Celebration of a century of insulin therapy in children with type 1 diabetes

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare ) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic.

Utility and precision evidence of technology in the treatment of type 1 diabetes: a systematic review

BACKGROUND

The greatest change in the treatment of people living with type 1 diabetes in the last decade has been the explosion of technology assisting in all aspects of diabetes therapy, from glucose monitoring to insulin delivery and decision making. As such, the aim of our systematic review was to assess the utility of these technologies as well as identify any precision medicine-directed findings to personalize care.

METHODS

Screening of 835 peer-reviewed articles was followed by systematic review of 70 of them (focusing on randomized trials and extension studies with ≥50 participants from the past 10 years).

RESULTS

We find that novel technologies, ranging from continuous glucose monitoring systems, insulin pumps and decision support tools to the most advanced hybrid closed loop systems, improve important measures like HbA1c, time in range, and glycemic variability, while reducing hypoglycemia risk. Several studies included person-reported outcomes, allowing assessment of the burden or benefit of the technology in the lives of those with type 1 diabetes, demonstrating positive results or, at a minimum, no increase in self-care burden compared with standard care. Important limitations of the trials to date are their small size, the scarcity of pre-planned or powered analyses in sub-populations such as children, racial/ethnic minorities, people with advanced complications, and variations in baseline glycemic levels. In addition, confounders including education with device initiation, concomitant behavioral modifications, and frequent contact with the healthcare team are rarely described in enough detail to assess their impact.

CONCLUSIONS

Our review highlights the potential of technology in the treatment of people living with type 1 diabetes and provides suggestions for optimization of outcomes and areas of further study for precision medicine-directed technology use in type 1 diabetes.

Systematic assessment of streptozotocin-induced diabetic metabolic alterations in rats using metabolomics

PURPOSE

Type 1 diabetes is characterized by elevated blood glucose levels, which negatively impacts multiple organs and tissues throughout the body, and its prevalence is on the rise. Prior reports primarily investigated the serum and urine specimen from diabetic patients. However, only a few studies examined the overall metabolic profile of diabetic animals or patients. The current systemic investigation will benefit the knowledge of STZ-based type 1 diabetes pathogenesis.

METHODS

Male SD rats were arbitrarily separated into control and streptozotocin (STZ)-treated diabetic rats (n = 7). The experimental rats received 50mg/kg STZ intraperitoneal injection daily for 2 consecutive days. Following 6 weeks, metabolites were assessed via gas chromatography-mass spectrometry (GC-MS), and multivariate analysis was employed to screen for differentially expressed (DE) metabolites between the induced diabetic and normal rats.

RESULTS

We identified 18, 30, 6, 24, 34, 27, 27 and 12 DE metabolites in the serum, heart, liver, kidney, cortex, renal lipid, hippocampus, and brown fat tissues of STZ-treated diabetic rats, compared to control rats. Based on our analysis, the largest differences were observed in the amino acids (AAs), B-group vitamin, and purine profiles. Using the metabolic pathway analysis, we screened 13 metabolic pathways related to the STZ-exposed diabetes pathogenesis. These pathways were primarily AA metabolism, followed by organic acids, sugars, and lipid metabolism.

CONCLUSION

Based on our GC-MS analysis, we identified potential metabolic alterations within the STZ-exposed diabetic rats, which may aid in the understanding of diabetes pathogenesis.