Serum metabolomic profile of incident diabetes

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

OBJECTIVE

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

DESIGN

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Untargeted metabolomics uncovers metabolic dysregulation and tissue sensitivity in ACE2 knockout mice

Angiotensin-converting enzyme 2 (ACE2) polymorphisms are associated with increased risk of type 2 diabetes mellitus (T2DM), obesity and dyslipidemia, which have been determined in various populations. Consistently, ACE2 knockout (ACE2 KO) mice display damaged energy metabolism in multiple tissues, especially the key metabolic tissues such as liver, skeletal muscle and epididymal white adipose tissue (eWAT) and show even more severe phenotype under high-fat diet (HFD) induced metabolic stress. However, the effects of ACE2 on global metabolomics profiling and the tissue sensitivity remain unclear. To understand how tissues independently and collectively respond to ACE2, we performed untargeted metabolomics in serum in ACE2 KO and control wild type (WT) mice both on normal diet (ND) and HFD, and in three key metabolic tissues (liver, skeletal muscle and eWAT) after HFD treatment. The results showed significant alterations in metabolic profiling in ACE2 KO mice. We identified 275 and 168 serum metabolites differing significantly between WT and ACE2 KO mice fed on ND and HFD, respectively. And the altered metabolites in the ACE2 KO group varied from 90 to 196 in liver, muscle and eWAT. The alterations in ND and HFD serum were most similar. Compared with WT mice, ACE2 KO mice showed an increase in N-phenylacetylglutamine (PAGln), methyl indole-3-acetate, 5-hydroxytryptophol, cholic acid, deoxycholic acid and 12(S)-HETE, while LPC (19:0) and LPE (16:1) decreased. Moreover, LPC (20:0), LPC (20:1) and PC (14:0e/6:0) were reduced in both ND and HFD serum, paralleling the decreases identified in HFD skeletal muscle. Interestingly, DL-tryptophan, indole and Gly-Phe decreased in both ND and HFD serum but were elevated in HFD liver of ACE2 KO mice. A low level of l-ergothioneine was observed among liver, muscle, and epididymal fat tissue of ACE2 KO mice. Pathway analysis demonstrated that different tissues exhibited different dysregulated metabolic pathways. In conclusion, these results revealed that ACE2 deficiency leads to an overall state of metabolic distress, which may provide a new insight into the underlying pathogenesis in metabolic disorders in both ACE2 KO mice and in patients with certain genetic variant of ACE2 gene.

Erythritol as a Potential Causal Contributor to Cardiometabolic Disease: A Mendelian Randomization Study

People with type 2 diabetes frequently use low-calorie sweeteners to manage glycemia and reduce caloric intake. Use of erythritol, a low-calorie sweetener, has increased recently. Higher circulating concentration associates with major cardiac events and metabolic disease in observational data, prompting some concern. As observational data may be prone to confounding and reverse causality, we undertook bidirectional Mendelian randomization (MR) to investigate potential causal associations between erythritol and coronary artery disease (CAD), BMI, waist-hip-ratio (WHR), and glycemic and renal traits in cohorts of European ancestry. Analyses were undertaken using instruments comprising genome-wide significant variants from three cohorts with erythritol measurement. Across instruments, we did not find supportive evidence that increased erythritol increases CAD (b = -0.033 ± 0.02, P = 0.14; b = 0.46 ± 0.37, P = 0.23). MR indicates erythritol may decrease BMI (b = -0.04 ± 0.018, P = 0.03; b = -0.04 ± 0.0085, P = 1.23 × 10-5; b = -0.083 ± 0.092, P = 0.036), with potential evidence from one instrument of increased BMI adjusted for WHR (b = 0.046 ± 0.022, P = 0.035). No evidence of causal association was found with other traits. In conclusion, we did not find supportive evidence from MR that erythritol increases cardiometabolic disease. These findings await confirmation in well-designed prospective studies.

ARTICLE HIGHLIGHTS

A novel preliminary metabolomic panel for IHD diagnostics and pathogenesis

Cardiovascular disease (CVD) represents one of the main causes of mortality worldwide and nearly a half of it is related to ischemic heart disease (IHD). The article represents a comprehensive study on the diagnostics of IHD through the targeted metabolomic profiling and machine learning techniques. A total of 112 subjects were enrolled in the study, consisting of 76 IHD patients and 36 non-CVD subjects. Metabolomic profiling was conducted, involving the quantitative analysis of 87 endogenous metabolites in plasma. A novel regression method of age-adjustment correction of metabolomics data was developed. We identified 36 significantly changed metabolites which included increased cystathionine and dimethylglycine and the decreased ADMA and arginine. Tryptophan catabolism pathways showed significant alterations with increased levels of serotonin, intermediates of the kynurenine pathway and decreased intermediates of indole pathway. Amino acid profiles indicated elevated branched-chain amino acids and increased amino acid ratios. Short-chain acylcarnitines were reduced, while long-chain acylcarnitines were elevated. Based on these metabolites data, machine learning algorithms: logistic regression, support vector machine, decision trees, random forest, and gradient boosting, were used for IHD diagnostic models. Random forest demonstrated the highest accuracy with an AUC of 0.98. The metabolites Norepinephrine; Xanthurenic acid; Anthranilic acid; Serotonin; C6-DC; C14-OH; C16; C16-OH; GSG; Phenylalanine and Methionine were found to be significant and may serve as a novel preliminary panel for IHD diagnostics. Further studies are needed to confirm these findings.

Altered metabolic profiles and targets relevant to the protective effect of acteoside on diabetic nephropathy in db/db mice based on metabolomics and network pharmacology studies

ETHNOPHARMACOLOGICAL RELEVANCE

Diabetic nephropathy (DN) was a major cause of end-stage renal failure and a common microvascular complication in patients with diabetes mellitus (DM). Acteoside (ACT) was the main ingredient extracted from the leaves of Rehmannia glutinosa, which had the functions of entering the lung, moisturizing the skin and relieving itching, nourishing yin and tonifying the kidney, cooling blood, and stopping bleeding. ACT had attracted worldwide interest because of its therapeutic effects on DM and its complications.

AIM OF THE STUDY

To clarify the metabolic profiles and targets of ACT in db/db mice based on metabolomics and network pharmacology studies.

MATERIALS AND METHODS

Db/db mice were used to observe the biochemical indices and histopathological changes in the kidney to evaluate the pharmacological effects of ACT on DN. Untargeted metabolomics studies were performed to investigate by UHPLC-LTQ-Orbitrap MS on urine, serum, and kidney samples. The key targets and pathways were analyzed by network pharmacology. For the pathways enriched by untargeted metabolomics, targeted metabolomics by UHPLC-QQQ-MS/MS was performed in kidney samples for validation. Sensitive biomarkers in kidney samples were evaluated. The effect of ACT on the improvement of DN from the perspective of metabolism of small molecules in vivo was described.

RESULTS

ACT could delay the progression of DN and improve the degree of histopathological damage to the kidney. The pathways were focused on amino acid metabolism by untargeted metabolomics. Through network pharmacology analysis, the effect pathways were related to signal transduction, carbohydrate, lipid, amino acid metabolism and mainly affected the endocrine and immune systems. Amino acid metabolism was disturbed in the kidney of db/db mice, which could be callback by ACT, such as tryptophan, glutamine, cysteine, leucine, threonine, proline, phenylalanine, histidine, serine, arginine, asparagine by targeted metabolomics.

CONCLUSIONS

In conclusion, this study provided strong support for ACT on DN treatment in clinics. Meanwhile, the Rehmannia glutinosa was used fully to raise the income level of farmers economically, while achieving the social benefit of empowering rural revitalization.

