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1
Szczerbinski, Lukasz, Gladys Wojciechowska, Adam Olichwier, Mark A. Taylor, Urszula Puchta, Paulina Konopka, Adam Paszko, et al. "Untargeted Metabolomics Analysis of the Serum Metabolic Signature of Childhood Obesity." Nutrients 14, no. 1 (January 4, 2022): 214. http://dx.doi.org/10.3390/nu14010214.
Abstract:
Obesity rates among children are growing rapidly worldwide, placing massive pressure on healthcare systems. Untargeted metabolomics can expand our understanding of the pathogenesis of obesity and elucidate mechanisms related to its symptoms. However, the metabolic signatures of obesity in children have not been thoroughly investigated. Herein, we explored metabolites associated with obesity development in childhood. Untargeted metabolomic profiling was performed on fasting serum samples from 27 obese Caucasian children and adolescents and 15 sex- and age-matched normal-weight children. Three metabolomic assays were combined and yielded 726 unique identified metabolites: gas chromatography–mass spectrometry (GC–MS), hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC LC–MS/MS), and lipidomics. Univariate and multivariate analyses showed clear discrimination between the untargeted metabolomes of obese and normal-weight children, with 162 significantly differentially expressed metabolites between groups. Children with obesity had higher concentrations of branch-chained amino acids and various lipid metabolites, including phosphatidylcholines, cholesteryl esters, triglycerides. Thus, an early manifestation of obesity pathogenesis and its metabolic consequences in the serum metabolome are correlated with altered lipid metabolism. Obesity metabolite patterns in the adult population were very similar to the metabolic signature of childhood obesity. Identified metabolites could be potential biomarkers and used to study obesity pathomechanisms.
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Bever, Alaina M., Dong Hang, Amit D. Joshi, Connor M. Geraghty, Dong Hoon Lee, Fred K. Tabung, Shuji Ogino, et al. "Abstract 3006: Metabolomic signatures of metabolic disturbance and inflammation in relation to colorectal cancer risk." Cancer Research 83, no. 7_Supplement (April 4, 2023): 3006. http://dx.doi.org/10.1158/1538-7445.am2023-3006.
Abstract:
Abstract Background: Metabolic disturbance and inflammation may explain observed associations between higher body mass index (BMI) and increased risk of colorectal cancer (CRC); however, the underlying mechanisms are not fully understood. Objectives: We characterized individual plasma metabolites and metabolomic signatures of metabolic disturbance and inflammation and evaluated their association with prospective CRC risk within the Nurses’ Health Study and the Health Professionals Follow-up Study. Methods: Among 686 colorectal cancer cases and 686 age-matched controls, we used reduced rank regression of markers of metabolic disturbance (BMI, waist circumference, C-peptide, and adiponectin) or inflammation (BMI, C-reactive protein, interleukin-6, and tumor necrosis factor receptor superfamily member 1B) with cross-sectional measures of 353 plasma metabolites to develop a Y-score for metabolic disturbance and Y-score for inflammation among men and women separately. We then used elastic net regression to derive a signature of metabolites, and multiple linear regression to identify individual metabolites, associated with each Y-score. We evaluated the association of individual metabolites and the metabolomic signatures with odds of CRC using conditional logistic regression adjusted for other CRC risk factors. Results: The metabolomic signature of metabolic disturbance consisted of 41 metabolites selected via elastic net regression in men and 72 in women; the metabolomic signature of inflammation consisted of 68 metabolites in men and 119 in women. The metabolic disturbance metabolomic signatures captured, on average, 36% of variation in markers of metabolic disturbance in women and 35% in men; the inflammation signature captured 35% of variation in inflammatory markers in women and 26% in men. The metabolomic signature of metabolic disturbance was associated with increased odds of CRC (odds ratio (OR) comparing highest to lowest quartile = 1.63; 95% confidence interval (CI), 0.92, 2.91; Ptrend = 0.31) and the metabolomic signature of inflammation was associated with increased odds of CRC (OR = 2.01; 95% CI, 1.14, 3.57; Ptrend = 0.008) among men. Neither signature was associated with CRC among women. Of the metabolites associated with metabolic disturbance and inflammation, 13 metabolites were also associated with CRC: 4 metabolites classified as uremic toxins (2 purine nucleosides and 2 amino acid derivates); 3 sphingolipids; 3 glycerophospholipids; 2 sterols related to cholesterol homeostasis; and 3-ureidopropanoate, a uracil metabolism substrate. Conclusion: We identified plasma metabolomic signatures and individual metabolites associated with metabolic disturbance, inflammation, and CRC risk, highlighting pathways such as protein metabolism and lipid homeostasis that may relate adiposity-related metabolic disturbance and inflammation to CRC development. Citation Format: Alaina M. Bever, Dong Hang, Amit D. Joshi, Connor M. Geraghty, Dong Hoon Lee, Fred K. Tabung, Shuji Ogino, Jeffrey A. Meyerhardt, Andrew T. Chan, Edward L. Giovannucci, A. Heather Eliassen, Liming Liang, Meir J. Stampfer, Mingyang Song. Metabolomic signatures of metabolic disturbance and inflammation in relation to colorectal cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3006.
3
Yang, Mingjia, Chen Zhu, Lingbin Du, Jianv Huang, Jiayi Lu, Jing Yang, Ye Tong, et al. "A Metabolomic Signature of Obesity and Risk of Colorectal Cancer: Two Nested Case–Control Studies." Metabolites 13, no. 2 (February 5, 2023): 234. http://dx.doi.org/10.3390/metabo13020234.
Abstract:
Obesity is a leading contributor to colorectal cancer (CRC) risk, but the metabolic mechanisms linking obesity to CRC are not fully understood. We leveraged untargeted metabolomics data from two 1:1 matched, nested case–control studies for CRC, including 223 pairs from the US Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial and 190 pairs from a prospective Chinese cohort. We explored serum metabolites related to body mass index (BMI), constructed a metabolomic signature of obesity, and examined the association between the signature and CRC risk. In total, 72 of 278 named metabolites were correlated with BMI after multiple testing corrections (p FDR < 0.05). The metabolomic signature was calculated by including 39 metabolites that were independently associated with BMI. There was a linear positive association between the signature and CRC risk in both cohorts (p for linear < 0.05). Per 1-SD increment of the signature was associated with 38% (95% CI: 9–75%) and 28% (95% CI: 2–62%) higher risks of CRC in the US and Chinese cohorts, respectively. In conclusion, we identified a metabolomic signature for obesity and demonstrated the association between the signature and CRC risk. The findings offer new insights into the underlying mechanisms of CRC, which is critical for improved CRC prevention.
4
Davis, Vanessa Wylie, Dan E. Schiller, and Michael B. Sawyer. "Metabolomic signature of esophageal cancer." Journal of Clinical Oncology 30, no. 4_suppl (February 1, 2012): 21. http://dx.doi.org/10.1200/jco.2012.30.4_suppl.21.
Abstract:
21 Background: Esophageal cancer is a pervasive malignancy, and early detection combined with newer therapeutic targets could alter the landscape of this condition. Metabolomic profiling offers one such innovative opportunity. We applied metabolomic techniques to identify urinary metabolites uniquely associated with this condition. Methods: Urine samples from patients with histologically confirmed esophageal cancer (n=66) and healthy volunteers (n=25) were collected and examined using 1H-NMR spectroscopy. Targeted profiling of spectra using Chenomx NMR Suite 7.0 software permitted detection and quantification of 66 distinct metabolites. Unsupervised (principal component analysis, PCA) and supervised (partial least-squares discriminant analysis, PLS-DA) multivariate pattern recognition techniques were applied to discriminate between sample spectra of esophageal cancer patients and healthy volunteers using SIMCA-P (version 11, Umetrics, Umeå, Sweden). Results: Significant differences were found when comparing concentrations of 59 metabolites in urines of healthy volunteers and esophageal cancer patients. Those metabolites contributing most class discriminating information included choline, urea, 2-aminobutyrate, and 3-hydoxybutyrate. Clear distinctions between patients with esophageal cancer and healthy controls were noted when PLS-DA was applied to the data set. Model parameters for both goodness of fit R2, and predictive capability Q2, were high (R2 = 0.867; Q2 = 0.732). Model validity was tested using response permutation and results were suggestive of excellent predictive power (see Figure). Conclusions: Urinary metabolomics identified a discrete signature associated with esophageal cancer compared to healthy controls. This profile has the potential to aid in diagnosis and development of new therapeutic targets.
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Lokhov, Petr G., Oxana P. Trifonova, Dmitry L. Maslov, and Elena E. Balashova. "In Situ Mass Spectrometry Diagnostics of Impaired Glucose Tolerance Using Label-Free Metabolomic Signature." Diagnostics 10, no. 12 (December 5, 2020): 1052. http://dx.doi.org/10.3390/diagnostics10121052.
Abstract:
In metabolomics, mass spectrometry is used to detect a large number of low-molecular substances in a single analysis. Such a capacity could have direct application in disease diagnostics. However, it is challenging because of the analysis complexity, and the search for a way to simplify it while maintaining the diagnostic capability is an urgent task. It has been proposed to use the metabolomic signature without complex data processing (mass peak detection, alignment, normalization, and identification of substances, as well as any complex statistical analysis) to make the analysis more simple and rapid. Methods: A label-free approach was implemented in the metabolomic signature, which makes the measurement of the actual or conditional concentrations unnecessary, uses only mass peak relations, and minimizes mass spectra processing. The approach was tested on the diagnosis of impaired glucose tolerance (IGT). Results: The label-free metabolic signature demonstrated a diagnostic accuracy for IGT equal to 88% (specificity 85%, sensitivity 90%, and area under receiver operating characteristic curve (AUC) of 0.91), which is considered to be a good quality for diagnostics. Conclusions: It is possible to compile label-free signatures for diseases that allow for diagnosing the disease in situ, i.e., right at the mass spectrometer without complex data processing. This achievement makes all mass spectrometers potentially versatile diagnostic devices and accelerates the introduction of metabolomics into medicine.
