Multicenter Evaluation of Ultra-Rapid Lispro Insulin with Control-IQ Technology in Adults, Adolescents, and Children with Type 1 Diabetes.

Objective: To evaluate the safety and explore the efficacy of use of ultra-rapid lispro (URLi, Lyumjev) insulin in the Tandem t:slim X2 insulin pump with Control-IQ 1.5 technology in children, teenagers, and adults living with type 1 diabetes (T1D). Methods: At 14 U.S. diabetes centers, youth and adults with T1D completed a 16-day lead-in period using lispro in a t:slim X2 insulin pump with Control-IQ 1.5 technology, followed by a 13-week period in which URLi insulin was used in the pump. Results: The trial included 179 individuals with T1D (age 6-75 years). With URLi, 1.7% (3 participants) had a severe hypoglycemia event over 13 weeks attributed to override boluses or a missed meal. No diabetic ketoacidosis events occurred. Two participants stopped URLi use because of infusion-site discomfort, and one stopped after developing a rash. Mean time 70-180 mg/dL increased from 65% ± 15% with lispro to 67% ± 13% with URLi (P = 0.004). Mean insulin treatment satisfaction questionnaire score improved from 75 ± 13 at screening to 80 ± 11 after 13 weeks of URLi use (mean difference = 6; 95% confidence interval 4-8; P < 0.001), with the greatest improvement reported for confidence avoiding symptoms of high blood sugar. Mean treatment-related impact measure-diabetes score improved from 74 ± 12 to 80 ± 12 (P < 0.001), and mean TRIM-Diabetes Device (score improved from 82 ± 11 to 86 ± 12 (P < 0.001). Conclusions: URLi use in the Tandem t:slim X2 insulin pump with Control-IQ 1.5 technology was safe for adult and pediatric participants with T1D, with quality-of-life benefits of URLi use perceived by the study participants. Clinicaltrials.gov registration: NCT05403502.

Correlation Between the Glycemia Risk Index and Longitudinal Hemoglobin A1c in Children and Young Adults With Type 1 Diabetes.

BACKGROUND: The glycemia risk index (GRI) is a composite metric developed and used to estimate quality of glycemia in adults with diabetes who use continuous glucose monitor (CGM) devices. In a cohort of youth with type 1 diabetes (T1D), we examined the utility of the GRI for evaluating quality of glycemia between clinic visits by analyzing correlations between the GRI and longitudinal glycated hemoglobin A1c (HbA1c) measures. METHOD: Using electronic health records and CGM data, we conducted a retrospective cohort study to analyze the relationship between the GRI and longitudinal HbA1c measures in youth (T1D duration ≥1 year; ≥50% CGM wear time) receiving care from a Midwest pediatric diabetes clinic network (March 2016 to May 2022). Furthermore, we analyzed correlations between HbA1c and the GRI high and low components, which reflect time spent with high/very high and low/very low glucose, respectively. RESULTS: In this cohort of 719 youth (aged = 2.5-18.0 years [median = 13.4; interquartile range [IQR] = 5.2]; 50.5% male; 83.7% non-Hispanic White; 68.0% commercial insurance), baseline GRI scores positively correlated with HbA1c measures at baseline and 3, 6, 9, and 12 months later (r = 0.68, 0.65, 0.60, 0.57, and 0.52, respectively). At all time points, strong positive correlations existed between HbA1c and time spent in hyperglycemia. Substantially weaker, negative correlations existed between HbA1c and time spent in hypoglycemia. CONCLUSIONS: In youth with T1D, the GRI may be useful for evaluating quality of glycemia between scheduled clinic visits. Additional CGM-derived metrics are needed to quantify risk for hypoglycemia in this population.

Predicting Hypoglycemia and Hyperglycemia Risk During and After Activity for Adolescents with Type 1 Diabetes.

