Alzheimer's disease is the most prevalent form of dementia. Cerebrospinal fluid biomarkers have been widely used to diagnose and follow the evolution of the disease. However, the way these data are interpreted and how are used to predict the onset or the prognosis of the disease is not clearly established. Therefore, in this study, a biologic scale of probabilities is developed to properly predict and stratify potential patients of Alzheimer's disease. The researchers use cerebrospinal fluid samples from several memory clinics and they use different models combining levels of amyloid-beta 42, tau and phosphorylated tau. A simple model based on numbered classification of the biomarkers is developed and shown to be very efficient to predict and stratify the patients from the memory clinics. This was validated computationally and with an independent dataset from different centres. Overall, here a mathematical predictive model is presented that can help diagnose patients of Alzheimer's disease in the very early stages of the disease, or even before the onset, and correctly stratify them for treatment or clinical research purposes.
A diagnostic scale for Alzheimer’s disease based on cerebrospinal fluid biomarker profiles
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