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Lu WH. Effect of Modifiable Lifestyle Factors on Biological Aging. JAR LIFE 2024; 13:88-92. [PMID: 38855439 PMCID: PMC11161669 DOI: 10.14283/jarlife.2024.13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Accepted: 05/31/2024] [Indexed: 06/11/2024]
Abstract
Biological age is a concept that uses bio-physiological parameters to account for individual heterogeneity in the biological processes driving aging and aims to enhance the prediction of age-related clinical conditions compared to chronological age. Although engaging in healthy lifestyle behaviors has been linked to a lower mortality risk and a reduced incidence of chronic diseases, it remains unclear to what extent these health benefits result from slowing the pace of the biological aging process. This short review summarized how modifiable lifestyle factors - including diet, physical activity, smoking, alcohol consumption, and the aggregate of multiple healthy behaviors - were associated with established estimates of biological age based on clinical or cellular/molecular markers, including Klemera-Doubal Method biological age, homeostatic dysregulation, phenotypic age, DNA methylation age, and telomere length. In brief, the available studies tend to show a consistent association of lifestyle factors with physiological measures of biological age, while findings regarding molecular-based metrics vary. The limited evidence highlights the need for further research in this field, particularly with a life-course approach.
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Affiliation(s)
- W-H Lu
- IHU HealthAge, Toulouse, France
- Institute on Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France
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2
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Kawamura T, Higuchi M, Ito T, Kawakami R, Usui C, McGreevy KM, Horvath S, Zsolt R, Torii S, Suzuki K, Ishii K, Sakamoto S, Oka K, Muraoka I, Tanisawa K. Healthy Japanese dietary pattern is associated with slower biological aging in older men: WASEDA'S health study. Front Nutr 2024; 11:1373806. [PMID: 38854166 PMCID: PMC11157009 DOI: 10.3389/fnut.2024.1373806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 05/14/2024] [Indexed: 06/11/2024] Open
Abstract
Aging is the greatest risk factor for numerous diseases and mortality, and establishing geroprotective interventions targeting aging is required. Previous studies have suggested that healthy dietary patterns, such as the Mediterranean diet, are associated with delayed biological aging; however, these associations depend on nationality and sex. Therefore, this study aimed to investigate the relationship between dietary patterns identified through principal component analysis and biological aging in older men of Japan, one of the countries with the longest life expectancies. Principal component analysis identified two dietary patterns: a healthy Japanese dietary pattern and a Western-style dietary pattern. Eight epigenetic clocks, some of the most accurate aging biomarkers, were identified using DNA methylation data from whole-blood samples. Correlation analyses revealed that healthy Japanese dietary patterns were significantly negatively or positively correlated with multiple epigenetic age accelerations (AgeAccel), including AgeAccelGrim, FitAgeAccel, and age-adjusted DNAm-based telomere length (DNAmTLAdjAge). Conversely, the Western-style dietary pattern was observed not to correlate significantly with any of the examined AgeAccels or age-adjusted values. After adjusting for covariates, the healthy Japanese dietary pattern remained significantly positively correlated with DNAmTLAdjAge. Regression analysis showed that healthy Japanese dietary pattern contributed less to epigenetic age acceleration than smoking status. These findings suggest that a Western-style dietary pattern may not be associated with biological aging, whereas a healthy Japanese dietary pattern is associated with delayed biological aging in older Japanese men. Our findings provide evidence that healthy dietary patterns may have mild beneficial effects on delayed biological aging in older Japanese men.
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Affiliation(s)
- Takuji Kawamura
- Waseda Institute for Sport Sciences, Waseda University, Saitama, Japan
- Research Center for Molecular Exercise Science, Hungarian University of Sports Science, Budapest, Hungary
| | - Mitsuru Higuchi
- Faculty of Sport Sciences, Waseda University, Saitama, Japan
| | - Tomoko Ito
- Waseda Institute for Sport Sciences, Waseda University, Saitama, Japan
- Department of Food and Nutrition, Tokyo Kasei University, Tokyo, Japan
| | - Ryoko Kawakami
- Waseda Institute for Sport Sciences, Waseda University, Saitama, Japan
- Physical Fitness Research Institute, Meiji Yasuda Life Foundation of Health and Welfare, Tokyo, Japan
| | - Chiyoko Usui
- Waseda Institute for Sport Sciences, Waseda University, Saitama, Japan
- Center for Liberal Education and Learning, Sophia University, Tokyo, Japan
| | - Kristen M. McGreevy
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States
| | - Steve Horvath
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Altos Labs, San Diego Institute of Science, San Diego, CA, United States
| | - Radak Zsolt
- Research Center for Molecular Exercise Science, Hungarian University of Sports Science, Budapest, Hungary
- Faculty of Sport Sciences, Waseda University, Saitama, Japan
| | - Suguru Torii
- Faculty of Sport Sciences, Waseda University, Saitama, Japan
| | | | - Kaori Ishii
- Faculty of Sport Sciences, Waseda University, Saitama, Japan
| | - Shizuo Sakamoto
- Faculty of Sport Sciences, Waseda University, Saitama, Japan
- Faculty of Sport Science, Surugadai University, Saitama, Japan
| | - Koichiro Oka
- Faculty of Sport Sciences, Waseda University, Saitama, Japan
| | - Isao Muraoka
- Faculty of Sport Sciences, Waseda University, Saitama, Japan
| | - Kumpei Tanisawa
- Faculty of Sport Sciences, Waseda University, Saitama, Japan
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Li DL, Hodge AM, Cribb L, Southey MC, Giles GG, Milne RL, Dugué PA. Body Size, Diet Quality, and Epigenetic Aging: Cross-Sectional and Longitudinal Analyses. J Gerontol A Biol Sci Med Sci 2024; 79:glae026. [PMID: 38267386 PMCID: PMC10953795 DOI: 10.1093/gerona/glae026] [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: 09/04/2023] [Indexed: 01/26/2024] Open
Abstract
Epigenetic age is an emerging marker of health that is highly predictive of disease and mortality risk. There is a lack of evidence on whether lifestyle changes are associated with changes in epigenetic aging. We used data from 1 041 participants in the Melbourne Collaborative Cohort Study with blood DNA methylation measures at baseline (1990-1994, mean age: 57.4 years) and follow-up (2003-2007, mean age: 68.8 years). The Alternative Healthy Eating Index-2010 (AHEI-2010), the Mediterranean Dietary Score, and the Dietary Inflammatory Index were used as measures of diet quality, and weight, waist circumference, and waist-to-hip ratio as measures of body size. Five age-adjusted epigenetic aging measures were considered: GrimAge, PhenoAge, PCGrimAge, PCPhenoAge, and DunedinPACE. Multivariable linear regression models including restricted cubic splines were used to assess the cross-sectional and longitudinal associations of body size and diet quality with epigenetic aging. Associations between weight and epigenetic aging cross-sectionally at both time points were positive and appeared greater for DunedinPACE (per SD: β ~0.24) than for GrimAge and PhenoAge (β ~0.10). The longitudinal associations with weight change were markedly nonlinear (U-shaped) with stable weight being associated with the lowest epigenetic aging at follow-up, except for DunedinPACE, for which only weight gain showed a positive association. We found negative, linear associations for AHEI-2010 both cross-sectionally and longitudinally. Other adiposity measures and dietary scores showed similar results. In middle-aged to older adults, declining diet quality and weight gain may increase epigenetic age, while the association for weight loss may require further investigation. Our study sheds light on the potential of weight management and dietary improvement in slowing aging processes.
