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Takasugi M, Nonaka Y, Takemura K, Yoshida Y, Stein F, Schwarz JJ, Adachi J, Satoh J, Ito S, Tombline G, Biashad SA, Seluanov A, Gorbunova V, Ohtani N. An atlas of the aging mouse proteome reveals the features of age-related post-transcriptional dysregulation. Nat Commun 2024; 15:8520. [PMID: 39353907 PMCID: PMC11445428 DOI: 10.1038/s41467-024-52845-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Accepted: 09/24/2024] [Indexed: 10/03/2024] Open
Abstract
To what extent and how post-transcriptional dysregulation affects aging proteome remains unclear. Here, we provide proteomic data of whole-tissue lysates (WTL) and low-solubility protein-enriched fractions (LSF) of major tissues collected from mice of 6, 15, 24, and 30 months of age. Low-solubility proteins are preferentially affected by age and the analysis of LSF doubles the number of proteins identified to be differentially expressed with age. Simultaneous analysis of proteome and transcriptome using the same tissue homogenates reveals the features of age-related post-transcriptional dysregulation. Post-transcriptional dysregulation becomes evident especially after 24 months of age and age-related post-transcriptional dysregulation leads to accumulation of core matrisome proteins and reduction of mitochondrial membrane proteins in multiple tissues. Based on our in-depth proteomic data and sample-matched transcriptome data of adult, middle-aged, old, and geriatric mice, we construct the Mouse aging proteomic atlas ( https://aging-proteomics.info/ ), which provides a thorough and integrative view of age-related gene expression changes.
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Affiliation(s)
- Masaki Takasugi
- Department of Pathophysiology, Osaka Metropolitan University, Graduate School of Medicine, Osaka, Japan.
| | - Yoshiki Nonaka
- Department of Pathophysiology, Osaka Metropolitan University, Graduate School of Medicine, Osaka, Japan
| | - Kazuaki Takemura
- Department of Pathophysiology, Osaka Metropolitan University, Graduate School of Medicine, Osaka, Japan
| | - Yuya Yoshida
- Department of Pathophysiology, Osaka Metropolitan University, Graduate School of Medicine, Osaka, Japan
| | - Frank Stein
- Proteomic Core Facility, EMBL Heidelberg, Heidelberg, Germany
| | | | - Jun Adachi
- Laboratory of Proteomics for Drug Discovery, Center for Drug Design Research, National Institute of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Junko Satoh
- Medical Research Support Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shinji Ito
- Medical Research Support Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Gregory Tombline
- Department of Biology, University of Rochester, Rochester, NY, USA
| | | | - Andrei Seluanov
- Department of Biology, University of Rochester, Rochester, NY, USA
| | - Vera Gorbunova
- Department of Biology, University of Rochester, Rochester, NY, USA
| | - Naoko Ohtani
- Department of Pathophysiology, Osaka Metropolitan University, Graduate School of Medicine, Osaka, Japan.
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2
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Wang S, Rao Z, Cao R, Blaes AH, Coresh J, Deo R, Dubin R, Joshu CE, Lehallier B, Lutsey PL, Pankow JS, Post WS, Rotter JI, Sedaghat S, Tang W, Thyagarajan B, Walker KA, Ganz P, Platz EA, Guan W, Prizment A. Development, characterization, and replication of proteomic aging clocks: Analysis of 2 population-based cohorts. PLoS Med 2024; 21:e1004464. [PMID: 39316596 DOI: 10.1371/journal.pmed.1004464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 08/22/2024] [Indexed: 09/26/2024] Open
Abstract
BACKGROUND Biological age may be estimated by proteomic aging clocks (PACs). Previous published PACs were constructed either in smaller studies or mainly in white individuals, and they used proteomic measures from only one-time point. In this study, we created de novo PACs and compared their performance to published PACs at 2 different time points in the Atherosclerosis Risk in Communities (ARIC) study of white and black participants (around 75% white and 25% black). MEDTHODS AND FINDINGS A total of 4,712 plasma proteins were measured using SomaScan in blood samples collected in 1990 to 1992 from 11,761 midlife participants (aged 46 to 70 years) and in 2011 to 2013 from 5,183 late-life participants (aged 66 to 90 years). The de novo ARIC PACs were constructed by training them against chronological age using elastic net regression in two-thirds of healthy participants in midlife and late life and validated in the remaining one-third of healthy participants at the corresponding time point. We also computed 3 published PACs. We estimated age acceleration for each PAC as residuals after regressing each PAC on chronological age. We also calculated the change in age acceleration from midlife to late life. We examined the associations of age acceleration and change in age acceleration with mortality through 2019 from all-cause, cardiovascular disease (CVD), cancer, and lower respiratory disease (LRD) using Cox proportional hazards regression in participants (irrespective of health) after excluding the training set. The model was adjusted for chronological age, smoking, body mass index (BMI), and other confounders. We externally validated the midlife PAC using the Multi-Ethnic Study of Atherosclerosis (MESA) Exam 1 data. The ARIC PACs had a slightly stronger correlation with chronological age than published PACs in healthy participants at each time point. Associations with mortality were similar for the ARIC PACs and published PACs. For late-life and midlife age acceleration for the ARIC PACs, respectively, hazard ratios (HRs) per 1 standard deviation were 1.65 and 1.38 (both p < 0.001) for all-cause mortality, 1.37 and 1.20 (both p < 0.001) for CVD mortality, 1.21 (p = 0.028) and 1.04 (p = 0.280) for cancer mortality, and 1.46 and 1.68 (both p < 0.001) for LRD mortality. For the change in age acceleration, HRs for all-cause, CVD, and LRD mortality were comparable to the HRs for late-life age acceleration. The association between the change in age acceleration and cancer mortality was not significant. The external validation of the midlife PAC in MESA showed significant associations with mortality, as observed for midlife participants in ARIC. The main limitation is that our PACs were constructed in midlife and late-life participants. It is unknown whether these PACs could be applied to young individuals. CONCLUSIONS In this longitudinal study, we found that the ARIC PACs and published PACs were similarly associated with an increased risk of mortality. These findings suggested that PACs show promise as biomarkers of biological age. PACs may be serve as tools to predict mortality and evaluate the effect of anti-aging lifestyle and therapeutic interventions.
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Affiliation(s)
- Shuo Wang
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Zexi Rao
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Rui Cao
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Anne H Blaes
- Division of Hematology, Oncology and Transplantation, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Josef Coresh
- Departments of Population Health and Medicine, New York University Glossman School of Medicine, New York, New York, United States of America
| | - Rajat Deo
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Ruth Dubin
- Division of Nephrology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Corinne E Joshu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland, United States of America
| | | | - Pamela L Lutsey
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Wendy S Post
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Sanaz Sedaghat
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Weihong Tang
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
| | - Peter Ganz
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland, United States of America
| | - Weihua Guan
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Anna Prizment
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America
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3
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Bartolomucci A, Kane AE, Gaydosh L, Razzoli M, McCoy BM, Ehninger D, Chen BH, Howlett SE, Snyder-Mackler N. Animal Models Relevant for Geroscience: Current Trends and Future Perspectives in Biomarkers, and Measures of Biological Aging. J Gerontol A Biol Sci Med Sci 2024; 79:glae135. [PMID: 39126297 PMCID: PMC11316208 DOI: 10.1093/gerona/glae135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Indexed: 08/12/2024] Open
Abstract
For centuries, aging was considered inevitable and immutable. Geroscience provides the conceptual framework to shift this focus toward a new view that regards aging as an active biological process, and the biological age of an individual as a modifiable entity. Significant steps forward have been made toward the identification of biomarkers for and measures of biological age, yet knowledge gaps in geroscience are still numerous. Animal models of aging are the focus of this perspective, which discusses how experimental design can be optimized to inform and refine the development of translationally relevant measures and biomarkers of biological age. We provide recommendations to the field, including: the design of longitudinal studies in which subjects are deeply phenotyped via repeated multilevel behavioral/social/molecular assays; the need to consider sociobehavioral variables relevant for the species studied; and finally, the importance of assessing age of onset, severity of pathologies, and age-at-death. We highlight approaches to integrate biomarkers and measures of functional impairment using machine learning approaches designed to estimate biological age as well as to predict future health declines and mortality. We expect that advances in animal models of aging will be crucial for the future of translational geroscience but also for the next chapter of medicine.
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Affiliation(s)
- Alessandro Bartolomucci
- Department of Integrative Biology and Physiology, University of Minnesota, Minneapolis, Minnesota, USA
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Alice E Kane
- Institute for Systems Biology, Seattle, Washington, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
| | - Lauren Gaydosh
- Department of Sociology, University of Texas at Austin, Austin, Texas, USA
| | - Maria Razzoli
- Department of Integrative Biology and Physiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Brianah M McCoy
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, USA
| | - Dan Ehninger
- German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Brian H Chen
- California Pacific Medical Center Research Institute, Sutter Health, San Francisco, CA, 94143, USA
| | - Susan E Howlett
- Departments of Pharmacology and Medicine (Geriatric Medicine), Dalhousie University, Halifax, Nova Scotia, Canada
| | - Noah Snyder-Mackler
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, USA
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4
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Argentieri MA, Xiao S, Bennett D, Winchester L, Nevado-Holgado AJ, Ghose U, Albukhari A, Yao P, Mazidi M, Lv J, Millwood I, Fry H, Rodosthenous RS, Partanen J, Zheng Z, Kurki M, Daly MJ, Palotie A, Adams CJ, Li L, Clarke R, Amin N, Chen Z, van Duijn CM. Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations. Nat Med 2024; 30:2450-2460. [PMID: 39117878 PMCID: PMC11405266 DOI: 10.1038/s41591-024-03164-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 06/27/2024] [Indexed: 08/10/2024]
Abstract
Circulating plasma proteins play key roles in human health and can potentially be used to measure biological age, allowing risk prediction for age-related diseases, multimorbidity and mortality. Here we developed a proteomic age clock in the UK Biobank (n = 45,441) using a proteomic platform comprising 2,897 plasma proteins and explored its utility to predict major disease morbidity and mortality in diverse populations. We identified 204 proteins that accurately predict chronological age (Pearson r = 0.94) and found that proteomic aging was associated with the incidence of 18 major chronic diseases (including diseases of the heart, liver, kidney and lung, diabetes, neurodegeneration and cancer), as well as with multimorbidity and all-cause mortality risk. Proteomic aging was also associated with age-related measures of biological, physical and cognitive function, including telomere length, frailty index and reaction time. Proteins contributing most substantially to the proteomic age clock are involved in numerous biological functions, including extracellular matrix interactions, immune response and inflammation, hormone regulation and reproduction, neuronal structure and function and development and differentiation. In a validation study involving biobanks in China (n = 3,977) and Finland (n = 1,990), the proteomic age clock showed similar age prediction accuracy (Pearson r = 0.92 and r = 0.94, respectively) compared to its performance in the UK Biobank. Our results demonstrate that proteomic aging involves proteins spanning multiple functional categories and can be used to predict age-related functional status, multimorbidity and mortality risk across geographically and genetically diverse populations.
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Affiliation(s)
- M Austin Argentieri
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, MA, USA.
| | - Sihao Xiao
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia
| | - Derrick Bennett
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Laura Winchester
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Alejo J Nevado-Holgado
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Upamanyu Ghose
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ashwag Albukhari
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia
- Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Pang Yao
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Mohsen Mazidi
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Iona Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hannah Fry
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Jukka Partanen
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Zhili Zheng
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Mitja Kurki
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Cassandra J Adams
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Robert Clarke
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Najaf Amin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cornelia M van Duijn
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- King Abdulaziz University and the University of Oxford Centre for Artificial Intelligence in Precision Medicine (KO-CAIPM), Jeddah, Saudi Arabia.
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5
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Sidorenko D, Pushkov S, Sakip A, Leung GHD, Lok SWY, Urban A, Zagirova D, Veviorskiy A, Tihonova N, Kalashnikov A, Kozlova E, Naumov V, Pun FW, Aliper A, Ren F, Zhavoronkov A. Precious2GPT: the combination of multiomics pretrained transformer and conditional diffusion for artificial multi-omics multi-species multi-tissue sample generation. NPJ AGING 2024; 10:37. [PMID: 39117678 PMCID: PMC11310469 DOI: 10.1038/s41514-024-00163-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 07/22/2024] [Indexed: 08/10/2024]
Abstract
Synthetic data generation in omics mimics real-world biological data, providing alternatives for training and evaluation of genomic analysis tools, controlling differential expression, and exploring data architecture. We previously developed Precious1GPT, a multimodal transformer trained on transcriptomic and methylation data, along with metadata, for predicting biological age and identifying dual-purpose therapeutic targets potentially implicated in aging and age-associated diseases. In this study, we introduce Precious2GPT, a multimodal architecture that integrates Conditional Diffusion (CDiffusion) and decoder-only Multi-omics Pretrained Transformer (MoPT) models trained on gene expression and DNA methylation data. Precious2GPT excels in synthetic data generation, outperforming Conditional Generative Adversarial Networks (CGANs), CDiffusion, and MoPT. We demonstrate that Precious2GPT is capable of generating representative synthetic data that captures tissue- and age-specific information from real transcriptomics and methylomics data. Notably, Precious2GPT surpasses other models in age prediction accuracy using the generated data, and it can generate data beyond 120 years of age. Furthermore, we showcase the potential of using this model in identifying gene signatures and potential therapeutic targets in a colorectal cancer case study.
