Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting

Plasma and urine metabolic fingerprinting of type 1 diabetic children

Type 1 diabetes mellitus is one of the most common chronic disorders of childhood. The metabolic control is lost due to the lack of insulin, which is the main treatment for the disease. Nevertheless, long-term complications appear even under good glycemic control. Metabolomics, an emerging strategy, can help in diagnosis, prognosis, and monitoring of metabolic disorders. The objective of the present study was to investigate the alterations in plasma (by LC-MS) and urine (CE-MS) of type 1 diabetic children that were under insulin treatment and good glycemic control. Even without remarkable biochemical differences between the two groups (diabetic and control) except for glucose level and glycosilated hemoglobin, metabolomic tools were able to capture subtle metabolic differences. The main changes in plasma were associated to lipidic metabolism (nonesterified fatty acids, lysophospholipids, and other derivatives of fatty acids), and some markers of the differential activity of the gut microflora were also found (bile acids, p-cresol sulfate). In urine, changes associated to protein and amino acid metabolism were found (amino acids, their metabolites and derivatives), and among them one advanced glycation end product (carboxyethylarginine) and one early glycation end product (fructosamine) were excreted in higher proportion in the diabetic group.

The role of metabonomics as a tool for augmenting nutritional information in epidemiological studies

Most chronic diseases have been demonstrated to have a link to nutrition. Within food and nutritional research there is a major driver to understand the relationship between diet and disease in order to improve health of individuals. However, the lack of accurate dietary intake assessment in free-living populations, makes accurate estimation of how diet is associated with disease risk difficulty. Thus, there is a pressing need to find solutions to the inaccuracy of dietary reporting. Metabolic profiling of urine or plasma can provide an unbiased approach to characterizing dietary intake and various high-throughput analytical platforms have been used in order to implement targeted and nontargeted assays in nutritional clinical trials and nutritional epidemiology studies. This review describes first the challenges presented in interpreting the relationship between diet and health within individual and epidemiological frameworks. Second, we aim to explore how metabonomics can benefit different types of nutritional studies and discuss the critical importance of selecting appropriate analytical techniques in these studies. Third, we propose a strategy capable of providing accurate assessment of food intake within an epidemiological framework in order establish accurate associations between diet and health.

Systems Epidemiology: A New Direction in Nutrition and Metabolic Disease Research

Systems epidemiology applied to the field of nutrition has potential to provide new insight into underlying mechanisms and ways to study the health effects of specific foods more comprehensively. Human intervention and population-based studies have identified i) common genetic factors associated with several nutrition-related traits and ii) dietary factors altering the expression of genes and levels of proteins and metabolites related to inflammation, lipid metabolism and/or gut microbial metabolism, results of high relevance to metabolic disease. System-level tools applied type 2 diabetes and related conditions have revealed new pathways that are potentially modified by diet and thus offer additional opportunities for nutritional investigations. Moving forward, harnessing the resources of existing large prospective studies within which biological samples have been archived and diet and lifestyle have been measured repeatedly within individual will enable systems-level data to be integrated, the outcome of which will be improved personalized optimal nutrition for prevention and treatment of disease.

Exploratory investigation reveals parallel alteration of plasma fatty acids and eicosanoids in coronary artery disease patients

Fatty acids and eicosanoids are two important classes of signaling lipid molecules involved in the pathogenesis of cardiovascular diseases. To investigate the physiological functions and interplay between fatty acids and eicosanoids in coronary artery disease (CAD) patients, we developed an analytical approach for parallel quantitative analysis of plasma fatty acids and eicosanoids, using gas chromatography-tandem mass spectrometry (GC-MS/MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). In this study, 26 fatty acids and 12 eicosanoids were confidently detected in 12 patients with confirmed coronary artery disease and 11 healthy subjects. Pattern recognition analysis (principal components analysis, orthogonal partial least-square discriminate analysis, and hierarchical clustering analysis) demonstrated that the plasma lipid profile of fatty acids and eicosanoids enabled robust discrimination of CAD patients versus healthy subjects. Significant differences in six fatty acids and five eicosanoids were noted among CAD patients and healthy subjects. The development of cardiovascular disease-induced metabolic change of fatty acids and eicosanoids, such as eicosapentaenoic acid, docosahexaenoic acid, arachidonic acid, hydroxyeicosatetraenoic acids and hydroxyoctadecadienoic acid, were consistent with previous isolated observations. Moderate-strong correlations between three plasma fatty acids and three eicosanoids from arachidonic acid metabolism were also observed. In brief, findings from this exploratory study offered a new insight on the roles of various bioactive lipid molecules in the development of coronary artery disease biomarkers.

Looking back into the future: 30 years of metabolomics at TNO

Metabolites have played an essential role in our understanding of life, health, and disease for thousands of years. This domain became much more important after the concept of metabolism was discovered. In the 1950s, mass spectrometry was coupled to chromatography and made the technique more application-oriented and allowed the development of new profiling technologies. Since 1980, TNO has performed system-based metabolic profiling of body fluids, and combined with pattern recognition has led to many discoveries and contributed to the field known as metabolomics and systems biology. This review describes the development of related concepts and applications at TNO in the biomedical, pharmaceutical, nutritional, and microbiological fields, and provides an outlook for the future.

Biomarker Discovery and Translation in Metabolomics

The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding, diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis, monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

Lipidomics in longevity and healthy aging

The role of classical lipids in aging diseases and human longevity has been widely acknowledged. Triglyceride and cholesterol concentrations are clinically assessed to infer the risk of cardiovascular disease while larger lipoprotein particle size and low triglyceride levels have been identified as markers of human longevity. The rise of lipidomics as a branch of metabolomics has provided an additional layer of accuracy to pinpoint specific lipids and its association with aging diseases and longevity. The molecular composition and concentration of lipid species determine their cellular localization, metabolism, and consequently, their impact in disease and health. For example, low density lipoproteins are the main carriers of sphingomyelins and ceramides, while high density lipoproteins are mostly loaded with ether phosphocholines, partly explaining their opposing roles in atherogenesis. Moreover, the identification of specific lipid species in aging diseases and longevity would aid to clarify how these lipids alter health and influence longevity. For instance, ether phosphocholines PC (O-34:1) and PC (O-34:3) have been positively associated with longevity and negatively with diabetes, and hypertension, but other species of phosphocholines show no effect or an opposite association with these traits confirming the relevance of the identification of molecular lipid species to tackle our understanding of healthy aging and disease. Up-to-date, a minor fraction of the human plasma lipidome has been associated to healthy aging and longevity, further research would pinpoint toward specific lipidomic profiles as potential markers of healthy aging and metabolic diseases.

