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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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2
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Gadd DA, Hillary RF, Kuncheva Z, Mangelis T, Cheng Y, Dissanayake M, Admanit R, Gagnon J, Lin T, Ferber KL, Runz H, Foley CN, Marioni RE, Sun BB. Blood protein assessment of leading incident diseases and mortality in the UK Biobank. NATURE AGING 2024:10.1038/s43587-024-00655-7. [PMID: 38987645 DOI: 10.1038/s43587-024-00655-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/22/2024] [Indexed: 07/12/2024]
Abstract
The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (n = 47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalized Cox regression. When applied to test sets, six ProteinScores improve the area under the curve estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c-a clinical marker used to monitor and diagnose type 2 diabetes. The performance of scores using metabolomic and proteomic features is also compared. These data characterize early proteomic contributions to major age-related diseases, demonstrating the value of the plasma proteome for risk stratification.
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Affiliation(s)
- Danni A Gadd
- Optima Partners, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Robert F Hillary
- Optima Partners, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Zhana Kuncheva
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Tasos Mangelis
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Yipeng Cheng
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Manju Dissanayake
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Romi Admanit
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Jake Gagnon
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Tinchi Lin
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Kyle L Ferber
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Heiko Runz
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Christopher N Foley
- Optima Partners, Edinburgh, UK.
- Bayes Centre, University of Edinburgh, Edinburgh, UK.
| | - Riccardo E Marioni
- Optima Partners, Edinburgh, UK.
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
| | - Benjamin B Sun
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA.
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
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3
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Ferrucci L, Tanaka T, Polidori MC. Can geroscience be translated into healthcare? Z Gerontol Geriatr 2024:10.1007/s00391-024-02326-z. [PMID: 38981884 DOI: 10.1007/s00391-024-02326-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 06/07/2024] [Indexed: 07/11/2024]
Abstract
As an introduction to this special issue on geroscience, the present work focuses on the complexity of disentangling biomolecular mechanisms of aging from biopsychosocial causes of accelerated aging. Due to this complexity, the biomolecular aging hallmarks of frailty and multimorbidity as predominant aging phenotypes in geriatrics reflect single aspects of the aging process. A possible approach to facilitate the integration of geroscience into healthcare might be to consider aging as the dynamic ratio between damage accumulation at the molecular and cellular level and resilience as strategies that prevent or repair such damage. There is a large body of evidence to show that geroscience has the potential to change healthcare; however, reaching a consensus and translating the best tool to measure aging needs more research on 1) the sensitivity of biomarkers to interventions and 2) the relationship between changes in biomarkers and changes in health trajectories.
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Affiliation(s)
- Luigi Ferrucci
- Translational Gerontology Branch, Intramural Research Program, National Institute on Aging, National Institutes of Health, 251 Bayview Boulevard, 21224, Baltimore, MD, USA.
| | - Toshiko Tanaka
- Translational Gerontology Branch, Intramural Research Program, National Institute on Aging, National Institutes of Health, 251 Bayview Boulevard, 21224, Baltimore, MD, USA
| | - M Cristina Polidori
- Department II of Internal Medicine and Center for Molecular Medicine Cologne, Ageing Clinical Research, Cologne, Germany
- Cluster of Excellence-Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany
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4
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Patel SK, Bons J, Rose JP, Chappel JR, Beres RL, Watson MA, Webster C, Burton JB, Bruderer R, Desprez PY, Reiter L, Campisi J, Baker ES, Schilling B. Exosomes Released from Senescent Cells and Circulatory Exosomes Isolated from Human Plasma Reveal Aging-associated Proteomic and Lipid Signatures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.22.600215. [PMID: 38979258 PMCID: PMC11230204 DOI: 10.1101/2024.06.22.600215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Senescence emerged as a significant mechanism of aging and age-related diseases, offering an attractive target for clinical interventions. Senescent cells release a senescence-associated secretory phenotype (SASP), including exosomes that may act as signal transducers between distal tissues, propagating secondary or bystander senescence and signaling throughout the body. However, the composition of exosome SASP remains underexplored, presenting an opportunity for novel unbiased discovery. Here, we present a detailed proteomic and lipidomic analysis of exosome SASP using mass spectrometry from human plasma from young and older individuals and from tissue culture of senescent primary human lung fibroblasts. We identified ~1,300 exosome proteins released by senescent fibroblasts induced by three different senescence inducers causing most exosome proteins to be differentially regulated with senescence. In parallel, a human plasma cohort from young and old individuals revealed over 1,350 exosome proteins and 171 plasma exosome proteins were regulated when comparing old vs young individuals. Of the age-regulated plasma exosome proteins, we observed 52 exosome SASP factors that were also regulated in exosomes from the senescent fibroblasts, including serine protease inhibitors (SERPINs), Prothrombin, Coagulation factor V, Plasminogen, and Reelin. In addition, 247 lipids were identified with high confidence in all exosome samples. Following the senescence inducers, a majority of the identified phosphatidylcholine, phosphatidylethanolamine, and sphingomyelin species increased significantly indicating cellular membrane changes. The most notable categories of significantly changed proteins were related to extracellular matrix remodeling and inflammation, both potentially detrimental pathways that can damage surrounding tissues and even induce secondary or bystander senescence. Our findings reveal mechanistic insights and potential senescence biomarkers, enabling a better approach to surveilling the senescence burden in the aging population and offering promising therapeutic targets for interventions.
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5
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Premeaux TA, Bowler S, Friday CM, Moser CB, Hoenigl M, Lederman MM, Landay AL, Gianella S, Ndhlovu LC. Machine learning models based on fluid immunoproteins that predict non-AIDS adverse events in people with HIV. iScience 2024; 27:109945. [PMID: 38812553 PMCID: PMC11134891 DOI: 10.1016/j.isci.2024.109945] [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: 10/30/2023] [Revised: 03/12/2024] [Accepted: 05/06/2024] [Indexed: 05/31/2024] Open
Abstract
Despite the success of antiretroviral therapy (ART), individuals with HIV remain at risk for experiencing non-AIDS adverse events (NAEs), including cardiovascular complications and malignancy. Several surrogate immune biomarkers in blood have shown predictive value in predicting NAEs; however, composite panels generated using machine learning may provide a more accurate advancement for monitoring and discriminating NAEs. In a nested case-control study, we aimed to develop machine learning models to discriminate cases (experienced an event) and matched controls using demographic and clinical characteristics alongside 49 plasma immunoproteins measured prior to and post-ART initiation. We generated support vector machine (SVM) classifier models for high-accuracy discrimination of individuals aged 30-50 years who experienced non-fatal NAEs at pre-ART and one-year post-ART. Extreme gradient boosting generated a high-accuracy model at pre-ART, while K-nearest neighbors performed poorly all around. SVM modeling may offer guidance to improve disease monitoring and elucidate potential therapeutic interventions.
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Affiliation(s)
- Thomas A. Premeaux
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Scott Bowler
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Courtney M. Friday
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Carlee B. Moser
- Center for Biostatistics in AIDS Research in the Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Martin Hoenigl
- Division of Infectious Diseases, Department of Medicine, University of California San Diego, San Diego, CA, USA
- Division of Infectious Diseases, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Michael M. Lederman
- Department of Medicine, Division of Infectious Diseases and HIV Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Alan L. Landay
- Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA
| | - Sara Gianella
- Division of Infectious Diseases, Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - Lishomwa C. Ndhlovu
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
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6
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Yusri K, Kumar S, Fong S, Gruber J, Sorrentino V. Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks. Int J Mol Sci 2024; 25:6793. [PMID: 38928497 PMCID: PMC11203944 DOI: 10.3390/ijms25126793] [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: 05/23/2024] [Revised: 06/16/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024] Open
Abstract
Aging is a complex and time-dependent decline in physiological function that affects most organisms, leading to increased risk of age-related diseases. Investigating the molecular underpinnings of aging is crucial to identify geroprotectors, precisely quantify biological age, and propose healthy longevity approaches. This review explores pathways that are currently being investigated as intervention targets and aging biomarkers spanning molecular, cellular, and systemic dimensions. Interventions that target these hallmarks may ameliorate the aging process, with some progressing to clinical trials. Biomarkers of these hallmarks are used to estimate biological aging and risk of aging-associated disease. Utilizing aging biomarkers, biological aging clocks can be constructed that predict a state of abnormal aging, age-related diseases, and increased mortality. Biological age estimation can therefore provide the basis for a fine-grained risk stratification by predicting all-cause mortality well ahead of the onset of specific diseases, thus offering a window for intervention. Yet, despite technological advancements, challenges persist due to individual variability and the dynamic nature of these biomarkers. Addressing this requires longitudinal studies for robust biomarker identification. Overall, utilizing the hallmarks of aging to discover new drug targets and develop new biomarkers opens new frontiers in medicine. Prospects involve multi-omics integration, machine learning, and personalized approaches for targeted interventions, promising a healthier aging population.
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Affiliation(s)
- Khalishah Yusri
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Sanjay Kumar
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Sheng Fong
- Department of Geriatric Medicine, Singapore General Hospital, Singapore 169608, Singapore
- Clinical and Translational Sciences PhD Program, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Jan Gruber
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Science Division, Yale-NUS College, Singapore 138527, Singapore
| | - Vincenzo Sorrentino
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Department of Medical Biochemistry, Amsterdam UMC, Amsterdam Gastroenterology Endocrinology Metabolism and Amsterdam Neuroscience Cellular & Molecular Mechanisms, University of Amsterdam, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
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7
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Suryadevara V, Hudgins AD, Rajesh A, Pappalardo A, Karpova A, Dey AK, Hertzel A, Agudelo A, Rocha A, Soygur B, Schilling B, Carver CM, Aguayo-Mazzucato C, Baker DJ, Bernlohr DA, Jurk D, Mangarova DB, Quardokus EM, Enninga EAL, Schmidt EL, Chen F, Duncan FE, Cambuli F, Kaur G, Kuchel GA, Lee G, Daldrup-Link HE, Martini H, Phatnani H, Al-Naggar IM, Rahman I, Nie J, Passos JF, Silverstein JC, Campisi J, Wang J, Iwasaki K, Barbosa K, Metis K, Nernekli K, Niedernhofer LJ, Ding L, Wang L, Adams LC, Ruiyang L, Doolittle ML, Teneche MG, Schafer MJ, Xu M, Hajipour M, Boroumand M, Basisty N, Sloan N, Slavov N, Kuksenko O, Robson P, Gomez PT, Vasilikos P, Adams PD, Carapeto P, Zhu Q, Ramasamy R, Perez-Lorenzo R, Fan R, Dong R, Montgomery RR, Shaikh S, Vickovic S, Yin S, Kang S, Suvakov S, Khosla S, Garovic VD, Menon V, Xu Y, Song Y, Suh Y, Dou Z, Neretti N. SenNet recommendations for detecting senescent cells in different tissues. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00738-8. [PMID: 38831121 DOI: 10.1038/s41580-024-00738-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/15/2024] [Indexed: 06/05/2024]
Abstract
Once considered a tissue culture-specific phenomenon, cellular senescence has now been linked to various biological processes with both beneficial and detrimental roles in humans, rodents and other species. Much of our understanding of senescent cell biology still originates from tissue culture studies, where each cell in the culture is driven to an irreversible cell cycle arrest. By contrast, in tissues, these cells are relatively rare and difficult to characterize, and it is now established that fully differentiated, postmitotic cells can also acquire a senescence phenotype. The SenNet Biomarkers Working Group was formed to provide recommendations for the use of cellular senescence markers to identify and characterize senescent cells in tissues. Here, we provide recommendations for detecting senescent cells in different tissues based on a comprehensive analysis of existing literature reporting senescence markers in 14 tissues in mice and humans. We discuss some of the recent advances in detecting and characterizing cellular senescence, including molecular senescence signatures and morphological features, and the use of circulating markers. We aim for this work to be a valuable resource for both seasoned investigators in senescence-related studies and newcomers to the field.
