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Audrey White,1 Elizabeth Buschur,2 Cara Harris,2 Michael L Pennell,3 Adam Soliman,2 Kathleen Wyne,2 Kathleen M Dungan2 1Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA; 2Division of Endocrinology, Diabetes & Metabolism, The Ohio State University, Columbus, OH, 43220, USA; 3The Ohio State University College of Public Health, Division of Biostatistics, Columbus, OH, 43210, USACorrespondence: Kathleen M Dungan, The Ohio State University, Division of Endocrinology, Diabetes and Metabolism, 5th Floor McCampbell Hall, 1581 Dodd Drive, Columbus, OH, 43210-1296, USA, Email kathleen.dungan@osumc.eduObjective: To evaluate the relationship between health literacy, social support, and self-efficacy as predictors of change in A1c and readmission among hospitalized patients with type 2 diabetes (T2D).Methods: This is a secondary analysis of patients with T2D (A1c > 8.5%) enrolled in a randomized trial in which health literacy (Newest Vital Sign), social support (Multidimensional Scale of Perceived Social Support), and empowerment (Diabetes Empowerment Scale-Short Form) was assessed at baseline. Multivariable models evaluated whether these concepts were associated with A1c reduction at 12 weeks (absolute change, % with > 1% reduction, % reaching individualized target) and readmission (14 and 30 days).Results: A1c (N=108) decreased > 1% in 60%, while individualized A1c target was achieved in 31%. After adjustment for baseline A1c and potential confounders, health literacy was associated with significant reduction in A1c (Estimate − 0.21, 95% CI − 0.40, − 0.01, p=0.041) and > 1% decrease in A1c (OR 1.37, 95% CI 1.08, 1.73, p=0.009). However, higher social support was associated with greater adjusted odds of reaching the individualized A1c target (OR 1.63, 95% CI 1.04, 2.55, p=0.32). Both higher empowerment (OR 0.23, 95% CI 0.08, 0.64, p=0.005) and social support (OR 0.57, 95% CI 0.36, 0.91, p=0.018) were associated with fewer readmissions by 14 days, but not 30 days.Conclusion: The study indicates that health literacy and social support may be important predictors of A1c reduction post-discharge among hospitalized patients with T2D. Social support and diabetes self-management skills should be addressed and early follow-up may be critical for avoiding readmissions.Clinical Trial: NCT03455985.Keywords: type 2 diabetes, hospital discharge, basal insulin, A1c, readmission, social support, self-efficacy, literacy

Currently patients with diabetes comprise up to 25-30% of the census of adult wards and critical care units in our hospitals. Although evidence suggests that avoidance of hyperglycemia (>180 mg/dL) and hypoglycemia (

Background: Glycemic variability (GV) is associated with hypoglycemia and possibly diabetes-related outcomes. We hypothesized that GV and glucose excursion risk may predict counterregulatory (CR) hormone responses to hypoglycemia.Research Design and Methods: This is a secondary analysis of a Diabetes Research in Children Network study containing continuous interstitial glucose monitoring records for 28 patients with type 1 diabetes between 3 to <8 or 12 to <18 years of age. GV and excursion measures, including continuous overall net glycemic action (CONGA), High Blood Glucose Index (HBGI), Low Blood Glucose Index (LBGI), and coefficient of variation (CV), were calculated 72 h prior to insulin-induced hypoglycemia. CR hormones were measured during the progressive fall in plasma glucose.Results: CV was inversely correlated with change in glucagon concentration (r=-0.41, P=0.046), but CONGA (log-transformed for better fit of the models) was not statistically significant in univariate analysis (r=-0.34, P=0.10). Other CR hormones were not significantly associated with measures of variability. In multivariate analysis, higher CONGA, but not CV, was associated with a smaller rise in glucagon following induced hypoglycemia (estimate=-9.73, P=0.048), independent of hemoglobin A1c, duration of diabetes, and insulin dose. HBGI, LBGI, and antecedent time spent in hypoglycemia were not significantly correlated with CR response to subsequent hypoglycemia.Conclusions: CV and CONGA may be predictors of impaired glucagon responses to insulin-induced hypoglycemia in patients with type 1 diabetes. Further study is indicated to characterize the role of GV and glycemic excursions on the defensive response to hypoglycemia. [ABSTRACT FROM AUTHOR]

BACKGROUND: Islet neogenesis associated protein (INGAP) has beta cell regenerating effects in experimental models. METHODS: Subjects with T1DM (N = 63) and T2DM (N = 126) received 300 or 600 mg/day of INGAP peptide in a 90 day, randomized, double-blind, placebo-controlled trial. RESULTS: In T1DM, on-treatment Arginine-stimulated C-peptide (AUC(0-30)) significantly increased from baseline in the 600 mg group (p = 0.0058 versus placebo); no significant changes were seen in the 300 mg group. In T2DM, stimulated C-peptide was significantly better preserved in the 600 mg group compared to placebo at day 120, 30 days after washout (p = 0.031 versus placebo), but did not reach statistical significance during treatment or in the 300 mg group. In T2DM, A1C decreased significantly more in the 600 mg group compared to placebo at day 90 (-0.94% versus -0.47%, respectively, p = 0.009) and day 120, 30 days after washout (-0.73% versus -0.24%, respectively, p = 0.013). This was accompanied by significant reductions in mean glucose. No difference from placebo was detected in the 300 mg group or in T1DM. Injection site reactions were the most common adverse event, occurring in 8 (36%) of placebo, 19 (90%) of 300 mg, and 15 (75%) of 600 mg groups (T1DM) and 14 (33%) of placebo, 27 (64%) of 300 mg, and 29 (69%) of 600 mg groups (T2DM). CONCLUSIONS: INGAP peptide increases C-peptide secretion in T1DM and improves glycaemic control in T2DM. Longer-term exposure, more frequent dosing, better tolerated formulations or combination with other therapies may be necessary to achieve optimal clinical response. [ABSTRACT FROM AUTHOR]

Background: Patients with diabetes often do not reach recommended lipid targets. This may be in part due to the difficulty of obtaining fasting measurements.Objective: To compare fasting and nonfasting low density lipoprotein cholesterol (LDLc), non-high density lipoprotein cholesterol (non-HDLc), and low density lipoprotein particle number (LDLP) patients with type 2 diabetes (T2DM) and to determine the effect on assessing treatment success.Research Design and Methods: Standard lipids and NMR lipoproteins were analyzed at baseline and following a standardized meal in patients with T2DM.Outcomes Measures: Primary outcomes were agreement between fasting and nonfasting LDLc, non-HDLc, and LDLP and the agreement between these measures for determining fasting LDLc treatment success. Direct LDL cholesterol (DLDL) was a secondary outcome.Results: Of 31 patients, only 56% of LDLc measurements were equivalent in the fasting and nonfasting states, compared to 97% of DLDLc (p < 0.0001), 94% of non-HDLc (p < 0.001) and 77% of LDLP (p < 0.01) measurements. Assessment of nonfasting lipid values would have resulted in misclassification by treatment target in 15% of LDLc, 3.2% of DLDLc assessments (p < 0.05 vs. LDLc), and 0% of non-HDLc and LDLP assessments (p < 0.01 vs. LDLc). Nonfasting LDLc, non-HDLc, LDLP, and DLDLc correctly identified fasting LDLc classification in 23/27 (85%), 26/30 (87%, p = NS vs. nonfasting LDLc), 16/30 (53%, p < 0.001 vs. nonfasting LDLc), and 24/30 (80%, p = NS vs. nonfasting LDLc), respectively.Conclusions: If confirmed in larger studies, nonfasting non-HDLc may be a suitable proxy for fasting LDLc in patients with T2DM. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the Ohio Diabetes Quality Improvement Project (QIP) equity aim to reduce the percentage of Non-Hispanic Black (NHB) and Hispanic patients with A1C >9% by ≥20% over 2 years., Research Design and Methods: The Ohio Department of Medicaid, Ohio Colleges of Medicine Government Resource Center, Ohio Medicaid managed care plans, and seven medical schools in Ohio formed the Diabetes QIP collaborative using the collective impact model to improve diabetes outcomes and equity in 20 practices across 11 health systems. The quality improvement (QI) strategies included data audit and feedback, peer-to-peer learning, QI coaching/practice facilitation, and subject matter expert consultation through coaching calls, monthly webinars, and annual virtual learning sessions. Electronic health record data were collected for preintervention (2019-2020) and intervention (2020-2022) periods. Assessments of improvements in A1C were based on prevalence of A1C >9% from preintervention, year 1, and year 2 with stratification by race and ethnicity., Results: The Diabetes QIP included 7,689 (54% female) sociodemographically diverse patients, self-identifying as non-Hispanic White (NHW) (42%), NHB (43%), Hispanic (8%), non-Hispanic Asian (4%), or other (3%). In year 2 compared with baseline, there were decreases in the proportion of patients with A1C >9% among NHW, NHB, and Hispanic patients (NHW from 19% to 12% [37% reduction], NHB 23% to 18% [22% reduction], and Hispanic 29% to 23% [20% reduction])., Conclusions: The Ohio Diabetes QIP, focused on multisector collaborative approaches, reduced the percentage of patients with A1C >9% by ≥20% among NHW, NHB, and Hispanic populations. Given the persistence of disparities, further equity-focused refinements are warranted to address disparities in diabetes control., (© 2024 by the American Diabetes Association.)