6
Li, Zhen, Yue Mu, Chunlan Guo, Xin You, Xiaoyan Liu, Qian Li, and Wei Sun. "Analysis of the saliva metabolic signature in patients with primary Sjögren’s syndrome." PLOS ONE 17, no. 6 (June 2, 2022): e0269275. http://dx.doi.org/10.1371/journal.pone.0269275.
Abstract:
Background The saliva metabolome has been applied to explore disease biomarkers. In this study we characterized the metabolic profile of primary Sjögren’s syndrome (pSS) patients and explored metabolomic biomarkers. Methods This work presents a liquid chromatography-mass spectrometry-based metabolomic study of the saliva of 32 patients with pSS and 38 age- and sex-matched healthy adults. Potential pSS saliva metabolite biomarkers were explored using test group saliva samples (20 patients with pSS vs. 25 healthy adults) and were then verified by a cross-validation group (12 patients with pSS vs. 13 healthy adults). Results Metabolic pathways, including tryptophan metabolism, tyrosine metabolism, carbon fixation, and aspartate and asparagine metabolism, were found to be significantly regulated and related to inflammatory injury, neurological cognitive impairment and the immune response. Phenylalanyl-alanine was discovered to have good predictive ability for pSS, with an area under the curve (AUC) of 0.87 in the testing group (validation group: AUC = 0.75). Conclusion Our study shows that salivary metabolomics is a useful strategy for differential analysis and biomarker discovery in pSS.
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Stockard, Bradley, Timothy Garrett, Soheil Meshinchi, and Jatinder K. Lamba. "Metabolomic Profiling Defines Distinct Metabolic Signature Associated with FLT3/ITD AML." Blood 128, no. 22 (December 2, 2016): 1692. http://dx.doi.org/10.1182/blood.v128.22.1692.1692.
Abstract:
Abstract AML is a hematological disorder resulting from proliferation and expansion of malignant myeloid cells. Clinical outcome for AML remains dismal despite intensive therapy in part due to the disease heterogeneity with various cytogenetic and molecular lesions. Fms-Like Tyrosine Kinase-3 (FLT3) is a receptor tyrosine kinase expressed hematopoietic stem/progenitor cells. Activating mutations of FLT3 gene due to internal tandem duplication of the juxtamembrane domain coding sequence (FLT3/ITD) causes autonomous cellular proliferations leading to disease progression. Metabolomic profiling has been successfully utilized to identify metabolic alterations in hematological disorders. However, no studies on metabolic alterations associated with pediatric AML have been reported at this time. In this study we propose to establish the metabolomic landscape in pediatric AML patients and identify differential expression of metabolites based on FLT3/ITD status. Cellular and plasma metabolomics profile was generated from 32 matching diagnostic material from 16 patients with and without FLT3/ITD (N=8 for each cohort and each sample was run in duplicate) treated on COG-AAML0531 study. Global metabolomics profiling was performed on a Thermo Q-Exactive Oribtrap mass spectrometer with Dionex UHPLC and autosampler. All samples were analyzed in duplicate in positive and negative heated electrospray ionization with a mass resolution of 35,000 at m/z 200 as separate injections. Separation was achieved on an ACE 18-pfp 100 x 2.1 mm, 2 µm column with mobile phase A as 0.1% formic acid in water and mobile phase B as acetonitrile. This is a polar embedded stationary phase that provides comprehensive coverage, but does have some limitation is the coverage of very polar species. The flow rate was 350 µL/min with a column temperature of 25¡C. 4 µL was injected for negative ions and 2 µL for positive ions. Statistical analysis was performed using MetaboAnalyst software using all metabolites (known and unknown) as well as only the annotated metabolites. Univariate analysis was performed by volcano plot and Multivariate analysis was performed using PCA, PLS-DA and OPLS-DA. Total of 2966 plasma metabolome (779 negative and 2187 positive) and 1742 (227 negative and 1515 positive) cellular metabolome features were identified. All subsequent data analyses were normalized to the sum of metabolites for each sample. Comparison of the cellular metabolome in patients with and without FLT3/ITD identified 12 known and 135 unknown metabolites that were significantly different between two cohorts (p<0.05). Similar comparison of the plasma metabolome identified19 known and 300 unknown metabolites in the patient with and without FLT3/ITD (top results are shown in Fig.1). Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed clear separation between the 2 groups (Fig.2). Some of the top known plasma metabolites (p<0.01) differentiating patients by FLT3 status include guanine, pyrimidine-2-3dicarboxylate, acetylglycine, acetyl-L-alanine, aminobutonate gaba, L-carnitine, methyl-2 oxovaleric acid, asparagine, acetyl arginine, Hydroxydecanoic acid, cysteic acid and glycocholic acid. Within leukemic cells top metabolites differentiating between FLT3 status included actyly carnitine, adenosine monophosphate, hypoxanthine, diaminohepatanedioate, guanine and sphingosine. Metaboanalyst Pathway analysis module mapped the differentiating metabolites to aminoacyl-tRNA biosynthesis, Glycerophospholipid metabolism, Cysteine and methionine metabolism, Pantothenate and CoA biosynthesis, Purine metabolism. This pilot study defines distinct metabolomics signature associated with genomic subtype of AML (FLT3/ITD). As metabolomics provides an insight into the ultimate metabolic destination of normal and malignant hematopoiesis, it has a potential to provide a unique insight into the altered metabolic pathways in AML and identify pathways and networks that might be shared by varied genomic subtypes of AML. Such data can help merge rare genomic variants based on shared metabolic signatures and more appropriately guide directed therapies. Disclosures No relevant conflicts of interest to declare.
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Serri, Orianne, Magalie Boguenet, Juan Manuel Chao de la Barca, Pierre-Emmanuel Bouet, Hady El Hachem, Odile Blanchet, Pascal Reynier, and Pascale May-Panloup. "A Metabolomic Profile of Seminal Fluid in Extremely Severe Oligozoopermia Suggesting an Epididymal Involvement." Metabolites 12, no. 12 (December 15, 2022): 1266. http://dx.doi.org/10.3390/metabo12121266.
Abstract:
Male infertility has increased in the last decade. Pathophysiologic mechanisms behind extreme oligospermia (EO) are not yet fully understood. In new “omics” approaches, metabolomic can offer new information and help elucidate these mechanisms. We performed a metabolomics study of the seminal fluid (SF) in order to understand the mechanisms implicated in EO. We realized a targeted quantitative analysis using high performance liquid chromatography and mass spectrometry to compare the SF metabolomic profile of 19 men with EO with that of 22 men with a history of vasectomy (V) and 20 men with normal semen parameters (C). A total of 114 metabolites were identified. We obtained a multivariate OPLS-DA model discriminating the three groups. Signatures show significantly higher levels of amino acids and polyamines in C group. The sum of polyunsaturated fatty acids and free carnitine progressively decrease between the three groups (C > EO > V) and sphingomyelins are significantly lower in V group. Our signature characterizing EO includes metabolites already linked to infertility in previous studies. The similarities between the signatures of the EO and V groups are clear evidence of epididymal dysfunction in the case of testicular damage. This study shows the complexity of the metabolomic dysfunction occurring in the SF of EO men and underlines the importance of metabolomics in understanding male infertility.
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Putluri, N., Y. Zhang, V. Putluri, S. Vareed, V. T. Vasu, S. M. Fischer, C. Chad, and A. Sreekumar. "Androgen-regulated metabolome in prostate cancer." Journal of Clinical Oncology 29, no. 7_suppl (March 1, 2011): 25. http://dx.doi.org/10.1200/jco.2011.29.7_suppl.25.
Abstract:
25 Background: Prostate cancer (PC) is the second most prevalent cancer among American men which is primarily treated by androgen ablation therapy. Although a number of patients respond to this regimen, a significant subset fail and the tumor invariably progresses into a hormone refractory metastatic state, which is lethal. Earlier we had reported the first unbiased metabolomic signature for localized and metastatic prostate cancer tissues. Advancing further, we attempt to delineate the subset of metabolome in prostate cancer which is regulated by androgen-action. Methods: Androgen responsive (R22V1, LnCap and VCAP) and independent (PC3, DU145) PC cells and benign prostate epithelial cells (RWPE) were profiled for their metabolomic alterations using mass spectrometry. Extracted metabolome from these cells were profiles using a combination quadrupole-time-of-flight (Q-TOF) and triple quadrupole (QQQ) mass spectrometers coupled to reverse phase and aqueous normal phase chromatography. The metabolomic profiles were analyzed to delineate class-specific signatures which were interrogated for altered bioprocesses using Oncomine Concept Map (OCM, www.oncomine.org ). The androgen receptor (AR) regulated metabolome was verified using treatment of PC cells with synthetic androgen, R1881. Results: A total of 3,092 metabolites (113 named) were detected across the 4 cells lines, of which 869 compounds were significantly (ANOVA P<0.01) different between androgen responsive and non-responsive cells. The differential compendia included 28 named metabolites, including sarcosine which was earlier shown to be elevated during PC development and progression. Bioprocess mapping of AR-regulated metabolome revealed enrichment of amino acid metabolism and methylation potential, both of which were earlier defined to be the hallmarks of PC development and progression. Conclusions: The study defines AR-regulated metabolic signature which portrays enrichment of amino acid metabolism and methylation potential that are hallmarks of PC development and progression. No significant financial relationships to disclose.
