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Abstract Dietary restriction (DR) is a potent method to enhance lifespan and healthspan, but individual responses are influenced by genetic variations. Understanding how metabolism-related genetic differences impact longevity and healthspan are unclear. To investigate this, we used metabolites as markers to reveal how different genotypes respond to diet to influence longevity and healthspan traits. We analyzed data from Drosophila Genetic Reference Panel (DGRP) strains raised under AL and DR conditions, combining metabolomic, phenotypic, and genome-wide information. We employed two computational and complementary methods across species—random forest modeling within the DGRP as our primary analysis and Mendelian randomization in human cohorts as a secondary analysis. We pinpointed key traits with cross-species relevance as well as underlying heterogeneity and pleiotropy that influence lifespan and healthspan. Notably, orotate was linked to parental age at death in humans and blocked the DR lifespan extension in flies, while threonine supplementation extended lifespan, in a strain- and sex-specific manner. Thus, utilizing natural genetic variation data from flies and humans, we employed a systems biology approach to elucidate potential therapeutic pathways and metabolomic targets for diet-dependent changes in lifespan and healthspan.

Metabolomics has been used extensively to capture the exposome. We investigated whether prospectively measured metabolites provided predictive power beyond well-established risk factors among 758 women with adjudicated cancers [n = 577 breast (BC) and n = 181 colorectal (CRC)] and n = 758 controls with available specimens (collected mean 7.2 years prior to diagnosis) in the Women’s Health Initiative Bone Mineral Density subcohort. Fasting samples were analyzed by LC-MS/MS and lipidomics in serum, plus GC-MS and NMR in 24 h urine. For feature selection, we applied LASSO regression and Super Learner algorithms. Prediction models were subsequently derived using logistic regression and Super Learner procedures, with performance assessed using cross-validation (CV). For BC, metabolites did not increase predictive performance over established risk factors (CV-AUCs~0.57). For CRC, prediction increased with the addition of metabolites (median CV-AUC across platforms increased from ~0.54 to ~0.60). Metabolites related to energy metabolism: adenosine, 2-hydroxyglutarate, N-acetyl-glycine, taurine, threonine, LPC (FA20:3), acetate, and glycerate; protein metabolism: histidine, leucic acid, isoleucine, N-acetyl-glutamate, allantoin, N-acetyl-neuraminate, hydroxyproline, and uracil; and dietary/microbial metabolites: myo-inositol, trimethylamine-N-oxide, and 7-methylguanine, consistently contributed to CRC prediction. Energy metabolism may play a key role in the development of CRC and may be evident prior to disease development.

Abstract Alterations in perioperative metabolic function, particularly hyperglycemia, are associated with increased post‐operative complications, even in patients without preexisting metabolic abnormalities. Anesthetic medications and the neuroendocrine stress response to surgery may both contribute to altered energy metabolism through impaired glucose and insulin homeostasis but the discrete pathways involved are unclear. Prior human studies, though informative, have been limited by analytic sensitivity or technique, preventing resolution of underlying mechanisms. We hypothesized that general anesthesia with a volatile agent would suppress basal insulin secretion without altering hepatic insulin extraction, and that surgical stress would promote hyperglycemia through gluconeogenesis, lipid oxidation, and insulin resistance. In order to address these hypotheses, we conducted an observational study of subjects undergoing multi‐level lumbar surgery with an inhaled anesthetic agent. We measured circulating glucose, insulin, c‐peptide, and cortisol frequently throughout the perioperative period and analyzed the circulating metabolome in a subset of these samples. We found volatile anesthetic agents suppress basal insulin secretion and uncouple glucose‐stimulated insulin secretion. Following surgical stimulus, this inhibition disappeared and there was gluconeogenesis with selective amino acid metabolism. No robust evidence of lipid metabolism or insulin resistance was observed. These results show that volatile anesthetic agents suppress basal insulin secretion, which results in reduced glucose metabolism. The neuroendocrine stress response to surgery ameliorates the inhibitory effect of the volatile agent on insulin secretion and glucose metabolism, promoting catabolic gluconeogenesis. A better understanding of the complex metabolic interaction between anesthetic medications and surgical stress is needed to inform design of clinical pathways aimed at improving perioperative metabolic function.

Human blood is the most widely used biospecimen in the clinic and the metabolomics field. While both mass spectrometry and NMR spectroscopy are the two premier analytical platforms in the metabolomics field, NMR exhibits several unsurpassed characteristics for blood metabolite analysis, the most important of which are its ability to identify unknown metabolites and its quantitative nature. However, the relatively small number of metabolites accessible by NMR has restricted the scope of its applications. Enhancing the limit of identified metabolites in blood will therefore greatly impact NMR-based metabolomics. Continuing our efforts to address this major issue, our current study describes the identification of 12 metabolites, which expands the number of quantifiable blood metabolites by ∼15%. These results, in combination with our earlier efforts, now provide access to nearly 90 metabolites, which is the highest to date for a simple 1D 1H NMR experiment that is widely used in the metabolomics field. Metabolites were identified based on the comprehensive investigation of human blood and plasma using 1D/2D NMR techniques. The newly identified metabolites were validated based on chemical shift databases, spectra of authentic compounds obtained under conditions identical to blood/plasma, and, finally, spiking experiments using authentic compounds. Considering the high reproducibility of NMR and the sensitivity of chemical shifts to altered sample conditions, experimental protocols and peak annotations are provided for the newly identified metabolites, which serve as a template for identification of blood metabolites for routine applications. Separately, the identified metabolites were evaluated for their sensitivity to preanalytical conditions. The results reveal that among the newly identified metabolites, inosine monophosphate (IMP) and nicotinamide are associated with labile coenzymes and their levels are sensitive to preanalytical conditions. The study demonstrates the expansion of quantifiable blood metabolites using NMR to a new height and is expected to greatly impact blood metabolomics.

