A Robust Blood-based Signature of Cerebrospinal Fluid Aß42 Status.

Early detection of Alzheimer's disease (AD) is of vital importance in the development of disease-modifying therapies. This necessitates the use of early pathological indicators of the disease such as amyloid abnormality to identify individuals at early disease stages where intervention is likely to be most effective. Recent evidence suggests that cerebrospinal fluid (CSF) amyloid ß1-42 (Aß42) level may indicate AD risk earlier compared to amyloid positron emission tomography (PET). However, the method of collecting CSF is invasive. Blood-based biomarkers indicative of CSF Aß42 status may remedy this limitation as blood collection is minimally invasive and inexpensive. In this study, we show that APOE4 genotype and blood markers comprising EOT3, APOC1, CGA, and Aß42 robustly predict CSF Aß42 with high classification performance (0.84 AUC, 0.82 sensitivity, 0.62 specificity, 0.81 PPV and 0.64 NPV) using machine learning approach. Due to the method employed in the biomarker search, the identified biomarker signature maintained high performance in more than a single machine learning algorithm, indicating potential to generalize well. A minimally invasive and cost-effective solution to detecting amyloid abnormality such as proposed in this study may be used as a first step in a multi-stage diagnostic workup to facilitate enrichment of clinical trials and population-based screening.

Identification of Optimum Panel of Blood-based Biomarkers for Alzheimer's Disease Diagnosis Using Machine Learning.

With the increasing number of people living with Alzheimer's disease (AD), there is a need for low-cost and easy to use methods to detect AD early to facilitate access to appropriate care pathways. Neuroimaging biomarkers (such as those based on PET and MRI) and biochemical biomarkers (such as those based on CSF) are recommended by international guidelines to facilitate diagnosis. However, neuroimaging is expensive and may not be widely available and CSF testing is invasive. Bloodbased biomarkers offer the potential for the development of a low-cost and more time efficient tool to detect AD to complement CSF and neuroimaging as blood is much easier to obtain. Although no single blood biomarker is yet able to detect AD, combinations of biomarkers (also called panels) have shown good results. However, a large number of biomarkers are often needed to achieve a satisfactory detection performance. In addition, it is difficult to reproduce reported results within and across different study cohorts because of data overfitting and lack of access to the datasets used in the studies. In this study, our focus is to identify an optimum panel (in terms of the least number of blood biomarkers to meet the specified diagnostic performance of 80% sensitivity and specificity) based on a widely accessible data set, and to demonstrate a testing methodology that reinforces reproducibility of results. Realizing a panel with reduced number of markers will have significant impact on the complexity and cost of diagnosis and potential development of cost-effective point of care devices.

Identification of blood biomarkers for use in point of care diagnosis tool for Alzheimer's disease.

Early diagnosis of Alzheimer's Disease (AD) is widely regarded as necessary to allow treatment to be started before irreversible damage to the brain occur and for patients to benefit from new therapies as they become available. Low-cost point-of-care (PoC) diagnostic tools that can be used to routinely diagnose AD in its early stage would facilitate this, but such tools require reliable and accurate biomarkers. However, traditional biomarkers for AD use invasive cerebrospinal fluid (CSF) analysis and/or expensive neuroimaging techniques together with neuropsychological assessments. Blood-based PoC diagnostics tools may provide a more cost and time efficient way to assess AD to complement CSF and neuroimaging techniques. However, evidence to date suggests that only a panel of biomarkers would provide the diagnostic accuracy needed in clinical practice and that the number of biomarkers in such panels can be large. In addition, the biomarkers in a panel vary from study to study. These issues make it difficult to realise a PoC device for diagnosis of AD. An objective of this paper is to find an optimum number of blood biomarkers (in terms of number of biomarkers and sensitivity/specificity) that can be used in a handheld PoC device for AD diagnosis. We used the Alzheimer's disease Neuroimaging Initiative (ADNI) database to identify a small number of blood biomarkers for AD. We identified a 6-biomarker panel (which includes A1Micro, A2Macro, AAT, ApoE, complement C3 and PPP), which when used with age as covariate, was able to discriminate between AD patients and normal subjects with a sensitivity of 85.4% and specificity of 78.6%.