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Nguyen S, McEvoy LK, Espeland MA, Whitsel EA, Lu A, Horvath S, Manson JE, Rapp SR, Shadyab AH. Associations of Epigenetic Age Estimators With Cognitive Function Trajectories in the Women's Health Initiative Memory Study. Neurology 2024; 103:e209534. [PMID: 38857479 PMCID: PMC11226313 DOI: 10.1212/wnl.0000000000209534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/05/2024] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Epigenetic age estimators indicating faster/slower biological aging vs chronological age independently associate with several age-related outcomes; however, longitudinal associations with cognitive function are understudied. We examined associations of epigenetic age estimators with cognitive function measured annually. METHODS This longitudinal study consisted of older women enrolled in the Women's Health Initiative Memory Study with DNA methylation (DNAm) collected at baseline (1995-1998) from 3 ancillary studies and were followed up to 13 years. Global cognitive function was measured annually by Modified Mini-Mental State Examination (3MS; baseline-2007) and by modified Telephone Interview for Cognitive Status (TICS-m, 2008-2021). We calculated 5 epigenetic age estimators: extrinsic AgeAccel, intrinsic AgeAccel, AgeAccelPheno, AgeAccelGrim2, Dunedin Pace of Aging Calculated From the Epigenome (DunedinPACE), and AgeAccelGrim2 components (DNA-based plasma protein surrogates). We estimated longitudinal epigenetic age estimator-cognitive function associations using linear mixed-effects models containing age, education, race or ethnicity, and subsequently alcohol, smoking, body mass index, and comorbidities. We examined effect modification by APOE ε4 carriage. RESULTS A total of 795 participants were enrolled. The mean baseline age was 70.8 ± 4 years (10.7% Black, 3.9% Hispanic or Latina, 85.4% White), A 1-SD (0.12) increment in DunedinPACE associated with faster annual declines in TICS-m scores in minimally adjusted (β = -0.118, 95% CI -0.202 to -0.034; p = 0.0006) and fully adjusted (β = -0.123, 95% CI -0.211 to -0.036; p = 0.006) models. AgeAccelPheno associated with faster annual declines in TICS-m with minimal adjustment (β = -0.091, 95% CI -0.176 to -0.006; p = 0.035) but not with full adjustment. No other epigenetic age estimators associated with changes in 3MS or TICS-m. Higher values of DNAm-based surrogates of growth differentiation factor 15, beta-2 microglobulin, Cystatin C, tissue inhibitor metalloproteinase 1, and adrenomedullin associated with faster annual declines in 3MS and TICS-m. Higher DNAm log A1c associated with faster annual declines in TICS-m only. DunedinPACE associated with faster annual declines in 3MS among APOE ε4 carriers but not among noncarriers (p-interaction = 0.020). DISCUSSION Higher DunedinPACE associated with faster declines in TICS-m and 3MS scores among APOE ε4 carriers. DunedinPACE may help identify older women at risk of future cognitive decline. Limitations include the ancillary studies that collected epigenetic data not designed to study epigenetics and cognitive function. We examined epigenetic age estimators with global cognitive function and not specific cognitive domains. Findings may not generalize to men and more diverse populations.
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Affiliation(s)
- Steve Nguyen
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Linda K McEvoy
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Mark A Espeland
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Eric A Whitsel
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Ake Lu
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Steve Horvath
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Joann E Manson
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Stephen R Rapp
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Aladdin H Shadyab
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
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Harris KM, Levitt B, Gaydosh L, Martin C, Meyer JM, Mishra AA, Kelly AL, Aiello AE. Sociodemographic and Lifestyle Factors and Epigenetic Aging in US Young Adults: NIMHD Social Epigenomics Program. JAMA Netw Open 2024; 7:e2427889. [PMID: 39073811 PMCID: PMC11287395 DOI: 10.1001/jamanetworkopen.2024.27889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/30/2024] Open
Abstract
Importance Epigenetic clocks represent molecular evidence of disease risk and aging processes and have been used to identify how social and lifestyle characteristics are associated with accelerated biological aging. However, most research is based on samples of older adults who already have measurable chronic disease. Objective To investigate whether and how sociodemographic and lifestyle characteristics are associated with biological aging in a younger adult sample across a wide array of epigenetic clock measures. Design, Setting, and Participants This cohort study was conducted using data from the National Longitudinal Study of Adolescent to Adult Health, a US representative cohort of adolescents in grades 7 to 12 in 1994 followed up for 25 years to 2018 over 5 interview waves. Participants who provided blood samples at wave V (2016-2018) were analyzed, with samples tested for DNA methylation (DNAm) in 2021 to 2024. Data were analyzed from February 2023 to May 2024. Exposure Sociodemographic (sex, race and ethnicity, immigrant status, socioeconomic status, and geographic location) and lifestyle (obesity status by body mass index [BMI] in categories of reference range or underweight [<25], overweight [25 to <30], obesity [30 to <40], and severe obesity [≥40]; exercise level; tobacco use; and alcohol use) characteristics were assessed. Main Outcome and Measure Biological aging assessed from banked blood DNAm using 16 epigenetic clocks. Results Data were analyzed from 4237 participants (mean [SD] age, 38.4 [2.0] years; percentage [SE], 51.3% [0.01] female and 48.7% [0.01] male; percentage [SE], 2.7% [<0.01] Asian or Pacific Islander, 16.7% [0.02] Black, 8.7% [0.01] Hispanic, and 71.0% [0.03] White). Sociodemographic and lifestyle factors were more often associated with biological aging in clocks trained to estimate morbidity and mortality (eg, PhenoAge, GrimAge, and DunedinPACE) than clocks trained to estimate chronological age (eg, Horvath). For example, the β for an annual income less than $25 000 vs $100 000 or more was 1.99 years (95% CI, 0.45 to 3.52 years) for PhenoAgeAA, 1.70 years (95% CI, 0.68 to 2.72 years) for GrimAgeAA, 0.33 SD (95% CI, 0.17 to 0.48 SD) for DunedinPACE, and -0.17 years (95% CI, -1.08 to 0.74 years) for Horvath1AA. Lower education, lower income, higher obesity levels, no exercise, and tobacco use were associated with faster biological aging across several clocks; associations with GrimAge were particularly robust (no college vs college or higher: β = 2.63 years; 95% CI, 1.67-3.58 years; lower vs higher annual income: <$25 000 vs ≥$100 000: β = 1.70 years; 95% CI, 0.68-2.72 years; severe obesity vs no obesity: β = 1.57 years; 95% CI, 0.51-2.63 years; no weekly exercise vs ≥5 bouts/week: β = 1.33 years; 95% CI, 0.67-1.99 years; current vs no smoking: β = 7.16 years; 95% CI, 6.25-8.07 years). Conclusions and Relevance This study found that important social and lifestyle factors were associated with biological aging in a nationally representative cohort of younger adults. These findings suggest that molecular processes underlying disease risk may be identified in adults entering midlife before disease is manifest and inform interventions aimed at reducing social inequalities in heathy aging and longevity.
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Affiliation(s)
- Kathleen Mullan Harris
- Department of Sociology, University of North Carolina at Chapel Hill
- Carolina Population Center, University of North Carolina at Chapel Hill
| | - Brandt Levitt
- Carolina Population Center, University of North Carolina at Chapel Hill
| | - Lauren Gaydosh
- Department of Sociology, University of Texas at Austin
- Population Research Center, University of Texas at Austin
| | - Chantel Martin
- Carolina Population Center, University of North Carolina at Chapel Hill
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
| | - Jess M Meyer
- Department of Population Health, University of Kansas Medical Center, Kansas City
| | | | - Audrey L Kelly
- Population Research Center, University of Texas at Austin
| | - Allison E Aiello
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
- Robert N. Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, New York
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Chen J, Moubadder L, Clausing ES, Kezios KL, Conneely KN, Hüls A, Baccarelli A, Factor-Litvak P, Cirrillo P, Shelton RC, Link BG, Suglia SF. Associations of childhood, adolescence, and midlife cognitive function with DNA methylation age acceleration in midlife. Aging (Albany NY) 2024; 16:9350-9368. [PMID: 38874516 PMCID: PMC11210249 DOI: 10.18632/aging.205943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/25/2024] [Indexed: 06/15/2024]
Abstract
Prior studies showed increased age acceleration (AgeAccel) is associated with worse cognitive function among old adults. We examine the associations of childhood, adolescence and midlife cognition with AgeAccel based on DNA methylation (DNAm) in midlife. Data are from 359 participants who had cognition measured in childhood and adolescence in the Child Health and Development study, and had cognition, blood based DNAm measured during midlife in the Disparities study. Childhood cognition was measured by Raven's Progressive Matrices and Peabody Picture Vocabulary Test (PPVT). Adolescent cognition was measured only by PPVT. Midlife cognition included Wechsler Test of Adult Reading (WTAR), Verbal Fluency (VF), Digit Symbol (DS). AgeAccel measures including Horvath, Hannum, PhenoAge, GrimAge and DunedinPACE were calculated from DNAm. Linear regressions adjusted for potential confounders were utilized to examine the association between each cognitive measure in relation to each AgeAccel. There are no significant associations between childhood cognition and midlife AgeAccel. A 1-unit increase in adolescent PPVT, which measures crystalized intelligence, is associated with 0.048-year decrease of aging measured by GrimAge and this association is attenuated after adjustment for adult socioeconomic status. Midlife crystalized intelligence measure WTAR is negatively associated with PhenoAge and DunedinPACE, and midlife fluid intelligence measure (DS) is negatively associated with GrimAge, PhenoAge and DunedinPACE. AgeAccel is not associated with VF in midlife. In conclusion, our study showed the potential role of cognitive functions at younger ages in the process of biological aging. We also showed a potential relationship of both crystalized and fluid intelligence with aging acceleration.
