Metabotyping reveals distinct metabolic alterations in ketotic cows and identifies early predictive serum biomarkers for the risk of disease

Introduction

Ketosis is a prevalent metabolic disease of transition dairy cows that affects milk yield and the development of other periparturient diseases.

Objectives

The objective of this study was to retrospectively metabotype the serum of dairy cows affected by ketosis before clinical signs of disease, during the diagnosis of ketosis, and after the diagnosis of disease and identify potential predictive and diagnostic serum metabolite biomarkers for the risk of ketosis.

Methods

Targeted metabolomics was used to identify and quantify 128 serum metabolites in healthy (CON, n?=?20) and ketotic (n?=?6) cows by DI/LC-MS/MS at ?8 and ?4 weeks prepartum, during the disease week, and at +4 and +8 weeks after parturition.

Results

Significant changes were detected in the levels of several metabolite groups including amino acids, glycerophospholipids, sphingolipids, acylcarnitines, and biogenic amines in the serum of ketotic cows during all time points studied.

Conclusions

Results of this study support the idea that ketosis is preceded and associated and followed by alterations in multiple metabolite groups. Moreover, two sets of predictive biomarker models and one set of diagnostic biomarker model with very high sensitivity and specificity were identified. Overall, these findings throw light on the pathobiology of ketosis and some of the metabolites identified might serve as predictive biomarkers for the risk of ketosis. The data must be considered as preliminary given the lower number of ketotic cows in this study and more research with a larger cohort of cows is warranted to validate the results.

     

Metallotyping of ketotic dairy cows reveals major alterations preceding,associating, and following the disease occurrence

Introduction

Ketosis is a common metabolic disorder, which is characterized by elevated concentrations of ketone bodies or ketoacids in three body fluids including blood, urine, and milk. Two of the ketones including β-hydroxybutyric acid and acetoacetic acid are strong acids which at high concentrations trigger ketoacidosis influencing physiological functions of various tissues and organs.

Objectives

The objectives of this study were to: (1) investigate mineral alterations in both serum and urine of preketotic, ketotic, and postketotic cows, (2) identify potential predictive and diagnostic mineral biomarkers for ketosis in serum and urine, and (3) better understand the role of minerals in the pathobiology of the disease.

Methods

Inductively coupled plasma mass spectrometry metallotyping was performed in the serum and urine of six cases of ketosis and 20 healthy controls cows at ?8 and ?4 weeks prepartum, at disease diagnosis week, and at +4 and +8 weeks postpartum.

Results

Data showed that concentrations of aluminum (Al), iron (Fe), manganese (Mn), and arsenic (As) were greater (P?<?0.001) in the serum of preketotic, ketotic and postketotic cows at most of the tested time points. Moreover, boron (B) and Al as well as calcium (Ca), phosphorus (P), potassium (K), and magnesium (Mg) were found to be elevated in the urine of preketotic and postketotic cows (P?<?0.001).

Conclusions

It is concluded that alterations of mineral elements observed in the serum and urine of preketotic, ketotic, and postketotic cows might be related to the state of chronic acidosis in those cows. The mineral elements identified in both serum and urine can be used as biomarkers to early diagnose ketosis at its pre-subclinical state and develop preventive interventions in the future.

     

Prepartal overfeeding alters the lipidomic profiles in the liver and the adipose tissue of transition dairy cows

Introduction

Physiological adaptations in the energy metabolism of dairy cows during the periparturient period are partly mediated by insulin resistance (IR), which may subsequently induce metabolic disorders postpartum. The molecular mechanisms underlying IR in dairy cows are largely unknown.

Objective

This study aimed to find a novel insight into the molecular mechanisms underlying IR in dairy cows during the periparturient period by analyzing the effects of prepartal overfeeding on the lipidomic profiles in the liver and adipose tissue (AT).

Methods

Sixteen cows were allocated to controlled-energy and high-energy feeding groups. Lipidomic profiling was conducted on liver and adipose tissue samples collected at 8 days prior to the predicted parturition, and 1 day (only AT) and 9 days after the actual parturition.

