Idiopathic pulmonary fibrosis is a progressive and often fatal condition that causes scarring of the lungs. Because the symptoms, such as a persistent dry cough and shortness of breath, overlap with many other respiratory illnesses, diagnosing the disease early is notoriously difficult. Currently, there is no cure. A new study attempts to address this diagnostic gap. It examines how genetic factors, specifically polygenic risk scores, relate to the disease when looking at real-world medical data.
Using Real-World Data
Prior research into idiopathic pulmonary fibrosis relied heavily on carefully curated research cohorts. These are small groups of patients selected specifically for clinical trials. While effective for controlled studies, this method often fails to show how the disease behaves in the general population. The recent study broke new ground by utilizing real-world electronic health record data and biobank datasets. The approach provides a more unfiltered view of IPF diagnosis, moving researchers closer to developing practical screening tools for clinical use.
The researchers analyzed the data to see if they could replicate the genetic risk factors known to be associated with IPF. They found that these factors do exist in large-scale datasets, which supports their validity. However, the results revealed a significant difference in the “effect size” compared to previous studies. The strength of the genetic link was diminished in the real-world data than it was in the curated cohorts. Dr. David Zhang, a pulmonary disease medicine specialist at Columbia University Irving Medical Center, explained that the reduction in effect size is a key finding.
When the results from this large-scale EHR and biobank study were compared to other clinical studies, the size of the genetic effect was smaller. Zhang noted that there are two possibilities for this discrepancy. One is that the biobank cohorts differ fundamentally from the groups used in previous research. The other, and potentially more complex, explanation is that the “precision of the phenotype” is not perfect. Clinicians in the field frequently debate the definition of a typical IPF diagnosis because symptoms often overlap with other conditions like chronic bronchitis or asthma.
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If the records do not perfectly capture the disease, the genetic signals can become diluted. The study suggests that diagnosing IPF in a real-world setting is more complicated than diagnosing it in a research lab because the symptoms are not always distinct. This highlights a major hurdle in using genetics for screening: defining exactly who has the disease is harder than it seems.
Despite the challenges, using these large data sets allows clinicians to look at the differences in how polygenic risk scores define an IPF diagnosis compared to real-world diagnoses. More research is still needed to fully understand the capabilities of these scores. Zhang emphasized that this study design could be used to shape future clinical studies and improve practice. “I think that’s probably one of the biggest sorts of lessons that I took away from this paper, which is that this is certainly an area that we have to address before this becomes implemented in a bigger way,” he said.
While the genetic markers exist, the current tools are not sharp enough for widespread clinical use. The study provides a framework for future inquiries, suggesting that refining how IPF is defined and diagnosed is essential before genetic risk scores can be integrated into standard medical care. The goal is to move from identifying risk factors to reliably identifying patients who will actually develop the disease.
Genetic risk scores have already become a standard part of preventive care for certain conditions, such as breast cancer and heart disease. Those fields operate with high accuracy because the biological markers are well-established and the phenotypes are easier to define. Idiopathic pulmonary fibrosis presents a steeper challenge because it is a complex disease where genetics, environment, and unique individual biology interact in ways that are difficult to isolate. The “diminished effect size” observed in this study shows that precision medicine in respiratory health is still in its infancy. It displays that while we can identify genetic risk, the current tools are not yet precise enough to replicate the success seen in other areas of medicine.
