Dataset Information

Plasma GFAP in Parkinson's disease with cognitive impairment and its potential to predict conversion to dementia.

ABSTRACT: Glial fibrillary acidic protein (GFAP) has been suggested as a biomarker for reactive astrogliosis. We measured the levels of plasma GFAP by Simoa in 60 patients with PD with normal cognition, 63 with mild cognitive impairment (PD-MCI), 24 with dementia (PDD) and 15 healthy controls. A subgroup of patients with PD-MCI (n = 31) was followed up for 4.1 ± 2.3 years. Compared with healthy controls, plasma GFAP levels were elevated in patients with PDD (adjusted P < 0.001) and PD-MCI (adjusted P = 0.013) and were negatively correlated with the Mini Mental State Examination (MMSE) score in PD participants. Plasma GFAP predicted MCI-to-dementia conversion with an AUC of 0.90, higher than NfL, Tau and pTau181. Our results support that plasma GFAP has potential value for distinguishing patients with PDD, and predicting MCI-to-dementia conversion in PD.

SUBMITTER: Tang Y

PROVIDER: S-EPMC9911758 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Plasma GFAP in Parkinson's disease with cognitive impairment and its potential to predict conversion to dementia.

Tang Yilin Y Han Linlin L Li Shiyu S Hu Tianyu T Xu Zhiheng Z Fan Yun Y Liang Xiaoniu X Yu Huan H Wu Jianjun J Wang Jian J

NPJ Parkinson's disease 20230209 1

Glial fibrillary acidic protein (GFAP) has been suggested as a biomarker for reactive astrogliosis. We measured the levels of plasma GFAP by Simoa in 60 patients with PD with normal cognition, 63 with mild cognitive impairment (PD-MCI), 24 with dementia (PDD) and 15 healthy controls. A subgroup of patients with PD-MCI (n = 31) was followed up for 4.1 ± 2.3 years. Compared with healthy controls, plasma GFAP levels were elevated in patients with PDD (adjusted P < 0.001) and PD-MCI (adjusted P = 0. ...[more]

PMID: 36759508

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Project description:BackgroundClinical markers of cognitive decline in Parkinson's disease (PD) encompass several mental non-motor symptoms such as hallucinations, apathy, anxiety, and depression. Furthermore, freezing of gait (FOG) and specific gait alterations have been associated with cognitive dysfunction in PD. Finally, although low cerebrospinal fluid levels of amyloid-β42 have been found to predict cognitive decline in PD, hitherto PET imaging of amyloid-β (Aβ) failed to consistently demonstrate the association between Aβ plaques deposition and mild cognitive impairment in PD (PD-MCI).AimFinding significant features associated with PD-MCI through a machine learning approach.Patients and methodsPatients were assessed with an extensive clinical and neuropsychological examination. Clinical evaluation included the assessment of mental non-motor symptoms and FOG using the specific items of the MDS-UPDRS I and II. Based on the neuropsychological examination, patients were classified as subjects without and with MCI (noPD-MCI, PD-MCI). All patients were evaluated using a motion analysis system. A subgroup of PD patients also underwent amyloid PET imaging. PD-MCI and noPD-MCI subjects were compared with a univariate statistical analysis on demographic data, clinical features, gait analysis variables, and amyloid PET data. Then, machine learning analysis was performed two times: Model 1 was implemented with age, clinical variables (hallucinations/psychosis, depression, anxiety, apathy, sleep problems, FOG), and gait features, while Model 2, including only the subgroup performing PET, was implemented with PET variables combined with the top five features of the former model.ResultsSeventy-five PD patients were enrolled (33 PD-MCI and 42 noPD-MCI). PD-MCI vs. noPD-MCI resulted in older and showed worse gait patterns, mainly characterized by increased dynamic instability and reduced step length; when comparing amyloid PET data, the two groups did not differ. Regarding the machine learning analyses, evaluation metrics were satisfactory for Model 1 overcoming 80% for accuracy and specificity, whereas they were disappointing for Model 2.ConclusionsThis study demonstrates that machine learning implemented with specific clinical features and gait variables exhibits high accuracy in predicting PD-MCI, whereas amyloid PET imaging is not able to increase prediction. Additionally, our results prompt that a data mining approach on certain gait parameters might represent a reliable surrogate biomarker of PD-MCI.

Dataset Information

Plasma GFAP in Parkinson's disease with cognitive impairment and its potential to predict conversion to dementia.

Publications

Plasma GFAP in Parkinson's disease with cognitive impairment and its potential to predict conversion to dementia.

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