Search

Lucrezia Ferrario,1 Fabrizio Schettini,1 Angelo Avogaro,2 Chiara Bellia,3 Federico Bertuzzi,4 Graziella Bonetti,5 Antonio Ceriello,6 Marcello Ciaccio,3,7 Massimiliano Corsi Romanelli,8,9 Elena Dozio,9 Luca Falqui,10 Angela Girelli,11 Antonio Nicolucci,12 Gianluca Perseghin,13,14 Mario Plebani,15 Umberto Valentini,11 Martina Zaninotto,15 Silvana Castaldi,9,16 Emanuela Foglia1 1Centre for Health Economics, Social and Health Care Management, Università Carlo Cattaneo - LIUC, Castellanza, Italy; 2Department of Medicine, University-Hospital of Padova, Padova, Italy; 3Section of Clinical Biochemistry and Clinical Molecular Medicine, Department of Biopathology and Medical Biotechnologies, University of Palermo, Palermo, Italy; 4Diabetology Unit, Grande Ospedale Metropolitano Niguarda Hospital, Milan, Italy; 5Department of Medicine Services, Valcamonica Hospital, Esine, Italy; 6Department of Cardiovascular and Metabolic Diseases, Multimedica Research Institute, Milan, Italy; 7Department of Laboratory Medicine, University-Hospital of Palermo, Palermo, Italy; 8Service of Laboratory Medicine 1-Clinical Pathology, Policlinico San Donato, Milan, Italy; 9Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy; 10Department of Medicine, Diabetes and Endocrinology, Multimedica Research Institute, Milan, Italy; 11Diabetes Care Unit, Spedali Civili Hospital, Brescia, Italy; 12Center for Outcomes Research and Clinical Epidemiology, Pescara, Italy; 13Department of Medicine and Surgery, Università degli Studi di Milano Bicocca, Milan, Italy; 14Department of Medicine and Rehabilitation, Unit of Metabolic Medicine, Policlinico di Monza, Monza, Italy; 15Department of Laboratory Medicine, University-Hospital of Padova, Padova, Italy; 16Fondazione Ca’ Granda Ospedale Maggiore Policlinico Research Institute of Milano, Milano, ItalyCorrespondence: Lucrezia FerrarioCentre for Health Economics, Social and Health Care Management, Università Carlo Cattaneo – LIUC, Corso Matteotti 22, Castellanza, 21053, VA, ItalyTel +39 033 1572504Fax +39 033 1572513Email lferrario@liuc.itPurpose: To investigate the glycated albumin (GA) introduction implications, as an add-on strategy to traditional glycemic control (Hb1Ac and fasting plasma glucose – FPG) instruments, considering insulin-naïve individuals with type 2 diabetes mellitus (T2DM), treated with oral therapies.Methods: A Health Technology Assessment was conducted in Italy, as a multi-dimensional approach useful to validate any innovative technology. The HTA dimensions, derived from the EUnetHTA Core Model, were deployed by means of literature evidence, health economics tools and qualitative questionnaires, filled-in by 15 professionals.Results: Literature stated that the GA introduction could lead to a higher number of individuals achieving therapeutic success after 3 months of therapy (97.0% vs 71.6% without GA). From an economic point of view, considering a projection of 1,955,447 T2DM insulin-naïve individuals, potentially treated with oral therapy, GA introduction would imply fewer individuals requiring a therapy switch (− 89.44%), with a 1.06% in costs reduction, on annual basis, thus being also the preferable solution from a cost-effectiveness perspective (cost-effectiveness value: 237.74 vs 325.53). According to experts opinions, lower perceptions on GA emerged with regard to equity aspects (0.13 vs 0.72, p-value> 0.05), whereas it would improve both individuals (2.17 vs 1.33, p-value=0.000) and caregivers quality of life (1.50 vs 0.83, p-value=0.000). Even if in the short term, GA required additional investments in training courses (− 0.80 vs 0.10, p-value = 0.036), in the long run, GA could become the preferable technology (0.30 vs 0.01, p-value=0.018) from an organisational perspective.Conclusion: Adding GA to traditional glycaemic control instruments could improve the clinical pathway of individuals with T2DM, leading to economic and organisational advantages for both hospitals and National Healthcare Systems.Keywords: glycated albumin, type 2 diabetes mellitus, T2DM, Health Technology Assessment, economic evaluation, multidimensional approach

Background Since 2010, more than half of World population lives in Urban Environments. Urban Diabetes has arisen as a novel nosological entity in Medicine. Urbanization leads to the accrual of a number of factors increasing the vulnerability to diabetes mellitus and related diseases. Herein we report clinical-epidemiological data of the Milano Metropolitan Area in the contest of the Cities Changing Diabetes Program. Since the epidemiological picture was taken in January 2020, on the edge of COVID-19 outbreak in the Milano Metropolitan Area, a perspective addressing potential interactions between diabetes and obesity prevalence and COVID-19 outbreak, morbidity and mortality will be presented. To counteract lock-down isolation and, in general, social distancing a pilot study was conducted to assess the feasibility and efficacy of tele-monitoring via Flash Glucose control in a cohort of diabetic patients in ASST North Milano. Methods Data presented derive from 1. ISTAT (National Institute of Statistics of Italy), 2. Milano ATS web site (Health Agency of Metropolitan Milano Area), which entails five ASST (Health Agencies in the Territories). A pilot study was conducted in 65 screened diabetic patients (only 40 were enrolled in the study of those 36 were affected by type 2 diabetes and 4 were affected by type 1 diabetes) of ASST North Milano utilizing Flash Glucose Monitoring for 3 months (mean age 65 years, HbA1c 7,9%. Patients were subdivided in 3 groups using glycemic Variability Coefficient (VC): a. High risk, VC > 36, n. 8 patients; Intermediate risk 20 Results In a total population of 3.227.264 (23% is over 65 y) there is an overall prevalence of 5.65% with a significant difference between Downtown ASST (5.31%) and peripheral ASST (ASST North Milano, 6.8%). Obesity and overweight account for a prevalence of 7.8% and 27.7%, respectively, in Milano Metropolitan Area. We found a linear relationship (R = 0.36) between prevalence of diabetes and aging index. Similarly, correlations between diabetes prevalence and both older people depending index and structural dependence index (R = 0.75 and R = 0.93, respectively), were found. A positive correlation (R = 0.46) with percent of unoccupied people and diabetes prevalence was also found. A reverse relationship between diabetes prevalence and University level instruction rate was finally identified (R = − 0.82). Our preliminary study demonstrated a reduction of Glycated Hemoglobin (p = 0.047) at 3 months follow-up during the lock-down period, indicating Flash Glucose Monitoring and remote control as a potential methodology for diabetes management during COVID-19 lock-down. Hypothesis and discussion The increase in diabetes and obesity prevalence in Milano Metropolitan Area, which took place over 30 years, is related to several environmental factors. We hypothesize that some of those factors may have also determined the high incidence and virulence of COVID-19 in the Milano area. Health Agencies of Milano Metropolitan Area are presently taking care of diabetic patients facing the new challenge of maintaining sustainable diabetes care costs in light of an increase in urban population and of the new life-style. The COVID-19 pandemic will modify the management of diabetic and obese patients permanently, via the implementation of approaches that entail telemedicine technology. The pilot study conducted during the lock-down period indicates an improvement of glucose control utilizing a remote glucose control system in the Milano Metropolitan Area, suggesting a wider utilization of similar methodologies during the present “second wave” lock-down.

Objective To evaluate sodium-glucose cotransporter-2 (SGLT-2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists in patients with type 2 diabetes at varying cardiovascular and renal risk. Design Network meta-analysis. Data sources Medline, Embase, and Cochrane CENTRAL up to 11 August 2020. Eligibility criteria for selecting studies Randomised controlled trials comparing SGLT-2 inhibitors or GLP-1 receptor agonists with placebo, standard care, or other glucose lowering treatment in adults with type 2 diabetes with follow up of 24 weeks or longer. Studies were screened independently by two reviewers for eligibility, extracted data, and assessed risk of bias. Main outcome measures Frequentist random effects network meta-analysis was carried out and GRADE (grading of recommendations assessment, development, and evaluation) used to assess evidence certainty. Results included estimated absolute effects of treatment per 1000 patients treated for five years for patients at very low risk (no cardiovascular risk factors), low risk (three or more cardiovascular risk factors), moderate risk (cardiovascular disease), high risk (chronic kidney disease), and very high risk (cardiovascular disease and kidney disease). A guideline panel provided oversight of the systematic review. Results 764 trials including 421 346 patients proved eligible. All results refer to the addition of SGLT-2 inhibitors and GLP-1 receptor agonists to existing diabetes treatment. Both classes of drugs lowered all cause mortality, cardiovascular mortality, non-fatal myocardial infarction, and kidney failure (high certainty evidence). Notable differences were found between the two agents: SGLT-2 inhibitors reduced admission to hospital for heart failure more than GLP-1 receptor agonists, and GLP-1 receptor agonists reduced non-fatal stroke more than SGLT-2 inhibitors (which appeared to have no effect). SGLT-2 inhibitors caused genital infection (high certainty), whereas GLP-1 receptor agonists might cause severe gastrointestinal events (low certainty). Low certainty evidence suggested that SGLT-2 inhibitors and GLP-1 receptor agonists might lower body weight. Little or no evidence was found for the effect of SGLT-2 inhibitors or GLP-1 receptor agonists on limb amputation, blindness, eye disease, neuropathic pain, or health related quality of life. The absolute benefits of these drugs vary substantially across patients from low to very high risk of cardiovascular and renal outcomes (eg, SGLT-2 inhibitors resulted in 3 to 40 fewer deaths in 1000 patients over five years; see interactive decision support tool ( https://magicevidence.org/match-it/200820dist/#!/ ) for all outcomes. Conclusions In patients with type 2 diabetes, SGLT-2 inhibitors and GLP-1 receptor agonists reduced cardiovascular and renal outcomes, with some differences in benefits and harms. Absolute benefits are determined by individual risk profiles of patients, with clear implications for clinical practice, as reflected in the BMJ Rapid Recommendations directly informed by this systematic review. Systematic review registration PROSPERO CRD42019153180.

International audience; Aims: To assess the effects of glycated haemoglobin (HbA(1c)) levels at time of glucose-lowering treatment intensification in DISCOVER, a global observational study of patients with type 2 diabetes (T2D) initiating second-line therapy. Outcomes of interest were glycaemic control, hypoglycaemia, and need for further intensification during 3 years of follow-up. Methods: We included patients who intensified treatment (add-on or insulin initiation) upon initiation of second-line therapy (baseline). Outcomes were assessed according to baseline HbA(1c): HbA(1c) 7.5% (late intensification). Factors associated with early or late intensification were assessed using multivariate logistic regression. Results: Of the 9575 patients included, 3275 (34.2%) intensified treatment early and 6300 (65.8%) intensified treatment late. During follow-up, mean (SD) HbA(1c) was lower in the early-than in the late-intensification group (6.9% [0.95%] vs 7.5% [1.28%] at 36 months). More patients had HbA(1c) < 7.0% in the early-than in the late-intensification group (61.8% vs 37.9% at 36 months; p < 0.001). The risk of further intensification was higher in the late-intensification group (hazard ratio 1.88 [95% confidence interval 1.68-2.09]). Occurrence of hypoglycaemia was similar in both groups. Conclusions: Late intensification of glucose-lowering therapy after first-line treatment failure reduces the likelihood of reaching recommended treatment goals.

