Idiopathic Intracranial Hypertension: Updates on Pathophysiology and Diagnosis

Idiopathic intracranial hypertension (IIH) is a neurological disorder characterized by elevated intracranial pressure (ICP) without a discernible structural cause. Historically referred to as pseudotumor cerebri, IIH primarily affects young women with obesity, reflecting its close association with metabolic and hormonal imbalances. Clinical manifestations include headaches, visual disturbances, and papilledema, which, if untreated, can lead to irreversible vision loss.Pathophysiological insights reveal a complex interplay of venous hypertension, glymphatic dysfunction, cerebrospinal fluid (CSF) dynamics, and metabolic and hormonal contributions. Transverse sinus stenosis exemplifies this complexity, acting as both a cause and consequence of elevated ICP. Dysfunction in the glymphatic system, impaired CSF reabsorption, and obesity-related hormonal dysregulation further elucidate IIH's multifactorial nature. Innovations like venous sinus stenting and pharmacological interventions targeting pathways like glucagon-like peptide-1 (GLP-1) show promise in managing refractory cases.Advances in diagnostics have refined IIH identification, integrating neuro-ophthalmological assessments, lumbar puncture, and imaging modalities such as magnetic resonance imaging (MRI), magnetic resonance venography (MRV), and optical coherence tomography (OCT). These tools enhance the detection of key findings like transverse sinus stenosis, optic nerve sheath dilation, and papilledema severity. Emerging non-invasive methods offer safer, real-time monitoring potential, including ultrasound-based optic nerve sheath diameter (ONSD) measurement and machine learning-based ICP estimation.Despite the significant and recent advances, challenges in standardizing diagnostic criteria and therapeutic approaches persist. This review integrates recent research to highlight critical developments, underscoring unresolved questions on pathophysiology and advocating for a multidisciplinary approach for the diagnosis. Future directions include hybrid diagnostic strategies and leveraging artificial intelligence to refine non-invasive monitoring and therapeutic precision.