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 |
REPOSITORIES: biostudies-literature
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Project description:DNA methylation aging clocks have become an invaluable tool in biogerontology research since their inception in 2013. Today, a variety of machine learning approaches have been tested for the purpose of predicting human age based on molecular-level features. Among these, deep learning, or neural networks, is an especially promising approach that has been used to construct accurate clocks using blood biochemistry, transcriptomics, and microbiomics data-feats unachieved by other algorithms. In this article, we explore how deep learning performs in a DNA methylation setting and compare it to the current industry standard-the 353 CpG clock published in 2013. The aging clock we are presenting (DeepMAge) is a neural network regressor trained on 4,930 blood DNA methylation profiles from 17 studies. Its absolute median error was 2.77 years in an independent verification set of 1,293 samples from 15 studies. DeepMAge shows biological relevance by assigning a higher predicted age to people with various health-related conditions, such as ovarian cancer, irritable bowel diseases, and multiple sclerosis.
| S-EPMC8279523 | biostudies-literature
Project description:The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host's age based on the gut community composition. Here we developed a method of predicting hosts' age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers. The described intestinal clock represents a unique quantitative model of gut microflora aging and provides a starting point for building host aging and gut community succession into a single narrative.
| S-EPMC7298543 | biostudies-literature
Project description:BACKGROUND:Using big data and the theory of cumulative deficits to develop the multimorbidity frailty index (mFI) has become a widely accepted approach in public health and health care services. However, constructing the mFI using the most critical determinants and stratifying different risk groups with dose-response relationships remain major challenges in clinical practice. OBJECTIVE:This study aimed to develop the mFI by using machine learning methods that select variables based on the optimal fitness of the model. In addition, we aimed to further establish 4 entities of risk using a machine learning approach that would achieve the best distinction between groups and demonstrate the dose-response relationship. METHODS:In this study, we used Taiwan's National Health Insurance Research Database to develop a machine learning multimorbidity frailty index (ML-mFI) using the theory of cumulative diseases/deficits of an individual older person. Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. To ensure that the survival curves showed a dose-response relationship with overlap during the follow-up, we developed the distance index and coverage index, which can be used at any time point to classify the ML-mFI of all subjects into the categories of fit, mild frailty, moderate frailty, and severe frailty. Survival analysis was conducted to evaluate the ability of the ML-mFI to predict adverse outcomes, such as unplanned hospitalizations, intensive care unit (ICU) admissions, and mortality. RESULTS:The final ML-mFI model contained 38 diseases/deficits. Compared with conventional mFI, both indices had similar distribution patterns by age and sex; however, among people aged 65 to 69 years, the mean mFI and ML-mFI were 0.037 (SD 0.048) and 0.0070 (SD 0.0254), respectively. The difference may result from discrepancies in the diseases/deficits selected in the mFI and the ML-mFI. A total of 86,133 subjects aged 65 to 100 years were included in this study and were categorized into 4 groups according to the ML-mFI. Both the Kaplan-Meier survival curves and Cox models showed that the ML-mFI significantly predicted all outcomes of interest, including all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions at 1, 5, and 8 years of follow-up (P<.01). In particular, a dose-response relationship was revealed between the 4 ML-mFI groups and adverse outcomes. CONCLUSIONS:The ML-mFI consists of 38 diseases/deficits that can successfully stratify risk groups associated with all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions in older people, which indicates that precise, patient-centered medical care can be a reality in an aging society.
| S-EPMC7317629 | biostudies-literature
Project description:AimsPhysical frailty is a commonly encountered geriatric syndrome among older adults without coronary heart disease (CHD). The impact of frailty on the incidence of long-term cardiovascular outcomes is not known.We aimed to evaluate the long-term association of frailty, measured by the Fried frailty phenotype, with all-cause-mortality and MACE among older adults without a history of CHD at baseline in the National Health and Aging Trends Study.Methods and resultsWe used the National Health and Aging Trends Study, a prospective cohort study linked to a Medicare sample. Participants with a prior history of CHD were excluded. Frailty was measured during the baseline visit using the Fried physical frailty phenotype. Cardiovascular outcomes were assessed during a 6-year follow-up.Of the 4656 study participants, 3259 (70%) had no history of CHD 1 year prior to their baseline visit. Compared to those without frailty, subjects with frailty were older (mean age 82.1 vs. 75.1 years, P < 0.001), more likely to be female (68.3% vs. 54.9%, P < 0.001), and belong to an ethnic minority. The prevalence of hypertension, falls, disability, anxiety/depression, and multimorbidity was much higher in the frail and pre-frail than the non-frail participants. In a Cox time-to-event multivariable model and during 6-year follow-up, the incidences of death and of each individual cardiovascular outcomes were all significantly higher in the frail than in the non-frail patients including major adverse cardiovascular event (MACE) [hazard ratio (HR) 1.77, 95% confidence interval (CI) 1.53, 2.06], death (HR 2.70, 95% CI 2.16, 3.38), acute myocardial infarction (HR 1.95, 95% CI 1.31, 2.90), stroke (HR 1.71, 95% CI 1.34, 2.17), peripheral vascular disease (HR 1.80, 95% CI 1.44, 2.27), and coronary artery disease (HR 1.35, 95% CI 1.11, 1.65).ConclusionIn patients without CHD, frailty is a risk factor for the development of MACEs. Efforts to identify frailty in patients without CHD and interventions to limit or reverse frailty status are needed and, if successful, may limit subsequent adverse cardiovascular events.
