Roche Diagnostics Corporation Research Articles

Lipid Profile Perturbations Associated With Subclinical Hypothyroidism: A Descriptive Study.

Background Hypothyroidism is a prevalent endocrine disorder associated with dyslipidemia, which increases cardiovascular risk. Our study aimed to estimate the prevalence of dyslipidemia and subclinical hypothyroidism (SCH) and their correlation in a diverse population. Methods A descriptive cross-sectional retrospective analysis was conducted to assess the prevalence of dyslipidemia in patients with SCH. Data were collected over 19 months from the Clinical Biochemistry Department of a Moroccan university hospital. A total of 447 patients were included based on comprehensive lipid profile andthyroid-stimulating hormone (TSH) assessments, and normal free thyroxine (FT4) levels. Lipid profile and TSHmeasurements followed standardized procedures using the Cobas Roche® 6000 system (Roche Diagnostics Corporation,Indianapolis,USA). Dyslipidemia and SCH were defined according to established thresholds recommended by reputable organizations. Statistical analyses were performed using SPSS version 23.0 (IBM Corp., Armonk, USA)and Microsoft Excel (Microsoft Corporation, Redmond, USA), with significance set at p <0.05. Results In the total population (447 individuals), the prevalence of dyslipidemia was approximately 42.05% (N = 188), with hypoHDLemia being most prevalent at approximately 31.31% (N = 140). The prevalence of SCHwas approximately 12.75% (N = 57), with women constituting approximately 7.6% and men approximately 5.15%. In the euthyroid group 1 (N = 390), the prevalence of dyslipidemia was approximately 40.76% (159 individuals), while in the hypothyroid group 2 (N = 57), it increased to approximately 50.87% (N = 29). Hypertriglyceridemia was more prevalent in Group 2, with a prevalence of approximately 21.05% (N = 12), compared to Group 1, which had a prevalence of approximately 13.84% (N = 54). Additionally, hypoHDLemia was notably higher in Group 2, with a prevalence of approximately 38.59% (N = 22), compared to Group 1, which had a prevalence of approximately 30.25% ( N = 118). The chi-square test revealed a significant association between SCH and dyslipidemia (χ2 = 1.427, p <0.05). The calculated odds ratio (OR) of 1.5 (p <0.05) indicates that individuals with SCHare 1.5 times more likely to have dyslipidemia compared to those without SCH. Conclusion In conclusion, our study provides valuable insights into the prevalence of dyslipidemia and its association with SCHin our patient population. We observed a notable prevalence of dyslipidemia among individuals with SCH, characterized by elevated levels of total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C). Importantly, while chi-square tests revealed a significant association between SCH and dyslipidemia, logistic regression analyses did not confirm a statistically significant correlation after adjusting for potential confounders.

Abstract C071: Keratin 17 excludes CD8-positive T cells and recruits CD163-positive macrophages in pancreatic ductal adenocarcinoma

Abstract Background: Keratin 17 (K17) is a negative prognostic biomarker, overexpressed in the biologically most aggressive forms of pancreatic ductal adenocarcinoma (PDAC). In other anatomic sites and disease processes, K17 expression also correlates with immune cell infiltrates and could block T-cell infiltration. Thus, we hypothesized that K17 expression correlates with the inflammatory microenvironment in PDAC. In this study, we aimed to determine the relationship between the stromal immune cell infiltrates and K17 expression, using multiplexed immunohistochemistry (mIHC) and our suite of deep learning tools to quantitatively evaluate the expression of four biomarkers of T-cells and macrophages in PDAC. Methods: mIHC was performed on representative sections of 201 primary PDACs from Stony Brook University Hospital, Thomas Jefferson University Hospital, Cedars Sinai Medical Center, and from a national cohort (KYT, Pancreatic Cancer Action Network, and Perthera). Antibodies for CD4 (helper T-cells), CD8 (cytotoxic T-cells), CD16 (pan-macrophage), CD163 (M2 macrophages), pancytokeratin, and K17 were provided by Roche Diagnostics Corporation through a sponsored research agreement. mIHC was performed on a Discovery Ultra Autostainer (Roche), using horseradish peroxidase (HRP) and alkaline phosphatase (AP)-based protocols with multiple chromogens (Red: CD4, Purple: CD8, Yellow: CD16, Green: CD163, Teal: pancytokeratin, and Brown: K17) to enable multispectral imaging of diverse immune cell populations within the cancer microenvironment. A deep learning analysis workflow was used to detect and classify stromal inflammatory cells, in whole slide images (WSIs), generated using an Olympus VS120 digital microscope (Olympus, Tokyo, Japan). Pixel-wise predictions from a color auto-encoder (ColorAE) union UNET anchor UNET model were combined to create multi-class masks that were further analyzed to perform detection and classification. Results: The analysis of the inflammatory microenvironment focused on defining immune cell infiltrates located within 25 microns of the closest K17-positive versus K17-negative tumor cell in each representative section. Across the sum of K17-positive and negative zones/section, CD4 cell counts ranged from 0-10,617 (mean 2,709), CD8 cell counts ranged from 63-28,596 (mean 6,745), CD16 cell counts ranged from 4-7,797 (mean 3,024), and CD163 cell counts ranged from 35-34,696 (mean 14,968). CD4 T-helper cells, CD8 cytotoxic T cells, and CD16 macrophages were more numerous (respectively, p=0.0012; p=&amp;lt;0.0001; p=&amp;lt;0.0001) in K17-negative tumor zones compared to K17-positive zones. By contrast, the number of CD163 (M2) tumor-promoting macrophages was greater in K17 positive zones (p=0.0019). Conclusion: K17 expression by tumor cells impacts the chronic inflammatory microenvironment, shielding tumor cells from immune cell mediated cytotoxic responses, while recruiting tumor-promoting M2 macrophages, indicating that K17 impacts the immune response as a fundamental hallmark of aggression in PDAC. Citation Format: Lyanne Oblein, Michael Horowitz, Mahmudul Hasan, Sruthi Babu, Mariana Torrente-Goncalves, Lucia Roa, Jaymie Oentoro, Jason Harper, Xin Yao Zheng, Wei Jiang, Andrew Hendifar, Natalie Moshayedi, Brent Larson, Veronica Placencio-Hickok, Edik Blais, Emmanuel Petricoin, Joel Saltz, Natalia D. Marchenko, Luisa F. Escobar-Hoyos, Kenneth Shroyer. Keratin 17 excludes CD8-positive T cells and recruits CD163-positive macrophages in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr C071.

Patients with Chronic Lymphocytic Leukemia Belonging to the B-Cell Receptor Stereotypy Subset #2 Have Long Progression Free Survival after Chemo- or Chemoimmunotherapy in the HOVON68 Trial

