A Metabolomic Approach to Understanding the Metabolic Link between Obesity and Diabetes

Metabolic signatures of greater body size and their associations with risk of colorectal and endometrial cancers in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

The mechanisms underlying the obesity-cancer relationship are incompletely understood. This study aimed to characterise metabolic signatures of greater body size and to investigate their association with two obesity-related malignancies, endometrial and colorectal cancers, and with weight loss within the context of an intervention study.

METHODS

Targeted mass spectrometry metabolomics data from 4326 participants enrolled in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort and 17 individuals from a single-arm pilot weight loss intervention (Intercept) were used in this analysis. Metabolic signatures of body size were first determined in discovery (N = 3029) and replication (N = 1297) sets among EPIC participants by testing the associations between 129 metabolites and body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) using linear regression models followed by partial least squares analyses. Conditional logistic regression models assessed the associations between the metabolic signatures with endometrial (N = 635 cases and 648 controls) and colorectal (N = 423 cases and 423 controls) cancer risk using nested case-control studies in EPIC. Pearson correlation between changes in the metabolic signatures and weight loss was tested among Intercept participants.

RESULTS

After adjustment for multiple comparisons, greater BMI, WC, and WHR were associated with higher levels of valine, isoleucine, glutamate, PC aa C38:3, and PC aa C38:4 and with lower levels of asparagine, glutamine, glycine, serine, lysoPC C17:0, lysoPC C18:1, lysoPC C18:2, PC aa C42:0, PC ae C34:3, PC ae C40:5, and PC ae C42:5. The metabolic signature of BMI (OR1-sd 1.50, 95% CI 1.30-1.74), WC (OR1-sd 1.46, 95% CI 1.27-1.69), and WHR (OR1-sd 1.54, 95% CI 1.33-1.79) were each associated with endometrial cancer risk. Risk of colorectal cancer was positively associated with the metabolic signature of WHR (OR1-sd: 1.26, 95% CI 1.07-1.49). In the Intercept study, a positive correlation was observed between weight loss and changes in the metabolic signatures of BMI (r = 0.5, 95% CI 0.06-0.94, p = 0.03), WC (r = 0.5, 95% CI 0.05-0.94, p = 0.03), and WHR (r = 0.6, 95% CI 0.32-0.87, p = 0.01).

CONCLUSIONS

Obesity is associated with a distinct metabolic signature comprising changes in levels of specific amino acids and lipids which is positively associated with both colorectal and endometrial cancer and is potentially reversible following weight loss.

Obesity Connected Metabolic Changes in Type 2 Diabetic Patients Treated With Metformin

Metformin is widely used in the treatment of Type 2 Diabetes Mellitus (T2DM). However, it is known to have beneficial effects in many other conditions, including obesity and cancer. In this study, we aimed to investigate the metabolic effect of metformin in T2DM and its impact on obesity. A mass spectrometry (MS)-based metabolomics approach was used to analyze samples from two cohorts, including healthy lean and obese control, and lean as well as obese T2DM patients on metformin regimen in the last 6 months. The results show a clear group separation and sample clustering between the study groups due to both T2DM and metformin administration. Seventy-one metabolites were dysregulated in diabetic obese patients (30 up-regulated and 41 down-regulated), and their levels were unchanged with metformin administration. However, 30 metabolites were dysregulated (21 were up-regulated and 9 were down-regulated) and then restored to obese control levels by metformin administration in obese diabetic patients. Furthermore, in obese diabetic patients, the level of 10 metabolites was dysregulated only after metformin administration. Most of these dysregulated metabolites were dipeptides, aliphatic amino acids, nucleic acid derivatives, and urea cycle components. The metabolic pattern of 62 metabolites was persistent, and their levels were affected by neither T2DM nor metformin in obesity. Interestingly, 9 metabolites were significantly dysregulated between lean and obese cohorts due to T2DM and metformin regardless of the obesity status. These include arginine, citrulline, guanidoacetic acid, proline, alanine, taurine, 5-hydroxyindoleacetic acid, and 5-hydroxymethyluracil. Understanding the metabolic alterations taking place upon metformin treatment would shed light on possible molecular targets of metformin, especially in conditions like T2DM and obesity.

The Effect of Exogenous Free Nε-(Carboxymethyl)Lysine on Diabetic-Model Goto-Kakizaki Rats: Metabolomics Analysis in Serum and Urine

The current study investigated the effects of exogenous free N^ε-(carboxymethyl) lysine (CML) from daily diet on diabetic-model Goto-Kakizaki rats. Rats were fed with free CML (2 mg/kg body weight) for 8 weeks, then metabolomics evaluation was performed on serum and urine, and biochemical and histopathologic examinations were conducted to verify metabolic results. Diabetic rats fed with free CML showed significantly increased (P < 0.05) fasting blood glucose (11.1 ± 1.07 mmol/L) and homeostasis model assessment values (homeostatic model assessment of insulin resistance: 16.0 ± 4.24; homeostatic model assessment of beta cell function: 6.66 ± 2.01; and modified beta cell function index: 11.5 ± 2.66) and a significantly altered (P < 0.05) oxidative stress level when compared to the control group. Serum and urine metabolomics showed a significantly altered (P < 0.05) level of aminomalonic acid, 2-oxoadipic acid, l-malic acid, β-alanine, 2-oxoglutaric acid, d-threitol, N-acetyl-leucine, methylmalonic acid, l-cysteine, thymine, glycine, l-alanine, 4-hydroxyproline, hexadecane, succinic acid, l-ornithine, gluconolactone, maleic acid, l-lactate, tryptophan, 5-methoxyindoleacetate, γ-aminobutyric acid, homoserine, maltose, and quinolinic acid. Our results indicated that these metabolites altered by exposure to exogenous free CML were mapped to the citric acid cycle and amino acid and carbohydrate metabolism, which might be related to increased progression of diabetes and some other diabetic complications, including diabetic brain and neurological diseases, retinopathy, nephropathy, and impaired wound healing.

The methanolic extract of Garcinia atroviridis (MeGa) reduces body weight and food intake, and improves lipid profiles by altering the lipid metabolism: a rat model

Garcinia species are widely used for their slimming effects via increased fat burning and suppression of satiety. However, scientific evidence for the biological effects of Garcinia atroviridis (GA) is lacking. We investigated the phytochemical composition, safety profiles, and antioxidant and antiobesity effects of methanolic extracts of Garcinia atroviridis (MeGa) in obese female rats. Repeated dose toxicity studies were conducted according to the OECD guidelines. Upon sacrifice, haematological, biochemical, lipid profile, and serum-based metabolomics analyses were performed to evaluate metabolic expression changes and their related pathways. MeGa contains several phytochemical groups and GA fruit acids. MeGa was found to be nontoxic in both male and female rats with an oral lethal dose (LD50) of 2000 mg/kg. After 9 weeks of treatment, MeGa-treated obese rats had lower weight gain and better lipid profiles (cholesterol and triglyceride), which correlated with the altered metabolic pathways involved in the metabolism of lipid (glycerophospholipid) and biosynthesis of unsaturated fatty acid. In addition, MeGa caused differential metabolism pathways of arachidonic acid and tryptophan that affect the inflammatory response and suppression of appetite. We concluded that MeGa is safe, and its slimming effects are due to the differential metabolism of lipids.

