Maternal hair metabolome analysis identifies a potential marker of lipid peroxidation in gestational diabetes mellitus

Mass spectrometry-based "omics" technologies for the study of gestational diabetes and the discovery of new biomarkers

Gestational diabetes (GDM) is one of the most common complications occurring during pregnancy. Diagnosis is performed by oral glucose tolerance test, but harmonized testing methods and thresholds are still lacking worldwide. Short-term and long-term effects include obesity, type 2 diabetes, and increased risk of cardiovascular disease. The identification and validation of sensitidve, selective, and robust biomarkers for early diagnosis during the first trimester of pregnancy are required, as well as for the prediction of possible adverse outcomes after birth. Mass spectrometry (MS)-based omics technologies are nowadays the method of choice to characterize various pathologies at a molecular level. Proteomics and metabolomics of GDM were widely investigated in the last 10 years, and various proteins and metabolites were proposed as possible biomarkers. Metallomics of GDM was also reported, but studies are limited in number. The present review focuses on the description of the different analytical methods and MS-based instrumental platforms applied to GDM-related omics studies. Preparation procedures for various biological specimens are described and results are briefly summarized. Generally, only preliminary findings are reported by current studies and further efforts are required to determine definitive GDM biomarkers.

Segmental hair metabolomics analysis in pregnant women with pregnancy complications

INTRODUCTION

Pregnancy complications, as preeclampsia (PE) and HELLP syndrome, occurring with similar pathophysiological mechanisms, have adverse effects on the health of both mother and fetus during pregnancy and thereafter, they are leading causes of maternal and fetal morbidity and mortality. The hair metabolome has been recognized as a valuable source of information in pregnancy research, as it provides stable metabolite information to be able to assist with studying biomarkers or metabolic mechanisms of pregnancy and its complications.

OBJECTIVE

The aim of this study was to investigate the hair metabolome profile of pregnant women with PE, HELLP syndrome and healthy women.

METHOD

Hair samples of new-borns' mothers (patients and controls) were investigated segmentally relevant to each trimester using a proper sample preparation and gas chromatography-mass spectrometry (GC-MS) to identify robust biomarkers that can be useful for screening, early detection, follow-up and treatment of PE and HELLP syndrome, the etiology of which are still unknown.

RESULTS

The results showed a significant change in the metabolome profiles of the patient and control groups regarding the trimesters. A striking decrease was observed in all 100 metabolites investigated in the patient group (p < 0.000). The metabolic pathways associated with significant metabolites have also been investigated, and the most affected pathways were observed to be the urea cycle, glycine, serine, aspartate, methionine and purine metabolism, ammonia cycle, and phosphatidylethanolamine biosynthesis.

CONCLUSION

The found metabolites provide us with extensive data on the ability to establish biomarkers for predicting, early detection and monitoring of PE.

Evaluating Different Extraction Approaches for GC-MS Based Metabolomics Analysis of the Giant Pandas' Fur

Giant pandas in zoo captivity are situated in residential areas, where environmental pollutants and anthropogenic factors have an impact on their health. Hair metabolomics has been applied in numerous environmental toxicological studies. Therefore, the panda fur metabolome could be a reliable approach to reflect endogenous and exogenous metabolic changes related to environmental exposure. However, there is no established extraction protocol to study the fur metabolome of pandas. The aim of this research was to optimize the extraction of panda fur metabolome for high-throughput metabolomics analysis using gas chromatography-mass spectrometry. Fur samples were collected from five pandas. Eight different extraction methods were investigated and evaluated for their reproducibility, metabolite coverage, and extraction efficiency, particularly in relation to the biochemical compound classes such as amino acids, tricarboxylic acid cycle derivatives, fatty acids, and secondary metabolites. Our results demonstrated that HCl + ACN were the superior extraction solvents for amino acid and secondary metabolite extraction, and NaOH + MeOH was ideal for fatty acid extraction. Interestingly, the metabolomic analysis of panda fur was capable of discriminating the longitudinal metabolite profile between black and white furs. These extraction protocols can be used in future study protocols for the analysis of the fur metabolome in pandas.

