Narrative review highlights mitigation strategies for M-protein-induced diagnostic inaccuracies, suggesting AI may enhance lab safety.
Monoclonal proteins (M-proteins) serve as critical biomarkers for plasma cell dyscrasias, yet their unique physicochemical properties paradoxically compromise the accuracy of routine laboratory diagnostics. Beyond causing well-documented anomalies in electrophoresis, M-protein interference pervasively affects coagulation, clinical chemistry, and immunoassay platforms, creating analytical artifacts that can fundamentally mislead clinical decision-making. The diagnostic landscape is further complicated by the widespread adoption of therapeutic monoclonal antibodies (t-mAbs), which introduce exogenous "drug spikes" mimicking disease markers and obscuring therapeutic response assessment. This narrative review elucidates the underlying mechanisms of these interferences, ranging from physical precipitation and chemical binding to immunological hook effects, and synthesizes evidence from recent clinical cohorts and technical evaluations. We critically assess the efficacy of mitigation strategies, highlighting the evolution from traditional techniques like polyethylene glycol precipitation to next-generation solutions involving mass spectrometry and artificial intelligence-driven algorithms. By establishing a comprehensive framework for the recognition and resolution of these artifacts, this article aims to equip clinicians and laboratorians with the tools necessary to ensure that laboratory diagnostics remain a pillar of patient safety.
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Liang et al. (2026) studied this question.
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