a Safe Easy and Objective Means to Monitor Exercise Intensity at Altitude is

• Journal List
• Front Physiol
• PMC7991828

Front Physiol. 2021; 12: 640302.

Altitude Cardiomyopathy Is Associated With Impaired Stress Electrocardiogram and Increased Circulating Inflammation Makers

Ya-Jun Shi,^† Jin-Li Wang,^† Ling Gao, Dong-Lin Wen, Qing Dan, Ying Dong, Ya-Tao Guo, Cheng-Hui Zhao, Teng-Jing Li, Jun Guo, Zong-Bin Li, and Yun-Dai Chen^*

Received 2020 Dec 11; Accepted 2021 Feb 19.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Abstract

Many sea-level residents suffer from acute mountain sickness (AMS) when first visiting altitudes above 4,000 m. Exercise tolerance also decreases as altitude increases. We observed exercise capacity at sea level and under a simulated hypobaric hypoxia condition (SHHC) to explore whether the response to exercise intensity represented by physiological variables could predict AMS development in young men. Eighty young men from a military academy underwent a standard treadmill exercise test (TET) and biochemical blood test at sea level, SHHC, and 4,000-m altitude, sequentially, between December 2015 and March 2016. Exercise-related variables and 12-lead electrocardiogram parameters were obtained. Exercise intensity and AMS development were investigated. After exposure to high altitude, the count of white blood cells, alkaline phosphatase and serum albumin were increased (P < 0.05). There were no significant differences in exercise time and metabolic equivalents (METs) between SHHC and high-altitude exposures (7.05 ± 1.02 vs. 7.22 ± 0.96 min, P = 0.235; 9.62 ± 1.11 vs. 9.38 ± 1.12, P = 0.126, respectively). However, these variables were relatively higher at sea level (8.03 ± 0.24 min, P < 0.01; 10.05 ± 0.31, P < 0.01, respectively). Thus, subjects displayed an equivalent exercise tolerance upon acute exposure to high altitude and to SHHC. The trends of cardiovascular hemodynamics during exercise under the three different conditions were similar. However, both systolic blood pressure and the rate–pressure product at every TET stage were higher at high altitude and under the SHHC than at sea level. After acute exposure to high altitude, 19 (23.8%) subjects developed AMS. Multivariate logistic regression analysis showed that METs under the SHHC {odds ratio (OR) 0.355 per unit increment [95% confidence intervals (CI) 0.159−0.793], P = 0.011}, diastolic blood pressure (DBP) at rest under SHHC [OR 0.893 per mmHg (95%CI 0.805−0.991), P = 0.030], and recovery DBP 3 min after exercise at sea level [OR 1.179 per mmHg (95%CI 1.043−1.333), P = 0.008] were independently associated with AMS. The predictive model had an area under the receiver operating characteristic curve of 0.886 (95%CI 0.803−0.969, P < 0.001). Thus, young men have similar exercise tolerance in acute exposure to high altitude and to SHHC. Moreover, AMS can be predicted with superior accuracy using characteristics easily obtainable with TET.

Keywords: treadmill exercise test, acute mountain sickness, simulated hypobaric hypoxia condition, prediction, inflammation

Introduction

Many people may be exposed to high altitude during their work, travel, training, or participation in competitions. Compared to sea level, high altitudes involve important environmental changes, such as decreases in temperature, barometric pressure, and partial oxygen concentration. Hypoxia and hypobaric exposure are associated with significant clinical disorders (Boos et al., 2016). Acute exposure to an altitude of more than 2,500 m puts individuals at risk of developing acute mountain sickness (AMS) (Luks et al., 2017).

Physiological adaptations to altitude are multidimensional and contribute to substantial individual variability (Chapman et al., 2011). Exercise tolerance decreases by about 2.5% with every 300-m increase in altitude above 1,500 m, but altitude training is also thought to be beneficial for athletic performance (Buskirk et al., 1967). However, the appropriate intensity of exercising at high altitudes for athletes is unclear. Furthermore, there is marked inter-individual variation in the occurrence of AMS after exposure to high altitudes. The ability to predict individual susceptibility to AMS would be useful for the purpose of athletes training at high altitude safely.

