Researchers at the University of Birmingham in the UK have identified two biomarkers that could potentially aid in the detection of atrial fibrillation in people with three specific ‘clinical risks’.
The research was conducted by scientists from the Institute of Cardiovascular Sciences and the Institute of Cancer and Genomic Sciences.
The team found that older male patients with a high body mass index are at a higher risk of atrial fibrillation.
They further observed that the heart condition in these patients can be identified by screening for increased levels of the brain natriuretic peptide (BNP) hormone and fibroblast growth factor-23 protein.
An ECG test is commonly used to screen patients for atrial fibrillation but is considered resource-intensive and burdensome.
The researchers believe that the newly discovered biomarkers can be potentially used in a blood test in community settings for selecting patients who require electrocardiogram (ECG) screening.
How well do you really know your competitors?
Access the most comprehensive Company Profiles on the market, powered by GlobalData. Save hours of research. Gain competitive edge.
Your download email will arrive shortly
Not ready to buy yet? Download a free sample
We are confident about the unique quality of our Company Profiles. However, we want you to make the most beneficial decision for your business, so we offer a free sample that you can download by submitting the below formBy GlobalData
Research senior author Larissa Fabritz said: “The research outcomes were surprising. While BNP is already a known and widely used in clinical practice biomarker, the results around the effectiveness of the FGF-23 biomarker was an unexpected and new finding.
“FGF-23 is only currently used in a research-based environment, but we have shown how its use could be invaluable in a clinical setting.”
During the study, the researchers evaluated 40 common cardiovascular biomarkers in 638 hospital patients recruited between September 2014 and August 2016.
Conventional statistical analysis was then combined with new machine learning techniques to obtain the results.
British Heart Foundation associate medical director professor Metin Avkiran said: “This research has used sophisticated statistical and machine learning methods to analyse patient data and provides encouraging evidence that a combination of easy-to-measure indices may be used to predict atrial fibrillation.
“The study may pave the way towards better detection of people with AF and their targeted treatment with blood-thinning medicines for the prevention of stroke and its devastating consequences.”
The research team is currently working on follow-up assessments of the patients in the study to improve the prevention and treatment of atrial fibrillation.