bluuebunny/crossref_metadata_2025_split 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 &lt;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&lt;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&lt;=.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&lt;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&lt;0.001; p&lt;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&gt;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 (&gt;-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&lt;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 &lt; 0.001 / r = 0.852, p&lt; 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 &lt; 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 &lt; 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 &lt; 0.01). The absolute difference between FSL-Pro and iPro2 glucose values was significantly higher among hyperglycemic values (&gt;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 &lt; 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&lt;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&lt;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&lt;0.006) vs. 8.9±1.2% (-1.9%, p&lt;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.