We here investigated whether the response to exercise intensity, represented by physiological variables, could predict AMS development in young men. Meanwhile, the change of circulating inflammation markers was studied. We used the treadmill exercise test (TET), which is a representative test for non-invasive testing of patterns (Miller, 2011). The TET allows adequate exercise for testees, while making it possible to obtain electrocardiograms and hemodynamic indexes during the exercise. Subjects underwent standard sub-maximal TET at simulated hypobaric hypoxia condition (SHHC) and high altitude, has never been performed. We hypothesized that young men would exhibit a similarly exercise reaction at high altitude and under a SHHC, as compared with sea level, while the response to exercise at sea level and SHHC would predict AMS at high altitude.

Materials and Methods

We conducted a prospective, self-controlled, case-series clinical trial that was approved by the institutional review board of the Chinese PLA General Hospital, Beijing, China (registry number S2014-070-01). This study was supported by the National Science and Technology Major Projects for Major Drugs Innovation and Development (2014ZX09J14102-02A). All subjects signed written informed consent forms.

Study Population

We recruited 80 male Chinese volunteers, aged 18–35 years, from the Medical School of the Chinese PLA between December 2015 and March 2016, none of whom had had a previous history of exposure to high altitudes. Exclusion criteria included: (1) symptoms similar to those of AMS at baseline; (2) any history of primary headache, cerebral neoplasm, heart failure, or chronic obstructive pulmonary disease; (3) absolute and relative contraindications to TET (Fletcher et al., 2013); and (4) any history of cardiovascular diseases.

Study Protocol

All subjects underwent standard sub-maximal TET and biochemical parameters testing in three different scenarios: at sea level, under the SHHC, and at 4,000-m high altitude. Furthermore, we allowed a washout period of >7 days between TET at each of these experimental settings. Based on responses to high altitude, subjects were grouped into those who developed AMS (AMS group) and those who did not (non-AMS group).

Treadmill Exercise Test

Treadmill exercise test procedures involved a predominant dynamic-aerobic component, which was characterized by increasing slope and velocity. TET was performed according to ACC/AHA practice guidelines using the Bruce protocol (Fletcher et al., 2001). The test was terminated for absolute or relative indications according to the Scientific Statement from the American Heart Association (Fletcher et al., 2001), or achieving an exercise workload of 10 metabolic equivalents (METs), or when the target heart rate was reached (submaximal workloads).

A 12-lead ECG was recorded continuously during the TET. The following measures were recorded every 3 min: exercise time, exercise capacity (METs), blood pressure, heart rate, and pressure–rate product (RPP) (T2100, GE Healthcare, Chicago, IL, United States). Resting and peak exercise recordings of oxygen saturation (SpO₂) were performed using a Nellcor N-20P pulse oximeter (Nellcor Puritan Bennett, Coventry, United Kingdom). SpO₂ recordings were made in 15-s continuous tests using the index finger of the right hand with the most consistent reading used for analysis.

Exercise tolerance was represented by exercise time, maximal heart rate (HR_max), heart rate reserve (HRR, i.e., the difference between the maximum and minimum heart rate during exercise), and METs. Adverse events, i.e., severe arrhythmia and exercise-induced ischemia, were recorded.

Myocardial ischemia is diagnosed if there is ≥1 mm horizontal or down-sloping ST depression in J-60/J-80 point (or between J-60 and J-80) (Fletcher et al., 2001).

Simulated Hypobaric Hypoxia Condition

The SHHC involved a setting where a PiO₂ of 96.3 ± 0.4 mmHg could be reached within 30 min after the volunteers entered the chamber (Center of Chinese Aviation, Peking, China).