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Affiliation(s)
- Denis Sidorenko
- Insilico Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Stefan Pushkov
- Insilico Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Akhmed Sakip
- Insilico Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Geoffrey Ho Duen Leung
- Insilico Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Sarah Wing Yan Lok
- Insilico Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Anatoly Urban
- Insilico Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Diana Zagirova
- Insilico Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Alexander Veviorskiy
- Insilico Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, Masdar City, Abu Dhabi, UAE
| | - Nina Tihonova
- Insilico Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, Masdar City, Abu Dhabi, UAE
| | - Aleksandr Kalashnikov
- Insilico Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, Masdar City, Abu Dhabi, UAE
| | - Ekaterina Kozlova
- Insilico Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Vladimir Naumov
- Insilico Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Frank W Pun
- Insilico Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Alex Aliper
- Insilico Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, Masdar City, Abu Dhabi, UAE
| | - Feng Ren
- Insilico Medicine Shanghai Ltd., Suite 902, Tower C, Changtai Plaza, 2889 Jinke Road, Pudong, Shanghai, 201203, China
| | - Alex Zhavoronkov
- Insilico Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W Hong Kong Science and Technology Park, Hong Kong SAR, China.
- Insilico Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, Masdar City, Abu Dhabi, UAE.
- Buck Institute for Research on Aging, Novato, CA, 94945, USA.
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6
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Casanova R, Walker KA, Justice JN, Anderson A, Duggan MR, Cordon J, Barnard RT, Lu L, Hsu FC, Sedaghat S, Prizment A, Kritchevsky SB, Wagenknecht LE, Hughes TM. Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort. GeroScience 2024; 46:3861-3873. [PMID: 38438772 PMCID: PMC11226584 DOI: 10.1007/s11357-024-01112-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/26/2024] [Indexed: 03/06/2024] Open
Abstract
Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.
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Affiliation(s)
- Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA.
| | | | - Jamie N Justice
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Andrea Anderson
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | | | | | - Ryan T Barnard
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | - Lingyi Lu
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | - Fang-Chi Hsu
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | - Sanaz Sedaghat
- School of Public Health, Oncology and Transplantation, University of Minnesota, Minneapolis, MN, USA
| | - Anna Prizment
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Stephen B Kritchevsky
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Timothy M Hughes
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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7
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Allen LH, Fenech M, LeVatte MA, West KP, Wishart DS. Multiomics: Functional Molecular Biomarkers of Micronutrients for Public Health Application. Annu Rev Nutr 2024; 44:125-153. [PMID: 39207879 DOI: 10.1146/annurev-nutr-062322-022751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Adequate micronutrient intake and status are global public health goals. Vitamin and mineral deficiencies are widespread and known to impair health and survival across the life stages. However, knowledge of molecular effects, metabolic pathways, biological responses to variation in micronutrient nutriture, and abilities to assess populations for micronutrient deficiencies and their pathology remain lacking. Rapidly evolving methodological capabilities in genomics, epigenomics, proteomics, and metabolomics offer unparalleled opportunities for the nutrition research community to link micronutrient exposure to cellular health; discover new, arguably essential micronutrients of microbial origin; and integrate methods of molecular biology, epidemiology, and intervention trials to develop novel approaches to assess and prevent micronutrient deficiencies in populations. In this review article, we offer new terminology to specify nutritional application of multiomic approaches and encourage collaboration across the basic to public health sciences to advance micronutrient deficiency prevention.
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Affiliation(s)
- Lindsay H Allen
- Western Human Nutrition Research Center, United States Department of Agriculture, Agricultural Research Service, Davis, California, USA
- Department of Nutrition, University of California, Davis, California, USA
| | - Michael Fenech
- Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
- Genome Health Foundation, North Brighton, South Australia, Australia
| | - Marcia A LeVatte
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Keith P West
- Center for Human Nutrition, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA;
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada
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8
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Félix J, Martínez de Toda I, Díaz-Del Cerro E, González-Sánchez M, De la Fuente M. Frailty and biological age. Which best describes our aging and longevity? Mol Aspects Med 2024; 98:101291. [PMID: 38954948 DOI: 10.1016/j.mam.2024.101291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/01/2024] [Accepted: 06/26/2024] [Indexed: 07/04/2024]
Abstract
Frailty and Biological Age are two closely related concepts; however, frailty is a multisystem geriatric syndrome that applies to elderly subjects, whereas biological age is a gerontologic way to describe the rate of aging of each individual, which can be used from the beginning of the aging process, in adulthood. If frailty reaches less consensus on the definition, it is a term much more widely used than this of biological age, which shows a clearer definition but is scarcely employed in social and medical fields. In this review, we suggest that this Biological Age is the best to describe how we are aging and determine our longevity, and several examples support our proposal.
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Affiliation(s)
- Judith Félix
- Department of Genetics, Physiology, and Microbiology (Unit of Animal Physiology), Faculty of Biological Sciences, Complutense University of Madrid, 28040 Madrid, Spain; Institute of Investigation Hospital 12 Octubre (imas12), 28041 Madrid, Spain.
| | - Irene Martínez de Toda
- Department of Genetics, Physiology, and Microbiology (Unit of Animal Physiology), Faculty of Biological Sciences, Complutense University of Madrid, 28040 Madrid, Spain; Institute of Investigation Hospital 12 Octubre (imas12), 28041 Madrid, Spain.
| | - Estefanía Díaz-Del Cerro
- Department of Genetics, Physiology, and Microbiology (Unit of Animal Physiology), Faculty of Biological Sciences, Complutense University of Madrid, 28040 Madrid, Spain; Institute of Investigation Hospital 12 Octubre (imas12), 28041 Madrid, Spain.
| | - Mónica González-Sánchez
- Department of Genetics, Physiology, and Microbiology (Unit of Genetics), Faculty of Biological Sciences, Complutense University of Madrid, 28040 Madrid, Spain; Institute of Investigation Hospital 12 Octubre (imas12), 28041 Madrid, Spain.
| | - Mónica De la Fuente
- Department of Genetics, Physiology, and Microbiology (Unit of Animal Physiology), Faculty of Biological Sciences, Complutense University of Madrid, 28040 Madrid, Spain; Institute of Investigation Hospital 12 Octubre (imas12), 28041 Madrid, Spain.
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9
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Reed ER, Chandler KB, Lopez P, Costello CE, Andersen SL, Perls TT, Li M, Bae H, Soerensen M, Monti S, Sebastiani P. Cross-platform proteomics signatures of extreme old age. GeroScience 2024:10.1007/s11357-024-01286-x. [PMID: 39048883 DOI: 10.1007/s11357-024-01286-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 07/10/2024] [Indexed: 07/27/2024] Open
Abstract
In previous work, we used a SomaLogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood generated in an independent set. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals. The comparison with blood transcriptomics also highlights a possible role for neutrophil degranulation in aging.
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Affiliation(s)
- Eric R Reed
- Data Intensive Study Center, Tufts University, Boston, MA, USA
| | - Kevin B Chandler
- Center for Biomedical Mass Spectrometry, Department of Biochemistry and Cell Biology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Cellular and Molecular Medicine, Florida International University, Miami, FL, USA
| | - Prisma Lopez
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
| | - Catherine E Costello
- Center for Biomedical Mass Spectrometry, Department of Biochemistry and Cell Biology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Stacy L Andersen
- Geriatric Section, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Thomas T Perls
- Geriatric Section, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Mengze Li
- Division of Computational Biomedicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Harold Bae
- Biostatistics Program, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Mette Soerensen
- Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Stefano Monti
- Division of Computational Biomedicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Paola Sebastiani
- Data Intensive Study Center, Tufts University, Boston, MA, USA.
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
- Department of Medicine, School of Medicine, Tufts University, Boston, MA, USA.
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10
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Melendez J, Sung YJ, Orr M, Yoo A, Schindler S, Cruchaga C, Bateman R. An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging. Aging Cell 2024:e14230. [PMID: 38923730 DOI: 10.1111/acel.14230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 06/28/2024] Open
Abstract
Machine learning can be used to create "biologic clocks" that predict age. However, organs, tissues, and biofluids may age at different rates from the organism as a whole. We sought to understand how cerebrospinal fluid (CSF) changes with age to inform the development of brain aging-related disease mechanisms and identify potential anti-aging therapeutic targets. Several epigenetic clocks exist based on plasma and neuronal tissues; however, plasma may not reflect brain aging specifically and tissue-based clocks require samples that are difficult to obtain from living participants. To address these problems, we developed a machine learning clock that uses CSF proteomics to predict the chronological age of individuals with a 0.79 Pearson correlation and mean estimated error (MAE) of 4.30 years in our validation cohort. Additionally, we analyzed proteins highly weighted by the algorithm to gain insights into changes in CSF and uncover novel insights into brain aging. We also demonstrate a novel method to create a minimal protein clock that uses just 109 protein features from the original clock to achieve a similar accuracy (0.75 correlation, MAE 5.41). Finally, we demonstrate that our clock identifies novel proteins that are highly predictive of age in interactions with other proteins, but do not directly correlate with chronological age themselves. In conclusion, we propose that our CSF protein aging clock can identify novel proteins that influence the rate of aging of the central nervous system (CNS), in a manner that would not be identifiable by examining their individual relationships with age.
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Affiliation(s)
- Justin Melendez
- Tracy Family SILQ Center, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Miranda Orr
- Department of Internal Medicine, Wake Forest School of Medicine Section of Gerontology and Geriatric Medicine Medical Center Boulevard, Winston-Salem, North Carolina, USA
| | - Andrew Yoo
- Department of Developmental Biology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Suzanne Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Carlos Cruchaga
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Randall Bateman
- Tracy Family SILQ Center, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
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11
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Seo D, Lee CM, Apio C, Heo G, Timsina J, Kohlfeld P, Boada M, Orellana A, Fernandez MV, Ruiz A, Morris JC, Schindler SE, Park T, Cruchaga C, Sung YJ. Sex and aging signatures of proteomics in human cerebrospinal fluid identify distinct clusters linked to neurodegeneration. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.18.24309102. [PMID: 38947020 PMCID: PMC11213043 DOI: 10.1101/2024.06.18.24309102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Sex and age are major risk factors for chronic diseases. Recent studies examining age-related molecular changes in plasma provided insights into age-related disease biology. Cerebrospinal fluid (CSF) proteomics can provide additional insights into brain aging and neurodegeneration. By comprehensively examining 7,006 aptamers targeting 6,139 proteins in CSF obtained from 660 healthy individuals aged from 43 to 91 years old, we subsequently identified significant sex and aging effects on 5,097 aptamers in CSF. Many of these effects on CSF proteins had different magnitude or even opposite direction as those on plasma proteins, indicating distinctive CSF-specific signatures. Network analysis of these CSF proteins revealed not only modules associated with healthy aging but also modules showing sex differences. Through subsequent analyses, several modules were highlighted for their proteins implicated in specific diseases. Module 2 and 6 were enriched for many aging diseases including those in the circulatory systems, immune mechanisms, and neurodegeneration. Together, our findings fill a gap of current aging research and provide mechanistic understanding of proteomic changes in CSF during a healthy lifespan and insights for brain aging and diseases.
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12
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Huang Q, Trumpff C, Monzel AS, Rausser S, Haahr R, Devine J, Liu CC, Kelly C, Thompson E, Kurade M, Michelson J, Shaulson ED, Li S, Engelstad K, Tanji K, Lauriola V, Wang T, Wang S, Zuraikat FM, St-Onge MP, Kaufman BA, Sloan R, Juster RP, Marsland AL, Gouspillou G, Hirano M, Picard M. Psychobiological regulation of plasma and saliva GDF15 dynamics in health and mitochondrial diseases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.19.590241. [PMID: 38659958 PMCID: PMC11042343 DOI: 10.1101/2024.04.19.590241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
GDF15 (growth differentiation factor 15) is a marker of cellular energetic stress linked to physical-mental illness, aging, and mortality. However, questions remain about its dynamic properties and measurability in human biofluids other than blood. Here, we examine the natural dynamics and psychobiological regulation of plasma and saliva GDF15 in four human studies representing 4,749 samples from 188 individuals. We show that GDF15 protein is detectable in saliva (8% of plasma concentration), likely produced by salivary glands secretory duct cells. Using a brief laboratory socio-evaluative stressor paradigm, we find that psychosocial stress increases plasma (+3.5-5.9%) and saliva GDF15 (+43%) with distinct kinetics, within minutes. Moreover, saliva GDF15 exhibits a robust awakening response, declining by ~40-89% within 30-45 minutes from its peak level at the time of waking up. Clinically, individuals with genetic mitochondrial OxPhos diseases show elevated baseline plasma and saliva GDF15, and post-stress GDF15 levels in both biofluids correlate with multi-system disease severity, exercise intolerance, and the subjective experience of fatigue. Taken together, our data establish that saliva GDF15 is dynamic, sensitive to psychological states, a clinically relevant endocrine marker of mitochondrial diseases. These findings also point to a shared psychobiological pathway integrating metabolic and mental stress.
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Affiliation(s)
- Qiuhan Huang
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Caroline Trumpff
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Anna S Monzel
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Shannon Rausser
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Rachel Haahr
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Jack Devine
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Cynthia C Liu
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Catherine Kelly
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Elizabeth Thompson
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Mangesh Kurade
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Jeremy Michelson
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Evan D Shaulson
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Shufang Li
- Department of Neurology, H. Houston Merritt Center, Neuromuscular Medicine Division, Columbia University Medical Center, New York, NY, USA
| | - Kris Engelstad
- Department of Neurology, H. Houston Merritt Center, Neuromuscular Medicine Division, Columbia University Medical Center, New York, NY, USA
| | - Kurenai Tanji
- Department of Neurology, H. Houston Merritt Center, Neuromuscular Medicine Division, Columbia University Medical Center, New York, NY, USA
- Department of pathology and cell biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Vincenzo Lauriola
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Tian Wang
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, United States
| | - Shuang Wang
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, United States
| | - Faris M Zuraikat
- Division of General Medicine and Center of Excellence for Sleep & Circadian Research, Department of Medicine, Columbia University Irving Medical Center, New York, USA
| | - Marie-Pierre St-Onge
- Division of General Medicine and Center of Excellence for Sleep & Circadian Research, Department of Medicine, Columbia University Irving Medical Center, New York, USA
| | - Brett A Kaufman
- Department of Medicine, Division of Cardiology, Center for Metabolism and Mitochondrial Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Richard Sloan
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Robert-Paul Juster
- Department of Psychiatry and Addiction, University of Montreal, Montreal, QC, Canada
| | - Anna L Marsland
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Gilles Gouspillou
- Département des Sciences de l'Activité Physique, Faculté des Sciences, UQAM, Montréal, Québec, Canada
| | - Michio Hirano
- Department of Neurology, H. Houston Merritt Center, Neuromuscular Medicine Division, Columbia University Medical Center, New York, NY, USA
| | - Martin Picard
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Department of Neurology, H. Houston Merritt Center, Neuromuscular Medicine Division, Columbia University Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
- Robert N Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA
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13
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Reed ER, Chandler KB, Lopez P, Costello CE, Andersen SL, Perls TT, Li M, Bae H, Soerensen M, Monti S, Sebastiani P. Cross-platform proteomics signatures of extreme old age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588876. [PMID: 38645061 PMCID: PMC11030369 DOI: 10.1101/2024.04.10.588876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
In previous work we used a Somalogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals.
