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Published in final edited form as: Eur Neuropsychopharmacol. 2015 Nov 14;26(4):787–795. doi: 10.1016/j.euroneuro.2015.11.004

Potential contribution of the Alzheimer’s disease risk locus BIN1 to episodic memory performance in cognitively normal Type 2 diabetes elderly

Lior Greenbaum a,b,*, Ramit Ravona-Springer b,c,d, Irit Lubitz b, James Schmeidler e, Itzik Cooper b, Mary Sano e, Jeremy M Silverman e, Anthony Heymann f,g, Michal Schnaider Beeri b,e
PMCID: PMC5753408  NIHMSID: NIHMS931081  PMID: 26947052

Abstract

In recent years, several promising susceptibility loci for late-onset Alzheimer’s disease (AD) were discovered, by implementing genome-wide association studies (GWAS) approach. Recent GWAS meta-analysis has demonstrated the association of 19 loci (in addition to the APOE locus) with AD in the European ancestry population at genome-wide significance level. Since Type 2 Diabetes (T2D) is a substantial risk factor for cognitive decline and dementia, the 19 single nucleotide polymorphisms (SNPs) that represent the 19 AD loci were studied for association with performance in episodic memory, a primary cognitive domain affected by AD, in a sample of 848 cognitively normal elderly Israeli Jewish T2D patients. We found a suggestive association of SNP rs6733839, located near the bridging integrator 1 (BIN1) gene, with this phenotype. Controlling for demographic (age, sex, education, disease duration and ancestry) covariates, carriers of two copies of the AD risk allele T (TT genotype) performed significantly worse (p=0.00576; p=0.00127 among Ashkenazi origin sub-sample) in episodic memory compared to carriers of the C allele (CT+CC genotypes). When including additional potential covariates (clinical and APOE genotype), results remained significant (p=0.00769; p=0.00148 among Ashkenazi). Interestingly, as validated in multiple large studies, BIN1 is one of the most established AD risk loci, with a high odds ratio. Although preliminary and require further replications, our findings support a contribution of BIN1 to individual differences in episodic memory performance among T2D patients.

Keywords: BIN1, Alzheimer’s disease, Episodic memory, Type 2 diabetes

1. Introduction

Traditionally, carriership of the APOE ε4 allele is considered the most established genetic risk factor for Alzheimer’s disease (AD) (Corder et al., 1993, 1995). Inherited mutations in other genes (APP, PSEN1 and PSEN2) are related to early-onset disease (Guerreiro et al., 2012). Recently, remarkable progress has been achieved in the field of AD genetics, in light of several large late-onset AD (henceforth AD) genome-wide association studies (GWAS), where several loci associated with this phenotype were discovered and replicated (Bettens et al., 2013; Guerreiro et al., 2012). The identified single nucleotide polymorphisms (SNPs) have small effect on AD risk, and are related to biological pathways such as cholesterol metabolism, endocytosis and immune response (Karch and Goate, 2015).

As part of the International Genomics of Alzheimer’s Project (IGAP), the largest AD GWAS meta-analysis was published by Lambert et al. (2013), including two stages and meta-analysis design study of 74,046 individuals from European ancestry (Lambert et al., 2013). 19 loci (in addition to the APOE locus) reached genome-wide significance level in the combined two stages analysis. Each of the 19 loci is represented by the SNP with best level of association within it. The 19 SNPs are divided into three sub-groups: 8 are in previously GWAS defined AD genes, 4 SNPs in new AD loci that reached genome-wide significance level (P<5 × 10−8) in the first analysis stage (17,008 cases and 37,154 controls from 4 GWAS data sets) and 7 SNPs that reached genome-wide significance only in the combined discovery and replication (8,572 cases and 11,312 controls) analysis (Lambert et al., 2013).

Episodic memory impairment is the hallmark of the AD, and is frequently the earliest clinical manifestation (Sperling et al., 2010). Neurobiologically, it is linked to the hippocampus and related structures in the medial temporal lobe, which are among the sites of early pathologic alterations in AD (Dickerson and Eichenbaum, 2010; Hyman et al., 1984). Episodic memory is highly influenced by genetic contribution (Wilson et al., 2011), and this sub-phenotype is frequently used as primary outcome measure in genetic studies aiming to evaluate the impact of AD related variants on cognitive performance among cognitively normal and impaired populations. Among GWAS confirmed AD risk loci that were associated with this intermediate phenotype are CR1, BIN1, CLU, PICALM, and APOE (Barnes et al., 2013; Barral et al., 2012; Ferencz et al., 2014; Keenan et al., 2012; Sweet et al., 2012).

Since Type 2 Diabetes (T2D) is a major risk factor to dementia (both AD and vascular dementia) (Biessels et al., 2008; Beeri et al., 2009) and cognitive decline—our aim in this study was to evaluate the potential effect of the described 19 AD associated SNPs (based on the Lambert et al. findings) on episodic memory performance in a sample of cognitively normal diabetic elderly. We hypothesized that the AD risk alleles will be associated with poorer cognitive performance in this group, which is at high risk for cognitive decline.

2. Experimental procedures

2.1. Sample

The Israel Diabetes and Cognitive Decline [(IDCD) (Beeri et al., 2014)] sample consists of elderly Israeli Jewish (≥65 years old) T2D subjects, cognitively normal at entry to the study, taking part in a longitudinal investigation for evaluation of the relationship of cognitive decline and long-term T2D characteristics. Participants, residing in Isreal’s central region, were randomly selected from the diabetes registry of the Maccabi Healthcare Services (MHS). Entry criteria to the registry are any of the four condition: (1) HbA1c>7.25%, (2) Glucose>200 mg/dl on two exams more than 3 months apart, (3) purchase of diabetic medication twice within 3 months, supported by a HbA1c>6.5% or Glucose>125mg/dl within half a year (4) clinical diagnosis of T2D by the primary or secondary practitioner, supported by a HbA1c>6.5% or Glucose>125 mg/dl within half a year.

All subjects speak Hebrew fluently, and gave informed consent to participate in the study. Importantly, IDCD’s participants did not suffer from major medical, psychiatric, or neurological conditions that may affect their cognitive status. Ancestry (Ashkenazi vs. non-Ashkenazi) was determined by self-report, as well as data regarding land of birth or parents’ land of birth. T2D medication data was extracted from the registry, and classified into three categories: (a) no medication, (b) hypoglycemic medication (among them metformin, rosiglitazone, glibenclamide, glyburide, exenatide, acarbose, liraglutide and repaglinide) and (c) insulin or insulin plus hypoglycemic medication. Subjects were categorized by having ever/never taken the medication (Table 1). For further information regarding the IDCD sample, see Beeri et al. (2014).

Table 1.

Demographic and clinical description of the analyzed sample.

