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Fong S, Pabis K, Latumalea D, Dugersuren N, Unfried M, Tolwinski N, Kennedy B, Gruber J. Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention. NATURE AGING 2024:10.1038/s43587-024-00646-8. [PMID: 38898237 DOI: 10.1038/s43587-024-00646-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 05/08/2024] [Indexed: 06/21/2024]
Abstract
Clocks that measure biological age should predict all-cause mortality and give rise to actionable insights to promote healthy aging. Here we applied dimensionality reduction by principal component analysis to clinical data to generate a clinical aging clock (PCAge) identifying signatures (principal components) separating healthy and unhealthy aging trajectories. We found signatures of metabolic dysregulation, cardiac and renal dysfunction and inflammation that predict unsuccessful aging, and we demonstrate that these processes can be impacted using well-established drug interventions. Furthermore, we generated a streamlined aging clock (LinAge), based directly on PCAge, which maintains equivalent predictive power but relies on substantially fewer features. Finally, we demonstrate that our approach can be tailored to individual datasets, by re-training a custom clinical clock (CALinAge), for use in the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study of caloric restriction. Our analysis of CALERIE participants suggests that 2 years of mild caloric restriction significantly reduces biological age. Altogether, we demonstrate that this dimensionality reduction approach, through integrating different biological markers, can provide targets for preventative medicine and the promotion of healthy aging.
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Affiliation(s)
- Sheng Fong
- Department of Geriatric Medicine, Singapore General Hospital, Singapore, Singapore
- Clinical and Translational Sciences PhD Program, Duke-NUS Medical School, Singapore, Singapore
| | - Kamil Pabis
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Center for Healthy Longevity, National University Health System, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Djakim Latumalea
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Center for Healthy Longevity, National University Health System, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | | | - Maximilian Unfried
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Center for Healthy Longevity, National University Health System, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Nicholas Tolwinski
- Science Division, Yale-NUS College, Singapore, Singapore
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, Singapore
| | - Brian Kennedy
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Center for Healthy Longevity, National University Health System, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Jan Gruber
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Center for Healthy Longevity, National University Health System, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Science Division, Yale-NUS College, Singapore, Singapore.
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Rose RA, Howlett SE. Preclinical Studies on the Effects of Frailty in the Aging Heart. Can J Cardiol 2024:S0828-282X(24)00200-9. [PMID: 38460611 DOI: 10.1016/j.cjca.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 02/20/2024] [Accepted: 03/04/2024] [Indexed: 03/11/2024] Open
Abstract
Age is a major risk factor for the development of cardiovascular diseases in men and in women. However, not all people age at the same rate and those who are aging rapidly are considered frail, compared with their fit counterparts. Frailty is an important clinical challenge because those who are frail are more likely to develop and die from illnesses, including cardiovascular diseases, than fit people of the same age. This increase in susceptibility to cardiovascular diseases in older individuals might occur as the cellular and molecular mechanisms involved in the aging process facilitate structural and functional damage in the heart. Consistent with this, recent studies in murine frailty models have provided strong evidence that maladaptive cardiac remodelling in older mice is the most pronounced in mice with a high level of frailty. For example, there is evidence that ventricular hypertrophy and contractile dysfunction increase as frailty increases in aging mice. Additionally, fibrosis and slowing of conduction in the sinoatrial node and atria are proportional to the level of frailty. These modifications could predispose frail older adults to diseases like heart failure and atrial fibrillation. This preclinical work also raises the possibility that emerging interventions designed to "treat frailty" might also treat or prevent cardiovascular diseases. These findings might help to explain why frail older people are most likely to develop these disorders as they age.
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Affiliation(s)
- Robert A Rose
- Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Department of Physiology and Pharmacology, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
| | - Susan E Howlett
- Department of Pharmacology, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; Department of Medicine (Geriatric Medicine), Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada.
