Evaluation of the neurotrophic peptide mixture in pathogenetic therapy of patients with Parkinson's disease.
📄 Abstract
This exploratory, single-group, open-label study investigated 17 patients with Parkinson's disease (PD) using a pre-post design. Motor and non-motor outcomes were assessed through clinical scales, biochemical and genetic analyses, and machine learning models (Gradient Boosting Machines, Random Forests). After treatment with a neurotrophic peptide mixture, improvements were observed in daily activity (16%), cognition (11%), depression (10% reduction), and reactive anxiety (23% reduction). Biological changes included a 45% increase in platelet δ-granules, higher mitochondrial counts, elevated gene expression (notably BDNF in women, p = 0.046), and modulation of oxidative stress markers (17% reduction in TBARS, 30% increase in GSH). Machine learning identified BDNF and PINK1 expression, along with MOCA and MMSE scores, as key predictors of UPDRS improvement. These findings suggest that neurotrophic peptide therapy may influence clinical, structural, and molecular domains in PD. Larger, controlled trials are warranted to confirm therapeutic potential and clarify associations with cognitive and neurotrophic parameters.