Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits

Investigating the mechanism of cornel iridoid glycosides on type 2 diabetes mellitus using serum and urine metabolites in rats

ETHNOPHARMACOLOGICAL RELEVANCE

Cornel iridoid glycosides (CIG) are extracted from Corni fructus, a herbal medicine used in traditional Chinese medicine to treat diabetes. However, the antidiabetic effects of CIG and the underlying metabolic mechanisms require further exploration.

AIM OF THE STUDY

This study aimed to assess the antidiabetic effects and metabolic mechanism of CIG by performing metabolomic analyses of serum and urine samples of rats.

MATERIALS AND METHODS

A rat model of type 2 diabetes mellitus (T2DM) was established by administering a low dose of streptozotocin (30 mg/kg) intraperitoneally after 4 weeks of feeding a high-fat diet. The model was evaluated based on several parameters, including fasting blood glucose (FBG), random blood glucose (RBG), urine volume, liver index, body weight, histopathological sections, and serum biochemical parameters. Subsequently, serum and urine metabolomics were analyzed using ultra-high-pressure liquid chromatography coupled with linear ion trap-Orbitrap tandem mass spectrometry (UHPLC-LTQ-Orbitrap-MS). Data were analyzed using unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA). Differential metabolites were examined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways to explore the underlying mechanisms.

RESULTS

After 4 weeks of treatment with different doses of CIG, varying degrees of antidiabetic effects were observed, along with reduced liver and pancreatic injury, and improved oxidative stress levels. Compared with the T2DM group, 19 and 23 differential metabolites were detected in the serum and urine of the CIG treatment group, respectively. The key metabolites involved in pathway regulation include taurine, chenodeoxycholic acid, glycocholic acid, and L-tyrosine in the serum and glycine, hippuric acid, phenylacetylglycine, citric acid, and D-glucuronic acid in the urine, which are related to lipid, amino acid, energy, and carbohydrate metabolism.

CONCLUSIONS

This study confirmed the antidiabetic effects of CIG and revealed that CIG effectively controlled metabolic disorders in T2DM rats. This seems to be meaningful for the clinical application of CIG, and can benefit further studies on CIG mechanism.

Serum metabolism characteristics of patients with myocardial injury after noncardiac surgery explored by the untargeted metabolomics approach

BACKGROUND

Myocardial injury after noncardiac surgery (MINS) is one of the most common complications associated with postoperative adverse cardiovascular outcomes and mortality. However, MINS often fails to be timely diagnosed due to the absence of clinical symptoms and limited diagnostic methods. The metabolomic analysis might be an efficient way to discover new biomarkers of MINS. Characterizing the metabolomic features of MINS patients may provide new insight into the diagnosis of MINS.

METHODS

In this study, serum samples from 20 matched patients with or without MINS (n = 10 per group) were subjected to untargeted metabolomics analysis to investigate comprehensive metabolic information. Differential metabolites were identified, and the enriched metabolic pathway was determined based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

RESULTS

A comprehensive analysis revealed 124 distinct metabolites, predominantly encompassing lipids, amino acids and other compounds. The observed modifications in metabolic pathways in patients with or without MINS showed significant clustering in cholesterol metabolism, aldosterone synthesis and secretion, primary bile acid biosynthesis, as well as cysteine and methionine metabolism. Four specific metabolites (taurocholic acid, L-pyroglutamic acid, taurochenodeoxycholic acid, and pyridoxamine) exhibited promising potential as biomarkers for prognosticating MINS.

CONCLUSIONS

This study contributes valuable insights into the metabolomic features of MINS and the discovery of potential biomarkers which may help the early diagnosis of MINS. The identified metabolites and altered pathways offer valuable insights into the molecular underpinnings of MINS, paving the way for improved diagnostic approaches and potential intervention strategies.

Compound chenpi tea consumption reduces obesity-related metabolic disorders by modulating gut microbiota and serum metabolites in mice

BACKGROUND

Compound chenpi tea (CCT) is a popular herbal beverage made from citri reticulatae pericarpium, Ganoderma lucidum, and pu-erh tea. This study aimed to investigate the anti-obesity effect of CCT in mice fed a high-fat diet (HFD) and to explore the potential mediators by 16S ribosomal RNA sequencing and serum metabolomic analysis.

RESULTS

The results showed that CCT supplementation can effectively reduce diet-induced obesity, hepatic steatosis, dyslipidemia, and insulin resistance. Further analyses revealed that CCT could reverse the changes of 28 metabolites induced by HFD, mainly affecting, amino acid metabolism, gut microbiota metabolism, and glycerophospholipid metabolism. CCT could also alleviate the intestinal dysbiosis by decreasing the abundance of Romboutsia, Gemella, [Eubacterium]_fissicatena_group, and Faecalibaculum, and increasing the abundance of Oscillibacter, Blautia, and Acetatifactor.

CONCLUSION

Collectively, these findings demonstrated that CCT supplementation modulated gut microbiota and obesity-related metabolic disorders in mice. © 2023 Society of Chemical Industry.

Camel milk peptides alleviate hyperglycemia by regulating gut microbiota and metabolites in type 2 diabetic mice

This study aimed to investigate the hypoglycemic effect of Camel milk peptides (CMPs) on Type 2 diabetes mellitus (T2DM) mice and reveal its related mechanism from the aspect of gut microbiota and metabolites. The administering CMPs significantly alleviated the weight loss, polydipsia and polyphagia, reduced fasting blood glucose (FBG), improved insulin resistance and sensitivity, and restored the level of serum hormones, lipopolysaccharide (LPS), lipid metabolic and tissue damage. Furthermore, CMPs intervention remarkably reversed gut microbiota dysbiosis in T2DM mice by reducing the relative abundance of Proteobacteria, Allobaculum, Clostridium, Shigella and the Firmicutes/Bacteroidetes ratio, while increasing the relative abundance of Bacteroidetes and Blautia. Metabolomic analysis identified 84 different metabolites between T2DM and CMPs-treated groups, participating in three pathways of Pantothenate and CoA biosynthesis, Phenylalanine metabolism and Linoleic acid metabolism. Ureidopropionic acid, pantothenic acid, hippuric acid, hydrocinnamic acid and linoleic acid were identified as key acidic metabolites closely related to hypoglycemic effect. Correlation analysis indicated that CMPs might have a hypoglycemic effect through their impact on gut microbiota, leading to variations in short-chain fatty acids (SCFAs), acidic metabolites and metabolic pathways. These findings suggest that CMPs could be a beneficial nutritional supplement for intervention T2DM.

High-Performance Liquid Chromatography-Mass Spectrometry-based Metabolic Profiling of Adult Growth Hormone Deficiency

CONTEXT

Patients with adult growth hormone deficiency (AGHD) are at increased risk of metabolic syndrome. Metabolic profiles in AGHD patients have been insufficiently evaluated.

OBJECTIVE

This work aims to explore serum metabolite profiles by metabolomics analysis and assess potential metabolites associated with recombinant human growth hormone (rhGH) treatment.

METHODS

Thirty-one AGHD patients and 31 healthy controls were enrolled. Untargeted ultra-performance liquid chromatography-coupled mass spectroscopy was conducted in all patients and controls at baseline and during 12 months of rhGH treatment in 11 AGHD patients. Data were processed by principal component analysis, variable importance in projection scoring, orthogonal partial least squares-discriminant analysis, and MetaboAnalyst 5.0. We further explored the associations between metabolites and clinical parameters.

