Search

Background\udPsychotic experiences (PEs) are associated with an increased risk of future psychotic and non-psychotic mental disorders. The identification of biomarkers of PEs may provide insights regarding the underlying pathophysiology.\ud\udMethods\udThe current study applied targeted lipidomic approaches to compare plasma lipid profiles in participants from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort who did (n = 206) or did not (n = 206) have PEs when aged approximately 24 years.\ud\udResults\udIn total, 202 lipids including 8 lipid classes were measured by using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS). Eight lipid clusters were generated. Thirteen individual lipids were nominally significantly higher in the PEs group compared to the control group. After correction for multiple comparisons, 9 lipids comprising 3 lysophosphatidylcholines (LPCs), 2 phosphatidylcholines (PCs) and 4 triacylglycerols (TGs) remained significant. In addition, PEs cases had increased levels of TGs and LPCs with a low double bond count.\ud\udConclusions\udThese findings indicate plasma lipidomic abnormalities in individuals experiencing PEs. The lipidomic profile measures could aid our understanding of the underlying pathophysiological mechanisms.

Publisher Copyright: © 2022 The Authors Background and aims: The susceptibility of low-density lipoprotein (LDL) to aggregation predicts atherosclerotic cardiovascular disease. However, causes of interindividual variation in LDL lipid composition and aggregation susceptibility remain unclear. We examined whether the lipid composition and aggregation susceptibility of LDL reflect the lipid composition of the human liver. Methods: Liver biopsies and blood samples for isolation of LDL particles were obtained from 40 obese subjects (BMI 45.9 ± 6.1 kg/m2, age 43 ± 8 years). LDL was isolated using sequential ultracentrifugation and lipidomic analyses of liver and LDL samples were determined using ultra-high performance liquid chromatography–mass spectrometry. LDL aggregation susceptibility ex vivo was analyzed by inducing aggregation by human recombinant secretory sphingomyelinase and following aggregate formation. Results: The composition (acyl carbon number and double bond count) of hepatic triglycerides, phosphatidylcholines, and sphingomyelins (SMs) was closely associated with that of LDL particles. Hepatic dihydroceramides and ceramides were positively correlated with concentrations of the corresponding SM species in LDL as well with LDL aggregation. These relationships remained statistically significant after adjustment for age, sex, and body mass index. Conclusions: Lipid composition of LDL reflects that of the human liver in obese patients. Changes in hepatic sphingolipid metabolism may contribute to interindividual variation of LDL lipid composition and susceptibility to aggregation.

The gut microbiota is crucial in the regulation of bile acid (BA) metabolism. However, not much is known about the regulation of BAs during progression to type 1 diabetes (T1D). Here, we analyzed serum and stool BAs in longitudinal samples collected at 3, 6,12,18, 24, and 36 months of age from children who developed a single islet autoantibody (AAb) (P1Ab; n = 23) or multiple islet AAbs (P2Ab; n = 13) and controls (CTRs; n = 38) who remained AAb negative. We also analyzed the stool microbiome in a subgroup of these children. Factor analysis showed that age had the strongest impact on both BA and microbiome profiles. We found that at an early age, systemic BAs and microbial secondary BA pathways were altered in the P2Ab group compared with the P1Ab and CTR groups. Our findings thus suggest that dysregulated BA metabolism in early life may contribute to the risk and pathogenesis of T1D.

The fields of human genetics and genomics have generated considerable knowledge about the mechanistic basis of many diseases. Genomic approaches to diagnosis, prognostication, prevention and treatment - genomic-driven precision medicine (GDPM) - may help optimize medical practice. Here, we provide a comprehensive review of GDPM of complex diseases across major medical specialties. We focus on technological readiness: how rapidly a test can be implemented into health care. Although these areas of medicine are diverse, key similarities exist across almost all areas. Many medical areas have, within their standards of care, at least one GDPM test for a genetic variant of strong effect that aids the identification/diagnosis of a more homogeneous subset within a larger disease group or identifies a subset with different therapeutic requirements. However, for almost all complex diseases, the majority of patients do not carry established single-gene mutations with large effects. Thus, research is underway that seeks to determine the polygenic basis of many complex diseases. Nevertheless, most complex diseases are caused by the interplay of genetic, behavioural and environmental risk factors, which will likely necessitate models for prediction and diagnosis that incorporate genetic and non-genetic data. Funding Agencies|SciLifeLab; Swedish Research Council (Vetenskapsradet)Swedish Research Council [D0886501]; US National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [MH077139, MH1095320]; European UnionEuropean Commission [610307, 733161, 825843]; Swedish Cancer SocietySwedish Cancer Society; Swedish Research CouncilSwedish Research CouncilEuropean Commission [2018-02837, 2014-03352, 2009-1039, 2018-03307]; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation; Karolinska Institutet, Karolinska University HospitalKarolinska Institutet; Radiumhemmets Forskningsfonder; European Research CouncilEuropean Research Council (ERC)European Commission [CoG-2015_681742_NASCENT]; Swedish Heart-Lung FoundationSwedish Heart-Lung Foundation [20160872]; Swedish Foundation for Strategic Research (IRC Center); Novo Nordisk FoundationNovo Nordisk FoundationNovocure Limited; ALF; Strategic Research Area Epidemiology at Karolinska Institutet; Swedish Rheumatism Association; Vth 80-year Foundation; Swedish Society of Medicine; Clinical Research Support (Avtal om Lakarutbildning och Forskning); Swedish government; county councils; ALF-agreement; Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research [15-0067]; IMI2 Joint Undertaking [115974]; European Federation of Pharmaceutical Industries and Associations with JDRF; H2020 Program ERA PerMed JTC 2018 Call (VR) [2018-05619]

This review focuses on the recent findings from lipidomics studies as related to nutrition and health research.Several lipidomics studies have investigated malnutrition, including both under- and overnutrition. Focus has been both on the early-life nutrition as well as on the impact of overfeeding later in life. Multiple studies have investigated the impact of different macronutrients in lipidome on human health, demonstrating that overfeeding with saturated fat is metabolically more harmful than overfeeding with polyunsaturated fat or carbohydrate-rich food. Diet rich in saturated fat increases the lipotoxic lipids, such as ceramides and saturated fatty-acyl-containing triacylglycerols, increasing also the low-density lipoprotein aggregation rate. In contrast, diet rich in polyunsaturated fatty acids, such as n-3 fatty acids, decreases the triacylglycerol levels, although some individuals are poor responders to n-3 supplementation.The results highlight the benefits of lipidomics in clinical nutrition research, also providing an opportunity for personalized nutrition. An area of increasing interest is the interplay of diet, gut microbiome, and metabolome, and how they together impact individuals' responses to nutritional challenges.

