title: A global immune deficit in Alzheimer's disease and mild cognitive impairment disclosed by a novel data mining process.
abstract: Alzheimer's disease (AD) is the most common form of dementia, while mild cognitive impairment (MCI) causes a slight but measurable decline in cognitive abilities. A person with MCI has an increased risk of developing AD or another dementia. Thus, it is of medical interest to develop predictive tools to assess this risk. A growing awareness exists that pro-oxidative state and neuro-inflammation are both involved in AD. However, the extent of this relationship is still a matter of debate. Due to the expected non-linear correlations between oxidative and inflammatory markers, traditional statistics is unsuitable to dissect their relationship with the disease. Artificial neural networks (ANNs) are computational models inspired by central nervous system networks, capable of machine learning and pattern recognition. The aim of this work was to disclose the relationship between immunological and oxidative stress markers in AD and MCI by the application of ANNs. Through a machine learning approach, we were able to construct an algorithm to classify MCI and AD with high accuracy. Such an instrument, requiring a small amount of immunological and oxidative-stress parameters, would be useful in the clinical practice. Moreover, applying an innovative non-linear mathematical technique, a global immune deficit was shown to be associated with cognitive impairment. Surprisingly, both adaptive and innate immunity were peripherally defective in AD and MCI patients. From this study, new pathogenetic aspects of these diseases could emerge.
authors.affiliation: CAM, Polidiagnostic Center, Monza, Italy INSPE, San Raffaele Scientific Institute, Milan, Italy.
authors.forename: Maira
authors.initials: M
authors.lastname: Gironi
authors.affiliation: CAM, Polidiagnostic Center, Monza, Italy INSPE, San Raffaele Scientific Institute, Milan, Italy.
authors.forename: Bruno
authors.initials: B
authors.lastname: Borgiani
authors.affiliation: IRCCS, Don Gnocchi Foundation, Milan, Italy.
authors.forename: Elisabetta
authors.initials: E
authors.lastname: Farina
authors.affiliation: CAM, Polidiagnostic Center, Monza, Italy.
authors.forename: Enrica
authors.initials: E
authors.lastname: Mariani
authors.affiliation: CAM, Polidiagnostic Center, Monza, Italy.
authors.forename: Cristina
authors.initials: C
authors.lastname: Cursano
authors.affiliation: IRCCS, Don Gnocchi Foundation, Milan, Italy.
authors.forename: Margherita
authors.initials: M
authors.lastname: Alberoni
authors.affiliation: IRCCS, Don Gnocchi Foundation, Milan, Italy.
authors.forename: Raffaello
authors.initials: R
authors.lastname: Nemni
authors.affiliation: INSPE, San Raffaele Scientific Institute, Milan, Italy.
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Comi
authors.affiliation: Semeion Research Center, Roma, Italy Department of the Thereotical and Statistical Sciences, University of Colorado, Denver, Colorado, USA.
authors.forename: Massimo
authors.initials: M
authors.lastname: Buscema
authors.affiliation: INSPE, San Raffaele Scientific Institute, Milan, Italy.
authors.forename: Roberto
authors.initials: R
authors.lastname: Furlan
authors.affiliation: Semeion Research Center, Roma, Italy.
authors.forename: Enzo
authors.initials: E
authors.lastname: Grossi
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000145:classification / Q000276:immunology*; D060825:Cognitive Dysfunction / Q000145:classification / Q000276:immunology*; D057225:Data Mining; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D018384:Oxidative Stress / Q000502:physiology
keywords: Artificial neural network; machine learning; neurodegeneration; oxidative-stress; pattern recognition; relationships