Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes

Anti-diabetes activity of (R)-gentiandiol in KKAy type 2 mice

Swertiamarin is a major component of many traditional Chinese Swertia herbs that show significant antidiabetic activity. (R)-Gentiandiol and (S)-gentiandiol are metabolites of swertiamarin found in vivo. The antidiabetic activity of swertiamarin and its nitrogen-containing metabolites (R)-gentiandiol and (S)-gentiandiol was evaluated in this research, and their mechanism of action was investigated after evaluating the serum metabolic profile of KK/Upj-Ay type 2 mice. The pharmaceutical effects of swertiamarin, (R)-gentiandiol, and (S)-gentiandiol were tested by biochemical indices and histopathological observations. Moreover, the mechanism underlying the action of three compounds against type 2 diabetes was elucidated using a metabolomic method. It was shown that (R)-gentiandiol significantly improved pathological changes in the kidney and pancreas. The levels of total cholesterol, triglyceride, and high-density and low-density lipoprotein cholesterol improved considerably after treatment with (R)-gentiandiol, compared to their levels in model mice. However, the levels of these compounds showed no improvement after treatment with (S)-gentiandiol. In total, 15 biomarkers were identified in KK/Upj-Ay type 2 mice, and the levels of 10 biomarkers were measured after treatment with (R)-gentiandiol. (R)-Gentiandiol reduced the abnormalities in metabolic pathways, including lipid metabolism, amino acid metabolism, carbohydrate metabolism, and nucleotide metabolism. Additionally, glycine, serine, and threonine metabolism related to the regulation of glycine was affected the most. The study indicated that the antidiabetic effects of Swertia herbs may due to (R)-gentiandiol which is a metabolite of swertiamarin in vivo. This study helps clarify the active metabolites of swertiamarin, provide greater insights into the clinical antidiabetic effects of Swertia herbs and bring novel ideas for developing new drugs from antidiabetic herbs.

The bile acid profile

As a large and structurally diverse family of small molecules, bile acids play a crucial role in regulating lipid, glucose, and energy metabolism. In the human body, bile acids share a similar chemical structure with many isomers, exhibit little difference in polarity, and possess various physiological activities. The types and contents of bile acids present in different diseases vary significantly. Therefore, comprehensive and accurate detection of the content of various types of bile acids in different biological samples can not only provide new insights into the pathogenesis of diseases but also facilitate the exploration of novel strategies for disease diagnosis, treatment, and prognosis. The detection of disease-induced changes in bile acid profiles has emerged as a prominent research focus in recent years. Concurrently, targeted metabolomics methods utilizing high-performance liquid chromatography-mass spectrometry (HPLC-MS) have progressively established themselves as the predominant technology for the separation and detection of bile acids. Bile acid profiles will increasingly play an important role in diagnosis and guidance in the future as the relationship between disease and changes in bile acid profiles becomes clearer. This highlights the growing diagnostic value of bile acid profiles and their potential to guide clinical decision-making. This review aims to explore the significance of bile acid profiles in clinical diagnosis from four perspectives: the synthesis and metabolism of bile acids, techniques for detecting bile acid profiles, changes in bile acid profiles associated with diseases, and the challenges and future prospects of applying bile acid profiles in clinical settings.

Postbiotic Impact on Host Metabolism and Immunity Provides Therapeutic Potential in Metabolic Disease

The gut microbiota influences aspects of metabolic disease, including tissue inflammation, adiposity, blood glucose, insulin, and endocrine control of metabolism. Prebiotics or probiotics are often sought to combat metabolic disease. However, prebiotics lack specificity and can have deleterious bacterial community effects. Probiotics require live bacteria to find a colonization niche sufficient to influence host immunity or metabolism. Postbiotics encompass bacterial-derived components and molecules, which are well-positioned to alter host immunometabolism without relying on colonization efficiency or causing widespread effects on the existing microbiota. Here, we summarize the potential for beneficial and detrimental effects of specific postbiotics related to metabolic disease and the underlying mechanisms of action. Bacterial cell wall components, such as lipopolysaccharides, muropeptides, lipoteichoic acids and flagellin, have context-dependent effects on host metabolism by engaging specific immune responses. Specific types of postbiotics within broad classes of compounds, such as lipopolysaccharides and muropeptides, can have opposing effects on endocrine control of host metabolism, where certain postbiotics are insulin sensitizers and others promote insulin resistance. Bacterial metabolites, such as short-chain fatty acids, bile acids, lactate, glycerol, succinate, ethanolamine, and ethanol, can be substrates for host metabolism. Postbiotics can fuel host metabolic pathways directly or influence endocrine control of metabolism through immunomodulation or mimicking host-derived hormones. The interaction of postbiotics in the host-microbe relationship should be considered during metabolic inflammation and metabolic disease.

Novel type 2 diabetes prediction score based on traditional risk factors and circulating metabolites: model derivation and validation in two large cohort studies

Background

We aimed to evaluate the incremental predictive value of metabolomic biomarkers for assessing the 10-year risk of type 2 diabetes when added to the clinical Cambridge Diabetes Risk Score (CDRS).

Methods

We utilized 86,232 UK Biobank (UKB) participants (recruited between 13 March 2006 and 1 October 2010) for model derivation and internal validation. Additionally, we included 4383 participants from the German ESTHER cohort (recruited between 1 July 2000 and 30 June 2002 for external validation). Participants were followed up for 10 years to assess the incidence of type 2 diabetes. A total of 249 NMR-derived metabolites were quantified using nuclear magnetic resonance (NMR) spectroscopy. Metabolites were selected with LASSO regression and model performance was evaluated with Harrell's C-index.

Findings

11 metabolomic biomarkers, including glycolysis related metabolites, ketone bodies, amino acids, and lipids, were selected. In internal validation within the UKB, adding these metabolites significantly increased the C-index (95% confidence interval (95% CI)) of the clinical CDRS from 0.815 (0.800, 0.829) to 0.834 (0.820, 0.847) and the continuous net reclassification index (NRI) with 95% CI was 39.8% (34.6%, 45.0%). External validation in the ESTHER cohort showed a comparable statistically significant C-index increase from 0.770 (0.750, 0.791) to 0.798 (0.779, 0.817) and a continuous NRI of 33.8% (26.4%, 41.2%). A concise model with 4 instead of 11 metabolites yielded similar results.

Interpretation

Adding 11 metabolites to the clinical CDRS led to a novel type 2 diabetes prediction model, we called UK Biobank Diabetes Risk Score (UKB-DRS), substantially outperformed the clinical CDRS. The concise version with 4 metabolites performed comparably. As only very few clinical information and a blood sample are needed for the UKB-DRS, and as high-throughput NMR metabolomics are becoming increasingly available at low costs, these models have considerable potential for routine clinical application in diabetes risk assessment.

Funding

The ESTHER study was funded by grants from the Baden-Württemberg state Ministry of Science, Research and Arts (Stuttgart, Germany), the Federal Ministry of Education and Research (Berlin, Germany), the Federal Ministry of Family Affairs, Senior Citizens, Women and Youth (Berlin, Germany), and the Saarland State Ministry of Health, Social Affairs, Women and the Family (Saarbrücken, Germany). The UK Biobank project was established through collaboration between various entities including the Wellcome Trust, the Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency. Additional funding was provided by the Welsh Assembly Government, British Heart Foundation, Cancer Research UK, and Diabetes UK, with support from the National Health Service (NHS). The German Diabetes Center is funded by the German Federal Ministry of Health (Berlin, Germany) and the Ministry of Culture and Science of the state North Rhine-Westphalia (Düsseldorf, Germany) and receives additional funding from the German Federal Ministry of Education and Research (BMBF) through the German Center for Diabetes Research (DZD e.V.).

Impact of Phenylketonuria on the Serum Metabolome and Plasma Lipidome: A Study in Early-Treated Patients

BACKGROUND

Data suggest that metabolites, other than blood phenylalanine (Phe), better and independently predict clinical outcomes in patients with phenylketonuria (PKU).

METHODS

To find new biomarkers, we compared the results of untargeted lipidomics and metabolomics in treated adult PKU patients to those of matched controls. Samples (lipidomics in EDTA-plasma (22 PKU and 22 controls) and metabolomics in serum (35 PKU and 20 controls)) were analyzed using ultra-high-performance liquid chromatography and high-resolution mass spectrometry. Data were subjected to multivariate (PCA, OPLS-DA) and univariate (Mann-Whitney U test, p < 0.05) analyses.

RESULTS

Levels of 33 (of 20,443) lipid features and 56 (of 5885) metabolite features differed statistically between PKU patients and controls. For lipidomics, findings include higher glycerolipids, glycerophospholipids, and sphingolipids species. Significantly lower values were found for sterols and glycerophospholipids species. Seven features had unknown identities. Total triglyceride content was higher. Higher Phe and Phe catabolites, tryptophan derivatives, pantothenic acid, and dipeptides were observed for metabolomics. Ornithine levels were lower. Twenty-six metabolite features were not annotated.

CONCLUSIONS

This study provides insight into the metabolic phenotype of PKU patients. Additional studies are required to establish whether the observed changes result from PKU itself, diet, and/or an unknown reason.

Metabolomics Panel Associated with Cystic Fibrosis-Related Diabetes toward Biomarker Discovery

The most prevalent comorbidity among cystic fibrosis (CF) patients is cystic fibrosis-related diabetes (CFRD). CFRD has been linked to one of the worse clinical outcomes and a higher mortality. Improved clinical results have been related to earlier diagnosis and treatment of CFRD. Therefore, the present study aimed to investigate the metabolome of human serum of patients with CFRD. This might aid in identifying novel biomarkers linked with the pathophysiology of CFRD and its diagnosis. The liquid chromatography-high-resolution mass spectrometry (LC-HRMS) metabolomics approach was utilized for serum samples from patients with CF (n = 36) and healthy controls (n = 36). Nine patients in the CF group had CFRD, and 27 were non-CFRD patients (nCFRD). A total of 2328 metabolites were significantly altered in CF compared with the healthy control. Among those, 799 significantly dysregulated metabolites were identified between CFRD and nCFRD. Arachidonic acid (AA), ascorbate, and aldarate metabolism were the most common metabolic pathways dysregulated in CF. l-Homocysteic acid (l-HCA) levels were significantly reduced in CF and CFRD compared to the control and nCFRD, respectively. In addition, gamma-glutamylglycine and l-5-hydroxytryptophan (5-HTP) had the highest discrimination between CFRD and nCFRD with AUC (0.716 and 0.683, respectively). These biomarkers might serve as diagnostic biomarkers and aid in understanding potential metabolic changes linked to CF and CFRD.

