Loss of Endothelial Annexin A1 Aggravates Inflammation-Induched Vascular Aging.

Chronic inflammation is increasingly considered as the most important component of vascular aging, contributing to the progression of age-related cardiovascular diseases. To delay the process of vascular aging, anti-inflammation may be an effective measure. The anti-inflammatory factor annexin A1 (ANXA1) is shown to participate in several age-related diseases; however, its function during vascular aging remains unclear. Here, an ANXA1 knockout (ANXA1-/-) and an endothelial cell-specific ANXA1 deletion mouse (ANXA1△EC) model are used to investigate the role of ANXA1 in vascular aging. ANXA1 depletion exacerbates vascular remodeling and dysfunction while upregulates age- and inflammation-related protein expression. Conversely, Ac2-26 (a mimetic peptide of ANXA1) supplementation reverses this phenomenon. Furthermore, long-term tumor necrosis factor-alpha (TNF-α) induction of human umbilical vein endothelial cells (HUVECs) increases cell senescence. Finally, the senescence-associated secretory phenotype and senescence-related protein expression, rates of senescence-ß-galactosidase positivity, cell cycle arrest, cell migration, and tube formation ability are observed in both ANXA1-knockdown HUVECs and overexpressed ANXA1-TNF-α induced senescent HUVECs. They also explore the impact of formyl peptide receptor 2 (a receptor of ANXA1) in an ANXA1 overexpression inflammatory model. These data provide compelling evidence that age-related inflammation in arteries contributes to senescent endothelial cells that promote vascular aging.

Single-Cell RNA Sequencing and Assay for Transposase-Accessible Chromatin Using Sequencing Reveals Cellular and Molecular Dynamics of Aortic Aging in Mice.

OBJECTIVE: The impact of vascular aging on cardiovascular diseases has been extensively studied; however, little is known regarding the cellular and molecular mechanisms underlying age-related vascular aging in aortic cellular subpopulations. Approach and Results: Transcriptomes and transposase-accessible chromatin profiles from the aortas of 4-, 26-, and 86-week-old C57/BL6J mice were analyzed using single-cell RNA sequencing and assay for transposase-accessible chromatin sequencing. By integrating the heterogeneous transcriptome and chromatin accessibility data, we identified cell-specific TF (transcription factor) regulatory networks and open chromatin states. We also determined that aortic aging affects cell interactions, inflammation, cell type composition, dysregulation of transcriptional control, and chromatin accessibility. Endothelial cells 1 have higher gene set activity related to cellular senescence and aging than do endothelial cells 2. Moreover, construction of senescence trajectories shows that endothelial cell 1 and fibroblast senescence is associated with distinct TF open chromatin states and an mRNA expression model. CONCLUSIONS: Our data provide a system-wide model for transcriptional and epigenetic regulation during aortic aging at single-cell resolution.

Development and Validation of a Prediction Model for Elevated Arterial Stiffness in Chinese Patients With Diabetes Using Machine Learning.

BACKGROUND: Arterial stiffness assessed by pulse wave velocity is a major risk factor for cardiovascular diseases. The incidence of cardiovascular events remains high in diabetics. However, a clinical prediction model for elevated arterial stiffness using machine learning to identify subjects consequently at higher risk remains to be developed. METHODS: Least absolute shrinkage and selection operator and support vector machine-recursive feature elimination were used for feature selection. Four machine learning algorithms were used to construct a prediction model, and their performance was compared based on the area under the receiver operating characteristic curve metric in a discovery dataset (n = 760). The model with the best performance was selected and validated in an independent dataset (n = 912) from the Dryad Digital Repository (https://doi.org/10.5061/dryad.m484p). To apply our model to clinical practice, we built a free and user-friendly web online tool. RESULTS: The predictive model includes the predictors: age, systolic blood pressure, diastolic blood pressure, and body mass index. In the discovery cohort, the gradient boosting-based model outperformed other methods in the elevated arterial stiffness prediction. In the validation cohort, the gradient boosting model showed a good discrimination capacity. A cutoff value of 0.46 for the elevated arterial stiffness risk score in the gradient boosting model resulted in a good specificity (0.813 in the discovery data and 0.761 in the validation data) and sensitivity (0.875 and 0.738, respectively) trade-off points. CONCLUSION: The gradient boosting-based prediction system presents a good classification in elevated arterial stiffness prediction. The web online tool makes our gradient boosting-based model easily accessible for further clinical studies and utilization.