Meningeal lymphatic dysfunction in sporadic cerebral small vessel diseases and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy by DCE-MRI.

Abstract

Background: The distinction between cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), a prevalent hereditary cerebrovascular disease, and sporadic cerebrovascular disease has sparked significant interest among researchers. Meningeal lymphatic vessels (mLVs) have become a new research topic in recent years. This study aimed to investigate the function of meningeal lymphatics in cerebral small vessel disease (CSVD) and to develop a predictive model to assist in the diagnosis of CADASIL patients.

Methods: We systematically examined the functional changes in the meningeal lymphatic system in both sporadic CSVD patients and CADASIL patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and the correlations among blood biomarkers were subsequently calculated. Finally, a nomogram was generated for the identification of CADASIL using multiple significant risk factors.

Results: This study enrolled 41 sporadic CSVD (S-CSVD) patients, 15 CADASIL patients, and 18 normal controls (NC). Both CSVD and CADASIL groups demonstrated mLVs dysfunction compared to controls, characterized by decreased mLVs wash-in rates (P<0.05) and prolonged time-to-peak (TTP, P<0.01). Significant metabolic correlations emerged specifically in CSVD: In the S-CSVD group, the parameters of DCE-MRI [wash-in rate, TTP, incremental area under the curve (IAUC), transfer rate constant (Ktrans), extravascular extracellular volume fraction (Ve)] correlated with total cholesterol (TC, TTP, r=-0.38), low-density lipoprotein cholesterol (LDL-C, TTP, r=-0.41), serum creatinine (SCr, Ktrans, r=0.34), uric acid (UA, Ve, r=0.34), homocysteine (Hcy, wash-in, r=-0.31, both P<0.05), and hemoglobin A1c (HbA1c, wash-in; r=-0.32, P<0.01, IAUC; r=-0.47, P<0.01). In the CADASIL group, the parameter of DCE-MRI correlated with folic acid (FA, TTP, r=-0.57, P=0.03), SCr (IAUC; r=0.54, P=0.04, Ktrans; r=0.45, P=0.01, Ve; r=0.61, P<0.01), and UA (Ktrans; r=0.45, Ve; r=0.71, both P<0.05). Additionally, we identified several critical risk factors for diagnosing CADASIL and developed a nomogram to differentiate CADASIL from S-CSVD (area under the curve 0.870, 95% confidence interval: 0.767-0.973).

Conclusions: This study confirmed that mLVs were dysfunctional in both S-CSVD patients and CADASIL patients. IAUC was identified as a key risk factor, and a nomogram was generated. This research enhances our understanding of the role of mLVs in CSVD and contributes to distinguishing between these S-CSVD and CADASIL.