Background In patients with type 2 diabetes (T2D), optimal management of cardiovascular (CV) risk factors is critical for primary prevention of CV disease. Purpose To describe the association of country income and patient socioeconomic factors with risk factor control in patients with T2D. Methods DISCOVER is a 37-country, prospective, observational study of 15,983 patients with T2D enrolled between January 2016 and December 2018 at initiation of 2nd-line glucose-lowering therapy and followed for 3 years. In patients without known CV disease with sub-optimally controlled risk factors at baseline, we examined achievement of risk factor control (HbA1c Results Among 9,613 patients with T2D but without CV disease (mean age 57.2 years, 47.9% women), 83.1%, 37.5%, and 66.3% did not have optimal control of glucose, BP, and statins, respectively, at baseline. Of these, 40.8%, 55.5%, and 28.6% achieved optimal control at 3 years of follow-up. There was substantial variability in achievement of risk factor control across countries (Figure) but no association of country GNI/capita on achievement of risk factor control (Table). Insurance status, which differed substantially by GNI group, was strongly associated with glycemic control, with no insurance and public insurance associated with lower odds of patients achieving HbA1c Conclusions In a global cohort of patients with T2D, a substantial proportion do not achieve risk factor control even after 3 years of follow-up. The variability across countries in risk factor control is not explained by the GNI/capita of the country. Proportion of patients at goal Funding Acknowledgement Type of funding source: Private company. Main funding source(s): The DISCOVER study is funded by AstraZeneca

Background:GLP-1 receptor agonists (G LP-1 RA) and SGLT-2 inhibitors (SGLT-2i) are recommended in type 2 diabetes subjects with established cardiovascular (CV) disease or at high CV risk. The efficacy and safety of GLP-1 RA and SGLT-2i compared with other anti-hyperglycemic agents (AHAs) was examined in large unselected populations of Lombardy and Apulia regions in Italy.Methods:An observational cohort study of new users of GLP-1 RA, SGLT-2i and other AHAs was conducted from 2010 to 2018. Death and cardiovascular events were evaluated using conditional Cox models in propensity-score matched populations. Adjusted hazard ratios (95%, CI) were calculated for each region and as meta-analysis for pooled risks.Results:After propensity-matching, the Lombardy cohort included 18,716 and 11,683 pairs and the Apulia cohort 9,772 and 6,046 pairs for the GLP-1 RA and SGLT-2i groups, respectively. Use of GLP-1 RA was associated with lower rates of death (HR 0.61, CI 0.56-0.65, Lombardy; HR 0.63, CI 0.55-0.71, Apulia), cerebrovascular disease and ischemic stroke (HR 0.70, CI 0.63-0.79; HR 0.72, CI 0.60-0.87, Lombardy) peripheral vascular disease (HR 0.72, CI 0.64-0.82, Lombardy; HR 0.80, CI 0.67-0.98, Apulia) and, lower limb complications (HR 0.67, CI 0.56-0.81, Lombardy; HR 0.69, CI 0.51-0.93, Apulia). Compared to other AHAs, SGLT-2i use decreased the risk of death (HR 0.47, CI 0.40-0.54, Lombardy; HR 0.43, CI 0.32-0.57, Apulia), cerebrovascular disease (HR 0.75, CI 0.61-0.91, Lombardy, HR 0.72, CI 0.54-0.96, Apulia), and heart failure (HR 0.56, CI 0.46-0.70, Lombardy, HR 0.57, CI 0.42-0.77, Apulia). In the pooled cohorts, a reduction in heart failure was also observed with GLP-1 RA (HR 0.89, 95% CI 0.82-0.97). Serious adverse events were quite low in frequency. Conclusions: Our findings from real world practice confirm the favorable effect of GLP-1 RA and SGLT-2i on death and CV outcomes consistent across both regions. Thus, these drug classes should be preferentially considered in a broad type 2 diabetes population beyond subjects with CV disease.

Issue The world is rapidly urbanizing, causing alarming health problems to their citizens. The Cities Changing Diabetes program aims to address the social factors and cultural determinants that can increase type 2 diabetes vulnerability among people living in cities. Rome joined the program in 2017, and a series of initiatives was launched with the aim of mapping the problem, sharing the learnings, and designing interventions. Description of the Problem The first phase of the project documented that a wide variation exists in the prevalence of diabetes among the districts of Rome, associated with social and cultural determinants. A linear correlation exists between the prevalence of diabetes in the districts, unemployment rate and use of private transportation rate, while an inverse correlation is present with aging index, school education level, and slow mobility rate. These findings were the base for the development of an action plan to be implemented in the next three years. A structured, multi-stakeholder approach was adopted to prioritize the areas of intervention. Politicians, healthcare policy makers, healthcare providers, epidemiologists, social scientists, and patient association representatives were involved. Results The following actions have been identified: To potentiate healthcare resources to meet the increasing needs associated with urban development and improve accessibility;To create and strengthen support networks in the territory, to meet the needs of elderly, fragile people, often living alone;To support sustainable mobility and improve the usability of shared and public transport networks;To increase information available to the most vulnerable subjects;To create a uniform network of specialist care through innovative solutions and increase the access to specialist care in suburban, underserved areas;To support the development of information and telemedicine systems, to promote integrated care. Key messages The aim of the initiative is triggering a virtuous circle in which prevention, access to care/innovation and sustainability of healthy lifestyles are the result of integrated actions in the territory. The experience of Rome can inspire other metropolitan areas in implementing effective strategies to reduce the burden of diabetes.

Background: Glucose variability has been associated with cardiovascular (CV) outcomes in type 2 diabetes, however, the interplay between glucose variability, empagliflozin and CV death has not been explored. In the EMPA-REG OUTCOME trial, empagliflozin reduced the risk of CV death by 38%. We explore post-hoc the association between HbA1c variability and CV death, and the potential mediating effects of HbA1c variability on empagliflozin’s CV death reductions.Methods: In total, 7,020 patients with type 2 diabetes and established CV disease received placebo, empagliflozin 10 mg or 25 mg. We defined within-patient HbA1c variability as standard deviation, coefficient of variation and range of HbA1c measurements (%) post-baseline. First, we compared HbA1c variability until week 28 and 52 by Wilcoxon tests. We explored the association between CV death and HbA1c variability in placebo and pooled empagliflozin arms separately with landmark analyses at week 28 and 52, and additionally with HbA1c variability as a time-dependent co-variate. We used Cox regression models adjusted for baseline risk factors including changes in HbA1c from baseline to week 12, and the interaction term HbA1c variability* treatment.Results: HbA1c variability was lower with empagliflozin compared to placebo. In all Cox analyses, high HbA1c variability increased the risk for CV death in both treatment arms with no interaction with treatment.Conclusions: HbA1c variability was reduced by empagliflozin and high values of HbA1c variability were associated with an increased risk of CV death. Empagliflozin’s reduction in CV death did not appear to be mediated by reductions in HbA1c variability.ClinicalTrials.gov number, NCT01131676. Registered May 27 2010. https://clinicaltrials.gov/ct2/show/NCT01131676

