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Chronological Age Affects MSC Senescence In Vitro-A Systematic Review.

ABSTRACT: The use of mesenchymal stromal cells (MSCs) in regenerative medicine and tissue engineering is well established, given their properties of self-renewal and differentiation. However, several studies have shown that these properties diminish with age, and understanding the pathways involved are important to provide regenerative therapies in an ageing population. In this PRISMA systematic review, we investigated the effects of chronological donor ageing on the senescence of MSCs. We identified 3023 studies after searching four databases including PubMed, Web of Science, Cochrane, and Medline. Nine studies met the inclusion and exclusion criteria and were included in the final analyses. These studies showed an increase in the expression of p21, p53, p16, ROS, and NF-κB with chronological age. This implies an activated DNA damage response (DDR), as well as increased levels of stress and inflammation in the MSCs of older donors. Additionally, highlighting the effects of an activated DDR in cells from older donors, a decrease in the expression of proliferative markers including Ki67, MAPK pathway elements, and Wnt/β-catenin pathway elements was observed. Furthermore, we found an increase in the levels of SA-β-galactosidase, a specific marker of cellular senescence. Together, these findings support an association between chronological age and MSC senescence. The precise threshold for chronological age where the reported changes become significant is yet to be defined and should form the basis for further scientific investigations. The outcomes of this review should direct further investigations into reversing the biological effects of chronological age on the MSC senescence phenotype.

SUBMITTER: Kapetanos K

PROVIDER: S-EPMC8348192 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Objective: Treatment of glioblastoma in elderly patients is particularly challenging due to their general condition and comorbidities. Treatment decisions are often based on chronological age. Frailty screening tests promise an assessment tool to stratify geriatric patients and identify those at risk for an unfavorable outcome. This study aims to evaluate the impact of age and frailty on the surgical outcome and overall survival in geriatric patients with glioblastoma. Methods: Data acquisition was conducted as a single-center retrospective analysis. From January 1st 2015, and December 31st 2019, 104 glioblastoma patients over 70 years of age were included in our study. Demographic data, tumor size, Karnofsky Performance Score (KPS), and Eastern Cooperative Oncology Group Performance Status (ECOG), as well as treatment modalities, were assessed. The Geriatric 8 health status screening tool (G8) and Groningen Frailty Index (GFI) were compiled pre-and postoperatively. Results: The mean patient age was 76.86 ± 4.11 years. Forty-nine (47%) patients were female, 55 (53%) male. Sixty-seven patients underwent microsurgical tumor resection, 37 received tumor biopsy alone. Mean G8 on admission was 12.4 ± 2.0, mean GFI 5.0 ± 2.5. In our cohort, frailty was independent of patient age, tumor size, or localization. Frailty, defined by G8 and GFI, is associated with shorter overall survival (G8: p = 0.0035; GFI: p = 0.0136) and higher numbers of surgical complications (G8: p = 0.0326; GFI: p = 0.0388). Frailer patients are more likely to receive best supportive care (p = 0.004). Nevertheless, frailty did not affect adjuvant treatment decision-making toward either single-use of chemo- or radiation therapy, stratified treatment, or concomitant therapy. The surgical decision on the extent of resection was not based on pre-operative frailty. Conclusion: In our study, frailty is a predictor of poorer surgical outcomes, post-operative complications, and impaired overall survival independent of chronological age. Frailty screening tests offer an additional assessment tool to stratify geriatric patients with glioblastoma and identify those at risk for a detrimental outcome and thus should be implemented in therapeutic decision making.

Project description:BackgroundEpigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture.MethodsFirst, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women's Health Initiative study).ResultsThrough the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10-52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10-60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations.ConclusionsThe integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age.

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Chronological Age Affects MSC Senescence In Vitro-A Systematic Review.

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