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Despite a large body of evidence, the implementation of guidelines on hemodynamic optimization and goal-directed therapy remains limited in daily routine practice. To facilitate/accelerate this implementation, a panel of experts in the field proposes an approach based on six relevant questions/answers that are frequently mentioned by clinicians, using a critical appraisal of the literature and a modified Delphi process. The mean arterial pressure is a major determinant of organ perfusion, so that the authors unanimously recommend not to tolerate absolute values below 65 mmHg during surgery to reduce the risk of postoperative organ dysfunction. Despite well-identified limitations, the authors unanimously propose the use of dynamic indices to rationalize fluid therapy in a large number of patients undergoing non-cardiac surgery, pending the implementation of a "validity criteria checklist" before applying volume expansion. The authors recommend with a good agreement mini- or non-invasive stroke volume/cardiac output monitoring in moderate to high-risk surgical patients to optimize fluid therapy on an individual basis and avoid volume overload. The authors propose to use fluids and vasoconstrictors in combination to achieve optimal blood flow and maintain perfusion pressure above the thresholds considered at risk. Although purchase of disposable sensors and stand-alone monitors will result in additional costs, the authors unanimously acknowledge that there are data strongly suggesting this may be counterbalanced by a sustained reduction in postoperative morbidity and hospital lengths of stay. Beside existing guidelines, knowledge and explicit clinical reasoning tools followed by decision algorithms are mandatory to implement individualized hemodynamic optimization strategies and reduce postoperative morbidity and duration of hospital stay in high-risk surgical patients. [ABSTRACT FROM AUTHOR]

In a cardiac output (CO) sub-study of the Restrictive versus Liberal Fluid Therapy in Major Abdominal Surgery (RELIEF) trial, it was shown that restrictive fluid management was associated with lower cardiac index at the end of surgery. However, the association of the fluid protocol with intraoperative blood pressure was less clear. This paper primarily compares rates of hypotension between the two fluid regimens. The haemodynamic effects of these protocols may increase our understanding of perioperative fluid prescription. Using a data set of arterial pressure and cardiac output measurements, this observational cohort study primarily compares intraoperative hypotension rates defined by a mean arterial pressure < 65 mmHg between liberal and restrictive fluid protocols. Secondary analyses explore predictors of invasive mean arterial pressure and doppler-derived cardiac output, including fluid volume regimens and surgical duration. 105 patients had a combined total of 835 haemodynamic data capture events from the beginning to the end of the surgery. Here we report that a restrictive regimen is not associated with a greater proportion of participants who experience at least one episode of hypotension than the liberal regimen 64.1% vs. 61.5% (mean difference 2.6%, 95% CI − 15.9% to 21%, p = 0.78). Duration of surgery was associated with an increased risk of hypotension (OR 1.05, 1 to 1.1, p = 0.038). A fluid restriction protocol compared to liberal fluid administration is not associated with lower blood pressure. [ABSTRACT FROM AUTHOR]

1.28 billion adults worldwide have high pressure, and only 21% of people have it under control. If high blood pressure (BP) is not diagnosed and treated properly, then there is a high risk for cardiovascular diseases, the main cause of mortality. Ultrasound has emerged as a potential medical imaging system to monitor cardiovascular health. It is comfortable, noninvasive, safe, and a very well-established and well-known technology. However, there are currently no commercial single ultrasound systems to directly quantify vascular parameters, without the need for complex imaging algorithms, additional software-based solutions, and high energy demands that limit portable and prolonged measurements. In this paper, we present the steps to design an image-free novel ultrasound device to continuously extract vital parameters, with a focus on BP. Moreover, as the BP waveforms are amplified toward the periphery (i.e., from the aorta to the radial artery), we applied mathematical models and algorithms, specific to the site of measurement, to accurately extract BP. We validated these algorithms in silico, in vitro, and ex vivo, where tissue and artery phantoms help emulate human physiology. In young subjects, an in silico pulse pressure (PP) correlation of 0.978 and a mean difference of (– 2.85 ± 2.57) mmHg at the radial artery were achieved. The ex vivo PP correlation was 0.986, with a mean difference of (1.72 ± 3.29) mmHg. Soon thereafter, in vivo measurements of BP and stiffness, and their correlation analysis will be performed to corroborate the accuracy of the developed proof-of-concept device. [ABSTRACT FROM AUTHOR]

Currently, learning physiological vital signs such as blood pressure (BP), hemoglobin levels, and oxygen saturation, from Photoplethysmography (PPG) signal, is receiving more attention. Despite successive progress that has been made so far, continuously revealing new aspects characterizes that field as a rich research topic. It includes a diverse number of critical points represented in signal denoising, data cleaning, employed features, feature format, feature selection, feature domain, model structure, problem formulation (regression or classification), and model combinations. It is worth noting that extensive research efforts are devoted to utilizing different variants of machine learning and deep learning models while transfer learning is not fully explored yet. So, in this paper, we are introducing a per-beat rPPG-to-BP mapping scheme based on transfer learning. An interesting representation of a 1-D PPG signal as a 2-D image is proposed for enabling powerful off-the-shelf image-based models through transfer learning. It resolves limitations about training data size due to strict data cleaning. Also, it enhances model generalization by exploiting underlying excellent feature extraction. Moreover, non-uniform data distribution (data skewness) is partially resolved by introducing logarithmic transformation. Furthermore, double cleaning is applied for training contact PPG data and testing rPPG beats as well. The quality of the segmented beats is tested by checking some of the related quality metrics. Hence, the prediction reliability is enhanced by excluding deformed beats. Varying rPPG quality is relaxed by selecting beats during intervals of the highest signal strength. Based on the experimental results, the proposed system outperforms the state-of-the-art systems in the sense of mean absolute error (MAE) and standard deviation (STD). STD for the test data is decreased to 5.4782 and 3.8539 for SBP and DBP, respectively. Also, MAE decreased to 2.3453 and 1.6854 for SBP and DBP, respectively. Moreover, the results for BP estimation from real video reveal that the STD reaches 8.027882 and 6.013052 for SBP and DBP, respectively. Also, MAE for the estimated BP from real videos reaches 7.052803 and 5.616028 for SBP and DBP, respectively. Proposed camera-based blood pressure monitoring system [ABSTRACT FROM AUTHOR]

The article highlights the study by Paul Elliott and colleagues which investigates the causes of high blood pressure. It relates that Elliott and colleagues have combined information about environmental influences with metabolic profiling, in which they gathered details about diet, alcohol, drug and supplement use from individuals aged between 40 and 59. It notes that the researchers have analysed the amounts of certain urinary constituents and found links between this urinary constituent and a person's diet and blood pressure.

