MRI evaluation of cerebral perivascular spaces predicts amyloid-related imaging abnormalities risk in preclinical Alzheimer's disease

脑血管周围间隙的MRI评估可预测临床前阿尔茨海默病中淀粉样蛋白相关影像学异常的风险

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Abstract

BACKGROUND AND PURPOSE: Amyloid-related imaging abnormalities (ARIA) are radiographic findings observed in the natural course of Alzheimer's disease and have been reported at higher rates in patients receiving anti-amyloid monoclonal antibody therapy. Identifying novel radiographic factors predicting ARIA risk may help prevent its occurrence, improve patient stratification, and provide insight on the underlying biological mechanisms. It remains unclear whether cerebral perivascular spaces (PVS) along with other quantitative radiographic markers of cerebral small vessel disease may help predict the risk of incident ARIA in patients diagnosed with preclinical Alzheimer's disease. METHODS: Participants from the A4 study were included. PVS and white matter hyperintensities (WMH) were segmented with robust fully-automated methods on T1-weighted and FLAIR images, respectively. Number of microhemorrhages and subcortical infarcts were previously recorded by expert radiologists. Baseline measurements of these markers were used in Cox proportional-hazards models to predict ARIA risk controlling for relevant demographic, clinical, and radiographic factors. RESULTS: Among 6,028 brain MRI from 1,088 participants (median age: 71-y.o.; 59.4% women), 356 ARIA were diagnosed (median study follow-up: 5.4 years). The volume fraction of PVS and WMH, and the number of microhemorrhages at baseline predicted higher ARIA risk (adjusted hazard ratio ranges: 1.32–1.55; adjusted p-values all <0.05). Importantly, the effect of PVS on ARIA with microhemorrhages risk was observed in individuals considered at low risk of ARIA according to current guidelines, i.e., APOE-ε4 non-carriers, low WMH burden, or no microhemorrhages. CONCLUSIONS: These results support the use of quantitative measurements of PVS in addition to WMH and microhemorrhages to assist clinicians in estimating an individual's risk of ARIA.

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