Endocrinology Meets Metabolomics: Achievements, Pitfalls, and Challenges

Metabolomics reveals altered metabolites in cirrhotic patients with severe portal hypertension in Tibetan population

Background

Portal hypertension (PHT) presents a challenging issue of liver cirrhosis. This study aims to identify novel biomarkers for severe PHT (SPHT) and explore the pathophysiological mechanisms underlying PHT progression.

Methods

Twenty-three Tibetan cirrhotic patients who underwent hepatic venous pressure gradient (HVPG) measurement were included. Eleven patients had an HVPG between 5 mmHg and 15 mmHg (MPHT), while 12 had an HVPG ≥16 mmHg (SPHT). Peripheral sera were analyzed using liquid chromatograph-mass spectrometer for metabolomic assessment. An additional 14 patients were recruited for validation of metabolites.

Results

Seven hundred forty-five metabolites were detected and significant differences in metabolomics between MPHT and SPHT patients were observed. Employing a threshold of p < 0.05 and a variable importance in projection score >1, 153 differential metabolites were identified. A significant number of these metabolites were lipids and lipid-like molecules. Pisumionoside and N-decanoylglycine (N-DG) exhibited the highest area under the curve (AUC) values (0.947 and 0.9091, respectively). Additional differential metabolites with AUC >0.8 included 6-(4-ethyl-2-methoxyphenoxy)-3,4,5-trihydroxyoxane-2-carboxylic acid, sphinganine 1-phosphate, 4-hydroxytriazolam, 4,5-dihydroorotic acid, 6-hydroxy-1H-indole-3-acetamide, 7alpha-(thiomethyl)spironolactone, 6-deoxohomodolichosterone, glutaminylisoleucine, taurocholic acid 3-sulfate, and Phe Ser. Enzyme-linked immunosorbent assay further confirmed elevated levels of sphinganine 1-phosphate, N-DG, and serotonin in SPHT patients. Significant disruptions in linoleic acid, amino acid, sphingolipid metabolisms, and the citrate cycle were observed in SPHT patients.

Conclusion

Pisumionoside and N-DG are identified as promising biomarkers for SPHT. The progression of PHT may be associated with disturbances in lipid, linoleic acid, and amino acid metabolisms, as well as alterations in the citrate cycle.

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications

Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography-tandem mass spectrometry (GC-MS/MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease.

Untargeted metabolomic profiling reveals molecular signatures associated with type 2 diabetes in Nigerians

BACKGROUND

Type 2 diabetes (T2D) has reached epidemic proportions globally, including in Africa. However, molecular studies to understand the pathophysiology of T2D remain scarce outside Europe and North America. The aims of this study are to use an untargeted metabolomics approach to identify: (a) metabolites that are differentially expressed between individuals with and without T2D and (b) a metabolic signature associated with T2D in a population of Sub-Saharan Africa (SSA).

METHODS

A total of 580 adult Nigerians from the Africa America Diabetes Mellitus (AADM) study were studied. The discovery study included 310 individuals (210 without T2D, 100 with T2D). Metabolites in plasma were assessed by reverse phase, ultra-performance liquid chromatography and mass spectrometry (RP)/UPLC-MS/MS methods on the Metabolon Platform. Welch's two-sample t-test was used to identify differentially expressed metabolites (DEMs), followed by the construction of a biomarker panel using a random forest (RF) algorithm. The biomarker panel was evaluated in a replication sample of 270 individuals (110 without T2D and 160 with T2D) from the same study.

RESULTS

Untargeted metabolomic analyses revealed 280 DEMs between individuals with and without T2D. The DEMs predominantly belonged to the lipid (51%, 142/280), amino acid (21%, 59/280), xenobiotics (13%, 35/280), carbohydrate (4%, 10/280) and nucleotide (4%, 10/280) super pathways. At the sub-pathway level, glycolysis, free fatty acid, bile metabolism, and branched chain amino acid catabolism were altered in T2D individuals. A 10-metabolite biomarker panel including glucose, gluconate, mannose, mannonate, 1,5-anhydroglucitol, fructose, fructosyl-lysine, 1-carboxylethylleucine, metformin, and methyl-glucopyranoside predicted T2D with an area under the curve (AUC) of 0.924 (95% CI: 0.845-0.966) and a predicted accuracy of 89.3%. The panel was validated with a similar AUC (0.935, 95% CI 0.906-0.958) in the replication cohort. The 10 metabolites in the biomarker panel correlated significantly with several T2D-related glycemic indices, including Hba1C, insulin resistance (HOMA-IR), and diabetes duration.