Purpose of Review: Diabetes mellitus (DM) is relatively common following acute pancreatitis (AP), even after mild acute pancreatitis (MAP), the most frequent AP presentation, in which there is no overt beta cell injury. Post-AP related diabetes is widely misdiagnosed, resulting in potentially inappropriate treatment and worse outcomes than type 2 diabetes (T2D). Thus, it is important to understand risk across the spectrum of AP severity., Recent Findings: Biological mechanisms are unclear and may include local and systemic inflammation leading to beta cell dysfunction and insulin resistance, altered gut barrier and/or gut peptides and possibly islet autoimmunity, though no studies have specifically focused on MAP. While studies examining clinical risk factors on MAP exclusively are lacking, there are studies which include MAP. These studies vary in scientific rigor, approaches to rule out preexisting diabetes, variable AP severity, diagnostic testing methods, and duration of follow-up. Overall, disease related factors, including AP severity, as well as established T2D risk factors are reported to contribute to the risk for DM following AP., Summary: Though numerous studies have explored risk factors for DM after AP, few studies specifically focused on MAP, highlighting a key knowledge gap that is relevant to the majority of patients with AP., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Clinical trials have demonstrated the efficacy and safety of hybrid closed-loop (HCL) systems, yet few studies have compared outcomes in the real-world setting., Method: This retrospective study analyzed patients from an academic endocrinology practice between January 1, 2018, and November 18, 2022. The inclusion criteria were diagnosis code for type I diabetes (T1D), >18 years of age, new to any HCL system [Medtronic 670G/770G (MT), Tandem Control IQ (CIQ), or Omnipod 5 (OP5)], and availability of a pump download within three months. The outcomes included %time in range (TIR) of 70 to 180 mg/dL, %time below range (TBR) <70 mg/dL at 90 days, and HbA1c for 91 to 180 days., Result: Of the 176 participants, 47 were MT, 74 CIQ, and 55 OP5. Median (25%, 75%) change in HbA1c was -0.1 (-0.8, 0.3), -0.6 (-1.1, -0.15), and -0.55 (-0.98, 0)% for MT, CIQ, and OP5, respectively, ( P = .04). TIR was 70 (57, 76), 67 (59, 75), and 68 (60, 76)% ( P = .95) at 90 days while TBR was 2 (1, 3), 1 (0, 2), and 1 (0, 1)%, respectively, ( P = .002). The %time in automated delivery was associated with TIR and change in HbA1c. After controlling other factors including %time in automated delivery, HCL type was not an independent predictor of change in HbA1c nor TIR but remained a significant predictor of TBR., Conclusion: There were significant reductions in HbA1c in CIQ and OP5. TIR was similar across pumps, but TBR was highest with MT. The %time in automated delivery likely explains differences in change in HbA1c but not TBR between HCL systems., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Kathleen Dungan reports research funding from Dexcom, Abbott, Insulet, Viacyte, and Sanofi; consulting from Dexcom, Eli Lilly, Insulet, Oppenheimer, and Elsevier; honoraria from Academy for Continued Healthcare Learning Med Learning Group and Medscape; and royalties from UpToDate. All other authors declare no conflicts of interest.

Background: Hypoglycemia from overtreatment is a serious but underrecognized complication among older adults with type 2 diabetes. However, diabetes treatment is seldom deintensified. We assessed the effectiveness of a Clinical Decision Support (CDS) tool and shared decision-making (SDM) in decreasing the number of patients at risk for hypoglycemia and reducing the impact of non-severe hypoglycemic events., Methods: HypoPrevent was a pre-post, single arm study at a five-site primary care practice. We identified at-risk patients (≥65 years-old, with type 2 diabetes, treated with insulin or sulfonylureas, and HbA 1c  < 7.0%). During three clinic visits over 6 months, clinicians used the CDS tool and SDM to assess hypoglycemic risk, set individualized HbA 1c goals, and adjust use of hypoglycemic agents. We assessed the number of patients setting individualized HbA 1c goals or modifying medication use, changes in the population at risk for hypoglycemia, and changes in impact of non-severe hypoglycemic events using a validated patient-reported outcome tool (TRIM-HYPO)., Results: We enrolled 94 patients (mean age-74; mean HbA 1c (±SD)-6.36% ± 0.43), of whom 94% set an individualized HbA 1c goal at either the baseline or first follow-up visit. Ninety patients completed the study. Insulin or sulfonylurea use was decreased or eliminated in 20%. An HbA 1c level before and after goal setting was obtained in 53% (N = 50). Among these patients, the mean HbA 1c increased 0.53% (p < 0.0001) and the number of patients at-risk decreased by 46% (p < 0.0001). Statistically significant reductions in the impact of hypoglycemia during daily activities occurred in both the total score and each functional domain of TRIM-HYPO., Conclusions: In a population of older patients at risk for hypoglycemia, the use of a CDS tool and SDM reduced the population at risk and decreased the use of insulin and sulfonylureas. Using a patient-reported outcome tool, we demonstrated significant reductions in the impact of hypoglycemia on daily life., (© 2023 The American Geriatrics Society.)

We used a collective impact model to form a statewide diabetes quality improvement collaborative to improve diabetes outcomes and advance diabetes health equity. Between 2020 and 2022, in collaboration with the Ohio Department of Medicaid, Medicaid Managed Care Plans, and Ohio's seven medical schools, we recruited 20 primary care practices across the state. The percentage of patients with hemoglobin A1c greater than 9% improved from 25% to 20% over two years. Applying our model more broadly could accelerate improvement in diabetes outcomes. ( Am J Public Health. 2023;113(12):1254-1257. https://doi.org/10.2105/AJPH.2023.307410).

