Skip directly to search Skip directly to A to Z list Skip directly to navigation Skip directly to page options Skip directly to site content


Hot Topics of the Day|PHGKB
Search PHGKB:

Archive

Hot Topics of the Day are picked by experts to capture the latest information and publications on public health genomics and precision health for various diseases and health topics. Sources include published scientific literature, reviews, blogs and popular press articles.

Search Archive:
90 hot topic(s) found with the query "Metabolomics"

The Multi-Omic Approach to Newborn Screening: Opportunities and Challenges
(Posted: Jun 26, 2024 7AM)

From the abstract: "Genomic screening could offer opportunities for lifelong care beyond the newborn period. For genomic newborn screening to be effective and ready for routine adoption, it must overcome barriers such as implementation cost, public acceptability, and scalability. Metabolomics approaches, on the other hand, can offer insight into disease phenotypes and could be used to identify known and novel biomarkers of disease. Given recent advances in metabolomic technologies, alongside advances in genomics including whole-genome sequencing, the combination of complementary multi-omic approaches may provide an exciting opportunity to leverage the best of both approaches and overcome their respective limitations. These techniques are described, along with the current outlook on multi-omic-based NBS research. "

Challenges and best practices in omics benchmarking.
Thomas G Brooks et al. Nat Rev Genet 2024 1 (Posted: Jan 15, 2024 10AM)

From the abstract: "Benchmarking refers to the process of objectively comparing and evaluating the performance of different computational or analytical techniques when processing and analysing large-scale biological data sets, such as transcriptomics, proteomics and metabolomics. With thousands of omics benchmarking studies published over the past 25 years, the field has matured to the point where the foundations of benchmarking have been established and well described. "

A framework for evaluating long-term impact of newborn screening
S Kalkman et al, EJHG, October 3, 2023 (Posted: Oct 03, 2023 9AM)

From the article: "Today, expansion of NBS is accompanied by even more uncertainty about the benefit-harm ratio than before. New pharmacotherapies and technological developments in metabolomics and genomics have led to a substantial expansion of the NBS panel in a relatively short period of time. These novel tests and treatment options have generally not been assessed in presymptomatic individuals and knowledge on long-term outcome of treatment is lacking. Periodic evaluation would thus create important opportunities for continuous quality improvement; if the benefit-harm ratio is unfavorable then there is a strong case for adjustments or even for removing a condition from the panel."

Power of Public Investment in Curated Big Health Data.
Paula Anne Newman-Casey et al. JAMA Ophthalmol 2023 9 (Posted: Sep 08, 2023 9AM)

From the paper: "Public investment from the US and the UK in creating the UK Biobank and the All of Us databases has resulted in the generation of critical new knowledge to better understand human health. Both projects have created publicly available data sets to encourage researchers to leverage large quantities of data to identify patterns and advance health care. Moreover, each database has its unique strengths. The UK Biobank data set goes deep into genomics, metabolomics, brain, heart, and ocular imaging, providing granular and specific measurements to inform many fields of study. The All of Us data set includes biospecimens, linkages to electronic health records, and survey results."

From Mendel to multi-omics: shifting paradigms
TB Mersha, EJHG, July 20, 2023 (Posted: Jul 20, 2023 7AM)

Multi-omics analysis is an emerging approach that aims to better understand health and disease through the convergence of different omics studies (genomics, transcriptomics, proteomics, metabolomics, metagenomics, phenomics, exposomics). Although technical limitations related to the analysis of high-dimensional multi-omics datasets and use of fairly small samples have hindered our ability to conduct multi-omic research, emerging technology and computational tools have facilitated impactful multi-omic research.

The artificial sweetener erythritol and cardiovascular event risk.
Marco Witkowski et al. Nature medicine 2023 2 (Posted: Mar 01, 2023 11AM)

In initial untargeted metabolomics studies in patients undergoing cardiac risk assessment (n?=?1,157; discovery cohort), circulating levels of multiple polyol sweeteners, especially erythritol, were associated with incident (3 year) risk for major adverse cardiovascular events (MACE; includes death or nonfatal myocardial infarction or stroke). Subsequent targeted metabolomics analyses in independent US (n?=?2,149) and European (n?=?833) validation cohorts of stable patients undergoing elective cardiac evaluation confirmed this association (fourth versus first quartile adjusted hazard ratio (95% confidence interval), 1.80 (1.18–2.77) and 2.21 (1.20–4.07), respectively).

Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease.
Raaj S Mehta et al. Nature medicine 2023 2 (Posted: Feb 24, 2023 7AM)

We developed a multi-omics workflow combining gut microbiome metagenomics, metatranscriptomics and metabolomics from the longitudinal IBDMDB cohort of 132 controls and patients with IBD. This associated 12 previously uncharacterized microbial acetyltransferases with 5-ASA inactivation, belonging to two protein superfamilies: thiolases and acyl-CoA N-acyltransferases. A cross-sectional analysis within the discovery cohort and subsequent prospective validation within the independent SPARC IBD cohort (n?=?208) found three of these microbial thiolases and one acyl-CoA N-acyltransferase to be epidemiologically associated with an increased risk of treatment failure among 5-ASA users.

Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases.
Chen Yiheng et al. Nature genetics 2023 1 (1) 44-53 (Posted: Jan 15, 2023 3PM)

By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases.

Identification of serum metabolome signatures associated with retinal and renal complications of type 2 diabetes.
Tomofuji Yoshihiko et al. Communications medicine 2023 1 (1) 5 (Posted: Jan 11, 2023 6AM)

We profiled serum metabolites of persons with type 2 diabetes with both DR and DKD (N?=?141) and without complications (N?=?159) using a comprehensive non-targeted metabolomics approach with mass spectrometry. Based on the serum metabolite profiles, case–control comparisons and metabolite set enrichment analysis (MSEA) were performed. Here we show that five metabolites (cyclohexylamine, P?=?4.5?×?10-6; 1,2-distearoyl-glycero-3-phosphocholine, P?=?7.3?×?10-6; piperidine, P?=?4.8?×?10-4; N-acetylneuraminic acid, P?=?5.1?×?10-4; stearoyl ethanolamide, P?=?6.8?×?10-4) are significantly increased in those with the complications. MSEA identifies fatty acid biosynthesis as the type 2 diabetes complications-associated biological pathway (P?=?0.0020).

Dietary metabolic signatures and cardiometabolic risk.
Shah Ravi V et al. European heart journal 2022 11 (Posted: Dec 17, 2022 9AM)

Observational studies of diet in cardiometabolic-cardiovascular disease (CM-CVD) focus on self-reported consumption of food or dietary pattern, with limited information on individual metabolic responses to dietary intake linked to CM-CVD. Here, machine learning approaches were used to identify individual metabolic patterns related to diet and relation to long-term CM-CVD in early adulthood. We found that metabolic signatures of diet are associated with long-term CM-CVD independent of lifestyle and traditional risk factors. Metabolomics improves precision to identify adverse consequences and pathways of diet-related CM-CVD.

Rare and common genetic determinants of metabolic individuality and their effects on human health
P Surendran et al, Nature Medicine, November 10, 2022 (Posted: Nov 11, 2022 7AM)

We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant–metabolite associations (P?<?1.25?×?10-11) within 330 genomic regions, with rare variants (minor allele frequency?=?1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant–metabolite clusters called genetically influenced metabotypes.

Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease
I W Wu et al, NPJ Digital Medicine, November 2, 2022 (Posted: Nov 03, 2022 8AM)

Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively.

Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome.
Chen Lianmin et al. Nature medicine 2022 10 (Posted: Oct 11, 2022 2PM)

The levels of the thousands of metabolites in the human plasma metabolome are strongly influenced by an individual’s genetics and the composition of their diet and gut microbiome. Here, by assessing 1,183 plasma metabolites in 1,368 extensively phenotyped individuals from the Lifelines DEEP and Genome of the Netherlands cohorts, we quantified the proportion of inter-individual variation in the plasma metabolome explained by different factors, characterizing 610, 85 and 38 metabolites as dominantly associated with diet, the gut microbiome and genetics.

