Metabolite profiling in identifying metabolic biomarkers in older people with late-onset type 2 diabetes mellitus

Fecal Microbiome and Urine Metabolome Profiling of Type 2 Diabetes

Type 2 diabetes is a prevalent metabolic disorder with serious health consequences, necessitating both enhanced diagnostic methodologies and comprehensive elucidation of its pathophysiological mechanisms. We compared fecal microbiome and urine metabolome profiles in type 2 diabetes patients versus healthy controls to evaluate their respective diagnostic potential. Using a cohort of 94 subjects (48 diabetics, 46 controls), this study employed 16S rRNA amplicon sequencing for fecal microbiome analysis and GC-MS for urinary metabolomics. While fecal microbiome alpha diversity showed no significant differences between groups, urinary metabolomics demonstrated distinct structural patterns and higher evenness in type 2 diabetes patients. The study identified several diabetes-associated urinary metabolites, including elevated levels of glucose and inositol, along with decreased levels of 6 urine metabolites including glycolic acid, hippurate, and 2-aminoethanol. In the fecal microbiome, genera such as Escherichia-Shigella showed positive correlation with type 2 diabetes, while Lacticaseibacillus demonstrated negative correlation. Receiver operating characteristic curve analyses revealed that urinary metabolites exhibited superior diagnostic potential compared to fecal microbiome features, with an area under the curve of 0.90 for the combined metabolite model versus 0.82 for the integrated bacterial taxa model. These findings suggest that urinary metabolomics may offer a more reliable approach for type 2 diabetes diagnosis compared to fecal 16S metabarcoding, while highlighting the potential of multi-marker panels for enhanced diagnostic accuracy.

Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies.

METHODS

We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN.

RESULTS

Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care.

CONCLUSION

Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.

Peanut skin procyanidins reduce intestinal glucose transport protein expression, regulate serum metabolites and ameliorate hyperglycemia in diabetic mice

One of diabetic characteristics is the postprandial hyperglycemia. Inhibiting glucose uptake may be beneficial for controlling postprandial blood glucose levels and regulating the glucose metabolism Peanut skin procyanidins (PSP) have shown a potential for lowering blood glucose; however, the underlying mechanism through which PSP regulate glucose metabolism remains unknown. In the current study, we investigated the effect of PSP on intestinal glucose transporters and serum metabolites using a mouse model of diabetic mice. Results showed that PSP improved glucose tolerance and systemic insulin sensitivity, which coincided with decreased expression of sodium-glucose cotransporter 1 and glucose transporter 2 in the intestinal epithelium induced by an activation of the phospholipase C β2/protein kinase C signaling pathway. Moreover, untargeted metabolomic analysis of serum samples revealed that PSP altered arachidonic acid, sphingolipid, glycerophospholipid, bile acids, and arginine metabolic pathways. The study provides new insight into the anti-diabetic mechanism of PSP and a basis for further research.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

Phenylacetylglutamine as a novel biomarker of type 2 diabetes with distal symmetric polyneuropathy by metabolomics

PURPOSE

Type 2 diabetes mellitus (T2DM) with distal symmetric polyneuropathy (DSPN) is a disease involving the nervous system caused by metabolic disorder, while the metabolic spectrum and key metabolites remain poorly defined.

METHODS

Plasma samples of 30 healthy controls, 30 T2DM patients, and 60 DSPN patients were subjected to nontargeted metabolomics. Potential biomarkers of DSPN were screened based on univariate and multivariate statistical analyses, ROC curve analysis, and logistic regression. Finally, another 22 patients with T2DM who developed DSPN after follow-up were selected for validation of the new biomarker based on target metabolomics.

RESULTS

Compared with the control group and the T2DM group, 6 metabolites showed differences in the DSPN group (P < 0.05; FDR < 0.1; VIP > 1) and a rising step trend was observed. Among them, phenylacetylglutamine (PAG) and sorbitol displayed an excellent discriminatory ability and associated with disease severity. The verification results demonstrated that when T2DM progressed to DSPN, the phenylacetylglutamine content increased significantly (P = 0.004).

