Hippocampus neuronal metabolic gene expression outperforms whole tissue data in accurately predicting Alzheimer's disease progression

Shiri Stempler, Yedael Y. Waldman, Lior Wolf, Eytan Ruppin

Research output: Contribution to journalArticlepeer-review

Abstract

Numerous metabolic alterations are associated with the impairment of brain cells in Alzheimer's disease (AD). Here we use gene expression microarrays of both whole hippocampus tissue and hippocampal neurons of AD patients to investigate the ability of metabolic gene expression to predict AD progression and its cognitive decline. We find that the prediction accuracy of different AD stages is markedly higher when using neuronal expression data (0.9) than when using whole tissue expression (0.76). Furthermore, the metabolic genes' expression is shown to be as effective in predicting AD severity as the entire gene list. Remarkably, a regression model from hippocampal metabolic gene expression leads to a marked correlation of 0.57 with the Mini-Mental State Examination cognitive score. Notably, the expression of top predictive neuronal genes in AD is significantly higher than that of other metabolic genes in the brains of healthy subjects. All together, the analyses point to a subset of metabolic genes that is strongly associated with normal brain functioning and whose disruption plays a major role in AD.

Original languageEnglish
Pages (from-to)2230.e13-2230.e21
JournalNeurobiology of Aging
Volume33
Issue number9
DOIs
StatePublished - Sep 2012

Keywords

  • Alzheimer's disease
  • Classification model
  • Gene expression
  • Hippocampus
  • Metabolism
  • Mini-Mental State Examination (MMSE)
  • Neuron
  • Regression

All Science Journal Classification (ASJC) codes

  • Clinical Neurology
  • Geriatrics and Gerontology
  • Ageing
  • General Neuroscience
  • Developmental Biology

Fingerprint

Dive into the research topics of 'Hippocampus neuronal metabolic gene expression outperforms whole tissue data in accurately predicting Alzheimer's disease progression'. Together they form a unique fingerprint.

Cite this