Abstract Background Infants with cystic fibrosis (CF) suffer from gastrointestinal (GI) complications, including pancreatic insufficiency and intestinal inflammation, which have been associated with impaired nutrition and growth. Recent evidence identified altered fecal microbiota taxonomic compositions in infants with CF relative to healthy infants that were characterized by differences in the abundances of taxa associated with GI health and nutrition. Furthermore, these taxonomic differences were more pronounced in low length infants with CF, suggesting a potential link to linear growth failure. We hypothesized that these differences would entail shifts in the microbiome’s functional capacities that could contribute to inflammation and nutritional failure in infants with CF. Results To test this hypothesis, we compared fecal microbial metagenomic content between healthy infants and infants with CF, supplemented with an analysis of fecal metabolomes in infants with CF. We identified notable differences in CF fecal microbial functional capacities, including metabolic and environmental response functions, compared to healthy infants that intensified during the first year of life. A machine learning-based longitudinal metagenomic age analysis of healthy and CF fecal metagenomic functional profiles further demonstrated that these differences are characterized by a CF-associated delay in the development of these functional capacities. Moreover, we found metagenomic differences in functions related to metabolism among infants with CF that were associated with diet and antibiotic exposure, and identified several taxa as potential drivers of these functional differences. An integrated metagenomic and metabolomic analysis further revealed that abundances of several fecal GI metabolites important for nutrient absorption, including three bile acids, correlated with specific microbes in infants with CF. Conclusions Our results highlight several metagenomic and metabolomic factors, including bile acids and other microbial metabolites, that may impact nutrition, growth, and GI health in infants with CF. These factors could serve as promising avenues for novel microbiome-based therapeutics to improve health outcomes in these infants.

Abstract Altered lipid metabolism has emerged as an important feature of ovarian cancer (OC), yet the translational potential of lipid metabolites to aid in diagnosis and triage remains unproven. We conducted a multi-level interrogation of lipid metabolic phenotypes in patients with adnexal masses, integrating quantitative lipidomics profiling of plasma and ascites with publicly-available tumor transcriptome data. Using Sciex Lipidyzer, we assessed concentrations of > 500 plasma lipids in two patient cohorts—(i) a pilot set of 100 women with OC (50) or benign tumor (50), and (ii) an independent set of 118 women with malignant (60) or benign (58) adnexal mass. 249 lipid species and several lipid classes were significantly reduced in cases versus controls in both cohorts (FDR

Background We showed that Beclin‐1‐dependent autophagy protects the heart in young and adult mice that underwent endotoxemia. Herein, we compared the potential therapeutic effects of Beclin‐1 activating peptide, TB‐peptide, on endotoxemia‐induced cardiac outcomes in young adult and aged mice. We further evaluated lipopolysaccharide (lipopolysaccharide)‐induced and TB‐peptide treatment‐mediated alterations in myocardial metabolism. Methods and Results C57BL/6J mice that were 10 weeks and 24 months old were challenged by lipopolysaccharide using doses at which cardiac dysfunction occurred. Following the treatment of TB‐peptide or control vehicle, heart contractility, circulating cytokines, and myocardial autophagy were evaluated. We detected that TB‐peptide boosted autophagy, attenuated cytokines, and improved cardiac performance in both young and aged mice during endotoxemia. A targeted metabolomics assay was designed to detect a pool of 361 known metabolites, of which 156 were detected in at least 1 of the heart tissue samples. Lipopolysaccharide‐induced impairments were found in glucose and amino acid metabolisms in mice of all ages, and TB‐peptide ameliorated these alterations. However, lipid metabolites were upregulated in the young group but moderately downregulated in the aged by lipopolysaccharide, suggesting an age‐dependent response. TB‐peptide mitigated lipopolysaccharide‐mediated trend of lipids in the young mice but had little effect on the aged. (Study registration: Project DOI: https://doi.org/10.21228/M8K11W). Conclusions Pharmacological activation of Beclin‐1 by TB‐peptide is cardiac protective in both young and aged population during endotoxemia, suggest a therapeutic potential for sepsis‐induced cardiomyopathy. Metabolomics analysis suggests that an age‐independent protection by TB‐peptide is associated with reprograming of energy production via glucose and amino acid metabolisms.

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.

Brain glucose hypometabolism is an early sign of Alzheimer’s disease (AD), and interventions which offset this deficit, such as ketogenic diets, show promise as AD therapeutics. Conversely, high-fat feeding may exacerbate AD risk. We analyzed the metabolomic profile of cerebrospinal fluid (CSF) in a pilot study of older adults who underwent saline and triglyceride (TG) infusions. Older adults (12 cognitively normal (CN), age 65.3 ± 8.1, and 9 with cognitive impairment (CI), age 70.9 ± 8.6) underwent a 5 h TG or saline infusion on different days using a random crossover design; CSF was collected at the end of infusion. Aqueous metabolites were measured using a targeted mass spectroscopy (MS) platform focusing on 215 metabolites from over 35 different metabolic pathways. Data were analyzed using MetaboAnalyst 4.0 and SAS. Of the 215 targeted metabolites, 99 were detectable in CSF. Only one metabolite significantly differed by treatment: the ketone body 3-hydroxybutyrate (HBA). Post hoc analyses showed that HBA levels were associated with age and markers of metabolic syndrome and demonstrated different correlation patterns for the two treatments. When analyzed by cognitive diagnosis group, TG-induced increases in HBA were over 3 times higher for those with cognitive impairment (change score CN +9.8 uM ± 8.3, CI +32.4 ± 7.4, p = 0.0191). Interestingly, individuals with cognitive impairment had higher HBA levels after TG infusion than those with normal cognition. These results suggest that interventions that increase plasma ketones may lead to higher brain ketones in groups at risk for AD and should be confirmed in larger intervention studies.

[This corrects the article DOI: 10.1371/journal.pgen.1008835.].

