New diagnostic tools such as machine learning and precision medicine may help identify tuberculosis patients with the highest risk of reactivation of the disease, a study has found.
Researchers from University of Michigan in the US showed that identifying multiple biomarkers can provide a more accurate diagnosis for patients.
"A multi-array test can provide a more detailed, disease specific glimpse into patient's infection and likely outcome," said Ryan Bailey, a professor at University of Michigan.
"Using a precision medicine approach reveals previously obscured diagnostic signatures and reactivation risk potential," said Bailey.
Latent tuberculosis infection affects nearly 2 billion individuals around the world and about 10 per cent of those cases result in active tuberculosis.
The reactivation from latency can happen anytime and the mechanism for it is not well-understood.
Currently, LTBI is tested through a skin scratch test or a blood test that can identify one biomarker but cannot distinguish between memory immune response, vaccine-initiated response, and non-tuberculous mycobacteria exposure.
The possibility of correctly identifying the disease through these tests is less than five per cent.
Tuberculosis is treated with an antibiotic regimen, but it also increases the potential side effects of antibiotic resistance.
The new diagnostic tools in the study will help identify patients with the highest risk of reactivation and who will benefit from therapy, and reduce some of the side effects of overtreatment.
By introducing multiple biomarker assays in blood tests with powerful analysis tools, the chances for correctly diagnosing TB increases dramatically.
The researchers used a precision normalization approach to correct for differences in individual basal immune function that revealed a high- and low-reactivation risk.
Using a precision medicine approach reveals previously obscured diagnostic signatures and reactivation risk potential.
"This high-level multiplexing, high-assay performance can be cost-effective and scalable," said Bailey, adding that it can also be used in the detection of other diseases like autoimmune diseases and cancer.
The study, published in Integrative Biology, was conducted in collaboration with the Mayo Clinic in the US, where a pool of 50 subjects were recruited, including those with positive LTBI status.
Researchers said that a larger cohort has been enrolled in the study and early results indicate that the initially identified biomarker signatures from this study are also robust across the second group.
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