Proof of concept for a non-invasive early diagnostic technology for Sporadic Alzheimer's Disease based on circulating exosomes

Currently, there are no reliable methods for the early diagnosis of sporadic Alzheimer's disease (98% cases). However, we have previously identified more than 500 mRNAs differentially expressed in the human hippocampus, with the additional property that their products are exportable to exosomes. Given that the permeability of the blood-brain barrier is altered by the inflammatory state typical of the disease, our working hypothesis is that circulating exosomes in peripheral blood may carry a plethora of potential biomarkers of the disease. WORK PLAN: A population of 150 patients (age ≥64 years) will be stratified according to gender and medical condition (Healthy Controls, Mild Cognitive Impairment, Early-Stage Alzheimer's, Moderate Alzheimer's, Other Central Neurological/Psychiatric Disorders not related to Alzheimer's). In addition to a clinical and neurological history, patients will be studied using a battery of psychometric tests and magnetic resonance imaging (resting and functional MRI). Blood samples will be processed to obtain four sub-fractions of interest, on which high-resolution transcriptomic (RNA sequencing) and proteomic (LC-MS/MS/MS) analyses will be performed. EXPECTED RESULTS: The interdisciplinary approach, combining diverse sources of information (clinical, psychometric, MRI, analytical laboratory) using machine learning techniques, will allow the establishment of a set of biomarkers sufficiently sensitive and specific to discriminate the various stages of the disease, thus enabling the development of a diagnostic kit. IMPACT OF THE RESULTS: The availability of an early, non-invasive test for the diagnosis of sporadic Alzheimer's and its precursor forms would help mitigate a disease that affects 30 million people worldwide. This also represents a huge market, with enormous consequences for healthcare policies of both governments and affected individuals and their families.

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ULL Team

• Daniel Victor Guebel
• Nestor Vicente Torres Darias