Using Multi-Scale Genetic, Neuroimaging and Clinical Data for Predicting Alzheimer’s Disease and Reconstruction of Relevant Biological Mechanisms
Autor: | Martin Hofmann-Apitius, Shashank Khanna, Mohammad Asif Emon, Anandhi Iyappan, Daniel Domingo-Fernández, Holger Fröhlich |
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Přispěvatelé: | Publica |
Rok vydání: | 2018 |
Předmět: |
Risk
0301 basic medicine MEDLINE lcsh:Medicine Neuroimaging Kaplan-Meier Estimate Disease Polymorphism Single Nucleotide Article Machine Learning 03 medical and health sciences Bayes' theorem Alzheimer Disease Databases Genetic Autophagy Humans Insulin Medicine Cognitive Dysfunction Cognitive decline lcsh:Science Aged Proportional Hazards Models Multidisciplinary business.industry Proportional hazards model lcsh:R Brain Bayes Theorem Adherens Junctions Models Theoretical Prognosis 3. Good health Killer Cells Natural Early Diagnosis 030104 developmental biology Blood-Brain Barrier Scale (social sciences) Baseline characteristics lcsh:Q business Neuroscience |
Zdroj: | Scientific Reports, Vol 8, Iss 1, Pp 1-13 (2018) Scientific Reports |
ISSN: | 2045-2322 |
DOI: | 10.1038/s41598-018-29433-3 |
Popis: | Alzheimer’s Disease (AD) is among the most frequent neuro-degenerative diseases. Early diagnosis is essential for successful disease management and chance to attenuate symptoms by disease modifying drugs. In the past, a number of cerebrospinal fluid (CSF), plasma and neuro-imaging based biomarkers have been proposed. Still, in current clinical practice, AD diagnosis cannot be made until the patient shows clear signs of cognitive decline, which can partially be attributed to the multi-factorial nature of AD. In this work, we integrated genotype information, neuro-imaging as well as clinical data (including neuro-psychological measures) from ~900 normal and mild cognitively impaired (MCI) individuals and developed a highly accurate machine learning model to predict the time until AD is diagnosed. We performed an in-depth investigation of the relevant baseline characteristics that contributed to the AD risk prediction. More specifically, we used Bayesian Networks to uncover the interplay across biological scales between neuro-psychological assessment scores, single genetic variants, pathways and neuro-imaging related features. Together with information extracted from the literature, this allowed us to partially reconstruct biological mechanisms that could play a role in the conversion of normal/MCI into AD pathology. This in turn may open the door to novel therapeutic options in the future. |
Databáze: | OpenAIRE |
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