Urinary Epinephrine Sulfate Can Predict Cardiovascular Risk in Moderate-to-Severe OSA: A Metabolomics-Based Study

Purpose

There are currently no ideal indicators for predicting the cardiovascular risk of obstructive sleep apnea (OSA). This study aimed to employ urinary metabolomics to detect early cardiovascular risk in patients with moderate-to-severe OSA.

Patients and Methods

Male participants who underwent polysomnography from November 2020 to May 2021 were screened. Clinical data, polysomnography data and urine samples were collected. Untargeted metabolomics analyses of urine were performed. Multivariate analyses and receiver operating characteristic (ROC) curve analyses were subsequently performed to identify potential biomarkers. Associations between metabolites and clinical indicators and cardiovascular risk were examined through linear regression analyses with interaction and mediation analyses.

Results

Thirty-six male participants were included in the study, comprising 22 males with moderate-to-severe OSA and 14 age-matched controls, with an average age of 39.6 ± 9.2 years. We identified 65 metabolites in the study, involving pathways including pyrimidine, androgen, estrogen, vitamin B6 and sulfate/sulfite metabolism. Among them, epinephrine sulfate was the most significantly altered metabolite. ROC analyses highlighted that epinephrine sulfate had the highest area under the curve (AUC=0.883) for detecting moderate-to-severe OSA. Epinephrine sulfate was statistically correlated with OSA severity, hypoxia-related indicators (apnea-hypopnea index: r=0.685; oxygen desaturation index: r=0.743, p<0.0001), arterial stiffness (arterial augmentation index: r=0.361, p=0.031) and long-term cardiovascular risk (Framingham cardiovascular risk: r=0.375, p=0.024). Linear regression analysis revealed that epinephrine sulfate was significantly associated with an increased in the Framingham risk (β = 0.004, 95% CI = 0.000-0.009, p = 0.049), with the effect partly mediated by systolic blood pressure (27.6%) and not moderated by other factors. Additionally, it also significantly associated with the increased in the arterial augmentation index (β = 0.019, 95% CI = 0.000-0.037, p = 0.046), with the effect fully mediated by blood pressure and not moderated by other indices statistically.

Conclusion

There are significant metabolic pathway alterations in moderate-to-severe OSA patients. Urinary epinephrine sulfate markedly predicts early cardiovascular risk in OSA patients.

Algorithmic detection of sleep-disordered breathing using respiratory signals: a systematic review

Background and Objective. Sleep-disordered breathing (SDB) poses health risks linked to hypertension, cardiovascular disease, and diabetes. However, the time-consuming and costly standard diagnostic method, polysomnography (PSG), limits its wide adoption and leads to underdiagnosis. To tackle this, cost-effective algorithms using single-lead signals (like respiratory, blood oxygen, and electrocardiogram) have emerged. Despite respiratory signals being preferred for SDB assessment, a lack of comprehensive reviews addressing their algorithmic scope and performance persists. This paper systematically reviews 2012-2022 literature, covering signal sources, processing, feature extraction, classification, and application, aiming to bridge this gap and provide future research references.Methods. This systematic review followed the registered PROSPERO protocol (CRD42022385130), initially screening 342 papers, with 32 studies meeting data extraction criteria.Results. Respiratory signal sources include nasal airflow (NAF), oronasal airflow (OAF), and respiratory movement-related signals such as thoracic respiratory effort (TRE) and abdominal respiratory effort (ARE). Classification techniques include threshold rule-based methods (8), machine learning models (13), and deep learning models (11). The NAF-based algorithm achieved the highest average accuracy at 94.11%, surpassing 78.19% for other signals. Hypopnea detection sensitivity with single-source respiratory signals remained modest, peaking at 73.34%. The TRE and ARE signals proved to be reliable in identifying different types of SDB because distinct respiratory disorders exhibited different patterns of chest and abdominal motion.Conclusions. Multiple detection algorithms have been widely applied for SDB detection, and their accuracy is closely related to factors such as signal source, signal processing, feature selection, and model selection.

