bluuebunny/crossref_metadata_2025 huggingface dataset:
HbA1c (You Later)—What Happens to Glycemic Control while Waiting for NICH?
Authors: WAGNER DAVID V. , SACHDEJ RAJKARAN , KOSKELA NATALIE C. , BARRY SAMANTHA A. , HANNAH LUZOD , JAMES-UNREIN ELIZA , HARRIS MICHAEL A.
Published: June 2018

Objective: Novel Interventions in Children’s Healthcare (NICH) is a community-based, family-focused intervention for youth with T1D who experience self-management difficulty due to social challenges. Previous findings suggest that NICH participation is associated with improved glycemic control. However, conclusions are limited due to possibility that these outcomes are influenced by regression to the mean and not reflective of intervention effect. This study examines whether glycemic control regresses during wait period following referral to NICH.

Methods: Youth with T1D and consistently poor glycemic control were referred to NICH and experienced a wait based on insurance approval and program capacity. Retrospective chart reviews collected HbA1c values in the year prior to referral, during wait period, and during NICH. Thirty-nine youth had lab values for all three time periods. Youth mean age was 15.4 years; 56% were female; and 85% were Caucasian. Mean days prior to referral, on wait list, and during NICH were 365, 161, and 394 days, respectively.

Results: Average youth HbA1c remained relatively stable during the wait-period (t(38)=.509, p=.61), going from 11.6 (SD=1.5) to 11.5 (SD=1.6). Average youth glycemic control significantly decreased to 10.9 (SD=1.6) during NICH intervention compared to prior to referral (t(38)=2.31, p=.03) and during wait period (t(38)=2.30, p=.03).

Conclusions: Youth glycemic control while waiting for NICH remained stable and high, only demonstrating significant and clinically meaningful improvement during NICH involvement. These findings suggest that, while regression to the mean should always be considered as a threat to validity, it may not apply to youth who have a history of significantly elevated A1cs. Thus, improvements experienced by these youth likely represent change associated with their involvement in NICH, and longer waits to access a program like NICH likely increase risk of short- and long-term diabetes complications.

Disclosure

D.V. Wagner: None. R. Sachdej: None. N.C. Koskela: None. S.A. Barry: None. H. Luzod: None. E. James-Unrein: None. M.A. Harris: Consultant; Self; Eli Lilly and Company.
Predictors of Depressive Symptoms in a Clinical Sample of Individuals with Type 1 Diabetes
Authors: STANEK KELLY R. , MAJIDI SHIDEH , VOGELI JO M. , DRISCOLL KIMBERLY A.
Published: June 2018

Background: Depression screening during routine type 1 diabetes (T1D) clinical care is increasing, but no studies have examined predictors of symptoms in a clinical sample of those with T1D.

Methods: Individuals with T1D ages 10-25 years completed the Patient Health Questionnaire-9 (PHQ-9) during routine T1D clinic visits. PHQ-9 scores range from 0-27 with higher scores reflecting more depressive symptoms. Demographic and T1D-specific characteristics were also collected.

Results: A total of 609 individuals with T1D were screened: 49% female; mean 15.2±3.1 years; 76% Caucasian; 11% Hispanic; 70% private insurance; mean A1C = 9.2±2.2%, mean T1D duration = 5.9±4.4 years; 49% insulin pump (CSII); 26% continuous glucose monitor (CGM). Average PHQ-9 score was 4.0±4.6 (range = 0-24; 67% minimal, 22% mild, 7% moderate, 3% moderately severe, 1% severe). Linear regression analyses revealed female sex, having public insurance and higher A1Cs, and lack of CGM use were significantly associated with higher depressive symptoms (Table). Older age and lack of CSII trended towards significance.

Conclusions: The results of this study demonstrate the importance of screening for depressive symptoms as part of routine T1D clinical care. Results further support potentially targeting individuals with specific characteristics (e.g., females, high A1Cs) for screening when clinic resources to implement screening are limited.