CONCLUSION

The discovered and verified endogenous metabolite PAG may be a novel potential biomarker of DSPN and involved in the disease pathogenesis.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

Effect of Acylated and Nonacylated Anthocyanins on Urine Metabolic Profile during the Development of Type 2 Diabetes in Zucker Diabetic Fatty Rats

The effect of nonacylated and acylated anthocyanins on urinary metabolites in diabetic rats was investigated. Nonacylated anthocyanins extract from bilberries (NAAB) or acylated anthocyanins extract from purple potatoes (AAPP) was given to Zucker diabetic fatty (ZDF) rats for 8 weeks at daily doses of 25 and 50 mg/kg body weight. ¹H NMR metabolomics was applied to study alterations in urinary metabolites from three time points (weeks 1, 4, and 8). Both types of anthocyanins modulated the metabolites associated with the tricarboxylic acid cycle, gut microbiota metabolism, and renal function at weeks 1 and 4, such as 2-oxoglutarate, fumarate, alanine, trigonelline, and hippurate. In addition, only a high dose of AAPP decreased monosaccharides, formate, lactate, and glucose levels at week 4, suggesting improvement in energy production in mitochondria, glucose homeostasis, and oxidative stress. This study suggested different impacts of AAPP and NAAB on the metabolic profile of urine in diabetes.

Candidate metabolite markers of peripheral neuropathy in Chinese patients with type 2 diabetes

OBJECTIVES

To analyze the serum and urine metabolites present in type 2 diabetes mellitus (T2DM) patients and T2DM patients with diabetic peripheral neuropathy (DPN) and to select differentially expressed biomarkers for early diagnosis of DPN.

METHODS

Serum and urine metabolites from 74 T2DM patients with peripheral neuropathy and 41 without peripheral neuropathy were analyzed using gas chromatograph system with time-of-flight mass spectrometer metabolomics to detect biomarkers of peripheral neuropathy in T2DM.

RESULTS

There were increased serum triglycerides, alanine aminotransferase, and decreased C-peptide, and total cholesterol levels in T2DM patients with DPN compared to those without peripheral neuropathy. Metabolomic analysis revealed visible differences in metabolic characteristics between two groups, and overall 53 serum differential metabolites and 56 urine differential metabolites were identified with variable influence on projection (VIP) >1 and P<0.05. To further analyze the correlation between the identified metabolites and DPN, four serum metabolites and six urine metabolites were selected with VIP>2, and fold change (FC) >1, including serum β-alanine, caproic acid, β-alanine/L-aspartic acid, and L-arabinose/L-arabitol, and urine gluconic acid, erythritol, galactonic acid, guanidoacetic acid, cytidine, and aminoadipic acid. Furthermore, five serum biomarkers and six urine biomarkers were found to show significant changes (P<0.05, VIP>1, and FC>1) respectively in patients with mild, moderate, and severe DPN. In addition, we found that glyoxylate and dicarboxylate metabolism was a differential metabolic pathway not only between T2DM and DPN, but also among different degrees of DPN. The differential metabolites such as β-alanine and caproic acid are expected to be biomarkers for DPN patients, and the significant changes in glyoxylate and dicarboxylate metabolism may be related to the pathogenesis of DPN.

CONCLUSION

There were serum and urine spectrum metabolomic differences in patients with DPN, which could serve as biomarkers for T2DM and DPN patients.

Pathological and protective roles of dendritic cells in Mycobacterium tuberculosis infection: Interaction between host immune responses and pathogen evasion

Dendritic cells (DCs) are principal defense components that play multifactorial roles in translating innate immune responses to adaptive immunity in Mycobacterium tuberculosis (Mtb) infections. The heterogeneous nature of DC subsets follows their altered functions by interacting with other immune cells, Mtb, and its products, enhancing host defense mechanisms or facilitating pathogen evasion. Thus, a better understanding of the immune responses initiated, promoted, and amplified or inhibited by DCs in Mtb infection is an essential step in developing anti-tuberculosis (TB) control measures, such as host-directed adjunctive therapy and anti-TB vaccines. This review summarizes the recent advances in salient DC subsets, including their phenotypic classification, cytokine profiles, functional alterations according to disease stages and environments, and consequent TB outcomes. A comprehensive overview of the role of DCs from various perspectives enables a deeper understanding of TB pathogenesis and could be useful in developing DC-based vaccines and immunotherapies.

Microbiome risk profiles as biomarkers for inflammatory and metabolic disorders

The intestine harbours a complex array of microorganisms collectively known as the gut microbiota. The past two decades have witnessed increasing interest in studying the gut microbiota in health and disease, largely driven by rapid innovation in high-throughput multi-omics technologies. As a result, microbial dysbiosis has been linked to many human pathologies, including type 2 diabetes mellitus and inflammatory bowel disease. Integrated analyses of multi-omics data, including metagenomics and metabolomics along with measurements of host response and cataloguing of bacterial isolates, have identified many bacteria and bacterial products that are correlated with disease. Nevertheless, insight into the mechanisms through which microbes affect intestinal health requires going beyond correlation to causation. Current understanding of the contribution of the gut microbiota to disease causality remains limited, largely owing to the heterogeneity of microbial community structures, interindividual differences in disease evolution and incomplete understanding of the mechanisms that integrate microbiota-derived signals into host signalling pathways. In this Review, we provide a broad insight into the microbiome signatures linked to inflammatory and metabolic disorders, discuss outstanding challenges in this field and propose applications of multi-omics technologies that could lead to an improved mechanistic understanding of microorganism-host interactions.

