bluuebunny/crossref_metadata_2025_split huggingface dataset:
917-P: Prediction Abilities for a Risk of Hypoglycemia in Glycemic Variability Metrics
Authors: TAKEISHI SOICHI , TSUBOI HIROKI 
Published: June 2019

We investigated prediction abilities for a risk of hypoglycemia (R-hypo) in intermediate-term glycemic variability metrics (GVM) using continuous glucose monitoring (CGM) data. We cross-sectionally investigated CGM (FreeStyle Libre Pro) data of 104 patients with type 2 diabetes whose 24 h glucose levels were measured continuously for 13 days during hospitalization. Values for 13 days in all GVM were evaluated. We have proposed GVM as follows: mean of daily difference 1 (MODD1) ÷ mean glucose level (MGL) × 100 (MODD1/M), mean absolute glucose (MAG) ÷ MGL × 100 (MAG/M), and glycemic variability percentage (GVP) ÷ MGL × 100 (GVP/M). The standard deviation (SD), MODD1, MAG, GVP, and MGL were significantly negatively correlated to the low blood glucose index (LBGI) (r=-0.34∼-0.68, p&lt;0.001). The coefficient of variation (CV), MODD1/M, MAG/M and GVP/M were significantly positively correlated to the LBGI (r=+0.22∼+0.53, p=0.03∼&lt;0.001). The prediction ability for LBGI&gt;2.5 in MAG/M was significantly higher than that in CV and GVP/M. The prediction ability for LBGI&gt;5.0 in MAG/M was significantly higher than that in CV, MODD1/M and GVP/M. The prediction ability for LBGI&gt;2.5 was higher than that for LBGI&gt;5.0 in MODD1/M (Table). The GVM which are divided by MGL should be used to predict a R-hypo. Among them, MAG/M has the highest prediction ability for a R-hypo. In addition, MODD1/M reflects a midium R-hypo more strongly than a high R-hypo.

Disclosure
S. Takeishi: None. H. Tsuboi: None.
919-P: Canada Real-World Analysis of Flash Glucose Monitoring and Impact on Time-in-Range and Hypoglycemia
Authors: BERARD LORI , VIRDI NAUNIHAL , DUNN TIMOTHY 
Published: June 2019

Prior analyses of global data from real world use of flash glucose monitoring have associated frequency of scanning with greater time in range and lower mean glucose, glucose variability, and hypoglycemia. These analyses did not include data from North America as flash glucose monitoring became available in late 2017. The objective of this follow-up analysis is to focus on real world outcomes in patients using flash glucose monitoring in Canada. A server collected anonymized data from patients whose flash glucose readers are uploaded (by patient or health care professional). Data from patients within Canada was analyzed through September 2018 (approximately 1 year). To understand the relationship between time in range and glucose variability with monitoring frequency, individuals were divided into 10 equal sized groups based on scanning frequency. Average ± SE time in range (glucose 70 mg/dL - 180 mg/dL [3.9 mmol/L - 10.0 mmol/L]) and time in glucose ≤ 54 mg/dL (∼3.0 mmol/L) was calculated for each group.
This analysis includes 15,424 readers, 95,103 sensors, and 108 million glucose measurements with an average of 12 scans per day. Patients in the lowest scanning frequency decile (3.7 scans per day) spent 12.6 ± 0.14 hours in range and 28.8 ± 1.2 minutes with a glucose ≤ 54 mg/dL. Patients in the highest scanning frequency decile (29.2 scans per day) spent 16.6 ± 0.12 hours in range and 22.1 ± 1.0 minutes with a glucose ≤ 54 mg/dL.
Real-world data from Canada demonstrates that higher frequency of scanning is associated with increased time in range and decreased hypoglycemia. This analysis is consistent with prior analyses and suggests that patients with diabetes who scan to obtain their glucose more frequently derive greater benefit than patients who scan less frequently.

