An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging.
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ABSTRACT:
SUBMITTER: Sayed N
PROVIDER: S-EPMC8654267 | biostudies-literature | 2021 Jul
REPOSITORIES: biostudies-literature
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An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging.
Sayed Nazish N Huang Yingxiang Y Nguyen Khiem K Krejciova-Rajaniemi Zuzana Z Grawe Anissa P AP Gao Tianxiang T Tibshirani Robert R Hastie Trevor T Alpert Ayelet A Cui Lu L Kuznetsova Tatiana T Rosenberg-Hasson Yael Y Ostan Rita R Monti Daniela D Lehallier Benoit B Shen-Orr Shai S SS Maecker Holden T HT Dekker Cornelia L CL Wyss-Coray Tony T Franceschi Claudio C Jojic Vladimir V Haddad François F Montoya José G JG Wu Joseph C JC Davis Mark M MM Furman David D
Nature aging 20210712
While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lacking. From the blood immunome of 1,001 individuals aged 8-96 years, we developed a deep-learning method based on patterns of systemic age-related inflammation. The resulting inflammatory clock of aging (iAge) tracked with multimorbidity, immunosenescence, frailty and cardiovascular aging, and is also associated with exceptional longevity in centenar ...[more]
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Project description:Chronic inflammation, a decline in immune responsiveness, and reduced cardiovascular function are all associated with aging, but the relationships among these phenomena remain unclear. Here, we longitudinally profiled a total of 84 signaling conditions in 91 young and older adults and observed an age-related reduction in cytokine responsiveness within four immune cell lineages, most prominently T cells. The phenotype can be partially explained by elevated baseline levels of phosphorylated STAT (pSTAT) proteins and a different response capacity of naive versus memory T cell subsets to interleukin 6 (IL-6), interferon α (IFN-α), and, to a lesser extent, IL-21 and IFN-γ. Baseline pSTAT levels tracked with circulating levels of C-reactive protein (CRP), and we derived a cytokine response score that negatively correlates with measures of cardiovascular disease, specifically diastolic dysfunction and atherosclerotic burden, outperforming CRP. Thus, we identified an immunological link between inflammation, decreased cell responsiveness in the JAK-STAT pathway, and cardiovascular aging. Targeting chronic inflammation may ameliorate this deficiency in cellular responsiveness and improve cardiovascular function.
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Project description:Background: Many biological clocks related to aging have been linked to the development of cancer. A recent study has identified that the inflammatory aging clock was an excellent indicator to track multiple diseases. However, the role of the inflammatory aging clock in glioblastoma (GBM) remains to be explored. This study aimed to investigate the expression patterns and the prognostic values of inflammatory aging (iAge) in GBM, and its relations with stem cells. Methods: Inflammation-related genes (IRG) and their relations with chronological age in normal samples from the Cancer Genome Atlas (TCGA) were identified by the Spearman correlation analysis. Then, we calculated the iAge and computed their correlations with chronological age in 168 patients with GBM. Next, iAge was applied to classify the patients into high- and low-iAge subtypes. Next, the survival analysis was performed. In addition, the correlations between iAge and stem cell indexes were evaluated. Finally, the results were validated in an external cohort. Results: Thirty-eight IRG were significantly associated with chronological age (|coefficient| > 0.5), and were used to calculate the iAge. Correlation analysis showed that iAge was positively correlated with chronological age. Enrichment analysis demonstrated that iAge was highly associated with immune cells and inflammatory activities. Survival analysis showed the patients in the low-iAge subtype had significantly better overall survival (OS) than those in the high-iAge subtype (p < 0.001). In addition, iAge outperformed the chronological age in revealing the correlations with stem cell stemness. External validation demonstrated that iAge was an excellent method to classify cancer subtypes and predict survival in patients with GBM. Conclusions: Inflammatory aging clock may be involved in the GBM via potential influences on immune-related activities. iAge could be used as biomarkers for predicting the OS and monitoring the stem cell.
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Project description:Several age predictors based on DNA methylation, dubbed epigenetic clocks, have been created in recent years, with the vast majority based on regularized linear regression. This study explores the improvement in the performance and interpretation of epigenetic clocks using deep learning. First, we gathered 142 publicly available data sets from several human tissues to develop AltumAge, a neural network framework that is a highly accurate and precise age predictor. Compared to ElasticNet, AltumAge performs better for within-data set and cross-data set age prediction, being particularly more generalizable in older ages and new tissue types. We then used deep learning interpretation methods to learn which methylation sites contributed to the final model predictions. We observe that while most important CpG sites are linearly related to age, some highly-interacting CpG sites can influence the relevance of such relationships. Using chromatin annotations, we show that the CpG sites with the highest contribution to the model predictions were related to gene regulatory regions in the genome, including proximity to CTCF binding sites. We also found age-related KEGG pathways for genes containing these CpG sites. Lastly, we performed downstream analyses of AltumAge to explore its applicability and compare its age acceleration with Horvath’s 2013 model. We show that our neural network approach predicts higher age acceleration for tumors, for cells that exhibit age-related changes in vitro, such as immune and mitochondrial dysfunction, and for samples from patients with multiple sclerosis, type 2 diabetes, and HIV, among other conditions. Altogether, our neural network approach provides significant improvement and flexibility compared to current epigenetic clocks for both performance and model interpretability.
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