Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting

Identifying critical age and gender-based metabolomic shifts in a Japanese population of the Tohoku Medical Megabank cohort

Understanding the physiological changes associated with aging and the associated disease risks is essential to establish biomarkers as indicators of biological aging. This study used the NMR-measured plasma metabolome to calculate age-specific metabolite indices. In doing so, the scope of the study was deliberately simplified to capture general trends and insights into age-related changes in metabolic patterns. In addition, changes in metabolite concentrations with age were examined in detail, with the period from 55-59 to 60-64 years being a period of significant metabolic change, particularly in men, and from 45-49 to 50-54 years in females. These results illustrate the different variations in metabolite concentrations by sex and provide new insights into the relationship between age and metabolic diseases.

Plasma metabolomic and lipidomic profiles accurately classify mothers of children with congenital heart disease: an observational study

INTRODUCTION

Congenital heart disease (CHD) is the most common congenital anomaly, representing a significant global disease burden. Limitations exist in our understanding of aetiology, diagnostic methodology and screening, with metabolomics offering promise in addressing these.

OBJECTIVE

To evaluate maternal metabolomics and lipidomics in prediction and risk factor identification for childhood CHD.

METHODS

We performed an observational study in mothers of children with CHD following pregnancy, using untargeted plasma metabolomics and lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). 190 cases (157 mothers of children with structural CHD (sCHD); 33 mothers of children with genetic CHD (gCHD)) from the children OMACp cohort and 162 controls from the ALSPAC cohort were analysed. CHD diagnoses were stratified by severity and clinical classifications. Univariate, exploratory and supervised chemometric methods were used to identify metabolites and lipids distinguishing cases and controls, alongside predictive modelling.

RESULTS

499 metabolites and lipids were annotated and used to build PLS-DA and SO-CovSel-LDA predictive models to accurately distinguish sCHD and control groups. The best performing model had an sCHD test set mean accuracy of 94.74% (sCHD test group sensitivity 93.33%; specificity 96.00%) utilising only 11 analytes. Similar test performances were seen for gCHD. Across best performing models, 37 analytes contributed to performance including amino acids, lipids, and nucleotides.

CONCLUSIONS

Here, maternal metabolomic and lipidomic analysis has facilitated the development of sensitive risk prediction models classifying mothers of children with CHD. Metabolites and lipids identified offer promise for maternal risk factor profiling, and understanding of CHD pathogenesis in the future.

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.

Causal relationships between blood metabolites and diabetic retinopathy: a two-sample Mendelian randomization study

Background

Diabetic retinopathy (DR) is a microvascular complication of diabetes, severely affecting patients' vision and even leading to blindness. The development of DR is influenced by metabolic disturbance and genetic factors, including gene polymorphisms. The research aimed to uncover the causal relationships between blood metabolites and DR.

Methods

The two-sample mendelian randomization (MR) analysis was employed to estimate the causality of blood metabolites on DR. The genetic variables for exposure were obtained from the genome-wide association study (GWAS) dataset of 486 blood metabolites, while the genetic predictors for outcomes including all-stage DR (All DR), non-proliferative DR (NPDR) and proliferative DR (PDR) were derived from the FinnGen database. The primary analysis employed inverse variance weighted (IVW) method, and supplementary analyses were performed using MR-Egger, weighted median (WM), simple mode and weighted mode methods. Additionally, MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were also conducted to guarantee the accuracy and robustness of the results. Subsequently, we replicated the MR analysis using three additional datasets from the FinnGen database and conducted a meta-analysis to determine blood metabolites associated with DR. Finally, reverse MR analysis and metabolic pathway analysis were performed.

Results

The study identified 13 blood metabolites associated with All DR, 9 blood metabolites associated with NPDR and 12 blood metabolites associated with PDR. In summary, a total of 21 blood metabolites were identified as having potential causal relationships with DR. Additionally, we identified 4 metabolic pathways that are related to DR.

Conclusion

The research revealed a number of blood metabolites and metabolic pathways that are causally associated with DR, which holds significant importance for screening and prevention of DR. However, it is noteworthy that these causal relationships should be validated in larger cohorts and experiments.

The metabolomic signature of weight loss and remission in the Diabetes Remission Clinical Trial (DiRECT)

AIMS/HYPOTHESIS

High-throughput metabolomics technologies in a variety of study designs have demonstrated a consistent metabolomic signature of overweight and type 2 diabetes. However, the extent to which these metabolomic patterns can be reversed with weight loss and diabetes remission has been weakly investigated. We aimed to characterise the metabolomic consequences of a weight-loss intervention in individuals with type 2 diabetes.

METHODS

We analysed 574 fasted serum samples collected within an existing RCT (the Diabetes Remission Clinical Trial [DiRECT]) (N=298). In the trial, participating primary care practices were randomly assigned (1:1) to provide either a weight management programme (intervention) or best-practice care by guidelines (control) treatment to individuals with type 2 diabetes. Here, metabolomics analysis was performed on samples collected at baseline and 12 months using both untargeted MS and targeted 1H-NMR spectroscopy. Multivariable regression models were fitted to evaluate the effect of the intervention on metabolite levels.

RESULTS

Decreases in branched-chain amino acids, sugars and LDL triglycerides, and increases in sphingolipids, plasmalogens and metabolites related to fatty acid metabolism were associated with the intervention (Holm-corrected p<0.05). In individuals who lost more than 9 kg between baseline and 12 months, those who achieved diabetes remission saw greater reductions in glucose, fructose and mannose, compared with those who did not achieve remission.

CONCLUSIONS/INTERPRETATION

We have characterised the metabolomic effects of an integrated weight management programme previously shown to deliver weight loss and diabetes remission. A large proportion of the metabolome appears to be modifiable. Patterns of change were largely and strikingly opposite to perturbances previously documented with the development of type 2 diabetes.

DATA AVAILABILITY

The data used for analysis are available on a research data repository ( https://researchdata.gla.ac.uk/ ) with access given to researchers subject to appropriate data sharing agreements. Metabolite data preparation, data pre-processing, statistical analyses and figure generation were performed in R Studio v.1.0.143 using R v.4.0.2. The R code for this study has been made publicly available on GitHub at: https://github.com/lauracorbin/metabolomics_of_direct .

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

Metabolomics signature of cardiovascular disease in patients with diabetes, a narrative review

Objectives

The exact underlying mechanism of developing diabetes-related cardiovascular disease (CVD) among patients with type 2 diabetes (T2D) is not clear. Metabolomics can provide a platform enabling the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The aim of this review is to summarize the available evidence on the relationship between metabolomics and cardiovascular diseases in patients with diabetes.

Methods

The literature was searched to find out studies that have investigated the relationship between the alteration of specific metabolites and cardiovascular diseases in patients with diabetes.

Results

Evidence proposed that changes in the metabolism of certain amino acids, lipids, and carbohydrates, independent of traditional CVD risk factors, are associated with increased CVD risk.

Conclusions

Metabolomics can provide a platform to enable the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The association of the alteration in specific metabolites with CVD may be considered in the investigations for the development of new therapeutic targets for the prevention of CVD in patients with diabetes mellitus.

Identification of Differential Metabolites Between Type 2 Diabetes and Postchronic Pancreatitis Diabetes (Type 3c) Based on an Untargeted Metabolomics Approach

OBJECTIVE

A nontargeted metabolomics approach was established to characterize serum metabolic profile in type 3c diabetes mellitus (T3cDM) secondary to chronic pancreatitis and compare with T2DM.

METHODS

Forty patients were recruited for metabolite analysis based on liquid chromatography-mass spectrometry. Cluster heatmap and KEGG metabolic pathway enrichment analysis were used to analyze the specific and differential metabolites. The receiver operating characteristics (ROCs) were generated and correlation analysis with clinical data was conducted.