Aims Cardiac injury and remodelling are associated with the rearrangement of cardiac lipids. Glycosphingolipids are membrane lipids that are important for cellular structure and function, and cardiac dysfunction is a characteristic of rare monogenic diseases with defects in glycosphingolipid synthesis and turnover. However, it is not known how cardiac glycosphingolipids regulate cellular processes in the heart. The aim of this study is to determine the role of cardiac glycosphingolipids in heart function. Methods and results Using human myocardial biopsies, we showed that the glycosphingolipids glucosylceramide and lactosylceramide are present at very low levels in non-ischaemic human heart with normal function and are elevated during remodelling. Similar results were observed in mouse models of cardiac remodelling. We also generated mice with cardiomyocyte-specific deficiency in Ugcg, the gene encoding glucosylceramide synthase (hUgcg–/– mice). In 9- to 10-week-old hUgcg–/– mice, contractile capacity in response to dobutamine stress was reduced. Older hUgcg–/– mice developed severe heart failure and left ventricular dilatation even under baseline conditions and died prematurely. Using RNA-seq and cell culture models, we showed defective endolysosomal retrograde trafficking and autophagy in Ugcg-deficient cardiomyocytes. We also showed that responsiveness to β-adrenergic stimulation was reduced in cardiomyocytes from hUgcg–/– mice and that Ugcg knockdown suppressed the internalization and trafficking of β1-adrenergic receptors. Conclusions Our findings suggest that cardiac glycosphingolipids are required to maintain β-adrenergic signalling and contractile capacity in cardiomyocytes and to preserve normal heart function., Graphical Abstract Cardiac glycosphingolipids are required to maintain β-adrenergic signaling and contractile capacity in cardiomyocytes and to preserve normal heart function.

BACKGROUND & AIMS:The enhanced liver fibrosis (ELF) test has been proposed for the non-invasive assessment of advanced fibrosis in patients with non-alcoholic fatty liver disease (NAFLD). We performed a systematic review to estimate the accuracy of this test against biopsy. METHODS:In this systematic review, we searched MEDLINE, Embase, Web of Science and the Cochrane Library for studies that included patients with NAFLD and that used both liver biopsy (as the reference standard) and the ELF test. Two authors independently screened the references, extracted the data and assessed the quality of included studies. Due to the variation in reported thresholds, we used a multiple thresholds random effects model for meta-analysis (diagmeta R-package). RESULTS:The meta-analysis of 11 studies reporting advanced fibrosis and 5 studies reporting significant fibrosis showed that the ELF test had a sensitivity of >0.90 for excluding fibrosis at a threshold of 7.7. However, as a diagnostic test at high thresholds, the test only achieved specificity and positive predictive value >0.80 in very high prevalence settings (>50%). To achieve a specificity of 0.90 for advanced and significant fibrosis, thresholds of 10.18 (sensitivity: 0.57) and 9.86 (sensitivity: 0.55) were required, respectively. CONCLUSION:The ELF test showed high sensitivity but limited specificity to exclude advanced and significant fibrosis at low cut-offs. The diagnostic performance of the test at higher thresholds was found to be more limited in low-prevalence settings. We conclude that clinicians should carefully consider the likely disease prevalence in their practice setting and adopt suitable test thresholds to achieve the desired performance. LAY SUMMARY:The enhanced liver fibrosis test has been suggested as a non-invasive blood test to aid the diagnosis of severe liver fibrosis in patients with non-alcoholic fatty liver disease (NAFLD). Our study results showed that the test has a high negative predictive value, especially in populations with low disease prevalence (likely encountered in primary care); so, it can exclude advanced fibrosis in patients with NAFLD. However, when prevalence is low, the positive predictive value of the enhanced liver fibrosis test is low, suggesting that additional strategies may be needed to make a positive diagnosis in such settings.

Aims/hypothesis Previous metabolomics studies suggest that type 1 diabetes is preceded by specific metabolic disturbances. The aim of this study was to investigate whether distinct metabolic patterns occur in peripheral blood mononuclear cells (PBMCs) of children who later develop pancreatic beta cell autoimmunity or overt type 1 diabetes. Methods In a longitudinal cohort setting, PBMC metabolomic analysis was applied in children who (1) progressed to type 1 diabetes (PT1D, n = 34), (2) seroconverted to ≥1 islet autoantibody without progressing to type 1 diabetes (P1Ab, n = 27) or (3) remained autoantibody negative during follow-up (CTRL, n = 10). Results During the first year of life, levels of most lipids and polar metabolites were lower in the PT1D and P1Ab groups compared with the CTRL group. Pathway over-representation analysis suggested alanine, aspartate, glutamate, glycerophospholipid and sphingolipid metabolism were over-represented in PT1D. Genome-scale metabolic models of PBMCs during type 1 diabetes progression were developed by using publicly available transcriptomics data and constrained with metabolomics data from our study. Metabolic modelling confirmed altered ceramide pathways, known to play an important role in immune regulation, as specifically associated with type 1 diabetes progression. Conclusions/interpretation Our data suggest that systemic dysregulation of lipid metabolism, as observed in plasma, may impact the metabolism and function of immune cells during progression to overt type 1 diabetes. Data availability The GEMs for PBMCs have been submitted to BioModels (www.ebi.ac.uk/biomodels/), under accession number MODEL1905270001. The metabolomics datasets and the clinical metadata generated in this study were submitted to MetaboLights (https://www.ebi.ac.uk/metabolights/), under accession number MTBLS1015.

Bipolar disorder (BD) is a mood disorder characterized by the occurrence of depressive episodes alternating with episodes of elevated mood (known as mania). There is also an increased risk of other medical comorbidities.This work uses a systems biology approach to compare BD treated patients with healthy controls (HCs), integrating proteomics and metabolomics data using partial correlation analysis in order to observe the interactions between altered proteins and metabolites, as well as proposing a potential metabolic signature panel for the disease.Data integration between proteomics and metabolomics was performed using GC-MS data and label-free proteomics from the same individuals (N = 13; 5 BD, 8 HC) using generalized canonical correlation analysis and partial correlation analysis, and then building a correlation network between metabolites and proteins. Ridge-logistic regression models were developed to stratify between BD and HC groups using an extended metabolomics dataset (N = 28; 14 BD, 14 HC), applying a recursive feature elimination for the optimal selection of the metabolites.Network analysis demonstrated links between proteins and metabolites, pointing to possible alterations in hemostasis of BD patients. Ridge-logistic regression model indicated a molecular signature comprising 9 metabolites, with an area under the receiver operating characteristic curve (AUROC) of 0.833 (95% CI 0.817-0.914).From our results, we conclude that several metabolic processes are related to BD, which can be considered as a multi-system disorder. We also demonstrate the feasibility of partial correlation analysis for integration of proteomics and metabolomics data in a case-control study setting.

Background & aims: Recent experimental models and epidemiological studies suggest that specific environmental contaminants (ECs) contribute to the initiation and pathology of nonalcoholic fatty liver disease (NAFLD). However, the underlying mechanisms linking EC exposure with NAFLD remain poorly understood and there is no data on their impact on the human liver metabolome. Herein, we hypothesized that exposure to ECs, particularly perfluorinated alkyl substances (PFAS), impacts liver metabolism, specifically bile acid metabolism. Methods: In a well-characterized human NAFLD cohort of 105 individuals, we investigated the effects of EC exposure on liver metabolism. We characterized the liver (via biopsy) and circulating metabolomes using 4 mass spectrometry-based analytical platforms, and measured PFAS and other ECs in serum. We subsequently compared these results with an exposure study in a PPARa-humanized mouse model. Results: PFAS exposure appears associated with perturbation of key hepatic metabolic pathways previously found altered in NAFLD, particularly those related to bile acid and lipid metabolism. We identified stronger associations between the liver metabolome, chemical exposure and NAFLD-associated clinical variables (liver fat content, HOMA-IR), in females than males. Specifically, we observed PFAS-associated upregulation of bile acids, triacylglycerols and ceramides, and association between chemical exposure and dysregulated glucose metabolism in females. The murine exposure study further corroborated our findings, vis-a-vis a sex-specific association between PFAS exposure and NAFLD-associated lipid changes. Conclusions: Females may be more sensitive to the harmful impacts of PFAS. Lipid-related changes subsequent to PFAS exposure may be secondary to the interplay between PFAS and bile acid metabolism. Lay summary: There is increasing evidence that specific environmental contaminants, such as perfluorinated alkyl substances (PFAS), contribute to the progression of non-alcoholic fatty liver disease (NAFLD). However, it is poorly understood how these chemicals impact human liver metabolism. Here we show that human exposure to PFAS impacts metabolic processes associated with NAFLD, and that the effect is different in females and males. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of European Association for the Study of the Liver.