Anti-diabetic effect of dicaffeoylquinic acids is associated with the modulation of gut microbiota and bile acid metabolism

INTRODUCTION

The human gut microbiome plays a pivotal role in health and disease, notably through its interaction with bile acids (BAs). BAs, synthesized in the liver, undergo transformation by the gut microbiota upon excretion into the intestine, thus influencing host metabolism. However, the potential mechanisms of dicaffeoylquinic acids (DiCQAs) from Ilex kudingcha how to modulate lipid metabolism and inflammation via gut microbiota remain unclear.

OBJECTIVES AND METHODS

The objectives of the present study were to investigate the regulating effects of DiCQAs on diabetes and the potential mechanisms of action. Two mice models were utilized to investigate the anti-diabetic effects of DiCQAs. Additionally, analysis of gut microbiota structure and functions was conducted concurrently with the examination of DiCQAs' impact on gut microbiota carrying the bile salt hydrolase (BSH) gene, as well as on the enterohepatic circulation of BAs and related signaling pathways.

RESULTS

Our findings demonstrated that DiCQAs alleviated diabetic symptoms by modulating gut microbiota carrying the BSH gene. This modulation enhanced intestinal barrier integrity, increased enterohepatic circulation of conjugated BAs, and inhibited the farnesoid X receptor-fibroblast growth factor 15 (FGF15) signaling axis in the ileum. Consequently, the protein expression of hepatic FGFR4 fibroblast growth factor receptor 4 (FGFR4) decreased, accompanied by heightened BA synthesis, reduced hepatic BA stasis, and lowered levels of hepatic and plasma cholesterol. Furthermore, DiCQAs upregulated glucolipid metabolism-related proteins in the liver and muscle, including v-akt murine thymoma viral oncogene homolog (AKT)/glycogen synthase kinase 3-beta (GSK3β) and AMP-activated protein kinase (AMPK), thereby ameliorating hyperglycemia and mitigating inflammation through the down-regulation of the MAPK signaling pathway in the diabetic group.

CONCLUSION

Our study elucidated the anti-diabetic effects and mechanism of DiCQAs from I. kudingcha, highlighting the potential of targeting gut microbiota, particularly Acetatifactor sp011959105 and Acetatifactor muris carrying the BSH gene, as a therapeutic strategy to attenuate FXR-FGF15 signaling and ameliorate diabetes.

Serum bile acid and unsaturated fatty acid profiles of non-alcoholic fatty liver disease in type 2 diabetic patients

BACKGROUND

The understanding of bile acid (BA) and unsaturated fatty acid (UFA) profiles, as well as their dysregulation, remains elusive in individuals with type 2 diabetes mellitus (T2DM) coexisting with non-alcoholic fatty liver disease (NAFLD). Investigating these metabolites could offer valuable insights into the pathophy-siology of NAFLD in T2DM.

AIM

To identify potential metabolite biomarkers capable of distinguishing between NAFLD and T2DM.

METHODS

A training model was developed involving 399 participants, comprising 113 healthy controls (HCs), 134 individuals with T2DM without NAFLD, and 152 individuals with T2DM and NAFLD. External validation encompassed 172 participants. NAFLD patients were divided based on liver fibrosis scores. The analytical approach employed univariate testing, orthogonal partial least squares-discriminant analysis, logistic regression, receiver operating characteristic curve analysis, and decision curve analysis to pinpoint and assess the diagnostic value of serum biomarkers.

RESULTS

Compared to HCs, both T2DM and NAFLD groups exhibited diminished levels of specific BAs. In UFAs, particular acids exhibited a positive correlation with NAFLD risk in T2DM, while the ω-6:ω-3 UFA ratio demonstrated a negative correlation. Levels of α-linolenic acid and γ-linolenic acid were linked to significant liver fibrosis in NAFLD. The validation cohort substantiated the predictive efficacy of these biomarkers for assessing NAFLD risk in T2DM patients.

CONCLUSION

This study underscores the connection between altered BA and UFA profiles and the presence of NAFLD in individuals with T2DM, proposing their potential as biomarkers in the pathogenesis of NAFLD.

Changes in bile acid subtypes and improvements in lipid metabolism and atherosclerotic cardiovascular disease risk: the Preventing Overweight Using Novel Dietary Strategies (POUNDS Lost) trial

BACKGROUND

Distinct circulating bile acid (BA) subtypes may play roles in regulating lipid homeostasis and atherosclerosis.

OBJECTIVES

We investigated whether changes in circulating BA subtypes induced by weight-loss dietary interventions were associated with improved lipid profiles and atherosclerotic cardiovascular disease (ASCVD) risk estimates.

METHODS

This study included adults with overweight or obesity (n = 536) who participated in a randomized weight-loss dietary intervention trial. Circulating primary and secondary unconjugated BAs and their taurine-/glycine-conjugates were measured at baseline and 6 mo after the weight-loss diet intervention. The ASCVD risk estimates were calculated using the validated equations.

RESULTS

At baseline, higher concentrations of specific BA subtypes were related to higher concentrations of atherogenic very low-density lipoprotein lipid subtypes and ASCVD risk estimates. Weight-loss diet-induced decreases in primary BAs were related to larger reductions in triglycerides and total cholesterol [every 1 standard deviation (SD) decrease of glycocholate, glycochenodeoxycholate, or taurochenodeoxycholate was related to β (standard error) -3.3 (1.3), -3.4 (1.3), or -3.8 (1.3) mg/dL, respectively; PFDR < 0.05 for all]. Greater decreases in specific secondary BA subtypes were also associated with improved lipid metabolism at 6 mo; there was β -4.0 (1.1) mg/dL per 1-SD decrease of glycoursodeoxycholate (PFDR =0.003) for changes in low-density lipoprotein cholesterol. We found significant interactions (P-interaction < 0.05) between dietary fat intake and changes in BA subtypes on changes in ASCVD risk estimates; decreases in primary and secondary BAs (such as conjugated cholate or deoxycholate) were significantly associated with improved ASCVD risk after consuming a high-fat diet, but not after consuming a low-fat diet.

CONCLUSIONS

Decreases in distinct BA subtypes were associated with improved lipid profiles and ASCVD risk estimates, highlighting the importance of changes in circulating BA subtypes as significant factors linked to improved lipid metabolism and ASCVD risk estimates in response to weight-loss dietary interventions. Habitual dietary fat intake may modify the associations of changes in BAs with ASCVD risk. This trial was registered at clinicaltrials.gov as NCT00072995.

Alterations in gut microbiota and bile acids by proton-pump inhibitor use and possible mediating effects on elevated glucose levels and insulin resistance

Several observational studies have suggested that proton-pump inhibitor (PPI) use might increase diabetes risk, but the mechanism remains unclear. This study aimed to investigate the effects of PPI use on gut microbiota and bile acids (BAs) profiles, and to explore whether these changes could mediate the association of PPIs use with fasting blood glucose (FBG) levels and insulin resistance (IR) in Chinese population. A cross-sectional study was conducted in Shenzhen, China, from April to August 2021, enrolled 200 eligible patients from the local hospital. Participants completed a questionnaire and provided blood and stool samples. Gut microbiome was measured by16S rRNA gene sequencing, and bile acids were quantified by UPLC-MS/MS. Insulin resistance (IR) was assessed using the Homeostasis Model Assessment 2 (HOMA2-IR). PPI use was positively associated with higher levels of FBG and HOMA2-IR after controlling for possible confounders. PPI users exhibited a decreased Firmicutes and an increase in Bacteroidetes phylum, alongside higher levels of glycoursodeoxycholic acid (GUDCA) and taurochenodeoxycholic acid (TCDCA). Higher abundances of Bacteroidetes and Fusobacterium as well as higher levels of TCDCA in PPI users were positively associated with elevated FBG or HOMA2-IR. Mediation analyses indicated that the elevated levels of FBG and HOMA2-IR with PPI use were partially mediated by the alterations in gut microbiota and specific BAs (i.e., Fusobacterium genera and TCDCA). Long-term PPI use may increase FBG and HOMA2-IR levels, and alterations in gut microbiota and BAs profiles may partially explain this association.

Plasma metabolite profile of legume consumption and future risk of type 2 diabetes and cardiovascular disease

BACKGROUND

Legume consumption has been linked to a reduced risk of type 2 diabetes (T2D) and cardiovascular disease (CVD), while the potential association between plasma metabolites associated with legume consumption and the risk of cardiometabolic diseases has never been explored. Therefore, we aimed to identify a metabolite signature of legume consumption, and subsequently investigate its potential association with the incidence of T2D and CVD.

METHODS

The current cross-sectional and longitudinal analysis was conducted in 1833 PREDIMED study participants (mean age 67 years, 57.6% women) with available baseline metabolomic data. A subset of these participants with 1-year follow-up metabolomics data (n = 1522) was used for internal validation. Plasma metabolites were assessed through liquid chromatography-tandem mass spectrometry. Cross-sectional associations between 382 different known metabolites and legume consumption were performed using elastic net regression. Associations between the identified metabolite profile and incident T2D and CVD were estimated using multivariable Cox regression models.

RESULTS

Specific metabolic signatures of legume consumption were identified, these included amino acids, cortisol, and various classes of lipid metabolites including diacylglycerols, triacylglycerols, plasmalogens, sphingomyelins and other metabolites. Among these identified metabolites, 22 were negatively and 18 were positively associated with legume consumption. After adjustment for recognized risk factors and legume consumption, the identified legume metabolite profile was inversely associated with T2D incidence (hazard ratio (HR) per 1 SD: 0.75, 95% CI 0.61-0.94; p = 0.017), but not with CVD incidence risk (1.01, 95% CI 0.86-1.19; p = 0.817) over the follow-up period.

CONCLUSIONS

This study identified a set of 40 metabolites associated with legume consumption and with a reduced risk of T2D development in a Mediterranean population at high risk of cardiovascular disease.

TRIAL REGISTRATION

ISRCTN35739639.

