Changes in the serum metabolite profile in obese children with weight loss

Predicting Disease-Metabolite Associations Based on the Metapath Aggregation of Tripartite Heterogeneous Networks

The exploration of the interactions between diseases and metabolites holds significant implications for the diagnosis and treatment of diseases. However, traditional experimental methods are time-consuming and costly, and current computational methods often overlook the influence of other biological entities on both. In light of these limitations, we proposed a novel deep learning model based on metapath aggregation of tripartite heterogeneous networks (MAHN) to explore disease-related metabolites. Specifically, we introduced microbes to construct a tripartite heterogeneous network and employed graph convolutional network and enhanced GraphSAGE to learn node features with metapath length 3. Additionally, we utilized node-level and semantic-level attention mechanisms, a more granular approach, to aggregate node features with metapath length 2. Finally, the reconstructed association probability is obtained by fusing features from different metapaths into the bilinear decoder. The experiments demonstrate that the proposed MAHN model achieved superior performance in five-fold cross-validation with Acc (91.85%), Pre (90.48%), Recall (93.53%), F1 (91.94%), AUC (97.39%), and AUPR (97.47%), outperforming four state-of-the-art algorithms. Case studies on two complex diseases, irritable bowel syndrome and obesity, further validate the predictive results, and the MAHN model is a trustworthy prediction tool for discovering potential metabolites. Moreover, deep learning models integrating multi-omics data represent the future mainstream direction for predicting disease-related biological entities.

Tangshen formula targets the gut microbiota to treat non-alcoholic fatty liver disease in HFD mice: A 16S rRNA and non-targeted metabolomics analyses

BACKGROUND

Tangshen formula (TSF) has an ameliorative effect on hepatic lipid metabolism in non-alcoholic fatty liver disease (NAFLD), but the role played by the gut microbiota in this process is unknown.

METHOD

We conducted three batches of experiments to explore the role played by the gut microbiota: TSF administration, antibiotic treatment, and fecal microbial transplantation. NAFLD mice were induced with a high-fat diet to investigate the ameliorative effects of TSF on NAFLD features and intestinal barrier function. 16S rRNA sequencing and serum untargeted metabolomics were performed to further investigate the modulatory effects of TSF on the gut microbiota and metabolic dysregulation in the body.

RESULTS

TSF ameliorated insulin resistance, hypercholesterolemia, lipid metabolism disorders, inflammation, and impairment of intestinal barrier function. 16S rRNA sequencing analysis revealed that TSF regulated the composition of the gut microbiota and increased the abundance of beneficial bacteria. Antibiotic treatment and fecal microbiota transplantation confirmed the importance of the gut microbiota in the treatment of NAFLD with TSF. Subsequently, untargeted metabolomics identified 172 differential metabolites due to the treatment of TSF. Functional predictions suggest that metabolisms of choline, glycerophospholipid, linoleic acid, alpha-linolenic acid, and arachidonic acid are the key metabolic pathways by which TSF ameliorates NAFLD and this may be influenced by the gut microbiota.

CONCLUSION

TSF treats the NAFLD phenotype by remodeling the gut microbiota and improving metabolic profile, suggesting that TSF is a functional gut microbial and metabolic modulator for the treatment of NAFLD.

Amino Acids Profile in Children with Acute Brucellosis

BACKGROUND

Many new cases of brucella infections are seen in Turkey every year, especially in March, April, and May, due to the consumption of local unpasteurized cheese. Amino acids profiles have not been studied in brucellosis so far.

AIMS

The amino acid profiles may be affected by infectious diseases. Our study aims to evaluate the plasma amino acid profile in the progression of acute brucellosis.

METHODS

Plasma amino acid profile was performed by an 8045 LC-MS / MS device (Shimadzu 8045, Japan) using JASEM amino acid kit.

RESULTS

Analysis of 45 amino acid profiles was made and results profiles showed significant differences in concentrations and types of amino acids in brucella patients. We observed a significant difference in terms of alanine, arginine, aspartic acid, glutamine, glutamic acid, glycine, isoleucine, ornithine, phenylalanine, proline, tyrosine, valine, alpha-aminoadipic acid, alpha-amino-pimelic acid, argininosuccinic acid, gamma-aminobutyric acid, thiaproline, 1-methylhistidine, 3-methylhistidine, hydroxylysine, hydroxyproline, cystine, serotonin, ethanolamine, and taurine (p-value < 0.05 for each). No significant differences were determined regarding asparagine, citrulline, histidine, leucine, alloisoleucine, lysine, methionine, serine, threonine, tryptophan, anserine, alpha aminobutyric acid, beta aminoisobutyric acid, beta-alanine, cystathionine, histamine, and 5-oh-trp (p-value > 0.05 for all).

CONCLUSION

Patients with brucellosis have a specific profile of amino acids which may reflect sequelae of pathological and metabolic biochemical changes in the disease process due to the growth of Brucella spp. in the human body leading to an imbalance of amino acid levels.

Lipidome Profiling in Childhood Obesity Compared to Adults: A Pilot Study

The objective is to assess the circulating lipidome of children with obesity before and after lifestyle intervention and to compare the data to the circulating lipidome of adults with obesity before and after bariatric surgery. Ten pediatric (PE) and thirty adult (AD) patients with obesity were prospectively recruited at a referral single center. The PE cohort received lifestyle recommendations. The AD cohort underwent bariatric surgery. Clinical parameters and lipidome were analyzed in serum before and after six months of metabolic intervention. The abundance of phosphatidylinositols in the PE cohort and phosphatidylcholines in the AD significantly increased, while O-phosphatidylserines in the PE cohort and diacyl/triacylglycerols in the AD decreased. Fifteen lipid species were coincident in both groups after lifestyle intervention and bariatric surgery. Five species of phosphatidylinositols, sphingomyelins, and cholesteryl esters were upregulated. Eight species of diacylglycerols, glycerophosphoglycerols, glycerophosphoethanolamines, and phosphatidylcholines were downregulated. Most matching species were regulated in the same direction except for two phosphatidylinositols: PI(O-36:2) and PI(O-34:0). A specific set of lipid species regulated after bariatric surgery in adult individuals was also modulated in children undergoing lifestyle intervention, suggesting they may constitute a core circulating lipid profile signature indicative of early development of obesity and improvement after clinical interventions regardless of individual age.

Salivary Metabolomic Signatures and Body Mass Index in Italian Adolescents: A Pilot Study

Context

Obesity surveillance is scarce in adolescents, and little is known on whether salivary metabolomics data, emerging minimally invasive biomarkers, can characterize metabolic patterns associated with overweight or obesity in adolescents.

Objective

This pilot study aims to identify the salivary molecular signatures associated with body mass index (BMI) in Italian adolescents.

Methods

Saliva samples and BMI were collected in a subset of n = 74 young adolescents enrolled in the Public Health Impact of Metal Exposure study (2007-2014). A total of 217 untargeted metabolites were identified using liquid chromatography-high resolution mass spectrometry. Robust linear regression was used to cross-sectionally determine associations between metabolomic signatures and sex-specific BMI-for-age z-scores (z-BMI).

Results

Nearly 35% of the adolescents (median age: 12 years; 51% females) were either obese or overweight. A higher z-BMI was observed in males compared to females (P = .02). One nucleoside (deoxyadenosine) and 2 lipids (18:0-18:2 phosphatidylcholine and dipalmitoyl-phosphoethanolamine) were negatively related to z-BMI (P < .05), whereas 2 benzenoids (3-hydroxyanthranilic acid and a phthalate metabolite) were positively associated with z-BMI (P < .05). In males, several metabolites including deoxyadenosine, as well as deoxycarnitine, hyodeoxycholic acid, N-methylglutamic acid, bisphenol P, and trigonelline were downregulated, while 3 metabolites (3-hydroxyanthranilic acid, theobromine/theophylline/paraxanthine, and alanine) were upregulated in relation to z-BMI (P < .05). In females, deoxyadenosine and dipalmitoyl-phosphoethanolamine were negatively associated with z-BMI while deoxycarnitine and a phthalate metabolite were positively associated (P < .05). A single energy-related pathway was enriched in the identified associations in females (carnitine synthesis, P = .04).

Conclusion

Salivary metabolites involved in nucleotide, lipid, and energy metabolism were primarily altered in relation to BMI in adolescents.

Lipid Profiling Reveals Lipidomic Signatures of Weight Loss Interventions

Obesity is an epidemic all around the world. Weight loss interventions that are effective differ from each other with regard to various lipidomic responses. Here, we aimed to find lipidomic biomarkers that are related to beneficial changes in weight loss. We adopted an untargeted liquid chromatography with tandem mass spectrometry (LC-MS/MS) method to measure 953 lipid species for Exercise (exercise intervention cohort, N = 25), 1388 lipid species for LSG (laparoscopic sleeve gastrectomy cohort, N = 36), and 886 lipid species for Cushing (surgical removal of the ACTH-secreting pituitary adenomas cohort, N = 25). Overall, the total diacylglycerol (DG), triacylglycerol (TG), phosphatidylethanolamine (PE), phosphatidylinositol (PI), phosphatidylserine (PS), and sphingomyelin (SM) levels were associated with changes in BMI, glycated hemoglobin (HbA1c), triglyceride, and total cholesterol according to weight loss interventions. We found that 73 lipid species changed among the three weight loss interventions. We screened 13 lipid species with better predictive accuracy in diagnosing weight loss situations in either Exercise, LSG, or Cushing cohorts (AUROC > 0.7). More importantly, we identified three phosphatidylcholine (PC) lipid species, PC (14:0_18:3), PC (31:1), and PC (32:2) that were significantly associated with weight change in three studies. Our results highlight potential lipidomic biomarkers that, in the future, could be used in personalized approaches involving weight loss interventions.

