bluuebunny/crossref_metadata_2025_split huggingface dataset:
2355-PUB: Discontinuation of Dapagliflozin in DEPICT-1 and DEPICT-2 Led to Clinically Meaningful Increases in HbA1c and Body Weight
Authors: GORDON JASON , DANNE THOMAS , BERESFORD-HULME LEE M. , BENNETT HAYLEY , TANK AMARJEET , EDMONDS CHRISTOPHER , THOREN FREDRIK A. , SCHEERER MARKUS F. , MCEWAN PHILIP 
Published: June 2019

Concerns about hypoglycaemia and weight gain are barriers to treatment adherence, and consequently glucose control, in type 1 diabetes (T1D). In adults inadequately controlled by insulin alone, DEPICT-1 and DEPICT-2 phase 3 trials showed statistically significant decreases in HbA1c, glycaemic variability and body weight for adjunct dapagliflozin (DAPA), along with moderate reduction in insulin requirements. As adherence to T1D treatment can be a daily challenge, understanding the implications of discontinuation is important. This study evaluated changes in HbA1c, weight and insulin dose following discontinuation of DAPA in the DEPICT trials. Data were analyzed from patients discontinuing DAPA (5 mg or 10 mg, added to insulin) at any point in DEPICT-1 or DEPICT-2 (unplanned or end of study). Annualized rates of change in HbA1c (%) and weight (kg) following discontinuation of DAPA were estimated via linear regression and trajectories derived from mean rates of change. Insulin doses were summarized before and after discontinuation and correlated to analyzes of weight and HbA1c. The main reasons for unplanned discontinuation of DAPA were adverse events and patient withdrawal of consent. Following DAPA discontinuation, annualized changes in HbA1c and weight were +0.99% (95% CI: 0.39, 1.59) and +3.75Kg (1.65, 5.86), respectively (N=91). Mean insulin dose two weeks post-discontinuation was higher than two weeks prior to discontinuation (5mg: +4.0 IU, 10mg: +4.4 IU). These estimates directly contrast to observed reductions in HbA1c, weight and insulin dose associated with DAPA treatment. Discontinuation of DAPA led to significant increases in HbA1c and weight, in addition to higher insulin doses soon after discontinuation. Enabling ongoing DAPA treatment may benefit patients due to the detrimental impact of poor glycaemic control and weight gain on retinopathy, nephropathy and cardiovascular disease shown in long-term T1D studies, such as DCCT and EDIC.

Disclosure
J. Gordon: Research Support; Self; AstraZeneca, Bristol-Myers Squibb Company, GlaxoSmithKline plc., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Takeda Canada, Takeda Pharmaceutical Company Limited. T. Danne: Advisory Panel; Self; Abbott, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Lilly Diabetes, Medtronic, Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis. Speaker's Bureau; Self; Dexcom, Inc. L.M. Beresford-Hulme: None. H. Bennett: Consultant; Self; AstraZeneca. Employee; Self; Health Economics and Outcomes Research Ltd. A. Tank: Employee; Self; AstraZeneca. C. Edmonds: Employee; Self; AstraZeneca. F.A. Thoren: Employee; Self; AstraZeneca. M.F. Scheerer: Employee; Self; AstraZeneca. P. McEwan: Consultant; Self; AstraZeneca. Employee; Self; Health Economics and Outcomes Research Ltd.
2372-PUB: An EHR-Based Tool to Aid Treatment Decisions and Combat Therapeutic Inertia in Patients with Uncontrolled Type 2 Diabetes—A Pilot Study
Authors: PANTALONE KEVIN M. , MISRA-HEBERT ANITA D. , RAJPATHAK SWAPNIL , WEISS TRACEY , HAMATY MARWAN , ZIMMERMAN ROBERT S. 
Published: June 2019

An electronic health record (EHR)-based tool was developed for EPIC My Practice at Cleveland Clinic to aid treatment decisions and combat therapeutic inertia in patients with type 2 diabetes (T2D) and suboptimal glycemic control (A1C ≥8%). The tool consists of three separate components activated during an office visit: a best practice alert (BPA) if a patient had an A1C ≥ 8% within the past 6 months, a Smartform (patient summary and order entry form), and a Smartset for T2D medications to achieve patient-centered treatment goals, supplies, laboratory, and consult orders. The provider may acknowledge the BPA and pursue therapy intensification or defer. The tool was piloted for 3 months (primary care providers (PCP) n=12, endocrinology providers n=5). BPA acknowledgement and utilization practices were recorded by specialty. The CollaboRATE survey (3 items, scale 0-9) was used to assess patient perceptions of shared decision-making afforded by the tool. The BPA appeared for 420/1,766 T2D patients at an office-based encounter with a PCP or endocrinology provider. While the BPA was acknowledged at most endocrinology visits (95%) and approximately 50% of primary care visits, use of all three components of the tool only occurred in ∼10% of eligible encounters. The CollaboRATE survey was administered to a subset of 83 patients exposed to the tool. Most chose the highest rating for questions assessing if efforts were made to: 1. help them better understand their health issue (87%), 2. listen to what matters most to them about their health issue (93%) and 3. integrate what matters to them into next steps (92%). Use of an EHR-based tool to aid treatment decisions is variable by specialty, though appears to lead to a high perception of shared decision-making for T2D patients. Additional provider education and improved incorporation of the tool into PCP work-flow may help to address therapeutic inertia in routine clinical practice.

