Comprehensive viewpoints on heart rate variability at high altitude

ABSTRACT

Objectives

Hypoxia is a physiological state characterized by reduced oxygen levels in organs and tissues. It is a common clinicopathological process and a major cause of health problems in highland areas.  Heart rate variability (HRV) is a measure of the balance in autonomic innervation to the heart. It provides valuable information on the regulation of the cardiovascular system by neurohumoral factors, and changes in HRV reflect the complex interactions between multiple systems. In this review, we provide a comprehensive overview of the relationship between high-altitude hypoxia and HRV. We summarize the different mechanisms of diseases caused by hypoxia and explore the changes in HRV across various systems. Additionally, we discuss relevant pharmaceutical interventions. Overall, this review aims to provide research ideas and assistance for in-depth studies on HRV. By understanding the intricate relationship between high-altitude hypoxia and HRV, we can gain insights into the underlying mechanisms and potential therapeutic approaches to mitigate the effects of hypoxia on cardiovascular and other systems.

Methods

The relevant literature was collected systematically from scientific database, including PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Baidu Scholar, as well as other literature sources, such as classic books of hypoxia.

Results

There is a close relationship between heart rate variability and high-altitude hypoxia. Heart rate variability is an indicator that evaluates the impact of hypoxia on the cardiovascular system and other related systems. By improving the observation of HRV, we can estimate the progress of cardiovascular diseases and predict the impact on other systems related to cardiovascular health. At the same time, changes in heart rate variability can be used to observe the efficacy of preventive drugs for altitude related diseases.

Conclusions

HRV can be used to assess autonomic nervous function under various systemic conditions, and can be used to predict and monitor diseases caused by hypoxia at high altitude. Investigating the correlation between high altitude hypoxia and heart rate variability can help make HRV more rapid, accurate, and effective for the diagnosis of plateau-related diseases.

KEYWORDS:

• Plateau
• hypoxia
• heart rate variability
• cardiovascular autonomic function monitoring
• drugs

Theoretical background

Traceability of HRV

Over the past decades, there has been a growing interest in researching diseases affecting various human organic systems, particularly in identifying reference indicators for circulatory system diseases. Heart rate variability (HRV) has gained popularity among researchers from diverse disciplines such as cardiology and neurology due to its role in evaluating autonomic nervous function. Initially, HRV was defined as the beat-to-beat time coefficient of variation, which captures the changes in the interval between each heartbeat (1). Subsequent research has expanded the understanding of HRV, considering it as an indicator of autonomic nervous function (2,3). It is now recognized that HRV arises from the interaction between the heart and brain, reflecting the dynamic and nonlinear nature of the autonomic nervous system. This is manifested as changes in the time intervals between adjacent heartbeats, which can be measured using the Respiration Rate (RR) interval of an electrocardiogram (4,5). In 1965, Hon and Lee observed differences in heart rate patterns between normally and abnormally delivered fetuses by monitoring fetal heart rates, leading to the term “HRV.” Since then, HRV has been used to monitor fetal heart rate as an indicator of the health status of newborns or unborn fetuses (6). During the 1970s, the study of HRV gradually became associated with pathological states of the body, and significant correlations were found between HRV changes and certain autonomic nervous diseases such as diabetic autonomic neuropathy. In 1976, Bennett discovered that monitoring heart rate in diabetic patients could help track the progression of diabetic autonomic neuropathy. This was accomplished by continuously recording the heart rate of 11 diabetic patients and comparing it with that of diabetic outpatients from 10 months earlier (7,8). In 1977, Lowensohn et al. conducted a study involving 10 patients with neurological deficits and found that severe brain injury, in the absence of drug intervention, resulted in a reduction of the normal cyclical variability in heart rate. Moreover, the presence of elevated intracranial pressure caused a rapid decrease in variability. The recovery rate of variability reflected subsequent neuronal functional status, suggesting that HRV may reflect the functional status of the central nervous system (9). With further in-depth research, starting in the 1980s, the study of HRV expanded to multiple systems of the body, including the nervous system, respiratory system, and endocrine system. Rhythmic changes in HRV were also observed simultaneously (10–13). In 1990, Malpas and colleagues observed the 24-hour dynamic electrocardiogram of patients with vagal neuropathy and found that the average heart rate variation in the vagal neuropathy group was significantly reduced, while HRV increased during sleep. This indicated a significant circadian rhythm change in HRV (14). In 1996, the European Society of Cardiology and the North American Society for Pacing and Electrophysiology classified HRV into four frequency bands: ultra-low frequency (ULF), very low frequency (VLF), low frequency (LF), and high frequency (HF). The LF (0.04–0.15 Hz) and HF (0.15–0.4 Hz) bands are used as separate indicators of sympathetic and vagal parasympathetic function (15). Since the heart is influenced by both sympathetic and parasympathetic nerves, HRV is often utilized as one of the indicators for monitoring cardiovascular disease in clinical settings.