Diabetes Technology Society organized an expert consensus panel to develop metrics for research in the use of continuous glucose monitors (CGMs) in a hospital setting. The experts met virtually in small groups both before and after an April 13, 2023 virtual meeting of the entire panel. The goal of the panel was to develop consensus definitions in anticipation of greater use of CGMs in hospital settings in the future. Establishment of consensus definitions of inpatient analytical metrics will be easier to compare outcomes between studies. Panelists defined terms related to 10 dimensions of measurements related to the use of CGMs including (1) hospital hypoglycemia, (2) hospital hyperglycemia, (3) hospital time in range, (4) hospital glycemic variability, (5) hospital glycemia risk index, (6) accuracy of CGM devices and reference methods for CGMs in the hospital, (7) meaningful time blocks for hospital glycemic goals, (8) hospital CGM data sufficiency, (9) using CGM data for insulin dosing, and (10) miscellaneous factors. The panelists voted on 51 proposed recommendations. Based on the panel vote, 51 recommendations were classified as either strong (43) or mild (8). Additional research is needed on CGM performance in the hospital. This consensus report is intended to support that type of research intended to improve outcomes for hospitalized people with diabetes., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: EKS was partially supported by the VA MERIT award (#1I01CX001825) and CSP #2002 from the United States (U.S.) Department of Veterans Affairs. EKS has received unrestricted research support from Dexcom, MannKind Cooperation and Tandem (to Baltimore VA Medical Center and to University of Maryland) for the conduction of clinical trials. EKS has received fees from the Medscape and the Endocrine Society (ESAP). JAA receives research funding from Abbott Diabetes. LB has product and research support for investigator-initiated studies from Dexcom and Ypsomed in addition to speaker honoraria from Dexcom and Ypsomed. GD has received research support from Insulet and has consulted for Medscape. KMD received research support from Abbott, Dexcom, Sanofi, Viacyte, and Insulet, consulting fees from Dexcom, Eli Lilly, advisory board fees from Elsevier, and honoraria from UpToDate, Medscape, Academy for Continued Healthcare Learning, Med Learning Group, Cardiometabolic Health Congress. FJP has received research support: Dexcom, Insulet, Tandem, Ideal Medical technologies, Novo Nordisk and consults for Dexcom. FJP has received research support: Dexcom, Insulet, Tandem, Ideal Medical technologies, Novo Nordisk and consulting fees from Dexcom. GEU is partly supported by research grants from National Institutes of Health (NIH/NATS UL 3UL1TR002378-05S2) from the Clinical and Translational Science Award program, and from National Institutes of Health and National Center for Research Resources (NIH/NIDDK 2P30DK111024-06). GEU has received research support (to Emory University) from Abbott, Bayer, Dexcom and Sanofi. DCK is a consultant for Better Therapeutics, EOflow, Integrity, Lifecare, Nevro, Novo, Sanofi, and Thirdwayv. The remaining authors have no disclosures.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data., Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation., Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals., Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Introduction: A post hoc analysis of the PIONEER 1-5 and 8 trials assessed the clinically relevant composite endpoints of HbA 1c (glycated haemoglobin) reduction ≥ 1% and body weight loss of ≥ 5% or ≥ 10% with orally administered semaglutide versus comparators., Methods: In the PIONEER trials, people with type 2 diabetes were randomised to orally administered semaglutide versus placebo (PIONEER 1, 4, 5 and 8), empagliflozin (PIONEER 2), sitagliptin (PIONEER 3) and liraglutide (PIONEER 4) for 26-78 weeks. This analysis assessed the proportion of people achieving an HbA 1c reduction of ≥ 1% and body weight loss of ≥ 5% at week 26 and at end of treatment, and the proportion of people achieving an HbA 1c reduction of ≥ 1% and body weight loss of ≥ 10% at end of treatment., Results: Overall, 3506 people in PIONEER 1-5 and 8 were included. At week 26 and at end of treatment, odds of achieving the composite endpoint of an HbA 1c reduction of ≥ 1% and body weight loss of ≥ 5% were significantly greater with orally administered semaglutide 14 mg than with placebo (PIONEER 1, 4, 5 and 8; all p < 0.0001), empagliflozin 25 mg (PIONEER 2, p < 0.0001), sitagliptin 100 mg (PIONEER 3, p < 0.0001) and liraglutide 1.8 mg (PIONEER 4, p < 0.0001). Odds of achieving the composite endpoint of HbA 1c reduction of ≥ 1% and body weight loss of ≥ 10% at end of treatment were also significantly greater with orally administered semaglutide versus comparators., Conclusion: In PIONEER 1-5 and 8, odds of achieving clinically relevant reductions in both HbA 1c and body weight were significantly greater with orally administered semaglutide versus comparators., (© 2023. The Author(s).)

Purpose of Review: The aim of this review is to identify the implementation approaches, strategies, and outcomes for continuous glucose monitoring (CGM) in the intensive care unit (ICU). Medline and Web of Science databases were searched to report relevant literature published between September 12, 2016 and September 12, 2021. Implementation outcomes and strategies, defined by the Expert Recommendations for Implementing Change (ERIC) project, were extracted., Recent Findings: Of the 324 titles reviewed, 16 articles were included in the review. While no studies were identified as implementation research, 14 of 16 identified implementation strategies that aligned with ERIC definitions. Included studies described a multi-disciplinary approach. Clinical outcomes included Mean Absolute Relative Difference (MARD), ranging from 7.5 to 15.3%, and 33-71% reduction in frequency of point-of-care (POC) blood glucose monitoring (BGM) using hybrid protocols. This scoping review provides valuable insight into the process of CGM implementation in the ICU. Continued research should include implementation outcomes to inform widespread utilization., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: The COVID-19 pandemic necessitated rapid implementation of continuous glucose monitoring (CGM) in the intensive care unit (ICU). Although rarely reported, perceptions from nursing staff who used the systems are critical for successful implementation and future expanded use of CGM in the inpatient setting., Methods: A 22-item survey focused on CGM use was distributed to ICU nurses at two large academic medical centers in the United States in 2022. Both institutions initiated inpatient CGM in the spring of 2020 using the same CGM+point of care (POC) hybrid protocol. The survey employed a 1- to 5-point Likert scale regarding CGM sensor insertion, accuracy, acceptability, usability, training, and perceptions on workload., Results: Of the 71 surveys completed, 68 (96%) nurses reported they cared for an ICU patient on CGM and 53% reported they had independently performed CGM sensor insertion. The ICU nurses overwhelmingly reported that CGM was accurate, reduced their workload, provided safer patient care, and was preferred over POC glucose testing alone. Interestingly, nearly half of nurses (49%) reported that they considered trend arrows in dosing decisions although trends were not included in the CGM+POC hybrid protocol. Nurses received training through multiple modalities, with the majority (80%) of nurses reporting that CGM training was sufficient and prepared them for its use., Conclusion: These results confirm nursing acceptance and preference for CGM use within a hybrid glucose monitoring protocol in the ICU setting. These data lay a blueprint for successful implementation and training strategies for future widespread use.

The use of diabetes technology, including insulin pumps, continuous glucose monitoring devices, and automated insulin delivery systems, has increased significantly in recent years. As more people with diabetes adopt technology in the outpatient setting, we are seeing these devices more frequently in the inpatient setting. This review offers best-practice guidelines for the continuation of personal diabetes technology use in the inpatient setting. It describes policy and guideline stipulations, roles and responsibilities, and device- and brand-specific considerations. Although these devices are not approved for inpatient use by the U.S. Food and Drug Administration, there is general expert consensus that the continuation of personal diabetes devices during hospitalization is appropriate for patients who have sufficient knowledge, are not critically ill, and retain sufficient mental capacity during an acute illness. Health care systems and inpatient providers need to understand the benefits and limitations of personal diabetes technology use during hospitalization., (© 2022 by the American Diabetes Association.)

Abstract: Acute pancreatitis (AP) is a disease characterized by an acute inflammatory phase followed by a convalescent phase. Diabetes mellitus (DM) was historically felt to be a transient phenomenon related to acute inflammation; however, it is increasingly recognized as an important late and chronic complication. There are several challenges that have prevented precisely determining the incidence rate of DM after AP and understanding the underlying mechanisms. The DREAM (Diabetes RElated to Acute Pancreatitis and its Mechanisms) Study is a prospective cohort study designed to address these and other knowledge gaps to provide the evidence needed to screen for, prevent, and treat DM after AP. In the following article, we summarize literature regarding the epidemiology of DM after AP and provide the rationale and an overview of the DREAM study., Competing Interests: E.A. is on the advisory board for Nestle. M.D.B. receives research support from Viacyte and Dexcom and is on advisory board for Insulet. K.M.D. receives research support from Sanofi, Viacyte, Abbott, and Dexcom; has consulting activities with Eli Lilly, Boehringer Ingelheim, Elsevier; and receives honoraria from UptoDate, Elsevier, Medscape, and Academy for Continued Healthcare Learning. C.E.F. receives research support from AbbVie and has consulting activities with Nestle. G.I.P. receives research support from AbbVie and has consulting activities with Nestle. C.S. is on the advisory board for Vertex Pharmaceuticals. F.G.S.T. receives research support from Dompé Pharmaceuticals and has consulting activities with Sanofi, Eli Lilly, and AstraZeneca. The other authors declare no conflict of interest., (Copyright © 2022 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a “work of the United States Government” for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government.)

