Prognostic performance across Alzheimer’s biomarkers, multi-modal physiological measures, and clinical history in asymptomatic individuals

Importance

Evaluating prognostic performance of Alzheimer’s biomarkers, multi-modal physiological measures, and clinical history in asymptomatic individuals versus established risk factors in asymptomatic individuals is can inform effcient screening strategies.

Objective

To determine and compare the prognostic performance of amyloid biomarkers, multi-modal physiological measures, and clinical/modifiable risk fac-tors ¹ , we conducted a modality-wide assessment of predictors of AD (MODAL-AD) in cognitively asymptomatic patients.

Design

We used clinical trials (A4/LEARN), longitudinal cohorts (ADNI, AIBL, HABS, NACC, OASIS), and the UK Biobank spanning 2004-2025 (median follow-up time range: 1.8-13.72 years) in time-varying survival and binary classification analyses.

Setting

Settings included a United States clinical trial, longitudinal cohort studies spread across medical centers in the United States and Australia, and the volunteer-based UK Biobank.

Participants

Patients were cognitively asymptomatic and age 65+ at baseline, and potentially progressed to either clinical impairment, clinical AD diagnosis, or incurred AD ICD-codes. Patients were volunteer or convenience samples.

Exposures

PTau-217, amyloid-PET, CSF markers (AB1-42, pTau-181, total-Tau), plasma proteomics, multimodal brain-imaging, and cognitive tests were evaluated as predictors, along with demographics (age, sex, education), APOE geno-type, and modifiable risk factors in the 2024 Lancet report ¹ .

Main Outcome(s) and Measure(s)

PTau-217 and amyloid-PET from A4/LEARN were used to predict clinical impairment (CDR score of 0.5+ on two consecutive visits). PTau-217, amyloid-PET imaging across five cohorts, and CSF markers were used to predict clinical AD diagnosis. Plasma proteomics, multimodal neuroimaging, and cognitive assessments from the UK Biobank were used to predict AD ICD-codes.

Results

Sample-sizes ranged from 356-28,533 (31-519 cases; female percentages: 48.45-67.39). Models of demographics, APOE genotype, and risk-factors as predic-tors did not show statistically significant differences in time-dependent area under the receiver operating characteristic curve (AUROC) compared to separate models using amyloid biomarkers. Predicting cognitive impairment in A4/LEARN, pTau-217 improved AUROC by 0.045–0.084 (best: 0.616 (CI: 0.51-0.723) vs. 0.7 (CI: 0.609-0.793)). Amyloid-PET improved AD prediction (maximum AUROC increase 0.074; 0.561 (CI: 0.468-0.653) vs. 0.635 (CI: 0.537-0.733)), and CSF biomarkers showed slightly larger gains (maximum AUROC increase 0.127; 0.627 (CI: 0.438-0.816) vs. 0.754 (CI: 0.577-0.931)). In UK Biobank analyses, mean AUROC improvements were minor across proteomics (0.044), neuroimaging (0.143, with 99.8%/0.2% class-balance), and cognitive tests (0.064).

Conclusions and Relevance

In cognitively asymptomatic populations, biomarkers offer limited advantage over demographics, APOE genotype, and modifi-able risk factors, supporting their importance in early AD screening strategies.

Key Points

Question

How does the prognostic performance of amyloid biomarkers (i.e., pTau-217, amyloid-PET, cerebrospinal fluid markers) and discovery-driven modalities (i.e., plasma protoemics, multimodal brain imaging, cognitive tests) compare to demo-graphics and modifiable risk factors for predicting clinical impairment, clinical AD diagnosis, and AD ICD code outcomes in asymptomatic patients?

Findings

In this prognostic study of > 300,000 patients, across cohorts, physiological modalities, and outcomes, predictive performance of demographics and modifiable risk factors did not statistically significantly differ from amyloid biomarkers, plasma proteomics, and other modalities.

Meaning

Alzheimer’s screening in asymptomatic patients can benefit from incorpo-rating modifiable risk factors as additional predictors to amyloid biomarkers.