CKD-related impairment in humoral and cellular immune response and potential correlation with long COVID-19: a systematic review
Bibliographic record
Abstract
Introduction Patients with chronic kidney disease (CKD) are at high risk of morbidity and mortality from SARS-CoV-2 infection (COVID-19). However, their immune response to vaccination may vary among individuals. The purpose of this review was to identify characteristics of alterations in humoral and cellular immune responses to the vaccination, and to provide insights into their immune dysfunctions for a better care of acute COVID-19 and prevention of long COVID-19. Methods PubMed, Embase, Scopus, Web of science and Cochrane Central were systematically searched. Eligible publications included clinical studies reporting immune response to COVID-19 vaccination in CKD patients without dialysis or KT, CKD patients undergoing dialysis, as well as CKD patients with KT. Demographics, measurements and results of their humoral and cellular response were evaluated, and the quality of studies were assessed using the Joanna Briggs Institute (JBI) critical appraisal tool and the Newcastle-Ottawa quality assessment scale (NOS). Results A total of 31 eligible studies were identified. A decreased proportion of patients with KT showed anti-S IgG positivity after the 2 nd (67%) and 3 rd (56.6%) dose of vaccination. Similarly, a decreased proportion of these patients presented S-specific T-cell response after the 2 nd (17.7%) and 3 rd (12.9%) dose. Though lower anti-S IgG titers in patients with CKD or on dialysis, as well as T-cell response in patients on dialysis were reported to be lower after the 2 nd or 3 rd dose of vaccination, conflicting results were reported by other studies. Limited studies on correlated change between humoral and cellular immune response revealed a low rate of co-presence of the two in patients with dialysis, though antibody level was correlated with rate of cellular response, while no such correlation was revealed in patients with KT. Conclusion The study provides crucial information on features of humoral and cellular immune responses to COVID-19 vaccinations in CKD patients, and suggests possible directions for strategy of management such as antibody monitoring, additional booster dose or immunomodulatory therapies not only for acute COVID-19 but also for long COVID-19.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".