A blood draw before the shot: ASU's 185-antigen panel flags weak vaccine responders

A team at Arizona State University's Biodesign Institute measured antibodies against 185 antigens in 8,687 blood samples from 4,089 people, then used AI to show that the antibody profile a person already carries predicts how strongly they respond to COVID-19 vaccination ^[1][2][3]. That matters because response is normally graded after the fact, by measuring whether the immune system produced antibodies against the target ^[10].

The cohort was deliberately split: 2,445 healthy volunteers and 1,644 people with conditions or treatments linked to immune suppression, including HIV, multiple myeloma, solid organ malignancy, autoimmune disease, inflammatory bowel disease and solid organ transplantation ^[2][13]. Those two groups account for the whole participant count ^[15], at roughly two samples per person ^[17].

The interesting result is not that immunosuppressed groups responded worse. Several did, but the clinical categories were imperfect predictors: some immunosuppressed participants mounted strong responses, while about 5% to 6% of healthy participants responded weakly ^[6]. On the reported healthy denominator, that is roughly 120 to 150 people who look fine on paper and are not ^[16]. Those are exactly the patients a category-based dosing rule misses.

What separated them, according to the ASU group, was pre-existing antibody levels against common microbes. Higher levels of antibodies to Staphylococcus aureus, respiratory syncytial virus and human respirovirus 3 were associated with stronger COVID-19 vaccine responses, and the signal held in both healthy and immunosuppressed participants ^[4][5]. The team calls these sentinel antibodies and argues they are not fighting the vaccine target at all; they read as a proxy for how responsive the antibody-producing arm of the immune system is, or in the authors' phrasing, a marker of "system-level humoral immune competence" ^[9]. The paper, "Pre-vaccine sentinel antibodies predict blunted vaccine responses," reports the profiles as "scalable biomarkers of humoral immune responsiveness" ^[8].

"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," said Joshua LaBaer, executive director of the Biodesign Institute and the study's lead. "This suggests that some people may be more immune-ready than others" ^[7].

The deployment argument is the strongest part. Age, sex, genetics, prior illness and underlying conditions have all been tied to response strength, and immune-compromised patients are already known to be at higher risk of blunted responses ^[11]. But unlike prediction methods built on genetic analysis, this one reads antibody patterns in blood, which the researchers say may be easier to adapt for clinical use ^[14]. A serology panel fits existing lab workflows in a way genotyping does not.

Three things are missing before this becomes a triage test. The account of the work does not report a predictive accuracy figure, an external validation cohort, or a test of whether people flagged as likely weak responders actually benefit from an extra dose ^[12]. Watch for those numbers, for whether 185 antigens can be cut to a handful that a hospital lab would run, and for whether a signature trained on COVID-19 vaccination transfers to influenza, hepatitis B or anything else where a booster decision is already routine.