ArticleNeurotoxicity research2026
Atorvastatin Shows Limited Disease-Modifying Effects in an A53T α-Synuclein Mouse Model of Parkinson's Disease.
Article in Neurotoxicity research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Neuroprotective effects of statins in Parkinson's disease (PD) remain uncertain, and their activity in genetic α-synuclein (αSyn) disease models has been insufficiently characterized. We evaluated atorvastatin (ATO) in a mouse model with nigral overexpression of human A53T-mutant αSyn. Mice received ATO by oral gavage at 10 mg/kg/day for 5 weeks and were assessed using behavioral testing, neuropathological assessment, brain transcriptomics, and molecular docking. ATO inhibited cholesterol biosynthesis-related transcriptional programs and broadly remodeled lipid metabolism-associated networks, but did not lead to functional or histopathological benefit. ATO did not ameliorate motor deficits, restore dopaminergic markers, or reduce αSyn protein levels or pSer129-αSyn immunoreactivity. Transcriptomic analysis further showed that ATO failed to reverse the core disease-associated signature induced by A53T αSyn overexpression and instead increased SNCA mRNA. Targeted RNA-seq and western blot analyses showed no parallel increase in Prkn, Gba1, or Lamp2 transcript abundance or in PARKIN, GBA1, or LAMP2A protein expression. Molecular docking, used here as an exploratory structural comparison, suggested a relatively weak predicted interaction between ATO and αSyn when compared with several other statins and provided supportive context for the lack of efficacy. Overall, our findings indicate limited efficacy of ATO in this αSyn-driven setting and support further comparative evaluation of individual statins across complementary PD models.
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