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Affiliation(s)
- Danmeng Lily Li
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Allison M Hodge
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Lachlan Cribb
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Roger L Milne
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
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Dye CK, Wu H, Jackson GL, Kidane A, Nkambule R, Lukhele NG, Malinga BP, Chekenyere R, El-Sadr WM, Baccarelli AA, Harris TG. Epigenetic aging in older people living with HIV in Eswatini: a pilot study of HIV and lifestyle factors and epigenetic aging. Clin Epigenetics 2024; 16:32. [PMID: 38403593 PMCID: PMC10895753 DOI: 10.1186/s13148-024-01629-7] [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: 09/26/2023] [Accepted: 01/12/2024] [Indexed: 02/27/2024] Open
Abstract
BACKGROUND People living with HIV (PLHIV) on effective antiretroviral therapy are living near-normal lives. Although they are less susceptible to AIDS-related complications, they remain highly vulnerable to non-communicable diseases. In this exploratory study of older PLHIV (OPLHIV) in Eswatini, we investigated whether epigenetic aging (i.e., the residual between regressing epigenetic age on chronological age) was associated with HIV-related parameters, and whether lifestyle factors modified these relationships. We calculated epigenetic aging focusing on the Horvath, Hannum, PhenoAge and GrimAge epigenetic clocks, and a pace of biological aging biomarker (DunedinPACE) among 44 OPLHIV in Eswatini. RESULTS Age at HIV diagnosis was associated with Hannum epigenetic age acceleration (EAA) (β-coefficient [95% Confidence Interval]; 0.53 [0.05, 1.00], p = 0.03) and longer duration since HIV diagnosis was associated with slower Hannum EAA (- 0.53 [- 1.00, - 0.05], p = 0.03). The average daily dietary intake of fruits and vegetables was associated with DunedinPACE (0.12 [0.03, 0.22], p = 0.01). The associations of Hannum EAA with the age at HIV diagnosis and duration of time since HIV diagnosis were attenuated when the average daily intake of fruits and vegetables or physical activity were included in our models. Diet and self-perceived quality of life measures modified the relationship between CD4+ T cell counts at participant enrollment and Hannum EAA. CONCLUSIONS Epigenetic age is more advanced in OPLHIV in Eswatini in those diagnosed with HIV at an older age and slowed in those who have lived for a longer time with diagnosed HIV. Lifestyle and quality of life factors may differentially affect epigenetic aging in OPLHIV. To our knowledge, this is the first study to assess epigenetic aging in OPLHIV in Eswatini and one of the few in sub-Saharan Africa.
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Affiliation(s)
- Christian K Dye
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, 630 West 168th St. Room 16-416, New York, NY, 10032, USA.
| | - Haotian Wu
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, 630 West 168th St. Room 16-416, New York, NY, 10032, USA
| | - Gabriela L Jackson
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, 630 West 168th St. Room 16-416, New York, NY, 10032, USA
| | - Altaye Kidane
- ICAP at Columbia University, Mailman School of Public Health, New York, NY, USA
| | | | | | | | | | - Wafaa M El-Sadr
- ICAP at Columbia University, Mailman School of Public Health, New York, NY, USA
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Andrea A Baccarelli
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, 630 West 168th St. Room 16-416, New York, NY, 10032, USA
| | - Tiffany G Harris
- ICAP at Columbia University, Mailman School of Public Health, New York, NY, USA
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
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Reynolds LM, Houston DK, Skiba MB, Whitsel EA, Stewart JD, Li Y, Zannas AS, Assimes TL, Horvath S, Bhatti P, Baccarelli AA, Tooze JA, Vitolins MZ. Diet Quality and Epigenetic Aging in the Women's Health Initiative. J Acad Nutr Diet 2024:S2212-2672(24)00002-9. [PMID: 38215906 DOI: 10.1016/j.jand.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 12/05/2023] [Accepted: 01/08/2024] [Indexed: 01/14/2024]
Abstract
BACKGROUND Higher diet quality scores are associated with a lower risk for many chronic diseases and all-cause mortality; however, it is unclear if diet quality is associated with aging biology. OBJECTIVE This study aimed to examine the association between diet quality and a measure of biological aging known as epigenetic aging. DESIGN A cross-sectional data analysis was used to examine the association between three diet quality scores based on self-reported food frequency questionnaire data and five measures of epigenetic aging based on DNA methylation (DNAm) data from peripheral blood. PARTICIPANTS/SETTING This study included 4,500 postmenopausal women recruited from multiple sites across the United States (1993-98), aged 50 to 79 years, with food frequency questionnaire and DNAm data available from the Women's Health Initiative baseline visit. MAIN OUTCOME MEASURES Five established epigenetic aging measures were generated from HumanMethylation450 Beadchip DNAm data, including AgeAccelHannum, AgeAccelHorvath, AgeAccelPheno, AgeAccelGrim, and DunedinPACE. STATISTICAL ANALYSES PERFORMED Linear mixed models were used to test for associations between three diet quality scores (Healthy Eating Index, Dietary Approaches to Stop Hypertension, and alternate Mediterranean diet scores) and epigenetic aging measures, adjusted for age, race and ethnicity, education, tobacco smoking, physical activity, Women's Health Initiative substudy from which DNAm data were obtained, and DNAm-based estimates of leukocyte proportions. RESULTS Healthy Eating Index, Dietary Approaches to Stop Hypertension, and alternate Mediterranean diet scores were all inversely associated with AgeAccelPheno, AgeAccelGrim, and DunedinPACE (P < 0.05), with the largest effects with DunedinPACE. A one standard deviation increment in diet quality scores was associated with a decrement (β ± SE) in DunedinPACE z score of -0.097 ± 0.014 (P = 9.70 x 10-13) for Healthy Eating Index, -0.107 ± 0.014 (P = 1.53 x 10-14) for Dietary Approaches to Stop Hypertension, and -0.068 ± 0.013 (P = 2.31 x 10-07) for the alternate Mediterranean diet. CONCLUSIONS In postmenopausal women, diet quality scores were inversely associated with DNAm-based measures of biological aging, particularly DunedinPACE.
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Affiliation(s)
- Lindsay M Reynolds
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
| | - Denise K Houston
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Meghan B Skiba
- Division of Biobehavioral Health Science, University of Arizona Cancer Center, University of Arizona, Tucson, Arizona
| | - Eric A Whitsel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina
| | - James D Stewart
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina; Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, North Carolina
| | - Yun Li
- Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, North Carolina; Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina
| | - Anthony S Zannas
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Themistocles L Assimes
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Palo Alto, California
| | - Steve Horvath
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California; Altos Labs, San Diego, California
| | - Parveen Bhatti
- Cancer Control Research, BC Cancer Research Institute, Vancouver, British Columbia, Canada; School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Andrea A Baccarelli
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York
| | - Janet A Tooze
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Mara Z Vitolins
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina
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He H, Chen X, Ding Y, Chen X, He X. Composite dietary antioxidant index associated with delayed biological aging: a population-based study. Aging (Albany NY) 2024; 16:15-27. [PMID: 38170244 PMCID: PMC10817368 DOI: 10.18632/aging.205232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/23/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVE The objective of this study was to explore the potential correlation between the composite dietary antioxidant index (CDAI) and biological aging, addressing the insufficient epidemiological evidence in this area. METHODS Participants meeting eligibility criteria were selected from the National Health and Nutrition Examination Surveys (NHANES) conducted between 2001 and 2018. CDAI was determined based on dietary antioxidants obtained from 24-hour dietary recalls. Biological age was determined using PhenoAge algorithms incorporating various clinical features. Weighted multiple models were employed to investigate and assess the association between CDAI and biological age. RESULTS Analysis of the CDAI quartile revealed disparities in terms of age, gender, ethnicity, educational level, marital status, poverty, dietary calories intakes, smoking, drinking status, BMI, physical activity, and PhenoAge. After adjusting for potential confounding factors, a significant inverse relationship was found between CDAI and Phenotypic Age, with each standard deviation increase in CDAI score correlating with a 0.18-year decrease in Phenotypic Age. These negative correlations between CDAI and PhenoAge advancement were observed regardless of age, gender, physical activity status, smoking status, and body mass index. CONCLUSIONS Our findings demonstrate a positive relationship between higher CDAI scores and delayed biological aging. These results have significant implications for public health initiatives aimed at promoting healthy aging through dietary interventions.
