Insulin Delivery and Glucose Variability Throughout the Menstrual Cycle on Closed Loop Control for Women with Type 1 Diabetes

The Effects of Resistance Exercise Training on Skeletal Muscle Metabolism and Insulin Resistance Development in Female Rodents with Type 1 Diabetes

The etiology of insulin resistance (IR) development in type 1 diabetes mellitus (T1DM) remains unclear; however, impaired skeletal muscle metabolism may play a role. While IR development has been established in male T1DM rodents, female rodents have yet to be examined in this context. Resistance exercise training (RT) has been shown to improve IR and is associated with a lower risk of hypoglycemia onset in T1DM compared to aerobic exercise. The purpose of this study was to investigate the effects of RT on IR development in female T1DM rodents. Forty Sprague Dawley eight-week-old female rats were divided into four groups: control sedentary (CS; n = 10), control trained (CT; n = 10), T1DM sedentary (DS; n = 10), and T1DM trained (DT; n = 10). Multiple low-dose streptozotocin injections were used to induce T1DM. Blood glucose levels were maintained in the 4-9 mmol/l range with intensive insulin therapy. CT and DT underwent weighted ladder climbing 5 days/week for six weeks. Intravenous glucose tolerance tests (IVGTT) were conducted on all animals following the six-week period. Results demonstrate that DS animals exhibited significantly increased weekly blood glucose measures compared to all groups including DT (p < 0.0001), despite similar insulin dosage levels. This was concomitant with a significant increase in insulin-adjusted area under the curve following IVGTT in DS (p < 0.05), indicative of a reduction in insulin sensitivity. Both DT and DS exhibited greater serum insulin concentrations compared to CT and CS (p < 0.05). DS animals also exhibited significantly greater glycogen content in white gastrocnemius muscle compared to CS and DT (p < 0.05), whereas DT and DS animals exhibited greater p-Akt: Akt ratio in the white vastus lateralis muscle and citrate synthase activity in the red vastus lateralis muscle compared to CS and CT (p < 0.05). These results indicate that female rodents with T1DM develop poor glycemic control and IR which can be attenuated with RT, possibly related to differences in intramyocellular glycogen content.

Menstrual Cycle, Glucose Control and Insulin Sensitivity in Type 1 Diabetes: A Systematic Review

The correlation between the menstrual cycle and glucose control in type 1 diabetes has been the focus of several studies since the 1920s, but a few critical aspects made it particularly challenging to reach conclusive evidence. The aim of this systematic review is to reveal more solid information about the impact of the menstrual cycle on glycaemic outcomes and insulin sensitivity in type 1 diabetes and highlight the less researched areas. The literature was searched by two authors independently using PubMed/MEDLINE, Embase and Scopus (last search on 2 November 2022). The retrieved data did not allow us to perform a meta-analysis. We included 14 studies published between 1990 and 2022, with sample sizes from 4 to 124 patients. We found a wide heterogeneity in the definition of the menstrual cycle phases, glucose metrics, techniques for determining insulin sensitivity, hormonal assessment and other interfering factors considered, with an overall high risk of bias. There is no conclusive evidence, and published data do not allow us to achieve quantitative results. In a subset of patients, a possible worsening of insulin sensitivity and hyperglycaemia in the luteal phase could be observed. From the clinical standpoint, a cautious strategy based on patient-specific patterns can be considered until new, solid evidence is obtained.

Type 1 Diabetes and the Menstrual Cycle: Where/How Does Exercise Fit in?

Regular exercise is associated with substantial health benefits for individuals with type 1 diabetes (T1D). However, the fear of hypoglycemia (low blood glucose) due to activity-induced declines in blood glucose levels acts as a major barrier to partaking in exercise in this population. For females with T1D, hormonal fluctuations during the menstrual cycle and their effects on blood glucose levels can act as an additional barrier. The impact that these cyclic changes may have on blood glucose and insulin needs and the consequent risk of hypoglycemia during or after exercise are still unknown in this population. Therefore, in this narrative review, we gathered existing knowledge about the menstrual cycle in T1D and the effects of different cyclic phases on substrate metabolism and glucose response to exercise in females with T1D to increase knowledge and understanding around exercise in this underrepresented population. This increased knowledge in such an understudied area can help to better inform exercise guidelines for females with T1D. It can also play an important role in eliminating a significant barrier to exercise in this population, which has the potential to increase activity, improve mental health and quality of life, and decrease the risk of diabetes-related complications.

Large-Scale Data Analysis for Glucose Variability Outcomes with Open-Source Automated Insulin Delivery Systems

Open-source automated insulin delivery (AID) technologies use the latest continuous glucose monitors (CGM), insulin pumps, and algorithms to automate insulin delivery for effective diabetes management. Early community-wide adoption of open-source AID, such as OpenAPS, has motivated clinical and research communities to understand and evaluate glucose-related outcomes of such user-driven innovation. Initial OpenAPS studies include retrospective studies assessing high-level outcomes of average glucose levels and HbA1c, without in-depth analysis of glucose variability (GV). The OpenAPS Data Commons dataset, donated to by open-source AID users with insulin-requiring diabetes, is the largest freely available diabetes-related dataset with over 46,070 days’ worth of data and over 10 million CGM data points, alongside insulin dosing and algorithmic decision data. This paper first reviews the development toward the latest open-source AID and the performance of clinically approved GV metrics. We evaluate the GV outcomes using large-scale data analytics for the n = 122 version of the OpenAPS Data Commons. We describe the data cleaning processes, methods for measuring GV, and the results of data analysis based on individual self-reported demographics. Furthermore, we highlight the lessons learned from the GV outcomes and the analysis of a rich and complex diabetes dataset and additional research questions that emerged from this work to guide future research. This paper affirms previous studies’ findings of the efficacy of open-source AID.