Definition of Acute Mountain Sickness

High altitude -related symptoms were assessed using the Lake Louis Scoring System (LLS) (Roach et al., 2018). The LLS allocates a score of 0–3 (representing symptoms not present to severe symptoms) for AMS symptoms (headache, gastrointestinal symptoms, fatigue/weakness, dizzy/light-headache, and difficulty sleeping). The diagnostic criteria for AMS included two aspects: (1) 6 h after arriving at an altitude above 2,500 m, or a higher altitude; (2) a headache occurs and the sum of the scores reaches three points.

Statistical Analysis

In the primary analyses, we compared the data in obtained under the three scenarios (sea level, SHHC, and high altitude) using analysis of variance, and/or non-parametric tests and paired t-tests for comparison between two groups.

For secondary analyses, we compared the data obtained under each scenario between the AMS and non-AMS groups, using t-tests for continuous variables and the χ² test for categorical variables. A binary logistic regression analysis was performed using high-altitude data. Relative risks with 95% confidence intervals (CIs) were computed.

All statistical analyses were performed using SPSS statistics, version 17.0 (IBM Corp., Armonk, NY, United States). A P value of <0.05 was considered statistically significant. Independent associations were expressed as the B coefficient and 95% CI.

Results

Study Cohort and Subjects Characteristics

Eighty young male volunteers were enrolled in the study. Their mean age was 26.0 years and their mean body mass index was 23.1 kg/m².

Biochemical Parameters of Acute Exposure to High Altitude and SHHC

Blood biochemical parameters and blood cell levels were examined in three different experimental settings, the sea level, SHHC and high altitude before exercise. Part of circulating inflammation makers were changed, respectively. The count of white blood cells, alkaline phosphatase and serum albumin were higher at high altitude (P < 0.05). However, C-reaction protein increased but no significance among three conditions (Table 1).

TABLE 1

Comparison of circulating inflammation markers in three scenarios.

Parameters	Sea level group	High altitude group	SHHC group	F value	P value
C-reactive protein (mg/dL)	0.140.24	0.080.09	0.120.24	1.734	0.179
Total protein (g/L)	75.553.10	77.444.19	71.93.66	45.78	< 0.001**
Urea (mmol/L)	4.470.95	4.920.89	4.730.97	4.74	0.01*
Serum uric acid (μmol/L)	353.5058.53	355.1175.92	352.5763.79	0.029	0.971
Homocysteine (μmol/L)	25.6220.12	26.8122.22	20.9414.80	2.006	0.137
Triglycerides (mmol/L)	1.030.41	1.710.94	1.631.02	15.55	< 0.001**
Low density lipoprotein cholesterol (mmol/L)	2.600.62	2.590.65	2.410.62	2.158	0.118
Sodium (mmol/L)	139.6414.50	143.312.05	140.271.67	4.221	0.016*
Magnesium (mmol/L)	0.880.05	0.850.05	0.860.05	8.153	<0.001
Direct bilirubin (μmol/L)	4.991.82	3.221.37	3.931.83	22.12	< 0.001**
Aspartic acid aminotransferase (U/L)	20.1311.25	18.405.33	18.247.67	1.205	0.302
Creatine kinase (U/L)	178.49147.42	144.4271.96	153.03122.04	1.778	0.171
γ-glutamine transferase (U/L)	18.587.52	20.7010.36	18.5810.42	1.304	0.273
Glucose (mmol/L)	4.860.43	5.490.68	5.660.73	35.5	< 0.001**
Serum albumin (g/L)	48.602.23	51.742.30	47.882.30	64.13	< 0.001**
Creatinine (μmol/L)	77.789.37	81.6313.59	74.8810.81	6.9	0.001**
Troponin T (ng/ml)	0.00600.011	0.0050.002	0.0050.004	1.048	0.352
Total cholesterol (mmol/L)	4.210.66	4.390.69	3.930.68	9.127	< 0.001**
High density lipoprotein cholesterol (mmol/L)	1.510.31	1.380.31	1.320.29	7.906	< 0.001**
Potassium (mmol/L)	4.160.30	4.130.34	4.130.23	0.276	0.759
Chloride (mmol/L)	98.792.44	102.491.74	101.901.83	75.16	< 0.001**
Total bilirubin (μmol/L)	14.716.01	8.103.88	10.645.71	31.5	< 0.001**
Alanine aminotransferase (U/L)	21.3715.99	22.5914.00	21.9716.15	0.123	0.884
Lactate dehydrogenase (U/L)	186.7445.82	181.2534.49	173.5137.10	2.206	0.112
Creatine kinase isoenzyme (U/L)	15.817.95	12.967.46	15.3513.90	1.797	0.168
Alkaline phosphatase (U/L)	69.9118.32	77.3620.90	69.8617.81	4.054	0.019*
Hemoglobin (g/L)	156.758.75	162.438.17	154.709.10	16.558	< 0.001**
Leukocyte (*109/L)	6.581.67	8.011.96	6.411.67	18.939	< 0.001**
Lymphocytes	0.360.08	2.530.73	0.310.07	693.092	< 0.001**
Eosinophils	0.0270.04	0.120.09	0.020.03	69.35	< 0.001**
Platelet count (pg)	222.7944.37	224.7848.46	236.044.47	1.903	0.151
Red blood cell count (fl)	5.200.35	5.220.38	5.080.34	3.539	0.031*
Neutrophils (*109/L)	0.540.08	4.951.89	0.600.11	414.174	< 0.001**
Monocyte (*109/L)	0.060.01	0.370.11	0.050.02	589.858	< 0.001**
Basophils (*109/L)	0.0060.003	0.0480.025	0.0060.003	212.854	< 0.001**