Objectives: The metabolic abnormalities that lead to diabetes mellitus (DM) after an episode of acute pancreatitis (AP) have not been extensively studied. This article describes the objectives, hypotheses, and methods of mechanistic studies of glucose metabolism that comprise secondary outcomes of the DREAM (Diabetes RElated to Acute pancreatitis and its Mechanisms) Study., Methods: Three months after an index episode of AP, participants without preexisting DM will undergo baseline testing with an oral glucose tolerance test. Participants will be followed longitudinally in three subcohorts with distinct metabolic tests. In the first and largest subcohort, oral glucose tolerance tests will be repeated 12 months after AP and annually to assess changes in β-cell function, insulin secretion, and insulin sensitivity. In the second, mixed meal tolerance tests will be performed at 3 and 12 months, then annually, and following incident DM to assess incretin and pancreatic polypeptide responses. In the third, frequently sampled intravenous glucose tolerance tests will be performed at 3 months and 12 months to assess the first-phase insulin response and more precisely measure β-cell function and insulin sensitivity., Conclusions: The DREAM study will comprehensively assess the metabolic and endocrine changes that precede and lead to the development of DM after AP., Competing Interests: K.M.D. has declared research support from Sanofi, Viacyte, Abbott, and Dexcom; consulting activities with Eli Lilly, Boehringer Ingelheim, Elsevier, and Dexcom; and honoraria from UptoDate, Medscape, Academy for Continued Healthcare Learning, and Cardiometabolic Health Congress. M.D.B. has declared research support from Viacyte and Dexcom and consulting activities with Insulet (advisory board). C.E.-M. has received research support from Lilly, Astellas Pharma, Bristol Myers Squibb, and Nimbus Therapeutics and consulting/advisory fees from Avotres, Inc, DiogenX, Isla Technologies, Provention Bio, Inc, MaiCell Therapeutics, and Dompe. R.E.P. has received grants (directed to his institution) from Hanmi Pharmaceutical Co, Ltd, Janssen, Metavention, Novo Nordisk, Poxel SA, and Sanofi; has received consulting fees (directed to his institution) from AstraZeneca, Corcept Therapeutics Incorporated, Glytec LLC, Hanmi Pharmaceutical Co, Ltd, Janssen, Merck & Co, Inc., Mundipharma, Novo Nordisk, Pfizer, Inc, Sanofi, Scohia Pharma, Inc, and Sun Pharmaceutical Industries; and has received support for attending meetings/travel (directed to his institution or to the travel provider) from AstraZeneca, Glytec LLC, Merck & Co, Inc, Mundipharma, Novo Nordisk, and Pfizer, Inc. E.K.S. has received honoraria from Medscape. C.S. has participated on an advisory board for Vertex Pharmaceuticals. F.G.S.T. has declared research support from Dompé Pharmaceuticals and previous consulting activities with Sanofi, Eli Lilly, and AstraZeneca. The other authors declare no conflict of interest., (Copyright © 2022 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a “work of the United States Government” for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government.)

Patients with diabetes are experts in the lived experience of self-management, making patient engagement beyond the role of research "subject" imperative for the creation of health care solutions that meaningfully address the problems they identify. We discuss our research team's relationship with our university's College Diabetes Network (CDN), an advocacy and support group for emerging adults with diabetes. Our collaborative research relationship has spanned three years, and multiple research studies with members serving as co-designers, consultants, and co-investigators. We discuss the CDN's role in two particular studies in which CDN members made substantive contributions to study design, instrument adaptation, and interpretation of findings. Key CDN members played a larger role in research activities and facilitated sustained engagement with the larger university CDN chapter. Barriers encountered included navigating research regulatory requirements while engaging CDN members in research and facilitating sustained engagement as CDN membership changes.

Cardiovascular disease (CVD) is the primary cause of morbidity and mortality in patients with type 2 diabetes (T2D). This review examines the impact of cardiovascular outcome trials (CVOTs) on clinical practice. To date, all CVOTs have shown non-inferiority versus placebo (both added to standard of care) against a primary endpoint of 3- or 4-point major adverse cardiovascular event (MACE), confirming CV safety of these treatments. Additionally, some CVOTs have shown superiority to placebo against the same MACE endpoint, suggesting a cardioprotective action for these treatments. This is reflected in guideline updates, which primary care physicians should consider when personalizing treatments., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Hyperglycemia is a common phenomenon in critically ill patients, even in those without diabetes. Two landmark studies established the benefits of tight glucose control (blood glucose target 80-110 mg/dL) in surgical and medical patients. Since then, literature has consistently demonstrated that both hyperglycemia and hypoglycemia are independently associated with increased morbidity and mortality in a variety of critically ill patients. However, tight glycemic control has subsequently come into question due to risks of hypoglycemia and increased mortality. More recently, strategies targeting euglycemia (blood glucose ≤180 mg/dL) have been associated with improved outcomes, although the risk of hypoglycemia remains. More complex targets (ie, glycemic variability and time within target glucose range) and the impact of individual patient characteristics (ie, diabetic status and prehospital glucose control) have more recently been shown to influence the relationship between glycemic control and outcomes in critically ill patients. Although our understanding has increased, the optimal glycemic target is still unclear and glucose management strategies may require adjustment for individual patient characteristics. As glucose management increases in complexity, we realize that traditional means of using meters and strips and paper insulin titration algorithms are potential limitations to our success. To achieve these complex goals for glycemic control, the use of continuous or near-continuous glucose monitoring combined with computerized insulin titration algorithms may be required. The purpose of this review is to discuss the evidence surrounding the various domains of glycemic control and the emerging data supporting the need for individualized glucose targets in critically ill patients.

Objective: Clinical trial data demonstrates improved glycemic control with hybrid close loop (HCL) insulin delivery systems, yet limited real-world data exists. Data from the inaugural cohort of patients initiating a HCL system (Medtronic MiniMed ™ 670G, Medtronic Canada, Brampton, ON) at a university medical center was used to examine real-world utilization and glycemic control following a standardized implementation process. Methods: Data from 34 adult patients with type 1 diabetes were obtained from pump downloads at 4 time points: ( 1 ) previous insulin pump, ( 2 ) HCL in manual-mode, ( 3 ) 2 weeks after HCL auto-mode transition, and ( 4 ) 6 to 12 weeks after initiation of HCL. In-person training by certified diabetes educators was performed across 3 sessions with phone and electronic messaging following auto-mode start. Results: Mean self-monitored blood glucose (SMBG) per day increased from 5.15 baseline to 6.49 at 6 to 12 weeks ( P <.05) with 3.26 sensor calibrations per day. Time-in-auto-mode was 79.3% at 2 weeks and 72.3% at final follow-up, with 82% of patients spending >50% of time in auto-mode. There were 8.2 auto-mode exits over the final 14-day download. Time-in-target was 67.3% in manual-mode, 73.4% at 2 weeks ( P = .09), and 71.7% by 6 to 12 weeks ( P = .06). Hemoglobin A1c (HbA1c) decreased by 0.51% ( P = .02), while total daily dose and % basal did not change. Patients with HbA1c <7.0% (53 mmol/mol) at baseline spent more time-in-target than those with HbA1c ≥7.0% (53 mmol/mol; 78.0% versus 67.5%) despite spending less time-in-auto-mode (66.5% versus 74.8%). Conclusion: These data illustrate real-world implementation of HCL technology using a structured education program within a major medical center. Overall benefit may vary based on baseline characteristics such as HbA1c. Abbreviations: CDE = certified diabetes educator; HbA1c = hemoglobin A1c; HCL = hybrid closed loop; SMBG = self-monitored blood glucose.

Hospitalized patients with heart failure (HF) undergoing bedside glucose monitoring with subcutaneous insulin orders were retrospectively identified over 2 years. Hypoglycaemia was defined as any glucose value <3.9 mmol/L (70 mg/dL) within 24 hours of admission (Hypo 1day ) or throughout the hospitalization (Hypo T ) or any glucose value <2.2 mmol/L (40 mg/dL) throughout the hospitalization (Hypo Severe ). A total of 13 424 patients were included, of whom 2484 had HF. Patients with HF were more likely to have Hypo 1day (9.1% vs 7.0%, P = .0003), Hypo T (28% vs 18.5%, P < .0001), or Hypo Severe (3.4% vs 2.1%, P = .0001). After controlling for other variables, the odds of Hypo 1day were similar between the HF and non-HF groups (odds ratio [OR] 1.14, 95% CI 0.94-1.39, P = .18, fully adjusted model), slightly lower for Hypo T (OR 0.85, 95% CI 0.73-0.99, P = .03, fully adjusted model), and similar for Hypo Severe (OR 1.25, 95% CI 0.91-1.70, P = .17). Hypo 1day , Hypo T and Hypo Severe were all associated with increased mortality; there was no evidence of an interaction by HF status. Hypoglycaemia occurred at a similar or lower frequency in hospitalized patients with HF compared to those without HF. Hypoglycaemia was associated with increased hospital mortality, regardless of HF status., (© 2018 John Wiley & Sons Ltd.)

