ABSTRACT
Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3-75% to 86-97% and reduces insulin infusion by 14-29%.
ABSTRACT
OBJECTIVE: To derive macronutrient recommendations for remission and prevention of type 2 diabetes (T2D) in Asian Indians using a data-driven optimization approach. RESEARCH DESIGN AND METHODS: Dietary, behavioral, and demographic assessments were performed on 18,090 adults participating in the nationally representative, population-based Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study. Fasting and 2-h postglucose challenge capillary blood glucose and glycosylated hemoglobin (HbA1c) were estimated. With HbA1c as the outcome, a linear regression model was first obtained for various glycemic categories: newly diagnosed diabetes (NDD), prediabetes (PD), and normal glucose tolerance (NGT). Macronutrient recommendations were formulated as a constrained quadratic programming problem (QPP) to compute optimal macronutrient compositions that would reduce the sum of the difference between the estimated HbA1c from the linear regression model and the targets for remission (6.4% for NDD and 5.6% for PD) and prevention of progression in T2D in PD and NGT groups. RESULTS: Four macronutrient recommendations (%E- Energy) emerged for 1) diabetes remission in NDD: carbohydrate, 49-54%; protein, 19-20%; and fat, 21-26%; 2) PD remission to NGT: carbohydrate, 50-56%; protein,18-20%; fat, 21-27%; 3 and 4) prevention of progression to T2D in PD and NGT: carbohydrate, 54-57% and 56-60%; protein, 16-20% and 14-17%, respectively; and fat 20-24% for PD and NGT. CONCLUSIONS: We recommend reduction in carbohydrates (%E) and an increase in protein (%E) for both T2D remission and for prevention of progression to T2D in PD and NGT groups. Our results underline the need for new dietary guidelines that recommend appropriate changes in macronutrient composition for reducing the burden due to diabetes in South Asia.
ABSTRACT
Diabetes technology (DT) has accomplished tremendous progress in the past decades, aiming to convert these technologies as viable treatment options for the benefit of patients with diabetes (PWD). Despite the advances, PWD face multiple challenges with the efficient management of type 1 diabetes. Most of the promising and innovative technological developments are not accessible to a larger proportion of PWD. The slow pace of development and commercialization, overpricing, and lack of peer support are few such factors leading to inequitable access to the innovations in DT. Highly motivated and tech-savvy members of the diabetes community have therefore come up with the #WeAreNotWaiting movement and started developing their own do-it-yourself artificial pancreas systems (DIYAPS) integrating continuous glucose monitoring (CGM), insulin pumps, and smartphone technology to run openly shared algorithms to achieve appreciable glycemic control and quality of life (QoL). These systems use tailor-made interventions to achieve automated insulin delivery (AID) and are not commercialized or regulated. Online social network megatrends such as GitHub, CGM in the Cloud, and Twitter have been providing platforms to share these open source technologies and user experiences. Observational studies, anecdotal evidence, and real-world patient stories revealed significant improvements in time in range (TIR), time in hypoglycemia (TIHypo), HbA1c levels, and QoL after the initiation of DIYAPS. But this unregulated do-it-yourself (DIY) approach is perceived with great circumspection by healthcare professionals (HCP), regulatory bodies, and device manufacturers, making users the ultimate risk-bearers. The use of the regularized CGM and insulin pump with unauthorized algorithms makes them off-label and has been a matter of great concern. Besides these, lack of safety data, funding or insurance coverage, ethical, and legal issues are roadblocks to the unanimous acceptance of these systems among patients with type 1 diabetes (T1D). A multi-agency approach is necessary to evaluate the risks, and to delineate the incumbency and liability of clinicians, regulatory bodies, and manufacturers associated with the use of DIYAPS. Understanding the potential of DIYAPS as the need of the present time, many regional and international agencies have come with strategies to appraise its safety as well as to support education and training on its use. Here we provide a comprehensive description of the DIYAPS-including their origin, existing literature, advantages, and disadvantages that can help the industry leaders, clinicians, and PWD to make the best use of these systems.