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Affiliation(s)
- Huiqin He
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Zhejiang University Medical School, Hangzhou 310016, China
| | - Xin Chen
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Zhejiang University Medical School, Hangzhou 310016, China
| | - Yiming Ding
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Zhejiang University Medical School, Hangzhou 310016, China
| | - Xiaoli Chen
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Zhejiang University Medical School, Hangzhou 310016, China
| | - Xingkang He
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Zhejiang University Medical School, Hangzhou 310016, China
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Ru L, Zheng H, Lian W, Zhao S, Fan Q. Knowledge mapping of idiopathic scoliosis genes and research hotspots (2002-2022): a bibliometric analysis. Front Pediatr 2023; 11:1177983. [PMID: 38111628 PMCID: PMC10725947 DOI: 10.3389/fped.2023.1177983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 11/06/2023] [Indexed: 12/20/2023] Open
Abstract
Background The etiology of idiopathic scoliosis (IS) remains unclear. Gene-based studies on genetic etiology and molecular mechanisms have improved our understanding of IS and guided treatment and diagnosis. Therefore, it is imperative to explicate and demarcate the preponderant areas of inquiry, key scholars, and their aggregate scholarly output, in addition to the collaborative associations amongst publications or researchers. Methods Documents were retrieved from the Web of Science Core Collection (WoSCC) with the following criteria: TS = ("idiopathic scoliosis" AND gene) refined by search operators (genomic OR "hereditary substance" OR "germ plasm" OR Cistrons OR genetics OR genetic OR genes OR Polygenic OR genotype OR genome OR allele OR polygenes OR Polygene) AND DOCUMENT TYPES (ARTICLE OR REVIEW), and the timespan of 2002-01-01 to 2022-11-26. The online bibliometric analysis platform (bibliometric), bibliographic item co-occurrence matrix builder (BICOMB), CiteSpace 6.1. R6 and VOS viewer were used to evaluate articles for publications, nations, institutions, journals, references, knowledge bases, keywords, and research hotspots. Results A total of 479 documents were retrieved from WoSCC. Fourty-four countries published relevant articles. The country with the most significant number of articles was China, and the institution with the most significant number of articles was Nanjing University. Citation analysis formed eight meaningful clusters and 16 high-frequency keywords. (2) The citation knowledge map included single nucleotide polymorphisms, whole exome sequencing, axonal dynamin, drug development, mesenchymal stem cells, dietary intake, curve progression, zebrafish development model, extracellular matrix, and rare variants were the current research hotspots and frontiers. Conclusions Recent research has focused on IS-related genes, whereas the extracellular matrix and unusual variants are research frontiers and hotspots. Functional analysis of susceptibility genes will prove to be valuable for identifying this disease.
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Affiliation(s)
- Like Ru
- School of Pediatrics, Henan University of Chinese Medicine, Zhengzhou, China
| | - Hong Zheng
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- School of Pediatrics, Henan University of Chinese Medicine, Zhengzhou, China
| | - Wenjun Lian
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Shuying Zhao
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Qimeng Fan
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
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Kresovich JK, O’Brien KM, Xu Z, Weinberg CR, Sandler DP, Taylor JA. Changes in methylation-based aging in women who do and do not develop breast cancer. J Natl Cancer Inst 2023; 115:1329-1336. [PMID: 37467056 PMCID: PMC10637033 DOI: 10.1093/jnci/djad117] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/09/2023] [Accepted: 06/16/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Breast cancer survivors have increased incidence of age-related diseases, suggesting that some survivors may experience faster biological aging. METHODS Among 417 women enrolled in the prospective Sister Study cohort, DNA methylation data were generated on paired blood samples collected an average of 7.7 years apart and used to calculate 3 epigenetic metrics of biological aging (PhenoAgeAccel, GrimAgeAccel, and Dunedin Pace of Aging Calculated from the Epigenome [DunedinPACE]). Approximately half (n = 190) the women sampled were diagnosed and treated for breast cancer between blood draws, whereas the other half (n = 227) remained breast cancer-free. Breast tumor characteristics and treatment information were abstracted from medical records. RESULTS Among women who developed breast cancer, diagnoses occurred an average of 3.5 years after the initial blood draw and 4 years before the second draw. After accounting for covariates and biological aging metrics measured at baseline, women diagnosed and treated for breast cancer had higher biological aging at the second blood draw than women who remained cancer-free as measured by PhenoAgeAccel (standardized mean difference [β] = 0.13, 95% confidence interval [CI) = 0.00 to 0.26), GrimAgeAccel (β = 0.14, 95% CI = 0.03 to 0.25), and DunedinPACE (β = 0.37, 95% CI = 0.24 to 0.50). In case-only analyses assessing associations with different breast cancer therapies, radiation had strong positive associations with biological aging (PhenoAgeAccel: β = 0.39, 95% CI = 0.19 to 0.59; GrimAgeAccel: β = 0.29, 95% CI = 0.10 to 0.47; DunedinPACE: β = 0.25, 95% CI = 0.02 to 0.48). CONCLUSIONS Biological aging is accelerated following a breast cancer diagnosis and treatment. Breast cancer treatment modalities appear to differentially contribute to biological aging.
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Affiliation(s)
- Jacob K Kresovich
- Departments of Cancer Epidemiology & Breast Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Katie M O’Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Zongli Xu
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Clarice R Weinberg
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
- Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
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9
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Koenigsberg SH, Chang CJ, Ish J, Xu Z, Kresovich JK, Lawrence KG, Kaufman JD, Sandler DP, Taylor JA, White AJ. Air pollution and epigenetic aging among Black and White women in the US. ENVIRONMENT INTERNATIONAL 2023; 181:108270. [PMID: 37890265 PMCID: PMC10872847 DOI: 10.1016/j.envint.2023.108270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 10/12/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023]
Abstract
BACKGROUND DNA methylation-based measures of biological aging have been associated with air pollution and may link pollutant exposures to aging-related health outcomes. However, evidence is inconsistent and there is little information for Black women. OBJECTIVE We examined associations of ambient particulate matter <2.5 μm and <10 μm in diameter (PM2.5 and PM10) and nitrogen dioxide (NO2) with DNA methylation, including epigenetic aging and individual CpG sites, and evaluated whether associations differ between Black and non-Hispanic White (NHW) women. METHODS Validated models were used to estimate annual average outdoor residential exposure to PM2.5, PM10, and NO2 in a sample of self-identified Black (n=633) and NHW (n=3493) women residing in the contiguous US. We used sampling-weighted generalized linear regression to examine the effects of pollutants on six epigenetic aging measures (primary: DunedinPACE, GrimAgeAccel, and PhenoAgeAccel; secondary: Horvath intrinsic epigenetic age acceleration [EAA], Hannum extrinsic EAA, and skin & blood EAA) and epigenome-wide associations for individual CpG sites. Wald tests of nested models with and without interaction terms were used to examine effect measure modification by race/ethnicity. RESULTS Black participants had higher median air pollution exposure than NHW participants. GrimAgeAccel was associated with both PM10 and NO2 among Black participants, (Q4 versus Q1, PM10: β=1.09, 95% CI: 0.16-2.03; NO2: β=1.01, 95% CI 0.08-1.94) but not NHW participants (p-for-heterogeneity: PM10=0.10, NO2=0.20). In Black participants, we also observed a monotonic exposure-response relationship between NO2 and DunedinPACE (Q4 versus Q1, NO2: β=0.029, 95% CI: 0.004-0.055; p-for-trend=0.03), which was not observed in NHW participants (p-for-heterogeneity=0.09). In the EWAS, pollutants were significantly associated with differential methylation at 19 CpG sites in Black women and one in NHW women. CONCLUSIONS In a US-wide cohort study, our findings suggest that air pollution is associated with DNA methylation alterations consistent with higher epigenetic aging among Black, but not NHW, women.
