Future Research Should Investigate Association Between Long Viral Clearance, Long COVID

Annie Antar, MD, PhD, Johns Hopkins Medicine, discussed the implications of COVID-19 on immunoglobulin A (IgA) antibody response and how it might affect the risk of long COVID.

This transcript has been lightly edited for clarity; captions were auto-generated.

Transcript

Could early detection of slow viral clearance help predict long COVID risk, and how might that shape clinical decision-making?

Yeah, it's a good question. That's something that we've thought about. When I say we, I'm talking about me and Yuka Manabe, MD, who work very closely together. [Manabe] is a world expert on diagnostics, so she thinks a lot about diagnostics and how can we get a test that can help you in the clinic.

Our collaborators—whose names are Nora Pisanic, PhD, MSc, and Chris Heaney, PhD, MS—they're at the School of Public Health at Johns Hopkins [and] we've been working closely with them—they have taken our cohort, not the test-at-home cohort, but a different cohort that we started in early 2020 and looked at secretory IgA antibodies. IgA antibodies should be helping you clear virus and protect your mucosal immune system. We show that for people who have persistent symptoms, you have lower and slower rates of secretory IgA secretion. That's probably a big part of why it's hard to clear the virus, is you're not mounting a good IgA response in the mucosa.

Measuring your IgA antibodies is something that can be done. A test we can clearly do it in the lab, and it is possible to design a test that could be a point of care in the clinic. That's kind of along the lines that we're thinking of. Also testing yourself at home with an antigen test is something that everyone can do nowadays, and so if you're someone that's still having virus in your nose 20 days after infection, then, for example, maybe that's someone that you might want to give Paxlovid if they haven't taken Paxlovid before in this acute infection. But I think we would need more studies to determine whether that's really going to have a benefit or not.