycaemia among individuals at increased risk of cardiovascular disease. Materials and methods: Two models were developed for the risk score based on data from the DEVOTE cardiovascular outcomes trials. The first, a data‐driven machine‐learning model, used stepwise regression with bidirectional elimination to identify risk factors for severe hypoglycaemia. The second, a risk score based on known clinical risk factors accessible in clinical practice identified from the data‐driven model, included: insulin treatment regimen; diabetes duration; sex; age; and glycated haemoglobin, all at baseline. Both the data‐driven model and simple risk score were evaluated for discrimination, calibration and generalizability using data from DEVOTE, and were validated against the external LEADER cardiovascular outcomes trial dataset. Results: Both the data‐driven model and the simple risk score discriminated between patients at higher and lower hypoglycaemia risk, and performed similarly well based on the time‐dependent area under the curve index (0.63 and 0.66, respectively) over a 2‐year time horizon. Conclusions: Both the data‐driven model and the simple hypoglycaemia risk score were able to discriminate between patients at higher and lower risk of severe hypoglycaemia, the latter doing so using easily accessible clinical data. The implementation of such a tool (http://www.hyporiskscore.com/) may facilitate improved recognition of, and education about, severe hypoglycaemia risk, potentially improving patient care. [ABSTRACT FROM AUTHOR]

Published
2020
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39. Risk of severe hypoglycaemia and its impact in type 2 diabetes in DEVOTE.

Author

Heller, Simon, Lingvay, Ildiko, Marso, Steven P., Philis‐Tsimikas, Athena, Pieber, Thomas R., Poulter, Neil R., Pratley, Richard E., Hachmann‐Nielsen, Elise, Kvist, Kajsa, Lange, Martin, Moses, Alan C., Andresen, Marie Trock, and Buse, John B.

Subjects
TYPE 2 diabetes, CARDIOVASCULAR diseases risk factors
Abstract

Aims: To undertake a post‐hoc analysis, utilizing a hypoglycaemia risk score based on DEVOTE trial data, to investigate if a high risk of severe hypoglycaemia was associated with an increased risk of cardiovascular events, and whether reduced rates of severe hypoglycaemia in patients identified as having the highest risk affected the risk of cardiovascular outcomes. Materials and Methods: The DEVOTE population was divided into quartiles according to patients' individual hypoglycaemia risk scores. For each quartile, the observed incidence and rate of severe hypoglycaemia, major adverse cardiovascular event (MACE) and all‐cause mortality were determined to investigate whether those with the highest risk of hypoglycaemia were also at the greatest risk of MACE and all‐cause mortality. In addition, treatment differences within each risk quartile [insulin degludec (degludec) vs. insulin glargine 100 units/mL (glargine U100)] in terms of severe hypoglycaemia, MACE and all‐cause mortality were investigated. Results: Patients with the highest risk scores had the highest rates of severe hypoglycaemia, MACE and all‐cause mortality. Treatment ratios between degludec and glargine U100 in the highest risk quartile were 95% confidence interval (CI) 0.56 (0.39; 0.80) (severe hypoglycaemia), 95% CI 0.76 (0.58; 0.99) (MACE) and 95% CI 0.77 (0.55; 1.07) (all‐cause mortality). Conclusions: The risk score demonstrated that a high risk of severe hypoglycaemia was associated with a high incidence of MACE and all‐cause mortality and that, in this high‐risk group, those treated with degludec had a lower incidence of MACE. These observations support the hypothesis that hypoglycaemia is a risk factor for cardiovascular events. [ABSTRACT FROM AUTHOR]

Published
2020
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40. Efficacy and Safety of Degludec versus Glargine in Type 2 Diabetes

