Accurate prediction of diabetes and pre-diabetes by AI + ECG heart trace

Published On 2022-08-12 06:00 GMT   |   Update On 2022-08-12 06:00 GMT

Structural and functional changes in the cardiovascular system occur early on even before indicative blood glucose changes, and these show up on an ECG heart trace. The researchers, therefore, wanted to see if machine learning (AI) techniques could be used to harness the screening potential of ECG to predict pre-diabetes and type 2 diabetes in people at high risk of the...

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Structural and functional changes in the cardiovascular system occur early on even before indicative blood glucose changes, and these show up on an ECG heart trace.

The researchers, therefore, wanted to see if machine learning (AI) techniques could be used to harness the screening potential of ECG to predict pre-diabetes and type 2 diabetes in people at high risk of the disease.

They drew on participants in the Diabetes in Sindhi Families in Nagpur (DISFIN) study, which looked at the genetic basis of type 2 diabetes and other metabolic traits in Sindhi families at high risk of the disease in Nagpur, India.

Families with at least one known case of type 2 diabetes and living in Nagpur, which has a high density of Sindhi people, were enrolled in the study.

Participants provided details of their personal and family medical histories, and their normal diet, and underwent a full range of blood tests and clinical assessments. Their average age was 48 and 61% of them were women.

Pre-diabetes and diabetes were identified from the diagnostic criteria specified by the American Diabetes Association.

The prevalence of both type 2 diabetes and pre-diabetes was high: around 30% and 14%, respectively. And the prevalence of insulin resistance was also high-35%---as was the prevalence of other influential coexisting conditions—high blood pressure (51%), obesity (around 40%), and disordered blood fats (36%).

A standard 12-lead ECG heart trace lasting 10 seconds was done for each of the 1262 participants included. And 100 unique structural and functional features for each lead were combined for each of the 10,461 single heartbeats recorded to generate a predictive algorithm (diabetes).

Important ECG features consistently matched the known biological triggers underpinning cardiac changes that are typical of diabetes and pre-diabetes.

Ref:

Dr Hemant Kulkarni et. al, Machine-learning algorithm to noninvasively detect diabetes and prediabetes from an electrocardiogram, BMJ Innovations, 9-Aug-2022,10.1136/bmjinnov-2021-000759

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Article Source : BMJ Innovations

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