Until relatively recently, a diagnosis of probable Alzheimer's disease (AD) and other neurodegenerative disorders was principally based on clinical presentation, with post-mortem examination remaining a gold standard for disease confirmation. This is in sharp contrast to other areas of medicine, where fluid biomarkers, such as troponin levels in myocardial infarction, form an integral part of the diagnostic and treatment criteria. There is a pressing need for such quantifiable and easily accessible tools in neurodegenerative diseases.In this paper, based on lectures given at the 2019 Biomarkers in Neurodegenerative Diseases Course, we provide an overview of a range of cerebrospinal fluid (CSF) and blood biomarkers in neurodegenerative disorders, including the 'core' AD biomarkers amyloid β (Aβ) and tau, as well as other disease-specific and general markers of neuroaxonal injury. We then highlight the main challenges in the field, and how those could be overcome with the aid of new methodological advances, such as assay automation, mass spectrometry and ultrasensitive immunoassays.As we hopefully move towards an era of disease-modifying treatments, reliable biomarkers will be essential to increase diagnostic accuracy, allow for earlier diagnosis, better participant selection and disease activity and treatment effect monitoring.
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http://dx.doi.org/10.1186/s13195-020-00586-6 | DOI Listing |
Alzheimers Dement
January 2025
Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, Texas, USA.
Introduction: The relationship between Alzheimer's disease (AD) plasma biomarkers, and physical functioning (PF) across diverse races and ethnicities remains unclear. This study aims to explore this association in an ethno-racially diverse sample of cognitively unimpaired community-dwelling adults.
Methods: Data clinical examinations, neuropsychological tests, blood draws, and PF exams (Timed Up and Go [TUG] and Short Physical Performance Battery [SPPB]) were analyzed.
Sci Adv
January 2025
Department of Biotechnology, College of Life Science, CHA University, Gyeonggi-do 13488, Republic of Korea.
The early detection of neurodegenerative diseases necessitates the identification of specific brain-derived biomolecules in peripheral blood. In this context, our investigation delineates the role of amyloid precursor-like protein 1 (APLP1)-a protein predominantly localized in oligodendrocytes and neurons-as a previously unidentified biomarker in extracellular vesicles (EVs). Through rigorous analysis, APLP1 EVs from human sera were unequivocally determined to be of cerebral origin.
View Article and Find Full Text PDFFront Neurol
December 2024
The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China.
Exp Neurol
December 2024
Department of Translational Medicine, All India Institute of Medical Sciences (AIIMS)Bhopal, Saket Nagar, Bhopal 462020, Madhya Pradesh, India.
Alzheimer's disease (AD), a diverse neurodegenerative disease, is the leading cause of dementia, accounting for 60-80 % of all cases. The pathophysiology of Alzheimer's disease is unknown, and there is no cure at this time. Recent developments in transcriptome-wide profiling have led to the identification of a number of non-coding RNAs (ncRNAs).
View Article and Find Full Text PDFNeurobiol Aging
December 2024
Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo 0373, Norway.
Structural brain changes underlie cognitive changes and interindividual variability in cognition in older age. By using structural MRI data-driven clustering, we aimed to identify subgroups of cognitively unimpaired older adults based on brain change patterns and assess how changes in cortical thickness, surface area, and subcortical volume relate to cognitive change. We tested (1) which brain structural changes predict cognitive change (2) whether these are associated with core cerebrospinal fluid (CSF) Alzheimer's disease biomarkers, and (3) the degree of overlap between clusters derived from different structural modalities in 1899 cognitively healthy older adults followed up to 16 years.
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