Fatty acid desaturation index in human plasma: comparison of different analytical methodologies for the evaluation of diet effects.

Stearoyl-CoA desaturase 1 (SCD1) plays a role in the development of obesity and related conditions, such as insulin resistance, and potentially also in neurological and heart diseases. The activity of SCD1 can be monitored using the desaturation index (DI), the ratio of product (16:1n-7 and 18:1n-9) to precursor (16:0 and 18:0) fatty acids. Here, different analytical strategies were applied to identify the method which best supports SCD1 biology. A novel effective approach was the use of the SCD1-independent fatty acid (16:1n-10) as a negative control. The first approach was based on a simple extraction followed by neutral loss triglyceride fatty acid analysis. The second approach was based on the saponification of triglycerides followed by fatty acid analysis (specific for the position of the double bond within monounsaturated fatty acids (MUFAs)). In addition to the analytical LC-MS assays, different matrices (plasma total triglyceride fraction and the very low-density lipoprotein (VLDL) fraction) were investigated to identify the best for studying changes in SCD1 activity. Samples from volunteers on a high-carbohydrate diet were analyzed. Both ultra HPLC (UHPLC)-MS-based assays showed acceptable accuracies (75-125% of nominal) and precisions (<20%) for the analysis of DI-specific fatty acids in VLDL and plasma. The most specific assay for the analysis of the liver SCD activity was then validated for specificity and selectivity, intra- and interday accuracy and precision, matrix effects, dilution effects, and analyte stability. After 3 days of high-carbohydrate diet, only the specific fatty acids in human plasma VLDL showed a significant increase in DI and associated SCD1 activity.

How unbiased is non-targeted metabolomics and is targeted pathway screening the solution?

Metabolomics is only truly unbiased if the whole metabolome is captured. Current metabolomics technologies capture only a part of the metabolome and therefore produce inherently biased results. Important factors that introduce such bias into a metabolomic analysis may include but are not limited to, timing of sample collection, the sample collection procedure, sample processing, stabilization, stability and storage, extraction procedures, dilution of sample, type and number of analytical methods used, preferences of analytical assays for metabolites with certain physico-chemical properties, ion suppression (LC-MS), derivatization (GC-MS), sensitivity of the assay, range of reliable response and the ability to allow at least for semi-quantitative comparison. Consideration of the many computational, chemometric and biostatistical steps required to link changes in metabolite patterns to metabolic pathways and the additional bias and risks that these steps entail, brings up the question of whether or not screening for changes in known metabolic pathways using a set of validated, quantitative multiplexing LC-MS assays (targeted pathway screening, TAPAS) would be a more robust and reliable approach. Instead of non-selectively screening for changes in metabolite patterns, TAPAS screens for changes in metabolic pathways. Since such assays are designed for specific groups of metabolites, TAPAS can cover a larger number of metabolic pathways including metabolites of a wide variety of physicochemical properties and concentration ranges and thus, although based on suite of targeted assays, TAPAS may ultimately be a less biased strategy than current non-targeted metabolomics technologies.