Routine Blood Biomarkers Reveal a Preclinical Continuum of Multiple Myeloma Risk

TL;DR

This study identifies blood biomarkers that can predict multiple myeloma risk years before diagnosis, enabling early risk stratification using routine clinical tests.

Contribution

It demonstrates that routinely measured blood analytes can reveal a preclinical continuum of MM risk and improve early detection models.

Findings

Blood biomarkers like total protein and albumin are strongly associated with future MM risk.

Preclinical deviations in blood markers emerge over a decade before diagnosis.

Incorporating biomarkers improves 10-year MM risk prediction accuracy.

Abstract

Multiple myeloma (MM) is preceded by a long preclinical phase spanning decades, yet scalable, non-specialist tools to identify individuals at elevated risk before end-organ damage are lacking. In a prospective analysis of 299,035 cancer-free UK Biobank participants followed for a median of 12.4 years, during which 768 developed incident MM, we conducted a biomarker-wide association scan across 61 routinely measured blood analytes spanning hematological, protein metabolism, renal, and immune categories. Markers of protein dysregulation-elevated total protein, depressed albumin, and a low albumin-to-globulin (A/G) ratio-showed the strongest preclinical associations (hazard ratios 0.61-1.54 per SD), consistent with progressive monoclonal immunoglobulin accumulation and suppression of normal polyclonal synthesis years before diagnosis. These signals were accompanied by indicators of erythropoietic suppression, morphological red cell dysregulation, and a shift toward lower neutrophil and higher lymphocyte fractions, reflecting coordinated perturbations across hematopoietic and immune compartments. Longitudinal trajectory analyses showed that these multi-system deviations emerge more than a decade before diagnosis and intensify as clinical onset approaches. Dose-response modelling revealed pronounced nonlinear associations for protein and erythrocytic markers, with risk concentrated among individuals with extreme values. Incorporating significant biomarkers into a clinical risk model improved 10-year MM discrimination from a C-index of 0.684 to 0.744, with the high-risk decile accumulating 0.79% cumulative incidence versus 0.47% under the clinical model alone. These findings provide a practical framework for biomarker-guided MM risk stratification and targeted surveillance using routinely available clinical tests.