Predictive value of cerebrospinal fluid Alzheimer's disease biomarkers and neuropsychological measures for cognitive and motor outcomes in Parkinson's disease

Cognitive impairment is a major non-motor feature of Parkinson’s disease (PD). Alzheimer’s disease CSF biomarkers—amyloid-β (Aβ42, Aβ42/40), phosphorylated tau (p-tau) and total tau (t-tau)—may influence both cognitive and motor outcomes, but their role in relation to Parkinson’s disease phenotype or progression remains unclear. Moreover, few studies have investigated these associations using extensive neuropsychological batteries. The aim of the study was to examine the associations between CSF Alzheimer’s disease biomarkers, domain-specific neuropsychological performance and motor impairment in Parkinson’s disease and to evaluate their predictive value for longitudinal changes. Seventy-eight Parkinson’s disease patients underwent extensive neuropsychological examination, along with motor evaluation and Alzheimer’s disease CSF biomarkers quantification. Fifty-two patients completed follow-up motor assessment and 43 repeated cognitive evaluation after 18–24 months. Correlation and regression models adjusted for demographic and clinical covariates were applied. Thirty-nine patients were classified as mild cognitive impaired (PD-MCI), mostly with multidomain impairment. Amyloid-β biomarkers were associated with poorer attention, working memory, executive and language performance. Tau proteins were inversely associated with motor scores. PD-MCI patients tended to experience greater motor decline than cognitive unimpaired subjects. Over time, higher p-tau and t-tau predicted global cognitive decline (MMSE), while lower Aβ42/40 predicted worsening in verbal memory and reasoning. Higher t-tau and lower baseline MMSE independently predicted greater motor deterioration. CSF Alzheimer’s disease biomarkers showed domain-specific relationships with cognitive function and predicted both neuropsychological decline and motor progression. Combining detailed neuropsychological profiling with Alzheimer’s disease biomarkers assessment may improve identification of high-risk patients and support integrated biological definitions of Parkinson’s disease.