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Keywords:

  • Alzheimer’s disease;
  • apolipoprotein E;
  • brain-derived neurotrophic factor;
  • magnetic resonance imaging;
  • polymorphism;
  • prognosis

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments
  9. Supporting Information

Genetic factors, such as apolipoprotein E (ApoE) polymorphisms, are thought to play an important role in the etiology of Alzheimer’s disease (AD). Recent association studies have suggested that the Val66Met polymorphism in the brain-derived neurotrophic factor (BDNF) gene could play a role in the development of AD. To identify genotypic effects of the BDNF and the ApoE genes on disease progression in preclinical AD, we assessed morphological changes using serial magnetic resonance imaging during the preclinical period of AD in 35 individuals. When all subjects were analyzed as one group, progressive atrophy was noted in the limbic, paralimbic and neocortical areas. Individuals of the BDNF Val/Val genotype showed progressive atrophy in the left medial temporal areas, whereas the BDNF Met allele carriers showed additional changes in the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC) and the precuneus. An interaction between the BDNF genotype and progressive morphological changes was found in the PCC. The noncarriers for the ApoE ɛ4 allele showed progressive atrophy in the bilateral medial temporal areas. In addition to changes in the medial temporal areas, ɛ4 carriers showed progressive atrophy in the PCC, ACC and precuneus. An interaction between the ApoE genotype and progressive morphological change was noted in the right medial temporal area. The present preliminary study indicates that polymorphisms of the ApoE and the BDNF genes could affect disease progression in preclinical AD and implies that the Met-BDNF polymorphism could be an additional risk factor for rapid disease progression in preclinical AD.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments
  9. Supporting Information

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive deterioration of memory and other cognitive abilities, progressive loss of neurons and synapses, and formation of amyloid plaques and neurofibrillary tangles (NFT). There are two types of AD, familial and sporadic AD. Sporadic AD, which constitutes at least 95% of AD, shows no clear familial pattern of inheritance; however, genetic factors are thought to play an important role in the etiology of sporadic AD (Ashford & Mortimer 2002).

The apolipoprotein E (ApoE) polymorphism is known to play a role in cholesterol management and is associated with AD (Puglielli et al. 2003). The ApoE ɛ4 allele is a well-demonstrated risk factor for both familial and sporadic AD cases and was associated with a progressively younger age at dementia onset in sporadic AD in a dose-dependent manner (Corder et al. 1993). Several studies have shown that the ApoE polymorphism may contribute to variations in brain morphology, in particular in the hippocampus of normal individuals, individuals with mild cognitive impairment and patients with AD. However, there are still some inconsistencies regarding dose effects of the ApoE ɛ4 allele on brain morphology (Farlow et al. 2004; Geroldi et al. 1999; den Heijer et al. 2002; Lehtovirta et al. 1995; Lemaitre et al. 2005).

The brain-derived neurotrophic factor (BDNF) gene is another promising risk factor for AD. A common single nucleotide polymorphism (SNP) in the BDNF gene produces a valine to methionine (Val66Met) amino acid substitution that affects intracellular packaging and activity-dependent secretion of BDNF and consequently affects human memory function (Chen et al. 2004; Egan et al. 2003; Hariri et al. 2003). Although some studies have shown an association between this Val66Met-BDNF polymorphism and the risk of AD (Bian et al. 2005; Matsushita et al. 2005; Ventriglia et al. 2002), this could not be confirmed by other studies (Bodner et al. 2005; Desai et al. 2005; Tsai et al. 2004). Recent studies showed that the Val66Met polymorphism of the BDNF gene was associated with variation in brain morphology in the hippocampus and parahippocampal gyrus of normal individuals and schizophrenics (Nemoto et al. 2006; Pezawas et al. 2004; Szeszko et al. 2005).

We hypothesized possible effects of the BDNF polymorphism on progressive morphological changes in preclinical AD because of the role of BDNF in neuroprotection and hippocampal plasticity. However, little is known about the effect of the BDNF and the ApoE polymorphisms, on the progressive morphological changes in preclinical AD. In this study, we examined the possible effect of the ApoE and the BDNF genes on time-dependent morphological changes during the preclinical AD using voxel-by voxel analysis with serial magnetic resonance imaging (MRI) over a 3-year follow-up period.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments
  9. Supporting Information

Subjects

The characteristics of subjects in the present study are summarized in Table 1. We retrospectively studied 35 individuals (20 men and 15 women, mean age 68.39 ± 9.88 years) with AD who visited our memory clinic with their chief complaint being memory disturbance. They fulfilled the diagnosis of probable AD according to the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria (McKhann et al. 1984) during the follow-up period after the initial visit. All subjects were not diagnosed with dementia at the initial visit to our clinic but displayed: (1) memory dysfunction noticed by the patient, family members or the physician, (2) normal daily living activities, (3) normal global cognitive function, (4) objective impairment in memory or in one other area of cognitive function with a score of <1.5 SD below the age appropriate mean, (5) a Clinical dementia rating score (Berg 1988) of 0.5 and (6) mini-mental state examination (MMSE) (Folstein et al. 1975) scores ranging from 24 to 29 (mean 25.8 ± 1.54). During the follow-up period of 3 years, all subjects showed a progressive cognitive decline and eventually converted to probable AD. The mean time from initial MRI scan to AD conversion was 534 days (±339; range: 190–1256 days) and the mean MMSE score at AD conversion was 20.4 (±4.93).

Table 1.  Subject characteristics
 Normal values of our clinicAll subjects (= 35)Categorized by ApoE genotypeCategorized by BDNF genotype
ɛ4 noncarriers (= 16)ɛ4 carriers (= 19, hetero 13, homo 6)dfP valueVal/Val-BDNF (= 12)Met-BDNF carriers (= 23, hetero 16, homo 7)dfP value
  • M, male; F, female.

