Frequency and Causes of Nocturnal Alarms in Youth and Young Adults With Type 1 Diabetes Using a First-Generation Hybrid Closed-Loop System.

Background: Meeting glycemic recommendations is challenging for youth with type 1 diabetes. Diabetes technology, including continuous glucose monitoring (CGM) and hybrid closed-loop (HCL) automated insulin delivery systems, significantly increase achievement of glycemic targets; however, many youth struggle to sustain use of early HCL systems. Nocturnal alarm fatigue contributes to disrupted sleep and device discontinuation. Methods: We examined the frequency and causes of nocturnal (10:00 p.m. to 6:00 a.m.) alarms in pediatric patients (N = 76, median age 14.5 years [interquartile range 11.8-17.0 years, range 7-24 years]) starting on a first-generation HCL system in a prospective observational study. Device data were analyzed with linear mixed-effects models to examine change across time at 3-month intervals for 12 months. Results: At baseline (HCL system in nonautomated mode), participants averaged 3.3 ± 0.6 alarms per night. In the 2 weeks after starting HCL (automated) mode, alarm frequency significantly increased to 5.4 ± 0.5 times per night (P <0.001). Alarm frequency decreased through the remainder of the observational period; however, CGM sensor and HCL system use also declined. The types of alarms were evenly distributed among sensor maintenance, sensor threshold, pump, and HCL-specific alarms. Conclusion: These data show that HCL system nocturnal alarms are frequent and may be barriers to sleep quality and device use. Further research is needed to assess the impact of diabetes technology on sleep and to determine method to improve sleep quality with technology use.

Propensity scores as a novel method to guide sample allocation and minimize batch effects during the design of high throughput experiments.

BACKGROUND: We developed a novel approach to minimize batch effects when assigning samples to batches. Our algorithm selects a batch allocation, among all possible ways of assigning samples to batches, that minimizes differences in average propensity score between batches. This strategy was compared to randomization and stratified randomization in a case-control study (30 per group) with a covariate (case vs control, represented as ß1, set to be null) and two biologically relevant confounding variables (age, represented as ß2, and hemoglobin A1c (HbA1c), represented as ß3). Gene expression values were obtained from a publicly available dataset of expression data obtained from pancreas islet cells. Batch effects were simulated as twice the median biological variation across the gene expression dataset and were added to the publicly available dataset to simulate a batch effect condition. Bias was calculated as the absolute difference between observed betas under the batch allocation strategies and the true beta (no batch effects). Bias was also evaluated after adjustment for batch effects using ComBat as well as a linear regression model. In order to understand performance of our optimal allocation strategy under the alternative hypothesis, we also evaluated bias at a single gene associated with both age and HbA1c levels in the 'true' dataset (CAPN13 gene). RESULTS: Pre-batch correction, under the null hypothesis (ß1), maximum absolute bias and root mean square (RMS) of maximum absolute bias, were minimized using the optimal allocation strategy. Under the alternative hypothesis (ß2 and ß3 for the CAPN13 gene), maximum absolute bias and RMS of maximum absolute bias were also consistently lower using the optimal allocation strategy. ComBat and the regression batch adjustment methods performed well as the bias estimates moved towards the true values in all conditions under both the null and alternative hypotheses. Although the differences between methods were less pronounced following batch correction, estimates of bias (average and RMS) were consistently lower using the optimal allocation strategy under both the null and alternative hypotheses. CONCLUSIONS: Our algorithm provides an extremely flexible and effective method for assigning samples to batches by exploiting knowledge of covariates prior to sample allocation.

Sodium glucose co-transporter 2 inhibition increases epidermal growth factor expression and improves outcomes in patients with type 2 diabetes.