Disclosure
L. Berard: Advisory Panel; Self; Eli Lilly and Company. Consultant; Self; Abbott, Ascensia Diabetes Care, AstraZeneca, Bayer AG, Becton, Dickinson and Company, Janssen Pharmaceuticals, Inc., LifeScan Canada, Mylan, Novo Nordisk Inc., Sanofi. Research Support; Self; Montmed Inc. Speaker's Bureau; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Merck & Co., Inc. N. Virdi: Employee; Self; Abbott, Proteus Digital Health. Stock/Shareholder; Self; Johnson & Johnson. T. Dunn: Employee; Self; Abbott Laboratories.
924-P: Pattern Mining of Trajectories of Glucose Values of Continuous Glucose Monitoring System by Artificial Intelligence in Type 2 Diabetes Patients
Authors: MAKINO MASAKI , YOSHIMOTO RYO , KONDO-ANDO MIZUHO , YOSHINO YASUMASA , HIRATSUKA IZUMI , MAKI WAKAKO , SEKIGUCHI-UEDA SAHOKO , KAKITA AYAKO , SHIBATA MEGUMI , SEINO YUSUKE , TAKAYANAGI TAKESHI , ONO MASAKI , KOSEKI AKIRA , KUDO MICHIHARU , HAIDA KYOICHI , YANAGIYA RYOSUKE , HAYAKAWA NOBUKI , SUZUKI ATSUSHI 
Published: June 2019

Background: Thanks to technological advancements for medical devices, we can measure glucose by the minute for weeks using a sensor called the continuous glucose monitoring (CGM) system. CGM is time-series data and has been available since devices with low measurement error appeared 10 years ago. CGM can be relied upon to help make treatment decisions. A major issue regarding CGM is clinical interpretation by physicians.
Methods: CGM data were obtained by flush glucose monitoring system in 156 type 2 diabetes patients. We divided the patients into 2 groups by mean HbA1c levels during 6 months before getting CGM data. High HbA1c group (High) had their mean HbA1c levels with equal to or above 7% and below 9%. Low HbA1c group (Low) had their mean HbA1c levels with equal to or above 5% and below 7%. The patients with their HbA1c levels equal to or above 9% was excluded from this study. We conducted the experiment with manually created dataset that is designed for evaluating the performance of trajectory extraction. Artificial intelligence (AI) performed pattern mining of raw data of flush glucose in 4 to 8 sequential data sets.
Results: Among 156 patients, there were 83 High group patients, while 58 patients were defined as Low group. We excluded 15 patients from this study due to high HbA1c levels, while there was no patient with HbA1c level below 5%. AI constructed 1292 patterns of glucose trajectories from CGM data. We found that 67 patterns were significantly different between High and Low groups 8p&lt;0.05).
Conclusion: In this session, we propose a method of extracting the trajectories of glucose values of CGM in type 2 diabetes patients. Our method could contribute to better CGM interpretation.

Disclosure
M. Makino: None. R. Yoshimoto: None. M. Kondo-Ando: None. Y. Yoshino: None. I. Hiratsuka: None. W. Maki: None. S. Sekiguchi-Ueda: None. A. Kakita: None. M. Shibata: None. Y. Seino: None. T. Takayanagi: None. M. Ono: None. A. Koseki: Employee; Self; IBM. M. Kudo: Employee; Self; IBM. K. Haida: None. R. Yanagiya: None. N. Hayakawa: None. A. Suzuki: Research Support; Self; Chugai Pharmaceutical Co., Ltd., Dai-ichi Life Insurance Company, IBM, MSD, Ono Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited. Speaker's Bureau; Self; Asahi Kasei Corporation, Daiichi Sankyo Company, Limited, Eli Lilly and Company, Mitsubishi Tanabe Pharma Corporation, Taisho Pharmaceutical Co., Ltd.
926-P: Frequency of Hypoglycemia by Gender in Healthy Individuals without Diabetes—Not So Different After All
Authors: DUBOSE STEPHANIE , LI ZOEY , SHAH VIRAL N. , SHERR JENNIFER , TAMBORLANE WILLIAM V. , BERGENSTAL RICHARD M. , BECK ROY 
Published: June 2019

