Background: Serum anti-glycopeptidolipid (GPL) core immunoglobuin A (IgA) antibody test has been proposed as a diagnostic tool for Mycobacterium avium complex pulmonary diseases. Cross-reactivity with other non-tuberculous mycobacteria (NTM), including M. abscessus, indicates that it may have a role as a broader screening test for NTM pulmonary disease (NTM-PD). NTM-PD is believed to be underdiagnosed in patients with bronchiectasis.
Research question: Can the serum anti-GPL core IgA antibody test be used to screen for NTM-PD in bronchiectasis?
Study design and methods: Patients from the prospective European Bronchiectasis Registry (EMBARC-BRIDGE; NCT03791086) were enrolled. Patients from the United Kingdom, Italy, Spain, Belgium, the Netherlands, and Germany were included. A control cohort of patients without any underlying lung disease was also recruited. The levels of serum IgA antibodies against the GPL core were measured using an enzyme immunoassay kit, and receiver operating characteristics curve analysis was conducted to evaluate the accuracy of the antibody level in screening for NTM-PD.
Results: 282 patients were enrolled (151 [53.6%] female, median age 68 years). Median (quartile 1-3) anti-GPL-core IgA antibody levels were 0.2 (0.1-0.3) U/mL in patients without NTM isolation and NTM-PD (n=238), 0.3 (0.2-0.4) U/mL in NTM isolation that were incompatible with the diagnosis of NTM-PD (n=18) and 1.5 (0.4-6.2) U/mL in NTM-PD (n=26) (P=0.0001). Antibody levels showed excellent accuracy in identifying patients with NTM-PD (area under the curve 0.886, 95% CI 0.800-0.973) in bronchiectasis cohort and also showed excellent discrimination of patients with NTM-PD from those with NTM isolation who did not meet the diagnostic criteria for NTM-PD (0.816, 95% CI 0.687-0.945).
Interpretation: The anti-GPL-core IgA antibody demonstrated excellent efficacy in screening for NTM-PD in a large bronchiectasis cohort.
Keywords: anti-glycopeptidolipid-core IgA test; bronchiectasis; nontuberculous mycobacteria; serodiagnosis.
Copyright © 2024. Published by Elsevier Inc.