Frontiers in Aging Neuroscience (Oct 2019)

Methodological Issues in the Clinical Validation of Biomarkers for Alzheimer’s Disease: The Paradigmatic Example of CSF

  • Marco Canevelli,
  • Marco Canevelli,
  • Ilaria Bacigalupo,
  • Giuseppe Gervasi,
  • Eleonora Lacorte,
  • Marco Massari,
  • Flavia Mayer,
  • Nicola Vanacore,
  • Matteo Cesari,
  • Matteo Cesari

DOI
https://doi.org/10.3389/fnagi.2019.00282
Journal volume & issue
Vol. 11

Abstract

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The use of biomarkers is profoundly transforming medical research and practice. Their adoption has triggered major advancements in the field of Alzheimer’s disease (AD) over the past years. For instance, the analysis of the cerebrospinal fluid (CSF) and neuroimaging changes indicative of neuronal loss and amyloid deposition has led to the understanding that AD is characterized by a long preclinical phase. It is also supporting the transition towards a biology-grounded framework and definition of the disease. Nevertheless, though sufficient evidence exists about the analytical validity (i.e., accuracy, reliability, and reproducibility) of the candidate AD biomarkers, their clinical validity (i.e., how well the test measures the clinical features, and the disease or treatment outcomes) and clinical utility (i.e., if and how the test improves the patient’s outcomes, confirms/changes the diagnosis, identifies at-risk individuals, influences therapeutic choices) have not been fully proven. In the present review, some of the methodological issues and challenges that should be addressed in order to better appreciate the potential benefits and limitations of AD biomarkers are discussed. The ultimate goal is to stimulate a constructive discussion aimed at filling the existing gaps and more precisely defining the directions of future research. Specifically, four main aspects of the clinical validation process are addressed and applied to the most relevant CSF biomarkers: (1) the definition of reference values; (2) the identification of reference standards for the disease of interest (i.e., AD); (3) the inclusion within the diagnostic process; and (4) the statistical process supporting the whole framework.

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