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Background Increased liver fat increases the risk of chronic metabolic diseases. This study is an exploratory secondary analysis aimed at (1) investigating whether transcriptomic responses of abdominal subcutaneous adipose tissue (SAT) to a high-fat-high-glucose meal challenge differ according to varying levels of liver fat accumulation and (2) identifying pathways in abdominal SAT metabolism that may be related to liver fat accumulation. We examined differences in abdominal SAT gene expression and pathway activity both at fasting and in response to a mixed-meal challenge, comparing individuals with varying levels of liver fat. Method From the subset of 66 of 110 middle-aged participants of a previous intervention study, we grouped participants by tertiles of intrahepatic lipids (IHL) into high liver fat group ( n  = 22, IHL: 8.0%-32.6%), middle liver fat group ( n  = 22, IHL: 2.5%-8.0%) and low liver fat group ( n  = 22, IHL: 0.1%-2.5%). Participants received a high-fat-high-glucose mixed-meal challenge (3833 kJ). Abdominal SAT samples were collected before and 4 h after the challenge for microarray gene expression analysis. Results At fasting, 87 gene sets were differently expressed (FDR < 0.25) between the high and the low liver fat group, and 66 gene sets were differently expressed between the high and middle liver fat group, pathways related to energy metabolism were lower expressed in the high compared to the low liver fat group. Postprandially, 17 gene sets responded differently to the mixed meal challenge, of which 7 changed within the high liver fat group, 2 changed within the middle liver fat group and 4 within the low liver fat group. The challenge increased the expression of genes involved in oxidative phosphorylation more in the high compared to the low liver fat group. Conclusions Compared to individuals with low liver fat, individuals with high liver fat have lower gene expression but a higher response of energy-related pathways in abdominal SAT at fasting and after a high-fat-high-glucose challenge. Whether this is the cause or consequence of increased liver fat storage or an early stage of insulin resistance needs to be investigated. Trial registration This trial was registered at clinicaltrials.gov as NCT02194504.

Cocoa consumption has beneficial cardiometabolic effects, but underlying mechanisms remain unclear. Epicatechin, the cocoa major monomeric flavan-3-ol, is considered to contribute to these cardio-protective effects. We investigated effects of pure epicatechin supplementation on gene expression profiles of immune cells in humans. In a double blind, placebo-controlled cross-over trial, 32 (pre)hypertensive subjects aged 30 to 80, received two 4-week interventions, i.e. epicatechin (100mg/day) or placebo with a 4-week wash-out between interventions. Gene expression profiles of peripheral blood mononuclear cells were determined before and after both interventions. Epicatechin regulated 1180 genes, of which 234 differed from placebo. Epicatechin upregulated gene sets involved in transcription and tubulin folding and downregulated gene sets involved in inflammation, PPAR signalling and adipogenesis. Several negatively enriched genes within these gene sets were involved in insulin signalling. Most inhibited upstream regulators within the epicatechin intervention were cytokines or involved in inflammation. No upstream regulators were identified compared to placebo. Epicatechin, a cocoa flavan-3-ol, reduces gene expression involved in inflammation, PPAR-signalling and adipogenesis in immune cells. Effects were mild but our findings increase our understanding and provide new leads on how epicatechin rich products like cocoa may affect immune cells and exert cardiometabolic protective effects.

