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.

Influence of the occurrence and duration of partial remission on short-term metabolic control in type 1 diabetes: the DIABHONEY pediatric study.

Objective: To evaluate the residual effect of partial remission (PR) on immediate post-PR glycemic control according to its occurrence and duration in a cohort of children with type 1 diabetes mellitus (T1DM). Patients and Methods: Values of glycemic control parameters [i.e. HbA1C, insulin dose-adjusted hemoglobin A1C (IDAA1C), glycemic target-adjusted HbA1C (GTAA1C)] and data from glucose monitoring devices from 189 pediatric patients with new-onset type 1 diabetes were collected retrospectively from 24 months. Patients were characterized according to their remission status (PR+ and PR-). PR+ patients were subdivided into three subgroups regarding PR duration [i.e. short (⩾3-⩽6 months), intermediate (>6-⩽12 months), and long PR (>12-⩽14 months)]. We compared glycemic control data from each PR+ subgroup at +6 and +12 months post-PR with PR- patients at the same postdiagnosis time. Second, PR+ subgroups were compared with each other. Results: PR+ patients showed improved glycemic control (i.e. HbA1C, IDAA1C, and GTAA1C) at + 6 months post-PR when compared with nonremitters (PR-), independently of the PR duration subgroups (p < 0.05). Interestingly, patients in long PR+ subgroup exhibited higher positive residual effect than short PR+ subgroup with lower GTAA1C scores (p = 0.02), better time in range (TIR) (p = 0.003), less time in hypoglycemia (10.45 versus 16.13%, p = 0.03) and less glycemic variability (83.1 mg/dl versus 98.84 mg/dl, p = 0.03). No significant differences were found for glucose control between PR+ and PR- patients at +12 months post-PR. Conclusion: This study supports the positive impact of PR occurrence and duration on short-term metabolic control (better HbA1C levels, IDAA1C and GTAA1C scores, TIR, and less glycemic variability) with the residual effect increasing according to PR duration.

Accuracy of Pancreatic Neuroendocrine Tumour Grading by Endoscopic Ultrasound-Guided Fine Needle Aspiration: Analysis of a Large Cohort and Perspectives for Improvement.

INTRODUCTION: Since the WHO Classification of Tumours of the Digestive System has been published in 2010, resected pancreatic neuroendocrine tumours (pNETs) are graded as grade 1 (G1), grade 2 (G2) or grade 3 (G3) using the Ki67 labelling index (Ki67-LI). Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) is often used for diagnosis, but few studies have assessed its value for grading. AIMS: The aims of this study were to compare the Ki67-LI obtained by cytological grading (cG) with that obtained by histological grading (hG) and to assess (1) the influence of tumour size and the number of counted cells on FNA grading as well as (2) the overall survival (OS) and progression-free survival based on cG. MATERIALS AND METHODS: EUS-FNA was performed for 102 pNETs (57 resected). cG (200 cells counted) was done on all FNAs. For 29 FNAs, >2,000 cells were counted (14 resected). A comparison was made between hG and cG for the 57 resected patients. Patients were followed up until June 2016. RESULTS: cG was consistent with hG in 39 of 57 patients with a concordance rate of 72% using a Ki67-LI cut-off of 5% for G1/G2. For Ki67-LI absolute values, the correlation was r = 0.443 and increased to r = 0.824 (p < 0.001) when only FNAs with >2,000 cells were counted. Twenty-one of 22 pNETs <2 cm had the same grading on cG and hG, whereas grading was discordant for 15 of 16 pNETs >2 cm. Thirty-eight patients died after 70.5 months of follow-up. OS for the whole cohort was 235 months and differed between cG1 (235 months), cG2 (36.3 months) and cG3 (10.9 months). CONCLUSION: cG of pNETs is more accurate when tumours measure <2 cm and more cells are counted on FNA. Discrepancies are seen between G2 tumours which are often considered G1 on FNA due to tumour heterogeneity. EUS-FNA is valuable to distinguish between patients with good (cG1) and poor (cG3) prognosis.