Background: Symptom relief, prolonging survival and avoiding complications are key goals in treating type 2 diabetes (T2D), but health-related quality of life (HRQoL) may be as or more important to patients. We used DISCOVER, a global observational study of people with T2D initiating a second-line glucose-lowering therapy, to examine factors associated with HRQoL over 3 years of follow-up. Methods: HRQoL was assessed using the 36-item Short-Form Health Survey (SF-36) v2 mental and physical component summary (MCS;PCS) scores (higher scores = better HRQoL) and the Hypoglycemia Fear Survey II (HFS-II;higher scores = greater fear). Factors associated with HRQoL over time were assessed using longitudinal multivariable regression models. Results: Of 14 691 DISCOVER patients from 37 countries, baseline and ≥ 1 follow-up MCS, PCS and HFS-II scores were available for 7880, 7854 and 5387 patients, respectively. Over time, SF-36 scores decreased (change per 6 months from baseline: MCS −0.04 [95% CI: −0.05 to −0.04];PCS −0.03 [95% CI: −0.03 to −0.02]), and HFS-II scores increased (change: 0.10 [95% CI: 0.09 to 0.12]). Many factors were associated with HRQoL (Table). Conclusions: HRQoL worsened during follow-up. Patient-, disease- and treatment-related factors were associated with HRQoL differences. Assessing factors associated with HRQoL over time may inform interventions to improve this important outcome. Disclosure: A. Nicolucci: Consultant;Self;AstraZeneca. H. Chen: None. A. Cooper: Employee;Self;AstraZeneca. M.B. Gomes: None. L. Ji: None. K. Khunti: Advisory Panel;Self;Amgen, AstraZeneca, Bayer AG, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Menarini Group, Merck Sharp & Dohme Corp., Napp Pharmaceuticals, Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi-Aventis, Servier. Board Member;Self;AstraZeneca, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi-Aventis. Consultant;Self;Amgen, AstraZeneca, Bayer AG, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Menarini Group, Merck Sharp & Dohme Corp., Napp Pharmaceuticals, Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi-Aventis, Servier. Research Support;Self;AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis, Servier. Speaker's Bureau;Self;Amgen, AstraZeneca, Bayer AG, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Menarini Group, Merck Sharp & Dohme Corp., Napp Pharmaceuticals, Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi-Aventis, Servier. M.N. Kosiborod: Consultant;Self;Amarin Corporation, Amgen, Applied Therapeutics, AstraZeneca, Bayer AG, Boehringer Ingelheim Pharmaceuticals, Inc., Eisai Inc., Eli Lilly and Company, GlaxoSmithKline plc., Glytec, Intarcia Therapeutics, Janssen Scientific Affairs, LLC., Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk Inc., Sanofi US, Vifor Pharma Group. Research Support;Self;AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc. P. Leigh: Employee;Self;AstraZeneca. Employee;Spouse/Partner;Merck Sharp & Dohme Corp. L. Ramirez: None. M.V. Shestakova: None. I. Shimomura: Advisory Panel;Self;AstraZeneca K.K., Daiichi Sankyo, Novo Nordisk Pharma Ltd., Taisho Pharmaceutical Co., Ltd. Consultant;Self;MSD K.K., Novo Nordisk Pharma Ltd. Research Support;Self;Astellas Pharma Inc., Daiichi Sankyo, Eli Lilly Japan K.K., Kowa Company, Ltd., Kyowa Kirin Co., Ltd., Mitsubishi Tanabe Pharma Corporation, MSD K.K., Novartis Pharma K.K., Novo Nordisk Pharma Ltd., Ono Pharmaceutical Co., Ltd., Sanofi K.K., Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. Speaker's Bureau;Self;Amgen Astellas BioPharma K.K., Astellas Pharma Inc., AstraZeneca K.K., Covidien Japan Inc., Daiichi Sankyo, Eli Lilly Japan K.K., KOBAYASHI Pharmaceutical Co., Ltd., Kowa Company, Ltd., Kyowa Kirin Co., Ltd., Mitsubishi T nabe Pharma Corporation, MSD K.K., Nippon Boehringer Ingelheim Co. Ltd., Nippon Chemiphar Co., Ltd., Novo Nordisk Pharma Ltd., Ono Pharmaceutical Co., Ltd., Rohto Pharmaceutical Co., Ltd., Sanofi K.K., Sanwa Kagaku Kenkyusho, Sumitomo Dainippon Pharma Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. A. Siddiqui: None. F. Tang: Research Support;Self;AstraZeneca. J. Vora: Other Relationship;Self;AstraZeneca. H. Watada: Advisory Panel;Self;Abbott, Ajinomoto, Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Fuji Film, Janssen Pharmaceuticals, Inc., Kowa Company, Ltd., Kyowa Hakko Kirin Co., Ltd., Mitsubishi Tanabe Pharma Corporation, Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Sanofi-Aventis, Takeda Pharmaceutical Company Limited, Terumo Medical Corporation. Research Support;Self;Astellas Pharma Inc., Bayer Yakuhin, Ltd., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo, Eli Lilly Japan K.K., Kissei Pharmaceutical Co., Ltd., Kowa Company, Ltd., Kyowa Hakko Kirin Co., Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novartis Pharma K.K., Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Otsuka Pharmaceutical Co., Ltd., Pfizer Japan Inc., Sanofi-Aventis, Sanwa Kagaku Kenkyusho, Shionogi & Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited, Yakult. Speaker's Bureau;Self;Astellas Pharma Inc., AstraZeneca, Bayer Yakuhin, Ltd., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo, Eli Lilly Japan K.K., Kissei Pharmaceutical Co., Ltd., Kowa Company, Ltd., Kyowa Hakko Kirin Co., Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novartis Pharmaceuticals Corporation, Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Sanofi-Aventis, Sanwa Kagaku Kenkyusho, Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited. S.V. Arnold: None. Funding: AstraZeneca [ABSTRACT FROM AUTHOR] Copyright of Diabetes is the property of American Diabetes Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: We used DISCOVER, a 3-year observational study of people with T2D, to assess the effects of second-line metformin combination therapies on weight, HbA1c level and risk of hypoglycemia. Methods: Adjusted changes from baseline in weight and HbA1c level at 6, 12, 24 and 36 months were assessed using linear mixed models. An interval censored analysis was used to assess the risk of hypoglycemia. Results: At baseline, 7613 patients from 37 countries received second-line therapy with metformin and a dipeptidyl peptidase-4 inhibitor (DPP-4i; 48.3%), a glucagon-like peptide-1 receptor agonist (GLP-1 RA; 2.4%), a sodium-glucose co-transporter 2 inhibitor (SGLT2i; 8.3%) or a sulfonylurea (SU; 40.9%). When used in combination with metformin, GLP-1 RAs, SGLT2is and DPP-4is were associated with greater weight loss than SUs at all time points (Figure). Beyond 12 months, changes in HbA1c from baseline were similar for all combinations. Patients prescribed metformin and an SU had a greater occurrence of hypoglycemia over 3 years (11.9%) compared with the other combinations (3.9-6.4%). Conclusions: A high proportion of patients received metformin and a DPP-4i or an SU as a second-line therapy. All combinations showed a similar reduction in HbA1c. However, combinations of metformin and an SU were associated with the highest risk of hypoglycemia and the lowest weight reduction. Disclosure K. Khunti: Advisory Panel; Self; Amgen, AstraZeneca, Bayer AG, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Menarini Group, Merck Sharp & Dohme Corp., Napp Pharmaceuticals, Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi-Aventis, Servier. Board Member; Self; AstraZeneca, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi-Aventis. Consultant; Self; Amgen, AstraZeneca, Bayer AG, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Menarini Group, Merck Sharp & Dohme Corp., Napp Pharmaceuticals, Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi-Aventis, Servier. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis, Servier. Speaker’s Bureau; Self; Amgen, AstraZeneca, Bayer AG, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Menarini Group, Merck Sharp & Dohme Corp., Napp Pharmaceuticals, Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi-Aventis, Servier. B. Charbonnel: Advisory Panel; Self; Merck Sharp & Dohme Corp., Mundipharma International, Novo Nordisk A/S, Sanofi. Speaker’s Bureau; Self; AstraZeneca. A. Cooper: Employee; Self; AstraZeneca. M.B. Gomes: None. L. Ji: None. P. Leigh: Employee; Self; AstraZeneca. Employee; Spouse/Partner; Merck Sharp & Dohme Corp. A. Nicolucci: Consultant; Self; AstraZeneca. W. Rathmann: Advisory Panel; Self; AstraZeneca. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Research Support; Self; Novo Nordisk Inc. Speaker’s Bureau; Self; Eli Lilly and Company, Novo Nordisk Inc. M.V. Shestakova: None. A. Siddiqui: None. F. Tang: Research Support; Self; AstraZeneca. H. Watada: Advisory Panel; Self; Abbott, Ajinomoto, Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Fuji Film, Janssen Pharmaceuticals, Inc., Kowa Company, Ltd., Kyowa Hakko Kirin Co., Ltd., Mitsubishi Tanabe Pharma Corporation, Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Sanofi-Aventis, Takeda Pharmaceutical Company Limited, Terumo Medical Corporation. Research Support; Self; Astellas Pharma Inc., Bayer Yakuhin, Ltd., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo, Eli Lilly Japan K.K., Kissei Pharmaceutical Co., Ltd., Kowa Company, Ltd., Kyowa Hakko Kirin Co., Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novartis Pharma K.K., Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Otsuka Pharmaceutical Co., Ltd., Pfizer Japan Inc., Sanofi-Aventis, Sanwa Kagaku Kenkyusho, Shionogi & Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited, Yakult. Speaker’s Bureau; Self; Astellas Pharma Inc., AstraZeneca, Bayer Yakuhin, Ltd., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo, Eli Lilly Japan K.K., Kissei Pharmaceutical Co., Ltd., Kowa Company, Ltd., Kyowa Hakko Kirin Co., Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novartis Pharmaceuticals Corporation, Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Sanofi-Aventis, Sanwa Kagaku Kenkyusho, Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited. H. Chen: None. Funding AstraZeneca

UNSTRUCTURED This study was performed to assess the system accuracy of the blood glucose monitoring system SD GlucoNavii Mentor (SD Biosensor Inc, Korea). The study procedures were based on International Organization for Standardization 15197:2013, in that capillary blood samples from 100 participants’ fingertips were measured with three reagent system lots of the self-monitoring blood glucose system. Samples were collected for comparison measurements on a hexokinase-based glucose analyzer (Cobas Integra400 Plus, Roche Instrument Center, Switzerland). Glucose concentrations were distributed as required by International Organization for Standardization 15197. For each of the 100 evaluable samples, duplicate measurements were taken from three different reagent lots, for a total of 600 measurements. Overall, 98.3% (590/600) of individual measurement results (185/186, 99.5% for glucose values

This study was performed to assess the system accuracy of the blood glucose monitoring system SD GlucoNavii Mentor (SD Biosensor Inc, Korea). The study procedures were based on International Organization for Standardization 15197:2013, in that capillary blood samples from 100 participants’ fingertips were measured with three reagent system lots of the self-monitoring blood glucose system. Samples were collected for comparison measurements on a hexokinase-based glucose analyzer (Cobas Integra400 Plus, Roche Instrument Center, Switzerland). Glucose concentrations were distributed as required by International Organization for Standardization 15197. For each of the 100 evaluable samples, duplicate measurements were taken from three different reagent lots, for a total of 600 measurements. Overall, 98.3% (590/600) of individual measurement results (185/186, 99.5% for glucose values

Insulin injection aspects, such as fear of injection and pain, directly affect glycemic control, patient adherence and quality of life. Use of thinner and shorter needles could increase acceptance of injections. The aim of the study is to evaluate the non-inferiority of the new 34G × 3.5 mm needle compared to a 32G × 4 mm in patients with diabetes treated with insulin.This is an open, randomized, two-period crossover, non-inferiority trial. Every treatment period lasted 3 weeks. Patients with type 1 or type 2 diabetes, treated with multiple daily insulin injections, were randomly assigned to receive a 34G × 3.5 mm or a 32G × 4 mm pen needle. The primary endpoint was the non-inferiority of the 34G × 3.5 mm in comparison with the 32G × 4 mm pen needle in terms of percentage absolute change of blood fructosamine (% |ΔFru|), using a non-inferiority margin of 20%.Overall 77 patients were randomized and 73 completed the study. Patients characteristics were: 52% male, 80.5% affected by type 1 diabetes, mean age 52 years (±14.6), mean BMI 24.5 kg/mThe 34G × 3.5 mm needle was non-inferior to the 32G × 4 mm needle regarding fructosamine levels and glycemic variability supporting the suitability of the 34G × 3.5 mm needle for insulin injection in patients with diabetes.NCT02690467.

Aims Up to one-third of patients with diabetes mellitus and heart failure (HF) are treated with insulin. As insulin causes sodium retention and hypoglycaemia, its use might be associated with worse outcomes. Methods and results We examined two datasets: 24 012 patients with HF from four large randomized trials and an administrative database of 4 million individuals, 103 857 of whom with HF. In the former, survival was examined using Cox proportional hazards models adjusted for baseline variables and separately for propensity scores. Fine-Gray competing risk regression models were used to assess the risk of hospitalization for HF. For the latter, a case-control nested within a population-based cohort study was conducted with propensity score. Prevalence of diabetes mellitus at study entry ranged from 25.5% to 29.5% across trials. Insulin alone or in combination with oral hypoglycaemic drugs was prescribed at randomization to 24.4% to 34.5% of the patients with diabetes. The rates of death from any cause and hospitalization for HF were higher in patients with vs. without diabetes, and highest of all in patients prescribed insulin [propensity score pooled hazard ratio for all-cause mortality 1.27 (1.16-1.38), for HF hospitalization 1.23 (1.13-1.33)]. In the administrative registry, insulin prescription was associated with a higher risk of all-cause death [odds ratio (OR) 2.02, 95% confidence interval (CI) 1.87-2.19] and rehospitalization for HF (OR 1.42, 95% CI 1.32-1.53). Conclusions Whether insulin use is associated with poor outcomes in HF should be investigated further with controlled trials, as should the possibility that there may be safer alternative glucose-lowering treatments for patients with HF and type 2 diabetes mellitus.