RESULTS

Metabolites including sphingosine, lipids, carnitine, bile acid, and hippuric acid were found to be different between T2DM and T3cDM, mainly enriched in bile acid biosynthesis, fatty acid biosynthesis, and sphingolipid metabolic pathways. The ROCs were generated with an area under the curve of 0.907 (95% confidence interval, 0.726-1) for the model with 15 metabolites.

CONCLUSION

T3cDM is characterized by increased sphingosine, carnitine, bile acid, and most lipids, providing novel biomarkers for clinical diagnosis and a future direction in research on pathophysiological mechanisms.

Integration of systematic review, lipidomics with experiment verification reveals abnormal sphingolipids facilitate diabetic retinopathy by inducing oxidative stress on RMECs

OBJECTIVE

This study aims to explore the potential biomarkers in the development of diabetes mellitus (DM) into diabetic retinopathy (DR).

METHODS

Systematic review of diabetic metabolomics was used to screen the differential metabolites and related pathways during the development of DM. Non-targeted lipidomics of rat plasma was performed to explore the differential metabolites in the development of DM into DR in vivo. To verify the effects of differential metabolites in inducing retinal microvascular endothelial cells (RMECs) injury by increasing oxidative stress, high glucose medium containing differential metabolites was used to induce rat RMECs injury and cell viability, malondialdehyde (MDA) contents, superoxide dismutase (SOD) activities, reactive oxygen species (ROS) levels and mitochondrial membrane potential (MMP) were evaluated in vitro. Network pharmacology was performed to explore the potential mechanism of differential metabolites in inducing DR.

RESULTS

Through the systematic review, 148 differential metabolites were obtained and the sphingolipid metabolic pathway attracted our attention. Plasma non-targeted lipidomics found that sphingolipids were accompanied by the development of DM into DR. In vitro experiments showed sphinganine and sphingosine-1-phosphate aggravated rat RMECs injury induced by high glucose, further increased MDA and ROS levels, and further decreased SOD activities and MMP. Network pharmacology revealed sphinganine and sphingosine-1-phosphate may induce DR by regulating the AGE-RAGE and HIF-1 signaling pathways.

CONCLUSIONS

Integrated systematic review, lipidomics and experiment verification reveal that abnormal sphingolipid metabolism facilitates DR by inducing oxidative stress on RMECs. Our study could provide the experimental basis for finding potential biomarkers for the diagnosis and treatment of DR.

Glucuronic acid is a novel source of pentosidine, associated with schizophrenia

Pentosidine (PEN) is an advanced glycation end-product (AGEs), where a fluorescent cross-link is formed between lysine and arginine residues in proteins. Accumulation of PEN is associated with aging and various diseases. We previously reported that a subpopulation of patients with schizophrenia showed PEN accumulation in the blood, having severe clinical features. PEN is thought to be produced from glucose, fructose, pentoses, or ascorbate. However, patients with schizophrenia with high PEN levels present no elevation of these precursors of PEN in their blood. Therefore, the molecular mechanisms underlying PEN accumulation and the molecular pathogenesis of schizophrenia associated with PEN accumulation remain unclear. Here, we identified glucuronic acid (GlcA) as a novel precursor of PEN from the plasma of subjects with high PEN levels. We demonstrated that PEN can be generated from GlcA, both in vitro and in vivo. Furthermore, we found that GlcA was associated with the diagnosis of schizophrenia. Among patients with high PEN, the proportion of those who also have high GlcA is 25.6%. We also showed that Aldo-keto reductase (AKR) activity to degrade GlcA was decreased in patients with schizophrenia, and its activity was negatively correlated with GlcA levels in the plasma. This is the first report to show that PEN is generated from GlcA. In the future, this finding will contribute to understanding the molecular pathogenesis of not only schizophrenia but also other diseases with PEN accumulation.

Effect of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and benzo[a]pyrene exposure on the development of metabolic syndrome in mice

AIM

The prevalence of metabolic syndrome (MetS), a cluster of serious medical conditions that raise the risk of lung cancer, has increased worldwide. Tobacco smoking (TS) potentially increases the risk of developing MetS. Despite the potential association of MetS with lung cancer, preclinical models that mimic human diseases, including TS-induced MetS, are limited. Here we evaluated the impact of exposure to tobacco smoke condensate (TSC) and two representative tobacco carcinogens, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNK) and benzo[a]pyrene (BaP), on MetS development in mice.

MATERIALS AND METHODS

FVB/N or C57BL/6 mice were exposed to vehicle, TSC, or NNK and BaP (NB) twice weekly for 5 months. The serum levels of total cholesterol (TCHO), triglycerides, high-density lipoprotein (HDL), blood glucose, and metabolites, along with glucose tolerance and body weight, were measured.

KEY FINDINGS

Compared with those of vehicle-treated mice, mice with TSC or NB exposure displayed major phenotypes associated with MetS, including increased serum levels of TCHO, triglycerides, and fasting and basal blood glucose and decreased glucose tolerance, and serum levels of HDL. These MetS-associated changes were found in both FVB/N and C57BL/6 mice that were susceptible or resistant to carcinogen-induced tumorigenesis, respectively, indicating that tumor formation is not involved in the TSC- or NB-mediated MetS. Moreover, oleic acid and palmitoleic acid, which are known to be associated with MetS, were significantly upregulated in the serum of TSC- or NB-treated mice compared with those in vehicle-treated mice.

SIGNIFICANCE

Both TSC and NB caused detrimental health problems, leading to the development of MetS in experimental mice.

A phenome-wide association study (PheWAS) to identify the health impacts of 4-cresol sulfate in the Nagahama Study

Gut-microbiota derived metabolites are important regulators of host biology and metabolism. To understand the impacts of the microbial metabolite 4-cresol sulfate (4-CS) on four chronic diseases [type 2 diabetes mellitus, metabolic syndrome (MetS), non-alcoholic fatty liver disease, and chronic kidney disease (CKD)], we conducted association analyses of plasma 4-CS quantified by liquid chromatography coupled to mass spectrometry (LC-MS) in 3641 participants of the Nagahama study. Our results validated the elevation of 4-CS in CKD and identified a reducing trend in MetS. To delineate the holistic effects of 4-CS, we performed a phenome-wide association analysis (PheWAS) with 937 intermediate biological and behavioral traits. We detected associations between 4-CS and 39 phenotypes related to blood pressure regulation, hepatic and renal functions, hematology, sleep quality, intraocular pressure, ion regulation, ketone and fatty acid metabolisms, disease history and dietary habits. Among them, 19 PheWAS significant traits, including fatty acids and 14 blood pressure indices, were correlated with MetS, suggesting that 4-CS is a potential biomarker for MetS. Consistent associations of this gut microbial-derived metabolite on multiple endophenotypes underlying distinct etiopathogenesis support its role in the overall host health, with prospects of probiotic-based therapeutic solutions in chronic diseases.

Sucrose Intake Elevates Erythritol in Plasma and Urine in Male Mice

BACKGROUND

Elevated serum erythritol concentration is a predictive biomarker of diabetes and cardiovascular incidence and complications. Erythritol is synthesized endogenously from glucose, but little is known regarding the origin of elevated circulating erythritol in vivo.

OBJECTIVES

In vitro evidence indicates that intracellular erythritol is elevated by high-glucose cell culture conditions and that final step of erythritol synthesis is catalyzed by the enzymes sorbitol dehydrogenase (SORD) and alcohol dehydrogenase (ADH) 1. The purpose of this study was to determine whether dietary intake and/or diet-induced obesity affect erythritol synthesis in mice and whether this relationship is modified by the loss of the enzymes SORD or ADH1.

