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

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%). Quantitative targeted metabol...

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Veröffentlicht in:Alzheimer's & dementia Jg. 12; H. 7; S. 815 - 822
Hauptverfasser: Casanova, Ramon, Varma, Sudhir, Simpson, Brittany, Kim, Min, An, Yang, Saldana, Santiago, Riveros, Carlos, Moscato, Pablo, Griswold, Michael, Sonntag, Denise, Wahrheit, Judith, Klavins, Kristaps, Jonsson, Palmi V, Eiriksdottir, Gudny, Aspelund, Thor, Launer, Lenore J, Gudnason, Vilmundur, Legido Quigley, Cristina, Thambisetty, Madhav
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.07.2016
Schlagworte:
Aged
Aging
Alzheimer Disease - blood
Baltimore
Biomarkers - blood
Humans
Longitudinal Studies
Metabolomics - methods
Prodromal Symptoms
Metabolomics
Biomarker
Phospholipids
Preclinical Alzheimer's disease
Machine learning
ISSN:1552-5279
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Zusammenfassung: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%). Quantitative targeted metabolomics in serum using an identical method as in the index study. 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. 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.
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ISSN:1552-5279
DOI:10.1016/j.jalz.2015.12.008