Human disease biomarker panels through systems biology

As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient's biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.

The effects of high-fat diet and metformin on urinary metabolites in diabetes and prediabetes rat models

High-fat diet (HFD) interferes with the dietary plan of patients with type 2 diabetes mellitus (T2DM). However, many diabetes patients consume food with higher fat content for a better taste bud experience. In this study, we examined the effect of HFD on rats at the early onset of diabetes and prediabetes by supplementing their feed with palm olein oil to provide a fat content representing 39% of total calorie intake. Urinary profile generated from liquid chromatography-mass spectrometry analysis was used to construct the orthogonal partial least squares discriminant analysis (OPLS-DA) score plots. The data provide insights into the physiological state of an organism. Healthy rats fed with normal chow (NC) and HFD cannot be distinguished by their urinary metabolite profiles, whereas diabetic and prediabetic rats showed a clear separation in OPLS-DA profile between the two diets, indicating a change in their physiological state. Metformin treatment altered the metabolomics profiles of diabetic rats and lowered their blood sugar levels. For prediabetic rats, metformin treatment on both NC- and HFD-fed rats not only reduced their blood sugar levels to normal but also altered the urinary metabolite profile to be more like healthy rats. The use of metformin is therefore beneficial at the prediabetes stage.

The role of metabolomics in personalized medicine for diabetes

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

Antidiabetic Effect of Noodles Containing Fermented Lettuce Extracts

The aim of the current study was to examine the antidiabetic effect of noodle containing fermented lettuce extract (FLE) on diabetic mice as a pre-clinical study. The γ-aminobutyric acid (GABA) content, antioxidant capacity, and total polyphenol content of the FLE noodles were analyzed and compared with those of standard noodles. In addition, oral glucose and sucrose tolerance, and fasting blood glucose tests were performed using a high-fat diet/streptozotocin-mediated diabetic mouse model. Serum metabolite profiling of mice feed standard or FLE noodles was performed using gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) to understand the mechanism changes induced by the FLE noodles. The GABA content, total polyphenols, and antioxidant activity were high in FLE noodles compared with those in the standard noodles. In vivo experiments also showed that mice fed FLE noodles had lower blood glucose levels and insulin resistance than those fed standard noodles. Moreover, glycolysis, purine metabolism, and amino acid metabolism were altered by FLE as determined by GC-TOF-MS-based metabolomics. These results demonstrate that FLE noodles possess significant antidiabetic activity, suggesting the applicability of fermented lettuce extract as a potential food additive for diabetic food products.

Non-invasive metabolic biomarkers for early diagnosis of diabetic nephropathy: Meta-analysis of profiling metabolomics studies

AIM

Diabetic nephropathy (DN) is one of the worst complications of diabetes. Despite a growing number of DN metabolite profiling studies, most studies are suffering from inconsistency in their findings. The main goal of this meta-analysis was to reach to a consensus panel of significantly dysregulated metabolites as potential biomarkers in DN.

DATA SYNTHESIS

To identify the significant dysregulated metabolites, meta-analysis was performed by "vote-counting rank" and "robust rank aggregation" strategies. Bioinformatics analyses were performed to identify the most affected genes and pathways. Among 44 selected studies consisting of 98 metabolite profiles, 17 metabolites (9 up-regulated and 8 down-regulated metabolites), were identified as significant ones by both the meta-analysis strategies (p-value<0.05 and OR>2 or <0.5) and selected as DN metabolite meta-signature. Furthermore, enrichment analyses confirmed the involvement of various effective biological pathways in DN pathogenesis, such as urea cycle, TCA cycle, glycolysis, and amino acid metabolisms. Finally, by performing a meta-analysis over existing time-course studies in DN, the results indicated that lactic acid, hippuric acid, allantoin (in urine), and glutamine (in blood), are the topmost non-invasive early diagnostic biomarkers.

CONCLUSION

The identified metabolites are potentially involved in diabetic nephropathy pathogenesis and could be considered as biomarkers or drug targets in the disease.

PROSPERO REGISTRATION NUMBER

CRD42020197697.

