Metabolomics in diabetes, a review

Metabolomics for the identification of biomarkers in endometriosis

BACKGROUND

Endometriosis affects the quality of life in women during their reproductive years, causing immense pain and can result in infertility. It is characterized by inflammation and the growth of the endometrium outside the uterine cavity. Metabolomics has the potential to resolve the major bottleneck of endometriosis which is delay in diagnosis due to the invasive diagnostic approach.In this review, the author has summarized the identified biomarkers of endometriosis from different bodily fluids. Metabolomics promises a non-invasive diagnostic approach for endometriosis that could aid in earlier diagnosis and prognosis.

METHODS

Patients with endometriosis keywords were searched in correspondence with the assigned keywords, including metabolomics from PubMed, from its inception to Dec 2023. The relevant studies from this search were extracted and included in the study.

RESULTS

This article provides information regarding metabolomics studies in endometrisis.

CONCLUSIONS

We demonstrated that metabolomics is about to change the world of endometriosis by analyzing and detecting the diagnosis, prognosis, mortality and treatment response biomarkers.

A nutrient responsive lipase mediates gut-brain communication to regulate insulin secretion in Drosophila

Pancreatic β cells secrete insulin in response to glucose elevation to maintain glucose homeostasis. A complex network of inter-organ communication operates to modulate insulin secretion and regulate glucose levels after a meal. Lipids obtained from diet or generated intracellularly are known to amplify glucose-stimulated insulin secretion, however, the underlying mechanisms are not completely understood. Here, we show that a Drosophila secretory lipase, Vaha (CG8093), is synthesized in the midgut and moves to the brain where it concentrates in the insulin-producing cells in a process requiring Lipid Transfer Particle, a lipoprotein originating in the fat body. In response to dietary fat, Vaha stimulates insulin-like peptide release (ILP), and Vaha deficiency results in reduced circulatory ILP and diabetic features including hyperglycemia and hyperlipidemia. Our findings suggest Vaha functions as a diacylglycerol lipase physiologically, by being a molecular link between dietary fat and lipid amplified insulin secretion in a gut-brain axis.

Global status and trends of metabolomics in diabetes: A literature visualization knowledge graph study

BACKGROUND

Diabetes is a metabolic disease characterized by hyperglycemia, which has increased the global medical burden and is also the main cause of death in most countries.

AIM

To understand the knowledge structure of global development status, research focus, and future trend of the relationship between diabetes and metabolomics in the past 20 years.

METHODS

The articles about the relationship between diabetes and metabolomics in the Web of Science Core Collection were retrieved from 2002 to October 23, 2023, and the relevant information was analyzed using CiteSpace6.2.2R (CiteSpace), VOSviewer6.1.18 (VOSviewer), and Bibliometrix software under R language.

RESULTS

A total of 3123 publications were included from 2002 to 2022. In the past two decades, the number of publications and citations in this field has continued to increase. The United States, China, Germany, the United Kingdom, and other relevant funds, institutions, and authors have significantly contributed to this field. Scientific Reports and PLoS One are the journals with the most publications and the most citations. Through keyword co-occurrence and cluster analysis, the closely related keywords are "insulin resistance", "risk", "obesity", "oxidative stress", "metabolomics", "metabolites" and "biomarkers". Keyword clustering included cardiovascular disease, gut microbiota, metabonomics, diabetic nephropathy, molecular docking, gestational diabetes mellitus, oxidative stress, and insulin resistance. Burst detection analysis of keyword depicted that "Gene", "microbiota", "validation", "kidney disease", "antioxidant activity", "untargeted metabolomics", "management", and "accumulation" are knowledge frontiers in recent years.

CONCLUSION

The relationship between metabolomics and diabetes is receiving extensive attention. Diabetic nephropathy, diabetic cardiovascular disease, and kidney disease are key diseases for future research in this field. Gut microbiota, molecular docking, and untargeted metabolomics are key research directions in the future. Antioxidant activity, gene, validation, mass spectrometry, management, and accumulation are at the forefront of knowledge frontiers in this field.

Therapeutic implications of dapagliflozin on the metabolomics profile of diabetic rats: A GC-MS investigation coupled with multivariate analysis

BACKGROUND

Diabetes mellitus is a complex metabolic disorder with systemic implications, necessitating the search for reliable biomarkers and therapeutic strategies. This study investigates the metabolomics profile alterations in diabetic rats, with a focus on the therapeutic effects of Dapagliflozin, a drug known to inhibit renal glucose reabsorption, using Gas Chromatography-Mass Spectrometry analysis.

METHODS

A GC-MS based metabolomics approach combined with multivariate and univariate statistical analyses was utilized to study serum samples from a diabetic model of Wistar rats, treated with dapagliflozin. Metabolomics pathways analysis was also performed to identify the altered metabolic pathways associated with the disease and the intervention.

RESULTS

Dapagliflozin treatment in diabetic rats resulted in normalized levels of metabolites associated with insulin resistance, notably branched-chain and aromatic amino acids. Improvements in glycine metabolism were observed, suggesting a modulatory role of the drug. Additionally, reduced palmitic acid levels indicated an alleviation of lipotoxic effects. The metabolic changes indicate a restorative effect of dapagliflozin on diabetes-induced metabolic perturbations.

CONCLUSIONS

The comprehensive metabolomics analysis demonstrated the potential of GC-MS in revealing significant metabolic pathway alterations due to dapagliflozin treatment in diabetic model rats. The therapy induced normalization of key metabolic disturbances, providing insights that could advance personalized diabetes mellitus management and therapeutic monitoring, highlighting the utility of metabolomics in understanding drug mechanisms and effects.

Metabolomics Signature in Prediabetes and Diabetes: Insights From Tandem Mass Spectrometry Analysis

OBJECTIVE

This study investigates the metabolic differences between normal, prediabetic and diabetic patients with good and poor glycaemic control (GGC and PGC).

DESIGN

In this study, 1102 individuals were included, and 50 metabolites were analysed using tandem mass spectrometry. The diabetes diagnosis and treatment standards of the American Diabetes Association (ADA) were used to classify patients.

METHODS

The nearest neighbour method was used to match controls and cases in each group on the basis of age, sex and BMI. Factor analysis was used to reduce the number of variables and find influential underlying factors. Finally, Pearson's correlation coefficient was used to check the correlation between both glucose and HbAc1 as independent factors with binary classes.

RESULTS

Amino acids such as glycine, serine and proline, and acylcarnitines (AcylCs) such as C16 and C18 showed significant differences between the prediabetes and normal groups. Additionally, several metabolites, including C0, C5, C8 and C16, showed significant differences between the diabetes and normal groups. Moreover, the study found that several metabolites significantly differed between the GGC and PGC diabetes groups, such as C2, C6, C10, C16 and C18. The correlation analysis revealed that glucose and HbA1c levels significantly correlated with several metabolites, including glycine, serine and C16, in both the prediabetes and diabetes groups. Additionally, the correlation analysis showed that HbA1c significantly correlated with several metabolites, such as C2, C5 and C18, in the controlled and uncontrolled diabetes groups.

CONCLUSIONS

These findings could help identify new biomarkers or underlying markers for the early detection and management of diabetes.

Omics to Unveil Diabetes Mellitus Pathogenesis and Biomarkers: Focus on Proteomics, Lipidomics, and Metabolomics

Diabetes mellitus (DM) is a chronic metabolic disorder characterized by elevated blood sugar levels, resulting from either body's inability to produce or effectively utilize insulin. There are several types of DM, but the most common are type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes mellitus (GDM). DM is a complex disease and a global health concern, and the current clinical markers, such as fasting glucose, are helpful in the diagnosis of DM, but are not specific and sensitive, especially when measured on the beginning of the pathogenesis. Therefore, there is a pressing need to discover new early biomarkers that can provide an early diagnosis. Omics is an important field for the discovery of potential new biomarkers, especially proteomics, metabolomics, and lipidomics, where techniques such as liquid chromatography, mass spectrometry, and nuclear magnetic resonance are utilized to identify novel DM biomarkers and their pathways. In this review, we report papers that applied omics in the context of DM to identify new markers and their relationship with this disease, with the aim of elucidating new diagnostic techniques for the main types of DM.