Exercise Tolerance and Safety of Acute Exposure to High Altitude and SHHC

Exercise tolerance was indicated by HR_max, HRR, exercise time, and METs. After a rapid entry into the plateau (high altitude) and SHHC, HRmax and HRR were increased significantly during TET as compared to that at sea level. There was no significant difference in exercise tolerance between SHHC and high-altitude exposures; however, HRmax and HRR were significantly higher than at sea level (Table 2).

TABLE 2

Comparison of exercise tolerance in three scenarios.

Indexes	1Sea level	2High altitude	3SHHC	P 1–2, P 1–3, and P 2–3
HRmax	143.49 ± 15.70	157.65 ± 9.94	155.16 ± 10.60	<0.001, <0.001, and 0.074
HRR	59.88 ± 16.53	65.66 ± 12.47	64.99 ± 12.08	0.008, 0.013, and 0.690
Exercise Time (min)	8.03 ± 0.24	7.22 ± 0.96	7.05 ± 1.02	<0.001, <0.001, and 0.235
METs	10.05 ± 0.31	9.38 ± 1.12	9.62 ± 1.11	<0.001, 0.001, and 0.126

Adverse events were severe arrhythmia and exercise-induced ischemia. Bland–Altman consistency analyses showed that 7.5% (6/80) of exercise time difference values were outside of the 95% CI, 2.5% (2/80) of HRR difference values were outside of the 95% CI, and 8.75% (7/80) of the METs difference values were outside of the 95% CI. These results were estimated as clinically permitted normal ranges. There were no significant differences in the occurrence of arrhythmia and exercise-induced ischemia between high altitude and SHHC exposure (7.5 vs. 16.25%, P = 0.655, 8.75 vs. 7.5%, P = 0.763, respectively), and no subject suffered from severe arrhythmia during exercise under either exposure condition.

Cardiovascular Hemodynamics During Exercise at Three Different Conditions

After acute entrance to high altitude and SHHC, the SO₂ before and immediately after exercise decreased significantly, as compared to sea level (Pre-exercise: High altitude vs. SHHC vs. Sea level, 81.20 ± 5.9 vs. 98.00 ± 1.36 vs. 84.53 ± 4.05, P < 0.001; Post-exercise: High altitude vs. SHHC vs. Sea level, 73.67 ± 6.88 vs. 76.88 ± 4.76 vs. 94.76 ± 6.66, P < 0.001) (Table 2).

