Navigating the Alzheimer's Biomarker Landscape: A Comprehensive Analysis of Fluid-Based Diagnostics

Alzheimer's disease (AD) affects a significant portion of the aging population, presenting a serious challenge due to the limited availability of effective therapies during its progression. The disease advances rapidly, underscoring the need for early diagnosis and the application of preventative measures. Current diagnostic methods for AD are often expensive and invasive, restricting access for the general public. One potential solution is the use of biomarkers, which can facilitate early detection and treatment through objective, non-invasive, and cost-effective evaluations of AD. This review critically investigates the function and role of biofluid biomarkers in detecting AD, with a specific focus on cerebrospinal fluid (CSF), blood-based, and saliva biomarkers.

CSF biomarkers have demonstrated potential for accurate diagnosis and valuable prognostic insights, while blood biomarkers offer a minimally invasive and cost-effective approach for diagnosing cognitive issues. However, while current biomarkers for AD show significant potential, none have yet achieved the precision needed to replace expensive PET scans and CSF assays. The lack of a single accurate biomarker underscores the need for further research to identify novel or combined biomarkers to enhance the clinical efficacy of existing diagnostic tests. In this context, artificial intelligence (AI) and deep-learning (DL) tools present promising avenues for improving biomarker analysis and interpretation, enabling more precise and timely diagnoses.

Further research is essential to confirm the utility of all AD biomarkers in clinical settings. Combining biomarker data with AI tools offers a promising path toward revolutionizing the personalized characterization and early diagnosis of AD symptoms.