Underlying molecular mechanisms of the kidney protective effects of sodium glucose co-transporter 2 (SGLT2) inhibitors are not fully elucidated. Therefore, we studied the association between urinary epidermal growth factor (uEGF), a mitogenic factor involved in kidney repair, and kidney outcomes in patients with type 2 diabetes (T2D). The underlying molecular mechanisms of the SGLT2 inhibitor canagliflozin on EGF using single-cell RNA sequencing from kidney tissue were examined. Urinary EGF-to-creatinine ratio (uEGF/Cr) was measured in 3521 CANagliflozin cardioVascular Assessment Study (CANVAS) participants at baseline and week 52. Associations of uEGF/Cr with kidney outcome were assessed using multivariable-adjusted Cox regression models. Single-cell RNA sequencing was performed using protocol kidney biopsy tissue from ten young patients with T2D on SGLT2i, six patients with T2D on standard care only, and six healthy controls (HCs). In CANVAS, each doubling in baseline uEGF/Cr was associated with a 12% (95% confidence interval 1-22) decreased risk of kidney outcome. uEGF/Cr decreased after 52 weeks with placebo and remained stable with canagliflozin (between-group difference +7.3% (2.0-12.8). In young persons with T2D, EGF mRNA was primarily expressed in the thick ascending loop of Henle. Expression in biopsies from T2D without SGLT2i was significantly lower compared to HCs, whereas treatment with SGLT2i increased EGF levels closer to the healthy state. In young persons with T2D without SGLT2i, endothelin-1 emerged as a key regulator of the EGF co-expression network. SGLT2i treatment was associated with a shift towards normal EGF expression. Thus, decreased uEGF represents increased risk of kidney disease progression in patients with T2D. Canagliflozin increased kidney tissue expression of EGF and was associated with a downstream signaling cascade linked to tubular repair and reversal of tubular injury.

Estimation of glomerular filtration rate in a pediatric population using non-contrast kidney phase contrast magnetic resonance imaging.

BACKGROUND: Glomerular filtration rate (GFR) is a key measure of kidney function but often inaccurately ascertained by serum creatinine and cystatin C in pediatrics. In this pilot trial, we evaluated the relationship between GFR calculated by using phase-contrast MRI (PC-MRI) biomarkers and GFR by 125I-iothalamate clearance in youth undergoing bone marrow transplantation (BMT). METHODS: A total of twenty-one pediatric BMT candidates (8-21 years of age) were recruited for a research kidney PC-MRI. After completion of 125I-iothalamate clearance, same-day PC-MRI measurements were completed of the kidney circulation without a gadolinium-based contrast agent. MRI included a non-contrast balanced-SSFP-triggered angiography to position ECG-gated breath-held 2D PC-MRI flow measurements (1.2 × 1.2 × 6 mm3). A multivariate model of MRI biomarkers estimating GFR (GFR-MRI) was selected using the elastic net approach. RESULTS: The GFR-MRI variables selected by elastic net included average heart rate during imaging (bpm), peak aorta flow below the kidney artery take-offs (ml/s), average kidney artery blood flow, average peak kidney vein blood flow, and average kidney vein blood flow (ml/s). The GFR-MRI model demonstrated strong agreement with GFR by 125I-iothalamate (R2 = 0.65), which was stronger than what was observed with eGFR by the full age spectrum and Chronic Kidney Disease in Children under 25 (CKiD U25) approaches. CONCLUSION: In this pilot study, noninvasive GFR-MRI showed strong agreement with gold standard GFR in youth scheduled for BMT. Further work is needed to evaluate whether non-contrast GFR-MRI holds promise to become a superior alternative to eGFR and GFR by clearance techniques. A higher resolution version of the Graphical abstract is available as Supplementary information.

Increasing Use of Diabetes Devices: What Do Health Care Professionals Need?

Despite evidence of improved diabetes outcomes with diabetes technology such as continuous glucose monitoring (CGM) systems, insulin pumps, and hybrid closed-loop (HCL) insulin delivery systems, these devices are underutilized in clinical practice for the management of insulin-requiring diabetes. This low uptake may be the result of health care providers' (HCPs') lack of confidence or time to prescribe and manage devices for people with diabetes. We administered a survey to HCPs in primary care, pediatric endocrinology, and adult endocrinology practices in the United States. Responding HCPs expressed a need for device-related insurance coverage tools and online data platforms with integration to electronic health record systems to improve diabetes technology uptake in these practice settings across the United States.

Novel predictors of daily fluctuations in glycemia and self-management in adolescents and young adults with type 1 diabetes.

