AI Can Predict Your Vaccine Response Before You Get the Shot, Study of 4,000+ People Reveals

AI may know how you'll respond to a vaccine before you get it

Vaccines prevent serious illness for many people, but the immune protection they produce can differ substantially from one person to another. New research led by Arizona State University offers clues about what may be behind those differences. The immune system may show signs of how strongly it will react even before vaccination. Researchers at ASU and collaborating institutions examined blood samples from more than 4,000 people, measuring antibodies that recognized 185 antigens. Those immune targets included common viruses and bacteria, along with targets connected to autoimmune diseases. Artificial intelligence was then used to search for patterns in blood samples taken before and after COVID-19 vaccination. The analysis uncovered antibody signatures that could help separate people who produced strong vaccine responses from those whose responses were weaker. The findings could eventually contribute to vaccination strategies tailored more closely to an individual's immune system. "What our study found is that certain biomarkers, when analyzed with AI, can predict who is likely to respond well to a vaccine, even before they receive it. This suggests that some people may be more immune-ready than others," says Joshua LaBaer, who led the study. LaBaer is executive director of the Biodesign Institute at ASU and director of the Virginia G. Piper Center for Personalized Diagnostics. The project also involved ASU researchers and collaborators from medical and research institutions around the United States. The study appears in the current issue of the journal Cell Press Blue. Blood Antibodies May Reveal Vaccine Readiness Scientists typically evaluate vaccine response after vaccination by measuring whether the immune system generated antibodies against the intended target. In this study, the researchers approached the problem from the opposite direction. They wanted to know whether immune patterns already present in the blood could reveal how someone would respond before receiving a vaccine. Many factors can influence vaccine response, including age, sex, genetics, previous illnesses and underlying health conditions. People with conditions that compromise the immune system are often more likely to produce weaker responses. However, vaccine outcomes can still vary widely among people who fall into the same general health categories. The researchers used one of the first approaches to examine a broad antibody "fingerprint" present before vaccination as a measure of immune readiness. While some other prediction strategies depend on genetic testing, this method analyzes antibody patterns in blood, potentially making it easier to translate into clinical practice. Health Status Alone Does Not Predict Response To investigate whether these antibody fingerprints could signal vaccine readiness, the team measured immune responses to 185 antigens. The targets included SARS-CoV-2, the virus responsible for COVID-19, as well as other widespread viruses and bacteria and targets associated with autoimmune diseases. Altogether, the researchers examined 8,687 samples from 4,089 participants. The group included healthy volunteers as well as people with diseases or treatments associated with immune suppression, including HIV, multiple myeloma, solid organ malignancy, autoimmune disease, inflammatory bowel disease and solid organ transplantation. Several immunosuppressed groups were more likely to show reduced responses to COVID-19 vaccination. Yet simply placing someone into an immunosuppressed or healthy category did not reliably predict the outcome. Some participants with suppressed immune systems still developed strong responses. At the same time, about 5% to 6% of healthy participants showed weak vaccine responses. "Sentinel" Antibodies Signal Immune Readiness Certain antibodies that were already present before vaccination stood out in the analysis. Higher levels of antibodies targeting common microbes, including Staphylococcus aureus, RSV and human respirovirus 3, were associated with stronger responses to COVID-19 vaccines. The researchers call these "sentinel" antibodies because they may serve as indicators of a person's underlying immune readiness. These antibodies are not necessarily acting directly against the vaccine target. Instead, their presence may provide information about how prepared the antibody-producing portion of the immune system is to mount a response. The team also investigated whether the complete antibody fingerprint could provide more predictive information than a small number of individual biomarkers. A deep learning model examined patterns across the entire antibody panel, combining numerous measurements to build a broader picture of each participant's immune state. AI Searches Millions of Immune Signals The results demonstrate one potential advantage of using AI in biomedical research. Machine learning systems can search millions of biological data points for subtle relationships that may be difficult to detect using conventional approaches. In this case, the findings suggest that understanding vaccine readiness may require looking at the immune system as an interconnected whole instead of concentrating on one antibody or one disease. The research also demonstrates the potential of newer technologies capable of measuring many antibody responses simultaneously. Rather than testing whether a person has antibodies against a single pathogen, researchers can examine a much broader immune landscape shaped by previous exposure to viruses, bacteria and other immune targets. Toward More Personalized Vaccination If the findings are confirmed in future studies and extended to additional vaccines, the approach could have uses well beyond COVID-19. Profiling sentinel antibodies might eventually support vaccine research, vaccine development and medical care for people who are especially vulnerable to weak immune responses. Doctors could potentially use this type of information to identify people who might benefit from additional vaccine doses, more careful follow-up or other protective strategies. It could also give scientists a clearer understanding of why vaccination produces powerful immune responses in some people but weaker ones in others. Ultimately, the research points toward a future in which vaccination decisions could be informed by an individual's own level of immune readiness.

