Anxiety in Turner syndrome: Engaging community to address barriers and facilitators to diagnosis and care.

Turner syndrome (TS), caused by complete or partial loss of the second sex chromosome, is associated with complex medical manifestations. The TS community identifies anxiety as a major contributor to reduced quality of life. The study aimed to improve understanding of anxiety symptomatology, diagnosis, and care in individuals with TS. A mixed methods design integrated community engagement, including community leaders as co-investigators and a community advisory board, an online survey (N = 135), and in-depth interviews (N = 10). The majority of respondents reported that anxiety symptoms occur two or more days per week, with self-advocates reporting more frequent symptoms than caregivers (p = 0.03). Self-advocates reported feeling anxious more often at school/work; both rater groups reported anxiety-related behaviors were most likely to be expressed at home. Insomnia was the most common symptom of anxiety endorsed across age and rater groups (>70%). Anxiety symptoms and triggers changed with age and often were undiagnosed or untreated during childhood. Therapy and medication were reported as helpful by most respondents who had tried these strategies. Qualitative themes included: 'Triggers for anxiety are related to TS', 'Anxiety impacts the whole family', and 'Opportunities for early identification and intervention'.

Impact of 6-months of an advanced hybrid closed-loop system on sleep and psychosocial outcomes in youth with type 1 diabetes and their parents.

INTRODUCTION: Youth with type 1 diabetes (T1D) and parents experience reduced quality of life and sleep quality due to nocturnal monitoring, hypoglycemia fear, and diabetes-related disruptions. This study examined the sleep and quality of life impact of advanced technology. METHODS: Thirty-nine youth with T1D, aged 2-17 years, starting an advanced hybrid closed-loop (HCL) system and a parent participated in an observational study. Surveys, actigraphy, sleep diaries, and glycemic data (youth) were captured prior to HCL, at one week, 3 months, and 6 months. Outcomes were modeled using linear mixed effects models with random intercepts to account for within-subject correlation, with least-squares means at each timepoint compared to baseline. RESULTS: Parents and youth reported improvements in health-related quality of life and fear of hypoglycemia after HCL initiation. Concurrently, nocturnal glycemia improved. Actigraphy-derived sleep outcomes showed improved 6 month adolescent efficiency and 3 and 6 month parent wake after sleep onset. Additionally, parents reported improved subjective sleep quality and child sleep-related impairment at 3 months. CONCLUSIONS: With nocturnal glycemic improvements in youth using HCL technology, some aspects of parent and youth sleep and quality of life improved. This may reflect decreased parental monitoring and worry and highlights benefits for youth beyond glycemia.

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.

Increased Technology Use Associated With Lower A1C in a Large Pediatric Clinical Population.

OBJECTIVE: While continuous glucose monitors (CGMs), insulin pumps, and hybrid closed-loop (HCL) systems each improve glycemic control in type 1 diabetes, it is unclear how the use of these technologies impacts real-world pediatric care. RESEARCH DESIGN AND METHODS: We found 1,455 patients aged <22 years, with type 1 diabetes duration >3 months, and who had data from a single center in between both 2016-2017 (n = 2,827) and 2020-2021 (n = 2,731). Patients were grouped by multiple daily injections or insulin pump, with or without an HCL system, and using a blood glucose monitor or CGM. Glycemic control was compared using linear mixed-effects models adjusting for age, diabetes duration, and race/ethnicity. RESULTS: CGM use increased from 32.9 to 75.3%, and HCL use increased from 0.3 to 27.9%. Overall A1C decreased from 8.9 to 8.6% (P < 0.0001). CONCLUSIONS: Adoption of CGM and HCL was associated with decreased A1C, suggesting promotion of these technologies may yield glycemic benefits.

Optimal Sampling Duration for Continuous Glucose Monitoring for the Estimation of Glycemia Risk Index.

Objective: To evaluate optimal continuous glucose monitoring (CGM) sampling duration to estimate glycemia risk index (GRI). Methods: Up to 90 days of CGM data from 225 nonpregnant adults with type 1 diabetes (median age 40 years, 60% females, and 20 years of diabetes duration) and not using hybrid closed-loop system were collected. The association between GRI from various sampling periods and GRI using all 90 days of data was determined using the squared value of the Pearson correlation coefficient (R2). Results: With increasing duration of the CGM sampling period, there was higher correlation with the 90-day GRI: R2 of 0.79 for 7 days, R2 of 0.88 for 14 days, and R2 of 0.93 for 30 days. In a sensitivity analysis, correlation (r) or correlation coefficient (R2) for CGM sampling period for GRI estimation was not different among participants with time <70 mg/dL of <4% and participants with time <70 mg/dL of >4%. Conclusion: Though 14 days of CGM sampling duration is optimal for estimation of GRI, 7 days of CGM data may be enough to estimate GRI to monitor change in quality of glycemia with intervention.