  • *

    Differences in clinical characteristics among genotypes were analyzed using the chi-square tests for categorical variables.

  • Differences in clinical characteristics among genotypes were analyzed using t-tests for continuous variables.

Distribution of other genotypes* Val/Val:Met = 3:13Val/Val:Met = 13:610.08E4(−):E4(+) = 3:9E4(−):E4(+) = 13:1010.08
Gender* (M:F = 20:15)(M:F = 7:9)(M:F = 13:6)10.14(M:F = 3:9)(M:F = 17:6)10.006*
Age70.4 ± 7.368.39 ± 9.8869.94 ± 7.8767.16 ± 7.61330.2967.25 ± 7.1869.61 ± 7.24330.3
Education level (years)12.2 ± 2.912.0 ± 3.112.08 ± 2.8911.93 ± 3.22330.6711.78 ± 3.112.43 ± 3.02330.57
Days of AD conversion 534 ± 339610 ± 453472 ± 201330.28462 ± 187.8566 ± 388330.46
MMSE (at initial scan)28.8 ± 1.525.8 ± 1.5426 ± 1.6325.68 ± 1.49330.5625.92 ± 1.4425.78 ± 1.62330.81
Time interval to second scan 359.7 ± 79.3361 ± 110358 ± 34330.9360 ± 23.4359.3 ± 97330.96
Time interval to third scan 733.6 ± 108.9711 ± 128750 ± 92330.34760 ± 95720 ± 114.8330.34
Second/third/fourth scan* 6/40/242/9/51/11/720.741/7/42/13/821.00
Digit span
 Forward5.3 ± 1.05.29 ± 1.065.55 ± 1.135.0 ± 0.94330.245.33 ± 1.225.25 ± 0.97330.86
 Backward4.1 ± 0.84.40 ± 1.054.45 ± 1.294.33 ± 0.71330.84.75 ± 0.714.17 ± 1.19330.23
Word-list learning
 Delayed recall (30 min)7.9 ± 1.20.92 ± 2.160.67 ± 1.611.15 ± 2.61330.581.10 ± 1.850.8 ± 2.4330.74
Story recall 
 Delayed recall (30 min)7.9 ± 2.50.88 ± 1.620.86 ± 1.950.9 ± 1.29330.960.71 ± 1.250.96 ± 1.82330.75
Rey-Osterrieth complex figure test
 Copy32.03 ± 3.2730.6 ± 7.4930.2 ± 8.9230.8 ± 7.2330.9731.2 ± 8.9231.8 ± 7.2330.77
 Delayed recall (30 min)14.47 ± 6.313.19 ± 6.881.3 ± 1.925.2 ± 9.6330.963.07 ± 3.613.25 ± 8.17330.96

All subjects were right-handed and were screened through a questionnaire and examination of the medical history to exclude those with neurological, psychiatric disorders and medical conditions that could potentially affect the central nervous system, such as alcoholism, substance abuse, atypical headache, head trauma with loss of consciousness, asymptomatic cerebral infarction detected by T2-weighted MRI, hypertension, chronic lung disease, kidney disease, chronic hepatic disease, cancer and diabetes mellitus. All subjects denied the use of psychotropic and other medications known to affect brain size, such as steroids. All subjects were free of depression with a score of greater than 8 on the Hamilton Depression Rating Scale (Hamilton 1980). After a description of the study, written informed consent was obtained from every subject. This study has been approved by the local ethics committee and has therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki.

Subjects were categorized according to their BDNF genotypes or ApoE genotypes (Table 1). Detail information for subjects are given in Appendix S1.

Genotyping

Genotyping of the ApoE genotype and the Val66Met SNP (dbSNP accession number: rs6265) was performed as described previously (Asada et al. 1996; Masui et al. 2006; Nemoto et al. 2006; Okada et al. 2006). Detailed information for genotyping is described in Appendix S1.

Brain MR procedures

All MR studies were performed on a 1.0 Tesla Siemens Magnetom Impact System (Siemens, Erlangen, Germany). A three-dimensional volumetric acquisition of a T1-weighted gradient echo sequence produced a gapless series of thin sagittal sections using a MPRage sequence (Echo Time (TE)/Repetition Time (TR); 4.4/11.4 ms, flip angle: 15°, acquisition matrix: 256 × 256, 1NEX, field of view: 31.5 cm, slice thickness: 1.23 mm, 1.23 mm × 1.23 mm × 1.23 mm). The first MR scans were performed at the initial visit and scans were taken during the next 3 years on subsequent visits to the clinic. The mean time interval for each follow-up scan was 336 ± 165 (days). During the follow-up period, all subjects converted to probable AD. In this study, we only analyzed MR data before the conversion to AD to map progressive morphological changes during the preclinical stage of the disease. The mean interval between the first and second scan was 359 ± 79.3 days and between the second and third scan 733.6 ± 108.9 days (Table 1). There was no significant difference in the interval times of follow-up scans between the ApoE and BDNF genotype groups (Table 1).

