Distinct biological ages of organs and systems identified from a multi-omics study

Elsevier BV - Tập 38 Số 10 - 2022
Chao Chao, Yan Yan, Rui Rui, Yizhen Yizhen, Detao Detao, Tao Tao, Zhiming Zhiming, Yuzhe Yuzhe, Hefu Hefu, Jiahong Jiahong, Ziyun Ziyun, Jianping Jianping, Yanfang Yanfang, Zhibo Zhibo, Yiran Yiran, Kaiye Kaiye, Yang Yang, Zhen Zhen, Rong Rong, Min Min, Xin Xin, Jian Jian, Huanming Huanming, Jing-Dong J. Jing-Dong J., Xiuqing Xiuqing, Claudio Claudio, Brian K. Brian K., Xun Xun

Tài liệu tham khảo

Ahadi, 2020, Personal aging markers and ageotypes revealed by deep longitudinal profiling, Nat. Med., 26, 83, 10.1038/s41591-019-0719-5 Akima, 2001, Muscle function in 164 men and women aged 20--84 yr, Med. Sci. Sports Exerc., 33, 220, 10.1097/00005768-200102000-00008 Almalki, 2016, Key transcription factors in the differentiation of mesenchymal stem cells, Differentiation, 92, 41, 10.1016/j.diff.2016.02.005 Bae, 2008, Development of models for predicting biological age (BA) with physical, biochemical, and hormonal parameters, Arch. Gerontol. Geriatr., 47, 253, 10.1016/j.archger.2007.08.009 Bakshi, 2016, Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits, Sci. Rep., 6, 32894, 10.1038/srep32894 Balding, 2006, A tutorial on statistical methods for population association studies, Nat. Rev. Genet., 7, 781, 10.1038/nrg1916 Chang, 2015, Second-generation PLINK: rising to the challenge of larger and richer datasets, Gigascience, 4, 7, 10.1186/s13742-015-0047-8 Chen, 2016, DNA methylation-based measures of biological age: meta-analysis predicting time to death, Aging (N Y), 8, 1844 Chen, 2020, Fight to the bitter end: DNA repair and aging, Ageing Res. Rev., 64, 101154, 10.1016/j.arr.2020.101154 Cho, 2010, An empirical comparative study on biological age estimation algorithms with an application of Work Ability Index (WAI), Mech. Ageing Dev., 131, 69, 10.1016/j.mad.2009.12.001 Choi, 2019, PRSice-2: polygenic risk score software for biobank-scale data, Gigascience, 8, giz082, 10.1093/gigascience/giz082 Clarke, 2019, Gut reactions: breaking down xenobiotic–microbiome interactions, Pharmacol. Rev., 71, 198, 10.1124/pr.118.015768 Comfort, 1969, Test-battery to measure ageing-rate in man, Lancet, 294, 1411, 10.1016/S0140-6736(69)90950-7 De Maesschalck, 2000, The mahalanobis distance, Chemometrics Intell. Lab. Syst., 50, 1, 10.1016/S0169-7439(99)00047-7 Delude, 2015, Deep phenotyping: the details of disease, Nature, 527, S14, 10.1038/527S14a Dubina, 1984, Biological age and its estimation. III. Introduction of a correction to the multiple regression model of biological age and assessment of biological age in cross-sectional and longitudinal studies, Exp. Gerontol., 19, 133, 10.1016/0531-5565(84)90016-0 Franceschi, 2018, Inflammaging: a new immune–metabolic viewpoint for age-related diseases, Nat. Rev. Endocrinol., 14, 576, 10.1038/s41574-018-0059-4 Harman, 1991, The aging process: major risk factor for disease and death, Proc. Natl. Acad. Sci. U S A, 88, 5360, 10.1073/pnas.88.12.5360 Hastings, 2019, Comparability of biological aging measures in the national health and nutrition examination study, 1999–2002, Psychoneuroendocrinology, 106, 171, 10.1016/j.psyneuen.2019.03.012 Hofecker, 1980, Models of the biological age of the rat. I. A factor model of age parameters, Mech. Ageing Dev., 14, 345, 10.1016/0047-6374(80)90008-1 Hollingsworth, 1965, Correlations between tests of aging in Hiroshima subjects--an attempt to define" physiologic age", Yale J. Biol. Med., 38, 11 Horvath, 2013, DNA methylation age of human tissues and cell types, Genome Biol., 14, 1, 10.1186/gb-2013-14-10-r115 Horvath, 2015, Accelerated epigenetic aging in down syndrome, Aging Cell, 14, 491, 10.1111/acel.12325 Horvath, 2016, An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease, Genome