bluuebunny/crossref_metadata_2025 huggingface dataset:
Determination of Risk for Suboptimal Glycemic Control in New-Onset Type 1 Diabetes
Authors: MAJIDI SHIDEH , VOGELI JO M. , DRISCOLL KIMBERLY A.
Published: June 2018

Background: Most youth with type 1 diabetes (T1D) do not meet current ADA recommendations for A1C, thus, it is important to identify at diagnosis, those at-risk for later suboptimal glycemic control (A1C ≥9.5%) and complications.

Methods: The Risk Index for Poor Glycemic Control (RI-PGC) was administered to 266 parents during children’s (ages <1-19 years) routine new onset T1D visit. The RI-PGC provides a single score (range = 0-9, higher scores = higher risk) based on psychosocial factors (e.g., insurance, child mood, parent stress/anxiety) to classify risk as “Low,” “Moderate,” or “High” in the 1-4 years after diagnosis. Demographic data and DKA occurrence at diagnosis were also collected.

Results: Of 266 children with new onset T1D (Mage 9.98 ± 4.42 years; 52% male; 68% Caucasian), 18.8% and 28.6% were Moderate and High Risk, respectively. DKA at onset (n=157; 59%) was significantly correlated with risk score (r=0.888, p<0.001). Children in DKA at onset were more than twice as likely to score high on RI-PGC (p=0.009; Table).

Conclusions: We use the RI-PGC in a prevention program to identify risk of future suboptimal glycemic control. These results are consistent with the only other study (Schwartz et al., 2014) to examine risk-most children were Low Risk and more children were High Risk than Moderate Risk. Presence of DKA at onset may be another predictor of future suboptimal glycemic control, DKA episodes, and complications.

Risk Classification at T1D New Onset for Later ComplicationsRI-PGC Risk Category (n; %)LowModerateHighDKA at Onset75 (54%)26 (52%)56 (74%)No DKA at Onset65 (46%)24 (48%)20 (26%)

Disclosure

S. Majidi: None. J.M. Vogeli: None. K.A. Driscoll: None.
Assessment of Diabetes Distress in Appalachian Ohio
Authors: BEVERLY ELIZABETH A. , GUSEMAN EMILY H. , COOK KARIE A. , HEALY AMBER M.
Published: June 2018

Diabetes rates in southeastern Ohio are more than double the national average (19.9% vs. 9.4%). Here, people battle a poverty rate 1.5 times that of the U.S. average, and suffer from higher unemployment, lower educational achievement, poorer health, and lower access to care. Despite the high presence of social determinants of health, the impact of diabetes is understudied and one critical outcome that has not been addressed is diabetes distress. We conducted a cross-sectional survey study with 121 students, faculty, and staff at a large university in Appalachian Ohio (A1C=7.3±1.4%, age=36±17years; female=60%; white=86%; type 2 diabetes=54%; duration=10±9 years, 48%=college degree or more). Diabetes distress was assessed using the Diabetes Distress Scale for type 2 diabetes and for type 1 diabetes; depressive symptoms were assessed using the Patient Health Questionnaire-9. Of the 65 type 2 diabetes participants, 34% (n=19) reported moderate to severe diabetes distress, with 48% (n=31) experiencing moderate to severe distress on the Regimen subscale. Among the 56 type 1 participants, 26% (n=15) reported moderate to severe diabetes distress, with 51% (n=28) experiencing moderate to severe distress on the Powerlessness subscale. Multiple regression analyses found high scores of diabetes distress (standardized b= 0.62, p=0.03) were independently associated with higher A1C levels after controlling for depressive symptoms (standardized b=0.01, p=79), age (standardized b= -0.03, p=0.14) and gender (standardized b=0.75, p<=.08). This model accounted for 29% of the variation in A1C. Multiple regression analyses using the same model found no association between type 2 diabetes, distress, and A1C; the lack of significance may be explained by well-educated, working adults with private insurance. Next steps include recruiting type 1 and type 2 patients from local specialty and primary care clinics to enhance representativeness. These data will help providers individualize clinical approaches for patient’s diabetes distress level.

Disclosure

E.A. Beverly: None. E.H. Guseman: None. K.A. Cook: None. A.M. Healy: None.
Shared Telemedicine Appointments for Young Adults with T1D and Depressive Symptoms—Improved Attendance and Symptom Regulation
Authors: REID MARK W. , RAYMOND JENNIFER , THOMAS JOHN F.
Published: June 2018

Background: Adherence to care recommendations for type 1 diabetes (T1D) may be complicated by the presence of depressive symptoms. Shared medical appointments (SMA) for T1D via home telemedicine may uniquely address the needs of depressed young adult T1D patients (YA), since the treatment model encourages social interaction and support while patients are safe at home.

Objective: Evaluate the efficacy of SMA via home telemedicine for improving care adherence and depressive symptom regulation in YA with T1D.

Methods: Participants in the Colorado Young Adults with T1D (CoYoT1) prospective study of SMA via home telemedicine completed the Center for Epidemiologic Studies Depression scale (CES-D) at baseline and after one year. We calculated somatic and cognitive CES-D subscale scores based on its theorized factor structure.

Results: Patients in CoYoT1 did not experience significant changes in depressive symptoms, but males in the control group, who only attended in-person appointments, reported significantly higher depressive symptoms after one year (see Figure 1, p = 0.03). Further, among those with depressive symptoms at baseline, CoYoT1 patients attended one more appointment on average than control patients (1.9 vs. 0.9, p = 0.008).

Conclusions: SMA via home telemedicine may help YA with T1D regulate depressive symptoms, while also promoting clinic attendance.

Disclosure

M.W. Reid: None. J. Raymond: None. J.F. Thomas: None.
Quality of Life Assessment in Patients with HNF1A- and GCK-MODY Compared with T1DM
Authors: SZOPA MAGDALENA
Published: June 2018

Maturity onset diabetes of the young (MODY) constitutes a few percent of this disease, with HNF1A- and GCK-MODY being its most common forms. We examined the impact on QoL (Quality of Life) of monogenic diabetes among patients with HNF1A-MODY or GCK mutation carrier status and compared it to type 1 diabetic (T1DM) individuals.

Methods: The study included 80 patients with HNF1A-MODY and 89 GCK gene mutation carriers (45 patients with diabetes and 44 prediabetic). We also examined 128 T1DM patients as a control group. Diabetes-specific QoL was assessed using the Audit of Diabetes Dependent Quality of Life questionnaire.