Harnessing the benefits of glycine supplementation for improved pancreatic microcirculation in type 1 diabetes mellitus

Type 1 diabetes mellitus (T1DM) is predominantly managed using insulin replacement therapy, however, pancreatic microcirculatory disturbances play a critical role in T1DM pathogenesis, necessitating alternative therapies. This study aimed to investigate the protective effects of glycine supplementation on pancreatic microcirculation in T1DM. Streptozotocin-induced T1DM and glycine-supplemented mice (n = 6 per group) were used alongside control mice. Pancreatic microcirculatory profiles were determined using a laser Doppler blood perfusion monitoring system and wavelet transform spectral analysis. The T1DM group exhibited disorganized pancreatic microcirculatory oscillation. Glycine supplementation significantly restored regular biorhythmic contraction and relaxation, improving blood distribution patterns. Further-more, glycine reversed the lower amplitudes of endothelial oscillators in T1DM mice. Ultrastructural deterioration of islet microvascular endothelial cells (IMECs) and islet microvascular pericytes, including membrane and organelle damage, collagenous fiber proliferation, and reduced edema, was substantially reversed by glycine supplementation. Additionally, glycine supplementation inhibited the production of IL-6, TNF-α, IFN-γ, pro-MMP-9, and VEGF-A in T1DM, with no significant changes in energetic metabolism observed in glycine-supplemented IMECs. A statistically significant decrease in MDA levels accompanied by an increase in SOD levels was also observed with glycine supplementation. Notably, negative correlations emerged between inflammatory cytokines and microhemodynamic profiles. These findings suggest that glycine supplementation may offer a promising therapeutic approach for protecting against pancreatic microcirculatory dysfunction in T1DM.

Re-evaluation of erythritol (E 968) as a food additive

This opinion addresses the re-evaluation of erythritol (E 968) as food additive and an application for its exemption from the laxative warning label requirement as established under Regulation (EU) No 1169/2011. Erythritol is a polyol obtained by fermentation with Moniliella pollinis BC or Moniliella megachiliensis KW3-6, followed by purifications and drying. Erythritol is readily and dose-dependently absorbed in humans and can be metabolised to erythronate to a small extent. Erythritol is then excreted unchanged in the urine. It does not raise concerns regarding genotoxicity. The dataset evaluated consisted of human interventional studies. The Panel considered that erythritol has the potential to cause diarrhoea in humans, which was considered adverse because its potential association with electrolyte and water imbalance. The lower bound of the range of no observed adverse effect levels (NOAELs) for diarrhoea of 0.5 g/kg body weight (bw) was identified as reference point. The Panel considered appropriate to set a numerical acceptable daily intake (ADI) at the level of the reference point. An ADI of 0.5 g/kg bw per day was considered by the Panel to be protective for the immediate laxative effect as well as potential chronic effects, secondary to diarrhoea. The highest mean and 95th percentile chronic exposure was in children (742 mg/kg bw per day) and adolescents (1532 mg/kg bw per day). Acute exposure was maximally 3531 mg/kg bw per meal for children at the 99th percentile. Overall, the Panel considered both dietary exposure assessments an overestimation. The Panel concluded that the exposure estimates for both acute and chronic dietary exposure to erythritol (E 968) were above the ADI, indicating that individuals with high intake may be at risk of experiencing adverse effects after single and repeated exposure. Concerning the new application, the Panel concluded that the available data do not support the proposal for exemption.

The link between obesity and insulin resistance among children: Effects of key metabolites

BACKGROUND

Childhood obesity became a severe public health challenge, and insulin resistance (IR) was one of the common complications. Both obesity and IR were considered as the basis of metabolic disorders. However, it is unclear which common key metabolites are associated with childhood obesity and IR.

METHODS

The children were divided into normal weight and overweight/obese groups. Fasting blood glucose and fasting insulin were measured, and homeostasis model assessment of insulin resistance was calculated. Liquid chromatography-tandem mass spectrometry was applied for metabonomic analysis. Multiple linear regression analysis and correlation analysis explored the relationships between obesity, IR, and metabolites. Random forests were used to rank the importance of differential metabolites, and relative operating characteristic curves were used for prediction.

RESULTS

A total of 88 normal-weight children and 171 obese/overweight children participated in the study. There was a significant difference between the two groups in 30 metabolites. Childhood obesity was significantly associated with 10 amino acid metabolites and 20 fatty acid metabolites. There were 12 metabolites significantly correlated with IR. The ranking of metabolites in random forest showed that glutamine, tyrosine, and alanine were important in amino acids, and pyruvic-ox-2, ethylmalonic-2, and phenyllactic-2 were important in fatty acids. The area under the curve of body mass index standard deviation  score (BMI-SDS) combined with key amino acid metabolites and fatty acid metabolites for predicting IR was 80.0% and 76.6%, respectively.

CONCLUSIONS

There are common key metabolites related to IR and obese children, and these key metabolites combined with BMI-SDS could effectively predict the risk of IR.

Elevated Erythritol: A Marker of Metabolic Dysregulation or Contributor to the Pathogenesis of Cardiometabolic Disease?

Erythritol is a non-nutritive sugar replacement that can be endogenously produced by humans. Witkowski et al. reported that elevated circulating erythritol is associated with adverse cardiovascular events in three independent cohorts, demonstrated in vitro and ex vivo that erythritol promotes platelet activation, and showed faster clotting time in mice injected with erythritol. It was concluded that erythritol fosters enhanced thrombosis. This narrative review presents additional evidence that needs to be considered when evaluating these data and conclusions. We conducted a search of all studies related to erythritol exposure with focus on those that reported vascular health outcomes. Patients with chronically elevated erythritol levels due to inborn errors of metabolism do not exhibit higher platelet activation or thrombosis risk. Most long-term studies in which animals consumed high levels of erythritol do not support its role in platelet activation and thrombosis formation. Clinical data on the effects of chronic intake of erythritol are limited. Erythritol may be merely a marker of dysregulation in the Pentose Phosphate Pathway caused by impaired glycemia. However, this suggestion and the findings of Witkowski et al. need to be further examined. Clinical trials examining the long-term effects of erythritol consumption on cardiometabolic outcomes are required to test the causality between dietary erythritol and cardiometabolic risk. Until supportive data from these trials are available, it cannot be concluded that dietary erythritol promotes platelet activation, thrombosis, and cardiometabolic risk.

Unlocking the Potential: Amino Acids' Role in Predicting and Exploring Therapeutic Avenues for Type 2 Diabetes Mellitus

Diabetes mellitus, particularly type 2 diabetes mellitus (T2DM), imposes a significant global burden with adverse clinical outcomes and escalating healthcare expenditures. Early identification of biomarkers can facilitate better screening, earlier diagnosis, and the prevention of diabetes. However, current clinical predictors often fail to detect abnormalities during the prediabetic state. Emerging studies have identified specific amino acids as potential biomarkers for predicting the onset and progression of diabetes. Understanding the underlying pathophysiological mechanisms can offer valuable insights into disease prevention and therapeutic interventions. This review provides a comprehensive summary of evidence supporting the use of amino acids and metabolites as clinical biomarkers for insulin resistance and diabetes. We discuss promising combinations of amino acids, including branched-chain amino acids, aromatic amino acids, glycine, asparagine and aspartate, in the prediction of T2DM. Furthermore, we delve into the mechanisms involving various signaling pathways and the metabolism underlying the role of amino acids in disease development. Finally, we highlight the potential of targeting predictive amino acids for preventive and therapeutic interventions, aiming to inspire further clinical investigations and mitigate the progression of T2DM, particularly in the prediabetic stage.