The clinical impact of maternal weight on offspring health: lights and shadows in breast milk metabolome

INTRODUCTION

Pre-pregnancy overweight and obesity, depending on maternal nutrition and metabolic state, can influence fetal, neonatal and long-term offspring health, regarding cardio-metabolic, respiratory, immunological and cognitive outcomes. Thus, maternal weight can act, through mechanisms that are not full understood, on the physiology and metabolism of some fetal organs and tissues, to adapt themselves to the intrauterine environment and nutritional reserves. These effects could occur by modulating gene expression, neonatal microbiome, and through breastfeeding.

AREAS COVERED

In this paper, we investigated the potential effects of metabolites found altered in breast milk (BM) of overweight/obese mothers, through an extensive review of metabolomics studies, and the potential short- and long-term clinical effects in the offspring, especially regarding overweight, glucose homeostasis, insulin resistance, oxidative stress, infections, immune processes, and neurodevelopment.

EXPERT OPINION

Metabolomics seems the ideal tool to investigate BM variation depending on maternal or fetal/neonatal factors. In particular, BM metabolome alterations according to maternal conditions were recently pointed out in cases of gestational diabetes, preeclampsia, intrauterine growth restriction and maternal overweight/obesity. In our opinion, even if BM is the food of choice in neonatal nutrition, the deepest comprehension of its composition in overweight/obese mothers could allow targeted supplementation, to improve offspring health and metabolic homeostasis.

Metabolomic profiling to reveal the therapeutic potency of Posidonia oceanica nanoparticles in diabetic rats

Posidonia oceanica is a sea grass belonging to the family Posidoniaceae, which stands out as a substantial reservoir of bioactive compounds. In this study, the secondary metabolites of the P. oceanica rhizome were annotated using UPLC-HRESI-MS/MS, revealing 86 compounds including simple phenolic acids, flavonoids, and their sulphated conjugates. Moreover, the P. oceanica butanol extract exhibited substantial antioxidant and antidiabetic effects in vitro. Thus, a reliable, robust drug delivery system was developed through the encapsulation of P. oceanica extract in gelatin nanoparticles to protect active constituents, control their release and enhance their therapeutic activity. To confirm these achievements, untargeted GC-MS metabolomics analysis together with biochemical evaluation was employed to investigate the in vivo anti-diabetic potential of the P. oceanica nano-extract. The results of this study demonstrated that the P. oceanica gelatin nanoparticle formulation reduced the serum fasting blood glucose level significantly (p < 0.05) in addition to improving the insulin level, together with the elevation of glucose transporter 4 levels. Besides, multivariate/univariate analyses of the GC-MS metabolomic dataset revealed several dysregulated metabolites in diabetic rats, which were restored to normalized levels after treatment with the P. oceanica gelatin nanoparticle formulation. These metabolites mainly originate from the metabolism of amino acids, fatty acids and carbohydrates, indicating that this type of delivery was more effective than the plain extract in regulating these altered metabolic processes. Overall, this study provides novel insight for the potential of P. oceanica butanol extract encapsulated in gelatin nanoparticles as a promising and effective antidiabetic therapy.

The potential of P. oceanica butanol extract encapsulated in gelatin nanoparticles as a promising and effective antidiabetic therapy has been investigated via metabolomics.

Tracking the carbons supplying gluconeogenesis

As the burden of type 2 diabetes mellitus (T2DM) grows in the 21st century, the need to understand glucose metabolism heightens. Increased gluconeogenesis is a major contributor to the hyperglycemia seen in T2DM. Isotope tracer experiments in humans and animals over several decades have offered insights into gluconeogenesis under euglycemic and diabetic conditions. This review focuses on the current understanding of carbon flux in gluconeogenesis, including substrate contribution of various gluconeogenic precursors to glucose production. Alterations of gluconeogenic metabolites and fluxes in T2DM are discussed. We also highlight ongoing knowledge gaps in the literature that require further investigation. A comprehensive analysis of gluconeogenesis may enable a better understanding of T2DM pathophysiology and identification of novel targets for treating hyperglycemia.