It is a common belief that females experience more hypoglycemia than males. We aimed to assess whether this was true in a cohort of healthy individuals without diabetes. In this multicenter study, healthy, nondiabetic children and adults (age 7 to 80 years, normal BMI, HbA1c &lt;5.7%, and no positive islet antibodies) were included. Each participant wore a blinded Dexcom G6 for ∼10 days. Overall, 153 participants were analyzed, 66% female, 84% non-Hispanic white, with a mean HbA1c of 5.1±0.3%. Race/ethnicity, BMI, and HbA1c were similar between females (n=101) and males (n=52) but females were older (mean age 34±21 years vs. 25±20 years). Mean average glucose, time in range (TIR) 70-140 mg/dL and coefficient of variation (CV) were similar between females and males (100 vs. 99 mg/dL; 95% TIR and 17% CV for both). There were no statistically significant differences in the percentage of participants with at least one hypoglycemic event (15 consecutive minutes &lt;54 mg/dL), the number of events, or the duration of events between females and males. Additionally, the percentages of time spent below various cutoffs of hypoglycemia were similar between females and males (Table). At least when it comes to frequency, severity and duration of hypoglycemia, these data support the contention that men and women are not so different after all.

Disclosure
S. DuBose: None. Z. Li: None. V.N. Shah: Advisory Panel; Self; Sanofi US. Consultant; Self; Dexcom, Inc. J. Sherr: Advisory Panel; Self; Bigfoot Biomedical, Eli Lilly and Company, Insulet Corporation. Consultant; Self; Lexicon Pharmaceuticals, Inc., Medtronic, Sanofi. Research Support; Self; Dexcom, Inc., Insulet Corporation. W.V. Tamborlane: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic MiniMed, Inc., Novo Nordisk Inc., Sanofi, Takeda Pharmaceutical Company Limited. R.M. Bergenstal: Research Support; Self; Abbott, Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Glooko, Inc., Hygieia, JDRF, Johnson & Johnson Medical Devices Companies, Lilly Diabetes, Medtronic MiniMed, Inc., Merck & Co., Inc., National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk Inc., Roche Diabetes Care, Sanofi. Other Relationship; Self; Abbott, Helmsley Charitable Trust, Hygieia, Lilly Diabetes, Novo Nordisk Inc., Onduo LLC, Sanofi, UnitedHealth Group Inc. R. Beck: Other Relationship; Self; Abbott Laboratories, Ascensia Diabetes Care, Bigfoot Biomedical, Dexcom, Inc., Insulet Corporation, Lilly Diabetes, Roche Diabetes Care, Tandem Diabetes Care.

Funding
The Leona M. and Harry B. Helmsley Charitable Trust
93-LB: Defining High Glycemic Variability in Type 1 Diabetes: Comparison of Multiple Indexes to Identify Patients at Risk of Hypoglycemia
Authors: GOMEZ ANA MARIA , HENAO DIANA , IMITOLA ANGELICA M. , TABOADA LUCIA B. , ROBLEDO MARÍA ALEJANDRA , CRUZ KERLY V. , RONDÓN MARTÍN , MUNOZ OSCAR M. , JARAMILLO MAIRA GARCIA , LEÓN-VARGAS FABIAN 
Published: June 2019

Background: International consensus on the use of continuous glucose monitoring (CGM) recommends coefficient of variation (CV) as the metric of choice to express glycemic variability (GV) with a cutoff of 36% to define unstable diabetes. Even though, CV is associated with hypoglycemia in T2DM patients, the evidence on the use of one particular measure of GV in T1DM patients as a predictor of hypoglycemia is limited.
Methods: A cohort of T1DM ambulatory patients was evaluated using CGM. Number and incidence rate of events &lt; 54 and &lt; 70 mg/dL were calculated. Bivariate and multivariate analysis of different glycemic indexes and clinical variables were performed to identify those associated with hypoglycemia. ROC curve analysis for each of the glycemic indexes were performed to define the best indexes and optimal cutoff thresholds to discriminate patients at risk of hypoglycemia.
Results: Seventy-three patients were included. A total of 128 events of hypoglycemia &lt; 54 mg/dL were recorded in 34 patients and 350 events &lt; 70 mg/dL were registered in 51 patients. CV was the only variable significantly associated with hypoglycemia &lt;54 mg/dL in the multivariate analysis (aRR: 1.44, 95% CI: 1.10-1.88; p=0.008). %CV, HbA1c and mean glucose were associated with events &lt;70 mg/dL. Analysis of ROC curves showed that, among GV metrics, CV had the best performance to discriminate patients with events &lt; 54 mg/dL (AUC: 0.87, 95% CI: 0.79-0.95) and events &lt;70 mg/dL (AUC: 0.79, 95% CI: 0.68-0.90) with optimal cutoff thresholds values of 34% and 31% respectively. Among glycemic risk indexes, LBGI showed the best performance.
Conclusions: This analysis shows that CV is the the best GV index, and LBGI the best glycemic risk index, to identify patients at risk of clinically significant hypoglycemia and hypoglycemia alert events in T1DM patients.

