Proteomic and clinical biomarkers for acute mountain sickness in a longitudinal cohort

Ascending to high-altitude by non-high-altitude natives is a well-suited model for studying acclimatization to extreme environments. Acute mountain sickness (AMS) is frequently experienced by visitors. The diagnosis of AMS mainly depends on a self-questionnaire, revealing the need for reliable bioma...

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Bibliographic Details
Published in:Communications biology 2022-06, Vol.5 (1), p.548-14, Article 548
Main Authors: Yang, Jing, Jia, Zhilong, Song, Xinyu, Shi, Jinlong, Wang, Xiaoreng, Zhao, Xiaojing, He, Kunlun
Format: Article
Language:English
Subjects:
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Acclimatization
Acute Disease
ADAM Proteins
Altitude
Altitude Sickness - diagnosis
Biology
Biomarkers
Biomedical and Life Sciences
High-altitude environments
Humans
Learning algorithms
Life Sciences
Machine learning
Membrane Proteins
Phenotypes
Precision medicine
Proteomics
TRAF2 protein
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Summary:Ascending to high-altitude by non-high-altitude natives is a well-suited model for studying acclimatization to extreme environments. Acute mountain sickness (AMS) is frequently experienced by visitors. The diagnosis of AMS mainly depends on a self-questionnaire, revealing the need for reliable biomarkers for AMS. Here, we profiled 22 AMS symptom phenotypes, 65 clinical indexes, and plasma proteomic profiles of AMS via a combination of proximity extension assay and multiple reaction monitoring of a longitudinal cohort of 53 individuals. We quantified 1069 proteins and validated 102 proteins. Via differential analysis, machine learning, and functional association analyses. We found and validated that RET played an important role in the pathogenesis of AMS. With high-accuracies (AUCs > 0.9) of XGBoost-based models, we prioritized ADAM15, PHGDH, and TRAF2 as protective, predictive, and diagnostic biomarkers, respectively. Our findings shed light on the precision medicine for AMS and the understanding of acclimatization to high-altitude environments. Potential acute mountain sickness diagnostic, predictive, protective biomarkers are established using plasma proteomic, clinical and symptom phenotype data with machine learning approaches in a longitudinal cohort of 53 individuals.
ISSN:2399-3642
2399-3642
DOI:10.1038/s42003-022-03514-6