Disclosure
K.M. Pantalone: Consultant; Self; Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Research Support; Self; Merck & Co., Inc., Novo Nordisk Inc. Speaker's Bureau; Self; AstraZeneca, Merck & Co., Inc., Novo Nordisk Inc. A.D. Misra-Hebert: Research Support; Self; Agency for Healthcare Research and Quality, Merck & Co., Inc., Novo Nordisk Inc. S. Rajpathak: None. T. Weiss: Employee; Self; Merck & Co., Inc. M. Hamaty: Consultant; Self; Applied Clinical Intelligence, LLC. R.S. Zimmerman: Consultant; Self; Novo Nordisk Inc. Research Support; Self; Merck & Co., Inc., Novo Nordisk Inc. Speaker's Bureau; Self; Johnson & Johnson Diabetes Institute, Merck & Co., Inc.
239-OR: Association between Hemoglobin A1c and Stroke Risk in Patients with Type 2 Diabetes
Authors: SHEN YUN , SHI LIZHENG , NAUMAN ELIZABETH , KATZMARZYK PETER , PRICE-HAYWOOD EBONI G. , BAZZANO ALESSANDRA N. , NIGAM SOMESH , HU GANG 
Published: June 2019

Clinical findings regarding the association between hemoglobin A1c (HbA1c) and stroke risk remain inconclusive, especially among patients with diabetes. We aimed to investigate the association between HbA1c and the risk of incident stroke in patients with type 2 diabetes using electronic medical record data from three large healthcare systems in Louisiana. We performed a retrospective cohort study of 27,113 African Americans and 40,431 whites with type 2 diabetes. Cox proportional hazards regression models were used to estimate the association of different levels of HbA1c with stroke risk. During a mean follow-up period of 3.74 years, 8986 patients developed stroke (8438 ischemic and 548 hemorrhagic). Multivariable-adjusted hazard ratios across levels of HbA1c at baseline (&lt;6.0, 6.0-6.9 [reference group], 7.0-7.9, 8.0-8.9, 9.0-9.9, and ≥10%) were 1.05, 1.00, 1.13, 1.21, 1.24, and 1.35 (Ptrend &lt;0.001) for total stroke, 1.04, 1.00, 1.13, 1.21, 1.24, and 1.35 (Ptrend &lt;0.001) for ischemic stroke, and 1.26, 1.00, 1.10, 1.26, 1.22, and 1.40 (Ptrend =0.284) for hemorrhagic stroke. When we used an updated mean value of HbA1c, the U-shaped association of HbA1c with stroke risk did not change. This U-shaped association was consistent among patients of different age, race, sex, body mass index, never and past or current smokers. The U-shaped association was more pronounced among patients taking antidiabetic, lipid-lowering and anti-hypertensive medications compared with those without these medications. In the U-shaped association between HbA1c and the risk of total, ischemic and hemorrhagic stroke among patients with type 2 diabetes, the lowest risk was found among patients with HbA1c at 6-7 mmol/L.

Disclosure
Y. Shen: None. L. Shi: None. E. Nauman: None. P. Katzmarzyk: None. E.G. Price-Haywood: None. A.N. Bazzano: None. S. Nigam: None. G. Hu: None.

