Peter G. Jacobs

Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH’s Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article’s cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH’s GSS principles.

Background: While automated insulin delivery (AIDs) systems have significantly improved glycemic control for individuals with type 1 diabetes (T1D), there remains a need for identifying and acting upon complex physiologic and behavioral patterns which consistently lead to hypo- and hyperglycemia. Prior methods have lacked the ability to automatically identify and extract patterns across mixed-type multidimensional data (eg, insulin, glucose, activity) without instilling bias from stipulations on time-lagged coupling, pattern length, or pre-defining patterns. Methods: We introduce a new pattern-detection technique—Block-based Recurrence Quantification Analysis (BlockRQA)—and preliminary results using BlockRQA in an AID on both in silico and in an outpatient feasibility study. We first introduce the BlockRQA algorithm, which extends Recurrence Quantification Analysis for use in categorical and continuous time-series data, while maintaining interpretable patterns in the domain of interest, in contrast to prior state-of-the-art approaches which require embeddings. Next, we demonstrate the feasibility of utilizing these patterns and BlockRQA with an existing AID system (BlockRQA+AID) to identify and dose for patterns leading to hyperglycemia in individuals with T1D. Results: We demonstrate how BlockRQA+AID can improve glucose outcomes in patterns leading to hyperglycemia in silico. And we show real-world results using BlockRQA+AID to reduce hyperglycemic events (>250 mg/dL) via an interim safety analysis of a small outpatient pilot study. For all cases, we show BlockRQA efficiently identifies, aggregates, and scores behavioral patterns which can be targeted for clinical intervention. Conclusions: The BlockRQA is a powerful pattern recognition tool that may be used to identify glucose outcome patterns to guide AID dosing.

OBJECTIVE: Nocturnal hypoglycemia is challenging for individuals with type 1 diabetes, particularly those who are physically active or using multiple daily injections (MDIs). We hypothesized that adding bedtime decision support to usual care with continuous glucose monitoring (CGM)–augmented MDI therapy could reduce nocturnal hypoglycemia. RESEARCH DESIGN AND METHODS: We developed the DailyDose Smart Snack (DDSS) app, which features CGM and physical activity logging and delivers personalized bedtime snack recommendations to help prevent nocturnal hypoglycemia. Recommendations are based on the probability and timing of low-glucose events forecasted by an evidential neural network. We conducted a randomized crossover trial comparing DDSS with CGM-augmented MDI for 4 weeks each under free-living conditions to evaluate the feasibility and effect of DDSS in reducing nocturnal hypoglycemia. RESULTS: Twenty participants completed the study (mean ± SD age 39 ± 15 years; 10 women; HbA1c 7.1% ± 1.0% [54 ± 11 mmol/mol]). There was no difference between arms in the proportion of nights with low-glucose events <70 mg/dL lasting ≥10 min (control 26.0% ± 14.1% vs. DDSS 23.6% ± 15.6%; odds ratio [OR] 0.83; P = 0.207) or in overnight CGM metrics. A post hoc analysis showed that the proportion of nights with low-glucose events <54 mg/dL lasting ≥10 min was significantly reduced in the DDSS arm by 3.5% (11.9% ± 12.9% vs. 8.3% ± 8.8%; OR 0.64; P = 0.040). CONCLUSIONS: DDSS did not significantly reduce the proportion of nights with low-glucose events <70 mg/dL lasting ≥10 min but reduced nocturnal low glucose <54 mg/dL without compromising other glycemic metrics.

Artificial pancreas (AP) systems, also called automated insulin delivery systems, have improved the time in range of glucose levels, reduced the daily burden of the user for glucose regulation, and improved their quality of life. Several commercially available AP systems operate in hybrid closed-loop mode that requires manual information from the user for meals and exercise. This article summarizes the progress on mathematical models of glucose-insulin dynamics, continuous glucose monitoring systems, and insulin pumps that form the building blocks of AP systems, the shift from animal studies to in silico clinical trials that accelerated the rate of progress in AP technologies and the efforts for developing the next-generation AP systems, and the fully automated AP that eliminates manual inputs and mitigates the effects of disturbances to glucose homeostasis—meals, physical activities, acute stress, and variations in sleep characteristics. A section is devoted to discuss the unique glycemic management challenges faced by women with diabetes across the lifespan (menstrual cycle, menopause, pregnancy) and summarize progress made to reduce their impact on glycemic management.

