Evidence map›Paper›PMID 41668150›Full record

ArticleAlzheimer's research & therapy2026

MRI-based atrophy subtypes in a young memory clinic cohort: associations with clinical and biomarker profiles.

Alessandro Zilioli, Rosaleena Mohanty, Anna Rosenberg, Anna Matton, Tobias Granberg, Göran Hagman, Marco Spallazzi, Daniel Ferreira, Miia Kivipelto, Eric Westman

Abstract read
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Article in Alzheimer's research & therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Alessandro ZilioliDepartment of Neurology, University-Hospital of Parma, Parma, Italy.
Rosaleena MohantyDivision of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Stockholm, Sweden.
Anna RosenbergDivision of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Stockholm, Sweden.
Anna MattonDivision of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Stockholm, Sweden.
Tobias GranbergDepartment of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Göran HagmanDivision of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Stockholm, Sweden.
Marco SpallazziDepartment of Neurology, University-Hospital of Parma, Parma, Italy.
Daniel FerreiraDivision of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Stockholm, Sweden.
Miia Kivipelto *Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Stockholm, Sweden.
Eric Westman *Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Stockholm, Sweden. eric.westman@ki.se.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBrain atrophy subtypes are increasingly recognized in Alzheimer’s disease (AD) dementia. However, their relevance across the real-world memory clinic spectrum, from subjective cognitive impairment (SCI) and mild cognitive impairment (MCI) to AD and non-AD dementias, remains unclear. This cross-sectional study aimed to identify MRI-based atrophy subtypes in a relatively young memory clinic and examine associations with demographic, cerebrospinal fluid (CSF) biomarkers, and cerebrovascular burden to inform precision medicine approaches.

methodsWe included all consecutive patients (SCI to dementia), evaluated at the Karolinska University-Hospital Memory Clinic (Stockholm, Sweden) between 2018 and 2023 with available clinical and 3T MRI data. Subtypes were defined using FreeSurfer-derived volumetric measures and a validated algorithm combining categorical classification (typical, limbic predominant, cortical predominant, minimal atrophy) with continuous indices of typicality (cortical predominant–limbic predominant) and severity (minimal atrophy–typical). Demographics, cognitive profiles, APOE ε4 status, CSF biomarkers (Aβ42, Aβ42/40, phosphorylated [p]-tau181, total tau, neurofilament light chain [NFL]), and cerebrovascular burden were compared across subtypes. Analyses were replicated in Aβ-positive individuals and those eligible for anti-Aβ therapy.

resultsAmong 809 patients (median age 60.0 years [interquartile-range 56.0–63.0], 56.1% female), 38.2% had SCI, 44.4% MCI, and 17.4% dementia. CSF biomarkers were available in 596 (73.7%). Limbic predominant and typical subtypes had more males (59.3% and 50.0%, respectively; group-wise p < 0.001), higher APOE ε4 frequency (47.7% and 41.0%, p = 0.02), greater cerebrovascular burden, and poorer memory. These subtypes were more often Aβ positive (46.1% and 46.5%, p = 0.01). A cortical predominant pattern was frequent in females (66.0%, p < 0.001), while minimal atrophy was associated with milder cognitive impairment (49.0% SCI, 45.5% MCI) and higher depressive symptoms. In Aβ-positive patients (n = 231), typical and limbic subtypes had higher p-tau181 (median: 83.0 and 84.5 pg/mL, respectively; p < 0.001), NFL (1120.0 and 1125.0 pg/mL, p < 0.001), and lower Aβ42/40 ratios (0.051 and 0.049, p = 0.02). Findings remained consistent across continuous atrophy measures and in the 14.9% (n = 89) eligible for anti-Aβ therapy. DISCUSSION: MRI-based atrophy subtypes exhibit distinct clinical and biomarker profiles, consistently observed in Aβ-positive and anti-Aβ-therapy-eligible patients. These findings support their diagnostic utility in memory clinics and relevance for biologically targeted AD trials.

Indexed as

BrainCognitive DysfunctionAgedAlzheimer DiseaseAmyloid beta-PeptidesAtrophyBiomarkersCohort StudiesCross-Sectional StudiesFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedSwedentau ProteinsAmyloid beta-PeptidesBiomarkerstau ProteinsAlzheimer’s diseaseBiological subtypesCerebrospinal fluid biomarkersMagnetic resonance imaging

Identifiers

PMID41668150
PMCPMC12895802

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.