Gastric Cancer and Intestinal Metaplasia: Differential Metabolic Landscapes and New Pathways to Diagnosis.

Gastric cancer (GC) is the fifth most common cause of cancer-related death worldwide. Early detection is crucial for improving survival rates and treatment outcomes. However, accurate GC-specific biomarkers remain unknown. This study aimed to identify the metabolic differences between intestinal metaplasia (IM) and GC to determine the pathways involved in GC. A metabolic analysis of IM and tissue samples from 37 patients with GC was conducted using ultra-performance liquid chromatography with tandem mass spectrometry. Overall, 665 and 278 significant features were identified in the aqueous and 278 organic phases, respectively, using false discovery rate analysis, which controls the expected proportion of false positives among the significant results. sPLS-DA revealed a clear separation between IM and GC samples. Steroid hormone biosynthesis, tryptophan metabolism, purine metabolism, and arginine and proline metabolism were the most significantly altered pathways. The intensity of 11 metabolites, including N1, N2-diacetylspermine, creatine riboside, and N-formylkynurenine, showed significant elevation in more advanced GC. Based on pathway enrichment analysis and cancer stage-specific alterations, we identified six potential candidates as diagnostic biomarkers: aldosterone, N-formylkynurenine, guanosine triphosphate, arginine, S-adenosylmethioninamine, and creatine riboside. These metabolic differences between IM and GC provide valuable insights into gastric carcinogenesis. Further validation is needed to develop noninvasive diagnostic tools and targeted therapies to improve the outcomes of patients with GC.

Transcriptome-metabolome-wide association study (TMWAS) in rats revealed a potential carcinogenic effect of DEHP in thyroid associated with eicosanoids.

The incidence of thyroid cancer (TC) has increased considerably in the last few decades. Environmental factors, including plasticizers, are recognized as potential risks leading to thyroid cancer in humans. In this study, we used a transcriptome-metabolome-wide association study to find the unidentified carcinogenic mechanism of di-2-ethylhexyl phthalate (DEHP) in thyroid and biomarkers for non-invasive diagnosis. Rats were treated with different doses of DEHP (0, 0.3, 3, 30, 150 mg DEHP/kg bw/day) for 13 weeks. Then, the thyroids were processed for Ki67 staining and RNA-seq. Also, 17-h urine samples were collected for high-resolution metabolomics analysis. After a high dose of DEHP exposure, the terminal body weights and the thyroid and parathyroid glands weights were not altered. However, the liver weights and numbers of Ki67-positive cells were increased. Further, multivariate statistical analysis revealed that metabolic shifts were considerably altered above 30 mg DEHP/kg bw/day. In RNA-seq analysis, some cancer-related genes were altered, including 18 upregulated and 9 downregulated transcripts. These cancer transcripts and whole metabolome data were integrated to uncover thyroid cancer-related metabolic pathways, which revealed that cancer-related transcripts had a network structure linked to eicosanoids such as leukotriene D4 and prostaglandin. In brief, our study demonstrated that DEHP can induce thyroid hyperplasia through the eicosanoid-associated pathway, providing further insight into the mechanism of DEHP-associated thyroid cancer.

Global metabolomics analysis of serum from humans at risk of thrombotic stroke.

We aimed to determine the serum concentrations of altered compounds to understand the changes in metabolism and pathophysiology that occur prior to thrombotic stroke. In this prospective cohort study, high-resolution metabolomics (HRM) was employed to analyze serum samples obtained from patients at risk of stroke (n = 99) and non-risk controls (n = 301). Partial least-squares discriminant analysis (PLS-DA), along with univariate analysis using a false discovery rate (FDR) of q = 0.05 were employed to identify the discriminant metabolic profiles and to determine significantly different metabolites between healthy control and stroke risk groups. PLS-DA satisfactorily separated the stroke risk sera from control sera. Additionally, these discriminant metabolic profiles were not related to hypertension, smoking, diabetes mellitus, or insulin sensitivity. A group of 35 metabolites, most of them amino acids, that were capable of discriminating stroke risk sera from controls were identified using untargeted metabolomics. Further, the targeted metabolomics approach confirmed that the quantified concentrations of l-tryptophan, 3-methoxytyramine, methionine, homocysteinesulfinic acid, cysteine, isoleucine, carnitine, arginine, linoleic acid, and sphingosine were specifically elevated in the sera of patients who were later diagnosed with stroke. Our untargeted and targeted metabolomics approaches support investigating these compounds as novel biomarkers for early and non-invasive detection of thrombotic stroke.

