A metabolomic study of low estimated GFR in non-proteinuric type 2 diabetes mellitus

Current updates on metabolites and its interlinked pathways as biomarkers for diabetic kidney disease: A systematic review

Diabetic kidney disease (DKD) is a major microvascular complication of diabetes mellitus (DM) that poses a serious risk as it can lead to end-stage renal disease (ESRD). DKD is linked to changes in the diversity, composition, and functionality of the microbiota present in the gastrointestinal tract. The interplay between the gut microbiota and the host organism is primarily facilitated by metabolites generated by microbial metabolic processes from both dietary substrates and endogenous host compounds. The production of numerous metabolites by the gut microbiota is a crucial factor in the pathogenesis of DKD. However, a comprehensive understanding of the precise mechanisms by which gut microbiota and its metabolites contribute to the onset and progression of DKD remains incomplete. This review will provide a summary of the current scenario of metabolites in DKD and the impact of these metabolites on DKD progression. We will discuss in detail the primary and gut-derived metabolites in DKD, and the mechanisms of the metabolites involved in DKD progression. Further, we will address the importance of metabolomics in helping identify potential DKD markers. Furthermore, the possible therapeutic interventions and research gaps will be highlighted.

Quantitative, Targeted Analysis of Gut Microbiota Derived Metabolites Provides Novel Biomarkers of Early Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients

Diabetic kidney disease (DKD) is one of the most debilitating complications of type 2 diabetes mellitus (T2DM), as it progresses silently to end-stage renal disease (ESRD). The discovery of novel biomarkers of early DKD becomes acute, as its incidence is reaching catastrophic proportions. Our study aimed to quantify previously identified metabolites from serum and urine through untargeted ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (UHPLC-QTOF-ESI+-MS) techniques, such as the following: arginine, dimethylarginine, hippuric acid, indoxyl sulfate, p-cresyl sulfate, L-acetylcarnitine, butenoylcarnitine and sorbitol. The study concept was based on the targeted analysis of selected metabolites, using the serum and urine of 20 healthy subjects and 90 T2DM patients with DKD in different stages (normoalbuminuria-uACR < 30 mg/g; microalbuminuria-uACR 30-300 mg/g; macroalbuminuria-uACR > 300 mg/g). The quantitative evaluation of metabolites was performed with pure standards, followed by the validation methods such as the limit of detection (LOD) and the limit of quantification (LOQ). The following metabolites from this study resulted as possible biomarkers of early DKD: in serum-arginine, dimethylarginine, hippuric acid, indoxyl sulfate, butenoylcarnitine and sorbitol and in urine-p-cresyl sulfate.

The effect of probiotic administration on metabolomics and glucose metabolism in CF patients

BACKGROUND AND OBJECTIVES

Cystic fibrosis (CF)-related diabetes (CFRD) affects 50% of CF adults. Gut microbial imbalance (dysbiosis) aggravates their inflammatory response and contributes to insulin resistance (IR). We hypothesized that probiotics may improve glucose tolerance by correcting dysbiosis.

METHODS

A single-center prospective pilot study assessing the effect of Vivomixx® probiotic (450 billion/sachet) on clinical status, spirometry, lung clearance index (LCI), and quality of life (QOL) questionnaires; inflammatory parameters (urine and stool metabolomics, blood cytokines); and glucose metabolism (oral glucose tolerance test [OGTT]), continuous glucose monitoring [CGM], and homeostasis model assessment of IR (HOMA-IR) in CF patients.

RESULTS

Twenty-three CF patients (six CFRD), mean age 17.7 ± 8.2 years. After 4 months of probiotic administration, urinary cysteine (p = 0.018), lactulose (p = 0.028), arabinose (p = 0.036), mannitol (p = 0.041), and indole 3-lactate (p = 0.046) significantly increased, while 3-methylhistidine (p = 0.046) and N-acetyl glutamine (p = 0.047) decreased. Stool 2-Hydroxyisobutyrate (p = 0.022) and 3-methyl-2-oxovalerate (p = 0.034) decreased. Principal component analysis, based on urine metabolites, found significant partitions between subjects at the end of treatment compared to baseline (p = 0.004). After 2 months of probiotics, the digestive symptoms domain of Cystic Fibrosis Questionnaire-Revised improved (p = 0.007). In the nondiabetic patients, a slight decrease in HOMA-IR, from 2.28 to 1.86, was observed. There was no significant change in spirometry results, LCI, blood cytokines and CGM.

CONCLUSIONS

Changes in urine and stool metabolic profiles, following the administration of probiotics, may suggest a positive effect on glucose metabolism in CF. Larger long-term studies are needed to confirm our findings. Understanding the interplay between dysbiosis, inflammation, and glucose metabolism may help preventing CFRD.

Novel biomarkers for diabetic kidney disease

Although diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in patients with diabetes mellitus; its prevalence has failed to decline over the past 30 years. To identify those at high risk of developing DKD and disease progression at an early stage, extensive research has been ongoing in the search for prognostic and surrogate endpoint biomarkers for DKD. Although biomarkers are not used routinely in clinical practice or prospective clinical trials, many biomarkers have been developed to improve the early identification and prognostication of patients with DKD. Novel biomarkers that capture one specific mechanism of the DKD disease process have been developed, and studies have evaluated the prognostic value of assay-based biomarkers either in small sets or in combinations involving multiple biomarkers. More recently, several studies have assessed the prognostic value of omics- based biomarkers that include proteomics, metabolomics, and transcriptomics. This review will first describe the biomarkers used in current practice and their limitations, and then summarize the current status of novel biomarkers for DKD with respect to assay- based protein biomarkers, proteomics, metabolomics, and transcriptomics.

Metabolomics as a tool for the early diagnosis and prognosis of diabetic kidney disease

Diabetic kidney disease (DKD) is one of the most prevalent comorbidities of diabetes mellitus and the leading cause of the end-stage renal disease (ESRD). DKD results from chronic exposure to hyperglycemia, leading to progressive alterations in kidney structure and function. The early development of DKD is clinically silent and when albuminuria is detected the lesions are often at advanced stages, leading to rapid kidney function decline towards ESRD. DKD progression can be arrested or substantially delayed if detected and addressed at early stages. A major limitation of current methods is the absence of albuminuria in non-albuminuric phenotypes of diabetic nephropathy, which becomes increasingly prevalent and lacks focused therapy. Metabolomics is an ever-evolving omics technology that enables the study of metabolites, downstream products of every biochemical event that occurs in an organism. Metabolomics disclosures complex metabolic networks and provide knowledge of the very foundation of several physiological or pathophysiological processes, ultimately leading to the identification of diseases' unique metabolic signatures. In this sense, metabolomics is a promising tool not only for the diagnosis but also for the identification of pre-disease states which would confer a rapid and personalized clinical practice. Herein, the use of metabolomics as a tool to identify the DKD metabolic signature of tubule interstitial lesions to diagnose or predict the time-course of DKD will be discussed. In addition, the proficiency and limitations of the currently available high-throughput metabolomic techniques will be discussed.

REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Clinical features are traditionally used to predict DKD, yet with low diagnostic efficacy. Most of the recent biomarkers used to predict DKD are based on transcriptomics and metabolomics; however, they also should be used in combination with many other predictive indicators. The purpose of this study was thus to identify a simplified class of blood biomarkers capable of predicting the risk of developing DKD. The Gene Expression Omnibus database was screened for DKD biomarkers, and differentially expressed genes (DEGs) in human blood and kidney were identified via gene expression analysis and the Least Absolute Shrinkage and Selection Operator regression. A comparison of the area under the curve (AUC) profiles on multiple receiver operating characteristic curves of the DEGs in DKD and other renal diseases revealed that REG1A and RUNX3 had the highest specificity for DKD diagnosis. The AUCs of the combined expression of REG1A and RUNX3 in kidney (AUC = 0.929) and blood samples (AUC = 0.917) of DKD patients were similar to each other. The AUC of blood samples from DKD patients and healthy individuals obtained for external validation further demonstrated that REG1A combined with RUNX3 had significant diagnostic efficacy (AUC=0.948). REG1A and RUNX3 expression levels were found to be positively and negatively correlated with urinary albumin creatinine ratio and estimated glomerular filtration rate, respectively. Kaplan-Meier curves also revealed the potential of REG1A and RUNX3 for predicting the risk of DKD. In conclusion, REG1A and RUNX3 may serve as biomarkers for predicting the risk of developing DKD.

Metabolomics reveals the impact of saturation of dietary lipids on aging and longevity of C. elegans

Saturation differences in dietary lipids modify their digestive and absorption profiles, endpoints that may influence the nutrition and health. This study tests the hypothesis that dietary with elevated unsaturated fats...

The Role of Gut Microbiota and Microbiota-Related Serum Metabolites in the Progression of Diabetic Kidney Disease

Objective: Diabetic kidney disease (DKD) has become the major cause of end-stage renal disease (ESRD) associated with the progression of renal fibrosis. As gut microbiota dysbiosis is closely related to renal damage and fibrosis, we investigated the role of gut microbiota and microbiota-related serum metabolites in DKD progression in this study.

Methods: Fecal and serum samples obtained from predialysis DKD patients from January 2017 to December 2019 were detected using 16S rRNA gene sequencing and liquid chromatography-mass spectrometry, respectively. Forty-one predialysis patients were divided into two groups according to their estimated glomerular filtration rate (eGFR): the DKD non-ESRD group (eGFR ≥ 15 ml/min/1.73 m²) (n = 22), and the DKD ESRD group (eGFR < 15 ml/min/1.73 m²) (n = 19). The metabolic pathways related to differential serum metabolites were obtained by the KEGG pathway analysis. Differences between the two groups relative to gut microbiota profiles and serum metabolites were investigated, and associations between gut microbiota and metabolite concentrations were assessed. Correlations between clinical indicators and both microbiota-related metabolites and gut microbiota were calculated by Spearman rank correlation coefficient and visualized by heatmap.

Results: Eleven different intestinal floras and 239 different serum metabolites were identified between the two groups. Of 239 serum metabolites, 192 related to the 11 different intestinal flora were mainly enriched in six metabolic pathways, among which, phenylalanine and tryptophan metabolic pathways were most associated with DKD progression. Four microbiota-related metabolites in the phenylalanine metabolic pathway [hippuric acid (HA), L-(−)-3-phenylactic acid, trans-3-hydroxy-cinnamate, and dihydro-3-coumaric acid] and indole-3 acetic acid (IAA) in the tryptophan metabolic pathway positively correlated with DKD progression, whereas L-tryptophan in the tryptophan metabolic pathway had a negative correlation. Intestinal flora g_Abiotrophia and g_norank_f_Peptococcaceae were positively correlated with the increase in renal function indicators and serum metabolite HA. G_Lachnospiraceae_NC2004_Group was negatively correlated with the increase in renal function indicators and serum metabolites [L-(−)-3-phenyllactic acid and IAA].

Conclusions: This study highlights the interaction among gut microbiota, serum metabolites, and clinical indicators in predialysis DKD patients, and provides new insights into the role of gut microbiota and microbiota-related serum metabolites that were enriched in the phenylalanine and tryptophan metabolic pathways, which correlated with the progression of DKD.

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.

Plasma Metabolome and Lipidome Associations with Type 2 Diabetes and Diabetic Nephropathy

We conducted untargeted metabolomics analysis of plasma samples from a cross-sectional case–control study with 30 healthy controls, 30 patients with diabetes mellitus and normal renal function (DM-N), and 30 early diabetic nephropathy (DKD) patients using liquid chromatography-mass spectrometry (LC-MS). We employed two different modes of MS acquisition on a high-resolution MS instrument for identification and semi-quantification, and analyzed data using an advanced multivariate method for prioritizing differentially abundant metabolites. We obtained semi-quantification data for 1088 unique compounds (~55% lipids), excluding compounds that may be either exogenous compounds or treated as medication. Supervised classification analysis over a confounding-free partial correlation network shows that prostaglandins, phospholipids, nucleotides, sugars, and glycans are elevated in the DM-N and DKD patients, whereas glutamine, phenylacetylglutamine, 3-indoxyl sulfate, acetylphenylalanine, xanthine, dimethyluric acid, and asymmetric dimethylarginine are increased in DKD compared to DM-N. The data recapitulate the well-established plasma metabolome changes associated with DM-N and suggest uremic solutes and oxidative stress markers as the compounds indicating early renal function decline in DM patients.