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Troisi, Jacopo, Laura Sarno, Annamaria Landolfi, Giovanni Scala, Pasquale Martinelli, Roberta Venturella, Annalisa Di Cello, Fulvio Zullo, and Maurizio Guida. "Metabolomic Signature of Endometrial Cancer." Journal of Proteome Research 17, no. 2 (January 2, 2018): 804–12. http://dx.doi.org/10.1021/acs.jproteome.7b00503.
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Pandey, Renu, Laura Caflisch, Alessia Lodi, Andrew J. Brenner, and Stefano Tiziani. "Metabolomic signature of brain cancer." Molecular Carcinogenesis 56, no. 11 (July 17, 2017): 2355–71. http://dx.doi.org/10.1002/mc.22694.
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Davis, Vanessa Wylie, Dan E. Schiller, and Michael B. Sawyer. "The metabolomic signature of pancreatic cancer in urine." Journal of Clinical Oncology 30, no. 4_suppl (February 1, 2012): 180. http://dx.doi.org/10.1200/jco.2012.30.4_suppl.180.
Abstract:
180 Background: Pancreatic cancer is one of the leading causes of cancer-related death, due partly to the lack of early detection and screening methods. Metabolomics, the newest of the “omics” sciences, provides a means for non-invasive screening of early tumor associated perturbations in cellular metabolism. We applied metabolomic techniques to identify urinary metabolites capable of facilitating diagnosis of pancreatic cancer. Methods: Urine samples from pancreatic cancer patients (n=55) and healthy volunteers (n=25) were collected and examined using 1H-NMR spectroscopy. Targeted profiling of spectra using Chenomx NMR Suite 7.0 software permitted quantification of 66 metabolites. Unsupervised (PCA) and supervised (PLS-DA) multivariate pattern recognition techniques were applied to discriminate between sample spectra of pancreatic cancer patients and healthy volunteers using SIMCA-P (version 11, Umetrics, Umeå, Sweden). Results: Significant differences were found when comparing concentrations of 66 metabolites in urines of healthy volunteers and pancreatic cancer patients. Those metabolites contributing the most class discriminating information included choline, 2-aminobutyrate, urea and 2-oxoglutarate. Clear distinctions between pancreatic cancer patients and healthy controls were noted when PLS-DA was applied to the data set. Model parameters for both goodness of fit R2, and predictive capability Q2, were high (R2 = 0.829; Q2 = 0.76). Model validity was tested using response permutation and results were suggestive of excellent predictive power. Application of PLS-DA to the data set also revealed clear discrimination of Stage I-III and Stage IV disease states, with the following model parameters, R2 = 0.62; Q2 =0.45. Conclusions: Urinary metabolomics detected clear differences in metabolic profiles of pancreatic cancer patients and healthy volunteers. Early results presented here suggest that metabolomic approaches may facilitate discovery of novel biomarkers capable of early disease detection.
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Volani, Chiara, Johannes Rainer, Vinicius Veri Hernandes, Viviana Meraviglia, Peter Paul Pramstaller, Sigurður Vidir Smárason, Giulio Pompilio, et al. "Metabolic Signature of Arrhythmogenic Cardiomyopathy." Metabolites 11, no. 4 (March 25, 2021): 195. http://dx.doi.org/10.3390/metabo11040195.
Abstract:
Arrhythmogenic cardiomyopathy (ACM) is a genetic-based cardiac disease accompanied by severe ventricular arrhythmias and a progressive substitution of the myocardium with fibro-fatty tissue. ACM is often associated with sudden cardiac death. Due to the reduced penetrance and variable expressivity, the presence of a genetic defect is not conclusive, thus complicating the diagnosis of ACM. Recent studies on human induced pluripotent stem cells-derived cardiomyocytes (hiPSC-CMs) obtained from ACM individuals showed a dysregulated metabolic status, leading to the hypothesis that ACM pathology is characterized by an impairment in the energy metabolism. However, despite efforts having been made for the identification of ACM specific biomarkers, there is still a substantial lack of information regarding the whole metabolomic profile of ACM patients. The aim of the present study was to investigate the metabolic profiles of ACM patients compared to healthy controls (CTRLs). The targeted Biocrates AbsoluteIDQ® p180 assay was used on plasma samples. Our analysis showed that ACM patients have a different metabolome compared to CTRLs, and that the pathways mainly affected include tryptophan metabolism, arginine and proline metabolism and beta oxidation of fatty acids. Altogether, our data indicated that the plasma metabolomes of arrhythmogenic cardiomyopathy patients show signs of endothelium damage and impaired nitric oxide (NO), fat, and energy metabolism.
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García Verdejo, Francisco José, Ana Laura Ortega Granados, Caridad Díaz Navarro, Natalia Luque Caro, David Fernández Garay, Maria Carmen Álamo de la Gala, María Ruiz Sanjuan, et al. "A metabolomic signature for predicting chemosensitivity in gastric cancer." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e15504-e15504. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e15504.
Abstract:
e15504 Background: Perioperative chemotherapy (QT) with platinum and fluoropyrimidines with or without anthracyclines is recommended option in patients with resectable gastric cancer (GC) at least cT2 or nodal involvement. Another option is surgery followed by QT with radiotherapy (QT/RT) or QT without RT in patients with D2 lymphadenectomy. Unfortunately, a considerable percentage of patients progress during neoadjuvant-QT (neo-QT) and some cases become inoperable cancer. These patients could benefit from curative surgery after diagnosis without neo-QT. Currently, histological/molecular markers have not been established to predict which patients can benefit from neo-QT. As potent analysis method, study of blood metabolites of resectable GC patients to establish a profile to differentiate responder patients (R-P) or not-responder (NR-P) to neoadjuvant-QT is promising. To establish a metabolomic profile or metabolomic signature and correlate with chemosensitivity, defined as pathological and clinical response is our endpoint. Methods: To this end we performend an untargeted metabolomic analysis by LC-HRMS of serum samples from resectable GC patients before neo-QT (n = 20 vs n = 10 healthy controls). Chemosensitive tumors were defined as those with good pathological response (Mandard 1 or 2) and partial response by TAC and chemoresistance tumors, defined as those with poor pathological response (Mandard 5) or/and progression by TAC. Reverse phase and HILIC chromatographic modes were applied to deal with highly polar as well as hydrophobic as required for untargeted metabolomics. For identification of potential biomarkers, we used in combination 2 independent variable selection techniques: principal component analysis and Student t test. Results: 11 patients were R-P and 9 patients were NR-P. We observed differences in metabolic profile between patients with GC & healthy controls and R-P & NR-P to neo-QT. Seven identified metabolites contributed most to the differentiating between R-P and NR-P. Conclusions: There are different metabolomic phenotypes among patients R-P and NR-P to neo-QT. It is necessary to validate a metabolomic signature to allow effective chemosensitivity prediction in patients with resectable GC.
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Davis, Vanessa Wylie, Dan E. Schiller, and Michael B. Sawyer. "The urinary metabolomic signature of Barrett's esophagus." Journal of Clinical Oncology 30, no. 4_suppl (February 1, 2012): 36. http://dx.doi.org/10.1200/jco.2012.30.4_suppl.36.
Abstract:
36 Background: Current screening and surveillance strategies for Barrett’s esophagus are inadequate. More reliable tools are needed. A unique urinary metabolomic signature could fill this niche. We applied metabolomic techniques to identify urinary metabolites capable of facilitating in the diagnosis of Barrett’s esophagus. Methods: Urine samples from patients with histologically confirmed Barrett’s esophagus (n=32) and normal, healthy volunteers (n=25) were collected and examined using 1H-NMR spectroscopy. Targeted profiling of spectra using Chenomx NMR Suite 7.0 software permitted the detection and quantification of 66 distinct metabolites. Unsupervised (principal component analysis, PCA) and supervised (partial-least squares discriminant analysis, PLS-DA) multivariate pattern recognition techniques were applied to discriminate between sample spectra of patients with Barrett’s esophagus and healthy volunteers using SIMCA-P (version 11, Umetrics, Umeå, Sweden). Results: Significant differences were found when comparing the concentrations of 59 metabolites in the urine of healthy volunteers and patients with Barrett’s esophagus. Those metabolites contributing the most class discriminating information included 3-hydoxybutyrate, adipate and choline. Clear distinction between patients with Barrett’s esophagus and healthy controls was noted when PLS-DA was applied to the data set. Model parameters for both the goodness of fit R2, and the predictive capability Q2, were high (R2 = 0.96; Q2 = 0.90). Model validity was tested using response permutation and results were suggestive of excellent predictive power. Conclusions: Urinary metabolomics identified a discrete signature associated with Barrett’s esophagus compared to healthy controls. This profile has the potential to aid in diagnosis and the development of new therapeutic targets.
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Haslam, Danielle, Jun Li, Marta Guasch-Ferre, Liming Liang, Clary B. Clish, JoAnn Manson, Deirdre Tobias, et al. "Plasma Metabolomic Signatures of Sugar-Sweetened Beverage Consumption and Risk of Type 2 Diabetes Among US Adults." Current Developments in Nutrition 5, Supplement_2 (June 2021): 1040. http://dx.doi.org/10.1093/cdn/nzab053_033.