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Affiliation(s)
- Eric R Reed
- Data Intensive Study Center, Tufts University, Boston, MA, USA
| | - Kevin B Chandler
- Center for Biomedical Mass Spectrometry, Department of Biochemistry and Cell Biology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Cellular and Molecular Medicine, Florida International University, Miami, FL, USA
| | - Prisma Lopez
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
| | - Catherine E Costello
- Center for Biomedical Mass Spectrometry, Department of Biochemistry and Cell Biology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Stacy L Andersen
- Geriatric Section, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Thomas T Perls
- Geriatric Section, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Mengze Li
- Division of Computational Biomedicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Harold Bae
- Biostatistics Program, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Mette Soerensen
- Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Stefano Monti
- Division of Computational Biomedicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Paola Sebastiani
- Data Intensive Study Center, Tufts University, Boston, MA, USA
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
- Department of Medicine, School of Medicine, Tufts University, Boston, MA, USA
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14
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Prattichizzo F, Frigé C, Pellegrini V, Scisciola L, Santoro A, Monti D, Rippo MR, Ivanchenko M, Olivieri F, Franceschi C. Organ-specific biological clocks: Ageotyping for personalized anti-aging medicine. Ageing Res Rev 2024; 96:102253. [PMID: 38447609 DOI: 10.1016/j.arr.2024.102253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/11/2024] [Accepted: 02/26/2024] [Indexed: 03/08/2024]
Abstract
Aging is a complex multidimensional, progressive remodeling process affecting multiple organ systems. While many studies have focused on studying aging across multiple organs, assessment of the contribution of individual organs to overall aging processes is a cutting-edge issue. An organ's biological age might influence the aging of other organs, revealing a multiorgan aging network. Recent data demonstrated a similar yet asynchronous inter-organs and inter-individuals progression of aging, thereby providing a foundation to track sources of declining health in old age. The integration of multiple omics with common clinical parameters through artificial intelligence has allowed the building of organ-specific aging clocks, which can predict the development of specific age-related diseases at high resolution. The peculiar individual aging-trajectory, referred to as ageotype, might provide a novel tool for a personalized anti-aging, preventive medicine. Here, we review data relative to biological aging clocks and omics-based data, suggesting different organ-specific aging rates. Additional research on longitudinal data, including young subjects and analyzing sex-related differences, should be encouraged to apply ageotyping analysis for preventive purposes in clinical practice.
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Affiliation(s)
| | | | | | - Lucia Scisciola
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Aurelia Santoro
- Department of Medical and Surgical Science, University of Bologna, Bologna, Italy
| | - Daniela Monti
- Department of Experimental and Clinical, Biomedical Sciences "Mario Serio" University of Florence, Florence, Italy
| | - Maria Rita Rippo
- Department of Clinical and Molecular Sciences, Università Politecnica delle Marche, Ancona, Italy
| | - Mikhail Ivanchenko
- Institute of Information Technologies, Mathematics and Mechanics, and Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Fabiola Olivieri
- Department of Clinical and Molecular Sciences, Università Politecnica delle Marche, Ancona, Italy; Clinic of Laboratory and Precision Medicine, IRCCS INRCA, Ancona, Italy.
| | - Claudio Franceschi
- Institute of Information Technologies, Mathematics and Mechanics, and Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia
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15
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Schmidt S. Speeding Up Time: New Urinary Peptide Clock Associates Greater Air Pollution Exposures with Faster Biological Aging. ENVIRONMENTAL HEALTH PERSPECTIVES 2024; 132:44001. [PMID: 38568857 PMCID: PMC10990112 DOI: 10.1289/ehp14528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 01/26/2024] [Indexed: 04/05/2024]
Abstract
A study in Belgium supports earlier findings on associations between higher air pollution exposures and markers of faster biological aging, this time by using urinary peptide levels instead of DNA-based markers.
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16
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Morandini F, Rechsteiner C, Perez K, Praz V, Lopez Garcia G, Hinte LC, von Meyenn F, Ocampo A. ATAC-clock: An aging clock based on chromatin accessibility. GeroScience 2024; 46:1789-1806. [PMID: 37924441 PMCID: PMC10828344 DOI: 10.1007/s11357-023-00986-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 10/14/2023] [Indexed: 11/06/2023] Open
Abstract
The establishment of aging clocks highlighted the strong link between changes in DNA methylation and aging. Yet, it is not known if other epigenetic features could be used to predict age accurately. Furthermore, previous studies have observed a lack of effect of age-related changes in DNA methylation on gene expression, putting the interpretability of DNA methylation-based aging clocks into question. In this study, we explore the use of chromatin accessibility to construct aging clocks. We collected blood from 159 human donors and generated chromatin accessibility, transcriptomic, and cell composition data. We investigated how chromatin accessibility changes during aging and constructed a novel aging clock with a median absolute error of 5.27 years. The changes in chromatin accessibility used by the clock were strongly related to transcriptomic alterations, aiding clock interpretation. We additionally show that our chromatin accessibility clock performs significantly better than a transcriptomic clock trained on matched samples. In conclusion, we demonstrate that the clock relies on cell-intrinsic chromatin accessibility alterations rather than changes in cell composition. Further, we present a new approach to construct epigenetic aging clocks based on chromatin accessibility, which bear a direct link to age-related transcriptional alterations, but which allow for more accurate age predictions than transcriptomic clocks.
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Affiliation(s)
- Francesco Morandini
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland
| | - Cheyenne Rechsteiner
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland
| | - Kevin Perez
- EPITERNA SA, Route de la Corniche 5, Epalinges, Switzerland
| | - Viviane Praz
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland
| | - Guillermo Lopez Garcia
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland
- Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain
| | - Laura C Hinte
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | | | - Alejandro Ocampo
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland.
- EPITERNA SA, Route de la Corniche 5, Epalinges, Switzerland.
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17
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Mitchell W, Goeminne LJE, Tyshkovskiy A, Zhang S, Chen JY, Paulo JA, Pierce KA, Choy AH, Clish CB, Gygi SP, Gladyshev VN. Multi-omics characterization of partial chemical reprogramming reveals evidence of cell rejuvenation. eLife 2024; 12:RP90579. [PMID: 38517750 PMCID: PMC10959535 DOI: 10.7554/elife.90579] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2024] Open
Abstract
Partial reprogramming by cyclic short-term expression of Yamanaka factors holds promise for shifting cells to younger states and consequently delaying the onset of many diseases of aging. However, the delivery of transgenes and potential risk of teratoma formation present challenges for in vivo applications. Recent advances include the use of cocktails of compounds to reprogram somatic cells, but the characteristics and mechanisms of partial cellular reprogramming by chemicals remain unclear. Here, we report a multi-omics characterization of partial chemical reprogramming in fibroblasts from young and aged mice. We measured the effects of partial chemical reprogramming on the epigenome, transcriptome, proteome, phosphoproteome, and metabolome. At the transcriptome, proteome, and phosphoproteome levels, we saw widescale changes induced by this treatment, with the most notable signature being an upregulation of mitochondrial oxidative phosphorylation. Furthermore, at the metabolome level, we observed a reduction in the accumulation of aging-related metabolites. Using both transcriptomic and epigenetic clock-based analyses, we show that partial chemical reprogramming reduces the biological age of mouse fibroblasts. We demonstrate that these changes have functional impacts, as evidenced by changes in cellular respiration and mitochondrial membrane potential. Taken together, these results illuminate the potential for chemical reprogramming reagents to rejuvenate aged biological systems and warrant further investigation into adapting these approaches for in vivo age reversal.
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Affiliation(s)
- Wayne Mitchell
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolBostonUnited States
| | - Ludger JE Goeminne
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolBostonUnited States
| | - Alexander Tyshkovskiy
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolBostonUnited States
| | - Sirui Zhang
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolBostonUnited States
| | - Julie Y Chen
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolBostonUnited States
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical SchoolBostonUnited States
| | - Kerry A Pierce
- Broad Institute of MIT and HarvardCambridgeUnited States
| | | | - Clary B Clish
- Broad Institute of MIT and HarvardCambridgeUnited States
| | - Steven P Gygi
- Department of Cell Biology, Harvard Medical SchoolBostonUnited States
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolBostonUnited States
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18
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Mitchell W, Goeminne LJ, Tyshkovskiy A, Zhang S, Chen JY, Paulo JA, Pierce KA, Choy AH, Clish CB, Gygi SP, Gladyshev VN. Multi-omics characterization of partial chemical reprogramming reveals evidence of cell rejuvenation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.30.546730. [PMID: 37425825 PMCID: PMC10327104 DOI: 10.1101/2023.06.30.546730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Partial reprogramming by cyclic short-term expression of Yamanaka factors holds promise for shifting cells to younger states and consequently delaying the onset of many diseases of aging. However, the delivery of transgenes and potential risk of teratoma formation present challenges for in vivo applications. Recent advances include the use of cocktails of compounds to reprogram somatic cells, but the characteristics and mechanisms of partial cellular reprogramming by chemicals remain unclear. Here, we report a multi-omics characterization of partial chemical reprogramming in fibroblasts from young and aged mice. We measured the effects of partial chemical reprogramming on the epigenome, transcriptome, proteome, phosphoproteome, and metabolome. At the transcriptome, proteome, and phosphoproteome levels, we saw widescale changes induced by this treatment, with the most notable signature being an upregulation of mitochondrial oxidative phosphorylation. Furthermore, at the metabolome level, we observed a reduction in the accumulation of aging-related metabolites. Using both transcriptomic and epigenetic clock-based analyses, we show that partial chemical reprogramming reduces the biological age of mouse fibroblasts. We demonstrate that these changes have functional impacts, as evidenced by changes in cellular respiration and mitochondrial membrane potential. Taken together, these results illuminate the potential for chemical reprogramming reagents to rejuvenate aged biological systems and warrant further investigation into adapting these approaches for in vivo age reversal.
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Affiliation(s)
- Wayne Mitchell
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 United States
| | - Ludger J.E. Goeminne
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 United States
| | - Alexander Tyshkovskiy
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 United States
| | - Sirui Zhang
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 United States
| | - Julie Y. Chen
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 United States
| | - Joao A. Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115 United States
| | - Kerry A. Pierce
- Broad Institute of MIT and Harvard, Cambridge, MA 01241 United States
| | - Angelina H. Choy
- Broad Institute of MIT and Harvard, Cambridge, MA 01241 United States
| | - Clary B. Clish
- Broad Institute of MIT and Harvard, Cambridge, MA 01241 United States
| | - Steven P. Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115 United States
| | - Vadim N. Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 United States
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19
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He M, Borlak J. A genomic perspective of the aging human and mouse lung with a focus on immune response and cellular senescence. Immun Ageing 2023; 20:58. [PMID: 37932771 PMCID: PMC10626779 DOI: 10.1186/s12979-023-00373-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 09/12/2023] [Indexed: 11/08/2023]
Abstract
BACKGROUND The aging lung is a complex process and influenced by various stressors, especially airborne pathogens and xenobiotics. Additionally, a lifetime exposure to antigens results in structural and functional changes of the lung; yet an understanding of the cell type specific responses remains elusive. To gain insight into age-related changes in lung function and inflammaging, we evaluated 89 mouse and 414 individual human lung genomic data sets with a focus on genes mechanistically linked to extracellular matrix (ECM), cellular senescence, immune response and pulmonary surfactant, and we interrogated single cell RNAseq data to fingerprint cell type specific changes. RESULTS We identified 117 and 68 mouse and human genes linked to ECM remodeling which accounted for 46% and 27%, respectively of all ECM coding genes. Furthermore, we identified 73 and 31 mouse and human genes linked to cellular senescence, and the majority code for the senescence associated secretory phenotype. These cytokines, chemokines and growth factors are primarily secreted by macrophages and fibroblasts. Single-cell RNAseq data confirmed age-related induced expression of marker genes of macrophages, neutrophil, eosinophil, dendritic, NK-, CD4+, CD8+-T and B cells in the lung of aged mice. This included the highly significant regulation of 20 genes coding for the CD3-T-cell receptor complex. Conversely, for the human lung we primarily observed macrophage and CD4+ and CD8+ marker genes as changed with age. Additionally, we noted an age-related induced expression of marker genes for mouse basal, ciliated, club and goblet cells, while for the human lung, fibroblasts and myofibroblasts marker genes increased with age. Therefore, we infer a change in cellular activity of these cell types with age. Furthermore, we identified predominantly repressed expression of surfactant coding genes, especially the surfactant transporter Abca3, thus highlighting remodeling of surfactant lipids with implications for the production of inflammatory lipids and immune response. CONCLUSION We report the genomic landscape of the aging lung and provide a rationale for its growing stiffness and age-related inflammation. By comparing the mouse and human pulmonary genome, we identified important differences between the two species and highlight the complex interplay of inflammaging, senescence and the link to ECM remodeling in healthy but aged individuals.
