Biomarkers in Alzheimer's and Parkinson's Diseases for IA

Overview

Investigate whether endogenous and exogenous risk factors, the presence of pathological amyloid aggregates and alpha-synuclein in the CSF, brain connectivity, and some clinical and biological indices such as vigilance, general motility, the sleep-wake cycle, genomic instability and possible DNA damage may correlate with (or predict) the clinical condition of mild cognitive impairment (MCI) and dementia in patients with Alzheimer's disease (AD) and Parkinson's disease (PD).

Study Description Brief Summary The study will investigate whether endogenous and exogenous risk factors, the presence of pathological amyloid aggregates and alpha-synuclein in cerebrospinal fluid (CSF), brain connectivity, and selected clinical and biological indices, including vigilance, general motility, sleep-wake cycle characteristics, genomic instability, and DNA damage, are associated with or predictive of mild cognitive impairment (MCI) and dementia in patients with Alzheimer's disease (AD) and Parkinson's disease (PD). Detailed Description Parkinson's disease (PD) and Alzheimer's disease (AD) are characterized by progressive neurodegeneration associated predominantly with alpha-synuclein and beta-amyloid pathology, respectively. In patients with PD, mild cognitive impairment (PD-MCI), characterized primarily by executive dysfunction, and Parkinson's disease dementia (PDD) are common and have been associated with functional abnormalities in frontal, temporal, and parietal neural networks as identified through resting-state electroencephalography (rsEEG) and magnetic resonance imaging (MRI). In patients with AD-related MCI (AD-MCI) and Alzheimer's disease dementia (ADD), these techniques have revealed predominant episodic memory deficits associated with alterations in default mode and visuospatial neural networks. Both disorders are also associated with frequently underrecognized abnormalities in vigilance and sleep regulation. Endogenous risk factors, including genetic profile, frailty, sex, and age, as well as exogenous factors such as lifestyle, may significantly influence cognitive impairment in PD and AD. Biomarkers derived from MRI and rsEEG may reflect these effects and contribute to variability in disease progression, prevention strategies, and therapeutic interventions. Artificial intelligence (AI) systems integrating genetic, clinical, neuropsychological, neuroimaging, and EEG data obtained in hospital settings and through home-based telemonitoring may improve the classification and prediction of clinical status in individuals with PD and AD presenting with MCI or dementia. Clinical and instrumental data will be collected both in hospital and at home during a one-week monitoring period and will be used as input for AI-based predictive models. Hospital-based assessments will include blood sampling, neurological and neuropsychological evaluations, MRI and EEG recordings in patients with PD, AD, and healthy control participants. Additional assessments in patients with PD and AD will include cerebrospinal fluid biomarkers, dopamine transporter single-photon emission computed tomography (DAT-SPECT), and home-based telemonitoring of vigilance, cognition, heart rate, and sleep. The expected duration of the study is approximately two years. Specific Aim 1 The study will evaluate whether genetic, neuropathological, neuroimaging, rsEEG, and neurophysiological markers derived from standard hospital-based procedures can be used as inputs for advanced linear regression analyses and artificial intelligence models to predict vigilance, cognitive, motor, autonomic, and clinical status (MCI and dementia) in patients with AD and PD, compared with healthy control participants, with an expected classification accuracy exceeding 80%. Specific Aim 2 The study will evaluate whether digital markers of motor function, sleep-wake cycle regulation, cognition, and overall stress derived from one week of home-based telemonitoring can provide accurate classification of patients with PD and AD according to clinical status (MCI or dementia), with an expected accuracy exceeding 70%. Furthermore, the integration of digital telemonitoring markers with hospital-derived biomarkers will be assessed to determine whether classification performance can be improved, with an expected accuracy exceeding 85%. Specific Aim 3 The study will evaluate the mediating and moderating effects of endogenous and exogenous risk factors on the relationships between disease-specific neuropathological biomarkers (CSF biomarkers and DAT-SPECT measures), EEG and neuroimaging markers, and the severity of cognitive impairment as reflected by MCI and dementia in patients with AD and PD. Incorporation of these risk factor effects into predictive models is expected to improve patient classification according to cognitive status, with an anticipated accuracy exceeding 90%.