CONCLUSIONS

We demonstrate that metabolomic dysregulation associated with T2D in Nigerians affects multiple processes, including glycolysis, free fatty acid and bile metabolism, and branched chain amino acid catabolism. Our study replicated previous findings in other populations and identified a metabolic signature that could be used as a biomarker panel of T2D risk and glycemic control thus enhancing our knowledge of molecular pathophysiologic changes in T2D. The metabolomics dataset generated in this study represents an invaluable addition to publicly available multi-omics data on understudied African ancestry populations.

Early changes of bone metabolites and lymphocyte subsets may participate in osteoporosis onset: a preliminary study of a postmenopausal osteoporosis mouse model

Purpose

Metabolic and immune changes in the early stages of osteoporosis are not well understood. This study aimed to explore the changes in bone metabolites and bone marrow lymphocyte subsets and their relationship during the osteoporosis onset.

Methods

We established OVX and Sham mouse models. After 5, 15, and 40 days, five mice in each group were sacrificed. Humeri were analyzed by microCT. The bone marrow cells of the left femur and tibia were collected for flow cytometry analysis. The right femur and tibia were analyzed by LC-MS/MS for metabolomics analysis.

Results

Bone microarchitecture was significantly deteriorated 15 days after OVX surgery. Analysis of bone metabolomics showed that obvious metabolite changes had happened since 5 days after surgery. Lipid metabolism was significant at the early stage of the osteoporosis. The proportion of immature B cells was increased, whereas the proportion of mature B cells was decreased in the OVX group. Metabolites were significantly correlated with the proportion of lymphocyte subsets at the early stage of the osteoporosis.

Conclusion

Lipid metabolism was significant at the early stage of the osteoporosis. Bone metabolites may influence bone formation by interfering with bone marrow lymphocyte subsets.

Integrative Analyses of Transcriptomics and Metabolomics in Immune Response of Leguminivora glycinivorella Mats to Beauveria bassiana Infection

This study utilized Beauveria bassiana to infect Leguminivora glycinivorella, analyzed the effects on the transcriptome and metabolome, and further investigated the antibacterial function of L. glycinivorella. We performed transcriptome and metabolome sequencing on the L. glycinivorella infected with B. bassiana and its control groups, and performed a joint analysis of transcriptome and metabolome results. Upon screening, 4560 differentially expressed genes were obtained in the transcriptome and 71 differentially expressed metabolites were obtained in the metabolome. On this basis, further integration of the use of transcriptomics and metabonomics combined an analysis of common enrichments of pathways of which there were three. They were glutathione S-transferase (GSTs) genes, heat shock protein (HSP) genes, and cytochrome P450 (CYP450) genes. These three pathways regulate the transport proteins, such as ppars, and thus affect the digestion and absorption of sugars and fats, thus regulating the development of pests. The above conclusion indicates that B. bassiana can affect the sugar metabolism, lipid metabolism, and amino acid metabolism pathways of L. glycinivorella, and can consume the necessary energy, protein, and lipids of L. glycinivorella. The research on the immune response mechanism of pests against pathogens can provide an important scientific basis and target for the development of immunosuppressants. This study laid an information foundation for the application of entomogenous fungi to control soybean borer at the molecular level.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

The role of multi-omics in the diagnosis of COVID-19 and the prediction of new therapeutic targets

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing COVID-19, has led to more than 170 million confirmed cases in 223 countries and regions, claiming 3,872,457 lives. Some patients with COVID-19 have mild clinical symptoms despite severe respiratory failure, which greatly increases the difficulty of diagnosis and treatment. It is therefore necessary to identify biological characteristics of SARS-CoV-2, screen novel diagnostic and prognostic biomarkers, as well as to explore potential therapeutic targets for COVID-19. In this comprehensive review, we discuss the current published literature on COVID-19. We find that the comprehensive application of genomics, transcriptomics, proteomics and metabolomics is becoming increasingly important in the treatment of COVID-19. Multi-omics analysis platforms are expected to revolutionize the diagnosis and classification of COVID-19. This review aims to provide a reference for diagnosis, surveillance and clinical decision making related to COVID-19.

