Elsevier

Personalized Medicine in Psychiatry

Volumes 7–8, March–April 2018, Pages 14-20
Personalized Medicine in Psychiatry

Cognitive gene risk profile for the prediction of cognitive decline in presymptomatic Alzheimer’s disease

https://doi.org/10.1016/j.pmip.2018.03.001Get rights and content

Highlights

  • A Cognitive Gene Risk Profile is defined in preclinical AD individuals.

  • Profile partitions individuals into groups with differential rates of cognitive decline.

  • Profile has utility in participant selection/stratification for preclinical trials.

Abstract

Introduction

In cognitively normal (CN) older adults, high levels of Aβ-amyloid are associated with significant decline in cognition, especially episodic memory. Several genes have previously been associated with cognition, including APOE, KIBRA, KLOTHO, BDNF, COMT, SPON1 and CSMD1. While some of this variation has been attributed to some of these genes individually, the combined effects of these genes on rates of cognitive decline, particularly in preclinical Alzheimer’s Disease remain largely unknown.

Methods

To elucidate if risk alleles within these genes can be suitably combined to predict cognitive decline 127 CN older adults with elevated PET-ascertained Aβ-amyloid were included in a decision tree analysis to define a “Cognitive Gene Risk Profile” for decline in a verbal episodic memory composite.

Results

The episodic memory-derived Cognitive Gene Risk Profile defined four groups: APOE ε4+ Risk, ε4+ Resilient, ε4− Risk, ε4− Resilient, with the ε4+ Risk group declining significantly faster than all other groups (ε4+ Resilient, p = 0.0008; ε4− Risk, p = 0.025; ε4− Resilient, p = 0.0006). The ε4+ Risk group also declined significantly faster than all other groups on Global, Clinical Progression and Pre-Alzheimer’s cognitive composites.

Discussion

The defined Cognitive Gene Risk Profile has potential utility in participant selection/stratification for preclinical AD trials that incorporate Aβ-amyloid and where decline in cognition is essential to determine therapeutic effectiveness.

Introduction

Evidence from prospective longitudinal cohort studies suggests that the pathological changes in Alzheimer’s Disease (AD) commence decades before the onset of clinical symptomology [1]. Further, it has been established that higher levels of Aβ-amyloid (Aβ) in cognitively normal (CN) older adults is associated with accelerated decline in cognition [2]. As such, cerebrospinal fluid (CSF) and imaging biomarkers of Aβ are used to define the preclinical stage of AD [3], [4]. However, at the preclinical stage of AD there is considerable interpersonal variability in the rate of cognitive decline, suggesting that while Aβ is a necessary condition for AD, other factors influence the relationship between this biomarker and clinical disease progression. Cognition has been shown to be both highly heritable and highly polygenic [5] and allelic variation in several genes associated with cognition has been shown to explain some variation in cognitive function in older adults and in Aβ related cognitive decline in early AD [6], [7], [8]. Thus suggesting that genetics could help inform and predict rates of cognitive decline and identify groups of CN older adults that are at a higher risk of a more rapid decline in cognition.

There have been several individual genes associated with cognitive performance and decline. The major genetic risk factor for AD, the ε4 allele of apolipoprotein E (APOE) [9], has been consistently associated with accelerated rates of episodic memory decline and hippocampal atrophy (reviewed in [10]). The non-synonymous rs6265 (Val66Met) single nucleotide polymorphism (SNP) in the brain derived neurotropic factor (BDNF), has been linked with altered rates of decline in several cognitive domains, and hippocampal atrophy [7], [8]. A further non-synonymous SNP that regulates dopamine availability in the central nervous system, rs4680 (Val158Met) within Catechol-O-methyltransferase (COMT), has also been associated with cognitive performance [11]. The Klotho gene (KL), initially discovered in transgenic mice with a phenotype resembling human aging [12], has a functional variant, KL-VS that has been associated with life expectancy [13], global cognition [14], processing speed [14], and brain volume [15].