Author

Marso, Steven P., McGuire, Darren K., Zinman, Bernard, Poulter, Neil R., Emerson, Scott S., Pieber, Thomas R., Pratley, Richard E., Haahr, Poul-Martin, Lange, Martin, Brown-Frandsen, Kirstine, Moses, Alan, Skibsted, Simon, Kvist, Kajsa, Buse, John B., Hansen, Liliana, Rabol, Rasmus, Bethel, Angelyn, de Lemos, James A., Senn, Stephen, Schwamm, Lee, Timmis, Adam, Alshekhlee, Amer, Bach, Richard G., Bertram, Therese M., Chaitman, Bernard R., Cruz-Flores, Salvador, Eckstein, Jane F., Feen, Eliahu S., Fisher, Simon J., Gosselin, Gilbert, Lim, Michael J., Rosenzweig, James L., Sherwin, Robert S., Tobin, Garry S., Vetrovec, George, Ziemer, David C., Daugaard, Gedske, Ozlem, Er, Pollak, Michael, Sengelov, Lisa, Ahn, Yu-Bae, Amod, Aslam, Benroubi, Mariana, Boonyavarakul, Apussanee, Canecki-Varzic, Silvija, Consoli, Agostino, Eliaschewitz, Freddy G., Franek, Edward, Jinnouchi, Hideaki, Khokhlov, Alexander L., Mithal, Ambrish, Philis-Tsimikas, Athena, Ong, Tiong Kiam, Pop, Lavinia, Reyna, Leobardo Sauque, Rodbard, Helena W., Roula, Daoud, Saravanan, Ponnusamy, Sesti, Giorgio, Sorli, Christopher, Sposetti, Georgina, Tinahones-Madueno, Francisco-Jose, Woo, Vincent, Berard, Lori, Caballero Mateos, Irene, Cooper, Alison, DeLuca, Clarisa, Fillary, Julie, Gan, Diyana Gan Abdul Rahman, Goondiwala, Amina, Tapia, Jessica, Busch, Robert, Chaykin, Louis, Hollander, Priscilla, Karounos, Dennis, Lane, Wendy, Lingvay, Ildiko, Odugbesan, A. Ola, Ohlson-Elliott, Theresa, Ovalle, Fernando, Pantalone, Kevin M., Phelps, Spencer, Pop-Busui, Rodica, Robertson, David, Shlesinger, Yshay, Silver, Robert, Warren, Mark, Arrar, Mohammed, Belkheir, Hassani, Benabbas, Youcef, Benferhat, Schahrazed, Benoun, Fatima, Bensalem, Samia, Bourezane, Samia, Elaoufi, Selmane Rafik, Gourine, Mouna, Hadjhabib, Mohamed, Hadjhamou, Fouzia, Hakem, Jamila, Kaouache, Nadjib, Khensal, Sabrina, Kitouni, Yacine, Lakhal, Lamine, Lezzar, Al-Kassem, Meftah, Imane Aicha, Mehenni, Lahouaria, Nouri, Nassim, Oumerzouk, Fatma, Sakouhi, Mounia, Talha, Kheira, Zemri, Zouaouia, Caeiro, Gabriela, Calveyra, Martha, Cluigt, Natalia, Geronazzo, Veronica, Lema, Silvina, Monferran, Pablo Martinez, Oviedo, Alejandra, Pozzi, Jose, Sala, Jorgelina, Schindler, Ana, Sernia, Virginia, Sicer, Maximiliano, Solis, Silvana, Varela, Patricia, Waitman, Jorge, Augusto, Gustavo Akerman, Alves, Breno, Alves, Erica, Barbosa, Luciana, Bona, Renata, Borges, Joao, Bosco, Adriana, Canani, Luis, Castro, Denise, Cavalcante, Lilian, Cechin, Laura, Cercato, Cintia, Chrisman, Carlos, Chrisman, Carolina, Cintra, Rebecca, Costa, Giselle, Fontenele, Ana Paula, Forti, Adriana, Francisco, Beatriz, Franco, Denise, Genestreti, Paulo, Gross, Jorge, Haddad, Ludmila, Halpern, Alfredo, Hissa, Miguel, Leanca, Camila, Leao, Beatriz, Marinho, Ana Elisabeth, Miranda, Isabela, Nunes, Adriano, Palhares, Fabiana, Poco, Christiani, Salles, Joao, Sampaio, Celia, Santomauro Jr., Augusto, Sartori, Carla, Sena, Rita, da Silva, Marina, da Silva, Paula, Siqueira, Katia, Turatti, Luiz, Valenti, Adriana, Vasques, Enio, Vencio, Sergio, Visconti, Guilherme, Belanger, Andre, Chilvers, Martyn, Dumas, Richard, Hurd, Carmen, Lafleche, Francois, Lam, Paul, Martinho, Valdemar, Palardy, Jean, Peterson, Sean, Raff, Errol, Sinnaeve, Linda, Sullivan, David, Baraban, Vedrana, Blaslov, Kristina, Bulum, Tomislav, Varzic, Silvija Canecki, Berkovic, Maja Cigrovski, Balen, Marica Jandric, Kruljac, Ivan, Lesnjakovic, Silvija, Lucijanic, Tomo, Majic, Ana, Rahelic, Dario, Smircic-Duvnjak, Lea, Vrkljan, Milan, Zibar, Karin, Zukanovic, Sidbela, Athanasopoulou, Elpida, Benis, Apostolos, Benroubi, Marian, Diakoumopoulou, Evanthia, Doupis, John, Griva, Theodora, Kazakos, Kyriakos, Kypraios, Nikolaos, Liatis, Stavros, Makrilakis, Konstantinos, Markou, Athina, Pagkalos, Emmanouil, Papanastasiou, Labrini, Piaditis, George, Sampanis, Christos, Siami, Evangelia, Tentolouris, Nikolaos, Tsiama, Vasiliki, Zografou, Ioanna, Adling, Pravishal, Ali, Mazher, Anjana, R. M., Arora, Arpita, Ayyar, Vageesh, Banerjee, Suvro, Bantwal, Ganapathi, Bhagwat, Nikhil, Bhattacharyya, Arpandev, Bhawarlal, Anandakumar, Chakraborty, Rabin, Chatterjee, Sanjay, Chaudhury, Tirthankar, Chowdhury, Subhankar, Damelia, Priyal, Das, Mrinal, Das, Sambit, Desai, Ankush, Devru, Nandani, Farooqui, Khalid, Ganie, Mohd Ashraf, George, Belinda, Ghosh, Debasis, Girithara, Gopalakrishnan, Haribhai, Zinzuwadia, Jain, Nupur, Jassar, Amandeep, Joshi, Ameya, Kale, Dilip, Kale, Shailaja, Kelwade, Jayant, Khatri, Ashok, Kulshreshtha, Bindu, Majumder, Bikas, Manikath, Neeraj, Margekar, Shubhalaxmi, Marwah, Tiven, Mathew, Vivek, Moulick, N. D., Mutha, Abhay, Muthu, Ramuu, Nalawade, Asmitha, Narvencar, Kedareshwar, Pandit, Kaushik, Pangtey, Ghanshyam, Parthasarathy, B., Phatak, Sanjeev, Prakash, Anupam, Prusty, Pitambar, Radhakrishnan, Chandni, Raja, Indu, Ravikumar, Ranjani, Saboo, Banshi, Sachdeva, Isha, Sangeetha, S., Selvam, Kasthuri, Sethi, Bipin, Shah, Asha, Shah, Harshal K., Shah, Priyanshu, Shah, Rushit, Shaikh, Sajid, Shaikh, Shehla, Singh, K. P., Sivagnanam, Nallaperumal, Tandon, Nikhil, Thonangi, Rajesh, Thulaseedharan, N., Toraskar, Kedar, Varthakavi, Premalatha, Yadav, Sameer, Andreozz, Francesco, Arturi, Franco, Baldassarre, Maria Pompea Antonia, Barone, Milena, Bonura, Alice, Bucciarelli, Loredana, Buscemi, Chiara, Buscemi, Silvio, Carnovale, Anna, Caselli, Chiara, Ceci, Gloria, Cimino, Elena, Colella, Alessandra, Crippa, Valentina, Cufone, Sabrina, D'Erasmo, Laura, De Remigis, Alessandra, Di Bartolo, Paolo, Dionisio, Rosa, Disoteo, Olga, Fabietti, Marcella, Febo, Fabrizio, Filetti, Sebastiano, Genovese, Stefano, Ginestra, Federica, Grancini, Valeria, Grieco, Elvira, Magistro, Andrea, Maranghi, Marianna, Monti, Lucilla, Orsi, Emanuela, Pellicano, Francesca, Pergolini, Daniela, Perticone, Francesca, Piatti, PierMarco, Pizzi, Gianluigi, Rondinelli, Maurizio, Rossi, Margherita, Rubino, Mariangela, Setola, Emanuela, Succurro, Elena, Tarquini, Giovanna, Tiseo, Giusy, Tonolo, Giancarlo, Villa, Valentina, Hong, Stefania Vo, Amano, Kazushi, Hachiya, Remi, Hieshima, Kunio, Hirao, Koichi, Hisatake, Tomoko, Honda, Ritsuko, Ihana, Noriko, Inoue, Kaori, Iwamoto, Yasuhiko, Jinnouchi, Tomio, Kajiwara, Keizou, Kawazu, Shoji, Kikuchi, Masatoshi, Kikuchi, Takako, Koike, Yoko, Komatsu, Mitsuhisa, Kumakura, Atsushi, Kurinami, Noboru, Kushiyama, Akifumi, Maeda, Hajime, Matsuda, Toshihisa, Nakamura, Shuji, Nakayama, Koujin, Nishio, Shin-ichi, Onishi, Yukiko, Sato, Nobuyuki, Sato, Yoshihiko, Shirabe, Shinichiro, Sugiyama, Seigo, Suzuki, Tomoko, Tahara, Tazu, Takao, Toshiko, Takei, Masahiro, Takemoto, Ikutaka, Takeshige, Keiko, Tanaka, Kentaro, Taniguchi, Shotaro, Tsujimoto, Tetsuro, Tsukuda, Katsunori, Wakabayashi, Sayaka, Yamazaki, Masanori, Yoshida, Yoko, Yoshimoto, Ayako, Abidin, Imran Zainal, Abdullah, Shahrin Tarmizi Che, Adam, Noor Lita, Ahmad, Madihah, Ahmad, Nik Nur Fatnoon Nik, Amin, Nor Hanim Mohd, Aw, Cheng Yew, Cham, Yee Ling, Chong, Hui Min, Chua, Seng Keong, Fauzi, Sharifah Aisyah Shahabudin Syed Ahmad, Fong, Alan Yean Yip, Ghapar, Abd Kahar Abd, Hanafi, Norliza, Abu Hassan, Muhammad Radzi, Iberahim, Rahimah, Khiew, Ning Zan, Lee, Gary Chin Keong, Lingam, Rakesh, Long, Richard Chay Shien, Mahendran, Kauthaman A., Oon, Yen Yee, Said, Asri, Sothiratnam, Radhakrishna, Sridhar, Ganiga Srinivasiah, Suan, Mohd Azri Mohd, Tan, Sian Kong, Tey, Julian Hock Chuan, Undok, Abdul Wahab, Waid, Anuar, Vijayasingham, Shalini, Voon, Chi Yen, Yew, Kuan Leong, Yong, Lit Sin, Aguilar-Parra, Lilia, Aguirre, Karen, Alvarez-Carrillo, Rebeca, Arechavaleta-Granell, Maria, Baez-Campos, Aurelia, Bayram Llamas, Edmundo Alfredo, Calvo-Vargas, Cesar, Cedano-Limon, Marielena, Beltran Jaramillo, Teresita de Jesus, de Jesus Marin-Lopez, Jose, Ruiz Cornejo, Maria de Jesus, Juarez Cervantes, Maria del Carmen, Estevez de Alba, Isabel, Estrada Garcia, Raul, Estrada, Suellen, Garza-Vergara, Marisol, Gonzalez-Perez, Rosa, Gonzalez-Valencia, Carlos, Guerrero-Reyes, Rosalinda, Haro Ortiz, Jose Alfredo, Hernandez Salazar, Eduardo, Illescas Diaz, Iris, Illescas Diaz, Jesus, Loredo, Imelda, Jimenez-Ramos, Silvia, Larios-Mora, Diana, Lopez Garcia, Maria Elena, Lopez-Serrano, Julieta, Martinez-Nolasco, Bertha, Nunez Marquez, Ricardo, Pacheco Aranda, Elizabeth, Peralta-Cantu, Irving, Perez Chagollan, Ana, Ramirez, Maria, Robledo Duron, Israel, Rodriguez Briones, Ignacio, Rodriguez, Maria, Sauque Reyna, Leobardo, Vazquez-Ceja, Adriana, Vera-Bocanegra, Alejandro, Vidrio Velazquez, Maricela, Contreras Sandoval, Aleida Yael, Bartelik, Karol, Bilska, Aleksandra, Biniszkiewicz, Tomasz, Bogdanski, Pawel, Chamienia, Agnieszka, Cieslak-Puchalska, Anna, Czochra, Wojciech, Gajewski, Bogdan, Gromniak, Elwira, Holda, Ewa, Jaskulska-Gorzelewska, Barbara, Jedynasty, Krystyna, Kloda, Elzbieta, Kmak-Balawender, Beata, Kobielusz-Gembala, Iwona, Konieczny, Marek, Kramarczuk, Elzbieta, Kuleta, Jerzy, Lesniewski, Robert, Lominska, Teresa, Lukaszewicz, Monika, Majewska, Izabella, Mlynarski, Tomasz, Mostowy, Aleksandra, Rybak, Anna, Skrypnik, Damian, Wolny, Malgorzata, Zmuda, Witold, Buduru, Alina Nicoleta, Cif, Adriana, Cozma, Mihaela, Crisan, Carmen, Barbonta, Cristian Gelu, Barbonta, Diana, Mihalcea, Madalina, Mociran, Mihaela, Muresan, Sorina, Popescu, Alexandrina, Pruna, Luchiana, Sandu, Nadia Ramona, Suleac, Oana Andreea, Vincze, Eva Cornelia, Andreeva, Elena, Berns, Svetlana, Barykina, Irina, Bernitcyna, Angela, Bezdenezhnykh, Natalia, Chumachek, Elena, Demarina, Svetlana, Didenko, Anna, Dubinina, Nadezhda, Dzherieva, Irina, Elsukova, Olga, Esenyan, Lev, Filippova, Ekaterina, Gabova, Nadezhda, Garina, Irina, Glikman, Yulia, Glova, Svetlana, Goncharova, Ekaterina, Gorbatova, Elena, Gordeev, Ivan, Grigorian, Inessa, Gromova, Svetlana, Ipatko, Irina, Karaban, Elena, Katushkina, Yulia, Kaygorodtseva, Olga, Kerova, Irina, Khaisheva, Larisa, Khmelnitskiy, Oleg, Khokhlov, Alexander, Khokhlov, Mikhail, Khorolets, Ekaterina, Klyuenkova, Elena, Konovalova, Olga, Korneva, Ksenia, Kosmacheva, Elena, Kunitsyna, Marina, Kupriyanova, Tatiana, Kuznetsova, Natalia, Lantseva, Olga, Ledyaeva, Alla, Lileeva, Elena, Markovich, Alexander, Martirosian, Narine, Maslova, Alisa, Milyukova, Galina, Mokhova, Irina, Nagirniak, Olga, Nastina, Elena, Nedogoda, Sergei, Orlova, Valeriia, Svetlana, Otarova, Ostapenko, Marina, Papaskiri, Nino, Pavlikova, Elena, Peskov, Andrey, Petrik, Galina, Petrov, Alexander, Petunina, Nina, Platonov, Dmitry, Poliakova, Yulia, Potapova, Zhanna, Pribylova, Svetlana, Razumovsky, Igor, Redkin, Alexander, Rodionova, Tatyana, Rudenko, Evgenia, Rykalina, Natalia, Salasyuk, Alla, Semikina, Tatiana, Serebryakova, Inna, Shanina, Larisa, Shmidt, Evgenia, Sinitsina, Olga, Smetanina, Svetlana, Smirnova, Marya, Smirnova, Victoria, Smolentseva, Nadezhda, Sobolev, Alexander, Soldatenkova, Anna, Soldatova, Yulia, Speshilova, Svetlana, Startseva, Olga, Strezhova, Larisa, Strongin, Leonid, Suplotova, Lyudmila, Suvorova, Daria, Tepaeva, Alisa, Trukhina, Liubov, Tyugaeva, Alisa, Vasyutkova, Olga, Vinokurova, Valentina, Yukhno, Elena, Yuzhakova, Natalia, Zagrebelnaya, Olga, Zalevskaya, Alsu, Zhukova, Elena, Aomar