Metabolomics of ischemic stroke: insights into risk prediction and mechanisms

Ischemic stroke (IS) is the most prevalent type of stroke. The early diagnosis and prognosis of IS are crucial for successful therapy and early intervention. Metabolomics, a tool in systems biology based on several innovative technologies, can be used to identify disease biomarkers and unveil underlying pathophysiological processes. Accordingly, in recent years, an increasing number of studies have identified metabolites from cerebral ischemia patients and animal models that could improve the diagnosis of IS and prediction of its outcome. In this paper, metabolomic research is comprehensively reviewed with a focus on describing the metabolic changes and related pathways associated with IS. Most clinical studies use biofluids (e.g., blood or plasma) because their collection is minimally invasive and they are ideal for analyzing changes in metabolites in patients of IS. We review the application of animal models in metabolomic analyses aimed at investigating potential mechanisms of IS and developing novel therapeutic approaches. In addition, this review presents the strengths and limitations of current metabolomic studies on IS, providing a reference for future related studies.

The Diurnal Blood Metabolome and Effects of Vitamin D Supplementation: A Randomised Crossover Trial in Postmenopausal Women

A way to maintain an adequate vitamin D status is through supplementation. Demonstration of blood-metabolome rhythmicity of vitamin D3 post-dosing effects is lacking in the pharmaco-metabonomics area. Thus, the overall aim of this study was to investigate the diurnal changes in the blood metabolome and how these are affected by vitamin D3 supplementation. The study was conducted as a crossover study, and the treatment included 200 µg (8000 IU) of vitamin D3 as compared with placebo with a washout period of at least 10 days. The participants were postmenopausal women aged 60−80 years (N = 29) with vitamin D insufficiency (serum 25-hydroxyvitamin D < 50 nmol/L) but otherwise healthy. During the intervention day, blood samples were taken at 0 h, 2 h, 4 h, 6 h, 8 h, 10 h, 12 h, and 24 h, and plasma was analysed by proton nuclear magnetic resonance (NMR) spectroscopy as a metabolomics approach. In general, diurnal effects were identified for the majority of the 20 quantified metabolites, and hierarchical cluster analysis revealed a change in the overall plasma metabolome around 12 AM (6 h after intervention), suggesting that the diurnal rhythm is reflected in two diurnal plasma metabolomes; a morning metabolome (8−12 AM) and an afternoon/evening metabolome (2−8 PM). Overall, the effect of vitamin D supplementation on the blood metabolome was minor, with no effect on the diurnal rhythm. However, a significant effect of the vitamin D supplementation on plasma acetone levels was identified. Collectively, our findings reveal an influence of diurnal rhythm on the plasma metabolome, while vitamin D supplementation appears to have minor influence on fluctuations in the plasma metabolome.

Metabolic changes associated with two endocrine abnormalities in dogs: elevated fructosamine and low thyroxine

INTRODUCTION

Metabolomics studies in canine endocrine abnormalities are sparse and basic information on these abnormalities must be generated.

OBJECTIVES

To characterize the metabolic changes associated with elevated fructosamine, reflecting poor glycemic control, and low thyroxine, a thyroid hormone controlling metabolism.

METHODS

Leftovers of clinical serum samples; 25 controls, 79 high fructosamine, and 47 low thyroxine, were analyzed using ¹H NMR and differences were evaluated using Firth logistic regression.

RESULTS

Both high fructosamine and low thyroxine were associated with changes in concentrations of multiple metabolites, including glycoprotein acetyls and lipids.

CONCLUSION

These findings suggest promising makers for further research and clinical validation.

Metabolomic profiling of peripheral plasma by GC-MS and correlation with size of uterine leiomyomas

Background

Uterine leiomyomas are benign monoclonal tumors originating from the myometrium. Little information exists concerning metabolomics and the presence of leiomyomas.

Objective

The present study evaluated circulating metabolites in the plasma and their correlation with the presence and size of leiomyomas.

Study Design

Cross-sectional observational study, including women divided into 3 groups: 37 with leiomyomas and uterus >500 cm³, 17 with leiomyomas and uterus ≤150 cm³, and 21 leiomyoma-free. Patients underwent peripheral blood collection using untargeted metabolic assessment by gas chromatography coupled to mass spectrometer.

Results

There was no statistical difference between patients’ anthropometric and demographic features and laboratory tests. Statistical differences in uterus volume (P < 0.0001) were found. Forty-six metabolites were identified (35% amino acids and derivatives, 22% fatty acids, and 18% carbohydrates). Statistically significant metabolic distinction (P < 0.05, false discovery rate< 0.05) was observed for 14 metabolites. Most amino acids (L-isoleucine, L-valine, and pyroglutamic acid) were significantly reduced in plasma levels of patients with large leiomyomas. The only exception was L-glutamine, with a significant increase. Fatty acids (arachidonic acid, alfa-tocopherol, palmitic acid, and stearic acid) were similarly reduced in large leiomyomas patients, except for alpha-linolenic acid, which increased. For carbohydrates (myo-inositol, D-threitol, and D-ribose), there was a decrease in the plasma of patients with leiomyomas.