Molecular biomarkers for gestational diabetes mellitus and postpartum diabetes

ABSTRACT

Gestational diabetes mellitus (GDM) is a growing public health problem worldwide that threatens both maternal and fetal health. Identifying individuals at high risk for GDM and diabetes after GDM is particularly useful for early intervention and prevention of disease progression. In the last decades, a number of studies have used metabolomics, genomics, and proteomic approaches to investigate associations between biomolecules and GDM progression. These studies clearly demonstrate that various biomarkers reflect pathological changes in GDM. The established markers have potential use as screening and diagnostic tools in GDM and in postpartum diabetes research. In the present review, we summarize recent studies of metabolites, single-nucleotide polymorphisms, microRNAs, and proteins associated with GDM and its transition to postpartum diabetes, with a focus on their predictive value in screening and diagnosis.

Complex metabolic interactions between ovary, plasma, urine, and hair in ovarian cancer

Ovarian cancer (OC) is the third most common malignant tumor of women accompanied by alteration of systemic metabolism, yet the underlying interactions between the local OC tissue and other system biofluids remain unclear. In this study, we recruited 17 OC patients, 16 benign ovarian tumor (BOT) patients, and 14 control patients to collect biological samples including ovary plasma, urine, and hair from the same patient. The metabolic features of samples were characterized using a global and targeted metabolic profiling strategy based on Gas chromatography-mass spectrometry (GC-MS). Principal component analysis (PCA) revealed that the metabolites display obvious differences in ovary tissue, plasma, and urine between OC and non-malignant groups but not in hair samples. The metabolic alterations in OC tissue included elevated glycolysis (lactic acid) and TCA cycle intermediates (malic acid, fumaric acid) were related to energy metabolism. Furthermore, the increased levels of glutathione and polyunsaturated fatty acids (linoleic acid) together with decreased levels of saturated fatty acid (palmitic acid) were observed, which might be associated with the anti-oxidative stress capability of cancer. Furthermore, how metabolite profile changes across differential biospecimens were compared in OC patients. Plasma and urine showed a lower concentration of amino acids (alanine, aspartic acid, glutamic acid, proline, leucine, and cysteine) than the malignant ovary. Plasma exhibited the highest concentrations of fatty acids (stearic acid, EPA, and arachidonic acid), while TCA cycle intermediates (succinic acid, citric acid, and malic acid) were most concentrated in the urine. In addition, five plasma metabolites and three urine metabolites showed the best specificity and sensitivity in differentiating the OC group from the control or BOT groups (AUC > 0.90) using machine learning modeling. Overall, this study provided further insight into different specimen metabolic characteristics between OC and non-malignant disease and identified the metabolic fluctuation across ovary and biofluids.

The relationship between hair metabolites, air pollution exposure and gestational diabetes mellitus: A longitudinal study from pre-conception to third trimester

BACKGROUND

Gestational diabetes mellitus (GDM) is a metabolic condition defined as glucose intolerance with first presentation during pregnancy. Many studies suggest that environmental exposures, including air pollution, contribute to the pathogenesis of GDM. Although hair metabolite profiles have been shown to reflect pollution exposure, few studies have examined the link between environmental exposures, the maternal hair metabolome and GDM. The aim of this study was to investigate the longitudinal relationship (from pre-conception through to the third trimester) between air pollution exposure, the hair metabolome and GDM in a Chinese cohort.

METHODS

A total of 1020 women enrolled in the Complex Lipids in Mothers and Babies (CLIMB) birth cohort were included in our study. Metabolites from maternal hair segments collected pre-conception, and in the first, second, and third trimesters were analysed using gas chromatography-mass spectrometry (GC-MS). Maternal exposure to air pollution was estimated by two methods, namely proximal and land use regression (LUR) models, using air quality data from the air quality monitoring station nearest to the participant's home. Logistic regression and mixed models were applied to investigate associations between the air pollution exposure data and the GDM associated metabolites.

RESULTS

Of the 276 hair metabolites identified, the concentrations of fourteen were significantly different between GDM cases and non-GDM controls, including some amino acids and their derivatives, fatty acids, organic acids, and exogenous compounds. Three of the metabolites found in significantly lower concentrations in the hair of women with GDM (2-hydroxybutyric acid, citramalic acid, and myristic acid) were also negatively associated with daily average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO and the exposure estimates of PM_2.5 and NO_2, and positively associated with O₃.

CONCLUSIONS

This study demonstrated that the maternal hair metabolome reflects the longitudinal metabolic changes that occur in response to environmental exposures and the development of GDM.

Association of circulating omega 3, 6 and 9 fatty acids with gestational diabetes mellitus: a systematic review

BACKGROUND

Gestational diabetes mellitus (GDM) is associated with increased risks of disease for mother and child during pregnancy and after that. Early diagnosis of GDM would promote both maternal and fetal health. Metabolomics can simplify and develop our understanding of the etiology, manifestation, or pathophysiology of the disease. This systematic review investigates the association of circulating omega 3, 6, and 9 fatty acids with GDM.

