Early detection of neurodegenerative diseases

Research group Early Detection of Neurodegenerative Diseases

Together, the research groups Early Detection of Neurodegenerative Diseases and IDA investigate markers and methods, which could enable the early identification of persons within the prodromal phase of Parkinson’s disease. The application of the Movement Disorder Society (MDS) research criteria for prodromal Parkinson’s disease and further refinement of these methods are key aspects of IDA research. Here, marker profiles and calculated probabilities of prodromal Parkinson’s disease are investigated in incident Parkinson patients and healthy controls of several prospective cohort studies.

German Parkinson Society (DPG)

As part of a DPG research cooperation of several German cohort studies (Depression-PD/Rostock; EPIPARK/Lübeck; PRIPS/Homburg und Tübingen; TREND/Tübingen) IDA investigates interactions of markers as well as age- and sex-dependent predictive values of markers. Objective is to enhance the sensitivity and specificity of predicting Parkinson’s disease.

InGef, Berlin

In cooperation with the InGef (http://www.ingef.de/), the MoPED consortium (Morbus Parkinson Epidemiology in Germany) and IDA updated prevalence and incidence of Parkinson in Germany in 2015 based on health insurance data of approx. 4 million insurants could be estimated. These longitudinal data on medical diagnoses years before the Parkinson diagnosis will now be analyzed with a scope on the prediction of Parkinson’s disease. Using such a comprehensive database previous findings of cohort studies in prodromal Parkinson’s disease could be replicated, revised and complemented with new markers and predictive factors.  

Parkinson‘s Progression Markers Initiative (PPMI)

Working together with Dr. Rezzak Yilmaz (Research group Early Detection of Neurodegenerative Diseases) IDA analyzes data of the PPMI study, a cohort of de-novo Parkinson patients. Research questions comprise the investigation of symptoms, markers and marker profiles at disease onset. Thereby, the predictive values of markers and symptom profiles could be further specified to improve the accuracy of Parkinson prediction.