Diabetes technology & therapeutics最新文献

Introduction: Youth obesity is a strong risk factor for prediabetes (PD) and type 2 diabetes. Current criteria for the diagnosis of PD/diabetes, including fasting glucose, 2-h blood glucose after oral glucose tolerance test (OGTT), and HbA1c, have some acknowledged limitations in youth. Continuous glucose monitoring (CGM) offers the opportunity to record daily glucose profiles in a free-living conditions. This study aims to explore how the CGM metrics are related to PD in youths with obesity. Method: Youths with obesity (BMI-for-age > 2SD, age 10-18 years) wore a Freestyle Libre 2 CGM sensor for 2 weeks. Several CGM metrics were measured, including time in tight ranges (TITR) 70-140 and 70-120 mg/dL. All subjects underwent OGTT, and normal glucose tolerance (NGT) and prediabetes (PD) were defined by American Diabetes Association criteria. A nonparametric Wilcoxon rank-sum test was used to compare NGT and PD youths, and logistic regression analysis was performed to investigate the ability of CGM metrics to predict PD. Results: Overall, 84 youths (age 12.6 ± 1.9 years, 42.4% female, BMI 32.8 ± 6.6 kg/m², HbA1c5.4 ± 0.2%, CGM use >80%) were recruited. HbA1c, blood glucose measured at baseline, 30, 90, and 120 min, and the area under the curve of glucose after glucose load were significantly higher (P value <0.05) in PD than in NGT youths. TITR 70-140 mg/dL and TITR 70-120 mg/dL were significantly (P < 0.05) lower in PD than in NGT youths. No other CGM metrics differed between the two groups. Both TITR 70-140 and 70-120 mg/dL significantly predict PD (P = 0.02), independent of age and sex, though with modest discriminative ability. Conclusions: This exploratory study showed that TITR measured in free-living may aid the identification of PD in youths with obesity, although the discriminative ability of CGM metrics was limited. Future works will focus on the analysis of the concordance of plasma glucose and CGM during OGTT, as well as their predictive performance.

Objective: Continuous glucose monitoring (CGM) and hemoglobin A1c (HbA1c) provide estimates of mean glycemia that may differ, in part, due to the effects of variation in red blood cell (RBC) age and turnover on HbA1c. Measurements derived from the complete blood count (CBC) may vary with RBC age and might be used to reduce the difference between glycemia estimates derived from CGM and HbA1c. Methods: We analyzed CBC measurements from 1,325 individuals with type 2 diabetes who participated in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE) CGM substudy. Mean glycemia was estimated from HbA1c (eAG_A1c) using the A1c-Derived Average Glucose (ADAG) formula and from CGM by averaging 10 days of measurements (eAG_CGM). We evaluated the association between CBC-derived data and the difference (eAG_A1c - eAG_CGM) using linear models, both unadjusted and adjusted for age and self-identified sex. Results: In adjusted analyses, several CBC-derived measurements were significantly associated with the difference between eAG_A1c and eAG_CGM. Platelet count and RBC distribution width (RDW) were positively associated, while hemoglobin concentration (HGB), reticulocyte fraction, mean corpuscular volume (MCV), mean corpuscular hemoglobin content (MCH), mean corpuscular hemoglobin concentration (MCHC), and reticulocyte MCHC were negatively associated. A linear model from HbA1c to eAG_CGM adjusted with all significantly associated CBC measurements (CBC_all-AG_A1c) provided modestly improved estimates of eAG_CGM compared with ADAG, with R² (SD) for ADAG of 0.68 (0.07) and for CBC_all-AG_A1c 0.72 (0.06). Conclusions: CBC measurements are associated with differences between estimates of glycemia derived from HbA1c and CGM. Further studies with longer periods of CGM are needed to determine whether CBCs can complement HbA1c and CGM and can help reconcile differences in estimates of mean glycemia provided by HbA1c and CGM.