Disclosure
A. Gomez: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Novo Nordisk Inc. Speaker’s Bureau; Self; Abbott, AstraZeneca, Medtronic MiniMed, Inc., Merck Sharp & Dohme Corp. D. Henao: Research Support; Self; Novo Nordisk Inc. Speaker’s Bureau; Self; Abbott, Medtronic MiniMed, Inc., Novo Nordisk Inc. A.M. Imitola: None. L.B. Taboada: Other Relationship; Self; Medtronic MiniMed, Inc. M. Robledo: None. K.V. Cruz: None. M. Rondón: None. O.M. Munoz: None. M. Garcia Jaramillo: None. F. León-Vargas: None.
93-OR: Expression of Genes Associated with Host Viral Responses in Insulitic Islets
Authors: NELSON GRACE , LENCHIK NATALIYA I. , CAMPBELL-THOMPSON MARTHA , MATHEWS CLAYTON E. , ATKINSON MARK A. , GERLING IVAN C. 
Published: June 2019

Background: Type 1 diabetes (T1D) is an autoimmune condition long hypothesized to be enhanced or triggered by viral infections. Islet pathology in T1D is characterized by destruction and subsequent loss of insulin producing pancreatic beta cells. In this study we evaluated specific physiological pathways associated with the disorder’s pathogenesis by examining differential expression of host genes within islets, with special emphasis on identifying evidence suggestive of responses to a viral infection.
Methods: Pancreatic tissue samples were obtained from nondiabetic donors, autoantibody positive donors, and donors with T1D through the Network for Pancreatic Organ donors with Diabetes (nPOD) program. Laser microdissection was used to isolate individual islets based on immunohistochemical documentation of presence or absence of insulin (INS+) and T-lymphocytes (CD3+). RNA was isolated and microarray used to assess transcriptomes.
Results: Increased expression in insulitic islets was seen in a variety of genes including TLR pathway inhibitors: LAIR1 (p=2.77E-8), SIGLEC7 (p=8.33E-6), SIGLEC9 (p=1.59E-4), and C1q (p=6.11E-16); as well as genes downstream of the TLR pathway: CD163 (p=2.34E-11), CTSS (p=9.89E-7) and IFNγ (p=5.9E-8). We also observed variances in the frequency of viral response genes, including those known to be associated with T1D at a GWAS level: STAT1 (p=1.64E-7) and OAS3 (p=1.04E-8). Pathway analysis demonstrated increased representation of antigen processing and presentation genes (p=3.0E-14).
Conclusions: In sum, these studies noted an increased expression of multiple host genes associated with anti-viral responses. The demonstration of specific protein involvement in host responses to infections are up-regulated in T1D islets, specifically in those with signs of immune activation and residual insulin positivity (INS+CD3+), supports the potential involvement of host viral response pathways in the development of T1D.

Disclosure
G. Nelson: None. N.I. Lenchik: None. M. Campbell-Thompson: None. M.A. Atkinson: None. I.C. Gerling: None.