Childhood Obesity and the Cryptic Language of the Microbiota: Metabolomics’ Upgrading

The growing obesity epidemic in childhood is increasingly concerning for the related physical and psychological consequences, with a significant impact on health care costs in both the short and the long term. Nonetheless, the scientific community has not yet completely clarified the complex metabolic mechanisms underlying body weight alterations. In only a small percentage of cases, obesity is the result of endocrine, monogenic, or syndromic causes, while in much more cases, lifestyle plays a crucial role in obesity development. In this context, the pediatric age appears to be of considerable importance as prevention strategies together with early intervention can represent important therapeutic tools not only to counteract the comorbidities that increasingly affect children but also to hinder the persistence of obesity in adulthood. Although evidence in the literature supporting the alteration of the microbiota as a critical factor in the etiology of obesity is abundant, it is not yet fully defined and understood. However, increasingly clear evidence is emerging regarding the existence of differentiated metabolic profiles in obese children, with characteristic metabolites. The identification of specific pathology-related biomarkers and the elucidation of the altered metabolic pathways would therefore be desirable in order to clarify aspects that are still poorly understood, such as the consequences of the interaction between the host, the diet, and the microbiota. In fact, metabolomics can characterize the biological behavior of a specific individual in response to external stimuli, offering not only an eventual effective screening and prevention strategy but also the possibility of evaluating adherence and response to dietary intervention.

Identification of potential biomarkers and metabolic insights for gestational diabetes prevention: A review of evidence contrasting gestational diabetes versus weight loss studies that may direct future nutritional metabolomics studies

Gestational diabetes mellitus (GDM) significantly increases maternal health risks and adverse effects for the offspring. Observational studies suggest that weight loss before pregnancy may be a promising GDM prevention method. Still, biochemical pathways linking preconception weight changes with subsequent development of GDM among women who are overweight or obese remain unclear. Metabolomic assessment is a powerful approach for understanding the global biochemical pathways linking preconception weight changes and subsequent GDM. We hypothesize that many of the alterations of metabolite levels associated with GDM will change in one direction in GDM studies but will change in the opposite direction in studies focusing on lifestyle interventions for weight loss. The present review summarizes available evidence from 21 studies comparing women with GDM with healthy participants and 12 intervention studies that investigated metabolite changes that occurred during weight loss using caloric restriction and behavioral interventions. We discuss 15 metabolites, including amino acids, lipids, amines, carbohydrates, and carbohydrate derivatives. Of particular note are the altered levels of branched-chain amino acids, alanine, palmitoleic acid, lysophosphatidylcholine 18:1, and hypoxanthine because of their mechanistic links to insulin resistance and weight change. Mechanisms that may explain how these metabolite modifications contribute to GDM development in those who are overweight or obese are proposed, including insulin resistance pathways. Future nutritional metabolomics preconception intervention studies in overweight or obese are necessary to investigate whether weight loss through lifestyle intervention can reduce GDM occurrence in association with these metabolite alterations and to test the value of these metabolites as potential diagnostic biomarkers of GDM development.

Changes in Plasma Metabolomic Profile Following Bariatric Surgery, Lifestyle Intervention or Diet Restriction-Insights from Human and Rat Studies

Although bariatric surgery is known to change the metabolome, it is unclear if this is specific for the intervention or a consequence of the induced bodyweight loss. As the weight loss after Roux-en-Y Gastric Bypass (RYGB) can hardly be mimicked with an evenly effective diet in humans, translational research efforts might be helpful. A group of 188 plasma metabolites of 46 patients from the randomized controlled Würzburg Adipositas Study (WAS) and from RYGB-treated rats (n = 6) as well as body-weight-matched controls (n = 7) were measured using liquid chromatography tandem mass spectrometry. WAS participants were randomized into intensive lifestyle modification (LS, n = 24) or RYGB (OP, n = 22). In patients in the WAS cohort, only bariatric surgery achieved a sustained weight loss (BMI -34.3% (OP) vs. -1.2% (LS), p ≤ 0.01). An explicit shift in the metabolomic profile was found in 57 metabolites in the human cohort and in 62 metabolites in the rodent model. Significantly higher levels of sphingolipids and lecithins were detected in both surgical groups but not in the conservatively treated human and animal groups. RYGB leads to a characteristic metabolomic profile, which differs distinctly from that following non-surgical intervention. Analysis of the human and rat data revealed that RYGB induces specific changes in the metabolome independent of weight loss.

A Metabolomics-Based Investigation of the Effects of a Short-Term Body Weight Reduction Program in a Cohort of Adolescents with Obesity: A Prospective Interventional Clinical Study

Metabolomics applied to assess the response to a body weight reduction program (BWRP) may generate valuable information concerning the biochemical mechanisms/pathways underlying the BWRP-induced cardiometabolic benefits. The aim of the present study was to establish the BWRP-induced changes in the metabolomic profile that characterizes the obese condition. In particular, a validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) targeted metabolomic approach was used to determine a total of 188 endogenous metabolites in the plasma samples of a cohort of 42 adolescents with obesity (female/male = 32/10; age = 15.94 ± 1.33 year; body mass index standard deviation score (BMI SDS) = 2.96 ± 0.46) who underwent a 3-week BWRP, including hypocaloric diet, physical exercise, nutritional education, and psychological support. The BWRP was capable of significantly improving body composition (e.g., BMI SDS, p < 0.0001), glucometabolic homeostasis (e.g., glucose, p < 0.0001), and cardiovascular function (e.g., diastolic blood pressure, p = 0.016). A total of 64 metabolites were significantly reduced after the intervention (at least p < 0.05), including 53 glycerophospholipids (23 PCs ae, 21 PCs aa, and 9 lysoPCs), 7 amino acids (tyrosine, phenylalanine, arginine, citrulline, tryptophan, glutamic acid, and leucine), the biogenic amine kynurenine, 2 sphingomyelins, and (free) carnitine (C0). On the contrary, three metabolites were significantly increased after the intervention (at least p < 0.05)-in particular, glutamine, trans-4-hydroxyproline, and the octadecenoyl-carnitine (C18:1). In conclusion, when administered to adolescents with obesity, a short-term BWRP is capable of changing the metabolomic profile in the plasma.

Low-dose radiation exaggerates HFD-induced metabolic dysfunction by gut microbiota through PA-PYCR1 axis

Co-exposure of High-fat-diet (HFD) behavior and environmental low-dose radiation (LDR) is common among majority occupational workers, but the synergism of this co-exposure in metabolic health is poorly understood. This study aimed to investigate the impact of gut microbiota and its metabolites on the regulation of HFD accompanied by LDR-associated with metabolic dysfunction and insulin resistance. Here, we reported that Parasutterella was markedly elevated in the gut microbiota of mice in co-exposure of HFD and LDR, accompanied by increased pyrrolidinecarboxylic acid (PA) level in both intestine and plasma. Transplantation of fecal microbiota from mice with co-exposure HFD and LDR with metabolic dysfunction resulted in increased disruption of metabolic dysfunction, insulin resistance and increased PYCR1 (Pyrroline-5-carboxylate reductase 1) expression. Mechanistically, intestinal barrier was damaged more serious in mice with co-exposure of HFD and LDR, leading high PA level in plasma, activating PYCR1 expression to inhibit insulin Akt/mTOR (AKT kinase-transforming protein/Serine threonine-protein kinase) signaling pathway to aggravate HFD-induced metabolic impairments. This study suggests a new avenue for interventions against western diet companied with low dose radiation exposure-driven metabolic impairments.

A deep learning method for predicting metabolite-disease associations via graph neural network

Metabolism is the process by which an organism continuously replaces old substances with new substances. It plays an important role in maintaining human life, body growth and reproduction. More and more researchers have shown that the concentrations of some metabolites in patients are different from those in healthy people. Traditional biological experiments can test some hypotheses and verify their relationships but usually take a considerable amount of time and money. Therefore, it is urgent to develop a new computational method to identify the relationships between metabolites and diseases. In this work, we present a new deep learning algorithm named as graph convolutional network with graph attention network (GCNAT) to predict the potential associations of disease-related metabolites. First, we construct a heterogeneous network based on known metabolite-disease associations, metabolite-metabolite similarities and disease-disease similarities. Metabolite and disease features are encoded and learned through the graph convolutional neural network. Then, a graph attention layer is used to combine the embeddings of multiple convolutional layers, and the corresponding attention coefficients are calculated to assign different weights to the embeddings of each layer. Further, the prediction result is obtained by decoding and scoring the final synthetic embeddings. Finally, GCNAT achieves a reliable area under the receiver operating characteristic curve of 0.95 and the precision-recall curve of 0.405, which are better than the results of existing five state-of-the-art predictive methods in 5-fold cross-validation, and the case studies show that the metabolite-disease correlations predicted by our method can be successfully demonstrated by relevant experiments. We hope that GCNAT could be a useful biomedical research tool for predicting potential metabolite-disease associations in the future.