Use of Metabolomic Profiling to Understand Variability in Adiposity Changes Following an Intentional Weight Loss Intervention in Older Adults

Inter-individual response to dietary interventions remains a major challenge to successful weight loss among older adults. This study applied metabolomics technology to identify small molecule signatures associated with a loss of fat mass and overall weight in a cohort of older adults on a nutritionally complete, high-protein diet. A total of 102 unique metabolites were measured using liquid chromatography-mass spectrometry (LC-MS) for 38 adults aged 65-80 years randomized to dietary intervention and 36 controls. Metabolite values were analyzed in both baseline plasma samples and samples collected following the six-month dietary intervention to consider both metabolites that could predict the response to diet and those that changed in response to diet or weight loss.Eight metabolites changed over the intervention at a nominally significant level: D-pantothenic acid, L-methionine, nicotinate, aniline, melatonin, deoxycarnitine, 6-deoxy-L-galactose, and 10-hydroxydecanoate. Within the intervention group, there was broad variation in the achieved weight-loss and dual-energy x-ray absorptiometry (DXA)-defined changes in total fat and visceral adipose tissue (VAT) mass. Change in the VAT mass was significantly associated with the baseline abundance of α-aminoadipate (p = 0.0007) and an additional mass spectrometry peak that may represent D-fructose, myo-inositol, mannose, α-D-glucose, allose, D-galactose, D-tagatose, or L-sorbose (p = 0.0001). This hypothesis-generating study reflects the potential of metabolomic biomarkers for the development of personalized dietary interventions.

Tracking the carbons supplying gluconeogenesis

As the burden of type 2 diabetes mellitus (T2DM) grows in the 21st century, the need to understand glucose metabolism heightens. Increased gluconeogenesis is a major contributor to the hyperglycemia seen in T2DM. Isotope tracer experiments in humans and animals over several decades have offered insights into gluconeogenesis under euglycemic and diabetic conditions. This review focuses on the current understanding of carbon flux in gluconeogenesis, including substrate contribution of various gluconeogenic precursors to glucose production. Alterations of gluconeogenic metabolites and fluxes in T2DM are discussed. We also highlight ongoing knowledge gaps in the literature that require further investigation. A comprehensive analysis of gluconeogenesis may enable a better understanding of T2DM pathophysiology and identification of novel targets for treating hyperglycemia.

Impact of Extreme Obesity and Diet-Induced Weight Loss on the Fecal Metabolome and Gut Microbiota

SCOPE

A limited number of human studies have characterized fecal microbiota and metabolome in extreme obesity and after diet-induced weight loss.

METHODS AND RESULTS

Fecal samples from normal-weight and extremely obese adults and from obese participants before and after moderate diet-induced weight loss are evaluated for their interaction with the intestinal adenocarcinoma cell line HT29 using an impedance-based in vitro model, which reveals variations in the interaction between the gut microbiota and host linked to obesity status. Microbiota composition, short chain fatty acids, and other intestinal metabolites are further analyzed to assess the interplay among diet, gut microbiota, and host in extreme obesity. Microbiota profiles are distinct between normal-weight and obese participants and are accompanied by fecal signatures in the metabolism of biliary compounds and catecholamines. Moderate diet-induced weight loss promotes shifts in the gut microbiota, and the primary fecal metabolomics features are associated with diet and the gut-liver and gut-brain axes.

CONCLUSIONS

Analyses of the fecal microbiota and metabolome enable assessment of the impact of diet on gut microbiota composition and activity, supporting the potential use of certain fecal metabolites or members of the gut microbiota as biomarkers for the efficacy of weight loss in extreme obesity.

Using liquid chromatography mass spectrometry (LC-MS) to assess the effect of age, high-fat diet, and rat strain on the liver metabolome

The goal of this study was to use liquid chromatography mass spectrometry to assess metabolic changes of two different diets in three distinct rat strains. Sprague-Dawley, Fischer 344, and Brown-Norway male rats were maintained on a high-fat, or regular diet for 24 weeks. Liver tissue was collected at 4, 12, and 24 weeks to assess global small molecule metabolite changes using high resolution accurate mass spectrometry coupled to ultra-high-performance liquid chromatography. The results of the global metabolomics analysis revealed significant changes based on both age and diet within all three strains. Principal component analysis revealed that the influence of diet caused a greater variation in significantly changing metabolites than that of age for the Brown Norway and Fisher 344 strains, whereas diet had the greatest influence in the Sprague Dawley strain only at the 4-week time point. As expected, metabolites involved in lipid metabolism were changed in the animals maintained on a high fat diet compared to the regular diet. There were also significant changes observed in the concentration of Tri carboxylic acid cycle intermediates that were extracted from the liver of all three strains based on diet. The results of this study showed that a high fat diet caused significant liver and metabolic changes compared to a regular diet in multiple rat strains. The inbred Fisher 344 and Brown Norway rats were more metabolically sensitive to the diet changes than outbred Sprague Dawley strain. The study also showed that age, as was the case for Sprague Dawley, is an important variable to consider when assessing metabolic changes.

Impact of Pre-blood Collection Factors on Plasma Metabolomic Profiles

Demographic, lifestyle and biospecimen-related factors at the time of blood collection can influence metabolite levels in epidemiological studies. Identifying the major influences on metabolite concentrations is critical to designing appropriate sample collection protocols and considering covariate adjustment in metabolomics analyses. We examined the association of age, sex, and other short-term pre-blood collection factors (time of day, season, fasting duration, physical activity, NSAID use, smoking and alcohol consumption in the days prior to collection) with 133 targeted plasma metabolites (acylcarnitines, amino acids, biogenic amines, sphingolipids, glycerophospholipids, and hexoses) among 108 individuals that reported exposures within 48 h before collection. The differences in mean metabolite concentrations were assessed between groups based on pre-collection factors using two-sided t-tests and ANOVA with FDR correction. Percent differences in metabolite concentrations were negligible across season, time of day of collection, fasting status or lifestyle behaviors at the time of collection, including physical activity or the use of tobacco, alcohol or NSAIDs. The metabolites differed in concentration between the age and sex categories for 21.8% and 14.3% metabolites, respectively. In conclusion, extrinsic factors in the short period prior to collection were not meaningfully associated with concentrations of selected endogenous metabolites in a cross-sectional sample, though metabolite concentrations differed by age and sex. Larger studies with more coverage of the human metabolome are warranted.

A comprehensive metabolic profiling of the metabolically healthy obesity phenotype

BACKGROUND

The ever-increasing prevalence of obesity constitutes a major health problem worldwide. A subgroup of obese individuals has been described as "metabolically healthy obese" (MHO). In contrast to metabolically unhealthy obese (MUO), the MHO phenotype has a favorable risk profile. Despite this, the MHO phenotype is still sub-optimally characterized with respect to a comprehensive risk assessment. Our aim was to increase the understanding of metabolic alterations associated with healthy and unhealthy obesity.

METHODS

In this cross-sectional study, men and women (18-70 years) with obesity (body mass index (BMI) ≥ 30 kg/m²) or normal weight (NW) (BMI ≤ 25 kg/m²) were classified with MHO (n = 9), MUO (n = 10) or NW (n = 11) according to weight, lipid profile and glycemic regulation. We characterized individuals by comprehensive metabolic profiling using a commercial available high-throughput proton NMR metabolomics platform. Plasma fatty acid profile, including short chain fatty acids, was measured using gas chromatography.