Technical aspects of cardiorespiratory estimation using subspace projections and cross entropy

BACKGROUND

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. Its quantification has been suggested as a biomarker to diagnose different diseases. Two state-of-the-art methods, based on subspace projections and entropy, are used to estimate the RSA strength and are evaluated in this paper. Their computation requires the selection of a model order, and their performance is strongly related to the temporal and spectral characteristics of the cardiorespiratory signals.

OBJECTIVE

To evaluate the robustness of the RSA estimates to the selection of model order, delays, changes of phase and irregular heartbeats as well as to give recommendations for their interpretation on each case.

APPROACH

Simulations were used to evaluate the model order selection when calculating the RSA estimates explained before, as well as 3 different scenarios that can occur in signals acquired in non-controlled environments and/or from patient populations: the presence of irregular heartbeats; the occurrence of delays between heart rate variability (HRV) and respiratory signals; and the changes over time of the phase between HRV and respiratory signals.

MAIN RESULTS

It was found that using a single model order for all the calculations suffices to characterize the RSA estimates correctly. In addition, the RSA estimation in signals containing more than 5 irregular heartbeats in a period of 5 minutes might be misleading. Regarding the delays between HRV and respiratory signals, both estimates are robust. For the last scenario, the two approaches tolerate phase changes up to 54°, as long as this lasts less than one fifth of the recording duration.

SIGNIFICANCE

Guidelines are given to compute the RSA estimates in non-controlled environments and patient populations.

Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions

Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.

Capacitively-coupled ECG and respiration for the unobtrusive detection of sleep apnea

OBJECTIVE

The performance of a novel unobtrusive system based on capacitively-coupled electrocardiography (ccECG) combined with different respiratory measurements is evaluated for the detection of sleep apnea.

APPROACH

A sleep apnea detection algorithm is proposed, which can be applied to electrocardiography (ECG) and ccECG, combined with different unobtrusive respiratory measurements, including ECG derived respiration (EDR), respiratory effort measured using the thoracic belt (TB) and capacitively-coupled bioimpedance (ccBioz). Several ECG, respiratory and cardiorespiratory features were defined, of which the most relevant ones were identified using a random forest based backwards wrapper. Using this relevant feature set, a least-squares support vector machine classifier was trained to decide if a one minute segment is apneic or not, based on the annotated polysomnography (PSG) data of 218 patients suspected of having sleep apnea. The obtained classifier was then tested on the PSG and capacitively-coupled data of 28 different patients.

MAIN RESULTS

On the PSG data, an AUC of 76.3% was obtained when the ECG was combined with the EDR. Replacing the EDR with the TB led to an AUC of 80.0%. Using the ccECG and ccBioz or the ccECG and TB resulted in similar performances as on the PSG data, while using the ccECG and ccECG-based EDR resulted in a drop in AUC to 67.4%.

SIGNIFICANCE

This is the first study which tests an apnea detection algorithm on capacitively-coupled ECG and bioimpedance signals and shows promising results on the capacitively-coupled data set. However, it was shown that the EDR could not be accurately estimated from the ccECG signals. Further research into the effect that respiration has on the ccECG is needed to propose alternative EDR estimates.

Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects. The quantification of the latter, in turn, has been suggested as a biomarker to improve the assessment of several conditions and diseases. In this study, a framework to quantify RSA using support vector machines is presented. The methods are based on multivariate autoregressive models, in which the present samples of the heart rate variability are predicted as combinations of past samples of the respiration. The selection and tuning of a kernel in these models allows to solve the regression problem taking into account only the linear components, or both the linear and the nonlinear ones. The methods are tested in simulated data as well as in a dataset of polysomnographic studies taken from 110 obstructive sleep apnea patients. In the simulation, the methods were able to capture the nonlinear components when a weak cardiorespiratory coupling occurs. When the coupling increases, the nonlinear part of the coupling is not detected and the interaction is found to be of linear nature. The trends observed in the application in real data show that, in the studied dataset, the proposed methods captured a more prominent linear interaction than the nonlinear one.