METHODS

We conducted a systematic search of PubMed, Scopus, Web of Science, and EMBASE databases up to May 8, 2020, using the key term combinations of all types of omega fatty acids with gestational diabetes mellitus. Additional articles were identified through searching the reference lists of included studies.

RESULTS

This systematic review included 15 articles. Five were cohort studies, four included nested case-control studies and four were case-control studies. The results of this study demonstrate an increasing trend in the amount of oleic acid and palmitoleic acid in the second trimester and an increase in decosahexanoic acid in the third trimester of GDM mothers. The changes in other fatty acids of interest are either not significant or if significant, their results are inconsistent with the other existing articles.

CONCLUSIONS

Omega fatty acids, as potential biomarkers, are considered to be associated with GDM risk and thus provide useful information regarding the prevention and early diagnosis of GDM. Moreover, existing metabolomic studies on GDM are shown to provide conflicting results about metabolite profile characteristics. This systematic review was registered at PROSPERO ( www.crd.york.ac.uk/PROSPERO ) as CRD42020196122.

Minimally Invasive Biospecimen Collection for Exposome Research in Children's Health

PURPOSE OF REVIEW

The advent of low-volume biosampling and novel biomarker matrices offers non- or minimally invasive approaches to sampling in children. These new technologies, combined with advancements in mass spectrometry that provide high sensitivity, robust measurements of low-concentration exposures, facilitate the application of untargeted metabolomics in children's exposome research. Here, we review emerging sampling technologies for alternative biomatrices-dried capillary blood, interstitial fluid, saliva, teeth, and hair-and highlight recent applications of these samplers to drive discovery in population-based exposure research.

RECENT FINDINGS

Biosampling and biomarker technologies demonstrate potential to directly measure exposures during key developmental time periods. While saliva is the most traditional of the reported biomatrices, each technology has key advantages and disadvantages. For example, hair and teeth provide retrospective analysis of past exposures, and dried capillary blood provides quantitative measurements of systemic exposures that can be more readily compared with traditional venous blood measurements. Importantly, all technologies can or have the potential to be used at home, increasing the convenience and parental support for children's biosampling. This review describes emerging sample collection technologies that hold promise for children's exposome studies. While applications in metabolomics are still limited, these novel matrices are poised to facilitate longitudinal exposome studies to discover key exposures and windows of susceptibility affecting children's health.

Spatial patterns of the exposure-response relationship between mercury and cortisol in the fur of river otter (Lontra canadensis)

Fur has been validated as a useful biomarker medium for chemical exposures and biological responses in wildlife. Mercury (Hg) is known to act as an endocrine disruptor by altering brain neurochemistry. In this study, we investigated the spatial patterns of relationships between total Hg (THg) and cortisol in the fur of river otter (Lontra canadensis). Geotagged fur samples were obtained from a wildlife biomonitoring program (n = 72) and the North American Fur Auction (n = 37) between 2014 and 2017. Fur THg was measured using direct thermal decomposition and fur cortisol was measured using an enzyme-linked immunosorbent assay (ELISA). The average fur THg concentration was 11.50 ± 12.40 μg/g fur weight (f.w.), and the fur cortisol concentration was 5.71 ± 8.24 pg/mg. Results from the global ordinary least squares regression show no relationship between THg and fur cortisol concentrations. However, both Hg and cortisol were heterogeneously distributed across the landscape. When a localized geographically weighted regression (GWR) was used, a geographically distinct bi-phasic relationship was observed. We suggest this bi-phasic relationship is associated with a threshold THg concentration, beyond which, there was a negative association with measured fur cortisol. Results of a break-point analysis, with one break, indicate that the threshold is 16 ± 1.27 μg/g f. w of THg in fur. This research highlights the need to use appropriate spatial methods when assessing exposure-response relationships in wildlife across large geographical areas. The identified threshold can be used for regulatory purposes.

