A team of researchers from the University of Michigan states that identifying multiple biomarkers can provide a more accurate diagnosis for the patients.
According to the study, some new diagnostic tools could help identify those patients who have a high risk of reactivation of the tuberculosis disease. Machine learning and precision medicine are such new tools.
The research was published in Integrative Biology, on 4 Feb 2019. The study was done in collaboration with Mayo Clinic in Rochester, Minnesota. In this study, they have enlisted 50 subjects. People with positive LTBI status were also included.
According to the reports, there are about 2 billion (200 crore) people around the world who suffer from latent tuberculosis out of which 10 percent cases are of activated tuberculosis. However, reactivation from latency can happen anytime and the researchers are still trying to find its reason.
Presently, the Latent tuberculosis infection (LTBI) is tested through two methods, one is a blood test and second is a skin scratch test. These tests can recognize one biomarker but they are unable to differentiate between memory immune response, vaccine-initiated response and non-tuberculous mycobacteria exposure.
There are less than 5 percent chances of correctly recognizing Latent tuberculosis infection (LTBI) through these tests. Tuberculosis can be cured using an antibiotic regimen, but there is a possibility of some potential side effects of antibiotic resistance using this method.
Ryan Bailey, study co-author and professor of Chemistry, said, “A multi-array test can provide a more detailed, disease-specific glimpse into patient’s infection and likely outcome, using a precision medicine approach reveals previously obscured diagnostic signatures and reactivation risk potential.”
Additionally, he said, “This high-level multiplexing, high-assay performance can be cost-effective and scalable. He claimed the device can also detect other diseases like autoimmune and cancer.”
According to the researchers, the new diagnostic tools will save the patients from some of the side effects of this overtreatment. They will also help in identifying patients with the highest risk of reactivation. Also, patients who will benefit from this therapy can be identified by using these tools.
This introduction of multiple biomarker analysis through powerful tools increases the chances of an accurate diagnosis of TB.