Alzheimer's disease (AD) is highly heritable and recent studies have identified over 20 diseaseassociated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed the largest genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg=0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.

BackgroundWe previously investigated low doses (105 or 225 mg) of gantenerumab, a fully human monoclonal antibody that binds and removes aggregated amyloid-β by Fc receptor-mediated phagocytosis, in the SCarlet RoAD (SR) and Marguerite RoAD (MR) phase 3 trials. Several lines of evidence suggested that higher doses may be necessary to achieve clinical efficacy. We therefore designed a positron emission tomography (PET) substudy to evaluate the effect of gantenerumab uptitrated to 1200 mg every 4 weeks on amyloid-β plaques as measured using florbetapir PET in patients with prodromal to moderate Alzheimer’s disease (AD).MethodsA subset of patients enrolled in the SR and MR studies who subsequently entered the open-label extensions (OLEs) were included in this substudy. Patients were aged 50 to 90 years with a clinical diagnosis of probable prodromal to moderate AD and were included based on a visual read of the original screening scan in the double-blind phase. Patients were assigned to 1 of 5 titration schedules (ranging from 2 to 10 months) with a target gantenerumab dose of 1200 mg every 4 weeks. The main endpoint of this substudy was change in amyloid-β plaque burden from OLE baseline to week 52 and week 104, assessed using florbetapir PET. Florbetapir global cortical signal was calculated using a prespecified standard uptake value ratio method converted to the Centiloid scale.ResultsSixty-seven of the 89 patients initially enrolled had ≥ 1 follow-up scan by August 15, 2018. Mean amyloid levels were reduced by 39 Centiloids by the first year and 59 Centiloids by year 2, a 3.5-times greater reduction than was seen after 2 years at 225 mg in SR. At years 1 and 2, 37% and 51% of patients, respectively, had amyloid-β plaque levels below the amyloid-β positivity threshold.ConclusionResults from this exploratory interim analysis of the PET substudy suggest that gantenerumab doses up to 1200 mg resulted in robust amyloid-β plaque removal at 2 years. PET amyloid levels were consistent with sparse-to-no neuritic amyloid-β plaques in 51% of patients after 2 years of therapy. Amyloid reductions were similar to those observed in other placebo-controlled studies that have suggested potential clinical benefit.Trial registrationClinicalTrials.gov, NCT01224106 (SCarlet RoAD) and NCT02051608 (Marguerite RoAD).

To elucidate the proteomic features of aging in plasma, the subproteome targeted by the SOMAscan assay was profiled in blood samples from 202 females from the TwinsUK cohort. Findings were replicated in 677 independent individuals from the AddNeuroMed, Alzheimer’s Research UK, and Dementia Case Registry cohorts. Results were further validated using RNAseq data from whole blood in TwinsUK and the most significant proteins were tested for association with aging-related phenotypes after adjustment for age. Eleven proteins were associated with chronological age and were replicated at protein level in an independent population. These were further investigated at gene expression level in 384 females from the TwinsUK cohort. The two most strongly associated proteins were chordin-like protein 1 (meta-analysis β [SE] = 0.013 [0.001], p = 3.66 × 10−46) and pleiotrophin (0.012 [0.005], p = 3.88 × 10−41). Chordin-like protein 1 was also significantly correlated with birthweight (0.06 [0.02], p = 0.005) and with the individual Framingham 10-years cardiovascular risk scores in TwinsUK (0.71 [0.18], p = 9.9 × 10−5). Pleiotrophin is a secreted growth factor with a plethora of functions in multiple tissues and known to be a marker for cardiovascular risk and osteoporosis. Our study highlights the importance of proteomics to identify some molecular mechanisms involved in human health and aging.

Background:Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer’s disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust.Objectives:This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts.Methods:Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis.Results:Gene and pathway level models performed similarly to each other and to a model based on demographic information only.Conclusions:Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.

Background: Four previously reported studies have tested for association of blood proteins with neocortical amyloid-β burden (NAB). If shown to be robust, these proteins could have utility as a blood test for enrichment in clinical trials of Alzheimer’s disease (AD) therapeutics.Objective: This study aimed to investigate whether previously identified blood proteins also show evidence for association with NAB in serum samples from the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL). The study considers candidate proteins seen in cohorts other than AIBL and candidates previously discovered in the AIBL cohort.Methods: Our study used the SOMAscan platform for protein quantification in blood serum. Linear and logistic regressions were used to model continuous NAB and dichotomized NAB respectively using single proteins as a predictor. Multiple protein models were built using stepwise regression techniques and support vectors machines. Age and APOE ɛ4 carriage were used as covariates for all analysis.Results: Of the 41 proteins previously reported, 15 AIBL candidates and 20 non-AIBL candidates were available for testing. Of these candidates, pancreatic polypeptide (PPY) and IgM showed a significant association with NAB. Notably, IgM was found to associate with continuous NAB across cognitively normal control subjects.Conclusions: We have further demonstrated the association of PPY and IgM with NAB, despite technical differences between studies. There are several reasons for a lack of significance for the other candidates including platform differences and the use of serum rather than plasma samples. To investigate the possibility of technical differences causing lack of replication, further studies are required.

We believe this is the first study to investigate associations between blood metabolites and neocortical amyloid burden (NAB) in the search for a blood-based biomarker for Alzheimer's disease (AD). Further, we present the first multi-modal analysis of blood markers in this field. We used blood plasma samples from 91 subjects enrolled in the University of California, San Francisco Alzheimer's Disease Research Centre. Non-targeted metabolomic analysis was used to look for associations with NAB using both single and multiple metabolic feature models. Five metabolic features identified subjects with high NAB, with 72% accuracy. We were able to putatively identify four metabolites from this panel and improve the model further by adding fibrinogen gamma chain protein measures (accuracy=79%). One of the five metabolic features was studied in the Alzheimer's Disease Neuroimaging Initiative cohort, but results were inconclusive. If replicated in larger, independent studies, these metabolic features and proteins could form the basis of a blood test with potential for enrichment of amyloid pathology in anti-amyloid trials.