14 results on '"Barupal, D."'
Search Results
2. A blood-based signature of cerebrospinal fluid A beta(1-42) status
- Author
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Goudey, B., Fung, B.J., Schieber, C., Faux, N.G., Weiner, M.W., Aisen, P., Petersen, R., Jack, C.R., Jagust, W., Trojanowki, J.Q., Toga, A.W., Beckett, L., Green, R.C., Saykin, A.J., Morris, J., Shaw, L.M., Kaye, J., Quinn, J., Silbert, L., Lind, B., Carter, R., Dolen, S., Schneider, L.S., Pawluczyk, S., Beccera, M., Teodoro, L., Spann, B.M., Brewer, J., Vanderswag, H., Fleisher, A., Heidebrink, J.L., Lord, J.L., Mason, S.S., Albers, C.S., Knopman, D., Johnson, K., Doody, R.S., Villanueva-Meyer, J., Chowdhury, M., Rountree, S., Dang, M., Stern, Y., Honig, L.S., Bell, K.L., Ances, B., Morris, J.C., Carroll, M., Creech, M.L., Franklin, E., Mintun, M.A., Schneider, S., Oliver, A., Marson, D., Griffth, R., Clark, D., Geldmacher, D., Brockington, J., Roberson, E., Natelson Love, M., Grossman, H., Mitsis, E., Shah, R.C., deToledo-Morrell, L., Duara, R., Varon, D., Greig, M.T., Roberts, P., Albert, M., Onyike, C., D'Agostino, D., Kielb, S., Galvin, J.E., Cerbone, B., Michel, C.A., Pogorelec, D.M., Rusinek, H., Leon, M.J. de, Glodzik, L., De Santi, S., Doraiswamy, P.M., Petrella, J.R., Borges-Neto, S., Wong, T.Z., Coleman, E., Smith, C.D., Jicha, G., Hardy, P., Sinha, P., Oates, E., Conrad, G., Porsteinsson, A.P., Goldstein, B.S., Martin, K., Makino, K.M., Ismail, M.S., Brand, C., Mulnard, R.A., Thai, G., McAdams-Ortiz, C., Womack, K., Mathews, D., Quiceno, M., Levey, A.I., Lah, J.J., Cellar, J.S., Burns, J.M., Swerdlow, R.H., Brooks, W.M., Apostolova, L., Tingus, K., Woo, E., Silverman, D.H.S., Lu, P.H., Bartzokis, G., Graff-Radford, N.R., Parftt, F., Kendall, T., Johnson, H., Farlow, M.R., Hake, A.M., Matthews, B.R., Brosch, J.R., Herring, S., Hunt, C., Dyck, .H. van, Carson, R.E., MacAvoy, M.G., Varma, P., Chertkow, H., Bergman, H., Hosein, C., Black, S., Stefanovic, B., Caldwell, C., Hsiung, Ging-Yuek Robin, Feldman, H., Mudge, B., Assaly, M., Finger, E., Pasternack, S., Rachisky, I., Trost, D., Kertesz, A., Bernick, C., Munic, D., Mesulam, M.-M., Lipowski, K., Weintraub, S., Bonakdarpour, B., Kerwin, D., Wu, C.-K., Johnson, N., Sadowsky, C., Villena, T., Turner, R.S., Reynolds, B., Sperling, R.A., Johnson, K.A., Marshall, G., Yesavage, J., Taylor, J.L., Lane, B., Rosen, A., Tinklenberg, J., Sabbagh, M.N., Belden, C.M., Jacobson, S.A., Sirrel, S.A., Kowall, N., Killiany, R., Budson, A.E., Norbash, A., Johnson, P.L., Obisesan, T.O., Wolday, S., Allard, J., Lerner, A., Ogrocki, P., Tatsuoka, C., Fatica, P., Fletcher, E., Maillard, P., Olichney, J., DeCarli, C., Carmichael, O., Kittur, S., Borrie, M., Lee, T.