A rare APOE variant APOE3 -Jac reduces self-aggregation and promotes healthy brain aging.

Background: The Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) are the most commonly used tools for cognitive impairment screening. The present study aimed to investigate the ability of the MOCA and MMSE to differentiate between cognitively normal elderly individuals, MCI patients and dementia patients at different ages and education levels and to establish the optimal cutoff scores of the MoCA and MMSE for MCI and dementia in the Chinese elderly.Methods: A total of 2,954 Chinese elderly individuals, including 1,746 normal controls, 599 MCI patients and 249 dementia patients, were consecutively recruited in the study. The optimal cutoffs for MoCA and MMSE were determined using receiver operating characteristic (ROC) analysis among the different age and education levels in the three groups. Furthermore, comparison of ROC curves were made to evaluate the performances of the two tests.Results: The area under the curve(AUC) of the MoCA (0.82) for detecting MCI was significantly higher than that of the MMSE (0.75) (P < 0.001). When the sample was divided according to age and education level, the AUC of the MoCA (0.84) was higher than those of the MMSE (0.71) for MCI (P < 0.001) in the younger and more highly-educated groups. The optimal cutoff scores of the MoCA for the groups aged ≤ 75 years old and education ≤ 6 years, aged > 75 years old and education ≤ 6 years, aged ≤ 75 years old and education > 6 years, aged > 75 years old and education > 6 years in screening for MCI were identified as 19.5, 15.5, 24.5 and 24.5, respectively, and the optimal cutoff scores for dementia were 18.5, 10.5, 18.5 and 20.5, respectively. For MMSE in the above four groups, the cutoff scores to detect MCI were 26.5, 22.5, 28.5 and 26.5, respectively, and the optimal cutoff scores for dementia were 23.5, 19.5, 23.5 and 23.5, respectively.Conclusion: Compared to MMSE, the MoCA is more suitable for discriminating MCI in younger and more highly educated elderly Chinese individuals. However, the MMSE has advantage over MoCA in screening MCI in individuals with lower education levels and the older groups of Chinese elderly.

Recently, the glymphatic system has been recognised as an important ‘waste solutes transport channel’ within the brain.1 Studies have shown that blockage of the glymphatic system leads to increased beta-amyloid deposits, accelerating the onset and progression of Alzheimer’s disease (AD).1 2 Given that cervical lymph nodes receive cerebrospinal fluid from the brain’s glymphatic system,3 4 we speculated that decompression of the lymphatic trunk and cervical lymphatic–venous anastomosis (LVA) could facilitate the flow of cerebrospinal fluid in the cranial glymphatic system, potentially accelerating the clearance of harmful beta-amyloid and tau proteins. We collaborated with surgeons who specialise in LVA supermicrosurgery for maxillofacial tumours and lymphoedema to develop a procedure to relieve the blockage of the glymphatic system. This surgery employs supermicrosurgery techniques to create LVA connecting the bilateral cervical, deep lymphatic vessels to the veins, resulting in lymphatic trunk decompression, which allows the lymph fluid in the high-pressure lymphatic vessels to flow into the low-pressure venous system. The goal of the minimally invasive surgery is to enhance the removal of proteins, such as beta-amyloid and tau, from the brain’s lymphatic systems to the maxillofacial lymphatic vessels, unclogging protein blockages within the brain. This extracranial procedure is safer than intracranial approaches. Following a thorough assessment of the safety and potential efficacy of the procedure, and securing ethics approval from the Ethics Committees of the Shanghai Mental Health Center and Shanghai Ninth People’s Hospital, we initiated an investigator-initiated trial of the ‘Cervical Shunting to Unclog cerebral Lymphatic Systems (CSULS) surgical procedure’ (registration number: ChiCTR2400084617).

Background: Late-onset Alzheimer’s disease (LOAD) and early-onset Alzheimer’s disease (EOAD) are different subtypes of AD. This study aimed to build and validate radiomics models of the hippocampus for EOAD and young controls (YCs), LOAD and old controls (OCs), as well as EOAD and LOAD.Methods: Thirty-six EOAD patients, 36 LOAD patients, 36 YCs, and 36 OCs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were enrolled and allocated to training and test sets of the EOAD-YC groups, LOAD-OC groups, and EOAD-LOAD groups. Independent external validation sets including 15 EOAD patients, 15 LOAD patients, 15 YCs, and 15 OCs from Shanghai Mental Health Center were constructed, respectively. Bilateral hippocampal segmentation and feature extraction were performed for each subject, and the least absolute shrinkage and selection operator (LASSO) method was used to select radiomic features. Support vector machine (SVM) models were constructed based on the identified features to distinguish EOAD from YC subjects, LOAD from OC subjects, and EOAD from LOAD subjects. The areas under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the models.Results: Three, three, and four features were selected for EOAD and YC subjects, LOAD and OC subjects, and EOAD and LOAD subjects, respectively. The AUC and accuracy of the SVM model were 0.90 and 0.77 in the test set and 0.91 and 0.87 in the validation set for EOAD and YC subjects, respectively; for LOAD and OC subjects, the AUC and accuracy were 0.94 and 0.86 in the test set and 0.92 and 0.78 in the validation set, respectively. For the SVM model of EOAD and LOAD subjects, the AUC was 0.87 and the accuracy was 0.79 in the test set; additionally, the AUC was 0.86 and the accuracy was 0.77 in the validation set.Conclusion: The findings of this study provide insights into the potential of hippocampal radiomic features as biomarkers to diagnose EOAD and LOAD. This study is the first to show that SVM classification analysis based on hippocampal radiomic features is a valuable method for clinical applications in EOAD.