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. 2025 Dec;21(12):e70983.
doi: 10.1002/alz.70983.

Epigenetic clocks and longitudinal plasma biomarkers of Alzheimer's disease

Affiliations

Epigenetic clocks and longitudinal plasma biomarkers of Alzheimer's disease

Bowei Zhang et al. Alzheimers Dement. 2025 Dec.

Abstract

Introduction: Chronological age is the strongest risk factor for Alzheimer's disease and related dementias (ADRD). However, the association of accelerated biological aging relative to chronological age with ADRD pathology is unclear.

Methods: In a cohort of 2366 (873 with longitudinal data) cognitively unimpaired older women, we examined associations of seven baseline measures of epigenetic age acceleration (EAA) and pace of aging with 15-year changes in plasma ADRD biomarkers.

Results: At baseline, higher AgeAccelHorvath and AgeAccelPheno were associated with lower amyloid beta (Aβ) 42 to Aβ40 (Aβ42:Aβ40) ratio, and higher AgeAccelGrim2, PCPhenoAge, and PCGrimAge were associated with elevated neurofilament light (NfL). Longitudinally, higher baseline DunedinPACE - capturing the pace of biological aging - was associated with faster increases in tau phosphorylated at threonine 181 (p-tau181), p-tau217, NfL, and glial fibrillary acidic protein (GFAP) over 15 years.

Discussion: Accelerated biological aging, particularly DunedinPACE, was associated with increasing levels of plasma ADRD biomarkers over time.

Highlights: We studied 2366 older women from the Women's Health Initiative Memory Study. AgeAccelHorvath and AgeAccelPheno were linked to lower plasma Aβ42:Aβ40 at baseline. AgeAccelGrim2, PCPhenoAge, and PCGrimAge were linked to higher plasma NfL at baseline. DunedinPACE was associated with faster increases in p-tau181, p-tau217, NfL, and GFAP.

Keywords: Alzheimer's disease; amyloid beta; biological aging; blood biomarkers; dementia; epigenetic clocks; glial fibrillary acidic protein; neurofilament light; plasma; p‐tau181; p‐tau217; women's health.

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Conflict of interest statement

The Regents of the University of California are the sole owner of patents and patent applications directed at epigenetic biomarkers for which S.H. and A.T.L. are named inventors. S.H. is a founder, paid consultant, and serves on the board of the non‐profit Epigenetic Clock Development Foundation that licenses these patents. S.H. reports funding from NIH 1U01AG060908‐01 and receiving royalty payments surrounding these patents. S.H. is a principal investigator at Altos Labs. A.T.L. reports royalties or licenses for the DNAm GrimAge2 clock. B.S. discloses the following relationships: the NHLBI grant R01 HL164485; funding from the American Heart Association and NINDS; consulting fees from the Women's Health Initiative; medicolegal malpractice review for various firms; and American Heart Association regional chapter president. M.M.M. reports funding from NIH: RF1 AG69052; RF1 AG077386; R01AG079397, U19 AG078109, and U24 AG082930; DOD: W81XWH2110490; and the Alzheimer's Association. M.M.M. has served on scientific advisory boards and/or has consulted for Acadia, Althira, Beckman Coulter, Biogen, Cognito Therapeutics, Eisai, Lilly, Merck, Neurogen Biomarking, Novo Nordisk, Roche, and Siemens Healthineers; received speaking honorariums from Roche, Novo Nordisk, Biogen, and Medscape; and participated in grant reviews for the Alzheimer's Drug Discovery Foundation. B.Z. reports grant funding from R01AG079149 and residual class settlement funds in the matter of April Krueger vs. Wyeth, Inc., Case No. 03‐cv‐2496 (US District Court, SD of Calif.). A.Z.L. reports funding from NIA R01AG079149 and R01AG074345 and NHLBI Contract No. 75N92021D00001. S.M.R. reports NIA/NIH funding as an NIA intramural employee, support from the McKnight Foundation as a keynote speaker, serving on the International Scientific Advisory Boards of the Canadian Consortium on Neurodegeneration in Aging and Dementia Platforms UK, and serving on the External Advisory Board of Adult Aging Brain Connectome. S.N. reports funding from NIA 5K99AG082863‐02. L.K.M. reports funding from NIA R01AG079149 and R01AG074345. M.A.E. reports funding from AG074345, AG058571, and AA‐POINTER‐19‐611541; service on a steering committee for Nestle; support for attending meetings and/or travel from the American Diabetes Association and European Association for the Study of Diabetes; and participation on a data safety monitoring board or advisory board for Acumen, Annovis Bio, and several NIH‐supported studies. A.H.S. reports funding from NIA R01AG079149 and R01AG074345 and residual class settlement funds in the matter of April Krueger v. Wyeth, Inc., Case No. 03‐cv‐2496 (US District Court, SD of Calif.), as well as consulting fees from the WHI as the chair of a scientific interest group and member of the Publications and Presentations Committee. S.R.R., C.M.N., A.X.M., K.B., D.L., J.E.M., and L.F. declare no conflicts of interest. Author disclosures are available in the Supporting Information.

