Establishment Of A Predictive Model For Parkinson’s Disease Progression Based On Inflammatory And Metabolic Indicators And The Predictive Value Of Plasma FN In Disease Progression

Objective:At present,there is still a lack of biomarkers that can dynamically reflect the progress of Parkinson’s disease(PD).It is known that neuroinflammation and metabolic profiles are associated with the progression of Parkinson’s disease.Therefore,we hope to establish a prediction model of Parkinson’s disease progression by screening the inflammatory and metabolic profiles.Based on the predictive model,we can find the key predictors and analyzed their relationship with Parkinson’s disease progression.Methods:This study is divided into three parts.In the first part,244 PD patients were enrolled.With motor complications as the primary outcome,Lasso-Logistic regression was used to screen inflammatory and metabolic indicators.Combined with demographic and clinical characteristics,a prediction model of PD motor complications based on inflammatory metabolic predictors was established.In the second part,we find the key indicators through the prediction model,and analyze the relationship between the key indicators and the severity of PD.In the third part,the above 244 patients were followed up,and the occurrence of milestones was used as the primary outcome to progress the late-stage Parkinson’s disease.218 cases completed the follow-up.Lasso-cox regression was used to screen inflammatory and metabolic indicators.Combined with demographic and clinical characteristics,a prediction model of late-stage PD based on inflammatory and metabolic indicators was established,and the survival curve of patients was established.Results:First the occurrence of motor complications is a sign of PD entering the advanced stage.Through Lasso-logistic regression,the influencing factors related to motor complications include disease duration,fibronectin,high-density lipoprotein cholesterol,albumin,lactate dehydrogenase,cystatin C,diabetes mellitus and equivalent dose of levodopa.Thus we construct a prediction model of PD motor complications based on inflammation and metabolic indicators.Second,based on the predictive model,we found for the first time that plasma fibronectin(pFN)is a key predictor of PD progression.(1)Low pFN levels(OR=0.989,95%CI=0.980-0.999,p=0.024)are associated with more severe motor complications;(2)low pFN levels(OR=0.985,95%CI=0.973-0.997,p=0.017)increase the risk of wearing-off of PD,but not dyskinesia;(3)low pFN levels(95%CI=-0.306-0.071,p=0.002)are associated with increased UPDRS Ⅲ scores of Parkinson’s disease;(4)low pFN levels do not increase the risk of cognitive impairment of PD.Third,the occurrence of milestones is a sign of late-stage PD.Through Lasso-Cox regression,the predictive indicators related to milestone events include age,fibronectin,uric acid,albumin and hypersensitive C-reactive protein.For the first time,low pFN levels(HR=0.98,95%CI 0.97-0.99,p=0)were found to be a significant independent risk factor for milestones progression of PD.Therefore,we established a predictive model of late-stage PD based on inflammatory and metabolic indicators.Conclusion:In our study,we found for the first time that lower pFN levels were associated with PD progression and disease severity.Low pFN levels were a significant independent risk factor for motor complications and milestones progression.In addition,Low pFN levels were associated with more severe motor symptoms,but did not increase the risk of cognitive impairment of PD.Based on the inflammatory and metabolic indicators,we established the prediction models of motor complications and late-stage PD respectively.The calibration of the model is reasonable and the prediction accuracy is acceptable.