Literature DB >> 25288138

APOE and BDNF polymorphisms moderate amyloid β-related cognitive decline in preclinical Alzheimer's disease.

Y Y Lim1,2,3, V L Villemagne1,4,5, S M Laws6,7,8, R H Pietrzak9, P J Snyder2,3, D Ames10,11, K A Ellis1,11, K Harrington1, A Rembach1, R N Martins6, C C Rowe4,5, C L Masters1, P Maruff1,12.   

Abstract

Accumulation of β-amyloid (Aβ) in the brain is associated with memory decline in healthy individuals as a prelude to Alzheimer's disease (AD). Genetic factors may moderate this decline. We examined the role of apolipoprotein E (ɛ4 carrier[ɛ4(+)], ɛ4 non-carrier[ɛ4(-)]) and brain-derived neurotrophic factor (BDNF(Val/Val), BDNF(Met)) in the extent to which they moderate Aβ-related memory decline. Healthy adults (n=333, Mage=70 years) enrolled in the Australian Imaging, Biomarkers and Lifestyle study underwent Aβ neuroimaging. Neuropsychological assessments were conducted at baseline, 18-, 36- and 54-month follow-ups. Aβ positron emission tomography neuroimaging was used to classify participants as Aβ(-) or Aβ(+). Relative to Aβ(-)ɛ4(-), Aβ(+)ɛ4(+) individuals showed significantly faster rates of cognitive decline over 54 months across all domains (d=0.40-1.22), while Aβ(+)ɛ4(-) individuals showed significantly faster decline only on verbal episodic memory (EM). There were no differences in rates of cognitive change between Aβ(-)ɛ4(-) and Aβ(-)ɛ4(+) groups. Among Aβ(+) individuals, ɛ4(+)/BDNF(Met) participants showed a significantly faster rate of decline on verbal and visual EM, and language over 54 months compared with ɛ4(-)/BDNF(Val/Val) participants (d=0.90-1.02). At least two genetic loci affect the rate of Aβ-related cognitive decline. Aβ(+)ɛ4(+)/BDNF(Met) individuals can expect to show clinically significant memory impairment after 3 years, whereas Aβ(+)ɛ4(+)/BDNF(Val/Val) individuals can expect a similar degree of impairment after 10 years. Little decline over 54 months was observed in the Aβ(-) and Aβ(+) ɛ4(-) groups, irrespective of BDNF status. These data raise important prognostic issues in managing preclinical AD, and should be considered in designing secondary preventative clinical trials.

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Year:  2014        PMID: 25288138      PMCID: PMC4759101          DOI: 10.1038/mp.2014.123

Source DB:  PubMed          Journal:  Mol Psychiatry        ISSN: 1359-4184            Impact factor:   15.992


Introduction

In healthy individuals, high β-amyloid (Aβ) levels suggest that preclinical Alzheimer's disease (AD) has begun.[1,2] However, variability in the extent of cognitive and clinical impairment in Aβ+ healthy individuals suggests other factors influence Aβ-related cognitive decline.[3,4] The major genetic risk factor for sporadic AD is the apolipoprotein E (APOE) ɛ4 allele:[5,6] apoE may be involved in AD pathogenesis directly, through increasing Aβ accumulation, reducing clearance of Aβ or modifying Aβ-synaptic toxicity,[5, 6, 7] or indirectly, through reducing synaptic plasticity, increasing neuroinflammation or affecting concurrence of cerebrovascular events.[3,8] In accord with this, a recent study of 490 healthy individuals aggregated from the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study, the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Harvard Aging Brain Study showed that carriage of the ɛ4 allele increased substantially the rate of memory decline in healthy individuals with high Aβ levels (Aβ+ɛ4+) over a median follow-up period of 1.5 years.[9] This analysis also showed that individuals who were ɛ4 carriers but with low Aβ (Aβ−ɛ4+) showed no memory decline compared with ɛ4 non-carriers with low Aβ (Aβ−ɛ4−), suggesting that, by itself, the APOE ɛ4 allele is not associated with memory decline. However, as the APOE ɛ4 allele is associated with increased cognitive decline in healthy individuals,[10] and earlier diagnosis of AD,[11] the effect of APOE ɛ4 on Aβ-related cognitive decline warrants further investigation over time intervals greater than 18 months. Another strong genetic candidate for moderating Aβ-related memory decline is the brain-derived neurotrophic factor (BDNF)Val66Met polymorphism. BDNF is important in the biological basis of learning and memory in animals and humans.[4,12, 13, 14] Prospective studies show that in healthy and mild cognitive impairment groups from both the AIBL and ADNI cohorts, BDNFMet carriage is associated with faster Aβ-related memory decline and hippocampal atrophy over 3 years but is unrelated to Aβ accumulation,[15, 16, 17] suggesting that BDNFVal66Met moderates the effects of Aβ on synaptic integrity in preclinical AD.[17] To our knowledge, no study has examined the interaction between BDNF, APOE and Aβ-related memory decline. The overarching aim of this study was to explore potential interactions between Aβ, APOE and BDNF on cognitive decline in 333 healthy individuals who had undergone Aβ neuroimaging, genetic testing and 54-month clinical follow-up as part of the AIBL study. First, we examined whether episodic memory (EM) and other aspects of cognition would remain stable over 54 months in healthy Aβ−ɛ4+ participants, where any cognitive decline would provide an estimate of Aβ-independent effects of ɛ4. We then examined whether the Aβ+ɛ4+ group, compared with Aβ−ɛ4− healthy individuals, would show faster rates of decline in EM. Finally, we explored whether BDNFMet moderated any relationship between Aβ, ɛ4 and cognition.

