ObjectiveObservational studies have shown that plasma lipids are associated with the development of neurodegenerative diseases (NDD), but the causal relationship is unclear. This study investigated the causal relationship between 179 liposomes and NDD using a two-sample Mendelian randomization (MR). MethodsA two-sample MR method was used to comprehensively analyze the causal relationship between liposomes and major NDD such as Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS). The two sample software package and Phenoscanner database were used to screen for appropriate instrumental variables (TV). In this study, inverse variance weighting (IVW) was used as the primary measure of MR analysis, and various sensitivity tests were performed. ResultsMR-IVW results showed that phosphatidylethanolamine (PE) (OR=0.90, 95%CI 0.83 to 0.99, P=0.03), phosphatidylcholine (PC) (OR=0.92, 95%CI 0.85 to 0.99, P=0.02) and phosphatidylinositol (PI) (OR=0.90, 95%CI 0.83 to 0.99, P=0.03) were protective factors for AD. Sterol ester (OR=1.18, 95%CI 1.04 to 1.34, P=0.01) and cholesterol (OR=1.26, 95%CI 1.02 to 1.56, P=0.03) increased the risk of PD. PE (OR=0.79, 95%CI 0.64 to 0.98, P=0.03) and PC (OR=0.83, 95%CI 0.69 to 0.98, P=0.03) reduced the risk of PD. Diacylglycerol (DAG) (OR=1.24, 95%CI 1.01 to 1.54, P=0.04) and sphingomyelin (SM) (OR=1.13, 95%CI 1.08 to 1.58, P=0.01) were the risk factors for MS. PI (OR=0.77, 95%CI 0.62 to 0.95, P=0.02) and PC (OR=0.74, 95%CI 0.88 to 0.95, P=0.02) were protective factors for MS. PI (OR=1.02, 95%CI 1.01 to 1.04, P=0.02) and triglyceride (TG) (OR=1.03, 95%CI 1.01 to 1.05, P=0.02) increased the risk of ALS, PC (OR=0.98, 95%CI 0.97 to 0.99, P=0.03) decreased the risk of ALS. ConclusionThere is a causal relationship between sterol ester, cholesterol, PC, PE, PI, DAG, SM, TG and different NDD, which provides a theoretical basis and support for further clinical studies.
Neurodegenerative diseases (NDDs) are a group of heterogeneous neurological disorders that can cause progressive loss of neurons in the central nervous system or peripheral nervous system, resulting in a decline in motor function. Motion capture, as a high-precision and high-resolution technology for capturing human motion data, drives NDDs motor assessment to effectively extract kinematic features and thus assess the patient’s motor ability or disease severity. This paper focuses on the recent research progress in motor assessment of NDDs driven by motion capture data. Based on a brief introduction of NDDs motor assessment datasets, we categorized the assessment methods into three types according to the way of feature extraction and processing: NDDs motor assessment methods based on statistical analysis, machine learning and deep learning. Then, we comparatively analyzed the technical points and characteristics of the three types of methods from the aspects of data composition, data preprocessing, assessment methods, assessment purposes and effects. Finally, we discussed and prospected the development trends of NDDs motor assessment.