Associations between metabolomic compounds and incident heart failure among African Americans: the ARIC Study

Heart failure is more prevalent among African Americans than in the general population. Metabolomic studies among African Americans may efficiently identify novel biomarkers of heart failure. We used untargeted methods to measure 204 stable serum metabolites and evaluated their associations with incident heart failure hospitalization (n = 276) after a median follow-up of 20 years (1987-2008) by using Cox regression in data from 1,744 African Americans aged 45-64 years without heart failure at baseline from the Jackson, Mississippi, field center of the Atherosclerosis Risk in Communities (ARIC) Study. After adjustment for established risk factors, we found that 16 metabolites (6 named with known structural identities and 10 unnamed with unknown structural identities, the latter denoted by using the format X-12345) were associated with incident heart failure (P < 0.0004 based on a modified Bonferroni procedure). Of the 6 named metabolites, 4 are involved in amino acid metabolism, 1 (prolylhydroxyproline) is a dipeptide, and 1 (erythritol) is a sugar alcohol. After additional adjustment for kidney function, 2 metabolites remained associated with incident heart failure (for metabolite X-11308, hazard ratio = 0.75, 95% confidence interval: 0.65, 0.86; for metabolite X-11787, hazard ratio = 1.23, 95% confidence interval: 1.10, 1.37). Further structural analysis revealed X-11308 to be a dihydroxy docosatrienoic acid and X-11787 to be an isoform of either hydroxyleucine or hydroxyisoleucine. Our metabolomic analysis revealed novel biomarkers associated with incident heart failure independent of traditional risk factors.

Short-chain fructo-oligosaccharides modulate intestinal microbiota and metabolic parameters of humanized gnotobiotic diet induced obesity mice

Prebiotic fibres like short-chain fructo-oligosaccharides (scFOS) are known to selectively modulate the composition of the intestinal microbiota and especially to stimulate Bifidobacteria. In parallel, the involvement of intestinal microbiota in host metabolic regulation has been recently highlighted. The objective of the study was to evaluate the effect of scFOS on the composition of the faecal microbiota and on metabolic parameters in an animal model of diet-induced obesity harbouring a human-type microbiota. Forty eight axenic C57BL/6J mice were inoculated with a sample of faecal human microbiota and randomly assigned to one of 3 diets for 7 weeks: a control diet, a high fat diet (HF, 60% of energy derived from fat)) or an isocaloric HF diet containing 10% of scFOS (HF-scFOS). Mice fed with the two HF gained at least 21% more weight than mice from the control group. Addition of scFOS partially abolished the deposition of fat mass but significantly increased the weight of the caecum. The analysis of the taxonomic composition of the faecal microbiota by FISH technique revealed that the addition of scFOS induced a significant increase of faecal Bifidobacteria and the Clostridium coccoides group whereas it decreased the Clostridium leptum group. In addition to modifying the composition of the faecal microbiota, scFOS most prominently affected the faecal metabolome (e.g. bile acids derivatives, hydroxyl monoenoic fatty acids) as well as urine, plasma hydrophilic and plasma lipid metabolomes. The increase in C. coccoides and the decrease in C. leptum, were highly correlated to these metabolic changes, including insulinaemia, as well as to the weight of the caecum (empty and full) but not the increase in Bifidobacteria. In conclusion scFOS induce profound metabolic changes by modulating the composition and the activity of the intestinal microbiota, that may partly explain their effect on the reduction of insulinaemia.

Clinical metabolomics paves the way towards future healthcare strategies

Metabolomics is recognized as a powerful top-down system biological approach to understand genetic-environment-health paradigms paving new avenues to identify clinically relevant biomarkers. It is nowadays commonly used in clinical applications shedding new light on physiological regulatory processes of complex mammalian systems with regard to disease aetiology, diagnostic stratification and, potentially, mechanism of action of therapeutic solutions. A key feature of metabolomics lies in its ability to underpin the complex metabolic interactions of the host with its commensal microbial partners providing a new way to define individual and population phenotypes. This review aims at describing recent applications of metabolomics in clinical fields with insight into diseases, diagnostics/monitoring and improvement of homeostatic metabolic regulation.

Metabolite profiles during oral glucose challenge

To identify distinct biological pathways of glucose metabolism, we conducted a systematic evaluation of biochemical changes after an oral glucose tolerance test (OGTT) in a community-based population. Metabolic profiling was performed on 377 nondiabetic Framingham Offspring cohort participants (mean age 57 years, 42% women, BMI 30 kg/m(2)) before and after OGTT. Changes in metabolite levels were evaluated with paired Student t tests, cluster-based analyses, and multivariable linear regression to examine differences associated with insulin resistance. Of 110 metabolites tested, 91 significantly changed with OGTT (P ≤ 0.0005 for all). Amino acids, β-hydroxybutyrate, and tricarboxylic acid cycle intermediates decreased after OGTT, and glycolysis products increased, consistent with physiological insulin actions. Other pathways affected by OGTT included decreases in serotonin derivatives, urea cycle metabolites, and B vitamins. We also observed an increase in conjugated, and a decrease in unconjugated, bile acids. Changes in β-hydroxybutyrate, isoleucine, lactate, and pyridoxate were blunted in those with insulin resistance. Our findings demonstrate changes in 91 metabolites representing distinct biological pathways that are perturbed in response to an OGTT. We also identify metabolite responses that distinguish individuals with and without insulin resistance. These findings suggest that unique metabolic phenotypes can be unmasked by OGTT in the prediabetic state.

Associations between thyroid hormones and serum metabolite profiles in an euthyroid population

The aim was to characterise associations between circulating thyroid hormones-free thyroxine (FT4) and thyrotropin (TSH)-and the metabolite profiles in serum samples from participants of the German population-based KORA F4 study. Analyses were based on the metabolite profile of 1463 euthyroid subjects. In serum samples, obtained after overnight fasting (≥8), 151 different metabolites were quantified in a targeted approach including amino acids, acylcarnitines (ACs), and phosphatidylcholines (PCs). Associations between metabolites and thyroid hormone concentrations were analysed using adjusted linear regression models. To draw conclusions on thyroid hormone related pathways, intra-class metabolite ratios were additionally explored. We discovered 154 significant associations (Bonferroni p < 1.75 × 10-04) between FT4 and various metabolites and metabolite ratios belonging to AC and PC groups. Significant associations with TSH were lacking. High FT4 levels were associated with increased concentrations of many ACs and various sums of ACs of different chain length, and the ratio of C2 by C0. The inverse associations observed between FT4 and many serum PCs reflected the general decrease in PC concentrations. Similar results were found in subgroup analyses, e.g., in weight-stable subjects or in obese subjects. Further, results were independent of different parameters for liver or kidney function, or inflammation, which supports the notion of an independent FT4 effect. In fasting euthyroid adults, higher serum FT4 levels are associated with increased serum AC concentrations and an increased ratio of C2 by C0 which is indicative of an overall enhanced fatty acyl mitochondrial transport and β-oxidation of fatty acids.

