Metabolite changes in risk of type 2 diabetes mellitus in cohort studies: A systematic review and meta-analysis

Alanine to glycine ratio is a novel predictive biomarker for type 2 diabetes mellitus

AIM

We aimed to evaluate the metabolite ratios that could predict the clinical incidence or remission of type 2 diabetes mellitus (T2D).

METHODS

The Cox proportional hazards regression model was used to assess 1813 individuals without T2D to test the predictive value of metabolite ratios for T2D incidence and 451 newly diagnosed T2D for remission. The receiver operating characteristic curve analysis was performed to determine the best cut-off values for the metabolite ratios. Survival analyses were performed to compare the four subgroups defined by baseline metabolite ratios and clinical status of obesity.

RESULTS

The alanine/glycine was the most significant marker for T2D incidence (hazard ratio per SD: 1.24; p < .001). On the other hand, metabolite hydroxy sphingomyelin C22:2 was most specific for T2D remission (hazard ratio per SD: 1.32; p = .029). Survival analysis of T2D incidence among the subgroups defined by the combination of alanine/glycine and obesity showed the group with a high alanine/glycine and obesity had the highest risk of T2D incidence (p < .001). The alanine/glycine as a T2D risk marker was also validated in the independent external data.

CONCLUSIONS

The combination of obesity and the alanine/glycine ratio can be used to evaluate the diabetes risk.

Metabolomic signature between diabetic and non-diabetic obese patients: A protocol for systematic review

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a chronic and progressive condition defined by hyperglycemia caused by abnormalities in insulin production, insulin receptor sensitivity, or both. Several studies have revealed that higher body mass index (BMI) is associated with increasing risk of developing diabetes. In this study, we perform a protocol for systematic review to explore metabolite biomarkers that could be used to identify T2DM in obese subjects.

METHODS

The protocol of this review was registered in PROSPERO (CRD42023405518). Three databases, EMBASE, PubMed, and Web of Science were selected to collect potential literature from their inceptions to July December 2023. Data for collection will include title, authors, study subjects, publication date, sample size, detection and analytical platforms, participant characteristics, biological samples, confounding factors, methods of statistical analysis, the frequency and directions of changes in potential metabolic biomarkers, and major findings. Pathway analysis of differential metabolites will be performed with MetaboAnalyst 5.0 based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Human Metabolome Database.

RESULTS

The results of this systematic review will be published in a peer-reviewed journal.

CONCLUSION

This systematic review will summarize the potential biomarkers and metabolic pathways to provide a new reference for the prevention and treatment of T2DM in obese subjects.

Effects of in vitro fermentation of Atractylodes chinensis (DC.) Koidz. polysaccharide on fecal microbiota and metabolites in patients with type 2 diabetes mellitus

Atractylodes chinensis (DC.) Koidz. polysaccharide (AKP) has been shown to have hypoglycemic activity. In this study, the effects of AKP on fecal microbiota and metabolites in healthy subjects and patients with type 2 diabetes mellitus (T2DM) were investigated using an in vitro simulated digestive fermentation model. AKP were isolated and purified from Atractylodes chinensis (DC.) Koidz. Its main component AKP1 (AKP-0 M, about 78 % of AKP) has an average molecular weight of 3.25 kDa with monosaccharide composition of rhamnose, arabinose, and galactosamine in a molar ratio of 1: 1.25: 2.88. Notably, AKP fermentation might improve the intestinal microbiota of T2DM patients by the enrichment of some specific bacteria rather than the increase of microbial diversity. The addition of AKP specifically enriched Bifidobacteriaceae and weakened the proportion of Escherichia-Shigella. Moreover, AKP also increased the levels of short-chain fatty acids without affecting total gut gas production, suggesting that AKP could have beneficial effects while avoiding flatulence. Metabolomic analysis revealed that ARP fermentation caused changes in some metabolites, which were mainly related to energy metabolism and amino acid metabolism. Importantly, ARP fermentation significantly increased the level of myo-inositol, an insulin sensitizer. In addition, a significant correlation was observed between specific microbiota and differential metabolites. This study has laid a theoretical foundation for AKP application in functional foods.