RESULTS

Metabolomics indicated a distinct metabolic pattern between AGHD patients and healthy controls. The perturbed pathways mainly include the biosynthesis of unsaturated fatty acids, sphingolipid metabolism, glycerophospholipid metabolism, fatty acid elongation, degradation, and biosynthesis. rhGH treatment increased the levels of specific glycerophospholipids compounds and reduced fatty acid ester compounds. Significant correlations existed between the 40 identified metabolites and insulin-like growth factor-1 SD score (IGF-1 SDS), body composition, and glucose and lipid metabolism plasma markers. During rhGH treatment, there was a statistically significant negative correlation between deoxycholic acid glycine conjugate and waist-to-hip ratio, while a statistically significant positive correlation existed between decanoylcarnitine and serum low-density lipoprotein levels.

CONCLUSION

AGHD patients have unique metabolomic profiles. rhGH treatment alters the serum levels of several fatty acid compounds/amino acids, which may contribute to the improvement of metabolic status in AGHD patients.

Metabolites Potentially Derived from Gut Microbiota Associated with Podocyte, Proximal Tubule, and Renal and Cerebrovascular Endothelial Damage in Early Diabetic Kidney Disease in T2DM Patients

Complications due to type 2 diabetes mellitus (T2DM) such as diabetic kidney disease (DKD) and cerebral small vessel disease (CSVD) have a powerful impact on mortality and morbidity. Our current diagnostic markers have become outdated as T2DM-related complications continue to develop. The aim of the investigation was to point out the relationship between previously selected metabolites which are potentially derived from gut microbiota and indicators of endothelial, proximal tubule (PT), and podocyte dysfunction, and neurosonological indices. The study participants were 20 healthy controls and 90 T2DM patients divided into three stages: normoalbuminuria, microalbuminuria, and macroalbuminuria. Serum and urine metabolites were determined by untargeted and targeted metabolomic techniques. The markers of endothelial, PT and podocyte dysfunction were assessed by ELISA technique, and the neurosonological indices were provided by an ultrasound device with high resolution (MYLAB 8-ESAOTE Italy). The descriptive statistical analysis was followed by univariable and multivariable linear regression analyses. In conclusion, in serum, arginine (sArg), butenoylcarnitine (sBCA), and indoxyl sulfate (sIS) expressed a biomarker potential in terms of renal endothelial dysfunction and carotid atherosclerosis, whereas sorbitol (sSorb) may be a potential biomarker of blood-brain barrier (BBB) dysfunction. In urine, BCA and IS were associated with markers of podocyte damage, whereas PCS correlated with markers of PT dysfunction.

Effects of Endurance Exercise and Vitamin D Supplementation on Insulin Resistance and Plasma Lipidome in Middle-Aged Adults with Type 2 Diabetes

(1) Background: We investigated the effects of a 12-week exercise intervention with or without vitamin D supplementation on insulin resistance and the plasma lipidome of participants with type 2 diabetes. We further explored whether the effects of the intervention on glycemic parameters could be associated with the baseline lipidome. (2) Methods: Sixty-one participants were randomly allocated to control (Con), exercise (EX), vitamin D (VD), and EX + VD groups. Multiple glycemic and anthropometric parameters were evaluated before and after intervention. The homeostasis model assessment of insulin resistance (HOMA-IR) was the primary outcome. The plasma lipidome was analyzed before, after, and at an additional 12-week follow-up. Machine learning was applied to establish prediction models for responsiveness of glycemic control. (3) Results: Our interventions failed to improve the HOMA-IR index while fasting glucose was reduced in the EX + VD group (change%, -11.9%; effect size, 0.65; p < 0.05). Both EX and VD interventions altered the plasma lipidome, with EX + VD intervention considerably affecting levels of lyso-phosphatidylcholines and triglycerols containing long-chain unsaturated fatty acids. Such effects could last until 12 weeks after intervention. Notably, there was high inter-individual variability in glycemic parameters including HOMA-IR in response to the interventions, which could be predicted with great accuracy using an optimal panel of baseline lipid predictors alone or in combination with clinical indices, as assessed by an area under the receiver operating characteristic curve value of over 0.9. (4) Conclusions: Although substantial alterations were observed in the plasma lipidome related to glycemic control, our intervention failed to improve HOMA-IR scores, which may have been predominately due to the large inter-individual variability in responses. Basal plasma lipid levels could potentially predict an individual's response to intervention, highlighting the necessity of personalized nutrition.

Derivatization of N-Acyl Glycines by 3-Nitrophenylhydrazine for Targeted Metabolomics Analysis and Their Application to the Study of Diabetes Progression in Mice

N-Acyl glycines (NAGlys) are an important class of metabolites in the detoxification system of the human body. They have been used in the diagnosis of several metabolic diseases. Liquid chromatography-mass spectrometry (LC-MS) is the most frequently used NAGlys detection platform. Here, we describe a simple and sensitive method of NAGlys detection by LC-MS in plasma and urine samples. This approach is based on the use of a derivatization reagent, 3-nitrophenylhydrazine. The reaction is quick in aqueous solution, and no quenching step is needed. To expand the coverage of NAGlys when standards are not available, NAGlys were first identified based on high-resolution LC-MS. Quantification was subsequently carried out on triple quadrupole LC-MS. This approach allowed a much broader measurement of NAGlys (41 NAGlys in total), especially when authentic standards are unavailable. Comprehensive analysis of NAGlys with this new method was applied in plasma and urine samples of db/db diabetic and non-diabetic db/m+ control mice. The majority of detected NAGlys were altered with high differentiation ability in plasma and urine samples from diabetic and non-diabetic mice. These identified NAGlys hold the potential to be diagnostic biomarkers for type II diabetes and diabetic complications.

Microbiome and Metabolome Variation as Indicator of Social Stress in Female Prairie Voles

Social isolation is detrimental to the health of social mammals inducing neurochemical and hormonal changes related to depression and anxiety, as well as impairments of cardiovascular and immune functioning. Likewise, perceptions of loneliness are increasingly recognized as detrimental to human psychological well-being, cognitive functioning, and physical health. Few studies, however, have examined the impact of social isolation on the intestinal microbiome and metabolome. To better understand the impact of social isolation on these systems, intestinal microbiota, and the systemic impact via the gut-brain axis, we employed prairie voles. Physiological stress on female prairie voles (n = 22) either with a same-sex sibling (n = 11) or in isolation (n = 11) for four weeks demonstrated behavioral indicators of increased anxiety and depression in isolated voles (p ≤ 0.01). Bacterial DNA from fecal and colon samples, collected at five time points (T0-4), were sequenced for all nine hypervariable regions of the 16S rRNA gene. Microbiome analyses revealed several differences in gut communities of paired and isolated voles with greater differences at T4. Notably, several taxa associated with host health including Anaerostipes and Lactobacillaceae were more prevalent in paired voles, whereas several taxa associated with known pathogens (e.g., Staphylococcaceae and Enterococcus) or disease were elevated in isolated animals. Similarly, metabolome analyses suggested isolated voles, when compared to paired animals, exhibited differences in metabolites associated with diabetes and colitis. These findings further contribute to our understanding of the harmful effects of social isolation, which cause perturbations in the gut microbiome and serum metabolites.

Metabolic remodeling of glycerophospholipids acts as a signature of dulaglutide and liraglutide treatment in recent-onset type 2 diabetes mellitus

Aims

As metabolic remodeling is a pathological characteristic in type 2 diabetes (T2D), we investigate the roles of newly developed long-acting glucagon-like peptide-1 receptor agonists (GLP-1RAs) such as dulaglutide and liraglutide on metabolic remodeling in patients with recent-onset T2D.

Methods

We recruited 52 cases of T2D and 28 control cases in this study. In the patient with T2D, 39 cases received treatment with dulaglutide and 13 cases received treatment with liraglutide. Using untargeted metabolomics analysis with broad-spectrum LC-MS, we tracked serum metabolic changes of the patients from the beginning to the end of follow-up (12th week).