Background Autosomal recessive hypercholesterolemia (ARH) is a rare lipid disorder characterized by premature atherosclerotic cardiovascular disease (ASCVD). There are sparse data for clinical management and cardiovascular outcomes in ARH. Objectives Evaluation of changes in lipid management, achievement of low-density lipoprotein cholesterol (LDL-C) goals and cardiovascular outcomes in ARH. Methods Published ARH cases were identified by electronic search. All corresponding authors and physicians known to treat these patients were asked to provide follow-up information, using a standardized protocol. Results We collected data for 52 patients (28 females, 24 males; 31.1 ± 17.1 years of age; baseline LDL-C: 571.9 ± 171.7 mg/dl). During a mean follow-up of 14.1 ± 7.3 years, there was a significant increase in the use of high-intensity statin and ezetimibe in combination with lipoprotein apheresis; in 6 patients, lomitapide was also added. Mean LDL-C achieved at nadir was 164.0 ± 85.1 mg/dl (−69.6% from baseline), with a better response in patients taking lomitapide (−88.3%). Overall, 23.1% of ARH patients reached LDL-C of Conclusions Despite intensive treatment, LDL-C in ARH patients remains far from targets, and this translates into a poor long-term cardiovascular prognosis. Our data highlight the importance of an early diagnosis and treatment and confirm the fact that an effective treatment protocol for ARH is still lacking.

Aim To investigate determinants of change in glycated haemoglobin (HbA1c) in patients with type 2 diabetes mellitus (T2DM) at 6 months after initiating uninterrupted second-line glucose-lowering therapies. Materials and Methods This cohort study utilized retrospective data from 10 256 patients with T2DM who initiated second-line glucose-lowering therapy (switch from or add-on to metformin) between 2011 and 2014 in Germany and the UK. Effects of pre-specified patient characteristics on 6-month HbA1c changes were assessed using analysis of covariance. Results Patients had a mean (standard error [SE]) baseline HbA1c of 8.68% (0.02); 28.5% of patients discontinued metformin and switched to an alternative therapy and the remainder initiated add-on therapy. Mean (SE) unadjusted 6-month HbA1c change was −1.27% (0.02). When adjusted for baseline HbA1c, 6-month changes depended markedly on the magnitude of the baseline HbA1c (HbA1c

OBJECTIVE Adherence to physical activity (PA) recommendations is hampered by the lack of effective strategies to promote behavior change. The Italian Diabetes and Exercise Study 2 (IDES_2) is a randomized controlled trial evaluating a novel behavioral intervention strategy for increasing PA and decreasing sedentary time (SED-time) in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS The study randomized 300 physically inactive and sedentary patients with type 2 diabetes 1:1 to receive theoretical and practical counseling once yearly for 3 years (intervention group [INT]) or standard care (control group [CON]). Here, we report the 4-month effects on objectively (accelerometer) measured daily light-intensity PA (LPA), moderate-to-vigorous–intensity PA (MVPA), and SED-time, and cardiovascular risk factors. RESULTS LPA and MVPA both increased, and SED-time decreased in both groups, although changes were significantly more marked in INT participants (approximately twofold for LPA and SED-time and approximately sixfold for MVPA). A significant reduction in HbA1c was observed only in INT subjects. An increase in LPA >0.92 h · day−1 and in MVPA >7.33 min · day−1 and a decrease in SED-time >1.05 h · day−1 were associated with an average decrease in HbA1c of ∼1% and also with significant improvements in fasting glucose, body weight, waist circumference, and hs-CRP. Changes in PA and SED-time were independent predictors of improvements in HbA1c. CONCLUSIONS This behavioral intervention is effective in the short term for increasing LPA and MVPA and reducing SED-time. Significant improvements in cardiometabolic risk profiles were observed in subjects experiencing the most pronounced changes in PA and SED-time, even if below the recommended level.

The increased mortality reported with intensive glycaemic control has been attributed to an increased risk of treatment-related hypoglycaemia. This study investigated the relationships of haemoglobin (Hb) A1c, anti-hyperglycaemic treatment, and potential risks of adverse effects with all-cause mortality in patients with type 2 diabetes. Patients (n = 15,773) were stratified into four categories according to baseline HbA1c and then assigned to three target categories, based on whether HbA1c was &le, 0.5% below or above (on-target), &gt, 0.5% below (below-target) or &gt, 0.5% above (above-target) their HbA1c goal, personalized according to the number of potential risks among age &gt, 70 years, diabetes duration &gt, 10 years, advanced complication(s), and severe comorbidity (ies). The vital status was retrieved for 15,656 patients (99.26%). Over a 7.4-year follow-up, mortality risk was increased among patients in the highest HbA1c category (&ge, 8.5%) (adjusted hazard ratio, 1.34 (95% confidence interval, 1.22&ndash, 1.47), p &lt, 0.001) and those above-target (1.42 (1.29&ndash, 1.57), p &lt, 0.001). Risk was increased among individuals in the lowest HbA1c category (&lt, 6.5%) and those below-target only if treated with agents causing hypoglycaemia (1.16 (1.03&ndash, 1.29), p = 0.01 and 1.10 (1.01&ndash, 1.22), p = 0.04, respectively). These data suggest the importance of setting both upper and lower personalized HbA1c goals to avoid overtreatment in high-risk individuals with type 2 diabetes treated with agents causing hypoglycaemia.

Background: Early and intensive glycaemic control provides long-term protection against the development of microvascular complications, a phenomenon defined legacy effect. Whether a legacy effect of high glucose exists also on macrovascular endpoints is uncertain. Methods and findings: We performed a systematic review of both randomized clinical trials (RCT)s and observational studies pertinent to the research question. We searched PubMed, Embase, Scopus and the Cochrane database up to January 31th 2020. Eligibility criteria for RCTs were: 1 - efficacy assessment of intensive glucose lowering treatment vs a less-stringent/conventional treatment; 2 - the inclusion of a post-active phase, observational follow-up; 3 - enrolment of patients with T1DM, pre-diabetes, and T2DM; and 4 - data report on major adverse cardiovascular events (MACE) incidence, which was the primary endpoint of this meta-analysis. We performed multiple meta-analyses of the available RCTs according to different scenarios considering the type of diabetes, diabetes duration, the presence of previous cardiovascular events, follow-up extension, and the incidence of MACE recorded only during the observational, post-active phase of the trials. Results from observational studies reporting the association between HbA1c levels during the first year after diabetes diagnosis and subsequent MACE incidence were also collected and are reported narratively. We included data from 7 RCTs and 40,346 patients. The intensive glucose-lowering approach significantly decrease the incidence of MACE compared with conventional treatment (OR 0.86, CI 0.77–0.96; p = 0.007) when considering all the available studies, with a more consistent effect (OR 0.73, CI 0.56–0.94; p = 0.01) in the case of RCTs enrolling patients with diabetes duration 10 years also yielded a relevant beneficial effect of the intensive approach (OR 0.71, CI 0.57, 0.88; p = 0.002). On the other hand, no effect was observed (OR 0.99, CI 0.92, 1.06; p = 0.81) when considering only the events recorded during the post-active, observational phases of the trials. Observational studies showed that HbA1c values >6.5% or 7% during the first year of diabetes diagnosis are associated with a higher incidence of late MACE with increased risk ranging from 19 up to 64%, according to the different study design and HbA1c stratification. Conclusions: These results support the recommendation regarding glucose-lowering treatment intensification in order to decrease the probability of having a macrovascular event in patients with short diabetes duration, no prevalent cardiovascular diseases, and long life-expectancy. On the other side, data from RCTs do not support the existence of a protective legacy effect on the macrovasculature beyond the period of intensive glycaemic treatment.

IntroductionIn addition to favoring renal disease progression, renal ‘hyperfiltration’ has been associated with an increased risk of death, though it is unclear whether and how excess mortality is related to increased renal function. We investigated whether renal hyperfiltration is an independent predictor of death in patients with type 2 diabetes from the Renal Insufficiency And Cardiovascular Events Italian multicenter study.Research design and methodsThis observational, prospective cohort study enrolled 15 773 patients with type 2 diabetes consecutively attending 19 Italian diabetes clinics in 2006–2008. Serum creatinine, albuminuria, cardiovascular risk factors, and complications/comorbidities were assessed at baseline. Vital status on 31 October 2015 was retrieved for 15 656 patients (99.26%). Patients were stratified (A) by absolute estimated glomerular filtration rate (eGFR) values in eGFR deciles or Kidney Disease: Improving Global Outcomes (KDIGO) categories and (B) based on age-corrected thresholds or age and gender-specific 95th and 5th percentiles in hyperfiltration, hypofiltration, and normofiltration groups.ResultsThe highest eGFR decile/category and the hyperfiltration group included (partly) different individuals with similar clinical features. Age and gender-adjusted death rates were significantly higher in deciles 1, 9, and 10 (≥103.9, 50.9–62.7, and 2, respectively) versus the reference decile 3 (92.9–97.5 mL/min/1.73 m2). Mortality risk, adjusted for multiple confounders, was also increased in deciles 1 (HR 1.461 (95% CI 1.175 to 1.818), p=0.001), 9 (1.312 (95% CI 1.107 to 1.555), p=0.002), and 10 (1.976 (95% CI 1.673 to 2.333), pConclusionsIn type 2 diabetes, both high-normal eGFR and hyperfiltration are associated with an increased risk of death from any cause, independent of confounders that may directly impact on mortality and/or affect GFR estimation. Further studies are required to clarify the nature of this relationship.Trial registration numberNCT00715481.

The affiliations listed for author Antonio Ceriello have been amended to a single affiliation note, …

IntroductionType 2 diabetes (T2D) is associated with an increased fracture risk despite normal-to-increased bone mineral density, suggesting reduced bone quality. Exercise may be effective in reducing fracture risk by ameliorating muscle dysfunction and reducing risk of fall, though it is unclear whether it can improve bone quality.Methods and analysisThe ‘Study to Weigh the Effect of Exercise Training on BONE quality and strength (SWEET BONE) in T2D’ is an open-label, assessor-blinded, randomised clinical trial comparing an exercise training programme of 2-year duration, specifically designed for improving bone quality and strength, with standard care in T2D individuals. Two hundred T2D patients aged 65–75 years will be randomised 1:1 to supervised exercise training or standard care, stratified by gender, age ≤ or >70 years and non-insulin or insulin treatment. The intervention consists of two weekly supervised sessions, each starting with 5 min of warm-up, followed by 20 min of aerobic training, 30 min of resistance training and 20 min of core stability, balance and flexibility training. Participants will wear weighted vests during aerobic and resistance training. The primary endpoint is baseline to end-of-study change in trabecular bone score, a parameter of bone quality consistently shown to be reduced in T2D. Secondary endpoints include changes in other potential measures of bone quality, as assessed by quantitative ultrasound and peripheral quantitative CT; bone mass; markers of bone turnover; muscle strength, mass and power; balance and gait. Falls and asymptomatic and symptomatic fractures will be evaluated over 7 years, including a 5-year post-trial follow-up. The superiority of the intervention will be assessed by comparing between-groups baseline to end-of-study changes.Ethics and disseminationThis study was approved by the institutional ethics committee. Written informed consent will be obtained from all participants. The study results will be submitted for peer-reviewed publication.Trial registration numberNCT02421393.