Image analysis

We performed tensor-based morphometry (TBM) to explore genotype effects on time-dependent morphological changes during the follow-up period. In a previous study, we applied TBM to detect interindividual morphological differences (Ohnishi et al. 2006). In this study, we applied TBM to detect intraindividual morphological changes. A similar method to detect intraindividual morphological changes was described as fluid-registered serial MRI and applied to clarify time-dependent morphological changes in familial AD (Scahill et al. 2002). Image analysis was carried out using Statistical Parametric Mapping 2 (spm2) running on matlab6 (Mathworks Inc., Sherborn, MA, USA) First, inhomogeneities in MR images were corrected using a bias correction function in SPM2, corrected images were scalp-edited by masking with a probability image of brain tissue obtained from each image using a segmentation function in spm2. Using a linear normalization algorithm in spm2, all brains were resized to a voxel size of 1.5 mm and adjusted for orientation and overall width, length and height. Brains were transformed to the anatomical space of a template brain based on Talairach space. After preprocessing for the high-dimensional warping mentioned above, each follow-up scan was warped to the initial scan of each subject. The nonlinear transformation was performed using a high-dimensional warping algorithm (Ashburner et al. 1999). From the resulting three-dimensional deformation fields (consisting of three-dimensional displacement vectors of every voxel in the image), we calculated the Jacobian determinant to obtain voxel-by-voxel parametric maps of local volume changes relative to the initial scan. After smoothing with an isotropic Gaussian kernel of 6-mm full width at half maximum, the smoothed parametric maps of Jacobian determinants were analyzed using SPM2.

First, we evaluated time-dependent morphological changes in all subjects. For this analysis, a simple regression model was used and the time intervals between follow-up scans and initial scans were used as covariates of interest. Subsequently, the effects were evaluated of each genotype on time-dependent morphological changes using a single subject condition and covariate model. Then, we evaluated interaction effects between genotype and time intervals of MRI scan on brain morphology (interaction effects of genotype on progressive morphological changes). Genotype effects were estimated by using condition (e.g. homozygotes for the Val-BDNF or Met-BDNF carriers) and covariates model of spm2 (this does not imply a within subject design).

To evaluate a possible genotype effect, the BDNF genotype was treated as two conditions (homozygotes for the Val-BDNF or Met-BDNF carriers). Time intervals between follow-up scans and initial scans were used as covariates of interest (interaction with condition), and the ApoE genotype and gender were treated as nuisance variables. To estimate the effects of the ApoE genotype, the ApoE genotype was treated as two conditions (ApoE ɛ4 carriers or noncarriers) and time intervals between follow-up scans and initial scans were used as covariates of interest (interaction with condition). To exclude an effect of the BDNF genotype, it treated as a nuisance variable. A statistical threshold of < 0.001 corrected for multiple comparisons with false discovery rate (FDR) < 0.05 was used. The resulting sets of t-values constituted SPM are rendered onto surface brain. Anatomic localization was determined according to both MNI coordinates and Talairach coordinates, obtained from M. Brett’s transformations and is represented as Talairach coordinates.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments
  9. Supporting Information

Time-dependent morphological changes in preclinical AD

When all subjects were treated as one group, the TBM revealed a significant time-dependent volume reduction (a negative correlation between time intervals from initial scan to each follow-up scan and Jacobian determinants) in the medial temporal structures, such as hippocampus, parahippocampal gyrus and the amygdala, and other limbic areas such as the posterior cingulate cortex (PCC), cingulate cortex and the anterior cingulate cortex (ACC) (Fig. 1, and Table 2). Additionally, we also found progressive atrophy of neocortical areas, such as the precuneus and temporoparietal association areas, fusiform gyrus and bilateral prefrontal areas (Fig. 1 and Table 2). However, a significant time-dependent expansion (a positive correlation between time intervals and Jacobian determinants) was noted in the cerebrospinal fluid space, such as the lateral ventricle, Sylvian fissures and the interhemispheric fissure (Fig. 2).

image

Figure 1. Time-dependent morphological changes in patients with preclinical AD. Top: The SPM {t} is displayed in a standard format as a maximum-intensity projection viewed from the right, the back and the top of the brain. The anatomical space corresponds to the atlas of Talairach and Tournoux. A representation is given in stereotaxic space of the regions with significant time-dependent reduction of volume in preclinical AD. A significant time-dependent volume reduction can be seen in the medial temporal structures, such as hippocampus, parahippocampal gyrus and the amygdala, other limbic areas such as the PCC, cingulate cortex, the ACC and neocortical areas, such as the precuneus and temporoparietal association areas, fusiform gyrus and bilateral prefrontal areas. Bottom: The SPM {t} is displayed and rendered onto T1-weighted MR images.

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Table 2.  Progressive atrophy in preclinical stage of patients with AD
Brain regionsBrodmann areaCluster sizedfT valueCorrected P value (FDR)Talairach coordinates
xyz
  1. B, both sides; L, left; R, right.

L parahippocampal gyrus, hippocampus, amygdala282486689.20.0000−22−11−20
L fusiform gyrus372486688.150.0000−42−57−11
R parahippocampal gyrus, hippocampus35735688.330.000030−20−17
R insula13133688.320.00003017−6
L subcallosal gyrus, ACC25/11/47650688.240.0000−813−14
L superior frontal gyrus101062687.320.0000−165517
L middle frontal gyrus8/9291686.630.0000−321643
L cingulate gyrus32321686.370.0000122139
R medial frontal gyrus8321684.90.0000123144
L precuneus, PCC7/31874686.130.0000−12−6644
L superior parietal lobule7874685.230.0000−14−6553
L inferior parietal lobule40215685.930.0000−44−5239
R medial frontal gyrus25/10231685.590.00001211−14
R inferior frontal gyrus47231684.110.00001815−19
L middle temporal gyrus19/39138685.440.0000−36−7919
L superior temporal gyrus39102685.390.0000−44−5017
R superior frontal gyrus8327685.320.0000144635
R inferior temporal gyrus20587685.310.000055−28−20
R middle temporal gyrus21587685.020.000048−3−20
R supramarginal gyrus, inferior parietal lobule40354685.260.000050−4333
R precuneus, PCC7/31167685.140.000010−5730
image

Figure 2. Time-dependent morphological changes in patients with preclinical AD. Top: The SPM {t} is displayed in a standard format as a maximum-intensity projection viewed from the right, the back and the top of the brain. A significantly increased volume can be seen in the cerebrospinal fluid space including the ventricular and cisternal system, Sylvian fissures and the interhemispheric fissure. Bottom: The SPM {t} is displayed onto axial T1-weighted MR images.

