- is focusing on the role of molecular mechanisms of aging in the pathogenesis of cardiovascular diseases, COVID19, hypertension, obesity and vascularhomeostasis. ‘ + Read more in the comments and submit📧 at the link⬇

Cell Biology of Vascular Aging.

Guest Editors: Prof. Zoltan Arany, Prof. Jalees Rehman and Prof. Gabor Csanyi.

Deputy Editor Dr. Stefano Tarantini and Editor-in-Chief Dr. Zoltan Ungvari, and the editorial team of GeroScience (Official Journal of the American Aging Association, published by Springer) invite submission of original research articles, opinion papers and review articles related to research focused on understanding the mechanisms involved in vascular aging, the factors promoting accelerated aging in vascular cells and the role of vascular cells in the pathogenesis of age-related diseases. This call-for-papers is aimed at providing a platform for the dissemination of critical novel ideas related to the mechanisms of vascular aging as well as mechanisms related to key phenotypes of vascular aging including.

If you looked at two snapshots of the same maple tree taken in July and December, you’d see a dramatic change from summer’s full green crown to winter’s bare branches. What those two photos don’t show you, however, is how the change occurred—gradually or all at once? In truth, deciduous trees tend to hold out for an environmental signal—a change in light or temperature—and then shed all their leaves within just a week or two.

When it comes to aging, we may be more like these trees than we realized.

According to new work from Rockefeller’s Laboratory of Single-Cell Genomics and Population Dynamics, mammals follow a similar aging trajectory at the cellular level. As described in a new paper in Science, lab head Junyue Cao and his colleagues used single-cell sequencing to simultaneously scan more than 21 million cells from every major mouse organ across five stages of life. This enormous collection is now the world’s largest cellular atlas within a single study.

Chronological age (CA) does not reflect individual variation in the aging process. However, existing biological age predictors are mostly based on European populations and overlook the widespread nonlinear effects of clinical biomarkers.

Using data from the prospective Dongfeng-Tongji (DFTJ) cohort of elderly Chinese, we propose a physiological aging index (PAI) based on 36 routine clinical biomarkers to measure aging progress. We first determined the optimal level of each biomarker by restricted cubic spline Cox models. For biomarkers with a U-shaped relationship with mortality, we derived new variables to model their distinct effects below and above the optimal levels. We defined PAI as a weighted sum of variables predictive of mortality selected by a LASSO Cox model. To measure aging acceleration, we defined ΔPAI as the residual of PAI after regressing on CA. We evaluated the predictive value of ΔPAI on cardiovascular diseases (CVD) in the DFTJ cohort, as well as nine major chronic diseases in the UK Biobank (UKB).

In the DFTJ training set (n = 12,769, median follow-up: 10.38 years), we identified 25 biomarkers with significant nonlinear associations with mortality, of which 11 showed insignificant linear associations. By incorporating nonlinear effects, we selected CA and 17 clinical biomarkers to calculate PAI. In the DFTJ testing set (n = 15,904, 5.87 years), PAI predict mortality with a concordance index (C-index) of 0.816 (95% confidence interval, [0.796, 0.837]), better than CA (C-index = 0.771 [0.755, 0.788]) and PhenoAge (0.799 [0.784, 0.814]). ΔPAI was predictive of incident CVD and its subtypes, independent of traditional risk factors. In the external validation set of UKB (n = 296,931, 12.80 years), PAI achieved a C-index of 0.749 (0.746, 0.752) to predict mortality, remaining better than CA (0.706 [0.702, 0.709]) and PhenoAge (0.743 [0.739, 0.746]).