A blood-based signature of cerebrospinal fluid A beta(1-42) status

It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fuid (CSF) amyloid β1−42 (Aβ1−42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specifcity). Refnement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1−42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1−42 levels transitioned to an AD diagnosis over 120 months signifcantly faster than those with predicted normal CSF Aβ1−42 levels and that the resulting model also validates reasonably across PET Aβ1−42 status (0.78 AUC). This is the frst study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1−42 status, the earliest risk indicator for AD, with high accurac

Predicting Alzheimer disease from a blood-based biomarker profile

Objective: We assessed a blood-based signature, which previously demonstrated high accuracy at stratifying individuals with high or low neocortical β-amyloid burden (NAB), to determine whether it could also identify individuals at risk of progression to Alzheimer disease (AD) within 54 months. Methods: We generated the blood-based signature for 585 healthy controls (HCs) and 74 participants with mild cognitive impairment (MCI) from the Australian Imaging, Biomarkers and Lifestyle Study who underwent clinical reclassification (blinded to biomarker findings) at 54-month follow-up. The individuals were split into estimated high and low NAB groups based on a cutoff of 1.5 standardized uptake value ratio. We assessed the predictive accuracy of the high and low NAB groupings based on progression to mild cognitive impairment or AD according to clinical reclassification at 54-month follow-up. Results: Twelve percent of HCs with estimated high NAB progressed in comparison to 5% of HCs with estimated low NAB (odds ratio = 2.4). Forty percent of the participants with MCI who had estimated high NAB progressed in comparison to 5% of the participants with MCI who had estimated low NAB (odds ratio = 12.3). These ratios are in line with those reported for Pittsburgh compound B–PET results. Individuals with estimated high NAB had faster rates of memory decline than those with estimated low NAB. Conclusion: These findings suggest that a simple blood-based signature not only provides estimates of NAB but also predicts cognitive decline and disease progression, identifying individuals at risk of progressing toward AD at the prodromal and preclinical stages

Plasma Amyloid-β as a Biomarker in Alzheimer's Disease: The AIBL Study of Aging

Amyloid-β (Aβ) plays a central role in the pathogenesis of Alzheimer's disease (AD) and has been postulated as a potential biomarker for AD. However, there is a lack of consensus as to its suitability as an AD biomarker. The objective of this study was to determine the significance of plasma Aβ as an AD biomarker and its relationship with Aβ load and to determine the effect of different assay methods on the interpretation of Aβ levels. Plasma Aβ1-40, Aβ1-42, and N-terminal cleaved fragments were measured using both a commercial multiplex assay and a well-documented ELISA in 1032 individuals drawn from the well-characterized Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Further, Aβ levels were compared to Aβ load derived from positron-emission tomography (PET) with the Pittsburgh compound B (PiB). Lower Aβ1-42 and Aβ1-42/1-40 ratio were observed in patients with AD and inversely correlated with PiB-PET derived Aβ load. However, assay methodology significantly impacted the interpretation of data. The cross-sectional analysis of plasma Aβ isoforms suggests that they may not be sufficient per se to diagnose AD. The value of their measurement in prognosis and monitoring of AD interventions needs further study, in addition to future longitudinal comparisons together with other predictors, which will determine whether plasma Aβ has diagnostic value in a panel of biomarkers