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.
With the discovery of cardiac stem cell, the conception of the heart considered to be a terminally differentiated organ was changed. Cardiac stem cells possess the common characteristics of self-renew, clone formation and differentiating into cardiomyocyte, smooth muscle cell, and endothelial cell. Because of the properties of tissue specificity and lineage commitment, cardiac stem cells are considered to have great advantages over other stem cells in the treatment of cardiovascular disease. However, the low rate of engraftment still remains a problem to be solved. In recent years, people attempted to combine stem cell therapy with other ways, such as tissue engineering, gene therapy, exosome therapy, to cure cardiovascular diseases, aiming at finding better ways to treat the cardiovascular disease. This article is mainly for the reviewing of the mechanisms underlying the stem cell therapy and the combinatory use of new technology emerged these years.
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.
Sclerostin, as a bone-derived secreted glycoprotein, is a suppressor of Wnt signaling pathway. Recently, adverse cardiovascular events in the treatment of osteoporosis with sclerostin inhibitors have raised concerns about the association of sclerostin with atherosclerotic heart disease. Whether the role of sclerostin in atherosclerotic heart disease is harmful or beneficial is not clear. This article reviews the progress of the mechanisms of sclerostin in vascular calcification and atherosclerotic heart disease, focusing on the relationship between sclerostin and vascular calcification, the impact of its concentration changes on atherosclerotic heart disease, and the effect of sclerostin inhibitor on cardiovascular events.
ObjectiveTo investigate the diagnostic value of CT-derived fractional flow reserve (CT-FFR) and fat attenuation index (FAI) based on artificial intelligence-assisted diagnostic software in coronary artery stenosis. MethodsA retrospective analysis was conducted on patients clinically suspected of coronary artery syndrome who underwent coronary computed tomography angiography at Guangdong Province Traditional Chinese and Western Medicine Hospital between June 2021 and May 2025. Patients were divided into two groups according to scanning protocols: group A underwent conventional retrospective electrocardiography-gated scanning, while group B used Flash_ChestPlin mode. Invasive coronary angiography data served as the gold standard for diagnosing vascular stenosis (stenosis rate<50% defined as negative group, ≥50% with clinical symptoms as positive group). Radiation dose was compared between the two scanning protocols. The diagnostic efficacy of CT-FFR, pericoronary FAI, and transluminal attenuation gradient (TAG) based on artificial intelligence system for coronary stenosis was analyzed, including sensitivity, specificity, and area under the curve (AUC). ResultsA total of 567 vessels from 189 patients were analyzed, including 105 males, 84 females with a mean age of (62.5±12.3) years and a mean body mass index of (24.21±3.5) kg/m2. There were 112 patients in the group A and 77 patients in the group B. The radiation dose in the group B was significantly lower than that in the group A [69.7 (58.1, 84.1) mGy·cm vs. 420.4 (338.6, 514.2) mGy·cm, P<0.001]. Significant differences in FAI and CT-FFR were observed between negative and positive groups under both scanning protocols (P<0.05), while no significant difference existed in TAG (P>0.05). In the group A, the AUC values for diagnosing stenosis were 0.925 for CT-FFR, 0.610 for FAI, and 0.516 for TAG. Corresponding values in the group B were 0.889, 0.677, and 0.548 respectively, with CT-FFR demonstrating optimal diagnostic performance. ConclusionUnder both conventional scanning and Flash scanning, the artificial intelligence-based CT-FFR demonstrates good diagnostic performance for coronary artery stenosis, and the Flash protocol significantly lowers radiation dose, indicating substantial potential for clinical application.
This paper tried to address how to apply the relative concepts and methods of evidence-based medicine in clinical practice of cardiology, especially in diagnosis and treatment, with an aim to promote the cardiovascular evidence-based medicine in China.
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 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.
In recent years, the diversity of gut microbiota and the role of its metabolites in cardiovascular disease (CVD) have attracted widespread attention. Gut microbiota metabolites not only play an important role in maintaining gut health, but may also influence cardiovascular health through a variety of mechanisms. As one of the important products of gut microbiota metabolism, sulfate’s biosynthetic pathway, metabolic dynamics and potential effects on cardiovascular system have become the focus of research. However, the current research on the relationship between sulfate and cardiovascular disease still has some shortcomings, including the mechanism is not clear, and clinical data are limited. This article reviewed the biosynthesis of sulfate and its mechanism of action in cardiovascular diseases, and combined with the existing clinical research results, aimed to provide new perspectives and ideas for future research, in order to promote the in-depth exploration and development of 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.
