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.

中文翻译：

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基于联合定量 NMR 和 LC-MS/MS 平台的 1018 种测量方法对脂蛋白和脂质代谢进行个性化分析


先进组学方法的应用在生物医学中越来越受欢迎。然而，针对临床转化的大规模研究通常孤立于单一技术。在这里，我们提出了第一个全面的大规模群体数据，结合了来自定量 NMR 波谱平台的 209 种脂蛋白测量值和来自定量 LC-MS/MS 平台的 809 种脂质类别和物种。这些数据和 1018 个分子测量在两个人群队列中进行了分析，共 7830 名参与者。关联和聚类分析揭示了方法学独立的数据域之间极好的连贯性，并证实了它们的定量兼容性和对大规模研究的适用性。分析阐明了异质循环大分子脂质运输系统的详细分子特征以及潜在的结构和组成关系。无监督神经网络分析——所谓的自组织图谱 （SOM）——揭示了这些深度分子和代谢数据与关键的生理和临床人群特征有着内在的关系。数据驱动的人群亚组发现多种心脏代谢危险因素的人群分布存在显着差异。这些包括多种脂蛋白脂质、载脂蛋白 B、神经酰胺和氧化脂质等。所有 79 种结构独特的甘油三酯物种在整个脂蛋白级联反应中显示出相似的关联，并表明颈动脉内膜中层增厚和其他动脉粥样硬化危险因素（包括肥胖和炎症）的风险系统性增加。 27 种胆固醇酯物种的代谢属性形成 6 个不同的簇，与较高的（例如 CE（16：1））和较低的（例如 CE（20：4）））的心脏代谢风险相关联，更加复杂。这些组合数据提供的分子细节对于分子流行病学来说是前所未有的，并为人群研究提供了新的潜在途径。