Accelerated midlife epigenetic age associates with poor later life brain health in the 1946 British Birth Cohort

Abstract Background Accelerated epigenetic ageing has been associated with various age‐related health outcomes, but its relevance for dementia risk prediction is unclear. We investigated whether accelerated midlife epigenetic age associates with poor later‐life brain health. Methods Participants were 230 individuals from Insight 46, drawn from the 1946 British Birth Cohort, a population‐based study of individuals born in the first week of March 1946. DNA methylation was measured at 53 years using Infinium MethylationEPIC BeadChip and GrimAge and PhenoAge were calculated. ‘Age acceleration’ was derived for each clock (AgeAccelGrim and AgeAccelPheno), as previously. At 69‐71 years, we measured MRI whole brain and white matter hyperintensity volume, amyloid‐PET burden, plasma neurofilament light, and MRI Brain Age (with chronological age subtracted to produce the Brain‐Predicted Age Difference/Brain‐PAD). For n=176 with follow‐up MRI aged 72‐75 (n=176), whole brain atrophy was calculated using the boundary shift integral. CSF p‐tau181 was measured in n=64. Results Despite participants’ highly similar chronological age (mean=53.4 years, SD=0.2), there was considerable variation in AgeAccelGrim (SD=4.8yr) and AgeAccelPheno (SD=5.6yr). Linear regression revealed that accelerated GrimAge was associated with smaller later‐life whole brain volume, (b=‐1.9ml, p=0.010), whilst accelerated PhenoAge was associated with greater whole brain atrophy (b=0.403, p=0.001). Neither GrimAge nor PhenoAge acceleration was associated with amyloid‐PET burden. Accelerated PhenoAge was associated with higher white matter hyperintensity burden (b=0.03, p=0.04). Acceleration in both GrimAge (b=0.4pg/ml, p=0.001) and PhenoAge (b=0.4pg/ml, p=0.001) was linked to higher plasma neurofilament light, and both AgeAccelGrim (r=0.32, p=0.010) and AgeAccelPheno (r=0.35, p=0.005) were positively correlated with CSF p‐Tau181. MRI brain age (Brain‐PAD) was significantly correlated with AgeAccelGrim (r=0.15, p=0.02) but not AgeAccelPheno (r=0.08, p=0.2). Conclusion Acceleration of midlife epigenetic age was associated with a wide range of features of poor brain health ∼20 years later, in a population‐based sample. Epigenetic clocks may hold promise as tools for future brain health prediction.

Authors

Institutions

Publication Details

Journal
Alzheimer s & Dementia
Published
2024-12-01
DOI
https://doi.org/10.1002/alz.084231
Primary Topic
Birth, Development, and Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accelerated midlife epigenetic age associates with poor later life brain health in the 1946 British Birth Cohort

James H. Cole, Jane Maddock, Jennifer M. Nicholas, Ashvini Keshavan et al.
Alzheimer s & Dementia
Birth, Development, and Health
article

Accelerated midlife epigenetic age associates with poor later life brain health in the 1946 British Birth Cohort

James H. Cole, Jane Maddock, Jennifer M. Nicholas, Ashvini Keshavan, David M. Cash, Jonathan M. Schott, Aaron Z. Wagen, Marcus Richards, William Coath, Frederik Barkhof, J. R. Groves
article en

Abstract

Abstract Background Accelerated epigenetic ageing has been associated with various age‐related health outcomes, but its relevance for dementia risk prediction is unclear. We investigated whether accelerated midlife epigenetic age associates with poor later‐life brain health. Methods Participants were 230 individuals from Insight 46, drawn from the 1946 British Birth Cohort, a population‐based study of individuals born in the first week of March 1946. DNA methylation was measured at 53 years using Infinium MethylationEPIC BeadChip and GrimAge and PhenoAge were calculated. ‘Age acceleration’ was derived for each clock (AgeAccelGrim and AgeAccelPheno), as previously. At 69‐71 years, we measured MRI whole brain and white matter hyperintensity volume, amyloid‐PET burden, plasma neurofilament light, and MRI Brain Age (with chronological age subtracted to produce the Brain‐Predicted Age Difference/Brain‐PAD). For n=176 with follow‐up MRI aged 72‐75 (n=176), whole brain atrophy was calculated using the boundary shift integral. CSF p‐tau181 was measured in n=64. Results Despite participants’ highly similar chronological age (mean=53.4 years, SD=0.2), there was considerable variation in AgeAccelGrim (SD=4.8yr) and AgeAccelPheno (SD=5.6yr). Linear regression revealed that accelerated GrimAge was associated with smaller later‐life whole brain volume, (b=‐1.9ml, p=0.010), whilst accelerated PhenoAge was associated with greater whole brain atrophy (b=0.403, p=0.001). Neither GrimAge nor PhenoAge acceleration was associated with amyloid‐PET burden. Accelerated PhenoAge was associated with higher white matter hyperintensity burden (b=0.03, p=0.04). Acceleration in both GrimAge (b=0.4pg/ml, p=0.001) and PhenoAge (b=0.4pg/ml, p=0.001) was linked to higher plasma neurofilament light, and both AgeAccelGrim (r=0.32, p=0.010) and AgeAccelPheno (r=0.35, p=0.005) were positively correlated with CSF p‐Tau181. MRI brain age (Brain‐PAD) was significantly correlated with AgeAccelGrim (r=0.15, p=0.02) but not AgeAccelPheno (r=0.08, p=0.2). Conclusion Acceleration of midlife epigenetic age was associated with a wide range of features of poor brain health ∼20 years later, in a population‐based sample. Epigenetic clocks may hold promise as tools for future brain health prediction.

Alzheimer s & DementiaVol. 20(S2)
The Francis Crick Institute (GB), London School of Hygiene & Tropical Medicine (GB), UK Dementia Research Institute (GB), MRC Unit for Lifelong Health and Ageing (GB), National Hospital for Neurology and Neurosurgery (GB), University College London (GB)
No poverty
Openalex Percentile: Top 61%
Birth, Development, and Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.