Background: Since the discovery of the life-saving hormone insulin in 1921 by Dr Frederick Banting in 1921, there have been many critical discoveries and technical breakthroughs that have enabled people living with type 1 diabetes (T1D) to live longer, healthier lives. The development of insulin pumps, continuous glucose monitoring (CGM) systems, and automated insulin delivery (AID) systems have enabled people living with T1D to safely manage their glucose, reduce their HbA1c, and improve their overall health and quality of life. Nevertheless, AID systems are not yet designed for all people with T1D, and they perform best during the overnight period when meals and exercise are not occurring. AID systems are not fully automated in that they require the person using the system to announce meals and exercise to the system to avoid dangerous hyper- or hypoglycemia, respectively. Methods: In this review, which is one of a collection of manuscripts to commemorate the 75th anniversary of the National Institute for Diabetes and Digestive and Kidney Diseases, we celebrate the commercialization of the AID and discuss the major challenges and research gaps that remain to be solved to enable single- and multi-hormone AID systems to more fully support glucose management in people living with T1D. Results: More research is required to design and evaluate more intelligent AID systems that do not require accurate carbohydrate estimations or announcements for meals and exercise. Current AID systems are also not designed to be used by older adults or pregnant people. Results are presented on new AID systems that can automatically respond to meals and exercise. Results are also presented on evaluations of AID systems in older adults and pregnant people. Conclusions: Next-generation AID systems will need to support all people, including older adults, people during pregnancy, athletes, and people who may be too busy to announce carbohydrates or exercise to the system. Solutions are now becoming available that will enable AID systems to support a broader range of people living with T1D by leveraging the latest technologies in artificial intelligence and adaptive control.

Since the discovery of the life-saving hormone insulin in 1921 by Dr. Frederick Banting in 1921, there have been many critical discoveries and technical breakthroughs that have enabled people living with type 1 diabetes (T1D) to live longer, healthier lives. The development of insulin pumps, continuous glucose monitoring systems, and automated insulin delivery (AID) systems has enabled people living with T1D to safely manage their glucose, reduce their HbA1c, and improve their overall health and quality of life. Nevertheless, AID systems are not yet designed for all people with T1D, and they perform best during the overnight period when meals and exercise are not occurring. AID systems are not fully automated in that they require the person using the system to announce meals and exercise to the system to avoid dangerous hyper- or hypoglycemia, respectively. In this review, which is one of a collection of articles to commemorate the 75th anniversary of the National Institute for Diabetes and Digestive and Kidney Diseases, we celebrate the commercialization of the AID and discuss the major challenges and research gaps that remain to be solved to enable single- and multihormone AID systems to more fully support glucose management in people living with T1D. More research is required to design and evaluate more intelligent AID systems that do not require accurate carbohydrate estimations or announcements for meals and exercise. Current AID systems are also not designed to be used by older adults or pregnant people. Results are presented on new AID systems that can automatically respond to meals and exercise. Results are also presented on evaluations of AID systems in older adults and pregnant people. Next-generation AID systems will need to support all people, including older adults, people during pregnancy, athletes, and people who may be too busy to announce carbohydrates or exercise to the system. Solutions are now becoming available that will enable AID systems to support a broader range of people living with T1D by leveraging the latest technologies in artificial intelligence and adaptive control.

Artificial pancreas (AP) systems, also called automated insulin delivery systems, have improved the time in range of glucose levels, reduced the daily burden of the user for glucose regulation, and improved their quality of life. Several commercially available AP systems operate in hybrid closed-loop mode that requires manual information from the user for meals and exercise. This article summarizes the progress on mathematical models of glucose-insulin dynamics, continuous glucose monitoring systems, and insulin pumps that form the building blocks of AP systems, the shift from animal studies to in silico clinical trials that accelerated the rate of progress in AP technologies and the efforts for developing the next-generation AP systems, and the fully automated AP that eliminates manual inputs and mitigates the effects of disturbances to glucose homeostasis—meals, physical activities, acute stress, and variations in sleep characteristics. A section is devoted to discuss the unique glycemic management challenges faced by women with diabetes across the lifespan (menstrual cycle, menopause, pregnancy) and summarize progress made to reduce their impact on glycemic management.