METHODS

First, 8-wk-old male Sord+/+, Sord-/-, Adh1+/+, and Adh1-/- mice were fed either low-fat diet (LFD) with 10% fat-derived calories or diet-induced obesity high-fat diet (HFD) with 60% fat-derived calories for 8 wk. Plasma and tissue erythritol concentrations were measured using gas chromatography-mass spectrometry. Second, male wild-type 8-wk-old C57BL/6J mice were fed LFD or HFD with plain drinking water or 30% sucrose water for 8 wk. Blood glucose and plasma and urinary erythritol concentrations were measured in nonfasted and fasted samples. Tissue erythritol was measured after killing. Finally, male Sord+/+ and Sord-/- mice were fed LFD with 30% sucrose water for 2 wk; then, nonfasted plasma, urine, and tissue erythritol concentrations were quantified.

RESULTS

Plasma and tissue erythritol concentrations were not affected by loss of Sord or Adh1 in mice fed LFD or HFD. In wild-type mice, consumption of 30% sucrose water significantly elevated plasma and urinary erythritol concentrations on both LFD-fed and HFD-fed mice compared with that of plain water. Sord genotype did not affect plasma or urinary erythritol concentration in response to sucrose feeding, but Sord-/- mice had reduced kidney erythritol content compared with wild-type littermates in response to sucrose.

CONCLUSIONS

Sucrose intake, not HFD, elevates erythritol synthesis and excretion in mice. Loss of ADH1 or SORD does not significantly affect erythritol concentration in mice.

Dose-dependent effects of xylooligosaccharides on glycemic regulation with L. rhamnosus CCFM1060 in diabetic mice

Dietary intervention with the probiotic Lactobacillus rhamnosus CCFM1060 has been proved to be effective on glycemic regulation in diabetic mice. Therefore characterization of the potential symbiotic effect of prebiotic xylooligosaccharides (XOS) with L. rhamnosus CCFM1060 would be desirable. In this study, we evaluated any dose-dependent relationship between XOS and L. rhamnosus CCFM1060, and the potential impact on glycemic regulation. Diabetic mice were randomly assigned to receive 5 × 10⁹ CFU mL^-1L. rhamnosus CCFM1060, 5 × 10⁹ CFU mL^-1L. rhamnosus CCFM1060 with 250 mg kg^-1 XOS (L-LXOS), or 5 × 10⁹ CFU mL^-1L. rhamnosus CCFM1060 with 500 mg kg^-1 XOS (L-HXOS) for 7 weeks. In addition to characterization of the host metabolism, the intestinal microbiota were analyzed using 16S rRNA gene sequencing. The results showed that L. rhamnosus alone and L-LXOS intervention significantly alleviated diabetes symptoms and increased the populations of short-chain fatty acid (SCFA)-producing bacteria. The intake of L-HXOS had an adverse effect on glucose metabolism, causing increased insulin resistance and inflammation. Although a significant increase in the relative abundance of Bifidobacterium was observed in the L-HXOS group, the abundance of SCFA-producing bacteria, such as Romboutsia and Clostrudium sensu stricto 1, decreased. KEGG pathway analysis revealed that the adverse effects of L-HXOS intervention might be attributed to the metabolic pathways involved in amino acid, cofactor, and vitamin metabolism. This study revealed that L. rhamnosus CCFM1060 combined with different doses of XOS exerted dose-dependent effects on glucose metabolism. Therefore, the type and dose of prebiotics should be carefully evaluated when developing individualized symbiotic formula.

Augmented region of interest for untargeted metabolomics mass spectrometry (AriumMS) of multi-platform-based CE-MS and LC-MS data

In mass spectrometry (MS)-based metabolomics, there is a great need to combine different analytical separation techniques to cover metabolites of different polarities and apply appropriate multi-platform data processing. Here, we introduce AriumMS (augmented region of interest for untargeted metabolomics mass spectrometry) as a reliable toolbox for multi-platform metabolomics. AriumMS offers augmented data analysis of several separation techniques utilizing a region-of-interest algorithm. To demonstrate the capabilities of AriumMS, five datasets were combined. This includes three newly developed capillary electrophoresis (CE)-Orbitrap MS methods using the recently introduced nanoCEasy CE-MS interface and two hydrophilic interaction liquid chromatography (HILIC)-Orbitrap MS methods. AriumMS provides a novel mid-level data fusion approach for multi-platform data analysis to simplify and speed up multi-platform data processing and evaluation. The key feature of AriumMS lies in the optimized data processing strategy, including parallel processing of datasets and flexible parameterization for processing of individual separation methods with different peak characteristics. As a case study, Saccharomyces cerevisiae (yeast) was treated with a growth inhibitor, and AriumMS successfully differentiated the metabolome based on the augmented multi-platform CE-MS and HILIC-MS investigation. As a result, AriumMS is proposed as a powerful tool to improve the accuracy and selectivity of metabolome analysis through the integration of several HILIC-MS/CE-MS techniques.

Food Polyphenols and Type II Diabetes Mellitus: Pharmacology and Mechanisms

Type II diabetes mellitus and its related complications are growing public health problems. Many natural products present in our diet, including polyphenols, can be used in treating and managing type II diabetes mellitus and different diseases, owing to their numerous biological properties. Anthocyanins, flavonols, stilbenes, curcuminoids, hesperidin, hesperetin, naringenin, and phenolic acids are common polyphenols found in blueberries, chokeberries, sea-buckthorn, mulberries, turmeric, citrus fruits, and cereals. These compounds exhibit antidiabetic effects through different pathways. Accordingly, this review presents an overview of the most recent developments in using food polyphenols for managing and treating type II diabetes mellitus, along with various mechanisms. In addition, the present work summarizes the literature about the anti-diabetic effect of food polyphenols and evaluates their potential as complementary or alternative medicines to treat type II diabetes mellitus. Results obtained from this survey show that anthocyanins, flavonols, stilbenes, curcuminoids, and phenolic acids can manage diabetes mellitus by protecting pancreatic β-cells against glucose toxicity, promoting β-cell proliferation, reducing β-cell apoptosis, and inhibiting α-glucosidases or α-amylase. In addition, these phenolic compounds exhibit antioxidant anti-inflammatory activities, modulate carbohydrate and lipid metabolism, optimize oxidative stress, reduce insulin resistance, and stimulate the pancreas to secrete insulin. They also activate insulin signaling and inhibit digestive enzymes, regulate intestinal microbiota, improve adipose tissue metabolism, inhibit glucose absorption, and inhibit the formation of advanced glycation end products. However, insufficient data are available on the effective mechanisms necessary to manage diabetes.

Identification of biomarkers for glycaemic deterioration in type 2 diabetes

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

Distinct metabolic features of genetic liability to type 2 diabetes and coronary artery disease: a reverse Mendelian randomization study

BACKGROUND

Type 2 diabetes (T2D) and coronary artery disease (CAD) both have known genetic determinants, but the mechanisms through which their associated genetic variants lead to disease onset remain poorly understood.

METHODS

We used large-scale metabolomics data in a two-sample reverse Mendelian randomization (MR) framework to estimate effects of genetic liability to T2D and CAD on 249 circulating metabolites in the UK Biobank (N = 118,466). We examined the potential for medication use to distort effect estimates by conducting age-stratified metabolite analyses.

FINDINGS

Using inverse variance weighted (IVW) models, higher genetic liability to T2D was estimated to decrease high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) (e.g.

, HDL-C

 -0.05 SD; 95% CI -0.07 to -0.03, per doubling of liability), whilst increasing all triglyceride groups and branched chain amino acids (BCAAs). IVW estimates for CAD liability suggested an effect on reducing HDL-C as well as raising very-low density lipoprotein cholesterol (VLDL-C) and LDL-C. In pleiotropy-robust models, T2D liability was still estimated to increase BCAAs, but several estimates for higher CAD liability reversed and supported decreased LDL-C and apolipoprotein-B. Estimated effects of CAD liability differed substantially by age for non-HDL-C traits, with higher CAD liability lowering LDL-C only at older ages when statin use was common.

INTERPRETATION

Overall, our results support largely distinct metabolic features of genetic liability to T2D and CAD, illustrating both challenges and opportunities for preventing these commonly co-occurring diseases.

