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Disease staging of Alzheimer’s disease using a CSF-based biomarker model

Salvadó, Gemma LU ; Horie, Kanta ; Barthélemy, Nicolas R. ; Vogel, Jacob W. LU ; Pichet Binette, Alexa LU ; Chen, Charles D. ; Aschenbrenner, Andrew J. ; Gordon, Brian A. ; Benzinger, Tammie L.S. and Holtzman, David M. , et al. (2024) In Nature Aging
Abstract

Biological staging of individuals with Alzheimer’s disease (AD) may improve diagnostic and prognostic workup of dementia in clinical practice and the design of clinical trials. In this study, we used the Subtype and Stage Inference (SuStaIn) algorithm to establish a robust biological staging model for AD using cerebrospinal fluid (CSF) biomarkers. Our analysis involved 426 participants from BioFINDER-2 and was validated in 222 participants from the Knight Alzheimer Disease Research Center cohort. SuStaIn identified a singular biomarker sequence and revealed that five CSF biomarkers effectively constituted a reliable staging model (ordered: Aβ42/40, pT217/T217, pT205/T205, MTBR-tau243 and non-phosphorylated mid-region tau). The CSF... (More)

Biological staging of individuals with Alzheimer’s disease (AD) may improve diagnostic and prognostic workup of dementia in clinical practice and the design of clinical trials. In this study, we used the Subtype and Stage Inference (SuStaIn) algorithm to establish a robust biological staging model for AD using cerebrospinal fluid (CSF) biomarkers. Our analysis involved 426 participants from BioFINDER-2 and was validated in 222 participants from the Knight Alzheimer Disease Research Center cohort. SuStaIn identified a singular biomarker sequence and revealed that five CSF biomarkers effectively constituted a reliable staging model (ordered: Aβ42/40, pT217/T217, pT205/T205, MTBR-tau243 and non-phosphorylated mid-region tau). The CSF stages (0–5) demonstrated a correlation with increased abnormalities in other AD-related biomarkers, such as Aβ-PET and tau-PET, and aligned with longitudinal biomarker changes reflective of AD progression. Higher CSF stages at baseline were associated with an elevated hazard ratio of clinical decline. This study highlights a common molecular pathway underlying AD pathophysiology across all patients, suggesting that a single CSF collection can accurately indicate the presence of AD pathologies and characterize the stage of disease progression. The proposed staging model has implications for enhancing diagnostic and prognostic assessments in both clinical practice and the design of clinical trials.

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publication status
epub
subject
in
Nature Aging
publisher
Springer
external identifiers
  • pmid:38514824
  • scopus:85188240159
DOI
10.1038/s43587-024-00599-y
language
English
LU publication?
yes
id
33da1f70-0870-405c-a466-2cb3d11b1906
date added to LUP
2024-04-10 12:31:36
date last changed
2024-04-24 15:26:45
@article{33da1f70-0870-405c-a466-2cb3d11b1906,
  abstract     = {{<p>Biological staging of individuals with Alzheimer’s disease (AD) may improve diagnostic and prognostic workup of dementia in clinical practice and the design of clinical trials. In this study, we used the Subtype and Stage Inference (SuStaIn) algorithm to establish a robust biological staging model for AD using cerebrospinal fluid (CSF) biomarkers. Our analysis involved 426 participants from BioFINDER-2 and was validated in 222 participants from the Knight Alzheimer Disease Research Center cohort. SuStaIn identified a singular biomarker sequence and revealed that five CSF biomarkers effectively constituted a reliable staging model (ordered: Aβ42/40, pT217/T217, pT205/T205, MTBR-tau243 and non-phosphorylated mid-region tau). The CSF stages (0–5) demonstrated a correlation with increased abnormalities in other AD-related biomarkers, such as Aβ-PET and tau-PET, and aligned with longitudinal biomarker changes reflective of AD progression. Higher CSF stages at baseline were associated with an elevated hazard ratio of clinical decline. This study highlights a common molecular pathway underlying AD pathophysiology across all patients, suggesting that a single CSF collection can accurately indicate the presence of AD pathologies and characterize the stage of disease progression. The proposed staging model has implications for enhancing diagnostic and prognostic assessments in both clinical practice and the design of clinical trials.</p>}},
  author       = {{Salvadó, Gemma and Horie, Kanta and Barthélemy, Nicolas R. and Vogel, Jacob W. and Pichet Binette, Alexa and Chen, Charles D. and Aschenbrenner, Andrew J. and Gordon, Brian A. and Benzinger, Tammie L.S. and Holtzman, David M. and Morris, John C. and Palmqvist, Sebastian and Stomrud, Erik and Janelidze, Shorena and Ossenkoppele, Rik and Schindler, Suzanne E. and Bateman, Randall J. and Hansson, Oskar}},
  language     = {{eng}},
  publisher    = {{Springer}},
  series       = {{Nature Aging}},
  title        = {{Disease staging of Alzheimer’s disease using a CSF-based biomarker model}},
  url          = {{http://dx.doi.org/10.1038/s43587-024-00599-y}},
  doi          = {{10.1038/s43587-024-00599-y}},
  year         = {{2024}},
}