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Predicting cognitive decline in amyloid-negative individuals with amnestic mild cognitive impairment

Annettesdotter, Amanda LU orcid ; Spotorno, Nicola LU ; Wuestefeld, Anika LU orcid ; Mattsson-Carlgren, Niklas LU orcid ; Strandberg, Olof LU ; Ossenkoppele, Rik LU ; Stomrud, Erik LU orcid ; Palmqvist, Sebastian LU orcid ; Wolk, David A. and Hansson, Oskar LU orcid , et al. (2025) In Alzheimer's & dementia : the journal of the Alzheimer's Association 21(S8).
Abstract

BACKGROUND: A considerable portion of patients with amnestic mild cognitive impairment (aMCI) have negative amyloid-β (Aβ) biomarkers and are therefore unlikely to have Alzheimer's disease (AD). Potential causes of cognitive decline in this heterogeneous group include limbic-predominant age-related TDP-43 encephalopathy (LATE), cardio/cerebrovascular diseases, primary age-related tauopathy (PART), and subthreshold Aβ. The prognosis of Aβ-negative (Aβ-) aMCI patients, putatively more benign, is unclear. We aim to investigate which predictors - including demographics, baseline cognition, fluid and imaging biomarkers - can best predict cognitive decline and progression to dementia in Aβ- aMCI. METHOD: We included 140 Aβ- aMCI patients (Aβ... (More)