Metabolomic profiles predict individual multidisease outcomes
T Buergel et al, Nature Medicine, September 22, 2022 (Posted: Sep 23, 2022 7AM)

We trained a neural network to learn disease-specific metabolomic states from 168?circulating metabolic markers measured in 117,981?participants with ~1.4?million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer.

Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk.
Yin Xianyong et al. American journal of human genetics 2022 9 (Posted: Sep 08, 2022 10AM)

Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated.

Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction
NA Fraunhoffer et al, NPJ Precision Oncology, August 17, 2022 (Posted: Aug 18, 2022 1PM)

Pancreatic ductal adenocarcinoma (PDAC), has recently been found to be a heterogeneous disease, although the extension of its diversity remains to be fully understood. Here, we harmonize transcriptomic profiles derived from both PDAC epithelial and microenvironment cells to develop a Master Regulators (MR)-Gradient model that allows important inferences on transcriptional networks, epigenomic states, and metabolomics pathways that underlies this disease heterogeneity.

The growing need for controlled data access models in clinical proteomics and metabolomics
TM Keane et al, Nature Communications, October 2021 (Posted: Oct 04, 2021 9AM)

More and more clinical studies include potentially sensitive human proteomics or metabolomics datasets, but bioinformatics resources for managing the access to these data are not yet available. This commentary discusses current best practices and future perspectives for the responsible handling of clinical proteomics and metabolomics data.

The blood metabolome of incident kidney cancer: A case–control study nested within the MetKid consortium
F Guida et al, PLOS Medicine, September 21, 2021 (Posted: Sep 21, 2021 9AM)

We looked at the association between kidney cancer and the levels of 1,416 metabolites measured in blood on average 8 years before the disease onset. The study included 1,305 kidney cancer cases and 1,305 healthy controls. We found 25 metabolites robustly associated with kidney cancer risk. Specifically, multiple glycerophospholipids (GPLs) were inversely associated with risk, while several amino acids were positively associated with risk. Accounting for BMI highlighted that some—but not all—metabolites associated with kidney cancer risk are influenced by BMI.

Comparison of Untargeted Metabolomic Profiling vs Traditional Metabolic Screening to Identify Inborn Errors of Metabolism
N Liu et al, JAMA Network Open, July 12, 2021 (Posted: Jul 13, 2021 8AM)

This cross-sectional analysis of 4464 traditional metabolic screening samples and 2000 plasma metabolomic screening samples received at a clinical biochemical laboratory between July 2014 and February 2019 found a?1.3% diagnostic rate for traditional metabolic screening, whereas clinical metabolomics supported diagnosis in?7.1% of cases, providing an approximately?6-fold higher diagnostic rate in screening for IEMs and identifying more disorders and more disease types compared with the traditional screening approach.

Untargeted saliva metabolomics reveals COVID-19 severity: Saliva Metabolomics for SARS-COV-2 Prognosis
M Bailey et al, MEDRXIV, July 7, 2021 (Posted: Jul 08, 2021 8AM)

COMETS Analytics: An online tool for analyzing and meta-analyzing metabolomics data in large research consortia
M Temprosa et al, AM J Epidemiology, April 2021 (Posted: May 02, 2021 0PM)

The application requires no specialized expertise and can be run locally to guarantee data protection or through a web-based server for convenience and speed. Unlike other web-based tools, COMETS Analytics enables standardized models to be run across all cohorts, using an algorithmic, reproducible approach to diagnose, document, and fix model issues.

Development and Validation of a Serum Metabolomic Signature for Endometrial Cancer Screening in Postmenopausal Women
J Troisi et al, JAMA Network Open, September 28, 2020 (Posted: Sep 29, 2020 8AM)

Is combining the blood metabolomic signature of endometrial carcinoma with an ensemble machine learning algorithm a useful system for screening test for endometrial cancer? In this study of 1550 postmenopausal women, the proposed screening test correctly identified all 16 women with endometrial cancer, with 2 false-positive results and 0 false-negative results.

Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis
JT ALaimo et al, Eur J Human Genetics, May 22, 2020 (Posted: May 22, 2020 0PM)

Metabolomic data contributed to the interpretation variants in 74 individuals (43.5%) over 73 different genes. The data allowed for the reclassification of 9 variants as likely benign, 15 variants as likely pathogenic, and 3 variants as pathogenic. Metabolomic data confirmed a clinical diagnosis in 21 cases, for a diagnostic rate of 12.3% in this population.

Metabolites and you- People leave molecular wakes that may give away their secrets
The Economist, February 2020 (Posted: Feb 15, 2020 9AM)

Precision medicine integrating whole-genome sequencing, comprehensive metabolomics, and advanced imaging.
Hou Ying-Chen Claire et al. Proceedings of the National Academy of Sciences of the United States of America 2020 Jan (Posted: Jan 29, 2020 8AM)

Genome sequencing has established clinical utility for rare disease diagnosis. While increasing numbers of individuals have undergone elective genome sequencing, a comprehensive study surveying genome-wide disease-associated genes in adults with deep phenotyping has not been reported

Real-time health monitoring through urine metabolomics
IJ Miller et al, NPJ Digital Medicine, November 11, 2019 (Posted: Nov 12, 2019 8AM)

Quantitative methods for metabolomic analyses evaluated in the Children’s Health Exposure Analysis Resource (CHEAR)
CHEAR team, J Exp Sci and Env Epi, September 2019 (Posted: Oct 01, 2019 8AM)

Enteric dysbiosis is associated with sepsis in patients
Z Liu et al, FASEB journal, August 28, 2019 (Posted: Aug 30, 2019 7AM)

Systematic analyses on clinical stool samples from patients with sepsis, including 16S rDNA sequencing, metabolomics, and metaproteomics analyses. The study found that the composition of gut microbiota was significantly disrupted in patients with sepsis compared with healthy individuals.

Leveraging -omics for asthma endotyping
SR Tyler et al, JACI, July 2019 (Posted: Jul 02, 2019 0PM)

Asthma is a highly heterogeneous disease, The eliciting factors, natural history, underlying molecular biology, and clinical management of asthma vary highly among affected subjects. Because of this variation, many efforts have gone into subtyping asthma. Endotypes are subtypes of disease based on distinct pathophysiologic mechanisms. We discuss the application of -omics approaches, including transcriptomics, epigenomics, microbiomics, metabolomics, and proteomics, to asthma endotyping. -Omics approaches have provided supporting evidence for many existing endotyping paradigms and also suggested novel ways to conceptualize asthma endotypes.

The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies
B Yu, Am J Epidemiology, February 2019 (Posted: Feb 25, 2019 9AM)

Metabolome-Wide Mendelian Randomization Analysis of Emotional and Behavioral Responses to Traumatic Stress
CM Carvalho et al, February 10, 2019 (Posted: Feb 11, 2019 10AM)

Genomics, microbiomics, proteomics, and metabolomics in bronchopulmonary dysplasia.
Lal Charitharth Vivek et al. Seminars in perinatology 2018 Nov (7) 425-431 (Posted: Jan 23, 2019 10AM)

Metabolomic Biomarkers in Gynecology: a Treasure Path or a False Path?
Igor Govorov et al. Current medicinal chemistry 2019 Jan (Posted: Jan 13, 2019 11AM)

Exposure to tobacco smoke and low birth weight: from epidemiology to metabolomics.
Dessì Angelica et al. Expert review of proteomics 2018 Jul (Posted: Jul 30, 2018 8AM)

Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review.
Graham Emma et al. Journal of inherited metabolic disease 2018 May (Posted: May 09, 2018 8AM)

Profound perturbation of the human metabolome by obesity
ET Cerulli et al, BioRXIV, Apr 2018 (Posted: Apr 10, 2018 8AM)

Metabolomics in Sepsis and Its Impact on Public Health.
Evangelatos Nikolaos et al. Public health genomics 2018 Jan (Posted: Feb 05, 2018 1PM)

Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study.
Varma Vijay R et al. PLoS medicine 2018 Jan (1) e1002482 (Posted: Jan 31, 2018 2PM)

Beyond genomics: understanding exposotypes through metabolomics.
Rattray Nicholas J W et al. Human genomics 2018 Jan 12(1) 4 (Posted: Jan 31, 2018 9AM)

The role of metabolomics in tuberculosis treatment research.
Luies Laneke et al. Biomarkers in medicine 2017 Oct (Posted: Oct 22, 2017 9AM)

Use of Metabolomics in Improving Assessment of Dietary Intake.
Guasch-Ferré Marta et al. Clinical chemistry 2017 Oct (Posted: Oct 22, 2017 9AM)

Objective Metabolomics Research
M Ala-Korpela, Clinical Chemistry, Oct 2017 (Posted: Oct 22, 2017 9AM)

Metabolomics as a Driver in Advancing Precision Medicine in Sepsis.
Eckerle Michelle et al. Pharmacotherapy 2017 Sep (9) 1023-1032 (Posted: Oct 05, 2017 3PM)

Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma.
Siskos Alexandros P et al. Analytical chemistry 2017 01 (1) 656-665 (Posted: Aug 23, 2017 5PM)

New frontiers in metabolomics: from measurement to insight.
Riekeberg Eli et al. F1000Research 2017 1148 (Posted: Aug 16, 2017 9AM)

Working Up a Good Sweat - The Challenges of Standardising Sweat Collection for Metabolomics Analysis.
Hussain Joy N et al. The Clinical biochemist. Reviews 2017 Feb (1) 13-34 (Posted: Aug 16, 2017 9AM)

Metabolomics Interest Group Events and Webinars
NIH Metabolomics Interest Group Brand (Posted: Aug 16, 2017 9AM)

Key elements of metabolomics in the study of biomarkers of diabetes.
Adamski Jerzy et al. Diabetologia 2016 Oct (Posted: Oct 08, 2016 9AM)

Metabolomics in amyotrophic lateral sclerosis: how far can it take us?
Blasco H et al. European journal of neurology 2016 Jan (Posted: Feb 24, 2016 1PM)

Metabolomics and Personalized Medicine.
Koen Nadia et al. Advances in protein chemistry and structural biology 2016 53-78 (Posted: Feb 24, 2016 1PM)

Radiation Metabolomics: Current Status and Future Directions.
Menon Smrithi S et al. Frontiers in oncology 2016 20 (Posted: Feb 24, 2016 1PM)

Metabolomics in diabetes, a review.
Pallares-Méndez Rigoberto et al. Annals of medicine 2016 Feb (1-2) 89-102 (Posted: Feb 24, 2016 1PM)

Metabolomics in diabetic complications.
Filla Laura A et al. Molecular bioSystems 2016 Feb (Posted: Feb 24, 2016 1PM)

Integration of omics: more than the sum of its parts.
Buescher Joerg Martin et al. Cancer & metabolism 2016 4 (Posted: Feb 24, 2016 1PM)

Targeted metabolomics in the expanded newborn screening for inborn errors of metabolism.
Scolamiero Emanuela et al. Molecular bioSystems 2015 Jun (6) 1525-35 (Posted: Feb 24, 2016 1PM)

Data standards can boost metabolomics research, and if there is a will, there is a way.
Rocca-Serra Philippe et al. Metabolomics : Official journal of the Metabolomic Society (1) 14 (Posted: Dec 16, 2015 1PM)

Prediction of Gestational Diabetes through NMR Metabolomics of Maternal Blood.
Pinto Joana et al. Journal of proteome research 2015 Jun (6) 2696-706 (Posted: Nov 12, 2015 2PM)

Metabolomics of Neurodegenerative Diseases.
Botas Alejandro et al. International review of neurobiology 2015 53-80 Brand (Posted: Oct 13, 2015 1PM)

Design and Analysis of Metabolomics Studies in Epidemiological Research: A Primer on -Omic Technologies
A Tzoulaki et al, AM J Epidemiology 2015 (Posted: Aug 19, 2015 11AM)

