Genetic discoveries of Alzheimer’s disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer’s disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer’s disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer’s disease.

Introduction: Large variability among Alzheimer's disease (AD) cases might impact genetic discoveries and complicate dissection of underlying biological pathways. Methods: Genome Research at Fundacio ACE (GR@ACE) is a genome-wide study of dementia and its clinical endophenotypes, defined based on AD's clinical certainty and vascular burden. We assessed the impact of known AD loci across endophenotypes to generate loci categories. We incorporated gene coexpression data and conducted pathway analysis per category. Finally, to evaluate the effect of heterogeneity in genetic studies, GR@ACE series were meta-analyzed with additional genome-wide association study data sets. Results: We classified known AD loci into three categories, which might reflect the disease clinical heterogeneity. Vascular processes were only detected as a causal mechanism in probable AD. The meta-analysis strategy revealed the ANKRD31-rs4704171 and NDUFAF6-rs10098778 and confirmed SCIMP-rs7225151 and CD33-rs3865444. Discussion: The regulation of vasculature is a prominent causal component of probable AD. GR@ACE meta-analysis revealed novel AD genetic signals, strongly driven by the presence of clinical heterogeneity in the AD series.

Chronic graft-versus-host disease (GVHD)

IntroductionAmyloid plaque deposition in the brain is an early pathological change in Alzheimer's disease (AD), causing disrupted synaptic connections. Brain network disruptions in AD have been demonstrated with eigenvector centrality (EC), a measure that identifies central regions within networks. Carrying an apolipoprotein (APOE)‐ε4 allele is a genetic risk for AD, associated with increased amyloid deposition. We studied whether APOE‐ε4 carriership is associated with EC disruptions in cognitively normal individuals.MethodsA total of 261 healthy middle‐aged to older adults (mean age 56.6 years) were divided into high‐risk (APOE‐ε4 carriers) and low‐risk (noncarriers) groups. EC was computed from resting‐state functional MRI data. Clusters of between‐group differences were assessed with a permutation‐based method. Correlations between cluster mean EC with brain volume, CSF biomarkers, and psychological test scores were assessed.ResultsDecreased EC in the visual cortex was associated with APOE‐ε4 carriership, a genetic risk factor for AD. EC differences were correlated with age, CSF amyloid levels, and scores on the trail‐making and 15‐object recognition tests.ConclusionOur findings suggest that the APOE‐ε4 genotype affects brain connectivity in regions previously found to be abnormal in AD as a sign of very early disease‐related pathology. These differences were too subtle in healthy elderly to use EC for single‐subject prediction of APOE genotype.

Alzheimer's disease (AD) is biologically heterogeneous, and detailed understanding of the processes involved in patients is critical for development of treatments. Cerebrospinal fluid (CSF) contains hundreds of proteins, with concentrations reflecting ongoing (patho)physiological processes. This provides the opportunity to study many biological processes at the same time in patients. We studied whether AD biological subtypes can be detected in cerebrospinal fluid (CSF) proteomics using the dual clustering technique non-negative matrix factorization. In two independent cohorts (EMIF-AD MBD and ADNI) we found that 705 (77% of 913 tested) proteins differed between AD (defined as having abnormal amyloid, n=425) and controls (defined as having normal CSF amyloid and tau and intact cognition, n=127). Using these proteins for data-driven clustering, we identified within each cohorts three robust pathophysiological AD subtypes showing 1) hyperplasticity and increased BACE1 levels; 2) innate immune activation; and 3) blood-brain barrier dysfunction with low BACE1 levels. In both cohorts, the majority of individuals was labelled as having subtype 1 (80, 36% in EMIF-AD MBD; 117, 59% in ADNI), 71 (32%) in EMIF-AD MBD and 41 (21%) in ADNI were labelled as subtype 2, 72 (32%) in EMIF-AD MBD and 39 (20%) individuals in ADNI were labelled as subtype 3. Genetic analyses showed that all subtypes had an excess of genetic risk for AD (all p>0.01). Additional pathological comparisons that were available for a subset in ADNI only further showed that subtypes showed similar severity of AD pathology, and did not differ in the frequencies of co-pathologies, providing further support that these differences truly reflect AD heterogeneity. Compared to controls all non-demented AD individuals had increased risk to show clinical progression, and compared to subtype 1, subtype 2 showed faster progression to after correcting for age, sex, level of education and tau levels (HR (95%CI) subtype 2 vs 1 = 2.5 (1.2, 5.1), p = 0.01), and subtype 3 at trend level (HR (95%CI) = 2.1 (1.0, 4.4)). Together, these results demonstrate the value of CSF proteomics to study biological heterogeneity in AD patients, and suggest that subtypes may require tailored therapy.