The proteome represents all the proteins expressed by a genome, a cell, a tissue, or an organism at any given time under defined physiological or pathological circumstances. Proteomic analysis has provided unparalleled opportunities for the discovery of expression patterns of proteins in a biological system, yielding precise and inclusive data about the system. Advances in the proteomics field opened the door to wider knowledge of the mechanisms underlying various post-translational modifications (PTMs) of proteins, including glycosylation. As of yet, the role of most of these PTMs remains unidentified. In this state-of-the-art review, we present a synopsis of glycosylation processes and the pathophysiological conditions that might ensue secondary to glycosylation shortcomings. The dynamics of protein glycosylation, a crucial mechanism that allows gene and pathway regulation, is described. We also explain how—at a biomolecular level—mutations in glycosylation-related genes may lead to neuropsychiatric manifestations and neurodegenerative disorders. We then analyze the shortcomings of glycoproteomic studies, putting into perspective their downfalls and the different advanced enrichment techniques that emanated to overcome some of these challenges. Furthermore, we summarize studies tackling the association between glycosylation and neuropsychiatric disorders and explore glycoproteomic changes in neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease, Huntington disease, multiple sclerosis, and amyotrophic lateral sclerosis. We finally conclude with the role of glycomics in the area of traumatic brain injury (TBI) and provide perspectives on the clinical application of glycoproteomics as potential diagnostic tools and their application in personalized medicine.

Traumatic brain injury (TBI) has the highest incidence of all common neurological disorders, and poses a substantial public health burden. TBI is increasingly documented not only as an acute condition but also as a chronic disease with long-term consequences, including an increased risk of late-onset neurodegeneration. The first Lancet Neurology Commission on TBI, published in 2017, called for a concerted effort to tackle the global health problem posed by TBI. Since then, funding agencies have supported research both in high-income countries (HICs) and in low-income and middle-income countries (LMICs). In November 2020, the World Health Assembly, the decision-making body of WHO, passed resolution WHA73.10 for global actions on epilepsy and other neurological disorders, and WHO launched the Decade for Action on Road Safety plan in 2021. New knowledge has been generated by large observational studies, including those conducted under the umbrella of the International Traumatic Brain Injury Research (InTBIR) initiative, established as a collaboration of funding agencies in 2011. InTBIR has also provided a huge stimulus to collaborative research in TBI and has facilitated participation of global partners. The return on investment has been high, but many needs of patients with TBI remain unaddressed. This update to the 2017 Commission presents advances and discusses persisting and new challenges in prevention, clinical care, and research.

ObjectiveGut microbial products are involved in regulation of host metabolism. In human and experimental studies, we explored the potential role of hippurate, a hepatic phase 2 conjugation product of microbial benzoate, as a marker and mediator of metabolic health.DesignIn 271 middle-aged non-diabetic Danish individuals, who were stratified on habitual dietary intake, we applied 1H-nuclear magnetic resonance (NMR) spectroscopy of urine samples and shotgun-sequencing-based metagenomics of the gut microbiome to explore links between the urine level of hippurate, measures of the gut microbiome, dietary fat and markers of metabolic health. In mechanistic experiments with chronic subcutaneous infusion of hippurate to high-fat-diet-fed obese mice, we tested for causality between hippurate and metabolic phenotypes.ResultsIn the human study, we showed that urine hippurate positively associates with microbial gene richness and functional modules for microbial benzoate biosynthetic pathways, one of which is less prevalent in the Bacteroides 2 enterotype compared with Ruminococcaceae or Prevotella enterotypes. Through dietary stratification, we identify a subset of study participants consuming a diet rich in saturated fat in which urine hippurate concentration, independently of gene richness, accounts for links with metabolic health. In the high-fat-fed mice experiments, we demonstrate causality through chronic infusion of hippurate (20 nmol/day) resulting in improved glucose tolerance and enhanced insulin secretion.ConclusionOur human and experimental studies show that a high urine hippurate concentration is a general marker of metabolic health, and in the context of obesity induced by high-fat diets, hippurate contributes to metabolic improvements, highlighting its potential as a mediator of metabolic health.

Environmental factors are strongly implicated in late-onset of inflammatory bowel disease. Here, we investigate whether high levels of perfluoroalkyl substances are associated with (1) late-onset inflammatory bowel disease, and (2) disturbances of the bile acid pool. We further explore the effect of the specific perfluoroalkyl substance perfluorooctanoic acid on intestinal barrier function in murine tissue.Serum levels of perfluoroalkyl substances and bile acids were assessed by ultra-performance liquid chromatography coupled to a triple-quadrupole mass spectrometer in matched samples from patients with ulcerative colitis (The total amount of perfluoroalkyl substances was significantly increased in patients with ulcerative colitis compared to healthy controls and patients with Crohn's disease (Our results demonstrate that perfluoroalkyl substances levels are increased in patients with late-onset ulcerative colitis and may contribute to the disease by inducing a dysfunctional intestinal barrier.

International audience; Knowledge about in vivo effects of human circulating C-6 hydroxylated bile acids (BAs), also called muricholic acids, is sparse. It is unsettled if the gut microbiome might contribute to their biosynthesis. Here, we measured a range of serum BAs and related them to markers of human metabolic health and the gut microbiome. We examined 283 non-obese and obese Danish adults from the MetaHit study. Fasting concentrations of serum BAs were quantified using ultra-performance liquid chromatographytandem mass-spectrometry. The gut microbiome was characterized with shotgun metagenomic sequencing and genome-scale metabolic modeling. We find that tauro-and glycohyocholic acid correlated inversely with body mass index (P = 4.1e-03, P = 1.9e-05, respectively), waist circumference (P = 0.017, P = 1.1e-04, respectively), body fat percentage (P = 2.5e-03, P = 2.3e-06, respectively), insulin resistance (P = 0.051, P = 4.6e-4, respectively), fasting concentrations of triglycerides (P = 0.06, P = 9.2e-4, respectively) and leptin (P = 0.067, P = 9.2e-4). Tauro-and glycohyocholic acids, and tauroa-muricholic acid were directly linked with a distinct gut microbial community primarily composed of Clostridia species (P = 0.037, P = 0.013, P = 0.027, respectively). We conclude that serum conjugated C-6-hydroxylated BAs associate with measures of human metabolic health and gut communities of Clostridia species. The findings merit preclinical interventions and human feasibility studies to explore the therapeutic potential of these BAs in obesity and type 2 diabetes. Bile acids (BAs) are a class of steroids produced from cholesterol in the liver where they are conjugated with either glycine or taurine and released into the bile. These primary BAs, which in humans predominantly comprise of cholic acid (CA) and chenodeoxycholic acid (CDCA) are absorbed by different intestinal bacteria, and conjugates of glycine or taurine are removed with subsequent alteration of hydroxyl groups to form secondary BAs 1. BAs emulsify dietary fats in the small intestine and facilitate their absorption 1,2. At the terminal ileum, the majority of BAs are absorbed by the apical sodium dependent BA transporters and enter the hepatic portal circulation. In total, about 95% of BAs are reabsorbed into intestinal enterocytes, whilst > 5% of BAs enter the systemic circulation and act on multiple organs throughout the body via several types of receptors 3. The most prominent of these are the farnesoid X receptor (FXR) and the Takeda G protein-coupled membrane receptor 5 (TGR5) which are expressed in a variety of peripheral tissues 4-7. Through these receptors, BAs regulate biological processes such as immunity, neuroprotection, metabolism, and energy expenditure 8-12 .