Abstract Introduction: About one third of all patients with Chronic Lymphocytic Leukemia (CLL) have quasi-identical, so-called stereotyped B-cell receptors (BCR) that can be divided into subsets (Agathangelidis et al. Blood 2012). At present, 19 major subsets have been described that account for approximately 12% of all patients with CLL. Of these, subsets #1 and #2 are associated with a poor prognosis (Baliakas et al. Lancet Hematology 2014). The HOVON68 trial compared fludarabine and cyclophosphamide (FC) chemotherapy to chemo-immunotherapy with low-dose alemtuzumab (FCA) as a first line treatment in selected high risk patients defined as either harboring 17p deletion, 11q deletion, trisomy 12, having unmutated mmunoglobulin heavy variable genes (IGHV) and/or VH3-21. The study showed a benefit of adding low-dose alemtuzumab to FC (Geisler et al. Blood 2014). Here, we studied the impact of BCR stereotypy subsets on the primary outcome progression free survival (PFS) and overall survival (OS). Methods: Sequences from IGHV mutational analyses were collected from participating centers in Sweden, Norway, Finland, Denmark, and the Netherlands. Subsets were assigned based on the ARResT/AssignSubsets software (Bystry et al. Bioinformatics 2015). Analysis for recurrent mutations were performed by next generation sequencing by a 454 based platform (Roche Diagnostics Corporation, Indianapolis, USA). All other clinical data were extracted from the HOVON database as of November 2016. Kaplan-Meier curves, log-rank tests, and Cox regression models were used for survival analysis. Fisher's exact test was used for contingency table analysis. P-values Results: A total of 187 sequences were available. Of these, 176 were suitable for analysis. We found a total of 37 patients (21%) that could be assigned to one of the 19 major subsets: Subset #2 was the most frequent (n=12, 6.8%), thereafter subset #8 (n=7, 4.0%), subset #6 (n=6, 3.4%), and subset #1 (n=5, 2.8%). The median follow-up time for patients still alive was 78.6 months. By November 2016, 148 patients (84%) had had an event (no response to treatment, progression, or death) and 77 patients (44%) had died. Compared to patients belonging to other major subsets, patients with subset #2 did not differ significantly as to: age group above 65 years, gender, treatment arm, cytogenetic aberrations by FISH, WHO performance status, beta2microglobulin level>3.5 mg/L, or in complete response rates, However, patients with subset #2 had more advanced disease (Binet stage C 83% vs. 52%, p=0.04) and as expected less often unmutated IGHV genes (67% vs. 100%, p=0.01). Nineteen of 37 patients (51%) was analyzed for recurrent mutations: Only SF3B1 mutations was found in patients with subset #2, and the frequency was slightly higher compared to other major subsets (57% vs. 17%, p=0.13), in whom also KRAS2 (17%) and BRAF15 (8%) mutations was found. Unexpectedly, patients with subset #2 had a longer PFS compared to patients with other major subsets (median PFS #2: 51.2 months vs. other major #: 31.1 months, p=0.002, Figure), or compared to patients with usage of the same IGHV gene (e.g. median PFS for 7 patients with VH3-21/non #2: 15.4 months, p Discussion and conclusions: As expected, stereotypy was found more frequently in the HOVON68 trial that selected for high risk patients as compared to patients from large multi-institutional studies (Stamatopoulos at al. Leukemia 2017). Surprisingly, patients belonging to subset #2 had a better PFS after chemo-(immuno) therapy compared to other major subsets, albeit the numbers are small. Our data suggest that, chemo-(immuno) therapy may still have a place for patients otherwise assessed as ‘high risk’ such as patients with B-cell receptor stereotypy subset #2. Thus, selection of patients for trials as well as the exact treatment regimen may have major impact on the significance of biomarkers. Download : Download high-res image (115KB) Download : Download full-size image Figure . Disclosures Vojdeman: Roche: Other: Travel support in 2015; Gilead: Other: Travel support. Kimby: Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria; Celgene: Research Funding; Pfizer: Research Funding. Van Oers: Roche: Consultancy; ISA therapeutics: Consultancy; Novartis: Consultancy; Immunicum: Consultancy. Kater: Celgene: Consultancy, Research Funding; Johnson & Johnson: Research Funding; Abbvie: Research Funding. Niemann: Roche: Consultancy, Other: Travel grant; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel grant; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel grant; Novo Nordisk Foundation: Research Funding; The Danish Cancer Society: Research Funding; Novartis: Other: Travel grant; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel grant, Research Funding.

416. Comparative Analytical Assessment of PCR Mastermixes for Detection of SARS-CoV-2 using the CDC Diagnostic Test and the LightMix Modular Test on the cobas® z 480 Analyzer

BackgroundThe novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a betacoronavirus responsible for the ongoing global pandemic and associated respiratory disease. Rapid development and implementation of molecular diagnostic testing solutions has been imperative to meet the enormous and urgent public health needs, and remains a key component of the US emergency response. Nucleic acid amplification tests (NAATs), with emergency use authorization (EUA) by the FDA, have been subject to significant supply chain shortages. This study aims to comparatively assess several commercially available substitutive mastermix reagents for the CDC SARS-CoV-2 EUA test and the TIB Mol Biol LightMix Modular (RUO) test.MethodsPositive control material included with each testing kit was used directly as DNA template for all manually assembled reactions and comparative evaluation. Additionally, these tests were evaluated similarly using the cobas omni Optimization kit, the first step in assessing suitability on the cobas® omni Utility Channel for high-volume user-defined molecular testing on the fully automated cobas® 6800/8800 Systems. All PCR was performed per the manufacturer’s instructions using the User Defined Workflow (UDF; open channel) on the cobas® z 480 analyzer.ResultsRobust amplification of the commercial control material was observed with each mastermix for all gene targets within the CDC and LightMix tests. Modest but significant (ANOVA, p< 0.05) target-specific Ct-value impacts were observed among the mastermixes assessed in this study. Using the cobas® omni optimization kit, Ct values for each target within the CDC and LightMix tests were consistently and significantly lower (ANOVA, p< 0.05) than the comparator mastermixes.ConclusionEach mastermix may be a useful alternative to the recommended mastermix for SARS-CoV-2 detection. Additionally, these findings suggest the CDC and LightMix tests may be adapted for fully-automated, high-throughput testing on the 6800/8800 Systems.DisclosuresSteven Cagas, PhD, Roche Diagnostics Corp (Employee) Stephen McCune, BS, Roche Diagnostics Corp (Employee) Pedro Rodriguez, Ph.D, Roche Diagnostics Corp (Employee) Ray Hein, PhD, Roche Diagnostics Corp (Employee) John Osiecki, PhD, Roche Diagnostics Corp (Employee) Nicole Robinson, Ph.D, Roche Diagnostics Corp (Employee) Chris L. McGowin, PhD, Roche Diagnostics Corp (Employee)

Validation of Soluble Fms-Like Tyrosine Kinase-1 (sFlt-1) and Placental Growth Factor (PlGF) Assays on Cobas e602 System

Abstract Background Preeclampsia is a leading hypertensive disorder in pregnant women. The angiogenic biomarkers, soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PlGF) ratio, have been shown to be associated with diagnosis and prediction of preeclampsia. The objective of this study is to validate the analytical performance of sFlt-1 and PlGF on the Cobas e602 system (Roche Diagnostics Corporation). Method Intra-day and inter-day precisions for both sFlt-1 and PlGF assays were assessed using quality control materials provided from Roche Diagnostics. The accuracies for both assays were assessed by running 60 patient samples, which have been previously analyzed on the Elecsys 411 analyzer (Roche Diagnostics Corporation) at the Beth Israel Deaconess Medical Center. Linearity studies for both assays were performed using patient plasma spiked with recombinant sFlt-1 and PlGF proteins (R&amp;D systems). Hemolysis, icterus, lipemia and biotin interference studies were performed by spiking hemolysate, bilirubin, intralipid or biotin into either pooled patient plasma with detectable levels of sFlt-1 and PlGF or otherwise, patient plasma spiked with recombinant sFlt-1 and PlGF proteins. Results Total precisions for both assays demonstrated CVs of &amp;lt;5.0%. The sFlt-1 and PlGF assays demonstrated analytical measuring ranges of 3060,000 pg/mL and 79,000 pg/mL, respectively (r2 &amp;gt; 0.98). Lower limit of quantitation (10% CV) was 30 pg/mL for sFlt-1 and 7 pg/mL for PlGF, respectively. Interference studies showed sFlt-1 and PlGF were not significantly affected by hemolysis up to H-indices of 500 and 1000 respectively; both assays were not affected by bilirubin up to an I-index of 60, and lipemia up to an L-index of 2800. Biotin at concentrations &amp;gt;30 ng/mL caused significant negative bias for both sFlt-1 and PlGF assays. Comparison studies showed the following: Cobas e602 sFLT-1 = 1.09 [Elecsys 411 sFLT-1] +203 (r2=0.97, Sy/x=1234, n=58); Cobas e602 PlGF = 1.10 [Elecsys 411 PlGF] +47 (r2=0.99, Sy/x=22.1, n=58); Cobas e602 sFLT-1/PlGF ratio = 0.94 [Elecsys 411 sFLT-1/PlGF ratio] +3.5 (r2=0.91, Sy/x=50, n=58). Conclusion sFlt-1 and PlGF measured on Roche Diagnostics Cobas e602 system demonstrated excellent analytical performance and are acceptable for clinical use once approved in the US.

Invited Product Profile – Cobas Liat System

From the Roche Diagnostics Corporation, Indianapolis, IN. Reprints: Jamie E. Phillips, PhD, Roche Diagnostics Corporation, Indianapolis, IN. E-mail: [email protected]. The author declares no conflict of interest.

