1
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Fang Z, Pan N, Liu S, Li H, Pan M, Zhang J, Li Z, Liu M, Ge X. Comparative Analysis of Brain Age Prediction Using Structural and Diffusion MRIs in Neonates. Neuroimage 2024:120815. [PMID: 39191358 DOI: 10.1016/j.neuroimage.2024.120815] [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: 07/03/2024] [Revised: 08/02/2024] [Accepted: 08/24/2024] [Indexed: 08/29/2024] Open
Abstract
Using machine learning techniques to predict brain age from multimodal data has become a crucial biomarker for assessing brain development. Among various types of brain imaging data, structural magnetic resonance imaging (sMRI) and diffusion magnetic resonance imaging (dMRI) are the most commonly used modalities. sMRI focuses on depicting macrostructural features of the brain, while dMRI reveals the orientation of major white matter fibers and changes in tissue microstructure. However, their differential capabilities in reflecting newborn age and clinical implications have not been systematically studied. This study aims to explore the impact of sMRI and dMRI on brain age prediction. Comparing predictions based on T2-weighted(T2w) and fractional anisotropy (FA) images, we found their mean absolute errors (MAE) in predicting infant age to be similar. Exploratory analysis revealed for T2w images, areas such as the cerebral cortex and ventricles contribute most significantly to age prediction, whereas FA images highlight the cerebral cortex and regions of the main white matter tracts. Despite both modalities focusing on the cerebral cortex, they exhibit significant region-wise differences, reflecting developmental disparities in macro- and microstructural aspects of the cortex. Additionally, we examined the effects of prematurity, gender, and hemispherical asymmetry of the brain on age prediction for both modalities. Results showed significant differences (p<0.05) in age prediction biases based on FA images across gender and hemispherical asymmetry, whereas no significant differences were observed with T2w images. This study underscores the differences between T2w and FA images in predicting infant brain age, offering new perspectives for studying infant brain development and aiding more effective assessment and tracking of infant development.
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Affiliation(s)
- Zhicong Fang
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Ningning Pan
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Shujuan Liu
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Hongzhuang Li
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Minmin Pan
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Jiong Zhang
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Zhuoshuo Li
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
| | - Mengting Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China.
| | - Xinting Ge
- School of Information Science and Engineering, Shandong Normal University, Shandong, China.
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2
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Wang X, Yan X, Li M, Cheng L, Qi X, Zhang J, Pan S, Xu X, Wei W, Li Y. U-shaped association between sleep duration and biological aging: Evidence from the UK Biobank study. Aging Cell 2024; 23:e14159. [PMID: 38556842 PMCID: PMC11258478 DOI: 10.1111/acel.14159] [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: 11/30/2023] [Revised: 03/11/2024] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
Previous research on sleep and aging largely has failed to illustrate the optimal dose-response curve of this relationship. We aimed to analyze the associations between sleep duration and measures of predicted age. In total, 241,713 participants from the UK Biobank were included. Habitual sleep duration was collected from the baseline questionnaire. Four indicators, homeostatic dysregulation (HD), phenoAge (PA), Klemera-Doubal method (KDM), and allostatic load (AL), were chosen to assess predicted age. Multivariate linear regression models were utilized. The association of sleep duration and predicted age followed a U-shape (All p for nonlinear <0.05). Compared with individuals who sleep for 7 h/day, the multivariable-adjusted beta of ≤5 and ≥9 h/day were 0.05 (95% CI 0.03, 0.07) and 0.03 (95% CI 0.02, 0.05) for HD, 0.08 (95% CI 0.01, 0.14) and 0.36 (95% CI 0.31, 0.41) for PA, and 0.21 (95% CI 0.12, 0.30) and 0.30 (95% CI 0.23, 0.37) for KDM. Significant independent and joint effects of sleep and cystatin C (CysC) and gamma glutamyltransferase (GGT) on predicted age metrics were future found. Similar results were observed when conducting stratification analyses. Short and long sleep duration were associated with accelerated predicted age metrics mediated by CysC and GGT.