-Y., Bartha, R., Johnson, S., Asthana, S., Carlsson, C.M., Potkin, S.G., Preda, A., Nguyen, D., Tariot, P., Burke, A., Trncic, N., Reeder, S., Bates, V., Capote, H., Rainka, M., Scharre, D.W., Kataki, M., Adeli, A., Zimmerman, E.A., Celmins, D., Brown, A.D., Pearlson, G.D., Blank, K., Anderson, K., Flashman, L.A., Seltzer, M., Hynes, M.L., Santulli, R.B., Sink, K.M., Gordineer, L., Williamson, J.D., Garg, P., Watkins, F., Ott, B.R., Querfurth, H., Tremont, G., Salloway, S., Malloy, P., Correia, S., Rosen, H.J., Miller, B.L., Perry, D., Mintzer, J., Spicer, K., Bachman, D., Pomara, N., Hernando, R., Sarrael, A., Relkin, N., Chaing, G., Lin, M., Ravdin, L., Smith, A., Raj, B.A., Fargher, K., Saykin, A., Nho, K., Kling, M., Toledo, J., Shaw, L., Trojanowski, J., Farrer, L., Kastsenmueller, G., Arnold, M., Wishart, D., Wurtz, P., Bhattcharyya, S., Duijin, C. van, Mangravite, L., Han, X., Hankemeier, T., Fiehn, O., Barupal, D., Thiele, I., Heinken, A., Meikle, P., Price, N., Funk, C., Jia, W., Kueider-Paisley, A., Tenebaum, J., Black, C., Moseley, A., Thompson, W., Mahmoudiandehkorki, S., Baillie, R., Welsh-Bohmer, K., Plassman, B., and Epidemiology
- Subjects
Male ,0301 basic medicine ,Apolipoprotein E ,Oncology ,medicine.medical_specialty ,Amyloid ,Amyloid beta ,lcsh:Medicine ,Article ,03 medical and health sciences ,Apolipoproteins E ,0302 clinical medicine ,Cerebrospinal fluid ,Alzheimer Disease ,Predictive Value of Tests ,Internal medicine ,medicine ,Humans ,Dementia ,Cognitive decline ,lcsh:Science ,Aged ,Aged, 80 and over ,Amyloid beta-Peptides ,Multidisciplinary ,biology ,Chemokine CCL26 ,business.industry ,lcsh:R ,Alzheimer’s Disease Metabolomics Consortium ,Alzheimer’s Disease Neuroimaging Initiative ,medicine.disease ,Peptide Fragments ,3. Good health ,030104 developmental biology ,biology.protein ,Chromogranin A ,Female ,lcsh:Q ,Alzheimer's disease ,business ,Biomarkers ,030217 neurology & neurosurgery ,Alzheimer's Disease Neuroimaging Initiative - Abstract
It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1−42 (Aβ1−42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1−42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1−42 levels and that the resulting model also validates reasonably across PET Aβ1−42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1−42 status, the earliest risk indicator for AD, with high accuracy.
- Published
- 2019
3. The impact of ambient air pollution on the human blood metabolome
- Author
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LS IRAS EEPI GRA (Gezh.risico-analyse), Dep IRAS, LS IRAS EEPI ME (Milieu epidemiologie), LS IRAS EEPI Inhalatie Toxicologie, Sub IRAS Tox ITX (immunotoxicologie), dIRAS RA-2, dIRAS RA-1, Vlaanderen, J.J., Janssen, N.A., Hoek, G., Keski-Rahkonen, P, Barupal, D K, Cassee, F.R., Gosens, I., Strak, M., Steenhof, M., Lan, Q., Brunekreef, B., Scalbert, Augustin, Vermeulen, R.C.H., LS IRAS EEPI GRA (Gezh.risico-analyse), Dep IRAS, LS IRAS EEPI ME (Milieu epidemiologie), LS IRAS EEPI Inhalatie Toxicologie, Sub IRAS Tox ITX (immunotoxicologie), dIRAS RA-2, dIRAS RA-1, Vlaanderen, J.J., Janssen, N.A., Hoek, G., Keski-Rahkonen, P, Barupal, D K, Cassee, F.R., Gosens, I., Strak, M., Steenhof, M., Lan, Q., Brunekreef, B., Scalbert, Augustin, and Vermeulen, R.C.H.
