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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

Bring Blood Glucose Down! An intervention to reduce fear of hypoglycemia in caregivers of adolescents with type 1 diabetes: Study design and participant characteristics.

Fear of hypoglycemia (FOH) is anxiety or extreme worry about having a low blood glucose and its consequences. FOH is common in individuals with type 1 diabetes (T1D) of all ages, as well as their caregivers, and can lead to inappropriate T1D self-management and suboptimal health outcomes. Despite its prevalence and serious health consequences, there has been very little attention focused on developing interventions to reduce FOH and its associated maladaptive T1D management behaviors. The primary aim of the present study, Bring BG Down!, was to implement a pilot intervention targeting FOH in mothers of adolescents with T1D. Exploratory aims included determining if the intervention had an impact on the adolescent's FOH, glycemia, as well as other generalized anxiety symptoms or symptoms of obsessive-compulsive disorder in both the mother and adolescent. Caregiver participants and their adolescents with T1D were randomized to either the Bring BG Down! group or the Control group. Individuals in the Bring BG Down! group participated in intervention sessions for 6 months via telehealth and they completed questionnaires, whereas those in the Control group only completed questionnaires. Follow-up occurred at 7-months and 10-months. The purpose of this paper is to describe the Bring BG Down! study design and rationale, and participant characteristics at the start of the study.

Structural Lesions on Kidney Biopsy in Youth-Onset and Adult-Onset Type 2 Diabetes.

OBJECTIVE: Type 2 diabetes (T2D) is a leading cause of end-stage kidney disease worldwide. Recent studies suggest a more aggressive clinical course of diabetic kidney disease in youth-onset compared with adult-onset T2D. We compared kidney structural lesions in youth- and adult-onset T2D to determine if youth onset was associated with greater early tissue injury. RESEARCH DESIGN AND METHODS: Quantitative microscopy was performed on kidney tissue obtained from research kidney biopsies in 161 Pima Indians (117 women, 44 men) with T2D. Onset of T2D was established by serial oral glucose tolerance testing, and participants were stratified as youth onset (age <25 years) or adult onset (age ≥25 years). Associations between clinical and morphometric parameters and age at onset were tested using linear models. RESULTS: At biopsy, the 52 participants with youth-onset T2D were younger than the 109 with adult-onset T2D (39.1 ± 9.9 vs. 51.4 ± 10.2 years; P < 0.0001), but their diabetes duration was similar (19.3 ± 8.1 vs. 17.0 ± 7.8 years; P = 0.09). Median urine albumin-to-creatinine ratio was higher in the youth-onset group (58 [25th-75th percentile 17-470] vs. 27 [13-73] mg/g; P = 0.02). Youth-onset participants had greater glomerular basement membrane (GBM) width (552 ± 128 vs. 490 ± 114 nm; P = 0.002) and mesangial fractional volume (0.31 ± 0.10 vs. 0.27 ± 0.08; P = 0.001) than adult-onset participants. Glomerular sclerosis percentage, glomerular volume, mesangial fractional volume, and GBM width were also inversely associated with age at diabetes onset as a continuous variable. CONCLUSIONS: Younger age at T2D onset strongly associates with more severe kidney structural lesions. Studies are underway to elucidate the pathways underlying these associations.

The Relationship Between Continuous Glucose Monitoring and OGTT in Youth and Young Adults With Cystic Fibrosis.

CONTEXT: Early glucose abnormalities in people with cystic fibrosis (PwCF) are commonly detected by continuous glucose monitoring (CGM). Relationships between these CGM abnormalities and oral glucose tolerance testing (OGTT) in PwCF have not been fully characterized. OBJECTIVE: This work aimed to determine the relationship between CGM and common OGTT-derived estimates of ß-cell function, including C-peptide index and oral disposition index (oDI) and to explore whether CGM can be used to screen for OGTT-defined prediabetes and cystic fibrosis-related diabetes (CFRD). METHODS: PwCF not on insulin and healthy controls aged 6 to 25 years were enrolled in a prospective study collecting OGTT and CGM. A subset underwent frequently sampled OGTTs (fsOGTT) with 7-point glucose, insulin, and C-peptide measurements. Pearson correlation coefficient was used to test the association between select CGM and fsOGTT measures. Receiver operating curve (ROC) analysis was applied to CGM variables to determine the cutoff optimizing sensitivity and specificity for detecting prediabetes and CFRD. RESULTS: A total of 120 participants (controls = 35, CF = 85), including 69 with fsOGTTs, were included. CGM coefficient of variation correlated inversely with C-peptide index (Cpeptide30-Cpeptide0/Glucose30-Glucose0) (r = -0.45, P < .001) and oDIcpeptide (C-peptide index)(1/cpep0) (r = -0.48, P < .0001). In PwCF, CGM variables had ROC - areas under the curve ranging from 0.43 to 0.57 for prediabetes and 0.47 to 0.6 for CFRD. CONCLUSION: Greater glycemic variability on CGM correlated with reduced ß-cell function. However, CGM performed poorly at discriminating individuals with and without OGTT-defined CFRD and prediabetes. Prospective studies are now needed to determine how well the different tests predict clinically relevant nonglycemic outcomes in PwCF.

