Departments of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Alzheimers Res Ther. 2024 Oct 15;16(1):226. doi: 10.1186/s13195-024-01585-7.
Dementia with Lewy Bodies (DLB) is a complex neurodegenerative disorder that often overlaps clinically with Alzheimer's disease (AD), presenting challenges in accurate diagnosis and underscoring the need for novel biomarkers. Lipidomic emerges as a promising avenue for uncovering disease-specific metabolic alterations and potential biomarkers, particularly as the lipidomics landscape of DLB has not been previously explored. We aim to identify potential diagnostic biomarkers and elucidate the disease's pathophysiological mechanisms.
This study conducted a lipidomic analysis of plasma samples from patients with DLB, AD, and healthy controls (HCs) at Xuanwu Hospital. Untargeted plasma lipidomic profiling was conducted via liquid chromatography coupled with mass spectrometry. Machine learning methods were employed to discern lipidomic signatures specific to DLB and to differentiate it from AD.
The study enrolled 159 participants, including 57 with AD, 48 with DLB, and 54 HCs. Significant differences in lipid profiles were observed between the DLB and HC groups, particularly in the classes of sphingolipids and phospholipids. A total of 55 differentially expressed lipid species were identified between DLB and HCs, and 17 between DLB and AD. Correlations were observed linking these lipidomic profiles to clinical parameters like Unified Parkinson's Disease Rating Scale III (UPDRS III) and cognitive scores. Machine learning models demonstrated to be highly effective in distinguishing DLB from both HCs and AD, achieving substantial accuracy through the utilization of specific lipidomic signatures. These include PC(15:0_18:2), PC(15:0_20:5), and SPH(d16:0) for differentiation between DLB and HCs; and a panel includes 13 lipid molecules: four PCs, two PEs, three SPHs, two Cers, and two Hex1Cers for distinguishing DLB from AD.
This study presents a novel and comprehensive lipidomic profile of DLB, distinguishing it from AD and HCs. Predominantly, sphingolipids (e.g., ceramides and SPHs) and phospholipids (e.g., PE and PC) were the most dysregulated lipids in relation to DLB patients. The lipidomics panels identified through machine learning may serve as effective plasma biomarkers for diagnosing DLB and differentiating it from AD dementia.
路易体痴呆症（DLB）是一种复杂的神经退行性疾病，其临床表现常与阿尔茨海默病（AD）重叠，这给准确诊断带来了挑战，也凸显了寻找新的生物标志物的必要性。脂质组学的出现为揭示疾病特异性代谢改变和潜在生物标志物提供了一个很有前途的途径，特别是因为之前尚未对 DLB 的脂质组学图谱进行过探索。我们旨在寻找潜在的诊断生物标志物并阐明其病理生理学机制。
本研究对宣武医院的 DLB、AD 患者和健康对照者（HC）的血浆样本进行了脂质组学分析。采用液相色谱-质谱联用技术进行非靶向性血浆脂质组学分析。采用机器学习方法来识别 DLB 特有的脂质组学特征，并将其与 AD 区分开来。
本研究共纳入 159 名参与者，其中 57 名 AD 患者，48 名 DLB 患者，54 名 HC。DLB 组与 HC 组的脂质谱存在显著差异，特别是鞘脂和磷脂类。共鉴定出 55 种 DLB 与 HC 之间差异表达的脂质，17 种 DLB 与 AD 之间差异表达的脂质。这些脂质组学特征与统一帕金森病评定量表第三部分（UPDRS III）和认知评分等临床参数存在相关性。机器学习模型在区分 DLB 与 HC 和 AD 方面表现出很高的准确性，通过利用特定的脂质组学特征，实现了较高的准确性。这些特征包括区分 DLB 与 HC 的 PC(15:0_18:2)、PC(15:0_20:5)和 SPH(d16:0)，以及区分 DLB 与 AD 的包含 13 种脂质分子的 panel：4 种 PC、2 种 PE、3 种 SPH、2 种 Cer 和 2 种 Hex1Cer。
本研究提供了一个新的、全面的 DLB 脂质组学图谱，将其与 AD 和 HC 区分开来。与 DLB 患者相关的最失调的脂质主要是鞘脂（如神经酰胺和 SPH）和磷脂（如 PE 和 PC）。通过机器学习识别的脂质组学特征可能成为诊断 DLB 和区分 AD 痴呆的有效血浆生物标志物。
