Asthma is thought to impact 8.6 percent of children and 7.4% of adults in the U.S.1 Despite its prevalence, mild-to-moderate asthma remains tricky to diagnose, one of the reasons scientists suspect asthma is underdiagnosed in all ages.2
Experts believe most patients tend to under-report and under-estimate the severity of their symptoms when visiting their primary care doctor. Wheezing, coughing, difficulty breathing, shortness of breath and chest tightness are all common experiences of asthma. In mild-to-moderate asthma, these symptoms occur less than 2 times a week.
So, when their doctor asks them about recent symptoms, performs a physical examination, and reviews their medical history, the patient is rarely experiencing symptoms at that moment.
Complicating this scenario, spirometry, the pulmonary function test that is the current definitive test to confirm asthma, is rarely performed in a primary care setting since it requires expertise and special equipment to administer. Spirometry results can also come back as normal even when a patient has a history of asthma symptoms.
With all these difficulties in accurately identifying asthma, it’s no surprise that recent studies estimate up to 30% of asthma diagnoses are incorrect.3,4
That’s why a group of researchers at the Icahn School of Medicine at Mount Sinai in New York sought to create a simple, yet accurate test that would perform well in a typical busy doctor’s office.
Their research, published recently in Nature5 found a genetic biomarker that can be easily tested to diagnose asthma far more accurately than current diagnostic methods.
How the test worked
The researchers brushed inside the noses of 150 people with and without asthma. RNA gathered from these nasal brushes (similar to a tiny mascara brush) was sequenced. Using the genetic data from these samples, the researchers harnessed machine learning to identify a biomarker that includes 90 genes that seem to play a role in asthma.
They then tested this biomarker on eight independent groups and found that the biomarker was highly accurate in identifying people who did or did not have asthma. In one test group of 40 subjects, the biomarker had 99.4% accuracy in distinguishing individuals with asthma from those without asthma.
Data analysis also showed that the nasal brush test correctly differentiated asthma from other respiratory conditions such as upper respiratory infection, allergic rhinitis, smoking or cystic fibrosis. The results were consistent across age, race and gender.
“Mild to moderate asthma can be difficult to diagnose because symptoms change over time and can be complicated by other respiratory conditions,” said Dr. Supinda Bunyavanich, physician and researcher at the Icahn School of Medicine told Science Daily.5 “Our nasal brush test takes seconds to collect — for time-strapped clinicians, particularly primary care providers at the frontlines of asthma diagnosis, this could greatly improve patient outcomes through early and accurate diagnosis.”
Since undiagnosed asthma leads to missed school, work, and other activities, emergency room visits, and hospitalizations, a simple, nasal-swab test could prove a major milestone in the improved diagnosis and treatment of this respiratory illness.
Next, the Mount Sinai team hopes to test a larger population of patients to further validate the accuracy of their asthma biomarker and the ease of using this simpler diagnostic method.
If you suspect asthma
If you think you or a family member may have mild-to-moderate asthma, talk to your doctor. Be ready to answer questions such as:
Are you experiencing shortness of breath, trouble breathing, chest tightness or wheezing? How often?
Do the symptoms happen at a particular time of day, during exercise, after a weather event, or when you’re around certain animals?
Do your symptoms wake you up at night?
Do you avoid certain activities because of your symptoms?
Do you or any close family members have allergies, eczema, chronic colds or asthma?
One reason so many people are misdiagnosed is that there is no standard test for asthma.
Current Asthma Prevalence Percents by Age, Sex, and Race/Ethnicity, United States. Asthma Surveillance Data. National Health Interview Survey, National Center for Health Statistics, Centers for Disease Control and Prevention. www.cdc.gov/asthma/asthmadata. Accessed 6/17/2018.
Yeatts, K., Shy, C., Sotir, M., et al. Health consequences for children with undiagnosed asthma-like symptoms. Archives of pediatrics & adolescent medicine 157, 540–544, https://doi.org/10.1001/archpedi.157.6.540 (2003).
Aaron SD, Vandemheen KL, Boulet LP, et al; Canadian Respiratory Clinical Research Consortium. Overdiagnosis of asthma in obese and nonobese adults. CMAJ. 2008;179:1121-1131. http://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19015563/
National Institute for Health and Care Excellence. Draft guideline to improve asthma diagnosis. Press release, 1/28/15. Accessed 2/7/15 at https://www.nice.org.uk/news/article/draft-guideline-to-improve-asthma-diagnosis.Pandev, G, Pandey, OP, et al. A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data. (2018)8:8826. doi.org/10.1038/s41598-018-27189-4.
The Mount Sinai Hospital / Mount Sinai School of Medicine. "Asthma diagnosed with nasal brush test: The team developed a biomarker of asthma using RNA sequencing and machine learning." ScienceDaily. ScienceDaily, 11 June 2018.