medRxiv : the preprint server for health sciences · 2026
Epigenetic Clock Trajectories and Brain Health in Midlife.
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Boeriu Ana I, Andrews Shea J, Hoang Tina, Bae Sejong, Yaffe Kristine
Аннотация
BACKGROUND: Accelerated biological aging can be assessed with DNA methylation (DNAm)-based epigenetic clocks. Research suggests that greater DNAm is associated with faster cognitive decline and risk of Alzheimer disease (AD) and other dementias. However, most studies have relied on single-time-point measurements of clocks, rather than evaluating dynamic changes over time. We examined the association between 15-year epigenetic aging trajectories and brain health outcomes in midlife.
METHODS: We analyzed 2,833 middle-aged adults (mean baseline age 40 years, 59% female and 44% Black) with ≥ 3 DunedinPACE (a recently developed epigenetic clock) measurements, collected over 15 years. Using mixed-effects modeling, we derived individual-specific slopes of epigenetic aging trajectories and categorized participants as Fast Agers (slopes > 1 SD above the mean), Slow Agers (slopes < 1 SD below the mean), or Typical Agers (within ±1 SD of the mean). We examined associations between trajectory group and cognition on five cognitive domains as well as on plasma AD biomarkers (NfL, p-tau217, Aβ42/Aβ40), all assessed 15-20 years post-baseline. Models were adjusted for demographics, education, physical activity and APOE*ε4 carrier status (with additional adjustments for eGFRcr for biomarker outcomes).
RESULTS: Epigenetic aging trajectories were associated with multiple domains of cognition and AD biomarkers (Figure 1). Compared to Typical Agers, Fast Agers showed worse processing speed, memory, executive function, and global cognition (all p<0.05), with no difference in verbal fluency. Slow Agers had better performance on memory and global cognition (both p < 0.05). Fast Agers also exhibited significantly lower Aβ42/Aβ40 levels (p = 0.011) compared to Typical agers; no significant associations with p-tau217 or NfL were observed in either group.
CONCLUSION: Middle-aged adults with faster 15-year epigenetic aging trajectories demonstrated worse cognitive performance, whereas those with slower biological aging trajectories exhibited cognitive resilience and more favorable AD biomarker profiles. By examining long-term trajectories rather than single timepoints, these findings identify individuals at differential risk for brain health outcomes.
METHODS: We analyzed 2,833 middle-aged adults (mean baseline age 40 years, 59% female and 44% Black) with ≥ 3 DunedinPACE (a recently developed epigenetic clock) measurements, collected over 15 years. Using mixed-effects modeling, we derived individual-specific slopes of epigenetic aging trajectories and categorized participants as Fast Agers (slopes > 1 SD above the mean), Slow Agers (slopes < 1 SD below the mean), or Typical Agers (within ±1 SD of the mean). We examined associations between trajectory group and cognition on five cognitive domains as well as on plasma AD biomarkers (NfL, p-tau217, Aβ42/Aβ40), all assessed 15-20 years post-baseline. Models were adjusted for demographics, education, physical activity and APOE*ε4 carrier status (with additional adjustments for eGFRcr for biomarker outcomes).
RESULTS: Epigenetic aging trajectories were associated with multiple domains of cognition and AD biomarkers (Figure 1). Compared to Typical Agers, Fast Agers showed worse processing speed, memory, executive function, and global cognition (all p<0.05), with no difference in verbal fluency. Slow Agers had better performance on memory and global cognition (both p < 0.05). Fast Agers also exhibited significantly lower Aβ42/Aβ40 levels (p = 0.011) compared to Typical agers; no significant associations with p-tau217 or NfL were observed in either group.
CONCLUSION: Middle-aged adults with faster 15-year epigenetic aging trajectories demonstrated worse cognitive performance, whereas those with slower biological aging trajectories exhibited cognitive resilience and more favorable AD biomarker profiles. By examining long-term trajectories rather than single timepoints, these findings identify individuals at differential risk for brain health outcomes.