Advantages of omics approaches for elucidating metabolic changes in diabetic peripheral neuropathy

Various animal and cell culture models of diabetes mellitus (DM) have been established and utilized to study diabetic peripheral neuropathy (DPN). The divergence of metabolic abnormalities among these models makes their etiology complicated despite some similarities regarding the pathological and neurological features of DPN. Thus, this study aimed to review the omics approaches toward DPN, especially on the metabolic states in diabetic rats and mice induced by chemicals (streptozotocin and alloxan) as type 1 DM models and by genetic mutations (MKR, db/db and ob/ob) and high-fat diet as type 2 DM models. Omics approaches revealed that the pathways associated with lipid metabolism and inflammation in dorsal root ganglia and sciatic nerves were enriched and controlled in the levels of gene expression among these animal models. Additionally, these pathways were conserved in human DPN, indicating the pivotal pathogeneses of DPN. Omics approaches are beneficial tools to better understand the association of metabolic changes with morphological and functional abnormalities in DPN.

Metabolomic Profiling in Children with Celiac Disease: Beyond the Gluten-Free Diet

Celiac disease (CD) is included in the group of complex or multifactorial diseases, i.e., those caused by the interaction of genetic and environmental factors. Despite a growing understanding of the pathophysiological mechanisms of the disease, diagnosis is still often delayed and there are no effective biomarkers for early diagnosis. The only current treatment, a gluten-free diet (GFD), can alleviate symptoms and restore intestinal villi, but its cellular effects remain poorly understood. To gain a comprehensive understanding of CD's progression, it is crucial to advance knowledge across various scientific disciplines and explore what transpires after disease onset. Metabolomics studies hold particular significance in unravelling the complexities of multifactorial and multisystemic disorders, where environmental factors play a significant role in disease manifestation and progression. By analyzing metabolites, we can gain insights into the reasons behind CD's occurrence, as well as better comprehend the impact of treatment initiation on patients. In this review, we present a collection of articles that showcase the latest breakthroughs in the field of metabolomics in pediatric CD, with the aim of trying to identify CD biomarkers for both early diagnosis and treatment monitoring. These advancements shed light on the potential of metabolomic analysis in enhancing our understanding of the disease and improving diagnostic and therapeutic strategies. More studies need to be designed to cover metabolic profiles in subjects at risk of developing the disease, as well as those analyzing biomarkers for follow-up treatment with a GFD.

Changes of intestinal flora in children with febrile seizure

Febrile seizure (FS) is a highly recurrent neuro-system disorder in children that affects their nervous system development and quality of life. However, the pathogenesis of febrile seizures remains unclear. Our study aims to investigate the potential differences in the intestinal flora and metabolomics between healthy children and those with FS. By examining the relationship between specific flora and different metabolites, we hope to shed light on the pathogenesis of FS. Fecal specimens were collected from healthy children (n = 15) and children with febrile seizures (n = 15), and 16S rDNA sequencing was conducted to characterize intestinal flora. Subsequently, fecal samples from healthy (n = 6) and febrile seizure children (n = 6) were used to characterize metabolomics using linear discriminant analysis of effect size, orthogonal partial least squares discriminant analysis, Kyoto Encyclopedia of Genes and Genomes (pathway enrichment analysis), and Kyoto encyclopedia of genes and genomes topology analysis. Liquid chromatography-mass spectrometry was used to identify metabolites in the fecal samples. The intestinal microbiome in the febrile seizure children significantly differed from that in the healthy children at the phylum level. Ten differentially accumulated metabolites (xanthosine, (S)-abscisic acid, N-palmitoylglycine, (+/-)-2-(5-methyl-5-vinyl-tetrahydrofuran-2-yl) propionaldehyde, (R)-3-hydroxybutyrylcarnitine, lauroylcarnitine, oleoylethanolamide, tetradecyl carnitine, taurine, and lysoPC [18:1 (9z)/0:0] were considered the potential febrile seizure markers. Three metabolic pathways (taurine metabolism; glycine, serine, and threonine metabolism; and arginine biosynthesis) were found essential in febrile seizure. Bacteroides were significantly correlated with the 4 differential metabolites. Adjusting the balance of intestinal flora may be an effective method for preventing and treating febrile seizures.

Pharmacometabolomics for the Study of Lipid-Lowering Therapies: Opportunities and Challenges

Lipid-lowering therapies are widely used to prevent the development of atherosclerotic cardiovascular disease (ASCVD) and related mortality worldwide. "Omics" technologies have been successfully applied in recent decades to investigate the mechanisms of action of these drugs, their pleiotropic effects, and their side effects, aiming to identify novel targets for future personalized medicine with an improvement of the efficacy and safety associated with the treatment. Pharmacometabolomics is a branch of metabolomics that is focused on the study of drug effects on metabolic pathways that are implicated in the variation of response to the treatment considering also the influences from a specific disease, environment, and concomitant pharmacological therapies. In this review, we summarized the most significant metabolomic studies on the effects of lipid-lowering therapies, including the most commonly used statins and fibrates to novel drugs or nutraceutical approaches. The integration of pharmacometabolomics data with the information obtained from the other "omics" approaches could help in the comprehension of the biological mechanisms underlying the use of lipid-lowering drugs in view of defining a precision medicine to improve the efficacy and reduce the side effects associated with the treatment.

Postmortem Metabolomics of Insulin Intoxications and the Potential Application to Find Hypoglycemia-Related Deaths

Postmortem metabolomics can assist death investigations by characterizing metabolic fingerprints differentiating causes of death. Hypoglycemia-related deaths, including insulin intoxications, are difficult to identify and, thus, presumably underdiagnosed. This investigation aims to differentiate insulin intoxication deaths by metabolomics, and identify a metabolic fingerprint to screen for unknown hypoglycemia-related deaths. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry data were obtained from 19 insulin intoxications (hypo), 19 diabetic comas (hyper), and 38 hangings (control). Screening for potentially unknown hypoglycemia-related deaths was performed using 776 random postmortem cases. Data were processed using XCMS and SIMCA. Multivariate modeling revealed group separations between hypo, hyper, and control groups. A metabolic fingerprint for the hypo group was identified, and analyses revealed significant decreases in 12 acylcarnitines, including nine hydroxylated-acylcarnitines. Screening of random postmortem cases identified 46 cases (5.9%) as potentially hypoglycemia-related, including six with unknown causes of death. Autopsy report review revealed plausible hypoglycemia-cause for five unknown cases. Additionally, two diabetic cases were found, with a metformin intoxication and a suspicious but unverified insulin intoxication, respectively. Further studies are required to expand on the potential of postmortem metabolomics as a tool in hypoglycemia-related death investigations, and the future application of screening for potential insulin intoxications.

Metabolic profiles of ultra-processed food consumption and their role in obesity risk in British children

BACKGROUND & AIMS

Higher consumption of ultra-processed foods (UPF) has been associated with childhood obesity, but underlying mechanisms remain unclear. We investigated plasma nuclear magnetic resonance metabolic profiles of higher UPF consumption and their role in obesity risk in the British ALSPAC cohort.

METHODS

We performed cross-sectional and prospective metabolome wide association analyses of UPF, calculated from food diaries using the NOVA classification. In cross-sectional analysis, we tested the association between UPF consumption and metabolic profile at 7 years (N = 4528), and in the prospective analysis we tested the association between UPF consumption at 13 years and metabolic profile at 17 years (N = 3086). Effects of UPF-associated metabolites at 7 years on subsequent fat mass accumulation were assessed using growth curve models.

RESULTS

At 7 years, UPF was associated with 115 metabolic traits including lower levels of branched-chain and aromatic amino acids and higher levels of citrate, glutamine, and monounsaturated fatty acids, which were also associated with greater fat mass accumulation. Reported intake of nutrients mediated associations with most metabolites, except for citrate.

CONCLUSIONS

UPF consumption among British children is associated with perturbation of multiple metabolic traits, many of which contribute to child obesity risk.

