Metabolic Phenotyping Studies Research Articles

Data for GAW20: genome-wide DNA sequence variation and epigenome-wide DNA methylation before and after fenofibrate treatment in a family study of metabolic phenotypes

GAW20 provided participants with an opportunity to comprehensively examine genetic and epigenetic variation among related individuals in the context of drug treatment response. GAW20 used data from 188 families (N = 1105) participating in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study (clinicaltrials.gov identifier NCT00083369), which included CD4+ T-cell DNA methylation at 463,995 cytosine-phosphate-guanine (CpG) sites measured before and after a 3-week treatment with fenofibrate, single-nucleotide variation at 906,600 loci, metabolic syndrome components ascertained before and after the drug intervention, and relevant covariates. All GOLDN participants were of European descent, with an average age of 48 years. In addition, approximately half were women and approximately 40% met the diagnostic criteria for metabolic syndrome. Unique advantages of the GAW20data set included longitudinal (3 weeks apart) measurements of DNA methylation, the opportunity to explore the contributions of both genotype and DNA methylation to the interindividual variability in drug treatment response, and the familial relationships between study participants. The principal disadvantage of GAW20/GOLDN data was the spurious correlation between batch effects and fenofibrate effects on methylation, which arose because the pre- and posttreatment methylation data were generated and normalized separately, and any attempts to remove time-dependent technical artifacts would also remove biologically meaningful changes brought on by fenofibrate. Despite this limitation, the GAW20 data set offered informative, multilayered omics data collected in a large population-based study of common disease traits, which resulted in creative approaches to integration and analysis of inherited human variation.

Response of skeletal muscle UCP2-expression during metabolic adaptation to caloric restriction.

Spendthrift vs. thrifty individuals expend more energy and experience greater weight loss during caloric restriction (CR). Adaptive mechanisms in skeletal muscle, adipose tissue, and on hormone level modulate energy expenditure (EE) during weight loss. Metabolic mechanisms underlying the variability in EE during CR are unclear. The present study explored whether during long-term CR (i) gene expression changes in skeletal muscle and adipose tissue relate with the individual EE response and weight loss, and (ii) altered catecholamine and FGF21-concentrations are associated with measures of metabolic adaptation. In a 10-week inpatient study, 24-h EE was measured before and after 6 weeks of 50% CR in 12 subjects using whole-room indirect calorimetry. Weight loss was assessed and repeated hormone measurements performed. Muscle and adipose tissue biopsies were taken before and after CR, and gene expression was assessed (RNA-Seq). Genes showing the most significant changes after CR were tested for association with EE and followed-up for further association with metabolic measures in a separate phenotyping study (n = 103). Muscle UCP2 showed the strongest change after CR (log2-fold change = -1.57, false discovery rate = 0.10) and was considered the best gene for exploration of metabolic adaptive processes. A greater decrease in UCP2-expression was associated with less weight loss (P = 0.03, r = 0.77) and relatively lower 24-h EE after CR (P = 0.001, r = -0.96). Post-CR changes in FGF21-plasma concentrations correlated with UCP2-expression change (P = 0.02, r = -0.89) and weight loss (P = 0.003, r = -0.83). In a separate metabolic phenotyping study, muscle UCP2-expression correlated with respiratory quotient and macronutrient oxidation. In adipose tissue, no candidate genes for metabolic exploration were found. Changes in muscle UCP2-expression reflect an inter-individual metabolic response to long-term CR and may influence EE and weight loss via modulation of substrate oxidation.

Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data.

Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.

A comparison of human serum and plasma metabolites using untargeted 1H NMR spectroscopy and UPLC-MS.

Differences in the metabolite profiles between serum and plasma are incompletely understood. To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes. We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG 1H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis. Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples. Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies.

Urinary metabolic phenotyping of mucopolysaccharidosis type I combining untargeted and targeted strategies with data modeling

