Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients

• Published in: PloS one Vol. 12; no. 10; p. e0187364
• Main Authors: Simic, Goran, Babic Leko, Mirjana, Dzeroski, Saso, Smuc, Tomislav, Lavrac, Nada, Mihelcic, Matej
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 31.10.2017; Public Library of Science (PLoS)
• Subjects: Abnormalities; Algorithms; Alzheimer Disease - pathology; Alzheimer's disease; Amyloid beta-protein; Amyloid precursor protein; Amyloidogenesis; Angiopoietin; Apolipoproteins; Atherosclerosis; Biology and Life Sciences; Brain; Brain natriuretic peptide; Brain research; Ciliary neurotrophic factor; Clustering; Cognition Disorders - pathology; Cognitive ability; Computer and Information Sciences; Data mining; Data processing; Dementia; Dementia disorders; Development and progression; Diabetes; Diagnostic imaging; Engineering and Technology; FasL protein; Health aspects; Homogeneity; Humans; Impairment; Insulin; Leptin; Medical imaging; Medicine and Health Sciences; Metabolism; Metalloproteinase; Mining; Neurodegenerative diseases; Neuroimaging; Neurology; Neurosciences; Older people; Pathogenesis; Patients; Pattern recognition; Physical Sciences; Pregnancy; Protein turnover; Proteins; Research and Analysis Methods; Secretase; Spatial discrimination; Statistical analysis; Studies; Substrates; Testosterone; Slovenia; Croatia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0187364

Based on a set of subjects and a collection of attributes obtained from the Alzheimer's Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, ciliary neurotrophic factor, brain natriuretic peptide, Fas ligand, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Moreover, applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p ≤ 0.01) were found between PAPP-A and clinical tests: Alzheimer's Disease Assessment Scale, Clinical Dementia Rating Sum of Boxes, Mini Mental State Examination, etc. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as α-secretase to physiologically cleave amyloid precursor protein (APP) in the non-amyloidogenic pathway, it could be directly involved in the metabolism of APP very early during the disease course. Therefore, further studies should investigate the role of PAPP-A in the development of AD more thoroughly.