Results: Both HNF1A-MODY and GCK-MODY groups had similar mean age (41.7 vs. 38.0 years, respectively) and BMI (24.1 vs. 24.3 kg/m2), whereas T1DM patients were younger (34.2 years) with similar BMI (25.0 kg/m2). Less than a third of GCK mutation carriers were on pharmacotherapy (31%), while the majority of HNF1A mutation carriers used oral drugs or insulin (82.5%) (p<0.0001). While QoL were similar in three groups (p=0.66), both the impact of diabetes on the patient’s QoL and the average weighted impact (AWI) differed between three groups (p<0.001; p<0.001, respectively). For MODY patients, both indices were larger in HNF1A-MODY patients than in GCK-MODY (-0.96 ± 1.vs. -0.63 ± 0.88, p>0.and -1.52 ± 1.44 vs. -0.95±1.06, p=0.01, respectively). However, the largest impact of diabetes on the patient’s QoL and AWI were observed in the T1DM patients (-1.6 ± 1.05; -1.99 ± 1.39, respectively). The highest impact of diabetes (>-1.5) was observed in all groups on ^‘freedom to eat‘, ‘feelings about future‘, and ‘freedom to drink‘. In the HNF1A-MODY group, all three major indices of QoL were more affected for patients on insulin as compared to the other individuals.

Conclusions: Diagnosis of the most frequent subtypes of MODY has a negative impact on QoL for affected individuals, although, it is smaller than in T1DM. Mode of treatment seems to influence QoL for HNF1A-MODY subjects.

Disclosure

M. Szopa: None.
The Relationship between A1C and Hypoglycemia in the HypoDE Study
Authors: OLIVER NICK , FRECKMANN GUIDO , GIMENEZ MARGA , REDDY MONIKA , HERMANNS NORBERT , HEINEMANN LUTZ
Published: June 2018

There is an inverse relationship between A1C and hypoglycemia which may be impacted by use of continuous glucose monitoring (CGM). Using data from the HypoDE clinical trial, we compared usual care to real-time CGM in subjects with type 1 diabetes using MDI and impaired hypoglycemia awareness or recent severe hypoglycemia. Data from 149 subjects (75 CGM/intervention and 74 SMBG/control) were analyzed. The percentages of sensor glucose (SG) values ≤70 mg/dL (≤3.9 mmol/L) (“%≤70”) and of those indicating clinically significant hypoglycemia (≤54 mg/dL [≤3.0 mmol/L]) (“%≤54”) during the blinded 4-week run-in and the final 4 weeks of the 26-week study were calculated for each subject and compared to corresponding baseline and 26-week A1C values. At baseline, the percent ≤70 and the percent ≤54 were similar for the two groups and there was a trend toward more hypoglycemia for subjects with lower A1C values (Figure A). At 26 weeks (Figure B), there were significant (p<0.01) between-group differences in favor of CGM at both thresholds, and the association between decreasing A1C and increasing exposure to SG values ≤54 mg/dL was attenuated. These data suggest patients at highest risk of hypoglycemia with access to CGM alerts and alarms take appropriate and timely measures to nearly eliminate exposure to clinically meaningful hypoglycemia. CGM allows safe intensification of MDI therapy and achievement of near-normal A1C levels in hypoglycemia-prone individuals.

Disclosure

N. Oliver: Advisory Panel; Self; Dexcom, Inc., Roche Diabetes Care Health and Digital Solutions, Medtronic. Research Support; Self; Dexcom, Inc., Roche Diabetes Care Health and Digital Solutions. Other Relationship; Self; Novo Nordisk A/S, Takeda Development Centre Europe Ltd. G. Freckmann: Speaker's Bureau; Self; Ascensia Diabetes Care. Research Support; Self; Ascensia Diabetes Care. Speaker's Bureau; Self; Roche Diabetes Care Health and Digital Solutions. Advisory Panel; Self; Roche Diabetes Care Health and Digital Solutions. Research Support; Self; Roche Diabetes Care Health and Digital Solutions. Advisory Panel; Self; Abbott, Novo Nordisk Inc.. Consultant; Self; Sensile Medical AG. Speaker's Bureau; Self; Ypsomed AG. M. Gimenez: None. M. Reddy: Advisory Panel; Self; Roche Diabetes Care Health and Digital Solutions. Other Relationship; Self; Dexcom, Inc. N. Hermanns: Speaker's Bureau; Self; Berlin-Chemie AG. Advisory Panel; Self; Abbott. Research Support; Self; Abbott. Speaker's Bureau; Self; Abbott. Research Support; Self; Berlin-Chemie AG. Advisory Panel; Self; Eli Lilly and Company. Speaker's Bureau; Self; Eli Lilly and Company. Advisory Panel; Self; Roche Diabetes Care Health and Digital Solutions. Research Support; Self; Ypsomed AG, Dexcom, Inc.. Speaker's Bureau; Self; Novo Nordisk Inc. L. Heinemann: Stock/Shareholder; Self; Profil Institute for Metabolic Research, ProSciento. Consultant; Self; Roche Diabetes Care Health and Digital Solutions.
Benefits of Use of Professional CGM in Low Socioeconomic Population
Authors: HASHMI SULMAN , MON SANN
Published: June 2018

Introduction: Self-monitoring blood glucose (BG) using a glucometer is the standard of care for patients with diabetes. However, up to 67% of patients fail to routinely monitor BG, which is one of the major challenges to optimize glycemic management. With an emergence of technology, continuous glucose monitoring devices (CGM) are now used by many patients for guiding therapeutic changes. However, such technology is not always available in low socioeconomic areas.

Objective: To examine the ability of professional CGM for guiding therapeutic changes in a population with limited access to technology to improve glycemic control as measured by A1c, patient glucometer data, hypoglycemia events, and total daily insulin requirements before and after the testing period.

Method: Professional CGM (Freestyle Libre Pro) was placed for 10-14 days in 19 patients age ≥ 18 years (mean age 47.6 years +/-19.6 SD, 58.42% female) with T1DM or T2DM on insulin therapy. CGM data was downloaded after the testing period, glucose results were retrospectively reviewed with the patient, and insulin dose adjustments were made.

Results: 66% of patients showed improvement in A1c with a median decrease of 0.8%. Mean A1c pre 9.2 and post 8.77 with the p value=0.13

Fewer hypoglycemic episodes and higher total daily use of insulin are noted in the post-CGM period. 7 episodes of severe hypoglycemic before and none following CGM use. There was an increase in the mean dose of insulin from 51.79 units to 56.93 units, p=0.14 post CGM.

