Media release

From: Springer Nature

Proteins in blood may help to predict Parkinson’s disease

Proteins in the blood may help to predict Parkinson’s disease up to seven years before the onset of motor symptoms, according to a study published in Nature Communications.

Parkinson’s disease is a neurodegenerative disorder defined by slowness of movement, rigidity, and resting tremor. Before motor symptoms develop, there is a period of non-motor symptoms including sleep disorders such as REM sleep behaviour disorder, which is an important predictor for developing Parkinson’s disease later in life. Studying individuals with REM sleep behaviour disorder provides an opportunity to gain insights into the early pathological events that occur before the development of Parkinson’s disease.  

Jenny Hällqvist and colleagues analysed blood samples from 99 individuals recently diagnosed with Parkinson’s disease, 72 individuals with REM sleep behaviour disorder but no motor symptoms associated with Parkinson’s disease, and 36 healthy controls. They identified 23 proteins involved in pathways of inflammation, coagulation cascade and Wnt-signalling that were consistently dysregulated in the blood of individuals with Parkinson’s disease. Of these proteins, six were also shown to be dysregulated in individuals with REM sleep behaviour disorder. The authors then applied a machine learning model to predict diagnosis based on blood protein composition. The model was able to identify 100% of individuals with Parkinson’s disease based on the expression of eight proteins. They then tested whether the machine learning model could predict whether an individual with REM sleep behaviour disorder would go on to develop Parkinson’s disease. The model was able to predict individuals that would go on to develop Parkinson’s disease with 79% accuracy up to 7 years before the onset of motor symptoms.

The authors note that identifying individuals with early Parkinson’s disease could allow greater recruitment into preventative clinical trials, improving both patient treatment options and research output. However, further validation in larger cohorts is needed before these findings could be translated into clinical settings, they conclude.