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Affiliation(s)
- Vidyani Suryadevara
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University, School of Medicine, Stanford, CA, USA
| | - Adam D Hudgins
- Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
| | - Adarsh Rajesh
- Sanford Burnham Prebys Medical Discovery Institute, Cancer Genome and Epigenetics Program, La Jolla, CA, USA
| | | | - Alla Karpova
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Amit K Dey
- National Institute on Aging, NIH, Baltimore, MD, USA
| | - Ann Hertzel
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN, USA
| | - Anthony Agudelo
- Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI, USA
- Center on the Biology of Aging, Brown University, Providence, RI, USA
| | - Azucena Rocha
- Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI, USA
- Center on the Biology of Aging, Brown University, Providence, RI, USA
| | - Bikem Soygur
- The Buck Institute for Research on Aging, Novato, CA, USA
| | | | - Chase M Carver
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center on Aging, Rochester, MN, USA
| | - Cristina Aguayo-Mazzucato
- Islet Cell Biology and Regenerative Medicine, Joslin Diabetes Center, Harvard Medical School, Boston, USA
| | - Darren J Baker
- Robert and Arlene Kogod Center on Aging, Rochester, MN, USA
- Department of Biochemistry and Molecular Biology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA
| | - David A Bernlohr
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN, USA
| | - Diana Jurk
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center on Aging, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Dilyana B Mangarova
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University, School of Medicine, Stanford, CA, USA
| | - Ellen M Quardokus
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | | | - Elizabeth L Schmidt
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN, USA
| | - Feng Chen
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Francesca E Duncan
- The Buck Institute for Research on Aging, Novato, CA, USA
- Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Gagandeep Kaur
- Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - George A Kuchel
- UConn Center on Aging, University of Connecticut Health Center, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
| | - Gung Lee
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center on Aging, Rochester, MN, USA
| | - Heike E Daldrup-Link
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University, School of Medicine, Stanford, CA, USA
| | - Helene Martini
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center on Aging, Rochester, MN, USA
| | - Hemali Phatnani
- New York Genome Center, New York, NY, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Iman M Al-Naggar
- UConn Center on Aging, University of Connecticut Health Center, Farmington, CT, USA
| | - Irfan Rahman
- Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Jia Nie
- Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - João F Passos
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center on Aging, Rochester, MN, USA
| | - Jonathan C Silverstein
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Judith Campisi
- The Buck Institute for Research on Aging, Novato, CA, USA
| | - Julia Wang
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Kanako Iwasaki
- Islet Cell Biology and Regenerative Medicine, Joslin Diabetes Center, Harvard Medical School, Boston, USA
| | - Karina Barbosa
- Sanford Burnham Prebys Medical Discovery Institute, Cancer Genome and Epigenetics Program, La Jolla, CA, USA
| | - Kay Metis
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Kerem Nernekli
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University, School of Medicine, Stanford, CA, USA
| | - Laura J Niedernhofer
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN, USA
| | - Li Ding
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Lichao Wang
- UConn Center on Aging, University of Connecticut Health Center, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
| | - Lisa C Adams
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University, School of Medicine, Stanford, CA, USA
| | - Liu Ruiyang
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Madison L Doolittle
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center on Aging, Rochester, MN, USA
- Division of Endocrinology, Diabetes and Metabolism, Mayo Clinic, Rochester, MN, USA
| | - Marcos G Teneche
- Sanford Burnham Prebys Medical Discovery Institute, Cancer Genome and Epigenetics Program, La Jolla, CA, USA
| | - Marissa J Schafer
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center on Aging, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Ming Xu
- UConn Center on Aging, University of Connecticut Health Center, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
| | - Mohammadjavad Hajipour
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University, School of Medicine, Stanford, CA, USA
| | | | | | - Nicholas Sloan
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Nikolai Slavov
- Center on the Biology of Aging, Brown University, Providence, RI, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Biology, Northeastern University, Boston, MA, USA
- Barnett Institute for Chemical and Biological Analysis, Northeastern University, Boston, MA, USA
| | - Olena Kuksenko
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Paul Robson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Paul T Gomez
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center on Aging, Rochester, MN, USA
| | - Periklis Vasilikos
- Department of Genetics and Development, Columbia University, New York, NY, USA
| | - Peter D Adams
- Sanford Burnham Prebys Medical Discovery Institute, Cancer Genome and Epigenetics Program, La Jolla, CA, USA
| | - Priscila Carapeto
- Islet Cell Biology and Regenerative Medicine, Joslin Diabetes Center, Harvard Medical School, Boston, USA
| | - Quan Zhu
- Center for Epigenomics, University of California, San Diego, CA, USA
| | | | | | - Rong Fan
- Yale-Center for Research on Aging, Yale School of Medicine, New Haven, CT, USA
| | - Runze Dong
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Ruth R Montgomery
- Yale-Center for Research on Aging, Yale School of Medicine, New Haven, CT, USA
| | - Sadiya Shaikh
- Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Sanja Vickovic
- New York Genome Center, New York, NY, USA
- Herbert Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Beijer Laboratory for Gene and Neuro Research, Uppsala University, Uppsala, Sweden
| | - Shanshan Yin
- Sanford Burnham Prebys Medical Discovery Institute, Cancer Genome and Epigenetics Program, La Jolla, CA, USA
| | - Shoukai Kang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Sonja Suvakov
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Sundeep Khosla
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center on Aging, Rochester, MN, USA
- Division of Endocrinology, Diabetes and Metabolism, Mayo Clinic, Rochester, MN, USA
| | - Vesna D Garovic
- Department of Obstetrics and Gynecology, Mayo Clinic, Rochester, MN, USA
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Vilas Menon
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Center for Translational and Computational Neuroimmunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Yanxin Xu
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yizhe Song
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Yousin Suh
- Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
- Department of Genetics and Development, Columbia University, New York, NY, USA
| | - Zhixun Dou
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Nicola Neretti
- Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI, USA.
- Center on the Biology of Aging, Brown University, Providence, RI, USA.
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8
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Sandalova E, Maier AB. Targeting the epigenetically older individuals for geroprotective trials: the use of DNA methylation clocks. Biogerontology 2024; 25:423-431. [PMID: 37968337 DOI: 10.1007/s10522-023-10077-4] [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: 09/08/2023] [Accepted: 10/15/2023] [Indexed: 11/17/2023]
Abstract
Chronological age is the most important risk factor for the incidence of age-related diseases. The pace of ageing determines the magnitude of that risk and can be expressed as biological age. Targeting fundamental pathways of human aging with geroprotectors has the potential to lower the biological age and therewith prolong the healthspan, the period of life one spends in good health. Target populations for geroprotective interventions should be chosen based on the ageing mechanisms being addressed and the expected effect of the geroprotector on the primary outcome. Biomarkers of ageing, such as DNA methylation age, can be used to select populations for geroprotective interventions and as a surrogate outcome. Here, the use of DNA methylation clocks for selecting target populations for geroprotective intervention is explored.
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Affiliation(s)
- Elena Sandalova
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore.
| | - Andrea B Maier
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore.
- Department of Human Movement Sciences, @AgeAmsterdam, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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9
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Faria M, Ganz A, Galkin F, Zhavoronkov A, Snyder M. Psychogenic Aging: A Novel Prospect to Integrate Psychobiological Hallmarks of Aging. Transl Psychiatry 2024; 14:226. [PMID: 38816369 PMCID: PMC11139997 DOI: 10.1038/s41398-024-02919-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 12/20/2023] [Accepted: 05/08/2024] [Indexed: 06/01/2024] Open
Abstract
Psychological factors are amongst the most robust predictors of healthspan and longevity, yet are rarely incorporated into scientific and medical frameworks of aging. The prospect of characterizing and integrating the psychological influences of aging is therefore an unmet step for the advancement of geroscience. Psychogenic Aging research is an emerging branch of biogerontology that aims to address this gap by investigating the impact of psychological factors on human longevity. It is an interdisciplinary field that integrates complex psychological, neurological, and molecular relationships that can be best understood with precision medicine methodologies. This perspective argues that psychogenic aging should be considered an integral component of the Hallmarks of Aging framework, opening the doors for future biopsychosocial integration in longevity research. By providing a unique perspective on frequently overlooked aspects of organismal aging, psychogenic aging offers new insights and targets for anti-aging therapeutics on individual and societal levels that can significantly benefit the scientific and medical communities.
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Affiliation(s)
- Manuel Faria
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Ariel Ganz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Alex Zhavoronkov
- Deep Longevity, Hong Kong, China
- Insilico Medicine, Hong Kong, China
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Michael Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
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10
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Wang X, Tazearslan C, Kim S, Guo Q, Contreras D, Yang J, Hudgins AD, Suh Y. In vitro heterochronic parabiosis identifies pigment epithelium-derived factor as a systemic mediator of rejuvenation by young blood. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592258. [PMID: 38746475 PMCID: PMC11092633 DOI: 10.1101/2024.05.02.592258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Several decades of heterochronic parabiosis (HCPB) studies have demonstrated the restorative impact of young blood, and deleterious influence of aged blood, on physiological function and homeostasis across tissues, although few of the factors responsible for these observations have been identified. Here we develop an in vitro HCPB system to identify these circulating factors, using replicative lifespan (RLS) of primary human fibroblasts as an endpoint of cellular health. We find that RLS is inversely correlated with serum donor age and sensitive to the presence or absence of specific serum components. Through in vitro HCPB, we identify the secreted protein pigment epithelium-derived factor (PEDF) as a circulating factor that extends RLS of primary human fibroblasts and declines with age in mammals. Systemic administration of PEDF to aged mice reverses age-related functional decline and pathology across several tissues, improving cognitive function and reducing hepatic fibrosis and renal lipid accumulation. Together, our data supports PEDF as a systemic mediator of the effect of young blood on organismal health and homeostasis and establishes our in vitro HCPB system as a valuable screening platform for the identification of candidate circulating factors involved in aging and rejuvenation.
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Affiliation(s)
- Xizhe Wang
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
- These authors contributed equally
| | - Cagdas Tazearslan
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY
- These authors contributed equally
| | - Seungsoo Kim
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
| | - Qinghua Guo
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
| | - Daniela Contreras
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
| | - Jiping Yang
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
| | - Adam D. Hudgins
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
| | - Yousin Suh
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
- Department of Genetics and Development, Columbia University Medical Center, New York, NY
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11
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Nyárády BB, Kiss LZ, Bagyura Z, Merkely B, Dósa E, Láng O, Kőhidai L, Pállinger É. Growth and differentiation factor-15: A link between inflammaging and cardiovascular disease. Biomed Pharmacother 2024; 174:116475. [PMID: 38522236 DOI: 10.1016/j.biopha.2024.116475] [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: 12/21/2023] [Revised: 03/13/2024] [Accepted: 03/19/2024] [Indexed: 03/26/2024] Open
Abstract
Age-related disorders are closely linked to the accumulation of senescent cells. The senescence-associated secretory phenotype (SASP) sustains and progresses chronic inflammation, which is involved in cellular and tissue dysfunction. SASP-related growth and differentiation factor-15 (GDF-15) is an immunoregulatory cytokine that is coupled to aging and thus may have a regulatory role in the development and maintenance of atherosclerosis, a major cause of cardiovascular disease (CVD). Although the effects of GDF-15 are tissue-specific and dependent on microenvironmental changes such as inflammation, available data suggest that GDF-15 has a significant role in CVD. Thus, GDF-15 is a promising biomarker and potential therapeutic target for atherosclerotic CVD.
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Affiliation(s)
- Balázs Bence Nyárády
- Heart and Vascular Center, Semmelweis University, Városmajor utca 68, Budapest H-1122, Hungary.
| | - Loretta Zsuzsa Kiss
- Heart and Vascular Center, Semmelweis University, Városmajor utca 68, Budapest H-1122, Hungary.
| | - Zsolt Bagyura
- Heart and Vascular Center, Semmelweis University, Városmajor utca 68, Budapest H-1122, Hungary.
| | - Béla Merkely
- Heart and Vascular Center, Semmelweis University, Városmajor utca 68, Budapest H-1122, Hungary.
| | - Edit Dósa
- Heart and Vascular Center, Semmelweis University, Városmajor utca 68, Budapest H-1122, Hungary.
| | - Orsolya Láng
- Department of Genetics, Cell- and Immunobiology, Semmelweis University, Nagyvárad tér 4, Budapest H-1089, Hungary.
| | - László Kőhidai
- Department of Genetics, Cell- and Immunobiology, Semmelweis University, Nagyvárad tér 4, Budapest H-1089, Hungary.
| | - Éva Pállinger
- Department of Genetics, Cell- and Immunobiology, Semmelweis University, Nagyvárad tér 4, Budapest H-1089, Hungary.
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12
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Bi S, Jiang X, Ji Q, Wang Z, Ren J, Wang S, Yu Y, Wang R, Liu Z, Liu J, Hu J, Sun G, Wu Z, Diao Z, Li J, Sun L, Izpisua Belmonte JC, Zhang W, Liu GH, Qu J. The sirtuin-associated human senescence program converges on the activation of placenta-specific gene PAPPA. Dev Cell 2024; 59:991-1009.e12. [PMID: 38484732 DOI: 10.1016/j.devcel.2024.02.008] [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: 03/12/2023] [Revised: 09/15/2023] [Accepted: 02/20/2024] [Indexed: 04/25/2024]
Abstract
Sirtuins are pro-longevity genes with chromatin modulation potential, but how these properties are connected is not well understood. Here, we generated a panel of isogeneic human stem cell lines with SIRT1-SIRT7 knockouts and found that any sirtuin deficiency leads to accelerated cellular senescence. Through large-scale epigenomic analyses, we show how sirtuin deficiency alters genome organization and that genomic regions sensitive to sirtuin deficiency are preferentially enriched in active enhancers, thereby promoting interactions within topologically associated domains and the formation of de novo enhancer-promoter loops. In all sirtuin-deficient human stem cell lines, we found that chromatin contacts are rewired to promote aberrant activation of the placenta-specific gene PAPPA, which controls the pro-senescence effects associated with sirtuin deficiency and serves as a potential aging biomarker. Based on our survey of the 3D chromatin architecture, we established connections between sirtuins and potential target genes, thereby informing the development of strategies for aging interventions.