The heartbeat is a complex and nonlinear process, allowing the heart to adapt flexibly to changing environments, resulting in corresponding changes in heart rate variability (HRV) (16). However, when the environment undergoes significant changes or the heart experiences abnormal conduction, such as atrial fibrillation or other pathological conditions that exceed the heart’s adaptability and self-regulation, HRV deviates from normal levels (4,17). Furthermore, studying the interaction between HRV and heart rate dynamics can enhance HRV’s predictive capability for cardiovascular mortality (18). It is important to note that HRV changes can also be influenced by multi-system injuries, including the autonomic nervous system, respiratory system, and digestive system. Therefore, HRV serves not only as an indicator of cardiac function but also reflects autonomic nerve regulation, lung capacity for gas exchange, and intestinal regulation, providing valuable insights for the clinical prevention and treatment of various diseases. Monitoring HRV changes can guide the selection and intensity of exercise (5,19,20). However, due to the influence of multiple systems and the lack of strong specificity, the clinical application of HRV remains limited. Currently, HRV is primarily used to evaluate autonomic nerve function at the sinus node and predict the risk of circulatory system diseases. It has been integrated into the clinical assessment of autonomic nerve function in cardiovascular diseases and is gradually expanding to include high altitude hypoxic diseases (21)(Figure 1).

Figure 1. Traceability of HRV.

Analysis method of HRV

Heart rate variability analysis encompasses various methods to study the patterns and characteristics of HRV. The main analysis methods currently used are time domain analysis, frequency domain analysis, and nonlinear analysis, which aim to quantify and describe HRV in different ways (22). Both time domain and frequency domain analyses are linear methods, but since human physiological activities are complex and nonlinear, simple linear analysis may not fully capture their inherent nonlinear properties in a comprehensive and objective manner (23,24). As a result, nonlinear analysis methods have been developed to better describe the characteristics of HRV. Time domain analysis is a linear method used to assess the regulation of the autonomic nervous system on heart rate. It involves statistical and geometric analysis of specific values of sinus R-R intervals arranged chronologically, typically using short-term records with a standard at least 5 minutes (5,15,25,26). Parameter indicators for time domain analysis include standard deviation of all normal to normal RR intervals (SDNN), coefficient of variation of NN intervals (CVNN), root mean square of the differences between adjacent NN intervals (RMSSD), pairs of adjacent NN intervals differing by more than 50 ms (NN50/pNN50), as well as triangular index and histogram analysis, which serve as reference indicators for evaluating HRV in the time domain (27). Frequency domain analysis is another linear method that involves mathematical transformations, such as Fast Fourier Transform or autoregressive modeling, to calculate the power density spectrum of heart rate variation signals. This analysis is commonly used to observe functional changes in the sympathetic and parasympathetic nervous systems and to complement the information obtained from time domain analysis. Frequency domain analysis divides HRV into four frequency bands: ULF, VLF, LF, and HF. Parameters such as LF, HF, and LF/HF ratio are widely used in frequency domain analysis (15,28). Nonlinear analysis methods include Poincaré plot analysis and approximate entropy analysis, among others. Poincaré plot analysis combines geometry and data analysis, allowing for the visualization of biological signals. Approximate entropy analysis calculates the approximate entropy using a formula. Mesin et al. elaborated on the calculation of approximate entropy in their article published in 2018 and analyzed that a higher approximate entropy value indicates greater complexity in the signal’s fluctuations and is used to evaluate the complexity of biological signals (29–31). However, the clinical application of nonlinear analysis is currently limited due to its involvement of multiple professional fields, complex content, lack of standardization, systematic approaches, and unified criteria. In addition to the traditional analysis methods based on RR interval observations from electrocardiograms, complementary analysis methods such as seismic electrocardiogram and gyro electrocardiogram have emerged (32). Seismic electrocardiogram is a noninvasive technique that records and analyzes cardiac activity by measuring the acceleration of the precordial area. It can be used for cardiac intervention, atrial fibrillation detection, and the diagnosis of heart failure and myocardial ischemia. Gyro electrocardiogram analyzes cardiac mechanical activity and function by capturing and analyzing changes in chest angular velocity related to cardiac activity. It provides evidence for the auxiliary diagnosis of cardiovascular diseases, including atrial fibrillation, coronary artery disease, and acute compensatory heart failure (33). Several studies have shown that HRV indicators can be effectively derived from seismic electrocardiogram and gyro electrocardiogram recordings (34–36)(Table 1).

Table 1. Analysis method of HRV.

