Abstract
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 β1-42 (Aβ42) 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 Aβ42 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 Aβ42 robustly predict CSF Aβ42 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.
Highlights
Alzheimer’s disease (AD) is the most common neurodegenerative disease accounting for over 60% of all dementia cases [1]
We explore the utility of blood-based proteins to predict cerebrospinal fluid (CSF) amyloid β1-42 (Aβ42) status using support vector machines with recursive feature elimination (SVM-RFE) that has shown effectiveness in similar research domains [14]
We investigated the utility of blood-based signature predictive of CSF Aβ42 status with a robust performance
Summary
Alzheimer’s disease (AD) is the most common neurodegenerative disease accounting for over 60% of all dementia cases [1] It is characterized in part by the accumulation of amyloid-beta (Aβ42) plaques in the brain – a condition known as amyloid pathology – that is present long before clinical symptoms (cognitive) are apparent [2, 3]. Amyloid screening is used in these trials to identify individuals with amyloid pathology and may be at the early stages of the disease before symptom onset. It may be beneficial in the future for population-based screening [5, 6]
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