Background: CGM is associated with improved diabetes management. Prior studies have evaluated its effects on health care utilization and costs among individuals using insulin, particularly those prescribed rapid- and short-acting regimens. The present study compared clinical and economic outcomes between CGM users and nonusers in a large, diverse, real-world population of rapid- and short-acting insulin users. Methods: Using the Mariner Commercial Claims Database, adults with diabetes and at least one claim for rapid- or short-acting insulin between January 1, 2010, and October 31, 2022, were identified. Two cohorts were defined based on receipt of CGM: those with CGM (wCGM) and those without CGM (xCGM). Direct matching was applied to ensure comparability between groups. Outcomes included total medical costs, emergency room (ER) days, inpatient (IP) days, ER and IP days associated with hypoglycemia, diabetic ketoacidosis (DKA), or mixed events, and the likelihood of achieving glycated hemoglobin (HbA1c) <9%. The National Committee for Quality Assurance considers HbA1c >9% as "poor control". Results: After applying exclusion criteria, 3,139,979 individuals met inclusion criteria. Of these, 536,512 received a CGM and 2,603,467 did not, meaning approximately 83% of eligible individuals had no evidence of CGM use. Total health care costs were significantly lower in the wCGM cohort ($6,245) compared with the xCGM cohort ($7,786; t(698,086) = -71.41, P < 0.001). The wCGM group also had significantly fewer ER days and IP days at 3, 6, 9, and 12 months. CGM users had 19% higher odds of achieving HbA1c <9% compared with nonusers (odds ratio [OR] = 1.19). A significantly smaller proportion of individuals in the wCGM cohort had ER/IP days associated with hypoglycemia, DKA, or both. Conclusions: These findings reinforce the clinical and economic value of CGM and support recent policy updates expanding access for insulin-treated populations.

Background: Physical activity (PA) poses significant challenges in glucose management for individuals with type 1 diabetes (T1D). Real-world PA is more frequent than structured PA, but remains underexplored. We analyzed 8171 real-world PA sessions comprising 45 activity types from the Type 1 Diabetes Exercise Initiative, examining hypoglycemia risk correlations with PA-level and population-level factors. Methods: Hypoglycemia risk was measured by change in continuous glucose monitoring (ΔCGM) from PA onset to end, low blood glucose index (LBGI), and hypoglycemia event occurrence. Primary analyses used analysis of variance and Tukey's range test to measure correlations. Secondary analyses compared risk across activity types and categories (aerobic, mixed, and anaerobic). Results: Higher hypoglycemia risk was associated with longer PA duration (median [Interquartile Range (IQR)] ΔCGM -24 [-60, 11] mg/dL for 60-120 min vs. -12 [-31, 5] mg/dL for 15-30 min), lower starting glucose (90% of sessions starting <50 mg/dL had hypoglycemia), and declining glucose rates before PA (all P < 0.05). Carbohydrate (CHO) intake 2-4 h before and during PA was associated with higher hypoglycemia risk (ΔCGM -37 [-67, -14] mg/dL with rescue CHO vs. -15 [-42, 8] mg/dL without, P < 0.05), but this paradoxical effect was explained by higher insulin on board (IoB) and lower starting glucose. Males had larger glucose drops (ΔCGM -20 [-46, 4] mg/dL vs. -16 [-44, 7] mg/dL in females, P < 0.05). Closed-loop users exhibited lower LBGI compared with open-loop users (P < 0.05). Secondary analyses showed significant glycemic variability across activity types (P < 0.05). Aerobic activities caused the greatest glucose drop, followed by mixed and anaerobic (P < 0.05), whereas LBGI differences were nonsignificant (P = 0.32). Conclusions: Real-world PA has a highly variable glycemic impact, with longer duration, lower starting glucose, and higher IoB increasing hypoglycemia risk. Glycemic responses differed significantly by activity type, with aerobic activities resulting in the greatest decline. These findings highlight the need for tailored strategies to mitigate PA-related hypoglycemia in T1D.

