Glymphatic dysfunction across sleep disorders: a meta-analysis of DTI-ALPS studies
1Department of Radiology, The Second Clinical Medical College, North Sichuan Medical College, Nanchong, China.
Background:
Sleep disorders are increasingly linked to glymphatic dysfunction, but whether this impairment is universal across different sleep pathologies remains unclear.
Objective:
This meta-analysis examined whether glymphatic dysfunction, assessed via the diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index, may represent a shared neural feature across a spectrum of sleep disorders.
Methods:
We systematically searched PubMed, EMBASE, and the Cochrane Library from inception to December 31, 2025, for observational studies comparing DTI-ALPS index between patients with sleep disorders and healthy controls. Data were synthesized from 19 studies (n = 2,315) using a random-effects model to calculate standardized mean differences (SMDs). Methodological quality was assessed with the Newcastle-Ottawa Scale. Subgroup analyses, meta-regression, sensitivity analyses, and publication bias assessment were performed.
Results:
The pooled analysis revealed significant global glymphatic impairment in patients with sleep disorders (SMD = -1.60, 95% CI [-2.65, -0.54], p = 0.003), indicating global glymphatic dysfunction. However, heterogeneity was extremely high (I2 = 94.7%). Disorder-specific analyses showed pronounced deficits in obstructive sleep apnea (SMD = -0.92, p < 0.001) and idiopathic REM sleep behavior disorder (SMD = -0.63, p = 0.004), as well as in PSQI-defined poor sleep (SMD = -0.50, p < 0.001). Results for insomnia and narcolepsy were non-significant and highly heterogeneous. Meta-regression identified no significant moderators; publication bias was detected (p = 0.01).
Conclusion:
Glymphatic dysfunction, as assessed by DTI-ALPS, is consistently observed across several sleep disorders, suggesting a potential shared pathway. However, extreme heterogeneity limits interpretability of the pooled effect, and cross-sectional data preclude causal inference.
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