Results

Five ceramides (Cers) were identified to be significantly increased by prepartal overfeeding in AT in the analysis of the variance between groups within time points. Principal component-linear discriminant analysis showed that lipidomic profiles between the feeding groups were mainly characterized by phosphatidylcholines (PC), phosphatidylethanolamines (PE), lysophophosphatidylcholines (LysoPC), and lysophosphatidylethanolamines (LysoPE) in the liver, and by Cer, PE, and phosphatidylinositols (PI) in AT. Lipid class levels indicated that prepartal overfeeding elevated the concentration of PE, PI, LysoPC, LysoPE, and sphingomyelin in the liver, and increased the concentration of Cer in AT during the periparturient period.

Conclusion

Prepartal overfeeding significantly altered the concentrations of various sphingolipids, phospholipids, and lysophospholipids in the liver and AT of dairy cows during the periparturient period.

     

Cinnamon: does it hold its promises in cows? Using non-targeted blood serum metabolomics profiling to test the effects of feeding cinnamon to dairy cows undergoing lactation-induced insulin resistance

Introduction

Cinnamon exerts insulin-enhancing activity in vitro and was demonstrated to improve blood glucose and lipid profiles in several human studies. Such effects may have an impact on metabolically stressed cows.

Objective

To study the effects of cinnamon supplementation during the transition from late pregnancy to early lactation on the metabolism in dairy cows.

Methods

Twenty-four Holstein cows (n?=?8/group) were assigned to either the control group (CTR; without supplementation) or the supplementation groups [supplemental cinnamon at 20 (LCIN) or 40 (HCIN) g/cow per day (d)] from 28 d before calving until 21 d thereafter. Blood samples were assayed for glucose, nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), and insulin; an index estimating insulin sensitivity (RQUICKI) was calculated. The serum metabolome was characterized in the samples collected from d 14 using a non-targeted approach.

Results

The serum concentrations of glucose and insulin did not differ among groups and followed a similar pattern over time. The serum NEFA concentrations were greater in LCIN (d 2, 7, and 14) and HCIN (d 14) than in CTR. On d 14 and 21, LCIN and HCIN had greater serum BHBA concentrations than CTR cows. The top 10 metabolites identified with significantly higher levels in the supplemented than the CTR cows were related to fatty acid metabolism.

Conclusion

The data suggest lipolytic and ketogenic effects of cinnamon supplementation in dairy cows during the transition from late gestation to early lactation. The fatty acid metabolites found elevated in the supplemented cows point towards impaired mitochondrial fatty acid β-oxidation.

     

The urinary metabolome in female mink (<Emphasis Type="Italic">Mustela neovison</Emphasis>) shows distinct changes in protein and lipid metabolism during the transition from diapause to implantation

Introduction

The mink exhibit an obligatory diapause. The metabolic changes during the transition from diapause to implantation and established pregnancy are currently unknown.

Objectives

The study aimed to characterize changes in the urinary metabolome in mink during the period from mating to early gestation and to identify the metabolites involved.

Methods

Urine samples were collected from 56 female mink on March 24, April 8, and April 15, covering the period from mating to early pregnancy. The urine samples were subjected to non-targeted LC-MS metabolomics. Processed data were evaluated by principal component analysis (PCA) and the peak area of identified metabolites were subjected to ANOVA.

Results

The samples showed clear clustering according to sampling date in a PCA scores plot, and 35 metabolites differing significantly between sampling days were identified. The excretion of dicarboxylic acids and acylcarnitines of dicarboxylic acids exhibited a decline on April 8, and the same trend was observed for four unidentified metabolites, two of which were putatively identified as acids of the furan fatty acid type. The decreased excretion of lipid components was suggested to be a result of increased oxidation of these compounds. In contrast, the excretion of amino acid-related metabolites showed an increase on April 8 which was attributed to increased metabolism of amino acids at this time point.

