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Record W4411472557 · doi:10.1038/s43856-025-00943-2

Determinants of antibody levels and protection against omicron BQ.1/XBB breakthrough infection

2025· article· en· W4411472557 on OpenAlexaff
Carla Martín Pérez, Anna Ramírez‐Morros, Alfons Jiménez, Marta Vidal, Edwards Pradenas, Diana Barrios, Mar Canyelles, Rocío Rubio, Inocência Cuamba, Luís Izquierdo, Pere Santamaría, Benjamin Trinité, Josep Vidal‐Alaball, Luis M. Molinos‐Albert, Julià Blanco, Ruth Aguilar, Anna Ruiz‐Comellas, Gemma Moncunill, Carlota Dobaño

Bibliographic record

VenueCommunications Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of Calgary
FundersAgencia Estatal de InvestigaciónGeneralitat de CatalunyaAgència de Gestió d'Ajuts Universitaris i de RecercaFundació Privada Daniel Bravo AndreuEuropean CommissionEuropean Social FundCentres de Recerca de Catalunya
KeywordsAntibodyNeutralizing antibodyNeutralizationTiterVaccinationVirologyImmune systemImmunologyBiologyCoronavirus disease 2019 (COVID-19)MedicineDiseaseInfectious disease (medical specialty)Internal medicine

Abstract

fetched live from OpenAlex

The ongoing evolution of SARS-CoV-2, particularly through the emergence of new variants, continues to challenge our understanding of immune protection. While antibody levels correlate with protection against earlier variants such as Alpha and Delta, their relationship with Omicron sub-variants remains unclear. To investigate the role of antibody levels and neutralizing activity in preventing breakthrough infections, we analyzed longitudinal SARS-CoV-2 humoral responses and neutralizing activity against the ancestral virus and major emerging variants in a well-characterized cohort of healthcare workers in Spain (N = 405). We find that antibody levels and neutralization titers are key indicators of protection against SARS-CoV-2, including the more evasive BQ.1 and XBB Omicron variants. Higher IgG and IgA levels are associated with protection over three 6-month follow-up periods sequentially dominated by BA.1, BA.2, BA.5, BQ.1, and XBB Omicron sub-variants, although the strength of the association between antibody levels and protection declines over time. Our findings demonstrate that binding antibody levels and neutralizing responses are valid correlates of protection against more evasive BQ.1 and XBB Omicron variants, although the strength of this association diminishes over time. Additionally, our results underscore the importance of continuous monitoring and updating vaccination strategies to maintain effective protection against emerging SARS-CoV-2 variants. The SARS-CoV-2 virus continues to change, creating new variants that can sometimes avoid the body’s immune defenses. Antibodies are proteins the body makes to recognize and help remove viruses and other foreign substances. We studied whether the amount and quality of antibodies in a person’s blood could predict how likely they are to avoid infection, especially when exposed to newer Omicron sub-variants like BQ.1 and XBB. We followed 405 Spanish healthcare workers, drawing blood every six months, as the dominant variant of SARS-CoV-2 shifted from BA.1 to BA.2, BA.5, BQ.1 and XBB. Higher antibody levels were associated with lower risk of infection, but the strength of this association weakened over time. Our results show regular antibody monitoring can signal when booster or updated vaccines are required to prevent infection by new virus variants, enabling health agencies to optimize vaccination schedules. Pérez et al. evaluate whether antibody levels and neutralization titers correlate with protection against SARS-CoV-2, including Omicron sub-variants BQ.1 and XBB, in a cohort of Spanish healthcare workers. They find an association that wanes over time, highlighting the need for updated vaccination strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.095
GPT teacher head0.441
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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