Ala-Korpela Mika, Lankinen Niko, Salminen Aino, Suna Teemu, Soininen Pasi, Laatikainen Reino, Ingman Petri, Jauhiainen Matti, Taskinen Marja-Riitta, Héberger Károly, Kaski Kimmo
Laboratory of Computational Engineering, Systems Biology and Bioinformation Technology, Helsinki University of Technology, Finland.
Atherosclerosis. 2007 Feb;190(2):352-8. doi: 10.1016/j.atherosclerosis.2006.04.020. Epub 2006 May 30.
Proton NMR spectroscopy as a means to quantify lipoprotein subclasses has received wide clinical interest. The experimental part is a fast routine procedure that contrasts favourably to other lipoprotein measurement protocols. The difficulties in using (1)H NMR, however, are in uncovering the subclass specific information from the overlapping data. The NMR-based quantification has been evaluated only in relation to biochemical measures, thereby leaving the inherent capability of NMR rather vague due to biological variation and diversity among the biochemical experiments. Here we will assess the use of (1)H NMR spectroscopy of plasma per se. This necessitates data for which the inherent parameters, namely the shapes and areas of the (1)H NMR signals of the subclasses are available. This was achieved through isolation and (1)H NMR experiments of 11 subclasses--VLDL1, VLDL2, IDL, LDL1, LDL2, LDL3, HDL(2b), HDL(2a), HDL(3a), HDL(3b) and HDL(3c)--and the subsequent modelling of the spectra. The subclass models were used to simulate biochemically representative sets of spectra with known subclass concentrations. The spectral analyses revealed 10-fold differences in the quantification accuracy of different subclasses by (1)H NMR. This finding has critical significance since the usage of (1)H NMR methodology in the clinical arena is rapidly increasing.
质子核磁共振波谱法作为一种定量脂蛋白亚类的手段已引起广泛的临床关注。实验部分是一个快速的常规程序,与其他脂蛋白测量方案相比具有优势。然而,使用氢核磁共振(1H NMR)的困难在于从重叠数据中揭示亚类特异性信息。基于核磁共振的定量仅相对于生化测量进行了评估,因此由于生化实验之间的生物学变异和多样性,核磁共振的内在能力仍相当模糊。在这里,我们将评估血浆本身的氢核磁共振波谱法的应用。这需要有亚类的固有参数即氢核磁共振信号的形状和面积的数据。这是通过分离11个亚类——极低密度脂蛋白1(VLDL1)、极低密度脂蛋白2(VLDL2)、中间密度脂蛋白(IDL)、低密度脂蛋白1(LDL1)、低密度脂蛋白2(LDL2)、低密度脂蛋白3(LDL3)、高密度脂蛋白2b(HDL(2b))、高密度脂蛋白2a(HDL(2a))、高密度脂蛋白3a(HDL(3a))、高密度脂蛋白3b(HDL(3b))和高密度脂蛋白3c(HDL(3c))并进行氢核磁共振实验以及随后的光谱建模来实现的。亚类模型用于模拟具有已知亚类浓度的生化代表性光谱集。光谱分析揭示了氢核磁共振对不同亚类定量准确性的10倍差异。这一发现具有关键意义,因为氢核磁共振方法在临床领域的应用正在迅速增加。