Two new biomarkers could help diagnose atrial fibrillation

7 January 2019 (Last Updated January 7th, 2019 11:18)

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’.

Two new biomarkers could help diagnose atrial fibrillation
Atrial fibrillation is the most common heart rhythm disturbance, affecting around 1.6 million people in the UK. Credit: University of Birmingham.

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.

“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.”

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.

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.