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Affiliation(s)
- Sarah H Koenigsberg
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 123 W. Franklin St., Chapel Hill, NC 27517, USA; Epidemiology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr, Research Triangle Park, NC 27709, USA.
| | - Che-Jung Chang
- Epidemiology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr, Research Triangle Park, NC 27709, USA
| | - Jennifer Ish
- Epidemiology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr, Research Triangle Park, NC 27709, USA
| | - Zongli Xu
- Epidemiology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr, Research Triangle Park, NC 27709, USA
| | - Jacob K Kresovich
- Epidemiology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr, Research Triangle Park, NC 27709, USA; Departments of Cancer Epidemiology and Breast Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL 33612, USA
| | - Kaitlyn G Lawrence
- Epidemiology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr, Research Triangle Park, NC 27709, USA
| | - Joel D Kaufman
- Departments of Environmental & Occupational Health Sciences, Medicine, and Epidemiology University of Washington, 4225 Roosevelt Way NE, Seattle, WA 98105, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr, Research Triangle Park, NC 27709, USA
| | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr, Research Triangle Park, NC 27709, USA
| | - Alexandra J White
- Epidemiology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr, Research Triangle Park, NC 27709, USA.
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10
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Dye CK, Wu H, Jackson GL, Kidane A, Nkambule R, Lukhele NG, Malinga BP, Chekenyere R, El-Sadr WM, Baccarelli AA, Harris TG. Epigenetic aging in older people living with HIV in Eswatini: a pilot study of HIV and lifestyle factors and epigenetic aging. RESEARCH SQUARE 2023:rs.3.rs-3389208. [PMID: 37886587 PMCID: PMC10602087 DOI: 10.21203/rs.3.rs-3389208/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Background People living with HIV (PLHIV) on effective antiretroviral therapy (ART) are living near-normal lives. Although they are less susceptible to AIDS-related complications, they remain highly vulnerable to non-communicable diseases (NCD). In this exploratory study of older PLHIV (OPLHIV) in Eswatini, we investigated whether biological aging (i.e., the difference between epigenetic age and chronological age, termed 'epigenetic age acceleration [EAA]') was associated with HIV-related parameters, and whether lifestyle factors modified these relationships. We calculated EAA focusing on the second-generation epigenetic clocks, PhenoAge and GrimAge, and a pace of aging biomarker (DunedinPACE) among 44 OPLHIV in Eswatini. Results Among participants, the PhenoAge clock showed older epigenetic age (68 years old [63, 77]) but a younger GrimAge epigenetic age (median=56 years old [interquartile range=50, 61]) compared to the chronological age (59 years old [54, 66]). Participants diagnosed with HIV at an older age showed slower DunedinPACE (β-coefficient [95% Confidence Interval]; -0.02 [-0.04, -0.01], p=0.002) and longer duration since HIV diagnosis was associated with faster DunedinPACE (0.02 [0.01, 0.04], p=0.002). The average daily dietary intake of fruits and vegetables was associated with faster DunedinPACE (0.12 [0.03, 0.22], p=0.01) and modified the relationship between HIV status variables (number of years living with HIV since diagnosis, age at HIV diagnosis, CD4+ T cell counts) and PhenoAge EAA, and DunedinPACE. Conclusions Biological age is accelerated in OPLHIV in Eswatini, with those living with HIV for a longer duration at risk for faster biological aging. Lifestyle factors, especially healthier diets, may attenuate biological aging in OPLHIV. To our knowledge, this is the first study to assess biological aging in Eswatini and one of the few in sub-Saharan Africa.
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Affiliation(s)
| | - Haotian Wu
- Columbia University Mailman School of Public Health
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11
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Yaskolka Meir A, Yun H, Stampfer MJ, Liang L, Hu FB. Nutrition, DNA methylation and obesity across life stages and generations. Epigenomics 2023; 15:991-1015. [PMID: 37933548 DOI: 10.2217/epi-2023-0172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023] Open
Abstract
Obesity is a complex multifactorial condition that often manifests in early life with a lifelong burden on metabolic health. Diet, including pre-pregnancy maternal diet, in utero nutrition and dietary patterns in early and late life, can shape obesity development. Growing evidence suggests that epigenetic modifications, specifically DNA methylation, might mediate or accompany these effects across life stages and generations. By reviewing human observational and intervention studies conducted over the past 10 years, this work provides a comprehensive overview of the evidence linking nutrition to DNA methylation and its association with obesity across different age periods, spanning from preconception to adulthood and identify future research directions in the field.
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Affiliation(s)
- Anat Yaskolka Meir
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Huan Yun
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Meir J Stampfer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Medicine, Channing Division of Network Medicine, Brigham & Women's Hospital & Harvard Medical School, Boston, MA 02115, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Medicine, Channing Division of Network Medicine, Brigham & Women's Hospital & Harvard Medical School, Boston, MA 02115, USA
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12
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Yaskolka Meir A, Keller M, Hoffmann A, Rinott E, Tsaban G, Kaplan A, Zelicha H, Hagemann T, Ceglarek U, Isermann B, Shelef I, Blüher M, Stumvoll M, Li J, Haange SB, Engelmann B, Rolle-Kampczyk U, von Bergen M, Hu FB, Stampfer MJ, Kovacs P, Liang L, Shai I. The effect of polyphenols on DNA methylation-assessed biological age attenuation: the DIRECT PLUS randomized controlled trial. BMC Med 2023; 21:364. [PMID: 37743489 PMCID: PMC10519069 DOI: 10.1186/s12916-023-03067-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 07/31/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND Epigenetic age is an estimator of biological age based on DNA methylation; its discrepancy from chronologic age warrants further investigation. We recently reported that greater polyphenol intake benefitted ectopic fats, brain function, and gut microbiota profile, corresponding with elevated urine polyphenols. The effect of polyphenol-rich dietary interventions on biological aging is yet to be determined. METHODS We calculated different biological aging epigenetic clocks of different generations (Horvath2013, Hannum2013, Li2018, Horvath skin and blood2018, PhenoAge2018, PCGrimAge2022), their corresponding age and intrinsic age accelerations, and DunedinPACE, all based on DNA methylation (Illumina EPIC array; pre-specified secondary outcome) for 256 participants with abdominal obesity or dyslipidemia, before and after the 18-month DIRECT PLUS randomized controlled trial. Three interventions were assigned: healthy dietary guidelines, a Mediterranean (MED) diet, and a polyphenol-rich, low-red/processed meat Green-MED diet. Both MED groups consumed 28 g walnuts/day (+ 440 mg/day polyphenols). The Green-MED group consumed green tea (3-4 cups/day) and Mankai (Wolffia globosa strain) 500-ml green shake (+ 800 mg/day polyphenols). Adherence to the Green-MED diet was assessed by questionnaire and urine polyphenols metabolomics (high-performance liquid chromatography quadrupole time of flight). RESULTS Baseline chronological age (51.3 ± 10.6 years) was significantly correlated with all methylation age (mAge) clocks with correlations ranging from 0.83 to 0.95; p < 2.2e - 16 for all. While all interventions did not differ in terms of changes between mAge clocks, greater Green-Med diet adherence was associated with a lower 18-month relative change (i.e., greater mAge attenuation) in Li and Hannum mAge (beta = - 0.41, p = 0.004 and beta = - 0.38, p = 0.03, respectively; multivariate models). Greater Li mAge attenuation (multivariate models adjusted for age, sex, baseline mAge, and weight loss) was mostly affected by higher intake of Mankai (beta = - 1.8; p = 0.061) and green tea (beta = - 1.57; p = 0.0016) and corresponded with elevated urine polyphenols: hydroxytyrosol, tyrosol, and urolithin C (p < 0.05 for all) and urolithin A (p = 0.08), highly common in green plants. Overall, participants undergoing either MED-style diet had ~ 8.9 months favorable difference between the observed and expected Li mAge at the end of the intervention (p = 0.02). CONCLUSIONS This study showed that MED and green-MED diets with increased polyphenols intake, such as green tea and Mankai, are inversely associated with biological aging. To the best of our knowledge, this is the first clinical trial to indicate a potential link between polyphenol intake, urine polyphenols, and biological aging. TRIAL REGISTRATION ClinicalTrials.gov, NCT03020186.