Objective: Few randomized controlled trials have focused on the optimal management of patients with type 2 diabetes (T2D) during the transition from the inpatient to outpatient setting. This multicenter open-label study explored a discharge strategy based on admission hemoglobin A1c (HbA1c) to guide therapy in general medicine and surgery patients with T2D., Methods: Patients with HbA1c ≤7% (53 mmol/mol) were discharged on sitagliptin and metformin; patients with HbA1c between 7 and 9% (53-75 mmol/mol) and those >9% (75 mmol/mol) were discharged on sitagliptinmetformin with glargine U-100 at 50% or 80% of the hospital daily dose. The primary outcome was change in HbA1c at 3 and 6 months after discharge., Results: Mean HbA1c on admission for the entire cohort (N = 253) was 8.70 ± 2.3% and decreased to 7.30 ± 1.5% and 7.30 ± 1.7% at 3 and 6 months ( P<.001). Patients with HbA1c <7% went from 6.3 ± 0.5% to 6.3 ± 0.80% and 6.2 ± 1.0% at 3 and 6 months. Patients with HbA1c between 7 and 9% had a reduction from 8.0 ± 0.6% to 7.3 ± 1.1% and 7.3 ± 1.3%, and those with HbA1c >9% from 11.3 ± 1.7% to 8.0 ± 1.8% and 8.0 ± 2.0% at 3 and 6 months after discharge (both P<.001). Clinically significant hypoglycemia (<54 mg/dL) was observed in 4%, 4%, and 7% among patients with a HbA1c <7%, 7 to 9%, and >9%, while a glucose <40 mg/dL was reported in <1% in all groups., Conclusion: The proposed HbA1c-based hospital discharge algorithm using a combination of sitagliptin-metformin was safe and significantly improved glycemic control after hospital discharge in general medicine and surgery patients with T2D., Abbreviations: BG = blood glucose; DPP-4 = dipeptidyl peptidase-4; eGFR = estimated glomerular filtration rate; HbA1c = hemoglobin A1c; T2D = type 2 diabetes.

Background: Although studies demonstrate techniques to limit hypoglycaemia in critically ill patients, there are limited data supporting methods to improve management of existing hypoglycaemia., Objective: Assess the impact and sustainability of a computerised, three tiered, nurse driven protocol for hypoglycaemia treatment., Design: Retrospective pre and post protocol study., Setting: Neurosciences and surgical intensive care units at a tertiary academic medical centre., Measurements: Patients with a hypoglycaemic episode were included during a pre-protocol or post-protocol implementation period. An additional six-month cohort was evaluated to assess sustainability., Results: Fifty-four patients were included for evaluation (35 pre- and 19 post-protocol); 122 patients were included in the sustainability cohort. Hypoglycaemia treatment significantly improved in the post-protocol cohort (20% vs. 52.6%, p=0.014); with additional improvement to 79.5% in the sustainability cohort. Time to follow-up blood glucose was decreased after treatment from 122 [Q1-Q3: 46-242] minutes pre-protocol to 25 [Q1-Q3: 9-48] minutes post protocol (p<0.0001). This reduction was maintained in the sustainability cohort [median of 29min (Q1-Q3: 20-51)]., Conclusion: Implementation of a nurse-driven, three-tiered protocol for treatment of hypoglyacemia significantly improved treatment rates, as well as reduced time to recheck blood glucose measurement. These benefits were sustained during a six-month period after protocol implementation., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Purpose: To determine prevalence, predictors, and outcomes of steroid-induced hyperglycemia in hospitalized patients with hematologic malignancies METHODS: We performed retrospective analysis of patients who were hospitalized on any of the four hematology services or bone marrow transplant service and who received systemic steroids during a 2-month period. Mean glucoses for days 1-4 and maximum glucose were calculated and the total daily steroid dose was converted to equivalent dose of dexamethasone. Hyperglycemia was defined as any glucose >9.917 mmol/l during days 1-4. We examined associations between variables using Spearman's correlations and multivariable linear regression RESULTS: 168 patients were included in the analysis. Mean age was 57.1 ± 14.4 years with 59% males and 90% Caucasians. The prevalence of hyperglycemia was 39%. Eight patients received intravenous insulin. In patients without diabetes, steroid dose equivalent to >12 mg dexamethasone and longer acting steroids caused greater degree of hyperglycemia compared to a dose <12 mg. Maximum glucose was a predictor of hospital length of stay among patients without diabetes (but not those with diabetes), and with acute leukemia or stem cell transplant (but not other hematologic malignancies). There were no significant differences in in mortality or other outcomes between the groups with and without hyperglycemia CONCLUSIONS: Hyperglycemia is common in patients with hematologic malignancies, which require frequent corticosteroids. Higher steroid dose and long-acting steroids caused a greater degree of hyperglycemia. Maximum glucose was a predictor of length of stay but this was only significant for patients without diabetes, those with acute hematologic malignancy or stem cell transplant.