Abstract Background Genetic association studies that seek to explain the inheritance of complex traits typically fail to explain a majority of the heritability of the trait under study. Thus, we are left with a gap in the map from genotype to phenotype. Several approaches have been used to fill this gap, including those that attempt to map endophenotype such as the transcriptome, proteome or metabolome, that underlie complex traits. Here we used metabolomics to explore the nature of genetic variation for hydrogen peroxide (H2O2) resistance in the sequenced inbred Drosophila Genetic Reference Panel (DGRP). Results We first studied genetic variation for H2O2 resistance in 179 DGRP lines and along with identifying the insulin signaling modulator u-shaped and several regulators of feeding behavior, we estimate that a substantial amount of phenotypic variation can be explained by a polygenic model of genetic variation. We then profiled a portion of the aqueous metabolome in subsets of eight ‘high resistance’ lines and eight ‘low resistance’ lines. We used these lines to represent collections of genotypes that were either resistant or sensitive to the stressor, effectively modeling a discrete trait. Across the range of genotypes in both populations, flies exhibited surprising consistency in their metabolomic signature of resistance. Importantly, the resistance phenotype of these flies was more easily distinguished by their metabolome profiles than by their genotypes. Furthermore, we found a metabolic response to H2O2 in sensitive, but not in resistant genotypes. Metabolomic data further implicated at least two pathways, glycogen and folate metabolism, as determinants of sensitivity to H2O2. We also discovered a confounding effect of feeding behavior on assays involving supplemented food. Conclusions This work suggests that the metabolome can be a point of convergence for genetic variation influencing complex traits, and can efficiently elucidate mechanisms underlying trait variation.

In metabolomics, pathway analysis normally refers to analysis of a pre-defined sets of metabolites (metabolite set) associated to the metabolic pathways. The metabolite set analysis is useful to facilitate biological interpretation of metabolomics data. The currently available methods may be divided into three generations: over-representation analysis, functional class scoring, and network topology analysis. Among the three generations of tools, the network topology methods have been shown to have lower false discovery rates and better biological interpretability than the other two earlier generations of tools. However, most of the current network topology methods focus the analysis only at the metabolite-level network. The interaction between pathways are not taken into consideration. To address this issue, we propose a new metabolite sets association network (MSAN) modelling scheme. In the developed method, the metabolite sets are defined based on the KEGG databases. By using the metabolite sets as vertexes, the MSAN network evaluated the relationships between pairs of metabolite sets based on their mutual information. The impact of a single metabolite set on the overall network was evaluated by the MSAN network, which may help to uncover differential metabolite sets relevant to the underlying biology mechanism of the study. A metabolomic dataset from a published colorectal cancer (CRC) study is used to evaluate the performance of MSAN network to identify perturbed metabolite sets in colorectal cancer patients. The current results are compared to that of two commonly used methods, NetGSA and MetaboAnalyst, which are based on the metabolite-level network approach. The current method highlights a number of metabolites sets consistent with recent published CRC reports. Taken together, the proposed method may provide an alternative tool for the identification of dysregulated pathways and facilitate biological interpretation of metabolomics data.

Inorganic polyphosphate (polyP) is an ancient, ubiquitous, and well-conserved polymer which is present in all the studied organisms. It is formed by individual subunits of orthophosphate which are linked by structurally similar bonds and isoenergetic to those found in ATP. While the metabolism and the physiological roles of polyP have already been described in some organisms, including bacteria and yeast, the exact role of this polymer in mammalian physiology still remains poorly understood. In these organisms, polyP shows a co-localization with mitochondria, and its role as a key regulator of the stress responses, including the maintenance of appropriate bioenergetics, has already been demonstrated by our group and others. Here, using Wild-type (Wt) and MitoPPX (cells enzymatically depleted of mitochondrial polyP) SH-SY5Y cells, we have conducted a comprehensive study of the status of cellular physiology, using proteomics and metabolomics approaches. Our results suggest a clear dysregulation of mitochondrial physiology, especially of bioenergetics, in MitoPPX cells when compared with Wt cells. Moreover, the effects induced by the enzymatic depletion of polyP are similar to those present in the mitochondrial dysfunction that is observed in neurodegenerative disorders and in neuronal aging. Based on our findings, the metabolism of mitochondrial polyP could be a valid and innovative pharmacological target in these conditions.

Male germ cell (GC) production is a metabolically driven and apoptosis-prone process. Here, we show that the glucose-sensing transcription factor (TF) MAX-Like protein X (MLX) and its binding partner MondoA are both required for male fertility in the mouse, as well as survival of human tumor cells derived from the male germ line. Loss of Mlx results in altered metabolism as well as activation of multiple stress pathways and GC apoptosis in the testes. This is concomitant with dysregulation of the expression of male-specific GC transcripts and proteins. Our genomic and functional analyses identify loci directly bound by MLX involved in these processes, including metabolic targets, obligate components of male-specific GC development, and apoptotic effectors. These in vivo and in vitro studies implicate MLX and other members of the proximal MYC network, such as MNT, in regulation of metabolism and differentiation, as well as in suppression of intrinsic and extrinsic death signaling pathways in both spermatogenesis and male germ cell tumors (MGCTs).

Consumption of dietary lignans has been associated with reduced risk of chronic diseases, although the underlying mechanisms are unclear. We sought to determine if urinary excretion of ENL, the predominant microbial metabolite of dietary lignans, was associated with plasma protein abundance using a cross-sectional design based on data and plasma collected from 80 healthy participants in a randomized crossover, controlled feeding study. Proteomic analysis was performed on plasma samples collected at the end of each of two diet periods (160 samples total) using a customized antibody array corresponding to 2072 unique proteins. GC-MS was used to measure ENL excretion in 24-h urine samples collected in tandem with plasma. Linear mixed models tested the association between urinary enterolignan excretion and plasma protein abundance. Subsequently, over-representation analysis was conducted considering 17 a priori pathways with putative associations with enterolignan bioactivity in an exploratory approach. Controlling the false discovery rate at 10%, ENL excretion was inversely associated with seven proteins (FCRL5, PSCA, GAB1, LAPTM5, CCS, REG4, CEACAM1), and positively associated with two proteins (WAS and FBLN5). Over-representation analysis revealed associations of ENL excretion with estrogen and TNF signaling pathways, which were not significant after adjustment for false discovery. These ENL-associated proteins and pathways have potential implications in cancer prevention.

Mutations in the gene HADHA result in mitochondrial tri-functional protein (MTP) deficiency and can result in sudden infant death syndrome for which there is no treatment. Here the authors show that the MTP deficient pathology in human cardiomyocytes leads to an abnormal cardiolipin pattern and suggests that cardiolipin affecting compounds may serve as a potential therapy.