Objective: To predict hypoglycemia and hyperglycemia risk during and after activity for adolescents with type 1 diabetes (T1D) using real-world data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study. Methods: Adolescents with T1D (n = 225; [mean ± SD] age = 14 ± 2 years; HbA1c = 7.1 ± 1.3%; T1D duration = 5 ± 4 years; 56% using hybrid closed loop), wearing continuous glucose monitors (CGMs), logged 3738 total activities over 10 days. Repeated Measures Random Forest (RMRF) and Repeated Measures Logistic Regression (RMLR) models were used to predict a composite risk of hypoglycemia (<70 mg/dL) and hyperglycemia (>250 mg/dL) within 2 h after starting exercise. Results: RMRF achieved high precision predicting composite risk and was more accurate than RMLR Area under the receiver operating characteristic curve (AUROC 0.737 vs. 0.661; P < 0.001). Activities with minimal composite risk had a starting glucose between 132 and 160 mg/dL and a glucose rate of change at activity start between -0.4 and -1.9 mg/dL/min. Time <70 mg/dL and time >250 mg/dL during the prior 24 h, HbA1c level, and insulin on board at activity start were also predictive. Separate models explored factors at the end of activity; activities with glucose between 128 and 133 mg/dL and glucose rate of change between 0.4 and -0.6 mg/dL/min had minimal composite risk. Conclusions: Physically active adolescents with T1D should aim to start exercise with an interstitial glucose between 130 and 160 mg/dL with a flat or slightly decreasing CGM trend to minimize risk for developing dysglycemia. Incorporating factors such as historical glucose and insulin can improve prediction modeling for the acute glucose responses to exercise.

The Association Between Diet Quality and Glycemic Outcomes Among People with Type 1 Diabetes.

Background: The amount and type of food consumed impacts the glycemic response and insulin needs of people with type 1 diabetes mellitus (T1DM). Daily variability in consumption, reflected in diet quality, may acutely impact glycemic levels and insulin needs. Objective: Type 1 Diabetes Exercise Initiative (T1DEXI) data were examined to evaluate the impact of daily diet quality on near-term glycemic control and interaction with exercise. Methods: Using the Remote Food Photography Method, ≤8 d of dietary intake data were analyzed per participant. Diet quality was quantified with the Healthy Eating Index-2015 (HEI), where a score of 100 indicates the highest-quality diet. Each participant day was classified as low HEI (≤57) or high HEI (>57) based on the mean of nationally reported HEI data. Within participants, the relationship between diet quality and subsequent glycemia measured by continuous glucose monitoring (CGM) and total insulin dose usage was evaluated using a paired t-test and robust regression models. Results: Two hundred twenty-three adults (76% female) with mean ± SD age, HbA1c, and body mass index (BMI) of 37 ± 14 y, 6.6% ± 0.7%, and 25.1 ± 3.6 kg/m2, respectively, were included in these analyses. The mean HEI score was 56 across all participant days. On high HEI days (mean, 66 ± 4) compared with low HEI days (mean, 47 ± 5), total time in range (70-180 mg/dL) was greater (77.2% ± 14% compared with 75.7% ± 14%, respectively, P = 0.01), whereas time above 180 mg/dL (19% ± 14% compared with 21% ± 15%, respectively, P = 0.004), mean glucose (143 ± 22 compared with 145 ± 22 mg/dL, respectively, P = 0.02), and total daily insulin dose (0.52 ± 0.18 compared with 0.54 ± 0.18 U/kg/d, respectively, P = 0.009) were lower. The interaction between diet quality and exercise on glycemia was not significant. Conclusions: Higher HEI scores correlated with improved glycemia and lower insulin needs, although the impact of diet quality was modest and smaller than the previously reported impact of exercise.

Factors Affecting Reproducibility of Change in Glucose During Exercise: Results From the Type 1 Diabetes and EXercise Initiative.

AIMS: To evaluate factors affecting within-participant reproducibility in glycemic response to different forms of exercise. METHODS: Structured exercise sessions ~30 minutes in length from the Type 1 Diabetes Exercise Initiative (T1DEXI) study were used to assess within-participant glycemic variability during and after exercise. The effect of several pre-exercise factors on the within-participant glycemic variability was evaluated. RESULTS: Data from 476 adults with type 1 diabetes were analyzed. A participant's change in glucose during exercise was reproducible within 15 mg/dL of the participant's other exercise sessions only 32% of the time. Participants who exercised with lower and more consistent glucose level, insulin on board (IOB), and carbohydrate intake at exercise start had less variability in glycemic change during exercise. Participants with lower mean glucose (P < .001), lower glucose coefficient of variation (CV) (P < .001), and lower % time <70 mg/dL (P = .005) on sedentary days had less variable 24-hour post-exercise mean glucose. CONCLUSIONS: Reproducibility of change in glucose during exercise was low in this cohort of adults with T1D, but more consistency in pre-exercise glucose levels, IOB, and carbohydrates may increase this reproducibility. Mean glucose variability in the 24 hours after exercise is influenced more by the participant's overall glycemic control than other modifiable factors.