Sucrose Intake Elevates Erythritol in Plasma and Urine in Male Mice

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Heterogeneity in the effect of marked weight loss on metabolic function in women with obesity

BACKGROUNDThere is considerable heterogeneity in the effect of weight loss on metabolic function in people with obesity.METHODSWe evaluated muscle and liver insulin sensitivity, body composition, and circulating factors associated with insulin action before and after approximately 20% weight loss in women identified as "Responders" (n = 11) or "Non-responders" (n = 11), defined as the top (>75% increase) and bottom (<5% increase) quartiles of the weight loss-induced increase in glucose disposal rate (GDR) during a hyperinsulinemic-euglycemic clamp procedure, among 43 women with obesity (BMI: 44.1 ± 7.9 kg/m2).RESULTSAt baseline, GDR, which provides an index of muscle insulin sensitivity, and the hepatic insulin sensitivity index were more than 50% lower in Responders than Non-responders, but both increased much more after weight loss in Responders than Non-responders, which eliminated the differences between groups. Weight loss also caused greater decreases in intrahepatic triglyceride content and plasma adiponectin and PAI-1 concentrations in Responders than Non-responders and greater insulin-mediated suppression of plasma free fatty acids, branched-chain amino acids, and C3/C5 acylcarnitines in Non-responders than Responders, so that differences between groups at baseline were no longer present after weight loss. The effect of weight loss on total body fat mass, intra-abdominal adipose tissue volume, adipocyte size, and circulating inflammatory markers were not different between groups.CONCLUSIONThe results from our study demonstrate that the heterogeneity in the effects of marked weight loss on muscle and hepatic insulin sensitivity in people with obesity is determined by baseline insulin action, and reaches a ceiling when "normal" insulin action is achieved.TRIAL REGISTRATIONNCT00981500, NCT01299519, NCT02207777.FUNDINGNIH grants P30 DK056341, P30 DK020579, P30 DK052574, UL1 TR002345, and T32 HL13035, the American Diabetes Association (1-18-ICTS-119), the Longer Life Foundation (2019-011), and the Atkins Philanthropic Trust.

Genetic loci of beta-aminoisobutyric acid are associated with aging-related mild cognitive impairment

We studied the genetic associations of a previously developed Metabolomic Risk Score (MRS) for Mild Cognitive Impairment (MCI) and beta-aminoisobutyric acid metabolite (BAIBA)-the metabolite highlighted by results from a genome-wide association study (GWAS) of the MCI-MRS, and assessed their association with MCI in datasets of diverse race/ethnicities. We first performed a GWAS for the MCI-MRS and BAIBA, in Hispanic/Latino adults (n = 3890) from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We identified ten independent genome-wide significant (p value <5 × 10-8) variants associated with MCI-MRS or BAIBA. Variants associated with the MCI-MRS are located in the Alanine-Glyoxylate Aminotransferase 2 (AGXT2 gene), which is known to be associated with BAIBA metabolism. Variants associated with BAIBA are located in the AGXT2 gene and in the SLC6A13 gene. Next, we tested the variants' association with MCI in independent datasets of n = 3178 HCHS/SOL older individuals, n = 3775 European Americans, and n = 1032 African Americans from the Atherosclerosis Risk In Communities (ARIC) study. Variants were considered associated with MCI if their p value <0.05 in the meta-analysis of the three datasets and their direction of association was consistent with expectation. Rs16899972 and rs37369 from the AGXT2 region were associated with MCI. Mediation analysis supported the mediation effect of BAIBA between the two genetic variants and MCI (p value = 0.004 for causal mediated effect). In summary, genetic variants in the AGXT2 region are associated with MCI in Hispanic/Latino, African, and European American populations in the USA, and their effect is likely mediated by changes in BAIBA levels.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

Metabolomic profiling of glucose homeostasis in African Americans: the Insulin Resistance Atherosclerosis Family Study (IRAS-FS)

INTRODUCTION

African Americans are at increased risk for type 2 diabetes.

OBJECTIVES

This work aimed to examine metabolomic signature of glucose homeostasis in African Americans.

METHODS

We used an untargeted liquid chromatography-mass spectrometry metabolomic approach to comprehensively profile 727 plasma metabolites among 571 African Americans from the Insulin Resistance Atherosclerosis Family Study (IRAS-FS) and investigate the associations between these metabolites and both the dynamic (S_I, insulin sensitivity; AIR, acute insulin response; DI, disposition index; and S_G, glucose effectiveness) and basal (HOMA-IR and HOMA-B) measures of glucose homeostasis using univariate and regularized regression models. We also compared the results with our previous findings in the IRAS-FS Mexican Americans.

RESULTS

We confirmed increased plasma metabolite levels of branched-chain amino acids and their metabolic derivatives, 2-aminoadipate, 2-hydroxybutyrate, glutamate, arginine and its metabolic derivatives, carbohydrate metabolites, and medium- and long-chain fatty acids were associated with insulin resistance, while increased plasma metabolite levels in the glycine, serine and threonine metabolic pathway were associated with insulin sensitivity. We also observed a differential ancestral effect of glutamate on glucose homeostasis with significantly stronger effects observed in African Americans than those previously observed in Mexican Americans.

CONCLUSION

We extended the observations that metabolites are useful biomarkers in the identification of prediabetes in individuals at risk of type 2 diabetes in African Americans. We revealed, for the first time, differential ancestral effect of certain metabolites (i.e., glutamate) on glucose homeostasis traits. Our study highlights the need for additional comprehensive metabolomic studies in well-characterized multiethnic cohorts.

The artificial sweetener erythritol and cardiovascular event risk

Artificial sweeteners are widely used sugar substitutes, but little is known about their long-term effects on cardiometabolic disease risks. Here we examined the commonly used sugar substitute erythritol and atherothrombotic disease risk. In initial untargeted metabolomics studies in patients undergoing cardiac risk assessment (n = 1,157; discovery cohort, NCT00590200 ), circulating levels of multiple polyol sweeteners, especially erythritol, were associated with incident (3 year) risk for major adverse cardiovascular events (MACE; includes death or nonfatal myocardial infarction or stroke). Subsequent targeted metabolomics analyses in independent US (n = 2,149, NCT00590200 ) and European (n = 833, DRKS00020915 ) validation cohorts of stable patients undergoing elective cardiac evaluation confirmed this association (fourth versus first quartile adjusted hazard ratio (95% confidence interval), 1.80 (1.18-2.77) and 2.21 (1.20-4.07), respectively). At physiological levels, erythritol enhanced platelet reactivity in vitro and thrombosis formation in vivo. Finally, in a prospective pilot intervention study ( NCT04731363 ), erythritol ingestion in healthy volunteers (n = 8) induced marked and sustained (>2 d) increases in plasma erythritol levels well above thresholds associated with heightened platelet reactivity and thrombosis potential in in vitro and in vivo studies. Our findings reveal that erythritol is both associated with incident MACE risk and fosters enhanced thrombosis. Studies assessing the long-term safety of erythritol are warranted.

Metabolomic Markers of Ultra-Processed Food and Incident CKD

BACKGROUND

High ultra-processed food consumption is associated with higher risk of CKD. However, there is no biomarker for ultra-processed food, and the mechanism through which ultra-processed food is associated with CKD is not clear. Metabolomics can provide objective biomarkers of ultra-processed food and provide important insights into the mechanisms by which ultra-processed food is associated with risk of incident CKD. Our objective was to identify serum metabolites associated with ultra-processed food consumption and investigate whether ultra-processed food-associated metabolites are prospectively associated with incident CKD.

METHODS

We used data from 3751 Black and White men and women (aged 45-64 years) in the Atherosclerosis Risk in Communities study. Dietary intake was assessed using a semiquantitative 66-item food frequency questionnaire, and ultra-processed food was classified using the NOVA classification system. Multivariable linear regression models were used to identify the association between 359 metabolites and ultra-processed food consumption. Cox proportional hazards models were used to investigate the prospective association of ultra-processed food-associated metabolites with incident CKD.

RESULTS

Twelve metabolites (saccharine, homostachydrine, stachydrine, N2, N2-dimethylguanosine, catechol sulfate, caffeine, 3-methyl-2-oxovalerate, theobromine, docosahexaenoate, glucose, mannose, and bradykinin) were significantly associated with ultra-processed food consumption after controlling for false discovery rate <0.05 and adjusting for sociodemographic factors, health behaviors, eGFR, and total energy intake. The 12 ultra-processed food-related metabolites significantly improved the prediction of ultra-processed food consumption (difference in C statistics: 0.069, P <1×10 -16 ). Higher levels of mannose, glucose, and N2, N2-dimethylguanosine were associated with higher risk of incident CKD after a median follow-up of 23 years.