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Affiliation(s)
- Junyu Chen
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Leah Moubadder
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Elizabeth S. Clausing
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
- School of Global Integrative Studies, University of Nebraska, Lincoln, NE 68508, USA
| | - Katrina L. Kezios
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Karen N. Conneely
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Anke Hüls
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Andrea Baccarelli
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Pam Factor-Litvak
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Piera Cirrillo
- Child Health and Development Studies, Public Health Institute, Washington, DC 20024, USA
| | - Rachel C. Shelton
- Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Bruce G. Link
- Department of Sociology, University of California Riverside, Riverside, CA 92507, USA
| | - Shakira F. Suglia
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
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Farina MP, Klopack ET, Umberson D, Crimmins EM. The embodiment of parental death in early life through accelerated epigenetic aging: Implications for understanding how parental death before 18 shapes age-related health risk among older adults. SSM Popul Health 2024; 26:101648. [PMID: 38596364 PMCID: PMC11002886 DOI: 10.1016/j.ssmph.2024.101648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/25/2024] [Accepted: 02/27/2024] [Indexed: 04/11/2024] Open
Abstract
Parental death in early life has been linked to various adverse health outcomes in older adulthood. This study extends prior research to evaluate how parental death in early life is tied to accelerated epigenetic aging, a potentially important biological mechanism from which social and environmental exposures impact age-related health. We used data from the 2016 Venous Blood Study (VBS), a component of the Health and Retirement Study (HRS), to examine the association between parental death in early life and accelerated epigenetic aging as measured by three widely used epigenetic clocks (PCPhenoAge, PCGrimAge, and DunedinPACE). We also assessed whether some of the association is explained by differences in educational attainment, depressive symptoms, and smoking behavior. Methods included a series of linear regression models and formal mediation analysis. Findings indicated that parental death in early life is associated with accelerated epigenetic aging for PCPhenoAge and DunedinPACE. The inclusion of educational attainment, depressive symptoms, and smoking behavior attenuated this association, with formal mediation analysis providing additional support for these observations. Parental death in early life may be one of the most difficult experiences an individual may face. The elevated biological risk associated with parental death in early life may operate through immediate changes but also through more downstream risk factors. This study highlights how early life adversity can set in motion biological changes that have lifelong consequences.
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Affiliation(s)
- Mateo P. Farina
- Department of Human Development and Family Sciences, University of Texas at Austin, United States
- Population Research Center, University of Texas at Austin, United States
| | - Eric T. Klopack
- Davis School of Gerontology, University of Southern California, United States
| | - Debra Umberson
- Population Research Center, University of Texas at Austin, United States
- Department of Sociology, University of Texas at Austin, United States
| | - Eileen M. Crimmins
- Davis School of Gerontology, University of Southern California, United States
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Harrison TC, Blozis SA, Taylor J, Mukherjee N, Ortega LC, Blanco N, Garcia AA, Brown SA. Mixed-Methods Study of Disability Self-Management in Mexican Americans With Osteoarthritis. Nurs Res 2024; 73:203-215. [PMID: 38652692 PMCID: PMC11045046 DOI: 10.1097/nnr.0000000000000721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
BACKGROUND Health disparities in osteoarthritis (OA) outcomes exist both in the occurrence and treatment of functional limitation and disability for Mexican Americans. Although the effect of self-management of chronic illness is well established, studies demonstrate little attention to self-management of function or disability, despite the strong potential effect on both and, consequently, on patients' lives. OBJECTIVE The purpose of this study pilot was to develop and test key variable relationships for a measure of disability self-management among Mexican Americans. METHODS In this sequential, two-phased, mixed-methods, biobehavioral pilot study of Mexican American women and men with OA, a culturally tailored measure of disability self-management was created, and initial relationships among key variables were explored. RESULTS First, a qualitative study of 19 adults of Mexican American descent born in Texas (United States) or Mexico was conducted. The Mexican American Disability Self-Management Scale was created using a descriptive content analysis of interview data. The scale was tested and refined, resulting in 18 items and a descriptive frequency of therapeutic management efforts. Second, correlations between study variables were estimated: Disability and function were negatively correlated. Disability correlated positively with social support and activity effort. Disability correlated negatively with disability self-management, pain, and C-reactive protein. Function was positively correlated with age, pain, and depression. Liver enzymes (alanine transaminase) correlated positively with pain and anxiety. DISCUSSION This mixed-methods study indicates directions for further testing and interventions for disability outcomes among Mexican Americans.
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Affiliation(s)
| | | | | | - Nandini Mukherjee
- College of Public Health the University of Arkansas for Medical Sciences
| | | | - Nancy Blanco
- School of Nursing Universidad de Guanajuato
- School of Nursing The University of Texas at Austin
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Harris KM, Levitt B, Gaydosh L, Martin C, Meyer JM, Mishra AA, Kelly AL, Aiello AE. The Sociodemographic and Lifestyle Correlates of Epigenetic Aging in a Nationally Representative U.S. Study of Younger Adults. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.21.585983. [PMID: 38585956 PMCID: PMC10996523 DOI: 10.1101/2024.03.21.585983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Importance Epigenetic clocks represent molecular evidence of disease risk and aging processes and have been used to identify how social and lifestyle characteristics are associated with accelerated biological aging. However, most of this research is based on older adult samples who already have measurable chronic disease. Objective To investigate whether and how sociodemographic and lifestyle characteristics are related to biological aging in a younger adult sample across a wide array of epigenetic clock measures. Design Nationally representative prospective cohort study. Setting United States (U.S.). Participants Data come from the National Longitudinal Study of Adolescent to Adult Health, a national cohort of adolescents in grades 7-12 in U.S. in 1994 followed for 25 years over five interview waves. Our analytic sample includes participants followed-up through Wave V in 2016-18 who provided blood samples for DNA methylation (DNAm) testing (n=4237) at Wave V. Exposure Sociodemographic (sex, race/ethnicity, immigrant status, socioeconomic status, geographic location) and lifestyle (obesity status, exercise, tobacco, and alcohol use) characteristics. Main Outcome Biological aging assessed from blood DNAm using 16 epigenetic clocks when the cohort was aged 33-44 in Wave V. Results While there is considerable variation in the mean and distribution of epigenetic clock estimates and in the correlations among the clocks, we found sociodemographic and lifestyle factors are more often associated with biological aging in clocks trained to predict current or dynamic phenotypes (e.g., PhenoAge, GrimAge and DunedinPACE) as opposed to clocks trained to predict chronological age alone (e.g., Horvath). Consistent and strong associations of faster biological aging were found for those with lower levels of education and income, and those with severe obesity, no weekly exercise, and tobacco use. Conclusions and Relevance Our study found important social and lifestyle factors associated with biological aging in a nationally representative cohort of younger-aged adults. These findings indicate that molecular processes underlying disease risk can be identified in adults entering midlife before disease is manifest and represent useful targets for interventions to reduce social inequalities in heathy aging and longevity. Key Points Question: Are epigenetic clocks, measures of biological aging developed mainly on older-adult samples, meaningful for younger adults and associated with sociodemographic and lifestyle characteristics in expected patterns found in prior aging research?Findings: Sociodemographic and lifestyle factors were associated with biological aging in clocks trained to predict morbidity and mortality showing accelerated aging among those with lower levels of education and income, and those with severe obesity, no weekly exercise, and tobacco use.Meaning: Age-related molecular processes can be identified in younger-aged adults before disease manifests and represent potential interventions to reduce social inequalities in heathy aging and longevity.
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Kawamura T, Radak Z, Tabata H, Akiyama H, Nakamura N, Kawakami R, Ito T, Usui C, Jokai M, Torma F, Kim H, Miyachi M, Torii S, Suzuki K, Ishii K, Sakamoto S, Oka K, Higuchi M, Muraoka I, McGreevy KM, Horvath S, Tanisawa K. Associations between cardiorespiratory fitness and lifestyle-related factors with DNA methylation-based ageing clocks in older men: WASEDA'S Health Study. Aging Cell 2024; 23:e13960. [PMID: 37584423 PMCID: PMC10776125 DOI: 10.1111/acel.13960] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/31/2023] [Accepted: 08/02/2023] [Indexed: 08/17/2023] Open
Abstract
DNA methylation-based age estimators (DNAm ageing clocks) are currently one of the most promising biomarkers for predicting biological age. However, the relationships between cardiorespiratory fitness (CRF), measured directly by expiratory gas analysis, and DNAm ageing clocks are largely unknown. We investigated the relationships between CRF and the age-adjusted value from the residuals of the regression of DNAm ageing clock to chronological age (DNAmAgeAcceleration: DNAmAgeAccel) and attempted to determine the relative contribution of CRF to DNAmAgeAccel in the presence of other lifestyle factors. DNA samples from 144 Japanese men aged 65-72 years were used to appraise first- (i.e., DNAmHorvath and DNAmHannum) and second- (i.e., DNAmPhenoAge, DNAmGrimAge, and DNAmFitAge) generation DNAm ageing clocks. Various surveys and measurements were conducted, including physical fitness, body composition, blood biochemical parameters, nutrient intake, smoking, alcohol consumption, disease status, sleep status, and chronotype. Both oxygen uptake at ventilatory threshold (VO2 /kg at VT) and peak oxygen uptake (VO2 /kg at Peak) showed a significant negative correlation with GrimAgeAccel, even after adjustments for chronological age and smoking and drinking status. Notably, VO2 /kg at VT and VO2 /kg at Peak above the reference value were also associated with delayed GrimAgeAccel. Multiple regression analysis showed that calf circumference, serum triglyceride, carbohydrate intake, and smoking status, rather than CRF, contributed more to GrimAgeAccel and FitAgeAccel. In conclusion, although the contribution of CRF to GrimAgeAccel and FitAgeAccel is relatively low compared to lifestyle-related factors such as smoking, the results suggest that the maintenance of CRF is associated with delayed biological ageing in older men.
