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      Modelling biological age based on plasma peptides in Han Chinese adults

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          Abstract

          Age-related disease burdens increased over time, and whether plasma peptides can be used to accurately predict age in order to explain the variation in biological indicators remains inadequately understood. Here we first developed a biological age model based on plasma peptides in 1890 Chinese Han adults. Based on mass spectrometry, 84 peptides were detected with masses in the range of 0.6-10.0 kDa, and 13 of these peptides were identified as known amino acid sequences. Five of these thirteen plasma peptides, including fragments of apolipoprotein A-I (m/z 2883.99), fibrinogen alpha chain (m/z 3060.13), complement C3 (m/z 2190.59), complement C4-A (m/z 1898.21), and breast cancer type 2 susceptibility protein (m/z 1607.84) were finally included in the final model by performing a multivariate linear regression with stepwise selection. This biological age model accounted for 72.3% of the variation in chronological age. Furthermore, the linear correlation between the actual age and biological age was 0.851 (95% confidence interval: 0.836-0.864) and 0.842 (95% confidence interval: 0.810-0.869) in the training and validation sets, respectively. The biological age based on plasma peptides has potential positive effects on primary prevention, and its biological meaning warrants further investigation.

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          Most cited references 37

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          Cellular senescence in aging and age-related disease: from mechanisms to therapy.

          Cellular senescence, a process that imposes permanent proliferative arrest on cells in response to various stressors, has emerged as a potentially important contributor to aging and age-related disease, and it is an attractive target for therapeutic exploitation. A wealth of information about senescence in cultured cells has been acquired over the past half century; however, senescence in living organisms is poorly understood, largely because of technical limitations relating to the identification and characterization of senescent cells in tissues and organs. Furthermore, newly recognized beneficial signaling functions of senescence suggest that indiscriminately targeting senescent cells or modulating their secretome for anti-aging therapy may have negative consequences. Here we discuss current progress and challenges in understanding the stressors that induce senescence in vivo, the cell types that are prone to senesce, and the autocrine and paracrine properties of senescent cells in the contexts of aging and age-related diseases as well as disease therapy.
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            The intersection between aging and cardiovascular disease.

            The average lifespan of humans is increasing, and with it the percentage of people entering the 65 and older age group is growing rapidly and will continue to do so in the next 20 years. Within this age group, cardiovascular disease will remain the leading cause of death, and the cost associated with treatment will continue to increase. Aging is an inevitable part of life and unfortunately poses the largest risk factor for cardiovascular disease. Although numerous studies in the cardiovascular field have considered both young and aged humans, there are still many unanswered questions as to how the genetic pathways that regulate aging in model organisms influence cardiovascular aging. Likewise, in the molecular biology of aging field, few studies fully assess the role of these aging pathways in cardiovascular health. Fortunately, this gap is beginning to close, and these two fields are merging together. We provide an overview of some of the key genes involved in regulating lifespan and health span, including sirtuins, AMP-activated protein kinase, mammalian target of rapamycin, and insulin-like growth factor 1 and their roles regulating cardiovascular health. We then discuss a series of review articles that will appear in succession and provide a more comprehensive analysis of studies carried out linking genes of aging and cardiovascular health, and perspectives of future directions of these two intimately linked fields.
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              Biological Age Predictors

              The search for reliable indicators of biological age, rather than chronological age, has been ongoing for over three decades, and until recently, largely without success. Advances in the fields of molecular biology have increased the variety of potential candidate biomarkers that may be considered as biological age predictors. In this review, we summarize current state-of-the-art findings considering six potential types of biological age predictors: epigenetic clocks, telomere length, transcriptomic predictors, proteomic predictors, metabolomics-based predictors, and composite biomarker predictors. Promising developments consider multiple combinations of these various types of predictors, which may shed light on the aging process and provide further understanding of what contributes to healthy aging. Thus far, the most promising, new biological age predictor is the epigenetic clock; however its true value as a biomarker of aging requires longitudinal confirmation.
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                Author and article information

                Journal
                Aging (Albany NY)
                Aging (Albany NY)
                Aging
                Aging (Albany NY)
                Impact Journals
                1945-4589
                15 June 2020
                05 June 2020
                : 12
                : 11
                : 10676-10686
                Affiliations
                [1 ]Department of Epidemiology and Health Statistics, School of Public Health, Beijing Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
                [2 ]The Second Affiliated Hospital of Shandong First Medical University, Tai’an 271000, China
                [3 ]Beijing Neurosurgical Institute, Beijing 100070, China
                [4 ]School of Medical and Health Sciences, Edith Cowan University, Perth 6027, Australia
                [5 ]School of Public Health, Shandong First Medical University and Academy of Medical Sciences of Shandong Province, Tai’an 271016, China
                Author notes
                [*]

                Equal contribution

                Correspondence to: Xizhu Xu; email: xzxu@tsmc.edu.cn
                Correspondence to: Youxin Wang; email: wangy@ccmu.edu.cn
                Article
                103286 103286
                10.18632/aging.103286
                7346055
                32501290
                Copyright © 2020 Cao et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Categories
                Research Paper

                Cell biology

                ageing, multiple linear regression, biological age, plasma peptide, primary prevention

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