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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.

There is an established, link between psychosis and metabolic abnormalities, such as altered glucose metabolism and dyslipidemia, which often precede the initiation of antipsychotic treatment. It is known that obesity-associated metabolic disorders are promoted by activation of specific cannabinoid targets (endocannabinoid system (ECS)). Our recent data suggest that there is a change in the circulating lipidome at the onset of first episode psychosis (FEP). With the aim of characterizing the involvement of the central and peripheral ECSs, and their mutual associations; here, we performed a combined neuroimaging and metabolomic study in patients with FEP and healthy controls (HC). Regional brain cannabinoid receptor type 1 (CB1R) availability was quantified in two, independent samples of patients with FEP (n = 20 and n = 8) and HC (n = 20 and n = 10), by applying three-dimensional positron emission tomography, using two radiotracers, [11C]MePPEP and [18F]FMPEP-d2. Ten endogenous cannabinoids or related metabolites were quantified in serum, drawn from these individuals during the same imaging session. Circulating levels of arachidonic acid and oleoylethanolamide (OEA) were reduced in FEP individuals, but not in those who were predominantly medication free. In HC, there was an inverse association between levels of circulating arachidonoyl glycerol, anandamide, OEA, and palmitoyl ethanolamide, and CB1R availability in the posterior cingulate cortex. This phenomenon was, however, not observed in FEP patients. Our data thus provide evidence of cross talk, and dysregulation between peripheral endocannabinoids and central CB1R availability in FEP.

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.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

BackgroundEnvironmental factors are strongly implicated in late-onset of inflammatory bowel disease. Here, we investigate whether high exposure to perfluoroalkyl substances correlates 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 perfluoronoctanoic acid on intestinal barrier function in murine tissue.MethodsSerum 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 (n=20) and Crohn’s disease (n=20) diagnosed at the age of ≥55 years. Age and sex-matched blood donors (n=20), were used as healthy controls. Ex vivo Ussing chamber experiments were performed to assess the effect of perfluoronoctanoic acid on ileal and colonic murine tissue (n=9).ResultsThe total amount of perfluoroalkyl substances was significantly increased in patients with ulcerative colitis compared to healthy controls and patients with Crohn’s disease (pEx vivo exposure to perfluoronoctanoic acid induced a significanlty altered ileal and colonic barrier function. The distribution of bile acids, as well as the correlation pattern between (1) perfluoroalkyl substances and (2) bile acids, differed between patient and control groups.ConclusionOur 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.

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

Free fatty acids (FFAs) are key intermediates of lipid metabolism that have a crucial role in many critical biological processes. The specific locations of carbon-carbon double bonds (C═C) in FFAs are often associated with distinct biological functions. Despite the rapid development of analytical techniques, identification of C═C locations in FFAs with more than three C═C bonds in complex biological matrices remains a challenge. Herein, we describe a double derivatization strategy, coupled with shotgun-mass spectrometry (MS), for unambiguous and sensitive determination of a high-coverage C═C bond (from 1 to 6) locations of FFAs. Our approach is based on combination of acetone labeling of C═C bonds and N,N-diethyl-1,2-ethanediamine (DEEA) labeling of carboxyl groups within FFAs. Acetone labeling of C═C bonds via photochemical reaction provides diagnostic ions, specific to C═C locations, and DEEA labeling of carboxyl groups significantly enhances MS response of diagnostic ions, by invoking a readily protonated tertiary amine group on FFA analytes. By exploiting this double derivatization strategy, the assignment of C═C locations of FFAs with more than three C═C bonds was achieved with high sensitivity (limit of quantitation (LOQ) 0.1-1.5 nmol/L). In contrast, such assignments were not possible by acetone labeling alone, because of the low sensitivity of diagnostic ions in negative ionization mode of MS. The applicability of our method was demonstrated by profiling of FFAs, including unsaturated FFA C═C positional isomers, in liver samples from mice with nonalcoholic fatty liver disease (NAFLD) and their lean controls. The study showed that the high-specificity and high-sensitivity method developed here is promising for accurate identification and quantitation of a wide array of FFAs in biological samples.