CONCLUSIONS

We identified 12 serum metabolites associated with ultra-processed food consumption and three of them were positively associated with incident CKD. Mannose and N2, N2-dimethylguanosine are novel markers of CKD that may explain observed associations between ultra-processed food and CKD.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_03_08_CJN08480722.mp3.

Metabolomic profiling in kidney cells treated with a sodium glucose-cotransporter 2 inhibitor

We aimed to determine the metabolomic profile of kidney cells under high glucose conditions and following sodium-glucose cotransporter 2 (SGLT2) inhibitor treatment. Targeted metabolomics using the Absolute IDQ-p180 kit was applied to quantify metabolites in kidney cells stimulated with high glucose (25 and 50 mM) and treated with SGLT2 inhibitor, dapagliflozin (2 µM). Primary cultured human tubular epithelial cells and podocytes were used to identify the metabolomic profile in high glucose conditions following dapagliflozin treatment. The levels of asparagine, PC ae C34:1, and PC ae C36:2 were elevated in tubular epithelial cells stimulated with 50 mM glucose and were significantly decreased after 2 µM dapagliflozin treatment. The level of PC aa C32:0 was significantly decreased after 50 mM glucose treatment compared with the control, and its level was significantly increased after dapagliflozin treatment in podocytes. The metabolism of glutathione, asparagine and proline was significantly changed in tubular epithelial cells under high-glucose stimulation. And the pathway analysis showed that aminoacyl-tRNA biosynthesis, arginine and proline metabolism, glutathione metabolism, valine, leucine and isoleucine biosynthesis, phenylalanine, tyrosine, and tryptophan biosynthesis, beta-alanine metabolism, phenylalanine metabolism, arginine biosynthesis, alanine, aspartate and glutamate metabolism, glycine, serine and threonine metabolism were altered in tubular epithelial cells after dapagliflozin treatment following 50 mM glucose compared to those treated with 50 mM glucose.

Metabolic modelling of the human gut microbiome in type 2 diabetes patients in response to metformin treatment

The human gut microbiome has been associated with several metabolic disorders including type 2 diabetes mellitus. Understanding metabolic changes in the gut microbiome is important to elucidate the role of gut bacteria in regulating host metabolism. Here, we used available metagenomics data from a metformin study, together with genome-scale metabolic modelling of the key bacteria in individual and community-level to investigate the mechanistic role of the gut microbiome in response to metformin. Individual modelling predicted that species that are increased after metformin treatment have higher growth rates in comparison to species that are decreased after metformin treatment. Gut microbial enrichment analysis showed prior to metformin treatment pathways related to the hypoglycemic effect were enriched. Our observations highlight how the key bacterial species after metformin treatment have commensal and competing behavior, and how their cellular metabolism changes due to different nutritional environment. Integrating different diets showed there were specific microbial alterations between different diets. These results show the importance of the nutritional environment and how dietary guidelines may improve drug efficiency through the gut microbiota.

Serum Metabolomics Reveals a Potential Benefit of Methionine in Type 1 Diabetes Patients with Poor Glycemic Control and High Glycemic Variability

Glycemic variability (GV) in some patients with type 1 diabetes (T1D) remains heterogeneous despite comparable clinical indicators, and whether other factors are involved is yet unknown. Metabolites in the serum indicate a broad effect of GV on cellular metabolism and therefore are more likely to indicate metabolic dysregulation associated with T1D. To compare the metabolomic profiles between high GV (GV-H, coefficient of variation (CV) of glucose ≥ 36%) and low GV (GV-L, CV < 36%) groups and to identify potential GV biomarkers, metabolomics profiling was carried out on serum samples from 17 patients with high GV, 16 matched (for age, sex, body mass index (BMI), diabetes duration, insulin dose, glycated hemoglobin (HbA1c), fasting, and 2 h postprandial C-peptide) patients with low GV (exploratory set), and another 21 (GV-H/GV-L: 11/10) matched patients (validation set). Subsequently, 25 metabolites were significantly enriched in seven Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the GV-H and GV-L groups in the exploratory set. Only the differences in spermidine, L-methionine, and trehalose remained significant after validation. The area under the curve of these three metabolites combined in distinguishing GV-H from GV-L was 0.952 and 0.918 in the exploratory and validation sets, respectively. L-methionine was significantly inversely related to HbA1c and glucose CV, while spermidine was significantly positively associated with glucose CV. Differences in trehalose were not as reliable as those in spermidine and L-methionine because of the relatively low amounts of trehalose and the inconsistent fold change sizes in the exploratory and validation sets. Our findings suggest that metabolomic disturbances may impact the GV of T1D. Additional in vitro and in vivo mechanistic studies are required to elucidate the relationship between spermidine and L-methionine levels and GV in T1D patients with different geographical and nutritional backgrounds.

Erythritol: An In-Depth Discussion of Its Potential to Be a Beneficial Dietary Component

The sugar alcohol erythritol is a relatively new food ingredient. It is naturally occurring in plants, however, produced commercially by fermentation. It is also produced endogenously via the pentose phosphate pathway (PPP). Consumers perceive erythritol as less healthy than sweeteners extracted from plants, including sucrose. This review evaluates that perspective by summarizing current literature regarding erythritol's safety, production, metabolism, and health effects. Dietary erythritol is 30% less sweet than sucrose, but contains negligible energy. Because it is almost fully absorbed and excreted in urine, it is better tolerated than other sugar alcohols. Evidence shows erythritol has potential as a beneficial replacement for sugar in healthy and diabetic subjects as it exerts no effects on glucose or insulin and induces gut hormone secretions that modulate satiety to promote weight loss. Long-term rodent studies show erythritol consumption lowers body weight or adiposity. However, observational studies indicate positive association between plasma erythritol and obesity and cardiometabolic disease. It is unlikely that dietary erythritol is mediating these associations, rather they reflect dysregulated PPP due to impaired glycemia or glucose-rich diet. However, long-term clinical trials investigating the effects of chronic erythritol consumption on body weight and risk for metabolic diseases are needed. Current evidence suggests these studies will document beneficial effects of dietary erythritol compared to caloric sugars and allay consumer misperceptions.

Nontargeted and Targeted Metabolomic Profiling Reveals Novel Metabolite Biomarkers of Incident Diabetes in African Americans

Nontargeted metabolomics methods have increased potential to identify new disease biomarkers, but assessments of the additive information provided in large human cohorts by these less biased techniques are limited. To diversify our knowledge of diabetes-associated metabolites, we leveraged a method that measures 305 targeted or "known" and 2,342 nontargeted or "unknown" compounds in fasting plasma samples from 2,750 participants (315 incident cases) in the Jackson Heart Study (JHS)-a community cohort of self-identified African Americans-who are underrepresented in omics studies. We found 307 unique compounds (82 known) associated with diabetes after adjusting for age and sex at a false discovery rate of <0.05 and 124 compounds (35 known, including 11 not previously associated) after further adjustments for BMI and fasting plasma glucose. Of these, 144 and 68 associations, respectively, replicated in a multiethnic cohort. Among these is an apparently novel isomer of the 1-deoxyceramide Cer(m18:1/24:0) with functional geonomics and high-resolution mass spectrometry. Overall, known and unknown metabolites provided complementary information (median correlation ρ = 0.29), and their inclusion with clinical risk factors improved diabetes prediction modeling. Our findings highlight the importance of including nontargeted metabolomics methods to provide new insights into diabetes development in ethnically diverse cohorts.

