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Alzheimers Res Ther. 2014 Jun 26;6(3):38. doi: 10.1186/alzrt267. eCollection 2014.

A diagnostic scale for Alzheimer's disease based on cerebrospinal fluid biomarker profiles.

Author information

1
CHU de Montpellier and Université Montpellier I, IRMB, CCBHM, Laboratoire de Biochimie Protéomique Clinique, 80 Avenue Augustin Fliche, 34295 Montpellier, France.
2
Centre Mémoire Ressources Recherche Paris Nord Ile de France and Histologie et Biologie du Vieillissement, Groupe Hospitalier Saint-Louis Lariboisiere Fernand-Widal APHP, INSERM U942, Universite Paris Diderot, France.
3
Inserm U837 and Neurobiology Unit, Centre de Biologie-Pathologie, CHU, Universite Lille Nord de France, 59045 Lille, France.
4
Inserm U1079, University of Rouen, Department of Neurology and Laboratoire de biochimie, Rouen University Hospital, Rouen, France.
5
Centre Mémoire Ressources Recherche, Alsace; Department of Neurology, University Hospital of Strasbourg, Strasbourg, France ; 2 ICube laboratory and FMTS (Fédération de Médecine Translationnelle de Strasbourg), team IMIS-Neurocrypto, University of Strasbourg and CNRS, Strasbourg, France.
6
Centre Mémoire Ressources Recherche Besancon Franche-Comté, Department of Neurology, CHU Besançon, Besançon, France.
7
Centre Mémoire Ressources Recherche, CHU, EA1040 Université Lille Nord de France, 59000 Lille, France.
8
Centre Mémoire Ressources Recherche, Alsace; Department of Neurology, University Hospital of Strasbourg, Strasbourg, France ; Laboratoire de Biochimie et de Biologie Moléculaire, Hôpital de Hautepierre, Hôpitaux Universitaire de Strasbourg, Strasbourg, France ; Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), UMR7364, Université de Strasbourg-CNRS, Strasbourg, France.
9
Centre Mémoire Ressources Recherche, Alsace; Department of Neurology, University Hospital of Strasbourg, Strasbourg, France.
10
Laboratoire de Biochimie Lariboisière-Fernand Widal Hospital, APHP, University Paris 7-Denis Diderot, University Paris Descartes, Paris, France.
11
Laboratoire de Biochimie, CHU de Besançon, Besançon, France.
12
Centre Mémoire Ressources Recherche Languedoc-Roussillon, CHU de Montpellier, Hôpital Gui de Chauliac, Montpellier, and Université Montpellier I, Montpellier, France.
13
CHU de Montpellier and Université Montpellier I, IRMB, CCBHM, Laboratoire de Biochimie Protéomique Clinique, 80 Avenue Augustin Fliche, 34295 Montpellier, France ; Centre Mémoire Ressources Recherche Languedoc-Roussillon, CHU de Montpellier, Hôpital Gui de Chauliac, Montpellier, and Université Montpellier I, Montpellier, France.

Abstract

INTRODUCTION:

The relevance of the cerebrospinal fluid (CSF) biomarkers for the diagnosis of Alzheimer's disease (AD) and related disorders is clearly established. However, the question remains on how to use these data, which are often heterogeneous (not all biomarkers being pathologic). The objective of this study is to propose to physicians in memory clinics a biologic scale of probabilities that the patient with cognitive impairments has an Alzheimer's disease (AD) pathologic process.

METHODS:

For that purpose, we took advantage of the multicenter data of our Paris-North, Lille, and Montpellier (PLM) study, which has emerged through the initial sharing of information from these memory centers. Different models combining the CSF levels of amyloid-β 42, tau, and p-tau(181) were tested to generate categories of patients with very low (<10%), low (<25%), high (>75%), and very high predictive values (>90%) for positive AD. In total, 1,273 patients (646 AD and 627 non-AD) from six independent memory-clinic cohorts were included.

RESULTS:

A prediction model based on logistic regressions achieved a very good stratification of the population but had the disadvantages of needing mathematical optimization and being difficult to use in daily clinical practice. Remarkably, a simple and intuitive model based on the number (from zero to three) of three pathologic CSF biomarkers resulted in a very efficient predictive scale for AD in patients seen in memory clinics. The scale's overall predictive value for AD for the different categories were as follows: class 0, 9.6% (95% confidence interval (CI), 6.0% to 13.2%); class 1, 24.7% (95% CI, 18.0% to 31.3%); class 2, 77.2% (95% CI, 67.8% to 86.5%); and class 3, 94.2% (95% CI, 90.7% to 97.7%). In addition, with this scale, significantly more patients were correctly classified than with the logistic regression. Its superiority in model performance was validated by the computation of the net reclassification index (NRI). The model was also validated in an independent multicenter dataset of 408 patients (213 AD and 195 non-AD).

CONCLUSIONS:

In conclusion, we defined a new scale that could be used to facilitate the interpretation and routine use of multivariate CSF data, as well as helping the stratification of patients in clinical research trials.

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