# Ménière’s disease and vestibular migraine: a narrative review of pathogenetic insights, diagnostic evolution, and clinical management advances

## TL;DR

This paper reviews Ménière’s disease and vestibular migraine, focusing on their overlapping symptoms, challenges in diagnosis, and recent advances in understanding their causes and potential biomarkers.

## Contribution

The paper proposes future research directions, including single-cell transcriptomics, animal models, and machine learning for improved diagnosis and treatment.

## Key findings

- Symptoms of Ménière’s disease and vestibular migraine overlap, making early differentiation difficult.
- Current biomarker studies for these disorders are limited by small sample sizes and lack of standardization.
- Machine learning integration of clinical, imaging, and molecular data is suggested to improve diagnostic accuracy.

## Abstract

Ménière’s disease (MD) and vestibular migraine (VM) are two common vestibular disorders with significant clinical overlap in their symptomatic presentations, including vertigo, hearing loss, tinnitus, and aural fullness. Although distinct diagnostic criteria exist for each, this symptomatic similarity often makes early-stage differentiation challenging. While recent studies have found potential biomarkers for MD and VM, their diagnostic utility remains limited by small sample sizes and lack of standardized validation protocols. This necessitates continued reliance on a synthesis of established guidelines (e.g., from the Bárány Society), detailed analysis of symptom temporal profiles, and ancillary examinations. This review presents a comparative analysis of the pathogenesis, clinical characteristics, and diagnostic criteria of MD and VM, summarizes recent research advances, and proposes key directions for future investigation. Major priorities include: (1) applying single-cell transcriptomics and genetically engineered animal models to further elucidate disease mechanisms underlying MD and VM; (2) establishing imaging-based specific biomarkers through high-resolution inner ear MRI; (3) validating candidate serum biomarkers using standardized proteomic platforms; and (4) integrating clinical features, imaging findings, and molecular biomarkers via machine learning approaches to improve diagnostic accuracy and enable personalized treatment strategies.