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Affiliation(s)
- Shijia Bi
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoyu Jiang
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qianzhao Ji
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zehua Wang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, 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; Key Laboratory of RNA Science and Engineering, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Si Wang
- Advanced Innovation Center for Human Brain Protection and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing 100053, China; Aging Translational Medicine Center, International Center for Aging and Cancer, Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; The Fifth People's Hospital of Chongqing, Chongqing 400062, China
| | - Yang Yu
- Department of Obstetrics and Gynecology, Center for Reproductive Medicine, Peking University Third Hospital, Beijing 100191, China
| | - Ruoqi Wang
- University of Chinese Academy of Sciences, Beijing 100049, China; National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zunpeng Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Junhang Liu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jianli Hu
- 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
| | - Guoqiang Sun
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zeming Wu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Organ Regeneration and Reconstruction, 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
| | - Zhiqing Diao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingyi Li
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Organ Regeneration and Reconstruction, 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
| | - Liang Sun
- NHC Beijing Institute of Geriatrics, NHC Key Laboratory of Geriatrics, Institute of Geriatric Medicine of Chinese Academy of Medical Sciences, National Center of Gerontology/Beijing Hospital, Beijing 100730, China; Department of Clinical Laboratory, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, 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; Aging Biomarker Consortium, Beijing 100101, China.
| | - Guang-Hui Liu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Organ Regeneration and Reconstruction, 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; Advanced Innovation Center for Human Brain Protection and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing 100053, China; Aging Translational Medicine Center, International Center for Aging and Cancer, Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Aging Biomarker Consortium, Beijing 100101, China.
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Organ Regeneration and Reconstruction, 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; Aging Biomarker Consortium, Beijing 100101, China.
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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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Neagu AN, Bruno P, Johnson KR, Ballestas G, Darie CC. Biological Basis of Breast Cancer-Related Disparities in Precision Oncology Era. Int J Mol Sci 2024; 25:4113. [PMID: 38612922 PMCID: PMC11012526 DOI: 10.3390/ijms25074113] [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: 03/03/2024] [Revised: 04/03/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024] Open
Abstract
Precision oncology is based on deep knowledge of the molecular profile of tumors, allowing for more accurate and personalized therapy for specific groups of patients who are different in disease susceptibility as well as treatment response. Thus, onco-breastomics is able to discover novel biomarkers that have been found to have racial and ethnic differences, among other types of disparities such as chronological or biological age-, sex/gender- or environmental-related ones. Usually, evidence suggests that breast cancer (BC) disparities are due to ethnicity, aging rate, socioeconomic position, environmental or chemical exposures, psycho-social stressors, comorbidities, Western lifestyle, poverty and rurality, or organizational and health care system factors or access. The aim of this review was to deepen the understanding of BC-related disparities, mainly from a biomedical perspective, which includes genomic-based differences, disparities in breast tumor biology and developmental biology, differences in breast tumors' immune and metabolic landscapes, ecological factors involved in these disparities as well as microbiomics- and metagenomics-based disparities in BC. We can conclude that onco-breastomics, in principle, based on genomics, proteomics, epigenomics, hormonomics, metabolomics and exposomics data, is able to characterize the multiple biological processes and molecular pathways involved in BC disparities, clarifying the differences in incidence, mortality and treatment response for different groups of BC patients.
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Affiliation(s)
- Anca-Narcisa Neagu
- Laboratory of Animal Histology, Faculty of Biology, "Alexandru Ioan Cuza" University of Iași, Carol I bvd. 20A, 700505 Iasi, Romania
| | - Pathea Bruno
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biochemistry, Clarkson University, Potsdam, NY 13699-5810, USA
| | - Kaya R Johnson
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biochemistry, Clarkson University, Potsdam, NY 13699-5810, USA
| | - Gabriella Ballestas
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biochemistry, Clarkson University, Potsdam, NY 13699-5810, USA
| | - Costel C Darie
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biochemistry, Clarkson University, Potsdam, NY 13699-5810, USA
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15
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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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16
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Sluiskes MH, Goeman JJ, Beekman M, Slagboom PE, Putter H, Rodríguez-Girondo M. Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data. BMC Med Res Methodol 2024; 24:58. [PMID: 38459475 PMCID: PMC10921716 DOI: 10.1186/s12874-024-02181-x] [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: 06/22/2023] [Accepted: 02/15/2024] [Indexed: 03/10/2024] Open
Abstract
BACKGROUND There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆. METHODS We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold. RESULTS The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random. CONCLUSIONS Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.
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Affiliation(s)
- Marije H Sluiskes
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.
| | - Jelle J Goeman
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
- Max Planck Institute for the Biology of Ageing, Cologne, Germany
| | - Hein Putter
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Mar Rodríguez-Girondo
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
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17
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Evans DS, Young D, Tanaka T, Basisty N, Bandinelli S, Ferrucci L, Campisi J, Schilling B. Proteomic Analysis of the Senescence-Associated Secretory Phenotype: GDF-15, IGFBP-2, and Cystatin-C Are Associated With Multiple Aging Traits. J Gerontol A Biol Sci Med Sci 2024; 79:glad265. [PMID: 37982669 PMCID: PMC10876076 DOI: 10.1093/gerona/glad265] [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: 03/30/2023] [Indexed: 11/21/2023] Open
Abstract
Cellular senescence, a hallmark of aging, results in a senescence-associated secretory phenotype (SASP) with an increased production of proinflammatory cytokines, growth factors, and proteases. Evidence from nonhuman models demonstrates that SASP contributes to tissue dysfunction and pathological effects of aging. However, there are relatively few human studies on the relationship between SASP and aging-related health outcomes. Proteins from the SASP Atlas were measured in plasma using aptamer-based proteomics (SomaLogic). Regression models were used to identify SASP protein associations with aging-related traits representing multiple aspects of physiology in 1 201 participants from 2 human cohort studies (BLSA/GESTALT and InCHIANTI). Traits examined were fasting glucose, C-reactive protein, interleukin-6, alkaline phosphatase, blood urea nitrogen, albumin, red blood cell distribution width, waist circumference, systolic and diastolic blood pressure, gait speed, and grip strength. Study results were combined with a fixed-effect inverse-variance weighted meta-analysis. In the meta-analysis, 28 of 77 SASP proteins were significantly associated with age. Of the 28 age-associated SASP proteins, 18 were significantly associated with 1 or more clinical traits, and 7 SASP proteins were significantly associated with 3 or more traits. Growth/differentiation factor 15, Insulin-like growth factor-binding protein 2, and Cystatin-C showed significant associations with inflammatory markers and measures of physical function (grip strength or gait speed). These results support the relevance of SASP proteins to human aging, identify specific traits that are potentially affected by SASP, and prioritize specific SASP proteins for their utility as biomarkers of human aging.
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Affiliation(s)
- Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, California, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - Danielle Young
- California Pacific Medical Center Research Institute, San Francisco, California, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - Toshiko Tanaka
- Longitudinal Studies Section, Translational Gerontology Branch, NIA, NIH, Baltimore, Maryland, USA
| | - Nathan Basisty
- Longitudinal Studies Section, Translational Gerontology Branch, NIA, NIH, Baltimore, Maryland, USA
| | | | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, NIA, NIH, Baltimore, Maryland, USA
| | - Judith Campisi
- Buck Institute for Research on Aging, Novato, California, USA
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18
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Maroun G, Fissoun C, Villaverde M, Brondello JM, Pers YM. Senescence-regulatory factors as novel circulating biomarkers and therapeutic targets in regenerative medicine for osteoarthritis. Joint Bone Spine 2024; 91:105640. [PMID: 37739212 DOI: 10.1016/j.jbspin.2023.105640] [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: 05/23/2023] [Revised: 09/07/2023] [Accepted: 09/12/2023] [Indexed: 09/24/2023]
Abstract
Recent discoveries reveal that the chronic presence of senescent cells in osteoarticular tissues provides a focal point of disease development for osteoarthritis (OA). Nevertheless, senescence-regulatory factors associated with OA still need to be identified. Furthermore, few diagnostic- and prognostic-validated biochemical markers (biomarkers) are currently used in clinics to evaluate OA patients. In the future, alongside imaging and clinical examination, detecting senescence-regulatory biomarkers in patient fluids could become a prospective method for disease: diagnosis, monitoring, progression and prognosis following treatment. This review summarizes a group of circulating OA biomarkers recently linked to senescence onset. Remarkably, these factors identified in proteomics, metabolomic and microRNA studies could also have deleterious or protective roles in osteoarticular tissue homeostasis. In addition, we discuss their potentially innovative modulation in combination with senotherapeutic approaches, for long-lasting OA treatment.
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Affiliation(s)
- Georges Maroun
- Institute for Regenerative Medicine and Biotherapy, University of Montpellier, INSERM UMR 1183, 34298 Montpellier, France
| | - Christina Fissoun
- Institute for Regenerative Medicine and Biotherapy, University of Montpellier, INSERM UMR 1183, 34298 Montpellier, France
| | - Marina Villaverde
- Institute for Regenerative Medicine and Biotherapy, University of Montpellier, INSERM UMR 1183, 34298 Montpellier, France; HCS Pharma, Biocentre Fleming, 250, rue Salvador-Allende, Bat A, 59120 Loos, France
| | - Jean-Marc Brondello
- Institute for Regenerative Medicine and Biotherapy, University of Montpellier, INSERM UMR 1183, 34298 Montpellier, France
| | - Yves-Marie Pers
- Institute for Regenerative Medicine and Biotherapy, University of Montpellier, INSERM UMR 1183, 34298 Montpellier, France; Clinical immunology and osteoarticular diseases Therapeutic Unit, Lapeyronie University Hospital, CHU Montpellier, IRMB, University of Montpellier, INSERM, Montpellier, France.
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19
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Fielding RA, Atkinson EJ, Aversa Z, White TA, Heeren AA, Mielke MM, Cummings SR, Pahor M, Leeuwenburgh C, LeBrasseur NK. Biomarkers of Cellular Senescence Predict the Onset of Mobility Disability and Are Reduced by Physical Activity in Older Adults. J Gerontol A Biol Sci Med Sci 2024; 79:glad257. [PMID: 37948612 PMCID: PMC10851672 DOI: 10.1093/gerona/glad257] [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: 07/30/2023] [Indexed: 11/12/2023] Open
Abstract
Studies in mice and cross-sectional studies in humans support the premise that cellular senescence is a contributing mechanism to age-associated deficits in physical function. We tested the hypotheses that circulating proteins secreted by senescent cells are (i) associated with the incidence of major mobility disability (MMD), the development of persistent mobility disability (PMMD), and decrements in physical functioning in older adults, and (ii) influenced by physical activity (PA). Using samples and data obtained longitudinally from the Lifestyle Interventions in Elders Study clinical trial, we measured a panel of 27 proteins secreted by senescent cells. Among 1 377 women and men randomized to either a structured PA intervention or a healthy aging (HA) intervention, we observed significant associations between several senescence biomarkers, most distinctly vascular endothelial growth factor A (VEGFA), tumor necrosis factor receptor 1 (TNFR1), and matrix metallopeptidase 7 (MMP7), and the onset of both MMD and PMMD. Moreover, VEGFA, GDF15, osteopontin, and other senescence biomarkers were associated with reductions in short physical performance battery scores. The change in senescence biomarkers did not differ between PA and HA participants. In the whole cohort, higher levels of PA were associated with significantly greater reductions in 10 senescence-related proteins at 12 and/or 24 months. These data reinforce cellular senescence as a contributing mechanism of age-associated functional decline and the potential for PA to attenuate this hallmark of aging. Clinical Trials Registration Number: NCT01072500.
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Affiliation(s)
- Roger A Fielding
- Nutrition, Exercise Physiology and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, Massachusetts, USA
| | - Elizabeth J Atkinson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Zaira Aversa
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physical Medicine and Rehabilitation, Mayo Clinic, Rochester, Minnesota, USA
| | - Thomas A White
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, Minnesota, USA
| | - Amanda A Heeren
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, Minnesota, USA
| | - Michelle M Mielke
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Steven R Cummings
- Departments of Medicine, Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
- Research Institute, California Pacific Medical Center, San Francisco, California, USA
| | - Marco Pahor
- Institute on Aging, University of Florida, Gainesville, Florida, USA
| | | | - Nathan K LeBrasseur
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physical Medicine and Rehabilitation, Mayo Clinic, Rochester, Minnesota, USA
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20
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Amor C, Fernández-Maestre I, Chowdhury S, Ho YJ, Nadella S, Graham C, Carrasco SE, Nnuji-John E, Feucht J, Hinterleitner C, Barthet VJA, Boyer JA, Mezzadra R, Wereski MG, Tuveson DA, Levine RL, Jones LW, Sadelain M, Lowe SW. Prophylactic and long-lasting efficacy of senolytic CAR T cells against age-related metabolic dysfunction. NATURE AGING 2024; 4:336-349. [PMID: 38267706 PMCID: PMC10950785 DOI: 10.1038/s43587-023-00560-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 12/18/2023] [Indexed: 01/26/2024]
Abstract
Senescent cells, which accumulate in organisms over time, contribute to age-related tissue decline. Genetic ablation of senescent cells can ameliorate various age-related pathologies, including metabolic dysfunction and decreased physical fitness. While small-molecule drugs that eliminate senescent cells ('senolytics') partially replicate these phenotypes, they require continuous administration. We have developed a senolytic therapy based on chimeric antigen receptor (CAR) T cells targeting the senescence-associated protein urokinase plasminogen activator receptor (uPAR), and we previously showed these can safely eliminate senescent cells in young animals. We now show that uPAR-positive senescent cells accumulate during aging and that they can be safely targeted with senolytic CAR T cells. Treatment with anti-uPAR CAR T cells improves exercise capacity in physiological aging, and it ameliorates metabolic dysfunction (for example, improving glucose tolerance) in aged mice and in mice on a high-fat diet. Importantly, a single administration of these senolytic CAR T cells is sufficient to achieve long-term therapeutic and preventive effects.
