Cerebrospinal Fluid Alpha-Synuclein Improves the Differentiation between Dementia with Lewy Bodies and Alzheimer's Disease in Clinical Practice.

Background: Alpha-synuclein, abnormally aggregated in Dementia with Lewy Bodies (DLB), could represent a potential biomarker to improve the differentiation between DLB and Alzheimer's disease (AD). Our main objective was to compare Cerebrospinal Fluid (CSF) alpha-synuclein levels between patients with DLB, AD and Neurological Control (NC) individuals. Methods: In a monocentric retrospective study, we assessed CSF alpha-synuclein concentration with a validated ELISA kit (ADx EUROIMMUN) in patients with DLB, AD and NC from a tertiary memory clinic. Between-group comparisons were performed, and Receiver Operating Characteristic analysis was used to identify the best CSF alpha-synuclein threshold. We examined the associations between CSF alpha-synuclein, other core AD CSF biomarkers and brain MRI characteristics. Results: We included 127 participants (mean age: 69.3 ± 8.1, Men: 41.7%). CSF alpha-synuclein levels were significantly lower in DLB than in AD (1.28 ± 0.52 ng/mL vs. 2.26 ± 0.91 ng/mL, respectively, p < 0.001) without differences due to the stage of cognitive impairment. The best alpha-synuclein threshold was characterized by an Area Under the Curve = 0.85, Sensitivity = 82.0% and Specificity = 76.0%. CSF alpha-synuclein was associated with CSF AT(N) biomarkers positivity (p < 0.01) but not with hippocampal atrophy or white matter lesions. Conclusion: CSF Alpha-synuclein evaluation could help to early differentiate patients with DLB and AD in association with existing biomarkers.

Positive effects of lumbar puncture simulation training for medical students in clinical practice.

BACKGROUND: Lumbar puncture (LP) is an invasive medical procedure that can be done by any doctor. Several simulation-based trainings have been built however the evaluations of the theoretical knowledge and the impact of the simulation-based training have never been performed in real life. The objective was to evaluate the impact of a LP training on the theoretical knowledge improvement and the performance of a LP in clinical practice. METHODS: Before and after medical students' training, theoretical knowledge and confidence level were assessed. Over a 6 months period, the impact of simulation training was evaluated by the success rate of students' first LP carried out in hospitalized patients and compared to the results of a no-training control. RESULTS: Students' theoretical knowledge and confidence level showed significant improvement after simulation training on 115 students (p < 0.0001). The evaluation in real life based on 41 students showed that the success rate of the first LP in patients was higher in the LP simulation group compared to the control group (67% vs 14%, p = 0.0025). The technical assistance was also less frequently needed in the LP simulation group (19% vs 57%, respectively, p = 0.017). The rate of students who participated in this educational study was low. DISCUSSION: Simulation-based teaching was an effective way to improve students' theoretical and practical knowledge. Whether this approach translates to other procedural skills in real clinical settings merits further study. The low participation rate in the study is due to the fact that students are not used to be included in educational studies and to the complexity of evaluation in routine clinical practice.

EEG evidence of compensatory mechanisms in preclinical Alzheimer's disease.

Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer's disease and to better understand the pathophysiological processes of disease progression. Preclinical Alzheimer's disease EEG changes would be non-invasive and cheap screening tools and could also help to predict future progression to clinical Alzheimer's disease. However, the impact of amyloid-ß deposition and neurodegeneration on EEG biomarkers needs to be elucidated. We included participants from the INSIGHT-preAD cohort, which is an ongoing single-centre multimodal observational study that was designed to identify risk factors and markers of progression to clinical Alzheimer's disease in 318 cognitively normal individuals aged 70-85 years with a subjective memory complaint. We divided the subjects into four groups, according to their amyloid status (based on 18F-florbetapir PET) and neurodegeneration status (evidenced by 18F-fluorodeoxyglucose PET brain metabolism in Alzheimer's disease signature regions). The first group was amyloid-positive and neurodegeneration-positive, which corresponds to stage 2 of preclinical Alzheimer's disease. The second group was amyloid-positive and neurodegeneration-negative, which corresponds to stage 1 of preclinical Alzheimer's disease. The third group was amyloid-negative and neurodegeneration-positive, which corresponds to 'suspected non-Alzheimer's pathophysiology'. The last group was the control group, defined by amyloid-negative and neurodegeneration-negative subjects. We analysed 314 baseline 256-channel high-density eyes closed 1-min resting state EEG recordings. EEG biomarkers included spectral measures, algorithmic complexity and functional connectivity assessed with a novel information-theoretic measure, weighted symbolic mutual information. The most prominent effects of neurodegeneration on EEG metrics were localized in frontocentral regions with an increase in high frequency oscillations (higher beta and gamma power) and a decrease in low frequency oscillations (lower delta power), higher spectral entropy, higher complexity and increased functional connectivity measured by weighted symbolic mutual information in theta band. Neurodegeneration was associated with a widespread increase of median spectral frequency. We found a non-linear relationship between amyloid burden and EEG metrics in neurodegeneration-positive subjects, either following a U-shape curve for delta power or an inverted U-shape curve for the other metrics, meaning that EEG patterns are modulated differently depending on the degree of amyloid burden. This finding suggests initial compensatory mechanisms that are overwhelmed for the highest amyloid load. Together, these results indicate that EEG metrics are useful biomarkers for the preclinical stage of Alzheimer's disease.

Exploring the link between GBA1 mutations and Dementia with Lewy bodies, A mini-review.

IMPORTANCE: Dementia with Lewy bodies (DLB) is a neurodegenerative disease linked to abnormal accumulation of phosphorylated α-synuclein. GBA1 is the gene encoding the lysosomal enzyme glucocerebrosidase (GCase), whose mutations are a risk factor of DLB. OBJECTIVE: To report all available data exploring the association between GBA1 mutations and DLB. EVIDENCE REVIEW: All publications focused on GCase and DLB in humans between 2003 and 2022 were identified on PubMed, Cochrane and ClinicalTrials.gov. FINDINGS: 29 studies were included and confirmed the strong association between GBA1 mutations and DLB (Odds Ratio [OR]: 8.28). GBA1 mutation carriers presented a more malignant phenotype, with earlier symptom onset, more severe motor and cognitive dysfunctions, more visual hallucinations and rapid eye movement sleep disorder. GBA1 mutations were associated with "purer" neuropathological DLB. No therapeutic recommendations exist and clinical trials targeting GCase are just starting in DLB patients. CONCLUSIONS AND RELEVANCE: This review reports a link between GBA1 mutations and the DLB phenotype with limited evidence due to the small number of studies.

A machine learning approach to screen for preclinical Alzheimer's disease.

Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on 18F-florbetapir and 18F-fluorodeoxyglucose PET, respectively. We used a nested cross-validation approach with non-invasive features (electroencephalography [EEG], APOE4 genotype, demographic, neuropsychological and MRI data) to predict: 1/ amyloid status; 2/ neurodegeneration status; 3/ decline to prodromal AD at 5-year follow-up. Importantly, EEG was most strongly predictive of neurodegeneration, even when reducing the number of channels from 224 down to 4, as 4-channel EEG best predicted neurodegeneration (negative predictive value [NPV] = 82%, positive predictive value [PPV] = 38%, 77% specificity, 45% sensitivity). The combination of demographic, neuropsychological data, APOE4 and hippocampal volumetry most strongly predicted amyloid (80% NPV, 41% PPV, 70% specificity, 58% sensitivity) and most strongly predicted decline to prodromal AD at 5 years (97% NPV, 14% PPV, 83% specificity, 50% sensitivity). Thus, machine learning can help to screen patients at high risk of preclinical AD using non-invasive and affordable biomarkers.