Statistically significant decrease in episodes of hyperglycemia Post CGM (Pre 11.66 to Post 4.18, p=0.03).

Conclusion: Professional CGM is useful for improving glycemic control in a low socioeconomic population with limited access to current technology. Despite a relatively small sample size, our study showed that there was an improvement in HbA1c and the decrease in the number of episodes of hyperglycemia and hypoglycemia after CGM placement.

Disclosure

S. Hashmi: None. S. Mon: None.
Comparison of Disparities between FGM/CGM Sensor Glucose Values and Blood Glucose Values in Type 1 Diabetic Patients on SAP Therapy
Authors: TANIZAWA MIKA , TAKAHASHI HIROSHI , MORI YUTAKA , UTSUNOMIYA KAZUNORI
Published: June 2018

Objective: Disparities were compared between flash glucose monitoring (FGM) sensor glucose (SG) and blood glucose (BG) values vs. between continuous glucose monitoring (CGM) SG and BG values to establish the accuracy of FGM SG values in patients with type 1 diabetes.

Methods: FGM (FreeStyle Libre Pro; Abbott Japan) and a sensor-augmented pump (SAP) with built-in CGM (MiniMed 620G; Medtronic Minimed) were simultaneously put in place in 8 outpatients with type 1 diabetes receiving SAP therapy (Age, 50.9 ± 6.3 years; BMI, 20.4±2.6 kg/m2; HbA1c, 7.4±0.3%; urinary C-peptide excretion, 1.5±0.7 µg/day; duration of SAP therapy, 11.9±3.7 months; insulin dose, 34.2±14.5 U/day). SMBG BG values at 385 points registered on Minimed 620G for calibration purposes and CGM/FGM SG values at the same time points, were selected to examine the accuracy of FGM SG values. Both these devices were evaluated for accuracy in terms of 1) simple correlation coefficients between CGM/FGM SG values and BG values; and 2) mean absolute relative differences (MARD) in SG values of CGM/FGM relative to BG values as reference.

Results: 1) A strong positive correlations were found between the CGM/FGM SG values and BG values (r = 0.984, p < 0.001 / r = 0.852, p< 0.001). 2) The MARD in CGM/FGM SG values were 5.81 ± 6.66% (0-39.4%) / 17.5 ± 24.8% (0-213.6%), and the frequency of MARD showing 10% or less accounted for 83% of all CGM SG values, and 48.3% of all FGM SG values. The relationship between the MARD of FGM SG values and the durations of FGM put in place varied depending on the number of days FGM put in place. Additionally, the disparity between the FGM SG values and the BG values varied greatly depending on the individual patient.

Conclusions: While FGM represents an innovative device for glucose monitoring, there is a wide disparity between the FGM SG and BG values in some patients with type 1 diabetes, suggesting that FGM may be appropriately positioned as an adjunct, but not an alternative, to SMBG.

Disclosure

M. Tanizawa: None. H. Takahashi: None. Y. Mori: None. K. Utsunomiya: None.
Comparison of Glucose Concentrations between Flash and Continuous Monitoring Systems in Patients with Diabetes Mellitus
Authors: KUMAGAI RYO , MURAMATSU AIKO , FUJII MASANAO , KATAKURA YUKINO , FUJIE KEIKO , NAKATA YOSHIO , HASHIMOTO KOICHI , YAGYU HIROAKI
Published: June 2018

Flash glucose (FGMS) and continuous glucose (CGMS) monitoring systems are becoming increasingly prevalent in clinical practice. We directly compared FGMS (FreeStyle Libre-Pro, FSL-Pro) and CGMS (iPro2) in a patient with diabetes mellitus (T1DM/T2DM = 3/10). Analysis of 7,365 paired values revealed a close correlation between FSL-Pro and iPro2 glucose values (r = 0.93, p < 0.01). Parkes error grid analysis showed that 93.3%, 6.2%, 0.5% and 0.04% of all glucose pairs fell within areas A, B, C and D, respectively, whereas no values was located in area E. Median glucose values were significantly lower for FSL-Pro than iPro2 (149.0 (115.0-187.0) vs. 171.0 (138.0-213.0) mg/dL, p < 0.01). Notably, 89.3% of all glucose values were lower for FSL-Pro than iPro2; therefore, the difference between them was considerable (Figure 1; r = -0.273, p < 0.01). The absolute difference between FSL-Pro and iPro2 glucose values was significantly higher among hyperglycemic values (>180 mg/dL) compared with normoglycemic values (70-180 mg/dL) (32.0 (19.0-47.0) vs. 23.0 (14.0-31.0) mg/dL, p < 0.05). We concluded that glucose values are lower when measured using FSL-Pro than iPro2 and this tendency became more pronounced under conditions of hyperglycemia.

Disclosure

R. Kumagai: None. A. Muramatsu: None. M. Fujii: None. Y. Katakura: None. K. Fujie: None. Y. Nakata: None. K. Hashimoto: None. H. Yagyu: None.
Using Hyperpolarized [2-13C] Dihydroxyacetone to Detect Hepatic Gluconeogenesis
Authors: MERRITT MATTHEW , RAGAVAN MUKUNDAN , TSAROVA TATSIANA
Published: June 2018

Diabetes afflicts millions of people worldwide and contributes to elevated risk of cardiovascular disease and stroke. Imaging of hepatic metabolic activity using MRI could prove advantageous for diagnosis, treatment monitoring, etc. With the advent of hyperpolarized (HP) carbon-13 based MRI, rapid imaging of metabolic flux is feasible without the use of ionizing radiation.

In this work, we have utilized HP [2-13C]dihydroxyacetone (DHA) to observe hepatic metabolism in real time in a perfused mouse (C57BLKS/J) liver model. We have utilized fatty acids and a 1:10 ratio of pyruvate to lactate in the perfusate to mimic physiological conditions. Figure 1 shows the various metabolites observed following the injection of DHA. Ratios of integrals of three-carbon intermediates and hexose sugars (and phosphates) are used to determine the rate of gluconeogenesis. Hepatic gluconeogenic rates are measured in parallel with gold standard tracer methods, confirming the accuracy of HP imaging.

Figure 1: Representative 13C NMR spectrum (sum) of hyperpolarized [2-13C] DHA metabolism in perfused liver at 14.1 T. Spectra were measured without 1H decoupling. Phosphoenolpyruvate (PEP) and glycerol-3-phosphate (G3P) are observed. Note that PEP is normally considered a low concentration metabolites, but it is easily detected here.