Plasma metabolomics supports non-fasted sampling for metabolic profiling across a spectrum of glucose tolerance in the Nile rat model for type 2 diabetes

Type 2 diabetes is a challenge in modern healthcare, and animal models are necessary to identify underlying mechanisms. The Nile rat (Arvicanthis niloticus) develops diet-induced diabetes rapidly on a conventional rodent chow diet without genetic or chemical manipulation. Unlike common laboratory models, the outbred Nile rat model is diurnal and has a wide range of overt diabetes onset and diabetes progression patterns in both sexes, better mimicking the heterogeneous diabetic phenotype in humans. While fasted blood glucose has historically been used to monitor diabetic progression, postprandial blood glucose is more sensitive to the initial stages of diabetes. However, there is a long-held assumption that ad libitum feeding in rodent models leads to increased variance, thus masking diabetes-related metabolic changes in the plasma. Here we compared repeatability within triplicates of non-fasted or fasted plasma samples and assessed metabolic changes relevant to glucose tolerance in fasted and non-fasted plasma of 8-10-week-old male Nile rats. We used liquid chromatography-mass spectrometry lipidomics and polar metabolomics to measure relative metabolite abundances in the plasma samples. We found that, compared to fasted metabolites, non-fasted plasma metabolites are not only more strongly associated with glucose tolerance on the basis of unsupervised clustering and elastic net regression model, but also have a lower replicate variance. Between the two sampling groups, we detected 66 non-fasted metabolites and 32 fasted metabolites that were associated with glucose tolerance using a combined approach with multivariable elastic net and individual metabolite linear models. Further, to test if metabolite replicate variance is affected by age and sex, we measured non-fasted replicate variance in a cohort of mature 30-week-old male and female Nile rats. Our results support using non-fasted plasma metabolomics to study glucose tolerance in Nile rats across the progression of diabetes.

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.

Metabolic remission precedes possible weight regain after gastric bypass surgery

OBJECTIVE

Some patients regain weight to a variable extent from 1 year after Roux-en-Y gastric bypass surgery (RYGB), though rarely reaching preoperative values. The aim of the present study was to investigate whether, when, and to what extent metabolic remission occurs.

METHODS

Fasting metabolite and lipid profiles were determined in blood plasma collected from a nonrandomized intervention study involving 148 patients before RYGB and at 2, 12, and 60 months post RYGB. Both short-term and long-term alterations in metabolism were assessed. Anthropometric and clinical variables were assessed at all study visits.

RESULTS

This study found that the vast majority of changes in metabolite levels occurred during the first 2 months post RYGB. Notably, thereafter the metabolome started to return toward the presurgical state. Consequently, a close-to-presurgical metabolome was observed at the time when patients reached their lowest weight and glucose level. Lipids with longer acyl chains and a higher degree of unsaturation were altered more dramatically compared with shorter and more saturated lipids, suggesting a systematic and reversible lipid remodeling.

CONCLUSIONS

Remission of the metabolic state was observed prior to notable weight regain. Further and more long-term studies are required to assess whether the extent of metabolic remission predicts future weight regain and glycemic deterioration.

Replication and mediation of the association between the metabolome and clinical markers of metabolic health in an adolescent cohort study

