Simultaneous evaluation of the effects of 62 risk factors on disease onset, progression and identification of clinical subtypes of Parkinson's disease.

Proponente Alfredo Berardelli - Professore Ordinario
Sottosettore ERC del proponente del progetto
Componenti gruppo di ricerca
Componente Categoria
Antonio Suppa Componenti strutturati del gruppo di ricerca
Giorgio Leodori Dottorando/Assegnista/Specializzando componente non strutturato del gruppo di ricerca
Giovanni Fabbrini Componenti strutturati del gruppo di ricerca
Componente Qualifica Struttura Categoria
Daniele Belvisi Medico, Neurologo, PhD IRCCS Neuromed Institute Altro personale aggregato Sapienza o esterni, titolari di borse di studio di ricerca

Parkinson's disease (PD) is a multi-factorial neurodegenerative disorder whose pathogenesis depends on a combination of genetic and environmental factors. PD is characterized by wide variability in the presentation and progression that is possibly linked to the contribution of different risk factors. In the current literature, however, there are no conclusive findings on the relationship between most of the risk factors investigated and PD. We performed a systematic database search and we identified 62 risk factors that may either trigger or prevent the development of PD. The aim of the present multicenter case-control study will be to simultaneously evaluate the effects of the 62 risk factors on disease development, clinical variability and progression of PD. For this purpose, we will enrol 1000 patients affected by PD and 1000 age- and sex-matched healthy controls. The presence of the 62 risk factors will be investigated by the administration of a semi-structured questionnaire specifically developed according to the results of our database search. We will collect the PD patients' demographic and clinical data. The clinical assessment will be performed at the baseline and after a 1-year follow-up period. To identify independent risk and protective factors for PD development, we will compute a multivariate logistic regression analysis by using the demographic and risk factors data of our population. To identify PD clinical subtypes, we will use the demographic and clinical variables to perform `data-driven' cluster analysis. Finally, by performing the 1 year- follow up assessment we will investigate the possible effects of the 62 risk factors examined on PD progression. The present study will allow to clarify the independent influence of 62 risk/protective factors on PD development and progression and to ascertain whether the existence of different clinical subtypes in PD may also depend on the contribution of different risk factors.


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