Total sample
Ashkenazi sub-sample
N %, SD N %, SD
Participants (N) 848 467
Ashkenazi origin (N) 467 55.07%
Females (N) 335 39.50% 189 40.47%
APOE ε4 carriers (N) 113 13.33% 75 16.06%
Age at cognitive evaluation (years) 71.98 4.72 72.47 4.91
Years of education 13.09 3.41 14.04 3.33
Hemoglobin A1c (%, mean) 6.79 0.78 6.76 0.72
Creatinine (mg/dl, mean) 1.00 0.25 1.01 0.27
BMI (kg/m2, mean) 28.37 4.34 28.45 4.56
Total cholesterol (mg/dl, mean) 180.23 24.67 179.33 24.71
HDL (mg/dl, mean) 47.63 10.76 48.15 10.98
LDL (mg/dl, mean) 101.3 19.6 100.37 19.16
Triglyceride (mg/dl, mean) 157.66 63.26 155.69 57.92
Duration of T2D (years) 8.72 2.61 8.79 2.57
Diastolic BP (mmHg, mean) 83.45 14.99 84.41 15.54
Systoloc BP (mmHg, mean) 165.9 9.41 167.07 9.26
No T2D medication (N) 113 13.33% 62 13.28%
Hypoglycemic medication only (N) 653 77.00% 357 76.45%
Insulin or Insulin plus hypoglycemic medication (N) 82 9.67% 48 10.28%
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Abbreviations: BMI=Body mass index; HDL=High-density lipoprotein; LDL=Low-density lipoprotein; BP=Blood pressure.

2.2. Cognitive assessment

A comprehensive cognitive battery was administrated to all eligible patients by a neuropsychologist, as well as additional questionnaires to the subject and informant, on cognitive and functional impairment. Subjects’ cognitive status (normal, mild cognitive impairment (MCI) or dementia) are discussed and defined by a multidisciplinary team. Only cognitively normal participants are included at baseline. The current study presents results based on single (baseline) cognitive assessment only—the first follow up wave is ongoing.

Similarly to our previous studies (Greenbaum et al., 2014; Ravona-Springer et al., 2013), factor analysis revealed four cognitive domains, which were then scored as totals of z scores: episodic memory factor (included word list immediate and delayed recall, and recognition from the CERAD (Consortium to Establish a Registry for Alzheimer’s Disease) performance neuropsychological battery), semantic categorization factor (included the letter and category fluency, and similarities), attention/working memory factor (included the diamond cancellation test, digit span forward and backward), and an executive factor (included the trails making A and B and the digit symbol test). In addition, an overall cognition measure was created by summarizing the four domains.

2.3. SNPs selection and genotyping

Based on Lambert et al. study results (Lambert et al., 2013), we genotyped in the current study the best level associated SNP (overall meta-analysis) within each of the 19 loci that surpassed genome-wide significance (in the combined stage 1 and 2 analysis). These SNPs represent (according to Lambert et al. (2013)) three sub-groups of variants (Table 2): Known GWAS-defined associated genes (rs6656401, rs6733839, rs10948363, rs11771145, rs9331896, rs983392, rs10792832, rs4147929); New loci reaching genome-wide significance in the discovery analysis (rs9271192, rs28834970, rs11218343, rs10498633); New loci reaching genome-wide significance in the combined discovery and replication analysis (rs35349669, rs190982, rs2718058, rs1476679, rs10838725, rs17125944, rs7274581) (Lambert et al, 2013). Although highly significant in the meta-analysis, we did not include the APOE locus in this study, since it’s relation to episodic memory and other cognitive phenotypes in the IDCD cohort was studied previously (Ravona-Springer et al., 2014). However, the APOE genotype was used as a covariate in the analysis.

Table 2.

Association of the 19 Alzheimer’s disease SNPs (three genotype levels) with episodic memory performance among Jewish elderly diabetic individuals. SNPs classification is according to Lambert et al. (2013).

SNP Chromosome Closest gene Minor/Major allele Minor allele fequency F P-value
Known GWAS-defined associated genes
rs6656401 1 CR1 A/G 0.172 0.381 0.683
rs6733839 2 BIN1 T/C 0.328 4.798 0.00847
rs10948363 6 CD2AP G/A 0.217 0.199 0.820
rs11771145 7 EPHA1 A/G 0.299 0.566 0.568
rs9331896 8 CLU C/T 0.393 3.52 0.030
rs983392 11 MS4A6A G/A 0.433 0.275 0.759
rs10792832 11 PICALM A/G 0.399 2.769 0.063
rs4147929 19 ABCA7 A/G 0.175 0.383 0.682
New loci reaching genome-wide significance in the discovery analysis
rs9271192 6 HLA-DRB5–HLA-DRB1 C/A 0.165 0.742 0.476
rs28834970 8 PTK2B C/T 0.306 0.463 0.629
rs11218343* 11 SORL1 C/T 0.045 0.829 0.363
rs10498633 14 SLC24A4-RIN3 T/G 0.219 0.378 0.685
New loci reaching genome-wide significance in the combined discovery and replication analysis
rs35349669 2 INPP5D T/C 0.349 0.057 0.944
rs190982 5 MEF2C G/A 0.455 0.400 0.670
rs2718058 7 NME8 G/A 0.359 0.007 0.993
rs1476679 7 ZCWPW1 C/T 0.238 0.24 0.786
rs10838725 11 CELF1 C/T 0.317 2.449 0.087
rs17125944* 14 FERMT2 C/T 0.056 2.04 0.154
rs7274581 20 CASS4 C/T 0.172 0.197 0.821
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*

Due to rarity of homozygous of minor allele (N=1 for rs11218343; N=4 for rs17125944), this group was combined with the heterozygous.

Genotyping was performed by LGC genomics, using KASP genotyping technology (www.lgcgenomics.com). Quality control measures were implemented.

3. Data analysis

3.1. Prioritization of SNPs according to three subgroups

Taking into account the limited sample size and relatively large number of SNPs (19), we decided a priori to perform separate analysis for each of the three SNPs sub-groups. This was performed in order to limit multiple testing. We consider this theoretically justified in light of the bold difference in previous support within scientific literature to the link between each subgroup of SNPs and AD. Currently, supporting evidences are extensively available for the majority of the “Known GWAS-defined associated genes”. These signals are located within established AD loci (CR1, BIN1, CD2AP, EPHA1, CLU, MS4A6A, PICALM and ABCA7), which were discovered by several GWAS before conducting the meta-analysis (Harold et al., 2009; Hollingworth et al., 2011; Lambert et al., 2009; Naj et al., 2011; Seshadri et al., 2010). In the context of AD, much less is known for most of the novel genes belonging to the other two sub-groups (“New loci reaching genome-wide significance in the discovery sample” and “New loci reaching genome-wide significance in the combined discovery and replication analysis”, representing novel AD loci, except for SORL1). The differentiation between the sub-groups is reflected also by the substantial gaps (for most SNPs) in p-values levels at both discovery and replication stages (mainly, none of the third sub-group SNPs reached genome-wide significance in the discovery stage, in contrast to the other sub-groups).

3.2. Statistical analysis

The relationship between the SNPs and episodic memory performance was evaluated by analysis of variance approach. At the initial screening stage, three genotype groups were considered to determine the most appropriate model of inheritance. For each SNP, separately, a one way ANCOVA was conducted (genotype group as independent variable), controlling for demographic covariates (sex, age at cognitive evaluation, years of education, T2D duration and ancestry (Ashkenazi vs. Non-Ashkenazi)). SNPs that met p<0.05 were taken for further investigation. Post-hoc analysis (using least significant difference (LSD) test) was performed to detect the most fitting and parsimonious genetic inheritance model, to be used for additional testing.