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Pridham G, Rutenberg AD. Network dynamical stability analysis reveals key "mallostatic" natural variables that erode homeostasis and drive age-related decline of health. Sci Rep 2023; 13:22140. [PMID: 38092834 PMCID: PMC10719307 DOI: 10.1038/s41598-023-49129-7] [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: 06/20/2023] [Accepted: 12/04/2023] [Indexed: 12/17/2023] Open
Abstract
Using longitudinal study data, we dynamically model how aging affects homeostasis in both mice and humans. We operationalize homeostasis as a multivariate mean-reverting stochastic process. We hypothesize that biomarkers have stable equilibrium values, but that deviations from equilibrium of each biomarker affects other biomarkers through an interaction network-this precludes univariate analysis. We therefore looked for age-related changes to homeostasis using dynamic network stability analysis, which transforms observed biomarker data into independent "natural" variables and determines their associated recovery rates. Most natural variables remained near equilibrium and were essentially constant in time. A small number of natural variables were unable to equilibrate due to a gradual drift with age in their homeostatic equilibrium, i.e. allostasis. This drift caused them to accumulate over the lifespan course and makes them natural aging variables. Their rate of accumulation was correlated with risk of adverse outcomes: death or dementia onset. We call this tendency for aging organisms to drift towards an equilibrium position of ever-worsening health "mallostasis". We demonstrate that the effects of mallostasis on observed biomarkers are spread out through the interaction network. This could provide a redundancy mechanism to preserve functioning until multi-system dysfunction emerges at advanced ages.
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Affiliation(s)
- Glen Pridham
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, B3H 4R2, Canada
| | - Andrew D Rutenberg
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, B3H 4R2, Canada.
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Fernandez ME, Martinez-Romero J, Aon MA, Bernier M, Price NL, de Cabo R. How is Big Data reshaping preclinical aging research? Lab Anim (NY) 2023; 52:289-314. [PMID: 38017182 DOI: 10.1038/s41684-023-01286-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/10/2023] [Indexed: 11/30/2023]
Abstract
The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.
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Affiliation(s)
- Maria Emilia Fernandez
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jorge Martinez-Romero
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Miguel A Aon
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Michel Bernier
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Nathan L Price
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Rafael de Cabo
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
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Zhu SP, Wang L, Luo C, Correia JAFO, De Jesus AMP, Berto F, Wang QY. Physics-informed machine learning and its structural integrity applications: state of the art. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20220406. [PMID: 37742705 DOI: 10.1098/rsta.2022.0406] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 08/16/2023] [Indexed: 09/26/2023]
Abstract
The development of machine learning (ML) provides a promising solution to guarantee the structural integrity of critical components during service period. However, considering the lack of respect for the underlying physical laws, the data hungry nature and poor extrapolation performance, the further application of pure data-driven methods in structural integrity is challenged. An emerging ML paradigm, physics-informed machine learning (PIML), attempts to overcome these limitations by embedding physical information into ML models. This paper discusses different ways of embedding physical information into ML and reviews the developments of PIML in structural integrity including failure mechanism modelling and prognostic and health management (PHM). The exploration of the application of PIML to structural integrity demonstrates the potential of PIML for improving consistency with prior knowledge, extrapolation performance, prediction accuracy, interpretability and computational efficiency and reducing dependence on training data. The analysis and findings of this work outline the limitations at this stage and provide some potential research direction of PIML to develop advanced PIML for ensuring structural integrity of engineering systems/facilities. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
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Affiliation(s)
- Shun-Peng Zhu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Lanyi Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Changqi Luo
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - José A F O Correia
- INEGI and CONSTRUCT, Faculty of Engineering, University of Porto, Porto 4200-465, Portugal
| | - Abílio M P De Jesus
- INEGI and CONSTRUCT, Faculty of Engineering, University of Porto, Porto 4200-465, Portugal
| | - Filippo Berto
- Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, 00184 Roma, Italy
| | - Qingyuan Y Wang
- MOE Key Laboratory of Deep Earth Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, People's Republic of China
- Advanced Research Institute, Chengdu University, Chengdu 610106, People's Republic of China
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Akagi K, Koizumi K, Kadowaki M, Kitajima I, Saito S. New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory. Cells 2023; 12:2297. [PMID: 37759519 PMCID: PMC10528308 DOI: 10.3390/cells12182297] [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: 08/20/2023] [Revised: 09/12/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
Aging is the slowest process in a living organism. During this process, mortality rate increases exponentially due to the accumulation of damage at the cellular level. Cellular senescence is a well-established hallmark of aging, as well as a promising target for preventing aging and age-related diseases. However, mapping the senescent cells in tissues is extremely challenging, as their low abundance, lack of specific markers, and variability arise from heterogeneity. Hence, methodologies for identifying or predicting the development of senescent cells are necessary for achieving healthy aging. A new wave of bioinformatic methodologies based on mathematics/physics theories have been proposed to be applied to aging biology, which is altering the way we approach our understand of aging. Here, we discuss the dynamical network biomarkers (DNB) theory, which allows for the prediction of state transition in complex systems such as living organisms, as well as usage of Raman spectroscopy that offers a non-invasive and label-free imaging, and provide a perspective on potential applications for the study of aging.