Disclosure

M. Merritt: None. M. Ragavan: None. T. Tsarova: None.
Impact of Continuous Glucose Monitors’ Accuracy on Their Clinical Utility—A Quantitative Assessment
Authors: KOZIEL CHRISTOPHER J. , BIALONCZYK DAMIAN , MOREL DIDIER , PETISCE JAMES , SALIU DRILON
Published: June 2018

Objective: Accuracy is recognized as an important attribute of continuous glucose monitors (CGMs). The objective of this research was to understand if improvements in CGM accuracy impact clinical outcomes in patients with T1DM or T2DM.

Research Design and Methods: A review of literature published from January 1999 to June 2017 was conducted using Medline. Articles were included if they reported desired outcome measures of clinical efficacy (HbA1c reduction, time-in-range [TIR], hypoglycemia detection or reduction) regardless of patient diabetes type. Simple linear and multiple regressions were utilized to evaluate the relationship between mean absolute relative difference (MARD) and measures of efficacy.

Results: The literature search yielded 574 results from which 20 articles met the inclusion criteria. MARD for commercialized CGM devices improved, on average, 0.75% per year (R2=0.87, p<0.0001). There was no significant correlation between HbA1c reduction and MARD (coefficient=0.464, R2=0, p=0.8857). A significant relationship was observed between MARD and the detection of hypoglycemic events (coefficient= -0.33, R2=0.63, p<0.0001). Insufficient data was available to assess the correlation between accuracy and TIR or hypoglycemia reduction.

Conclusion: CGM accuracy correlates with an increased detection of hypoglycemic events, but not with HbA1c reduction.

Disclosure

C.J. Koziel: None. D. Bialonczyk: Employee; Self; Becton, Dickinson and Company. D. Morel: Employee; Self; Becton, Dickinson and Company. J. Petisce: None. D. Saliu: Employee; Self; Becton, Dickinson and Company.
Evaluation of a New Protocol of Insulin Dose Adjustment in a Low-Resource Setting
Authors: BAPTISTE EDDY JEAN , LARCO PHILIPPE , CHARLES LARCO MARIE-NANCY , VON OETTINGEN JULIA E. , DUBOIS EDDLYS , PAUL RODOLPHE , FLEURY-MILFORT EVELYNE , CHARLES RENE , OGLE GRAHAM D.
Published: June 2018

Glycemic control (GC) is difficult to achieve in settings where only NPH, R and pre-mixed insulins are available, test strips are scarce, and food insecurity is common.

We evaluated the impact of a sliding-scale based insulin adjustment protocol in Haiti. Thirty youth aged 11-28 years with T1D treated with 70/30 Mixtard were randomized to remain on 70/30 (G70, n=15) or switch to self-mixed NPH+R (GNR, n=15). Carbohydrate (CHO) ratio and insulin sensitivity factor were calculated based on total daily insulin dose (TDD), and translated into sliding scales with either insulin correction dose (ICD) only (G70), or the sum of a 60 g CHO meal dose and ICD (GNR). Self-monitored blood glucose (SMBG) and insulin were administered before AM and PM meals. All had bi-monthly visits for 12 weeks. In G70 vs. GNR, age (19.3±4.5 vs. 19.2±5.4 years), sex (40 vs. 53% m), normal BMI (87 vs. 80%), diabetes duration (5.3±4.2 vs. 5.3±4.5 years), and baseline A1c (9.8±2.1 vs. 10.8±2.9%, p=0.27) did not differ. A1c declined to 8.0±1.3% (-1.8%, p<0.006) vs. 8.9±1.2% (-1.9%, p<0.02), p=0.83, without severe acute complication. Skipped meals per week (3.7±2.9 vs. 4.1±3.3), missed SMBG (3.5±3.4 vs. 7.4±7.6%, p=0.07), and increase of TDD (7.7±7.8 vs. 6.6±9.6 units) were similar. Use of sliding scales adjusted for SMBG and meals omission, and frequent clinic visits significantly improved GC in youth with T1D in a low-resource setting, regardless of pre- or self-mixed insulin.

Disclosure

E. Jean Baptiste: None. P. Larco: None. M. Charles Larco: None. J.E. von Oettingen: None. E. Dubois: None. R. Paul: None. E. Fleury-Milfort: None. R. Charles: None. G.D. Ogle: None.
A Wireless, Integrated, Extremely Miniaturized Continuous Glucose Monitoring System
Authors: MUJEEB-U-RAHMAN MUHAMMAD , HONARVAR NAZARI MEISAM , SENCAN MEHMET
Published: June 2018

Miniaturization is a key performance driver for CGM sensors for diabetes management. We have combined semiconductor and nanotechnology to realize an extremely miniaturized (0.1mmx0.6mmx3mm i.e., smaller than a sesame seed) fully wireless implant for electrochemical glucose sensing in interstitial fluid. This small size minimizes foreign body response, thus increasing longevity and decreasing calibration frequency. The implant is inserted under the skin using a custom injector and is removed via a simple procedure under local anesthesia. The implant is powered via an external wearable transmitter using standard RFID technology, also enabling low power communication between the two. The transmitter uses a bluetooth link to communicate with a smartphone reader for data visualization and further analytics. The smartphone sends data securely to an online database where it is shared with caregivers.

We have verified our digital health system's operation through extensive in-vitro and in-vivo testing (Figure). We have de-risked sensor fabrication and sterilization schemes. The wound healing response and histopathology studies indicate favorable safety and biocompatibility profile for the sensor as it uses standard materials being used in other implants. We are optimizing glucose oxidase system’s chemistry to achieve optimal in-vivo performance leading to human feasibility testing.

Disclosure

M. Mujeeb-U-Rahman: None. M. Honarvar Nazari: None. M. Sencan: Employee; Self; Integrated Medical Sensors, INC.
Performance of the GLUCOCARD® Shine XL Blood Glucose Monitoring System Compared with FDA 2016 Guidance Accuracy Criteria
Authors: WALKER JULIE , GILL PATRICIA , MAHER DANIELLE , GLEISNER JOHN M.
Published: June 2018

Background: Blood Glucose Monitoring Systems (BGMS) are important tools used in the management of diabetes mellitus. Accurate BGMS readings are needed to prevent potential micro and macrovascular complications due to uncontrolled blood glucose levels. The FDA’s 2016 Guidance, “Self-Monitoring Blood Glucose Test Systems for Over-the-Counter (OTC) Use”, is FDA’s recommendation for measuring the accuracy of OTC BGMS. The accuracy boundaries of the guidance require that 95% of all results fall within ±15% of reference and 99% within ±20% of reference.