Background: The role of incretin-based drugs in the treatment of patients with type 2 diabetes admitted to hospital has not been extensively assessed. In this study, we compared the safety and efficacy of a dipeptidyl peptidase-4 inhibitor (sitagliptin) plus basal insulin with a basal-bolus insulin regimen for the management of patients with type 2 diabetes in general medicine and surgery in hospitals., Methods: We did a multicentre, prospective, open-label, non-inferiority randomised clinical trial (Sita-Hospital) in five hospitals in the USA, enrolling patients aged 18-80 years with type 2 diabetes and a random blood glucose concentration of 7·8-22·2 mmol/L who were being treated with diet or oral antidiabetic drugs or had a total daily insulin dose of 0·6 units per kg or less, admitted to general medicine and surgery services. We randomly assigned patients (1:1) to receive either sitagliptin plus basal glargine once daily (the sitagliptin-basal group) or a basal-bolus regimen with glargine once daily and rapid-acting insulin lispro or aspart before meals (the basal-bolus group) during the hospital stay. All other antidiabetic drugs were discontinued on admission. The randomisation was achieved by computer-generated tables with block stratification according to randomisation blood glucose concentrations (ie, higher or lower than 11·1 mmol/L). The primary endpoint of the trial was non-inferiority in mean differences between groups in their daily blood glucose concentrations during the first 10 days of therapy (point-of-care measurements; non-inferiority was deemed a difference <1 mmol/L). The safety endpoints included hypoglycaemia and uncontrolled hyperglycaemia leading to treatment failure. All participants who received at least one dose of study drug were included in the analysis. This study is registered with ClinicalTrials.gov, number NCT01845831., Findings: Between Aug 23, 2013, and July 27, 2015, we recruited 279 patients, and randomly assigned 277 to treatment; 138 to sitagliptin-basal and 139 to basal-bolus. The length of stay in hospital was similar for both groups (median 4 days [IQR 3-8] vs 4 [3-8] days, p=0·54). The mean daily blood glucose concentration in the sitagliptin-basal group (9·5 mmol/L [SD 2·7]) was not inferior to that in the basal-bolus group 9·4 mmol/L [2·7]) with a mean blood glucose difference of 0·1 mmol/L (95% CI -0·6 to 0·7). No deaths occurred in this trial. Treatment failure occurred in 22 patients (16%) in the sitagliptin-basal group versus 26 (19%) in the basal-bolus group (p=0·54). Hypoglycaemia occurred in 13 patients (9%) in the sitagliptin-basal group and in 17 (12%) in the basal-bolus group (p=0·45). No differences in hospital complications were noted between groups. Seven patients (5%) developed acute kidney injury in the sitagliptin-basal group and six (4%) in the basal-bolus group. One patient (0·7%) developed acute pancreatitis (in the basal-bolus group)., Interpretation: The trial met the non-inferiority threshold for the primary endpoint, because there was no significant difference between groups in mean daily blood glucose concentrations. Treatment with sitagliptin plus basal insulin is as effective and safe as, and a convenient alternative to, the labour-intensive basal-bolus insulin regimen for the management of hyperglycaemia in patients with type 2 diabetes admitted to general medicine and surgery services in hospital in the non-intensive-care setting., Funding: Merck., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Purpose: Hypoglycemia is associated with increased mortality, but the role of its etiology is unclear. This study aimed to examine the impact of hypoglycemia etiology on mortality risk among critically ill patients., Methods: This single-center, retrospective, cohort study evaluated adult patients admitted to the medical or surgical intensive care unit, who experienced medication-induced or spontaneous hypoglycemia (blood glucose <70 mg/dL) during intensive care unit admission. Patients who became hypoglycemic following receipt of glucose-lowering therapy within a predefined time period were categorized in the medication-induced group. Periods were determined for each agent based on expected pharmacokinetics in critically ill patients. Patients who became hypoglycemic with no identifiable cause were categorized in the spontaneous group. Primary analysis compared medication-induced and spontaneous hypoglycemia with a primary endpoint of all-cause hospital mortality. Secondary analyses stratified patients by diabetes, severity of hypoglycemia, and glycemic variability., Results: A total of 642 patients were eligible for inclusion (305 medication-induced and 337 spontaneous). When adjusted for covariates, no difference in hospital mortality was observed based on hypoglycemia etiology (odds ratio, 1.22 [0.77-1.93]; P=.39). Regardless of etiology, hypoglycemic severity, frequency, and glycemic variability were significantly associated with higher odds of hospital mortality. Hypoglycemic etiology did not impact hospital mortality when patients were stratified by presence or absence of diabetes., Conclusions: Medication-induced hypoglycemia appears to be equally harmful as spontaneous hypoglycemia during critical illness. Future studies should aim to identify strategies to minimize hypoglycemia regardless of etiology, while also minimizing glycemic variability associated with hypoglycemia treatment., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: Electronic messaging (EM) is increasingly utilized among patients with diabetes, but it is unclear whether it is associated with improved glycemic control, hospital admissions, or emergency visits., Methods: Patients who were seen over a 1 year period at an academic endocrinology clinic with a diagnosis of diabetes were categorized according to portal activation and whether EMs were actually sent. The association between EM and HbA1c and inpatient or emergency department (ED) visits was further characterized using multivariable (MV) linear or logistic regression models., Results: A total of 867 patients were using EM (active user group), 1207 patients had activated the patient portal but did not use the EM function (active nonuser group), and 1542 patients had not activated the patient portal (inactive group). There were important disparities in race, age, income, and other variables across groups. The HbA1c was 7.7% ± 1.5%, 8.4% ± 1.9%, and 8.2% ± 1.8% among the active user, active nonuser, and inactive groups, respectively (P < 0.0001). After controlling for other factors, EM was associated with a 0.25% (SE 0.04%) lower HbA1c compared with the active nonuser group and a 0.19% (SE 0.04%) lower HbA1c compared with the inactive group (P < 0.0001 for both). However, EM frequency was not associated with HbA1c. EM use was not associated with inpatient or ED visits in MV analysis., Conclusions: EM use was associated with improved glycemic control, even after controlling for electronic portal access and other variables, but not with hospitalizations or emergency visits. EM frequency was not associated with glycemic control, justifying the need for prospective studies., Competing Interests: Author Disclosure Statement No competing financial interests exist.

Aims: To evaluate the safety and efficacy of once-weekly dulaglutide 1.5 mg, a long-acting glucagon-like peptide-1 receptor agonist, compared with placebo in patients with type 2 diabetes (T2D) on glimepiride monotherapy., Methods: This phase III, randomized (4 : 1; dulaglutide:placebo), double-blind, placebo-controlled, 24-week study compared the safety and efficacy of once-weekly dulaglutide 1.5 mg with placebo in sulphonylurea-treated (≥half-maximal dose, stable ≥3 months) patients (N = 300) with T2D and inadequate glycaemic control [glycated haemoglobin (HbA1c) ≥7.5 and ≤9.5% (≥58 mmol/mol and ≤80 mmol/mol)]. Analysis was carried out according to intention-to-treat., Results: At baseline, the mean participant age was 58 years; mean HbA1c was 8.4% (68 mmol/mol) and mean weight was 85.5 kg. Dulaglutide 1.5 mg was superior to placebo at 24 weeks for HbA1c reduction from baseline with a between-group HbA1c difference of -1.3% [95% confidence interval (CI) -1.6, -1.0] or -14 mmol/mol (95% CI -17, -11); p < 0.001. A greater proportion of participants in the dulaglutide group reached an HbA1c level of <7.0% (53 mmol/mol) compared with placebo (55.3% vs 18.9%; p < 0.001). Dulaglutide significantly decreased fasting serum glucose from baseline compared with placebo (between-group difference -1.86 mmol/l (95% CI -2.58, -1.14) or -33.54 mg/dl (95% CI -46.55, -20.53); p < 0.001. Weight was decreased significantly from baseline in the dulaglutide group (p < 0.001); the between-group difference was not significant. The most common treatment-emergent adverse events for dulaglutide 1.5 mg were gastrointestinal: nausea (10.5%), diarrhoea (8.4%) and eructation (5.9%). Total hypoglycaemia was higher with dulaglutide 1.5 mg vs placebo (2.37 and 0.07 events/participant/year, respectively; p = 0.025). No severe hypoglycaemia was reported., Conclusions: Once-weekly dulaglutide 1.5 mg had a favourable benefit/risk profile when added to glimepiride monotherapy., (© 2016 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

Aim: To compare the effectiveness of dulaglutide 1.5 and 0.75 mg with active comparators and placebo with regard to a composite endpoint of glycated haemoglobin (HbA1c), weight and hypoglycaemia, using post hoc analyses., Methods: A logistic regression analysis was performed on the intention-to-treat population, using data from the last observation carried forward, and the composite endpoint of HbA1c <7.0% (53 mmol/mol), no weight gain (≤0 kg) and no hypoglycaemia (glucose <3.0 mmol/l or severe hypoglycaemia) after 26 weeks for each trial in the AWARD programme separately., Results: At 26 weeks, within each study, 37-58% of patients on dulaglutide 1.5 mg, 27-49% of patients on dulaglutide 0.75 mg, and 9-61% of patients on active comparators achieved the composite endpoint. Significantly more patients reached the composite endpoint with dulaglutide 1.5 mg than with metformin, sitagliptin, exenatide twice daily or insulin glargine: odds ratio (OR) 1.5 [95% confidence interval (CI) 1.0, 2.2; p < 0.05], OR 4.5 (95% CI 3.0, 6.6; p < 0.001), OR 2.6 (95% CI 1.8, 3.7; p < 0.001) and OR 7.4 (95% CI 4.4, 12.6; p < 0.001), respectively, with no difference between dulaglutide 1.5 mg and liraglutide 1.8 mg. In addition, significantly more patients reached the composite endpoint with dulaglutide 0.75 mg than with sitagliptin or insulin glargine: OR 3.3 (95% CI 2.2, 4.8; p < 0.001) and OR 4.5 (95% CI 2.7, 7.8; p < 0.001), respectively., Conclusions: Dulaglutide is an effective treatment option, resulting in a similar or greater proportion of patients reaching the HbA1c target of <7.0% (53 mmol/mol), without weight gain or hypoglycaemia compared with active comparators., (© 2015 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

Patients with hematologic malignancies are at high risk for hyperglycemia due to factors such as frequent exposure to glucocorticoids, immunosuppressants, total parenteral nutrition, and medical stress. Hyperglycemia in these patients has been associated with poor outcomes including increased risk of infection, organ dysfunction, durability of remission, graft-versus-host disease, and mortality. However, the appropriate glucose targets are not well established, and there are few prospective data assessing whether glucose control improves outcomes. HbA1c should be interpreted with caution in patients with hematologic malignancies, due to inaccuracies imposed by disordered hematopoiesis and frequent transfusions, and short-term perturbations imposed by acute illness or medications. Management of diabetes or glucocorticoid-induced hyperglycemia in the hospital generally requires insulin therapy, which is tailored based upon nutritional needs, baseline glucose control, and concomitant factors such as type and dose of glucocorticoid administration. Close follow-up and adjustment of therapy, ideally with the assistance of patient self-titration algorithms, is required after discharge. Patients are at increased long-term risk for developing diabetes and therefore should undergo regular screening.