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Affiliation(s)
- Meng He
- Centre for Pharmacology and Toxicology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Jürgen Borlak
- Centre for Pharmacology and Toxicology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany.
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20
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Wang S, Rao Z, Cao R, Blaes AH, Coresh J, Joshu CE, Lehallier B, Lutsey PL, Pankow JS, Sedaghat S, Tang W, Thyagarajan B, Walker KA, Ganz P, Platz EA, Guan W, Prizment A. Development and Characterization of Proteomic Aging Clocks in the Atherosclerosis Risk in Communities (ARIC) Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.06.23295174. [PMID: 37732184 PMCID: PMC10508816 DOI: 10.1101/2023.09.06.23295174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Biological age may be estimated by proteomic aging clocks (PACs). Previous published PACs were constructed either in smaller studies or mainly in White individuals, and they used proteomic measures from only one-time point. In the Atherosclerosis Risk in Communities (ARIC) study of about 12,000 persons followed for 30 years (around 75% White, 25% Black), we created de novo PACs and compared their performance to published PACs at two different time points. We measured 4,712 plasma proteins by SomaScan in 11,761 midlife participants, aged 46-70 years (1990-92), and 5,183 late-life pariticpants, aged 66-90 years (2011-13). All proteins were log2-transformed to correct for skewness. We created de novo PACs by training them against chronological age using elastic net regression in two-thirds of healthy participants in midlife and late life and compared their performance to three published PACs. We estimated age acceleration (by regressing each PAC on chronological age) and its change from midlife to late life. We examined their associations with mortality from all-cause, cardiovascular disease (CVD), cancer, and lower respiratory disease (LRD) using Cox proportional hazards regression in all remaining participants irrespective of health. The model was adjusted for chronological age, smoking, body mass index (BMI), and other confounders. The ARIC PACs had a slightly stronger correlation with chronological age than published PACs in healthy participants at each time point. Associations with mortality were similar for the ARIC and published PACs. For late-life and midlife age acceleration for the ARIC PACs, respectively, hazard ratios (HRs) per one standard deviation were 1.65 and 1.38 (both p<0.001) for all-cause mortality, 1.37 and 1.20 (both p<0.001) for CVD mortality, 1.21 (p=0.03) and 1.04 (p=0.19) for cancer mortality, and 1.46 and 1.68 (both p<0.001) for LRD mortality. For the change in age acceleration, HRs for all-cause, CVD, and LRD mortality were comparable to those observed for late-life age acceleration. The association between the change in age acceleration and cancer mortality was insignificant. In this prospective study, the ARIC and published PACs were similarly associated with an increased risk of mortality and advanced testing in relation to various age-related conditions in future studies is suggested.
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Affiliation(s)
- Shuo Wang
- Department of Laboratory Medicine and Pathology, Medical School, University of Minnesota, Minneapolis, MN
| | - Zexi Rao
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Rui Cao
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Anne H. Blaes
- Division of Hematology, Oncology and Transplantation, Medical School, University of Minnesota, Minneapolis, MN
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Corinne E. Joshu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD
| | - Benoit Lehallier
- Alkahest Inc, San Carlos, CA, United States, Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA
| | - Pamela L. Lutsey
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - James S. Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Sanaz Sedaghat
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Weihong Tang
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, Medical School, University of Minnesota, Minneapolis, MN
| | - Keenan A. Walker
- Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, Baltimore, MD
| | - Peter Ganz
- Division of Cardiology, Zuckerberg San Francisco General Hospital and Department of Medicine, University of California, San Francisco, CA
| | - Elizabeth A. Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD
| | - Weihua Guan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Anna Prizment
- Department of Laboratory Medicine and Pathology, Medical School, University of Minnesota, Minneapolis, MN
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21
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Wolf SE, Shalev I. The shelterin protein expansion of telomere dynamics: Linking early life adversity, life history, and the hallmarks of aging. Neurosci Biobehav Rev 2023; 152:105261. [PMID: 37268182 PMCID: PMC10527177 DOI: 10.1016/j.neubiorev.2023.105261] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/10/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023]
Abstract
Aging is characterized by functional decline occurring alongside changes to several hallmarks of aging. One of the hallmarks includes attrition of repeated DNA sequences found at the ends of chromosomes called telomeres. While telomere attrition is linked to morbidity and mortality, whether and how it causally contributes to lifelong rates of functional decline is unclear. In this review, we propose the shelterin-telomere hypothesis of life history, in which telomere-binding shelterin proteins translate telomere attrition into a range of physiological outcomes, the extent of which may be modulated by currently understudied variation in shelterin protein levels. Shelterin proteins may expand the breadth and timing of consequences of telomere attrition, e.g., by translating early life adversity into acceleration of the aging process. We consider how the pleiotropic roles of shelterin proteins provide novel insights into natural variation in physiology, life history, and lifespan. We highlight key open questions that encourage the integrative, organismal study of shelterin proteins that enhances our understanding of the contribution of the telomere system to aging.
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Affiliation(s)
- Sarah E Wolf
- Department of Biobehavioral Health, Penn State University, University Park, PA 16802, USA.
| | - Idan Shalev
- Department of Biobehavioral Health, Penn State University, University Park, PA 16802, USA
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22
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Manyilov VD, Ilyinsky NS, Nesterov SV, Saqr BMGA, Dayhoff GW, Zinovev EV, Matrenok SS, Fonin AV, Kuznetsova IM, Turoverov KK, Ivanovich V, Uversky VN. Chaotic aging: intrinsically disordered proteins in aging-related processes. Cell Mol Life Sci 2023; 80:269. [PMID: 37634152 PMCID: PMC11073068 DOI: 10.1007/s00018-023-04897-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 07/03/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023]
Abstract
The development of aging is associated with the disruption of key cellular processes manifested as well-established hallmarks of aging. Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) have no stable tertiary structure that provide them a power to be configurable hubs in signaling cascades and regulate many processes, potentially including those related to aging. There is a need to clarify the roles of IDPs/IDRs in aging. The dataset of 1702 aging-related proteins was collected from established aging databases and experimental studies. There is a noticeable presence of IDPs/IDRs, accounting for about 36% of the aging-related dataset, which is however less than the disorder content of the whole human proteome (about 40%). A Gene Ontology analysis of the used here aging proteome reveals an abundance of IDPs/IDRs in one-third of aging-associated processes, especially in genome regulation. Signaling pathways associated with aging also contain IDPs/IDRs on different hierarchical levels, revealing the importance of "structure-function continuum" in aging. Protein-protein interaction network analysis showed that IDPs present in different clusters associated with different aging hallmarks. Protein cluster with IDPs enrichment has simultaneously high liquid-liquid phase separation (LLPS) probability, "nuclear" localization and DNA-associated functions, related to aging hallmarks: genomic instability, telomere attrition, epigenetic alterations, and stem cells exhaustion. Intrinsic disorder, LLPS, and aggregation propensity should be considered as features that could be markers of pathogenic proteins. Overall, our analyses indicate that IDPs/IDRs play significant roles in aging-associated processes, particularly in the regulation of DNA functioning. IDP aggregation, which can lead to loss of function and toxicity, could be critically harmful to the cell. A structure-based analysis of aging and the identification of proteins that are particularly susceptible to disturbances can enhance our understanding of the molecular mechanisms of aging and open up new avenues for slowing it down.
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Affiliation(s)
- Vladimir D Manyilov
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia
| | - Nikolay S Ilyinsky
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia.
| | - Semen V Nesterov
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia
- Institute of Cytology, Russian Academy of Sciences, Saint Petersburg, 194064, Russia
| | - Baraa M G A Saqr
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia
| | - Guy W Dayhoff
- Department of Chemistry, University of South Florida, Tampa, FL, USA
| | - Egor V Zinovev
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia
| | - Simon S Matrenok
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia
| | - Alexander V Fonin
- Institute of Cytology, Russian Academy of Sciences, Saint Petersburg, 194064, Russia
| | - Irina M Kuznetsova
- Institute of Cytology, Russian Academy of Sciences, Saint Petersburg, 194064, Russia
| | | | - Valentin Ivanovich
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia
| | - Vladimir N Uversky
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia.
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Blvd., MDC07, Tampa, FL, 33612, USA.
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23
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Lathe R, St Clair D. Programmed ageing: decline of stem cell renewal, immunosenescence, and Alzheimer's disease. Biol Rev Camb Philos Soc 2023; 98:1424-1458. [PMID: 37068798 DOI: 10.1111/brv.12959] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/19/2023]
Abstract
The characteristic maximum lifespan varies enormously across animal species from a few hours to hundreds of years. This argues that maximum lifespan, and the ageing process that itself dictates lifespan, are to a large extent genetically determined. Although controversial, this is supported by firm evidence that semelparous species display evolutionarily programmed ageing in response to reproductive and environmental cues. Parabiosis experiments reveal that ageing is orchestrated systemically through the circulation, accompanied by programmed changes in hormone levels across a lifetime. This implies that, like the circadian and circannual clocks, there is a master 'clock of age' (circavital clock) located in the limbic brain of mammals that modulates systemic changes in growth factor and hormone secretion over the lifespan, as well as systemic alterations in gene expression as revealed by genomic methylation analysis. Studies on accelerated ageing in mice, as well as human longevity genes, converge on evolutionarily conserved fibroblast growth factors (FGFs) and their receptors, including KLOTHO, as well as insulin-like growth factors (IGFs) and steroid hormones, as key players mediating the systemic effects of ageing. Age-related changes in these and multiple other factors are inferred to cause a progressive decline in tissue maintenance through failure of stem cell replenishment. This most severely affects the immune system, which requires constant renewal from bone marrow stem cells. Age-related immune decline increases risk of infection whereas lifespan can be extended in germfree animals. This and other evidence suggests that infection is the major cause of death in higher organisms. Immune decline is also associated with age-related diseases. Taking the example of Alzheimer's disease (AD), we assess the evidence that AD is caused by immunosenescence and infection. The signature protein of AD brain, Aβ, is now known to be an antimicrobial peptide, and Aβ deposits in AD brain may be a response to infection rather than a cause of disease. Because some cognitively normal elderly individuals show extensive neuropathology, we argue that the location of the pathology is crucial - specifically, lesions to limbic brain are likely to accentuate immunosenescence, and could thus underlie a vicious cycle of accelerated immune decline and microbial proliferation that culminates in AD. This general model may extend to other age-related diseases, and we propose a general paradigm of organismal senescence in which declining stem cell proliferation leads to programmed immunosenescence and mortality.
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Affiliation(s)
- Richard Lathe
- Division of Infection Medicine, Chancellor's Building, University of Edinburgh Medical School, Little France, Edinburgh, EH16 4SB, UK
| | - David St Clair
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, AB25 2ZD, UK
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24
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Turrini S, Wong B, Eldaief M, Press DZ, Sinclair DA, Koch G, Avenanti A, Santarnecchi E. The multifactorial nature of healthy brain ageing: Brain changes, functional decline and protective factors. Ageing Res Rev 2023; 88:101939. [PMID: 37116664 DOI: 10.1016/j.arr.2023.101939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 04/14/2023] [Accepted: 04/24/2023] [Indexed: 04/30/2023]
Abstract
As the global population faces a progressive shift towards a higher median age, understanding the mechanisms underlying healthy brain ageing has become of paramount importance for the preservation of cognitive abilities. The first part of the present review aims to provide a comprehensive look at the anatomical changes the healthy brain endures with advanced age, while also summarizing up to date findings on modifiable risk factors to support a healthy ageing process. Subsequently, we describe the typical cognitive profile displayed by healthy older adults, conceptualizing the well-established age-related decline as an impairment of four main cognitive factors and relating them to their neural substrate previously described; different cognitive trajectories displayed by typical Alzheimer's Disease patients and successful agers with a high cognitive reserve are discussed. Finally, potential effective interventions and protective strategies to promote cognitive reserve and defer cognitive decline are reviewed and proposed.
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Affiliation(s)
- Sonia Turrini
- Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Centro studi e ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestrari", Alma Mater Studiorum Università di Bologna, Campus di Cesena, Cesena, Italy
| | - Bonnie Wong
- Neuropsychology Program, Frontotemporal Disorders Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA , USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mark Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel Z Press
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - David A Sinclair
- Blavatnik Institute, Department of Genetics, Paul F. Glenn Center for Biology of ageing Research, Harvard Medical School, Boston, MA, USA
| | - Giacomo Koch
- Stroke Unit, Department of Systems Medicine, University of Tor Vergata, Rome, Italy; Department of Clinical and Behavioural Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Alessio Avenanti
- Centro studi e ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestrari", Alma Mater Studiorum Università di Bologna, Campus di Cesena, Cesena, Italy; Centro de Investigación en Neuropsicología y Neurociencias Cognitivas, Universidad Católica del Maule, Talca, Chile
| | - Emiliano Santarnecchi
- Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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25
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Gaylord A, Cohen A, Kupsco A. Biomarkers of aging through the life course: A Recent Literature Update. CURRENT OPINION IN EPIDEMIOLOGY AND PUBLIC HEALTH 2023; 2:7-17. [PMID: 38130910 PMCID: PMC10732539 DOI: 10.1097/pxh.0000000000000018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Purpose of review The development of biomarkers of aging has greatly advanced epidemiological studies of aging processes. However, much debate remains on the timing of aging onset and the causal relevance of these biomarkers. In this review, we discuss the most recent biomarkers of aging that have been applied across the life course. Recent findings The most recently developed aging biomarkers that have been applied across the life course can be designated into three categories: epigenetic clocks, epigenetic markers of chronic inflammation, and mitochondrial DNA copy number. While these have been applied at different life stages, the development, validation, and application of these markers has been largely centered on populations of older adults. Few studies have examined trajectories of aging biomarkers across the life course. As the wealth of molecular and biochemical data increases, emerging biomarkers may be able to capture complex and system-specific aging processes. Recently developed biomarkers include novel epigenetic clocks; clocks based on ribosomal DNA, transcriptomic profiles, proteomics, metabolomics, and inflammatory markers; clonal hematopoiesis of indeterminate potential gene mutations; and multi-omics approaches. Summary Attention should be placed on aging at early and middle life stages to better understand trajectories of aging biomarkers across the life course. Additionally, novel biomarkers will provide greater insight into aging processes. The specific mechanisms of aging reflected by these biomarkers should be considered when interpreting results.