RESULTS

The concentrations of very low density lipoprotein (VLDL), intermediate density lipoprotein (IDL) and low density lipoprotein (LDL) subclasses were overall significantly higher, and high density lipoprotein (HDL) subclasses lower in MUO compared with MHO. VLDL and IDL subclasses were significantly lower and HDL subclasses were higher in NW compared with MHO. The concentration of isoleucine, leucine and valine was significantly higher in MUO compared with MHO, and the concentration phenylalanine was lower in NW subjects compared with MHO. The fatty acid profile in MHO was overall more favorable compared with MUO.

CONCLUSIONS

Comprehensive metabolic profiling supports that MHO subjects have intermediate-stage cardiovascular disease risk marker profile compared with NW and MUO subjects.

CLINICAL TRIAL REGISTRATION NUMBER

NCT01034436, Fatty acid quality and overweight (FO-study).

Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

Chinese hamster ovary (CHO) cells are the most commonly used cell lines in biopharmaceutical manufacturing. Genome-scale metabolic models have become a valuable tool to study cellular metabolism. Despite the presence of reference global genome-scale CHO model, context-specific metabolic models may still be required for specific cell lines (for example, CHO-K1, CHO-S, and CHO-DG44), and for specific process conditions. Many integration algorithms have been available to reconstruct specific genome-scale models. These methods are mainly based on integrating omics data (i.e., transcriptomics, proteomics, and metabolomics) into reference genome-scale models. In the present study, we aimed to investigate the impact of time points of transcriptomics integration on the genome-scale CHO model by assessing the prediction of growth rates with each reconstructed model. We also evaluated the feasibility of applying extracted models to different cell lines (generated from the same parental cell line). Our findings illustrate that gene expression at various stages of culture slightly impacts the reconstructed models. However, the prediction capability is robust enough on cell growth prediction not only across different growth phases but also in expansion to other cell lines.

Metabolomic and Lipidomic Signatures of Metabolic Syndrome and its Physiological Components in Adults: A Systematic Review

The aim of this work was to conduct a systematic review of human studies on metabolite/lipid biomarkers of metabolic syndrome (MetS) and its components, and provide recommendations for future studies. The search was performed in MEDLINE, EMBASE, EMB Review, CINHAL Complete, PubMed, and on grey literature, for population studies identifying MetS biomarkers from metabolomics/lipidomics. Extracted data included population, design, number of subjects, sex/gender, clinical characteristics and main outcome. Data were collected regarding biological samples, analytical methods, and statistics. Metabolites were compiled by biochemical families including listings of their significant modulations. Finally, results from the different studies were compared. The search yielded 31 eligible studies (2005–2019). A first category of articles identified prevalent and incident MetS biomarkers using mainly targeted metabolomics. Even though the population characteristics were quite homogeneous, results were difficult to compare in terms of modulated metabolites because of the lack of methodological standardization. A second category, focusing on MetS components, allowed comparing more than 300 metabolites, mainly associated with the glycemic component. Finally, this review included also publications studying type 2 diabetes as a whole set of metabolic risks, raising the interest of reporting metabolomics/lipidomics signatures to reflect the metabolic phenotypic spectrum in systems approaches.

Near-roadway air pollution exposure and altered fatty acid oxidation among adolescents and young adults - The interplay with obesity

BACKGROUND

Air pollution exposure has been shown to increase the risk of obesity and metabolic dysfunction in animal models and human studies. However, the metabolic pathways altered by air pollution exposure are unclear, especially in adolescents and young adults who are at a critical period in the development of cardio-metabolic diseases.

OBJECTIVES

The aim of this study was to examine the associations between air pollution exposure and indices of fatty acid and amino acid metabolism.

METHODS

A total of 173 young adults (18-23 years) from eight Children's Health Study (CHS) Southern California communities were examined from 2014 to 2018. Near-roadway air pollution (NRAP) exposure (freeway and non-freeway) and regional air pollution exposure (nitrogen dioxide, ozone and particulate matter) during one year before the study visit were estimated based on participants' residential addresses. Serum concentrations of 64 targeted metabolites including amino acids, acylcarnitines, non-esterified fatty acid (NEFA) and glycerol were measured in fasting serum samples. Principal component analysis of metabolites was performed to identify metabolite clusters that represent key metabolic pathways. Mixed effects models were used to analyze the associations of air pollution exposure with metabolomic principal component (PC) scores and individual metabolite concentrations adjusting for potential confounders.

RESULTS

Higher lagged one-year averaged non-freeway NRAP exposure was associated with higher concentrations of NEFA oxidation byproducts and higher NEFA-related PC score (all p's ≤ 0.038). The effect sizes were larger among obese individuals (interaction p = 0.047). Among females, higher freeway NRAP exposure was also associated with a higher NEFA-related PC score (p = 0.042). Among all participants, higher freeway NRAP exposure was associated with a lower PC score for lower concentrations of short- and median-chain acylcarnitines (p = 0.044).

CONCLUSIONS

Results of this study indicate that NRAP exposure is associated with altered fatty acid metabolism, which could contribute to the metabolic perturbation in obese youth.

A Metabolomics Analysis of Body Mass Index and Postmenopausal Breast Cancer Risk

Background

Elevated body mass index (BMI) is associated with increased risk of postmenopausal breast cancer. The underlying mechanisms, however, remain elusive.

Methods

In a nested case-control study of 621 postmenopausal breast cancer case participants and 621 matched control participants, we measured 617 metabolites in prediagnostic serum. We calculated partial Pearson correlations between metabolites and BMI, and then evaluated BMI-associated metabolites (Bonferroni-corrected α level for 617 statistical tests = P < 8.10 × 10-5) in relation to invasive breast cancer. Odds ratios (ORs) of breast cancer comparing the 90th vs 10th percentile (modeled on a continuous basis) were estimated using conditional logistic regression while controlling for breast cancer risk factors, including BMI. Metabolites with the lowest P values (false discovery rate < 0.2) were mutually adjusted for one another to determine those independently associated with breast cancer risk.

Results

Of 67 BMI-associated metabolites, two were independently associated with invasive breast cancer risk: 16a-hydroxy-DHEA-3-sulfate (OR = 1.65, 95% confidence interval [CI] = 1.22 to 2.22) and 3-methylglutarylcarnitine (OR = 1.67, 95% CI = 1.21 to 2.30). Four metabolites were independently associated with estrogen receptor-positive (ER+) breast cancer risk: 16a-hydroxy-DHEA-3-sulfate (OR = 1.84, 95% CI = 1.27 to 2.67), 3-methylglutarylcarnitine (OR = 1.91, 95% CI = 1.23 to 2.96), allo-isoleucine (OR = 1.76, 95% CI = 1.23 to 2.51), and 2-methylbutyrylcarnitine (OR = 1.89, 95% CI = 1.22 to 2.91). In a model without metabolites, each 5 kg/m2 increase in BMI was associated with a 14% higher risk of breast cancer (OR = 1.14, 95% CI = 1.01 to 1.28), but adding 16a-hydroxy-DHEA-3-sulfate and 3-methylglutarylcarnitine weakened this association (OR = 1.06, 95% CI = 0.93 to 1.20), with the logOR attenuating by 57.6% (95% CI = 21.8% to 100.0+%).

Conclusion

These four metabolites may signal metabolic pathways that contribute to breast carcinogenesis and that underlie the association of BMI with increased postmenopausal breast cancer risk. These findings warrant further replication efforts.

