Changes in Plasma Metabolite Concentrations after a Low-Glycemic Index Diet Intervention

Artemisinin counteracts Edwardsiella tarda-induced liver inflammation and metabolic changes in juvenile fat greenling Hexagrammos otakii

Emerging evidence suggests that artemisinin (ART) can modulate pathogen-induced immune responses and metabolic dysregulation. However, whether this modulation is associated with metabolic pathways related to oxidative stress and inflammation remains unclear. The aim of this study was to investigate the antioxidant and anti-inflammatory effects on the ART-fed juvenile fat greenling Hexagrammos otakii and the associated metabolic pathways in response to ART administration using an integrated biochemical and metabolomic approach. Biochemical analysis and histological examination showed that ART significantly increased body weight gain and improved tissue structure. ART effectively attenuated reactive oxygen species (ROS), malondialdehyde (MDA) and inflammatory responses (NFκB, TNF-α, IL-6, and MCP-1) in the Edwardsiella tarda-induced H. otakii model. Liver metabolomics analysis revealed that twenty-nine metabolites were up-regulated and twenty-one metabolites were down-regulated after ART administration compared to those in pathogen-induced fish. Pathway analysis indicated that ART alleviated the E. tarda-induced inflammation and oxidative stress through two major pathways, namely lipid metabolism and amino acid metabolism. Taken together, ART showed great potential as a natural feed additive against pathogen-induced oxidative stress and inflammation.

Low glycaemic index or low glycaemic load diets for people with overweight or obesity

BACKGROUND

The prevalence of obesity is increasing worldwide, yet nutritional management remains contentious. It has been suggested that low glycaemic index (GI) or low glycaemic load (GL) diets may stimulate greater weight loss than higher GI/GL diets or other weight reduction diets. The previous version of this review, published in 2007, found mainly short-term intervention studies. Since then, randomised controlled trials (RCTs) with longer-term follow-up have become available, warranting an update of this review.

OBJECTIVES

To assess the effects of low glycaemic index or low glycaemic load diets on weight loss in people with overweight or obesity.

SEARCH METHODS

We searched CENTRAL, MEDLINE, one other database, and two clinical trials registers from their inception to 25 May 2022. We did not apply any language restrictions.

SELECTION CRITERIA

We included RCTs with a minimum duration of eight weeks comparing low GI/GL diets to higher GI/GL diets or any other diets in people with overweight or obesity.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. We conducted two main comparisons: low GI/GL diets versus higher GI/GL diets and low GI/GL diets versus any other diet. Our main outcomes included change in body weight and body mass index, adverse events, health-related quality of life, and mortality. We used GRADE to assess the certainty of the evidence for each outcome.