Information is the most potent protective weapon we have to combat a pandemic, at both the individual and global level. For individuals, information can help us make personal decisions and provide a sense of security. For the global community, information can inform policy decisions and offer critical insights into the epidemic of COVID-19 disease. Fully leveraging the power of information, however, requires large amounts of data and access to it. To achieve this, we are making steps to form an international consortium, Coronavirus Census Collective (CCC, coronaviruscensuscollective.org), that will serve as a hub for integrating information from multiple data sources that can be utilized to understand, monitor, predict, and combat global pandemics. These sources may include self-reported health status through surveys (including mobile apps), results of diagnostic laboratory tests, and other static and real-time geospatial data. This collective effort to track and share information will be invaluable in predicting hotspots of disease outbreak, identifying which factors control the rate of spreading, informing immediate policy decisions, evaluating the effectiveness of measures taken by health organizations on pandemic control, and providing critical insight on the etiology of COVID-19. It will also help individuals stay informed on this rapidly evolving situation and contribute to other global efforts to slow the spread of disease.In the past few weeks, several initiatives across the globe have surfaced to use daily self-reported symptoms as a means to track disease spread, predict outbreak locations, guide population measures and help in the allocation of healthcare resources. The aim of this paper is to put out a call to standardize these efforts and spark a collaborative effort to maximize the global gain while protecting participant privacy.

Patients with schizophrenia have a lower than average life span, largely due to the increased prevalence of cardiometabolic co-morbidities. Identification of individuals with psychotic disorders with a high risk of rapid weight gain, and the associated development of metabolic complications, is an unmet need as regards public health. Here, we applied mass spectrometry-based lipidomics in a prospective study comprising 48 controls (CTR), 44 first-episode psychosis (FEP) patients and 22 individuals at clinical-high-risk (CHR) for psychosis, from two study centers (Turku/Finland and London/UK). Baseline serum samples were analyzed by lipidomics, while body mass index (BMI) was assessed at baseline and after 12 months. We found that baseline triacylglycerols 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 two triacylglycerols (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 co-morbidities.

A key clinical challenge in the management of individuals at clinical high risk for psychosis (CHR) is that it is difficult to predict their future clinical outcomes. Here, we investigated if the levels of circulating molecular lipids are related to adverse clinical outcomes in this group. Serum lipidomic analysis was performed in 263 CHR individuals and 51 healthy controls (HC), who were then clinically monitored for up to five years. Machine learning was used to identify lipid profiles that discriminated between CHR subjects and HC, and between subgroups of CHR subjects with distinct clinical outcomes. At baseline, compared to HC, CHR subjects (independent of outcome) had higher levels of triacylglycerols (TGs) with a low acyl carbon number and a double bond count, as well as higher levels of lipids in general. CHR subjects who subsequently developed psychosis (n=50) were distinguished from those that did not (n=213) on the basis of lipid profile at baseline, using a model with an AUC = 0.81 (95% CI = 0.69-0.93). CHR subjects who became psychotic had lower levels of ether phospholipids than CHR individuals who did not (p

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.

Human blood is a self-regenerating lipid-rich biological fluid that is routinely collected in hospital settings. The inventory of lipid molecules found in blood plasma (plasma lipidome) offers insights into individual metabolism and physiology in health and disease. Disturbances in the plasma lipidome also occur in conditions that are not directly linked to lipid metabolism; therefore, plasma lipidomics based on MS is an emerging tool in an array of clinical diagnostics and disease management. However, challenges exist in the translation of such lipidomic data to clinical applications. These relate to the reproducibility, accuracy, and precision of lipid quantitation, study design, sample handling, and data sharing. This position paper emerged from a workshop that initiated a community-led process to elaborate and define a set of generally accepted guidelines for quantitative MS-based lipidomics of blood plasma or serum, with harmonization of data acquired on different instrumentation platforms across independent laboratories as an ultimate goal. We hope that other fields may benefit from and follow such a precedent.

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.