Millan, Ismael, Beltran Romero, Luis M., Ignacio Fernandez Navarro, Jose, Carlos Ferrer Garcia, Juan, Garcia Blasco, Lourdes, Garcia Garcia, Clara, Garcia Puig, Juan, Girbes Borras, Juan, Inigo Zaera, Pilar, Lopez de la Torre, Mart-n, Martinez Alfaro, Baldomera, Morales Portillo, Cristobal, Romero Sanchez, Paloma, Sebastian Ochoa, Nerea, Serrano Olmedo, Isabel, Tinahones Madueno, Francisco, Torres Jimenez, Rosa, Adeyemo, A. O., Bassa, Ayesha, Bhorat, A., Bhorat, Qasim, Blacking, Laura, Burgess, Lesley, Coetzee, Kathleen, Conradie, Hillet, Cyster, Henry, Distiller, Larry, Ellis, G. C., Gani, Mashra, Gordon, Debra, Govender, Parimala, Govender, Thirumani, Govender, Vimladhevi, Jivan, Daksha, Joffe, Barry, Jooma, Yaseer, Kaplan, Hilton, Komati, Stephanus, Kramer, Brian, Kruger, Sieglinde, Landau, Stanley, Latiff, Gulam, Maritz, L., Matthews, Peter, Middlemost, S. J., Ngcakani, Nomangesi, Nortje, Hennie, Osman, Yasmin, Padayachee, Trevenesan, Podgorski, Gracjan, Potts, Jenny, Pretorius, Catharina, Prozesky, Hans, Snyman, Hans, Tayob, Mohammed, Urbach, Dorothea, van der Merwe, Maryke, Vorajee, Haroon, Wilson, Nadine, Wing, J. R., Lee, Jie Eun, Ahn, Chang Ho, Kim, Nam Hoon, Kim, Sun Hwa, Hwangbo, Yul, Ko, Sun Hye, An, Jee Hyun, Ko, Seung Hyun, Lee, Ji Hyun, Noh, Jung Hyun, Ji, Myeong Jin, Kim, Kyeong Jin, Jung, Chan-Hyeon, Kim, DongJun, Kim, Kyongyoung, Kim, Lee-Kyung, Kim, SinGon, Kim, Eun Ky, Lee, Dong-Hwa, Cho, Young Min, Moon, Shin-Je, Oh, Chang-Myung, Hong, A. Ram, Park, Kyeong Seon, Yun, Jae Seung, Hong, Jae Won, Kim, Hee Young, Buranapin, Supawan, Deerochanawong, Chaicharn, Hengjeerajarus, Natavuth, Kosachunhanun, Natapong, Kuanprasert, Srun, Kunavisarut, Tada, Laortanakul, Rungnapa, Mangklabruks, Ampica, Phornphutkul, Mattabhorn, Prasitdamrong, Hutsaya, Snabboon, Thiti, Sriwijitkamol, Apiradee, Suwanwalaikorn, Sompongse, Tantiwong, Puntip, Tejavanija, Sirakarn, Vorasettakarnkit, Yongkasem, Alasabbagh, Samer, Bagais, Christos, Bingley, Polly, Borthwick, Les, Brett, Barbara, Davidson, Sue, Ewing, Jane, Garesse, Robert, Gillani, Syed, Gkastaris, Konstantinos, Herring, Roselle, Illsley, Graham, Johnson, Andrew, Kaplan, Felicity, Lonnen, Kathryn, Malcolm, Ellen, McCrimmon, Rory, Mcknight, John, Nair, Rajiv, Patel, Vinod, Pearson, Ewan, Raghavan, Rajeev, Raj, Suchitra, Russell-Jones, David, Sampson, Mike, Singh, Baldev, Sukamer, Nithya, Turner, Jeremy, Venkataraman, Hema, Viljoen, Adie, Wake, Deborah, Barnes, Cassandra, Boyd, Gary, Childress, Gwen, Coleman, Debora, Combs, Judy, Davis, Leon, Davison, Kentress, DeAtkine, David, Dunaway, Linda, Duncan, Janet, Hibbard, Nina, Martin, Melissa, Nichols, Melissa, Painter, Karolynn, Perkins, Tammy, Quinney, Rosylen, Raymond, Katrina, Roney, Christopher, Sanchez, Gilberto, Sosnowchik, James, Sultan, Farah, Talley, Shellie, Trippe, Bruce, Tuck, Pamela, Vaughan, T., Warriner, Amy, Watts, Tamara, Williams, Hayes, Abidov, Aiden, Abidov, Yulia, Anderson, Corey, Avila, Vanessa, Baraniak, James, Berry, Barbara, Bessette, Lynn, Blackwell, Gina, Burrows, Jennifer, Caldron, Meghan, Chang, Chui-An, Collins, Elizabeth, Crawford, Meli'sa, Delfin, Katherine, De'Loera, Jessie, Dokken, Betsy, Feld, Lawrence, Gill, Ryan, Gordon, James, Gordon, Rachel, Gullick, Nicole, Gutierrez, Diane, Harrison, Larissa, Haught, Annamarie, Hemmila, Karin, Hendrickson, Darell, Horsley, Craig, Hsu, Connie, Jerrell, Maggie, Kertesz, Briana, Khananisho, Paulina, Lenzmeier, Sonia, Lenzmeier, Thomas, Lipschitz, Barbara, Lonquist, Mark, Mather, Dana, (Shelley) Miceli, Michelene, Norris, Gwenth, Padilla, Ann Marie, Petersen, Dena, Riffer, Ernie, Scott, Susan, Sheridan, Kathryn, Smith, Misty, Stump, Craig, Thomson, Stephen, Torok, Ernese, Vesely, Kelly, Vincent, Tom, Wilson, Christy, Wilson, Sandra, Agarwal, Monica, Appleberry, Kady, Barden, M. Gregg, Brickell, Lorrie, Bravo, Eric, Cassis, Cady, Covington, Becky, Devine, De'Andrea, Dishongh, Katherine, Dugger, Joseph, Evans, Lauren, Francis, Spencer, Johnson, David, Koneru, Kalyana, Kurck, Sheryl, Lingisetty, Chandra, Menke, Jennifer, Miller, Beatrice, Montgomery, Cristal, Nimmo, Teresa, Pace, Daniel, Pellegrino, Richard, Rose, Joseph, Straub, Karl, Stubbs, Amanda, Thomas, Leslie, Thrasher, James, Thrasher, Lesa, Wijewardane, Priyantha, Williamson, Kimberly, Acevedo, Lizeth, Ahmad, Aakif, Andresen, Teri, Anderson-Berman, Nancy, Ayele, Etsegenet, Baker, Janice, Banares, Arlene, Barsalou, Thea, Becker, Steven, Benavidez, JoAnn, Benitez, Guadalupe, Bernstein, Richard, Bhasker, Kala, Blain, Arthur, Blustin, Joy, Bughi, Stephanie, Carney, Kimberly, Castro, Gerardo, Catangay, Jennifer, Chambers, Joseph, Chan, Eve, Chang, Amy, Chang, Anna, Chavous Jr., Donald, Chen, Constance, Chiu, Ken, Chochinov, Ronald, Chu, James, Cleghorn, Monica, Cohen-Gadol, Shariar, Cokely, David, Collins, Michelle, Collins, Yolanda, Colomy, Carissa, Cruz-Corona, Yolanda, Cunnea, Virginia, Dailey, George, Dang, Andy, Dang, Trinh, De La Luz, Mabel, Delgadillo, Rico, Delgado, Michael, Desta, Taddese, Doriguzzi, David, Dulgeroff, Anthony, Einhorn, Daniel, Emery, Pamela, Figueroa, Martin, Fink, Raymond, Flowe, Chelsea, Flowers, Francesca, Fregosi, Julie, Garcia, Gabriel, Garcia, Maria, Gaudiani, Linda, Gawad, Michael, Ghattas, Sam, Gilroy, Christine, Gonzalez, Viviana, Gonzalez, Yadira, Guadron-Hernandez, Liliana, Ha, Thu, Haas, Terry, Harbison, Judy, Hernandez, Graciela, Hernandez, Robert, Herrera, Nancy, Ho, Vincent, Hsu, Kang, Humphries, Denise, Huynh, Vincent, Jacobs, Shahram, Jardula, Michael, Jarman, Cheryl, Kabongo, Martin, Kafri, Hassan, Kaplan, Roy, Kim, Grace, Kobayashi, Gary, Korff, Gary, Kowalick, Alexandra, Levin, Ellis, Lewis, Erin, Lisovskiy, Svetlana, Luu, Hieu, Malkhassian, Daniel, Maletz, Louis, Martinez, Geraldine, Martinez, Gilbert, Martinez-Miss, Salomon, McCallum, James, Melikian, George, Metcalf, Francine, Michlin, Bernard, Michlin, Shira, Mitchell, Anitha, Moattari, Ali Reza, Montano-Pereira, Cynthia, Moon, Mia, Naderi, Bahar, Nagata, Jenna, Nguyen, Christopher, Norwood, Paul, Garcia, Sagrario Olmos, Olsen, Sharon, Pardino, Aarika, Parkes, Kathleen, Pascal, Aileen, Patel, Mihir, Paulson, Carolyn, Pernia, Kay, Perez, Marisol, Pham, David, Presant, Larry, Preston, Krista, Reeves, Laurie, Richards, Jessica, Rios-Santiago, Jessica, Risser, Joseph, Rodriguez, Silvia, (Cherrie) Rosal, Rosario, Rosenblit, Paul, Rosero, Raquel, Sadler, Chris, Samoa, Raynald, Sanchez, Liliana, Schaeffer, Cynthia, Schwartz, Alan, Scott, Christopher, Scott, Courtney, Scott, Cranford, Shartel, Jo-Ann, Shaw, Sylvia, Sivalingam, Kanagaratnam, Sligh, Scott, Sligh, Teresa, Smith, Bradley, Soler, Daniel, Spencer, Torre, Stalters, Sophia, Stone, Jenine, Szlachcic, Yaga, Thorsell, Ashley, Tu, Zhongheng, Unger, Jeffrey, Vela, Teresa, Walton, Anita, Whittle, Jessica, Wilson, Lou Ellen, Wing, Zandra, Wright, Philippa, Wyman, Shannan, Yao, Ni, Zlock, Douglas, Baker, Chelsea, Bakkum, Robyn, Bird, C. David, Carangi, Gwen, Cohen, Kenneth, Daza-Taylor, Kristen, Diesburg-Stanwood, Amy, Douglass, Sara, Finck, Lani, Karaboitis, Krisoula, Kerstein, Howard, Lesh, Kurt, Loesch, Ryan, McClure, Lori, Nevarez, Max, Pavol, Bristol, Rasouli, Neda, Rockler, Briana, Sanagorski, Rebecca, Solano, Royce, Underkofler, Chantal, Van Winkle, Rebecca, Weiss, Sarah, White, Katherine, Wirtemburg, Patricia, Zarrabi, Lida, Zemel, Leonard, Barchini, George, Casagni, Kimberely, Cordisco, Marie Elena, Davis, Amanda, Duffy-Hidalgo, Bobbi, Huntley, Richard, McNicol, Donald, Miller, Neil, Mukwaya, Margaret, Northrup, Patricia, Platt, Jonathan, Rabideau, Diane, Randolph, Pamela, Rose, Suzanne, Samuel, James, Savino, Robert, Soufer, Joseph, Torello, Carole, Abinader, Azarel, Agosto, Milagos, Aguirre, Frank, Alexander, Lori, Almanza, Orlando, Alvarez, Dalia, Anees, Muhammad, Aqua, Keith, Arazo, Luz, Asberry, Claudia, Ashley, Alyssa, Balentine, Tracy, Barta, Melissa, Bertot, Ivan, Binns-Leon, Michelle, Borello, Nora, Bostick, Janette, Boucher, Loria, Braddy, Dalton, Breton, Cristian, Broxton, Bridgitte, Burke, Deborah, Cabielles, Tania, Calderon, Juan, Canaan, Yamil, Canelon, Gustavo, Cardona, Jose, Castillo-Gonzalez, Sheila, Castro, Kenia, Chacon, Jessica, Chehade, Joe, Church, Lara, Cohen, Lisa, Crews, Erin, Cromer, Carol, Cromer, Michael, Cuello, Jorge, Cusi, Kenneth, de Leon, Karel, Delgado, Edmund, Delima, Selma, Denson, Janneral, DeVries, Benjamin, Diaz, Maritza, Digon, Beningo, Dowell, Michelle, Dunn, Kelly, Ellington, Dickson, Emerick, George, Encarnacion, Frances, Ezell, Leslie, Fahmie, Ashley, Fernandez, Noel, Figueredo, Rommel, Flores, Patricia, Fonseca, Juan, Freeman, Alain, Freeman, Erica, Frias, Juan, Garcia, Lazaro, Geronilla, Giovanni, Hamilton, Maxine, Hanabergh, Rodolfo, Hanson, Lenita, Harris, Kim, Hasan, Syed, Hendrick, Mary, Hicks, Melissa, Hoffman, Lara, Holt, Antoniwa, Hutchinson, Rachel, Iparraguirre, Harold, Janicka, Ania, Janovitz, Richard, Jones, Dawn, Jones, Sarah, Kaplun, Polina, Kelijman, Mirtha, Kersey, Angela, Khan, Ashraf, Khan, Misal, Kirk, Gregory, Konwai, Ada, Kovar, Meghan, Kutner, Mark, Lafaille, Jimmy, Leach, Dana, Levenson, David, Lewy-Alterbaum, Lorena, Light, Timothy, Lo, Margaret, Loman, Lusiana, Long, Sarah, Love, Jeremy, Madera, Arelis, Magee, James, Mallitz, Michelle, Marquez, Farid, Marshall, Richard, Mary, James, Mateu, Janet, McKenzie, Leanna, McKenzie, Rona, McKenzie, Wilfred, Mendelson, Reina, Mendez, Janet, Merlos, Ledis, Mierau, Jennifer, Miller, Robert, Miranda-Palma, Bresta, Montoya, Carla, Navarro, Eduardo, Neller, Christian, Nossa, Janette, Osborn, Tracey, Patel, Bijal, Penabad, Jesus, Perez, Gilberto, Pineda, Daniel, Pinero-Pilona, Antonio, Platt, George, Prestwood, Elfrida, Quintero, Luis, Rengifo, Rodney, Reyes, Dannier, Rivas, Aolani, Rivero, Mileydis, Robinson, Sonya, Robison, Dawn, Rodriguez, Dalier, Rodriguez, Ivete, Rodriguez, Toni, Rosario, Cristobal, Rosenwasser, Rebecca, Roura, Miguel, Schramm, Erich, Schramm, Rashi, Schweppe, Laurie, Segel, Scott, Sepulveda, Maria, Sheets, Lori, Shields, Karen, Smith, Krystal, Smith, Meridith, Somerstein, Claudia, Solano, Maria, Suarez, Manuel, Suarez, Omar, Suarez, Rogelio, Suarez, Victor, Sultan, Senan, Sutton, David, Szilagyi, Istvan, Thorne, Margaret, Travaglini, Stefano, Trevino, Miguel, Uriarte, Oscar, Vizcaino, Luis, Wadud, Khurram, Wehmeier, Kent, Weinstein, Debra, Welch, Celeste, White, Alexander, White, Basil, Williams, Tammy, Williamson, Cathi, Zwick, Lisa, Adams, Cynthia, Arkin, David, Bode, Bruce, Bonner, Sheila, Bonner, Wes, Bowen, Danielle, Bradley, Brooke, Bradley, Paul, Brennan, Elizaebeth, Brown, Cynthia, Brown, De'Aira, Burgess, Elizabeth, Collett, Taryn, Cook, Jennifer, Corse, Steven, Dukes, Kaitlyn, Durrence, Jenna, Dutton, Robin, Estes, Susie, Falcone, Laura, Gaskin, Katherine, Goswami, Ketan, Hart, Mari, Jester, Donni, Kies, Staci, Leiker, Abigail, Lu, Peter, Mahakala, Aparna, Maher, Maxine, Miller, Alan, Miller, Todd, Munoz, Miguel, Musto, Michele, Odugbesan, Adeniyi, Olson, Darin, Osborn, Nicole, Pace, Kerri, Pajkovic, Gordana, Paulus, Carolyn, Phillips, Lawrence, Reddick, William, Reed, John, Rowe, Shasta, Sadler, Barney, Sapp, Tabby, Savoye, Louise, Shore, David, Shuman, Susan, Sittle, Jessica, Standard, Shana, Wardell, Karla, Workman, Amy, Carreira, Tabitha, Fitz-Patrick, David, Hean, Vutheary, Limcolioc, April, Nihei, Megan, Park, Youngmee, Pau, Cindy, Shannon, Lindsey, Trotter, Alanna, Williams, Sierra, Coughenour, Tiffany, Liljenquist, John, Vance, Carl, Williams, Jonathan, Agaiby, Eva, Agaiby, John, Argo, Kelly, Azad, Nasrin, Backus, Arnetta, Bartlett, Andrew, Bellucci-Jackson, Jennifer, Boncu, Otilia, Brodt, Pamela, Carlson, Hope, Collins, Kathleen, Delibasic, Maja, Dor, Isaac, Dukett, Mary, Emanuele, Nicholas, Gabel, Faith, Giardino, Alexandra, Hoelzer, David, Hsieh, Annming, Jayaramaiah, Poornima, Kearney, Tracy, Kelley, Kathleen, Kravarusic, Jelena, Lawless, Andrea, Maheshwari, Hiralal, Martin, Elonia, McCall, Linda, Mikell, Frank, Nallala, Deepika, Said, Sufyan, Sakla, Nadia, Schiferi, Joanna, Smith, Patricia, Stokes, John, Sugimoto, Danny, Sugimoto, Michael, Swan, Delores, Alfata, Sarab, Beavins, Jill, Bogardus, Christine, Bogardus, Donald, Calvert, Brittany, Calvin, Krystle, Carlton, Kristin, Condit, Jonathan, Cornett, George, Covert, Carissa, Dela Llana, Alexander, Dixon, Rosie, Felker, Dean, Fogle, Sara, Harris, Leslie, Hoebeke, Roberta E., Silva, Ricardo, Johnson, Tabitha, Johnston, Kelly, King, Jennifer, Lang, Amber, Maynard, Kenneth, McDaniel, Vickie, McGrath, Caitlin, Moretto, Thomas, Ndukwu, Ikeadi, O'Connor, Leo, O'Connor, Thomas, Oliver, Tonya, Overmars, Stephanie, Patel, Minesh, Patterson, Geraldine, Powell, Talessa, Raphael, Annmarie, Raphael, Rod, Reedy, Mark, Reyes, Hubert S., Ridge, Terry, Rutherford, Mary S., Sebastian, Mike, Shaeffer, Laura, Sheets, Michelle, Thomas, Karen, Trueblood, Nancy, Tucker, Tiffany, White, Adrian, Wooldridge, Evan, Wright, Alison, Zapinski, Dawn, Cieloha, Megan, Coker, Clarine, Fletcher, Cherisa, Kay, Jennifer, Larsen, Katelyn, Marar, Isam, Motz, Zachary, Paul, Jamie, Pokorski, Alan, Rabadi-Marar, Diana, Scheideler, Kylie, Sobczyk, Jill, Sweeney, Kolbie, Brink, Nathalie, Buth, Dennis, Grimes, Katlyn, Lakin, Gregory, Loewen, Barbara, Secrist, Neal, Simon, William, Steere, Diane, Talbot, Kimberly, Wheeler, Christina, Adams, Derick, Armour, Wilhelmina, Asher, Whitney, Bays, Harold, Behl, Noreen, Conn, Brittany, Dye, Lyndsey, Earl, Jennifer, Farris, Neil, Figueroa, Sergio, Fruit, Olivia, Hobbs, Coury, Howard, Kristen, Jackson, Tamara, Kain, Angela, Karounos, Chrisi, Keiran, Sarah, Kennedy, Ryan, King, Kara, Krishnasamy, Sathya, Lambdin, Winona, Micklewright, Gezina, Mokshagundam, Sri, Mooney, Stephen, Moore, Melanie, Morgan, Denise, Morley, Robert, Newsome, Stacy, Nichols, Jennifer, Nims, Diana, Nockerts, Nichole, Oremus, Ruth, Ritchey, Barbara Michelle, Shelton, Christina, Smith, Danielle, Walker, Debra, Weiter, Kathleen, Whitehead, Rebecca, Wood, Robert, Woodruff, Robin, Young, J. DeShay, Alper, Arnold, Berry, Martha, Gauthier, Nadine, Griffin, Catina, Haynes, Elvia, Lawrence, Jereta, McCormack, Teraina (Terry), Midyett, Catherine, Rangaraj, Uma, Smith, William, Stricker, Makenzie, Tandron, Ileana, Vargas, Ramon, Wagner, Michele, Albert, Marie, Baillargeon, Gail, Stephens, John, Aroda, Vanita, Dempsey, Michael, Junker, Herman, Lopez, Meyling, Shepard, Marc, Ciccarelli, Carol, Datu, Joycelyn, Gallagher-Dorval, Karen, Hartigan, Celia, Kowaloff, Edward, Lock, John-Paul, Rosano, Nina, Shaw, Bernadette, Thompson, Michael, Abbo, Camerin, Anton, Salwan, Baker, Lydia, Bhan, Arti, Blackmer, Karla, Boley, Bryon, Hyde, Desirae, Bonema, John, Carlin, Tamara, Curran, Nancy, Dandois, Bethani, Dermyer, Abigail, Dubeck, Janet, Elkins, Jennifer, Ferris, Pamela, Fraser, Neil, Kozma, Julia Gargan, Gasparovich, David, Grodman, Robert, Hampson, Jaison, Hewitt, Mitzie, Hiatt, Judy, Hopkins, Ashley, Howard, Jennifer, Joliat, John, Joliat, Jonathan, Kalt, Steven, Katzman, Steven, Kogelman, Jesse, Kruger, Davida, Kurkowski, April, Lambert, Jodi, Levcovici, Elisabeta, Lockwood, Karen, Lumberg, Michael, McMullen, Lindsay, Meyers, Peter, Minto, Carrie, Nelson, Tina, Neuner, Gregory, Nurmi, Kevin, Perkins, Judy, Plunkett, Cynthia, Ruoff, Gary, Samantray, Julie, Sawah, Ahmed, Secord, Samantha, Seyoum, Berhane, Sherman, Sarah, Simpson, Michael, Sinkoff, Mark, Skoney, Joseph, Small, Jami, Szerlong, Heidi, Tinetti, Timothy, Toomey, Adam, VanDerWerff, Holly, Whitehouse, Fred, Wilkins, Raechelle, Wolf, Stephanie, Yarows, Steven, Eldred, Edward, Hester, Kim, Huling, Randall, Moore, Kerry, Preslar, Kristina, Proge, Nakia, Smith, Lora, Young, Gerri, Bybee, Kevin, Carr, George, Case, Christopher, Forker, Alan, Goeke, Lindsay, Kocher, Darlene, Lin, Oscar, Littlefield, James, Mendoza, Robinson, Moore, Michelle, Oberhaus, Barbara, Reed, Larry, Rutherford, Cheryl, Windsor, Sheryl, Ackerman, Kim, Christensen, Reed, De Souza, Jose, Douglas, Kirsten, Hrncirik, Therese, Lefkowicz, Magen, McDonnell, Lindsey, Severa, Larry, Tjaden, Jeffrey, Ward, Laura, Addison, Shayla, Anderson, Robert, Baker, Claire, Baum, Robert, Belitz, Jenny, Boerner, Shannon, Desouza, Cyrus, Drincic, Andjela, Fitzgibbons, William, Francis, Jessica, Garvin, Maureen, Goldner, Whitney, Goodrich, Melissa, Grubbs, Lisa, Hatcher, Theresa, Hochstein, Renee, Hunter, Christine, Keuchel, Janet, Konigsberg, Sarah, Kuechenmeister, Lisa, Mack, Lynn, McKnight, Thomas, Mensik, Hanna, Mitchell, Jan, Moeller, Jennifer, Montanez, Edward, Montoya, Paul, Nemecek, Tiffany, Neumeister, Amy, Newcomb, Jefferey, Pracht, Shelby, Radke, Kaitlin, Rahman, Jennifer, Renter, Samantha, Reynolds, Jill, Grace Rodriguez, Maria, Satorie, Jessica, Schiermann, Lynnda, Severson, Megan, Shivaswamy, Vijay, Snyder, Janice, Tillson, Renee, Vasey, Andrew, Wahl, Timothy, Weeks, William, Wolf, Thomas, Springer, Brian, Mitchell, Janice, Woodruff, Mark, Alvarez, Isis, Bowers, Rachel, Cala-Anaya, Franklin, Castellanos-Rodriguez, Rudiber, Christopher, Janice, Concepcion, Gelvis, Fernandez, Dareana, Ghanem, Paula, Harmon, Nicolette, Kaiser, Heather, Kaplan, Westbrook, Klugh, Serena, Le, Nicholas, Lehrner, Stephanie, Lopez, Robert, Martin, Olga, Martinez, Tina, Martini, Nicole, Molin, Clifford, Nakhle, Samer, Newsome, Yvonne, Neylon, Ellen, Olsen, Jarvis, Palal, Betsy, Philander, Peter, Rosario, Adriana Ruiz, Sanchez, Mary, Sierra-Garcia, Miguel, Smith, Myesha, Trenche, Samuel, Trippi, Danna, Vernetti, Nicholas, Villegas, Laura, White, Briana, Wilcox, Simmon, Carmolli-Cornette, Lisa, Chuang, Ellie, Hoffman, Sheila, Laliberte, Alison, Marshall, Lisa, Meroski, Pamela, O'Neil, Christine, Shippey, Jeanette, Spector, Linda, Cam, Jenny, Chen, Tina, Damle, Jagadish, Holman, Rebecca, Liu, Jenny, Milazzo, Carmelo, Quach, Brenda, Yang, Bin, Ahmad, Basharat, Aro, Dominic, Azeem, Fouzal, Bonaccorso, Antoinette, Brancato, Lenore, Brautigam, Donald, Charatz, Seth, Chiarot, June, Cunningham, Linda, DiCerbo, Nancy, DiGiovanna, Michael, Dluge-Aungst, Dawn, Dolbeer, Traci, Donovan, Daniel, Duma, Carol, Ellwood, Rebecca, Freedman, Zachary, Gorman, Timothy, Gunther, David, Hogan, Eileen, Hoffman, Lisa, Kapoor, Anoop, Kelly, Michelle, Kitchen, Timothy, Lam, David, Levister, Camilla, Levy, Carol, Lopez, Carlos, Lyons-Patterson, Jane, Makam, Sashi, Mauceri, Diana, McClary, Carole, Murak, Daniel, Nolan, Kelly, Nunez, Carlos, Elliott, Theresa Ohlson, Ostrowski, Phillip, Pallai, Ajish, Porges, Andrew, Rodes, Alfredo, Rosenberg, Richard, Rubel, Lance, Serrano, Lelanie, Shannon, Daniel, Sinha, Rabi, Snyder, Brian, Sy, Claude, Tang, Rocky, Varavenkataraman, Raghupathy, Winterberger, Amy, Allen, Stacy, Anderson, Marshall, Asbill, Mark, Beasley, Amanda, Bennett, Chrisitian, Bergamo, Katherine, Bilbro, Scott, Brannock, Sarah, Brown, Laura, Bryant, Danielle, Buck, Genena, Burdette, Tara, Buse, John, Butler, Laura, Caldwell, Ronald, Cannon, Daniel, Cannon, Kevin, Carter, Casey, Cherry, Amanda, Comer, Zoe, Crittenden, Susan, Cuffee, Juanita, Culmer, Dawn, Daly-Barnes, Kristie, Evensen, Erick, Saavedra, Rubin, Dean, John, Dezube, Milana, Diner, Jaime, Dodis, Regina, Dostou, Jean, Duclos, Michelle, Elmore, Miles, Ferree, Kristin, Foster, Leslie, Fuller, Gail, Garner, Susan, Godwin, Sarah, Goley, April, Gore, Whitney, Green, Brenda, Harris, Elizabeth, Harris, Joy, Harris, Rebecca, Hatharasinghe, Roger, Herring, Charles, Hill, Chernessa, Holmes, Robert, Hutchens, Erin, Jennings, Stuart, Kaake, Ashley, Kaygun, Handan, Kirkman, Marian Sue, Kretzschmar, Heather, Lang, Jaqueline, Largay, Joseph, Lewis, Melissa, Long, Maria Ann, Lucas, Kathryn, McCall, Roshanda, McDaniel, Catrina, McGinnis, Lee Ann, Mogabgab, Edward, Montero, Manuel, Montgomery, Richard, Moran, Joseph, Morgan, Katie, Motley, Taylor, Narendran, Mahendra, Narron, Brooke, Newman, Walter, Nicholson, John, Norris, Stephanie, Norton, Jennifer, Nowell, Erin, O'Brien, Heather, Otero, Heydin, Perkins, Victoria, Quesada, Reyna, Raad, George, Raley, Rosalie, Raman, Sujatha, Rappaport, Jonathan, Ray, Robert, Ray, Shannon, Rice, Lucian, Roberts, Christine, Ross, Kaylee, Scanlon, Erica, Schroder, Derek, Schulte, Michelle, Schultzaberger, Richard, Shoffner, Sylvia, Skrzynski, Michael, Smith, Brian, Smith, Holly, Smith, Jennifer, Smith, Justin, Smith, Kendall, Smith, Leigh, Sparks, Jeffrey, Speece, Jessica, Spivey, Jessica, Stebich, Andrew, Temple, Karen, Tessena, Boryana, Thompson, Casey, Traylor, Henry, Turner, Juliana, Turner, Sh-Rhonda, Van Cleeff, Martin, Vukojicic, Katarina, Wanchick, Leslie, Weinrib, Stephen, Werner, Devorah, Wishon, Candace, Wilson, Jonathan, Wilson, Stephanie, Wright, Heather, Young, Laura, Brown, Jamie, Lillestol, Michael, Allen, Mary