Noninvasive Serum Metabolomic Profiling Reveals Elevated Kynurenine Pathway's Metabolites in Humans with Prostate Cancer.

This study aimed to apply high-resolution metabolomics to detect compounds that may contribute significantly to prostate cancer (PCa) development. The test population's sera for evaluating the metabolic differences consisted of healthy control ( n = 96) and PCa ( n = 50) groups. PCa patients were further divided into two groups based on whether their PSA level was >4 ( n = 25) or <4 ( n = 25). Univariate analysis was performed with the false discovery rate (FDR) at q = 0.05 to determine significantly different metabolites. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) clearly distinguished healthy subjects from PCa groups, while no significant difference was observed in PCa patients with PSA level < 4 or > 4. Mummichog, in combination with the KEGG and MetaboAnalyst, showed that tryptophan metabolism along the kynurenine pathway was most significantly enriched, with -log ( p) < 0.05. l-Tryptophan, kynurenine, anthranilate, isophenoxazine, glutaryl-CoA, ( S)-3-hydroxybutanoyl-CoA, acetoacetyl-CoA, and acetyl-CoA were upregulated in correlation with the PSA level of PCa patients; in contrast, indoxyl, indolelactate, and indole-3-ethanol, involved in the alternative pathway, were downregulated in the PCa patients. Validation and quantification of potential metabolites by MS/MS further confirmed the disruption of tryptophan, kynurenine, and anthranilate, suggesting that the metabolites of this pathway are potential biomarkers in patients with PCa.

High-resolution metabolomics to identify urine biomarkers in corticosteroid-resistant asthmatic children.

BACKGROUND: Corticosteroid (CS) treatment has been established as the first anti-inflammatory treatment for adults and children with asthma. However, a subset of patients fails to respond to combined systemic and inhaled CS treatment. OBJECTIVE: This study was aimed at further understanding CS resistance among children with severe asthma. METHODS: High-resolution metabolomics was performed on urine samples from CS-respondent (n = 15) and CS-nonrespondent (n = 15) children to determine possible urine biomarkers related to CS resistance. The metabolic phenotypes of CS responders and CS nonresponders were analyzed using bioinformatics including Manhattan plot with false- discovery rate, hierarchical cluster analysis, Kyoto Encyclopedia Genes and Genomes, and Mummichog pathway analysis. RESULTS: The 2-way hierarchical cluster analysis study determined 30 metabolites showing significantly different levels between CS responders and CS nonresponders. The important metabolites annotated were 3,6-dihydronicotinic acid (126.05 m/z, RT: 106, [M+H]+), 3-methoxy-4-hydroxyphenyl(ethylene)glycol (185.05 m/z, RT: 155, [M+H]+), 3,4-dihydroxy-phenylalanine (198.07 m/z, RT: 446, [M+H]+), γ-glutamylcysteine (236.06 m/z, RT: 528, [M+S(34)+H]+), Cys-Gly, (253.06 m/z, RT: 528, [M-NH3+H]+), and reduced Flavin mononucleotide (517.0794 m/z, RT: 533, [M+NaCl]+). Tyrosine metabolism, degradation of aromatic compounds, and glutathione metabolism are suggested to be significant pathways relating to CS resistance. CONCLUSIONS: High-resolution metabolomics is a promising approach in asthma research. Five candidate markers were identified to be related to CS-resistant children with severe asthma. These compounds, upon validation, may contribute further in the understanding of CS resistance among children with severe asthma through the use of urine.

High resolution metabolomics to discriminate compounds in serum of male lung cancer patients in South Korea.