Metabolomics-derived metabolites (henceforth metabolites) may mediate the relationship between modifiable risk factors and clinical biomarkers of metabolic health (henceforth clinical biomarkers). We set out to study the associations of metabolites with clinical biomarkers and a potential mediation effect in a population of young adults. First, we conducted a systematic literature review searching for metabolites associated with 11 clinical biomarkers (inflammation markers, glucose, blood pressure or blood lipids). Second, we replicated the identified associations in a study population of n = 218 (88 males and 130 females, average age of 18 years) participants of the DONALD Study. Sex-stratified linear regression models adjusted for age and BMI and corrected for multiple testing were calculated. Third, we investigated our previously reported metabolites associated with anthropometric and dietary factors mediators in sex-stratified causal mediation analysis. For all steps, both urine and blood metabolites were considered. We found 41 metabolites in the literature associated with clinical biomarkers meeting our inclusion criteria. We were able to replicate an inverse association of betaine with CRP in women, between body mass index and C-reactive protein (CRP) and between body fat and leptin. There was no evidence of mediation by lifestyle-related metabolites after correction for multiple testing. We were only able to partially replicate previous findings in our age group and did not find evidence of mediation. The complex interactions between lifestyle factors, the metabolome, and clinical biomarkers warrant further investigation.

Several Metabolite Families Display Inflexibility during Glucose Challenge in Patients with Type 2 Diabetes: An Untargeted Metabolomics Study

Metabolic inflexibility is a hallmark of insulin resistance and can be extensively explored with high-throughput metabolomics techniques. However, the dynamic regulation of the metabolome during an oral glucose tolerance test (OGTT) in subjects with type 2 diabetes (T2D) is largely unknown. We aimed to identify alterations in metabolite responses to OGTT in subjects with T2D using untargeted metabolomics of both plasma and subcutaneous adipose tissue (SAT) samples. Twenty subjects with T2D and twenty healthy controls matched for sex, age, and body mass index (BMI) were profiled with untargeted metabolomics both in plasma (755 metabolites) and in the SAT (588) during an OGTT. We assessed metabolite concentration changes 90 min after the glucose load, and those responses were compared between patients with T2D and controls. Post-hoc analyses were performed to explore the associations between glucose-induced metabolite responses and markers of obesity and glucose metabolism, sex, and age. During the OGTT, T2D subjects had an impaired reduction in plasma levels of several metabolite families, including acylcarnitines, amino acids, acyl ethanolamines, and fatty acid derivates (p < 0.05), compared to controls. Additionally, patients with T2D had a greater increase in plasma glucose and fructose levels during the OGTT compared to controls (p < 0.05). The plasma concentration change of most metabolites after the glucose load was mainly associated with indices of hyperglycemia rather than insulin resistance, insulin secretion, or BMI. In multiple linear regression analyses, hyperglycemia indices (glucose area under the curve (AUC) during OGTT and glycosylated hemoglobin (HbA1c)) were the strongest predictors of plasma metabolite changes during the OGTT. No differences were found in the adipose tissue metabolome in response to the glucose challenge between T2D and controls. Using a metabolomics approach, we show that T2D patients display attenuated responses in several circulating metabolite families during an OGTT. Besides the well-known increase in monosaccharides, the glucose-induced lowering of amino acids, acylcarnitines, and fatty acid derivatives was attenuated in T2D subjects compared to controls. These data support the hypothesis of inflexibility in several metabolic pathways, which may contribute to dysregulated substrate partitioning and turnover in T2D. These findings are not directly associated with changes in adipose tissue metabolism; therefore, other tissues, such as muscle and liver, are probably of greater importance.

Chemotherapy-Induced Peripheral Neuropathy

Chemotherapy-induced peripheral neuropathy (CIPN) is a debilitating side effect of many common anti-cancer agents that can lead to dose reduction or treatment discontinuation, which decrease chemotherapy efficacy. Long-term CIPN can interfere with activities of daily living and diminish the quality of life. The mechanism of CIPN is not yet fully understood, and biomarkers are needed to identify patients at high risk and potential treatment targets. Metabolomics can capture the complex behavioral and pathophysiological processes involved in CIPN. This chapter is to review the CIPN metabolomics studies to find metabolic pathways potentially involved in CIPN. These potential CIPN metabolites are then investigated to determine whether there is evidence from studies of other neuropathy etiologies such as diabetic neuropathy and Leber hereditary optic neuropathy to support the importance of these pathways in peripheral neuropathy. Six potential biomarkers and their putative mechanisms in peripheral neuropathy were reviewed. Among these biomarkers, histidine and phenylalanine have clear roles in neurotransmission or neuroinflammation in peripheral neuropathy. Further research is needed to discover and validate CIPN metabolomics biomarkers in large clinical studies.

