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Early CRAB-like Biomarker Signatures Reveal a Preclinical Susceptibility Continuum for Multiple Myeloma

Bingjie Li, Jiadai Xu, Yiqing Sun, Peng Liu, Zhigang Yao

TL;DR

It is suggested that subtle yet quantifiable deviations in common laboratory tests reflect early microenvironmental changes that precede malignant plasma cell expansion, offering opportunities for risk stratification and targeted surveillance.

Abstract

Multiple myeloma (MM) evolves over decades, yet robust tools for identifying individuals at risk long before clinical onset remain limited. Using data from 378,930 UK Biobank participants, we systematically characterized the longitudinal dynamics and predictive value of routinely measured "CRAB-like" biomarkers, including hematologic indices, protein metabolism markers, renal function, and serum calcium. Across multivariable models, biomarkers reflecting anemia and protein imbalance (including hemoglobin, red blood cell indices, total protein, albumin, and the albumin/globulin ratio) showed strong and consistent associations with future MM, independent of demographic, lifestyle, clinical, and genetic risk factors. These markers displayed pronounced non-linear dose-response relationships and contributed substantially to 5- and 10-year MM risk discrimination, with the C-index improving from 0.66 to 0.76. Longitudinal analyses revealed progressive shifts in red cell morphology and protein metabolism profiles up to a decade before diagnosis, supporting the existence of a preclinical susceptibility continuum detectable in the general population. Our findings suggest that subtle yet quantifiable deviations in common laboratory tests reflect early microenvironmental changes that precede malignant plasma cell expansion, offering opportunities for risk stratification and targeted surveillance.

Early CRAB-like Biomarker Signatures Reveal a Preclinical Susceptibility Continuum for Multiple Myeloma

TL;DR

It is suggested that subtle yet quantifiable deviations in common laboratory tests reflect early microenvironmental changes that precede malignant plasma cell expansion, offering opportunities for risk stratification and targeted surveillance.

Abstract

Multiple myeloma (MM) evolves over decades, yet robust tools for identifying individuals at risk long before clinical onset remain limited. Using data from 378,930 UK Biobank participants, we systematically characterized the longitudinal dynamics and predictive value of routinely measured "CRAB-like" biomarkers, including hematologic indices, protein metabolism markers, renal function, and serum calcium. Across multivariable models, biomarkers reflecting anemia and protein imbalance (including hemoglobin, red blood cell indices, total protein, albumin, and the albumin/globulin ratio) showed strong and consistent associations with future MM, independent of demographic, lifestyle, clinical, and genetic risk factors. These markers displayed pronounced non-linear dose-response relationships and contributed substantially to 5- and 10-year MM risk discrimination, with the C-index improving from 0.66 to 0.76. Longitudinal analyses revealed progressive shifts in red cell morphology and protein metabolism profiles up to a decade before diagnosis, supporting the existence of a preclinical susceptibility continuum detectable in the general population. Our findings suggest that subtle yet quantifiable deviations in common laboratory tests reflect early microenvironmental changes that precede malignant plasma cell expansion, offering opportunities for risk stratification and targeted surveillance.

Paper Structure

This paper contains 18 sections, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Results
  3. Discussion
  4. Methods
  5. Acknowledgements

Figures (1)

  • Figure 1: Nonlinear associations between baseline biomarker levels and multiple myeloma incidence. Restricted cubic spline (RCS) models were used to estimate hazard ratios (HRs) for incident multiple myeloma across the distribution of each biomarker, adjusted for demographic, lifestyle, clinical, and genetic covariates (Fully adjusted Model 3). HRs are shown relative to the median biomarker level (vertical dotted line). Shaded grey areas indicate 95% confidence intervals. Green density curves represent the population distribution of each biomarker, allowing comparison between exposure prevalence and risk regions. Black tick marks at the top of each panel denote incident MM cases, illustrating where cases occur along the biomarker spectrum. Colored lines indicate biomarker category groups. Overall $P$-values reflect the significance of the biomarker--MM association, and $P_{\text{nonlinear}}$ denotes evidence for nonlinear effects.