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May 1, 2026Scientia Sinica Vitae

Urinary proteomic biomarkers for pre-ascent prediction of susceptibility to acute mountain sickness

Liwen Xu Jin Ma Miaoying Cao Jingjie Jin Wang Xiangjie Tingkai Zheng Gong Zhang

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Key Points

This research aims to develop a non-invasive prediction strategy for acute mountain sickness (AMS) susceptibility using urinary proteomic biomarkers.
Enrolled 15 healthy volunteers who underwent physical training before ascending to high altitude.
Classified participants into AMS and non-AMS groups based on Lake Louise scoring.
Performed data independent acquisition (DIA) proteomic analysis to identify differences in urinary proteins between groups.
Identified significant differences in urinary proteomic profiles between AMS and non-AMS groups.
Developed a predictive model with an area under the curve (AUC) of 0.97, indicating excellent discriminative ability.
Validation showed high consistency between model predictions and participants' actual acclimatization status.

Implication

Evaluates urinary proteomics for predicting acute mountain sickness risk in healthy volunteers, suggesting a non-invasive screening tool.

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Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a2ba3d18101cf8926f025fe https://doi.org/10.1360/ssv-2025-0389

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Prediction of acute mountain sickness risk by pre-plateau uric acid and albumin levels in Chinese young males: a prospective cohort studyCohort Study · 2026
2Urinary proteomics for noninvasive monitoring of biomarkers of chronic mountain sickness in a young adult population using data-independent acquisition (DIA)-based mass spectrometry2024 · 1 citations
3Baseline indices of cardiorespiratory fitness for predicting acute mountain sickness: an interpretable four-task ensemble modelcohort · 2026
4Recent advances in predicting acute mountain sickness: from multidimensional cohort studies to cutting-edge model applications2024 · 23 citations
5Prediction of acute mountain sickness from overnight pulse oximetry during ascent to Everest Base Camp: a field-based machine learning studyOriginal Research · 2026

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