BackgroundApplication of metabolic phenotyping could expand the pathophysiological knowledge of mucopolysaccharidoses (MPS) and may reveal the comprehensive metabolic impairments in MPS. However, few studies applied this approach to MPS. MethodsWe applied targeted and untargeted metabolic profiling in urine samples obtained from a French cohort comprising 19 MPS I and 15 MPS I treated patients along with 66 controls. For that purpose, we used ultra-high-performance liquid chromatography combined with ion mobility and high-resolution mass spectrometry following a protocol designed for large-scale metabolomics studies regarding robustness and reproducibility. Furthermore, 24 amino acids have been quantified using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Keratan sulfate, Heparan sulfate and Dermatan sulfate concentrations have also been measured using an LC-MS/MS method. Univariate and multivariate data analyses have been used to select discriminant metabolites. The mummichog algorithm has been used for pathway analysis. ResultsThe studied groups yielded distinct biochemical phenotypes using multivariate data analysis. Univariate statistics also revealed metabolites that differentiated the groups. Specifically, metabolites related to the amino acid metabolism. Pathway analysis revealed that several major amino acid pathways were dysregulated in MPS. Comparison of targeted and untargeted metabolomics data with in silico results yielded arginine, proline and glutathione metabolisms being the most affected. ConclusionThis study is one of the first metabolic phenotyping studies of MPS I. The findings might help to generate new hypotheses about MPS pathophysiology and to develop further targeted studies of a smaller number of potentially key metabolites.

Application of an In Vivo Hepatic Triacylglycerol Production Method in the Setting of a High-Fat Diet in Mice.

High-fat (HF) diets typically promote diet-induced obesity (DIO) and metabolic dysfunction (i.e., insulin resistance, hypertriglyceridemia, and hepatic steatosis). Dysfunction of triacylglycerol (TAG) metabolism may contribute to the development of hepatic steatosis, via increased de novo lipogenesis or repackaging of circulating nonesterified fatty acids (NEFAs). Hepatic TAG production (HTP) rate can be assessed through injecting mice with nonionic detergents that inhibit tissue lipoprotein lipase. Potential confounding effects of detergent-based HTP tests (HTPTs) used in longitudinal studies—including the impact on food intake, energy balance, and weight gain—have not been reported. To examine this, male C57BL/6J mice were fed a 10% or 60% kcal diet. After 4 weeks, the mice underwent an HTPT via poloxamer 407 intraperitoneal injections (1000 mg/kg). Weight gain, energy intake, and postabsorptive TAG levels normalized 7–10 days post-HTPT. The post-HTPT recovery of body weight and energy intake suggest that, in metabolic phenotyping studies, any additional sample collection should occur at least 7–10 days after the HTPT to reduce confounding effects. Diet-specific effects on HTP were also observed: HF-fed mice had reduced HTP, plasma TAG, and NEFA levels compared to controls. In conclusion, the current study highlights the procedural and physiological complexities associated with studying lipid metabolism using a HTPT in the DIO mouse model.

Metabolomics enables precision medicine: "A White Paper, Community Perspective".

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.

Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping.

To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided.

Increased susceptibility to metabolic dysregulation in a mouse model of Alzheimer's disease is associated with impaired hypothalamic insulin signaling and elevated BCAA levels

IntroductionEpidemiologic studies have demonstrated an association between diabetes and dementia. Insulin signaling within the brain, in particular within the hypothalamus regulates carbohydrate, lipid, and branched chain amino acid (BCAA) metabolism in peripheral organs such as the liver and adipose tissue. We hypothesized that cerebral amyloidosis impairs central nervous system control of metabolism through disruption of insulin signaling in the hypothalamus, which dysregulates glucose and BCAA homeostasis resulting in increased susceptibility to diabetes. MethodsWe examined whether APP/PS1 mice exhibit increased susceptibility to aging or high-fat diet (HFD)-induced metabolic impairment using metabolic phenotyping and insulin-signaling studies. ResultsAPP/PS1 mice were more susceptible to high-fat feeding and aging-induced metabolic dysregulation including disrupted BCAA homeostasis and exhibited impaired hypothalamic insulin signaling. DiscussionOur data suggest that AD pathology increases susceptibility to diabetes due to impaired hypothalamic insulin signaling, and that plasma BCAA levels could serve as a biomarker of hypothalamic insulin action in patients with AD.

Solid phase extraction methodology for UPLC‐MS based metabolic profiling of urine samples

In general, when performing untargeted metabolic phenotyping (metabolomics/metabonomics) studies on biological samples for example urine or food, sample preparation should be kept to a minimum. However, there are circumstances when desalting, preconcentration, or the fractionation of samples into polar and nonpolar metabolites is of value for enabling the subsequent analysis. Because of its simplicity and ease of automation SPE is well suited to such applications prior to analysis by ultra-performance LC-TOF-MS. In the present study, the properties of a range of SPE phases have been investigated with respect to the range of metabolites that can be extracted from urine. The phases include alkyl modified (C8 and, C18-OH and C18) silica and polymeric materials. The results show that the C18 phase was well suited to fractionating urine into samples suitable for separate analysis of polar and nonpolar constituents via HILIC and RPLC, respectively, while the polymeric materials were best for concentrating and desalting samples.