Analysis method		Measure method	Significance	Reference Indicators
Linear analysis	Time domain analysis (5,15,25)	Specific values of the R-R interval of sinus rhythm in chronological order	Evaluate the regulation of autonomic nerve on heart rate	SDNN, CVNN, RMSSD, NN50/pNN50, Triangle index, Histogram analysis
	Frequency domain analysis (15,26)	Calculate the power density spectrum of heart rate variability signals using mathematical transformation methods such as fast Fourier transform or autoregressive modeling	To observe the functional changes of Sympathetic nervous system and Vagus nerve to make up for the shortcomings of time-domain analysis	LF, HF, LF/HF
Nonlinear analysis	Poincar é diagram analysis (27–29)	Combining Geometry and Data Analysis	Can achieve visualization of biological signals	＼
	Approximate entropy analysis (27–29)	Calculate the approximate entropy using a formula	The higher the approximate entropy value, the more complex the changes are, used to evaluate the complexity of biological signals	＼
Seismic electrocardiogram		A noninvasive technique for recording and analyzing cardiac activity by measuring precordial acceleration (31)	Assisted diagnosis for cardiac intervention, atrial fibrillation detection, heart failure, and myocardial ischemia	＼
Gyroscopic electrocardiogram		Analyze the mechanical activity and cardiac function of the heart by collecting and analyzing changes in chest angular velocity related to cardiac activity (31)	Provide evidence for auxiliary diagnosis of cardiovascular diseases such as Atrial fibrillation, Coronary artery disease, acute compensatory heart failure, etc	＼

The role of HRV in pathology

HRV has a certain predictive value of many common diseases, but there is no comprehensive summary of its pathological role. Current studies have shown that a variety of inflammatory factors are closely related to HRV changes, C-reactive protein (CRP) is an acute phase inflammatory factor with dual effects of anti-inflammatory and pro-inflammatory (37–47). High-sensitivity C-reactive protein (hs-CRP) is a kind of CRP with higher sensitivity in plasma. In most cases, CRP and hs-CRP often appear as indicators of the prevention, diagnosis and treatment of cardiovascular diseases. In hypertensive patients, HRV parameters were negatively correlated with CRP and hs-CRP, indicating that subclinical inflammatory response in hypertensive patients was related to autonomic nervous dysfunction. Similarly, hs-CRP was also negatively associated with HRV in patients with perinatal cardiomyopathy, elderly coronary heart disease, and unstable angina (48–52). In patients with atrial fibrillation, HRV parameters were positively correlated with CRP and hs-CRP (37,38). In addition, HRV changes are also related to inflammatory factors such as fibrinogen, IL-6, white blood cell count, lipoprotein-associated phospholipase A2 (Lp-PLA2) and other inflammatory factors (37,38,49). It can be seen that the changes of CRP and hs-CRP have an impact on the changes of HRV parameters, which also indicated the trend of HRV changes and suggested the severity of cardiovascular diseases. Similarly, studies have shown that the pathogenesis of diabetes is closely related to the inflammatory response, and HRV can also be used as a diagnostic tool to reflect the severity of diabetes. In type 1 diabetes, inflammatory factors IL-1α, IL-4, IL-12p70, TNF-α and adhesion molecule E-selectin have been shown to be negatively correlated with 24-hour HRV parameters (53). For type 2 diabetes, the increase of classical inflammation factor interleukin-12/23p40 is closely related to the decrease of VLF. The adhesion factor intercellular adhesion molecule (ICAM −1), soluble vascular cell adhesion molecule-1 (SCVAM-1), soluble intercellular adhesion molecule-1 (SCVAM-1), soluble Adhesion molecule-1 (sICAM-1) and soluble E-selectin, which mediate inflammation, have stable negative correlations with VLF, HRVI, SDANN and RMSSD, respectively (54,55). The anti-inflammatory factor, adiponectin, was positively correlated with HRV frequency domain indicators SDNN, RMSSD, and time domain indicators LF, but negatively correlated with VLF (39,55).

Nitric oxide (NO) is widely present in various cells and tissues, and is associated with many cardiovascular diseases due to its effects on reducing platelet aggregation, inhibiting neutrophil adhesion, and regulating myocardial contractile function. Numerous studies have shown that it plays an important role in affecting HRV. In animal experiments, Markos et al showed a fold decrease of HRV in healthy dogs treated with TRIM, a NO synthase inhibitor (40,41). Another type of NO synthase inhibitor, L-NAME, was used in rats, resulting in HRV time-domain and frequency-domain parameters are both decreased. Clinical trials related to NO focused on hypertensive patients. Sun Caihong et al. showed that serum NO levels in patients with essential hypertension were negatively correlated with LF, LF/HF, and positively correlated with HF and SDANN. Similarly, HRV attenuation was also associated with lower NO levels in pregnant women at risk of hypertension during pregnancy (56). In addition, the transforming growth factor-β superfamily, which is involved in regulating cell growth and differentiation are important mediators of HRV changes induced by air pollutants such as polycyclic aromatic hydrocarbons, acrylamide, styrene and ethylbenzene, it was negatively correlated with HRV in chronic kidney disease (CKD) patients (42–45). Another key molecule that regulates the sympathetic nervous system, norepinephrine (NE), increased in patients with unstable angina, while HRV parameters decreased, showing a negative correlation (57). NE has also been shown to promote tumor cell invasion and migration, and has a significant correlation with HRV changes in liver cancer patients. Endothelin (ET), a long-acting vasoconstriction regulator, is one of the more well-studied substances involved in HRV, the changes of HRV may indicate the quantitative change of ET-1 in patients with slow coronary flow, normotensive glaucoma (46,47).