Materials and methods

Participants

Participants were recruited from the AIBL healthy adult group, the recruitment of which has been described previously.[18,19] Briefly, exclusion criteria included the following: schizophrenia, depression (15-item Geriatric Depression Score ⩾6), Parkinson's disease, symptomatic stroke, uncontrolled diabetes and alcohol use exceeding two standard drinks per day for women or four per day for men. Participants underwent medical, psychiatric and neuropsychological assessments at baseline, 18-, 36- and 54-month follow-up.[19] At each assessment, a clinical review panel considered all available medical, psychiatric and neuropsychological information to classify clinical status.[19] Clinical classification was blinded to neuroimaging results. Group demographic and clinical characteristics are provided in Table 1, with the number of participants whose diagnostic classification changed or who withdrew from the study shown in Figure 1. The study was approved by and complied with the regulations of three institutional research and ethics committees.[19] All participants provided written informed consent.
Table 1

Demographic and clinical characteristics of the full sample and of study groups

 Full sample (n=333) ɛ4 (n=188) ɛ4+ (n=61)+ ɛ4 (n=36)+ ɛ4+ (n=48)P-value
 N (%) or mean (s.d.)N (%) or mean (s.d.)N (%) or mean (s.d.)N (%) or mean (s.d.)N (%) or mean (s.d.) 
N (%) Female173 (52.0%)95 (50.5%)33 (54.1%)19 (52.8%)26 (54.2%)0.947
Age (years)69.95 (6.80)69.22 (6.28)66.98 (5.20)76.06 (7.27)72.04 (7.03)0.000
Premorbid IQ108.59 (7.07)108.51 (6.84)106.98 (7.75)111.47 (6.55)108.75 (6.93)0.000
HADS depression subscale2.58 (2.24)2.58 (2.26)2.73 (2.11)1.83 (1.42)2.92 (2.70)0.151
HADS anxiety subscale4.20 (2.78)4.18 (2.72)4.12 (2.82)3.26 (1.99)5.10 (3.21)0.026
CDR sum of boxes0.04 (0.16)0.04 (0.18)0.04 (0.14)0.06 (0.16)0.02 (0.10)0.777
MMSE28.87 (1.19)28.94 (1.18)28.84 (1.23)28.69 (1.26)28.75 (1.14)0.578
N (%) high Aβ+44 (13.2%)n.a.n.a.18 (50.0%)26 (54.2%)0.705
N (%) progressed at 54 months23/296 (7.8%)11/178 (6.2%)2/57 (3.5%)3/26 (11.5%)7/35 (20.0%)0.020

Abbreviations: CDR, Clinical Dementia Rating scale; HADS, Hospital Anxiety and Depression Scale; MMSE, Mini Mental State Examination; PET, positron emission tomography; SUVr, standardized uptake value ratio.

Bolded values are significant at the P<0.05 or the P<0.001 level; of the 333 healthy older adults who underwent PET neuroimaging, 183 were scanned using 11C Pittsburgh Compound B, 76 using 18F florbetapir and 74 using 18F flutemetamol; high Aβ+ was classified when SUVr PiB >1.9, flutemetamol >0.82 and florbetapir >1.29.

Figure 1

Clinical classification and disease progression of β-amyloid negative (Aβ−) and β-amyloid positive (Aβ+) participants over 54 months.

