Extracellular Vesicles for Alzheimer’s Disease and Dementia Diagnosis

Abstract

Accurate differential diagnosis of dementia disorders including Alzheimer’s disease (AD), frontotemporal dementia (FTD), dementia with Lewy bodies (DLB), Parkinson’s disease dementia (PDD), and vascular cognitive impairment and dementia (VCID), along with conditions like prodromal mild cognitive impairment (MCI) or negative controls (NCs), continues to challenge neurologists. The nuanced and sometimes shared pathophysiological features underscore the need for precision in developing disease-modifying therapies. In the pursuit of reliable antemortem biomarkers, extracellular vesicles (EVs) have emerged as a popular tool for their capacity to encapsulate disease-specific signatures, particularly in neurodegenerative and neurological disorders. To this end, we have performed a comprehensive, PRISMA-guided systematic review and meta-analysis, utilizing sophisticated statistical modeling to determine the diagnostic accuracy, explore between-study variance and heterogeneity (I2), and investigate potential publication bias using various statistical tests.Biomarkers derived from general EVs demonstrated superior diagnostic accuracy, less between-study variance, heterogeneity, and publication bias than those from speculative CNS-enriched EVs. The trim-and-fill method suggested a potential overestimation of diagnostic effectiveness for biomarkers derived from CNS-enriched EVs due to four hypothesized missing studies with low diagnostic results, but none for general EVs. Meta-regressions revealed that studies using cerebrospinal fluid or serum, involving non-fasting individuals, sampling in the afternoon, employing citrate instead of EDTA for blood collection, using thrombin for coagulation factor depletion, isolating EVs with purer methods such as combined ultracentrifugation and size-exclusion chromatography, not freezing EVs post-isolation, and quantifying miRNA biomarkers, achieved better diagnostic accuracy and less heterogeneity. The diagnostic accuracy was low in differentiating among different dementia disorders. However, the analysis for diagnosing persons with AD vs. VCID achieved the highest diagnostic accuracy, suggesting that further studies may focus on this comparison. Additionally, we highlight several limitations in the included studies and recommend the following: Implement the use of appropriate negative controls, thorough documentation of preanalytical factors, inclusion of more dementia groups beyond AD, comprehensive reporting on pharmacological treatments, consideration of racial and ethnic minority groups, adherence to ISEV guidelines, application of the A-T-N framework, detailed documentation of dementia stages, extension of studies beyond differential diagnosis, reanalysis when postmortem definitive diagnostics become available, evaluation of prodromal conversion rates, and commitment to accurate statistical modeling and data transparency. We hope that lessons learned from this comprehensive meta-analysis can be beneficial for those attempting to discover biomarkers for AD and related dementias through EVs or alternative approaches.