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%.

No 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.

No abstract

No abstract