Glucose Variables in T1D Studies with Dapagliflozin—Pooled Analysis of Continuous Glucose Monitoring Data from DEPICT-1 and 2

Improved glycemic control and weight loss, without increased hypoglycemia, were demonstrated in two short-term, 24-week, Phase 3 studies of the SGLT2 inhibitor dapagliflozin (DAPA) as adjunct to adjustable insulin in patients with inadequately controlled T1D (DEPICT-1 and 2). In this post-hoc analysis we pooled continuous glucose monitoring (CGM) data at baseline and week 24 from both studies. In total, 1591 patients were included (DAPA 5 mg N=530; DAPA 10 mg N=529; placebo N=532). Baseline characteristics were comparable between the study groups. DAPA treatment significantly reduced mean interstitial glucose, mean amplitude of glucose excursions (MAGE), and postprandial glucose, while expanding the time in glycemic target range (&amp;gt;70 mg/dL to ≤180 mg/dL)(Table). In addition, DAPA treatment did not increase the percent of glucose readings ≤70 mg/dL or ≤54 mg/dL, or the percent of readings ≤70 mg/dL in the nocturnal period (00:00 to 05:59). These results demonstrate that DAPA as adjunct to insulin in patients with T1D reduced glycemic variability without increasing time in the hypoglycemia range.Table. Pooled CGM parametersCGM parameterTreatment armMean baseline value (SD)Mean Week 24 value (SD)Adjusted mean change from baseline (SE)Difference vs. placebo (95% CI)Mean 24-hour glucose readings (mg/dL)DAPA 5 mg193.00 (28.78)179.93 (32.41)-8.40 (1.30)−15.48 (−18.82, −12.13)DAPA 10 mg191.00 (28.11)174.87 (26.59)–11.82 (1.32)−18.90 (−22.25, − 15.54)Placebo191.64 (29.04)194.34 (32.62)7.07 (1.33)MAGE (mg/dL)DAPA 5 mg170.53 (30.55)154.67 (34.21)–12.48 (1.37)−13.36 (−16.89, −9.83)DAPA 10 mg171.01 (31.59)154.52 (34.52)–13.06 (1.39)−13.94 (−17.48, −10.40)Placebo168.99 (31.46)166.94 (31.62)0.88 (1.40)Percent of 24-hour glucose values within &amp;gt;70 – ≤180 mg/dL (%)DAPA 5 mg43.38 (12.22)51.69 (14.49)6.48 (0.60)9.07 (7.55, 10.59)DAPA 10 mg43.98 (12.32)53.89 (13.26)8.08 (0.60)10.67 (9.15, 12.20)Placebo43.66 (12.64)43.10 (13.97)–2.59 (0.61)Percent of glucose readings ≤70 mg/dL over 24-hours (%)DAPA 5 mg4.96 (4.64)4.85 (4.89)–0.43 (0.21)0.06 (–0.47, 0.59)DAPA 10 mg5.30 (4.90)5.10 (4.44)–0.33 (0.21)0.16 (–0.38, 0.69)Placebo5.14 (5.56)4.83 (4.72)–0.49 (0.21)Percent of glucose readings ≤54 mg/dL (%)DAPA 5 mg2.22 (2.87)2.02 (2.83)–0.32 (0.13)–0.14 (–0.47, 0.19)DAPA 10 mg2.34 (3.13)2.05 (2.63)–0.34 (0.13)–0.16 (–0.48, 0.17)Placebo2.28 (3.82)2.15 (3.15)–0.19 (0.13)Postprandial glucose readings by CGM (mg/dL)DAPA 5 mg199.42 (40.51)192.97 (43.90)–0.85 (1.99)–8.55 (–13.70, –3.41)DAPA 10 mg196.61 (38.57)186.93 (36.21)–5.05 (2.02)–12.76 (–17.93, –7.58)Placebo197.35 (42.32)199.68 (44.36)7.70 (2.03)Percent of nocturnal (00:00–05:59) glucose values ≤70 mg/dL (%)DAPA 5 mg5.94 (7.64)5.95 (8.26)–0.38 (0.36)–0.15 (–1.07, 0.78)DAPA 10 mg5.52 (7.22)6.00 (7.60)–0.18 (0.37)0.06 (–0.87, 0.99)Placebo5.83 (7.74)5.85 (7.64)–0.24 (0.37) Disclosure C. Mathieu: Research Support; Self; Novo Nordisk A/S. Advisory Panel; Self; Novo Nordisk A/S. Speaker's Bureau; Self; Novo Nordisk A/S. Research Support; Self; Sanofi. Speaker's Bureau; Self; Sanofi. Advisory Panel; Self; Sanofi. Research Support; Self; Merck Sharp &amp; Dohme Corp.. Speaker's Bureau; Self; Merck Sharp &amp; Dohme Corp.. Advisory Panel; Self; Merck Sharp &amp; Dohme Corp.. Research Support; Self; Eli Lilly and Company. Speaker's Bureau; Self; Eli Lilly and Company. Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Novartis AG. Speaker's Bureau; Self; Novartis AG. Advisory Panel; Self; Novartis AG, Bristol-Myers Squibb Company. Speaker's Bureau; Self; AstraZeneca. Advisory Panel; Self; AstraZeneca, Pfizer Inc., Janssen Pharmaceuticals, Inc., Boehringer Ingelheim GmbH. Speaker's Bureau; Self; Boehringer Ingelheim GmbH. Advisory Panel; Self; Hanmi Pharmaceutical. Research Support; Self; Roche Diagnostics Corporation. Advisory Panel; Self; Roche Diagnostics Corporation. Research Support; Self; Medtronic. Advisory Panel; Self; Medtronic, MannKind Corporation. Research Support; Self; Intrexon. Advisory Panel; Self; Intrexon, Dianax, UCB, Inc.. Research Support; Self; Abbott. P. Dandona: Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca. Research Support; Self; AstraZeneca. M. Phillip: Other Relationship; Self; Sanofi, Medtronic MiniMed, Inc., Novo Nordisk A/S. Speaker's Bureau; Self; Eli Lilly and Company. Research Support; Self; Merck &amp; Co., Inc., Bristol-Myers Squibb Company, Kamada, Lexicon Pharmaceuticals, Inc.. Other Relationship; Self; DreaMed Diabetes, Ltd.. Speaker's Bureau; Self; Abbott. T. Oron: None. L. Hansen: Employee; Self; MedImmune. F.A. Thoren: Employee; Self; AstraZeneca. J. Xu: Employee; Self; AstraZeneca. A. Langkilde: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca.

Improvements in Patient Care by an Integrated Personalized Diabetes Management (iPDM) Approach May Be Driven by the Structured Process and How Physicians Use Data Sources

Aim: The PDM-ProValue study program showed the benefit of iPDM for people with type 2 diabetes mellitus (T2D) on insulin therapy. Here, we analyze which treatment process conditions may be key for this success. Methods: The study program was conducted over 12 months in a prospective, controlled, cluster-randomized setting. 101 medical practices were randomized to the iPDM arm (n=53) or the control (CNL) arm (n=48). In addition to medical and patient related outcomes, parameters of process quality and data sources for therapy decisions were monitored e.g., by means of Likert-scaled requests. Results: After 12 months, HbA1c reduction vs. baseline was higher for patients in iPDM (0.5%, p&amp;lt;0.0001) compared to those in CNL (0.3%, p&amp;lt;0.0001; between-group difference=0.2%, p&amp;lt;0.05). The degree of using both patient reports and SMBG data to assess glycemic status increased: after 12 months, 94% of physicians in the iPDM group stated to use a combination of data sources vs. 75% in CNL (Baseline: 56% iPDM, 59% CNL). Similarly, doctors’ appreciation of the combined information of SMBG and HbA1c (iPDM group only) increased in the course of the study. (Baseline: 43%, 12 months: 61%). After 12 months, 82% of physicians rated the usefulness of all information sources high or very high in the iPDM group compared to 49% in CNL (baseline: 28% vs. 28%). Physicians rated the iPDM process as more structured and better adaptable to the individual situation of the patient compared to before iPDM implementation. Discussion: A more holistic and beneficial use of data sources by implementing iPDM may be key for the improvements observed in this difficult to treat patient group. A more structured treatment process applied by the physicians and better personalization of therapy seem to enhance physician-patient interaction. Physicians’ positive perception of iPDM may facilitate more focused treatment decisions, thereby overcoming clinical inertia. Disclosure L. Heinemann: Stock/Shareholder; Self; Profil Institute for Metabolic Research, ProSciento. Consultant; Self; Roche Diabetes Care Health and Digital Solutions. D. Messinger: Other Relationship; Self; Roche Diagnostics Corporation, Roche Diagnostics Corporation, Roche Pharma, AbbVie Inc., Merck KGaA. W. Schramm: Advisory Panel; Self; Roche Diabetes Care Health and Digital Solutions. Research Support; Self; Roche Diabetes Care Health and Digital Solutions. I. Vesper: None. J. Weissmann: Employee; Self; Roche Diabetes Care Deutschland GmbH. B. Kulzer: Research Support; Self; Berlin-Chemie AG. Speaker's Bureau; Self; Berlin-Chemie AG, Novo Nordisk Inc.. Advisory Panel; Self; Roche Diabetes Care Health and Digital Solutions. Speaker's Bureau; Self; Roche Diabetes Care Health and Digital Solutions. Research Support; Self; Abbott. Speaker's Bureau; Self; Abbott, Eli Lilly and Company. Advisory Panel; Self; Novo Nordisk Inc., Medtronic, Ascensia Diabetes Care. Speaker's Bureau; Self; Ascensia Diabetes Care.