T cell activation, proliferation, and differentiation involve metabolic reprogramming resulting from the interplay of genes, proteins, and metabolites. Here, we aim to understand the metabolic pathways involved in the activation and functional differentiation of human CD4

The composition of human breast milk is highly variable inter- and intra-individually. Environmental factors are suspected to contribute to such compositional variation, however, their impact on breast milk composition is currently poorly understood. We sought to (1) define the impact of maternal exposure to per- and polyfluoroalkyl substances (PFAS) on lipid composition of human breast milk, and (2) to study the combined impact of maternal PFAS exposure and breast milk lipid composition on the growth of the infants.In a mother-infant study (n = 44) we measured the levels of PFAS and lipids in maternal serum and conducted lipidomics analysis of breast milk collect 2–4 days after the delivery and at 3 months of infant age, by using ultra high performance liquid chromatography combined with quadrupole-time-of-flight mass spectrometry. Gastrointestinal biomarkers fecal calprotectin and human beta defensin 2 were measured in the stool samples at the age of 3, 6, 9, and 12 months. Maternal diet was studied by a validated food frequency questionnaire. PFAS levels were inversely associated with total lipid levels in the breast milk collected after the delivery. In the high exposure group, the ratio of acylated saturated and polyunsaturated fatty acids in triacylglycerols was increased. Moreover, high exposure to PFAS associated with the altered phospholipid composition, which was indicative of unfavorable increase in the size of milk fat globules. These changes in the milk lipid composition were further associated with slower infant growth and with elevated intestinal inflammatory markers. Our data suggest that the maternal exposure to PFAS impacts the nutritional quality of the breast milk, which, in turn, may have detrimental impact on the health and growth of the children later in life. publishedVersion

Ceramides are bioactive sphingolipids, comprised of sphingosine and a fatty acyl chain. They have been recognized as key mediators of lipotoxicity; a phenomenon where excess fat in adipose tissue l ...

Nonalcoholic fatty liver disease (NAFLD) is one of the most common liver diseases worldwide and is often associated with aspects of metabolic syndrome. Despite its prevalence and the importance of early diagnosis, there is a lack of robustly validated biomarkers for diagnosis, prognosis and monitoring of disease progression in response to a given treatment. In this Review, we provide an overview of the contribution of metabolomics and lipidomics in clinical studies to identify biomarkers associated with NAFLD and nonalcoholic steatohepatitis (NASH). In addition, we highlight the key metabolic pathways in NAFLD and NASH that have been identified by metabolomics and lipidomics approaches and could potentially be used as biomarkers for non-invasive diagnostic tests. Overall, the studies demonstrated alterations in amino acid metabolism and several aspects of lipid metabolism including circulating fatty acids, triglycerides, phospholipids and bile acids. Although we report several studies that identified potential biomarkers, few have been validated.

International audience; The mechanisms that drive nonalcoholic fatty liver disease (NAFLD) remain incompletely understood. This large multicenter study characterized the transcriptional changes that occur in liver tissue across the NAFLD spectrum as disease progresses to cirrhosis to identify potential circulating markers. We performed high-throughput RNA sequencing on a discovery cohort comprising histologically characterized NAFLD samples from 206 patients. Unsupervised clustering stratified NAFLD on the basis of disease activity and fibrosis stage with differences in age, aspartate aminotransferase (AST), type 2 diabetes mellitus, and carriage of PNPLA3 rs738409 , a genetic variant associated with NAFLD. Relative to early disease, we consistently identified 25 differentially expressed genes as fibrosing steatohepatitis progressed through stages F2 to F4. This 25-gene signature was independently validated by logistic modeling in a separate replication cohort ( n = 175), and an integrative analysis with publicly available single-cell RNA sequencing data elucidated the likely relative contribution of specific intrahepatic cell populations. Translating these findings to the protein level, SomaScan analysis in more than 300 NAFLD serum samples confirmed that circulating concentrations of proteins AKR1B10 and GDF15 were strongly associated with disease activity and fibrosis stage. Supporting the biological plausibility of these data, in vitro functional studies determined that endoplasmic reticulum stress up-regulated expression of AKR1B10 , GDF15 , and PDGFA , whereas GDF15 supplementation tempered the inflammatory response in macrophages upon lipid loading and lipopolysaccharide stimulation. This study provides insights into the pathophysiology of progressive fibrosing steatohepatitis, and proof of principle that transcriptomic changes represent potentially tractable and clinically relevant markers of disease progression.

Background & Aims\ud \ud The I148M variant in PNPLA3 is the major genetic risk factor for non‐alcoholic fatty liver disease (NAFLD). The liver is enriched with polyunsaturated triglycerides (PUFA‐TGs) in PNPLA3‐I148M carriers. Gene expression data indicate that PNPLA3 is liver‐specific in humans, but whether it functions in adipose tissue (AT) is unknown. We investigated whether PNPLA3‐I148M modifies AT metabolism in human NAFLD.\ud Methods\ud \ud Profiling of the AT lipidome and fasting serum non‐esterified fatty acid (NEFA) composition was conducted in 125 volunteers (PNPLA3 148MM/MI, n = 63; PNPLA3 148II, n = 62). AT fatty acid composition was determined in 50 volunteers homozygous for the variant (PNPLA3 148MM, n = 25) or lacking the variant (PNPLA3 148II, n = 25). Whole‐body insulin sensitivity of lipolysis was determined using [2H5]glycerol, and PNPLA3 mRNA and protein levels were measured in subcutaneous AT and liver biopsies in a subset of the volunteers.\ud Results\ud \ud PUFA‐TGs were significantly increased in AT in carriers versus non‐carriers of PNPLA3‐I148M. The variant did not alter the rate of lipolysis or the composition of fasting serum NEFAs. PNPLA3 mRNA was 33‐fold higher in the liver than in AT (P < .0001). In contrast, PNPLA3 protein levels per tissue protein were three‐fold higher in AT than the liver (P < .0001) and nine‐fold higher when related to whole‐body AT and liver tissue masses (P < .0001).\ud Conclusions\ud \ud Contrary to previous assumptions, PNPLA3 is highly abundant in AT. PNPLA3‐I148M locally remodels AT TGs to become polyunsaturated as it does in the liver, without affecting lipolysis or composition of serum NEFAs. Changes in AT metabolism do not contribute to NAFLD in PNPLA3‐I148M carriers.

Patients with schizophrenia have a lower than average life span, largely due to the increased prevalence of cardiometabolic comorbidities. There is an unmet public health need to identify individuals with psychotic disorders who have a high risk of rapid weight gain and who are at risk of developing metabolic complications. Here, we applied mass spectrometry-based lipidomics in a prospective study comprising 48 healthy controls (CTR), 44 first-episode psychosis (FEP) patients, and 22 individuals at clinical high risk (CHR) for psychosis, from 2 study centers (Turku, Finland and London, UK). Baseline serum samples were analyzed using lipidomics, and body mass index (BMI) was assessed at baseline and after 12 months. We found that baseline triacylglycerols (TGs) with low double-bond counts and carbon numbers were positively associated with the change in BMI at follow-up. In addition, a molecular signature comprised of 2 TGs (TG[48:0] and TG[45:0]) was predictive of weight gain in individuals with a psychotic disorder, with an area under the receiver operating characteristic curve (AUROC) of 0.74 (95% CI: 0.60–0.85). When independently tested in the CHR group, this molecular signature predicted said weight change with AUROC = 0.73 (95% CI: 0.61–0.83). We conclude that molecular lipids may serve as a predictor of weight gain in psychotic disorders in at-risk individuals and may thus provide a useful marker for identifying individuals who are most prone to developing cardiometabolic comorbidities.