Branched-Chain and Aromatic Amino Acids in Relation to Fat Mass and Fat-Free Mass Changes among Adolescents: A School-Based Intervention

Plasma levels of branched-chain amino acids (BCAA) and aromatic amino acids (AAA) are considered early metabolic markers of obesity and insulin resistance (IR). This study aimed to assess changes in plasma concentrations of BCAA/AAA and HOMA-IR2 (homeostasis model assessment of IR) after intervention-induced modifications in fat mass (FM) and fat-free mass (FFM) among French Polynesian adolescents. FM, FFM, plasma levels of BCAA and AAA, HOMA-IR2 were recorded at baseline and post intervention among 226 adolescents during a 5-month school-based intervention on diet and physical activity. Participants were divided into two subgroups according to their college attendance status which determined their intervention adherence: externs/half-residents (n = 157) and residents (n = 69). Four ordinal categories of body composition changes post-intervention were created for the analysis (FMgain/FFMlost < FMgain/FFMgain < FMlost/FFMlost < FMlost/FFMgain). After 5 months, changes in BCAA (p−trend < 0.001) and AAA (p−trend = 0.007) concentrations were positively associated with ordinal categories of body composition. HOMA-IR2 significantly decreased with FMlost (−0.40; 95% CI, −0.60 to −0.20) and increased with FMgain (0.23; 95% CI, 0.11 to 0.36). Our results suggest that FM loss is associated with a decrease in concentrations of obesity and IR metabolic markers which is more substantial when FM loss is accompanied with FFM gain.

Novel Potential Diagnostic Serum Biomarkers of Metabolomics in Osteoarticular Tuberculosis Patients: A Preliminary Study

Osteoarticular tuberculosis is one of the extrapulmonary tuberculosis, which is mainly caused by direct infection of Mycobacterium tuberculosis or secondary infection of tuberculosis in other parts. Due to the low specificity of the current detection method, it is leading to a high misdiagnosis rate and subsequently affecting the follow-up treatment and prognosis. Metabolomics is mainly used to study the changes of the body’s metabolites in different states, so it can serve as an important means in the discovery of disease-related metabolic biomarkers and the corresponding mechanism research. Liquid chromatography tandem mass spectrometry (LC-MS/MS) was used to detect and analyze metabolites in the serum with osteoarticular tuberculosis patients, disease controls, and healthy controls to find novel metabolic biomarkers that could be used in the diagnosis of osteoarticular tuberculosis. Our results showed that 68 differential metabolites (p<0.05, fold change>1.0) were obtained in osteoarticular tuberculosis serum after statistical analysis. Then, through the evaluation of diagnostic efficacy, PC[o-16:1(9Z)/18:0], PC[20:4(8Z,11Z,14Z,17Z)/18:0], PC[18:0/22:5(4Z,7Z,10Z,13Z,16Z)], SM(d18:1/20:0), and SM[d18:1/18:1(11Z)] were found as potential biomarkers with high diagnostic efficacy. Using bioinformatics analysis, we further found that these metabolites share many lipid metabolic signaling pathways, such as choline metabolism, sphingolipid signaling, retrograde endocannabinoid signaling, and sphingolipid and glycerophospholipid metabolism; these results suggest that lipid metabolism plays an important role in the pathological process of tuberculosis. This study can provide certain reference value for the study of metabolic biomarkers of osteoarticular tuberculosis and the mechanism of lipid metabolism in osteoarticular tuberculosis and even other tuberculosis diseases.

Metabolomic Profiles in Childhood and Adolescence Are Associated with Fetal Overnutrition

Fetal overnutrition predisposes offspring to increased metabolic risk. The current study used metabolomics to assess sustained differences in serum metabolites across childhood and adolescence among youth exposed to three typologies of fetal overnutrition: maternal obesity only, gestational diabetes mellitus (GDM) only, and obesity + GDM. We included youth exposed in utero to obesity only (BMI ≥ 30; n = 66), GDM only (n = 56), obesity + GDM (n = 25), or unexposed (n = 297), with untargeted metabolomics measured at ages 10 and 16 years. We used linear mixed models to identify metabolites across both time-points associated with exposure to any overnutrition, using a false-discovery-rate correction (FDR) <0.20. These metabolites were included in a principal component analysis (PCA) to generate profiles and assess metabolite profile differences with respect to overnutrition typology (adjusted for prenatal smoking, offspring age, sex, and race/ethnicity). Fetal overnutrition was associated with 52 metabolites. PCA yielded four factors accounting for 17−27% of the variance, depending on age of measurement. We observed differences in three factor patterns with respect to overnutrition typology: sphingomyelin-mannose (8−13% variance), skeletal muscle metabolism (6−10% variance), and 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF; 3−4% variance). The sphingomyelin-mannose factor score was higher among offspring exposed to obesity vs. GDM. Exposure to obesity + GDM (vs. GDM or obesity only) was associated with higher skeletal muscle metabolism and CMPF scores. Fetal overnutrition is associated with metabolic changes in the offspring, but differences between typologies of overnutrition account for a small amount of variation in the metabolome, suggesting there is likely greater pathophysiological overlap than difference.

Lipidomic signatures from physically frail and robust older adults at hospital admission

Identifying serum biomarkers that can predict physical frailty in older adults would have tremendous clinical value for primary care, as this condition is inherently related to poor quality of life and premature mortality. We compared the serum lipid profile of physically frail and robust older adults to identify specific lipid biomarkers that could be used to assess physical frailty in older patients at hospital admission. Forty-three older adults (58.1% male), mean (range) age 86.4 (78–100 years) years, were classified as physically frail (n = 18) or robust (n = 25) based on scores from the Short Physical Performance Battery (≤ 6 points). Non-targeted metabolomic study by ultra-high performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) analysis with later bioinformatics data analysis. Once the significantly different metabolites were identified, the KEGG database was used on them to establish which were the metabolic pathways mainly involved. Area under receiver-operating curve (AUROC) analysis was used to test the discriminatory ability of lipid biomarkers for frailty based on the Short Physical Performance Battery. We identified a panel of five metabolites including ceramides Cer (40:2), Cer (d18:1/20:0), Cer (d18:1/23:0), cholesterol, and phosphatidylcholine (PC) (14:0/20:4) that were significantly increased in physically frail older adults compared with robust older adults at hospital admission. The most interesting in the physically frail metabolome study found with the KEGG database were the metabolic pathways, vitamin digestion and absorption, AGE-RAGE signaling pathway in diabetic complications, and insulin resistance. In addition, Cer (40:2) (AUROC 0.747), Cer (d18:1/23:0) (AUROC 0.720), and cholesterol (AUROC 0.784) were identified as higher values of physically frail at hospital admission. The non-targeted metabolomic study can open a wide view of the physically frail features changes at the plasma level, which would be linked to the physical frailty phenotype at hospital admission. Also, we propose that metabolome analysis will have a suitable niche in personalized medicine for physically frail older adults.

Metabolic Status of Lean and Obese Zucker Rats Based on Untargeted and Targeted Metabolomics Analysis of Serum

Obesity is growing worldwide epidemic. Animal models can provide some clues about the etiology, development, prevention, and treatment of obesity. We examined and compared serum metabolites between seven lean (L) and seven obese (O) female Zucker rats to investigate the individual serum metabolic profile. A combination of HPLC-UV, HPLC-ECD, and LC-MS revealed more than 400 peaks. The 50 highest quality peaks were selected as the focus of our study. Untargeted metabolomics analysis showed significantly higher mean peak heights for 20 peaks in L rats, generally distributed randomly, except for a cluster (peaks 44–50) where L showed stable dominancy over O. Only eight peaks were significantly higher in O rats. Peak height ratios between pairs of L and O rats were significantly higher at 199 positions in L rats and at 123 positions in O rats. Targeted metabolomics analysis showed significantly higher levels of methionine, cysteine, tryptophan, kynurenic acid, and cysteine/cystine ratio in L rats and significantly higher levels of cystine and tyrosine in O rats. These results contribute to a better understanding of systemic metabolic perturbations in the obese Zucker rat model, emphasizing the value of both whole metabolome and individual metabolic profiles in the design and interpretation of studies using animal models.