Gene-Environment Interactions on Body Fat Distribution

The prevalence of obesity has been increasing markedly in the U.S. and worldwide in the past decades; and notably, the obese populations are signified by not only the overall elevated adiposity but also particularly harmful accumulation of body fat in the central region of the body, namely, abdominal obesity. The profound shift from “traditional” to “obesogenic” environments, principally featured by the abundance of palatable, energy-dense diet, reduced physical activity, and prolonged sedentary time, promotes the obesity epidemics and detrimental body fat distribution. Recent advances in genomics studies shed light on the genetic basis of obesity and body fat distribution. In addition, growing evidence from investigations in large cohorts and clinical trials has lent support to interactions between genetic variations and environmental factors, e.g., diet and lifestyle factors, in relation to obesity and body fat distribution. This review summarizes the recent discoveries from observational studies and randomized clinical trials on the gene–environment interactions on obesity and body fat distribution.

Tissue-Specific Sample Dilution: An Important Parameter to Optimise Prior to Untargeted LC-MS Metabolomics

When developing a sample preparation protocol for LC–MS untargeted metabolomics of a new sample matrix unfamiliar to the laboratory, selection of a suitable injection concentration is rarely described. Here we developed a simple workflow to address this issue prior to untargeted LC–MS metabolomics using pig adipose tissue and liver tissue. Bi-phasic extraction was performed to enable simultaneous optimisation of parameters for analysis of both lipids and polar extracts. A series of diluted pooled samples were analysed by LC–MS and used to evaluate signal linearity. Suitable injected concentrations were determined based on both the number of reproducible features and linear features. With our laboratory settings, the optimum concentrations of tissue mass to reconstitution solvent of liver and adipose tissue lipid fractions were found to be 125 mg/mL and 7.81 mg/mL respectively, producing 2811 (ESI+) and 4326 (ESI−) linear features from liver, 698 (ESI+) and 498 (ESI−) linear features from adipose tissue. For analysis of the polar fraction of both tissues, 250 mg/mL was suitable, producing 403 (ESI+) and 235 (ESI−) linear features from liver, 114 (ESI+) and 108 (ESI−) linear features from adipose tissue. Incorrect reconstitution volumes resulted in either severe overloading or poor linearity in our lipid data, while too dilute polar fractions resulted in a low number of reproducible features (<50) compared to hundreds of reproducible features from the optimum concentration used. Our study highlights on multiple matrices and multiple extract and chromatography types, the critical importance of determining a suitable injected concentration prior to untargeted LC–MS metabolomics, with the described workflow applicable to any matrix and LC–MS system.

1H Nuclear Magnetic Resonance (NMR)-Based Cerebrospinal Fluid and Plasma Metabolomic Analysis in Type 2 Diabetic Patients and Risk Prediction for Diabetic Microangiopathy

Insulin resistance and metabolic derangement are present in patients with type 2 diabetes mellitus (T2DM). However, the metabolomic signature of T2DM in cerebrospinal fluid (CSF) has not been investigated thus far. In this prospective metabolomic study, fasting CSF and plasma samples from 40 T2DM patients to 36 control subjects undergoing elective surgery with spinal anesthesia were analyzed by ¹H nuclear magnetic resonance (NMR) spectroscopy. NMR spectra of CSF and plasma metabolites were analyzed and correlated with the presence of T2DM and diabetic microangiopathy (retinopathy, nephropathy, and neuropathy) using an area under the curve (AUC) estimation. CSF metabolomic profiles in T2DM patients vs. controls revealed significantly increased levels of alanine, leucine, valine, tyrosine, lactate, pyruvate, and decreased levels of histidine. In addition, a combination of alanine, histidine, leucine, pyruvate, tyrosine, and valine in CSF showed a superior correlation with the presence of T2DM (AUC:0.951), diabetic retinopathy (AUC:0.858), nephropathy (AUC:0.811), and neuropathy (AUC:0.691). Similar correlations also appeared in plasma profiling. These metabolic alterations in CSF suggest decreasing aerobic metabolism and increasing anaerobic glycolysis in cerebral circulation of patients with T2DM. In conclusion, our results provide clues for the metabolic derangements in diabetic central neuropathy among T2DM patients; however, their clinical significance requires further exploration.

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.

Atlas of Circadian Metabolism Reveals System-wide Coordination and Communication between Clocks

Metabolic diseases are often characterized by circadian misalignment in different tissues, yet how altered coordination and communication among tissue clocks relate to specific pathogenic mechanisms remains largely unknown. Applying an integrated systems biology approach, we performed 24-hr metabolomics profiling of eight mouse tissues simultaneously. We present a temporal and spatial atlas of circadian metabolism in the context of systemic energy balance and under chronic nutrient stress (high-fat diet [HFD]). Comparative analysis reveals how the repertoires of tissue metabolism are linked and gated to specific temporal windows and how this highly specialized communication and coherence among tissue clocks is rewired by nutrient challenge. Overall, we illustrate how dynamic metabolic relationships can be reconstructed across time and space and how integration of circadian metabolomics data from multiple tissues can improve our understanding of health and disease.

The impact of early-life sub-therapeutic antibiotic treatment (STAT) on excessive weight is robust despite transfer of intestinal microbes

The high-fat, high-calorie diets of westernized cultures contribute to the global obesity epidemic, and early life exposure to antibiotics may potentiate those dietary effects. Previous experiments with mice had shown that sub-therapeutic antibiotic treatment (STAT)-even restricted to early life-affected the gut microbiota, altered host metabolism, and increased adiposity throughout the lifetime of the animals. Here we carried out a large-scale cohousing experiment to investigate whether cohousing STAT and untreated (Control) mice would transfer the STAT-perturbed microbiota and transmit its impact on weight. We exposed pregnant dams and their young offspring to either low-dose penicillin (STAT) or water (Control) until weaning, and then followed the offspring as they grew and endured a switch from normal to high-fat diet at week 17 of life. Cohousing, which started at week 4, rapidly approximated the microbiota within cages, lowering the weight of STAT mice relative to non-cohoused mice. The effect, however, varied between cages, and was restricted to the first 16 weeks when diet consisted of normal chow. Once mice switched to high-fat diet, the microbiota α- and β-diversity expanded and the effect of cohousing faded: STAT mice, again, were heavier than control mice independently of cohousing. Metabolomics revealed serum metabolites associated with STAT exposure, but no significant differences were detected in glucose or insulin tolerance. Our results show that cohousing can partly ameliorate the impact of STAT on the gut microbiota but not prevent increased weight with high-fat diet. These observations have implications for microbiota therapies aimed to resolve the collateral damage of antibiotics and their load on human obesity.

The Metabolomic Signatures of Weight Change

Obesity represents a major health concern, not just in the West but increasingly in low and middle income countries. In order to develop successful strategies for losing weight, it is essential to understand the molecular pathogenesis of weight change. A number of pathways, implicating oxidative stress but also the fundamental regulatory of insulin, have been implicated in weight gain and in the regulation of energy expenditure. In addition, a considerable body of work has highlighted the role of metabolites generated by the gut microbiome, in particular short chain fatty acids, in both processes. The current review provides a brief understanding of the mechanisms underlying the associations of weight change with changes in lipid and amino acid metabolism, energy metabolism, dietary composition and insulin dynamics, as well as the influence of the gut microbiome. The changes in metabolomic profiles and the models outlined can be used as an accurate predictor for obesity and obesity related disorders.