Summary 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+ T cell subsets (T helper [Th]1, Th2, Th17, and induced regulatory T [iTreg] cells). Here, we combine genome-scale metabolic modeling, gene expression data, and targeted and non-targeted lipidomics experiments, together with in vitro gene knockdown experiments, and show that human CD4+ T cells undergo specific metabolic changes during activation and functional differentiation. In addition, we confirm the importance of ceramide and glycosphingolipid biosynthesis pathways in Th17 differentiation and effector functions. Through in vitro gene knockdown experiments, we substantiate the requirement of serine palmitoyltransferase (SPT), a de novo sphingolipid pathway in the expression of proinflammatory cytokines (interleukin [IL]-17A and IL17F) by Th17 cells. Our findings provide a comprehensive resource for selective manipulation of CD4+ T cells under disease conditions characterized by an imbalance of Th17/natural Treg (nTreg) cells.

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 Environmental factors have been implicated in the pathogenesis of inflammatory bowel disease (IBD), particularly late onset disease. Per- and polyfluoroalkyl substances (PFAS) are man-made chemicals with a long biological half-life that have been extensively used since the 1950s and have been proposed to interfere with the bile acid synthesis. Therefore, to investigate if late onset IBD correlates with higher PFAS levels, we measured serum levels of PFAS and bile acids in patients diagnosed with IBD later in life. Methods Serum samples were collected from patients diagnosed with ulcerative colitis (n = 20) and Crohn’s disease (n = 20) at the age of ≥55 years. Blood donors (n = 20) were used as healthy controls and were matched by gender and age. The levels of PFAS and bile acids were assessed by ultra performance liquid chromatography coupled to a triple quadrupole mass spectrometer. Results The total amount of PFAS was significantly higher in patients with ulcerative colitis compared with healthy controls (p = 0.021) or patients with Crohn’s disease (p = 0.015). No difference was found in total PFAS levels between Crohn’s disease patients and healthy controls (p = 0.841). Seven out of 30 bile acids correlated to the total PFAS level. Conclusion Our results demonstrate that PFAS levels are increased in patients with late-onset of ulcerative colitis compared with Crohn’s disease patients and healthy controls. This finding indicates that PFAS might represent an environmental risk factor for ulcerative colitis. However, additional studies assessing the functional consequences of increased PFAS in late-onset ulcerative colitis are required to confirm this hypothesis.

The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.

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.

BackgroundSeveral small molecule biomarkers have been reported in the literature for prediction and diagnosis of (pre)diabetes, its co-morbidities and complications. Here, we report the development and validation of a novel, quantitative, analytical method for use in the diabetes clinic. This method enables the determination of a selected panel of 36 metabolite biomarkers from human plasma.MethodsBased on a review of the literature and our own data, we selected a panel of metabolites indicative of various clinically-relevant pathogenic stages of diabetes. We combined these candidate biomarkers into a single ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method and optimized it, prioritizing simplicity of sample preparation and time needed for analysis, enabling high-throughput analysis in clinical laboratory settings.ResultsWe validated the method in terms of limit of (a) detection (LOD), (b) limit of quantitation (LOQ), (c) linearity (R2), (d) linear range, and (e) intra- and inter-day repeatability of each metabolite. The method’s performance was demonstrated in the analysis of selected samples from a diabetes cohort study. Metabolite levels were associated with clinical measurements and kidney complications in type 1 diabetes (T1D) patients. Specifically, both amino acids and amino acid-related analytes were associated with macro-albuminuria. Additionally, specific bile acids were associated with kidney function, anti-hypertensive medication, statin medication and clinical lipid measurements.ConclusionsThe developed analytical method is suitable for robust determination of selected plasma metabolites in the diabetes clinic.

Previous metabolomics studies suggest that type 1 diabetes (T1D) is preceded by specific metabolic disturbances. Here we asked whether distinct metabolic patterns occur in peripheral blood mononuclear cells (PBMCs) of children later developing pancreaticβ-cell autoimmunity or overt T1D. In a longitudinal cohort setting, PBMC metabolomic analysis was applied in children who either (1) progressed to T1D (PT1D, n=34), (2) seroconverted to ≥1 islet autoantibody without progressing to T1D (P1Ab, n=27), or (3) remained autoantibody negative during follow-up (CTRL, n=10). During the first year of life, levels of most lipids and polar metabolites were lower in PT1D and P1Ab, versus CTRLs. Pathway overrepresentation analysis suggested alanine, aspartate, glutamate, glycerophospholipid and sphingolipid metabolism were overrepresented in PT1D. Genome-scale metabolic models of PBMCs in T1D progression were developed using available transcriptomics data and constrained with metabolomics data from our study. Metabolic modeling confirmed altered ceramide pathways as specifically associated with T1D progression.

