Connection between heart rate variability alterations and cancer in tumor patients

Abstract

BACKGROUND

A significant proportion of cancer patients experience autonomic dysfunction, and cancer treatments such as chemotherapy and radiation therapy can exacerbate impairments in the cardiac autonomic nervous system. This study sought to investigate the characteristics of heart rate variability (HRV) in individuals with cancer.

AIM

To evaluate the relationship between HRV and cancer patients, providing insights and references for cancer treatment.

METHODS

The study included 127 cancer patients with available 24-hour dynamic electrocardiogram data. HRV differences were analyzed using both time domain and frequency domain methods. These findings were then compared to HRV data from reference individuals, sourced from literature that utilized the same HRV computing algorithm.

RESULTS

Our findings revealed that cancer patients generally exhibited abnormal HRV compared to the reference group. HRV was found to be correlated with age and clinical type (P < 0.05), but no significant correlation was observed with tumor site or gender (P > 0.05).

CONCLUSION

This study indicates that cancer patients have significantly abnormal HRV compared to reference individuals, suggesting the presence of a certain level of cardiac autonomic dysfunction in this patient population.

Key Words: Cancer patients; Heart rate variability; Domain and frequency domain methods; Autonomic dysfunction; Dynamic electrocardiogram

Core Tip: Heart rate variability serves as an indicator of autonomic nervous system function. A reduction in heart rate variability (HRV) is associated with system imbalances and potential health concerns. A study involving 127 cancer patients revealed that both the disease and its treatments can lead to a decrease in HRV. Additionally, psychological factors such as stress and anxiety also influence HRV levels. Therefore, monitoring HRV can aid in the assessment of cancer patients’ health status, prognosis, and the formulation of personalized treatment plans.

INTRODUCTION

Cancer is a generic term used for a large group of diseases that can affect any part of the body. Although the scientific community has achieved notable advancements in cancer treatment, 20 million new cancer cases were detected in 2022, and there were almost 9.7 million related deaths. Cancer has become one of the leading causes of mortality worldwide, and according to data provided by the World Health Organization, the number of patients with cancer is expected to increase by approximately 70% over the following two decades. Regardless of the cancer treatment approach, whether it involves traditional cytotoxic chemotherapy, targeted therapy, immunotherapy, radiation therapy, or surgical procedures, arrhythmia may emerge as one of the potential adverse effects. Therefore, in the process of tumor treatment, it is necessary to pay close attention to the cardiovascular condition of patients, which may directly affect the survival and mortality rates of patients with cancer[1,2].

Heart rate variability (HRV) is a well-known physiological phenomenon in which the time interval between heart beats varies. It is a parameter measuring the time variation of the sinus rhythm, reflecting the regulatory effects of the nervous system and humoral factors on the sinoatrial node. Through the analysis of HRV, the balance of the sympathetic and parasympathetic nerves can be evaluated, and the functional status of the supporting nerves can also be evaluated. Neuroregulation plays a critical role in the diagnosis of tumors, usually related to tumor occurrence, development, local and systemic immune response, tolerance, vascular infiltration, and lymph node metastasis. Therefore, there is a mutual correlation between autonomic nervous system function, heart rate variability and tumor assessment. The measurement of HRV is performed using traditional methods. The normal-to-normal (N-N) interval dates are usually recorded using the dynamic electrocardiogram (ECG) over a long period of time, such as 24 hours. With the development of research on the role of the vagus nerve activity, the application of HRV data is widely used, not only in the treatment and prognosis of patients with coronary heart disease and diabetes, but is also indexed in cancer prognosis[3-5]. Although there have been previous studies comparing ECG findings before and after cancer treatment, the majority of them are limited to specific cancer type or treatment modalities. There is a lack of comprehensive research that systematically compares ECG changes across various cancer types and stages. The aim of the present study is to explore the application of dynamic ECG technology for the detection of arrhythmia in patients with cancer, and to determine the differences in HRV between patients with cancer and reference individuals[6].

