Estimating the time course of biomarker changes in Alzheimer's disease.
The 2 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
- [1] § Materials and methods › Participants › Inclusion criteria ↔ R/data.R, the whole file · a weak match · score 0.55 · subjective cognitive, ADAS cog, baseline, status, dementia, MCI
- [2] § Materials and methods › Model validation ↔ R/data.R, the whole file · a weak match · score 0.54 · ADAS cog, predicted disease, scores, longitudinal, dementia, MCI
Paper
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R · 34 lines · 1.4 KB · no license · 2 matches
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Overview
13 affiliations
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund 223 62, Sweden
- Eli Lilly and Company, Indianapolis, IN 46285, USA
- Department of Neurology, Skåne University Hospital, Lund University, Lund 221 85, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund 223 62, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal 431 39, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal 431 39, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London WC1N 3BG, UK
- UK Dementia Research Institute at UCL, London NW1 3BT, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong 999077, China
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA
- Banner Alzheimer’s Institute and University of Arizona, Phoenix, AZ 85006, USA
- Banner Sun Health Research Institute, Sun City, AZ 85351, USA
- Memory Clinic, Skåne University Hospital, Malmö 211 46, Sweden
Abstract
Recent advancements in biomarkers have transformed Alzheimer’s disease (AD) diagnosis from being purely symptom-based to include biological criteria. With new treatments targeting the core biology of Alzheimer’s disease, understanding the timeline of biological changes is crucial as the disease progresses over decades.
Longitudinal data from amyloid-beta (Aβ) PET and cognitive tests [Mini-Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale–Cognitive Subscale (ADAS-cog)] from the Alzheimer’s Disease Neuroimaging Initiative (n = 1448) and BioFINDER (n = 2088) were used to stage patients against an estimated continuous disease timeline (predicted time since Aβ-PET positivity). The estimated timeline was validated by comparing correlations with unseen biomarkers and cognitive measures against alternative staging approaches. Trajectories for plasma, CSF, MRI and PET biomarkers, measuring Aβ, tau and neurodegeneration, were mapped along this Alzheimer’s disease continuum.
The proposed staging approach was found to produce stronger correlations with unseen cognitive measures and biomarkers compared to alternative staging methods, including amyloid and tau PET clocks (all pairwise P < 0.05). Findings related to biomarker trajectories were highly consistent across cohorts. The period from Aβ-PET positivity to end-stage Alzheimer’s disease dementia (MMSE = 0) was estimated at 20–25 years, with a presymptomatic phase of 7–11 years. CSF Aβ42/
The progression from initial biomarker abnormality to severe Alzheimer’s disease spans two decades. Disease progression modelling elucidates the evolution of AD biomarkers and cognition, highlighting the relative timing of biomarker abnormalities. These models can determine disease stages, aiding in prognosis and the evaluation of disease-modifying treatments.
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d374fb34a87d469891ab675f2c7294676e62dec9, 5 April 2022Availability: 1 check, the latest on 27 September 2026: the link answers
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Data availability
ADNI data is available to qualified academic investigators submitting an online application for access. For more information, please see the ADNI website http://
Pseudonymized data from BioFINDER will be made available by request from a qualified academic investigator for the sole purpose of replicating procedures and results presented in the article and if data transfer is in agreement with EU legislation on the general data protection regulation and decisions by the Ethical Review Board of Sweden and Region Skåne, which should be regulated in a material transfer agreement.
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 6 keywords, 14 MeSH terms, 17 funders, 62 references.
Cite
This paper
Raket, L. L., Binette, A. P., Mattsson-Carlgren, N., Janelidze, S., Zetterberg, H., Ashton, N. J., Blennow, K., Stomrud, E., Palmqvist, S., & Hansson, O. (2026). Estimating the time course of biomarker changes in Alzheimer's disease. Brain : a journal of neurology, 149(6), 1929-1943. https://
BibTeX
@article{raket2026estima
author = {Raket, Lars Lau and Binette, Alexa Pichet and Mattsson-Carlgren, Niklas and Janelidze, Shorena and Zetterberg, Henrik and Ashton, Nicholas J and Blennow, Kaj and Stomrud, Erik and Palmqvist, Sebastian and Hansson, Oskar},
title = {{Estimating the time course of biomarker changes in Alzheimer's disease}},
journal = {Brain : a journal of neurology},
year = {2026},
month = jun,
volume = {149},
number = {6},
pages = {1929--1943},
publisher = {Oxford University Press},
issn = {0006-8950},
doi = {10.1093/
url = {https://
pmid = {41178353},
pmcid = {PMC13232038}
}
RIS
TY - JOUR
AU - Raket, Lars Lau
AU - Binette, Alexa Pichet
AU - Mattsson-Carlgren, Niklas
AU - Janelidze, Shorena
AU - Zetterberg, Henrik
AU - Ashton, Nicholas J
AU - Blennow, Kaj
AU - Stomrud, Erik
AU - Palmqvist, Sebastian
AU - Hansson, Oskar
TI - Estimating the time course of biomarker changes in Alzheimer's disease
T2 - Brain : a journal of neurology
J2 - Brain
PY - 2026
DA - 2026/
VL - 149
IS - 6
SP - 1929
EP - 1943
SN - 0006-8950
PB - Oxford University Press
DO - 10.1093/
UR - https://
LA - en
ER -
CSL-JSON
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