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Grants
- R01 CA190092 NCI NIH HHS
- DP5 OD033055 NIH HHS
- U01 CA224013 NCI NIH HHS
- R35 CA197594 NCI NIH HHS
- P30 CA045508 NCI NIH HHS
- U01 AG077921 NIA NIH HHS
- R01 CA188134 NCI NIH HHS
- R01 AG065396 NIA NIH HHS
- R01 CA229699 NCI NIH HHS
- P30 CA008748 NCI NIH HHS
- R01 AG082800 NIA NIH HHS
- U01 AG077925 NIA NIH HHS
- S10 OD028632 NIH HHS
- U01 CA210240 NCI NIH HHS
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
- NIH-NIA: 1R01 AG082800-01 NIH-Common Fund: 1DP5OD033055-01 Longevity Impetus Grant
- European Research Council (ERC-StG-949667).
- JLM Benevolent Fund. Cancer Research Institute.
- Netherlands Organization for Scientific Research Cancer Research Institute
- Lustgarten Foundation, Thompson Foundation, the Pershing Square Foundation, the Cold Spring Harbor Laboratory and Northwell Health Affiliation, the Northwell Health Tissue Donation Program, the Cold Spring Harbor Laboratory Association, the Simons Foundation (552716), and the National Institutes of Health (5P30CA45508, U01CA210240, R01CA229699, U01CA224013, 1R01CA188134, and 1R01CA190092).
- NIH-NCI (R35CA197594) NIH-NIA (U01AG077925)
- NIH: S10OD028632-01 and P30 CA008748 NIH-NIA: AG065396 Pasteur-Weizmann/Servier Award Leopold Griffuel Award Stephen and Barbara Friedman Chair at MSKCC
- NIH: S10OD028632-01 and P30 CA008748 NIH-NIA: AG065396 Technology Development Fund project grant from MSKCC Geoffrey Beene Chair of Cancer Biology at MSKCC Howard Hughes Medical Institute
- La Caixa Foundation.Mark Foundation.
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Affiliation(s)
- Corina Amor
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Inés Fernández-Maestre
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Louis V. Gerstner Jr Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Yu-Jui Ho
- Department of Cancer Biology and Genetics. Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Courtenay Graham
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sebastian E Carrasco
- Laboratory of Comparative Pathology. Weill Cornell Medicine, Memorial Sloan Kettering Cancer Center, and Rockefeller University, New York, NY, USA
| | - Emmanuella Nnuji-John
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Cold Spring Harbor School of Biological Sciences, Cold Spring Harbor, NY, USA
| | - Judith Feucht
- Center for Cell Engineering, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Cluster of Excellence iFIT, University Children's Hospital Tuebingen, Tuebingen, Germany
| | - Clemens Hinterleitner
- Department of Cancer Biology and Genetics. Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Valentin J A Barthet
- Department of Cancer Biology and Genetics. Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jacob A Boyer
- Lewis Sigler Institute for Integrative Genomics and Department of Chemistry, Princeton University, Princeton, NJ, USA
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton, NJ, USA
| | - Riccardo Mezzadra
- Department of Cancer Biology and Genetics. Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew G Wereski
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Ross L Levine
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Lee W Jones
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Michel Sadelain
- Center for Cell Engineering, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Scott W Lowe
- Department of Cancer Biology and Genetics. Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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21
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Straiton J. Age is just a number: can proteomic analysis provide insight into the aging body? Biotechniques 2024; 76:81-82. [PMID: 38386391 DOI: 10.2144/btn-2024-0008] [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: 01/25/2024] [Accepted: 01/25/2024] [Indexed: 02/23/2024] Open
Abstract
Recent proteomic studies have increased our understanding of the molecular process of aging, potentially enabling age-related diseases to be treated before the appearance of symptoms. [Formula: see text].
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22
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Hattangady NG, Carter K, Maroni-Rana B, Wang T, Ayers JL, Yu M, Grady WM. Mapping the core senescence phenotype of primary human colon fibroblasts. Aging (Albany NY) 2024; 16:3068-3087. [PMID: 38385965 PMCID: PMC10929841 DOI: 10.18632/aging.205577] [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: 05/16/2023] [Accepted: 01/15/2024] [Indexed: 02/23/2024]
Abstract
Advanced age is the largest risk factor for many diseases and several types of cancer, including colorectal cancer (CRC). Senescent cells are known to accumulate with age in various tissues, where they can modulate the surrounding tissue microenvironment through their senescence associated secretory phenotype (SASP). Recently, we showed that there is an increased number of senescent cells in the colons of CRC patients and demonstrated that senescent fibroblasts and their SASP create microniches in the colon that are conducive to CRC onset and progression. However, the composition of the SASP is heterogenous and cell-specific, and the precise senescence profile of colon fibroblasts has not been well-defined. To generate a SASP atlas of human colon fibroblasts, we induced senescence in primary human colon fibroblasts using various in vitro methods and assessed the resulting transcriptome. Using RNASequencing and further validation by quantitative RT-PCR and Luminex assays, we define and validate a 'core senescent profile' that might play a significant role in shaping the colon microenvironment. We also performed KEGG analysis and GO analyses to identify key pathways and biological processes that are differentially regulated in colon fibroblast senescence. These studies provide insights into potential driver proteins involved in senescence-associated diseases, like CRC, which may lead to therapies to improve overall health in the elderly and to prevent CRC.
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Affiliation(s)
- Namita Ganesh Hattangady
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Kelly Carter
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Brett Maroni-Rana
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Ting Wang
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Jessica Lee Ayers
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Ming Yu
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - William M. Grady
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Division of Gastroenterology, University of Washington School of Medicine, Seattle, WA 98195, USA
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23
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Moqri M, Herzog C, Poganik JR, Ying K, Justice JN, Belsky DW, Higgins-Chen AT, Chen BH, Cohen AA, Fuellen G, Hägg S, Marioni RE, Widschwendter M, Fortney K, Fedichev PO, Zhavoronkov A, Barzilai N, Lasky-Su J, Kiel DP, Kennedy BK, Cummings S, Slagboom PE, Verdin E, Maier AB, Sebastiano V, Snyder MP, Gladyshev VN, Horvath S, Ferrucci L. Validation of biomarkers of aging. Nat Med 2024; 30:360-372. [PMID: 38355974 PMCID: PMC11090477 DOI: 10.1038/s41591-023-02784-9] [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: 09/07/2023] [Accepted: 12/19/2023] [Indexed: 02/16/2024]
Abstract
The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.
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Affiliation(s)
- Mahdi Moqri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
- Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Chiara Herzog
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
| | - Jesse R Poganik
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kejun Ying
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jamie N Justice
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Daniel W Belsky
- Department of Epidemiology, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | | | - Brian H Chen
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, USA
| | - Alan A Cohen
- Department of Environmental Health Sciences, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Martin Widschwendter
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, London, UK
- Department of Women's and Children's Health, Division of Obstetrics and Gynaecology, Karolinska Institutet, Stockholm, Sweden
| | | | | | | | - Nir Barzilai
- Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jessica Lasky-Su
- Department of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Douglas P Kiel
- Musculoskeletal Research Center, Hinda and Arthur Marcus Institute for Aging Research and Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Brian K Kennedy
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore
| | - Steven Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - P Eline Slagboom
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Eric Verdin
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Andrea B Maier
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore
- Department of Human Movement Sciences, @AgeAmsterdam, Amsterdam Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Vittorio Sebastiano
- Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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24
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Aversa Z, White TA, Heeren AA, Hulshizer CA, Saul D, Zhang X, Molina AJA, Redman LM, Martin CK, Racette SB, Huffman KM, Bhapkar M, Khosla S, Das SK, Fielding RA, Atkinson EJ, LeBrasseur NK. Calorie restriction reduces biomarkers of cellular senescence in humans. Aging Cell 2024; 23:e14038. [PMID: 37961856 PMCID: PMC10861196 DOI: 10.1111/acel.14038] [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: 10/02/2023] [Revised: 10/19/2023] [Accepted: 10/22/2023] [Indexed: 11/15/2023] Open
Abstract
Calorie restriction (CR) with adequate nutrient intake is a potential geroprotective intervention. To advance this concept in humans, we tested the hypothesis that moderate CR in healthy young-to-middle-aged individuals would reduce circulating biomarkers of cellular senescence, a fundamental mechanism of aging and aging-related conditions. Using plasma specimens from the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE™) phase 2 study, we found that CR significantly reduced the concentrations of several senescence biomarkers at 12 and 24 months compared to an ad libitum diet. Using machine learning, changes in biomarker concentrations emerged as important predictors of the change in HOMA-IR and insulin sensitivity index at 12 and 24 months, and the change in resting metabolic rate residual at 12 months. Finally, using adipose tissue RNA-sequencing data from a subset of participants, we observed a significant reduction in a senescence-focused gene set in response to CR at both 12 and 24 months compared to baseline. Our results advance the understanding of the effects of CR in humans and further support a link between cellular senescence and metabolic health.
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Affiliation(s)
- Zaira Aversa
- Robert and Arlene Kogod Center on Aging, Mayo ClinicRochesterMinnesotaUSA
- Department of Physical Medicine and RehabilitationMayo ClinicRochesterMinnesotaUSA
| | - Thomas A. White
- Robert and Arlene Kogod Center on Aging, Mayo ClinicRochesterMinnesotaUSA
| | - Amanda A. Heeren
- Robert and Arlene Kogod Center on Aging, Mayo ClinicRochesterMinnesotaUSA
| | | | - Dominik Saul
- Robert and Arlene Kogod Center on Aging, Mayo ClinicRochesterMinnesotaUSA
- Department of Trauma and Reconstructive SurgeryEberhard Karls University Tübingen, BG Trauma Center TübingenTübingenGermany
| | - Xu Zhang
- Robert and Arlene Kogod Center on Aging, Mayo ClinicRochesterMinnesotaUSA
| | | | | | - Corby K. Martin
- Pennington Biomedical Research CenterBaton RougeLouisianaUSA
| | - Susan B. Racette
- College of Health SolutionsArizona State UniversityPhoenixArizonaUSA
- Program in Physical TherapyWashington University School of MedicineSt. LouisMissouriUSA
| | - Kim M. Huffman
- Duke Clinical Research Institute and Molecular Physiology Institute, School of MedicineDurhamNorth CarolinaUSA
| | - Manjushri Bhapkar
- Duke Clinical Research Institute and Molecular Physiology Institute, School of MedicineDurhamNorth CarolinaUSA
| | - Sundeep Khosla
- Robert and Arlene Kogod Center on Aging, Mayo ClinicRochesterMinnesotaUSA
- Division of EndocrinologyMayo Clinic College of MedicineRochesterMinnesotaUSA
| | - Sai Krupa Das
- Energy Metabolism Team, Jean Mayer USDA Human Nutrition Research Center on AgingTufts UniversityBostonMassachusettsUSA
| | - Roger A. Fielding
- Nutrition, Exercise Physiology and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center on AgingTufts UniversityBostonMassachusettsUSA
| | | | - Nathan K. LeBrasseur
- Robert and Arlene Kogod Center on Aging, Mayo ClinicRochesterMinnesotaUSA
- Department of Physical Medicine and RehabilitationMayo ClinicRochesterMinnesotaUSA
- Paul F. Glenn Center for the Biology of Aging at Mayo ClinicRochesterMinnesotaUSA
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25
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Zhang Y, Liu C. Transcriptomic analysis of mRNAs in human whole blood identified age-specific changes in healthy individuals. Medicine (Baltimore) 2023; 102:e36486. [PMID: 38065846 PMCID: PMC10713173 DOI: 10.1097/md.0000000000036486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/14/2023] [Indexed: 12/18/2023] Open
Abstract
Older age is one of the most important shared risk factors for multiple chronic diseases, increasing the medical burden to contemporary societies. Current research focuses on identifying aging biomarkers to predict aging trajectories and developing interventions aimed at preventing and delaying the progression of multimorbidity with aging. Here, a transcriptomic changes analysis of whole blood genes with age was conducted. The age-related whole blood gene-expression profiling datasets were downloaded from the Gene Expression Omnibus (GEO) database. We screened the differentially expressed genes (DEGs) between healthy young and old individuals and performed functional enrichment analysis. Cytoscape with Cytohubba and MCODE was used to perform an interaction network of DEGs and identify hub genes. In addition, ROC curves and correlation analysis were used to evaluate the accuracy of hub genes. In total, we identified 29 DEGs between young and old samples that were enriched mainly in immunoglobulin binding and complex, humoral immune response, and immune response-activating signaling pathways. In combination with the PPI network and topological analysis, 4 hub genes (IGLL5, Jchain, POU2AF1, and Bach2) were identified. Pearson analysis showed that the expression changes of these hub genes were highly correlated with age. Among them, 3 hub genes (IGLL5, POU2AF1, and Bach2) were identified with good accuracy (AUC score > 0.7), indicating that these genes were the best indicators of age. Together, our results provided potential biomarkers IGLL5, POU2AF1, and Bach2 to identify individuals at high early risk of age-related disease to be targeted for early interventions and contribute to understanding the molecular mechanisms in the progression of aging.