Conclusion

There are different plasma metabolites levels of amino acids, fatty acids, and carbohydrates among patients with leiomyomas, most of them reduced, but some significantly increased in large leiomyomas, compared to leiomyoma-free patients.

Dysregulation in Sphingolipid Signaling Pathways is Associated With Symptoms and Functional Connectivity of Pain Processing Brain Regions in Provoked Vestibulodynia

Provoked vestibulodynia (PVD) is a chronic pain disorder characterized by local hypersensitivity and severe pain with pressure localized to the vulvar vestibule. Despite decades of study, the lack of identified biomarkers has slowed the development of effective therapies. The primary aim of this study was to use metabolomics to identify novel biochemical mechanisms in vagina and blood underlying brain biomarkers and symptoms in PVD, thereby closing this knowledge gap. Using a cross-sectional case-control observational study design, untargeted and unbiased metabolomic profiling of vaginal fluid and plasma was performed in women with PVD compared to healthy controls. In women with PVD, we also obtained assessments of vulvar pain, vestibular and vaginal muscle tenderness, and 24-hour symptom intensity alongside resting-state brain functional connectivity of brain regions involved in pain processing and modulation. Compared to healthy controls, women with PVD demonstrated differences primarily in vaginal (but not plasma) concentrations of metabolites of the sphingolipid signaling pathways, suggesting localized effects in vagina and vulvar vestibule rather than systemic effects. Our findings reveal that dysregulation of sphingolipid metabolism in PVD is associated with increased vulvar pain and muscle tenderness, sexual dysfunction, and decreased functional connectivity strength in pain processing/modulatory brain regions. This data collectively suggests that alterations in sphingolipid signaling pathways are likely an important molecular biomarker in PVD that could lead to new targets for therapeutic intervention. PERSPECTIVE: This manuscript presents the results of a robust, unbiased molecular assessment of plasma and vaginal fluid samples in women with provoked vestibulodynia compared to healthy controls. The findings suggest that alterations in sphingolipid signaling pathways are associated with symptoms and brain biomarkers and may be an important molecular marker that could provide new targets for therapeutic intervention.

Metabolomics comparison of serum and urine in dairy cattle using proton nuclear magnetic resonance spectroscopy

OBJECTIVE

The aim of the study was to conduct metabolic profiling of dairy cattle serum and urine using proton nuclear magnetic resonance (1H-NMR) spectroscopy and to compare the results obtained with those of other dairy cattle herds worldwide so as to provide a basic dataset to facilitate research on metabolites in serum and urine.

METHODS

Six dairy cattle were used in this study; all animals were fed the same diet, which was composed of total mixed ration; the fed amounts were based on voluntary intake. Blood from the jugular neck vein of each steer was collected at the same time using a separate serum tube. Urine samples were collected by hand sweeping the perineum. The metabolites were determined by 1H-NMR spectroscopy, and the obtained data were statistically analyzed by performing principal component analysis, partial least squares-discriminant analysis, variable importance in projection scores, and metabolic pathway data using Metaboanalyst 4.0.

RESULTS

The total number of metabolites in the serum and urine was measured to be 115 and 193, respectively, of which 47 and 81, respectively were quantified. Lactate (classified as an organic acid) and urea (classified as an aliphatic acylic compound) exhibited the highest concentrations in serum and urine, respectively. Some metabolites that have been associated with diseases such as ketosis, bovine respiratory disease, and metritis, and metabolites associated with heat stress were also found in the serum and urine samples.

CONCLUSION

The metabolites measured in the serum and urine could potentially be used to detect diseases and heat stress in dairy cattle. The results could also be useful for metabolomic research on the serum and urine of ruminants in Korea.

Metabolomics as a potential tool for the diagnosis of growth hormone deficiency (GHD): a review

Metabolomics uses several analytical tools to identify the chemical diversity of metabolites present in organisms. These metabolites are low molecular weight molecules (<1500 Da) classified as a final or intermediary product of metabolic processes. The application of this omics technology has become prominent in inferring physiological conditions through reporting on the phenotypic state; therefore, the introduction of metabolomics into clinical studies has been growing in recent years due to its efficiency in discriminating pathophysiological states. Regarding endocrine diseases, there is a great interest in verifying comprehensive and individualized physiological scenarios, in particular for growth hormone deficiency (GHD). The current GHD diagnostic tests are laborious and invasive and there is no exam with ideal reproducibility and sensitivity for diagnosis neither standard GH cut-off point. Therefore, this review was focussed on articles that applied metabolomics in the search for new biomarkers for GHD. The present work shows that the applications of metabolomics in GHD are still limited, since the little complementarily of analytical techniques, a low number of samples, GHD combined to other deficiencies, and idiopathic diagnosis shows a lack of progress. The results of the research are relevant and similar; however, their results do not provide an application for clinical practice due to the lack of multidisciplinary actions that would be needed to mediate the translation of the knowledge produced in the laboratory, if transferred to the medical setting.

