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1
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Beasley-Green A, Heckert NA. Estimation of measurement uncertainty for the quantification of protein by ID-LC-MS/MS. Anal Bioanal Chem 2023:10.1007/s00216-023-04705-8. [PMID: 37231301 DOI: 10.1007/s00216-023-04705-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/13/2023] [Accepted: 04/18/2023] [Indexed: 05/27/2023]
Abstract
The emergence of mass spectrometry (MS)-based methods to quantify proteins for clinical applications has led to the need for accurate and consistent measurements. To meet the clinical needs of MS-based protein results, it is important that the results are traceable to higher-order standards and methods and have defined uncertainty values. Therefore, we outline a comprehensive approach for the estimation of measurement uncertainty of a MS-based procedure for the quantification of a protein biomarker. Using a bottom-up approach, which is the model outlined in the "Guide to the Expression of Uncertainty of Measurement" (GUM), we evaluated the uncertainty components of a MS-based measurement procedure for a protein biomarker in a complex matrix. The cause-and-effect diagram of the procedure is used to identify each uncertainty component, and statistical equations are derived to determine the overall combined uncertainty. Evaluation of the uncertainty components not only enables the calculation of the measurement uncertainty but can also be used to determine if the procedure needs improvement. To demonstrate the use of the bottom-up approach, the overall combined uncertainty is estimated for the National Institute of Standards and Technology (NIST) candidate reference measurement procedure for albumin in human urine. The results of the uncertainty approach are applied to the determination of uncertainty for the certified value for albumin in candidate NIST Standard Reference Material® (SRM) 3666. This study provides a framework for measurement uncertainty estimation of a MS-based protein procedure by identifying the uncertainty components of the procedure to derive the overall combined uncertainty.
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Affiliation(s)
- Ashley Beasley-Green
- Material Measurement Laboratory (Biomolecular Measurement Division), National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD, 20899-8390, USA.
| | - N Alan Heckert
- Information Technology Laboratory (Statistical Engineering Division), National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD, 20899-8390, USA
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2
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Lang BE, Molloy JL, Vetter TW, Kotoski SP, Possolo A. Value assignment and uncertainty evaluation for anion and single-element reference solutions incorporating historical information. Anal Bioanal Chem 2023; 415:1657-1673. [PMID: 36847795 PMCID: PMC10680025 DOI: 10.1007/s00216-022-04410-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/16/2022] [Accepted: 10/27/2022] [Indexed: 03/01/2023]
Abstract
The National Institute of Standards and Technology, which is the national metrology institute of the USA, assigns certified values to the mass fractions of individual elements in single-element solutions, and to the mass fractions of anions in anion solutions, based on gravimetric preparations and instrumental methods of analysis. The instrumental method currently is high-performance inductively coupled plasma optical emission spectroscopy for the single-element solutions, and ion chromatography for the anion solutions. The uncertainty associated with each certified value comprises method-specific components, a component reflecting potential long-term instability that may affect the certified mass fraction during the useful lifetime of the solutions, and a component from between-method differences. Lately, the latter has been evaluated based only on the measurement results for the reference material being certified. The new procedure described in this contribution blends historical information about between-method differences for similar solutions produced previously, with the between-method difference observed when a new material is characterized. This blending procedure is justified because, with only rare exceptions, the same preparation and measurement methods have been used historically: in the course of almost 40 years for the preparation methods, and of 20 years for the instrumental methods. Also, the certified values of mass fraction, and the associated uncertainties, have been very similar, and the chemistry of the solutions also is closely comparable within each series of materials. If the new procedure will be applied to future SRM lots of single-element or anion solutions routinely, then it is expected that it will yield relative expanded uncertainties that are about 20 % smaller than the procedure for uncertainty evaluation currently in use, and that it will do so for the large majority of the solutions. However, more consequential than any reduction in uncertainty, is the improvement in the quality of the uncertainty evaluations that derives from incorporating the rich historical information about between-method differences and about the stability of the solutions over their expected lifetimes. The particular values listed for several existing SRMs are given merely as retrospective illustrations of the application of the new method, not to suggest that the certified values or their associated uncertainties should be revised.