BACKGROUND: The cancer death rate escalated during 20th century. In South Korea, lung cancer is expected to contribute 12,736 deaths in men, the highest amount among all cancers. Several risk factors may increase the chance to acquiring lung cancer, with mostly related to exogenous compounds found in cigarette smoke and synthetic manufacturing materials. As the mortality rate of lung cancer increases, deeper understanding is necessary to explore risk factors that may lead to this malignancy. In this regard, this study aims to apply high resolution metabolomics (HRM) using LC-MS to detect significant compounds that might contribute in inducing lung cancer and find the correlation of these compounds to the subjects' smoking habit. METHODS: The comparison was made between healthy control and lung cancer groups for metabolic differences. Further analyses to determine if these differences are related to tobacco-induced lung cancer (past-smoker control vs. past-smoker lung cancer patients (LCPs) and non-smoker control vs. current-smoker LCPs) were selected. The univariate analysis was performed, including a false discovery rate (FDR) of q = 0.05, to determine the significant metabolites between the analyses. Hierarchical clustering analysis (HCA) was done to discriminate metabolites between the control and case subjects. Selected compounds based on significant m/z features of human serum then experienced MS/MS examination, showing that for many m/z, the patterns of ion dissociation matched with standards. Then, the significant metabolites were identified using Metlin database and features were mapped on the human metabolic pathway mapping tool of the Kyoto Encyclopedia of Genes and Genomes (KEGG). RESULTS: Using metabolomics-wide association studies, metabolic changes were observed among control group and lung cancer patients. Bisphenol A (211.11, [M + H-H2O](+)), retinol (287.23, [M + H](+)) and L-proline (116.07, [M + H](+)) were among the significant compounds found to have contributed in the discrimination between these groups, suggesting that these compounds might be related in the development of lung cancer. Retinol has been seen to have a correlation with smoking while both bisphenol A and L-proline were found to be unrelated. CONCLUSIONS: Two potential biomarkers, retinol and L-proline, were identified and these findings may create opportunities for the development of new lung cancer diagnostic tools.

High-resolution metabolomics to discover potential parasite-specific biomarkers in a Plasmodium falciparum erythrocytic stage culture system.

BACKGROUND: Current available malaria diagnostic methods each have some limitations to meet the need for real-time and large-scale screening of asymptomatic and low density malaria infection at community level. It was proposed that malaria parasite-specific low molecular-weight metabolites could be used as biomarkers for the development of a malaria diagnostic tool aimed to address this diagnostic challenge. In this study, high resolution metabolomics (HRM) was employed to identify malaria parasite-specific metabolites in Plasmodium falciparum in vitro culture samples. METHODS: Supernatants were collected at 12 hours interval from 3% haematocrit in vitro 48-hour time-course asynchronized culture system of P. falciparum. Liquid chromatography coupled with high resolution mass spectrometry was applied to discover potential parasite-specific metabolites in the cell culture supernatant. A metabolome-wide association study was performed to extract metabolites using Manhattan plot with false discovery rate (FDR) and hierarchical cluster analysis. The significant metabolites based on FDR cutoff were annotated using Metlin database. Standard curves were created using corresponding chemical compounds to accurately quantify potential Plasmodium-specific metabolites in culture supernatants. RESULTS: The number of significant metabolite features was 1025 in the supernatant of the Plasmodium infected culture based on Manhattan plot with FDR q=0.05. A two way hierarchical cluster analysis showed a clear segregation of the metabolic profile of parasite infected supernatant from non-infected supernatant at four time points during the 48 hour culture. Among the 1025 annotated metabolites, the intensities of four molecules were significantly increased with culture time suggesting a positive association between the quantity of these molecules and level of parasitaemia: i) 3-methylindole, a mosquito attractant, ii) succinylacetone, a haem biosynthesis inhibitor, iii) S-methyl-L-thiocitrulline, a nitric oxide synthase inhibitor, and iv) O-arachidonoyl glycidol, a fatty acid amide hydrolase inhibitor, The highest concentrations of 3-methylindole and succinylacetone were 178 ± 18.7 pmoles at 36 hours and 157±30.5 pmoles at 48 hours respectively in parasite infected supernatant. CONCLUSION: HRM with bioinformatics identified four potential parasite-specific metabolite biomarkers using in vitro culture supernatants. Further study in malaria infected human is needed to determine presence of the molecules and its relationship with parasite densities.