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Affiliation(s)
- Anat Yaskolka Meir
- The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O. Box 653, 8410501, Be'er Sheva, Israel
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
| | - Maria Keller
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Center Munich at the University of Leipzig and University Hospital Leipzig, 04103, Leipzig, Germany
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, University of Leipzig, Liebigstrasse 21, 04103, Leipzig, Germany
| | - Anne Hoffmann
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Center Munich at the University of Leipzig and University Hospital Leipzig, 04103, Leipzig, Germany
| | - Ehud Rinott
- The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O. Box 653, 8410501, Be'er Sheva, Israel
| | - Gal Tsaban
- The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O. Box 653, 8410501, Be'er Sheva, Israel
- Soroka University Medical Center, 84101, Be'er Sheva, Israel
| | - Alon Kaplan
- The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O. Box 653, 8410501, Be'er Sheva, Israel
| | - Hila Zelicha
- The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O. Box 653, 8410501, Be'er Sheva, Israel
| | - Tobias Hagemann
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Center Munich at the University of Leipzig and University Hospital Leipzig, 04103, Leipzig, Germany
| | - Uta Ceglarek
- Institute of Laboratory Medicine, Clinical Chemistry, and Molecular Diagnostics, University of Leipzig Medical Center, 04103, Leipzig, Germany
| | - Berend Isermann
- Institute of Laboratory Medicine, Clinical Chemistry, and Molecular Diagnostics, University of Leipzig Medical Center, 04103, Leipzig, Germany
| | - Ilan Shelef
- Soroka University Medical Center, 84101, Be'er Sheva, Israel
| | - Matthias Blüher
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Center Munich at the University of Leipzig and University Hospital Leipzig, 04103, Leipzig, Germany
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, University of Leipzig, Liebigstrasse 21, 04103, Leipzig, Germany
| | - Michael Stumvoll
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Center Munich at the University of Leipzig and University Hospital Leipzig, 04103, Leipzig, Germany
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, University of Leipzig, Liebigstrasse 21, 04103, Leipzig, Germany
| | - Jun Li
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and, Harvard Medical School, Boston, MA, 02115, USA
| | - Sven-Bastian Haange
- Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research GmbH, 04318, Leipzig, Germany
| | - Beatrice Engelmann
- Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research GmbH, 04318, Leipzig, Germany
| | - Ulrike Rolle-Kampczyk
- Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research GmbH, 04318, Leipzig, Germany
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research GmbH, 04318, Leipzig, Germany
- Institute of Biochemistry, Faculty of Life Sciences, University of Leipzig, 04103, Leipzig, Germany
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Meir J Stampfer
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Peter Kovacs
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, University of Leipzig, Liebigstrasse 21, 04103, Leipzig, Germany.
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA.
| | - Iris Shai
- The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O. Box 653, 8410501, Be'er Sheva, Israel.
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Center Munich at the University of Leipzig and University Hospital Leipzig, 04103, Leipzig, Germany.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
- Faculty of Medicine, Leipzig University, Leipzig, 04103, Germany.
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13
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Thomas A, Belsky D, Gu Y. Healthy Lifestyle Behaviors and Biological Aging in the U.S. National Health and Nutrition Examination Surveys 1999-2018. J Gerontol A Biol Sci Med Sci 2023; 78:1535-1542. [PMID: 36896965 PMCID: PMC10460553 DOI: 10.1093/gerona/glad082] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Indexed: 03/11/2023] Open
Abstract
People who have a balanced diet and engage in more physical activity live longer, healthier lives. This study aimed to test the hypothesis that these associations reflect a slowing of biological processes of aging. We analyzed data from 42 625 participants (aged 20-84 years, 51% female participants) from the National Health and Nutrition Examination Surveys (NHANES), 1999-2018. We calculated adherence to a Mediterranean diet (MeDi) and level of leisure time physical activity (LTPA) using standard methods. We measured biological aging by applying the PhenoAge algorithm, developed using clinical and mortality data from NHANES-III (1988-94), to clinical chemistries measured from a blood draw at the time of the survey. We tested the associations of diet and physical activity measures with biological aging, explored synergies between these health behaviors, and tested heterogeneity in their associations across strata of age, sex, and body mass index. Participants who adhered to the MeDi and who did more LTPA had younger biological ages compared with those who had less-healthy lifestyles (high vs low MeDi tertiles: β = 0.14 standard deviation [SD] [95% confidence interval {CI}: -0.18, -0.11]; high vs sedentary LTPA, β = 0.12 SD [-0.15, -0.09]), in models controlled for demographic and socioeconomic characteristics. Healthy diet and regular physical activity were independently associated with lower clinically defined biological aging, regardless of age, sex, and BMI category.
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Affiliation(s)
- Aline Thomas
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, New York, USA
| | - Daniel W Belsky
- Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, New York, USA
- Department of Epidemiology, Joseph P. Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Yian Gu
- Department of Epidemiology, Joseph P. Mailman School of Public Health, Columbia University, New York, New York, USA
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Gertrude H. Sergievsky Center, and Department of Neurology, Columbia University, New York, New York, USA
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14
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Wang S, Li W, Li S, Tu H, Jia J, Zhao W, Xu A, Xu W, Tsai MK, Chu DTW, Wen CP, Wu X. Association between plant-based dietary pattern and biological aging trajectory in a large prospective cohort. BMC Med 2023; 21:310. [PMID: 37592257 PMCID: PMC10433678 DOI: 10.1186/s12916-023-02974-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 07/06/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND Aging is a dynamic and heterogeneous process that may better be captured by trajectories of aging biomarkers. Biological age has been advocated as a better biomarker of aging than chronological age, and plant-based dietary patterns have been found to be linked to aging. However, the associations of biological age trajectories with mortality and plant-based dietary patterns remained unclear. METHODS Using group-based trajectory modeling approach, we identified distinctive aging trajectory groups among 12,784 participants based on a recently developed biological aging measure acquired at four-time points within an 8-year period. We then examined associations between aging trajectories and quintiles of plant-based dietary patterns assessed by overall plant-based diet index (PDI), healthful PDI (hPDI), and unhealthful PDI (uPDI) among 10,191 participants who had complete data on dietary intake, using multivariable multinomial logistics regression adjusting for sociodemographic and lifestyles factors. Cox proportional hazards regression models were applied to investigate the association between aging trajectories and all-cause mortality. RESULTS We identified three latent classes of accelerated aging trajectories: slow aging, medium-degree, and high-degree accelerated aging trajectories. Participants who had higher PDI or hPDI had lower odds of being in medium-degree (OR = 0.75, 95% CI: 0.65, 0.86 for PDI; OR = 0.73, 95% CI: 0.62, 0.85 for hPDI) or high-degree (OR = 0.63, 95% CI: 0.46, 0.86 for PDI; OR = 0.62, 95% CI: 0.44, 0.88 for hPDI) accelerated aging trajectories. Participants in the highest quintile of uPDI were more likely to be in medium-degree (OR = 1.72, 95% CI: 1.48, 1.99) or high-degree (OR = 1.70, 95% CI: 1.21, 2.38) accelerated aging trajectories. With a mean follow-up time of 8.40 years and 803 (6.28%) participants died by the end of follow-up, we found that participants in medium-degree (HR = 1.56, 95% CI: 1.29, 1.89) or high-degree (HR = 3.72, 95% CI: 2.73, 5.08) accelerated aging trajectory groups had higher risks of death than those in the slow aging trajectory. CONCLUSIONS We identified three distinctive aging trajectories in a large Asian cohort and found that adopting a plant-based dietary pattern, especially when rich in healthful plant foods, was associated with substantially lowered pace of aging.
