Metabolomics: unraveling the chemical individuality of common human diseases

Genetic regulation of carnitine metabolism controls lipid damage repair and aging RBC hemolysis in vivo and in vitro

ABSTRACT

Recent large-scale multiomics studies suggest that genetic factors influence the chemical individuality of donated blood. To examine this concept, we performed metabolomics analyses of 643 blood units from volunteers who donated units of packed red blood cells (RBCs) on 2 separate occasions. These analyses identified carnitine metabolism as the most reproducible pathway across multiple donations from the same donor. We also measured l-carnitine and acyl-carnitines in 13 091 packed RBC units from donors in the Recipient Epidemiology and Donor Evaluation study. Genome-wide association studies against 879 000 polymorphisms identified critical genetic factors contributing to interdonor heterogeneity in end-of-storage carnitine levels, including common nonsynonymous polymorphisms in genes encoding carnitine transporters (SLC22A16, SLC22A5, and SLC16A9); carnitine synthesis (FLVCR1 and MTDH) and metabolism (CPT1A, CPT2, CRAT, and ACSS2), and carnitine-dependent repair of lipids oxidized by ALOX5. Significant associations between genetic polymorphisms on SLC22 transporters and carnitine pools in stored RBCs were validated in 525 Diversity Outbred mice. Donors carrying 2 alleles of the rs12210538 SLC22A16 single-nucleotide polymorphism exhibited the lowest l-carnitine levels, significant elevations of in vitro hemolysis, and the highest degree of vesiculation, accompanied by increases in lipid peroxidation markers. Separation of RBCs by age, via in vivo biotinylation in mice, and Percoll density gradients of human RBCs, showed age-dependent depletions of l-carnitine and acyl-carnitine pools, accompanied by progressive failure of the reacylation process after chemically induced membrane lipid damage. Supplementation of stored murine RBCs with l-carnitine boosted posttransfusion recovery, suggesting this could represent a viable strategy to improve RBC storage quality.

Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures

The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry and the machine learning approach to analyze metabolites in 173 pairs of cancer samples and matched normal tissue samples to build robust metabolic signature models for diagnostic purposes. Screening and independent validation of metabolic signatures from colorectal cancers via machine learning methods (Logistic Regression_L1 for feature selection and eXtreme Gradient Boosting for classification) was performed to generate a panel of seven signatures with good diagnostic performance (the accuracy of 87.74%, sensitivity of 85.82%, and specificity of 89.66%). Moreover, seven signatures were evaluated according to their ability to distinguish between cancer and normal tissues, with the metabolic molecule PC (30:0) showing good diagnostic performance. In addition, genes associated with PC (30:0) were identified by multiomics analysis (combining metabolic data with transcriptomic data analysis) and our results showed that PC (30:0) could promote the proliferation of colorectal cancer cell SW480, revealing the correlation between genetic changes and metabolic dysregulation in cancer. Overall, our results reveal potential determinants affecting metabolite dysregulation, paving the way for a mechanistic understanding of altered tissue metabolites in colorectal cancer and design interventions for manipulating the levels of circulating metabolites.

Metabolomics: A useful tool for ischemic stroke research

Ischemic stroke (IS) is a multifactorial and heterogeneous disease. Despite years of studies, effective strategies for the diagnosis, management and treatment of stroke are still lacking in clinical practice. Metabolomics is a growing field in systems biology. It is starting to show promise in the identification of biomarkers and in the use of pharmacometabolomics to help patients with certain disorders choose their course of treatment. The development of metabolomics has enabled further and more biological applications. Particularly, metabolomics is increasingly being used to diagnose diseases, discover new drug targets, elucidate mechanisms, and monitor therapeutic outcomes and its potential effect on precision medicine. In this review, we reviewed some recent advances in the study of metabolomics as well as how metabolomics might be used to identify novel biomarkers and understand the mechanisms of IS. Then, the use of metabolomics approaches to investigate the molecular processes and active ingredients of Chinese herbal formulations with anti-IS capabilities is summarized. We finally summarized recent developments in single cell metabolomics for exploring the metabolic profiles of single cells. Although the field is relatively young, the development of single cell metabolomics promises to provide a powerful tool for unraveling the pathogenesis of IS.