Identification of Novel Biomarkers for Early Diagnosis of Atherosclerosis Using High-Resolution Metabolomics.

Atherosclerosis (AS) is a metabolic disorder and the pre-stage of several cardiovascular diseases, including myocardial infarction, stroke, and angina pectoris. Early detection of AS can provide the opportunity for effective management and better clinical results, along with the prevention of further progression of the disease. In the current study, an untargeted and targeted metabolomic approach was used to identify possible metabolic signatures that have altered levels in AS patients. A total of 200 serum samples from individuals with AS and normal were analyzed via liquid chromatography-high-resolution mass spectrometry. Univariate and multivariate analysis approaches were used to identify differential metabolites. A group of metabolites associated with bile acids, amino acids, steroid hormones, and purine metabolism were identified that are capable of distinguishing AS-risk sera from normal. Further, the targeted metabolomics approach confirmed that six metabolites, namely taurocholic acid, cholic acid, cortisol, hypoxanthine, trimethylamine N-oxide (TMAO), and isoleucine, were found to be significantly upregulated, while the concentrations of glycoursodeoxycholic acid, glycocholic acid, testosterone, leucine, methionine, phenylalanine, tyrosine, and valine were found to be significantly downregulated in the AS-risk sera. The receiver operating characteristic curves of three metabolites, including cortisol, hypoxanthine, and isoleucine, showed high sensitivity and specificity. Taken together, these findings suggest cortisol, hypoxanthine, and isoleucine as novel biomarkers for the early and non-invasive detection of AS. Thus, this study provides new insights for further investigations into the prevention and management of AS.

The effects of dietary self-monitoring intervention on anthropometric and metabolic changes via a mobile application or paper-based diary: a randomized trial.

BACKGROUND/OBJECTIVES: Weight loss via a mobile application (App) or a paper-based diary (Paper) may confer favorable metabolic and anthropometric changes. SUBJECTS/METHODS: A randomized parallel trial was conducted among 57 adults whose body mass indices (BMIs) were 25 kg/m2 or greater. Participants randomly assigned to either the App group (n = 30) or the Paper group (n = 27) were advised to record their foods and supplements through App or Paper during the 12-week intervention period. Relative changes of anthropometries and biomarker levels were compared between the 2 intervention groups. Untargeted metabolic profiling was identified to discriminate metabolic profiles. RESULTS: Out of the 57 participants, 54 participants completed the trial. Changes in body weight and BMI were not significantly different between the 2 groups (P = 0.11). However, body fat and low-density lipoprotein (LDL)-cholesterol levels increased in the App group but decreased in the Paper group, and the difference was statistically significant (P = 0.03 for body fat and 0.02 for LDL-cholesterol). In the metabolomics analysis, decreases in methylglyoxal and (S)-malate in pyruvate metabolism and phosphatidylcholine (lecithin) in linoleic acid metabolism from pre- to post-intervention were observed in the Paper group. CONCLUSIONS: In the 12-week randomized parallel trial of weight loss through a App or a Paper, we found no significant difference in change in BMI or weight between the App and Paper groups, but improvement in body fatness and LDL-cholesterol levels only in the Paper group under the circumstances with minimal contact by dietitians or health care providers. Trial Registration: Clinical Research Information Service Identifier: KCT0004226.

High-resolution metabolomics study revealing l-homocysteine sulfinic acid, cysteic acid, and carnitine as novel biomarkers for high acute myocardial infarction risk.

