Nocturnal Hypoglycaemia in Patients with Diabetes Mellitus: Database Analysis of a Cohort Using Telemedicine Support for Self-Monitoring of Blood Glucose over a 10-Year-Long Period

How to evaluate over 60 million blood glucose data - The design of the MÉRY Diabetes Database

AIMS

The aim of the article is to describe the method for creating a close to ideal diabetes database. The MÉRY Diabetes Database (MDD) consists of a large quantity of reliable, well-maintained, precise and up-to-date data suited for clinical research with the intention to improve diabetes care in terms of maintaining targeted blood glucose levels, avoiding hypoglycemic episodes and complications and improving patient compliance and quality of life.

METHODS

Based on the analysis of the databases found in the literature and the experience of our research team, nine important characteristics were identified as critical to an ideal diabetes database. The data for our database is collected using MÉRYkék glucometers, a device that meets all requirements of international regulations and measures blood glucose levels within the normal range with appropriate precision (10 %).

RESULTS

Using the key characteristics defined, we were able to create a database suitable for the analysis of a large amount of data regarding diabetes care and outcomes.

CONCLUSIONS

The MDD is a reliable and ever growing database which provides stable and expansive foundation for extensive clinical investigations that hold the potential to significantly influence the trajectory of diabetes care and enhance patient outcomes.

Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice

Background and objective

Hypoglycemia is a key barrier to achieving optimal glycemic control in people with diabetes, which has been proven to cause a set of deleterious outcomes, such as impaired cognition, increased cardiovascular disease, and mortality. Hypoglycemia prediction has come to play a role in diabetes management as big data analysis and machine learning (ML) approaches have become increasingly prevalent in recent years. As a result, a review is needed to summarize the existing prediction algorithms and models to guide better clinical practice in hypoglycemia prevention.

Materials and methods

PubMed, EMBASE, and the Cochrane Library were searched for relevant studies published between 1 January 2015 and 8 December 2022. Five hypoglycemia prediction aspects were covered: real-time hypoglycemia, mild and severe hypoglycemia, nocturnal hypoglycemia, inpatient hypoglycemia, and other hypoglycemia (postprandial, exercise-related).

Results

From the 5,042 records retrieved, we included 79 studies in our analysis. Two major categories of prediction models are identified by an overview of the chosen studies: simple or logistic regression models based on clinical data and data-based ML models (continuous glucose monitoring data is most commonly used). Models utilizing clinical data have identified a variety of risk factors that can lead to hypoglycemic events. Data-driven models based on various techniques such as neural networks, autoregressive, ensemble learning, supervised learning, and mathematical formulas have also revealed suggestive features in cases of hypoglycemia prediction.

Conclusion

In this study, we looked deep into the currently established hypoglycemia prediction models and identified hypoglycemia risk factors from various perspectives, which may provide readers with a better understanding of future trends in this topic.

Electrospun nanofiber-based glucose sensors for glucose detection

Diabetes is a chronic, systemic metabolic disease that leads to multiple complications, even death. Meanwhile, the number of people with diabetes worldwide is increasing year by year. Sensors play an important role in the development of biomedical devices. The development of efficient, stable, and inexpensive glucose sensors for the continuous monitoring of blood glucose levels has received widespread attention because they can provide reliable data for diabetes prevention and diagnosis. Electrospun nanofibers are new kinds of functional nanocomposites that show incredible capabilities for high-level biosensing. This article reviews glucose sensors based on electrospun nanofibers. The principles of the glucose sensor, the types of glucose measurement, and the glucose detection methods are briefly discussed. The principle of electrospinning and its applications and advantages in glucose sensors are then introduced. This article provides a comprehensive summary of the applications and advantages of polymers and nanomaterials in electrospun nanofiber-based glucose sensors. The relevant applications and comparisons of enzymatic and non-enzymatic nanofiber-based glucose sensors are discussed in detail. The main advantages and disadvantages of glucose sensors based on electrospun nanofibers are evaluated, and some solutions are proposed. Finally, potential commercial development and improved methods for glucose sensors based on electrospinning nanofibers are discussed.