Metabolic Profiles in Madin-Darby Canine Kidney Cell Lines Infected with H3N2 Canine Influenza Viruses

Virus replication and host cell growth require host cell metabolic networks to provide energy and precursors for the synthesis of macromolecules. The aim of this study was to investigate the most direct changes in energy metabolism and small-molecule metabolism of Madin-Darby canine kidney (MDCK) cells infected with H3N2 canine influenza virus (CIV) and to determine whether small metabolites contribute to the pathogenesis of CIV. To study the metabolomics of MDCK cells infected with H3N2 CIV, we used liquid chromatography-tandem mass spectrometry combined with multivariate statistical analysis. The results showed that 798 positive ions were detected, among which 33 were upregulated and 11 were downregulated, and 406 negative ions were detected, among which 33 were upregulated and 9 were downregulated. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, we found that these differentially expressed molecules were mainly concentrated in the steroid hormone biosynthesis, amino sugar and nucleotide sugar metabolism, sphingolipid metabolism, vitamin B6 metabolism, cysteine and methionine metabolism, vitamin digestion and absorption, arginine and proline metabolism, biosynthesis of amino acids, and folate biosynthesis metabolic pathways. These pathways are involved in energy metabolism and nucleic acid and protein synthesis, which are essential for virus replication. Our experimental data suggest that H3N2 CIV infection reconstitutes/influences cellular metabolic processes, which in turn may contribute to viral replication. These findings are important for the development of enzyme inhibitors or metabolites for the identification of antiviral drugs. In addition, understanding the metabolic interaction between CIV and host cells is also very important for the complex pathogenicity of CIV, providing certain guidance for the treatment of canine influenza.

The Human Breast Milk Metabolome in Overweight and Obese Mothers

Pre-pregnancy body mass index (BMI) is a major relevance factor, since maternal overweight and obesity can impair the pregnancy outcome and represent risk factors for several neonatal, childhood, and adult conditions, including excessive weight gain, cardiovascular disease, diabetes mellitus, and even behavioral disorders. Currently, breast milk (BM) composition in such category of mothers was not completely defined. In this field, metabolomics represents the ideal technology, able to detect the whole profile of low molecular weight molecules in BM. Limited information is available on human BM metabolites differences in overweight or obese compared to lean mothers. Analyzing all the metabolomics studies published on Medline in English language, this review evaluated the effects that 8 specific types of metabolites found altered by maternal overweight and obesity (nucleotide derivatives, 5-methylthioadenosine, sugar-alcohols, acylcarnitine and amino acids, polyamines, mono-and oligosaccharides, lipids) can exert on the risk of offspring obesity development and other potentially associated health outcomes and complications. However, metabolites variations in samples collected from overweight and obese mothers and the potentially correlated effects highlighted below still need further investigations and should be confirmed in future metabolomics studies on larger samples. Finally, the positive or negative influence of maternal overweight and obesity on the offspring, potentially exerted by breastfeeding, should be analyzed in close correlation with maternal age, genetic and environmental factors, including diet, and taking into account the interactions occurring between BM metabolites and lactobiome. The evaluation of all the factors affecting BM metabolites in overweight and obese mothers can lead to the comprehensive description of such biofluid and the related effects on breastfed subjects, potentially highlighting personalized needs of BM supplementation or short- and long-term prevention strategies to optimize offspring health.

Serum and urine metabolomics reveal potential biomarkers of T2DM patients with nephropathy

BACKGROUND

Diabetes is a metabolic disease and is often accompanied by severe microvascular and macrovascular complications. A comprehensive understanding of its complex mechanisms can help prevent type 2 diabetes mellitus (T2DM) complications, such as diabetic nephropathy (DN).

METHODS

To reveal the systemic metabolic changes related to renal injury, clinical information of T2DM patients with or without nephropathy was collected, and it was found that serum urea levels of DN patients were significantly higher in T2DM patients without nephropathy. Further along the disease progression, the serum urea levels also gradually increased. We used gas chromatograph coupled with time-of-flight mass spectrometry (GC-TOFMS) metabolomics to analyze the serum and urine metabolites of T2DM patients with or without nephropathy to study the metabolic changes associated with the disease.

RESULTS

Finally, we identified 61 serum metabolites and 46 urine metabolites as potential biomarkers related to DN (P<0.05, VIP >1). In order to determine which metabolic pathways were major altered in DN, we summarized pathway analysis based on P values from their impact values and enrichment. There were 9 serum metabolic pathways and 12 urine metabolic pathways with significant differences in serum and urine metabolism, respectively.

CONCLUSIONS

This study emphasizes that GC-TOFMS-based metabolomics provides insight into the potential pathways in the pathogenesis and progression of DN.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Omics-based biomarkers in the diagnosis of diabetes

Diabetes mellitus (DM) is a group of metabolic diseases related to the dysfunction of insulin, causing hyperglycaemia and life-threatening complications. Current early screening and diagnostic tests for DM are based on changes in glucose levels and autoantibody detection. This review evaluates recent studies on biomarker candidates in diagnosing type 1, type 2 and gestational DM based on omics classification, whilst highlighting the relationship of these biomarkers with the development of diabetes, diagnostic accuracy, challenges and future prospects. In addition, it also focuses on possible non-invasive biomarker candidates besides common blood biomarkers.

