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Bioprofile Analysis: A New Approach for the Analysis of Biomedical Data in Alzheimer's Disease

Article type: Research Article

Authors: Escudero, Javiera; * | Ifeachor, Emmanuela | Zajicek, John P.b | for the Alzheimer's Disease Neuroimaging Initiative1

Affiliations: [a] Signal Processing and Multimedia Communications Research Group, School of Computing and Mathematics, Plymouth University, Plymouth, UK | [b] Clinical Neurology Research Group, Peninsula College of Medicine and Dentistry, Plymouth University, Derriford, UK

Correspondence: [*] Correspondence to: Javier Escudero, School of Computing and Mathematics, Plymouth University, Drake Circus, Plymouth, PL4 8AA, UK. Tel.: +44 1752 586295; E-mail: [email protected].

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.ucla.edu/). As such, the investigators within the 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.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Keywords: Alzheimer's disease, Bioindex, biomarkers, Bioprofile, machine learning, mild cognitive impairment, unsupervised techniques

DOI: 10.3233/JAD-2012-121024

Journal: Journal of Alzheimer's Disease, vol. 32, no. 4, pp. 997-1010, 2012

Accepted 3 July 2012
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Published: 14 November 2012
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Supplementary Materials:

Supplementary Materials.

Abstract

This article presents a new approach for the analysis of biomedical data to support the management and care of patients with Alzheimer's disease (AD). The increase in prevalence of neurodegenerative disorders such as AD has led to a need for objective means to assist clinicians with the analysis and interpretation of complex biomedical data. To this end, we propose a “Bioprofile” analysis to reveal the pattern of disease in the subject's biodata. From the Bioprofile, personal “Bioindices” that indicate how closely a subject's data resemble the pattern of AD can be derived. We used an unsupervised technique (k-means) to cluster variables of the ADNI database so that subjects are divisible into those with the Bioprofile of AD and those without it. Results revealed that there is an “AD pattern” in the biodata of most AD and mild cognitive impairment (MCI) patients and some controls. This pattern agrees with a recent hypothetical model of AD evolution. We also assessed how the Bioindices changed with time and we found that the Bioprofile was associated with the risk of progressing from MCI to AD. Hence, the Bioprofile analysis is a promising methodology that may potentially provide a complementary new way of interpreting biomedical data. Furthermore, the concept of the Bioprofile could be extended to other neurodegenerative diseases.

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