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Affiliation(s)
- Takuji Kawamura
- Waseda Institute for Sport Sciences, Waseda UniversitySaitamaJapan
- Research Centre for Molecular Exercise ScienceHungarian University of Sports ScienceBudapestHungary
| | - Zsolt Radak
- Research Centre for Molecular Exercise ScienceHungarian University of Sports ScienceBudapestHungary
- Faculty of Sport SciencesWaseda UniversitySaitamaJapan
| | - Hiroki Tabata
- Waseda Institute for Sport Sciences, Waseda UniversitySaitamaJapan
- Sportology CentreJuntendo University Graduate School of MedicineTokyoJapan
| | - Hiroshi Akiyama
- Graduate School of Sport SciencesWaseda UniversitySaitamaJapan
| | | | - Ryoko Kawakami
- Waseda Institute for Sport Sciences, Waseda UniversitySaitamaJapan
- Physical Fitness Research Institute, Meiji Yasuda Life Foundation of Health and WelfareTokyoJapan
| | - Tomoko Ito
- Waseda Institute for Sport Sciences, Waseda UniversitySaitamaJapan
- Department of Food and NutritionTokyo Kasei UniversityTokyoJapan
| | - Chiyoko Usui
- Faculty of Sport SciencesWaseda UniversitySaitamaJapan
| | - Matyas Jokai
- Research Centre for Molecular Exercise ScienceHungarian University of Sports ScienceBudapestHungary
| | - Ferenc Torma
- Faculty of Health and Sport SciencesUniversity of TsukubaIbarakiJapan
| | - Hyeon‐Ki Kim
- Research Centre for Molecular Exercise ScienceHungarian University of Sports ScienceBudapestHungary
| | | | - Suguru Torii
- Faculty of Sport SciencesWaseda UniversitySaitamaJapan
| | | | - Kaori Ishii
- Faculty of Sport SciencesWaseda UniversitySaitamaJapan
| | - Shizuo Sakamoto
- Faculty of Sport SciencesWaseda UniversitySaitamaJapan
- Faculty of Sport ScienceSurugadai UniversitySaitamaJapan
| | - Koichiro Oka
- Faculty of Sport SciencesWaseda UniversitySaitamaJapan
| | | | - Isao Muraoka
- Faculty of Sport SciencesWaseda UniversitySaitamaJapan
| | - Kristen M. McGreevy
- Department of Biostatistics, Fielding School of Public HealthUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Steve Horvath
- Department of Biostatistics, Fielding School of Public HealthUniversity of California Los AngelesLos AngelesCaliforniaUSA
- Department of Human Genetics, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA
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8
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Meier HCS, Mitchell C, Karadimas T, Faul JD. Systemic inflammation and biological aging in the Health and Retirement Study. GeroScience 2023; 45:3257-3265. [PMID: 37501048 PMCID: PMC10643484 DOI: 10.1007/s11357-023-00880-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 07/16/2023] [Indexed: 07/29/2023] Open
Abstract
Chronic, low-level systemic inflammation associated with aging, or inflammaging, is a risk factor for several chronic diseases and mortality. Using data from the Health and Retirement Study, we generated a continuous latent variable for systemic inflammation from seven measured indicators of inflammation and examined associations with another biomarker of biological aging, DNA methylation age acceleration measured by epigenetic clocks, and 4-year mortality (N = 3,113). We found that greater systemic inflammation was positively associated with DNA methylation age acceleration for 10 of the 13 epigenetic clocks, after adjustment for sociodemographics and chronic disease risk factors. The latent variable for systemic inflammation was associated with 4-year mortality independent of DNA methylation age acceleration and was a better predictor of 4-year mortality than any of the epigenetic clocks examined, as well as mortality risk factors, including obesity and multimorbidity. Inflammaging and DNA methylation age acceleration may represent different biological processes contributing to mortality risk. Leveraging multiple measured inflammation markers to capture inflammaging is important for biology of aging research.
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Affiliation(s)
- Helen C S Meier
- Survey Research Center, Institute for Social Research, University of Michigan, 426 Thompson Street, Ann Arbor, MI, 48106-1248, USA.
| | - Colter Mitchell
- Survey Research Center, Institute for Social Research, University of Michigan, 426 Thompson Street, Ann Arbor, MI, 48106-1248, USA
| | - Thomas Karadimas
- Survey Research Center, Institute for Social Research, University of Michigan, 426 Thompson Street, Ann Arbor, MI, 48106-1248, USA
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, 426 Thompson Street, Ann Arbor, MI, 48106-1248, USA
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9
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Topriceanu CC, Dev E, Ahmad M, Hughes R, Shiwani H, Webber M, Direk K, Wong A, Ugander M, Moon JC, Hughes AD, Maddock J, Schlegel TT, Captur G. Accelerated DNA methylation age plays a role in the impact of cardiovascular risk factors on the human heart. Clin Epigenetics 2023; 15:164. [PMID: 37853450 PMCID: PMC10583368 DOI: 10.1186/s13148-023-01576-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/29/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND DNA methylation (DNAm) age acceleration (AgeAccel) and cardiac age by 12-lead advanced electrocardiography (A-ECG) are promising biomarkers of biological and cardiac aging, respectively. We aimed to explore the relationships between DNAm age and A-ECG heart age and to understand the extent to which DNAm AgeAccel relates to cardiovascular (CV) risk factors in a British birth cohort from 1946. RESULTS We studied four DNAm ages (AgeHannum, AgeHorvath, PhenoAge, and GrimAge) and their corresponding AgeAccel. Outcomes were the results from two publicly available ECG-based cardiac age scores: the Bayesian A-ECG-based heart age score of Lindow et al. 2022 and the deep neural network (DNN) ECG-based heart age score of Ribeiro et al. 2020. DNAm AgeAccel was also studied relative to results from two logistic regression-based A-ECG disease scores, one for left ventricular (LV) systolic dysfunction (LVSD), and one for LV electrical remodeling (LVER). Generalized linear models were used to explore the extent to which any associations between biological cardiometabolic risk factors (body mass index, hypertension, diabetes, high cholesterol, previous cardiovascular disease [CVD], and any CV risk factor) and the ECG-based outcomes are mediated by DNAm AgeAccel. We derived the total effects, average causal mediation effects (ACMEs), average direct effects (ADEs), and the proportion mediated [PM] with their 95% confidence intervals [CIs]. 498 participants (all 60-64 years) were included, with the youngest ECG heart age being 27 and the oldest 90. When exploring the associations between cardiometabolic risk factors and Bayesian A-ECG cardiac age, AgeAccelPheno appears to be a partial mediator, as ACME was 0.23 years [0.01, 0.52] p = 0.028 (i.e., PM≈18%) for diabetes, 0.34 [0.03, 0.74] p = 0.024 (i.e., PM≈15%) for high cholesterol, and 0.34 [0.03, 0.74] p = 0.024 (PM≈15%) for any CV risk factor. Similarly, AgeAccelGrim mediates ≈30% of the relationship between diabetes or high cholesterol and the DNN ECG-based heart age. When exploring the link between cardiometabolic risk factors and the A-ECG-based LVSD and LVER scores, it appears that AgeAccelPheno or AgeAccelGrim mediate 10-40% of these associations. CONCLUSION By the age of 60, participants with accelerated DNA methylation appear to have older, weaker, and more electrically impaired hearts. We show that the harmful effects of CV risk factors on cardiac age and health, appear to be partially mediated by DNAm AgeAccelPheno and AgeAccelGrim. This highlights the need to further investigate the potential cardioprotective effects of selective DNA methyltransferases modulators.
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Affiliation(s)
- Constantin-Cristian Topriceanu
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
- Cardiac MRI Unit, Barts Heart Centre, West Smithfield, London, UK
| | - Eesha Dev
- UCL Medical School, Gower Street, London, UK
| | - Mahmood Ahmad
- Centre for Inherited Heart Muscle Conditions, The Royal Free Hospital, Pond Street, Hampstead, London, UK
| | - Rebecca Hughes
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
- Cardiac MRI Unit, Barts Heart Centre, West Smithfield, London, UK
| | - Hunain Shiwani
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
- Cardiac MRI Unit, Barts Heart Centre, West Smithfield, London, UK
| | - Matthew Webber
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
| | - Kenan Direk
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK
| | - Andrew Wong
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK
| | - Martin Ugander
- Kolling Institute Royal North Shore Hospital, and Charles Perkins Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - James C Moon
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
- Cardiac MRI Unit, Barts Heart Centre, West Smithfield, London, UK
| | - Alun D Hughes
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
| | - Jane Maddock
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
| | - Todd T Schlegel
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
- Nicollier-Schlegel SARL, Trélex, Switzerland
| | - Gabriella Captur
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK.
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK.
- Centre for Inherited Heart Muscle Conditions, The Royal Free Hospital, Pond Street, Hampstead, London, UK.
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10
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Yannatos I, Stites SD, Boen C, Xie SX, Brown RT, McMillan CT. Epigenetic age and socioeconomic status contribute to racial disparities in cognitive and functional aging between Black and White older Americans. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.29.23296351. [PMID: 37873230 PMCID: PMC10592997 DOI: 10.1101/2023.09.29.23296351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Epigenetic age, a biological aging marker measured by DNA methylation, is a potential mechanism by which social factors drive disparities in age-related health. Epigenetic age gap is the residual between epigenetic age measures and chronological age. Previous studies showed associations between epigenetic age gap and age-related outcomes including cognitive capacity and performance on some functional measures, but whether epigenetic age gap contributes to disparities in these outcomes is unknown. We use data from the Health and Retirement Study to examine the role of epigenetic age gap in racial disparities in cognitive and functional outcomes and consider the role of socioeconomic status (SES). Epigenetic age measures are GrimAge or Dunedin Pace of Aging methylation (DPoAm). Cognitive outcomes are cross-sectional score and two-year change in Telephone Interview for Cognitive Status (TICS). Functional outcomes are prevalence and incidence of limitations performing Instrumental Activities of Daily Living (IADLs). We find, relative to White participants, Black participants have lower scores and greater decline in TICS, higher prevalence and incidence rates of IADL limitations, and higher epigenetic age gap. Age- and gender-adjusted analyses reveal that higher GrimAge and DPoAm gap are both associated with worse cognitive and functional outcomes and mediate 6-11% of racial disparities in cognitive outcomes and 19-39% of disparities in functional outcomes. Adjusting for SES attenuates most DPoAm associations and most mediation effects. These results support that epigenetic age gap contributes to racial disparities in cognition and functioning and may be an important mechanism linking social factors to disparities in health outcomes.