AI predicts vaccine response based on pre-existing antibody patterns

Arizona State UniversityAug 20 2026Reviewed Vaccines protect most people from serious illness, but the strength of that protection can vary considerably from one person to another. A new study led by Arizona State University helps us understand why. Before a vaccine ever enters your body, your immune system may already hold clues to how strongly it will respond. In blood samples from more than 4,000 people, ASU researchers and their colleagues measured antibodies against 185 antigens-targets recognized by the immune system, including those from common viruses and bacteria as well as targets associated with autoimmune diseases. They then used artificial intelligence to analyze patterns in samples collected before and after COVID-19 vaccination, identifying antibody signatures that helped distinguish strong vaccine responders from weak ones. The research opens a possible path toward more personalized vaccination strategies. "What our study found is that certain biomarkers, when analyzed with AI, can predict who is likely to respond well to a vaccine, even before they receive it. This suggests that some people may be more immune-ready than others," says Joshua LaBaer, who led the study. LaBaer is the executive director of the Biodesign Institute at ASU and director of the Virginia G. Piper Center for Personalized Diagnostics. The research was conducted with ASU colleagues and collaborators from medical and research institutions across the country. The study appears in the current issue of the journal Cell Press Blue. Antibody clues reveal immune readiness Usually, scientists evaluate vaccine response after the shot, by measuring whether the immune system produced antibodies against the target. Here, the researchers asked a different question: Could patterns already present in the blood predict the response before vaccination? Age, sex, genetics, prior illnesses and underlying health conditions have all been linked to how strongly people respond to vaccines. People with immune-compromising conditions are often at higher risk of weaker responses. But even within these groups, outcomes can differ sharply. The new approach is one of the first to use a broad, pre-vaccine antibody "fingerprint" to assess immune readiness. Unlike some prediction methods that rely on genetic analyses, this strategy uses antibody patterns in blood, which may be easier to adapt for clinical use. Beyond immune categories To test whether that antibody fingerprint could reveal vaccine readiness, the researchers analyzed antibody responses to 185 antigens. These included SARS-CoV-2, the virus that causes COVID-19, other common viruses and bacteria, and targets associated with autoimmune diseases. The study included 8,687 samples from 4,089 participants, spanning healthy volunteers and people with conditions or treatments linked to immune suppression, such as HIV, multiple myeloma, solid organ malignancy, autoimmune disease, inflammatory bowel disease and solid organ transplantation. The researchers found that several immunosuppressed groups were more likely to have blunted responses to COVID-19 vaccination. But those categories were imperfect predictors. Some immunosuppressed participants mounted strong responses, while about 5% to 6% of healthy participants had weak responses. Sentinel antibodies The study found that higher levels of certain preexisting antibodies, including antibodies to common microbes such as Staphylococcus aureus, RSV and human respirovirus 3, were associated with stronger COVID-19 vaccine responses. The researchers describe these as "sentinel" antibodies because they may indicate a person's baseline immune readiness. They are not necessarily fighting the vaccine target directly. Instead, they may reflect how responsive the antibody-producing arm of the immune system is likely to be. The researchers then asked whether the full antibody fingerprint, not just a few individual markers, could help identify people likely to have weak vaccine responses. Their deep-learning model analyzed patterns across the antibody panel, combining many measurements into a broader immune profile. The study highlights a key strength of AI in health research: its ability to find subtle, predictive patterns in millions of biological data points that might otherwise remain hidden. The approach suggests that vaccine readiness may be better understood by looking at the immune system as a whole, rather than focusing only on a single disease or a single antibody. The work also highlights the value of newer technologies that can measure large numbers of antibody responses at once. Instead of asking whether someone has antibodies to one pathogen, the method can scan a wider immune landscape, capturing patterns formed by many previous encounters with viruses, bacteria and other immune targets. The researchers say the findings could have implications beyond COVID-19, if they are validated in additional studies and with other vaccines. Sentinel antibody profiling could help guide vaccine testing, vaccine development and clinical care for people at risk of weak immune responses. The approach might eventually help doctors identify patients who need additional vaccine doses, closer follow-up or alternative protective measures. It could also help researchers better understand why some people respond well to vaccination while others do not. The work points toward a future in which vaccine decisions could be guided by a person's own immune readiness. Source: Arizona State University Journal reference: Song, L., et al. (2026). Pre-vaccine sentinel antibodies predict blunted vaccine responses. Cell Press Blue. DOI: 10.1016/j.cpblue.2026.100088. https://www.cell.com/cell-press-blue/fulltext/S3051-3839(26)00086-1