Associations between daily step count classifications and continuous glucose monitoring metrics in adults with type 1 diabetes: analysis of the Type 1 Diabetes Exercise Initiative (T1DEXI) cohort.

AIMS/HYPOTHESIS: Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics. METHODS: In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9-10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l). RESULTS: A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7%]; 73% female; 45% hybrid closed-loop system, 38% standard insulin pump, 17% multiple daily insulin injections) were included in the study. Between-participant analyses showed that individuals who exceeded the mean daily step count goal over the 4 week period had a similar TIR (75±14%) to those meeting (74±14%) or below (75±16%) the step count goal (p>0.05). In the within-participant comparisons, TIR was higher on days when the step count goal was exceeded or met (both 75±15%) than on days below the step count goal (73±16%; both p<0.001). The TBR was also higher when individuals exceeded the step count goals (3.1%±3.2%) than on days when they met or were below step count goals (difference in means -0.3% [p=0.006] and -0.4% [p=0.001], respectively). The total daily insulin dose was lower on days when step count goals were exceeded (0.52±0.18 U/kg; p<0.001) or were met (0.53±0.18 U/kg; p<0.001) than on days when step counts were below the current recommendation (0.55±0.18 U/kg). Step count had a larger effect on CGM-based metrics in participants with a baseline HbA1c ≥53 mmol/mol (≥7.0%). CONCLUSIONS/INTERPRETATION: Our results suggest that, compared with days with low step counts, days with higher step counts are associated with slight increases in both TIR and TBR, along with small reductions in total daily insulin requirements, in adults living with type 1 diabetes. DATA AVAILABILITY: The data that support the findings reported here are available on the Vivli Platform (ID: T1-DEXI; https://doi.org/10.25934/PR00008428 ).

Comparing Survival Extrapolation within All-Cause and Relative Survival Frameworks by Standard Parametric Models and Flexible Parametric Spline Models Using the Swedish Cancer Registry.

BACKGROUND: In health technology assessment, restricted mean survival time and life expectancy are commonly evaluated. Parametric models are typically used for extrapolation. Spline models using a relative survival framework have been shown to estimate life expectancy of cancer patients more reliably; however, more research is needed to assess spline models using an all-cause survival framework and standard parametric models using a relative survival framework. AIM: To assess survival extrapolation using standard parametric models and spline models within relative survival and all-cause survival frameworks. METHODS: From the Swedish Cancer Registry, we identified patients diagnosed with 5 types of cancer (colon, breast, melanoma, prostate, and chronic myeloid leukemia) between 1981 and 1990 with follow-up until 2020. Patients were categorized into 15 cancer cohorts by cancer and age group (18-59, 60-69, and 70-99 y). We right-censored the follow-up at 2, 3, 5, and 10 y and fitted the parametric models within an all-cause and a relative survival framework to extrapolate to 10 y and lifetime in comparison with the observed Kaplan-Meier survival estimates. All cohorts were modeled with 6 standard parametric models (exponential, Weibull, Gompertz, log-logistic, log-normal, and generalized gamma) and 3 spline models (on hazard, odds, and normal scales). RESULTS: For predicting 10-y survival, spline models generally performed better than standard parametric models. However, using an all-cause or a relative survival framework did not show any distinct difference. For lifetime survival, extrapolating from a relative survival framework agreed better with the observed survival, particularly using spline models. CONCLUSIONS: For extrapolation to 10 y, we recommend spline models. For extrapolation to lifetime, we suggest extrapolating in a relative survival framework, especially using spline models. HIGHLIGHTS: For survival extrapolation to 10 y, spline models generally performed better than standard parametric models did. However, using an all-cause or a relative survival framework showed no distinct difference under the same parametric model.Survival extrapolation to lifetime within a relative survival framework agreed well with the observed data, especially using spline models.Extrapolating parametric models within an all-cause survival framework may overestimate survival proportions at lifetime; models for the relative survival approach may underestimate instead.

Exploring Factors That Influence Postexercise Glycemia in Youth With Type 1 Diabetes in the Real World: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study.

