Plasma esterified and non-esterified fatty acids metabolic profiling using gas chromatography–mass spectrometry and its application in the study of diabetic mellitus and diabetic nephropathy

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Abstract

Using gas chromatography–mass spectrometry (GC–MS), a new metabolic profiling method was established to assess the levels of non-esterified fatty acids (NEFAs) and esterified fatty acids (EFAs) in plasma. The extraction method was simple and robust without removing protein process. With this method 25 fatty acids (FAs), both EFAs and NEFAs, can be recognized simultaneously with only 10 μL plasma. 15 of the 25 can be precisely quantified. The method was validated and then applied into clinical metabonomics research. Five clinical groups including 150 cases were involved. The relationship between FA levels and diabetic mellitus (DM) as well as diabetic nephropathy (DN) pathology was speculated. Furthermore, the possible pathological causes and effects were discussed in detail. Potential biomarkers (p value <0.01) were screened with Student's t-test. With the application of partial least squares-discriminant analysis (PLS-DA), different stages were distinguished. The result may be useful for the pathology study of metabolic syndromes, and may also be helpful for monitoring the progression of DM and DN.

Introduction

It has been commonly recognized that the explosive increase of metabolic syndromes and their attendant staggering public health costs are currently afflicting developed and developing countries. The medical and socioeconomic burden of the disease is often caused by the associated complications, which impose enormous strains on health-care systems. People are always hoping to find out the pathogenesis of the disease thus to detect them in their early stage and to remedy them in time, but few results had been obtained. Researchers considered that insulin resistance (IR) and lipid metabolic disorder are the two main aspects of pathology in metabolic syndrome [1], [2]. Fatty acids (FAs) could potentially contribute to the pathogenesis of insulin resistance [3].

Diabetic mellitus (DM) are typical metabolic syndromes. The incidence of DM has increased almost 10-fold over the past ten years, which means approximately 3% of the world population suffers from the disease and the numbers is still rising year by year. Moreover, 30–50% of DM patients evolve towards diabetic nephropathy (DN), which is the leading cause of chronic renal disease and a major cause of cardiovascular mortality [4], [5].

It has been reported that plasma FAs have direct impacts on the occurrence and development of diabetes. With special attention to the incidence of diabetes and its mechanism of secondary disease, lipotoxicity hypothesis has gained a broad recognition although a large number of clinical trial data is still required [6], [7], [8], [9], [10], [11], [12]. Lipidomics as a branch of metabonomics has become a hot topic recently [13], [14], [15], and may be helpful for the understanding of the disease.

Gas chromatography–mass spectrometry (GC–MS) is one of the most popular techniques for metabolic profiling analysis. Metabolic profiling based on GC–MS can reflect the metabolites and their levels clearly. Although lots of research articles have discussed about GC methods for FAs analysis [16], [17], few articles offered disease metabolic profiling method for analyses both of EFAs and NEFAs. We have shortly reported such a method as a letter [18]. Plasma NEFAs and EFAs of a sample are separated by a single operation step of liquid–liquid extraction without removing protein. After derivatization, qualification and accurate quantification of EFAs and NEFAs were conducted separately by GC–MS. In this paper, we report the detailed method validation and its further clinical application. The plasma EFAs and NEFAs levels of different states of DN were reported here. These data uphold the lipotoxicity hypothesis. Based on the results of quantification, the possible pathology of DM and DN was discussed.

Reducing the sample volume is undoubtedly very important, especially for precious clinical samples. This strategy requires only 10 μL plasma. DM and DN were included in this study. Five-group samples were involved, that is, control, type 2 DM, DNIII, DNIV, and DNV (three different stages of DN). Precise quantification data for each sample was obtained and the mean values of each group were given. Possible relationships between the difference of fatty acid concentrations and DM as well as DN were discussed. Partial least squares-discriminant analysis (PLS-DA) was conducted based on quantification results and Student's t-test was adopted to obtain the potential biomarkers.

Section snippets

Chemicals and reagents

Decanoic acid (C10:0), lauric acid (C12:0), myristic acid (C14:0), palmitic acid (C16:0), palmitoleic acid (C16:1n-9), stearic acid (C18:0), oleic acid (C18:1n-9), petroleic acid (C18:1n-11), linoleic acid (C18:2), arachidonic acid (C20:4), eicosapentaenoic acid (C20:5), eicosatrienoic acid (C20:3), eicosadienoic acid (C20:2), arachidic acid (C20:0), docosahexaenoic acid (C22:6) and monostearin were purchased from Sigma (St. Louis, MO, USA); all standards purity were above 99.5% (v/v) H2SO4/CH3

Extraction, separation and derivatization of EFAs and NEFAs

Plasma NEFAs and EFAs exist in different statuses and play different physiological roles. Thus, it is necessary to analyze them separately. In this experiment, EFAs and NEFAs were extracted and separated by a single participation operation. EFAs are mainly composed by phospholipids, sterol lipids and glycol lipids. In EFAs, non-polar fatty acyl moieties are covalently linked to polar moieties (phosphoric acid, glycerol or sterols). They should be hydrolyzed to the form of FFAs before analysis.

Conclusions

GC–MS based metabolic profiling using is a technique whose potential in the field of clinical metabonomics is largely untapped. Based on GC–MS, plasma FAs profiling was generated for the simultaneous determination of EFAs and NEFAs with only a small volume of samples. This is very beneficial when the sample is rare or difficult to obtain.

In the study of typical metabolic syndrome DM and DN, our results uphold the lipotoxicity hypothesis. The PLS-DA scores plot showed that, based on the

Acknowledgements

The authors acknowledge the support of the China-Japan Friendship Hospital (Beijing, China) who provided the samples used in this work. This study was supported by grants from the National Basic Research Development Program of China (973 Program, No. 2005CB523503) and from the National Natural Science Foundation of China (No. 90709045 and No. 20805026).

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