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- Title
A Signature of Circulating microRNAs Predicts the Susceptibility of Acute Mountain Sickness.
- Authors
Bao Liu; He Huang; Gang Wu; Gang Xu; Bing-Da Sun; Er-Long Zhang; Jian Chen; Yu-Qi Gao; Giugno, Rosalba; Russo, Francesco
- Abstract
Background: Acute mountain sickness (AMS) is a common disabling condition in individuals experiencing high altitudes, which may progress to life-threatening high altitude cerebral edema. Today, no established biomarkers are available for prediction the susceptibility of AMS.MicroRNAs emerge as promising sensitive and specific biomarkers for a variety of diseases. Thus, we sought to identify circulating microRNAs suitable for prediction the susceptible of AMS before exposure to high altitude. Methods: We enrolled 109 healthy man adults and collected blood samples before their exposure to high altitude. Then we took them to an elevation of 3648m for 5 days. Circulating microRNAs expression was measured by microarray and quantitative reverse-transcription polymerase chain reaction (qRT-PCR). AMS was defined as Lake Louise score ≥3 and headache using Lake Louise Acute Mountain Sickness Scoring System. Results: A total of 31 microRNAs were differentially expressed between AMS and Non-AMS groups, 15 up-regulated and 16 down-regulated. Up-regulation of miR-369-3p, miR-449b-3p, miR-136-3p, and miR-4791 in patients with AMS compared with Non-AMS individuals were quantitatively confirmed using qRT-PCR (all, P < 0.001). With multiple logistic regression analysis, a unique signature encompassing miR-369-3p, miR-449b-3p, and miR-136-3p discriminate AMS from Non-AMS (area under the curve 0.986, 95%CI 0.970-1.000, P < 0.001, LR+: 14.21, LR-: 0.08). This signature yielded a 92.68% sensitivity and a 93.48% specificity for AMS vs. Non-AMS. Conclusion: The study here, for the first time, describes a signature of three circulating microRNAs as a robust biomarker to predict the susceptibility of AMS before exposure to high altitude.
- Subjects
MICRORNA; MOUNTAIN sickness; CEREBRAL edema; MICROARRAY technology; LOGISTIC regression analysis
- Publication
Frontiers in Physiology, 2017, Vol 8, p1
- ISSN
1664-042X
- Publication type
Article
- DOI
10.3389/fphys.2017.00055