Measures

Neuroimaging

Aβ imaging with positron emission tomography (PET) was conducted using either 11C-Pittsburgh Compound B (PiB), 18F-florbetapir or 18F-flutemetamol. PET methodology has been described in detail previously.[18,20,21] A 30-min acquisition was started 40 min post injection of PiB and a 20-min acquisition was performed 50 min post injection of florbetapir and 90 min post injection of flutemetamol. For PiB-PET, standardized uptake value (SUV) data were summed and normalized to the cerebellar cortex SUV, resulting in a region-to-cerebellar ratio termed SUV ratio (SUVr). The whole cerebellum was the reference region for florbetapir,[21] while for flutemetamol the reference region was the pons. Consistent with these studies, SUVr was classified dichotomously as either negative (Aβ−) or positive (Aβ+). PiB studies were classified Aβ+ when SUVr⩾1.5,[18] florbetapir, when SUVr⩾1.11 ref. 21 and for flutemetamol when SUVr⩾0.62.[20] Aβ+ levels were further classified as being ‘high' Aβ+ (SUVr PiB>1.9; flutemetamol>0.82; florbetapir>1.29) or ‘low' Aβ+ (SUVr PiB=1.5–1.9; flutemetamol=0.62–0.82; florbetapir=1.11–1.29).[22,23]

Genotyping

A blood sample was taken from each participant for genotyping. The BDNFVal66Met polymorphism (rs6265) was included in a custom Illumina GoldenGate assay, which included 1536 single-nucleotide polyorphisms, and was performed by the Beijing Genomics Institute. Of the 333 healthy individuals who had undergone PET neuroimaging for Aβ, BDNFVal66Met data was available for 314 healthy individuals, of which 191 were BDNFVal/Val homozygotes and 123 were BDNFMet carriers (111 BDNFMet/Val heterozygotes and 12 BDNFMet/Met homozygotes).

Cognitive assessments

Composite cognitive scores were computed by standardizing outcome measures for each neuropsychological test against the baseline mean and s.d. for the Aβ− group. Standardized scores were averaged to form composite scores for verbal EM (Logical Memory delayed recall, California Verbal Learning Test, Second Edition [CVLT-II] long delay, CVLT-II d'); visual EM (Rey Complex Figure Test [RCFT ] 3-min delayed recall, RCFT 30-min delayed recall, CogState One-Card Learning); executive function (CogState One-Back, Letter Fluency, Category Fluency Switching [Fruit/Furniture]); language (Category Fluency [Animals/Boys' Names], Boston Naming Test); and attention (Digit Symbol, CogState Detection, CogState Identification). The development and validation of each cognitive composite score has been described previously.[23,24]

Data analysis

For each composite cognitive score, three planned comparisons were constructed using repeated-measures linear mixed-effects model with maximum likelihood estimation and an unstructured covariance matrix. Linear mixed modeling was employed because of its ability to model both fixed and random effects, which accounts for multiple sources of variability in longitudinal studies. In addition, both empirical and theoretical models of AD show that once the threshold for Aβ positivity is reached, there is a linear trend in cognitive decline, neurodegeneration and amyloid accumulation until a clinical diagnosis of AD is reached.[1,2,25] In these analyses, Aβ status (Aβ−, Aβ+), APOE status (ɛ4+, ɛ4−), time, APOE × Aβ interaction, APOE × time interaction, Aβ × time interaction and APOE × Aβ × time interaction were entered as fixed factors; participant as a random factor; age, premorbid intelligence and anxiety levels as covariates; and cognitive composite score as the dependent variable. Within the model, the magnitude of difference from the Aβ−ɛ4− group was expressed using Cohen's d.[26] Where the planned comparisons indicated differences between group trajectories, group means (95% confidence intervals (95% CIs)) for each assessment were estimated from the model and differences in these between Aβ+ɛ4− and Aβ+ɛ4+ groups at each assessment were determined by the extent of overlap between the 95% CIs associated with those means. To examine the effect of BDNFVal66Met, separate linear mixed-effects models were conducted for each composite score in Aβ+ individuals. In these analyses, APOE status, BDNF status (BDNFVal/Val, BDNFMet), time, APOE × BDNF interaction, APOE × time interaction, BDNF × time interaction and APOE × BDNF × time interaction were entered as fixed factors; participant as a random factor; age, premorbid intelligence and anxiety levels as covariates and composite cognitive test score as the dependent variable. Within this model, rate of change over 18-month intervals in each group (ɛ4− BDNFMet; ɛ4+BDNFVal/Val; ɛ4+ BDNFMet) was compared with that for the ɛ4− BDNFVal/Val group. For each comparison, the magnitude of difference from the ɛ4− BDNFVal/Val group was expressed using Cohen's d.[26] Group means (95% CIs) at each assessment were estimated from the linear mixed-effects model, and differences in performance between ɛ4+ BDNFVal/Val and ɛ4+ BDNFMet groups at each assessment was determined by the extent of overlap of 95% CIs associated with those means. Bonferonni correction was applied to all pairwise comparisons. To estimate the clinical meaning for the effect of each genetic risk factor on decline in cognition, a group mean of 1.5 s.d. below the Aβ−ɛ4− group was defined as clinically important cognitive impairment.[27] The time to reach this criterion was estimated for each group based on linear mixed-effects model-derived linear functions.

