bluuebunny/crossref_metadata_2025_split huggingface dataset:
Long-Term Use of Closed-Loop Control—Patient-Reported Experience
Authors: GONDER-FREDERICK LINDA , BROWN SUE A. , SHEPARD JACLYN , KOLLAR LAURA L. , KOVATCHEV BORIS 
Published: June 2018

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.
Interim Analysis of an Implantable CGM System Longevity during a 365-Day Study
Authors: IOACARA SORIN , RASTOGI RAVI , MDINGI COLLEEN , CHEN XIAOXIAO , TWEDEN KATHERINE 
Published: June 2018

Background: Research has been conducted to extend the life of a subcutaneously implanted CGM System (Eversense®, Senseonics, Maryland U.S.A) using several strategies including the use of different configurations of an anti-inflammatory agent and improvement in glucose sensing and calculation methods. The current study investigated the longevity of the Eversense® XL CGM over the first 250 days of the planned 365-day evaluation period.
Methods: Thirty-four adult participants with T1D were enrolled in a prospective, unblinded, single-center 365-day study. Sensors were inserted in the upper arm and assessed every 30-60 (±14) days for sensor longevity, safety and effectiveness. An interim analysis of sensor longevity was performed using the Kaplan-Meier method to estimate the probability of sensor survival through 250 days. CGM wear duration was also assessed to monitor subject compliance. Serious adverse events related to the device or insertion or removal procedures were assessed.
Results: Participant baseline demographics were 22 males/12 females, 30 ± 7.9 years of age, with BMI of 24 ± 3.6 kg/m2 (mean ± SD). At this interim analysis, all sensors had a range of 124-365 days of use. Survival analysis at post-implant 90, 120, 150, 180 and 250 days showed an estimated probability of sensor survival of 97%, 94%, 84%, 80% and 51%, respectively. Median wear compliance of the subjects was 23.1 hours/day (96% of time). No insertion, removal or device-related serious adverse events were reported.
Conclusions: The Eversense XL CGM demonstrated good survival and safety up to 250 days of sensor wear.

Disclosure
S. Ioacara: Research Support; Self; Senseonics. R. Rastogi: Employee; Self; Senseonics. C. Mdingi: Employee; Self; Senseonics. X. Chen: Employee; Self; Senseonics. K. Tweden: Employee; Self; Senseonics.
Activation of PKM2 Protects against Diabetic Kidney Disease from Mice to Men
Authors: GORDIN DANIEL , SHINJO TAKANORI , ST-LOUIS RONALD , QI WEIER , TINSLEY LIANE J. , POBER DAVID M. , BRISSETT MEGAN J. , NIEWCZAS MONIKA , DREYFUSS JONATHAN , PAN HUI , SADOWSKI THORSTEN , KANNT AIMO , SHAH HETAL , KEENAN HILLARY A. , KROLEWSKI ANDRZEJ , KING GEORGE L. 
Published: June 2018

We have reported that up-regulation of PKM2 in renal glomeruli isolated from patients with more than 50 years of type 1 diabetes (Joslin Medalist Study) correlated with preservation of renal function even in the presence of chronic hyperglycemia. Further, pharmacological activation of PKM2 in T1D mice and podocyte-specific deletion of PKM2 in cultured cells and in vivo strongly showed that PKM2 activation protects renal glomeruli from hyperglycemia/diabetes induced damage (Nat. Med 2017). To confirm these findings in type 1 and type 2 diabetes, we performed the following:
1. We showed the protein expressions of glycolytic (PKM1, PKM2, ENO1) and mitochondrial (MTCO2) enzymes to be significantly elevated in glomeruli of post mortem kidneys in people with T2D without DN compared to those with DN, or nondiabetic controls.
2. Plasma PKM2 levels (SomaLogic aptamer assay) in the Medalists (n=180) correlated with albumin-to-creatinine ratio (ACR;p=0.03) and eGFR (p=0.0002) at baseline. In a replication cohort (n=219) of patients with T1D in the longitudinal Joslin Kidney Study (JKS) baseline plasma PKM2 also correlated with ACR (p=0.0001). High baseline plasma PKM2 in JKS was independently associated with a slower decline in eGFR (p=0.004) and a slower progression to end-stage renal disease (p=0.0002).
3. TEPP-46, a PKM2 activator, prevented diabetes induced increases in mRNA expression of pro-fibrotic (Col3a1, Fn1, TGFb1) and inflammatory (Ccl2) genes, and enhanced key mitochondrial protein (Ppargc1a) in uninephrectomized T2D db/db mice. No differences in ACR were observed between TEPP-46 treated and untreated diabetic mice.
These findings support the hypothesis that PKM2 activation may protect or even reverse the initiation and progression of diabetic glomerular dysfunction and pathology in both T1D and T2D, suggesting that activating PKM2 could be a clinically effective therapeutic target to stop the progression of DN.