Revealing Metabolic Perturbation Following Heavy Methamphetamine Abuse by Human Hair Metabolomics and Network Analysis

Methamphetamine (MA) is a highly addictive central nervous system stimulant. Drug addiction is not a static condition but rather a chronically relapsing disorder. Hair is a valuable and stable specimen for chronic toxicological monitoring as it retains toxicants and metabolites. The primary focus of this study was to discover the metabolic effects encompassing diverse pathological symptoms of MA addiction. Therefore, metabolic alterations were investigated in human hair following heavy MA abuse using both targeted and untargeted mass spectrometry and through integrated network analysis. The statistical analyses (t-test, variable importance on projection score, and receiver-operator characteristic curve) demonstrated that 32 metabolites (in targeted metabolomics) as well as 417 and 224 ion features (in positive and negative ionization modes of untargeted metabolomics, respectively) were critically dysregulated. The network analysis showed that the biosynthesis or metabolism of lipids, such as glycosphingolipids, sphingolipids, glycerophospholipids, and ether lipids, as well as the metabolism of amino acids (glycine, serine and threonine; cysteine and methionine) is affected by heavy MA abuse. These findings reveal crucial metabolic effects caused by MA addiction, with emphasis on the value of human hair as a diagnostic specimen for determining drug addiction, and will aid in identifying robust diagnostic markers and therapeutic targets.

Omics-based biomarkers in the diagnosis of diabetes

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

Trace biomarkers associated with spontaneous preterm birth from the maternal serum metabolome of asymptomatic nulliparous women - parallel case-control studies from the SCOPE cohort

Prediction of spontaneous preterm birth (sPTB) in asymptomatic women remains a great challenge; accurate and reproducible screening tools are still not available in clinical practice. We aimed to investigate whether the maternal serum metabolome together with clinical factors could be used to identify asymptomatic women at risk of sPTB. We conducted two case-control studies using gas chromatography-mass spectrometry to analyse maternal serum samples collected at 15- and 20-weeks' gestation from 164 nulliparous women from Cork, and 157 from Auckland. Smoking and vaginal bleeding before 15 weeks were the only significant clinical predictors of sPTB for Auckland and Cork subsets, respectively. Decane, undecane, and dodecane were significantly associated with sPTB (FDR < 0.05) in the Cork subset. An odds ratio of 1.9 was associated with a one standard deviation increase in log (undecane) in a multiple logistic regression which also included vaginal bleeding as a predictor. In summary, elevated serum levels of the alkanes decane, undecane, and dodecane were associated with sPTB in asymptomatic nulliparous women from Cork, but not in the Auckland cohort. The association is not strong enough to be a useful clinical predictor, but suggests that further investigation of the association between oxidative stress processes and sPTB risk is warranted.

Differences in Pregnancy Metabolic Profiles and Their Determinants between White European and South Asian Women: Findings from the Born in Bradford Cohort

There is widespread metabolic disruption in women upon becoming pregnant. South Asians (SA) compared to White Europeans (WE) have more fat mass and are more insulin-resistant at a given body mass index (BMI). Whether these are reflected in other gestational metabolomic differences is unclear. Our aim was to compare gestational metabolic profiles and their determinants between WE and SA women. We used data from a United Kingdom (UK) cohort to compare metabolic profiles and associations of maternal age, education, parity, height, BMI, tricep skinfold thickness, gestational diabetes (GD), pre-eclampsia, and gestational hypertension with 156 metabolic measurements in WE (n = 4072) and SA (n = 4702) women. Metabolic profiles, measured in fasting serum taken between 26–28 weeks gestation, were quantified by nuclear magnetic resonance. Distributions of most metabolic measures differed by ethnicity. WE women had higher levels of most lipoprotein subclasses, cholesterol, glycerides and phospholipids, monosaturated fatty acids, and creatinine but lower levels of glucose, linoleic acid, omega-6 and polyunsaturated fatty acids, and most amino acids. Higher BMI and having GD were associated with higher levels of several lipoprotein subclasses, triglycerides, and other metabolites, mostly with stronger associations in WEs. We have shown differences in gestational metabolic profiles between WE and SA women and demonstrated that associations of exposures with these metabolites differ by ethnicity.

Hair Metabolomics in Animal Studies and Clinical Settings

Metabolomics is a powerful tool used to understand comprehensive changes in the metabolic response and to study the phenotype of an organism by instrumental analysis. It most commonly involves mass spectrometry followed by data mining and metabolite assignment. For the last few decades, hair has been used as a valuable analytical sample to investigate retrospective xenobiotic exposure as it provides a wider window of detection than other biological samples such as saliva, plasma, and urine. Hair contains functional metabolomes such as amino acids and lipids. Moreover, segmental analysis of hair based on its growth rate can provide information on metabolic changes over time. Therefore, it has great potential as a metabolomics sample to monitor chronic diseases, including drug addiction or abnormal conditions. In the current review, the latest applications of hair metabolomics in animal studies and clinical settings are highlighted. For this purpose, we review and discuss the characteristics of hair as a metabolomics sample, the analytical techniques employed in hair metabolomics and the consequence of hair metabolome alterations in recent studies. Through this, the value of hair as an alternative biological sample in metabolomics is highlighted.