MATERIALS AND METHODS

Clinical data

127 patients (from May to October, 2024) who were confirmed to have cancer by pathological analysis, and for 24-hour dynamic ECG data were available, were enrolled in the present study. The patients included 73 males and 54 females, with a mean age of 62.82 ± 12.35 years. During the dynamic ECG examination, the movements of the patients were not restricted. After 24 hours, the ECG recorder was removed, and the results of the dynamic ECG were obtained through automatic analysis and manual modification by professional staff. Patients who had a history of cardiac disease, such as coronary heart disease, arrhythmia and hyperthyroidism, or obvious excessive atrial fibrillation, which may affect the HRV analysis were excluded. The clinical and pathological data for each patient were recorded according to the clinical cases. The basic clinical data of the patients with cancer are presented in Table 1.

Table 1 Basic clinical data of the patients with cancer in the present study.

Characteristics	n	%
Male	73	57.5
Female	54	42.5
Age < 65 years	48	37.8
Age ≥ 65 years	79	62.2
Head and neck tumor	37	29.1
Thoracic tumor	62	48.8
Digestive system tumor	16	12.6
Others tumor	12	9.5
Stage I	15	11.8
Stage II	27	21.3
Stage III	52	40.9
Stage Ⅳ	33	26.0

HRV analysis

ECG monitoring was conducted for all patients for 24 hours using an ECG with a DMS 300-4A Holter recorder (DM Software Inc. United States), the device adopt 12-lead system of international standard, which can continuously record ECG waveform for 24-hour and analyze ECG waveform by the Holter analysis software which installed on PC. All HRV indicators were recorded and statistically analyzed by for analysis. The HRV indicators included time domain analysis and frequency domain analysis. In time domain analysis, the N-N intervals for each 5-minute period within 24 hours, the SD of the all N-N intervals (SDNN), the SD of the average values of N-N intervals (SDANN), the mean of SD of N-N intervals for 5 minutes and the percentage of N-N intervals differing from each other by > 50 ms were calculated. In frequency domain analysis, the very low frequency, low-frequency power and the ratio of low frequency to high frequency were calculated. Furthermore, the triangle index was calculated.

Statistical analysis

The data obtained in the present study were analyzed using SPSS 26.0 professional data software (IBM Corp.), and the measurement data consistent with normal distribution are expressed as the mean ± SD. Data were analyzed using the independent samples t-test method or the paired t-test inter-group comparisons. Data which were non-normally distributed are expressed as the median and interquartile range (Q1, Q3), and inter-group comparisons were performed using the Mann-Whitney U-test. Count data are expressed by the number of cases and the percentage, and the two samples χ²-test and paired χ²-test were used for inter-group comparisons. A value of P < 0.05 was considered to indicate a statistically significant difference.

RESULTS

Compared with the normal patients, the patients with cancer exhibited a significant decrease in SDNN, SDANN, the mean of SD of N-N intervals for 5 minutes, triangle index, the percentage of N-N intervals differing from each other by > 50 ms, very low frequency, low frequency and low frequency/high frequency indicators (Table 2)[7,8]. As shown in Table 3, the age or sex of the patients did not differ significantly in association with the HRV data; however, tumor staging exhibited a significant association with HRV. The patients with III-IV stage tumors had a lower SDNN, SDANN and low frequency/high frequency than those with I-II stage tumors. Of note, none of the HRV indicators exhibited an association with tumor type. At the same time, the resting ECG of all the patients who were included in the study was analyzed and compared with the dynamic ECG. For the T-segment changes, the dynamic ECG was found in 51 (30.4%) cases with T-segment changes, and the resting ECG was found in 22 (13.1%) cases (P < 0.05). For the ST-T segment changes, the dynamic ECG was found in 69 (41.1%) cases and the resting ECG was found in 19 (11.3%) cases (P < 0.05).

Table 2 Results of the heart rate variability for cancer patients.