Objective: Using a multistep machine-learning approach, the aim is to create virtual continuous glucose monitoring (CGM) traces from glycemic data collected in the Diabetes Control and Complications Trial (DCCT) to assess the relationship between CGM metrics and DCCT cardiovascular (CV) outcomes in people with type 1 diabetes. Research Design and Methods: Utilizing the virtual CGM traces created for each DCCT participant, as previously published, discrete Cox proportional hazard models were used to calculate hazard ratios (HRs) for the association between glycemic metrics (hemoglobin A1c [HbA1c] and virtual CGM) and 3 separate DCCT CV outcome definitions: (1) all DCCT-recorded events; (2) a restricted set of "hard" CV end points; and (3) a restricted set of major CV and major peripheral vascular events. Results: Mean HbA1c and CGM metrics reflective of hyperglycemia were consistently higher, and time-in-range (70-180 mg/dL) and time-in-tight-range (70-140 mg/dL) were consistently lower, in DCCT participants who experienced a CV outcome versus those who did not. For the outcome definition encompassing all CV events, specific adjusted HRs for a CV outcome per a 1 standard deviation (SD) change in glucose metrics were 1.29 for HbA1c with nearly identical values of 1.29-1.31 for relevant CGM metrics. A similar pattern was seen when assuming a 0.5 SD change in glucose metrics. Notably, there was no increased risk for experiencing a CV outcome as time-below-range increased, and in fact, there was a trend toward a slightly protective effect when assuming either a 1- or 0.5-SD change in virtual hypoglycemia metrics. Conclusions: Virtual CGM metrics are associated with CV outcomes in people with type 1 diabetes. These findings support the case for CGM metrics to be included as clinical trial primary endpoints for this population.

Continuous glucose monitors (CGMs) offer insight into glycemic control but have not been established as predictors of acute diabetes complications. Using data from 120 youth with type 1 diabetes (ages 8-17) enrolled in a 24-month study, we investigated associations of CGM-derived metrics (time-in-range [TIR] 70-180 mg/dL, time <70, time >180, time >250, mean glucose, glucose coefficient of variation [CV]) with incidence rates of severe hypoglycemia and diabetic ketoacidosis (DKA)/severe hyperglycemia. Over 285 person-years of follow-up, there were 75 events of severe hypoglycemia and 15 events of DKA/severe hyperglycemia. TIR and CV were significantly associated with severe hypoglycemia. Those with <45% TIR had 2.09 times the rate of severe hypoglycemia than those with ≥45% TIR (P = 0.003). Those with CV ≥41% had 2.03 times the rate of severe hypoglycemia than those with CV <41% (P = 0.006). No CGM metrics were significantly associated with DKA/severe hyperglycemia. CGM data could serve as additional predictors for acute complications, particularly severe hypoglycemia.

Diabetic retinopathy (DR) is a common and potentially sight-threatening complication of diabetes. Early detection of DR through screening can prevent visual loss. Handheld fundus cameras combined with artificial intelligence (AI) technology may improve DR screening. We evaluated the Aireen AI algorithm's performance in grading DR in fundus images captured by the handheld Optomed Aurora. Two retina specialists and Aireen graded 624 fundus images for DR. Sensitivity, specificity, and predictive values were measured against the ophthalmologists' grading. Overall, 97% of images were sufficient for DR classification. Aireen demonstrated 94.8% sensitivity, 91.4% specificity, and 92.7% diagnostic accuracy for DR. Aireen showed high diagnostic accuracy in detecting DR in Optomed Aurora images, suggesting its potential for effective screening. The validated use of AI with a handheld fundus camera may streamline the screening process, reduce the burden on health care professionals, and improve access to screening and patient outcomes through enhanced diagnostic accuracy.