Analysis of plasma metabolic profiling and evaluation of the effect of the intake of Angelica keiskei using metabolomics and lipidomics

ETHNOPHARMACOLOGICAL RELEVANCE

Angelica keiskei contains many bioactive components with anti-oxidative and anti-inflammatory effects. It is also effective for the treatment of diabetes mellitus, hypertension, and arteriosclerosis, but the relationships between these effects and the active components in the herb have not been studied.

AIM OF THE STUDY

We aimed to confirm the effects of Angelica keiskei on humans.

MATERIALS AND METHODS

A metabolomics and lipidomics study was performed using human plasma samples from 20 subjects after the intake of Angelica keiskei, and the components of Angelica keiskei in the plasma were profiled. UPLC-Orbitrap-MS was used to analyze the plasma and plant extracts, and multivariate analysis and correlation studies between the exogenous components from plant and endogenous metabolite in plasma were performed.

RESULTS

The levels of the 14 metabolites including kynurenic acid, prostaglandin E1, chenodeoxycholic acid, lysoPC (18:1), lysoPC (18:2), lysoPC (20:3), lysoPC (20:4), lysoPC (22:6), PC (34:1), PC (34:2), PC (38:3), PC (38:4), PC (38:6) and PC (40:7) in the plasma were changed. By monitoring the components originating from Angelica keiskei in plasma, five components including 5-methoxypsoralen, 8-methoxypsoralen, 4-hydroxyderricin, xanthoangelol B and xanthoangelol F were detected and they reduced the levels of bile acids and fatty acids.

CONCLUSIONS

The levels of the metabolites, including bile acids, amino acids, glycerophospholipids and fatty acids, in the plasma were changed, and 14 significantly changed metabolites were closely related to the preventive effect against liver diseases, type 2 diabetes, anemia, obesity, atherosclerosis, depression and anti-inflammatory effects. The five components of Angelica keiskei were related the modulatory activity of reducing the levels of bile acids and fatty acids.

Intra- and inter-individual metabolic profiling highlights carnitine and lysophosphatidylcholine pathways as key molecular defects in type 2 diabetes

Type 2 diabetes (T2D) mellitus is a complex metabolic disease commonly caused by insulin resistance in several tissues. We performed a matched two-dimensional metabolic screening in tissue samples from 43 multi-organ donors. The intra-individual analysis was assessed across five key metabolic tissues (serum, visceral adipose tissue, liver, pancreatic islets and skeletal muscle), and the inter-individual across three different groups reflecting T2D progression. We identified 92 metabolites differing significantly between non-diabetes and T2D subjects. In diabetes cases, carnitines were significantly higher in liver, while lysophosphatidylcholines were significantly lower in muscle and serum. We tracked the primary tissue of origin for multiple metabolites whose alterations were reflected in serum. An investigation of three major stages spanning from controls, to pre-diabetes and to overt T2D indicated that a subset of lysophosphatidylcholines was significantly lower in the muscle of pre-diabetes subjects. Moreover, glycodeoxycholic acid was significantly higher in liver of pre-diabetes subjects while additional increase in T2D was insignificant. We confirmed many previously reported findings and substantially expanded on them with altered markers for early and overt T2D. Overall, the analysis of this unique dataset can increase the understanding of the metabolic interplay between organs in the development of T2D.

Maternal obesity and the human milk metabolome: associations with infant body composition and postnatal weight gain

BACKGROUND

Maternal obesity is a risk factor for childhood obesity; this is a major public health concern given that ∼40% of pregnant women are either overweight or obese. Whether differences in milk composition in lean compared with obese women contribute to childhood obesity is unclear.

OBJECTIVES

We aimed to analyze relationships between maternal obesity and human milk metabolites, infant body composition, and postnatal weight gain.

METHODS

This was a prospective study in which mothers intending to breastfeed exclusively, and their newborn infants, were enrolled at delivery (n = 35 mother-infant pairs). We excluded mothers with diabetes, other medical conditions, or pregnancy complications. Participants were grouped by maternal prepregnancy BMI <25 (lean) or ≥25 kg/m2 (overweight/obese). We analyzed infant body composition by dual-energy X-ray absorptiometry and used untargeted liquid chromatography-gas chromatography-mass spectrometry to measure the milk content of 275 metabolites at 1 and 6 mo postpartum.

