Xingang Zhang

BackgroundHypertension is a major risk factor for cardiovascular disease (CVD), with a high prevalence in rural northeastern China. This study assesses the cardiovascular risk associated with different blood pressure patterns: systolic dominant, diastolic dominant, and parallel elevation.MethodsWe analyzed data from the Northeast Rural Cardiovascular Health Study (NCRCHS), which included 8,189 participants aged 35 and above. Baseline surveys from 2012 to 2013 and follow-ups in 2015 and 2018 provided a median follow-up of 4.66 years. Participants were categorized into ten subgroups based on systolic and diastolic blood pressure elevations. Kaplan-Meier curves and Cox regression models were used to examine CVD incidence and cardiovascular risk across these groups.ResultsThe incidence of CVD varied significantly among hypertension categories. Patients with grade 1 hypertension had no significant increase in cardiovascular risk at nearly 5 years. Notably, parallel elevations in systolic and diastolic pressures posed the highest cardiovascular risk, while a predominant rise in diastolic pressure alone did not significantly increase risk. This highlights the importance of analyzing blood pressure comprehensively for cardiovascular risk stratification and suggests rethinking treatment strategies for diastolic dominant hypertension.ConclusionsOur findings call for a nuanced approach to cardiovascular risk assessment in hypertension, taking into account distinct patterns of systolic and diastolic blood pressure. The study supports personalized treatment interventions and reinforces current hypertension treatment guidelines. We advocate for prioritizing non-pharmacological management in grade 1 hypertension and further clinical evaluation of treatment thresholds for diastolic dominant hypertension.

In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.

The ubiquitin‐proteasome system is a crucial mechanism for regulating protein levels in cells, with substrate‐specific E3 ubiquitin ligases serving as an integral component of this system. Among these ligases are SMAD‐specific E3 ubiquitin‐protein ligase 1 (SMURF1) and SMAD‐specific E3 ubiquitin‐protein ligase 2 (SMURF2), which belong to the neural precursor cell‐expressed developmentally downregulated 4 (NEDD4) subfamily of Homologous to E6‐AP COOH terminus (HECT)‐type E3 ligases. As E3 ligases, SMURFs have critical functions in regulating the stability of multiple proteins, thereby maintaining physiological processes such as cell migration, proliferation, and apoptosis. The occurrence of many diseases is attributed to abnormal cell physiology and an imbalance in cell homeostasis. It is noteworthy that SMURFs play pivotal roles in disease progression, with the regulatory functions being complex and either facilitative or inhibitory. In this review, we elucidate the mechanisms by which SMURF1 and SMURF2 can regulate disease progression in non‐cancerous diseases. These significant findings offer potential novel therapeutic targets for various diseases and new avenues for research on SMURF proteins.

Background and objective: Obesity has become a serious public health problem and brings a heavy burden of cardiovascular disease. Metabolically healthy obesity (MHO) is defined as individuals with obesity with no or only minor metabolic complications. Whether individuals with MHO have a lower cardiovascular risk remains controversial. In this study, a new criterion was used to define MHO and assess its predictive value for cardiovascular events and death. At the same time, the new criterion and the traditional criterion are compared to analyze the differences between different diagnostic criteria.Methods: A prospective cohort was established in northeast rural China from 2012 to 2013. Follow-up was conducted in 2015 and 2018 to investigate the incidence of cardiovascular events and survival. Subjects were grouped according to the metabolic health and obesity status. Kaplan-Meier curves were drawn to describe the cumulative risk of endpoint events in the four groups. Cox regression analysis model was constructed to evaluate the risk of endpoint events. Analysis of variance and post hoc analyses were used to calculate and compare differences in metabolic markers between MHO subjects diagnosed by novel and traditional criteria.Results: A total of 9345 participants 35 years of age or older without a history of cardiovascular disease were included in this study. After a median follow-up of 4.66 years, the data showed that participants in the MHO group had no significant increase in the risk of composite cardiovascular events and stroke, but had a 162% increase in the risk of coronary heart disease (HR: 2.62; 95%CI: 1.21-5.67). However, when using conventional criteria for metabolic health, mMHO group had a 52% increase in combined CVD risk (HR: 1.52; 95%CI: 1.14-2.03). By comparing the differences of metabolic indicators between MHO subjects diagnosed by the two criteria, MHO subjects diagnosed by the new criterion had higher WC, WHR, TG, FPG, and lower HDL-C levels except for lower blood pressure, showing more exposure to cardiovascular risk factors.Conclusions: The risk of combined CVD and stroke was not increased in MHO subjects. The new metabolic health criterion is superior to the traditional criterion and can effectively identify individuals with obesity with a lower risk of combined CVD. Blood pressure levels may be responsible for the inconsistent risk of combined CVD in MHO subjects diagnosed with both criteria.