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Affiliation(s)
- Brian E Lang
- Inorganic Chemical Metrology Group, Chemical Sciences Division, Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Drive, Stop 8391, Gaithersburg, MD, 20899-8391, USA.
| | - John L Molloy
- Inorganic Chemical Metrology Group, Chemical Sciences Division, Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Drive, Stop 8391, Gaithersburg, MD, 20899-8391, USA
| | - Thomas W Vetter
- Inorganic Chemical Metrology Group, Chemical Sciences Division, Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Drive, Stop 8391, Gaithersburg, MD, 20899-8391, USA
| | - Shaun P Kotoski
- Inorganic Chemical Metrology Group, Chemical Sciences Division, Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Drive, Stop 8391, Gaithersburg, MD, 20899-8391, USA
| | - Antonio Possolo
- Statistical Engineering Division, Information Technology Laboratory, National Institute of Standards and Technology, 100 Bureau Drive, Stop 8980, Gaithersburg, MD, 20899-8980, USA
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3
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Ghorasaini M, Mohammed Y, Adamski J, Bettcher L, Bowden JA, Cabruja M, Contrepois K, Ellenberger M, Gajera B, Haid M, Hornburg D, Hunter C, Jones CM, Klein T, Mayboroda O, Mirzaian M, Moaddel R, Ferrucci L, Lovett J, Nazir K, Pearson M, Ubhi BK, Raftery D, Riols F, Sayers R, Sijbrands EJG, Snyder MP, Su B, Velagapudi V, Williams KJ, de Rijke YB, Giera M. Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Anal Chem 2021; 93:16369-16378. [PMID: 34859676 PMCID: PMC8674878 DOI: 10.1021/acs.analchem.1c02826] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 11/18/2021] [Indexed: 12/15/2022]
Abstract
Modern biomarker and translational research as well as personalized health care studies rely heavily on powerful omics' technologies, including metabolomics and lipidomics. However, to translate metabolomics and lipidomics discoveries into a high-throughput clinical setting, standardization is of utmost importance. Here, we compared and benchmarked a quantitative lipidomics platform. The employed Lipidyzer platform is based on lipid class separation by means of differential mobility spectrometry with subsequent multiple reaction monitoring. Quantitation is achieved by the use of 54 deuterated internal standards and an automated informatics approach. We investigated the platform performance across nine laboratories using NIST SRM 1950-Metabolites in Frozen Human Plasma, and three NIST Candidate Reference Materials 8231-Frozen Human Plasma Suite for Metabolomics (high triglyceride, diabetic, and African-American plasma). In addition, we comparatively analyzed 59 plasma samples from individuals with familial hypercholesterolemia from a clinical cohort study. We provide evidence that the more practical methyl-tert-butyl ether extraction outperforms the classic Bligh and Dyer approach and compare our results with two previously published ring trials. In summary, we present standardized lipidomics protocols, allowing for the highly reproducible analysis of several hundred human plasma lipids, and present detailed molecular information for potentially disease relevant and ethnicity-related materials.
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Affiliation(s)
- Mohan Ghorasaini
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Albinusdreef 2, Leiden 2333ZA, The Netherlands
| | - Yassene Mohammed
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Albinusdreef 2, Leiden 2333ZA, The Netherlands
- Genome
BC Proteomics Centre, University of Victoria, Victoria, British Columbia V8Z 7X8, Canada
| | - Jerzy Adamski
- Institute
of Experimental Genetics, German Research Center for Environmental
Health, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, Neuherberg 85764, Germany
- Department
of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
- Institute
of Biochemistry, Faculty of Medicine, University
of Ljubljana, Vrazov
Trg 2, Ljubljana 1000, Slovenia
| | - Lisa Bettcher
- Northwest
Metabolomics Research Center, Department of Anesthesiology, University of Washington, Seattle, Washington 98109, United States
| | - John A. Bowden
- Department
of Physiological Sciences, College of Veterinary Medicine, University of Florida, 1333 Center Drive, Gainesville, Florida 32610, United States
| | - Matias Cabruja
- Department
of Genetics, School of Medicine, Stanford
University, 300 Pasteur Drive, Stanford, California 94305, United States
| | - Kévin Contrepois
- Department
of Genetics, School of Medicine, Stanford