The Association between Circulating Branched Chain Amino Acids and the Temporal Risk of Developing Type 2 Diabetes Mellitus: A Systematic Review & Meta-Analysis

Introduction: Recent studies have concluded that elevated circulating branched chain amino acids (BCAA) are associated with the pathogenesis of type 2 diabetes mellitus (T2DM) and obesity. However, the development of this association over time and the quantification of the strength of this association for individual BCAAs prior to T2DM diagnosis remains unexplored. Methods: A systematic search was conducted using the Healthcare Databases Advance Search (HDAS) via the National Institute for Health and Care Excellence (NICE) website. The data sources included EMBASE, MEDLINE and PubMed for all papers from inception until November 2021. Nine studies were identified in this systematic review and meta-analysis. Stratification was based on follow-up times (0−6, 6−12 and 12 or more years) and controlling of body mass index (BMI) through the specific assessment of overweight cohorts was also undertaken. Results: The meta-analysis revealed a statistically significant positive association between BCAA concentrations and the development of T2DM, with valine OR = 2.08 (95% CI = 2.04−2.12, p < 0.00001), leucine OR = 2.25 (95% CI = 1.76−2.87, p < 0.00001) and isoleucine OR = 2.12, 95% CI = 2.00−2.25, p < 0.00001. In addition, we demonstrated a positive consistent temporal association between circulating BCAA levels and the risk of developing T2DM with differentials in the respective follow-up times of 0−6 years, 6−12 years and ≥12 years follow-up for valine (OR = 2.08, 1.86 and 2.14, p < 0.05 each), leucine (OR = 2.10, 2.25 and 2.16, p < 0.05 each) and isoleucine (OR = 2.12, 1.90 and 2.16, p < 0.05 each) demonstrated. Conclusion: Plasma BCAA concentrations are associated with T2DM incidence across all temporal subgroups. We suggest the potential utility of BCAAs as an early biomarker for T2DM irrespective of follow-up time.

Metabolic signatures of insulin resistance in non-diabetic individuals

BACKGROUND

Insulin resistance (IR) evolved from excessive energy intake and poor energy expenditure, affecting the patient's quality of life. Amino acid and acylcarnitine metabolomic profiles have identified consistent patterns associated with metabolic disease and insulin sensitivity. Here, we have measured a wide array of metabolites (30 acylcarnitines and 20 amino acids) with the MS/MS and investigated the association of metabolic profile with insulin resistance.

METHODS

The study population (n = 403) was randomly chosen from non-diabetic participants of the Surveillance of Risk Factors of NCDs in Iran Study (STEPS 2016). STEPS 2016 is a population-based cross-sectional study conducted periodically on adults aged 18-75 years in 30 provinces of Iran. Participants were divided into two groups according to the optimal cut-off point determined by the Youden index of HOMA-IR for the diagnosis of metabolic syndrome. Associations were investigated using regression models adjusted for age, sex, and body mass index (BMI).

RESULTS

People with high IR were significantly younger, and had higher education level, BMI, waist circumference, FPG, HbA1c, ALT, triglyceride, cholesterol, non-HDL cholesterol, uric acid, and a lower HDL-C level. We observed a strong positive association of serum BCAA (valine and leucine), AAA (tyrosine, tryptophan, and phenylalanine), alanine, and C0 (free carnitine) with IR (HOMA-IR); while C18:1 (oleoyl L-carnitine) was inversely correlated with IR.

CONCLUSIONS

In the present study, we identified specific metabolites linked to HOMA-IR that improved IR prediction. In summary, our study adds more evidence that a particular metabolomic profile perturbation is associated with metabolic disease and reemphasizes the significance of understanding the biochemistry and physiology which lead to these associations.

Metabolomic profiling of the Dietary Approaches to Stop Hypertension diet provides novel insights for the nutritional epidemiology of type 2 diabetes mellitus

Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet is inversely associated with type 2 diabetes mellitus (T2DM) risk. Metabolic changes due to DASH adherence and their potential relationship with incident T2DM have not been described. The objective is to determine metabolite clusters associated with adherence to a DASH-like diet in the Insulin Resistance Atherosclerosis Study cohort and explore if the clusters predicted 5-year incidence of T2DM. The current study included 570 non-diabetic multi-ethnic participants aged 40–69 years. Adherence to a DASH-like diet was determined a priori through an eighty-point scale for absolute intakes of the eight DASH food groups. Quantitative measurements of eighty-seven metabolites (acylcarnitines, amino acids, bile acids, sterols and fatty acids) were obtained at baseline. Metabolite clusters related to DASH adherence were determined through partial least squares (PLS) analysis using R. Multivariable-adjusted logistic regression was used to explore the associations between metabolite clusters and incident T2DM. A group of acylcarnitines and fatty acids loaded strongly on the two components retained under PLS. Among strongly loading metabolites, a select group of acylcarnitines had over 50 % of their individual variance explained by the PLS model. Component 2 was inversely associated with incident T2DM (OR: 0·89; (95 % CI 0·80, 0·99), P-value = 0·043) after adjustment for demographic and metabolic covariates. Component 1 was not associated with T2DM risk (OR: 1·02; (95 % CI 0·88, 1·19), P-value = 0·74). Adherence to a DASH-type diet may contribute to reduced T2DM risk in part through modulations in acylcarnitine and fatty acid physiology.

Metabolomic Analysis of Severe Osteoarthritis in a Spanish Population of Women Compared to Healthy and Osteoporotic Subjects

Bone pathologies such as osteoporosis (OTP) and osteoarthritis (OA) are rising in incidence with the worldwide rise in life expectancy. The diagnosis is usually obtained using imaging techniques such as densitometry, but with both being multifactorial diseases, several molecular mechanisms remain to be understood. Metabolomics offers the potential to detect global changes which can lead to the identification of biomarkers and a better insight in the progress of the diseases. Our aim was to compare the metabolic profiles of a cohort of 100 postmenopausal women, including subcapital hip fragility fracture patients, women with severe OA of the hip that required the implantation of a hip prosthesis and controls, to find altered metabolites and networks. Nuclear magnetic resonance (NMR) spectroscopy was used to obtain the metabolomic profiles of peripheral blood derived serum, and statistical analysis was performed using MATLAB V.6.5. 30 of the 73 metabolites analysed showed statistically significant differences in a 3-way ANOVA, and 11 of them were present in the comparison between OA and controls after adjustment by covariates, including amino acids, energy metabolism metabolites and phospholipid precursors. PLS-DA analysis shows a good discrimination between controls and fracture subjects with OA patients, and ROC curve analysis demonstrates that control and fracture subjects were accurately discriminated using the metabolome, but not OA. These results point to OA as an intermediate metabolic state between controls and fracture, and suggest that some metabolic shifts that happen after a fracture are also present at weaker intensity in the OA process.

Acylcarnitines: Nomenclature, Biomarkers, Therapeutic Potential, Drug Targets, and Clinical Trials

Acylcarnitines are fatty acid metabolites that play important roles in many cellular energy metabolism pathways. They have historically been used as important diagnostic markers for inborn errors of fatty acid oxidation and are being intensively studied as markers of energy metabolism, deficits in mitochondrial and peroxisomal β -oxidation activity, insulin resistance, and physical activity. Acylcarnitines are increasingly being identified as important indicators in metabolic studies of many diseases, including metabolic disorders, cardiovascular diseases, diabetes, depression, neurologic disorders, and certain cancers. The US Food and Drug Administration-approved drug L-carnitine, along with short-chain acylcarnitines (acetylcarnitine and propionylcarnitine), is now widely used as a dietary supplement. In light of their growing importance, we have undertaken an extensive review of acylcarnitines and provided a detailed description of their identity, nomenclature, classification, biochemistry, pathophysiology, supplementary use, potential drug targets, and clinical trials. We also summarize these updates in the Human Metabolome Database, which now includes information on the structures, chemical formulae, chemical/spectral properties, descriptions, and pathways for 1240 acylcarnitines. This work lays a solid foundation for identifying, characterizing, and understanding acylcarnitines in human biosamples. We also discuss the emerging opportunities for using acylcarnitines as biomarkers and as dietary interventions or supplements for many wide-ranging indications. The opportunity to identify new drug targets involved in controlling acylcarnitine levels is also discussed. SIGNIFICANCE STATEMENT: This review provides a comprehensive overview of acylcarnitines, including their nomenclature, structure and biochemistry, and use as disease biomarkers and pharmaceutical agents. We present updated information contained in the Human Metabolome Database website as well as substantial mapping of the known biochemical pathways associated with acylcarnitines, thereby providing a strong foundation for further clarification of their physiological roles.