Abstract:
Abstract Objectives Sugar-sweetened beverage (SSB) consumption is associated with a higher risk of type 2 diabetes (T2D), but the metabolic changes linking SSB consumption to T2D are not fully understood. Thus, we aimed to identify a plasma metabolomic signature of SSB consumption and evaluate its association with incident T2D. Methods We used liquid chromatography–mass spectrometry to measure plasma metabolites (>200) among 3,434 participants from three US cohorts: Nurses’ Health Study (NHS), NHS II, and Health Professionals Follow-up Study (HPFS). SSB consumption (servings/day; sodas, fruit punches, and other sugary drinks) was estimated from food frequency questionnaires. We used elastic net regression with 10-fold-cross-validation to identify metabolites associated with higher SSB consumption among a training set of participants (n = 2068) and replicated the association in a testing set (n = 1366). A metabolomic signature score was calculated as the weighted sum of SSB-associated metabolites. Pearson correlation (r) coefficients and 95% confidence intervals (CI) between the metabolomic signature and self-reported SSB consumption were calculated. We used multivariable Cox regression models to estimate hazard ratios (HR) and CI of the identified metabolomic signature with incident T2D among all participants. Results We identified an SSB plasma metabolomic signature of 71 metabolites, primarily lipids and amino acids. Pearson correlation (r) coefficients between self-reported SSBs and the plasma metabolomic signature were 0.18 (95% CI: 0.14, 0.22; P < 0.0001) and 0.19 (95% CI: 0.14, 0.24; P < 0.0001) in the training and testing sets, respectively. After a median follow-up of 22 years, the metabolomic signature was significantly associated with higher T2D risk [HR for quartile (Q) 1 versus 4 (95% CI): 1.45 (1.02, 2.05); P = 0.04] in models adjusting for factors related to demographics, lifestyle, diet, and body mass index. The association persisted when further adjusting for self-reported SSB consumption [HR for Q1 versus Q4 (95% CI): 1.42 (1.00, 2.02); P = 0.05]. Conclusions We identified a novel metabolomic signature of SSB consumption in US adults that associated with elevated incident T2D risk. This signature may reflect both SSB consumption and metabolic changes related to T2D risk, although residual confounding cannot be ruled out. Funding Sources NIH.
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Yuan, Fangcheng, Danxia Yu, Wanqing Wen, and Wei Zheng. "Abstract 6452: A plasma metabolomic signature of healthy lifestyles and risk of colorectal cancer in the UK Biobank." Cancer Research 83, no. 7_Supplement (April 4, 2023): 6452. http://dx.doi.org/10.1158/1538-7445.am2023-6452.
Abstract:
Abstract Background: Lifestyle factors contribute to the etiology of colorectal cancer (CRC). We aimed to derive a metabolomic signature of adherence to healthy lifestyles and examine its association with CRC risk. Methods: In the analysis, we included 98,898 UK Biobank participants with 168 metabolomic biomarkers measured by nuclear magnetic resonance spectroscopy from baseline plasma samples. A healthy lifestyle score (HLS) was derived from 8 lifestyle factors according to the American Cancer Society (ACS) guidelines. We applied elastic net regularized regressions to identify a metabolomic signature from 130 biomarkers. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of metabolomic signature with CRC risk after adjusting for known confounders. Results: During a median follow-up time of 8.2 years, 818 incident primary CRC cases were identified after participants were enrolled for more than two years. We observed a strong partial correlation between the metabolomic signature and HLS (r = 0.34), as well as waist-to-hip ratio, sedentary time, red and processed meat intake, and tobacco smoking aspects of the HLS (r = 0.14-0.33). A higher HLS was characterized by a signature of increased levels of linoleic acid, total cholines, and polyunsaturated fatty acids and decreased levels of omega-6 fatty acids, phospholipids in intermediate-density lipoprotein, and phosphatidylcholines. The metabolomic signature showed a significantly inverse association with CRC risk (HR = 0.84; 95% CI = 0.80-0.89 per standard deviation increment). The significantly inverse associations were observed irrespective of sex, educational attainment, and CRC subsite. Stronger associations were seen among participants aged between 50 and 60 years at study enrollment, and those diagnosed with CRC at 60 years old or later. Conclusion: We identified a metabolomic signature characterizing adherence to the ACS guidelines and showed that the signature was inversely associated with CRC risk. Citation Format: Fangcheng Yuan, Danxia Yu, Wanqing Wen, Wei Zheng. A plasma metabolomic signature of healthy lifestyles and risk of colorectal cancer in the UK Biobank [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6452.
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D’Andréa, Grégoire, Lun Jing, Isabelle Peyrottes, Jean-Marie Guigonis, Fanny Graslin, Sabine Lindenthal, Julie Sanglier, et al. "Pilot Study on the Use of Untargeted Metabolomic Fingerprinting of Liquid-Cytology Fluids as a Diagnostic Tool of Malignancy for Thyroid Nodules." Metabolites 13, no. 7 (June 23, 2023): 782. http://dx.doi.org/10.3390/metabo13070782.
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Although it is the gold standard for assessing the malignancy of thyroid nodules (TNs) preoperatively, the cytological analysis of fine-needle aspiration cytology (FNAC) samples results in 20–30% of cases in indeterminate lesions (ITNs). As two-thirds of these lesions will appear benign after diagnostic surgery, improved preoperative diagnostic methods need to be developed. In this pilot study, we evaluate if the metabolomic profiles of liquid-based (CytoRich®) FNAC samples of benign and malignant nodules can allow the molecular diagnosis of TNs. We performed untargeted metabolomic analyses with CytoRich® FNAC in a monocentric retrospective study. The cohort was composed of cytologically benign TNs, histologically benign or papillary thyroid carcinomas (PTCs) cytologically ITNs, and suspicious/malignant TNs histologically confirmed as PTCs. The diagnostic performance of the identified metabolomic signature was assessed using several supervised classification methods. Seventy-eight patients were enrolled in the study. We identified 7690 peaks, of which 2697 ions were included for further analysis. We selected a metabolomic signature composed of the top 15 metabolites. Among all the supervised classification methods, the supervised autoencoder deep neural network exhibited the best performance, with an accuracy of 0.957 (0.842–1), an AUC of 0.945 (0.833–1), and an F1 score of 0.947 (0.842–1). Here, we report a promising new ancillary molecular technique to differentiate PTCs from benign TNs (including among ITNs) based on the metabolomic signature of FNAC sample fluids. Further studies with larger cohorts are now needed to identify a larger number of biomarkers and obtain more robust signatures.
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Loras, Alba, M. Carmen Martínez-Bisbal, Guillermo Quintás, Salvador Gil, Ramón Martínez-Máñez, and José Luis Ruiz-Cerdá. "Urinary Metabolic Signatures Detect Recurrences in Non-Muscle Invasive Bladder Cancer." Cancers 11, no. 7 (June 29, 2019): 914. http://dx.doi.org/10.3390/cancers11070914.
Abstract:
Patients with non-muscle invasive bladder cancer (NMIBC) undergo lifelong monitoring based on repeated cystoscopy and urinary cytology due to the high recurrence rate of this tumor. Nevertheless, these techniques have some drawbacks, namely, low accuracy in detection of low-grade tumors, omission of pre-neoplastic lesions and carcinomas in situ (CIS), invasiveness, and high costs. This work aims to identify a urinary metabolomic signature of recurrence by proton Nuclear Magnetic Resonance (1H NMR) spectroscopy for the follow-up of NMIBC patients. To do this, changes in the urinary metabolome before and after transurethral resection (TUR) of tumors are analyzed and a Partial Least Square Discriminant Analysis (PLS-DA) model is developed. The usefulness of this discriminant model for the detection of tumor recurrences is assessed using a cohort of patients undergoing monitoring. The trajectories of the metabolomic profile in the follow-up period provide a negative predictive value of 92.7% in the sample classification. Pathway analyses show taurine, alanine, aspartate, glutamate, and phenylalanine perturbed metabolism associated with NMIBC. These results highlight the potential of 1H NMR metabolomics to detect bladder cancer (BC) recurrences through a non-invasive approach.
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Malagelada, Carolina, Teodora Pribic, Barbara Ciccantelli, Nicolau Cañellas, Josep Gomez, Nuria Amigo, Anna Accarino, Xavier Correig, and Fernando Azpiroz. "Metabolomic signature of the postprandial experience." Neurogastroenterology & Motility 30, no. 12 (August 13, 2018): e13447. http://dx.doi.org/10.1111/nmo.13447.
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Allegra, Alessandro, Vanessa Innao, Demetrio Gerace, Oriana Bianco, and Caterina Musolino. "The metabolomic signature of hematologic malignancies." Leukemia Research 49 (October 2016): 22–35. http://dx.doi.org/10.1016/j.leukres.2016.08.002.
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Walker, Maura, Thomas Travison, Joanne Murabito, Yi-Hsiang Hsu, Marian Hannan, Douglas Kiel, Paul Jacques, Debby Ngo, and Shivani Sahni. "Association of Serum Metabolites With Frailty in Community-Dwelling Older Adults: The Framingham Offspring Study." Current Developments in Nutrition 5, Supplement_2 (June 2021): 62. http://dx.doi.org/10.1093/cdn/nzab033_062.