The blood flow in human arterial system can be considered as a fluid dynamics problem. Simulation of blood flow will provide a better understanding of the physiology of human body. Simulation studies of blood flow in the diseased condition can help to diagnose the health problem easily and also have many applications in the areas such as surgical planning and design of medical devices. This paper presents a synthetic electrocardiogram (ECG), blood pressure signals (BP) and ballistocardiographic signal (BCG). Dynamical models of electrocardiogram and cardiovascular system are important in medicine because they can be used as approximation of the real patient. An example is the Windkessel model, which is often used for simulation. ECG, BP and BCG signals can be generated with different sampling frequencies, with different noise levels, with different shapes, filters etc. The paper is based on real data (Real data and identification methods can be used to create models), which are then used for models based on coupled oscillators. Models of the above-mentioned signals are generated by a microcontroller, which allows easy control and adjustment of the output signal and other experiments. The presented paper describes a device that was developed and used for educational purposes, especially for biomedical engineering. [ABSTRACT FROM AUTHOR]

Due to the reactive medical approach applied to disease management, stroke has reached an epidemic scale worldwide. In 2019, the global stroke prevalence was 101.5 million people, wherefrom 77.2 million (about 76%) suffered from ischemic stroke; 20.7 and 8.4 million suffered from intracerebral and subarachnoid haemorrhage, respectively. Globally in the year 2019 — 3.3, 2.9 and 0.4 million individuals died of ischemic stroke, intracerebral and subarachnoid haemorrhage, respectively. During the last three decades, the absolute number of cases increased substantially. The current prevalence of stroke is 110 million patients worldwide with more than 60% below the age of 70 years. Prognoses by the World Stroke Organisation are pessimistic: globally, it is predicted that 1 in 4 adults over the age of 25 will suffer stroke in their lifetime. Although age is the best known contributing factor, over 16% of all strokes occur in teenagers and young adults aged 15–49 years and the incidence trend in this population is increasing. The corresponding socio-economic burden of stroke, which is the leading cause of disability, is enormous. Global costs of stroke are estimated at 721 billion US dollars, which is 0.66% of the global GDP. Clinically manifested strokes are only the "tip of the iceberg": it is estimated that the total number of stroke patients is about 14 times greater than the currently applied reactive medical approach is capable to identify and manage. Specifically, lacunar stroke (LS), which is characteristic for silent brain infarction, represents up to 30% of all ischemic strokes. Silent LS, which is diagnosed mainly by routine health check-up and autopsy in individuals without stroke history, has a reported prevalence of silent brain infarction up to 55% in the investigated populations. To this end, silent brain infarction is an independent predictor of ischemic stroke. Further, small vessel disease and silent lacunar brain infarction are considered strong contributors to cognitive impairments, dementia, depression and suicide, amongst others in the general population. In sub-populations such as diabetes mellitus type 2, proliferative diabetic retinopathy is an independent predictor of ischemic stroke. According to various statistical sources, cryptogenic strokes account for 15 to 40% of the entire stroke incidence. The question to consider here is, whether a cryptogenic stroke is fully referable to unidentifiable aetiology or rather to underestimated risks. Considering the latter, translational research might be of great clinical utility to realise innovative predictive and preventive approaches, potentially benefiting high risk individuals and society at large. In this position paper, the consortium has combined multi-professional expertise to provide clear statements towards the paradigm change from reactive to predictive, preventive and personalised medicine in stroke management, the crucial elements of which are: Consolidation of multi-disciplinary expertise including family medicine, predictive and in-depth diagnostics followed by the targeted primary and secondary (e.g. treated cancer) prevention of silent brain infarction Application of the health risk assessment focused on sub-optimal health conditions to effectively prevent health-to-disease transition Application of AI in medicine, machine learning and treatment algorithms tailored to robust biomarker patterns Application of innovative screening programmes which adequately consider the needs of young populations Stroke is a severe brain disease which has reached an epidemic scale worldwide: in 2019, the global stroke prevalence was 101.5 million people. The World Stroke Organisation predicted that globally, 1 in 4 adults over the age of 25 will get a stroke in their lifetime. Not only old people but also teenagers and young adults are affected. Current global costs of stroke are estimated at 721 billion US dollars. Due to undiagnosed so-called "silent" brain infarction, the number of affected individuals is about 14 times greater in the population than clinically recorded. If it remains untreated, silent brain infarction may cause many severe and fatal disorders such as dementia, depression and even suicide. In this position paper, the consortium describes how the rudimental approach to treating severely diseased people could be replaced by an innovative predictive and preventive one to protect people against the health-to-disease transition. [ABSTRACT FROM AUTHOR]

Post-pandemic health operations have become a near-term reality, discussions around wearables are on the rise. How do wearable health solutions effectively deploy and use this opportunity to fill the gap between wellness and healthcare? In this paper, we will talk about wearable healthcare diagnosis, with a particular focus on monitoring skin hydration using optical multi-wavelength sensor fusion. Continuous monitoring of human skin hydration is a task of paramount importance for maintaining water loss dynamics for fitness lovers as well as for skin beauty, integrity and the health of the entire body. Preserving the appropriate levels of hydration ensures consistency of weight, positively affects psychological state, and proven to result in a decrease in blood pressure as well as the levels of "bad" cholesterol while slowing down the aging processes. Traditional methods for determining the state of water content in the skin do not allow continuous and non-invasive monitoring, which is required for variety of consumer, clinical and cosmetic applications. We present novel sensing technology and a pipeline for capturing, modeling and analysis of the skin hydration phenomena and associated changes therein. By expanding sensing capabilities built into the SmartWatch sensor and combining them with advanced modeling and Machine Learning (ML) algorithms, we identified several important characteristics of photoplethysmography (PPG) signal and spectral sensitivity corresponding to dynamics of skin water content. In a hardware aspect, we newly propose the expansion of SmartWatch capabilities with InfraRed light sources equipped with wavelengths of 970 nm and 1450 nm. Evaluation of the accuracy and characteristics of PPG sensors has been performed with biomedical optics-based simulation framework using Monte Carlo simulations. We performed rigorous validation of the developed technology using experimental and clinical studies. The developed pipeline serves as a tool in the ongoing studies of the next generation of optical sensing technology. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) is among one of the notable menaces to society worldwide. CVD causes the highest number of deaths each year making it one of the most life-threatening diseases across the globe. Most deaths from CVD are sudden therefore patients do not have a chance to get medical assistance in time. Consequently, an immense need for a smart real-time system arises that can be used to monitor heart patients' activities affecting their cardiac health. This system acts as a life-saving tool during serious health emergencies. Data analysis in real-time will proves to be a substantial enhancement in innovative healthcare practices, by which in the near future we can develop an effective, faster, and smarter diagnosis system for doctors. If we talk about real-time data monitoring possibilities, Internet of Things (IoT) empowered systems can provide one of the better solutions. IoT-enabled intelligent healthcare system include a variety of applications, such as Blood Pressure (BP) check, Heart Rate (HR) monitoring, Electrocardiography (ECG) observation, etc. This paper recommends an IoT-enabled ECG monitoring system for data generation (with the help of Node MCU ESP32 and heart rate sensor AD8232) and an intelligent hybrid classification model for data classification. The dataset used has two classes where class 1 represents healthy patients and class 2 represents cardiac ill patients. A comparison among state-of-the-art algorithms and recommended hybrid models has been carried out to establish the accurateness and suitableness of our recommended model. The recommended model attains the highest accuracy of 99.7% under different validation criteria among all the state-of-the-art algorithms, i.e. Adaboost (91.88%), Bagging (92.40%), random forest (92.48%), K-Nearest Neighbor (92.38%), and support vector machine (91.98%). The recommended hybrid model not only handles the complexities of class imbalance for electrocardiogram datasets but will also help in building intelligent and accurate IoT-enabled healthcare systems. [ABSTRACT FROM AUTHOR]

