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Background & Aims: Intake assessment in multicenter trials is challenging, yet important for accurate outcome evaluation. The present study aimed to characterize a multicenter randomized controlled trial with a healthy Nordic diet (HND) compared to a Control diet (CD) by plasma and urine metabolic profiles and to associate them with cardiometabolic markers., Methods: During 18-24 weeks of intervention, 200 participants with metabolic syndrome were advised at six centres to eat either HND (e.g. whole-grain products, berries, rapeseed oil, fish and low-fat dairy) or CD while being weight stable. Of these 166/159 completers delivered blood/urine samples. Metabolic profiles of fasting plasma and 24 h pooled urine were analysed to identify characteristic diet-related patterns. Principal components analysis (PCA) scores (i.e. PC1 and PC2 scores) were used to test their combined effect on blood glucose response (primary endpoint), serum lipoproteins, triglycerides, and inflammatory markers., Results: The profiles distinguished HND and CD with AUC of 0.96 ± 0.03 and 0.93 ± 0.02 for plasma and urine, respectively, with limited heterogeneity between centers, reflecting markers of key foods. Markers of fish, whole grain and polyunsaturated lipids characterized HND, while CD was reflected by lipids containing palmitoleic acid. The PC1 scores of plasma metabolites characterizing the intervention is associated with HDL (β = 0.05; 95% CI: 0.02, 0.08; P = 0.001) and triglycerides (β = -0.06; 95% CI: -0.09, -0.03; P < 0.001). PC2 scores were related with glucose metabolism (2 h Glucose, β = 0.1; 95% CI: 0.05, 0.15; P < 0.001), LDL (β = 0.06; 95% CI: 0.01, 0.1; P = 0.02) and triglycerides (β = 0.11; 95% CI: 0.06, 0.15; P < 0.001). For urine, the scores were related with LDL cholesterol., Conclusions: Plasma and urine metabolite profiles from SYSDIET reflected good compliance with dietary recommendations across the region. The scores of metabolites characterizing the diets associated with outcomes related with cardio-metabolic risk. Our analysis therefore offers a novel way to approach a per protocol analysis with a balanced compliance assessment in larger multicentre dietary trials. The study was registered at clinicaltrials.gov with NCT00992641., Competing Interests: Conflict of interest The authors declare they have no conflict of interest., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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)., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2019.)

Metabolomics is utilized for comprehensive investigations on the structure, function and interactions of low molecular weight metabolites. The method provides a momentary overall picture of metabolism in the organ system. Investigations into metabolic disturbances aim at understanding the etiology of the disease, studying the effects of therapy, as well as identifying biological marker substances applicable to the prediction of the course of the disease and treatment response. Among psychotic disorders, schizophrenia is in particular associated with significant changes in the metabolism of glucose, amino acids, lipids and energy. Of these changes only some are related to the disease process itself, while some are explained by the effect of drug therapy and habits of life.

The metabolome is sensitive to genetic and environmental factors contributing to complex diseases such as type 1 diabetes (T1D). Metabolomics is the study of biochemical and physiological processes involving metabolites. It is therefore one of the key platforms for the discovery and study of pathophysiological phenomena leading to T1D and the development of T1D-associated complications. Although the application of metabolomics in T1D research is still rare, metabolomic research has already advanced across the full spectrum, from disease progression to the development of diabetic complications. Metabolomic studies in T1D have contributed to an improved etiopathogenic understanding and demonstrated their potential in the clinic. For example, metabolomic data from recent T1D studies suggest that a specific metabolic profile, or metabotype, precedes islet autoimmunity and the development of overt T1D. These early metabolic changes are attributed to many biochemical pathways, thus suggesting a systemic change in metabolism which may be inborn. Based on this evidence, the role of the metabolome in the progression to T1D is therefore to facilitate specific biochemical processes associated with T1D, and to contribute to the development of a vulnerable state in which disease is more likely to be triggered. This may have important implications for the understanding of T1D pathophysiology and early disease detection and prevention.

Primary obesity and psychotic disorders are similar with respect to the associated changes in energy balance and co-morbidities, including metabolic syndrome. Such similarities do not necessarily demonstrate causal links, but instead suggest that specific causes of and metabolic disturbances associated with obesity play a pathogenic role in the development of co-morbid disorders, potentially even before obesity develops. Metabolomics - the systematic study of metabolites, which are small molecules generated by the process of metabolism - has been important in elucidating the pathways underlying obesity-associated co-morbidities. This review covers how recent metabolomic studies have advanced biomarker discovery and the elucidation of mechanisms underlying obesity and its co-morbidities, with a specific focus on metabolic syndrome and psychotic disorders. The importance of identifying metabolic markers of disease-associated intermediate phenotypes - traits modulated but not encoded by the DNA sequence - is emphasized. Such markers would be applicable as diagnostic tools in a personalized healthcare setting and might also open up novel therapeutic avenues.

