Background: The brain age gap (BAG), the difference between neuroimaging-predicted and chronological age, captures inter-individual variation in brain aging. Although sensitive to Alzheimer's disease (AD) pathology, its longitudinal patterns across the clinical AD continuum and prognostic relevance remain unclear. Methods: 577 participants from the DELCODE cohort (>2,100 MRI scans) were analysed: healthy controls individuals (HC, N=202), and patients with subjective cognitive decline (SCD, N=248), mild cognitive impairment (N=93), and AD dementia (N=34). All underwent structural MRI, amyloid (Ab42/40) and phosphorylated tau181 assessment, and lifestyle-related dementia risk profiling (LIBRA). BAG was derived using brainageR. Associations with baseline cognition, cognitive decline, and clinical progression (up to eight years) were examined using mixed-effects and Cox models. Mediation analyses tested whether BAG accounted for LIBRA-cognition associations. Biomarker-related and clinical findings were replicated in ADNI (N=461). Findings: BAG showed excellent short-term reliability, increased stepwise across the clinical spectrum and was elevated in amyloid-positive SCD, but not in asymptomatic amyloid-positive HC. Longitudinal BAG increases were strongest in amyloid- and tau-positive participants (Ab+T+). Higher BAG was associated with poorer baseline cognition and predicted cognitive decline, with strongest effects in Ab+T+. All main findings replicated in ADNI. BAG was associated with LIBRA only in biomarker-negative participants and partly mediated associations with cognitive outcomes in DELCODE. Interpretation: BAG is a reliable non-invasive marker of structural brain health sensitive to AD pathology and to modifiable AD risk. Detectable divergence prior to objective cognitive impairment supports its relevance for early risk stratification and prevention-oriented research. Funding: Helmholtz AI Cooperation Unit (ZT-I-PF-5-163).
E. Kuhn, G. Antopoulos, L. Kleineidam et al.· medRxiv· 0 citations
Mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), remains undiagnosed in > 90% of individuals, delaying access to timely evaluation and interventions. Self‐administered digital cognitive assessments (SA‐DCAs) offer scalable approaches for early detection, yet their real‐world validation and clinical readiness remain uncertain. We developed a use‐case–specific framework to evaluate SA‐DCAs intended for community and primary‐care MCI screening and applied it to a comprehensive scoping review of published evidence (2012‐2025). Among 79 identified SA‐DCAs, only four tools met predefined framework criteria across nine eligible studies. Common limitations included restricted population representativeness, inconsistent diagnostic performance reporting, limited biomarker anchoring, and reliance on prefiltered cohorts. Overall, the current evidence base is methodologically heterogeneous and incomplete for clinical deployment. The proposed framework characterizes requirements including anchoring strength, prevalence‐adjusted performance reporting, and representative sampling establishing a foundation for advancing robust real‐world evidence needed to translate SA‐DCAs from research to clinical practice.
H. Hampel, Yosuke Nakamura, J. Bell et al.· Alzheimer's & Dementia· 0 citations