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Alzheimer's disease (AD) is the most commonly diagnosed form of dementia in the elderly. Predominantly this disease is sporadic in nature with only a small percentage of patients exhibiting a familial trait. Early-onset AD may be explained by single gene defects; however, most AD cases are late onset (> 65 years) and, although there is no known definite cause for this form of the disease, there are several known risk factors. Of these, the e4 allele of the apolipoprotein E (apoE) gene (APOE) is a major risk factor. The e4 allele of APOE is one of three (e2 e3 and e4) common alleles generated by cysteine/arginine substitutions at two polymorphic sites. The possession of the e4 allele is recognized as the most common identifiable genetic risk factor for late-onset AD across most populations. Unlike the pathogenic mutations in the amyloid precursor or those in the presenilins, APOE e4 alleles increase the risk for AD but do not guarantee disease, even when present in homozygosity. In addition to the cysteine/arginine polymorphisms at the e2/e3/e4 locus, polymorphisms within the proximal promoter of the APOE gene may lead to increased apoE levels by altering transcription of the APOE gene. Here we review the genetic and biochemical evidence supporting the hypothesis that regulation of apoE protein levels may contribute to the risk of AD, distinct from the well known polymorphisms at the e2/e3/e4 locus.

Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer’s disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (Poptimal) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD.

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Dementia is a global epidemic with Alzheimer's disease (AD) being the leading cause. Early identification of patients at risk of developing AD is now becoming an international priority. Neocortical Aβ (extracellular β-amyloid) burden (NAB), as assessed by positron emission tomography (PET), represents one such marker for early identification. These scans are expensive and are not widely available, thus, there is a need for cheaper and more widely accessible alternatives. Addressing this need, a blood biomarker-based signature having efficacy for the prediction of NAB and which can be easily adapted for population screening is described. Blood data (176 analytes measured in plasma) and Pittsburgh Compound B (PiB)-PET measurements from 273 participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study were utilised. Univariate analysis was conducted to assess the difference of plasma measures between high and low NAB groups, and cross-validated machine-learning models were generated for predicting NAB. These models were applied to 817 non-imaged AIBL subjects and 82 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for validation. Five analytes showed significant difference between subjects with high compared to low NAB. A machine-learning model (based on nine markers) achieved sensitivity and specificity of 80 and 82%, respectively, for predicting NAB. Validation using the ADNI cohort yielded similar results (sensitivity 79% and specificity 76%). These results show that a panel of blood-based biomarkers is able to accurately predict NAB, supporting the hypothesis for a relationship between a blood-based signature and Aβ accumulation, therefore, providing a platform for developing a population-based screen.

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 (BDNFVal/Val, BDNFMet) 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+/BDNFMet participants showed a significantly faster rate of decline on verbal and visual EM, and language over 54 months compared with ɛ4−/BDNFVal/Val participants (d=0.90–1.02). At least two genetic loci affect the rate of Aβ-related cognitive decline. Aβ+ɛ4+/BDNFMet individuals can expect to show clinically significant memory impairment after 3 years, whereas Aβ+ɛ4+/BDNFVal/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.