Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture

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 (P) 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

A blood-based biomarker panel indicates IL-10 and IL-12/23p40 are jointly associated as predictors of β-amyloid load in an AD cohort

Alzheimer\u27s Disease (AD) is the most common form of dementia, characterised by extracellular amyloid deposition as plaques and intracellular neurofibrillary tangles of tau protein. As no current clinical test can diagnose individuals at risk of developing AD, the aim of this project is to evaluate a blood-based biomarker panel to identify individuals who carry this risk. We analysed the levels of 22 biomarkers in clinically classified healthy controls (HC), mild cognitive impairment (MCI) and Alzheimer\u27s participants from the well characterised Australian Imaging, Biomarker and Lifestyle (AIBL) study of aging. High levels of IL-10 and IL-12/23p40 were significantly associated with amyloid deposition in HC, suggesting that these two biomarkers might be used to detect at risk individuals. Additionally, other biomarkers (Eotaxin-3, Leptin, PYY) exhibited altered levels in AD participants possessing the APOE ϵ4 allele. This suggests that the physiology of some potential biomarkers may be altered in AD due to the APOE ϵ4 allele, a major risk factor for AD. Taken together, these data highlight several potential biomarkers that can be used in a blood-based panel to allow earlier identification of individuals at risk of developing AD and/or early stage AD for which current therapies may be more beneficial

High content, multi-parameter analyses in buccal cells to identify Alzheimer's disease

Alzheimer’s disease (AD) is a degenerative brain disorder and is the most common form of dementia. Minimally invasive approaches are required that combine biomarkers to identify individuals who are at risk of developing mild cognitive impairment (MCI) and AD, to appropriately target clinical trials for therapeutic discovery as well as lifestyle strategies aimed at prevention. Buccal mucosa cells from the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing cohort (n=60) were investigated for cytological markers that could be used to identify both MCI and AD individuals. Visual scoring of the buccal cytome demonstrated a significantly lower frequency of basal and karyorrhectic cells in the MCI group compared with controls. A high content, automated assay was developed using laser scanning cytometry to simultaneously measure cell types, nuclear DNA content and aneuploidy, neutral lipid content, putative Tau and amyloid-β (Aβ) in buccal cells. DNA content, aneuploidy, neutral lipids and Tau were similar in all groups. However, there was significantly lower Tau protein in both basal and karyolytic buccal cell types compared with differentiated buccal cells. Aβ, as measured by frequency of cells containing Aβ signal, as well as area and integral of Aβ signal, was significantly higher in the AD group compared with the control group. Buccal cell Aβ was correlated with mini-mental state examination (MMSE) scores (r = -0.436, P=0.001) and several blood-based biomarkers. Combining newly identified biomarkers from buccal cells with those already established may offer a potential route for more specific biomarker panels which may substantially increase the likelihood of better predictive markers for earlier diagnosis of AD

Rates of diagnostic transition and cognitive change at 18-month follow-up among 1,112 participants in the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing (AIBL)

Background: The Australian Imaging, Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing is a prospective study of 1,112 individuals (211 with Alzheimer's disease (AD), 133 with mild cognitive impairment (MCI), and 768 healthy controls (HCs)). Here we report diagnostic and cognitive findings at the first (18-month) follow-up of the cohort. The first aim was to compute rates of transition from HC to MCI, and MCI to AD. The second aim was to characterize the cognitive profiles of individuals who transitioned to a more severe disease stage compared with those who did not. Methods: Eighteen months after baseline, participants underwent comprehensive cognitive testing and diagnostic review, provided an 80 ml blood sample, and completed health and lifestyle questionnaires. A subgroup also underwent amyloid PET and MRI neuroimaging. Results: The diagnostic status of 89.9% of the cohorts was determined (972 were reassessed, 28 had died, and 112 did not return for reassessment). The 18-month cohort comprised 692 HCs, 82 MCI cases, 197 AD patients, and one Parkinson's disease dementia case. The transition rate from HC to MCI was 2.5%, and cognitive decline in HCs who transitioned to MCI was greatest in memory and naming domains compared to HCs who remained stable. The transition rate from MCI to AD was 30.5%. Conclusion: There was a high retention rate after 18 months. Rates of transition from healthy aging to MCI, and MCI to AD, were consistent with established estimates. Follow-up of this cohort over longer periods will elucidate robust predictors of future cognitive decline.12 page(s