Search

The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognostication and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to apply technological advances to clinical needs, for instance in the development of precision biomarkers for personalised medicine. Via omics technologies, biological processes from the genes to circulating protein, as well as the microbiome - including bacteria, viruses and fungi, can be investigated on an axis. However, there are important barriers to omics-based biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate them across diverse populations presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression at different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated has enabled the hypothesis-free discovery of a plethora of candidate biomarkers that warrant further validation. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Infant adiposity may be related to later metabolic health. Maternal metabolite profiling reflects both genetic and environmental influences and allows elucidation of metabolic pathways associated with infant adiposity. In this multi-ethnic Asian cohort, we aimed to (i) identify maternal plasma metabolites associated with infant adiposity and other birth outcomes and (ii) investigate the maternal characteristics associated with those metabolites. In 940 mother-offspring pairs, we performed gas chromatography-mass spectrometry and identified 134 metabolites in maternal fasting plasma at 26-28 weeks of gestation. At birth, neonatal triceps and subscapular skinfold thicknesses were measured by trained research personnel, while weight and length measures were abstracted from delivery records. Gestational age was estimated from first-trimester dating ultrasound. Associations were assessed by multivariable linear regression, with p-values corrected using the Benjamini-Hochberg approach. At a false discovery rate of 5%, we observed associations between 28 metabolites and neonatal sum of skinfold thicknesses (13 amino acid-related, 4 non-esterified fatty acids, 6 xenobiotics, and 5 unknown compounds). Few associations were observed with gestational duration, birth weight, or birth length. Maternal ethnicity, pre-pregnancy BMI, and diet quality during pregnancy had the strongest associations with the specific metabolome related to infant adiposity. Further studies are warranted to replicate our findings and to understand the underlying mechanisms.

Remodeling of the gut microbiota is implicated in various metabolic and inflammatory diseases of the gastrointestinal tract. We hypothesized that the gut microbiota affects the DNA methylation profile of intestinal epithelial cells (IECs) which could, in turn, alter intestinal function. In this study, we used mass spectrometry and methylated DNA capture to respectively investigate global and genome-wide DNA methylation of intestinal epithelial cells from germ-free (GF) and conventionally raised mice. In colonic IECs from GF mice, DNA was markedly hypermethylated. This was associated with a dramatic loss of ten-eleven-translocation activity, a lower DNA methyltransferase activity and lower circulating levels of the 1-carbon metabolite, folate. At the gene level, we found an enrichment for differentially methylated regions proximal to genes regulating the cytotoxicity of NK cells (false-discovery rate < 8.9E -6 ), notably genes regulating the cross-talk between NK cells and target cells, such as members of the NK group 2 member D ligand superfamily Raet. This distinct epigenetic signature was associated with a marked decrease in Raet1 expression and a loss of CD56 + /CD45 + cells in the intestine of GF mice. Thus, our results indicate that altered activity of methylation-modifying enzymes in GF mice influences the IEC epigenome and modulates the crosstalk between IECs and NK cells. Epigenetic reprogramming of IECs may modulate intestinal function in diseases associated with altered gut microbiota.-Poupeau, A., Garde, C., Sulek, K., Citirikkaya, K., Treebak, J. T., Arumugam, M., Simar, D., Olofsson, L. E., Bäckhed, F., Barrès, R. Genes controlling the activation of natural killer lymphocytes are epigenetically remodeled in intestinal cells from germ-free mice.

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.

Background: Frequent binge drinking is a known contributor to alcohol‐related harm, but its impact on systemic and hepatic inflammation is not fully understood. We hypothesize that changes in immune markers play a central role in adverse effects of acute alcohol intake, especially in patients with early liver disease. Aim: To investigate the effects of acute alcohol intoxication on inflammation‐related markers in hepatic and systemic venous plasma in people with alcohol‐related liver disease (ArLD), non‐alcoholic fatty liver disease (NAFLD) and healthy controls. Methods: Thirty‐eight participants (13 with ArLD, 15 with NAFLD and 10 healthy controls) received 2.5 mL of 40% ethanol per kg body weight via a nasogastric tube. Seventy‐two inflammation‐related markers were quantified in plasma from hepatic and systemic venous blood, at baseline, 60 and 180 min after intervention. Results: Alcohol intervention altered the levels of 31 of 72 and 14 of 72 markers in the systemic and hepatic circulation. All changes observed in the hepatic circulation were also identified in the systemic circulation after 180 min. Only FGF21 and IL6 were increased after alcohol intervention, while the remaining 29 markers decreased. Differences in response to acute alcohol between the groups were observed for 8 markers, and FGF21 response was blunted in individuals with steatosis. Conclusion: Acute alcohol intoxication induced changes in multiple inflammation‐related markers, implicated in alcohol metabolism and hepatocellular damage. Differences identified between marker response to binge drinking in ArLD, NAFLD and healthy controls may provide important clues to disease mechanisms and potential targets for treatment. Clinical trial number: NCT03018990 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In our study, we used a mass spectrometry-based metabolomic approach to search for biomarkers that may act as early indicators of spontaneous preterm birth (sPTB). Samples were selected as a nested case-control study from the Screening for Pregnancy Endpoints (SCOPE) biobank in Auckland, New Zealand. Cervicovaginal swabs were collected at 20 weeks from women who were originally assessed as being at low risk of sPTB. Samples were analysed using gas chromatography-mass spectrometry (GC-MS). Despite the low amount of biomass (16-23 mg), 112 compounds were detected. Statistical analysis showed no significant correlations with sPTB. Comparison of reported infection and plasma inflammatory markers from early pregnancy showed two inflammatory markers were correlated with reported infection, but no correlation with any compounds in the metabolite profile was observed. We hypothesise that the lack of biomarkers of sPTB in the cervicovaginal fluid metabolome is simply because it lacks such markers in early pregnancy. We propose alternative biofluids be investigated for markers of sPTB. Our results lead us to call for greater scrutiny of previously published metabolomic data relating to biomarkers of sPTB in cervicovaginal fluids, as the use of small, high risk, or late pregnancy cohorts may identify metabolite biomarkers that are irrelevant for predicting risk in normal populations. [ABSTRACT FROM AUTHOR]

Current early pregnancy screening tools to identify women at risk of developing gestational diabetes mellitus lack both specificity and sensitivity. As a result, the foetus and mother are often subjected to insult during disease progression, prior to diagnosis and treatment in later pregnancy. Metabolomics is an analytical approach, which allows for appraisal of small molecular mass compounds in a biofluid. The aim of this pilot study was to investigate the relationship between the early gestation serum metabolite profile and the subsequent development of gestational diabetes mellitus in the search for early pregnancy biomarkers and potential metabolic mechanisms. Our nested case-control study analysed maternal serum at 20 weeks' gestation, obtained from the New Zealand cohort of the Screening for Pregnancy Endpoints study. Metabolomic profiling was performed using gas chromatography coupled to mass spectrometry, and metabolites were identified using R software and an in-house mass spectral library. Statistical analysis was performed using SPSS version 21.0. Forty-eight metabolites were identified in the serum samples. Itaconic acid ( P = 0.0003), with a false discovery rate of 0.012, was found to be significantly more abundant in women who subsequently developed gestational diabetes mellitus, when compared to controls with uncomplicated pregnancies. The current pilot study found that itaconic acid may have potential as a novel biomarker in early pregnancy to predict the subsequent development of gestational diabetes mellitus. However, the findings from this pilot study require validation with a larger, diverse population before translation into the clinical setting. [ABSTRACT FROM AUTHOR]