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Guan L, Tuttle CSL, Galkin F, Zhavoronkov A, Maier AB. Higher blood biochemistry-based biological age developed by advanced deep learning techniques is associated with frailty in geriatric rehabilitation inpatients: RESORT. Exp Gerontol 2024; 190:112421. [PMID: 38588752 DOI: 10.1016/j.exger.2024.112421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/27/2024] [Accepted: 04/05/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND Accelerated biological ageing is a major underlying mechanism of frailty development. This study aimed to investigate if the biological age measured by a blood biochemistry-based ageing clock is associated with frailty in geriatric rehabilitation inpatients. METHODS Within the REStORing health of acutely unwell adulTs (RESORT) cohort, patients' biological age was measured by an ageing clock based on completed data of 30 routine blood test variables measured at rehabilitation admission. The delta of biological age minus chronological age (years) was calculated. Ordinal logistic regression and multinomial logistic regression were performed to evaluate the association of the delta of ages with frailty assessed by the Clinical Frailty Scale. Effect modification of Cumulative Illness Rating Scale (CIRS) score was tested. RESULTS A total of 1187 geriatric rehabilitation patients were included (median age: 83.4 years, IQR: 77.7-88.5; 57.4 % female). The biochemistry-based biological age was strongly correlated with chronological age (Spearman r = 0.883). After adjustment for age, sex and primary reasons for acute admission, higher biological age (per 1 year higher in delta of ages) was associated with more severe frailty at admission (OR: 1.053, 95 % CI:1.012-1.096) in patients who had a CIRS score of <12 not in patients with a CIRS score >12. The delta of ages was not associated with frailty change from admission to discharge. The specific frailty manifestations as cardiac, hematological, respiratory, renal, and endocrine conditions were associated with higher biological age. CONCLUSION Higher biological age was associated with severe frailty in geriatric rehabilitation inpatients with less comorbidity burden.
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Affiliation(s)
- Lihuan Guan
- Department of Medicine and Aged Care, @AgeMelbourne, The Royal Melbourne Hospital, The University of Melbourne, Victoria, Australia; Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore.
| | - Camilla S L Tuttle
- Department of Medicine and Aged Care, @AgeMelbourne, The Royal Melbourne Hospital, The University of Melbourne, Victoria, Australia; Department of Surgery, St Vincent's Hospital, The University of Melbourne, Victoria, Australia.
| | | | - Alex Zhavoronkov
- Deep longevity, Hong Kong; Insilico Medicine, Hong Kong Science and Technology Park, Hong Kong; The Buck Institute for Research on Aging, Novato, CA, USA.
| | - Andrea B Maier
- Department of Medicine and Aged Care, @AgeMelbourne, The Royal Melbourne Hospital, The University of Melbourne, Victoria, Australia; Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Amsterdam Movement Sciences, Amsterdam, the Netherlands.; Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore.
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2
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Lu JK, Guan L, Wang W, Rojer AGM, Galkin F, Goh J, Maier AB. The association between blood biological age at rehabilitation admission and physical activity during rehabilitation in geriatric inpatients: RESORT. GeroScience 2024:10.1007/s11357-024-01152-w. [PMID: 38589672 DOI: 10.1007/s11357-024-01152-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 04/02/2024] [Indexed: 04/10/2024] Open
Abstract
Geriatric rehabilitation inpatients have high levels of sedentary behaviour (SB) and low levels of physical activity (PA). Biological age predicted by blood biomarkers is indicative of adverse outcomes. The objective was to determine the association between blood biological age at rehabilitation admission and levels of SB and PA during rehabilitation in geriatric inpatients. Inpatients admitted to geriatric rehabilitation wards at the Royal Melbourne Hospital (Melbourne, Australia) from October 22, 2019, to March 29, 2020, in the REStORing health of acute unwell adulTs (RESORT) observational cohort were included. Blood biological age was predicted using SenoClock-BloodAge, a hematological ageing clock. Patients wore an inertial sensor to measure SB and PA. Logistic regression analyses were conducted. A total of 111 patients (57.7% female) with mean age 83.3 ± 7.5 years were included in the analysis. The mean blood biological age was 82.7 ± 8.4 years. Patients with 1-year higher blood biological age had higher odds of having high SB measured as non-upright time greater than 23 h/day (odds ratio (OR): 1.050, 95% confidence interval (CI): 1.000-1.102). Individuals having 1-year higher age deviation trended towards lower odds of having high levels of PA measured as stepping time greater than 7.4 min/day (OR: 0.916, CI: 0.836-1.005) and as greater than 19.5 sit-to-stand transitions/day (OR: 0.915, CI: 0.836-1.002). In conclusion, higher biological age was associated with higher levels of SB and trended towards lower PA. Incorporating blood biological age could facilitate resource allocation and the development of more tailored rehabilitation plans.