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Effect of the genotype on time-dependent morphological changes in preclinical AD

The individuals with a homozygotic Val-BDNF genotype showed a significant time-dependent volume reduction in the left medial temporal areas such as the hippocampus, parahippocampal gyrus and the amygdala (Fig. 3, top and Table 3). However, the Met-BDNF carriers showed a significant time-dependent volume reduction in the limbic and paralimbic areas, such as hippocampus, parahippocampal gyrus, amygdala, ACC, PCC and the precuneus (Fig. 3, middle and Table 3). We found a significant interaction effect of the BDNF genotype on progressive morphological change (more rapid reduction of volume in Met-BDNF carriers than in the Val-BDNF homozygotes) in the bilateral PCC and the cingulate gyrus (< 0.001, small volume correction (SVC) < 0.05, search volume: 10 mm) (Fig. 3, bottom and Table 3). The noncarriers for the ApoE ɛ4 allele showed a significant time-dependent volume reduction in the medial temporal structures, such as hippocampus, parahippocampal gyrus and the amygdala (Fig. 4, top and Table 4). However, ɛ4 carriers also showed a significant time-dependent volume reduction in the hippocampus, parahippocampal gyrus and the amygdala (Fig. 4, middle and Table 4). In addition to morphological changes in the medial temporal structures, there was also a significant time-dependent volume reduction in the PCC, ACC and the precuneus (Fig. 4, middle and Table 4). We found a significant interaction between the ApoE ɛ4 genotype and progressive morphological changes (more rapid reduction of volume in ApoE ɛ4 carriers than noncarriers) in the right parahippocampal gyrus and hippocampus (< 0.001, SVC < 0.05, search volume: 10 mm) (Fig. 4, bottom and Table 4).

image

Figure 3. Genotype effects of the BDNF gene on time-dependent morphological changes in preclinical AD. Top: Time-dependent reduction of volumes in preclinical AD patients carrying homozygous for the Val-BDNF allele. A significant time-dependent volume reduction can be seen in the left hippocampus, parahippocampal gyrus and the amygdala. Middle: Time-dependent reduction of volume in preclinical AD patients carrying the Met-BDNF allele. Met-BDNF carriers display a significant time-dependent volume reduction in the limbic and paralimbic areas and the precuneus. Bottom: Interaction effect between the BDNF genotype and time-dependent morphological changes. A significant interaction effect (a more rapid reduction of volume in Met-BDNF carriers than in the Val-BDNF homozygotes) can be seen in the bilateral PCC and the cingulate gyrus.

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Table 3.  Effect of the BDNF genotype on atrophy progression in preclinical AD
Brain regionsBrodmann areaCluster sizedfT valueCorrected P value (FDR)Talairach coordinates
xyz
  1. B, both sides; L, left; R, right.

Progressive atrophy in preclinical AD carrying the Val/Val-BDNF polymorphism
 L parahippocampal gyrus, hippocampus, uncus, amygdala28/35/36187684.670.000−20−8−16
Progressive atrophy in preclinical AD carrying the Met-BDNF polymorphism 
 L parahippocampal gyrus, hippocampus, uncus, amygdala35/36741687.620.000−22−12−24
 R parahippocampal gyrus, hippocampus, uncus, amygdala28/36558687.620.00032−20−22
 R medial frontal gyrus, ACC10/32275685.690.000164612
 L subcallosal gyrus, ACC11/32520685.40.000−123416
 L precuneus, PCC7/23/311318685.220.000−14−7042
 R precuneus, PCC, cingulate gyrus7/31117683.910.0028−3836
Met-BDNF carrier more than Val/Val-BDNF 
 B PCC, cingulate gyrus31120684.780.000−10−4036
image

Figure 4. Effect of the ApoE genotype on time-dependent morphological changes in preclinical AD. Top: Time-dependent volume reduction in preclinical AD patients without the ApoE ɛ4 allele. A significant time-dependent volume reduction can be seen in the bilateral hippocampus, parahippocampal gyrus and amygdala. Middle: Time-dependent reduction of volume in preclinical AD patients carrying the ApoE ɛ4 allele. ApoE ɛ4 carriers show a significant time-dependent volume reduction in the medial temporal structures, PCC, ACC and the precuneus. Bottom: Interaction effect between the ApoE genotype and time-dependent morphological changes. A significant interaction effect (a more rapid volume reduction in ApoE ɛ4 carriers than noncarriers) in the right parahippocampal gyrus.

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Table 4.  Effects of the ApoE genotype on atrophy progression in preclinical AD
Brain regionsBrodmann areaCluster SizedfT valueCorrected P value (FDR)Talairach coordinates
xyz
  1. B, both sides; L, left; R, right.

Progressive atrophy in preclinical AD without ApoE ɛ 4 allele 
 L parahippocampal gyrus, hippocampus, uncus, amygdala28361685.420.001−22−4−24
 R parahippocampal gyrus, hippocampus, uncus, amygdala20/28/35467685.340.00130−16−26
 L subcallosal gyrus, ACC11/3293685.30.001−1024−10
 L precuneus7135684.210.003−10−6844
Progressive atrophy in preclinical AD carrying ApoE ɛ 4 allele 
 L parahippocampal gyrus, hippocampus, amygdala35826687.650.000−28−6−14
 B ACC32361686.140.000−102426
 R parahippocampal gyrus, hippocampus, uncus, amygdala36/28455685.940.00030−22−22
 B precuneus7/19843685.770.000−24−7638
 R medial frontal gyrus10145684.790.000164612
 B posterior cingulate cortex, cingulate gyrus3197683.650.00416−2642
Interaction (ApoE genotype × progressive atrophy) 
 R parahippocampal gyrus, hippocampus35139684.380.00224−24−16