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Affiliation(s)
- Yan Zhang
- Department of Ophthalmology, the Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Chonghui Liu
- College of Life Science, Northeast Forestry University, Harbin, China
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26
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Silva N, Rajado AT, Esteves F, Brito D, Apolónio J, Roberto VP, Binnie A, Araújo I, Nóbrega C, Bragança J, Castelo-Branco P. Measuring healthy ageing: current and future tools. Biogerontology 2023; 24:845-866. [PMID: 37439885 PMCID: PMC10615962 DOI: 10.1007/s10522-023-10041-2] [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: 04/05/2023] [Accepted: 05/23/2023] [Indexed: 07/14/2023]
Abstract
Human ageing is a complex, multifactorial process characterised by physiological damage, increased risk of age-related diseases and inevitable functional deterioration. As the population of the world grows older, placing significant strain on social and healthcare resources, there is a growing need to identify reliable and easy-to-employ markers of healthy ageing for early detection of ageing trajectories and disease risk. Such markers would allow for the targeted implementation of strategies or treatments that can lessen suffering, disability, and dependence in old age. In this review, we summarise the healthy ageing scores reported in the literature, with a focus on the past 5 years, and compare and contrast the variables employed. The use of approaches to determine biological age, molecular biomarkers, ageing trajectories, and multi-omics ageing scores are reviewed. We conclude that the ideal healthy ageing score is multisystemic and able to encompass all of the potential alterations associated with ageing. It should also be longitudinal and able to accurately predict ageing complications at an early stage in order to maximize the chances of successful early intervention.
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Affiliation(s)
- Nádia Silva
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - Ana Teresa Rajado
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - Filipa Esteves
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - David Brito
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - Joana Apolónio
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - Vânia Palma Roberto
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal
| | - Alexandra Binnie
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal
- Department of Critical Care, William Osler Health System, Etobicoke, ON, Canada
| | - Inês Araújo
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal
- Champalimaud Research Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Clévio Nóbrega
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal
- Champalimaud Research Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - José Bragança
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal
- Champalimaud Research Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Pedro Castelo-Branco
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal.
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal.
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal.
- Champalimaud Research Program, Champalimaud Centre for the Unknown, Lisbon, Portugal.
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27
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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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28
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McIlvenna LC, Whitham M. Exercise, healthy ageing, and the potential role of small extracellular vesicles. J Physiol 2023; 601:4937-4951. [PMID: 35388915 PMCID: PMC10952297 DOI: 10.1113/jp282468] [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: 11/30/2021] [Accepted: 03/29/2022] [Indexed: 11/08/2022] Open
Abstract
Extracellular vesicles (EVs) can be released from most cells in the body and act as intercellular messengers transferring information in their cargo to affect cellular function. A growing body of evidence suggests that a subset of EVs, referred to here as 'small extracellular vesicles' (sEVs), can accelerate or slow the processes of ageing and age-related diseases dependent on their molecular cargo and cellular origin. Continued exploration of the vast complexity of the sEV cargo aims to further characterise these systemic vehicles that may be targeted to ameliorate age-related pathologies. Marked progress in the development of mass spectrometry-based technologies means that it is now possible to characterise a significant proportion of the proteome of sEVs (surface and cargo) via unbiased proteomics. This information is vital for identifying biomarkers and the development of sEV-based therapeutics in the context of ageing. Although exercise and physical activity are prominent features in maintaining health in advancing years, the mechanisms responsible are unclear. A potential mechanism by which plasma sEVs released during exercise could influence ageing and senescence is via the increased delivery of cargo proteins that function as antioxidant enzymes or inhibitors of senescence. These have been observed to increase in sEVs following acute and chronic exercise, as identified via independent interrogation of high coverage, publicly available proteomic datasets. Establishing tropism and exchange of functionally active proteins by these processes represents a promising line of enquiry in implicating sEVs as biologically relevant mediators of the ageing process.
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Affiliation(s)
- Luke C. McIlvenna
- School of Sport, Exercise and Rehabilitation SciencesUniversity of BirminghamBirminghamUK
| | - Martin Whitham
- School of Sport, Exercise and Rehabilitation SciencesUniversity of BirminghamBirminghamUK
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29
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Mahoney SA, Dey AK, Basisty N, Herman AB. Identification and functional analysis of senescent cells in the cardiovascular system using omics approaches. Am J Physiol Heart Circ Physiol 2023; 325:H1039-H1058. [PMID: 37656130 PMCID: PMC10908411 DOI: 10.1152/ajpheart.00352.2023] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/28/2023] [Accepted: 08/28/2023] [Indexed: 09/02/2023]
Abstract
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and senescent cells have emerged as key contributors to its pathogenesis. Senescent cells exhibit cell cycle arrest and secrete a range of proinflammatory factors, termed the senescence-associated secretory phenotype (SASP), which promotes tissue dysfunction and exacerbates CVD progression. Omics technologies, specifically transcriptomics and proteomics, offer powerful tools to uncover and define the molecular signatures of senescent cells in cardiovascular tissue. By analyzing the comprehensive molecular profiles of senescent cells, omics approaches can identify specific genetic alterations, gene expression patterns, protein abundances, and metabolite levels associated with senescence in CVD. These omics-based discoveries provide insights into the mechanisms underlying senescence-induced cardiovascular damage, facilitating the development of novel diagnostic biomarkers and therapeutic targets. Furthermore, integration of multiple omics data sets enables a systems-level understanding of senescence in CVD, paving the way for precision medicine approaches to prevent or treat cardiovascular aging and its associated complications.
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Affiliation(s)
- Sophia A Mahoney
- Department of Integrative Physiology, University of Colorado at Boulder, Boulder, Colorado, United States
| | - Amit K Dey
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States
| | - Nathan Basisty
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States
| | - Allison B Herman
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States
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30
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Perry AS, Zhao S, Gajjar P, Murthy VL, Lehallier B, Miller P, Nair S, Neill C, Carr JJ, Fearon W, Kapadia S, Kumbhani D, Gillam L, Lindenfeld J, Farrell L, Marron MM, Tian Q, Newman AB, Murabito J, Gerszten RE, Nayor M, Elmariah S, Lindman BR, Shah R. Proteomic architecture of frailty across the spectrum of cardiovascular disease. Aging Cell 2023; 22:e13978. [PMID: 37731195 PMCID: PMC10652351 DOI: 10.1111/acel.13978] [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: 06/05/2023] [Revised: 08/14/2023] [Accepted: 08/16/2023] [Indexed: 09/22/2023] Open
Abstract
While frailty is a prominent risk factor in an aging population, the underlying biology of frailty is incompletely described. Here, we integrate 979 circulating proteins across a wide range of physiologies with 12 measures of frailty in a prospective discovery cohort of 809 individuals with severe aortic stenosis (AS) undergoing transcatheter aortic valve implantation. Our aim was to characterize the proteomic architecture of frailty in a highly susceptible population and study its relation to clinical outcome and systems-wide phenotypes to define potential novel, clinically relevant frailty biology. Proteomic signatures (specifically of physical function) were related to post-intervention outcome in AS, specifying pathways of innate immunity, cell growth/senescence, fibrosis/metabolism, and a host of proteins not widely described in human aging. In published cohorts, the "frailty proteome" displayed heterogeneous trajectories across age (20-100 years, age only explaining a small fraction of variance) and were associated with cardiac and non-cardiac phenotypes and outcomes across two broad validation cohorts (N > 35,000) over ≈2-3 decades. These findings suggest the importance of precision biomarkers of underlying multi-organ health status in age-related morbidity and frailty.
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Affiliation(s)
- Andrew S. Perry
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Shilin Zhao
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Priya Gajjar
- Cardiovascular Medicine Section, Department of MedicineBoston University School of MedicineBostonMassachusettsUSA
| | | | | | - Patricia Miller
- Department of Medicine, and Department of BiostatisticsBoston University School of MedicineBostonMassachusettsUSA
| | - Sangeeta Nair
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Colin Neill
- Department of Medicine, Division of Cardiovascular MedicineUniversity of Wisconsin Hospital and ClinicsMadisonWisconsinUSA
| | - J. Jeffrey Carr
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - William Fearon
- Department of Medicine, Division of CardiologyStanford Medical CenterPalo AltoCaliforniaUSA
| | - Samir Kapadia
- Department of Medicine, Division of CardiologyCleveland Clinic FoundationClevelandOhioUSA
| | - Dharam Kumbhani
- Department of Medicine, Division of CardiologyUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | - Linda Gillam
- Department of Cardiovascular MedicineMorristown Medical CenterMorristownNew JerseyUSA
| | - JoAnn Lindenfeld
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Laurie Farrell
- Broad Institute of Harvard and MITCambridgeMassachusettsUSA
| | - Megan M. Marron
- Department of Epidemiology, Graduate School of Public HealthUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Qu Tian
- National Institute on Aging, National Institutes of HealthBaltimoreMarylandUSA
| | - Anne B. Newman
- Department of Epidemiology, Graduate School of Public HealthUniversity of PittsburghPittsburghPennsylvaniaUSA
- Departments of Medicine and Clinical and Translational ScienceUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Joanne Murabito
- Sections of Cardiovascular Medicine and Preventive Medicine and Epidemiology, Department of MedicineBoston University School of MedicineBostonMassachusettsUSA
| | - Robert E. Gerszten
- Broad Institute of Harvard and MITCambridgeMassachusettsUSA
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical SchoolBostonMassachusettsUSA
| | - Matthew Nayor
- Sections of Cardiovascular Medicine and Preventive Medicine and Epidemiology, Department of MedicineBoston University School of MedicineBostonMassachusettsUSA
| | - Sammy Elmariah
- Department of Medicine, Division of CardiologyThe University of CaliforniaSan FranciscoCaliforniaUSA
| | - Brian R. Lindman
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Ravi Shah
- Vanderbilt Translational and Clinical Cardiovascular Research CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
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31
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Liu F, Austin TR, Schrack JA, Chen J, Walston J, Mathias RA, Grams M, Odden MC, Newman A, Psaty BM, Ramonfaur D, Shah AM, Windham BG, Coresh J, Walker KA. Late-life plasma proteins associated with prevalent and incident frailty: A proteomic analysis. Aging Cell 2023; 22:e13975. [PMID: 37697678 PMCID: PMC10652348 DOI: 10.1111/acel.13975] [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: 05/18/2023] [Revised: 08/06/2023] [Accepted: 08/16/2023] [Indexed: 09/13/2023] Open
Abstract
Proteomic approaches have unique advantages in the identification of biological pathways that influence physical frailty, a multifactorial geriatric syndrome predictive of adverse health outcomes in older adults. To date, proteomic studies of frailty are scarce, and few evaluated prefrailty as a separate state or examined predictors of incident frailty. Using plasma proteins measured by 4955 SOMAmers in the Atherosclerosis Risk in Community study, we identified 134 and 179 proteins cross-sectionally associated with prefrailty and frailty, respectively, after Bonferroni correction (p < 1 × 10-5 ) among 3838 older adults aged ≥65 years, adjusting for demographic and physiologic factors and chronic diseases. Among them, 23 (17%) and 82 (46%) were replicated in the Cardiovascular Health Study using the same models (FDR p < 0.05). Notably, higher odds of prefrailty and frailty were observed with higher levels of growth differentiation factor 15 (GDF15; pprefrailty = 1 × 10-15 , pfrailty = 2 × 10-19 ), transgelin (TAGLN; pprefrailty = 2 × 10-12 , pfrailty = 6 × 10-22 ), and insulin-like growth factor-binding protein 2 (IGFBP2; pprefrailty = 5 × 10-15 , pfrailty = 1 × 10-15 ) and with a lower level of growth hormone receptor (GHR, pprefrailty = 3 × 10-16 , pfrailty = 2 × 10-18 ). Longitudinally, we identified 4 proteins associated with incident frailty (p < 1 × 10-5 ). Higher levels of triggering receptor expressed on myeloid cells 1 (TREM1), TAGLN, and heart and adipocyte fatty-acid binding proteins predicted incident frailty. Differentially regulated proteins were enriched in pathways and upstream regulators related to lipid metabolism, angiogenesis, inflammation, and cell senescence. Our findings provide a set of plasma proteins and biological mechanisms that were dysregulated in both the prodromal and the clinical stage of frailty, offering new insights into frailty etiology and targets for intervention.