OBJECTIVE: To explore 24-h postexercise glycemia and hypoglycemia risk, data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were analyzed to examine factors that may influence glycemia. RESEARCH DESIGN AND METHODS: This was a real-world observational study with participant self-reported physical activity, food intake, and insulin dosing (multiple daily injection users). Heart rate, continuous glucose data, and available pump data were collected. RESULTS: A total of 251 adolescents (42% females), with a mean ± SD age of 14 ± 2 years, and hemoglobin A1c (HbA1c) of 7.1 ± 1.3% (54 ± 14.2 mmol/mol), recorded 3,319 activities over ∼10 days. Trends for lower mean glucose after exercise were observed in those with shorter disease duration and lower HbA1c; no difference by insulin delivery modality was identified. Larger glucose drops during exercise were associated with lower postexercise mean glucose levels, immediately after activity (P < 0.001) and 12 to <16 h later (P = 0.02). Hypoglycemia occurred on 14% of nights following exercise versus 12% after sedentary days. On nights following exercise, more hypoglycemia occurred when average total activity was ≥60 min/day (17% vs. 8% of nights, P = 0.01) and on days with longer individual exercise sessions. Higher nocturnal hypoglycemia rates were also observed in those with longer disease duration, lower HbA1c, conventional pump use, and if time below range was ≥4% in the previous 24 h. CONCLUSIONS: In this large real-world pediatric exercise study, nocturnal hypoglycemia was higher on nights when average activity duration was higher. Characterizing both participant- and event-level factors that impact glucose in the postexercise recovery period may support development of new guidelines, decision support tools, and refine insulin delivery algorithms to better support exercise in youth with diabetes.

The origin and speciation of orchids.

Orchids constitute one of the most spectacular radiations of flowering plants. However, their origin, spread across the globe, and hotspots of speciation remain uncertain due to the lack of an up-to-date phylogeographic analysis. We present a new Orchidaceae phylogeny based on combined high-throughput and Sanger sequencing data, covering all five subfamilies, 17/22 tribes, 40/49 subtribes, 285/736 genera, and c. 7% (1921) of the 29 524 accepted species, and use it to infer geographic range evolution, diversity, and speciation patterns by adding curated geographical distributions from the World Checklist of Vascular Plants. The orchids' most recent common ancestor is inferred to have lived in Late Cretaceous Laurasia. The modern range of Apostasioideae, which comprises two genera with 16 species from India to northern Australia, is interpreted as relictual, similar to that of numerous other groups that went extinct at higher latitudes following the global climate cooling during the Oligocene. Despite their ancient origin, modern orchid species diversity mainly originated over the last 5 Ma, with the highest speciation rates in Panama and Costa Rica. These results alter our understanding of the geographic origin of orchids, previously proposed as Australian, and pinpoint Central America as a region of recent, explosive speciation.

Repeated Prostate Cancer Screening Using Prostate-Specific Antigen Testing and Magnetic Resonance Imaging: A Secondary Analysis of the STHLM3-MRI Randomized Clinical Trial.