Plasma metabolomic signatures of obesity and risk of type 2 diabetes

OBJECTIVE

The mechanisms linking obesity to type 2 diabetes (T2D) are not fully understood. This study aimed to identify obesity-related metabolomic signatures (MESs) and evaluated their relationships with incident T2D.

METHODS

In a nested case-control study of 2076 Chinese adults, 140 plasma metabolites were measured at baseline, linear regression was applied with the least absolute shrinkage and selection operator to identify MESs for BMI and waist circumference (WC), and conditional logistic regression was applied to examine their associations with T2D risk.

RESULTS

A total of 32 metabolites associated with BMI or WC were identified and validated, among which 14 showed positive associations and 3 showed inverse associations with T2D; 8 and 18 metabolites were selected to build MESs for BMI and WC, respectively. Both MESs showed strong linear associations with T2D: odds ratio (95% CI) comparing extreme quartiles was 4.26 (2.00-9.06) for BMI MES and 9.60 (4.22-21.88) for WC MES (both p-trend < 0.001). The MES-T2D associations were particularly evident among individuals with normal WC: odds ratio (95% CI) reached 6.41 (4.11-9.98) for BMI MES and 10.38 (6.36-16.94) for WC MES. Adding MESs to traditional risk factors and plasma glucose improved C statistics from 0.79 to 0.83 (p < 0.001).

CONCLUSIONS

Multiple obesity-related metabolites and MESs strongly associated with T2D in Chinese adults were identified.

Erythritol synthesis is elevated in response to oxidative stress and regulated by the non-oxidative pentose phosphate pathway in A549 cells

Background

Erythritol is a predictive biomarker of cardiometabolic diseases and is produced from glucose metabolism through the pentose phosphate pathway (PPP). Little is known regarding the regulation of endogenous erythritol synthesis in humans.

Objective

In the present study, we investigated the stimuli that promote erythritol synthesis in human lung carcinoma cells and characterized potential points of regulation along the PPP.

Methods

Human A549 lung carcinoma cells were chosen for their known ability to synthesize erythritol. A549 cells were treated with potential substrates for erythritol production, including glucose, fructose, and glycerol. Using siRNA knockdown, we assessed the necessity of enzymes G6PD, TKT, TALDO, and SORD for erythritol synthesis. We also used position-specific ¹³C-glucose tracers to determine whether the carbons for erythritol synthesis are derived directly from glycolysis or through the oxidative PPP. Finally, we assessed if erythritol synthesis responds to oxidative stress using chemical and genetic models.

Results

Intracellular erythritol was directly associated with media glucose concentration. In addition, siRNA knockdown of TKT or SORD inhibited erythritol synthesis, whereas siG6PD did not. Both chemically induced oxidative stress and constitutive activation of the antioxidant response transcription factor NRF2 elevated intracellular erythritol.

Conclusion

Our findings indicate that in A549 cells, erythritol synthesis is proportional to flux through the PPP and is regulated by non-oxidative PPP enzymes.

The role of NMR-based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetes

Associations of circulating metabolic biomarkers with type 2 diabetes (T2D) and their added value for risk prediction are uncertain among Chinese adults. A case-cohort study included 882 T2D cases diagnosed during 8-years' follow-up and a subcohort of 789 participants. NMR-metabolomic profiling quantified 225 plasma biomarkers in stored samples taken at recruitment into the study. Cox regression yielded adjusted hazard ratios (HRs) for T2D associated with individual biomarkers, with a set of biomarkers incorporated into an established T2D risk prediction model to assess improvement in discriminatory ability. Mean baseline BMI (SD) was higher in T2D cases than in the subcohort (25.7 [3.6] vs. 23.9 [3.6] kg/m²). Overall, 163 biomarkers were significantly and independently associated with T2D at false discovery rate (FDR) controlled p < 0.05, and 138 at FDR-controlled p < 0.01. Branched chain amino acids (BCAA), apolipoprotein B/apolipoprotein A1, triglycerides in VLDL and medium and small HDL particles, and VLDL particle size were strongly positively associated with T2D (HRs 1.74-2.36 per 1 SD, p < 0.001). HDL particle size, cholesterol concentration in larger HDL particles and docosahexaenoic acid levels were strongly inversely associated with T2D (HRs 0.43-0.48, p < 0.001). With additional adjustment for plasma glucose, most associations (n = 147 and n = 129 at p < 0.05 and p < 0.01, respectively) remained significant. HRs appeared more extreme among more centrally adipose participants for apolipoprotein B/apolipoprotein A1, BCAA, HDL particle size and docosahexaenoic acid (p for heterogeneity ≤ 0.05). Addition of 31 selected biomarkers to an established T2D risk prediction model modestly, but significantly, improved risk discrimination (c-statistic 0.86 to 0.91, p < 0.001). In relatively lean Chinese adults, diverse metabolic biomarkers are associated with future risk of T2D and can help improve established risk prediction models.

Impaired Amino Acid Metabolism and Its Correlation with Diabetic Kidney Disease Progression in Type 2 Diabetes Mellitus

Background: Metabolomics is useful in elucidating the progression of diabetes; however, the follow-up changes in metabolomics among health, diabetes mellitus, and diabetic kidney disease (DKD) have not been reported. This study was aimed to reveal metabolomic signatures in diabetes development and progression. Methods: In this cross-sectional study, we compared healthy (n = 30), type 2 diabetes mellitus (T2DM) (n = 30), and DKD (n = 30) subjects with the goal of identifying gradual altering metabolites. Then, a prospective study was performed in T2DM patients to evaluate these altered metabolites in the onset of DKD. Logistic regression was conducted to predict rapid eGFR decline in T2DM subjects using altered metabolites. The prospective association of metabolites with the risk of developing DKD was examined using logistic regression and restricted cubic spline regression models. Results: In this cross-sectional study, impaired amino acid metabolism was the main metabolic signature in the onset and development of diabetes, which was characterized by increased N-acetylaspartic acid, L-valine, isoleucine, asparagine, betaine, and L-methionine levels in both the T2DM and DKD groups. These candidate metabolites could distinguish the DKD group from the T2DM group. In the follow-up study, higher baseline levels of L-valine and isoleucine were significantly associated with an increased risk of rapid eGFR decline in T2DM patients. Of these, L-valine and isoleucine were independent risk factors for the development of DKD. Notably, nonlinear associations were also observed for higher baseline levels of L-valine and isoleucine, with an increased risk of DKD among patients with T2DM. Conclusion: Amino acid metabolism was disturbed in diabetes, and N-acetylaspartic acid, L-valine, isoleucine, asparagine, betaine, and L-methionine could be biomarkers for the onset and progression of diabetes. Furthermore, high levels of L-valine and isoleucine may be risk factors for DKD development.

Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies

OBJECTIVE

Tryptophan can be catabolised to various metabolites through host kynurenine and microbial indole pathways. We aimed to examine relationships of host and microbial tryptophan metabolites with incident type 2 diabetes (T2D), host genetics, diet and gut microbiota.

METHOD

We analysed associations between circulating levels of 11 tryptophan metabolites and incident T2D in 9180 participants of diverse racial/ethnic backgrounds from five cohorts. We examined host genome-wide variants, dietary intake and gut microbiome associated with these metabolites.

RESULTS

Tryptophan, four kynurenine-pathway metabolites (kynurenine, kynurenate, xanthurenate and quinolinate) and indolelactate were positively associated with T2D risk, while indolepropionate was inversely associated with T2D risk. We identified multiple host genetic variants, dietary factors, gut bacteria and their potential interplay associated with these T2D-relaetd metabolites. Intakes of fibre-rich foods, but not protein/tryptophan-rich foods, were the dietary factors most strongly associated with tryptophan metabolites. The fibre-indolepropionate association was partially explained by indolepropionate-associated gut bacteria, mostly fibre-using Firmicutes. We identified a novel association between a host functional LCT variant (determining lactase persistence) and serum indolepropionate, which might be related to a host gene-diet interaction on gut Bifidobacterium, a probiotic bacterium significantly associated with indolepropionate independent of other fibre-related bacteria. Higher milk intake was associated with higher levels of gut Bifidobacterium and serum indolepropionate only among genetically lactase non-persistent individuals.