AIMS: To understand morning biopsychosocial factors that predict glycemia, adherence, and goal attainment in adolescents and young adults (AYA) with type 1 diabetes (T1D) on a daily basis. METHODS: Eight-eight AYA (mean 17.6 ± 2.6 years, 54% female, HbA1c 7.9 ± 1.4%, diabetes duration 8.5 ± 4.5 years) with T1D who use Continuous Glucose Monitoring (CGM) completed a 2-week prospective study. Participants chose a self-management goal to focus on during participation. For six days, participants prospectively completed a 25-item Engagement Prediction Survey to assess biopsychosocial factors to predict daily diabetes outcomes and an end-of-day Goal Survey. Lasso and mixed-model regression were used to determine items in the Engagement Prediction Survey most predictive of perceived goal attainment, CGM Time-in-Range (TIR, 70-180 mg/dl), sensor mean glucose, number of insulin boluses and hyperglycemia response (bolus within 30 min of high alert or glucose <200 mg/dl within 2 hours). RESULTS: A 7-item model (including current glucose, planning/wanting to manage diabetes, wanting to skip self-management, feeling good about self, health perception and support needs) explained 16.7% of the daily variance in TIR, 18.6% of mean sensor glucose, 2.1% of the number of boluses, 14% of hyperglycemia response, and 28.7% of goal attainment perceptions. The mean absolute change in day-to-day TIR was 16%, sensor glucose was 30 mg/dl, and the number of boluses was 2. AYA reported more positive Engagement Prediction Survey responses on mornings when they awoke with lower glucose levels. CONCLUSIONS: Morning biopsychosocial state factors predict glycemic and adherence outcomes in AYA with diabetes and could be a novel intervention target for future behavioural interventions.

Ultra Rapid-Acting Inhaled Insulin Improves Glucose Control in Patients With Type 2 Diabetes Mellitus.

OBJECTIVE: To determine whether the use of an inhaled insulin would improve HbA1c. METHODS: This study was performed in 20 type 2 diabetes mellitus (T2DM) participants with HbA1c values ≥7.5 (58) to ≤11.5% (102 mmol/mol) on a variety of glucose-lowering regimens. Prandial Technosphere insulin (TI) was rapidly titrated based on a treatment algorithm using postprandial blood glucose to calculate premeal doses. A 2-week baseline period was followed by 12 weeks of active treatment with TI. The primary outcome was change in HbA1c. Secondary outcomes included glucose time in range (time in range: 70-180 mg/dL) obtained by a blinded continuous glucose monitoring during the baseline period and at the end of 12 weeks. Goals were to assess how to rapidly and safely initiate TI intensification, determine dosing requirements, and establish an effective dose range in uncontrolled T2DM. RESULTS: Mean HbA1c decreased by -1.6% (-17 mmol/mol) from 9.0% (75 mmol/mol) at baseline to 7.4% (57 mmol/mol) at 12 weeks (P < .0001). Mean time in range increased from 42.2% to 65.7% (P < .0002). Mean prandial doses of TI were 18 or 19 units for all meals. Time below range was 1.1% baseline and 2.6% post treatment (P = .01). CONCLUSION: Treatment with inhaled TI dosed using a simple algorithm improved glycemic control measured by both HbA1c and time in range, with low rates of hypoglycemia. These data add significantly to understanding TI in the management of T2DM patients for whom prandial insulin is a consideration.

Real world hybrid closed-loop discontinuation: Predictors and perceptions of youth discontinuing the 670G system in the first 6 months.

OBJECTIVE: To describe predictors of hybrid closed loop (HCL) discontinuation and perceived barriers to use in youth with type 1 diabetes. SUBJECTS: Youth with type 1 diabetes (eligible age 2-25 y; recruited age 8-25 y) who initiated the Minimed 670G HCL system were followed prospectively for 6 mo in an observational study. RESEARCH DESIGN AND METHODS: Demographic, glycemic (time-in-range, HbA1c), and psychosocial variables [Hypoglycemia Fear Survey (HFS); Problem Areas in Diabetes (PAID)] were collected for all participants. Participants who discontinued HCL (<10% HCL use at clinical visit) completed a questionnaire on perceived barriers to HCL use. RESULTS: Ninety-two youth (15.7 ± 3.6 y, HbA1c 8.8 ± 1.3%, 50% female) initiated HCL, and 28 (30%) discontinued HCL, with the majority (64%) discontinuing between 3 and 6 mo after HCL start. Baseline HbA1c predicted discontinuation (P = .026) with the odds of discontinuing 2.7 times higher (95% CI: 1.123, 6.283) for each 1% increase in baseline HbA1c. Youth who discontinued HCL rated difficulty with calibrations, number of alarms, and too much time needed to make the system work as the most problematic aspects of HCL. Qualitatively derived themes included technological difficulties (error alerts, not working correctly), too much work (calibrations, fingersticks), alarms, disappointment in glycemic control, and expense (cited by parents). CONCLUSIONS: Youth with higher HbA1c are at greater risk for discontinuing HCL than youth with lower HbA1c, and should be the target of new interventions to support device use. The primary reasons for discontinuing HCL relate to the workload required to use HCL.