Diagnosis of obesity and use of obesity biomarkers in science and clinical medicine

The global epidemic of obesity is a major public health problem today. Obesity increases the risk of many chronic diseases, such as type 2 diabetes, coronary heart disease, and certain types of cancer, and is associated with lower life expectancy. The body mass index (BMI), which is currently used to classify obesity, is only an imperfect measure of abnormal or excessive body fat accumulation. Studies have shown that waist circumference as a measure of fat distribution may improve disease prediction. More elaborate techniques such as magnetic resonance imaging are increasingly available to assess body fat distribution, but these measures are not readily available in routine clinical practice, and health-relevant cut-offs not yet been established. The measurement of biomarkers that reflect the underlying biological mechanisms for the increased disease risk may be an alternative approach to characterize the relevant obesity phenotype. The insulin/insulin-like growth factor (IGF) axis and chronic low-grade inflammation have been identified as major pathways. In addition, specific adipokines such as leptin, adiponectin and resistin have been related to obesity-associated health outcomes. This biomarker research, which is currently further developed with the application of high throughput methods, gives important insights in obesity-related disease etiology and pathophysiological pathways and may be used to better characterize obese persons at high risk of disease development and target disease-causing biomarkers in personalized prevention strategies.

Overview of genomics and post-genomics research on type 2 diabetes mellitus: Future perspectives and a framework for further studies

In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes. Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies. We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease. We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.

Profound Perturbation of the Metabolome in Obesity Is Associated with Health Risk

Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). There is a need for a more precise yet portable method of phenotyping and categorizing risk in large numbers of people with obesity to advance clinical care and drug development. Here, we used non-targeted metabolomics and whole-genome sequencing to identify metabolic and genetic signatures of obesity. We find that obesity results in profound perturbation of the metabolome; nearly a third of the assayed metabolites associated with changes in BMI. A metabolome signature identifies the healthy obese and lean individuals with abnormal metabolomes-these groups differ in health outcomes and underlying genetic risk. Specifically, an abnormal metabolome associated with a 2- to 5-fold increase in cardiovascular events when comparing individuals who were matched for BMI but had opposing metabolome signatures. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.

1H NMR based serum metabolic profiling reveals differentiating biomarkers in patients with diabetes and diabetes-related complication

BACKGROUND

Diabetes is among the most prevalent diseases worldwide, of all the affected individuals a significant proportion of the population remains undiagnosed due to lack of specific symptoms early in this disorder and inadequate diagnostics. Diabetes and its associated sequela, i.e., comorbidity are associated with microvascular and macrovascular complications. As diabetes is characterized by an altered metabolism of key metabolites and regulatory pathways. Metabolic phenotyping can provide us with a better understanding of the unique set of regulatory perturbations that predispose to diabetes and its associated complication/comorbidities.

METHODOLOGY

The present study utilizes the analytical platform NMR spectroscopy coupled with Random Forest statistical analysis to identify the discriminatory metabolites in diabetes (DB = 38) vs. diabetes-related complication (DC = 35) along with the healthy control (HC = 50) subjects. A combined and pairwise analysis was performed to identify the discriminatory metabolites responsible for class separation. The perturbed metabolites were further rigorously validated using t-test, AUROC analysis to examine the statistical significance of the identified metabolites.

RESULTS

The DB and DC patients were well discriminated from HC. However, 15 metabolites were found to be significantly perturbed in DC patients compared to DB, the identified panel of metabolites are TCA cycle (succinate, citrate), methylamine metabolism (trimethylamine, methylamine, betaine), -intermediates; energy metabolites (glucose, lactate, pyruvate); and amino acids (valine, arginine, glutamate, methionine, proline, and threonine).

CONCLUSION

The ¹H NMR metabolomics may prove a promising technique to differentiate and predict diabetes and its complication on their onset or progression by determining the altered levels of the metabolites in serum.

High-Fat Diet, Betaine, and Polydextrose Induce Changes in Adipose Tissue Inflammation and Metabolism in C57BL/6J Mice

SCOPE

High-fat diets are a likely cause of low-grade inflammation and obesity-related pathologies. This study measures the effects of a high-fat diet, in combination with two dietary supplements-betaine and polydextrose-on metabolism and inflammation in the adipose tissue of diet-induced obese mice.

METHODS AND RESULTS

Forty male C57BL/6J mice are fed a high-fat diet for 8 weeks and compared with low-fat-diet-fed control animals (n = 10). For the last 4 weeks, the high-fat-diet-fed animals are supplemented with 1% betaine, 3.33% polydextrose, their combination, or plain water. Fat depots from subcutaneous and visceral adipose tissue are analyzed for inflammatory markers and nontargeted metabolomics by quantitative PCR and LC-QTOF-MS. The high-fat diet significantly increases adipose tissue inflammation in both fat depots. By metabolic profiling, clear differences are noted between low-fat-diet and high-fat-diet groups with regard to the levels of several metabolite species-primarily carnitines, lipids, and amino acids. Dietary betaine mitigates the high-fat-diet-induced IL-6 expression and significantly increases betaine and butyrobetaine levels in adipose tissue.

CONCLUSIONS

The high-fat diet induces patent changes in carnitine and lipid metabolism in adipose tissue. Betaine supplementation elevates the levels of betaine and its derivatives and certain carnitine species, as reported in muscle and liver, and moderately reduces inflammation.

Urinary organic acids as biomarkers in the assessment of pulmonary function in children with asthma

Childhood asthma prevalence continues to rise despite advancements in prevention and medical management strategies. The purpose of this study was to investigate correlations between urinary organic acids and pulmonary diagnostic tests, asthma control in Greek asthmatic children. We hypothesized that urinary organic acids are positively associated with poor pulmonary function in children with asthma. Seventy-two children, 5 to 12 years old with asthma were recruited from a pediatric asthma clinic in Athens, Greece. Pulmonary function was assessed using spirometry and exhaled nitric oxide analysis. Asthma control was measured qualitatively using the Asthma Control Questionnaire. Targeted metabolomic analysis of 34 urinary organic acids in children was conducted by gas chromatography-mass spectrometry. A statistically significant difference between girls and boys was found for asthma control score (P = .02), lactic acid (P = .03), but not for any other organic acids (P > .05). Statistically significant correlations were found between lactic acid and Forced Expiratory Volume in 1 second (FEV₁) (P = .02), Forced Vital Capacity (FVC) (P = .03); 4- hydroxyphenylacetic acid and FEV₁ (P = .01), FVC (P = .01); 5-hydroxyindoleacetic acid and FEV₁/FVC (P = .03), eNO (P = .05); glycolic acid with Peak Expiratory Flow (PEF) (P = .03); and malic acid with asthma control (P = .02). In conclusion, metabolomics was used to determine correlations between urinary organic acids and conventional pulmonary diagnostic tests in Greek asthmatic children. Metabolomics could be a promising approach for asthma research and in detection of novel biomarkers for asthma monitoring and therapeutic targets for childhood asthma. This study contributes towards a better understanding of the biochemical pathways involved in asthma.