Background Diabetic patients with on-treatment high platelet reactivity (HPR) show an increased risk of thrombotic events. Whether measuring glycated haemoglobin (HbA1c) levels and/or glycaemic variability (GV) may help identifying diabetic patients at higher risk deserving tailored antiplatelet and/or glucose lowering strategies is unknown. We aimed to investigate the relationship between GV, HbA1c levels and platelet reactivity in patients with type 2 diabetes mellitus (DM) undergoing percutaneous coronary intervention (PCI). Methods Platelet reactivity was measured in type 2 DM patients using VerifyNow P2Y12 assay. HPR was defined as P2Y12 Reaction Unit (PRU) > 240. GV was expressed through mean amplitude of glycaemic excursions (MAGE) and coefficient of variance (CV) by using the iPro™ continuous glucose recorder. Results Thirty-five patients (age 70 ± 9 years, 86% male, mean HbA1c 7.2 ± 1.0%) on clopidogrel therapy were enrolled. HbA1c was independently associated with HPR (OR 7.25, 95% CI 1.55–33.86, p = 0.012). Furthermore, when factored into the model, GV indexes provided independent (OR 1.094, 95% CI 1.007–1.188, p Conclusions Glyco-metabolic state significantly correlates with HPR in well-controlled type 2 DM patients on clopidogrel therapy. HbA1c identifies patients at higher thrombotic risk but the highest diagnostic accuracy is achieved by combining GV and HbA1c. Whether individualized antithrombotic and glucose-lowering therapies based on the assessment of these parameters may reduce the incidence of thrombotic events in patients undergoing PCI should be further investigated.

Background and Aim: Costs for the treatment of diabetes and its comorbidities are a major international issue. A recent randomized clinical trial showed that the introduction of color range indicator (CRI)-based glucose meters (GMs) positively affects the HbA1c of patients with type 1 and type 2 diabetes, when compared to GMs without a CRI. This budget impact analysis aimed to translate this beneficial effect of CRI-based GMs, OneTouch Verio Flex and OneTouch Verio, into potential monetary impact for the healthcare systems of five European countries, Germany, Spain, Italy, France, and the United Kingdom. Material and Methods: Data from a randomized controlled trial, evaluating the effect of CRI-based GMs, were used to estimate the ten-year risk of patients for fatal myocardial infarction (MI) as calculated by the UK Prospective Diabetes Study (UKPDS) risk engine. On the basis of assessed risks for MI, the potential monetary impact for the healthcare systems in five European countries was modeled. Results: Based on a mean HbA1c reduction of 0.36%, as demonstrated in a randomized controlled trial, the UKPDS risk engine estimated a reduction of 2.4% of the ten-year risk of patients for fatal MI. When applied to our economic model, substantial potential cost savings for the healthcare systems of five European countries were calculated: €547 472 (France), €9.0 million (Germany), €6.0 million (Italy), €841 799 (Spain), and €421 069 (United Kingdom) per year. Conclusion: Improving metabolic control in patients with diabetes by the utilization of CRI-based GMs may have substantial positive effects on the expenditure of the healthcare systems of several European countries.

Background/objective In 1993, the Diabetes Control and Complications Trial (DCCT) found that intensive antihyperglycemic therapy was effective in the primary and secondary prevention of microvascular complications in patients with type 1 diabetes (T1D) but was associated with a 3-fold greater rate of severe hypoglycemia (SH) than conventional therapy. The aim of this analysis was to determine whether, in the real-world setting, the incidence of SH in pediatric patients with T1D has changed since 1993. Methods A systematic literature search of PubMed for prospective or retrospective observational studies (≥250 participants) on SH epidemiology or related topics in pediatric patients with T1D, published between October 1993 and June 2016, identified 35 articles (involving >55 000 participants). SH incidence data were analyzed in approximate 5-year blocks: 1993-2000, 2001-2005, 2006-2010, and 2011-2016. Information on factors that might influence the incidence of SH was also collected. Results A trend for a marked reduction in the incidence of SH in the post-DCCT setting (from 62.0 per 100 patient-years to 1.21-30 per 100 patient-years) was apparent. Factors that could have influenced this temporal trend in SH incidence included the increased use of new types of, and methods of administering, insulin, in particular rapid-acting insulin analogs and continuous subcutaneous insulin infusion. Conclusions SH in pediatric patients with T1D has declined in incidence since the DCCT but remains a common problem. The optimal use of new insulin therapies/regimens/technologies, improved education, and dedicated specialized management teams are needed to help reduce the risk of SH in this population.

Background: DISCOVER is a 3-year, observational study of patients with type 2 diabetes (T2D) initiating second-line glucose-lowering therapy in 38 countries. We assessed socioeconomic factors associated with poor glycemic control (HbA1c ≥ 9.0%) at baseline. Methods: In total, 15 342 participants from 36 countries were evaluated. HbA1c levels were recorded in 12 261 participants (79.9%). Factors associated with an increased likelihood of having HbA1c ≥ 9.0% were assessed using a hierarchical logistic regression model. Results: The mean HbA1c level was 8.4% (SD: 1.7%); 48.9%, 23.4% and 27.7% of patients had HbA1c < 8.0%, ≥ 8.0 to < 9.0%, and ≥ 9.0%, respectively. Socioeconomic factors associated with an increased likelihood of having HbA1c ≥ 9.0% (Figure) included living in lower middle-income (vs. high-income) countries, being treated at a site with public/governmental or mixed (vs. private) funding, having public or no (vs. private) health insurance and having primary or no formal (vs. secondary or higher) education. Male sex and being younger than 50 years of age were also associated with an increased likelihood of having HbA1c ≥ 9.0%. Conclusions: Lower socioeconomic status is associated with an increased likelihood of having HbA1c ≥ 9.0% in people with T2D initiating second-line therapy. Efforts are needed to address social determinants of health in patients with T2D. Disclosure M.B. Gomes: Advisory Panel; Self; AstraZeneca. Consultant; Self; Merck KGaA. Research Support; Self; CNPq, FAPERJ. F. Tang: Employee; Self; Mid America Heart Institute. Research Support; Self; AstraZeneca. H. Chen: Employee; Self; AstraZeneca. J. Cid-Ruzafa: Employee; Self; Evidera. P. Fenici: Employee; Self; AstraZeneca. K. Khunti: Advisory Panel; Self; Amgen Inc., AstraZeneca, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi. Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis, Servier, Takeda Pharmaceutical Company Limited. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis. Speaker's Bureau; Self; AstraZeneca, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Menarini Group, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi, Servier, Takeda Pharmaceutical Company Limited. S. Pocock: Consultant; Self; AstraZeneca. W. Rathmann: Advisory Panel; Self; AstraZeneca. Consultant; Self; Boehringer Ingelheim International GmbH. Research Support; Self; Novo Nordisk A/S. Speaker's Bureau; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Novo Nordisk A/S. M.V. Shestakova: Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi, Servier. Research Support; Self; Novo Nordisk A/S, Sanofi. F. Surmont: Employee; Self; AstraZeneca. H. Watada: Research Support; Self; Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Kissei Pharmaceutical Co., Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novartis Pharmaceuticals Corporation, Novo Nordisk A/S, Pfizer Inc., Sanofi, Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. Speaker's Bureau; Self; Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Eli Lilly and Company, Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novo Nordisk A/S, Ono Pharmaceutical Co., Ltd., Sanofi, Takeda Pharmaceutical Company Limited, Terumo Medical Corporation. Other Relationship; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Kowa Pharmaceutical Europe Co. Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Ono Pharmaceutical Co., Ltd., Sanwa Chemical Industry Co. Ltd., Takeda Pharmaceutical Company Limited. J. Medina: Employee; Self; AstraZeneca. S.M. Sadikot: Consultant; Self; AstraZeneca. I. Shimomura: Advisory Panel; Self; AstraZeneca, Novo Nordisk Pharma Ltd., Sanwa Kagaku Kenkyusho. Consultant; Self; MSD K.K., Novo Nordisk Pharma Ltd., Taisho Pharmaceutical Co., Ltd. Research Support; Self; Astellas Pharma Inc., AstraZeneca, Daiichi Sankyo Company, Limited, Eli Lilly Japan K.K., Kaken Pharmaceutical Co., Ltd., Kowa Pharmaceutical Co., Ltd., Kyowa Hakko Kirin Co., Ltd., Mitsubishi Tanabe Pharma Corporation, MSD K.K., Novartis Pharma K.K., Novo Nordisk Pharma Ltd., Shionogi & Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. Speaker's Bureau; Self; Astellas Pharma Inc., AstraZeneca, Daiichi Sankyo Company, Limited, Eli Lilly Japan K.K., Kowa Pharmaceutical Co., Ltd., Lotte Co., Ltd., Mitsubishi Tanabe Pharma Corporation, MSD K.K., Novartis Pharma K.K., Novo Nordisk Pharma Ltd., Ono Pharmaceutical Co., Ltd., Sanofi K.K., Sanwa Kagaku Kenkyusho, Taisho Toyama Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. G. Luporini Saraiva: Employee; Self; AstraZeneca. A. Nicolucci: Consultant; Self; AstraZeneca, Eli Lilly and Company, Medtronic, Novo Nordisk A/S. Research Support; Self; Artsana S.p.A., Dexcom, Inc., Novo Nordisk A/S, Sanofi-Aventis. Funding AstraZeneca