Allostasis and Resilience of the Human Individual Metabolic Phenotype

The urine metabotype of 12 individuals was followed over a period of 8-10 years, which provided the longest longitudinal study of metabolic phenotypes to date. More than 2000 NMR metabolic profiles were analyzed. The majority of subjects have a stable metabotype. Subjects who were exposed to important pathophysiological stressful conditions had a significant metabotype drift. When the stress conditions ceased, the original metabotypes were regained, while an irreversible stressful condition resulted in a permanent metabotype change. These results suggest that each individual occupies a well-defined region in the broad metabolic space, within which a limited degree of allostasis is permitted. The insurgence of significant stressful conditions causes a shift of the metabotype to another distinct region. The spontaneous return to the original metabolic region when the stressful conditions are removed suggests that the original metabotype has some degree of resilience. In this picture, precision medicine should aim at reinforcing the patient's metabolic resilience, that is, his or her ability to revert to his or her specific metabotype rather than to a generic healthy one.

Hematopoietic androgen receptor deficiency promotes visceral fat deposition in male mice without impairing glucose homeostasis.

Androgen deficiency in men increases body fat, but the mechanisms by which testosterone suppresses fat deposition have not been elucidated fully. Adipose tissue macrophages express the androgen receptor (AR) and regulate adipose tissue remodeling. Thus, testosterone signaling in macrophages could alter the paracrine function of these cells and thereby contribute to the metabolic effects of androgens in men. A metabolic phenotyping study was performed to determine whether the loss of AR signaling in hematopoietic cells results in greater fat accumulation in male mice. C57BL/6J male mice (ages 12-14weeks) underwent bone marrow transplant from either wild-type (WT) or AR knockout (ARKO) donors (n=11-13 per group). Mice were fed a high-fat diet (60% fat) for 16weeks. At baseline, 8 and 16weeks, glucose and insulin tolerance tests were performed, and body composition was analyzed with fat-water imaging by MRI. No differences in body weight were observed between mice transplanted with WT bone marrow [WT(WTbm)] or ARKO bone marrow [WT(ARKObm)] prior to initiation of the high-fat diet. After 8weeks of high-fat feeding, WT(ARKObm) mice exhibited significantly more visceral and total fat mass than WT(WTbm) animals. Despite this, no differences between groups were observed in glucose tolerance, insulin sensitivity, or plasma concentrations of insulin, glucose, leptin, or cholesterol, although WT(ARKObm) mice had higher plasma levels of adiponectin. Resultant data indicate that AR signaling in hematopoietic cells influences body fat distribution in male mice, and the absence of hematopoietic AR plays a permissive role in visceral fat accumulation. These findings demonstrate a metabolic role for AR signaling in marrow-derived cells and suggest a novel mechanism by which androgen deficiency in men might promote increased adiposity. The relative contributions of AR signaling in macrophages and other marrow-derived cells require further investigation.

Sample size calculation in metabolic phenotyping studies.

The number of samples needed to identify significant effects is a key question in biomedical studies, with consequences on experimental designs, costs and potential discoveries. In metabolic phenotyping studies, sample size determination remains a complex step. This is due particularly to the multiple hypothesis-testing framework and the top-down hypothesis-free approach, with no a priori known metabolic target. Until now, there was no standard procedure available to address this purpose. In this review, we discuss sample size estimation procedures for metabolic phenotyping studies. We release an automated implementation of the Data-driven Sample size Determination (DSD) algorithm for MATLAB and GNU Octave. Original research concerning DSD was published elsewhere. DSD allows the determination of an optimized sample size in metabolic phenotyping studies. The procedure uses analytical data only from a small pilot cohort to generate an expanded data set. The statistical recoupling of variables procedure is used to identify metabolic variables, and their intensity distributions are estimated by Kernel smoothing or log-normal density fitting. Statistically significant metabolic variations are evaluated using the Benjamini-Yekutieli correction and processed for data sets of various sizes. Optimal sample size determination is achieved in a context of biomarker discovery (at least one statistically significant variation) or metabolic exploration (a maximum of statistically significant variations). DSD toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) for Kernel and log-normal estimates, and in GNU Octave for log-normal estimates (Kernel density estimates are not robust enough in GNU octave). It is available at http://www.prabi.fr/redmine/projects/dsd/repository, with a tutorial at http://www.prabi.fr/redmine/projects/dsd/wiki.