Importance: Magnetic resonance imaging (MRI) has been proposed to enhance the benefit-to-harm ratio of prostate cancer screening, but data on repeated screening outcomes are lacking. Objective: To describe outcomes of prostate-specific antigen (PSA)-based screening with MRI and prostate biopsies at repeat screening. Design, Setting, and Participants: This secondary analysis examined the population-based, screen-by-invitation STHLM3-MRI randomized clinical trial, which recruited Swedish men aged 50 to 74 years. Men were eligible for repeat screening at 2 to 3 years if they had PSA levels of 1.5 ng/mL or greater at trial inclusion, were randomized to the MRI-targeted group (including screening using biomarkers and MRI), and were not diagnosed with prostate cancer after the first screening round. Repeat screening was performed between November 10, 2021, and February 20, 2023. Data analysis was performed between May and August 2023. Intervention: Participants underwent blood sampling, including PSA testing. A biparametric MRI scan was performed if PSA levels were 3 ng/mL or greater, and men with lesions with a Prostate Imaging-Reporting and Data System (PI-RADS) score of 3 or greater were referred for targeted and systematic biopsies. Main Outcomes and Measures: The primary outcome was clinically significant prostate cancer (Gleason score of ≥3 + 4). Secondary outcomes included the proportion of men with clinically insignificant cancer (Gleason score of 6), the number of elevated PSA tests, MRI scans, and biopsy procedures. Results: Of 7609 men from the first screening round, 2078 (27.3%) were eligible for and were invited for rescreening. Among the invitees, 1500 (72.2%) participated. Their median age was 67 (IQR, 61-72) years. Of 1094 men with PSA levels between 1.5 and 2.9 ng/mL in the first screening round, 326 (29.8%) had levels of 3 ng/mL or greater in the second round. Overall, 667 men (44.5%) had PSA levels of 3 ng/mL or greater: 617 underwent MRI (92.5%), revealing 51 (7.6%) with equivocal lesions (PI-RADS score of 3) and 33 (4.9%) with suspicious lesions (PI-RADS score of ≥4). Only 10 of 383 men (2.6%) with a prior negative MRI result had a lesion with a PI-RADS score of 4 or greater. Among the 1500 rescreened men, 48 (3.2%) had a Gleason score of 3 + 4 or greater, including 19 (1.3%) with a score of 4 + 3 or greater and 11 (0.7%) with a score of 6. Conclusions and Relevance: In this secondary analysis of the STHLM3-MRI randomized clinical trial, cancer detection during the second screening round in biennial PSA and MRI-based prostate cancer screening was limited, and the detection of low-grade tumors remained low. A substantial proportion of men exhibited elevated PSA levels during rescreening, and a considerable portion of MRI scans performed lacked lesions suggestive of cancer. Future studies should explore strategies to reduce MRI-related resource use. Trial Registration: ClinicalTrials.gov Identifier: NCT03377881.

Improving Planning for Transition to Adult Care in a Pediatric Diabetes Clinic.

Quality Improvement Success Stories are published by the American Diabetes Association in collaboration with the American College of Physicians and the National Diabetes Education Program. This series is intended to highlight best practices and strategies from programs and clinics that have successfully improved the quality of care for people with diabetes or related conditions. Each article in the series is reviewed and follows a standard format developed by the editors of Clinical Diabetes. The following article describes efforts at a large midwestern pediatric health system to improve planning for patients' transition from pediatric to adult diabetes care settings.

Benchmarking Diabetes Technology Use Among 21 U.S. Pediatric Diabetes Centers.

The American Diabetes Association's Standards of Care in Diabetes recommends the use of diabetes technology such as continuous glucose monitoring systems and insulin pumps for people living with type 1 diabetes. Unfortunately, there are multiple barriers to uptake of these devices, including local diabetes center practices. This study aimed to examine overall change and center-to-center variation in uptake of diabetes technology across 21 pediatric centers in the T1D Exchange Quality Improvement Collaborative. It found an overall increase in diabetes technology use for most centers from 2021 to 2022 with significant variation.

Postprandial Glucose Variability Following Typical Meals in Youth Living with Type 1 Diabetes.

We explored the association between macronutrient intake and postprandial glucose variability in a large sample of youth living with T1D and consuming free-living meals. In the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study, youth took photographs before and after their meals on 3 days during a 10 day observation period. We used the remote food photograph method to obtain the macronutrient content of youth's meals. We also collected physical activity, continuous glucose monitoring, and insulin use data. We measured glycemic variability using standard deviation (SD) and coefficient of variation (CV) of glucose for up to 3 h after meals. Our sample included 208 youth with T1D (mean age: 14 ± 2 years, mean HbA1c: 54 ± 14.2 mmol/mol [7.1 ± 1.3%]; 40% female). We observed greater postprandial glycemic variability (SD and CV) following meals with more carbohydrates. In contrast, we observed less postprandial variability following meals with more fat (SD and CV) and protein (SD only) after adjusting for carbohydrates. Insulin modality, exercise after meals, and exercise intensity did not influence associations between macronutrients and postprandial glycemic variability. To reduce postprandial glycemic variability in youth with T1D, clinicians should encourage diversified macronutrient meal content, with a goal to approximate dietary guidelines for suggested carbohydrate intake.

The Relationship Between Parent Fear of Hypoglycemia and Youth Glycemic Control Across the Recent-Onset Period in Families of Youth with Type 1 Diabetes.