Metabolomics in Population-Based Research
NCI Information (Posted: Aug 19, 2015 8AM)

Innovation: Metabolomics: the apogee of the omics trilogy
GJ Patti, Nature April, 2012 (Posted: Aug 19, 2015 8AM)

Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects
Boca Raton (FL):CRC Press 2015 (Posted: Aug 18, 2015 0PM)

Sequencing and beyond: integrating molecular 'omics' for microbial community profiling.
Franzosa Eric A et al. Nat. Rev. Microbiol. 2015 Jun (6) 360-72 (Posted: Aug 17, 2015 7PM)

Metabolomics - the complementary field in systems biology: a review on obesity and type 2 diabetes.
Abu Bakar Mohamad Hafizi et al. Mol Biosyst 2015 Jul (7) 1742-74 (Posted: Aug 17, 2015 7PM)

Systems biology of host-microbe metabolomics.
Heinken Almut et al. Wiley Interdiscip Rev Syst Biol Med 2015 Jul-Aug (4) 195-219 (Posted: Aug 17, 2015 7PM)

Metabolomics in cardiovascular diseases.
Kordalewska Marta et al. J Pharm Biomed Anal 2015 Sep 10. 121-36 (Posted: Aug 17, 2015 7PM)

Metabolic Signatures of Human Breast Cancer.
Mishra Prachi et al. Mol Cell Oncol 2015 Jul-Sep (3) (Posted: Aug 17, 2015 7PM)

Metabolomics and renal disease.
Rhee Eugene P et al. Curr. Opin. Nephrol. Hypertens. 2015 Jul (4) 371-9 (Posted: Aug 17, 2015 6PM)

A Metabolomic Approach to Understanding the Metabolic Link between Obesity and Diabetes.
Park Seokjae et al. Mol. Cells 2015 Jul 31. (7) 587-96 (Posted: Aug 17, 2015 6PM)

Metabolomics for Biomarker Discovery: Moving to the Clinic.
Zhang Aihua et al. Biomed Res Int 2015 354671 (Posted: Aug 17, 2015 6PM)

Serum metabolomics in animal models and human disease.
James Emma L et al. Curr Opin Clin Nutr Metab Care 2015 Sep (5) 478-83 (Posted: Aug 17, 2015 6PM)

Two elephants in the room: new hybrid nuclear magnetic resonance and mass spectrometry approaches for metabolomics.
Bingol Kerem et al. Curr Opin Clin Nutr Metab Care 2015 Sep (5) 471-7 (Posted: Aug 17, 2015 6PM)

Genetics of human metabolism: an update.
Kastenmüller Gabi et al. Hum. Mol. Genet. 2015 Jul 9. (Posted: Aug 17, 2015 6PM)

Metabolomic profiling for the identification of novel diagnostic markers in prostate cancer.
Lucarelli Giuseppe et al. Expert Rev. Mol. Diagn. 2015 Jul 15. 1-14 (Posted: Aug 17, 2015 6PM)

High-Resolution Metabolomics: Review of the Field and Implications for Nursing Science and the Study of Preterm Birth.
Li Shuzhao et al. Biol Res Nurs 2015 Jul 16. (Posted: Aug 17, 2015 6PM)

Metabolomics in the natural products field - a gateway to novel antibiotics.
Wu Changsheng et al. Drug Discov Today Technol 2015 Jun 11-7 (Posted: Aug 17, 2015 6PM)

The emergence of metabolomics as a key discipline in the drug discovery process.
Fillet Marianne et al. Drug Discov Today Technol 2015 Jun 19-24 (Posted: Aug 17, 2015 6PM)

Metabolomics in the pharmaceutical industry.
Reily Michael D et al. Drug Discov Today Technol 2015 Jun 25-31 (Posted: Aug 17, 2015 6PM)

Exploration of individuality in drug metabolism by high-throughput metabolomics: The fast line for personalized medicine.
Trifonova Oxana et al. Drug Discov. Today 2015 Jul 26. (Posted: Aug 17, 2015 6PM)