Characterization of different fat depots in NAFLD using inflammation-associated proteome, lipidome and metabolome

Non-alcoholic fatty liver disease (NAFLD) is recognized as a liver manifestation of metabolic syndrome, accompanied with excessive fat accumulation in the liver and other vital organs. Ectopic fat accumulation was previously associated with negative effects at the systemic and local level in the human body. Thus, we aimed to identify and assess the predictive capability of novel potential metabolic biomarkers for ectopic fat depots in non-diabetic men with NAFLD, using the inflammation-associated proteome, lipidome and metabolome. Myocardial and hepatic triglycerides were measured with magnetic spectroscopy while function of left ventricle, pericardial and epicardial fat, subcutaneous and visceral adipose tissue were measured with magnetic resonance imaging. Measured ectopic fat depots were profiled and predicted using a Random Forest algorithm, and by estimating the Area Under the Receiver Operating Characteristic curves. We have identified distinct metabolic signatures of fat depots in the liver (TAG50:1, glutamate, diSM18:0 and CE20:3), pericardium (N-palmitoyl-sphinganine, HGF, diSM18:0, glutamate, and TNFSF14), epicardium (sphingomyelin, CE20:3, PC38:3 and TNFSF14), and myocardium (CE20:3, LAPTGF-β1, glutamate and glucose). Our analyses highlighted non-invasive biomarkers that accurately predict ectopic fat depots, and reflect their distinct metabolic signatures in subjects with NAFLD.

Metabolomics-Based Clinical Efficacy and Effect on the Endogenous Metabolites of Tangzhiqing Tablet, a Chinese Patent Medicine for Type 2 Diabetes Mellitus with Hypertriglyceridemia

Tangzhiqing tablet (TZQ) is derived from Tangzhiqing formula, which has been used to regulate glucose and lipid metabolism in China for hundreds of years. However, as a new Chinese patent medicine, its clinical indication is not clear. To explore the clinical indication and effect on the patients with type 2 diabetes mellitus (T2DM), a pilot clinical trial and metabolomics study were carried out. In the clinical study, T2DM patients were divided into three groups and treated with TZQ, placebo, or acarbose for 12 weeks, respectively. The metabolomic study based on UPLC Q-TOF MS was performed including patients with hypertriglyceridemia in TZQ and placebo groups and healthy volunteers. The clinical results showed that TZQ could reduce glycosylated hemoglobin (HbA1c) and fasting insulin. For patients with hypertriglyceridemia in TZQ group, the levels of HbA1c all decreased and were correlated with the baseline level of triglyceride. Metabonomics data showed a significant difference between patients and healthy volunteers, and 17 biomarkers were identified. After 12-week treatment with TZQ, 11 biomarkers decreased significantly (p<0.05), suggesting that TZQ could improve the metabolomic abnormalities in these participants. In conclusion, the clinical indication of TZQ was T2DM with hypertriglyceridemia, and its target was related to glycerophospholipid metabolism.

Metabolic fingerprint of insulin resistance in human polymorphonuclear leucocytes

The present study was aimed at determining the metabolic profile of PMNs in obese subjects, and to explore its potential relationship with insulin resistance (IR). To achieve this goal, a pilot clinical study was performed using PMNs from 17 patients with obesity and IR, and 17 lean controls without IR, which was validated in an additional smaller cohort (consisting of 10 patients and 10 controls). PMNs were isolated from peripheral blood and nuclear magnetic resonance was used to perform the metabolomic analysis. A total of 48 metabolites were quantified. The main metabolic change found in PMNs was a significant increase in 2-aminoisobutyric acid with a direct correlation with HOMA-IR (p<0.001), BMI (p<0.000001) and waist circumference (p<0.000001). By contrast, a decrease of 3-hydroxyisovalerate was observed with an inverse correlation with HOMA-IR (p = 0.001), BMI (p = 0.001) and waist circumference (p = 0.0001). Notably, the metabolic profile in plasma was different than that obtained in PMNs. In summary, our results suggest that the change in 3-hydroxyisovalerate and 2-aminoisobutyric is the key metabolic fingerprint in PMNs of obese subjects with IR. In addition, our methodology could be an easy and reliable tool for monitoring the effect of treatments in the setting of precision medicine.

Animal research for type 2 diabetes mellitus, its limited translation for clinical benefit, and the way forward

Obesity and type 2 diabetes mellitus (T2DM) have reached pandemic proportions worldwide, and considerable research efforts have been dedicated to investigating disease pathology and therapeutic options. The two hallmark features of T2DM, insulin resistance and pancreatic dysfunction, have been studied extensively by using various animal models. Despite the knowledge acquired from such models, particularly mechanistic discoveries that sometimes mimic human T2DM mechanisms or pathways, many details of human T2DM pathogenesis remain unknown, therapeutic options remain limited, and a cure has eluded research. Emerging human data have raised concern regarding inter-species differences at many levels (e.g. in gene regulation, pancreatic cytoarchitecture, glucose transport, and insulin secretion regulation), and the subsequent impact of these differences on the clinical translation of animal research findings. Therefore, it is important to recognise and address the translational gap between basic animal-based research and the clinical advances needed to prevent and treat T2DM. The purpose of this report is to identify some limitations of T2DM animal research, and to propose how greater human relevance and applicability of hypothesis-driven basic T2DM research could be achieved through the use of human-based data acquisition at various biological levels. This report addresses how in vitro, in vivo and in silico technologies could be used to investigate particular aspects of human glucose regulation. We do not propose that T2DM animal research has been without value in the identification of mechanisms, pathways, or potential targets for therapies, nor do we claim that human-based methods can provide all the answers. We recognise that the ultimate goal of T2DM animal research is to identify ways to advance the prevention, recognition and treatment of T2DM in humans, but postulate that this is where the use of animal models falls short, despite decades of effort. The best way to achieve this goal is by prioritising human-centred research.

Plasma fatty acyl-carnitines during 8 weeks of overfeeding: relation to diet energy expenditure and body composition: the PROOF study

OBJECTIVE

Overfeeding is a strategy for evaluating the effects of excess energy intake. In this secondary analysis we tested the possibility that different levels of dietary protein might differentially modify the response of fatty acyl-carnitines to overfeeding.

METHODS

Twenty-three healthy adult men and women were overfed by 40% for 8 weeks while in-patients with diets containing 5% (LPD), 15% (NPD) or 25% (HPD) protein. Plasma fatty acyl-carnitines were measured by gas chromatography/mass spectrometry (GC/MS) at baseline and after 8 weeks of overfeeding. Measurements included: body composition by DXA, energy expenditure by ventilated hood and doubly-labeled water, fat cell size from subcutaneous fat biopsies, and fat distribution by CT scan.

RESULTS

Analysis was done on 5 groups of fatty acyl-carnitines identified by principal components analysis and 6 individual short-chain fatty acyl carnitines. Higher protein intake was associated with significantly lower 8 week levels of medium chain fatty acids and C2, C4-OH and C 6:1, but higher values of C3 and C5:1 acyl-carnitines derived from essential amino acids. In contrast energy and fat intake were only weakly related to changes in fatty acyl-carnitines. A decease or smaller rise in 8 week medium chain acyl-carnitines was associated with an increase in sleeping energy expenditure (P = 0.0004), and fat free mass (P < 0.0001) and a decrease in free fatty acid concentrations (FFA) (P = 0.0067). In contrast changes in short-chain fatty acyl-carnitines were related to changes in resting energy expenditure (P = 0.0026), and fat free mass (P = 0.0007), and C4-OH was positively related to FFA (P = 0006).

