The clinical manifestation of Parkinson’s disease exhibits significant heterogeneity in the prevalence of non-motor symptoms and the rate of progression of motor symptoms, suggesting that Parkinson’s disease can be classified into distinct subtypes. In this study, we aimed to explore this heterogeneity by identifying a set of subtypes with distinct patterns of spatiotemporal trajectories of neurodegeneration. We applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning algorithm that combined disease progression modelling with clustering methods, to cortical and subcortical neurodegeneration visible on 3 T structural MRI of a large cross-sectional sample of 504 patients and 279 healthy controls. Serial longitudinal data were available for a subset of 178 patients at the 2-year follow-up and for 140 patients at the 4-year follow-up. In a subset of 210 patients, concomitant Alzheimer’s disease pathology was assessed by evaluating amyloid-β concentrations in the CSF or via the amyloid-specific radiotracer 18F-flutemetamol with PET. The SuStaIn analysis revealed three distinct subtypes, each characterized by unique patterns of spatiotemporal evolution of brain atrophy: neocortical, limbic and brainstem. In the neocortical subtype, a reduction in brain volume occurred in the frontal and parietal cortices in the earliest disease stage and progressed across the entire neocortex during the early stage, although with relative sparing of the striatum, pallidum, accumbens area and brainstem. The limbic subtype represented comparative regional vulnerability, which was characterized by early volume loss in the amygdala, accumbens area, striatum and temporal cortex, subsequently spreading to the parietal and frontal cortices across disease stage. The brainstem subtype showed gradual rostral progression from the brainstem extending to the amygdala and hippocampus, followed by the temporal and other cortices. Longitudinal MRI data confirmed that 77.8% of participants at the 2-year follow-up and 84.0% at the 4-year follow-up were assigned to subtypes consistent with estimates from the cross-sectional data. This three-subtype model aligned with empirically proposed subtypes based on age at onset, because the neocortical subtype demonstrated characteristics similar to those found in the old-onset phenotype, including older onset and cognitive decline symptoms (P < 0.05). Moreover, the subtypes correspond to the three categories of the neuropathological consensus criteria for symptomatic patients with Lewy pathology, proposing neocortex-, limbic- and brainstem-predominant patterns as different subgroups of α-synuclein distributions. Among the subtypes, the prevalence of biomarker evidence of amyloid-β pathology was comparable. Upon validation, the subtype model might be applied to individual cases, potentially serving as a biomarker to track disease progression and predict temporal evolution.

中文翻译：

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描绘帕金森病脑萎缩的三种不同的时空模式


帕金森病的临床表现在非运动症状的患病率和运动症状的进展速度上表现出显著的异质性，表明帕金森病可分为不同的亚型。在这项研究中，我们旨在通过识别一组具有不同神经退行性时空轨迹模式的亚型来探索这种异质性。我们将亚型和分期推理 （SuStaIn） 应用于 504 名患者和 279 名健康对照者的大型横截面样本的 3 T 结构 MRI 上可见的皮质和皮质下神经变性。在 2 年随访中，有 178 名患者的子集，在 4 年随访中，有 140 名患者的系列纵向数据。在 210 名患者的亚组中，通过评估 CSF 中淀粉样蛋白β浓度或通过淀粉样蛋白特异性放射性示踪剂 18F-flutemetamol 与 PET 来评估伴随的阿尔茨海默病病理。SuStaIn 分析揭示了三种不同的亚型，每种亚型都以脑萎缩的独特时空演变模式为特征：新皮层、边缘和脑干。在新皮质亚型中，脑容量减少发生在疾病早期的额叶和顶叶皮层，而在早期则在整个新皮层中进展，尽管纹状体、苍白球、伏隔肌区和脑干相对较少。边缘亚型代表相对区域脆弱性，其特征是杏仁核、伏隔区、纹状体和颞叶皮层的早期体积损失，随后在整个疾病阶段扩散到顶叶和额叶皮层。 脑干亚型显示从脑干延伸到杏仁核和海马体的逐渐向前进展，然后是颞叶和其他皮层。纵向 MRI 数据证实，在 2 年随访中，77.8% 的参与者在 4 年随访中被分配到亚型，在 4 年随访中，84.0% 的参与者被分配到亚型，与横断面数据的估计一致。这种三亚型模型与基于发病年龄的经验提出的亚型一致，因为新皮质亚型表现出与老年发病表型相似的特征，包括老年发病和认知能力下降症状 （P < 0.05）。此外，这些亚型对应于有症状的 Lewy 病理患者的神经病理学共识标准的三类，将新皮层、边缘和脑干为主的模式提出为 α-突触核蛋白分布的不同亚组。在亚型中，淀粉样蛋白β病理的生物标志物证据的流行率相当。经过验证，亚型模型可能适用于个别病例，可能作为跟踪疾病进展和预测时间演变的生物标志物。