Stroke is the leading cause of death in China (Zhou et al. in The Lancet, 2019). A dataset from Shanxi Province is analyzed to predict the risk of patients at four states (low/medium/high/attack) and to estimate transition probabilities between various states via a SHAP DeepExplainer. To handle the issues related to an imbalanced sample set, the quadratic interactive deep model (QIDeep) was first proposed by flexible selection and appending of quadratic interactive features. The experimental results showed that the QIDeep model with 3 interactive features achieved the state-of-the-art accuracy 83.33%(95% CI (83.14%; 83.52%)). Blood pressure, physical inactivity, smoking, weight, and total cholesterol are the top five most important features. For the sake of high recall in the attack state, stroke occurrence prediction is considered an auxiliary objective in multi-objective learning. The prediction accuracy was improved, while the recall of the attack state was increased by 17.79% (to 82.06%) compared to QIDeep (from 71.49%) with the same features. The prediction model and analysis tool in this paper provided not only a prediction method but also an attribution explanation of the risk states and transition direction of each patient, a valuable tool for doctors to analyze and diagnose the disease. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Perceived social support has been linked to lower rates of morbidity and mortality. However, more information is needed on the biological mechanisms potentially responsible for such links. The main aim of this paper was to conduct a meta-analytic review of the association between perceived social support and awake ambulatory blood pressure (ABP) which is linked to cardiovascular morbidity and mortality. The review identified 12 studies with a total of 3254 participants. The omnibus meta-analysis showed that higher perceived social support was not significantly related to lower ABP (Zr = −0.052, [ −0.11, 0.01]). In addition, there was evidence of significant bias across several indicators. Future research will be needed to explore the boundary conditions linking social support to ABP and its implications for theoretical models and intervention development. [ABSTRACT FROM AUTHOR]

This paper proposes a modeling method based on selective ensemble BP neural networks for the thermal error of machine tool spindle. The fuzzy c-means clustering (FCM) algorithm and the correlation analysis were utilized in combination to select the temperature-sensitive points. A certain number of individual BP neural network models but with instable prediction performance were generated using different training sets and random initial parameters. Then, each individual BP neural network model was assigned a weight, and the genetic algorithm was used to evolve the weights. Afterwards, a prespecified threshold value was applied to select the individual BP neural network models to constitute the ensemble model. Taking a horizontal machining center THM6380 as an example, the thermal error experiment was carried out, and the prediction performance and generalization performance of the selective ensemble BP neural network model were compared with the single BP neural network model, the multiple linear regression (MLR) model, and the least-square support vector machine (LS-SVM) model. The comparison results indicated the superiority of the proposed modeling method, which provides a new idea for thermal error modeling and subsequent thermal error compensation of machine tool spindle. [ABSTRACT FROM AUTHOR]

This paper proposes a method that automatically measures non-invasive blood pressure (BP) based on an auscultatory approach using Korotkoff sounds (K-sounds). There have been methods utilizing K-sounds that were more accurate in general than those using cuff pressure signals only under well-controlled environments, but most were vulnerable to the measurement conditions and to external noise because blood pressure is simply determined based on threshold values in the sound signal. The proposed method enables robust and precise BP measurements by evaluating the probability that each sound pulse is an audible K-sound based on a deep learning using a convolutional neural network (CNN). Instead of classifying sound pulses into two categories, audible K-sounds and others, the proposed CNN model outputs probability values. These values in a Korotkoff cycle are arranged in time order, and the blood pressure is determined. The proposed method was tested with a dataset acquired in practice that occasionally contains considerable noise, which can degrade the performance of the threshold-based methods. The results demonstrate that the proposed method outperforms a previously reported CNN-based classification method using K-sounds. With larger amounts of various types of data, the proposed method can potentially achieve more precise and robust results. [ABSTRACT FROM AUTHOR]

The pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices. [ABSTRACT FROM AUTHOR]

Background: Evidence suggests that arteriosclerosis and sarcopenia (decreased muscle mass) share some of their many causes. However, after controlling for confounding factors, it is unclear whether the presence of arteriosclerosis correlates with sarcopenia. Objectives: The aim of this paper is to explore whether sarcopenia is associated with arteriosclerosis. Design: A multicenter cross-sectional community-based study. Materials and methods: A total of 2511 elderly subjects from six Chinese community health service centers in Anhui province were surveyed through an e-health promotion system to collect basic data and measurements of brachial-ankle pulse wave (baPWV), body composition, and handgrip strength (HGS). Pearson's correlation and binary logistic regression analyses were performed to identify associations between sarcopenia and high baPWV. Results: The prevalence rates of sarcopenia were 12.9% in men and 15.3% in women according to the 2019 standard of Asian Working Group for Sarcopenia. Among subjects with high baPWV, the proportion of sarcopenia was higher compared to those with normal baPWV (men: 17.7% vs. 3.7%; women: 20.4% vs. 4.9%, both p<0.001). Binary logistic regression analysis revealed that sarcopenia was associated with high baPWV (p<0.0001, odds ratio=1.619) after adjusting for confounding factors. HGS slightly and negatively correlated with baPWV (−0.19 in men and −0.18 in women). Conclusions: The intertwined pathophysiological mechanisms shared by arteriosclerosis and sarcopenia are potential targets for future interventions to reduce morbimortality in subjects with both disorders. Upcoming prospective studies and clinical trials are expected to advance these findings. [ABSTRACT FROM AUTHOR]