Conclusion

The urinary metabolic profile of mink showed distinct changes during the period studied. The major changes were observed at the time of implantation where increases in the lipid and protein metabolism were evident.

     

Metabolite profiling: development and application of an UHR-QTOF-MS(/MS) method approach for the assessment of metabolic changes in high fat diet fed mice

Introduction

The metabolic alterations accompanying the development of insulin resistance and type 2 diabetes mellitus (T2DM) are complex, not coherently understood and only partially represented by conventional clinical tests like the oral glucose tolerance test. Changes in plasma metabolite concentrations preceding insulin resistance or overt T2DM may help understand the etiology of metabolic disorders and they are potential predictive risk markers.

Objectives

Here, we describe a non-targeted metabolomics platform based on UPLC-UHR-QToF-MS(/MS) for the assessment of plasma non-polar metabolites.

Methods

This method was applied to a longitudinal mouse obesity study comparing mice on control and high fat diet (HFD), respectively. Plasma metabolites were assessed 2, 4, 8 and 16 weeks after initiation of feeding. Multivariate analysis of the metabolite dataset showed clear differentiation of the feeding groups after 8 weeks when the HFD-fed mice exhibited clear signs of insulin resistance.

Results

The discrimination of the groups was due to changes in various metabolic pathways including, among others, glycerophospholipid, sphingolipid and cholesterol metabolism.

Conclusion

From 81 compounds with a p-value lower than 0.05, a total of 19 metabolites could be putatively identified due to their accurate mass, isotope and fragmentation pattern. Thirteen of these observed metabolites are known key metabolites to diabetes or its secondary diseases like diabetic nephropathy and neuropathy (Meiss, Werner, John, Scheja, Herbach, Heeren, Fischer 2015). The compounds putatively identified here may provide valuable starting points for further investigations and developments of clinical diagnostics and prediagnostics for T2DM and related diseases.

     

Comparative metabolome analysis of ruminal changes in Holstein dairy cows fed low- or high-concentrate diets

Introduction

Currently, information on the comprehensive changes in the ruminal metabolites of dairy cows fed high-concentrate diet is limited.

Objectives

This study aimed to compare the composition of whole-ruminal metabolites in dairy cows that were fed a low concentrate diet or a high concentrate diet using modern metabolome analysis.

Methods

Cows were fed a low-concentrate diet (LC; 40% concentrate feeds, dry matter (DM) basis) or a high-concentrate diet (HC; 70% concentrate feeds, DM basis). GC/MS was used to analyze rumen fluid samples.

Results

As compared with the LC group, HC diet significantly increased the concentration of bacterial degradation products (included xanthine, hypoxanthine, uracil, etc.), some toxic compounds (included lipopolysaccharide, biogenic amines, ethanolamine, etc.) and 15 amino acids (included alanine, leucine, glycine, etc.). The enrichment analysis of differentially expressed metabolites indicated that three pathways, including aminoacyl-tRNA biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; and valine, leucine and isoleucine biosynthesis, were significantly enriched after the diet treatments. Correlation network analysis revealed that HC diets altered the ruminal metabolic pattern, and the metabolites in the HC group were more complicated than those in the LC group. The correlations between ruminal metabolites and blood parameters were mainly centralized in the ruminal metabolites and albumin (40 metabolites), followed by globulin (18 metabolites) and total protein (6 metabolites).

Conclusions

These findings revealed that HC feeding altered the concentrations of ruminal metabolites as well as the metabolic pattern, and the rumen metabolism could be reflected by blood metabolism to a certain degree.

     

Fungi as a source of marker compounds for the control of illicit use of drugs: mesterolone as a case study

Introduction

Anabolic steroids are frequently misused for performance enhancement during sports competitions. One of the major bottlenecks in the confident analysis of steroids and their metabolites is the non-availability/cost of standard reference compounds.

Objective

The study objective was to identify the common metabolites of prohibited anabolic steroids that are produced in both fungi and human and thus can be synthesized in bulk using fungal cultures. Mesterolone is used as a case study.