Metabolomic Changes Are Predictive of Aging in Laying Hens

Aging in vertebrates is an extremely complex process that is still poorly understood. One confining factor to studying vertebrate aging is the lack of appropriate models. The laying hen is a good model to study vertebrate aging, as it can be maintained under standard housing conditions, its breeds are genetically well defined and it exhibits significant aging phenotypes at around 18 months of age. Furthermore, laying hens are maintained in a challenging realistic environment and possess a fully functional immune system. Here we used, for the first time, metabolomic profiling of laying hens' blood for identifying biomarkers of aging. Random forest classifier was used to quantify the quality of the markers and found that the markers can predict the correct age group of individuals with 90% accuracy. Animals under time-restricted feeding, a condition known to increase health span, appeared younger under the markers, indicating that the aging biomarkers can also predict the effectiveness of environmental treatments. Additionally, we found that noise, defined as the ratio between the standard deviation and the mean, is an exceptionally robust and universal biomarker of aging, as metabolomic noise increases significantly with age in laying hens, humans, and mice. Our study suggests the laying hen as a useful model to study aging in vertebrates and establishes metabolomic noise as a novel, universal biomarker of aging.

Seasonally related metabolic changes and energy allocation associated with growth and reproductive phases in the liver of male goldfish (Carassius auratus)

Reproduction and growth follow a seasonal pattern in many fish species involving changes in gonadal development, growth, and metabolism. Significant metabolic energy is needed during gametogenesis in both female and male to produce hundreds of eggs and billions of sperms. Seasonal variations are controlled by the hormones of brain-pituitary-peripheral axis and are accompanied by significant metabolic changes. There is evidence that GnRH and GnIH are among the key neurohormones that regulate the reciprocal control of growth and reproduction. The objective of this study was to investigate changes in metabolic profile and energy allocation patterns at different stages of reproduction, using goldfish as a model organism and LC-MS as analytical platform for metabolic analysis. Goldfish undergoes a clear seasonal cycle of growth and reproduction. In vivo experiments were conducted at three different time point of the annual cycle: regressed gonadal phase (peak growth phase), mid gametogenesis and late gametogenesis. Emphasis is placed on changes in liver metabolic pathways to energetically sustain the physiological processes related to growth and reproduction. Moreover, we tested the hypothesis that GnRH and GnIH may play a role in the regulation of metabolism by investigating acute effects of these peptides at different stages of reproductive cycle. SIGNIFICANCE: The findings in this paper provide novel information on the seasonal changes in basal metabolism during different stages of reproductive cycle, and evidence for differential allocation of energy during reciprocal control of reproduction and growth in goldfish. Chemometrics combined with pathway-driven bioinformatics elucidated a shift in the metabolic profile, indicating distinct patterns of energy allocation in the reproductive and growth seasons. Furthermore, to our knowledge this is the first study to provide evidence for a possible regulatory role of GnRH and GnIH in liver metabolism and energy allocation patterns associated with growth and reproductive processes. Together our findings present a framework for better understanding of the hormonally induced changes in metabolism to energetically sustain growth and reproduction in fish and other oviparous species undergoing seasonal cycle.

Endogenous cortisol excess confers a unique lipid signature and metabolic network