Determination of Major Endogenous FAHFAs in Healthy Human Circulation: The Correlations with Several Circulating Cardiovascular-Related Biomarkers and Anti-Inflammatory Effects on RAW 264.7 Cells

Fatty acid esters of hydroxy fatty acids (FAHFAs) are newly discovered long-chain fatty acids. However, the major endogenous FAHFAs in healthy human circulation, their correlation with cardiovascular (CV) biomarkers, and their anti-inflammatory effects have not been investigated and remain unclear. In the present study, a total of 57 healthy subjects were recruited. Liquid chromatography–mass spectrometry (LC-MS) was developed for the simultaneous determination of seven FAHFAs, four long-chain fatty acids, and four non-traditional circulating CV-related biomarkers. We found two major types of FAHFAs in healthy human circulation, palmitoleic acid ester of 9-hydroxystearic acid (9-POHSA), and oleic acid ester of 9-hydroxystearic acid (9-OAHSA). Both 9-POHSA and 9-OAHSA had a strong positive correlation with each other and were negatively correlated with fasting blood glucose, S-adenosyl-l-homocysteine (SAH), and trimethylamine N-oxide (TMAO), but not with l-homocysteine. 9-POHSA was also positively correlated with l-carnitine. Moreover, we confirmed that both 9-POHSA and 9-OAHSA exhibited an anti-inflammatory effect by suppressing LPS stimulated cytokines, including IL-1β and IL-6 in RAW 264.7 cells. In addition, palmitoleic acid also had a positive correlation with 9-POHSA and 9-OAHSA. As far as we know, this is the first report showing the major endogenous FAHFAs in healthy subjects and their CV protection potential which might be correlated with SAH and TMAO reduction, l-Carnitine elevation, and their anti-inflammatory effects.

Metabolite biomarkers of type 2 diabetes mellitus and pre-diabetes: a systematic review and meta-analysis

BACKGROUND

We aimed to explore metabolite biomarkers that could be used to identify pre-diabetes and type 2 diabetes mellitus (T2DM) using systematic review and meta-analysis.

METHODS

Four databases, the Cochrane Library, EMBASE, PubMed and Scopus were selected. A random effect model and a fixed effect model were applied to the results of forest plot analyses to determine the standardized mean difference (SMD) and 95% confidence interval (95% CI) for each metabolite. The SMD for every metabolite was then converted into an odds ratio to create an metabolite biomarker profile.

RESULTS

Twenty-four independent studies reported data from 14,131 healthy individuals and 3499 patients with T2DM, and 14 included studies reported 4844 healthy controls and a total of 2139 pre-diabetes patients. In the serum and plasma of patients with T2DM, compared with the healthy participants, the concentrations of valine, leucine, isoleucine, proline, tyrosine, lysine and glutamate were higher and that of glycine was lower. The concentrations of isoleucine, alanine, proline, glutamate, palmitic acid, 2-aminoadipic acid and lysine were higher and those of glycine, serine, and citrulline were lower in prediabetic patients. Metabolite biomarkers of T2DM and pre-diabetes revealed that the levels of alanine, glutamate and palmitic acid (C16:0) were significantly different in T2DM and pre-diabetes.

CONCLUSIONS

Quantified multiple metabolite biomarkers may reflect the different status of pre-diabetes and T2DM, and could provide an important reference for clinical diagnosis and treatment of pre-diabetes and T2DM.

Exploring blood alterations in chronic kidney disease and haemodialysis using metabolomics

Chronic kidney disease (CKD) is characterized by retention of uremic solutes. Compared to patients with non-dialysis dependent CKD, those requiring haemodialysis (HD) have increased morbidity and mortality. We wished to characterise metabolic patterns in CKD compared to HD patients using metabolomics. Prevalent non-HD CKD KDIGO stage 3b-4 and stage 5 HD outpatients were screened at a single tertiary hospital. Various liquid chromatography approaches hyphenated with mass spectrometry were used to identify 278 metabolites. Unsupervised and supervised data analyses were conducted to characterize metabolic patterns. 69 patients were included in the CKD group and 35 in the HD group. Unsupervised data analysis showed clear clustering of CKD, pre-dialysis (preHD) and post-dialysis (postHD) patients. Supervised data analysis revealed qualitative as well as quantitative differences in individual metabolites profiles between CKD, preHD and postHD states. An original metabolomics framework could discriminate between CKD stages and highlight HD effect based on 278 identified metabolites. Significant differences in metabolic patterns between CKD and HD patients were found overall as well as for specific metabolites. Those findings could explain clinical discrepancies between patients requiring HD and those with earlier stage of CKD.

Association of Serum Indoxyl Sulfate Levels with Skeletal Muscle Mass and Strength in Chronic Hemodialysis Patients: A 2-year Longitudinal Analysis

Sarcopenia is highly prevalent in patients undergoing chronic hemodialysis (HD). This study investigated the relationship among serum indoxyl sulfate (IS) levels, muscle mass, and strength in HD patients. A total of 108 HD patients were enrolled. Skeletal muscle mass and handgrip strength (HGS) were assessed, using bioimpedance analysis and a hand-held dynamometer, respectively. Skeletal muscle index (SMI) was defined as skeletal muscle mass/height² (kg/m²). Serum IS, p-cresol sulfate (PCS), and trimethylamine N-oxide (TMAO) levels were determined using liquid chromatography-mass spectrometry. Patients were classified into two groups based on median serum IS values. HGS measurement was repeated after 2 years. Patients in the high IS group had longer HD duration and higher serum TMAO levels than those in the low IS group. Log-normalized IS level was negatively correlated with SMI (r = - 0.227; p = 0.018), but PCS and TMAO levels were not. Among 78 patients who completed 2-year follow-up, those in the high IS group (n = 41) showed greater absolute (- 2.48 kg versus - 0.25 kg, p = 0.035) and relative HGS loss (- 9.1% versus 1.4%, p = 0.036) than those in the low IS group, after adjustment for potential confounders. Indoxyl sulfate (IS) may play a significant role in uremic sarcopenia. Further large-scale studies are needed to confirm our preliminary findings.

UPLC-MS-based urine nontargeted metabolic profiling identifies dysregulation of pantothenate and CoA biosynthesis pathway in diabetic kidney disease

AIMS

Diabetic kidney disease (DKD) is a major prevalent chronic microvascular complication of type 2 diabetes (T2D). However, the present diagnostic indicators have limitations in the early diagnosis of DKD. This study concentrated on the sensitive and specific biomarkers in early diagnosis of DKD by metabolomics.

MATERIALS AND METHODS

In this cross-sectional study, we performed a UPLC-MS based nontargeted metabolomics assay to profile the urinary metabolites in patients with DKD. Principal Component Analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA) were used for screening out the metabolomic variables.

KEY FINDINGS

A total of 147 urinary metabolites were identified and 5 metabolic pathways were correlated with DKD pathophysiology. Pantothenate and coenzyme A biosynthesis pathway alteration was found the most prominent in DKD subjects. 4 metabolites, including dihydrouracil, ureidopropionic acid, pantothenic acid (PA), and adenosine 3',5'-diphosphate involved in pantothenate and CoA biosynthesis were significantly down-regulated.

SIGNIFICANCE

Our finding indicates that PA would be served as a novel predictive biomarker associated with DKD development and progression. Furthermore, our results provide a promising prospect that PA and CoA biosynthesis pathway can be potential therapeutic targets for DKD treatment.

Cohort profile: the Singapore diabetic cohort study

PURPOSE

The diabetic cohort (DC) was set up to study the determinants of complications in individuals with type 2 diabetes and examine the role of genetic, physiological and lifestyle factors in the development of complications in these individuals.