Results

We identified 198 metabolites that were differentially expressed in the patients with T2D, compared to the control group, in which 23 metabolites were significantly associated with fasting plasma glucose. Compared to pre-treatment, a total of 46 and 45 differentially regulated metabolites were identified after treatments with dulaglutide and liraglutide, respectively, in which the most differentially regulated metabolites belong to glycerophospholipids. Furthermore, a longitudinal integration analysis concurrent with diabetes case-control status revealed that metabolic pathways, such as the insulin resistance pathway and type 2 diabetes mellitus, were enriched after dulaglutide and liraglutide treatments. Proteins such as GLP-1R, GNAS, and GCG were speculated as potential targets of dulaglutide and liraglutide.

Conclusions

In total, a metabolic change in lipids existed in the early stage of T2D was ameliorated after the treatments of GLP-1RAs. In addition to similar effects on improving glycemic control, remodeling of glycerophospholipid metabolism was identified as a signature of dulaglutide and liraglutide treatments.

Overall metabolic network analysis of urine in hyperlipidemic rats treated with Bidens bipinnata L

Hyperlipidemia has been highlighted as one of the most prominent and global chronic conditions nowadays. Bidens bipinnata L. (BBL), a folk medicine in contemporary China, has efficacy in the treatment of hyperlipidemia (HLP) in China. Although some physiological and pathological function parameters of hyperlipidemia have been investigated, little information about the changes in small metabolites in biofluids has been reported. In the present study, global metabolic profiling with high-performance liquid chromatography-linear ion trap/Orbitrap high-resolution mass spectrometry (HPLC-LTQ/Orbitrap MS) combined with a pattern recognition method was performed to discover the underlying lipid-regulating mechanisms of BBL on hyperlipidemic rats induced by high-fat diet (HFD). The total of four metabolites, up- or down-regulated (p < 0.05 or 0.01), were identified and contributed to the progression of hyperlipidemia. These promising identified biomarkers underpin the metabolic pathway, including glyoxylate and dicarboxylate metabolism, the TCA cycle, sphingolipid metabolism and purine metabolism. They are disturbed in hyperlipidemic rats, and are identified using pathway analysis with MetPA. The altered metabolite indices could be regulated closer to normal levels after BBL intervention. The results demonstrated that urinary metabolomics is a powerful tool in the clinical diagnosis and treatment of hyperlipidemia to provide information on changes in metabolite pathways.

Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era

The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way.

Plasma Metabolic Signatures of Healthy Overweight Subjects Challenged With an Oral Glucose Tolerance Test

Insulin secretion following ingestion of a carbohydrate load affects a multitude of metabolic pathways that simultaneously change direction and quantity of interorgan fluxes of sugars, lipids and amino acids. In the present study, we aimed at identifying markers associated with differential responses to an OGTT a population of healthy adults. By use of three metabolite profiling platforms, we assessed these postprandial responses of a total of 202 metabolites in plasma of 72 healthy volunteers undergoing comprehensive phenotyping and of which half enrolled into a weight-loss program over a three-month period. A standard oral glucose tolerance test (OGTT) served as dietary challenge test to identify changes in postprandial metabolite profiles. Despite classified as healthy according to WHO criteria, two discrete clusters (A and B) were identified based on the postprandial glucose profiles with a balanced distribution of volunteers based on gender and other measures. Cluster A individuals displayed 26% higher postprandial glucose levels, delayed glucose clearance and increased fasting plasma concentrations of more than 20 known biomarkers of insulin resistance and diabetes previously identified in large cohort studies. The volunteers identified by canonical postprandial responses that form cluster A may be called pre-pre-diabetics and defined as “at risk” for development of insulin resistance. Moreover, postprandial changes in selected fatty acids and complex lipids, bile acids, amino acids, acylcarnitines and sugars like mannose revealed marked differences in the responses seen in cluster A and cluster B individuals that sustained over the entire challenge test period of 240 min. Almost all metabolites, including glucose and insulin, returned to baseline values at the end of the test (at 240 min), except a variety of amino acids and here those that have been linked to diabetes development. Analysis of the corresponding metabolite profile in a fasting blood sample may therefore allow for early identification of these subjects at risk for insulin resistance without the need to undergo an OGTT.

Metabolome alterations in Clonorchis sinensis after treatment with tribendimidine and praziquante in vivo

Tribendimidine (TBD) is a broad-spectrum anthelmintic drug that is also significantly effective in treating clonorchiasis. In this study, the altered metabolomes of Clonorchis sinensis (C. sinensis) in rats after TBD administration were quantified by using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and gas chromatography-mass spectrometry (GC-MS) to explore the possible active sites of TBD against clonorchiasis through altered metabolites and metabolic pathway analysis, and the results are expected to provide a target for the future design of anti-Clonorchis sinensis drugs. The worm burden reduction rate and scanning electron microscopy demonstrated that praziquantel (PZQ, positive control drug) and TBD had significant effects on C. sinensis in rats after treatment at a single dose of 200 mg/kg for 24 h. For the MS-based metabolomic analysis, a total of 173 standard metabolites (126 amino acids, 10 phospholipids and 37 fatty acids) were utilized as a reference metabolite database for metabolome identification. In total, 32 amino acids, 71 phospholipids and 27 fatty acids were detected in the C. sinensis of each group. Among these metabolites, 10 amino acids were significantly decreased in both drug-treated groups. Four lysophosphatidyl cholines (LPCs), six lysophosphatidyl ethanolamines (LPEs) and one phosphatidyl inositol (PI) were significantly increased after treatment with TBD. There were no significant changes in fatty acids among the control group and the two drug-treated groups. The results indicated that TBD administration caused a decrease in amino acids involved in the metabolic pathways of energy consumption and an increase in lysophospholipids, which are the hydrolysis products of phospholipase2 (PLA2) in the phospholipid metabolic pathways. The increased lysophospholipid content can destroy the cell membrane, increase membrane permeability, and even cause exposure to internal antigens that can be attacked by host antibodies. Perhaps the destroyed membrane, the exposed internal antigens and the consumed energy are the cause of the damage and death of C. sinensis after TBD administration. This is an interesting problem that can be examined in future research.

Novel Lipidomic Signature Associated With Metabolic Risk in Women With and Without Polycystic Ovary Syndrome

CONTEXT

Dyslipidemia is a feature of polycystic ovary syndrome (PCOS) and may augment metabolic dysfunction in this population.

OBJECTIVE

Using comprehensive lipidomic profiling and gold-standard metabolic measures, we examined whether distinct lipid biomarkers were associated with metabolic risk in women with and without PCOS.

METHODS

Using preexisting data and biobanked samples from 76 women (n = 42 with PCOS), we profiled > 700 lipid species by mass spectrometry. Lipids were compared between women with and without PCOS and correlated with direct measures of adiposity (dual x-ray absorptiometry and computed tomography) and insulin sensitivity (hyperinsulinemic-euglycemic clamp), as well as fasting insulin, HbA1c, and hormonal parameters (luteinizing and follicle-stimulating hormones; total and free testosterone; sex hormone-binding globulin [SHBG]; and free androgen index [FAI]). Multivariable linear regression was used with correction for multiple testing.

RESULTS

Despite finding no differences by PCOS status, lysophosphatidylinositol (LPI) species esterified with an 18:0 fatty acid were the strongest lipid species associated with all the metabolic risk factors measured in women with and without PCOS. Across the cohort, higher concentrations of LPI(18:0) and lower concentrations of lipids containing docosahexaenoic acid (DHA, 22:6) n-3 polyunsaturated fatty acids were associated with higher adiposity, insulin resistance, fasting insulin, HbA1c and FAI, and lower SHBG.

CONCLUSION

Our data indicate that a distinct lipidomic signature comprising high LPI(18:0) and low DHA-containing lipids are associated with key metabolic risk factors that cluster in PCOS, independent of PCOS status. Prospective studies are needed to corroborate these findings in larger cohorts of women with varying PCOS phenotypes.