MAIN RESULTS

In this updated review, we included 10 studies (1210 participants); nine were newly-identified studies. We included only one study from the previous version of this review, following a revision of inclusion criteria. We listed five studies as 'awaiting classification' and one study as 'ongoing'. Of the 10 included studies, seven compared low GI/GL diets (233 participants) with higher GI/GL diets (222 participants) and three studies compared low GI/GL diets (379 participants) with any other diet (376 participants). One study included children (50 participants); one study included adults aged over 65 years (24 participants); the remaining studies included adults (1136 participants). The duration of the interventions varied from eight weeks to 18 months. All trials had an unclear or high risk of bias across several domains.  Low GI/GL diets versus higher GI/GL diets Low GI/GL diets probably result in little to no difference in change in body weight compared to higher GI/GL diets (mean difference (MD) -0.82 kg, 95% confidence interval (CI) -1.92 to 0.28; I2 = 52%; 7 studies, 403 participants; moderate-certainty evidence). Evidence from four studies reporting change in body mass index (BMI) indicated low GI/GL diets may result in little to no difference in change in BMI compared to higher GI/GL diets (MD -0.45 kg/m2, 95% CI -1.02 to 0.12; I2 = 22%; 186 participants; low-certainty evidence)at the end of the study periods. One study assessing participants' mood indicated that low GI/GL diets may improve mood compared to higher GI/GL diets, but the evidence is very uncertain (MD -3.5, 95% CI -9.33 to 2.33; 42 participants; very low-certainty evidence). Two studies assessing adverse events did not report any adverse events; we judged this outcome to have very low-certainty evidence. No studies reported on all-cause mortality.    For the secondary outcomes, low GI/GL diets may result in little to no difference in fat mass compared to higher GI/GL diets (MD -0.86 kg, 95% CI -1.52 to -0.20; I2 = 6%; 6 studies, 295 participants; low certainty-evidence). Similarly, low GI/GL diets may result in little to no difference in fasting blood glucose level compared to higher GI/GL diets (MD 0.12 mmol/L, 95% CI 0.03 to 0.21; I2 = 0%; 6 studies, 344 participants; low-certainty evidence).  Low GI/GL diets versus any other diet Low GI/GL diets probably result in little to no difference in change in body weight compared to other diets (MD -1.24 kg, 95% CI -2.82 to 0.34; I2 = 70%; 3 studies, 723 participants; moderate-certainty evidence). The evidence suggests that low GI/GL diets probably result in little to no difference in change in BMI compared to other diets (MD -0.30 kg in favour of low GI/GL diets, 95% CI -0.59 to -0.01; I2 = 0%; 2 studies, 650 participants; moderate-certainty evidence). Two adverse events were reported in one study: one was not related to the intervention, and the other, an eating disorder, may have been related to the intervention. Another study reported 11 adverse events, including hypoglycaemia following an oral glucose tolerance test. The same study reported seven serious adverse events, including kidney stones and diverticulitis. We judged this outcome to have low-certainty evidence. No studies reported on health-related quality of life or all-cause mortality. For the secondary outcomes, none of the studies reported on fat mass. Low GI/GL diets probably do not reduce fasting blood glucose level compared to other diets (MD 0.03 mmol/L, 95% CI -0.05 to 0.12; I2 = 0%; 3 studies, 732 participants; moderate-certainty evidence).  AUTHORS' CONCLUSIONS: The current evidence indicates there may be little to no difference for all main outcomes between low GI/GL diets versus higher GI/GL diets or any other diet. There is insufficient information to draw firm conclusions about the effect of low GI/GL diets on people with overweight or obesity. Most studies had a small sample size, with only a few participants in each comparison group. We rated the certainty of the evidence as moderate to very low. More well-designed and adequately-powered studies are needed. They should follow a standardised intervention protocol, adopt objective outcome measurement since blinding may be difficult to achieve, and make efforts to minimise loss to follow-up. Furthermore, studies in people from a wide range of ethnicities and with a wide range of dietary habits, as well as studies in low- and middle-income countries, are needed.

Serum metabolomics analysis reveals amelioration effects of sea cucumber ether phospholipids on oxidative stress and inflammation in high-fat diet-fed mice

Emerging evidence suggests that sea cucumber ether phospholipids (ether-PLs) can modulate high-fat diet (HFD)-induced metabolic disorders. However, whether this modulation is associated with metabolic pathways related to oxidative stress and inflammation remains unclear. This study aimed to investigate the antioxidative and anti-inflammatory effects on HFD-fed mice and the associated metabolism pathways in response to administration with sea cucumber ether-PLs using integrated biochemistry and a metabolomics approach. Biochemistry analysis and histological examinations showed that sea cucumber ether-PLs significantly decreased body weight gain and fat deposition in tissues. PE-P was superior to PC-O in alleviating reactive oxygen species (ROS), malondialdehyde (MDA) and inflammatory responses (IL-6, TNF-α and MCP-1) in the HFD-induced mouse model. Serum metabolomics analysis revealed that it upregulated four metabolites and downregulated twenty-four metabolites compared to those in HFD mice after ether-PL administration. Pathway analysis indicated that sea cucumber ether-PLs alleviate the HFD-induced inflammation and oxidative stress by three main metabolic pathways, namely fatty acid metabolism, branched-chain amino acid (BCAA) metabolism, and trichloroacetic acid (TCA) metabolism. Taken together, sea cucumber ether-PLs showed great potential to become a natural functional food against oxidative stress and inflammation caused by HFD.