Results

Demographic and clinical characteristics

At baseline, statistically significant differences between groups were observed for age, premorbid intelligence and anxiety symptoms (Table 1). No other demographic or clinical characteristics differed between groups. There was no difference in the proportion of individuals in the ɛ4+ or ɛ4− groups who were classified as high Aβ+ (Table 1).

Effect of Aβ levels and ɛ4 on cognitive change

Group mean slopes for each Aβ-ɛ4 group for each composite cognitive score are summarized in Table 2. Relative to Aβ−ɛ4−, the Aβ+ɛ4+ group showed a significantly faster decline on all cognitive composites, with these differences moderate to large in magnitude (Table 2). Extrapolation of the rate of decline in verbal EM in the Aβ+ɛ4+ group indicated it would meet criterion for clinically significant impairment (<1.5 s.d. from controls) in ~9 years (Supplementary Table 1). Compared with Aβ−ɛ4−, the Aβ+ɛ4− group showed a faster rate of decline only for verbal EM composite (Table 2). Inspection of performance on each assessment indicated no overlap between 95% CIs for group mean verbal and visual EM composites between Aβ+ɛ4+ and Aβ+ɛ4− groups at 18-, 36- and 54-month assessments (Supplementary Table 1). Extrapolation of the rate of verbal EM decline in the Aβ+ɛ4− group indicated it would meet criterion for clinically significant impairment in 27 years (Supplementary Table 1). Group mean slopes did not differ significantly for any cognitive composite between the Aβ−ɛ4− and Aβ−ɛ4+ groups.
Table 2

Mean slopes (s.d.) per 18-month interval for each cognitive composite score and magnitudes of difference (Cohen's d) in slopes

 Mean slope (s.d.)
Cohen's d (95% CIs) (vs Aβ ɛ4)
  ɛ4 (n=188) ɛ4+ (n=61)+ ɛ4 (n=36)+ ɛ4+(n=48) ɛ4++ ɛ4+ ɛ4+
Verbal EM0.021 (0.239)0.034 (0.206)−0.075 (0.197)−0.263 (0.206)−0.06 (−0.34, 0.23)0.41 (0.05, 0.77)1.22 (0.88, 1.55)
Visual EM0.026 (0.276)0.030 (0.238)−0.001 (0.229)−0.198 (0.237)−0.01 (−0.30, 0.27)0.10 (−0.26, 0.46)0.83 (0.51, 1..16)
Executive Function−0.011 (0.220)−0.003 (0.190)−0.051 (0.180)−0.103 (0.188)−0.04 (−0.33, 0.25)0.19 (−0.17, 0.54)0.43 (0.11, 0.75)
Language−0.033 (0.252)−0.035 (0.217)−0.086 (0.206)−0.176 (0.216)0.01 (−0.28, 0.30)0.22 (−0.14, 0.57)0.58 (0.26, 0.90)
Attention−0.101 (0.201)−0.125 (0.174)−0.100 (0.164)−0.180 (0.177)0.12 (−0.17, 0.41)−0.01 (−0.36, 0.35)0.40 (0.08, 0.72)

Abbreviations: CI, confidence interval; EM, episodic memory.

Bolded values are significant at the P<0.05 or P<0.001 level; values are adjusted for age, premorbid intelligence and anxiety.