Funding
Patient-Centered Outcomes Research Institute (NEN-1508-32257); National Institute of General Medical Sciences (U54GM104940 to G.H., P.K.)
240-OR: Impact of Insulin Treatment on Primary Outcomes in Cardiovascular Outcome Trials
Authors: KHATIB JOANNA , SHAO YIXUE , SHI LIZHENG , FONSECA VIVIAN 
Published: June 2019

Objective: Several Cardiovascular Outcome Trials (CVOTs) have demonstrated benefits compared to placebo. However, the patients included were heterogeneous, including several with long duration diabetes, insulin treatment may be a surrogate for duration and severity of disease. We conducted a meta-analysis to evaluate the impact of concurrent insulin treatment on the odds of meeting the primary outcome in recent CVOTs.
Methods: PubMed and google scholar were searched for CVOTs with new drugs for diabetes. We found 10 publications, of which 7 provided outcomes according to insulin use. We compared the risk ratio of the primary endpoint in the group taking insulin with those not taking insulin by extracting the number of events in both groups along with total number of patients. We also removed the 2 trials that used dipeptidyl peptidase 4 inhibitors, since none of the drugs had shown benefit in the trials. The I-square was used to assess between-study heterogeneity and all analyses were performed using STATA program version 15.1. Fixed effect models were used in these analyses due to low heterogeneity. This study was conducted in compliance with PRISMA guidelines.
Results: When all 7 trials were included in the meta-analysis (EXSCEL, SUSTAIN6, SAVORTIMI, EMPAREG, LEADER, EXAMINE, and DECLARE), the risk ratio of the primary outcome was significantly increased indicating that patients using insulin with drug are more likely to have the primary cardiovascular outcome than the group of patients using drug without insulin. The RR was 1.43 (95% confidence interval: 1.32, 1.55).
Conclusion: In recent CVOTs which have demonstrated a benefit of the study drug on the primary outcome compared to usual care, the concomitant use of Insulin along with the study drugs led to a higher risk of cardiovascular events than in patients who were on the drugs without insulin. The reason for this increased risk is unclear but may be related to the likely longer duration of diabetes and poorer antecedent control which led to the use of insulin.

Disclosure
J. Khatib: None. Y. Shao: None. L. Shi: None. V. Fonseca: Board Member; Self; American Association of Clinical Endocrinologists. Consultant; Self; Abbott, Asahi Kasei Corporation, Novo Nordisk Inc., Sanofi US, Takeda Pharmaceutical Company Limited. Research Support; Self; Bayer US. Stock/Shareholder; Self; Amgen Inc., BRAVO4HEALTH, Mellitus Health.
2479-PUB: Mesenchymal Stromal Cells Improves Motor Function and Increases Collagen Macromolecular Organization in Muscles of Diabetic Rats
Authors: LUNA GENOVEVA L. , SABADINE MARIA AUGUSTA , ESTRADA-BONILLA YISEL CAROLINA , FREITAS ANÍBAL FERNANDA , LEAL ÂNGELA M. 
Published: June 2019

Diabetes mellitus affects the skeletal muscle connective tissue and leads to significant physical performance impairment. The use of mesenchymal stromal cells (MSCs) is an attractive strategy to modify the microenvironment and promote tissue repair and regeneration. Male Wistar rats (225 ± 25 g) were randomly assigned to 3 groups: Control (C), Diabetic (DM) and Diabetic treated with MSCs (DM-MSCs). Diabetes was induced by Streptozotocin (50 µg/kg). Bone marrow cells were isolated from the tibia and femur. After 10 weeks of DM induction, DM-MSC rats received 4 i.p. injections of MSCs (1 x 106). Ten weeks after MSC transplantation motor performance was evaluated by Rota Rod test and the anterior tibial (TA) muscles were collected for morphometric and quantification of collagen birefringence by polarizing microscopy analysis. Statistical significance was set at 0.05. Fasting glycemia was significantly higher and body mass was significantly lower in the Diabetic (DM) and Diabetic treated with MSCs (DM + MSCs) groups compared with control group. Motor performance of the DM group (30.1 ± 9.6 s) was significantly reduced when compared to the C group (727.2 ± 92 s) and increased significantly in DM + MSCs group (786.8 ± 109.5 s). The TA muscle mass was significantly reduced in DM and DM + MSCs groups compared with C group. The muscle fiber cross sectional area followed the same pattern. The connective tissue increased in DM group compared with C group and decreased in DM + MSCs. The percentage collagen birefringence quantified in the connective tissue of the TA muscle decreased significantly in DM group when compared to C group and increased in DM + MSCs group. Motor performance was positively correlated with collagen birefringence (r = 0.83; P &lt; 0.0001) and negatively correlated with percentage of connective tissue (r = -0.94; P &lt; 0.0001). The results indicate that improves both motor function and the collagen macromolecular organization in type 1 DM.

Disclosure
G.L. Luna: None. M.A. Sabadine: None. Y. Estrada-Bonilla: None. F. Freitas Aníbal: None. Â.M. Leal: None.

