Alzheimer's Disease: Genes, pathogenesis and risk prediction

With the aging of western society the contribution to morbidity of diseases of the elderly, such as dementia, will increase exponentially. Thorough preventative and curative strategies are needed to constrain the increasing prevalence of these disabling diseases. Better understanding of the pathogenesis of disease will enable development of therapy, prevention and the identification of high-risk groups in the population. Here, we review the genetic epidemiology of Alzheimer's disease, the most common cause of dementia in the western world. The search for genetic risk factors, though far from completed, has been of major importance for understanding the pathogenesis of Alzheimer's disease. Although effective therapy is still awaited, these findings have led to new avenues for the development of drugs

The Secure Anonymised Information Linkage databank Dementia e-cohort (SAIL-DeC)

Introduction: The rising burden of dementia is a global concern, and there is a need to study its causes, natural history and outcomes. The Secure Anonymised Information Linkage (SAIL) Databank contains anonymised, routinely-collected healthcare data for the population of Wales, UK. It has potential to be a valuable resource for dementia research owing to its size, long follow-up time and prospective collection of data during clinical care. Objectives:We aimed to apply reproducible methods to create the SAIL dementia e-cohort (SAIL-DeC). We created SAIL-DeC with a view to maximising its utility for a broad range of research questions whilst minimising duplication of effort for researchers. Methods:SAIL contains individual-level, linked primary care, hospital admission, mortality and demographic data. Data are currently available until 2018 and future updates will extend participant follow-up time. We included participants who were born between 1st January 1900 and 1st January 1958 and for whom primary care data were available. We applied algorithms consisting of International Classification of Diseases (versions 9 and 10) and Read (version 2) codes to identify participants with and without all-cause dementia and dementia subtypes. We also created derived variables for comorbidities and risk factors. Results:From 4.4 million unique participants in SAIL, 1.2 million met the cohort inclusion criteria, resulting in 18.8 million person-years of follow-up. Of these, 129,650 (10%) developed all-cause dementia, with 77,978 (60%) having dementia subtype codes. Alzheimer's disease was the most common subtype diagnosis (62%). Among the dementia cases, the median duration of observation time was 14 years. Conclusion:We have created a generalisable, national dementia e-cohort, aimed at facilitating epidemiological dementia research

A 22-single nucleotide polymorphism Alzheimer's disease risk score correlates with family history, onset age, and cerebrospinal fluid Aβ42

Introduction: The ability to identify individuals at increased genetic risk for Alzheimer's disease (AD) may streamline biomarker and drug trials and aid clinical and personal decision making. Methods: We evaluated the discriminative ability of a genetic risk score (GRS) covering 22 published genetic risk loci forADin 1162 Flanders-BelgianADpatients and 1019 controls and assessed correlations with family history, onset age, and cerebrospinal fluid (CSF) biomarkers (A beta(1-42), T-Tau, P-Tau(181P)). Results: A GRS including all single nucleotide polymorphisms (SNPs) and age-specific APOE epsilon 4 weights reached area under the curve (AUC) 0.70, which increased to AUC 0.78 for patients with familial predisposition. Risk of AD increased with GRS (odds ratio, 2.32 (95% confidence interval 2.08-2.58 per unit; P < 1.0e(-15)). Onset age and CSF Ab1-42 decreased with increasing GRS (P-onset_age 5 9.0e(-11); P-A beta = 8.9e(-7)). Discussion: The discriminative ability of this 22-SNP GRS is still limited, but these data illustrate that incorporation of age-specific weights improves discriminative ability. GRS-phenotype correlations highlight the feasibility of identifying individuals at highest susceptibility. (C) 2015 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association

A 22-single nucleotide polymorphism Alzheimer's disease risk score correlates with family history, onset age, and cerebrospinal fluid A beta(42)

Introduction: The ability to identify individuals at increased genetic risk for Alzheimer's disease (AD) may streamline biomarker and drug trials and aid clinical and personal decision making. Methods: We evaluated the discriminative ability of a genetic risk score (GRS) covering 22 published genetic risk loci forADin 1162 Flanders-BelgianADpatients and 1019 controls and assessed correlations with family history, onset age, and cerebrospinal fluid (CSF) biomarkers (A beta(1-42), T-Tau, P-Tau(181P)). Results: A GRS including all single nucleotide polymorphisms (SNPs) and age-specific APOE epsilon 4 weights reached area under the curve (AUC) 0.70, which increased to AUC 0.78 for patients with familial predisposition. Risk of AD increased with GRS (odds ratio, 2.32 (95% confidence interval 2.08-2.58 per unit; P <1.0e(-15)). Onset age and CSF Ab1-42 decreased with increasing GRS (P-onset_age 5 9.0e(-11); P-A beta = 8.9e(-7)). Discussion: The discriminative ability of this 22-SNP GRS is still limited, but these data illustrate that incorporation of age-specific weights improves discriminative ability. GRS-phenotype correlations highlight the feasibility of identifying individuals at highest susceptibility. (C) 2015 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association. This is an open access article under the CC BY-NC-ND licens

Amyloid-β1–43 cerebrospinal fluid levels and the interpretation of APP, PSEN1 and PSEN2 mutations