The Effect of Dietary Carbohydrate and Fat Manipulation on the Metabolome and Markers of Glucose and Insulin Metabolism: A Randomised Parallel Trial

High carbohydrate, lower fat (HCLF) diets are recommended to reduce cardiometabolic disease (CMD) but low carbohydrate high fat (LCHF) diets can be just as effective. The effect of LCHF on novel insulin resistance biomarkers and the metabolome has not been fully explored. The aim of this study was to investigate the impact of an ad libitum 8-week LCHF diet compared with a HCLF diet on CMD markers, the metabolome, and insulin resistance markers. n = 16 adults were randomly assigned to either LCHF (n = 8, <50 g CHO p/day) or HCLF diet (n = 8) for 8 weeks. At weeks 0, 4 and 8, participants provided fasted blood samples, measures of body composition, blood pressure and dietary intake. Samples were analysed for markers of cardiometabolic disease and underwent non-targeted metabolomic profiling. Both a LCHF and HCLF diet significantly (p < 0.01) improved fasting insulin, HOMA IR, rQUICKI and leptin/adiponectin ratio (p < 0.05) levels. Metabolomic profiling detected 3489 metabolites with 78 metabolites being differentially regulated, for example, an upregulation in lipid metabolites following the LCHF diet may indicate an increase in lipid transport and oxidation, improving insulin sensitivity. In conclusion, both diets may reduce type 2 diabetes risk albeit, a LCHF diet may enhance insulin sensitivity by increasing lipid oxidation.

Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling

The quality of automatic metabolite profiling in NMR datasets from complex matrices can be affected by the numerous sources of variability. These sources, as well as the presence of multiple low-intensity signals, cause uncertainty in the metabolite signal parameters. Lineshape fitting approaches often produce suboptimal resolutions to adapt them in a complex spectrum lineshape. As a result, the use of software tools for automatic profiling tends to be restricted to specific biological matrices and/or sample preparation protocols to obtain reliable results. However, the analysis and modelling of the signal parameters collected during initial iteration can be further optimized to reduce uncertainty by generating narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted, and the performance of automatic profiling can be maximized. Our proposed workflow can learn and model the sample properties; therefore, restrictions in the biological matrix, or sample preparation protocol, and limitations of lineshape fitting approaches can be overcome.

Associations of plasma glycerophospholipid profile with modifiable lifestyles and incident diabetes in middle-aged and older Chinese

AIMS/HYPOTHESIS

Glycerophospholipid (GPL) perturbance was linked to the pathogenesis of diabetes in animal studies but prospective studies in humans are rare, particularly in Asians. We aimed to investigate the associations between plasma GPLs and incident diabetes and to explore effects of lifestyle on the associations in a Chinese population.

METHODS

The study included 1877 community-dwelling Chinese individuals aged 50-70 years (751 men and 1126 women), free of diabetes at baseline and followed for 6 years. A total of 160 GPL species were quantified in plasma at baseline by using high-throughput targeted lipidomics. Log-Poisson regression was used to assess the associations between GPLs and incidence of diabetes.

