Core Metabolomics and Lipidomics Laboratory, Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Level 4 Pathology, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Centre for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
Metabolomics. 2020 Jul 24;16(8):83. doi: 10.1007/s11306-020-01703-0.
Blood-based sample collection is a challenge, and dried blood spots (DBS) represent an attractive alternative. However, for DBSs to be an alternative to venous blood it is important that these samples are able to deliver comparable associations with clinical outcomes. To explore this we looked to see if lipid profile data could be used to predict the concentration of triglyceride, HDL, LDL and total cholesterol in DBSs using markers identified in plasma.
To determine if DBSs can be used as an alternative to venous blood in both research and clinical settings, and to determine if machine learning could predict 'clinical lipid' concentration from lipid profile data.
Lipid profiles were generated from plasma (n = 777) and DBS (n = 835) samples. Random forest was applied to identify and validate panels of lipid markers in plasma, which were translated into the DBS cohort to provide robust measures of the four 'clinical lipids'.
In plasma samples panels of lipid markers were identified that could predict the concentration of the 'clinical lipids' with correlations between estimated and measured triglyceride, HDL, LDL and total cholesterol of 0.920, 0.743, 0.580 and 0.424 respectively. When translated into DBS samples, correlations of 0.836, 0.591, 0.561 and 0.569 were achieved for triglyceride, HDL, LDL and total cholesterol.
DBSs represent an alternative to venous blood, however further work is required to improve the combined lipidomics and machine learning approach to develop it for use in health monitoring.
血液样本采集具有挑战性，而干血斑（DBS）则是一种很有吸引力的替代方法。然而，为了使 DBS 成为静脉血的替代品，重要的是这些样本能够与临床结果产生可比的关联。为了探讨这一点，我们试图了解是否可以使用脂质谱数据来预测 DBS 中甘油三酯、HDL、LDL 和总胆固醇的浓度，方法是使用血浆中鉴定出的标志物。
确定 DBS 是否可用于研究和临床环境中的静脉血替代物，并确定机器学习是否可以从脂质谱数据预测“临床脂质”浓度。
从血浆（n=777）和 DBS（n=835）样本中生成脂质谱。随机森林用于鉴定和验证血浆中脂质标志物的面板，然后将其转化为 DBS 队列，以提供四个“临床脂质”的稳健测量值。
在血浆样本中，确定了可以预测“临床脂质”浓度的脂质标志物面板，估计和测量的甘油三酯、HDL、LDL 和总胆固醇之间的相关性分别为 0.920、0.743、0.580 和 0.424。当转化为 DBS 样本时，甘油三酯、HDL、LDL 和总胆固醇的相关性分别达到 0.836、0.591、0.561 和 0.569。
DBS 代表了静脉血的替代品，然而，需要进一步的工作来改进脂质组学和机器学习的综合方法，以开发其用于健康监测。