In addition to the factors mentioned above, many gene mutations in cardiovascular diseases can also be suggested by HRV, such as N-type calcium gene expression, HCN4 gene overexpression, methyltransferase METTL3 knockdown, and deletion of anchoring protein B gene in cardiac sympathetic postganglionic neurons (CSP) can cause HRV abnormalities (58–61). Some factors that regulate cardiac sympathetic and parasympathetic nerves, such as Rho kinase, G protein signaling regulator RGS6, angiotensin II can reflect their abnormalities by means of HRV changes (62–64). The high expression of homocysteine can be judged by HRV in diabetics (65). But notably, lower insulin levels and higher insulin sensitivity were related to increased HRV in nondiabetic patients (66). Although HRV changes are related to variety of inflammatory factors and gene mutations, however, its specificity and sensitivity are poor, and further research is needed.

Relationship between HRV and Plateau hypoxia -related diseases

Hypoxia is a prominent characteristic of high-altitude areas and plays a significant role in the development of plateau-related diseases (67,68). It has been established that hypoxia is a contributing factor to changes in HRV, which serves as an index reflecting autonomic nervous function. Research has shown that high HRV variability is often indicative of adaptability and good health, whereas reduced HRV variability is a sign of inadequate adaptability and physiological dysfunction (22,69,70). Upon entering a high-altitude environment, the oxygen saturation (SaO2) in the blood decreases, leading to corresponding changes in autonomic nervous system regulation (71). When the body fails to adapt to the high-altitude environment, it can result in high-altitude illnesses, characterized by decreased HRV frequencies compared to normal levels (72,73). Therefore, there may be a correlation between the high-altitude environment and changes in HRV. Although alterations in HRV alone cannot provide a specific disease diagnosis, they serve as a valuable indicator of changes in health status (74). Consequently, HRV can be utilized to assess the prevention, occurrence, and prognosis of high-altitude-related diseases (Table 2)(Figure 2).

Figure 2. High altitude hypoxia-related diseases.

The diagram illustrates cardiovascular diseases, respiratory system and endocrine system diseases associated with high altitude hypoxia, the cardiovascular diseases include hypertension, cardiac hypertrophy, pulmonary hypertension, atrial fibrillation, myocardial infarction, and respiratory diseases include high altitude pulmonary edema and obstructive sleep apnea syndrome, endocrine system diseases include hypothyroidism and type 2 diabetes.

Table 2. Relationship between high altitude hypoxia related diseases and HRV.

System	Diseases	Mechanism	HRV Changes
Circulatory	Hypertension (72–80)	Decreased vascular compliance, sympathetic excitation, and cardiac organic damage	The frequency decreased significantly
	Supraventricular arrhythmia and atrial fibrillation caused by pulmonary hypertension (81–86)	Long-term low pressure and hypoxia activate sympathetic, causing changes in cardiac rhythm and autonomic tone	Increased LF and LF/HF, decreased HF in patients
	Myocardial infarction (87–90)	Hypertension, long-term coronary artery spasm	ST segment specific elevation, LF/HF ratio decreased
	Cardiomyopathy (91)	Under the stimulation of high altitude hypoxia, mitochondria may undergo autophagy and certain inflammatory mediators are activated, such as NLRP3 inflammasome	Both LF and HF are significantly suppressed and the amplitude of the QRS complex attenuates instantaneously
Other systems	High altitude pulmonary edema(92–96)	Increased pulmonary artery pressure, coupled with acute hypoxia, lead to changes in autonomic nervous function, decreased heart rate and oxygen saturation, ventricular diastolic dysfunction.	HRV decreases, LF/HF increases
	Obstructive sleep apnea syndrome (97–102)	Endothelial dysfunction, sympathetic activation, increased oxidative stress	With the prolongation of RR interval, LF/HF also decreased significantly
	Diabetes106	\	Low HF and LF
	Hypothyroidism (103)	\	Ectopic pulsation, heart rate changes, RR interval changes

Remarks: HF = high frequency, LF = low frequency.