Serum Metabolomics of Incident Diabetes and Glycemic Changes in a Population With High Diabetes Burden: The Hispanic Community Health Study/Study of Latinos

Metabolomic signatures of incident diabetes remain largely unclear for the U.S. Hispanic/Latino population, a group with high diabetes burden. We evaluated the associations of 624 known serum metabolites (measured by a global, untargeted approach) with incident diabetes in a subsample (n = 2,010) of the Hispanic Community Health Study/Study of Latinos without diabetes and cardiovascular disease at baseline (2008-2011). Based on the significant metabolites associated with incident diabetes, metabolite modules were detected using topological network analysis, and their associations with incident diabetes and longitudinal changes in cardiometabolic traits were further examined. There were 224 incident cases of diabetes after an average 6 years of follow-up. After adjustment for sociodemographic, behavioral, and clinical factors, 134 metabolites were associated with incident diabetes (false discovery rate-adjusted P < 0.05). We identified 10 metabolite modules, including modules comprising previously reported diabetes-related metabolites (e.g., sphingolipids, phospholipids, branched-chain and aromatic amino acids, glycine), and 2 reflecting potentially novel metabolite groups (e.g., threonate, N-methylproline, oxalate, and tartarate in a plant food metabolite module and androstenediol sulfates in an androgenic steroid metabolite module). The plant food metabolite module and its components were associated with higher diet quality (especially higher intakes of healthy plant-based foods), lower risk of diabetes, and favorable longitudinal changes in HOMA for insulin resistance. The androgenic steroid module and its component metabolites decreased with increasing age and were associated with a higher risk of diabetes and greater increases in 2-h glucose over time. We replicated the associations of both modules with incident diabetes in a U.S. cohort of non-Hispanic Black and White adults (n = 1,754). Among U.S. Hispanic/Latino adults, we identified metabolites across various biological pathways, including those reflecting androgenic steroids and plant-derived foods, associated with incident diabetes and changes in glycemic traits, highlighting the importance of hormones and dietary intake in the pathogenesis of diabetes.

Is there a role for small molecule metabolite biomarkers in the development of a diagnostic test for endometriosis?

Endometriosis is a disease defined by the presence of benign lesions of endometrial-like glands and stroma outside the endometrial cavity. Affecting an estimated 11.4% of Australian women, symptoms include chronic pelvic pain, dysmenorrhea and infertility. The current gold standard of diagnosis requires an expensive and invasive laparoscopic surgery, resulting in delayed time to treatment. The identification of a non-invasive endometriosis biomarker - a measurable factor correlating with disease presence or activity - has therefore become a priority in endometriosis research, although no biomarker has yet been validated. As small molecule metabolites and lipids have emerged as a potential focus, this review with systematic approach, aims to summarize studies examining metabolomic biomarkers of endometriosis in order to guide future research. EMBASE, PubMed and Web of Science were searched using keywords: lipidomics OR metabolomics OR metabolome AND diagnostic tests OR biomarkers AND endometriosis, and only studies written in English from August 2000 to August 2020 were included. Twenty-nine studies met inclusion and exclusion criteria and were included. These studies identified potential biomarkers in serum, ectopic tissue, eutopic endometrium, peritoneal fluid, follicular fluid, urine, cervical swabs and endometrial fluid. Glycerophospholipids were identified as potential biomarkers in all specimens, except urine and cervical swab specimens. However, no individual molecule or metabolite combination has reached clinical diagnostic utility. Further research using large study populations with robust patient phenotype and specimen characterisation is required if we are to make progress in identifying and validating a non-invasive diagnostic test for endometriosis.

The Potential of Metabolomics in Biomedical Applications

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.

Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus Through Machine-Learning Techniques

Gestational diabetes mellitus (GDM) is a type of diabetes that usually resolves at the end of the pregnancy but exposes to a higher risk of developing type 2 diabetes mellitus (T2DM). This study aimed to unravel the factors, among those that quantify specific metabolic processes, which determine progression to T2DM by using machine-learning techniques. Classification of women who did progress to T2DM (labeled as PROG, n = 19) vs. those who did not (labeled as NON-PROG, n = 59) progress to T2DM has been performed by using Orange software through a data analysis procedure on a generated data set including anthropometric data and a total of 34 features, extracted through mathematical modeling/methods procedures. Feature selection has been performed through decision tree algorithm and then Naïve Bayes and penalized (L2) logistic regression were used to evaluate the ability of the selected features to solve the classification problem. Performance has been evaluated in terms of area under the operating receiver characteristics (AUC), classification accuracy (CA), precision, sensitivity, specificity, and F1. Feature selection provided six features, and based on them, classification was performed as follows: AUC of 0.795, 0.831, and 0.884; CA of 0.827, 0.813, and 0.840; precision of 0.830, 0.854, and 0.834; sensitivity of 0.827, 0.813, and 0.840; specificity of 0.700, 0.821, and 0.662; and F1 of 0.828, 0.824, and 0.836 for tree algorithm, Naïve Bayes, and penalized logistic regression, respectively. Fasting glucose, age, and body mass index together with features describing insulin action and secretion may predict the development of T2DM in women with a history of GDM.

Metabolomics based biomarker identification of anti-diabetes and anti-obesity properties of Malaysian herbs

BACKGROUND

Today, obesity affects over one-third of the global population and is hugely considered the Industrial Revolution's side effect. This multi-factorial disease is continuously spreading across developing countries, including the Middle East and Southeast Asia region, where Malaysia and Darussalam Brunei are the most affected. The sedentary lifestyle and availability of surplus foods have dramatically increased the number of individuals with type 2 diabetes in these countries. Thus, an adequate medical strategy must be developed urgently to address and remedy these diseases. Natural sources have been attracting attention, especially in Malaysia, where most land areas are under plant cover. Metabolomics, as a prophylactic technique, has been used extensively in Malaysia to investigate the potential use and benefits of herbs to combat obesity and diabetes.

AIM OF REVIEW

This review aims to explain the application of the metabolomics approach in the study of anti-diabetes and anti-obesity activity of Malaysian herbs to identify the stand-up point for future advancement in using these herbs as a primary source for drug exploration.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review provides an overview of using metabolomics technique in studying the anti-diabetes and anti-obesity activity of Malaysian herbs. Specific emphasis is given to the changed metabolites in both in vivo and in vitro treatment of Malaysia herbs that might be future drugs for treating diabetes and obesity.

Lifestyle correlates of eight breast cancer-related metabolites: a cross-sectional study within the EPIC cohort

BACKGROUND

Metabolomics is a promising molecular tool for identifying novel etiological pathways leading to cancer. In an earlier prospective study among pre- and postmenopausal women not using exogenous hormones, we observed a higher risk of breast cancer associated with higher blood concentrations of one metabolite (acetylcarnitine) and a lower risk associated with higher blood concentrations of seven others (arginine, asparagine, phosphatidylcholines (PCs) aa C36:3, ae C34:2, ae C36:2, ae C36:3, and ae C38:2).

METHODS

To identify determinants of these breast cancer-related metabolites, we conducted a cross-sectional analysis to identify their lifestyle and anthropometric correlates in 2358 women, who were previously included as controls in case-control studies nested within the European Prospective Investigation into Cancer and Nutrition cohort and not using exogenous hormones at blood collection. Associations of each metabolite concentration with 42 variables were assessed using linear regression models in a discovery set of 1572 participants. Significant associations were evaluated in a validation set (n = 786).

RESULTS

For the metabolites previously associated with a lower risk of breast cancer, concentrations of PCs ae C34:2, C36:2, C36:3, and C38:2 were negatively associated with adiposity and positively associated with total and saturated fat intakes. PC ae C36:2 was also negatively associated with alcohol consumption and positively associated with two scores reflecting adherence to a healthy lifestyle. Asparagine concentration was negatively associated with adiposity. Arginine and PC aa C36:3 concentrations were not associated to any of the factors examined. For the metabolite previously associated with a higher risk of breast cancer, acetylcarnitine, a positive association with age was observed.

CONCLUSIONS

These associations may indicate possible mechanisms underlying associations between lifestyle and anthropometric factors, and risk of breast cancer. Further research is needed to identify potential non-lifestyle correlates of the metabolites investigated.