A limited number of papers have addressed the association between non-dipping-blood pressure (BP) obstructive sleep apnea (OSA), and no study has assessed BP-dipping during rapid eye movement (REM) and non-REM sleep in OSA patients. This study sought to noninvasively assess BP-dipping during REM and non-REM (NREM)-sleep using a beat-by-beat measurement method (pulse-transit-time (PTT)). Thirty consecutive OSA patients (men = 50%) who had not been treated for OSA before and who had > 20-min of REM-sleep were included. During sleep, BP was indirectly determined via PTT. Patients were divided into dippers and non-dippers based on the average systolic-BP during REM and NREM-sleep. The studied group had a a median age of 50 (42–58.5) years and a body mass index of 33.8 (27.6–37.5) kg/m2. The median AHI of the study group was 32.6 (20.1–58.1) events/h (range: 7–124), and 89% of them had moderate-to-severe OSA. The prevalence of non-dippers during REM-sleep was 93.3%, and during NREM-sleep was 80%. During NREM sleep, non-dippers had a higher waist circumference and waist-hip-ratio, higher severity of OSA, longer-time spent with oxygen saturation < 90%, and a higher mean duration of apnea during REM and NREM-sleep. Severe OSA (AHI ≥ 30) was defined as an independent predictor of non-dipping BP during NREM sleep (OR = 19.5, CI: [1.299–292.75], p-value = 0.03). This short report demonstrated that BP-dipping occurs during REM and NREM-sleep in patients with moderate-to-severe OSA. There was a trend of more severe OSA among the non-dippers during NREM-sleep, and severe OSA was independently correlated with BP non-dipping during NREM sleep. [ABSTRACT FROM AUTHOR]

The article discusses a study on the overall mean impact of adherence interventions on blood pressure outcomes among subjects with hypertension. Study highlights include ancestry searches on eligible studies and review papers, summary measure and method of analyses, and a table that outlines characteristics of primary studies included in blood pressure meta-analyses.

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Eight years ago we have designed and implemented an initial prototype of an agent-based glider pilot support system (the GliderAgent). Our aim was to validate correctness of the initial assumption that an agent-based system, combined with sensor data, can help pilots in various situations that occur during a flight. For instance, the GliderAgent was capable of detecting certain dangers, warn the pilot and, autonomously, send notification(s) to the ground station. Due to the continuous and rapid development of mobile technologies and sensors, our initial prototype has evolved. First, we moved the system, from an emulated environment, to real devices. Second, a semantic-rule-based decision making system was integrated with the GliderAgent, to analyze feeds from sensors (altitude, temperature, blood pressure, etc.) and, based on received information, to trigger appropriate behaviors. The result of our work provided a foundations for development of a general-purpose, cyber-physical system framework, which will be described in this paper. Furthermore, the developed system illustrates an interesting approach to integration of heterogeneous IoT devices (potentially also IoT platforms). [ABSTRACT FROM AUTHOR]

In this paper, we investigate the possibility of using image-based pulse transit time (iPTT) to estimate blood pressure using an RGB (red, green, blue)-camera video taken from the face only. It is generally known that there is a correlation between iPTT and blood pressure. iPTT refers to the difference between the times when a pulse wave reaches two different parts of the body. Therefore, it is possible to estimate blood pressure without contact, with iPTTs obtained from the RGB camera. However, this method requires simultaneous capture of images of the face and palm. Recording of such videos is limited by the subject's posture. Hence, it is very difficult to achieve blood pressure estimation in a real-world environment. To solve these problems, this study examined whether it is possible to measure pulse wave propagation based on a single part of the body. The results showed that there was a time difference between the pulse waves at the forehead and the chin, thus it was possible to measure iPTT using only the face. Pulse waves were obtained from the chin and forehead, and the correlations between iPTT and blood pressure from the acquired pulse waves were examined. The results showed a negative correlation between these iPTT measures and blood pressure. The results suggest that simple, non-contact blood pressure estimation will be possible in the future. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method for predicting acute clinical deterioration triggered by hypotension, ventricular fibrillation, and an undiagnosed multiple disease condition using biological signals, such as heart rate, RR interval, and blood pressure. Efforts trying to predict such acute clinical deterioration events have received much attention from researchers lately, but most of them are targeted to a single symptom. The distinctive feature of the proposed method is that the occurrence of the event is manifested as a probability by applying a recurrent probabilistic neural network, which is embedded with a hidden Markov model and a Gaussian mixture model. Additionally, its machine learning scheme allows it to learn from the sample data and apply it to a wide range of symptoms. The performance of the proposed method was tested using a dataset provided by Physionet and the University of Tokyo Hospital. The results show that the proposed method has a prediction accuracy of 92.5% for patients with acute hypotension and can predict the occurrence of ventricular fibrillation 5 min before it occurs with an accuracy of 82.5%. In addition, a multiple disease condition can be predicted 7 min before they occur, with an accuracy of over 90%. [ABSTRACT FROM AUTHOR]

In Caucasian and Asian populations, evidence suggests that 24-h blood pressures (BP) are more predictive of long-term cardiovascular events than clinic BP. However, few long-term studies have evaluated the predictive value of 24-h BP phenotypes (24-h, daytime, nighttime) among African Americans (AA). The purpose of this study is to evaluate the added value of 24-h BP phenotypes compared to clinic BP in predicting the subsequent fatal and non-fatal cardiovascular/renal disease events in AA subjects. AA subjects (n = 270) were initially studied between 1994 and 2006 and standardized clinic BP measurements were obtained during screening procedures for a 3-day inpatient clinical study during which 24-h BP measurements were obtained. To assess the subsequent incidence of cardiovascular and renal disease events, follow-up information was obtained and confirmed by review of paper and electronic medical records between 2015 and 2017. During a mean follow-up of 14 ± 4 years, 50 subjects had one or more fatal or non-fatal cardiovascular/renal disease events. After adjustment for covariates, clinic systolic and diastolic BP were strongly associated with cardiovascular/renal disease events and all-cause mortality (p < 0.0001). Twenty-four-hour BP phenotypes conferred a small incremental advantage over clinic BP in predicting cardiovascular/renal events, which was limited to making a difference of one predicted event in 250–1,000 predictions depending on the 24-h BP phenotype. Nocturnal BP was no more predictive than the other 24-h BP phenotypes. In AA, 24-h BP monitoring provides limited added value as a predictor of cardiovascular/renal disease events. Larger studies are needed in AA to confirm these findings. [ABSTRACT FROM AUTHOR]