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Affiliation(s)
- Sicong Wang
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, Zhejiang, China
| | - Wenyuan Li
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Shu Li
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Huakang Tu
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Junlin Jia
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenting Zhao
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Andi Xu
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenxin Xu
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Min Kuang Tsai
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | | | - Chi Pang Wen
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.
- Graduate Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung, Taiwan.
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
| | - Xifeng Wu
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
- Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China.
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15
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Aurich S, Müller L, Kovacs P, Keller M. Implication of DNA methylation during lifestyle mediated weight loss. Front Endocrinol (Lausanne) 2023; 14:1181002. [PMID: 37614712 PMCID: PMC10442821 DOI: 10.3389/fendo.2023.1181002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 07/18/2023] [Indexed: 08/25/2023] Open
Abstract
Over the past 50 years, the number of overweight/obese people increased significantly, making obesity a global public health challenge. Apart from rare monogenic forms, obesity is a multifactorial disease, most likely resulting from a concerted interaction of genetic, epigenetic and environmental factors. Although recent studies opened new avenues in elucidating the complex genetics behind obesity, the biological mechanisms contributing to individual's risk to become obese are not yet fully understood. Non-genetic factors such as eating behaviour or physical activity are strong contributing factors for the onset of obesity. These factors may interact with genetic predispositions most likely via epigenetic mechanisms. Epigenome-wide association studies or methylome-wide association studies are measuring DNA methylation at single CpGs across thousands of genes and capture associations to obesity phenotypes such as BMI. However, they only represent a snapshot in the complex biological network and cannot distinguish between causes and consequences. Intervention studies are therefore a suitable method to control for confounding factors and to avoid possible sources of bias. In particular, intervention studies documenting changes in obesity-associated epigenetic markers during lifestyle driven weight loss, make an important contribution to a better understanding of epigenetic reprogramming in obesity. To investigate the impact of lifestyle in obesity state specific DNA methylation, especially concerning the development of new strategies for prevention and individual therapy, we reviewed 19 most recent human intervention studies. In summary, this review highlights the huge potential of targeted interventions to alter disease-associated epigenetic patterns. However, there is an urgent need for further robust and larger studies to identify the specific DNA methylation biomarkers which influence obesity.
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Affiliation(s)
- Samantha Aurich
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Center Munich at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Luise Müller
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Peter Kovacs
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
- Deutsches Zentrum für Diabetesforschung e.V., Neuherberg, Germany
| | - Maria Keller
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Center Munich at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
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16
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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] [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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17
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Kresovich JK, Sandler DP, Taylor JA. Methylation-Based Biological Age and Hypertension Prevalence and Incidence. Hypertension 2023; 80:1213-1222. [PMID: 36974720 PMCID: PMC10192055 DOI: 10.1161/hypertensionaha.122.20796] [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: 12/12/2022] [Accepted: 03/08/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND Hypertension is common in older individuals and is a major risk factor for cardiovascular disease. Blood DNA methylation profiles have been used to derive metrics of biological age that capture age-related physiological change, disease risk, and mortality. The relationships between hypertension and DNA methylation-based biological age metrics have yet to be carefully described. METHODS Among 4419 women enrolled in the prospective Sister Study cohort, DNA methylation data generated from whole blood samples collected at baseline were used to calculate 3 biological age metrics (PhenoAgeAccel, GrimAgeAccel, DunedinPACE). Women were classified as hypertensive at baseline if they had high blood pressure (systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg) or reported current use of antihypertensive medication. New incident cases of hypertension during follow-up were identified via self-report on annual health questionnaires. RESULTS All 3 DNA methylation metrics of biological age were positively associated with prevalent hypertension at baseline (per 1-SD increase; PhenoAgeAccel, adjusted odds ratio, 1.16 [95% CI, 1.05-1.28]; GrimAgeAccel, adjusted odds ratio, 1.28 [95% CI, 1.14-1.45]; DunedinPACE, adjusted odds ratio, 1.16 [95% CI, 1.03-1.30]). Among 2610 women who were normotensive at baseline, women with higher biological age were more likely to be diagnosed with incident hypertension (per 1-SD increase; PhenoAgeAccel, adjusted hazard ratio, 1.09 [95% CI, 0.97-1.23]; GrimAgeAccel, adjusted hazard ratio, 1.16 [95% CI, 0.99-1.36]; DunedinPACE, adjusted hazard ratio, 1.16 [95% CI, 1.01-1.33]). CONCLUSIONS Methylation-based biological age metrics increase before a hypertension diagnosis and appear to remain elevated in the years after clinical diagnosis and treatment.
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Affiliation(s)
- Jacob K Kresovich
- Departments of Cancer Epidemiology & Breast Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL (J.K.K.)
| | - Dale P Sandler
- Epidemiology Branch (D.P.S., J.A.T.), National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC
| | - Jack A Taylor
- Epigenetic and Stem Cell Biology Laboratory (J.A.T.), National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC
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18
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Thomas A, Ryan CP, Caspi A, Moffitt TE, Sugden K, Zhou J, Belsky DW, Gu Y. Diet, pace of biological aging, and risk of dementia in the Framingham Heart Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.24.23290474. [PMID: 37398353 PMCID: PMC10312831 DOI: 10.1101/2023.05.24.23290474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
INTRODUCTION We tested the hypothesis that healthy diet protects against dementia because it slows the pace of biological aging. METHODS We analyzed Framingham Offspring Cohort data (≥60y). We measured healthy diet using the Dietary Guideline for Americans (DGA, 3 visits 1991-2008), pace of aging using the DunedinPACE epigenetic clock (2005-2008), and incident dementia and mortality using records (compiled 2005-2018). RESULTS Of n=1,525 included participants (mean age 69.7, 54% female), n=129 developed dementia and n=432 died over follow-up. Greater DGA adherence was associated with slower DunedinPACE and reduced risks for dementia and mortality. Slower DunedinPACE was associated with reduced risks for dementia and mortality. Slower DunedinPACE accounted for 15% of the DGA association with dementia and 39% of the DGA association with mortality. DISCUSSION Findings suggest that slower pace of aging mediates part of the relationship of healthy diet with reduced dementia risk. Monitoring pace of aging may inform dementia prevention.