Metabolic signatures of cardiorenal dysfunction in plasma from sickle cell patients, as a function of therapeutic transfusion and hydroxyurea treatment

Metabolomics studies in sickle cell disease (SCD) have been so far limited to tens of samples, owing to technical and experimental limitations. To overcome these limitations, we performed plasma metabolomics analyses on 596 samples from patients with sickle cell sickle cell disease (SCD) enrolled in the WALK-PHaSST study. Clinical covariates informed the biological interpretation of metabolomics data, including genotypes (hemoglobin SS, hemoglobin SC), history of recent transfusion (HbA%), response to hydroxyurea treatment (HbF%). We investigated metabolic correlates to the degree of hemolysis, cardiorenal function, as determined by tricuspid regurgitation velocity (TRV), estimated glomerular filtration rate (eGFR), and overall hazard ratio (unadjusted or adjusted by age). Recent transfusion events or hydroxyurea treatment were associated with elevation in plasma free fatty acids and decreases in acyl-carnitines, urate, kynurenine, indoles, carboxylic acids, and glycine- or taurine-conjugated bile acids. High levels of these metabolites, along with low levels of plasma S1P and L-arginine were identified as top markers of hemolysis, cardiorenal function (TRV, eGFR), and overall hazard ratio. We thus uploaded all omics and clinical data on a novel online portal that we used to identify a potential mechanism of dysregulated red cell S1P synthesis and export as a contributor to the more severe clinical manifestations in patients with the SS genotype compared to SC. In conclusion, plasma metabolic signatures - including low S1P, arginine and elevated kynurenine, acyl-carnitines and bile acids - are associated with clinical manifestation and therapeutic efficacy in SCD patients, suggesting new avenues for metabolic interventions in this patient population.

Role of kisspeptin in polycystic ovarian syndrome: A metabolomics study

OBJECTIVE

Polycystic ovary syndrome (PCOS) is a pathophysiological disease affecting reproductive and metabolic indicators. Research has shown that kisspeptin might be involved in the regulation of pituitary hormone secretion and energy metabolism. The aim of this study was to investigate the relationship between serum kisspeptin levels and abnormal metabolism in PCOS.

METHODS

Fifty patients with PCOS and 50 control patients were recruited for this study. Serum kisspeptin levels were measured via ELISA. High-performance liquid chromatography-tandem mass spectrometry metabolomics was used to study the changes in serum metabolism between the PCOS and control groups.

RESULTS

Serum kisspeptin levels were significantly elevated in individuals with PCOS compared with those in healthy controls (p = 0.011) and positively correlated with LH, T, FFA, BA, and LEP levels (p < 0.05). Significantly dysregulated expression of several metabolites was observed in the intergroup comparisons of the high-kisspeptin PCOS, low-kisspeptin PCOS, and healthy control groups. These primarily consisted of lipid, amino acid, and carbohydrate metabolites, among which palmitic acid and N-formylkynurenine levels were lower in the high-kisspeptin group than in controls. Metabolite set enrichment analysis was also performed based on metabolites in the KEGG database. The results showed that owing to the differences in kisspeptin concentrations in individuals with PCOS, there was a significant difference in amino acid and pyruvate metabolism.

CONCLUSIONS

Kisspeptin could be a potential biomarker for the diagnosis of PCOS and plays an important role in metabolic regulation in individuals with PCOS. In addition, metabolomics provides a promising method for the study of metabolic abnormalities in individuals with PCOS, which might contribute to our understanding of its mechanisms.