An Erratum to this paper has been published: https://doi.org/10.1134/S1061934822370018 [ABSTRACT FROM AUTHOR]

Introduction: Bariatric surgery (BS) has emerged an effective intervention in achieving significant and sustained weight loss in patients with type 2 diabetes (T2D). However, comprehensive data on the long-term impact of BS on hypertension is scarce. We aimed to investigate the long-term impact of BS on blood pressure management in individuals within a T2D cohort. Methods: This retrospective cohort study was conducted on 119 patients who underwent BS between 2009 and 2012. Baseline and follow-up observations, including blood pressure, HbA1c, BMI, and antihypertensive medication use were obtained from electronic patient records at regular intervals up to and beyond 10-year follow-up. Results: The median follow-up period for the 119 patients was 11.5 years. Mean fall in BMI 4–8 weeks post-surgery was 12%. A sustained reduction in systolic BP was observed up to 10 years post-surgery (154.5 mmHg pre-op vs. 132.8 mmHg at 10 years; p < 0.0001. From 5 years onwards, there were increases in mean glycated hemoglobin (HbA1c) and body mass index (BMI). At latest follow-up (> 5 years after bariatric surgery), the number of individuals prescribed an antihypertensive agent started to increase. This is in the context of the number of the number of individuals on 2–3 antihypertensive agents declining up until 5 years post-BS. Specifically, there was a reduction in the number of prescriptions of an antihypertensive agent over time from 164 prescriptions pre-operatively to 81 at 8 weeks post-operatively, 78 at 6 months, 72 at 1 year, 66 at 5 years before rising at 10 years to 95 prescriptions. Conclusions: Our study shows an overall benefit in the years after bariatric surgery in terms of blood pressure and requirement for antihypertensive medication. However, at 5 years and beyond after surgery, the beneficial effect of bariatric surgery diminishes with respect to an increase in number of antihypertensive medication prescriptions, BMI, and HbA1c. [ABSTRACT FROM AUTHOR]

The Na+-Cl− cotransporter (NCC) drives salt reabsorption in the kidney and plays a decisive role in balancing electrolytes and blood pressure. Thiazide and thiazide-like diuretics inhibit NCC-mediated renal salt retention and have been cornerstones for treating hypertension and edema since the 1950s. Here we determine NCC co-structures individually complexed with the thiazide drug hydrochlorothiazide, and two thiazide-like drugs chlorthalidone and indapamide, revealing that they fit into an orthosteric site and occlude the NCC ion translocation pathway. Aberrant NCC activation by the WNKs-SPAK kinase cascade underlies Familial Hyperkalemic Hypertension, but it remains unknown whether/how phosphorylation transforms the NCC structure to accelerate ion translocation. We show that an intracellular amino-terminal motif of NCC, once phosphorylated, associates with the carboxyl-terminal domain, and together, they interact with the transmembrane domain. These interactions suggest a phosphorylation-dependent allosteric network that directly influences NCC ion translocation. The Na+-Cl− cotransporter (NCC) drives salt reabsorption in the kidney. Here the authors determine NCC co-structures individually complexed with the thiazide drug hydrochlorothiazide, and two thiazide-like drugs chlorthalidone and indapamide, revealing that they occlude the NCC ion translocation pathway. [ABSTRACT FROM AUTHOR]

Continuous hemodynamic monitoring in a wearable means can play a crucial role in managing hypertension and preventing catastrophic cardiovascular events. In this study, we have described the fully wearable tonometric device, referred to as flexible adaptive sensing tonometry (FAST), which is capable of continuous and accurate monitoring of hemodynamic parameters within the medical-grade precision. In particular, the FAST system integrates a 1 × 8 unit array of highly sensitive and highly flexible iontronic sensing (FITS) with 1 mm spatial resolution and a closed-loop motion system. The flexible tonometric architecture has been used to determine the radial arterial position with high sensitivity and high conformability, which simplifies the biaxial searching process of the traditional applanation tonometry into a highly efficient uniaxial applanation while keeping the medical-precision assessments. Importantly, a self-calibration algorithm can be automatically implemented during the applanation process, from which the intra-arterial blood pressure wave can be continuously predicted within the medical-grade precision, and subsequently, multi-parametric hemodynamic analysis can be performed in real-time. Experimental validations on health volunteers have demonstrated that the FAST measurements are all within the required accuracy of the clinical standards for continuous pulse wave assessments, blood pressure monitoring as well as other key hemodynamic parameter evaluations. Therefore, the FAST system, by integrating the flexible iontronic sensing array, provides a real-time, medical-grade hemodynamic monitoring solution in a continuously wearable manner, from which remote patient-centered monitoring can be delivered with both medical precision and convenience. [ABSTRACT FROM AUTHOR]

Abdominal aortic aneurysm has a high heritability and often co-occurs with other cardiometabolic disorders, suggesting shared genetic susceptibility. We investigate this commonality leveraging recent GWAS studies of abdominal aortic aneurysm and 32 cardiometabolic traits. We find significant genetic correlations between abdominal aortic aneurysm and 21 of the cardiometabolic traits investigated, including causal relationships with coronary artery disease, hypertension, lipid traits, and blood pressure. For each trait pair, we identify shared causal variants, genes, and pathways, revealing that cholesterol metabolism and inflammation are shared most prominently. Additionally, we show the tissue and cell type specificity in the shared signals, with strong enrichment across traits in the liver, arteries, adipose tissues, macrophages, adipocytes, and fibroblasts. Finally, we leverage drug-gene databases to identify several lipid-lowering drugs and antioxidants with high potential to treat abdominal aortic aneurysm with comorbidities. Our study provides insight into the shared genetic mechanism between abdominal aortic aneurysm and cardiometabolic traits, and identifies potential targets for pharmacological intervention. Here, the authors identify SNPs, genes, pathways, cells and tissues shared between abdominal aortic aneurysm and 21 cardiometabolic traits, generating a reference for common mechanisms and identifying drugs for comorbid conditions. [ABSTRACT FROM AUTHOR]