RESULTS

Over the 6 years of follow-up, 499 participants (26.6%) developed diabetes. After multivariable adjustment, eight GPLs were positively associated with incident diabetes (RR_{per SD} 1.13-1.25; all false-discovery rate [FDR]-corrected p < 0.05), including five novel GLPs, namely phosphatidylcholines (PCs; 16:0/18:1, 18:0/16:1, 18:1/20:3), lysophosphatidylcholine (LPC; 20:3) and phosphatidylethanolamine (PE; 16:0/16:1), and three reported GPLs (PCs 16:0/16:1, 16:0/20:3 and 18:0/20:3). In network analysis, a PC-containing module was positively associated with incident diabetes (RR_{per SD} 1.16 [95% CI 1.06, 1.26]; FDR-corrected p < 0.05). Notably, three of the diabetes-associated PCs (16:0/16:1, 16:0/18:1 and 18:0/16:1) and PE (16:0/16:1) were associated not only with fatty acids in the de novo lipogenesis (DNL) pathway, especially 16:1n-7 (Spearman correlation coefficients = 0.35-0.62, p < 0.001), but also with an unhealthy dietary pattern high in refined grains and low in fish, dairy and soy products (|factor loadings| ≥0.2). When stratified by physical activity levels, the associations of the eight GPLs and the PC module with incident diabetes were stronger in participants with lower physical activity (RR_{per SD} 1.24-1.49, FDR-corrected p < 0.05) than in those with the median and higher physical activity levels (RR_{per SD} 1.03-1.12, FDR-corrected p ≥ 0.05; FDR-corrected p_interaction < 0.05).

CONCLUSIONS/INTERPRETATION

Eight GPLs, especially PCs associated with the DNL pathway, were positively associated with incident diabetes in a cohort of Chinese men and women. The associations were most prominent in participants with a low level of physical activity.

Mendelian Randomization Identifies the Potential Causal Impact of Dietary Patterns on Circulating Blood Metabolites

Nutrition plays an important role in the development and progress of several health conditions, but the exact mechanism is often still unclear. Blood metabolites are likely candidates to be mediating these relationships, as their levels are strongly dependent on the frequency of consumption of several foods/drinks. Understanding the causal effect of food on metabolites is thus of extreme importance. To establish these effects, we utilized two-sample Mendelian randomization using the genetic variants associated with dietary traits as instrumental variables. The estimates of single-nucleotide polymorphisms' effects on exposures were obtained from a recent genome-wide association study (GWAS) of 25 individual and 15 principal-component dietary traits, whereas the ones for outcomes were obtained from a GWAS of 123 blood metabolites measured by nuclear magnetic resonance spectroscopy. We identified 413 potentially causal links between food and metabolites, replicating previous findings, such as the association between increased oily fish consumption and higher DHA, and highlighting several novel associations. Most of the associations were related to very-low-density, intermediate-density (IDL), and low-density lipoproteins (LDL). For example, we found that constituents of IDL particles and large LDL particles were raised by coffee and alcohol while lowered by an overall healthier diet and fruit consumption. Our findings provide a strong base of evidence for planning future RCTs aimed at understanding the role of diet in determining blood metabolite levels.

A Scoping Review of the Application of Metabolomics in Nutrition Research: The Literature Survey 2000-2019

Nutrimetabolomics is an emerging field in nutrition research, and it is expected to play a significant role in deciphering the interaction between diet and health. Through the development of omics technology over the last two decades, the definition of food and nutrition has changed from sources of energy and major/micro-nutrients to an essential exposure factor that determines health risks. Furthermore, this new approach has enabled nutrition research to identify dietary biomarkers and to deepen the understanding of metabolic dynamics and the impacts on health risks. However, so far, candidate markers identified by metabolomics have not been clinically applied and more efforts should be made to validate those. To help nutrition researchers better understand the potential of its application, this scoping review outlined the historical transition, recent focuses, and future prospects of the new realm, based on trends in the number of human research articles from the early stage of 2000 to the present of 2019 by searching the Medical Literature Analysis and Retrieval System Online (MEDLINE). Among them, objective dietary assessment, metabolic profiling, and health risk prediction were positioned as three of the principal applications. The continued growth will enable nutrimetabolomics research to contribute to personalized nutrition in the future.