Plateau hypoxia -related circulatory diseases

Adaptation to the harsh environment of hypoxia, severe cold, and low pressure in plateau areas triggers a series of compensatory reactions in the body. Among the organs, the heart is particularly sensitive to hypoxia. In the early stage of hypoxia, the heart adjusts cardiac output to ensure tissue and organ support by increasing heart rate and systolic blood pressure. However, if arterial oxygen saturation falls below 50%, it can cause severe damage to myocardial function and directly lead to various types of arrhythmias. Chronic hypoxia, which is prevalent at high altitudes, is a significant risk factor for cardiogenic diseases, including hypertension, coronary heart disease, and myocardial infarction. Paradoxically, these cardiovascular diseases can further worsen tissue and organ hypoxia, creating a detrimental cycle. Hypoxia and low pressure at high altitudes stimulate chemoreceptors and baroreceptors, resulting in the activation of the sympathetic nervous system and renin-angiotensin system. This activation leads to increased vasoconstriction and blood pressure (75,76). Prolonged hypertension at high altitudes reduces vascular compliance, impairing the body’s ability to regulate blood pressure and causing increased cardiac afterload and compensatory hypertrophy. Consequently, the heart may experience organic damage, such as myocardial alignment disorder, exacerbated fibrosis, myocardial remodeling, and alterations in HRV, as observed in studies (77–83). Thus, the changes in HRV associated with high-altitude hypertension primarily stem from sympathetic nervous system activation, decreased vascular compliance, and organic damage to the heart induced by high-altitude hypoxia.

Pulmonary hypertension is another prevalent cardiovascular disease in plateau areas, sharing a similar pathogenesis with high-altitude hypertension. The environment of high-altitude hypoxia leads to a decrease in pulmonary vascular compliance, lumen stenosis, and increased resistance, ultimately triggering pulmonary vascular remodeling and the development of pulmonary hypertension (84,85). High-altitude pulmonary hypertension increases ventricular afterload, leading to structural and functional changes in the heart, such as ventricular hypertrophy and decreased ventricular compliance. In advanced stages, it may further result in atrial enlargement and the onset of atrial fibrillation. Atrial fibrillation is a common arrhythmia in clinical practice and is influenced by various factors. Changes in atrial anatomical structure disrupt the electrophysiological mechanism, and abnormal electrophysiological activity promotes remodeling of the atrial anatomical structure, thus creating a vicious circle (86). Additionally, dysfunction of the cardiac autonomic nerve caused by high-altitude hypoxia not only contributes to the development of atrial fibrillation but also promotes its progression. HRV, an effective tool for evaluating autonomic nervous function, can be improved through intervention and is gradually being applied to assess autonomic nervous function in patients with atrial fibrillation (87). Numerous studies have demonstrated that the LF and LF/HF ratios in patients with arrhythmias are higher than those in non-arrhythmic patients, while HF is significantly lower (84,88,89). Although research on HRV and atrial fibrillation is gradually increasing, large-scale studies investigating the relationship between HRV, atrial fibrillation, and high-altitude hypoxia have not yet been reported.

Myocardial infarction is a common clinical cardiovascular disease characterized by the occlusion or rupture of coronary lipid plaques or continuous spasm of the coronary artery, resulting in coronary stenosis or even complete occlusion, leading to myocardial ischemia and injury. In high-altitude environments, the likelihood of inducing coronary artery spasm and myocardial infarction is higher. In addition to specific ST-segment elevation observed on dynamic electrocardiograms, changes in HRV become more pronounced, and the LF/HF ratio decreases (90,91,104,105). High-altitude hypoxia can stimulate the activation of certain inflammatory mediators, such as NLRP3 inflammatory corpuscles, which may contribute to cardiomyopathy (92). The underlying mechanism may be related to mitochondrial autophagy under hypoxia (93). Kujime et al. reported a case of Takotsubo cardiomyopathy syndrome in which HRV’s LF and HF components were significantly suppressed, and these changes were observed earlier than alterations in ECG waveforms. Compared with the ECG during the healing period, the abnormality in HRV on the actual onset day was more pronounced (94). However, this study was a case report, and its accuracy requires further investigation.

The plateau environment can induce various cardiovascular diseases, including hypertension, pulmonary hypertension, atrial fibrillation, and myocardial infarction, all of which are accompanied by changes in HRV. Therefore, it is theoretically possible to evaluate autonomic nervous function and disease progression in cardiovascular diseases at high altitudes by monitoring HRV changes, and to assess the prognosis of these conditions. However, current research on HRV changes caused by plateau hypoxia lacks systematic and in-depth investigations, necessitating further reliable evidence to support these findings.

Plateau hypoxia -related respiratory diseases

Autonomic nerves are extensively distributed throughout various tissues and organs, playing a crucial role in regulating the physiological functions of the human body. Dysfunctions of autonomic nerves are associated with the development of numerous diseases when the body experiences hypoxia. High altitude pulmonary edema (HAPE) is a severe non-cardiogenic condition characterized by pulmonary edema, which can be life-threatening and is caused by low atmospheric pressure and hypoxia at high altitudes. When individuals rapidly ascend to high altitudes, their lungs face excessive stress within a short period, which can trigger HAPE (95). Exposure to high altitude hypoxia results in increased sensitivity of the pulmonary vascular system to the sympathetic nervous system and endothelin, while the response to vasodilators diminishes, leading to hypoxic pulmonary vasoconstriction. This constriction increases pulmonary artery pressure, potentially exacerbating HAPE (86,96). Furthermore, acute hypoxia and decreased oxygen saturation can cause ventricular diastolic dysfunction, resulting in HAPE. This condition is characterized by a decrease in HRV, an increase in the LF/HF ratio, and other symptoms (97–99).