Background: Guidelines endorse a target HbA1c of < 7% for most people with T2D, but less stringent goals (e.g., HbA1c < 8%) and medications with a low risk of causing hypoglycemia are recommended for older patients. We examined the prevalence of potential overtreatment in a global cohort of older patients with T2D. Methods: DISCOVER is an ongoing, observational study of 15 992 patients with T2D initiating second-line glucose-lowering therapy in 38 countries. HbA1c levels and glucose-lowering therapies were recorded at baseline (initiation of second-line therapy), 6, 12 and 24 months. The analysis includes patients aged ≥ 65 years with a recorded baseline HbA1c level. Insulin, sulfonylureas, and meglitinides are medications associated with risk of hypoglycemia. Results: Among 3492 older patients, 2110 (60.4%) and 812 (23.3%) had an HbA1c level of < 8% and < 7% at baseline, respectively. The proportions of patients with HbA1c < 7% increased to more than 50% during follow-up (Figure). A third or more of these patients received medications associated with risk of hypoglycemia at baseline and during follow-up. Conclusions: In this global cohort, including patients from both lower-middle- and higher-income countries, many older patients with good glycemic control (HbA1c < 7%) received medications that are associated with increased risk of hypoglycemia. A more tailored management of hyperglycemia may be needed in this population. Disclosure B. Bongaerts: Advisory Panel; Self; AstraZeneca. S.V. Arnold: None. B. Charbonnel: Consultant; Self; AstraZeneca, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi, Servier. Speaker's Bureau; Self; AstraZeneca, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi, Takeda Pharmaceutical Company Limited. H. Chen: Employee; Self; AstraZeneca. J. Cid-Ruzafa: Employee; Self; Evidera. P. Fenici: Employee; Self; AstraZeneca. M.B. Gomes: Advisory Panel; Self; AstraZeneca. Consultant; Self; Merck KGaA. Research Support; Self; CNPq, FAPERJ. L. Ji: Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Bristol-Myers Squibb Company, Eli Lilly and Company, Merck KGaA, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi, Takeda Pharmaceutical Company Limited. Research Support; Self; AstraZeneca, Bristol-Myers Squibb Company, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novartis AG, Roche Pharma, Sanofi. K. Khunti: Advisory Panel; Self; Amgen Inc., AstraZeneca, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi. Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis, Servier, Takeda Pharmaceutical Company Limited. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis. Speaker's Bureau; Self; AstraZeneca, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Menarini Group, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi, Servier, Takeda Pharmaceutical Company Limited. M.N. Kosiborod: Consultant; Self; Amgen Inc., AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Eisai Co., Ltd., GlaxoSmithKline plc., Glytec, LLC, Intarcia Therapeutics, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novartis AG, Novo Nordisk A/S, Sanofi. Research Support; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc. G. Luporini Saraiva: Employee; Self; AstraZeneca. J. Medina: Employee; Self; AstraZeneca. A. Nicolucci: Consultant; Self; AstraZeneca, Eli Lilly and Company, Medtronic, Novo Nordisk A/S. Research Support; Self; Artsana S.p.A., Dexcom, Inc., Novo Nordisk A/S, Sanofi-Aventis. S. Pocock: Consultant; Self; AstraZeneca. S.M. Sadikot: Consultant; Self; AstraZeneca. M.V. Shestakova: Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi, Servier. Research Support; Self; Novo Nordisk A/S, Sanofi. I. Shimomura: Advisory Panel; Self; AstraZeneca, Novo Nordisk Pharma Ltd., Sanwa Kagaku Kenkyusho. Consultant; Self; MSD K.K., Novo Nordisk Pharma Ltd., Taisho Pharmaceutical Co., Ltd. Research Support; Self; Astellas Pharma Inc., AstraZeneca, Daiichi Sankyo Company, Limited, Eli Lilly Japan K.K., Kaken Pharmaceutical Co., Ltd., Kowa Pharmaceutical Co., Ltd., Kyowa Hakko Kirin Co., Ltd., Mitsubishi Tanabe Pharma Corporation, MSD K.K., Novartis Pharma K.K., Novo Nordisk Pharma Ltd., Shionogi & Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. Speaker's Bureau; Self; Astellas Pharma Inc., AstraZeneca, Daiichi Sankyo Company, Limited, Eli Lilly Japan K.K., Kowa Pharmaceutical Co., Ltd., Lotte Co., Ltd., Mitsubishi Tanabe Pharma Corporation, MSD K.K., Novartis Pharma K.K., Novo Nordisk Pharma Ltd., Ono Pharmaceutical Co., Ltd., Sanofi K.K., Sanwa Kagaku Kenkyusho, Taisho Toyama Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. F. Surmont: Employee; Self; AstraZeneca. F. Tang: Employee; Self; Mid America Heart Institute. Research Support; Self; AstraZeneca. H. Watada: Research Support; Self; Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Kissei Pharmaceutical Co., Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novartis Pharmaceuticals Corporation, Novo Nordisk A/S, Pfizer Inc., Sanofi, Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. Speaker's Bureau; Self; Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Eli Lilly and Company, Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novo Nordisk A/S, Ono Pharmaceutical Co., Ltd., Sanofi, Takeda Pharmaceutical Company Limited, Terumo Medical Corporation. Other Relationship; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Kowa Pharmaceutical Europe Co. Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Ono Pharmaceutical Co., Ltd., Sanwa Chemical Industry Co. Ltd., Takeda Pharmaceutical Company Limited. W. Rathmann: Advisory Panel; Self; AstraZeneca. Consultant; Self; Boehringer Ingelheim International GmbH. Research Support; Self; Novo Nordisk A/S. Speaker's Bureau; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Novo Nordisk A/S. Funding AstraZeneca

Markers of GV have been associated with CV outcomes in type 2 diabetes (T2D), however, the association of GV with HF outcomes remains unclear. In EMPA-REG OUTCOME, empagliflozin reduced the risk of CV death by 38% and HF hospitalizations (HHF) by 35%. Here we explore the association between GV and CV death/HHF outcomes. In EMPA-REG OUTCOME, 7,020 patients with T2D and established CV disease received placebo (PBO), empagliflozin (EMPA) 10 mg or 25 mg and were observed for a median period of 3.1 years. We defined within-patient GV as range and standard deviation of HbA1c measurements on-treatment until week 28. The associations with CV death, first HHF, and HHF or CV death were explored in PBO and pooled EMPA arms separately with landmark (LM) analyses at week 28 by Cox regression models adjusted for baseline risk factors including changes in HbA1c from baseline to week 12. GV levels were lower with EMPA (Table). In the LM analysis, higher GV increased the risk for CV death and CV death or HHF in both treatment arms. For HHF, no significant association could be shown (Table). In EMPA-REG OUTCOME, EMPA reduced GV. An increase in GV correlated with increased risk of subsequent CV death and CV death or HHF. The association with HHF alone was not significant maybe due to a lower numbers of HHF events, or the assumption that the effects of EMPA on HHF are largely glucose-independent. Disclosure A. Ceriello: Advisory Panel; Self; Abbott Laboratories, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Novo Nordisk A/S, Vifor Pharma. Research Support; Self; Mitsubishi Tanabe Pharma Corporation. Speaker's Bureau; Self; A. Menarini Diagnostics, AstraZeneca, Berlin-Chemie AG, Boehringer Ingelheim Pharmaceuticals, Inc. A. Ofstad: Employee; Self; Boehringer Ingelheim International GmbH. I. Zwiener: Employee; Self; Boehringer Ingelheim Pharma GmbH & Co KG, Merck KGaA. S. Kaspers: Employee; Self; Boehringer Ingelheim International GmbH. J. George: Employee; Self; Boehringer Ingelheim International GmbH. A. Nicolucci: Consultant; Self; AstraZeneca, Eli Lilly and Company, Medtronic, Novo Nordisk A/S. Research Support; Self; Artsana S.p.A., Dexcom, Inc., Novo Nordisk A/S, Sanofi-Aventis. Funding Boehringer Ingelheim; Eli Lilly and Company

Background: DISCOVER is an ongoing, observational study of people with T2D initiating second-line glucose-lowering therapy in 38 countries in six regions. We report changes in glucose-lowering therapies and HbA1c levels from the first to the second year of follow-up. Methods: HbA1c levels and changes in therapies were recorded at baseline, 1 and 2 years; 15 988 patients were included in the analysis. Results: Mean HbA1c levels at baseline, 1 and 2 years were 8.3%, 7.2% and 7.3%, respectively. At 1 and 2 years, mean HbA1c levels were close to 7.0% in patients receiving one or two oral drugs at 1 year, but were higher than 7.0% for those receiving more than two oral drugs or an injectable drug (Figure). The proportion of patients with HbA1c ≥ 8.0% was similar at 1 and 2 years (19.8% vs. 19.1%). Overall, 16.7% of patients changed therapy during the second year of follow-up. Treatment was intensified by the addition of an oral drug in 6.2% of patients receiving one or two oral drugs, and 3.2% of patients receiving oral therapies initiated an injectable drug. Results were consistent across regions. Conclusions: Glycemic levels were well controlled after 1 and 2 years of follow-up in participants receiving oral therapies, and treatment intensification occurred in a small proportion of people in this group. By contrast, glycemic control was suboptimal in people receiving injectable drugs. Disclosure B. Charbonnel: Consultant; Self; AstraZeneca, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi, Servier. Speaker's Bureau; Self; AstraZeneca, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi, Takeda Pharmaceutical Company Limited. H. Chen: Employee; Self; AstraZeneca. J. Cid-Ruzafa: Employee; Self; Evidera. P. Fenici: Employee; Self; AstraZeneca. M.B. Gomes: Advisory Panel; Self; AstraZeneca. Consultant; Self; Merck KGaA. Research Support; Self; CNPq, FAPERJ. G. Luporini Saraiva: Employee; Self; AstraZeneca. J. Medina: Employee; Self; AstraZeneca. A. Nicolucci: Consultant; Self; AstraZeneca, Eli Lilly and Company, Medtronic, Novo Nordisk A/S. Research Support; Self; Artsana S.p.A., Dexcom, Inc., Novo Nordisk A/S, Sanofi-Aventis. S. Pocock: Consultant; Self; AstraZeneca. M.V. Shestakova: Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi, Servier. Research Support; Self; Novo Nordisk A/S, Sanofi. I. Shimomura: Advisory Panel; Self; AstraZeneca, Novo Nordisk Pharma Ltd., Sanwa Kagaku Kenkyusho. Consultant; Self; MSD K.K., Novo Nordisk Pharma Ltd., Taisho Pharmaceutical Co., Ltd. Research Support; Self; Astellas Pharma Inc., AstraZeneca, Daiichi Sankyo Company, Limited, Eli Lilly Japan K.K., Kaken Pharmaceutical Co., Ltd., Kowa Pharmaceutical Co., Ltd., Kyowa Hakko Kirin Co., Ltd., Mitsubishi Tanabe Pharma Corporation, MSD K.K., Novartis Pharma K.K., Novo Nordisk Pharma Ltd., Shionogi & Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. Speaker's Bureau; Self; Astellas Pharma Inc., AstraZeneca, Daiichi Sankyo Company, Limited, Eli Lilly Japan K.K., Kowa Pharmaceutical Co., Ltd., Lotte Co., Ltd., Mitsubishi Tanabe Pharma Corporation, MSD K.K., Novartis Pharma K.K., Novo Nordisk Pharma Ltd., Ono Pharmaceutical Co., Ltd., Sanofi K.K., Sanwa Kagaku Kenkyusho, Taisho Toyama Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. S.M. Sadikot: Consultant; Self; AstraZeneca. F. Surmont: Employee; Self; AstraZeneca. F. Tang: Employee; Self; Mid America Heart Institute. Research Support; Self; AstraZeneca. J. Vora: Employee; Self; AstraZeneca. Research Support; Self; Abbott, AstraZeneca, Boehringer Ingelheim International GmbH, Bristol-Myers Squibb Company, Eli Lilly and Company, GlaxoSmithKline plc., Merck KGaA, Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi, Takeda Pharmaceutical Company Limited. H. Watada: Research Support; Self; Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Kissei Pharmaceutical Co., Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novartis Pharmaceuticals Corporation, Novo Nordisk A/S, Pfizer Inc., Sanofi, Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. Speaker's Bureau; Self; Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Eli Lilly and Company, Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novo Nordisk A/S, Ono Pharmaceutical Co., Ltd., Sanofi, Takeda Pharmaceutical Company Limited, Terumo Medical Corporation. Other Relationship; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Kowa Pharmaceutical Europe Co. Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Ono Pharmaceutical Co., Ltd., Sanwa Chemical Industry Co. Ltd., Takeda Pharmaceutical Company Limited. K. Khunti: Advisory Panel; Self; Amgen Inc., AstraZeneca, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi. Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis, Servier, Takeda Pharmaceutical Company Limited. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis. Speaker's Bureau; Self; AstraZeneca, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Menarini Group, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi, Servier, Takeda Pharmaceutical Company Limited. Funding AstraZeneca