Purpose: This study evaluated the GLUCOCARD® Shine XL BGMS performance against the accuracy boundaries set by the FDA’s 2016 Guidance.

Methods: Three lots of test strips were evaluated in a side-by-side comparison at the ARKRAY Factory in Minneapolis, Minnesota. Blood samples were drawn from the fingertip of people with diabetes (n=180) by trained laboratory professionals between November and December 2017. Reference values were obtained using the YSI Model 2300 Analyzer. Data was evaluated against the accuracy boundaries of the FDA’s 2016 Guidance for Self-Monitoring Blood Glucose Test Systems for OTC Use.

Results: 98.3% (177/180) of the combined results for the three lots of GLUCOCARD® Shine XL BGMS fell within the ±15% of reference and 99.4% (179/180) were within ±20% of reference. Overall bias was 2.2% and the correlation coefficient was 0.99 (r).

Conclusion: The GLUCOCARD® Shine XL BGMS performed within the accuracy boundaries of the FDA’s 2016 Guidance, Self-Monitoring Blood Glucose Test Systems for OTC Use.

Disclosure

J. Walker: Employee; Self; Arkray, Inc. P. Gill: Employee; Self; Arkray, Inc. D. Maher: Employee; Self; Arkray, Inc. J.M. Gleisner: Employee; Self; Arkray, Inc..
Improvement of Glycemic Control after Three Months’ Use of the Predictive Low-Glucose Management System and Persisting for 18 Months in Patients with Diabetes Mellitus Type 1
Authors: DIDANDELOS TRIANTAFYLLOS , GIANNOULAKI PARTHENA , KARLAFTI ELENI , KOTZAKIOULAFI EVANGELIA , KONTONINAS ZISIS , TZIOMALOS KONSTANTINOS , HATZITOLIOS APOSTOLOS I.
Published: June 2018

Aim: To study the efficacy of the Medtronic MiniMed 640G insulin pump (CSII with Suspend before low (Predictive Low-Glucose Management [PLGM]) on glycemic control in patients with diabetes mellitus type 1 (DMT1) at baseline, after the first trimester and after 18 months’ use.

Patients/Methods: Thirteen patients with DMT1 participated who were on PLGM system (Minimed 640G system-Medtronic), 8 females, age 45.8±12.5 years, DM duration 28.1±8.5 years and PLGM duration 17.5±7.4 months. HbA1c, anthropometric measurements and medical history were recorded at baseline, after 3 months and after 18 months’ use of the PLGM system. The continuous glucose measurements (CGM) data of the last two weeks of each interval were downloaded using the software Carelink Pro and Personal.

Results: The glycemic control after using PLGM system was significantly better compared with baseline at 3 and 18 months’ use (HbA1c 7.2±0.7% vs. 6.6±0.5 vs. 6.7±0.5, p=0.006). There was an improvement at first trimester compared with baseline (HbA1c 6.6±0.5 vs. 7.2±0.7, p=0.027) and at 18 months’ use compared with baseline (HbA1c 6.7±0.5 vs. 7.2±0.7, p=0.046). There was no difference in glycemic control between 3 and 18 months’ use. There was no difference of time duration < 3.9 mmol/l (min/day), hypoglycemic episodes and AUC < 3.9 mmol/l compared with baseline at 3 and 18 months’ use. There was a reduction of the time duration > 9.9 mmol/l (min/day) between baseline and after 18 months’ use (3281.3±2175.9 vs. 1891.6±555.7, p=0.037). Hyperglycemic events were reduced significantly after 18 months compared to baseline (median 28.5, interquartile range (IQR) 14 vs. median 39.5, IQR 19, p=0.045).

Conclusion: Patients with DMT1 using CSII with PLGM system for 18 months improved their glycemic control due to the reduction of hyperglycemic fluctuation. Glycemic control improved after 3 months and that improvement persisted for 18 months of follow-up.

Disclosure

T. Didandelos: None. P. Giannoulaki: None. E. Karlafti: None. E. Kotzakioulafi: None. Z. Kontoninas: None. K. Tziomalos: None. A.I. Hatzitolios: None.
Characteristics of Children Discontinuing Flash Glucose Monitoring
Authors: MESSAAOUI ANISSA , TENOUTASSE SYLVIE , CRENIER LAURENT
Published: June 2018

Aims: To study characteristics of type 1 diabetic patients who stop Flash Glucose Monitoring (FGM) during the first year of use in a pediatric hospital.

Methods: This retrospective study included all type 1 diabetic children and adolescents who switched to FGM in our center and for whom A1c, FGM data, sensor related adverse events and satisfaction scores were available.

Results: On the 278 subjects (aged 4-20 years) included in the study, 44 (15.8%) came back to SMBG during the first 12.7 (11.9-13.5) months [median (IQR)] of follow-up. Adverse events were more often reported, including premature loss of the sensor (31.8% vs. 12.4%; p=0.001), skin reactions (18.2% vs. 2.6%; p<0.001) and local pain (6.8% vs. 0%; p<0.001). They were also less satisfied by the FGM than continuing users (p<0.001). Subjects who discontinued FGM had a diabetes of longer duration (7.3 (4.5-10.5) vs. 5.2 (3.1-7.7) years; p=0.002) and a higher A1c (7.9% (7.5-8.4) vs. 7.5 (6.8-8.0); p=0.002). After two months, they scanned less often (6.0 [4.0-7.8] vs. 7.0 [5.0-10.0] per day; p=0.022) and those scanning less than 7 times a day stopped FGM more quickly (p<0.001).

Conclusions: In a pediatric population FGM is discontinued more often in patients experiencing adverse events, with higher A1c and scanning less. These patients are also older and have a diabetes of longer duration.