CONCLUSION

Higher milk intake among lactase non-persistent individuals, and higher fibre intake were associated with a favourable profile of circulating tryptophan metabolites for T2D, potentially through the host-microbial cross-talk shifting tryptophan metabolism toward gut microbial indolepropionate production.

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

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

Targeted metabolomics analysis of amino acids and acylcarnitines as risk markers for diabetes by LC-MS/MS technique

Diabetes is a common chronic disease affecting millions of people worldwide. It underlies various complications and imposes many costs on individuals and society. Discovering early diagnostic biomarkers takes excellent insight into preventive plans and the best use of interventions. Therefore, in the present study, we aimed to evaluate the association between the level of amino acids and acylcarnitines and diabetes to develop diabetes predictive models. Using the targeted LC-MS/MS technique, we analyzed fasting plasma samples of 206 cases and 206 controls that were matched by age, sex, and BMI. The association between metabolites and diabetes was evaluated using univariate and multivariate regression analysis with adjustment for systolic and diastolic blood pressure and lipid profile. To deal with multiple comparisons, factor analysis was used. Participants' average age and BMI were 61.6 years, 28.9 kg/m2, and 55% were female. After adjustment, Factor 3 (tyrosine, valine, leucine, methionine, tryptophan, phenylalanine), 5 (C3DC, C5, C5OH, C5:1), 6 (C14OH, C16OH, C18OH, C18:1OH), 8 (C2, C4OH, C8:1), 10 (alanine, proline) and 11 (glutamic acid, C18:2OH) were positively associated with diabetes. Inline, factor 9 (C4DC, serine, glycine, threonine) and 12 (citrulline, ornithine) showed a reverse trend. Some amino acids and acylcarnitines were found as potential risk markers for diabetes incidents that reflected the disturbances in the several metabolic pathways among the diabetic population and could be targeted to prevent, diagnose, and treat diabetes.

Metabolomics of Dietary Acid Load and Incident Chronic Kidney Disease

OBJECTIVE

Blood biomarkers of dietary intake are more objective than self-reported dietary intake. Metabolites associated with dietary acid load were previously identified in 2 chronic kidney disease (CKD) populations. We aimed to extend these findings to a general population, replicating their association with dietary acid load, and investigating whether the individual biomarkers were prospectively associated with incident CKD.

METHODS

Among 15,792 participants in the Atherosclerosis Risk in Communities cohort followed up from 1987 to 1989 (baseline) to 2019, we evaluated 3,844 black and white men and women with dietary and metabolomic data in cross-sectional and prospective analyses. We hypothesized that a higher dietary acid load (using equations for potential renal acid load and net endogenous acid production) was associated with lower serum levels of 12 previously identified metabolites: indolepropionylglycine, indolepropionate, N-methylproline, N-δ-acetylornithine, threonate, oxalate, chiro-inositol, methyl glucopyranoside, stachydrine, catechol sulfate, hippurate, and tartronate. In addition, we hypothesized that lower serum levels of these 12 metabolites were associated with higher risk of incident CKD.

RESULTS

Eleven out of 12 metabolites were significantly inversely associated with dietary acid load, after adjusting for demographics, socioeconomic status, health behaviors, health status, and estimated glomerular filtration rate: indolepropionylglycine, indolepropionate, N-methylproline, threonate, oxalate, chiro-inositol, catechol sulfate, hippurate, methyl glucopyranoside (α + β), stachydrine, and tartronate. N-methylproline was inversely associated with incident CKD (hazard ratio: 0.95, 95% confidence interval: 0.91, 0.99, P = .01). The metabolomic biomarkers of dietary acid load significantly improved prediction of elevated dietary acid load estimated using dietary data, beyond covariates (difference in C statistics: 0.021-0.077, P ≤ 1.08 × 10-3).

CONCLUSION

Inverse associations between candidate biomarkers of dietary acid load were replicated in a general population. N-methylproline, representative of citrus fruit consumption, is a promising marker of dietary acid load and could represent an important pathway between dietary acid load and CKD.

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.

Amino Acid-Related Metabolic Signature in Obese Children and Adolescents

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

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

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

Individual and joint effects of trehalose and glutamate on diabetic retinopathy: a propensity score-matched case-control study

Although previous studies demonstrate that trehalose can help maintain glucose homeostasis in healthy humans, its role and joint effect with glutamate on diabetic retinopathy (DR) remain unclear. We aimed to comprehensively quantify the associations of trehalose and glutamate with DR. This study included 69 pairs of DR and matched type 2 diabetic (T2D) patients. Serum trehalose and glutamate were determined via ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system. Covariates were collected by a standardized questionnaire, clinical examinations and laboratory assessments. Individual and joint association of trehalose and glutamate with DR were quantified by multiple conditional logistic regression models. The adjusted odds of DR averagely decreased by 86% (odds ratio (OR): 0.14; 95% CI: 0.06, 0.33) with per interquartile range increase of trehalose. Comparing with the lowest quartile, adjusted OR (95% CI) were 0.20 (0.05, 0.83), 0.14 (0.03, 0.63) and 0.01 (<0.01, 0.05) for participants in the second, third and fourth quartiles of trehalose, respectively. In addition, as compared to their counterparts, T2D patients with lower trehalose (<median) and higher glutamate (≥median) had the highest odds of DR (OR: 36.81; 95% CI: 6.75, 200.61). An apparent super-multiplicative effect of trehalose and glutamate on DR was observed, whereas relative excess risk due to interaction was not significant. The study suggests that trehalose is beneficial to inhibit the occurrence of DR and synergistically decreases the risk of DR with reduced glutamate. Our findings also provide new insights into the mechanisms of DR and further longitudinal studies are required to confirm these findings.

A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research

Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.

Using Metabolomics in Diabetes Management with Traditional Chinese Medicine: A Review

The incidence of diabetes worldwide continues to rise, placing a huge economic and medical burden on human society. More than 90% of diabetic cases are type 2 diabetes (T2D). At present, the pathogenesis of T2D is not yet fully understood. Metabolomics uses high-resolution analytical techniques (typically NMR and MS) to help identify biomarkers associated with the risk of T2D and reveal potential pathogenesis. Many metabolites such as branched-chain amino acids (BCAAs), aromatic amino acids, glycine, 2-hydroxybutyric acid (2-HB), lysophosphatidylcholine (LPC) (18:2), and trehalose have proven to be biomarkers of T2D. Insulin resistance (IR) induced by BCAA in T2D mice is related to the activation of mammalian target of rapamycin (mTOR) and phosphorylation of insulin receptor substrate-1 (IRS1). Incomplete LCFA [Formula: see text]-oxidation promote acylcarnitine byproduct accumulation and stimulates proinflammatory NF[Formula: see text]B-related pathways to inhibit insulin action. Traditional Chinese Medicine (TCM) presents unique advantages in the treatment of T2D. Multiple metabolites and metabolic pathways have been identified in the treatment of TCM, providing valuable biomarkers and novel targets for drug therapy and pharmacological mechanism. Therefore, this paper reviews the modern achievements of metabolomics in T2D research and the progress of TCM management in recent years, in order to provide valuable information for related research.