Altered Gut Microbiota as a Potential Risk Factor for Coronary Artery Disease in Diabetes: A Two-Sample Bi-Directional Mendelian Randomization Study

The current body of research points to a notable correlation between an imbalance in gut microbiota and the development of type 2 diabetes mellitus (T2D) as well as its consequential ailment, coronary artery disease (CAD). The complexities underlying the association, especially in the context of diabetic coronary artery disease (DCAD), are not yet fully understood, and the causal links require further clarification. In this study, a bidirectional Mendelian randomization (MR) methodology was utilized to explore the causal relationships between gut microbiota, T2D, and CAD. By analyzing data from the DIAGRAM, GERA, UKB, FHS, and mibioGen cohorts and examining GWAS databases, we sought to uncover genetic variants linked to T2D, CAD, and variations in gut microbiota and metabolites, aiming to shed light on the potential mechanisms connecting gut microbiota with DCAD. Our investigation uncovered a marked causal link between the presence of Oxalobacter formigenes and an increased incidence of both T2D and CAD. Specifically, a ten-unit genetic predisposition towards T2D was found to be associated with a 6.1% higher probability of an increase in the Oxalobacteraceae family's presence (β = 0.061, 95% CI = 0.002-0.119). In a parallel finding, an augmented presence of Oxalobacter was related to an 8.2% heightened genetic likelihood of CAD (β = 0.082, 95% CI = 0.026-0.137). This evidence indicates a critical pathway by which T2D can potentially raise the risk of CAD via alterations in gut microbiota. Additionally, our analyses reveal a connection between CAD risk and Methanobacteria, thus providing fresh perspectives on the roles of TMAO and carnitine in the etiology of CAD. The data also suggest a direct causal relationship between increased levels of certain metabolites - proline, lysophosphatidylcholine, asparagine, and salicylurate - and the prevalence of both T2D and CAD. Sensitivity assessments reinforce the notion that changes in Oxalobacter formigenes could pose a risk for DCAD. There is also evidence to suggest that DCAD may, in turn, affect the gut microbiota's makeup. Notably, a surge in serum TMAO levels in individuals with CAD, coinciding with a reduced presence of methanogens, has been identified as a potentially significant factor for future examination.

Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders

Metabolic disorders are increasingly prevalent worldwide, leading to high rates of morbidity and mortality. The variety of metabolic illnesses can be addressed through personalized medicine. The goal of personalized medicine is to give doctors the ability to anticipate the best course of treatment for patients with metabolic problems. By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. To achieve this goal, real-time modeling of clinical data and multiple omics is essential to pinpoint underlying biological mechanisms, risk factors, and possibly useful data to promote early diagnosis and prevention of complex diseases. Incorporating cutting-edge technologies like artificial intelligence and machine learning is crucial for consolidating diverse forms of data, examining multiple variables, establishing databases of clinical indicators to aid decision-making, and formulating ethical protocols to address concerns. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. It focuses on the recent advancements in genomics, epigenomics, proteomics, metabolomics, and nutrigenomics, emphasizing their role in revolutionizing personalized medicine.

Cholesterol-rich dietary pattern during early pregnancy and genetic variations of cholesterol metabolism genes in predicting gestational diabetes mellitus: a nested case-control study

BACKGROUND

Higher dietary cholesterol intake during pregnancy increases risk of gestational diabetes mellitus (GDM). However, no studies have investigated interindividual variability in cholesterol metabolism and the association of genetics and diet on GDM.

OBJECTIVE

; To prospectively evaluate the joint association of cholesterol-rich dietary patterns and polymorphisms of genes coding for cholesterol metabolism pathway proteins with GDM.

METHODS

A total of 1116 pregnant females from the Tongji Birth Cohort were enrolled. GDM was diagnosed according to a 75-g 2-h oral glucose tolerance test at 24-28 wk of gestation. Dietary data were collected by a validated food frequency questionnaire. The reduced-rank regression method was used to identify dietary patterns using dietary cholesterol as the response variable. Time-of-flight mass spectrometry was used for genotyping. The genetic risk score (GRS) for GDM was constructed with genetic variants in 28 cholesterol metabolism-related single-nucleotide polymorphisms (SNPs). Conditional logistic regression models were used to assess the odds ratio (OR) for GDM.

RESULTS

The cholesterol-rich dietary pattern was rich in livestock and poultry meat and eggs but lower in cereals. The multivariable-adjusted ORs for GDM were 1.24 (95% confidence interval: 1.06-1.44) per SD increment of cholesterol-rich pattern scores and 1.28 (1.09-1.49) per tertile GRS. The variants of the CYP7A1 rs3808607 G→T/rs8192871 G→A/rs7833904 A→T, as well as AGGG and TTGA haplotypes of 4 CYP7A1-spanning SNPs, were significantly associated with GDM. For the joint effect, the OR was 3.53 (1.71-7.31) in the highest categories of both dietary pattern scores and GRS compared with individuals with the lowest strata without significant interaction (P for interaction = 0.101).

CONCLUSIONS

Both a cholesterol-rich dietary pattern and genetic variants of cholesterol metabolism genes are associated with risk of GDM. Adherence to a cholesterol-rich dietary pattern during early pregnancy promotes the chance of GDM, especially in women with higher GRS.

CLINICAL TRIAL REGISTRY

This trial was registered at http://www.chictr.org.cn (Registration number: ChiCTR1800016908). URL: =https://www.chictr.org.cn/showprojEN.html?proj=28081.

Metabolite profiles of diabetes mellitus and response to intervention in anti-hyperglycemic drugs

Type 2 diabetes mellitus (T2DM) has become a major health problem, threatening the quality of life of nearly 500 million patients worldwide. As a typical multifactorial metabolic disease, T2DM involves the changes and interactions of various metabolic pathways such as carbohydrates, amino acid, and lipids. It has been suggested that metabolites are not only the endpoints of upstream biochemical processes, but also play a critical role as regulators of disease progression. For example, excess free fatty acids can lead to reduced glucose utilization in skeletal muscle and induce insulin resistance; metabolism disorder of branched-chain amino acids contributes to the accumulation of toxic metabolic intermediates, and promotes the dysfunction of β-cell mitochondria, stress signal transduction, and apoptosis. In this paper, we discuss the role of metabolites in the pathogenesis of T2DM and their potential as biomarkers. Finally, we list the effects of anti-hyperglycemic drugs on serum/plasma metabolic profiles.

CYP4F2 is a human-specific determinant of circulating N-acyl amino acid levels

N-acyl amino acids are a large family of circulating lipid metabolites that modulate energy expenditure and fat mass in rodents. However, little is known about the regulation and potential cardiometabolic functions of N-acyl amino acids in humans. Here, we analyze the cardiometabolic phenotype associations and genomic associations of four plasma N-acyl amino acids (N-oleoyl-leucine, N-oleoyl-phenylalanine, N-oleoyl-serine, and N-oleoyl-glycine) in 2,351 individuals from the Jackson Heart Study. We find that plasma levels of specific N-acyl amino acids are associated with cardiometabolic disease endpoints independent of free amino acid plasma levels and in patterns according to the amino acid head group. By integrating whole genome sequencing data with N-acyl amino acid levels, we identify that the genetic determinants of N-acyl amino acid levels also cluster according to amino acid head group. Furthermore, we identify the CYP4F2 locus as a genetic determinant of plasma N-oleoyl-leucine and N-oleoyl-phenylalanine levels in human plasma. In experimental studies, we demonstrate that CYP4F2-mediated hydroxylation of N-oleoyl-leucine and N-oleoyl-phenylalanine results in metabolic diversification and production of many previously unknown lipid metabolites with varying characteristics of the fatty acid tail group, including several that structurally resemble fatty acid hydroxy fatty acids (FAHFAs). These studies provide a structural framework for understanding the regulation and disease-associations of N-acyl amino acids in humans and identify that the diversity of this lipid signaling family can be significantly expanded through CYP4F-mediated ω-hydroxylation.

CYP4F2 is a human-specific determinant of circulating N-acyl amino acid levels

N-acyl amino acids are a large family of circulating lipid metabolites that modulate energy expenditure and fat mass in rodents. However, little is known about the regulation and potential cardiometabolic functions of N-acyl amino acids in humans. Here, we analyze the cardiometabolic phenotype associations and genetic regulation of four plasma N-fatty acyl amino acids (N-oleoyl-leucine, N-oleoyl-phenylalanine, N-oleoyl-serine, and N-oleoyl-glycine) in 2,351 individuals from the Jackson Heart Study. N-oleoyl-leucine and N-oleoyl-phenylalanine were positively associated with traits related to energy balance, including body mass index, waist circumference, and subcutaneous adipose tissue. In addition, we identify the CYP4F2 locus as a human-specific genetic determinant of plasma N-oleoyl-leucine and N-oleoyl-phenylalanine levels. In vitro, CYP4F2-mediated hydroxylation of N-oleoyl-leucine and N-oleoyl-phenylalanine results in metabolic diversification and production of many previously unknown lipid metabolites with varying characteristics of the fatty acid tail group, including several that structurally resemble fatty acid hydroxy fatty acids (FAHFAs). By contrast, FAAH-regulated N-oleoyl-glycine and N-oleoyl-serine were inversely associated with traits related to glucose and lipid homeostasis. These data uncover a human-specific enzymatic node for the metabolism of a subset of N-fatty acyl amino acids and establish a framework for understanding the cardiometabolic roles of individual N-fatty acyl amino acids in humans.

Oat β-glucan ameliorates diabetes in high fat diet and streptozotocin-induced mice by regulating metabolites

Oats are widely distributed worldwide and oat β-glucan has positive effects on human health. Particularly, oat β-glucan is reported to be beneficial in the management of type 2 diabetes. The aim of the present study is to investigate the effects of oat β-glucan and its possible underlying mechanisms on diabetes in type 2 diabetic mice that was induced by streptozotocin/high-fat diet (STZ/HFD). The data indicated that oat β-glucan significantly reduced the fasting blood glucose, improved glucose tolerance, and insulin sensitivity. The results further showed that oat β-glucan remarkably decreased the levels of total cholesterol (TCHO), total triglyceride (TG), low-density lipoprotein cholesterol (LDL-C) and free fatty acids. Moreover, oat β-glucan remarkably increased the hepatic glycogen content, but largely decreased the levels of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in STZ/HFD-induced diabetic mice. Histological analysis showed that oat β-glucan alleviated visceral lesions. Finally, the metabolomic analysis indicated that the metabolic profile was remarkably changed after oat β-glucan intervention in diabetic mice. There were 88 and 106 differential metabolites screened as biomarkers in negative ion mode (NEG) and positive ion mode (POS) after oat β-glucan treatment, respectively. In addition, oat β-glucan significantly affected the serum metabolites of amino acids, organic acids and bile acids. Collectively, the current study elucidates oat β-glucan displays an effective nutritional intervention in diabetes.

