Context-Specific Montreal Cognitive Assessment Cutoffs for Cognitive Impairment in Parkinson’s Disease

Background The Montreal Cognitive Assessment (MoCA) is widely used in Parkinson’s disease (PD) research to measure cognition, but published cutoffs for mild cognitive impairment (PD-MCI) and dementia (PDD) vary. Objective To identify context-specific MoCA cutoffs for PD-MCI, PDD, and impairment on formal neuropsychological testing, and to assess whether combining MoCA with routinely collected clinical measures improves discrimination. Methods Data from 1,094 people with PD were analysed. 39.0% were diagnosed with PD-MCI, 6.2% with PDD. Normative ranges for five neuropsychological tests were derived from healthy controls. Mixed-effects logistic regression accounted for repeated measures and predicted (1) impairment on formal neuropsychological testing and (2) clinician-derived cognitive diagnoses. Cutoffs were derived using Youden’s J, a prevalence-matching approach (cutoff where the proportion below threshold equals outcome prevalence), and standard screening and diagnostic criteria. Results Education-adjusted MoCA predicted impairment in two or more cognitive domains with an AUC of 0.86 (95% CI: 0.84–0.88). A cutoff of ≤24 maximized Youden’s J (sensitivity 74.7% [70.5–79.3], specificity 83.1% [82.0–84.2]); the prevalence-matched cutoff was ≤21. For 2 PD-MCI, the Youden’s J cutoff was ≤25 and the prevalence-matched cutoff ≤24; PDD cutoffs were ≤23 and ≤17. Functional impairment (MDS-UPDRS 1.1) showed greater concordance with clinician-assigned diagnosis than MoCA alone. Conclusion Optimal MoCA cutoffs vary by intended use. For research stratification, prevalence-matched cutoffs can align classification with observed outcome prevalence. Generalizability may be limited, particularly to routine clinical populations. Cutoffs derived in the PPMI cohort may over-identify impairment in broader populations.