Objectives: Despite the widespread use of manganese (Mn) in industrial settings and its association with adverse neurological outcomes, a validated and reliable biomarker for Mn exposure is still elusive. Here, we utilize targeted metabolomics to investigate metabolic differences between Mn-exposed and -unexposed workers, which could inform a putative biomarker for Mn and lead to increased understanding of Mn toxicity.Methods: End of shift spot urine samples collected from Mn exposed (n = 17) and unexposed (n = 15) workers underwent a targeted assay of 362 metabolites using LC-MS/MS; 224 were quantified and retained for analysis. Differences in metabolite abundances between exposed and unexposed workers were tested with a Benjamini-Hochberg adjusted Wilcoxon Rank-Sum test. We explored perturbed pathways related to exposure using a pathway analysis.Results: Seven metabolites were significantly differentially abundant between exposed and unexposed workers (FDR ≤ 0.1), including n-isobutyrylglycine, cholic acid, anserine, beta-alanine, methionine, n-isovalerylglycine, and threonine. Three pathways were significantly perturbed in exposed workers and had an impact score >0.5: beta-alanine metabolism, histidine metabolism, and glycine, serine, and threonine metabolism.Conclusion: This is one of few studies utilizing targeted metabolomics to explore differences between Mn-exposed and -unexposed workers. Metabolite and pathway analysis showed amino acid metabolism was perturbed in these Mn-exposed workers. Amino acids have also been shown to be perturbed in other occupational cohorts exposed to Mn. Additional research is needed to characterize the biological importance of amino acids in the Mn exposure-disease continuum, and to determine how to appropriately utilize and interpret metabolomics data collected from occupational cohorts.

Abnormal protein aggregation within neurons is a key pathologic feature of Parkinson's disease (PD). The spread of brain protein aggregates is associated with clinical disease progression, but how this occurs remains unclear. Mutations in glucosidase, beta acid 1 (GBA), which encodes glucocerebrosidase (GCase), are the most penetrant common genetic risk factor for PD and dementia with Lewy bodies and associate with faster disease progression. To explore how GBA mutations influence pathogenesis, we previously created a Drosophila model of GBA deficiency (Gba1b) that manifests neurodegeneration and accelerated protein aggregation. Proteomic analysis of Gba1b mutants revealed dysregulation of proteins involved in extracellular vesicle (EV) biology, and we found altered protein composition of EVs from Gba1b mutants. Accordingly, we hypothesized that GBA may influence pathogenic protein aggregate spread via EVs. We found that accumulation of ubiquitinated proteins and Ref(2)P, Drosophila homologue of mammalian p62, were reduced in muscle and brain tissue of Gba1b flies by ectopic expression of wildtype GCase in muscle. Neuronal GCase expression also rescued protein aggregation both cell-autonomously in brain and non-cell-autonomously in muscle. Muscle-specific GBA expression reduced the elevated levels of EV-intrinsic proteins and Ref(2)P found in EVs from Gba1b flies. Perturbing EV biogenesis through neutral sphingomyelinase (nSMase), an enzyme important for EV release and ceramide metabolism, enhanced protein aggregation when knocked down in muscle, but did not modify Gba1b mutant protein aggregation when knocked down in neurons. Lipidomic analysis of nSMase knockdown on ceramide and glucosylceramide levels suggested that Gba1b mutant protein aggregation may depend on relative depletion of specific ceramide species often enriched in EVs. Finally, we identified ectopically expressed GCase within isolated EVs. Together, our findings suggest that GCase deficiency promotes accelerated protein aggregate spread between cells and tissues via dysregulated EVs, and EV-mediated trafficking of GCase may partially account for the reduction in aggregate spread.

Inorganic polyphosphate (polyP) is an ancient biopolymer that is well preserved throughout evolution and present in all studied organisms. In mammals, it shows a high co-localization with mitochondria, and it has been demonstrated to be involved in the homeostasis of key processes within the organelle, including mitochondrial bioenergetics. However, the exact extent of the effects of polyP on the regulation of cellular bioenergetics, as well as the mechanisms explaining these effects, still remain poorly understood. Here, using HEK293 mammalian cells under Wild-type (Wt) and MitoPPX (cells enzymatically depleted of mitochondrial polyP) conditions, we show that depletion of polyP within mitochondria increased oxidative stress conditions. This is characterized by enhanced mitochondrial O2− and intracellular H2O2 levels, which may be a consequence of the dysregulation of oxidative phosphorylation (OXPHOS) that we have demonstrated in MitoPPX cells in our previous work. These findings were associated with an increase in basal peroxiredoxin-1 (Prx1), superoxide dismutase-2 (SOD2), and thioredoxin (Trx) antioxidant protein levels. Using 13C-NMR and immunoblotting, we assayed the status of glycolysis and the pentose phosphate pathway (PPP) in Wt and MitoPPX cells. Our results show that MitoPPX cells display a significant increase in the activity of the PPP and an increase in the protein levels of transaldolase (TAL), which is a crucial component of the non-oxidative phase of the PPP and is involved in the regulation of oxidative stress. In addition, we observed a trend towards increased glycolysis in MitoPPX cells, which corroborates our prior work. Here, for the first time, we show the crucial role played by mitochondrial polyP in the regulation of mammalian redox homeostasis. Moreover, we demonstrate a significant effect of mitochondrial polyP on the regulation of global cellular bioenergetics in these cells.

In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies.