Six months of hybrid closed loop in the real-world: An evaluation of children and young adults using the 670G system.

OBJECTIVE: To describe glycemic and psychosocial outcomes in youth with type 1 diabetes using a hybrid closed loop (HCL) system. SUBJECTS: Youth with type 1 diabetes (2-25 years) starting the 670G HCL system for their diabetes care were enrolled in an observational study. METHODS: Prospective data collection occurred during routine clinical care and included glycemic variables (sensor time in range [70-180 mg/dL], HbA1c), and psychosocial variables (Hypoglycemia Fear Survey [HFS]; Problem Areas in Diabetes [PAID]). Mixed models were used to analyze change across time. RESULTS: Ninety-two youth (mean age 15.7 ± 3.6 years, 50% female, HbA1c 8.8% ± 1.8%) started HCL for their diabetes care. Youth used Auto Mode 65.5% ± 3.0% of the time at month 1, which decreased to 51.2% ± 3.4% at month 6 (P = .001). Sensor time in range increased from 50.7% ± 1.8% at baseline to 56.9% ± 2.1% at 6 months (P = .007). HbA1c decreased from 8.7% ± 0.2% at baseline to 8.4% ± 0.2% after 6 months of use (P ≤ .0001), with the greatest HbA1c decline in participants with high baseline HbA1c. Increased percent time in auto mode was associated with lower HbA1c (P = .02). Thirty percent of youth discontinued HCL in the first 6 months of use. There were no changes in the HFS or PAID scores across time. CONCLUSIONS: HCL use is associated with improved glycemic control and no change in psychosocial outcomes in this clinical sample. The decline in HCL use across time suggests that youth experience barriers in sustaining use of HCL. Further research is needed to understand reasons for HCL discontinuation and determine intervention strategies.

Clinical prediction score of nonalcoholic fatty liver disease in adolescent girls with polycystic ovary syndrome (PCOS-HS index).

OBJECTIVE: Nonalcoholic fatty liver disease (NAFLD) is common in obese adolescents with polycystic ovary syndrome (PCOS), but there are no inexpensive ways to accurately identify NAFLD in PCOS. The objective was to develop a simple clinical score to screen for NAFLD risk in obese adolescents with PCOS. DESIGN: This is a secondary analysis of 3 cross-sectional studies on metabolic characterization of obese adolescents with PCOS. 108 overweight and obese adolescents with PCOS (BMI > 90th percentile, age 12-19 years) were enrolled from 2012 to 2018. METHODS: Magnetic resonance imaging was used to quantify hepatic fat fraction (HFF). A development cohort of 87 girls were divided by presence of NAFLD (HFF > 5.5%). A logistic regression model with the outcome of NAFLD and candidate predictor variables was fit. A simplified model (PCOS-HS index) was created using backwards stepdown elimination. Validation was performed using 200 bootstrapped sample and in a second cohort of 21 PCOS participants. RESULTS: 52% of the development cohort had NAFLD. The PCOS-HS index that included BMI percentile, waist circumference, ALT and SHBG had an AUCROC of 0.81, sensitivity 82%, specificity 69%, negative predictive value (NPV) 78% and positive predictive value 74%, using a threshold of 0.44 to predict HS. A threshold of 0.15 ruled out NAFLD with a NPV 90%. In the validation cohort, the model showed an accuracy of 81%, sensitivity of 91% and specificity of 70%. CONCLUSIONS: We developed a clinical index to identify NAFLD in girls with PCOS who would need further evaluation and treatment.

Screening for cystic fibrosis-related diabetes and prediabetes: Evaluating 1,5-anhydroglucitol, fructosamine, glycated albumin, and hemoglobin A1c.