University, 300 Pasteur Drive, Stanford, California 94305, United States
| | - Mathew Ellenberger
- Department
of Genetics, School of Medicine, Stanford
University, 300 Pasteur Drive, Stanford, California 94305, United States
| | - Bharat Gajera
- Metabolomics
Unit, Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Tukholmankatu 8, Biomedicum 2U, Helsinki 00014, Finland
| | - Mark Haid
- Metabolomics
and Proteomics Core, German Research Center for Environmental Health, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, Neuherberg 85764, Germany
| | - Daniel Hornburg
- Department
of Genetics, School of Medicine, Stanford
University, 300 Pasteur Drive, Stanford, California 94305, United States
| | | | - Christina M. Jones
- Material Measurement Laboratory, National
Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Theo Klein
- Department
of Clinical Chemistry, University Medical Center, Erasmus MC, Rotterdam, 3000CA, The Netherlands
| | - Oleg Mayboroda
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Albinusdreef 2, Leiden 2333ZA, The Netherlands
| | - Mina Mirzaian
- Department
of Clinical Chemistry, University Medical Center, Erasmus MC, Rotterdam, 3000CA, The Netherlands
| | - Ruin Moaddel
- National Institute on Aging, National Institutes of
Health, Baltimore, Maryland 21224, United
States
| | - Luigi Ferrucci
- National Institute on Aging, National Institutes of
Health, Baltimore, Maryland 21224, United
States
| | - Jacqueline Lovett
- National Institute on Aging, National Institutes of
Health, Baltimore, Maryland 21224, United
States
| | - Kenneth Nazir
- Metabolomics
Unit, Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Tukholmankatu 8, Biomedicum 2U, Helsinki 00014, Finland
| | | | | | - Daniel Raftery
- Northwest
Metabolomics Research Center, Department of Anesthesiology, University of Washington, Seattle, Washington 98109, United States
| | - Fabien Riols
- Metabolomics
and Proteomics Core, German Research Center for Environmental Health, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, Neuherberg 85764, Germany
| | | | - Eric J. G. Sijbrands
- Department of Internal Medicine, University
Medical Center, Erasmus MC, Rotterdam 3000CA, The Netherlands
| | - Michael P. Snyder
- Department
of Genetics, School of Medicine, Stanford
University, 300 Pasteur Drive, Stanford, California 94305, United States
| | - Baolong Su
- Department of Biological
Chemistry, University
of California, Los Angeles, California 90095, United States
| | - Vidya Velagapudi
- Metabolomics
Unit, Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Tukholmankatu 8, Biomedicum 2U, Helsinki 00014, Finland
| | - Kevin J. Williams
- Department of Biological
Chemistry, University
of California, Los Angeles, California 90095, United States
| | - Yolanda B. de Rijke
- Department
of Clinical Chemistry, University Medical Center, Erasmus MC, Rotterdam, 3000CA, The Netherlands
| | - Martin Giera
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Albinusdreef 2, Leiden 2333ZA, The Netherlands
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4
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Michael H, Thornton S, Xie M, Tian L. Exact inference on the random-effects model for meta-analyses with few studies. Biometrics 2019; 75:485-493. [PMID: 30430540 DOI: 10.1111/biom.12998] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 10/24/2018] [Indexed: 11/29/2022]
Abstract
We describe an exact, unconditional, non-randomized procedure for producing confidence intervals for the grand mean in a normal-normal random effects meta-analysis. The procedure targets meta-analyses based on too few primary studies, ≤ 7 , say, to allow for the conventional asymptotic estimators, e.g., DerSimonian and Laird (1986), or non-parametric resampling-based procedures, e.g., Liu et al. (2017). Meta-analyses with such few studies are common, with one recent sample of 22,453 heath-related meta-analyses finding a median of 3 primary studies per meta-analysis (Davey et al., 2011). Reliable and efficient inference procedures are therefore needed to address this setting. The coverage level of the resulting CI is guaranteed to be above the nominal level, up to Monte Carlo error, provided the meta-analysis contains more than 1 study and the model assumptions are met. After employing several techniques to accelerate computation, the new CI can be easily constructed on a personal computer. Simulations suggest that the proposed CI typically is not overly conservative. We illustrate the approach on several contrasting examples of meta-analyses investigating the effect of calcium intake on bone mineral density.
