A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer's disease

Heterogeneous diseases such as Alzheimer's disease (AD) manifest a variety of phenotypes among populations. Early diagnosis and effective treatment offer cost benefits. Many studies on biochemical and imaging markers have shown potential promise in improving diagnosis, yet establishing quantita...

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Published inGigascience Vol. 7; no. 7
Main Authors Zhang, Hongjiu, Zhu, Fan, Dodge, Hiroko H, Higgins, Gerald A, Omenn, Gilbert S, Guan, Yuanfang
Format Journal Article
LanguageEnglish
Published United States Oxford University Press 01.07.2018
Subjects
Algorithms
Alzheimer Disease - diagnosis
Alzheimer Disease - genetics
Alzheimer's disease
Big Data
Biomarkers
Cognition Disorders - diagnosis
Cognitive ability
Cognitive Dysfunction - diagnosis
Cohort Studies
Diagnosis
Diagnosis, Computer-Assisted
Diagnostic systems
Disease
Disease Progression
Gaussian process
Heterogeneity
Humans
Impairment
Machine Learning
Magnetic Resonance Imaging
Medical Informatics - methods
Movement disorders
Neurodegenerative diseases
Normal Distribution
Parkinson Disease - diagnosis
Parkinson's disease
Patients
Phenotype
Phenotypes
Principal Component Analysis
Prognosis
ROC Curve
Sensitivity and Specificity
Similarity
Online AccessGet full text
ISSN2047-217X
2047-217X
DOI10.1093/gigascience/giy085