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Affiliation(s)
- Xuanyang Wang
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of EducationHarbin Medical UniversityHarbinHeilongjiangChina
| | - Xuemin Yan
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of EducationHarbin Medical UniversityHarbinHeilongjiangChina
| | - Mengdi Li
- Department of Endodontics, The First HospitalHarbin Medical UniversityHarbinChina
| | - Licheng Cheng
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of EducationHarbin Medical UniversityHarbinHeilongjiangChina
| | - Xiang Qi
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of EducationHarbin Medical UniversityHarbinHeilongjiangChina
| | - Jia Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of EducationHarbin Medical UniversityHarbinHeilongjiangChina
| | - Sijia Pan
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of EducationHarbin Medical UniversityHarbinHeilongjiangChina
| | - Xiaoqing Xu
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of EducationHarbin Medical UniversityHarbinHeilongjiangChina
| | - Wei Wei
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of EducationHarbin Medical UniversityHarbinHeilongjiangChina
- Department of Pharmacology, College of Pharmacy, Key Laboratory of Cardiovascular Research, Ministry of EducationHarbin Medical UniversityHarbinChina
| | - Ying Li
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of EducationHarbin Medical UniversityHarbinHeilongjiangChina
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3
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Cui F, Tang L, Li D, Ma Y, Wang J, Xie J, Su B, Tian Y, Zheng X. Early-life exposure to tobacco, genetic susceptibility, and accelerated biological aging in adulthood. SCIENCE ADVANCES 2024; 10:eadl3747. [PMID: 38701212 PMCID: PMC11068008 DOI: 10.1126/sciadv.adl3747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 04/03/2024] [Indexed: 05/05/2024]
Abstract
Early-life tobacco exposure serves as a non-negligible risk factor for aging-related diseases. To understand the underlying mechanisms, we explored the associations of early-life tobacco exposure with accelerated biological aging and further assessed the joint effects of tobacco exposure and genetic susceptibility. Compared with those without in utero exposure, participants with in utero tobacco exposure had an increase in Klemera-Doubal biological age (KDM-BA) and PhenoAge acceleration of 0.26 and 0.49 years, respectively, but a decrease in telomere length of 5.34% among 276,259 participants. We also found significant dose-response associations between the age of smoking initiation and accelerated biological aging. Furthermore, the joint effects revealed that high-polygenic risk score participants with in utero exposure and smoking initiation in childhood had the highest accelerated biological aging. There were interactions between early-life tobacco exposure and age, sex, deprivation, and diet on KDM-BA and PhenoAge acceleration. These findings highlight the importance of reducing early-life tobacco exposure to improve healthy aging.
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Affiliation(s)
- Feipeng Cui
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, Hubei, PR China
| | - Linxi Tang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, Hubei, PR China
| | - Dankang Li
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, Hubei, PR China
| | - Yudiyang Ma
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, Hubei, PR China
| | - Jianing Wang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, Hubei, PR China
| | - Junqing Xie
- Center for Statistics in Medicine, NDORMS, University of Oxford, The Botnar Research Centre, Oxford, UK
| | - Binbin Su
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences/Peking Union Medical College, No. 31, Beijige-3, Dongcheng District, Beijing 100730, PR China
| | - Yaohua Tian
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, Hubei, PR China
| | - Xiaoying Zheng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences/Peking Union Medical College, No. 31, Beijige-3, Dongcheng District, Beijing 100730, PR China
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4
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Wang X, Peng Y, Liu F, Wang P, Si C, Gong J, Zhou H, Zhang M, Song F. Joint association of biological aging and lifestyle with risks of cancer incidence and mortality: A cohort study in the UK Biobank. Prev Med 2024; 182:107928. [PMID: 38471624 DOI: 10.1016/j.ypmed.2024.107928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
BACKGROUND Aging is a risk factor for cancer incidence and mortality. Biological aging can reflect the aging degree of the body better than chronological age and can be aggravated by unhealthy lifestyle factors. We aimed to assess the joint effect of biological aging and lifestyle with risks of cancer incidence and mortality. METHODS This study included a total of 281,889 participants aged 37 to 73 from the UK Biobank database. Biological age was derived from chronological age and 9 clinical blood indicators, and lifestyle score was constructed by body mass index, smoking status, alcohol consumption, physical activity, and diet. Multivariate Cox hazard proportional regression model was used to analyze the independent and joint association of biological aging and lifestyle with risks of cancer incidence and mortality, respectively. RESULTS Over a median follow-up period of 12.3 years, we found that older biological age was associated with increased risks of overall cancer, digestive system cancers, lung, breast and renal cancers incidence and mortality (HRs: 1.12-2.25). In the joint analysis of biological aging and lifestyle with risks of cancer incidence and mortality, compared with unhealthy lifestyle and younger biological age, individuals with healthy lifestyle and older biological age had decreased risks of incidence (8% ∼ 60%) and mortality (20% ∼ 63%) for overall, esophageal, colorectal, pancreatic and lung cancers. CONCLUSIONS Biological aging may be an important risk factor for cancer morbidity and mortality. A healthier lifestyle is more likely to mitigate the adverse effects of biological aging on overall cancer and some site-specific cancers.
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Affiliation(s)
- Xixuan Wang
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China
| | - Yu Peng
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China
| | - Fubin Liu
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China
| | - Peng Wang
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China
| | - Changyu Si
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China
| | - Jianxiao Gong
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China
| | - Huijun Zhou
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China
| | - Ming Zhang
- Comprehensive Management Department of Occupational Health, Shenzhen Prevention and Treatment Center for Occupational Diseases, Shenzhen 518020, China
| | - Fangfang Song
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China.