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Affiliation(s)
- Aline Thomas
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, USA
- Department of Neurology, Columbia University, New York, NY, USA
| | - Calen P Ryan
- Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Avshalom Caspi
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Terrie E. Moffitt
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Karen Sugden
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Jiayi Zhou
- Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Daniel W. Belsky
- Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA
- Department of Epidemiology, Joseph P. Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Yian Gu
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, USA
- Department of Neurology, Columbia University, New York, NY, USA
- Department of Epidemiology, Joseph P. Mailman School of Public Health, Columbia University, New York, NY, USA
- Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA
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19
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Bao H, Cao J, Chen M, Chen M, Chen W, Chen X, Chen Y, Chen Y, Chen Y, Chen Z, Chhetri JK, Ding Y, Feng J, Guo J, Guo M, He C, Jia Y, Jiang H, Jing Y, Li D, Li J, Li J, Liang Q, Liang R, Liu F, Liu X, Liu Z, Luo OJ, Lv J, Ma J, Mao K, Nie J, Qiao X, Sun X, Tang X, Wang J, Wang Q, Wang S, Wang X, Wang Y, Wang Y, Wu R, Xia K, Xiao FH, Xu L, Xu Y, Yan H, Yang L, Yang R, Yang Y, Ying Y, Zhang L, Zhang W, Zhang W, Zhang X, Zhang Z, Zhou M, Zhou R, Zhu Q, Zhu Z, Cao F, Cao Z, Chan P, Chen C, Chen G, Chen HZ, Chen J, Ci W, Ding BS, Ding Q, Gao F, Han JDJ, Huang K, Ju Z, Kong QP, Li J, Li J, Li X, Liu B, Liu F, Liu L, Liu Q, Liu Q, Liu X, Liu Y, Luo X, Ma S, Ma X, Mao Z, Nie J, Peng Y, Qu J, Ren J, Ren R, Song M, Songyang Z, Sun YE, Sun Y, Tian M, Wang S, Wang S, Wang X, Wang X, Wang YJ, Wang Y, Wong CCL, Xiang AP, Xiao Y, Xie Z, Xu D, Ye J, Yue R, Zhang C, Zhang H, Zhang L, Zhang W, Zhang Y, Zhang YW, Zhang Z, Zhao T, Zhao Y, Zhu D, Zou W, Pei G, Liu GH. Biomarkers of aging. SCIENCE CHINA. LIFE SCIENCES 2023; 66:893-1066. [PMID: 37076725 PMCID: PMC10115486 DOI: 10.1007/s11427-023-2305-0] [Citation(s) in RCA: 71] [Impact Index Per Article: 71.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 04/21/2023]
Abstract
Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.
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Affiliation(s)
- Hainan Bao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Jiani Cao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Mengting Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Min Chen
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Wei Chen
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Third Military Medical University, Chongqing, 400042, China
| | - Yanhao Chen
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Yutian Chen
- The Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Zhiyang Chen
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China
| | - Jagadish K Chhetri
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yingjie Ding
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junlin Feng
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jun Guo
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China
| | - Mengmeng Guo
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Chuting He
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Yujuan Jia
- Department of Neurology, First Affiliated Hospital, Shanxi Medical University, Taiyuan, 030001, China
| | - Haiping Jiang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Ying Jing
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Dingfeng Li
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyi Li
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Qinhao Liang
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China
| | - Rui Liang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China
| | - Feng Liu
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaoqian Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Zuojun Liu
- School of Life Sciences, Hainan University, Haikou, 570228, China
| | - Oscar Junhong Luo
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Jianwei Lv
- School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Jingyi Ma
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Jiawei Nie
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinhua Qiao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinpei Sun
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China
| | - Xiaoqiang Tang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianfang Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siyuan Wang
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
| | - Xuan Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China
| | - Yaning Wang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuhan Wang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Rimo Wu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China
| | - Kai Xia
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Fu-Hui Xiao
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Lingyan Xu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yingying Xu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Haoteng Yan
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Liang Yang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China
| | - Ruici Yang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yuanxin Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Yilin Ying
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China
| | - Le Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Weiwei Zhang
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China
| | - Wenwan Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xing Zhang
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Zhuo Zhang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Min Zhou
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Qingchen Zhu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Zhengmao Zhu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Feng Cao
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China.
| | - Zhongwei Cao
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Piu Chan
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
| | - Chang Chen
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Guobing Chen
- Department of Microbiology and Immunology, School of Medicine, Jinan University, Guangzhou, 510632, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, Guangzhou, 510000, China.
| | - Hou-Zao Chen
- Department of Biochemistryand Molecular Biology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
| | - Jun Chen
- Peking University Research Center on Aging, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, Department of Integration of Chinese and Western Medicine, School of Basic Medical Science, Peking University, Beijing, 100191, China.
| | - Weimin Ci
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Bi-Sen Ding
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Qiurong Ding
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Feng Gao
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Kai Huang
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Zhenyu Ju
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China.
| | - Qing-Peng Kong
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
| | - Ji Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jian Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China.
| | - Xin Li
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Baohua Liu
- School of Basic Medical Sciences, Shenzhen University Medical School, Shenzhen, 518060, China.
| | - Feng Liu
- Metabolic Syndrome Research Center, The Second Xiangya Hospital, Central South Unversity, Changsha, 410011, China.
| | - Lin Liu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China.
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Institute of Translational Medicine, Tianjin Union Medical Center, Nankai University, Tianjin, 300000, China.
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300350, China.
| | - Qiang Liu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China.
| | - Qiang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- Tianjin Institute of Immunology, Tianjin Medical University, Tianjin, 300070, China.
| | - Xingguo Liu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China.
| | - Yong Liu
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China.
| | - Xianghang Luo
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China.
| | - Shuai Ma
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Xinran Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
| | - Zhiyong Mao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Jing Nie
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Yaojin Peng
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ruibao Ren
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Center for Aging and Cancer, Hainan Medical University, Haikou, 571199, China.
| | - Moshi Song
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Zhou Songyang
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China.
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
| | - Yi Eve Sun
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China.
| | - Yu Sun
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Department of Medicine and VAPSHCS, University of Washington, Seattle, WA, 98195, USA.
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
| | - Shusen Wang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China.
| | - Si Wang
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| | - Xia Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
| | - Xiaoning Wang
- Institute of Geriatrics, The second Medical Center, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Yan-Jiang Wang
- Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
| | - Yunfang Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China.
| | - Catherine C L Wong
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China.
| | - Andy Peng Xiang
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China.
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Yichuan Xiao
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Zhengwei Xie
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China.
- Beijing & Qingdao Langu Pharmaceutical R&D Platform, Beijing Gigaceuticals Tech. Co. Ltd., Beijing, 100101, China.
| | - Daichao Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
| | - Jing Ye
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China.
| | - Rui Yue
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Cuntai Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China.
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Hongbo Zhang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Liang Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yong Zhang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Yun-Wu Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Zhuohua Zhang
- Key Laboratory of Molecular Precision Medicine of Hunan Province and Center for Medical Genetics, Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, 410078, China.