Pharmacomicrobiomics: Exploiting the Drug-Microbiota Interactions in Antihypertensive Treatment

Hypertension is a leading risk factor for cardiovascular diseases and can reduce life expectancy. Owing to the widespread use of antihypertensive drugs, patients with hypertension have improved blood pressure control over the past few decades. However, for a considerable part of the population, these drugs still cannot significantly improve their symptoms. In order to explore the reasons behind, pharmacomicrobiomics provide unique insights into the drug treatment of hypertension by investigating the effect of bidirectional interaction between gut microbiota and antihypertensive drugs. This review discusses the relationship between antihypertensive drugs and the gut microbiome, including changes in drug pharmacokinetics and gut microbiota composition. In addition, we highlight how our current knowledge of antihypertensive drug-microbiota interactions to develop gut microbiota-based personalized ways for disease management, including antihypertensive response biomarker, microbial-targeted therapies, probiotics therapy. Ultimately, a better understanding of the impact of pharmacomicrobiomics in the treatment of hypertension will provide important information for guiding rational clinical use and individualized use.

Serum metabolomic profiling reveals potential biomarkers in assessing the management of women with polycystic ovary syndrome: a randomized controlled trial

BACKGROUND

As one of the most common endocrinal disorders for women at childbearing age, the diagnostic criteria of polycystic ovary syndrome (PCOS) have been defined differently among different international health organizations. Phenotypic heterogeneity of PCOS also brings about difficulties for its diagnosis and management assessment. Therefore, more efficient biomarkers representing the progression of PCOS are expected to be integrated into the monitoring of management process using metabolomic approaches.

METHODS

In this prospective randomized controlled trial, 117 PCOS patients were enrolled from December 2016 to September 2017. Classical diagnostic parameters, blood glucose, and metabolome were measured in these patients before and at 2 months and 3 months of different medical interventions. The receiver operating characteristic (ROC) curves were built based on multivariate statistical analysis using data at baseline and 3 months' management, and combinational biomarkers with appreciable sensitivity and specificity were selected, which then validated with data collected at 2 months.

RESULTS

A set of metabolites including glutamic acid, aspartic acid, 1-methylnicotinamide, acetylcarnitine, glycerophosphocholine, and oleamide were filtered out with high performance in representing the improvement through 3-month management of PCOS with high sensitivity and specificity in ROC analysis and validation with other two groups showed an appreciable area under the curve over 0.96.

CONCLUSIONS

The six metabolites were representative of the remission of PCOS through medical intervention, making them a set of potential biomarkers for assessing the outcome of PCOS management.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03264638.

Diagnostic Performance of Sex-Specific Modified Metabolite Patterns in Urine for Screening of Prediabetes

AIMS/HYPOTHESIS

Large-scale prediabetes screening is still a challenge since fasting blood glucose and HbA_1c as the long-standing, recommended analytes have only moderate diagnostic sensitivity, and the practicability of the oral glucose tolerance test for population-based strategies is limited. To tackle this issue and to identify reliable diagnostic patterns, we developed an innovative metabolomics-based strategy deviating from common concepts by employing urine instead of blood samples, searching for sex-specific biomarkers, and focusing on modified metabolites.

METHODS

Non-targeted, modification group-assisted metabolomics by liquid chromatography-mass spectrometry (LC-MS) was applied to second morning urine samples of 340 individuals from a prediabetes cohort. Normal (n = 208) and impaired glucose-tolerant (IGT; n = 132) individuals, matched for age and BMI, were randomly divided in discovery and validation cohorts. ReliefF, a feature selection algorithm, was used to extract sex-specific diagnostic patterns of modified metabolites for the detection of IGT. The diagnostic performance was compared with conventional screening parameters fasting plasma glucose (FPG), HbA_1c, and fasting insulin.

RESULTS

Female- and male-specific diagnostic patterns were identified in urine. Only three biomarkers were identical in both. The patterns showed better AUC and diagnostic sensitivity for prediabetes screening of IGT than FPG, HbA_1c, insulin, or a combination of FPG and HbA_1c. The AUC of the male-specific pattern in the validation cohort was 0.889 with a diagnostic sensitivity of 92.6% and increased to an AUC of 0.977 in combination with HbA_1c. In comparison, the AUCs of FPG, HbA_1c, and insulin alone reached 0.573, 0.668, and 0.571, respectively. Validation of the diagnostic pattern of female subjects showed an AUC of 0.722, which still exceeded the AUCs of FPG, HbA_1c, and insulin (0.595, 0.604, and 0.634, respectively). Modified metabolites in the urinary patterns include advanced glycation end products (pentosidine-glucuronide and glutamyl-lysine-sulfate) and microbiota-associated compounds (indoxyl sulfate and dihydroxyphenyl-gamma-valerolactone-glucuronide).

CONCLUSIONS/INTERPRETATION

Our results demonstrate that the sex-specific search for diagnostic metabolite biomarkers can be superior to common metabolomics strategies. The diagnostic performance for IGT detection was significantly better than routinely applied blood parameters. Together with recently developed fully automatic LC-MS systems, this opens up future perspectives for the application of sex-specific diagnostic patterns for prediabetes screening in urine.

Changes in the gut microbiome influence the hypoglycemic effect of metformin through the altered metabolism of branched-chain and nonessential amino acids

AIMS

Although metformin has been reported to affect the gut microbiome, the mechanism has not been fully determined. We explained the potential underlying mechanisms of metformin through a multiomics approach.

METHODS

An open-label and single-arm clinical trial involving 20 healthy Korean was conducted. Serum glucose and insulin concentrations were measured, and stool samples were collected to analyze the microbiome. Untargeted metabolomic profiling of plasma, urine, and stool samples was performed by GC-TOF-MS. Network analysis was applied to infer the mechanism of the hypoglycemic effect of metformin.

RESULTS

The relative abundances of Escherichia, Romboutsia, Intestinibacter, and Clostridium were changed by metformin treatment. Additionally, the relative abundances of metabolites, including carbohydrates, amino acids, and fatty acids, were changed. These changes were correlated with energy metabolism, gluconeogenesis, and branched-chain amino acid metabolism, which are major metabolic pathways related to the hypoglycemic effect.

CONCLUSIONS

We observed that specific changes in metabolites may affect hypoglycemic effects through both pathways related to AMPK activation and microbial changes. Energy metabolism was mainly related to hypoglycemic effects. In particular, branched-chain amino acid metabolism and gluconeogenesis were related to microbial metabolites. Our results will help uncover the potential underlying mechanisms of metformin through AMPK and the microbiome.

Roles of Gut Microbial Metabolites in Diabetic Kidney Disease

Diabetes is a highly prevalent metabolic disease that has emerged as a global challenge due to its increasing prevalence and lack of sustainable treatment. Diabetic kidney disease (DKD), which is one of the most frequent and severe microvascular complications of diabetes, is difficult to treat with contemporary glucose-lowering medications. The gut microbiota plays an important role in human health and disease, and its metabolites have both beneficial and harmful effects on vital physiological processes. In this review, we summarize the current findings regarding the role of gut microbial metabolites in the development and progression of DKD, which will help us better understand the possible mechanisms of DKD and explore potential therapeutic approaches for DKD.