After the acute entrance to high altitude and SHHC, the trend of changes in systolic blood pressure (SBP) during exercise in volunteers was similar to that at sea level, but both were higher than the SBP at sea level (P < 0.001). DBP was significantly lower during SHHC exposure than during the other two exposures (P = 0.001) (Figure 1).

Blood pressure changes during exercise.

After rapid exercise at high altitude, RPP gradually increased with the increase in exercise intensity, and recovered after exercise. Both RPP values were higher than that at sea level (P < 0.001) (Figure 2).

Rate–pressure product (RPP) changes during exercise.

A New Method for Predicting AMS Based on Exercise Test Data

Soon after exposure to 4,000 m (high altitude), 19 (23.8%) subjects developed AMS. Table 3 shows the details of the clinical profiles, including SO₂, heart rate, blood pressure, RPP, and HRR, along with exercise test data, and basic subject characteristics. As expected, exercise time at sea level was shorter in the AMS group than in the non-AMS group. Notably, DBP at rest under the SHHC was lower in the AMS group than in the non-AMS group.

TABLE 3

Comparison of exercise test parameters between AMS group and non-AMS group.

Parameters	non-AMS (N = 61)	AMS (N = 19)	P value
Age (year)	26.082.55	25.531.90	0.411
Height (cm)	175.035.00	175.216.12	0.704
Weight (kg)	71.237.41	70.329.01	0.292
Pre-SO2 1 (%)	98.111.35	97.671.37	0.232
Post-SO2 1 (%)	94.936.94	94.225.81	0.684
HRrest 1 (bpm)	81.2111.85	77.4510.62	0.220
HRmax 1 (bpm)	143.3115.46	144.0516.87	0.859
Exercise time1 (min sec)	8.060.17	7.920.38	0.022**
METs1–[3.5 ml/(kg⋅min)]	10.080.10	9.940.60	0.318
SBPrest1 (mmHg)	121.4912.02	123.216.10	0.552
DBPrest1 (mmHg)	75.748.89	76.637.70	0.694
RPPrest1 (mmHg*bpm)	9860.611803.92	9523.951325.05	0.455
HR3min1 (bpm)	104.2114.79	97.5315.52	0.093*
SBP3min1 (bpm)	131.1613.80	128.7411.78	0.491
DBP3min1 (mmHg)	70.8212.38	74.7918.18	0.282
RPP3min1 (mmHg)	13671.522553.54	12587.842434.65	0.104*
HR6min1 (bpm)	120.8213.93	117.5812.88	0.370
SBP6min1 (mmHg)	135.9216.26	137.749.22	0.644
DBP6min1 (mmHg)	65.087.91	64.479.27	0.780
RPP6min1 (mmHg*bpm)	16450.442935.74	16268.162349.59	0.806
HR9min1 (bpm)	128.7915.19	128.0015.35	0.845
SBP9min1 (mmHg)	139.8016.94	136.1614.46	0.400
DBP9min1 (mmHg)	62.747.59	63.327.22	0.770
RPP9min1 (mmHg*bpm)	17785.773549.93	17117.892716.86	0.454
Recovery–HR3min1 (bpm)	89.3016.43	85.4016.67	0.370
Recovery–SBP3min1 (mmHg)	131.1314.71	129.4214.17	0.657
Recovery–DBP3min1 (mmHg)	74.519.22	78.005.53	0.123*
Recovery–RPP3min1 (mmHg*bpm)	11879.333171.93	10990.442583.83	0.310
Recovery–HR6min1 (bpm)	83.8013.46	80.4211.77	0.328
Recovery–SBP6min1 (mmHg)	122.4414.74	118.588.59	0.282