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Affiliation(s)
- Fangyu Liu
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Thomas R. Austin
- Department of EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
| | - Jennifer A. Schrack
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Center on Aging and HealthJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Jingsha Chen
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Jeremy Walston
- Department of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Rasika A. Mathias
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Department of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Morgan Grams
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Division of Precision MedicineNew York University Grossman School of MedicineNew YorkNew YorkUSA
| | - Michelle C. Odden
- Department of Epidemiology and Population HealthStanford University School of MedicineStanfordCaliforniaUSA
| | - Anne Newman
- Department of EpidemiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Systems and Population HealthUniversity of WashingtonSeattleWashingtonUSA
| | - Diego Ramonfaur
- Brigham and Women's Hospital, Harvard Medical School, Cardiovascular MedicineBostonMassachusettsUSA
| | - Amil M. Shah
- Brigham and Women's Hospital, Harvard Medical School, Cardiovascular MedicineBostonMassachusettsUSA
| | - B. Gwen Windham
- Department of Medicine, MIND CenterUniversity of Mississippi Medical CenterJacksonMississippiUSA
| | - Josef Coresh
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Keenan A. Walker
- Laboratory of Behavioral NeuroscienceNational Institute on AgingBaltimoreMarylandUSA
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32
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Dey AK, Banarjee R, Boroumand M, Rutherford DV, Strassheim Q, Nyunt T, Olinger B, Basisty N. Translating Senotherapeutic Interventions into the Clinic with Emerging Proteomic Technologies. BIOLOGY 2023; 12:1301. [PMID: 37887011 PMCID: PMC10604147 DOI: 10.3390/biology12101301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/25/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
Cellular senescence is a state of irreversible growth arrest with profound phenotypic changes, including the senescence-associated secretory phenotype (SASP). Senescent cell accumulation contributes to aging and many pathologies including chronic inflammation, type 2 diabetes, cancer, and neurodegeneration. Targeted removal of senescent cells in preclinical models promotes health and longevity, suggesting that the selective elimination of senescent cells is a promising therapeutic approach for mitigating a myriad of age-related pathologies in humans. However, moving senescence-targeting drugs (senotherapeutics) into the clinic will require therapeutic targets and biomarkers, fueled by an improved understanding of the complex and dynamic biology of senescent cell populations and their molecular profiles, as well as the mechanisms underlying the emergence and maintenance of senescence cells and the SASP. Advances in mass spectrometry-based proteomic technologies and workflows have the potential to address these needs. Here, we review the state of translational senescence research and how proteomic approaches have added to our knowledge of senescence biology to date. Further, we lay out a roadmap from fundamental biological discovery to the clinical translation of senotherapeutic approaches through the development and application of emerging proteomic technologies, including targeted and untargeted proteomic approaches, bottom-up and top-down methods, stability proteomics, and surfaceomics. These technologies are integral for probing the cellular composition and dynamics of senescent cells and, ultimately, the development of senotype-specific biomarkers and senotherapeutics (senolytics and senomorphics). This review aims to highlight emerging areas and applications of proteomics that will aid in exploring new senescent cell biology and the future translation of senotherapeutics.
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Affiliation(s)
| | | | | | | | | | | | | | - Nathan Basisty
- Translational Geroproteomics Unit, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA; (A.K.D.); (R.B.); (M.B.); (D.V.R.); (Q.S.); (T.N.); (B.O.)
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33
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Arif M, Matyas C, Mukhopadhyay P, Yokus B, Trojnar E, Paloczi J, Paes-Leme B, Zhao S, Lohoff FW, Haskó G, Pacher P. Data-driven transcriptomics analysis identifies PCSK9 as a novel key regulator in liver aging. GeroScience 2023; 45:3059-3077. [PMID: 37726433 PMCID: PMC10643490 DOI: 10.1007/s11357-023-00928-w] [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: 07/11/2023] [Accepted: 08/29/2023] [Indexed: 09/21/2023] Open
Abstract
The liver, as a crucial metabolic organ, undergoes significant pathological changes during the aging process, which can have a profound impact on overall health. To gain a comprehensive understanding of these alterations, we employed data-driven approaches, along with biochemical methods, histology, and immunohistochemistry techniques, to systematically investigate the effects of aging on the liver. Our study utilized a well-established rat aging model provided by the National Institute of Aging. Systems biology approaches were used to analyze genome-wide transcriptomics data from liver samples obtained from young (4-5 months old) and aging (20-21 months old) Fischer 344 rats. Our findings revealed pathological changes occurring in various essential biological processes in aging livers. These included mitochondrial dysfunction, increased oxidative/nitrative stress, decreased NAD + content, impaired amino acid and protein synthesis, heightened inflammation, disrupted lipid metabolism, enhanced apoptosis, senescence, and fibrosis. These results were validated using independent datasets from both human and rat aging studies. Furthermore, by employing co-expression network analysis, we identified novel driver genes responsible for liver aging, confirmed our findings in human aging subjects, and pointed out the cellular localization of the driver genes using single-cell RNA-sequencing human data. Our study led to the discovery and validation of a liver-specific gene, proprotein convertase subtilisin/kexin type 9 (PCSK9), as a potential therapeutic target for mitigating the pathological processes associated with aging in the liver. This finding envisions new possibilities for developing interventions aimed to improve liver health during the aging process.
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Affiliation(s)
- Muhammad Arif
- Laboratory of Cardiovascular Physiology and Tissue Injury, National Institute On Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
- Section On Fibrotic Disorders, National Institute On Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Csaba Matyas
- Laboratory of Cardiovascular Physiology and Tissue Injury, National Institute On Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Partha Mukhopadhyay
- Laboratory of Cardiovascular Physiology and Tissue Injury, National Institute On Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Burhan Yokus
- Laboratory of Cardiovascular Physiology and Tissue Injury, National Institute On Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Eszter Trojnar
- Laboratory of Cardiovascular Physiology and Tissue Injury, National Institute On Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Janos Paloczi
- Laboratory of Cardiovascular Physiology and Tissue Injury, National Institute On Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Bruno Paes-Leme
- Laboratory of Cardiovascular Physiology and Tissue Injury, National Institute On Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Suxian Zhao
- Laboratory of Cardiovascular Physiology and Tissue Injury, National Institute On Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Falk W Lohoff
- Section On Clinical Genomics and Experimental Therapeutics, National Institute On Alcohol Abuse and Alcoholism National Institutes of Health, Bethesda, MD, USA
| | - György Haskó
- Department of Anesthesiology, Columbia University, New York, NY, USA
| | - Pal Pacher
- Laboratory of Cardiovascular Physiology and Tissue Injury, National Institute On Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA.
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Clayton ZS, Rossman MJ, Mahoney SA, Venkatasubramanian R, Maurer GS, Hutton DA, VanDongen NS, Greenberg NT, Longtine AG, Ludwig KR, Brunt VE, LaRocca TJ, Campisi J, Melov S, Seals DR. Cellular Senescence Contributes to Large Elastic Artery Stiffening and Endothelial Dysfunction With Aging: Amelioration With Senolytic Treatment. Hypertension 2023; 80:2072-2087. [PMID: 37593877 PMCID: PMC10530538 DOI: 10.1161/hypertensionaha.123.21392] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/02/2023] [Indexed: 08/19/2023]
Abstract
BACKGROUND Here, we assessed the role of cellular senescence and the senescence associated secretory phenotype (SASP) in age-related aortic stiffening and endothelial dysfunction. METHODS We studied young (6-8 mo) and old (27-29 mo) p16-3MR mice, which allows for genetic-based clearance of senescent cells with ganciclovir (GCV). We also treated old C57BL/6N mice with the senolytic ABT-263. RESULTS In old mice, GCV reduced aortic stiffness assessed by aortic pulse wave velocity (PWV; 477±10 vs. 382±7 cm/s, P<0.05) to young levels (old-GCV vs. young-vehicle, P=0.35); ABT-263 also reduced aortic PWV in old mice (446±9 to 356±11 cm/s, P<0.05). Aortic adventitial collagen was reduced by GCV (P<0.05) and ABT-263 (P=0.12) in old mice. To show an effect of the circulating SASP, we demonstrated that plasma exposure from Old-vehicle p16-3MR mice, but not from Old-GCV mice, induced aortic stiffening assessed ex vivo (elastic modulus; P<0.05). Plasma proteomics implicated glycolysis in circulating SASP-mediated aortic stiffening. In old p16-3MR mice, GCV increased endothelial function assessed via peak carotid artery endothelium-dependent dilation (EDD; Old-GCV, 94±1% vs. Old-vehicle, 84±2%, P<0.05) to young levels (Old-GCV vs. young-vehicle, P=0.98), and EDD was higher in old C57BL/6N mice treated with ABT-263 vs. vehicle (96±1% vs. 82±3%, P<0.05). Improvements in endothelial function were mediated by increased nitric oxide (NO) bioavailability (P<0.05) and reduced oxidative stress (P<0.05). Circulating SASP factors related to NO signaling were associated with greater NO-mediated EDD following senescent cell clearance. CONCLUSIONS Cellular senescence and the SASP contribute to vascular aging and senolytics hold promise for improving age-related vascular function.
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Affiliation(s)
- Zachary S. Clayton
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO
| | - Matthew J. Rossman
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO
| | - Sophia A. Mahoney
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO
| | | | - Grace S. Maurer
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO
| | - David A. Hutton
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO
| | | | - Nathan T. Greenberg
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO
| | - Abigail G. Longtine
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO
| | - Katelyn R. Ludwig
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO
| | - Vienna E. Brunt
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO
| | - Thomas J. LaRocca
- Department of Health & Exercise Science, Colorado State University, Fort Collins, CO
- Center for Healthy Aging, Colorado State University, Fort Collins, CO
| | - Judith Campisi
- The Buck Institute for Research on Aging, Novato, CA
- Lawrence Berkeley National Laboratory, Berkeley, CA
| | - Simon Melov
- The Buck Institute for Research on Aging, Novato, CA
| | - Douglas R. Seals
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO
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Amor C, Fernández-Maestre I, Chowdhury S, Ho YJ, Nadella S, Graham C, Carrasco SE, Nnuji-John E, Feucht J, Hinterleitner C, Barthet VJ, Boyer JA, Mezzadra R, Wereski MG, Tuveson DA, Levine RL, Jones LW, Sadelain M, Lowe SW. Prophylactic and long-lasting efficacy of senolytic CAR T cells against age-related metabolic dysfunction. RESEARCH SQUARE 2023:rs.3.rs-3385749. [PMID: 37841853 PMCID: PMC10571605 DOI: 10.21203/rs.3.rs-3385749/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Senescent cells accumulate in organisms over time because of tissue damage and impaired immune surveillance and contribute to age-related tissue decline1,2. In agreement, genetic ablation studies reveal that elimination of senescent cells from aged tissues can ameliorate various age-related pathologies, including metabolic dysfunction and decreased physical fitness3-7. While small-molecule drugs capable of eliminating senescent cells (known as 'senolytics') partially replicate these phenotypes, many have undefined mechanisms of action and all require continuous administration to be effective. As an alternative approach, we have developed a cell-based senolytic therapy based on chimeric antigen receptor (CAR) T cells targeting uPAR, a cell-surface protein upregulated on senescent cells, and previously showed these can safely and efficiently eliminate senescent cells in young animals and reverse liver fibrosis8. We now show that uPAR-positive senescent cells accumulate during physiological aging and that they can be safely targeted with senolytic CAR T cells. Treatment with anti uPAR CAR T cells ameliorates metabolic dysfunction by improving glucose tolerance and exercise capacity in physiological aging as well as in a model of metabolic syndrome. Importantly, a single administration of a low dose of these senolytic CAR T cells is sufficient to achieve long-term therapeutic and preventive effects.