Continuous and non-invasive measurement of blood pressure (BP) is of great importance particularly for patients in critical state. To achieve continuous and cuffless BP monitoring, pulse transit time (PTT) has been reported as a potential parameter. Nevertheless, this approach remains very sensitive, cumbersome and disagreeable in ambulatory measurement. This paper proposes a new approach to estimate blood pressure through PCG signal by exploring the correlation between PTT and diastolic duration (S21). In this purpose, an artificial neural network was developed using as input data: (systolic duration, diastolic duration, heart rate, sex, height and weight). According to the NN decision, the mean blood pressure was measured and consequently the systolic and the diastolic pressures were estimated. The proposed method is evaluated on 37 subjects. The obtained results are satisfactory, where, the error in the estimation of the systolic and the diastolic pressures compared to the commercial blood pressure device was in the order of 6.48 ± 4.48 mmHg and 3.91 ± 2.58 mmHg, respectively, which are very close to the AAMI standard, 5 ± 8 mmHg. This shows the feasibility of estimating of blood pressure using PCG. [ABSTRACT FROM AUTHOR]

Blood counting is one of the most commonly ordered clinical assays, and is often part of the basis for initial diagnosis and screening for disease. While substantial prior research has shown the ability of portable instruments to accurately produce blood counts through image- or flow-based cytometry, these methods require complex sample preparation using either costly commercial imaging chambers or complicated reagents. To address these issues, in this paper we developed a method to prepare trace volumes of whole blood aimed at portable blood counting. The strategy is based on pre-storing dry-form reagents and fabricating a specifically designed cell counter. In order to obtain total cell counts for red blood cells, platelets, and 3-part differentials of white blood cells, two parallel counting chambers with different depths are made from cost- and environmentally friendly materials using soft lithography. As little as 1 μl of whole blood is prepared with pre-stored reagents in centrifuge vials, whereas red blood cells are sphered and white blood cells are stained at the same time. Driven by the capillary force, prepared blood samples enter the hydrophilic chambers automatically. Monolayers of cells are formed when the blood dilution factors and the chamber depths are co-optimized. Combined with our previous custom-built instrument and automated analysis algorithm, the sample preparation strategy allows producing counting results with excellent agreement to a gold-standard clinical hematology instrument. The success of this preparation method may further advance applications of our technology for global use in low-resource settings where central hematology laboratories are not accessible. [ABSTRACT FROM AUTHOR]

This study aimed to develop a new simple and effective prognostic model using artificial intelligence (AI)-based chest radiograph (CXR) results to predict the outcomes of pneumonia. Patients aged > 18 years, admitted the treatment of pneumonia between March 2020 and August 2021 were included. We developed prognostic models, including an AI-based consolidation score in addition to the conventional CURB-65 (confusion, urea, respiratory rate, blood pressure, and age ≥ 65) and pneumonia severity index (PSI) for predicting pneumonia outcomes, defined as 30-day mortality during admission. A total of 489 patients, including 310 and 179 patients in training and test sets, were included. In the training set, the AI-based consolidation score on CXR was a significant variable for predicting the outcome (hazard ratio 1.016, 95% confidence interval [CI] 1.001–1.031). The model that combined CURB-65, initial O2 requirement, intubation, and the AI-based consolidation score showed a significantly high C-index of 0.692 (95% CI 0.628–0.757) compared to other models. In the test set, this model also demonstrated a significantly high C-index of 0.726 (95% CI 0.644–0.809) compared to the conventional CURB-65 and PSI (p < 0.001 and 0.017, respectively). Therefore, a new prognostic model incorporating AI-based CXR results along with traditional pneumonia severity score could be a simple and useful tool for predicting pneumonia outcomes in clinical practice. [ABSTRACT FROM AUTHOR]

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations. [ABSTRACT FROM AUTHOR]

We have developed a digital twin-based CKD identification and prediction model that leverages generalized metabolic fluxes (GMF) for patients with Type 2 Diabetes Mellitus (T2DM). GMF digital twins utilized basic clinical and physiological biomarkers as inputs for identification and prediction of CKD. We employed four diverse multi-ethnic cohorts (n = 7072): a Singaporean cohort (EVAS, n = 289) and a North American cohort (NHANES, n = 1044) for baseline CKD identification, and two multi-center Singaporean cohorts (CDMD, n = 2119 and SDR, n = 3627) for 3-year CKD prediction and risk stratification. We subsequently conducted a comprehensive study utilizing a single dataset to evaluate the clinical utility of GMF for CKD prediction. The GMF-based identification model performed strongly, achieving an AUC between 0.80 and 0.82. In prediction, the GMF generated with complete parameters attained high performance with an AUC of 0.86, while with incomplete parameters, it achieved an AUC of 0.75. The GMF-based prediction model utilizing complete inputs is the standard implementation of our algorithm: HealthVector Diabetes®. We have established the GMF digital twin-based model as a robust clinical tool capable of predicting and stratifying the risk of future CKD within a 3-year time horizon. We report the correlation of GMF with basic input parameters, their ability to differentiate between future health states and medication status at baseline, and their capability to quantify CKD progression rates. This holistic methodology provides insights into patients' health states and CKD progression rates based on GMF metabolic profile differences, enabling personalized care plans. [ABSTRACT FROM AUTHOR]

Long-term exposures to environmental factors including airborne as well as noise pollutants, are associated with cardiovascular risk. However, the influence of environmental pollution on the young population is controversial. Accordingly, we aimed to investigate the relationships between long-term exposures to different environmental factors and major cardiovascular and inflammatory parameters and biomarkers in young, healthy subjects. Representative sample of permanent residents of two cities differing in air and noise pollution levels, aged 15–21 years, were recruited. Krakow and Lublin, both located in southern Poland, were chosen in relation to their similarities in demographic and geopolitical characteristics, but differences in air pollution (higher in Krakow) and noise parameters (higher in Lublin). A total of 576 subjects were studied: 292 in Krakow and 284 in Lublin. All subjects underwent health questionnaire, blood pressure measurements and biomarker determinations. Inflammatory biomarkers, such as CRP, hs-CRP, fibrinogen as well as homocysteine were all significantly higher in subjects living in Krakow as opposed to subjects living in Lublin (for hsCRP: 0.52 (0.32–0.98) mg/l vs. 0.35 (0.22–0.67) mg/l; p < 0.001). Increased inflammatory biomarker levels were observed in Krakow in both male and female young adults. Interestingly, significant differences were observed in blood pressure between male and female subjects. Males from Krakow had significantly higher mean systolic blood pressure (127.7 ± 10.4 mm/Hg vs. 122.4 ± 13.0 mm/Hg; p = 0.001), pulse pressure (58.7 ± 8.9 mm/Hg vs. 51.4 ± 12.3 mm/Hg; p < 0.001) and lower heart rate (p < 0.001) as compared to males living in Lublin. This was not observed in young adult females. Long-term exposure to environmental factors related to the place of residence can significantly influence inflammatory and cardiovascular parameters, even in young individuals. Interestingly, among otherwise healthy young adults, blood pressure differences exhibited significant variations based on biological sex. [ABSTRACT FROM AUTHOR]

