Stepwise approach to alzheimer's disease diagnosis in primary care using cognitive screening, risk factors, neuroimaging and plasma biomarkers

Abstract

Early identification of Alzheimer's disease (AD) pathology is essential for timely intervention, particularly in primary care. We evaluated the diagnostic performance of a scalable, multimodal framework in a real-world, population-based cohort. A total of 277 community-dwelling individuals aged ≥ 60 years from the STOP-ALZHEIMER DEBA study (Basque Country, Spain) underwent brief cognitive screening (MMSE, M@T, Fototest, AD8) with optimized cut-offs, along with clinical risk assessment. Among them, 181 participants also completed structural MRI, plasma biomarker profiling (p-tau181, Aβ42/40, GFAP, NfL), and cerebrospinal fluid (CSF) analysis. We assessed performance for detecting cognitive impairment, CSF amyloid positivity (A+), and combined amyloid-tau positivity (A + T+). Optimized cognitive tests showed moderate accuracy (AUC 0.66-0.77), with the Fototest performing best. For biological outcomes, GFAP and p-tau181 had the highest predictive value (AUCs: 0.813 and 0.755 for A+; 0.852 and 0.710 for A + T+), and their combination further improved accuracy (AUC = 0.842). Fully adjusted models incorporating optimized cognitive scores, plasma biomarkers, APOE genotype, MRI, and demographics achieved high diagnostic performance (AUC = 0.886 for A+; 0.893 for A + T+). Results were consistent across sex and age strata. These findings support a stepwise diagnostic strategy combining brief, minimally invasive tools to enhance early AD detection in community settings.

Keywords: ATN framework; Alzheimer’s disease; Cognitive screening; Mild cognitive impairment; Plasma biomarkers; Primary care.

Fig. 1

Flowchart of participant inclusion and evaluation steps in the STOP-ALZHEIMER project.

Fig. 2

Forest plot showing the area under the curve (AUC) and 95% confidence intervals for each optimized cognitive screening test in detecting cognitive impairment.

Fig. 3

AUC values for brief cognitive screening tests to detect CSF-defined amyloid positivity (A+) and combined amyloid and tau positivity (A + T+).

Fig. 4

Forest plot showing area under the curve (AUC) and 95% confidence intervals for individual and combined plasma biomarker models in predicting CSF-defined amyloid and tau positivity (A + T+).

Fig. 5

ROC curves for sequential assessment levels predicting MCI + A+.