Background: Micro- and macrovascular complications are a major cause of morbidity and mortality in patients with T2D. We sought to understand the global pattern and predictors of these complications. Methods: Incidence of non-adjudicated complications was examined over 2 years of follow-up of DISCOVER, a global, observational study of patients with T2D initiating second-line glucose-lowering therapy. Hierarchical logistic regression models examined factors associated with development of complications during follow-up. Results: In 10 718 patients from 33 countries, median T2D duration was 4.1 years (interquartile range [IQR] 2.0-7.7) and median HbA1c was 8.1% (IQR 7.3-9.2). Prevalent micro- and macrovascular complications at baseline were noted in 18.2% and 12.7% of patients, respectively. Over 2 years of follow-up, 12.2% and 4.8% of patients had new micro- and macrovascular complications, respectively (Figure). Male sex, higher blood pressure and longer T2D duration were associated with a greater odds of incident microvascular disease whereas older age and known atherosclerosis were associated with greater odds of incident macrovascular disease. Conclusion: Both the prevalence and the 2-year incidence of vascular complications were remarkably high in patients with relatively short T2D duration, highlighting opportunities for early aggressive risk-factor modification. Disclosure S.V. Arnold: None. H. Chen: Employee; Self; AstraZeneca. J. Cid-Ruzafa: Employee; Self; Evidera. P. Fenici: Employee; Self; AstraZeneca. M.B. Gomes: Advisory Panel; Self; AstraZeneca. Consultant; Self; Merck KGaA. Research Support; Self; CNPq, FAPERJ. K. Khunti: Advisory Panel; Self; Amgen Inc., AstraZeneca, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi. Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis, Servier, Takeda Pharmaceutical Company Limited. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis. Speaker's Bureau; Self; AstraZeneca, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Menarini Group, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi, Servier, Takeda Pharmaceutical Company Limited. G. Luporini Saraiva: Employee; Self; AstraZeneca. J. Medina: Employee; Self; AstraZeneca. A. Nicolucci: Consultant; Self; AstraZeneca, Eli Lilly and Company, Medtronic, Novo Nordisk A/S. Research Support; Self; Artsana S.p.A., Dexcom, Inc., Novo Nordisk A/S, Sanofi-Aventis. S. Pocock: Consultant; Self; AstraZeneca. S.M. Sadikot: Consultant; Self; AstraZeneca. M.V. Shestakova: Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi, Servier. Research Support; Self; Novo Nordisk A/S, Sanofi. I. Shimomura: Advisory Panel; Self; AstraZeneca, Novo Nordisk Pharma Ltd., Sanwa Kagaku Kenkyusho. Consultant; Self; MSD K.K., Novo Nordisk Pharma Ltd., Taisho Pharmaceutical Co., Ltd. Research Support; Self; Astellas Pharma Inc., AstraZeneca, Daiichi Sankyo Company, Limited, Eli Lilly Japan K.K., Kaken Pharmaceutical Co., Ltd., Kowa Pharmaceutical Co., Ltd., Kyowa Hakko Kirin Co., Ltd., Mitsubishi Tanabe Pharma Corporation, MSD K.K., Novartis Pharma K.K., Novo Nordisk Pharma Ltd., Shionogi & Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. Speaker's Bureau; Self; Astellas Pharma Inc., AstraZeneca, Daiichi Sankyo Company, Limited, Eli Lilly Japan K.K., Kowa Pharmaceutical Co., Ltd., Lotte Co., Ltd., Mitsubishi Tanabe Pharma Corporation, MSD K.K., Novartis Pharma K.K., Novo Nordisk Pharma Ltd., Ono Pharmaceutical Co., Ltd., Sanofi K.K., Sanwa Kagaku Kenkyusho, Taisho Toyama Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. F. Surmont: Employee; Self; AstraZeneca. F. Tang: Employee; Self; Mid America Heart Institute. Research Support; Self; AstraZeneca. J. Vora: Employee; Self; AstraZeneca. Research Support; Self; Abbott, AstraZeneca, Boehringer Ingelheim International GmbH, Bristol-Myers Squibb Company, Eli Lilly and Company, GlaxoSmithKline plc., Merck KGaA, Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi, Takeda Pharmaceutical Company Limited. H. Watada: Research Support; Self; Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Kissei Pharmaceutical Co., Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novartis Pharmaceuticals Corporation, Novo Nordisk A/S, Pfizer Inc., Sanofi, Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited. Speaker's Bureau; Self; Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Eli Lilly and Company, Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novo Nordisk A/S, Ono Pharmaceutical Co., Ltd., Sanofi, Takeda Pharmaceutical Company Limited, Terumo Medical Corporation. Other Relationship; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Kowa Pharmaceutical Europe Co. Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Ono Pharmaceutical Co., Ltd., Sanwa Chemical Industry Co. Ltd., Takeda Pharmaceutical Company Limited. M.N. Kosiborod: Consultant; Self; Amgen Inc., AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Eisai Co., Ltd., GlaxoSmithKline plc., Glytec, LLC, Intarcia Therapeutics, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novartis AG, Novo Nordisk A/S, Sanofi. Research Support; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc. Funding AstraZeneca

BACKGROUND Despite the prophylactic use of platelet transfusion, hemorrhagic complications still represent an important cause of morbidity and mortality in patients with hematologic malignancies. Patient-related factors and characteristics of the transfused product can affect transfusion efficacy. STUDY DESIGN AND METHODS The aim of this study was to develop and validate the Platelet Efficacy Score (PEscore), based on patient and product characteristics, to predict the likelihood of a satisfactory platelet transfusion (absolute increment ≥10.5 × 109/L). This study utilized data relative to 16,265 platelet transfusions performed in 1592 oncohematologic patients. The whole sample was divided into two random samples: a training set, in which different patient-related and transfusion-related characteristics were included in a predictive model to develop the PEscore; and a validation set, in which the predictive properties of the PEscore were confirmed. In the training set, multilevel logistic regression analysis was performed in which the likelihood of attaining a satisfactory transfusion was modeled. RESULTS The predictive score ranged between 0 and 30. Predictive properties of the PEscore were confirmed by the observed rates of satisfactory transfusions in the validation sample; the probability of a satisfactory transfusion was less than 10% for a score less than 12 and exceeded 50% if the score was 22 or higher. The likelihood of a satisfactory transfusion increased by 29% for a 1-unit increase in the PEscore (odds ratio, 1.29; 95% confidence interval, 1.27-1.31). CONCLUSION The availability of a prediction score can increase transfusion efficacy, help the transfusion medicine specialist in the choice of the best product for the individual patient, and avoid waste of resources.

Aims: To compare iBGStar™+DMApp (experimental meter+telemedicine system) (iBGStar) with a traditional glucose meter (Control) in type 1 diabetes adolescents/young adults. Methods: i-NewTrend was a multicenter, open-label, randomized trial involving subjects aged 14–24years, on basal–bolus insulin, HbA1c≥8.0%, and poorly compliant with SMBG (i.e.

AIMS To assess country- and individual-level correlates of psychological outcomes, and differences among countries in the associations of individual characteristics with psychological outcomes among adults with diabetes. METHODS The second Diabetes Attitudes, Wishes and Needs (DAWN2(™) ) study assessed self-reported characteristics of people with diabetes in 17 countries, including 1368 adults with Type 1 diabetes and 7228 with Type 2 diabetes. In each country, a sample of 500 adults, stratified by diabetes type and treatment, completed a questionnaire incorporating the validated WHO-5 wellbeing index, the WHOQOL-BREF, and the five-item Problem Areas in Diabetes Scale, as well as the newly developed Diabetes Impact on Life Dimensions that assessed impact ranging from very positive to very negative, with no impact as the midpoint. Multilevel regression analyses identified significant (P < 0.05) independent correlates of psychological outcomes. RESULTS There were significant variations in all outcomes across countries before adjustment for individual-level factors; adjustment reduced between-country disparities. Worse psychological outcomes were associated with more complications, incidence of hypoglycaemia, hypoglycaemic medication, perceived burden of diabetes, family conflict and experience of discrimination. Better psychological outcomes were associated with higher self-rated health, greater access to diabetes education and healthcare, and more psychosocial support from others. The associations of many factors with the outcomes were mediated by modifiable factors. The association of all factors with the outcomes varied across (interacted with) countries, highlighting the need for country-specific analyses. CONCLUSIONS Improvements in modifiable risk factors (reductions in burden and increases in support) may lead to better psychological outcomes in adults with diabetes.

OBJECTIVE An association between variability in clinical parameters (HbA1c, blood pressure, cholesterol, and uric acid) and risk of complications in type 2 diabetes has been reported. In this analysis, we investigated to what extent such variability is associated with overall quality of care. RESEARCH DESIGN AND METHODS The quality of care summary score (Q-score) represents a validated, overall quality of care indicator ranging between 0 and 40; the higher the score, the better the quality of care provided by the diabetes center. We identified patients with five or more measurements of clinical parameters after the assessment of the Q-score. Multiple linear regression analyses assessed the role of the Q-score in predicting the variability of the different parameters. RESULTS Overall, 273,888 patients were analyzed. The variability of all the parameters systematically increased with decreasing Q-score values. At multivariate linear regression analysis, compared with a Q-score >25, a score CONCLUSIONS The variability of risk factors for diabetic complications is associated with quality of care. Quality of care improvement initiatives should be targeted to increase the achievement of the recommended target while reducing such variability.

Context Hypoglycemic drugs with proven cardiovascular (CV) benefits are recommended for patients with type 2 diabetes and CV disease. Whether the beneficial effects extend to those at lower risk remains unclear. Aim We investigated the long-term CV effects of pioglitazone or sulfonylureas (SUs) across the spectrum of pretreatment CV risk. Methods Among 2820 participants of the TOSCA.IT trial, four subgroups with different risk of outcome-a composite of all-cause death, nonfatal myocardial infarction, nonfatal stroke, urgent coronary revascularization-were identified by the RECursive Partitioning and AMalgamation (RECPAM) method. Within each group, the effect of SUs or pioglitazone on the outcome was evaluated. Results Sex was the first splitting variable, followed by urinary albumin-to-creatinine ratio (UACR) (>9 mg/g or ≤9 mg/g) and body mass index (BMI) (>28.7 or ≤28.7 kg/m2). Female patients had the lowest risk (reference); male patients with UACR >9 mg/g and BMI >28.7 kg/m2 had the highest risk [hazard ratio (HR), 5.58; 95% CI, 3.32 to 9.69]. Patients in this group present a cluster of conditions suggestive of marked insulin resistance (higher BMI, waist circumference, triglycerides, blood pressure, and UACR and lower high-density lipoprotein cholesterol) than the other groups. Treatment with pioglitazone in this group was associated with a significantly lower occurrence of the outcome than SUs (HR, 0.48; 95% CI, 0.25 to 0.76). No significant difference between study treatments was observed in the other RECPAM classes. Conclusions It is possible to identify patients with type 2 diabetes early in the stage of their disease and who are largely free from evident CV disease in whom add-on pioglitazone to metformin confers CV protection as compared with SUs.

