Back

Kinetics of antibody responses dictate COVID-19 outcome

Lucas, C.; Klein, J.; Sundaram, M.; Lu, F.; Wong, P.; Silva, J.; Mao, T.; Oh, J. E.; Tokuyama, M.; Lu, P.; Venkataraman, A.; Park, A.; Israelow, B.; Wyllie, A.; Vogels, C.; Muenker, M. C.; Casanovas-Massana, A.; Schulz, W.; Zell, J.; Campbell, M.; Fournier, J.; Grubaugh, N.; Dela Cruz, C.; Farhadian, S.; Wisnewski, A.; Omer, S.; Ko, A.; Ring, A.; Iwasaki, A.

2020-12-22 allergy and immunology
10.1101/2020.12.18.20248331 medRxiv
Show abstract

Recent studies have provided insights into innate and adaptive immune dynamics in coronavirus disease 2019 (COVID-19). Yet, the exact feature of antibody responses that governs COVID-19 disease outcomes remain unclear. Here, we analysed humoral immune responses in 209 asymptomatic, mild, moderate and severe COVID-19 patients over time to probe the nature of antibody responses in disease severity and mortality. We observed a correlation between anti-Spike (S) IgG levels, length of hospitalization and clinical parameters associated with worse clinical progression. While high anti-S IgG levels correlated with worse disease severity, such correlation was time-dependent. Deceased patients did not have higher overall humoral response than live discharged patients. However, they mounted a robust, yet delayed response, measured by anti-S, anti-RBD IgG, and neutralizing antibody (NAb) levels, compared to survivors. Delayed seroconversion kinetics correlated with impaired viral control in deceased patients. Finally, while sera from 89% of patients displayed some neutralization capacity during their disease course, NAb generation prior to 14 days of disease onset emerged as a key factor for recovery. These data indicate that COVID-19 mortality does not correlate with the cross-sectional antiviral antibody levels per se, but rather with the delayed kinetics of NAb production.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.