A semi-mechanism approach based on MRI and proteomics for prediction of conversion from mild cognitive impairment to Alzheimer's disease

Haochen Liu; Xiaoting Zhou; Hao Jiang; Hua He; Xiaoquan Liu; Alzheimer’s Disease Neuroimaging Initiative

doi:10.1038/srep26712

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A semi-mechanism approach based on MRI and proteomics for prediction of conversion from mild cognitive impairment to Alzheimer's disease

Journal article

Open access

Peer reviewed

A semi-mechanism approach based on MRI and proteomics for prediction of conversion from mild cognitive impairment to Alzheimer's disease

Haochen Liu, Xiaoting Zhou, Hao Jiang, Hua He, Xiaoquan Liu and Alzheimer’s Disease Neuroimaging Initiative

Scientific reports, Vol.6(1), pp.26712-26712

06/07/2016

DOI: 10.1038/srep26712

PMCID: PMC4896009

PMID: 27273250

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Abstract

Mild cognitive impairment (MCI) is a precursor phase of Alzheimer's disease (AD). As current treatments may be effective only at the early stages of AD, it is important to track MCI patients who will convert to AD. The aim of this study is to develop a high performance semi-mechanism based approach to predict the conversion from MCI to AD and improve our understanding of MCI-to-AD conversion mechanism. First, analysis of variance (ANOVA) test and lasso regression are employed to identify the markers related to the conversion. Then the Bayesian network based on selected markers is established to predict MCI-to-AD conversion. The structure of Bayesian network suggests that the conversion may start with fibrin clot formation, verbal memory impairment, eating pattern changing and hyperinsulinemia. The Bayesian network achieves a high 10-fold cross-validated prediction performance with 96% accuracy, 95% sensitivity, 65% specificity, area under the receiver operating characteristic curve of 0.82 on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The semi-mechanism based approach provides not only high prediction performance but also clues of mechanism for MCI-to-AD conversion.

Magnetic Resonance Imaging

Proteomics

Predictive Value of Tests

Alzheimer Disease - physiopathology

Humans

Male

Cognitive Dysfunction - blood

Cognitive Dysfunction - physiopathology

Models, Biological

Aged, 80 and over

Female

Aged

Alzheimer Disease - diagnostic imaging

Cognitive Dysfunction - diagnostic imaging

Alzheimer Disease - blood

Details

Title: Subtitle: A semi-mechanism approach based on MRI and proteomics for prediction of conversion from mild cognitive impairment to Alzheimer's disease
Creators: Haochen Liu - Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, 210009, China
Xiaoting Zhou - Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, 210009, China
Hao Jiang - Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, 210009, China
Hua He - Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, 210009, China
Xiaoquan Liu - Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, 210009, China
Alzheimer’s Disease Neuroimaging Initiative
Contributors: Laura L Boles-Ponto (Contributor) - University of Iowa, Radiology
Resource Type: Journal article
Publication Details: Scientific reports, Vol.6(1), pp.26712-26712
DOI: 10.1038/srep26712
PMID: 27273250
PMCID: PMC4896009
NLM abbreviation: Sci Rep
ISSN: 2045-2322
eISSN: 2045-2322
Publisher: England
Grant note: U24 AG021886 / NIA NIH HHS P50 AG005142 / NIA NIH HHS U01 AG024904 / NIA NIH HHS
Language: English
Date published: 06/07/2016
Academic Unit: Radiology; Pharmaceutical Sciences and Experimental Therapeutics
Record Identifier: 9984051561302771

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