Chronic cortisol excess induces several alterations on protein, lipid and carbohydrate metabolism resembling those found in the metabolic syndrome. However, patients exposed to prolonged high levels of cortisol in Cushing syndrome (CS) present exceeding cardiometabolic alterations not reflected by conventional biomarkers. Using 3 ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS) platforms, we aimed to characterise the serum metabolome of 25 patients with active endogenous CS and 25 control subjects matched by propensity score (sex, BMI, diabetes mellitus type 2 (T2D), high blood pressure (HBP) and dyslipidaemia) to search for potential disease-specific biomarkers and pathways associated to the clinical comorbidities. A total of 93 metabolites were significantly altered in patients with CS. Increased levels of sulfur amino acids (AA), triacylglycerols, glycerophospholipids, ceramides and cholesteryl esters were observed. Contrarily, concentrations of essential and non-essential AA, polyunsaturated fatty acids, conjugated bile acids and second messenger glycerolipids were decreased. Twenty-four-hour urinary free cortisol (24h-UFC) independently determined the concentration of 21 lipids and 4 AA. A metabolic signature composed by 10 AA and 10 lipid metabolites presented an AUC-ROC of 95% for the classification of CS patients. Through differential network analysis, 152 aberrant associations between metabolites involved in the Lands cycle and Kennedy pathway were identified. Our data indicates that chronic hypercortisolemia confers a unique lipidomic signature and several alterations in numerous AA even when compared to patients with similar metabolic comorbidities providing novel insights of the increased cardiometabolic burden of CS. KEY MESSAGES: • Cortisol excess induces metabolic alterations beyond conventional biomarkers. • The hypercortisolism extent determines the concentration of 21 lipids and 5 aa. • Cortisol excess confers a unique metabolic signature of 20 metabolites. • Kennedy and Lands cycle are profoundly disturbed by cortisol excess.

Intrauterine Hyperglycemia Alters the Metabolomic Profile in Fetal Mouse Pancreas in a Gender-Specific Manner

Mounting evidence has shown that intrauterine hyperglycemia exposure during critical stages of development may be contributing to the increasing prevalence of diabetes. However, little is known about the mechanisms responsible for offspring metabolic disorder. In this present study, we explored intrauterine hyperglycemia exposure on fetal pancreatic metabolome, and its potential link to impaired glucose tolerance in adult offspring. Here, using a GDM mouse model, we found the metabolome profiling of pancreas from male and female fetus showing altered metabolites in several important pathways, including 5-methylcytosine, α-KG, branched-chain amino acids, and cystine, which are associated with epigenetic modification, insulin secretion, and intracellular redox status, respectively. This finding suggests that intrauterine exposure to hyperglycemia could cause altered metabolome in pancreas, which might be a metabolism-mediated mechanism for GDM-induced intergenerational diabetes predisposition.

Reduced concentrations of vaginal metabolites involved in steroid hormone biosynthesis are associated with increased vulvar vestibular pain and vaginal muscle tenderness in provoked vestibulodynia: An exploratory metabolomics study

Provoked vestibulodynia (PVD) is a chronic vulvar pain disorder characterized by hypersensitivity and severe pain with pressure localized to the vulvar vestibule. Knowledge regarding pathophysiological mechanisms contributing to the etiology and production of symptoms in PVD remains incomplete but is considered multifactorial. Using a cross-sectional observational study design, data from untargeted metabolomic profiling of vaginal fluid and plasma in women with PVD and healthy women was combined with pain testing and brain imaging in women with PVD to test the hypotheses that women with PVD compared to healthy women show differences in vaginal and plasma metabolites involved in steroid hormone biosynthesis. Steroid hormone metabolites showing group differences were correlated with vulvar vestibular pain and vaginal muscle tenderness and functional connectivity of brain regions involved in pain processing in women with PVD to provide insight into the functional mechanisms linked to the identified alterations. Sensitivity analyses were also performed to determine the impact of hormonal contraceptive use on the study findings. Women with PVD compared to healthy controls had significant reductions primarily in vaginal fluid concentrations of androgenic, pregnenolone and progestin metabolites involved in steroidogenesis, suggesting localized rather than systemic effects in vagina and vulvar vestibule. The observed reductions in androgenic metabolite levels showed large effect size associations with increased vulvar vestibular pain and vulvar muscle tenderness and decreases in androgenic and progestin metabolites were associated with decreased connectivity strength in primary sensorimotor cortices. Women with PVD showed symptom-associated reductions in vaginal fluid concentrations of metabolites involved in the biosynthesis of steroid hormones previously shown to affect the integrity of vulvar and vaginal tissue and nociceptive processing. Deficiency of certain steroids may be an important mechanism contributing to the pathophysiology of symptoms in PVD may provide potential diagnostic markers that could lead to new targets for therapeutic intervention.