Multiomics Analysis-Based Biomarkers in Diagnosis of Polycystic Ovary Syndrome

Polycystic ovarian syndrome is an utmost communal endocrine, psychological, reproductive, and metabolic disorder that occurs in women of reproductive age with extensive range of clinical manifestations. This may even lead to long-term multiple morbidities including obesity, diabetes mellitus, insulin resistance, cardiovascular disease, infertility, cerebrovascular diseases, and ovarian and endometrial cancer. Women affliction from PCOS in midst assemblage of manifestations allied with menstrual dysfunction and androgen exorbitance, which considerably affects eminence of life. PCOS is recognized as a multifactorial disorder and systemic syndrome in first-degree family members; therefore, the etiology of PCOS syndrome has not been copiously interpreted. The disorder of PCOS comprehends numerous allied health conditions and has influenced various metabolic processes. Due to multifaceted pathophysiology engaging several pathways and proteins, single genetic diagnostic tests cannot be supportive to determine in straight way. Clarification of cellular and biochemical pathways and various genetic players underlying PCOS could upsurge our consideration of pathophysiology of this syndrome. It is requisite to know pathophysiological relationship between biomarker and their reflection towards PCOS disease. Biomarkers deliver vibrantly and potent ways to apprehend the spectrum of PCOS with applications in screening, diagnosis, characterization, and monitoring. This paper relies on the endeavor to point out many candidates as potential biomarkers based on omics technologies, thus highlighting correlation between PCOS disease with innovative technologies. Therefore, the objective of existing review is to encapsulate more findings towards cutting-edge advances in prospective use of biomarkers for PCOS disease. Discussed biomarkers may be fruitful in guiding therapies, addressing disease risk, and predicting clinical outcomes in future.

A review of study designs and data analyses in metabolomics studies in myopia

Metabolomics analyzes the entire range of small molecule metabolites in biological systems to reveal the response signals that are transmitted from "genetics and environment", which could help us understand complex phenotypes of diseases. Metabolomics has been successfully applied to the study of eye diseases including age-related macular degeneration, glaucoma, and diabetic retinopathy. In this review, we summarize the findings of myopic metabolomics and discuss them from a design and analysis perspective. Finally, we provide new ideas for the future development of myopia metabolomics research based on the broader ocular metabolomics study.

Advancing Cancer Treatment by Targeting Glutamine Metabolism-A Roadmap

Tumor growth and metastasis strongly depend on adapted cell metabolism. Cancer cells adjust their metabolic program to their specific energy needs and in response to an often challenging tumor microenvironment. Glutamine metabolism is one of the metabolic pathways that can be successfully targeted in cancer treatment. The dependence of many hematological and solid tumors on glutamine is associated with mitochondrial glutaminase (GLS) activity that enables channeling of glutamine into the tricarboxylic acid (TCA) cycle, generation of ATP and NADPH, and regulation of glutathione homeostasis and reactive oxygen species (ROS). Small molecules that target glutamine metabolism through inhibition of GLS therefore simultaneously limit energy availability and increase oxidative stress. However, some cancers can reprogram their metabolism to evade this metabolic trap. Therefore, the effectiveness of treatment strategies that rely solely on glutamine inhibition is limited. In this review, we discuss the metabolic and molecular pathways that are linked to dysregulated glutamine metabolism in multiple cancer types. We further summarize and review current clinical trials of glutaminolysis inhibition in cancer patients. Finally, we put into perspective strategies that deploy a combined treatment targeting glutamine metabolism along with other molecular or metabolic pathways and discuss their potential for clinical applications.

Omics technologies in personalized combination therapy for cardiovascular diseases: challenges and opportunities

The primary purpose of 'omics' technologies is to understand the intricacy of genomics, proteomics, metabolomics and other molecular mechanisms to reveal the complex traits of human diseases. The significant use of omics technologies and their applications in medicine gear up the study of the pathogenesis of several disorders. The detection of biomarkers in the early onset of diseases is challenging; still, omics can discover novel molecular mechanisms and biomarkers. In this review, the different types of omics and their technologies are explicated and aimed to provide their emerging applications in cardiovascular precision medicine. These technologies significantly impact optimizing medical treatment for individuals to reach a higher level in precision medicine.

