Frontiers in Cardiovascular Medicine (Jan 2022)

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

  • Jie Yang,
  • Hu Tan,
  • Mengjia Sun,
  • Renzheng Chen,
  • Jihang Zhang,
  • Chuan Liu,
  • Yuanqi Yang,
  • Xiaohan Ding,
  • Shiyong Yu,
  • Wenzhu Gu,
  • Jingbin Ke,
  • Yang Shen,
  • Chen Zhang,
  • Xubin Gao,
  • Chun Li,
  • Lan Huang

DOI
https://doi.org/10.3389/fcvm.2021.719776
Journal volume & issue
Vol. 8

Abstract

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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 (SpO2) of post-submaximal exercise at SL and EPAS1 rs13419896-A and EGLN1 rs508618-G variants were associated with HA-CRFi (SpO2, 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 SpO2 at SL of <95.5% and the 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 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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