From case-control averages to validated subgroups: Interpreting inflammatory and neuroaxonal-injury biomarkers in schizophrenia and major depression.

Psychiatric biomarker research is characterized by subtle but potentially informative biological signals that often lie close to methodological noise. Compared with neurodegenerative or infectious diseases, candidate blood and cerebrospinal fluid (CSF) markers in major psychiatric disorders often show small-to-moderate group differences, substantial patient-control overlap, and a more demanding path to clinical use. Nevertheless, such signals may help identify biologically-meaningful subgroups and contribute to a shift from symptom-based diagnoses toward neuro-immune-endocrine stratification frameworks. This review examines why such findings occur and summarizes technical sources of noise that can generate false-positive biomarker claims, including limited analytical sensitivity, misinterpretation of assay detection limits, multiplex trade-offs, and pre-analytical factors such as collection timing, season, hemolysis, freeze-thaw cycles, and batch effects. We also discuss how modest effects may become more informative when integrated using validated multivariate approaches. To calibrate interpretation, we compare psychiatric disorders (e.g., altered neutrophils, cytokines, neurofilament light chain, S100B) with benchmarks from infection and neurodegeneration, and illustrate common pitfalls through laboratory and clinical vignettes. We also outline an analysis-to-synthesis pathway in which biomarkers are evaluated as tools for clinically relevant biological stratification. Finally, we address publication bias and outcome switching and provide a concise quality checklist for researchers. We argue that progress is more likely to come from prospectively-validated subgrouping strategies than from averaged case-control differences alone, helping move psychoneuroimmunology toward reproducible biomarkers with validated contexts of use.