PARTICIPANTS

A total of 14 033 adult participants with type 2 diabetes were recruited from multiple public sector polyclinics and hospital outpatient clinics in Singapore between November 2004 and November 2010. The first round of follow-up was conducted for 4131 participants between 2012 and 2016; the second round of follow-up started in 2016 and is expected to end in 2021. A questionnaire survey, physical assessments, blood and urine sample collection were conducted at recruitment and each follow-up visit. The data set also includes genetic data and linkage to medical and administrative records for recruited participants.

FINDINGS TO DATE

Data from the cohort have been used to identify determinants of diabetes and related complications. The longitudinal data of medical records have been used to analyse diabetes control over time and its related outcomes. The cohort has also contributed to the identification of genetic loci associated with type 2 diabetes and diabetic kidney disease in collaboration with other large cohort studies. About 25 scientific papers based on the DC data have been published up to May 2019.

FUTURE PLANS

The rich data in DC can be used for various types of research to study disease-related complications in patients with type 2 diabetes. We plan to further investigate disease progression and new biomarkers for common diabetic complications, including diabetic kidney disease and diabetic neuropathy.

Foodomics Revealed the Effects of Extract Methods on the Composition and Nutrition of Peanut Oil

Processing technology has a significant effect on the functional quality of vegetable oil, but the exact mechanism is not yet very well known so far. The purpose of this study was to investigate the effects of extract methods on the composition and nutrition of peanut oil. Peanut oil was prepared by cold pressing, hot pressing, and enzyme-assisted aqueous extraction, and their trace components were determined by liquid chromatography-mass spectrometry (LC-MS). Serum and liver samples from Sprague-Dawley (SD) rats fed with different extract oils were profiled by gas chromatography-mass spectrometry (GC-MS) and LC-MS. The component analysis showed that different process technologies cause differentiation of trace active ingredients. Metabolomics analysis revealed that a high-fat diet causes serum and hepatic metabolic disorders, which can be ameliorated by hot-pressed and hydroenzymatic peanut oil, including downregulation of partial amino acids, fatty acids, phospholipids, and carbohydrates in cold-pressed peanut oil as well as the upregulation of palmitic acid, uric acid, and pyrimidine in enzyme-assisted aqueous oils. Canonical correspondence analysis (CCA) uncovered strong associations between specific metabolic alterations and peanut oil trace components. The data obtained in this study offers a new insight on the roles of oil processing.

Application of Metabolomics to Renal and Cardiometabolic Diseases

Metabolomics has been increasingly applied to study renal and related cardiometabolic diseases, including diabetes and cardiovascular diseases. These studies span cross-sectional studies correlating metabolites with specific phenotypes, longitudinal studies to identify metabolite predictors of future disease, and physiologic/interventional studies to probe underlying causal relationships. This chapter provides a description of how metabolomic profiling is being used in these contexts, with an emphasis on study design considerations as a practical guide for investigators who are new to this area. Research in kidney diseases is underlined to illustrate key principles. The chapter concludes by discussing the future potential of metabolomics in the study of renal and cardiometabolic diseases.

Statistical Workflow for Feature Selection in Human Metabolomics Data

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.

A comparison of L-carnitine and several cardiovascular-related biomarkers between healthy vegetarians and omnivores

OBJECTIVE

A plant-based diet has been associated with a reduced risk of cardiovascular (CV) diseases. This study aimed to determine the levels and correlations of CV-related biomarkers and the beneficial role of dietary habits.

METHODS

A total of 63 healthy vegetarians (n = 32) and omnivores (n = 31) were recruited. The baseline characteristics were recorded and measured (including lipid profiles, blood glucose, etc.). Liquid chromatography-mass spectrometry method was developed for the simultaneous determination of seven circulating CV-related biomarkers.

RESULTS

L-carnitine (L-Car), L-methionine, and ascorbic acid (AA) were significantly higher in vegetarians than in omnivores. In the vegetarians, L-Car had a negative correlation with triacylglycerols (P = 0.042) and blood glucose (P = 0.048) and a positive correlation with high-density lipoprotein cholesterol (P = 0.049). L-Car was also positively correlated with L-lysine (P = 0.009), L-methionine (P = 0.006), and AA (P = 0.035). The vegetarians' AA also had a negative correlation with L-homocysteine (P = 0.028). In the omnivores, L-Car was negatively correlated with total cholesterol (P = 0.008), low-density lipoprotein cholesterol (P = 0.004), and high-density lipoprotein cholesterol (P = 0.038). Omnivores' body mass index was positively correlated with L-homocysteine (P = 0.033), and age was positively correlated with trimethylamine N-oxide (P < 0.001) and blood glucose (P = 0.007), but not in vegetarians.

CONCLUSIONS

Our results suggest that vegetarians have an elevated level of L-Car, which might be associated with endogenous biosynthesis and diet composition. Circulating L-Car might play an important role in CV protection, especially in vegetarians.

Toward a better understanding of chronic kidney disease with complementary chromatographic methods hyphenated with mass spectrometry for improved polar metabolome coverage

The prevalence of chronic kidney disease (CKD) is increasing worldwide. New technical approaches are needed to improve early diagnosis, disease understanding and patient monitoring, and to evaluate new therapies. Metabolomics, as a prime candidate in the field of CKD research, aims to comprehensively analyze the metabolic complexity of biological systems. An extensive analysis of the metabolites contained in biofluids is therefore needed, and the combination of data obtained from multiple analytical platforms constitutes a promising methodological approach. This study presents an original workflow based on complementary chromatographic conditions, reversed-phase and hydrophilic interaction chromatography hyphenated to mass spectrometry to improve the polar metabolome coverage coupled with a univocal metabolite annotation strategy enabling a rapid access to the biological interpretation. This multiplatform workflow was applied in a CKD cohort study to assess plasma metabolic profile modifications related to renal disease. Multivariate analysis of 278 endogenous annotated metabolites enabled patient stratification with respect to CKD stages and helped to generate new biological insights, while also confirming the relevance of tryptophan metabolism pathway in this condition.