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Affiliation(s)
- Abigail Gaylord
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Alan Cohen
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec, Canada
- Research Center on Aging and Research Center of Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec, Canada
- Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Allison Kupsco
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
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26
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Bao H, Cao J, Chen M, Chen M, Chen W, Chen X, Chen Y, Chen Y, Chen Y, Chen Z, Chhetri JK, Ding Y, Feng J, Guo J, Guo M, He C, Jia Y, Jiang H, Jing Y, Li D, Li J, Li J, Liang Q, Liang R, Liu F, Liu X, Liu Z, Luo OJ, Lv J, Ma J, Mao K, Nie J, Qiao X, Sun X, Tang X, Wang J, Wang Q, Wang S, Wang X, Wang Y, Wang Y, Wu R, Xia K, Xiao FH, Xu L, Xu Y, Yan H, Yang L, Yang R, Yang Y, Ying Y, Zhang L, Zhang W, Zhang W, Zhang X, Zhang Z, Zhou M, Zhou R, Zhu Q, Zhu Z, Cao F, Cao Z, Chan P, Chen C, Chen G, Chen HZ, Chen J, Ci W, Ding BS, Ding Q, Gao F, Han JDJ, Huang K, Ju Z, Kong QP, Li J, Li J, Li X, Liu B, Liu F, Liu L, Liu Q, Liu Q, Liu X, Liu Y, Luo X, Ma S, Ma X, Mao Z, Nie J, Peng Y, Qu J, Ren J, Ren R, Song M, Songyang Z, Sun YE, Sun Y, Tian M, Wang S, Wang S, Wang X, Wang X, Wang YJ, Wang Y, Wong CCL, Xiang AP, Xiao Y, Xie Z, Xu D, Ye J, Yue R, Zhang C, Zhang H, Zhang L, Zhang W, Zhang Y, Zhang YW, Zhang Z, Zhao T, Zhao Y, Zhu D, Zou W, Pei G, Liu GH. Biomarkers of aging. SCIENCE CHINA. LIFE SCIENCES 2023; 66:893-1066. [PMID: 37076725 PMCID: PMC10115486 DOI: 10.1007/s11427-023-2305-0] [Citation(s) in RCA: 90] [Impact Index Per Article: 90.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 04/21/2023]
Abstract
Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.
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Affiliation(s)
- Hainan Bao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Jiani Cao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Mengting Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Min Chen
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Wei Chen
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Third Military Medical University, Chongqing, 400042, China
| | - Yanhao Chen
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Yutian Chen
- The Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Zhiyang Chen
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China
| | - Jagadish K Chhetri
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yingjie Ding
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junlin Feng
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jun Guo
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China
| | - Mengmeng Guo
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Chuting He
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Yujuan Jia
- Department of Neurology, First Affiliated Hospital, Shanxi Medical University, Taiyuan, 030001, China
| | - Haiping Jiang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Ying Jing
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Dingfeng Li
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyi Li
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Qinhao Liang
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China
| | - Rui Liang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China
| | - Feng Liu
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaoqian Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Zuojun Liu
- School of Life Sciences, Hainan University, Haikou, 570228, China
| | - Oscar Junhong Luo
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Jianwei Lv
- School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Jingyi Ma
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Jiawei Nie
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinhua Qiao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinpei Sun
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China
| | - Xiaoqiang Tang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianfang Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siyuan Wang
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
| | - Xuan Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China
| | - Yaning Wang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuhan Wang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Rimo Wu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China
| | - Kai Xia
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Fu-Hui Xiao
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Lingyan Xu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yingying Xu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Haoteng Yan
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Liang Yang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China
| | - Ruici Yang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yuanxin Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Yilin Ying
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China
| | - Le Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Weiwei Zhang
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China
| | - Wenwan Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xing Zhang
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Zhuo Zhang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Min Zhou
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Qingchen Zhu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Zhengmao Zhu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Feng Cao
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China.
| | - Zhongwei Cao
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Piu Chan
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
| | - Chang Chen
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Guobing Chen
- Department of Microbiology and Immunology, School of Medicine, Jinan University, Guangzhou, 510632, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, Guangzhou, 510000, China.
| | - Hou-Zao Chen
- Department of Biochemistryand Molecular Biology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
| | - Jun Chen
- Peking University Research Center on Aging, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, Department of Integration of Chinese and Western Medicine, School of Basic Medical Science, Peking University, Beijing, 100191, China.
| | - Weimin Ci
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Bi-Sen Ding
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Qiurong Ding
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Feng Gao
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Kai Huang
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Zhenyu Ju
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China.
| | - Qing-Peng Kong
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
| | - Ji Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jian Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China.
| | - Xin Li
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Baohua Liu
- School of Basic Medical Sciences, Shenzhen University Medical School, Shenzhen, 518060, China.
| | - Feng Liu
- Metabolic Syndrome Research Center, The Second Xiangya Hospital, Central South Unversity, Changsha, 410011, China.
| | - Lin Liu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China.
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Institute of Translational Medicine, Tianjin Union Medical Center, Nankai University, Tianjin, 300000, China.
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300350, China.
| | - Qiang Liu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China.
| | - Qiang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- Tianjin Institute of Immunology, Tianjin Medical University, Tianjin, 300070, China.
| | - Xingguo Liu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China.
| | - Yong Liu
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China.
| | - Xianghang Luo
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China.
| | - Shuai Ma
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Xinran Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
| | - Zhiyong Mao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Jing Nie
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Yaojin Peng
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ruibao Ren
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Center for Aging and Cancer, Hainan Medical University, Haikou, 571199, China.
| | - Moshi Song
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Zhou Songyang
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China.
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
| | - Yi Eve Sun
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China.
| | - Yu Sun
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Department of Medicine and VAPSHCS, University of Washington, Seattle, WA, 98195, USA.
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
| | - Shusen Wang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China.
| | - Si Wang
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| | - Xia Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
| | - Xiaoning Wang
- Institute of Geriatrics, The second Medical Center, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Yan-Jiang Wang
- Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
| | - Yunfang Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China.
| | - Catherine C L Wong
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China.
| | - Andy Peng Xiang
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China.
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Yichuan Xiao
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Zhengwei Xie
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China.
- Beijing & Qingdao Langu Pharmaceutical R&D Platform, Beijing Gigaceuticals Tech. Co. Ltd., Beijing, 100101, China.
| | - Daichao Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
| | - Jing Ye
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China.
| | - Rui Yue
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Cuntai Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China.
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Hongbo Zhang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Liang Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yong Zhang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Yun-Wu Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Zhuohua Zhang
- Key Laboratory of Molecular Precision Medicine of Hunan Province and Center for Medical Genetics, Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, 410078, China.
- Department of Neurosciences, Hengyang Medical School, University of South China, Hengyang, 421001, China.
| | - Tongbiao Zhao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Dahai Zhu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Weiguo Zou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Gang Pei
- Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Biomedicine, The Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200070, China.
| | - Guang-Hui Liu
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
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Ubaida-Mohien C, Tanaka T, Tian Q, Moore Z, Moaddel R, Basisty N, Simonsick EM, Ferrucci L. Blood Biomarkers for Healthy Aging. Gerontology 2023; 69:1167-1174. [PMID: 37166337 PMCID: PMC11137618 DOI: 10.1159/000530795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/22/2023] [Indexed: 05/12/2023] Open
Abstract
Measuring the abundance of biological molecules and their chemical modifications in blood and tissues has been the cornerstone of research and medical diagnoses for decades. Although the number and variety of molecules that can be measured have expanded exponentially, the blood biomarkers routinely assessed in medical practice remain limited to a few dozen, which have not substantially changed over the last 30-40 years. The discovery of novel biomarkers would allow, for example, risk stratification or monitoring of disease progression or the effectiveness of treatments and interventions, improving clinical practice in myriad ways. In this review, we combine the biomarker discovery concept with geroscience. Geroscience bridges aging research and translation to clinical applications by combining the framework of medical gerontology with high-technology medical research. With the development of geroscience and the rise of blood biomarkers, there has been a paradigm shift from disease prevention and cure to promoting health and healthy aging. New -omic technologies have played a role in the development of blood biomarkers, including epigenetic, proteomic, metabolomic, and lipidomic markers, which have emerged as correlates or predictors of health status, from disease to exceptional health.
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Affiliation(s)
- Ceereena Ubaida-Mohien
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Toshiko Tanaka
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Qu Tian
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Zenobia Moore
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Ruin Moaddel
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Nathan Basisty
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Eleanor M Simonsick
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
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Salazar A, von Hagen J. Circadian Oscillations in Skin and Their Interconnection with the Cycle of Life. Int J Mol Sci 2023; 24:ijms24065635. [PMID: 36982706 PMCID: PMC10051430 DOI: 10.3390/ijms24065635] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/08/2023] [Accepted: 03/14/2023] [Indexed: 03/18/2023] Open
Abstract
Periodically oscillating biological processes, such as circadian rhythms, are carefully concerted events that are only beginning to be understood in the context of tissue pathology and organismal health, as well as the molecular mechanisms underlying these interactions. Recent reports indicate that light can independently entrain peripheral circadian clocks, challenging the currently prevalent hierarchical model. Despite the recent progress that has been made, a comprehensive overview of these periodic processes in skin is lacking in the literature. In this review, molecular circadian clock machinery and the factors that govern it have been highlighted. Circadian rhythm is closely linked to immunological processes and skin homeostasis, and its desynchrony can be linked to the perturbation of the skin. The interplay between circadian rhythm and annual, seasonal oscillations, as well as the impact of these periodic events on the skin, is described. Finally, the changes that occur in the skin over a lifespan are presented. This work encourages further research into the oscillating biological processes occurring in the skin and lays the foundation for future strategies to combat the adverse effects of desynchrony, which would likely have implications in other tissues influenced by periodic oscillatory processes.
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Affiliation(s)
- Andrew Salazar
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
- Correspondence:
| | - Jörg von Hagen
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
- Department of Life Science Engineering, University Applied Sciences, Wiesenstrasse 14, 35390 Gießen, Germany
- ryon—GreenTech Accelerator Gernsheim GmbH, Mainzer Str. 41, 64579 Gernsheim, Germany
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29
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Aging Hallmarks and the Role of Oxidative Stress. Antioxidants (Basel) 2023; 12:antiox12030651. [PMID: 36978899 PMCID: PMC10044767 DOI: 10.3390/antiox12030651] [Citation(s) in RCA: 72] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/26/2023] [Accepted: 03/01/2023] [Indexed: 03/08/2023] Open
Abstract
Aging is a complex biological process accompanied by a progressive decline in the physical function of the organism and an increased risk of age-related chronic diseases such as cardiovascular diseases, cancer, and neurodegenerative diseases. Studies have established that there exist nine hallmarks of the aging process, including (i) telomere shortening, (ii) genomic instability, (iii) epigenetic modifications, (iv) mitochondrial dysfunction, (v) loss of proteostasis, (vi) dysregulated nutrient sensing, (vii) stem cell exhaustion, (viii) cellular senescence, and (ix) altered cellular communication. All these alterations have been linked to sustained systemic inflammation, and these mechanisms contribute to the aging process in timing not clearly determined yet. Nevertheless, mitochondrial dysfunction is one of the most important mechanisms contributing to the aging process. Mitochondria is the primary endogenous source of reactive oxygen species (ROS). During the aging process, there is a decline in ATP production and elevated ROS production together with a decline in the antioxidant defense. Elevated ROS levels can cause oxidative stress and severe damage to the cell, organelle membranes, DNA, lipids, and proteins. This damage contributes to the aging phenotype. In this review, we summarize recent advances in the mechanisms of aging with an emphasis on mitochondrial dysfunction and ROS production.
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30
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Xiao P, Shi Z, Liu C, Hagen DE. Characteristics of circulating small noncoding RNAs in plasma and serum during human aging. Aging Med (Milton) 2023; 6:35-48. [PMID: 36911092 PMCID: PMC10000275 DOI: 10.1002/agm2.12241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/05/2023] [Accepted: 01/11/2023] [Indexed: 02/24/2023] Open
Abstract
Objective Aging is a complicated process that triggers age-related disease susceptibility through intercellular communication in the microenvironment. While the classic secretome of senescence-associated secretory phenotype (SASP) including soluble factors, growth factors, and extracellular matrix remodeling enzymes are known to impact tissue homeostasis during the aging process, the effects of novel SASP components, extracellular small noncoding RNAs (sncRNAs), on human aging are not well established. Methods Here, by utilizing 446 small RNA-seq samples from plasma and serum of healthy donors found in the Extracellular RNA (exRNA) Atlas data repository, we correlated linear and nonlinear features between circulating sncRNAs expression and age by the maximal information coefficient (MIC) relationship determination. Age predictors were generated by ensemble machine learning methods (Adaptive Boosting, Gradient Boosting, and Random Forest) and core age-related sncRNAs were determined through weighted coefficients in machine learning models. Functional investigation was performed via target prediction of age-related miRNAs. Results We observed the number of highly expressed transfer RNAs (tRNAs) and microRNAs (miRNAs) showed positive and negative associations with age respectively. Two-variable (sncRNA expression and individual age) relationships were detected by MIC and sncRNAs-based age predictors were established, resulting in a forecast performance where all R 2 values were greater than 0.96 and root-mean-square errors (RMSE) were less than 3.7 years in three ensemble machine learning methods. Furthermore, important age-related sncRNAs were identified based on modeling and the biological pathways of age-related miRNAs were characterized by their predicted targets, including multiple pathways in intercellular communication, cancer and immune regulation. Conclusion In summary, this study provides valuable insights into circulating sncRNAs expression dynamics during human aging and may lead to advanced understanding of age-related sncRNAs functions with further elucidation.