Good vaccine response? Your blood might hold the secret

Patterns in the blood, present before vaccination, could help predict how strongly a person's immune system will respond, a new study has found. Not everyone reacts to vaccines in the same way. Immune responses vary in strength depending on age, sex, prior illnesses and genetics. In a new study, published in the Cell Press Blue journal, researchers at the United States National Cancer Institute's SeroNet program investigated how pre-existing antibodies to common microbes can predict a person's response to new vaccines. "Certain biomarkers, when analyzed with AI, can predict who is likely to respond well to a vaccine, even before they receive it," said Joshua LaBaer, who led the study, which suggests that some people may be more immune-ready than others. Immunosuppression -- having a weak immune system -- is associated with a weaker antibody response to vaccination, which increases the risk of infection, disease severity and mortality. The researchers found that, before receiving a vaccine, the body's immune system already hints at how it will react and how robust the response will be. The study analysed over 8,000 blood samples from more than 4,000 people, testing for antibodies against 185 antigens from common viruses, bacteria and autoimmune-linked targets. Participants included healthy volunteers as well as people with weakened immune systems -- due to conditions such as HIV, multiple myeloma and solid organ transplantation -- all of whom received the COVID-19 vaccine. The researchers then used artificial intelligence to analyse patterns in samples collected before and after COVID-19 vaccination, identifying antibody signatures that helped categorise patients as either strong or poor vaccine responders. They identified what they call "sentinel antibodies," which act as indicators of a person's likely immune response to a given vaccine, helping predict how an individual's body will react to the virus. "We identified universal antimicrobial signatures that were positively associated with the top 25% of high COVID-19 vaccine responders," the researchers noted. The study found that higher levels of certain pre-existing antibodies, especially those against common organisms such as Staphylococcus aureus, Respiratory Syncytial Virus (RSV) and human parainfluenza virus 3 (HPIV-3), were associated with stronger COVID-19 vaccine responses. Transforming vaccination strategies By using pre-existing antimicrobial antibody profiles as predictive biomarkers, clinicians could identify individuals likely to develop a weak immune response before they are immunised. The study applied machine learning to construct predictive models to classify individuals with suboptimal vaccine response. "Predicting which individuals will mount poor antibody responses before vaccination could improve personalised vaccination strategies," the researchers wrote. This would allow for targeted interventions such as tailored dosing schedules, different vaccine types or additional booster requirements for those at higher risk.