BACKGROUND: Identifying changes in serum metabolites before the occurrence of acute myocardial infarction (AMI) is an important approach for finding novel biomarkers of AMI. METHODS: In this prospective cohort study, serum samples obtained from patients at risk of AMI (n = 112) and non-risk controls (n = 89) were tested using high-resolution metabolomics (HRM). Partial least-squares discriminant analysis (PLS-DA), along with univariate analysis using a false discovery rate (FDR) of q = 0.05 were performed to discriminate metabolic profiles and to determine significantly different metabolites between healthy control and AMI risk groups. RESULTS: PLS-DA significantly separated the AMI risk sera from control sera. The metabolites associated with amino acid biosynthesis, 2-oxocarboxylic acid, tryptophan, and amino sugar and nucleotide sugar metabolism pathways were mainly elevated in patients at risk of AMI. Further validation and quantification by MS/MS showed that tryptophan, carnitine, L-homocysteine sulfinic acid (L-HCSA), and cysteic acid (CA) were upregulated, while L-cysteine and L-cysteine sulfinic acid (L-CSA) were downregulated, specifically among AMI risk sera. Additionally, these discriminant metabolic profiles were not related to hypertension, smoking or alcoholism. CONCLUSION: In conclusion, detecting upregulated L-HCSA and CA along with carnitine among patients at risk for AMI could serve as promising non-invasive biomarkers for early AMI detection.

Treadmill Exercise Improves Motor Function and Short-term Memory by Enhancing Synaptic Plasticity and Neurogenesis in Photothrombotic Stroke Mice.

PURPOSE: Thrombotic stroke is a type of ischemic stroke characterized by motor dysfunction and memory impairments. In the present study, the effect of treadmill exercise on motor function and short-term memory was evaluated in relation with synaptic plasticity in the mice with photothrombotic stroke. METHODS: Photothrombotic stroke was induced by cortical photothrombotic vascular occlusion. The mice in the treadmill exercise groups performed running on a motorized treadmill for 28 days. Motor function was determined using rota-rod test and foot fault test. Step-through avoidance task was conducted to evaluate short-term memory. Immunohistochemistry for 5-bromo-2'-deoxyuridine and doublecortin was conducted to detect new cell generation. Postsynaptic density protein 95, synaptophysin, brain-derived neurotrophic factor (BDNF), and tyrosine kinase B receptor (TrkB) were determined using western blot. The number of dendritic spines was determined using Golgi stain. RESULTS: Treadmill exercise improved motor function and short-term memory in mice with the photothrombotic stroke. The infarct size was reduced and the number of dendritic spines and expression of postsynaptic density protein 95 and synaptophysin in the peri-infarct cortex and hippocampus were increased by treadmill exercise in photothrombotic stroke mice. Treadmill exercise enhanced neurogenesis through increasing the expression of the hippocampal BDNF and TrkB in photothrombotic stroke mice. CONCLUSION: Treadmill exercise improved motor function and short-term memory through increasing synaptic plasticity and neurogenesis in photothrombotic stroke mice. Treadmill exercise can be used as an effective treatment strategy to improve brain function related to stroke.

A biplot correlation range for group-wise metabolite selection in mass spectrometry.

BACKGROUND: Analytic methods are available to acquire extensive metabolic information in a cost-effective manner for personalized medicine, yet disease risk and diagnosis mostly rely upon individual biomarkers based on statistical principles of false discovery rate and correlation. Due to functional redundancies and multiple layers of regulation in complex biologic systems, individual biomarkers, while useful, are inherently limited in disease characterization. Data reduction and discriminant analysis tools such as principal component analysis (PCA), partial least squares (PLS), or orthogonal PLS (O-PLS) provide approaches to separate the metabolic phenotypes, but do not offer a statistical basis for selection of group-wise metabolites as contributors to metabolic phenotypes. METHODS: We present a dimensionality-reduction based approach termed 'biplot correlation range (BCR)' that uses biplot correlation analysis with direct orthogonal signal correction and PLS to provide the group-wise selection of metabolic markers contributing to metabolic phenotypes. RESULTS: Using a simulated multiple-layer system that often arises in complex biologic systems, we show the feasibility and superiority of the proposed approach in comparison of existing approaches based on false discovery rate and correlation. To demonstrate the proposed method in a real-life dataset, we used LC-MS based metabolomics to determine spectrum of metabolites present in liver mitochondria from wild-type (WT) mice and thioredoxin-2 transgenic (TG) mice. We select discriminatory variables in terms of increased score in the direction of class identity using BCR. The results show that BCR provides means to identify metabolites contributing to class separation in a manner that a statistical method by false discovery rate or statistical total correlation spectroscopy can hardly find in complex data analysis for predictive health and personalized medicine.