Untargeted metabolomic approach to study the serum metabolites in women with polycystic ovary syndrome

BACKGROUND

Polycystic ovary syndrome (PCOS) is not only a kind of common endocrine syndrome but also a metabolic disorder, which harms the reproductive system and the whole body metabolism of the PCOS patients worldwide. In this study, we aimed to investigate the differences in serum metabolic profiles of the patients with PCOS compared to the healthy controls.

MATERIAL AND METHODS

31 PCOS patients and 31 matched healthy female controls were recruited in this study, the clinical characteristics data were recorded, the laboratory biochemical data were detected. Then, we utilized the metabolomics approach by UPLC-HRMS technology to study the serum metabolic changes between PCOS and controls.

RESULTS

The metabolomics analysis showed that there were 68 downregulated and 78 upregulated metabolites in PCOS patients serum compared to those in the controls. These metabolites mainly belong to triacylglycerols, glycerophosphocholines, acylcarnitines, diacylglycerols, peptides, amino acids, glycerophosphoethanolamines and fatty acid. Pathway analysis showed that these metabolites were enriched in pathways including glycerophospholipid metabolism, fatty acid degradation, fatty acid biosynthesis, ether lipid metabolism, etc. Diagnosis value assessed by ROC analysis showed that the changed metabolites, including Leu-Ala/Ile-Ala, 3-(4-Hydroxyphenyl) propionic acid, Ile-Val/Leu-Val, Gly-Val/Val-Gly, aspartic acid, DG(34:2)_DG(16:0/18:2), DG(34:1)_DG(16:0/18:1), Phe-Trp, DG(36:1)_DG(18:0/18:1), Leu-Leu/Leu-Ile, had higher AUC values, indicated a significant role in PCOS.

CONCLUSION

The present study characterized the difference of serum metabolites and related pathway profiles in PCOS patients, this finding hopes to provide potential metabolic markers for the prognosis and diagnosis of this disease.

UHPLC-MS-based metabolomics and chemoinformatics study reveals the neuroprotective effect and chemical characteristic in Parkinson's disease mice after oral administration of Wen-Shen-Yang-Gan decoction

Parkinson's disease (PD), the typical neurodegenerative disease, is characterized by the progressive loss of dopaminergic neurons in the substantia nigra (SN). However, no therapeutic agent used currently could slow down neuronal cell loss so as to decelerate or halt the progression of PD. Traditional Chinese medicine (TCM) has been utilized to treat the dysfunction of the autonomic nervous system. Wen-Shen-Yang-Gan decoction (WSYGD) has a good effect on the clinical treatment of PD with constipation. However, it is not clear which ingredients and what mechanism are responsible for the therapeutic effect. In this study, the pharmacodynamic study of WSYGD in PD mice was applied. Concurrently, a novel method for the identification of metabolic profiles of WSYGD has been developed. Finally, we found that WSYGD could protect the PD mice induced by rotenone. The underlying mechanism of the protective effect may be related to the reduction of the DA neurons apoptosis via reducing inflammatory reaction. By virtue of UPLC-MS and chemoinformatics method, 35 prototype compounds and 27 metabolites were filtered out and tentatively characterized. In conclusion, this study provides an insight into the metabolism of WSYGD in vivo to enable understanding of the metabolic process and therapeutic mechanism of PD.

Individual variability in human urinary metabolites identifies age-related, body mass index-related, and sex-related biomarkers

Background

Metabolites present in human urine can be influenced by individual physiological parameters (e.g., body mass index [BMI], age, and sex). Observation of altered metabolites concentrations could provide insight into underlying disease pathology, disease prognosis and diagnosis, and facilitate discovery of novel biomarkers.