- Published
- 2017
4. The impact of ambient air pollution on the human blood metabolome
- Author
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Vlaanderen, J.J., Janssen, N.A., Hoek, G., Keski-Rahkonen, P, Barupal, D K, Cassee, F.R., Gosens, I., Strak, M., Steenhof, M., Lan, Q., Brunekreef, B., Scalbert, Augustin, Vermeulen, R.C.H., LS IRAS EEPI GRA (Gezh.risico-analyse), Dep IRAS, LS IRAS EEPI ME (Milieu epidemiologie), LS IRAS EEPI Inhalatie Toxicologie, Sub IRAS Tox ITX (immunotoxicologie), dIRAS RA-2, dIRAS RA-1, LS IRAS EEPI GRA (Gezh.risico-analyse), Dep IRAS, LS IRAS EEPI ME (Milieu epidemiologie), LS IRAS EEPI Inhalatie Toxicologie, Sub IRAS Tox ITX (immunotoxicologie), dIRAS RA-2, and dIRAS RA-1
- Subjects
0301 basic medicine ,Spirometry ,Male ,Time Factors ,Adolescent ,Air pollution ,Particulate matter components ,010501 environmental sciences ,medicine.disease_cause ,01 natural sciences ,Biochemistry ,03 medical and health sciences ,Young Adult ,Metabolite profiling ,Metabolome ,medicine ,Humans ,0105 earth and related environmental sciences ,General Environmental Science ,Netherlands ,Air Pollutants ,Ambient air pollution ,Human blood ,medicine.diagnostic_test ,Chemistry ,Environmental Exposure ,Particulates ,Peripheral blood ,Cardio-respiratory health effects ,030104 developmental biology ,Blood ,Environmental chemistry ,Female ,Environmental Monitoring - Abstract
Background Biological perturbations caused by air pollution might be reflected in the compounds present in blood originating from air pollutants and endogenous metabolites influenced by air pollution (defined here as part of the blood metabolome). We aimed to assess the perturbation of the blood metabolome in response to short term exposure to air pollution. Methods We exposed 31 healthy volunteers to ambient air pollution for 5 h. We measured exposure to particulate matter, particle number concentrations, absorbance, elemental/organic carbon, trace metals, secondary inorganic components, endotoxin content, gaseous pollutants, and particulate matter oxidative potential. We collected blood from the participants 2 h before and 2 and 18 h after exposure. We employed untargeted metabolite profiling to monitor 3873 metabolic features in 493 blood samples from these volunteers. We assessed lung function using spirometry and six acute phase proteins in peripheral blood. We assessed the association of the metabolic features with the measured air pollutants and with health markers that we previously observed to be associated with air pollution in this study. Results We observed 89 robust associations between air pollutants and metabolic features two hours after exposure and 118 robust associations 18 h after exposure. Some of the metabolic features that were associated with air pollutants were also associated with acute health effects, especially changes in forced expiratory volume in 1 s. We successfully identified tyrosine, guanosine, and hypoxanthine among the associated features. Bioinformatics approach Mummichog predicted enriched pathway activity in eight pathways, among which tyrosine metabolism. Conclusions This study demonstrates for the first time the application of untargeted metabolite profiling to assess the impact of air pollution on the blood metabolome.
- Published
- 2016
5. High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage.
- Author
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Lai Y, Koelmel JP, Walker DI, Price EJ, Papazian S, Manz KE, Castilla-Fernández D, Bowden JA, Nikiforov V, David A, Bessonneau V, Amer B, Seethapathy S, Hu X, Lin EZ, Jbebli A, McNeil BR, Barupal D, Cerasa M, Xie H, Kalia V, Nandakumar R, Singh R, Tian Z, Gao P, Zhao Y, Froment J, Rostkowski P, Dubey S, Coufalíková K, Seličová H, Hecht H, Liu S, Udhani HH, Restituito S, Tchou-Wong KM, Lu K, Martin JW, Warth B, Godri Pollitt KJ, Klánová J, Fiehn O, Metz TO, Pennell KD, Jones DP, and Miller GW
- Subjects
- Humans, Exposome, Metabolomics, Proteomics methods, Environmental Exposure, Mass Spectrometry methods
- Abstract
In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.
- Published
- 2024
- Full Text
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6. Advisory Group recommendations on priorities for the IARC Monographs.