OBJECTIVE: Dysglycemia is prevalent in cystic fibrosis (CF) but screening with annual oral glucose tolerance tests (OGTT) can be burdensome. We investigated alternate glycemic markers-hemoglobin A1c (HbA1c), 1,5-anhydroglucitol (1,5AG), fructosamine (FA), and glycated albumin (GA)-as screening tests for CF-related diabetes (CFRD) and pre-diabetes (CFPD) in youth with CF as defined by the gold-standard OGTT 2-hour glucose (2hG). METHODS: Youth 10 to 18 years with CF had a 1,5AG, FA, GA, HbA1c, and 2-hour OGTT collected. Correlations between all glycemic markers and 2hG were evaluated. Area under the receiver operative characteristic (ROC-AUC) curves were generated. Optimal cut points for predicting CFPD (2hG ≥ 140 mg/dL) and CFRD (2hG ≥ 200 mg/dL) were determined. RESULTS: Fifty-eight youth with CF were included (2hG < 140, n = 16; CFPD, n = 33; CFRD, n = 9; 41% male, mean ± SD age 14.2 ± 3.6 years, BMI z-score 0.0 ± 0.8, % predicted forced expiratory volume in 1 second [FEV1] 89.9 ± 15.1, % predicted forced vital capacity [FVC] 103.2 ± 14.6). ROC-AUC's for all alternate markers were low for CFPD (0.52-0.67) and CFRD (0.56-0.61). At a cut point of 5.5%, HbA1c had 78% sensitivity (95% CI: 0.45-0.94) and 41% specificity (95% CI: 0.28-0.55) for identifying CFRD, correlating to a ROC-AUC of 0.61 (95% CI: 0.42-0.8). CONCLUSIONS: All alternate markers tested demonstrate poor diagnostic accuracy for identifying CFRD by 2hG.

Reduced insulin sensitivity is correlated with impaired sleep in adolescents with cystic fibrosis.

BACKGROUND: Prevalence of cystic fibrosis-related diabetes (CFRD) rises sharply in adolescence/young-adulthood and is associated with increased morbidity/mortality. Sleep may be a modifiable risk factor for diabetes but its relationship with metabolic function has not been fully examined in youth with CF. The aim of the study was to examine the relationship between objectively measured sleep and glucose metabolism in youth with CF. METHODS: Adolescents (43 with CF and 11 healthy controls) completed 1-week of concurrent home continuous glucose monitoring (CGM) and actigraphy. Fasting labs and an oral glucose tolerance test were obtained. T-tests and analysis of variance (ANOVA) were used to test differences between actigraphy outcomes in CF participants and controls. Spearman's rank correlation coefficients were used to test for correlations between actigraphy, CGM, and insulin sensitivity (IS) measures. RESULTS: All participants averaged insufficient sleep (mean = 7.5 hours per night) compared to the 8 to 10 hours recommended for this age group. CF participants had poorer sleep by actigraphy measures than healthy controls. Higher minimum daytime glucoses on CGM correlated with shorter total sleep time (TST) and worse sleep efficiency (SE). Reduced IS in CF participants with dysglycemia was correlated with shorter TST, longer sleep latency, more wake after sleep onset, and poorer SE. CONCLUSIONS: Poor sleep appears to correlate with higher blood glucose and lower IS in CF adolescents with dysglycemia. Further research is needed to better understand the mechanisms and directionality behind this relationship.

Glycoprotein Acetyls Associate With Intraglomerular Hemodynamic Dysfunction, Albuminuria, Central Adiposity, and Insulin Resistance in Youth With Type 1 Diabetes.