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Abstract Objectives Metabolomic markers may provide deeper insight into the underlying biology of frailty. Yet, little data exist characterizing metabolomic correlates of frailty. The objective of this cross-sectional study was to determine the association of serum metabolites with frailty. Methods We evaluated 2187 participants from the Framingham Offspring Study (mean age; 62y; 53% women) with metabolomic and frailty data. Metabolites (n = 217) were quantified using liquid chromatography/tandem mass spectrometry (1990–95). Frailty was assessed using Fried's frailty phenotype (1998–01). Multivariable logistic regression models were used to relate individual metabolites with odds of frailty. In 1,815 participants, we created a metabolomic signature: sum of (serum metabolite concentration*β coefficient). Metabolites were included based on the strength of their individual association with frailty (FDR ≤ 0.05). Logistic regression was used to relate the metabolomic signature with odds of frailty. C-statistics determined if the metabolomic signature improved the prediction of frailty beyond a standard model. Models were adjusted for age, sex, BMI, and smoking. Results Adjusting for age and sex, 24 metabolites were significantly associated with frailty (22 positive and 2 negative; all FDR ≤ 0.05). A majority of metabolites were lipids (n = 19) of the phosphatidylcholine (n = 7) and triacylglycerol (n = 10) sub-classes with high degree of saturation (n = 13 with <2 carbon double-bonds). Additional metabolites have roles in amino acid and cellular metabolism (lactate, asparagine, phosphocreatine, glycerol, and isocitrate). In an age and sex-adjusted model, a weighted signature of the 24 metabolites was associated with increased odds of frailty (OR: 2.0, CI: 1.5, 2.7). C-statistics to predict frailty improved beyond a standard model by the addition of the weighted metabolomic signature (C-statistics: 0.70 vs 0.61). Results were similar with further adjustment for BMI and smoking. Conclusions Frailty was associated with 24 distinct metabolites and with a metabolomic signature in this study of older adults. These findings may inform future investigations on the progression of frailty and warrant replication in independent populations. Funding Sources AHA and Boston Pepper Center OAIC
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Sebastiani, Paola, Zeyuan Sog, Michael Lustgarten, Thomas Perls, Stefano Monti, Stacy Andersen, Michaela Schwaiger-Haber, and Gary Patti. "METABOLOMIC SIGNATURES OF AGE, EXTREME OLD AGE, AND LONGEVITY IN LONG LIFE FAMILY STUDY PARTICIPANTS." Innovation in Aging 7, Supplement_1 (December 1, 2023): 631. http://dx.doi.org/10.1093/geroni/igad104.2057.
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Abstract Serum metabolomics of aging has been a growing area of interest and several studies have identified metabolites that correlate with chronological age. The Long Life Family Study (LLFS) has generated serum metabolomics of individuals aged 30 to 110 years and several years of follow up, and offers a unique opportunity to identify (1) a robust signature of age; (2) a signature of extreme old age that differs from the age-related signature; and (3) a signature that predict survival. We analyzed 409 general metabolites and lipid species in approximately 2700 LLFS participants and used a state-of-the-art computational approach with mixed effect models and whole genome sequence data to model within family relation. The analysis identified 305 metabolites that correlate with age at 5% false discovery rate (FDR), 30 metabolites that are not age related and differ in centenarians compared to younger individuals, and 144 metabolites that predict survival at 5%FDR. The aging signature included well known markers: eg ergothioneine and tryptophan that decreased with older age, and was enriched for carbohydrates, organic acids, and several lipid classes. The extreme old age signature was enriched for glycerolipids and glycerophospholipids. The metabolomics signature of survival was enriched for nucleic and organic acids. Comparison with other studies showed strong agreement of results and also highlighted unique finding in extreme old individuals. The analysis also showed substantial variability of serum metabolomics at different ages, thus confirming the heterogeneity of molecular signatures of age and the opportunity to discover specific molecular profiles that promote heathy aging.
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Yang, Kaifeng, Zhiyu Kang, Weihua Guan, Sahar Lotfi-Emran, Zachary J. Mayer, Candace R. Guerrero, Brian T. Steffen, et al. "Developing A Baseline Metabolomic Signature Associated with COVID-19 Severity: Insights from Prospective Trials Encompassing 13 U.S. Centers." Metabolites 13, no. 11 (October 24, 2023): 1107. http://dx.doi.org/10.3390/metabo13111107.
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Metabolic disease is a significant risk factor for severe COVID-19 infection, but the contributing pathways are not yet fully elucidated. Using data from two randomized controlled trials across 13 U.S. academic centers, our goal was to characterize metabolic features that predict severe COVID-19 and define a novel baseline metabolomic signature. Individuals (n = 133) were dichotomized as having mild or moderate/severe COVID-19 disease based on the WHO ordinal scale. Blood samples were analyzed using the Biocrates platform, providing 630 targeted metabolites for analysis. Resampling techniques and machine learning models were used to determine metabolomic features associated with severe disease. Ingenuity Pathway Analysis (IPA) was used for functional enrichment analysis. To aid in clinical decision making, we created baseline metabolomics signatures of low-correlated molecules. Multivariable logistic regression models were fit to associate these signatures with severe disease on training data. A three-metabolite signature, lysophosphatidylcholine a C17:0, dihydroceramide (d18:0/24:1), and triacylglyceride (20:4_36:4), resulted in the best discrimination performance with an average test AUROC of 0.978 and F1 score of 0.942. Pathways related to amino acids were significantly enriched from the IPA analyses, and the mitogen-activated protein kinase kinase 5 (MAP2K5) was differentially activated between groups. In conclusion, metabolites related to lipid metabolism efficiently discriminated between mild vs. moderate/severe disease. SDMA and GABA demonstrated the potential to discriminate between these two groups as well. The mitogen-activated protein kinase kinase 5 (MAP2K5) regulator is differentially activated between groups, suggesting further investigation as a potential therapeutic pathway.
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Ismaeel, Ahmed, Marco E. Franco, Ramon Lavado, Evlampia Papoutsi, George P. Casale, Matthew Fuglestad, Constance J. Mietus, et al. "Altered Metabolomic Profile in Patients with Peripheral Artery Disease." Journal of Clinical Medicine 8, no. 9 (September 14, 2019): 1463. http://dx.doi.org/10.3390/jcm8091463.
Abstract:
Peripheral artery disease (PAD) is a common atherosclerotic disease characterized by narrowed or blocked arteries in the lower extremities. Circulating serum biomarkers can provide significant insight regarding the disease progression. Here, we explore the metabolomics signatures associated with different stages of PAD and investigate potential mechanisms of the disease. We compared the serum metabolites of a cohort of 26 PAD patients presenting with claudication and 26 PAD patients presenting with critical limb ischemia (CLI) to those of 26 non-PAD controls. A difference between the metabolite profiles of PAD patients from non-PAD controls was observed for several amino acids, acylcarnitines, ceramides, and cholesteryl esters. Furthermore, our data demonstrate that patients with CLI possess an altered metabolomic signature different from that of both claudicants and non-PAD controls. These findings provide new insight into the pathophysiology of PAD and may help develop future diagnostic procedures and therapies for PAD patients.
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Liu, Yin Allison, Orwa Aboud, Lina A. Dahabiyeh, Orin Bloch, and Oliver Fiehn. "Metabolomic characterization of human glioblastomas and patient plasma: a pilot study." F1000Research 13 (February 16, 2024): 98. http://dx.doi.org/10.12688/f1000research.143642.1.
Abstract:
Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.
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Liu, Yin Allison, Orwa Aboud, Lina A. Dahabiyeh, Orin Bloch, and Oliver Fiehn. "Metabolomic characterization of human glioblastomas and patient plasma: a pilot study." F1000Research 13 (June 25, 2024): 98. http://dx.doi.org/10.12688/f1000research.143642.2.
Abstract:
Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.
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Diab, Joseph, Terkel Hansen, Rasmus Goll, Hans Stenlund, Einar Jensen, Thomas Moritz, Jon Florholmen, and Guro Forsdahl. "Mucosal Metabolomic Profiling and Pathway Analysis Reveal the Metabolic Signature of Ulcerative Colitis." Metabolites 9, no. 12 (November 27, 2019): 291. http://dx.doi.org/10.3390/metabo9120291.
Abstract:
The onset of ulcerative colitis (UC) is characterized by a dysregulated mucosal immune response triggered by several genetic and environmental factors in the context of host–microbe interaction. This complexity makes UC ideal for metabolomic studies to unravel the disease pathobiology and to improve the patient stratification strategies. This study aims to explore the mucosal metabolomic profile in UC patients, and to define the UC metabolic signature. Treatment- naïve UC patients (n = 18), UC patients in deep remission (n = 10), and healthy volunteers (n = 14) were recruited. Mucosa biopsies were collected during colonoscopies. Metabolomic analysis was performed by combined gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS). In total, 177 metabolites from 50 metabolic pathways were identified. The most prominent metabolome changes among the study groups were in lysophosphatidylcholine, acyl carnitine, and amino acid profiles. Several pathways were found perturbed according to the integrated pathway analysis. These pathways ranged from amino acid metabolism (such as tryptophan metabolism) to fatty acid metabolism, namely linoleic and butyrate. These metabolic changes during UC reflect the homeostatic disturbance in the gut, and highlight the importance of system biology approaches to identify key drivers of pathogenesis which prerequisite personalized medicine.
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Collet, Tinh-Hai, Takuhiro Sonoyama, Elana Henning, Julia M. Keogh, Brian Ingram, Sarah Kelway, Lining Guo, and I. Sadaf Farooqi. "A Metabolomic Signature of Acute Caloric Restriction." Journal of Clinical Endocrinology & Metabolism 102, no. 12 (September 28, 2017): 4486–95. http://dx.doi.org/10.1210/jc.2017-01020.
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Surendran, Arun, Andrea Edel, Mahesh Chandran, Pascal Bogaert, Pedram Hassan-Tash, Aneesh Kumar Asokan, Brett Hiebert, et al. "Metabolomic Signature of Human Aortic Valve Stenosis." JACC: Basic to Translational Science 5, no. 12 (December 2020): 1163–77. http://dx.doi.org/10.1016/j.jacbts.2020.10.001.
