Search

Abstract Accurate diagnosis and monitoring of neurodegenerative diseases require reliable biomarkers. Cerebrospinal fluid (CSF) proteins are promising candidates for reflecting brain pathology; however, their diagnostic utility may be compromised by natural variability between individuals, weakening their association with disease. Here, we measured the levels of 69 pre-selected proteins in cerebrospinal fluid using antibody-based suspension bead array technology in a multi-disease cohort of 499 individuals with neurodegenerative disorders including Alzheimer’s disease (AD), behavioral variant frontotemporal dementia, primary progressive aphasias, amyotrophic lateral sclerosis (ALS), corticobasal syndrome, primary supranuclear palsy, along with healthy controls. We identify significant inter-individual variability in overall CSF levels of brain-derived proteins, which could not be attributed to specific disease associations. Using linear modelling, we show that adjusting for median CSF levels of brain-derived proteins increases the diagnostic accuracy of proteins previously identified as altered in CSF in the context of neurodegenerative disorders. We further demonstrate a simplified approach for the adjustment using pairs of correlated proteins with opposite alteration in the diseases. With this approach, the proteins adjust for each other and further increase the biomarker performance through additive effect. When comparing the diseases, two proteins—neurofilament medium and myelin basic protein—showed increased levels in ALS compared to other diseases, and neurogranin showed a specific increase in AD. Several other proteins showed similar trends across the studied diseases, indicating that these proteins likely reflect shared processes related to neurodegeneration. Overall, our findings suggest that accounting for inter-individual variability is crucial in future studies to improve the identification and performance of relevant biomarkers. Importantly, we highlight the need for multi-disease studies to identify disease-specific biomarkers.

Introduction: A rapid and reliable detection of glial fibrillary acidic protein (GFAP) in biological samples could assist in the diagnostic evaluation of neurodegenerative disorders. Sensitive assays applicable in the routine setting are needed to validate the existing GFAP tests. This study aimed to develop a highly sensitive and clinically applicable microfluidic immunoassay for the measurement of GFAP in blood.Methods: A microfluidic GFAP assay was developed and validated regarding its performance. Subsequently, serum and cerebrospinal fluid (CSF) of Alzheimer’s disease (AD), Multiple Sclerosis (MS) and control patients were analyzed with the established assay, and levels were compared to the commercial GFAP Simoa discovery kit.Results: The developed GFAP assay showed a good performance with a recovery of 85% of spiked GFAP in serum and assay variations below 15%. The established assay was highly sensitive with a calculated lower limit of quantification and detection of 7.21 pg/mL and 2.37 pg/mL, respectively. GFAP levels were significantly increased in AD compared to control patients with advanced age (p = 0.002). However, GFAP levels revealed no significant increase in MS compared to control patients in the same age range (p = 0.140). Furthermore, serum GFAP levels evaluated with the novel microfluidic assay strongly correlated with Simoa concentrations (r = 0.88 (95% CI: 0.81–0.93), p < 0.0001).Conclusion: We successfully developed a sensitive and easy-to-use microfluidic assay to measure GFAP in blood. Furthermore, we could confirm previous findings of elevated GFAP levels in AD by applying the assay in a cohort of clinically characterized patients.

Proteomics studies have shown differential expression of numerous proteins in dementias but have rarely led to novel biomarker tests for clinical use. The Marie Curie MIRIADE project is designed to experimentally evaluate development strategies to accelerate the validation and ultimate implementation of novel biomarkers in clinical practice, using proteomics-based biomarker development for main dementias as experimental case studies. We address several knowledge gaps that have been identified in the field. First, there is the technology-translation gap of different technologies for the discovery (e.g., mass spectrometry) and the large-scale validation (e.g., immunoassays) of biomarkers. In addition, there is a limited understanding of conformational states of biomarker proteins in different matrices, which affect the selection of reagents for assay development. In this review, we aim to understand the decisions taken in the initial steps of biomarker development, which is done via an interim narrative update of the work of each ESR subproject. The results describe the decision process to shortlist biomarkers from a proteomics to develop immunoassays or mass spectrometry assays for Alzheimer's disease, Lewy body dementia, and frontotemporal dementia. In addition, we explain the approach to prepare the market implementation of novel biomarkers and assays. Moreover, we describe the development of computational protein state and interaction prediction models to support biomarker development, such as the prediction of epitopes. Lastly, we reflect upon activities involved in the biomarker development process to deduce a best-practice roadmap for biomarker development.