Glymphatic function in humans: indicators, modulators, and measurement strategies

The glymphatic system is an important pathway for brain waste clearance and is increasingly implicated in neurodegenerative, cerebrovascular, traumatic, and sleep-related disorders. Clinical translation remains challenging because glymphatic function cannot currently be comprehensively or directly quantified in vivo in humans, and assessment relies on heterogeneous measures differing in physiological specificity and measurement directness. This review integrates human glymphatic indicators, physiological modulators, and measurement strategies within a unified framework.

A narrative literature review was conducted using human studies and key foundational research identified through PubMed and Google Scholar. Assessment methods were organized according to the authors' proposed three-domain framework of glymphatic physiology (transport, exchange, and clearance outcome), recognizing that modalities may span multiple domains. Major physiological modulators and confounders were summarized, and measurement strategies were evaluated based on feasibility, reproducibility, and translational applicability.

Human glymphatic assessment relies on complementary approaches with different levels of measurement directness, including tracer-informed kinetic MRI; neurofluid proxies such as BOLD-CSF coupling and CSF flow MRI; indirect imaging correlates such as DTI-ALPS and enlarged perivascular spaces; and downstream biological correlates such as CSF solute biomarkers. Measurements can be influenced by sleep, circadian timing, respiration, cardiovascular pulsatility, posture, medications, vascular risk factors, and aging. A domain-first, tiered framework is proposed to support multimodal evaluation and interpretation.

The proposed framework organizes multimodal glymphatic-related measures according to physiological domain and measurement directness. Standardized acquisition, harmonized analytical methods, rigorous confounder reporting, and prospective validation may improve reproducibility and support future clinical translation.