Search

BackgroundThe aim of the present study was to identify the metabolomic signature of responders and non-responders to an omega-3 fatty acid (n-3 FA) supplementation, and to test the ability of a multi-omics classifier combining genomic, lipidomic, and metabolomic features to discriminate plasma triglyceride (TG) response phenotypes.MethodsA total of 208 participants of the Fatty Acid Sensor (FAS). Study took 5 g per day of fish oil, providing 1.9–2.2 g eicosapentaenoic acid (EPA) and 1.1 g docosahexaenoic (DHA) daily over a 6-week period, and were further divided into two subgroups: responders and non-responders, according to the change in plasma TG levels after the supplementation. Changes in plasma levels of 6 short-chain fatty acids (SCFA) and 25 bile acids (BA) during the intervention were compared between subgroups using a linear mixed model, and the impact of SCFAs and BAs on the TG response was tested in a mediation analysis. Genotyping was conducted using the Illumina Human Omni-5 Quad BeadChip. Mass spectrometry was used to quantify plasma TG and cholesterol esters levels, as well as plasma SCFA and BA levels. A classifier was developed and tested within the DIABLO framework, which implements a partial least squares-discriminant analysis to multi-omics analysis. Different classifiers were developed by combining data from genomics, lipidomics, and metabolomics.ResultsPlasma levels of none of the SCFAs or BAs measured before and after the n-3 FA supplementation were significantly different between responders and non-responders. SCFAs but not BAs were marginally relevant in the classification of plasma TG responses. A classifier built by adding plasma SCFAs and lipidomic layers to genomic data was able to even the accuracy of 85% shown by the genomic predictor alone.ConclusionThese results inform on the marginal relevance of SCFA and BA plasma levels as surrogate measures of gut microbiome in the assessment of the interindividual variability observed in the plasma TG response to an n-3 FA supplementation. Genomic data still represent the best predictor of plasma TG response, and the inclusion of metabolomic data added little to the ability to discriminate the plasma TG response phenotypes.

BackgroundMany studies show that the intake of raspberries is beneficial to immune-metabolic health, but the responses of individuals are heterogeneous and not fully understood.MethodsIn a two-arm parallel-group, randomized, controlled trial, immune-metabolic outcomes and plasma metabolite levels were analyzed before and after an 8-week red raspberry consumption. Based on partial least squares discriminant analysis (PLS-DA) on plasma xenobiotic levels, adherence to the intervention was first evaluated. A second PLS-DA followed by hierarchical clustering was used to classify individuals into response subgroups. Clinical immune and metabolic outcomes, including insulin resistance (HOMA-IR) and sensitivity (Matsuda, QUICKI) indices, during the intervention were assessed and compared between response subgroups.ResultsTwo subgroups of participants, type 1 responders (n = 17) and type 2 responders (n = 5), were identified based on plasma metabolite levels measured during the intervention. Type 1 responders showed neutral to negative effects on immune-metabolic clinical parameters after raspberry consumption, and type 2 responders showed positive effects on the same parameters. Changes in waist circumference, waist-to-hip ratio, fasting plasma apolipoprotein B, C-reactive protein and insulin levels as well as Matsuda, HOMA-IR and QUICKI were significantly different between the two response subgroups. A deleterious effect of two carotenoid metabolites was also observed in type 1 responders but these variables were significantly associated with beneficial changes in the QUICKI index and in fasting insulin levels in type 2 responders. Increased 3-ureidopropionate levels were associated with a decrease in the Matsuda index in type 2 responders, suggesting that this metabolite is associated with a decrease in insulin sensitivity for those subjects, whereas the opposite was observed for type 1 responders.ConclusionThe beneficial effects associated with red raspberry consumption are subject to inter-individual variability. Metabolomics-based clustering appears to be an effective way to assess adherence to a nutritional intervention and to classify individuals according to their immune-metabolic responsiveness to the intervention. This approach may be replicated in future studies to provide a better understanding of how interindividual variability impacts the effects of nutritional interventions on immune-metabolic health.

