Genetic report abstractQuantitative EEG during normal aging: association with the Alzheimer's disease genetic risk variant in PICALM gene
Introduction
Genetic predisposition and increasing age are the greatest known risk factors for AD. Mutations of the APP, presenilin-1 (PSEN1), and presenilin-2 (PSEN2) genes are causative factors for early onset AD (Goate et al., 1991, Levy-Lahad et al., 1995, Rogaev et al., 1995, Sherrington et al., 1995). Polymorphisms of the apolipoprotein E (ApoE) gene are the most prevalent genetic risk factors for late onset AD in Caucasian ethnic groups, including the Russian population (Farrer et al., 1997, Rogaev, 1999, Saunders et al., 1993, Schmechel et al., 1993). Recent genome-wide association studies have identified novel risk variants for AD (Benitez et al., 2014, Chauhan et al., 2015, Harold et al., 2009, Lambert et al., 2009, Liu et al., 2013; Naj et al., 2014). The putative epistatic interaction of the novel risk variants with the APOE ε4 variant in the risk for AD has been demonstrated (Golenkina et al., 2010, Naj et al., 2014). Among the identified genes, phosphatidylinositol clathrin assembly lymphoid-myeloid leukemia (PICALM, chrm 11q14) is currently one of the top 6 risk genes for AD in the AlzGene database (http://www.AlzGene.org).
Understanding how the genes identified in association studies influence AD pathogenesis can potentially contribute to the earlier prediction of AD and the use of personalized prevention strategies in individuals at risk for AD.
The PICALM protein has been implicated in clathrin-mediated endocytosis (Ford et al., 2001). Clathrin-mediated endocytosis is responsible for the internalization of receptors, the recycling of membrane components, and the regulation of autophagic processes (Xu et al., 2015). Convincing data indicate that genetically directed changes in PICALM function lead to alterations in APP processing through endocytic pathways, amyloid beta (Aβ) production (Xiao et al., 2012) and Aβ clearance into the bloodstream (Ando et al., 2013, Baig et al., 2010, Parikh et al., 2014). PICALM may also contribute to AD development due to defects in autophagy and the clearance of tau, which is an autophagy substrate (Moreau et al., 2014). The PICALM protein is associated with neurofibrillary tangles and is abnormally cleaved in Alzheimer's brains (Ando et al., 2013). PICALM may cause synaptic perturbations by modulating the abundance of the glutamate receptor subunit GluR2 (Harel et al., 2011, Harold et al., 2009) and may influence AD risk by disrupting iron homeostasis and lipid metabolism (Xu et al., 2015).
The influence of the PICALM genotype on hippocampal volume and the thickness of the entorhinal cortex was found in patients with AD and mild cognitive impairment (MCI), and in normal controls (Biffi et al., 2010). Gene-wide scoring highlighted PICALM as the most significant gene associated with entorhinal cortical thickness (Furney et al., 2011).
However, the mechanism by which PICALM polymorphisms influence brain function in nondemented subjects remain largely unknown. The SNP rs3851179, which was the first PICALM SNP associated with AD risk (Harold et al., 2009), is located in a noncoding region ∼80 kb 5′ of PICALM. There are several PICALM SNPs (e.g., rs543293, rs659023, rs7110631, rs7941541, and rs3851179) in linkage disequilibrium with each other and associated with AD (Harold et al., 2009, Raj et al., 2012, Xu et al., 2016). Hence, the variant rs3851179 investigated in the present study is tagging the AD-associated haplotype.
Electroencephalography (EEG) is a powerful and cost-effective method for studying alterations in brain function during normal and pathological aging. EEG reflects the integrated synaptic activity of large populations of neurons, which progressively deteriorates in normal and physiological aging (Buzsaki, 2006). Recent data showed that EEG may be a valuable biomarker of the development of the pathologic processes in AD (Babiloni et al., 2006a, Jeong, 2004, Prichep et al., 2006, Moretti et al., 2012). Such biomarkers can be helpful to estimate the effect of potential therapies to prevent or delay the onset of neurodegenerative diseases (Illarioshkin et al., 2004, Masdeu et al., 2012).