FUNDING

Wellcome Trust [218495/Z/19/Z], UK MRC [MC_UU_00011/1; MC_UU_00011/4], the University of Bristol, Diabetes UK [17/0005587], World Cancer Research Fund [IIG_2019_2009].

Effect of Frozen-Thawed Embryo Transfer on the Metabolism of Children in Early Childhood

Background: As a routine procedure in assisted reproductive technology (ART), it is crucial to assess the safety of frozen and thawed embryo transfer (FET). We aimed to investigate the metabolic profile of children conceived through FET in their early childhood. Method: A total of 147 children between the age of 1.5 and 4 years old, conceived through FET or naturally conceived (NC), were recruited. A total of 89 children, 65 in the FET group and 24 in the NC group (matched with the FET group based on children’s BMI) were included in the final statistical analysis of biochemical markers and metabolomics. Results: Children conceived through FET had a lower level of fasting insulin level and HORM-IR and a higher level of fasting glucose and APOE as compared to children naturally conceived. Metabolomics showed that there were 16 small differential metabolites, mainly including amino acids, carnitines, organic acids, butyric, and secondary bile acid between two groups, which enriched in Nitrogen metabolism, Butanoate metabolism, Phenylalanine metabolism, and D-Arginine and D-ornithine metabolism pathways. Conclusion: Although the FET group had a significantly higher level of APOE and fasting glucose, it cannot yet be considered that children in the FET group had an obvious disorder of glucose and lipid metabolism. However, the potentially more active intestinal flora and lower carnitine levels of children in the FET group suggested by metabolomics are worth further exploration.

Global and Partial Effect Assessment in Metabolic Syndrome Explored by Metabolomics

In nutrition and health research, untargeted metabolomics is actually analyzed simultaneously with clinical data to improve prediction and better understand pathological status. This can be modeled using a multiblock supervised model with several input data blocks (metabolomics, clinical data) being potential predictors of the outcome to be explained. Alternatively, this configuration can be represented with a path diagram where the input blocks are each connected by links directed to the outcome—as in multiblock supervised modeling—and are also related to each other, thus allowing one to account for block effects. On the basis of a path model, we show herein how to estimate the effect of an input block, either on its own or conditionally to other(s), on the output response, respectively called “global” and “partial” effects, by percentages of explained variance in dedicated PLS regression models. These effects have been computed in two different path diagrams in a case study relative to metabolic syndrome, involving metabolomics and clinical data from an older men′s cohort (NuAge). From the two effects associated with each path, the results highlighted the complementary information provided by metabolomics to clinical data and, reciprocally, in the metabolic syndrome exploration.

Crosstalk between COVID-19 Infection and Kidney Diseases: A Review on the Metabolomic Approaches

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a respiratory disorder. Various organ injuries have been reported in response to this virus, including kidney injury and, in particular, kidney tubular injury. It has been discovered that infection with the virus does not only cause new kidney disease but also increases treatment difficulty and mortality rates in people with kidney diseases. In individuals hospitalized with COVID-19, urinary metabolites from several metabolic pathways are used to distinguish between patients with acute kidney injury (AKI) and those without. This review summarizes the pathogenesis, pathophysiology, treatment strategies, and role of metabolomics in relation to AKI in COVID-19 patients. Metabolomics is likely to play a greater role in predicting outcomes for patients with kidney disease and COVID-19 with varying levels of severity in the near future as data on metabolic profiles expand rapidly. Here, we also discuss the correlation between COVID-19 and kidney diseases and the available metabolomics approaches.

Markers for obese and non-obese Type 2 diabetes identified using whole blood metabolomics

Definitive differences in blood metabolite profiles between obese and non-obese Type 2 diabetes (T2D) have not been established. We performed an LC-MS-based non-targeted metabolomic analysis of whole blood samples collected from subjects classified into 4 types, based on the presence or absence of obesity and T2D. Of the 125 compounds identified, 20, comprising mainly nucleobases and glucose metabolites, showed significant increases or decreases in the T2D group. These included cytidine, UDP-glucuronate, UMP, 6-phosphogluconate, and pentose-phosphate. Among those 20 compounds, 11 enriched in red blood cells (RBCs) have rarely been studied in the context of diabetes, indicating that RBC metabolism is more extensively disrupted than previously known. Correlation analysis revealed that these T2D markers include 15 HbA1c-associated and 5 irrelevant compounds that may reflect diabetic conditions by a different mechanism than that of HbA1c. In the obese group, enhanced protein and fatty acid catabolism causes increases in 13 compounds, including methylated or acetylated amino acids and short-chain carnitines. Our study, which may be considered a pilot investigation, suggests that changes in blood metabolism due to obesity and diabetes are large, but essentially independent.

The relationship between islet β-cell function and metabolomics in overweight patients with Type 2 diabetes

A cross-sectional study was performed using metabolomics in overweight patients with Type 2 diabetes (T2D) at different stages of the disease. We aimed to identify potential metabolites for assessing islet β-cell function in order to investigate the correlation between islet β-cell dysfunction and metabolite changes in overweight patients with T2D. We selected 60 overweight adults (24 ≤ body mass index [BMI] < 28 kg/m2) with T2D who had been admitted to our hospital. The participants were equally divided into three groups according to disease duration: H1 (duration ≤ 5 years), H2 (5 years < duration ≤ 10 years), and H3 (duration > 10 years). Questionnaires, physical examinations, laboratory tests, and imaging studies were administered to all participants. The modified homeostasis model of assessment (HOMA) index was calculated using fasting C-peptide levels, and metabolite assays were performed using mass spectrometry. The results showed that HOMA-β and visceral fat area (VFA) were negatively correlated with diabetes duration. The VFA was positively correlated with arginine, cysteine, methionine, proline, and succinyl/methylmalonylcarnitine levels. The HOMA-β was negatively correlated with the serine and tetradecanoyldiacylcarnitine levels, and positively correlated with the aspartic acid, cysteine, homocysteine, piperamide, proline, and valine levels. The HOMA-IR was negatively correlated with hydroxypalmitoylcarnitine levels and positively correlated with the myristoylcarnitine levels. Thus, at different stages of T2D progression in overweight patients, serine, aspartic acid, cysteine, homocysteine, piperamide, proline, valine, and tetradecanoyldiacylcarnitine may be associated with HOMA-β and represent potential novel biomarkers for evaluating islet β-cell function.

Pathophysiology of type 2 diabetes and the impact of altered metabolic interorgan crosstalk

Diabetes is a complex and multifactorial disease that affects millions of people worldwide, reducing the quality of life significantly, and results in grave consequences for our health care system. In type 2 diabetes (T2D), the lack of β-cell compensatory mechanisms overcoming peripherally developed insulin resistance is a paramount factor leading to disturbed blood glucose levels and lipid metabolism. Impaired β-cell functions and insulin resistance have been studied extensively resulting in a good understanding of these pathways but much less is known about interorgan crosstalk, which we define as signaling between tissues by secreted factors. Besides hormones and organokines, dysregulated blood glucose and long-lasting hyperglycemia in T2D is associated with changes in metabolism with metabolites from different tissues contributing to the development of this disease. Recent data suggest that metabolites, such as lipids including free fatty acids and amino acids, play important roles in the interorgan crosstalk during the development of T2D. In general, metabolic remodeling affects physiological homeostasis and impacts the development of T2D. Hence, we highlight the importance of metabolic interorgan crosstalk in this review to gain enhanced knowledge of the pathophysiology of T2D, which may lead to new therapeutic approaches to treat this disease.

Circulating Metabolites Associated with Albuminuria in a Hispanic/Latino Population

BACKGROUND

Albuminuria is associated with metabolic abnormalities, but these relationships are not well understood. We studied the association of metabolites with albuminuria in Hispanic/Latino people, a population with high risk for metabolic disease.