Mixtures of environmental contaminants and diabetes

BACKGROUND

Many studies have been published on the relationships between different environmental contaminants and diabetes. In these studies, the environmental contaminants have most often been evaluated one by one, but in real life we are exposed to a mixture of contaminants that interact with each other.

OBJECTIVE

The major aim of this study was to see if a mixture of contaminants could improve the prediction of incident diabetes, using machine learning.

METHODS

In the Prospective Investigation of the Vasculature in Uppsala (PIVUS) study (988 men and women aged 70 years), circulating levels of 42 contaminants from several chemical classes were measured at baseline. Incident diabetes was followed for 15 years. Six different machine-learning models were used to predict prevalent diabetes (n = 115). The variables with top importance were thereafter used to predict incident diabetes (n = 83).

RESULTS

Boosted regression trees performed best regarding prediction of prevalent diabetes (area under the ROC-curve = 0.70). Following removal of correlated contaminants, the addition of nine selected contaminants (Cd, Pb, Trans-nonachlor the phthalate MiBP, Hg, Ni, PCB126, PCB169 and PFOS) resulted in a significant improvement of 6.0 % of the ROC curve (from 0.66 to 0.72, p = 0.018) regarding incident diabetes (n = 51) compared with a baseline model including sex and BMI when the first 5 years of the follow-up was used. No such improvement in prediction was seen over 15 years follow-up. The single contaminant being most closely related to incident diabetes over 5 years was Nickel (odds ratio 1.44 for a SD change, 95 % CI 1.05-1.95, p = 0.022).

CONCLUSION

This study supports the view that machine learning was useful in finding a mixture of important contaminants that improved prediction of incident diabetes. This improvement in prediction was seen only during the first 5 years of follow-up.

Gamma-glutamyl-leucine levels are causally associated with elevated cardio-metabolic risks

Objective

Gamma-glutamyl dipeptides are bioactive peptides involved in inflammation, oxidative stress, and glucose regulation. Gamma-glutamyl-leucine (Gamma-Glu-Leu) has been extensively reported to be associated with the risk of cardio-metabolic diseases, such as obesity, metabolic syndrome, and type 2 diabetes. However, the causality remains to be uncovered. The aim of this study was to explore the causal-effect relationships between Gamma-Glu-Leu and metabolic risk.

Materials and methods

In this study, 1,289 subjects were included from a cross-sectional survey on metabolic syndrome (MetS) in eastern China. Serum Gamma-Glu-Leu levels were measured by untargeted metabolomics. Using linear regressions, a two-stage genome-wide association study (GWAS) for Gamma-Glu-Leu was conducted to seek its instrumental single nucleotide polymorphisms (SNPs). One-sample Mendelian randomization (MR) analyses were performed to evaluate the causality between Gamma-Glu-Leu and the metabolic risk.

Results

Four SNPs are associated with serum Gamma-Glu-Leu levels, including rs12476238, rs56146133, rs2479714, and rs12229654. Out of them, rs12476238 exhibits the strongest association (Beta = -0.38, S.E. = 0.07 in discovery stage, Beta = -0.29, S.E. = 0.14 in validation stage, combined P-value = 1.04 × 10^-8). Each of the four SNPs has a nominal association with at least one metabolic risk factor. Both rs12229654 and rs56146133 are associated with body mass index, waist circumference (WC), the ratio of WC to hip circumference, blood pressure, and triglyceride (5 × 10^-5 < P < 0.05). rs56146133 also has nominal associations with fasting insulin, glucose, and insulin resistance index (5 × 10^-5 < P < 0.05). Using the four SNPs serving as the instrumental SNPs of Gamma-Glu-Leu, the MR analyses revealed that higher Gamma-Glu-Leu levels are causally associated with elevated risks of multiple cardio-metabolic factors except for high-density lipoprotein cholesterol and low-density lipoprotein cholesterol (P > 0.05).

Conclusion

Four SNPs (rs12476238, rs56146133, rs2479714, and rs12229654) may regulate the levels of serum Gamma-Glu-Leu. Higher Gamma-Glu-Leu levels are causally linked to cardio-metabolic risks. Future prospective studies on Gamma-Glu-Leu are required to explain its role in metabolic disorders.

Response of circulating metabolites to an oral glucose challenge and risk of cardiovascular disease and mortality in the community

Background

New biomarkers to identify cardiovascular disease (CVD) risk earlier in its course are needed to enable targeted approaches for primordial prevention. We evaluated whether intraindividual changes in blood metabolites in response to an oral glucose tolerance test (OGTT) may provide incremental information regarding the risk of future CVD and mortality in the community.

Methods

An OGTT (75 g glucose) was administered to a subsample of Framingham Heart Study participants free from diabetes (n = 361). Profiling of 211 plasma metabolites was performed from blood samples drawn before and 2 h after OGTT. The log2(post/pre) metabolite levels (Δmetabolites) were related to incident CVD and mortality in Cox regression models adjusted for age, sex, baseline metabolite level, systolic blood pressure, hypertension treatment, body mass index, smoking, and total/high-density lipoprotein cholesterol. Select metabolites were related to subclinical cardiometabolic phenotypes using Spearman correlations adjusted for age, sex, and fasting metabolite level.

Results

Our sample included 42% women, with a mean age of 56 ± 9 years and a body mass index of 30.2 ± 5.3 kg/m². The pre- to post-OGTT changes (Δmetabolite) were non-zero for 168 metabolites (at FDR ≤ 5%). A total of 132 CVD events and 144 deaths occurred during median follow-up of 24.9 years. In Cox models adjusted for clinical risk factors, four Δmetabolites were associated with incident CVD (higher glutamate and deoxycholate, lower inosine and lysophosphatidylcholine 18:2) and six Δmetabolites (higher hydroxyphenylacetate, triacylglycerol 56:5, alpha-ketogluturate, and lower phosphatidylcholine 32:0, glucuronate, N-monomethyl-arginine) were associated with death (P < 0.05). Notably, baseline metabolite levels were not associated with either outcome in models excluding Δmetabolites. The Δmetabolites exhibited varying cross-sectional correlation with subclinical risk factors such as visceral adiposity, insulin resistance, and vascular stiffness, but overall relations were modest. Significant Δmetabolites included those with established roles in cardiometabolic disease (e.g., glutamate, alpha-ketoglutarate) and metabolites with less defined roles (e.g., glucuronate, lipid species).

Conclusions

Dynamic changes in metabolite levels with an OGTT are associated with incident CVD and mortality and have potential relevance for identifying CVD risk earlier in its development and for discovering new potential therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01647-w.

Advances in multi-omics study of biomarkers of glycolipid metabolism disorder

Glycolipid metabolism disorder are major threats to human health and life. Genetic, environmental, psychological, cellular, and molecular factors contribute to their pathogenesis. Several studies demonstrated that neuroendocrine axis dysfunction, insulin resistance, oxidative stress, chronic inflammatory response, and gut microbiota dysbiosis are core pathological links associated with it. However, the underlying molecular mechanisms and therapeutic targets of glycolipid metabolism disorder remain to be elucidated. Progress in high-throughput technologies has helped clarify the pathophysiology of glycolipid metabolism disorder. In the present review, we explored the ways and means by which genomics, transcriptomics, proteomics, metabolomics, and gut microbiomics could help identify novel candidate biomarkers for the clinical management of glycolipid metabolism disorder. We also discuss the limitations and recommended future research directions of multi-omics studies on these diseases.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study

BACKGROUND

Effective targeted prevention of type 2 diabetes (T2D) depends on accurate prediction of disease risk. We assessed the role of metabolomic profiling in improving T2D risk prediction beyond conventional risk factors.

METHODS

Nuclear magnetic resonance (NMR) metabolomic profiling was undertaken on baseline plasma samples in 65,684 UK Biobank participants without diabetes and not taking lipid-lowering medication. Among a subset of 50,519 participants with data available on all relevant co-variates (sociodemographic characteristics, parental history of diabetes, lifestyle-including dietary-factors, anthropometric measures and fasting time), Cox regression yielded adjusted hazard ratios for the associations of 143 individual metabolic biomarkers (including lipids, lipoproteins, fatty acids, amino acids, ketone bodies and other low molecular weight metabolic biomarkers) and 11 metabolic biomarker principal components (PCs) (accounting for 90% of the total variance in individual biomarkers) with incident T2D. These 11 PCs were added to established models for T2D risk prediction among the full study population, and measures of risk discrimination (c-statistic) and reclassification (continuous net reclassification improvement [NRI], integrated discrimination index [IDI]) were assessed.

RESULTS

During median 11.9 (IQR 11.1-12.6) years' follow-up, after accounting for multiple testing, 90 metabolic biomarkers showed independent associations with T2D risk among 50,519 participants (1211 incident T2D cases) and 76 showed associations after additional adjustment for HbA1c (false discovery rate controlled p < 0.01). Overall, 8 metabolic biomarker PCs were independently associated with T2D. Among the full study population of 65,684 participants, of whom 1719 developed T2D, addition of PCs to an established risk prediction model, including age, sex, parental history of diabetes, body mass index and HbA1c, improved T2D risk prediction as assessed by the c-statistic (increased from 0.802 [95% CI 0.791-0.812] to 0.830 [0.822-0.841]), continuous NRI (0.44 [0.38-0.49]) and relative (15.0% [10.5-20.4%]) and absolute (1.5 [1.0-1.9]) IDI. More modest improvements were observed when metabolic biomarker PCs were added to a more comprehensive established T2D risk prediction model additionally including waist circumference, blood pressure and plasma lipid concentrations (c-statistic, 0.829 [0.819-0.838] to 0.837 [0.831-0.848]; continuous NRI, 0.22 [0.17-0.28]; relative IDI, 6.3% [4.1-9.8%]; absolute IDI, 0.7 [0.4-1.1]).

CONCLUSIONS

When added to conventional risk factors, circulating NMR-based metabolic biomarkers modestly enhanced T2D risk prediction.

Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies

BACKGROUND

Due to the rapidly increasing availability of metabolomics data in prospective studies, an update of the meta evidence on metabolomics and type 2 diabetes risk is warranted.

PURPOSE

To conduct an updated systematic review and meta-analysis of plasma, serum, and urine metabolite markers and incident type 2 diabetes.

DATA SOURCES

We searched PubMed and Embase until 6 March 2021.

STUDY SELECTION

We selected prospective observational studies where investigators used high-throughput techniques to investigate the relationship between plasma, serum, or urine metabolites and incident type 2 diabetes.

DATA EXTRACTION

Baseline metabolites per-SD risk estimates and 95% CIs for incident type 2 diabetes were extracted from all eligible studies.

DATA SYNTHESIS

A total of 61 reports with 71,196 participants and 11,771 type 2 diabetes cases/events were included in the updated review. Meta-analysis was performed for 412 metabolites, of which 123 were statistically significantly associated (false discovery rate-corrected P < 0.05) with type 2 diabetes risk. Higher plasma and serum levels of certain amino acids (branched-chain, aromatic, alanine, glutamate, lysine, and methionine), carbohydrates and energy-related metabolites (mannose, trehalose, and pyruvate), acylcarnitines (C4-DC, C4-OH, C5, C5-OH, and C8:1), the majority of glycerolipids (di- and triacylglycerols), (lyso)phosphatidylethanolamines, and ceramides included in meta-analysis were associated with higher risk of type 2 diabetes (hazard ratio 1.07-2.58). Higher levels of glycine, glutamine, betaine, indolepropionate, and (lyso)phosphatidylcholines were associated with lower type 2 diabetes risk (hazard ratio 0.69-0.90).

LIMITATIONS

Substantial heterogeneity (I2 > 50%, τ2 > 0.1) was observed for some of the metabolites.

CONCLUSIONS

Several plasma and serum metabolites, including amino acids, lipids, and carbohydrates, are associated with type 2 diabetes risk.

Lipidomics in diabetes

PURPOSE OF REVIEW

Multiple studies have shown a strong association between lipids and diabetes. These are usually described through the effects of cholesterol content of lipid particles and in particular low-density lipoprotein. However, lipoprotein particles contain other components, such as phospholipids and more complex lipid species, such as ceramides and sphingolipids. Ceramides, such as sphingolipids are also produced intracellularly and have signalling actions in regulating cell metabolism including effects on inflammation, and potentially have a mechanistic role in the development of insulin resistance.

RECENT FINDINGS

Recently, techniques have been developed to analyse detailed molecular profiles of lipid particles - lipidomics. Proteomics has confirmed the different proteins associated with different particles but far less is known about the relationship of individual lipid species with diabetes and cardiovascular risk. A number of studies have now shown that the plasma lipidome, and in particular, ceramides and sphingolipids may predict the development of diabetes.

SUMMARY

Lipidomics had identified ceramides and sphingolipids as potential mediators of cellular dysfunction in diabetes. Further work is required to ascertain whether they have clinical utility.

Effect of General Adiposity and Central Body Fat Distribution on the Circulating Metabolome: A Multicohort Nontargeted Metabolomics Observational and Mendelian Randomization Study

Obesity is associated with adverse health outcomes, but the metabolic effects have not yet been fully elucidated. We aimed to investigate the association between adiposity and circulating metabolites and to address causality with Mendelian randomization (MR). Metabolomics data were generated with nontargeted ultraperformance liquid chromatography coupled to time-of-flight mass spectrometry in plasma and serum from three population-based Swedish cohorts: ULSAM (N = 1,135), PIVUS (N = 970), and TwinGene (N = 2,059). We assessed associations of general adiposity measured as BMI and central body fat distribution measured as waist-to-hip ratio adjusted for BMI (WHRadjBMI) with 210 annotated metabolites. We used MR analysis to assess causal effects. Lastly, we attempted to replicate the MR findings in the KORA and TwinsUK cohorts (N = 7,373), the CHARGE Consortium (N = 8,631), the Framingham Heart Study (N = 2,076), and the DIRECT Consortium (N = 3,029). BMI was associated with 77 metabolites, while WHRadjBMI was associated with 11 and 3 metabolites in women and men, respectively. The MR analyses in the Swedish cohorts suggested a causal association (P value <0.05) of increased general adiposity and reduced levels of arachidonic acid, dodecanedioic acid, and lysophosphatidylcholine (P-16:0) as well as with increased creatine levels. The results of the replication effort provided support for a causal association of adiposity with reduced levels of arachidonic acid (P value = 0.03). Adiposity is associated with variation of large parts of the circulating metabolome; however, further investigation of causality is required in well-powered cohorts.

Proteomic Analysis of Mouse Kidney Tissue Associates Peroxisomal Dysfunction with Early Diabetic Kidney Disease

Background: The absence of efficient inhibitors for diabetic kidney disease (DKD) progression reflects the gaps in our understanding of DKD molecular pathogenesis. Methods: A comprehensive proteomic analysis was performed on the glomeruli and kidney cortex of diabetic mice with the subsequent validation of findings in human biopsies and omics datasets, aiming to better understand the underlying molecular biology of early DKD development and progression. Results: LC–MS/MS was employed to analyze the kidney proteome of 2 DKD models: Ins2Akita (early and late DKD) and db/db mice (late DKD). The abundance of detected proteins was defined. Pathway analysis of differentially expressed proteins in the early and late DKD versus the respective controls predicted dysregulation in DKD hallmarks (peroxisomal lipid metabolism and β-oxidation), supporting the functional relevance of the findings. Comparing the observed protein changes in early and late DKD, the consistent upregulation of 21 and downregulation of 18 proteins was detected. Among these were downregulated peroxisomal and upregulated mitochondrial proteins. Tissue sections from 16 DKD patients were analyzed by IHC confirming our results. Conclusion: Our study shows an extensive differential expression of peroxisomal proteins in the early stages of DKD that persists regardless of the disease severity, providing new perspectives and potential markers of diabetic kidney dysfunction.

Changes in metabolomics profiles over ten years and subsequent risk of developing type 2 diabetes: Results from the Nurses' Health Study

BACKGROUND

Metabolomics profiles were consistently associated with type 2 diabetes (T2D) risk, but evidence on long-term metabolite changes and T2D incidence is lacking. We examined the associations of 10-year plasma metabolite changes with subsequent T2D risk.

METHODS

We conducted a nested T2D case-control study (n=244 cases, n=244 matched controls) within the Nurses' Health Study. Repeated metabolomics profiling (170 targeted metabolites) was conducted in participant blood specimens from 1989/1990 and 2000/2001, and T2D occurred between 2002 and 2008. We related 10-year metabolite changes (Δ-values) to subsequent T2D risk using conditional logistic models, adjusting for baseline metabolite levels and baseline levels and concurrent changes of BMI, diet quality, physical activity, and smoking status.

FINDINGS

The 10-year changes of thirty-one metabolites were associated with subsequent T2D risk (false discovery rate-adjusted p-values [FDR]<0.05). The top three high T2D risk-associated 10-year changes were (odds ratio [OR] per standard deviation [SD], 95%CI): Δisoleucine (2.72, 1.97-3.79), Δleucine (2.53, 1.86-3.47), and Δvaline (1.93, 1.52-2.44); other high-risk-associated metabolite changes included alanine, tri-/diacylglycerol-fragments, short-chain acylcarnitines, phosphatidylethanolamines, some vitamins, and bile acids (ORs per SD between 1.31and 1.82). The top three low T2D risk-associated 10-year metabolite changes were (OR per SD, 95% CI): ΔN-acetylaspartic acid (0.54, 0.42-0.70), ΔC20:0 lysophosphatidylethanolamine (0.68, 0.56-0.82), and ΔC16:1 sphingomyelin (0.68, 0.56-0.83); 10-year changes of other sphingomyelins, plasmalogens, glutamine, and glycine were also associated with lower subsequent T2D risk (ORs per SD between 0.66 and 0.78).

INTERPRETATION

Repeated metabolomics profiles reflecting the long-term deterioration of amino acid and lipid metabolism are associated with subsequent risk of T2D.

Alleviating the effect of quinoa and the underlying mechanism on hepatic steatosis in high-fat diet-fed rats

Background

Nonalcoholic fatty liver disease (NAFLD) is considered the hepatic component of metabolic syndrome and has attracted widespread attention due to its increased prevalence. Daily dietary management is an effective strategy for the prevention of NAFLD. Quinoa, a nutritious pseudocereal, is abundant in antioxidative bioactive phytochemicals. In the present study, the effects of different amounts of quinoa on the progression of NAFLD and the related molecular mechanism were investigated.

Methods

Male SD rats were simultaneously administered a high fat diet (HF) and different amounts of quinoa (equivalent to 100 g/day and 300 g/day of human intake, respectively). After 12 weeks of the intervention, hepatic TG (triglyceride) and TC (total cholesterol) as well as serum antioxidative parameters were determined, and hematoxylin–eosin staining (H&E) staining was used to evaluate hepatic steatosis. Differential metabolites in serum and hepatic tissue were identified using UPLC-QTOF-MS^E. The mRNA expression profile was investigated using RNA-Seq and further verified using real-time polymerase chain reaction (RT-PCR).

Results

Low amounts of quinoa (equivalent to 100 g/d of human intake) effectively controlled the weight of rats fed a high-fat diet. In addition, quinoa effectively inhibited the increase in hepatic TG and TC levels, mitigated pathological injury, promoted the increase in SOD and GSH-Px activities, and decreased MDA levels. Nontarget metabolic profile analysis showed that quinoa regulated lipid metabolites in the circulation system and liver such as LysoPC and PC. RNA-Seq and RT-PCR verification revealed that a high amount of quinoa more effectively upregulated genes related to lipid metabolism [Apoa (apolipoprotein)5, Apoa4, Apoc2] and downregulated genes related to the immune response [lrf (interferon regulatory factor)5, Tlr6 (Toll-like receptor), Tlr10, Tlr11, Tlr12].

Conclusion

Quinoa effectively prevented NAFLD by controlling body weight, mitigating oxidative stress, and regulating the lipid metabolic profile and the expression of genes related to lipid metabolism and the immune response.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12986-021-00631-7.