In most organisms, dietary restriction (DR) increases lifespan. However, several studies have found that genotypes within the same species vary widely in how they respond to DR. To explore the mechanisms underlying this variation, we exposed 178 inbred Drosophila melanogaster lines to a DR or ad libitum (AL) diet, and measured a panel of 105 metabolites under both diets. Twenty four out of 105 metabolites were associated with the magnitude of the lifespan response. These included proteinogenic amino acids and metabolites involved in α-ketoglutarate (α-KG)/glutamine metabolism. We confirm the role of α-KG/glutamine synthesis pathways in the DR response through genetic manipulations. We used covariance network analysis to investigate diet-dependent interactions between metabolites, identifying the essential amino acids threonine and arginine as "hub" metabolites in the DR response. Finally, we employ a novel metabolic and genetic bipartite network analysis to reveal multiple genes that influence DR lifespan response, some of which have not previously been implicated in DR regulation. One of these is CCHa2R, a gene that encodes a neuropeptide receptor that influences satiety response and insulin signaling. Across the lines, variation in an intronic single nucleotide variant of CCHa2R correlated with variation in levels of five metabolites, all of which in turn were correlated with DR lifespan response. Inhibition of adult CCHa2R expression extended DR lifespan of flies, confirming the role of CCHa2R in lifespan response. These results provide support for the power of combined genomic and metabolomic analysis to identify key pathways underlying variation in this complex quantitative trait.

Hypertrophic cardiomyocytes switch their metabolism from fatty acid oxidation to glucose use, but the functional role of this change is unclear. Here the authors show that high intracellular glucose inhibits the degradation of branched-chain amino acids, which is required for the activation of pro-growth mTOR signaling.

Colorectal cancer (CRC) tends to occur at older age; however, CRC incidence rates have been rising sharply among young age groups. The increasing prevalence of obesity is recognized as a major risk, yet the mechanistic underpinnings remain poorly understood. Using a diet-induced obesity mouse model, we identified obesity-associated molecular changes in the colonic epithelium of young and aged mice, and we further investigated whether the changes were reversed after weight loss. Transcriptome analysis indicated that obesity-related colonic cellular metabolic switch favoring long-chain fatty acid oxidation happened in young mice, while obesity-associated downregulation of negative feedback regulators of pro-proliferative signaling pathways occurred in older mice. Strikingly, colonic DNA methylome was pre-programmed by obesity at young age, priming for a tumor-prone gene signature after aging. Furthermore, obesity-related changes were substantially preserved after short-term weight loss, but they were largely reversed after long-term weight loss. We provided mechanistic insights into increased CRC risk in obesity.

Chronic kidney disease (CKD) is a public health problem with very high prevalence and mortality. Yet, there is a paucity of effective treatment options, partly due to insufficient knowledge of underlying pathophysiology. We combined metabolomics (GCMS) with kidney gene expression studies to identify metabolic pathways that are altered in adults with non-diabetic stage 3–4 CKD versus healthy adults. Urinary excretion rate of 27 metabolites and plasma concentration of 33 metabolites differed significantly in CKD patients versus controls (estimate range − 68% to +113%). Pathway analysis revealed that the citric acid cycle was the most significantly affected, with urinary excretion of citrate, cis-aconitate, isocitrate, 2-oxoglutarate and succinate reduced by 40–68%. Reduction of the citric acid cycle metabolites in urine was replicated in an independent cohort. Expression of genes regulating aconitate, isocitrate, 2-oxoglutarate and succinate were significantly reduced in kidney biopsies. We observed increased urine citrate excretion (+74%, p = 0.00009) and plasma 2-oxoglutarate concentrations (+12%, p = 0.002) in CKD patients during treatment with a vitamin-D receptor agonist in a randomized trial. In conclusion, urinary excretion of citric acid cycle metabolites and renal expression of genes regulating these metabolites were reduced in non-diabetic CKD. This supports the emerging view of CKD as a state of mitochondrial dysfunction.

In mass spectrometry (MS)-based metabolomics, missing values (NAs) may be due to different causes, including sample heterogeneity, ion suppression, spectral overlap, inappropriate data processing, and instrumental errors. Although a number of methodologies have been applied to handle NAs, NA imputation remains a challenging problem. Here, we propose a non-negative matrix factorization (NMF)-based method for NA imputation in MS-based metabolomics data, which makes use of both global and local information of the data. The proposed method was compared with three commonly used methods: k-nearest neighbors (kNN), random forest (RF), and outlier-robust (ORI) missing values imputation. These methods were evaluated from the perspectives of accuracy of imputation, retrieval of data structures, and rank of imputation superiority. The experimental results showed that the NMF-based method is well-adapted to various cases of data missingness and the presence of outliers in MS-based metabolic profiles. It outperformed kNN and ORI and showed results comparable with the RF method. Furthermore, the NMF method is more robust and less susceptible to outliers as compared with the RF method. The proposed NMF-based scheme may serve as an alternative NA imputation method which may facilitate biological interpretations of metabolomics data.

Abstract Sepsis‐associated acute kidney injury (AKI) is a significant problem in critically ill children and adults resulting in increased morbidity and mortality. Fundamental mechanisms contributing to sepsis‐associated AKI are poorly understood. Previous research has demonstrated that peroxisome proliferator‐activated receptor α (PPARα) expression is associated with reduced organ system failure in sepsis. Using an experimental model of polymicrobial sepsis, we demonstrate that mice deficient in PPARα have worse kidney function, which is likely related to reduced fatty acid oxidation and increased inflammation. Ultrastructural evaluation with electron microscopy reveals that the proximal convoluted tubule is specifically injured in septic PPARα deficient mice. In this experimental group, serum metabolomic analysis reveals unanticipated metabolic derangements in tryptophan‐kynurenine‐NAD+ and pantothenate pathways. We also show that a subgroup of children with sepsis whose genome‐wide expression profiles are characterized by repression of the PPARα signaling pathway has increased incidence of severe AKI. These findings point toward interesting associations between sepsis‐associated AKI and PPARα‐driven fatty acid metabolism that merit further investigation.