Similar hypoglycemic effects of glucomannan and its enzyme degraded products from Amorphophallus albus on type 2 diabetes mellitus in mice and potential mechanisms

In the present study, the hypoglycemic effects of glucomannan (AGM) and its enzyme-degraded products from Amorphophallus albus were investigated. Four degraded products were prepared through ultrafiltration of β-glucanase-degraded products of AGM. The hypoglycemic activities were evaluated in HFD-STZ-induced type 2 diabetes mellitus (T2DM) mice, and the diversity of gut bacteria was analyzed by 16S rRNA gene sequencing; the fecal short chain fatty acids (SCFAs) and endogenous metabolites were determined by UPLC-QTOF-MS/MS. It was found that AGM and its enzyme-degraded products, though with different molecular weights, had similar β-glycosidic bonds and monosaccharide compositions, exerted similar strength of hypoglycemic effects, and reinstated with a similar extent the disordered gut microbiota and the contents of SCFAs and endogenous metabolites. It was speculated that the hypoglycemic activity of AGM is decided by not the molecular weight but the glycosidic bonds/monosaccharide composition of AGM, which might be structurally specific to the gut bacteria, and thus certain SCFAs and endogenous metabolites that are related to the occurrence and therapy of T2DM. This study provides a scientific basis for using AGM as potential prebiotics beneficial for prevention or therapeutic treatment of T2DM.

Metabolomic Biomarkers in Polycystic Ovary Syndrome: A Review of the Evidence

Polycystic ovary syndrome (PCOS) is an endocrinologic condition affecting one in five women of reproductive age. PCOS is often characterized by disruptions to the menstrual cycle, development of male-pattern hair growth (hirsutism), and polycystic ovary morphology. Recently, PCOS has been linked to metabolic dysfunction, with 40 to 80% of women characterized as overweight or obese. Despite these well-known negative health effects of PCOS, 75% of sufferers remain undiagnosed. This is most likely due to the variability in symptom presentation and the lack of a definitive test for the condition. Metabolomics, which is a platform used to analyze and characterize a large number of metabolites, has recently been proposed as a potential tool for investigating the metabolic pathways that could be involved in the pathophysiology of PCOS. In doing so, novel biomarkers could be identified to improve diagnosis and treatment of PCOS. This review aims to summarize the findings of recent metabolomic studies that highlight metabolic-specific molecules which are deranged in PCOS, to identify potential biomarkers for the condition. Current limitations for metabolomic studies are discussed, as well as future directions to progress the field toward further validation and integration into clinical practice.

Invited review: Milk fat globule membrane-A possible panacea for neurodevelopment, infections, cardiometabolic diseases, and frailty

Milk is an evolutionary benefit for humans. For infants, it offers optimal nutrients for normal growth, neural development, and protection from harmful microbes. Humans are the only mammals who drink milk throughout their life. Lipids in colostrum originate mostly from milk fat globule membrane (MFGM) droplets extruded from the mammary gland. The MFGM gained much interest as a potential nutraceutical, due to their high phospholipid (PL), ganglioside (GD), and protein contents. In this review, we focused on health effects of MFGM ingredients and dairy food across the life span, especially on neurodevelopment, cardiometabolic health, and frailty in older adults. The MFGM supplements to infants and children reduced gastrointestinal and respiratory tract infections and improved neurodevelopment due to the higher content of protein, PL, and GD in MFGM. The MFGM formulas containing PL and GD improved brain myelination and fastened nerve conduction speed, resulting in improved behavioral developments. Administration of MFGM-rich ingredients improved insulin sensitivity and decreased inflammatory markers, LDL-cholesterol, and triglycerides by lowering intestinal absorption of cholesterol and increasing its fecal excretion. The MFGM supplements, together with exercise, improved ambulatory activities, leg muscle mass, and muscle fiber velocity in older adults. There are great variations in the composition of lipids and proteins in MFGM products, which make comparisons of the different studies impossible. In addition, investigations of the individual MFGM components are required to evaluate their specific effects and molecular mechanisms. Although we are currently only beginning to understand the possible health effects of MFGM products, the current MFGM supplementation trials as presented in this review have shown significant clinical health benefits across the human life span, which are worth further investigation.

Combination of urine and faeces metabolomics to reveal the intervention mechanism of Polygala tenuifolia compatibility with Magnolia officinalis on gastrointestinal motility disorders

OBJECTIVES

To explore the intervention mechanism of combining Polygala tenuifolia (PT) with Magnolia officinalis (MO) on gastrointestinal motility disorders caused by PT.

METHODS

Urine and faeces of rats were collected; the effects of PT and MO on the gastric emptying and small intestine advancing rates in mice were analysed via ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) to determine the potential metabolites. Changes in the metabolic profiles of the urine and faeces were revealed by untargeted metabolomics, followed by multivariate statistical analysis. The integration of urine and faeces was applied to reveal the intervention mechanism of PT-MO on PT-induced disorders.

KEY FINDINGS

PT + MO (1:2) improved the gastrointestinal function in mice suffering from PT-induced gastrointestinal motility disorder. Metabolomics indicated that the PT-MO mechanism was mainly associated with the regulations of 17 and 12 metabolites and 11 and 10 pathways in urine and faeces, respectively. The common metabolic pathways were those of tyrosine, purine, tricarboxylic acid cycle, pyruvate and gluconeogenesis, which were responsible for the PT-MO intervention mechanism.

CONCLUSIONS

The PT-MO (1:2) couple mechanism mitigated the PT-induced disorders, which were related to the energy, amino acid and fatty metabolisms.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

Mass Spectrometry-based Metabolomics in Translational Research

Metabolomics is the systematic study of metabolite profiles of complex biological systems, and involves the systematic identification and quantification of metabolites. Metabolism is integrated with all biochemical reactions in biological systems; thus metabolite profiles provide collective information on biochemical processes induced by genetic or environmental perturbations. Transcriptomes or proteomes may not be functionally active and not always reflect phenotypic variations. The metabolome, however, consists of the biomolecules closest to the phenotype of living organisms, and is often called the molecular phenotype of biological systems. Thus, metabolome alterations can easily result in disease states, providing important clues to understand pathophysiological mechanisms contributing to various biomedical symptoms. The metabolome and metabolomics have been emphasized in translational research related to biomarker discovery, drug target discovery, drug responses, and disease mechanisms. This review describes the basic concepts, workflows, and applications of mass spectrometry-based metabolomics in translational research.

Identification of metabolic markers predictive of prediabetes in a Korean population

Prediabetes (PD) is a high-risk state of developing type 2 diabetes, and cardiovascular and metabolic diseases. Metabolomics-based biomarker studies can provide advanced opportunities for prediction of PD over the conventional methods. Here, we aimed to identify metabolic markers and verify their abilities to predict PD, as compared to the performance of the traditional clinical risk factor (CRF) and previously reported metabolites in other population-based studies. Targeted metabolites quantification was performed in 1723 participants in the Korea Association REsource (KARE) cohort, from which 500 normal individuals were followed up for 6 years. We selected 12 significant metabolic markers, including five amino acids, four glycerophospholipids, two sphingolipids, and one acylcarnitine, at baseline, resulting in a predicted incidence of PD with an area under the curve (AUC) of 0.71 during follow-up. The performance of these metabolic markers compared to that of fasting glucose was significantly higher in obese patients (body mass index: BMI ≥ 25 kg/m², 0.79 vs. 0.58, P < 0.001). The combination with metabolic markers, CRF, and fasting glucose yielded the best prediction performance (AUC = 0.86). Our results revealed that metabolic markers were not only associated with the risk of PD, but also improved the prediction performance in combination with conventional approaches.