RESULTS

At 1 mo postpartum, 10 metabolites differed between overweight/obese and lean groups with nominal P < 0.05, but none was altered with a false discovery rate <0.25. Many differentially abundant metabolites belonged to the same chemical class; e.g., 4/10 metabolites were nucleotide derivatives, and 3/10 were human milk oligosaccharides. Milk adenine correlated positively with both continuously distributed maternal BMI and with infant adiposity and fat accrual. Analysis of milk composition at 6 mo postpartum revealed 20 differentially abundant metabolites (P < 0.05) in overweight/obese compared with lean women, including 6 metabolites with a false discovery rate of <0.25. At both 1 and 6 mo, human milk abundance of 1,5-anhydroglucitol, which has not previously been described in milk, was positively associated with maternal BMI.

CONCLUSIONS

Maternal obesity is associated with changes in the human milk metabolome. While only a subset of metabolites correlated with both maternal and infant weight, these point to potential milk-dependent mechanisms for mother-child transmission of obesity. This trial was registered at www.clinicaltrials.gov as NCT02535637.

Metabolic signatures suggest o-phosphocholine to UDP-N-acetylglucosamine ratio as a potential biomarker for high-glucose and/or palmitate exposure in pancreatic β-cells

INTRODUCTION

Chronic exposure to high-glucose and free fatty acids (FFA) alone/or in combination; and the resulting gluco-, lipo- and glucolipo-toxic conditions, respectively, have been known to induce dysfunction and apoptosis of β-cells in Diabetes. The molecular mechanisms and the development of biomarkers that can be used to predict similarities and differences behind these conditions would help in easier and earlier diagnosis of Diabetes.

OBJECTIVES

This study aims to use metabolomics to gain insight into the mechanisms by which β-cells respond to excess-nutrient stress and identify associated biomarkers.

METHODS

INS-1E cells were cultured in high-glucose, palmitate alone/or in combination for 24 h to mimic gluco-, lipo- and glucolipo-toxic conditions, respectively. Biochemical and cellular experiments were performed to confirm the establishment of these conditions. To gain molecular insights, abundant metabolites were identified and quantified using ¹H-NMR.

RESULTS

No loss of cellular viability was observed in high-glucose while exposure to FFA alone/in combination with high-glucose was associated with increased ROS levels, membrane damage, lipid accumulation, and DNA double-strand breaks. Forty-nine abundant metabolites were identified and quantified using ¹H-NMR. Chemometric pair-wise analysis in glucotoxic and lipotoxic conditions, when compared with glucolipotoxic conditions, revealed partial overlap in the dysregulated metabolites; however, the dysregulation was more significant under glucolipotoxic conditions.

CONCLUSION

The current study compared gluco-, lipo- and glucolipotoxic conditions in parallel and elucidated differences in metabolic pathways that play major roles in Diabetes. o-phosphocholine and UDP-N-acetylglucosamine were identified as common dysregulated metabolites and their ratio was proposed as a potential biomarker for these conditions.

An untargeted metabolomic strategy based on liquid chromatography-mass spectrometry to study high glucose-induced changes in HK-2 cells

Diabetes mellitus is a major health concern nowadays. It is estimated that 40% of diabetics are affected by diabetic nephropathy, one of the complications derived from high glucose blood levels which can lead to chronic loss of kidney function. It is now clear that the renal proximal tubule plays a critical role in the progression of diabetic nephropathy but research focused on studying the molecular mechanisms involved is still needed. The aim of this work was to develop a liquid chromatography-mass spectrometry platform to carry out, for the first time, the untargeted metabolomic analysis of high glucose-induced changes in cultured human proximal tubular HK-2 cells. In order to find the metabolites which were affected by high glucose and to expand the metabolite coverage, intra- and extracellular fluid from HK-2 cells exposed to high glucose (25 mM), normal glucose (5.5 mM) or osmotic control (5.5 mM glucose +19.5 mM mannitol) were analyzed by two complementary chromatographic modes: hydrophilic interaction and reversed-phase liquid chromatography. Non-supervised principal components analysis showed a good separation among the three groups of samples. Statistically significant variables were chosen for further metabolite identification. Different metabolic pathways were affected mainly those derived from amino acidic, polyol, and nitrogenous bases metabolism.

