Two biomarkers can aid in atrial fibrillation screening, researchers find
A team of researchers at the University of Birmingham has identified a pair of biomarkers which it says could aid in quicker diagnosis of atrial fibrillation (AF) in specific patient populations.
Affecting around 1.6 million in the UK alone, AF is the most common form of arrhythmia of the heart. “People with atrial fibrillation are much more likely to develop blood clots and suffer from strokes,” said first author Dr Winnie Chua. “To avoid strokes it is important for them to take anticoagulant drugs to prevent blood clotting. However, atrial fibrillation is often only diagnosed after a patient has suffered a stroke. Therefore it is important that patients at risk are screened so that they can begin taking anticoagulants to prevent potentially life-threatening complications.”
The team, based at the Institute of Cardiovascular Sciences and the Institute of Cancer and Genomic Sciences at the University’s College of Medical and Dental Sciences, investigated 40 common cardiovascular biomarkers in a study sample of 638 participants. It was found that the hormone brain natriuretic peptide (BNP) and the protein fibroblast growth factor-23 (FGF-23) could be used to screen for AF in patients with three “clinical risks”: old age, being male, and having a high body mass index (BMI).
“An electrocardiogram (ECG), a test which measures the electrical activity of your heart to show whether or not it is working normally, is usually used to screen patients for atrial fibrillation,” commented joint first author Yanish Purmah. “ECG screening is resource-intensive and burdensome for patients therefore it is important that the right patients are selected for this type of screening. The biomarkers we have identified have the potential to be used in a blood test in community settings such as in GP practices to simplify patient selection for ECG screening.”
Professor Metin Avkiran, Associate Medical Director at the British Heart Foundation, added: “Atrial fibrillation increases the risk of stroke, a serious condition that causes over 36,000 deaths in the UK each year, but is often detected too late. 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.”