Integrative Omics Reveals Metabolic and Transcriptomic Alteration of Nonalcoholic Fatty Liver Disease in Catalase Knockout Mice.

The prevalence of nonalcoholic fatty liver disease (NAFLD) has increased with the incidence of obesity; however, the underlying mechanisms are unknown. In this study, high-resolution metabolomics (HRM) along with transcriptomics were applied on animal models to draw a mechanistic insight of NAFLD. Wild type (WT) and catalase knockout (CKO) mice were fed with normal fat diet (NFD) or high fat diet (HFD) to identify the changes in metabolic and transcriptomic profiles caused by catalase gene deletion in correspondence with HFD. Integrated omics analysis revealed that cholic acid and 3ß, 7α-dihydroxy-5-cholestenoate along with cyp7b1 gene involved in primary bile acid biosynthesis were strongly affected by HFD. The analysis also showed that CKO significantly changed all-trans-5,6-epoxy-retinoic acid or all-trans-4-hydroxy-retinoic acid and all-trans-4-oxo-retinoic acid along with cyp3a41b gene in retinol metabolism, and α/γ-linolenic acid, eicosapentaenoic acid and thromboxane A2 along with ptgs1 and tbxas1 genes in linolenic acid metabolism. Our results suggest that dysregulated primary bile acid biosynthesis may contribute to liver steatohepatitis, while up-regulated retinol metabolism and linolenic acid metabolism may have contributed to oxidative stress and inflammatory phenomena in our NAFLD model created using CKO mice fed with HFD.

Effect of developmental exposure to bisphenol A on steroid hormone and vitamin D3 metabolism.

High exposure to bisphenol A (BPA) in children has been associated with the outcomes of several diseases, including those related to developmental problems. To elucidate the mechanism of BPA mediated developmental toxicity, plasma and urine from rats exposed to BPA was analyzed with high resolution metabolomics, beginning from post-natal day 9, for 91 days. Female and male rats were orally administered 5 different BPA doses to elucidate dose- and sex-specific BPA effects. Regarding dose-specific effects, multivariate statistical analysis showed that metabolic shifts were considerably altered between 5, 50 and 250 mg BPA/kg bw/day in treated rats. A nonmonotonicity and monotonicity between BPA dose and metabolic response were major trajectories, showing overall metabolic changes in plasma and urine, respectively. Metabolic perturbation in the steroid hormone biosynthesis pathway was significantly associated with dose- and sex-specific BPA effects. Intermediate metabolites in the rate-limiting step of steroid hormone biosynthesis down-regulated steroid hormones in the 250 mg treatment. Further, our study identified that BPA increased urinary excretion of vitamin D3 and decreased its concentration in blood, suggesting that perturbation of vitamin D3 metabolism may be mechanistically associated with neurodevelopmental disorders caused by BPA. Three metabolites showed a decrease in sex difference with high BPA dose because female rats were more affected than males, which can be related with early puberty onset in female. In brief, the results demonstrated that BPA induces dose- and sex-specific metabolic shifts and that perturbation of metabolism can explain developmental problems.

Screening and identification of neuroprotective compounds from Scrophularia buergeriana using cell extraction coupled with LC-MS.

In the cell extraction_LC-MS method, when cells are incubated with natural product extracts, bioactive compounds selectively bind to extracellular or intracellular targets. The extracts and major compounds (phenylpropanoids and iridoid glycosides) of Scrophularia buergeriana Miquel have been reported to show neuroprotective effects both in vitro and in vivo. In this study, the cell extraction_LC-MS strategy was applied to screen and identify potential neuroprotective compounds from S. buergeriana by using immortalized mouse hippocampal HT22 cells. The results showed that two known compounds from S. buergeriana selectively bound HT22 cells. Additionally, metabolomics analyses were performed using the Mass Profiler Professional and Limma differential expression package of R to identify significant differences between HT22 cells treated with S. buergeriana and untreated cells. The cell extraction approach more accurately reflects in vivo conditions compared with other methods and can be readily used for screening bioactive components from natural products.

