Artificial neural networks identify the predictive values of risk factors on the conversion of amnestic mild cognitive impairment.
The search for markers that are able to predict the conversion of amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) is crucial for early mechanistic therapies. Using artificial neural networks (ANNs), 22 variables that are known risk factors of AD were analyzed in 80 patients with aMCI, for a period spanning at least 2 years. The cases were chosen from 195 aMCI subjects recruited by four Italian Alzheimer's disease units. The parameters of glucose metabolism disorder, female gender, and apolipoprotein E epsilon3/epsilon4 genotype were found to be the biological variables with high relevance for predicting the conversion of aMCI. The scores of attention and short term memory tests also were predictors. Surprisingly, the plasma concentration of amyloid-beta (42) had a low predictive value. The results support the utility of ANN analysis as a new tool in the interpretation of data from heterogeneous and distinct sources.
Department of Internal Medicine and Medical Specialties, University of Genova, Genova, Italy. mtabaton@neurologia.unige.it
Massimo
M
Tabaton
Patrizio
P
Odetti
Sergio
S
Cammarata
Roberta
R
Borghi
Fiammetta
F
Monacelli
Carlo
C
Caltagirone
Paola
P
Bossù
Massimo
M
Buscema
Enzo
E
Grossi
D000368:Aged; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000503:physiopathology*; D000647:Amnesia / Q000175:diagnosis*; D001185:Artificial Intelligence; D015415:Biomarkers; D001921:Brain / Q000503:physiopathology*; D003072:Cognition Disorders / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D009483:Neuropsychological Tests; D011237:Predictive Value of Tests; D012720:Severity of Illness Index
D015415:Biomarkers