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Relationship between Plasma Analytes and SPARE-AD Defined Brain Atrophy Patterns in ADNI

Overview of attention for article published in PLOS ONE, February 2013
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Title
Relationship between Plasma Analytes and SPARE-AD Defined Brain Atrophy Patterns in ADNI
Published in
PLOS ONE, February 2013
DOI 10.1371/journal.pone.0055531
Pubmed ID
Authors

Jon B. Toledo, Xiao Da, Priyanka Bhatt, David A. Wolk, Steven E. Arnold, Leslie M. Shaw, John Q. Trojanowski, Christos Davatzikos, Alzheimer’s Disease Neuroimaging Initiative

Abstract

Different inflammatory and metabolic pathways have been associated with Alzheimeŕs disease (AD). However, only recently multi-analyte panels to study a large number of molecules in well characterized cohorts have been made available. These panels could help identify molecules that point to the affected pathways. We studied the relationship between a panel of plasma biomarkers (Human DiscoveryMAP) and presence of AD-like brain atrophy patterns defined by a previously published index (SPARE-AD) at baseline in subjects of the ADNI cohort. 818 subjects had MRI-derived SPARE-AD scores, of these subjects 69% had plasma biomarkers and 51% had CSF tau and Aβ measurements. Significant analyte-SPARE-AD and analytes correlations were studied in adjusted models. Plasma cortisol and chromogranin A showed a significant association that did not remain significant in the CSF signature adjusted model. Plasma macrophage inhibitory protein-1α and insulin-like growth factor binding protein 2 showed a significant association with brain atrophy in the adjusted model. Cortisol levels showed an inverse association with tests measuring processing speed. Our results indicate that stress and insulin responses and cytokines associated with recruitment of inflammatory cells in MCI-AD are associated with its characteristic AD-like brain atrophy pattern and correlate with clinical changes or CSF biomarkers.

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Geographical breakdown

Country Count As %
Chile 1 1%
Belgium 1 1%
Unknown 76 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 14%
Other 10 13%
Student > Ph. D. Student 10 13%
Professor 8 10%
Student > Bachelor 5 6%
Other 15 19%
Unknown 19 24%
Readers by discipline Count As %
Medicine and Dentistry 17 22%
Neuroscience 17 22%
Psychology 5 6%
Computer Science 4 5%
Agricultural and Biological Sciences 3 4%
Other 9 12%
Unknown 23 29%