Metabolomics Reveals Metabolic Changes Caused by Low-Dose 4-Tert-Octylphenol in Mice Liver

Background: Humans are constantly exposed to low concentrations of 4-tert-octylphenol (OP). However, studies investigating the effects of low-dose OP on the liver are scarce, and the mechanism of these effects has not been thoroughly elucidated to date. Methods: Adult male institute of cancer research (ICR) mice were exposed to low-dose OP (0, 0.01 and 1 μg/kg/day) for 7 consecutive days. Weights of mice were recorded daily during the experiment. Blood serum levels of OP, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were determined, and haematoxylin-eosin (HE) staining of the liver was performed. We applied an integrated metabolomic and enzyme gene expression analysis to investigate liver metabolic changes, and the gene expression of related metabolic enzymes was determined by real-time PCR and ELISA. Results: OP in blood serum was increased after OP exposure, while body weights of mice were unchanged. Liver weight and its organ coefficient were decreased significantly in the OP (1 μg/kg/day) group, but ALT and AST, as well as the HE staining results, were unchanged after OP treatment. The levels of cytidine, uridine, purine and N-acetylglutamine were increased significantly, and the level of vitamin B6 was decreased significantly in mice treated with OP (1 μg/kg/day). The mRNA and protein levels of Cda and Shmt1 were both increased significantly in OP (1 μg/kg/day)-treated mice. Conclusions: Through metabolomic analysis, our study firstly found that pyrimidine and purine synthesis were promoted and that N-acetylglutamine was upregulated after low-dose OP treatment, indicating that the treatment disturbed nucleic acid and amino acid metabolism in mice liver.

Might hyperbaric oxygen therapy (HBOT) reduce renal injury in diabetic people with diabetes mellitus? From preclinical models to human metabolomics

Diabetic kidney disease (DKD) is the leading cause of end-stage renal failure in the western world. Current treatment of diabetic kidney disease relies on nutritional management and drug therapies to achieve metabolic control. Here, we discuss the potential application of hyperbaric oxygen therapy (HBOT) for the treatment of diabetic kidney disease (DKD), a treatment which requires patients to breathe in 100% oxygen at elevated ambient pressures. HBOT has traditionally been used to diabetic foot ulcers (DFU) refractory to conventional medical treatments. Successful clinic responses seen in the DFU provide the underlying therapeutic rationale for testing HBOT in the setting of DKD. Both the DFU and DKD have microvascular endothelial disease as a common underlying pathologic feature. Supporting evidence for HBOT of DKD comes from previous animal studies and from our preliminary prospective clinical trial reported here. We report urinary metabolomic data obtained from patients undergoing HBOT for DFU, before and after exposure to 6 weeks of HBOT. The preliminary data support the concept that HBOT can reduce biomarkers of renal injury, oxidant stress, and mitochondrial dysfunction in patients receiving HBOT for DFU. Further studies are needed to confirm these initial findings and correlate them with simultaneous measures of renal function. HBOT is a safe and effective treatment for DFU and could also be for individuals with DKD.

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.

Serum metabolites are associated with all-cause mortality in chronic kidney disease

Chronic kidney disease (CKD) involves significant metabolic abnormalities and has a high mortality rate. Because the levels of serum metabolites in patients with CKD might provide insight into subclinical disease states and risk for future mortality, we determined which serum metabolites reproducibly associate with mortality in CKD using a discovery and replication design. Metabolite levels were quantified via untargeted liquid chromatography and mass spectroscopy from serum samples of 299 patients with CKD in the Modification of Diet in Renal Disease (MDRD) study as a discovery cohort. Six among 622 metabolites were significantly associated with mortality over a median follow-up of 17 years after adjustment for demographic and clinical covariates, including urine protein and measured glomerular filtration rate. We then replicated associations with mortality in 963 patients with CKD from the African American Study of Kidney Disease and Hypertension (AASK) cohort over a median follow-up of ten years. Three of the six metabolites identified in the MDRD cohort replicated in the AASK cohort: fumarate, allantoin, and ribonate, belonging to energy, nucleotide, and carbohydrate pathways, respectively. Point estimates were similar in both studies and in meta-analysis (adjusted hazard ratios 1.63, 1.59, and 1.61, respectively, per doubling of the metabolite). Thus, selected serum metabolites were reproducibly associated with long-term mortality in CKD beyond markers of kidney function in two well characterized cohorts, providing targets for investigation.

Biomarkers of diabetic kidney disease

Diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in diabetes. The quest for both prognostic and surrogate endpoint biomarkers for advanced DKD and end-stage renal disease has received major investment and interest in recent years. However, at present no novel biomarkers are in routine use in the clinic or in trials. This review focuses on the current status of prognostic biomarkers. First, we emphasise that albuminuria and eGFR, with other routine clinical data, show at least modest prediction of future renal status if properly used. Indeed, a major limitation of many current biomarker studies is that they do not properly evaluate the marginal increase in prediction on top of these routinely available clinical data. Second, we emphasise that many of the candidate biomarkers for which there are numerous sporadic reports in the literature are tightly correlated with each other. Despite this, few studies have attempted to evaluate a wide range of biomarkers simultaneously to define the most useful among these correlated biomarkers. We also review the potential of high-dimensional panels of lipids, metabolites and proteins to advance the field, and point to some of the analytical and post-analytical challenges of taking initial studies using these and candidate approaches through to actual clinical biomarker use.

Identifying diseases-related metabolites using random walk

Background

Metabolites disrupted by abnormal state of human body are deemed as the effect of diseases. In comparison with the cause of diseases like genes, these markers are easier to be captured for the prevention and diagnosis of metabolic diseases. Currently, a large number of metabolic markers of diseases need to be explored, which drive us to do this work.

Methods

The existing metabolite-disease associations were extracted from Human Metabolome Database (HMDB) using a text mining tool NCBO annotator as priori knowledge. Next we calculated the similarity of a pair-wise metabolites based on the similarity of disease sets of them. Then, all the similarities of metabolite pairs were utilized for constructing a weighted metabolite association network (WMAN). Subsequently, the network was utilized for predicting novel metabolic markers of diseases using random walk.

Results

Totally, 604 metabolites and 228 diseases were extracted from HMDB. From 604 metabolites, 453 metabolites are selected to construct the WMAN, where each metabolite is deemed as a node, and the similarity of two metabolites as the weight of the edge linking them. The performance of the network is validated using the leave one out method. As a result, the high area under the receiver operating characteristic curve (AUC) (0.7048) is achieved. The further case studies for identifying novel metabolites of diabetes mellitus were validated in the recent studies.

Conclusion

In this paper, we presented a novel method for prioritizing metabolite-disease pairs. The superior performance validates its reliability for exploring novel metabolic markers of diseases.

Metabolic profiling of asthma in mice and the interventional effects of SPA using liquid chromatography and Q-TOF mass spectrometry

Asthma is a chronic inflammatory lung disease that leads to 250 000 deaths annually. There is a need to better understand asthma by identifying new pathogenic molecules. We conducted a liquid-chromatography time-of-flight mass spectrometry (LC-Q-TOF-MS)-based metabolomics study to test for asthma and investigate the interventional mechanisms of surfactant protein A (SPA) in OVA-induced asthma mice. The results revealed that asthma disturbed 32 metabolites in 9 metabolic pathways. After SPA treatment, the metabolomics profile found in asthma was significantly reversed, shifting much closer to that of the control group, indicating that SPA has therapeutic effects against asthma. Metabolomic pathway analysis by the ingenuity pathway analysis demonstrated that several pathways including fatty acid metabolism, lipid metabolism, and purine metabolism were significantly altered in asthma. This study offers new methodologies for the understanding of asthma and the mechanisms of SPA in treating asthma.

