Reducing HbA1c in Type 2 Diabetes Using Digital Twin Technology-Enabled Precision Nutrition: A Retrospective Analysis.

Paramesh ShamannaBanshi SabooSuresh DamodharanJahangir MohammedMaluk MohamedTerrence PoonNathan L KleinmanMohamed Thajudeen

Published in: Diabetes therapy : research, treatment and education of diabetes and related disorders (2020)

The results provide evidence that daily precision nutrition guidance based on CGM, food intake data, and machine learning algorithms can benefit patients with type 2 diabetes. Adherence for 3 months to the TPN Program resulted in patients achieving a 1.9 percentage point decrease in HbA1c, a 6.1% drop in weight, a 56.9% reduction in HOMA-IR, a significant decline in glucose time below range, and, in most patients, the elimination of diabetes medication use.

Keyphrases

type diabetes
machine learning
physical activity
newly diagnosed
end stage renal disease
ejection fraction
glycemic control
cardiovascular disease
prognostic factors
big data
blood pressure
metabolic syndrome
patient reported outcomes
quality improvement
electronic health record