Ye Yongxin, Markussen Bo, Engelsen Søren Balling, Khakimov Bekzod
Department of Food Science, University of Copenhagen, Rolighedsvej 26, DK-1958, Frederiksberg C, Denmark.
Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen OE, Denmark.
Comput Biol Med. 2025 Jan;184:109379. doi: 10.1016/j.compbiomed.2024.109379. Epub 2024 Nov 26.
Low-density lipoprotein (LDL) cholesterol (chol) subfractions are risk biomarkers for cardiovascular diseases (CVD). A reference analysis, ultracentrifugation (UC), is laborious and may be replaced with a rapid prediction using proton NMR spectra of human blood plasma. However, the quality and uniqueness of these prediction models of biologically related subfractions remains unknown. This study, using two independent cohorts (n = 277), investigates the inter-correlations between LDL cholesterol in the main fraction and five subfractions, as well as the independence of their NMR-based prediction models. The results reveal that the prediction models utilize both shared and unique spectral information from the NMR spectra to determine concentrations of LDL subfractions. Analysis of variance contributions for prediction and causality assessments demonstrate that the NMR spectra contain unique predictive information for the LDL1chol, LDL2chol, and LDL5chol subfractions. In contrast, the spectral signatures for LDL3chol and LDL4chol are either insufficient or confounded. Our findings indicate that these five CVD biomarkers represent two independent clusters, reflecting their biosynthetic pathways, and confirm the presence of causal relationships between certain LDL chol subfractions. This highlights the importance of employing caution when interpreting the concentrations of specific LDL subfractions as standalone biomarkers for CVD risk.
低密度脂蛋白（LDL）胆固醇亚组分是心血管疾病（CVD）的风险生物标志物。一种参考分析方法，即超速离心法（UC），操作繁琐，或许可用基于人体血浆质子核磁共振（NMR）谱的快速预测方法取而代之。然而，这些与生物相关亚组分的预测模型的质量和独特性仍不明确。本研究使用两个独立队列（n = 277），调查了主要组分中的低密度脂蛋白胆固醇与五个亚组分之间的相互关系，以及基于核磁共振的预测模型的独立性。结果显示，预测模型利用核磁共振谱中共享的和独特的光谱信息来确定低密度脂蛋白亚组分的浓度。预测和因果关系评估的方差贡献分析表明，核磁共振谱包含LDL1胆固醇、LDL2胆固醇和LDL5胆固醇亚组分的独特预测信息。相比之下，LDL3胆固醇和LDL4胆固醇的光谱特征要么不足，要么相互混淆。我们的研究结果表明，这五种心血管疾病生物标志物代表两个独立的簇，反映了它们的生物合成途径，并证实了某些低密度脂蛋白胆固醇亚组分之间存在因果关系。这突出了在将特定低密度脂蛋白亚组分的浓度解释为心血管疾病风险的独立生物标志物时要谨慎的重要性。