Novel Risk Panel for CAD and All-Cause Mortality in Type 1 Diabetes

Coronary artery disease (CAD) prevalence is increased and life expectancy is reduced by more than a decade in people with type 1 diabetes (T1D) compared to nondiabetics (non-DM), but traditional risk factors do not explain all of the increased risk. We examined whether three novel risk markers, high-sensitive cardiac troponin T (hs-cTnT), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and growth differentiation factor 15 (GDF-15), are elevated in T1D and predict CAD and mortality. 1,416 adults age 19-56 (652 T1D, 754 non-DM) were enrolled in the Coronary Artery Calcification in type 1 diabetes (CACTI) study from 2000-2002. We assessed CAD events (fatal or non-fatal myocardial infarction, coronary artery bypass graft, or angioplasty) with annual follow-up questionnaires. In-person visits occurred at 3, 6, and 12 years after baseline. Serum samples from the visit prior to a CAD event or death or from the last in-person visit were available for 1,389 participants and were assayed for hs-cTnT, NT-proBNP, and GDF-15 at the University of Maryland. Elevated marker levels were defined as those above the 95th percentile among non-DM participants. Biomarker levels were compared by T1D status in linear regression, and logistic regression was used to examine associations with CAD and all-cause mortality. Age- and sex-adjusted LSmeans ± SE for log hs-cTnT (2.0±0.02 vs. 1.6±0.02, p&amp;lt;0.0001), log NT-proBNP (3.8±0.04 vs. 3.5±0.03, p#x003C;0.0001), and log GDF-15 (6.5±0.02 vs. 6.2±0.02, p#x003C;0.0001) were significantly higher in T1D than non-DM participants. Over the follow-up period, there were 72 CAD events (53 T1D, 19 non-DM) and 76 deaths (58 T1D, 18 non-DM). Having elevated levels of all 3 vs. none of the markers was a significant predictor of both CAD (OR [95% CI] 6.4 [2.0-20.7]) and all-cause mortality (OR [95% CI] 10.7 [3.3-35.4]), independent of age, sex, renal function and T1D status. A panel of novel markers, hs-cTnT, NT-proBNP, and GDF-15, are elevated in T1D and may help identify those at risk for CAD events and mortality. Disclosure A. Keshawarz: None. R.M. Sippl: None. H. Rebuck: None. S. Donald: None. S.Y. Huang: None. R.H. Christenson: Consultant; Self; Roche Diagnostics Corporation, Siemens, Becton, Dickinson and Company. J.K. Snell-Bergeon: Stock/Shareholder; Self; Abbott. Research Support; Self; Roche Diagnostics Corporation.

Modulation of Dapagliflozin-Associated Genital Tract Infections by Saxagliptin—A Pooled Safety Analysis

Incidence of genital tract infections (GTIs), a common adverse event during SGLT2 inhibitor therapy, may be modified by concomitant DPP-4 inhibitor treatment. We evaluated GTI incidence across randomized trials of dapagliflozin (DAPA) ± saxagliptin (SAXA) as add-on to metformin (MET). Safety data were pooled from seven randomized phase 3 trials; patients with type 2 diabetes (N=3134) receiving DAPA 5/10 mg, SAXA 5 mg or DAPA 5/10 mg + SAXA 5 mg as add-on to MET for 24-52 weeks were included. Data from patients with 52 weeks of follow-up across five of the studies were pooled separately (long-term [LT]-study pool; N=1719). In the 7-study pool, GTI incidence was lower with add-on DAPA 10 mg + SAXA 5 mg to MET vs. add-on of DAPA 10 mg alone (3.6% vs. 6.6%); the most common events in both groups were vulvovaginal mycotic infections (DAPA + SAXA, 1.5%; DAPA, 2.0%) and balanoposthitis (DAPA + SAXA, 0.9%; DAPA, 1.8%) (Table). This finding was confirmed in the individual studies with the exception of one study comparing GTI incidence with low-dose DAPA 5 mg + SAXA 5 mg (3.1%) vs. DAPA 5 mg (1.4%). With LT treatment, GTI incidence decreased over time with DAPA 10 mg + SAXA 5 mg and DAPA 10 mg (month 6, 3.1% and 5.3%; month 12, 0.9% and 2.1%). In conclusion, add-on of DAPA 10 mg + SAXA 5 mg to MET led to lower GTI incidence than with DAPA 10 mg alone; this effect was maintained after 12 months of treatment. Disclosure S. Del Prato: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, GlaxoSmithKline plc., Intarcia Therapeutics, Inc., Merck &amp; Co., Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk A/S, Servier, Sanofi, Takeda Pharmaceuticals U.S.A., Inc.. Research Support; Self; Merck &amp; Co., Inc., Novartis Pharmaceuticals Corporation, Boehringer Ingelheim Pharmaceuticals, Inc., AstraZeneca. Speaker's Bureau; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Novartis Pharmaceuticals Corporation, Takeda Pharmaceuticals U.S.A., Inc.. Advisory Panel; Self; Janssen Biotech, Inc., Abbott. E.K. Johnsson: Employee; Self; AstraZeneca. R. Garcia-Sanchez: Employee; Self; AstraZeneca. C. Morgan: Employee; Self; AstraZeneca. P. Sartipy: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca, Novo Nordisk A/S. R. Kurlyandskaya: Employee; Self; AstraZeneca. N. Iqbal: Employee; Self; AstraZeneca. C. Mathieu: Research Support; Self; Novo Nordisk A/S. Advisory Panel; Self; Novo Nordisk A/S. Speaker's Bureau; Self; Novo Nordisk A/S. Research Support; Self; Sanofi. Speaker's Bureau; Self; Sanofi. Advisory Panel; Self; Sanofi. Research Support; Self; Merck Sharp &amp; Dohme Corp.. Speaker's Bureau; Self; Merck Sharp &amp; Dohme Corp.. Advisory Panel; Self; Merck Sharp &amp; Dohme Corp.. Research Support; Self; Eli Lilly and Company. Speaker's Bureau; Self; Eli Lilly and Company. Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Novartis AG. Speaker's Bureau; Self; Novartis AG. Advisory Panel; Self; Novartis AG, Bristol-Myers Squibb Company. Speaker's Bureau; Self; AstraZeneca. Advisory Panel; Self; AstraZeneca, Pfizer Inc., Janssen Pharmaceuticals, Inc., Boehringer Ingelheim GmbH. Speaker's Bureau; Self; Boehringer Ingelheim GmbH. Advisory Panel; Self; Hanmi Pharmaceutical. Research Support; Self; Roche Diagnostics Corporation. Advisory Panel; Self; Roche Diagnostics Corporation. Research Support; Self; Medtronic. Advisory Panel; Self; Medtronic, MannKind Corporation. Research Support; Self; Intrexon. Advisory Panel; Self; Intrexon, Dianax, UCB, Inc.. Research Support; Self; Abbott.

