S1099 Comparison of Serological Responses to SARS-CoV-2 Vaccination in Patients With Inflammatory Bowel Disease Between Smokers and Non-Smokers
Notice bibliographique
Résumé
Introduction: Smoking (SMK) has been associated with reduced IgG antibody responses to two-dose regimens of SARS-COV-2 immunization in the general population1. The impact of SMK on serological responses (SR) to additional SARS-CoV-2 doses is lacking within the IBD population. We aim to describe the impact of SMK status and SR to additional doses (>2) of SARS-CoV-2 vaccination within a cohort of IBD patients. Methods: Patients were recruited from a cohort of SARS-CoV-2 vaccinated adults with a diagnosis of IBD (STOP COVID-19 in IBD)2, and all had a minimum of two doses of the vaccine. SMK status was documented at recruitment; individuals were stratified into “current smokers” (active smokers at baseline) and “non-smokers” (never smoked or former smokers). SR were assessed via concentration of IgG antibodies to the spike protein of SARS-CoV-2 (anti-S) using the Abbott Architect IgG II Quant assay at several timepoints following vaccination: 1–8 weeks after 1st dose vaccination, and both 1–8 weeks and 8+ weeks after 2nd, 3rd, and 4th dose. Sex, age, and medication status at 1st dose vaccination were collected through chart review. Sex and medication class frequencies between SMK groups were compared using Chi-square tests; mean age was compared using a Mann-Whitney U test. SR rates, defined as the proportion of individuals with anti-S titres of ≥50 AU/mL, were compared between SMK groups using two-sample proportion tests. Anti-S concentrations stratified by SMK status were reported as geometric mean titres (GMT) with 95% confidence intervals and compared using Mann Whitney-U tests. Results: There were 23 current smokers, 370 and non-smokers. The mean age for smokers was significantly higher than non-smokers (58.2 vs. 47.1 years; P< 0.001). SR rates were similar between SMK groups across all vaccine doses. Anti-S titres were significantly decreased for smokers compared to non-smokers 8+ weeks after 3rd dose vaccination (1804 AU/mL vs. 4741 AU/mL, P=0.019). When assessing GMTs there were no statistically significant differences between SMK groups for other timepoints (Table 1, Figure 1). Conclusion: In this cohort, active smokers had significantly reduced antibody responses following 3rd dose vaccination compared to non-smokers. This difference was not observed following 4th dose vaccination. Therefore, a 4th dose of SARS-CoV-2 vaccine should be recommended for patients with IBD, particularly active smokers. These data also reinforce the importance of advising SMK cessation within the IBD population. Table 1. - Overall patient characteristics, seroconversion, and GMT with associated 95% CIs stratified by current smoking status and vaccination timepoint and associated univariate analyses Characteristic Time point Current smoker (n = 23) Non-smoker (n = 370) P-value Male sex, n (%) Overall 9 (39.1%) 182 (49.2%) 0.349 Mean age (SD) 58.2 (10.2) 47.1 (14.3) < 0.001 Medication class, n (%) No immunosuppressives 3 (13.0%) 38 (10.3%) – Anti-TNF only 8 (34.8%) 126 (34.1%) 0.756 Immunomodulator only – 10 (2.7%) 0.378 Vedolizumab only 1 (4.4%) 45 (12.2%) 0.253 Ustekinumab only 8 (34.8%) 68 (18.4%) 0.570 Tofacitinib only – 5 (1.3%) 0.532 Combination therapy † 3 (13.0%) 69 (18.6%) 0.473 Corticosteroids‡ – 9 (2.4%) 0.403 IBD type, n (%) Crohn’s disease 21 (91.3%) 257 (69.5%) 0.081 Ulcerative colitis 2 (8.7%) 106 (28.6%) IBD-Unclassified – 7 (1.9%) Seroconversion, n/N (%) Post-1st 8/10 (80.0%) 148/182 (81.3%) 0.917 Post-2nd (1–8 weeks) 14/14 (100.0%) 237/241 (98.3%) 0.627 Post-2nd (8+ weeks) 14/14 (100.0%) 170/178 (95.5%) 0.418 Post-3rd (1–8 weeks) 13/13 (100.0%) 182/183 (99.5%) 0.789 Post-3rd (8+ weeks) 15/15 (100.0%) 239/241 (99.2%) 0.723 Post-4th (1–8 weeks) 10/10 (100.0%) 57/58 (98.3%) 0.676 Post-4th (8+ weeks) 5/5 (100.0%) 58/60 (96.7%) 0.678 GMT (95% CI) Post-1st 168 (61, 459) 291 (225, 377) 0.362 Post-2nd (1–8 weeks) 2429 (985, 5992) 4030 (3320, 4891) 0.112 Post-2nd (8+ weeks) 651 (315, 1347) 1170 (919, 1491) 0.145 Post-3rd (1–8 weeks) 8450 (4240, 16837) 12253 (10180, 14748) 0.248 Post-3rd (8+ weeks) 1804 (729, 4464) 4741 (3838, 5857) 0.019 Post-4th (1–8 weeks) 12003 (3696, 38974) 14869 (10406, 21244) 0.788 Post-4th (8+ weeks) 6370 (796, 50957) 5070 (3267, 7871) 0.749 *Indicates reference group.†Combination therapy refers to any combination of two or more of the following therapies: anti-TNF, immunomodulators, vedolizumab, ustekinumab, and tofacitinib.‡Oral prednisone at any dose or with any other drug class. Figure 1.: Anti-SARS-CoV-2 antibody concentration per vaccine category stratified by smoking status. Black circles represent GMTs while narrow black bars represent bounds of 95% CI associated with each GMT. Solid blue line represents threshold for positive seroconversion (50 AU/mL).
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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) | 0,003 | 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 ».