BACKGROUND: This study aims to examine the relationship between parents' fear of hypoglycemia (FH) over a 1-year period and child glucose metrics in 126 families of youth recently diagnosed with type 1 diabetes (T1D). METHODS: Parents completed the Hypoglycemia Fear Survey for Parents (HFS-P) and uploaded 14 days of glucose data at a baseline, 6-month, and 12-month assessment. RESULTS: Parents' HFS-P total and worry scores increased to a clinically meaningful degree from baseline to 6-month assessment, while multilevel models revealed within- and between-person variability in parents' HFS-P worry and behavior scores over time associated with child glycemia. Specifically, a significant negative relationship for within-person worry scores suggested that when parents reported higher than their average worry scores, their children recorded fewer glucose values in the target range, while within-person behavior scores suggested that when parents reported lower than their average behavior scores, their children recorded more values above the target range. There was also a negative relationship for between-person behavior scores with child glycated hemoglobin and a positive relationship for between-person behavior scores with child glucose values in the target range. CONCLUSIONS: In the recent-onset period of T1D, parental FH worry and behavior associated with child glycemia possibly due to changes in parents' perceptions of their child's hypoglycemia risk. The clinically meaningful increases in parent FH in the recent-onset period and the negative association for between-person behavior scores with child glycated hemoglobin suggest that clinics should consider screening parents for FH, especially among parents of children with lower glycemic levels.

The Acute Effects of Real-World Physical Activity on Glycemia in Adolescents With Type 1 Diabetes: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study.

OBJECTIVE: Data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were evaluated to understand glucose changes during activity and identify factors that may influence changes. RESEARCH DESIGN AND METHODS: In this real-world observational study, adolescents with type 1 diabetes self-reported physical activity, food intake, and insulin dosing (multiple-daily injection users) using a smartphone application. Heart rate and continuous glucose monitoring data were collected, as well as pump data downloads. RESULTS: Two hundred fifty-one adolescents (age 14 ± 2 years [mean ± SD]; HbA1c 7.1 ± 1.3% [54 ± 14.2 mmol/mol]; 42% female) logged 3,738 activities over ∼10 days of observation. Preactivity glucose was 163 ± 66 mg/dL (9.1 ± 3.7 mmol/L), dropping to 148 ± 66 mg/dL (8.2 ± 3.7 mmol/L) by end of activity; median duration of activity was 40 min (20, 75 [interquartile range]) with a mean and peak heart rate of 109 ± 16 bpm and 130 ± 21 bpm. Drops in glucose were greater in those with lower baseline HbA1c levels (P = 0.002), shorter disease duration (P = 0.02), less hypoglycemia fear (P = 0.04), and a lower BMI (P = 0.05). Event-level predictors of greater drops in glucose included self-classified "noncompetitive" activities, insulin on board >0.05 units/kg body mass, glucose already dropping prior to the activity, preactivity glucose >150 mg/dL (>8.3 mmol/L) and time 70-180 mg/dL >70% in the 24 h before the activity (all P < 0.001). CONCLUSIONS: Participant-level and activity event-level factors can help predict the magnitude of drop in glucose during real-world physical activity in youth with type 1 diabetes. A better appreciation of these factors may improve decision support tools and self-management strategies to reduce activity-induced dysglycemia in active adolescents living with the disease.

Red blood cell alloimmunization in myelodysplastic syndromes: Associations with sex, DAT-positivity, and increased transfusion needs.

BACKGROUND: Many patients with myelodysplastic syndromes (MDS) need repeated red blood cell transfusions which entails a risk of immunization and antibody formation. Associations between alloantibodies, autoantibodies and increased transfusion requirements have been reported, but their relationship remains unclear. In this study, we analyzed factors potentially associated with red blood cell alloimmunization, as well as changes in transfusion intensity and post-transfusion hemoglobin increments. METHODS: In a retrospective cohort study, we linked Swedish MDS patients diagnosed between 2003 and 2017 to transfusion and immunohematology data. Potentially associated factors were analyzed using Cox proportional hazards regression. The transfusion rate after detected alloimmunization was analyzed using a fixed effects Poisson regression. Post-transfusion hemoglobin increments before and after alloimmunization were compared using a mixed effects regression. RESULTS: Alloantibodies following MDS diagnosis were detected in 50 out of 429 patients (11.7%). Female sex and a positive direct antiglobulin test (DAT) were independently associated with alloimmunization, with hazard ratios of 2.02 (95% confidence interval [CI] 1.08-3.78) and 9.72 (95% CI, 5.31-17.74), respectively. The transfusion rate following alloimmunization was increased with an incidence rate ratio of 1.33 (95% CI, 0.98-1.80) and the post-transfusion hemoglobin increment after alloimmunization was 1.40 g/L (95% CI, 0.52-2.28) lower per red blood cell unit (p = .002) compared to before alloimmunization, in multivariable analyses. DISCUSSION: Alloimmunization against blood group antigens was associated with sex, DAT-positivity, increased transfusion needs, and lower post-transfusion hemoglobin increments. These findings warrant further investigation to evaluate the clinical significance of up-front typing and prophylactic antigen matching in patients with MDS.