Plasma metabolomic profiling in subclinical atherosclerosis: the Diabetes Heart Study

BACKGROUND

Incidence rates of cardiovascular disease (CVD) are increasing, partly driven by the diabetes epidemic. Novel prediction tools and modifiable treatment targets are needed to enhance risk assessment and management. Plasma metabolite associations with subclinical atherosclerosis were investigated in the Diabetes Heart Study (DHS), a cohort enriched for type 2 diabetes (T2D).

METHODS

The analysis included 700 DHS participants, 438 African Americans (AAs), and 262 European Americans (EAs), in whom coronary artery calcium (CAC) was assessed using ECG-gated computed tomography. Plasma metabolomics using liquid chromatography-mass spectrometry identified 853 known metabolites. An ancestry-specific marginal model incorporating generalized estimating equations examined associations between metabolites and CAC (log-transformed (CAC + 1) as outcome measure). Models were adjusted for age, sex, BMI, diabetes duration, date of plasma collection, time between plasma collection and CT exam, low-density lipoprotein cholesterol (LDL-C), and statin use.

RESULTS

At an FDR-corrected p-value < 0.05, 33 metabolites were associated with CAC in AAs and 36 in EAs. The androgenic steroids, fatty acid, phosphatidylcholine, and bile acid metabolism subpathways were associated with CAC in AAs, whereas fatty acid, lysoplasmalogen, and branched-chain amino acid (BCAA) subpathways were associated with CAC in EAs.

CONCLUSIONS

Strikingly different metabolic signatures were associated with subclinical coronary atherosclerosis in AA and EA DHS participants.

Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants

Background

Metabolic syndrome (MetS) is a cluster of multiple cardiometabolic risk factors that increase the risk of type 2 diabetes and cardiovascular diseases. Identifying novel biomarkers of MetS and their genetic associations could provide insights into the mechanisms of cardiometabolic diseases.

Methods

Potential MetS-associated metabolites were screened and internally validated by untargeted metabolomics analyses among 693 patients with MetS and 705 controls. External validation was conducted using two well-established targeted metabolomic methods among 149 patients with MetS and 253 controls. The genetic associations of metabolites were determined by linear regression using our previous genome-wide SNP data. Causal relationships were assessed using a one-sample Mendelian Randomization (MR) approach.

Findings

Nine metabolites were ultimately found to be associated with MetS or its components. Five metabolites, including LysoPC(14:0), LysoPC(15:0), propionyl carnitine, phenylalanine, and docosapentaenoic acid (DPA) were selected to construct a metabolite risk score (MRS), which was found to have a dose-response relationship with MetS and metabolic abnormalities. Moreover, MRS displayed a good ability to differentiate MetS and metabolic abnormalities. Three SNPs (rs11635491, rs7067822, and rs1952458) were associated with LysoPC(15:0). Two SNPs, rs1952458 and rs11635491 were found to be marginally correlated with several MetS components. MR analyses showed that a higher LysoPC(15:0) level was causally associated with the risk of overweight/obesity, dyslipidaemia, high uric acid, high insulin and high HOMA-IR.

Interpretation

We identified five metabolite biomarkers of MetS and three SNPs associated with LysoPC(15:0). MR analyses revealed that abnormal LysoPC metabolism may be causally linked the metabolic risk.

Funding

This work was supported by grants from the National Key Research and Development Program of China (2017YFC0907004).

Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies

The increasing prevalence of diabetes and its complications, such as cardiovascular and kidney disease, remains a huge burden globally. Identification of biomarkers for the screening, diagnosis, and prognosis of diabetes and its complications and better understanding of the molecular pathways involved in the development and progression of diabetes can facilitate individualized prevention and treatment. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner. Providing information on underlying metabolic pathways, metabolomics can further identify mechanisms of diabetes and its progression. The application of metabolomics in epidemiological studies have identified novel biomarkers for type 2 diabetes (T2D) and its complications, such as branched-chain amino acids, metabolites of phenylalanine, metabolites involved in energy metabolism, and lipid metabolism. Metabolomics have also been applied to explore the potential pathways modulated by medications. Investigating diabetes using a systems biology approach by integrating metabolomics with other omics data, such as genetics, transcriptomics, proteomics, and clinical data can present a comprehensive metabolic network and facilitate causal inference. In this regard, metabolomics can deepen the molecular understanding, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The current review focused on metabolomic biomarkers for kidney and cardiovascular disease in T2D identified from epidemiological studies, and will also provide a brief overview on metabolomic investigations for T2D.

Myocardial Infarction and Coronary Artery Disease in Menopausal Women With Type 2 Diabetes Mellitus Negatively Correlate With Total Serum Bile Acids

Background

As metabolic molecules, bile acids (BAs) not only promote the absorption of fat-soluble nutrients, but they also regulate many metabolic processes, including the homeostasis of glucose and lipids. Although total serum BA (TBA) measurement is a readily available clinical test related to coronary artery disease (CAD), myocardial infarction (MI), and type 2 diabetes mellitus (T2DM), the relationship between TBA and these pathological conditions remain unclear, and research on this topic is inconclusive.

Methods

This study enrolled 20,255 menopausal women aged over 50 years, including 6,421 T2DM patients. The study population was divided into different groups according to the median TBA level in order to explore the clinical characteristics of menopausal women with different TBA levels. Spline analyses, generalized additive model (GAM) model and regression analyses based on TBA level were used to explore the relationship between TBA and different diseases independently, including CAD and MI, or in combination with T2DM.

Results

Both in the general population and in the T2DM subgroup, the TBA level was significantly lower in CAD patients than in non-CAD patients. Spline analyses indicated that within normal clinical range of TBA concentration (0-10 µmol/L), the presence of CAD and MI showed similar trends in total and T2DM population. Similarly, the GAM model indicated that within the 0-10 μmol/L clinical range, the predicted probability for CAD and MI alone and in combination with T2DM was negatively correlated with TBA concentration. Multivariate regression analysis suggested that low TBA level was positively associated with the occurrence of CAD combined with T2DM (OR: 1.451; 95%CI: 1.141-1.847).

Conclusions

In menopausal women, TBA may represent a valuable clinical serum marker with negative correlation for CAD and MI in patients with T2DM.

Comparison of the blood, bone marrow, and cerebrospinal fluid metabolomes in children with b-cell acute lymphoblastic leukemia

Metabolomics may shed light on treatment response in childhood acute lymphoblastic leukemia (ALL), however, most assessments have analyzed bone marrow or cerebrospinal fluid (CSF), which are not collected during all phases of therapy. Blood is collected frequently and with fewer risks, but it is unclear whether findings from marrow or CSF biomarker studies may translate. We profiled end-induction plasma, marrow, and CSF from N = 10 children with B-ALL using liquid chromatography-mass spectrometry. We estimated correlations between plasma and marrow/CSF metabolite abundances detected in ≥ 3 patients using Spearman rank correlation coefficients (r_s). Most marrow metabolites were detected in plasma (N = 661; 81%), and we observed moderate-to-strong correlations (median r_s 0.62, interquartile range [IQR] 0.29–0.83). We detected 328 CSF metabolites in plasma (90%); plasma-CSF correlations were weaker (median r_s 0.37, IQR 0.07–0.70). We observed plasma-marrow correlations for metabolites in pathways associated with end-induction residual disease (pyruvate, asparagine) and plasma-CSF correlations for a biomarker of fatigue (gamma-glutamylglutamine). There is considerable overlap between the plasma, marrow, and CSF metabolomes, and we observed strong correlations for biomarkers of clinically relevant phenotypes. Plasma may be suitable for biomarker studies in B-ALL.

Metabolite patterns link diet, obesity, and type 2 diabetes in a Hispanic population

INTRODUCTION

Obesity is a precursor of type 2 diabetes (T2D).

OBJECTIVES

Our aim was to identify metabolic signatures of T2D and dietary factors unique to obesity.

METHODS

We examined a subsample of the Boston Puerto Rican Health Study (BPRHS) population with a high prevalence of obesity and T2D at baseline (n = 806) and participants (without T2D at baseline) at 5-year follow-up (n = 412). We determined differences in metabolite profiles between T2D and non-T2D participants of the whole sample and according to abdominal obesity status. Enrichment analysis was performed to identify metabolic pathways that were over-represented by metabolites that differed between T2D and non-T2D participants. T2D-associated metabolites unique to obesity were examined for correlation with dietary food groups to understand metabolic links between dietary intake and T2D risk. False Discovery Rate method was used to correct for multiple testing.

RESULTS

Of 526 targeted metabolites, 179 differed between T2D and non-T2D in the whole sample, 64 in non-obese participants and 120 unique to participants with abdominal obesity. Twenty-four of 120 metabolites were replicated and were associated with T2D incidence at 5-year follow-up. Enrichment analysis pointed to three metabolic pathways that were overrepresented in obesity-associated T2D: phosphatidylethanolamine (PE), long-chain fatty acids, and glutamate metabolism. Elevated intakes of three food groups, energy-dense takeout food, dairy intake and sugar-sweetened beverages, associated with 13 metabolites represented by the three pathways.

CONCLUSION

Metabolic signatures of lipid and glutamate metabolism link obesity to T2D, in parallel with increased intake of dairy and sugar-sweetened beverages, thereby providing insight into the relationship between dietary habits and T2D risk.

Genetic variants associated with beta-cell function and insulin sensitivity potentially influence bile acid metabolites and gestational diabetes mellitus in a Chinese population

INTRODUCTION

To investigate associations between genetic variants related to beta-cell (BC) dysfunction or insulin resistance (IR) in type 2 diabetes (T2D) and bile acids (BAs), as well as the risk of gestational diabetes mellitus (GDM).

RESEARCH DESIGN AND METHODS

We organized a case-control study of 230 women with GDM and 217 without GDM nested in a large prospective cohort of 22 302 Chinese women in Tianjin, China. Two weighted genetic risk scores (GRSs), namely BC-GRS and IR-GRS, were established by combining 39 and 23 single nucleotide polymorphisms known to be associated with BC dysfunction and IR, respectively. Regression and mediation analyses were performed to evaluate the relationship of GRSs with BAs and GDM.