Table.Predictors of Depressive Symptoms. *p<0.05, **p<0.01, ***p<0.001ßSEp-valueAge 0.0660.0590.096Sex -0.0840.3630.036*Insurance-0.1000.3040.013*A1C0.1500.083<0.001***Pump Use -0.0740.3050.085CGM Use -0.0990.2510.021*

Disclosure

K.R. Stanek: None. S. Majidi: None. J.M. Vogeli: None. K.A. Driscoll: None.
RCT of Tailored Cognitive Behavioral Intervention Reduces Regimen-Related Distress—Relationship to Change in Medication Adherence and Glycemic Control at 12-Month Follow-Up
Authors: LUTES LESLEY , CUMMINGS DOYLE M. , HAMBIDGE BERTHA , CARRAWAY MARISSA , PATIL SHIVAJIRAO , ADAMS ALYSSA , SOLAR CHELSEY , LITTLEWOOD KERRY , EDWARDS SHEILA , GATLIN PEGGY
Published: June 2018

Elevated regimen related distress (RRD) in type 2 diabetes (T2D) is associated with poor glycemic control and lower medication adherence but the strategy for and impact of reducing RRD in primary care is unclear. Data for this study comes from a prospective randomized controlled trial that evaluated the effectiveness of a 16-session severity tailored cognitive behavioral intervention plus lifestyle change counseling (n = 67; IG=intervention group) delivered by trained staff compared to usual care (n = 72; CG=control group), in 139 rural adult patients (mean age = 52.6 ± 9.5 years.; 72% black; BMI = 37.0 ± 9.0) with uncontrolled (mean A1c = 9.6 ± 2.0) T2D (52% on insulin) and co-morbid depressive (PHQ-2) or distress (DDS-2) symptoms at screening in an academic primary care clinic. At baseline and at 12-month follow-up: A1c, regimen-related distress (sub-score of DDS-17), depressive symptoms (PHQ-9), self-care behaviors (SDSCA), and medication adherence (ModMAS) were measured using validated instruments. There were no differences between groups at baseline in mean age, race, gender, or mean values for A1c, body mass index (BMI), PHQ-9, or RRD scores. At 12-month follow-up patients in the behavioral intervention group had significantly greater reduction in RRD scores than those in the control arm (-1.12 ± 1.vs. -0.31 ± 1.22; p = 0.001). Among intervention group patients only, those with an improvement in RRD score of ≥ 1 (n = 32) had substantially greater improvement in A1c [-1.3 ± 1.7 vs. -0.56 ± 1.9; p = 0.09] and self-care behaviors [+1.33 ± 1.38 vs. + 0.88 ± 1.2; p = 0.16] as well as significantly greater improvement in medication adherence [+1.6 ± 1.7 vs. +0.47 ± 2.2; p = 0.02]. A tailored cognitive behavioral intervention reduces RRD in patients with uncontrolled T2D and co-morbid behavioral problems and is associated with improved medication adherence that may be associated with improved glycemic control.

Disclosure

L. Lutes: None. D.M. Cummings: None. B. Hambidge: None. M. Carraway: None. S. Patil: None. A. Adams: None. C. Solar: None. K. Littlewood: None. S. Edwards: None. P. Gatlin: None.
Evaluation of Glomerular Hemodynamic Function by Empagliflozin in Diabetic Mice Using In Vivo Imaging
Authors: KIDOKORO KENGO , SOGAWA YUJI , CHERNEY DAVID , KULASINGAM VATHANY , NAGASU HAJIME , SATOH MINORU , SASAKI TAMAKI , KASHIHARA NAOKI
Published: June 2018