We call upon the research community to standardize efforts to use daily self-reported data about COVID-19 symptoms in the response to the pandemic and to form a collaborative consortium to maximize global gain while protecting participant privacy.

Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of ‘big data’, including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.

MARC1 variant rs2642438 increases hepatic phosphatidylcholines and decreases severity of non-alcoholic fatty liver disease in humans

OBJECTIVESCeliac disease (CD) is a systemic immune-mediated disorder with increased frequency in the developed countries over the last decades implicating the potential causal role of various environmental triggers in addition to gluten. Herein, we apply determination of perfluorinated alkyl substances (PFAS) and combine the results with the determination of bile acids (BAs) and molecular lipids, with the aim to elucidate the impact of prenatal exposure on risk of progression to CD in a prospective series of children prior the first exposure to gluten (at birth and at 3 months of age).METHODSWe analyzed PFAS, BAs and lipidomic profiles in 76 plasma samples at birth and at 3 months of age in the Type 1 Diabetes Prediction and Prevention (DIPP) study (n=17 progressors to CD, n=16 healthy controls, HCs).RESULTSPlasma PFAS levels showed a significant inverse association with the age of CD diagnosis in infants who later progressed to the disease. Associations between BAs and triacylglycerols (TGs) showed different patterns already at birth in CD progressors, indicative of different absorption of lipids in these infants.DISCUSSIONPFAS exposure may modulate lipid and BA metabolism, and the impact is different in the infants who develop CD later in life, in comparison to HCs. The results indicate more efficient uptake of PFAS in such infants. Higher PFAS exposure during prenatal and early life may accelerate the progression to CD in the genetically predisposed children.Study HighlightsWHAT IS KNOWNSeveral observational studies have implicated a role of early life environmental triggers other than gluten in the development of CD. This is supported by the findings showing dysregulation of lipids already prior to the first introduction of gluten.WHAT IS NEW HEREWe show that prenatal exposure to perfluorinated compounds is associated with changes in the lipid metabolism, most likely through the bile acids, and that a high exposure during prenatal and early life may accelerate the progression to CD in the genetically predisposed children.TRANSLATIONAL IMPACTExposure to environmental chemicals may impact the rate of progression to CD and should be assessed as a potential risk factor of CD in larger clinical cohort settings.

The effects of gut microbiota on the central nervous system, along its possible role in mental disorders, have received increasing attention. Here we investigated differences in fecal microbiota between 28 patients with first-episode psychosis (FEP) and 16 healthy matched controls and explored whether such differences were associated with response after up to 12 months of treatment. Numbers of Lactobacillus group bacteria were elevated in FEP-patients and significantly correlated with severity along different symptom domains. A subgroup of FEP patients with the strongest microbiota differences also showed poorer response after up to 12 months of treatment. The present findings support the involvement of microbiota alterations in psychotic illness and may provide the basis for exploring the benefit of their modulation on treatment response and remission. (C) 2017 Elsevier B.V. All rights reserved.

Background: Carriers of the transmembrane 6 superfamily member 2 E167K gene variant (TM6SF2(EK/KK)) have decreased expression of the TM6SF2 gene and increased risk of NAFLD and NASH. Unlike common 'obese/metabolic' NAFLD, these subjects lack hypertriglyceridemia and have lower risk of cardiovascular disease. In animals, phosphatidylcholine (PC) deficiency results in a similar phenotype. PCs surround the core of VLDL consisting of triglycerides (TGs) and cholesteryl-esters (CEs). We determined the effect of the TM6SF2 E167K on these lipids in the human liver and serum and on hepatic gene expression and studied the effect of TM6SF2 knockdown on hepatocyte handling of these lipids. Methods: Liver biopsies were taken from subjects characterized with respect to the TM6SF2 genotype, serum and liver lipidome, gene expression and histology. In vitro, after TM6SF2 knockdown in HuH-7 cells, we compared incorporation of different fatty acids into TGs, CEs, and PCs. Results: The TM6SF2(EK/KK) and TM6SF2EE groups had similar age, gender, BMI and HOMA-IR. Liver TGs and CEs were higher and liver PCs lower in the TM6SF2(EK/KK) than the TM6SF2EE group (p Conclusions: Hepatic lipid synthesis from PUFAs is impaired and could contribute to deficiency in PCs and increased intrahepatic TG in TM6SF2 E167K variant carriers. (C) 2017 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Sphingolipids in general and ceramides in particular, contribute to pathophysiological mechanisms by modifying signalling and metabolic pathways. Here, we present the available evidence for a bidirectional homeostatic crosstalk between sphingolipids and glycerophospholipids, whose dysregulation contributes to lipotoxicity induced metabolic stress. The initial evidence for this crosstalk originates from simulated models designed to investigate the biophysical properties of sphingolipids in plasma membrane representations. In this review, we reinterpret some of the original findings and conceptualise them as a sort of "ying/yang" interaction model of opposed/complementary forces, which is consistent with the current knowledge of lipid homeostasis and pathophysiology. We also propose that the dysregulation of the balance between sphingolipids and glycerophospholipids results in a lipotoxic insult relevant in the pathophysiology of common metabolic diseases, typically characterised by their increased ceramide/sphingosine pools.

As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950-Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each laboratory using a different lipidomics workflow. A total of 1,527 unique lipids were measured across all laboratories and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra- and interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.

Background The identification of early biomarkers of psychotic experiences (PEs) is of interest because early diagnosis and treatment of those at risk of future disorder is associated with improved outcomes. The current study investigated early lipidomic and coagulation pathway protein signatures of later PEs in subjects from the Avon Longitudinal Study of Parents and Children cohort. Methods Plasma of 115 children (12 years of age) who were first identified as experiencing PEs at 18 years of age (48 cases and 67 controls) were assessed through integrated and targeted lipidomics and semitargeted proteomics approaches. We assessed the lipids, lysophosphatidylcholines (n = 11) and phosphatidylcholines (n = 61), and the protein members of the coagulation pathway (n = 22) and integrated these data with complement pathway protein data already available on these subjects. Results Twelve phosphatidylcholines, four lysophosphatidylcholines, and the coagulation protein plasminogen were altered between the control and PEs groups after correction for multiple comparisons. Lipidomic and proteomic datasets were integrated into a multivariate network displaying a strong relationship between most lipids that were significantly associated with PEs and plasminogen. Finally, an unsupervised clustering approach identified four different clusters, with one of the clusters presenting the highest case-control ratio (p < .01) and associated with a higher concentration of smaller low-density lipoprotein cholesterol particles. Conclusions Our findings indicate that the lipidome and proteome of subjects who report PEs at 18 years of age are already altered at 12 years of age, indicating that metabolic dysregulation may contribute to an early vulnerability to PEs and suggesting crosstalk between these lysophosphatidylcholines, phosphatidylcholines, and coagulation and complement proteins.