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Affiliation(s)
- Isabel Yannatos
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Shana D. Stites
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, USA
| | - Courtney Boen
- Department of Sociology, University of Pennsylvania, Philadelphia, USA
| | - Sharon X. Xie
- Deptartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, USA
| | - Rebecca T. Brown
- Division of Geriatric Medicine, Perelman School of Medicine, Philadelphia, USA
- Geriatrics and Extended Care Program, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, USA
- Center for Health Equity Research and Promotion, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, USA
| | - Corey T. McMillan
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
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11
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Rentscher KE, Bethea TN, Zhai W, Small BJ, Zhou X, Ahles TA, Ahn J, Breen EC, Cohen HJ, Extermann M, Graham DM, Jim HS, McDonald BC, Nakamura ZM, Patel SK, Root JC, Saykin AJ, Van Dyk K, Mandelblatt JS, Carroll JE. Epigenetic aging in older breast cancer survivors and noncancer controls: preliminary findings from the Thinking and Living with Cancer Study. Cancer 2023; 129:2741-2753. [PMID: 37259669 PMCID: PMC10659047 DOI: 10.1002/cncr.34818] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 03/22/2023] [Accepted: 03/29/2023] [Indexed: 06/02/2023]
Abstract
BACKGROUND Cancer and its treatments may accelerate aging in survivors; however, research has not examined epigenetic markers of aging in longer term breast cancer survivors. This study examined whether older breast cancer survivors showed greater epigenetic aging than noncancer controls and whether epigenetic aging related to functional outcomes. METHODS Nonmetastatic breast cancer survivors (n = 89) enrolled prior to systemic therapy and frequency-matched controls (n = 101) ages 62 to 84 years provided two blood samples to derive epigenetic aging measures (Horvath, Extrinsic Epigenetic Age [EEA], PhenoAge, GrimAge, Dunedin Pace of Aging) and completed cognitive (Functional Assessment of Cancer Therapy-Cognitive Function) and physical (Medical Outcomes Study Short Form-12) function assessments at approximately 24 to 36 and 60 months after enrollment. Mixed-effects models tested survivor-control differences in epigenetic aging, adjusting for age and comorbidities; models for functional outcomes also adjusted for racial group, site, and cognitive reserve. RESULTS Survivors were 1.04 to 2.22 years biologically older than controls on Horvath, EEA, GrimAge, and DunedinPACE measures (p = .001-.04) at approximately 24 to 36 months after enrollment. Survivors exposed to chemotherapy were 1.97 to 2.71 years older (p = .001-.04), and among this group, an older EEA related to worse self-reported cognition (p = .047) relative to controls. An older epigenetic age related to worse physical function in all women (p < .001-.01). Survivors and controls showed similar epigenetic aging over time, but Black survivors showed accelerated aging over time relative to non-Hispanic White survivors. CONCLUSION Older breast cancer survivors, particularly those exposed to chemotherapy, showed greater epigenetic aging than controls that may relate to worse outcomes. If replicated, measurement of biological aging could complement geriatric assessments to guide cancer care for older women.
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Affiliation(s)
- Kelly E. Rentscher
- Department of Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee WI
- Norman Cousins Center for Psychoneuroimmunology, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA
| | - Traci N. Bethea
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
| | - Wanting Zhai
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
| | - Brent J. Small
- School of Aging Studies, University of South Florida, Tampa, FL
| | - Xingtao Zhou
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
| | - Tim A. Ahles
- Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Jaeil Ahn
- Department of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
| | - Elizabeth C. Breen
- Norman Cousins Center for Psychoneuroimmunology, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA
| | - Harvey Jay Cohen
- Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, NC
| | | | - Deena M.A. Graham
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ
| | | | - Brenna C. McDonald
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine and Indiana University Melvin and Bren Simon Cancer Center, Indianapolis, IN
| | - Zev M. Nakamura
- Department of Psychiatry, University of North Carolina–Chapel Hill, Chapel Hill, NC
| | | | - James C. Root
- Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine and Indiana University Melvin and Bren Simon Cancer Center, Indianapolis, IN
| | - Kathleen Van Dyk
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA
| | | | - Judith E. Carroll
- Norman Cousins Center for Psychoneuroimmunology, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA
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12
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Liu R, Zhao E, Yu H, Yuan C, Abbas MN, Cui H. Methylation across the central dogma in health and diseases: new therapeutic strategies. Signal Transduct Target Ther 2023; 8:310. [PMID: 37620312 PMCID: PMC10449936 DOI: 10.1038/s41392-023-01528-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 08/26/2023] Open
Abstract
The proper transfer of genetic information from DNA to RNA to protein is essential for cell-fate control, development, and health. Methylation of DNA, RNAs, histones, and non-histone proteins is a reversible post-synthesis modification that finetunes gene expression and function in diverse physiological processes. Aberrant methylation caused by genetic mutations or environmental stimuli promotes various diseases and accelerates aging, necessitating the development of therapies to correct the disease-driver methylation imbalance. In this Review, we summarize the operating system of methylation across the central dogma, which includes writers, erasers, readers, and reader-independent outputs. We then discuss how dysregulation of the system contributes to neurological disorders, cancer, and aging. Current small-molecule compounds that target the modifiers show modest success in certain cancers. The methylome-wide action and lack of specificity lead to undesirable biological effects and cytotoxicity, limiting their therapeutic application, especially for diseases with a monogenic cause or different directions of methylation changes. Emerging tools capable of site-specific methylation manipulation hold great promise to solve this dilemma. With the refinement of delivery vehicles, these new tools are well positioned to advance the basic research and clinical translation of the methylation field.
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Affiliation(s)
- Ruochen Liu
- State Key Laboratory of Resource Insects, Medical Research Institute, Southwest University, Chongqing, 400715, China
- Jinfeng Laboratory, Chongqing, 401329, China
- Chongqing Engineering and Technology Research Center for Silk Biomaterials and Regenerative Medicine, Chongqing, 400716, China
- Engineering Research Center for Cancer Biomedical and Translational Medicine, Southwest University, Chongqing, 400715, China
| | - Erhu Zhao
- State Key Laboratory of Resource Insects, Medical Research Institute, Southwest University, Chongqing, 400715, China
- Jinfeng Laboratory, Chongqing, 401329, China
- Chongqing Engineering and Technology Research Center for Silk Biomaterials and Regenerative Medicine, Chongqing, 400716, China
- Engineering Research Center for Cancer Biomedical and Translational Medicine, Southwest University, Chongqing, 400715, China
| | - Huijuan Yu
- State Key Laboratory of Resource Insects, Medical Research Institute, Southwest University, Chongqing, 400715, China
| | - Chaoyu Yuan
- State Key Laboratory of Resource Insects, Medical Research Institute, Southwest University, Chongqing, 400715, China
| | - Muhammad Nadeem Abbas
- State Key Laboratory of Resource Insects, Medical Research Institute, Southwest University, Chongqing, 400715, China
- Jinfeng Laboratory, Chongqing, 401329, China
- Chongqing Engineering and Technology Research Center for Silk Biomaterials and Regenerative Medicine, Chongqing, 400716, China
- Engineering Research Center for Cancer Biomedical and Translational Medicine, Southwest University, Chongqing, 400715, China
| | - Hongjuan Cui
- State Key Laboratory of Resource Insects, Medical Research Institute, Southwest University, Chongqing, 400715, China.
- Jinfeng Laboratory, Chongqing, 401329, China.
- Chongqing Engineering and Technology Research Center for Silk Biomaterials and Regenerative Medicine, Chongqing, 400716, China.
- Engineering Research Center for Cancer Biomedical and Translational Medicine, Southwest University, Chongqing, 400715, China.
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13
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Loh KP, Sanapala C, Jensen-Battaglia M, Rana A, Sohn MB, Watson E, Gilmore N, Klepin HD, Mendler JH, Liesveld J, Huselton E, LoCastro M, Susiarjo M, Netherby-Winslow C, Williams AM, Mustian K, Vertino P, Janelsins MC. Exercise and epigenetic ages in older adults with myeloid malignancies. Eur J Med Res 2023; 28:180. [PMID: 37254221 PMCID: PMC10227405 DOI: 10.1186/s40001-023-01145-z] [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: 12/27/2022] [Accepted: 05/19/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND Older adults with myeloid malignancies are susceptible to treatment-related toxicities. Accelerated DNAm age, or the difference between DNA methylation (DNAm) age and chronological age, may be used as a biomarker of biological age to predict individuals at risk. In addition, cancer treatment can also lead to accelerated DNAm age. Exercise is a promising intervention to reduce or prevent functional, psychological, and cognitive impairments in older patients with myeloid malignancies, yet there is little evidence of the effects of exercise on DNAm age. We explored (1) the associations of accelerated DNAm age with physical, psychological, and cognitive functions at baseline; (2) changes in DNAm age from baseline to post-intervention; and (3) the associations of changes in accelerated DNAm age with changes in functions from baseline to post-intervention. METHODS We enrolled older patients with myeloid malignancies to a single-arm pilot study testing a mobile health (mHealth) exercise intervention that combines an exercise program (EXCAP©®) with a mobile application over 2 cycles of chemotherapy (8-12 weeks). Patients completed measures of physical, psychological, and cognitive functions and provided blood samples for analyses of DNAm age at baseline and post-intervention. Paired t-tests or Wilcoxon signed rank tests assessed changes in DNAm ages, and Spearman's correlation assessed the relationships between accelerated ages and functions. RESULTS We included 20 patients (mean age: 72 years, range 62-80). Accelerated GrimAge, accelerated PhenoAge, and DunedinPACE were stable from baseline to post-intervention. At baseline, DunedinPACE was correlated with worse grip strength (r = -0.41, p = 0.08). From baseline to post-intervention, decreases in accelerated GrimAge (r = -0.50, p = 0.02), accelerated PhenoAge (r = - 0.39, p = 0.09), and DunedinPace (r = - 0.43, p = 0.06) were correlated with increases in distance walked on 6-min walk test. Decreases in accelerated GrimAge (r = - 0.49, p = 0.03), accelerated PhenoAge (r = - 0.40, p = 0.08), and DunedinPace (r = - 0.41, p = 0.07) were correlated with increases in in grip strength. CONCLUSIONS Among older adults with myeloid malignancies receiving chemotherapy, GrimAge and PhenoAge on average are stable after a mHealth exercise intervention. Decreases in accelerated GrimAge, accelerated PhenoAge, and DunedinPACE over 8-12 weeks of exercise were correlated with increased physical performance. Future trials assessing the effects of exercise on treatment-related toxicities should evaluate DNAm age. Trial registration Clinicaltrials.gov identifier: NCT04981821.