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5
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Lua CZB, Gao Y, Li J, Cao X, Lyu X, Tu Y, Jin S, Liu Z. Influencing Factors of Healthy Aging Risk Assessed Using Biomarkers: A Life Course Perspective. China CDC Wkly 2024; 6:219-224. [PMID: 38532748 PMCID: PMC10961214 DOI: 10.46234/ccdcw2024.044] [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: 11/04/2023] [Accepted: 01/23/2024] [Indexed: 03/28/2024] Open
Abstract
Assessing individual risks of healthy aging using biomarkers and identifying associated factors have become important areas of research. In this study, we conducted a literature review of relevant publications between 2018 and 2023 in both Chinese and English databases. Previous studies have predominantly used single biomarkers, such as C-reactive protein, or focused on specific life course stages and factors such as socioeconomic status, mental health, educational levels, and unhealthy lifestyles. By summarizing the progress in this field, our study provides valuable insights and future directions for promoting healthy aging from a life course perspective.
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Affiliation(s)
- Cedric Zhang Bo Lua
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou City, Zhejiang Province, China
| | - Yajie Gao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou City, Zhejiang Province, China
| | - Jinming Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou City, Zhejiang Province, China
| | - Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou City, Zhejiang Province, China
| | - Xinwei Lyu
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Yinuo Tu
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou City, Zhejiang Province, China
| | - Shuyi Jin
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou City, Zhejiang Province, China
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou City, Zhejiang Province, China
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6
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Ferrucci L, Wilson DM, Donega S, Montano M. Enabling translational geroscience by broadening the scope of geriatric care. Aging Cell 2024; 23:e14034. [PMID: 38038340 PMCID: PMC10776120 DOI: 10.1111/acel.14034] [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: 09/28/2023] [Revised: 10/28/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023] Open
Abstract
Geroscience poses that core biological mechanisms of aging contribute to chronic diseases and disabilities in late life and that health span and longevity can be modulated by pharmacological and behavioral interventions. Despite strong evidence from studies in model organisms and great potentials for translation, most geriatricians remain skeptical that geroscience will help them in the day-by-day battle with the consequences of aging in their patients. We believe that a closer collaboration between gerontologists and geriatricians is the key to overcome this impasse. There is evidence that trajectories of health with aging are rooted in intrinsic and extrinsic exposures that occur early in life and affect the pace of molecular and cellular damage accumulation with aging, also referred to as the "pace" of biological aging. Tools that measure the pace of aging currently allow for the identification of individuals experiencing accelerated aging and at higher risk of multimorbidity and disability. What we term "Translational Geroscience", i.e., the merger of fundamental and translational science with clinical practice, is thus poised to extend the action of geriatric care to a life course perspective. By targeting core mechanisms of aging, gerotherapeutics should be effective in treating patients with multimorbidity and disability, phenotypes that are all too common among geriatric patients nowadays. We call for initiatives that enhance the flow of ideas between gerontologists and geriatricians to facilitate the growth of translational geroscience. This approach can widen the scope of geriatric care, including a new role for geroscience in the promotion and operationalization of healthy longevity.
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Affiliation(s)
- Luigi Ferrucci
- Intramural Research Program of the National Institute on Aging, NIHBaltimoreMarylandUSA
| | - David M. Wilson
- Biomedical Research Institute, Faculty of Medicine and Life SciencesHasselt UniversityDiepenbeekBelgium
| | - Stefano Donega
- Intramural Research Program of the National Institute on Aging, NIHBaltimoreMarylandUSA
| | - Monty Montano
- Department of MedicineHarvard Medical SchoolBostonMassachusettsUSA
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7
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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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8
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Bernard D, Doumard E, Ader I, Kemoun P, Pagès J, Galinier A, Cussat‐Blanc S, Furger F, Ferrucci L, Aligon J, Delpierre C, Pénicaud L, Monsarrat P, Casteilla L. Explainable machine learning framework to predict personalized physiological aging. Aging Cell 2023; 22:e13872. [PMID: 37300327 PMCID: PMC10410015 DOI: 10.1111/acel.13872] [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: 12/22/2022] [Revised: 04/17/2023] [Accepted: 05/03/2023] [Indexed: 06/12/2023] Open
Abstract
Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter-parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty-six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age-specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow-up. These data show that PPA is a robust, quantitative and explainable ML-based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.