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Affiliation(s)
- Ping Xiao
- Department of Animal and Food SciencesOklahoma State UniversityStillwaterOklahomaUSA
| | - Zhangyue Shi
- School of Industrial Engineering and ManagementOklahoma State UniversityStillwaterOklahomaUSA
| | - Chenang Liu
- School of Industrial Engineering and ManagementOklahoma State UniversityStillwaterOklahomaUSA
| | - Darren E. Hagen
- Department of Animal and Food SciencesOklahoma State UniversityStillwaterOklahomaUSA
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31
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Vitamin D as a Shield against Aging. Int J Mol Sci 2023; 24:ijms24054546. [PMID: 36901976 PMCID: PMC10002864 DOI: 10.3390/ijms24054546] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/17/2023] [Accepted: 02/22/2023] [Indexed: 03/02/2023] Open
Abstract
Aging can be seen as a physiological progression of biomolecular damage and the accumulation of defective cellular components, which trigger and amplify the process, toward whole-body function weakening. Senescence initiates at the cellular level and consists in an inability to maintain homeostasis, characterized by the overexpression/aberrant expression of inflammatory/immune/stress responses. Aging is associated with significant modifications in immune system cells, toward a decline in immunosurveillance, which, in turn, leads to chronic elevation of inflammation/oxidative stress, increasing the risk of (co)morbidities. Albeit aging is a natural and unavoidable process, it can be regulated by some factors, like lifestyle and diet. Nutrition, indeed, tackles the mechanisms underlying molecular/cellular aging. Many micronutrients, i.e., vitamins and elements, can impact cell function. This review focuses on the role exerted by vitamin D in geroprotection, based on its ability to shape cellular/intracellular processes and drive the immune response toward immune protection against infections and age-related diseases. To this aim, the main biomolecular paths underlying immunosenescence and inflammaging are identified as biotargets of vitamin D. Topics such as heart and skeletal muscle cell function/dysfunction, depending on vitamin D status, are addressed, with comments on hypovitaminosis D correction by food and supplementation. Albeit research has progressed, still limitations exist in translating knowledge into clinical practice, making it necessary to focus attention on the role of vitamin D in aging, especially considering the growing number of older individuals.
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32
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Coenen L, Lehallier B, de Vries HE, Middeldorp J. Markers of aging: Unsupervised integrated analyses of the human plasma proteome. FRONTIERS IN AGING 2023; 4:1112109. [PMID: 36911498 PMCID: PMC9992741 DOI: 10.3389/fragi.2023.1112109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/08/2023] [Indexed: 02/24/2023]
Abstract
Aging associates with an increased susceptibility for disease and decreased quality of life. To date, processes underlying aging are still not well understood, leading to limited interventions with unknown mechanisms to promote healthy aging. Previous research suggests that changes in the blood proteome are reflective of age-associated phenotypes such as frailty. Moreover, experimentally induced changes in the blood proteome composition can accelerate or decelerate underlying aging processes. The aim of this study is to identify a set of proteins in the human plasma associated with aging by integration of the data of four independent, large-scaled datasets using the aptamer-based SomaScan platform on the human aging plasma proteome. Using this approach, we identified a set of 273 plasma proteins significantly associated with aging (aging proteins, APs) across these cohorts consisting of healthy individuals and individuals with comorbidities and highlight their biological functions. We validated the age-associated effects in an independent study using a centenarian population, showing highly concordant effects. Our results suggest that APs are more associated to diseases than other plasma proteins. Plasma levels of APs can predict chronological age, and a reduced selection of 15 APs can still predict individuals' age accurately, highlighting their potential as biomarkers of aging processes. Furthermore, we show that individuals presenting accelerated or decelerated aging based on their plasma proteome, respectively have a more aged or younger systemic environment. These results provide novel insights in the understanding of the aging process and its underlying mechanisms and highlight potential modulators contributing to healthy aging.
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Affiliation(s)
- L. Coenen
- Department of Neurobiology and Aging, Biomedical Primate Research Centre, Rijswijk, Netherlands
- Department of Molecular Cell Biology and Immunology, Amsterdam Neuroscience, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | | | - H. E. de Vries
- Department of Molecular Cell Biology and Immunology, Amsterdam Neuroscience, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - J. Middeldorp
- Department of Neurobiology and Aging, Biomedical Primate Research Centre, Rijswijk, Netherlands
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33
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Casanova R, Anderson AM, Barnard RT, Justice JN, Kucharska-Newton A, Windham BG, Palta P, Gottesman RF, Mosley TH, Hughes TM, Wagenknecht LE, Kritchevsky SB. Is an MRI-derived anatomical measure of dementia risk also a measure of brain aging? GeroScience 2023; 45:439-450. [PMID: 36050589 PMCID: PMC9886771 DOI: 10.1007/s11357-022-00650-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 08/22/2022] [Indexed: 02/03/2023] Open
Abstract
Machine learning methods have been applied to estimate measures of brain aging from neuroimages. However, only rarely have these measures been examined in the context of biologic age. Here, we investigated associations of an MRI-based measure of dementia risk, the Alzheimer's disease pattern similarity (AD-PS) scores, with measures used to calculate biological age. Participants were those from visit 5 of the Atherosclerosis Risk in Communities Study with cognitive status adjudication, proteomic data, and AD-PS scores available. The AD-PS score estimation is based on previously reported machine learning methods. We evaluated associations of the AD-PS score with all-cause mortality. Sensitivity analyses using only cognitively normal (CN) individuals were performed treating CNS-related causes of death as competing risk. AD-PS score was examined in association with 32 proteins measured, using a Somalogic platform, previously reported to be associated with age. Finally, associations with a deficit accumulation index (DAI) based on a count of 38 health conditions were investigated. All analyses were adjusted for age, race, sex, education, smoking, hypertension, and diabetes. The AD-PS score was significantly associated with all-cause mortality and with levels of 9 of the 32 proteins. Growth/differentiation factor 15 (GDF-15) and pleiotrophin remained significant after accounting for multiple-testing and when restricting the analysis to CN participants. A linear regression model showed a significant association between DAI and AD-PS scores overall. While the AD-PS scores were created as a measure of dementia risk, our analyses suggest that they could also be capturing brain aging.
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Affiliation(s)
- Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA.
| | - Andrea M Anderson
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Ryan T Barnard
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jamie N Justice
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | | | - Priya Palta
- School of Public Health, Columbia University, New York, NY, USA
| | | | | | - Timothy M Hughes
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Stephen B Kritchevsky
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
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Abstract
Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.
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Affiliation(s)
- Jarod Rutledge
- Department of Genetics, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Paul F. Glenn Center for the Biology of Ageing, Stanford University School of Medicine, Stanford, CA, USA
| | - Hamilton Oh
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Paul F. Glenn Center for the Biology of Ageing, Stanford University School of Medicine, Stanford, CA, USA
- Graduate Program in Stem Cell and Regenerative Medicine, Stanford University, Stanford, CA, USA
| | - Tony Wyss-Coray
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Paul F. Glenn Center for the Biology of Ageing, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
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35
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Zinovkin RA, Kondratenko ND, Zinovkina LA. Does Nrf2 Play a Role of a Master Regulator of Mammalian Aging? BIOCHEMISTRY. BIOKHIMIIA 2022; 87:1465-1476. [PMID: 36717440 DOI: 10.1134/s0006297922120045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
For a long time Nrf2 transcription factor has been attracting attention of researchers investigating phenomenon of aging. Numerous studies have investigated effects of Nrf2 on aging and cell senescence. Nrf2 is often considered as a key player in aging processes, however this needs to be proven. It should be noted that most studies were carried out on invertebrate model organisms, such as nematodes and fruit flies, but not on mammals. This paper briefly presents main mechanisms of mammalian aging and role of inflammation and oxidative stress in this process. The mechanisms of Nrf2 activity regulation, its involvement in aging and development of the senescence-associated secretory phenotype (SASP) are also discussed. Main part of this review is devoted to critical analysis of available experimental data on the role of Nrf2 in mammalian aging.
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Affiliation(s)
- Roman A Zinovkin
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia.
- Russian Clinical Research Center for Gerontology, Ministry of Healthcare of the Russian Federation, Pirogov Russian National Research Medical University, Moscow, 129226, Russia
| | - Natalia D Kondratenko
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia
- Russian Clinical Research Center for Gerontology, Ministry of Healthcare of the Russian Federation, Pirogov Russian National Research Medical University, Moscow, 129226, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Ludmila A Zinovkina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia
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36
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Liu X, Pan S, Xanthakis V, Vasan RS, Psaty BM, Austin TR, Newman AB, Sanders JL, Wu C, Tracy RP, Gerszten RE, Odden MC. Plasma proteomic signature of decline in gait speed and grip strength. Aging Cell 2022; 21:e13736. [PMID: 36333824 PMCID: PMC9741503 DOI: 10.1111/acel.13736] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 09/27/2022] [Accepted: 10/21/2022] [Indexed: 11/08/2022] Open
Abstract
The biological mechanisms underlying decline in physical function with age remain unclear. We examined the plasma proteomic profile associated with longitudinal changes in physical function measured by gait speed and grip strength in community-dwelling adults. We applied an aptamer-based platform to assay 1154 plasma proteins on 2854 participants (60% women, aged 76 years) in the Cardiovascular Health Study (CHS) in 1992-1993 and 1130 participants (55% women, aged 54 years) in the Framingham Offspring Study (FOS) in 1991-1995. Gait speed and grip strength were measured annually for 7 years in CHS and at cycles 7 (1998-2001) and 8 (2005-2008) in FOS. The associations of individual protein levels (log-transformed and standardized) with longitudinal changes in gait speed and grip strength in two populations were examined separately by linear mixed-effects models. Meta-analyses were implemented using random-effects models and corrected for multiple testing. We found that plasma levels of 14 and 18 proteins were associated with changes in gait speed and grip strength, respectively (corrected p < 0.05). The proteins most strongly associated with gait speed decline were GDF-15 (Meta-analytic p = 1.58 × 10-15 ), pleiotrophin (1.23 × 10-9 ), and TIMP-1 (5.97 × 10-8 ). For grip strength decline, the strongest associations were for carbonic anhydrase III (1.09 × 10-7 ), CDON (2.38 × 10-7 ), and SMOC1 (7.47 × 10-7 ). Several statistically significant proteins are involved in the inflammatory responses or antagonism of activin by follistatin pathway. These novel proteomic biomarkers and pathways should be further explored as future mechanisms and targets for age-related functional decline.
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Affiliation(s)
- Xiaojuan Liu
- Department of Epidemiology and Population HealthStanford University School of MedicineStanfordCaliforniaUSA
| | - Stephanie Pan
- Framingham Heart Study and Section of Preventive Medicine and EpidemiologyBoston University School of MedicineBostonMassachusettsUSA
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Vanessa Xanthakis
- Framingham Heart Study and Section of Preventive Medicine and EpidemiologyBoston University School of MedicineBostonMassachusettsUSA
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Ramachandran S. Vasan
- Framingham Heart Study and Section of Preventive Medicine and EpidemiologyBoston University School of MedicineBostonMassachusettsUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMassachusettsUSA
- Section of Cardiovascular Medicine, Department of MedicineBoston University School of MedicineBostonMassachusettsUSA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Systems and Population HealthUniversity of WashingtonSeattleWashingtonUSA
| | - Thomas R. Austin
- Department of EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
| | - Anne B. Newman
- Department of EpidemiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | | | - Chenkai Wu
- Global Health Research CenterDuke Kunshan UniversityKunshanChina
| | - Russell P. Tracy
- Department of Pathology and Laboratory Medicine, The Robert Larner M.D. College of MedicineUniversity of VermontBurlingtonVermontUSA
- Department of Biochemistry, The Robert Larner M.D. College of MedicineUniversity of VermontBurlingtonVermontUSA
| | - Robert E. Gerszten
- Division of Cardiovascular MedicineBeth Israel Deaconess Medical CenterBostonMassachusettsUSA
| | - Michelle C. Odden
- Department of Epidemiology and Population HealthStanford University School of MedicineStanfordCaliforniaUSA
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Brinkley TE, Justice JN, Basu S, Bauer SR, Loh KP, Mukli P, Ng TKS, Turney IC, Ferrucci L, Cummings SR, Kritchevsky SB. Research priorities for measuring biologic age: summary and future directions from the Research Centers Collaborative Network Workshop. GeroScience 2022; 44:2573-2583. [PMID: 36242692 PMCID: PMC9768050 DOI: 10.1007/s11357-022-00661-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/09/2022] [Indexed: 01/07/2023] Open
Abstract
Biologic aging reflects the genetic, molecular, and cellular changes underlying the development of morbidity and mortality with advancing chronological age. As several potential mechanisms have been identified, there is a growing interest in developing robust measures of biologic age that can better reflect the underlying biology of aging and predict age-related outcomes. To support this endeavor, the Research Centers Collaborative Network (RCCN) conducted a workshop in January 2022 to discuss emerging concepts in the field and identify opportunities to move the science forward. This paper presents workshop proceedings and summarizes the identified research needs, priorities, and recommendations for measuring biologic age. The highest priorities identified were the need for more robust measures, longitudinal studies, multidisciplinary collaborations, and translational approaches.