A systematic review of metabolomic studies of childhood obesity: State of the evidence for metabolic determinants and consequences

Childhood obesity has become a global epidemic and carries significant long-term consequences to physical and mental health. Metabolomics, the global profiling of small molecules or metabolites, may reveal the mechanisms of development of childhood obesity and clarify links between obesity and metabolic disease. A systematic review of metabolomic studies of childhood obesity was conducted, following Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines, searching across Scopus, Ovid, Web of Science and PubMed databases for articles published from January 1, 2005 to July 8, 2020, retrieving 1271 different records and retaining 41 articles for qualitative synthesis. Study quality was assessed using a modified Newcastle-Ottawa Scale. Thirty-three studies were conducted on blood, six on urine, three on umbilical cord blood, and one on saliva. Thirty studies were primarily cross-sectional, five studies were primarily longitudinal, and seven studies examined effects of weight-loss following a life-style intervention. A consistent metabolic profile of childhood obesity was observed including amino acids (particularly branched chain and aromatic), carnitines, lipids, and steroids. Although the use of metabolomics in childhood obesity research is still developing, the identified metabolites have provided additional insight into the pathogenesis of many obesity-related diseases. Further longitudinal research is needed into the role of metabolic profiles and child obesity risk.

Paediatric obesity: a systematic review and pathway mapping of metabolic alterations underlying early disease processes

BACKGROUND

The alarming trend of paediatric obesity deserves our greatest awareness to hinder the early onset of metabolic complications impacting growth and functionality. Presently, insight into molecular mechanisms of childhood obesity and associated metabolic comorbidities is limited. This systematic review aimed at scrutinising what has been reported on putative metabolites distinctive for metabolic abnormalities manifesting at young age by searching three literature databases (Web of Science, Pubmed and EMBASE) during the last 6 years (January 2015-January 2021). Global metabolomic profiling of paediatric obesity was performed (multiple biological matrices: blood, urine, saliva and adipose tissue) to enable overarching pathway analysis and network mapping. Among 2792 screened Q1 articles, 40 met the eligibility criteria and were included to build a database on metabolite markers involved in the spectrum of childhood obesity. Differential alterations in multiple pathways linked to lipid, carbohydrate and amino acid metabolisms were observed. High levels of lactate, pyruvate, alanine and acetate marked a pronounced shift towards hypoxic conditions in children with obesity, and, together with distinct alterations in lipid metabolism, pointed towards dysbiosis and immunometabolism occurring early in life. Additionally, aberrant levels of several amino acids, most notably belonging to tryptophan metabolism including the kynurenine pathway and its relation to histidine, phenylalanine and purine metabolism were displayed. Moreover, branched-chain amino acids were linked to lipid, carbohydrate, amino acid and microbial metabolism, inferring a key role in obesity-associated insulin resistance.

CONCLUSIONS

This systematic review revealed that the main metabolites at the crossroad of dysregulated metabolic pathways underlying childhood obesity could be tracked down to one central disturbance, i.e. impending insulin resistance for which reference values and standardised measures still are lacking. In essence, glycolytic metabolism was evinced as driving energy source, coupled to impaired Krebs cycle flux and ß-oxidation. Applying metabolomics enabled to retrieve distinct metabolite alterations in childhood obesity(-related insulin resistance) and associated pathways at early age and thus could provide a timely indication of risk by elucidating early-stage biomarkers as hallmarks of future metabolically unhealthy phenotypes.

Blood plasma metabolomics of children and adolescents with sickle cell anaemia treated with hydroxycarbamide: a new tool for uncovering biochemical alterations

Sickle cell anaemia (SCA) is a debilitating genetic haemoglobinopathy predominantly affecting the disenfranchised strata of society in Africa and the Americas. The most common pharmacological treatment for this disease is the administration of hydroxycarbamide (HC) for which questions remain regarding its mechanism of action, efficacy and long-term toxicity specifically in paediatric individuals. A multiplatform metabolomics approach was used to assess the metabolome of plasma samples from a population of children and adolescents with SCA with and without HC treatment along with non-SCA individuals. Fifty-three metabolites were identified by ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) and ¹ H nuclear magnetic resonance (NMR) with a predominance of membrane lipids, amino acids and organic acids. The partial least-squares discriminant analysis (PLS-DA) analysis allowed a clear discrimination between the different studied groups, revealing clear effects of the HC treatment in the patients' metabolome including rescue of specific metabolites to control levels. Increased creatine/creatinine levels under HC treatment suggests a possible increase in the arginine pool and increased NO synthesis, supporting existing models for HC action in SCA. The metabolomics results extend the current knowledge on the models for SCA pathophysiology including impairment of Lands' cycle and increased synthesis of sphingosine 1-phosphate. Putative novel biomarkers are suggested.

Metabolic features of adiposity and glucose homoeostasis among school-aged inuit children from Nunavik (Northern Quebec, Canada)

In contrast to most Indigenous people in Canada, Inuit appeared until recently to have been protected from type 2 diabetes (T2D) related to obesity. We assessed the associations of metabolites (amino acids, acylcarnitines) with adiposity and biomarkers of T2D in school-aged Inuit children of Nunavik (Canada). Concentrations of metabolite were measured in plasma samples from a cross-sectional analysis of 248 children (mean age = 10.8 years). We assessed associations of plasma metabolites with adiposity measures (BMI, skinfold thicknesses) and T2D markers (insulin, glucose, adiponectin). Plasma concentrations of valine and tyrosine were higher in obese and overweight children compared to those of normal weight children (P < 0.05). An increment of 1-SD in BMI (SD = 3.3 kg/m²) was statistically associated with an increment of 0.21 (95% CI: 0.08, 0.33) for valine, 0.15 (95% CI: 0.02, 0.27) for isoleucine and 0.17 (95% CI: 0.04, 0.29) for tyrosine. Insulin concentration increased with concentrations of all amino acids (P < 0.05) except methionine. None of the acylcarnitines measured were statistically significantly associated with adiposity or T2D biomarkers A signature of metabolites, particularly higher levels of branched-chain amino acids, might allow for early detection of T2D among school-aged Inuit children.

Intergenerational Metabolomic Analysis of Mothers with a History of Gestational Diabetes Mellitus and Their Offspring

Shared metabolomic patterns at delivery have been suggested to underlie the mother-to-child transmission of adverse metabolic health. This study aimed to investigate whether mothers with gestational diabetes mellitus (GDM) and their offspring show similar metabolomic patterns several years postpartum. Targeted metabolomics (including 137 metabolites) was performed in plasma samples obtained during an oral glucose tolerance test from 48 mothers with GDM and their offspring at a cross-sectional study visit 8 years after delivery. Partial Pearson's correlations between the area under the curve (AUC) of maternal and offspring metabolites were calculated, yielding so-called Gaussian graphical models. Spearman's correlations were applied to investigate correlations of body mass index (BMI), Matsuda insulin sensitivity index (ISI-M), dietary intake, and physical activity between generations, and correlations of metabolite AUCs with lifestyle variables. This study revealed that BMI, ISI-M, and the AUC of six metabolites (carnitine, taurine, proline, SM(-OH) C14:1, creatinine, and PC ae C34:3) were significantly correlated between mothers and offspring several years postpartum. Intergenerational metabolite correlations were independent of shared BMI, ISI-M, age, sex, and all other metabolites. Furthermore, creatinine was correlated with physical activity in mothers. This study suggests that there is long-term metabolic programming in the offspring of mothers with GDM and informs us about targets that could be addressed by future intervention studies.

Metabolic Signatures of Gestational Weight Gain and Postpartum Weight Loss in a Lifestyle Intervention Study of Overweight and Obese Women

Background: Overweight and obesity amongst women of reproductive age are increasingly common in developed economies and are shown to adversely affect birth outcomes and both childhood and adulthood health risks in the offspring. Metabolic profiling in conditions of overweight and obesity in pregnancy could potentially be applied to elucidate the molecular basis of the adverse effects of gestational weight gain (GWG) and postpartum weight loss (WL) on future risks for cardiovascular disease (CVD) and other chronic diseases. Methods: Biofluid samples were collected from 114 ethnically diverse pregnant women with body mass index (BMI) between 25 and 40 kg/m² from Chicago (US), as part of a randomized lifestyle intervention trial (Maternal Offspring Metabolics: Family Intervention Trial; NCT01631747). At 15 weeks, 35 weeks of gestation, and at 1 year postpartum, the blood plasma lipidome and metabolic profile of urine samples were analyzed by liquid chromatography mass spectrometry (LC-MS) and ¹H nuclear magnetic resonance spectroscopy (¹H NMR) respectively. Results: Urinary 4-deoxyerythronic acid and 4-deoxythreonic acid were found to be positively correlated to BMI. Seventeen plasma lipids were found to be associated with GWG and 16 lipids were found to be associated with WL, which included phosphatidylinositols (PI), phosphatidylcholines (PC), lysophospholipids (lyso-), sphingomyelins (SM) and ether phosphatidylcholine (PC-O). Three phospholipids found to be positively associated with GWG all contained palmitate side-chains, and amongst the 14 lipids that were negatively associated with GWG, seven were PC-O. Six of eight lipids found to be negatively associated with WL contained an 18:2 fatty acid side-chain. Conclusions: Maternal obesity was associated with characteristic urine and plasma metabolic phenotypes, and phospholipid profile was found to be associated with both GWG and postpartum WL in metabolically healthy pregnant women with overweight/obesity. Postpartum WL may be linked to the reduction in the intake of linoleic acid/conjugated linoleic acid food sources in our study population.