Biol., 17, 1, 10.1186/s13059-016-1030-0 Horvath, 2018, DNA methylation-based biomarkers and the epigenetic clock theory of ageing, Nat. Rev. Genet., 19, 371, 10.1038/s41576-018-0004-3 Jaslove, 2018, Smooth muscle: a stiff sculptor of epithelial shapes, Philos. Trans. R. Soc. B: Biol. Sci., 373, 20170318, 10.1098/rstb.2017.0318 Jee, 2019, Selection of a set of biomarkers and comparisons of biological age estimation models for Korean men, J. Exerc. Rehabil., 15, 31, 10.12965/jer.1836644.322 Joossens, 2019, Gut microbiota dynamics and uraemic toxins: one size does not fit all, Gut, 68, 2257, 10.1136/gutjnl-2018-317561 Klemera, 2006, A new approach to the concept and computation of biological age, Mech. Ageing Dev., 127, 240, 10.1016/j.mad.2005.10.004 Krøll, 2000, On the use of regression analysis for the estimation of human biological age, Biogerontology, 1, 363, 10.1023/A:1026594602252 Kuh, 2014, A life-course approach to healthy ageing: maintaining physical capability, Proc. Nutr. Soc., 73, 237, 10.1017/S0029665113003923 Lee, 2017, Association between body mass index and quality of life in elderly people over 60 years of age, Korean J. Fam. Med., 38, 181, 10.4082/kjfm.2017.38.4.181 Lehallier, 2019, Undulating changes in human plasma proteome profiles across the lifespan, Nat. Med., 25, 1843, 10.1038/s41591-019-0673-2 Levine, 2013, Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age?, J. Gerontol. Biol. Med. Sci., 68, 667, 10.1093/gerona/gls233 Li, 2009, Fast and accurate short read alignment with Burrows–Wheeler transform, Bioinformatics, 25, 1754, 10.1093/bioinformatics/btp324 Li, 2014, An integrated catalog of reference genes in the human gut microbiome, Nat. Biotechnol., 32, 834, 10.1038/nbt.2942 Li, 2009, SOAP2: an improved ultrafast tool for short read alignment, Bioinformatics, 25, 1966, 10.1093/bioinformatics/btp336 Liberti, 2014, Euclidean distance geometry and applications, SIAM Rev., 56, 3, 10.1137/120875909 Marteijn, 2014, Understanding nucleotide excision repair and its roles in cancer and ageing, Nat. Rev. Mol. Cel. Biol., 15, 465, 10.1038/nrm3822 McFadden, 1979 McKenna, 2010, The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data, Genome Res., 20, 1297, 10.1101/gr.107524.110 Nakamura, 2007, A method for identifying biomarkers of aging and constructing an index of biological age in humans, J. Gerontol. Biol. Med. Sci., 62, 1096, 10.1093/gerona/62.10.1096 Nakamura, 1988, Assessment of biological age by principal component analysis, Mech. Ageing Dev., 46, 1, 10.1016/0047-6374(88)90109-1 Neale, 2004, The future of association studies: gene-based analysis and replication, Am. J. Hum. Genet., 75, 353, 10.1086/423901 Niedernhofer, 2018, Nuclear genomic instability and aging, Annu. Rev. Biochem., 87, 295, 10.1146/annurev-biochem-062917-012239 Rampelli, 2019, Shotgun metagenomics of human gut microbiota up to extreme longevity and the increasing role of xenobiotics degradation, mSystems, 5, e00124 Stegeman, 2017, Transcriptional signatures of aging, J. Mol. Biol., 429, 2427, 10.1016/j.jmb.2017.06.019 Tabibzadeh, 2021, Signaling pathways and effectors of aging, Growth, 3, 53 Truong, 2015, MetaPhlAn2 for enhanced metagenomic taxonomic profiling, Nat. Methods, 12, 902, 10.1038/nmeth.3589 Wallach, 2007 Wang, 2020, Aberrant gut microbiota alters host metabolome and impacts renal failure in humans and rodents, Gut, 69, 2131, 10.1136/gutjnl-2019-319766 Wilmanski, 2019, Blood metabolome predicts gut microbiome α-diversity in humans, Nat. Biotechnol., 37, 1217, 10.1038/s41587-019-0233-9 Zeng, 2016, Novel loci and pathways significantly associated with longevity, Sci. Rep., 6, 1 Zhang, 2015, IMonitor: a robust pipeline for TCR and BCR repertoire analysis, Genetics, 201, 459, 10.1534/genetics.115.176735