Abstract
Abstract
Dementia is a major global health challenge; however, current diagnostic approaches often rely on invasive or costly procedures. Non-invasive molecular approaches using Raman spectroscopy and sweat-derived extracellular vesicles (EVs) may provide an accessible strategy for characterizing dementia-associated molecular changes. To determine whether Raman spectral profiling of sweat EVs can discriminate individuals with dementia from cognitively healthy controls (HC) and distinguish between Alzheimer disease (AD) and frontotemporal dementia (FTD) including the assessment of a sex-specific spectral signatures.
This proof-of-concept cross-sectional study included 88 participants: 26 HC, 37 patients with AD, and 25 patients FTD, comprising female and male participants. Sweat EVs were isolated from sweat patches and characterized using Raman spectroscopy. Differences in Raman spectral signatures between groups and diagnostic performance assessed by receiver operating characteristic analysis.
Distinct Raman spectral fingerprints were identified in sweat EVs from dementia patients compared with HC. Participant-level Raman classification discriminated dementia patients from HC with an AUC of 0.76 in the full cohort and 0.83 in a balanced sensitivity analysis. Distinct spectral differences were also observed between AD and FTD, achieving modest classification performance of 0.73 AUC. Sex-stratified analyses further revealed Raman spectral heterogeneity between diagnostic groups, highlighting additional molecular differences that were not observed in the overall cohort.
These findings provide proof-of-concept evidence that Raman spectroscopy of sweat EVs can identify dementia-associated molecular signatures potentially related to differences in protein, lipid, and metabolic composition. Furthermore, Raman profiling showed modest ability to differentiate AD from FTD. As a non-invasive approach, our strategy may represent a promising platform for molecular characterization of dementia, warranting validation in larger longitudinal cohorts.