Disclosure
D. Gordin: None. T. Shinjo: None. R. St-Louis: None. W. Qi: None. L.J. Tinsley: None. D.M. Pober: None. M.J. Brissett: None. M. Niewczas: None. J. Dreyfuss: None. H. Pan: None. T. Sadowski: Employee; Self; Sanofi. A. Kannt: Employee; Self; Sanofi. Stock/Shareholder; Self; Sanofi. H. Shah: None. H.A. Keenan: Research Support; Self; Sanofi. Employee; Self; Sanofi Genzyme. A. Krolewski: None. G.L. King: Research Support; Self; Sanofi-Aventis.
Near-Infrared Spectroscopic Method for Noninvasive Blood Glucose Prediction without Using Multivariate Analyses
Authors: MARUO KATSUHIKO , YAMADA YUKIO 
Published: June 2018

A near-infrared spectroscopy method for noninvasive blood glucose prediction without using multivariate analyses is proposed and examined. Near-infrared diffuse reflection spectra with a wavelength range from 1400 to 1850 nm were measured at the skin tissue using an optical fiber probe. The difference spectra obtained by subtracting the initially measured spectrum from the other measured spectra were reconstructed from the spectra of four major components in the human skin (water, protein, glucose, and fat) and the spectrum of a scattering equivalent component called baseline, based on the modified Beer’s law. Then the blood glucose contents were predicted from the content index of the glucose component appearing in calculation of the reconstructed spectrum. We assumed that one of the origins of the errors in the blood glucose prediction using near-infrared spectroscopy was the similarity of the shapes of the absorption spectrum between glucose and baseline. To distinguish the glucose contribution from those of baseline and fat in the difference spectra, an imaginary component combining baseline and fat was introduced by considering that both the change in the fat contribution and the generation of baseline originated from the change in scattering in the skin. The imaginary component enabled us to reduce the errors in the blood glucose prediction. In contrast to the conventional methods using multivariate analyses, the calculation process of the proposed method is not so called "a black box." Thus, it is easy to estimate the origins of the changes and contributions of the components in the difference spectra. The proposed method may become a useful tool for realization of noninvasive blood glucose prediction.

Disclosure
K. Maruo: None. Y. Yamada: None.
Development and Validation of a Pattern-Recognition Engine for Visualization of Glycemic Patterns in Individuals Performing Low-Frequency SMBG
Authors: VESPASIANI GIACOMO , NICOLUCCI ANTONIO , SALEH MILENA , SIEBER JOCHEN , PROSPERINI GIUSEPPE , SURACI CONCETTA , GALETTA MARIANNA , GRASSI GIORGIO 
Published: June 2018

Background and Aims: Innovative technologies supporting healthcare professionals (HCPs) and patients in the review of self-monitoring blood glucose (SMBG) data may aid clinical and educational approach to diabetes management. MyStar Connect® (MSC) is a diabetes data management software that helps HCPs review patient data and adjust diabetes therapy. Filtering mechanisms are needed to visualize data efficiently. We developed a pattern recognition engine (PRE) for visualization of glycemic patterns of SMBG data in MSC.
Method: 3-month of SMBG data from 184 insulin- and non-insulin treated patients, at low testing frequency (0.5-2 tests per day), were independently evaluated by three expert assessors. Daily and weekly hypo- and hyperglycemic patterns were identified, and compared to those identified by the PRE. Discrepancies were reconciled through discussion between the assessors. The rate of agreement between assessors and PRE was evaluated. The analyses were conducted separately for hypoglycemic and hyperglycemic patterns.
Results: Daily patterns: 56 hypoglycemic and 247 hyperglycemic patterns were identified by the PRE. No disagreement was noted in the assessment of hypoglycemic patterns between the PRE and the assessors. In 23 cases of hyperglycemic patterns, a disagreement was noted, due to the PRE missing a pattern considered as present by the assessors. After further clarification, 18 cases were reconciled. Therefore, the rate of agreement between the PRE and the assessors was 100% for hypoglycemic patterns and 98% for hyperglycemic patterns.
Weekly patterns: The assessors recognized that the PRE provided information not easily inferable by simple review of SMBG data. PRE was therefore considered reliable for all the patterns identified.
Conclusion: The implementation of PRE can represent a useful tool to guide HCPs’ treatment decisions and educate patients performing SMBG at a low frequency.

