Longitudinal follow up of data‐driven cognitive subtypes in Parkinson's disease.

Aim: The dual syndrome hypothesis proposes that there are two cognitive subtypes in Parkinson's disease (PD): a frontal subtype with executive/attention impairment and gradual cognitive decline, and a posterior‐cortical subtype with memory/visuospatial deficits and rapid cognitive decline. We aimed to compare the rate of global cognitive decline between subtypes derived using data‐driven methods and explore their longitudinal performance within specific cognitive domains to better understand the prognosis of each subtype. Method: Frontal, posterior‐cortical, globally impaired, and cognitively intact PD subtypes were identified at baseline using k‐means clustering (N = 85), and 29 participants (34%) returned for follow‐up assessments on average 4.87 years from baseline. Linear mixed effects models compared progression of subtypes on global cognition; psychological symptoms; parkinsonism; and the memory, attention, executive, language, and visuospatial cognitive domains. Results: The frontal subtype was lost to attrition. While rate of change in parkinsonism, anxiety, and apathy differed between subtypes, there was no difference in the rate of global cognitive decline. However, the posterior‐cortical subtype declined most rapidly in verbal memory, card sorting, trail making, and judgement of line orientation (JLO), while the cognitively intact group declined most rapidly on verbal memory and semantic fluency. The globally impaired subtype declined most rapidly in JLO, although this should be interpreted with caution due to high attrition. Conclusion: Despite limited sample size, the present study supports the differential progression of the posterior‐cortical subtype compared to cognitively intact and globally impaired PD. These results encourage further, large‐scale longitudinal investigations of cognitive subtypes in PD. Data‐driven machine learning was used to derive cognitive subtypes in Parkinsons' disease, and their cognition was assessed at a five year follow up. Rates of decline differed across subtypes on specific domains of cognition. Results demonstrate that machine learning can identify patients with distinct cognitive trajectories over time. [ABSTRACT FROM AUTHOR]