Human metabolomics: strategies to understand biology

Metabolomics provides a direct functional read-out of the physiological status of an organism and is in principle ideally suited to describe someone's health status. Whereas only a limited number of small metabolites are used in the clinics, in inborn errors of metabolism an extensive repertoire of metabolites are used as biomarkers. We discuss that the proper clinical phenotyping is crucial to find biomarkers and obtain biological insights for multifactorial diseases. This requires to study the phenotype dynamics including the concepts of homeostasis and allostasis, that is, the ability to adapt and cope with a challenge. We also elaborate that biology-driven metabolomics platforms (i.e. development of metabolomics technology driven by the need of studying and answering important biomedical questions) addressing clinically relevant pathways and at the same time providing absolute concentrations are key to allow discovery and validation of biomarkers across studies and labs. Following individuals over years will require high throughput metabolomics approaches, which are emerging for nuclear magnetic resonance spectroscopy and direct-infusion mass spectrometry, but should also include the biochemical networks needed for personalized health monitoring.

Metabolomics and incident hypertension among blacks: the atherosclerosis risk in communities study

Development of hypertension is influenced by genes, environmental effects, and their interactions, and the human metabolome is a measurable manifestation of gene-environment interaction. We explored the metabolomic antecedents of developing incident hypertension in a sample of blacks, a population with a high prevalence of hypertension and its comorbidities. We examined 896 black normotensives (565 women; aged, 45-64 years) from the Atherosclerosis Risk in Communities study, whose metabolome was measured in serum collected at the baseline examination and analyzed by high-throughput methods. The analyses presented here focus on 204 stably measured metabolites during a period of 4 to 6 weeks. Weibull parametric models considering interval censored data were used to assess the hazard ratio for incident hypertension. We used a modified Bonferroni correction accounting for the correlations among metabolites to define a threshold for statistical significance (P<3.9 × 10(-4)). During 10 years of follow-up, 38% of baseline normotensives developed hypertension (n=344). With adjustment for traditional risk factors and estimated glomerular filtration rate, each +1SD difference in baseline 4-hydroxyhippurate, a product of gut microbial fermentation, was associated with 17% higher risk of hypertension (P=2.5 × 10(-4)), which remained significant after adjusting for both baseline systolic and diastolic blood pressure (P=3.8 × 10(-4)). After principal component analyses, a sex steroids pattern was significantly associated with risk of incident hypertension (highest versus lowest quintile hazard ratio, 1.72; 95% confidence interval, 1.05-2.82; P for trend, 0.03), and stratified analyses suggested that this association was consistent in both sexes. Metabolomic analyses identify novel pathways in the pathogenesis of hypertension.

GC/TOFMS analysis of metabolites in serum and urine reveals metabolic perturbation of TCA cycle in db/db mice involved in diabetic nephropathy

Early diagnosis of diabetic nephropathy (DN) is difficult although it is of crucial importance to prevent its development. To probe potential markers and the underlying mechanism of DN, an animal model of DN, the db/db mice, was used and serum and urine metabolites were profiled using gas chromatography/time-of-flight mass spectrometry. Metabolic patterns were evaluated based on serum and urine data. Principal component analysis of the data revealed an obvious metabonomic difference between db/db mice and controls, and db/db mice showed distinctly different metabolic patterns during the progression from diabetes to early, medium, and later DN. The identified metabolites discriminating between db/db mice and controls suggested that db/db mice have perturbations in the tricarboxylic acid cycle (TCA, citrate, malate, succinate, and aconitate), lipid metabolism, glycolysis, and amino acid turnover. The db/db mice were characterized by acidic urine, high TCA intermediates in serum at week 6 and a sharp decline thereafter, and gradual elevation of free fatty acids in the serum. The sharp drop of serum TCA intermediates from week 6 to 8 indicated the downregulated glycolysis and insulin resistance. However, urinary TCA intermediates did not decrease in parallel with those in the serum from week 6 to 10, and an increased portion of TCA intermediates in the serum was excreted into the urine at 8, 10, and 12 wk than at 6 wk, indicating kidney dysfunction occurred. The relative abundances of TCA intermediates in urine relative to those in serum were suggested as an index of renal damage.

Lipidomics of familial longevity

Middle-aged offspring of nonagenarians, as compared to their spouses (controls), show a favorable lipid metabolism marked by larger LDL particle size in men and lower total triglyceride levels in women. To investigate which specific lipids associate with familial longevity, we explore the plasma lipidome by measuring 128 lipid species using liquid chromatography coupled to mass spectrometry in 1526 offspring of nonagenarians (59 years ± 6.6) and 675 (59 years ± 7.4) controls from the Leiden Longevity Study. In men, no significant differences were observed between offspring and controls. In women, however, 19 lipid species associated with familial longevity. Female offspring showed higher levels of ether phosphocholine (PC) and sphingomyelin (SM) species (3.5–8.7%) and lower levels of phosphoethanolamine PE (38:6) and long-chain triglycerides (TG) (9.4–12.4%). The association with familial longevity of two ether PC and four SM species was independent of total triglyceride levels. In addition, the longevity-associated lipid profile was characterized by a higher ratio of monounsaturated (MUFA) over polyunsaturated (PUFA) lipid species, suggesting that female offspring have a plasma lipidome less prone to oxidative stress. Ether PC and SM species were identified as novel longevity markers in females, independent of total triglycerides levels. Several longevity-associated lipids correlated with a lower risk of hypertension and diabetes in the Leiden Longevity Study cohort. This sex-specific lipid signature marks familial longevity and may suggest a plasma lipidome with a better antioxidant capacity, lower lipid peroxidation and inflammatory precursors, and an efficient beta-oxidation function.