Circulating Metabolites Associated with Body Fat and Lean Mass in Adults with Overweight/Obesity

The interplay between fat mass and lean mass within human metabolism is not completely understood. We aimed to identify specific circulating metabolomic profiles associated with these body composition compartments. Cross-sectional analyses were conducted over 236 adults with overweight/obesity from the Satiety Innovation (SATIN) study. Body composition was assessed by dual-energy X-ray absorptiometry. A targeted multiplatform metabolite profiling approach was applied. Associations between 168 circulating metabolites and the body composition measures were assessed using elastic net regression analyses. The accuracy of the multimetabolite weighted models was evaluated using a 10-fold cross-validation approach and the Pearson's correlation coefficients between metabolomic profiles and body compartments were estimated. Two different profiles including 86 and 65 metabolites were selected for % body fat and lean mass. These metabolites mainly consisted of lipids (sphingomyelins, phosphatidylcholines, lysophosphatidylcholines), acylcarnitines, and amino acids. Several metabolites overlapped between these body composition measures but none of them towards the same direction. The Pearson correlation coefficients between the metabolomic profiles and % body fat or lean mass were 0.80 and 0.79, respectively. Our findings suggest alterations in lipid metabolism, fatty acid oxidation, and protein degradation with increased adiposity and decreased lean body mass. These findings could help us to better understand the interplay between body composition compartments with human metabolic processes.

Nutrigenomics: lessons learned and future perspectives

The omics technologies of metabolomics, transcriptomics, proteomics, and metagenomics are playing an increasingly important role in nutrition science. With the emergence of the concept of precision nutrition and the need to understand individual responses to dietary interventions, it is an opportune time to examine the impact of these tools to date in human nutrition studies. Advances in our mechanistic understanding of dietary interventions were realized through incorporation of metabolomics, proteomics, and, more recently, metagenomics. A common observation across the studies was the low intra-individual variability of the omics measurements and the high inter-individual variation. Harnessing this data for use in the development of precision nutrition will be important. Metabolomics in particular has played a key role in the development of biomarkers of food intake in an effort to enhance the accuracy of dietary assessments. Further work is needed to realize the full potential of such biomarkers and to demonstrate integration with current strategies, with the goal of overcoming the well-established limitations of self-reported approaches. Although many of the nutrigenomic studies performed to date were labelled as proof-of-concept or pilot studies, there is ample evidence to support the use of these technologies in nutrition science. Incorporating omic technologies from the start of study designs will ensure that studies are sufficiently powered for such data. Furthermore, multi-disciplinary collaborations are likely to become even more important to aid analyses and interpretation of the data.

Plasma Metabolomic Profiles of Glycemic Index, Glycemic Load, and Carbohydrate Quality Index in the PREDIMED Study

BACKGROUND

The quality of carbohydrate consumed, assessed by the glycemic index (GI), glycemic load (GL), or carbohydrate quality index (CQI), affects the postprandial glycemic and insulinemic responses, which have been implicated in the etiology of several chronic diseases. However, it is unclear whether plasma metabolites involved in different biological pathways could provide functional insights into the role of carbohydrate quality indices in health.

OBJECTIVES

We aimed to identify plasma metabolomic profiles associated with dietary GI, GL, and CQI.

METHODS

The present study is a cross-sectional analysis of 1833 participants with overweight/obesity (mean age = 67 y) from 2 case-cohort studies nested within the PREDIMED (Prevención con Dieta Mediterránea) trial. Data extracted from validated FFQs were used to estimate the GI, GL, and CQI. Plasma concentrations of 385 metabolites were profiled with LC coupled to MS and associations of these metabolites with those indices were assessed with elastic net regression analyses.

RESULTS

A total of 58, 18, and 57 metabolites were selected for GI, GL, and CQI, respectively. Choline, cotinine, γ-butyrobetaine, and 36:3 phosphatidylserine plasmalogen were positively associated with GI and GL, whereas they were negatively associated with CQI. Fructose-glucose-galactose was negatively and positively associated with GI/GL and CQI, respectively. Consistent associations of 21 metabolites with both GI and CQI were found but in opposite directions. Negative associations of kynurenic acid, 22:1 sphingomyelin, and 38:6 phosphatidylethanolamine, as well as positive associations of 32:1 phosphatidylcholine with GI and GL were also observed. Pearson correlation coefficients between GI, GL, and CQI and the metabolomic profiles were 0.30, 0.22, and 0.27, respectively.

