Alzheimer's & dementia : the journal of the Alzheimer's Association · 2016
Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАResearch Support, N.I.H., IntramuralНазвание и аннотация: Перевод пока не подготовлен
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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
Аннотация
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.
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.