Background: The world is rapidly urbanizing, causing alarming health problems to their citizens. The Cities Changing Diabetes program aims to address the social factors and cultural determinants that can increase type 2 diabetes (T2D) vulnerability among people living in cities. Methods: Public data of Italian Institute for Statistics (ISTAT) and available scientific reports were reviewed and findings integrated. The prevalence of T2D in the 8 health districts of Rome was mapped and the correlation between prevalence and social and cultural determinants was assessed. Results: The metropolitan area of Rome has 4.3 million inhabitants. People over 65 has increased by 136,000 units in the last decade, reaching 631,000 citizens in 2015. Elderly people living alone are 28.4%. The obesity prevalence is 9.3%, as compared to 8.2% in the year 2000. The prevalence of T2D is 6.6%, varying in the different 8 health districts between 5.9% and 7.3%. A linear correlation exists between the prevalence of diabetes in the districts, unemployment rate and use of private transportation rate (Pearson R 0.52 and 0.60, respectively), while an inverse correlation is present with aging index, school education level, and slow mobility rate (Person R -0.57, -0.52, and -0.52, respectively). Conclusions: Important socio-demographic changes have occurred in Rome during the last decades with a raise in the prevalence of obesity and diabetes. A wide variation exists in the prevalence of T2D among the districts of Rome, associated with social and cultural determinants. This study model can help rethinking diabetes in an urban setting. (www.actabiomedica.it)

Background The global prevalence of type 2 diabetes-related complications is not well described. We assessed prevalence of vascular complications at baseline in DISCOVER (NCT02322762; NCT02226822), a global, prospective, observational study program of 15,992 patients with type 2 diabetes initiating second-line therapy, conducted across 38 countries. Methods Patients were recruited from primary and specialist healthcare settings. Data were collected using a standardized case report form. Prevalence estimates of microvascular and macrovascular complications at baseline were assessed overall and by country and region, and were standardized for age and sex. Modified Poisson regression was used to assess factors associated with the prevalence of complications. Results The median duration of type 2 diabetes was 4.1 years (interquartile range [IQR]: 1.9–7.9 years), and the median glycated hemoglobin (HbA1c) level was 8.0% (IQR: 7.2–9.1%). The crude prevalences of microvascular and macrovascular complications were 18.8% and 12.7%, respectively. Common microvascular complications were peripheral neuropathy (7.7%), chronic kidney disease (5.0%), and albuminuria (4.3%). Common macrovascular complications were coronary artery disease (8.2%), heart failure (3.3%) and stroke (2.2%). The age- and sex-standardized prevalence of microvascular complications was 17.9% (95% confidence interval [CI] 17.3–18.6%), ranging from 14.2% in the Americas to 20.4% in Europe. The age- and sex-standardized prevalence of macrovascular complications was 9.2% (95% CI 8.7–9.7%), ranging from 4.1% in South-East Asia to 18.8% in Europe. Factors positively associated with vascular complications included age (per 10-year increment), male sex, diabetes duration (per 1-year increment), and history of hypoglycemia, with rate ratios (95% CIs) for microvascular complications of 1.14 (1.09–1.19), 1.30 (1.20–1.42), 1.03 (1.02–1.04) and 1.45 (1.25–1.69), respectively, and for macrovascular complications of 1.41 (1.34–1.48), 1.29 (1.16–1.45), 1.02 (1.01–1.02) and 1.24 (1.04–1.48), respectively. HbA1c levels (per 1.0% increment) were positively associated with microvascular (1.05 [1.02–1.08]) but not macrovascular (1.00 [0.97–1.04]) complications. Conclusions The global burden of microvascular and macrovascular complications is substantial in these patients with type 2 diabetes who are relatively early in the disease process. These findings highlight an opportunity for aggressive early risk factor modification, particularly in regions with a high prevalence of complications. Trial registration ClinicalTrials.gov; NCT02322762. Registered 23 December 2014. https://clinicaltrials.gov/ct2/show/NCT02322762. ClinicalTrials.gov; NCT02226822. Registered 27 August 2014. https://clinicaltrials.gov/ct2/show/NCT02226822

Background: Gram-negative Multi-Drug-Resistant Organisms (GNMDROs) cause an increasing burden of disease in Intensive Care Units (ICUs). We deployed a multifaceted intervention to control selection and transmission of GNMDROs and to estimate at which rate GNMDROs would decline with our interventional bundle. Methods: Interventions implemented in 2015: in-ward Antimicrobial-Stewardship-Program for appropriate management of antimicrobial prescription; infection monitoring with nasal/rectal swabs and repeated procalcitonin assays; 24 h microbiological support (since 2016); prevention of catheter-related infections, VAPs and in-ward GNMDROs transmission; education of ICU personnel. In May 2017, epidemiological, clinical and microbiological data were collected and retrospectively analyzed. Rates of resistance in Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii, as well as percentages of resistance among all Gram-negative bacteria were compared during the study period. Results: Of 668 patients, at least one isolate was obtained from 399 patients. The proportions of patients with infection and with Gram-negative isolates were even across the 5 semesters (p = 0.8). For Klebsiella pneumoniae, the number of strains resistant to carbapenems fell from 94% to 6% (p

Marco DiBonaventura,1 Antonio Nicolucci,2 Henrik Meincke,3 Agathe Le Lay,3 Janine Fournier4 1Kantar Health, Health Outcomes Practice, New York, NY, USA; 2Center for Outcomes Research and Clinical Epidemiology, Pescara, Italy; 3Novo Nordisk A/S, Soeborg, Denmark; 4Novo Nordisk, Plainsboro, NJ, USA Purpose: This study investigated the association between body mass index (BMI) and three comorbid conditions (type 2 diabetes [T2D], prediabetes, and hypertension) on humanistic and economic outcomes.Patients and methods: This retrospective observational study collected data from German (n=14286) and Italian (n=9433) respondents to the 2013 European Union National Health and Wellness Survey, a cross-sectional, nationally representative online survey of the general adult population. Respondents were grouped, based on their self-reported BMI, and stratified into three other comorbid conditions (T2D, prediabetes, and hypertension). Generalized linear models, controlling for demographics and health characteristics, tested the relationship between BMI and health status, work productivity loss, and health care resource utilization. Indirect and direct costs were calculated based on overall work productivity loss and health care resource utilization, respectively. The same generalized linear models were also performed separately for those with T2D, prediabetes, and hypertension.Results: The sample of German respondents was 50.16% male, with a mean age of 46.68years (SD =16.05); 35.24% were classified as overweight and 21.29% were obese. In Italy, the sample was 48.34% male, with a mean age of 49.27years (SD =15.75); 34.85% were classified as overweight, and 12.89% were obese. Multivariable analyses demonstrated that, in both countries, higher BMI was associated with worse humanistic outcomes and only those from Germany also reported greater direct and indirect costs. Differences in the impact of BMI on outcomes by country were additionally found when the sample was stratified into those with prediabetes, T2D, and hypertension.Conclusion: The high percentage of patients who are overweight or obese in Germany and Italy remains problematic. Better elucidating the impact of overweight or obese BMI, as well as the incremental effects of relevant comorbid conditions, on humanistic and economic outcomes is critical to quantify the multifaceted burden on individuals and society. Keywords: body mass index, costs, health care resource utilization, health status, work productivity loss, weight loss

Background and Aims: Innovative technologies supporting healthcare professionals (HCPs) and patients in the review of self-monitoring blood glucose (SMBG) data may aid clinical and educational approach to diabetes management. MyStar Connect® (MSC) is a diabetes data management software that helps HCPs review patient data and adjust diabetes therapy. Filtering mechanisms are needed to visualize data efficiently. We developed a pattern recognition engine (PRE) for visualization of glycemic patterns of SMBG data in MSC. Method: 3-month of SMBG data from 184 insulin- and non-insulin treated patients, at low testing frequency (0.5-2 tests per day), were independently evaluated by three expert assessors. Daily and weekly hypo- and hyperglycemic patterns were identified, and compared to those identified by the PRE. Discrepancies were reconciled through discussion between the assessors. The rate of agreement between assessors and PRE was evaluated. The analyses were conducted separately for hypoglycemic and hyperglycemic patterns. Results: Daily patterns: 56 hypoglycemic and 247 hyperglycemic patterns were identified by the PRE. No disagreement was noted in the assessment of hypoglycemic patterns between the PRE and the assessors. In 23 cases of hyperglycemic patterns, a disagreement was noted, due to the PRE missing a pattern considered as present by the assessors. After further clarification, 18 cases were reconciled. Therefore, the rate of agreement between the PRE and the assessors was 100% for hypoglycemic patterns and 98% for hyperglycemic patterns. Weekly patterns: The assessors recognized that the PRE provided information not easily inferable by simple review of SMBG data. PRE was therefore considered reliable for all the patterns identified. Conclusion: The implementation of PRE can represent a useful tool to guide HCPs’ treatment decisions and educate patients performing SMBG at a low frequency. Disclosure G. Vespasiani: Consultant; Self; METEDA Srl. Other Relationship; Spouse/Partner; METEDA Srl. A. Nicolucci: None. M. Saleh: Employee; Self; Sanofi. J. Sieber: Employee; Self; Sanofi. Stock/Shareholder; Self; Sanofi. G. Prosperini: None. C. Suraci: None. M. Galetta: None. G. Grassi: None.

To assess use of self-monitoring of blood glucose (SMBG) in type 2 diabetes (T2DM) in the context of a continuous quality improvement initiative (AMD Annals).14 quality-of-care indicators were developed, including frequency of SMBG, fasting blood glucose (FBG), and post-prandial glucose (PPG) levels, and hypoglycemia and hyperglycemia episodes. Clinical data and SMBG values downloaded from any glucose meter were obtained from electronic medical records. The most frequently used glucose-lowering treatment regimens were identified and the indicators were assessed separately by regimen.Overall, 21 Italian centers and 13,331 patients (accounting for 35,657 HbA1c tests and 8.44 million SMBG values collected during 2014 and 2015) were included in the analysis; 11 therapeutic regimens were selected. Patients in regimens not including insulin performed 15-23 measurements per patient-month, those treated with basal insulin 32.1 tests/patient-month, and those treated with basal and short-acting insulin 53-58 tests/patient-month. In all treatment regimens, PPG measurements represented a minority of all tests; pre-breakfast measurements accounted for about 50% of all FBG values. Mean FBG levels exceeded 130 mg/dl in 49.3-88.3% of the cases in the different treatment regimens, while PPG levels were over 140 mg/dl in 46.7-81.0%. From 5.7 to 32.7%, patients in the different regimens had at least one episode of hypoglycemia ( 70 mg/dl), while from 3.7 to 47.7% had at least one episode of hyperglycemia ( 300 mg/dl).SMBG is underutilized in patients with T2DM treated or not with insulin. In all treatment groups, PPG is seldom investigated. Poor metabolic control and rates of hyper- and hypoglycemia deserve consideration in all treatment groups.

The purpose of this study was to quantify the metabolic equivalents (METs) of resistance exercise in obese patients with type 2 diabetes (T2DM) and healthy young subjects and to evaluate whether there were differences between sessions executed at low- versus high-intensity resistance exercise. Twenty obese patients with T2DM (62.9±6.1 years) and 22 young subjects (22.6±1.9 years) performed two training sessions: one at vigorous intensity (80% of 1-repetition maximum (1RM)) and one at moderate intensity (60% of 1RM). Both groups carried out three strength exercises with a 2-day recovery between sessions. Oxygen consumption was continuously measured 15 min before, during and after each training session. Obese T2DM patients showed lower METs values compared with young healthy participants at the baseline phase (F= 2043.86; P