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments
  9. Supporting Information

The two main findings of this preliminary study were that in comparison with homozygotic individuals for the Val-BDNF allele, the Met-BDNF carriers showed a more rapid atrophy in the bilateral PCC and cingulate gyrus. Furthermore, in comparison with noncarriers for the ApoE ɛ4 allele, ApoE ɛ4 carriers showed a more rapid atrophy in the right parahippocampal gyrus. Despite the small sample size, serial MR analysis can minimize intersubject variances and estimate the pure disease process. Our data suggest that polymorphisms in the ApoE and the BDNF genes could affect disease progression in preclinical AD. This is the first study showing a significant effect of the Val66Met polymorphism in the BDNF gene on disease progression in preclinical AD as shown by the serial MR study.

When all subjects were treated as one group, we found a significant time-dependent volume loss in the limbic areas, such as the parahippocampal gyrus, hippocampus, amygdale, PCC, ACC and temporal and parietal association areas including the precuneus. This spatial distribution of progressive atrophy is consistent with the hierarchical distribution of NFT-tau pathology present in aging and AD brain (Braak et al. 1994; Delacourte et al. 1999). Using essentially the same method for image analysis, one group reported similar results to ours in a small sample of individuals with familial AD (Fox et al. 2001; Scahill et al. 2002). A longitudinal study of brain volume in normal aging, particularly after 70 years of age, showed marked atrophy in hippocampi (Scahill et al. 2003). The present study generalizes and strengthens these previous results and a time-dependent volume loss in the limbic, and temporal and parietal association areas could be, at least in part, specific to disease progression.

Importantly, the present study suggests that the Val66Met polymorphism is associated with disease progression in preclinical AD. The Met-BDNF carriers displayed a more extensive time-dependent atrophy in the limbic and paralimbic areas including the PCC and cingulate gyrus. A significant genotype–disease progression interaction was noted in the PCC. Medial temporal structures and the PCC are involved in episodic memory and AD pathology (Reiman et al. 2005); therefore, it seems plausible that the genotype effect on progression of atrophy was observed in the PCC. Because the number of men and women was different in the two groups (there was a significantly greater number of men in the Met-BDNF carrier group), one would argue that the observed results could be caused by gender differences rather than a genotype effect of the BDNF gene. However, during MRI analysis, gender was used as a nuisance variable. Furthermore, several studies suggested that AD pathology is more likely to present as dementia in women than in men (Barnes et al. 2005b; Fleisher et al. 2005). In our previous study, the genotype effect of the Val66Met polymorphism on age-related brain morphology was more prominent in women than in men (Nemoto et al. 2006). Taken together, it seems unlikely that the observed result is attributable to the higher number of men in the Met-BDNF carrier group. Although the underlying mechanism remains to be clarified, we assume that the Val66Met polymorphism may be associated with variances in the neuroprotective and/or stress-resistant function of BDNF. BDNF, a member of the neurotrophin family, activates a high-affinity cell surface receptor that is coupled to activation of phosphatidylinositol-3-kinase and protein kinase (Mattson et al. 2004). It plays an important role in preventing neuronal death during development and promotes cell survival during stressful conditions such as ischemia and trauma in the adult brain (Larsson et al. 1999; Mattson et al. 2004). Furthermore, several studies have suggested an associations between AD pathology and BDNF, for example, reduction of BNDF expression in the hippocampus of AD patients (Phillips et al. 1991), decreased trkB protein, a BDNF receptor, in the temporal and frontal cortex of AD patients and impaired cyclic adenosine monophosphate response element-binding protein signaling in AD (Allen et al. 1999; Mattson et al. 2004). These data seem to support our hypothesis, possible effects of BDNF on disease progression in preclinical AD; however, further in vitro studies are needed to clarify whether the Val66Met polymorphism contributes to a functional change in the neuroprotective and/or stress-resistant role of BDNF.

Someone may argue that genotype effects of the Val66Met and the ApoE polymorphisms on rapid reduction of certain brain volumes observed in this study seem inconsistent with the result of days of the AD conversion. In general, it is considered that the difference of a missense polymorphism, that is the BDNF Val66Met polymorphism and the ApoE polymorphism, first causes the difference of function in the protein level and then that the distinct protein function influences neuronal functions in the cellular level. Distinct cellular functions might affect brain morphologies and these differences of brain morphologies could result in the distinct disease progression. Thus, genotype effects could be stronger in order of the protein level, the cellular level, the brain morphology level and the disease level. The brain morphology might reflect genotype effects more directly than the disease progression does in our study.

In addition to the BDNF gene polymorphism, we found an effect of the ApoE ɛ4 polymorphism on disease progression in preclinical AD. When subjects were classified by ApoE genotypes, ɛ4 carriers displayed a more extensive time-dependent atrophy in the limbic and paralimbic areas and showed a significantly faster atrophy in the right parahippocampal gyrus compared with noncarriers. Although there was no significant difference between ɛ4 carriers and noncarriers regarding duration from the initial visit to AD conversion, our data suggest that carrying the ɛ4 allele could be a risk for rapid disease progression in preclinical AD. The ApoE ɛ4 allele is associated with increased amyloid deposition and plaque formation, and abnormal neurite maintenance (Ashford & Mortimer 2002). Furthermore, the parahippocampal gyrus is the earliest brain region involved in AD pathology (Braak et al. 1994; Delacourte et al. 1999). Taken together, it is plausible that the ɛ4 polymorphism of the ApoE gene contributes to progressive morphological changes in this region. However, there has been some controversy on whether the ApoE ɛ4 polymorphism is associated with clinical course and/or disease progression in preclinical AD. In the preclinical phase of AD, decreased memory performance and a faster cognitive decline have been associated with the ApoE ɛ4 allele in some studies (Bondi et al. 1999) but not in others (Tierney et al. 1996). Further studies have shown an association between the ApoE ɛ4 allele and a faster rate of cognitive decline (Corder et al. 1993; Craft et al. 1998), but these results have not been confirmed (Frisoni et al. 1995; Stern et al. 1997). Neuroimaging studies have also shown inconsistent results. Some studies showed that the ɛ4 allele is associated with a faster rate of atrophy in the hippocampus (Moffat et al. 2000), whereas other studies showed that the presence of an ɛ4 allele had no significant effect on the rate of atrophy in AD and normal individuals (Barnes et al. 2005a; Du et al. 2006). We speculate that differences in background of each cohort, such as genetic variance of the BDNF gene and other genes, may have contributed to such discrepancies.