Increased plasma citrulline in mice marks diet-induced obesity and may predict the development of the metabolic syndrome

In humans, plasma amino acid concentrations of branched-chain amino acids (BCAA) and aromatic amino acids (AAA) increase in states of obesity, insulin resistance and diabetes. We here assessed whether these putative biomarkers can also be identified in two different obesity and diabetic mouse models. C57BL/6 mice with diet-induced obesity (DIO) mimic the metabolic impairments of obesity in humans characterized by hyperglycemia, hyperinsulinemia and hepatic triglyceride accumulation. Mice treated with streptozotocin (STZ) to induce insulin deficiency were used as a type 1 diabetes model. Plasma amino acid profiling of two high fat (HF) feeding trials revealed that citrulline and ornithine concentrations are elevated in obese mice, while systemic arginine bioavailability (ratio of plasma arginine to ornithine + citrulline) is reduced. In skeletal muscle, HF feeding induced a reduction of arginine levels while citrulline levels were elevated. However, arginine or citrulline remained unchanged in their key metabolic organs, intestine and kidney. Moreover, the intestinal conversion of labeled arginine to ornithine and citrulline in vitro remained unaffected by HF feeding excluding the intestine as prime site of these alterations. In liver, citrulline is mainly derived from ornithine in the urea cycle and DIO mice displayed reduced hepatic ornithine levels. Since both amino acids share an antiport mechanism for mitochondrial import and export, elevated plasma citrulline may indicate impaired hepatic amino acid handling in DIO mice. In the insulin deficient mice, plasma citrulline and ornithine levels also increased and additionally these animals displayed elevated BCAA and AAA levels like insulin resistant and diabetic patients. Therefore, type 1 diabetic mice but not DIO mice show the “diabetic fingerprint” of plasma amino acid changes observed in humans. Additionally, citrulline may serve as an early indicator of the obesity-dependent metabolic impairments.

Metabolomics in adult and pediatric nephrology

Metabolomics, the latest of the “omics” sciences, has a non-selective approach and can thus lead to the identification of all the metabolites (molecules < 1 kDa) in a biological system. The metabolomic profile can be considered the most predictive phenotype capable of evaluating epigenetic modifications determined by external factors. It is so close to the phenotype as to be considered the phenotype itself in its unique individuality (fingerprinting), both in health (phenome), and disease (diseasome). Urine, compared to other biological liquids, has the advantage of being a complex fluid with many components, including intermediate metabolites. Metabolomics may thus play a role in the study of different kidney diseases and overcome diagnostic difficulties. We shall present the studies that to our knowledge have been published on Nephrology and Pediatric Nephrology. Some are experimental while others are clinical. We have not considered carcinomas and transplantations. Although scarce, the data on adults and the very few ones in pediatrics are quite interesting. Further studies on kidneys are needed to determine the practical clinical impact of metabolomics in kidney renal pathologies. The “multiplatform” “omic” study of urine and namely metabolomics can contribute to improving early diagnosis and the outcome of kidney diseases.

Verification of skin autofluorescence values by mass spectrometry in adolescents with type 1 diabetes: brief report

BACKGROUND

Accumulation of advanced glycation end products (AGEs) in tissues is a major risk factor for diabetes-associated complications. Skin autofluorescence (SAF) values measured by a specific noninvasive approach (AGE Reader; DiagnOptics Technologies B.V., Gröningen, The Netherlands) reflect the overall AGE exposure in skin.

SUBJECTS AND METHODS

In 16 adolescents with type 1 diabetes (age range, 11-18 years) we tested the association between SAF measured with an AGE Reader and the presence of glucuronic acid, 3-indoxyl sulfate, 3-hydroxybutyrate, phenol sulfate, and pentosidine in skin tissue determined with desorption electrospray ionization mass spectrometry (DESI-MS). These compounds are implicated in long-term diabetes complications.

RESULTS

SAF values significantly correlated with levels of compounds measured by DESI-MS (r>0.9 and P<0.001 for each).

CONCLUSIONS

The strong correlation between adolescents' SAF values measured with the AGE Reader and some glycation products measured with DESI-MS indicates that SAF values may be used as surrogate markers of skin exposure to glycemic end products in type 1 diabetes.

Metabolomics as a tool for personalizing medicine: 2012 update

Numerous factors in conjunction with an individual’s genetic make up will determine predisposition to disease, adverse or beneficial effects of drug treatment or therapy, and disease progression. A major limitation of current clinical measures is that the disease phenotype, which is comprised of the genotype and other environmental factors, is underestimated. Rather, each disease is treated similarly even though the disease process is highly complex. Methods that evaluate the interaction of genotype and environmental factors would likely be a better indicator of patients’ response to medical treatments. The omics technologies, specifically metabolomics, will play a major role in the movement towards personalized medicine. Metabolomics is phenotype driven and should provide better clinical biomarkers. Furthermore, recent studies have shown that associations between genetic variants and downstream metabolite changes can provide a unique description of an individual’s genotype and phenotype, which will further enhance the movement towards personalized medicine.

Adiponectin corrects high-fat diet-induced disturbances in muscle metabolomic profile and whole-body glucose homeostasis

We provide here a detailed and comprehensive analysis of skeletal muscle metabolomic profiles in response to adiponectin in adiponectin knockout (AdKO) mice after high-fat-diet (HFD) feeding. Hyperinsulinemic-euglycemic clamp studies showed that adiponectin administration corrected HFD-induced defects in post/basal insulin stimulated R(d) and insulin signaling in skeletal muscle. Lipidomic profiling of skeletal muscle from HFD-fed mice indicated elevated triacylglycerol and diacylglycerol species (16:0-18:1, 18:1, and 18:0-18:2) as well as acetyl coA, all of which were mitigated by adiponectin. HFD induced elevated levels of various ceramides, but these were not significantly altered by adiponectin. Adiponectin corrected the altered branched-chain amino acid metabolism caused by HFD and corrected increases across a range of glycerolipids, fatty acids, and various lysolipids. Adiponectin also reversed induction of the pentose phosphate pathway by HFD. Analysis of muscle mitochondrial structure indicated that adiponectin treatment corrected HFD-induced pathological changes. In summary, we show an unbiased comprehensive metabolomic profile of skeletal muscle from AdKO mice subjected to HFD with or without adiponectin and relate these to changes in whole-body glucose handling, insulin signaling, and mitochondrial structure and function. Our data revealed a key signature of relatively normalized muscle metabolism across multiple metabolic pathways with adiponectin supplementation under the HFD condition.