Background Body composition and body fat distribution are important predictors of cardiometabolic diseases. The etiology of cardiometabolic diseases is heterogenous, and partly driven by inter-individual differences in tissue-specific insulin sensitivity. Objectives To investigate (1) the associations between body composition and whole-body, liver and muscle insulin sensitivity, and (2) changes in body composition and insulin sensitivity and their relationship after a 12-week isocaloric diet high in mono-unsaturated fatty acids (HMUFA) or a low-fat, high-protein, high-fiber (LFHP) diet. Methods This subcohort analysis of the PERSON study includes 93 individuals (53% women, BMI 25–40 kg/m2, 40–75 years) who participated in this randomized intervention study. At baseline and after 12 weeks of following the LFHP, or HMUFA diet, we performed a 7-point oral glucose tolerance test to assess whole-body, liver, and muscle insulin sensitivity, and whole-body magnetic resonance imaging to determine body composition and body fat distribution. Both diets are within the guidelines of healthy nutrition. Results At baseline, liver fat content was associated with worse liver insulin sensitivity (β [95%CI]; 0.12 [0.01; 0.22]). Only in women, thigh muscle fat content was inversely related to muscle insulin sensitivity (-0.27 [-0.48; -0.05]). Visceral adipose tissue (VAT) was inversely associated with whole-body, liver, and muscle insulin sensitivity. Both diets decreased VAT, abdominal subcutaneous adipose tissue (aSAT), and liver fat, but not whole-body and tissue-specific insulin sensitivity with no differences between diets. Waist circumference, however, decreased more following the LFHP diet as compared to the HMUFA diet (-3.0 vs. -0.5 cm, respectively). After the LFHP but not HMUFA diet, improvements in body composition were positively associated with improvements in whole-body and liver insulin sensitivity. Conclusions Liver and muscle insulin sensitivity are distinctly associated with liver and muscle fat accumulation. Although both LFHP and HMUFA diets improved in body fat, VAT, aSAT, and liver fat, only LFHP-induced improvements in body composition are associated with improved insulin sensitivity. Trial registration NCT03708419 (clinicaltrials.gov).

Continuous glucose monitoring (CGM) is a promising, minimally invasive alternative to plasma glucose measurements for calibrating physiology-based mathematical models of insulin-regulated glucose metabolism, reducing the reliance on in-clinic measurements. However, the use of CGM glucose, particularly in combination with insulin measurements, to develop personalized models of glucose regulation remains unexplored. Here, we simultaneously measured interstitial glucose concentrations using CGM as well as plasma glucose and insulin concentrations during an oral glucose tolerance test (OGTT) in individuals with overweight or obesity to calibrate personalized models of glucose-insulin dynamics. We compared the use of interstitial glucose with plasma glucose in model calibration, and evaluated the effects on model fit, identifiability, and model parameters’ association with clinically relevant metabolic indicators. Models calibrated on both plasma and interstitial glucose resulted in good model fit, and the parameter estimates associated with metabolic indicators such as insulin sensitivity measures in both cases. Moreover, practical identifiability of model parameters was improved in models estimated on CGM glucose compared to plasma glucose. Together these results suggest that CGM glucose may be considered as a minimally invasive alternative to plasma glucose measurements in model calibration to quantify the dynamics of glucose regulation.

Background Tissue-specific insulin resistance (IR) predominantly in muscle (muscle IR) or liver (liver IR) has previously been linked to distinct fasting metabolite profiles, but postprandial metabolite profiles have not been investigated in tissue-specific IR yet. Given the importance of postprandial metabolic impairments in the pathophysiology of cardiometabolic diseases, we compared postprandial plasma metabolite profiles in response to a high-fat mixed meal between individuals with predominant muscle IR or liver IR. Methods This cross-sectional study included data from 214 women and men with BMI 25–40 kg/m 2 , aged 40–75 years, and with predominant muscle IR or liver IR. Tissue-specific IR was assessed using the muscle insulin sensitivity index (MISI) and hepatic insulin resistance index (HIRI), which were calculated from the glucose and insulin responses during a 7-point oral glucose tolerance test. Plasma samples were collected before (T = 0) and after (T = 30, 60, 120, 240 min) consumption of a high-fat mixed meal and 247 metabolite measures, including lipoproteins, cholesterol, triacylglycerol (TAG), ketone bodies, and amino acids, were quantified using nuclear magnetic resonance spectroscopy. Differences in postprandial plasma metabolite iAUCs between muscle and liver IR were tested using ANCOVA with adjustment for age, sex, center, BMI, and waist-to-hip ratio. P -values were adjusted for a false discovery rate (FDR) of 0.05 using the Benjamini–Hochberg method. Results Sixty-eight postprandial metabolite iAUCs were significantly different between liver and muscle IR. Liver IR was characterized by greater plasma iAUCs of large VLDL ( p  = 0.004), very large VLDL ( p  = 0.002), and medium-sized LDL particles ( p  = 0.026), and by greater iAUCs of TAG in small VLDL ( p  = 0.025), large VLDL ( p  = 0.003), very large VLDL ( p  = 0.002), all LDL subclasses (all p  < 0.05), and small HDL particles ( p  = 0.011), compared to muscle IR. In liver IR, the postprandial plasma fatty acid (FA) profile consisted of a higher percentage of saturated FA ( p  = 0.013), and a lower percentage of polyunsaturated FA ( p  = 0.008), compared to muscle IR. Conclusion People with muscle IR or liver IR have distinct postprandial plasma metabolite profiles, with more unfavorable postprandial metabolite responses in those with liver IR compared to muscle IR.

