Metabolomics reveals impaired maturation of HDL particles in adolescents with hyperinsulinaemic androgen excess

Obesity and Dyslipidemia: A Review of Current Evidence

PURPOSE OF REVIEW

Obesity is accompanied by atherogenic dyslipidemia, a specific lipid disorder characterized by both quantitative and qualitative changes of plasma lipoproteins. The main alterations in the lipid profile include hypertriglyceridemia, reduced high-density lipoprotein (HDL) cholesterol level, and elevated small dense low-density lipoprotein (LDL) particles. Epidemiological data show that obesity is more common in women and is a frequent risk factor for reproductive disorders, metabolic complications in pregnancy, and cardiometabolic disease later in life. The aim of this narrative review is to discuss recent advances in the research of dyslipidemia in obesity, with an emphasis on female-specific disorders and cardiometabolic risk.

RECENT FINDINGS

The focus of current research on dyslipidemia in obesity is moving toward structurally and functionally modified plasma lipoproteins. Special attention is paid to the pro-atherogenic role of triglyceride-rich lipoproteins and their remnants. Introduction of advanced analytical techniques enabled identification of novel lipid biomarkers with potential clinical applications. In particular, proteomic and lipidomic studies have provided significant progress in the comprehensive research of HDL's alterations in obesity. Obesity-related dyslipidemia is a widespread metabolic disturbance in polycystic ovary syndrome patients and high-risk pregnancies, but is seldom evaluated with respect to its impact on future cardiometabolic health. Obesity and associated cardiometabolic diseases require a more depth insight into the quality of lipoprotein particles. Further application of omics-based techniques would enable a more comprehensive evaluation of dyslipidemia in order to reduce an excessive cardiovascular risk attributable to increased body weight. However, more studies on obesity-related female reproductive disorders are needed for this approach to be adopted in daily clinical practice.

MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data

BACKGROUND

Modern mass spectrometry has revolutionized the detection and analysis of metabolites but likewise, let the data skyrocket with repositories for metabolomics data filling up with thousands of datasets. While there are many software tools for the analysis of individual experiments with a few to dozens of chromatograms, we see a demand for a contemporary software solution capable of processing and analyzing hundreds or even thousands of experiments in an integrative manner with standardized workflows.

RESULTS

Here, we introduce MetHoS as an automated web-based software platform for the processing, storage and analysis of great amounts of mass spectrometry-based metabolomics data sets originating from different metabolomics studies. MetHoS is based on Big Data frameworks to enable parallel processing, distributed storage and distributed analysis of even larger data sets across clusters of computers in a highly scalable manner. It has been designed to allow the processing and analysis of any amount of experiments and samples in an integrative manner. In order to demonstrate the capabilities of MetHoS, thousands of experiments were downloaded from the MetaboLights database and used to perform a large-scale processing, storage and statistical analysis in a proof-of-concept study.

CONCLUSIONS

MetHoS is suitable for large-scale processing, storage and analysis of metabolomics data aiming at untargeted metabolomic analyses. It is freely available at: https://methos.cebitec.uni-bielefeld.de/ . Users interested in analyzing their own data are encouraged to apply for an account.

CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network

Motivation

The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, e.g. how many metabolites are there in a given sample.

Results

Here, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools.

Availability and implementation

https://CRAN.R-project.org/package=cliqueMS and https://github.com/osenan/cliqueMS.

Supplementary information

Supplementary data are available at Bioinformatics online.

Metabolomic change due to combined treatment with myo-inositol, D-chiro-inositol and glucomannan in polycystic ovarian syndrome patients: a pilot study

Background

Polycystic ovarian syndrome (PCOS) is a highly variable syndrome and one of the most common female endocrine disorders. Although the association inositols-glucomannan may represent a good therapeutic strategy in the treatment of PCOS women with insulin resistance, the effect of inositols on the metabolomic profile of these women has not been described yet.

Results

Fifteen PCOS-patients and 15 controls were enrolled. Patients were treated with myo-inositol (1.75 g/day), D-chiro-inositol (0.25 g/day) and glucomannan (4 g/day) for 3 months. Blood concentrations of glucose, insulin, triglycerides and cholesterol, and ovary volumes and antral follicles count, as well as metabolomic profiles, were evaluated for control subjects and for cases before and after treatment.

PCOS-patients had higher BMI compared with Controls, BMI decreased significantly after 3 months of treatment although it remained significantly higher compared to controls. 3-methyl-1-hydroxybutyl-thiamine-diphosphate, valine, phenylalanine, ketoisocapric, linoleic, lactic, glyceric, citric and palmitic acid, glucose, glutamine, creatinine, arginine, choline and tocopherol emerged as the most relevant metabolites for distinguishing cases from controls.

Conclusion

Our pilot study has identified a complex network of serum molecules that appear to be correlated with PCOS, and with a combined treatment with inositols and glucomannan.

Trial registration

ClinicalTial.gov, NCT03608813. Registered 1st August 2018 - Retrospectively registered, .