Obstructive Sleep Apnea Syndrome (OSAS) is a common sleep disorder characterized by interrupted breathing, leading to varying degrees of hypoxia. It causes endothelial dysfunction, autonomic nervous system disorders, increased oxidative stress, and other physiological abnormalities. OSAS is also a significant risk factor for cardiovascular diseases (100,101). Through analyzing multiple clinical records of OSAS patients, Khoo et al. discovered a close relationship between OSAS and cardiovascular autonomic nerves (102). The hypoxic environment at high altitudes undoubtedly exacerbates the occurrence of OSAS. Gammoudi et al. conducted retrospective studies and found a strong correlation between changes in HRV and the severity of OSAS. They observed that the RR interval significantly prolonged in mild and moderate OSAS cases, while the LF/HF ratio was significantly reduced (103). OSAS places an excessive aerobic demand on the body, significantly increasing the incidence and mortality of cardiovascular diseases (106,107).

Plateau hypoxia -related endocrine system diseases

The plateau hypoxic environment triggers a series of compensatory reactions that affect endocrine systems such as the pituitary-thyroid axis and hypothalamus-pituitary-adrenal axis. Hoshi et al. evaluated cardiac autonomic nervous function in patients with hypothyroidism and euthyroid individuals by measuring time-domain and frequency-domain reference indicators, as well as utilizing nonlinear analysis methods. They discovered that hypothyroidism patients exhibited ectopic beats, heart rate changes, and alterations in RR intervals (108).

Type 1 diabetes is generally believed to be initiated by the presentation of β cell peptides by antigen-presenting cells. These cells migrate to the pancreatic lymph nodes and interact with CD4+ T lymphocytes, leading to the activation of CD8+ T lymphocytes. These activated CD8+ T lymphocytes then attack and destroy β cells that express immunogenic autoantigens in the pancreatic islets. The release of pro-inflammatory cytokines and reactive oxygen species by innate immune cells further exacerbates the destruction of β cells, while other endocrine cells in the islets remain unaffected. Consequently, as beta cell loss continues, hyperglycemia becomes detectable. Limberg et al. conducted a study by randomly assigning 13 subjects with type 1 diabetes to hypoxic and normoxic environments during hypoglycemia. They observed a reduction in SDNN (standard deviation of normal-to-normal intervals) and the mean NN interval, which are indicators of HRV (109).

It is worth noting that while plateau hypoxia can affect multiple systems and organs, and research on the relationship between HRV and various systemic diseases is gradually advancing, there is still a lack of large-scale and high-quality studies investigating HRV, high altitude hypoxia, and multiple systemic diseases. The specific mechanisms underlying their interactions remain unclear.

The relationship between HRV and anti-altitude sickness medicines

Hypoxia is a crucial factor contributing to the development of diseases at high altitudes. Previous studies have demonstrated a close relationship between HRV and hypoxia, suggesting that monitoring HRV can provide insights into the effects of hypoxia on cardiovascular and autonomic functions. Consequently, implementing proactive measures to prevent high altitude hypoxia and treat hypoxia-related conditions is of significant importance. Currently, anti-altitude response medications can partially alleviate hypoxia, as evidenced by changes in HRV (Table 3).

Table 3. Effects of anti-altitude sickness drugs on HRV.