METHODS

We used data from 3736 participants from the Hispanic Community Health Study/Study of Latinos, of which 16% had diabetes and 9% had an increased urine albumin-to-creatinine ratio (UACR). Metabolites were quantified in fasting serum through nontargeted mass spectrometry (MS) analysis using ultra-performance liquid chromatography-MS/MS. Spot UACR was inverse normally transformed and tested for the association with each metabolite or combined, correlated metabolites, in covariate-adjusted models that accounted for the study design. In total, 132 metabolites were available for replication in the Hypertension Genetic Epidemiology Network study ( n =300), and 29 metabolites were available for replication in the Malmö Offspring Study ( n =999).

RESULTS

Among 640 named metabolites, we identified 148 metabolites significantly associated with UACR, including 18 novel associations that replicated in independent samples. These metabolites showed enrichment for D-glutamine and D-glutamate metabolism and arginine biosynthesis, pathways previously reported for diabetes and insulin resistance. In correlated metabolite analyses, we identified two modules significantly associated with UACR, including a module composed of lipid metabolites related to the biosynthesis of unsaturated fatty acids and alpha linolenic acid and linoleic acid metabolism.

CONCLUSIONS

Our study identified associations of albuminuria with metabolites involved in glucose dysregulation, and essential fatty acids and precursors of arachidonic acid in Hispanic/Latino population.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_02_08_CJN09070822.mp3.

Berberis microphylla G. Forst Intake Reduces the Cardiovascular Disease Plasmatic Markers Associated with a High-Fat Diet in a Mice Model

Polyphenols are bioactive substances that participate in the prevention of chronic illnesses. High content has been described in Berberis microphylla G. Forst (calafate), a wild berry extensively distributed in Chilean-Argentine Patagonia. We evaluated its beneficial effect through the study of mouse plasma metabolome changes after chronic consumption of this fruit. Characterized calafate extract was administered in water, for four months, to a group of mice fed with a high-fat diet and compared with a control diet. Metabolome changes were studied using UHPLC-DAD-QTOF-based untargeted metabolomics. The study was complemented by the analysis of protein biomarkers determined using Luminex technology, and quantification of OH radicals by electron paramagnetic resonance spectroscopy. Thirteen features were identified with a maximum annotation level-A, revealing an increase in succinic acid, activation of tricarboxylic acid and reduction of carnitine accumulation. Changes in plasma biomarkers were related to inflammation and cardiovascular disease, with changes in thrombomodulin (-24%), adiponectin (+68%), sE-selectin (-34%), sICAM-1 (-24%) and proMMP-9 (-31%) levels. The production of OH radicals in plasma was reduced after calafate intake (-17%), especially for the group fed with a high-fat diet. These changes could be associated with protection against atherosclerosis due to calafate consumption, which is discussed from a holistic and integrative point of view.

A Review of the Impact of Pharmacogenetics and Metabolomics on the Efficacy of Metformin in Type 2 Diabetes

Metformin is the most often prescribed drug for people with type 2 diabetes (T2D). More than 120 million patients with T2D use metformin worldwide. However, monotherapy fails to achieve glycemic control in a third of the treated patients. Genetics contribute to some of the inter-individual variations in glycemic response to metformin. Numerous pharmacogenetic studies have demonstrated that variations in genes related to pharmacokinetics and pharmacodynamics of metformin's encoding transporters are mainly associated with metformin response. The goal of this review is to evaluate the current state of metformin pharmacogenetics and metabolomics research, discuss the clinical and scientific issues that need to be resolved in order to increase our knowledge of patient response variability to metformin, and how to improve patient outcomes. Metformin's hydrophilic nature and absorption as well as its action mechanism and effectiveness on T2D initiation are discussed. The impacts of variations associated with various genes are analysed to identify and evaluate the effect of genetic polymorphisms on the therapeutic activity of metformin. The metabolic pattern of T2D and metformin is also indicated. This is to emphasise that studies of pharmacogenetics and metabolomics could expand our knowledge of metformin response in T2D.

Alterations of the gut microbiota in type 2 diabetics with or without subclinical hypothyroidism

Background

Diabetes and thyroid dysfunction are two closely related endocrine diseases. Increasing evidences show that gut microbiota plays an important role in both glucose metabolism and thyroid homeostasis. Meanwhile, copy number variation (CNV) of host salivary α-amylase gene (AMY1) has been shown to correlate with glucose homeostasis. Hence, we aim to characterize the gut microbiota and CNV of AMY1 in type 2 diabetes (T2D) patients with or without subclinical hypothyroidism (SCH).

Methods

High-throughput sequencing was used to analyze the gut microbiota of euthyroid T2D patients, T2D patients with SCH and healthy controls. Highly sensitive droplet digital PCR was used to measure AMY1 CN.

Results

Our results revealed that T2D patients have lower gut microbial diversity, no matter with or without SCH. The characteristic taxa of T2D patients were Coriobacteriales, Coriobacteriaceae, Peptostreptococcaceae, Pseudomonadaceae, Collinsella, Pseudomonas and Romboutsia. Meanwhile, Escherichia/Shigella, Lactobacillus_Oris, Parabacteroides Distasonis_ATCC_8503, Acetanaerobacterium, Lactonifactor, uncultured bacterium of Acetanaerobacterium were enriched in T2D patients with SCH. Moreover, serum levels of free triiodothyronine (FT3) and free thyroxine (FT4) in T2D patients were both negatively correlated with richness of gut microbiota. A number of specific taxa were also associated with clinical parameters at the phylum and genus level. In contrast, no correlation was found between AMY1 CN and T2D or T2D_SCH.

Conclusion

This study identified characteristic bacterial taxa in gut microbiota of T2D patients with or without SCH, as well as the taxa associated with clinical indices in T2D patients. These results might be exploited in the prevention, diagnosis and treatment of endocrine disorders in the future.

Gut microbiota links with cognitive impairment in amyotrophic lateral sclerosis: a multi-omics study

Recently, cognitive impairments (CI) and behavioral abnormalities in patients with amyotrophic lateral sclerosis (ALS) have been reported. However, the underlying mechanisms have been poorly understood. In the current study, we explored the role of gut microbiota in CI of ALS patients. We collected fecal samples from 35 ALS patients and 35 healthy controls. The cognitive function of the ALS patients was evaluated using the Edinburgh Cognitive and Behavioral ALS Screen. We analyzed these samples by using 16S rRNA gene sequencing as well as both untargeted and targeted (bile acids) metabolite mapping between patients with CI and patients with normal cognition (CN). We found altered gut microbial communities and a lower ratio of Firmicutes/ Bacteroidetes in the CI group, compared with the CN group. In addition, the untargeted metabolite mapping revealed that 26 and 17 metabolites significantly increased and decreased, respectively, in the CI group, compared with the CN group. These metabolites were mapped to the metabolic pathways associated with bile acids. We further found that cholic acid and chenodeoxycholic acid were significantly lower in the CI group than in the CN group. In conclusion, we found that the gut microbiota and its metabolome profile differed between ALS patients with and without CI and that the altered bile acid profile in fecal samples was significantly associated with CI in ALS patients. These results need to be replicated in larger studies in the future.

Longitudinal associations between metabolites and long-term exposure to ambient air pollution: Results from the KORA cohort study

BACKGROUND

Long-term exposure to air pollution has been associated with cardiopulmonary diseases, while the underlying mechanisms remain unclear.

OBJECTIVES

To investigate changes in serum metabolites associated with long-term exposure to air pollution and explore the susceptibility characteristics.

