EDN1 gene potentially involved in the development of acute mountain sickness

Abstract

Previous investigations have indicated that environmental and genetic factors collectively contribute to the development of acute mountain sickness (AMS), but whether the EDN1 gene is involved in AMS remains to be elucidated. A total of 356 healthy male soldiers who had not traveled to high altitudes in the previous 12 months were enrolled in our study. All participants were taken by plane from 500 m (Chengdu in Sichuan Province) to a 3700 m highland (Lhasa) within 2 hours. Clinical data were collected within 24 hours, and pulmonary function parameters were completed simultaneously. Genotypes were obtained by using iMLDR genotyping assays. A total of 237 soldiers (66.57%) presented AMS symptoms, including headache, dizziness, gastrointestinal upset and fatigue. Soldiers with AMS showed an increase in heart rate (HR), plasma tryptophan and serotonin, and a decrease in SaO2, FEV1, PEF, FVC, V75, V50, V25 and MMF (all P < 0.01). Notably, allele T in single nucleotide polymorphism (SNP) rs2070699 showed a positive correlation with the occurrence of AMS. A general linear regression analysis showed that rs2060799, Mean Arterial Pressure (MAP), SaO2, FVC, tryptophan and serotonin were independent predictors for the occurrence of AMS. Importantly, the area under the curve (AUC) values for tryptophan (0.998), serotonin (0.912) and FVC (0.86) had diagnostic specificity and sensitivity. Our results demonstrated that AMS is accompanied by changes in lung function parameters, increased plasma tryptophan and serotonin levels, and that the EDN1 polymorphism is a potential risk factor for AMS.

Establishing a prediction model of severe acute mountain sickness using machine learning of support vector machine recursive feature elimination

“…Despite the variety of the ascent plan and the individual baseline medical conditions, genetic susceptibility is addressed to explain why AMS and the related events may still occur in certain groups 15 . Genetic issues concerning AMS have been previously discussed [16][17][18][19][20] , however, evidence for the genetic susceptibilities to AMS is still very rare, even less to sAMS. In the study, microarray data of GSE103927 21 were explored for the genetic background of AMS, and a prediction model of sAMS was established by machine learning of the support vector machine recursive feature elimination (SVM-RFE) method 22 , which was clinically applicable as tested within the timeline of the GSE103927 cohort and validated in an isolated cohort GSE52209 23 .…”