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Affiliation(s)
- David Bernard
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
- Université Toulouse 1 – Capitole, Institute of Research in Informatics (IRIT) of Toulouse, CNRSToulouseFrance
| | - Emmanuel Doumard
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
| | - Isabelle Ader
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
| | - Philippe Kemoun
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
- Oral Medicine Department and Hospital of ToulouseToulouse Institute of Oral Medicine and Science, CHU de ToulouseToulouseFrance
| | - Jean‐Christophe Pagès
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
- UFR Santé, Département Médecine, Institut Fédératif de Biologie, CHU de ToulouseToulouseFrance
| | - Anne Galinier
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
- UFR Santé, Département Médecine, Institut Fédératif de Biologie, CHU de ToulouseToulouseFrance
| | - Sylvain Cussat‐Blanc
- Université Toulouse 1 – Capitole, Institute of Research in Informatics (IRIT) of Toulouse, CNRSToulouseFrance
- Artificial and Natural Intelligence Toulouse Institute ANITIToulouseFrance
| | - Felix Furger
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
| | - Luigi Ferrucci
- Biomedical Research Centre, National Institute on AgingNIHBaltimoreMarylandUSA
| | - Julien Aligon
- Université Toulouse 1 – Capitole, Institute of Research in Informatics (IRIT) of Toulouse, CNRSToulouseFrance
| | | | - Luc Pénicaud
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
| | - Paul Monsarrat
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
- Oral Medicine Department and Hospital of ToulouseToulouse Institute of Oral Medicine and Science, CHU de ToulouseToulouseFrance
- Artificial and Natural Intelligence Toulouse Institute ANITIToulouseFrance
| | - Louis Casteilla
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
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9
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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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10
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Gaylord A, Cohen A, Kupsco A. Biomarkers of aging through the life course: A Recent Literature Update. CURRENT OPINION IN EPIDEMIOLOGY AND PUBLIC HEALTH 2023; 2:7-17. [PMID: 38130910 PMCID: PMC10732539 DOI: 10.1097/pxh.0000000000000018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Purpose of review The development of biomarkers of aging has greatly advanced epidemiological studies of aging processes. However, much debate remains on the timing of aging onset and the causal relevance of these biomarkers. In this review, we discuss the most recent biomarkers of aging that have been applied across the life course. Recent findings The most recently developed aging biomarkers that have been applied across the life course can be designated into three categories: epigenetic clocks, epigenetic markers of chronic inflammation, and mitochondrial DNA copy number. While these have been applied at different life stages, the development, validation, and application of these markers has been largely centered on populations of older adults. Few studies have examined trajectories of aging biomarkers across the life course. As the wealth of molecular and biochemical data increases, emerging biomarkers may be able to capture complex and system-specific aging processes. Recently developed biomarkers include novel epigenetic clocks; clocks based on ribosomal DNA, transcriptomic profiles, proteomics, metabolomics, and inflammatory markers; clonal hematopoiesis of indeterminate potential gene mutations; and multi-omics approaches. Summary Attention should be placed on aging at early and middle life stages to better understand trajectories of aging biomarkers across the life course. Additionally, novel biomarkers will provide greater insight into aging processes. The specific mechanisms of aging reflected by these biomarkers should be considered when interpreting results.
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Affiliation(s)
- Abigail Gaylord
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Alan Cohen
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec, Canada
- Research Center on Aging and Research Center of Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec, Canada
- Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Allison Kupsco
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
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11
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Allen JP, Danoff JS, Costello MA, Loeb EL, Davis AA, Hunt GL, Gregory SG, Giamberardino SN, Connelly JJ. Adolescent peer struggles predict accelerated epigenetic aging in midlife. Dev Psychopathol 2023; 35:912-925. [PMID: 35379374 PMCID: PMC9532470 DOI: 10.1017/s0954579422000153] [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] [Indexed: 11/07/2022]
Abstract
This study examined struggles to establish autonomy and relatedness with peers in adolescence and early adulthood as predictors of advanced epigenetic aging assessed at age 30. Participants (N = 154; 67 male and 87 female) were observed repeatedly, along with close friends and romantic partners, from ages 13 through 29. Observed difficulty establishing close friendships characterized by mutual autonomy and relatedness from ages 13 to 18, an interview-assessed attachment state of mind lacking autonomy and valuing of attachment at 24, and self-reported difficulties in social integration across adolescence and adulthood were all linked to greater epigenetic age at 30, after accounting for chronological age, gender, race, and income. Analyses assessing the unique and combined effects of these factors, along with lifetime history of cigarette smoking, indicated that each of these factors, except for adult social integration, contributed uniquely to explaining epigenetic age acceleration. Results are interpreted as evidence that the adolescent preoccupation with peer relationships may be highly functional given the relevance of such relationships to long-term physical outcomes.