Combining small-volume metabolomic and transcriptomic approaches for assessing brain chemistry

The integration of disparate data types provides a more complete picture of complex biological systems. Here we combine small-volume metabolomic and transcriptomic platforms to determine subtle chemical changes and to link metabolites and genes to biochemical pathways. Capillary electrophoresis–mass spectrometry (CE–MS) and whole-genome gene expression arrays, aided by integrative pathway analysis, were utilized to survey metabolomic/transcriptomic hippocampal neurochemistry. We measured changes in individual hippocampi from the mast cell mutant mouse strain, C57BL/6 Kit^W-sh/W-sh. These mice have a naturally occurring mutation in the white spotting locus that causes reduced c-Kit receptor expression and an inability of mast cells to differentiate from their hematopoietic progenitors. Compared with their littermates, the mast cell-deficient mice have profound deficits in spatial learning, memory, and neurogenesis. A total of 18 distinct metabolites were identified in the hippocampus that discriminated between the C57BL/6 Kit^W-sh/W-sh and control mice. The combined analysis of metabolite and gene expression changes revealed a number of altered pathways. Importantly, results from both platforms indicated that multiple pathways are impacted, including amino acid metabolism, increasing the confidence in each approach. Because the CE–MS and expression profiling are both amenable to small-volume analysis, this integrated analysis is applicable to a range of volume-limited biological systems.

Metabolomics in epidemiology: sources of variability in metabolite measurements and implications

BACKGROUND

Metabolite levels within an individual vary over time. This within-individual variability, coupled with technical variability, reduces the power for epidemiologic studies to detect associations with disease. Here, the authors assess the variability of a large subset of metabolites and evaluate the implications for epidemiologic studies.

METHODS

Using liquid chromatography/mass spectrometry (LC/MS) and gas chromatography-mass spectroscopy (GC/MS) platforms, 385 metabolites were measured in 60 women at baseline and year-one of the Shanghai Physical Activity Study, and observed patterns were confirmed in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening study.

RESULTS

Although the authors found high technical reliability (median intraclass correlation = 0.8), reliability over time within an individual was low. Taken together, variability in the assay and variability within the individual accounted for the majority of variability for 64% of metabolites. Given this, a metabolite would need, on average, a relative risk of 3 (comparing upper and lower quartiles of "usual" levels) or 2 (comparing quartiles of observed levels) to be detected in 38%, 74%, and 97% of studies including 500, 1,000, and 5,000 individuals. Age, gender, and fasting status factors, which are often of less interest in epidemiologic studies, were associated with 30%, 67%, and 34% of metabolites, respectively, but the associations were weak and explained only a small proportion of the total metabolite variability.

CONCLUSION

Metabolomics will require large, but feasible, sample sizes to detect the moderate effect sizes typical for epidemiologic studies.

IMPACT

We offer guidelines for determining the sample sizes needed to conduct metabolomic studies in epidemiology.

ω-6 Polyunsaturated fatty acids extend life span through the activation of autophagy

Adaptation to nutrient scarcity depends on the activation of metabolic programs to efficiently use internal reserves of energy. Activation of these programs in abundant food regimens can extend life span. However, the common molecular and metabolic changes that promote adaptation to nutritional stress and extend life span are mostly unknown. Here we present a response to fasting, enrichment of ω-6 polyunsaturated fatty acids (PUFAs), which promotes starvation resistance and extends Caenorhabditis elegans life span. Upon fasting, C. elegans induces the expression of a lipase, which in turn leads to an enrichment of ω-6 PUFAs. Supplementing C. elegans culture media with these ω-6 PUFAs increases their resistance to starvation and extends their life span in conditions of food abundance. Supplementation of C. elegans or human epithelial cells with these ω-6 PUFAs activates autophagy, a cell recycling mechanism that promotes starvation survival and slows aging. Inactivation of C. elegans autophagy components reverses the increase in life span conferred by supplementing the C. elegans diet with these fasting-enriched ω-6 PUFAs. We propose that the salubrious effects of dietary supplementation with ω-3/6 PUFAs (fish oils) that have emerged from epidemiological studies in humans may be due to a similar activation of autophagic programs.

Metabolomics in human type 2 diabetes research

The high prevalence of diabetes and diabetic complications has caused a huge burden on the modern society. Although scientific advances have led to effective strategies for preventing and treating diabetes over the past several decades, little progress has been made toward curing the disease or even getting it under control, from a public health and overall societal standpoint. There is still a lack of reliable biomarkers indicative of metabolic alterations associated with diabetes and different drug responses, highlighting the need for the development of early diagnostic and prognostic markers for diabetes and diabetic complications. The emergence of metabolomics has allowed researchers to systemically measure the small molecule metabolites, which are sensitive to the changes of both environmental and genetic factors and therefore, could be regarded as the link between genotypes and phenotypes. During the last decade, the progression made in metabolomics has provided insightful information on disease development and disease onset prediction. Recent studies using metabolomics approach coupled with statistical tools to predict incident diabetes revealed a number of metabolites that are significantly altered, including branched-chain and aromatic amino acids, such as isoleucine, leucine, valine, tyrosine and phenylalanine, as diagnostic or highly-significant predictors of future diabetes. This review summarizes the current findings of metabolomic studies in human investigations with the most common form of diabetes, type 2 diabetes.

Probiotics as potential biotherapeutics in the management of type 2 diabetes - prospects and perspectives

Diabetes mellitus is a looming epidemic worldwide, affecting almost all major sections of society, creating burdens on global health and economy. A large number of studies have identified a series of multiple risk factors such as genetic predisposition, epigenetic changes, unhealthy lifestyle, and altered gut microbiota that cause increased adiposity, β-cell dysfunction, hyperglycemia, hypercholesterolemia, adiposity, dyslipidaemia, metabolic endotoxemia, systemic inflammation, intestinal permeability (leaky gut), defective secretion of incretins and oxidative stress associated with type 2 diabetes (T2D). Recent studies have proposed multifactorial interventions including dietary manipulation in the management of T2D. The same interventions have also been recommended by many national and international diabetes associations. These studies are aimed at deciphering the gut microbial influence on health and disease. Interestingly, results from several genomic, metagenomic and metabolomic studies have provided substantial information to target gut microbiota by dietary interventions for the management of T2D. Probiotics particularly lactobacilli and bifidobacteria have recently emerged as the prospective biotherapeutics with proven efficacy demonstrated in various in vitro and in vivo animal models adequately supported with their established multifunctional roles and mechanism of action for the prevention and disease treatment. The dietary interventions in conjunction with probiotics - a novel multifactorial strategy to abrogate progression and development of diabetes - hold considerable promise through improving the altered gut microbial composition and by targeting all the possible risk factors. This review will highlight the new developments in probiotic interventions and future prospects for exploring probiotic therapy in the prevention and control of lifestyle diseases like T2D.

Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach

Metabolomic discovery of biomarkers of type 2 diabetes (T2D) risk may reveal etiological pathways and help to identify individuals at risk for disease. We prospectively investigated the association between serum metabolites measured by targeted metabolomics and risk of T2D in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (27,548 adults) among all incident cases of T2D (n = 800, mean follow-up 7 years) and a randomly drawn subcohort (n = 2,282). Flow injection analysis tandem mass spectrometry was used to quantify 163 metabolites, including acylcarnitines, amino acids, hexose, and phospholipids, in baseline serum samples. Serum hexose; phenylalanine; and diacyl-phosphatidylcholines C32:1, C36:1, C38:3, and C40:5 were independently associated with increased risk of T2D and serum glycine; sphingomyelin C16:1; acyl-alkyl-phosphatidylcholines C34:3, C40:6, C42:5, C44:4, and C44:5; and lysophosphatidylcholine C18:2 with decreased risk. Variance of the metabolites was largely explained by two metabolite factors with opposing risk associations (factor 1 relative risk in extreme quintiles 0.31 [95% CI 0.21-0.44], factor 2 3.82 [2.64-5.52]). The metabolites significantly improved T2D prediction compared with established risk factors. They were further linked to insulin sensitivity and secretion in the Tübingen Family study and were partly replicated in the independent KORA (Cooperative Health Research in the Region of Augsburg) cohort. The data indicate that metabolic alterations, including sugar metabolites, amino acids, and choline-containing phospholipids, are associated early on with a higher risk of T2D.

Current metabolomics: practical applications

The field of metabolomics continues to grow rapidly over the last decade and has been proven to be a powerful technology in predicting and explaining complex phenotypes in diverse biological systems. Metabolomics complements other omics, such as transcriptomics and proteomics and since it is a 'downstream' result of gene expression, changes in the metabolome is considered to best reflect the activities of the cell at a functional level. Thus far, metabolomics might be the sole technology capable of detecting complex, biologically essential changes. As one of the omics technology, metabolomics has exciting applications in varied fields, including medical science, synthetic biology, medicine, and predictive modeling of plant, animal and microbial systems. In addition, integrated applications with genomics, transcriptomics, and proteomics provide greater understanding of global system biology. In this review, we discuss recent applications of metabolomics in microbiology, plant, animal, food, and medical science.

Application of metabolomics approaches to the study of respiratory diseases

Metabolomics is the global unbiased analysis of all the small-molecule metabolites within a biological system, under a given set of conditions. These methods offer the potential for a holistic approach to clinical medicine, as well as improving disease diagnosis and understanding of pathological mechanisms. Respiratory diseases including asthma and chronic obstructive pulmonary disorder are increasing globally, with the latter predicted to become the third leading cause of global mortality by 2020. The root causes for disease onset remain poorly understood and no cures are available. This review presents an overview of metabolomics followed by in-depth discussion of its application to the study of respiratory diseases, including the design of metabolomics experiments, choice of clinical material collected and potentially confounding experimental factors. Particular challenges in the field are presented and placed within the context of the future of the applications of metabolomics approaches to the study of respiratory diseases.

Applications of metabolomics for kidney disease research: from biomarkers to therapeutic targets

Metabolomics is one of the relative newcomers of the omics techniques and is likely the one most closely related to actual real-time disease pathophysiology. Hence, it has the power to yield not only specific biomarkers but also insight into the pathophysiology of disease. Despite this power, metabolomics as applied to kidney disease is still in its early adolescence and has not yet reached the mature stage of clinical application, i.e., specific biomarker and therapeutic target discovery. On the other hand, the insight gained from hints into what makes these diseases tick, as is evident from the metabolomics pathways which have been found to be altered in kidney cancer, are now beginning to bear fruit in leading to potential therapeutic targets. It is quite likely that, with greater numbers of clinical materials and with more investigators jumping into the field, metabolomics may well change the course of kidney disease research.

Comparison of metabolic profiles of acutely ill and short-term weight recovered patients with anorexia nervosa reveals alterations of 33 out of 163 metabolites

Starvation represents an extreme physiological state and entails numerous endocrine and metabolic adaptations. The large-scale application of metabolomics to patients with acute anorexia nervosa (AN) should lead to the identification of state markers characteristic of starvation in general and of the starvation specifically associated with this eating disorder. Novel metabolomics technology has not yet been applied to this disorder. Using a targeted metabolomics approach, we analysed 163 metabolite concentrations in 29 patients with AN in the acute stage of starvation (T0) and after short-term weight recovery (T1). Of the 163 metabolites of the respective kit, 112 metabolites were quantified within restrictive quality control limits. We hypothesized that concentrations are different in patients in the acute stage of starvation (T0) and after weight gain (T1). Furthermore, we compared all 112 metabolite concentrations of patients at the two time points (T0, T1) with those of 16 age and gender matched healthy controls. Thirty-three of the metabolite serum levels were found significantly different between T0 and T1. At the acute stage of starvation (T0) serum concentrations of 90 metabolites differed significantly from those of healthy controls. Concentrations of controls mostly differed even more strongly from those of AN patients after short-term weight recovery than at the acute stage of starvation. We conclude that AN entails profound and longer lasting alterations of a large number of serum metabolites. Further studies are warranted to distinguish between state and trait related alterations and to establish diagnostic sensitivity and specificity of the thus altered metabolites.

Effects of type 2 diabetes and insulin on whole-body, splanchnic, and leg protein metabolism

CONTEXT

Type 2 diabetes (T2D) is characterized by insulin resistance to glucose metabolism. Most studies suggest that protein metabolism is unaffected by T2D, but regional protein metabolism and response to multiple doses of insulin have not been examined.

OBJECTIVE

Our objective was to determine whether insulin regulation of splanchnic and leg protein metabolism are affected by T2D during hyperglycemia and graded insulin levels.

DESIGN AND SETTING

We conducted a cross-sectional study at an academic medical center.

PARTICIPANTS

T2D and non-T2D adults were matched for age (62 yr) and body mass index (30 kg/m(2)).

INTERVENTIONS

Glucose was maintained at approximately 9 mmol/liter while insulin was infused at three progressively higher rates, achieving circulating concentrations of approximately 150, 350, and 700 pmol/liter, respectively.

MAIN OUTCOME MEASURES

Protein kinetics were measured using labeled phenylalanine (Phe) and tyrosine (Tyr).