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Abstract Heterogeneous diseases such as Alzheimer's disease (AD) manifest a variety of phenotypes among populations. Early diagnosis and effective treatment offer cost benefits. Many studies on biochemical and imaging markers have shown potential promise in improving diagnosis, yet establishing quantitative diagnostic criteria for ancillary tests remains challenging. We have developed a similarity-based approach that matches individuals to subjects with similar conditions. We modeled the disease with a Gaussian process, and tested the method in the Alzheimer's Disease Big Data DREAM Challenge. Ranked the highest among submitted methods, our diagnostic model predicted cognitive impairment scores in an independent dataset test with a correlation score of 0.573. It differentiated AD patients from control subjects with an area under the receiver operating curve of 0.920. Without knowing longitudinal information about subjects, the model predicted patients who are vulnerable to conversion from mild-cognitive impairment to AD through the similarity network. This diagnostic framework can be applied to other diseases with clinical heterogeneity, such as Parkinson's disease.
AbstractList Heterogeneous diseases such as Alzheimer's disease (AD) manifest a variety of phenotypes among populations. Early diagnosis and effective treatment offer cost benefits. Many studies on biochemical and imaging markers have shown potential promise in improving diagnosis, yet establishing quantitative diagnostic criteria for ancillary tests remains challenging. We have developed a similarity-based approach that matches individuals to subjects with similar conditions. We modeled the disease with a Gaussian process, and tested the method in the Alzheimer's Disease Big Data DREAM Challenge. Ranked the highest among submitted methods, our diagnostic model predicted cognitive impairment scores in an independent dataset test with a correlation score of 0.573. It differentiated AD patients from control subjects with an area under the receiver operating curve of 0.920. Without knowing longitudinal information about subjects, the model predicted patients who are vulnerable to conversion from mild-cognitive impairment to AD through the similarity network. This diagnostic framework can be applied to other diseases with clinical heterogeneity, such as Parkinson's disease.
Motivation Heterogeneous diseases such as Alzheimer's disease (AD) manifest a variety of phenotypes among populations. Early diagnosis and effective treatment offer cost benefits. Many studies on biochemical and imaging markers have shown potential promise in improving diagnosis, yet establishing quantitative diagnostic criteria for ancillary tests remains challenging. Results We have developed a similarity-based approach that matches individuals to subjects with similar conditions. We modeled the disease with a Gaussian process, and tested the method in the Alzheimer's Disease Big Data DREAM Challenge. Ranked the highest among submitted methods, our diagnostic model predicted cognitive impairment scores in an independent dataset test with a correlation score of 0.573. It differentiated AD patients from control subjects with an area under the receiver operating curve of 0.920. Without knowing longitudinal information about subjects, the model predicted patients who are vulnerable to conversion from mild-cognitive impairment to AD through the similarity network. This diagnostic framework can be applied to other diseases with clinical heterogeneity, such as Parkinson's disease.
Heterogeneous diseases such as Alzheimer's disease (AD) manifest a variety of phenotypes among populations. Early diagnosis and effective treatment offer cost benefits. Many studies on biochemical and imaging markers have shown potential promise in improving diagnosis, yet establishing quantitative diagnostic criteria for ancillary tests remains challenging.MotivationHeterogeneous diseases such as Alzheimer's disease (AD) manifest a variety of phenotypes among populations. Early diagnosis and effective treatment offer cost benefits. Many studies on biochemical and imaging markers have shown potential promise in improving diagnosis, yet establishing quantitative diagnostic criteria for ancillary tests remains challenging.We have developed a similarity-based approach that matches individuals to subjects with similar conditions. We modeled the disease with a Gaussian process, and tested the method in the Alzheimer's Disease Big Data DREAM Challenge. Ranked the highest among submitted methods, our diagnostic model predicted cognitive impairment scores in an independent dataset test with a correlation score of 0.573. It differentiated AD patients from control subjects with an area under the receiver operating curve of 0.920. Without knowing longitudinal information about subjects, the model predicted patients who are vulnerable to conversion from mild-cognitive impairment to AD through the similarity network. This diagnostic framework can be applied to other diseases with clinical heterogeneity, such as Parkinson's disease.ResultsWe have developed a similarity-based approach that matches individuals to subjects with similar conditions. We modeled the disease with a Gaussian process, and tested the method in the Alzheimer's Disease Big Data DREAM Challenge. Ranked the highest among submitted methods, our diagnostic model predicted cognitive impairment scores in an independent dataset test with a correlation score of 0.573. It differentiated AD patients from control subjects with an area under the receiver operating curve of 0.920. Without knowing longitudinal information about subjects, the model predicted patients who are vulnerable to conversion from mild-cognitive impairment to AD through the similarity network. This diagnostic framework can be applied to other diseases with clinical heterogeneity, such as Parkinson's disease.
Author Zhang, Hongjiu
Guan, Yuanfang
Dodge, Hiroko H
Higgins, Gerald A
Omenn, Gilbert S
Zhu, Fan
AuthorAffiliation 1 Department of Computational Medicine and Bioinformatics, University of Michigan, 2017G Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI, USA 48109
2 Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fangzheng Avenue, Shuitu Hi-tech Industrial Park, Shuitu Town, Beibei District, Chongqing, China 400714
4 Department of Neurology, University of Michigan, 1500 E. Medical Center Dr., 1914 Taubman Center SPC 5316, Ann Arbor, MI, USA 48109
5 Layton Aging and Alzheimer's Disease Center and Department of Neurology, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, L226, Portland, OR, USA 97239
3 Michigan Alzheimer's Disease Center, University of Michigan, 2101 Commonwealth Blvd, Ann Arbor, MI, USA 48105
7 Department of Human Genetics, University of Michigan, 4909 Buhl Building, 1241 E. Catherine St., Ann Arbor, MI, USA 48109
8 School of Public Health, University of Michigan,
AuthorAffiliation_xml – name: 5 Layton Aging and Alzheimer's Disease Center and Department of Neurology, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, L226, Portland, OR, USA 97239
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CitedBy_id crossref_primary_10_1093_gigascience_giy085
crossref_primary_10_1093_bib_bbac207
crossref_primary_10_1093_jamiaopen_ooab052
crossref_primary_10_2196_18389
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Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp+content/up+loads/how_to_apply/ADNI_Acknowledgement_List.pdf.
H.Z. and F.Z. equally contributed to the work.
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SSID ssj0000778873
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Snippet Heterogeneous diseases such as Alzheimer's disease (AD) manifest a variety of phenotypes among populations. Early diagnosis and effective treatment offer cost...
Motivation Heterogeneous diseases such as Alzheimer's disease (AD) manifest a variety of phenotypes among populations. Early diagnosis and effective treatment...
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pubmedcentral
proquest
pubmed
crossref
SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
SubjectTerms Algorithms
Alzheimer Disease - diagnosis
Alzheimer Disease - genetics
Alzheimer's disease
Big Data
Biomarkers
Cognition Disorders - diagnosis
Cognitive ability
Cognitive Dysfunction - diagnosis
Cohort Studies
Diagnosis
Diagnosis, Computer-Assisted
Diagnostic systems
Disease
Disease Progression
Gaussian process
Heterogeneity
Humans
Impairment
Machine Learning
Magnetic Resonance Imaging
Medical Informatics - methods
Movement disorders
Neurodegenerative diseases
Normal Distribution
Parkinson Disease - diagnosis
Parkinson's disease
Patients
Phenotype
Phenotypes
Principal Component Analysis
Prognosis
ROC Curve
Sensitivity and Specificity
Similarity
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Title A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer's disease
URI https://www.ncbi.nlm.nih.gov/pubmed/30010762
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