Characteristic of Metabolic Status in Heart Failure and Its Impact in Outcome Perspective

Metabolic alterations have been documented in peripheral tissues in heart failure (HF). Outcomes might be improved by early identification of risk. However, the prognostic information offered is still far from enough. We hypothesized that plasma metabolic profiling potentially provides risk stratification for HF patients. Of 61 patients hospitalized due to acute decompensated HF, 31 developed HF-related events in one year after discharge (Event group), and the other 30 patients did not (Non-event group). The plasma collected during hospital admission was analyzed by an ultra-high performance liquid chromatography time-of-flight mass spectrometry (UPLC-TOFMS)-based metabolomic approach. The orthogonal projection to latent structure discriminant analysis (OPLS-DA) reveals that the metabolomics profile is able to distinguish between events in HF. Levels of 19 metabolites including acylcarnitines, lysophospholipids, dimethylxanthine, dimethyluric acid, tryptophan, phenylacetylglutamine, and hypoxanthine are significantly different between patients with and without event (p < 0.05). Established risk prediction models of event patients by using receiver operating characteristics analysis reveal that the combination of tetradecenoylcarnitine, dimethylxanthine, phenylacetylglutamine, and hypoxanthine has better discrimination than B-type natriuretic peptide (BNP) (AUC 0.871 and 0.602, respectively). These findings suggest that metabolomics-derived metabolic profiling have the potential of identifying patients with high risk of HF-related events and provide insights related to HF outcome.

A combined LC-MS and NMR approach to reveal metabolic changes in the hemolymph of honeybees infected by the gut parasite Nosema ceranae

Nosema ceranae is an emerging and invasive gut pathogen in Apis mellifera and is considered as a factor contributing to the decline of honeybee populations. Here, we used a combined LC-MS and NMR approach to reveal the metabolomics changes in the hemolymph of honeybees infected by this obligate intracellular parasite. For metabolic profiling, hemolymph samples were collected from both uninfected and N. ceranae-infected bees at two time points, 2 days and 10 days after the experimental infection of emergent bees. Hemolymph samples were individually analyzed by LC-MS, whereas each NMR spectrum was obtained from a pool of three hemolymphs. Multivariate statistical PLS-DA models clearly showed that the age of bees was the parameter with the strongest effect on the metabolite profiles. Interestingly, a total of 15 biomarkers were accurately identified and were assigned as candidate biomarkers representative of infection alone or combined effect of age and infection. These biomarkers included carbohydrates (α/β glucose, α/β fructose and hexosamine), amino acids (histidine and proline), dipeptides (Glu-Thr, Cys-Cys and γ-Glu-Leu/Ile), metabolites involved in lipid metabolism (choline, glycerophosphocholine and O-phosphorylethanolamine) and a polyamine compound (spermidine). Our study demonstrated that this untargeted metabolomics-based approach may be useful for a better understanding of pathophysiological mechanisms of the honeybee infection by N. ceranae.

Exploring the Diversity of Sugar Compounds in Healthy, Prediabetic, and Diabetic Volunteers

SCOPE

Diabetes is thought to primarily represent a disturbance of carbohydrate metabolism; however, population studies employing metabolomics have mainly identified plasma amino acids and lipids, or their products, as biomarkers. In this pilot study, the aim is to analyze a wide spectrum of sugar compounds in the fasting state and during an oral glucose tolerance test (OGTT) in healthy, prediabetic, and type 2 diabetic volunteers.

METHODS AND RESULTS

The three volunteer groups underwent a standard OGTT. Plasma samples obtained in the fasting state, 30 and 90 min after the OGTT, are subjected to a semitargeted GC-MS (gas chromatography-mass spectrometry) sugar profiling. Overall, 40 sugars are detected in plasma, of which some are yet unknown to change during an OGTT. Several sugars (e.g., trehalose) reveal significant differences between the volunteer groups both in fasting plasma and in distinct time courses after the OGTT. This suggests an endogenous production from orally absorbed glucose and/or an insulin-dependent production/removal from plasma.

CONCLUSION

It is demonstrated that more sugars than expected can be found in human plasma. Since some of these show characteristic differences depending on health status, it may be worthwhile to assess their usability as biomarkers for diagnosing early-stage insulin resistance and type 2 diabetes.

Metabolomic profiling of metoprolol hypertension treatment reveals altered gut microbiota-derived urinary metabolites

Introduction

Metoprolol succinate is a long-acting beta-blocker prescribed for the management of hypertension (HTN) and other cardiovascular diseases. Metabolomics, the study of end-stage metabolites of upstream biologic processes, yield insight into mechanisms of drug effectiveness and safety. Our aim was to determine metabolomic profiles associated with metoprolol effectiveness for the treatment of hypertension.

Methods

We performed a prospective pragmatic trial (NCT02293096) that enrolled patients between 30 and 80 years with uncontrolled HTN. Patients were started on metoprolol succinate at a dose based upon systolic blood pressure (SBP). Urine and blood pressure measurements were collected weekly. Individuals with a 10% decline in SBP or heart rate (HR) were considered responsive. Genotype for the CYP2D6 enzyme, the primary metabolic pathway for metoprolol, was evaluated for each subject. Unbiased metabolomic analyses were performed on urine samples using UPLC-QTOF mass spectrometry.

Results

Urinary metoprolol metabolite ratios are indicative of patient CYP2D6 genotypes. Patients taking metoprolol had significantly higher urinary levels of many gut microbiota-dependent metabolites including hydroxyhippuric acid, hippuric acid, and methyluric acid. Urinary metoprolol metabolite profiles of normal metabolizer (NM) patients more closely correlate to ultra-rapid metabolizer (UM) patients than NM patients. Metabolites did not predict either 10% SBP or HR decline.

Conclusion

In summary, urinary metabolites predict CYP2D6 genotype in hypertensive patients taking metoprolol. Metoprolol succinate therapy affects the microbiome-derived metabolites.

Associations of dairy product consumption with mortality in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Italy cohort

BACKGROUND

The relation of dairy product consumption to health and mortality is controversial.

OBJECTIVES

We investigated associations of consumption of various dairy products with mortality in the Italian cohort of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Italy study.

METHODS

Dairy product consumption was assessed by validated semiquantitative FFQs. Multivariable Cox models stratified by center, age, and sex and adjusted for confounders estimated associations of milk (total, full fat, and reduced fat), yogurt, cheese, butter, and dairy calcium consumption with mortality for cancer, cardiovascular disease, and all causes. Nonlinearity was tested by restricted cubic spline regression.

RESULTS

After a median follow-up of 14.9 y, 2468 deaths were identified in 45,009 participants: 59% from cancer and 19% from cardiovascular disease. No significant association of consumption of any dairy product with mortality was found in the fully adjusted models. A 25% reduction in risk of all-cause mortality was found for milk intake from 160 to 120 g/d (HR: 0.75; 95% CI: 0.61, 0.91) but not for the highest (>200 g/d) category of intake (HR: 0.95; 95% CI: 0.84, 1.08) compared with nonconsumption. Associations of full-fat and reduced-fat milk consumption with all-cause and cause-specific mortality were similar to those for milk as a whole.

CONCLUSIONS

In this Italian cohort characterized by low to average milk consumption, we found no evidence of a dose-response association between milk consumption and mortality and also no association of consumption of other dairy products investigated with mortality.