Objective: To evaluate trends in insulin delivery and day-to-day variability of insulin requirements over 48 months of hybrid closed-loop use following diagnosis of type 1 diabetes (T1D) in individuals aged 10-16 years. Methods: A secondary analysis of the closed-loop arm of an open-label, multicenter, randomized, parallel hybrid closed-loop trial assessing closed-loop insulin delivery in newly diagnosed children and adolescents with T1D was conducted. Mean total daily dose (TDD) over 24 h and during the night, as well as mean total basal and bolus insulin over 24 h, were calculated. Day-to-day variability of insulin requirements was evaluated over 24 h and at night. Results: TDD increased from 27.2 ± 16.1 units/d (mean ± standard deviation) at 0-3 months following diagnosis to 65.7 ± 24.9 units/d at 42-48 months. The proportion of total daily insulin delivered as basal insulin rose from 41% to 61% over 48 months. Day-to-day variability of insulin requirements after diagnosis was high (coefficient of variation at 0-3 months: 23.3 ± 0.9%) and remained stable over 48 months. No clinically relevant sex-based differences were observed in insulin requirements. Conclusions: During the first 48 months after diagnosis of T1D, insulin requirements in children and adolescents more than double with hybrid closed-loop insulin delivery. Over time, a greater proportion of insulin is administered via the closed-loop algorithm, and the high day-to-day variability in insulin needs underscores the importance of initiating adaptive closed-loop systems from diagnosis.

In this study, we aimed to explore the impact of meal carbohydrate (CHO) content on postprandial hyperglycemia in hospitalized patients receiving fully automated insulin delivery (AID). We performed a post-hoc analysis of two trials and analyzed 844 postprandial periods from 48 adults treated with fully AID (FlorenceD2W-T2 or CamAPS HX) in hospital using generalized additive regression models. Meal CHO content had a nonlinear effect on postprandial hyperglycemia risk (P < 0.001). Postprandial hyperglycemia was more likely at breakfast compared with lunch and dinner (odds ratio or OR [95% confidence interval or CI] 1.8 [1.2, 2.6], P = 0.006; and 1.5 [1.1, 2.2], P = 0.05, respectively) and more frequent on days with glucocorticoid administration (OR [95% CI] 3.3 [2.1, 5.1]; P < 0.001). In conclusion, during fully AID in hospitalized patients, the risk of postprandial hyperglycemia remained <50% for meals ≤50 g CHO. The CHO tolerance was lowest at breakfast and with concomitant glucocorticoid therapy across all meals.

Introduction: Consensus guidelines recommend reviewing 14 days of continuous glucose monitor (CGM) data when assessing glycemia in people with type 1 diabetes (T1D). Adult studies have shown that 7 days of CGM data provide a reliable assessment of glycemia. Objectives: To understand the minimum amount of CGM data required to assess glycemia in the pediatric T1D population. Methods: Real-world Dexcom G6 CGM data were extracted from cloud-based CGM software for 8 time windows (3, 5, 7, 10, 14, 30, 60, and 90 days), all starting on March 1, 2023. Youth <21 years with T1D and ≥70% CGM active time in each window were included. Pearson correlation and interclass correlation coefficients (ICCs) between 14-day data and other windows were calculated. Differences in the percentage of youth within predetermined thresholds of 14-day CGM metrics (±0.3% glucose management indicator [GMI]; ±5% time in range [TIR]/time in tight range; ±1% time below range <70 and <54 mg/dL) were assessed using chi-squared analyses. Sub-analyses were conducted according to categorical groupings of 14-day TIR, coefficient of variation (CV), and age. Results: A total of 1316 youth were included (45.0% female, 76.9% non-Hispanic White, median age 14.6 years). Median 14-day CGM active time was 97.2% and GMI and TIR were 7.4% (7.0, 7.9) and 60.5% (48.6, 70.6), respectively. Pearson correlation coefficients and ICCs between 14-day and GMI and TIR for all 8 windows were >0.9; however, categorical agreement as defined by the percentage of subjects acceptable thresholds for GMI and TIR only exceeded 90% at 10 days. Although there was no difference in agreement for CGM metrics according to categorical groupings of age, agreement was stronger for youth with TIR ≥70% and CV <36%. Conclusions: Although 14 days of CGM data are considered the gold standard, assessing ∼9.6 days of data in youth with T1D provides a reliable assessment of glycemia. For youth with higher TIR (≥70%) and lower CV (<36%), 7-day CGM data may prove sufficient.