Funding
National Institutes of Health (UC4DK104155)
931-P: Hyperglycemia following Early vs. Late Meal Boluses
Authors: NORLANDER LISA M. , NYKAZA EDWARD T. , ARBITER BRANDON , BUCKINGHAM BRUCE A. , LAL RAYHAN 
Published: June 2019

Background: Late-meal blousing increases the risk of hyperglycemia following a meal using meter blood glucose data. We used data from the Donors to the Tidepool Big Data Donation project to evaluate real-life bolusing and glycemic excursions using continuous glucose monitoring (CGM).
Methods: We analyzed 214,087 meal boluses from 120 subjects, mean age 22 years (range 3-66), average CGM glucose 155mg/dL (range 103-239 mg/dL). We screened for meals meeting the following criteria: carbs of ≥20g, premeal glucose between 70 and 200mg/dL, no glucose &lt;70mg/dL in the hour prior to bolus, no other insulin bolus 2 hours prior and 4 hours post bolus, 95% of CGM readings available 30 min before and 4 hour after the meal bolus. A pre-meal bolus was defined by the glucose rising &lt;0.4mg/dL/min and a late meal boluses by a glucose rise of ≥1mg/dL/min in the 20 minutes prior to the bolus.
Results: The post meal bolus had significantly higher CGM glucose levels at the time of bolus (p&lt;0.001), and a significantly higher post prandial peak glucose (p&lt;0.001). There was no statistical difference in the average amount of meal carbs for the two groups.
Conclusions: Using integrated pump and CGM data from a large data base, late meal bolusing occurred with 12% of selected meal boluses and was associated with higher CGM values at the time of bolus, and higher peak post prandial CGM values.

Disclosure
L.M. Norlander: None. E.T. Nykaza: None. B. Arbiter: Employee; Self; Tidepool Project. Stock/Shareholder; Self; Abbott Laboratories, Dexcom, Inc. B.A. Buckingham: Advisory Panel; Self; ConvaTec Inc., Novo Nordisk Inc., Profusa, Inc. Consultant; Self; Medtronic MiniMed, Inc. Research Support; Self; Beta Bionics, ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Medtronic MiniMed, Inc., Tandem Diabetes Care. Other Relationship; Self; Insulet Corporation, Tandem Diabetes Care. R. Lal: Consultant; Self; Abbott.
940-P: Toward a Novel Tiny Continuous Glucose Monitor (CGM): Step 3, Building an Enhanced Ratiometric Detector Using a Buried Dual Junction Diode (BDJ)
Authors: PENNATHUR SUMITA 
Published: June 2019

Continuous glucose monitors (CGM) improve overall glucose control in patients with diabetes, but have many disadvantages for users. We are developing a novel CGM that will be painless to insert and wear, and easy to use. Our goal is an on-body, wirelessly transmitting CGM device that extracts interstitial fluid with a dime-sized patch of microneedles and optically detects glucose from extremely small fluid volumes. Our first step in 2016 was developing an innovative method for fabricating silicon microneedles, sharp and small enough to penetrate skin without pain (160 µm in height, 50 µm in diameter, and 100 µm in width). The next year we not only demonstrated that our microneedle fabrication is repeatable and reliable but also developed an entirely new, stable, non-enzymatic glucose-sensing method based on a variation of the well-known fluorescent-based boronic acid sensor. Since then, we have further refined our sensor for conductivity sensing as well as demonstrated a novel integrated silicon buried dual-junction (BDJ) photodiode-based system with which to detect glucose with our sensor.
Specifically, we built a buried dual junction (BDJ) photodiode for color or ratiometric optical sensing. We employed a simple polycarbonate UV filter to block stray 380 nm pump light from reaching the BDJ detector, and the signal-to-background ratio is further enhanced by incorporating a yellow band filter between the LED and sample reservoir. To test the optical performance of our photosensor, cationic polymer PFP and fluorescein/coumarin are excited at 380 nm and measured at the peak 425 and 525 nm. These data prove that our device is capable of detecting ∼µM of fluorophores in a ∼7 µL of volume. Next, we demonstrated the diffusion of molecules via hollow silicon microneedles. These data show that integration if all components can enable for the first time, a compact, wearable optical biosensor.