Metabolite profiling of the post-ovulatory oocytes of the common carp, Cyprinus carpio: A 1H NMR-based metabolomics approach

A metabolomics study was conducted to investigate the molecular bases of oocyte over-ripening in common carp, Cyprinus carpio from a metabolic point of view. The ovulation was induced in fish brooders by intramuscular injection of pituitary extract and oocytes were collected four times post-ovulation with 30 min intervals. A set of 32 metabolites were identified on the NMR spectra of the oocytes, which mainly included energy-linked metabolites, amino acids, methylated metabolites and citric acid cycle (TCA) intermediates. PCA and PLS-DA models clearly separated the post ovulations times, indicating the effects of post-ovulation time on oocyte metabolome content. Based on the loading plot outputs, 15 metabolites including tryptophan, cysteine, AMP, tyrosine, valine, creatine phosphate (PCr), ATP, leucine, inosine, malate, acetate, TMAO, glucose, fumarate and lysine had more effects on the separation of post ovulation times. According to the results of metabolite profiling, the concentrations of glutamine, alanine, tryptophan, lysine and cysteine mostly significantly (P < 0.01) increased at 90 and 120 min post-ovulation. The concentrations of PCr, ATP, inosine and guanosine were relatively stable until 60 min post-ovulation, while significantly (P < 0.01) decreased at 90 and 120 min post ovulation. The TCA metabolites succinate, malate and fumarate significantly (P < 0.01) elevated at 90 and 120 min post-ovulation. AMP concentrations remained relatively unchanged until 30 min and then progressively decreased with time post ovulation (P < 0.01). The concentrations of lactate showed significant elevations at 90 and 120 min post ovulation (P < 0.01). In conclusion, the energetic potentials of the oocytes reduced with time post ovulation. There were apparent elevations in the concentrations of free amino acids, which may be associated with the onset of proteolytic activities in the post ovulatory oocytes. In addition, we found some changes in the apoptotic-related metabolites, which may support the results of previous studies regarding the oxidative stress and following apoptosis in post ovulatory oocytes of fish.

Genetic predisposition to impaired metabolism of the branched chain amino acids, dietary intakes, and risk of type 2 diabetes

Background and objectives

Circulating branched chain amino acids (BCAAs) increase the risk of type 2 diabetes (T2D). The genetic variants in the BCAA metabolic pathway influence the individual metabolic ability of BCAAs and may affect circulating BCAA levels together with dietary intakes. So, we investigated whether genetic predisposition to impaired BCAA metabolism interacts with dietary BCAA intakes on the risk of type 2 diabetes and related parameters.

Methods

We estimated dietary BCAA intakes among 434 incident T2D cases and 434 age-matched controls from The Harbin Cohort Study on Diet, Nutrition and Chronic Non-Communicable Diseases. The genetic risk score (GRS) was calculated on the basis of 5 variants having been identified in the BCAA metabolic pathway. Multivariate logistic regression models and general linear regression models were used to assess the interaction between dietary BCAAs and GRS on T2D risk and HbA1c.

Results

Dietary BCAAs significantly interact with metabolism related GRS on T2D risk and HbA1c (p for interaction = 0.038 and 0.015, respectively). A high intake of dietary BCAAs was positively associated with diabetes incidence only among high GRS (OR 2.40, 95% CI 1.39, 4.12, P for trend = 0.002). Dietary BCAAs were associated with 0.14% elevated HbA1c (p = 0.003) and this effect increased to 0.21% in high GRS (p = 0.003). Furthermore, GRS were associated with 9.19 μmol/L higher plasma BCAA levels (p = 0.006, P for interaction = 0.015) only among the highest BCAA intake individuals.

Conclusions

Our study suggests that genetic predisposition to BCAA metabolism disorder modifies the effect of dietary BCAA intakes on T2D risk as well as HbA1c and that higher BCAA intakes exert an unfavorable effect on type 2 diabetes risk and HbA1c only among those with high genetic susceptibility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12263-021-00695-3.

Metabolomics to understand placental biology: Where are we now?

Metabolomics, the application of analytical chemistry methodologies to survey the chemical composition of a biological system, is used to globally profile and compare metabolites in one or more groups of samples. Given that metabolites are the terminal end-products of cellular metabolic processes, or 'phenotype' of a cell, tissue, or organism, metabolomics is valuable to the study of the maternal-fetal interface as it has the potential to reveal nuanced complexities of a biological system as well as differences over time or between individuals. The placenta acts as the primary site of maternal-fetal exchange, the success of which is paramount to growth and development of offspring during pregnancy and beyond. Although the study of metabolomics has proven moderately useful for the screening, diagnosis, and understanding of the pathophysiology of pregnancy complications, the placental metabolome in the context of a healthy pregnancy remains poorly characterized and understood. Herein, we discuss the technical aspects of metabolomics and review the current literature describing the placental metabolome in human and animal models, in the context of health and disease. Finally, we highlight areas for future opportunities in the emerging field of placental metabolomics.

Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview

Metabolomics is a discipline that offers a comprehensive analysis of metabolites in biological samples. In the last decades, the notable evolution in liquid chromatography and mass spectrometry technologies has driven an exponential progress in LC-MS-based metabolomics. Targeted and untargeted metabolomics strategies are important tools in health and medical science, especially in the study of disease-related biomarkers, drug discovery and development, toxicology, diet, physical exercise, and precision medicine. Clinical and biological problems can now be understood in terms of metabolic phenotyping. This overview highlights the current approaches to LC-MS-based metabolomics analysis and its applications in the clinical research.

The role of metabolomics in personalized medicine for diabetes

Metabolomics is rapidly evolving omics technology in personalized medicine, it offers a new avenue for identification of multiple novel metabolic mediators of impaired glucose tolerance and dysglycemia. Liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy are most commonly used analytical methods in the field of metabolomics. Recent evidences showed that metabolomic profiles are link to the incidence of diabetes. In this review, an overview of metabolomics studies in diabetes revealed several diabetes-associated metabolites including 1,5-anhydroglycitol, branch chain amino acids, glucose, α-hydroxybutyric acid, 3-hydroundecanoyl-carnitine and phosphatidylcholine that could be potential biomarkers associated with diabetes. These identified metabolites can be used to develop personalized prognostics and diagnostic, and help in diabetes management.

Metabolic Changes in Mouse Plasma after Acute Diquat Poisoning by UPLC-MS/MS

Introduction:

Diquat is a fast-acting contact herbicide and plant dehydrating agent. The oral lethal dose 50 (LD50) of diquat in mice is about 125 mg/kg. The purpose of this study is to research the metabolomics in mouse plasma after acute diquat poisoning.

Method:

These mice were divided into two groups (the control group and acute diquat poisoning group). The control group was given normal saline by gavage. The acute diquat poisoning group was given 50 mg/kg diquat. UPLC-MS/MS was used to determinate the small molecule organic acid in mouse plasma.

Results:

Compare to the control group, the L-lysine, Adenine, L-Alanine, L-Valine, Lactic acid, Inosine, Adenosine, LTryptophan, L-Tyrosine, L-Arginine, L-Phenylalanine, L-Methionine, Citric acid, Fructose, L-Glutamine, Malic acid, LAspartic acid and Pyruvic acid increased in the acute diquat poisoning group (p<0.05); while the L-Histidine decreased (p<0.05).

Conclusion:

The results of metabolites increased or decreased, indicating that acute diquat poisoning induced amino acid metabolism and energy metabolism perturbations in mice.

Optimization of Sample Preparation for Metabolomics Exploration of Urine, Feces, Blood and Saliva in Humans Using Combined NMR and UHPLC-HRMS Platforms

Currently, most clinical studies in metabolomics only consider a single type of sample such as urine, plasma, or feces and use a single analytical platform, either NMR or MS. Although some studies have already investigated metabolomics data from multiple fluids, the information is limited to a unique analytical platform. On the other hand, clinical studies investigating the human metabolome that combine multi-analytical platforms have focused on a single biofluid. Combining data from multiple sample types for one patient using a multimodal analytical approach (NMR and MS) should extend the metabolome coverage. Pre-analytical and analytical phases are time consuming. These steps need to be improved in order to move into clinical studies that deal with a large number of patient samples. Our study describes a standard operating procedure for biological specimens (urine, blood, saliva, and feces) using multiple platforms (¹H-NMR, RP-UHPLC-MS, and HILIC-UHPLC-MS). Each sample type follows a unique sample preparation procedure for analysis on a multi-platform basis. Our method was evaluated for its robustness and was able to generate a representative metabolic map.

Influence of atorvastatin on metabolic pattern of rats with pulmonary hypertension

BACKGROUND

Metabonomics has been widely used to analyze the initiation, progress, and development of diseases. However, application of metabonomics to explore the mechanism of pulmonary arterial hypertension (PAH) are poorly reported. This study aimed to investigate the influence of atorvastatin (Ato) on metabolic pattern of rats with pulmonary hypertension.