Erythritol modulates the polarization of macrophages: Potential role of tumor necrosis factor-α and Akt pathway

Low-calorie sweeteners are substitutes for sugar and frequently used by patients with cardiometabolic diseases. Erythritol, a natural low-calorie sugar alcohol, was linked to cardiometabolic diseases in several recent metabolomics studies. However, the characterization of its role in disease development is lacking. Macrophage polarization orchestrates the immune response in various inflammatory conditions, most notably cardiometabolic disease. Therefore, the physiological effects of Erythritol on THP-1 macrophages were investigated. We observed an increased cellular abundance of proinflammatory M1 macrophages, characterized by CD11c, TNF-α, CD64, CD38, and HLA-DR markers and decreased anti-inflammatory M2 macrophages, characterized by mannose receptor CD206. The, Erythritol increased ROS generation, and the activation of the AKT pathway, cytosolic calcium overload, and cell cycle arrest at the G1 phase. Concomitantly, an increased population of necroptotic macrophages was observed. In conclusion, we provide evidence that Erythritol induced the proinflammatory phenotype in THP-1 macrophages and this was associated with an increased population of necroptotic macrophages. PRACTICAL APPLICATIONS: This assessment provides evidence of the effects of Erythritol on macrophages, particularly THP-1-derived macrophages. Our results support the role of Erythritol in driving the inflammation that is associated with cardiometabolic diseases and provide insights in the role of Erythritol as an inducer of necroptosis in THP-1 derived macrophages that could be associated the disease.

Effect of Metabolite Levels on Type 2 Diabetes Mellitus and Glycemic Traits: A Mendelian Randomization Study

OBJECTIVE

To assess the causal associations of plasma levels of metabolites with type 2 diabetes mellitus (T2DM) and glycemic traits.

METHODS

Two-sample mendelian randomization (MR) was conducted to assess the causal associations. Genetic variants strongly associated with metabolites at genome-wide significance level (P < 5 × 10-8) were selected from public genome-wide association studies, and single-nucleotide polymorphisms of outcomes were obtained from the Diabetes Genetics Replication and Meta-analysis consortium for T2DM and from the Meta-Analyses of Glucose and Insulin-related Traits Consortium for fasting glucose, insulin, and glycated hemoglobin (HbA1c). The Wald ratio and inverse-variance weighted methods were used for analyses, and MR-Egger was used for sensitivity analysis.

RESULTS

The β estimates per 1-SD increase of arachidonic acid (AA) level was 0.16 (95% CI, 0.078-0.242; P < 0.001). Genetic predisposition to higher plasma AA levels were associated with higher fasting glucose levels (β 0.10 [95% CI, 0.064-0.134], P < 0.001), higher HbA1c levels (β 0.04 [95% CI, 0.027-0.061]), and lower fasting insulin levels (β -0.025 [95% CI, -0.047 to -0.002], P = 0.033). Besides, 2-hydroxybutyric acid (2-HBA) might have a positive causal effect on glycemic traits.

CONCLUSIONS

Our findings suggest that AA and 2-HBA may have causal associations on T2DM and glycemic traits. This is beneficial for clarifying the pathogenesis of T2DM, which would be valuable for early identification and prevention for T2DM.

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.

Saliva and Plasma Reflect Metabolism Altered by Diabetes and Periodontitis

Periodontitis is an inflammatory disorder caused by disintegration of the balance between the periodontal microbiome and host response. While growing evidence suggests links between periodontitis and various metabolic disorders including type 2 diabetes (T2D), non-alcoholic liver disease, and cardiovascular disease (CVD), which often coexist in individuals with abdominal obesity, factors linking periodontal inflammation to common metabolic alterations remain to be fully elucidated. More detailed characterization of metabolomic profiles associated with multiple oral and cardiometabolic traits may provide better understanding of the complexity of oral-systemic crosstalk and its underlying mechanism. We performed comprehensive profiling of plasma and salivary metabolomes using untargeted gas chromatography/mass spectrometry to investigate multivariate covariation with clinical markers of oral and systemic health in 31 T2D patients with metabolic comorbidities and 30 control subjects. Orthogonal partial least squares (OPLS) results enabled more accurate characterization of associations among 11 oral and 25 systemic clinical outcomes, and 143 salivary and 78 plasma metabolites. In particular, metabolites that reflect cardiometabolic changes were identified in both plasma and saliva, with plasma and salivary ratios of (mannose + allose):1,5-anhydroglucitol achieving areas under the curve of 0.99 and 0.92, respectively, for T2D diagnosis. Additionally, OPLS analysis of periodontal inflamed surface area (PISA) as the numerical response variable revealed shared and unique responses of metabolomic and clinical markers to PISA between healthy and T2D groups. When combined with linear regression models, we found a significant correlation between PISA and multiple metabolites in both groups, including threonate, cadaverine and hydrocinnamate in saliva, as well as lactate and pentadecanoic acid in plasma, of which plasma lactate showed a predominant trend in the healthy group. Unique metabolites associated with PISA in the T2D group included plasma phosphate and salivary malate, while those in the healthy group included plasma gluconate and salivary adenosine. Remarkably, higher PISA was correlated with altered hepatic lipid metabolism in both groups, including higher levels of triglycerides, aspartate aminotransferase and alanine aminotransferase, leading to increased risk of cardiometabolic disease based on a score summarizing levels of CVD-related biomarkers. These findings revealed the potential utility of saliva for evaluating the risk of metabolic disorders without need for a blood test, and provide evidence that disrupted liver lipid metabolism may underlie the link between periodontitis and cardiometabolic disease.

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.

Oleic Acid and Insulin as Key Characteristics of T2D Promote Colorectal Cancer Deterioration in Xenograft Mice Revealed by Functional Metabolomics

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with high mortality worldwide. Type 2 diabetes mellitus (T2D), known as a risk factor of CRC, can promote the deterioration of CRC, but the underlying mechanism is elusive. In this study, we aimed to reveal the relationship between CRC and T2D from the perspective of small-molecule metabolism. First, a list of common dysregulated metabolites in CRC and T2D was obtained by retrieving existing metabolomics publications. Among these metabolites, oleic acid (OA) was found to be able to promote the proliferation and migration of colon carcinoma cell HCT116. Further experiments proved that insulin could significantly strengthen this promotion and showed a synergistic effect with OA. Mechanism study found that OA and insulin acted synergistically through the extracellular signal-regulated kinase (ERK)1/2/c-Myc/cyclin D1 pathway. In addition, the combination of ERK1/2 inhibitor SCH772984 and cyclin-dependent kinase (CDK)4/6 inhibitor palbociclib showed a remarkable inhibitory effect on tumor growth in vivo. Taken together, the current study found that OA plays an important role in CRC development by using a functional metabolomics approach. More importantly, insulin and OA were confirmed to synergistically promote the deterioration of CRC in vitro and in vivo via ERK1/2/c-Myc/cyclin D1 pathway. Our findings may shed light on CRC treatment among the T2D population.

Chronic Dietary Erythritol Exposure Elevates Plasma Erythritol Concentration in Mice but Does Not Cause Weight Gain or Modify Glucose Homeostasis

BACKGROUND

Erythritol is both a common nonnutritive sweetener and an endogenous product of glucose metabolism. Recent reports suggest that elevated plasma erythritol is a predictive biomarker of cardiometabolic disease onset and complications.

OBJECTIVES

Although short-term erythritol consumption has been evaluated, the effect of chronically elevated circulating erythritol on adiposity and glucose metabolism has not. This study investigated the effect of longer-term erythritol consumption on weight gain and glucose tolerance in young/adolescent mice.

METHODS

Four erythritol supplementation experiments were completed and analyzed separately in male C57BL/6J mice. In experiments 1 and 2, mice aged 8 wk or 20 wk, respectively, were randomly allocated to consume 16% fat diet (LFD) or LFD with 40 g/kg erythritol. In experiments 3 and 4, mice aged 8 wk or 20 wk were fed 45% fat diet (HFD) or HFD with 40 g/kg erythritol (HFD + ERY). In each experiment, we compared the effect of erythritol consumption on plasma erythritol, body weight and composition, glucose tolerance, and brown adipose tissue (BAT) uncoupling protein 1 (UCP1) expression. We also investigated relative endogenous tissue erythritol concentrations in a subset of control (LFD or HFD) mice in experiments 1 and 3.