RESULTS

Whole-body protein breakdown and synthesis rates were higher in T2D but declined with increasing insulin in both groups. Leg Phe and Tyr appearance and disappearance and estimates of protein breakdown and synthesis, respectively, were higher in T2D but did not decline significantly with insulin, resulting in similar net balance between groups. Splanchnic response to insulin was blunted in T2D, shown by a smaller reduction in rates of disappearance and net balance of Phe and Tyr as insulin increased. Splanchnic conversion of Phe to Tyr was lower in T2D and less sensitive to insulin, whereas nonsplanchnic Phe to Tyr tended to be higher in T2D.

CONCLUSIONS

T2D results in higher whole-body, splanchnic, and leg protein turnover and blunts the insulin-mediated suppression of splanchnic protein anabolism under hyperglycemic, hyperinsulinemic conditions.

Metabolic phenotyping in clinical and surgical environments

Metabolic phenotyping involves the comprehensive analysis of biological fluids or tissue samples. This analysis allows biochemical classification of a person's physiological or pathological states that relate to disease diagnosis or prognosis at the individual level and to disease risk factors at the population level. These approaches are currently being implemented in hospital environments and in regional phenotyping centres worldwide. The ultimate aim of such work is to generate information on patient biology using techniques such as patient stratification to better inform clinicians on factors that will enhance diagnosis or the choice of therapy. There have been many reports of direct applications of metabolic phenotyping in a clinical setting.

Metabolomics platforms for genome wide association studies--linking the genome to the metabolome

Genome-wide association studies (GWAS) reveal links between genetic variance and predisposition to disease. With the advent of modern 'omics-technologies', GWAS can now identify the genetic factors that influence intermediate traits on pathways to disease, such as blood concentrations of carbohydrates, lipids, amino acids, and secondary metabolites, hormones and signal molecules. At the example of recent GWAS with metabolic traits (mGWAS) we review the high-throughput screening approaches that are available to further advance the field.

Genetic variation in metabolic phenotypes: study designs and applications

Many complex disorders are linked to metabolic phenotypes. Revealing genetic influences on metabolic phenotypes is key to a systems-wide understanding of their interactions with environmental and lifestyle factors in their aetiology, and we can now explore the genetics of large panels of metabolic traits by coupling genome-wide association studies and metabolomics. These genome-wide association studies are beginning to unravel the genetic contribution to human metabolic individuality and to demonstrate its relevance for biomedical and pharmaceutical research. Adopting the most appropriate study designs and analytical tools is paramount to further refining the genotype-phenotype map and eventually identifying the part played by genetic influences on metabolic phenotypes. We discuss such design considerations and applications in this Review.

A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

The metabolome, defined as the reflection of metabolic dynamics derived from parameters measured primarily in easily accessible body fluids such as serum, plasma, and urine, can be considered as the omics data pool that is closest to the phenotype because it integrates genetic influences as well as nongenetic factors. Metabolic traits can be related to genetic polymorphisms in genome-wide association studies, enabling the identification of underlying genetic factors, as well as to specific phenotypes, resulting in the identification of metabolome signatures primarily caused by nongenetic factors. Similarly, correlation of metabolome data with transcriptional or/and proteome profiles of blood cells also produces valuable data, by revealing associations between metabolic changes and mRNA and protein levels. In the last years, the progress in correlating genetic variation and metabolome profiles was most impressive. This review will therefore try to summarize the most important of these studies and give an outlook on future developments.

Metabolomics in diabetes research

Diabetes represents one of the most important global health problems because it is associated with a large economic burden on the health systems of many countries. Whereas the diagnosis and treatment of manifest diabetes have been well investigated, the identification of novel pathways or early biomarkers indicative of metabolic alterations or insulin resistance related to the development of diabetes is still in progress. Over half of the type 2 diabetes patients show manifestations of diabetes-related diseases, which highlight the need for early screening markers of diabetes. During the last decade, the rapidly growing research field of metabolomics has introduced new insights into the pathology of diabetes as well as methods to predict disease onset and has revealed new biomarkers. Recent epidemiological studies first used metabolism to predict incident diabetes and revealed branched-chain and aromatic amino acids including isoleucine, leucine, valine, tyrosine and phenylalanine as highly significant predictors of future diabetes. This review summarises the current findings of metabolic research regarding diabetes in animal models and human investigations.

Serum metabolic signatures of fulminant type 1 diabetes

Fulminant type 1 diabetes (FT1DM) is a relatively new clinical entity featured by acute destruction of pancreatic beta cells. Clinical consequences of FT1DM could be fatal when timely medications are not provided, suggesting the particular importance of rapid and accurate diagnosis. Here we report a serum metabonomics study of FT1DM patients, together with healthy control subjects (NC), type 2 diabetes (T2DM), classic type 1 diabetes (T1DM), and diabetic ketoacidosis (DKA) patients, with the aim of discovering metabolic markers associated with FT1DM. A total of 79 subjects were enrolled (22 NC, 22 T1DM, 22 T2DM, 8 DKA and 5 FT1DM) and the serum metabolic profiling of fasting blood samples was performed using gas chromatography time-of-flight mass spectrometry (GC-TOFMS) coupled with multivariate and univariate statistical analyses. Serum metabolites differentially expressed in FT1DM relative to NC, or to T2DM, T1DM and DKA were identified. Three metabolite markers, 5-oxoproline, glutamate, and homocysteine, were significantly altered among FT1DM, T2DM, T1DM, and DKA. In addition, the three metabolite markers, 5-oxoproline, glutamate, and homocysteine, presented similar patterns of distribution across groups. The results showed that the metabolic signatures of FT1DM identified in this study could be of potential clinical significance for the accurate diagnosis of FT1DM.

Changing metabolic signatures of amino acids and lipids during the prediabetic period in a pig model with impaired incretin function and reduced β-cell mass

Diabetes is generally diagnosed too late. Therefore, biomarkers indicating early stages of β-cell dysfunction and mass reduction would facilitate timely counteraction. Transgenic pigs expressing a dominant-negative glucose-dependent insulinotropic polypeptide receptor (GIPR(dn)) reveal progressive deterioration of glucose control and reduction of β-cell mass, providing a unique opportunity to study metabolic changes during the prediabetic period. Plasma samples from intravenous glucose tolerance tests of 2.5- and 5-month-old GIPR(dn) transgenic and control animals were analyzed for 163 metabolites by targeted mass spectrometry. Analysis of variance revealed that 26 of 163 parameters were influenced by the interaction Genotype × Age (P ≤ 0.0001) and thus are potential markers for progression within the prediabetic state. Among them, the concentrations of seven amino acids (Phe, Orn, Val, xLeu, His, Arg, and Tyr) were increased in 2.5-month-old but decreased in 5-month-old GIPR(dn) transgenic pigs versus controls. Furthermore, specific sphingomyelins, diacylglycerols, and ether phospholipids were decreased in plasma of 5-month-old GIPR(dn) transgenic pigs. Alterations in plasma metabolite concentrations were associated with liver transcriptome changes in relevant pathways. The concentrations of a number of plasma amino acids and lipids correlated significantly with β-cell mass of 5-month-old pigs. These metabolites represent candidate biomarkers of early phases of β-cell dysfunction and mass reduction.

