Type 2 diabetes reversal with digital twin technology-enabled precision nutrition and staging of reversal: a retrospective cohort study

Abstract

Background Type 2 diabetes reversal has been viewed in the literature primarily as a dichotomous event (reversed or not reversed), even though this viewpoint may not be optimal for clinicians or patients. This cohort study’s objectives were to define stages of type 2 diabetes reversal and measure changes in reversal stages before and after 90 days of digital twin-enabled precision nutrition therapy. Methods This study defines seven stages of diabetes reversal. The study is a retrospective pre/post comparison of changes in reversal stage, hemoglobin A1c (HbA1c), weight, body mass index (BMI), and other metrics measured before and after precision nutrition therapy. Reversal stages were defined as Stage 0: HbA1c < 5.7% without medication for > 1 year, Stage 1: HbA1c < 5.7% without medication for < 1 year, Stage 2: HbA1c < 6.5% without medication, Stage 3: estimated HbA1c (eA1c) between 5.7 and 6.4% without medication, Stage 4: estimated HbA1c (eA1c) between 5.7 and 6.4% with metformin monotherapy, Stage 5: dual oral therapy, Stage 6: > = 3 medications. Results Reversal stage information was available for 463 patients at baseline and 90 days. At baseline, the proportions of patients in each reversal stage were Stages 1 and 2: 0%, Stage 3: 1%, Stage 4: 8%, Stage 5: 6%, and Stage 6: 85%. After 90 days, the proportions in each reversal stage were Stage 1: 2%, Stage 2: 9%, Stage 3: 32%, Stage 4: 39%, Stage 5: 7%, and Stage 6: 11%, indicating significant progress. Reversal stage progression rates varied by patient subgroup. Conclusions Type 2 diabetes patients reached differing reversal stages during 90 days of precision nutrition therapy. Use of reversal stages may benefit patients during therapy. Trial registration This was a retrospective study that was approved by the Medisys Clinisearch Ethical Review Board (without registration number) in 2019.

Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems

“…Neural network (NN) decisions are unreliable because they lack expressiveness and transparency [ 73 ]. An NN cannot understand or resonate with the content of the data it is trained on and cannot explain its decisions [ 74 , 75 ].…”

“…It contains several features of digital twins, including collecting and analyzing patient-specific online data and generating clinically meaningful outputs [ 70 , 71 , 72 ]. For patients with type 2 diabetes mellitus, data on blood sugar levels, vital signs, lifestyle, and daily nutritional habits are incorporated into a model that generates recommendations regarding dietary modifications and drug prescriptions [ 73 , 74 , 75 ]. The model follows weight reduction, improved glycemic control, and insulin sensitivity, reducing the need for anti-diabetic medications [ 73 , 74 , 75 , 76 ].…”

“…For patients with type 2 diabetes mellitus, data on blood sugar levels, vital signs, lifestyle, and daily nutritional habits are incorporated into a model that generates recommendations regarding dietary modifications and drug prescriptions [ 73 , 74 , 75 ]. The model follows weight reduction, improved glycemic control, and insulin sensitivity, reducing the need for anti-diabetic medications [ 73 , 74 , 75 , 76 ].…”

Human‐based new approach methodologies to accelerate advances in nutrition research

“…The DTs were able to accurately predict the progression of diabetes in over 80% of cases, and they identified several key risk factors that were previously unknown. This information could be used to develop more effective prevention and treatment strategies for diabetes patients (Shamanna et al., 2021).…”

Maternal and fetal health in the digital twin era

“…The current literature for DTs applications in healthcare is relatively new compared to other fields. DTs has been reported in various area of precision medicine including management of asthma ( 5 ), diabetes ( 6 , 7 ), precision cancer care ( 8 ), personalized cardiovascular system model ( 9 ), predictive simulations of treatment response in infectious diseases ( 10 , 11 ). Eventhough, there are many challenges and barriers that must be overcome before a DTs can be used in health care ( 12 ), DTs may have an increasing future use and will become a new platform for personal health management and healthcare services ( 13 ).…”