Characterization and validation of biomarkers for an improved classification of Alzheimer's disease pathology
6 July 2018
Auditorium S1 (Campus Drie Eiken, Building S) - Universiteitsplein 1 - 2610 Wilrijk (Antwerp) (route: UAntwerpen, Campus Drie Eiken
2:00 PM - 4:00 PM
Sebastiaan Engelborghs, Peter Paul De Deyn
PhD defence Hanne Struyfs - Department of Biomedical Sciences
For many years, Alzheimer’s disease (AD) could only be diagnosed in the presence of symptoms and by excluding other diseases. Nowadays, biomarkers are used to measure and confirm the presence of AD pathology during clinical disease and even before symptoms occur. Although the currently existing AD biomarkers have enabled a shift from a clinical exclusion to a biomarker-based diagnosis of AD over the past years, they still need further improvement as they are also changed (to a lesser extent) in non-AD disorders, do not always detect ongoing AD pathology, and have limited power to predict speed of clinical progression. As such, we aimed at an improved classification of AD pathology by the characterisation and validation of existing and candidate biomarkers.
Biomarkers for AD are categorised according to the pathology they are supposed to represent: amyloid plaques, neurofibrillary tangles composed of hyperphosphorylated tau protein, and neurodegeneration. Improving the detection and classification of AD pathology will most probably benefit from combining biomarkers of different categories and modalities. Based on the studies presented in this PhD work, we suggest such a biomarker model should include the Aβ1-42/Aβ1-40 ratio and pTau181 as CSF biomarkers of amyloid plaques and neurofibrillary pathology, respectively. The value of neurodegeneration biomarkers in a biomarker model might depend on whether a patient has abnormal levels of biomarkers of amyloid plaques and neurofibrillary pathology. Useful additions to a future biomarker model would be grey and white matter volume and CSF neurofilament light as measures of neurodegeneration. Consequently, further progress is needed to validate such a biomarker model with regard to its power to differentiate AD from non-AD disorders as well as to predict speed of clinical progression.