Metabolomics applied to maternal and perinatal health: a review of new frontiers with a translation potential

The prediction or early diagnosis of maternal complications is challenging mostly because the main conditions, such as preeclampsia, preterm birth, fetal growth restriction, and gestational diabetes mellitus, are complex syndromes with multiple underlying mechanisms related to their occurrence. Limited advances in maternal and perinatal health in recent decades with respect to preventing these disorders have led to new approaches, and "omics" sciences have emerged as a potential field to be explored. Metabolomics is the study of a set of metabolites in a given sample and can represent the metabolic functioning of a cell, tissue or organism. Metabolomics has some advantages over genomics, transcriptomics, and proteomics, as metabolites are the final result of the interactions of genes, RNAs and proteins. Considering the recent "boom" in metabolomic studies and their importance in the research agenda, we here review the topic, explaining the rationale and theory of the metabolomic approach in different areas of maternal and perinatal health research for clinical practitioners. We also demonstrate the main exploratory studies of these maternal complications, commenting on their promising findings. The potential translational application of metabolomic studies, especially for the identification of predictive biomarkers, is supported by the current findings, although they require external validation in larger datasets and with alternative methodologies.

Hsa_circRNA_0054633 is highly expressed in gestational diabetes mellitus and closely related to glycosylation index

Background

Circular RNA (circRNA) is involved in the pathological processes of various diseases. CircRNA is more stable than linear RNAs and is expressed in high levels in tissues, making it a better biomarker candidate than linear RNAs. In this study, we aimed to identify potential circRNA biomarkers of gestational diabetes mellitus (GDM).

Methods

A retrospective case–control study was conducted using data and samples from women treated at a hospital in China between July 10, 2017, and February 15, 2018. We collected serum samples from 40 healthy pregnant women (controls) and 40 women with GDM (cases) during the second trimester as well as 65 controls and 65 cases during the third trimester of pregnancy. Placenta tissues and neonatal cord blood were each from another 20 cases and 20 controls. We selected six circRNAs (hsa_circRNA_0054633, hsa_circRNA_103410, hsa_circRNA_063981, hsa_circRNA_102682, hsa_circRNA_0018508, and hsa_circRNA_406918) as candidate biomarkers and used quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) to measure their concentrations in the serum and placental tissues. The Pearson correlation test was used to assess the correlation between various circRNAs and between circRNA and clinical variables. The area under the receiver operating characteristic (ROC) curve was used to assess the diagnostic value of circRNAs for GDM at each stage.

Results

Hsa_circRNA_0054633 was highly expressed in the blood during the second and third trimesters; its expression was also high in the placenta but low in the cord blood (P < 0.05). Hsa_cirRNA_0054633 was highly correlated with GHBA1 and GHBA1c levels in maternal blood samples at various stages of the GDM group (including placental tissue and umbilical cord blood) (P < 0.05). Hsa_circRNA_063981, hsa_circRNA_102682, and hsa_circRNA_103410 were also differentially expressed between the case and control groups at different stages (P < 0.05). There was a strong correlation between hsa_circRNA_0054633 and hsa_circRNA_103410 levels in third-trimester maternal blood (P = 0.000, r = 0.554) and in neonatal umbilical cord blood (P = 0.000, r = 0.866). Hsa_circRNA_0054633 showed a significant diagnostic value in the second and third trimesters of pregnancy, placenta, and cord blood (AUC = 0.793, 0.664, 0.747, and 0.783, respectively, P < 0.001).

Conclusion

This study suggests that hsa_cirRNA_0054633 is abnormally expressed in GDM patients and may play a potential role in the development of GDM. The possibility of using circRNAs for the diagnosis of GDM requires additional investigation in future studies.

Electronic supplementary material

The online version of this article (10.1186/s13148-019-0610-8) contains supplementary material, which is available to authorized users.

