S1099 Comparison of Serological Responses to SARS-CoV-2 Vaccination in Patients With Inflammatory Bowel Disease Between Smokers and Non-Smokers
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".