Distinct Characteristics of Suspected Non-Alzheimer Pathophysiology in Relation to Cognitive Status and Cerebrovascular Burden.

Purpose of the Report: This study investigated the prevalence and clinical characteristics of suspected non-Alzheimer disease pathophysiology (SNAP) across varying cognitive statuses and cerebral small vessel disease (CSVD) burden.
Patients and Methods: We included 1992 participants with cognitive status categorized as cognitively unimpaired, mild cognitive impairment, or dementia. β-amyloid (Aβ, A) positivity was assessed by Aβ PET, and neurodegeneration (N) positivity was determined through hippocampal volume. Participants were further divided by the presence or absence of severe CSVD. The clinical and imaging characteristics of A-N+ (SNAP) group were compared with those of the A-N- and A+N+ groups.
Results: SNAP participants were older and had more vascular risk factors compared with A-N- and A+N+ in the CSVD(-) cohort. SNAP and A+N+ showed similar cortical thinning. At the dementia stage, SNAP had a cognitive trajectory similar to A+N+ in the CSVD(-) cohort. However, SNAP exhibited less cognitive decline than A+N+ in the CSVD(+) cohort.
Conclusions: SNAP is characterized by distinct clinical and imaging characteristics; however, it does not necessarily indicate a benign prognosis, particularly at the dementia stage. These findings highlight the need to assess SNAP in relation to the cognitive stage and CSVD presence to better understand its progression and guide interventions.
Competing Interests: Conflicts of interest and sources of funding: This research was supported by a grant of the Korea Dementia Research Project through the Korea Dementia Research Center (KDRC), funded by the Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea (grant number: RS-2020-KH106434 and RS-2020-KH107436); a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea (grant number: RS-2022-KH127756 and RS-2022-KH125667); Future Medicine 2030 Project of the Samsung Medical Center [#SMX1250081]; the "Korea National Institute of Health" research project (2024-ER1003-01); partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2021-II212068, Artificial Intelligence Innovation Hub); and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A5A2027340 and NRF-2020R1A2C1009778).
(Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc.)