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October 16, 2025Frontiers in Neurology5 citationsOpen Access

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

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HSHongwei SunGZGang ZhangYZYingxin Zhang

Key Points

  • Diagnosing ménière's disease and vestibular migraine remains challenging due to overlapping symptoms, including vertigo and hearing loss.
  • Recent studies highlight potential biomarkers for both conditions, but limitations include small sample sizes and lack of standardized protocols.
  • Future research should focus on using single-cell transcriptomics and animal models to reveal the underlying disease mechanisms.
  • Combining clinical features and biomarkers via machine learning could enhance diagnostic accuracy and tailor treatment options.

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.

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Cite This Study

Sun et al. (2025) studied this question.

synapsesocial.com/papers/68f0492fe559138a1a06e0a5https://doi.org/10.3389/fneur.2025.1653509
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