CONCLUSIONS

The GI, GL, and CQI were associated with specific metabolomic profiles in a Mediterranean population at high cardiovascular disease risk. Our findings may help in understanding the role of dietary carbohydrate indices in the development of cardiometabolic disorders. This trial was registered at isrctn.com as ISRCTN35739639.

Mass spectrometry-based lipidomics in food science and nutritional health: A comprehensive review

With the advance in science and technology as well as the improvement of living standards, the function of food is no longer just to meet the needs of survival. Food science and its associated nutritional health issues have been increasingly debated. Lipids, as complex metabolites, play a key role both in food and human health. Taking advantages of mass spectrometry (MS) by combining its high sensitivity and accuracy with extensive selective determination of all lipid classes, MS-based lipidomics has been employed to resolve the conundrum of addressing both qualitative and quantitative aspects of high-abundance and low-abundance lipids in complex food matrices. In this review, we systematically summarize current applications of MS-based lipidomics in food field. First, common MS-based lipidomics procedures are described. Second, the applications of MS-based lipidomics in food science, including lipid composition characterization, adulteration, traceability, and other issues, are discussed. Third, the application of MS-based lipidomics for nutritional health covering the influence of food on health and disease is introduced. Finally, future research trends and challenges are proposed. MS-based lipidomics plays an important role in the field of food science, promoting continuous development of food science and integration of food knowledge with other disciplines. New methods of MS-based lipidomics have been developed to improve accuracy and sensitivity of lipid analysis in food samples. These developments offer the possibility to fully characterize lipids in food samples, identify novel functional lipids, and better understand the role of food in promoting healt.

Starch sources influence lipidaemia of diabetic dogs

BACKGROUND

Hyperlipidaemia is considered a cause of other diseases that are clinically important and potentially life threatening. Combination of pea and barley as exclusive starch sources is known to interfere with glycemic control in diabetic dogs, but their effect on lipid profile of hiperlipidaemic dogs is yet to be evaluated. Twelve adult diabetic dogs were fed three dry extruded diets with different starch sources and different fat levels: peas and barley (PB), maize (Mi), and peas, barley and rice (Ba) with 15.7, 15.6 and 9.0% of their dry matter as fat, respectively. Plasmatic cholesterol and triglycerides concentration curves over 10 h were obtained after 60 days on each diet and with the same NPH insulin dose. ANOVA test or Friedman test were used to compare the dietary effects on triglycerides and cholesterol variables among the diets.

RESULTS

Dogs presented lower mean (p = 0.05), fasting (p = 0.03), and time 8-h postprandial (p = 0.05) triglyceridemia after PB diet period than Ba diet period and time 4-h postprandial (p = 0.02) lower after PB than Mi diet. Cholesterolemia mean, minimum, maximum, area under the cholesterol curve and times points: 2, 4, 6, 8 and 10-h postprandial, had lower values after PB ingestion in comparison to Mi, without difference to Ba diet.

CONCLUSION

Inclusion of pea and barley, as exclusive starch sources, in therapeutic diets for diabetic dogs can minimize plasmatic triglycerides and cholesterol concentration at fasting and at different postprandial time, compared to the maize diet or diet with lesser fat content.

Impact of a High Protein Intake on the Plasma Metabolome in Elderly Males: 10 Week Randomized Dietary Intervention

High protein diets may improve the maintenance of skeletal muscle mass in the elderly, although it remains less clear what broader impact such diets have on whole body metabolic regulation in the elderly. Non-targeted polar metabolomics analysis using HILIC HPLC-MS was used to profile the circulating plasma metabolome of elderly men (n = 31; 74.7 ± 4.0 years) who were randomized to consume for 10 weeks a diet designed to achieve either protein (RDA; 0.8·g⁻¹·kg⁻¹) or that doubled this recommend intake (2RDA; 1.6.g.kg⁻¹). A limited number of plasma metabolites (n = 24) were significantly differentially regulated by the diet. These included markers of protein anabolism, which increased by the 2RDA diet, including; urea, creatine, and glutarylcarnitine. Whilst in response to the RDA diet; glutamine, glutamic acid, and proline were increased, relative to the 2RDA diet (p < 0.05). Metaboanalyst identified six major metabolic pathways to be influenced by the quantity of protein intake, most notably the arginine and proline pathways. Doubling of the recommended protein intake in older males over 10 weeks exerted only a limited impact on circulating metabolites, as determined by LC-MS. This metabolomic response was almost entirely due to increased circulating abundances of metabolites potentially indicative of altered protein anabolism, without evidence of impact on pathways for metabolic health.

