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Precision protection: antibody testing offers insights into predicting SARS-CoV-2 infection risk in immunocompromised patients

In a recent article published in The Lancet, researchers used antibody testing to predict the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in immunocompromised people.

Study: Predicting COVID-19 infection risk in people who are immunocompromised by antibody testing. Image Credit: tommaso79/


Like other disease groups, blood cancer patients mount highly heterogeneous immune responses to coronavirus disease 2019 (COVID-19) vaccination, which raises the risk of severe illness.

Yet, studies have not defined a direct association between antibody titers post-vaccination and SARS-CoV-2 infection risk. When researchers identify a laboratory correlate of increased SARS-CoV-2 infection risk, clinicians and policymakers could target specific COVID-19 treatments toward people at the highest risk.

About the study

In the present study, researchers enrolled 592 participants with malignant lymphoma, a type of blood cancer, between March 11, 2021, and September 9, 2022, for longitudinal peripheral blood sampling before and post-COVID-19 vaccination from nine hospitals in England. They also collected each participant's demographic and clinical data. 

The researchers assessed antibody and cellular responses to the COVID-19 vaccines. To this end, first, they sampled the peripheral blood of all participants at median three, five, and six weeks after two-, three-, and four doses of vaccine. Next, they quantified their anti-SARS-CoV-2 spike (S) immunoglobulin G (IgGs).

They also assessed pseudovirus neutralization and T-cell interferon-gamma (IFNγ) response to S peptides of the ancestral SARS-CoV-2 Wuhan strain. Further, they examined the association between COVID-19-related hospital admission and antibody and cellular responses. 

The team evaluated the risk factors associated with breakthrough SARS-CoV-2 infection. They conducted a multivariable logistic regression analysis, which they repeated to assess whether these risks changed with the number of vaccine doses accounting for the time of the breakthrough infection.

Furthermore, the team performed receiver operating curve analyses to establish the optimal antibody threshold that best discriminated between participants with and without breakthrough infection.

Finally, the researchers used questionnaires to determine the social behavior of participants social behavior before the breakthrough infection. 


In total, 524 (89%) participants met the eligibility criteria for analysis, of which only 75%, i.e., 396 of 524 participants, responded to a follow-up questionnaire that helped the researchers measure COVID-19 cases and their preceding social behaviors. Of these 396 people, 334, 315, and 266 had received two, three, and four vaccine doses, respectively. 

Plasma was available for analysis in 273 (82%) of 334 participants after the second vaccine dose, 237 (75%) of 315 participants after the third vaccine dose, and 177 (67%) of 266 participants after the fourth vaccine dose at the time of data cutoff.

Among 334 two vaccine dose recipients, 20 developed a breakthrough infection, i.e., a SARS-CoV-2 infection two weeks or more after vaccination confirmed by a rapid antigen test or reverse transcription-polymerase chain reaction (RT-PCR).

Other 40 and 36 individuals, 13%, and 14% of 315 and 266 three and four dose recipients also developed a breakthrough infection. The median interval between the second, third, and fourth vaccine doses and breakthrough infections was 22.2, 12.5, and 11 weeks, respectively.

The dominant infecting variant for breakthrough infection after the second vaccine dose were the Alpha and Delta strains, B.1.1.7 and B.1.617.2; Omicron sublineages B.1.1.529, BA.1\BA.2 caused breakthrough infections after the third and fourth vaccine doses, which have increased transmissibility, possibly due to a shorter incubation period.

Despite three or four vaccine doses, 12 of 96 participants sought hospital admission after breakthrough infections. Five percent with breakthrough infections also needed mechanical oxygen supplementation, but none sought admission to the intensive care unit, and none died due to COVID-19. On average, an inpatient stay in the hospital lasted two days. 

Even after three and four vaccine doses, 13% of participants with breakthrough infections sought hospital admission. Five of nine participants under treatment in the hospital did not exhibit T-cell responses versus 16% of 45 participants who were not, highlighting the significance of risk stratification by cellular testing. 

Per the current understanding of antibody affinity maturation, increased antibody avidity over time with repeated vaccinations facilitates the generation of higher-quality antibodies.

The optimal antibody titer predicting breakthrough and no breakthrough infection is 20-fold lower after four versus three vaccine doses, which means that lower antibody titers are needed to confer protection against breakthrough infection with increasing vaccine doses. 

The authors noted no marked variations across the type of contact and duration with SARS-CoV-2-infected people or COVID-19 mitigation measures between participants with and without breakthrough infection.

