Insulin Glargine in Type 1 Diabetes Mellitus: A Review of Clinical Trials and Real-world Evidence Across Two Decades.

BACKGROUND: Over the past two decades, insulin glargine 100 U/mL (Gla-100) has emerged as the "standard of care" basal insulin for the management of type 1 diabetes mellitus (T1DM). Both formulations, insulin glargine 100 U/mL (Gla-100) and glargine 300 U/mL (Gla- 300) have been extensively studied against various comparator basal insulins across various clinical and real-world studies. In this comprehensive article, we reviewed the evidence on both insulin glargine formulations in T1DM across clinical trials and real-world studies. METHODS: Evidence in T1DM for Gla-100 and Gla-300 since their approvals in 2000 and 2015, respectively, were reviewed. RESULTS: Gla-100 when compared to the second-generation basal insulins, Gla-300 and IDeg-100, demonstrated a comparable risk of overall hypoglycemia, but the risk of nocturnal hypoglycemia was higher with Gla-100. Additional benefits of Gla-300 over Gla-100 include a prolonged (>24- hours) duration of action, a more stable glucose-lowering profile, improved treatment satisfaction, and greater flexibility in the dose administration timing. CONCLUSION: Both glargine formulations are largely comparable to other basal insulins in terms of glucose-lowering properties in T1DM. Further, risk of hypoglycemia is lower with Gla-100 than Neutral Protamine Hagedorn but comparable to insulin detemir.

Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study.

BACKGROUND: Research studies are establishing the use of smartphone sensing to measure mental well-being. Smartphone sensor information captures behavioral patterns, and its analysis helps reveal well-being changes. Depression in diabetes goes highly underdiagnosed and underreported. The comorbidity has been associated with increased mortality and worse clinical outcomes, including poor glycemic control and self-management. Clinical-only intervention has been found to have a very modest effect on diabetes management among people with depression. Smartphone technologies could play a significant role in complementing comorbid care. OBJECTIVE: This study aimed to analyze the association between smartphone-sensing parameters and symptoms of depression and to explore an approach to risk-stratify people with diabetes. METHODS: A cross-sectional observational study (Project SHADO-Analyzing Social and Health Attributes through Daily Digital Observation) was conducted on 47 participants with diabetes. The study's smartphone-sensing app passively collected data regarding activity, mobility, sleep, and communication from each participant. Self-reported symptoms of depression using a validated Patient Health Questionnaire-9 (PHQ-9) were collected once every 2 weeks from all participants. A descriptive analysis was performed to understand the representation of the participants. A univariate analysis was performed on each derived sensing variable to compare behavioral changes between depression states-those with self-reported major depression (PHQ-9>9) and those with none (PHQ-9≤9). A classification predictive modeling, using supervised machine-learning methods, was explored using derived sensing variables as input to construct and compare classifiers that could risk-stratify people with diabetes based on symptoms of depression. RESULTS: A noticeably high prevalence of self-reported depression (30 out of 47 participants, 63%) was found among the participants. Between depression states, a significant difference was found for average activity rates (daytime) between participant-day instances with symptoms of major depression (mean 16.06 [SD 14.90]) and those with none (mean 18.79 [SD 16.72]), P=.005. For average number of people called (calls made and received), a significant difference was found between participant-day instances with symptoms of major depression (mean 5.08 [SD 3.83]) and those with none (mean 8.59 [SD 7.05]), P<.001. These results suggest that participants with diabetes and symptoms of major depression exhibited lower activity through the day and maintained contact with fewer people. Using all the derived sensing variables, the extreme gradient boosting machine-learning classifier provided the best performance with an average cross-validation accuracy of 79.07% (95% CI 74%-84%) and test accuracy of 81.05% to classify symptoms of depression. CONCLUSIONS: Participants with diabetes and self-reported symptoms of major depression were observed to show lower levels of social contact and lower activity levels during the day. Although findings must be reproduced in a broader randomized controlled study, this study shows promise in the use of predictive modeling for early detection of symptoms of depression in people with diabetes using smartphone-sensing information.

Creativity and diabetes education: Essentiality, impact and way forward.

The changing diabetes in children (CDiC) program is a unique program aimed at children suffering from type 1 diabetes. The whole focus of CDiC is to provide comprehensive care including diabetes education. Various innovative and creative diabetes educational materials have been developed, which makes learning fun. Lot of diabetes camps are held at CDiC, focusing on diabetes education, experience sharing and fun activities. CDiC faces many challenges in an effort to cater to the needs of most deserving children with type 1 diabetes mellitus (T1DM) throughout the country, to provide comprehensive care including self-sufficiency, to serve children for as long as possible and to ultimately have better outcomes for all children with T1DM. The CDiC program aims to make the child more positive, secure and hopeful and initiate and strive for comprehensive diabetes care for the economically underprivileged children with T1DM.

Type 1 diabetes: Awareness, management and challenges: Current scenario in India.

Type 1 diabetes mellitus (T1DM) has a wide presence in children and has a high mortality rates. The disease, if left unmanaged, poses various challenges to the patient and healthcare providers, including development of diabetic complications and thus decreasing the life expectancy of the affected child. The challenges of T1DM include awareness of the disease that is very poor among the general public and also in parents of T1DM children along with the health care professionals. The challenge of lack of awareness of T1DM can be met by increasing public awareness programs, conducting workshops for diabetes educators regarding T1DM in children, newsletters, CMEs, online courses, and by structured teaching modules for diabetes educators. Diagnosis of T1DM was a challenge a few decades ago but the situation has improved today with diagnostic tests and facilities, made available even in villages. Investigation facilities and infrastructure, however, are very poor at the primary care level, especially in rural areas. Insulin availability, acceptability, and affordability are also major problems, compounded by the various types of insulin that are available in the market with a varied price range. But effective use of insulin remains a matter of utmost importance.