Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals

Ramon Casanova, Sudhir Varma, Brittany Simpson, Min Kim, Yang An, Santiago Saldana, Carlos Riveros, Pablo Moscato, Michael Griswold, Denise Sonntag, Judith Wahrheit, Kristaps Klavins, Palmi V. Jonsson, Gudny Eiriksdottir, Thor Aspelund, Lenore J. Launer, Vilmundur Gudnason, Cristina Legido Quigley, Madhav Thambisetty

Research output: Contribution to journalArticlepeer-review


Introduction: Recently, quantitative metabolomics identified a panel of 10 plasma lipids that were highly predictive of conversion to Alzheimer's disease (AD) in cognitively normal older individuals (n = 28, area under the curve [AUC] = 0.92, sensitivity/specificity of 90%/90%). Methods: Quantitative targeted metabolomics in serum using an identical method as in the index study. Results: We failed to replicate these findings in a substantially larger study from two independent cohorts-the Baltimore Longitudinal Study of Aging ([BLSA], n = 93, AUC = 0.642, sensitivity/specificity of 51.6%/65.7%) and the Age, Gene/Environment Susceptibility-Reykjavik Study ([AGES-RS], n = 100, AUC = 0.395, sensitivity/specificity of 47.0%/36.0%). In analyses applying machine learning methods to all 187 metabolite concentrations assayed, we find a modest signal in the BLSA with distinct metabolites associated with the preclinical and symptomatic stages of AD, whereas the same methods gave poor classification accuracies in the AGES-RS samples. Discussion: We believe that ours is the largest blood biomarker study of preclinical AD to date. These findings underscore the importance of large-scale independent validation of index findings from biomarker studies with relatively small sample sizes.

Original languageEnglish (US)
JournalAlzheimer's and Dementia
StateAccepted/In press - 2016
Externally publishedYes


  • Biomarker
  • Machine learning
  • Metabolomics
  • Phospholipids
  • Preclinical Alzheimer's disease

ASJC Scopus subject areas

  • Clinical Neurology
  • Developmental Neuroscience
  • Cellular and Molecular Neuroscience
  • Psychiatry and Mental health
  • Geriatrics and Gerontology
  • Epidemiology
  • Health Policy


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