Altered metabolomic profiling of overweight and obese adolescents after combined training is associated with reduced insulin resistance

Exercise training and a healthy diet are the main non-pharmacological strategies for treating chronic conditions, such as obesity and insulin resistance (IR), in adolescents. However, the isolated metabolic changes caused by exercise training without dietary intervention have not yet been established. We investigated how combined training (CT) without dietary intervention altered the concentrations of serum metabolites, biochemical, anthropometric and functional parameters in overweight and obese adolescents. Thirty-seven adolescents (14.6 ± 1.05 years), of both sexes, were randomly assigned to the control group (CG, n = 19) or the training group (TG, n = 18). The CT was composed by resistance training and aerobic training performed in the same session (~ 60 min), three times a week, for 12 weeks. All assessments were performed pre and post-intervention. Metabolomics analyses were conducted using nuclear magnetic resonance spectroscopy (1H NMR) in a 600 MHz spectrometer. There was a decrease in body weight (BW), body mass index (BMI), waist circumference (WC), % body fat (%BF), fasting glucose, insulin levels, and insulin resistance (IR), by HOMA-IR, in the TG. An increase in fat-free mass (FFM) was also observed in the CG. The metabolic changes were given mainly by changes in the levels of metabolites 2-oxoisocaproate (↓TG), 3-hydroxyisobutyrate (↑CG and ↓TG), glucose (↓TG), glutamine (↓CG and ↑TG) and pyruvate (↓TG). These findings demonstrate the positive effects of CT program without dietary intervention on metabolomic profile, body composition, biochemical markers, and glucose metabolism in overweight and obese adolescents.

Integration of Transcriptome and Metabolome Provides Unique Insights to Pathways Associated With Obese Breast Cancer Patients

Information regarding transcriptome and metabolome has significantly contributed to identifying potential therapeutic targets for the management of a variety of cancers. Obesity has profound effects on both cancer cell transcriptome and metabolome that can affect the outcome of cancer therapy. The information regarding the potential effects of obesity on breast cancer (BC) transcriptome, metabolome, and its integration to identify novel pathways related to disease progression are still elusive. We assessed the whole blood transcriptome and serum metabolome, as circulating metabolites, of obese BC patients compared them with non-obese BC patients. In these patients' samples, 186 significant differentially expressed genes (DEGs) were identified, comprising 156 upregulated and 30 downregulated. The expressions of these gene were confirmed by qRT-PCR. Furthermore, 96 deregulated metabolites were identified as untargeted metabolomics in the same group of patients. These detected DEGs and deregulated metabolites enriched in many cellular pathways. Further investigation, by integration analysis between transcriptomics and metabolomics data at the pathway levels, revealed seven unique enriched pathways in obese BC patients when compared with non-obese BC patients, which may provide resistance for BC cells to dodge the circulating immune cells in the blood. In conclusion, this study provides information on the unique pathways altered at transcriptome and metabolome levels in obese BC patients that could provide an important tool for researchers and contribute further to knowledge on the molecular interaction between obesity and BC. Further studies are needed to confirm this and to elucidate the exact underlying mechanism for the effects of obesity on the BC initiation or/and progression.

The effect of eight-week walking program on plasma levels of amino acids in early/mid pubertal obese girls

Background: Altered circulating amino acids levels have been observed in metabolic disorders, like obesity, type-2 diabetes, and other insulin-resistant states. This study aimed to investigate the effect of 8-week walking on plasma amino acids (PAAs) in obese girls. Methods: This clinical trial study (IRCT20180928041160N1) was conducted on 32 early/mid pubertal obese girls which they divided into interval-walking (IWG, n=12), continuous-walking (CWG, n=11) and control (CG, n=9) groups. The walking program (3- sessions/week for 8-weeks) consists of 30-min walking with 70-85%HRmax and 60-75%HRmax, respectively in the IWG (2-min walking and 1-min active rest) and CWG. The concentration of PAAs was measured at baseline and 72-hours after the last session in fasting state, using high-performance liquid chromatography. A repeated measures ANCOVA (group (3) * time (2)) with post hoc Bonferroni was used to analyze the data. Results: More the PAAs were not affected by interval or continuous walking training. A significant increase in lysine (p=0.003, 95%CI 24.08, 108.97) was observed only in the CG, and there was a significant difference between the CG and CWG (p=0.032). Global arginine bioavailability (GABA) significantly decreased in the CG (P<0.001, 95%CI -0.65, -0.21) and the IWG (p=0.004, 95%CI -0.60, -0.21). A significant increase in weight (p=0.043, 95%CI 0.27, 1.46), insulin (p=0.046, 95%CI -0.91, 9.01), and HOMA-IR (p=0.007, 95%CI 0.26, 2.63) were found only in the CG, and both insulin and HOMA-IR tended to decline in the CWG. Conclusion: Except for lysine and GABA, all groups roughly showed similar changes in more amino acids. Continuous-walking could improve the plasma level of lysine and GABA, which along with an improvement of fasting insulin levels and HOMA-IR.

Changes of Differential Urinary Metabolites after High-Intensive Training in Teenage Football Players

Objective

The mechanism underlying the fatigue of football players is closely related to the energy depletion and accumulation of metabolites; the present study tries to explore the metabolic mechanism in teenage football players during exercise-induced fatigue.

Methods

12 teenage football players were subjected to three groups of combined training by using a cycle ergometer, with the subjective Rating of Perceived Exertion (RPE) as a fatigue criterion. The following indicators were measured in each group after training: maximum oxygen uptake (VO_2max), anaerobic power, and average anaerobic power. Urine samples were collected before and after the training. Gas chromatography-mass spectrometry (GC-MS) was performed for the metabonomics analysis of the samples. The metabolism data was analyzed by using principal component analysis (PCA) and orthogonal partial least squares analysis (OPLS-DA), through the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to confirm the potential differences between metabolites, and the MetPA database was used to analyze the related metabolic pathways.

Results

There was no significant difference between the maximal oxygen uptakes among the three groups. Compared with group 1, the maximum and average anaerobic power in group 3 significantly decreased (p < 0.05) at the end of training. GC-MS detected 635 metabolites in the urine samples. Through PCA, OPLS-DA analysis, and KEGG matching, 25 different metabolites (3↑22↓) that met the conditions were finally selected. These different metabolites belonged to 5 metabolic pathways: glycine-serine-threonine metabolism, citrate cycle, tyrosine metabolism, nitrogen metabolism, and glycerophospholipid metabolism.

Conclusions

During the combined exercise of aerobic and anaerobic metabolism, teenage football players show a significant decrease in anaerobic capacity after fatigue. The metabolic mechanism of exercise fatigue was related to disorders in amino acid and energy metabolism.

Lipidomic Profile Revealed the Association of Plasma Lysophosphatidylcholines with Adolescent Obesity

Objective

The human lipidomic profile reflects lipid metabolism, including the early phase of pathophysiological changes associated with diseases. An investigation of the association between the plasma lipidomic profile and adolescent obesity might provide new insights into the biological mechanisms of obesity. Therefore, we aimed to investigate the association of the plasma lipidome with obesity in Chinese adolescents using lipidomics.

Methods

Using a combination of liquid chromatography and electrospray ionization tandem mass spectrometry, we quantified 328 lipid species from 24 lipid classes and subclasses in 100 male adolescents aged 14–16 years who were categorized into four groups: (1) normal weight with traditional normal clinical plasma lipid levels (NN); (2) normal weight with traditional abnormal clinical plasma lipid levels (NA); (3) obese with traditional normal clinical plasma lipid levels (ON); and (4) obese with traditional abnormal clinical plasma lipid levels (OA). The concentrations of all the lipid species were compared between obese and normal-weight adolescents at different traditional clinical plasma lipid levels using the Kruskal–Wallis test followed by the Mann–Whitney U test. A partial least squares discriminant analysis (PLS-DA) was applied to select lipids with a significant ability to discriminate adolescent obesity.

Results

The lipidomic profile distinguished obese adolescents from normal-weight subjects. Regardless of whether traditional clinical plasma lipid levels were normal or abnormal, we observed a significant reduction in the levels of five lysophosphatidylcholines (LPC) species (LPC18:2, LPC18:1, LPC20:2, LPC20:1, and LPC20:0) in the obese group compared with the normal-weight group (difference = −31.29% to −13.19%; P=9.91 × 10⁻⁵ to 2.28 × 10⁻²). The ability of these five LPC species to discriminate adolescent obesity was confirmed in the PLS-DA model.

Conclusions

The findings provided evidence for the association of some LPC species with adolescent obesity. The discriminatory effects of five LPC species were identified between normal-weight and obese adolescents, independent of traditional clinical plasma lipid levels. These results will provide a basis for validation in subsequent studies.