Finally, there are some limitations to the current study. First, because of the limited number of subjects, we could not evaluate the dose effect of each gene or a possible interaction between the two genes. The later would be a possible confounding factor in this study. For example, we found a significant interaction effect of genotype-disease progression on the PCC and cingulate morphology but not on the medial temporal morphology. We treated the ApoE genotype as a nuisance variable in the analysis of the effect of the Val66Met-BDNF polymorphism. Because of the effect of the ApoE polymorphism on the medial temporal morphology, such a procedure could exclude the effects of the BDNF polymorphism on the medial temporal structures. This could explain why the genotype–disease progression interaction of the BDNF gene was not found in the hippocampus. It is entirely possible that one gene modulates the effects of the other. There is another possibility of a polygenic effect on morphological changes and preclinical AD. There is a methodological limitation that could account for the lateralization of findings such as a genotype effect on right medial temporal lobe structures but not the same structures on the left medial temporal lobe structures. However, we found the genotype effect in the other side, when we loosened the statistical threshold. These data suggest that lateralization could be false-negative results because of the small sample size.

A further study with larger sample size could clarify the effect of genotype–genotype interactions on disease progression in preclinical AD. Second, this study is a retrospective study; therefore, all patients had not been scanned and rescanned at a similar time period in a prospective fashion. However, the time interval from the initial MR study to each follow-up study was the same for each genotype group and it would be unlikely that the observed results were affected by this.