Improvement of glomerular hyperfiltration via tubuloglomerular feedback (TGF) is considered to be one of the possible pathways for renal protection with SGLT2 inhibition (SGLT2i) in diabetic kidney disease (DKD). However, the mechanisms that regulate renal function in response to SGLT2i have not been fully elucidated. We explored the renal protective mechanisms of SGLT2i with empagliflozin (EMPA), with a focus on glomerular hemodynamic effects and TGF using in vivo multiphoton microscope (MPM) imaging techniques. C57BL/6 mice and spontaneously diabetic Ins2 Akita (AKITA) mice were used. The mice were treated with EMPA (20mg/kg/day) and insulin for four weeks, and single-nephron GFR (SNGFR) was measured using MPM. An nNOS inhibitor (7-nitroindazole, 20mg/kg/day) or a COX2 inhibitor (SC58236, 6mg/L), or an adenosine A1 receptor (A1aR) antagonist (8-cyclopentyl-1,3-dipropylxanthine, 1mg/kg/day) were administered to elucidate mechanisms of TGF signaling and SNGFR regulation. The urinary excretion of adenosine, nitric oxide metabolites (NOx) and the prostanoid PGE2 was also quantified. SNGFR in the AKITA group was higher compared to controls (C57BL/6; 4.9±1.3 nl/min vs. AKITA; 15.8±6.8 nl/min) and decreased in AKITA/EMPA to 8.0±3.3 nl/min (p<0.01). In vivo imaging also revealed concomitant afferent arteriolar dilation (p<0.01) and increased glomerular permeability of albumin in the AKITA group. EMPA ameliorated these changes (p<0.01). Urinary adenosine excretion in the AKITA/EMPA group was higher compared to AKITA (AKITA; 3.4±1.4 nmol/day, AKITA/EMPA; 11.2±3.0 nmol/day, p<0.05), while NOx and PGE2 did not differ. A1aR antagonism, but not nNOS or COX2 inhibition, blocked the effect of EMPA on renal function. EMPA increased urinary adenosine excretion and reduced hyperfiltration via afferent arteriolar constriction, effects that were abolished by A1aR blockade. The adenosine/A1aR system plays a pivotal role in the regulation of SNGFR via TGF mechanisms in DKD.

Disclosure

K. Kidokoro: Research Support; Spouse/Partner; Sanwa Kagaku Kenkyusho Co., Ltd., Kyowa Hakko Kirin Co., Ltd., Chugai Pharmaceutical Co., Ltd., Pfizer Inc., MSD K.K., Boehringer Ingelheim Pharmaceuticals, Inc., Sanofi K.K., Takeda Pharmaceutical Co., Astellas Pharma Inc., TORII PHARMACEUTICAL Co.. Y. Sogawa: None. D. Cherney: Consultant; Self; AbbVie Inc.. Other Relationship; Self; AstraZeneca, Boehringer Ingelheim GmbH, Eli Lilly and Company. Consultant; Self; Sanofi. Other Relationship; Self; Merck & Co., Inc.. Consultant; Self; Mitsubishi Tanabe Pharma Corporation. Other Relationship; Self; Janssen Pharmaceuticals, Inc.. V. Kulasingam: None. H. Nagasu: None. M. Satoh: None. T. Sasaki: None. N. Kashihara: None.
Insulin Adherence Is a Mechanism Underlying Disparities in A1C for Younger Adults with Type 2 Diabetes
Authors: SEARS LINDSAY , HIRSCH ASHLEY , DACHIS JEFF , RAYMOND JENNIFER , HEYMAN MARK , HEAD RACHEL , OSBORN CHANDRA Y.
Published: June 2018

Background: Diabetes disparities are well-documented. Racial/ethnic minority groups consistently have worse self-care and higher A1cs relative to non-Hispanic whites (NHWs). Less known are the underlying mechanisms contributing to disparities in diabetes outcomes that, if addressed, would mitigate disparities in A1c.

Objective: We tested whether insulin adherence is a mechanism underlying racial/ethnic disparities in A1c.

Methods: Adults (age 18-75 years) with type 2 diabetes (T2D) self-selected to be in a randomized control trial comparing inhalable, ultra-rapid-acting insulin (Afrezza) with One Drop vs. injectable, rapid-acting insulin with One Drop. Eligible persons completed an online survey and NGSP certified A1c test. Survey items collected demographic information and responses to the Adherence to Refills and Medications Scale for Diabetes (ARMS-D) administered for insulin only. SPSS and PROCESS v3.0 were used for descriptive statistics and unadjusted and adjusted mediation models with bootstrapped (10K) CIs for tests of indirect effects.