The evidence of the beneficial health effects of dietary fiber and whole grain consumption is strong, but the underlying mechanisms are not completely understood. Here, we investigate how the consumption of high-fiber rye bread (RB) or white-wheat bread (WB) modifies the plasma metabolomic profiles in postmenopausal women. The study was a randomized crossover trial consisting of 8-wk intervention periods and an 8-wk washout period. The study included 39 postmenopausal women with elevated serum total cholesterol (5.0-8.5 mmol/L) and BMI 20-33 kg/m(2). During the intervention periods, the study breads contributed to least 20% of total energy intake. Two analytical platforms for metabolomics were applied. Lipidomic analysis was performed using ultra performance liquid chromatography coupled to electrospray ionization MS and the other metabolites, including sterols, organic acids, and alcohols, were analyzed by 2-dimensional GC coupled to time-of-flight MS. Altogether, 540 metabolites were profiled. Ribitol (P < 0.001), ribonic acid (P < 0.001), and indoleacetic acid (P < 0.001) increased during the RB consumption period. Ribonic acid correlated positively with tryptophan (r = 0.40; P = 0.003), which is a precursor for the biosynthesis of hunger-depressing serotonin. There were no changes in plasma lipidomic profiles during the RB or WB intervention periods. The results suggest that 8-wk consumption of high-fiber rye bread increases metabolites that might mediate positive effects of rye bread on satiety and weight maintenance.

Lipids are a highly diverse class of molecules with crucial roles in cellular energy storage, structure and signaling. Lipid homeostasis is fundamental to maintain health, and lipid defects are central to the pathogenesis of important and devastating diseases. Newly emerging advances have facilitated the development of so-called lipidomics technologies and offer an opportunity to elucidate the mechanisms leading to disease. Furthermore, these advances also provide the tools to unravel the complexity of the 'allostatic forces' that allow maintenance of normal cellular/tissue phenotypes through the application of bioenergetically inefficient adaptive mechanisms. An alternative strategy is to focus on tissues with limited allostatic capacity, such as the eye, that could be used as readouts of metabolic stress over time. Identification of these allostatic mechanisms and pathological 'scares' might provide a window to unknown pathogenic mechanisms, as well as facilitate identification of early biomarkers of disease.

Introduction background to metabolomicsMetabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates.Objectives of white paper—expected treatment outcomes and metabolomics enabling tool for precision medicineWe anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine.Conclusions key scientific concepts and recommendations for precision medicineOur metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.

Angiotensin II (Ang II) induces mitochondrial dysfunction. We tested whether Ang II alters the "metabolomic" profile. We harvested hearts from 8-week-old double transgenic rats harboring human renin and angiotensinogen genes (dTGRs) and controls (Sprague-Dawley), all with or without Ang II type 1 receptor (valsartan) blockade. We used gas chromatography coupled with time-of-flight mass spectrometry to detect 247 intermediary metabolites. We used a partial least-squares discriminate analysis and identified 112 metabolites that differed significantly after corrections (false discovery rate q <0.05). We found great differences in the use of fatty acids as an energy source, namely, decreased levels of octanoic, oleic, and linoleic acids in dTGR (all P<0.01). The increase in cardiac hypoxanthine levels in dTGRs suggested an increase in purine degradation, whereas other changes supported an increased ketogenic amino acid tyrosine level, causing energy production failure. The metabolomic profile of valsartan-treated dTGRs more closely resembled Sprague-Dawley rats than untreated dTGRs. Mitochondrial respiratory chain activity of cytochrome C oxidase was decreased in dTGRs, whereas complex I and complex II were unaltered. Mitochondria from dTGR hearts showed morphological alterations suggesting increased mitochondrial fusion. Cardiac expression of the redox-sensitive and the cardioprotective metabolic sensor sirtuin 1 was increased in dTGRs. Interestingly, valsartan changed the level of 33 metabolites and induced mitochondrial biogenesis in Sprague-Dawley rats. Thus, distinct patterns of cardiac substrate use in Ang II-induced cardiac hypertrophy are associated with mitochondrial dysfunction. The finding underscores the importance of Ang II in the regulation of mitochondrial biogenesis and cardiac metabolomics, even in healthy hearts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little "arm twisting" in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption. [ABSTRACT FROM AUTHOR]