Arg287Gln variant of EPHX2 and epoxyeicosatrienoic acids are associated with insulin sensitivity in humans

Epoxyeicosatrienoic acids (EETs) protect against the development of insulin resistance in rodents. EETs are hydrolyzed to less biologically active diols by soluble epoxide hydrolase (encoded for by EPHX2). Functional variants of EPHX2 encode for enzymes with increased (Lys55Arg) or decreased (Arg287Gln) hydrolase activity. This study tested the hypothesis that variants of EPHX2 are associated with insulin sensitivity or secretion in humans. Subjects participating in metabolic phenotyping studies were genotyped. Eighty-five subjects underwent hyperglycemic clamps. There was no relationship between the Lys55Arg genotype and insulin sensitivity or secretion. In contrast, the EPHX2 287Gln variant was associated with higher insulin sensitivity index (p=0.019 controlling for body mass index and metabolic syndrome). Also, there was an interactive effect of EPHX2 Arg287Gln genotype and body mass index on insulin sensitivity index (p=0.029). There was no relationship between EPHX2 Arg287Gln genotype and acute or late-phase glucose-stimulated insulin secretion, but disposition index was higher in 287Gln carriers compared with Arg/Arg (p=0.022). Plasma EETs correlated with insulin sensitivity index (r=0.64, p=0.015 for total EETs) and were decreased in the metabolic syndrome. A genetic variant that results in decreased soluble epoxide hydrolase activity is associated with increased insulin sensitivity, as are higher EETs.

μHigh resolution-magic-angle spinning NMR spectroscopy for metabolic phenotyping of Caenorhabditis elegans.

Analysis of model organisms, such as the submillimeter-size Caenorhabditis elegans, plays a central role in understanding biological functions across species and in characterizing phenotypes associated with genetic mutations. In recent years, metabolic phenotyping studies of C. elegans based on (1)H high-resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy have relied on the observation of large populations of nematodes, requiring labor-intensive sample preparation that considerably limits high-throughput characterization of C. elegans. In this work, we open new platforms for metabolic phenotyping of C. elegans mutants. We determine rich metabolic profiles (31 metabolites identified) from samples of 12 individuals using a (1)H NMR microprobe featuring high-resolution magic-angle coil spinning (HR-MACS), a simple conversion of a standard HR-MAS probe to μHR-MAS. In addition, we characterize the metabolic variations between two different strains of C. elegans (wild-type vs slcf-1 mutant). We also acquire a NMR spectrum of a single C. elegans worm at 23.5 T. This study represents the first example of a metabolomic investigation carried out on a small number of submillimeter-size organisms, demonstrating the potential of NMR microtechnologies for metabolomics screening of small model organisms.

Metabolic Phenotyping of Traumatized Patients Reveals a Susceptibility to Sepsis

Sepsis is one of the leading causes of morbidity and mortality in patients admitted in intensive care units (ICU) for trauma. The identification of biochemical mechanisms and prediction of patients at risk of early sepsis remain unsolved. Metabolic phenotyping allows the recovery of coordinated metabolic concentration variations. There are no predictive metabolic phenotyping studies based on noninvasive human samples to identify the later development of sepsis in traumatized patients. The aim of this study was to investigate whether the metabolic phenotype could help in the discrimination of patients according to the later development of sepsis. Plasma samples were taken from severely injured patients in the hours following their admission in the ICU. Nuclear magnetic resonance (NMR) based metabolic phenotyping was performed on this prospective cohort. Statistical analyses were run on NMR spectra to discriminate patients according to the later development of sepsis. Twenty-two patients were included. One was excluded because of aberrant metabolic phenotype. Orthogonal partial least-squares analysis allowed the recovery of a predictive metabolic phenotype identifying patients with a later development of sepsis (1 + 4 component model, R(2) = 0.855, Q(2) = 0.384). A cross-validated receiver operator characteristic curve showed a remarkable prediction capacity (AUC = 0.778). Eight metabolic hotspots were identified. NMR-based metabolic phenotyping allows the prediction of patients at high risk of early sepsis after ICU admission for trauma. A larger cohort is necessary to validate and complete this study, understand biochemical mechanisms promoting sepsis development, and identify patients at risk.