Then, at second stage, we repeated the ANCOVA (with the same covariates) only for the significant SNPs, implementing the selected inheritance model. We further included an additional group of clinical variables as covariates in the analysis (mean hemoglobin A1c level, systolic and diastolic blood pressure, creatinine level, total cholesterol, triglycerides, HDL and LDL level, BMI and T2D medication). Adjusting for these cardiovascular covariates is consistent with our previous genetic studies in this cohort (Greenbaum et al., 2014; Ravona-Springer et al., 2013), and based on evidences that these factors are associated with cognitive decline and dementia (Beeri et al., 2009). We also added the carriership status of the APOE ε4 allele as a covariate, to account for a possible effect on cognition. The same analysis was implemented for the Ashkenazi sub-sample. This is based on the assumption that Ashkenazi Jews represent an isolated population that have less genetic heterogneity, which increases the power for genetic association studies (Shifman and Darvasi, 2001).

The primary outcome of this study was episodic memory, but data was also available regarding additional cognitive domains. In a secondary analysis, multivariate analysis of covariance (MANCOVA) was employed to evaluate the association of the previously found significant SNPs with cognitive performance in the five domains of interest (episodic memory, and also executive function, attention/working memory, semantic categorization and overall cognition). As a comprehensive evaluation of significance for multiple domains, MANCOVA takes into account the covariate structure of the cognitive measures, and is therefore superior to analysis with each domain separately.

In order to assess whether characteristics reflecting severity of T2D affect the direction of the genotype effect on episodic memory, we employed a linear regression model (including all covariates) to calculate the unstandardized and standardized (β) coefficients in the whole sample, and across tertiles of mean HbA1c level and duration of T2D, and ever consumption of antidiabetic drugs [simplified Yes/No definition].

A p-value of 0.05 (two sided) was used to determine statistical significance level. Required p-value for Bonferroni correction for multiple testing was calculated both for the global SNPs number (19), and also separately for each of the three SNPs sub-groups, based on SNPs number within each (8 for the first, 4 in the second and 7 in the third subgroup). For statistical analysis, we used SPSS version 17.0 (SPSS Inc., Chicago, IL, USA). Hardy–Weinberg calculations were performed with PLINK (Purcell et al., 2007) (http://pngu.mgh.harvard.edu/purcell/plink).

4. Results

All 19 SNPs were successfully genotyped in 848 participants, and none showed deviation from Hardy–Weinberg equilibrium (p>0.05), except MS4A6A rs983392 in the Ashkenazi group (p=0.013). Rate of genotyping failure was approximately 1% for all SNPs. The sample’s demographic and cardiovascular details are shown in Table 1.

For the first sub-group of 8 SNPs of interest (“Known GWAS-defined associated genes” in Lambert et al. study), we studied the association with performance in episodic memory, the primary outcome. For exploratory analysis, we implemented ANCOVA procedure separately for each SNP, using the genotype (homozygous major allele, heterozygous and homozygous minor allele) as independent variable. As explained above, we included relevant demographic variables as covariates: sex, age at cognitive assessment, years of education, disease duration and ancestry.

For SNP rs6733839, located near the BIN1 gene, a nominal significant association was demonstrated (F(2,833)=4.798, p=0.00847) (Table 2). In the post-hoc analysis, significant differences were observed between carriers of the TT genotype (worst performance group) compared to CT (p=0.00216), and of carriers of the TT genotype compared to CC (p=0.02814), but not between CC and CT (p=0.16581). Among the Ashkenazi group, results were even stronger in the main analysis (F(2,459)=5.415, p=0.00474), as well as in the post-hoc (p=0.00125; 0.0046; 0.56156). Therefore, we implemented the recessive model of inheritance (TT vs. CT+CC) as the best model for further analysis, using ANCOVA with the same covariates.

Individuals who carry two copies of rs6733839 AD risk allele T (TT, N=100; 60 among Ashkenazi) performed significantly worse in episodic memory compared to carriers of the C allele (CT+CC genotypes, N=742; 406 in the Ashkenazi sub-sample) (F(1,834)=7.664, p=0.00576; F (1,460)=10.509, p=0.00127 among Ashkenazi), in line with the direction of initial hypothesis. At the level of this SNPs sub-group, the significant association among the Ashkenazi sub-sample withstands (although marginally) Bonferroni correction for multiple testing (8 SNPs × 2 models × 2 samples (general and Ashkenazi), required p-value: p=0.05/32=0.00156). If a more rigorous correction which includes the global number of SNPs within this study is employed, than p<0.001 is required, and the association is considered significant at the nominal level.

Even when including the additional set of clinical covariates (mean hemoglobin A1c level, systolic and diastolic blood pressure, creatinine level, total cholesterol, triglyceride, HDL and LDL level, BMI and T2D medication [no medication, hypoglycaemic medication, and insulin or insulin+hypoglycaemic medication]), as well as carriership of the APOE ε4 allele –the analysis results were essentially the same (F(1,814)=7.139, p=0.00769; F(1,445)=10.232, p=0.00148 among Ashkenazi).

In secondary analysis, we examined the contribution of severity of T2D to the associations found, using three proxies of glycemic control and disease severity: HbA1c level, duration of disease and consumption of Antidiabetic drugs. We split the sample to three tertiles according to mean HbA1c level: low (mean 6.029, SD=0.354) medium (6.678, SD=0.157) and high (7.66, SD=0.582). In a similar way, we also spilt the sample to tertiles according to T2D duration years (low – mean 5.492, SD=1.815; medium –9.692, SD=0.712; high – 10.962, SD=0.531), as well as ever consumption of diabetic medication (Yes/No dichotomized definition (N=113 and 735, respectively)). Employing linear regression, the unstandardized and standardized (β) coefficients in each tertile/group were calculated, as presented in Supplementary Table 1. Although not significant in each tertile/group separately (probably due to much smaller sample sizes), results demonstrate that the direction of the BIN1 genotype effect on episodic memory is consistent in the same direction in all tertiles (HbA1c and T2D duration) and the drug use groups, as in the whole sample. This finding suggests that rs6733839 effect on episodic memory is not influenced by T2D characteristics or severity. Interestingly, and taking into account a simplistic definition of T2D medication use (ever/never), we did not find statistically significant difference in episodic memory performance in the medicated vs. non-medicated groups (both whole sample and Ashkenazi). Similarly, no significant differences in this phenotype were observed when comparing T2D medication defined as no drugs, hypoglycemic medication, and insulin or insulin+hypoglycemic medication (controlling for background variables).

