Abstract
Clinical and biochemical diversity of Parkinson’s disease (PD) presents a major challenge for accurate diagnosis and prediction of its progression. We propose, develop and optimize PD clinical scores as efficient integrated progression biomarkers for prediction of the likely rate of cognitive decline in PD patients. We considered 269 drug-naïve participants from the Parkinson’s Progression Marker Initiative database, diagnosed with idiopathic PD and observed between 4 and 6 years. Nineteen baseline clinical and pathological measures were systematically considered. Relative variable importance and logistic regressions were used to optimize combinations of significant baseline measures as integrated biomarkers. Parkinson’s disease cognitive decline scores were designed as new clinical biomarkers using optimally categorized baseline measures. Specificities and sensitivities of the biomarkers reached ~93% for prediction of severe rate of cognitive decline (with more than 5 points decline in 4 years on the Montreal Cognitive Assessment scale), and up to ~73% for mild-to-moderate decline (between 1 and 5 points decline). The developed biomarkers and clinical scores could resolve the long-standing clinical problem about reliable prediction of PD progression into cognitive deterioration. The outcomes also provide insights into the contributions of individual clinical and pathological measures to PD progression, and will assist with better-targeted treatment regiments, stratification of clinical trial and their evaluation.
Highlights
Clinical and biochemical diversity of Parkinson’s disease (PD) presents a major challenge for accurate diagnosis and prediction of its progression
dopamine transporter (DaT) imaging was recently indicated by the European Medicines Agency and Food and Drug Administration as an ‘enrichment biomarker’ for inclusion in clinical trials[24], significant deficiencies and lack of reliability of this biomarker have been highlighted[25]
Global cognitive function of the study participants and its decline over time were evaluated using the Montreal Cognitive Assessment (MoCA) scale, which is often used for the evaluation of global cognitive function in PD patients[29,30]
Summary
Clinical and biochemical diversity of Parkinson’s disease (PD) presents a major challenge for accurate diagnosis and prediction of its progression. The difficulties with the development of biomarkers for this disorder are largely related to heterogeneity of PD, its poor clinicopathological correlation and instability of the clinical phenotypes, and significant overlaps of the clinical and biochemical characteristics with healthy controls and patients suffering from other neurodegenerative disorders[1,2,5,6,7,8,9] This significantly impedes optimal therapy advice and evaluation of new drugs and therapies for PD. Despite the apparent success with identifying numerous individual measures that could aid with PD diagnosis and prognosis, it has become apparent that no such measure could be an efficient biomarker for this disorder and its progression[2,6,23] This is because no individual measure is capable of reflecting the vast heterogeneity of the clinical and biochemical presentation of this disease. Failure of individual measures as PD markers for a particular patient could be effectively compensated by other better-performing measures
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