Jo, Barker, Bruce, Bedel, Gary, Beuerlein, Shawn, Bhati, Amar, Breslaw, Nicole, Brown, April, Burtch, Brian, Check, Ellen, Chiodi, Shawnie, Colosimo, Linda, Cope, Kathie, Coressel, Denise, Cramer, Bethany, Crowel, Amy, Daboul, Nizar, DeLong, Rebecca, Dirkes, William, Farooki, Zia, Fixler, Don, Friedhof, Linda, Griffen, Julie, Hagan, Anjani, Haney, Kevin, Harris, Kira, Hoffmann, Amanda, Hsieh, Ronald, Ismail-Beigi, Faramarz, Kelso, Kim, Khamousia, Nidaa, Kremer, Gerald, Krol, Adrienne, Kulow, Tanya, Kuttler, Mary, Lee, Andy, Leksan, Jane, McElroy-Marcus, Susan, McNeil, Donald, Mulligan, Guy, Pantalone, Kevin, Perrino, Frank, Peysha, Lori, Pickett, Alicia, Pritchard, James, Provanzana, Kathleen, Rancitelli, Philip, Raupp, Randa, Schnall, Adrian, Siddiqui, Jaweriah, Smith, Cynthia, Sood, Ajay, Sortor, Jennifer, Spinelli, Kristin, Taylor, Diana, Thornton, Aaron, Tiktin, Margaret, Twine, Melissa, Urbank, Ann, Voegele, Francis, Wang, Lawrence, Weiss, Daniel, Werner, Amanda, Williams, Daniel, Wilson, Charles, Aberle, Teresa, Arcon-Rios, Sirlys, Arodak, Bassam, Aziz, Ammara, Bixler, Lacey, Brisko, Carolann, Cannon, Melanie, Choe, James, Connery, Lisa, Elkosseifi, Myriam, George, Aja, Hummer, Kimberly, LaBryer, Lauren, Lane, James, Lim, Jonea, Mirza, Lubna, Mittadodla, Saritha, Priest, Diane, Rao, Veitla, Ricci, Cassie, Salazar, Jennifer, Shaw, Jessica, St John, Jennifer, Talsania, Mitali, Joarder, Farahnaz, Riddle, Matthew, Yuen, Kevin, Ahmed, Intekhab, Austin, Barry, Bagnata, Linda, Bartkowiak, Anthony, Batrus, Jennifer, Baumgarten, Thomas, Belejchak, Paula, Belucher, Christine, Benton, Julia, Berger, Peter, Biscoveanu, Mihaela, Blasick, Abbie, Blyler, Kay, Casey, Linda, Chandler, Bronell, Chapman, Kim, Chou, Brian, Cisar, Jennifer, Clatterbuck, Tabetha, Cook, Richard, Cook II, Richard, Dardi, Inderpreet, DeSalle, Dina, Edevane, Shari, Elliott, Kelsey, Everhart, Brian, Furlong, Kevin J., Geishauser, Jennifer, Goisse, Marcy, Goodman, Yvonne, Gordon, Murray, Gutowski, Catherine, Harris, Ronald, Hu, Ying, Hull, Jenny, Iachini, Kathleen, Iannamorelli, Anthony, Jabbour, Serge, Jackson, Timothy, Jameson, Brian, Janosov, Paula, Jordan, Terry, Kennedy, John, Kurtzer, Gayle, Labuda, Joseph, LaRochelle, Anne, Levinson, Lawrence, Madder, Robert, Marconnet, Kari, Mareda, Patricia, Michael, Donna, Michaels, Debra, Miller, Cathy, Miller, Jeffrey, Minnock, Ann, Mitchell, Michele, Moturi, Hemlata, Moyer, Heather, Nagelberg, Steven, Oliver, Michael, Pepe, Constance, Porter, Patricia, Posey, Dora, Purighalla, Raman, Redmond, Jessica, Reiling, Joseph, Reiling, Susan, Rine, Kathy, Rust, William, Sandutch, Jill, Shaffer, Anette, Shalongo, Annette, Shawley, Sarah, Shonk, Pamela, Singh, Vinay, Slucki, Andrea, Spiller, Kellie, Springer, Nina, Sprouse, Heidi, Sun, Albert, Taylor, Judith, Watine, Robert, Watkinson, Jean, Weaver, Nancy, Wenocur, Howard, Yeoman, Gary, Ackermann, Jeremy, Averill, Nathan, Ballard, Thomas, Byars, William, Carter, Caroline, Castay, Cathleen, Cheek, Shonda, Collins, Jessica, Collins-Jamison, Traci, Cooksey, Erin, Crockford, Nicole, Cutshall, Christopher, Dar, Moahad, Drake, Almond, Dugat, Kameron, Durham, John, Durham, Nancy, Eagerton, Donald, Edmonds, Nataish, Farmer III, Joseph, Fogarty, Charles, Gatlin, June, Geisberg, Harry, Grossman, Margaret, Harrell, Wendy, Harrison, John, Harrison, Kristina, Heincelman, Sarah, Holladay, Lauren, Huberty, Chad, Isakov, Ingrid, Jordan, Norma, Kunkel, Michael, Laserna, Susan, Lawrence, Amanda, Lawrence, Andrew, Livingston, Yolanda, Lloyd, Eric, Lopez, Lina, Manuel, Joyce, Mayfield, Ronald, McDonald, Margaret, Morgan, Caroleann, Munn, Joanie, Murphy, Lauren, Nicol, Brent, Nicol, Philip, Norris, Rebecca, Phillips, Bunny, Reeves, Angela, Roach, Jacqueline, Ross, Irene, Santi, Jeffrey, Saunders, Jacqueline, Shugart, Henry, Singleton, Monnieque, Slee, Erin, Smalley, Kathleen, Smith, Kristy, Sofley, Carl, Solheim, Vesna, Spell, Samantha, Tanenberg, Robert, Whitley, Chester, Widel, Michele, Wilhoit, Gordon, Wood, Ruth, Wright, Jaime, Wulf, Alissa, Zapkowski, Julie, Adams, Kristi, Adams, Roberta, Ananthula, Parvati, Bass, Caitlin, Bates, Chad, Baum, Howard, Bays, Anna, Beck, Teresa, Bilbrey, Brett, Brown, Ashley, Carlton, Anna, Carter, Jenny, Cheri, Laura, Childress, Richard, Cloud, Mark, Cohen, A. Jay, Coulter, Alice, Dotson, Kimberly, Green, Christopher, Helton, Ashley, Houstrup, Jody, Huffman, David, Hurst, Fred, Hurst, James, Jantzi, Curtis, Jerkins, Randy, Jerkins, Terri, Jones, Mandy, Kelley, Shane, Kroulek, Judy, Lawhun, Dennis, Lee, Marcus, Louthan, James, Manning, Rickey, May, Michael, Means, Katherine, Monroe, Jeanabe, Morawski, Emily, Morin, David, Morris, Barbara, Murray, John, Nichols, Robert, Olivi, Stacie, Pennington, Kristina, Poe, Jenny, Pour, Omid Rad, Reeves, Michael, Reeves, Pamela, Root, Connie, Rose, Karen, Rutledge, Lily, Ryan, Eugene, Schwartz, Patricia, Shafer, Jennifer, Singh, Chitra, Smith, Ashley, Snow, Rodney, Stamps, Henry, Staton, Peggy, Stevens, Jeffrey, Stovall, Cathy, Sturgeon, Rodney, Tapp, John, Tapp, Stacy, Warner, Carla, Weiss, Melissa, White, Judith, Wierum, Craig, Williams, Konya, Wright, Tiffanie, Abreu, Marconi, Agarwal, Vandana, Ahmad, Aftab, Ahmad, Asma, Ahmed, Faiz, Ahn, Lily, Alaoui, Rachid, Alarcon, Stephanie, Allison, Dale, Annamalai, Rajasekaran, Arain, Khadija, Arellano, Angelica, Arispe, Barbara, Aronoff, Stephen, Arroyo, Jesus, Bachand, Trista, Bailey, Becky, Bajaj, Chris, Banarer, Salomon, Barnella, Christine, Bartz, Mary, Bates, Jason, Becerra, Karen, Benavides, Melissa, Bhuchar, Subodh, Blevins, Thomas, Bonazzi, Amanda, Brinson, Cynthia, Brown, Timiki, Buckley, Brandee, Bunker, Christy, Burbano, Jose, Busch, Vickey, Bustamante, Rosibel, Calais, Natalee, Camacho, Allison, Camp, Shana, Canada, Nancy, Canchola, Daniel, Cano, Monica, Cardenas, Veronica, Carl, Leighton, Casaubon, Luis, Castaneda, Jessica, Castro, Cindy, Ceniceros, Diego, Chaicha-Brom, Tira, Chaudhuri, Sudipta, Chavez-Velazquez, Alberto, Chen, Jocelyn, Cheyne, Theresa, Chilka, Sapna, Chionh, Kristen, Chunawala, Imran, Cope, Pamela, Cotton, Sonja, Daudjee, Munib, Davidson, Tichina, De Loa, Geraldine, Deniega, Ann, Dillow, Naco, Dominguez, Michael, Dorfman, Steven, Doshi, Ankur, Dunnam, Theresa, Dutta, Papiya, Eiler-Prater, Megann, Eizensmits, Christina, Espinosa, Valerie, Evans, Charles, Frisinger, Cathy, Ganta, Shylesh, Galtney, Kirsten, Gammon, John, Garcia, Analisa Marie, Garcia, Ronald, Glaspie, Tina, Golici, Laura, Gonzalez, Julian, Grant, David, Green, Carrie, Groce, Lindsay, Guevara, Stephanie, Gutierrez, Mary, Hance, Cortney, Harden, Tiffany, Harrison, Lindsay, Herrera, Carlos, Hettler, Evelyn, Holcomb, Michelle, Hoste, Laura, Hudson, Tina, Huerta, Leticia, Jain, Mahendra, Jayaram, Preeth, Johnson, Monique, Johnson, Olivia, Jones, Laurie, Joseph, John, Juarez, Misty, Karimjee, Najmuddin, Kephart, Ashley, Khan, Armgan, Khan, Jalil, Killian, Michael, Kniffen, Wendy, Koops, Maureen, Kwan, Louise, La, John, Laabs, Jessica, Ledesma, Gilbert, Lee, Monica, Leuck, Kathryn, Livingston, Carrie, Llanas, Teresa, Longoria, Andy, Lorta, Jesse, Lothringer, Larry, MacAdams, Maria, MacAdams, Michael, MacGillivray, Brian, Mageno, Melissa, Malek, Megan, Manley, Meghan, Marler, Karen, Martinez, Cristina, Massie, Jodi, Mateos, Glenda, Mathew, Reena, Maya, Claudia, Mazour, Meg, Mbogua, Caroline, McClendon, Robert, McMullen, Deirdre, Meisner, Carl, Menchaca, Ethelina, Meyers, Linda, Miklius, Audrey, Milam, Alissa, Milam, Ronnie, Miller, Tracey, Mize, Lisa, Moczygemba, Roger, Nafegar, Alexandria, Naidu, Jayaram, Nair, Archana, Nguyen, Alison, Nielsen, Douglas, Nisnisan, Josier, Nisnisan, Maria, Oden, Wendy, Orsak, Jessica, Ozer, Kerem, Papacostea, Olivia, Panchal, Ravi, Parikh, Manish, Parthiban, Beneta, Perrin, Brittney, Perkins, Harvonya, Poduri, Madhuri, Pop, Laurentiu, Portales, Nancy, Porter-Tucci, Linda, Postell, Sharon, Pucillo, Ronald, Reddy, Madhavi, Reddy, Rajneesh, Almodovar, Ramon Gilberto Reyes, Richard, Timiki, Richardson, Michele, Riesch, Louie, Rivera-Flores, Samuel, Rizvi, Abid, Rizvi, Samana, Rodas, Kristen, Rodgers, Mark, Roe, Erin, Rogers, Rebecca, Romero, Reina, Rooker-Morris, Leslie, Rosenstock, Julio, Sachson, Richard, Salcedo, Alfredo, Schill, Joseph, Schuh, Kristy, Schuster, Randall, Scribner, Anita, Shah, Arunkumar, Shaw, Stephanie, Shirley, Jefferey, Shook, Mindy, Siddiqui, Muhammad, Sims, Jamie, Singh, Kamalpreet, Skero, Jennifer, Smejdir, Debra, Stacey, Amy, Strzinek, Robert, Tan, Anjanette, Tayabali, Khadija, Tercero, Almundena, Terry, Jessica, Tinney, Irene, Toe, Florence, Toler, Sara, Tripathy, Devjit, Tumyan, Anna, Ulrich, Michelle, Underwood, Krissandra, Valdez, Christina, Valdivieso, April, Vanderheiden, Anna, Vandre, Morgan, Vardeman, Megan, Vargas, Richard, Vilchis, Monica, Warne, Desiree, Wallace, Jeannine, Weindorff, Kathleen, Welch, Heather, Wellmon, Brandy, Werntz, Gary, White, Leta, Williams, Tearani, Wilson, Maria, Wilson, Robyn, Wilson, Ronald, Wood, John, Wright, David, Yeatts, Rhonda, Zahir, Rezwana, Crockett, Becky, Fawson, Ashlee, Graham, Timothy, Mitchell, Zachary, Quinn, Sally, Reddy, Deepika, Rhudy, Jackson, Rosen, Priscilla, Shakespear, Kaylynn, Simmons, Debra, Turner, Ashley, Vanderhoof, Erin, Wahl, Matthew, Zubair, Imran, Algus, Michael, Gilbert, Matthew, Gorson, David, Marney, Annis, Rodriguez, Juan Pablo Perdomo, Roth, Marilynn, Scofield, Sheilah, Thottan, Anna, Bittorf, Margaret, Botros, Emad, Carpenter, Jeanette, Dashiell, Carie, Dimascio-Johnson, Nancy, Fredrickson, Sonja, Gruenther, M., Hall, Lisa, Hartman, Aaron, Hiner, Janette, Kelley, Donna, Kimmel, Melissa, Larrick, Laura, Lawson, Marie, Levy, James, Lewis, Teneshia, Lovell, Charles, Newby, James, Nichols, Michelle, O'Donnell, Philip, Orengo, Sheyla, Pecsok, Thomas, Reiss, Lind, Tarkington, Phillip, Yeatts, Donald, Zieve, Franklin, Ahmed, Imtiaz, Ali, Sajid, Anwar, Sami, Cathcart, Harold, Dawson, Lucia, Floresca, Rolando, Galvan, Ashley, Gardner, Timothy, Halsey, Kami, Hashmi, Farrukh, Hegel, Jackie, Landon, Megan, Moulton, Katlynn, Nand, Cheta, Sakic, Amra, Sevey, Brandie, Wilson, Bruce, Ahl, Scott, Blank, Robert, Coly, Gerard, Hoenig, Jennifer, Jain, Rajeev, Joseph, Elizabeth, Kasprzak, Debra, Kisiel, Lalaine, Maaske, Maurene, Manadhar, Smriti, Patel, Shailendra, Sickler, Whitney, Grp, D.E.V.O.T.E. Study, Acibadem University Dspace, Marso S.P., Mcguire D.K., Zinman B., Poulter N.R., Emerson S.S., Pieber T.R., Pratley R.E., Haahr P.-M., Lange M., Brown-Frandsen K., Moses A., Skibsted S., Kvist K., Buse J.B., and Buscemi S