In order to evaluate the link between rs6733839 and additional cognitive factors available for this cohort, we performed MANCOVA procedure, including five dependent cognitive variables: episodic memory, executive function, attention/working memory, semantic categorization and overall cognitive score. When comparing carriers of the TT genotype to carriers of the CT+CC genotypes (controlling for all demographic and clinical variables, as well as the APOE status), the whole model Wilk’s lambda was statistically significant, at nominal level (F(4,809)=2.733; p=0.02803). The TT group performed significantly worse in domains of episodic memory (F(1,812)=6.825, p=0.00915) and attention/working memory (F(1,812)=5.174, p=0.02319) and in the overall cognitive measure (F(1,812)=4.891, p=0.02728), compared to the CT+CC group. No significant associations were found for executive function and semantic categorization. Among the Ashkenazi individuals, statistical significance was shown for the whole MANCOVA model (F(4,441)=3.18, p=0.01360) and for episodic memory (F(1,444)=10.189, p=0.00151), but not for the other domains.

As seen in Table 2, the association of the rs9331896 SNP with episodic memory performance reached significance. However, the direction of the effect was opposite to expected one based on Lamberts et al. meta-analysis (2013) (the AD risk allele is related to poorer results). Two additional SNPs (rs10792832 and rs10838725) showed trend level of significance, not justifying further post-hoc analysis.

5. Discussion

Although our findings should be regarded as preliminary, they suggest that rs6733839 SNP, located upstream of the BIN1 gene, may contribute to individual differences in episodic memory performance among T2D elderly. Carriers of two copies of the AD risk allele T (TT genotype) performed significantly worse in episodic memory compared to carriers of the C allele (CT+CC genotypes), when controlling for relevant demographic and clinical covariates and APOE ε4 allele carriership status. The direction of the association found in our study is consistent with the literature and the a priori hypothesis (AD risk allele is associated with poorer episodic memory). In a secondary analysis, MANCOVA including additional cognitive domains (episodic memory, executive function, attention/working memory, semantic categorization and overall measure), was nominally significant. Findings were more pronounced among the sub-sample of Ashkenazi ancestry individuals, a group which is considered as more genetically homogenous. However, the association only partially withstood correction for multiple testing, therefore requiring further replications in the future.

To the best of our knowledge, this is the first report which supports direct contribution of genetic variants to episodic memory function among T2D patients, showing that AD risk variants are relevant to this phenotype among T2D individuals.

The bridging integrator 1 (BIN1) gene, also known as amphiphysin 2, is considered one of the most well-established AD risk loci, with odds ratio of 1.22 and population attributable fraction (PAF) of 8.1%, calculated for rs6733839 (the highest among AD associated genes, after APOE) (Lambert et al., 2013; Tan et al., 2014). Several GWAS have reported association of multiple BIN1 SNPs with AD (Kamboh et al., 2012; Naj et al., 2011; Seshadri et al., 2010), and these findings were further validated at additional studies (Carrasquillo et al., 2011; Hu et al., 2011). Among them are rs744373 (Seshadri et al., 2010) and rs7561528 (Naj et al., 2011), located approximately 30 kilobases upstream to the gene coding region. These and other SNPs were associated with relevant AD phenotypes, such as thickness of the entorhinal and temporal pole cortex (Biffi et al., 2010), mRNA expression and tau load (Chapuis et al., 2013). The rs6733839 SNP (also located upstream of the gene, in proximity to rs744373) was found to be associated with AD in a meta-analysis of 74,046 individuals, with overall p-value of 6.9 ×10−44 (Lambert et al., 2013).

Several limitations of the current study should be discussed. First, it is possible that the association of rs6733839 with episodic memory is a spurious result, derived from multiple testing. In this study, we genotyped 19 AD SNPs, based on Lambert et al. meta-analysis findings (2013). Due to the relatively large number of genotyped SNPs and the limited available sample size, we decided a priori to perform a separate analysis for each of the three SNPs sub-groups reported by the meta-analysis, in order to limit multiple testing. The first sub-group includes SNPs located within well established AD loci identified by GWAS before the meta-analysis, while the two others include novel AD loci (except for SORL1), discovered at genome-wide level in the meta-analysis itself. Therefore, the association of rs6733839 TT genotype with poorer episodic memory performance withstands correction for multiple testing only among the Ashkenazi sub-sample for the “Known GWAS-defined associated genes” 8 SNPs sub-group. Had we used the more rigorous and conservative correction in our analysis, taking into account all 19 genotyped SNPs, our results are rendered at the nominal significant level, since a p-value <0.001 would have been required. A larger sample size might have strengthened the findings. A second limitation is the cross-sectional design of the study, which includes only a single cognitive assessment, thus not enabling analyzing the relationships of the genetic variants with rate of cognitive decline over time. This will be addressed in the future, since longitudinal follow-up of the IDCD cohort is ongoing.

Nevertheless, we consider the association of the rs6733839 with episodic memory to be true positive, with a relatively high degree of confidence. This is due to the consistency of our findings with multiple previous reports regarding the contribution of BIN1 to AD (see above), and in particular the consistency with same SNP and risk allele reported by Lambert et al. (2013). rs6733839 has stronger effect size (odds ratio of 1.22) compared to other SNPs reported by the meta-analysis, rendering better statistical power for detecting the association. Further, in agreement with our findings, BIN1 was previously shown to be associated with episodic memory performance (in the context of a genotyping pattern involving combination with additional AD genes) (Barral et al., 2012; Ferencz et al., 2014). This is also in line with recent studies reporting BIN1 associations with several cognitive functions in healthy, non-demented elderly. For example, among healthy young Chinese participants, BIN1 rs744373 (which is in incomplete linkage disequilibrium (LD) with rs6733839, and the relationship between them is not clear) was associated with working memory, hippocampal volume and functional connectivity (Zhang et al., 2015). The same SNP was associated with rate of change in global cognition in a large prospective study of French elderly dementia-free individuals (Vivot et al., 2015).

Finally, this study includes only T2D participants without healthy controls, and we could not study the possible interaction between rs6733839 and T2D status with episodic memory. However, our results indicate that BIN1 effect on cognition is not related to the severity of T2D (as measured by Hb1Ac levels, disease duration and use of diabetes medication) – suggesting that the effect of the gene on cognitive performance may be relevant to the elderly population as a whole, and does not depend on T2D per se.

This study has several unique strengths, among them the homogeneity of the sample (Israelis from Jewish ancestry), normal cognitive status (ascertained by a multidisciplinary consensus conference) with an extensive cognitive evaluation, and validated T2D diagnosis for each subject. An additional major advantage is availability of data regarding possible demographic and clinical confounder variables, as well as APOE genotype. This includes cardiovascular risk factors which contribute by themselves to cognitive decline, as well as data regarding diabetic control (HbA1c level). Taking them into account (based on multiple measurements, representing the course of T2D better than a single risk factor measurements) in the statistical analysis is therefore crucial.

Originally, BIN1 was identified as MYC interacting tumor surpressor gene (Sakamuro et al., 1996), expressed abundantly in brain and muscles (Wechsler-Reya et al., 1997). Mutations within BIN1 were found in autosomal recessive centronuclear myopathy (Nicot et al., 2007). In brain tissue from AD patients, BIN1 expression levels were associated with disease progression (Karch et al., 2012), and with neurofibrillary tangle (NFT) pathology (Holler et al., 2014). Alternation of the protein levels was reported in aging mice brains (Yang et al., 2008).