Effect of BDNFVal66Met on the relationship between Aβ, ɛ4 and cognitive change

In Aβ− participants, mean slopes between ɛ4− BDNFVal/Val and the three subgroups (ɛ4− BDNFMet, ɛ4+ BDNFVal/Val and ɛ4+BDNFMet) did not differ for any other composite (data not shown). In Aβ+participants, relative to the ɛ4− BDNFVal/Val group, the ɛ4+ BDNFMet group showed a faster decline on the verbal and visual EM and language composites, and differences between slopes were moderate to large in magnitude (Table 3). Inspection of group means for individual assessments indicated no overlap between 95% CIs for the mean verbal and visual EM and language composite between ɛ4+ BDNFMet and ɛ4+BDNFVal/Val groups at the 36- and 54-month assessments (Supplementary Table 1). The rate of verbal EM decline in the ɛ4+ BDNFMet group indicated it met criterion for clinically significant impairment within 3 years from enrolment (Figure 2b). In contrast, extrapolation of the rate of verbal EM decline suggested that ɛ4+ BDNFVal/Val group would meet criterion for clinically significant impairment within 10 years. Groups did not differ in the rate of decline on the executive function and attention composites (Table 3). Finally, relative to the ɛ4− BDNFVal/Val group, the ɛ4− BDNFMet group did not show a significantly faster decline on any cognitive composite.
Table 3

Mean slopes (s.d.) per 18-month interval for each cognitive composite score and magnitudes of difference (Cohen's d) in slopes in Aβ+ healthy individuals

 Mean slope (s.d.)Cohen's d (95% CIs) (vs ɛ4 BDNFVal/Val)
 ɛ4 BDNFVal/Val (n=19)ɛ4 BDNFMet (n=11)ɛ4+ BDNFVal/Val (n=27)ɛ4+ BDNFMet (n=14)ɛ4 BDNFMetɛ4+ BDNFVal/Valɛ4+ BDNFMet
Verbal EM−0.058 (0.341)−0.046 (0.326)−0.223 (0.451)−0.400 (0.423)−0.04 (−0.76, 0.69)0.40 (−0.16, 0.96)0.91 (0.19, 1.59)
Visual EM0.039 (0.325)−0.091 (0.315)−0.146 (0.428)−0.328 (0.407)0.40 (−0.32, 1.12)0.48 (−0.09, 1.03)1.02 (0.29, 1.71)
Executive Function−0.017 (0.262)−0.087 (0.249)−0.018 (0.345)−0.181 (0.323)0.27 (−0.46, 0.99)0.00 (−0.55, 0.56)0.57 (−0.12, 1.24)
Language−0.063 (0.282)−0.141 (0.269)−0.130 (0.372)−0.341 (0.349)0.28 (−0.45, 1.00)0.20 (−0.36, 0.75)0.90 (0.18, 1.58)
Attention−0.028 (0.218)−0.143 (0.200)−0.207 (0.279)−0.134 (0.263)0.54 (−0.21, 1.26)0.70 (0.12, 1.26)0.45 (−0.24, 1.12)

Abbreviations: CI, confidence interval; EM, episodic memory.

Bolded values are significant at the P<0.05 or P<0.001 level; values are adjusted for age, premorbid intelligence and anxiety.

Figure 2

(a) Trajectories of change over 54 months on the Verbal Episodic Memory composite for Aβ−ɛ4−, Aβ−ɛ4+, Aβ+ɛ4− and Aβ+ɛ4+groups, with age and premorbid IQ as covariates (error bars represent 95% confidence intervals). Dotted line indicates 1.5 s.d. decline for clinically significant memory impairment. (b) Trajectories of change over 54 months in Aβ+ healthy individuals on the Verbal Episodic Memory composite for ɛ4−/BDNFVal/Val, ɛ4−/BDNFMet, ɛ4+/BDNFVal/Val and ɛ4+/BDNFMet groups, with age and premorbid IQ as covariates (error bars represent 95% confidence intervals). Dotted line indicates 1.5 s.d. decline for clinically significant memory impairment.

