HomeLifestyleHealth & FitnessThis New Test Can Detect Diabetes And Pre-diabetes Much Earlier - Experts

This New Test Can Detect Diabetes And Pre-diabetes Much Earlier – Experts

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According to 2019 estimates, 463 million adults have diabetes. Additionally, detecting the illness in its early stages is essential for avoiding subsequent significant health issues. But the measurement of blood glucose plays a significant role in diagnosis.

Now, a preliminary study published in the journal BMJ Innovations indicates that an artificial intelligence (AI) system generated from the characteristics of individual heartbeats captured on an ECG (electrocardiogram) can effectively identify diabetes and pre-diabetes.

Researchers say that if the method works in larger studies, it could be used to check for the disease in places with few resources.

According to 2019 estimates, 463 million adults have diabetes. Additionally, detecting the illness in its early stages is essential for avoiding subsequent significant health issues. But the measurement of blood glucose plays a significant role in diagnosis.

The researchers point out that not only is this uncomfortable, but it is also difficult to implement as a mass screening test in low resource situations.

Changes in the structure and function of the cardiovascular system happen early on, even before blood sugar levels change in a noticeable way. These changes can be seen on an ECG heart trace.

In order to predict pre-diabetes and type 2 diabetes in those who are at high risk for the condition, the researchers wanted to examine if machine learning (AI) approaches might be utilized to tap into the screening potential of the ECG.

They used information from people who took part in the Diabetes in Sindhi Families in Nagpur (DISFIN) study. This study looked at the genetic causes of type 2 diabetes and other metabolic traits in Sindhi families in Nagpur, India, who had a high risk of getting the disease.

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

Participants gave specifics about their own and their families’ medical histories, described their usual diets, and underwent a full range of clinical evaluations and blood testing. They were 61 percent female and had an average age of 48.

The diagnostic standards outlined by the American Diabetes Association allowed for the identification of pre-diabetes and diabetes.

Pre-diabetes and type 2 diabetes were both very common, with prevalence rates of roughly 30% and 14%, respectively. Additionally, the prevalence of insulin resistance was considerable (35%) as well as that of other significant concomitant diseases, such as high blood pressure (51%), obesity (about 40%), and disordered blood fats (36%).

Each of the 1262 subjects underwent a conventional 10-second 12-lead ECG cardiac tracing. And for each of the 10,461 single heartbeats that were captured, 100 distinct structural and functional features for each lead were merged to create a predictive algorithm (DiaBeats).

Independent of influencing factors, such as age, gender, and underlying metabolic illnesses, the DiaBeats algorithm quickly identified diabetes and prediabetes with an overall accuracy of 97 percent and a precision of 97 percent.

Important ECG characteristics consistently matched the underlying mechanisms known to underlie the cardiac alterations linked to diabetes and pre-diabetes.

The researchers admit that the individuals in the trial were all at a high risk for diabetes and other metabolic problems, and therefore are not representative of the general population. Those who were taking prescription medications for diabetes, high blood pressure, high cholesterol, etc. found DiaBeats to be a little less accurate.

Also, there was no information about people who became pre-diabetic or diabetic, so it was impossible to figure out how early screening would help.

In their conclusion, they state that their study “provides a relatively inexpensive, non-invasive, and accurate alternative [to current diagnostic methods] which can be used as a gatekeeper to effectively detect diabetes and pre-diabetes early in its course.”

“Nevertheless, adoption of this algorithm into routine practice will need robust validation on external, independent datasets,” they caution.

Image Credit: Getty

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