June 11 (UPI) — Researchers have developed a simple nasal brush test than can accurately identify mild to moderate asthma in a cheaper way than pulmonary diagnostics.
The most common tests for asthma are spirometry, which measures how much and how quickly air can blow out of lungs, and methacholine, which is an agent that, when inhaled, causes the airways to spasm and narrow if asthma is present.
But the equipment is not usually available in primary care settings and they can’t differentiate between asthma and other respiratory diseases, researchers say. In addition, people may feel dizzy and faint doing the breathing tests.
The nasal brush — which works the way it sounds — can more easily and comfortably differentiate an asthma diagnosis from other respiratory conditions, including allergic rhinitis, smoking, upper respiratory infection and cystic fibrosis. Researchers at Mount Sinai in New York published their findings Monday on the new test in the journal Scientific Reports.
“Mild to moderate asthma can be difficult to diagnose because symptoms change over time and can be complicated by other respiratory conditions,” Dr. Supinda Bunyavanich, a physician and researcher at the Icahn School of Medicine at Mount Sinai, said in a press release. “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.”
In the United States, asthma affects 8.3 percent of children and adults, according to the Centers for Disease Control and Prevention. The condition can lead to emergency room visits, hospitalizations and reduced activities if not treated with medication.
“We’re hopeful that further studies can help bring this test into primary care settings, transforming the ease and accuracy of diagnosing asthma and our ability as doctors to appropriately treat our patients,” Dunyavanich said.
Nasal brushing was performed on 190 participants, including 66 with well-defined mild to moderate persistent asthma and 124 subjects without asthma based on no personal or family history of the condition.
Data scientists used machine learning algorithms to analyze genetic data — RNA — acquired from the nasal brushes of patients. They identified a 90-gene biomarker for asthma.
Similar genetic biomarker tests are detecting other diseases, including MammaPrint and Oncotype DX, both used for certain types of breast cancer prognosis. The Oncotype DX tool demonstrated that mammography testing is unnecessary to diagnose breast cancer among many breast cancer patients, researchers say.
“One of the most exciting components of this study is demonstrating the power of machine learning when applied to biomedical data,” said Dr. Gaurav Pandey, a Mount Sinai data science researcher. “Collaborations between computational scientists and biomedical researchers and clinicians are advancing medicine at an inspiring pace. We have the power of insights we didn’t have many of in the past and that opens a window to an entirely new world of diagnostic tools and treatments.”