Background: Subarachnoid hemorrhage (SAH) patients with cerebral autoregulation (CA) impairment at an early post-SAH period are at high risk of unfavorable outcomes due to delayed cerebral ischemia (DCI) or other complications. Limited evidence exists for an association between early-stage CA impairments and SAH patient outcomes. The objective of this prospective study was to explore associations between CA impairments detected in early post-SAH snapshot examinations and patient outcomes. Methods: The pilot observational study included 29 SAH patients whose CA status was estimated 2–3 days after spontaneous aneurysm rupture and a control group of 15 healthy volunteers for comparison. Inflatable leg recovery boots (reboots.com, Germany) were used for the safe controlled generation of arterial blood pressure (ABP) changes necessary for reliable CA examination. At least 5 inflation‒deflation cycles of leg recovery boots with a 2–3 min period were used during examinations. CA status was assessed according to the delay time (∆TCBFV) measured between ABP(t) and cerebral blood flow velocity (CBFV(t)) signals during artificially induced ABP changes at boot deflation cycle. CBFV was measured in middle cerebral artery by using transcranial Doppler device. Results: Statistically significant differences in ∆TCBFV were found between SAH patients with unfavorable outcomes (∆TCBFV = 1.37 ± 1.23 s) and those with favorable outcomes (∆TCBFV = 2.86 ± 0.99 s) (p < 0.001). Early assessment of baroreflex sensitivity (BRS) during the deflation cycle showed statistically significant differences between the DCI and non-DCI patient groups (p = 0.039). Conclusions: A relatively small delay of ∆TCBFV <1.6 s between CBFV(t) and ABP(t) waves could be an early warning sign associated with unfavorable outcomes in SAH patients. The BRS during boot deflation can be used as a biomarker for the prediction of DCI. Trial registration: ClinicalTrials.gov Identifier: NCT06028906. Registered 31 August 2023 - Retrospectively registered, https://www.clinicaltrials.gov/study/NCT06028906. [ABSTRACT FROM AUTHOR]

Background: Hypertension is one of the burning topics in today's world. Natural product can open a new window in the treatment as they are lesser side effect compared to synthetic compounds. Paederia foetida a naturally occurring plant has proven its biological importance in many aspects. In this present study, the ethanolic extract of Paederia foetida has effectively proven its antihypertensive activity against Amphetamine-induced hypertension. Results: The study was carried out for 4 weeks with five different groups where the groups receiving Paederia foetida (400 mg/kg) for 4 weeks result in decrease in blood pressure and was found helpful in maintaining the sodium and potassium balance in rat's serum. Amphetamine induces decreasing sodium level that can be countered by Paederia foetida whole plant extract. Geniposide, an active ingredient present in this plant, is having antihypertensive activity, so it was docked against different PDB IDs (3OLL, 3OLS, 5DX3, 5DXE & 6PIT), to find its anti-hypertension effectiveness through computational chemistry. The docking investigations identified that estrogen receptor (PDB ID: 3OLS) exhibited the highest possibility to be the site of action. Docking score of Geniposide with 3OLS was -8.91 which is quit comparable with the internal ligand Estradiol. Conclusion: To assess the binding affinity of Geniposide with the estrogen receptor, an additional molecular dynamics simulation was conducted. The result strongly suggests that Geniposide has the potential to function as an activator of estrogen receptor through of β-ligand binding. This key finding reveals that Geniposide may serve as a potential in the treatment of hypertension by modulating the activity of the estrogen receptor. [ABSTRACT FROM AUTHOR]

Abstract  We investigated the effects of redecoration of a hospital isolation room with natural materials on thermoregulatory, cardiovascular and hormonal parameters of healthy subjects staying in the room. Two isolation rooms with almost bilaterally-symmetrical arrangements were used. One room (RD) was redecorated with wood paneling and Japanese paper, while the other (CN) was unchanged (with concrete walls). Seven healthy male subjects stayed in each room for over 24 h in the cold season. Their rectal temperature (Tre) and heart rate, and the room temperature (Ta) and relative humidity were continuously measured. Arterial blood pressures, arterial vascular compliance, thermal sensation and thermal comfort were measured every 4 h except during sleeping. Blood was sampled after the stay in the rooms. In RD, Ta was significantly higher by about 0.4°C and relative humidity was lower by about 5% than in CN. Diurnal Tre levels of subjects in RD significantly differed from those in CN, i.e., Tres were significantly higher in RD than in CN especially in the evening. In RD, the subjects felt more thermally-comfortable than in CN. Redecoration had minimal effects on cardiovascular parameters. Plasma levels of catecholamines and antidiuretic hormone did not differ, while plasma cortisol level was significantly lower after staying in RD than in CN by nearly 20%. The results indicate that, in the cold season, redecoration with natural materials improves the thermal environment of the room and contributes to maintaining core temperature of denizens at preferable levels. It also seems that redecoration of room could attenuate stress levels of isolated subjects. [ABSTRACT FROM AUTHOR]

This paper aims to identify how many young adults on antihypertensive treatment have been misclassified as hypertensive. We identified subjects aged under 35 on antihypertensive treatment, from the Health Surveys for England, 1998–2004. Pretreatment systolic and diastolic blood pressures were calculated by adjusting on-treatment blood pressures for the effects of treatment. Treatment effects were derived from meta-analysis. Subjects were classified as hypertensive if pretreatment blood pressure was 160/100 mm Hg, or was 140/90 mm Hg in conjunction with high cardiovascular risk. We then identified the proportion of treated subjects on antihypertensive treatment who were truly eligible for treatment. From the survey data we identified 65 adults (25 men and 40 women) under 35 on diuretics, β-blockers, angiotensin converting enzyme inhibitors, calcium blockers or other antihypertensives. Average pretreatment blood pressure was 164/100 mm Hg in those eligible for treatment, and 136/79 mm Hg in those not eligible. The analysis indicated that 29.2% of adults aged 16–34 (95% confidence interval (CI): 18.6–41.8%) were truly eligible for antihypertensive treatment: 32.0% (95% CI: 14.9–53.5%) of men and 25.0% (95% CI: 12.7–41.2%) of women. A total of 73.7% (14 of 19) of subjects eligible and 41.3% (19 of 46) of subjects not eligible for treatment either had a body mass index>30 kg m−2 or kidney disease (χ2-test P=0.018). Because of biological variation in blood pressure, most young adults on treatment for hypertension have been misclassified as hypertensive. Most who have been correctly diagnosed are either clinically obese or have kidney disease.Journal of Human Hypertension (2008) 22, 96–101; doi:10.1038/sj.jhh.1002291; published online 13 September 2007 [ABSTRACT FROM AUTHOR]