Methods

The study was conducted in three steps; we first studied the fungal transformation of mesterolone. In the second step, these metabolites were used as references to detect in human urine after the oral use of mesterolone using LC-ESI-QqQ-MS/MS. In the third step, 12 fungal cultures were screened to evaluate their potential to produce reference markers.

Results

This led to the detection of two metabolites, 6α-hydroxymesterolone (M1) and 7α-hydroxymesterolone (M2) that were found to be common in both, fungal cultures and human urine samples. Moreover, Rhizopus stolonifer and Beauveria bassiana can be considered as good candidates to produce M1 and M2 metabolites, respectively.

Conclusion

This approach can be employed for the synthesis of marker compounds of other prohibited anabolic steroids thus can be detected efficiently during national and international sports competitions.

     

Metabolomic profiling of maternal hair suggests rapid development of intrahepatic cholestasis of pregnancy

Introduction

Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.

Objective

Investigate the maternal hair metabolome for predictive biomarkers of ICP.

Methods

The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.

Results

Of 105 metabolites detected in hair, none were significantly associated with ICP.

Conclusion

Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.

     

Comprehensive <Superscript>1</Superscript>H NMR metabolic profiling of body fluids for differentiation of meningitis in adults

Introduction

Meningitis, a morbidly infectious central nervous system pathology is accompanied by acute inflammation of the meninges, causing raised intracranial pressure linked with serious neurological sequelae.

Objective

To observe the variation in the metabolic profile, that may occur in serum and urine along with CSF in adults using ¹H NMR spectroscopy, with an attempt of appropriate and timely treatment regimen.

Methods

The ¹H NMR-based metabolomics has been performed in 115 adult subjects for differentiating bacterial meningitis (BM) and tubercular meningitis (TBM).

Results

The discriminant function analysis (DFA) of the three bio-fluids collectively identified 3-hydroxyisovalerate, lactate, glucose, formate, valine, alanine, ketonic bodies, malonate and choline containing compounds (choline and GPC) as significant metabolites among cases versus control group. The differentiation of bacterial meningitis and tuberculous meningitis (BM vs. TBM) can be done on the basis of identification of 3-hydroxyisovalerate, isobutyrate and formate in case of CSF (with a correct classification of 78 %), alanine in serum (correct classification 60 %), valine and acetone in case of urine (correct classification 89.1 %). The NMR spectral bins based orthogonal signal correction principal component analysis score plots of significant metabolites obtained from DFA also provided group classification among cases versus control group in CSF, serum and urine samples. The variable importance in projection scores also identified similar significant metabolites as obtained from DFA, collectively in CSF, serum and urine samples, responsible for differentiation of meningitis.

Conclusion

The CSF contained metabolites which are formed during infection and inflammation, and these were also found in significant quantity in serum and urine samples.

     

Effect of yeast culture on milk production and metabolic and reproductive performance of early lactation dairy cows

Background

The main objective of this study was to estimate the effect of supplementation with Saccaromyces cerevisiae (SC) (Yea-Sacc^® 1026) on milk production, metabolic parameters and the resumption of ovarian activity in early lactation dairy cows.

Methods

The experiment was conducted during 2005/2006 in a commercial tied-house farm with an average of 200 milking Estonian Holstein Friesian cows. The late pregnant multiparous cows (n = 46) were randomly divided into two groups; one group received 10 g yeast culture from two weeks before to 14 weeks after calving. The groups were fed a total mixed ration with silages and concentrates. Milk recording data and blood samples for plasma metabolites were taken. Resumption of luteal activity was determined using milk progesterone (P₄) measurements. Uterine bacteriology and ovarian ultrasonography (US) were performed and body condition scores (BCS) and clinical disease occurrences were recorded. For analysis, the statistical software Stata 9.2 and R were used to compute Cox proportional hazard and linear mixed models.