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Diab, J., T. Hansen, R. Goll, H. Stenlund, E. Jensen, T. Moritz, J. Florholmen, and G. Forsdahl. "P003 Metabolomics for improved patient stratification in inflammatory bowel disease: Characterisation of the ulcerative colitis metabolome." Journal of Crohn's and Colitis 14, Supplement_1 (January 2020): S130—S131. http://dx.doi.org/10.1093/ecco-jcc/jjz203.132.
Abstract:
Abstract Background The onset of ulcerative colitis (UC) is characterised by a dysregulated mucosal immune response triggered by several genetic and environmental factors in the context of host-microbe interaction. This complexity makes UC ideal for metabolomic studies to unravel the disease pathobiology and to improve the patient stratification strategies toward personalised medicine. This study aims to explore the mucosal metabolomic profile in treatment-naïve and deep remission UC patients, and to define the metabolic signature of UC. Methods Treatment-naive UC patients (n = 18), UC patients in deep remission (n = 10), and healthy volunteers (n = 14) were recruited. Mucosa biopsies were collected during colonoscopy. The UC activity and the state of deep remission were assessed by endoscopy, histology, and by measuring TNF gene expression. Metabolomic analysis was performed by combined gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS). In total, 177 metabolites from 50 metabolic pathways were identified. Results Multivariate data analysis revealed a distinct metabolomic profile in inflamed mucosa taken from treatment- naïve UC patients compared with non-inflamed mucosa taken from UC remission patients and healthy controls. The mucosal metabolome in UC remission patients differed to a lesser extent from the healthy controls. The most prominent metabolome changes among the study groups were in lysophosphatidylcholine, acylcarnitine, and amino acid profiles. Several metabolic pathways were perturbed, ranging from amino acid metabolism (such as tryptophan metabolism, and alanine, aspartate and glutamate metabolism) to antioxidant defence pathway (glutathione pathway). Furthermore, the pathway analysis revealed a disruption in the long-and short-chain fatty acid (LCFA and SCFA) metabolism, namely linoleic metabolism and butyrate metabolism. Conclusion The mucosal metabolomic profiling revealed a metabolic signature during the onset of UC, and reflected the homeostatic disturbance in the gut. The altered metabolic pathways highlight the importance of system biology approaches to identify key drivers of IBD pathogenesis which prerequisite personalised treatment.
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Kang, Jihyun, Jeong Yeon Kim, Youjin Jung, Seon Uk Kim, Eun Young Lee, and Joo-Youn Cho. "Identification of Metabolic Signature Associated with Idiopathic Inflammatory Myopathy Reveals Polyamine Pathway Alteration in Muscle Tissue." Metabolites 12, no. 10 (October 21, 2022): 1004. http://dx.doi.org/10.3390/metabo12101004.
Abstract:
Idiopathic inflammatory myopathy (IIM) is hard to diagnose without a muscle biopsy. We aimed to identify a metabolite panel for IIM detection by metabolomics approach in serum samples and to explore the metabolomic signature in tissue samples from a mouse model. We obtained serum samples from IIM patients, ankylosing spondylitis (AS) patients, healthy volunteers and muscle tissue samples from IIM murine model. All samples were subjected to a targeted metabolomic approach with various statistical analyses on serum and tissue samples to identify metabolic alterations. Three machine learning methods, such as logistic regression (LR), support vector machine (SVM), and random forest (RF), were applied to build prediction models. A set of 7 predictive metabolites was calculated using backward stepwise selection, and the model was evaluated within 5-fold cross-validation by using three machine algorithms. The model produced an area under the receiver operating characteristic curve values of 0.955 (LR), 0.908 (RF) and 0.918 (SVM). A total of 68 metabolites were significantly changed in mouse tissue. Notably, the most influential pathways contributing to the inflammation of muscle were the polyamine pathway and the beta-alanine pathway. Our metabolomic approach offers the potential biomarkers of IIM and reveals pathologically relevant metabolic pathways that are associated with IIM.
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Bailleux, Caroline, David Chardin, Jocelyn Gal, Jean-Marie Guigonis, Sabine Lindenthal, Fanny Graslin, Laurent Arnould, et al. "Metabolomic Signatures of Scarff–Bloom–Richardson (SBR) Grade in Non-Metastatic Breast Cancer." Cancers 15, no. 7 (March 23, 2023): 1941. http://dx.doi.org/10.3390/cancers15071941.
Abstract:
Purpose: Identification of metabolomic biomarkers of high SBR grade in non-metastatic breast cancer. Methods: This retrospective bicentric metabolomic analysis included a training set (n = 51) and a validation set (n = 49) of breast cancer tumors, all classified as high-grade (grade III) or low-grade (grade I–II). Metabolomes of tissue samples were studied by liquid chromatography coupled with mass spectrometry. Results: A molecular signature of the top 12 metabolites was identified from a database of 602 frequently predicted metabolites. Partial least squares discriminant analyses showed that accuracies were 0.81 and 0.82, the R2 scores were 0.57 and 0.55, and the Q2 scores were 0.44431 and 0.40147 for the training set and validation set, respectively; areas under the curve for the Receiver Operating Characteristic Curve were 0.882 and 0.886. The most relevant metabolite was diacetylspermine. Metabolite set enrichment analyses and metabolic pathway analyses highlighted the tryptophan metabolism pathway, but the concentration of individual metabolites varied between tumor samples. Conclusions: This study indicates that high-grade invasive tumors are related to diacetylspermine and tryptophan metabolism, both involved in the inhibition of the immune response. Targeting these pathways could restore anti-tumor immunity and have a synergistic effect with immunotherapy. Recent studies could not demonstrate the effectiveness of this strategy, but the use of theragnostic metabolomic signatures should allow better selection of patients.
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Troisi, Jacopo, Federica Belmonte, Antonella Bisogno, Luca Pierri, Angelo Colucci, Giovanni Scala, Pierpaolo Cavallo, et al. "Metabolomic Salivary Signature of Pediatric Obesity Related Liver Disease and Metabolic Syndrome." Nutrients 11, no. 2 (January 26, 2019): 274. http://dx.doi.org/10.3390/nu11020274.
Abstract:
Pediatric obesity-related metabolic syndrome (MetS) and nonalcoholic fatty liver disease (NAFLD) are increasingly frequent conditions with a still-elusive diagnosis and low-efficacy treatment and monitoring options. In this study, we investigated the salivary metabolomic signature, which has been uncharacterized to date. In this pilot-nested case-control study over a transversal design, 41 subjects (23 obese patients and 18 normal weight (NW) healthy controls), characterized based on medical history, clinical, anthropometric, and laboratory data, were recruited. Liver involvement, defined according to ultrasonographic liver brightness, allowed for the allocation of the patients into four groups: obese with hepatic steatosis ([St+], n = 15) and without hepatic steatosis ([St–], n = 8), and with (n = 10) and without (n = 13) MetS. A partial least squares discriminant analysis (PLS-DA) model was devised to classify the patients’ classes based on their salivary metabolomic signature. Pediatric obesity and its related liver disease and metabolic syndrome appear to have distinct salivary metabolomic signatures. The difference is notable in metabolites involved in energy, amino and organic acid metabolism, as well as in intestinal bacteria metabolism, possibly reflecting diet, fatty acid synthase pathways, and the strict interaction between microbiota and intestinal mucins. This information expands the current understanding of NAFLD pathogenesis, potentially translating into better targeted monitoring and/or treatment strategies in the future.
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di Meo, Nicola Antonio, Davide Loizzo, Savio Domenico Pandolfo, Riccardo Autorino, Matteo Ferro, Camillo Porta, Alessandro Stella, et al. "Metabolomic Approaches for Detection and Identification of Biomarkers and Altered Pathways in Bladder Cancer." International Journal of Molecular Sciences 23, no. 8 (April 10, 2022): 4173. http://dx.doi.org/10.3390/ijms23084173.
Abstract:
Metabolomic analysis has proven to be a useful tool in biomarker discovery and the molecular classification of cancers. In order to find new biomarkers, and to better understand its pathological behavior, bladder cancer also has been studied using a metabolomics approach. In this article, we review the literature on metabolomic studies of bladder cancer, focusing on the different available samples (urine, blood, tissue samples) used to perform the studies and their relative findings. Moreover, the multi-omic approach in bladder cancer research has found novel insights into its metabolic behavior, providing excellent start-points for new diagnostic and therapeutic strategies. Metabolomics data analysis can lead to the discovery of a “signature pathway” associated with the progression of bladder cancer; this aspect could be potentially valuable in predictions of clinical outcomes and the introduction of new treatments. However, further studies are needed to give stronger evidence and to make these tools feasible for use in clinical practice.
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Troisi, Jacopo, Angelo Colucci, Pierpaolo Cavallo, Sean Richards, Steven Symes, Annamaria Landolfi, Giovanni Scala, et al. "A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer." Applied Sciences 11, no. 6 (March 22, 2021): 2835. http://dx.doi.org/10.3390/app11062835.
Abstract:
Bladder cancer has a high incidence and is marked by high morbidity and mortality. Early diagnosis is still challenging. The objective of this study was to create a metabolomics-based profile of bladder cancer in order to provide a novel approach for disease screening and stratification. Moreover, the study characterized the metabolic changes associated with the disease. Serum metabolomic profiles were obtained from 149 bladder cancer patients and 81 healthy controls. Different ensemble machine learning models were built in order to: (1) differentiate cancer patients from controls; (2) stratify cancer patients according to grading; (3) stratify patients according to cancer muscle invasiveness. Ensemble machine learning models were able to discriminate well between cancer patients and controls, between high grade (G3) and low grade (G1-2) cancers and between different degrees of muscle invasivity; ensemble model accuracies were ≥80%. Relevant metabolites, selected using the partial least square discriminant analysis (PLS-DA) algorithm, were included in a metabolite-set enrichment analysis, showing perturbations primarily associated with cell glucose metabolism. The metabolomic approach may be useful as a non-invasive screening tool for bladder cancer. Furthermore, metabolic pathway analysis can increase understanding of cancer pathophysiology. Studies conducted on larger cohorts, and including blind trials, are needed to validate results.