Subjects
Insulin degludec, Blood Glucose, Male, medicine.medical_treatment, DEVOTE Study Group, Insulin Glargine, Type 2 diabetes, Kaplan-Meier Estimate, 030204 cardiovascular system & hematology, law.invention, diabetes, insulin, 0302 clinical medicine, Randomized controlled trial, law, Cardiovascular Disease, GLUCOSE CONTROL, 11 Medical and Health Sciences, RISK, COMPLICATIONS, OUTCOMES, Incidence, General Medicine, Middle Aged, Insulin, Long-Acting, VARIABILITY, Cardiovascular Diseases, diabetes mellitus, Female, Life Sciences & Biomedicine, Human, medicine.drug, medicine.medical_specialty, 030209 endocrinology & metabolism, Aged, Diabetes Mellitus, Type 2, Double-Blind Method, Humans, Hypoglycemia, Hypoglycemic Agents, Medicine (all), Bedtime, Article, EVENTS, 03 medical and health sciences, HYPOGLYCEMIA, Medicine, General & Internal, Internal medicine, Diabetes mellitus, General & Internal Medicine, medicine, Intensive care medicine, METAANALYSIS, Science & Technology, Hypoglycemic Agent, Insulin glargine, business.industry, Insulin, medicine.disease, business, BASAL INSULIN
Abstract