Disclosure
S. Pennathur: Board Member; Self; Laxmi Therapeutic Devices. Consultant; Self; Alveo Technologies.

Funding
American Diabetes Association/Pathway to Stop Diabetes (1-17-VSN-18)
947-P: Glucose Variability: Insulin Pump vs. Multiple Daily Injection in Type 1 Diabetes Evaluated by Continuous Glucose Monitoring
Authors: PROIETTI ADRIAN E. , DAGHERO ANDREA V. , SCAPUZZI MARIA LUJAN , IGLESIAS MARIA L. , NOGUEIRA JUAN PATRICIO 
Published: June 2019

Background: This study compared the effects of insulin pump (IP) vs. multiple daily injection therapy (MDI) on glucose variability (GV) evaluated by professional Continuous Glucose Monitoring (CGM) in real-life condition of patients with type 1 diabetes (T1D).
Methods: This was a parallel study in 120 T1D patients, with a mean age of 31.2 years, and hemoglobin A1c (HbA1c) level of 8.3%; 99 patients were treated MDI (66 patients with Glargine U100, 13 with Detemir, 20 with Degludec U100) and 21 with IP. The primary end point was the coefficient of variation (CV) of blood glucose. Secondary end points included HbA1c, mean amplitude of glycemic excursions (MAGE). We compared IP vs. MDI.
Results: IP was not different to MDI respect to CV (37.94±1.68 vs. 40.32±075; p=0.12) and HbA1c (8.59±0.20 vs. 8.23±0.15; p=0.39). There were also no significant differences between treatment groups with respect to MAGE (308.75±11.28 vs. 309.22±5.17; p=0.92) and total doses of insulin (35.14±4.77 vs. 42.88±2,22; p=0.36). In multiple regression analyses, the CV was positively associated with MAGE and negatively with HbA1c only 22% (R2: 0,22, p = 0.01).
Conclusion: In our population in real world data, no significant difference was observed in the GV indicators in patients treated with MDI vs. IP.

Disclosure
A.E. Proietti: None. A.V. Daghero: None. M.L. Scapuzzi: None. M.L. Iglesias: None. J.P. Nogueira: None.
948-P: Analysis of Key Metrics for Continuous Glucose Monitoring Data in Adults without Diabetes
Authors: FRECKMANN GUIDO , PLEUS STEFAN , ULBRICH SINA , WALDENMAIER DELIA , BAUMSTARK ANNETTE , LINK MANUELA , HAUG CORNELIA 
Published: June 2019

Continuous glucose monitoring (CGM) may help to improve diabetes therapy. An international consensus paper calls for standards in the CGM data analysis, suggesting reports of standardized “key metrics.” Data from people without diabetes might be useful in the interpretation of the suggested key metrics.
In this study, 39 adults without diabetes used two iscCGM systems (FreeStyle Libre) in parallel for the 14-day sensor life. Based on collected iscCGM data the key metrics mean glucose, standard deviation, glycemic variability (CV), time in various glucose ranges, low blood glucose index (LBGI) and high blood glucose index (HBGI) were calculated. Instead of estimated A1C, its recently proposed replacement, the glucose management index (GMI), was calculated.
Out of 78 sensors, 73 sensors achieved a data capture rate of at least 80%, the minimum required by the consensus statement. Direct comparison of the two sensors worn in parallel was possible for 35 subjects. Specific results are provided in the Table.
In this study, some key metrics calculated from iscCGM data of adults without diabetes showed considerable variation between subjects as well as within subjects. Although the reliability of these key metrics depends on how accurately CGM data depict “true” glucose levels, these findings may be helpful in the interpretation of these key metrics in diabetes patients.

Disclosure
G. Freckmann: Advisory Panel; Self; Abbott, Novo Nordisk Pharma GmbH, Sensile Medical AG. Consultant; Self; Metronom Health Europe, Roche Diabetes Care, Sanofi. Speaker's Bureau; Self; Ascensia Diabetes Care, Dexcom, Inc., Ypsomed AG. S. Pleus: None. S. Ulbrich: None. D. Waldenmaier: None. A. Baumstark: None. M. Link: None. C. Haug: None.