METHODS

PAH animal model was established using monocrotaline (MCT). The mean pulmonary artery pressure (mPAP) and right ventricular hypertrophy index (RVHI) were measured. The microstructure of pulmonary arterioles was observed by HE staining. Nuclear magnetic resonance was used to detect and analyze the serum metabolites. The levels of glycogen synthase kinase-3β (GSK-3β), hexokinase 2 (HK-2), sterol regulatory element-binding protein 1c (SREBP-1c), and carnitine palmitoyltransferase I (CPT-1) in the lung tissues were measured.

RESULTS

Ato significantly improved lung function by decreasing mPAP, RVHI, wall thickness, and wall area. Differences in metabolic patterns were observed among normal, PAH, and Ato group. The levels of GSK-3β and SREBP-1c were decreased, but HK-2 and CPT-1 were increased in the group PAH. Ato treatment markedly reversed the influence of MCT.

CONCLUSION

Ato significantly improved the pulmonary vascular remodeling and pulmonary hypertension of PAH rats due to its inhibition on Warburg effect and fatty acid β oxidation.

Mass spectrometry-based metabolomics diagnostics - myth or reality?

ABSTACTIntroduction: Metabolomics, one of the most high-promising technologies, is the most recently developed post-genomics discipline for developing new diagnostic tests for future implementation in medicine. More than 2,000 scientific papers, using mass spectrometry-based (MS-based) metabolomics analysis for human disease diagnostics, have been published during the past two decades, and almost every metabolomics study shows high diagnostic accuracy. However, despite the great results and promising perspectives, there are currently no diagnostic tests based on metabolomics that have been approved and introduced into clinics.Areas covered: In this report, the advantages and challenges of MS-based metabolomics are discussed with a focus on its developing role in diagnostics, and the current trends in implementing metabolomics diagnostics in the clinic.Expert opinion: In the development of new clinical diagnostics tests, MS-based metabolomics has potential as both a preliminary discovery base for routine testing and a multi-test prototype, which is hoped to be introduced into clinical practice in the near future. A laboratory-developed test (LDT) is one possible way that multi-testing could be developed.

Metabolic impact of weight variations in Icelandic horses

Background

Insulin dysregulation (ID) is an equine endocrine disorder, which is often accompanied by obesity and various metabolic perturbations. The relationship between weight variations and fluctuations of the insulin response to oral glucose tests (OGT) as well as the metabolic impact of ID have been described previously. The present study seeks to characterize the concomitant metabolic impact of variations in the insulin response and bodyweight during repeated OGTs using a metabolomics approach.

Methods

Nineteen Icelandic horses were subjected to five OGTs over one year and their bodyweight, insulin and metabolic response were monitored. Analysis of metabolite concentrations depending on time (during the OGT), relative bodyweight (rWeight; defined as the bodyweight at one OGT divided by the mean bodyweight across all OGTs) and relative insulin response (rAUC_ins; defined accordingly from the area under the insulin curve during OGT) was performed using linear models. Additionally, the pathways significantly associated with time, rWeight and rAUC_ins were identified by rotation set testing.

Results

The results suggested that weight gain and worsening of ID activate distinct metabolic pathways. The metabolic profile associated with weight gain indicated an increased activation of arginase, while the pathways associated with time and rAUC_ins were consistent with the expected effect of glucose and insulin, respectively. Overall, more metabolites were significantly associated with rWeight than with rAUC_ins.

Revealing hypoglycemic and hypolipidemic mechanism of Xiaokeyinshui extract combination on streptozotocin-induced diabetic mice in high sucrose/high fat diet by metabolomics and lipidomics

Type 2 diabetic mellitus (T2DM), often accompanied by disorders of glucose and lipid metabolism, has troubled hundreds of millions of people. Xiaokeyinshui extract combination (XEC), derived from traditional Chinese medicines formula, has exerted hypoglycemic effects against T2DM. However, its mechanism of metabolic level is still unclear. In this study, a T2DM mice model, induced by a high sucrose/high fat diet combined with low-dose streptozotocin (STZ) injections, was adopted. The biochemical index was determined and a combination of metabolomics and lipidomics analyses of plasma were performed. The results showed that XEC increased secretion of insulin and level of HDL-C, decreased levels of FBG, HbA1c, TC, TG, LDL-C and repaired islet structure in diabetic mice. In addition, the metabolic profiles of plasma were analyzed and 54 potential biomarkers were screened out, mainly including carbohydrates, lipids and amino acids. These potential biomarkers were found to be correlated with the following pathways: galactose metabolism, fructose and mannose metabolism, TCA cycle, arachidonic acid metabolism, glycerolipid metabolism, glycerophospholipid metabolism, sphingolipid metabolism and amino acid metabolism. In conclusion, we speculated that carbohydrate metabolism, lipid metabolism and amino acid metabolism played roles in the therapeutic mechanisms of XEC on T2DM.

Clinical serum metabolomics study on fluoxetine hydrochloride for depression

BACKGROUND

Fluoxetine hydrochloride is one of the familiar antidepressants of the second generation and has the effect of inhibiting the reuptake of 5-hydroxytryptamine by central nervous system. Both clinical trials and animal experiments show that it has good antidepressant effect, but there are few reports on its clinical efficacy in treating depression patients from the perspective of metabolomics. This study aimed at evaluating the antidepressant effect of fluoxetine hydrochloride by metabolomics, so that to find out its specific biomarkers and related metabolic characteristics of depression in the treatment of depression and analyze the intervention mechanism of fluoxetine hydrochloride in depression.

METHOD

Twenty depression patients and twenty healthy volunteers were recruited in clinical. Using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) to analyze serum metabolites of depression patients pretherapy and post-treatment and compared with healthy people.

RESULT

Finally, we have detected 16 specific biomarkers of depression. Compared with the healthy group, the level of 10 biomarkers in the depression group was significantly increased (P < 0.05) and 6 biomarkers were significantly decreased (P < 0.01). After 8 weeks of fluoxetine hydrochloride treatment, all the biomarkers have showed a tendency of callback. The metabolic pathways involved amino acid metabolism, energy metabolism and lipid metabolism.

CONCLUSION

In our study, the antidepressant effect of fluoxetine hydrochloride in clinic was proved by metabolomics and provided basis for clinical use of fluoxetine hydrochloride. At the same time, the biomarkers that may be related to the occurrence of depression are determined to provide objective basis for the diagnosis of depression.

Evaluation of Saliva Stability for NMR Metabolomics: Collection and Handling Protocols

Maintaining a salivary metabolic profile upon sample collection and preparation is determinant in metabolomics. Nuclear magnetic resonance (NMR) spectroscopy was used to identify metabolite changes during short-term storage, at room temperature (RT)/4 °C/-20 °C, and after sample preparation, at RT/4 °C (mimicking typical clinical/laboratory settings). Interestingly, significant metabolic inter-individual and inter-day variability were noted, probably determining sample stability to some extent. After collection, no changes were noted at -20 °C (at least for 4 weeks). RT storage induced decreases in methylated macromolecules (6 h); lactate (8 h); alanine (12 h); galactose, hypoxanthine, pyruvate (24 h); sarcosine, betaine, choline, N-acetyl-glycoproteins (48 h), while acetate increased (48 h). Less, but different, changes were observed at 4 °C, suggesting different oral and microbial status at different temperatures (with a possible contribution from inter-individual and inter-day variability), and identifying galactose, hypoxanthine, and possibly, choline esters, as potential general stability indicators. After preparation, addition of NaN3 did not impact significantly on saliva stabilization, neither at RT nor at 4 °C, although its absence was accompanied by slight increases in fucose (6.5 h) and proline (8 h) at RT, and in xylose (24 h) at 4 °C. The putative metabolic origins of the above variations are discussed, with basis on the salivary microbiome. In summary, after collection, saliva can be stored at RT/4 °C for up to 6 h and at -20 °C for at least 4 weeks. Upon preparation for NMR analysis, samples are highly stable at 25 °C up to 8 h and at 4 °C up to 48 h, with NaN3 addition preventing possible early changes in fucose, proline (6-8 h), and xylose (24 h) levels.

Metabolic changes induced by oral glucose tests in horses and their diagnostic use

Background

Little is known about the implications of hyperinsulinemia on energy metabolism, and such knowledge might help understand the pathophysiology of insulin dysregulation.

Objectives

Describe differences in the metabolic response to an oral glucose test, depending on the magnitude of the insulin response.

Animals

Twelve Icelandic horses in various metabolic states.