Background: The study aims to determine whether the route of insulin administration influences glycaemic variability and inflammatory or neurohormonal markers in patients with type 2 diabetes and congestive heart failure (CHF) exacerbation., Methods: Patients (n = 65) were randomized to intravenous (IV) insulin (duration 48 h) or subcutaneous (SQ) insulin. Inflammatory cytokines and markers of lipid oxidation, high-frequency heart rate variability (n = 27) and cardiac impedance (pre-ejection period, n = 28) were used to estimate parasympathetic and sympathetic tone in patients with valid cardiac data. Glycaemic variability was measured using a continuous glucose monitor., Results: Mean glucose was lower (7.7 ± 1.2 vs 9.4 ± 2.7 mmol/L, p = 0.004), coefficient of variation was higher (p = 0.03) and glycaemic lability index was similar on day 1 in the IV group compared with the SQ group, but groups were similar by day 2. The IV group had more confirmed hypoglycaemia (p = 0.005). There were no differences in hospital readmission or hospital length of stay between groups. There were no differences in CHF biomarkers, heart rate variability or pre-ejection period between groups. Increasing log glycaemic lability index was associated with lower on-treatment pre-ejection period (p = 0.03) while increasing coefficient of variation was associated with increasing brain natriuretic peptide (p = 0.004) and paroxonase-1 (p = 0.02). Other univariable analyses were not significant., Conclusions: There were modest, transient differences in glucose control between IV and SQ insulin in hospitalized CHF patients. However, the analyses do not support a link between insulin route and inflammatory markers or autonomic tone. Further study is needed to assess outcomes in hospitalized CHF patients., (Copyright © 2014 John Wiley & Sons, Ltd.)

Monitoring of glycemic control is a key component of the diabetes treatment plan. Patients who are not meeting targets often require more intensive monitoring, ranging from frequent self-monitored glucose to continuous glucose monitoring in order to facilitate medication and lifestyle changes. However, more intensive monitoring demands more training and a structured plan for interpretation and use of the data. Better patient and provider tools to support decision-making and progress toward an artificial pancreas may help to alleviate this burden., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: Dulaglutide and liraglutide, both glucagon-like peptide-1 (GLP-1) receptor agonists, improve glycaemic control and reduce weight in patients with type 2 diabetes. In a head-to-head trial, we compared the safety and efficacy of once-weekly dulaglutide with that of once-daily liraglutide in metformin-treated patients with uncontrolled type 2 diabetes., Methods: We did a phase 3, randomised, open-label, parallel-group study at 62 sites in nine countries between June 20, 2012, and Nov 25, 2013. Patients with inadequately controlled type 2 diabetes receiving metformin (≥1500 mg/day), aged 18 years or older, with glycated haemoglobin (HbA1c) 7·0% or greater (≥53 mmol/mol) and 10·0% or lower (≤86 mmol/mol), and body-mass index 45 kg/m(2) or lower were randomly assigned to receive once-weekly dulaglutide (1·5 mg) or once-daily liraglutide (1·8 mg). Randomisation was done according to a computer-generated random sequence with an interactive voice response system. Participants and investigators were not masked to treatment allocation. The primary outcome was non-inferiority (margin 0·4%) of dulaglutide compared with liraglutide for change in HbA1c (least-squares mean change from baseline) at 26 weeks. Safety data were collected for a further 4 weeks' follow-up. Analysis was by intention to treat. This study is registered with ClinicalTrials.gov, number NCT01624259., Findings: We randomly assigned 599 patients to receive once-weekly dulaglutide (299 patients) or once-daily liraglutide (300 patients). 269 participants in each group completed treatment at week 26. Least-squares mean reduction in HbA1c was -1·42% (SE 0·05) in the dulaglutide group and -1·36% (0·05) in the liraglutide group. Mean treatment difference in HbA1c was -0·06% (95% CI -0·19 to 0·07, pnon-inferiority<0·0001) between the two groups. The most common gastrointestinal adverse events were nausea (61 [20%] in dulaglutide group vs 54 [18%] in liraglutide group), diarrhoea (36 [12%] vs 36 [12%]), dyspepsia (24 [8%] vs 18 [6%]), and vomiting (21 [7%] vs 25 [8%]), with similar rates of study or study drug discontinuation because of adverse events between the two groups (18 [6%] in each group). The hypoglycaemia rate was 0·34 (SE 1·44) and 0·52 (3·01) events per patient per year, respectively, and no severe hypoglycaemia was reported., Interpretation: Once-weekly dulaglutide is non-inferior to once-daily liraglutide for least-squares mean reduction in HbA1c, with a similar safety and tolerability profile., Funding: Eli Lilly and Company., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Background: Despite the plethora of data in the critical care setting, there are few studies to guide clinicians in the hospitalwide care of patients with hyperglycemia., Methods: Patients 18 years of age and older who had a discharge diagnosis code for diabetes were admitted between January 1, 2005, and December 31, 2010, and received an insulin infusion for any reason were included in the analysis. Patients were receiving noncritical care or cardiac care (with interchangeable critical and noncritical care capacity). The effect of an insulin infusion guideline introduced in 2006 with a target glucose of 5.6-8.3 mmol/L was measured. Hyperglycemic (> 11.1 mmol/L) and hypoglycemic (< 3.9 or < 2.8 mmol/L) events were analyzed using multivariable models., Results: After adjusting for age, gender, race, and nutrition, there was a significant decrease in time to first glucose < 8.3 mmol/L in hours (T8.3; p = .01) and hyperglycemia (p < .0001) in the year following implementation of the guideline in cardiac but not noncritical beds, which persisted through 2010. There was a significant decrease in hypoglycemic events by Year 3 in cardiac beds and by Year 5 in noncritical beds. Compared with patients who received nothing by mouth, patients eating discrete meals had significantly longer T8.3, greater variability, and more hyperglycemic and hypoglycemic events in cardiac and noncritical beds., Conclusions: Following the hospitalwide implementation of a nursing-run insulin infusion guideline, rapid, stable reduction in hyperglycemia was achieved in cardiac beds (having interchangeable ICU and non-ICU status), and the frequency of hypoglycemia steadily decreased over time in both cardiac and noncardiac beds. Oral intake and enteral feeding were associated with worse glycemic control.

Current guidelines advocate the use of insulin for the management of hyperglycemia in the hospital setting. However, insulin is limited by a narrow therapeutic window, frequent errors, a need for expertise and systems-based monitoring, and lack of specificity for metabolic abnormalities that occur during critical illness. As a result, non-insulin alternatives have garnered increasing interest for managing hyperglycemia in the hospital. However, non-insulin therapies have had safety and tolerability concerns, patients may still need insulin for glycemic control, and there have been limited outcomes data supporting their use. In the study by Christiansen and colleagues in the previous issue of Critical Care, pre-admission metformin therapy was associated with reduced mortality in critically ill patients with type 2 diabetes. The mortality benefit persisted after controlling for other variables, and was particularly prominent when metformin was continued during admission. Furthermore, the reduction in mortality was observed despite a slightly increased prevalence of lactic acidosis in metformin users. The protective effects of metformin are purported to be related to pleiotropic, possibly anti-inflammatory mechanisms, raising the question of benefit in patients without diabetes. Thus, the findings warrant a re-appraisal of the risks and benefits of metformin use during critical illness. However, in order to justify the revision of multiple guidelines and changes in product labeling, clinical trials in carefully selected patient populations are indicated.