Planning, Implementing, and Running a Multicentre Preterm Birth Study with Biobank Resources in Brazil: The Preterm SAMBA Study

Background

Our aim was to describe the steps in planning, implementing, and running a multicentre cohort study of maternal and perinatal health using a high-quality biobank comprised of maternal serum, plasma, and hair samples collected from five sites in Brazil. The Preterm SAMBA study, conducted by the Brazilian Network for Studies on Reproductive and Perinatal Health, was an innovative approach used to identify women at higher risk for preterm birth. It is also of great importance in the study of other maternal and perinatal complications in the context of Brazil, which is a middle-income country.

Methods

We described phases of planning, implementing, and running the Preterm SAMBA study, a multicentre Brazilian cohort study of low-risk nulliparous pregnant women, to validate a set of metabolite biomarkers for preterm birth identified in an external cohort. Procedures and strategies used to plan, implement, and maintain this multicentre preterm birth study are described in detail. Barriers and experience cited in the current narrative are not usually discussed in the scientific literature or published study protocols.

Results

Several barriers and strategies were identified in different phases of the Preterm SAMBA study at different levels of the study framework (steering committee; coordinating and local centres). Strategies implemented and resources used in the study are a legacy of the Brazilian Network, aimed at training collaborators in such complex settings.

Conclusion

The Brazilian Network for Studies on Reproductive and Perinatal Health has gained some experience in conducting a multicentre cohort study using a resourceful biobank which may be helpful to other research groups and maternal/perinatal health networks that plan on employing a similar approach to a similar background.

Metabolomics of Pregnancy Complications: Emerging Application of Maternal Hair

In recent years, the study of metabolomics has begun to receive increasing international attention, especially as it pertains to medical research. This is due in part to the potential for discovery of new biomarkers in the metabolome and to a new understanding of the "exposome", which refers to the endogenous and exogenous compounds that reflect external exposures. Consequently, metabolomics research into pregnancy-related issues has increased. Biomarkers discovered through metabolomics may shed some light on the etiology of certain pregnancy-related complications and their adverse effects on future maternal health and infant development and improve current clinical management. The discoveries and methods used in these studies will be compiled and summarized within the following paper. A further focus of this paper is the use of hair as a biological sample, which is gaining increasing attention across diverse fields due to its noninvasive sampling method and the metabolome stability. Its significance in exposome studies will be considered in this review, as well as the potential to associate exposures with adverse pregnancy outcomes. Currently, hair has been used in only two metabolomics studies relating to fetal growth restriction (FGR) and gestational diabetes mellitus (GDM).

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.

Metabolomic profiling of women with gestational diabetes mellitus and their offspring: Review of metabolomics studies

BACKGROUND

Gestational diabetes mellitus (GDM) reflects an increased risk of developing type 2 diabetes (T2D) after pregnancy in women. Offspring born to mothers with GDM are at an elevated risk of obesity and T2D at a young age. Currently, there are lack of ways for identifying women in early pregnancy who are at risk of developing GDM. As a result, both mothers and fetus are not treated until late in the second trimester when GDM is diagnosed. The recent advance in metabolomics, a new approach of systematic investigation of the metabolites, provides an opportunity for early detection of GDM, and classifying the risk of subsequent chronic diseases among women and their offspring.

METHODS

We reviewed the literatures published in the past 20 years on studies using high-throughput metabolomics technologies to investigate women with GDM and their offspring.

CONCLUSIONS

Despite the inconsistent results, previous studies have identified biomarkers that involved in specific metabolite groups and several pathways, including amino acid metabolism, steroid hormone biosynthesis, glycerophospholipid metabolism, and fatty acid metabolism. However, most studies have small sample sizes. Further research is warranted to determine if metabolomics will result in new indicators for the diagnosis, management, and prognosis of GDM and related complications.

Association between maternal exposure to phthalates and lower language ability in offspring derived from hair metabolome analysis

The fetus undergoes a crucial period of neurodevelopment in utero. The maternal hair metabolome provides an integrated record of the metabolic state of the mother prior to, and during pregnancy. We investigated whether variation in the maternal hair metabolome was associated with neurodevelopmental differences across infants. Maternal hair samples and infant neurocognitive assessments (using the Bayley III Scales of Infant Development at 24 months) were obtained for 373 infant-mother dyads between 26–28 weeks’ gestation from the Growing Up in Singapore Towards Healthy Outcomes cohort. The hair metabolome was analysed using gas chromatography-mass spectrometry. Intensity measurements were obtained for 276 compounds. After controlling for maternal education, ethnicity, and infant sex, associations between metabolites and expressive language skills were detected, but not for receptive language, cognitive or motor skills. The results confirm previous research associating higher levels of phthalates with lower language ability. In addition, scores were positively associated with a cluster of compounds, including adipic acid and medium-chain fatty acids. The data support associations between the maternal hair metabolome and neurodevelopmental processes of the fetus. The association between phthalates and lower language ability highlights a modifiable risk factor that warrants further investigation.

