Comparison of Insulin Dose Adjustments Made by Artificial Intelligence-Based Decision Support Systems and by Physicians in People with Type 1 Diabetes Using Multiple Daily Injections Therapy.

Objective: Artificial intelligence-based decision support systems (DSS) need to provide decisions that are not inferior to those given by experts in the field. Recommended insulin dose adjustments on the same individual data set were compared among multinational physicians, and with recommendations made by automated Endo.Digital DSS (ED-DSS). Research Design and Methods: This was a noninterventional study surveying 20 physicians from multinational academic centers. The survey included 17 data cases of individuals with type 1 diabetes who are treated with multiple daily insulin injections. Participating physicians were asked to recommend insulin dose adjustments based on glucose and insulin data. Insulin dose adjustments recommendations were compared among physicians and with the automated ED-DSS. The primary endpoints were the percentage of comparison points for which there was agreement on the trend of insulin dose adjustments. Results: The proportion of agreement and disagreement in the direction of insulin dose adjustment among physicians was statistically noninferior to the proportion of agreement and disagreement observed between ED-DSS and physicians for basal rate, carbohydrate-to insulin ratio, and correction factor (P < 0.001 and P ≤ 0.004 for all three parameters for agreement and disagreement, respectively). The ED-DSS magnitude of insulin dose change was consistently lower than that proposed by the physicians. Conclusions: Recommendations for insulin dose adjustments made by automatization did not differ significantly from recommendations given by expert physicians regarding the direction of change. These results highlight the potential utilization of ED-DSS as a useful clinical tool to manage insulin titration and dose adjustments.

Adjustment of Insulin Pump Settings in Type 1 Diabetes Management: Advisor Pro Device Compared to Physicians' Recommendations.

AIMS: To compare insulin dose adjustments made by physicians to those made by an artificial intelligence-based decision support system, the Advisor Pro, in people with type 1 diabetes (T1D) using an insulin pump and self-monitoring blood glucose (SMBG). METHODS: This was a multinational, non-interventional study surveying 17 physicians from 11 countries. Each physician was asked to provide insulin dose adjustments for the settings of the pump including basal rate, carbohydrate-to-insulin ratios (CRs), and correction factors (CFs) for 15 data sets of pumps and SMBG of people with T1D (mean age 18.4 ± 4.8 years; eight females; mean glycated hemoglobin 8.2% ± 1.4% [66 ± 11mmol/mol]). The recommendations were compared among the physicians and between the physicians and the Advisor Pro. The study endpoint was the percentage of comparison points for which there was an agreement on the direction of insulin dose adjustments. RESULTS: The percentage (mean ± SD) of agreement among the physicians on the direction of insulin pump dose adjustments was 51.8% ± 9.2%, 54.2% ± 6.4%, and 49.8% ± 11.6% for the basal, CR, and CF, respectively. The automated recommendations of the Advisor Pro on the direction of insulin dose adjustments were comparable )49.5% ± 6.4%, 55.3% ± 8.7%, and 47.6% ± 14.4% for the basal rate, CR, and CF, respectively( and noninferior to those provided by physicians. The mean absolute difference in magnitude of change between physicians was 17.1% ± 13.1%, 14.6% ± 8.4%, and 23.9% ± 18.6% for the basal, CR, and CF, respectively, and comparable to the Advisor Pro 11.7% ± 9.7%, 10.1% ± 4.5%, and 25.5% ± 19.5%, respectively, significant for basal and CR. CONCLUSIONS: Considerable differences in the recommendations for changes in insulin dosing were observed among physicians. Since automated recommendations by the Advisor Pro were similar to those given by physicians, it could be considered a useful tool to manage T1D.

Prevalence of diabetes distress and depression and their association with glycemic control in adolescents with type 1 diabetes in Dubai, United Arab Emirates.

OBJECTIVE: Diabetes distress and depression have been shown to be prevalent among adolescents with type 1 diabetes and screening for these parameters should be a routine part of diabetes care. To assess the prevalence of diabetes distress and depression and their association with glycemic control in a sample of adolescents with type 1 diabetes attending a diabetes center in Dubai, United Arab Emirates. All adolescents aged 13 to 18 years with type 1 diabetes that were seeking treatment at the Dubai Diabetes Center from the period of September 1, 2018 to May 1, 2019. A total of 72 participants completed the study. RESEARCH DESIGN AND METHODS: Adolescents were asked to fill in questionnaires assessing diabetes distress and depression. Multivariate linear regression analysis was used to assess the relationships between the subsets of socio-demographic and clinical characteristics, and the scores of the questionnaires. RESULTS: The mean HbA1c of the study sample was 9.61% [82 mmol/mol] with higher levels found in females as compared with males (p<0.05). Females showed significantly greater levels of distress as compared with males. Although adolescents with HbA1c≥7.5% scored higher for diabetes distress and depression, the difference was not statistically significant to those with an HbA1c of <7.5%. Higher levels of diabetes distress were highly correlated with depressive symptoms, with distress and depression both being significant predictors of one another. CONCLUSIONS: Our results highlight the importance of implementing and sustaining psycho-educational interventions to aid in alleviating diabetes distress and depression in this subgroup of the population.

PP1 phosphatase-binding motif in Reg1 protein of Saccharomyces cerevisiae is required for interaction with both the PP1 phosphatase Glc7 and the Snf1 protein kinase.

In Saccharomyces cerevisiae, Snf1 kinase, the ortholog of the mammalian AMP-activated protein kinase, is activated by an increase in the phosphorylation of the conserved threonine residue in its activation loop. The phosphorylation status of this key site is determined by changes in the rate of dephosphorylation catalyzed by the yeast PP1 phosphatase Glc7 in a complex with the Reg1 protein. Reg1 and many PP1 phosphatase regulatory subunits utilize some variation of the conserved RVxF motif for interaction with PP1. In the Snf1 pathway, the exact role of the Reg1 protein is uncertain since it binds to both the Glc7 phosphatase and to Snf1, the Glc7 substrate. In this study we sought to clarify the role of Reg1 by separating the Snf1- and Glc7-binding functions. We generated a series of Reg1 proteins, some with deletions of conserved domains and one with two amino acid changes in the RVxF motif. The ability of Reg1 to bind Snf1 and Glc7 required the same domains of Reg1. Further, the RVxF motif that is essential for Reg1 binding to Glc7 is also required for binding to Snf1. Our data suggest that the regulation of Snf1 dephosphorylation is imparted through a dynamic competition between the Glc7 phosphatase and the Snf1 kinase for binding to the PP1 regulatory subunit Reg1.