Figures

FIGURE 1
FIGURE 1
Associations of epigenetic clocks with plasma biomarkers of ADRD at baseline. Associations between epigenetic clocks and plasma biomarkers were based on linear regression models. Standardized log2‐transformed biomarker levels are shown with 95% confidence intervals per 1‐SD increase in epigenetic clocks. Epigenetic clocks include AgeAccelHorvath (SD 5.38), AgeAccelHannum (SD 5.01), AgeAccelPheno (SD 6.80), AgeAccelGrim2 (SD 4.37), DunedinPACE (SD 0.11), PCPhenoAge (SD 6.47), and PCGrimAge (SD 3.20). Biomarkers include Aβ42:Aβ40, p‐tau181, p‐tau217, NfL, and GFAP. Standard deviation of log2‐transformed biomarker values used in model fitting: Aβ42:Aβ40 (SD 0.31), p‐tau181 (SD 0.55), p‐tau217 (SD 0.81), NfL (SD 0.59), and GFAP (SD 0.63). Models were adjusted for chronological age, hormone therapy treatment arm, education, smoking status, race, ethnicity, physical activity, body mass index, diabetes, cardiovascular disease, hypertension, total cholesterol, HDL cholesterol, estimated glomerular filtration rate, and white blood cell counts (CD8T, CD4T, natural killer cells, B cells, monocytes, and neutrophils). ADRD, Alzheimer's diseases and related dementias; Aβ, amyloid beta; CI, confidence interval; GFAP, glial fibrillary acidic protein; NfL, neurofilament light; p‐tau181, tau phosphorylated at threonine 181; p‐tau217, tau phosphorylated at threonine 217; SD, standard deviation.
FIGURE 2
FIGURE 2
Changes in plasma biomarkers over an average of 15 years. Distribution of plasma biomarker levels at baseline and a second time point an average of 15 years later among participants with both measurements (N = 873). Biomarkers include plasma Aβ42:Aβ40, p‐tau181, p‐tau217, GFAP, and NfL. Violin plots display the distribution density. Boxplots indicate median and interquartile ranges. Means and SDs are presented above each group. Aβ, amyloid beta; GFAP, glial fibrillary acidic protein; NfL, neurofilament light; p‐tau181, tau phosphorylated at threonine 181; p‐tau217, tau phosphorylated at threonine 217; SD, standard deviation.
FIGURE 3
FIGURE 3
Associations of baseline epigenetic clocks with changes in plasma biomarkers of ADRD over an average of 15 years. Associations between baseline epigenetic clocks and rates of changes per decade in plasma biomarkers based on linear mixed‐effects regression models. Standardized log2‐transformed rates of change in plasma biomarkers are shown with 95% confidence intervals per 1‐SD increase in epigenetic clocks at baseline. Epigenetic clocks include AgeAccelHorvath (SD 5.36 years), AgeAccelHannum (SD 4.93), AgeAccelPheno (SD 6.72), AgeAccelGrim2 (SD 4.29), DunedinPACE (SD 0.11 years of physiologic decline), PCPhenoAge (SD 6.43), and PCGrimAge (SD 3.15). Biomarkers include Aβ42:Aβ40, GFAP, NfL, p‐tau181, and p‐tau217. Standard deviation of log2‐transformed biomarker values used in model fitting: Aβ42:Aβ40 (SD 0.32), p‐tau181 (SD 0.62), p‐tau217 (SD 0.92), NfL (SD 0.73), and GFAP (SD 0.68). Models were adjusted for chronological age, time since baseline in decades, hormone therapy treatment arm, education, smoking status, race, ethnicity, physical activity, body mass index, diabetes, cardiovascular disease, hypertension, total cholesterol, high‐density lipoprotein cholesterol, estimated glomerular filtration rate, and white blood cell counts (CD8T, CD4T, natural killer cells, B cells, monocytes, and neutrophils). ADRD, Alzheimer's diseases and related dementias; Aβ, amyloid beta; CI, confidence interval; GFAP, glial fibrillary acidic protein; p‐tau181, tau phosphorylated at threonine 181; p‐tau217, tau phosphorylated at threonine 217; NfL, neurofilament light; SD, standard deviation.
FIGURE 4
FIGURE 4
Estimated changes in plasma biomarkers of ADRD over an average of 15 years across 3 levels of DunedinPACE from a fully adjusted linear mixed‐effects model. Linear mixed‐effects models contained random intercepts and slopes adjusted for chronological age, time since baseline in decades, hormone therapy treatment arm, education, smoking status, race, ethnicity, physical activity, body mass index, diabetes, cardiovascular disease, hypertension, total cholesterol, HDL cholesterol, estimated glomerular filtration rate, and white blood cell counts (CD8T, CD4T, natural killer cells, B cells, monocytes, and neutrophils). An interaction term between DunedinPACE and time was included. Results are Z‐transformed DunedinPACE set to the mean (1.05), 1 SD below (0.94) and above the mean (1.16) at baseline. Biomarkers include GFAP, NfL, p‐tau181, and p‐tau217. ADRD, Alzheimer's disease and related dementias; GFAP, glial fibrillary acidic protein; NfL, neurofilament light; p‐tau181, tau phosphorylated at threonine 181; p‐tau217, tau phosphorylated at threonine 217; SD, standard deviation.

Update of

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