Published reports of 30 separate sets of analyses from 29 observational studies relating dietary intake of magnesium to blood pressure (BP) were identified through a comprehensive search using MEDLINE and BIDS-EMBASE. Three studies were prospective, 24 cross-sectional (25 reports), of which four also contained a longitudinal component, and two were obtained from baseline data in a trial. Various dietary methodologies were used: 24-h dietary recall (n = 12), food-frequency questionnaire (8), food record (7), and duplicate diet (2). Twelve reports compared magnesium intake or BP level between subgroups. Seven showed a negative association between magnesium intake and BP level, and five reported no association. From 18 of the 30 sets of analyses either a regression estimate or a Pearson correlation coefficient was reported. Many reports also allowed identification of subgroups by sex, age and race. Ninety population samples and subgroups could thus be identified from the 30 reports. All 11 Pearson-r correlation coefficients reported for systolic BP (SBP) (three significant, P < 0.05) and 10 (out of 12) Pearson-r correlation coefficients reported for diastolic BP (DBP) (four significant) were negative. Seven reports (13 subgroups for SBP, 11 subgroups for DBP) gave partial regression coefficients after adjustment; 10 (seven significant) and eight (six significant) were negative for SBP and DBP, respectively. For 13 subgroups in five papers, Pearson-r correlation coefficients were reported after adjustment for confounding factors. Eight (out of 13) showed a negative relationship for SBP and DBP. This review points to a negative association between dietary magnesium intake and BP. A systematic quantitative overview is needed to reconcile the inconsistencies of the results of individual studies and to quantify the size of such relationship. [ABSTRACT FROM AUTHOR]

Background: As patient-reported outcome measures (PROMs) become available to clinicians for routine clinical decision-making, many wonder how to define a meaningful change in a patient's PROM score. Some PROMs have a specific threshold that indicates meaningful change, but since those numbers are based on population averages, they do not necessarily apply to the varying experiences of each individual patient. Rather than viewing this as a weakness of PROMs, it is worth considering how clinicians use other existing measures in clinical decision-making—and whether PROMs can be used similarly. Body: An informal survey of 43 clinicians reported using measures such as weight, blood pressure, and blood chemistry to inform clinical decision-making. Although clinicians were very consistent with what constituted a meaningful change for some measures (e.g., ECOG performance status), other measures had considerable variability (e.g., weight), often informed by their specialization (for example, differing thresholds for meaningful weight change for adult primary care, pediatrics, and oncology). For interpreting change in measures, they relied on clinical experience (44%), published literature (38%), and established guidelines (35%). In open-response comments, many clarified that the results of any measure had to be taken in the context of each individual patient before making treatment decisions. In short, clinicians already apply individualized clinical judgment when interpreting score changes in existing clinical measures. As clinicians gain familiarity with PROMs, PROMs will likely be utilized in the same way. Conclusion: Like other clinical measures from weight to blood chemistry, change in a PROM score is but one piece of a patient's clinical story. Rather than relying on a hard-and-fast number for defining clinically meaningful change in a PROM score, providers should—and many already do—consider the full scope of a patient's experience as they make treatment decisions. [ABSTRACT FROM AUTHOR]

Genetic mechanisms of blood pressure (BP) regulation remain poorly defined. Using kidney-specific epigenomic annotations and 3D genome information we generated and validated gene expression prediction models for the purpose of transcriptome-wide association studies in 700 human kidneys. We identified 889 kidney genes associated with BP of which 399 were prioritised as contributors to BP regulation. Imputation of kidney proteome and microRNAome uncovered 97 renal proteins and 11 miRNAs associated with BP. Integration with plasma proteomics and metabolomics illuminated circulating levels of myo-inositol, 4-guanidinobutanoate and angiotensinogen as downstream effectors of several kidney BP genes (SLC5A11, AGMAT, AGT, respectively). We showed that genetically determined reduction in renal expression may mimic the effects of rare loss-of-function variants on kidney mRNA/protein and lead to an increase in BP (e.g., ENPEP). We demonstrated a strong correlation (r = 0.81) in expression of protein-coding genes between cells harvested from urine and the kidney highlighting a diagnostic potential of urinary cell transcriptomics. We uncovered adenylyl cyclase activators as a repurposing opportunity for hypertension and illustrated examples of BP-elevating effects of anticancer drugs (e.g. tubulin polymerisation inhibitors). Collectively, our studies provide new biological insights into genetic regulation of BP with potential to drive clinical translation in hypertension. Genetic mechanisms of blood pressure regulation remain poorly defined. Here, the authors perform a kidney multi-omics study to identify 399 kidney effector genes, 11 miRNAs and 97 proteins for blood pressure and hypertension. [ABSTRACT FROM AUTHOR]

Despite the high prevalence of snoring in Asia, little is known about the genetic etiology of snoring and its causal relationships with cardiometabolic traits. Based on 100,626 Chinese individuals, a genome-wide association study on snoring was conducted. Four novel loci were identified for snoring traits mapped on SLC25A21, the intergenic region of WDR11 and FGFR, NAA25, ALDH2, and VTI1A, respectively. The novel loci highlighted the roles of structural abnormality of the upper airway and craniofacial region and dysfunction of metabolic and transport systems in the development of snoring. In the two-sample bi-directional Mendelian randomization analysis, higher body mass index, weight, and elevated blood pressure were causal for snoring, and a reverse causal effect was observed between snoring and diastolic blood pressure. Altogether, our results revealed the possible etiology of snoring in China and indicated that managing cardiometabolic health was essential to snoring prevention, and hypertension should be considered among snorers. A genome-wide association study in the China Kadoorie Biobank identified four novel loci related to snoring, and highlights the relevance of cardiometabolic health to snoring prevention. [ABSTRACT FROM AUTHOR]