BACKGROUND Degludec is an ultralong-Acting, once-daily basal insulin that is approved for use in adults, adolescents, and children with diabetes. Previous open-label studies have shown lower day-To-day variability in the glucose-lowering effect and lower rates of hypoglycemia among patients who received degludec than among those who received basal insulin glargine. However, data are lacking on the cardiovascular safety of degludec. METHODS We randomly assigned 7637 patients with type 2 diabetes to receive either insulin degludec (3818 patients) or insulin glargine U100 (3819 patients) once daily between dinner and bedtime in a double-blind, treat-To-Target, event-driven cardiovascular outcomes trial. The primary composite outcome in the time-To-event analysis was the first occurrence of an adjudicated major cardiovascular event (death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke) with a prespecified noninferiority margin of 1.3. Adjudicated severe hypoglycemia, as defined by the American Diabetes Association, was the prespecified, multiplicity-Adjusted secondary outcome. RESULTS Of the patients who underwent randomization, 6509 (85.2%) had established cardiovascular disease, chronic kidney disease, or both. At baseline, the mean age was 65.0 years, the mean duration of diabetes was 16.4 years, and the mean (±SD) glycated hemoglobin level was 8.4±1.7%; 83.9% of the patients were receiving insulin. The primary outcome occurred in 325 patients (8.5%) in the degludec group and in 356 (9.3%) in the glargine group (hazard ratio, 0.91; 95% confidence interval, 0.78 to 1.06; P

