Continuous glucose monitoring versus blood glucose monitoring for risk of severe hypoglycaemia and diabetic ketoacidosis in children, adolescents, and young adults with type 1 diabetes: a population-based study.

BACKGROUND: The effect of continuous glucose monitoring on the risk of severe hypoglycaemia and ketoacidosis in patients with diabetes is unclear. We investigated whether rates of acute diabetes complications are lower with continuous glucose monitoring, compared with blood glucose monitoring, and which metrics predict its risk in young patients with type 1 diabetes. METHODS: In this population-based cohort study, patients were identified from 511 diabetes centres across Austria, Germany, Luxembourg, and Switzerland participating in the Diabetes Prospective Follow-up initiative. We included people with type 1 diabetes aged 1·5-25·0 years, with a diabetes duration of more than 1 year, who had been treated between Jan 1, 2014, and June 30, 2021, and had an observation time of longer than 120 days in the most recent treatment year. Severe hypoglycaemia and ketoacidosis rates during the most recent treatment year were examined in people using continuous glucose monitoring and in those using blood glucose monitoring. Adjustments of statistical models included age, sex, diabetes duration, migration background, insulin therapy (pump or injections), and treatment period. Rates of severe hypoglycaemia and diabetic ketoacidosis were evaluated by several continuous glucose monitoring metrics, including percentage of time below target glucose range (<3·9 mmol/L), glycaemic variability (measured as the coefficient of variation), and mean sensor glucose. FINDINGS: Of 32 117 people with type 1 diabetes (median age 16·8 years [IQR 13·3-18·1], 17 056 [53·1%] males), 10 883 used continuous glucose monitoring (median 289 days per year), and 21 234 used blood glucose monitoring. People using continuous glucose monitoring had lower rates of severe hypoglycaemia than those using blood glucose monitoring (6·74 [95% CI 5·90-7·69] per 100 patient-years vs 8·84 [8·09-9·66] per 100 patient-years; incidence rate ratio 0·76 [95% CI 0·64-0·91]; p=0·0017) and diabetic ketoacidosis (3·72 [3·32-4·18] per 100 patient-years vs 7·29 [6·83-7·78] per 100 patient-years; 0·51 [0·44-0·59]; p<0·0001). Severe hypoglycaemia rates increased with percentage of time below target glucose range (incidence rate ratio 1·69 [95% CI 1·18-2·43]; p=0·0024, for 4·0-7·9% vs <4·0% and 2·38 [1·51-3·76]; p<0·0001, for ≥8·0% vs <4·0%) and glycaemic variability (coefficient of variation ≥36% vs <36%; incidence rate ratio 1·52 [95% CI 1·06-2·17]; p=0·022). Diabetic ketoacidosis rates increased with mean sensor glucose (incidence rate ratio 1·77 [95% CI 0·89-3·51], p=0·13, for 8·3-9·9 mmol/L vs <8·3 mmol/L; 3·56 [1·83-6·93], p<0·0001, for 10·0-11·6 mmol/L vs <8·3 mmol/L; and 8·66 [4·48-16·75], p<0·0001, for ≥11·7 mmol/L vs <8·3 mmol/L). INTERPRETATION: These findings provide evidence that continuous glucose monitoring can reduce severe hypoglycaemia and ketoacidosis risk in young people with type 1 diabetes on insulin therapy. Continuous glucose monitoring metrics might help to identify those at risk for acute diabetes complications. FUNDING: German Center for Diabetes Research, German Federal Ministry of Education and Research, German Diabetes Association, and Robert Koch Institute.

Use of Continuous Glucose Monitoring in Pump Therapy Sensor Augmented Pump or Automated Insulin Delivery in Different Age Groups (0.5 to <26 Years) With Type 1 Diabetes From 2018 to 2021: Analysis of the German/Austrian/Swiss/Luxemburg DPV Registry.

AIM: Insulin pump, continuous glucose monitoring (CGM), and sensor augmented pump (SAP) technology have evolved continuously leading to the development of automated insulin delivery (AID) systems. Evaluation of the use of diabetes technologies in people with T1D from January 2018 to December 2021. METHODS: A patient registry (Diabetes Prospective Follow-up Database [DPV]) was analyzed for use of SAP (insulin pump + CGM ≥90 days, no automated dose adjustment) and AID (HCL or LGS/PLGS). In total 46,043 people with T1D aged 0.5 to <26 years treated in 416 diabetes centers (Germany, Austria, Luxemburg, and Switzerland) were included and stratified into 4 groups A-D according to age. Additionally, TiR and HbA1c were analyzed. RESULTS: From 2018 to 2021, there was a significant increase from 28.7% to 32.9% (sensor augmented pump [SAP]) and 3.5% to 16.6% (AID) across all age groups, with the most frequent use in group A (<7 years, 38.8%-40.2% and 10.3%-28.5%). A similar increase in SAP and AID use was observed in groups B (7 to <11 years) and C (11 to <16 years): B: +15.8 PP, C: +15.9 PP. HbA1c improved significantly in groups C and D (16 to <26 years) (both P < .01). Time in range (TiR) increased in all groups (A: +3 PP; B: +5 PP; C: +5 PP; D: +5 PP; P < 0.01 for each group). Insulin pumps (61.0% versus 53.4% male) and SAP (33.5% versus 28.9% male) are used more frequently in females. CONCLUSION: In recent years, we found an increasing use of new diabetes technologies and an improvement in metabolic control (TiR) across all age groups.

