P-2052. Pre-existing Humoral Immunity to Seasonal Coronaviruses and Effect on SARS-CoV-2 Antibody Responses on SARS-CoV-2 Vaccination and Infection
Notice bibliographique
Résumé
Abstract Background High homology and potential cross-reactive immune responses between SARS-CoV-2 and seasonal human coronaviruses (HCoVs) have been described by several studies. However, the role of pre-existing immunity to HCoVs in the outcome of SARS-CoV-2 infection and vaccination is still unclear. Anti-spike IgG titers against human coronavirus (HCoV)-229E, -HKU1, -NL63, and -OC43 by age of participants N=1,675 serum samples were collected from participants aged 2 to 95 years (median 44). anti-spike IgG titers against HCoV-229E, -HKU1, -NL63, and -OC43 were quantified by electrochemiluminescent immunoassay. Each dot represents the anti-HCoV spike IgG titer and age of the participant. Methods Pediatric and adult participants were enrolled. We collected demographic data and vaccination status, as well as serum samples at a single time point between August 2020 and August 2023. Anti-spike IgG titers against SARS-CoV-2 and HCoV-229E, -HKU1, -NL63, and -OC43 were quantified by electrochemiluminescent immunoassay. SARS-CoV-2 infection status was determined by the presence of anti-SARS-CoV-2 nucleocapsid antibodies. Correlations were assessed by two-sided Spearman rank-correlation tests. Correlations between SARS-CoV-2 and hCoV-229E, -HKU1, -NL63, and -OC43 spike IgG titers among SARS-CoV-2 unvaccinated participants To evaluate the effect of immunity against HCoVs on SARS-CoV-2 infection, correlations between SARS-CoV-2 and HCoV-229E, -HKU1, -NL63, and -OC43 spike IgG titers among SARS-CoV-2 unvaccinated participants are shown (N=380). Each dot represents individual participants. Solid and dotted lines respectively indicate linear regression and 95% confidence interval. Correlations were assessed by two-sided Spearman rank-correlation tests. Results Sera were collected from N=1,675 participants of which 5.7% were ≤10 years (age range: 2-95 yrs, median: 44 yrs). HCoV titers rapidly increased in early childhood and the majority of adults had immunity against HCoVs. We first analyzed N=380 sera from SARS-CoV-2 unvaccinated participants. SARS-CoV-2 titers positively correlated with HCoV-OC43, -HKU1, and -NL63 titers. Furthermore, HCoV-OC43 titers were significantly higher in participants post-SARS-CoV-2 infection, determined by the presence of SARS-CoV-2 nucleocapsid antibodies, as compared to non-infected participants (geometric mean, 49,256 vs 29,613; P< 0.01). Next, to evaluate the correlation between HCoV immunity and SARS-CoV-2 titers on vaccination, sera from N=1,059 SARS-CoV-2 non-infected participants were analyzed. Notably, positive correlations between SARS-CoV-2 and HCoV-OC43, and -HKU1 anti-spike IgG titers were demonstrated (r=0.43 and 0.27; both P< 0.0001). Although higher numbers of SARS-CoV-2 vaccinations were associated with more SARS-CoV-2 antibodies, HCoV-OC43 titers did not show a correlation. Correlations between SARS-CoV-2 and hCoV-229E, -HKU1, -NL63, and -OC43 spike IgG titers among SARS-CoV-2 non-infected participants To evaluate the effect of immunity against HCoVs on SARS-CoV-2 vaccination, correlations between SARS-CoV-2 and HCoV-229E, -HKU1, -NL63, and -OC43 spike IgG titers among SARS-CoV-2 non-infected participants, determined by the absence of SARS-CoV-2 nucleocapsid antibodies, are shown (N=1,059). Each dot represents individual participants. Solid and dotted lines respectively indicate linear regression and 95% confidence intervals. Correlations were assessed by two-sided Spearman rank-correlation tests. Conclusion HCoVs immunity was acquired in early childhood. HCoV-OC43 titers were higher in SARS-CoV-2 infected participants compared to non-infected. In SARS-CoV-2 non-infected participants, positive correlations were seen between SARS-CoV-2 and HCoV-OC43 and -HKU1 titers. Our data indicates that immunity against HCoVs may enhance SARS-CoV-2 immune responses on infection and vaccination. Anti-spike IgG titers against SARS-CoV-2 and HCoVs by numbers of previous SARS-CoV-2 vaccines SARS-CoV-2 and HCoV-229E, -HKU1, -NL63, and -OC43 spike IgG titers among SARS-CoV-2 non-infected participants, determined by the absence of SARS-CoV-2 nucleocapsid antibodies, are shown by numbers of previous SARS-CoV-2 vaccination (N=780). Geometric mean titers are listed. Data were analyzed by Kruskal–Wallis test. ** P<0.01. Disclosures Julia Upton, MD, ALK Abello: Advisor/Consultant|ALK Abello: Grant/Research Support|Bausch Health: Advisor/Consultant|DBV Technologies: Grant/Research Support|Pfizer: Advisor/Consultant|Pharming: Advisor/Consultant|Regeneron: Grant/Research Support|Sanofi: Grant/Research Support
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».