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).

In the last decade, the increasing incidence of type 1 diabetes (T1D) stabilized in Finland, coinciding with tighter regulation of per- and polyfluoroalkyl substances (PFAS). Here we applied lipidomics and quantification of PFAS to examine their effect, during pregnancy, on lipid-related markers of T1D risk in children. In a well-characterized mother-infant cohort (264 pairs), high PFAS exposure during pregnancy associated with decreased phospholipids in the offspring. This association was exacerbated with iwncreased human leukocyte antigen-conferred risk of T1D in infants. Their lipid profiles proved similar to those observed in earlier studies in young children progressing to T1D later in life. Exposure to a single PFAS compound or a PFAS-containing mixture of organic pollutants in non-obese diabetic mice resulted in their offspring seeing a similar decrease in phospholipids, with early signs of insulitis. Our findings suggest that high PFAS exposure during pregnancy contributes to risk and pathogenesis of T1D in children.

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.

There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.

Lipid metabolism has been an area of increased interest in psychosis research, not only due to its link to metabolic comorbidities, but also due to its putative role in the pathophysiology of psychosis. Lipid disturbances are observed already in the period preceding the onset of psychosis. For example, we performed mass spectrometry based lipidomics in a cohort of individuals at clinical high risk for psychosis (the EU-GEI study) and found that the individuals who transitioned to psychosis within a 2-year follow-up period displayed decreased levels of ether phospholipids. This finding may be of direct (patho)physiological relevance, as ether phospholipids (particularly plasmalogens, a major subgroup of ether phospholipids) are highly enriched in the brain, are supplied to the brain by the liver, have many structural and functional roles, and may act as endogenous antioxidants. Accumulating evidence also suggests that lipid disturbances play a crucial role in the development of metabolic comorbidities associated with psychotic disorders. Our lipidomic studies have shown that psychotic patients who rapidly gain weight during follow-up have elevated triglycerides (TGs) with low double bond count and carbon number at baseline. These TGs are known to be associated with non-alcoholic fatty liver disease (NAFLD) and with increased risk of type 2 diabetes. In conclusion, although the mechanisms linking dysregulation of lipid metabolism with the pathophysiology of psychosis are currently poorly understood, findings by us and others suggest that metabolic abnormalities are evident in people who are vulnerable to psychosis, and to the associated metabolic comorbidities.DisclosureNo significant relationships.

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.

Aims The aim of this study was to investigate the longitudinal plasma metabolic profiles in healthy infants and the potential association with breastfeeding duration and islet autoantibodies predictive of type 1 diabetes. Method Up to four longitudinal plasma samples from age 3 months from case children who developed islet autoimmunity (n = 29) and autoantibody-negative control children (n = 29) with the HLA DR4-DQ8/DR3-DQ2 genotype were analyzed using two-dimensional gas chromatography coupled to a time-of-flight mass spectrometer for detection of small polar metabolites. Results Plasma metabolite levels were found to depend strongly on age, with fold changes varying up to 50% from age 3 to 24 months (p

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.