Analysis of sequential hair segments reflects changes in the metabolome across the trimesters of pregnancy

The hair metabolome has been recognized as a valuable source of information in pregnancy research, as it provides stable metabolite information that could assist with studying biomarkers or metabolic mechanisms of pregnancy and its complications. We tested the hypothesis that hair segments could be used to reflect a metabolite profile containing information from both endogenous and exogenous compounds accumulated during the nine months of pregnancy. Segments of hair samples corresponding to the trimesters were collected from 175 pregnant women in New Zealand. The hair samples were analysed using gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry. In healthy pregnancies, 56 hair metabolites were significantly different between the first and second trimesters, while 62 metabolites were different between the first and third trimesters (p < 0.05). Additionally, three metabolites in the second trimester hair samples were significantly different between healthy controls and women who delivered small-for-gestational-age infants (p < 0.05), and ten metabolites in third trimester hair were significantly different between healthy controls and women with gestational diabetes mellitus (p < 0.01). The findings from this pilot study provide improved insight into the changes of the hair metabolome during pregnancy, as well as highlight the potential of the maternal hair metabolome to differentiate pregnancy complications from healthy pregnancies.

Curcumin attenuates oxidative stress in liver in Type 1 diabetic rats

We investigated the effect of curcumin on liver anti-oxidative stress in the type 1 diabetic rat model induced by streptozotocin (STZ). Experimental diabetic rats were induced by STZ intraperitoneally. All rats were fed for 21 days including three groups of control (NC), diabetic model (DC) and curcumin-treated (Cur, 1.5 g/kg by gavage). The results showed that curcumin-treatment significantly decreased the blood glucose and plasma malondialdehyde levels, but significantly increased the plasma superoxide dismutase, glutathione peroxidase and reduced glutathione levels. Curcumin treatment decreased the activity of aldose reductase, but increased the plasma glucose-6-phosphate dehydrogenase, glucose synthetase and glucose-polymerizing activities. Curcumin treatment significantly decreased the protein of protein kinase C (PKC) and poly ADP ribose polymerase (PARP) expression in the Cur group compared with the DC group. Moreover, the sorbitol dehydrogenase activity was significantly decreased and deterred glucose enters into the polyol pathway leading to an increased NADPH content in the Cur group compared with the DC group. Our data provides evidence that oxidative stress in diabetic rats may be attenuated by curcumin by inhibiting polyol pathway associated with down-regulated expression of PKC and PARP, as evidenced by both an increase the antioxidant enzymes levels and glycogen biosynthesis enzymes activities.

Curcumin alleviates liver oxidative stress in type 1 diabetic rats

The aim of the present study was to determine the effects of curcumin on antioxidants using a rat model of type 1 diabetes. Seven-week-old male Sprague-Dawley rats were injected with Streptozotocin (STZ) intraperitoneally to induce this model, and then treated with 1.0% curcumin (weight ratio) mixed in their diet for 21 days. The present study included three groups: Control group (NC), diabetic rat model group (DC) and a curcumin treated group (Diab-Cur). The results demonstrated that curcumin treatment markedly decreased the blood glucose levels, plasma malondialdehyde concentration and plasma activity of glutathione peroxidase (GSH-Px) and catalase (CAT); however, it increased the plasma superoxide dismutase (SOD) and insulin levels. Curcumin treatment increased the expression of the CAT, GSH-Px, HO-1 and norvegicus NAD(P)H quinone dehydrogenase 1, and decreased the SOD1 expression, which, led to a diminished oxidative stress status. In addition, curcumin treatment significantly increased the protein expression of Keap1 in the Diab-Cur group when compared with the DC group, decreased cytosolic concentrations of Nrf2 while increasing nuclear accumulation of Nrf2. The results provide evidence that oxidative stress in the STZ-induced diabetic rat model may be attenuated by curcumin via the activation of the Keap1-Nrf2-ARE signaling pathway, as evidenced by a decrease in the blood glucose concentration and an increase in the transcription of several antioxidant genes.

Plasma fatty acid-binding protein 4 (FABP4) as a novel biomarker to predict gestational diabetes mellitus

AIMS

Fatty acid-binding protein 4 (FABP4) is mainly expressed in adipocytes and macrophages and is demonstrated to be elevated in diabetes patients. The aim of this study was to evaluate the possible role of FABP4 in the diagnosis of GDM and to investigate the relationship between FABP4 and overweight, insulin resistance and inflammatory marker TNF-α.