- Author
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Berrington de González A, Masten SA, Bhatti P, Fortner RT, Peters S, Santonen T, Yakubovskaya MG, Barouki R, Barros SBM, Barupal D, Beane Freeman LE, Calaf GM, Dillner J, El Rhazi K, Fritschi L, Fukushima S, Godderis L, Kogevinas M, Lachenmeier DW, Mandrioli D, Muchengeti MM, Niemeier RT, Pappas JJ, Pi J, Purdue MP, Riboli E, Rodríguez T, Schlünssen V, Benbrahim-Tallaa L, de Conti A, Facchin C, Pasqual E, Wedekind R, Ahmadi A, Chittiboyina S, Herceg Z, Kulasingam S, Lauby-Secretan B, MacLehose R, Sanaa M, Schüz J, Suonio E, Zavadil J, Mattock H, Madia F, and Schubauer-Berigan MK
- Subjects
- Humans, Advisory Committees, Health Priorities, Neoplasms therapy
- Published
- 2024
- Full Text
- View/download PDF
7. IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets.
- Author
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Mahajan P, Fiehn O, and Barupal D
- Subjects
- Female, Humans, Gene Ontology, Databases, Factual, Computational Biology, Metabolomics, Proteins genetics
- Abstract
Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2393 metabolic GO terms and associated 3144 genes, 1,492 EC annotations, and 2621 metabolites. IDSL.GOA analysis of a case study of older versus young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR < 0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/ ., (© 2024. The Author(s).)
- Published
- 2024
- Full Text
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8. PFAS Exposures and the Human Metabolome: A Systematic Review of Epidemiological Studies.
- Author
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India-Aldana S, Yao M, Midya V, Colicino E, Chatzi L, Chu J, Gennings C, Jones DP, Loos RJF, Setiawan VW, Smith MR, Walker RW, Barupal D, Walker DI, and Valvi D
- Abstract
Purpose of Review: There is a growing interest in understanding the health effects of exposure to per- and polyfluoroalkyl substances (PFAS) through the study of the human metabolome. In this systematic review, we aimed to identify consistent findings between PFAS and metabolomic signatures. We conducted a search matching specific keywords that was independently reviewed by two authors on two databases (EMBASE and PubMed) from their inception through July 19, 2022 following PRISMA guidelines., Recent Findings: We identified a total of 28 eligible observational studies that evaluated the associations between 31 different PFAS exposures and metabolomics in humans. The most common exposure evaluated was legacy long-chain PFAS. Population sample sizes ranged from 40 to 1,105 participants at different stages across the lifespan. A total of 19 studies used a non-targeted metabolomics approach, 7 used targeted approaches, and 2 included both. The majority of studies were cross-sectional ( n = 25), including four with prospective analyses of PFAS measured prior to metabolomics., Summary: Most frequently reported associations across studies were observed between PFAS and amino acids, fatty acids, glycerophospholipids, glycerolipids, phosphosphingolipids, bile acids, ceramides, purines, and acylcarnitines. Corresponding metabolic pathways were also altered, including lipid, amino acid, carbohydrate, nucleotide, energy metabolism, glycan biosynthesis and metabolism, and metabolism of cofactors and vitamins. We found consistent evidence across studies indicating PFAS-induced alterations in lipid and amino acid metabolites, which may be involved in energy and cell membrane disruption., Competing Interests: Conflict of Interest The authors declared no conflicts of interest.
- Published
- 2023
- Full Text
- View/download PDF
9. An actionable annotation scoring framework for gas chromatography-high-resolution mass spectrometry.
- Author
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Koelmel JP, Xie H, Price EJ, Lin EZ, Manz KE, Stelben P, Paige MK, Papazian S, Okeme J, Jones DP, Barupal D, Bowden JA, Rostkowski P, Pennell KD, Nikiforov V, Wang T, Hu X, Lai Y, Miller GW, Walker DI, Martin JW, and Godri Pollitt KJ
- Abstract
Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here, we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography HRMS (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false-positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar isomers are not considered false positives). This framework is easily adaptable to various workflows and provides a concise means to communicate confidence in annotations. Further validation, refinements, and adoption of this framework will ideally lead to harmonization across the field, helping to improve the quality and interpretability of compound annotations obtained in GC-HRMS., (© The Author(s) 2022. Published by Oxford University Press.)
- Published
- 2022
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10. Comparison of untargeted and targeted perfluoroalkyl acids measured in adolescent girls.
