SummaryGut microbiota can influence the aging process and may modulate aging‐related changes in cognitive function. Trimethylamine‐N‐oxide (TMAO), a metabolite of intestinal flora, has been shown to be closely associated with cardiovascular disease and other diseases. However, the relationship between TMAO and aging, especially brain aging, has not been fully elucidated. To explore the relationship between TMAO and brain aging, we analysed the plasma levels of TMAO in both humans and mice and administered exogenous TMAO to 24‐week‐old senescence‐accelerated prone mouse strain 8 (SAMP8) and age‐matched senescence‐accelerated mouse resistant 1 (SAMR1) mice for 16 weeks. We found that the plasma levels of TMAO increased in both the elderly and the aged mice. Compared with SAMR1‐control mice, SAMP8‐control mice exhibited a brain aging phenotype characterized by more senescent cells in the hippocampal CA3 region and cognitive dysfunction. Surprisingly, TMAO treatment increased the number of senescent cells, which were primarily neurons, and enhanced the mitochondrial impairments and superoxide production. Moreover, we observed that TMAO treatment increased synaptic damage and reduced the expression levels of synaptic plasticity‐related proteins by inhibiting the mTOR signalling pathway, which induces and aggravates aging‐related cognitive dysfunction in SAMR1 and SAMP8 mice, respectively. Our findings suggested that TMAO could induce brain aging and age‐related cognitive dysfunction in SAMR1 mice and aggravate the cerebral aging process of SAMP8 mice, which might provide new insight into the effects of intestinal microbiota on the brain aging process and help to delay senescence by regulating intestinal flora metabolites.

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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.

BackgroundArterial 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.MethodsLeast 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.ResultsThe 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.ConclusionThe 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.