Comorbidity of type 1 diabetes and juvenile idiopathic arthritis.

OBJECTIVE: To analyze the prevalence of juvenile idiopathic arthritis (JIA) and diabetes end points in pediatric patients with type 1 diabetes. STUDY DESIGN: Patients with type 1 diabetes, recorded from 1995 up to September 2013 in the Diabetes Patienten Verlaufsdokumentation database (n = 54,911, <16 years of age, 47% girls), were analyzed. The patients' height, weight, and body mass index SDS, glycosylated hemoglobin A1c (HbA1c); insulin dose; hypertension and dyslipidemia prevalence; rate of hypoglycemic events; and ketoacidosis were compared between patients with and without JIA. To adjust for age, sex, diabetes duration, and migration background, data were analyzed in hierarchic multivariable regression models. RESULTS: The prevalence of JIA in type 1 diabetes was 106 of 54,911 patients; 66% were girls. Diabetes onset was earlier in children with JIA (7.2 years vs 8.3 years, P = .04). Children with JIA were smaller (SDS: -0.22 vs 0.09, P = .004). Correspondingly, weight SDS was lower in patients with JIA (-0.02 vs 0.22, P = .01). Body mass index SDS did not differ. HbA1c was marginally lower in children with JIA (63 mmol/mol [8.0%] vs 67 mmol/mol [8.3%], P = .06). Insulin requirement was greater in patients with JIA (1.03 vs 0.93 insulin units/weight/day, P = .003). Hypertension and dyslipidemia were comparable in both groups. CONCLUSIONS: The JIA-prevalence in patients with type 1 diabetes (0.19%) was considerably greater than in the general population (0.05%). Growth is influenced negatively by JIA. Surprisingly, HbA1c was somewhat lower in children with JIA, possibly because of a more intensive treatment or a latent hemolysis caused by the inflammation.

Basal rates and circadian profiles in continuous subcutaneous insulin infusion (CSII) differ for preschool children, prepubertal children, adolescents and young adults.

OBJECTIVE: Initiation of continuous subcutaneous insulin therapy (CSII) requires an appropriate basal rate profile. Different approaches exist; however, there is a lack of evidence-based recommendations, especially in young children. STUDY DESIGN: In this large multicenter survey, 5941 CSII patients from the German/Austrian prospective documentation system (DPV) were analyzed. Patients were divided into four age groups: <6 yr (n = 837), 6 to <12 yr (n = 1739), 12 to <18 yr (n = 2985) and 18 to <25 yr (n = 380). Basal insulin requirement and diurnal distribution were evaluated based on the most recent documentation for each patient. RESULTS: Basal insulin requirement differed significantly between the four age groups (<6: 0.25 ± 0.12; 6 to <12: 0.33 ± 0.12; 12 to <18: 0.43 ± 0.15; 18 to <25: 0.35 ± 0.13 U/kg; p < 0.001). Circadian insulin profiles were markedly different between the younger and older age groups. In addition to age, longer diabetes duration, female gender, higher HbA1c and lower body mass index standard deviation score (BMI-SDS) were related to higher basal insulin requirement per kilogram of body weight. CONCLUSIONS: Age of the patient is the primary factor that influences both total daily requirement and circadian distribution of basal insulin in CSII. Experience from a large database may therefore facilitate the initiation of pump therapy in pediatric patients.

A cross-sectional international survey of continuous subcutaneous insulin infusion in 377 children and adolescents with type 1 diabetes mellitus from 10 countries.

OBJECTIVE: To document current practices using continuous subcutaneous insulin infusion (CSII) by downloading electronically the 90-d pump data held within the pump memory and relating that to clinical data from children and adolescents in different pediatric diabetes centers from Europe and Israel. METHODS: Data of patients (1-18 yr) treated with CSII in 23 centers from nine European countries and Israel were recorded with the encapture software (PEC International, Frankfurt, Germany). The number of patients who participated was 377 (48% female; mean diabetes duration +/- SD: 6.8 +/- 3.7 yr; age: 12.9 +/- 3.8 yr, preschool n = 33; prepubertal n = 95; adolescent n = 249; CSII duration: 1.6 +/- 1.2 yr; local HbA1c: 8.1 +/- 1.2%). RESULTS: The total insulin dose was lower than previously reported for injection therapy (0.79 +/- 0.20 U/kg/d). Covariance coefficient of daily total insulin was high in all age groups (adolescents 19 +/- 9%, prepubertal 18 +/- 8 and preschool 17 +/- 8). The distribution of basal insulin infusion rates over 24 hr (48 +/- 12% of total dose) varied significantly between centers and age groups. The number of boluses per day (7 +/- 3) was not significantly different between the age groups (average daily bolus amount: 0.42 +/- 0.16 U/kg). The rate of severe hypoglycemia (coma/convulsions) was 12.4 episodes per 100 patient-years and the number of diabetes-related hospital days was 124 per 100 patient-years. DISCUSSION: Pediatric CSII patients show a high variability in their insulin therapy. This relates both to age-dependent differences in the distribution of basal insulin as to the age-independent day-to-day variation in prandial insulin.