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Affiliation(s)
- Kah Poh Loh
- James P. Wilmot Cancer Institute, Rochester, NY USA
- Division of Hematology/Oncology, Department of Medicine, University of Rochester Medical Center, 601 Elmwood Avenue, Box 704, Rochester, NY 14642 USA
| | | | | | - Anish Rana
- School of Medicine and Dentistry, University of Rochester, Rochester, NY USA
| | - Michael B. Sohn
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY USA
| | - Erin Watson
- Department of Psychology, Princeton University, Princeton, NJ USA
| | - Nikesha Gilmore
- Division of Cancer Control, Department of Surgery, University of Rochester Medical Center, Rochester, NY USA
| | - Heidi D. Klepin
- Wake Forest Baptist Comprehensive Cancer Center, Medical Center Blvd, Winston-Salem, NC USA
| | - Jason H. Mendler
- James P. Wilmot Cancer Institute, Rochester, NY USA
- Division of Hematology/Oncology, Department of Medicine, University of Rochester Medical Center, 601 Elmwood Avenue, Box 704, Rochester, NY 14642 USA
| | - Jane Liesveld
- James P. Wilmot Cancer Institute, Rochester, NY USA
- Division of Hematology/Oncology, Department of Medicine, University of Rochester Medical Center, 601 Elmwood Avenue, Box 704, Rochester, NY 14642 USA
| | - Eric Huselton
- James P. Wilmot Cancer Institute, Rochester, NY USA
- Division of Hematology/Oncology, Department of Medicine, University of Rochester Medical Center, 601 Elmwood Avenue, Box 704, Rochester, NY 14642 USA
| | - Marissa LoCastro
- James P. Wilmot Cancer Institute, Rochester, NY USA
- School of Medicine and Dentistry, University of Rochester, Rochester, NY USA
| | - Martha Susiarjo
- Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY USA
| | - Colleen Netherby-Winslow
- Division of Cancer Control, Department of Surgery, University of Rochester Medical Center, Rochester, NY USA
| | - AnnaLynn M. Williams
- Division of Cancer Control, Department of Surgery, University of Rochester Medical Center, Rochester, NY USA
| | - Karen Mustian
- James P. Wilmot Cancer Institute, Rochester, NY USA
- Division of Cancer Control, Department of Surgery, University of Rochester Medical Center, Rochester, NY USA
| | - Paula Vertino
- James P. Wilmot Cancer Institute, Rochester, NY USA
- Department of Biomedical Genetics, University of Rochester Medical Center, Rochester, NY USA
| | - Michelle C. Janelsins
- James P. Wilmot Cancer Institute, Rochester, NY USA
- Division of Cancer Control, Department of Surgery, University of Rochester Medical Center, Rochester, NY USA
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14
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Merchant JP, Zhu K, Henrion MYR, Zaidi SSA, Lau B, Moein S, Alamprese ML, Pearse RV, Bennett DA, Ertekin-Taner N, Young-Pearse TL, Chang R. Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease. Commun Biol 2023; 6:503. [PMID: 37188718 PMCID: PMC10185548 DOI: 10.1038/s42003-023-04791-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/31/2023] [Indexed: 05/17/2023] Open
Abstract
Despite decades of genetic studies on late-onset Alzheimer's disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer's pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer's-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer's disease.
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Affiliation(s)
- Julie P Merchant
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Neuroscience Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kuixi Zhu
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Marc Y R Henrion
- Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, Pembroke Place, L3 5QA, UK
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, PO Box 30096, Blantyre, Malawi
| | - Syed S A Zaidi
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Branden Lau
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
- Arizona Research Labs, Genetics Core, University of Arizona, Tucson, AZ, USA
| | - Sara Moein
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Melissa L Alamprese
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Richard V Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Tracy L Young-Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Harvard Stem Cell Institute, Harvard University, Boston, MA, USA.
| | - Rui Chang
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA.
- Department of Neurology, University of Arizona, Tucson, AZ, USA.
- INTelico Therapeutics LLC, Tucson, AZ, USA.
- PATH Biotech LLC, Tucson, AZ, USA.
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15
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Stephan Y, Sutin AR, Luchetti M, Aschwanden D, Terracciano A. The Mediating Role of Biomarkers in the Association Between Subjective Aging and Episodic Memory. J Gerontol B Psychol Sci Soc Sci 2023; 78:242-252. [PMID: 36179098 PMCID: PMC9938926 DOI: 10.1093/geronb/gbac155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES Subjective aging, indexed by subjective age and self-perceptions of aging (SPA), is consistently related to cognition in adulthood. The present study examined whether blood biomarkers mediate the longitudinal associations between subjective aging indices and memory. METHODS Data of 5,369 individuals aged 50-94 years (mean = 66.89 years, SD = 9.22; 60% women) were drawn from the Health and Retirement Study (HRS). Subjective age, SPA, and demographic factors were assessed in 2012/2014. Interleukin-6, C-reactive protein, albumin, cystatin C, N-terminal pro B-type natriuretic peptide (NT-proBNP), fasting glucose, Vitamin D, hemoglobin, red cells distribution width, and epigenetic aging were assessed as part of the HRS Venuous Blood Study in 2016. Memory was measured in 2018. The mediators (except for epigenetic aging, which was assessed in a subsample) were tested simultaneously in models that accounted for demographic covariates. RESULTS An older subjective age was related to worse memory partially through higher fasting glucose, higher cystatin C, higher NT-proBNP, and accelerated epigenetic aging. Negative SPA was related to worse memory through lower Vitamin D3, higher fasting glucose, higher cystatin C, higher NT-proBNP, and accelerated epigenetic aging. The biomarkers explained between 2% and 10% of subjective age and between 1% and 8% of SPA associations with memory. Additional analysis revealed that biomarkers continued to be significant mediators when physical inactivity and depressive symptoms were included as additional mediators. CONCLUSION The present study adds to existing research on the association between subjective aging and memory by providing new evidence on the biological mediators of this association.
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Affiliation(s)
| | - Angelina R Sutin
- Department of Behavioral Sciences and Social Medicine, College of Medicine, Florida State University, Tallahassee, Florida, USA
| | - Martina Luchetti
- Department of Behavioral Sciences and Social Medicine, College of Medicine, Florida State University, Tallahassee, Florida, USA
| | - Damaris Aschwanden
- Department of Geriatrics, College of Medicine, Florida State University, Tallahassee, Florida, USA
| | - Antonio Terracciano
- Department of Geriatrics, College of Medicine, Florida State University, Tallahassee, Florida, USA
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16
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Lin WY. Epigenetic clocks derived from western samples differentially reflect Taiwanese health outcomes. Front Genet 2023; 14:1089819. [PMID: 36814906 PMCID: PMC9939687 DOI: 10.3389/fgene.2023.1089819] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 01/23/2023] [Indexed: 02/09/2023] Open
Abstract
Introduction: Several epigenetic clocks have been developed, with five measures of epigenetic age acceleration (EAA) especially receiving extensive investigations: HannumEAA, IEAA, PhenoEAA, GrimEAA, and DunedinPACE. These epigenetic clocks were mainly developed by individuals of European or Hispanic ancestry. It remains unclear whether they can reflect disease morbidity and physiological conditions in Asian populations. Methods: I here investigated five measures of EAA of 2,474 Taiwan Biobank participants with DNA methylation data. Using logistic regressions, I sequentially regressed various health outcomes on each of the five measures of EAA while adjusting for chronological age, sex, body mass index, the number of smoking pack-years, drinking status, regular exercise, educational attainment, and six cell-type proportions. Results: Except for IEAA, all measures of EAA reflected the obesity of Taiwanese (p < 4.0E-4). Diabetes was reflected by DunedinPACE (p = 5.4E-6) and GrimEAA (p = 5.8E-5). Moreover, DunedinPACE was associated with dyslipidemia, including hypertriglyceridemia (p = 1.1E-5), low high-density lipoprotein cholesterol (HDL-C) (p = 4.0E-5), and high triglyceride to HDL-C ratio (p = 1.6E-7). Discussion: This is one of the first studies to show that epigenetic clocks (developed by individuals of European or Hispanic ancestry) can reflect Taiwanese physiological conditions. DunedinPACE was associated with more Taiwanese health outcomes than the other four measures of EAA.
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Affiliation(s)
- Wan-Yu Lin
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan,Master of Public Health Degree Program, College of Public Health, National Taiwan University, Taipei, Taiwan,Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan,*Correspondence: Wan-Yu Lin,
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17
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Zhai J, Kongsberg WH, Pan Y, Hao C, Wang X, Sun J. Caloric restriction induced epigenetic effects on aging. Front Cell Dev Biol 2023; 10:1079920. [PMID: 36712965 PMCID: PMC9880295 DOI: 10.3389/fcell.2022.1079920] [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: 10/25/2022] [Accepted: 12/31/2022] [Indexed: 01/15/2023] Open
Abstract
Aging is the subject of many studies, facilitating the discovery of many interventions. Epigenetic influences numerous life processes by regulating gene expression and also plays a crucial role in aging regulation. Increasing data suggests that dietary changes can alter epigenetic marks associated with aging. Caloric restriction (CR)is considered an intervention to regulate aging and prolong life span. At present, CR has made some progress by regulating signaling pathways associated with aging as well as the mechanism of action of intercellular signaling molecules against aging. In this review, we will focus on autophagy and epigenetic modifications to elaborate the molecular mechanisms by which CR delays aging by triggering autophagy, epigenetic modifications, and the interaction between the two in caloric restriction. In order to provide new ideas for the study of the mechanism of aging and delaying aging.