Trial Registration: This trial was registered on 3rd March 2016 at the Australia New Zealand Clinical Trial Registry (www.anzctr.org.au) at ACTRN 12616000310460.

Plasma metabolomics profiles suggest beneficial effects of a low-glycemic load dietary pattern on inflammation and energy metabolism

BACKGROUND

Low-glycemic load dietary patterns, characterized by consumption of whole grains, legumes, fruits, and vegetables, are associated with reduced risk of several chronic diseases.

METHODS

Using samples from a randomized, controlled, crossover feeding trial, we evaluated the effects on metabolic profiles of a low-glycemic whole-grain dietary pattern (WG) compared with a dietary pattern high in refined grains and added sugars (RG) for 28 d. LC-MS-based targeted metabolomics analysis was performed on fasting plasma samples from 80 healthy participants (n = 40 men, n = 40 women) aged 18-45 y. Linear mixed models were used to evaluate differences in response between diets for individual metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG)-defined pathways and 2 novel data-driven analyses were conducted to consider differences at the pathway level.

RESULTS

There were 121 metabolites with detectable signal in >98% of all plasma samples. Eighteen metabolites were significantly different between diets at day 28 [false discovery rate (FDR) < 0.05]. Inositol, hydroxyphenylpyruvate, citrulline, ornithine, 13-hydroxyoctadecadienoic acid, glutamine, and oxaloacetate were higher after the WG diet than after the RG diet, whereas melatonin, betaine, creatine, acetylcholine, aspartate, hydroxyproline, methylhistidine, tryptophan, cystamine, carnitine, and trimethylamine were lower. Analyses using KEGG-defined pathways revealed statistically significant differences in tryptophan metabolism between diets, with kynurenine and melatonin positively associated with serum C-reactive protein concentrations. Novel data-driven methods at the metabolite and network levels found correlations among metabolites involved in branched-chain amino acid (BCAA) degradation, trimethylamine-N-oxide production, and β oxidation of fatty acids (FDR < 0.1) that differed between diets, with more favorable metabolic profiles detected after the WG diet. Higher BCAAs and trimethylamine were positively associated with homeostasis model assessment-insulin resistance.

CONCLUSIONS

These exploratory metabolomics results support beneficial effects of a low-glycemic load dietary pattern characterized by whole grains, legumes, fruits, and vegetables, compared with a diet high in refined grains and added sugars on inflammation and energy metabolism pathways. This trial was registered at clinicaltrials.gov as NCT00622661.

Plasma metabolites associated with homeostatic model assessment of insulin resistance: metabolite-model design and external validation

Different plasma metabolites have been related to insulin resistance (IR). However, there is a lack of metabolite models predicting IR with external validation. The aim of this study is to identify a multi-metabolite model associated to the homeostatic model assessment (HOMA)-IR values. We performed a cross-sectional metabolomics analysis of samples collected from overweight and obese subjects from two independent studies. The training step was performed in 236 subjects from the SATIN study and validated in 102 subjects from the GLYNDIET study. Plasma metabolomics profile was analyzed using three different approaches: GC/quadrupole-TOF, LC/quadrupole-TOF, and nuclear magnetic resonance (NMR). Associations between metabolites and HOMA-IR were assessed using elastic net regression analysis with a leave-one-out cross validation (CV) and 100 CV runs. HOMA-IR was analyzed both as linear and categorical (median or lower versus higher than the median). Receiver operating characteristic curves were constructed based on metabolites’ weighted models. A set of 30 metabolites discriminating extremes of HOMA-IR were consistently selected. These metabolites comprised some amino acids, lipid species and different organic acids. The area under the curve (AUC) for the discrimination between HOMA-IR extreme categories was 0.82 (95% CI: 0.74–0.90), based on the multi-metabolite model weighted with the regression coefficients of metabolites in the validation dataset. We identified a set of metabolites discriminating between extremes of HOMA-IR and able to predict HOMA-IR with high accuracy.

