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. 2021 Nov 23;19(1):309.
doi: 10.1186/s12916-021-02184-1.

Antibody conversion rates to SARS-CoV-2 in saliva from children attending summer schools in Barcelona, Spain

Affiliations

Antibody conversion rates to SARS-CoV-2 in saliva from children attending summer schools in Barcelona, Spain

Carlota Dobaño et al. BMC Med. .

Abstract

Background: Surveillance tools to estimate viral transmission dynamics in young populations are essential to guide recommendations for school opening and management during viral epidemics. Ideally, sensitive techniques are required to detect low viral load exposures among asymptomatic children. We aimed to estimate SARS-CoV-2 infection rates in children and adult populations in a school-like environment during the initial COVID-19 pandemic waves using an antibody-based field-deployable and non-invasive approach.

Methods: Saliva antibody conversion defined as ≥ 4-fold increase in IgM, IgA, and/or IgG levels to five SARS-CoV-2 antigens including spike and nucleocapsid constructs was evaluated in 1509 children and 396 adults by high-throughput Luminex assays in samples collected weekly in 22 summer schools and 2 pre-schools in 27 venues in Barcelona, Spain, from June 29th to July 31st, 2020.

Results: Saliva antibody conversion between two visits over a 5-week period was 3.22% (49/1518) or 2.36% if accounting for potentially cross-reactive antibodies, six times higher than the cumulative infection rate (0.53%) assessed by weekly saliva RT-PCR screening. IgG conversion was higher in adults (2.94%, 11/374) than children (1.31%, 15/1144) (p=0.035), IgG and IgA levels moderately increased with age, and antibodies were higher in females. Most antibody converters increased both IgG and IgA antibodies but some augmented either IgG or IgA, with a faster decay over time for IgA than IgG. Nucleocapsid rather than spike was the main antigen target. Anti-spike antibodies were significantly higher in individuals not reporting symptoms than symptomatic individuals, suggesting a protective role against COVID-19.

Conclusion: Saliva antibody profiling including three isotypes and multiplexing antigens is a useful and user-friendlier tool for screening pediatric populations to detect low viral load exposures among children, particularly while they are not vaccinated and vulnerable to highly contagious variants, and to recommend public health policies during pandemics.

Keywords: Antibody conversion; Children; SARS-CoV-2; Saliva; Schools.

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Conflict of interest statement

The authors declare that they have no competing interests. Professor Carlota Dobaño is Editorial Adviser for BMC Medicine.

Figures

Fig. 1
Fig. 1
Evolution of IgM, IgA, and IgG levels to SARS-CoV-2 antigens between the first and last visit in paired samples. Individuals who decreased or increased IgM (A), IgA (B), or IgG (C) levels per each isotype and antigen are shown in different plots. Gray lines mean < 3-fold-change, blue lines mean 3–4-fold change, and red lines mean ≥4-fold-change. Table 1 indicates the number and proportion of individuals in each category. The levels of antibodies in individuals with only one sample are depicted in Figure S3
Fig. 2
Fig. 2
Antibody levels by age and symptoms. Radar charts representing the median of antibody levels (in log10MFI) in saliva collected in the last or single visits, comparing children (< 15 years old) versus adults (A), and symptomatic (n=43, blue) versus asymptomatic (n=2635, red) children (B). Group medians were compared through Mann-Whitney U test. Statistically significant differences between comparisons are highlighted with asterisks. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001
Fig. 3
Fig. 3
Heatmap analysis of antibody responses per individual. Fold change antibody levels (MFI) with hierarchical clustering (Canberra), including all individuals with paired first and last visit samples, showing decreasers (blue scale), maintainers and increasers (red scale) (A) or including only individuals who increased or decreased antibody levels ≥4-fold between the two visits (B). Antibody levels (log10 MFI) with hierarchical clustering (Euclidean) in all individuals (C)

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