Results: Participants (N=269) were 50 & 11 years old, 51% male, 30% non-white, with 13 & 8 years since a T2D diagnosis, and A1c 8.8% & 1.8%. Compared to NHWs, non-whites were less adherent to their insulin regimen (ARMS-D = 35.4 vs. 37.2, p<.05) and had a higher A1c (9.2% vs. 8.6%, p < .05). Among younger adults (<49.94 years old), not taking enough insulin was a mechanism linking non-white race/ethnicity to having a higher A1c (unadj. coef.=.40, 95% CI: .09, .79; adj. coef.=.33, 95% CI: .01, .77). Among people over age 50, there was no indirect effect of non-white race/ethnicity on higher A1c via less adherence to insulin.

Discussion: Reducing disparities in insulin adherence may reduce disparities in A1c for younger adults, but not older adults. Other mechanisms underlying diabetes disparities may be age- or cohort-specific. Understanding these nuances can inform interventions aimed at reducing disparities in diabetes.

Disclosure

L. Sears: Employee; Self; Informed Data Systems Inc.. Research Support; Self; MannKind Corporation. A. Hirsch: Employee; Self; One Drop. Research Support; Self; MannKind Corporation. J. Dachis: Stock/Shareholder; Self; One Drop. Board Member; Self; One Drop. Employee; Self; One Drop. J. Raymond: None. M. Heyman: Employee; Self; One Drop. R. Head: None. C.Y. Osborn: Employee; Self; One Drop. Research Support; Self; MannKind Corporation. Stock/Shareholder; Self; One Drop.
Experiences of Enrolling in the U.S. Monogenic Diabetes Registry
Authors: LADSARIA SALLY S. , MONTGOMERY JAZZMYNE , LINDAUER KATHERINE L. , NAYLOR ROCHELLE N.
Published: June 2018

The U.S. Monogenic Diabetes Registry (Registry) facilitates accurate genetic diagnosis, gene-directed management and longitudinal follow-up of monogenic diabetes. Through the Registry, we currently follow 569 individuals with MODY, 74% of who have had genetic testing through Registry-related studies. We aimed to understand participants’ experiences of accessing, enrolling and participating in the Registry.

Through one-on-one semi-structured interviews participants were queried on how they were diagnosed with MODY, understanding of their diagnosis, and their experiences as Registry participants. An iterative process of coding, analyzing, and summarizing transcripts was completed using thematic analysis.

Data collection and analysis are ongoing. To date, we have conducted 8 interviews. Prolonged misclassification of diabetes type and delayed consideration of and referral for monogenic diabetes evaluation were commonly reported. Participants reported negative effects from diabetes misclassification, including emotional, financial and treatment burden. Participants were generally positive regarding interactions with the Registry. However, long turnaround times for research-based genetic testing results negatively impacted some participants. Levels of provider knowledge, healthcare access, socioeconomic status (SES), culture, language and self-efficacy were identified as important facilitators or barriers to Registry referral and enrollment for accurate MODY diagnosis.

Our study demonstrates multiple negative impacts of delay in MODY diagnosis. It also shows the utility of the Registry in aiding in MODY diagnosis. Efforts should focus on increased visibility of the Registry to diabetes providers and directly to patients. Specific measures to increase knowledge of and facilitate enrollment in the U.S. Monogenic Diabetes Registry among lower SES patients, non-native English speakers and racial and ethnic minorities can help address apparent ascertainment bias within the Registry.

Disclosure

S.S. Ladsaria: None. J. Montgomery: None. K.L. Lindauer: None. R.N. Naylor: None.
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 <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 <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<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 <7%, respectively. In Main group, mean BI dose was 26.43 ± 12.35 IU/day (p<0.05 vs. baseline); mean PI dose was 10.16 ± 7.90 IU/day (p <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 <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 < 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<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.
Effects of Basal Doses of Insulin Treatment in Type 1 Diabetes Evaluated by Continuous Glucose Monitoring on Glucose Variability
Authors: PROIETTI ADRIAN E. , DAGHERO ANDREA V. , PANEI NICOLAS , SCAPUZZI MARIA L. , JOKIEL ANDREA E. , GLESIAS MARIA LAURA , VELAZQUEZ JULIETA , NOGUEIRA JUAN P.
Published: June 2018

Background: This study compared the effects of insulin glargine and insulin detemir or degludec on glucose variability with CGM technology in real-life condition of patients with T1D.