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Affiliation(s)
- Jessica K Lu
- Centre for Healthy Longevity, National University Health System, Singapore, Singapore
- Healthy Longevity Translational Research Program, @Age Singapore, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Lihuan Guan
- Centre for Healthy Longevity, National University Health System, Singapore, Singapore
- Healthy Longevity Translational Research Program, @Age Singapore, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Weilan Wang
- Centre for Healthy Longevity, National University Health System, Singapore, Singapore
- Healthy Longevity Translational Research Program, @Age Singapore, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Anna G M Rojer
- Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Van Der Boechorstsraat 7, 1081 BT, Amsterdam, The Netherlands
| | | | - Jorming Goh
- Centre for Healthy Longevity, National University Health System, Singapore, Singapore
- Healthy Longevity Translational Research Program, @Age Singapore, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Andrea B Maier
- Centre for Healthy Longevity, National University Health System, Singapore, Singapore.
- Healthy Longevity Translational Research Program, @Age Singapore, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Van Der Boechorstsraat 7, 1081 BT, Amsterdam, The Netherlands.
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Galkin F, Kovalchuk O, Koldasbayeva D, Zhavoronkov A, Bischof E. Stress, diet, exercise: Common environmental factors and their impact on epigenetic age. Ageing Res Rev 2023; 88:101956. [PMID: 37211319 DOI: 10.1016/j.arr.2023.101956] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/13/2023] [Accepted: 05/15/2023] [Indexed: 05/23/2023]
Abstract
Epigenetic aging clocks have gained significant attention as a tool for predicting age-related health conditions in clinical and research settings. They have enabled geroscientists to study the underlying mechanisms of aging and assess the effectiveness of anti-aging therapies, including diet, exercise and environmental exposures. This review explores the effects of modifiable lifestyle factors' on the global DNA methylation landscape, as seen by aging clocks. We also discuss the underlying mechanisms through which these factors contribute to biological aging and provide comments on what these findings mean for people willing to build an evidence-based pro-longevity lifestyle.
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Affiliation(s)
| | - Olga Kovalchuk
- Department of Biological Sciences, University of Lethbridge, Canada
| | | | - Alex Zhavoronkov
- Deep Longevity, Hong Kong; Insilico Medicine, Hong Kong; Buck Institute for Research on Aging, Novato, CA, USA
| | - Evelyne Bischof
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Department of Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China; Shanghai University of Medicine and Health Sciences, Shanghai, China; Division of Cardiology, Department of Advanced Biomedical Sciences, Federico II University, Via S. Pansini, 580131, Naples, Italy
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4
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Galkin F, Kochetov K, Keller M, Zhavoronkov A, Etcoff N. Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability. Aging (Albany NY) 2022; 14:4935-4958. [PMID: 35723468 PMCID: PMC9271294 DOI: 10.18632/aging.204061] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 04/25/2022] [Indexed: 12/18/2022]
Abstract
In this article, we present a deep learning model of human psychology that can predict one’s current age and future well-being. We used the model to demonstrate that one’s baseline well-being is not the determining factor of future well-being, as posited by hedonic treadmill theory. Further, we have created a 2D map of human psychotypes and identified the regions that are most vulnerable to depression. This map may be used to provide personalized recommendations for maximizing one’s future well-being.
