Prediction of High-Altitude Cardiorespiratory Fitness Impairment Using a Combination of Physiological Parameters During Exercise at Sea Level and Genetic Information in an Integrated Risk Model
FRONTIERS IN CARDIOVASCULAR MEDICINE(2022)
摘要
Insufficient cardiorespiratory compensation is closely associated with acute hypoxic symptoms and high-altitude (HA) cardiovascular events. To avoid such adverse events, predicting HA cardiorespiratory fitness impairment (HA-CRFi) is clinically important. However, to date, there is insufficient information regarding the prediction of HA-CRFi. In this study, we aimed to formulate a protocol to predict individuals at risk of HA-CRFi. We recruited 246 volunteers who were transported to Lhasa (HA, 3,700 m) from Chengdu (the sea level [SL], <500 m) through an airplane. Physiological parameters at rest and during post-submaximal exercise, as well as cardiorespiratory fitness at HA and SL, were measured. Logistic regression and receiver operating characteristic (ROC) curve analyses were employed to predict HA-CRFi. We analyzed 66 pulmonary vascular function and hypoxia-inducible factor- (HIF-) related polymorphisms associated with HA-CRFi. To increase the prediction accuracy, we used a combination model including physiological parameters and genetic information to predict HA-CRFi. The oxygen saturation (SpO(2)) of post-submaximal exercise at SL and EPAS1 rs13419896-A and EGLN1 rs508618-G variants were associated with HA-CRFi (SpO(2), area under the curve (AUC) = 0.736, cutoff = 95.5%, p < 0.001; EPAS1 A and EGLN1 G, odds ratio [OR] = 12.02, 95% CI = 4.84-29.85, p < 0.001). A combination model including the two risk factors-post-submaximal exercise SpO(2) at SL of EPAS1 rs13419896-A and EGLN1 rs508618-G variants-was significantly more effective and accurate in predicting HA-CRFi (OR = 19.62, 95% CI = 6.42-59.94, p < 0.001). Our study employed a combination of genetic information and the physiological parameters of post-submaximal exercise at SL to predict HA-CRFi. Based on the optimized prediction model, our findings could identify individuals at a high risk of HA-CRFi in an early stage and reduce cardiovascular events.
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关键词
cardiorespiratory fitness (CRF), hypoxia, single nucleotide polymorphism, SpO(2), prediction
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