Diabetes Healthcare Professionals Use Multiple Continuous Glucose Monitoring Data Indicators to Assess Glucose Management

Silicone-containing thermoresponsive membranes to form an optical glucose biosensor

Glucose biosensors that could be subcutaneously injected and interrogated without a physically connected electrode and transmitter affixed to skin would represent a major advancement in reducing the user burden of...

Network pharmacology-based analysis of the anti-hyperglycemic active ingredients of roselle and experimental validation

Diabetes mellitus is one of the top four leading causes of death among noncommunicable diseases worldwide, according to the World Hibiscus sabdariffa 2019. Roselle (Hibiscus sabdariffa L.), a traditional herbal medicine, has shown significant clinical anti-hyperglycemic efficacy. However, the mechanism of the treatment is not yet clear. We found that Roselle has a certain protective effect on vascular endothelial cells through this study. This study was based on network pharmacology and experimental validation. The present study made a comprehensive analysis by combining active ingredient screening, target prediction and signaling pathway analysis to elucidate the active ingredients and possible molecular mechanism of roselle for the first time, which provided theoretical and experimental basis for the development and application of roselle as an antidiabetic drug.

Machine Learning-Based Adherence Detection of Type 2 Diabetes Patients on Once-Daily Basal Insulin Injections

BACKGROUND

Lack of treatment adherence can lead to life-threatening health complications for people with type 2 diabetes (T2D). Recent improvements and availability in continuous glucose monitoring (CGM) technology have enabled various possibilities to monitor diabetes treatment. Detection of missed once-daily basal insulin injections can be used to provide feedback to patients, thus improving their diabetes management. In this study, we explore how machine learning (ML) based on CGM data can be used for detecting adherence to once-daily basal insulin injections.

METHODS

In-silico CGM data were generated to simulate a cohort of T2D patients on once-daily insulin injection (Tresiba®). Deep learning methods within ML based on automatic feature extraction including convolutional neural networks were explored and compared with simple feature-engineered ML classification models for adherence detection. It was further investigated whether fused expert-dependent and automatically learned features could improve performance, resulting in a comparison of six different detection models. Adherence was detected throughout each day with an increasing amount of CGM data available.

RESULTS

The adherence detection accuracy improved as more CGM data became available on the day of classification. The three classification models based on expert-engineered features obtained mean accuracies of 78.6%, 78.2%, and 78.3%. The classification model based purely on learned features obtained a mean accuracy of 79.7%. The two classification models fusing expert-engineered and learned features obtained mean accuracies of 79.7% and 79.8%. All the mentioned results were obtained 16 hours after time of injection.

CONCLUSION

The results suggest that adherence detection based on CGM data is feasible. Even though our study based on in-silico data indicates only slightly improved performance of more complex models, the question remains whether advanced models would outperform the simple in a real-world setting. Thus, future studies on adherence monitoring using real CGM data are relevant.