Decoding Parkinson's disease: A multifaceted approach to diagnosis and biomarker discovery

Abstract

We all get older, and our brains get older, but only some of us get neurodegenerative disorders like Parkinson's disease (PD). Age, which is a dynamic process, is the largest risk factor for the development and progression of PD. Decreased dopaminergic activity, tangential Lewy body illnesses, and degenerative pathologies are predisposing factors leading to PD. Due to the significant clinical overlap across parkinsonian illnesses, the misdiagnosis rate is still high (16%-20%) even when the new diagnostic standards are correctly applied. Misdiagnosis and delayed diagnosis militate against the therapeutic benefits of disease-modifying therapies. Thus, there is an urgent need to discover and develop precise and reliable PD biomarkers. Discovering accurate biomarkers for early diagnoses, such as prodromal diagnosis and preclinical diagnosis, is necessary for better clinical intervention and treatment at the onset of disease. Further, these reliable biomarkers can be used to evaluate the progression of PD disorder. In this book chapter, we will discuss current progress in the development of PD biomarkers from various perspectives including imaging biomarkers, clinical, genetic, RNA-based biomarkers, biochemical, metabolomics, artificial intelligence, deep learning, and machine learning aspects that play critical roles as diagnostic biomarkers for PD. © 2025 Elsevier Inc. All rights reserved