Objective: To compare a modified fixed meal dosing strategy to flexible meal dosing in hospitalized patients with type 2 diabetes., Research Design and Methods: Patients (N = 126) with refractory hyperglycemia or requiring at least 20 units of insulin per day were randomly assigned to fixed meal dosing (including withholding the dose if less than half of the meal tray was consumed) or flexible meal dosing based upon carbohydrate intake. The inpatient diabetes management team made all treatment adjustments. Outcomes included day 3 mean glucose, 72-h glucose trend analysis, hypoglycemia (<3.9 mmol/L), and inpatient diabetes treatment satisfaction., Results: The mean glucose on day 3 was 9.5 and 8.8 mmol/L in the fixed and flexible meal groups, respectively (P = 0.26). The frequency of hypoglycemia was 23 and 39% overall in the fixed and flexible meal groups (P = 0.08), with half of events occurring in the morning. There was a wide range of carbohydrate intake (median 51 g/meal, 10-90% range 26-72 g on day 3). The fixed dose group required significantly more prandial insulin overall and more correction insulin over time. There was no difference in composite treatment satisfaction or dosing miscalculations between groups., Conclusions: A fixed meal dosing strategy provided similar glucose control as flexible meal dosing, when managed by an inpatient diabetes treatment team. However, a larger sample size would be needed to definitively evaluate a treatment effect of flexible meal dosing in the hospital. Further study is needed to improve the delivery of bolus insulin in hospitalized patients.

Objective: To explore the relationship between inpatient diabetes education (IDE) and hospital readmissions in patients with poorly controlled diabetes., Research Design and Methods: Patients with a discharge diagnosis of diabetes (ICD-9 code 250.x) and HbA1c>9% who were hospitalized between 2008 and 2010 were retrospectively identified. All-cause first readmissions were determined within 30 days and 180 days after discharge. IDE was conducted by a certified diabetes educator or trainee. Relationships between IDE and hospital readmission were analyzed with stepwise backward logistic regression models., Results: In all, 2,265 patients were included in the 30-day analysis and 2,069 patients were included in the 180-day analysis. Patients who received IDE had a lower frequency of readmission within 30 days than did those who did not (11 vs. 16%; P=0.0001). This relationship persisted after adjustment for sociodemographic and illness-related factors (odds ratio 0.66 [95% CI 0.51-0.85]; P=0.001). Medicaid insurance and longer stay were also independent predictors in this model. IDE was also associated with reduced readmissions within 180 days, although the relationship was attenuated. In the final 180-day model, no IDE, African American race, Medicaid or Medicare insurance, longer stay, and lower HbA1c were independently associated with increased hospital readmission. Further analysis determined that higher HbA1c was associated with lower frequency of readmission only among patients who received a diabetes education consult., Conclusions: Formal IDE was independently associated with a lower frequency of all-cause hospital readmission within 30 days; this relationship was attenuated by 180 days. Prospective studies are needed to confirm this association.

Currently patients with diabetes comprise up to 25-30% of the census of adult wards and critical care units in our hospitals. Although evidence suggests that avoidance of hyperglycemia (>180 mg/dL) and hypoglycemia (<70 mg/dL) is beneficial for positive outcomes in the hospitalized patient, much of this evidence remains controversial and at times somewhat contradictory. We have recently formed a consortium for Planning Research in Inpatient Diabetes (PRIDE) with the goal of promoting clinical research in the area of management of hyperglycemia and diabetes in the hospital. In this article, we outline eight aspects of inpatient glucose management in which randomized clinical trials are needed. We refer to four as system-based issues and four as patient-based issues. We urge further progress in the science of inpatient diabetes management. We hope this call to action is supported by the American Diabetes Association, The Endocrine Society, the American Association of Clinical Endocrinologists, the American Heart Association, the European Association for the Study of Diabetes, the International Diabetes Federation, and the Society of Hospital Medicine. Appropriate federal research funding in this area will help ensure high-quality investigations, the results of which will advance the field. Future clinical trials will allow practitioners to develop optimal approaches for the management of hyperglycemia in the hospitalized patient and lessen the economic and human burden of poor glycemic control and its associated complications and comorbidities in the inpatient setting.

Aims: Glycaemic variability (GV) is associated with mortality in acutely ill patients, but the mechanism is unknown. The objective of this study is to determine whether common approaches to insulin therapy have distinct effects on GV and autonomic tone., Methods: Hospitalized patients with diabetes were randomized to short-term intravenous (IV) or physiologic subcutaneous (SQ) insulin. Heart rate variability (HRV) and cardiac impedance (pre-ejection period, PEP) were used to estimate parasympathetic and sympathetic tone, respectively. GV was measured using a continuous glucose monitor., Results: Mean glucose tended to be lower initially in the SQ group (N = 16) compared with the IV group (N = 17) on day 1 (10.5 vs. 8.6 mmol/l, p = 0.05), but became non-significant during the transition off of the infusion. There was no difference in glycaemic lability index (GLI), continuous overlapping net glycaemic action (CONGA) or coefficient of variation (CV) on day 1, but by day 2, these measures were higher in the IV group (p < 0.05 for all). PEP was higher in the SQ group during (110 vs. 123 ms, p = 0.02) and after the intervention (104 vs. 126 ms, p = 0.004). Hypoglycaemia was similar in both groups. There were only small differences in HRV. Post-treatment PEP was inversely correlated with log GLI (r = -0.41, p = 0.03) but not other measures., Conclusions: Short-term IV insulin is associated with an increase in multiple GV measures compared with optimal SQ insulin. However, GLI was the only predictor of PEP. Further research is needed to determine if interventions that minimize GV improve outcomes in the hospital., (© 2013 Blackwell Publishing Ltd.)

Hospital readmission is an important contributor to total medical expenditures and is an emerging indicator of quality of care. Diabetes, similar to other chronic medical conditions, is associated with increased risk of hospital readmission. Risk factors include previous hospitalization, extremes in age, and socioeconomic barriers. Preliminary studies suggest that acute and/or chronic glycemic control may be of importance when diabetes is the primary diagnosis or when it is a comorbidity. Very limited evidence from prospective randomized controlled trials aimed at improving glycemic control is available. However, whether one concludes that inpatient or outpatient glycemic control is partly responsible for reduced hospitalizations, attention to glycemic control in the hospital may facilitate sustained glycemic control post-discharge. Limited prospective and retrospective evidence suggest that the involvement of a diabetes specialist team may improve readmission rates, but attention to more generalized comprehensive approaches may also be worthwhile. Prospective interventional studies targeting interventions for improving glycemic control are needed to determine whether glycemic control impacts readmission rates., (© 2012 Diabetes Technology Society.)

Background: The accuracy of continuous glucose monitoring (CGM) in non-critically ill hospitalized patients with heart failure or severe hyperglycemia (SH) is unknown., Methods: Hospitalized patients with congestive heart failure (CHF) exacerbation (receiving IV or subcutaneous insulin) or SH requiring insulin infusion were compared to outpatients referred for retrospective CGM., Results: Forty-three patients with CHF, 15 patients with SH, and 88 outpatients yielded 470, 164, and 2150 meter-sensor pairs, respectively. Admission glucose differed (188 versus 509 mg/dl in CHF and SH, p < .001) but not the first sensor glucose (p = .35). In continuous glucose error grid analysis, 67-78% of pairs during hypoglycemia were in zones A+B (p = .63), compared with 98-100% in euglycemia (p < .001) and 98%, 92%, and 99% (p = .001) during hyperglycemia for the CHF, SH, and outpatient groups, respectively. Mean absolute relative difference (MARD) was lower in the CHF versus the SH group in glucose strata above 100 mg/dl, but there was no difference between the CHF and outpatient groups. Linear regression models showed that CHF versus outpatient, SH versus CHF, and coefficient of variation were significant predictors of higher MARD. Among subjects with CHF, MARD was not associated with brain natriuretic peptide or change in plasma volume, but it was significantly higher in subjects randomized to IV insulin (p = .04)., Conclusions: The results suggest that SH and glycemic variability are more important determinants of CGM accuracy than known CHF status alone in hospitalized patients., (© 2012 Diabetes Technology Society.)

Endpoint HbA(1c) <7.0% was achieved by 80 (73.4%) lispro mix 25 (LM25)-treated patients and 67 (60.9%) glargine-treated patients (p=0.027) with baseline 1,5 anhydroglucitol (1,5AG) below median and 75 (70.8%) LM25-treated patients and 72 (63.7%) glargine-treated patients (p=0.238) with 1,5AG≥median, suggesting, 1,5AG may offer therapeutic insight when starting insulin therapy., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)