Funding
Conselho Nacional de Desenvolvimen to Científico e Tecnológico Processo (447518/2014-3)
250-LB: Affinity Purification and Identification of Unique, Shared, Anti-IA-2 B-Cell Clonotypes: A Proteomic Approach to Understanding Pathophysiological Mechanisms Underpinning Humoral Autoimmunity in Type 1 Diabetes
Authors: MENDIS THILINI , WANG JING JING , PIETROPAOLO MASSIMO , JACKSON MICHAEL W. 
Published: June 2019

Immune destruction of beta (β) cells is a feature of type 1 diabetes (T1D), yet the pathophysiological role of autoantibodies (Abs) is poorly understood. Recently, proteomic interrogation of Abs indicates that restricted heavy (H) and light (L) chain gene usage underpins targeting of classic autoantigens in systemic autoimmunity. In the current study, we have taken a similar approach to characterise B cell clonotypes resulting in targeting of the major T1D autoantigen, IA-2. Firstly, we developed a novel ELISA assay for the major IA-2 epitopes using an MBP-fusion protein (FP) incorporating intracellular domain of IA-2. We next established ELISA-based affinity purification (AP) method using the IA-2-MBP FP to isolate enriched IA-2 specific Abs from 2 patients with verified (by RSR commercial ELISA) anti-IA-2 autoimmunity. The AP IA-2 Abs were then analysed for Ig H and L chain clonality, variable region gene usage and AA sequence using high-res electrophoretic fractionation and de-novo/database-driven analysis of AA peptides (TripleTOF 5600+ MS). We report the presence of 2 unique Ig Kappa-restricted clonotypes giving rise to IA-2 autoimmunity; namely a IGHV3.23 H chain paired with either a IGKV1.9 or IGKV3.20 L chain. Remarkably, both clones were present in AP Ig from 2 unrelated T1D patients, with shared AA replacement mutations. The Ig signatures were not present in healthy, age/sex matched controls, T1D patients negative for anti-IA-2, or Lupus. These extraordinary findings suggest the pathogenic mechanisms leading to IA-2 autoimmunity may be virtually identical across individuals, leading to the intriguing possibility of intervention in the processes underpinning the disease. Furthermore, the ability to AP and identify genuine human clonal IA-2 Abs will unlock functional studies aimed at establishing the role of these Abs in the pathophysiology of β cell loss.

Disclosure
T. Mendis: None. J. Wang: None. M. Pietropaolo: None. M.W. Jackson: None.
256-LB: Adipose Tissue T-Cells Assume a Dysfunctional Phenotype in Obese Mice and Humans
Authors: PORSCHE CARA E. , DELPROPOSTO JENNIFER , GELETKA LYNN M. , O’ROURKE ROBERT W. , LUMENG CAREY 
Published: June 2019

Adipose tissue induced chronic low-grade inflammation is a critical link between obesity and insulin resistance. Despite studies demonstrating the importance of adipose tissue T cells (ATT) in insulin resistance, fundamental gaps in knowledge regarding CD4+ ATT function impedes progress towards understanding their role in diabetes. Little is known about the mechanisms of ATT activation and chemokine profiles. We hypothesized that high fat diet induced obesity would potentiate ATT inflammatory properties. To maximize the viability and number of ATTs in-vitro, T cell activation assays were performed by adding α-CD3/CD28 beads to the entire stromal vascular fraction (SVF). Three days after T cell receptor (TCR) stimulation, supernatants were collected and cells were analyzed using flow cytometry. Surprisingly, ATTs from obese epididymal white adipose tissue had senescent characteristics. Stimulation failed to induce CD25 upregulation, ATT proliferation, or heightened IFNγ and IL-2 secretion, which is seen in lean samples. Human samples collected from diabetic bariatric surgery patients also have an exhausted phenotype unlike nondiabetic controls. Both cell intrinsic and extrinsic factors are implicated in ATT dysfunction. Diabetic humans and mice both have significantly increased expression of the co-inhibitory receptor, PD1, on the cell surface. Despite experiments showing that T cell intrinsic factors, such as major-histocompatibility complex II signals are necessary for ATT dysfunction in obese mice, blocking PD1 fails to rescue this phenotype. Most likely this is due to additional extrinsic factors, since co-culturing lean ATTs in obese SVF results in reduced pro-inflammatory cytokine secretion post-TCR stimulation. Further studies are required to elucidate whether ATT dysfunction is a protective mechanism that reduces inflammation, and whether relieving intrinsic and extrinsic blocks on ATTs results in a more robust inflammatory response and worsened glucose tolerance.

Disclosure
C.E. Porsche: None. J. DelProposto: None. L.M. Geletka: None. R.W. O’Rourke: None. C. Lumeng: None.