Ethnic disparities attributed to the manifestation in and response to type 2 diabetes: insights from metabolomics

Type 2 diabetes (T2D) associated health disparities among different ethnicities have long been known. Ethnic variations also exist in T2D related comorbidities including insulin resistance, vascular complications and drug response. Genetic heterogeneity, dietary patterns, nutrient metabolism and gut microbiome composition attribute to ethnic disparities in both manifestation and progression of T2D. These factors differentially regulate the rate of metabolism and metabolic health. Metabolomics studies have indicated significant differences in carbohydrate, lipid and amino acid metabolism among ethnicities. Interestingly, genetic variations regulating lipid and amino acid metabolism might also contribute to inter-ethnic differences in T2D. Comprehensive and comparative metabolomics analysis between ethnicities might help to design personalized dietary regimen and newer therapeutic strategies. In the present review, we explore population based metabolomics data to identify inter-ethnic differences in metabolites and discuss how (a) genetic variations, (b) dietary patterns and (c) microbiome composition may attribute for such differences in T2D.

Effects of pharmacological treatment on metabolomic alterations in animal models of depression

Numerous studies have investigated metabolite alterations resulting from pharmacological treatment in depression models although few quantitative studies explored metabolites exhibiting constant alterations. This study aimed to identify consistently dysregulated metabolites across such studies using a knowledgebase-driven approach. This study was based on 157 studies that identified an assembly of 2757 differential metabolites in the brain, blood, urine, liver, and feces samples of depression models with pharmacological medication. The use of a vote-counting approach to identify consistently upregulated and downregulated metabolites showed that serotonin, dopamine, norepinephrine, gamma-aminobutyric acid, anandamide, tryptophan, hypoxanthine, and 3-methoxytyramine were upregulated in the brain, while quinolinic acid, glutamic acid, 5-hydroxyindoleacetic acid, myo-inositol, lactic acid, and the kynurenine/tryptophan ratio were downregulated. Circulating levels of trimethylamine N-oxide, isoleucine, leucine, tryptophan, creatine, serotonin, valine, betaine, and low-density lipoprotein were elevated. In contrast, levels of alpha-D-glucose, lactic acid, N-acetyl glycoprotein, glutamine, beta-D-glucose, corticosterone, alanine, phenylacetylglycine, glycine, high-density lipoprotein, arachidonic acid, myo-inositol, allantoin, and taurine were decreased. Moreover, 12 metabolites in urine and nine metabolites in the liver were dysregulated after treatment. Pharmacological treatment also increased fecal levels of butyric acid, acetic acid, propionic acid, and isovaleric acid. Collectively, metabolite disturbances induced by depression were reversed by pharmacological treatment. Pharmacological medication reversed the reduction of brain neurotransmitters caused by depression, modulated disturbance of the tryptophan-kynurenine pathway and inflammatory activation, and alleviated abnormalities of amino acid metabolism, energy metabolism, lipid metabolism, and gut microbiota-derived metabolites.

Circulating Fatty Acids and Genetic Predisposition to Type 2 Diabetes: Gene-Nutrient Interaction Analysis

OBJECTIVE

To assess the relationship of circulating fatty acids (FA) with risk of type 2 diabetes (T2D) and potential interactions with genetic risk.

RESEARCH DESIGN AND METHODS

A total of 95,854 participants with complete data on plasma FA from the UK Biobank were enrolled between 2006 and 2010 and were followed up to the end of 2020. Plasma concentrations of saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA) were analyzed by a high-throughput nuclear magnetic resonance-based biomarker profiling platform. The genetic risk scores (GRS) were calculated on the basis of 424 variants associated with T2D. Pathway-specific GRS were calculated based on robust clusters of T2D loci.