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Affiliation(s)
- Corina Amor
- Cold Spring Harbor Laboratory. Cold Spring Harbor, NY, USA
| | - Inés Fernández-Maestre
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Louis V. Gerstner Jr Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Yu-Jui Ho
- Department of Cancer Biology and Genetics. Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Courtenay Graham
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sebastian E. Carrasco
- Laboratory of Comparative Pathology. Weill Cornell Medicine, Memorial Sloan Kettering Cancer Center, and Rockefeller University, New York, NY, USA
| | - Emmanuella Nnuji-John
- Cold Spring Harbor Laboratory. Cold Spring Harbor, NY, USA
- Cold Spring Harbor School of Biological Sciences, Cold Spring Harbor, NY, USA
| | - Judith Feucht
- Center for Cell Engineering, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Cluster of Excellence iFIT, University Children’s Hospital Tuebingen, Tuebingen, Germany
| | - Clemens Hinterleitner
- Department of Cancer Biology and Genetics. Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Valentin J.A. Barthet
- Department of Cancer Biology and Genetics. Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jacob A. Boyer
- Lewis Sigler Institute for Integrative Genomics and Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Riccardo Mezzadra
- Department of Cancer Biology and Genetics. Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew G Wereski
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Ross L. Levine
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Lee W Jones
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Michel Sadelain
- Center for Cell Engineering, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Scott W Lowe
- Department of Cancer Biology and Genetics. Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, USA
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Wang S, Prizment A, Moshele P, Vivek S, Blaes AH, Nelson HH, Thyagarajan B. Aging measures and cancer: Findings from the Health and Retirement Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.20.23295845. [PMID: 37790462 PMCID: PMC10543046 DOI: 10.1101/2023.09.20.23295845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Background Compared to cancer-free persons, cancer survivors of the same chronological age (CA) have increased physiological dysfunction, i.e., higher biological age (BA), which may lead to higher morbidity and mortality. We estimated BA using eight aging metrics: BA computed by Klemera Doubal method (KDM-BA), phenotypic age (PhenoAge), five epigenetic clocks (ECs, Horvath, Hannum, Levine, GrimAge, and pace of aging (POA)), and subjective age (SA). We tested if aging constructs were associated with total cancer prevalence and all-cause mortality in cancer survivors and controls, i.e., cancer-free persons, in the Health and Retirement Study (HRS), a large population-based study. Methods In 2016, data on BA-KDM, PhenoAge, and SA were available for 946 cancer survivors and 4,555 controls; data for the five ECs were available for 582 cancer survivors and 2,805 controls. Weighted logistic regression was used to estimate the association between each aging construct and cancer prevalence (odds ratio, OR, 95%CI). Weighted Cox proportional hazards regression was used to estimate the associations between each aging construct and cancer incidence as well as all-cause mortality (hazard ratio, HR, 95%CI). To study all BA metrics (except for POA) independent of CA, we estimated age acceleration as residuals of BA regressed on CA. Results Age acceleration for each aging construct and POA were higher in cancer survivors than controls. In a multivariable-adjusted model, five aging constructs (age acceleration for Hannum, Horvath, Levine, GrimAge, and SA) were associated with cancer prevalence. Among all cancer survivors, age acceleration for PhenoAge and four ECs (Hannum, Horvath, Levine, and GrimAge), was associated with higher all-cause mortality over 4 years of follow-up. PhenoAge, Hannum, and GrimAge were also associated with all-cause mortality in controls. The highest HR was observed for GrimAge acceleration in cancer survivors: 2.03 (95% CI, 1.58-2.60). In contrast, acceleration for KDM-BA and POA was significantly associated with mortality in controls but not in cancer survivors. When all eight aging constructs were included in the same model, two of them (Levine and GrimAge) were significantly associated with mortality among cancers survivors. None of the aging constructs were associated with cancer incidence. Conclusion Variations in the associations between aging constructs and mortality in cancer survivors and controls suggests that aging constructs may capture different aspects of aging and that cancer survivors may be experiencing age-related physiologic dysfunctions differently than controls. Future work should evaluate how these aging constructs predict mortality for specific cancer types.
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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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Moqri M, Herzog C, Poganik JR, Justice J, Belsky DW, Higgins-Chen A, Moskalev A, Fuellen G, Cohen AA, Bautmans I, Widschwendter M, Ding J, Fleming A, Mannick J, Han JDJ, Zhavoronkov A, Barzilai N, Kaeberlein M, Cummings S, Kennedy BK, Ferrucci L, Horvath S, Verdin E, Maier AB, Snyder MP, Sebastiano V, Gladyshev VN. Biomarkers of aging for the identification and evaluation of longevity interventions. Cell 2023; 186:3758-3775. [PMID: 37657418 PMCID: PMC11088934 DOI: 10.1016/j.cell.2023.08.003] [Citation(s) in RCA: 59] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 09/03/2023]
Abstract
With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice.
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Affiliation(s)
- Mahdi Moqri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Chiara Herzog
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
| | - Jesse R Poganik
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jamie Justice
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Daniel W Belsky
- Department of Epidemiology, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | | | - Alexey Moskalev
- Institute of Biogerontology, Lobachevsky University, Nizhny Novgorod, Russia
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany; School of Medicine, University College Dublin, Dublin, Ireland
| | - Alan A Cohen
- Department of Environmental Health Sciences, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Ivan Bautmans
- Gerontology Department, Vrije Universiteit Brussel, Brussels, Belgium; Frailty in Ageing Research Department, Vrije Universiteit Brussel, Brussels, Belgium
| | - Martin Widschwendter
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria; Department of Women's Cancer, EGA Institute for Women's Health, University College London, London, UK; Department of Women's and Children's Health, Division of Obstetrics and Gynaecology, Karolinska Institutet, Stockholm, Sweden
| | - Jingzhong Ding
- Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | | | - Jing-Dong Jackie Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology, Peking University, Beijing, China
| | - Alex Zhavoronkov
- Insilico Medicine Hong Kong, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Nir Barzilai
- Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Matt Kaeberlein
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Steven Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Brian K Kennedy
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | | | | | - Eric Verdin
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Andrea B Maier
- Department of Human Movement Sciences, @AgeAmsterdam, Amsterdam Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA.
| | - Vittorio Sebastiano
- Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA, USA.
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Forman DE, Kuchel GA, Newman JC, Kirkland JL, Volpi E, Taffet GE, Barzilai N, Pandey A, Kitzman DW, Libby P, Ferrucci L. Impact of Geroscience on Therapeutic Strategies for Older Adults With Cardiovascular Disease: JACC Scientific Statement. J Am Coll Cardiol 2023; 82:631-647. [PMID: 37389519 PMCID: PMC10414756 DOI: 10.1016/j.jacc.2023.05.038] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/09/2023] [Accepted: 05/31/2023] [Indexed: 07/01/2023]
Abstract
Geroscience posits that cardiovascular disease (CVD) and other chronic diseases result from progressive erosion of the effectiveness of homeostatic mechanisms that oppose age-related accumulation of molecular damage. This hypothetical common root to chronic diseases explains why patients with CVD are often affected by multimorbidity and frailty and why older age negatively affects CVD prognosis and treatment response. Gerotherapeutics enhance resilience mechanisms that counter age-related molecular damage to prevent chronic diseases, frailty, and disability, thereby extending healthspan. Here, we describe the main resilience mechanisms of mammalian aging, with a focus on how they can affect CVD pathophysiology. We next present novel gerotherapeutic approaches, some of which are already used in management of CVD, and explore their potential to transform care and management of CVD. The geroscience paradigm is gaining traction broadly in medical specialties, with potential to mitigate premature aging, reduce health care disparities, and improve population healthspan.
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Affiliation(s)
- Daniel E Forman
- Department of Medicine (Geriatrics and Cardiology) University of Pittsburgh, Pittsburgh, Pennsylvania, USA; GRECC, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA.
| | - George A Kuchel
- UConn Center on Aging, University of Connecticut School of Medicine, UConn Health, Farmington, Connecticut, USA
| | - John C Newman
- Buck Institute for Research on Aging, Novato California, USA; Division of Geriatrics, University of California San Francisco, San Francisco, California, USA
| | - James L Kirkland
- Division of General Internal Medicine, Department of Medicine and Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Elena Volpi
- Sealy Center on Aging, University of Texas Medical Branch, Galveston, Texas, USA
| | - George E Taffet
- Department of Medicine (Geriatrics and Cardiovascular Sciences), Baylor College of Medicine, Houston, Texas, USA
| | - Nir Barzilai
- Einstein Institute for Aging Research, Bronx, New York, USA; Einstein-NSC and Glenn Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Ambarish Pandey
- Division of Cardiology, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Dalane W Kitzman
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Peter Libby
- Cardiovascular Medicine and Geriatrics, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
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Donlon TA, Morris BJ, Chen R, Lim E, Morgen EK, Fortney K, Shah N, Masaki KH, Willcox BJ. Proteomic basis of mortality resilience mediated by FOXO3 longevity genotype. GeroScience 2023; 45:2303-2324. [PMID: 36881352 PMCID: PMC10651822 DOI: 10.1007/s11357-023-00740-6] [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: 11/10/2022] [Accepted: 01/23/2023] [Indexed: 03/08/2023] Open
Abstract
FOXO3 is a ubiquitous transcription factor expressed in response to cellular stress caused by nutrient deprivation, inflammatory cytokines, reactive oxygen species, radiation, hypoxia, and other factors. We showed previously that the association of inherited FOXO3 variants with longevity was the result of partial protection against mortality risk posed by aging-related life-long stressors, particularly cardiometabolic disease. We then referred to the longevity-associated genotypes as conferring "mortality resilience." Serum proteins whose levels change with aging and are associated with mortality risk may be considered as "stress proteins." They may serve as indirect measures of life-long stress. Our aims were to (1) identify stress proteins that increase with aging and are associated with an increased risk of mortality, and (2) to determine if FOXO3 longevity/resilience genotype dampens the expected increase in mortality risk they pose. A total of 4500 serum protein aptamers were quantified using the Somalogic SomaScan proteomics platform in the current study of 975 men aged 71-83 years. Stress proteins associated with mortality were identified. We then used age-adjusted multivariable Cox models to investigate the interaction of stress protein with FOXO3 longevity-associated rs12212067 genotypes. For all the analyses, the p values were corrected for multiple comparisons by false discovery rate. This led to the identification of 44 stress proteins influencing the association of FOXO3 genotype with reduced mortality. Biological pathways were identified for these proteins. Our results suggest that the FOXO3 resilience genotype functions by reducing mortality in pathways related to innate immunity, bone morphogenetic protein signaling, leukocyte migration, and growth factor response.
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Affiliation(s)
- Timothy A Donlon
- Department of Research, NIH Center of Biomedical Research Excellence for Clinical and Translational Research on Aging, Kuakini Medical Center, Honolulu, Hawaii, 96817, USA
- Department of Cell and Molecular Biology, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
| | - Brian J Morris
- Department of Research, NIH Center of Biomedical Research Excellence for Clinical and Translational Research on Aging, Kuakini Medical Center, Honolulu, Hawaii, 96817, USA.
- Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA.
- School of Medical Sciences, University of Sydney, Sydney, New South Wales, Australia.
| | - Randi Chen
- Department of Research, NIH Center of Biomedical Research Excellence for Clinical and Translational Research on Aging, Kuakini Medical Center, Honolulu, Hawaii, 96817, USA
| | - Eunjung Lim
- Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
| | - Eric K Morgen
- BioAge Labs Inc., 1445A S 50th St, Richmond, California, USA
| | - Kristen Fortney
- BioAge Labs Inc., 1445A S 50th St, Richmond, California, USA
| | - Naisha Shah
- BioAge Labs Inc., 1445A S 50th St, Richmond, California, USA
| | - Kamal H Masaki
- Department of Research, NIH Center of Biomedical Research Excellence for Clinical and Translational Research on Aging, Kuakini Medical Center, Honolulu, Hawaii, 96817, USA
- Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
| | - Bradley J Willcox
- Department of Research, NIH Center of Biomedical Research Excellence for Clinical and Translational Research on Aging, Kuakini Medical Center, Honolulu, Hawaii, 96817, USA
- Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
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Parnell LD, McCaffrey KS, Brooks AW, Smith CE, Lai CQ, Christensen JJ, Wiley CD, Ordovas JM. Rate-Limiting Enzymes in Cardiometabolic Health and Aging in Humans. Lifestyle Genom 2023; 16:124-138. [PMID: 37473740 DOI: 10.1159/000531350] [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: 02/14/2023] [Accepted: 05/24/2023] [Indexed: 07/22/2023] Open
Abstract
INTRODUCTION Rate-limiting enzymes (RLEs) are innate slow points in metabolic pathways, and many function in bio-processes related to nutrient sensing. Many RLEs carry causal mutations relevant to inherited metabolic disorders. Because the activity of RLEs in cardiovascular health is poorly characterized, our objective was to assess their involvement in cardiometabolic health and disease and where altered biophysical and biochemical functions can promote disease. METHODS A dataset of 380 human RLEs was compared to protein and gene datasets for factors likely to contribute to cardiometabolic disease, including proteins showing significant age-related altered expression in blood and genetic loci with variants that associate with common cardiometabolic phenotypes. The biochemical reactions catalyzed by RLEs were evaluated for metabolites enriched in RLE subsets associating with various cardiometabolic phenotypes. Most significance tests were based on Z-score enrichment converted to p values with a normal distribution function. RESULTS Of 380 RLEs analyzed, 112 function in mitochondria, and 53 are assigned to inherited metabolic disorders. There was a depletion of RLE proteins known as aging biomarkers. At the gene level, RLEs were assessed for common genetic variants that associated with important cardiometabolic traits of LDL-cholesterol or any of the five outcomes pertinent to metabolic syndrome. This revealed several RLEs with links to cardiometabolic traits, from a minimum of 26 for HDL-cholesterol to a maximum of 45 for plasma glucose. Analysis of these GWAS-linked RLEs for enrichment of the molecular constituents of the catalyzed reactions disclosed a number of significant phenotype-metabolite links. These included blood pressure with acetate (p = 2.2 × 10-4) and NADP+ (p = 0.0091), plasma HDL-cholesterol and triglyceride with diacylglycerol (p = 2.6 × 10-5, 6.4 × 10-5, respectively) and diolein (p = 2.2 × 10-6, 5.9 × 10-6), and waist circumference with d-glucosamine-6-phosphate (p = 1.8 × 10-4). CONCLUSION In the context of cardiometabolic health, aging, and disease, these results highlight key diet-derived metabolites that are central to specific rate-limited processes that are linked to cardiometabolic health. These metabolites include acetate and diacylglycerol, pertinent to blood pressure and triglycerides, respectively, as well as diacylglycerol and HDL-cholesterol.