Disclosure

A. Messaaoui: None. S. Tenoutasse: None. L. Crenier: None.
Retrospective Flash Glucose Monitoring Analysis in Patients with Type 2 DM, Closer to Target
Authors: CHAWLA MANOJ S. , CHAWLA PURVI M. , KOTHARI MIKHIL C. , SHAIKH AMBARI F. , WADHWA NAVNEET
Published: June 2018

Undiagnosed or poorly managed diabetes may be associated with several complications including cardiac ailments, lower limb amputations, blindness or renal disease. Projections state that India would lead this increasing public health concern with a staggering incidence of 134.3 million patients by the year, 2045 based on current trends. We utilized the FreeStyleLibre Pro Flash Glucose Monitoring (FGM) system, a novel automated ambulatory glucose profile (AGP) reporting system to identify precise hyperglycemic spikes in a specific patient population. There were 53 T2DM patients with an AGP-estimated HbA1c close to target levels (HbA1c between 7-8%). The 24-hour glycemic patterns were divided into 2-hour time slot patterns for review and analyses; thus, identifying precise spikes contributing to hyperglycemia in patients not achieving their target HbA1c. The highest glycemic peaks were observed between 10 am to 12noonand between 10 pm-12 am [mean interstitial glucose (mg/dL) ± SD of 204 ± 36 and 186 ± 23] coinciding with the post-breakfast and post-dinner periods, respectively. As shown in Table 1, the variation in glycemic spikes ofeach of the 2-hour slots was statistically significant (p<0.0001). FGM helped us identify glycemic variability and specifically, the contribution of post-breakfast hyperglycemia in precluding patients from achieving their target metabolic goals.

Disclosure

M.S. Chawla: Speaker's Bureau; Self; IPCA Laboratories, Eli Lilly and Company, Novo Nordisk A/S, MSD Pharmaceutical Pvt. Ltd., Boehringer Ingelheim Pharmaceuticals, Inc., AstraZeneca. Speaker's Bureau; Spouse/Partner; Sanofi. P.M. Chawla: Speaker's Bureau; Self; Sanofi. Speaker's Bureau; Spouse/Partner; Eli Lilly and Company, IPCA LABORATORIES, Novo Nordisk A/S, MSD PHARMACEUTICALS PVT. LTD., Boehringer Ingelheim Pharmaceuticals, Inc., AstraZeneca. M.C. Kothari: None. A.F. Shaikh: None. N. Wadhwa: None.
Use of Continuous Glucose Monitoring Data for Defining Glucose Control in Insulin-Treated Patients
Authors: TOTOMIROVA TZVETELINA , DASKALOVA IVONA , ARNAUDOVA MILA
Published: June 2018

Glycated haemoglobin (HbA1c) and self-monitoring of blood glucose (SMBG) are usually used to define glucose control in diabetic patients, but in many cases they are insufficient to present overall control. Continuous Glucose Monitoring (CGM) is used for adjustment of insulin doses but derived data could help to define glucose control. We assess the use of CGM data for defining control in subgroup of patients treated with insulin. We studied 75 diabetic patients (43 men, 32 women; age 55.23±8.99 years, disease duration 12.84±7.02 years) - 33 with type 2 diabetes on pre-mixed insulin, 21 with type 2 on multiple insulin injection (MII), 21 with type 1 on MMI. Patients did multiple daily blood glucose measurements of fasting and prandial blood glucose for three month period. HbA1c was measured and CGM by using iProTM for seven days was performed at the end of this period. Based on HbA1c assessment 36.36% of patients on premixed insulin, 19.05% of type 2 patients on MII and 9.52% of type 1 patients on MII were with good control. After estimation of results from SMBG these percentage were respectively 28.14%, 12.11%, 4.51%. CGM defined 27.27% of patients on premixed insulin, 23.80% of type 2 patients on MII and 2.38% of type 1 patients on MII as well controlled. In addition CGM gave information about insulin excursion - they were with no significant difference in the three groups (17.09±7.01, p=0.231). CGM was more informative than SMBG in presenting hypoglycemic episode (p=0.01) including night hypoglycemia and especially their duration (p=0.001). No difference was found in different groups with no dependence on type of diabetes or insulin regimen. Nevertheless of short observed period CGM data could give much information about overall glucose control that is comparable to three month blood glucose measurement and are better than other methods in estimating hypoglycemic episodes. Performing of CGM should be included in recommendations for assessment of glucose control.

Disclosure

T. Totomirova: None. I. Daskalova: None. M. Arnaudova: None.
Safety of Microdose Dexamethasone Acetate in the Eversense Long-Term Implantable Continuous Glucose Monitoring System
Authors: KELLEY E. L. , HARIDAS HARITHA , LORENZ CARRIE
Published: June 2018

Background: Dexamethasone (DEX) is used in a variety of implantable medical devices to decrease inflammation and improve outcomes. The implantable Eversense® CGM System (Senseonics, Inc.) includes a silicone collar which slowly releases DEX acetate throughout the lifetime of the sensor. Studies were performed to characterize the potential user risk to dexamethasone acetate exposure.

Methods: Clinical trial subjects underwent frequent blood sampling for analysis of plasma DEX levels. Chemical testing of 90-day sensor explants was used to measure residual DEX content. Finally, a post market clinical follow-up study (PMCF) is currently ongoing which monitors the rate of serious device-related, procedure-related, or drug (DEX) related adverse events through approximately 24 months.

Results: In two trials (N=161, 86 with 2 sensors, 75 with 1 sensor), blood tests were performed to the 2 ng/mL level with no detectable plasma DEX. In a third trial (N=35), the limit of detection was reduced to 0.050 ng/mL. Blood samples from 8 patients inserted with one sensor had no measurable DEX plasma levels. Among 27 patients inserted with two sensors, the highest level of DEX detected was 0.114 ng/mL on day 2 post-insertion. All samples were below the detectable limit by day 8. Literature review states at plasma levels < 1 ng/mL no systemic effect occurs. Testing of sensor explants determined that 0.3 mg of DEX is eluted over 90 days. No adverse events related to exposure to DEX have been reported to date in the PMCF

Conclusions: Dexamethasone acetate, which was selected to reduce the inflammatory response of the implantable sensor, was found to be safe. Plasma DEX levels after extended use are well below the concentration necessary for a pharmacological response, demonstrating safety of the Eversense Sensor with regard to clinical adverse effects attributable to chronic DEX exposure and that this risk should not grow over time.

Disclosure

E.L. Kelley: Employee; Self; Senseonics. H. Haridas: Employee; Self; Senseonics. C. Lorenz: Employee; Self; Senseonics.
Real-World Data from the MiniMed™ 670G System Commercial Launch
Authors: AGRAWAL PRATIK , STONE MICHAEL , GOPALAKRISHNAN SHWETA , FOGEL CATHERINE , LEE SCOTT W. , KAUFMAN FRANCINE R.
Published: June 2018

The Medtronic MiniMed™ 670G hybrid closed-loop system with SmartGuard™ technology, which automatically adjusts basal insulin delivery, was released for commercial use in the U.S. in March 2017. Data from the first 13,9patients to use the system are reported.