Electronic supplementary material

The online version of this article (10.1186/s13048-019-0500-x) contains supplementary material, which is available to authorized users.

Minor lipids profiling in subcutaneous and epicardial fat tissue using LC/MS with an optimized preanalytical phase

Analysis of bioactive lipids in adipose tissue could lead to better understanding of the pathogenesis of obesity and its complications. However, current MS methods are limited by a high content of triacylglycerols (TAGs), which markedly surpasses the amount of other lipids and suppresses their ionization. The aim of our study was thus to optimize the preanalytical phase of lipid analysis in adipose tissue, focusing in particular on less-abundant lipids. Next, the optimized method was used to describe the differences between epicardial and subcutaneous adipose tissues obtained from patients undergoing cardiac surgery. Lipids were extracted using a modified Folch method with subsequent detachment of TAGs by thin layer chromatography (TLC). The extracts with/without TAGs were analyzed by tandem LC/MS. The repeatability of the presented method expressed by the median of the coefficients of variation was 12/5% for analysis with/without TAGs separation, respectively. The difference in the relative abundance of TAGs gained with/without TLC was, on average, 19% and did not reach significance (p value > 0.05) for any identified TAG. The novel preanalytical step allowed us to detect 37 lipids, which could not have been detected without TAG separation, because their signal to noise ratio is <5 in current methods of untargeted lipidomics. These lipids belong predominately to ceramides, glycerophosphatidylserines, glycerophosphatidylinsitols, sphingomyelins, glycerophosphatidylcholines, glycerophosphatidylethanolamines, diacylglycerols. The two adipose tissue depots differed mainly in the following lipid classes: glycerophosphatidylcholines, glycerophosphatidylinositols, glycerophosphatidylethanolamine, and sphingomyelins. Moreover, other major lipids showed distinctly different distributions between the two adipose tissues. Among these, the changes in TAGs were the most striking, which correspond to previously published data describing the differences between omental and subcutaneous adipose tissue. Implementation of the TLC step for the elimination of TAGs was crucial for enhancing the MS detection limit of minor lipids in adipose tissue. The differences between the overall lipid profiles of subcutaneous and epicardial tissue reflect their different functions arising from their location.

A metabolome-wide characterization of the diabetic phenotype in ZDF rats and its reversal by pioglitazone

Type 2 diabetes (T2D) is a complex metabolic disease associated with alterations in glucose, lipid and protein metabolism. In order to characterize the biochemical phenotype of the Zucker diabetic fatty (ZDF) rat, the most common animal model for the study of T2D, and the impact of the insulin sensitizer pioglitazone, a global, mass spectrometry-based analysis of the metabolome was conducted. Overall, 420 metabolites in serum, 443 in the liver and 603 in the intestine were identified at study end. In comparison to two control groups, obese diabetic ZDF rats showed characteristic metabolic signatures that included hyperglycemia, elevated β-oxidation, dyslipidemia—featured by an increase in saturated and monounsaturated fatty acids and a decrease of medium chain and of polyunsaturated fatty acids in serum–and decreased amino acid levels, consistent with their utilization in hepatic gluconeogenesis. A 13-week treatment with the PPARγ agonist pioglitazone reversed most of these signatures: Pioglitazone improved glycemic control and the fatty acid profile, elevated amino acid levels in the liver, but decreased branched chain amino acids in serum. The hitherto most comprehensive metabolic profiling study identified a biochemical blueprint for the ZDF diabetic model and captured the impact of genetic, nutritional and pharmacological perturbations. The in-depth characterization on the molecular level deepens the understanding and further validates the ZDF rat as a suitable preclinical model of diabetes in humans.

Joint Bounding of Peaks Across Samples Improves Differential Analysis in Mass Spectrometry-Based Metabolomics

As mass spectrometry-based metabolomics becomes more widely used in biomedical research, it is important to revisit existing data analysis paradigms. Existing data preprocessing efforts have largely focused on methods which start by extracting features separately from each sample, followed by a subsequent attempt to group features across samples to facilitate comparisons. We show that this preprocessing approach leads to unnecessary variability in peak quantifications that adversely impacts downstream analysis. We present a new method, bakedpi, for the preprocessing of both centroid and profile mode metabolomics data that relies on an intensity-weighted bivariate kernel density estimation on a pooling of all samples to detect peaks. This new method reduces this unnecessary quantification variability and increases power in downstream differential analysis.

eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics

Gas chromatography coupled to mass spectrometry (GC/MS) has been a long-standing approach used for identifying small molecules due to the highly reproducible ionization process of electron impact ionization (EI). However, the use of GC-EI MS in untargeted metabolomics produces large and complex data sets characterized by coeluting compounds and extensive fragmentation of molecular ions caused by the hard electron ionization. In order to identify and extract quantitative information on metabolites across multiple biological samples, integrated computational workflows for data processing are needed. Here we introduce eRah, a free computational tool written in the open language R composed of five core functions: (i) noise filtering and baseline removal of GC/MS chromatograms, (ii) an innovative compound deconvolution process using multivariate analysis techniques based on compound match by local covariance (CMLC) and orthogonal signal deconvolution (OSD), (iii) alignment of mass spectra across samples, (iv) missing compound recovery, and (v) identification of metabolites by spectral library matching using publicly available mass spectra. eRah outputs a table with compound names, matching scores and the integrated area of compounds for each sample. The automated capabilities of eRah are demonstrated by the analysis of GC-time-of-flight (TOF) MS data from plasma samples of adolescents with hyperinsulinaemic androgen excess and healthy controls. The quantitative results of eRah are compared to centWave, the peak-picking algorithm implemented in the widely used XCMS package, MetAlign, and ChromaTOF software. Significantly dysregulated metabolites are further validated using pure standards and targeted analysis by GC-triple quadrupole (QqQ) MS, LC-QqQ, and NMR. eRah is freely available at http://CRAN.R-project.org/package=erah .

Urinary metabolomic profiling in mice with diet-induced obesity and type 2 diabetes mellitus after treatment with metformin, vildagliptin and their combination

Metformin, vildagliptin and their combination are widely used for the treatment of diabetes, but little is known about the metabolic responses to these treatments. In the present study, NMR-based metabolomics was applied to detect changes in the urinary metabolomic profile of a mouse model of diet-induced obesity in response to these treatments. Additionally, standard biochemical parameters and the expression of enzymes involved in glucose and fat metabolism were monitored. Significant correlations were observed between several metabolites (e.g., N-carbamoyl-β-alanine, N1-methyl-4-pyridone-3-carboxamide, N1-methyl-2-pyridone-5-carboxamide, glucose, 3-indoxyl sulfate, dimethylglycine and several acylglycines) and the area under the curve of glucose concentrations during the oral glucose tolerance test. The present study is the first to present N-carbamoyl-β-alanine as a potential marker of type 2 diabetes mellitus and consequently to demonstrate the efficacies of the applied antidiabetic interventions. Moreover, the elevated acetate level observed after vildagliptin administration might reflect increased fatty acid oxidation.

The improvement of large High-Density Lipoprotein (HDL) particle levels, and presumably HDL metabolism, depend on effects of low-carbohydrate diet and weight loss

Depressed levels of atheroprotective large HDL particles are common in obesity and cardiovascular disease (CVD). Increases in large HDL particles are favourably associated with reduced CVD event risk and coronary plaque burden. The objective of the study is to compare the effectiveness of low-carbohydrate diets and weight loss for increasing blood levels of large HDL particles at 1 year. This study was performed by screening for body mass index (BMI) and metabolic syndrome in 160 consecutive subjects referred to our out-patient Metabolic Unit in South Italy. We administered dietary advice to four small groups rather than individually. A single team comprised of a dietitian and physician administered diet-specific advice to each group. Large HDL particles at baseline and 1 year were measured using two-dimensional gel electrophoresis. Dietary intake was assessed via 3-day diet records. Although 1-year weight loss did not differ between diet groups (mean 4.4 %), increases in large HDL particles paralleled the degree of carbohydrate restriction across the four diets (p<0.001 for trend). Regression analysis indicated that magnitude of carbohydrate restriction (percentage of calories as carbohydrate at 1 year) and weight loss were each independent predictors of 1-year increases in large HDL concentration. Changes in HDL cholesterol concentration were modestly correlated with changes in large HDL particle concentration (r=0.47, p=.001). In conclusion, reduction of excess dietary carbohydrate and body weight improved large HDL levels. Comparison trials with cardiovascular outcomes are needed to more fully evaluate these findings.

The Consumption of Bicarbonate-Rich Mineral Water Improves Glycemic Control

Hot spring water and natural mineral water have been therapeutically used to prevent or improve various diseases. Specifically, consumption of bicarbonate-rich mineral water (BMW) has been reported to prevent or improve type 2 diabetes (T2D) in humans. However, the molecular mechanisms of the beneficial effects behind mineral water consumption remain unclear. To elucidate the molecular level effects of BMW consumption on glycemic control, blood metabolome analysis and fecal microbiome analysis were applied to the BMW consumption test. During the study, 19 healthy volunteers drank 500 mL of commercially available tap water (TW) or BMW daily. TW consumption periods and BMW consumption periods lasted for a week each and this cycle was repeated twice. Biochemical tests indicated that serum glycoalbumin levels, one of the indexes of glycemic controls, decreased significantly after BMW consumption. Metabolome analysis of blood samples revealed that 19 metabolites including glycolysis-related metabolites and 3 amino acids were significantly different between TW and BMW consumption periods. Additionally, microbiome analysis demonstrated that composition of lean-inducible bacteria was increased after BMW consumption. Our results suggested that consumption of BMW has the possible potential to prevent and/or improve T2D through the alterations of host metabolism and gut microbiota composition.