In conclusion, our preliminary data suggest that polymorphisms of the ApoE and BDNF genes may affect disease progression in preclinical AD. In addition to carrying the ApoE ɛ4 allele, carrying the Met-BDNF polymorphism could be a risk factor for rapid disease progression in individuals with preclinical AD.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments
  9. Supporting Information
  • Allen, S.J., Wilcock, G.K. & Dawbarn, D. (1999) Profound and selective loss of catalytic TrkB immunoreactivity in Alzheimer’s disease. Biochem Biophys Res Commun 264, 648651.
  • Asada, T., Yamagata, Z., Kinoshita, T., Kinoshita, A., Kariya, T., Asaka, A. & Kakuma, T. (1996) Prevalence of dementia and distribution of ApoE alleles in Japanese centenarians: an almost-complete survey in Yamanashi Prefecture, Japan. J Am Geriatr Soc 44, 151155.
  • Ashburner, J., Andersson, J.L. & Friston, K.J. (1999) High-dimensional image registration using symmetric priors. Neuroimage 9, 619628.
  • Ashford, J.W. & Mortimer, J.A. (2002) Non-familial Alzheimer’s disease is mainly due to genetic factors. J Alzheimers Dis 4, 169177.
  • Barnes, J., Scahill, R.I., Schott, J.M., Frost, C., Rossor, M.N. & Fox, N.C. (2005a) Does Alzheimer’s disease affect hippocampal asymmetry? Evidence from a cross-sectional and longitudinal volumetric MRI study. Dement Geriatr Cogn Disord 19, 338344.
  • Barnes, L.L., Wilson, R.S., Bienias, J.L., Schneider, J.A., Evans, D.A. & Bennett, D.A. (2005b) Sex differences in the clinical manifestations of Alzheimer disease pathology. Arch Gen Psychiatry 62, 685691.
  • Berg, L. (1988) Clinical dementia rating (CDR). Psychopharmacol Bull 24, 637639.
  • Bian, J.T., Zhang, J.W., Zhang, Z.X. & Zhao, H.L. (2005) Association analysis of brain-derived neurotrophic factor (BDNF) gene 196 A/G polymorphism with Alzheimer’s disease (AD) in mainland Chinese. Neurosci Lett 387, 1116.
  • Bodner, S.M., Berrettini, W., Van Deerlin, V., Bennett, D.A., Wilson, R.S., Trojanowski, J.Q. & Arnold, S.E. (2005) Genetic variation in the brain derived neurotrophic factor gene in Alzheimer’s disease. Am J Med Genet B Neuropsychiatr Genet 134, 15.
  • Bondi, M.W., Salmon, D.P., Galasko, D., Thomas, R.G. & Thal, L.J. (1999) Neuropsychological function and apolipoprotein E genotype in the preclinical detection of Alzheimer’s disease. Psychol Aging 14, 295303.
  • Braak, E., Braak, H. & Mandelkow, E.M. (1994) A sequence of cytoskeleton changes related to the formation of neurofibrillary tangles and neuropil threads. Acta Neuropathol 87, 554567.
  • Chen, Z.Y., Patel, P.D., Sant, G., Meng, C.X., Teng, K.K., Hempstead, B.L. & Lee, F.S. (2004) Variant brain-derived neurotrophic factor (BDNF) (Met66) alters the intracellular trafficking and activity-dependent secretion of wild-type BDNF in neurosecretory cells and cortical neurons. J Neurosci 24, 44014411.
  • Corder, E.H., Saunders, A.M., Strittmatter, W.J., Schmechel, D.E., Gaskell, P.C., Small, G.W., Roses, A.D., Haines, J.L. & Pericak-Vance, M.A. (1993) Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 261, 921923.
  • Craft, S., Teri, L., Edland, S.D., Kukull, W.A., Schellenberg, G., McCormick, W.C., Bowen, J.D. & Larson, E.B. (1998) Accelerated decline in apolipoprotein E-epsilon4 homozygotes with Alzheimer’s disease. Neurology 51, 149153.
  • Delacourte, A., David, J.P., Sergeant, N., Buee, L., Wattez, A., Vermersch, P., Ghozali, F., Fallet-Bianco, C., Pasquier, F., Lebert, F., Petit, H. & Di Menza, C. (1999) The biochemical pathway of neurofibrillary degeneration in aging and Alzheimer’s disease. Neurology 52, 11581165.
  • Desai, P., Nebes, R., DeKosky, S.T. & Kamboh, M.I. (2005) Investigation of the effect of brain-derived neurotrophic factor (BDNF) polymorphisms on the risk of late-onset Alzheimer’s disease (AD) and quantitative measures of AD progression. Neurosci Lett 379, 229234.
  • Du, A.T., Schuff, N., Chao, L.L., Kornak, J., Jagust, W.J., Kramer, J.H., Reed, B.R., Miller, B.L., Norman, D., Chui, H.C. & Weiner, M.W. (2006) Age effects on atrophy rates of entorhinal cortex and hippocampus. Neurobiol Aging 27, 733740.
  • Egan, M.F., Kojima, M., Callicott, J.H., Goldberg, T.E., Kolachana, B.S., Bertolino, A., Zaitsev, E., Gold, B., Goldman, D., Dean, M., Lu, B. & Weinberger, D.R. (2003) The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell 112, 257269.
  • Farlow, M.R., He, Y., Tekin, S., Xu, J., Lane, R. & Charles, H.C. (2004) Impact of APOE in mild cognitive impairment. Neurology 63, 18981901.
  • Fleisher, A., Grundman, M., Jack, C.R. Jr, Petersen, R.C., Taylor, C., Kim, H.T., Schiller, D.H., Bagwell, V., Sencakova, D., Weiner, M.F., DeCarli, C., DeKosky, S.T., Van Dyck, C.H. & Thal, L.J. (2005) Sex, apolipoprotein E epsilon 4 status, and hippocampal volume in mild cognitive impairment. Arch Neurol 62, 953957.
  • Folstein, M.F., Folstein, S.E. & McHugh, P.R. (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12, 189198.
  • Fox, N.C., Crum, W.R., Scahill, R.I., Stevens, J.M., Janssen, J.C. & Rossor, M.N. (2001) Imaging of onset and progression of Alzheimer’s disease with voxel-compression mapping of serial magnetic resonance images. Lancet 358, 201205.
  • Frisoni, G.B., Govoni, S., Geroldi, C., Bianchetti, A., Calabresi, L., Franceschini, G. & Trabucchi, M. (1995) Gene dose of the epsilon 4 allele of apolipoprotein E and disease progression in sporadic late-onset Alzheimer’s disease. Ann Neurol 37, 596604.
  • Geroldi, C., Pihlajamaki, M., Laakso, M.P., DeCarli, C., Beltramello, A., Bianchetti, A., Soininen, H., Trabucchi, M. & Frisoni, G.B. (1999) APOE-epsilon4 is associated with less frontal and more medial temporal lobe atrophy in AD. Neurology 53, 18251832.
  • Hamilton, M. (1980) Rating depressive patients. J Clin Psychiatry 41, 2124.
  • Hariri, A.R., Goldberg, T.E., Mattay, V.S., Kolachana, B.S., Callicott, J.H., Egan, M.F. & Weinberger, D.R. (2003) Brain-derived neurotrophic factor val66met polymorphism affects human memory-related hippocampal activity and predicts memory performance. J Neurosci 23, 66906694.