Methods

Quantitative metabolomics analysis in the urine of 183 healthy individuals was performed based on high‐resolution liquid chromatography–mass spectrometry (LC–MS). Coefficients of variation were obtained for 109 urine metabolites of all the 183 human healthy subjects.

Results

Three urine metabolites (such as dehydroepiandrosterone sulfate, acetaminophen glucuronide, and p‐anisic acid) with CV₁₈₃ > 0.3, for which metabolomics studies have been scarce, are considered highly variable here. We identified 30 age‐related metabolites, 18 BMI‐related metabolites, and 42 sex‐related metabolites. Among the identified metabolites, three metabolites were found to be associated with all three physiological parameters (age, BMI, and sex), which included dehydroepiandrosterone sulfate, 3‐methylcrotonylglycine and N‐acetyl‐aspartic acid. Pearson's coefficients demonstrated that some age‐, BMI‐, and sex‐related compounds are strongly correlated, suggesting that age, BMI, and sex could affect them concomitantly.

Conclusion

Metabolic differences between distinct physiological statuses were found to be related to several metabolic pathways (such as the caffeine metabolism, the amino acid metabolism, and the carbohydrate metabolism), and these findings may be key for the discovery of new diagnostics and treatments as well as new understandings on the mechanisms of some related diseases.

Application of omics- and multi-omics-based techniques for natural product target discovery

Natural products continue to be an unparalleled source of pharmacologically active lead compounds because of their unprecedented structures and unique biological activities. Natural product target discovery is a vital component of natural product-based medicine translation and development and is required to understand and potentially reduce mechanisms that may be associated with off-target side effects and toxicity. Omics-based techniques, including genomics, transcriptomics, proteomics, metabolomics, and bioinformatics, have become recognized as effective tools needed to construct innovative strategies to discover natural product targets. Although considerable progress has been made, the successful discovery of natural product targets remains a challenging time-consuming process that has come to increasingly rely on the effective integration of multi-omics-based technologies to create emerging panomics (a.k.a., integrative omics, pan-omics, multiomics)-based strategies. This review summarizes a series of successful studies regarding the application of integrative omics-based methods in natural product target discovery. The advantages and disadvantages of each technique are discussed, with a particular focus on the systematic integration of multi-omics strategies. Further, emerging micro-scale single-cell-based techniques are introduced, especially to deal with minute natural product samples.

Supercritical fluid chromatography - Mass spectrometry in metabolomics: Past, present, and future perspectives

Metabolomics, which consists of the comprehensive analysis of metabolites within a biological system, has been playing a growing role in the implementation of personalized medicine in modern healthcare. A wide range of analytical approaches are used in metabolomics, notably mass spectrometry (MS) combined to liquid chromatography (LC), gas chromatography (GC), or capillary electrophoresis (CE). However, none of these methods enable a comprehensive analysis of the metabolome, due to its extreme complexity and the large differences in physico-chemical properties between metabolite classes. In this context, supercritical fluid chromatography (SFC) represents a promising alternative approach to improve the metabolome coverage, while further increasing the analysis throughput. SFC, which uses supercritical CO₂ as mobile phase, leads to numerous advantages such as improved kinetic performance and lower environmental impact. This chromatographic technique has gained a significant interest since the introduction of advanced instrumentation, together with the introduction of dedicated interfaces for hyphenating SFC to MS. Moreover, new developments in SFC column chemistry (including sub-2 µm particles), as well as the use of large amounts of organic modifiers and additives in the CO₂-based mobile phase, significantly extended the application range of SFC, enabling the simultaneous analysis of a large diversity of metabolites. Over the last years, several applications have been reported in metabolomics using SFC-MS - from lipophilic compounds, such as steroids and other lipids, to highly polar compounds, such as carbohydrates, amino acids, or nucleosides. With all these advantages, SFC-MS is promised to a bright future in the field of metabolomics.

