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. 2016 Mar 9;6:22769.
doi: 10.1038/srep22769.

Distinctive Pattern of Serum Elements During the Progression of Alzheimer's Disease

Free PMC article

Distinctive Pattern of Serum Elements During the Progression of Alzheimer's Disease

Giuseppe Paglia et al. Sci Rep. .
Free PMC article


Element profiling is an interesting approach for understanding neurodegenerative processes, considering that compelling evidences show that element toxicity might play a crucial role in the onset and progression of Alzheimer's disease (AD). Aim of this study was to profile 22 serum elements in subjects with or at risk of AD. Thirtyfour patients with probable AD, 20 with mild cognitive impairment (MCI), 24 with subjective memory complaint (SMC) and 40 healthy subjects (HS) were included in the study. Manganese, iron, copper, zinc, selenium, thallium, antimony, mercury, vanadium and molybdenum changed significantly among the 4 groups. Several essential elements, such as manganese, selenium, zinc and iron tended to increase in SMC and then progressively to decrease in MCI and AD. Toxic elements show a variable behavior, since some elements tended to increase, while others tended to decrease in AD. A multivariate model, built using a panel of six essential elements (manganese, iron, copper, zinc, selenium and calcium) and their ratios, discriminated AD patients from HS with over 90% accuracy. These findings suggest that essential and toxic elements contribute to generate a distinctive signature during the progression of AD, and their monitoring in elderly might help to detect preclinical stages of AD.


Figure 1
Figure 1. Principal Component Analysis (PCA) (a) PCA separates AD samples from the other groups.
(b) Loading biplot shows how essential elements and toxic elements contribute in a different way to AD samples clustering.
Figure 2
Figure 2. Profiles of selected essential elements.
Essential elements show a similar profile with highest values in SMC samples and lowest values in AD samples. Dot line represents the average value.
Figure 3
Figure 3. Heatmap.
Essential elements showed a characteristic pattern, which was different from the one of toxic elements. The average values were used after data normalization as described in methods.
Figure 4
Figure 4. Correlation analysis.
Essential elements strong correlate between each other. In the table are reported positive and negative significant correlations.
Figure 5
Figure 5. Univariate ROC curves analysis individuated Cu and the ratio Cu/Mn as two potential biomarkers for discriminating between AD and HS samples.
AUC = Area under the curves. The confidential interval is also provided. Plot bars were obtained using normalized concentration as described in methods.
Figure 6
Figure 6. Multivariate ROC curves analysis was performed by generating a model with 6 essential elements (Cu, Ca, Mn, Zn, Fe and Se) and their ratios.
(a) Random Forest algorithm was used during this analysis. AUC = Area under the curve. CI = Confidential Intervals. The model was then validated by cross validation and permutation test. (b) Selected features included in the model (Supplementary Table S2).

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