Personalized Profiling of Lipoprotein and Lipid Metabolism Based on 1018 Measures from Combined Quantitative NMR and LC-MS/MS Platforms

Applications of advanced omics methodologies are increasingly popular in biomedicine. However, large-scale studies aiming at clinical translation are typically siloed to single technologies. Here, we present the first comprehensive large-scale population data combining 209 lipoprotein measures from a quantitative NMR spectroscopy platform and 809 lipid classes and species from a quantitative LC-MS/MS platform. These data with 1018 molecular measures were analyzed in two population cohorts totaling 7830 participants. The association and cluster analyses revealed excellent coherence between the methodologically independent data domains and confirmed their quantitative compatibility and suitability for large-scale studies. The analyses elucidated the detailed molecular characteristics of the heterogeneous circulatory macromolecular lipid transport system and the underlying structural and compositional relationships. Unsupervised neural network analysisthe so-called self-organizing maps (SOMs)revealed that these deep molecular and metabolic data are inherently related to key physiological and clinical population characteristics. The data-driven population subgroups uncovered marked differences in the population distribution of multiple cardiometabolic risk factors. These include, e.g., multiple lipoprotein lipids, apolipoprotein B, ceramides, and oxidized lipids. All 79 structurally unique triglyceride species showed similar associations over the entire lipoprotein cascade and indicated systematically increased risk for carotid intima media thickening and other atherosclerosis risk factors, including obesity and inflammation. The metabolic attributes for 27 individual cholesteryl ester species, which formed six distinct clusters, were more intricate with associations both with highere.g., CE(16:1)and lowere.g., CE(20:4)cardiometabolic risk. The molecular details provided by these combined data are unprecedented for molecular epidemiology and demonstrate a new potential avenue for population studies.

先进组学（Omics）技术方法在生物医学领域的应用日益普及。然而，旨在推动临床转化的大规模研究通常局限于单一技术平台。本研究首次公布了一套综合大规模人群数据集，整合了来自定量核磁共振（Nuclear Magnetic Resonance, NMR）光谱平台的209项脂蛋白检测指标，以及来自液相色谱-串联质谱（Liquid Chromatography-Tandem Mass Spectrometry, LC-MS/MS）定量平台的809种脂质类别与分子物种。这套包含1018项分子检测指标的数据集，在总计7830名参与者的两个人群队列中开展了分析。关联分析与聚类分析结果显示，这两个技术方法独立的数据域之间具有极佳的一致性，证实了二者的定量兼容性及其适用于大规模研究的潜力。本研究阐明了异质性循环大分子脂质转运系统的精细分子特征，以及其背后的结构与组成关联。无监督神经网络分析——即所谓的自组织映射（Self-Organizing Maps, SOMs）——结果表明，这些深度分子与代谢数据与人群关键生理及临床特征存在内在关联。基于数据驱动得到的人群亚组，揭示了多种心血管代谢危险因素在人群分布中的显著差异。此类危险因素包括多种脂蛋白脂质、载脂蛋白B（Apolipoprotein B, ApoB）、神经酰胺以及氧化脂质。所有79种结构独特的甘油三酯分子物种在整个脂蛋白级联反应中均表现出相似的关联特征，并提示颈动脉内膜中层增厚及其他动脉粥样硬化危险因素（包括肥胖与炎症）的风险呈系统性升高。形成6个不同聚类的27种胆固醇酯分子物种的代谢特征更为复杂，它们分别与更高（例如CE(16:1)）与更低（例如CE(20:4)）的心血管代谢风险存在关联。这套整合数据所提供的分子细节在分子流行病学领域尚属首次，同时为人群研究开辟了全新的潜在方向。