- Author
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Petrick LM, Wolff MS, Barupal D, and Teitelbaum SL
- Subjects
- Adolescent, Biological Monitoring, Environmental Exposure, Female, Humans, Plasma chemistry, Alkanesulfonic Acids, Environmental Pollutants, Fluorocarbons analysis
- Abstract
Quantitative biomonitoring (e.g., targeted analysis) has served as the gold standard for environmental exposure biomonitoring for several decades. Recent advancements to broaden exposomic research brought new semi-quantitative untargeted assays that capture a wide range of endogenous metabolites and exogenous exposures in a single assay for discovery, though usually at the expense of absolute quantitation. The high-resolution mass spectrometers (HRMS) typically used in untargeted workflows are sensitive and robust, but there do not yet exist comprehensive comparisons between environmental chemicals at population exposure levels measured using targeted and untargeted assays. Using liquid chromatography (LC)-HRMS, we measured per- and polyfluoroalkyl substances (PFAS) including perfluorohexane sulfonate (PFHxS), n-perfluorooctanoic acid (PFOA), n-perfluorooctanesulfonic acid (PFOS), and perfluorononanoic acid (PFNA) in plasma of 180 girls from New York City, and compared them to previously obtained targeted measures using correlation and rank order methods. We showed high agreement between the methods with Spearman Rhos ranging from 0.69 to 0.92 and weighted Kappa's from 0.62 to 0.82 for tertiles among the PFAS. This finding demonstrates that semi-quantitative data from untargeted assays designed for exposomics can be reliably used to estimate environmental exposures occurring in the general population, providing an economic alternative to targeted assays. We also describe an approach that can be used to compare relative quantitation measurements from an untargeted assay to traditional targeted measures to establish fit-for-purpose usability and validation. These results suggest that environmental exposure measures from untargeted assays can serve as reliable inputs into statistical analysis for discovery and for determining their resultant biological impacts. Future efforts to develop new statistical approaches for standardization and merging with targeted measures-toward harmonization-will further enhance the utility of untargeted assays in environmental epidemiology., (Copyright © 2021 Elsevier Ltd. All rights reserved.)
- Published
- 2022
- Full Text
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11. Functional Microbiomics Reveals Alterations of the Gut Microbiome and Host Co-Metabolism in Patients With Alcoholic Hepatitis.
- Author
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Gao B, Duan Y, Lang S, Barupal D, Wu TC, Valdiviez L, Roberts B, Choy YY, Shen T, Byram G, Zhang Y, Fan S, Wancewicz B, Shao Y, Vervier K, Wang Y, Zhou R, Jiang L, Nath S, Loomba R, Abraldes JG, Bataller R, Tu XM, Stärkel P, Lawley TD, Fiehn O, and Schnabl B
- Abstract
Alcohol-related liver disease is a major public health burden, and the gut microbiota is an important contributor to disease pathogenesis. The aim of the present study is to characterize functional alterations of the gut microbiota and test their performance for short-term mortality prediction in patients with alcoholic hepatitis. We integrated shotgun metagenomics with untargeted metabolomics to investigate functional alterations of the gut microbiota and host co-metabolism in a multicenter cohort of patients with alcoholic hepatitis. Profound changes were found in the gut microbial composition, functional metagenome, serum, and fecal metabolomes in patients with alcoholic hepatitis compared with nonalcoholic controls. We demonstrate that in comparison with single omics alone, the performance to predict 30-day mortality was improved when combining microbial pathways with respective serum metabolites in patients with alcoholic hepatitis. The area under the receiver operating curve was higher than 0.85 for the tryptophan, isoleucine, and methionine pathways as predictors for 30-day mortality, but achieved 0.989 for using the urea cycle pathway in combination with serum urea, with a bias-corrected prediction error of 0.083 when using leave-one-out cross validation. Conclusion: Our study reveals changes in key microbial metabolic pathways associated with disease severity that predict short-term mortality in our cohort of patients with alcoholic hepatitis., (© 2020 The Authors. Hepatology Communications published by Wiley Periodicals, Inc., on behalf of the American Association for the Study of Liver Diseases.)
- Published
- 2020
- Full Text
- View/download PDF
12. A Pilot Study on the Effect of Prebiotic on Host-Microbial Co-metabolism in Peritoneal Dialysis Patients.
- Author
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Gao B, Alonzo-Palma N, Brooks B, Jose A, Barupal D, Jagadeesan M, Nobakht E, Collins A, Ramezani A, Omar B, Amdur RL, and Raj DS
