帕金森病（Parkinson’s disease, PD）作为仅次于阿尔茨海默病的第二大神经退行性疾病，其眼部表现近年来逐渐成为跨学科研究热点。以往医生多关注运动迟缓、静止性震颤和肌强直等PD典型症状，但大量临床研究表明，眼睑异常、眼球运动障碍、视觉功能异常等眼部表现不仅普遍存在于PD患者中，更可能在典型运动症状出现前就已显现。长期以来，这些眼部症状因其他症状的掩盖往往被忽视，进一步降低了患者的生活质量。本综述系统梳理PD患者眼部表现的三大方面：首先，眼睑异常方面，PD患者瞬目频率降低，61.1%患者出现干眼症状，导致PD患者的生活质量进一步下降。其次，眼球运动障碍表现为特征性的阶梯式方波急跳、集合功能减退以及反向扫视错误率增加，其中反向扫视潜伏期延长对步态冻结的发生具有预测价值。最后，视觉功能障碍方面，PD患者可出现视敏度下降、色觉异常、对比敏感度受损和视幻觉。影像学检查观察到视网膜神经节细胞层变薄，伴随视网膜微血管密度降低，这些结构性改变与PD患者的视觉功能障碍有关，作为生物标志物具有独特潜力。神经内科-眼科联合诊疗模式不仅有助于PD的早期诊断和预后评估，更有助于临床医生全面理解PD的疾病机制和表现，为未来诊疗策略的优化提供客观依据。
Parkinson’s disease (PD), the second most common neurodegenerative disorder after Alzheimer’s disease, has increasingly garnered interdisciplinary research attention due to its ocular manifestations. While the classical triad of motor symptoms—bradykinesia, resting tremor, and rigidity—remains the diagnostic hallmark, accumulating clinical evidence indicates that ocular abnormalities, including eyelid dysfunction, oculomotor disturbances, and visual impairments, are not only prevalent in PD patients but may also precede the onset of typical motor symptoms. Historically overlooked due to masking by other clinical features, these ocular manifestations contribute to the deterioration of patients' quality of life. This review systematically examines PD-related ocular abnormalities across three key domains: First, eyelid dysfunction manifests as reduced blink frequency, with 61.1% of PD patients reporting dry eye symptoms, further exacerbating their life quality impairment. Second, oculomotor disturbances are characterized by staircase-pattern square-wave jerks, convergence insufficiency, and increased error rates in antisaccade tasks, with prolonged antisaccade latency serving as a predictive marker for freezing of gait. Third, visual dysfunction encompasses diminished visual acuity, dyschromatopsia, impaired contrast sensitivity, and visual hallucinations. Imaging studies reveal structural alterations such as retinal ganglion cell layer thinning and reduced retinal microvascular density, which correlate with visual deficits and hold promise as potential biomarkers. The establishment of a neuro-ophthalmological collaborative framework not only facilitates early PD diagnosis and prognostic assessment but also enhances clinicians' comprehensive understanding of disease mechanisms. Such an approach provides an objective foundation for optimizing future therapeutic strategies.
目的：利用超高效液相色谱串联四极杆-静电场轨道阱高分辨质谱(ultra-high per formance liquid chromatography tandem quadrupole-electrostatic field orbitrap high resolution mass spectrometer, UHPLC- HRMS)代谢组学技术结合机器学习识别与糖尿病视网膜病变(diabetic retinopathy, DR)进展过程中的房水代谢差异，以寻找DR进展相关生物标志物。方法：本研究共纳入78例2型糖尿病(type 2 diabetes mellitus, T2DM)患者以及30名年龄性别匹配健康对照人群。使用UHPLC- HRMS检测所有患者及对照人群房水中的代谢物丰度，结合机器学习筛选T2DM和DR进展相关代谢物标志物并建立预测模型。结果：在校正混杂因素后，与健康人群对比1, 5-脱水山梨醇、硫酸十四烷基酯和N,N,N-三甲基-5-氨基戊酸在T2DM患者中表现出显著差异(均P < 0.05)；而N-乙酰色氨酸、亚油酰胺、油酰胺、棕榈酰胺、戊酸(游离脂肪酸(5:0)和琥珀酸与DR进展显著相关(均P < 0.001)。代谢通路分析表明，“缬氨酸、亮氨酸和异亮氨酸的生物合成”“精氨酸的生物合成”和“半胱氨酸及蛋氨酸代谢”是T2DM差异代谢途径。基于生物标志物的随机森林预测模型显示，差异代谢产物对T2DM和DR进展的预测准确率分别为81.3%和74%。结论：代谢组学结合机器学习方法有效揭示了T2DM及与DR进展相关的代谢特征，亚油酰胺和油酰胺有望成为DR进展的生物标志物，为DR的诊断和个体化治疗提供了新的可能性。
Objective: To identify aqueous humor metabolic profiles associated with the progression of type 2 diabetes mellitus (T2DM) and diabetic retinopathy (DR), aiming to discover potential biomarkers for DR progression. Ultra-high performance liquid chromatography tandem quadrupole-electrostatic field orbitrap high-resolution mass spectrometry (UHPLC-HRMS) will be utilized in conjunction with machine learning (ML) for comprehensive analysis. Methods: A total of 78 patients with T2DM and 30 age- and gender-matched healthy controls were included. UHPLC-HRMS was used to identify metabolites in the aqueous humor of all participants. ML was employed to screen for metabolites associated with T2DM and DR progression, and predictive models were established. Results: After adjusting for covariates, 1,5-anhydroglucitol, tetradecyl sulfate, and n,n,n-trimethyl-5-aminovaleric acid identified as significant indicators for T2DM compared to controls (all P < 0.05). N-acetyltryptophan, linoleamide, oleamide, palmitic amide, valeric acid(FFA(5:0), and succinic acid emerged as predictors for DR progression (all P < 0.001). Metabolic pathway analysis revealed that "valine, leucine and isoleucine biosynthesis", "arginine biosynthesis," and "cysteine and methionine metabolism" were the most enriched pathways for T2DM. Predictive models achieved R² values of 81.3%, and 74% for T2DM and DR progression, respectively. Conclusions: Metabolomic combined with ML effectively uncovered metabolic characteristics associated with T2DM and DR progression. Linoleamide and oleamide represent promising potential biomarkers for DR progression, offering new opportunities for diagnosis and personalized treatment of DR.