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

Nonalcoholic fatty liver disease (NAFLD) is categorized based on histological severity into nonalcoholic fatty liver (NAFL) or nonalcoholic steatohepatitis (NASH). We used a multiplatform metabolomics approach to identify metabolite markers and metabolic pathways that distinguish NAFL from early NASH and advanced NASH. We analyzed fasting serum samples from 57 prospectively-recruited patients with histologically-proven NAFLD, including 12 with NAFL, 31 with early NASH and 14 with advanced NASH. Metabolite profiling was performed using a combination of liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy analyzed with multivariate statistical and pathway analysis tools. We targeted 237 metabolites of which 158 were quantified. Multivariate analysis uncovered metabolite profile clusters for patients with NAFL, early NASH, and advanced NASH. Also, multiple individual metabolites were associated with histological severity, most notably spermidine which was more than 2-fold lower in advanced fibrosis vs. early fibrosis, in advanced NASH vs. NAFL and in advanced NASH vs. early NASH, suggesting that spermidine exercises a protective effect against development of fibrosing NASH. Furthermore, the results also showed metabolic pathway perturbations between early-NASH and advanced-NASH. In conclusion, using a combination of two reliable analytical platforms (LC-MS and NMR spectroscopy) we identified individual metabolites, metabolite clusters and metabolic pathways that were significantly different between NAFL, early-NASH, and advanced-NASH. These differences provide mechanistic insights as well as potentially important metabolic biomarker candidates that may noninvasively distinguish patients with NAFL, early-NASH, and advanced-NASH. The associations of spermidine levels with less advanced histology merit further assessment of the potential protective effects of spermidine in NAFLD.

Although the gut microbiome has been associated with dietary patterns linked to health, microbial metabolism is not well characterized. This ancillary study was a proof of principle analysis for a novel application of metaproteomics to study microbial protein expression in a controlled dietary intervention. We measured the response of the microbiome to diet in a randomized crossover dietary intervention of a whole-grain, low glycemic load diet (WG) and a refined-grain, high glycemic load diet (RG). Total proteins in stools from 9 participants at the end of each diet period (n = 18) were analyzed by LC MS/MS and proteins were identified using the Human Microbiome Project (HMP) human gut microbiome database and UniProt human protein databases. T-tests, controlling for false discovery rate (FDR)

The Warburg effect is a well-known phenomenon in cancer, but the glutamine addiction in which cancer cells utilize glutamine as an alternative source of energy is less well known. Recent efforts have focused on preventing cancer cell proliferation associated with glutamine addiction by targeting glutaminase using the inhibitor BPTES (bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)ethyl sulfide). In the current study, an investigation of the BPTES induced changes in metabolism was made in two human breast cancer cell lines, MCF7 (an estrogen receptor dependent cell line) and MDA-MB231 (a triple negative cell line), relative to the non-cancerous cell line, MCF10A. NMR spectroscopy combined with a recently established smart-isotope tagging approach enabled quantitative analysis of 41 unique metabolites representing numerous metabolite classes including carbohydrates, amino acids, carboxylic acids and nucleotides. BPTES induced metabolism changes in the cancer cell lines were especially pronounced under hypoxic conditions with up to 1/3 of the metabolites altered significantly (p < 0.05) relative to untreated cells. The BPTES induced changes were more pronounced for MCF7 cells, with 14 metabolites altered significantly (p < 0.05) compared to seven for MDA-MB231. Analyses of the results indicate that BPTES affected numerous metabolic pathways including glycolysis, TCA cycle, nucleotide and amino acid metabolism in cancer. The distinct metabolic responses to BPTES treatment determined in the two breast cancer cell lines offer valuable metabolic information for the exploration of the therapeutic responses to breast cancer.

Birt-Hogg-Dube' Syndrome (BHDS) is a rare genetic disorder in humans characterized by skin hamartomas, lung cysts, pneumothorax, and increased risk of renal tumors. BHDS is caused by mutations in the BHD gene, which encodes for Folliculin, a cytoplasmic adapter protein that binds to Folliculin interacting proteins-1 and -2 (Fnip1, Fnip2) as well as the master energy sensor AMP kinase (AMPK). Whereas kidney-specific deletion of the Bhd gene in mice is known to result in polycystic kidney disease (PKD) and renal cell carcinoma, the roles of Fnip1 in renal cell development and function are unclear. In this study, we utilized mice with constitutive deletion of the Fnip1 gene to show that the loss of Fnip1 is sufficient to result in renal cyst formation, which was characterized by decreased AMPK activation, increased mTOR activation, and metabolic hyperactivation. Using RNAseq, we found that Fnip1 disruption resulted in many cellular and molecular changes previously implicated in the development of PKD in humans, including alterations in the expression of ion and amino acid transporters, increased cell adhesion, and increased inflammation. Loss of Fnip1 synergized with Tsc1 loss to hyperactivate mTOR, increase Erk activation, and greatly accelerate the development of PKD. Our results collectively define roles for Fnip1 in regulating kidney development and function, and provide a model for how loss of Fnip1 contributes to PKD and perhaps renal cell carcinoma.

Oral cavity squamous cell carcinoma (OCC) and oropharyngeal squamous cell carcinoma (OPC) are among the most common cancers worldwide and are associated with high mortality and morbidity. The purpose of this study is to identify potential biomarkers to distinguish OCC/OPC from normal controls and to distinguish OCC patients with and without nodal metastasis. We tested saliva samples from 101 OCC, 58 OPC, and 35 normal controls using four analytical platforms (NMR, targeted aqueous by LC-MS/MS, global aqueous and global lipidomics by LC-Q-TOF). Samples from OCC and normal controls were divided into discovery and validation sets. Using linear regression adjusting for age, sex, race and experimental batches, we found the levels of two metabolites (glycine and proline) to be significantly different between OCC and controls (FDR < 0.1 for both discovery and validation sets) but did not find any appreciable differences in metabolite levels between OPC and controls or between OCC with and without nodal metastasis. Four metabolites, including glycine, proline, citrulline, and ornithine were associated with early stage OCC in both discovery and validation sets. Further study is warranted to confirm these results in the development of salivary metabolites as diagnostic markers.