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Affiliation(s)
| | | | | | | | | | - Jie Sun
- *Correspondence: Xiaojing Wang, ; Jie Sun,
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18
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Yang GS, Yang K, Weaver MT, Lynch Kelly D, Dorsey SG, Jackson-Cook CK, Lyon DE. Exploring the relationship between DNA methylation age measures and psychoneurological symptoms in women with early-stage breast cancer. Support Care Cancer 2022; 31:65. [PMID: 36538110 DOI: 10.1007/s00520-022-07519-z] [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: 09/14/2021] [Accepted: 11/14/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE The epigenetic clock has been acknowledged as an indicator for molecular aging, but few studies have examined possible associations of DNA methylation (DNAm) age or age acceleration (AA) with symptom burden in individuals who are treated for cancer. This study explored the association of DNAm age or AA with psychoneurological (PN) symptoms, including cognitive impairment, fatigue, sleep disturbances, pain, and depressive symptoms, in breast cancer survivors over a 2-year period. METHODS We measured PN symptoms using reliable instruments and DNAm levels by Infinium HumanMethylation450K BeadChip (N = 72). DNAm age was calculated by the Horvath, Grim, and Hannum-based intrinsic and extrinsic age estimations. AA was defined by the residual regressing estimated epigenetic age on chronological age. Mixed regression models were fitted for AA and changes in AA to study the association over time. Separate linear regression models and a mixed-effects model were fitted for AA at each time point. RESULTS Horvath-AA, Grim-AA, and extrinsic epigenetic AA were significantly changed over time, while intrinsic epigenetic AA did not exhibit any temporal changes. Increased AA was associated with greater anxiety and fatigue, as well as worse cognitive memory, adjusting for race, BMI, income, chemotherapy, radiation therapy, and chronological age. Increased DNAm age was associated with greater anxiety over 2 years. CONCLUSION Our findings suggest DNAm age and AA may be associated with PN symptoms over the course of cancer treatment and survivorship. Some PN symptoms may be amenable to preventive interventions targeted to epigenetic clocks that influence aging-associated processes.
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Affiliation(s)
- Gee Su Yang
- University of Connecticut School of Nursing, Storrs, CT, USA
| | - Kai Yang
- Medical College of Wisconsin Department of Institute for Health and Equity Division of Biostatistics, Milwaukee, WI, USA
| | | | | | - Susan G Dorsey
- School of Nursing Department of Pain and Translational Symptom Science, University of Maryland, Baltimore, MD, USA
| | - Colleen K Jackson-Cook
- Department of Pathology & Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Debra E Lyon
- University of Florida College of Nursing, Gainesville, FL, USA.
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19
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Richards M. The Power of Birth Cohorts to Study Risk Factors for Cognitive Impairment. Curr Neurol Neurosci Rep 2022; 22:847-854. [PMID: 36350423 PMCID: PMC9643995 DOI: 10.1007/s11910-022-01244-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2022] [Indexed: 11/10/2022]
Abstract
PURPOSE OF REVIEW Birth cohorts are studies of people the same time; some of which have continuously followed participants across the life course. These are powerful designs for studying predictors of age-related outcomes, especially when information on predictors is collected before these outcomes are known. This article reviews recent findings from these cohorts for the outcomes of cognitive function, cognitive impairment, and risk of dementia, in relation to prior cognitive function, and social and biological predictors. RECENT FINDINGS Cognitive function and impairment are predicted by a wide range of factors, including childhood cognition, education, occupational status and complexity, and biological factors, including genetic and epigenetic. The particular importance of high and rising blood pressure in midlife is highlighted, with some insight into brain mechanisms involved. Some limitations are noted, including sources of bias in the data. Despite these limitations, birth cohorts have provided valuable insights into factors across the life course associated with cognitive impairment.
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Affiliation(s)
- Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
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20
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Engelbrecht HR, Merrill SM, Gladish N, MacIsaac JL, Lin DTS, Ecker S, Chrysohoou CA, Pes GM, Kobor MS, Rehkopf DH. Sex differences in epigenetic age in Mediterranean high longevity regions. FRONTIERS IN AGING 2022; 3:1007098. [PMID: 36506464 PMCID: PMC9726738 DOI: 10.3389/fragi.2022.1007098] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/21/2022] [Indexed: 11/24/2022]
Abstract
Sex differences in aging manifest in disparities in disease prevalence, physical health, and lifespan, where women tend to have greater longevity relative to men. However, in the Mediterranean Blue Zones of Sardinia (Italy) and Ikaria (Greece) are regions of centenarian abundance, male-female centenarian ratios are approximately one, diverging from the typical trend and making these useful regions in which to study sex differences of the oldest old. Additionally, these regions can be investigated as examples of healthy aging relative to other populations. DNA methylation (DNAm)-based predictors have been developed to assess various health biomarkers, including biological age, Pace of Aging, serum interleukin-6 (IL-6), and telomere length. Epigenetic clocks are biological age predictors whose deviation from chronological age has been indicative of relative health differences between individuals, making these useful tools for interrogating these differences in aging. We assessed sex differences between the Horvath, Hannum, GrimAge, PhenoAge, Skin and Blood, and Pace of Aging predictors from individuals in two Mediterranean Blue Zones and found that men displayed positive epigenetic age acceleration (EAA) compared to women according to all clocks, with significantly greater rates according to GrimAge (β = 3.55; p = 1.22 × 10-12), Horvath (β = 1.07; p = 0.00378) and the Pace of Aging (β = 0.0344; p = 1.77 × 10-08). Other DNAm-based biomarkers findings indicated that men had lower DNAm-predicted serum IL-6 scores (β = -0.00301, p = 2.84 × 10-12), while women displayed higher DNAm-predicted proportions of regulatory T cells than men from the Blue Zone (p = 0.0150, 95% Confidence Interval [0.00131, 0.0117], Cohen's d = 0.517). All clocks showed better correlations with chronological age in women from the Blue Zones than men, but all clocks showed large mean absolute errors (MAE >30 years) in both sexes, except for PhenoAge (MAE <5 years). Thus, despite their equal survival to older ages in these Mediterranean Blue Zones, men in these regions remain biologically older by most measured DNAm-derived metrics than women, with the exception of the IL-6 score and proportion of regulatory T cells.
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Affiliation(s)
- Hannah-Ruth Engelbrecht
- Edwin S. H. Leong Healthy Aging Program, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,British Columbia Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada,Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Sarah M. Merrill
- Edwin S. H. Leong Healthy Aging Program, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,British Columbia Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada,Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Nicole Gladish
- Department of Epidemiology and Population Health, School of Medicine, Stanford University, Palo Alto, CA, United States
| | - Julie L. MacIsaac
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,British Columbia Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada,Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - David T. S. Lin
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,British Columbia Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada,Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Simone Ecker
- UCL Cancer Institute, University College London, London, United Kingdom
| | | | - Giovanni M. Pes
- Department of Clinical and Experimental Medicine, University of Sassari, Sassari, Italy
| | - Michael S. Kobor
- Edwin S. H. Leong Healthy Aging Program, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,British Columbia Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada,Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada,*Correspondence: Michael S. Kobor, ; David H. Rehkopf,
| | - David H. Rehkopf
- Department of Epidemiology and Population Health, School of Medicine, Stanford University, Palo Alto, CA, United States,*Correspondence: Michael S. Kobor, ; David H. Rehkopf,
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21
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Reed RG, Carroll JE, Marsland AL, Manuck SB. DNA methylation-based measures of biological aging and cognitive decline over 16-years: preliminary longitudinal findings in midlife. Aging (Albany NY) 2022; 14:9423-9444. [PMID: 36374219 PMCID: PMC9792211 DOI: 10.18632/aging.204376] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 10/29/2022] [Indexed: 11/13/2022]
Abstract
DNA methylation-based (DNAm) measures of biological aging associate with increased risk of morbidity and mortality, but their links with cognitive decline are less established. This study examined changes over a 16-year interval in epigenetic clocks (the traditional and principal components [PC]-based Horvath, Hannum, PhenoAge, GrimAge) and pace of aging measures (Dunedin PoAm, Dunedin PACE) in 48 midlife adults enrolled in the longitudinal arm of the Adult Health and Behavior project (56% Female, baseline AgeM = 44.7 years), selected for discrepant cognitive trajectories. Cognitive Decliners (N = 24) were selected based on declines in a composite score derived from neuropsychological tests and matched with participants who did not show any decline, Maintainers (N = 24). Multilevel models with repeated DNAm measures within person tested the main effects of time, group, and group by time interactions. DNAm measures significantly increased over time generally consistent with elapsed time between study visits. There were also group differences: overall, Cognitive Decliners had an older PC-GrimAge and faster pace of aging (Dunedin PoAm, Dunedin PACE) than Cognitive Maintainers. There were no significant group by time interactions, suggesting accelerated epigenetic aging in Decliners remained constant over time. Older PC-GrimAge and faster pace of aging may be particularly sensitive to cognitive decline in midlife.
