Modeling the heterogeneity of cognitive dysfunction in Parkinson’s disease using partially ordered set models

Traditional binary classifications of Parkinson’s disease (PD) cognitive dysfunction fail to capture its inherent heterogeneity. This study introduces the Partially Ordered Set (POSET) model, a Bayesian framework, to analyze cognitive heterogeneity using Parkinson’s Precision Medicine Initiative (PPMI) data. Five cognitive domains: Attention, Visuospatial Judgement, Executive Functioning, Working Memory, and Episodic Memory, were mapped onto nine neuropsychological measures to calculate Cognitive Performance Scores (CPS).

Of 264 participants without baseline cognitive dysfunction, 21.2% developed dysfunction by Year 3. After demographic adjustment and Bonferroni correction, cognitive dysfunction at Year 3 was inversely associated with median CPS values for: Attention (across all visits); Executive Functioning and Working Memory (Year 1 onward); and Visuospatial Judgement (baseline only). In multivariable logistic regression, both baseline Attention and Visuospatial Judgement CPS were significant predictors of future impairment without adjustment for baseline disease duration, however, only Visuospatial Judgement CPS remained a significant predictor after the adjustment.

POSET modeling provides a sophisticated approach to characterizing PD cognitive decline, offering greater granularity than conventional schemes. Further large-cohort studies are needed to confirm these findings. We thank the PPMI – a public-private partnership – which is funded by the Michael J.

Fox Foundation for Parkinson’s Research, and funding partners. Multiple authors are employees of The VA Parkinson’s Disease Research, Education, and Clinical Centers (PADRECC). This study was funded by the project titled “Cognitive Functions Impairment as a Novel Paradigm for Delineating Cognitive Dysfunction in Parkinson’s Disease (PD-CFI)”, funded by the Investigator Initiative Research Award (IIRA) of the Parkinson's Research Program (PRP) of the Congressionally Directed Medical Research Programs (CDMRP) of the United States (US) Department of Defense (DoD).

Office of Medical Student Education, College of Medicine, University of Vermont, Burlington, VT, USA Geriatric Research Education and Clinical Center (GRECC), VA Puget Sound Health Care System, Seattle, WA, USA Parkinson’s Disease Research Education and Clinical Center (PADRECC), VA Puget Sound Health Care System, Seattle, WA, USA Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA Department of Neurological Sciences, College of Medicine, University of Vermont, 1 South Prospect Street, Arnold, Level 2, Burlington, VT, 05401-5505, USA Department of Neurology, Oregon Health & Science University, Portland, OR, USA Department of Population & Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA Department of Psychiatry, University of Vermont Medical Center, Burlington, VT, USA Department of Epidemiology & Public Health, School of Medicine, University of Maryland, 655 West Baltimore Street, Baltimore, MD, 21201, USA Clinical Informatics, University of Vermont Health Network Medical Group, Burlington, VT, USA Correspondence to Curtis Tatsuoka or Deepak K. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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