Development and validation of a machine learning model for prediction of comorbid major depression disorder among narcolepsy type 1.

BACKGROUND: Major depression disorder (MDD) forms a common psychiatric comorbidity among patients with narcolepsy type 1 (NT1), yet its impact on patients with NT1 is often overlooked by neurologists. Currently, there is a lack of effective methods for accurately predicting MDD in patients with NT1. OBJECTIVE: This study utilized machine learning (ML) algorithms to identify critical variables and developed the prediction model for predicting MDD in patients with NT1. METHODS: The study included 267 NT1 patients from four sleep centers. The diagnosis of comorbid MDD was based on Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5). ML models, including six full models and six compact models, were developed using a training set. The performance of these models was compared in the testing set, and the optimal model was evaluated in the testing set. Various evaluation metrics, such as Area under the receiver operating curve (AUC), precision-recall (PR) curve and calibration curve were employed to assess and compare the performance of the ML models. Model interpretability was demonstrated using SHAP. RESULT: In the testing set, the logistic regression (LG) model demonstrated superior performance compared to other ML models based on evaluation metrics such as AUC, PR curve, and calibration curve. The top eight features used in the LG model, ranked by feature importance, included social impact scale (SIS) score, narcolepsy severity scale (NSS) score, total sleep time, body mass index (BMI), education years, age of onset, sleep efficiency, sleep latency. CONCLUSION: The study yielded a straightforward and practical ML model for the early identification of MDD in patients with NT1. A web-based tool for clinical applications was developed, which deserves further verification in diverse clinical settings.

The neurovascular protective effect of alogliptin in murine MCAO model and brain endothelial cells.

Endothelial damage and blood brain barrier disruption contribute to ischemic stroke and brain injury. Gliptins are a novel class of treatment agents for diabetes, and recent studies have linked the use of gliptins to neuroprotection. Alogliptin is a type of orally available gliptin that was approved for clinical use by the FDA in 2013. In this study, we investigated the neurovascular protective effects of alogliptin both in vivo and in vitro. In a murine middle cerebral artery occlusion (MCAO) stroke model, administration of alogliptin ameliorated cerebral infarction and disruption of brain vascular permeability, and restored expression of the endothelial tight junction proteins occludin and zona occludens 1 (ZO-1). In brain vascular endothelial cells exposed to oxygen and glucose deprivation/reperfusion (OGD/R), alogliptin prevented OGD/R-induced high permeability of the endothelial monolayer. Alogliptin treatment recovered the reduction in occludin and ZO-1 induced by OGD/R. Moreover, alogliptin treatment prevented OGD/R-induced induction of metalloproteinase (MMP)-2 and MMP-9, and restored expression of tissue inhibitor of metalloproteinase (TIMP)-1 and TIMP-2. Collectively, our data indicate that alogliptin can improve neurovascular integrity and exerts neuroprotective effects.