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Gamma-aminobutyric acid (GABA) is an important inhibitory neurotransmitter in the central nervous system (CNS) and outside of the CNS, found in the highest concentrations in immune cells and pancreatic beta-cells. GABA is gaining increasing interest in diabetes research due to its immune-modulatory and beta-cell stimulatory effects and is a highly interesting drug candidate for the treatment of type 1 diabetes (T1D). GABA is synthesized from glutamate by glutamic acid decarboxylase (GAD), one of the targets for autoantibodies linked to T1D. Using mass spectrometry, we have quantified the endogenous circulating levels of GABA in patients with new-onset and long-standing T1D and found that the levels are unaltered when compared to healthy controls, i.e., T1D patients do not have a deficit of systemic GABA levels. In T1D, GABA levels were negatively correlated with IL-1 beta, IL-12, and IL-15 15 and positively correlated to levels of IL-36 beta and IL-37. Interestingly, GABA levels were also correlated to the levels of GAD-autoantibodies. The unaltered levels of GABA in T1D patients suggest that the GABA secretion from beta-cells only has a minor impact on the circulating systemic levels. However, the local levels of GABA could be altered within pancreatic islets in the presence of GAD-autoantibodies.

Gamma-amino butyric acid (GABA) has in experimental studies been found to promote beta-cell proliferation, enhance insulin secretion and reduce inflammation, positioning it as a candidate drug for type 1 diabetes (T1D) therapy. This phase I/II randomized controlled trial assessed the safety and efficacy of long-term treatment with Remygen® (Diamyd Medical), a controlled-release oral GABA formulation, as a potential beta-cell regenerative therapy in adults with long-standing T1D. Thirty-five male subjects with T1D (≥ 5 years) were randomized into three arms receiving the study drug(s) once daily for 6 months: GABA 200 mg (Arm 1), GABA 600 mg (Arm 2) and GABA 600 mg + alprazolam 0.5 mg for 3 months followed by GABA 600 mg alone for 3 months (Arm 3). Safety measures, hormonal counter-regulation during hypoglycemic clamps, fasting- and stimulated C-peptide levels, were assessed at multiple timepoints. Safety concerns included elevated aspartate aminotransferase (AST) in nine subjects, leading to the withdrawal of two subjects. Most elevations were, however, transient with no dose-differences. No effects were observed on fasting- or stimulated C-peptide levels, CGM metrics or HbA1c. Hypoglycemic hormonal counter-regulation was unaltered. To conclude, we found no clinical evidence of a beta-cell regenerative effect of GABA, but side effects were commonly observed.

Introduction: Hypoglycemia composes an always present risk in the treatment of type 1 diabetes (T1D) and can be a fatal complication. Many studies on hypoglycemic events are based on self-reported data or focused on the aggregated time below range. We have processed continuous glucose monitoring (CGM) data in children and adolescents with T1D in order to examine all occurring hypoglycemic events.

Research design and methods: CGM data (mean 168 +/- 3 days) from 214 children and adolescents with T1D were analyzed using computer-based algorithms. Patients were divided into three groups based on estimated HbA1c (eHbA1c): (1) <= 48 mmol/mol (n=58); (2) 49-64 mmol/ mol (n=113); (3) >= 65 mmol/mol (n=43). The groups were compared concerning descriptive data and CGM metrics with emphasis on the frequency of hypoglycemic events.

Results: Only one self-reported event of severe hypoglycemia was registered, while 54 390 hypoglycemic events (<3.9 mmol/L (<70 mg/dL)) were identified from CGM data out of which 11 740 were serious (<3.0 mmol/L (<54 mg/dL)). On average there were 1.5 +/- 0.1 hypoglycemic events per 24 hours out of which 1.2 +/- 0.1 were mild (3.0-3.9 mmol/L) and 0.3 +/- 0.02 serious. Group 1 had a higher frequency of both total and mild hypoglycemic events compared with both groups 2 and 3. However, the frequency of serious hypoglycemic events was similar in all groups. A negative correlation was observed for eHbA1c and total daily and mild hypoglycemic events (r=-0.57 and r=-0.66, respectively, p<0.0001), whereas for serious hypoglycemic events there was only a borderline significance (r=-0.13, p=0.05).

Conclusions: This study shows that hypoglycemic events are a frequent phenomenon in children and adolescents with T1D, occurring regardless of overall metabolic control. Although patients with an HbA1c =48 mmol/mol had a higher frequency of mild hypoglycemic events there was no increase in serious hypoglycemic events.

Introduction Despite the improvements in diabetes management by continuous glucose monitoring (CGM) it is difficult to capture the complexity of CGM data in one metric. We aimed to develop a clinically relevant multidimensional scoring model with the capacity to identify the most alarming CGM episodes and/or patients from a large cohort.Research design and methods Retrospective CGM data from 2017 to 2020 available in electronic medical records were collected from n=613 individuals with type 1 diabetes (total 82 114 days). A scoring model was developed based on three metrics; glycemic variability percentage, low blood glucose index and high blood glucose index. Values for each dimension were normalized to a numeric score between 0-100. To identify the most representative score for an extended time period, multiple ways to combine the mean score of each dimension were evaluated. Correlations of the scoring model with CGM metrics were computed. The scoring model was compared with interpretations of a clinical expert board (CEB).Results The dimension of hypoglycemia must be weighted to be representative, whereas the other two can be represented by their overall mean. The scoring model correlated well with established CGM metrics. Applying a score of >= 80 as the cut-off for identifying time periods with a 'true' target fulfillment (ie, reaching all targets for CGM metrics) resulted in an accuracy of 93.4% and a specificity of 97.1%. The accuracy of the scoring model when compared with the CEB was high for identifying the most alarming CGM curves within each dimension of glucose control (overall 86.5%).Conclusions Our scoring model captures the complexity of CGM data and can identify both the most alarming dimension of glycemia and the individuals in most urgent need of assistance. This could become a valuable tool for population management at diabetes clinics to enable healthcare providers to stratify care to the patients in greatest need of clinical attention.