Vemuri Prashanthi, Graff-Radford Jonathan, Lesnick Timothy G, Przybelski Scott A, Reid Robert I, Reddy Ashritha L, Lowe Val J, Mielke Michelle M, Machulda Mary M, Petersen Ronald C, Knopman David S, Jack Clifford R
Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA.
Brain Commun. 2021 Apr 12;3(2):fcab076. doi: 10.1093/braincomms/fcab076. eCollection 2021.
While cerebrovascular disease can be observed using MRI, the multiplicity and heterogeneity in the mechanisms of cerebrovascular damage impede accounting for these measures in ageing and dementia studies. Our primary goal was to investigate the key sources of variability across MRI markers of cerebrovascular disease and evaluate their impact in comparison to amyloidosis on cognitive decline in a population-based sample. Our secondary goal was to evaluate the prognostic utility of a cerebrovascular summary measure from all markers. We included both visible lesions seen on MRI (white matter hyperintensities, cortical and subcortical infarctions, lobar and deep microbleeds) and early white matter damage due to systemic vascular health using diffusion changes in the genu of the corpus callosum. We identified 1089 individuals aged ≥60 years with concurrent amyloid-PET and MRI scans from the population-based Mayo Clinic Study of Aging. We divided these into discovery and validation datasets. Using the discovery dataset, we conducted principal component analyses and ascertained the main sources of variability in cerebrovascular disease markers. Using linear regression and mixed effect models, we evaluated the utility of these principal components and combinations of these components for the prediction of cognitive performance along with amyloidosis. Our main findings were (i) there were three primary sources of variability among the CVD measures-white matter changes are driven by white matter hyperintensities and diffusion changes; number of microbleeds (lobar and deep); and number of infarctions (cortical and subcortical); (ii) Components of white matter changes and microbleeds but not infarctions significantly predicted cognition trajectories in all domains with greater contributions from white matter; and (iii) The summary vascular score explained 3-5% of variability in baseline global cognition in comparison to 3-6% variability explained by amyloidosis. Across all cognitive domains, the vascular summary score had the least impact on memory performance (∼1%). Though there is mechanistic heterogeneity in the cerebrovascular disease markers measured on MRI, these changes can be grouped into three components and together explain variability in cognitive performance equivalent to the impact of amyloidosis on cognition. White matter changes represent dynamic ongoing damage, predicts future cognitive decline across all domains and diffusion measurements help capture white matter damage due to systemic vascular changes. Therefore, measuring and accounting for white matter changes using diffusion MRI and white matter hyperintensities along with microbleeds will allow us to capture vascular contributions to cognitive impairment and dementia.
虽然可以使用磁共振成像（MRI）观察脑血管疾病，但脑血管损伤机制的多样性和异质性阻碍了在衰老和痴呆研究中对这些指标的考量。我们的主要目标是研究脑血管疾病MRI标志物变异性的关键来源，并在基于人群的样本中评估它们与淀粉样变性相比对认知衰退的影响。我们的次要目标是评估所有标志物的脑血管综合测量指标的预后效用。我们纳入了MRI上可见的病变（白质高信号、皮质和皮质下梗死、脑叶和深部微出血）以及利用胼胝体膝部的扩散变化反映的因全身血管健康导致的早期白质损伤。我们从基于人群的梅奥诊所衰老研究中确定了1089名年龄≥60岁且同时进行了淀粉样蛋白PET和MRI扫描的个体。我们将这些个体分为发现数据集和验证数据集。利用发现数据集，我们进行了主成分分析，并确定了脑血管疾病标志物变异性的主要来源。使用线性回归和混合效应模型，我们评估了这些主成分及其组合对预测认知表现以及淀粉样变性的效用。我们的主要发现是：（i）脑血管疾病测量指标存在三个主要变异性来源——白质变化由白质高信号和扩散变化驱动；微出血数量（脑叶和深部）；梗死数量（皮质和皮质下）；（ii）白质变化和微出血的成分而非梗死显著预测了所有领域的认知轨迹，白质的贡献更大；（iii）与淀粉样变性解释的3 - 6%的变异性相比，血管综合评分解释了基线整体认知中3 - 5%的变异性。在所有认知领域中，血管综合评分对记忆表现的影响最小（约1%）。尽管MRI测量的脑血管疾病标志物存在机制上的异质性，但这些变化可分为三个成分，共同解释了认知表现的变异性，相当于淀粉样变性对认知的影响。白质变化代表动态的持续损伤，预测所有领域未来的认知衰退，扩散测量有助于捕捉因全身血管变化导致的白质损伤。因此，使用扩散MRI、白质高信号以及微出血来测量和考量白质变化，将使我们能够捕捉血管对认知障碍和痴呆的影响。
