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

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. 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. 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). 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.