Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02144, USA; Harquail Centre for Neuromodulation and Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada; Department of Medicine (Neurology), University of Toronto, Toronto, ON M5S 3H2, Canada.
Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02144, USA; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Neuroimage Clin. 2020;28:102400. doi: 10.1016/j.nicl.2020.102400. Epub 2020 Aug 28.
To identify a parsimonious set of markers that optimally predicts subsequent clinical progression from normal to mild cognitive impairment (MCI).
250 clinically normal adults (mean age = 73.6 years, SD = 6.0) from the Harvard Aging Brain Study were assessed at baseline on a wide set of markers, including magnetic resonance imaging markers of gray matter thickness and volume, white matter lesions, fractional anisotropy, resting state functional connectivity, positron emission tomography markers of glucose metabolism and β-amyloid (Aβ) burden, and a measure of vascular risk. Participants were also tested annually on a battery of clinical and cognitive tests (median follow-up = 5.0 years, SD = 1.66). We applied least absolute shrinkage and selection operator (LASSO) Cox models to determine the minimum set of non-redundant markers that predicts subsequent clinical progression from normal to MCI, adjusting for age, sex, and education.
23 participants (9.2%) progressed to MCI over the study period (mean years of follow-up to diagnosis = 3.96, SD = 1.89). Progression was predicted by several brain markers, including reduced entorhinal thickness (hazard ratio, HR = 1.73), greater Aβ burden (HR = 1.58), lower default network connectivity (HR = 1.42), and smaller hippocampal volume (HR = 1.30). When cognitive test scores were added to the model, the aforementioned neuroimaging markers remained significant and lower striatum volume as well as lower scores on baseline memory and processing speed tests additionally contributed to progression.
Among a large set of brain, vascular and cognitive markers, a subset of markers independently predicted progression from normal to MCI. These markers may enhance risk stratification by identifying clinically normal individuals who are most likely to develop clinical symptoms and would likely benefit most from therapeutic intervention.
确定一组最优的标记物,以最佳预测从正常认知到轻度认知障碍(MCI)的后续临床进展。
哈佛衰老大脑研究中的 250 名临床正常成年人(平均年龄 73.6 岁,标准差 6.0)在基线时接受了广泛的标记物评估,包括磁共振成像标记物的灰质厚度和体积、白质病变、各向异性分数、静息状态功能连接、正电子发射断层扫描标记物的葡萄糖代谢和β-淀粉样蛋白(Aβ)负荷,以及血管风险的测量。参与者还每年接受一系列临床和认知测试(中位随访时间为 5.0 年,标准差为 1.66)。我们应用最小绝对收缩和选择算子(LASSO)Cox 模型来确定预测从正常到 MCI 的后续临床进展的最小非冗余标记物集,调整年龄、性别和教育。
在研究期间,23 名参与者(9.2%)进展为 MCI(平均随访至诊断的年限为 3.96 年,标准差为 1.89 年)。进展预测了几个大脑标记物,包括海马旁回厚度减少(危险比,HR=1.73)、Aβ负荷增加(HR=1.58)、默认网络连接降低(HR=1.42)和海马体积减小(HR=1.30)。当将认知测试分数添加到模型中时,上述神经影像学标记物仍然具有显著性,而纹状体体积较小以及基线记忆和处理速度测试得分较低也有助于进展。
在一大组脑、血管和认知标记物中,一组标记物独立预测了从正常认知到 MCI 的进展。这些标记物可能通过识别最有可能出现临床症状的临床正常个体并可能从治疗干预中获益最大,从而增强风险分层。