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Affiliation(s)
- Laurence D Parnell
- US Department of Agriculture, Nutrition and Genomics Laboratory, Agricultural Research Service, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts, USA
| | | | | | - Caren E Smith
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts, USA
| | - Chao-Qiang Lai
- US Department of Agriculture, Nutrition and Genomics Laboratory, Agricultural Research Service, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts, USA
| | - Jacob J Christensen
- Norwegian National Advisory Unit on Familial Hypercholesterolemia, Oslo University Hospital, Oslo, Norway
- Department of Nutrition, University of Oslo, Oslo, Norway
| | - Christopher D Wiley
- Vitamin K Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts, USA
| | - Jose M Ordovas
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts, USA
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Silva N, Rajado AT, Esteves F, Brito D, Apolónio J, Roberto VP, Binnie A, Araújo I, Nóbrega C, Bragança J, Castelo-Branco P, Andrade RP, Calado S, Faleiro ML, Matos C, Marques N, Marreiros A, Nzwalo H, Pais S, Palmeirim I, Simão S, Joaquim N, Miranda R, Pêgas A, Sardo A. Measuring healthy ageing: current and future tools. Biogerontology 2023. [DOI: https:/doi.org/10.1007/s10522-023-10041-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 05/23/2023] [Indexed: 09/01/2023]
Abstract
AbstractHuman ageing is a complex, multifactorial process characterised by physiological damage, increased risk of age-related diseases and inevitable functional deterioration. As the population of the world grows older, placing significant strain on social and healthcare resources, there is a growing need to identify reliable and easy-to-employ markers of healthy ageing for early detection of ageing trajectories and disease risk. Such markers would allow for the targeted implementation of strategies or treatments that can lessen suffering, disability, and dependence in old age. In this review, we summarise the healthy ageing scores reported in the literature, with a focus on the past 5 years, and compare and contrast the variables employed. The use of approaches to determine biological age, molecular biomarkers, ageing trajectories, and multi-omics ageing scores are reviewed. We conclude that the ideal healthy ageing score is multisystemic and able to encompass all of the potential alterations associated with ageing. It should also be longitudinal and able to accurately predict ageing complications at an early stage in order to maximize the chances of successful early intervention.
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Picca A, Lozanoska-Ochser B, Calvani R, Coelho-Júnior HJ, Leewenburgh C, Marzetti E. Inflammatory, mitochondrial, and senescence-related markers: Underlying biological pathways of muscle aging and new therapeutic targets. Exp Gerontol 2023; 178:112204. [PMID: 37169101 DOI: 10.1016/j.exger.2023.112204] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/04/2023] [Accepted: 05/09/2023] [Indexed: 05/13/2023]
Abstract
The maintenance of functional health is pivotal for achieving independent life in older age. The aged muscle is characterized by ultrastructural changes, including loss of type I and type II myofibers and a greater proportion of cytochrome c oxidase deficient and succinate dehydrogenase positive fibers. Both intrinsic (e.g., altered proteostasis, DNA damage, and mitochondrial dysfunction) and extrinsic factors (e.g., denervation, altered metabolic regulation, declines in satellite cells, and inflammation) contribute to muscle aging. Being a hub for several cellular activities, mitochondria are key to myocyte viability and mitochondrial dysfunction has been implicated in age-associated physical decline. The maintenance of functional organelles via mitochondrial quality control (MQC) processes is, therefore, crucial to skeletal myofiber viability and organismal health. The autophagy-lysosome pathway has emerged as a critical step of MQC in muscle by disposing organelles and proteins via their tagging for autophagosome incorporation and delivery to the lysosome for clearance. This pathway was found to be altered in muscle of physically inactive older adults. A relationship between this pathway and muscle tissue composition of the lower extremities as well as physical performance was also identified. Therefore, integrating muscle structure and myocyte quality control measures in the evaluation of muscle health may be a promising strategy for devising interventions fostering muscle health.
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Affiliation(s)
- Anna Picca
- Department of Medicine and Surgery, LUM University, Casamassima, 70100 Bari, Italy; Fondazione Policlinico Universitario "Agostino Gemelli" IRCSS, 00168 Rome, Italy
| | - Biliana Lozanoska-Ochser
- Department of Medicine and Surgery, LUM University, Casamassima, 70100 Bari, Italy; DAHFMO Unit of Histology and Medical Embryology, Sapienza University of Rome, 00161 Rome, Italy
| | - Riccardo Calvani
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCSS, 00168 Rome, Italy; Department of Geriatrics and Orthopedics, Università Cattolica del Sacro Cuore, 00168 Rome, Italy.
| | - Hélio José Coelho-Júnior
- Department of Geriatrics and Orthopedics, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | | | - Emanuele Marzetti
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCSS, 00168 Rome, Italy; Department of Geriatrics and Orthopedics, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
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44
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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: 77] [Impact Index Per Article: 77.0] [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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46
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Kocsmár É, Schmid M, Cosenza-Contreras M, Kocsmár I, Föll M, Krey L, Barta BA, Rácz G, Kiss A, Werner M, Schilling O, Lotz G, Bronsert P. Proteome alterations in human autopsy tissues in relation to time after death. Cell Mol Life Sci 2023; 80:117. [PMID: 37020120 PMCID: PMC10075177 DOI: 10.1007/s00018-023-04754-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 02/17/2023] [Accepted: 03/07/2023] [Indexed: 04/07/2023]
Abstract
Protein expression is a primary area of interest for routine histological diagnostics and tissue-based research projects, but the limitations of its post-mortem applicability remain largely unclear. On the other hand, tissue specimens obtained during autopsies can provide unique insight into advanced disease states, especially in cancer research. Therefore, we aimed to identify the maximum post-mortem interval (PMI) which is still suitable for characterizing protein expression patterns, to explore organ-specific differences in protein degradation, and to investigate whether certain proteins follow specific degradation kinetics. Therefore, the proteome of human tissue samples obtained during routine autopsies of deceased patients with accurate PMI (6, 12, 18, 24, 48, 72, 96 h) and without specific diseases that significantly affect tissue preservation, from lungs, kidneys and livers, was analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). For the kidney and liver, significant protein degradation became apparent at 48 h. For the lung, the proteome composition was rather static for up to 48 h and substantial protein degradation was detected only at 72 h suggesting that degradation kinetics appear to be organ specific. More detailed analyses suggested that proteins with similar post-mortem kinetics are not primarily shared in their biological functions. The overrepresentation of protein families with analogous structural motifs in the kidney indicates that structural features may be a common factor in determining similar postmortem stability. Our study demonstrates that a longer post-mortem period may have a significant impact on proteome composition, but sampling within 24 h may be appropriate, as degradation is within acceptable limits even in organs with faster autolysis.
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Affiliation(s)
- Éva Kocsmár
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary
| | - Marlene Schmid
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Miguel Cosenza-Contreras
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Ildikó Kocsmár
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary
- Department of Urology, Semmelweis University, Budapest, Hungary
| | - Melanie Föll
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| | - Leah Krey
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Bálint András Barta
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Gergely Rácz
- Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest, Hungary
| | - András Kiss
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary
| | - Martin Werner
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oliver Schilling
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany.
- Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Gábor Lotz
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary
| | - Peter Bronsert
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany.
- Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Biobank Comprehensive Cancer Center Freiburg, University Medical Center, Freiburg, Germany.
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47
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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: 46] [Impact Index Per Article: 46.0] [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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48
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Tanaka T, Talegawkar SA, Jin Y, Candia J, Fantoni G, Bandinelli S, Ferrucci L. Proteomic Mediators of Overall Cardiovascular Health on All-Cause Mortality. Nutrients 2023; 15:781. [PMID: 36771486 PMCID: PMC9921082 DOI: 10.3390/nu15030781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/25/2023] [Accepted: 01/29/2023] [Indexed: 02/05/2023] Open
Abstract
Measures of cardiovascular health (CVH) assessed by a combination of behavioral and biological factors has shown protective associations with all-cause mortality. The mechanisms underlying these associations have not been fully elucidated. In this study, we characterized the plasma proteomics profile of CVH and tested whether specific proteins mediated the associations between CVH and all-cause mortality in participants of the InCHIANTI study. Of the 1301 proteins tested, 92 proteins were associated with CVH (22 positively, 70 negatively). Proteins most strongly associated with CVH included leptin (LEP), fatty acid binding protein 3 (FABP3), Angiopoietin-2 (ANGPT2), and growth-differential factor 15 (GDF15). Of the 92 CVH-associated proteins, 33 proteins significantly mediated the associations between CVH and all-cause mortality, with percent mediation ranging from 5 to 30%. The most significant mediating proteins were GDF15 and insulin-like growth factor 2 (IGFBP2). Proteins associated with better CVH were enriched for proteins that reflect the suppression of the complement coagulation and GH/IGF pathways.
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Affiliation(s)
- Toshiko Tanaka
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA
| | - Sameera A. Talegawkar
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Yichen Jin
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Julián Candia
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA
| | - Giovanna Fantoni
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA
| | | | - Luigi Ferrucci
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
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49
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Dietary Responses of Dementia-Related Genes Encoding Metabolic Enzymes. Nutrients 2023; 15:nu15030644. [PMID: 36771351 PMCID: PMC9921944 DOI: 10.3390/nu15030644] [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: 09/29/2022] [Revised: 01/19/2023] [Accepted: 01/24/2023] [Indexed: 01/31/2023] Open
Abstract
The age-related loss of the cognitive function is a growing concern for global populations. Many factors that determine cognitive resilience or dementia also have metabolic functions. However, this duality is not universally appreciated when the action of that factor occurs in tissues external to the brain. Thus, we examined a set of genes involved in dementia, i.e., those related to vascular dementia, Alzheimer's disease, Parkinson's disease, and the human metabolism for activity in 12 metabolically active tissues. Mining the Genotype-Tissue Expression (GTEx) data showed that most of these metabolism-dementia (MD) genes (62 of 93, 67%) exhibit a higher median expression in any of the metabolically active tissues than in the brain. After identifying that several MD genes served as blood-based biomarkers of longevity in other studies, we examined the impact of the intake of food, nutrients, and other dietary factors on the expression of MD genes in whole blood in the Framingham Offspring Study (n = 2134). We observed positive correlations between flavonoids and HMOX1, taurine and UQCRC1, broccoli and SLC10A2, and myricetin and SLC9A8 (p < 2.09 × 10-4). In contrast, dairy protein, palmitic acid, and pie were negatively correlated, respectively, with the expression of IGF1R, CSF1R, and SLC9A8, among others (p < 2.92 × 10-4). The results of this investigation underscore the potential contributions of metabolic enzyme activity in non-brain tissues to the risk of dementia. Specific epidemiological or intervention studies could be designed using specific foods and nutrients or even dietary patterns focused on these foods and nutrients that influence the expression of some MD genes to verify the findings presented here.
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50
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Ruangritchankul S, Sumananusorn C, Sirivarasai J, Monsuwan W, Sritara P. Association between Dietary Zinc Intake, Serum Zinc Level and Multiple Comorbidities in Older Adults. Nutrients 2023; 15:nu15020322. [PMID: 36678192 PMCID: PMC9865239 DOI: 10.3390/nu15020322] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/24/2022] [Accepted: 12/29/2022] [Indexed: 01/10/2023] Open
Abstract
Zinc is one of the essential micronutrients in the geriatric population, but the importance of zinc status and dietary zinc intake has been poorly characterized. We aimed to explore the relationships among dietary zinc intake, serum zinc concentrations and multimorbidity in a cross-sectional study of 300 employees of Electric Generating Authority of Thailand aged ≥ 60 years. Comprehensive questionnaires were completed, and clinical and laboratory assessments were performed. Factors associated with low serum zinc concentrations were identified using multivariate multinomial logistic regression analyses. The mean serum zinc level was 80.5 (12.8) µg/dL. After adjustment for baseline characteristics, being female and having been in education for ≤12 years were independent risk factors for the lowest tertile (T1) of serum zinc. After additional adjustment for clinical and biochemical parameters, there was a significant association between depression (Thai Geriatric Depression Scale-15 score > 5) and low serum zinc levels (T1 vs. T3, odds ratio (OR): 2.24; 95% confidence interval (CI): 1.06−4.77). Furthermore, as serum albumin increased, serum zinc concentration substantially increased (T1 vs. T3, OR: 0.01; 95% CI: 0.002−0.070). Therefore, the early detection of risk factors and the further management of depression and low serum albumin may assist physicians in preventing low serum concentrations.
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Affiliation(s)
- Sirasa Ruangritchankul
- Division of Geriatric Medicine, Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand
- Correspondence: ; Tel.: +66-81-640-4373
| | - Chutima Sumananusorn
- Master of Science Program in Nutrition, Faculty of Medicine, Ramathibodi Hospital and Institute of Nutrition, Mahidol University, Bangkok 10400, Thailand
| | - Jintana Sirivarasai
- Graduate Program in Nutrition, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand
| | - Wutarak Monsuwan
- Graduate Program in Nutrition, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand
| | - Piyamitr Sritara
- Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand
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