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Affiliation(s)
- Rebecca G. Reed
- Department of Psychology, Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Judith E. Carroll
- Cousins Center for Psychoneuroimmunology, Department of Psychiatry and Biobehavioral Science, Jane and Terry Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Anna L. Marsland
- Department of Psychology, Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Stephen B. Manuck
- Department of Psychology, Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
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22
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Sommerer Y, Dobricic V, Schilling M, Ohlei O, Bartrés-Faz D, Cattaneo G, Demuth I, Düzel S, Franzenburg S, Fuß J, Lindenberger U, Pascual-Leone Á, Sabet SS, Solé-Padullés C, Tormos JM, Vetter VM, Wesse T, Franke A, Lill CM, Bertram L. Epigenome-Wide Association Study in Peripheral Tissues Highlights DNA Methylation Profiles Associated with Episodic Memory Performance in Humans. Biomedicines 2022; 10:2798. [PMID: 36359320 PMCID: PMC9687249 DOI: 10.3390/biomedicines10112798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 10/24/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022] Open
Abstract
The decline in episodic memory (EM) performance is a hallmark of cognitive aging and an early clinical sign in Alzheimer’s disease (AD). In this study, we conducted an epigenome-wide association study (EWAS) using DNA methylation (DNAm) profiles from buccal and blood samples for cross-sectional (n = 1019) and longitudinal changes in EM performance (n = 626; average follow-up time 5.4 years) collected under the auspices of the Lifebrain consortium project. The mean age of participants with cross-sectional data was 69 ± 11 years (30−90 years), with 50% being females. We identified 21 loci showing suggestive evidence of association (p < 1 × 10−5) with either or both EM phenotypes. Among these were SNCA, SEPW1 (both cross-sectional EM), ITPK1 (longitudinal EM), and APBA2 (both EM traits), which have been linked to AD or Parkinson’s disease (PD) in previous work. While the EM phenotypes were nominally significantly (p < 0.05) associated with poly-epigenetic scores (PESs) using EWASs on general cognitive function, none remained significant after correction for multiple testing. Likewise, estimating the degree of “epigenetic age acceleration” did not reveal significant associations with either of the two tested EM phenotypes. In summary, our study highlights several interesting candidate loci in which differential DNAm patterns in peripheral tissue are associated with EM performance in humans.
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Affiliation(s)
- Yasmine Sommerer
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Marcel Schilling
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Olena Ohlei
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - David Bartrés-Faz
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Campus Clínic August Pi i Sunyer, Casanova, 143, 08036 Barcelona, Spain
| | - Gabriele Cattaneo
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Garcilaso, 57, 08027 Barcelona, Spain
- Departament de Medicina, Universitat Autònoma de Barcelona, Plaça Cívica, Bellaterra, 08193 Barcelona, Spain
- Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Camí de les Escoles, Badalona, 08916 Barcelona, Spain
| | - Ilja Demuth
- Biology of Aging Working Group, Department of Endocrinology and Metabolic Diseases, Division of Lipid Metabolism, Charité—Universitätsmedizin Berlin (corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin), Augustenburger Platz 1, 13353 Berlin, Germany
- Berlin Institute of Health Center for Regenerative Therapies, Charité—Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Sören Franzenburg
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
| | - Janina Fuß
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Álvaro Pascual-Leone
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Garcilaso, 57, 08027 Barcelona, Spain
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Harvard Medical School, 1200 Centre St., Boston, MA 02131, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215, USA
| | - Sanaz Sedghpour Sabet
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
| | - Cristina Solé-Padullés
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Campus Clínic August Pi i Sunyer, Casanova, 143, 08036 Barcelona, Spain
| | - Josep M. Tormos
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Garcilaso, 57, 08027 Barcelona, Spain
- Departament de Medicina, Universitat Autònoma de Barcelona, Plaça Cívica, Bellaterra, 08193 Barcelona, Spain
- Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Camí de les Escoles, Badalona, 08916 Barcelona, Spain
| | - Valentin Max Vetter
- Biology of Aging Working Group, Department of Endocrinology and Metabolic Diseases, Division of Lipid Metabolism, Charité—Universitätsmedizin Berlin (corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin), Augustenburger Platz 1, 13353 Berlin, Germany
| | - Tanja Wesse
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
| | - Christina M. Lill
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Institute of Epidemiology and Social Medicine, University of Münster, Domagkstr. 3, 48149 Münster, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, Charing Cross Hospital, St Dunstan's Road, London W68RP, UK
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Forskningsveien 3A, 0373 Oslo, Norway
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23
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Tzemah-Shahar R, Hochner H, Iktilat K, Agmon M. What can we learn from physical capacity about biological age? A systematic review. Ageing Res Rev 2022; 77:101609. [PMID: 35306185 DOI: 10.1016/j.arr.2022.101609] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/02/2022] [Accepted: 03/14/2022] [Indexed: 01/16/2023]
Abstract
OBJECTIVE To systematically investigate the relationship between objective measures of physical capacity (e.g., cardio-respiratory fitness or daily step count) and biological age, measured in different ways. DATA SOURCE PubMed; SCOPUS - Elsevier API; and Web of Science - ISI 1984-present, as well as contextual search engines used to identify additional relevant publications. STUDY SELECTION Cross-sectional and longitudinal studies that assessed the association between objectively measured physical capacity and biological aging in adult individuals (age>18). RESULTS Analysis of 28 studies demonstrated that physical capacity is positively associated with biological aging; the most dominant measures of physical capacity are muscular strength or gait speed. The majority of the studies estimated biological aging by a single methodology - either Leukocyte Telomere Length or DNA methylation levels. CONCLUSIONS This systematic review of the objective physical capacity measures used to estimate aging finds that the current literature is limited insofar as it overlooks the potential contribution of many feasible markers. We recommend measuring physical capacity in the context of aging using a wide range of modifiable behavioral markers, beyond simple muscle strength or simple gait speed. Forming a feasible and diversified method for estimating physical capacity through which it will also be possible to estimate biological aging in wide population studies is essential for the development of interventions that may alleviate the burden of age-related disease.
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Affiliation(s)
- Roy Tzemah-Shahar
- The Cheryl Spencer Institute for Nursing Research, Faculty of Health and Social Welfare, University of Haifa, Haifa, Israel
| | - Hagit Hochner
- Epidemiology unit, Hebrew University School of Public Health, Jerusalem, Israel
| | - Khalil Iktilat
- Department of Gerontology, Faculty of Health and Social Welfare, University of Haifa, Haifa, Israel
| | - Maayan Agmon
- The Cheryl Spencer Institute for Nursing Research, Faculty of Health and Social Welfare, University of Haifa, Haifa, Israel
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24
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Zheng Y, Habes M, Gonzales M, Pomponio R, Nasrallah I, Khan S, Vaughan DE, Davatzikos C, Seshadri S, Launer L, Sorond F, Sedaghat S, Wainwright D, Baccarelli A, Sidney S, Bryan N, Greenland P, Lloyd-Jones D, Yaffe K, Hou L. Mid-life epigenetic age, neuroimaging brain age, and cognitive function: coronary artery risk development in young adults (CARDIA) study. Aging (Albany NY) 2022; 14:1691-1712. [PMID: 35220276 PMCID: PMC8908939 DOI: 10.18632/aging.203918] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 02/08/2022] [Indexed: 11/25/2022]
Abstract
The proportion of aging populations affected by dementia is increasing. There is an urgent need to identify biological aging markers in mid-life before symptoms of age-related dementia present for early intervention to delay the cognitive decline and the onset of dementia. In this cohort study involving 1,676 healthy participants (mean age 40) with up to 15 years of follow up, we evaluated the associations between cognitive function and two classes of novel biological aging markers: blood-based epigenetic aging and neuroimaging-based brain aging. Both accelerated epigenetic aging and brain aging were prospectively associated with worse cognitive outcomes. Specifically, every year faster epigenetic or brain aging was on average associated with 0.19-0.28 higher (worse) Stroop score, 0.04-0.05 lower (worse) RAVLT score, and 0.23-0.45 lower (worse) DSST (all false-discovery-rate-adjusted p <0.05). While epigenetic aging is a more stable biomarker with strong long-term predictive performance for cognitive function, brain aging biomarker may change more dynamically in temporal association with cognitive decline. The combined model using epigenetic and brain aging markers achieved the highest accuracy (AUC: 0.68, p<0.001) in predicting global cognitive function status. Accelerated epigenetic age and brain age at midlife may aid timely identification of individuals at risk for accelerated cognitive decline and promote the development of interventions to preserve optimal functioning across the lifespan.
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Affiliation(s)
- Yinan Zheng
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Mohamad Habes
- Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Mitzi Gonzales
- Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Raymond Pomponio
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ilya Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sadiya Khan
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Douglas E. Vaughan
- Feinberg Cardiovascular Research Institute, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sudha Seshadri
- Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Lenore Launer
- Laboratory of Epidemiology and Population Science, Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
| | - Farzaneh Sorond
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Sanaz Sedaghat
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Derek Wainwright
- Departments of Neurological Surgery, Medicine-Hematology and Oncology, Microbiology-Immunology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Andrea Baccarelli
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY 10032, USA
| | - Stephen Sidney
- Kaiser Permanente Division of Research, Oakland, CA 94612, USA
| | - Nick Bryan
- Department of Diagnostic Medicine, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
| | - Philip Greenland
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Kristine Yaffe
- Departments of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA 94143, USA
- Department of Neurology University of California, San Francisco, CA 94143, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, CA 94143, USA
- San Francisco VA Medical Center, San Francisco, CA 94143, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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25
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Macdonald-Dunlop E, Taba N, Klarić L, Frkatović A, Walker R, Hayward C, Esko T, Haley C, Fischer K, Wilson JF, Joshi PK. A catalogue of omics biological ageing clocks reveals substantial commonality and associations with disease risk. Aging (Albany NY) 2022; 14:623-659. [PMID: 35073279 PMCID: PMC8833109 DOI: 10.18632/aging.203847] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 12/20/2021] [Indexed: 11/25/2022]
Abstract
Biological age (BA), a measure of functional capacity and prognostic of health outcomes that discriminates between individuals of the same chronological age (chronAge), has been estimated using a variety of biomarkers. Previous comparative studies have mainly used epigenetic models (clocks), we use ~1000 participants to compare fifteen omics ageing clocks, with correlations of 0.21-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 95% of the variance in chronAge being shared among clocks. The difference between BA and chronAge - omics clock age acceleration (OCAA) - often associates with health measures. One year’s OCAA typically has the same effect on risk factors/10-year disease incidence as 0.09/0.25 years of chronAge. Epigenetic and IgG glycomics clocks appeared to track generalised ageing while others capture specific risks. We conclude BA is measurable and prognostic and that future work should prioritise health outcomes over chronAge.