RESULTS

There were 3,052 instances of T2D documented after an average follow-up of 11.6 years. Plasma concentrations of SFA and MUFA were positively associated with T2D risk, while plasma PUFA were inversely associated. After adjustment for major risk factors, hazard ratios (95% CI) of T2D for 1-SD increment were 1.03 (1.02-1.04) for SFA, 1.03 (1.02-1.05) for MUFA, 0.62 (0.56-0.68) for PUFA, 0.67 (0.61-0.73) for n-6 PUFA, 0.90 (0.85-0.95) for n-3 PUFA, and 1.01 (0.98-1.04) for n-6-to-n-3 ratio. Plasma MUFA had significant interactions with the overall GRS and GRS for proinsulin and liver/lipid clusters on T2D risk. The protective associations of n-3 PUFA with T2D risk were weaker among individuals with higher obesity GRS (P interaction = 0.040) and liver/lipid GRS (P interaction = 0.012). Additionally, increased plasma n-3 PUFA concentration was associated with more reductions in T2D risk among participants carrying more docosapentaenoic acid-associated alleles (P interaction = 0.007).

CONCLUSIONS

Plasma concentrations of SFA and MUFA were associated with a higher T2D risk, whereas plasma PUFA and n-6 and n-3 PUFA were related to a lower risk. Circulating MUFA and n-3 PUFA had significant interactions with genetic predisposition to T2D and FA-associated variants.

The Association Between Leucine and Diabetic Retinopathy in Different Genders: A Cross-Sectional Study in Chinese Patients With Type 2 Diabetes

OBJECTIVE

To explore the association between serum leucine (leu) and diabetic retinopathy (DR) in patients with type 2 diabetes (T2D) and then to analyze the influence of gender on the association.

METHOD

The electronic medical records of 1,149 T2D patients who met inclusion and exclusion criteria were retrieved from the Second Affiliated Hospital of Dalian Medical University and the First Affiliated Hospital of Jinzhou Medical University. Serum leu levels of all subjects were measured by liquid chromatography-mass spectrometry. Logistic regression was used to obtain the odds ratio (OR) and CI of leu-DR risk in multiple models. When using these models, restricted cubic spline (RCS) was used to test the potential non-linear relationship between multiple continuous independent variables, such as leu and DR (classification), and dependent variables. We also used the additive interaction method to evaluate the interaction effect between leu and gender on DR.

RESULTS

Leu was a protective factor of DR [0.78 (0.66, 0.92)]. When gender was divided into male and female, the above relationship was statistically significant only in men [0.73 (0.58, 0.94)]. Three indicators of additive interaction-RERI, AP, and S-suggested that there is no interaction between gender and leu on the risk of DR.

CONCLUSIONS

Male T2D patients with high leu levels may have a lower risk of DR.

Association of circulating metabolites with incident type 2 diabetes in an obese population from a national cohort

AIMS

Obesity is the most common risk factor for type 2 diabetes. However, not all obese individuals develop diabetes. In the era of precision medicine, metabolomics may reveal the fundamental metabolic status of an individual. Our aim was to assess the association of metabolites with incident type 2 diabetes in obese individuals using Korean Genome and Epidemiology Cohort Study.

METHODS

Using 12 years of metabolomic data from 2,580 individuals, we performed a metabolomic study to define metabolically healthy obesity in an obese population (n = 704) with incident type 2 diabetes. Cox proportional hazards regression model and survival analysis were performed adjusted for the traditional risk factors of type 2 diabetes.

RESULTS

Our study revealed that spermine, acyl-alkyl phosphatidylcholines (C34:3, C36:3, C42:1), hydroxy sphingomyelin (C22:2, C14:1), and sphingomyelin (C16:0) were associated with incident type 2 diabetes in obese individuals after the adjustment for risk factors and correction of multiple comparisons by Bonferroni method. Five metabolites (except hydroxy sphingomyelin C14:1 and sphingomyelin C16:0) were also significantly associated with incident type 2 diabetes in lean individuals.