Disclosure
G. Vespasiani: Consultant; Self; METEDA Srl. Other Relationship; Spouse/Partner; METEDA Srl. A. Nicolucci: None. M. Saleh: Employee; Self; Sanofi. J. Sieber: Employee; Self; Sanofi. Stock/Shareholder; Self; Sanofi. G. Prosperini: None. C. Suraci: None. M. Galetta: None. G. Grassi: None.
Stepwise Intensification of Prandial Insulin in Taiwanese Patients with T2DM—Final Analysis Report of the SPIRIT Study
Authors: HUNG YI-JEN , HUANG YU YAO , CHEN HARN-SHEN , LIU RUE-TSUAN , YU NENG C. , LAM HING-CHUNG , HUANG CHEN-LING , CHEN CHUNG-SEN , HSIAO PI-JUNG , CHEN HUA FEN , CHIEN MING-NAN , LEE TAO-CHUN , YANG CHWEN Y. , CHEN CHING-CHU , TU SHIH TE , CHEN HUNG-LIN , HUEY-HERNG SHEU WAYNE 
Published: June 2018

Aim: Addition of 1-3 dose of bolus insulin is recommended by ADA/EASD guidelines when basal insulin (BI) therapy becomes insufficient for T2DM patients to achieve HbA1c target of &lt;7%. This prospective registry study examined the effect of stepwise intensification of prandial insulin (PI) on top of BI over 48 weeks across multi-centers in Taiwan.
Method: 328 T2DM patients completed 48 weeks of stepwise intensification of PI + BI therapy (Main group). The primary objective of the study was to determine the mean change in HbA1c. Secondary objectives included; evaluation of change in FPG, 2h-PPG, PI and BI dose and the rate of HbA1c &lt;7% achievement and hypoglycemic events. Results of BI intensification with PI were retrospectively analyzed against a BI alone group (Control group, n=113). Statistical analysis was performed using T-tests.
Result: Mean HbA1c was 9.16% in Main group at baseline. At week 48, significant difference in mean HbA1c change (-0.59 ± 1.16% vs. -0.07 ± 1.06%; p&lt;0.0001) but not in mean FPG change (-10.99 ± 70.06 mg/dL vs. -6.53 ± 50.58 mg/dL; p=0.054) was observed between Main group and Control group; 5.49% and 2.65% (p=0.0318) of patients achieved HbA1c &lt;7%, respectively. In Main group, mean BI dose was 26.43 ± 12.35 IU/day (p&lt;0.05 vs. baseline); mean PI dose was 10.16 ± 7.90 IU/day (p &lt;0.0001 vs. baseline). In Control group, mean BI dose was 24.95 ± 13.92 IU/day (p=0.0986 vs. Main group). Overall, 31 hypoglycemic episodes related to PI (9) and BI (22) use were reported as treatment-emergent adverse events in Main group. No significant difference was observed in the frequency of documented symptomatic or severe hypoglycemic episodes between Main group and Control group at baseline and at week 48.
Conclusion: This study demonstrated the introduction of stepwise intensification of prandial insulin leads to greater improvements in HbA1c levels than basal insulin monotherapy in T2DM patients in Taiwan and this effect can be maintained during the study period of 48 weeks.

Disclosure
Y. Hung: Advisory Panel; Self; Sanofi. Y. Huang: Research Support; Self; Sanofi. H. Chen: Research Support; Self; Sanofi. R. Liu: Research Support; Self; Sanofi. N.C. Yu: None. H. Lam: None. C. Huang: Research Support; Self; Sanofi. C. Chen: Research Support; Self; Sanofi. P. Hsiao: Advisory Panel; Self; Sanofi. H. Chen: None. M. Chien: Research Support; Self; Sanofi. T. Lee: None. C.Y. Yang: None. C. Chen: Advisory Panel; Self; Sanofi-Aventis. S. Tu: None. H. Chen: None. W. Sheu: Advisory Panel; Self; Sanofi.
Use of Automated Clinical Decision Support (CDS) to Effect Glycemic Control in Elderly Patients with T1D
Authors: STEIL GARRY M. , OLADUNJOYE ADEOLU O. , WALD JONATHAN S. , SLYNE CHRISTINE , ATAKOV-CASTILLO ASTRID , GREENBERG JORDAN , GREAVES TORI , TOSCHI ELENA , MUNSHI MEDHA 
Published: June 2018