| S-EPMC8487013 | biostudies-literature
Project description:BackgroundMultimorbidity increases with aging, but risk factors beyond age are unknown.ObjectiveTo investigate the association of inflammatory and anabolic hormonal biomarkers with presence and prospective development of multimorbidity.MethodsNine-year longitudinal study of 1018 participants aged 60 years or older (InCHIANTI Study). Multimorbidity was evaluated at baseline and follow-up visits as number of diagnosed diseases from a predefined list of 15 candidate chronic conditions, defined according to standard clinical criteria. Linear mixed models were used to test cross-sectional and longitudinal associations between candidate biomarkers and multimorbidity.ResultsAt baseline, multimorbidity was significantly higher in older participants (p < .001) and higher IL-6, IL-1ra, TNF-α receptor II (TNFAR2), and lower dehydroepiandrosterone sulfate were associated with higher number of diseases, independent of age, sex, body mass index, and education. The rate of longitudinal increase in number of chronic diseases was significantly steeper in participants who were older at baseline (p < .001). In addition, higher baseline IL-6 and steeper increase of IL-6 levels were significantly and independently associated with a steeper increase in multimorbidity over time (p < .001 and p = .003, respectively). Sensitivity analyses, performed using 15 different models obtained by removing each of 15 conditions included in the original list of candidate diseases, confirmed that results were not driven by any specific condition.ConclusionsAccumulation of chronic diseases accelerates at older ages and in persons with higher baseline levels and steeper increase over time of IL-6. High IL-6 and increase in IL-6 may serve as early warning sign to better target interventions aimed at reducing the burden of multimorbidity.
| S-EPMC4296167 | biostudies-literature
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.
| S-EPMC5358544 | biostudies-literature
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.
| S-EPMC9158789 | biostudies-literature
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.
| S-EPMC9402943 | biostudies-literature
Project description:The problem of aging is mainly the increase of age-related diseases, and elderly patients have longer hospitalization and worse prognosis. Poorer nutritional status and immunosenescence may be predisposing and severe factors. The mechanism of the high incidence of diseases and poor prognosis behind aging is complex. Finding suitable aging models is of great significance to find strategies to prevent aging related events. In this study, the relationship between thyrotoxicosis and aging was investigated in mice. The results of routine blood tests and flow cytometry showed that immunosenescence occurred in thyrotoxicosis mice, which was characterized by a significant decrease in neutrophils, lymphocytes, CD4+/CD8+ and CD4+IFN-γ+ lymphocytes. Biochemical examination results showed that there were hypocholesterolemia, hypolipoproteinemia, and hyperlipidemia in thyrotoxicosis mice. Serum proteomics analysis showed that the downregulation of complement and coagulation proteins was another manifestation of declined immunity. Moreover, proteomics analysis showed that many downregulated proteins were related to homeostasis, mainly transport proteins. Their downregulation led to the disturbance of osmotic pressure, ion homeostasis, vitamin utilization, lipid transport, hyaluronic acid processing, and pH maintenance. Serum metabolomics analysis provided more detailed evidence of homeostasis disturbance, especially lipid metabolism disorder, including the downregulation of cholesterol, vitamin D, bile acids, docosanoids, and the upregulation of glucocorticoids, triglycerides, sphingolipids, and free fatty acids. The upregulated lipid metabolites were related to lipotoxicity, which might be one cause of immunosenescence and many aging related syndromes. This study provides evidence for the aging model of thyrotoxicosis mice, which can be used for exploring anti-aging drugs and strategies.
| S-EPMC9201349 | biostudies-literature
Project description:AimsAging entails profound immunological transformations that can impact myocardial homeostasis and predispose to heart failure. However, preclinical research in the immune-cardiology field is mostly conducted in young healthy animals, which potentially weakens its translational relevance. Herein, we sought to investigate how the aging T-cell compartment associates with changes in myocardial cell biology in aged mice.Methods and resultsWe phenotyped the antigen-experienced effector/memory T cells purified from heart-draining lymph nodes of 2-, 6-, 12-, and 18-month-old C57BL/6J mice using single-cell RNA/T cell receptor (TCR) sequencing (sc-seq). Simultaneously, we profiled all non-cardiomyocyte cell subsets purified from 2- and 18-month-old hearts and integrated our data with publicly available cardiomyocyte sc-seq datasets. Some of these findings were confirmed at the protein level by flow cytometry. With aging, the heart-draining lymph node and myocardial T cells underwent clonal expansion and exhibited an up-regulated pro-inflammatory transcription signature, marked by an increased interferon-γ (IFN-γ) production. In parallel, all major myocardial cell populations showed increased IFN-γ responsive signature with aging. In the aged cardiomyocytes, a stronger IFN-γ response signature was paralleled by the dampening of expression levels of transcripts related to most metabolic pathways, especially oxidative phosphorylation. Likewise, induced pluripotent stem cells-derived cardiomyocytes (iPSC-CM) exposed to chronic, low grade IFN-γ treatment showed a similar inhibition of metabolic activity.ConclusionsBy investigating the paired age-related alterations in the T cells found in the heart and its draining lymph nodes, we provide evidence for increased myocardial IFN-γ signaling with age, which is associated with inflammatory and metabolic shifts typically seen in heart failure.
| S-EPMC10651211 | biostudies-literature