Contextualizing Blood-Based Biomarkers for Dementia Globally

J Neurochem. 2026 Apr;170(4):e70443. doi: 10.1111/jnc.70443.

ABSTRACT

Blood-based biomarkers (BBMs) are transforming the diagnostic landscape of Alzheimer's disease by enabling scalable, less invasive, and potentially earlier biological characterization. However, most evidence supporting their performance, interpretation, and clinical integration derives from highly selected cohorts in high-income settings, raising concerns about external validity, threshold transportability, and equitable implementation across diverse populations. In this Opinion, we argue that advancing BBMs from analytical validity to real-world use requires a shift from biomarker-centric accuracy toward context-aware interpretation frameworks that explicitly account for social, environmental, and health system determinants. Using the amyloid, tau, and neurodegeneration (AT(N)) system as a conceptual anchor, we discuss how BBMs should be positioned according to clearly defined contexts of use, including triage, diagnostic support, prognosis, and clinical trial readiness, rather than treated as universal diagnostic substitutes. We examine how social determinants of health, life-course exposures, and the cumulative exposome interact with comorbidity burden, systemic physiological stress, and health system readiness to shape biomarker distributions, trajectories, and clinical meaning. Evidence from Latin America and other underrepresented settings illustrates how cardiometabolic, vascular, and inflammatory load can modify baseline biomarker levels, challenging the uncritical transfer of cutoffs, reference ranges, and predictive models developed in high-income settings. We conclude that BBMs hold substantial potential to expand access to biological characterization of Alzheimer's disease, but their responsible adoption depends on aligning biological signals with clinical context, social and environmental conditions, and system capacity. Without this alignment, large-scale deployment risks misclassification, inequitable access to care, biased trial enrollment, and distorted estimates of disease burden.

PMID:42003482 | DOI:10.1111/jnc.70443