OBJECTIVES: Glycoprotein acetyls (GlycA's) are biomarkers of systemic inflammation and cardiovascular disease, yet little is known about their role in type 1 diabetes (T1D). In this study we examined the associations among GlycA's, central adiposity, insulin resistance, and early kidney injury in youth with T1D. METHODS: Glomerular filtration rate and renal plasma flow by iohexol and p-aminohippurate clearance, urine albumin-to-creatinine ratio (UACR), central adiposity by dual-energy x-ray absorptiometry, and estimated insulin sensitivity were assessed in 50 youth with T1D (16±3.0 years of age, 50% female, glycated hemoglobin 8.7%±1.3%, T1D duration 5.7±2.6 years). Concentrations of GlycA were quantified by targeted nuclear magnetic resonance spectroscopy. Correlation and multivariable linear regression analyses were performed. RESULTS: GlycA's were higher in girls vs boys (1.05±0.26 vs 0.84±0.15 mmol/L, p=0.001) and in participants living with overweight/obesity vs normal weight (1.12±0.23 vs 0.87±0.20 mmol/L, p=0.0004). GlycA's correlated positively with estimated intraglomerular pressure (r=0.52, p=0.001), UACR (r=0.53, p<0.0001), and trunk mass (r=0.45, p=0.001), and inversely with estimated insulin sensitivity (r=-0.36, p=0.01). All relationships remained significant after adjustment for age, sex, and glycated hemoglobin. CONCLUSIONS: As biomarkers of inflammation, GlycA's were higher in girls and those with overweight or obese body habitus in T1D. GlycA's associated with parameters of early kidney dysfunction, central adiposity, and insulin resistance.

Insulin Secretion, Sensitivity, and Kidney Function in Young Individuals With Type 2 Diabetes.

OBJECTIVE: ß-Cell dysfunction and insulin resistance magnify the risk of kidney injury in type 2 diabetes. The relationship between these factors and intraglomerular hemodynamics and kidney oxygen availability in youth with type 2 diabetes remains incompletely explored. RESEARCH DESIGN AND METHODS: Fifty youth with type 2 diabetes (mean age ± SD 16 ± 2 years; diabetes duration 2.3 ± 1.8 years; 60% female; median HbA1c 6.4% [25th, 75th percentiles 5.9, 7.6%]; BMI 36.4 ± 7.4 kg/m2; urine albumin-to-creatinine ratio [UACR] 10.3 [5.9, 58.0] mg/g) 21 control participants with obesity (OCs; age 16 ± 2 years; 29% female; BMI 37.6 ± 7.4 kg/m2), and 20 control participants in the normal weight category (NWCs; age 17 ± 3 years; 70% female; BMI 22.5 ± 3.6 kg/m2) underwent iohexol and p-aminohippurate clearance to assess glomerular filtration rate (GFR) and renal plasma flow, kidney MRI for oxygenation, hyperglycemic clamp for insulin secretion (acute C-peptide response to glucose [ACPRg]) and disposition index (DI; ×103 mg/kg lean/min), and DXA for body composition. RESULTS: Youth with type 2 diabetes exhibited lower DI (0.6 [0.0, 1.6] vs. 3.8 [2.4, 4.5] × 103 mg/kg lean/min; P < 0.0001) and ACPRg (0.6 [0.3, 1.4] vs. 5.3 [4.3, 6.9] nmol/L; P < 0.001) and higher UACR (10.3 [5.9, 58.0] vs. 5.3 [3.4, 14.3] mg/g; P = 0.003) and intraglomerular pressure (77.8 ± 11.5 vs. 64.8 ± 5.0 mmHg; P < 0.001) compared with OCs. Youth with type 2 diabetes and OCs had higher GFR and kidney oxygen availability (relative hyperoxia) than NWCs. DI was associated inversely with intraglomerular pressure and kidney hyperoxia. CONCLUSIONS: Youth with type 2 diabetes demonstrated severe ß-cell dysfunction that was associated with intraglomerular hypertension and kidney hyperoxia. Similar but attenuated findings were found in OCs.

Discordance Between Glucose Management Indicator and Glycated Hemoglobin in People Without Diabetes.

Background: In recent years, continuous glucose monitor (CGM) use is increasing in people without diabetes to promote healthy lifestyle. CGM metrics such as glucose management indicator (GMI), a statistical formula to estimate glycated hemoglobin (HbA1c) from sensor glucose, is commonly used to approximate HbA1c. This study was aimed to evaluate discordance between GMI and HbA1c in people without diabetes. Methods: Children and nonpregnant adults (age ≥6 years) without diabetes (laboratory HbA1c <5.7% and negative islet antibodies) were invited to participate in a multicenter prospective study aimed to evaluate glycemic profiles in nondiabetic individuals. Each participant wore a blinded Dexcom G6 for up to 10 days. GMI was calculated from mean sensor glucose and discordance between GMI and HbA1c was analyzed. Results: Of 201 screened participants, 153 participants (mean age 31.2 ± 21.0 years, 66.0% female, HbA1c 5.1% ± 0.3%) were included in the analysis. Mean GMI was 0.59% higher than laboratory HbA1c in participants without diabetes. The discordance between GMI and HbA1c of 0.4% or greater was 71% in participants without diabetes compared with 39% in the original GMI development cohort. Conclusion: GMI does not accurately estimate HbA1c in healthy people without diabetes. Clinical trial registration number is: NCT00717977.