Results

The average milk production per cow did not differ between the groups (32.7 ± 6.4 vs 30.7 ± 5.3 kg/day in the SC and control groups respectively), but the production of milk fat (P < 0.001) and milk protein (P < 0.001) were higher in the SC group. There was no effect of treatment on BCS. The analysis of energy-related metabolites in early lactation showed no significant differences between the groups. In both groups higher levels of β-hydroxybutyrate (BHB) appeared from days 14 to 28 after parturition and the concentration of non-esterfied fatty acid (NEFA) was higher from days 1–7 post partum (PP). According to US and P₄ results, all cows in both groups ovulated during the experimental period. The resumption of ovarian activity (first ovulations) and time required for elimination of bacteria from the uterus did not differ between the groups.

Conclusion

Supplementation with SC had an effect on milk protein and fat production, but did not influence the milk yield. No effects on PP metabolic status, bacterial elimination from the uterus nor the resumption of ovarian activity were found.

     

Metabolic profiling of maternal urine can aid clinical management of gestational diabetes mellitus

Introduction

The clinical management of Gestational diabetes mellitus (GDM) would benefit from enhanced metabolic knowledge both at the time of diagnosis and during therapy.

Objectives

This work aimed at unveiling metabolic markers of GDM and of the subjects’ response to therapy.

Methods

Urine NMR metabolomics was used with a variable selection methodology to reduce uninformative variability. The NMR data was analysed by multivariate and univariate analysis methodologies.

Results

The results showed that urine NMR metabolomics enables a metabolic signature of GDM to be identified at the time of diagnosis. This signature comprises relevant changes in 12 NMR metabolites/resonances and qualitative variations in a number of additional metabolites. The metabolite changes characterizing GDM suggest adaptations in a number of different pathways and highlight the relevance of gut microflora disturbances in relation to the disease. The impact of diet and insulin treatments on the excreted metabolome of pregnant GDM women was measured and enabled responsive and resistant metabolic pathways to be identified, as well as side-effects of treatment i.e. metabolic changes induced by treatment and previously unrelated to the disease (including changes in the gut microflora). Furthermore, treatment duration was found to be associated to urine metabolic profile, thus emphasizing the possible future use of urine metabolomics in treatment follow-up and efficacy evaluation. Finally, a possible association of a priori urinary metabolome with future treatment requirements is reported, albeit requiring demonstration in larger cohorts. This result supports the hypothesis of different metabotypes characterizing different subjects and relating to individual response to treatment.

Conclusion

A 12-resonance metabolic signature of GDN at the time of diagnosis was identified and the evaluation of the impact of insulin and/or diet therapies enabled responsive/resistant metabolic pathways and treatment side-effects to be identified.

     

Stability of targeted metabolite profiles of urine samples under different storage conditions

Introduction

Few studies have investigated the influence of storage conditions on urine samples and none of them used targeted mass spectrometry (MS).

Objectives

We investigated the stability of metabolite profiles in urine samples under different storage conditions using targeted metabolomics.

Methods

Pooled, fasting urine samples were collected and stored at ?80 °C (biobank standard), ?20 °C (freezer), 4 °C (fridge), ~9 °C (cool pack), and ~20 °C (room temperature) for 0, 2, 8 and 24 h. Metabolite concentrations were quantified with MS using the AbsoluteIDQ? p150 assay. We used the Welch-Satterthwaite-test to compare the concentrations of each metabolite. Mixed effects linear regression was used to assess the influence of the interaction of storage time and temperature.

Results

The concentrations of 63 investigated metabolites were stable at ?20 and 4 °C for up to 24 h when compared to samples immediately stored at ?80 °C. When stored at ~9 °C for 24 h, few amino acids (Arg, Val and Leu/Ile) significantly decreased by 40% in concentration (P < 7.9E?04); for an additional three metabolites (Ser, Met, Hexose H1) when stored at ~20 °C reduced up to 60% in concentrations. The concentrations of four more metabolites (Glu, Phe, Pro, and Thr) were found to be significantly influenced when considering the interaction between exposure time and temperature.