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Affiliation(s)
| | | | | | - Alex Zhavoronkov
- Deep Longevity Limited, Hong Kong.,Insilico Medicine, Hong Kong.,Buck Institute for Research on Aging, Novato, CA 94945, USA
| | - Nancy Etcoff
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
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Galkin F, Mamoshina P, Kochetov K, Sidorenko D, Zhavoronkov A. DeepMAge: A Methylation Aging Clock Developed with Deep Learning. Aging Dis 2021; 12:1252-1262. [PMID: 34341706 PMCID: PMC8279523 DOI: 10.14336/ad.2020.1202] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 12/02/2020] [Indexed: 12/26/2022] Open
Abstract
DNA methylation aging clocks have become an invaluable tool in biogerontology research since their inception in 2013. Today, a variety of machine learning approaches have been tested for the purpose of predicting human age based on molecular-level features. Among these, deep learning, or neural networks, is an especially promising approach that has been used to construct accurate clocks using blood biochemistry, transcriptomics, and microbiomics data-feats unachieved by other algorithms. In this article, we explore how deep learning performs in a DNA methylation setting and compare it to the current industry standard-the 353 CpG clock published in 2013. The aging clock we are presenting (DeepMAge) is a neural network regressor trained on 4,930 blood DNA methylation profiles from 17 studies. Its absolute median error was 2.77 years in an independent verification set of 1,293 samples from 15 studies. DeepMAge shows biological relevance by assigning a higher predicted age to people with various health-related conditions, such as ovarian cancer, irritable bowel diseases, and multiple sclerosis.
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Affiliation(s)
- Fedor Galkin
- Deep Longevity Limited, Hong Kong.
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
| | | | | | - Denis Sidorenko
- Insilico Medicine Hong Kong Limited, Hong Kong Science and Technology Park, Hong Kong.
| | - Alex Zhavoronkov
- Deep Longevity Limited, Hong Kong.
- Insilico Medicine Hong Kong Limited, Hong Kong Science and Technology Park, Hong Kong.
- Buck Institute for Research on Aging, Novato, CA, USA.
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Galkin F, Kochetov K, Mamoshina P, Zhavoronkov A. Adapting Blood DNA Methylation Aging Clocks for Use in Saliva Samples With Cell-type Deconvolution. Front Aging 2021; 2:697254. [PMID: 35822029 PMCID: PMC9261380 DOI: 10.3389/fragi.2021.697254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/16/2021] [Indexed: 11/20/2022]
Abstract
DeepMAge is a deep-learning DNA methylation aging clock that measures the organismal pace of aging with the information from human epigenetic profiles. In blood samples, DeepMAge can predict chronological age within a 2.8 years error margin, but in saliva samples, its performance is drastically reduced since aging clocks are restricted by the training set domain. However, saliva is an attractive fluid for genomic studies due to its availability, compared to other tissues, including blood. In this article, we display how cell type deconvolution and elastic net can be used to expand the domain of deep aging clocks to other tissues. Using our approach, DeepMAge’s error in saliva samples was reduced from 20.9 to 4.7 years with no retraining.
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Affiliation(s)
- Fedor Galkin
- Deep Longevity Limited, Hong Kong, China
- *Correspondence: Alex Zhavoronkov, ; Fedor Galkin,
| | | | | | - Alex Zhavoronkov
- Deep Longevity Limited, Hong Kong, China
- Insilico Medicine Hong Kong Limited, Hong Kong Science and Technology Park, Hong Kong, China
- The Buck Institute for Research on Aging, Novato, CA, United States
- *Correspondence: Alex Zhavoronkov, ; Fedor Galkin,
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Galkin F, Parish A, Bischof E, Zhang J, Mamoshina P, Zhavoronkov A. Increased Pace of Aging in COVID-Related Mortality. Life (Basel) 2021; 11:730. [PMID: 34440474 PMCID: PMC8401657 DOI: 10.3390/life11080730] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/19/2021] [Accepted: 06/29/2021] [Indexed: 02/07/2023] Open
Abstract
Identifying prognostic biomarkers and risk stratification for COVID-19 patients is a challenging necessity. One of the core survival factors is patient age. However, chronological age is often severely biased due to dormant conditions and existing comorbidities. In this retrospective cohort study, we analyzed the data from 5315 COVID-19 patients (1689 lethal cases) admitted to 11 public hospitals in New York City from 1 March 2020 to 1 December. We calculated patients' pace of aging with BloodAge-a deep learning aging clock trained on clinical blood tests. We further constructed survival models to explore the prognostic value of biological age compared to that of chronological age. A COVID-19 score was developed to support a practical patient stratification in a clinical setting. Lethal COVID-19 cases had higher predicted age, compared to non-lethal cases (Δ = 0.8-1.6 years). Increased pace of aging was a significant risk factor of COVID-related mortality (hazard ratio = 1.026 per year, 95% CI = 1.001-1.052). According to our logistic regression model, the pace of aging had a greater impact (adjusted odds ratio = 1.09 ± 0.00, per year) than chronological age (1.04 ± 0.00, per year) on the lethal infection outcome. Our results show that a biological age measure, derived from routine clinical blood tests, adds predictive power to COVID-19 survival models.
