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J Alzheimers Dis. 2019;67(2):639-651. doi: 10.3233/JAD-180855.

Improved Differential Diagnosis of Alzheimer's Disease by Integrating ELISA and Mass Spectrometry-Based Cerebrospinal Fluid Biomarkers.

Author information

Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden.
Department of Chemistry-BMC, Analytical Chemistry, Uppsala University, Uppsala, Sweden.
Division of Affinity Proteomics, SciLifeLab, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala, Sweden.
Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.
Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.
UK Dementia Research Institute at UCL, London, United Kingdom.
Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, United Kingdom.



Alzheimer's disease (AD) is diagnosed based on a clinical evaluation as well as analyses of classical biomarkers: Aβ42, total tau (t-tau), and phosphorylated tau (p-tau) in cerebrospinal fluid (CSF). Although the sensitivities and specificities of the classical biomarkers are fairly good for detection of AD, there is still a need to develop novel biochemical markers for early detection of AD.


We explored if integration of novel proteins with classical biomarkers in CSF can better discriminate AD from non-AD subjects.


We applied ELISA, mass spectrometry, and multivariate modeling to investigate classical biomarkers and the CSF proteome in subjects (n = 206) with 76 AD patients, 74 mild cognitive impairment (MCI) patients, 11 frontotemporal dementia (FTD) patients, and 45 non-dementia controls. The MCI patients were followed for 4-9 years and 21 of these converted to AD, whereas 53 remained stable.


By combining classical CSF biomarkers with twelve novel markers, the area of the ROC curves (AUROCS) of distinguishing AD and MCI/AD converters from non-AD were 93% and 96%, respectively. The FTDs and non-dementia controls were identified versus all other groups with AUROCS of 96% and 87%, respectively.


Integration of new and classical CSF biomarkers in a model-based approach can improve the identification of AD, FTD, and non-dementia control subjects.


Alzheimer’s disease; ELISA; cerebrospinal fluid; mass spectrometry; mild cognitive impairment; proteomics

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