By Sola Ogundipe

A simple blood test could revolutionize disease detection, according to scientists who have identified unique protein “signatures” in the blood that might predict the risk of developing over 60 different diseases.

The study, published in Nature Medicine, raises hope for earlier diagnosis, particularly for complex or rare conditions that can take years to identify.

The lead author and director of PHURI at Queen Mary University of London, Professor Claudia Langenberg, explains the significance: “We are extremely excited about the opportunity to identify new markers for screening and diagnosis from the thousands of proteins circulating and now measurable in human blood.

“Traditionally, doctors measure specific proteins for known conditions. For example, we use troponin to detect heart attacks. This research suggests analyzing a broader protein profile might predict a vast array of diseases, even before symptoms appear.”

This paves the way for personalized preventative measures and potentially earlier treatment interventions and has the potential to revolutionize healthcare. Early detection allows for earlier intervention and potentially better treatment outcomes. The ability to predict rare diseases, often taking years to diagnose, could be life-changing for many patients.

In the study, scientists used advanced techniques to pinpoint a signature of between five and 20 of the most important proteins – found in blood plasma – for the prediction of 67 different diseases.

They used data from the UK Biobank Pharma Proteomics Project (UKB-PPP), the largest proteomics (large-scale study of proteins) study to date with measurements from more than 40,000 randomly selected people from the UK Biobank.

According to the findings, protein signatures can predict the onset of 67 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neurone disease, and dilated cardiomyopathy – a heart muscle disease.

The researchers found that models based on protein prediction were better than models based on clinically recorded information.

Prediction based on blood cell counts, cholesterol, kidney function and diabetes tests did not perform as well as the protein prediction models for most examples, the researchers found.