A serum nuclear magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer

Breast cancer (BC) displays a high heterogeneity from histology to prognosis, metastatic evolution and treatment responses. We report here a 1H NMR-based metabolic phenotyping study aiming at identifying coordinated metabolic serum changes associated with advanced metastatic breast cancer (MBC) in comparison to the localized early disease (EBC). A model discriminating EBC and MBC patients is obtained (n=85: 46 EBC and 39 MBC), and validated with an independent cohort (n=112: 61 EBC and 51 MBC; 89.8% sensitivity, 79.3% specificity). We identify 9 statistically significant metabolites involved in this discrimination: histidine, acetoacetate, glycerol, pyruvate, glycoproteins (N-acetyl), mannose, glutamate and phenylalanine. This work illustrates the strong potential of NMR metabolic phenotyping for the diagnosis, prognosis, and management of cancer patients.

Data-Driven Sample Size Determination for Metabolic Phenotyping Studies

Sample size determination is a key question in the experimental design of medical studies. The number of patients to include in a clinical study is actually critical to evaluate costs and inclusion requirements to achieve a sufficient statistical power of test and the identification of significant variations among the factors under study. Metabolic phenotyping is an expanding field of translational research in medicine, focusing on the identification of metabolism rearrangements due to various pathophysiological conditions. This top-down hypothesis-free approach uses analytical chemistry methods, coupled to statistical analysis, to quantify subtle and coordinated metabolite concentration variations and eventually identify candidate biomarkers. The sample size determination in metabolic phenotyping studies is difficult considering the absence of a priori metabolic target. This technical note introduces a data-driven sample size determination for metabolic phenotyping studies. Starting from nuclear magnetic resonance (NMR) spectra belonging to a small cohort, metabolic NMR variables are identified by the statistical recoupling of variables (SRV) procedure. A larger data set is then generated on the basis of Kernel density estimation of SRV variable distributions. Statistically significant variations of metabolic NMR signals identified by SRV are assessed by the Benjamini-Yekutieli correction for simulated data sets of variable sizes. Simulated model robustness is evaluated by receiver operating characteristic analysis (sensitivity and specificity) on an independent cohort and cross-validation. Sample size determination is obtained by identifying the optimal data set size, depending on the purpose of the study: at least one statistically significant variation (biomarker discovery) or a maximum of statistically significant variations (metabolic exploration).

Serum Metabolic Signatures of Four Types of Human Arthritis

Similar symptoms of the different types of arthritis have continued to confound the clinical diagnosis and represent a clinical dilemma making treatment choices with a more personalized or generalized approach. Here we report a mass spectrometry-based metabolic phenotyping study to identify the global metabolic defects associated with arthritis as well as metabolic signatures of four major types of arthritis--rheumatoid arthritis (n = 27), osteoarthritis (n = 27), ankylosing spondylitis (n = 27), and gout (n = 33)--compared with healthy control subjects (n = 60). A total of 196 metabolites were identified from serum samples using a combined gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF MS) and ultraperformance liquid chromatography quadrupole-time-of-flight mass spectrometry (UPLC-QTOF MS). A global metabolic profile is identified from all arthritic patients, suggesting that there are common metabolic defects resulting from joint inflammation and lesion. Meanwhile, differentially expressed serum metabolites are identified constituting an unique metabolic signature of each type of arthritis that can be used as biomarkers for diagnosis and patient stratification. The results highlight the applicability of metabonomic phenotyping as a novel diagnostic tool for arthritis complementary to existing clinical modalities.

SRV: an open-source toolbox to accelerate the recovery of metabolic biomarkers and correlations from metabolic phenotyping datasets

Supervised multivariate statistical analyses are often required to analyze the high-density spectral information in metabolic datasets acquired from complex mixtures in metabolic phenotyping studies. Here we present an implementation of the SRV-Statistical Recoupling of Variables-algorithm as an open-source Matlab and GNU Octave toolbox. SRV allows the identification of similarity between consecutive variables resulting from the high-resolution bucketing. Similar variables are gathered to restore the spectral dependency within the datasets and identify metabolic NMR signals. The correlation and significance of these new NMR variables for a given effect under study can then be measured and represented on a loading plot to allow a visual and efficient identification of candidate biomarkers. Further on, correlations between these candidate biomarkers can be visualized on a two-dimensional pseudospectrum, representing a correlation map, helping to understand the modifications of the underlying metabolic network. SRV toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) and in GNU Octave. It is available free of charge at http://www.prabi.fr/redmine/projects/srv/repository with a tutorial. benjamin.blaise@chu-lyon.fr or vincent.navratil@univ-lyon1.fr.