BACKGROUND: A considerable portion of patients with amnestic mild cognitive impairment (aMCI) have negative amyloid-β (Aβ) biomarkers and are therefore unlikely to have Alzheimer's disease (AD). Potential causes of cognitive decline in this heterogeneous group include limbic-predominant age-related TDP-43 encephalopathy (LATE), cardio/cerebrovascular diseases, primary age-related tauopathy (PART), and subthreshold Aβ. The prognosis of Aβ-negative (Aβ-) aMCI patients, putatively more benign, is unclear. We aim to investigate which predictors - including demographics, baseline cognition, fluid and imaging biomarkers - can best predict cognitive decline and progression to dementia in Aβ- aMCI. METHOD: We included 140 Aβ- aMCI patients (Aβ status based on cerebrospinal fluid (CSF) and positron emission tomography, when available; 'amnestic' based on norm scores for AD Assessment Scale delayed word recall) from BioFINDER-1/2 with longitudinal Mini-Mental State Examination (MMSE), and subsets with longitudinal data on Clinical Dementia Rating Sum of Boxes (CDR-SB; n = 67) and progression to dementia (n = 134, 43% progressors; Table 1). Predictors included global and regional atrophy measures, specific for LATE and PART, CSF Aβ42/40 and p-tau181, hypertension, white matter hyperintensities and global cognition. Individual MMSE and CDR-SB slopes were estimated with linear mixed-effects models. Associations of predictors with MMSE/CDR-SB slopes and progression to dementia were tested. Significant predictors and demographic variables were included in the model selection process using R package MuMIn, which tests linear combinations of variables and ranks models by the Akaike information criterion (AIC). RESULTS: Figure 1 shows individual associations for the identification of significant predictors for the model selection process. For MMSE (AIC: 350.57; Figure 2a), the selected most parsimonious model included baseline MMSE and whole-brain cortical thickness. For CDR-SB (AIC: 176.73; Figure 2b), baseline CDR-SB, amygdala volume, and middle frontal gyrus cortical thickness were included. For progression to dementia (AIC: 97.65; Figure 2c), the selected model included MMSE, lateral ventricles volume, entorhinal and whole-brain cortical thickness, sex and CSF Aβ42/40. CONCLUSIONS: Baseline cognition, global and regional atrophy measures are valuable predictors of cognitive decline in Aβ- aMCI, with regional brain measures hinting at specific pathologies. These prediction models are relevant for the new LATE clinical criteria. We aim to validate our findings in ADNI.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Alzheimer's & dementia : the journal of the Alzheimer's Association
volume
21
issue
S8
article number
e110861
publisher
Wiley
external identifiers
  • pmid:41433399
  • scopus:105025740966
ISSN
1552-5279
DOI
10.1002/alz70862_110861
language
English
LU publication?
yes
id
2d9b28b3-68cc-427f-bb99-8d96a14df5bc
date added to LUP
2026-02-11 14:07:04
date last changed
2026-02-12 03:45:22
@misc{2d9b28b3-68cc-427f-bb99-8d96a14df5bc,
  abstract     = {{<p>BACKGROUND: A considerable portion of patients with amnestic mild cognitive impairment (aMCI) have negative amyloid-β (Aβ) biomarkers and are therefore unlikely to have Alzheimer's disease (AD). Potential causes of cognitive decline in this heterogeneous group include limbic-predominant age-related TDP-43 encephalopathy (LATE), cardio/cerebrovascular diseases, primary age-related tauopathy (PART), and subthreshold Aβ. The prognosis of Aβ-negative (Aβ-) aMCI patients, putatively more benign, is unclear. We aim to investigate which predictors - including demographics, baseline cognition, fluid and imaging biomarkers - can best predict cognitive decline and progression to dementia in Aβ- aMCI. METHOD: We included 140 Aβ- aMCI patients (Aβ status based on cerebrospinal fluid (CSF) and positron emission tomography, when available; 'amnestic' based on norm scores for AD Assessment Scale delayed word recall) from BioFINDER-1/2 with longitudinal Mini-Mental State Examination (MMSE), and subsets with longitudinal data on Clinical Dementia Rating Sum of Boxes (CDR-SB; n = 67) and progression to dementia (n = 134, 43% progressors; Table 1). Predictors included global and regional atrophy measures, specific for LATE and PART, CSF Aβ42/40 and p-tau181, hypertension, white matter hyperintensities and global cognition. Individual MMSE and CDR-SB slopes were estimated with linear mixed-effects models. Associations of predictors with MMSE/CDR-SB slopes and progression to dementia were tested. Significant predictors and demographic variables were included in the model selection process using R package MuMIn, which tests linear combinations of variables and ranks models by the Akaike information criterion (AIC). RESULTS: Figure 1 shows individual associations for the identification of significant predictors for the model selection process. For MMSE (AIC: 350.57; Figure 2a), the selected most parsimonious model included baseline MMSE and whole-brain cortical thickness. For CDR-SB (AIC: 176.73; Figure 2b), baseline CDR-SB, amygdala volume, and middle frontal gyrus cortical thickness were included. For progression to dementia (AIC: 97.65; Figure 2c), the selected model included MMSE, lateral ventricles volume, entorhinal and whole-brain cortical thickness, sex and CSF Aβ42/40. CONCLUSIONS: Baseline cognition, global and regional atrophy measures are valuable predictors of cognitive decline in Aβ- aMCI, with regional brain measures hinting at specific pathologies. These prediction models are relevant for the new LATE clinical criteria. We aim to validate our findings in ADNI.</p>}},
  author       = {{Annettesdotter, Amanda and Spotorno, Nicola and Wuestefeld, Anika and Mattsson-Carlgren, Niklas and Strandberg, Olof and Ossenkoppele, Rik and Stomrud, Erik and Palmqvist, Sebastian and Wolk, David A. and Hansson, Oskar and Wisse, Laura E.M.}},
  issn         = {{1552-5279}},
  language     = {{eng}},
  note         = {{Conference Abstract}},
  number       = {{S8}},
  publisher    = {{Wiley}},
  series       = {{Alzheimer's & dementia : the journal of the Alzheimer's Association}},
  title        = {{Predicting cognitive decline in amyloid-negative individuals with amnestic mild cognitive impairment}},
  url          = {{http://dx.doi.org/10.1002/alz70862_110861}},
  doi          = {{10.1002/alz70862_110861}},
  volume       = {{21}},
  year         = {{2025}},
}