No clear evidence of neuropathy among patients with high risk for the development of prediabetes/diabetes-a pilot study.

Introduction: Autonomic and sensory neuropathy have been observed in both prediabetes and manifest diabetes mellitus. However, there is a lack of available data regarding whether patients at a moderate or high risk of developing diabetes, yet without a current diagnosis of prediabetes or diabetes, exhibit an increased prevalence of neuropathy. Methods: FINDRISC (Finnish Diabetes Risk Score) was used to classify individuals at risk (≥12 points, n = 44; control <12 points, n = 28). HbA1c levels >5.6% served as exclusion criteria, and patients with known medical conditions predisposing to neuropathy were also excluded. Cardiac autonomic function (Ewing tests) and peripheral sensory neuropathy (Neurometer and Q-sense) were assessed by standardized protocols, and their potential association with increased FINDRISC points was analyzed using a regression model. Results: Mean age was 46.7 ± 14.3 years in the control and 55.7 ± 14.1 years in the increased risk group. Male/female ratio did not differ. Individuals with increased risk of diabetes were more obese (BMI: 29.9 ± 12.5 kg/m2 vs. 25.9 ± 8.9 kg/m2). Additionally, hypertension was more frequent among them (68.2% vs. 17.9%), and their lipid parameters were also less favorable. Parasympathetic neuropathy was present in both groups (56.8% vs. 32.1%, respectively). Sympathetic neuropathy was not found. Sensory nerve dysfunction was of low prevalence in the high-risk group and did not occur in healthy controls. In multiple logistic regression analysis, HbA1c exhibited an independent association with parasympathetic neuropathy (OR: 5.9; 95% CI: 1.08-32.68; p < 0.041). Discussion: An increased risk of developing prediabetes/diabetes does not appear to have a strong correlation with an increased likelihood of developing autonomic or sensory neuropathy. However, the etiology behind the occurrence of parasympathetic autonomic neuropathy in healthy individuals remains unknown.

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.