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Affiliation(s)
- Fedor Galkin
- Deep Longevity, Hong Kong, China; (P.M.); (A.Z.)
| | - Austin Parish
- Department of Emergency Medicine, Lincoln Medical and Mental Health Center, Bronx, NY 10451, USA;
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA 94305, USA
| | - Evelyne Bischof
- International Center for Multimorbidity and Complexity in Medicine (ICMC), Universität Zürich, 8006 Zürich, Switzerland;
- Basic and Clinical Medicine Department, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
| | - John Zhang
- NYC Health + Hospitals, Lincoln Medical Center, Bronx, NY 10451, USA;
| | | | - Alex Zhavoronkov
- Deep Longevity, Hong Kong, China; (P.M.); (A.Z.)
- Insilico Medicine, Hong Kong Science and Technology Park, Hong Kong, China
- The Buck Institute for Research on Aging, Novato, CA 94945, USA
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8
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Naumov V, Putin E, Pushkov S, Kozlova E, Romantsov K, Kalashnikov A, Galkin F, Tihonova N, Shneyderman A, Galkin E, Zinkevich A, Cope SM, Sethuraman R, Oprea TI, Pearson AT, Tay S, Agrawal N, Dubovenko A, Vanhaelen Q, Ozerov I, Aliper A, Izumchenko E, Zhavoronkov A. COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity. PLoS Comput Biol 2021; 17:e1009183. [PMID: 34260589 PMCID: PMC8312936 DOI: 10.1371/journal.pcbi.1009183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 07/26/2021] [Accepted: 06/14/2021] [Indexed: 01/08/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in December 2019 in Wuhan, China. It was quickly established that both the symptoms and the disease severity may vary from one case to another and several strains of SARS-CoV-2 have been identified. To gain a better understanding of the wide variety of SARS-CoV-2 strains and their associated symptoms, thousands of SARS-CoV-2 genomes have been sequenced in dozens of countries. In this article, we introduce COVIDomic, a multi-omics online platform designed to facilitate the analysis and interpretation of the large amount of health data collected from patients with COVID-19. The COVIDomic platform provides a comprehensive set of bioinformatic tools for the multi-modal metatranscriptomic data analysis of COVID-19 patients to determine the origin of the coronavirus strain and the expected severity of the disease. An integrative analytical workflow, which includes microbial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification, allows to analyze the presence of the most common microbial organisms, their antibiotic resistance, the severity of the infection and the set of the most probable geographical locations from which the studied strain could have originated. The online platform integrates a user friendly interface which allows easy visualization of the results. We envision this tool will not only have immediate implications for management of the ongoing COVID-19 pandemic, but will also improve our readiness to respond to other infectious outbreaks.