Funding
National Institutes of Health (F31DK118811, T32HL125242 to C.E.P.), R01DK090262-06A1 (to C.L.)
269-LB: PVT1 Induces the Insulin Resistance of Obesity-Related Metabolic Diseases via Modulation of Transcription Factor Foxo1 through Binding with PTBP1
Authors: LIN XIHUA , GUI WEIWEI , LI HONG 
Published: June 2019

Insulin resistance (IR) is the pathophysiological basis of obesity-related metabolic diseases. PVT1 (Plasmacytoma Variant Translocation-1) has been confirmed to be involved in the occurrence and progression of diabetic nephropathy, but its role and mechanism in IR remain unclear. Our study found that:1)The expression of PVT1 was increased in high-fat diet-induced obese IR mice; 2) Specific overexpression of PVT1 by tissue fixed point injection of adenovirus in epididymal adipose tissue promoted IR in obese DIO mice, increased heterotopic lipid deposition, and damaged liver function; 3) Systemic knockdown of PVT1 by tail vein injection of PVT1 shRNA lentivirus effectively improved the IR status of db/db obesity model mice, reduced lipid deposition, reduced liver weight and alleviated liver function injury; 4) PVT1 activated the transcriptional activity of the transcription factor FoxO1 (Forkhead box protein O1), and regulated its downstream target genes such as PEPCK, G6Pase, Gck and Glut4; 5) Knockdown of PVT1 inhibited IRS-PI3K-Akt insulin signaling pathway by activating the phosphorylation of Akt, thus regulated FoxO1 trans-localization; 6) Biotin-RNA Pull-down assay followed by mass spectrometry detection and combined with the CLIP-seq data of RBP-RNA binding in starBaseV3 database showed that PVT1 combines with PTBP1 (Polypyrimidine tract-binding protein-1), proving that PTBP1 was necessary in the process of PVT1 regulating FoxO1 to improve IR, and exploring its specific molecular regulatory mechanism. To sum up, we hope to elucidate the specific molecular mechanism of the regulation of transcription factor FoxO1 by PVT1 combined with PTBP1, so as to provide a new target for the prevention and treatment of IR.

Disclosure
X. Lin: None. W. Gui: None. H. Li: None.

Funding
National Natural Science Foundation of China
280-OR: Machine Learning to Predict Hypoglycemic Events from Continuous Glucose Monitoring Data
Authors: AGRAWAL PRATIK , CAO LINGTAO , CHANG YUAN-CHI , JACKSON GRETCHEN P. , KEFAYATI SARAH , PAVULURI VENKATA , SATHE SAKET , TURAGA DEEPAK S. , NGUEYEP RODRIGUE , VITKIN EDWARD , VU LONG , ZHONG YUXIANG 
Published: June 2019

Introduction: Hypoglycemia is a potentially life-threatening complication of diabetes that can result in seizures, coma, and death, if left untreated. We have built a machine learning system that can predict occurrences of low glucose events from continuous glucose monitoring (CGM) data within 2 to 4 hours before an event.
Methods: IBM Research scientists developed a machine learning system based on a random forest model to predict from data, provided by Medtronic Guardian™ Connect CGM, whether or not a patient’s glucose level will fall below 70 mg/dl, which was defined as a hypoglycemic event. The model was built based on de-identified data collected from voluntary uploads that measured sensor glucose (SG) levels of users every 5 minutes. The system employed 120 million SG data points collected across many years from approximately 10,000 patients. All the patients in the data set were type 1 diabetics who had used a CGM device for at least 11 days and for a median of 272 days. The training features of the ensemble classifier were extracted from the users’ temporal glucose measurements and past event patterns. This was engineered to account for short- and long-term effects of glucose and seasonal elements observed in multiple patients. The random forest method was selected for its robustness, its accuracy, and its ease of maintenance. The data were divided into 80% training and 20% holdout test sets. The model performance was assessed by area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the lead time, which is the time elapsed between the first alert and the time of occurrence of the predicted event.
Results: The model performed with AUC of 0.9, and it is capable of alerting patients more than an hour lead time in 80% of events.
Conclusions: Managing and avoiding hypoglycemia is significant challenge for patients. Systems using modern machine learning techniques have the potential to improve the care of individuals with diabetes and reduce life-threatening complications for these patients.

Disclosure
P. Agrawal: Employee; Self; Medtronic MiniMed, Inc. Stock/Shareholder; Self; Medtronic. L. Cao: None. Y. Chang: None. G.P. Jackson: Employee; Self; IBM. S. Kefayati: None. V. Pavuluri: None. S. Sathe: None. D.S. Turaga: Other Relationship; Self; Medtronic. R. Ngueyep: None. E. Vitkin: Employee; Self; IBM. L. Vu: None. Y. Zhong: None.