Design and evaluation of standard lipid prediction models based on 1H-NMR spectroscopy of human serum/plasma samples

Abstract

New approaches are increasingly being used for studying and evaluating coronary heart disease (CHD), especially since the irruption of metabolomics. The classical approach is to use enzymatically-measured standard lipids and these are still the main markers for assessing risk of CHD. Since metabolomics relies on advanced analytical technologies, such as MS and NMR, using them to estimate standard lipids would be of great interest because there is no need for additional biochemical measures. The present study evaluates partial least squares and N-way partial least squares regression models to predict standard lipid concentrations by using serum and plasma sample sets from various clinical centres. Information provided by editing NMR techniques and 2D diffusion NMR was incorporated in these models using four different data structures. Firstly, the models were calibrated and validated with three of the four sample sets (n = 591) involved. Then the best estimation models were selected and applied to the left-out sample set. This evaluation of a new sample set gave correlation coefficients of predicted versus biochemical variables above 0.86 and %rRMSE lower than 18 %. These values are similar to those found by other studies although, in our case, the results are more general because we used a higher number of samples (n = 785) from different sample sets, different clinical centres and different blood matrices (serum and plasma). Finally, we compared the performance of NMR predicted lipids and enzymatically measured lipids in a clinical case study.

LC-MS based cell metabolic profiling of tumor cells: a new predictive method for research on the mechanism of action of anticancer candidates

“… 7 Hu analyzed the metabolic changes induced by celastrol treatment in human cervical cancer cells and provided new evidence of the pharmacological mechanism of celastrol. 8 Furthermore, some metabolic profile based discrimination models have been reported in evaluating clinical therapy, diagnosing disease, 9 revealing dose effects, 10 determining disease stage, 11 and improving avian productivity. 12 For example, a study has demonstrated the novel strategy that the metabolic profiling approach may be applicable to show the discrimination of metabolites between treated and control groups in human endothelial cells, and thus contribute to the assessment of the clinical therapeutic effect.…”