Capturing silent oxidative stress in early Alzheimer's disease: prediction of CSF biomarkers from sleep qEEG data.

Oxidative stress is a central pathogenic process in the earliest stages of Alzheimer's disease (AD), promoting non-enzymatic protein modifications that accumulate in cerebrospinal fluid (CSF) before measurable neurodegeneration. These alterations impair proteostasis and disrupt sleep-regulating neural circuits, producing characteristic changes in sleep electroencephalographic patterns. Because CSF sampling is invasive, quantitative electroencephalography (qEEG) has emerged as a promising non-invasive proxy for early oxidative processes. Here, we investigated whether nonlinear and time-domain sleep qEEG features can estimate CSF oxidative stress biomarkers in early AD using machine learning (ML) models. Forty-two mild-to-moderate AD patients underwent overnight polysomnography, from which sleep qEEG features were extracted. CSF protein oxidation biomarkers-glutamic semialdehyde, aminoadipic semialdehyde, N-carboxyethyl-lysine, N-carboxymethyl-lysine, and N-malondialdehyde-lysine-were quantified by gas chromatography/mass spectrometry, and ML models were trained to predict CSF biomarker levels from qEEG features. The best-performing model was a random forest trained on the first principal component, achieving an