Index	Reference group[7,8]	Cancer group	P value
Total		127
Time domain analysis, 24 hours
SDNN, ms	141 ± 39	102.12 ± 23.48	< 0.05
SDANN, ms	127 ± 35	95.3 ± 31.25	< 0.05
SDNNIDX, ms	50 ± 13	38.3 ± 21.4	< 0.05
Triangle index	37 ± 15	24.0 ± 5.12	< 0.05
pNN50, %	7.95 ± 8.15	4.06 ± 0.59	< 0.05
Frequency domain analysis, 24 hours			< 0.05
VLF, ms2	1124 ± 316	631.9 ± 275.3	< 0.05
LF, ms2	1170 ± 416	426 ± 153	< 0.05
LF/HF	1.75 ± 0.67	1.29 ± 0.48	< 0.05

SDNN: SD of the all normal-to-normal intervals; SDANN: SD of the average values of normal-to-normal intervals; SDNNIDX: mean of SD of normal-to-normal intervals for 5 minutes; pNN50: Percentage of normal-to-normal intervals differing from each other by > 50 ms; VLF: Very low frequency; LF: Low frequency; HF: High frequency.

Table 3 Association of clinical factors with heart rate variability.

Factor	SDNN, ms	pNN50, %	VLF, ms2	LF/HF
Sex	P > 0.05	P > 0.05	P > 0.05	P > 0.05
Male	104.67 ± 32.57	4.22 ± 0.75	657.6 ± 271.3	1.35 ± 0.49
Female	101.67 ± 35.35	4.12 ± 0.71	612.5 ± 281.3	1.23 ± 0.51
Age, years	P < 0.05	P < 0.05	P < 0.05	P < 0.05
< 65	111.43 ± 32.51	4.58 ± 0.73	665.6 ± 289.1	1.39 ± 0.43
≥ 65	92.12 ± 31.31	3.38 ± 0.56	553.6 ± 252.1	1.12 ± 0.46
Tumor location	P > 0.05	P > 0.05	P > 0.05	P > 0.05
Cardia	104.28 ± 33.67	4.18 ± 0.65	633.8 ± 275.3	1.31 ± 0.42
Body	103.12 ± 33.7	4.15 ± 0.76	635.3 ± 276.2	1.35 ± 0.49
Antrum	102.34 ± 35.1	4.06 ± 0.62	611.6 ± 289.2	1.18 ± 0.51
Diffuse	103.36 ± 35.18	4.16 ± 0.78	631.8 ± 279.4	1.32 ± 0.48
Clinical type	P < 0.05	P < 0.05	P < 0.05	P < 0.05
I	111.96 ± 32.98	4.87 ± 0.32	698.3 ± 254.6	1.49 ± 0.52
II	99.86 ± 31.98	4.07 ± 0.62	658.3 ± 224.3	1.41 ± 0.35
III	87.95 ± 31.58	3.57 ± 1.22	572.1 ± 289.6	1.38 ± 0.39
Ⅳ	80.15 ± 29.68	3.27 ± 0.85	522.1 ± 259.4	1.15 ± 0.48

SDNN: SD of the all normal-to-normal intervals; pNN50: Percentage of normal-to-normal intervals differing from each other by > 50 ms; VLF: Very low frequency; LF: Low frequency; HF: High frequency.

DISCUSSION

The present study observed the HRV in 127 consecutive patients with cancer. HRV is the change of heart beat speed or the time-related variations in R-R intervals and reflects the regulation of the cardiovascular system by the autonomic nervous system and subsequent responses. The HRV can be used to evaluate the condition of the parasympathetic nervous system and sympathetic nervous system; it has been considered as the most simple and valuable non-invasive diagnostic method for evaluating the functional status of the autonomic nervous system[9]. Time-domain analysis and frequency-domain analysis are the commonly used analysis methods for HRV. In the present study, the HRV of patients with cancer was analyzed and compared with standard reference values. The results revealed that the patients with cancer had a lower HRV index, which suggests that patients with cancer have an abnormal autonomic nervous system function. Based on clinical perspective reports, a number of patients with cancer exhibit an imbalance in autonomic nervous system regulation, which becomes more severe as the tumor condition worsens. In the present study, there was a significant decrease in HRV in patients with stage III and IV tumors compared with patients with I and II stage tumors (Table 3). With the increase in age, the indicators of HRV also decrease. However, compared to the impact of cancer type, the impact of age is less prominent. Herein, no significant differences were found as regards sex, tumor location and HRV. Thus, the HRV can be used as a key reference value in determining the clinical staging of patients with cancer[10,11].