RESULTS

We found that the BC-GRS was inversely associated with taurodeoxycholic acid (TDCA) after adjustment for confounders (Beta (SE)=-0.177 (0.048); p=2.66×10^-4). The BC-GRS was also associated with the risk of GDM (OR (95% CI): 1.40 (1.10 to 1.77); p=0.005), but not mediated by TDCA. Compared with individuals in the low tertile of BC-GRS, the OR for GDM was 2.25 (95% CI 1.26 to 4.01) in the high tertile. An interaction effect of IR-GRS with taurochenodeoxycholic acid (TCDCA) on the risk of GDM was evidenced (p=0.005). Women with high IR-GRS and low concentration of TCDCA had a markedly higher OR of 14.39 (95% CI 1.59 to 130.16; p=0.018), compared with those with low IR-GRS and high TCDCA.

CONCLUSIONS

Genetic variants related to BC dysfunction and IR in T2D potentially influence BAs at early pregnancy and the development of GDM. The identification of both modifiable and non-modifiable risk factors may facilitate the identification of high-risk individuals to prevent GDM.

How Perturbated Metabolites in Diabetes Mellitus Affect the Pathogenesis of Hypertension?

The presence of hypertension (HTN) in type 2 diabetes mellitus (DM) is a common phenomenon in more than half of the diabetic patients. Since HTN constitutes a predictor of vascular complications and cardiovascular disease in type 2 DM patients, it is of significance to understand the molecular and cellular mechanisms of type 2 DM binding to HTN. This review attempts to understand the mechanism via the perspective of the metabolites. It reviewed the metabolic perturbations, the biological function of perturbated metabolites in two diseases, and the mechanism underlying metabolic perturbation that contributed to the connection of type 2 DM and HTN. DM-associated metabolic perturbations may be involved in the pathogenesis of HTN potentially in insulin, angiotensin II, sympathetic nervous system, and the energy reprogramming to address how perturbated metabolites in type 2 DM affect the pathogenesis of HTN. The recent integration of the metabolism field with microbiology and immunology may provide a wider perspective. Metabolism affects immune function and supports immune cell differentiation by the switch of energy. The diverse metabolites produced by bacteria modified the biological process in the inflammatory response of chronic metabolic diseases either. The rapidly evolving metabolomics has enabled to have a better understanding of the process of diseases, which is an important tool for providing some insight into the investigation of diseases mechanism. Metabolites served as direct modulators of biological processes were believed to assess the pathological mechanisms involved in diseases.

Multi-omics profiling: the way towards precision medicine in metabolic diseases

Metabolic diseases including type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and metabolic syndrome (MetS) are alarming health burdens around the world, while therapies for these diseases are far from satisfying as their etiologies are not completely clear yet. T2DM, NAFLD, and MetS are all complex and multifactorial metabolic disorders based on the interactions between genetics and environment. Omics studies such as genetics, transcriptomics, epigenetics, proteomics, and metabolomics are all promising approaches in accurately characterizing these diseases. And the most effective treatments for individuals can be achieved via omics pathways, which is the theme of precision medicine. In this review, we summarized the multi-omics studies of T2DM, NAFLD, and MetS in recent years, provided a theoretical basis for their pathogenesis and the effective prevention and treatment, and highlighted the biomarkers and future strategies for precision medicine.

Plasma metabolites, especially lipid metabolites, are altered in pregnant women with gestational diabetes mellitus

BACKGROUND AND AIMS

Gestational diabetes mellitus (GDM) is a pathological condition of glucose intolerance associated with adverse pregnancy outcomes and increased risk of developing maternal type 2 diabetes later in life. Metabolomics is finding increasing use in the study of GDM. To date, GDM-specific metabolomic changes have not been completely elucidated.

MATERIALS AND METHODS

In this pilot study, metabolomics fingerprinting data, obtained by ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS), of 54 healthy pregnant women and 49 patients with GDM at the second and third gestational trimesters were analyzed. Multilevel statistical methods were used to process complex metabolomic data from the retrospective cohorts.

RESULTS

Using univariate analysis (p < 0.05), 41 metabolites were identified as having the most significant differences between these two groups. Lipid metabolites, particularly glycerophospholipids, were the most prevalent class of altered compounds. In addition, metabolites with previously unknown connection to GDM - such as monoacylglycerol, dihydrobiopterin, and 13S-hydroxyoctadecadienoic acid - were identified with strong discriminative power. The main metabolic pathways affected by GDM included glycerophospholipid metabolism, linoleic acid metabolism, and D-arginine and D-ornithine metabolism.

CONCLUSION

Our data provide a comprehensive overview of metabolite changes at different stages of pregnancy, which offers further insights into the pathogenesis of GDM.

A plasma lipid signature predicts incident coronary artery disease

BACKGROUND

Dyslipidemia is a hallmark of cardiovascular disease but is characterized by crude measurements of triglycerides, HDL- and LDL cholesterol. Lipidomics enables more detailed measurements of plasma lipids, which may help improve risk stratification and understand the pathophysiology of cardiovascular disease.

METHODS

Lipidomics was used to measure 184 lipids in plasma samples from the Malmö Diet and Cancer - Cardiovascular Cohort (N = 3865), taken at baseline examination. During an average follow-up time of 20.3 years, 536 participants developed coronary artery disease (CAD). Least absolute shrinkage and selection operator (LASSO) were applied to Cox proportional hazards models in order to identify plasma lipids that predict CAD.

RESULTS

Eight plasma lipids improved prediction of future CAD on top of traditional cardiovascular risk factors. Principal component analysis of CAD-associated lipids revealed one principal component (PC2) that was associated with risk of future CAD (HR per SD increment =1.46, C·I = 1.35-1.48, P < 0.001). The risk increase for being in the highest quartile of PC2 (HR = 2.33, P < 0.001) was higher than being in the top quartile of systolic blood pressure. Addition of PC2 to traditional risk factors achieved an improvement (2%) in the area under the ROC-curve for CAD events occurring within 10 (P = 0.03), 15 (P = 0.003) and 20 (P = 0.001) years of follow-up respectively.

CONCLUSIONS

A lipid pattern improve CAD prediction above traditional risk factors, highlighting that conventional lipid-measures insufficiently describe dyslipidemia that is present years before CAD. Identifying this hidden dyslipidemia may help motivate lifestyle and pharmacological interventions early enough to reach a substantial reduction in absolute risk.

Association of Serum Bile Acids Profile and Pathway Dysregulation With the Risk of Developing Diabetes Among Normoglycemic Chinese Adults: Findings From the 4C Study

OBJECTIVE

Comprehensive assessment of serum bile acids (BAs) aberrations before diabetes onset remains inconclusive. We examined the association of serum BA profile and coregulation with the risk of developing type 2 diabetes mellitus (T2DM) among normoglycemic Chinese adults.

RESEARCH DESIGN AND METHODS

We tested 23 serum BA species in subjects with incident diabetes (n = 1,707) and control subjects (n = 1,707) matched by propensity score (including age, sex, BMI, and fasting glucose) from the China Cardiometabolic Disease and Cancer Cohort (4C) Study, which was composed of 54,807 normoglycemic Chinese adults with a median follow-up of 3.03 years. Multivariable-adjusted odds ratios (ORs) for associations of BAs with T2DM were estimated using conditional logistic regression.

RESULTS

In multivariable-adjusted logistic regression analysis, per SD increment of unconjugated primary and secondary BAs were inversely associated with incident diabetes, with an OR (95% CI) of 0.89 (0.83-0.96) for cholic acid, 0.90 (0.84-0.97) for chenodeoxycholic acid, and 0.90 (0.83-0.96) for deoxycholic acid (P < 0.05 and false discovery rate <0.05). On the other hand, conjugated primary BAs (glycocholic acid, taurocholic acid, glycochenodeoxycholic acid, taurochenodeoxycholic acid, and sulfated glycochenodeoxycholic acid) and secondary BA (tauroursodeoxycholic acid) were positively related with incident diabetes, with ORs ranging from 1.11 to 1.19 (95% CIs ranging between 1.05 and 1.28). In a fully adjusted model additionally adjusted for liver enzymes, HDL cholesterol, diet, 2-h postload glucose, HOMA-insulin resistance, and waist circumference, the risk estimates were similar. Differential correlation network analysis revealed that perturbations in intraclass (i.e., primary and secondary) and interclass (i.e., unconjugated and conjugated) BA coregulation preexisted before diabetes onset.

CONCLUSIONS

These findings reveal novel changes in BAs exist before incident T2DM and support a potential role of BA metabolism in the pathogenesis of diabetes.

Associations among circulating sphingolipids, β-cell function, and risk of developing type 2 diabetes: A population-based cohort study in China

BACKGROUND

Animal studies suggest vital roles of sphingolipids, especially ceramides, in the pathogenesis of type 2 diabetes (T2D) via pathways involved in insulin resistance, β-cell dysfunction, and inflammation, but human studies are limited. We aimed to evaluate the associations of circulating sphingolipids with incident T2D and to explore underlying mechanisms.

METHODS AND FINDINGS

The current study included 826 men and 1,148 women who were aged 50-70 years, from Beijing and Shanghai, and without T2D in 2005 and who were resurveyed in 2011. Cardiometabolic traits were measured at baseline and follow-up surveys. A total of 76 sphingolipids were quantified using high-coverage targeted lipidomics. Summary data for 2-sample Mendelian randomization were obtained from genome-wide association studies of circulating sphingolipids and the China Health and Nutrition Survey (n = 5,731). During the 6-year period, 529 participants developed T2D. Eleven novel and 3 reported sphingolipids, namely ceramides (d18:1/18:1, d18:1/20:0, d18:1/20:1, d18:1/22:1), saturated sphingomyelins (C34:0, C36:0, C38:0, C40:0), unsaturated sphingomyelins (C34:1, C36:1, C42:3), hydroxyl-sphingomyelins (C34:1, C38:3), and a hexosylceramide (d18:1/20:1), were positively associated with incident T2D (relative risks [RRs]: 1.14-1.21; all P < 0.001), after multivariate adjustment including lifestyle characteristics and BMI. Network analysis further identified 5 modules, and 2 modules containing saturated sphingomyelins showed the strongest associations with increased T2D risk (RRQ4 versus Q1 = 1.59 and 1.43; both Ptrend < 0.001). Mediation analysis suggested that the detrimental associations of 13 sphingolipids with T2D were largely mediated through β-cell dysfunction, as indicated by HOMA-B (mediation proportion: 11.19%-42.42%; all P < 0.001). Moreover, Mendelian randomization evidenced a positive association between a genetically instrumented ceramide (d18:1/20:1) and T2D (odds ratio: 1.15 [95% CI 1.05-1.26]; P = 0.002). Main limitations in the current study included potential undiagnosed cases and lack of an independent population for replication.