Comparison of Cgmanalysis, a Free Open-Source Continuous Glucose Monitoring Data Management and Analysis Software, with Commercially Available CGM Platforms: Data Standardization for Diabetes Technology Research.

Background: Cgmanalysis is an open-source software based on the R programming language for data management and descriptive analysis of data from continuous glucose monitoring (CGM). We sought to validate the summary measures calculated by cgmanalysis against the results from proprietary software associated with four CGM commercially available models. Methods: Two weeks of data from 188 patients with type 1 diabetes using commercially available CGMs. Freestyle Libre Gen 1 (n = 53), Medtronic Guardian 3 (n = 52), Dexcom G6 reported by Dexcom Clarity (n = 48), and Dexcom G6 reported by Tandem (n = 35) were analyzed using proprietary software and cgmanalysis. Agreement was assessed using scatterplots, Bland-Altman plots, and equivalence tests. Results: Good agreement was obtained for all glycemic summary measures for all CGMs assessed. None of the differences between the cgmanalysis package and the manufacturers' software were outside the prespecified bounds of equivalence. Conclusions: Cgmanalysis is a validated open-source software to analyze commercially available CGM data and can be used to standardize diabetes technology research.

Predicting Success with a First-Generation Hybrid Closed-Loop Artificial Pancreas System Among Children, Adolescents, and Young Adults with Type 1 Diabetes: A Model Development and Validation Study.

Background: Hybrid Closed-Loop (HCL) systems aid individuals with type 1 diabetes in improving glycemic control; however, sustained use over time has not been consistent for all users. This study developed and validated prognostic models for successful 12-month use of the first commercial HCL system based on baseline and 1- or 3-month data. Methods and Materials: Data from participants at the Barbara Davis Center (N = 85) who began use of the MiniMed 670G HCL were used to develop prognostic models using logistic regression and Lasso model selection. Candidate factors included sex, age, duration of diabetes, baseline hemoglobin A1c (HbA1c), race, ethnicity, insurance status, history of insulin pump and continuous glucose monitor use, 1- or 3-month Auto Mode use, boluses per day, and time in range (TIR; 70-180 mg/dL), and scores on behavioral questionnaires. Successful use of HCL was predefined as Auto Mode use ≥60%. The 3-month model was then externally validated against a sample from Stanford University (N = 55). Results: Factors in the final model included baseline HbA1c, sex, ethnicity, 1- or 3-month Auto Mode use, Boluses per Day, and TIR. The 1- and 3-month prognostic models had very good predictive ability with area under the curve values of 0.894 and 0.900, respectively. External validity was acceptable with an area under the curve of 0.717. Conclusions: Our prognostic models use clinically accessible baseline and early device-use factors to identify risk for failure to succeed with 670G HCL technology. These models may be useful to develop targeted interventions to promote success with new technologies.

A Mediation Approach to Discovering Causal Relationships between the Metabolome and DNA Methylation in Type 1 Diabetes.

Environmental factors including viruses, diet, and the metabolome have been linked with the appearance of islet autoimmunity (IA) that precedes development of type 1 diabetes (T1D). We measured global DNA methylation (DNAm) and untargeted metabolomics prior to IA and at the time of seroconversion to IA in 92 IA cases and 91 controls from the Diabetes Autoimmunity Study in the Young (DAISY). Causal mediation models were used to identify seven DNAm probe-metabolite pairs in which the metabolite measured at IA mediated the protective effect of the DNAm probe measured prior to IA against IA risk. These pairs included five DNAm probes mediated by histidine (a metabolite known to affect T1D risk), one probe (cg01604946) mediated by phostidyl choline p-32:0 or o-32:1, and one probe (cg00390143) mediated by sphingomyelin d34:2. The top 100 DNAm probes were over-represented in six reactome pathways at the FDR <0.1 level (q = 0.071), including transport of small molecules and inositol phosphate metabolism. While the causal pathways in our mediation models require further investigation to better understand the biological mechanisms, we identified seven methylation sites that may improve our understanding of epigenetic protection against T1D as mediated by the metabolome.