High Levels of Baseline HbA1c, FPG, and 2-Hour Postprandial Glucose Are Key Predictors for Not Achieving HbA1c &amp;lt; 7.0% at Six Months in People with T2DM Starting Insulin Glargine 100 U/mL

Pooled participant-level data from 16 RCTs (≥ 24 weeks) were used to explore the association of demographic and clinical markers with different levels of HbA1c achieved at Week 24 in insulin-naive people with T2DM on oral agents after starting insulin glargine 100 U/mL (Gla-100). A stepwise logistic regression analysis was performed to identify potential predictive and exploratory factors for attaining an HbA1c &amp;lt; 7.0%. In total, 46%, 37%, and 17% of 3,415 participants (53% male) included in the analysis achieved Week 24 HbA1c &amp;lt; 7.0%, 7.0-8.0%, and &amp;gt; 8.0%, respectively. Key clinical findings and multivariate analysis results are shown in the Table. Sex, diabetes duration, baseline BMI, HbA1c, FPG, and overall 2h-PPG were significant predictors of attaining HbA1c &amp;lt; 7.0% at Week 24, whereas change in FPG or 2h-PPG were not. FPG and 2h-PPG levels in the HbA1c &amp;gt; 8.0% group remained highest despite a higher Gla-100 dose (0.52 U/kg) and lower overall hypoglycaemia (PG &amp;lt; 3.9 mmol/L) rate at Week 24. In people with T2DM, high baseline HbA1c, FPG, and PPG levels are important predictors of failure of basal insulin/OAD therapy to achieve desired glycaemic goals. A more active treatment strategy targeting both fasting and postprandial hyperglycaemia appears to be indicated for people with poor glycaemic control after starting basal insulin therapy. Disclosure D.R. Owens: Consultant; Self; Boehringer Ingelheim GmbH, Roche Diagnostics Corporation, Eli Lilly and Company, Sanofi, Takeda Development Centre Europe Ltd.. Speaker's Bureau; Self; Boehringer Ingelheim GmbH, Roche Diagnostics Corporation, Eli Lilly and Company, Sanofi, Takeda Development Centre Europe Ltd. W. Landgraf: Employee; Self; Sanofi-Aventis Deutschland GmbH. Stock/Shareholder; Self; Sanofi. M. Zhang: Employee; Self; Sanofi US. Stock/Shareholder; Self; Sanofi US. B.M. Frier: Advisory Panel; Self; Novo Nordisk A/S, Eli Lilly and Company. Consultant; Self; Locemia Solutions, Zucara Therapeutics. Speaker's Bureau; Self; Novo Nordisk A/S, Eli Lilly and Company, Roche Pharma, AstraZeneca. L. Meneghini: Advisory Panel; Self; Novo Nordisk Inc., Sanofi US. Consultant; Self; Sanofi US, Novo Nordisk Inc.. Advisory Panel; Self; Intarcia Therapeutics, Inc.. Other Relationship; Self; American Diabetes Association. P. Home: Consultant; Self; AstraZeneca. Other Relationship; Self; Biocon, Boehringer Ingelheim GmbH. Research Support; Self; GlaxoSmithKline plc.. Consultant; Self; Hanmi Pharmaceutical. Other Relationship; Self; Janssen Research &amp; Development. Consultant; Self; Janssen Research &amp; Development, Merck &amp; Co., Inc.. Other Relationship; Self; Merck &amp; Co., Inc.. Research Support; Self; Merck &amp; Co., Inc.. Other Relationship; Self; Novo Nordisk A/S. Consultant; Self; AntriaBio, Inc., Earlysign, Roche Diabetes Care Health and Digital Solutions. Research Support; Self; Sanofi. Consultant; Self; Sanofi. G.B. Bolli: Speaker's Bureau; Self; Menarini Group, Sanofi. Research Support; Self; Sanofi.

Impact of Switching from Twice-Daily Basal Insulin to Once-Daily Insulin Glargine 300 U/mL (Gla-300) in Patients with Type 1 Diabetes (T1DM)—Phase 4 OPTIMIZE Study, Belgian Cohort

The 28-week, prospective, multicenter, open-label OPTIMIZE study, conducted in Belgium and Canada, evaluated treatment optimization with once-daily (OD) Gla-300 in combination with a prandial rapid-acting insulin analog in adults with T1DM previously uncontrolled (HbA1c 8-10%) on twice-daily (BID) basal insulin as part of basal-bolus therapy. The intent-to-treat (ITT) and safety populations of the Belgian cohort comprised 48 patients (52.1% women); mean (SD) age 51.7 (12.8) years and mean (SD) body mass index 28.2 (4.9) kg/m². Prior to the study, 37.5% were on insulin glargine 100 U/mL and 62.5% were on insulin detemir. There was a statistically significant reduction in HbA1c from baseline to month 6 (primary endpoint, p&amp;lt;0.0001). No significant changes were observed in basal or prandial insulin dose, or in total or nocturnal hypoglycemic event rates. Statistically significant improvements were seen in the Diabetes Treatment Satisfaction Questionnaire (DTSQ) total and perceived hyperglycemia scores (p&amp;lt;0.05) (Table), and in the patient satisfaction score for number of injections (p=0.004). Switching from a BID basal insulin analog to OD Gla-300 in this challenging T1DM population was associated with a statistically significant improvement in HbA1c levels and better overall treatment satisfaction. Disclosure C. Mathieu: Research Support; Self; Novo Nordisk A/S. Advisory Panel; Self; Novo Nordisk A/S. Speaker's Bureau; Self; Novo Nordisk A/S. Research Support; Self; Sanofi. Speaker's Bureau; Self; Sanofi. Advisory Panel; Self; Sanofi. Research Support; Self; Merck Sharp &amp; Dohme Corp.. Speaker's Bureau; Self; Merck Sharp &amp; Dohme Corp.. Advisory Panel; Self; Merck Sharp &amp; Dohme Corp.. Research Support; Self; Eli Lilly and Company. Speaker's Bureau; Self; Eli Lilly and Company. Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Novartis AG. Speaker's Bureau; Self; Novartis AG. Advisory Panel; Self; Novartis AG, Bristol-Myers Squibb Company. Speaker's Bureau; Self; AstraZeneca. Advisory Panel; Self; AstraZeneca, Pfizer Inc., Janssen Pharmaceuticals, Inc., Boehringer Ingelheim GmbH. Speaker's Bureau; Self; Boehringer Ingelheim GmbH. Advisory Panel; Self; Hanmi Pharmaceutical. Research Support; Self; Roche Diagnostics Corporation. Advisory Panel; Self; Roche Diagnostics Corporation. Research Support; Self; Medtronic. Advisory Panel; Self; Medtronic, MannKind Corporation. Research Support; Self; Intrexon. Advisory Panel; Self; Intrexon, Dianax, UCB, Inc.. Research Support; Self; Abbott. P. Stella: Employee; Self; Sanofi. Stock/Shareholder; Self; Sanofi. J. Bruhwyler: Other Relationship; Self; Sanofi. K. Alexandre: Employee; Self; Sanofi. Stock/Shareholder; Self; Sanofi.

STAT1 Ablation in CD11c+ Cells Protects Mice from Obesity-Induced Insulin Resistance

Background: Obesity is a global epidemic and major risk factor for insulin resistance and type 2 diabetes. Obesity is associated with low grade chronic inflammation of adipose tissue (AT). F4/80+CD11c+ macrophages/dendritic cells are increased and polarized into M1-like phenotypes in AT and may contribute to insulin resistance in mouse models of obesity. STAT1 is a transcription factor that play key roles in macrophage polarization into M1-like phenotypes. Hence, we investigated the role of CD11c+ macrophage/dendritic cell STAT1 in obesity-induced AT inflammation and insulin resistance. Methods: Mice with specific knockout of STAT1 in CD11c+ cells were generated by crossbreeding STAT1 fl/fl and CD11c-Cre mice. CD11c/STAT1 KO and littermate controls were fed either high fat diet (HFD, for 16 weeks) to induce obesity or normal diet (ND) as lean controls. We evaluated body composition, insulin sensitivity, gene expression of inflammatory markers, various immune cells and brown/beige adipogenesis markers in AT. Results: Perigonadal white AT (PWAT) and body weight were not significantly different between KO and control groups but liver weight and liver-to-body weight ratio were significantly reduced in obese KO mice (p&amp;lt;0.05). STAT1 KO mice on HFD exhibited improved insulin sensitivity examined by insulin tolerance test (p&amp;lt;0.05) and reduced expression of inflammatory markers TNFα, IFNγ, IL-12 and MCP1 in PWAT and subcutaneous AT (SAT) compared to littermate controls (p&amp;lt;0.05). AT immune cell analysis revealed that STAT1 ablation caused a decrease in M1-like proinflammatory polarization and increase in M2-like polarization of F4/80+ macrophages, reduced number of total CD3+ T cells and CD8+T cells in PWAT. In addition, brown/beige adipogenesis markers UCP1, CIDEA and Prdm16 also upregulated (p&amp;lt;0.05) in SAT of obese KO mice. Conclusion: Our results show the critical role of macrophage/dendritic cell STAT1 in obesity-induced AT inflammation and insulin resistance. Disclosure A. Kalathookunnel Antony: None. X.D. Perrard: None. Z. Lian: None. J. Perrard: None. L. Hennighausen: None. C.W. Smith: None. C.M. Ballantyne: Research Support; Self; Abbott, Amarin Corporation, Amgen Inc., Esperion Therapeutics, Ionis Pharmaceuticals, Inc., Novartis Pharmaceuticals Corporation, Pfizer Inc., Regeneron Pharmaceuticals, Inc., Roche Diagnostics Corporation, Sanofi. Consultant; Self; Abbott, Amarin Corporation, Amgen Inc., AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Esperion Therapeutics, Ionis Pharmaceuticals, Inc., Matinas BioPharma, Merck &amp; Co., Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk Inc., Pfizer Inc., Regeneron Pharmaceuticals, Inc., Roche Diagnostics Corporation, Sanofi. H. Wu: None.

