OBJECTIVE: Evaluate HV and WMLs as predictive markers of VaD in VMCI patients by assessing: (i)baseline differences in HV and WMLs between converters to VaD and non-converters, (ii) predictive power of HV and WMLs for VaD, (iii) associations between HV, WMLs, and cognitive decline, (iv)the role of WMLs on HV.
METHODS: This longitudinal multicenter study included 110 VMCI subjects (mean age:74.33 ± 6.63 years, 60males/50females) from the VMCI-Tuscany Study database. Subjects underwent brain MRI and cognitive testing, with 2-year follow-up data on VaD progression. HV and WMLs were semi-automatically segmented and measured. ANCOVA assessed group differences, while linear and logistic regression models evaluated predictive power.
RESULTS: After 2 years, 32/110 VMCI patients progressed to VaD. Converting patients had lower HV(p = 0.015) and higher lesion volumes in the posterior thalamic radiation (p = 0.046), splenium of the corpus callosum (p = 0.016), cingulate gyrus (p = 0.041), and cingulum hippocampus(p = 0.038). HV alone did not fully explain progression (p = 0.059), but combined with WMLs volume, the model was significant (p = 0.035). The best prediction model (p = 0.001) included total HV (p = 0.004) and total WMLs volume of the posterior thalamic radiation (p = 0.005) and cingulate gyrus (p = 0.005), achieving 80% precision, 81% specificity, and 74% sensitivity. Lower HV were linked to poorer performance on the Rey Auditory-Verbal Learning Test delayed recall (RAVLT) and Mini Mental State Examination (MMSE).
CONCLUSIONS: HV and WMLs are significant predictors of progression from VMCI to VaD. Lower HV correlate with worse cognitive performance on RAVLT and MMSE tests.
目的:通过评估:(i)在VMCI患者中,HV和WML作为VaD的预测标志物,(Ii)HV和WML对VaD的预测能力,(iii)HV之间的关联,WMLs,和认知能力下降,(Iv)WML对HV的作用。
方法:这项纵向多中心研究包括110名VMCI受试者(平均年龄:74.33±6.63岁,60名男性/50名女性)来自VMCI-托斯卡纳研究数据库。受试者接受了脑部MRI和认知测试,关于VaD进展的2年随访数据。HV和WML被半自动分割和测量。ANCOVA评估了组间差异,而线性和逻辑回归模型评估预测能力。
结果:2年后,32/110名VMCI患者进展为VaD。转换患者在后丘脑辐射中具有较低的HV(p=0.015)和较高的病变体积(p=0.046),call体的脾(p=0.016),扣带回(p=0.041),和海马扣带(p=0.038)。单独的HV不能完全解释进展(p=0.059),但结合WMLs的体积,模型显著(p=0.035).最佳预测模型(p=0.001)包括后丘脑辐射(p=0.005)和扣带回(p=0.005)的总HV(p=0.004)和总WMLs体积,达到80%的精度,81%的特异性,74%的灵敏度。较低的HV与Rey听觉言语学习测试延迟回忆(RAVLT)和迷你精神状态检查(MMSE)的表现较差有关。
结论:HV和WML是VMCI向VaD进展的重要预测因子。在RAVLT和MMSE测试中,较低的HV与较差的认知表现相关。