Capillary ultrahigh-pressure liquid chromatography-mass spectrometry for fast and high resolution metabolomics separations

LC-MS is an important tool for metabolomics due its high sensitivity and broad metabolite coverage. The goal of improving resolution and decreasing analysis time in HPLC has led to the use of 5 - 15 cm long columns packed with 1.7 - 1.9 µm particles requiring pressures of 8 - 12 kpsi. We report on the potential for capillary LC-MS based metabolomics utilizing porous C18 particles down to 1.1 µm diameter and columns up to 50 cm long with an operating pressure of 35 kpsi. Our experiments show that it is possible to pack columns with 1.1 µm porous particles to provide predicted improvements in separation time and efficiency. Using kinetic plots to guide the choice of column length and particle size, we packed 50 cm long columns with 1.7 µm particles and 20 cm long columns with 1.1 µm particles, which should produce equivalent performance in shorter times. Columns were tested by performing isocratic and gradient LC-MS analyses of small molecule metabolites and extracts from plasma. These columns provided approximately 100,000 theoretical plates for metabolite standards and peak capacities over 500 in 100 min for a complex plasma extract with robust interfacing to MS. To generate a given peak capacity, the 1.1 µm particles in 20 cm columns required roughly 75% of the time as 1.7 µm particles in 50 cm columns with both operated at 35 kpsi. The 1.1 µm particle packed columns generated a given peak capacity nearly 3 times faster than 1.7 µm particles in 15 cm columns operated at ~10 kpsi. This latter condition represents commercial state of the art for capillary LC. To consider practical benefits for metabolomics, the effect of different LC-MS variables on mass spectral feature detection was evaluated. Lower flow rates (down to 700 nL/min) and larger injection volumes (up to 1 µL) increased the features detected with modest loss in separation performance. The results demonstrate the potential for fast and high resolution separations for metabolomics using 1.1 µm particles operated at 35 kpsi for capillary LC-MS.

Emerging Insights into the Metabolic Alterations in Aging Using Metabolomics

Metabolomics is the latest 'omics' technology and systems biology science that allows for comprehensive profiling of small-molecule metabolites in biological systems at a specific time and condition. Metabolites are cellular intermediate products of metabolic reactions, which reflect the ultimate response to genomic, transcriptomic, proteomic, or environmental changes in a biological system. Aging is a complex biological process that is characterized by a gradual and progressive decline in molecular, cellular, tissue, organ, and organismal functions, and it is influenced by a combination of genetic, environmental, diet, and lifestyle factors. The precise biological mechanisms of aging remain unknown. Metabolomics has emerged as a powerful tool to characterize the organism phenotypes, identify altered metabolites, pathways, novel biomarkers in aging and disease, and offers wide clinical applications. Here, I will provide a comprehensive overview of our current knowledge on metabolomics led studies in aging with particular emphasis on studies leading to biomarker discovery. Based on the data obtained from model organisms and humans, it is evident that metabolites associated with amino acids, lipids, carbohydrate, and redox metabolism may serve as biomarkers of aging and/or longevity. Current challenges and key questions that should be addressed in the future to advance our understanding of the biological mechanisms of aging are discussed.

Plasma lipidomics as a diagnostic tool for peroxisomal disorders

Peroxisomes are ubiquitous cell organelles that play an important role in lipid metabolism. Accordingly, peroxisomal disorders, including the peroxisome biogenesis disorders and peroxisomal single-enzyme deficiencies, are associated with aberrant lipid metabolism. Lipidomics is an emerging tool for diagnosis, disease-monitoring, identifying lipid biomarkers, and studying the underlying pathophysiology in disorders of lipid metabolism. In this study, we demonstrate the potential of lipidomics for the diagnosis of peroxisomal disorders using plasma samples from patients with different types of peroxisomal disorders. We show that the changes in the plasma profiles of phospholipids, di- and triglycerides, and cholesterol esters correspond with the characteristic metabolite abnormalities that are currently used in the metabolic screening for peroxisomal disorders. The lipidomics approach, however, gives a much more detailed overview of the metabolic changes that occur in the lipidome. Furthermore, we identified novel unique lipid species for specific peroxisomal diseases that are candidate biomarkers. The results presented in this paper show the power of lipidomics approaches to enable the specific diagnosis of different peroxisomal disorders.