Microbiota-Related Changes in Unconjugated Fecal Bile Acids Are Associated With Naturally Occurring, Insulin-Dependent Diabetes Mellitus in Dogs

Diabetes mellitus (DM) in humans has recently been associated with altered intestinal microbiota. The consequences of intestinal dysbiosis, such as increased intestinal permeability and altered microbial metabolites, are suspected to contribute to the host inflammatory state and peripheral insulin resistance. Human diabetics have been shown to have changes in bile acid (BA) metabolism which may be detrimental to glycemic control. The purpose of this study was to examine BA metabolism in dogs with naturally-occurring, insulin-dependent DM and to relate these findings to changes in the intestinal microbiota. A prospective observational study of adult dogs with a clinical diagnosis of DM (n = 10) and healthy controls (HC, n = 10) was performed. The fecal microbiota were analyzed by 16S rRNA gene next-generation (Illumina) sequencing. Concentrations of fecal unconjugated BA (fUBA) were measured using gas chromatography and mass spectrometry. Analysis of bacterial communities showed no significant difference for any of the alpha-diversity measures between DM vs. HC dogs. Principal coordinate analysis based on unweighted Unifrac distance metric failed to show significant clustering between dog groups (ANOSIM_Unweighted: R = 0.084; p = 0.114). However, linear discriminate analysis effects size (LEfSe) detected differentially abundant bacterial taxa (α = 0.01, LDA score >2.0) on various phylogenetic levels. While Enterobacteriaceae was overrepresented in dogs with DM, the proportions of Erysipelotrichia, Mogibacteriaceae, and Anaeroplasmataceae were increased in HC dogs. Dogs with DM had increased concentration of total primary fUBA compared to HC dogs (p = 0.028). The concentrations of cholic acid and the cholic acid percentage of the total fUBA were increased (p = 0.028 and p = 0.035, respectively) in the feces of DM dogs relative to HC dogs. The levels of lithocholic acid (both absolute value and percentage of the total fUBA) were decreased (p = 0.043 and p < 0.01, respectively) in DM dogs vs. HC dogs. Results indicate that dogs with DM have both intestinal dysbiosis and associated fUBA alterations. The pattern of dysbiosis and altered BA composition is similar to that seen in humans with Type 2 DM. The dog represents a novel large animal model for advancing translational medicine research efforts (e.g., investigating pathogenesis and therapeutics) in DM affecting humans.

Metabolomics profiling of haloperidol and validation of thromboxane-related signaling in the early development of zebrafish

Haloperidol is a common butyrophenone-derivative antipsychotic drug that is used clinically to treat schizophrenia and to control Tourette disorder. Haloperidol has been shown to be an embryonic toxicant and to cause a variety of adverse effects that affect human embryonic development. However, the pathway impaired by haloperidol during the developmental stages remains unclear. To elucidate the innate toxicological pathway of haloperidol, we investigated the lethality of haloperidol during the embryonic development of zebrafish. We observed that haloperidol caused serious morphological changes, with an LD₅₀ of 9.7 x 10^-6 ± 2.4 x 10^-6 μg/L. Next, we established a systematic approach to perform metabolite profiling in embryonic zebrafish with various concentrations of haloperidol and analyzed the metabolites using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS). A total of 304 metabolites were identified and 86 metabolites were chosen to predict potential pathways. Among the metabolites, we found through prediction that numerous metabolomics-biological pathways are associated with haloperidol, including peroxisome-proliferator-activated receptor (ppar), thromboxane, and mTOR signaling. Quantitative real time-qPCR was then used to validate the gene expression potentially associated with the thromboxane, which is a metabolic product of arachidonic acid and considered to be important for cell proliferation and the inflammatory response. To sum up, analysis of metabolites in the zebrafish model provides a system for mining biomarkers that reflect biological significance and highlight the therapeutic potency in humans. In addition, it may show potential for application to other pharmaceuticals to identify their various activities and clarify functional mechanisms in the future.

Longitudinal multi-omics of host-microbe dynamics in prediabetes

Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host-microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states.

Comparison of the microbiome, metabolome, and lipidome of obese and non-obese horses

Metabolic diseases such as obesity and type 2 diabetes in humans have been linked to alterations in the gastrointestinal microbiota and metabolome. Knowledge of these associations has improved our understanding of the pathophysiology of these diseases and guided development of diagnostic biomarkers and therapeutic interventions. The cellular and molecular pathophysiology of equine metabolic syndrome (EMS) and obesity in horses, however, remain ill-defined. Thus, the objectives of this study were to characterize the fecal microbiome, fecal metabolome, and circulating lipidome in obese and non-obese horses. The fecal microbiota, fecal metabolome, and serum lipidome were evaluated in obese (case) horses (n = 20) and non-obese (control) horses (n = 20) matched by farm of origin (n = 7). Significant differences in metabolites of the mitochondrial tricarboxylic acid cycle and circulating free fatty acids were identified in the obese horses compared to the non-obese horses. These results indicate that the host and bacterial metabolism should be considered important in obese horses. Further studies to determine whether these associations are causal and the mechanistic basis of the association are warranted because they might reveal diagnostic biomarkers and therapeutic interventions to mitigate obesity, EMS, and sequelae including laminitis.

Current Progress of Lipid Analysis in Metabolic Diseases by Mass Spectrometry Methods

BACKGROUND

Obesity, insulin resistance, diabetes, and metabolic syndrome are associated with lipid alterations, and they affect the risk of long-term cardiovascular disease. A reliable analytical instrument to detect changes in the composition or structures of lipids and the tools allowing to connect changes in a specific group of lipids with a specific disease and its progress, is constantly lacking. Lipidomics is a new field of medicine based on the research and identification of lipids and lipid metabolites present in human organism. The primary aim of lipidomics is to search for new biomarkers of different diseases, mainly civilization diseases.

OBJECTIVE

We aimed to review studies reporting the application of mass spectrometry for lipid analysis in metabolic diseases.

METHOD

Following an extensive search of peer-reviewed articles on the mass spectrometry analysis of lipids the literature has been discussed in this review article.

RESULTS

The lipid group contains around 1.7 million species; they are totally different, in terms of the length of aliphatic chain, amount of rings, additional functional groups. Some of them are so complex that their complex analyses are a challenge for analysts. Their qualitative and quantitative analysis of is based mainly on mass spectrometry.

CONCLUSION

Mass spectrometry techniques are excellent tools for lipid profiling in complex biological samples and the combination with multivariate statistical analysis enables the identification of potential diagnostic biomarkers.

Serum metabolomics analysis of patients with polycystic ovary syndrome by mass spectrometry

Polycystic ovary syndrome (PCOS) is a set of symptoms caused by elevated androgens (male hormones) in females. PCOS is the most common endocrine disorder among women between 18 and 44 years. Currently, the pathogenesis of PCOS remains unclear. Liquid chromatography-mass spectrometry (LC/MS)-based metabolomics is becoming more and more useful for medical research, especially in revealing the mechanism of the disease. The aim of this study was to investigate the difference of serum metabolic profiles in patients with PCOS and healthy control to better understand the mechanism of this disease. Ten patients with PCOS and 10 healthy people were recruited for this study. The serum samples were collected for LC/MS analysis. Multivariate statistical analysis was performed to discover and identify the potential biomarkers. Six biomarkers were found and identified. The biomarkers belonged to different metabolic pathway including lipid metabolism, carnitine metabolism, androgen metabolism, and bile acid metabolism. Those biomarkers also played different roles in disease progression. Metabolomics is a powerful tool used in research of the mechanism involved in this disease to provide useful information for better understanding of PCOS.

The levels of urine CTX-II, C2C, and PYD in children patients with Kashin-Beck disease in Qinghai Province of China

BACKGROUND

Kashin-Beck disease (KBD) is an endemic and chronic osteoarthropathy. At present, the diagnosis of KBD mainly depends on the X-ray examination and which could not reflect early damage of cartilage sensitively. So, the aim of this study was to find effective and sensitive biomarkers for early diagnosis of pediatric KBD.

METHODS

A total of 122 children aged 7-15 years old from 3 villages of Qinghai Province were eligible for the study. Thirty-one, 41, and 50 children were assigned in case, internal, and external control groups, respectively. The levels of CTX-II, C2C, and PYD in urine were measured by using ELISA and compared statistically. In addition, the receiver operating characteristic curve (ROC) analysis was used to assess the performance of diagnostic biomarkers.

RESULTS

There were significant differences in levels of CTX-II, C2C, and PYD in urine of subjects among three groups. The levels of CTX-II and PYD in the case group were significantly higher than those in external and internal control groups. On the contrary, the level of C2C in the case group was lower than that in the external control group. Compared to the external control group, the area under the curve (AUC) of CTX-II, C2C, and PYD were 0.857, 0.837, and 0.79, and the AUC of CTX-II significantly higher than that of PYD. Compared to the internal control group, the AUC of CTX-II, C2C, and PYD were 0.911, 0.875, and 0.839, and there were no significant differences in the AUC among three indicators.