CONCLUSIONS

In this study, we observed that a panel of novel sphingolipids with unique structures were positively associated with incident T2D, largely mediated through β-cell dysfunction, in Chinese individuals.

QiDiTangShen granules modulated the gut microbiome composition and improved bile acid profiles in a mouse model of diabetic nephropathy

QiDiTangShen granules (QDTS), a traditional Chinese herbal medicine, have been used in clinical practice for treating diabetic kidney disease for several years. In our previous study, we have demonstrated that QDTS displayed good efficacy on reducing proteinuria in mice with diabetic nephropathy (DN). However, the exact mechanism by which QDTS exerts its reno-protection remains largely unknown. To ascertain whether QDTS could target the gut microbiota-bile acid axis, the db/db mice were adopted as a mouse model of DN. After a 12-week of treatment, we found that QDTS significantly reduced urinary albumin excretion (UAE), and attenuated the pathological injuries of kidney in the db/db mice, while the body weight and blood glucose levels of those mice were not affected. In addition, we found that QDTS significantly altered the gut microbiota composition, and decreased serum levels of total bile acid (TBA) and BA profiles such as β-muricholic acid (β-MCA), taurocholic acid (TCA), tauro β-muricholic acid (Tβ-MCA) and deoxycholic acid (DCA). These BAs are associated with the activation of farnesoid X receptor (FXR), which is highly expressed in kidney. However, there was no significant difference between QDTS-treated and -untreated db/db mice regarding the renal expression of FXR, indicating that other mechanisms may be involved. Conclusively, our study revealed that QDTS significantly alleviated renal injuries in mice with DN. The gut microbiota-bile acid axis may be an important target for the reno-protection of QDTS in DN, but the specific mechanism merits further study.

Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes

Better risk prediction and new molecular targets are key priorities in type 2 diabetes (T2D) research. Little is known about the role of the urine metabolome in predicting the risk of T2D. We aimed to use non-targeted urine metabolomics to discover biomarkers and improve risk prediction for T2D. Urine samples from two community cohorts of 1,424 adults were analyzed by ultra-performance liquid chromatography/mass spectrometry (UPLC-MS). In a discovery/replication design, three out of 62 annotated metabolites were associated with prevalent T2D, notably lower urine levels of 3-hydroxyundecanoyl-carnitine. In participants without diabetes at baseline, LASSO regression in the training set selected six metabolites that improved prediction of T2D beyond established risk factors risk over up to 12 years' follow-up in the test sample, from C-statistic 0.866 to 0.892. Our results in one of the largest non-targeted urinary metabolomics study to date demonstrate the role of the urine metabolome in identifying at-risk persons for T2D and suggest urine 3-hydroxyundecanoyl-carnitine as a biomarker candidate.

Underlying dyslipidemia postpartum in women with a recent GDM pregnancy who develop type 2 diabetes

Approximately, 35% of women with Gestational Diabetes (GDM) progress to Type 2 Diabetes (T2D) within 10 years. However, links between GDM and T2D are not well understood. We used a well-characterised GDM prospective cohort of 1035 women following up to 8 years postpartum. Lipidomics profiling covering >1000 lipids was performed on fasting plasma samples from participants 6–9 week postpartum (171 incident T2D vs. 179 controls). We discovered 311 lipids positively and 70 lipids negatively associated with T2D risk. The upregulation of glycerolipid metabolism involving triacylglycerol and diacylglycerol biosynthesis suggested activated lipid storage before diabetes onset. In contrast, decreased sphingomyelines, hexosylceramide and lactosylceramide indicated impaired sphingolipid metabolism. Additionally, a lipid signature was identified to effectively predict future diabetes risk. These findings demonstrate an underlying dyslipidemia during the early postpartum in those GDM women who progress to T2D and suggest endogenous lipogenesis may be a driving force for future diabetes onset.

Global Plasma Metabolomics to Identify Potential Biomarkers of Blood Pressure Progression

Objective:

The pathophysiology of hypertension remains incompletely understood. We investigated associations of circulating metabolites with longitudinal blood pressure (BP) changes in the Prospective Investigation of the Vasculature in Uppsala Seniors cohort and validated the findings in the Uppsala Longitudinal Study of Adult Men cohort.

Approach and Results:

Circulating metabolite levels were assessed with liquid- and gas-chromatography coupled to mass spectrometry among persons without BP-lowering medication at baseline. We studied associations of baseline levels of metabolites with changes in BP levels and the clinical BP stage between baseline and a follow-up examination 5 years later. In the discovery cohort, we investigated 504 individuals that contributed with 757 observations of paired BP measurements. The mean baseline systolic and diastolic BPs were 144 (19.7)/76 (9.7) mm Hg, and change in systolic and diastolic BPs were 3.7 (15.8)/−0.5 (8.6) mm Hg over 5 years. The metabolites associated with diastolic BP change were ceramide, triacylglycerol, total glycerolipids, oleic acid, and cholesterylester. No associations with longitudinal changes in systolic BP or BP stage were observed. Metabolites with similar structures to the 5 top findings in the discovery cohort were investigated in the validation cohort. Diacylglycerol (36:2) and monoacylglycerol (18:0), 2 glycerolipids, were associated with diastolic BP change in the validation cohort.

Conclusions:

Circulating baseline levels of ceramide, triacylglycerol, total glycerolipids, and oleic acid were positively associated with longitudinal diastolic BP change, whereas cholesterylester levels were inversely associated with longitudinal diastolic BP change. Two glycerolipids were validated in an independent cohort. These metabolites may point towards pathophysiological pathways of hypertension.

Serum total bile acids associate with risk of incident type 2 diabetes and longitudinal changes in glucose-related metabolic traits

BACKGROUND

Bile acids have been found to be related to changes in gut microbiota and multiple metabolic disorders, including type 2 diabetes (T2D). We aimed to prospectively investigate associations of serum total bile acids (TBAs) with risk of incident T2D and longitudinal changes in glycemic traits.

METHODS

A community-based study was conducted at baseline in 2010, including 4968 nondiabetic participants aged ≥40 years followed up for an average of 4.3 years. Incident T2D was defined by using the 1999 WHO criteria based on 75-g oral glucose tolerance tests. Multivariate Cox proportional hazards regression was used to examine the association of serum TBAs with incident T2D. Fasting plasma glucose (FPG), 2-hour postload plasma glucose (2-h PPG), and fasting serum insulin (FSI) were measured at baseline and follow-up.

RESULTS

During 21 653.7 person-years of follow-up, 605 cases of incident diabetes were identified (incidence rate 2.8%). Comparing to quartile 1 of serum TBAs, quartile 2, 3, and 4 were significantly associated with a 14.2%, 15.0%, and 31.4% higher risk of incident T2D (P = .029). Each one unit of log-TBAs was associated with an increase of 0.034 mmol/L in FPG, 0.111 mmol/L in 2-h PPG, 0.023 in log-FSI, and 0.012 in log-HOMA-IR (homeostasis model assessment of insulin resistance) (all P ≤ .024). The association was attenuated after further adjustment for HOMA-IR. Mediation analysis showed that insulin resistance indicated by HOMA-IR might mediate 28.5% of indirect effect on the association of TBAs with T2D (P = .0004).

CONCLUSIONS

Baseline serum TBAs were significantly associated with incident T2D and longitudinal changes in glycemic traits. Insulin resistance might partially mediate the association of TBAs and T2D.

Microbiota-Related Metabolites and the Risk of Type 2 Diabetes

OBJECTIVE

Recent studies have highlighted the significance of the microbiome in human health and disease. Changes in the metabolites produced by microbiota have been implicated in several diseases. Our objective was to identify microbiome metabolites that are associated with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Our study included 5,181 participants from the cross-sectional Metabolic Syndrome in Men (METSIM) study that included Finnish men (age 57 ± 7 years, BMI 26.5 ± 3.5 kg/m²) having metabolomics data available. Metabolomics analysis was performed based on fasting plasma samples. On the basis of an oral glucose tolerance test, Matsuda ISI and disposition index values were calculated as markers of insulin sensitivity and insulin secretion. A total of 4,851 participants had a 7.4-year follow-up visit, and 522 participants developed type 2 diabetes.

RESULTS

Creatine, 1-palmitoleoylglycerol (16:1), urate, 2-hydroxybutyrate/2-hydroxyisobutyrate, xanthine, xanthurenate, kynurenate, 3-(4-hydroxyphenyl)lactate, 1-oleoylglycerol (18:1), 1-myristoylglycerol (14:0), dimethylglycine, and 2-hydroxyhippurate (salicylurate) were significantly associated with an increased risk of type 2 diabetes. These metabolites were associated with decreased insulin secretion or insulin sensitivity or both. Among the metabolites that were associated with a decreased risk of type 2 diabetes, 1-linoleoylglycerophosphocholine (18:2) significantly reduced the risk of type 2 diabetes.

CONCLUSIONS

Several novel and previously reported microbial metabolites related to the gut microbiota were associated with an increased risk of incident type 2 diabetes, and they were also associated with decreased insulin secretion and insulin sensitivity. Microbial metabolites are important biomarkers for the risk of type 2 diabetes.

Metabolomics and Proteomics in Type 2 Diabetes

The persistent increase in the worldwide burden of type 2 diabetes mellitus (T2D) and the accompanying rise of its complications, including cardiovascular disease, necessitates our understanding of the metabolic disturbances that cause diabetes mellitus. Metabolomics and proteomics, facilitated by recent advances in high-throughput technologies, have given us unprecedented insight into circulating biomarkers of T2D even over a decade before overt disease. These markers may be effective tools for diabetes mellitus screening, diagnosis, and prognosis. As participants of metabolic pathways, metabolite and protein markers may also highlight pathways involved in T2D development. The integration of metabolomics and proteomics with genomics in multiomics strategies provides an analytical method that can begin to decipher causal associations. These methods are not without their limitations; however, with careful study design and sample handling, these methods represent powerful scientific tools that can be leveraged for the study of T2D. In this article, we aim to give a timely overview of circulating metabolomics and proteomics findings with T2D observed in large human population studies to provide the reader with a snapshot into these emerging fields of research.