Discussion

EM and all other aspects of cognition remained stable over 54 months in Aβ− individuals, irrespective of ɛ4 status, which replicates and extends previous observations from AIBL[23,28] and other cohorts.[29, 30, 31] The absence of any ɛ4-related cognitive decline in the current Aβ− individuals is also consistent with findings of a recent study[9] and supports the hypothesis that there are no Aβ-independent effects of APOE on cognitive decline in healthy individuals, even when studied over more than 4 years. Compared with the Aβ−ɛ4− group, Aβ+ individuals showed faster decline in EM, and this decline was increased by ɛ4+ (Figure 2a). Recently, the exacerbation of Aβ-related memory decline by ɛ4 carriage in healthy individuals was shown over 1.5 years.[9] The current findings support and extend this report by demonstrating that APOE ɛ4 carriage does exacerbate Aβ-related cognitive decline in healthy individuals, and persists over more than 4 years. Neurologically, APOE can affect both intrinsic (for example, synaptic plasticity and neuroinflammation) and extrinsic (for example, cerebrovascular disease) factors.[3,8,32] As all participants in the AIBL study have well-controlled risk factors for cardiovascular disease,[19] the risk of concomitant cerebrovascular events over the period of observation was reduced. Further, as there is increasing experimental evidence that APOE isoforms have a direct effect on Aβ deposition, clearance and Aβ-mediated synaptotoxicity,[5, 6, 7] a likely explanation for the cognitive decline seen in the Aβ+ɛ4+ group is that APOE moderates the direct effects of Aβ accumulation. These results differ from previous reports, where we and others found no effect of ɛ4 carriage on Aβ-related cognitive decline.[23,28,30,33] The most likely reason for this lack of effect was that the smaller sample sizes used in these studies resulted in reduced power to detect any effects of ɛ4 on Aβ-related cognitive decline.[23,28,30,33] We reported previously that BDNFVal66Met moderated Aβ-related changes in cognition and neurodegeneration in both healthy older and mild cognitive impairment groups in AIBL and ADNI, although it did not moderate the rate of Aβ deposition.[15, 16, 17] Consistent with evidence indicating that BDNF is necessary for synaptic plasticity in the hippocampus,[4] we proposed that BDNFVal66Met affects the clinical manifestation of early AD by influencing the ability of the brain to tolerate Aβ toxicity.[16,17] In the current study, we explored whether BDNFVal66Met moderated any relationship between Aβ, ɛ4 and cognition. The Aβ+ɛ4+BDNFMet group showed faster decline in EM and language compared with both the ɛ4− BDNFVal/Val and ɛ4− BDNFMet groups (Figure 2b). The ɛ4+ BDNFVal/Val group showed a moderate rate of cognitive decline. Consistent with our previous observations,[15, 16, 17] BDNFVal66Met, such as APOE, did not moderate any cognitive variable in Aβ− individuals. These results raise some important prognostic issues in managing preclinical AD and the implications for communicating these group data to individuals. The data demonstrates clearly that the rate of cognitive decline in preclinical AD is moderated by the combination of at least two genetic loci: ɛ4+ and BDNFMet. Aβ+ɛ4+ individuals showed significantly greater EM decline than Aβ+ɛ4− individuals and this difference became evident 18 months after enrolment. BDNFMet carriage increased the rate of memory decline related to Aβ and ɛ4, with differences in memory between Aβ+ɛ4+BDNFVal/Val and Aβ+ɛ4+BDNFMet groups becoming evident 36 months after enrolment (Supplementary Table 1). Another way of expressing these observations is to consider the length of time between establishing an individual's Aβ, ɛ4 and BDNF status and a criterion for clinically significant cognitive impairment (performance <1.5 s.d. below controls,[27] dashed horizontal line in Figure 2). In the Aβ− group, irrespective of ɛ4 status, there was no evidence of decline over 4.5 years (Figure 2a). In contrast, extrapolation of the EM decline observed in the Aβ+ɛ4+group showed that clinically significant memory impairment would be met ~9 years after enrolment, while the Aβ+ɛ4− group would take ~27 years. Taking into consideration the additional effect of BDNF in Aβ+ individuals, the effect of BDNF is seen clearly in the ɛ4+ subgroup where BDNFVal/Val homozygotes would meet criteria for clinically significant impairment after ~10 years (similar rate based on ɛ4+ status alone) but the accelerated rate of memory decline in the BDNFMet group meant that they met this criteria after only 3 years (Figure 2b). Although there is some evidence in the model that even at baseline the Aβ+ɛ4+BDNFMet group performs worse than the other three groups, this difference was not statistically significant. One limitation of natural history cohorts is that the baseline performance of each individual is defined by their first visit, rather than symptom onset. As such, the data presented here can be interpreted as suggesting that the combination of Aβ+, ɛ4+ and BDNFMet does accelerate cognitive decline significantly such that even at the first assessment this decline is already evident. A diagnosis of mild cognitive impairment is typically made when objective evidence of clinically significant memory impairment is accompanied by individuals' acknowledgement of that impairment, usually corroborated by an informant.[27] General cognitive function and functional activities are also typically preserved.[27] Over the course of this study, relatively few healthy individuals were classified as meeting clinical criteria for mild cognitive impairment/AD (Figure 1). This indicates that although some individuals met criteria for clinically significant impairment, these individuals, or their caregivers, had not acknowledged any problems with cognition. We have reported previously that subjective memory impairment in the AIBL healthy cohort does not predict objectively defined cognitive impairment or Aβ levels.[34] Presumably, individuals with clinically significant memory decline observed here will begin to report subjective memory complaints in the future. An important caveat is that as three radioligands were used to measure Aβ, SUVr data could not be integrated to form a single continuous measure of Aβ burden. However, we found no relationship between the proportions of individuals who were high and low Aβ+ in the Aβ+ɛ4+and Aβ+ɛ4− groups, suggesting that the faster decline observed in Aβ+ɛ4+was not due to more advanced disease at enrolment. Second, although the large number of healthy individuals who have undergone Aβ PET neuroimaging in the AIBL cohort has allowed for this report to investigate the effects of APOE and BDNF on the clinical manifestation of high Aβ, the resultant sample sizes remain relatively small. Further, the AIBL study is also not a representative population sample. Healthy participants in the AIBL study were highly educated, were of Caucasian backgrounds and had few existing or untreated medical, neurological or psychiatric illnesses. Participants selected for neuroimaging were also enriched for APOE ɛ4 carriers. As such, these results need to be replicated in other more representative and ethnically diverse prospective cohorts of healthy individuals. Finally, APOE and BDNF are unlikely to be the only factors that moderate the clinical manifestation of Aβ rather, other co-morbidities (for example, cerebrovascular disease) and lifestyle and genetic factors will need to be considered in future work.
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Journal:  Neurology       Date:  2008-04-09       Impact factor: 9.910

