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Endocrine Abstracts (2020) 70 AEP1027 | DOI: 10.1530/endoabs.70.AEP1027

ECE2020 Audio ePoster Presentations Hot topics (including COVID-19) (110 abstracts)

Utilising internet of things and artificial intelligence to enable twin precision treatment for reversal of type 2 Diabetes

Paramesh Shamanna 1 , Suresh Damodharan 2 , Banshi Saboo 3 , Rajeev Chawla 4 , Jahangir Mohammed 5 , Maluk Mohamed 5 & Mohamad Thajudeen 5

1Bangalore Diabetes Centre, Bengaluru, India; 2Ramakrishna Hospital, Coimbatore, India; 3Dia Care Diabetes Care Center, Ahmedabad, India; 4North Delhi Diabetes Center, Delhi, India; 5Twin Health Inc, Bengaluru, India

Introduction: We evaluated Twin Precision Treatment (TPT) approach, a cluster of Internet of Things (IoT) and Artificial Intelligence (AI), validated biosensors and Continuous Glucose Monitoring (CGM) through Ambulatory Glucose Profile (AGP) integrated with machine learning algorithms, enabling physicians to empower patients to reverse diabetes.

Methods: 64 T2DM (19 males, 45 female), registered on Twin Health TM service, managed by standard care of approach with TPT, were monitored for three months for change in glycemic and non-glycemic parameters with changed pharmacotherapeutic approach. Patients achieving HbA1c > 6.5% and ≤ 6.5 were classified as responders (RG) – demonstrating reversal of diabetes and non-responders (NRG), respectively. Twin Platform was used to correlate and analyse billions of data points to understand the drivers of the glucose response to specific foods for each participant. ANOVA was utilised for statistical analysis.

Results: The mean duration diabetes was 7 years (95% CI 0.1 to 30.0), with mean duration in the RG (n = 25) was 4 years (95% 0.1 to 15.0) as compared to the mean duration of diabetes in NRG (n = 39) was 10 years (95% CI 0.1 to 30.0); (P = 0.009). The rates of diabetes reversal were 10%, 75% and 41% in HbA1c tertiles of HbA1c of > 9.5, 8.1–9.5, 6.5–8, respectively. HbA1c reduced by 1.9 (8.8 ± 2.23 to 6.9 ± 1.07) (P < 0.001). There was non-significant, but numerically superior improvement in non glycemic parameters in the RG Vs NRG with body weight reduced by 5.8 kg to 79.7 ± 15.90, SBP by 8.68 mmHg to 122.92 ± 10.06. In RG, C-peptide, LBGI (Low Blood Glucose Index), FPG, and HOMA-IR decreased by 0.71 ng/ml to 1.97, 1.19 to 0.67, 21 mg/dl to 112.4, 4.26 to 3.04, respectively. Glucose Variability of patients was maintained at 17%. 45 out of 57 patients who were on at least one anti-diabetic medication at baseline, were off the medication.

Conclusion: Technology enabled precision nutrition, a combination of macro, micro and biota nutrients, along with serial HbA1c evaluation are key for reversal of diabetes. Physician enabled adoption and integration of the technology, empowers patients to achieve better glycemic control. Our study with limited dataset with short duration validates robustness of TPT approach for a precise and effective metabolic control beyond hyperglycaemia.

Volume 70

22nd European Congress of Endocrinology

05 Sep 2020 - 09 Sep 2020

European Society of Endocrinology 

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