Likewise, their anti-spike IgG levels were not markedly different after two vaccine doses with a geomean of 80·4 binding antibody units (BAU)/mL vs. 38·1 BAU/mL. 

However, anti-spike IgG levels changed for participants who had breakthrough infection more than those who did not after three and four vaccine doses (50.2 BAU/mL vs. 141 BAU/mL and 30.9 BAU/ mL vs. 305.7 BAU/mL).

Furthermore, there were no differences in cellular responses between participants with and without breakthrough infections. Irrespective of the time of breakthrough infection, cancer treatment, more vaccine doses, anti-S IgG levels, and pseudoneutralisation antibody titers reduced the SARS-CoV-2 infection risk.

The factors associated with breakthrough infection after the third and fourth doses of vaccines were anti-S IgG levels, with the odds ratio (ORs) after the third and fourth doses of 1.59 and 2.26, and 95% CI, respectively.

Likewise, pseudo-neutralization titers after the third and fourth doses had ORs of 2·41 and 3·77, respectively. Accordingly, after three and four vaccine doses, the breakthrough infection risk was 1·6 and 2.3 times less for every 10-fold higher anti-S IgG titers. 

The antibody cutoff values post-three and four vaccine doses were 820 and 41 BAU/mL, with AUC of 0.61 and 0.70 and sensitivity of 46.6% and 73.5%, respectively.

After four vaccine doses, anti-S IgG levels of more than 41 BAU/mL lowered the risk of a breakthrough infection by 13.1-fold. However, anti-S IgG levels of more than 820 BAU/mL after receipt of three vaccine doses reduced this risk by 8.9-fold, as assessed using thresholds in the multivariable analysis.

Among hospital-admitted patients, anti-S IgG levels were lower than those who did not require hospital admission after breakthrough infection, with a geomean of 5·5 BAU/mL vs. 58·9 BAU/mL. 

Furthermore, a higher proportion of hospitalized patients had undetectable antibodies and cellular response versus those who did not [44% vs. 4%].


According to the authors, this study is the first to establish a relationship between antibody and T-cell responses and clinical manifestations of COVID-19 in immunocompromised people. 

It established that finding a clinically relevant correlate of SARS-CoV-2 infection risk is possible without variant analysis. The researchers did not assess mucosal antibody responses correlated to protection against SARS-CoV-2 infection.

Moreover, they showed that the defined sensitivity and specificity of the antibody thresholds were relatively low. However, these thresholds need validation in different diseased populations as they might vary depending on the circulating viral variant and with successive vaccinations. Since the risk of breakthrough infection decreases with increasing antibody levels, a range of thresholds might serve as an alternative to specific antibody threshold cutoffs.

Nevertheless, the antibody thresholds provided in this study are informative to guide the use of anti-S IgG levels for COVID-19 risk assessment in immunocompromised people and for identifying people at the most risk.

In this study, the rates of breakthrough infections after two, three, and four vaccine doses in lymphoma patients were 6%, 13%, and 14%, respectively.

Social mixing shielded people from the worry of developing breakthrough infections as they experienced more care. Accordingly, participants who reported they were worried about COVID-19 experienced markedly fewer breakthrough infections than those who were not (61\241 vs. 35\93). 

The researchers hypothesized that less SARS-CoV-2 exposure during the lockdown in England and varying infectivity of the circulating variants lowered the infection rate after two vaccine doses.

Overall, the study data favored the need to promote the uptake of booster vaccination, especially among immunocompromised people, such as those with lymphoid malignancies.

The researchers also advocated for the standardization and beginning of routine antibody testing in these patients to enable distinguishing individuals and focus efforts to shield the most susceptible groups.

Journal reference:
  • Ratna Wijaya, Marina Johnson, Nicola Campbell, Beth Stuart, Adam Kelly, Nicole Tipler, et al., (2023) Predicting COVID-19 infection risk in people who are immunocompromised by antibody testing, The Lancet., doi: 10.1016/S0140-6736(23)01180-7.

Posted in: Medical Science News | Medical Research News | Medical Condition News | Disease/Infection News

Tags: Antibodies, Antibody, Antigen, Blood, Blood Cancer, Cancer, Cancer Treatment, Cell, Coronavirus, covid-19, Hospital, Immunoglobulin, Intensive Care, Interferon, Interferon-gamma, Laboratory, Lymphoma, Malignant, Omicron, Oxygen, Peptides, Polymerase, Polymerase Chain Reaction, Pseudovirus, Respiratory, SARS, SARS-CoV-2, Severe Acute Respiratory, Severe Acute Respiratory Syndrome, Syndrome, T-Cell, Transcription, Vaccine

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Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.

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