High resolution metabolomics to determines the risk associated with bisphenol A exposure in humans.

Although high BPA exposure has been correlated with several metabolic diseases, the underlying mechanisms are unclear. In the present study, a metabolomics approach was used to explore the metabolic variations caused by low or high BPA exposure in female (n = 96) and male (n = 98) urine. Fatty acid elongation and sphingolipid metabolism were affected by high BPA exposure in males and females. Fatty acid elongation and sphingolipid metabolism were further investigated among age groups consisted of 30-39 yrs old, 40-49 yrs old, and 50-59 yrs old males and females with high or low urinary BPA. High BPA-exposed males in 30 s and females in 40 s were found with significant disturbance in fatty acid elongation and sphingolipid metabolism, respectively. Additionally, females in 40 s showed elevated inflammatory metabolites: 6-ketoprostaglandin E1 and thromboxane. In the present study, we have demonstrated that environmental metabolomics is useful to elucidate the health effects of BPA exposure.

High-resolution metabolomics determines the mode of onset of type 2 diabetes in a 3-year prospective cohort study.

Type 2 diabetes mellitus (DM) is a progressive disease and the rate of progression from non-diabetes to DM varies considerably between individuals, ranging from a few months to many years. It is important to understand the mechanisms underlying the progression of diabetes. In the present study, a high-resolution metabolomics (HRM) analysis was performed to detect potential biomarkers and pathways regulating the mode of onset by comparing subjects who developed and did not develop type 2 DM at the second year in a 3-year prospective cohort study. Metabolic profiles correlated with progression to DM were examined. The subjects (n=98) were classified into four groups: Control (did not develop DM for 3 years), DM (diagnosed with DM at the start of the study), DM onset at the third year and DM onset at the second year. The focus was on the comparison of serum samples of the DM groups with onset at the second and third year from the first year, where these two groups had not developed DM, yet. Analyses involved sample examination using liquid chromatography-mass spectrometry-based HRM and multivariate statistical analysis of the data. Metabolic differences were identified across all analyses with the affected pathways involved in metabolism associated with steroid biosynthesis and bile acid biosynthesis. In the first year, higher levels of cholesterol {mass-to charge ratio (m/z) 369.35, (M+H-H2O)+}, 25-hydroxycholesterol [m/z 403.36, (M+H)+], 3α,7α-dihydroxy-5ß-cholestane [m/z 443.33, (M+K)+], 4α-methylzymosterol-4-carboxylate [m/z 425.34, (M+H­H2O)+], and lower levels of 24,25-dihydrolanosterol [m/z 429.40, (M+H)+] were evident in the group with DM onset at the second year compared with those in the group with DM onset at the third year. These results, with a focus on the cholesterol biosynthesis pathway, point to important aspects in the development of DM and may aid in the development of more effective means of treatment and prevention.

Increased urinary l-histidine in patients with asthma-COPD overlap: a pilot study.

Purpose: Asthma-COPD overlap (ACO) is heterogeneous in nature and requires a unified diagnostic approach. We investigated the urinary levels of l-histidine, a precursor of histamine related to inflammatory responses, as a new candidate biomarker for diagnosing this condition. Patients and methods: We performed a prospective multicenter cohort study with retrospective analysis of 107 patients, who were divided into three groups: asthma, COPD, and ACO, according to the Spanish guidelines algorithm. Urinary l-histidine levels were measured using liquid chromatography-mass spectrometry. High-resolution metabolomic analysis, coupled with liquid chromatography-mass spectrometry and followed by multivariate statistical analysis, was performed on urine samples to discriminate between the metabolic profiles of the groups. Results: Urinary l-histidine levels were significantly higher in patients with ACO than in those with asthma or COPD, but the subgroups of ACO, classified according to disease origin, did not differ significantly. High urinary l-histidine level was a significant factor for the diagnosis of ACO even after adjusting for age, sex, and smoking amount. Among patients with airflow obstruction, the urinary l-histidine levels were elevated in patients with a documented history of asthma before the age of 40 years or bronchodilator responsiveness ≥400 mL; bronchodilator responsiveness ≥200 mL of forced expiratory volume in 1 second and exceeding baseline values by 12% on two or more visits; blood eosinophil count ≥300 cells·mm-3; and frequent exacerbations (P < 0.05). Conclusion: Urinary l-histidine could be a potential biomarker for ACO, regardless of the diversity of diagnostic definitions used.