Methods: This was a paralell trial in 81 T1D patients: 27 men and 51 women, with an average age of 31.33 years, and hemoglobin A1c (HbA1c) level of 8.4%. 56 patients were treated with glargine and 25 with detemir/degludec. The primary end point was the coefficient of variation (CV) of blood glucose. Secondary end points included HbA1c, mean amplitude of glycemic excursions (MAGE) and total doses of basal insulin and rapid analogs.

Results: insulin degludec/detemir was not different to insulin glargine with respect to CV (39.42±7.64 vs. 40.37±7.38, p=0.95) and HbA1c (8.03±1.42 vs. 8.47±1.68, p=0.26). There were also no significant differences between treatment groups with respect to MAGE (305.77±7.50 vs. 300.83±10.64, p=0.71) and rapids analogs (17.92±1.34 vs. 17.50±1.88, p=0.85). Only significantly higher doses were found with insulin detemir/degludec vs. insulin glargine (33.30±5.60 vs. 21.70±1.53, p=0.01). In multiple regression analyses, the total dose of basal insulin was the only parameter positively associated with levels of HbA1c (p = 0.01).

Conclusion: the increased doses of insulin determir/degludec related to insulin glargine do not lead to a better metabolic control regarding CV or HbA1c levels.

Disclosure

A.E. Proietti: None. A.V. Daghero: None. N. Panei: None. M.L. Scapuzzi: None. A.E. Jokiel: None. M. Iglesias: None. J. Velazquez: None. J.P. Nogueira: None.
Flash Glucose Monitoring in Children–One-Year Experience
Authors: MESSAAOUI ANISSA , TENOUTASSE SYLVIE , CRENIER LAURENT
Published: June 2018

Aims: To characterize the use of Flash Glucose Monitoring (FGM) in type 1 diabetic patients in a pediatric hospital one year after reimbursement by the public health insurance in Belgium.

Methods: This retrospective study included all type 1 diabetic children and adolescents who switched to FGM with specific education in our center and for whom A1c, FGM data, and severe hypoglycemic records were available.

Results: On the 334 subjects (aged 4-20 years) included, 56 (16.8%) refused to use FGM. At baseline, users were younger [median (IQR)] (13.6 (10.9-16.3) vs. 15.2 (12.2-17.5) years; p=0.012) and performed more SMBG tests (4.3 (3.8-5.0) vs. 4.1 (3.5-4.4) per day; p=0.008) than non-users. During follow-up, FGM users increased glucose monitoring from 4.3 (3.8-5.0) to 7.0 (5.0-10.0) time per day (p=0.011) while severe hypoglycemic events decreased from 6.8% to 0.9% (p=0.011). At the end of follow-up (12.7 (11.9-13.5) months), there were less severe hypoglycemic events in FGM users than in non-users (0.9% vs. 8.9%; p=0.026) but no difference in A1c. In multivariate analysis, ΔA1c during follow-up was independently related to A1c at baseline (R=0.419; p<0.001) for FGM users, and negatively with the frequency of glucose scans (R=0.160; p<0.001).

Conclusions: FGM is well accepted in our pediatric population and decreased the risk of severe hypoglycemic events, without changing A1c after one-year follow-up in youth with good glycemic control at baseline.

Disclosure

A. Messaaoui: None. S. Tenoutasse: None. L. Crenier: None.
Noninvasive Tissue Glucose Prediction in Patients with Type 1 Diabetes
Authors: PFÜTZNER ANDREAS , DEMIRCIK FILIZ , LIER ALEXANDER , RAMLJAK SANJA
Published: June 2018

Background: Pain-free assessment of the glucose information required for treatment monitoring is one of the most desired unmet medical needs in insulin treated patients with type 1 diabetes. TensorTip CoG, is a non-invasive tissue glucose monitoring device (NI-CoG) with an additional built-in invasive glucose meter (Inv-CoG), which operates based on optical methods in the near-infrared and visible wavelength range. Here we report about the results obtained from type 1 patients in the course of two clinical studies.