Metabolomics for Diagnosis and Prognosis of Uterine Diseases? A Systematic Review

This systematic review analyses the contribution of metabolomics to the identification of diagnostic and prognostic biomarkers for uterine diseases. These diseases are diagnosed invasively, which entails delayed treatment and a worse clinical outcome. New options for diagnosis and prognosis are needed. PubMed, OVID, and Scopus were searched for research papers on metabolomics in physiological fluids and tissues from patients with uterine diseases. The search identified 484 records. Based on inclusion and exclusion criteria, 44 studies were included into the review. Relevant data were extracted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) checklist and quality was assessed using the QUADOMICS tool. The selected metabolomics studies analysed plasma, serum, urine, peritoneal, endometrial, and cervico-vaginal fluid, ectopic/eutopic endometrium, and cervical tissue. In endometriosis, diagnostic models discriminated patients from healthy and infertile controls. In cervical cancer, diagnostic algorithms discriminated patients from controls, patients with good/bad prognosis, and with/without response to chemotherapy. In endometrial cancer, several models stratified patients from controls and recurrent from non-recurrent patients. Metabolomics is valuable for constructing diagnostic models. However, the majority of studies were in the discovery phase and require additional research to select reliable biomarkers for validation and translation into clinical practice. This review identifies bottlenecks that currently prevent the translation of these findings into clinical practice.

SMfinder: Small Molecules Finder for Metabolomics and Lipidomics Analysis

Metabolomics and lipidomics studies are becoming increasingly popular but available tools for automated data analysis are still limited. The major issue in untargeted metabolomics is linked to the lack of efficient ranking methods allowing accurate identification of metabolites. Herein, we provide a user-friendly open-source software, named SMfinder, for the robust identification and quantification of small molecules. The software introduces an MS2 false discovery rate approach, which is based on single spectral permutation and increases identification accuracy. SMfinder can be efficiently applied to shotgun and targeted analysis in metabolomics and lipidomics without requiring extensive in-house acquisition of standards as it provides accurate identification by using available MS2 libraries in instrument independent manner. The software, downloadable at www.ifom.eu/SMfinder, is suitable for untargeted, targeted, and flux analysis.

Circulating Levels of Muscle-Related Metabolites Increase in Response to a Daily Moderately High Dose of a Vitamin D3 Supplement in Women with Vitamin D Insufficiency-Secondary Analysis of a Randomized Placebo-Controlled Trial

Recently, we demonstrated negative effects of vitamin D supplementation on muscle strength and physical performance in women with vitamin D insufficiency. The underlying mechanism behind these findings remains unknown. In a secondary analysis of the randomized placebo-controlled trial designed to investigate cardiovascular and musculoskeletal health, we employed NMR-based metabolomics to assess the effect of a daily supplement of vitamin D3 (70 µg) or an identically administered placebo, during wintertime. We assessed the serum metabolome of 76 postmenopausal, otherwise healthy, women with vitamin D (25(OH)D) insufficiency (25(OH)D < 50 nmol/L), with mean levels of 25(OH)D of 33 ± 9 nmol/L. Compared to the placebo, vitamin D3 treatment significantly increased the levels of 25(OH)D (−5 vs. 59 nmol/L, respectively, p < 0.00001) and 1,25(OH)₂D (−10 vs. 59 pmol/L, respectively, p < 0.00001), whereas parathyroid hormone (PTH) levels were reduced (0.3 vs. −0.7 pmol/L, respectively, p < 0.00001). Analysis of the serum metabolome revealed a significant increase of carnitine, choline, and urea and a tendency to increase for trimethylamine-N-oxide (TMAO) and urinary excretion of creatinine, without any effect on renal function. The increase in carnitine, choline, creatinine, and urea negatively correlated with muscle health and physical performance. Combined with previous clinical findings reporting negative effects of vitamin D on muscle strength and physical performance, this secondary analysis suggests a direct detrimental effect on skeletal muscle of moderately high daily doses of vitamin D supplements.

Metabolomics for predicting hyperglycemia in pregnancy: a protocol for a systematic review and potential meta-analysis

BACKGROUND

Hyperglycemia in pregnancy (HIP) has been recently differentiated between diabetes in pregnancy (DIP) and gestational diabetes mellitus (GDM). The proposed protocol is relevant, and clinical concern is due to the higher risk of adverse pregnancy outcomes (APO) and long-term effects on both the mother and the fetus. Fasting plasma glucose level (FPG) and oral glucose tolerance test (OGTT) are current diagnostic tools. However, controversy persists concerning diagnostic criteria, cut-off points, and even selective or universal screening. The objective of this systematic review is to assess the performance of metabolomic markers in the prediction of HIP.

