Search

Abstract Background Gangliosides are highly enriched in the brain and are critical for its normal development and function. However, in some rare neurometabolic diseases, a deficiency in lysosomal ganglioside hydrolysis is pathogenic and leads to early-onset neurodegeneration, neuroinflammation, demyelination, and dementia. Increasing evidence also suggests that more subtle ganglioside accumulation contributes to the pathogenesis of more common neurological disorders including Alzheimer’s disease (AD). Notably, ganglioside GM3 levels are elevated in the brains of AD patients and in several mouse models of AD, and plasma GM3 levels positively correlate with disease severity in AD patients. Methods Tg2576 AD model mice were fed chow formulated with a small molecule inhibitor of glucosylceramide synthase (GCSi) to determine whether reducing glycosphingolipid synthesis affected aberrant GM3 accumulation, amyloid burden, and disease manifestations in cognitive impairment. GM3 was measured with LC-MS, amyloid burden with ELISA and amyloid red staining, and memory was assessed using the contextual fear chamber test. Results GCSi mitigated soluble Aβ42 accumulation in the brains of AD model mice when treatment was started prophylactically. Remarkably, GCSi treatment also reduced soluble Aβ42 levels and amyloid plaque burden in aged (i.e., 70 weeks old) AD mice with preexisting neuropathology. Our analysis of contextual memory in Tg2576 mice showed that impairments in remote (cortical-dependent) memory consolidation preceded deficits in short-term (hippocampal-dependent) contextual memory, which was consistent with soluble Aβ42 accumulation occurring more rapidly in the cortex of AD mice compared to the hippocampus. Notably, GCSi treatment significantly stabilized remote memory consolidation in AD mice—especially in mice with enhanced cognitive training. This finding was consistent with GCSi treatment lowering aberrant GM3 accumulation in the cortex of AD mice. Conclusions Collectively, our results indicate that glycosphingolipids regulated by GCS are important modulators of Aβ neuropathology and that glycosphingolipid homeostasis plays a critical role in the consolidation of remote memories.

Abstract Background Characterization of prediagnostic Parkinson’s Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. Methods We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. Results We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. Conclusions Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.