Authors
Junda Huang, Zewen Li, Enzhong Chen, Guangqing Liang, Xin Zhao, Meng Lan, Shaocong Liang, Yifeng Ou, Mengxue Zou, Xuguang Bao, Yamei Lv, Zhencheng Fang, Hongwei Zhou, Nianyi Zeng
Published in
iScience. Volume 29. Issue 8. Pages 116923. Aug 21, 2026. Epub Jul 24, 2026.
Abstract
Monoclonal gammopathy of undetermined significance is a common precursor of multiple myeloma, yet its prevalence and clinical distribution remain poorly defined due to the limited scalability of conventional electrophoretic workflows. Here, we developed an artificial intelligence-augmented matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) platform that integrates machine learning, rule-based detection of weak monoclonal signals and glycosylation assessment for automated M-protein screening. Following training and validation on 5,218 retrospective serum samples, the platform was deployed in a real-world cohort of 12,263 adult patients. Screening identified M-proteins in 7.5% of patients, reaching 10.1% among individuals aged ≥ 50 years, and revealed a substantial burden of previously undetected monoclonal protein abnormalities. Beyond hematologic disorders, M-protein positivity was associated with a broad spectrum of infectious, neoplastic, and metabolic conditions. These findings establish a scalable strategy for population-level M-protein screening and highlight adult patients as an underrecognized high-risk population that may benefit from systematic surveillance.
PMID:
42564530
Bibliographic data and abstract were imported from PubMed on 07 Aug 2026.
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