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Araújo, Ana Margarida, Maria Enea, Félix Carvalho, Maria de Lourdes Bastos, Márcia Carvalho, and Paula Guedes de Pinho. "Hepatic Metabolic Derangements Triggered by Hyperthermia: An In Vitro Metabolomic Study." Metabolites 9, no. 10 (October 15, 2019): 228. http://dx.doi.org/10.3390/metabo9100228.
Abstract:
Background and aims: Liver toxicity is a well-documented and potentially fatal adverse complication of hyperthermia. However, the impact of hyperthermia on the hepatic metabolome has hitherto not been investigated. Methods: In this study, gas chromatography-mass spectrometry (GC-MS)-based metabolomics was applied to assess the in vitro metabolic response of primary mouse hepatocytes (PMH, n = 10) to a heat stress stimulus, i.e., after 24 h exposure to 40.5 °C. Metabolomic profiling of both intracellular metabolites and volatile metabolites in the extracellular medium of PMH was performed. Results: Multivariate analysis showed alterations in levels of 22 intra- and 59 extracellular metabolites, unveiling the capability of the metabolic pattern to discriminate cells exposed to heat stress from cells incubated at normothermic conditions (37 °C). Hyperthermia caused a considerable loss of cell viability that was accompanied by significant alterations in the tricarboxylic acid cycle, amino acids metabolism, urea cycle, glutamate metabolism, pentose phosphate pathway, and in the volatile signature associated with the lipid peroxidation process. Conclusion: These results provide novel insights into the mechanisms underlying hyperthermia-induced hepatocellular damage.
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Fabrile, Maria Pia, Sergio Ghidini, Augusta Caligiani, Federico Scali, Maria Olga Varrà, Veronica Lolli, Giovanni Loris Alborali, Adriana Ianieri, and Emanuela Zanardi. "1H NMR Metabolomics on Pigs’ Liver Exposed to Antibiotics Administration: An Explorative Study." Foods 12, no. 23 (November 25, 2023): 4259. http://dx.doi.org/10.3390/foods12234259.
Abstract:
An untargeted Nuclear Magnetic Resonance (NMR) spectroscopy-based metabolomics approach was applied as a first attempt to explore the metabolome of pigs treated with antibiotics. The final goal was to investigate the possibility of discriminating between antibiotic-treated (TX group) and untreated pigs (CTRL group), with the further perspective of identifying the authentication tools for antibiotic-free pork supply chains. In particular, 41 samples of pig liver were subjected to a biphasic extraction to recover both the polar and the non-polar metabolites, and the 1H NMR spectroscopy analysis was performed on the two separate extracts. Unsupervised (principal component analysis) and supervised (orthogonal partial least squares discriminant analysis) multivariate statistical analysis of 1H NMR spectra data in the range 0–9 ppm provided metabolomic fingerprinting useful for the discrimination of pig livers based on the antibiotic treatment to which they were exposed. Moreover, within the signature patterns, significant discriminating metabolites were identified among carbohydrates, choline and derivatives, amino acids and some lipid-class molecules. The encouraging findings of this exploratory study showed the feasibility of the untargeted metabolomic approach as a novel strategy in the authentication framework of pork supply chains and open a new horizon for a more in-depth investigation.
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Rattigan, Kevin M., Andrew W. Pountain, Clement Regnault, Fiona Achcar, Isabel M. Vincent, Carl S. Goodyear, and Michael P. Barrett. "Metabolomic profiling of macrophages determines the discrete metabolomic signature and metabolomic interactome triggered by polarising immune stimuli." PLOS ONE 13, no. 3 (March 14, 2018): e0194126. http://dx.doi.org/10.1371/journal.pone.0194126.
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Vonica, Camelia Larisa, Anca Daniela Farcas, Gabriela Roman, Andrada Alina Muresan, Adriana Fodor, Simona Cernea, and Carmen Emanuela Georgescu. "Metabolomic biomarkers of polycystic ovary syndrome related-obesity: a review of the literature." Revista Romana de Medicina de Laborator 28, no. 3 (July 1, 2020): 241–55. http://dx.doi.org/10.2478/rrlm-2020-0017.
Abstract:
AbstractBackground and objectives: Polycystic ovary syndrome (PCOS) displays a phenotype-dependent cardio-metabolic risk. By performing a systematic search of the literature, we aimed to summarize metabolomic signatures associated with obesity in PCOS women.Data sources and study eligibility criteria: We conducted a comprehensive search including: Embase, PubMed, and Web of Science until 31st of May 2019. We used the terms: metabolomics and polycystic ovary syndrome. We excluded the following papers: animal studies, studies that included only lean PCOS women, reviews, meta-analyses, results of interventional studies, those that did not apply metabolomic techniques.Results: The lipid signature in obese women with PCOS showed increased levels of free fatty acids (carnitine, adipic acid, linoleic acid, oleic acid) and lower levels of lysophosphatidylcholines and glycerolphosphocholine compared with non-obese PCOS women. Regarding carbohydrate metabolism, a decrease in citric and lactic acid levels characterized obese PCOS women. Decreased lactic acid in obese PCOS women suggests augmented insulin stimulated glucose muscle use in lean, but not in obese women. Considering amino acid metabolomic markers, valine, glycine, serine, threonine, isoleucine and lysine were higher in obese PCOS women. Patients with visceral obesity presented a diminished uptake of essential amino acids, BCAA, leucine and serine in the skeletal muscle. α-ketoglutarate was significantly higher in obese women with PCOS in comparison with lean women with PCOS, distinguishing these 2 subgroups of PCOS with high ‘predictive accuracy’.Limitations: Overall, a small number of studies have focused on the impact of obesity on the metabolic fingerprints of PCOS women. There is need for properly controlled, high-quality studies.Conclusions: There is compelling evidence of significant alterations in carbohydrate, lipid, and amino acid metabolism in women with PCOS and obesity. Metabolomics may identify new metabolic pathways involved in PCOS and improve our understanding of the complex relation between PCOS and obesity in order to personalize PCOS therapy.
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Jové, Mariona, Ricardo Collado, José Luís Quiles, Mari-Carmen Ramírez-Tortosa, Joaquim Sol, Maria Ruiz-Sanjuan, Mónica Fernandez, et al. "A plasma metabolomic signature discloses human breast cancer." Oncotarget 8, no. 12 (January 5, 2017): 19522–33. http://dx.doi.org/10.18632/oncotarget.14521.
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Zurowietz, A., Patrick P. Lehr, M. Kleb, N. Merkt, V. Gödde, H. Bednarz, K. Niehaus, and C. Zörb. "Training grapevines generates a metabolomic signature of wine." Food Chemistry 368 (January 2022): 130665. http://dx.doi.org/10.1016/j.foodchem.2021.130665.
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Gawlik, Aneta, Michael Shmoish, Michaela F. Hartmann, Ewa Malecka-Tendera, Stefan A. Wudy, and Ze’ev Hochberg. "Steroid Metabolomic Disease Signature of Nonsyndromic Childhood Obesity." Journal of Clinical Endocrinology & Metabolism 101, no. 11 (August 9, 2016): 4329–37. http://dx.doi.org/10.1210/jc.2016-1754.
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Heckmann, Matthias, Anna S. Runkel, Donna E. Sunny, Michaela F. Hartmann, Till Ittermann, and Stefan A. Wudy. "Steroid Metabolomic Signature in Term and Preterm Infants." Biomolecules 14, no. 2 (February 17, 2024): 235. http://dx.doi.org/10.3390/biom14020235.
Abstract:
Adrenal function is essential for survival and well-being of preterm babies. In addition to glucocorticoids, it has been hypothesized that C19-steroids (DHEA-metabolites) from the fetal zone of the adrenal gland may play a role as endogenous neuroprotective steroids. In 39 term-born (≥37 weeks gestational age), 42 preterm (30–36 weeks) and 51 early preterm (<30 weeks) infants 38 steroid metabolites were quantified by GC-MS in 24-h urinary samples. In each gestational age group, three distinctive cluster were identified by pattern analysis (k-means clustering). Individual steroidal fingerprints and clinical phenotype were analyzed at the 3rd day of life. Overall, the excretion rates of C21-steroids (glucocorticoid precursors, cortisol, and cortisone metabolites) were low (<99 μg/kg body weight/d) whereas the excretion rates of C19-steroids were up to 10 times higher. There was a shift to higher excretion rates of C19-steroids in both preterm groups compared to term infants but only minor differences in the distribution of C21-steroids. Comparable metabolic patterns were found between gestational age groups: Cluster 1 showed mild elevation of C21- and C19-steroids with the highest incidence of neonatal morbidities in term and severe intraventricular hemorrhage in early preterm infants. In cluster 2 lowest excretion in general was noted but no clinically unique phenotype. Cluster 3 showed highest elevation of C21-steroids and C19-steroids but no clinically unique phenotype. Significant differences in steroid metabolism between clusters are only partly reflected by gestational age and disease severity. In early preterm infants, higher excretion rates of glucocorticoids and their precursors were associated with severe cerebral hemorrhage. High excretion rates of C19-steroids in preterm infants may indicate a biological significance.