Minimum Sampling Duration for Continuous Glucose Monitoring Metrics to Achieve Representative Glycemic Outcomes in Suboptimal Continuous Glucose Monitor Use.

BACKGROUND: Two weeks of continuous glucose monitoring (CGM) sampling with >70% CGM use is recommended to accurately reflect 90 days of glycemic metrics. However, minimum sampling duration for CGM use <70% is not well studied. We investigated the minimum duration of CGM sampling required for each CGM metric to achieve representative glycemic outcomes for <70% CGM use over 90 days. METHODS: Ninety days of CGM data were collected in 336 real-life CGM users with type 1 diabetes. CGM data were grouped in 5% increments of CGM use (45%-95%) over 90 days. For each CGM metric and each CGM use category, the correlation between the summary statistic calculated using each sampling period and all 90 days of data was determined using the squared value of the Spearmen correlation coefficient (R2). RESULTS: For CGM use 45% to 95% over 90 days, minimum sampling period is 14 days for mean glucose, time in range (70-180 mg/dL), time >180 mg/dL, and time >250 mg/dL; 28 days for coefficient of variation, and 35 days for time <54 mg/dL. For time <70 mg/dL, 28 days is sufficient between 45 and 80% CGM use, while 21 days is required >80% CGM use. CONCLUSION: We defined minimum sampling durations for all CGM metrics in suboptimal CGM use. CGM sampling of at least 14 days is required for >45% CGM use over 90 days to sufficiently reflect most of the CGM metrics. Assessment of hypoglycemia and coefficient of variation require a longer sampling period regardless of CGM use duration.

A provider survey of cystic fibrosis related diabetes screening and management practices at North American CF centers.

Background: Cystic Fibrosis Foundation (CFF) Guidelines recommend annual screening for cystic fibrosis related diabetes (CFRD) with an oral glucose tolerance test (OGTT). However, screening rates remain consistently low. We conducted surveys of 1) US CF center directors and 2) Endocrinologists affiliated with the CFF-sponsored EnVision program to characterize CFRD screening practices, describe provider perceived barriers to screening, and identify strategies for improving screening. Methods: The surveys queried OGTT protocols, alternate screening strategies, and perceived barriers to screening. CF center characteristics and procedures for coordinating OGTTs were compared between centers achieving ≥50% versus <50% OGTT completion. Endocrinologists received additional questions regarding OGTT interpretation and management. Results: The survey response rate was 18% (51/290) from CF Centers and 63% (25/40) from Endocrinologists. The majority (57%) of CF centers utilized 2 OGTT timepoints (0,120 min). The majority (72%) of Endocrinologists utilized 3 timepoints (0,60,120 min). Four percent of CF centers and 8% of Endocrinologists utilized other timepoints. Forty-nine percent of CF centers reported ≥50% OGTT completion in the past year. Completion of ≥50% OGTT was 5 times more likely when patient reminders were consistently provided (p = 0.017). Both CF Centers and Endocrinologists employed alternative screening strategies including HbA1c (64%, 92%), fasting plasma glucose (49%, 67%), continuous glucose monitoring (30%, 58%), and home fingerstick monitoring (55%, 50%). Discussion: OGTT is the gold standard screening method for CFRD, but completion rates remain suboptimal, practice variation exists, and many providers utilize alternate screening strategies. Systematic reminders may improve completion rates. Studies to improve our approach to CFRD screening are urgently needed.

Composite Metric of Glycemic Control Q-Score Is Elevated in Pediatric and Adolescent/Young Adult Hematopoietic Stem Cell Transplant Recipients.