MiniMed™ 670G data from patients who voluntarily uploaded to CareLink™ Personal software, from 3/17/17 to 12/14/17, were de-identified and analyzed if there were ≥7 days of sensor wear. Comparisons of glycemic metrics between 258,415 patient-days in the initial open-loop Manual Mode and 724,220 patient-days after starting closed-loop Auto Mode were made. Data were compared for patients 7-13 years, 14-21 years, 22-60 years, and ≥60 years.

The Table shows glycemic metrics, including time in target range (TIR, 70-180mg/dL), during Manual Mode and after Auto Mode start for all users and by age group. Children and teens had lower baseline TIR and more hyperglycemia in Manual Mode vs. adults, but a similar magnitude of improvement when in Auto Mode. Older adults had the most optimal glycemia with Auto Mode. TIR was higher in patients with greater time in Auto Mode. For those in Auto Mode 50-59% of the time, mean TIR was 65%; for Auto Mode 60-80% of the time, TIR was 68%; and for Auto Mode >80% of the time, TIR was 74%.

Overall, data from the commercial launch of the MiniMed™ 670G system mirrored the pivotal trial1 results for all ages and showed an increase in TIR with less hypoglycemia and hyperglycemia.

All Patients7-13 years14-21 years22-60 years≥ 60 yearsManual ModeAfter Auto Mode startManual ModeAfter Auto Mode startManual ModeAfter Auto Mode startManual ModeAfter Auto Mode startManual ModeAfter Auto Mode startPatients, N13,906479126293781946Auto Mode Use, %-80.05%-77.58%-66.07%-80.89%-84.95%Mean SG, mg/dL163.53154.87183.44163.44179.55166.98161.50153.55156.91150.38SD SG, mg/dL54.2251.0463.1560.5563.0560.2953.3550.0649.9447.22Percentage of time in SG range, mg/dL<500.38%0.31%0.22%0.25%0.38%0.37%0.41%0.33%0.29%0.22%<540.60%0.49%0.36%0.40%0.59%0.57%0.64%0.51%0.47%0.36%<702.59%2.05%1.71%2.03%2.42%2.21%2.77%2.12%2.21%1.76%70-18063.16%71.35%52.31%64.86%53.49%62.68%64.12%72.13%68.27%75.26%>18034.24%26.60%45.98%33.11%44.09%35.11%33.11%25.75%29.52%22.98%>2509.58%6.18%17.22%10.37%16.49%11.42%8.79%5.53%6.69%4.55%>3003.22%1.86%6.94%3.73%6.57%4.34%2.82%1.53%1.91%1.20%

Disclosure

P. Agrawal: None. M. Stone: None. S. Gopalakrishnan: Employee; Self; Medtronic. C. Fogel: Employee; Self; Medtronic. S.W. Lee: Employee; Self; Medtronic. F.R. Kaufman: Employee; Self; Medtronic.
Automatic Tuning of CIR and ISF with the Adaptive Bolus Calculator (ABC) Approach Is Safe and Reliable—Results from a Hybrid In Silico Evaluation
Authors: REITERER FLORIAN , RE LUIGI DEL
Published: June 2018

Introduction: T1DM patients usually determine their bolus insulin requirements using a patient and daytime specific carbohydrate to insulin ratio (CIR) and insulin sensitivity factor (ISF). These factors are tuned by the patients themselves with assistance of their MDs. Recently, the Adaptive Bolus Calculator (ABC) method has been proposed for the automatic tuning of CIRs and ISFs based on data that is routinely collected by T1DM patients.

Methods: Data from a recent clinical trial with 12 patients are used for evaluating the ABC method. CIR and ISF values are identified using the ABC method based on the first 6 days of the data, whereas the 7th day is used for the evaluation. The effect of the estimated CIRs and ISFs on the glycemic outcomes is simulated using a deviation based method. The simulation outcomes are compared to the outcomes obtained with the clinically used settings (in the figure below abbreviated as CUS) that the patients employed on the previous 6 days for calculating their bolus insulin needs.

Results: The CIRs and ISFs estimated by the ABC approach lead in average to a 6.8% reduction for time in hypoglycemia (BG<70 mg/dl) as compared to the clinically used CIR/ISF settings (avg. time in hypoglycemia: from 9.8% to 3.0%, p<0.01). Time in hyperglycemia (BG>180 mg/dl) on the other hand increases only slightly (avg. time in hyperglycemia: 10.9% vs. 9.3%, p=0.08).

Disclosure

F. Reiterer: None. L. del Re: None.
Intradermal Administration Improves the Kinetics of Faster-Acting Fiasp® Insulin
Authors: RANAMUKHA SAHAN , MANSOOR IMAN , FENG KIMBERLEY , STOEBER BORIS , WEHBE MOHAMED , CLAIR RORY ST , PICHE CLAUDE , MOREAU JEAN-PIERRE
Published: June 2018

Fiasp® (insulin aspart, Novo Nordisk) is a faster-acting injectable insulin formulation that is administered subcutaneously (SC) for blood glucose management. We hypothesized that intradermal (ID) administration could improve the pharmacokinetic profile of Fiasp® when compared to traditional SC injection.

Herein, we describe a simple, scalable and cost-effective hollow microneedle platform to evaluate the effects of ID administration on Fiasp® pharmacokinetics. Three groups of 8 rats received 1 IU/kg by SC injection or by ID injection using either a single or quadruple microneedle projection design. Blood was collected from 5-240 mins post-injection and assayed for insulin (ELISA, Crystal Chem) and blood glucose (hand-held glucometer) concentrations.

The results (below) show that plasma insulin levels were comparable for all groups with mean values ranging from 464 to 480 µU/mL. Yet, the peak plasma concentrations were 1.5-3-fold earlier for rats treated using the microneedle designs. This preliminary data suggests that ID injection of Fiasp® using these novel hollow microneedle designs results in faster absorption and thereby could lead to a more rapid onset of action.

Disclosure

S. Ranamukha: None. I. Mansoor: Stock/Shareholder; Self; Microdermics. K. Feng: Employee; Self; Microdermics Inc. B. Stoeber: Other Relationship; Self; Microdermics Inc.. M. Wehbe: None. R. St Clair: Employee; Self; Microdermics Inc. C. Piche: Employee; Self; Locemia Solutions. Stock/Shareholder; Self; Locemia Solutions. Consultant; Self; Microdermics Inc. Stock/Shareholder; Self; Diasome Pharmaceuticals, Inc., Bigfoot Biomedical. J. Moreau: Consultant; Self; Microdermics.
Glucose Control Using a Standard vs. an Enhanced Hybrid Closed-Loop System—A Pilot Study
Authors: PALDUS BARBORA , LEE MELISSA H. , JONES HANNAH , MCAULEY SYBIL A. , HORSBURGH JODIE C. , ROEM KERRYN L. , WARD GLENN , MACISAAC RICHARD , COHEN NEALE , COLMAN PETER G. , JENKINS ALICIA , O'NEAL DAVID N.
Published: June 2018

Background: Hybrid closed loop (HCL) insulin delivery with the Medtronic MiniMed 670G system is safe and effective in improving glycemia for people with type 1 diabetes (T1D).

