Y. Liu, H. Wang, Y. Zhang, R. Lu, W. Xu, W. Hou, Y. Zhu, X. Li, Q. Chen, Y. Gao, S. Zhang, Q. Chen, Z. An, J. Yu, W. Ying, X. Yu, J. Wu, Z. Fan, X. Yao, Z. Li, H. Shan, T. Guo, Z. Ye, X. Yu, G. Liu, Y. Yu, Z. Peng, C. Ding, Q. Tian, R. Zhang, J. Li, X. Fang, F. He, L. Jin, X. Chen, L. Shi, Y. Zheng
Plasma proteomics is expanding across platforms and cohorts, and integrating these data for AI demands comparability at the protein level, not merely concordant associations1,2. Affinity and MS platforms use distinct probes (antibodies, aptamers, or peptides) and signal readouts, yielding contradictory cross-platform results3,4. Without a known quantitative truth, we cannot distinguish biology from measurement distortion, leaving no gold standard for integration. Here we introduce Plasmix--a plasma reference suite with predefined male:female ratios (M, 1:0; Y, 3:1; P, 1:1; X, 1:3; F, 0:1)--and show that preserving this quantitative titration gradient, not just technical repeatability, predicts cross-platform concordance and identifies protein measurements suitable for integration. Profiling Plasmix across five platforms (Olink, SomaScan, NULISA, AAgAtlas, and MS-DIA) and 12 protocols across 17 batches, we found discordance is dominated by signal generation, not sample identity, and platforms distort signals in a protein-specific manner. Crucially, proteins retaining the titration response showed stronger agreement in an independent cohort; anchoring to the Plasmix midpoint (P) via sample-to-reference ratios reduced distortions, extending harmonizable coverage by 10-20%. Plasmix thus provides a physical ruler to benchmark accuracy, identifying genuinely integrable measurements before pooling datasets or training AI models--a critical bottleneck for plasma proteomics.