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Blood-based multivariate methylation risk score for cognitive impairment and dementia.

Jarno KoetsierRachel CavillRick ReijndersJoshua HarveyJan HomannMorteza KouhsarKay DeckersSebastian KöhlerLars M T EijssenDaniel L A van den HoveIlja DemuthSandra Düzelnull nullRebecca G SmithAdam R SmithJoe BurrageEmma M WalkerGemma ShirebyEilis HannonEmma DempsterTim FraylingJonathan MillValerija DobricicPeter JohannsenMichael WittigAndre FrankeRik VandenbergheJolien SchaeverbekeYvonne Freund-LeviLutz FrölichPhilip ScheltensCharlotte E TeunissenGiovanni FrisoniOlivier BlinJill C RichardsonRégis BordetSebastiaan EngelborghsEllen de RoeckPablo Martinez-LageMikel TaintaAlberto LleóIsabel SalaJulius PoppGwendoline PeyratoutFrans VerheyMagda TsolakiUlf AndreassonKaj BlennowHenrik ZetterbergJohannes StrefferStephanie J B VosSimon LovestonePieter-Jelle VisserChristina M LillLars BertramKatie LunnonEhsan Pishva
Published in: Alzheimer's & dementia : the journal of the Alzheimer's Association (2024)
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.
Keyphrases
  • cognitive impairment
  • dna methylation
  • machine learning
  • genome wide
  • risk factors
  • data analysis
  • gene expression
  • big data
  • artificial intelligence
  • single cell
  • deep learning