The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).

openaire: EC/H2020/634413/EU//EPoS | openaire: EC/H2020/777377/EU//LITMUS The common patatin-like phospholipase domain-containing protein 3 (PNPLA3) variant I148M predisposes to nonalcoholic liver disease but not its metabolic sequelae. We compared the handling of labeled polyunsaturated fatty acids (PUFAs) and saturated fatty acids (SFA) in vivo in humans and in cells harboring different PNPLA3 genotypes. In 148M homozygous individuals, triglycerides (TGs) in very low-density lipoproteins (VLDL) were depleted of PUFAs both under fasting and postprandial conditions compared with 148I homozygotes, and the PUFA/SFA ratio in VLDL-TGs was lower relative to the chylomicron precursor pool. In human PNPLA3-148M and PNPLA3-KO cells, PUFA but not SFA incorporation into TGs was increased at the expense of phosphatidylcholines, and under lipolytic conditions, PUFA-containing diacylglycerols (DAGs) accumulated compared with PNPLA3-148I cells. Polyunsaturated TGs were increased, while phosphatidylcholines (PCs) were decreased in the human liver in 148M homozygous individuals as compared with 148I homozygotes. We conclude that human PNPLA3-I148M is a loss-of-function allele that remodels liver TGs in a polyunsaturated direction by impairing hydrolysis/transacylation of PUFAs from DAGs to feed phosphatidylcholine synthesis.

Previous studies suggest that children who progress to type 1 diabetes (T1D) later in life already have an altered serum lipid molecular profile at birth. Here, we compared cord blood lipidome across the three study groups: children who progressed to T1D (PT1D, n = 30), children who developed at least one islet autoantibody but did not progress to T1D during the follow-up (P1Ab, n = 33), and their age-matched controls (CTR, n = 38). We found that phospholipids, specifically sphingomyelins, were lower in T1D progressors when compared to P1Ab and the CTR. Cholesterol esters remained higher in PT1D when compared to other groups. A signature comprising five lipids was predictive of the risk of progression to T1D, with an area under the receiver operating characteristic curve (AUROC) of 0.83. Our findings provide further evidence that the lipidomic profiles of newborn infants who progress to T1D later in life are different from lipidomic profiles in P1Ab and CTR.

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.

Background & Aims: Recent data in mice have identified de novo ceramide synthesis as the key mediator of hepatic insulin resistance (IR) that in humans characterizes increases in liver fat due to IR ('Metabolic NAFLD' but not that due to the I148M gene variant in PNPLA3 ('PNPLA3 NAFLD'). We determined which bioactive lipids co-segregate with IR in the human liver. Methods: Liver lipidome was profiled in liver biopsies from 125 subjects that were divided into equally sized groups based on median HOMA-IR ('High and Low HOMA-IR', n = 62 and n = 63) or PNPLA3 genotype (PNPIA3(148MM/MI), n = 61 vs. PNPLA3(148II), n = 64). The subjects were also divided into 4 groups who had either IR, the I148M gene variant, both of the risk factors or neither. Results: Steatosis and NASH prevalence were similarly increased in 'High HOMA-IR' and PNPLA3(148MM/MI) groups compared to their respective control groups. The 'High HOMA-IR' but not the PNPLA3(148MM/MI) group had features of IR. The liver in 'High HOMA-IR' vs. low HOMA-IR' was markedly enriched in saturated and monounsaturated triacylglycerols and free fatty acids, dihydroceramides (markers of de novo ceramide synthesis) and ceramides. Markers of other ceramide synthetic pathways were unchanged. In PNPLA3(148MM/MI) vs. PNPLA3(148II), the increase in liver fat was due to polyunsaturated triacylglycerols while other lipids were unchanged. Similar changes were observed when data were analyzed using the 4 subgroups. Conclusions: Similar increases in liver fat and NASH are associated with a metabolically harmful saturated, ceramide-enriched liver lipidome in 'Metabolic NAFLD' but not in 'PNPLA3 NAFLD'. This difference may explain why metabolic but not PNPLA3 NAFLD increases the risk of type 2 diabetes and cardiovascular disease. (C) 2016 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Lipidomic analysis aims at comprehensive characterization of molecular lipids in biological systems. Due to the central role of lipid metabolism in many devastating diseases, lipidomics is being increasingly applied in biomedical research. Over the past years, advances in analytical techniques and bioinformatics enabled increasingly comprehensive and accurate coverage of lipids both in tissues and biofluids, yet many challenges remain. This review highlights recent progress in the domain of analytical lipidomics, with main emphasis on non-targeted methodologies for large scale clinical applications, as well as discusses some of the key challenges and opportunities in this field.

Traumatic brain injury (TBI) is a major cause of death and disability worldwide, especially in children and young adults. TBI is an example of a medical condition where there are still major lacks in diagnostics and outcome prediction. Here we apply comprehensive metabolic profiling of serum samples from TBI patients and controls in two independent cohorts. The discovery study included 144 TBI patients, with the samples taken at the time of hospitalization. The patients were diagnosed as severe (sTBI; n = 22), moderate (moTBI; n = 14) or mild TBI (mTBI; n = 108) according to Glasgow Coma Scale. The control group (n = 28) comprised of acute orthopedic non-brain injuries. The validation study included sTBI (n = 23), moTBI (n = 7), mTBI (n = 37) patients and controls (n = 27). We show that two medium-chain fatty acids (decanoic and octanoic acids) and sugar derivatives including 2,3-bisphosphoglyceric acid are strongly associated with severity of TBI, and most of them are also detected at high concentrations in brain microdialysates of TBI patients. Based on metabolite concentrations from TBI patients at the time of hospitalization, an algorithm was developed that accurately predicted the patient outcomes (AUC = 0.84 in validation cohort). Addition of the metabolites to the established clinical model (CRASH), comprising clinical and computed tomography data, significantly improved prediction of patient outcomes. The identified ‘TBI metabotype’ in serum, that may be indicative of disrupted blood-brain barrier, of protective physiological response and altered metabolism due to head trauma, offers a new avenue for the development of diagnostic and prognostic markers of broad spectrum of TBIs., Graphical Abstract Image 1, Highlights • The study reports that serum metabolites are sensitive to severity of TBI as well as predict the patient outcomes. • The findings are indicative of disruption in blood brain barrier and of protective response and altered TBI metabolism. • Metabolites significantly improved the prediction of patient outcomes when added to the established clinical model. Traumatic brain injury (TBI) is an example of a medical condition where there are still major lacks in diagnostics, outcome prediction, and the therapy development. Since the blood-brain barrier prevents diffusion of most water-soluble molecules with molecular mass over 500 Da, here we hypothesized that circulating small molecules (metabolites) are a potential source of TBI markers. Based on serum metabolomic studies in two independent cohorts, we found that metabolites are sensitive to severity of TBI as well as predict the patient outcomes. The findings of this study may pave the way for new diagnostic tools for TBI.