CONCLUSIONS

This study highlights the need for defining metabolically healthy obesity based on serum metabolites and elucidates potential biomarkers for type 2 diabetes in an obese population.

Fasting status and metabolic health in relation to plasma branched chain amino acid concentrations in women

BACKGROUND

Circulating branched chain amino acids (BCAA) are associated with cardiometabolic risk, although the mechanisms leading to their accumulation remain uncertain. Examining the relationship between fasting status, metabolic syndrome, and type 2 diabetes (T2D) with circulating BCAA levels may provide insights into their metabolic handling.

METHODS

We conducted cross-sectional analyses among 25,740 Women's Health Study participants (mean age 55 years).

RESULTS

In multivariable linear regression models, fasting was associated with lower plasma BCAAs vs. non-fasting in women without metabolic syndrome or T2D (% mean difference = -5.1%; 95% CI = -5.8, -4.5) and among women with metabolic syndrome only (-3.7%; -4.9, -2.6), p_interaction = 0.002. However, there was no difference in BCAAs by fasting status among women with T2D (0.4%; -3.7, 4.7).

CONCLUSIONS

We observed higher BCAAs with worsening metabolic health status. Fasting is modestly associated with lower plasma BCAAs, except among women with T2D. These findings support hypotheses that impaired BCAA catabolism may be a feature of T2D pathophysiology.

Biomarkers for type 2 diabetes

Background

The prevalence and incidence of type 2 diabetes (T2D), representing >90% of all cases of diabetes, are increasing rapidly worldwide. Identification of individuals at high risk of developing diabetes is of great importance as early interventions might delay or even prevent full-blown disease. T2D is a complex disease caused by multiple genetic loci in interplay with lifestyle and environmental factors. Recently over 400 distinct association signals were published; these explain 18% of the risk of T2D.

Scope of review

In this review there is a major focus on risk factors and genetic and non-genetic biomarkers for the risk of T2D identified especially in large prospective population-based studies, and studies testing causality of the biomarkers for T2D in Mendelian randomization studies. Another focus is on understanding genome-phenome interplay in the classification of individuals with T2D into subgroups.

Major conclusions

Several recent large population-based studies and their meta-analyses have identified multiple potential genetic and non-genetic biomarkers for the risk of T2D. Combination of genetic variants and physiologically characterized pathways improves the classification of individuals with T2D into subgroups, and is also paving the way to a precision medicine approach, in T2D.

Amino acid and lipid metabolism in post-gestational diabetes and progression to type 2 diabetes: A metabolic profiling study

BACKGROUND

Women with a history of gestational diabetes mellitus (GDM) have a 7-fold higher risk of developing type 2 diabetes (T2D) during midlife and an elevated risk of developing hypertension and cardiovascular disease. Glucose tolerance reclassification after delivery is recommended, but fewer than 40% of women with GDM are tested. Thus, improved risk stratification methods are needed, as is a deeper understanding of the pathology underlying the transition from GDM to T2D. We hypothesize that metabolites during the early postpartum period accurately distinguish risk of progression from GDM to T2D and that metabolite changes signify underlying pathophysiology for future disease development.