Combining uncertainty-aware predictive modeling and a bedtime Smart Snack intervention to prevent nocturnal hypoglycemia in people with type 1 diabetes on multiple daily injections.

OBJECTIVE: Nocturnal hypoglycemia is a known challenge for people with type 1 diabetes, especially for physically active individuals or those on multiple daily injections. We developed an evidential neural network (ENN) to predict at bedtime the probability and timing of nocturnal hypoglycemia (0-4 vs 4-8 h after bedtime) based on several glucose metrics and physical activity patterns. We utilized these predictions in silico to prescribe bedtime carbohydrates with a Smart Snack intervention specific to the predicted minimum nocturnal glucose and timing of nocturnal hypoglycemia. MATERIALS AND METHODS: We leveraged free-living datasets collected from 366 individuals from the T1DEXI Study and Glooko. Inputs to the ENN used to model nocturnal hypoglycemia were derived from demographic information, continuous glucose monitoring, and physical activity data. We assessed the accuracy of the ENN using area under the receiver operating curve, and the clinical impact of the Smart Snack intervention through simulations. RESULTS: The ENN achieved an area under the receiver operating curve of 0.80 and 0.71 to predict nocturnal hypoglycemic events during 0-4 and 4-8 h after bedtime, respectively, outperforming all evaluated baseline methods. Use of the Smart Snack intervention reduced probability of nocturnal hypoglycemia from 23.9 ± 14.1% to 14.0 ± 13.3% and duration from 7.4 ± 7.0% to 2.4 ± 3.3% in silico. DISCUSSION: Our findings indicate that the ENN-based Smart Snack intervention has the potential to significantly reduce the frequency and duration of nocturnal hypoglycemic events. CONCLUSION: A decision support system that combines prediction of minimum nocturnal glucose and proactive recommendations for bedtime carbohydrate intake might effectively prevent nocturnal hypoglycemia and reduce the burden of glycemic self-management.

Parental diabetes distress is a stronger predictor of child HbA1c than diabetes device use in school-age children with type 1 diabetes.

INTRODUCTION: Diabetes distress (DD) describes the unrelenting emotional and behavioral challenges of living with, and caring for someone living with, type 1 diabetes (T1D). We investigated associations between parent-reported and child-reported DD, T1D device use, and child glycated hemoglobin (HbA1c) in 157 families of school-age children. RESEARCH DESIGN AND METHODS: Parents completed the Parent Problem Areas in Diabetes-Child (PPAID-C) and children completed the Problem Areas in Diabetes-Child (PAID-C) to assess for DD levels. Parents also completed a demographic form where they reported current insulin pump or continuous glucose monitor (CGM) use (ie, user/non-user). We measured child HbA1c using a valid home kit and central laboratory. We used correlations and linear regression for our analyses. RESULTS: Children were 49% boys and 77.1% non-Hispanic white (child age (mean±SD)=10.2±1.5 years, T1D duration=3.8±2.4 years, HbA1c=7.96±1.62%). Most parents self-identified as mothers (89%) and as married (78%). Parents' mean PPAID-C score was 51.83±16.79 (range: 16-96) and children's mean PAID-C score was 31.59±12.39 (range: 11-66). Higher child HbA1c correlated with non-pump users (r=-0.16, p<0.05), higher PPAID-C scores (r=0.36, p<0.001) and higher PAID-C scores (r=0.24, p<0.001), but there was no association between child HbA1c and CGM use. A regression model predicting child HbA1c based on demographic variables, pump use, and parent-reported and child-reported DD suggested parents' PPAID-C score was the strongest predictor of child HbA1c. CONCLUSIONS: Our analyses suggest parent DD is a strong predictor of child HbA1c and is another modifiable treatment target for lowering child HbA1c.