Methods: The data from 6 patients with type 1 diabetes (3 male/3 female, age: 43±16 years, HbA1c: 8.0±0.5%), who had participated in a standard meal study experiment, and the results from 29 type 1 patients, who had participated in a system accuracy evaluation in accordance with ISO15197:2015 (13 male/16 female, age: 42±14 years, HbA1c: 7.5±0.5%) were combined for this analysis.

Results: The meal study patients (n = 66 data points) and the ISO study patients (n = 29) showed good agreement of the NI-CoG predicted tissue glucose with the capillary reference method (YSI Stat2300 plus). Mean absolute relative difference (values >100 mg/dL) was calculated to be 13.3% (mean absolute difference for values<100 mg/dL: 16 mg/dL). The consensus error grid (version for type 1 diabetes) revealed 78% of the data points to be in zone A (B: 18%, C: 4%). Zone C values were only observed in patients without proper device calibration over the entire measurement range of the device.

Conclusions: The results of this new non-invasive glucose prediction device are encouraging for routine use in patients with type type 1 diabetes. This non-invasive technology has reached an accuracy level for glucose prediction that is comparable to the results published for needle sensors working for flash or continuous glucose monitoring.

Disclosure

A. Pfützner: Advisory Panel; Self; Novo Nordisk A/S, Sanofi. Consultant; Self; Lifecare. Speaker's Bureau; Self; Berlin-Chemie AG. Research Support; Self; Boehringer Ingelheim GmbH. F. Demircik: None. A. Lier: None. S. Ramljak: None.
Unique Digital Care Interactions of Diabetes Patients with Remote Health Coach through Apollo Sugar App to Achieve Positive Glycemic Control Outcomes
Authors: SHAH SANJIV , MEHROTRA RABINDER NATH , DAS SAMBIT , ERUKULAPATI RAVI S. , REDDY SURYA P. , JAYAPRAKASHSAI JANA , DASH KALPANA , JAGANMOHAN BALAJI , AYYAGARI USHA , TIPPISETTY SUREKHA , KOLUKULA VAMSI K. , JOSHI SHASHANK
Published: June 2018

Aim: To evaluate the quality and meaningful interactions of patients with type 2 diabetes mellitus (T2DM) with health coach through Apollo Sugar App and its effects on clinical outcomes.

Methods: Apollo Sugar Clinics is a pan-India chain of state of art, integrated care network of diabetes specialty clinics. The current analysis includes digital data of health interactions captured through a novel, unique and well-designed Apollo Sugar App. of great utility value. These live health interactions were between Sugar health coaches and 10,000 patients with T2DM, registered at Apollo Sugar Clinics who downloaded the Apollo Sugar App from Google Play store for the last 6 months. Meaningful health interactions with health coach were identified in 317 patients which were categorized into medication, diet, exercise, and self-monitoring blood glucose (SMBG). Descriptive statistics was applied to analyze the data, to assess clinical outcomes.

Results: A total of 10800 health interactions were captured, of which 2000 quality interactions were categorized as meaningful interactions. Among these interactions 29% on diet, 21% on exercise, 19% on medication change, 4% on insulin dose, 2% on hypoglycemia and 25% on meaningful use of SMBG data. SMBG was performed >2times/week by 65% of study population. Pre-meal and post-meal SMBG target were achieved in 35% and 24% of patients, respectively. 28% achieved control reductions in <2 weeks and no hypoglycemic episodes occurred in any of the patients. There was a considerable reduction in mean HbA1c (-0.7%), fasting plasma glucose (-41.5mg/dL) and post-prandial glucose (-100.7mg/dL) at 3 months follow-up.

Conclusions: The current analysis indicates that patient interactions with health coach through an Apollo Sugar app is a modern improved method of behavioral and clinical care, to achieve long term continuity of care for change in outcome of hard end points.

Disclosure

S. Shah: None. R. Mehrotra: None. S. Das: None. R.S. Erukulapati: None. S.P. Reddy: None. J. Jayaprakashsai: None. K. Dash: None. B. Jaganmohan: None. U. Ayyagari: None. S. Tippisetty: None. V.K. Kolukula: None. S. Joshi: None.