Biomarkers are an efficient means to examine intakes or exposures and their biological effects and to assess system susceptibility. Aided by novel profiling technologies, the biomarker research field is undergoing rapid development and new putative biomarkers are continuously emerging in the scientific literature. However, the existing concepts for classification of biomarkers in the dietary and health area may be ambiguous, leading to uncertainty about their application. In order to better understand the potential of biomarkers and to communicate their use and application, it is imperative to have a solid scheme for biomarker classification that will provide a well-defined ontology for the field. In this manuscript, we provide an improved scheme for biomarker classification based on their intended use rather than the technology or outcomes (six subclasses are suggested: food compound intake biomarkers (FCIBs), food or food component intake biomarkers (FIBs), dietary pattern biomarkers (DPBs), food compound status biomarkers (FCSBs), effect biomarkers, physiological or health state biomarkers). The application of this scheme is described in detail for the dietary and health area and is compared with previous biomarker classification for this field of research.

Identification of new biomarkers of food and nutrient intake has developed fast over the past two decades and could potentially provide important new tools for compliance monitoring and dietary intake assessment in nutrition and health science. In recent years, metabolomics has played an important role in identifying a large number of putative biomarkers of food intake (BFIs). However, the large body of scientific literature on potential BFIs outside the metabolomics area should also be taken into account. In particular, we believe that extensive literature reviews should be conducted and that the quality of all suggested biomarkers should be systematically evaluated. In order to cover the literature on BFIs in the most appropriate and consistent manner, there is a need for appropriate guidelines on this topic. These guidelines should build upon guidelines in related areas of science while targeting the special needs of biomarker methodology. This document provides a guideline for conducting an extensive literature search on BFIs, which will provide the basis to systematically validate BFIs. This procedure will help to prioritize future work on the identification of new potential biomarkers and on validating these as well as other biomarker candidates, thereby providing better tools for future studies in nutrition and health.

High fat meal challenges are known to induce postprandial low-grade inflammation and endothelial dysfunction. This assumption is largely based on studies performed in older populations or in populations with a progressed disease state and an appropriate control meal is often lacking. Young healthy individuals might be more resilient to such challenges. We therefore aimed to characterize the vascular and inflammatory response after a high fat meal in young healthy individuals. In a double-blind randomized cross-over intervention study, we used a comprehensive phenotyping approach to determine the vascular and inflammatory response after consumption of a high fat shake and after an average breakfast shake in 20 young healthy subjects. Both interventions were performed three times. Many features of the vascular postprandial response, such as FMD, arterial stiffness and micro-vascular skin blood flow were not different between shakes. High fat/high energy shake consumption was associated with a more pronounced increase in blood pressure, heart rate, plasma concentrations of IL-8 and PBMCs gene expression of IL-8 and CD54 (ICAM-1), whereas plasma concentrations of sVCAM1 were decreased compared to an average breakfast. Whereas no difference in postprandial response were observed on classical markers of endothelial function, we did observe differences between consumption of a HF/HE and an average breakfast meal on blood pressure and IL-8 in young healthy volunteers. IL-8 might play an important role in dealing with high fat challenges and might be an early marker for endothelial stress, a stage preceding endothelial dysfunction.