- Published
- 2020
- Full Text
- View/download PDF
13. Perspective: Dietary Biomarkers of Intake and Exposure-Exploration with Omics Approaches.
- Author
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Maruvada P, Lampe JW, Wishart DS, Barupal D, Chester DN, Dodd D, Djoumbou-Feunang Y, Dorrestein PC, Dragsted LO, Draper J, Duffy LC, Dwyer JT, Emenaker NJ, Fiehn O, Gerszten RE, B Hu F, Karp RW, Klurfeld DM, Laughlin MR, Little AR, Lynch CJ, Moore SC, Nicastro HL, O'Brien DM, Ordovás JM, Osganian SK, Playdon M, Prentice R, Raftery D, Reisdorph N, Roche HM, Ross SA, Sang S, Scalbert A, Srinivas PR, and Zeisel SH
- Subjects
- Biomarkers blood, Biomarkers urine, Food, Genomics, Humans, Metagenomics, Nutritional Physiological Phenomena genetics, Nutritional Sciences methods, Nutritional Status, Reproducibility of Results, Biomarkers analysis, Diet, Metabolomics methods
- Abstract
While conventional nutrition research has yielded biomarkers such as doubly labeled water for energy metabolism and 24-h urinary nitrogen for protein intake, a critical need exists for additional, equally robust biomarkers that allow for objective assessment of specific food intake and dietary exposure. Recent advances in high-throughput MS combined with improved metabolomics techniques and bioinformatic tools provide new opportunities for dietary biomarker development. In September 2018, the NIH organized a 2-d workshop to engage nutrition and omics researchers and explore the potential of multiomics approaches in nutritional biomarker research. The current Perspective summarizes key gaps and challenges identified, as well as the recommendations from the workshop that could serve as a guide for scientists interested in dietary biomarkers research. Topics addressed included study designs for biomarker development, analytical and bioinformatic considerations, and integration of dietary biomarkers with other omics techniques. Several clear needs were identified, including larger controlled feeding studies, testing a variety of foods and dietary patterns across diverse populations, improved reporting standards to support study replication, more chemical standards covering a broader range of food constituents and human metabolites, standardized approaches for biomarker validation, comprehensive and accessible food composition databases, a common ontology for dietary biomarker literature, and methodologic work on statistical procedures for intake biomarker discovery. Multidisciplinary research teams with appropriate expertise are critical to moving forward the field of dietary biomarkers and producing robust, reproducible biomarkers that can be used in public health and clinical research., (Published by Oxford University Press on behalf of the American Society for Nutrition 2019.)
- Published
- 2020
- Full Text
- View/download PDF
14. The impact of ambient air pollution on the human blood metabolome.
- Author
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Vlaanderen JJ, Janssen NA, Hoek G, Keski-Rahkonen P, Barupal DK, Cassee FR, Gosens I, Strak M, Steenhof M, Lan Q, Brunekreef B, Scalbert A, and Vermeulen RCH
- Subjects
- Adolescent, Environmental Monitoring, Female, Humans, Male, Netherlands, Time Factors, Young Adult, Air Pollutants toxicity, Blood metabolism, Environmental Exposure, Metabolome drug effects
- Abstract
Background: Biological perturbations caused by air pollution might be reflected in the compounds present in blood originating from air pollutants and endogenous metabolites influenced by air pollution (defined here as part of the blood metabolome). We aimed to assess the perturbation of the blood metabolome in response to short term exposure to air pollution., Methods: We exposed 31 healthy volunteers to ambient air pollution for 5h. We measured exposure to particulate matter, particle number concentrations, absorbance, elemental/organic carbon, trace metals, secondary inorganic components, endotoxin content, gaseous pollutants, and particulate matter oxidative potential. We collected blood from the participants 2h before and 2 and 18h after exposure. We employed untargeted metabolite profiling to monitor 3873 metabolic features in 493 blood samples from these volunteers. We assessed lung function using spirometry and six acute phase proteins in peripheral blood. We assessed the association of the metabolic features with the measured air pollutants and with health markers that we previously observed to be associated with air pollution in this study., Results: We observed 89 robust associations between air pollutants and metabolic features two hours after exposure and 118 robust associations 18h after exposure. Some of the metabolic features that were associated with air pollutants were also associated with acute health effects, especially changes in forced expiratory volume in 1s. We successfully identified tyrosine, guanosine, and hypoxanthine among the associated features. Bioinformatics approach Mummichog predicted enriched pathway activity in eight pathways, among which tyrosine metabolism., Conclusions: This study demonstrates for the first time the application of untargeted metabolite profiling to assess the impact of air pollution on the blood metabolome., (Copyright © 2017 Elsevier Inc. All rights reserved.)
- Published
- 2017
- Full Text
- View/download PDF
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