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Abstract BACKGROUNDLimited research has explored the effect of cardiovascular risk and amyloid interplay on cognitive decline in East Asians. METHODSVascular burden was quantified using Framingham's General Cardiovascular Risk Score (FRS) in 526 Korean Brain Aging Study (KBASE) participants. Cognitive differences in groups stratified by FRS and amyloid positivity were assessed at baseline and longitudinally. RESULTSBaseline analyses revealed that amyloid‐negative (Aβ–) cognitively normal (CN) individuals with high FRS had lower cognition compared to Aβ– CN individuals with low FRS (p < 0.0001). Longitudinally, amyloid pathology predominantly drove cognitive decline, while FRS alone had negligible effects on cognition in CN and mild cognitive impairment (MCI) groups. CONCLUSIONOur findings indicate that managing vascular risk may be crucial in preserving cognition in Aβ– individuals early on and before the clinical manifestation of dementia. Within the CN and MCI groups, irrespective of FRS status, amyloid‐positive individuals had worse cognitive performance than Aβ– individuals. HighlightsVascular risk significantly affects cognition in amyloid‐negative older Koreans.Amyloid‐negative CN older adults with high vascular risk had lower baseline cognition.Amyloid pathology drives cognitive decline in CN and MCI, regardless of vascular risk.The study underscores the impact of vascular health on the AD disease spectrum.
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“Brain age” predicts disability accumulation in multiple sclerosis
Abstract ObjectiveNeurodegenerative conditions often manifest radiologically with the appearance of premature aging. Multiple sclerosis (MS) biomarkers related to lesion burden are well developed, but measures of neurodegeneration are less well‐developed. The appearance of premature aging quantified by machine learning applied to structural MRI assesses neurodegenerative pathology. We assess the explanatory and predictive power of “brain age” analysis on disability in MS using a large, real‐world dataset. MethodsBrain age analysis is predicated on the over‐estimation of predicted brain age in patients with more advanced pathology. We compared the performance of three brain age algorithms in a large, longitudinal dataset (>13,000 imaging sessions from >6,000 individual MS patients). Effects of MS, MS disease course, disability, lesion burden, and DMT efficacy were assessed using linear mixed effects models. ResultsMS was associated with advanced predicted brain age cross‐sectionally and accelerated brain aging longitudinally in all techniques. While MS disease course (relapsing vs. progressive) did contribute to advanced brain age, disability was the primary correlate of advanced brain age. We found that advanced brain age at study enrollment predicted more disability accumulation longitudinally. Lastly, a more youthful appearing brain (predicted brain age less than actual age) was associated with decreased disability. InterpretationBrain age is a technically tractable and clinically relevant biomarker of disease pathology that correlates with and predicts increasing disability in MS. Advanced brain age predicts future disability accumulation.
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- Award ID(s):
- 2054199
- PAR ID:
- 10410047
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Annals of Clinical and Translational Neurology
- Volume:
- 10
- Issue:
- 6
- ISSN:
- 2328-9503
- Page Range / eLocation ID:
- p. 990-1001
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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