Modulation of the lipidomic profile due to a lipid challenge and fitness level: a postprandial study

Background

The lipid composition of plasma is known to vary due to both phenotypic factors such as age, gender and BMI as well as with various diseases including cancer and neurological disorders. However, there is little investigation into the variation in the lipidome due to exercise and/ or metabolic challenges. The objectives of this present study were (i) To identify the glycerophospholipid, sphingolipids and ceramide changes in response to an oral lipid tolerance test (OLTT) in healthy adults and (ii) To identify the effect of aerobic fitness level on lipidomic profiles.

Methods

214 healthy adults aged 18–60 years were recruited as part of a metabolic challenge study. A sub-group of 40 volunteers were selected for lipidomic analysis based on their aerobic fitness level. Ceramides, glycerophospholipids and sphingomyelins were quantified in baseline fasting plasma samples as well as at 60, 120, 180, 240 and 300 min following a lipid challenge using high-throughput flow injection ESI-MS/MS.

Results

Mixed model repeated measures analysis identified lipids which were significantly changing over the time course of the lipid challenge. Included in these lipids were lysophosphoethanolamines (LPE), phosphoethanolamines (PE), phosphoglycerides (PG) and ceramides (Cer). Five lipids (LPE a C18:2, LPE a C18:1, PE aa C36:2, PE aa C36:3 and N-C16:1-Cer) had a fold change > 1.5 at 120 min following the challenge and these lipids remained elevated. Furthermore, three of these lipids (LPE a C18:2, PE aa C36:2 and PE aa C36:3) were predictive of fasting and peak plasma TAG concentrations following the OLTT. Further analysis revealed that fitness level has a significant impact on the response to the OLTT: in particular significant differences between fitness groups were observed for phosphatidylcholines (PC), sphingomyelins (SM) and ceramides.

Conclusion

This study identified specific lipids which were modulated by an acute lipid challenge. Furthermore, it identified a series of lipids which were modulated by fitness level. Future lipidomic studies should take into account environmental factors such as diet and fitness level during biomarker discovery work.

Trial registration

Data, clinicaltrials.gov, NCT01172951

Electronic supplementary material

The online version of this article (doi:10.1186/s12944-015-0062-x) contains supplementary material, which is available to authorized users.

Habitual dietary intake impacts on the lipidomic profile

Reliable dietary assessments are essential when attempting to understand the complex links between diet and health. Traditional methods for collecting dietary exposure can be unreliable, therefore there is an increasing interest in identifying biomarkers to provide a more accurate measurement. Metabolomics is a technology that offers great promise in this area. The aim of this study was to use a multivariate statistical strategy to link lipidomic patterns with dietary data in an attempt to identify dietary biomarkers. We assessed the relationship between lipidomic profiles and dietary data in volunteers (n=34) from the Metabolic Challenge Study (MECHE). Principal component analysis (PCA), linear regression and receiver operating characteristic (ROC) analysis were used to (1) reduce the lipidomic data into lipid patterns (LPs), (2) investigate relationships between these patterns and dietary data and (3) identify biomarkers of dietary intake. Our study identified a total of 6 novel LPs. LP1 was highly predictive of dietary fat intake (area under the curve AUC=0.82). A random forest (RF) classification model used to discriminate between low and high consumers resulted with an error rate of >10%, with a panel of six metabolites identified as the most predictive. LP4 was highly predictive of alcohol intake (AUC=0.81) with lysophosphatidylcholine alkyl C18:0 (LPCeC18:0) identified as a potential biomarker of alcohol consumption. LP6 had a reasonably good ability to predict dietary fish intake (AUC=0.76), with lysophosphatidylethanolamine acyl C18:2 (LPEaC18:2) phoshatidylethanolamine diaclyl C38:4 (PEaaC38:4) identified as potential biomarkers. The identification of these LPs and specific biomarkers will help in better classifying a persons dietary intake and in turn will improve the assessment of the relationship between diet and disease. Linking these LPs and specific biomarkers with health parameters will be an important future step.

Relationship between the lipidome, inflammatory markers and insulin resistance

The objectives of the present study were to (1) examine the effects of the phenotypic factors age, gender and BMI on the lipidomic profile and (2) investigate the relationship between the lipidome, inflammatory markers and insulin resistance.

PBMCs reflect the immune component of the WAT transcriptome--implications as biomarkers of metabolic health in the postprandial state

SCOPE

Food and nutrition studies often require accessing metabolically active tissues, including adipose tissue. This can involve invasive biopsy procedures that can be a limiting factor in study design. In contrast, peripheral blood mononuclear cells (PBMCs) are a population of circulating immune cells that are easily accessible through venipuncture. As transcriptomics is of growing importance in food and metabolism research, understanding the transcriptomic relationship between these tissue types can provide insight into the utility of PBMCs in this field.

METHODS AND RESULTS

We examine this relationship within eight subjects, in two postprandial states (following oral lipid tolerance test and oral glucose tolerance test). Multivariate analysis techniques were used to examine variation between tissues, samples, and subjects in order to define which genes havecommon/disparate expression profiles associated with highly defined metabolic phenotypes. We demonstrate global similarities in gene expression between PBMCs and white adipose tissue, irrespective of the metabolic challenge type. Closer examination of individual genes revealed this similarity to be strongest in pathways related to immune response/inflammation. Notably, the expression of metabolism-related nuclear receptors, including PPARs, LXR, etc. was discordant between tissues

CONCLUSION

The PBMC transcriptome may therefore provide a unique insight into the inflammatory component of metabolic health, as opposed to directly reflecting the metabolic component of the adipose tissue transcriptome.

Within-person variation in the postprandial lipemic response of healthy adults

BACKGROUND

The response to dietary fat plays a key role in metabolic health. Although this can vary widely between individuals, variation within an individual and the associated contribution of phenotypic and genotypic factors to this variation are less defined.

OBJECTIVES

The objectives were to quantify within-person variation in triacylglycerol response by means of a novel variation score (S(v)) and to explore the phenotypic and genotypic factors associated with this score.

DESIGN

Two consecutive 5-h oral-lipid-tolerance tests (OLTTs) were conducted in 51 healthy adults aged 18-60 y with a BMI (in kg/m²) of 18.5 to 49.8. Detailed body composition, physical function, biochemistry, and genotype data were gathered.

RESULTS

The postprandial triacylglycerol response profile did not differ (P = 0.64) across OLTTs for the group; nor did average concentrations of functional markers apolipoprotein C2 (P = 0.73) and apolipoprotein C3 (P = 0.74). S(v) was low in most (82%) of the adults and was significantly (P < 0.05) associated with age, fasting triacylglycerol, triacylglycerol AUC, and fasting nonessential fatty acids. Significant associations were also observed between S(v) and single nucleotide polymorphisms in 7 genes (APOA1, IL1α, IL1β, TLR4, TCF7L2, CCK1Rec, and STAT3) after correction for phenotypic differences.

CONCLUSIONS

This work showed that the within-person variability in postprandial lipemic response is low in most healthy adults. It also showed that variability in this response is associated with a defined set of phenotypic and genotypic characteristics.