BACKGROUND: A healthy Nordic diet is associated with improvements in cardiometabolic risk factors, but the effect on lipidomic profile is not known.OBJECTIVE: The aim was to investigate how a healthy Nordic diet affects the fasting plasma lipidomic profile in subjects with metabolic syndrome.METHODS: Men and women (n = 200) with features of metabolic syndrome [mean age: 55 y; body mass index (in kg/m(2)): 31.6] were randomly assigned to either a healthy Nordic (n = 104) or a control (n = 96) diet for 18 or 24 wk at 6 centers. Of the participants, 156 completed the study with plasma lipidomic measurements. The healthy Nordic diet consisted of whole grains, fruits, vegetables, berries, vegetable oils and margarines, fish, low-fat milk products, and low-fat meat. An average Nordic diet served as the control diet and included low-fiber cereal products, dairy fat-based spreads, regular-fat milk products, and a limited amount of fruits, vegetables, and berries. Lipidomic profiles were measured at baseline, week 12, and the end of the intervention (18 or 24 wk) by using ultraperformance liquid chromatography mass spectrometry. The effects of the diets on the lipid variables were analyzed with linear mixed-effects models. Data from centers with 18- or 24-wk duration were also analyzed separately.RESULTS: Changes in 21 plasma lipids differed significantly between the groups at week 12 (false discovery rate P < 0.05), including increases in plasmalogens and decreases in ceramides in the healthy Nordic diet group compared with the control group. At the end of the study, changes in lipidomic profiles did not differ between the groups. However, when the intervention lasted 24 wk, changes in 8 plasma lipids that had been identified at 12 wk, including plasmalogens, were sustained. There were no differences in changes in plasma lipids between groups with an intervention of 18 wk. By the dietary biomarker score, adherence to diet did not explain the difference in the results related to the duration of the study.CONCLUSIONS: A healthy Nordic diet transiently modified the plasma lipidomic profile, specifically by increasing the concentrations of antioxidative plasmalogens and decreasing insulin resistance-inducing ceramides. This trial was registered at clinicaltrials.gov as NCT00992641.

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.

Perfluorooctanesulfonate (PFOS) is a well-known contaminant in the environment and it has shown to disrupt multiple biological pathways, particularly those related with lipid metabolism. In this study, we have studied the impact of in ovo exposure to PFOS on lipid metabolism in livers in developing chicken embryos using lipidomics for detailed characterization of the liver lipidome. We used an avian model (Gallus gallus domesticus) for in ovo treatment at two levels of PFOS. The lipid profile of the liver of the embryo was investigated by ultra-high performance liquid chromatography combined with quadrupole-time-of-flight mass spectrometry and by gas chromatography mass spectrometry. Over 170 lipids were identified, covering phospholipids, ceramides, di- and triacylglycerols, cholesterol esters and fatty acid composition of the lipids. The PFOS exposure caused dose dependent changes in the lipid levels, which included upregulation of specific phospholipids associated with the phosphatidylethanolamine N-methyltransferase (PEMT) pathway, triacylglycerols with low carbon number and double bond count as well as of lipotoxic ceramides and diacylglycerols. Our data suggest that at lower levels of exposure, mitochondrial fatty acid β-oxidation is suppressed while the peroxisomal fatty acid β -oxidation is increased. At higher doses, however, both β -oxidation pathways are upregulated.

Background and AimsCeliac disease (CD) is a chronic enteropathy characterized by an autoimmune reaction in the small intestine in genetically-susceptible individuals. Gluten is the required environmental trigger of clinical CD, but the underlying causes of the autoimmune reaction remain unknown. Herein, we apply lipidomics to elucidate the early events preceding clinical CD in a prospective study of children observed from birth until diagnosis of CD and subsequent introduction of a gluten-free diet.MethodsMass spectrometry–based lipidomics profiling was applied to a longitudinal series of 233 plasma samples from the Type 1 Diabetes Prediction and Prevention (DIPP) study, spanning the period between birth and the introduction of a gluten–free diet following CD diagnosis (n=23 CD progressors, n=23 controls matched for gender, HLA risk, period of birth, and age).Results23 children progressed to CD at a mean age of 4.8 years. They showed increased amounts of triacylglycerols (TGs) of low carbon number and double bond count and a decreased level of phosphatidylcholines by 3 months of age as compared to controls. These differences were exacerbated with age but were not observed at birth. No significant differences were observed in essential (dietary) TGs such as those containing polyunsaturated fatty acids.ConclusionOur findings suggest that abnormal lipid metabolism associated with development of clinical CD may occur prior to the introduction of gluten to the diet. Moreover, our data suggest that the specific TGs found elevated in CD progressors may be due to a host response to compromised intake of essential lipids in the small intestine, requiring de novo lipogenesis.

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.