CONCLUSION

Protein intake was the major factor influencing changes in fatty acyl carnitines during overfeeding with higher values of most acyl-fatty acids on the low protein diet. The association of dietary protein and fat intake may explain the changes in energy expenditure and metabolic variables resulting in the observed patterns of fatty acyl carnitines.

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

SCOPE

To examine whether a low-glycemic index (LGI) diet improves a set of plasma metabolites related to different metabolic diseases, and comparison to a high-glycemic index (HGI) diet and a low-fat (LF) diet.

METHODS AND RESULTS

A parallel, randomized trial with three intervention diets: an LGI diet, an HGI diet, and an LF diet. A total of 122 adult overweight and obese subjects were enrolled in the study for 6 months. Blood samples were collected at baseline and at the end of the intervention. The plasma metabolomic profile of 102 subjects was analyzed using three different approaches: GC/quadrupole-TOF, LC/quadrupole-TOF, and nuclear magnetic resonance. Both univariate and multivariate analysis were performed. Serine levels were significantly higher following the LGI diet compared to both the HGI and LF diets (q = 0.002), whereas leucine (q = 0.015) and valine (q = 0.024) were lower in the LGI diet compared to the LF diet. A set of two sphingomyelins, two lysophosphatidylcholines, and six phosphatidylcholines were significantly modulated after the LGI diet compared to the HGI and LF diets (q < 0.05). Significant correlations between changes in plasma amino acids and lipid species with changes in body weight, glucose, insulin, and some inflammatory markers are also reported.

CONCLUSION

These results suggest that an LGI diet modulates certain circulating amino acids and lipid levels. These findings may explain the health benefits attributed to LGI diets in metabolic diseases such as type 2 diabetes.

Metabolic perturbations in Welsh Ponies with insulin dysregulation, obesity, and laminitis

Background

Metabolomics, the study of small‐molecule metabolites, has increased understanding of human metabolic diseases, but has not been used to study equine metabolic syndrome (EMS).

Objectives

(1) To examine the serum metabolome of Welsh Ponies with and without insulin dysregulation before and during an oral sugar test (OST). (2) To identify differences in metabolites in ponies with insulin dysregulation, obesity, or history of laminitis.

Animals

Twenty Welsh Ponies (mean ± SD; 13.8 ± 9.0 years) classified as non‐insulin dysregulated [CON] (n = 10, insulin < 30 mU/L) or insulin dysregulated [ID] (n = 10, insulin > 60 mU/L) at 75 minutes after administration of Karo syrup, obese (n = 6) or nonobese (n = 14), and history of laminitis (n = 9) or no history of laminitis (n = 11).

Methods

Case‐control study. Metabolomic analysis was performed on serum obtained at 0 minutes (baseline) and 75 minutes during the OST. Data were analyzed with multivariable mixed linear models with significance set at P ≤ .05.

Results

Metabolomic analysis of 646 metabolites (506 known) detected significant metabolite differences. At baseline, 55 metabolites (insulin response), 91 metabolites (obesity status), and 136 metabolites (laminitis history) were different. At 75 minutes, 51 metabolites (insulin response), 102 metabolites (obesity status), and 124 metabolites (laminitis history) were different.

Conclusions and Clinical Importance

Use of metabolomics could have diagnostic utility for early detection of EMS and provide new knowledge regarding the pathophysiology of metabolic perturbations associated with this condition that might lead to improved clinical management.

Integrated Metabolomics and Network Pharmacology Approach to Explain Possible Action Mechanisms of Xin-Sheng-Hua Granule for Treating Anemia

As a well-known traditional Chinese medicine (TCM) prescription, Xin-Sheng-Hua Granule (XSHG) has been applied in China for more than 30 years to treat postpartum diseases, especially anemia. However, underlying therapeutic mechanisms of XSHG for anemia were still unclear. In this study, plasma metabolomics profiling with UHPLC-QTOF/MS and multivariate data method was firstly analyzed to discover the potential regulation mechanisms of XSHG on anemia rats induced by bleeding from the orbit. Afterward, the compound-target-pathway network of XSHG was constructed by the use of network pharmacology, thus anemia-relevant signaling pathways were dissected. Finally, the crucial targets in the shared pathways of metabolomics and network pharmacology were experimentally validated by ELISA and Western Blot analysis. The results showed that XSHG could exert excellent effects on anemia probably through regulating coenzyme A biosynthesis, sphingolipids metabolism and HIF-1α pathways, which was reflected by the increased levels of EPOR, F2, COASY, as well as the reduced protein expression of HIF-1α, SPHK1, and S1PR1. Our work successfully explained the polypharmcological mechanisms underlying the efficiency of XSHG on treating anemia, and meanwhile, it probed into the potential treatment strategies for anemia from TCM prescription.

Plasma Amino Acids During 8 Weeks of Overfeeding: Relation to Diet Body Composition and Fat Cell Size in the PROOF Study

OBJECTIVE

Different amounts of dietary protein during overfeeding produced similar fat gain but different amounts of gain in fat-free body mass. Protein and energy intake may have differential effects on amino acids during overfeeding.

METHODS

Twenty-three healthy adult men and women were overfed by 40% for 8 weeks with 5%, 15%, or 25% protein diets. Plasma amino acids were measured by gas chromatography and mass spectrometry at baseline and week 8. Body composition was measured by dual-energy x-ray absorptiometry, fat cell size (FCS) from subcutaneous fat biopsies, and insulin resistance by euglycemic-hyperinsulinemic clamp.

RESULTS

The following three amino acid patterns were seen: increasing concentration of five essential and three nonessential amino acids with increasing protein intake, higher levels of six nonessential amino acids with the low-protein diet, and a pattern that was flat or "V" shaped. Dietary fat and protein were both correlated with changes in valine, leucine/isoleucine/norleucine, phenylalanine, and tyrosine, but energy intake was not. The change in fat mass and weight was related to the change in several amino acids. Baseline FCS and the interaction between glucose disposal and FCS were associated with changes in several amino acids during overfeeding.

CONCLUSIONS

Overfeeding dietary protein affects the levels of both essential and nonessential amino acids.

Depot-Specific Adipose Tissue Metabolite Profiles and Corresponding Changes Following Aerobic Exercise

Objectives: Total, visceral, and abdominal subcutaneous adipose tissue (AT) depots have distinct associations with cardiometabolic health; however, the metabolite profiles that characterize each AT depot and its reduction following exercise are poorly understood. Our objectives were to (1) assess the independent associations between identified metabolites and total, visceral and abdominal subcutaneous AT; and (2) examine whether changes in metabolite concentrations and AT mass following aerobic exercise are associated. Methods: A secondary analysis was performed in 103 middle-aged abdominally obese men and women {[mean (SD)], 52.4 (8.0) years} randomized into one of four groups varying in exercise amount and intensity for 6 months duration: high amount high intensity, high amount low intensity, low amount low intensity, and control. One hundred and forty seven metabolites were profiled by liquid chromatography-tandem mass spectrometry. AT mass was measured by magnetic resonance imaging (MRI). Results: Individual metabolite associations with AT depots confirmed several established cross-sectional relationships between the obesity phenotype and metabolic pathways. Collapsed across exercise groups, reduction in visceral AT predicted increases in pyroglutamic acid (B = -0.41) and TCA cycle intermediates [succinic (B = -0.41) and fumaric acid (B = -0.20)], independent of change in total AT. Changes in UDP-GlcNAc (B = 0.43), pyroglutamic acid (B = -0.35), histidine (B = 0.20), citric acid/isocitric acid (B = -0.20), and creatine (B = 0.27) were significantly associated with changes in total AT (false discovery rate = 0.1). Conclusions: Our findings point to potential biomarkers of depot-specific AT reduction that may play a direct role in mediating cardiometabolic improvements.