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12
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Gao X, Geng T, Jiang M, Huang N, Zheng Y, Belsky DW, Huang T. Accelerated biological aging and risk of depression and anxiety: evidence from 424,299 UK Biobank participants. Nat Commun 2023; 14:2277. [PMID: 37080981 PMCID: PMC10119095 DOI: 10.1038/s41467-023-38013-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 04/11/2023] [Indexed: 04/22/2023] Open
Abstract
Theory predicts that biological processes of aging may contribute to poor mental health in late life. To test this hypothesis, we evaluated prospective associations between biological age and incident depression and anxiety in 424,299 UK Biobank participants. We measured biological age from clinical traits using the KDM-BA and PhenoAge algorithms. At baseline, participants who were biologically older more often experienced depression/anxiety. During a median of 8.7 years of follow-up, participants with older biological age were at increased risk of incident depression/anxiety (5.9% increase per standard deviation [SD] of KDM-BA acceleration, 95% confidence intervals [CI]: 3.3%-8.5%; 11.3% increase per SD of PhenoAge acceleration, 95% CI: 9.%-13.0%). Biological-aging-associated risk of depression/anxiety was independent of and additive to genetic risk measured by genome-wide-association-study-based polygenic scores. Advanced biological aging may represent a potential risk factor for incident depression/anxiety in midlife and older adults and a potential target for risk assessment and intervention.
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Affiliation(s)
- Xu Gao
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China.
| | - Tong Geng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Meijie Jiang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Ninghao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yinan Zheng
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Daniel W Belsky
- Department of Epidemiology & Butler Columbia Aging Center, Columbia University, New York, NY, USA.
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
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13
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Li Z, Zhang W, Duan Y, Niu Y, Chen Y, Liu X, Dong Z, Zheng Y, Chen X, Feng Z, Wang Y, Zhao D, Sun X, Cai G, Jiang H, Chen X. Progress in biological age research. Front Public Health 2023; 11:1074274. [PMID: 37124811 PMCID: PMC10130645 DOI: 10.3389/fpubh.2023.1074274] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 03/16/2023] [Indexed: 05/02/2023] Open
Abstract
Biological age (BA) is a common model to evaluate the function of aging individuals as it may provide a more accurate measure of the extent of human aging than chronological age (CA). Biological age is influenced by the used biomarkers and standards in selected aging biomarkers and the statistical method to construct BA. Traditional used BA estimation approaches include multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal's method (KDM), and, in recent years, deep learning methods. This review summarizes the markers for each organ/system used to construct biological age and published literature using methods in BA research. Future research needs to explore the new aging markers and the standard in select markers and new methods in building BA models.
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Affiliation(s)
- Zhe Li
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Weiguang Zhang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Yuting Duan
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Yue Niu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Yizhi Chen
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
- Department of Nephrology, Hainan Hospital of Chinese PLA General Hospital, Hainan Academician Team Innovation Center, Sanya, China
| | - Xiaomin Liu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Zheyi Dong
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Ying Zheng
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Xizhao Chen
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Zhe Feng
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Yong Wang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Delong Zhao
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Xuefeng Sun
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Guangyan Cai
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Hongwei Jiang
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
- *Correspondence: Hongwei Jiang,
| | - Xiangmei Chen
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
- Xiangmei Chen,
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14
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Gao X, Huang J, Cardenas A, Zhao Y, Sun Y, Wang J, Xue L, Baccarelli AA, Guo X, Zhang L, Wu S. Short-Term Exposure of PM 2.5 and Epigenetic Aging: A Quasi-Experimental Study. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:14690-14700. [PMID: 36197060 DOI: 10.1021/acs.est.2c05534] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Epigenetic age (EA) is an emerging DNA methylation-based biomarker of biological aging, but whether EA is causally associated with short-term PM2.5 exposure remains unknown. We conducted a quasi-experimental study of 26 healthy adults to test whether short-term PM2.5 exposure accelerates seven EAs with three health examinations performed before, during, and after multiple PM2.5 pollution waves. Seven EAs were derived from the DNA methylation profiles of the Illumina HumanMethylationEPIC BeadChip from CD4+ T-helper cells. We found that an increase of 10 μg/m3 in the 0-24 h personal PM2.5 exposure prior to health examinations was associated with a 0.035, 0.035, 0.050, 0.055, 0.052, and 0.037-unit increase in the changes of z-scored DNA methylation age acceleration (AA,Horvath), AA (Hannum), AA (GrimAge), DunedinPoAm, mortality risk score (MS), and epiTOC, respectively (p-values < 0.05). The same increase in the 24-48 h average personal PM2.5 exposure yielded smaller effects but was still robustly associated with the changes in AA (GrimAge), DunedinPoAm, and MS. Such acute aging effects of PM2.5 were mediated by the changes in several circulating biomarkers, including EC-SOD and sCD40L, with up to ∼28% mediated proportions. Our findings demonstrated that short-term PM2.5 exposure could accelerate aging reflected by DNA methylation profiles via blood coagulation, oxidative stress, and systematic inflammation.