A metabolomic study on high-risk stroke patients determines low levels of serum lysine metabolites: a retrospective cohort study.

Identifying changes in serum metabolites during cerebral ischemia is an important approach for early diagnosis of thrombotic stroke. Herein, we highlight novel biomarkers for early diagnosis of patients at high risk of thrombotic stroke using high resolution metabolomics (HRM). In this retrospective cohort study, serum samples obtained from patients at risk of thrombotic stroke (n = 62) and non-risk individuals (n = 348) were tested using HRM, coupled with LC-MS/MS, to discriminate between metabolic profiles of control and stroke risk patients. Multivariate analysis and orthogonal partial least square-discriminant analysis (OPLS-DA) were performed to determine the top 5% metabolites within 95% group identities, followed by filtering with p-value <0.05 and annotating significant metabolites using a Metlin database. Mapping identified features from Kyoto Encyclopedia of Genes and Genomes (KEGG) and Mummichog resulted in 341 significant features based on OPLS-DA with p-value <0.05. Among these 341 features, nine discriminated the thrombotic stroke risk group from the control group: low levels of N6-acetyl-l-lysine, 5-aminopentanoate, cadaverine, 2-oxoglutarate, nicotinamide, l-valine, S-(2-methylpropionyl)-dihydrolipoamide-E and ubiquinone, and elevated levels of homocysteine sulfinic acid. Further analysis showed that these metabolite biomarkers are specifically related to stroke occurrence, and unrelated to other factors such as diabetes or smoking. Lower levels of lysine catabolites in thrombotic stroke risk patients, as compared to the control, supports targeting these compounds as novel biomarkers for early and non-invasive detection of a thrombotic stroke.

A pilot study identifying a potential plasma biomarker for determining EGFR mutations in exons 19 or 21 in lung cancer patients.

The most common type of lung cancer is non­small cell lung cancer (NSCLC), which is frequently characterized by a mutation in the epidermal growth factor receptor (EGFR). Determining the presence of an EGFR mutation in lung cancer is important, as it determines the type of treatment that a patients will receive. Therefore, the aim of the present study was to apply high­resolution metabolomics (HRM) using liquid chromatography­mass spectrometry to identify significant compounds in human plasma samples obtained from South Korean NSCLC patients, as potential biomarkers for providing early detection and diagnosis of minimally­invasive NSCLC. The metabolic differences between lung cancer patients without EGFR mutations were compared with patients harboring EGFR mutations. Univariate analysis was performed, with a false discovery rate of q=0.05, in order to identify significant metabolites between the two groups. In addition, hierarchical clustering analysis was performed to discriminate between the metabolic profiles of the two groups. Furthermore, the significant metabolites were identified and mapped using Mummichog software, in order to generate a potential metabolic network model. Using metabolome­wide association studies, metabolic alterations were identified. Linoleic acid [303.23 m/z, (M+Na)+], 5­methyl tetrahydrofolate [231.10 m/z, (M+2H)+] and N­succinyl­L­glutamate­5 semialdehyde [254.06 m/z, (M+Na)+], were observed to be elevated in patients harboring EGFR mutations, whereas tetradecanoyl carnitine [394.29 m/z, (M+Na)+] was observed to be reduced. This suggests that these compounds may be affected by the EGFR mutation. In conclusion, the present study identified four potential biomarkers in patients with EGFR mutations, using HRM combined with pathway analysis. These results may facilitate the development of novel diagnostic tools for EGFR mutation detection in patients with lung cancer.