Recovery–DBP6min1 (mmHg)	75.5212.19	76.266.22	0.801
Recovery–RPP6min1 (mmHg*bpm)	10250.702172.81	9470.841255.01	0.142*
HRR1 (bpm)	60.2816.42	59.2117.34	0.871
Pre-SO2 3 (%)	84.574.16	84.003.46	0.588
Post-SO2 3 (%)	76.974.93	76.324.62	0.612
HRrest 3 (bpm)	92.3411.83	95.7415.07	0.634
HRmax 3 (bpm)	154.9210.36	155.9511.60	0.714
Exercise Time3 (min sec)	7.160.90	6.721.28	0.178
METs3–[3.5ml/(kg⋅min)]	9.751.01	9.201.32	0.112*
SBPrest3 (mmHg)	121.3016.82	120.5312.04	0.854
DBPrest3 (mmHg)	65.3911.19	58.639.53	0.020**
RPPrest3 (mmHg*bpm)	11133.612199.39	11042.211707.01	0.869
HR3min3 (bpm)	123.5413.58	128.7415.35	0.162
SBP3min3 (mmHg)	138.2621.42	143.4717.55	0.338
DBP3min3 (mmHg)	58.5413.17	52.379.79	0.063*
RPP3min3 (mmHg*bpm)	16774.342712.07	17301.684139.02	0.519
HR6min3 (bpm)	134.0817.71	138.1116.14	0.380
SBP6min3 (mmHg)	139.7721.11	141.1120.09	0.808
DBP6min3 (mmHg)	58.4911.52	59.0511.77	0.854
RPP6min3 (mmHg*bpm)	19184.163129.94	19904.793596.28	0.400
HR9min3 (bpm)	136.4312.12	142.5019.47	0.534
SBP9min3 (mmHg)	139.4328.30	150.0026.43	0.527
DBP9min3 (mmHg)	57.5712.31	56.605.32	0.873
RPP9min3 (bpm)	18858.143387.94	19868.252986.06	0.633
Recovery-HR3min3 (bpm)	98.9211.91	102.1611.67	0.301
Recovery–SBP3min3 (mmHg)	135.5716.11	135.7916.74	0.960
Recovery–DBP3min3 (mmHg)	65.208.58	60.689.38	0.054*
Recovery–RPP3min3 (mmHg*bpm)	13347.442085.65	13932.532292.61	0.300
Recovery–HR6min3 (bpm)	92.6613.21	96.479.90	0.249
Recovery–SBP6min3 (mmHg)	125.0714.34	130.4214.27	0.159
Recovery–DBP6min3 (mmHg)	66.898.12	62.5310.83	0.064*
Recovery–RPP6min3 (mmHg*bpm)	12002.431674.82	12593.111800.22	0.191
HRR3 (bpm)	64.7011.76	65.8913.34	0.710
Exercise result3 (n)	5	1	0.561

Multivariate logistic regression model showed that METs under the SHHC [odds ratio (OR) 0.355 per unit increment (95% CI 0.159−0.793), P = 0.011], DBP at rest under the SHHC [OR 0.893 per mmHg (95%CI 0.805−0.991), P = 0.030], and recovery DBP 3 min after exercise at sea level [OR 1.179 per mmHg (95% CI 1.043−1.333), P = 0.008] were independently associated with AMS (Table 4).

TABLE 4

Binary logistic regression of AMS.

	B	S.E.	Walds	P value	Odds ratio (95% CI)
METs3	−1.035	0.410	6.387	0.011*	0.355 (0.159–0.793)
DBPrest3	−0.113	0.053	4.500	0.034*	0.893 (0.805–0.991)
DBP3min3	−0.011	0.043	0.064	0.800	0.989 (0.909–1.077)
Recovery–DBP3min3	−0.053	0.054	0.962	0.327	0.948 (0.852–1.055)
Recovery–DBP6min3	−0.045	0.055	0.671	0.413	0.956 (0.859–1.065)
Exercise time1	−6.300	3.811	2.732	0.098	0.002 (0.000–3.222)
HR3min1	0.085	0.052	2.655	0.103	1.089 (0.983–1.207)
RPP3min1	0.000	0.000	3.755	0.053	0.999 (0.998–1.000)
Recovery–DBP3min1	0.165	0.062	6.977	0.008*	1.179 (1.043–1.333)
Recovery–RPP6min1	0.000	0.000	1.360	0.244	1.000 (0.999–1.000)