METHODS AND FINDINGS

The study utilized fasting plasma samples collected from a well-characterized prospective research study of 1,035 women diagnosed with GDM. The cohort included racially/ethnically diverse pregnant women (aged 20-45 years-33% primiparous, 37% biparous, 30% multiparous) who delivered at Kaiser Permanente Northern California hospitals from 2008 to 2011. Participants attended in-person research visits including 2-hour 75-g oral glucose tolerance tests (OGTTs) at study baseline (6-9 weeks postpartum) and annually thereafter for 2 years, and we retrieved diabetes diagnoses from electronic medical records for 8 years. In a nested case-control study design, we collected fasting plasma samples among women without diabetes at baseline (n = 1,010) to measure metabolites among those who later progressed to incident T2D or did not develop T2D (non-T2D). We studied 173 incident T2D cases and 485 controls (pair-matched on BMI, age, and race/ethnicity) to discover metabolites associated with new onset of T2D. Up to 2 years post-baseline, we analyzed samples from 98 T2D cases with 239 controls to reveal T2D-associated metabolic changes. The longitudinal analysis tracked metabolic changes within individuals from baseline to 2 years of follow-up as the trajectory of T2D progression. By building prediction models, we discovered a distinct metabolic signature in the early postpartum period that predicted future T2D with a median discriminating power area under the receiver operating characteristic curve of 0.883 (95% CI 0.820-0.945, p < 0.001). At baseline, the most striking finding was an overall increase in amino acids (AAs) as well as diacyl-glycerophospholipids and a decrease in sphingolipids and acyl-alkyl-glycerophospholipids among women with incident T2D. Pathway analysis revealed up-regulated AA metabolism, arginine/proline metabolism, and branched-chain AA (BCAA) metabolism at baseline. At follow-up after the onset of T2D, up-regulation of AAs and down-regulation of sphingolipids and acyl-alkyl-glycerophospholipids were sustained or strengthened. Notably, longitudinal analyses revealed only 10 metabolites associated with progression to T2D, implicating AA and phospholipid metabolism. A study limitation is that all of the analyses were performed with the same cohort. It would be ideal to validate our findings in an independent longitudinal cohort of women with GDM who had glucose tolerance tested during the early postpartum period.

CONCLUSIONS

In this study, we discovered a metabolic signature predicting the transition from GDM to T2D in the early postpartum period that was superior to clinical parameters (fasting plasma glucose, 2-hour plasma glucose). The findings suggest that metabolic dysregulation, particularly AA dysmetabolism, is present years prior to diabetes onset, and is revealed during the early postpartum period, preceding progression to T2D, among women with GDM.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01967030.

Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas

Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).

Plasma Metabolomics to Identify and Stratify Patients With Impaired Glucose Tolerance

OBJECTIVE

Impaired glucose tolerance (IGT) is one of the presymptomatic states of type 2 diabetes mellitus and requires an oral glucose tolerance test (OGTT) for diagnosis. Our aims were twofold: (i) characterize signatures of small molecules predicting the OGTT response and (ii) identify metabolic subgroups of participants with IGT.

METHODS

Plasma samples from 827 participants of the Study of Health in Pomerania free of diabetes were measured using mass spectrometry and proton-nuclear magnetic resonance spectroscopy. Linear regression analyses were used to screen for metabolites significantly associated with the OGTT response after 2 hours, adjusting for baseline glucose and insulin levels as well as important confounders. A signature predictive for IGT was established using regularized logistic regression. All cases with IGT (N = 159) were selected and subjected to unsupervised clustering using a k-means approach.

RESULTS AND CONCLUSION

In total, 99 metabolites and 22 lipoprotein measures were significantly associated with either 2-hour glucose or 2-hour insulin levels. Those comprised variations in baseline concentrations of branched-chain amino ketoacids, acylcarnitines, lysophospholipids, or phosphatidylcholines, largely confirming previous studies. By the use of these metabolites, subjects with IGT segregated into two distinct groups. Our IGT prediction model combining both clinical and metabolomics traits achieved an area under the curve of 0.84, slightly improving the prediction based on established clinical measures. The present metabolomics approach revealed molecular signatures associated directly to the response of the OGTT and to IGT in line with previous studies. However, clustering of subjects with IGT revealed distinct metabolic signatures of otherwise similar individuals, pointing toward the possibility of metabolomics for patient stratification.