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Affiliation(s)
- Xu Gao
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing100191, China
| | - Jing Huang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing100191, China
| | - Andres Cardenas
- Division of Environmental Health Sciences, School of Public Health and Center for Computational Biology, University of California, Berkeley, California94720, United States
| | - Yan Zhao
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing100191, China
| | - Yanyan Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing100069, China
| | - Jiawei Wang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing100191, China
| | - Lijun Xue
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing100191, China
| | - Andrea A Baccarelli
- Laboratory of Environmental Precision Health, Mailman School of Public Health, Columbia University, New York, New York10032, United States
| | - Xinbiao Guo
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing100191, China
| | - Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing100069, China
| | - Shaowei Wu
- Department of Occupational and Environmental Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an710061, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, Shaanxi710061, China
- Key Laboratory of Trace Elements and Endemic Diseases in Ministry of Health, Xi'an, Shaanxi710061, China
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15
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Gao X, Huang N, Guo X, Huang T. Role of sleep quality in the acceleration of biological aging and its potential for preventive interaction on air pollution insults: Findings from the UK Biobank cohort. Aging Cell 2022; 21:e13610. [PMID: 35421261 PMCID: PMC9124313 DOI: 10.1111/acel.13610] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 03/03/2022] [Accepted: 03/25/2022] [Indexed: 12/16/2022] Open
Abstract
Sleep has been associated with aging and relevant health outcomes, but the causal relationship remains inconclusive. In this study, we investigated the associations of sleep behaviors with biological ages (BAs) among 363,886 middle and elderly adults from UK Biobank. Sleep index (0 [worst]-6 [best]) of each participant was retrieved from the following six sleep behaviors: snoring, chronotype, daytime sleepiness, sleep duration, insomnia, and difficulties in getting up. Two BAs, the KDM-biological age and PhenoAge, were estimated by corresponding algorithms based on clinical traits, and their residual discrepancies with chronological age were defined as the age accelerations (AAs). We first observed negative associations between the sleep index and the two AAs, and demonstrated that the change of AAs could be the consequence of sleep quality using Mendelian randomization with genetic risk scores of sleep index and BAs. Particularly, a one-unit increase in sleep index was associated with 0.104- and 0.119-year decreases in KDM-biological AA and PhenoAge acceleration, respectively. Air pollution is another key driver of aging. We further observed significant independent and joint effects of sleep and air pollution (PM2.5 and NO2 ) on AAs. Sleep quality also showed a modifying effect on the associations of elevated PM2.5 and NO2 levels with accelerated AAs. For instance, an interquartile range increase in PM2.5 level was associated with 0.009-, 0.044-, and 0.074-year increase in PhenoAge acceleration among people with high (5-6), medium (3-4), and low (0-2) sleep index, respectively. Our findings elucidate that better sleep quality could lessen accelerated biological aging resulting from air pollution.
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Affiliation(s)
- Xu Gao
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Ninghao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Xinbiao Guo
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
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16
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Kalia V, Belsky DW, Baccarelli AA, Miller GW. An exposomic framework to uncover environmental drivers of aging. EXPOSOME 2022; 2:osac002. [PMID: 35295547 PMCID: PMC8917275 DOI: 10.1093/exposome/osac002] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 01/19/2022] [Accepted: 01/24/2022] [Indexed: 01/02/2023]
Abstract
The exposome, the environmental complement of the genome, is an omics level characterization of an individual's exposures. There is growing interest in uncovering the role of the environment in human health using an exposomic framework that provides a systematic and unbiased analysis of the non-genetic drivers of health and disease. Many environmental toxicants are associated with molecular hallmarks of aging. An exposomic framework has potential to advance understanding of these associations and how modifications to the environment can promote healthy aging in the population. However, few studies have used this framework to study biological aging. We provide an overview of approaches and challenges in using an exposomic framework to investigate environmental drivers of aging. While capturing exposures over a life course is a daunting and expensive task, the use of historical data can be a practical way to approach this research.
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Affiliation(s)
- Vrinda Kalia
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Daniel W Belsky
- Department of Epidemiology and Robert N. Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Andrea A Baccarelli
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Gary W Miller
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
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17
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Belsky DW, Caspi A, Corcoran DL, Sugden K, Poulton R, Arseneault L, Baccarelli A, Chamarti K, Gao X, Hannon E, Harrington HL, Houts R, Kothari M, Kwon D, Mill J, Schwartz J, Vokonas P, Wang C, Williams BS, Moffitt TE. DunedinPACE, a DNA methylation biomarker of the pace of aging. eLife 2022; 11:e73420. [PMID: 35029144 PMCID: PMC8853656 DOI: 10.7554/elife.73420] [Citation(s) in RCA: 258] [Impact Index Per Article: 129.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 12/13/2021] [Indexed: 01/09/2023] Open
Abstract
Background Measures to quantify changes in the pace of biological aging in response to intervention are needed to evaluate geroprotective interventions for humans. Previously, we showed that quantification of the pace of biological aging from a DNA-methylation blood test was possible (Belsky et al., 2020). Here, we report a next-generation DNA-methylation biomarker of Pace of Aging, DunedinPACE (for Pace of Aging Calculated from the Epigenome). Methods We used data from the Dunedin Study 1972-1973 birth cohort tracking within-individual decline in 19 indicators of organ-system integrity across four time points spanning two decades to model Pace of Aging. We distilled this two-decade Pace of Aging into a single-time-point DNA-methylation blood-test using elastic-net regression and a DNA-methylation dataset restricted to exclude probes with low test-retest reliability. We evaluated the resulting measure, named DunedinPACE, in five additional datasets. Results DunedinPACE showed high test-retest reliability, was associated with morbidity, disability, and mortality, and indicated faster aging in young adults with childhood adversity. DunedinPACE effect-sizes were similar to GrimAge Clock effect-sizes. In analysis of incident morbidity, disability, and mortality, DunedinPACE and added incremental prediction beyond GrimAge. Conclusions DunedinPACE is a novel blood biomarker of the pace of aging for gerontology and geroscience. Funding This research was supported by US-National Institute on Aging grants AG032282, AG061378, AG066887, and UK Medical Research Council grant MR/P005918/1.
