Quantitative proteomics has emerged as a pivotal technique in precision medicine, offering significant potential to support clinical diagnostics and disease treatments. Despite its promise, the clinical validation of protein biomarkers remains costly, limiting their integration into routine clinical practice. Addressing this gap, our study presents a novel data-driven approach designed to enhance the discovery and validation of protein biomarkers, making the process more efficient and cost-effective.
In our study, the proposed data-driven approach combines parametric and nonparametric statistical techniques and machine learning algorithms to process high-dimensional quantitative proteomics data. We propose reference intervals for plasma proteomics value, critical for identifying deviations associated with disease states. We validate our method using a case study on Alzheimer's disease (AD) in the UK Biobank. We also performed pharmacoproteomics analysis, where we focus on individual proteomics profiles in response to medications. This ensures the relevance and reliability of the identified biomarkers, as medication data can precisely indicate the disease onset and progression. In addition to proteomics data, we incorporate clinical biochemistry tests focusing on lipid profiles, such as apolipoproteins and cholesterol levels, which are known to be associated with a higher risk of AD. This holistic approach allows us to explore the interplay between protein and lipid profiles.
Our study identified five candidate protein biomarkers for AD. The individual protein profiles of these biomarkers are aligned with both medication responses and lipid profiles, providing a comprehensive understanding of their potential clinical relevance. The integration of lipid profile data further substantiates the link between these biomarkers and AD risk. The proposed data-driven approach offers a rapid, efficient, cost-effective alternative for advancing proteomics biomarker studies. Our findings highlight the potential of this approach to revolutionize the field of precision medicine, particularly in the context of AD, facilitating the transition of protein biomarkers from research to routine clinical practice.