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Background The diagnosis of Parkinson disease (PD) is mainly based on clinical symptoms, and there is a lack of objective methods for correct diagnosis. At present, there have been studies on retinal structural changes as a biomark for early diagnosis of PD, but machine learning based on retinal structural changes for predicting early PD has not yet been studied. Objective To construct a machine learning model based on the characteristics of retinal structural changes, explore its value in early PD diagnosis, and the accuracy of different machine learning algorithms for early PD diagnosis. Methods From October 2021 to September 2022, 49 PD patients aged 40 to 70 years old (PD group) who attended outpatient clinics and were hospitalized in the department of neurology of Henan Provincial People's Hospital (PD group) and 39 healthy people with matching age and sex (healthy control group) who came to the hospital for physical examination were collected. All study subjects underwent swept-source optical coherence tomography and swept-source optical coherence tomography angiography, the thickness and vessel density of the macular retina were also quantitatively analyzed. The 88 subjects were randomly divided into the 62 training sets and 26 validation set according to the ratio of 7∶3. Variables with significant differences between the PD group and healthy control group were selected as the characteristic variables for inclusion in the machine learning model, and Logistic regression (LR) , K-nearest neighbor algorithm (KNN) , decision tree (DT) , random forest (RF) and extreme gradient boosting (XGboost) models were constructed in the training set. The area under the curve (AUC) , accuracy, sensitivity and specificity of the receiver operating characteristic (ROC) curve were used to evaluate the predictive value of the machine learning model based on retinal structural changes for early PD. Results Compared with the healthy control group, the density of the upper outer ring (A6) , the outer temporal outer ring (A7) , the lower outer ring (A8) and the outer nasal ring (A9) of the superficial capillaries in the PD group were reduced, the thickness of the upper inner ring (A2) , the inner temporal inner ring (A3) , the inferior inner ring (A4) , the inner ring of the nasal side (A5) of the retinal layer, A6, A7, A8 and A9, the thickness of A6 of the ganglion cell complex layer, the thickness of A7 of the nerve fiber layer, A2 and A4, A5, A6, A7, A8, A9 became thinner (P

Background Parkinson's disease (PD) is frequently accompanied with anxiety, depression, sleep disorders, autonomic dysfunction, cognitive impairment, and other non-motor symptoms, among which autonomic dysfunction and cognitive impairment severely impair the quality of life. The relationship between autonomic dysfunction and cognitive impairment in PD is still controversial. Objective To analyze the characteristic of autonomic dysfunction and its correlation with cognitive impairment in PD patients. Methods A total of 156 patients with idiopathic PD admitted to Henan Provincial People's Hospital between September 2018 and November 2020 were enrolled. General data of patients, including age, sex, duration of disease, age of onset and years of education, were collected. According to the prevalence and duration of autonomic dysfunction, they were divided into autonomic dysfunction group (AutD group, n=107) and non-autonomic dysfunction group (NAutD group, n=49) . The part Ⅲ of the Unified Parkinson's Disease Rating Scale (UPDRS-Ⅲ) was used to assess motor symptoms. The Hoehn-Yahr (H-Y) scale was used to assess the severity of PD. The non-motor symptoms were assessed by the Non-motor Symptoms Scale (NMSS) . The Hamilton Anxiety Scale (HAMA) and Hamilton Rating Scale for Depression 24-item (HAMD-24) were used to evaluate anxiety and depression symptoms, respectively. The 39-item Parkinson's Disease Questionnaire (PDQ-39) was used to evaluate PD-specific health related quality of life. Autonomic function was evaluated by Scales for Outcomes in Parkinson's disease-Autonomic (SCOPA-AUT) . Cognitive function was evaluated by Mini-mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) . Spearman correlation analysis was used to explore the correlation between autonomic function (assessed using the SCOPA-AUT score) and cognitive function (assessed using the MMSE score and MoCA score) . Results Among the 156 patients, 83 were male and 73 were female, with a mean age of (63.9±0.7) years. AutD group had greater mean age (t=-4.391, P