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Affiliation(s)
- Daniel W Belsky
- Department of Epidemiology & Butler Columbia Aging Center, Columbia UniversityNew YorkUnited States
| | - Avshalom Caspi
- Center for Genomic and Computational Biology, Duke UniversityDurhamUnited States
| | - David L Corcoran
- Center for Genomic and Computational Biology, Duke UniversityDurhamUnited States
| | - Karen Sugden
- Department of Psychology and Neuroscience, Duke UniversityDurhamUnited States
| | - Richie Poulton
- Department of Psychology, University of OtagoOtagoNew Zealand
| | - Louise Arseneault
- Social, Genetic, and Developmental Psychiatry Centre, King's College LondonLondonUnited Kingdom
| | - Andrea Baccarelli
- Department of Environmental Health Sciences, Columbia UniversityNew YorkUnited States
| | - Kartik Chamarti
- Department of Psychology and Neuroscience, Duke UniversityDurhamUnited States
| | - Xu Gao
- Department of Occupational and Environmental Health, Peking UniversityBeijingChina
| | - Eilis Hannon
- Complex Disease Epigenetics Group, University of ExeterExeterUnited Kingdom
| | - Hona Lee Harrington
- Department of Psychology and Neuroscience, Duke UniversityDurhamUnited States
| | - Renate Houts
- Department of Psychology and Neuroscience, Duke UniversityDurhamUnited States
| | - Meeraj Kothari
- Robert N Butler Columbia Aging Center, Columbia UniversityBrooklynUnited States
| | - Dayoon Kwon
- Robert N Butler Columbia Aging Center, Columbia UniversityNew YorkUnited States
| | - Jonathan Mill
- Complex Disease Epigenetics Group, University of ExeterExeterUnited Kingdom
| | - Joel Schwartz
- Department of Environmental Health Sciences, Harvard TH Chan School of Public Health, Harvard UniversityBostonUnited States
| | - Pantel Vokonas
- Department of Medicine, VA Boston Healthcare SystemBostonUnited States
| | - Cuicui Wang
- Department of Environmental Health Sciences, Harvard TH Chan School of Public Health, Harvard UniversityBostonUnited States
| | - Benjamin S Williams
- Department of Psychology and Neuroscience, Duke UniversityDurhamUnited States
| | - Terrie E Moffitt
- Department of Psychology and Neuroscience, Duke UniversityDurhamUnited States
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18
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McCrory C, Fiorito G, Hernandez B, Polidoro S, O'Halloran AM, Hever A, Ni Cheallaigh C, Lu AT, Horvath S, Vineis P, Kenny RA. GrimAge Outperforms Other Epigenetic Clocks in the Prediction of Age-Related Clinical Phenotypes and All-Cause Mortality. J Gerontol A Biol Sci Med Sci 2021; 76:741-749. [PMID: 33211845 DOI: 10.1093/gerona/glaa286] [Citation(s) in RCA: 198] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Indexed: 12/18/2022] Open
Abstract
The aging process is characterized by the presence of high interindividual variation between individuals of the same chronical age prompting a search for biomarkers that capture this heterogeneity. Epigenetic clocks measure changes in DNA methylation levels at specific CpG sites that are highly correlated with calendar age. The discrepancy resulting from the regression of DNA methylation age on calendar age is hypothesized to represent a measure of biological aging with a positive/negative residual signifying age acceleration (AA)/deceleration, respectively. The present study examines the associations of 4 epigenetic clocks-Horvath, Hannum, PhenoAge, GrimAge-with a wide range of clinical phenotypes (walking speed, grip strength, Fried frailty, polypharmacy, Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA), Sustained Attention Reaction Time, 2-choice reaction time), and with all-cause mortality at up to 10-year follow-up, in a sample of 490 participants in the Irish Longitudinal Study on Ageing (TILDA). HorvathAA and HannumAA were not predictive of health; PhenoAgeAA was associated with 4/9 outcomes (walking speed, frailty MOCA, MMSE) in minimally adjusted models, but not when adjusted for other social and lifestyle factors. GrimAgeAA by contrast was associated with 8/9 outcomes (all except grip strength) in minimally adjusted models, and remained a significant predictor of walking speed, .polypharmacy, frailty, and mortality in fully adjusted models. Results indicate that the GrimAge clock represents a step-improvement in the predictive utility of the epigenetic clocks for identifying age-related decline in an array of clinical phenotypes promising to advance precision medicine.