Long-Term Efficacy and Safety of Dapagliflozin in Patients with Inadequately Controlled Type 1 Diabetes—The DEPICT-1 Study

In the short term (24-week) period of the Phase 3 study DEPICT-1, treatment with the SGLT2 inhibitor dapagliflozin (DAPA) as adjunct to adjustable insulin (INS) improved glycemic control and was well-tolerated in patients with inadequately controlled T1D (HbA1c 7.5-10.5%) (Lancet Diabetes Endocrinol. 2017;5:864-76). Here we describe the 28-week extension of DEPICT-1, assessing the 52-week efficacy and safety of DAPA in patients who completed the 24-week period. 747 patients in the DAPA 5 mg (n=250), 10 mg (n=270), or placebo (PBO; n=227) plus INS groups entered the long-term period (90% of the randomized patients); 85% completed the study. Reductions in HbA1c and body weight were maintained in the DAPA groups vs. PBO over 52 weeks (Table). The moderate decrease in HbA1c with DAPA was accompanied by a more substantial dose-dependent reduction in body weight. Total events of adjudicated definite DKA increased in the long-term period, with more in the DAPA groups vs. PBO at Week 52. Most were mild or moderate, with the primary cause related to missed insulin doses or pump failure. Adverse events (AEs), serious AEs, and hypoglycemic events were balanced between groups (Table). In conclusion, DAPA plus INS provided a sustained reduction in HbA1c and body weight, and was well-tolerated, but increased events of DKA over 52 weeks in patients with inadequately controlled T1D.Efficacy outcomes at Week 52DAPA 5 mg + INS n=259*DAPA 10 mg + INS n=259*Placebo + INS n=260*A1c, % Week 52 adjusted mean change from baseline (SE) Difference vs PBO (95% CI). -0.27 (0.06) -0.33 (-0.49, -0.17). -0.31 (0.06) -0.36 (-0.53, -0.20). 0.06 (0.06) -Total body weight Week 52 adjusted mean percent change from baseline (SE) Difference vs PBO (95% CI). -2.80 (0.32) -2.95 (-3.83, -2.06). -4.39 (0.31) -4.54 (-5.40, -3.66). 0.15 (0.33) -Safety outcomes at Week 52, n (%)n=277†n=296†n=260†≥1 AE ≥1 SAE ≥1 related SAE ≥1 event of any hypoglycaemia ≥1 event of severe hypoglycaemia Events adjudicated as definite DKA ...Mild ...Moderate ...Severe215 (77.6) 37 (13.4) 8 (2.9) 227 (81.9) 29 (10.5) 12 (4.3) ...5 (1.8) ...3 (1.1) ...4 (1.4)236 (79.7) 40 (13.5) 13 (4.4) 241 (81.4) 25 (8.4) 10 (3.4) ...2 (0.7) ...6 (2.0) ...2 (0.7)189 (72.7) 30 (11.5) 2 (0.8) 212 (81.5) 30 (11.5) 5 (1.9) ...3 (1.2) ...1 (0.4) ...1 (0.4)AE, adverse event; CI, confidence interval; DAPA, dapagliflozin; PBO, placebo; INS, insulin; SAE, serious adverse event; SE, standard error. *n is the number of patients who received 52 weeks of treatment in the full analysis set (excludes the first 55 patients allocated to only DAPA groups due to an interactive voice response system randomization system error). †n is the number of patients who initiated the 24-week period in the safety analysis set (includes the first 55 patients incorrectly randomized to only DAPA groups). ‡Includes events monitored in both 24-week short-term period and 28-week extension. Disclosure P. Dandona: Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca. Research Support; Self; AstraZeneca. C. Mathieu: Research Support; Self; Novo Nordisk A/S. Advisory Panel; Self; Novo Nordisk A/S. Speaker's Bureau; Self; Novo Nordisk A/S. Research Support; Self; Sanofi. Speaker's Bureau; Self; Sanofi. Advisory Panel; Self; Sanofi. Research Support; Self; Merck Sharp &amp; Dohme Corp.. Speaker's Bureau; Self; Merck Sharp &amp; Dohme Corp.. Advisory Panel; Self; Merck Sharp &amp; Dohme Corp.. Research Support; Self; Eli Lilly and Company. Speaker's Bureau; Self; Eli Lilly and Company. Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Novartis AG. Speaker's Bureau; Self; Novartis AG. Advisory Panel; Self; Novartis AG, Bristol-Myers Squibb Company. Speaker's Bureau; Self; AstraZeneca. Advisory Panel; Self; AstraZeneca, Pfizer Inc., Janssen Pharmaceuticals, Inc., Boehringer Ingelheim GmbH. Speaker's Bureau; Self; Boehringer Ingelheim GmbH. Advisory Panel; Self; Hanmi Pharmaceutical. Research Support; Self; Roche Diagnostics Corporation. Advisory Panel; Self; Roche Diagnostics Corporation. Research Support; Self; Medtronic. Advisory Panel; Self; Medtronic, MannKind Corporation. Research Support; Self; Intrexon. Advisory Panel; Self; Intrexon, Dianax, UCB, Inc.. Research Support; Self; Abbott. M. Phillip: Other Relationship; Self; Sanofi, Medtronic MiniMed, Inc., Novo Nordisk A/S. Speaker's Bureau; Self; Eli Lilly and Company. Research Support; Self; Merck &amp; Co., Inc., Bristol-Myers Squibb Company, Kamada, Lexicon Pharmaceuticals, Inc.. Other Relationship; Self; DreaMed Diabetes, Ltd.. Speaker's Bureau; Self; Abbott. L. Hansen: Employee; Self; MedImmune. D. Tschoepe: Advisory Panel; Self; AstraZeneca. Speaker's Bureau; Self; AstraZeneca. Advisory Panel; Self; Amgen Inc.. Speaker's Bureau; Self; Amgen Inc.. Advisory Panel; Self; Eli Lilly and Company. Speaker's Bureau; Self; Eli Lilly and Company. Advisory Panel; Self; Novo Nordisk A/S. Speaker's Bureau; Self; Novo Nordisk A/S. Advisory Panel; Self; Servier. Speaker's Bureau; Self; Servier, Sanofi, Novartis Pharmaceuticals Corporation. Research Support; Self; AstraZeneca, Bayer AG, Eli Lilly and Company, Novo Nordisk A/S, Novartis Pharmaceuticals Corporation, Sanofi. F.A. Thoren: Employee; Self; AstraZeneca. J. Xu: Employee; Self; AstraZeneca. A. Langkilde: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca.

Comparative Cardiovascular Effectiveness of SGLT2 Inhibitors vs. Liraglutide in Routine Care

Both SGLT2 inhibitors (canagliflozin and empagliflozin) and liraglutide have shown cardiovascular (CV) benefits in CV outcome trials. Whether these agents are associated with differential CV effects in routine care remains unexplored. In a large commercial U.S. health insurance database (4/2013-9/2015), we assessed the comparative CV risk of SGLT2 inhibitors (SGLT2i) vs. liraglutide in patients with type 2 diabetes. We used 1:1 propensity score (PS) matching to balance over 100 baseline characteristics. We estimated the hazard ratio (HR) of a combined CV outcome (hospitalization for myocardial infarction (MI) or stroke) and heart failure hospitalization (HHF). Secondary outcomes included an expanded combined CV outcome (hospitalization for MI, stroke, unstable angina, or coronary revascularization) and individual MI or stroke hospitalization. Over 30 months, SGLT2i initiators had no significant difference in the risk of the combined CV outcome (HR= 1.01 [95% CI 0.76-1.34]) or other secondary CV outcomes, but had an approximately 30% decrease in the risk of HHF (HR= 0.68 [0.52-0.88]), compared with liraglutide (Table). Subgroup analyses in patients with and without baseline CV disease produced largely consistent results. In conclusion, this study rules out large differences in atherosclerotic CV risk between SGLT2i and liraglutide, but supports larger benefits of SGLT2i vs. liraglutide for HHF. Disclosure E. Patorno: Research Support; Self; National Institute on Aging. Other Relationship; Self; Boehringer Ingelheim GmbH, GlaxoSmithKline plc. B.M. Everett: Research Support; Self; Roche Diagnostics Corporation, Novartis Pharmaceuticals Corporation. Consultant; Self; Roche Diagnostics Corporation, Abbott Laboratories, Novartis Pharmaceuticals Corporation, U.S. Food and Drug Administration, UpToDate. Other Relationship; Self; American College of Cardiology. S. Schneeweiss: Consultant; Self; WHISCON, LLC. Other Relationship; Self; Aetion, Inc.. Research Support; Self; Genentech, Inc., Bayer AG, Boehringer Ingelheim GmbH, U.S. Food and Drug Administration, Patient-Centered Outcomes Research Institute. R.J. Glynn: Research Support; Self; Pfizer Inc., Novartis AG, Kowa Pharmaceuticals America, Inc.. J. Liu: None. S.C. Kim: None.