Effect of Obesity and Exercise Training on Plasma Amino Acids and Amino Metabolites in American Indian Adolescents

CONTEXT

Amino acids (AAs) and their metabolites are altered with obesity and may be predictive of future diabetes in adults, but there are fewer studies on AAs, as well as conflicting findings on how they vary with obesity, in adolescents.

OBJECTIVE

To determine whether plasma AAs vary with body composition and insulin sensitivity and are altered in response to exercise training.

DESIGN

Cross-sectional, and an exercise intervention.

SETTING

Tribal wellness center.

PARTICIPANTS

American Indian boys and girls, 11 to 17 years of age with obesity (Ob, n = 58) or normal weight (NW, n = 36).

INTERVENTION

The Ob group completed 16 weeks of aerobic exercise training.

MAIN OUTCOME MEASURE

A panel of 42 plasma AAs.

RESULTS

Compared with the NW group, the Ob group had lower aerobic fitness and insulin sensitivity (interactive homeostasis model assessment 2), 17 AAs that were higher, and 7 AAs that were lower. Branched-chain AAs (+10% to 16%), aromatic AAs (+15% to 32%), and glutamate were among the higher AAs; all were positively correlated with body fat and negatively correlated with insulin sensitivity. The lysine metabolite 2-aminoadipic acid (2-AAA) and the valine metabolite β-aminoisobutyric acid (BAIBA) were 47% higher and 29% lower, respectively, in the Ob group, and were positively (2-AAA) and negatively (BAIBA) correlated with insulin sensitivity. Exercise training increased aerobic fitness by 10%, but body composition, insulin sensitivity, and AAs were not significantly changed.

CONCLUSIONS

Several plasma AAs are altered in American Indian adolescents with obesity and are associated with insulin sensitivity, but they were not altered with this exercise intervention.

Microbial and metabolomic remodeling by a formula of Sichuan dark tea improves hyperlipidemia in apoE-deficient mice

Medicine-food homology is a long-standing concept in traditional Chinese medicine. YiNianKangBao (YNKB) tea is a medicine-food formulation based on Sichuan dark tea (Ya'an Tibetan tea), which is traditionally used for its lipid-lowering properties. In this study, we evaluated the effects of YNKB on dyslipidemia and investigated the mechanism underlying its correlation with gut microbiota and serum metabolite regulation. Wild-type mice were fed a normal diet as a control. Male ApoE-/- mice were randomly divided into three high-fat diet (HFD) groups, a model group, and two treated groups (100, 400 mg/kg/d for low, high-dose), and fed by gavage for 12 weeks. Serum lipid levels, composition of gut microbiota, and serum metabolites were then analyzed before treatment with YNKB. We extracted the ingredients of YNKB in boiled water for one hour. YNKB supplementation at a high dose of 400 mg/kg/day reduced bodyweight gains (relative epididymal fat pad and liver weight), and markedly attenuated serum lipid profiles and atherosclerosis index, with no significant differences present between the low-dose treatment and HFD groups. Gut microbiota and serum metabolic analysis indicated that significant differences were observed between normal, HFD, and YNKB treatment groups. These differences in gut microbiota exhibited strong correlations with dyslipidemia-related indexes and serum metabolite levels. Oral administration of high-dose YNKB also showed significant lipid-lowering activity against hyperlipidemia in apoE-deficient mice, which might be associated with composition alterations of the gut microbiota and changes in serum metabolite abundances. These findings highlight that YNKB as a medicine-food formulation derived from Sichuan dark tea could prevent dyslipidemia and improve the understanding of its mechanisms and the pharmacological rationale for preventive use.

Analysis of serum metabolomics among biopsy-proven diabetic nephropathy, type 2 diabetes mellitus and healthy controls

Type 2 diabetes mellitus (T₂DM) has a rising prevalence and diabetic nephropathy (DN) is a major complication of T₂DM. Metabolomics could provide novel insights into the pathogenesis, so we aimed to explore serum metabolomic profiles from DN to T₂DM. Serum samples were collected from 14 biopsy-proven DNs, 14 age/gender-matched T₂DMs without renal diseases (DM), 14 age/gender-matched healthy controls (CTRL) and household contacts of DM group (HH). Serum metabolomics was analyzed by untargeted liquid chromatography-tandem mass spectrometry (LC/MS) assays. There were a total of 1470 metabolites identified from all serum samples. 45 metabolites with significantly different intensity were found between DN and DM, e.g., biliverdin and taurine were reduced while l-arginine was increased in DN comparing to DM. DN could be distinguished from age/gender matched DM patients by l-arginine (AUC = 0.824) or taurine levels (AUC = 0.789). The metabolic pathways affected by metabolite distinctions between DN and DM also existed, among which taurine and hypotaurine metabolism exhibited the highest pathway impact. l-Methionine, deethylatrazine, l-tryptophan and fumaric acid were reduced in DM comparing with those of CTRL, but had no different intensity in DM and HH groups. The changes were demonstrated in the metabolomic profiles of biopsy-proven DN compared to DM. Biopsy-proven DN patients could be distinguished from age/gender matched DM by l-arginine or taurine levels in serum metabolomic profiles. Taurine and hypotaurine metabolism pathway had the highest impact in pathway set enrichment analysis, which potentially affected the pathogenesis of DN from T₂DM.

Metabolites between healthy controls (CTRL)/type 2 diabetes mellitus without renal diseases (DM), and DM/diabetic nephropathy (DN).

Glutamate interactions with obesity, insulin resistance, cognition and gut microbiota composition

AIMS

To investigate the interactions among fecal and plasma glutamate levels, insulin resistance cognition and gut microbiota composition in obese and non-obese subjects.

METHODS

Gut microbiota composition (shotgun) and plasma and fecal glutamate, glutamine and acetate (NMR) were analyzed in a pilot study of obese and non-obese subjects (n = 35). Neuropsychological tests [Trail making test A (TMT-A) and Trail making test B (TMT-B)] scores measured cognitive information about processing speed, mental flexibility and executive function.

RESULTS

Trail-making test score was significantly altered in obese compared with non-obese subjects. Fecal glutamate and glutamate/glutamine ratio tended to be lower among obese subjects while fecal glutamate/acetate ratio was negatively associated with BMI and TMT-A scores. Plasma glutamate/acetate ratio was negatively associated with TMT-B. The relative abundance (RA) of some bacterial families influenced glutamate levels, given the positive association of fecal glutamate/glutamine ratio with Corynebacteriaceae, Coriobacteriaceae and Burkholderiaceae RA. In contrast, Streptococaceae RA, that was significantly higher in obese subjects, negatively correlated with fecal glutamate/glutamine ratio. To close the circle, Coriobacteriaceae/Streptococaceae ratio and Corynebacteriaceae/Streptococaceae ratio were associated both with TMT-A scores and fecal glutamate/glutamine ratio.

CONCLUSIONS

Gut microbiota composition is associated with processing speed and mental flexibility in part through changes in fecal and plasma glutamate metabolism.

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.

Human White Adipose Tissue Metabolome: Current Perspective

OBJECTIVE

Interest in metabolites produced by adipose tissue has increased substantially in the past several decades. Previously regarded as an inert energy storage depot, adipose tissue is now viewed as a complex metabolically active organ with considerable impact on human health. The emerging field of mass spectrometry-based metabolomics presents a powerful tool for the study of processes in complex biological matrices including adipose tissue.

RESULTS

A large number of structurally distinct metabolites can be analyzed to facilitate the investigation of differences between physiological and pathophysiological metabolic profiles associated with adipose tissue. Understanding the molecular basis of adipose tissue regulation can thereby provide insight into the monitoring of obesity-related metabolic disorders and lead to the development of novel diagnostic and prognostic biomarkers.

CONCLUSIONS

This review provides the current state of knowledge, recent progress, and critical evaluation of metabolomics approaches in the context of white adipose tissue and obesity. An overview of basic principles and resources describing individual groups of metabolites analyzed in white adipose tissue and biological fluids is given. The focus is on metabolites that can serve as reliable biomarkers indicative of metabolic alterations associated with obesity.

Lifestyle Intervention Decreases Urine Trimethylamine N-Oxide Levels in Prepubertal Children with Obesity

OBJECTIVE

Early lifestyle interventions in children with obesity decrease risk of obesity and metabolic disorders during adulthood. This study aimed to identify metabolic signatures associated with lifestyle intervention in urine samples from prepubertal children with obesity.

METHODS

Thirty-four prepubertal children with obesity were studied before and after a 6-month lifestyle intervention program, and anthropometric, metabolic, and nutritional variables were collected. A nuclear magnetic resonance approach was applied to obtain the metabolomic profile from urine samples. Partial least squares-discriminant analysis (PLS-DA) was used to achieve group classification and variable importance on projection (VIP) for biomarker selection.

RESULTS

The intervention reduced caloric intake by 10% (P < 0.05) and BMI standard deviation score by 0.47 SD (P < 0.001). PLS-DA identified trimethylamine N-oxide (TMAO, VIP = 2.21) as the metabolite with the highest discrimination properties between groups. Urine TMAO levels were reduced after the intervention (P < 0.05). TMAO is a biomarker of cardiovascular disease risk and is a product of gut microbiota-dependent metabolism of certain dietary compounds, including choline. Notably, changes in TMAO levels after the intervention did not correlate to differences in choline intake but were inversely associated with fiber intake (P < 0.05).