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Affiliation(s)
- Cathal McCrory
- Department of Medical Gerontology, Trinity College Dublin, Ireland
| | - Giovanni Fiorito
- Department of Biomedical Sciences, University of Sassari, Italy.,MRC Centre for Environment and Health, School of Public Medicine, Imperial College London, UK
| | | | | | | | - Ann Hever
- Department of Medical Gerontology, Trinity College Dublin, Ireland
| | | | - Ake T Lu
- Department of Human Genetics, David Geffen School of Medicine, Department of Biostatistics Fielding School of Public Health, University of California Los Angeles
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, Department of Biostatistics Fielding School of Public Health, University of California Los Angeles
| | - Paolo Vineis
- MRC Centre for Environment and Health, School of Public Medicine, Imperial College London, UK
| | - Rose Anne Kenny
- Department of Medical Gerontology, Trinity College Dublin, Ireland
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19
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Han LKM, Schnack HG, Brouwer RM, Veltman DJ, van der Wee NJA, van Tol MJ, Aghajani M, Penninx BWJH. Contributing factors to advanced brain aging in depression and anxiety disorders. Transl Psychiatry 2021; 11:402. [PMID: 34290222 PMCID: PMC8295382 DOI: 10.1038/s41398-021-01524-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 05/26/2021] [Accepted: 07/05/2021] [Indexed: 02/07/2023] Open
Abstract
Depression and anxiety are common and often comorbid mental health disorders that represent risk factors for aging-related conditions. Brain aging has shown to be more advanced in patients with major depressive disorder (MDD). Here, we extend prior work by investigating multivariate brain aging in patients with MDD, anxiety disorders, or both, and examine which factors contribute to older-appearing brains. Adults aged 18-57 years from the Netherlands Study of Depression and Anxiety underwent structural MRI. A pretrained brain-age prediction model based on >2000 samples from the ENIGMA consortium was applied to obtain brain-predicted age differences (brain PAD, predicted brain age minus chronological age) in 65 controls and 220 patients with current MDD and/or anxiety. Brain-PAD estimates were associated with clinical, somatic, lifestyle, and biological factors. After correcting for antidepressant use, brain PAD was significantly higher in MDD (+2.78 years, Cohen's d = 0.25, 95% CI -0.10-0.60) and anxiety patients (+2.91 years, Cohen's d = 0.27, 95% CI -0.08-0.61), compared with controls. There were no significant associations with lifestyle or biological stress systems. A multivariable model indicated unique contributions of higher severity of somatic depression symptoms (b = 4.21 years per unit increase on average sum score) and antidepressant use (-2.53 years) to brain PAD. Advanced brain aging in patients with MDD and anxiety was most strongly associated with somatic depressive symptomatology. We also present clinically relevant evidence for a potential neuroprotective antidepressant effect on the brain-PAD metric that requires follow-up in future research.
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Affiliation(s)
- Laura K. M. Han
- grid.484519.5Department of Psychiatry, Amsterdam University Medical Centers, Vrije Universiteit and GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Hugo G. Schnack
- grid.7692.a0000000090126352Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Rachel M. Brouwer
- grid.7692.a0000000090126352Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, Netherlands ,grid.12380.380000 0004 1754 9227Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands
| | - Dick J. Veltman
- grid.484519.5Department of Psychiatry, Amsterdam University Medical Centers, Vrije Universiteit and GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Nic J. A. van der Wee
- grid.5132.50000 0001 2312 1970Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands ,grid.10419.3d0000000089452978Department of Psychiatry, University Medical Center Leiden, Leiden, The Netherlands
| | - Marie-José van Tol
- grid.4830.f0000 0004 0407 1981Cognitive Neuroscience Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Moji Aghajani
- grid.484519.5Department of Psychiatry, Amsterdam University Medical Centers, Vrije Universiteit and GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands ,grid.5132.50000 0001 2312 1970Institute of Education & Child Studies, Section Forensic Family & Youth Care, Leiden University, Leiden, The Netherlands
| | - Brenda W. J. H. Penninx
- grid.484519.5Department of Psychiatry, Amsterdam University Medical Centers, Vrije Universiteit and GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands
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