The Promise of Systems Biology for Diabetic Kidney Disease

Diabetic kidney disease (DKD) has a complex and prolonged pathogenesis involving many cell types in the kidney as well as extrarenal factors. It is clinically silent for many years after the onset of diabetes and usually progresses over decades. Given this complexity, a comprehensive and unbiased molecular approach is best suited to help identify the most critical mechanisms responsible for progression of DKD and those most suited for targeted intervention. Systems biological investigations provide such an approach since they examine the entire network of molecular changes that occur in a disease process in a comprehensive way instead of focusing on a single abnormal molecule or pathway. Systems biological studies can also start with analysis of the disease in humans, not in animal or cell culture models that often poorly reproduce the changes in human DKD. Indeed, in the last decade, systems biological approaches have led to the identification of critical molecular abnormalities in DKD and have directly led to development of new biomarkers and potential treatments for DKD.

Metabolic profiling reveals new serum biomarkers of lupus nephritis

Metabolomics has been applied to explore altered metabolite profiles in disease and identify unique metabolic signatures specific to certain pathologies. The aim of the current study is to characterize the metabolic profile of patients diagnosed with lupus nephritis (LN) and explore new insights into underlying disease processes. A metabolomic approach using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS) was developed in serum samples from 32 LN patients, 30 idiopathic nephrotic syndrome (INS) patients and 28 healthy controls (HCs). Potential biomarkers were screened from orthogonal projection to latent structures discriminate analysis (OPLS-DA) and further evaluated by receiver operating characteristic analysis (ROC). A total of 14 potential biomarkers were screened and tentatively identified for LN patients compared to HCs. Compared to HCs and INS patients, the LN patients had increased serum levels of sorbitol and glycocholic acid metabolites and decreased levels of cortisol, creatinine and L-aspartyl-L-phenylalanine. A panel of three metabolomics (theophylline, oxidized glutathione and capric acid) was identified as biomarkers of LN with a sensitivity of 87.50% and a specificity of 67.86% using ROC analysis. Our results suggest that UPLC-HRMS based quantification of circulating metabolites was a useful tool for identification of biomarkers with the ability to segregate LN patients from INS patients and HCs. The potential biomarkers indicated that the LN metabolic disturbance may be closely associated with inflammation injury, oxidative stress and phospholipid metabolism.

Profiling of Plasma Metabolites Suggests Altered Mitochondrial Fuel Usage and Remodeling of Sphingolipid Metabolism in Individuals With Type 2 Diabetes and Kidney Disease

Introduction

Pathophysiology of diabetic kidney disease (DKD) is incompletely understood. We aim to elucidate metabolic abnormalities associated with DKD in type 2 diabetes mellitus (T2DM) by targeted plasma metabolomics.

Methods

A total of 126 T2DM participants with early DKD (urinary albumin-to-creatinine ratio [ACR] 30−299 mg/g and eGFR ≥ 60 ml/min/1.73 m²), 154 overt DKD (ACR ≥ 300 mg/g or eGFR < 60 ml/min/1.73 m²), and 129 non-DKD T2DM controls (ACR < 30 mg/g and eGFR ≥ 60 ml/min/1.73 m²) were included in discovery study. Findings were subsequently validated in 149 T2DM with macroalbuminuria (ACR ≥ 300 mg/g) and 149 matched non-DKD T2DM controls. Plasma amino acid, acylcarnitine, Krebs cycle organic acid, and sphingolipids/ceramide levels were quantified by liquid chromatography−mass spectrometry and gas chromatography−mass spectrometry.

Results

Of 123 metabolites included in the data analysis, 24 differed significantly between DKD and controls in the same direction in both discovery and validation subpopulations. A number of short acylcarnitines including their dicarboxylic derivatives (C2−C6) were elevated in DKD, suggesting abnormalities in fatty acids and amino acids metabolic pathways. Five phosphatidylcholines were lower whereas 4 metabolites in the sphingomyelin−ceramide subfamily were higher in DKD. Principal component regression revealed that long-chain ceramides were independently associated with ACR but not eGFR. Conversely, essential amino acids catabolism and short dicarboxylacylcarnitine accumulation were associated with eGFR but not ACR.

Discussion

DKD is associated with altered fuel substrate use and remodeling of sphingolipid metabolism in T2DM with DKD. Associations of albuminuria and impaired filtration function with distinct metabolomic signatures suggest different pathophysiology underlying these 2 manifestations of DKD.

NMR spectroscopy-based metabolomic study of serum in sulfur mustard exposed patients with lung disease

Sulfur mustard (SM) is a vesication chemical warfare agent for which there is currently no antidote. Despite years of research, there is no common consensus about the pathophysiological basis of chronic pulmonary disease caused by this chemical warfare agent. In this study, we combined chemometric techniques with nuclear magnetic resonance (NMR) spectroscopy to explore the metabolic profile of sera from SM-exposed patients. A total of 29 serum samples obtained from 17 SM-injured patients, and 12 healthy controls were analyzed by Random Forest. Increased concentrations of seven amino acids, glycerol, dimethylamine, ketone bodies, lactate, acetate, citrulline and creatine together with the decreased very low-density lipoproteins (VLDL) levels were observed in patients compared with control subjects. Our study reveals the metabolic profile of sera from SM-injured patients and indicates that NMR-based methods can distinguish these patients from healthy controls.

Metabolomics in Diabetic Kidney Disease: Unraveling the Biochemistry of a Silent Killer

The development of new therapies for chronic diseases, such as diabetic kidney disease (DKD), will continue to be hampered by lack of sufficient biomarkers that will provide insights and will be responsive to treatment interventions. The recent application of metabolomic technologies, such as nuclear magnetic resonance and mass spectroscopy, has allowed large-scale analysis of small molecules to be interrogated in a targeted or untargeted manner. Recent advances from both human and animal studies that have arisen from metabolomic analysis have recognized that mitochondrial function and fatty acid oxidation play key roles in the development and progression of DKD. Although many challenges in the technology for clinical chronic kidney disease (CKD) are yet to be validated, there will very likely be ongoing major contributions of metabolomics to develop new biochemical understanding for diabetic and CKD. The clinical application of metabolomics and accompanying bioinformatic tools will likely be a cornerstone of personalized medicine triumphs for CKD.