CONCLUSIONS

These results indicate that lifestyle intervention decreases TMAO levels in children with obesity.

Changes in Plasma Acylcarnitine and Lysophosphatidylcholine Levels Following a High-Fructose Diet: A Targeted Metabolomics Study in Healthy Women

Background: The consumption of high amounts of fructose is associated with metabolic diseases. However, the underlying mechanisms are largely unknown. Objective: To determine the effects of high fructose intake on plasma metabolomics. Study design: We enrolled 12 healthy volunteers (six lean and six obese women, age 24–35 years) in a crossover intervention study. All participants carried out three diets: (1) low fructose (<10 g/day); (2) high fructose (100 g/day) from natural food sources (fruit); and (3) high fructose (100 g/day) from high fructose syrup (HFS). Outcome measures: The primary outcome was changes in plasma metabolites measured by targeted metabolomics. Results: High compared to low fructose diets caused a marked metabolite class separation, especially because of changes in acylcarnitine and lysophosphatidylcholine levels. Both high fructose diets resulted in a decrease in mean acylcarnitine levels in all subjects, and an increase in mean lysophosphatidylcholine and diacyl-phosphatidylcholine levels in obese individuals. Medium chain acylcarnitines were negatively correlated with serum levels of liver enzymes and with the fatty liver index. Discussion: The metabolic shifts induced by high fructose consumption suggest an inhibition of mitochondrial β-oxidation and an increase in lipid peroxidation. The effects tended to be more pronounced following the HFS than the fruit diet.

Effectiveness of vitamin D therapy in improving metabolomic biomarkers in obesity phenotypes: Two randomized clinical trials

BACKGROUND

Uncertainty remains about the effect of vitamin D therapy on biomarkers of health status in obesity. The molecular basis underlying this controversy is largely unknown.

OBJECTIVE

To address the existing gap, our study sought to compare changes in metabolomic profiles of obesity phenotypes (metabolically healthy obese (MHO) and metabolically unhealthy obese (MUHO)) patients with sub-optimal levels of vitamin D following vitamin D supplementation.

METHODS

We conducted two randomized double-blind clinical trials on participants with either of the two obesity phenotypes from Tehran province. These phenotypes were determined by the Adult Treatment Panel-III criteria. Patients in each of the MHO (n = 110) and MUHO (n = 105) groups were separately assigned to receive either vitamin D (4000 IU/d) or placebo for 4 months. Pre- and post-supplementation plasma metabolomic profiling were performed using Liquid chromatography coupled to a triple quadrupole mass spectrometry. Multivariable linear regression was used to explore the association of change in each metabolite with the trial assignment (vitamin D/placebo) across obesity phenotypes.

RESULTS

Metabolites (n = 104) were profiled in 82 MHO and 78 MUHO patients. After correction for multiple comparisons, acyl-lysophosphatidylcholines C16:0, C18:0, and C18:1, diacyl-phosphatidylcholines C32:0, C34:1, C38:3, and C38:4, and sphingomyelin C40:4 changed significantly in response to vitamin D supplementation only in MUHO phenotype. The interaction analysis revealed that vitamin D therapy was different between the two obesity phenotypes based on acyl-lysophosphatidylcholines C16:0 and C16:1 and citrulline which were altered significantly after supplementation. Changes in metabolites were associated with changes in cardiometabolic biomarkers after the intervention.

CONCLUSIONS

Vitamin D treatment influenced the obesity-related plasma metabolites only in adults with obesity and metabolically unhealthy phenotype. Therefore, not all patients with obesity may benefit from an identical strategy for vitamin D therapy. These findings provide mechanistic basis highlighting the potential of precision medicine to mitigate diseases in health-care settings.

Identification of metabolic phenotypes in childhood obesity by 1H NMR metabolomics of blood plasma

Aim:

To identify the plasma metabolic profile associated with childhood obesity and its metabolic phenotypes.

Materials & methods:

The plasma metabolic profile of 65 obese and 37 normal-weight children was obtained using proton NMR spectroscopy. NMR spectra were rationally divided into 110 integration regions, which reflect relative metabolite concentrations, and were used as statistical variables.

Results:

Obese children show increased levels of lipids, N-acetyl glycoproteins, and lactate, and decreased levels of several amino acids, α-ketoglutarate, glucose, citrate, and cholinated phospholipids as compared with normal-weight children. Metabolically healthy children show lower levels of lipids and lactate, and higher levels of several amino acids and cholinated phospholipids, as compared with unhealthy children.

Conclusion:

This study reveals new valuable findings in the field of metabolomics and childhood obesity. Although validation should be performed, the proof of principle looks promising and justifies a deeper investigation of the diagnostic possibilities of proton NMR metabolomics in follow-up studies.

Trial registration: NCT03014856. Registered January 9, 2017.

The plasma metabolic profile of childhood obesity and its metabolic phenotypes was identified using untargeted proton NMR spectroscopy combined with multivariate statistics. Obese children show increased plasma levels of lipids, N-acetyl glycoproteins and lactate, next to decreased levels of several amino acids, α-ketoglutarate, glucose, citrate and cholinated phospholipids as compared with normal-weight children. In addition, the metabolic profile of healthy and unhealthy obese children could be discriminated and although further validation should be performed, these findings might pave the way to a detailed diagnostic metabolic signature in children.

Untargeted metabolomics identifies a plasma sphingolipid-related signature associated with lifestyle intervention in prepubertal children with obesity

OBJECTIVE

Childhood obesity is a strong risk factor for adult obesity and metabolic diseases, including type 2 diabetes and cardiovascular disease. Early lifestyle intervention in children with obesity reduces future disease risk. The objective of this study is to identify metabolic signatures associated with lifestyle intervention in prepubertal children with obesity.

METHODS

Thirty-five prepubertal children (7-10 years) with obesity (body mass index (BMI)>2 standard deviations) were enrolled in the study and participated in a 6-month-long lifestyle intervention program. Physiological and biochemical data and blood samples were collected both at baseline and after the intervention. A liquid chromatography-mass spectrometry (LC-MS)-based metabolomics approach was applied to obtain a comprehensive profiling of plasma samples, identifying 2581 distinct metabolite. Principal component analysis (PCA) was performed to consolidate all features into 8 principal components. Associations between metabolites and physiological and biochemical variables were investigated.

RESULTS

The intervention program significantly decreased mean (95% CI) BMI standard deviation score from 3.56 (3.29-3.84) to 3.11 (2.88-3.34) (P<0.001). PCA identified one component (PC1) significantly altered by the intervention (Bonferroni adjusted P=0.008). A sphingolipid metabolism-related signature was identified as the major contributor to PC1. Sphingolipid metabolites were decreased by the intervention, and included multiple sphingomyelin, ceramide, glycosylsphingosine and sulfatide species. Changes in several sphingolipid metabolites were associated with intervention-induced improvements in HbA1c levels.

CONCLUSIONS

Decreased circulating sphingolipid-related metabolites were associated with lifestyle intervention in prepubertal children with obesity, and correlated to improvements in HbA1c.

Obesity, adipokines, and C-peptide are associated with distinct plasma phospholipid profiles in adult males, an untargeted lipidomic approach

Obesity is associated with dysregulated lipid metabolism and adipokine secretion. Our group has previously reported obesity and adipokines are associated with % total fatty acid (FA) differences in plasma phospholipids. The objective of our current study was to identify in which complex lipid species (i.e., phosphatidylcholine, sphingolipids, etc) these FA differences occur. Plasma lipidomic profiling (n = 126, >95% Caucasian, 48–65 years) was performed using chromatographic separation and high resolution tandem mass spectrometry. The responses used in the statistical analyses were body mass index (BMI), waist circumference (WC), serum adipokines, cytokines, and a glycemic marker. High-dimensional statistical analyses were performed, all models were adjusted for age and smoking, and p-values were adjusted for false discovery. In Bayesian models, the lipidomic profiles (over 1,700 lipids) accounted for >60% of the inter-individual variation of BMI, WC, and leptin in our population. Across statistical analyses, we report 51 individual plasma lipids were significantly associated with obesity. Obesity was inversely associated lysophospholipids and ether linked phosphatidylcholines. In addition, we identify several unreported lipids associated with obesity that are not present in lipid databases. Taken together, these results provide new insights into the underlying biology associated with obesity and reveal new potential pathways for therapeutic targeting.

Metabolite profiling of obese individuals before and after a one year weight loss program

OBJECTIVE

We and others have previously characterized changes in circulating metabolite levels following diet-induced weight loss. Our aim was to investigate whether baseline metabolite levels and weight-loss-induced changes in these are predictive of or associated with changes in body mass index (BMI) and metabolic risk traits.