Methods

Horses were subjected to 3 oral glucose tests (OGT; 0.5 g/kg body weight glucose). Basal, 120 and 180 minutes samples were analyzed using a combined liquid chromatography tandem mass spectrometry and flow injection analysis tandem mass spectrometry metabolomic assay. Insulin concentrations were measured using an ELISA. Analysis was performed using linear models and partial least‐squares regression.

Results

The kynurenine : tryptophan ratio increased over time during the OGT (adjusted P‐value = .001). A high insulin response was associated with lower arginine (adjusted P‐value = .02) and carnitine (adjusted P‐value = .03) concentrations. A predictive model using only baseline samples performed well with as few as 7 distinct metabolites (sensitivity, 86%; 95% confidence interval [CI], 81%‐90%; specificity, 88%; 95% CI, 84%‐92%).

Conclusions and Clinical Importance

Our results suggest induction of low‐grade inflammation during the OGT. Plasma arginine and carnitine concentrations were lower in horses with high insulin response and could constitute potential therapeutic targets. Development of screening tools to identify insulin‐dysregulated horses using only baseline blood sample appears promising.

Predictive diagnosis of chronic obstructive pulmonary disease using serum metabolic biomarkers and least-squares support vector machine

Objective

Development of biofluid‐based biomarkers is attractive for the diagnosis of chronic obstructive pulmonary disease (COPD) but still lacking. Thus, here we aimed to identify serum metabolic biomarkers for the diagnosis of COPD.

Methods

In this study, we investigated serum metabolic features between COPD patients (n = 54) and normal individuals (n = 74) using a ¹H NMR‐based metabolomics approach and developed an integrated method of least‐squares support vector machine (LS‐SVM) and serum metabolic biomarkers to assist COPD diagnosis.

Results

We observed a hypometabolic state in serum of COPD patients, as indicated by decreases in N‐acetyl‐glycoprotein (NAG), lipoprotein (LOP, mainly LDL/VLDL), polyunsaturated fatty acid (pUFA), glucose, alanine, leucine, histidine, valine, and lactate. Using an integrated method of multivariable and univariate analyses, NAG and LOP were identified as two important metabolites for distinguishing between COPD patients and controls. Subsequently, we developed a LS‐SVM classifier using these two markers and found that LS‐SVM classifiers with linear and polynomial kernels performed better than the classifier with RBF kernel. Linear and polynomial LS‐SVM classifiers can achieve the total accuracy rates of 80.77% and 84.62% and the AUC values of 0.87 and 0.90 for COPD diagnosis, respectively.

Conclusions

This study suggests that artificial intelligence integrated with serum metabolic biomarkers has a great potential for auxiliary diagnosis of COPD.

Multiorgan Metabolomics and Lipidomics Provide New Insights Into Fat Infiltration in the Liver, Muscle Wasting, and Liver-Muscle Crosstalk Following Burn Injury

Burn injury mediated hypermetabolic syndrome leads to increased mortality among severe burn victims, due to liver failure and muscle wasting. Metabolic changes may persist up to 2 years following the injury. Thus, understanding the underlying mechanisms of the pathology is crucially important to develop appropriate therapeutic approaches. We present detailed metabolomic and lipidomic analyses of the liver and muscle tissues in a rat model with a 30% body surface area burn injury located at the dorsal skin. Three hundred and thirty-eight of 1587 detected metabolites and lipids in the liver and 119 of 1504 in the muscle tissue exhibited statistically significant alterations. We observed excessive accumulation of triacylglycerols, decreased levels of S-adenosylmethionine, increased levels of glutamine and xenobiotics in the liver tissue. Additionally, the levels of gluconeogenesis, glycolysis, and tricarboxylic acid cycle metabolites are generally decreased in the liver. On the other hand, burn injury muscle tissue exhibits increased levels of acyl-carnitines, alpha-hydroxyisovalerate, ophthalmate, alpha-hydroxybutyrate, and decreased levels of reduced glutathione. The results of this preliminary study provide compelling observations that liver and muscle tissues undergo distinctly different changes during hypermetabolism, possibly reflecting liver-muscle crosstalk. The liver and muscle tissues might be exacerbating each other's metabolic pathologies, via excessive utilization of certain metabolites produced by each other.

Untargeted Metabolomic Approach Shows No Differences in Subcutaneous Adipose Tissue of Diabetic and Non-Diabetic Subjects Undergoing Bariatric Surgery: An Exploratory Study

BACKGROUND

Obesity plays a major role in the development of insulin resistance (IR) and diabetes (T2DM). Increased adipose tissue (AT) is particularly of interest because it activates a chronic inflammatory response in adipocytes and other tissues. AT plays key endocrine and metabolic functions, acting in the regulation of insulin sensitivity and energy homeostasis. Additionally, it can be easily collected during bariatric surgery. The purpose of this pilot study was to explore the potential differences in AT metabolism, through comparing the untargeted metabolomic profiles of diabetic and non-diabetic obese patients undergoing bariatric surgery.

METHODS

For this exploratory study, samples were collected from 17 subjects. Subcutaneous AT (SAT) samples from obese-diabetic (n = 8) and Obese-non-Diabetic (n = 9) subjects were obtained from the Human Metabolic Tissue Bank. Untargeted metabolomic profiling was performed by Metabolon® Inc. Statistical analysis was performed using the MetaboAnalyst 4.0 platform.

RESULTS

Among the 421 metabolites identified and analyzed there were no significant differences between the Obese-Diabetics and the Obese-non-Diabetics. Small changes were observed by fold change analysis mainly in lipid (n = 12; e.g. NEFAs) and amino acid (n = 8; e.g. BCAAs) metabolic pathways. Dysregulation of these metabolites has been associated with IR and other T2DM-related pathophysiological processes.

CONCLUSION

Obesity may influence SAT metabolism masking T2DM-dependent dysregulation. Better understanding the metabolic differences within SAT in diabetic populations may help identify potential biomarkers for diagnosis and monitoring of T2DM in patients undergoing bariatric surgery.

Progress in the Treatment of Diabetes Mellitus Based on Intestinal Flora Homeostasis and the Advancement of Holistic Analysis Methods

Diabetes mellitus (DM) is a complex metabolic disorder characterized by abnormal glucose metabolism, which is accompanied by alterations in energy metabolism, intestinal bacterial metabolism, amino acid metabolism, lipid metabolism, nucleotide metabolism, and others. However, intestinal flora metabolism plays a fundamental role in host metabolism; they are complementary to each other and help maintain homeostasis, thus ensuring the normal operation of the host metabolic system. This suggests that a holistic analysis method would be of great use in the study of the overall metabolism in patients with DM. With this in mind, this review summarizes the mechanism of intestinal flora metabolism regarding the occurrence of DM and assesses the effects of drug treatments on the intestinal flora of patients with diabetes. Based on these results, we combined intestinal flora metabolism with host metabolism to evaluate the necessity and the advantages of holistic metabonomics analyses in the treatment of DM and its complications.

Metabolic profiling of tissue-specific insulin resistance in human obesity: results from the Diogenes study and the Maastricht Study

BACKGROUND

Recent evidence indicates that insulin resistance (IR) in obesity may develop independently in different organs, representing different etiologies toward type 2 diabetes and other cardiometabolic diseases. The aim of this study was to investigate whether IR in the liver and IR in skeletal muscle are associated with distinct metabolic profiles.

METHODS

This study includes baseline data from 634 adults with overweight or obesity (BMI ≥ 27 kg/m²) (≤65 years; 63% women) without diabetes of the European Diogenes Study. Hepatic insulin resistance index (HIRI) and muscle insulin sensitivity index (MISI), were derived from a five-point OGTT. At baseline 17 serum metabolites were identified and quantified by nuclear-magnetic-resonance spectroscopy. Linear mixed model analyses (adjusting for center, sex, body mass index (BMI), waist-to-hip ratio) were used to associate HIRI and MISI with these metabolites. In an independent sample of 540 participants without diabetes (BMI ≥ 27 kg/m²; 40-65 years; 46% women) of the Maastricht Study, an observational prospective population-based cohort study, 11 plasma metabolites and a seven-point OGTT were available for validation.

RESULTS

Both HIRI and MISI were associated with higher levels of valine, isoleucine, oxo-isovaleric acid, alanine, lactate, and triglycerides, and lower levels of glycine (all p < 0.05). HIRI was also associated with higher levels of leucine, hydroxyisobutyrate, tyrosine, proline, creatine, and n-acetyl and lower levels of acetoacetate and 3-OH-butyrate (all p < 0.05). Except for valine, these results were replicated for all available metabolites in the Maastricht Study.

