RESUMO
Although the expression of many genes is associated with adaptation to high-altitude hypoxic environments, the role of epigenetics in the response to this harsh environmental stress is currently unclear. We explored whether abnormal DNA promoter methylation levels of six genes, namely, ABCA1, SOD2, AKT1, VEGFR2, TGF-ß, and BMPR2, affect the occurrence and development of high-altitude polycythemia (HAPC) in Tibetans. The methylation levels of HAPC and the control group of 130 Tibetans from very high altitudes (> 4500 m) were examined using quantitative methylation-specific real-time PCR (QMSP). Depending on the type of data, the Pearson chi-square test, Wilcoxon rank-sum test, and Fisher exact test were used to assess the differences between the two groups. The correlation between the methylation levels of each gene and the hemoglobin content was explored using a linear mixed model. Our experiment revealed that the methylation levels of the TGF-ß and BMPR2 genes differed significantly in the two groups (p < 0.05) and linear mixed model analysis showed that the correlation between the hemoglobin and methylation of ABCA1, TGF-ß, and BMPR2 was statistically significant (p < 0.05). Our study suggests that levels of TGF-ß and BMPR2 methylation are associated with the occurrence of HAPC in extreme-altitude Tibetan populations among 6 selected genes. Epigenetics may be involved in the pathogenesis of HAPC, and future experiments could combine gene and protein levels to verify the diagnostic value of TGF-ß and BMPR2 methylation levels in HAPC.
RESUMO
OBJECTIVE: To develop the first prediction model based on the common clinical symptoms of high-altitude pulmonary edema (HAPE), enabling early identification and an easy-to-execute self-risk prediction tool. METHODS: A total of 614 patients who consulted People's Hospital of Tibet Autonomous Region between January 2014 and April 2022 were enrolled. Out of those, 508 patients (416 males and 92 females) were diagnosed with HAPE and 106 were patients without HAPE (33 females and 72 males). They were randomly distributed into training (n=431) and validation (n=182) groups. Univariate and multivariate analysis were used to screen predictors of HAPE selected from the 36 predictors; nomograms were established based on the results of multivariate analysis. The receiver operating characteristic curve (ROC) was developed to obtain the area under the ROC curve (AUC) of the predictive model, and its predictive power was further evaluated by calibrating the curve, while the Decision Curve Analysis (DCA) was developed to evaluate the clinical applicability of the model, which was visualised by nomogram. RESULTS: All six predictors were significantly associated with the incidence of HAPE, and two models were classified according to whether the value of SpO2 (percentage of oxygen in the blood) was available in the target population. Both could accurately predict the risk of HAPE. In the validation cohort, the AUC of model 1 was 0.934 with 95% CI (0.848 to 1.000), and model 2 had an AUC of 0.889, 95% CI (0.779 to 0.999). Calibration plots showed that the predicted and actual HAPE probabilities fitted well with internal validation, and the clinical decision curve shows intervention in the risk range of 0.01-0.98, resulting in a net benefit of nearly 99%. CONCLUSION: The recommended prediction model (nomogram) could estimate the risk of HAPE with good precision, high discrimination and possible clinical applications for patients with HAPE. More importantly, it is an easy-to-execute scoring tool for individuals without medical professionals' support.
