Journal of translational medicine · 2026
Identification of a 10-protein plasma signature for preclinical gout risk beyond serum urate: a robustness-first proteomic study in 33,147 normouricaemic individuals.
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Yuan Yue, Zhang Huan, Zhang Kun, Wang Mingzhu, Yan Hao, Zhang Junyang, Wen Chengping, Guo Zheshan, Huang Lin, Wang Zhaoxiang
Аннотация
BACKGROUND: Gout risk assessment is currently reactive, relying on hyperuricaemia. However, a significant clinical gap exists for individuals who develop gout despite maintaining "normal" serum urate levels. We aimed to identify a plasma proteomic signature that defines a susceptibility state for gout years before clinical onset, specifically within individuals who are normouricaemic at baseline.
METHODS: We analyzed large-scale plasma proteomic data (2,920 proteins) from 33,147 normouricaemic individuals (baseline serum urate < 360 μmol/L) in the UK Biobank. To ensure findings were not artifacts of specific algorithms, we employed a "robustness-first" machine-learning framework encompassing 756 model configurations. Longitudinal protein trajectories were traced up to 15 years prior to diagnosis. Incident osteoarthritis was used as a negative control to evaluate specificity.
RESULTS: A 10-protein signature (including VSIG4, UBQLN3, and MB) predicted incident gout with high discrimination (ROC-AUC = 0.892). This signature identified a high-risk subgroup with a 4.46-fold increased hazard, independent of baseline serum urate and conventional risk factors. Notably, protein trajectories diverged between future cases and controls 10-15 years before clinical diagnosis, even while serum urate remained within the normal range. The signature showed minimal discrimination for osteoarthritis (AUC ≈ 0.60).
CONCLUSIONS: In 33,147 normouricaemic individuals, plasma proteomics identifies a stable preclinical susceptibility state for gout that exists beyond the traditional serum urate paradigm. This 10-protein signature enables early risk stratification and offers a significant window for precision prevention a decade before clinical onset.
METHODS: We analyzed large-scale plasma proteomic data (2,920 proteins) from 33,147 normouricaemic individuals (baseline serum urate < 360 μmol/L) in the UK Biobank. To ensure findings were not artifacts of specific algorithms, we employed a "robustness-first" machine-learning framework encompassing 756 model configurations. Longitudinal protein trajectories were traced up to 15 years prior to diagnosis. Incident osteoarthritis was used as a negative control to evaluate specificity.
RESULTS: A 10-protein signature (including VSIG4, UBQLN3, and MB) predicted incident gout with high discrimination (ROC-AUC = 0.892). This signature identified a high-risk subgroup with a 4.46-fold increased hazard, independent of baseline serum urate and conventional risk factors. Notably, protein trajectories diverged between future cases and controls 10-15 years before clinical diagnosis, even while serum urate remained within the normal range. The signature showed minimal discrimination for osteoarthritis (AUC ≈ 0.60).
CONCLUSIONS: In 33,147 normouricaemic individuals, plasma proteomics identifies a stable preclinical susceptibility state for gout that exists beyond the traditional serum urate paradigm. This 10-protein signature enables early risk stratification and offers a significant window for precision prevention a decade before clinical onset.