MENDA: a comprehensive curated resource of metabolic characterization in depression

Depression is a seriously disabling psychiatric disorder with a significant burden of disease. Metabolic abnormalities have been widely reported in depressed patients and animal models. However, there are few systematic efforts that integrate meaningful biological insights from these studies. Herein, available metabolic knowledge in the context of depression was integrated to provide a systematic and panoramic view of metabolic characterization. After screening more than 10 000 citations from five electronic literature databases and five metabolomics databases, we manually curated 5675 metabolite entries from 464 studies, including human, rat, mouse and non-human primate, to develop a new metabolite-disease association database, called MENDA (http://menda.cqmu.edu.cn:8080/index.php). The standardized data extraction process was used for data collection, a multi-faceted annotation scheme was developed, and a user-friendly search engine and web interface were integrated for database access. To facilitate data analysis and interpretation based on MENDA, we also proposed a systematic analytical framework, including data integration and biological function analysis. Case studies were provided that identified the consistently altered metabolites using the vote-counting method, and that captured the underlying molecular mechanism using pathway and network analyses. Collectively, we provided a comprehensive curation of metabolic characterization in depression. Our model of a specific psychiatry disorder may be replicated to study other complex diseases.

1-Hour Post-OGTT Glucose Improves the Early Prediction of Type 2 Diabetes by Clinical and Metabolic Markers

CONTEXT

Early prediction of dysglycemia is crucial to prevent progression to type 2 diabetes. The 1-hour postload plasma glucose (PG) is reported to be a better predictor of dysglycemia than fasting plasma glucose (FPG), 2-hour PG, or glycated hemoglobin (HbA1c).

OBJECTIVE

To evaluate the predictive performance of clinical markers, metabolites, HbA1c, and PG and serum insulin (INS) levels during a 75-g oral glucose tolerance test (OGTT).

DESIGN AND SETTING

We measured PG and INS levels at 0, 30, 60, and 120 minutes during an OGTT in 543 participants in the Botnia Prospective Study, 146 of whom progressed to type 2 diabetes within a 10-year follow-up period. Using combinations of variables, we evaluated 1527 predictive models for progression to type 2 diabetes.

RESULTS

The 1-hour PG outperformed every individual marker except 30-minute PG or mannose, whose predictive performances were lower but not significantly worse. HbA1c was inferior to 1-hour PG according to DeLong test P value but not false discovery rate. Combining the metabolic markers with PG measurements and HbA1c significantly improved the predictive models, and mannose was found to be a robust metabolic marker.

CONCLUSIONS

The 1-hour PG, alone or in combination with metabolic markers, is a robust predictor for determining the future risk of type 2 diabetes, outperforms the 2-hour PG, and is cheaper to measure than metabolites. Metabolites add to the predictive value of PG and HbA1c measurements. Shortening the standard 75-g OGTT to 1 hour improves its predictive value and clinical usability.

Lipidomic Response to Coffee Consumption

Coffee is widely consumed and contains many bioactive compounds, any of which may impact pathways related to disease development. Our objective was to identify individual lipid changes in response to coffee drinking. We profiled the lipidome of fasting serum samples collected from a previously reported single blinded, three-stage clinical trial. Forty-seven habitual coffee consumers refrained from drinking coffee for 1 month, consumed 4 cups of coffee/day in the second month and 8 cups/day in the third month. Samples collected after each coffee stage were subject to quantitative lipidomic profiling using ion-mobility spectrometry–mass spectrometry. A total of 853 lipid species mapping to 14 lipid classes were included for univariate analysis. Three lysophosphatidylcholine (LPC) species including LPC (20:4), LPC (22:1) and LPC (22:2), significantly decreased after coffee intake (p < 0.05 and q < 0.05). An additional 72 species mapping to the LPC, free fatty acid, phosphatidylcholine, cholesteryl ester and triacylglycerol classes of lipids were nominally associated with coffee intake (p < 0.05 and q > 0.05); 58 of these decreased after coffee intake. In conclusion, coffee intake leads to lower levels of specific LPC species with potential impacts on glycerophospholipid metabolism more generally.