METHODS

Serum metabolites were analyzed with gas and liquid chromatography/mass spectrometry in 91 obese individuals at baseline and after participating in a 1 year non-surgical weight loss program.ResultsA total of 137 metabolites were identified and semi-quantified at baseline (BMI 42.7±5.8, mean±s.d.) and at follow-up (BMI 36.3±6.6). Weight-loss-induced modification was observed for levels of 57 metabolites in individuals with ⩾10% weight loss. Lower baseline levels of xylitol was predictive of a greater decrease in BMI (β=0.06, P<0.01) and ⩾10% weight loss (odds ratio (OR)=0.2, confidence interval (CI)=0.07-0.7, P=0.01). Decreases in levels of isoleucine, leucine, valine and tyrosine were associated with decrease in BMI (β>0.1, P<0.05) and ⩾10% weight loss (isoleucine: OR=0.08, CI=0.01-0.3, leucine: OR=0.1, CI=0.01-0.6, valine: OR=0.1, CI=0.02-0.5, tyrosine: OR=0.1, CI=0.03-0.6, P<0.02).

CONCLUSIONS

Diet-induced weight loss leads to mainly reduced levels of metabolites that are elevated in obese insulin resistant individuals. We identified multiple new associations with metabolic risk factors and validated several previous findings related to weight loss-mediated metabolite changes. Levels of specific metabolites, such as xylitol, may be predictive of the response to non-surgical weight loss already at baseline.

Livestock metabolomics and the livestock metabolome: A systematic review

Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular “omics” approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at http://www.lmdb.ca). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed.

Sex differences in the association of phospholipids with components of the metabolic syndrome in young adults

BACKGROUND

There are differences in the prevalence and severity of diseases between males, females not taking hormonal contraceptives (non-HC females) and females taking hormonal contraceptives (HC females). The aim of this study was to identify sex-specific differences in the metabolome and its relation to components of the metabolic syndrome in a young adult population.

METHODS

The subjects analysed are from the 20-year follow-up of the Western Australian Pregnancy Cohort (Raine) Study. Two hundred fifteen plasma metabolites were analysed in 1021 fasted plasma samples by a targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) metabolomics approach. Principal component analysis between males (n = 550), non-HC females (n = 199) and HC females (n = 269) was applied. Regression analysis with a sex × metabolite concentration interaction was performed on components of the MetS, namely waist circumference, systolic blood pressure, and plasma HDL-C, triglycerides and glucose concentration, as outcome to select the significant metabolites of the interaction. Those selected metabolites were used as predictors in a sex group stratified analysis to compare the different β coefficients and therefore the sex group-dependent associations.

RESULTS

Principal component analysis between males, non-HC females, and HC females showed a general discriminating trend between males and HC females. One hundred twenty-seven metabolites were significantly different between males and non-HC females, whereas 97 differed between non-HC females and HC females. Males and non-HC females mainly differed in sphingomyelin, lyso-phosphatidylcholine, acyl-carnitine and amino acid species, whilst non-HC females and HC females mainly differed in phosphatidylcholine, lyso-phosphatidylcholine and acyl-carnitine concentrations. Forty-one metabolites (phosphatidylcholines, sphingomyelines, lyso-phosphatidylcholine) were significantly differently associated with the MetS factors in the different groups.

CONCLUSIONS

We have shown clear differences between plasma metabolite concentrations in males, and HC or non-HC females, especially in lyso-phosphatidylcholine, sphingomyelin and phosphatidylcholine, which have been shown to associate with obesity in other studies. The association of these metabolites differed between sexes with components of the metabolic syndrome, which means that development of diseases like obesity and diabetes may differ between the sexes. Our findings highlight the importance of considering sex differences when conducting a metabolomics study and the need to account for the effect of HC usage in females in future studies.

Direct infusion mass spectrometry for metabolomic phenotyping of diseases

Metabolomics based on direct mass spectrometry (MS) analysis, either by direct infusion or flow injection of crude sample extracts, shows a great potential for metabolic fingerprinting because of its high-throughput screening capability, wide metabolite coverage and reduced time of analysis. Considering that numerous metabolic pathways are significantly perturbed during the initiation and progression of diseases, these metabolomic tools can be used to get a deeper understanding about disease pathogenesis and discover potential biomarkers for early diagnosis. In this work, we describe the most common metabolomic platforms used in biomedical research, with special focus on strategies based on direct MS analysis. Then, a comprehensive review on the application of direct MS fingerprinting in clinical issues is provided.

Cord Blood Metabolome Is Highly Associated with Birth Weight, but Less Predictive for Later Weight Development

BACKGROUND/AIMS

Fetal metabolism may be changed by the exposure to maternal factors, and the route to obesity may already set in utero. Cord blood metabolites might predict growth patterns and later obesity. We aimed to characterize associations of cord blood with birth weight, postnatal weight gain, and BMI in adolescence.

METHODS

Over 700 cord blood samples were collected from infants participating in the German birth cohort study LISAplus. Glycerophospholipid fatty acids (GPL-FA), polar lipids, non-esterified fatty acids (NEFA), and amino acids were analyzed with a targeted, liquid chromatography-tandem mass spectrometry based metabolomics platform. Cord blood metabolites were related to growth factors by linear regression models adjusted for confounding variables.

RESULTS

Cord blood metabolites were highly associated with birth weight. Lysophosphatidylcholines C16:1, C18:1, C20:3, C18:2, C20:4, C14:0, C16:0, C18:3, GPL-FA C20:3n-9, and GPL-FA C22:5n-6 were positively related to birth weight, while higher cord blood concentrations of NEFA C22:6, NEFA C20:5, GPL-FA C18:3n-3, and PCe C38:0 were associated with lower birth weight. Postnatal weight gain and BMI z-scores in adolescents were not significantly associated with cord blood metabolites after adjustment for multiple testing.

CONCLUSION

Potential long-term programming effects of the intrauterine environment and metabolism on later health cannot be predicted with profiling of the cord blood metabolome.

Lipidomics: Techniques, Applications, and Outcomes Related to Biomedical Sciences

Lipidomics is a newly emerged discipline that studies cellular lipids on a large scale based on analytical chemistry principles and technological tools, particularly mass spectrometry. Recently, techniques have greatly advanced and novel applications of lipidomics in the biomedical sciences have emerged. This review provides a timely update on these aspects. After briefly introducing the lipidomics discipline, we compare mass spectrometry-based techniques for analysis of lipids and summarize very recent applications of lipidomics in health and disease. Finally, we discuss the status of the field, future directions, and advantages and limitations of the field.

Metabolomics-identified metabolites associated with body mass index and prospective weight gain among Mexican American women

Objective

Obesity is a metabolic disease. However, the underlying molecular mechanisms linking metabolic profiles and weight gain are largely unknown.

Methods

Here, we used semi‐targeted metabolomics to assay 156 metabolites selected from 25 key metabolic pathways in plasma samples from 300 non‐smoking healthy women identified from Mano‐A‐Mano, the Mexican American Cohort study. The study subjects were randomly divided into two cohorts: training (N = 200) and testing (N = 100) cohorts. Linear regression and Cox proportional hazard regression were used to assess the effect of body mass index (BMI) at baseline on metabolite levels and the effects of metabolites on significant weight gain during a 5‐year follow‐up.

Results

At baseline, we observed 7 metabolites significantly associated with BMI in both training and testing cohorts. They were Methyl succinate, Asparagine, Urate, Kynurenic acid, Glycine, Glutamic acid, and Serine. In further analysis, we identified 6 metabolites whose levels at baseline predicted significant weight gain during 5‐year follow‐up in both cohorts. They were Acetylcholine, Leucine, Hippuric acid, Acetylglycine, Urate, and Xanthine.

Conclusions

The findings establish the baseline metabolic profiles for BMI, and suggest new metabolic targets for researchers attempting to understand the molecular mechanisms of weight gain and obesity.

Modeling Longitudinal Metabonomics and Microbiota Interactions in C57BL/6 Mice Fed a High Fat Diet

Longitudinal studies aim typically at following populations of subjects over time and are important to understand the global evolution of biological processes. When it comes to longitudinal omics data, it will often depend on the overall objective of the study, and constraints imposed by the data, to define the appropriate modeling tools. Here, we report the use of multilevel simultaneous component analysis (MSCA), orthogonal projection on latent structures (OPLS), and regularized canonical correlation analysis (rCCA) to study associations between specific longitudinal urine metabonomics data and microbiome data in a diet-induced obesity model using C57BL/6 mice. (1)H NMR urine metabolic profiling was performed on samples collected weekly over a period of 13 weeks, and stool microbial composition was assessed using 16S rRNA gene sequencing at three specific time periods (baseline, first week response, end of study). MSCA and OPLS allowed us to explore longitudinal urine metabonomics data in relation to the dietary groups, as well as dietary effects on body weight. In addition, we report a data integration strategy based on regularized CCA and correlation analyses of urine metabonomics data and 16S rRNA gene sequencing data to investigate the functional relationships between metabolites and gut microbial composition. Thanks to this workflow enabling the breakdown of this data set complexity, the most relevant patterns could be extracted to further explore physiological processes at an anthropometric, cellular, and molecular level.