Perspectives on systems biology applications in diabetic kidney disease

Diabetic kidney disease (DKD) is a microvascular complication of type 1 and 2 diabetes with a devastating impact on individuals with the disease, their families, and society as a whole. DKD is the single most frequent cause of incident chronic kidney disease cases and accounts for over 40% of the population with end-stage renal disease. Contributing factors for the high prevalence are the increase in obesity and subsequent diabetes combined with an improved long-term survival with diabetes. Environment and genetic variations contribute to DKD susceptibility and progressive loss of kidney function. How the molecular mechanisms of genetic and environmental exposures interact during DKD initiation and progression is the focus of ongoing research efforts. The development of standardized, unbiased high-throughput profiling technologies of human DKD samples opens new avenues in capturing the multiple layers of DKD pathobiology. These techniques routinely interrogate analytes on a genome-wide scale generating comprehensive DKD-associated fingerprints. Linking the molecular fingerprints to deep clinical phenotypes may ultimately elucidate the intricate molecular interplay in a disease stage and subtype-specific manner. This insight will form the basis for accurate prognosis and facilitate targeted therapeutic interventions. In this review, we present ongoing efforts from large-scale data integration translating "-omics" research efforts into improved and individualized health care in DKD.

Bayesian independent component analysis recovers pathway signatures from blood metabolomics data

Interpreting the complex interplay of metabolites in heterogeneous biosamples still poses a challenging task. In this study, we propose independent component analysis (ICA) as a multivariate analysis tool for the interpretation of large-scale metabolomics data. In particular, we employ a Bayesian ICA method based on a mean-field approach, which allows us to statistically infer the number of independent components to be reconstructed. The advantage of ICA over correlation-based methods like principal component analysis (PCA) is the utilization of higher order statistical dependencies, which not only yield additional information but also allow a more meaningful representation of the data with fewer components. We performed the described ICA approach on a large-scale metabolomics data set of human serum samples, comprising a total of 1764 study probands with 218 measured metabolites. Inspecting the source matrix of statistically independent metabolite profiles using a weighted enrichment algorithm, we observe strong enrichment of specific metabolic pathways in all components. This includes signatures from amino acid metabolism, energy-related processes, carbohydrate metabolism, and lipid metabolism. Our results imply that the human blood metabolome is composed of a distinct set of overlaying, statistically independent signals. ICA furthermore produces a mixing matrix, describing the strength of each independent component for each of the study probands. Correlating these values with plasma high-density lipoprotein (HDL) levels, we establish a novel association between HDL plasma levels and the branched-chain amino acid pathway. We conclude that the Bayesian ICA methodology has the power and flexibility to replace many of the nowadays common PCA and clustering-based analyses common in the research field.

Metabolomic profiling in blood from umbilical cords of low birth weight newborns

BACKGROUND

Low birth weight has been linked to an increased risk to develop obesity, type 2 diabetes, and hypertension in adult life, although the mechanisms underlying the association are not well understood. The objective was to determine whether the metabolomic profile of plasma from umbilical cord differs between low and normal birth weight newborns.

METHODS

Fifty healthy pregnant women and their infants were selected. The eligibility criteria were being born at term and having a normal pregnancy. Pairs were grouped according to their birth weight: low birth weight (LBW, birth weight < 10th percentile, n = 20) and control (control, birth weight between the 75th-90th percentiles, n = 30). Nuclear Magnetic Resonance (NMR) was used to generate metabolic fingerprints of umbilical cord plasma samples. Simultaneously, the metabolomic profiles of the mothers were analysed. The resulting data were subjected to chemometric, principal component and partial least squares discriminant analyses.

RESULTS

Umbilical cord plasma from LBW and control newborns displayed a clearly differentiated metabolic profile. Seven metabolites were identified that discriminate the LBW from the control group. LBW newborns had lower levels of choline, proline, glutamine, alanine and glucose than did the control newborns, while plasma levels of phenylalanine and citrulline were higher in LBW newborns (p < 0.05). No significant differences were found between the two groups of mothers.

CONCLUSIONS

Low birth weight newborns display a differential metabolomic profile than those of normal birth weight, a finding not present in the mothers. The meaning and the potential utility of the findings as biomarkers of risk need to be addressed in future studies.

Body fat free mass is associated with the serum metabolite profile in a population-based study

OBJECTIVE

To characterise the influence of the fat free mass on the metabolite profile in serum samples from participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) S4 study.

SUBJECTS AND METHODS

Analyses were based on metabolite profile from 965 participants of the S4 and 890 weight-stable subjects of its seven-year follow-up study (KORA F4). 190 different serum metabolites were quantified in a targeted approach including amino acids, acylcarnitines, phosphatidylcholines (PCs), sphingomyelins and hexose. Associations between metabolite concentrations and the fat free mass index (FFMI) were analysed using adjusted linear regression models. To draw conclusions on enzymatic reactions, intra-metabolite class ratios were explored. Pairwise relationships among metabolites were investigated and illustrated by means of Gaussian graphical models (GGMs).

RESULTS

We found 339 significant associations between FFMI and various metabolites in KORA S4. Among the most prominent associations (p-values 4.75 × 10(-16)-8.95 × 10(-06)) with higher FFMI were increasing concentrations of the branched chained amino acids (BCAAs), ratios of BCAAs to glucogenic amino acids, and carnitine concentrations. For various PCs, a decrease in chain length or in saturation of the fatty acid moieties could be observed with increasing FFMI, as well as an overall shift from acyl-alkyl PCs to diacyl PCs. These findings were reproduced in KORA F4. The established GGMs supported the regression results and provided a comprehensive picture of the relationships between metabolites. In a sub-analysis, most of the discovered associations did not exist in obese subjects in contrast to non-obese subjects, possibly indicating derangements in skeletal muscle metabolism.

CONCLUSION

A set of serum metabolites strongly associated with FFMI was identified and a network explaining the relationships among metabolites was established. These results offer a novel and more complete picture of the FFMI effects on serum metabolites in a data-driven network.