Prognostic clinical and molecular biomarkers of renal disease in type 2 diabetes

Diabetic kidney disease occurs in ∼ 25-40% of patients with type 2 diabetes. Given the high risk of progressive renal function loss and end-stage renal disease, early identification of patients with a renal risk is important. Novel biomarkers may aid in improving renal risk stratification. In this review, we first focus on the classical panel of albuminuria and estimated glomerular filtration rate as the primary clinical predictors of renal disease and then move our attention to novel biomarkers, primarily concentrating on assay-based multiple/panel biomarkers, proteomics biomarkers and metabolomics biomarkers. We focus on multiple biomarker panels since the molecular processes of renal disease progression in type 2 diabetes are heterogeneous, rendering it unlikely that a single biomarker significantly adds to clinical risk prediction. A limited number of prospective studies of multiple biomarkers address the predictive performance of novel biomarker panels in addition to the classical panel in type 2 diabetes. However, the prospective studies conducted so far have small sample sizes, are insufficiently powered and lack external validation. Adequately sized validation studies of multiple biomarker panels are thus required. There is also a paucity of studies that assess the effect of treatments on novel biomarker panels and determine whether initial treatment-induced changes in novel biomarkers predict changes in long-term renal outcomes. Such studies can not only improve our healthcare but also our understanding of the mechanisms of actions of existing and novel drugs and may yield biomarkers that can be used to monitor drug response. We conclude that this will be an area to focus research on in the future.

Urinary Biomarkers of Brain Diseases

Biomarkers are the measurable changes associated with a physiological or pathophysiological process. Unlike blood, urine is not subject to homeostatic mechanisms. Therefore, greater fluctuations could occur in urine than in blood, better reflecting the changes in human body. The roadmap of urine biomarker era was proposed. Although urine analysis has been attempted for clinical diagnosis, and urine has been monitored during the progression of many diseases, particularly urinary system diseases, whether urine can reflect brain disease status remains uncertain. As some biomarkers of brain diseases can be detected in the body fluids such as cerebrospinal fluid and blood, there is a possibility that urine also contain biomarkers of brain diseases. This review summarizes the clues of brain diseases reflected in the urine proteome and metabolome.

Metabolomic biomarkers in diabetic kidney diseases--A systematic review

Diabetic kidney disease (DKD) is generally characterized by increasing albuminuria in diabetic patients; however, few biomarkers are available to facilitate early diagnosis of this disease. The application of metabolomics has shown promises addressing this need. In this review, we conducted a search about metabolomic biomarkers in DKD patients through MEDLINE, EMBASE, and Cochrane Database up to the end of March, 2015. 12 eligible studies were selected and evaluated subsequently through the use of QUADOMICS, a quality assessment tool. 7 of the 12 included studies were classified as 'high quality'. We also recorded specific study characteristics including participants' characteristics, metabolomic techniques, sample types, and significantly altered metabolites between DKD and control groups. Products of lipid metabolisms including esterified and non-esterified fatty acids, carnitines, phospholipids and metabolites involved in branch-chained amino acids and aromatic amino acids metabolisms were frequently affected biomarkers of DKD. Other differential metabolites were also found, while some of their associations with DKD were unclear. Further more studies are required to test these findings in larger, diverse ethnic populations with elaborate study designs, and finally we could translate them into the benefits of DKD patients.

Metabolomics Reveals Inflammatory-Linked Pulmonary Metabolic Alterations in a Murine Model of House Dust Mite-Induced Allergic Asthma

Although the house dust mite (HDM) is a major environmental aeroallergen that promotes the pathogenesis and severity of allergic asthma, it remains elusive if HDM exposures can induce global metabolism aberrations during allergic airway inflammation. Using an integrated gas and liquid chromatography mass spectrometry-based metabolomics and multiplex cytokine profile analysis, metabolic alterations and cytokine changes were investigated in the bronchoalveolar lavage fluid (BALF), serum, and lung tissues in experimental HDM-induced allergic asthma. Allergic pulmonary HDM exposures lead to pronounced eosinophilia, neutrophilia, and increases in inflammatory cytokines. Metabolomics analysis of the BALF, serum, and lung tissues revealed distinctive compartmental metabolic signatures, which included depleted carbohydrates, increased energy metabolites, and consistent losses of sterols and phosphatidylcholines. Pearson correlation analysis uncovered strong associations between specific metabolic alterations and inflammatory cells and cytokines, linking altered pulmonary metabolism to allergic airway inflammation. The clinically prescribed glucocorticoid prednisolone could modulate airway inflammation but was ineffective against the reversal of many HDM-induced metabolic alterations. Collectively, metabolomics reveal comprehensive pulmonary metabolic signatures in HDM-induced allergic asthma, with specific alterations in carbohydrates, lipids, sterols, and energy metabolic pathways. Altered pulmonary metabolism may be a major underlying molecular feature involved during HDM-induced allergic airway inflammation, linked to inflammatory cells and cytokines changes.

Identifying early urinary metabolic changes with long-term environmental exposure to cadmium by mass-spectrometry-based metabolomics

Cadmium (Cd) is a common environmental pollutant, and urinary Cd (UCd) is generally used as a marker of exposure; however, our understanding on the related urinary metabolic changes caused by Cd exposure is still not clear. In this study, we applied a mass-spectrometry-based metabolomic approach to assess the urinary metabolic changes in human with long-term environmental Cd exposure, aimed to identify early biomarkers to assess Cd nephrotoxicity. Urine samples from 94 female never smokers aged 44-70 with UCd in the range of 0.20-68.67 μg/L were analyzed by liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-ToF-MS) and gas chromatography-mass spectrometry (GC-MS). It was found that metabolites related to amino acid metabolism (L-glutamine, L-cystine, L-tyrosine, N-methyl-L-histidine, L-histidinol, taurine, phenylacetylglutamine, hippurate, and pyroglutamic acid), galactose metabolism (D-galactose and myo-inositol), purine metabolism (xanthine, urea, and deoxyadenosine monophosphate), creatine pathway (creatine and creatinine), and steroid hormone biosynthesis (17-α-hydroxyprogesterone, tetrahydrocortisone, estrone, and corticosterone) were significantly higher among those with a UCd level higher than 5 μg/L. Moreover, we noticed that the level of N-methyl-L-histidine had already started to elevate among individuals with a UCd concentration of ≥2 μg/L. The overall findings illustrate that metabolomics offer a useful approach for revealing metabolic changes as a result of Cd exposure.