The potential role of BIN1 in AD pathogenesis is yet unknown, but several pathways might be involved, as reviewed by Karch and Goate (2015) and Tan et al. (2013). Since BIN1 is involved in Clathrin mediated endocytosis pathways and intracellular trafficking (Wigge and McMahon, 1998; Karch and Goate, 2015), it may affect AD pathogenesis through amyloid precursor protein (APP) and APOE, which are influenced by these mechanisms (Tan et al., 2013). In addition, BIN1 might be implicated in tau pathology, since BIN1 and tau proteins interact in both in vivo and in vitro models (Chapuis et al., 2013; Kingwell, 2013). Based on our results, these mechanisms could be relevant irrespective of T2D status.

To conclude, our study suggests association of the well established AD susceptibility SNP rs6733839, located near the BIN1 gene, with episodic memory performance among cognitively normal elderly T2D individuals. This relationship seems not to be affected by T2D characteristics. At the moment, understanding the contribution of BIN1 to AD is only at the initial steps, but may point to biological mechanisms for cognitive decline and open novel opportunities for therapeutic venues.

Supplementary Material

Supplemental

Acknowledgments

Role of the funding source

This study was supported by the National Institute on Aging (R01-AG-034087 and P50-AG-05138 to M.S.B.), the Leroy Schecter Foundation (to M.S.B.), and the Irma T. Hirschl Scholar Award (to M.S.B.).

Appendix A. Supplementary material

Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.euroneuro.2015.11.004.

Footnotes

Contributors

L.G. and M.S.B. researched data, performed statistical analysis and wrote the manuscript. R.R.S. and A.H. contributed to research design and reviewed the manuscript. J.S. contributed to statistical analysis and interpretation of data. I.L. and I.C. contributed to interpretation of data. M.S. and J.M.S. reviewed the manuscript.

Conflict of interests

The authors have no disclosures or conflict of interests.