METHODS

We used data from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) S4 survey (1999-2001) and two follow-up examinations (F4: 2006-08 and FF4: 2013-14). Mass-spectrometry-based targeted metabolomics was used to quantify metabolites among serum samples. Only participants with repeated metabolites measurements were included in the current analysis. Land-use regression (LUR) models were used to estimate annual average concentrations of ultrafine particles, particulate matter (PM) with an aerodynamic diameter less than 10 μm (PM₁₀), coarse particles (PM_coarse), fine particles, PM_2.5 absorbance (a proxy of elemental carbon related to traffic exhaust, PM_2.5abs), nitrogen oxides (NO₂, NO_x), and ozone at individuals' residences. We applied confounder-adjusted mixed-effects regression models to examine the associations between long-term exposure to air pollution and metabolites.

RESULTS

Among 9,620 observations from 4,261 KORA participants, we included 5,772 (60.0%) observations from 2,583 (60.6%) participants in this analysis. Out of 108 metabolites that passed stringent quality control across three study points in time, we identified nine significant negative associations between phosphatidylcholines (PCs) and ambient pollutants at a Benjamini-Hochberg false discovery rate (FDR) corrected p-value < 0.05. The strongest association was seen for an increase of 0.27 μg/m³ (interquartile range) in PM_2.5abs and decreased phosphatidylcholine acyl-alkyl C36:3 (PC ae C36:3) concentrations [percent change in the geometric mean: -2.5% (95% confidence interval: -3.6%, -1.5%)].

CONCLUSIONS

Our study suggested that long-term exposure to air pollution is associated with metabolic alterations, particularly in PCs with unsaturated long-chain fatty acids. These findings might provide new insights into potential mechanisms for air pollution-related adverse outcomes.

Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population

Type 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of circulating metabolites and proteins were established, revealing subtype-specific molecular mechanisms, including activation of the complement system with features of autoimmune diabetes and reduced 1,5-anhydroglucitol in SIDD, impaired insulin signaling in SIRD, and elevated leptin and fatty acid binding protein levels in MOD. The MARD cluster was the healthiest with metabolomic and proteomic profiles most similar to the controls. We have translated the T2D subtypes to an Arab population and identified distinct molecular signatures to further our understanding of the etiology of these subtypes.

Multi-omics research strategies in ischemic stroke: A multidimensional perspective

Ischemic stroke (IS) is a multifactorial and heterogeneous neurological disorder with high rate of death and long-term impairment. Despite years of studies, there are still no stroke biomarkers for clinical practice, and the molecular mechanisms of stroke remain largely unclear. The high-throughput omics approach provides new avenues for discovering biomarkers of IS and explaining its pathological mechanisms. However, single-omics approaches only provide a limited understanding of the biological pathways of diseases. The integration of multiple omics data means the simultaneous analysis of thousands of genes, RNAs, proteins and metabolites, revealing networks of interactions between multiple molecular levels. Integrated analysis of multi-omics approaches will provide helpful insights into stroke pathogenesis, therapeutic target identification and biomarker discovery. Here, we consider advances in genomics, transcriptomics, proteomics and metabolomics and outline their use in discovering the biomarkers and pathological mechanisms of IS. We then delineate strategies for achieving integration at the multi-omics level and discuss how integrative omics and systems biology can contribute to our understanding and management of IS.

Higher body mass index and disease duration are associated with increased risk of left ventricular diastolic dysfunction in women with systemic lupus erythematosus

BACKGROUND

Patients with systemic lupus erythematosus (SLE) have an increased cardiovascular (CV) risk. Insulin resistance (IR), which is higher in patients with SLE, adversely impacts left ventricular (LV) remodeling and function. The aims were to determine LV dysfunction and evaluate the influence of potential risk factors on subclinical LV dysfunction in women with SLE, including IR.

METHODS

This cross-sectional study included adult women with SLE without diabetes mellitus (DM), hypertension or severe obesity. Diastolic dysfunction (DD) was verified according to current guidelines. Insulin resistance was estimated using the Quantose score.

RESULTS

We included 77 women. The frequency of IR was 65%. All participants had a normal ejection fraction (EF), and 11 (15.7%) had abnormal LV global longitudinal strain (GLS). Twenty-three (32.8%) had DD. The GLS% and global circumferential strain (GCS)% did not differ in patients with and without IR (-20.8 ± 3.1 vs -20.5 ± 2.1; p = 0.61 and -27.9 ± 4.4 vs -27.4 ± 3.7; p = 0.57, respectively). The prevalence of DD was 38.1% in patients with IR versus 25% in those without (p = 0.30). E/e' and E/A ratios did not differ between groups (6.6 ± 1.9 vs 6.6 ± 1.5; p = 0.98 and 1.3 ± 0.3 vs 1.3 ± 0.2; p = 0.27). Higher BMI (OR: 1.2, 95% CI 1.1-1.5) and disease duration (OR: 1.2, 95% CI 1.1-1.4) were associated with DD.

CONCLUSIONS

Patients with overweight/obesity may be at higher risk of LV dysfunction. Although IR was high in our patients with SLE was not associated with systolic dysfunction or DD. Body mass index and disease duration were associated with an increased risk of DD.

Untargeted approach to investigating the metabolomics profile of type 2 diabetes emiratis

Type 2 Diabetes (T2D) is expected to be the seventh most significant cause of death worldwide by 2030. Although research into its mechanism has received the attention it deserves, our understanding of T2D is still limited. This case-control study employs untargeted metabolomics to explore novel T2D plasma biomarkers in the Emirati population. Ninety-two UAE nationals were included in the cohort, with fifty T2D and forty-two non-T2D profiles. Participants were then stratified into three groups based on metabolic profiles, clinically verified diabetic status, and current HbA1c values: namely controlled diabetics, uncontrolled diabetics and prediabetics, and non-diabetics. The study identified fifteen significant differentially abundant metabolites between the uncontrolled diabetics group and the prediabetics or controlled diabetics group. Interestingly, some metabolites essential for the corticosteroid and thyroid signaling pathways were found to be significantly elevated in poorly controlled T2D, including cortisol, glycocholic acid, bile acids, thyroxine, and the tryptophan metabolite, 5-hydroxyindoleacetic acid. These findings align with those from prior western cohorts and suggest an intriguing linkage between T2D glycemic control and thyroid and adrenal signaling that may provide new diagnostic and prognostic indicators. RESEARCH SIGNIFICANCE: This study investigates the underlooked metabolomic role and correlation with T2D in the UAE population. The report indicates fifteen significant differentially abundant metabolites between on diabetics, uncontrolled diabetics and or controlled diabetics or prediabetics. This panel of metabolites such as thyroxine and corticosteroids should be considered further as potential diagnostic or prognostic biomarkers for T2D in the region.

Genetics of Type 2 Diabetes: Past, Present, and Future

Diabetes has reached epidemic proportions worldwide. Currently, approximately 537 million adults (20–79 years) have diabetes, and the total number of people with diabetes is continuously increasing. Diabetes includes several subtypes. About 80% of all cases of diabetes are type 2 diabetes (T2D). T2D is a polygenic disease with an inheritance ranging from 30 to 70%. Genetic and environment/lifestyle factors, especially obesity and sedentary lifestyle, increase the risk of T2D. In this review, we discuss how studies on the genetics of diabetes started, how they expanded when genome-wide association studies and exome and whole-genome sequencing became available, and the current challenges in genetic studies of diabetes. T2D is heterogeneous with respect to clinical presentation, disease course, and response to treatment, and has several subgroups which differ in pathophysiology and risk of micro- and macrovascular complications. Currently, genetic studies of T2D focus on these subgroups to find the best diagnoses and treatments for these patients according to the principles of precision medicine.