Background: Endothelial injury caused by Type 2 diabetes mellitus (T2DM) is considered as a mainstay in the pathophysiology of diabetic vascular complications (DVCs). However, the molecular mechanism of T2DM-induced endothelial injury remains largely unknown. Here, we found that endothelial WW domain-containing E3 ubiquitin protein ligase 2 (WWP2) act as a novel regulator for T2DM-induced vascular endothelial injury through modulating ubiquitination and degradation of DEAD-box helicase 3 X-linked (DDX3X). Methods: Single-cell transcriptome analysis was used to evaluate WWP2 expression in vascular endothelial cells of T2DM patients and healthy controls. Endothelial-specific Wwp2 knockout mice were used to investigate the effect of WWP2 on T2DM-induced vascular endothelial injury. In vitro loss- and gain-of-function studies were performed to assess the function of WWP2 on cell proliferation and apoptosis of human umbilical vein endothelial cells. The substrate protein of WWP2 was verified using mass spectrometry, coimmunoprecipitation assays and immunofluorescence assays. The mechanism of WWP2 regulation on substrate protein was investigated by pulse-chase assay and ubiquitination assay. Results: The expression of WWP2 was significantly down-regulated in vascular endothelial cells during T2DM. Endothelial-specific Wwp2 knockout in mice significantly aggravated T2DM-induced vascular endothelial injury and vascular remodeling after endothelial injury. Our in vitro experiments showed that WWP2 protected against endothelial injury by promoting cell proliferation and inhibiting apoptosis in ECs. Mechanically, we found that WWP2 is down-regulated in high glucose and palmitic acid (HG/PA)-induced ECs due to c-Jun N-terminal kinase (JNK) activation, and uncovered that WWP2 suppresses HG/PA-induced endothelial injury by catalyzing K63-linked polyubiquitination of DDX3X and targeting it for proteasomal degradation. Conclusion: Our studies revealed the key role of endothelial WWP2 and the fundamental importance of the JNK-WWP2-DDX3X regulatory axis in T2DM-induced vascular endothelial injury, suggesting that WWP2 may serve as a new therapeutic target for DVCs.

UNSTRUCTURED In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.

Acute lymphoblastic leukemia (ALL) has been a huge threat for people's health and finding effective target therapy is urgent and important. WWP2, as one of E3 ubiquitin ligase, is involved in many biological processes by specifically binding to substrates. PARP1 plays a role in cell apoptosis and is considered as a therapeutic target of certain cancers. In this study, we firstly found that WWP2 expressed higher in newly diagnosed ALL patients comparing with complete remission (CR) ALL patients and normal control people, and WWP2 in relapse ALL patients expressed higher than normal control people. WWP2 expression was related with the FAB subtype of ALL and the proportion of blast cells in bone marrow blood tested by flow cytometry. We demonstrated knockout WWP2 inhibited the ALL growth and enhanced apoptosis induced by Dox in vitro and vivo for the first time. WWP2 negatively regulated and interacted with PARP1 and WWP2 mechanically degraded PARP1 through polyubiquitin-proteasome pathway in ALL. These findings suggested WWP2 played a role in ALL development as well as growth and apoptosis, and also displayed a regulatory pathway of PARP1, which provided a new potential therapeutic target for the treatment of ALL.

Background: Various anthropometric indices have been proved to be useful to predict metabolic syndrome(MetS), but the association between changes in anthropometric indices and the onset of MetS is unclear. This study selected six indices that are easy to measure and calculate in daily life and evaluated the relationships.Methods: We established a prospective cohort in rural China during 2012-2013 and involved 5,221 participants without MetS. The follow-up visit was conducted in 2015 to repeat anthropometric indices measurements and assess MetS onset. Binary logistic regression model was used to calculate the association between changes in anthropometric indices and MetS onset. Receiver operating characteristic (ROC) curve was drawn to compare their abilities in MetS prediction.Results: Over a median follow-up time of 2.42 years, 1,367 participants (26.2%) developed MetS. The increase in all the six indices is associated with an increased risk of MetS. Changes in WC and WHtR are the strongest predictors, with a 5 cm increase in WC and a 0.025 increase in WHtR giving the best prediction of MetS onset.Conclusions: People should be aware of changes in these six anthropometric indices in daily life, as their increase is closely related to an increased risk of MetS, especially WC and WHtR. We recommend an increase of 5 cm in WC and 0.025 in WHtR as the optimal cut-off for the MetS prediction.