Plasma proteomic aging clocks and pseudotime trajectories may complement Alzheimer's disease biomarkers and diagnostic classification.
Evidence
An observational plasma proteomics analysis of 498 Indiana AD Research Center participants used SomaScan 7k data to test Alzheimer's risk scores against diagnosis, plasma biomarkers, imaging biomarkers, and classification performance.
Caveat
The abstract reports association and classification performance in one research-center dataset, so clinical use would need external and prospective validation.
Simplified
INTRODUCTION: Alzheimer's disease (AD) involves progressive cognitive decline. Plasma proteomics data may provide insights into disease risk and mechanisms.
METHODS: Plasma proteomics data (N = 498) by the SomaScan 7k platform from the Indiana AD Research Center was used to calculate proteomics-based Alzheimer's risk scores: organ-specific aging clocks and . The Alzheimer's risk scores were investigated for associations with plasma- and imaging-based biomarkers for AD and diagnosis and evaluated for classification performance for diagnosis and biomarker positivity status.
RESULTS: Cognition-optimized brain and liver age acceleration showed significant associations with AD diagnosis and biomarkers. Pseudotime showed a molecular trajectory from cognitively normal to AD individuals, in association with plasma and imaging biomarkers. Previously known plasma biomarkers yielded better classification performance with inclusion of proteomics-based Alzheimer's risk scores.
DISCUSSION: The findings highlight proteomics-derived biological aging clocks and pseudotime trajectory as potential biomarkers to complement current biomarker frameworks and identify disease mechanisms.
HIGHLIGHTS: Plasma proteomic aging clocks and pseudotime provide potential markers for Alzheimer's disease (AD) risk. Cognition-optimized brain aging and pseudotime link to diagnosis and AD biomarkers. Brain aging clock and pseudotime improve the diagnosis classification performance. Pseudotime outperformed established plasma biomarkers for diagnosis classification.
Key numbers
498
Participants
Total participants from the Indiana Alzheimer's Disease Research Center cohort.
22%
- improvement
Increase in - for vs. classification when incorporating and cognition-optimized age measures.
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