CONCLUSION

Both CTX-II and PYD in urine could be used as biomarkers for early diagnosis of pediatric KBD, and the prediction accuracy of CTX-II was relatively superior.

LC-MS/MS-Based Metabolomics for Cell Cultures

Metabolomics, a comprehensive analysis of metabolites in biological specimens (e.g., cells, body fluids, tissues, exhaled air, plants), offers promising tools in health, nutrition, biotechnology, and food sciences. Here we describe methods of LC-MS/MS-based analyses for cell metabolomics. Using methods employed in this section, over 1000 endogenous and exogenous metabolites can be detected, annotated, and quantified relatively by nontargeted analysis approach, whereas targeted metabolomics analysis enables us to quantify 188 endogenous metabolites.

Plasma Lipidomic Profiling and Risk of Type 2 Diabetes in the PREDIMED Trial

OBJECTIVE

Specific lipid molecular changes leading to type 2 diabetes (T2D) are largely unknown. We assessed lipidome factors associated with future occurrence of T2D in a population at high cardiovascular risk.

RESEARCH DESIGN AND METHODS

We conducted a case-cohort study nested within the PREDIMED trial, with 250 incident T2D cases diagnosed during 3.8 years of median follow-up, and a random sample of 692 participants (639 noncases and 53 overlapping cases) without T2D at baseline. We repeatedly measured 207 plasma known lipid metabolites at baseline and after 1 year of follow-up. We built combined factors of lipid species using principal component analysis and assessed the association between these lipid factors (or their 1-year changes) and T2D incidence.

RESULTS

Baseline lysophosphatidylcholines and lysophosphatidylethanolamines (lysophospholipids [LPs]), phosphatidylcholine-plasmalogens (PC-PLs), sphingomyelins (SMs), and cholesterol esters (CEs) were inversely associated with risk of T2D (multivariable-adjusted P for linear trend ≤0.001 for all). Baseline triacylglycerols (TAGs), diacylglycerols (DAGs), and phosphatidylethanolamines (PEs) were positively associated with T2D risk (multivariable-adjusted P for linear trend <0.001 for all). One-year changes in these lipids showed associations in similar directions but were not significant after adjustment for baseline levels. TAGs with odd-chain fatty acids showed inverse associations with T2D after adjusting for total TAGs.

CONCLUSIONS

Two plasma lipid profiles made up of different lipid classes were found to be associated with T2D in participants at high cardiovascular risk. A profile including LPs, PC-PLs, SMs, and CEs was associated with lower T2D risk. Another profile composed of TAGs, DAGs, and PEs was associated with higher T2D risk.

Molecular Fingerprints of Iron Parameters among a Population-Based Sample

Iron deficiency is the most frequent deficiency disease and parameters of iron metabolism appear to be linked to major metabolic and cardiovascular diseases. We screened a large set of small molecules in plasma for associations with iron status among apparently healthy subjects to elucidate subclinical profiles which may provide a link between iron status and onset of diseases. Based on mass spectrometry and nuclear magnetic resonance spectroscopy we determined 613 plasma metabolites and lipoprotein subfractions among 820 apparently healthy individuals. Associations between ferritin, transferrin, haemoglobin and myoglobin and metabolite levels were tested by sex-specific linear regression analyses controlling for common confounders. Far more significant associations in women (82 out of 102) compared to men became obvious. The majority of the metabolites associated with serum ferritin and haemoglobin in women comprising fatty acid species, branched-chain amino acid catabolites and catabolites of heme. The latter was also obvious among men. Positive associations between serum transferrin and VLDL and IDL particle measures seen in women were observed in men with respect to serum ferritin. We observed a sexual-dimorphic fingerprint of surrogates of iron metabolism which may provide a link for the associations between those parameters and major metabolic and cardiovascular disease.

Identification of Urinary Metabolite Biomarkers of Type 2 Diabetes Nephropathy Using an Untargeted Metabolomic Approach

Diabetic nephropathy (DN) is one of the most common complications of diabetes mellitus (DM). To discover early stage biomarkers of DN, untargeted liquid chromatography-mass spectrometry-based metabolomic analysis was performed in urine samples from healthy subjects and patients with micro- or macroalbuminuria due to nondiabetic disease (macro), type 2 DM without microalbuminuria (T2DM), and type 2 DM with microalbuminuria (T2DM+micro). Levels of four metabolites were significantly different among groups, and they were quantified in a larger group of 267 urine samples. Two metabolites were also discovered and validated in targeted metabolic study of amino acids. For diagnosis of nephropathy, N1-methylguanosine had the highest area-under-the-curve (AUC) value of 0.75 when compared to those of valine (0.68), xanthosine (0.67), and 7-methyluric acid (0.69). After combining fasting blood glucose and diastolic blood pressure (DBP) with N1-methylguanosine, the AUC increased to 0.987. To distinguish between T2DM and T2DM+micro conditions, xanthosine and N1-methylguanosine have AUC value of 0.612 and 0.624, respectively. After adjustment of HbA1c and DBP, AUC values of xanthosine and N1-methylguanosine increased to 0.716 and 0.723, respectively, and could be used to predict the development of nephropathy in T2DM patients.

Lysophosphatidylcholine and its phosphorothioate analogues potentiate insulin secretion via GPR40 (FFAR1), GPR55 and GPR119 receptors in a different manner

Lysophosphatidylcholine (LPC) is an endogenous ligand for GPR119 receptor, mediating glucose-stimulated insulin secretion (GSIS). We demonstrate that LPC facilitates GSIS in MIN6 pancreatic β-cell line and murine islets of Langerhans by recognizing not only GPR119 but also GPR40 (free fatty acid receptor 1) and GPR55 activated by lysophosphatidylinositol. Natural LPCs are unstable when administered in vivo limiting their therapeutic value and therefore, we present phosphorothioate LPC analogues with increased stability. All the modified LPCs under study (12:0, 14:0, 16:0, 18:0, and 18:1) significantly enhanced GSIS. The 16:0 sulfur analogue was the most potent, evoking 2-fold accentuated GSIS compared to the native counterpart. Interestingly, LPC analogues evoked GPR40-, GPR55-and GPR119-dependent [Ca²⁺]_i signaling, but did not stimulate cAMP accumulation as in the case of unmodified molecules. Thus, introduction of a phosphorothioate function not only increases LPC stability but also modulates affinity towards receptor targets and evokes different signaling pathways.

A Pharmacometabolomic Approach to Predict Response to Metformin in Early-Phase Type 2 Diabetes Mellitus Patients

Metformin is a first-line medication for type 2 diabetes mellitus (T2DM). Based on its universal use, the consideration of inter-individual variability and development of predictive biomarkers are clinically significant. We aimed to identify endogenous markers of metformin responses using a pharmacometabolomic approach. Twenty-nine patients with early-phase T2DM were enrolled and orally administered metformin daily for 6 months. A total of 22 subjects were included in the final analysis. Patients were defined as responders or non-responders based on changes in their glycated haemoglobin A1c (HbA1c) from baseline, over 3 months. Urine metabolites at baseline, as well as at the 3 and 6 month follow-ups after the start of treatment were analysed using gas chromatography-mass spectrometry and evaluated with multivariate analyses. Metabolites distinguishable between the two response groups were obtained at baseline, as well as at the 3 and 6 month follow-ups, and significantly different metabolites were listed as markers of metformin response. Among the identified metabolites, citric acid, myoinositol, and hippuric acid levels showed particularly significant differences between the non-responder and responder groups. We thus identified different metabolite profiles in the two groups of T2DM patients after metformin administration, using pharmacometabolomics. These results might facilitate a better understanding and prediction of metformin response and its variability in individual patients.