Older adults with T1D can require additional support to achieve glycemic goals as they may have cognitive impairment and be at high risk for hypoglycemia. The Technological Advances in Glucose Management in Older Adults trial (TANGO) is a RCT comparing risk of hypoglycemia (primary outcome: time glucose &lt;70 mg/dl per day) in patients older than 65 yo managed with pump or pen, randomized to receive CGM with automated CDS (intervention; INT) or equal clinical attention (control; CON). Patients are randomized after a 2-week period confirming ability to use CGM (2 successful insertions; Dexcom CGM) and baseline hypoglycemia (2 incidence of CGM &lt; 70 mg/dL for 20 minutes). Sixteen subjects have been enrolled (median [IQR] age 70 [68 73] yo), all 16 eligible for randomization (9 pump and 4 pen patients randomized to INT; 3 to CON). Insulin delivery, meal, and activity (FitBit) data are obtained daily from the cloud (glooko; see Figure) and an explicit CDS algorithm is used to review the data and recommend changes in CIR, ISF, and basal rates (pump patients), or long and short acting insulin doses (pen patients). CDS has reviewed 1194 days of patient data and generated 15 recommendations (average 1 every 79.6 days), all approved or modified. We conclude that hypoglycemia is common in this population and that patients can be managed with the aid of an automated CDS algorithm reviewing data at the end of each day.

Disclosure
G.M. Steil: Consultant; Self; Profusa, Eli Lilly and Company. A.O. Oladunjoye: None. J.S. Wald: None. C. Slyne: None. A. Atakov-Castillo: None. J. Greenberg: None. T. Greaves: None. E. Toschi: None. M. Munshi: Consultant; Self; Sanofi.
Mobile Technology for Behavioral Feedback on Glycemic Excursions—A Qualitative Study
Authors: RITHOLZ MARILYN D. , HENN OWEN , ATAKOV-CASTILLO ASTRID , FISHER LAWRENCE , TOSCHI ELENA 
Published: June 2018

Little is known about how adults with type 1 diabetes (T1D) engage with technology to make positive changes in diabetes self-management. We explored the qualitative experiences of T1D adults after a clinical intervention that included FreeStyle Libre (FSL) linked to “Sugar Sleuth,” a mobile-based system that prompted patients to reflect on the causes of daily glycemic excursions in real time. Ten T1D adults (baseline A1c 8.0% ± 0.4; 60% male, 52±16 years old, T1D duration 31±14 years, 40% insulin pump) met with clinicians 5 times over 14 weeks initially to learn to use Sugar Sleuth and then to collaboratively review glucose data. They also completed an online nutrition education module focused on the impact of food on postprandial glycemia. Research psychologist conducted semi-structured interviews that were transcribed, coded, and analyzed using content analysis to derive themes. Three main themes emerged regarding the integrated FSL + Sugar Sleuth system: Empowering Tool, provided constant information for preemptive actions with hypo/hyperglycemia, immediate feedback for food choices, and useful data to discuss with clinicians; Psychosocial Support, promoted a sense of normalcy and relief from worry, and aided diabetes coping; Approaches to Glycemic Data, 50% described using FSL + Sugar Sleuth to review data, problem-solve, understand cause and effect, and plan for future self-care; the remainder focused only on immediate glucose numbers, trend arrows, and graphs but rarely reviewed data retrospectively. At study end, A1c was reduced by 0.4±0.14% (P&lt;0.005) without a significant change in total daily insulin dose.
Overall, participants perceived distinct benefits from this intervention, which incorporated the use of FSL + Sugar Sleuth and clinical support to facilitate changes in diabetes self-management. These qualitative results are supported by improvement in A1c. Further study of FSL + Sugar Sleuth can inform more personalized clinical and mobile-based interventions for T1D adults.

Disclosure
M.D. Ritholz: None. O. Henn: None. A. Atakov-Castillo: None. L. Fisher: Consultant; Self; Eli Lilly and Company, Abbott, Merck & Co., Inc.. E. Toschi: None.
Fourth-Generation Glucose Sensor with an Advanced Algorithm Featuring No Calibrations and Optional Calibration Modes of Operation
Authors: ENGEL TALY , AJEMBA PETER , NISHIDA JEFFREY , NOGUEIRA KEITH , GROSMAN BENYAMIN , TSAI ANDY , LU YUNFENG , SULLIVAN ASHLEY , VARSAVSKY ANDREA 
Published: June 2018

