Evaluating prognostic performance of Alzheimer's biomarkers, multi-modal physiological measures, and clinical history in asymptomatic individuals versus established risk factors in asymptomatic indivi Show more
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 efficient screening strategies. To determine and compare the prognostic performance of amyloid biomarkers, multi-modal physiological measures, and clinical/modifiable risk factors 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. 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. 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. PTau-217, amyloid-PET, CSF markers (AB1-42, pTau-181, total-Tau), plasma proteomics, multi-modal brain-imaging, and cognitive tests were evaluated as predictors, along with demographics (age, sex, education), APOE genotype, and modifiable risk factors in the 2024 Lancet report 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. 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 predictors 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). In cognitively asymptomatic populations, biomarkers offer limited advantage over demographics, APOE genotype, and modifiable risk factors, supporting their importance in early AD screening strategies. Show less