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Affiliation(s)
- Vladimir Naumov
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Evgeny Putin
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Stefan Pushkov
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Ekaterina Kozlova
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | | | | | - Fedor Galkin
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Nina Tihonova
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
- School of Biology, Lomonosov Moscow State University, Moscow, Russia
| | | | - Egor Galkin
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Arsenii Zinkevich
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Stephanie M. Cope
- Intel Corporation, Santa Clara, California, United States of America
| | | | - Tudor I. Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico, United States of America
- University of New Mexico Comprehensive Cancer Center, Albuquerque, New Mexico, United States of America
- Autophagy Inflammation and Metabolism Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, United States of America
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Alexander T. Pearson
- Department of Medicine, Section of Hematology and Oncology, University of Chicago, Chicago, Ilinois, United States of America
| | - Savas Tay
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Ilinois, United States of America
| | - Nishant Agrawal
- Department of Surgery, Section of Otolaryngology-Head and Neck Surgery, University of Chicago, Chicago, Ilinois, United States of America
| | - Alexey Dubovenko
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Quentin Vanhaelen
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Ivan Ozerov
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Alex Aliper
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Evgeny Izumchenko
- Department of Medicine, Section of Hematology and Oncology, University of Chicago, Chicago, Ilinois, United States of America
| | - Alex Zhavoronkov
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
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Galkin F, Mamoshina P, Aliper A, Putin E, Moskalev V, Gladyshev VN, Zhavoronkov A. Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning. iScience 2020; 23:101199. [PMID: 32534441 PMCID: PMC7298543 DOI: 10.1016/j.isci.2020.101199] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 04/14/2020] [Accepted: 05/21/2020] [Indexed: 12/14/2022] Open
Abstract
The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host's age based on the gut community composition. Here we developed a method of predicting hosts' age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers. The described intestinal clock represents a unique quantitative model of gut microflora aging and provides a starting point for building host aging and gut community succession into a single narrative. DNNs are the most appropriate model to predict host age from gut microflora profiles Our DNN models reach MAE of 5.9 years in independent verification Feature importance analysis gives a starting point for anti-aging intervention design
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Affiliation(s)
- Fedor Galkin
- Deep Longevity Inc, Hong Kong Science and Technology Park, Hong Kong; Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - Polina Mamoshina
- Deep Longevity Inc, Hong Kong Science and Technology Park, Hong Kong; Insilico Medicine Ltd, Hong Kong Science and Technology Park, Hong Kong
| | - Alex Aliper
- Insilico Medicine Ltd, Hong Kong Science and Technology Park, Hong Kong
| | - Evgeny Putin
- Insilico Medicine Ltd, Hong Kong Science and Technology Park, Hong Kong
| | - Vladimir Moskalev
- Insilico Medicine Ltd, Hong Kong Science and Technology Park, Hong Kong
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, MA, USA
| | - Alex Zhavoronkov
- Deep Longevity Inc, Hong Kong Science and Technology Park, Hong Kong; Insilico Medicine Ltd, Hong Kong Science and Technology Park, Hong Kong; Buck Institute for Research on Aging, Novato, CA, USA; Biogerontology Research Foundation, London, UK.
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Abstract
Most multicellular organisms are known to age, due to accumulation of damage and other deleterious changes over time. These changes are often irreversible, as organisms, humans included, evolved fully differentiated, irreplaceable cells (e.g. neurons) and structures (e.g. skeleton). Hence, deterioration or loss of at least some cells and structures should lead to inevitable aging of these organisms. Yet, some cells may escape this fate: adult somatic cells may be converted to partially reprogrammed cells or induced pluripotent stem cells (iPSCs). By their nature, iPSCs are the cells representing the early stages of life, indicating a possibility of reversing the age of cells within the organism. Reprogramming strategies may be accomplished both in vitro and in vivo, offering opportunities for rejuvenation in the context of whole organisms. Similarly, older organs may be replaced with the younger ones prepared ex vivo, or grown within other organisms or even other species. How could the irreversibility of aging of some parts of the organism be reconciled with the putative reversal of aging of the other parts of the same organism? Resolution of this question holds promise for dramatically extending lifespan, which is currently not possible with traditional genetic, dietary and pharmacological approaches. Critical issues in this challenge are the nature of aging, relationship between aging of an organism and aging of its parts, relationship between cell dedifferentiation and rejuvenation, and increased risk of cancer that goes hand in hand with rejuvenation approaches.
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Affiliation(s)
- Fedor Galkin
- Belozersky Institute of Physico-Chemical Biology, Moscow State University, Moscow, 119234, Russia; Insilico Medicine, Rockville, Maryland 20850, United States
| | - Bohan Zhang
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Sergey E Dmitriev
- Belozersky Institute of Physico-Chemical Biology, Moscow State University, Moscow, 119234, Russia
| | - Vadim N Gladyshev
- Belozersky Institute of Physico-Chemical Biology, Moscow State University, Moscow, 119234, Russia; Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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Filonenko K, Kriachok I, Galkin F, Grabovoy A. OR57 Coincidence of multiple myeloma and non-Hodgkin's lymphoma in female. Crit Rev Oncol Hematol 2012. [DOI: 10.1016/s1040-8428(12)70069-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Hochbaum M, Galkin F. Medicaid patients need not apply. Soc Policy 1987; 17:40-2. [PMID: 10301755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
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