Proteomic profiling of olfactory exfoliates from people with subjective cognitive complaints reveal networks of olfactory biomarkers of cognitive performance

Introduction: Partly due to the inaccessibility of olfactory brain regions vulnerable to early Alzheimer's Disease (AD) for repeated sampling, proteomic networks underlying progressive cognitive decline remain poorly understood. The olfactory mucosa (OM), an accessible part of the olfactory system, reflects central nervous system physiology and pathology, and represents a promising site for biomarker discovery. This study aimed to identify olfactory proteomic markers and pathways associated with performance in the logical memory II recognition (LM II_recog) subtest of the Wechsler Memory Scale among older adults with subjective cognitive complaints. Methods: < 0.1) was used to identify proteins associated with LM II_recog performance, and ingenuity pathway analysis (IPA) was applied to determine functional pathways. Results: < 0.1 threshold were found to be linearly correlated with LM II_recog scores. Of the top 10 most significant proteins, six (PLOD1, MFN2, NGFR, PPP2R5E, C4A/C4B, and ITGAV) have previously been linked to AD and/or cognitive function, underscoring their potential as biomarkers of cognitive impairment. Ingenuity pathway analysis using the knowledge base machine learning (ML) platform revealed several disease pathways highly represented among the significant proteins. These included Hyperactive Behavior, Neuromuscular Disease, Tauopathy, Behavioral Deficits, Alzheimer's Disease, Progressive Dementia, Degenerative Dementia, Alzheimer's or Frontotemporal Dementia, all of which were associated with LM II_recog performance in the elderly population. Discussion: This study demonstrates the feasibility of using OM-derived proteomics to identify molecular signatures associated with cognitive performance and highlights the OM as a potential site for non-invasive biomarker discovery. These findings provide a foundation for future studies integrating OM profiling with established AD biomarkers.