References

  1. Barnes LL, Arvanitakis Z, Yu L, Kelly J, De Jager PL, Bennett DA. Apolipoprotein E and change in episodic memory in blacks and whites. Neuroepidemiology. 2013;40:211–219. doi: 10.1159/000342778. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Barral S, Bird T, Goate A, Farlow MR, Diaz-Arrastia R, Bennett DA, Graff-Radford N, Boeve BF, Sweet RA, Stern Y, Wilson RS, Foroud T, Ott J, Mayeux R National Institute on Aging Late-Onset Alzheimer’s Disease Genetics Study. Genotype patterns at PICALM, CR1, BIN1, CLU, and APOE genes are associated with episodic memory. Neurology. 2012;78:1464–1471. doi: 10.1212/WNL.0b013e3182553c48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Beeri MS, Ravona-Springer R, Moshier E, Schmeidler J, Godbold J, Karpati T, Leroith D, Koifman K, Kravitz E, Price R, Hoffman H, Silverman JM, Heymann A. The Israel Diabetes and Cognitive Decline (IDCD) study: design and baseline characteristics. Alzheimers Dement. 2014;10:769–778. doi: 10.1016/j.jalz.2014.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Beeri MS, Ravona-Springer R, Silverman JM, Haroutunian V. The effects of cardiovascular risk factors on cognitive compromise. Dialog- Clin Neurosci. 2009;11:201–212. doi: 10.31887/DCNS.2009.11.2/msbeeri. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bettens K, Sleegers K, Van Broeckhoven C. Genetic insights in Alzheimer’s disease. Lancet Neurol. 2013;12:92–104. doi: 10.1016/S1474-4422(12)70259-4. [DOI] [PubMed] [Google Scholar]
  6. Biessels GJ, Deary IJ, Ryan CM. Cognition and diabetes: a lifespan perspective. Lancet Neurol. 2008;7:184–190. doi: 10.1016/S1474-4422(08)70021-8. [DOI] [PubMed] [Google Scholar]
  7. Biffi A, Anderson CD, Desikan RS, Sabuncu M, Cortellini L, Schmansky N, Salat D, Rosand J Alzheimer’s Disease Neuroimaging Initiative (ADNI) Genetic variation and neuroimaging measures in Alzheimer disease. Arch Neurol. 2010;67:677–685. doi: 10.1001/archneurol.2010.108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Carrasquillo MM, Belbin O, Hunter TA, Ma L, Bisceglio GD, Zou F, Crook JE, Pankratz VS, Sando SB, Aasly JO, Barcikowska M, Wszolek ZK, Dickson DW, Graff-Radford NR, Petersen RC, Morgan K, Younkin SG. Replication of BIN1 association with Alzheimer’s disease and evaluation of genetic interactions. J Alzheimers Dis. 2011;24:751–758. doi: 10.3233/JAD-2011-101932. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chapuis J, Hansmannel F, Gistelinck M, Mounier A, Van Cauwenberghe C, Kolen KV, Geller F, Sottejeau Y, Harold D, Dourlen P, Grenier-Boley B, Kamatani Y, Delepine B, Demiautte F, Zelenika D, Zommer N, Hamdane M, Bellenguez C, Dartigues JF, Hauw JJ, Letronne F, Ayral AM, Sleegers K, Schellens A, Broeck LV, Engelborghs S, De Deyn PP, Vandenberghe R, O’Donovan M, Owen M, Epelbaum J, Mercken M, Karran E, Bantscheff M, Drewes G, Joberty G, Campion D, Octave JN, Berr C, Lathrop M, Callaerts P, Mann D, Williams J, Buee L, Dewachter I, Van Broeckhoven C, Amouyel P, Moechars D, Dermaut B, Lambert JC GERAD Consortium. Increased expression of BIN1 mediates Alzheimer genetic risk by modulating tau pathology. Mol Psychiatry. 2013;18:1225–1234. doi: 10.1038/mp.2013.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Corder EH, Saunders AM, Pericak-Vance MA, Roses AD. There is a pathologic relationship between ApoE-epsilon 4 and Alzheimer’s disease. Arch Neurol. 1995;52:650–651. doi: 10.1001/archneur.1995.00540310012003. [DOI] [PubMed] [Google Scholar]
  11. Corder EH, Saunders AM, Strittmatter WJ, Schmechel DE, Gaskell PC, Small GW, Roses AD, Haines JL, Pericak-Vance MA. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science. 1993;261:921–923. doi: 10.1126/science.8346443. [DOI] [PubMed] [Google Scholar]
  12. Dickerson BC, Eichenbaum H. The episodic memory system: neurocircuitry and disorders. Neuropsychopharmacology. 2010;35:86–104. doi: 10.1038/npp.2009.126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Ferencz B, Laukka EJ, Welmer AK, Kalpouzos G, Angleman S, Keller L, Graff C, Lovden M, Backman L. The benefits of staying active in old age: physical activity counteracts the negative influence of PICALM, BIN1, and CLU risk alleles on episodic memory functioning. Psychol Aging. 2014;29:440–449. doi: 10.1037/a0035465. [DOI] [PubMed] [Google Scholar]
  14. Greenbaum L, Springer RR, Lutz MW, Heymann A, Lubitz I, Cooper I, Kravitz E, Sano M, Roses AD, Silverman JM, Saunders AM, Beeri MS. The TOMM40 poly-T rs10524523 variant is associated with cognitive performance among non-demented elderly with type 2 diabetes. Eur Neuropsychopharmacol. 2014;24:1492–1499. doi: 10.1016/j.euroneuro.2014.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Guerreiro RJ, Gustafson DR, Hardy J. The genetic architecture of Alzheimer’s disease: beyond APP, PSENs and APOE. Neurobiol Aging. 2012;33:437–456. doi: 10.1016/j.neurobiolaging.2010.03.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A, Hamshere ML, Pahwa JS, Moskvina V, Dowzell K, Williams A, Jones N, Thomas C, Stretton A, Morgan AR, Lovestone S, Powell J, Proitsi P, Lupton MK, Brayne C, Rubinsztein KS, Gill M, Lawlor B, Lynch A, Morgan K, Brown KS, Passmore PA, Craig D, McGuinness B, Todd S, Holmes C, Mann D, Smith AD, Love S, Kehoe PG, Hardy J, Mead S, Fox N, Rossor M, Collinge J, Maier W, Jessen F, Schurmann B, Heun R, van den Bussche H, Heuser I, Kornhuber J, Wiltfang J, Dichgans M, Frolich L, Hampel H, Hull M, Rujescu D, Goate AM, Kauwe JS, Cruchaga C, Nowotny P, Morris JC, Mayo K, Sleegers K, Bettens K, Engelborghs S, De Deyn PP, Van Broeckhoven C, Livingston G, Bass NJ, Gurling H, McQuillin A, Gwilliam R, Deloukas P, Al-Chalabi A, Shaw CE, Tsolaki M, Singleton AB, Guerreiro R, Muhleisen TW, Nothen MM, Moebus S, Jockel KH, Klopp N, Wichmann HE, Carrasquillo MM, Pankratz VS, Younkin SG, Holmans PA, O’Donovan M, Owen MJ, Williams J. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet. 2009;41:1088–1093. doi: 10.1038/ng.440. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Holler CJ, Davis PR, Beckett TL, Platt TL, Webb RL, Head E, Murphy MP. Bridging integrator 1 (BIN1) protein expression increases in the Alzheimer’s disease brain and correlates with neurofibrillary tangle pathology. J Alzheimers Dis. 2014;42:1221–1227. doi: 10.3233/JAD-132450. