Long-Term Glucose-Lowering Effect of Intermittently Scanned Continuous Glucose Monitoring for Type 1 Diabetes Patients in Poor Glycaemic Control from Region North Denmark:An Observational Real-World Cohort Study

BACKGROUND: Lowering glucose levels is a complex task for patients with type 1 diabetes, and they often lack contact with health care professionals. Intermittently scanned continuous glucose monitoring (isCGM) has the potential to aid them with blood glucose management at home. The aim of this study was to investigate the long-term effect of isCGM on HbA(1c) in type 1 diabetes patients with poor glycaemic control in a region-wide real-world setting. METHODS: All patients with type 1 diabetes receiving an isCGM due to poor glycaemic control (≥70 mmol/mol [≥8.6%]) in the period of 2020–21 in Region North Denmark (“T1D-CGM”) were compared with all type 1 diabetes patients without isCGM (“T1D-NOCGM”) in the same period. A multiple linear regression model adjusted for age, sex, diabetes duration and use of continuous subcutaneous insulin infusion was constructed to estimate the difference in change from baseline HbA(1c) between the two groups and within subgroups of T1D-CGM. RESULTS: A total of 2,527 patients (T1D-CGM: 897; T1D-NOCGM: 1,630) were included in the study. The estimated adjusted difference in change from baseline HbA(1c) between T1D-CGM vs T1D-NOCGM was -5.68 mmol/mol (95% CI: (-6.69 to -4.67 mmol/mol; p<0.0001)). Older patients using isCGM dropped less in HbA(1c). CONCLUSIONS: Our results indicate that patients with type 1 diabetes in poor glycaemic control from Region North Denmark in general benefit from using isCGM with a sustained 24-month improvement in HbA(1c), but the effect on HbA(1c) may be less pronounced for older patients

Prediction of pancreatic cancer risk in patients with new-onset diabetes using a machine learning approach based on routine biochemical parameters; Prediction of Pancreatic Cancer Risk in New Onset Diabetes

ObjectiveTo develop a machine-learning model that can predict the risk of pancreatic ductal adenocarcinoma (PDAC) in people with new-onset diabetes (NOD).MethodsFrom a population-based sample of individuals with NOD aged &gt;50 years, patients with pancreatic cancer-related diabetes (PCRD), defined as NOD followed by a PDAC diagnosis within 3 years, were included (n = 716). These PCRD patients were randomly matched in a 1:1 ratio with individuals having NOD. Data from Danish national health registries were used to develop a random forest model to distinguish PCRD from Type 2 diabetes. The model was based on age, gender, and parameters derived from feature engineering on trajectories of routine biochemical variables. Model performance was evaluated using receiver operating characteristic curves (ROC) and relative risk scores.ResultsThe most discriminative model included 20 features and achieved a ROC-AUC of 0.78 (CI:0.75–0.83). Compared to the general NOD population, the relative risk for PCRD was 20-fold increase for the 1 % of patients predicted by the model to have the highest cancer risk (3-year cancer risk of 12 % and sensitivity of 20 %). Age was the most discriminative single feature, followed by the rate of change in haemoglobin A1c and the latest plasma triglyceride level. When the prediction model was restricted to patients with PDAC diagnosed six months after diabetes diagnosis, the ROC-AUC was 0.74 (CI:0.69–0.79).ConclusionIn a population-based setting, a machine-learning model utilising information on age, sex and trajectories of routine biochemical variables demonstrated good discriminative ability between PCRD and Type 2 diabetes

Identification of Individuals with Diabetes who are Eligible for Continuous Glucose Monitoring Forecasting

Background and objectives: Predicting glucose levels in individuals with diabetes offers potential improvements in glucose control. However, not all patients exhibit predictable glucose dynamics, which may lead to ineffective treatment strategies. We sought to investigate the efficacy of a 7-day blinded screening test in identifying diabetes patients suitable for glucose forecasting. Methods: Participants with type 1 diabetes (T1D) were stratified into high and low initial error groups based on screening results (eligible and non-eligible). Long-term glucose predictions (30/60 min lead time) were evaluated among 334 individuals who underwent continuous glucose monitoring (CGM) over a total of 64,460,560 min. Results: A strong correlation was observed between screening accuracy and long-term mean absolute relative difference (MARD) (0.661–0.736; p &lt; 0.001), suggesting significant predictability between screening and long-term errors. Group analysis revealed a notable reduction in predictions falling within zone D of the Clark Error Grid by a factor of three and in zone C by a factor of two. Conclusions: The identification of eligible patients for glucose prediction through screening represents a practical and effective strategy. Implementation of this approach could lead to a decrease in adverse glucose predictions.</p