Based on the high-quality Parkinson's disease cohort population and biological sample database in the early stage of the team, multi-dimensional intelligent wearable device technology and machine learning were used to screen and classify digital biomarkers in the prodromal PD cohort, early PD cohort and healthy population cohort. Using peptide nanoprobes, metabolomics and Simoa technology to find the pathological molecular biomarkers of high-purity blood neurogenic exosomes in the prodromal stage of PD cohort, early PD cohort and healthy population cohort, and conduct classification and combination study. The association analysis was used to explore the specific internal relationship between digital biomarkers and neurogenic exosome molecular biomarkers, to explore the potential relationship between the two types of biomarkers, and to further apply machine learning methods to establish a fusion model for early diagnosis of PD including markers related to digital phenotype and pathological molecular mechanism, and to verify it clinically. The research results of this project are expected to bring new strategies for the early diagnosis of PD biomarkers, provide theoretical support for clinical transformation, and have important clinical significance for achieving the goal of early diagnosis and early treatment.
1. Healthy Control Group
No severe mental illness;
Mini-Mental State Examination (MMSE) score >24 points; 2. Prodromal PD Group
Meets the diagnostic criteria for rapid eye movement sleep behavior disorder;
Presence of mental, cognitive, or psychological disorders, unable to sign informed consent;
Presence of other diseases affecting walking distance, such as lower limb joint lesions, spinal lesions, neurological disorders, or severe cardiopulmonary diseases;
Presence of intracranial structural changes, cerebrovascular disease, or other neurological disorders;
Presence of tumors, severe liver or kidney dysfunction (indicators exceeding three times the normal value), or other conditions that significantly impact health;
This study comprises a combined retrospective and prospective cohort of patients with idiopathic Parkinson's disease (PD), selected from the PD registry database maintained by our team at Fujian Medical University Union Hospital. The study population includes individuals who meet all of the following criteria: (1) diagnosed with idiopathic PD based on the Movement Disorder Society (MDS) clinical diagnostic criteria; (2) Hoehn-Yahr (H-Y) stage ≤ 2.0 during off-medication periods; and (3) Mini-Mental State Examination (MMSE) score \> 24 points. Eligible participants will be identified from the existing database for retrospective data collection, while prospective follow-up data will be collected for the same cohort to enable comprehensive longitudinal analyses.
Case-control
Other
Not listed
Mini-mental State Examination
Mini-Mental State Examination (MMSE) is a 30-item cognitive screening tool assessing orientation, memory, attention, calculation, and language, with total scores ranging from 0 to 30. Higher scores indicate better cognitive function. Scores ≥27 generally indicate normal cognition, while lower scores suggest varying degrees of impairment, with cutoffs adjusted for education and age.
Montreal Cognitive Assessment
Montreal Cognitive Assessment (MoCA) is a 10-15 minute screening tool covering 8 cognitive domains through 12 tasks, with total scores ranging from 0 to 30. Higher scores indicate better cognitive function. Scores ≥26 are generally considered normal, with a 1-point adjustment for education ≤12 years; scores \<26 suggest possible cognitive impairment.
The 39-Item Parkinson's Disease Questionnaire
Non-Motor Symptoms Scale (NMSS) assesses frequency and severity of non-motor symptoms across all stages of Parkinson's disease, covering autonomic, affective, cognitive, sleep, and other domains. Total scores range from 0 to 360. Higher scores indicate worse outcome (greater symptom burden).
Hamilton Depression Rating Scale
Hamilton Depression Rating Scale (HAM-D) is a clinician-rated instrument assessing depression severity across multiple dimensions including mood, somatic symptoms, anxiety, and sleep disturbance. The 17-item version (HAM-D17) yields total scores ranging from 0 to 52. Higher scores indicate worse outcome (greater depressive symptom severity), with scores ≥23 indicating very severe depression.
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