Branched-chain and aromatic amino acid levels response to an oral glucose load associated with gestational diabetes mellitus

Serum branched chain amino acids (BCAAs) and aromatic amino acids (AAAs) are associated with obesity, insulin resistance and type 2 diabetes mellitus (T2DM). We investigated the levels of these amino acids in women with gestational diabetes mellitus (GDM) and examined their changes in response to an oral glucose tolerance test (OGTT). 110 women were enrolled and underwent a 75-g OGTT during their second trimester; 43 women were diagnosed with GDM and 67 women did not have GDM (non-GDM women). During the OGTT, fasting, 1-h, and 2-h blood samples were obtained. BCAA and AAA levels were measured by liquid chromatography-tandem mass spectrometry. The differences in BCAA and AAA levels between GDM and non-GDM women were not evident during fasting but became significant after glucose loading. Glucose ingestion decreased the levels of BCAAs and AAAs in both groups. Notably, GDM women showed a delayed and blunted decrease in these amino acids compared to non-GDM women. The risks of 2-h changes in BCAAs and AAAs for GDM women were significant. We identified that the differences in BCAA and AAA levels between GDM women and controls, which were not evident during fasting, could be provoked by performing an OGTT.

Serum and Soleus Metabolomics Signature of Klf10 Knockout Mice to Identify Potential Biomarkers

The transcription factor Krüppel-like factor 10 (Klf10), also known as Tieg1 for TGFβ (Inducible Early Gene-1) is known to control numerous genes in many cell types that are involved in various key biological processes (differentiation, proliferation, apoptosis, inflammation), including cell metabolism and human disease. In skeletal muscle, particularly in the soleus, deletion of the Klf10 gene (Klf10 KO) resulted in ultrastructure fiber disorganization and mitochondrial metabolism deficiencies, characterized by muscular hypertrophy. To determine the metabolic profile related to loss of Klf10 expression, we analyzed blood and soleus tissue using UHPLC-Mass Spectrometry. Metabolomics analyses on both serum and soleus revealed profound differences between wild-type (WT) and KO animals. Klf10 deficient mice exhibited alterations in metabolites associated with energetic metabolism. Additionally, chemical classes of aromatic and amino-acid compounds were disrupted, together with Krebs cycle intermediates, lipids and phospholipids. From variable importance in projection (VIP) analyses, the Warburg effect, citric acid cycle, gluconeogenesis and transfer of acetyl groups into mitochondria appeared to be possible pathways involved in the metabolic alterations observed in Klf10 KO mice. These studies have revealed essential roles for Klf10 in regulating multiple metabolic pathways whose alterations may underlie the observed skeletal muscle defects as well as other diseases.

Isobaric 4-Plex Tagging for Absolute Quantitation of Biological Acids in Diabetic Urine Using Capillary LC-MS/MS

Isobaric labeling in mass spectrometry enables multiplexed absolute quantitation and high throughput, while minimizing full scan spectral complexity. Here, we use 4-plex isobaric labeling with a fixed positive charge tag to improve quantitation and throughput for polar carboxylic acid metabolites. The isobaric tag uses an isotope-encoded neutral loss to create mass-dependent reporters spaced 2 Da apart and was validated for both single- and double-tagged analytes. Tags were synthesized in-house using deuterated formaldehyde and methyl iodide in a total of four steps, producing cost-effective multiplexing. No chromatographic deuterium shifts were observed for single- or double-tagged analytes, producing consistent reporter ratios across each peak. Perfluoropentanoic acid was added to the sample to drastically increase retention of double-tagged analytes on a C18 column. Excess tag was scavenged and extracted using hexadecyl chloroformate after reaction completion. This allowed for removal of excess tag that typically causes ion suppression and column overloading. A total of 54 organic acids were investigated, producing an average linearity of 0.993, retention time relative standard deviation (RSD) of 0.58%, and intensity RSD of 12.1%. This method was used for absolute quantitation of acid metabolites comparing control and type 1 diabetic urine. Absolute quantitation of organic acids was achieved by using one isobaric lane for standards, thereby allowing for analysis of six urine samples in two injections. Quantified acids showed good agreement with previous work, and six significant changes were found. Overall, this method demonstrated 4-plex absolute quantitation of acids in a complex biological sample.

Lipidomics Analysis of Free Fatty Acids in Human Plasma of Healthy and Diabetic Subjects by Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS)

Targeted analytical methods for the determination of free fatty acids (FFAs) in human plasma are of high interest because they may help in identifying biomarkers for diseases and in monitoring the progress of a disease. The determination of FFAs is of particular importance in the case of metabolic disorders because FFAs have been associated with diabetes. We present a liquid chromatography-high resolution mass spectrometry (LC-HRMS) method, which allows the simultaneous determination of 74 FFAs in human plasma. The method is fast (10-min run) and straightforward, avoiding any derivatization step and tedious sample preparation. A total of 35 standard saturated and unsaturated FFAs, as well as 39 oxygenated (either hydroxy or oxo) saturated FFAs, were simultaneously detected and quantified in plasma samples from 29 subjects with type 2 diabetes mellitus (T2D), 14 with type 1 diabetes mellitus (T1D), and 28 healthy subjects. Alterations in the levels of medium-chain FFAs (C6:0 to C10:0) were observed between the control group and T2D and T1D patients.

Metabolomic Analysis of Serum and Tear Samples from Patients with Obesity and Type 2 Diabetes Mellitus

Metabolomics strategies are widely used to examine obesity and type 2 diabetes (T2D). Patients with obesity (n = 31) or T2D (n = 26) and sex- and age-matched controls (n = 28) were recruited, and serum and tear samples were collected. The concentration of 23 amino acids and 10 biogenic amines in serum and tear samples was analyzed. Statistical analysis and Pearson correlation analysis along with network analysis were carried out. Compared to controls, changes in the level of 6 analytes in the obese group and of 10 analytes in the T2D group were statistically significant. For obesity, the energy generation, while for T2D, the involvement of NO synthesis and its relation to insulin signaling and inflammation, were characteristic. We found that BCAA and glutamine metabolism, urea cycle, and beta-oxidation make up crucial parts of the metabolic changes in T2D. According to our data, the retromer-mediated retrograde transport, the ethanolamine metabolism, and, consequently, the endocannabinoid signaling and phospholipid metabolism were characteristic of both conditions and can be relevant pathways to understanding and treating insulin resistance. By providing potential therapeutic targets and new starting points for mechanistic studies, our results emphasize the importance of complex data analysis procedures to better understand the pathomechanism of obesity and diabetes.

Guide to Metabolomics Analysis: A Bioinformatics Workflow

Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach's ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

Longitudinal associations of pre-pregnancy BMI and gestational weight gain with maternal urinary metabolites: an NYU CHES study

Background/Objectives:

Excessive gestational weight gain (GWG) and pre-pregnancy obesity affect a significant portion of the US pregnant population and are linked with negative maternal and child health outcomes. The objective of this study was to explore associations of pre-pregnancy body mass index (pBMI) and GWG with longitudinally measured maternal urinary metabolites throughout pregnancy.

Subjects/Methods:

Among 652 participants in the New York University Children’s Health and Environment Study, a longitudinal pregnancy cohort, targeted metabolomics were measured in serially collected urine samples throughout pregnancy. Metabolites were measured at median 10 (T1), 21 (T2), and 29 (T3) weeks gestation using the Biocrates AbsoluteIDQ® p180 Urine Extension kit. Acylcarnitine, amino acid, biogenic amine, phosphatidylcholine, lysophosphatidylcholine, sphingolipid, and sugar levels were quantified. Pregnant people 18 years or older, without type 1 or 2 diabetes and with singleton live births and valid pBMI and metabolomics data were included. GWG and pBMI were calculated using weight and height data obtained from electronic health records. Linear mixed effects models with interactions with time were fit to determine the gestational age-specific associations of categorical pBMI and continuous interval-specific GWG with urinary metabolites. All analyses were corrected for false discovery rate.

Results:

Participants with obesity had lower long-chain acylcarnitine levels throughout pregnancy and lower phosphatidylcholine and glucogenic amino acids and higher phenylethylamine concentrations in T2 and T3 compared with participants with normal/underweight pBMI. GWG was associated with taurine in T2 and T3 and C5 acylcarnitine species, C5:1, C5-DC, and C5-M-DC, in T2.

