- Title
- Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals
- Creator
- Casanova, Ramon; Varma, Sudhir; Wahrheit, Judith; Klavins, Kristaps; Jonsson, Palmi V.; Eiriksdottir, Gudny; Aspelund, Thor; Launer, Lenore J.; Gudnason, Vilmundur; Quigley, Cristinia Legido; Thambisetty, Madhav; Simpson, Brittany; Kim, Min; An, Yang; Saldana, Santiago; Riveros, Carlos; Moscato, Pablo; Griswold, Michael; Sonntag, Denise
- Relation
- Alzheimers & Dementia Vol. 12, Issue 7, p. 815-822
- Publisher Link
- http://dx.doi.org/10.1016/j.jalz.2015.12.008
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2016
- Description
- 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.
- Subject
- preclinical Alzheimer’s disease; biomarker;; metabolomics; phospholipids; machine learning
- Identifier
- http://hdl.handle.net/1959.13/1326845
- Identifier
- uon:25518
- Identifier
- ISSN:1552-5260
- Language
- eng
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