Researchers from the Luxembourg Institution of Health (LIH) – an EATRIS institute – have used advanced artificial intelligence and machine learning to classify people into distinct risk groups, paving the way for more targeted strategies for the prevention of cardiometabolic diseases at a population level. The findings were published in the Nature Portfolio journal “Scientific Reports”.
The rapid increase in the incidence of cardiometabolic conditions, such as type 2 diabetes and hypertension, urgently calls for better prevention strategies, moving from a one-size-fits-all to a precision approach in the general population. This should take into account the high variability observed in individuals in terms of genetic profiles, inflammation, oxidative stress, insulin resistance and sugar levels, and the ensuing risk of developing cardiometabolic disorders.
“In this context, artificial intelligence and machine-learning approaches can prove to be very valuable to identify subgroups of the population with different cardiometabolic risk profiles”, explains Dr Guy Fagherazzi, Director of DoPH and first author of the publication.
To this end, the research team leveraged a technique known as semi-supervised cluster analysis in the general population and at a large scale. Using the unique set of 29 different cardiometabolic characteristics available from the nationwide population-based ORISCAV-LUX 2 study, they set about classifying the Luxembourgish population in terms of cardiometabolic profiles, driven mainly by body mass index (BMI) − the most frequently used indicator of adiposity and an established risk factor in numerous cardiometabolic disorders − and glycated haemoglobin (HbA1c), a reliable indicator of blood sugar levels also correlated with many cardiometabolic conditions.
The scientists observed that the 1356 participants considered could be grouped into four distinct clusters. Specifically, 729 individuals (i.e. 53.8% of the study population) belonged to Cluster 1, generally characterised by a young age, low blood sugar levels, low BMI, low adiposity, healthy cardiovascular parameters and better lifestyle indicators. “Individuals in this ‘cardiovascularly healthy’ cluster, therefore, reported the lowest cardiovascular age and a 0% average 10-year cardiovascular risk”, says Dr Fagherazzi. The 508 participants in Cluster 2 “Family history – Overweight – High Cholesterol” were mainly overweight, with low HbA1c levels, elevated total and low-density lipoprotein (LDL) cholesterol levels and a high frequency of family history of diabetes and high blood pressure. “The average 10-year cardiovascular risk for Cluster 2 was, therefore, higher than Cluster 1, at 2%”, adds Dr Fagherazzi. 91 participants belonged to Cluster 3 “Severe Obesity – Prediabetes – Inflammation”, characterised by obesity and even higher BMI and HbA1c levels, as well as the most elevated level of inflammation, while the 28 Cluster 4 “Diabetes – Hypertension – Poor Cardiovascular Health” members were mainly overweight or obese, displaying high blood sugar and fat levels and suffering from diabetes and hypertension. They, therefore, reported the highest cardiovascular age, with the average 10-year cardiovascular risk reaching 15%.
This article has been referred from the Luxembourg Institute of Health blog here.