The World Health Organization (WHO) released the “Global report on hypertension” on September 19, 2023. This report systematically summarizes the prevalence, mortality, diagnosis and treatment of hypertension in various countries, and elucidates the current situation of hypertension management, and gives a series of suggestions on how to manage hypertension, providing new thinking and inspiration for countries to optimize hypertension management. Through the summary of relevant studies and reports, this paper further reviews the present situation, early identification and management of hypertension.
Cardiovascular disease has caused a huge burden of disease worldwide, and the rapid advancement of smart wearable devices has provided new means for early diagnosis, real-time monitoring, and event prevention of cardiovascular disease. Smart wearable devices can be classified into various categories based on detection signals and physical carrier types. Based on an overview of the composition of such devices, this article further introduces the current cutting-edge research and related market products related to smart blood pressure monitoring, electrocardiogram monitoring, and ultrasound monitoring. It also discusses the future development and challenges of such devices, aiming to provide evidence support for the research and development of smart wearable devices in the diagnosis and treatment of cardiovascular diseases in the future.
There are various examination methods for cardiovascular diseases. Non-invasive diagnosis and prognostic information acquisition are the current research hotspots of related imaging examinations. Positron emission tomography (PET)/magnetic resonance imaging (MRI) is a new advanced fusion imaging technology that combines the molecular imaging of PET with the soft tissue contrast function of MRI to achieve their complementary advantages. This article briefly introduces several major aspects of cardiac PET/MRI in the diagnosis of cardiovascular disease, including atherosclerosis, ischemic cardiomyopathy, nodular heart disease, and myocardial amyloidosis, in order to promote cardiac PET/MRI to be more widely used in precision medicine in this field.
Objective To investigate the relationship between estimated glucose disposal rate (eGDR) and the incidence of cardiovascular disease (CVD) in individuals without diabetes and those with diabetes. Methods Participants were drawn from the China Health and Retirement Longitudinal Study from 2011 to 2018. Participants were divided into four subgroups based on quartiles of baseline eGDR. In this study, data were analyzed using Kaplan-Meier survival curves, Cox proportional hazards models, restricted cubic spline curves, subgroup analyses, and receiver operator characteristic curves. Results A total of 6 283 participants were included. Among them, 47.2% are male, with an average age of (59.6±9.5) years; 285 cases (4.5%) had diabetes; there were 1 571 cases in Q1 group, 1 572 cases in Q2 group, 1 583 cases in Q3 group, and 1 557 cases in Q4 group. A total of 761 CVD events occurred. According to the multivariate-adjusted model, baseline eGDR levels were significantly associated with the risk of CVD events (P<0.05). Baseline eGDR was associated with the risk of CVD events in individuals without diabetes (P<0.05), but the results were not entirely consistent for those with diabetes [CVD: hazard ratio (HR)=0.85, 95% confidence interval (CI) (0.75, 0.96), P=0.012; heart disease: HR=0.91, 95%CI (0.78, 1.06), P=0.211; stroke: HR=0.74, 95%CI (0.58, 0.93), P=0.012]. Restricted cubic spline curves revealed significant negative linear relationships between baseline eGDR and CVD, heart disease, and stroke. Subgroup analyses with interaction testing revealed that the association between baseline eGDR and CVD was not modified by age, sex, smoking status, alcohol consumption, or dyslipidemia. Receiver operator characteristic curves further demonstrated that baseline eGDR exhibited significantly better predictive performance than the triglyceride-glucose (TyG) index, obesity indices, and the TyG index-obesity composite. Conclusions Low level baseline eGDR is associated with an increased risk of CVD in individuals without diabetes. This finding may help improve risk stratification to guide preventive measures and enhance the prognosis of CVD.