Emerging applications of metabolomics in drug discovery and precision medicine

Metabolomics is an emerging 'omics' science involving the comprehensive characterization of metabolites and metabolism in biological systems. Recent advances in metabolomics technologies are leading to a growing number of mainstream biomedical applications. In particular, metabolomics is increasingly being used to diagnose disease, understand disease mechanisms, identify novel drug targets, customize drug treatments and monitor therapeutic outcomes. This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases (such as atherosclerosis, cancer and diabetes), the growing role of metabolomics in drug discovery and its potential effect on precision medicine.

Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids

BACKGROUND

Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits.

METHODS

We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another.

RESULTS

A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci.

CONCLUSIONS

These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits.

The effects of long- or medium-chain fat diets on glucose tolerance and myocellular content of lipid intermediates in rats

Accumulation of triacylglycerols (TAGs) and acylcarnitines in skeletal muscle upon high-fat (HF) feeding is the resultant of fatty acid uptake and oxidation and is associated with insulin resistance. As medium-chain fatty acids (MCFAs) are preferentially β-oxidized over long-chain fatty acids, we examined the effects of medium-chain TAGs (MCTs) and long-chain TAGs (LCTs) on muscle lipid storage and whole-body glucose tolerance. Rats fed a low-fat (LF), HFLCT, or an isocaloric HFMCT diet displayed a similar body weight gain over 8 weeks of treatment. Only HFLCT increased myocellular TAG (42.3 ± 4.9, 71.9 ± 6.7, and 48.5 ± 6.5 µmol/g for LF, HFLCT, and HFMCT, respectively, P < 0.05) and long-chain acylcarnitine content (P < 0.05). Neither HF diet increased myocellular diacylglycerol (DAG) content. Intraperitoneal (IP) glucose tolerance tests (1.5 g/kg) revealed a significantly decreased glucose tolerance in the HFMCT compared to the HFLCT-fed rats (802 ± 40, 772 ± 18, and 886 ± 18 area under the curve for LF, HFLCT, and HFMCT, respectively, P < 0.05). Finally, no differences in myocellular insulin signaling after bolus insulin injection (10 U/kg) were observed between LF, HFLCT, or HFMCT-fed rats. These results show that accumulation of TAGs and acylcarnitines in skeletal muscle in the absence of body weight gain do not impede myocellular insulin signaling or whole-body glucose intolerance.

The use of two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOF-MS) for metabolomic analysis of polar metabolites

Metabolites produced by an organism can be quite extensive, and one analytical technique alone is not capable of their comprehensive detection and identification. The majority of environmental metabolomic studies have implemented proton nuclear magnetic resonance ((1)H-NMR) spectroscopy with little attention given to mass spectrometry (MS) techniques. In this chapter, an analytical technique is outlined that incorporates two-dimensional gas chromatography-time-of-flight MS (GC×GC-TOF-MS) for the identification and quantification of polar metabolites.

Web-enabled and improved software tools and data are needed to measure nutrient intakes and physical activity for personalized health research

Food intake, physical activity (PA), and genetic makeup each affect health and each factor influences the impact of the other 2 factors. Nutrigenomics describes interactions between genes and environment. Knowledge about the interplay between environment and genetics would be improved if experimental designs included measures of nutrient intake and PA. Lack of familiarity about how to analyze environmental variables and ease of access to tools and measurement instruments are 2 deterrents to these combined studies. This article describes the state of the art for measuring food intake and PA to encourage researchers to make their tools better known and more available to workers in other fields. Information presented was discussed during a workshop on this topic sponsored by the USDA, NIH, and FDA in the spring of 2009.