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Affiliation(s)
- Tina E Brinkley
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
| | - Jamie N Justice
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Shubhashrita Basu
- Center for Demography of Health and Aging, University of Wisconsin, Madison, WI, USA
| | - Scott R Bauer
- Departments of Medicine and Urology, University of California and San Francisco VA Medical Center, San Francisco, CA, USA
| | - Kah Poh Loh
- Division of Hematology/Oncology, Department of Medicine, University of Rochester Medical Center, James P. Wilmot Cancer Institute, Rochester, NY, USA
| | - Peter Mukli
- Department of Biochemistry/Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Ted Kheng Siang Ng
- Edson College of Nursing and Health Innovation, Arizona State University, Tempe, AZ, USA
| | - Indira C Turney
- Department of Neurology, Columbia University Medical Center, New York City, NY, USA
| | - Luigi Ferrucci
- Intramural Research Program of the National Institute On Aging, NIH, Baltimore, MD, USA
| | - Steven R Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute and the Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Stephen B Kritchevsky
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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38
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Tabibzadeh S. Resolving Geroplasticity to the Balance of Rejuvenins and Geriatrins. Aging Dis 2022; 13:1664-1714. [PMID: 36465174 PMCID: PMC9662275 DOI: 10.14336/ad.2022.0414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 04/14/2022] [Indexed: 09/29/2024] Open
Abstract
According to the cell centric hypotheses, the deficits that drive aging occur within cells by age dependent progressive damage to organelles, telomeres, biologic signaling pathways, bioinformational molecules, and by exhaustion of stem cells. Here, we amend these hypotheses and propose an eco-centric model for geroplasticity (aging plasticity including aging reversal). According to this model, youth and aging are plastic and require constant maintenance, and, respectively, engage a host of endogenous rejuvenating (rejuvenins) and gero-inducing [geriatrin] factors. Aging in this model is akin to atrophy that occurs as a result of damage or withdrawal of trophic factors. Rejuvenins maintain and geriatrins adversely impact cellular homeostasis, cell fitness, and proliferation, stem cell pools, damage response and repair. Rejuvenins reduce and geriatrins increase the age-related disorders, inflammatory signaling, and senescence and adjust the epigenetic clock. When viewed through this perspective, aging can be successfully reversed by supplementation with rejuvenins and by reducing the levels of geriatrins.
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Affiliation(s)
- Siamak Tabibzadeh
- Frontiers in Bioscience Research Institute in Aging and Cancer, Irvine, CA 92618, USA
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39
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An evaluation of aging measures: from biomarkers to clocks. Biogerontology 2022; 24:303-328. [PMID: 36418661 DOI: 10.1007/s10522-022-09997-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/21/2022] [Indexed: 11/25/2022]
Abstract
With the increasing number of aged population and growing burden of healthy aging demands, a rational standard for evaluation aging is in urgent need. The advancement of medical testing technology and the prospering of artificial intelligence make it possible to evaluate the biological status of aging from a more comprehensive view. In this review, we introduced common aging biomarkers and concluded several famous aging clocks. Aging biomarkers reflect changes in the organism at a molecular or cellular level over time while aging clocks tend to be more of a generalization of the overall state of the organism. We expect to construct a framework for aging evaluation measurement from both micro and macro perspectives. Especially, population-specific aging clocks and multi-omics aging clocks may better fit the demands to evaluate aging in a comprehensive and multidimensional manner and make a detailed classification to represent different aging rates at tissue/organ levels. This framework will promisingly provide a crucial basis for disease diagnosis and intervention assessment in geroscience.
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40
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Shokhirev MN, Johnson AA. An integrative machine-learning meta-analysis of high-throughput omics data identifies age-specific hallmarks of Alzheimer's disease. Ageing Res Rev 2022; 81:101721. [PMID: 36029998 DOI: 10.1016/j.arr.2022.101721] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/15/2022] [Accepted: 08/19/2022] [Indexed: 02/06/2023]
Abstract
Alzheimer's disease (AD) is an incredibly complex and presently incurable age-related brain disorder. To better understand this debilitating disease, we collated and performed a meta-analysis on publicly available RNA-Seq, microarray, proteomics, and microRNA samples derived from AD patients and non-AD controls. 4089 samples originating from brain tissues and blood remained after applying quality filters. Since disease progression in AD correlates with age, we stratified this large dataset into three different age groups: < 75 years, 75-84 years, and ≥ 85 years. The RNA-Seq, microarray, and proteomics datasets were then combined into different integrated datasets. Ensemble machine learning was employed to identify genes and proteins that can accurately classify samples as either AD or control. These predictive inputs were then subjected to network-based enrichment analyses. The ability of genes/proteins associated with different pathways in the Molecular Signatures Database to diagnose AD was also tested. We separately identified microRNAs that can be used to make an AD diagnosis and subjected the predicted gene targets of the most predictive microRNAs to an enrichment analysis. The following key themes emerged from our machine learning and bioinformatics analyses: cell death, cellular senescence, energy metabolism, genomic integrity, glia, immune system, metal ion homeostasis, oxidative stress, proteostasis, and synaptic function. Many of the results demonstrated unique age-specificity. For example, terms highlighting cellular senescence only emerged in the earliest and intermediate age ranges while the majority of results relevant to cell death appeared in the youngest patients. Existing literature corroborates the importance of these hallmarks in AD.
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Affiliation(s)
- Maxim N Shokhirev
- Razavi Newman Integrative Genomics and Bioinformatics Core, Salk Institute for Biological Studies, La Jolla, CA, USA.
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41
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Li A, Koch Z, Ideker T. Epigenetic aging: Biological age prediction and informing a mechanistic theory of aging. J Intern Med 2022; 292:733-744. [PMID: 35726002 DOI: 10.1111/joim.13533] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Numerous studies have shown that epigenetic age-an individual's degree of aging based on patterns of DNA methylation-can be computed and is associated with an array of factors including diet, lifestyle, genetics, and disease. One can expect that still further associations will emerge with additional aging research, but to what end? Prediction of age was an important first step, but-in our view-the focus must shift from chasing increasingly accurate age computations to understanding the links between the epigenome and the mechanisms and physiological changes of aging. Here, we outline emerging areas of epigenetic aging research that prioritize biological understanding and clinical application. First, we survey recent progress in epigenetic clocks, which are beginning to predict not only chronological age but aging outcomes such as all-cause mortality and onset of disease, or which integrate aging signals across multiple biological processes. Second, we discuss research that exemplifies how investigation of the epigenome is building a mechanistic theory of aging and informing clinical practice. Such examples include identifying methylation sites and the genes most strongly predictive of aging-a subset of which have shown strong potential as biomarkers of neurodegenerative disease and cancer; relating epigenetic clock predictions to hallmarks of aging; and using longitudinal studies of DNA methylation to characterize human disease, resulting in the discovery of epigenetic indications of type 1 diabetes and the propensity for psychotic experiences.
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Affiliation(s)
- Adam Li
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Zane Koch
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Trey Ideker
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, California, USA
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42
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A set of common buccal CpGs that predict epigenetic age and associate with lifespan-regulating genes. iScience 2022; 25:105304. [PMID: 36304118 PMCID: PMC9593711 DOI: 10.1016/j.isci.2022.105304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/11/2022] [Accepted: 10/02/2022] [Indexed: 11/23/2022] Open
Abstract
Epigenetic aging clocks are computational models that use DNA methylation sites to predict age. Since cheek swabs are non-invasive and painless, collecting DNA from buccal tissue is highly desirable. Here, we review 11 existing clocks that have been applied to buccal tissue. Two of these were exclusively trained on adults and, while moderately accurate, have not been used to capture health-relevant differences in epigenetic age. Using 130 common CpGs utilized by two or more existing buccal clocks, we generate a proof-of-concept predictor in an adult methylomic dataset. In addition to accurately estimating age (r = 0.95 and mean absolute error = 3.88 years), this clock predicted that Down syndrome subjects were significantly older relative to controls. A literature and database review of CpG-associated genes identified numerous genes (e.g., CLOCK, ELOVL2, and VGF) and molecules (e.g., alpha-linolenic acid, glycine, and spermidine) reported to influence lifespan and/or age-related disease in model organisms. 130 CpGs have been used by two or more aging clocks applied to human buccal tissue Common CpG genes are linked to the adaptive immune system and telomere maintenance Common CpGs can be used to build a novel, proof-of-concept epigenetic aging clock Several compounds associated with common CpG genes regulate lifespan in animals
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43
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Caliskan A, Crouch SAW, Giddins S, Dandekar T, Dangwal S. Progeria and Aging-Omics Based Comparative Analysis. Biomedicines 2022; 10:2440. [PMID: 36289702 PMCID: PMC9599154 DOI: 10.3390/biomedicines10102440] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/09/2022] [Accepted: 09/21/2022] [Indexed: 10/21/2023] Open
Abstract
Since ancient times aging has also been regarded as a disease, and humankind has always strived to extend the natural lifespan. Analyzing the genes involved in aging and disease allows for finding important indicators and biological markers for pathologies and possible therapeutic targets. An example of the use of omics technologies is the research regarding aging and the rare and fatal premature aging syndrome progeria (Hutchinson-Gilford progeria syndrome, HGPS). In our study, we focused on the in silico analysis of differentially expressed genes (DEGs) in progeria and aging, using a publicly available RNA-Seq dataset (GEO dataset GSE113957) and a variety of bioinformatics tools. Despite the GSE113957 RNA-Seq dataset being well-known and frequently analyzed, the RNA-Seq data shared by Fleischer et al. is far from exhausted and reusing and repurposing the data still reveals new insights. By analyzing the literature citing the use of the dataset and subsequently conducting a comparative analysis comparing the RNA-Seq data analyses of different subsets of the dataset (healthy children, nonagenarians and progeria patients), we identified several genes involved in both natural aging and progeria (KRT8, KRT18, ACKR4, CCL2, UCP2, ADAMTS15, ACTN4P1, WNT16, IGFBP2). Further analyzing these genes and the pathways involved indicated their possible roles in aging, suggesting the need for further in vitro and in vivo research. In this paper, we (1) compare "normal aging" (nonagenarians vs. healthy children) and progeria (HGPS patients vs. healthy children), (2) enlist genes possibly involved in both the natural aging process and progeria, including the first mention of IGFBP2 in progeria, (3) predict miRNAs and interactomes for WNT16 (hsa-mir-181a-5p), UCP2 (hsa-mir-26a-5p and hsa-mir-124-3p), and IGFBP2 (hsa-mir-124-3p, hsa-mir-126-3p, and hsa-mir-27b-3p), (4) demonstrate the compatibility of well-established R packages for RNA-Seq analysis for researchers interested but not yet familiar with this kind of analysis, and (5) present comparative proteomics analyses to show an association between our RNA-Seq data analyses and corresponding changes in protein expression.
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Affiliation(s)
- Aylin Caliskan
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Samantha A. W. Crouch
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Sara Giddins
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Seema Dangwal
- Stanford Cardiovascular Institute, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
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Zhang Z, Liu N, Guo Z, Jiao L, Fenster A, Jin W, Zhang Y, Chen J, Yan C, Gou S. Ageing and degeneration analysis using ageing-related dynamic attention on lateral cephalometric radiographs. NPJ Digit Med 2022; 5:151. [PMID: 36168038 PMCID: PMC9515216 DOI: 10.1038/s41746-022-00681-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 08/22/2022] [Indexed: 11/25/2022] Open
Abstract
With the increase of the ageing in the world’s population, the ageing and degeneration studies of physiological characteristics in human skin, bones, and muscles become important topics. Research on the ageing of bones, especially the skull, are paid much attention in recent years. In this study, a novel deep learning method representing the ageing-related dynamic attention (ARDA) is proposed. The proposed method can quantitatively display the ageing salience of the bones and their change patterns with age on lateral cephalometric radiographs images (LCR) images containing the craniofacial and cervical spine. An age estimation-based deep learning model based on 14142 LCR images from 4 to 40 years old individuals is trained to extract ageing-related features, and based on these features the ageing salience maps are generated by the Grad-CAM method. All ageing salience maps with the same age are merged as an ARDA map corresponding to that age. Ageing salience maps show that ARDA is mainly concentrated in three regions in LCR images: the teeth, craniofacial, and cervical spine regions. Furthermore, the dynamic distribution of ARDA at different ages and instances in LCR images is quantitatively analyzed. The experimental results on 3014 cases show that ARDA can accurately reflect the development and degeneration patterns in LCR images.
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Affiliation(s)
- Zhiyong Zhang
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, 710004, Shaanxi, China.,College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, China.,Department of Orthodontics, the Affiliated Stomatological Hospital of Xi'an Jiaotong University, Xi'an, 710004, Shaanxi, China
| | - Ningtao Liu
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, 710071, Shaanxi, China.,Robarts Research Institute, Western University, London, N6A 3K7, ON, Canada
| | - Zhang Guo
- Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Licheng Jiao
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Aaron Fenster
- Robarts Research Institute, Western University, London, N6A 3K7, ON, Canada
| | - Wenfan Jin
- Department of Radiology, the Affiliated Stomatological Hospital of Xi'an Jiaotong University, Xi'an, 710004, Shaanxi, China
| | - Yuxiang Zhang
- College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, China
| | - Jie Chen
- College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, China
| | - Chunxia Yan
- College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, China.
| | - Shuiping Gou
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, 710071, Shaanxi, China.