Conclusions:

pBMI and GWG were associated with the metabolic environment of pregnant individuals, particularly in relation to mid-pregnancy. These results highlight the importance of both preconception and prenatal maternal health.

Assessing Dietary Pesticide Intake and Potential Health Effects: The Application of Global Metabolomics Analysis

Scientific information is not yet available to provide insight into how individual metabolome might be affected by the presence of pesticides in regular diets. This study aimed to evaluate the perturbation of metabolomic pathways in children who switched their diets from conventional foods to mostly organic foods for five consecutive days. We selected 46 child-matched spot urine samples with distinct differences of urinary pesticide metabolite levels between the conventional and organic eating days and then analyzed those urine samples on three analytical platforms to perform global metabolomics analysis. We found statistically significant perturbations of metabolic pathways relevant to inflammation, oxidative stress, and the demands of xenobiotic detoxification when children switched their conventional diets to mostly organic foods. The outcomes of this study allow us to extend the current understanding beyond organophosphate pesticides' acute toxicity of cholinesterase inhibition to the perturbation of metabolic pathways at dietary intake levels.

Metabolic changes induced by dapagliflozin, an SGLT2 inhibitor, in Japanese patients with type 2 diabetes treated by oral anti-diabetic agents: A randomized, clinical trial

AIM

We aimed to determine whether SGLT2 inhibitor dapagliflozin treatment affects body composition and amino acid (AA) metabolism.

METHODS

Fifty-two overweight patients treated by oral antidiabetic agents were randomly assigned to dapagliflozin (Dapa) or a standard treatment (Con) and followed for 24 weeks. The primary outcome was the change in body mass (BM) between baseline and week 24. Body composition, intrahepatic triglyceride (IHTG) content, and plasma AA concentrations were examined as secondary outcomes.

RESULTS

The change in BM was significantly larger in the Dapa than in the Con group, with a difference in the mean change of -1.72 kg (95 %CI: -2.85, -0.59; P = 0.004) between the groups. Total fat mass was reduced by dapagliflozin treatment, but fat-free mass was maintained. IHTG content was significantly reduced in the Dapa than in the Con (P = 0.033). Changes in AAs showed small differences between the groups, but only serine concentrations were significantly reduced in the Dapa. Intra-group analysis showed that positive associations were observed between changes in branched chain AA concentrations and body composition only in the Dapa.

CONCLUSIONS

Dapagliflozin treatment causes a reduction in BM mainly by reducing fat mass. AA metabolism shows subtle changes with dapagliflozin treatment.

Plasma metabolomic profiles for colorectal cancer precursors in women

How metabolome changes influence the early process of colorectal cancer (CRC) development remains unknown. We conducted a 1:2 matched nested case-control study to examine the associations of pre-diagnostic plasma metabolome (profiled using LC-MS) with risk of CRC precursors, including conventional adenomas (n = 586 vs. 1141) and serrated polyps (n = 509 vs. 993), in the Nurses' Health Study (NHS) and NHSII. Conditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). We used the permutation-based Westfall and Young approach to account for multiple testing. Subgroup analyses were performed for advanced conventional adenomas (defined as at least one adenoma of ≥ 10 mm or with high-grade dysplasia, or tubulovillous or villous histology) and high-risk serrated polyps that were located in the proximal colon or with size of ≥ 10 mm. After multiple testing correction, among 207 metabolites, higher levels of C36:3 phosphatidylcholine (PC) plasmalogen were associated with lower risk of conventional adenomas, with the OR (95% CI) comparing the 90th to the 10th percentile of 0.62 (0.48-0.81); C54:8 triglyceride (TAG) was associated with higher risk of serrated polyps (OR = 1.79, 95% CI: 1.31-2.43), and phenylacetylglutamine (PAG) was associated with lower risk (OR = 0.57, 95% CI:0.43-0.77). PAG was also inversely associated with advanced adenomas (OR = 0.57, 95% CI: 0.36-0.89) and high-risk serrated polyps (OR = 0.54, 95% CI: 0.32-0.89), although the multiple testing-corrected p value was > 0.05. Our findings suggest potential roles of lipid metabolism and phenylacetylglutamine, a microbial metabolite, in the early stage of colorectal carcinogenesis, particularly for the serrated pathway.

Amino Acid-Related Metabolic Signature in Obese Children and Adolescents

The growing interest in metabolomics has spread to the search for suitable predictive biomarkers for complications related to the emerging issue of pediatric obesity and its related cardiovascular risk and metabolic alteration. Indeed, several studies have investigated the association between metabolic disorders and amino acids, in particular branched-chain amino acids (BCAAs). We have performed a revision of the literature to assess the role of BCAAs in children and adolescents' metabolism, focusing on the molecular pathways involved. We searched on Pubmed/Medline, including articles published until February 2022. The results have shown that plasmatic levels of BCAAs are impaired already in obese children and adolescents. The relationship between BCAAs, obesity and the related metabolic disorders is explained on one side by the activation of the mTORC1 complex-that may promote insulin resistance-and on the other, by the accumulation of toxic metabolites, which may lead to mitochondrial dysfunction, stress kinase activation and damage of pancreatic cells. These compounds may help in the precocious identification of many complications of pediatric obesity. However, further studies are still needed to better assess if BCAAs may be used to screen these conditions and if any other metabolomic compound may be useful to achieve this goal.

Gut microbiota and fermentation-derived branched chain hydroxy acids mediate health benefits of yogurt consumption in obese mice

Meta-analyses suggest that yogurt consumption reduces type 2 diabetes incidence in humans, but the molecular basis of these observations remains unknown. Here we show that dietary yogurt intake preserves whole-body glucose homeostasis and prevents hepatic insulin resistance and liver steatosis in a dietary mouse model of obesity-linked type 2 diabetes. Fecal microbiota transplantation studies reveal that these effects are partly linked to the gut microbiota. We further show that yogurt intake impacts the hepatic metabolome, notably maintaining the levels of branched chain hydroxy acids (BCHA) which correlate with improved metabolic parameters. These metabolites are generated upon milk fermentation and concentrated in yogurt. Remarkably, diet-induced obesity reduces plasma and tissue BCHA levels, and this is partly prevented by dietary yogurt intake. We further show that BCHA improve insulin action on glucose metabolism in liver and muscle cells, identifying BCHA as cell-autonomous metabolic regulators and potential mediators of yogurt's health effects.

Recent Developments in Biomarkers for Diagnosis and Screening of Type 2 Diabetes Mellitus

PURPOSE OF REVIEW

Diabetes mellitus is a complex, chronic illness characterized by elevated blood glucose levels that occurs when there is cellular resistance to insulin action, pancreatic β-cells do not produce sufficient insulin, or both. Diabetes prevalence has greatly increased in recent decades; consequently, it is considered one of the fastest-growing public health emergencies globally. Poor blood glucose control can result in long-term micro- and macrovascular complications such as nephropathy, retinopathy, neuropathy, and cardiovascular disease. Individuals with diabetes require continuous medical care, including pharmacological intervention as well as lifestyle and dietary changes.

RECENT FINDINGS

The most common form of diabetes mellitus, type 2 diabetes (T2DM), represents approximately 90% of all cases worldwide. T2DM occurs more often in middle-aged and elderly adults, and its cause is multifactorial. However, its incidence has increased in children and young adults due to obesity, sedentary lifestyle, and inadequate nutrition. This high incidence is also accompanied by an estimated underdiagnosis prevalence of more than 50% worldwide. Implementing successful and cost-effective strategies for systematic screening of diabetes mellitus is imperative to ensure early detection, lowering patients' risk of developing life-threatening disease complications. Therefore, identifying new biomarkers and assay methods for diabetes mellitus to develop robust, non-invasive, painless, highly-sensitive, and precise screening techniques is essential. This review focuses on the recent development of new clinically validated and novel biomarkers as well as the methods for their determination that represent cost-effective alternatives for screening and early diagnosis of T2DM.

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.