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Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease

Author

Listed:
  • Konstantinos Poulakis

    (Karolinska Institutet)

  • Joana B. Pereira

    (Karolinska Institutet
    Lund University)

  • J.-Sebastian Muehlboeck

    (Karolinska Institutet)

  • Lars-Olof Wahlund

    (Karolinska Institutet)

  • Örjan Smedby

    (KTH Royal Institute of Technology)

  • Giovanni Volpe

    (University of Gothenburg)

  • Colin L. Masters

    (The University of Melbourne)

  • David Ames

    (Academic Unit for Psychiatry of Old Age, St George’s Hospital, University of Melbourne
    National Ageing Research Institute)

  • Yoshiki Niimi

    (The University of Tokyo Hospital)

  • Takeshi Iwatsubo

    (The University of Tokyo Hospital)

  • Daniel Ferreira

    (Karolinska Institutet
    Mayo Clinic)

  • Eric Westman

    (Karolinska Institutet
    King’s College London)

Abstract

Understanding Alzheimer’s disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.

Suggested Citation

  • Konstantinos Poulakis & Joana B. Pereira & J.-Sebastian Muehlboeck & Lars-Olof Wahlund & Örjan Smedby & Giovanni Volpe & Colin L. Masters & David Ames & Yoshiki Niimi & Takeshi Iwatsubo & Daniel Ferre, 2022. "Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32202-6
    DOI: 10.1038/s41467-022-32202-6
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    References listed on IDEAS

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    1. Alexandra L Young & Razvan V Marinescu & Neil P Oxtoby & Martina Bocchetta & Keir Yong & Nicholas C Firth & David M Cash & David L Thomas & Katrina M Dick & Jorge Cardoso & John Swieten & Barbara Borr, 2018. "Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference," Nature Communications, Nature, vol. 9(1), pages 1-16, December.
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