Noninvasive diagnosis of Alzheimer’s disease: a review of emerging biomarkers and multimodal approaches

Abstract

Noninvasive biomarkers are expanding access to biological assessment of Alzheimer’s disease (AD). This review evaluates blood-based, neuroimaging, retinal, electrophysiological, digital, and behavioral measures alongside structured cognitive and functional assessment, and examines multimodal and machine-learning approaches for detection, differential diagnosis, prognosis, and treatment monitoring. Plasma phosphorylated tau, particularly p-tau217, currently offers the strongest scalable evidence for identifying amyloid and advanced tau pathology, whereas PET and MRI retain complementary roles in pathological confirmation, regional staging, exclusion of alternative causes, and characterization of atypical phenotypes. Retinal, electrophysiological, speech, sleep, and eye-tracking measures remain promising but require greater standardization and external validation. Across modalities, biomarker trajectories are temporally staggered, assay and platform differences affect interpretation, and pathology–phenotype mapping is modified by disease stage, cognitive reserve, copathology, vascular factors, and population characteristics. We therefore propose a phenotype-anchored, stepped pathway that begins with patient and informant history, objective cognitive and functional assessment, structural imaging, and routine laboratory evaluation; adds validated blood biomarkers when they resolve a defined uncertainty; and reserves CSF or PET for unresolved, atypical, or treatment-relevant cases. Discordant findings should prompt reassessment rather than automatic reclassification. We describe the literature-search and descriptive evidence-categorization approach used to select and categorize the evidence reviewed, and we place the findings within the emerging regulatory context of blood-based testing. Clinical translation will depend on harmonized thresholds, representative prospective cohorts, calibration, independent validation, and evidence that multimodal systems improve decisions and patient outcomes rather than benchmark accuracy alone.