TRIENNIAL GROWTH AND DEVELOPMENT SYMPOSIUM: Molecular mechanisms related to bovine intramuscular fat deposition in the longissimus muscle

The intramuscular fat (IMF) content of the LM, also known as marbling, is particularly important in determining the price of beef in Korea, Japan, and the United States. Deposition of IMF is influenced by both genetic (e.g., breed, gender, and genotype) and nongenetic factors (e.g., castration, nutrition, stressors, animal weight, and age). Castration of bulls markedly increases deposition of IMF, resulting in improved beef quality. Here, we present a comparative gene expression approach between bulls and steers. Transcriptomic and proteomic studies have demonstrated that the combined effects of increases in lipogenesis, fatty acid uptake, and fatty acid esterification and decreased lipolysis are associated with increased IMF deposition in the LM. Several peripheral tissues (LM, adipose tissues, and the liver) are involved in lipid metabolism. Therefore, understanding the significance of the tissue network in lipid metabolism is important. Here, we demonstrate that lipid metabolism in LM tissues is crucial for IMF deposition, whereas lipid metabolism in the liver plays only a minor role. Metabolism of body fat and IMF deposition in bovine species has similarities with these processes in metabolic diseases, such as obesity in humans and rodents. Extensive studies on metabolic diseases using epigenome modification (DNA methylation, histone modification, and microRNA), microbial metagenomics, and metabolomics have been performed in humans and rodents, and new findings have been reported using these technologies. The importance of applying "omics" fields (epigenomics, metagenomics, and metabolomics) to the study of IMF deposition in cattle is described. New information on the molecular mechanisms of IMF deposition may be used to design nutritional or genetic methods to manipulate IMF deposition and to modify fatty acid composition in beef cattle. Applying nutrigenomics could maximize the expression of genetic potential of economically important traits (e.g., marbling) in animals.

Endocrinology Meets Metabolomics: Achievements, Pitfalls, and Challenges

The metabolome, although very dynamic, is sufficiently stable to provide specific quantitative traits related to health and disease. Metabolomics requires balanced use of state-of-the-art study design, chemical analytics, biostatistics, and bioinformatics to deliver meaningful answers to contemporary questions in human disease research. The technology is now frequently employed for biomarker discovery and for elucidating the mechanisms underlying endocrine-related diseases. Metabolomics has also enriched genome-wide association studies (GWAS) in this area by providing functional data. The contributions of rare genetic variants to metabolome variance and to the human phenotype have been underestimated until now.

Biological determinants of health: Genes, microbes, and metabolism exemplars of nursing science

BACKGROUND

Increasingly, nurse scientists are incorporating "omics" measures (e.g., genomics, transcriptomics, proteomics, and metabolomics) in studies of biologic determinants of health and behavior. The role of omics in nursing science can be conceptualized in several ways: (a) as a portfolio of biological measures (biomarkers) to monitor individual risk, (b) as a set of combined data elements that can generate new knowledge based on large and complex patient data sets, (c) as baseline information that promotes health education and potentially personalized interventions, and (d) as a platform to understand how environmental parameters (e.g., diet) interact with the individual's physiology.

PURPOSE

In this article, we provide exemplars of nursing scientists who use omics to better understand specific health conditions.

METHODS

We highlight various ongoing nursing research investigations incorporating omics technologies to study chronic pain vulnerability, risk for a pain-related condition, cardiometabolic complications associated with pregnancy, and as biomarkers of response to a dietary intervention.

DISCUSSION

Omics technologies add an important dimension to nursing science across many foci of investigation. However, there are also challenges and opportunities for nurse scientists who consider using omics in their research.

CONCLUSION

The integration of omics holds promise for increasing the impact of nursing research and practice on population health outcomes.

Metabolomic Profiling of Long-Term Weight Change: Role of Oxidative Stress and Urate Levels in Weight Gain

OBJECTIVE

To investigate the association between long-term weight change and blood metabolites.

METHODS

Change in BMI over 8.6 ± 3.79 years was assessed in 3,176 females from the TwinsUK cohort (age range: 18.3-79.6, baseline BMI: 25.11 ± 4.35) measured for 280 metabolites at follow-up. Statistically significant metabolites (adjusting for covariates) were included in a multivariable least absolute shrinkage and selection operator (LASSO) model. Findings were replicated in the Cooperative Health Research in the Region of Augsburg (KORA) study (n = 1,760; age range: 25-70, baseline BMI: 27.72 ± 4.53). The study examined whether the metabolites identified could prospectively predict weight change in KORA and in the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) study (n = 471; age range: 55-74, baseline BMI: 27.24 ± 5.37).

RESULTS

Thirty metabolites were significantly associated with change in BMI per year in TwinsUK using Bonferroni correction. Four were independently associated with weight change in the multivariable LASSO model and replicated in KORA: namely, urate (meta-analysis β [95% CI] = 0.05 [0.040 to 0.063]; P = 1.37 × 10^-19 ), gamma-glutamyl valine (β [95% CI] = 0.06 [0.046 to 0.070]; P = 1.23 × 10^-20 ), butyrylcarnitine (β [95% CI] = 0.04 [0.028 to 0.051]; P = 6.72 × 10^-12 ), and 3-phenylpropionate (β [95% CI] = -0.03 [-0.041 to -0.019]; P = 9.8 × 10^-8 ), all involved in oxidative stress. Higher levels of urate at baseline were associated with weight gain in KORA and PLCO.

CONCLUSIONS

Metabolites linked to higher oxidative stress are associated with increased long-term weight gain.

Lipidomic profiling reveals distinct differences in plasma lipid composition in healthy, prediabetic, and type 2 diabetic individuals

The relationship between dyslipidemia and type 2 diabetes mellitus (T2D) has been extensively reported, but the global lipid profiles, especially in the East Asia population, associated with the development of T2D remain to be characterized. Liquid chromatography coupled to tandem mass spectrometry was applied to detect the global lipidome in the fasting plasma of 293 Chinese individuals, including 114 T2D patients, 81 prediabetic subjects, and 98 individuals with normal glucose tolerance (NGT). Both qualitative and quantitative analyses revealed a gradual change in plasma lipid features with T2D patients exhibiting characteristics close to those of prediabetic individuals, whereas they differed significantly from individuals with NGT. We constructed and validated a random forest classifier with 28 lipidomic features that effectively discriminated T2D from NGT or prediabetes. Most of the selected features significantly correlated with diabetic clinical indices. Hydroxybutyrylcarnitine was positively correlated with fasting plasma glucose, 2-hour postprandial glucose, glycated hemoglobin, and insulin resistance index (HOMA-IR). Lysophosphatidylcholines such as lysophosphatidylcholine (18:0), lysophosphatidylcholine (18:1), and lysophosphatidylcholine (18:2) were all negatively correlated with HOMA-IR. The altered plasma lipidome in Chinese T2D and prediabetic subjects suggests that lipid features may play a role in the pathogenesis of T2D and that such features may provide a basis for evaluating risk and monitoring disease development.