An advanced algorithm was developed for the Medtronic fourth-generation glucose sensor to eliminate the need for blood glucose meter calibrations. The algorithm uses electrochemical impedance spectroscopy and other measurements to self-calibrate the sensor and adjust CGM system sensitivity over time. Self-calibration adjusts for potential sensor-to-sensor variability, as well as subject-dependent sensitivity changes over time.
The new algorithm was designed to not require self-monitored blood glucose (SMBG) input during use, but accepts calibrations at any time to improve CGM system performance. It contains multiple independent sensor glucose (SG) calculation units that implement a distinct dynamic model of interstitial glucose that accounts for differences in response caused by sensor-dependent and time-dependent nonlinearities. This approach is particularly useful during the post-sensor insertion period and during periods of hypoglycemia. Each dynamic model was selected for its unique pattern of strengths, which complement each other to augment overall performance. The output of each model is combined to provide a final SG value and a measure of confidence in the calculated value.
Performance was evaluated on 31 subjects with type 1 and type 2 diabetes against SMBG values (2441 points over 80 sensors). Sensors were inserted in the arm. The overall (7-day) mean absolute relative difference was 10.8% with no calibrations, 9.5% with 1 calibration/day. There were 86.7% of points that met the 20% agreement rate with no calibrations, 90.0% with 1 calibration/day.
A new advanced algorithm for the fourth-generation glucose sensor enables the CGM system to function with good accuracy without SMBG calibrations.

Disclosure
T. Engel: Employee; Self; Medtronic MiniMed, Inc. P. Ajemba: Employee; Self; Medtronic. J. Nishida: Employee; Self; Medtronic MiniMed, Inc. K. Nogueira: Employee; Self; Medtronic. B. Grosman: Employee; Self; Medtronic. A. Tsai: None. Y. Lu: None. A. Sullivan: None. A. Varsavsky: Employee; Self; Medtronic MiniMed, Inc..
Continuous Glucose Monitoring (CGM) Provides Enhanced Sensitivity Compared with Self-Monitored Blood Glucose (SMBG) for Detecting Hypoglycemia during T2D Clinical Trials
Authors: TESTA MARCIA A. , SALDIVAR-SALAZAR SERGIO , SU MAXWELL , HAYES JOHANNA F. , SIMONSON DONALD C. 
Published: June 2018

Documenting hypoglycemia during clinical trials relies primarily on SMBG ≤ 70 mg/dL (3.9 mmol/L) with or without symptoms; however, hypoglycemia unawareness, sleep, and prandial-based sampling might lead to underestimation and lower detection of treatment differences. To quantify sensitivity and detection rates using CGM vs. SMBG, we analyzed hypoglycemia (Hypo: glucose ≤ 70 mg/dL) data from 73,941 SMBG samples collected simultaneously with masked CGM (iPro®2) during four 6-day CGM sessions (10,830 person-days) in 682 T2D patients participating in three 20-26 week trials testing insulin regimens [glargine with/without metformin, lispro, and peglispro (n = 539)] and triple oral combination therapy (n = 143). For SMBG vs. CGM, total Hypo events were 864 (n = 296 patients) vs. 2,935 (n = 494) at any time of day (daily), and 1(n = 61) vs. 1,209 (n = 383) nocturnally 12:01-6:00 AM. Poisson regression of Hypos and exposure time, with study regimen, time of day and monitoring method as independent variables, indicated CGM nocturnal Hypo risk [IRR (SE), 95% CI] was higher than CGM daily Hypo risk [1.665 (0.057), 1.558 to 1.781]. However, SMBG detected only 29.4% of CGM daily Hypos [0.294 (0.011), 0.273 to 0.318]. The interaction term indicated that the SMBG nocturnal detection bias resulted in 70% fewer Hypo events [0.303 (0.033), 0.246 to 0.375] than CGM, thereby detecting only 8.9% of those detected by CGM [0.089 (0.009), 0.073 to 0.109]. CGM also detected higher Hypo risk for insulin and metformin compared to triple oral combination therapy [daily = 1.16 (0.085), 1.007 to 1.340, p = 0.04; nocturnal = 1.247 (0.131), 1.015 to 1.531, p = 0.04] when A1C levels were comparable. SMBG did not detect these differences (p = 0.09 and p = 0.94). Using CGM vs. SMBG to detect hypoglycemia can improve comparative safety and risk-benefit assessments during diabetes clinical trials by providing greater sensitivity and representative 24-hour sampling.

Disclosure
M.A. Testa: Stock/Shareholder; Spouse/Partner; GI Windows, Inc.. Stock/Shareholder; Self; Phase V Technologies, Inc.. S. Saldivar-Salazar: None. M. Su: None. J.F. Hayes: None. D.C. Simonson: Advisory Panel; Self; GI Windows, Inc.. Stock/Shareholder; Self; GI Windows, Inc.. Stock/Shareholder; Spouse/Partner; Phase V Technologies, Inc.