OBJECTIVES:The co-primary objectives of this study were to determine the human pharmacokinetics (PK) of oral NR and the effect of NR on whole blood nicotinamide adenine dinucleotide (NAD+) levels. BACKGROUND:Though mitochondrial dysfunction plays a critical role in the development and progression of heart failure, no mitochondria-targeted therapies have been translated into clinical practice. Recent murine studies have reported associations between imbalances in the NADH/NAD+ ratio with mitochondrial dysfunction in multiple tissues, including myocardium. Moreover, an NAD+ precursor, nicotinamide mononucleotide, improved cardiac function, while another NAD+ precursor, nicotinamide riboside (NR), improved mitochondrial function in muscle, liver and brown adipose. Thus, PK studies of NR in humans is critical for future clinical trials. METHODS:In this non-randomized, open-label PK study of 8 healthy volunteers, 250 mg NR was orally administered on Days 1 and 2, then uptitrated to peak dose of 1000 mg twice daily on Days 7 and 8. On the morning of Day 9, subjects completed a 24-hour PK study after receiving 1000 mg NR at t = 0. Whole-blood levels of NR, clinical blood chemistry, and NAD+ levels were analyzed. RESULTS:Oral NR was well tolerated with no adverse events. Significant increases comparing baseline to mean concentrations at steady state (Cave,ss) were observed for both NR (p = 0.03) and NAD+ (p = 0.001); the latter increased by 100%. Absolute changes from baseline to Day 9 in NR and NAD+ levels correlated highly (R2 = 0.72, p = 0.008). CONCLUSIONS:Because NR increases circulating NAD+ in humans, NR may have potential as a therapy in patients with mitochondrial dysfunction due to genetic and/or acquired diseases.

There is a lack of experimental reference materials and standards for metabolomics measurements, such as urine, plasma, and other human fluid samples. Reasons include difficulties with supply, distribution, and dissemination of information about the materials. Additionally, there is a long lead time because reference materials need their compositions to be fully characterized with uncertainty, a labor-intensive process for material containing thousands of relevant compounds. Furthermore, data analysis can be hampered by different methods using different software by different vendors. In this work, we propose an alternative implementation of reference materials. Instead of characterizing biological materials based on their composition, we propose using untargeted metabolomic data such as nuclear magnetic resonance (NMR) or gas and liquid chromatography-mass spectrometry (GC-MS and LC-MS) profiles. The profiles are then distributed with the material accompanying the certificate, so that researchers can compare their own metabolomic measurements with the reference profiles. To demonstrate this approach, we conducted an interlaboratory study (ILS) in which seven National Institute of Standards and Technology (NIST) urine Standard Reference Material®s (SRM®s) were distributed to participants, who then returned the metabolomic data to us. We then implemented chemometric methods to analyze the data together to estimate the uncertainties in the current measurement techniques. The participants identified similar patterns in the profiles that distinguished the seven samples. Even when the number of spectral features is substantially different between platforms, a collective analysis still shows significant overlap that allows reliable comparison between participants. Our results show that a urine suite such as that used in this ILS could be employed for testing and harmonization among different platforms. A limited quantity of test materials will be made available for researchers who are willing to repeat the protocols presented here and contribute their data.

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

Cellular senescence displays a heterogeneous set of phenotypes linked to tumor suppression; however, after drug treatment, senescence may also be involved in stable or recurrent cancer. Metabolic changes during senescence can provide detailed information on cellular status and may also have implications for the development of effective treatment strategies. The metabolic response to Adriamycin (ADR) treatment, which causes senescence as well as cell death, was obtained with the aid of metabolic profiling and isotope tracing in two human breast cancer cell lines, MCF7 and MDA-MB-231. After 5 days of ADR treatment, more than 60% of remaining, intact cells entered into a senescent state, characterized by enlarged and flattened morphology and positive blue staining using SA-β-gal. Metabolic trajectory analysis showed that the two cell lines’ responses were significantly different and were divided into two distinct stages. The metabolic shift from the first stage to the second was reflected by a partial recovery of the TCA cycle, as well as amino acid and lipid metabolisms. Isotope tracing analysis indicated that the higher level of glutamine metabolism helped maintain senescence. The results suggest that the dynamic changes during senescence indicate a multi-step process involving important metabolic pathways which might allow breast cancer cells to adapt to persistent ADR treatment, while the higher level of anapleurosis may be important for maintaining the senescent state. Ultimately, a better understanding of metabolic changes during senescence might provide targets for cancer therapy and tumor eradication.

The complementary use of liquid chromatography (LC) and nuclear magnetic resonance (NMR) has shown high utility in a variety of fields. While the significant benefit of spectral simplification can be achieved for the analysis of complex samples, other limitations remain. For example, 1H LC-NMR suffers from pH dependent chemical shift variations, especially during urine analysis, owing to the high physiological variation of urine pH. Additionally, large solvent signals from the mobile phase in LC can obscure lower intensity signals and severely limit the number of metabolites detected. These limitations, along with sample dilution, hinder the ability to make reliable chemical shift assignments. Recently, stable isotopic labeling has been used to detect quantitatively specific classes of metabolites of interest in biofluids. Here we present a strategy that explores the combined use of two-dimensional hydrophilic interaction chromatography (HILIC) and isotope tagged NMR for the unambiguous identification of carboxyl containing metabolites present in human urine. The ability to separate structurally related compounds chromatographically, in off-line mode, followed by detection using 1H-15N 2D HSQC (two-dimensional heteronuclear single quantum coherence) spectroscopy, resulted in the assignment of low concentration carboxyl-containing metabolites from a library of isotope labeled compounds. The quantitative nature of this strategy is also demonstrated.

Dereplication based on hyphenated techniques has been extensively applied in plant metabolomics, avoiding re-isolation of known natural products. However, due to the complex nature of biological samples and their large concentration range, dereplication requires the use of chemometric tools to comprehensively extract information from the acquired data. In this work we developed a reliable GC-MS-based method for the identification of non-targeted plant metabolites by combining the Ratio Analysis of Mass Spectrometry deconvolution tool (RAMSY) with Automated Mass Spectral Deconvolution and Identification System software (AMDIS). Plants species from Solanaceae, Chrysobalanaceae and Euphorbiaceae were selected as model systems due to their molecular diversity, ethnopharmacological potential and economical value. The samples were analyzed by GC-MS after methoximation and silylation reactions. Dereplication initiated with the use of a factorial design of experiments to determine the best AMDIS configuration for each sample, considering linear retention indices and mass spectral data. A heuristic factor (CDF, compound detection factor) was developed and applied to the AMDIS results in order to decrease the false-positive rates. Despite the enhancement in deconvolution and peak identification, the empirical AMDIS method was not able to fully deconvolute all GC-peaks, leading to low MF values and/or missing metabolites. RAMSY was applied as a complementary deconvolution method to AMDIS to peaks exhibiting substantial overlap, resulting in recovery of low-intensity co-eluted ions. The results from this combination of optimized AMDIS with RAMSY attested to the ability of this approach as an improved dereplication method for complex biological samples such as plant extracts.

