Abstract
Background: Meta-analyses show that percentages of non-Cp-Cu—copper that is not bound to ceruloplasmin (also known as ‘free’ copper)—in serum are higher in Alzheimer’s disease (AD) patients. Genetic heterogeneity in AD patients stratified on the basis of non-Cp-Cu cut-off sustains the existence of a copper AD metabolic subtype. Non-Cp-Cu abnormalities correlated with alterations of electroencephalographic rhythms (EEG).
Objective: We aimed to determine whether an EEG-derived brain cortical rhythm’s heterogeneity between two AD groups stratified on the basis of a copper marker.
Method: We assessed levels of copper, ceruloplasmin, Non-Cp-Cu, and the APOE4 genotype in 67 AD patients and compared resting EEG-derived eLORETA cortical rhythms between AD groups stratified in terms of ‘Normal’ and ‘High’ non-Cp-Cu.
Results: The High non-Cp-Cu group experienced a lower power in all bands (0.2-48 Hz) in the parietal cortices (p=0.019) and a more limited alpha band (8-13 Hz) power in the sensory lobes (temporal, occipital, and parietal p<0.05 consistently) than the Normal non-Cp-Cu AD group. When corrected for MMSE, the non-Cp-Cu levels correlated with a reduction of high-frequency brain activity (from high alpha to gamma, 10.5-48 Hz).
Conclusion: This neurophysiological heterogeneity in EEG-derived brain cortical rhythms between the two AD groups sustains a copper AD metabolic subtype; Non-Cp-Cu is a marker of this copper AD.
Keywords: Alzheimer's disease (AD) subtype, ceruloplasmin, cortical lobes, Electroencephalography (EEG), free copper.
Current Alzheimer Research
Title:Non-Ceruloplasmin Copper Distinguishes A Distinct Subtype of Alzheimer’s Disease: A Study of EEG-Derived Brain Activity
Volume: 13 Issue: 12
Author(s): Franca Tecchio, Fabrizio Vecchio, Mariacarla Ventriglia, Camillo Porcaro, Francesca Miraglia, Mariacristina Siotto, Paolo M. Rossini, Mauro Rongioletti and Rosanna Squitti
Affiliation:
Keywords: Alzheimer's disease (AD) subtype, ceruloplasmin, cortical lobes, Electroencephalography (EEG), free copper.
Abstract: Background: Meta-analyses show that percentages of non-Cp-Cu—copper that is not bound to ceruloplasmin (also known as ‘free’ copper)—in serum are higher in Alzheimer’s disease (AD) patients. Genetic heterogeneity in AD patients stratified on the basis of non-Cp-Cu cut-off sustains the existence of a copper AD metabolic subtype. Non-Cp-Cu abnormalities correlated with alterations of electroencephalographic rhythms (EEG).
Objective: We aimed to determine whether an EEG-derived brain cortical rhythm’s heterogeneity between two AD groups stratified on the basis of a copper marker.
Method: We assessed levels of copper, ceruloplasmin, Non-Cp-Cu, and the APOE4 genotype in 67 AD patients and compared resting EEG-derived eLORETA cortical rhythms between AD groups stratified in terms of ‘Normal’ and ‘High’ non-Cp-Cu.
Results: The High non-Cp-Cu group experienced a lower power in all bands (0.2-48 Hz) in the parietal cortices (p=0.019) and a more limited alpha band (8-13 Hz) power in the sensory lobes (temporal, occipital, and parietal p<0.05 consistently) than the Normal non-Cp-Cu AD group. When corrected for MMSE, the non-Cp-Cu levels correlated with a reduction of high-frequency brain activity (from high alpha to gamma, 10.5-48 Hz).
Conclusion: This neurophysiological heterogeneity in EEG-derived brain cortical rhythms between the two AD groups sustains a copper AD metabolic subtype; Non-Cp-Cu is a marker of this copper AD.
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Tecchio Franca, Vecchio Fabrizio, Ventriglia Mariacarla, Porcaro Camillo, Miraglia Francesca, Siotto Mariacristina, Rossini M. Paolo, Rongioletti Mauro and Squitti Rosanna, Non-Ceruloplasmin Copper Distinguishes A Distinct Subtype of Alzheimer’s Disease: A Study of EEG-Derived Brain Activity, Current Alzheimer Research 2016; 13 (12) . https://dx.doi.org/10.2174/1567205013666160603001131
DOI https://dx.doi.org/10.2174/1567205013666160603001131 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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Alzheimer's disease (AD) poses a significant global health challenge, with an increasing number of individuals affected yearly. Deep learning, a subfield of artificial intelligence, has shown immense potential in various domains, including healthcare. This thematic issue of Current Alzheimer Research explores the application of deep learning techniques in advancing our ...read more
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