Predicting cognitive impairment in Parkinson's disease using neurophysiology and biochemical parameters as biomarkers

Abstract

Parkinson’s disease (PD) is a common neurodegenerative condition with multiple associated non-motor symptoms. Of these, dementia is a frequent debilitating complication of the disorder, with significant morbidity and mortality. Some forms of mild cognitive impairment in PD (PD-MCI) may represent a pre-dementia state and certain clinical, laboratory and neurophysiological parameters may increase the accuracy of prediction of cognitive decline. If validated, these markers would offer the opportunity for disease modification and therapeutic intervention at a critical early stage of the illness, when the viable neuronal population is greater. The key aim of this thesis was to characterise cognitive impairment in PD in a cohort of newly diagnosed cases, and evaluate how a panel of biomarkers correlated with cognitive phenotypes to predict risk of future cognitive decline. The main findings were that PD-MCI was common, and was associated with a distinct clinical phenotype. Memory impairment was the most common single domain affected, although the majority of those with PD-MCI were classified as nonamnestic single domain subtype. A significant correlation was found between pattern recognition memory, sensitive to temporal lobe impairments, and cerebrospinal amyloid-β 1-42 levels, thought to represent amyloid-β metabolism and deposition Both amyloid-β 1-42 and 1-40 levels were significantly lower in those with impaired cognition. In addition, short latency afferent inhibition, a neurophysiological in vivo non-invasive measurement of cholinergic function, was also reduced in participants with mild cognitive impairment. These findings suggest that cholinergic dysfunction and amyloid deposition may contribute to the underlying pathophysiology of early PD- MCI. The major conclusion from this thesis is that PD-MCI is heterogeneous and more frequent than previously reported in early disease. This is associated with abnormalities of amyloid processing and cholinergic dysfunction, and may highlight those at risk of developing dementia. Longitudinal assessment of these individuals will enable us to determine and better model those measures predictive of cognitive decline at an early disease stage.