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hollingworth P, Harold D, Sims R, Gerrish A, Lambert JC, Carrasquillo MM, Abraham R, Hamshere ML, Pahwa JS, Moskvina V, Dowzell K, Jones N, Stretton A, Thomas C, Richards A, Ivanov D, Widdowson C, Chapman J, Lovestone S, Powell J, Proitsi P, Lupton MK, Brayne C, Rubinsztein DC, Gill M, Lawlor B, Lynch A, Brown KS, Passmore PA, Craig D, McGuinness B, Todd S, Holmes C, Mann D, Smith AD, Beaumont H, Warden D, Wilcock G, Love S, Kehoe PG, Hooper NM, Vardy ER, Hardy J, Mead S, Fox NC, Rossor M, Collinge J, Maier W, Jessen F, Ruther E, Schurmann B, Heun R, Kolsch H, van den Bussche H, Heuser I, Kornhuber J, Wiltfang J, Dichgans M, Frolich L, Hampel H, Gallacher J, Hull M, Rujescu D, Giegling I, Goate AM, Kauwe JS, Cruchaga C, Nowotny P, Morris JC, Mayo K, Sleegers K, Bettens K, Engelborghs S, De Deyn PP, Van Broeckhoven C, Livingston G, Bass NJ, Gurling H, McQuillin A, Gwilliam R, Deloukas P, Al-Chalabi A, Shaw CE, Tsolaki M, Singleton AB, Guerreiro R, Muhleisen TW, Nothen MM, Moebus S, Jockel KH, Klopp N, Wichmann HE, Pankratz VS, Sando SB, Aasly JO, Barcikowska M, Wszolek ZK, Dickson DW, Graff-Radford NR, Petersen RC, van Duijn CM, Breteler MM, Ikram MA, DeStefano AL, Fitzpatrick AL, Lopez O, Launer LJ, Seshadri S, Berr C, Campion D, Epelbaum J, Dartigues JF, Tzourio C, Alperovitch A, Lathrop M, Feulner TM, Friedrich P, Riehle C, Krawczak M, Schreiber S, Mayhaus M, Nicolhaus S, Wagenpfeil S, Steinberg S, Stefansson H, Stefansson K, Snaedal J, Bjornsson S, Jonsson PV, Chouraki V, Genier-Boley B, Hiltunen M, Soininen H, Combarros O, Zelenika D, Delepine M, Bullido MJ, Pasquier F, Mateo I, Frank-Garcia A, Porcellini E, Hanon O, Coto E, Alvarez V, Bosco P, Siciliano G, Mancuso M, Panza F, Solfrizzi V, Nacmias B, Sorbi S, Bossu P, Piccardi P, Arosio B, Annoni G, Seripa D, Pilotto A, Scarpini E, Galimberti D, Brice A, Hannequin D, Licastro F, Jones L, Holmans PA, Jonsson T, Riemenschneider M, Morgan K, Younkin SG, Owen MJ, O’Donovan M, Amouyel P, Williams J Alzheimer’s Disease Neuroimaging Initiative, CHARGE consortium, EADI1 consortium. Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet. 2011;43:429–435. doi: 10.1038/ng.803. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hu X, Pickering E, Liu YC, Hall S, Fournier H, Katz E, Dechairo B, John S, Van Eerdewegh P, Soares H the Alzheimer’s Disease Neuroimaging Initiative. Meta-analysis for genome-wide association study identifies multiple variants at the BIN1 locus associated with late-onset Alzheimer’s disease. PLoS One. 2011;6:e16616. doi: 10.1371/journal.pone.0016616. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hyman BT, Van Hoesen GW, Damasio AR, Barnes CL. Alzheimer’s disease: cell-specific pathology isolates the hippocampal formation. Science. 1984;225:1168–1170. doi: 10.1126/science.6474172. [DOI] [PubMed] [Google Scholar]
  21. Kamboh MI, Demirci FY, Wang X, Minster RL, Carrasquillo MM, Pankratz VS, Younkin SG, Saykin AJ, Baldwin C, Logue MW, Buros J, Farrer L, Pericak-Vance MA, Haines JL, Sweet RA, Ganguli M, Feingold E, Dekosky ST, Lopez OL, Barmada MM Alzheimer’s Disease Neuroimaging Initiative Jun G. Genome-wide association study of Alzheimer’s disease. Transl Psychiatry. 2012;2:e117. doi: 10.1038/tp.2012.45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Karch CM, Goate AM. Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol Psychiatry. 2015;77:43–51. doi: 10.1016/j.biopsych.2014.05.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Karch CM, Jeng AT, Nowotny P, Cady J, Cruchaga C, Goate AM. Expression of novel Alzheimer’s disease risk genes in control and Alzheimer’s disease brains. PLoS One. 2012;7:e50976. doi: 10.1371/journal.pone.0050976. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Keenan BT, Shulman JM, Chibnik LB, Raj T, Tran D, Sabuncu MR, Corneveaux JJ, Hardy JA, Huentelman MJ, Lemere CA, Myers AJ, Nicholson-Weller A, Reiman EM, Evans DA, Bennett DA, De Jager PL Alzheimer’s Disease Neuroimaging Initiative Allen AN. A coding variant in CR1 interacts with APOE-epsilon4 to influence cognitive decline. Hum Mol Genet. 2012;21:2377–2388. doi: 10.1093/hmg/dds054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kingwell K. Alzheimer disease: BIN1 variant increases risk of Alzheimer disease through tau. Nat Rev Neurol. 2013;9:184. doi: 10.1038/nrneurol.2013.34. [DOI] [PubMed] [Google Scholar]
  26. Lambert JC, Heath S, Even G, Campion D, Sleegers K, Hiltunen M, Combarros O, Zelenika D, Bullido MJ, Tavernier B, Letenneur L, Bettens K, Berr C, Pasquier F, Fievet N, Barberger-Gateau P, Engelborghs S, De Deyn P, Mateo I, Franck A, Helisalmi S, Porcellini E, Hanon O, Lendon C, Dufouil C, Jaillard C, Leveillard T, Alvarez V, Bosco P, Mancuso M, Panza F, Nacmias B, Bossu P, Piccardi P, Annoni G, Seripa D, Galimberti D, Hannequin D, Licastro F, Soininen H, Ritchie K, Blanche H, Dartigues JF, Tzourio C, Gut I, Van Broeckhoven C, Alperovitch A, Lathrop M, Amouyel P European Alzheimer’s Disease Initiative Investigators de Pancorbo MM. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet. 2009;41:1094–1099. doi: 10.1038/ng.439. [DOI] [PubMed] [Google Scholar]
  27. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, Beecham GW, Grenier-Boley B, Russo G, Thorton-Wells TA, Jones N, Smith AV, Chouraki V, Thomas C, Ikram MA, Zelenika D, Vardarajan BN, Kamatani Y, Lin CF, Gerrish A, Schmidt H, Kunkle B, Dunstan ML, Ruiz A, Bihoreau MT, Choi SH, Reitz C, Pasquier F, Cruchaga C, Craig D, Amin N, Berr C, Lopez OL, De Jager PL, Deramecourt V, Johnston JA, Evans D, Lovestone S, Letenneur L, Moron FJ, Rubinsztein DC, Eiriksdottir G, Sleegers K, Goate AM, Fievet N, Huentelman MW, Gill M, Brown K, Kamboh MI, Keller L, Barberger-Gateau P, McGuiness B, Larson EB, Green R, Myers AJ, Dufouil C, Todd S, Wallon D, Love S, Rogaeva E, Gallacher St J, George-Hyslop P, Clarimon J, Lleo A, Bayer A, Tsuang DW, Yu L, Tsolaki M, Bossu P, Spalletta G, Proitsi P, Collinge J, Sorbi S, Sanchez-Garcia F, Fox NC, Hardy J, Deniz Naranjo MC, Bosco P, Clarke R, Brayne C, Galimberti D, Mancuso M, Matthews F, Mecocci P, Del Zompo M, Maier W, Hampel H, Pilotto A, Bullido M, Panza F, Caffarra P, Nacmias B, Gilbert JR, Mayhaus M, Lannefelt L, Hakonarson H, Pichler S, Carrasquillo MM, Ingelsson M, Beekly D, Alvarez V, Zou F, Valladares O, Younkin SG, Coto E, Hamilton-Nelson KL, Gu W, Razquin C, Pastor P, Mateo I, Owen MJ, Faber KM, Jonsson PV, Combarros O, O’Donovan MC, Cantwell LB, Soininen H, Blacker D, Mead T, Mosley TH, Jr, Bennett DA, Harris TB, Fratiglioni L, Holmes C, de Bruijn RF, Passmore P, Montine TJ, Bettens K, Rotter JI, Brice A, Morgan K, Foroud TM, Kukull WA, Hannequin D, Powell JF, Nalls MA, Ritchie K, Lunetta KL, Kauwe JS, Boerwinkle E, Riemenschneider M, Boada M, Hiltuenen M, Martin ER, Schmidt R, Rujescu D, Wang LS, Dartigues JF, Mayeux R, Tzourio C, Hofman A, Nothen MM, Graff C, Psaty BM, Jones L, Haines JL, Holmans PA, Lathrop M, Pericak-Vance MA, Launer LJ, Farrer LA, van Duijn CM, Van Broeckhoven C, Moskvina V, Seshadri S, Williams J, Schellenberg GD, Amouyel P European Alzheimer’s Disease Initiative (EADI) Genetic, Environmental Risk in Alzheimer’s Disease Alzheimer’s Disease Genetic Consortium Cohorts for Heart, Aging Research in Genomic Epidemiology Moebus S. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013;45:1452–1458. doi: 10.1038/ng.2802. