In an aging population dementias are a serious threat. Currently 30 million people worldwide suffer from Alzheimer´s disease (AD) and the World Health Organization projects that this number will triple over the next 20 years. The cumulative incidence of AD has been estimated to rise from about 5% by age 70 to 50% by age 90. The clinical diagnosis of dementias is established late in the course of the disease process with poor sensitivity and specificity making the differentiation between various dementias like AD, frontotemporal dementia (FTD), and vascular dementia difficult.

Figure 1: All types of dementia are progressive. This means that the structure and chemistry of the brain become increasingly damaged over time. People who have Mild Cognitive Impairment (MCI) are at an increased risk of going on to develop AD (or another form of dementia). Unfortunately first signs of a decreasing cognitive level become evident at the time when most of the plaque precipitation has already been occurred. Our approach enables us to predict with high confidence those patients with MCI, who subsequently convert to AD.

People are often not diagnosed with dementia until their symptoms begin to affect their quality of life and their ability to carry out everyday activities. It is important that people with AD are identified as early as possible so that they can benefit from medicamentous and/or cognitive treatments in the future. Identifying people with MCI is one way to try to achieve this (Figure 1).

Therefore, we used cerebrospinal fluid (CSF) of 159 out-patients of a memory-clinic at a University Hospital suffering from neurodegenerative disorders and 17 cognitively-healthy controls to create differential peptide pattern for dementias and prospective blinded-comparison of sensitivity and specificity for AD diagnosis against the Criterion standard in a naturalistic prospective sample of patients. Sensitivity and specificity of proteome analysis compared to standard diagnostic procedures and identification of new putative biomarkers for AD was the main outcome measure. CE-MS was used to reliably detect 1104 low-molecular-weight peptides in CSF. Training-sets of patients with clinically secured sporadic AD, FTD, and cognitively healthy controls allowed establishing discriminative biomarker pattern for diagnosis of AD (Figure 2). This pattern was already detectable in patients with MCI. The AD-pattern was tested in a prospective sample of patients (n = 100) and AD was diagnosed with a sensitivity of 87% and a specificity of 83%. Using CSF measurements of beta-amyloid1-42, total-tau, and phospho181-tau, AD-diagnosis had a sensitivity of 88% and a specificity of 67% in the same sample. This method allows early differential diagnosis of various dementias using specific peptide patterns and identification of incipient AD in patients suffering from MCI.

Figure 2: A) Compiled 3-D protein contour plot from CSF samples of 34 patients with AD. B) Compiled 3-D protein contour plot for healthy controls (n = 17). C) Discriminative biomarker pattern for subjects suffering from Alzheimer’s disease. The normalized CE-migration time (in min) is plotted on the x-axis and the relative molecular mass (in kDa) on the y-axis. As a third dimension, the signal intensity is color coded (blue lowest and white highest signal intensity). Each dot represents one peptide.

REFERECES

Jahn H, Wittke S, Zürbig P, Raedler TJ, Arlt S, Kellmann M, Mullen W, Eichenlaub M, Mischak H, Wiedemann K. Peptide fingerprinting of Alzheimer's disease in cerebrospinal fluid: identification and prospective evaluation of new synaptic biomarkers. PLoS One. 2011;6(10):e26540.

Zürbig P, Jahn H. Use of proteomic methods in the analysis of human body fluids in Alzheimer research. Electrophoresis. 2012;33(24):3617-30.

Barrera-Ocampo A, Arlt S, Matschke J, Hartmann U, Puig B, Ferrer I, Zürbig P, Glatzel M, Sepulveda-Falla D, Jahn H. Amyloid-β Precursor Protein Modulates the Sorting of Testican-1 and Contributes to Its Accumulation in Brain Tissue and Cerebrospinal Fluid from Patients with Alzheimer Disease. J Neuropathol Exp Neurol. 2016;75(9):903-16.