- Department of Neurosciences, Hengyang Medical School, University of South China, Hengyang, 421001, China.
| | - Tongbiao Zhao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Dahai Zhu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Weiguo Zou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Gang Pei
- Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Biomedicine, The Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200070, China.
| | - Guang-Hui Liu
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
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20
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Isaev FI, Sadykov AR, Moskalev A. Blood Markers of Biological Age Evaluates Clinic Complex Medical Spa Programs. Biomedicines 2023; 11:biomedicines11020625. [PMID: 36831161 PMCID: PMC9953453 DOI: 10.3390/biomedicines11020625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 02/12/2023] [Accepted: 02/17/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND Kivach Clinic has developed a special medical spa program to prevent aging-related conditions in metabolic, cardio-vascular, and neurological states. Spa programs modify diet, physical activity, and lymphatic drainage, as it deteriorates with aging. We investigated its influence on the blood markers of biological age of patients during their stay to objectify the potential of spa treatment for influencing the risk of age-related events. METHODS The artificial deep learning model Aging.ai 3.0 was based on blood parameters. The change in the biological age of 43 patients was assessed after their 14-day spa treatment at Kivach Clinic. RESULTS Biological age decreased in 29 patients (median decrease: 8 years, mean: 8.83 years), increased in 10 patients (median increase: 3 years, mean: 5.33 years) and remained unchanged in 4 patients. Overall mean values for the entire patient group were as follows: median value was -3 years, and mean was -4.79 ± 1.2 years (p-value = 0.00025, t-test). CONCLUSIONS The capability of specially selected medical spa treatment to reduce human biological age (assessed by Aging.AI 3.0) has been established.
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Affiliation(s)
| | - Arsenii R. Sadykov
- Laboratory of Metabolomic Diagnostics of Meta-Metrix, 117630 Moscow, Russia
| | - Alexey Moskalev
- Institute of Biogerontology, Lobachevsky State University of Nizhny Novgorod, 603146 Nizhny Novgorod, Russia
- Russian Research Clinical Center of Gerontology of the Russian National Research Medical University Named after N.I. Pirogov, 129226 Moscow, Russia
- Correspondence:
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21
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Laupèze B, Doherty TM. Maintaining a 'fit' immune system: the role of vaccines. Expert Rev Vaccines 2023; 22:256-266. [PMID: 36864769 DOI: 10.1080/14760584.2023.2185223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Abstract
INTRODUCTION Conventionally, vaccines are thought to induce a specific immune response directed against a target pathogen. Long recognized but poorly understood nonspecific benefits of vaccination, such as reduced susceptibility to unrelated diseases or cancer, are now being investigated and may be due in part to "trained immunity'. AREAS COVERED We discuss 'trained immunity' and whether vaccine-induced 'trained immunity' could be leveraged to prevent morbidity due to a broader range of causes. EXPERT OPINION The prevention of infection i.e. maintaining homeostasis by preventing the primary infection and resulting secondary illnesses, is the pivotal strategy used to direct vaccine design and may have long-term, positive impacts on health at all ages. In the future, we anticipate that vaccine design will change to not only prevent the target infection (or related infections) but to generate positive modifications to the immune response that could prevent a wider range of infections and potentially reduce the impact of immunological changes associated with aging. Despite changing demographics, adult vaccination has not always been prioritized. However, the SARS-CoV-2 pandemic has demonstrated that adult vaccination can flourish given the right circumstances, demonstrating that harnessing the potential benefits of life-course vaccination is achievable for all.
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22
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Woldeamanuel YW, Shrivastava S, Vila-Pueyo M. Editorial: Lifestyle modifications to manage migraine. Front Neurol 2022; 13:966424. [PMID: 36105771 PMCID: PMC9465452 DOI: 10.3389/fneur.2022.966424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 06/27/2022] [Indexed: 11/17/2022] Open
Affiliation(s)
- Yohannes W. Woldeamanuel
- Division of Headache & Facial Pain, Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- *Correspondence: Yohannes W. Woldeamanuel
| | | | - Marta Vila-Pueyo
- Headache and Neurological Pain Research Group, Department of Medicine, Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona, Barcelona, Spain
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23
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Johnson AA, English BW, Shokhirev MN, Sinclair DA, Cuellar TL. Human age reversal: Fact or fiction? Aging Cell 2022; 21:e13664. [PMID: 35778957 PMCID: PMC9381899 DOI: 10.1111/acel.13664] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 05/23/2022] [Accepted: 06/13/2022] [Indexed: 12/19/2022] Open
Abstract
Although chronological age correlates with various age‐related diseases and conditions, it does not adequately reflect an individual's functional capacity, well‐being, or mortality risk. In contrast, biological age provides information about overall health and indicates how rapidly or slowly a person is aging. Estimates of biological age are thought to be provided by aging clocks, which are computational models (e.g., elastic net) that use a set of inputs (e.g., DNA methylation sites) to make a prediction. In the past decade, aging clock studies have shown that several age‐related diseases, social variables, and mental health conditions associate with an increase in predicted biological age relative to chronological age. This phenomenon of age acceleration is linked to a higher risk of premature mortality. More recent research has demonstrated that predicted biological age is sensitive to specific interventions. Human trials have reported that caloric restriction, a plant‐based diet, lifestyle changes involving exercise, a drug regime including metformin, and vitamin D3 supplementation are all capable of slowing down or reversing an aging clock. Non‐interventional studies have connected high‐quality sleep, physical activity, a healthy diet, and other factors to age deceleration. Specific molecules have been associated with the reduction or reversal of predicted biological age, such as the antihypertensive drug doxazosin or the metabolite alpha‐ketoglutarate. Although rigorous clinical trials are needed to validate these initial findings, existing data suggest that aging clocks are malleable in humans. Additional research is warranted to better understand these computational models and the clinical significance of lowering or reversing their outputs.
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Affiliation(s)
- Adiv A Johnson
- Longevity Sciences, Inc. (dba Tally Health), Greenwich, Connecticut, USA
| | - Bradley W English
- Blavatnik Institute, Department of Genetics, Paul F. Glenn Center for Biology of Aging Research, Harvard Medical School, Boston, Massachusetts, USA
| | - Maxim N Shokhirev
- Longevity Sciences, Inc. (dba Tally Health), Greenwich, Connecticut, USA
| | - David A Sinclair
- Blavatnik Institute, Department of Genetics, Paul F. Glenn Center for Biology of Aging Research, Harvard Medical School, Boston, Massachusetts, USA
| | - Trinna L Cuellar
- Longevity Sciences, Inc. (dba Tally Health), Greenwich, Connecticut, USA
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24
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Reale A, Tagliatesta S, Zardo G, Zampieri M. Counteracting aged DNA methylation states to combat ageing and age-related diseases. Mech Ageing Dev 2022; 206:111695. [PMID: 35760211 DOI: 10.1016/j.mad.2022.111695] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 06/09/2022] [Accepted: 06/22/2022] [Indexed: 12/18/2022]
Abstract
DNA methylation (DNAm) overwrites information about multiple extrinsic factors on the genome. Age is one of these factors. Age causes characteristic DNAm changes that are thought to be not only major drivers of normal ageing but also precursors to diseases, cancer being one of these. Although there is still much to learn about the relationship between ageing, age-related diseases and DNAm, we now know how to interpret some of the effects caused by age in the form of changes in methylation marks at specific loci. In fact, these changes form the basis of the so called "epigenetic clocks", which translate the genomic methylation profile into an "epigenetic age". Epigenetic age does not only estimate chronological age but can also predict the risk of chronic diseases and mortality. Epigenetic age is believed to be one of the most accurate metrics of biological age. Initial evidence has recently been gathered pointing to the possibility that the rate of epigenetic ageing can be slowed down or even reversed. In this review, we discuss some of the most relevant advances in this field. Expected outcome is that this approach can provide insights into how to preserve health and reduce the impact of ageing diseases in humans.
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Affiliation(s)
- Anna Reale
- Department of Experimental Medicine, Sapienza University of Rome, 00161 Rome, Italy.
| | - Stefano Tagliatesta
- Department of Biology and Biotechnology "Charles Darwin", Sapienza University of Rome, 00161 Rome, Italy.
| | - Giuseppe Zardo
- Department of Experimental Medicine, Sapienza University of Rome, 00161 Rome, Italy.
| | - Michele Zampieri
- Department of Experimental Medicine, Sapienza University of Rome, 00161 Rome, Italy.
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