Performance of Clinical/Proteomic Biomarker Panels to Predict Coronary Artery Disease Presence or Cardiovascular Prognosis in Patients With and Without Diabetes Mellitus

Background: Patients with diabetes mellitus (DM) are at increased risk for prevalent coronary artery disease (CAD) and incident major adverse cardiac events (MACE) such as cardiovascular death, myocardial infarction, and stroke. Prediction of risk in those with DM may be challenging. Using unbiased proteomics we recently described biomarker panels to predict prevalent CAD (HART-CAD) and incident MACE (HART-CVE; both Prevencio, Kirkland, WA) in unselected patients undergoing diagnostic coronary angiography. In this study, we sought to compare accuracy of these panels in patients with and without DM. Methods: The HART-CAD panel includes 2 clinical variables (male sex and prior PCI) and 4 biomarkers: adiponectin, apolipoprotein CI, kidney injury molecule 1 (KIM 1), and midkine. The HART-CVE panel includes 4 biomarkers (amino-terminal pro-B type natriuretic peptide, KIM 1, osteopontin, and tissue inhibitor of matrix metalloproteinase-1). These panels were derived in 167 DM and 482 non-DM patients; performance of each was validated in a second cohort of patients (N=278) with and without DM. Results: In patients with DM, the HART-CAD panel had excellent performance for prediction of coronary stenosis ≥ 70%, with area under the curve (AUC) of 0.81 (p&amp;lt;0.001), sensitivity 85%, specificity 69%, positive predictive value (PPV) 90%, and negative predictive value (NPV) 58% for detection of CAD, results comparable to those without DM. HART-CVE was highly predictive of incident 1 year MACE in those with DM: AUC of 0.80 (p=0.002), sensitivity 78%, specificity 69%, PPV 47%, and NPV of 90%, again, comparable to those without DM. In Kaplan-Meier analyses, an elevated HART-CVE score predicted shorter time to first MACE (p&amp;lt;0.001). Conclusion: Previously described multivariate proteomic biomarker panels effectively predict prevalent CAD and risk for incident MACE in at-risk patients with DM. Disclosure S. Shrestha: None. C.P. McCarthy: None. N.E. Ibrahim: None. R. van Kimmenade: None. H. Gaggin: Research Support; Self; Roche Diagnostics Corporation, Portola. Consultant; Self; Roche Diagnostics Corporation, Amgen Inc., Ortho clinical. Other Relationship; Self; EchoSense, Radiometer. R. Mukai: None. C.A. Magaret: Consultant; Self; Prevencio. G. Barnes: Employee; Self; Prevencio. R.F. Rhyne: None. J. Januzzi: Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc.. Board Member; Self; Jana Care Inc.. Research Support; Self; Prevencio.

Real-World Distribution of Hematocrit Values in the Netherlands and the Czech Republic—A Cross-Sectional Study

Many self-monitoring of blood glucose (SMBG)-systems demonstrate positive glucose bias at lower and negative bias at higher hematocrit (HCT) levels. Variations can thus lead to measurement errors and possibly to over- or underdosage of insulin. ISO 15197:2013 demands that manufacturers publish HCT-ranges within which BG-meters perform within certain tolerance limits. But how wide should these ranges be? In this cross-sectional study, we collect real-world data from hospital outpatients to assess the frequency of out-of-range HCT-values compared to healthy individuals. Data from more than 1 million patients were collected from hospitals in Leeuwarden (NL) and Prague (CZ) and stratified by outpatient status, sex and age. Data were compared to common HCT labelled range limits and data from 1780 healthy Czech subjects to assess differences in distribution. Compared to healthy subjects, outpatients from Prague were distributed with significantly higher dispersion of values. Independent of the location, low HCT-values are common in age groups known to also have high prevalence of diabetes. Real-world data indicate that BG-meters labelled to perform only within the frequently used 30-55% range would leave thousands of patients at risk of falsely high or low results. Adequate SMBG-systems should be chosen carefully, particularly for patients with increased and decreased HCT-values.Age groupLocationTotal number Female / MaleNumber &amp;lt;30% HCT F / MPercentage &amp;lt;30% HCT F / MPrevalence of Diabetes (Eurostat) F / M55-64NL27329 / 335461043 / 13243.8% / 3.9%6.2% / 10.3%55-64CZ22212 / 19035748 / 9703.4% / 5.1%10.9% / 11.6%65-74NL32004 / 441811958 / 28496.1% / 6.4%12.0% / 15.0%65-74CZ33598 / 313211987 / 19245.9% / 6.1%18.1% / 19.5%75+NL42435 / 450803431 / 45358.1% / 10.1%14.4% / 19.7%75+CZ26645 / 227061923 / 20417.2% / 9.0%27.3% / 25.3% Disclosure T. Zima: Research Support; Self; Roche Diabetes Care Health and Digital Solutions. H. Storm: None. M. Spacek: None. S. Wardat: Employee; Self; Roche Diagnostics Corporation. O. Hauss: Consultant; Self; Roche Diabetes Care GmbH. R. Hinzmann: Employee; Self; Roche Diagnostics Corporation.

Long-Term Use of Closed-Loop Control—Patient-Reported Experience

Objective: Assess patient experience via semi-structured interviews after long-term outpatient use of closed loop control (CLC). Methods: Eleven patients with T1D diagnosed for at least one year participated (10 females), with a mean (SD) age of 47.3 years (12.9), diabetes duration of 26.6 years (17.7), and pre-study HbA1c 7.6% (0.5) or 60 mmol/mol. The CLC system was inControl, composed of a CGM, insulin pump, and control algorithms housed on a smartphone. Participants used inControl for 8-weeks after which they were interviewed. Interviews followed a semi-structured format based on theories of technology acceptance. Numerical ratings were obtained for difficulty, complexity, trust, and long-term use probability on a 5-point Likert scale. Results: Ratings were difficulty - 2.0 (0.8), complexity - 1.9 (0.8), trust - 3.8 (1.0), and 4) probability of long-term use - 3.3 (1.6). Difficult aspects of use were managing the three components and alarms for out of range BGs. The major complexities were finding ways to stop alarms and understanding insulin bolus calculation. Nine patients reported that the device was very useful and had improved BG control, including decreasing low BGs and reducing HbA1c. All reported “over-riding“ the device, most often to give more insulin for high BG correction, and also detaching the system at times to avoid alarms. Trust in the device was high, with 8 patients rating ≥ 4 but there was belief that not enough insulin was given for high BGs. Probability of long-term use varied, with the alarms and burden of multiple components as factors. Conclusions: Patient-reported experiences varied across individuals. The majority did not find the system difficult or complex, found it useful for better BG control, and trusted it. However, there is need for technology improvement to reduce equipment burden and alarms. Findings indicate that users might benefit from patient education before device use to help them understand how the system calculates insulin doses. Disclosure L. Gonder-Frederick: Other Relationship; Self; HFS-Global LLC. S.A. Brown: Research Support; Self; Roche Diagnostics Corporation, Dexcom, Inc., Tandem Diabetes Care, Inc., Medtronic, Ascensia Diabetes Care, TypeZero Technologies, Inc.. J. Shepard: None. L.L. Kollar: None. B. Kovatchev: Consultant; Self; Sanofi-Aventis. Speaker's Bureau; Self; Dexcom, Inc.. Stock/Shareholder; Self; TypeZero Technologies, Inc.. Research Support; Self; Dexcom, Inc., Roche Diagnostics Corporation, Tandem Diabetes Care, Inc., Ascensia Diabetes Care.