METHODS

A total of 46 women with GDM and 55 age-matched pregnant women without GDM (non-GDM) were eligible for the study. Demographic and biochemical parameters and fasting venous blood samples of two groups were collected from all cases. Serum concentrations of FABP4 were determined using enzyme-linked immunosorbent assay (ELISA). The predictive value of Serum FABP4 level was evaluated using receiver operating characteristic curve (ROC curve) analysis.

RESULTS

We found that the serum FABP4 levels were significantly higher in GDM compared to the non-GDM group. The area under the ROC curve assay yielded a satisfactory result of 0.94 (95 % confidence interval 0.90-0.98; p < 0.001). The best compromise between 86.96 % specificity and 89.09 % sensitivity was obtained with a cutoff value of 1.96 ng/mL for GDM diagnosis. Moreover, a significant positive correlation was observed between FABP4 and overweight, insulin resistance and TNF-α in pregnant women with GDM.

CONCLUSIONS

These results suggest that serum FABP4 may potentially serve as a novel biomarker for the prediction of GDM.

Use of metabolomics for the identification and validation of clinical biomarkers for preterm birth: Preterm SAMBA

Background

Spontaneous preterm birth is a complex syndrome with multiple pathways interactions determining its occurrence, including genetic, immunological, physiologic, biochemical and environmental factors. Despite great worldwide efforts in preterm birth prevention, there are no recent effective therapeutic strategies able to decrease spontaneous preterm birth rates or their consequent neonatal morbidity/mortality. The Preterm SAMBA study will associate metabolomics technologies to identify clinical and metabolite predictors for preterm birth. These innovative and unbiased techniques might be a strategic key to advance spontaneous preterm birth prediction.

Methods/design

Preterm SAMBA study consists of a discovery phase to identify biophysical and untargeted metabolomics from blood and hair samples associated with preterm birth, plus a validation phase to evaluate the performance of the predictive modelling. The first phase, a case–control study, will randomly select 100 women who had a spontaneous preterm birth (before 37 weeks) and 100 women who had term birth in the Cork Ireland and Auckland New Zealand cohorts within the SCOPE study, an international consortium aimed to identify potential metabolomic predictors using biophysical data and blood samples collected at 20 weeks of gestation. The validation phase will recruit 1150 Brazilian pregnant women from five participant centres and will collect blood and hair samples at 20 weeks of gestation to evaluate the performance of the algorithm model (sensitivity, specificity, predictive values and likelihood ratios) in predicting spontaneous preterm birth (before 34 weeks, with a secondary analysis of delivery before 37 weeks).

Discussion

The Preterm SAMBA study intends to step forward on preterm birth prediction using metabolomics techniques, and accurate protocols for sample collection among multi-ethnic populations. The use of metabolomics in medical science research is innovative and promises to provide solutions for disorders with multiple complex underlying determinants such as spontaneous preterm birth.

Early second-trimester plasma protein profiling using multiplexed isobaric tandem mass tag (TMT) labeling predicts gestational diabetes mellitus

AIMS

Gestational diabetes mellitus (GDM) is associated with an increased risk of serious complications for mother and child during pregnancy. The main option for diagnosis of GDM is 75 g oral glucose tolerance test (OGTT) at 24-28 gestation weeks, when harms to both mother and child have already potentially occurred. The aim of this study was to investigate new biomarkers for earlier detection and assessment of GDM at early second trimester (16-18 gestation weeks).

METHODS

We systematically used multiplexed isobaric tandem mass tag labeling combined with liquid chromatography mass spectrometry (LC-MS/MS) to screen differentially expressed proteins in plasma collected at 16-18 gestational weeks between pregnant women with and without GDM outcome.

RESULTS

A total of 828 proteins were identified, of which 36 proteins implicated in immune response, inflammation, transport, platelet aggregation, catalyze and defense response were identified as differentially regulated proteins in GDM. To assess the validity of the results, four selected proteins including C-reactive protein, sex hormone-binding globulin, Ficolin 3 and pregnancy-specific beta-1-glycoprotein 4 were selected for subsequent Western blot analysis.

CONCLUSIONS

This is the first comprehensive study that integrates multiple state-of-the-art proteomic technologies to discover the earlier potential plasma biomarkers for GDM.