MRI-derived brain aging trajectories across the Alzheimer’s continuum: links to cognition, pathology and lifestyle risk factors

  1. Elizabeth Kuhn
  2. Georgios Antonopoulos
  3. Luca Kleineidam
  4. Melina Stark
  5. Sandra Roeske
  6. Felix Hoffstaedter
  7. Laura Waite
  8. Oliver Peters
  9. Julian Hellmann-Regen
  10. Lukas Preis
  11. Daria Gref
  12. Josef Priller
  13. Eike Jakob Spruth
  14. Maria Gemenetzi
  15. Anja Schneider
  16. Klaus Fliessbach
  17. Jens Wiltfang
  18. Björn H. Schott
  19. Franziska Maier
  20. Emrah Düzel
  21. Wenzel Glanz
  22. Enise Incesoy
  23. Renat Yakupov
  24. Falk Lüsebrink
  25. Katharina Buerger
  26. Daniel Janowitz
  27. Sophia Stöcklein
  28. Robert Perneczky
  29. Boris-Stephan Rauchmann
  30. Stefan J. Teipel
  31. Ingo Kilimann
  32. Christoph Laske
  33. Sebastian Sodenkamp
  34. Annika Spottke
  35. Frederic Brosseron
  36. Aflredo Ramirez
  37. Matthias C. Schmid
  38. Stefen Hetzer
  39. Peter Dechent
  40. Frank Jessen
  41. Simon B. Eickhoff
  42. Kaustubh R. Patil
  43. Michael Wagner
  44. the Alzheimer’s Disease Neuroimaging Initiative
  45. the DELCODE study group
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Abstract

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 (Aβ 42/40 ) 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 (Aβ+T+). Higher BAG was associated with poorer baseline cognition and predicted cognitive decline, with strongest effects in Aβ+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).

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  1. Version published to 10.64898/2026.08.19.26360525 on medRxiv