Background Lysinuric protein intolerance (LPI [MIM 222700]) is an aminoaciduria with defective transport of cationic amino acids in epithelial cells in the small intestine and proximal kidney tubules due to mutations in the SLC7A7 gene. LPI is characterized by protein malnutrition, failure to thrive and hyperammonemia. Many patients also suffer from combined hyperlipidemia and chronic kidney disease (CKD) with an unknown etiology. Methods Here, we studied the plasma metabolomes of the Finnish LPI patients (n = 26) and healthy control individuals (n = 19) using a targeted platform for analysis of amino acids as well as two analytical platforms with comprehensive coverage of molecular lipids and polar metabolites. Results Our results demonstrated that LPI patients have a dichotomy of amino acid profiles, with both decreased essential and increased non-essential amino acids. Altered levels of metabolites participating in pathways such as sugar, energy, amino acid and lipid metabolism were observed. Furthermore, of these metabolites, myo-inositol, threonic acid, 2,5-furandicarboxylic acid, galactaric acid, 4-hydroxyphenylacetic acid, indole-3-acetic acid and beta-aminoisobutyric acid associated significantly (P < 0.001) with the CKD status. Lipid analysis showed reduced levels of phosphatidylcholines and elevated levels of triacylglycerols, of which long-chain triacylglycerols associated (P

Seroconversion to islet autoimmunity is preceded by metabolic disturbances in children who later progress to overt type 1 diabetes (T1D). The underlying metabolic pathways and the interaction of metabolic and immune system factors involved in progression to the disease are however poorly understood. There is a clear need for mathematical models which capture the temporal and spatial complexity of early pathogenesis of T1D. Here we review the early attempts to model the development of islet autoimmunity and T1D, including the models which emphasize the potential beneficial role of autoimmune response in specific circumstances, such as to 'correct' for the early metabolic disturbances. We also highlight the genome-scale metabolic modeling as a promising new avenue to study metabolism and its interactions with the immune system in T1D.

International audience; We recently presented a three-pronged association study that integrated human intestinal microbiome data derived from shotgun-based sequencing with untargeted serum metabolome data and measures of host physiology. Metabolome and microbiome data are high dimensional, posing a major challenge for data integration. Here, we present a step-by-step computational protocol that details and discusses the dimensionality-reduction techniques used and methods for subsequent integration and interpretation of such heterogeneous types of data. Dimensionality reduction was achieved through a combination of data normalization approaches, binning of co-abundant genes and metabolites, and integration of prior biological knowledge. The use of prior knowledge to overcome functional redundancy across microbiome species is one central advance of our method over available alternative approaches. Applying this framework, other investigators can integrate various '-omics' readouts with variables of host physiology or any other phenotype of interest (e.g., connecting host and microbiome readouts to disease severity or treatment outcome in a clinical cohort) in a three-pronged association analysis to identify potential mechanistic links to be tested in experimental settings. Although we originally developed the framework for a human metabolome-microbiome study, it is generalizable to other organisms and environmental metagenomes, as well as to studies including other -omics domains such as transcriptomics and proteomics. The provided R code runs in similar to 1 h on a standard PC.

There is a need to rapidly detect patients with traumatic brain injury (TBI) who require head computed tomography (CT). Given the energy crisis in the brain following TBI, we hypothesized that serum metabolomics would be a useful tool for developing a set of biomarkers to determine the need for CT and to distinguish among different types of injuries observed. Logistical regression models using metabolite data from the discovery cohort (n = 144, Turku, Finland) were used to distinguish between patients with traumatic intracranial findings and those with negative findings on head CT. The resultant models were then tested in the validation cohort (n = 66, Cambridge, United Kingdom). The levels of glial fibrillary acidic protein and ubiquitin C-terminal hydrolase-L1 were also quantified in the serum from the same patients. Despite there being significant differences in the protein biomarkers in patients with TBI, the model that determined the need for a CT scan validated poorly (area under the curve [AUC] = 0.64: Cambridge patients). However, using a combination of six metabolites (two amino acids, three sugar derivatives, and one ketoacid) it was possible to discriminate patients with intracranial abnormalities on CT and patients with a normal CT (AUC = 0.77 in Turku patients and AUC = 0.73 in Cambridge patients). Further, a combination of three metabolites could distinguish between diffuse brain injuries and mass lesions (AUC = 0.87 in Turku patients and AUC = 0.68 in Cambridge patients). This study identifies a set of validated serum polar metabolites, which associate with the need for a CT scan. Additionally, serum metabolites can also predict the nature of the brain injury. These metabolite markers may prevent unnecessary CT scans, thus reducing the cost of diagnostics and radiation load.

Type 1 diabetes (T1D) is one of the most prevalent autoimmune diseases among children in Western countries. Earlier metabolomics studies suggest that T1D is preceded by dysregulation of lipid metabolism. Here we used a lipidomics approach to analyze molecular lipids in a prospective series of 428 plasma samples from 40 children who progressed to T1D (PT1D), 40 children who developed at least a single islet autoantibody but did not progress to T1D during the follow-up (P1Ab) and 40 matched controls (CTR). Sphingomyelins were found to be persistently downregulated in PT1D when compared to the P1Ab and CTR groups. Triacylglycerols and phosphatidylcholines were mainly downregulated in PT1D as compared to P1Ab at the age of 3 months. Our study suggests that children who progressed to islet autoimmunity or overt T1D are characterized by distinct lipidomic signatures, which may be helpful in the identification of at-risk children before the initiation of autoimmunity.

Psychotic disorders are associated with metabolic abnormalities including alterations in glucose and lipid metabolism. A major challenge in the treatment of psychosis is to identify patients with vulnerable metabolic profiles who may be at risk of developing cardiometabolic co-morbidities. It is established that both central and peripheral metabolic organs use lipids to control energy balance and regulate peripheral insulin sensitivity. The endocannabinoid system, implicated in the regulation of glucose and lipid metabolism, has been shown to be dysregulated in psychosis. It is currently unclear how these endocannabinoid abnormalities relate to metabolic changes in psychosis. Here we review recent research in the field of metabolic co-morbidities in psychotic disorders as well as the methods to study them and potential links to the endocannabinoid system. We also describe the bioinformatics platforms developed in the EU project METSY for the investigations of the biological etiology in patients at risk of psychosis and in first episode psychosis patients. The METSY project was established with the aim to identify and evaluate multi-modal peripheral and neuroimaging markers that may be able to predict the onset and prognosis of psychiatric and metabolic symptoms in patients at risk of developing psychosis and first episode psychosis patients. Given the intrinsic complexity and widespread role of lipid metabolism, a systems biology approach which combines molecular, structural and functional neuroimaging methods with detailed metabolic characterisation and multi-variate network analysis is essential in order to identify how lipid dysregulation may contribute to psychotic disorders. A decision support system, integrating clinical, neuropsychological and neuroimaging data, was also developed in order to aid clinical decision making in psychosis. Knowledge of common and specific mechanisms may aid the etiopathogenic understanding of psychotic and metabolic disorders, facilitate early disease detection, aid treatment selection and elucidate new targets for pharmacological treatments.

It is well established that gut microbes and their metabolic products regulate host metabolism. The interactions between the host and its gut microbiota are highly dynamic and complex. In this review we present and discuss the metabolomic strategies to study the gut microbial ecosystem. We highlight the metabolic profiling approaches to study faecal samples aimed at deciphering the metabolic product derived from gut microbiota. We also discuss how metabolomics data can be integrated with metagenomics data derived from gut microbiota and how such approaches may lead to better understanding of the microbial functions. Finally, the emerging approaches of genome-scale metabolic modelling to study microbial co-metabolism and host–microbe interactions are highlighted.

BACKGROUND: Supplementation of formula with bovine milk fat globule membranes has been shown to narrow the gap in immunological and cognitive development between breast-fed and formula-fed infants. METHOD: In a double-blinded randomized controlled trial 160 formula-fed infants received an experimental formula (EF), supplemented with bovine milk fat globule membranes, or standard formula until 6 months of age. A breast-fed reference group was recruited. Lipidomic analyses were performed on plasma and erythrocyte membranes at 6 months and on serum at 4 and 12 months of age. RESULTS: At 6 months of age, we observed a significant separation in the plasma lipidome between the two formula groups, mostly due to differences in concentrations of sphingomyelins (SM), phosphatidylcholines (PC), and ceramides, and in the erythrocyte membrane lipidome, mostly due to SMs, PEs and PCs. Already at 4 months, a separation in the serum lipidome was evident where SMs and PCs contributed. The separation was not detected at 12 months. CONCLUSIONS: The effect of MFGM supplementation on the lipidome is likely part of the mechanisms behind the positive cognitive and immunological effects of feeding the EF previously reported in the same study population.