Objective To explore the relationship between uric acid (UA) level and cardiovascular disease in patients with OSAHS and its clinical significance. Methods The electronic medical record system of the First hospital of Lanzhou University was used to collect 475 subjects who completed polysomnography (PSG) during hospitalization from January 2019 to May 2020. According to the Guidelines for the Diagnosis and Treatment of Obstructive Sleep Apnea Hypopnea Syndrome (Basic Version), the patients were divided into four group: control group [apnea-hypopnea index (AHI) <5 times/h, n=96], mild group (5≤AHI≤15 times/h, n=130), moderate group (15<AHI≤30 times/h, n=112), and severe group (AHI>30 times/h, n=137). The age, gender, body mass index (BMI), smoking history, drinking history, hypertension, diabetes mellitus, cardiovascular disease and biochemical indexes [including triglyceride, total cholesterol, high density lipoprotein cholesterol, low density lipoprotein cholesterol, glucose, UA, blood urea nitrogen (BUN), serum creatinine, lactate dehydrogenase, homocysteine], PSG indexes were observed and compared among the four groups, and the differences were compared by appropriate statistical methods. Binary logistic regression model was used to evaluate the correlation between various risk factors and cardiovascular disease. Results There were statistically significant differences in age, gender, BMI, drinking history, hypertension and cardiovascular disease among the 4 groups (P<0.05). The levels of UA and BUN in mild, moderate and severe groups were higher than those in the control group, with statistical significance (P<0.05). With the increasing of OSAHS severity, the level of UA increased. There was statistical significance in the incidence of cardiovascular disease among the four groups (P<0.05), and the highest incidence of arrhythmia was found among the four groups. And the incidence of cardiovascular disease increases with the increasing of OSAHS severity. Binary Logistic regression analysis showed that the risk factors for cardiovascular disease in OSAHS patients were age, UA and BUN (P<0.05). Conclusions The occurrence of cardiovascular disease in OSAHS patients is positively correlated with the severity of OSAHS. The level of UA can be used as an independent risk factor for cardiovascular disease in OSAHS patients. Therefore, reducing the level of UA may have positive significance for the prevention and control of the prevalence and mortality of cardiovascular disease in OSAHS patients.
Circular RNA (circRNA) is a non-coding RNA which exists widely in eukaryotic cells with a structure of covalently closed continuous loop. Its generation, characteristics and functions have received extensive attention, making it one of the hot spots in the field of non-coding RNA research. Many studies have found that circRNA plays an important role in the development of various diseases including cardiovascular disease, nervous system disease and cancer. Cardiovascular disease is a worldwide common disease with high incidence and poor prognosis. Its exact pathogenesis has not been found, which blocks the development of cardiovascular disease treatment. In this review, we summarize the loop-forming mechanisms, the functions and the progress of current researches of circRNA in cardiovascular diseases.
Cardiovascular disease (CVD) has caused a huge burden of disease worldwide, and accurate diagnosis and assessment of CVD has a clear significance for improving the prognosis of patients. The development of artificial intelligence (AI) and its rapid application in the medical field have enabled new approaches for the analysis and fitting of various CVD data. At present, in addition to structured medical records, the CVD field also includes a large number of non-linear data brought by imaging and electrophysiological examinations. How to use AI to process such multi-source data has been explored by a large number of studies. Therefore, this review discusses the existing ways of processing various multi-source heterogeneous data with existing artificial intelligence technologies by summarizing various existing studies, and analyzes their possible advantages and disadvantages, in order to provide a basis for the future application of AI in CVD.
ObjectiveWearable devices refer to a class of monitoring devices that can be tightly integrated with the human body and are designed to continuously monitor individual's activity without impeding or restricting the user's normal activities in the process. With the rapid advancement of chips, sensors, and artificial intelligence technologies, such devices have been widely used for patients with cardiovascular diseases who require continuous health monitoring. These patients require continuous monitoring of a number of physiological indicators to assess disease progression, treatment efficacy, and recovery in the early stages of the disease, during the treatment, and in the recovery period. Traditional monitoring methods require patients to see a doctor on a regular basis with the help of fixed devices and analysis by doctors, which not only increases the financial burden of patients, but also consumes medical resources and time. However, wearable devices can collect data in real time and transmit it directly to doctors via the network, thus providing an efficient and cost-effective monitoring solution for patients. In this paper, we will review the applications, advantages and challenges of wearable devices in the treatment of cardiovascular diseases, as well as the outlook for their future applications.
The peak period of cardiovascular disease (CVD) is around the time of awakening in the morning, which may be related to the surge of sympathetic activity at the end of nocturnal sleep. This paper chose 140 participants as study object, 70 of which had occurred CVD events while the rest hadn’t during a two-year follow-up period. A two-layer model was proposed to investigate whether hypnopompic heart rate variability (HRV) was informative to distinguish these two types of participants. In the proposed model, the extreme gradient boosting algorithm (XGBoost) was used to construct a classifier in the first layer. By evaluating the feature importance of the classifier, those features with larger importance were fed into the second layer to construct the final classifier. Three machine learning algorithms, i.e., XGBoost, random forest and support vector machine were employed and compared in the second layer to find out which one can achieve the highest performance. The results showed that, with the analysis of hypnopompic HRV, the XGBoost+XGBoost model achieved the best performance with an accuracy of 84.3%. Compared with conventional time-domain and frequency-domain features, those features derived from nonlinear dynamic analysis were more important to the model. Especially, modified permutation entropy at scale 1 and sample entropy at scale 3 were relatively important. This study might have significance for the prevention and diagnosis of CVD, as well as for the design of CVD-risk assessment system.