Low circulating concentrations of insulin-like growth factor binding protein-2 (IGFBP-2) have been associated with dyslipidemia, notably with high triglyceride (TG) levels. However, the determinants by which IGFBP-2 influences lipoprotein metabolism, especially that of TG-rich lipoproteins (TRLs), are poorly understood. Here, we aimed to assess the relationships between IGFBP-2 levels and lipoprotein production and catabolism in human subjects. Fasting IGFBP-2 concentrations were measured in the plasma of 219 men pooled from previous lipoprotein kinetics studies. We analyzed production rate and fractional catabolic rates of TRLapoB-48, and LDL-, IDL-, and VLDLapoB-100 by multicompartmental modeling of l-[5,5,5-D3] leucine enrichment data after a 12 h primed constant infusion in individuals kept in a constant nutritional steady state. Subjects had an average BMI of 30 kg/m2, plasma IGFBP-2 levels of 157 ng/ml, and TG of 2.2 mmol/l. After adjustments for age and BMI, IGFBP-2 levels were negatively associated with plasma TG (r = −0.29; P < 0.0001) and positively associated with HDL-cholesterol (r = 0.26; P < 0.0001). In addition, IGFBP-2 levels were positively associated with the fractional catabolic rate of VLDLapoB-100 (r = 0.20; P < 0.01) and IDLapoB-100 (r = 0.19; P < 0.05) and inversely with the production rate of TRLapoB-48 (r = −0.28; P < 0.001). These correlations remained statistically significant after adjustments for age, BMI, and the amount of fat given during the tracer infusion. These findings show that the association between low plasma IGFBP-2 and high TG concentrations could be due to both an impaired clearance of apoB-100-containing VLDL and IDL particles and an increased production of apoB-48-containing chylomicrons. Additional studies are necessary to investigate whether and how IGFBP-2 directly impacts the kinetics of TRL.

In 2019, the European Society of Cardiology/European Atherosclerosis Society stated that apolipoprotein B (apoB) was a more accurate marker of cardiovascular risk than low‐density lipoprotein cholesterol (LDL‐C) and non–high‐density lipoprotein cholesterol. Since then, the evidence has continued to mount in favor of apoB. This review explicates the physiological mechanisms responsible for the superiority of apoB as a marker of the cardiovascular risk attributable to the atherogenic apoB lipoprotein particles chylomicron remnants, very low‐density lipoprotein, and low‐density lipoprotein particles. First, the nature and relative numbers of these different apoB particles will be outlined. This will make clear why low‐density lipoprotein particles are almost always the major determinants of cardiovascular risk and why the concentrations of triglycerides and LDL‐C may obscure this relation. Next, the mechanisms that govern the number of very low‐density lipoprotein and low‐density lipoprotein particles will be outlined because, except for dysbetalipoproteinemia, the total number of apoB particles determines cardiovascular risk, Then, the mechanisms that govern the cholesterol mass within very low‐density lipoprotein and low‐density lipoprotein particles will be reviewed because these are responsible for the discordance between the mass of cholesterol within apoB particles, measured either as LDL‐C or non–high‐density lipoprotein cholesterol, and the number of apoB particles measured as apoB, which creates the superior predictive power of apoB over LDL‐C and non–high‐density lipoprotein cholesterol. Finally, the major apoB dyslipoproteinemias will be briefly outlined. Our objective is to provide a physiological framework for health care givers to understand why apoB is a more accurate marker of cardiovascular risk than LDL‐C or non–high‐density lipoprotein cholesterol.

BackgroundLittle is known about the impact of mango consumption on metabolic pathways assessed by changes in gene expression.MethodsIn this single-arm clinical trial, cardiometabolic outcomes and gene expression levels in whole blood samples from 26 men and women were examined at baseline and after 8 weeks of mango consumption and differential gene expression changes were determined. Based on changes in gene expression profiles, partial least squares discriminant analysis followed by hierarchical clustering were used to classify participants into subgroups of response and differences in gene expression changes and in cardiometabolic clinical outcomes following the intervention were tested.ResultsTwo subgroups of participants were separated based on the resemblance of gene expression profiles in response to the intervention and as responders (n = 8) and non-responders (n = 18). A total of 280 transcripts were significantly up-regulated and 603 transcripts down-regulated following the intervention in responders, as compared to non-responders. Several metabolic pathways, mainly related to oxygen and carbon dioxide transport as well as oxidative stress, were found to be significantly enriched with differentially expressed genes. In addition, significantly beneficial changes in hip and waist circumference, c-reactive protein, HOMA-IR and QUICKI indices were observed in responders vs. non-responders, following the intervention.ConclusionThe impact of mango consumption on cardiometabolic health appears to largely rely on interindividual variability. The novel transcriptomic-based clustering analysis used herein can provide insights for future research focused on unveiling the origins of heterogeneous responses to dietary interventions.Clinical Trial Registration[clinicaltrials.gov], identifier [NCT03825276].