Characteristics of heart rate variability in women with polycystic ovary syndrome: A retrospective cross-sectional study

The role of the autonomic nervous system in polycystic ovary syndrome

This article reviewed the relationship between the autonomic nervous system and the development of polycystic ovary syndrome (PCOS). PCOS is the most common reproductive endocrine disorder among women of reproductive age. Its primary characteristics include persistent anovulation, hyperandrogenism, and polycystic ovarian morphology, often accompanied by disturbances in glucose and lipid metabolism. The body's functions are regulated by the autonomic nervous system, which consists mainly of the sympathetic and parasympathetic nervous systems. The autonomic nervous system helps maintain homeostasis in the body. Research indicates that ovarian function in mammals is under autonomic neural control. The ovaries receive central nervous system information through the ovarian plexus nerves and the superior ovarian nerves. Neurotransmitters mediate neural function, with acetylcholine and norepinephrine being the predominant autonomic neurotransmitters. They influence the secretion of ovarian steroids and follicular development. In animal experiments, estrogen, androgens, and stress-induced rat models have been used to explore the relationship between PCOS and the autonomic nervous system. Results have shown that the activation of the autonomic nervous system contributes to the development of PCOS in rat. In clinical practice, assessments of autonomic nervous system function in PCOS patients have been gradually employed. These assessments include heart rate variability testing, measurement of muscle sympathetic nerve activity, skin sympathetic response testing, and post-exercise heart rate recovery evaluation. PCOS patients exhibit autonomic nervous system dysfunction, characterized by increased sympathetic nervous system activity and decreased vagal nerve activity. Abnormal metabolic indicators in PCOS women can also impact autonomic nervous system activity. Clinical studies have shown that various effective methods for managing PCOS regulate patients' autonomic nervous system activity during the treatment process. This suggests that improving autonomic nervous system activity may be an effective approach in treating PCOS.

Assessment of Cardiac Autonomic Function in Women With Polycystic Ovary Syndrome Through Ewing's Battery, Heart Rate Variability Analysis, and Composite Autonomic Symptom Score-31 Scale

Introduction Polycystic ovary syndrome (PCOS) is a frequently occurring endocrine condition prevalent in women of reproductive age characterized by chronic anovulation, hyperandrogenism, insulin resistance, and a low-grade inflammatory state. Patients with PCOS are more vulnerable to developing cardiac and metabolic co-morbidities. Sympathetic overactivity is also reported in PCOS patients. Objective This study aimed to assess cardiac autonomic function in PCOS by Heart Rate Variability Analysis, Ewing's Test, and Composite Autonomic Symptom Scale-31 (COMPASS-31). Methods Thirty female PCOS patients and 30 age-matched control females were enrolled in the study. Both cases and controls were subdivided into sub-groups based on body mass index (BMI) and waist-to-hip ratio (WHR). The cardiac autonomic functions were assessed by the COMPASS-31 questionnaire, Ewing's battery tests, and short-term heart rate variability analysis. Results There was no significant difference in age, BMI, and WHR of both groups. Additionally, cases had a significantly higher low frequency to high frequency (LF/HF) ratio and COMPASS-31 score and also reported more derangement in Ewing's battery test, indicating cardiac autonomic dysfunction in PCOS patients. Conclusion Patients with PCOS are more prone to developing cardiac and metabolic co-morbidities. Early assessment of cardiac autonomic function can prevent future complications with timely interventions. Altered autonomic function in PCOS patients can be due to hyperandrogenism and insulin resistance.

Machine-Aided Self-diagnostic Prediction Models for Polycystic Ovary Syndrome: Observational Study

Background

Artificial intelligence and digital health care have substantially advanced to improve and enhance medical diagnosis and treatment during the prolonged period of the COVID-19 global pandemic. In this study, we discuss the development of prediction models for the self-diagnosis of polycystic ovary syndrome (PCOS) using machine learning techniques.

Objective

We aim to develop self-diagnostic prediction models for PCOS in potential patients and clinical providers. For potential patients, the prediction is based only on noninvasive measures such as anthropomorphic measures, symptoms, age, and other lifestyle factors so that the proposed prediction tool can be conveniently used without any laboratory or ultrasound test results. For clinical providers who can access patients’ medical test results, prediction models using all predictor variables can be adopted to help health providers diagnose patients with PCOS. We compare both prediction models using various error metrics. We call the former model the patient model and the latter, the provider model throughout this paper.

Methods

In this retrospective study, a publicly available data set of 541 women’s health information collected from 10 different hospitals in Kerala, India, including PCOS status, was acquired and used for analysis. We adopted the CatBoost method for classification, K-fold cross-validation for estimating the performance of models, and SHAP (Shapley Additive Explanations) values to explain the importance of each variable. In our subgroup study, we used k-means clustering and Principal Component Analysis to split the data set into 2 distinct BMI subgroups and compared the prediction results as well as the feature importance between the 2 subgroups.

Results

We achieved 81% to 82.5% prediction accuracy of PCOS status without any invasive measures in the patient models and achieved 87.5% to 90.1% prediction accuracy using both noninvasive and invasive predictor variables in the provider models. Among noninvasive measures, variables including acanthosis nigricans, acne, hirsutism, irregular menstrual cycle, length of menstrual cycle, weight gain, fast food consumption, and age were more important in the models. In medical test results, the numbers of follicles in the right and left ovaries and anti-Müllerian hormone were ranked highly in feature importance. We also reported more detailed results in a subgroup study.

Conclusions

The proposed prediction models are ultimately expected to serve as a convenient digital platform with which users can acquire pre- or self-diagnosis and counsel for the risk of PCOS, with or without obtaining medical test results. It will enable women to conveniently access the platform at home without delay before they seek further medical care. Clinical providers can also use the proposed prediction tool to help diagnose PCOS in women.

Intensive chemotherapy perturbs heart rate variability in children with cancer

OBJECTIVES

In children, cancer chemotherapy may impair the functioning of the cardiac autonomic nervous system. Moreover, it is not known whether there are any differences between intensive and maintenance phases of chemotherapy. Therefore, the aim of the current study was to assess autonomic nervous system activity using heart rate variability, in children receiving intensive or maintenance cancer chemotherapy.

METHODS

For that purpose, children who were healthy (healthy control, n = 30), receiving intensive chemotherapy (chemotherapy, n = 30), and receiving maintenance chemotherapy (maintenance, n = 25) were included in the study. Autonomic nervous system activity was measured by means of heart rate variability. Electrocardiogram recordings were used to calculate time- and frequency-domain heart rate variability parameters.

RESULTS

Time-domain parameters such as standard deviation of NN intervals and frequency-domain parameters such as total power were lower during the intensive chemotherapy but not during maintenance phase (standard deviation of NN intervals: 50±5, 33±3, and 48±3 ms, and total power: 2613±504, 1379±296 and 2295±264 ms², respectively for healthy control, chemotherapy and maintenance groups, P<0.001 for both standard deviation of NN intervals and total power).

DISCUSSION

The present results indicate that intensive chemotherapy perturbs the function of heart rate variability in children, with recovery during the maintenance phase. This suggests that intensive chemotherapy is likely to affect the autonomic nervous system but this effect does not appear to be permanent.