Efficacy and Safety of Dapagliflozin in Patients with Inadequately Controlled Type 1 Diabetes—DEPICT-2 Study

The DEPICT-2 trial (NCT02460978) evaluated the efficacy and safety of dapagliflozin (DAPA) as add-on to adjustable insulin (INS) in patients (pts) with inadequately controlled T1D (A1c 7.5-10.5%) over 24 weeks. This phase 3 study randomized pts 1:1:1 to DAPA 5 mg (n=271), 10 mg (n=270) or placebo (PBO; n=272) plus INS. INS dose could be adjusted by the investigator according to self-monitored blood glucose readings, local guidance and individual circumstances. At Week 24, DAPA 5 and 10 mg significantly decreased A1c (0.37% and 0.42% reductions in the DAPA 5 mg and 10 mg groups respectively), total daily insulin dose (TDD), and body weight (Table). As measured by masked continuous glucose monitoring (CGM), mean interstitial glucose, mean amplitude of glucose excursion (MAGE) and mean percent of readings within target glycemic range (>70-≤180 mg/dL) vs. PBO were improved. There was an increase in pts who reduced their A1c by ≥0.5% without severe hypoglycemia (odds ratios [95% CI]: 2.71 [1.81, 4.06] and 3.07 [2.05, 4.60] for DAPA 5 and 10 mg respectively). Hypoglycemic events, including severe hypoglycemia were balanced between treatment groups. There were more adjudicated definite diabetic ketoacidosis (DKA) events on DAPA. In conclusion, DAPA vs. PBO as add-on to INS in pts with T1D was well tolerated, improved glycemic control and decreased variability without increasing hypoglycemia but with more DKA events. Disclosure C. Mathieu: Research Support; Self; Novo Nordisk A/S. Advisory Panel; Self; Novo Nordisk A/S. Speaker9s Bureau; Self; Novo Nordisk A/S. Research Support; Self; Sanofi. Speaker9s Bureau; Self; Sanofi. Advisory Panel; Self; Sanofi. Research Support; Self; Merck Sharp & Dohme Corp.. Speaker9s Bureau; Self; Merck Sharp & Dohme Corp.. Advisory Panel; Self; Merck Sharp & Dohme Corp.. Research Support; Self; Eli Lilly and Company. Speaker9s Bureau; Self; Eli Lilly and Company. Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Novartis AG. Speaker9s Bureau; Self; Novartis AG. Advisory Panel; Self; Novartis AG, Bristol-Myers Squibb Company. Speaker9s Bureau; Self; AstraZeneca. Advisory Panel; Self; AstraZeneca, Pfizer Inc., Janssen Pharmaceuticals, Inc., Boehringer Ingelheim GmbH. Speaker9s Bureau; Self; Boehringer Ingelheim GmbH. Advisory Panel; Self; Hanmi Pharmaceutical. Research Support; Self; Roche Diagnostics Corporation. Advisory Panel; Self; Roche Diagnostics Corporation. Research Support; Self; Medtronic. Advisory Panel; Self; Medtronic, MannKind Corporation. Research Support; Self; Intrexon. Advisory Panel; Self; Intrexon, Dianax, UCB, Inc.. Research Support; Self; Abbott. P. Dandona: Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca. Research Support; Self; AstraZeneca. P. Gillard: None. P.A. Senior: Consultant; Self; Abbott, AstraZeneca. Research Support; Self; AstraZeneca, Prometic Life Sciences Inc., Novo Nordisk Inc., Sanofi. Consultant; Self; Eli Lilly and Company. Speaker9s Bureau; Self; Eli Lilly and Company, Novo Nordisk Inc.. Consultant; Self; Novo Nordisk Inc., Janssen Pharmaceuticals, Inc.. Speaker9s Bureau; Self; Janssen Pharmaceuticals, Inc.. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc.. Speaker9s Bureau; Self; AstraZeneca, Merck & Co., Inc., Abbott. Research Support; Self; Viacyte, Inc.. C. Hasslacher: None. E. Araki: Speaker9s Bureau; Self; Astellas Pharma US, Inc., MSD K.K., Kowa Pharmaceuticals America, Inc., Sanofi, Novo Nordisk Inc.. Research Support; Self; Astellas Pharma US, Inc., MSD K.K., Ono Pharmaceutical Co., Ltd., Shionogi & Co., Ltd., Takeda Pharmaceuticals U.S.A., Inc., Daiichi Sankyo Company, Limited, Nippon Boehringer Ingelheim Co. Ltd., Novartis Pharma K.K., Novo Nordisk Inc., Sanofi, Mitsubishi Tanabe Pharma Corporation, Sumitomo Dainippon Pharma Co., Ltd., Taisho Toyama?Pharmaceutical Co. M. Lind: Consultant; Self; AstraZeneca. Research Support; Self; AstraZeneca. Consultant; Self; Novo Nordisk Inc.. Advisory Panel; Self; Novo Nordisk Inc.. Research Support; Self; Novo Nordisk Inc.. Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc.. Consultant; Self; Eli Lilly and Company. Advisory Panel; Self; MSD K.K.. Research Support; Self; Pfizer Inc.. Consultant; Self; Medtronic. S.C. Bain: Research Support; Self; Novo Nordisk Inc., AstraZeneca. S. Jabbour: None. N. Arya: Employee; Self; AstraZeneca. F.A. Thoren: Employee; Self; AstraZeneca. A. Langkilde: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca.

Lower Risk of CV Events and Death Associated with Initiation of SGLT2 vs. DPP-4 Inhibitors—Analysis from the CVD-REAL 2 Study

DPP-4 inhibitors and SGLT2 inhibitors are widely used in T2D. Clinical trials demonstrated lower risk of CV events with SGLT2i, and a neutral effect on CV events with DPP-4i. However, large comparative studies are lacking. We compared the risk of death, hospitalization for heart failure (HHF), MI and stroke in patients starting the SGLT2i dapagliflozin (DAPA) vs. any DPP-4i, using real world data from S. Korea, Japan, Israel, and Canada. Patients initiating DAPA or DPP-4i were identified via national registries, claims, and medical records. Propensity scores for SGLT2i initiation were developed in each country, with 1:1 matching. Hazard ratios were assessed by country and pooled using weighted meta-analysis, with an intent-to-treat approach. In total, 128,066 patients were included (mean age 55 years, 46% women, 25% with history of CVD). Post-match, baseline characteristics were balanced across matched groups. Initiation of DAPA vs. DPP-4i was associated with significantly lower risk of death, HHF, MI and stroke (Figure 1). In a large cohort of T2D patients seen in clinical practice across 4 countries, 75% without established CVD, initiation of DAPA was associated with lower risk of CV events (including stroke) and death compared with DPP-4i. Disclosure S. Kohsaka: Research Support; Self; Bayer Yakuhin, Ltd.. Speaker's Bureau; Self; Bayer Yakuhin, Ltd.. Research Support; Self; Daiichi Sankyo Company, Limited. Speaker's Bureau; Self; Bristol-Myers Squibb Company. C.S. Lam: Research Support; Self; Boston Scientific Corporation, Bayer AG, Thermofisher, Medtronic, Vifor Pharma. Consultant; Self; Bayer AG, Novartis AG, AstraZeneca, Janssen Research &amp; Development, Menarini Group, Abbott, Roche Diagnostics Corporation, Boehringer Ingelheim GmbH, Merck &amp; Co., Inc.. D. Kim: None. A. Karasik: Stock/Shareholder; Self; Novo Nordisk A/S. Research Support; Self; Novo Nordisk A/S. Advisory Panel; Self; Novo Nordisk A/S, AstraZeneca. Research Support; Self; AstraZeneca. Consultant; Self; Boehringer Ingelheim GmbH. Advisory Panel; Self; Merck &amp; Co., Inc., GlucoMe. N. Tangri: Research Support; Self; AstraZeneca. Advisory Panel; Self; AstraZeneca, Otsuka Holdings Co., Ltd. S. Goh: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim GmbH, Amgen Inc., Novo Nordisk A/S, Sanofi-Aventis, Servier. M. Thuresson: Consultant; Self; AstraZeneca. H. Chen: Employee; Self; AstraZeneca. F. Surmont: Employee; Self; AstraZeneca. N. Hammar: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca. P. Fenici: Employee; Self; AstraZeneca. M. Kosiborod: Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca, Amgen Inc., Sanofi, Boehringer Ingelheim GmbH, GlaxoSmithKline plc., Glytec Systems, Merck &amp; Co., Inc., Novo Nordisk A/S, Janssen Pharmaceuticals, Inc., ZS Pharma, Inc., Intarcia Therapeutics, Inc., Novartis AG. Research Support; Self; AstraZeneca, Boehringer Ingelheim GmbH.