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Central Nervous System and Peripheral Inflammatory Processes in Alzheimer's Disease: Biomarker Profiling Approach - PubMed

  • ️Thu Jan 01 2015

Central Nervous System and Peripheral Inflammatory Processes in Alzheimer's Disease: Biomarker Profiling Approach

Constance Delaby et al. Front Neurol. 2015.

Abstract

Brain inflammation is one of the hallmarks of Alzheimer disease (AD) and a current trend is that inflammatory mediators, particularly cytokines and chemokines, may represent valuable biomarkers for early screening and diagnosis of the disease. Various studies have reported differences in serum level of cytokines, chemokines, and growth factors in patients with mild cognitive impairment or AD. However, data were often inconsistent and the exact function of inflammation in neurodegeneration is still a matter of debate. In the present work, we measured the expression of 120 biomarkers (corresponding to cytokines, chemokines, growth factors, and related signaling proteins) in the serum of 49 patients with the following diagnosis distribution: 15 controls, 14 AD, and 20 MCI. In addition, we performed the same analysis in the cerebrospinal fluid (CSF) of 20 of these patients (10 AD and 10 controls). Among the biomarkers tested, none showed significant changes in the serum, but 13 were significantly modified in the CSF of AD patients. Interestingly, all of these biomarkers were implicated in neurogenesis or neural stem cells migration and differentiation. In the second part of the study, 10 of these putative biomarkers (plus 4 additional) were quantified using quantitative multiplex ELISA methods in the CSF and the serum of an enlarged cohort composed of 31 AD and 24 control patients. Our results confirm the potential diagnosis interest of previously published blood biomarkers, and proposes new ones (such as IL-8 and TNFR-I). Further studies will be needed to validate these biomarkers which could be used alone, combined, or in association with the classical amyloid and tau biomarkers.

Keywords: Alzheimer disease; biomarkers; cerebrospinal fluid; inflammation; serum.

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Figures

Figure 1
Figure 1

Cohort patients and study outline. (A) A total of 49 patients were included in the preliminary study for protein-arrays analysis: 15 control subjects (A), 14 AD late-stage patients (B) and 20 MCI (mild cognitive impairment): 10 MCI with non-AD profile (C) and 10 MCI presenting AD profile (D). Serum was analyzed for every patient included; CSF was analyzed for 20 of them (10 patients from groups A and B). Time between collection of samples (serum and CSF) and their analysis is indicated (in months): results are mean ± SD. Mini-mental state examination (MMSE) is indicated (MMSE/30) as mean ± SD. (B) Sera, CSF, and our internal quality control (CQI, corresponding to a pool of five control sera) were hybridized on the protein arrays. Homogeneity of the slides was controlled thanks to the CQI: non-homogeneous slide identified was extracted before proceeding to the analysis of the arrays. Normalized array data of 120 serum signaling proteins were analyzed in the training set with statistical Student’s t-test to discover differences in protein abundance between samples (strategy 1). As no predictor could be identified, serum data were pooled for groups A–C and B–D (strategy 2). To discover predictors for classification, the training set was analyzed through prediction analysis for microarrays (PAM) approach.

Figure 2
Figure 2

Predictors discovery. (A) Predictor discovery by prediction analysis for microarrays (PAM) was performed with normalized array measurements of 120 signaling proteins in the training set. Internal cross-validation (redline) decreasing the centroid threshold (lower x-axis) resulted in an increase in the number of markers (inserted upper x-axis) that were used for classification and calculation of the classification error (y-axis). This led to the discovery of an optimal set of 13 predictors with lowest possible classification error. (B) The 13 predictors identified through PAM analysis are presented. Proteins are arranged in columns, with d-score corresponding in each group. Control group corresponded to control subjects and MCI non-AD patients (groups A–C defined for protein-arrays analysis); AD group corresponded to AD late-stage patients and MCI presenting AD profile (groups B–D defined for protein-arrays analysis). Positive d-score is indicative of increased expression and negative d-score reflects decrease in the expression of the proteins analyzed.

Figure 3
Figure 3

Clinical biochemical characterization of patients. The 55 patients (AD, n = 31 and control subjects, n = 24) of the enlarged cohort used for the second part of the study were characterized clinically and quantified for CSF biomarkers: Aβ42, tau, and p-tau, using Fujirebio ELISA quantification kits (A–C). IATI ratio [Aβ42/(240 + 1,18tau)] (D) was calculated for every subject of the cohort. Outliers are indicated with black circle. Outliers are defined as a value that is smaller than the lower quartile minus three times the interquartile range, or larger than the upper quartile plus three times the interquartile range.

Figure 4
Figure 4

Predictors quantification in the CSF and the serum. sIL-6R, TIMP-1, and sTNFR-I were quantified in the CSF (A–C) and in the serum (D–F) of 55 patients (AD, n = 31 and control subjects, n = 24). Outliers are indicated with black circle. Outliers are defined as a value that is smaller than the lower quartile minus three times the interquartile range, or larger than the upper quartile plus three times the interquartile range.

Figure 5
Figure 5

Area under ROC curve (AUC) of Tau, p-Tau, and sTNFR-I. ROC curves (A) of the three most efficient biomarkers for AD diagnosis sIL-6R, TIMP-1, and sTNFR-I were plotted along with their combination (logistic regression, see text). The classification tree for CSF biomarkers (B) defined as AD samples those with IGFBP6 >300,000 pg/mL and MIP-3beta >160 pg/mL. Using serum biomarkers (C), the criteria selected for AD diagnosis was: IL8 <23 pg/mL and TNFR-I <5260 pg/mL.

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