The predictive model based on these variables had an area under the curve (AUC) of 0.886. The ROC curve analysis for these variables as a predictor of AMS is shown in Figure 3 [AUC = 0.886 (95% CI 0.803−0.969), P < 0.001].

Receiver-operating characteristic (ROC) curve of the predictive model as a predictor of acute mountain sickness (AMS) in young men as determined by logistic regression analysis. The area under the ROC curve for the model was 0.886.

Discussion

After acute exposure to hypobaric hypoxia, the cardiovascular system adapts to the new environment (i.e., acclimatization), which may cause AMS. In order to exercise safely and enhance aerobic performance, it is recommended that the appropriate exercise tolerance of individuals be predicted. TET is widely used for the estimation of exercise intensity in hospitals. In addition, exercise tests are commonly used on site and at equivalent altitudes (Richalet, 2012). Therefore, TET could be used to analyze exercise tolerance and safety. In this study, a standard submaximal TET was used while subjects were acutely exposed to a 4,000-m high altitude and to a SHHC, as well as to sea level. The young male subjects displayed equivalent exercise tolerance when acutely exposed to high altitude and to SHHC. The trends of cardiovascular hemodynamics during exercise at the high altitude, under the SHHC, and sea level were similar. However, both SBP and RPP were higher at high altitude and under the SHHC than at sea level. DBP was significantly lower under the SHHC than with the other two exposures. Furthermore, results showed significant circulating inflammation related markers increases in the count of white blood cells, alkaline phosphatase and serum albumin, but not C-reactive protein after acute exposure to high altitude. The main findings of this study indicate that DBP during recovery 3 min at sea level and METs and rest DBP under the SHHC can independently predict AMS. Using our model, we could predict AMS development with an accuracy of 88.6%.

Our research found that the circulating inflammation markers were increased after exposure to high altitude. An elevated white blood cell count could be caused by stressors, such as exercise, trauma and emotional stress (Riley and Rupert, 2015). The proliferation and circulation of immune cells in response to acute stress results in association with increased circulating levels of epinephrine (Dragoş and Tănăsescu, 2010). We got the results in increase of alkaline phosphatase and albumin activity upon induction to the high altitude stress. This result is consistent with the findings of previous study (Rawal et al., 1999). The increase of albumin was observed after acute active and passive ascent to high altitude (Imoberdorf et al., 2001).

We found that RPP was significantly higher at high altitudes and under the SHHC at every stage than at sea level. Higher RPP is due to a higher heart rate and SBP, imposing a greatly increased myocardial oxygen demand. Moreover, sub-maximal exercise is safe when the exercise intensity was slightly decreased at high altitudes and under the SHHC than at sea level, but activities requiring an energy expenditure of eight METS and above are considered to be of high intensity (Jetté et al., 1990). At high altitudes, submaximal heart rate and cardiac output can rise as much as 50% above sea level values, whereas the heart's stroke volume remains unchanged (Insalaco et al., 1996). In the young men in our study, exercise tolerance was equivalent upon acute exposure to high altitude and to SHHC. In addition, it has been previously demonstrated that genuine high altitude and SHHC produce similar cardiac adaptations at rest and during exercise (Boos et al., 2016).