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Naj AC, Jun G, Beecham GW, Wang LS, Vardarajan BN, Buros J, Gallins PJ, Buxbaum JD, Jarvik GP, Crane PK, Larson EB, Bird TD, Boeve BF, Graff-Radford NR, De Jager PL, Evans D, Schneider JA, Carrasquillo MM, Ertekin-Taner N, Younkin SG, Cruchaga C, Kauwe JS, Nowotny P, Kramer P, Hardy J, Huentelman MJ, Myers AJ, Barmada MM, Demirci FY, Baldwin CT, Green RC, Rogaeva St E, George-Hyslop P, Arnold SE, Barber R, Beach T, Bigio EH, Bowen JD, Boxer A, Burke JR, Cairns NJ, Carlson CS, Carney RM, Carroll SL, Chui HC, Clark DG, Corneveaux J, Cotman CW, Cummings JL, DeCarli C, DeKosky ST, Diaz-Arrastia R, Dick M, Dickson DW, Ellis WG, Faber KM, Fallon KB, Farlow MR, Ferris S, Frosch MP, Galasko DR, Ganguli M, Gearing M, Geschwind DH, Ghetti B, Gilbert JR, Gilman S, Giordani B, Glass JD, Growdon JH, Hamilton RL, Harrell LE, Head E, Honig LS, Hulette CM, Hyman BT, Jicha GA, Jin LW, Johnson N, Karlawish J, Karydas A, Kaye JA, Kim R, Koo EH, Kowall NW, Lah JJ, Levey AI, Lieberman AP, Lopez OL, Mack WJ, Marson DC, Martiniuk F, Mash DC, Masliah E, McCormick WC, McCurry SM, McDavid AN, McKee AC, Mesulam M, Miller BL, Miller CA, Miller JW, Parisi JE, Perl DP, Peskind E, Petersen RC, Poon WW, Quinn JF, Rajbhandary RA, Raskind M, Reisberg B, Ringman JM, Roberson ED, Rosenberg RN, Sano M, Schneider LS, Seeley W, Shelanski ML, Slifer MA, Smith CD, Sonnen JA, Spina S, Stern RA, Tanzi RE, Trojanowski JQ, Troncoso JC, Van Deerlin VM, Vinters HV, Vonsattel JP, Weintraub S, Welsh-Bohmer KA, Williamson J, Woltjer RL, Cantwell LB, Dombroski BA, Beekly D, Lunetta KL, Martin ER, Kamboh MI, Saykin AJ, Reiman EM, Bennett DA, Morris JC, Montine TJ, Goate AM, Blacker D, Tsuang DW, Hakonarson H, Kukull WA, Foroud TM, Haines JL, Mayeux R, Pericak-Vance MA, Farrer LA, Schellenberg GD. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet. 2011;43:436–441. doi: 10.1038/ng.801. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Nicot AS, Toussaint A, Tosch V, Kretz C, Wallgren-Pettersson C, Iwarsson E, Kingston H, Garnier JM, Biancalana V, Oldfors A, Mandel JL, Laporte J. Mutations in amphiphysin 2 (BIN1) disrupt interaction with dynamin 2 and cause autosomal recessive centronuclear myopathy. Nat Genet. 2007;39:1134–1139. doi: 10.1038/ng2086. [DOI] [PubMed] [Google Scholar]
  30. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am J Hum Genet. 2007;81:559–575. doi: 10.1086/519795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Ravona-Springer R, Heymann A, Schmeidler J, Sano M, Preiss R, Koifman K, Hoffman H, Silverman JM, Beeri MS. The ApoE4 genotype modifies the relationship of long-term glycemic control with cognitive functioning in elderly with type 2 diabetes. Eur Neuropsychopharmacol. 2014;24:1303–1308. doi: 10.1016/j.euroneuro.2014.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Ravona-Springer R, Heymann A, Schmeidler J, Guerrero-Berroa E, Sano M, Preiss R, Koifman K, Hoffman H, Levy A, Silverman JM, Schnaider-Beeri M. Haptoglobin 1-1 genotype is associated with poorer cognitive functioning in the elderly with type 2 diabetes. Diabetes Care. 2013;36:3139–3145. doi: 10.2337/dc12-2250. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Sakamuro D, Elliott KJ, Wechsler-Reya R, Prendergast GC. BIN1 is a novel MYC-interacting protein with features of a tumour suppressor. Nat Genet. 1996;14:69–77. doi: 10.1038/ng0996-69. [DOI] [PubMed] [Google Scholar]
  34. Seshadri S, Fitzpatrick AL, Ikram MA, DeStefano AL, Gudnason V, Boada M, Bis JC, Smith AV, Carassquillo MM, Lambert JC, Harold D, Schrijvers EM, Ramirez-Lorca R, Debette S, Longstreth WT, Jr, Janssens AC, Pankratz VS, Dartigues JF, Hollingworth P, Aspelund T, Hernandez I, Beiser A, Kuller LH, Koudstaal PJ, Dickson DW, Tzourio C, Abraham R, Antunez C, Du Y, Rotter JI, Aulchenko YS, Harris TB, Petersen RC, Berr C, Owen MJ, Lopez-Arrieta J, Varadarajan BN, Becker JT, Rivadeneira F, Nalls MA, Graff-Radford NR, Campion D, Auerbach S, Rice K, Hofman A, Jonsson PV, Schmidt H, Lathrop M, Mosley TH, Au R, Psaty BM, Uitterlinden AG, Farrer LA, Lumley T, Ruiz A, Williams J, Amouyel P, Younkin SG, Wolf PA, Launer LJ, Lopez OL, van Duijn CM, Breteler MM CHARGE Consortium GERAD1 Consortium EADI1 Consortium. Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA. 2010;303:1832–1840. doi: 10.1001/jama.2010.574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Shifman S, Darvasi A. The value of isolated populations. Nat Genet. 2001;28:309–310. doi: 10.1038/91060. [DOI] [PubMed] [Google Scholar]
  36. Sperling RA, Dickerson BC, Pihlajamaki M, Vannini P, LaViolette PS, Vitolo OV, Hedden T, Becker JA, Rentz DM, Selkoe DJ, Johnson KA. Functional alterations in memory networks in early Alzheimer’s disease. Neuromol Med. 2010;12:27–43. doi: 10.1007/s12017-009-8109-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Sweet RA, Seltman H, Emanuel JE, Lopez OL, Becker JT, Bis JC, Weamer EA, DeMichele-Sweet MA, Kuller LH. Effect of Alzheimer’s disease risk genes on trajectories of cognitive function in the Cardiovascular Health Study. Am J Psychiatry. 2012;169:954–962. doi: 10.1176/appi.ajp.2012.11121815. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Tan MS, Yu JT, Jiang T, Zhu XC, Guan HS, Tan L. Genetic variation in BIN1 gene and Alzheimer’s disease risk in Han Chinese individuals. Neurobiol Aging. 2014;35(1781):e1781–1788. doi: 10.1016/j.neurobiolaging.2014.01.151. [DOI] [PubMed] [Google Scholar]
  39. Tan MS, Yu JT, Tan L. Bridging integrator 1 (BIN1): form, function, and Alzheimer’s disease. Trends Mol Med. 2013;19:594–603. doi: 10.1016/j.molmed.2013.06.004. [DOI] [PubMed] [Google Scholar]
  40. Vivot A, Glymour MM, Tzourio C, Amouyel P, Chene G, Dufouil C. Association of Alzheimer’s related genotypes with cognitive decline in multiple domains: results from the Three-City Dijon study. Mol Psychiatry. 2015;20:1173–1178. doi: 10.1038/mp.2015.62. [DOI] [PubMed] [Google Scholar]
  41. Wechsler-Reya R, Sakamuro D, Zhang J, Duhadaway J, Prendergast GC. Structural analysis of the human BIN1 gene. Evidence for tissue-specific transcriptional regulation and alternate RNA splicing. J Biol Chem. 1997;272:31453–31458. doi: 10.1074/jbc.272.50.31453. [DOI] [PubMed] [Google Scholar]
  42. Wigge P, McMahon HT. The amphiphysin family of proteins and their role in endocytosis at the synapse. Trends Neurosci. 1998;21:339–344. doi: 10.1016/s0166-2236(98)01264-8. [DOI] [PubMed] [Google Scholar]
  43. Wilson RS, Barral S, Lee JH, Leurgans SE, Foroud TM, Sweet RA, Graff-Radford N, Bird TD, Mayeux R, Bennett DA. Heritability of different forms of memory in the Late Onset Alzheimer’s Disease Family Study. J Alzheimers Dis. 2011;23:249–255. doi: 10.3233/JAD-2010-101515. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Yang S, Liu T, Li S, Zhang X, Ding Q, Que H, Yan X, Wei K, Liu S. Comparative proteomic analysis of brains of naturally aging mice. Neuroscience. 2008;154:1107–1120. doi: 10.1016/j.neuroscience.2008.04.012. [DOI] [PubMed] [Google Scholar]
  45. Zhang X, Yu JT, Li J, Wang C, Tan L, Liu B, Jiang T. Bridging Integrator 1 (BIN1) genotype effects on working memory, hippocampal volume, and functional connectivity in young healthy individuals. Neuropsychopharmacology. 2015;40:1794–1803. doi: 10.1038/npp.2015.30. [DOI] [PMC free article] [PubMed] [Google Scholar]

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