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Farshidfar, Farshad, Aalim M. Weljie, Karen A. Kopciuk, Robert Hilsden, S. Elizabeth McGregor, W. Donald Buie, Anthony MacLean, Hans J. Vogel, and Oliver F. Bathe. "A validated metabolomic signature for colorectal cancer: exploration of the clinical value of metabolomics." British Journal of Cancer 115, no. 7 (August 25, 2016): 848–57. http://dx.doi.org/10.1038/bjc.2016.243.
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Elshenawy, Summer, Sara E. Pinney, Tami Stuart, Paschalis-Thomas Doulias, Gabriella Zura, Samuel Parry, Michal A. Elovitz, et al. "The Metabolomic Signature of the Placenta in Spontaneous Preterm Birth." International Journal of Molecular Sciences 21, no. 3 (February 4, 2020): 1043. http://dx.doi.org/10.3390/ijms21031043.
Abstract:
The placenta is metabolically active and supports the growth of the fetus. We hypothesize that deficits in the capacity of the placenta to maintain bioenergetic and metabolic stability during pregnancy may result in spontaneous preterm birth (SPTB). To explore this hypothesis, we performed a nested cased control study of metabolomic signatures in placentas from women with SPTB (<36 weeks gestation) compared to normal pregnancies (≥38 weeks gestation). To control for the effects of gestational age on placenta metabolism, we also studied a subset of metabolites in non-laboring preterm and term Rhesus monkeys. Comprehensive quantification of metabolites demonstrated a significant elevation in the levels of amino acids, prostaglandins, sphingolipids, lysolipids, and acylcarnitines in SPTB placenta compared to term placenta. Additional quantification of placental acylcarnitines by tandem mass spectrometry confirmed the significant elevation in SPTB human, with no significant differences between midgestation and term placenta in Rhesus macaque. Fatty acid oxidation as measured by the flux of 3H-palmitate in SPTB placenta was lower than term. Collectively, significant and biologically relevant alterations in the placenta metabolome were identified in SPTB placenta. Altered acylcarnitine levels and fatty acid oxidation suggest that disruption in normal substrate metabolism is associated with SPTB.
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Bourgonje, A. R., S. Ibing, C. Argmann, B. E. Sands, M. Dubinsky, H. A. Jacobsen, L. Larsen, et al. "P439 Mild Crohn’s disease is characterized by a unique serum metabolomic signature." Journal of Crohn's and Colitis 18, Supplement_1 (January 1, 2024): i897—i898. http://dx.doi.org/10.1093/ecco-jcc/jjad212.0569.
Abstract:
Abstract Background Approximately 20-30% of patients with Crohn's disease (CD) experience a mild disease course following initial diagnosis. Balancing the effectiveness and safety of medical treatments is crucial in the management of mild CD. However, the main clinical challenge currently lies in identifying patients likely to maintain this mild disease course, underscoring the urgent need for biomarkers of mild CD. In this study, we therefore aimed to identify potential circulating biomarkers for patients with mild CD leveraging a metabolomics approach. Methods Patients with mild CD were retrospectively identified from participants of the Mount Sinai Crohn's and Colitis Registry (MSSCR). Baseline screenings in MSSCR included assessments of the use of biologicals and immunomodulators, history of IBD-related surgeries, and the presence of disease complications (including stricturing, penetrating, and perianal CD). Mild CD was defined as the absence of all these variables at baseline as well as after a 5-year follow-up period. Untargeted serum metabolomic profiling was performed to measure the circulating metabolome of these patients. Multivariable logistic regression analyses, adjusting for age, sex, measurement batch, and smoking behavior, were conducted to identify metabolites associated with mild CD. Results In total, 114 patients with CD were characterized in which 1,456 different metabolites were profiled, with 32 patients (28.1%) meeting the criteria for mild CD. The remaining 82 patients (71.9%) partially met criteria for mild CD; these were all biologic-naïve and did not undergo IBD-related surgeries. Multivariable logistic regression revealed 45 distinct metabolites that were significantly associated with the mild CD group (nominal P<0.05). Sphingomyelin compounds and ceramides exhibited the most prominent associations with mild CD (odds ratios [OR] all >2.0), while tryptophan, pantothenate, and salicylate metabolites were also linked to mild CD. Conversely, primary and secondary bile acid metabolites and amino acid derivatives from lysine-, tyrosine-, and proline metabolism were all inversely associated with mild CD. Conclusion Mild CD is associated with distinct alterations in metabolic pathways compared to patients with moderate-to-severe CD. Future research should focus on longitudinal assessments of these metabolites for disease monitoring and determining optimal therapeutic strategies for this clinical subgroup.
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Ciocan, Dragos, Anne-Marie Cassard, Laurent Becquemont, Céline Verstuyft, Cosmin Sebastian Voican, Khalil El Asmar, Romain Colle, et al. "Blood microbiota and metabolomic signature of major depression before and after antidepressant treatment: a prospective case–control study." Journal of Psychiatry & Neuroscience 46, no. 3 (May 1, 2021): E358—E368. http://dx.doi.org/10.1503/jpn.200159.
Abstract:
Background: The microbiota interacts with the brain through the gut–brain axis, and a distinct dysbiosis may lead to major depressive episodes. Bacteria can pass through the gut barrier and be found in the blood. Using a multiomic approach, we investigated whether a distinct blood microbiome and metabolome was associated with major depressive episodes, and how it was modulated by treatment. Methods: In this case–control multiomic study, we analyzed the blood microbiome composition, inferred bacterial functions and metabolomic profile of 56 patients experiencing a current major depressive episode and 56 matched healthy controls, before and after treatment, using 16S rDNA sequencing and liquid chromatography coupled to tandem mass spectrometry. Results: The baseline blood microbiome in patients with a major depressive episode was distinct from that of healthy controls (patients with a major depressive episode had a higher proportion of Janthinobacterium and lower levels of Neisseria) and changed after antidepressant treatment. Predicted microbiome functions confirmed by metabolomic profiling showed that patients who were experiencing a major depressive episode had alterations in the cyanoamino acid pathway at baseline. High baseline levels of Firmicutes and low proportions of Bosea and Tetrasphaera were associated with response to antidepressant treatment. Based on inferred baseline metagenomic profiles, bacterial pathways that were significantly associated with treatment response were related to xenobiotics, amino acids, and lipid and carbohydrate metabolism, including tryptophan and drug metabolism. Metabolomic analyses showed that plasma tryptophan levels are independently associated with response to antidepressant treatment. Limitations: Our study has some limitations, including a lack of information on blood microbiome origin and the lack of a validation cohort to confirm our results. Conclusion: Patients with depression have a distinct blood microbiome and metabolomic signature that changes after treatment. Dysbiosis could be a new therapeutic target and prognostic tool for the treatment of patients who are experiencing a major depressive episode.
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Halper-Stromberg, Eitan, Lucas Gillenwater, Charmion Cruickshank-Quinn, Wanda Kay O’Neal, Nichole Reisdorph, Irina Petrache, Yonghua Zhuang, et al. "Bronchoalveolar Lavage Fluid from COPD Patients Reveals More Compounds Associated with Disease than Matched Plasma." Metabolites 9, no. 8 (July 25, 2019): 157. http://dx.doi.org/10.3390/metabo9080157.
Abstract:
Smoking causes chronic obstructive pulmonary disease (COPD). Though recent studies identified a COPD metabolomic signature in blood, no large studies examine the metabolome in bronchoalveolar lavage (BAL) fluid, a more direct representation of lung cell metabolism. We performed untargeted liquid chromatography–mass spectrometry (LC–MS) on BAL and matched plasma from 115 subjects from the SPIROMICS cohort. Regression was performed with COPD phenotypes as the outcome and metabolites as the predictor, adjusted for clinical covariates and false discovery rate. Weighted gene co-expression network analysis (WGCNA) grouped metabolites into modules which were then associated with phenotypes. K-means clustering grouped similar subjects. We detected 7939 and 10,561 compounds in BAL and paired plasma samples, respectively. FEV1/FVC (Forced Expiratory Volume in One Second/Forced Vital Capacity) ratio, emphysema, FEV1 % predicted, and COPD exacerbations associated with 1230, 792, eight, and one BAL compounds, respectively. Only two plasma compounds associated with a COPD phenotype (emphysema). Three BAL co-expression modules associated with FEV1/FVC and emphysema. K-means BAL metabolomic signature clustering identified two groups, one with more airway obstruction (34% of subjects, median FEV1/FVC 0.67), one with less (66% of subjects, median FEV1/FVC 0.77; p < 2 × 10−4). Associations between metabolites and COPD phenotypes are more robustly represented in BAL compared to plasma.
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Ghini, Veronica, Walter Vieri, Tommaso Celli, Valentina Pecchioli, Nunzia Boccia, Tania Alonso-Vásquez, Lorenzo Pelagatti, et al. "COVID-19: A complex disease with a unique metabolic signature." PLOS Pathogens 19, no. 11 (November 9, 2023): e1011787. http://dx.doi.org/10.1371/journal.ppat.1011787.
Abstract:
Plasma of COVID-19 patients contains a strong metabolomic/lipoproteomic signature, revealed by the NMR analysis of a cohort of >500 patients sampled during various waves of COVID-19 infection, corresponding to the spread of different variants, and having different vaccination status. This composite signature highlights common traits of the SARS-CoV-2 infection. The most dysregulated molecules display concentration trends that scale with disease severity and might serve as prognostic markers for fatal events. Metabolomics evidence is then used as input data for a sex-specific multi-organ metabolic model. This reconstruction provides a comprehensive view of the impact of COVID-19 on the entire human metabolism. The human (male and female) metabolic network is strongly impacted by the disease to an extent dictated by its severity. A marked metabolic reprogramming at the level of many organs indicates an increase in the generic energetic demand of the organism following infection. Sex-specific modulation of immune response is also suggested.