The primary EEG abnormalities in AD patients consist of a shift of the power spectrum to lower frequencies and a decrease in the coherence of fast rhythms (Jelic et al., 1997, Jeong, 2004). AD patients exhibit a slowing of the dominant EEG frequency, increased delta and theta power, and decreased alpha power compared to healthy age-matched controls (Babiloni et al., 2014, Rossini et al., 2007, Van Straaten et al., 2014). MCI, which is in most cases a prodromal stage of AD, has EEG characteristics intermediate of those of normal subjects and AD patients (Babiloni et al., 2006a). Longitudinal studies have revealed EEG-based predictors of future decline in MCI patients and even in healthy elderly subjects (Babiloni et al., 2014, Prichep et al., 2006, Van der Hiele et al., 2008).
Healthy aging is associated with an amplitude decrease in the posterior alpha rhythm, with the anteriorization of the alpha activity and the slowing of alpha frequency (Babiloni et al., 2006b, Klimesch, 1999, Ponomareva et al., 2013, Tsuno et al., 2002, Volf and Gluhih, 2011). Several, but not all, studies also reported a decrease in delta activity and an increase in beta activity during physiological aging (Babiloni et al., 2006b, Vlahou et al., 2014).
EEG patterns have been suggested as promising tools to assess endophenotypes—basic heritable quantitative biological traits that more directly reflect the influence of specific genetic abnormalities than a complex disorder. Resting-state EEG characteristics are among the most heritable traits in humans, and the heritability of the spectral power of different EEG bands is in the range 70%–90% (van Beijsterveldt et al., 1996). EEG endophenotypes might help to clarify the role of genetic variants in brain function and disease development (De Geus, 2010).
Recent studies have demonstrated an association between EEG characteristics and AD risk variants in the ApoE and CLU genes in AD and MCI patients and even in healthy adults (Babiloni et al., 2006a, Jelic et al., 1997, Lee et al., 2012, Lehtovirta et al., 2000, Ponomareva et al., 2008, Ponomareva et al., 2012, Ponomareva et al., 2013, Stam et al., 2003).
The effect of the PICALM genotype on EEG characteristics has not been previously investigated.
The present study aimed to determine whether the PICALM genotype influences EEG characteristics in nondemented adults, and to estimate whether this possible effect is modified over the course of aging.
Section snippets
Participants
The enrolled cohort included 137 nondemented individuals (47 men and 90 women; age range: 20–79 years).
The subjects were of Russian descent from Moscow and the Moscow region. The participants underwent a neurologic examination and cognitive screening. The recruited subjects were free of dementia and other medical, psychiatric, and neurologic conditions. The exclusion criteria included a history of neurologic and psychiatric diseases, any type of memory impairment, signs of clinical depression
Results
Table 1 shows the demographic information for the participants. There were no differences in age and sex between the PICALM GG and PICALM AA and AG subgroups in either the young or the old subgroups, or in the whole sample (p > 0.05). There were no significant differences in sex between the young and the old subgroups with the same PICALM genotype.
Statistical examination of the normalized EEG relative power values yielded a significant main effect for the PICALM genotype [F(1,131) = 4.09, p =
Discussion
This study shows that the PICALM rs3851179 polymorphism is associated with beta relative power in the resting-state EEG of nondemented adults. An increase in beta 1 and beta 2 relative power was observed in the carriers of the homozygous AD risk variant PICALM GG compared to the carriers of the protective PICALM A allele (PICALM AA and AG genotypes).
This study also showed that the main effect of age was significant for the relative power in the delta and beta 1 frequency bands. Delta power was
Disclosure statement
The authors have no actual or potential conflicts of interest.
Acknowledgements
Research was supported by Russian Science Foundation grant no. 14-44-00077 (Aging-related genetic-EEG association study); co-authors were supported, in part, by Russian Science Foundation grant no. 14-15-01121 (genotyping of AD-gene), the grants from the Government of the Russian Federation (No 14.B25.31.0033), and NIH/NIA AG029360, N.V.P. was supported by RFBR grants No 11-04-01896-a and No 15-04-08744-a.
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