Metabolomic analysis of diet-induced type 2 diabetes using UPLC/MS integrated with pattern recognition approach

Metabolomics represents an emerging discipline concerned with comprehensive assessment of small molecule endogenous metabolites in biological systems and provides a powerful approach insight into the mechanisms of diseases. Type 2 diabetes (T2D), called the burden of the 21^st century, is growing with an epidemic rate. However, its precise molecular mechanism has not been comprehensively explored. In this study, we applied urinary metabolomics based on the UPLC/MS integrated with pattern recognition approaches to discover differentiating metabolites, to characterize and explore metabolic pathway disruption in an experimental model for high-fat-diet induced T2D. Six differentiating urinary metabolites were found in the negative mode, and two (2-(4-hydroxy-3-methoxy-phenyl) acetaldehyde sulfate, 2-phenylethanol glucuronide) of which were identified involving the key metabolic pathways linked to pentose and glucuronate interconversions, starch, sucrose metabolism and tyrosine metabolism. Our study provides new insight into pathophysiologic mechanisms and may enhance the understanding of T2D pathogenesis.

Exploratory investigation of plasma metabolomics in human lung adenocarcinoma

Globally lung cancer is common among males and recently also noted with increasing incidences in females, especially adenocarcinoma. Further, most lung cancers are not easily detected until the late stage. Metabolic profiling of plasma low molecular weight metabolites may help unveil the complex pathophysiological changes during early lung adenocarcinoma development. Here we used a combination of gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) methods to investigate the metabolic signatures in the plasma of 31 stage I human lung adenocarcinoma patients and 28 healthy controls. The metabolic profiles were assayed using orthogonal projections to latent structures discriminant analysis (OPLS-DA), and were further analyzed to identify the associated marker metabolites. The OPLS-DA models derived from both GC-MS and LC-MS showed significant discriminations in metabolic profiles between cases and healthy controls. It was found that around 37 metabolites contributed to the differences. The alterations of these metabolites implied disturbances in amino acids, lipids, fatty acids and glutaminolysis metabolism in human lung adenocarcinoma, even after removal of influencing factors such as age, gender and smoking habits. Of particular interest, the sex hormone metabolic pathway involving the sulfate conjugate of testosterone, androsterone and pregnenolone was found to be disturbed considerably. All these metabolic perturbations occur at an early stage of lung adenocarcinoma and thus could act as biomarkers for its early diagnosis. These exploratory findings suggest that integration of two sensitive and complementary metabolomic approaches enables a comprehensive metabolite profiling for human lung adenocarcinoma, although a more extensive study is needed to confirm the findings.

Serum metabolome and lipidome changes in adult patients with primary dengue infection

Background

Dengue virus (DENV) is the most widespread arbovirus with an estimated 100 million infections occurring every year. Endemic in the tropical and subtropical areas of the world, dengue fever/dengue hemorrhagic fever (DF/DHF) is emerging as a major public health concern. The complex array of concurrent host physiologic changes has hampered a complete understanding of underlying molecular mechanisms of dengue pathogenesis.

Methodology/Principle Findings

Systems level characterization of serum metabolome and lipidome of adult DF patients at early febrile, defervescence, and convalescent stages of DENV infection was performed using liquid chromatography- and gas chromatography-mass spectrometry. The tractability of following metabolite and lipid changes in a relatively large sample size (n = 44) across three prominent infection stages allowed the identification of critical physiologic changes that coincided with the different stages. Sixty differential metabolites were identified in our metabolomics analysis and the main metabolite classes were free fatty acids, acylcarnitines, phospholipids, and amino acids. Major perturbed metabolic pathways included fatty acid biosynthesis and β-oxidation, phospholipid catabolism, steroid hormone pathway, etc., suggesting the multifactorial nature of human host responses. Analysis of phospholipids and sphingolipids verified the temporal trends and revealed association with lymphocytes and platelets numbers. These metabolites were significantly perturbed during the early stages, and normalized to control levels at convalescent stage, suggesting their potential utility as prognostic markers.

Conclusions/Significance

DENV infection causes temporally distinct serum metabolome and lipidome changes, and many of the differential metabolites are involved in acute inflammatory responses. Our global analyses revealed early anti-inflammatory responses working in concert to modulate early pro-inflammatory processes, thus preventing the host from development of pathologies by excessive or prolonged inflammation. This study is the first example of how an omic- approach can divulge the extensive, concurrent, and dynamic host responses elicited by DENV and offers plausible physiological insights to why DF is self limiting.

Dengue virus is the most widespread arbovirus and a major public health threat in the tropical and subtropical areas of the world. As yet, little is known about the molecular mechanisms underlying infection, and there is no specific treatment or vaccine that is currently effective against the disease. Metabolomics and lipidomics provide global views of metabolome and lipidome landscapes and implicate metabolic to disease phenotype. We performed serum metabolic and lipidomic profiling on a cohort of dengue patients with three sampling time points at early febrile, defervescence, and convalescent stages via mass spectrometry-based analytical platforms. Compared with healthy subjects, approximately two hundred metabolites showed significant difference in dengue patients, and 60 were identified. This study revealed that in primary dengue infection, the host metabolome is tightly regulated, with active, early anti-inflammatory processes modulating the pro-inflammatory processes, suggesting the self-limiting phenotype of dengue fever. Major perturbed metabolic pathways included fatty acid biosynthesis, fatty acid β-oxidation, phospholipid catabolism, steroid hormone pathway, etc. This represents a first report on the characterization of the serum metabolome and significantly advances our understanding on host and dengue virus interactions. These differential metabolites have the potential as biomarkers for disease monitoring and evaluation of therapeutic interventions.

Metabolic profiling of plasma from benign and malignant pulmonary nodules patients using mass spectrometry-based metabolomics

Solitary pulmonary nodule (SPN or coin lesion) is a mass in the lung and can be commonly found in chest X-rays or computerized tomography (CT) scans. However, despite the advancement of imaging technologies, it is still difficult to distinguish malignant cancer from benign SPNs. Here we investigated the metabolic profiling of patients with benign and malignant pulmonary nodules. A combination of gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS) was used to profile the plasma metabolites in 17 patients with malignant SPNs, 15 patients with benign SPNs and 20 healthy controls. The metabolic profiles were assayed using OPLS-DA, and further analyzed to identify marker metabolites related to diseases. Both GC/MS- and LC/MS-derived models showed clear discriminations in metabolic profiles among three groups. It was found that 63 metabolites (12 from GC/MS, 51 from LC/MS) contributed to the differences. Of these, 48 metabolites showed same change trend in both malignant and benign SPNs as compared with healthy controls, indicating some common pathways including inflammation and oxidative injury shared by two diseases. In contrast, 14 metabolites constituted distinct profiles that differentiated malignant from benign SPNs, which might be a unique biochemical feature associated with lung cancer. Overall, our data suggested that integration of two highly sensitive and complementary metabolomics platforms could enable a comprehensive metabolic profiling and assist in discrimination malignant from benign SPNs.