CONCLUSIONS

In persons with obesity without diabetes, both liver and muscle IR show a circulating metabolic profile of elevated (branched-chain) amino acids, lactate, and triglycerides, and lower glycine levels, but only liver IR associates with lower ketone body levels and elevated ketogenic amino acids in circulation, suggestive of decreased ketogenesis. This knowledge might enhance developments of more targeted tissue-specific interventions to prevent progression to more severe disease stages.

Current metabolic perspective on malnutrition in obesity: towards more subgroup-based nutritional approaches?

Lifestyle intervention may be effective in reducing type 2 diabetes mellitus incidence and cardiometabolic risk. A more personalised nutritional approach based on an individual or subgroup-based metabolic profile may optimise intervention outcome. Whole body insulin resistance (IR) reflects defective insulin action in tissues such as muscle, liver, adipose tissue, gut and brain, which may precede the development of cardiometabolic diseases. IR may develop in different organs but the severity may vary between organs. Individuals with more pronounced hepatic IR have a distinct plasma metabolome and lipidome profile as compared with individuals with more pronounced muscle IR. Additionally, genes related to extracellular modelling were upregulated in abdominal subcutaneous adipose tissue in individuals with more pronounced hepatic IR, whilst genes related to inflammation as well as systemic low-grade inflammation were upregulated in individuals with primarily muscle IR. There are indications that these distinct IR phenotypes may also respond differentially to dietary macronutrient composition. Besides metabolic phenotype, microbial phenotype may be of importance in personalising the response to diet. In particular fibres or fibre mixtures, leading to a high distal acetate and SCFA production may have more pronounced effects on metabolic health. Notably, individuals with prediabetes may have a reduced response to diet-induced microbiota modulation with respect to host insulin sensitivity and metabolic health outcomes. Overall, we need more research to relate metabolic subphenotypes to intervention outcomes to define more optimal diets for individuals with or predisposed to chronic metabolic diseases.

Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers

Weight gain is a metabolic disorder that often culminates in the development of obesity and other comorbidities such as diabetes. Obesity is characterized by the development of a chronic, subclinical systemic inflammation, and is regarded as a remarkably important factor that contributes to the development of such comorbidities. Therefore, laboratory methods that allow the identification of subjects at higher risk for severe weight-associated morbidity are of utter importance, considering the health, and safety of populations. This contribution analyzed the plasma of 180 Brazilian individuals, equally divided into a eutrophic control group and case group, to assess the presence of biomarkers related to weight gain, aiming at characterizing the phenotype of this population. Samples were analyzed by mass spectrometry and most discriminant features were determined by a machine learning approach using Random Forest algorithm. Five biomarkers related to the pathogenesis and chronicity of inflammation in weight gain were identified. Two metabolites of arachidonic acid were upregulated in the case group, indicating the presence of inflammation, as well as two other molecules related to dysfunctions in the cycle of nitric oxide (NO) and increase in superoxide production. Finally, a fifth case group marker observed in this study may indicate the trigger for diabetes in overweight and obesity individuals. The use of mass spectrometry combined with machine learning analyses to prospect and characterize biomarkers associated with weight gain will pave the way for elucidating potential therapeutic and prognostic targets.

Plasma metabolomics in tuberculosis patients with and without concurrent type 2 diabetes at diagnosis and during antibiotic treatment

Tuberculosis (TB) and type 2 diabetes mellitus (DM), a major TB risk factor, are both accompanied by marked alterations in metabolic processes. Dissecting the specific metabolic changes induced by disease through metabolomics has shown potential to improve our understanding of relevant pathophysiological mechanisms of disease, which could lead to improved treatment. Targeted tandem liquid chromatography–mass spectrometry (LC-MS/MS) was used to compare amine and acylcarnitine levels in plasma samples of patients with TB or TB-DM from Indonesia at time of diagnosis and during antibiotic treatment. Partial least squares discrimination analysis (PLS-DA) showed good separation of patient groups. Amine levels were strongly altered in both disease groups compared to healthy controls, including low concentrations of citrulline and ornithine. Several amino acid ratios discriminated TB from controls (phenylalanine/histidine; citrulline/arginine; kynurenine/tryptophan), possibly reflecting changes in indoleamine-pyrrole 2,3-dioxygenase (IDO) and nitric oxide synthase (NOS) activity. Choline, glycine, serine, threonine and homoserine levels were lower in TB-DM compared to TB, and, in contrast to other analytes, did not normalize to healthy control levels during antibiotic treatment. Our results not only provide important validation of previous studies but also identify novel biomarkers, and significantly enhance our understanding of metabolic changes in human TB and TB-DM.

Metabolomics and Lipidomics Profiling Reveals Hypocholesterolemic and Hypolipidemic Effects of Arabinoxylan on Type 2 Diabetic Rats

Type 2 diabetes (T2D) is a pandemic disease chiefly characterized by hyperglycemia. In this study, the combination of serum lipidomic and metabolomic approach was employed to investigate the effect of arabinoxylan on type 2 diabetic rats and identify the critical biomarkers of T2D. Metabolomics analysis revealed that branched-chain amino acids, 12α-hydroxylated bile acids, ketone bodies, and several short- and long-chain acylcarnitines were significantly increased in T2D, whereas lysophosphatidylcholines (LPCs) were significantly decreased. Lipidomics analysis indicated T2D-related dyslipidemia was mainly associated with the increased levels of acetylcarnitine, free fatty acids (FFA), diacylglycerols, triacylglycerols, and cholesteryl esters and the decreased levels of some unsaturated phosphatidylcholines (less than 22 carbons). These variations indicated the disturbed amino acid and lipid metabolism in T2D, and the accumulation of incompletely oxidized lipid species might eventually contribute to impaired insulin action and glucose homeostasis. Arabinoxylan treatment decreased the concentrations of 12α-hydroxylated bile acids, carnitines, and FFAs and increased the levels of LPCs. The improved bile acid and lipid metabolism by arabinoxylan might be involved in the alleviation of hypercholesterolemia and hyperlipidemia in T2D.

A latent unknown clustering integrating multi-omics data (LUCID) with phenotypic traits

MOTIVATION

Epidemiologic, clinical and translational studies are increasingly generating multiplatform omics data. Methods that can integrate across multiple high-dimensional data types while accounting for differential patterns are critical for uncovering novel associations and underlying relevant subgroups.

RESULTS

We propose an integrative model to estimate latent unknown clusters (LUCID) aiming to both distinguish unique genomic, exposure and informative biomarkers/omic effects while jointly estimating subgroups relevant to the outcome of interest. Simulation studies indicate that we can obtain consistent estimates reflective of the true simulated values, accurately estimate subgroups and recapitulate subgroup-specific effects. We also demonstrate the use of the integrated model for future prediction of risk subgroups and phenotypes. We apply this approach to two real data applications to highlight the integration of genomic, exposure and metabolomic data.

AVAILABILITY AND IMPLEMENTATION

The LUCID method is implemented through the LUCIDus R package available on CRAN (https://CRAN.R-project.org/package=LUCIDus).

SUPPLEMENTARY INFORMATION

Supplementary materials are available at Bioinformatics online.

Metabolomics and Cell Therapy in Diabetes Mellitus

Diabetes with a broad spectrum of complications has become a global epidemic metabolic disorder. Till now, several pharmaceutical and non-pharmaceutical therapeutic approaches were applied for its treatment. Cell-based therapies have become promising methods for diabetes treatment. Better understanding of diabetes pathogenesis and identification of its specific biomarkers along with evaluation of different treatments efficacy, can be possible by clarification of specific metabolic modifications during the diabetes progression. Subsequently, metabolomics technology can support this goal as an effective tool. The present review tried to show how metabolomics quantifications can be useful for diabetic monitoring before and after cell therapy. Cell therapy is an alternative approach to achieve diabetes treatments goals including insulin resistance amelioration, insulin independence reparation, and control of glycemia. OMICs approaches provide a comprehensive insight into the molecular mechanisms of cells features and functional mechanism of their genomics, transcriptomics, proteomics, and metabolomics profile which can be useful for their therapeutic application. As a modern technology for the detection and analysis of metabolites in biological samples, metabolomica can identify many of the metabolic and molecular pathways associated with diabetes and its following complications.

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.