To date, the main risk factors for AMS have been variables related to hypoxia (Tannheimer et al., 2009; Karinen et al., 2010; Canouï-Poitrine et al., 2014; Sutherland et al., 2017). Hypoxia is one of the main triggers for AMS. It contributes to sympathetic activation and circulatory changes, such as greatly increased myocardial oxygen demand and cardiac work (Schmid et al., 2006; Boos et al., 2016). Multiple compensatory changes due to hypoxia, such as decreased maximal oxygen consumption and aerobic exercise capacity, may be present to varying extents among individuals who may be susceptible to AMS (Canouï-Poitrine et al., 2014; Khodaee et al., 2016). Recent study showed that the history of Severe high altitude Illness, ventilatory, and cardiac responses to hypoxia during exercise, speed of ascent, desaturation during hypoxic exercise, history of migraine, geographical location, female sex, age under 46 years, and regular physical activity were associated with AMS incidence (Canouï-Poitrine et al., 2014). We examined SO₂ before and after exercise and found that it did not contribute to the AMS prediction model. We used TET to observe cardiac responses at sea level and under the SHHC and found that exercise intensity significantly contributed to the model, reinforcing that such subjects with low METs and rest DBP at SHHC, and higher DBP during recovery at sea level likely suffered AMS. Furthermore, exercise tolerance is affected by both hypoxia and hypobaric conditions (Boos et al., 2016). Thus, hypoxia exercise testing may not be sufficient to simulate exercise at high altitude.

Other models for prediction of AMS have been proposed. Karinen et al. (2010) and Faulhaber et al. (2014) found that arterial oxygen saturation at rest and during ascent are predictors of AMS. However, Leichtfried et al. (2016) found no strong altitude-independent association between AMS and SPO₂ during the first week of high-altitude adaptation. The implementation of pulse oximetry during trekking for detecting and predicting AMS remains questionable (Faulhaber et al., 2014). The major limitation of these two prediction models is the relatively low or ineffective prediction value before acute entrance to high altitudes. In our analyses, we found that SO₂ was not associated with the risk of AMS (Karinen et al., 2010; Faulhaber et al., 2014).

Age was thought to be an important factor in a previous AMS prediction model (Canouï-Poitrine et al., 2014), but a meta-analysis suggested that there was no association between age and the risk of AMS (Wu et al., 2018). Several studies found that smoking, heart rate variability, and anxiety might be other risk factors associated with AMS (Sánchez-Mascuñano et al., 2017; Boos et al., 2018); however, the results remain controversial (Masuet-Aumatell et al., 2017). Despite the large number of patients, the discrimination of prediction model was more than 0.85, which was similar to our findings (Canouï-Poitrine et al., 2014). For a prediction model to be adopted in clinical practice, it must not only be statistically valid, but also computationally simple. Our prediction model involves a simpler scoring system than previous models and relies less on complicated data.

Study Limitations

Our results and conclusions are limited to healthy young men. We excluded women and those with disease in order to focus on a homogeneous group acutely exposed to a high altitude for the first time. Although the overall accuracy of the prediction was high (AUC = 0.886), our model needs to be assessed in another group for outside validation, as performance of a model based on a single institution tends to decrease when used in a new setting. Moreover, we found circulating inflammation markers and impaired exercise capacity response to high altitude, but can't able to result in their relationship.

Conclusion

We found that young men display a similar exercise reaction when acutely exposed to high altitudes and to SHHC. We have developed a model for predicting AMS based on a standard TET, which includes METs under the SHHC, DBP at rest under the SHHC, and recovery DBP 3 min after exercise at sea level. This model can bu used to predict which athletes would develop AMS during high altitude training.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Ethics Statement

The studies involving human participants were reviewed and approved by Chinese PLA General Hospital. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

Y-JS, J-LW, and Y-DC contributed to the experiments design and data analysis. J-LW and QD contributed to the data collection and manuscript writing. LG and D-LW contributed to the data analysis. Y-TG, C-HZ, YD, JG, Z-BL, and T-JL contributed to the manuscript writing. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

Funding. This study was supported by the National Science and Technology Major Projects for Major New Drugs Innovation and Development (Grant 2014ZX09J14102-02A) and the National Natural Science Foundation of China (81170249).

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7991828/

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