Background/Aim Lipidomic and metabolomic techniques become more and more important in human health research. Recent developments in analytical techniques enable the investigation of high amounts of substances. The high numbers of metabolites and lipids that are detected with among others mass spectrometric techniques challenge in most cases the statistical processes to bring out stable and interpretable results. This study targets to use the novel non-established statistical method treelet transform (TT) to investigate high numbers of metabolites and lipids and to compare the results with the established method principal component analysis (PCA). Serum lipid and metabolite profiles are investigated regarding their association to anthropometric parameters associated to obesity. Methods From 226 participants of the EPIC (European Prospective Investigation into Cancer and Nutrition)-Potsdam study blood samples were investigated with an untargeted metabolomics approach regarding serum metabolites and lipids. Additionally, participants were surveyed anthropometrically to assess parameters of obesity, such as body mass index (BMI), waist-to-hip-ratio (WHR) and body fat mass. TT and PCA are used to generate treelet components (TCs) and factors summarizing serum metabolites and lipids in new, latent variables without too much loss of information. With partial correlations TCs and factors were associated to anthropometry under the control for relevant parameters, such as sex and age. Results TT with metabolite variables (p = 121) resulted in 5 stable and interpretable TCs explaining 18.9% of the variance within the data. PCA on the same variables generated 4 quite complex, less easily interpretable factors explaining 37.5% of the variance. TT on lipidomic data (p = 353) produced 3 TCs as well as PCA on the same data resulted in 3 factors; the proportion of explained variance was 17.8% for TT and 39.8% for PCA. In both investigations TT ended up with stable components that are easier to interpret than the factors from the PCA. In general, the generated TCs and factors were similar in their structure when the factors are considered regarding the original variables loading high on them. Both TCs and factors showed associations to anthropometric measures. Conclusions TT is a suitable statistical method to generate summarizing, latent variables in data sets with more variables than observations. In the present investigation it resulted in similar latent variables compared to the established method of PCA. Whereby less variance is explained by the summarizing constructs of TT compared to the factors of PCA, TCs are easier to interpret. Additionally the resulting TCs are quite stable in bootstrap samples.

To critically review current knowledge on the positive and negative predictive value of blood biomarkers for concussion; to illustrate the clinical and biological contexts that help evaluate the use of these markers in sport-related traumatic brain injuries (TBIs).This systematic review was performed in accordance with PRISMA guidelines. We reviewed the measurement, clinical utility, endpoint, and biological significance of blood biomarkers in concussion.A total of 4352 publications were identified. Twenty-six articles relating to blood biomarkers were included in the review. Four common blood biomarkers, namely S100B, tau, neuron-specific enolase (NSE), and glial fibrillary acidic protein (GFAP), were examined. Overall, the studies showed S100B measurement and use, either acutely or at several time points, can distinguish injured from noninjured patients with an uncertain degree of utility in predicting mortality. At present, S100B has largely become an acceptable biomarker of TBI; however, studies have begun to highlight the need to incorporate clinical symptoms instead of S100B concentration in isolation on the basis of inconsistent results and lack of specificity across published studies. Further research is needed to evaluate and validate the use of tau, NSE, and GFAP as a diagnostic aid in the management of concussion and TBI.At present, blood biomarkers have only a limited role in the evaluation and management of concussion. Although several biomarkers of brain injury have been identified, continued research is required. S100B holds promise as the most clinically useful diagnostic biomarker. Blood biomarkers, in combination with other clinical data, such as head computed tomography, would maximize the diagnostic accuracy. The methodological limitations evident in blood biomarker research results in the need for the clinical utility of blood biomarker use in concussion to be further explored.

The identification of an early biomarker of psychotic disorder is important as early treatment is associated with improved patient outcome. Metabolomic and lipidomic approaches in combination with multivariate statistical analysis were applied to identify plasma alterations in children (age 11) (38 cases vs 67 controls) and adolescents (age 18) (36 cases vs 117 controls) preceeding or coincident with the development of psychotic disorder (PD) at age 18 in the Avon Longitudinal Study of Parents and Children (ALSPAC). Overall, 179 lipids were identified at age 11, with 32 found to be significantly altered between the control and PD groups. Following correction for multiple comparisons, 8 of these lipids remained significant (lysophosphatidlycholines (LPCs) LPC(18:1), LPC(18:2), LPC(20:3); phosphatidlycholines (PCs) PC(32:2; PC(34:2), PC(36:4), PC(0-34-3) and sphingomyelin (SM) SM(d18:1/24:0)), all of which were elevated in the PD group. At age 18, 23 lipids were significantly different between the control and PD groups, although none remained significant following correction for multiple comparisons. In conclusion, the findings indicate that the lipidome is altered in the blood during childhood, long before the development of psychotic disorder. LPCs in particular are elevated in those who develop PD, indicating inflammatory abnormalities and altered phospholipid metabolism. These findings were not found at age 18, suggesting there may be ongoing alterations in the pathophysiological processes from prodrome to onset of PD. European Research Council Health Research Board Wellcome Trust UK Medical Council

Statins are the frontline in cholesterol reduction therapies; however, their use in combination with agents that possess complimentary mechanisms of action may achieve further reductions in low-density lipoprotein cholesterol. Thirty-nine patients were treated with either 80 mg simvastatin (n=20) or 10 mg simvastatin plus 10 mg ezetimibe (n=19) for 6 weeks. Dosing was designed to produce comparable low-density lipoprotein cholesterol reductions, while enabling assessment of potential simvastatin-associated pleiotropic effects. Baseline and post-treatment plasma were analyzed for lipid mediators (eg, eicosanoids and endocannabinoids) and structural lipids by liquid chromatography tandem mass spectrometry. After statistical analysis and orthogonal projections to latent structures multivariate modeling, no changes were observed in lipid mediator levels, whereas global structural lipids were reduced in response to both monotherapy ( R 2 Y =0.74; Q 2 =0.66; cross-validated ANOVA P =7.0×10 −8 ) and combination therapy ( R 2 Y =0.67; Q 2 =0.54; cross-validated ANOVA P =2.6×10 −5 ). Orthogonal projections to latent structures modeling identified a subset of 12 lipids that classified the 2 treatment groups after 6 weeks ( R 2 Y =0.65; Q 2 =0.61; cross-validated ANOVA P =5.4×10 −8 ). Decreases in the lipid species phosphatidylcholine (15:0/18:2) and hexosyl-ceramide (d18:1/24:0) were the strongest discriminators of low-density lipoprotein cholesterol reductions for both treatment groups ( q q =0.017). Shifts in lipid composition were similar for high-dose simvastatin and simvastatin/ezetimibe combination therapy, but the magnitude of the reduction was linked to simvastatin dosage. Simvastatin therapy did not affect circulating levels of lipid mediators, suggesting that pleiotropic effects are not associated with eicosanoid production. Only high-dose simvastatin reduced the relative proportion of sphingomyelin and ceramide to phosphatidylcholine ( q =0.008), suggesting a pleiotropic effect previously associated with a reduced risk of cardiovascular disease.