Metabolic biomarkers for chronic kidney disease.
Breit Marc et al. Arch. Biochem. Biophys. 2015 Jul 31. (Posted: Aug 17, 2015 6PM)

Metabolomic profiling of hormone-dependent cancers: a bird's eye view.
Lloyd Stacy M et al. Trends Endocrinol. Metab. 2015 Aug 1. (Posted: Aug 17, 2015 6PM)

Metabolomics of cocaine: implications in toxicity.
Dinis-Oliveira Ricardo Jorge et al. Toxicol. Mech. Methods 2015 Aug 7. 1-7 (Posted: Aug 17, 2015 6PM)

Metabolomic biomarkers in diabetic kidney diseases-A systematic review.
Zhang Yumin et al. J. Diabetes Complicat. 2015 Jul 9. (Posted: Aug 17, 2015 6PM)

Potential of metabolomics to reveal Burkholderia cepacia complex pathogenesis and antibiotic resistance.
Shommu Nusrat S et al. Front Microbiol 2015 668 (Posted: Aug 11, 2015 3PM)

Distinguishing between the metabolome and xenobiotic exposome in environmental field samples analysed by direct-infusion mass spectrometry based metabolomics and lipidomics.
Southam Andrew D et al. Metabolomics 2014 (6) 1050-1058 (Posted: May 20, 2015 2PM)

Metabolomics and diabetes: analytical and computational approaches.
Sas Kelli M et al. Diabetes 2015 Mar (3) 718-32 (Posted: Apr 10, 2015 2PM)

The Newest "Omics"-Metagenomics and Metabolomics-Enter the Battle against the Neglected Tropical Diseases.
Preidis Geoffrey A et al. PLoS Negl Trop Dis 2015 Feb (2) e0003382 (Posted: Mar 06, 2015 2PM)

Application of metabolomics in drug resistant breast cancer research.
Shajahan-Haq Ayesha N et al. Metabolites 2015 (1) 100-18 (Posted: Mar 04, 2015 8PM)

Comprehensive Metabolomics Identifies the Alarmin Uric Acid as a Critical Signal for the Induction of Peanut Allergy.
Kong Joshua et al. Allergy 2015 Feb 3. (Posted: Feb 26, 2015 7PM)

Metabolomics in Population-Based Research
Brand (Posted: Jan 11, 2014 11AM)

Metabolomics is the study of small molecules of both endogenous and exogenous origin, such as metabolic substrates and their products, lipids, small peptides, vitamins and other protein cofactors generated by metabolism, which are downstream from genes. This approach has received more attention in recent years as an ideal methodology to unravel signals closer to the culmination of the disease process. The compounds identified through metabolomic profiling represent a range of intermediate metabolic pathways that may serve as biomarkers of exposure, susceptibility, or disease. In short, it is a valuable approach for deciphering metabolic outcomes with a phenotypic change. Until recently, metabolomics and other post-genomic platforms, such as proteomics and transcriptomics, have not been suitable for large-scale, high-throughput epidemiologic applications. Studies that employed metabolomics technologies have focused on toxicological, physiological, and disease responses in animal models and small-scale human studies. This has been due mainly to the limited capacity of the analytical platforms for sample throughput and the processing requirements for the enormous amounts of data created. Improved sample preparation, robotic sample-delivery systems, automated data processing, and use of multivariate statistical and chemometric methods, with associated reductions in cost, are now allowing researchers to realize the potential for metabolic phenotyping in epidemiology. Investigators have begun to extend these studies to larger-scale population studies for biomarker discovery. With these studies comes the challenge of applying metabolomics technologies in a manner that generates meaningful results. Epidemiologists must strive to comprehensively understand the principles of metabolomics to determine when it is appropriate to use biomarkers identified using this technology, which includes the ability to determine when biomarkers have been validated sufficiently. Cancer


Disclaimer: Articles listed in Hot Topics of the Day are selected by Public Health Genomics Branch to provide current awareness of the scientific literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the Clips, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
TOP