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Affiliation(s)
- Erin Macdonald-Dunlop
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Nele Taba
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia.,Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia
| | - Lucija Klarić
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - Azra Frkatović
- Genos Glycoscience Research Laboratory, Zagreb 10000, Croatia
| | - Rosie Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia.,Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
| | - Chris Haley
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia.,Institute of Mathematics and Statistics, University of Tartu, Tartu 51009, Estonia
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK.,MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
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26
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McCrory C, Fiorito G, Hernandez B, Polidoro S, O'Halloran AM, Hever A, Ni Cheallaigh C, Lu AT, Horvath S, Vineis P, Kenny RA. GrimAge Outperforms Other Epigenetic Clocks in the Prediction of Age-Related Clinical Phenotypes and All-Cause Mortality. J Gerontol A Biol Sci Med Sci 2021; 76:741-749. [PMID: 33211845 DOI: 10.1093/gerona/glaa286] [Citation(s) in RCA: 198] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Indexed: 12/18/2022] Open
Abstract
The aging process is characterized by the presence of high interindividual variation between individuals of the same chronical age prompting a search for biomarkers that capture this heterogeneity. Epigenetic clocks measure changes in DNA methylation levels at specific CpG sites that are highly correlated with calendar age. The discrepancy resulting from the regression of DNA methylation age on calendar age is hypothesized to represent a measure of biological aging with a positive/negative residual signifying age acceleration (AA)/deceleration, respectively. The present study examines the associations of 4 epigenetic clocks-Horvath, Hannum, PhenoAge, GrimAge-with a wide range of clinical phenotypes (walking speed, grip strength, Fried frailty, polypharmacy, Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA), Sustained Attention Reaction Time, 2-choice reaction time), and with all-cause mortality at up to 10-year follow-up, in a sample of 490 participants in the Irish Longitudinal Study on Ageing (TILDA). HorvathAA and HannumAA were not predictive of health; PhenoAgeAA was associated with 4/9 outcomes (walking speed, frailty MOCA, MMSE) in minimally adjusted models, but not when adjusted for other social and lifestyle factors. GrimAgeAA by contrast was associated with 8/9 outcomes (all except grip strength) in minimally adjusted models, and remained a significant predictor of walking speed, .polypharmacy, frailty, and mortality in fully adjusted models. Results indicate that the GrimAge clock represents a step-improvement in the predictive utility of the epigenetic clocks for identifying age-related decline in an array of clinical phenotypes promising to advance precision medicine.
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Affiliation(s)
- Cathal McCrory
- Department of Medical Gerontology, Trinity College Dublin, Ireland
| | - Giovanni Fiorito
- Department of Biomedical Sciences, University of Sassari, Italy.,MRC Centre for Environment and Health, School of Public Medicine, Imperial College London, UK
| | | | | | | | - Ann Hever
- Department of Medical Gerontology, Trinity College Dublin, Ireland
| | | | - Ake T Lu
- Department of Human Genetics, David Geffen School of Medicine, Department of Biostatistics Fielding School of Public Health, University of California Los Angeles
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, Department of Biostatistics Fielding School of Public Health, University of California Los Angeles
| | - Paolo Vineis
- MRC Centre for Environment and Health, School of Public Medicine, Imperial College London, UK
| | - Rose Anne Kenny
- Department of Medical Gerontology, Trinity College Dublin, Ireland
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27
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Maddock J, Castillo-Fernandez J, Wong A, Ploubidis GB, Kuh D, Bell JT, Hardy R. Childhood growth and development and DNA methylation age in mid-life. Clin Epigenetics 2021; 13:155. [PMID: 34372922 PMCID: PMC8351141 DOI: 10.1186/s13148-021-01138-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 07/20/2021] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND In the first study of its kind, we examine the association between growth and development in early life and DNAm age biomarkers in mid-life. METHODS Participants were from the Medical Research Council National Survey of Health and Development (n = 1376). Four DNAm age acceleration (AgeAccel) biomarkers were measured when participants were aged 53 years: AgeAccelHannum; AgeAccelHorvath; AgeAccelLevine; and AgeAccelGrim. Exposure variables included: relative weight gain (standardised residuals from models of current weight z-score on current height, and previous weight and height z-scores); and linear growth (standardised residuals from models of current height z-score on previous height and weight z-scores) during infancy (0-2 years, weight gain only), early childhood (2-4 years), middle childhood (4-7 years) and late childhood to adolescence (7-15 years); age at menarche; and pubertal stage for men at 14-15 years. The relationship between relative weight gain and linear growth and AgeAccel was investigated using conditional growth models. We replicated analyses from the late childhood to adolescence period and pubertal timing among 240 participants from The National Child and Development Study (NCDS). RESULTS A 1SD increase in relative weight gain in late childhood to adolescence was associated with 0.50 years (95% CI 0.20, 0.79) higher AgeAccelGrim. Although the CI includes the null, the estimate was similar in NCDS [0.57 years (95% CI - 0.01, 1.16)] There was no strong evidence that relative weight gain and linear growth in childhood was associated with any other AgeAccel biomarker. There was no relationship between pubertal timing in men and AgeAccel biomarkers. Women who reached menarche ≥ 12 years had 1.20 years (95% CI 0.15, 2.24) higher AgeAccelGrim on average than women who reached menarche < 12 years; however, this was not replicated in NCDS and was not statistically significant after Bonferroni correction. CONCLUSIONS Our findings generally do not support an association between growth and AgeAccel biomarkers in mid-life. However, we found rapid weight gain during pubertal development, previously related to higher cardiovascular disease risk, to be associated with older AgeAccelGrim. Given this is an exploratory study, this finding requires replication.
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Affiliation(s)
- Jane Maddock
- MRC Unit for Lifelong Health and Ageing at UCL, Faculty of Population Health, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
| | | | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, Faculty of Population Health, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - George B Ploubidis
- Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at UCL, Faculty of Population Health, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Rebecca Hardy
- CLOSER, UCL Institute of Education, University College London, London, WC1H 0NU, UK
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28
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Xu R, Li S, Li S, Wong EM, Southey MC, Hopper JL, Abramson MJ, Guo Y. Surrounding Greenness and Biological Aging Based on DNA Methylation: A Twin and Family Study in Australia. ENVIRONMENTAL HEALTH PERSPECTIVES 2021; 129:87007. [PMID: 34460342 PMCID: PMC8404778 DOI: 10.1289/ehp8793] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 08/06/2021] [Accepted: 08/10/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND High surrounding greenness has many health benefits and might contribute to slower biological aging. However, very few studies have evaluated this from the perspective of epigenetics. OBJECTIVES We aimed to evaluate the association between surrounding greenness and biological aging based on DNA methylation. METHODS We derived Horvath's DNA methylation age (DNAmAge), Hannum's DNAmAge, PhenoAge, and GrimAge based on DNA methylation measured in peripheral blood samples from 479 Australian women in 130 families. Measures of DNAmAge acceleration (DNAmAgeAC) were derived from the residuals after regressing each DNAmAge metric on chronological age. Greenness was represented by satellite-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) metrics within 300-, 500-, 1,000-, and 2,000-m buffers surrounding participant addresses. Greenness-DNAmAgeAC associations were estimated using a within-sibship design fitted by linear mixed effect models, adjusting for familial clustering and important covariates. RESULTS Greenness metrics were associated with significantly lower DNAmAgeAC based on GrimAge acceleration, suggesting slower biological aging with higher greenness based on both NDVI and EVI in 300-2,000m buffer areas. For example, each interquartile range increase in NDVI within 1,000m was associated with a 0.59 (95% CI: 0.18, 1.01)-year decrease in GrimAge acceleration. Greenness was also inversely associated with three of the eight components of GrimAge, specifically, DNA methylation-based surrogates of serum cystatin-C, serum growth differentiation factor 15, and smoking pack years. Associations between greenness and biological aging measured by Horvath's and Hannum's DNAmAgeAC were less consistent, and depended on neighborhood socioeconomic status. No significant associations were estimated for PhenoAge acceleration. DISCUSSION Higher surrounding greenness was associated with slower biological aging, as indicated by GrimAge age acceleration, in Australian women. Associations were also evident for three individual components of GrimAge, but were inconsistent for other measures of biological aging. Additional studies are needed to confirm our results. https://doi.org/10.1289/EHP8793.
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Affiliation(s)
- Rongbin Xu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Victoria, Australia
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Michael J. Abramson
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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29
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Crimmins EM, Thyagarajan B, Levine ME, Weir DR, Faul J. Associations of Age, Sex, Race/Ethnicity, and Education With 13 Epigenetic Clocks in a Nationally Representative U.S. Sample: The Health and Retirement Study. J Gerontol A Biol Sci Med Sci 2021; 76:1117-1123. [PMID: 33453106 PMCID: PMC8140049 DOI: 10.1093/gerona/glab016] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Many DNA methylation-based indicators have been developed as summary measures of epigenetic aging. We examine the associations between 13 epigenetic clocks, including 4 second generation clocks, as well as the links of the clocks to social, demographic, and behavioral factors known to be related to health outcomes: sex, race/ethnicity, socioeconomic status, obesity, and lifetime smoking pack-years. METHODS The Health and Retirement Study is the data source which is a nationally representative sample of Americans over age 50. Assessment of DNA methylation was based on the EPIC chip and epigenetic clocks were developed based on existing literature. RESULTS The clocks vary in the strength of their relationships with age, with each other and with independent variables. Second generation clocks trained on health-related characteristics tend to relate more strongly to the sociodemographic and health behaviors known to be associated with health outcomes in this age group. CONCLUSIONS Users of this publicly available data set should be aware that epigenetic clocks vary in their relationships to age and to variables known to be related to the process of health change with age.
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Affiliation(s)
- Eileen M Crimmins
- Davis School of Gerontology, University of Southern California, Los Angeles, USA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, USA
| | - Morgan E Levine
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
| | - David R Weir
- Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, USA
| | - Jessica Faul
- Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, USA
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30
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Levine ME. Assessment of Epigenetic Clocks as Biomarkers of Aging in Basic and Population Research. J Gerontol A Biol Sci Med Sci 2020; 75:463-465. [PMID: 31995162 PMCID: PMC7328198 DOI: 10.1093/gerona/glaa021] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Indexed: 12/16/2022] Open
Affiliation(s)
- Morgan E Levine
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
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