Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy.
Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy.
复制标题
突触前多巴胺能成像表征了因突触核蛋白病导致的快速眼动睡眠行为障碍患者。
DOI：
10.1002/ana.26902
复制
发表时间：
2024
影响因子：
11.2
通讯作者：
Plac
中科院分区：
文献类型：
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作者：
Arnaldi,Dario;Mattioli,Pietro;Raffa,Stefano;Pardini,Matteo;Massa,Federico;Iranzo,Alex;Perissinotti,Andres;Niñerola-Baizán,Aida;Gaig,Carles;Serradell,Monica;Muñoz-Lopetegi,Amaia;Mayà,Gerard;Liguori,Claudio;Fernandes,Mariana;Plac
目的应用机器学习分析快速眼动（REM）睡眠行为障碍（RBD）患者的临床和突触前多巴胺能成像数据，以预测帕金森病（PD）和路易体痴呆（DLB）的发展。入组最终表型转化为明显α-突触核蛋白病（由于突触核蛋白病导致的RBD）的多导睡眠图证实的RBD患者（平均年龄70.5 ± 6.3岁，70.5%男性），并进行基线突触前多巴胺能成像和临床评估，包括运动、认知、嗅觉和便秘评估。为了进行比较，入组了232例RBD非表型患者（67.6 ± 7.1岁，78.4%男性）和160例对照（68.2 ± 7.2岁，53.1%男性）。影像学和临床特征进行了分析，通过机器学习，以确定预测phenoconversion.ResultsMachine学习分析表明，临床数据本身预测表型转换不佳。突触前多巴胺能成像显著改善了预测，特别是结合临床数据，在区分由突触核蛋白病引起的RBD与非表型转化的RBD患者方面具有77%的灵敏度和85%的特异性，在区分PD转化者与DLB转化者方面具有85%的灵敏度和86%的特异性。突触前多巴胺能成像的定量显示，突触核蛋白病患者受影响最严重的大脑半球壳核的Apricicalz评分临界值为-1.0，是RBD的特征，而受影响最严重的大脑半球壳核/尾状核比值的临界值为-1.0，是PD转换者的特征。相反，突触前多巴胺能成像可以很好地预测即将到来的表型转换诊断。这一发现可用于设计未来的疾病改善试验。神经网络2024;95：1178-1192
ObjectiveTo apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB).MethodsIn this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.3 years, 70.5% males) with polysomnography‐confirmed RBD who eventually phenoconverted to overt alpha‐synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non‐phenoconvertor patients (67.6 ± 7.1 years, 78.4% males) and 160 controls (68.2 ± 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion.ResultsMachine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD‐converters from DLB‐converters. Quantification of presynaptic dopaminergic imaging showed that an empiricalz‐score cutoff of −1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of −1.0 at the most affected hemisphere putamen/caudate ratio characterized PD‐converters.InterpretationClinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease‐modifying trials. ANN NEUROL 2024;95:1178–1192