Wang Meng-Yun, Xin Ran, Shao Jing-Yu, Wang Sheng-Hui, Yang Hong-Qi, Zhang Hong-Ju, Zhang Jie-Wen, Chen Shuai
Department of Neurology, Henan University People's Hospital, Zhengzhou, 450003, Henan, China.
School of Nursing, China Medical University, Shenyang, 110122, China.
Neurol Sci. 2025 Jun;46(6):2627-2635. doi: 10.1007/s10072-024-07953-3. Epub 2025 Jan 8.
Longitudinal cognitive changes in Parkinson's disease (PD) exhibit considerable heterogeneity.Predicting cognitive trajectories in early PD patients can improve prognostic counseling and guide clinical trials.
This study included 337 early PD patients with 6-year follow-up in the Parkinson's Progression Markers Initiative (PPMI) database.Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) to identify subtypes of longitudinal cognitive trajectories, and a nomogram predictive model was constructed using baseline clinical variables.
The 337 PD patients had a mean age of 61.0 years, mean disease duration of 0.55 years, and mean MoCA score of 27.1 points. Latent class mixed models (LCMM) identified two longitudinal cognitive subtypes: cognitive stable (276 cases, 81.9%) and cognitivel deteriorating (61 cases, 18.1%). The cognitively deteriorating subtype presented poorer baseline cognition, older age, and more severe motor and non-motor symptoms. On biomarkers, the cognitively deteriorating subtype revealed higher serum NFL levels and lower mean striatum DAT uptake. Six baseline clinical variables (age, Letter Number Sequencing score, Symbol Digit Modalities Test score, Benton Judgment of Line Orientation Test score, Hopkins Verbal Learning Test-Revised score, and REM Sleep Behavior Disorder) were selected to construct the nomogram predictive model which achieved an AUC of 0.92.The calibration curve demonstrated high consistency between predicted and observed probabilities.The predictive model has potential utility in disease-modifying clinical trials by pre-screening patients at high risk for cognitive deterioration.
This study identified two longitudinal cognitive subtypes: cognitive stable and cognitive deterioration within 6-year follow-up, and eighteen percent of early PD patients shared the cognitive deterioration subtype The predictive model, incorporating six baseline variables could estimate the risk of longitudinal cognitive deterioration in PD.
帕金森病（PD）的纵向认知变化表现出相当大的异质性。预测早期PD患者的认知轨迹可以改善预后咨询并指导临床试验。
本研究纳入了帕金森病进展标志物倡议（PPMI）数据库中337例接受了6年随访的早期PD患者。使用蒙特利尔认知评估（MoCA）评估认知功能，以确定纵向认知轨迹的亚型，并使用基线临床变量构建列线图预测模型。
337例PD患者的平均年龄为61.0岁，平均病程为0.55年，平均MoCA评分为27.1分。潜在类别混合模型（LCMM）确定了两种纵向认知亚型：认知稳定型（276例，81.9%）和认知恶化型（61例，18.1%）。认知恶化型亚型表现出较差的基线认知、年龄较大以及更严重的运动和非运动症状。在生物标志物方面，认知恶化型亚型显示血清神经丝轻链（NFL）水平较高，纹状体多巴胺转运体（DAT）摄取平均值较低。选择六个基线临床变量（年龄、字母数字序列得分、符号数字模式测试得分、本顿线方向判断测试得分、霍普金斯词语学习测试修订版得分和快速眼动睡眠行为障碍）构建列线图预测模型，其曲线下面积（AUC）为0.92。校准曲线显示预测概率与观察概率之间具有高度一致性。该预测模型通过对认知恶化高风险患者进行预筛选，在疾病修饰临床试验中具有潜在效用。
本研究在6年随访中确定了两种纵向认知亚型：认知稳定型和认知恶化型，18%的早期PD患者属于认知恶化型亚型。纳入六个基线变量的预测模型可以估计PD患者纵向认知恶化的风险。