Classification	Drugs	Research object	indicators
Chinese medicine	Rhodiola rosea	Patients with acute coronary syndrome (97)	SDNN, SDANN, RMSSD ↑
		Elderly patients with unstable angina pectoris (98)	SDNN, SDANN, RMSSD, PNN50 ↑
		Exhausted training rats (110)	TP, LF, HF, LF/HF ↑
	Salvia miltiorrhiza polyphenol acid	Elderly patients with coronary heart disease (100)	HF ↑,LF, LF/HF ↓
	Compound Salvia miltiorrhiza injection	Patients with chronic heart failure (101)	SDNN, SDANN, RMSSD, PNN50 ↑
	Buqi Zhitong decoction	Patients with stable angina pectoris (102)	SDNN, SDANN, SDNN, RMSSD, PNN50 ↑
	Tongluo Anxin decoction	Patients with unstable angina pectoris of coronary heart disease (103)	SDNN ↑
Western medicine	Acetazolamide	Patients with acute mountain sickness (106)	NN50, PNN50, HF ↑
		Patients with central apnea caused by opiate abuse (107)	LF, LF/HF ↓
	Metoprolol	Patients with miocardial infraction (108)	SDRR, SDANN, RMSSD, PNN50, TP, LFP, HFP, HF ↑，LF/HF ↓
		Patients with essential hypertension (109)	RRI, LF variability, HF variability ↑
		Patients with coronary disease (111)	SDNN, HRVTI, LF, HF, TP ↑，LF/HF, VLF ↓
	Trimetazidine	Patients with ischemic heart failure (112)	SDNN, SDANN ↑
		Patients with acute coronary syndrome (113)	SDNN, SDANN, RMSSD, PNN50, HF ↑，LF, LF/HF ↓
		Patients with coronary heart disease after percutaneous coronary intervention (114)	RMSSD, SDSD, PNN50, HF ↑ SDNN, LF, LF/HF ↓
		Patients with acute myocardial infarction (114)	SDSD, RMSSD, HF ↑ LF/HF ↓
		Left ventricular ejection fraction LVEF ≤ 40% in patients with old myocardial infarction and in patients with type 2 diabetes mellitus (115)	SDNN, SDANN, Triangular-index ↑
		Patients with ischemic cardiomyopathy (116)	SDNN, SDANN, RMSSD, PNN50 ↑
		Patients with chronic heart failure (117)	SDNN, SDANN, RMSSD, PNN50 ↑
		Patients with non ST segment elevation acute coronary syndrome	SDNN, SDANN, RMSSD, PNN50 ↑ LF, LF/HF ↓
	Nifedipine	Patients with acute hypertension (118)	LF/HF ↑，HF ↓
		Patients with hypertensive coronary heart disease (119)	LF ↓
		Patients with chronic heart failure and ventricular arrhythmia (120)	SDNN, SDANN, RMSSD, PNN50 ↑
		Patients with essential hypertension (121)	LF/HF ↑ SDNN, RMSSD, PNN50 ↑，LF/HF ↓
Chinese medicine combined with western medicine	Metoprolol combined with nifedipine	Patients with systematic hypertension (122)	RRI, SDNN, LFP, HFP ↑,LF/HF ↓
		Patients with essential hypertension (123)	SDNN, SDANN, RMSSD ↑
		Patients with hypertension with coronary heart disease (124)	SDNN, SDANN, RMSSD, PNN50 ↑
	Metoprolol combined with Belopril	Patients with chronic heart failure (125)	SDNN, SDANN, SDNN index, RMSSD, PNN50 ↑
		Elderly patients with essential hypertension with chronic heart failure (42)	SDNN, SDANN, RMSSD, PNN50 ↑
	Metoprolol combined with trimetazidine	Patients with unstable angina pectoris (126)	SDNN, SDANN, RMSSD, PNN50 ↑
		Patients with stable angina pectoris (127)
		Patients with acute myocardial infarction (128)
	Metoprolol combined with Wenxin granule	Patients with acute myocardial infarction (129)	SDNN, SDANN, PNN50 ↑
	Yindan Xintai Dropping Pills	Patients with supraventricular arrhythmia (130)	SDNN, SDANN, SDNN index, RMSSD, PNN50 ↑
	Ginkgo damole combined with mecobalamin	Patients with diabetic cardiovascular autonomic neuropathy (42)	SDNN, SDANN, RMSSD, PNN50, HF, LF, LF/HF ↑
	Rhodiola rosea combined with levocarnitine	Patients with coronary heart disease with diabetes (131)	SDNN, SDANN, RMSSD, PNN50, LF, HF ↑
	Xinkeshu combined with metoprolol	Young Patients with non organic ventricular premature contraction (132)	SDNN, SDANN, HF ↑，LF ↓

Notes: SDNN=standard deviation of all normal to normal RR intervals, SDANN=standard deviation of all mean 5 minute NN intervals, RMSSD=the root mean square successive differences, PNN50=percentage of adjacent RR intervals more than 50 ms different for the total number of RR intervals, TP=Total power, LF=low frequency, HF=high frequency, NN50=the number of differences between adjacent normal RR intervals higher than 50 ms, SDRR=the standard deviation of the detrended RR intervals, LFP=low-frequency power, HFP=high-frequency power, RRI=RR interval, HRVTI=heart variability time index, SDSD=the standard deviation of differences between adjacent RR intervals, Triangular-index=heart rate variability Triangular-index, VLF=very low frequency.

Anti-altitude sickness traditional Chinese medicine

Chinese herbal medicines are widely utilized in the prevention and treatment of hypoxia-related diseases due to their notable therapeutic effects and minimal adverse reactions. Rhodiola rosea is a prominent anti-altitude sickness drug that is extensively used for its ability to reduce oxygen consumption and enhance oxygen-carrying capacity. Chinese herbs like Salvia miltiorrhiza, Dracocephalum heterophyllum Benth, and Lycium chinense Miller also possess properties that reduce tissue and organ damage caused by hypoxia. They can alleviate the degree of hypoxia in the body, thus mitigating the symptoms of altitude sickness. Both animal and clinical trials have demonstrated that Rhodiola rosea and its extract can improve HRV (111–113). Similarly, Salvia polyphenolic acid salt and Compound Salvia injection have shown improvements in HRV in cases of elderly coronary heart disease and chronic heart failure (114,115). Apart from individual herbal medicines with notable efficacy, compound medications and combination therapies offer a more comprehensive approach for addressing high altitude hypoxia-related diseases. Time-Domain-Analysis-Methods have indicated that Buqi Zhitong decoction and Tongluo Anxin decoction have positive effects on improving HRV (116,117). However, there is still a lack of direct research on high altitude HRV specifically related to natural anti-altitude sickness drugs such as Cordyceps, Salvia, and Dracocephalum tanguticum.

