Blood-Based Biomarkers in Alzheimer's Disease: Advancing Non-Invasive Diagnostics and Prognostics

Abstract

Alzheimer's disease (AD), the most prevalent form of dementia, is expected to rise dramatically in incidence due to the global population aging. Traditional diagnostic approaches, such as cerebrospinal fluid analysis and positron emission tomography, are expensive and invasive, limiting their routine clinical use. Recent advances in blood-based biomarkers, including amyloid-beta, phosphorylated tau, and neurofilament light, offer promising non-invasive alternatives for early AD detection and disease monitoring. This review synthesizes current research on these blood-based biomarkers, highlighting their potential to track AD pathology and enhance diagnostic accuracy. Furthermore, this review uniquely integrates recent findings on protein-protein interaction networks and microRNA pathways, exploring novel combinations of proteomic, genomic, and epigenomic biomarkers that provide new insights into AD's molecular mechanisms. Additionally, we discuss the integration of these biomarkers with advanced neuroimaging techniques, emphasizing their potential to revolutionize AD diagnostics. Although large-scale validation is still needed, these biomarkers represent a critical advancement toward more accessible, cost-effective, and early diagnostic tools for AD.

Keywords: Alzheimer’s disease (AD); abnormal protein accumulation; advanced neuroimaging; amyloid-beta (Aβ); blood-based biomarkers; dementia; early detection; neurodegeneration; neurofilament light chain (NfL); neuroinflammation; non-invasive diagnostics; phosphorylated tau (p-tau); prognostics; vascular pathology.

Figure 1

(A): From ATN to AT1T2NIVS biomarker categorization of fluid analytes: The proposed new criteria by the NIA-AA 2024 working group emphasize ‘A’ and ‘T’ as the core biomarkers for the diagnosis and staging of AD. In addition, the revised scheme recognizes an expanded set of additional markers that detect non-specific biomarkers involved in AD pathophysiology (categorized under ‘N’ and ‘I’) and non-AD co-pathological biomarkers (categorized under ‘V’ and ‘S’). The core biomarkers are further divided into Core 1 and Core 2 biomarkers to reflect different stages of AD-related changes. (B): This figure depicts the biomarker profile and corresponding categorization based on the “A”, “T”, and “N” systems. By binarizing the three AT(N) biomarker types, eight distinct biomarker profiles are generated. Based on these profiles, individuals can be placed into one of three general categories: standard AD biomarkers, the Alzheimer’s continuum, or non-AD pathological changes.

Figure 2

Timeline of patients’ journey integrated with blood-based biomarkers (BBBM). The figure illustrates the patient journey, starting with population screening and preventive strategies aimed at healthy aging. As AD-related pathology develops, BBBMs such as Aβ42/40, p-tau, and NfL are introduced during visits to a primary care physician for cognitive screening and early detection. Patients are then referred to specialists (neurologists or dementia experts) for clinical evaluation, which integrates BBBMs, CSF biomarkers, and PET imaging to confirm diagnosis. Biomarkers are also used for therapeutic intervention and monitoring disease progression, supporting treatment strategies like anti-Aβ or anti-tau therapies.

Figure 3

An approximate relationship between biomarker abnormality and the Alzheimer’s disease (AD) continuum (pre-clinical, prodromal, and AD dementia). The figure demonstrates the progressive increase in biomarker abnormalities across the AD continuum. In the pre-clinical phase, changes in CSF Aβ42, Aβ42/Aβ40 ratio, and plasma GFAP occur early. As the disease progresses to the prodromal phase, abnormalities in Aβ PET, CSF p-tau181, total-tau, and plasma p-tau181 become evident. In the later stages, including AD dementia, abnormalities in tau PET, MRI/FDG-PET, and cognitive impairment markers become pronounced. The figure illustrates the evolving nature of these biomarkers in the context of AD progression.

Figure 4

The STRING protein-protein interaction (PPI) network for AD-related blood-based biomarkers. The STRING database was queried with a subset of proteins relevant as blood biomarkers in AD, generating four distinct clusters: core AD proteins, mitochondrial OXPHOS proteins, neuroinflammatory proteins, and vascular pathology-related proteins. The PPI network highlights significant overlaps and interactions between pathways common to both blood and brain cells, reflecting the complex molecular mechanisms driving AD pathogenesis.

Figure 5

The figure illustrates the microRNA interaction networks for predicted microRNA markers of AD in peripheral blood and brain tissue. (A) Depicts the network for four predicted microRNA markers in peripheral blood. The network is characterized by a densely interconnected structure, with four prominent green square nodes representing the predicted microRNAs. These square nodes vary in size, indicating differences in the number of gene interactions each microRNA has. Surrounding these microRNAs are numerous circular nodes in shades of red, representing target genes. The darker red nodes signify genes without interactions with other genes, while lighter red nodes indicate genes interacting with multiple others. (B) Shows the network for eight predicted microRNA markers in brain tissue. This network appears more dispersed compared to A, with eight green square nodes of varying sizes representing the predicted microRNAs. The circular nodes in this network are in shades of brown, with darker brown indicating isolated genes and lighter brown showing genes with multiple interactions.