An untargeted metabolomics approach reveals further insights of Lycium barbarum polysaccharides in high fat diet and streptozotocin-induced diabetic rats

Lycium barbarum polysaccharide (LBP), as one bioactive macromolecular abstracted from goji berry, has shown an abundance of potential function. The present study aimed to evaluate the metabolic effects of LBP on the urine and liver metabolomics on a high-fat diet and streptozotocin-induced diabetic rat model. After 8 weeks of high-fat diet and streptozotocin induction of diabetes, 24 diabetic rats were randomly allocated to the diabetic control (DC) group, LBP low, moderate, and high dosage (LBP-L, LBP-M, LBP-H) groups and 6 non-diabetic rats were established as the non-diabetic control (NDC) group for 30 days' intervention. Metabolomics was performed on liver and urine. LBP positively regulated fasting blood glucose, hemoglobin-A1c, homeostasis model assessment for insulin resistance, liver glycogen and SOD levels significantly, as compared to the DC group. Liver metabolomics showed higher levels of myo-inositol and lower levels of L-malic acid, fumaric acid, D-arabitol, L-allothreonine 1, xylitol, O-phosphorylethanolamine, ribitol, 5-methoxytryptamine 2 and digitoxose 2 in the LBP-H group vs. the DC group, which indicates that LBP may regulate the citrate cycle, alanine, aspartate and glutamate metabolism, glyoxylate and dicarboxylate metabolism. Urine metabolomics showed increased levels of creatinine, D-galacturonic acid 2, 2,3-dihydroxybutyric acid and citric acid, and decreased levels of methylmalonic acid, benzoic acid and xylitol between the LBP-H and DC groups. The present study exhibited the effects of LBP on the urine and liver metabolomics in a high-fat diet and streptozotocin-induced rat model, which not only provides a better understanding of the anti-diabetic effects of LBP but also supplies a useful database for further specific mechanism study.

Microbiome Alteration in Type 2 Diabetes Mellitus Model of Zebrafish

Understanding the gut microbiota in metabolic disorders, including type 2 diabetes mellitus (T2DM), is now gaining importance due to its potential role in disease risk and progression. We previously established a zebrafish model of T2DM, which shows glucose intolerance with insulin resistance and responds to anti-diabetic drugs. In this study, we analysed the gut microbiota of T2DM zebrafish by deep sequencing the 16S rRNA V3-V4 hypervariable regions, and imputed a functional profile using predictive metagenomic tools. While control and T2DM zebrafish were fed with the same kind of feed, the gut microbiota in T2DM group was less diverse than that of the control. Predictive metagenomics profiling using PICRUSt revealed functional alternation of the KEGG pathways in T2DM zebrafish. Several amino acid metabolism pathways (arginine, proline, and phenylalanine) were downregulated in the T2DM group, similar to what has been previously reported in humans. In summary, we profiled the gut microbiome in T2DM zebrafish, which revealed functional similarities in gut bacterial environments between these zebrafish and T2DM affected humans. T2DM zebrafish can become an alternative model organism to study host-bacterial interactions in human obesity and related diseases.

Clopidogrel utilization in patients with coronary artery disease and diabetes mellitus: should we determine CYP2C19*2 genotype?

PURPOSE

Clopidogrel non-responsiveness is multifactorial; several genetic and non-genetic factors may contribute to impaired platelet inhibition. The goal of this study is to determine the effect of the cytochrome P450 CYP2C19*2 polymorphism on the platelet response to clopidogrel in patients with and without diabetes mellitus (DM).

METHODS

We conducted an observational study in patients with coronary artery disease and consequent exposure to clopidogrel therapy (75 mg/day for at least 7 consecutive days). We have analyzed two groups of patients: group I (DM patients) and group II (non-diabetes mellitus patients). Platelet reactivity was assessed by the VerifyNow P2Y12 assay and high on clopidogrel platelet reactivity (HPR) was defined as P2Y12 reaction units (PRU) ≥ 208. Genotyping for CYP2C19*2 polymorphism was performed by PCR-RFLP.

RESULTS

We have included 150 subjects (76 DM and 74 non-diabetes mellitus patients). The carriage of CYP2C19*2 allele, in DM patients, was significantly associated to HPR (odds ratio (OR) 4.437, 95% confidence interval (CI) 1.134 to 17.359; p = 0.032). Furthermore, 8.4% of the variability in percent inhibition by clopidogrel could be attributed to CYP2C19*2 carrier status. However, in non-diabetes mellitus patients, there was no significant difference in platelet response to clopidogrel according to the presence or absence of CYP2C19*2 allele carriage (OR 1.260, 95% CI 0.288 to 5.522; p = 0.759).

CONCLUSIONS

Our study suggests that the carriage of CYP2C19*2 polymorphism, in DM patients, might be a potential predictor of persisting HPR in these high-risk individuals.

TRIAL REGISTRATION

Clinical Trials.gov NCT03373552 (Registered 13 December 2017).