Abstract
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 language | English (US) |
|---|---|
| Journal | Alzheimer's and Dementia |
| DOIs | |
| State | Accepted/In press - 2016 |
| Externally published | Yes |
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Keywords
- 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
Cite this
Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals. / 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; Quigley, Cristina Legido; Thambisetty, Madhav.
In: Alzheimer's and Dementia, 2016.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals
AU - Casanova, Ramon
AU - Varma, Sudhir
AU - Simpson, Brittany
AU - Kim, Min
AU - An, Yang
AU - Saldana, Santiago
AU - Riveros, Carlos
AU - Moscato, Pablo
AU - Griswold, Michael
AU - Sonntag, Denise
AU - Wahrheit, Judith
AU - Klavins, Kristaps
AU - Jonsson, Palmi V.
AU - Eiriksdottir, Gudny
AU - Aspelund, Thor
AU - Launer, Lenore J.
AU - Gudnason, Vilmundur
AU - Quigley, Cristina Legido
AU - Thambisetty, Madhav
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Biomarker
KW - Machine learning
KW - Metabolomics
KW - Phospholipids
KW - Preclinical Alzheimer's disease
UR - http://www.scopus.com/inward/record.url?scp=84960884135&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84960884135&partnerID=8YFLogxK
U2 - 10.1016/j.jalz.2015.12.008
DO - 10.1016/j.jalz.2015.12.008
M3 - Article
JO - Alzheimer's and Dementia
JF - Alzheimer's and Dementia
SN - 1552-5260
ER -