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. 2025 Jun 20;5(1):243.
doi: 10.1038/s43856-025-00943-2.

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

Carla Martín Pérez  1   2 , Anna Ramírez-Morros  3 , Alfons Jiménez  1   4 , Marta Vidal  1   2 , Edwards Pradenas  5 , Diana Barrios  1 , Mar Canyelles  1   2 , Rocío Rubio  1   2 , Inocencia Cuamba  1   2   6 , Luis Izquierdo  1   2   7 , Pere Santamaria  8   9 , Benjamin Trinité  5 , Josep Vidal-Alaball  3   10   11 , Luis M Molinos-Albert  1   2 , Julià Blanco  5   7   11   12 , Ruth Aguilar  1   2 , Anna Ruiz-Comellas #  3   10   11   13 , Gemma Moncunill #  14   15   16 , Carlota Dobaño #  17   18   19
Affiliations

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

Carla Martín Pérez et al. Commun Med (Lond). .

Abstract

Background: 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.

Methods: 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).

Results: 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.

Conclusions: 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.

Plain language summary

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.

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Conflict of interest statement

Competing interests: The authors declare the following competing interests: P. Santamaria is founder, scientific officer and stock holder of Parvus Therapeutics and receives funding from the company. He also has a consulting agreement with Sanofi. Carlota Dobaño is an Editorial Board Member for Communications Medicine, but was not involved in the editorial review or peer review, nor in the decision to publish this article. The other authors declare no competing interests.

Figures

Fig. 1
Fig. 1. CCC study sample collection and vaccination timepoints, SARS-CoV-2 cases in Catalonia, and main SARS-CoV-2 lineages circulating in Spain over the study period.
The grey plot represents the number of SARS-CoV-2 cases at a given time in Catalonia according to the official data available from IDESCAT (Statistical Institute of Catalonia, https://www.idescat.cat/dades/covid19/?lang=es (accessed on 03 July 2024)). The bottom timeline depicts the main SARS-CoV-2 lineages circulating in Spain at those time intervals according to GISAID. The orange lines represent the three follow-up periods (6 months each) for breakthrough infections in this study. The top grey lines represent the cohort of participants recruited during the first wave of the COVID-19 pandemic (March–April 2020, n = 247) with symptomatic SARS-CoV-2 infection confirmed by rRT-PCR and/or RDT (first-infected), and the cohort recruited in March–April 2021 after completing full primary vaccination (n = 200) without evidence of prior infection (first-vaccinated). T: Timepoint.
Fig. 2
Fig. 2. Association of clinic-demographic factors with IgG antibody levels at T9 (red), T10 (green) and T11 (blue) using multivariable linear regression models in vaccinated individuals.
Beta (β) and CI values have been transformed to a percentage for an easier interpretation. Filled dots indicate p <0.05, while unfilled dots indicate non-significant p-values after adjustment for multiple testing by Benjamini-Hochberg. Error bars represent the 95% confidence intervals (CI). aAdjusted by sexb Adjusted by age. c Adjusted by age and sex. d Adjusted by age, sex, smoking, number of doses and number of infections. e Adjusted by age, sex, comorbidities, first exposure type, time since last exposure, number of doses and number of infections. f Adjusted by age, sex, comorbidities, smoking, and number of infections. g Adjusted by age, sex, comorbidities, smoking, and number of doses. Full-length (Fl), Nucleocapsid (N), Receptor-binding domain (RBD), Spike (S). T: timepoint.
Fig. 3
Fig. 3. Association of clinic-demographic factors with protection against SARS-CoV-2 infection.
a. Association with symptomatic infections were estimated using using multivariable Cox regression models. b. Association with asymptomatic infections were estimated using multivariable logistic regression models. The color of the dots represents the P value, where black represents < 0.001, dark grey < 0.01, light grey < 0.05, and white non-significant. Error bars represent the 95% confidence intervals (CI). a Adjusted by sex. b Adjusted by age. c Adjusted by smoking, age and sex. dAdjusted by age and sex. e Adjusted by age, sex, comorbidities, smoking, and number of doses. f Adjusted by age, sex, comorbidities, smoking, and number of infections. g Adjusted by age, sex, comorbidities, first exposure type, time since last exposure, number of doses and number of infections. T: timepoint.
Fig. 4
Fig. 4. Association of antibody levels with protection against symptomatic breakthrough infections in vaccinated individuals at T8 – T9, T9 – T10 and T10 – T11 periods.
a Forest plot of multivariable Cox Regression models. Models were adjusted by age, comorbidities, hybrid immunity, number of infections, number of doses, sex, smoking, and time since last exposure. The color of the dots represents the P value, where black represents < 0.001, dark grey < 0.01, light grey < 0.05, and white non-significant. Error bars represent the 95% confidence intervals (CI). b, c Kaplan–Meier survival curves of risk of breakthrough infection by tertiles of anti-RBD IgG (b) and IgA levels (c). Tertile T1 corresponds to the lowest antibody levels, whereas T3 denotes the highest. Shaded areas represent the 95% confidence intervals. Full-length (Fl), Nucleocapsid (N), Receptor-binding domain (RBD), Spike (S). Kaplan–Meier curves were compared using the log-rank test. T: tertile.
Fig. 5
Fig. 5. Correlation between antibody levels and plasma neutralization capacity at T9.
Pearson correlation R values and correlation p-values (* p < 0.05, ** p < 0.01, *** p < 0.001, ns) are shown in the heatmap.
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