Anti-altitude sickness western medicines

A wide range of Western drugs is available for the prevention and treatment of altitude sickness. One representative drug is acetazolamide, which exhibits certain antioxidant effects. Hung et al. conducted a study where acetazolamide was administered prophylactically to individuals with a history of acute altitude sickness. Upon reentering high altitude areas, acetazolamide increased parasympathetic activity, thereby expediting the acclimatization process to high altitude, accompanied by changes in HRV (118). Naghan et al. discovered that acetazolamide improved the prognosis of patients with central apnea resulting from opioid abuse, leading to decreased LF and LF/HF of HRV (119). Nevertheless, the precise mechanism and further clinical trials regarding the effects of acetazolamide on HRV need to be investigated. Certain drugs used for cardiovascular diseases also exhibit anti-high altitude sickness effects, and their correlation with HRV has been well established. Studies have demonstrated that metoprolol (a beta-blocker) and trimetazidine (an anti-anginal agent) positively affect HRV in cardiovascular conditions such as essential hypertension, acute myocardial infarction, and coronary artery disease (120–127,133). Nifedipine (a calcium channel blocker) inhibits calcium influx and dilates arterial blood vessels. Nifedipine shows potential anti-high altitude sickness effects and can influence HRV in cardiovascular conditions such as hypertension (128–131). Furthermore, several studies have indicated that the combination of anti-high altitude sickness drugs yields greater improvements in HRV. For instance, the combination of metoprolol with nifedipine, felodipine, beloprim, or trimetazidine has demonstrated more significant enhancements in HRV (110,132,134–137).

Combined Chinese herbs and western medicines for anti-altitude sickness

In recent years, the benefits of combining Chinese herbal medicines with Western medicines in clinical practice have become increasingly prominent. For instance, the combination of metoprolol with Wenxin granule or Yindan Xintai Dropping Pills has shown improvements in the time domain indicators of HRV (138,139). Similarly, the combination of ginkgo damole with mecobalamin, rhodiola with levocarnitine, and Xinkeshu in combination with metoprolol have demonstrated effectiveness in improving both time-domain and frequency-domain indicators of HRV (140–142). Although existing studies have identified numerous drugs for the prevention and treatment of high altitude hypoxia-related diseases, the specific mechanisms involved are not yet fully understood, and there is a scarcity of research involving HRV. Further extensive experimentation is still required.

Conclusion

HRV, calculated from electrocardiogram recordings, is widely utilized as an index to assess the autonomic nerve’s regulation of the cardiovascular system. Its sensitivity, non-invasiveness, and ease of use have contributed to its widespread application. As research has advanced, it has been discovered that autonomic nervous system dysfunction is implicated in numerous systemic diseases. Consequently, HRV is increasingly being employed to evaluate autonomic nervous function in various systemic conditions, providing effective assessments of disease severity, progression, and prognosis. Hypoxia is a defining characteristic of the challenging plateau environment. It can lead to the development of diverse diseases through various pathological pathways, with impaired autonomic function induced by hypoxia being one of the underlying mechanisms. Therefore, HRV can be used for early diagnosis, disease progression assessment, and prognosis evaluation of high altitude hypoxia-related conditions. However, existing studies on hypoxia-related diseases and HRV at high altitudes have primarily focused on cardiovascular diseases, while research on the endocrine system, respiratory system, and digestive system remains limited. Moreover, even within the extensively studied cardiovascular diseases, most investigations have primarily examined the correlation between plateau hypoxia-related diseases and changes in HRV. Specific studies elucidating the mechanisms by which plateau hypoxia influences HRV are still lacking. Consequently, there is a scarcity of research on the mechanisms underlying preventive drugs for plateau-related diseases. Overall, comprehensive studies encompassing altitude hypoxia, diseases, and heart rate variability are still insufficient, and further large-scale, high-quality research is necessary to fully comprehend the specific interactions among these factors. This will enable the more rapid, accurate, and effective utilization of HRV as an aid in diagnosing plateau-related diseases.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (81970241 to Haifeng Pei; 81900339 to Jun Hou), Key Projects of Hospital Management of the General Hospital of the Western Theater Command of PLA (2021-XZYG-A03 to Haifeng Pei), Tianfu Qingcheng Project-Tianfu Science and Technology Elite (No.1358 to Haifeng Pei), The General Hospital of Western Theater Command-Spark Young Innovative Talents (to HaifengPei), The Fundamental Research Funds for the Central Universities (2682022TPY052 to Jun Hou), Chengdu Medical Research Project(2022138 to Jun Hou), Key Research and Development Program of Science and Technology of Tibet Province (XZ202201ZR0036G to Jun Hou) and Chengdu High-level Key Clinical Specialty Construction Project.