9.  The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease.

Authors:  Kathryn A Ellis; Ashley I Bush; David Darby; Daniela De Fazio; Jonathan Foster; Peter Hudson; Nicola T Lautenschlager; Nat Lenzo; Ralph N Martins; Paul Maruff; Colin Masters; Andrew Milner; Kerryn Pike; Christopher Rowe; Greg Savage; Cassandra Szoeke; Kevin Taddei; Victor Villemagne; Michael Woodward; David Ames
Journal:  Int Psychogeriatr       Date:  2009-05-27       Impact factor: 3.878

10.  Enhanced risk for Alzheimer disease in persons with type 2 diabetes and APOE epsilon4: the Cardiovascular Health Study Cognition Study.

Authors:  Fumiko Irie; Annette L Fitzpatrick; Oscar L Lopez; Lewis H Kuller; Rita Peila; Anne B Newman; Lenore J Launer
Journal:  Arch Neurol       Date:  2008-01
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  47 in total

1.  Alzheimer disease: the landscape of ageing--insights from AD imaging markers.

Authors:  Victor L Villemagne; Colin L Masters
Journal:  Nat Rev Neurol       Date:  2014-11-04       Impact factor: 42.937

Review 2.  Amyloid Accumulation and Cognitive Decline in Clinically Normal Older Individuals: Implications for Aging and Early Alzheimer's Disease.

Authors:  Elizabeth C Mormino; Kathryn V Papp
Journal:  J Alzheimers Dis       Date:  2018       Impact factor: 4.472

Review 3.  Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans.

Authors:  Andrew J Saykin; Li Shen; Xiaohui Yao; Sungeun Kim; Kwangsik Nho; Shannon L Risacher; Vijay K Ramanan; Tatiana M Foroud; Kelley M Faber; Nadeem Sarwar; Leanne M Munsie; Xiaolan Hu; Holly D Soares; Steven G Potkin; Paul M Thompson; John S K Kauwe; Rima Kaddurah-Daouk; Robert C Green; Arthur W Toga; Michael W Weiner
Journal:  Alzheimers Dement       Date:  2015-07       Impact factor: 21.566

Review 4.  Sexual dimorphism in predisposition to Alzheimer's disease.

Authors:  Daniel W Fisher; David A Bennett; Hongxin Dong
Journal:  Neurobiol Aging       Date:  2018-04-17       Impact factor: 4.673

5.  BDNF Val66Met moderates memory impairment, hippocampal function and tau in preclinical autosomal dominant Alzheimer's disease.

