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 - Yang, Jie; Tan, Hu; Sun, Mengjia; Chen, Renzheng; Zhang, Jihang; Liu, Chuan; Yang, Yuanqi; Ding, Xiaohan; Yu, Shiyong; Gu, Wenzhu; Ke, Jingbin; Shen, Yang; et al. (Frontiers in Cardiovascular Medicine, 2022)
Insufficient cardiorespiratory compensation is closely associated with acute hypoxic symptoms and high-altitude (HA) cardiovascular events. To avoid such adverse events, predicting high-altitude cardiorespiratory fitness impairment (HA-CRFi) is clinically important. However, to date, there is insufficient information regarding HA-CRFi prediction. 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, 3700 m) from Chengdu (sea level [SL], <500 m) by plane. Physiological parameters at rest and during post-submaximal exercise, as well as cardiorespiratory fitness, were measured at HA and SL. Logistic regression and receiver operating characteristic curve analyses were employed to predict HA-CRFi. We also analyzed 66 pulmonary vascular function and hypoxia-inducible factor-related polymorphisms associated with HA-CRFi. To increase the predictive strength, we used a combination model that included physiological parameters and genetic information to predict HA-CRFi. The oxygen saturation (SpO2) of post-submaximal exercise at SL and EPAS1 rs13419896-A and EGLN1 rs508618-G variants were associated with HA-CRFi (SpO2, area under curve=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 two risk factors—post-submaximal exercise SpO2 at SL of <95.5% and presence 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 physiological parameters of post-submaximal exercise at SL to predict HA-CRFi. Based on this optimized prediction model, our findings could identify individuals at high risk of HA-CRFi at an early stage and reduce cardiovascular events.