RESULTS

There was no effect of erythritol supplementation on body weight or glucose tolerance in experiments 1-3. In experiment 4, in the 20-wk-old mice fed HFD or HFD + ERY, there was a significant interaction of time and erythritol on body weight (P < 0.0001), but the main effect of diet was not significant. Plasma erythritol was elevated 40-fold in mice consuming erythritol-supplemented diets relative to mice consuming LFD or HFD controls. We found no effect of chronic erythritol consumption on BAT UCP1 protein concentrations. Liver and kidney tissue contained significantly higher endogenous erythritol than quadriceps and visceral adipose (P < 0.001) in young mice fed LFD and HFD.

CONCLUSIONS

In young/adolescent mice, prolonged erythritol consumption did not significantly affect body weight, composition, or glucose tolerance.

Associations of network-derived metabolite clusters with prevalent type 2 diabetes among adults of Puerto Rican descent

INTRODUCTION

We investigated whether network analysis revealed clusters of coregulated metabolites associated with prevalent type 2 diabetes (T2D) among Puerto Rican adults.

RESEARCH DESIGN AND METHODS

We used liquid chromatography-mass spectrometry to measure fasting plasma metabolites (>600) among participants aged 40-75 years in the Boston Puerto Rican Health Study (BPRHS; discovery) and San Juan Overweight Adult Longitudinal Study (SOALS; replication), with (n=357; n=77) and without (n=322; n=934) T2D, respectively. Among BPRHS participants, we used unsupervised partial correlation network-based methods to identify and calculate metabolite cluster scores. Logistic regression was used to assess cross-sectional associations between metabolite clusters and prevalent T2D at the baseline blood draw in the BPRHS, and significant associations were replicated in SOALS. Inverse-variance weighted random-effect meta-analysis was used to combine cohort-specific estimates.

RESULTS

Six metabolite clusters were significantly associated with prevalent T2D in the BPRHS and replicated in SOALS (false discovery rate (FDR) <0.05). In a meta-analysis of the two cohorts, the OR and 95% CI (per 1 SD increase in cluster score) for prevalent T2D were as follows for clusters characterized primarily by glucose transport (0.21 (0.16 to 0.30); FDR <0.0001), sphingolipids (0.40 (0.29 to 0.53); FDR <0.0001), acyl cholines (0.35 (0.22 to 0.56); FDR <0.0001), sugar metabolism (2.28 (1.68 to 3.09); FDR <0.0001), branched-chain and aromatic amino acids (2.22 (1.60 to 3.08); FDR <0.0001), and fatty acid biosynthesis (1.54 (1.29 to 1.85); FDR <0.0001). Three additional clusters characterized by amino acid metabolism, cell membrane components, and aromatic amino acid metabolism displayed significant associations with prevalent T2D in the BPRHS, but these associations were not replicated in SOALS.

CONCLUSIONS

Among Puerto Rican adults, we identified several known and novel metabolite clusters that associated with prevalent T2D.

Tear metabolomics highlights new potential biomarkers for differentiating between Sjögren's syndrome and other causes of dry eye

PURPOSE

The lacrimal exocrinopathy of primary Sjögren's syndrome (pSS) is one of the main causes of severe dry eye syndrome and a burden for patients. Early recognition and treatment could prevent irreversible damage to lacrimal glands. The aim of this study was to find biomarkers in tears, using metabolomics and data mining approaches, in patients with newly-diagnosed pSS compared to other causes of dry eye syndrome.

METHODS

A prospective cohort of 40 pSS and 40 non-pSS Sicca patients with dryness was explored through a standardized targeted metabolomic approach using liquid chromatography coupled with mass spectrometry. A metabolomic signature predictive of the pSS status was sought out using linear (logistic regression with elastic-net regularization) and non-linear (random forests) machine learning architectures, after splitting the studied population into training, validation and test sets.

RESULTS

Among the 104 metabolites accurately measured in tears, we identified a discriminant signature composed of nine metabolites (two amino acids: serine, aspartate; one biogenic amine: dopamine; six lipids: Lysophosphatidylcholine C16:1, C18:1, C18:2, sphingomyelin C16:0 and C22:3, and the phoshatidylcholine diacyl PCaa C42:4), with robust performances (ROC-AUC = 0.83) for predicting the pSS status. Adjustment for age, sex and anti-SSA antibodies did not disrupt the link between the metabolomic signature and the pSS status. The non-lipidic components also remained specific for pSS regardless of the dryness severity.

CONCLUSION

Our results reveal a metabolomic signature for tears that distinguishes pSS from other dry eye syndromes and further highlight nine key metabolites of potential interest for early diagnosis and therapeutics of pSS.

Associations between serum amino acids and incident type 2 diabetes in Chinese rural adults

BACKGROUND AND AIMS

Some amino acids (AAs) may be associated with type 2 diabetes (T2DM). This study aimed to determine the associations of individual AAs with the development of T2DM in rural Chinese adults.

METHODS AND RESULTS

A cohort study of 1199 individuals aged 18 years or older was conducted from 2006 to 2008 in a rural community of Deqing, China, a repeated survey was done in 2015 and data linkage with the electronic health records system was performed each year for identifying new T2DM cases. A high-performance liquid chromatography approach was used to measure the baseline serum concentrations of 15 AAs. Cox proportional hazards models were used to examine the associations between AAs and the risk of incident T2DM. A total of 98 new T2DM cases were identified during the follow-up of 12 years on average. Among 15 AAs, proline was associated with an increased risk of incident T2DM after adjusted for age, sex, body mass index, fasting plasma glucose, family history of T2DM, smoking status, alcohol use, and history of hypertension, the adjusted hazard ratio for 1-standard deviation increment was 1.20 (95% confidence interval: 1.00, 1.43). The association tended to be more marked in subjects younger than 60 years and overweight/obese subjects. Among participants without hypertension, proline and phenylalanine were associated with an increased risk of incident T2DM, while aspartic acid was associated with a decreased risk.

CONCLUSION

Serum proline was associated with the risk of incident T2DM in rural Chinese adults and might be a potential predictor.

Mass spectrometry-based metabolomics diagnostics - myth or reality?

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

Changes on proteomic and metabolomic profile in serum of mice induced by chronic exposure to tramadol

Tramadol is an opioid used as an analgesic for treating moderate or severe pain. The long-term use of tramadol can induce several adverse effects. The toxicological mechanism of tramadol abuse is unclear. Limited literature available indicates the change of proteomic profile after chronic exposure to tramadol. In this study, we analyzed the proteomic and metabolomic profile by TMT-labeled quantitative proteomics and untargeted metabolomics between the tramadol and the control group. Proteomic analysis revealed 31 differential expressed serum proteins (9 increased and 22 decreased) in tramadol-treated mice (oral, 50 mg/kg, 5 weeks) as compared with the control ones. Bioinformatics analysis showed that the dysregulated proteins mainly included: enzyme inhibitor-associated proteins (i.e. apolipoprotein C-III (Apoc-III), alpha-1-antitrypsin 1–2 (Serpina 1b), apolipoprotein C-II (Apoc-II), plasma protease C1 inhibitor, inter-alpha-trypsin inhibitor heavy chain H3 (itih3)); mitochondria-related proteins (i.e. 14-3-3 protein zeta/delta (YWHAZ)); cytoskeleton proteins (i.e. tubulin alpha-4A chain (TUBA4A), vinculin (Vcl)). And we found that the differential expressed proteins mainly involved in the pathway of the protein digestion and absorption. Metabolomics analysis revealed that differential expressed metabolites mainly involved in protein ingestion and absorption, fatty acid biosynthesis, steroid hormone biosynthesis and bile secretion. Our overall findings revealed that chronic exposure to tramadol changed the proteomic and metabolomic profile of mice. Moreover, integrated proteomic and metabolomic revealed that the protein digestion and absorption is the common enrichment KEGG pathway. Thus, the combination of proteomics and metabolomics opens new avenues for the research of the molecular mechanisms of tramadol toxicity.