Conclusion

Our findings indicate that 78% of quantified metabolites were stable for all examined storage conditions. Particularly, some amino acid concentrations were sensitive to changes after prolonged storage at room temperature. Shipping or storing urine samples on cool packs or at room temperature for more than 8 h and multiple numbers of freeze and thaw cycles should be avoided.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Identification of a urine metabolite constellation characteristic for kidney allograft rejection

Introduction

Allograft rejection is still an important complication after kidney transplantation. Currently, monitoring of these patients mostly relies on the measurement of serum creatinine and clinical evaluation. The gold standard for diagnosing allograft rejection, i.e. performing a renal biopsy is invasive and expensive. So far no adequate biomarkers are available for routine use.

Objectives

We aimed to develop a urine metabolite constellation that is characteristic for acute renal allograft rejection.

Methods

NMR-Spectroscopy was applied to a training cohort of transplant recipients with and without acute rejection.

Results

We obtained a metabolite constellation of four metabolites that shows promising performance to detect renal allograft rejection in the cohorts used (AUC of 0.72 and 0.74, respectively).

Conclusion

A metabolite constellation was defined with the potential for further development of an in-vitro diagnostic test that can support physicians in their clinical assessment of a kidney transplant patient.

     

Extraction of natural products from bark of <Emphasis Type="Italic">Betula pendula</Emphasis> using ionic liquids

Introduction

Aqueous–methanol mixtures have successfully been applied to extract a broad range of metabolites from plant tissue. However, a certain amount of material remains insoluble.

Objectives

To enlarge the metabolic compendium, two ionic liquids were selected to extract the methanol insoluble part of trunk from Betula pendula.

Methods

The extracted compounds were analyzed by LC/MS and GC/MS.

Results

The results show that 1-butyl-3-methylimidazolium acetate (IL-Ac) predominantly resulted in fatty acids, whereas 1-ethyl-3-methylimidazolium tosylate (IL-Tos) mostly yielded phenolic structures. Interestingly, bark yielded more ionic liquid soluble metabolites compared to interior wood.

Conclusion

From this one can conclude that the application of ionic liquids may expand the metabolic snapshot.

     

Urinary antihypertensive drug metabolite screening using molecular networking coupled to high-resolution mass spectrometry fragmentation

Introduction

Mass spectrometry is the current technique of choice in studying drug metabolism. High-resolution mass spectrometry in combination with MS/MS gas-phase experiments has the potential to contribute to rapid advances in this field. However, the data emerging from such fragmentation spectral files pose challenges to downstream analysis, given their complexity and size.

Objectives

This study aims to detect and visualize antihypertensive drug metabolites in untargeted metabolomics experiments based on the spectral similarity of their fragmentation spectra. Furthermore, spectral clusters of endogenous metabolites were also examined.

Methods

Here we apply a molecular networking approach to seek drugs and their metabolites, in fragmentation spectra from urine derived from a cohort of 26 patients on antihypertensive therapy. The mass spectrometry data was collected on a Thermo Q-Exactive coupled to pHILIC chromatography using data dependent analysis (DDA) MS/MS gas-phase experiments.

Results

In total, 165 separate drug metabolites were found and structurally annotated (17 by spectral matching and 122 by classification based on a clustered fragmentation pattern). The clusters could be traced to 13 drugs including the known antihypertensives verapamil, losartan and amlodipine. The molecular networking approach also generated clusters of endogenous metabolites, including carnitine derivatives, and conjugates containing glutamine, glutamate and trigonelline.

Conclusions

The approach offers unprecedented capability in the untargeted identification of drugs and their metabolites at the population level and has great potential to contribute to understanding stratified responses to drugs where differences in drug metabolism may determine treatment outcome.