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45
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Salignon J, Rizzuto D, Calderón-Larrañaga A, Zucchelli A, Fratiglioni L, Riedel CG, Vetrano DL. Beyond Chronological Age: A Multidimensional Approach to Survival Prediction in Older Adults. J Gerontol A Biol Sci Med Sci 2022; 78:158-166. [PMID: 36075209 PMCID: PMC9879753 DOI: 10.1093/gerona/glac186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND There is a growing interest in generating precise predictions of survival to improve the assessment of health and life-improving interventions. We aimed to (a) test if observable characteristics may provide a survival prediction independent of chronological age; (b) identify the most relevant predictors of survival; and (c) build a metric of multidimensional age. METHODS Data from 3 095 individuals aged ≥60 from the Swedish National Study on Aging and Care in Kungsholmen. Eighty-three variables covering 5 domains (diseases, risk factors, sociodemographics, functional status, and blood tests) were tested in penalized Cox regressions to predict 18-year mortality. RESULTS The best prediction of mortality at different follow-ups (area under the receiver operating characteristic curves [AUROCs] 0.878-0.909) was obtained when 15 variables from all 5 domains were tested simultaneously in a penalized Cox regression. Significant prediction improvements were observed when chronological age was included as a covariate for 15- but not for 5- and 10-year survival. When comparing individual domains, we find that a combination of functional characteristics (ie, gait speed, cognition) gave the most accurate prediction, with estimates similar to chronological age for 5- (AUROC 0.836) and 10-year (AUROC 0.830) survival. Finally, we built a multidimensional measure of age by regressing the predicted mortality risk on chronological age, which displayed a stronger correlation with time to death (R = -0.760) than chronological age (R = -0.660) and predicted mortality better than widely used geriatric indices. CONCLUSIONS Combining easily accessible characteristics can help in building highly accurate survival models and multidimensional age metrics with potentially broad geriatric and biomedical applications.
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Affiliation(s)
| | | | - Amaia Calderón-Larrañaga
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden,Stockholm Gerontology Research Center, Stockholm, Sweden
| | - Alberto Zucchelli
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden,Department of Information Engineering, University of Brescia, Brescia, Italy
| | - Laura Fratiglioni
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden,Stockholm Gerontology Research Center, Stockholm, Sweden
| | - Christian G Riedel
- Address correspondence to: Christian G. Riedel, PhD, Department of Biosciences and Nutrition, Karolinska Institutet, Blickagången 16, 141 52 Huddinge, Sweden. E-mail:
| | - Davide L Vetrano
- Address correspondence to: Davide L. Vetrano, MD, PhD, Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Tomtebodavägen 18 A, 171 65 Solna, Sweden. E-mail:
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Abstract
Paleoproteomics, the study of ancient proteins, is a rapidly growing field at the intersection of molecular biology, paleontology, archaeology, paleoecology, and history. Paleoproteomics research leverages the longevity and diversity of proteins to explore fundamental questions about the past. While its origins predate the characterization of DNA, it was only with the advent of soft ionization mass spectrometry that the study of ancient proteins became truly feasible. Technological gains over the past 20 years have allowed increasing opportunities to better understand preservation, degradation, and recovery of the rich bioarchive of ancient proteins found in the archaeological and paleontological records. Growing from a handful of studies in the 1990s on individual highly abundant ancient proteins, paleoproteomics today is an expanding field with diverse applications ranging from the taxonomic identification of highly fragmented bones and shells and the phylogenetic resolution of extinct species to the exploration of past cuisines from dental calculus and pottery food crusts and the characterization of past diseases. More broadly, these studies have opened new doors in understanding past human-animal interactions, the reconstruction of past environments and environmental changes, the expansion of the hominin fossil record through large scale screening of nondiagnostic bone fragments, and the phylogenetic resolution of the vertebrate fossil record. Even with these advances, much of the ancient proteomic record still remains unexplored. Here we provide an overview of the history of the field, a summary of the major methods and applications currently in use, and a critical evaluation of current challenges. We conclude by looking to the future, for which innovative solutions and emerging technology will play an important role in enabling us to access the still unexplored "dark" proteome, allowing for a fuller understanding of the role ancient proteins can play in the interpretation of the past.
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Affiliation(s)
- Christina Warinner
- Department
of Anthropology, Harvard University, Cambridge, Massachusetts 02138, United States
- Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany
| | - Kristine Korzow Richter
- Department
of Anthropology, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Matthew J. Collins
- Department
of Archaeology, Cambridge University, Cambridge CB2 3DZ, United Kingdom
- Section
for Evolutionary Genomics, Globe Institute,
University of Copenhagen, Copenhagen 1350, Denmark
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47
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Liu JF, Wu Y, Yang YH, Wu SF, Liu S, Xu P, Yang JT. Phosphoproteome profiling of mouse liver during normal aging. Proteome Sci 2022; 20:12. [PMID: 35932011 PMCID: PMC9354360 DOI: 10.1186/s12953-022-00194-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 07/24/2022] [Indexed: 08/30/2023] Open
Abstract
Background Aging is a complex biological process accompanied by a time-dependent functional decline that affects most living organisms. Omics studies help to comprehensively understand the mechanism of aging and discover potential intervention methods. Old mice are frequently obese with a fatty liver. Methods We applied mass spectrometry-based phosphoproteomics to obtain a global phosphorylation profile of the liver in mice aged 2 or 18 months. MaxQuant was used for quantitative analysis and PCA was used for unsupervised clustering. Results Through phosphoproteome analysis, a total of 5,685 phosphosites in 2,335 proteins were filtered for quantitative analysis. PCA analysis of both the phosphoproteome and transcriptome data could distinguish young and old mice. However, from kinase prediction, kinase-substrate interaction analysis, and KEGG functional enrichment analysis done with phosphoproteome data, we observed high phosphorylation of fatty acid biosynthesis, β-oxidation, and potential secretory processes, together with low phosphorylation of the Egfr-Sos1-Araf/Braf-Map2k1-Mapk1 pathway and Ctnnb1 during aging. Proteins with differentially expressed phosphosites seemed more directly related to the aging-associated fatty liver phenotype than the differentially expressed transcripts. The phosphoproteome may reveal distinctive biological functions that are lost in the transcriptome. Conclusions In summary, we constructed a phosphorylation-associated network in the mouse liver during normal aging, which may help to discover novel antiaging strategies. Supplementary Information The online version contains supplementary material available at 10.1186/s12953-022-00194-2. The first phosphoproteome profiling of mouse livers during normal aging. A total of 5,685 phosphosites in 2,335 proteins were quantified in this study. A phosphorylation-regulated pathway network was constructed. Metabolism, secretion, and the cell cycle might be dysregulated during normal aging.
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Affiliation(s)
- Jiang-Feng Liu
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
| | - Yue Wu
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China.,School of Statistics and Data Science, Nankai University, Tianjin, 300071, China
| | - Ye-Hong Yang
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
| | - Song-Feng Wu
- State Key Laboratory of ProteomicsResearch Unit of Proteomics & ResearchDevelopment of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing, 102206, China
| | - Shu Liu
- State Key Laboratory of ProteomicsResearch Unit of Proteomics & ResearchDevelopment of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing, 102206, China
| | - Ping Xu
- State Key Laboratory of ProteomicsResearch Unit of Proteomics & ResearchDevelopment of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing, 102206, China.
| | - Jun-Tao Yang
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China.
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48
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Contini C, Serrao S, Manconi B, Olianas A, Iavarone F, Bizzarro A, Masullo C, Castagnola M, Messana I, Diaz G, Cabras T. Salivary Proteomics Reveals Significant Changes in Relation to Alzheimer's Disease and Aging. J Alzheimers Dis 2022; 89:605-622. [PMID: 35912740 DOI: 10.3233/jad-220246] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Aging is a risk factor for several pathologies as Alzheimer's disease (AD). Great interest exists, therefore, in discovering diagnostic biomarkers and indicators discriminating biological aging and health status. To this aim, omic investigations of biological matrices, as saliva, whose sampling is easy and non-invasive, offer great potential. OBJECTIVE Investigate the salivary proteome through a statistical comparison of the proteomic data by several approaches to highlight quali-/quantitative variations associated specifically either to aging or to AD occurrence, and, thus, able to classify the subjects. METHODS Salivary proteomic data of healthy controls under-70 (adults) and over-70 (elderly) years old, and over-70 AD patients, obtained by liquid chromatography/mass spectrometry, were analyzed by multiple Mann-Whitney test, Kendall correlation, and Random-Forest (RF) analysis. RESULTS Almost all the investigated proteins/peptides significantly decreased in relation to aging in elderly subjects, with or without AD, in comparison with adults. AD subjects exhibited the highest levels of α-defensins, thymosin β4, cystatin B, S100A8 and A9. Correlation tests also highlighted age/disease associated differences. RF analysis individuated quali-/quantitative variations in 20 components, as oxidized S100A8 and S100A9, α-defensin 3, P-B peptide, able to classify with great accuracy the subjects into the three groups. CONCLUSION The findings demonstrated a strong change of the salivary protein profile in relation to the aging. Potential biomarkers candidates of AD were individuated in peptides/proteins involved in antimicrobial defense, innate immune system, inflammation, and in oxidative stress. RF analysis revealed the feasibility of the salivary proteome to discriminate groups of subjects based on age and health status.
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Affiliation(s)
- Cristina Contini
- Department of Life and Environmental Sciences, University of Cagliari, Cagliari, Italy
| | - Simone Serrao
- Department of Life and Environmental Sciences, University of Cagliari, Cagliari, Italy
| | - Barbara Manconi
- Department of Life and Environmental Sciences, University of Cagliari, Cagliari, Italy
| | - Alessandra Olianas
- Department of Life and Environmental Sciences, University of Cagliari, Cagliari, Italy
| | - Federica Iavarone
- Department of Basic Biotechnological Sciences, Intensive and Perioperative Clinics, Catholic University of the Sacred Heart, Rome, Italy.,Policlinico Universitario "A. Gemelli" Foundation -IRCCS, Rome, Italy
| | | | - Carlo Masullo
- Department of Neuroscience, Section Neurology, Catholic University of the Sacred Heart, Rome, Italy
| | - Massimo Castagnola
- Proteomics laboratory, European Centre for Research on the Brain, "Santa Lucia" Foundation -IRCCS, Rome, Italy
| | - Irene Messana
- Institute of Chemical Sciences and Technologies "Giulio Natta", National Research Council, Rome, Italy
| | - Giacomo Diaz
- Department of Biomedical Sciences University of Cagliari Cagliari, Italy
| | - Tiziana Cabras
- Department of Life and Environmental Sciences, University of Cagliari, Cagliari, Italy
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49
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Brink-Kjaer A, Leary EB, Sun H, Westover MB, Stone KL, Peppard PE, Lane NE, Cawthon PM, Redline S, Jennum P, Sorensen HBD, Mignot E. Age estimation from sleep studies using deep learning predicts life expectancy. NPJ Digit Med 2022; 5:103. [PMID: 35869169 PMCID: PMC9307657 DOI: 10.1038/s41746-022-00630-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 06/10/2022] [Indexed: 11/11/2022] Open
Abstract
Sleep disturbances increase with age and are predictors of mortality. Here, we present deep neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging was modeled using 2500 PSGs and tested in 10,699 PSGs from men and women in seven different cohorts aged between 20 and 90. Ages were estimated with a mean absolute error of 5.8 ± 1.6 years, while basic sleep scoring measures had an error of 14.9 ± 6.29 years. After controlling for demographics, sleep, and health covariates, each 10-year increment in age estimate error (AEE) was associated with increased all-cause mortality rate of 29% (95% confidence interval: 20-39%). An increase from -10 to +10 years in AEE translates to an estimated decreased life expectancy of 8.7 years (95% confidence interval: 6.1-11.4 years). Greater AEE was mostly reflected in increased sleep fragmentation, suggesting this is an important biomarker of future health independent of sleep apnea.
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Affiliation(s)
- Andreas Brink-Kjaer
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Denmark.
- Stanford Center for Sleep Sciences and Medicine, Stanford University, Palo Alto, CA, USA.
| | - Eileen B Leary
- Stanford Center for Sleep Sciences and Medicine, Stanford University, Palo Alto, CA, USA
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Katie L Stone
- Research Institute, California Pacific Medical Center, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Paul E Peppard
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Nancy E Lane
- Department of Medicine, University of Davis School of Medicine, Sacramento, CA, USA
| | - Peggy M Cawthon
- Research Institute, California Pacific Medical Center, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Poul Jennum
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Denmark
| | - Helge B D Sorensen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Emmanuel Mignot
- Stanford Center for Sleep Sciences and Medicine, Stanford University, Palo Alto, CA, USA.
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50
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Solary E, Abou-Zeid N, Calvo F. Ageing and cancer: a research gap to fill. Mol Oncol 2022; 16:3220-3237. [PMID: 35503718 PMCID: PMC9490141 DOI: 10.1002/1878-0261.13222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/01/2022] [Accepted: 05/02/2022] [Indexed: 12/03/2022] Open
Abstract
The complex mechanisms of ageing biology are increasingly understood. Interventions to reduce or delay ageing‐associated diseases are emerging. Cancer is one of the diseases promoted by tissue ageing. A clockwise mutational signature is identified in many tumours. Ageing might be a modifiable cancer risk factor. To reduce the incidence of ageing‐related cancer and to detect the disease at earlier stages, we need to understand better the links between ageing and tumours. When a cancer is established, geriatric assessment and measures of biological age might help to generate evidence‐based therapeutic recommendations. In this approach, patients and caregivers would include the respective weight to give to the quality of life and survival in the therapeutic choices. The increasing burden of cancer in older patients requires new generations of researchers and geriatric oncologists to be trained, to properly address disease complexity in a multidisciplinary manner, and to reduce health inequities in this population of patients. In this review, we propose a series of research challenges to tackle in the next few years to better prevent, detect and treat cancer in older patients while preserving their quality of life.
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Affiliation(s)
- Eric Solary
- Fondation « Association pour la Recherche sur le Cancer », Villejuif, France.,Université Paris Saclay, Faculté de Médecine, Le Kremlin-Bicêtre, France.,Gustave Roussy Cancer Center, INSERM U1287, Villejuif, France
| | - Nancy Abou-Zeid
- Fondation « Association pour la Recherche sur le Cancer », Villejuif, France
| | - Fabien Calvo
- Fondation « Association pour la Recherche sur le Cancer », Villejuif, France.,Université de Paris, Paris, France
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