ALZHEIMER’S DISEASE DIAGNOSTIC MODEL
The real etiology of Alzheimer’s disease (AD) is still unclear while several risk factors have been recognized to catalytically affect the early onset and the progression of the disease. According to latest studies, AD can be categorized according to risk factors, symptoms, and pathophysiological lesions into 8 different categories. Furthermore, these 8 categories can be analyzed in depth, by adding potential biomarkers in each category which have been proved to affect the severity of the disease.
AD Dementia: The social function, the composite activities of the daily life are obstructed. This state is the threshold between memory changes and in one more cognitive factor.
Alzheimer’s Pathology: Senile Plaques and neurofibrillary tangles, loss of neuronal synapses, amyloid deficits in the vascular cerebral cortex.
Atypical AD: Progressive aphasia, Logopenic aphasia, frontal AD morphology and cortical atrophy at the posterior section. Also, is supported from amyloidosis biomarkers in brain or CSF.
MCI: Individuals that abstain from the clinicobiological character of AD and also have measurable MCI. Those individuals may suffer from AD, but there is no evidence for AD.
Mixed AD: Incidents that validate the diagnostic AD requirements for the typical AD and there are disorders such as cerebrovascular disease or Lewy Bodies disease.
Preclinical States of AD: This state includes an in vivo amyloidosis evidence of the brain, or individuals whose families have the autosomal dominant mutation of AD.
Prodromal AD: Clinical Symptoms, memory disorders, Hippocampal volume loss and biomarkers of CSF that lead to AD pathology.
Typical AD: Progressive memory loss, cognitive disorders, and neuropsychiatric modifications.
While several attempts of reducing AD severity have been already presented mainly targeting the symptomatic treatment , there is still no holistic therapy that can efficient reverse AD. For many scientists and pharmaceuticals companies there are several and different treatment approaches like cholinesterase inhibitors, NMDA receptor antagonist, b-secretase inhibitors, c-secretase inhibitors, a-secretase stimulators, tau inhibitors, immunotherapy, naturaceuticals and nanodrugs even though the more secure solution seems to be the early diagnosis of brain lesions, pathophysiological alterations and obvious the case of early and secure prediction.
How it works
The proposed Bayesian Network has been designed according to the latest ‘Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria’ and the model exports for every AD category the maximum probability value is given the biomarkers evidence. The proposed statistical model is multi-parametric, relating several heterogeneous data like plasma and CSF tests, behavioral or imaging tests as categorical variables through prior categorical distributions.
The probabilistic model is based on conditional probabilities, therefore it must be noted that the calculated error is the Monte Carlo error that measures the variability of each estimation due to the simulation. The AD Bayesian model uses the WinBUGS 1.4.3 software, which cannot be used online. Therefore users are able to fill AD results in the form of YES or NO and receive the exported statistics in their email account.
The preferred reference for citing this work and the WinBUGS as well, in scientific papers are:
 Alexiou A, Mantzavinos VD, Greig NH and Kamal MA (2017) A Bayesian Model for the Prediction and Early Diagnosis of Alzheimer’s Disease. Front. Aging Neurosci. 9:77. doi: 10.3389/fnagi.2017.00077
 Mantzavinos V, Alexiou A (2017) Biomarkers for Alzheimer’s disease diagnosis. Curr Alzheimer Res 14(11): 1149-54.
 Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10:325-337.