multibiomarker

A multibiomarker refers to the use of multiple biological markers measured together to assess a health condition, estimate risk, predict treatment response, or monitor disease progression. Unlike single-marker tests, multibiomarker approaches aim to capture different aspects of a disease process, such as inflammation, metabolism, and tissue damage, to improve diagnostic accuracy and prognostic precision.

The rationale for multibiomarker strategies is that complex diseases often involve heterogeneous and overlapping biological pathways. By combining information from several markers, clinicians can better stratify patients, tailor therapies, and track changes over time. Multibiomarker data are typically integrated using statistical models, risk scores, or machine learning algorithms to produce a clinically actionable result.

Common formats include biomarker panels and gene-expression signatures. Panels may rely on multiplex assays that quantify multiple proteins or nucleic acids in a single sample, while signatures often derive from omics data (genomics, proteomics, metabolomics) analyzed to produce a composite score. Applications span various fields, including cardiology (risk prediction using troponin, BNP, inflammatory markers), oncology (gene-expression panels to guide chemotherapy or targeted therapy), neurology (biomarkers for neurodegeneration and patient stratification), and infectious disease.

Benefits of multibiomarker approaches include enhanced sensitivity and specificity, better risk stratification, and more individualized treatment decisions. Limitations involve higher costs, need for standardized assays, complex data interpretation, and the necessity for robust clinical validation to demonstrate incremental value beyond existing tests. Regulatory approval and integration into clinical guidelines are ongoing considerations for widespread adoption.