NMR-based metabolic profiling in healthy individuals overfed different types of fat: links to changes in liver fat accumulation and lean tissue mass

Abstract

Background:Overeating different dietary fatty acids influence the amount of liver fat stored during weight gain, however, the mechanisms responsible are unclear. We aimed to identify non-lipid metabolites that may differentiate between saturated (SFA) and polyunsaturated fatty acid (PUFA) overfeeding using a non-targeted metabolomic approach. We also investigated the possible relationships between plasma metabolites and body fat accumulation.Methods:In a randomized study (LIPOGAIN study), n=39 healthy individuals were overfed with muffins containing SFA or PUFA. Plasma samples were precipitated with cold acetonitrile and analyzed by nuclear magnetic resonance (NMR) spectroscopy. Pattern recognition techniques were used to overview the data, identify variables contributing to group classification and to correlate metabolites with fat accumulation.Results:We previously reported that SFA causes a greater accumulation of liver fat, visceral fat and total body fat, whereas lean tissue levels increases less compared with PUFA, despite comparable weight gain. In this study, lactate and acetate were identified as important contributors to group classification between SFA and PUFA (P<0.05). Furthermore, the fat depots (total body fat, visceral adipose tissue and liver fat) and lean tissue correlated (P(corr)>0.5) all with two or more metabolites (for example, branched amino acids, alanine, acetate and lactate). The metabolite composition differed in a manner that may indicate higher insulin sensitivity after a diet with PUFA compared with SFA, but this needs to be confirmed in future studies.Conclusion:A non-lipid metabolic profiling approach only identified a few metabolites that differentiated between SFA and PUFA overfeeding. Whether these metabolite changes are involved in depot-specific fat storage and increased lean tissue mass during overeating needs further investigation.

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment

“…Unsaturated fatty acids can be further divided by the number of double bounds as mono-(MUFAs; a single double bound) and poly-unsaturated fatty acids (PUFAs; more than one double bound) [163]. SFAs, MUFAs and PUFAs present different biological properties.…”

“…Numerous beneficial healthy effects have been attributed to unsaturated fatty acids, including protection from obesity, diabetes, cancer, and atherosclerosis [162,164]. The most abundant MUFA in typical diets is oleic acid (C18:1n-9) which is effective in lowering the inflammatory response and LDL levels; that together contribute in the reduction of CVD risk [162,163]. High levels of MUFAs were described in the prevention of abdominal fat accumulation.…”

“…High levels of MUFAs were described in the prevention of abdominal fat accumulation. Moreover, the substitution of carbohydrates with MUFA cause a decrease on total blood cholesterol an TGs, reducing the levels of HDL-C [162,163]. The mechanism involved in the anti-inflammatory effect of MUFAs is the inhibition of NF-kB activity [167].…”

Recent Advances in NMR-Based Metabolomics

“…Focused on understanding the mechanism of such weight gain, a metabolomics study investigated the effect of overfeeding of saturated fatty acids (SFA) and polyunsaturated fatty acids (PUFA). 153 Here polar metabolites were evaluated for differentiation between SFA and PUFA diet-based body fat deposition. Finally, a metabolomics study focused on identifying dietary markers for coffee consumption identified 2-furoylglycine as a reliable marker.…”

Unique identifiers for small molecules enable rigorous labeling of their atoms

“…Atom-specific information generated by quantum mechanical calculations or by experimental data from NMR spectroscopy or X-ray diffraction needs to be associated with standard atom nomenclature. One well-known application of such atom specific data is in NMR metabolic profiling, a method widely used for enhancing our understanding of cellular mechanisms and for identifying metabolic biomarkers 38 , 39 , 40 , 41 , 42 , 43 , 44 . The process of quantifying a metabolite from an 1D- 1 H NMR spectrum requires that NMR peaks from the experiment be matched against those of a reference spectrum, for example from the Biological Magnetic Resonance Data Bank (BMRB 21 ) or the Human Metabolome Data Bank (HMDB 22 , 23 , 24 ).…”