Characteristics of heart rate variability in women with polycystic ovary syndrome

Ji, Hae Ri; Woo, Hye Lin; Park, Young Jae; Hwang, Deok Sang; J, Lee; Lee, Chang Hoon; Jang, Jun Bock; Park, Kyoung Sun

doi:10.1097/md.0000000000012510

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…However, it is worth noting that rMSSD, which reflects parasympathetic modulation of HRV, exhibited a reduction in the cases studied. The results presented here align with the findings reported by Ji et al (2018) [24]. Yildirir et al [25] observed a decrease in high frequency (HF) and an increase in low frequency (LF) in individuals with polycystic ovary syndrome (PCOS) compared to the control group.…”

Section: Discussionsupporting

confidence: 92%

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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

Shrivastava,

Pathak,

Shrivastava

et al. 2023

Cureus

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IntroductionPolycystic ovary syndrome (PCOS) is a frequently occurring endocrine condition prevalent in women of reproductive age characterized by chronic anovulation, hyperandrogenism, insulin resistance, and a lowgrade inflammatory state. Patients with PCOS are more vulnerable to developing cardiac and metabolic comorbidities. Sympathetic overactivity is also reported in PCOS patients. ObjectiveThis 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). MethodsThirty 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. ResultsThere 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. ConclusionPatients 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.

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Section: Discussionsupporting

confidence: 92%

“…Studies have incorporated the COMPASS-31 scale in the assessment of diabetic autonomic neuropathy (DAN), cardiac autonomic neuropathy (CAN), SFPNs, etc. [24].…”

Section: Discussionmentioning

confidence: 99%

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

Shrivastava,

Pathak,

Shrivastava

et al. 2023

Cureus

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“…Hence, Sverrisdóttir et al found increased sympathetic nerve activity in patients with PCOS by direct measurement [ 12 ]. Other authors obtained similar results using the heart rate variability for indirect evaluation of autonomous function [ 13 , 14 ]. However, few studies have investigated the plasma levels of catecholamines and their metabolites in PCOS patients with somewhat contradictory results [ 15 – 17 ].…”

Section: Introductionsupporting

confidence: 61%

Plasma-free metanephrines, nerve growth factor, and renalase significance in patients with PCOS

et al. 2023

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Purpose Polycystic ovarian syndrome (PCOS) is a common heterogeneous condition with probably multifactorial genesis. Animal studies have proven the essential role of the sympathetic nervous system in the syndrome development, while human studies are still contradictory. The present study aims to investigate the possible influence of plasma-free metanephrine (MN), and normetanephrine (NMN), nerve growth factor (NGF), and renalase (RNL) on the hormonal and metabolic parameters in women with PCOS and healthy controls. Methods Fifty patients with PCOS and 30 healthy women participated in the study. The plasma-free MN and NMN, NGF, RNL, anti-Mullerian hormone (AMH), gonadotropin, androgen levels, and metabolic parameters were investigated. Results Plasma-free NMN and NGF concentrations were increased in PCOS individuals, while RNL levels were decreased compared to healthy volunteers. Increased plasma-free NMN (OR = 1.0213 [95%CI 1.0064–1.0364], p = 0.005) and NGF (OR = 1.0078 [95%CI 1.0001–1.0155], p = 0.046) but not MN or RNL levels were associated with a higher risk of PCOS after adjustment for age. Plasma-free NMN levels were positively associated with the LH ( r = +0.253; p = 0.039). androstenedione ( r = +0.265; p = 0.029), 17-OH progesterone ( r = +0.285; p = 0.024), NGF ( r = +0.320; p = 0.008), and AMH ( r = +0.417; p < 0.001) concentrations of the investigated women. RNL levels were inversely related to the BMI ( r = −0.245; p = 0.029), HOMA-IR ( r = −0.250; p = 0.030), free testosterone ( r = −0.303; p = 0.006) levels. systolic ( r = −0.294; p = 0.008) and diastolic ( r = −0.342; p = 0.002) blood pressure. Conclusions Increased sympathetic noradrenergic activity and NGF synthesis might be related to the increased AMH and delta-4 androgen levels in a subgroup of PCOS patients. RNL levels might influence the metabolic status of PCOS patients. Further studies are needed to explore the significance of adrenal medullar and autonomic dysfunction for developing different PCOS phenotypes and their subsequent cardiovascular complications.

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“…In comparison between subgroup 1 and subgroup 2, pulse rate and respiratory rate are more important features in subgroup 1 in Figure 16 , and AMH is a more important feature (also note that the SHAP values of AMH are more spread out) in subgroup 2 than in the other subgroup, as shown in Figure 17 . Regarding pulse rate and respiratory rate being important in subgroup 1, heart rate variability in normal weight women with PCOS has been subject to debate in the literature [ 35 , 36 ].…”

Section: Resultsmentioning

confidence: 99%

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

Zigarelli¹,

Jia²,

Lee³

2022

JMIR Form Res

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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.

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Characteristics of heart rate variability in women with polycystic ovary syndrome

Cited by 10 publications

References 27 publications

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

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

Plasma-free metanephrines, nerve growth factor, and renalase significance in patients with PCOS

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

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