Researchers from the University of Leicester have found a smarter way to include more people in studies of lung health—without sacrificing data quality.
Traditionally, lung function tests must meet strict clinical quality checks, which often exclude more than one-third of participants. While these rules are essential for patient care in hospitals, they can be overly rigid for large-scale research studies, limiting what scientists can learn.
The new method, published in the International Journal of Population Data Science (IJPDS) uses genetic risk scores to guide quality checks on lung function tests. A genetic risk score is like a health “fingerprint” based on your DNA. It combines tiny genetic markers linked to lung health into a single score. By comparing these scores with lung function test results, researchers can spot when a test is likely to be accurate enough for research purposes, even if it doesn’t meet the strictest technical standards.
When applied to data from the UK Biobank, this approach allowed researchers to include nearly 30% more participants compared to the strictest current guidelines. More participants mean stronger studies and better insights into how our lungs work and what affects them.
This breakthrough could reshape how big health studies are done, making them more inclusive and efficient. In the long run, it may help scientists uncover new ways to prevent and treat lung diseases.
Lead author Jing Chen explained, “Our approach uses genetic clues to make research for lung health more inclusive without compromising quality. By leveraging genetic risk scores, we can identify reliable test results for large-scale health studies even when they don’t meet the strictest clinical standards. This means more participants, stronger studies, and ultimately better insights into lung disease prevention and treatment.”