Published
2017

42. Efficacy and Safety of Degludec versus Glargine in Type 2 Diabetes

Author

Marso, Steven P., primary, McGuire, Darren K., additional, Zinman, Bernard, additional, Poulter, Neil R., additional, Emerson, Scott S., additional, Pieber, Thomas R., additional, Pratley, Richard E., additional, Haahr, Poul-Martin, additional, Lange, Martin, additional, Brown-Frandsen, Kirstine, additional, Moses, Alan, additional, Skibsted, Simon, additional, Kvist, Kajsa, additional, and Buse, John B., additional

Published
2017
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45. DEVOTE 3: temporal relationships between severe hypoglycaemia, cardiovascular outcomes and mortality.

Author

Pieber, Thomas R., Marso, Steven P., McGuire, Darren K., Zinman, Bernard, Poulter, Neil R., Emerson, Scott S., Pratley, Richard E., Woo, Vincent, Heller, Simon, Lange, Martin, Brown-Frandsen, Kirstine, Moses, Alan, Barner Lekdorf, Jesper, Lehmann, Lucine, Kvist, Kajsa, Buse, John B., and on behalf of the DEVOTE Study Group

Abstract

Aims/hypothesis: The double-blind Trial Comparing Cardiovascular Safety of Insulin Degludec vs Insulin Glargine in Patients with Type 2 Diabetes at High Risk of Cardiovascular Events (DEVOTE) assessed the cardiovascular safety of insulin degludec. The incidence and rates of adjudicated severe hypoglycaemia, and all-cause mortality were also determined. This paper reports a secondary analysis investigating associations of severe hypoglycaemia with cardiovascular outcomes and mortality. Methods: In DEVOTE, patients with type 2 diabetes were randomised to receive either insulin degludec or insulin glargine U100 (100 units/ml) once daily (between dinner and bedtime) in an event-driven, double-blind, treat-to-target cardiovascular outcomes trial. The primary outcome was the first occurrence of an adjudicated major adverse cardiovascular event (MACE; cardiovascular death, non-fatal myocardial infarction or non-fatal stroke). Adjudicated severe hypoglycaemia was the pre-specified secondary outcome. In the present analysis, the associations of severe hypoglycaemia with both MACE and all-cause mortality was evaluated in the pooled trial population using time-to-event analyses, with severe hypoglycaemia as a time-dependent variable and randomised treatment as a fixed factor. An investigation with interaction terms indicated that the effect of severe hypoglycaemia on the risk of MACE and all-cause mortality were the same for both treatment arms, and so the temporal association for severe hypoglycaemia with subsequent MACE and all-cause mortality is reported for the pooled population. Results: There was a non-significant difference in the risk of MACE for individuals who had vs those who had not experienced severe hypoglycaemia during the trial (HR 1.38, 95% CI 0.96, 1.96; p = 0.080) and therefore there was no temporal relationship between severe hypoglycaemia and MACE. There was a significantly higher risk of all-cause mortality for patients who had vs those who had not experienced severe hypoglycaemia during the trial (HR 2.51, 95% CI 1.79, 3.50; p < 0.001). There was a higher risk of all-cause mortality 15, 30, 60, 90, 180 and 365 days after experiencing severe hypoglycaemia compared with not experiencing severe hypoglycaemia in the same time interval. The association between severe hypoglycaemia and all-cause mortality was maintained after adjustment for the following baseline characteristics: age, sex, HbA, BMI, diabetes duration, insulin regimen, hepatic impairment, renal status and cardiovascular risk group. Conclusions/interpretation: The results from these analyses demonstrate an association between severe hypoglycaemia and all-cause mortality. Furthermore, they indicate that patients who experienced severe hypoglycaemia were particularly at greater risk of death in the short term after the hypoglycaemic episode. These findings indicate that severe hypoglycaemia is associated with higher subsequent mortality; however, they cannot answer the question as to whether severe hypoglycaemia serves as a risk marker for adverse outcomes or whether there is a direct causal effect. Trial registration: NCT01959529 [ABSTRACT FROM AUTHOR]

Published
2018
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46. Day-to-day fasting glycaemic variability in DEVOTE: associations with severe hypoglycaemia and cardiovascular outcomes (DEVOTE 2).

Author

Zinman, Bernard, Marso, Steven P., Poulter, Neil R., Emerson, Scott S., Pieber, Thomas R., Pratley, Richard E., Lange, Martin, Brown-Frandsen, Kirstine, Moses, Alan, Ocampo Francisco, Ann Marie, Barner Lekdorf, Jesper, Kvist, Kajsa, Buse, John B., and on behalf of the DEVOTE Study Group

Abstract

Aims/hypothesis: The Trial Comparing Cardiovascular Safety of Insulin Degludec vs Insulin Glargine in Patients with Type 2 Diabetes at High Risk of Cardiovascular Events (DEVOTE) was a double-blind, randomised, event-driven, treat-to-target prospective trial comparing the cardiovascular safety of insulin degludec with that of insulin glargine U100 (100 units/ml) in patients with type 2 diabetes at high risk of cardiovascular events. This paper reports a secondary analysis investigating associations of day-to-day fasting glycaemic variability (pre-breakfast self-measured blood glucose [SMBG]) with severe hypoglycaemia and cardiovascular outcomes. Methods: In DEVOTE, patients with type 2 diabetes were randomised to receive insulin degludec or insulin glargine U100 once daily. The primary outcome was the first occurrence of an adjudicated major adverse cardiovascular event (MACE). Adjudicated severe hypoglycaemia was the pre-specified secondary outcome. In this article, day-to-day fasting glycaemic variability was based on the standard deviation of the pre-breakfast SMBG measurements. The variability measure was calculated as follows. Each month, only the three pre-breakfast SMBG measurements recorded before contact with the site were used to determine a day-to-day fasting glycaemic variability measure for each patient. For each patient, the variance of the three log-transformed pre-breakfast SMBG measurements each month was determined. The standard deviation was determined as the square root of the mean of these monthly variances and was defined as day-to-day fasting glycaemic variability. The associations between day-to-day fasting glycaemic variability and severe hypoglycaemia, MACE and all-cause mortality were analysed for the pooled trial population with Cox proportional hazards models. Several sensitivity analyses were conducted, including adjustments for baseline characteristics and most recent HbA. Results: Day-to-day fasting glycaemic variability was significantly associated with severe hypoglycaemia (HR 4.11, 95% CI 3.15, 5.35), MACE (HR 1.36, 95% CI 1.12, 1.65) and all-cause mortality (HR 1.58, 95% CI 1.23, 2.03) before adjustments. The increased risks of severe hypoglycaemia, MACE and all-cause mortality translate into 2.7-, 1.2- and 1.4-fold risk, respectively, when a patient's day-to-day fasting glycaemic variability measure is doubled. The significant relationships of day-to-day fasting glycaemic variability with severe hypoglycaemia and all-cause mortality were maintained after adjustments. However, the significant association with MACE was not maintained following adjustment for baseline characteristics with either baseline HbA (HR 1.19, 95% CI 0.96, 1.47) or the most recent HbA measurement throughout the trial (HR 1.21, 95% CI 0.98, 1.49). Conclusions/interpretation: Higher day-to-day fasting glycaemic variability is associated with increased risks of severe hypoglycaemia and all-cause mortality. Trial registration: NCT01959529 [ABSTRACT FROM AUTHOR]

Published
2018
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