TY - JOUR
T1 - Blood-based multivariate methylation risk score for cognitive impairment and dementia
AU - for the Alzheimer's Disease Neuroimaging Initiative
AU - Koetsier, Jarno
AU - Cavill, Rachel
AU - Reijnders, Rick
AU - Harvey, Joshua
AU - Homann, Jan
AU - Kouhsar, Morteza
AU - Deckers, Kay
AU - Köhler, Sebastian
AU - Eijssen, Lars M.T.
AU - van den Hove, Daniel L.A.
AU - Demuth, Ilja
AU - Düzel, Sandra
AU - Smith, Rebecca G.
AU - Smith, Adam R.
AU - Burrage, Joe
AU - Walker, Emma M.
AU - Shireby, Gemma
AU - Hannon, Eilis
AU - Dempster, Emma
AU - Frayling, Tim
AU - Mill, Jonathan
AU - Dobricic, Valerija
AU - Johannsen, Peter
AU - Wittig, Michael
AU - Franke, Andre
AU - Vandenberghe, Rik
AU - Schaeverbeke, Jolien
AU - Freund-Levi, Yvonne
AU - Frölich, Lutz
AU - Scheltens, Philip
AU - Teunissen, Charlotte E.
AU - Frisoni, Giovanni
AU - Blin, Olivier
AU - Richardson, Jill C.
AU - Bordet, Régis
AU - Engelborghs, Sebastiaan
AU - de Roeck, Ellen
AU - Martinez-Lage, Pablo
AU - Tainta, Mikel
AU - Lleó, Alberto
AU - Sala, Isabel
AU - Popp, Julius
AU - Peyratout, Gwendoline
AU - Verhey, Frans
AU - Tsolaki, Magda
AU - Andreasson, Ulf
AU - Blennow, Kaj
AU - Zetterberg, Henrik
AU - Lill, Christina M.
AU - Bertram, Lars
N1 - Publisher Copyright:
© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
PY - 2024/10
Y1 - 2024/10
N2 - INTRODUCTION: The established link between DNA methylation and pathophysiology of dementia, along with its potential role as a molecular mediator of lifestyle and environmental influences, positions blood-derived DNA methylation as a promising tool for early dementia risk detection. METHODS: In conjunction with an extensive array of machine learning techniques, we employed whole blood genome-wide DNA methylation data as a surrogate for 14 modifiable and non-modifiable factors in the assessment of dementia risk in independent dementia cohorts. RESULTS: We established a multivariate methylation risk score (MMRS) for identifying mild cognitive impairment cross-sectionally, independent of age and sex (P = 2.0 × 10−3). This score significantly predicted the prospective development of cognitive impairments in independent studies of Alzheimer's disease (hazard ratio for Rey's Auditory Verbal Learning Test (RAVLT)-Learning = 2.47) and Parkinson's disease (hazard ratio for MCI/dementia= 2.59). DISCUSSION: Our work shows the potential of employing blood-derived DNA methylation data in the assessment of dementia risk. Highlights: We used whole blood DNA methylation as a surrogate for 14 dementia risk factors. Created a multivariate methylation risk score for predicting cognitive impairment. Emphasized the role of machine learning and omics data in predicting dementia. The score predicts cognitive impairment development at the population level.
AB - INTRODUCTION: The established link between DNA methylation and pathophysiology of dementia, along with its potential role as a molecular mediator of lifestyle and environmental influences, positions blood-derived DNA methylation as a promising tool for early dementia risk detection. METHODS: In conjunction with an extensive array of machine learning techniques, we employed whole blood genome-wide DNA methylation data as a surrogate for 14 modifiable and non-modifiable factors in the assessment of dementia risk in independent dementia cohorts. RESULTS: We established a multivariate methylation risk score (MMRS) for identifying mild cognitive impairment cross-sectionally, independent of age and sex (P = 2.0 × 10−3). This score significantly predicted the prospective development of cognitive impairments in independent studies of Alzheimer's disease (hazard ratio for Rey's Auditory Verbal Learning Test (RAVLT)-Learning = 2.47) and Parkinson's disease (hazard ratio for MCI/dementia= 2.59). DISCUSSION: Our work shows the potential of employing blood-derived DNA methylation data in the assessment of dementia risk. Highlights: We used whole blood DNA methylation as a surrogate for 14 dementia risk factors. Created a multivariate methylation risk score for predicting cognitive impairment. Emphasized the role of machine learning and omics data in predicting dementia. The score predicts cognitive impairment development at the population level.
UR - https://www.scopus.com/pages/publications/85202550809
U2 - 10.1002/alz.14061
DO - 10.1002/alz.14061
M3 - Journal articles
C2 - 39193899
AN - SCOPUS:85202550809
SN - 1552-5260
VL - 20
SP - 6682
EP - 6698
JO - Alzheimer's and Dementia
JF - Alzheimer's and Dementia
IS - 10
ER -