     

TarMet: a reactive GUI tool for efficient and confident quantification of MS based targeted metabolic and stable isotope tracer analysis

Introduction

Untargeted and targeted analyses are two classes of metabolic study. Both strategies have been advanced by high resolution mass spectrometers coupled with chromatography, which have the advantages of high mass sensitivity and accuracy. State-of-art methods for mass spectrometric data sets do not always quantify metabolites of interest in a targeted assay efficiently and accurately.

Objectives

TarMet can quantify targeted metabolites as well as their isotopologues through a reactive and user-friendly graphical user interface.

Methods

TarMet accepts vendor-neutral data files (NetCDF, mzXML and mzML) as inputs. Then it extracts ion chromatograms, detects peak position and bounds and confirms the metabolites via the isotope patterns. It can integrate peak areas for all isotopologues automatically.

Results

TarMet detects more isotopologues and quantify them better than state-of-art methods, and it can process isotope tracer assay well.

Conclusion

TarMet is a better tool for targeted metabolic and stable isotope tracer analyses.

     

Applications of metabolomics in the study and management of preeclampsia: a review of the literature

Introduction

Preeclampsia represents a major public health burden worldwide, but predictive and diagnostic biomarkers are lacking. Metabolomics is emerging as a valuable approach to generating novel biomarkers whilst increasing the mechanistic understanding of this complex condition.

Objectives

To summarize the published literature on the use of metabolomics as a tool to study preeclampsia.

Methods

PubMed and Web of Science were searched for articles that performed metabolomic profiling of human biosamples using either Mass-spectrometry or Nuclear Magnetic Resonance based approaches and which included preeclampsia as a primary endpoint.

Results

Twenty-eight studies investigating the metabolome of preeclampsia in a variety of biospecimens were identified. Individual metabolite and metabolite profiles were reported to have discriminatory ability to distinguish preeclamptic from normal pregnancies, both prior to and post diagnosis. Lipids and carnitines were among the most commonly reported metabolites. Further work and validation studies are required to demonstrate the utility of such metabolites as preeclampsia biomarkers.

Conclusion

Metabolomic-based biomarkers of preeclampsia have yet to be integrated into routine clinical practice. However, metabolomic profiling is becoming increasingly popular in the study of preeclampsia and is likely to be a valuable tool to better understand the pathophysiology of this disorder and to better classify its subtypes, particularly when integrated with other omic data.

     

Urinary Metabolomic Signatures in Obstructive Sleep Apnea through Targeted Metabolomic Analysis: A Pilot Study

Introduction

Obstructive sleep apnea (OSA) is very common sleep problem, and it is associated with serious morbidities such as cardiovascular diseases and metabolic diseases. Overnight polysomnography (PSG) is the gold standard test for OSA, but it is expensive and requires specific facilities and equipment. Thus, novel screening methods are needed for effective diagnosis and follow-up in OSA.

Objectives

The aims of the study were to investigate the urinary metabolic signatures and identify potential urine markers for OSA using a mass spectrometry (MS)-based assay for targeted metabolomics.

Methods

Urine samples were collected from 48 male subjects who visited a sleep clinic for suspicious OSA. All underwent overnight in-laboratory polysomnography. The Biocrates AbsoluteIDQ p180 kit was used for targeted metabolomics.

Results

Among the 86 metabolites quantified, three acylcarnitines, one biogenic amine, two glycerophospholipids, and two sphingomyelins were differently expressed in OSA patients [apnea-hypopnea index (AHI) ≥5] compared with control groups (AHI <5 and/or simple snoring with no other sleep disorders). Additional partial correlation and multivariate logistic regression analysis revealed that long-chain acylcarnitine C14:1, symmetric dimethylarginine, and sphingomyelin C18:1 might be potential biomarkers for OSA. Receiver operating characteristic analysis showed favorable predictive properties of these metabolites. Furthermore, a combination of the metabolites exceeding cutoff values yielded further improved sensitivity or specificity.

Conclusions

MS-based targeted metabolomics identified specific classes of urinary metabolites that were up-regulated in OSA patients. Further assessments in large populations are required to clarify the screening values of these metabolite markers.