  • Den Heijer, T., Oudkerk, M., Launer, L.J., Van Duijn, C.M., Hofman, A. & Breteler, M.M. (2002) Hippocampal, amygdalar, and global brain atrophy in different apolipoprotein E genotypes. Neurology 59, 746748.
  • Larsson, E., Nanobashvili, A., Kokaia, Z. & Lindvall, O. (1999) Evidence for neuroprotective effects of endogenous brain-derived neurotrophic factor after global forebrain ischemia in rats. J Cereb Blood Flow Metab 19, 12201228.
  • Lehtovirta, M., Laakso, M.P., Soininen, H., Helisalmi, S., Mannermaa, A., Helkala, E.L., Partanen, K., Ryynanen, M., Vainio, P., Hartikainen, P. & Riekkinen, P.J. Sr. (1995) Volumes of hippocampus, amygdala and frontal lobe in Alzheimer patients with different apolipoprotein E genotypes. Neuroscience 67, 6572.
  • Lemaitre, H., Crivello, F., Dufouil, C., Grassiot, B., Tzourio, C., Alperovitch, A. & Mazoyer, B. (2005) No epsilon4 gene dose effect on hippocampal atrophy in a large MRI database of healthy elderly subjects. Neuroimage 24, 12051213.
  • Masui, T., Hashimoto, R., Kusumi, I., Suzuki, K., Tanaka, T., Nakagawa, S., Suzuki, T., Iwata, N., Ozaki, N., Kato, T., Kunugi, H. & Koyama, T. (2006) Lithium response and Val66Met polymorphism of the brain-derived neurotrophic factor gene in Japanese patients with bipolar disorder. Psychiatr Genet 16, 4950.
  • Matsushita, S., Arai, H., Matsui, T., Yuzuriha, T., Urakami, K., Masaki, T. & Higuchi, S. (2005) Brain-derived neurotrophic factor gene polymorphisms and Alzheimer’s disease. J Neural Transm 112, 703711.
  • Mattson, M.P., Maudsley, S. & Martin, B. (2004) BDNF and 5-HT: a dynamic duo in age-related neuronal plasticity and neurodegenerative disorders. Trends Neurosci 27, 589594.
  • McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D. & Stadlan, E.M. (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s disease. Neurology 34, 939944.
  • Moffat, S.D., Szekely, C.A., Zonderman, A.B., Kabani, N.J. & Resnick, S.M. (2000) Longitudinal change in hippocampal volume as a function of apolipoprotein E genotype. Neurology 55, 134136.
  • Nemoto, K., Ohnishi, T., Mori, T., Moriguchi, Y., Hashimoto, R., Asada, T. & Kunugi, H. (2006) The Val66Met polymorphism of the brain-derived neurotrophic factor gene affects age-related brain morphology. Neurosci Lett 397, 2529.
  • Ohnishi, T., Hashimoto, R., Mori, T., Nemoto, K., Moriguchi, Y., Iida, H., Noguchi, H., Nakabayashi, T., Hori, H., Ohmori, M., Tsukue, R., Anami, K., Hirabayashi, N., Harada, S., Arima, K., Saitoh, O. & Kunugi, H. (2006) The association between the Val158Met polymorphism of the catechol-O-methyl transferase gene and morphological abnormalities of the brain in chronic schizophrenia. Brain 129, 399410.
  • Okada, T., Hashimoto, R., Numakawa, T., Iijima, Y., Kosuga, A., Tatsumi, M., Kamijima, K., Kato, T. & Kunugi, H. (2006) A complex polymorphic region in the brain-derived neurotrophic factor (BDNF) gene confers susceptibility to bipolar disorder and affects transcriptional activity. Mol Psychiatry 11, 695703.
  • Pezawas, L., Verchinski, B.A., Mattay, V.S., Callicott, J.H., Kolachana, B.S., Straub, R.E., Egan, M.F., Meyer-Lindenberg, A. & Weinberger, D.R. (2004) The brain-derived neurotrophic factor val66met polymorphism and variation in human cortical morphology. J Neurosci 24, 1009910102.
  • Phillips, H.S., Hains, J.M., Armanini, M., Laramee, G.R., Johnson, S.A. & Winslow, J.W. (1991) BDNF mRNA is decreased in the hippocampus of individuals with Alzheimer’s disease. Neuron 7, 695702.
  • Puglielli, L., Tanzi, R.E. & Kovacs, D.M. (2003) Alzheimer’s disease: the cholesterol connection. Nat Neurosci 6, 345351.
  • Reiman, E.M., Chen, K., Alexander, G.E., Caselli, R.J., Bandy, D., Osborne, D., Saunders, A.M. & Hardy, J. (2005) Correlations between apolipoprotein E epsilon4 gene dose and brain-imaging measurements of regional hypometabolism. Proc Natl Acad Sci U S A 102, 82998302.
  • Scahill, R.I., Schott, J.M., Stevens, J.M., Rossor, M.N. & Fox, N.C. (2002) Mapping the evolution of regional atrophy in Alzheimer’s disease: unbiased analysis of fluid-registered serial MRI. Proc Natl Acad Sci U S A 99, 47034707.
  • Scahill, R.I., Frost, C., Jenkins, R., Whitwell, J.L., Rossor, M.N. & Fox, N.C. (2003) A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. Arch Neurol 60, 989994.
  • Stern, Y., Brandt, J., Albert, M., Jacobs, D.M., Liu, X., Bell, K., Marder, K., Sano, M., Albert, S., Del-Castillo Castenada, C., Bylsma, F., Tycko, B. & Mayeux, R. (1997) The absence of an apolipoprotein epsilon4 allele is associated with a more aggressive form of Alzheimer’s disease. Ann Neurol 41, 615620.
  • Szeszko, P.R., Lipsky, R., Mentschel, C., Robinson, D., Gunduz-Bruce, H., Sevy, S., Ashtari, M., Napolitano, B., Bilder, R.M., Kane, J.M., Goldman, D. & Malhotra, A.K. (2005) Brain-derived neurotrophic factor val66met polymorphism and volume of the hippocampal formation. Mol Psychiatry 10, 631636.
  • Tierney, M.C., Szalai, J.P., Snow, W.G., Fisher, R.H., Nores, A., Nadon, G., Dunn, E. & St George-Hyslop, P.H. (1996) Prediction of probable Alzheimer’s disease in memory-impaired patients: a prospective longitudinal study. Neurology 46, 661665.
  • Tsai, S.J., Hong, C.J., Liu, H.C., Liu, T.Y., Hsu, L.E. & Lin, C.H. (2004) Association analysis of brain-derived neurotrophic factor Val66Met polymorphisms with Alzheimer’s disease and age of onset. Neuropsychobiology 49, 1012.
  • Ventriglia, M., Bocchio Chiavetto, L., Benussi, L., Binetti, G., Zanetti, O., Riva, M.A. & Gennarelli, M. (2002) Association between the BDNF 196 A/G polymorphism and sporadic Alzheimer’s disease. Mol Psychiatry 7, 136137.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments
  9. Supporting Information

This work was supported in part by Grants-in-Aid from the Japanese Ministry of Health, Labor and Welfare (H17-kokoro-007 and H16-kokoro-002) and the Program for Promotion of Fundamental Studies in Health Science of the Organization for Pharmaceuticals and Medical Devices Agency to T.O.

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments
  9. Supporting Information

Additional supporting information may be found in the online version of this article.

Appendix S1. Methods

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