Blood biomarkers plus genomics is better at predicting common disease risk

Blood biomarkers can improve risk prediction for twelve diseases, over using genomics alone, according to research presented at the European Society of Human Genetics conference.

The analysis, involving over 200 biomarkers collected from around half a million participants across three large-scale biobanks, demonstrates that 'metabolomic' risk scores developed using these blood markers are generally stronger predictors of disease risk than genomic information alone. While the results have not yet been peer-reviewed, they represent a potentially substantial improvement in preventative healthcare.

'We found that in all the diseases, both genetics and biomarkers could provide useful information about disease risk, even ten years into the future. And the blood biomarkers provided better prediction in nearly all cases', said Dr Jeffrey Barrett, chief scientific officer of Nightingale Health in Helsinki, Finland, who led the study.

He added, 'For example, the ten percent of individuals with the highest risk of lung cancer based on the biomarkers had four times the risk of an average person, whereas the top ten percent based on genetics had only 1.8 times the risk. And for liver disease, the same numbers are ten times and two times respectively.'

One of the key challenges in preventative medicine is identifying patients at the highest risk of developing disease. Current interest has focused on genetic predictions developed from whole genome studies, known as polygenic risk scores. However, their broader use has faced practical challenges due to difficulties in clinical implementation.

Using these biomarkers, which are easily obtainable from blood samples, the researchers built statistical models to predict individuals' future risks for the disorders considered by the Word Health Organisation as the largest contributors to disability-adjusted life-years in high-income countries. For the diseases where polygenic risk scores were available, the researchers showed that including blood biomarkers alongside this information consistently improved risk prediction.

'It means that it is relatively easy to find the individuals at greatest risk of many diseases and offer them ways to reduce their risk, keeping them healthier and at the same time reducing the financial burden on healthcare systems', said Dr Barrett.

An additional benefit of using biomarkers for risk prediction is that, unlike polygenic risk scores which are fixed from birth, the blood markers were shown to be useful for near-term risk prediction. Across three diseases – myocardial infarction, chronic obstructive pulmonary disease, and type 2 diabetes – patients whose high metabolomic scores were maintained were at higher risk than those who initially displayed high scores, but which decreased over time. This may have occurred as the biomarkers could reflect the underlying biology behind the disease, thereby enabling at-risk patients to be monitored.

Consequently, the metabolomic models showed the greatest predictive potential for diseases where the biomarkers will have been relevant, such as liver cirrhosis and type 2 diabetes, and less predictive of diseases where causation is less well understood, such as depression or Alzheimer's disease.