The SDNN can reflect the overall variability of the heart; a significant reduction in SDNN, particularly one < 100 ms, may indicate an increasing mortality rate[12,13]. The decrease in HRV in patients with cancer may be related to the following factors: (1) the deterioration of tumors represents an increase in the metabolic abnormalities of fat cells, which affect the reflex and regulation of sympathetic and valgus nerves, leading to a marked decrease in HRV. Patients with advanced-stage cancer generally experience varying degrees of fever, fatigue, and metabolic and immune system disorders, which are also manifestations of autonomic dysfunction; and (2) The diagnosis of tumors can cause emotional changes, such as anxiety and tension in patients. The abnormal changes in emotions can lead to the excitation of the sympathetic nervous system through neurohumoral regulation, leading to an imbalance in autonomic nervous system regulation, which then causes changes in HRV.

The present study also compared the diagnostic rates of the dynamic ECG and resting ECG in arrhythmia. The resting ECG instrument is a 12-lead ECG instrument, model Beijing Medex Inc, MECG-300 ECG. The dynamic ECG instrument is also a 12-lead ECG instrument, DMS 300-4A Holter recorder. During the testing process, the operation requirements of the relevant inspection room are strictly followed to eliminate the interference factors affecting the test results and ensure the effectiveness of the test results. The results revealed that the detection rate of the dynamic ECG was higher than that of the resting ECG. Moreover, relatively speaking, the time recorded by the dynamic ECG is 24 hours and can be defined as wakefulness (6:00 am to 10:00 pm) and sleep (10:00 pm to 6:00 am the following day). With advancements being made in cancer treatment methods, the survival rate of patients with cancer has greatly improved. However, the cardiovascular risks brought to patients by processes, such as radiotherapy and chemotherapy are also increasing synchronously, and the quality of life of patients also requires further evaluation[14]. Compared to other examinations, the dynamic ECG is a more economical, simple and non-invasive examination. From the aforementioned analysis, it can be seen that dynamic ECG can not only more effectively detect heart rate abnormalities, but can also be used in association with HRV indicators to predict the malignancy of tumors and the quality of life of patients.

So, HRV holds great significance in cancer assessment and diagnosis. Firstly, the HRV serve as a non-invasive method used to test the autonomic nervous function, widely applied in evaluating autonomic nervous function for patients in clinical treatment. Cancer patients often experience disruptions in their autonomic nervous system function, which can be reflected in changes in HRV. Secondly, HRV values have been found to be closely associated with the prognosis of cancer patients. Lower HRV values often indicate a poorer prognosis. In patients with malignant tumors, HRV values are significantly reduced, and the later the cancer stage, the more pronounced the decrease in HRV. So HRV can therefore serve as a reliable marker for assessing the prognosis of cancer patients, allowing for more informed treatment decisions and patient management. What’s more, the HRV changes can be used as an indicator to monitor the effectiveness of cancer treatments. If a patient’s HRV value improves after treatment, it may suggest that the treatment is effective and the autonomic nervous system function is recovering. Conversely, a continued decrease in HRV may indicate that the treatment is not working well or that the disease is progressing. At last, the HRV can provide useful information for developing individualized treatment plans for cancer patients. By understanding a patient’s autonomic nervous system function, healthcare providers can tailor treatments to better meet the patient’s specific needs and improve treatment outcomes.

CONCLUSION

Our study shows that the HRV plays an important role in cancer diagnosis by reflecting the state of the autonomic nervous system, predicting and assessing prognosis, monitoring treatment effectiveness, and guiding individualized treatment plans. By considering HRV along with other diagnostic tests and clinical information, healthcare providers can make more informed decisions and provide better care for cancer patients.

ACKNOWLEDGEMENTS

The authors would like to thank the team of the Department of Electrocardiogram, Hefei Cancer Hospital, Chinese Academy of Sciences, for their assistance with the data collection and organization.