Effect of a low glycaemic index diet during pregnancy on maternal and cord blood metabolomic profiles: results from the ROLO randomized controlled trial

Background

Elevated post-prandial blood glucose during pregnancy has been associated with adverse pregnancy and offspring outcomes, such as maternal gestational diabetes and excessive foetal growth. The ROLO Study is a randomized controlled trial (RCT) investigating the effect of a low glycaemic index (GI) diet in pregnancy to prevent foetal macrosomia (birth weight > 4000 g). We described the impact of a low-GI diet on the maternal and feto-placental unit metabolism by studying how the ROLO intervention affected maternal and cord blood metabolomes.

Methods

Fasting maternal plasma samples pre- and post-intervention of 51 pregnant women and 132 cord blood samples were measured with a targeted metabolomics approach using liquid-chromatography coupled to tandem mass spectrometry. The differences between RCT groups were explored via multivariate models with covariates correction. Significance was set at Bonferroni-corrected level of 0.05.

Results

A total of 262 metabolites species, sums and ratios were investigated. While no metabolite reached statistical significance after Bonferroni correction, many maternal phospholipids and acylcarnitines were elevated in the intervention group at uncorrected 0.05 alpha level. Most species contained saturated and monounsaturated fatty acid chains with 16 or 18 carbon atoms. In cord blood, no differences were identified between RCT groups.

Conclusions

A low-GI diet in pregnancy was associated with a trend to modest but consistent changes in maternal lipid and fatty acid metabolism. The intervention seemed not to affect foetal metabolism. Our exploratory findings may be used to direct further investigations about low GI diets before and during pregnancy, to improve patient care for pre-conceptional and pregnant women with lipid dysregulations and potentially modulate the offspring's risk for future metabolic diseases.

Trial registration

Current Controlled Trials ISRCTN54392969.

Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies

INTRODUCTION

Obesity is a disorder characterized by a disproportionate increase in body weight in relation to height, mainly due to the accumulation of fat, and is considered a pandemic of the present century by many international health institutions. It is associated with several non-communicable chronic diseases, namely, metabolic syndrome, type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), and cancer. Metabolomics is a useful tool to evaluate changes in metabolites due to being overweight and obesity at the body fluid and cellular levels and to ascertain metabolic changes in metabolically unhealthy overweight and obese individuals (MUHO) compared to metabolically healthy individuals (MHO).

OBJECTIVES

We aimed to conduct a systematic review (SR) of human studies focused on identifying metabolomic signatures in obese individuals and obesity-related metabolic alterations, such as inflammation or oxidative stress.

METHODS

We reviewed the literature to identify studies investigating the metabolomics profile of human obesity and that were published up to May 7th, 2019 in SCOPUS and PubMed through an SR. The quality of reporting was evaluated using an adapted of QUADOMICS.

RESULTS

Thirty-three articles were included and classified according to four types of approaches. (i) studying the metabolic signature of obesity, (ii) studying the differential responses of obese and non-obese subjects to dietary challenges (iii) studies that used metabolomics to predict weight loss and aimed to assess the effects of weight loss interventions on the metabolomics profiles of overweight or obese human subjects (iv) articles that studied the effects of specific dietary patterns or dietary compounds on obesity-related metabolic alterations in humans.

CONCLUSION

The present SR provides state-of-the-art information about the use of metabolomics as an approach to understanding the dynamics of metabolic processes involved in human obesity and emphasizes metabolic signatures related to obesity phenotypes.