Deep Analysis of Clinical Parameters and Temporal Evolution of Glycemic Parameters Based on CGM Data for the Characterization of Severe Hypoglycemia in a Cohort of Children and Adolescents with Type 1 Diabetes.

This study aims to evaluate the determinants and clinical markers of patients at risk for severe hypoglycemia (SH) in children and adolescents with type 1 diabetes. In the EPI-GLUREDIA study, clinical parameters and continuous glucose monitoring metrics from children and adolescents with type 1 diabetes were retrospectively analyzed between July 2017 and June 2022. Their clinical parameters were collected during traditional and quarterly medical consultations according to whether they experienced severe hypoglycemia or not. Then, continuous glucose monitoring metrics were analyzed on days surrounding SH during specific periods. According to the glycemic parameters, glycemic hemoglobin and glycemic mean were significantly lower in the three months preceding a SH compared with during three normal months (p < 0.05). Moreover, the time spent in hypoglycemia(time below the range, TBR<3.3) and its strong correlation (R = 0.9, p < 0.001) with the frequency of SH represent a sensitive and specific clinical parameter to predict SH (cut-off: 9%, sensitivity: 71%, specificity: 63%). The second finding of the GLUREDIA study is that SH is not an isolated event in the glycemic follow-up of our T1DM patients. Indeed, most of the glycemic parameters (i.e., glycemic mean, glycemic variability, frequency of hypoglycemia, and glycemic targets) vary considerably in the month preceding an SH (all p < 0.05), whereas most of these studied glycemic parameters remain stable in the absence of a severe acute complication (all p > 0.05). Furthermore, the use of ROC curves allowed us to determine for each glycemic parameter a sensitive or specific threshold capable of more accurately predicting SH. For example, a 10% increase in the frequency of hypoglycemia predicts a risk of near SH with good combination of sensitivity and specificity (sensitivity: 80%, specificity: 60%). The GLUREDIA study aimed to target clinical and glycemic parameters to predict patients at risk for SH. First, we identified TBR<3.3 < 9% as a sensitive and specific tool to reduce the frequency of SH. In addition, SH was not an isolated event but rather it was accompanied by glycemic disturbances in the 30 days before SH.

Determinants and Characteristics of Insulin Dose Requirements in Children and Adolescents with New-Onset Type 1 Diabetes: Insights from the INSENODIAB Study.

Aims: New-onset type 1 diabetes mellitus (T1D) in pediatric patients represents a clinical challenge for initial total daily insulin dosing (TIDD) due to substantial heterogeneity in practice and lack of consensus on the optimal starting dose. Our INSENODIAB (INsulin SEnsitivity in New Onset type 1 DIABetes) study is aimed at (1) exploring the influence of patient-specific characteristics on insulin requirements in pediatric patients with new-onset T1D; (2) constructing a predictive model for the recommended TIDD tailored to individual patient profiles; and (3) assessing potential associations between TIDD and patient outcomes at follow-up intervals of 3 and 12 months. Methods: We conducted a comprehensive analysis of medical records for children aged 6 months to 18 years, hospitalized for new-onset T1D from 2013 to 2022. The study initially involved multivariable regression analysis on a retrospective cohort (rINSENODIAB), incorporating baseline variables. Subsequently, we validated the model robustness on a prospective cohort (pINSENODIAB) with a significance threshold of 5%. The model accuracy was assessed by Pearson's correlation. Results: Our study encompassed 103 patients in the retrospective cohort and 80 in the prospective cohort, with median TIDD at diagnosis of 1.1 IU/kg BW/day (IQR 0.5). The predictive model for optimal TIDD was established using baseline characteristics, resulting in the following formula: TIDD (IU/d) = ([0.09 × Age2] + [0.68 × %Weight Loss] + [28.60 × Veinous pH] - [1.03 × Veinous bicarbonates] + [0.81 × Weight] - 194.63). Validation of the model using the pINSENODIAB cohort demonstrated a significant Pearson correlation coefficient of 0.74. Notably, no significant correlation was observed between TIDD at diagnosis and partial remission markers (IDAA1C, C-peptide) at 3- and 12-months postdiagnosis time points. Conclusions: In the context of new-onset T1D in pediatric patients, we identified key influencing factors for determining optimal TIDD, including age, percentage of weight loss, weight, veinous pH, and bicarbonates. These findings have paved the way for the development of a dosing algorithm to potentially expedite glycemic control stabilization and facilitate a more individualized approach to treatment regimens.