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Affiliation(s)
- Kazutaka Akagi
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Keiichi Koizumi
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Makoto Kadowaki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Isao Kitajima
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Shigeru Saito
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
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Poganik JR, Zhang B, Baht GS, Tyshkovskiy A, Deik A, Kerepesi C, Yim SH, Lu AT, Haghani A, Gong T, Hedman AM, Andolf E, Pershagen G, Almqvist C, Clish CB, Horvath S, White JP, Gladyshev VN. Biological age is increased by stress and restored upon recovery. Cell Metab 2023; 35:807-820.e5. [PMID: 37086720 PMCID: PMC11055493 DOI: 10.1016/j.cmet.2023.03.015] [Citation(s) in RCA: 49] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/22/2022] [Accepted: 03/20/2023] [Indexed: 04/24/2023]
Abstract
Aging is classically conceptualized as an ever-increasing trajectory of damage accumulation and loss of function, leading to increases in morbidity and mortality. However, recent in vitro studies have raised the possibility of age reversal. Here, we report that biological age is fluid and exhibits rapid changes in both directions. At epigenetic, transcriptomic, and metabolomic levels, we find that the biological age of young mice is increased by heterochronic parabiosis and restored following surgical detachment. We also identify transient changes in biological age during major surgery, pregnancy, and severe COVID-19 in humans and/or mice. Together, these data show that biological age undergoes a rapid increase in response to diverse forms of stress, which is reversed following recovery from stress. Our study uncovers a new layer of aging dynamics that should be considered in future studies. The elevation of biological age by stress may be a quantifiable and actionable target for future interventions.
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Affiliation(s)
- Jesse R Poganik
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Bohan Zhang
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Gurpreet S Baht
- Department of Orthopaedic Surgery, Duke University, Durham, NC 27701, USA; Duke Molecular Physiology Institute, Duke University, Durham, NC 27701, USA
| | - Alexander Tyshkovskiy
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA 01241, USA
| | - Csaba Kerepesi
- Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network, Budapest, 1111, Hungary
| | - Sun Hee Yim
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ake T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Altos Labs, San Diego, CA, USA
| | - Amin Haghani
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Altos Labs, San Diego, CA, USA
| | - Tong Gong
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anna M Hedman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ellika Andolf
- Department of Clinical Sciences, Division of Obstetrics and Gynaecology, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Göran Pershagen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Catarina Almqvist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA 01241, USA
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Altos Labs, San Diego, CA, USA; Department of Biostatistics, School of Public Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - James P White
- Duke Molecular Physiology Institute, Duke University, Durham, NC 27701, USA; Department of Medicine, Duke University School of Medicine, Durham, NC 27701, USA.
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 01241, USA.
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Mikhalsky AI. On the Paper by Leonid A. Gavrilov and Natalia S. Gavrilova entitled “Trends in Human Species-Specific Lifespan and Actuarial Aging Rate” Published in Biochemistry (Moscow), Vol. 87, Nos. 12-13, pp. 1622-1633 (2022). BIOCHEMISTRY (MOSCOW) 2023; 88:162-163. [PMID: 37068880 DOI: 10.1134/s0006297923010145] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
The methodology used for analyzing the survival process should keep in mind heterogeneity in empirical data. Cross-sectional data are more heterogeneous in comparison with birth-cohort data.
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Affiliation(s)
- Anatoly I Mikhalsky
- Institute of Control Sciences, Russian Academy of Sciences, Moscow, 117997, Russia.
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