Humoral and Cellular Immune Responses Against SARS-CoV-2 Following COVID-19 Vaccination in Older Adults: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Evidence on the humoral and cellular immune responses to SARS-CoV-2 following COVID-19 vaccination in older adults is warranted. AIMS: To synthesize and analyze the current evidence on humoral and cellular immune responses to both standard and booster COVID-19 vaccination in individuals aged 60 years and older. METHODS: Clinical trials and observational studies were included. Reviews, case series, letters to the editor, and similar publications were excluded. A selective literature search was conducted in the following databases: PubMed, Scopus, EMBASE, and Web of Science. The risk of bias and methodological quality of the included studies were assessed using the Newcastle-Ottawa Scale (NOS) and the Risk of Bias 2.0 (RoB 2) tool. Statistical analysis was conducted using Stata version 18 and Review Manager version 5.4.1. RESULTS: Thirteen studies were included: eleven observational studies and two randomized clinical trials, evaluating humoral and cellular immune responses in 782 older adults. Messenger RNA vaccines were the most administered, particularly Pfizer-BioNTech (76.9%) and Moderna mRNA-1273 (23%). In most cases, immune responses were assessed after the second dose and booster doses. Most studies (61.5%) reported increased IgG titers specific to the SARS-CoV-2 Spike protein, while 23.1% reported a decrease. Regarding cellular immunity, 46.2% of the studies reported low interferon-gamma (IFN-γ) levels post-vaccination, whereas 38.5% showed increases. These findings highlight the need for tailored vaccination strategies to address emerging variants, particularly in vulnerable populations such as older adults. CONCLUSIONS: In older adults receiving COVID-19 vaccination, humoral immunity tends to increase, whereas cellular responses are frequently diminished, reflecting age-related immunosenescence that may limit the durability and breadth of protection following vaccination in older adults.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".