Background: Malglycemia in pediatric, adolescent and young adult (AYA) patients who undergo hematopoietic stem cell transplant (HSCT) is associated with increased infection and mortality rate. Continuous glucose monitoring (CGM) has been safely used in pediatric/AYA HSCT recipients, but there is a need for a composite metric that can easily be used in clinical settings to assess the glycemic control and identify high-risk patients who needs therapeutic intervention. Composite metrics derived from CGM have not been studied in pediatric/AYA HSCT patients. Methods: Patients aged 2-30 years old who are admitted inpatient while undergoing HSCT at Children's Hospital Colorado underwent CGM using the Abbot Freestyle Libre Pro device from up to 7 days before and 60 days after HSCT. A composite metric Q-score, comprising five primary factors of CGM profiles (central tendency, hyperglycemia, hypoglycemia, intradaily variations, and interdaily variations), was calculated for each patient for the duration of CGM wear. Results: Twenty-nine patients received CGM for an average of 25 days per participant. The median Q-score was 10.2 (interquartile range [IQR]: 8.3, 14.3). Sixty-nine percent of patients had Q-scores that would be categorized into the Fair or Poor category. There was no difference in the Q-score by sources of stem cell, types of primary disease, types of preparative regimen, need for PICU admission, presence of documented infections, and total parenteral nutrition use in the peri-HSCT period. Conclusions: Most pediatric/AYA HSCT recipients have Q-scores indicating suboptimal glycemic control in the peri-HSCT period. Future study should focus on developing screening and treatment strategies to improve malglycemia and its associated adverse clinical outcomes. This study was registered at clinicaltrials.gov (NCT03482154).

Relationship Between Daytime Versus Nighttime Continuous Glucose Monitoring Metrics with A1C in Adults with Type 1 Diabetes.

Objective: To evaluate influence of daytime versus nighttime continuous glucose monitoring (CGM)-based metrics on A1C in adults with type 1 diabetes (T1D). Research Design and Methods: CGM data from 407 adults with T1D (age 39 ± 15 years, diabetes duration 20 ± 12 years, A1C 7.3% ± 1.4% and 53% female) from two studies were included in this analysis. The association between daytime (6 AM-10.59 PM) and nighttime (11 PM-5.59 AM) CGM variables such as mean glucose, time in range (TIR; 70-180 mg/dL), time in tight target range (TTIR; 70-140 mg/dL), and time above range (TAR >180 mg/dL) was examined within five A1C categories (<7%, 7%-7.9%, 8%-8.9%, 9%-9.9%, and ≥10%). Results: Although mean glucose was increasing with higher A1C, there was no statistical difference in mean glucose between daytime versus nighttime within five A1C groups (143.2 ± 22.7 vs. 143.6 ± 25.0 for A1C <7%, 171.4 ± 17.3 vs. 175.3 ± 28.8 for A1C 7.0%-7.9%, 193.4 ± 19.4 vs. 195.3 ± 29.5 for A1C 8.0%-8.9%, 214.9 ± 28.8 vs. 219.7 ± 36.1 for A1C 9.0%-9.9% and 244.0 ± 39.0 vs. 239.9 ± 50.9 for A1C ≥10%, P > 0.05). Similarly, there was no difference between various CGM metrics by daytime versus nighttime within five A1C groups. Differences between five A1C groups' daytime versus nighttime mean glucose, TIR, TTIR, and TAR were also not statistically significant (all P > 0.05) Conclusion: Daytime versus nighttime glycemic control has similar influence on A1C in adults with T1D.

A randomized controlled clinical trial to improve health outcomes in youth with type 1 diabetes: Study design and baseline characteristics.

Most adolescents with T1D do not meet glycemic recommendations or consistently perform the required self-management behaviors to prevent acute- and long-term deleterious health outcomes. In addition, most youth with T1D do not have access to behavioral health services to address T1D management barriers. Thus, delivering behavioral interventions during routine medical appointments may hold promise for improving T1D outcomes in adolescents. The overall objective of this study was to examine the effect of behavioral interventions, either a Personalized T1D Self-Management Behaviors Feedback Report or Problem-Solving Skills, delivered by a T1D behavioral health provider and a T1D medical provider during a joint, fully integrated appointment to improve health outcomes in youth with T1D. This paper describes the study rationale, design, and baseline characteristics for the 109 adolescent-caregiver dyads who participated. Primary and secondary outcomes include hemoglobin A1c (A1C), T1D self-management behaviors, and biological indicators of complications.