Hepatocellular carcinoma (HCC) accounts for most liver cancer cases worldwide. Contraction of the hepatitis C virus (HCV) is considered a major risk factor for liver cancer. In order to identify the risk of cancer, metabolic profiling of serum samples from patients with HCC (n=40) and HCV (n=22) was performed by 1H nuclear magnetic resonance spectroscopy. Multivariate statistical analysis showed a distinct separation of the two patient cohorts, indicating a distinct metabolic difference between HCC and HCV patient groups based on signals from lipids and other individual metabolites. Univariate analysis showed that three metabolites (choline, valine and creatinine) were significantly altered in HCC. A PLS-DA model based on these three metabolites showed a sensitivity of 80%, specificity of 71% and an area under the receiver operating curve of 0.83, outperforming the clinical marker alpha-fetoprotein (AFP). The robustness of the model was tested using Monte-Carlo cross validation (MCCV). This study showed that metabolite profiling could provide an alternative approach for HCC screening in HCV patients, many of whom have high risk for developing liver cancer.

ABSTRACT Bacterial vaginosis (BV) is characterized by shifts in the vaginal microbiota from Lactobacillus dominant to a microbiota with diverse anaerobic bacteria. Few studies have linked specific metabolites with bacteria found in the human vagina. Here, we report dramatic differences in metabolite compositions and concentrations associated with BV using a global metabolomics approach. We further validated important metabolites using samples from a second cohort of women and a different platform to measure metabolites. In the primary study, we compared metabolite profiles in cervicovaginal lavage fluid from 40 women with BV and 20 women without BV. Vaginal bacterial representation was determined using broad-range PCR with pyrosequencing and concentrations of bacteria by quantitative PCR. We detected 279 named biochemicals; levels of 62% of metabolites were significantly different in women with BV. Unsupervised clustering of metabolites separated women with and without BV. Women with BV have metabolite profiles marked by lower concentrations of amino acids and dipeptides, concomitant with higher levels of amino acid catabolites and polyamines. Higher levels of the signaling eicosanoid 12-hydroxyeicosatetraenoic acid (12-HETE), a biomarker for inflammation, were noted in BV. Lactobacillus crispatus and Lactobacillus jensenii exhibited similar metabolite correlation patterns, which were distinct from correlation patterns exhibited by BV-associated bacteria. Several metabolites were significantly associated with clinical signs and symptoms (Amsel criteria) used to diagnose BV, and no metabolite was associated with all four clinical criteria. BV has strong metabolic signatures across multiple metabolic pathways, and these signatures are associated with the presence and concentrations of particular bacteria. IMPORTANCE Bacterial vaginosis (BV) is a common but highly enigmatic condition that is associated with adverse outcomes for women and their neonates. Small molecule metabolites in the vagina may influence host physiology, affect microbial community composition, and impact risk of adverse health outcomes, but few studies have comprehensively studied the metabolomics profile of BV. Here, we used mass spectrometry to link specific metabolites with particular bacteria detected in the human vagina by PCR. BV was associated with strong metabolic signatures across multiple pathways affecting amino acid, carbohydrate, and lipid metabolism, highlighting the profound metabolic changes in BV. These signatures were associated with the presence and concentrations of particular vaginal bacteria, including some bacteria yet to be cultivated, thereby providing clues as to the microbial origin of many metabolites. Insights from this study provide opportunities for developing new diagnostic markers of BV and novel approaches for treatment or prevention of BV.

BACKGROUND: Esophageal adenocarcinoma (EAC) is a rarely curable disease and is rapidly rising worldwide in incidence. Barret's esophagus (BE) and high-grade dysplasia (HGD) are considered major risk factors for invasive adenocarcinoma. In the current study, unbiased global metabolic profiling methods were applied to serum samples from patients with EAC, BE and HGD, and healthy individuals, in order to identify metabolite based biomarkers associated with the early stages of EAC with the goal of improving prognostication. METHODOLOGY/PRINCIPAL FINDINGS: Serum metabolite profiles from patients with EAC (n = 67), BE (n = 3), HGD (n = 9) and healthy volunteers (n = 34) were obtained using high performance liquid chromatography-mass spectrometry (LC-MS) methods. Twelve metabolites differed significantly (p

Insulin is as a major postprandial hormone with profound effects on carbohydrate, fat, and protein metabolism. In the absence of exogenous insulin, patients with type 1 diabetes exhibit a variety of metabolic abnormalities including hyperglycemia, glycosurea, accelerated ketogenesis, and muscle wasting due to increased proteolysis. We analyzed plasma from type 1 diabetic (T1D) humans during insulin treatment (I+) and acute insulin deprivation (I-) and non-diabetic participants (ND) by (1)H nuclear magnetic resonance spectroscopy and liquid chromatography-tandem mass spectrometry. The aim was to determine if this combination of analytical methods could provide information on metabolic pathways known to be altered by insulin deficiency. Multivariate statistics differentiated proton spectra from I- and I+ based on several derived plasma metabolites that were elevated during insulin deprivation (lactate, acetate, allantoin, ketones). Mass spectrometry revealed significant perturbations in levels of plasma amino acids and amino acid metabolites during insulin deprivation. Further analysis of metabolite levels measured by the two analytical techniques indicates several known metabolic pathways that are perturbed in T1D (I-) (protein synthesis and breakdown, gluconeogenesis, ketogenesis, amino acid oxidation, mitochondrial bioenergetics, and oxidative stress). This work demonstrates the promise of combining multiple analytical methods with advanced statistical methods in quantitative metabolomics research, which we have applied to the clinical situation of acute insulin deprivation in T1D to reflect the numerous metabolic pathways known to be affected by insulin deficiency.