Unexpected Immune Biomarker Separation by Recorded Long COVID Status in an Exploratory Cross-Sectional Study

Background: Long COVID is clinically and immunologically heterogeneous. We describe unexpected biomarker separation between recorded groups and limitations that prevent a disease-specific interpretation. Methods: This exploratory cross-sectional secondary analysis included 191 post-COVID records: 158 labeled long COVID and 33 originally designated “No adverse events.” The comparator designation did not establish a clinically verified absence of post-COVID sequelae. Eight biomarkers were evaluated after correction of confirmed reporting errors at common assay lower limits. Analyses included rank-based comparisons, Firth penalized logistic regression, internal resampling, censoring sensitivity analyses and exploratory clustering. Results: Seven biomarkers were higher in the recorded long COVID group (all false-discovery-rate q < 0.001). Apparent areas under the receiver operating characteristic curve (AUCs) were 0.9942 for CXCL9 and 0.9914 for IL-6. The seven-marker model had an apparent AUC of 1.0000 and a mean cross-validated AUC of 0.9989. CXCL9 was at or below the reporting limit in 30/33 comparator records versus 1/158 long COVID records. All individual coefficients in the full model had 95% profile intervals including the null. Clustering did not establish distinct biological subtypes. Conclusions: The unexpectedly strong separation is a hypothesis-generating observation requiring independent investigation. Incomplete verification of group definitions and sampling times limits disease-specific interpretation. These findings do not establish a long COVID-specific immune signature or a clinically applicable diagnostic test.