Aims: To compare glucose control, CL exits and alarm frequency with standard HCL vs. enhanced HCL (eHCL) systems.

Methods: Pump experienced adults with T1D (n=11; 9 women; mean[SD] age: 51[15]Y; HbA1c 7.5[1.0]%) were assigned in random order HCL and eHCL for 1 week each in a supervised live-in setting. eHCL incorporated enhanced bolus reminders and iterative changes broadening glucose and insulin delivery parameters permitting persistence in CL. For both HCL and eHCL insulin delivery was by a Medtronic pump with identical interventions (missed bolus, exercise, high GI and high fat meals), insulin action times and insulin-carbohydrate ratios implemented. The primary outcome was CGM time in target range.

Results: eHCL resulted in fewer CL alerts and exits. Time in target and mean glucose favored eHCL but did not reach significance (Table). No episodes of severe hypoglycemia or ketoacidosis occurred.

Conclusions: Iterative changes to the Medtronic HCL system resulted in trends towards improved glycemia, fewer CL exits and alerts without compromising safety, despite multiple food and exercise challenges during the study periods. Longer term studies at home are required to confirm these findings.

Table: All results are Mean (SD); *NS= Not Significant.HCL (n=11)eHCL (n=11)pInsulin DeliveryTotal daily insulin (units/day)44 (18)47 (24)NS*Daily basal (%)55 (8)55 (7)NS*Daily bolus (%)45 (8)45 (7)NS*Daily insulin to carbohydrate ratio9.6 (3.1)9.6 (3.1)NS*Insulin action time (hrs)3.7 (0.6)3.7 (0.6)NS*Glucose Metrics% time in 70-180mg/dL69 (11)74 (10)0.16Mean sensor glucose (mg/dL)160 (13)151 (11)0.091Time <70mg/dL (%)1.9 (2.4)1.2 (1.2)0.42Time <50mg/dL (%)0.4 (0.5)0.2 (0.3)0.21Time>180mg/dL (%)29 (11)24 (9)0.37Time >250mg/dL (%)7.3 (5.6)6.1 (4.7)0.48Algorithm related Alerts and Dropout Alerts (n/week)8.6 (5.8)3.9 (2.8)0.01CL exits (n/week)3.5 (3.1)0 (0)0.004

Disclosure

B. Paldus: None. M.H. Lee: None. H. Jones: None. S.A. McAuley: Research Support; Self; JDRF. Speaker's Bureau; Self; Novo Nordisk Inc., Australian Diabetes Society. J.C. Horsburgh: None. K.L. Roem: None. G. Ward: None. R. MacIsaac: None. N. Cohen: None. P.G. Colman: None. A. Jenkins: Research Support; Self; Medtronic, Mylan, Sanofi-Aventis. D.N. O'Neal: Research Support; Self; Medtronic MiniMed, Inc.. Advisory Panel; Self; Medtronic MiniMed, Inc.. Speaker's Bureau; Self; Medtronic MiniMed, Inc.. Advisory Panel; Self; Abbott. Speaker's Bureau; Self; Novo Nordisk Inc.. Advisory Panel; Self; Novo Nordisk Inc.. Research Support; Self; Novo Nordisk Inc.. Advisory Panel; Self; Sanofi. Research Support; Self; Sanofi.
Fasting Plasma Glucose Indicates Reversibility of Acute Insulin Response after Short-Term Intensive Insulin Therapy in Patients with Type 2 Diabetes of Various Duration
Authors: LIU LIEHUA , YANG SIYUE , LIU JIANBIN , HAI LI , LIU JUAN , LI YANBING
Published: June 2018

Objective: In patients with early type 2 diabetes, short-term intensive insulin therapy (SIIT) reverses ß cell dysfunction measured by acute insulin response (AIR). However, the complicated procedures limit large-scale use of AIR. Also unclear is whether SIIT is effective in patients with longer duration. This study is performed to assess fasting indicators estimating recovery of AIR after SIIT for type 2 diabetes of various duration.

Research Design and Methods: Patients with type 2 diabetes (n=62) who had poor glycemic control were enrolled. Near-nomoglycaemia was achieved and maintained for 7 days with insulin pump. AIR was measured by intravenous tolerance tests before and after SIIT.

Results: Both fasting plasma glucose (FPG, 12.0 ± 3.0 vs. 7.71± 1.6 mmol/L, P<0.001) and 2h-postprandial glucose (17.3 ± 5.2 vs. 11.5 ± 3.7 mmol/L, P<0.001) decreased significantly after SIIT. AIR was improved from -16.7 (-117.4, 52.4) pmol/L·min to 178.7 (31.8, 390.7) pmol/L·min (P<0.001). Increase of AIR (?AIR)was observed in all disease duration categories (329.2±380.6, 312.8±347.8, and 123.8 ±186.3 pmol/L·min for patients with disease duration of <2 years, 2-6 years, and >6 years respectively, P=0.10). AIR was almost absent when FPG was over 10 mmol/L. However, when FPG after SIIT was less than 10 mmol/L it was negatively associated with AIR (R= -0.53, P<0.001) and ?AIR (R= -0.52, P<0.001). In multivariate analysis, AIR after the therapy and ?AIR could be estimated as follows: Estimated AIR (pmol/L·min) = 447.6 × baseline fasting C peptide (nmol/L) - 138.0× FPG after SIIT (mmol/L) + 925.2 (R2=0.42); estimated ?AIR (pmol/L·min) = 401.4 × baseline fasting C peptide (nmol/L) - 136.8 × FPG after SIIT (mmol/L) -357.6 × HDL-C after SIIT (mmol/L) + 1380.0(R2=0.50).

Conclusions: Recovery of AIR could be obtained in patients with various duration of type 2 diabetes, and it could be estimated conveniently by FPG and other fasting indicators.

Disclosure

L. Liu: None. S. Yang: None. J. Liu: None. L. Hai: None. J. Liu: None. Y. Li: None.