A further gene, KIBRA, that encodes the KIdney and BRAin expressed protein has recently been shown to be involved in the mediation of tau-induced memory loss and synaptic plasticity [16]. Allelic variation in the KIBRA gene, specifically a substitution of C for T in the 9th intron (rs17070145), has been reported to be associated with memory performance [17], hippocampal atrophy [18] and measurable differences in brain activation [17]. We have described recently how this gene contributes to moderating Aβ driven cognitive decline [19]. Additionally, several SNPs in the CSMD1 (CUB and Sushi Multiple Domains 1) gene, involved in the regulation of complement and inflammation [20], have been associated with episodic memory and general cognition in a cognitively normal sample [21]. Finally, multiple SNPs within the Spondin 1 (SPON1) gene, involved in the processing of amyloid precursor protein (APP) [22], have been associated with disease severity [23] and rates of cognitive decline [24], though only in AD individuals.

Several studies have investigated the extent to which combinations of genes can influence cognitive decline and clinical progression in AD [25], [26], [27], [28]. However, most of these studies focused on genes shown previously to be associated with risk for AD, with gene weighting based on AD risk [25], [26]. Thus these polygenic approaches may have resulted in exclusion of genes associated with cognitive performance, or if included, their influence diluted due to a disease risk based weighting [26]. Further, few studies have taken brain Aβ burden into consideration and investigated combining genes associated with cognitive performance in preclinical AD [8], [29].

This study hypothesised that combining genes shown to be associated with cognition would explain variance in Aβ related cognitive decline in preclinical AD. This study aimed to combine these genes into a straightforward profile able to discriminate individuals based on cognition, and particularly episodic memory, which is one of the earliest cognitive domains to decline [30]. The profile was created in CN older adults, signified at risk of cognitive decline based on brain imaging biomarkers, enrolled in the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study. Extensive 18-monthly assessment, including cognitive and neuroimaging, within the AIBL Study allows for the longitudinal evaluation of this profile. Such a genetic profile could be easily implemented for the identification of individuals with accelerated rates of cognitive decline, which could have utilisation for clinical trial design, leading to more efficient clinical trials and secondary prevention studies.

Section snippets

Study participants

One hundred and thirty-three CN biomarker positive (based on brain imaging) older adults enrolled in the AIBL Study, a prospective longitudinal study of ageing, were included in this study. The study design, enrolment process, neuropsychological assessments, and diagnostic criteria of the AIBL Study have been previously described [31]. Approval of the AIBL Study has been granted by each of the ethics committees of each of the member institutions: Austin Health, St Vincent’s Health, Hollywood

high cognitively normal adults baseline demographics, genotype frequencies and cognitive slopes

Table 1 shows the demographics, genotype frequencies and cognitive slopes of the 127 Aβhigh CN older adults included in the study. The statistically driven global composite (−0.0901 SD/year), clinical progression (−0.0484 SD/year), and verbal episodic (−0.0774 SD/year) composites all presented with a negative rate of change when investigating Aβhigh CN older adults.

Defining the Cognitive Gene Risk Profile (Cog-GRP) and group stratification

The “Rpart” package in R was used to calculate the decision tree that defined the Cog-GRP. The decision tree was constructed using

Discussion

Results from this study support the hypothesis that combining genes previously associated with cognitive performance allows for the identification of groups of individuals with accelerated rates of cognitive decline. In CN older adults with high Aβ burden at baseline a decision tree was created driven by decline in a composite score of verbal episodic memory to define a Cog-GRP. This profile combined the effects of APOE, BDNF, KIBRA, KLOTHO, SPON1 and CSMD1. COMT dropped out of the model due to

Acknowledgements

Funding for the AIBL study was provided in part by the study partners [Commonwealth Scientific Industrial and Research Organization (CSIRO), Edith Cowan University (ECU), Mental Health Research Institute (MHRI), National Ageing Research Institute (NARI), Austin Health, CogState Ltd.]. The AIBL study has also received support from the National Health and Medical Research Council (NHMRC) and the Dementia Collaborative Research Centres program (DCRC2), as well as funding from the Science and

References (51)

  • C.C. Rowe et al.

    Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging

    Neurobiol Aging

    (2010)
  • P. Bourgeat et al.

    Comparison of MR-less PiB SUVR quantification methods

    Neurobiol Aging

    (2015)
  • S.C. Burnham et al.

    Clinical and cognitive trajectories in cognitively healthy elderly individuals with suspected non-Alzheimer’s disease pathophysiology (SNAP) or Alzheimer’s disease pathology: a longitudinal study

    Lancet Neurol

    (2016)
  • E.E. Tripoliti et al.

    A six stage approach for the diagnosis of the Alzheimer’s disease based on fMRI data

    J Biomed Inform

    (2010)
  • S.L. Mestizo Gutiérrez et al.

    Decision trees for the analysis of genes involved in Alzheimer’s disease pathology

    J Theor Biol

    (2014)
  • V.L. Villemagne et al.

    Longitudinal assessment of Abeta and cognition in aging and Alzheimer disease

    Ann Neurol

    (2011)
  • L. Kulic et al.

    Recent advances in cerebrospinal fluid biomarkers for the detection of preclinical Alzheimer’s disease

    Curr Opin Neurol

    (2016)
  • R.M. Kirkpatrick et al.

    Results of a “GWAS plus:” general cognitive ability is substantially heritable and massively polygenic

    PLoS ONE

    (2014)
  • Y.Y. Lim et al.

    Abeta-related memory decline in APOE epsilon4 noncarriers: implications for Alzheimer disease

    Neurology

    (2016)
  • Y.Y. Lim et al.

    APOE and BDNF polymorphisms moderate amyloid beta-related cognitive decline in preclinical Alzheimer’s disease

    Mol Psychiatry

    (2015)
  • E.H. Corder et al.

    Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families

    Science

    (1993)
  • Y. Liu et al.

    APOE genotype and neuroimaging markers of Alzheimer’s disease: systematic review and meta-analysis

    J Neurol Neurosurg Psychiatry

    (2015)
  • M. Kuro-o et al.

    Mutation of the mouse klotho gene leads to a syndrome resembling ageing

    Nature

    (1997)
  • D.E. Arking et al.

    Association of human aging with a functional variant of klotho

    PNAS

    (2002)
  • J.S. Yokoyama et al.

    Variation in longevity gene KLOTHO is associated with greater cortical volumes

    Ann Clin Transl Neurol

    (2015)
  • Cited by (17)

    • Resistance training enhances delayed memory in healthy middle-aged and older adults: A randomised controlled trial

      2019, Journal of Science and Medicine in Sport
      Citation Excerpt :

      DNA was extracted from whole blood as per manufacturer instructions using QIAamp DNA Blood Mini Kits (Qiagen, Hilden, Germany). Genotyping was performed as described previously.24,25 Briefly, TaqMan® genotyping assays were used to determine apolipoprotein E (APOE) genotype (Life Technologies, Carlsbad, CA, U.S.A.) and were performed on a QuantStudio 12 K Flex™ Real-Time-PCR system (Applied Biosystems, Foster City, CA, U.S.A.) using the TaqMan® GTXpress™ Master Mix (Life Technologies) methodology as per manufacturer instructions.

    • COMT val158met is not associated with Aβ-amyloid and APOE ε4 related cognitive decline in cognitively normal older adults

      2019, IBRO Reports
      Citation Excerpt :

      These SUVRs were then classified as either low (Aβlow) or high (Aβhigh) Aβ burden, based on a tracer-specific SUVR threshold: ≥1.4, ≥1.05 and ≥0.55 for PiB, florbetapir and flutemetamol, respectively (Rowe et al., 2013). Methods for DNA extraction and genotyping have been previously described (Brown et al., 2014; Porter et al., 2018a, b). Briefly, QIAamp DNA Blood Maxi Kits (Qiagen, Hilden, Germany) were used for DNA extraction from whole blood as per manufacturer’s instructions.

    View all citing articles on Scopus
    1

    SML and SCB are joint senior authors.

    2

    http://aibl.csiro.au/about/aibl-research-team.

    3

    http://www.mentalhealthcrc.com.

    View full text