RESUMO
Identifying biological factors which contribute to the clinical progression of heterogeneous motor and non-motor phenotypes in Parkinson's disease may help to better understand the disease process. Several lipid-related genetic risk factors for Parkinson's disease have been identified, and the serum lipid signature of Parkinson's disease patients is significantly distinguishable from controls. However, the extent to which lipid profiles are associated with clinical outcomes remains unclear. Untargeted high-performance liquid chromatography-tandem mass spectrometry identified >900 serum lipids in Parkinson's disease subjects at baseline (n = 122), and the potential for machine learning models using these lipids to predict motor and non-motor clinical scores after 2 years (n = 67) was assessed. Machine learning models performed best when baseline serum lipids were used to predict the 2-year future Unified Parkinson's disease rating scale part three (UPDRS III) and Geriatric Depression Scale scores (both normalised root mean square error = 0.7). Feature analysis of machine learning models indicated that species of lysophosphatidylethanolamine, phosphatidylcholine, platelet-activating factor, sphingomyelin, diacylglycerol and triacylglycerol were top predictors of both motor and non-motor scores. Serum lipids were overall more important predictors of clinical outcomes than subject sex, age and mutation status of the Parkinson's disease risk gene LRRK2. Furthermore, lipids were found to better predict clinical scales than a panel of 27 serum cytokines previously measured in this cohort (The Michael J. Fox Foundation LRRK2 Clinical Cohort Consortium). These results suggest that lipid changes may be associated with clinical phenotypes in Parkinson's disease.
RESUMO
OBJECTIVE: Low-density lipoprotein receptor-related protein-1 (LRP1) regulates energy homeostasis, blood-brain barrier integrity, and metabolic signaling in the brain. Deficiency of LRP1 in inhibitory gamma-aminobutyric acid (GABA)ergic neurons causes severe obesity in mice. However, the impact of LRP1 in inhibitory neurons on memory function and cognition in the context of obesity is poorly understood. METHODS: Mice lacking LRP1 in GABAergic neurons (Vgat-Cre; LRP1loxP/loxP) underwent behavioral tests for locomotor activity and motor coordination, short/long-term and spatial memory, and fear learning/memory. This study evaluated the relationships between behavior and metabolic risk factors and followed the mice at 16 and 32 weeks of age. RESULTS: Deletion of LRP1 in GABAergic neurons caused a significant impairment in memory function in 32-week-old mice. In the spatial Y-maze test, Vgat-Cre; LRP1loxP/loxP mice exhibited decreased travel distance and duration in the novel arm compared with controls (LRP1loxP/loxP mice). In addition, GABAergic neuron-specific LRP1-deficient mice showed a diminished capacity for performing learning and memory tasks during the water T-maze test. Moreover, reduced freezing time was observed in these mice during the contextual and cued fear conditioning tests. These effects were accompanied by increased neuronal necrosis and satellitosis in the hippocampus. Importantly, the distance and duration in the novel arm, as well as the performance of the reversal water T-maze test, negatively correlated with metabolic risk parameters, including body weight, serum leptin, insulin, and apolipoprotein J. However, in 16-week-old Vgat-Cre; LRP1loxP/loxP mice, there were no differences in the behavioral tests or correlations between metabolic parameters and cognition. CONCLUSIONS: Our findings demonstrate that LRP1 from GABAergic neurons is important in regulating normal learning and memory. Metabolically, obesity caused by GABAergic LRP1 deletion negatively regulates memory and cognitive function in an age-dependent manner. Thus, LRP1 in GABAergic neurons may play a crucial role in maintaining normal excitatory/inhibitory balance, impacting memory function, and reinforcing the potential importance of LRP1 in neural system integrity.