METHODS

This is a protocol for a systematic review with potential meta-analysis. The primary outcome is GDM, defined as glucose intolerance identified in the second and third trimesters of pregnancy (any FPG ≥ 92 mg/dL and < 126 mg/dL OR when 75-g OGTT shows one altered value among these: FPG ≥ 92 mg/dL or 1-h post glucose load ≥ 180 mg/dL or 2-h post glucose load ≥ 153 mg/dL); the secondary outcome is HIP, defined as hyperglycemia detected in the first trimester of pregnancy (any FPG ≥ 126 mg/dL). A detailed systematic literature search will be carried out in electronic databases and conference abstracts, using the keywords "gestational diabetes mellitus," "metabolomics," "pregnancy," and "screening" (and their variations). We will include original peer-reviewed articles published from Jan 1, 1999, to Dec 31, 2018. Original studies including diabetes diagnosed before pregnancy (T2DM and T1DM), multiple pregnancies, and congenital malformations will be excluded. All results regarding samples, participant characteristics, metabolomic techniques, and diagnostic accuracy measures will be retrieved and analyzed. Since this is a systematic review, no ethical approval is necessary.

DISCUSSION

This systematic review may have the potential to provide significant evidence-based findings on the prediction performance of metabolomics. There are short and long-term repercussions for the mother and the newborn. Therefore, both may benefit from an accurate prediction technique for HIP.

SYSTEMATIC REVIEW REGISTRATION

This protocol was registered in the PROSPERO platform under number CRD42018100175 .

Metabolomics for Animal Models of Rare Human Diseases: An Expert Review and Lessons Learned

Rare diseases occur with a frequency ≤1:1500-1:2500 depending on the location and applicable definitions across countries. Although individually rare, they collectively affect as much as 4-8% of population imposing a large burden on public health. Rarity in prevalence means prolonged path to accurate diagnosis, lack of treatment options, and also limited chances for preclinical studies of pathogenesis. I discuss in this expert review (1) what metabolomics, as a high throughput systems sciences technology, offers for rare disease studies, (2) why animal models are important for the study of rare human diseases and what should be kept in mind while using animal models, and finally, (3) provide examples of recent research to highlight how metabolomics on animal models of rare diseases perform, and how these results can lead to the knowhow, which raises genome, metabolome, and phenotype integration to a whole new level. In sum, metabolomics has been for years in clinical use for diagnosis of certain types of rare diseases. Determination of pathogenesis of more complex diseases and testing of treatment strategies is where animal models and systems biology analytical approaches are necessary. From gathered data, it is possible to go back to diagnostic and prognostic markers for rare diseases, which so far lack reliable and robust diagnosis and therapeutic options. In the future, a major challenge is to reveal the links between genotype, metabolism, and phenotype. Rare diseases could be the key in that process.

Gestational Diabetes Alters the Metabolomic Profile in 2nd Trimester Amniotic Fluid in a Sex-Specific Manner

Maternal diabetes and obesity induce marked abnormalities in glucose homeostasis and insulin secretion in the fetus, and are linked to obesity, diabetes, and metabolic disease in the offspring, with specific metabolic characterization based on offspring sex. Gestational diabetes (GDM) has profound effects on the intrauterine milieu, which may reflect and/or modulate the function of the maternal–fetal unit. In order to characterize metabolic factors that affect offspring development, we profiled the metabolome of second trimester amniotic fluid (AF) from women who were subsequently diagnosed with gestational diabetes (GDM) using a targeted metabolomics approach, profiling 459 known biochemicals through gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS) assays. Using a nested case-control study design, we identified 69 total biochemicals altered by GDM exposure, while sex-specific analysis identified 44 and 58 metabolites in male and female offspring, respectively. The most significant changes were in glucose, amino acid, glutathione, fatty acid, sphingolipid, and bile acid metabolism with specific changes identified based on the offspring sex. Targeted isotope dilution LC/MS confirmatory assays measured significant changes in docosahexaenoic acid and arachidonic acid. We conclude that the sex-specific alterations in GDM maternal–fetal metabolism may begin to explain the sex-specific metabolic outcomes seen in offspring exposed to GDM in utero.

Metabolomics of Major Depressive Disorder and Bipolar Disorder: Overview and Future Perspective

Major depressive disorder (MDD) and bipolar disorder (BD) are the most common mood disorders. They are etiologically related, but clinically distinct psychiatric illnesses. Their shared clinical features result in high rates of misdiagnosis due to a lack of biomarkers that allow their differentiation. BD is more frequently misdiagnosed as MDD because of overlapping symptomology, often later onset of mania, and frequent occurrence of depressive episodes in patients with BD. Misdiagnosis is also increased when patients with BD present symptoms indicative of a clinically significant depressive episode, but are premorbid for manic symptoms, or previous manic states not recognized. Therefore, the development of specific biomarkers for these disorders would be invaluable for establishing the correct diagnosis and treatment of MDD and BD. This chapter presents an overview and future perspective of the identification of biomarkers for mood disorders using metabolomics.