Background: To unravel true links between diet and health, it is important that dietary exposure is accurately measured. Currently, mainly self-reporting methods (e.g. food frequency questionnaires and 24-h recalls) are used to assess food intake in epidemiological studies. However, these traditional instruments are subjective measures and contain well-known biases. Especially, estimating the intake of the group of confectionary products, such as products containing cocoa and liquorice, remains a challenge. The use biomarkers of food intake (BFIs) may provide a more objective measurement. However, an overview of current candidate biomarkers and their validity is missing for both cocoa- and liquorice-containing foods. Objective: The purpose of the current study was to (1) identify currently described candidate BFIs for cocoa (products) and liquorice, (2) to evaluate the validity of these identified candidate BFIs and (3) to address further validation and/or identification work to be done. Methods: This systematic review was based on a comprehensive literature search of three databases (PubMed, Scopus and ISI web of Science), to identify candidate BFIs. Via a second search step in the Human Metabolome Database (HMDB), the Food Database (FooDB) and Phenol-Explorer, the specificity of the candidate BFIs was evaluated, followed by an evaluation of the validity of the specific candidate BFIs, via pre-defined criteria. Results: In total, 37 papers were included for cocoa and 8 papers for liquorice. For cocoa, 164 unique candidate BFIs were obtained, and for liquorice, four were identified in total. Despite the high number of identified BFIs for cocoa, none of the metabolites was specific. Therefore, the validity of these compounds was not further examined. For liquorice intake, 18-glycyrrhetinic acid (18-GA) was found to have the highest assumed validity. Conclusions: For cocoa, specific BFIs were missing, mainly because the individual BFIs were also found in foods having a similar composition, such as tea (polyphenols) or coffee (caffeine). However, a combination of individual BFIs might lead to discriminating profiles between cocoa (products) and foods with a similar composition. Therefore, studies directly comparing the consumption of cocoa to these similar products are needed, enabling efforts to find a unique profile per product. For liquorice, we identified 18-GA as a promising BFI; however, important information on its validity is missing; thus, more research is necessary. Our findings indicate a need for more studies to determine acceptable BFIs for both cocoa and liquorice. Keywords: Licorice, Liquorice, Cocoa, Cacao, Chocolate, Metabolites, Metabolomics, Biomarkers

The ability of subjects to respond to nutritional challenges can reflect the flexibility of their biological system. Nutritional challenge tests could be used as an indicator of health status but more knowledge on metabolic and immune responses of different subjects to nutritional challenges is needed. The aim of this study was to compare the responses to high-fat challenges varying in fat type in subjects with different metabolic risk phenotypes. In a cross-over design 42 men (age 50-70 y) consumed three high-fat shakes containing saturated fat (SFA), monounsaturated fat (MUFA) or n-3 polyunsaturated (PUFA). Men were selected on BMI and health status (lean, obese or obese diabetic) and phenotyped with MRI for adipose tissue distribution. Before and 2 and 4 h after shake consumption blood was drawn for measurement of expression of metabolic and inflammation-related genes in peripheral blood mononuclear cells (PBMCs), plasma triglycerides (TAG), glucose, insulin, cytokines and ex vivo PBMC immune response capacity. The MUFA and n-3 PUFA challenge, compared to the SFA challenge, induced higher changes in expression of inflammation genes MCP1 and IL1β in PBMCs. Obese and obese diabetic subjects had different PBMC gene expression and metabolic responses to high-fat challenges compared to lean subjects. The MUFA challenge induced the most pronounced TAG response, mainly in obese and obese diabetic subjects. The PBMC gene expression response and metabolic response to high-fat challenges were affected by fat type and metabolic risk phenotype. Based on our results we suggest using a MUFA challenge to reveal differences in response capacity of subjects. ClinicalTrials.gov NCT00977262.