EOAD-Signature Atrophy Predicts Dementia in Early-Onset MCI due to Alzheimer Disease

Background and ObjectivesEarly-onset Alzheimer disease (EOAD) is associated with substantial variability in clinical progression, and reliable biomarkers to predict the transition from mild cognitive impairment (MCI) to dementia remain limited. Structural MRI measures have demonstrated prognostic value in late-onset Alzheimer disease, but their utility for predicting progression in EOAD is less well understood. The goal was to examine whether baseline cortical atrophy predicts progression to dementia in patients with MCI because of EOAD. MethodsThis study included a well-characterized cohort of patients with EOAD enrolled in the large multisite natural history Longitudinal Early-Onset Alzheimer's Disease Study. Participants underwent standardized clinical assessments and structural MRI at baseline. Participants were aged between 40 and 64 years with biomarker-supported sporadic EOAD at the MCI stage. Cortical atrophy was measured within the EOAD-signature, a set of predominantly parieto-temporal regions showing greater atrophy in EOAD than in controls. Clinical severity was measured with the global Clinical Dementia Rating. Cox proportional hazards models estimated the association between baseline EOAD-signature atrophy burden and the hazard of progression to dementia over time. We evaluated whether EOAD-signature atrophy improved prognostic performance beyond baseline clinical severity using likelihood ratio tests, Akaike Information Criterion (AIC), and Harrell concordance index. ResultsA total of 130 patients with MCI due to EOAD (mean age 59.6 ± 4.1 years; 49% female) and 97 cognitively normal controls (mean age 56.9 ± 6.0 years; 64% female) were included. Greater baseline atrophy within the EOAD-signature predicted faster progression to dementia (hazard ratio [HR] = 1.24 per 1-SD increase in atrophy; 95% CI 1.13–1.37; p < 0.002). Adding EOAD-signature atrophy burden to a model including baseline clinical severity significantly improved model fit (ΔAIC = −4.5; likelihood ratio test p = 0.011). DiscussionBaseline cortical atrophy within the EOAD-signature predicts progression from MCI to dementia in EOAD and provides prognostic information beyond baseline clinical severity. These findings support the potential value of EOAD-signature atrophy as an MRI-based biomarker for individualized prognostication and clinical trial stratification.

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Publication Details

Journal
Neurology
Published
2026-08-26
DOI
https://doi.org/10.1212/wnl.0000000000218519
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
0.00

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article

EOAD-Signature Atrophy Predicts Dementia in Early-Onset MCI due to Alzheimer Disease

Alexandra Touroutoglou, Stephen Salloway, Lawrence S. Honig, Clifford R. Jack et al.
Neurology
Dementia and Cognitive Impairment Research
article

EOAD-Signature Atrophy Predicts Dementia in Early-Onset MCI due to Alzheimer Disease

Alexandra Touroutoglou, Stephen Salloway, Lawrence S. Honig, Clifford R. Jack, Alireza Atri, Arthur W. Toga, Renaud La Joie, Thiago Paranhos, Joseph C. Masdeu, Erik S. Musiek, Ian Grant, Liana G. Apostolova, Paul S. S Aisen, David A. Wolk, Erik C. B. Johnson, Prashanthi Vemuri, Chiadi U. Onyike, Gil Dan Rabinovici, Mark C. Eldaief, Dustin B. Hammers, Kyle Womack, Laurel Beckett, Sharon J. Sha, Walter A. Kukull, Gregory Scott Day, David Thomas Jones, Yuta Katsumi, Mario F. Mendez, Ani Eloyan, Ranjan Duara, Bradford Clark Dickerson, Raymond Scott Turner, Ryan Eckbo, David Clark, Alexander Zaitsev, Thomas S. Wingo, Meghan Riddle, Alex Taurone, Anna Du, Neill R. Graff-Radford, Robert Koeppe, Maryanne Thangarajah, Emily Rogalski, Maria C. Carrillo, Michael Joseph Brickhouse
article en

Abstract

Background and ObjectivesEarly-onset Alzheimer disease (EOAD) is associated with substantial variability in clinical progression, and reliable biomarkers to predict the transition from mild cognitive impairment (MCI) to dementia remain limited. Structural MRI measures have demonstrated prognostic value in late-onset Alzheimer disease, but their utility for predicting progression in EOAD is less well understood. The goal was to examine whether baseline cortical atrophy predicts progression to dementia in patients with MCI because of EOAD. MethodsThis study included a well-characterized cohort of patients with EOAD enrolled in the large multisite natural history Longitudinal Early-Onset Alzheimer's Disease Study. Participants underwent standardized clinical assessments and structural MRI at baseline. Participants were aged between 40 and 64 years with biomarker-supported sporadic EOAD at the MCI stage. Cortical atrophy was measured within the EOAD-signature, a set of predominantly parieto-temporal regions showing greater atrophy in EOAD than in controls. Clinical severity was measured with the global Clinical Dementia Rating. Cox proportional hazards models estimated the association between baseline EOAD-signature atrophy burden and the hazard of progression to dementia over time. We evaluated whether EOAD-signature atrophy improved prognostic performance beyond baseline clinical severity using likelihood ratio tests, Akaike Information Criterion (AIC), and Harrell concordance index. ResultsA total of 130 patients with MCI due to EOAD (mean age 59.6 ± 4.1 years; 49% female) and 97 cognitively normal controls (mean age 56.9 ± 6.0 years; 64% female) were included. Greater baseline atrophy within the EOAD-signature predicted faster progression to dementia (hazard ratio [HR] = 1.24 per 1-SD increase in atrophy; 95% CI 1.13–1.37; p < 0.002). Adding EOAD-signature atrophy burden to a model including baseline clinical severity significantly improved model fit (ΔAIC = −4.5; likelihood ratio test p = 0.011). DiscussionBaseline cortical atrophy within the EOAD-signature predicts progression from MCI to dementia in EOAD and provides prognostic information beyond baseline clinical severity. These findings support the potential value of EOAD-signature atrophy as an MRI-based biomarker for individualized prognostication and clinical trial stratification.

NeurologyVol. 107(7)
Northwestern University (US), Alzheimer's Association (US), Harvard University (US), Johns Hopkins University (US), Emory University (US), University of California, Los Angeles (US), Palo Alto University (US), University of California, San Francisco (US), Georgetown University (US), Washington University in St. Louis (US), Cornell University (US), Brown University (US), University of Michigan (US), Columbia University Irving Medical Center (US), Johns Hopkins Medicine (US), Keck Hospital of USC (US), Harvard University Press (US), WinnMed (US), University of Chicago (US), Massachusetts General Hospital (US), Mount Sinai Medical Center (US), Cancer Research And Biostatistics (US), Methodist Hospital (US), Michigan Medicine (US), Mayo Clinic in Arizona (US), Banner Sun Health Research Institute (US), Mayo Clinic in Florida (US), Department of Public Health (US), Biostatistical Consulting (United States) (US), Indiana University – Purdue University Indianapolis (US), University of Pennsylvania (US), University of California, Davis (US), Southern States University (US), Stanford University (US)
Alzheimer's Association, Massachusetts General Hospital, Centre d'Imagerie BioMédicale, National Institutes of Health, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering
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Openalex Percentile: Top 9%
Dementia and Cognitive Impairment Research
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