Authors:  Yen Ying Lim; Jason Hassenstab; Carlos Cruchaga; Alison Goate; Anne M Fagan; Tammie L S Benzinger; Paul Maruff; Peter J Snyder; Colin L Masters; Ricardo Allegri; Jasmeer Chhatwal; Martin R Farlow; Neill R Graff-Radford; Christoph Laske; Johannes Levin; Eric McDade; John M Ringman; Martin Rossor; Stephen Salloway; Peter R Schofield; David M Holtzman; John C Morris; Randall J Bateman
Journal:  Brain       Date:  2016-08-12       Impact factor: 13.501

Review 6.  One protein, multiple pathologies: multifaceted involvement of amyloid β in neurodegenerative disorders of the brain and retina.

Authors:  Vivek Gupta; Veer B Gupta; Nitin Chitranshi; Sumudu Gangoda; Roshana Vander Wall; Mojdeh Abbasi; Mojtaba Golzan; Yogita Dheer; Tejal Shah; Alberto Avolio; Roger Chung; Ralph Martins; Stuart Graham
Journal:  Cell Mol Life Sci       Date:  2016-06-22       Impact factor: 9.261

7.  GWAS of longitudinal amyloid accumulation on 18F-florbetapir PET in Alzheimer's disease implicates microglial activation gene IL1RAP.

Authors:  Vijay K Ramanan; Shannon L Risacher; Kwangsik Nho; Sungeun Kim; Li Shen; Brenna C McDonald; Karmen K Yoder; Gary D Hutchins; John D West; Eileen F Tallman; Sujuan Gao; Tatiana M Foroud; Martin R Farlow; Philip L De Jager; David A Bennett; Paul S Aisen; Ronald C Petersen; Clifford R Jack; Arthur W Toga; Robert C Green; William J Jagust; Michael W Weiner; Andrew J Saykin
Journal:  Brain       Date:  2015-08-11       Impact factor: 13.501

8.  Effect of BDNFVal66Met on disease markers in dominantly inherited Alzheimer's disease.

Authors:  Yen Ying Lim; Jason Hassenstab; Alison Goate; Anne M Fagan; Tammie L S Benzinger; Carlos Cruchaga; Eric McDade; Jasmeer Chhatwal; Johannes Levin; Martin R Farlow; Neill R Graff-Radford; Christoph Laske; Colin L Masters; Stephen Salloway; Peter Schofield; John C Morris; Paul Maruff; Randall J Bateman
Journal:  Ann Neurol       Date:  2018-08-25       Impact factor: 10.422

9.  The Role of Apolipoprotein E and Ethanol Exposure in Age-Related Changes in Choline Acetyltransferase and Brain-Derived Neurotrophic Factor Expression in the Mouse Hippocampus.

Authors:  Mostofa Jamal; Asuka Ito; Naoko Tanaka; Takanori Miki; Ayaka Takakura; Shingo Suzuki; Kiyoshi Ameno; Hiroshi Kinoshita
Journal:  J Mol Neurosci       Date:  2018-05-01       Impact factor: 3.444

10.  The BDNFVal66Met SNP modulates the association between beta-amyloid and hippocampal disconnection in Alzheimer's disease.

Authors:  Nicolai Franzmeier; Jinyi Ren; Alexander Damm; Gemma Monté-Rubio; Mercè Boada; Agustín Ruiz; Alfredo Ramirez; Frank Jessen; Emrah Düzel; Octavio Rodríguez Gómez; Tammie Benzinger; Alison Goate; Celeste M Karch; Anne M Fagan; Eric McDade; Katharina Buerger; Johannes Levin; Marco Duering; Martin Dichgans; Marc Suárez-Calvet; Christian Haass; Brian A Gordon; Yen Ying Lim; Colin L Masters; Daniel Janowitz; Cihan Catak; Steffen Wolfsgruber; Michael Wagner; Esther Milz; Sonia Moreno-Grau; Stefan Teipel; Michel J Grothe; Ingo Kilimann; Martin Rossor; Nick Fox; Christoph Laske; Jasmeer Chhatwal; Peter Falkai; Robert Perneczky; Jae-Hong Lee; Annika Spottke; Henning Boecker; Frederic Brosseron; Klaus Fliessbach; Michael T Heneka; Peter Nestor; Oliver Peters; Manuel Fuentes; Felix Menne; Josef Priller; Eike J Spruth; Christiana Franke; Anja Schneider; Christine Westerteicher; Oliver Speck; Jens Wiltfang; Claudia Bartels; Miguel Ángel Araque Caballero; Coraline Metzger; Daniel Bittner; Stephen Salloway; Adrian Danek; Jason Hassenstab; Igor Yakushev; Peter R Schofield; John C Morris; Randall J Bateman; Michael Ewers
Journal:  Mol Psychiatry       Date:  2019-03-21       Impact factor: 15.992

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