Background Alzheimer's disease (AD) mutations in amyloid precursor protein (APP) and presenilins (PSENs) could potentially lead to the production of longer amyloidogenic A beta peptides. Amongst these, A beta(1-43)is more prone to aggregation and has higher toxic properties than the long-known A beta(1-42). However, a direct effect on A beta(1-43)in biomaterials of individuals carrying genetic mutations in the known AD genes is yet to be determined. Methods N = 1431 AD patients (n = 280 early-onset (EO) andn = 1151 late-onset (LO) AD) and 809 control individuals were genetically screened forAPPandPSENs. For the first time, A beta(1-43)levels were analysed in cerebrospinal fluid (CSF) of 38 individuals carrying pathogenic or unclear rare mutations or the commonPSEN1p.E318G variant and compared with A beta(1-42)and A beta 1-40CSF levels. The soluble sAPP alpha and sAPP beta species were also measured for the first time in mutation carriers. Results A known pathogenic mutation was identified in 5.7% of EOAD patients (4.6%PSEN1, 1.07%APP) and in 0.3% of LOAD patients. Furthermore, 12 known variants with unclear pathogenicity and 11 novel were identified. Pathogenic and unclear mutation carriers showed a significant reduction in CSF A beta(1-43)levels compared to controls (p = 0.037; < 0.001). CSF A beta(1-43)levels positively correlated with CSF A beta(1-42)in both pathogenic and unclear carriers and controls (allp < 0.001). The p.E318G carriers showed reduced A beta(1-43)levels (p < 0.001), though genetic association with AD was not detected. sAPP alpha and sAPP beta CSF levels were significantly reduced in the group of unclear (p = 0.006; 0.005) and p.E318G carriers (p = 0.004; 0.039), suggesting their possible involvement in AD. Finally, using A beta(1-43)and A beta(1-42)levels, we could re-classify as "likely pathogenic" 3 of the unclear mutations. Conclusion This is the first time that A beta(1-43)levels were analysed in CSF of AD patients with genetic mutations in the AD causal genes. The observed reduction of A beta(1-43)inAPPandPSENscarriers highlights the pathogenic role of longer A beta peptides in AD pathogenesis. Alterations in A beta(1-43)could prove useful in understanding the pathogenicity of unclearAPPandPSENsvariants, a critical step towards a more efficient genetic counselling

Both common variations and rare non-synonymous substitutions and small insertion/deletions in CLU are associated with increased Alzheimer risk.

BACKGROUND: We have followed-up on the recent genome-wide association (GWA) of the clusterin gene (CLU) with increased risk for Alzheimer disease (AD), by performing an unbiased resequencing of all CLU coding exons and regulatory regions in an extended Flanders-Belgian cohort of Caucasian AD patients and control individuals (n = 1930). Moreover, we have replicated genetic findings by targeted resequencing in independent Caucasian cohorts of French (n = 2182) and Canadian (n = 573) origin and by performing meta-analysis combining our data with previous genetic CLU screenings. RESULTS: In the Flanders-Belgian cohort, we identified significant clustering in exons 5-8 of rare genetic variations leading to non-synonymous substitutions and a 9-bp insertion/deletion affecting the CLU β-chain (p = 0.02). Replicating this observation by targeted resequencing of CLU exons 5-8 in 2 independent Caucasian cohorts of French and Canadian origin identified identical as well as novel non-synonymous substitutions and small insertion/deletions. A meta-analysis, combining the datasets of the 3 cohorts with published CLU sequencing data, confirmed that rare coding variations in the CLU β-chain were significantly enriched in AD patients (OR(MH) = 1.96 [95% CI = 1.18-3.25]; p = 0.009). Single nucleotide polymorphisms (SNPs) association analysis indicated the common AD risk association (GWA SNP rs11136000, p = 0.013) in the 3 combined datasets could not be explained by the presence of the rare coding variations we identified. Further, high-density SNP mapping in the CLU locus mapped the common association signal to a more 5' CLU region. CONCLUSIONS: We identified a new genetic risk association of AD with rare coding CLU variations that is independent of the 5' common association signal identified in the GWA studies. At this stage the role of these coding variations and their likely effect on the β-chain domain and CLU protein functioning remains unclear and requires further studies.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Predicting AT(N) pathologies in Alzheimer’s disease from blood-based proteomic data using neural networks

Background and objective: Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD. Methods: We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis. Results: Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway. Conclusion: Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size. Keywords: Alzheimer’s disease; amyloid β; artificial neural networks; machine learning; neurodegeneration; plasma proteomics; ta

An intronic VNTR affects splicing of ABCA7 and increases risk of Alzheimer's disease

Mutations leading to premature termination codons in ATP-Binding Cassette Subfamily A Member 7 (ABCA7) are high penetrant risk factors of Alzheimer's disease (AD). The influence of other genetic variants in ABCA7 and downstream functional mechanisms, however, is poorly understood. To address this knowledge gap, we investigated tandem repetitive regions in ABCA7 in a Belgian cohort of 1529 AD patients and control individuals and identified an intronic variable number tandem repeat (VNTR). We observed strong association between VNTR length and a genome-wide associated signal for AD in the ABCA7 locus. Expanded VNTR alleles were highly enriched in AD patients [odds ratio = 4.5 (1.3-24.2)], and VNTR length inversely correlated with amyloid beta(1-42) in cerebrospinal fluid and ABCA7 expression. In addition, we identified three novel ABCA7 alternative splicing events. One isoform in particular-which is formed through exon 19 skipping-lacks the first nucleotide binding domain of ABCA7 and is abundant in brain tissue. We observed a tight correlation between exon 19 skipping and VNTR length. Our findings underline the importance of studying repetitive DNA in complex disorders and expand the contribution of genetic and transcript variation in ABCA7 to AD