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Affiliation(s)
- Haben Michael
- Department of Statistics, Stanford University, Stanford, California
| | - Suzanne Thornton
- Department of Statistics, Rutgers University, New Brunswick, New Jersey
| | - Minge Xie
- Department of Statistics, Rutgers University, New Brunswick, New Jersey
| | - Lu Tian
- Department of Biomedical Data Science, Stanford University, Stanford, California
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5
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Abstract
Interlaboratory studies are common in toxicology, particularly for the introduction of alternative assays. Numerous papers are available on the statistical analysis of interlaboratory studies, but these deal primarily with the case of a replicated single sample studied in several laboratories. This approach can be used for some assays, but for the majority, the results will be unsatisfactory, i.e. involving great variability between both the dose groups and the laboratories. However, the primary objective of toxicological assays is to achieve similarity between the sizes of effects, rather than to determine absolute values. In the parametric model, the sizes of effects are the studentised differences from the negative control or, for the commonly used dose-response designs, the similarity of the slopes of the dose-response curves. Standard approaches for the estimation of intralaboratory and interlaboratory variability, including Mandel plots, are introduced, and new approaches are presented for demonstrating similarity of effect sizes, with or without assuming a dose-response model. One approach is based on a modification of the parallel-line assay, the other is based on a modification of the interaction contrasts of the analysis of variance. SAS programs are given for all approaches, and real data from an interlaboratory immunotoxicological study are analysed as a demonstration.
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Affiliation(s)
- Ludwig A Hothorn
- Bioinformatics Unit, University of Hannover, 30419 Hannover, Germany
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6
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Liu Y, He A, Liu B, Zhong Y, Liao X, Yang J, Chen J, Wu J, Mei H. rs11614913 polymorphism in miRNA-196a2 and cancer risk: an updated meta-analysis. Onco Targets Ther 2018; 11:1121-1139. [PMID: 29535537 PMCID: PMC5840307 DOI: 10.2147/ott.s154211] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Several epidemiological studies have reported that polymorphisms in microRNA-196a2 (miR-196a2) were associated with various cancers. However, the results remained unverified and were inconsistent in different cancers. Therefore, we carried out an updated meta-analysis to elaborate the effects of rs11614913 polymorphism on cancer susceptibility. A total of 84 articles with 35,802 cases and 41,541 controls were included to evaluate the association between the miR-196a2 rs11614913 and cancer risk by pooled odds ratios (ORs) and 95% confidence intervals (CIs). The results showed that miR-196a2 rs11614913 polymorphism is associated with cancer susceptibility, especially in lung cancer (homozygote comparison, OR =0.840, 95% CI =0.734-0.961; recessive model, OR =0.858, 95% CI =0.771-0.955), hepatocellular carcinoma (allelic contrast, OR =0.894, 95% CI =0.800-0.998; homozygote comparison, OR =0.900, 95% CI =0.813-0.997; recessive model, OR =0.800, 95% CI =0.678-0.944), and head and neck cancer (allelic contrast, OR =1.076, 95% CI =1.006-1.152; homozygote comparison, OR =1.214, 95% CI =1.043-1.413). In addition, significant association was found among Asian populations (allele model, OR =0.847, 95% CI =0.899-0.997, P=0.038; homozygote model, OR =0.878, 95% CI =0.788-0.977, P=0.017; recessive model, OR =0.895, 95% CI =0.824-0.972, P=0.008) but not in Caucasians. The updated meta-analysis confirmed the previous results that miR-196a2 rs11614913 polymorphism may serve as a risk factor for patients with cancers.
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Affiliation(s)
- Yuhan Liu
- Department of Urology, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Anbang He
- Department of Urology, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
- Department of Urology, Peking University First Hospital, The Institute of Urology, Peking University, National Urological Cancer Centre, Beijing, China
| | - Baoer Liu
- Department of Urology, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Yucheng Zhong
- Department of Urology, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Xinhui Liao
- Department of Urology, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Jiangeng Yang
- Department of Urology, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Jieqing Chen
- Department of Urology, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Jianting Wu
- Department of Urology, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Hongbing Mei
- Department of Urology, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
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7
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Mei Q, Qu J. Interleukin-13 +2044 G/A and +1923C/T polymorphisms are associated with asthma susceptibility in Asians: A meta-analysis. Medicine (Baltimore) 2017; 96:e9203. [PMID: 29390465 PMCID: PMC5758167 DOI: 10.1097/md.0000000000009203] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
A number of studies have reported that the interleukin 13 (IL-13) gene is associated with asthma susceptibility. However, the reported relationships between the +2044G/A and +1923C/T polymorphisms and asthma susceptibility are inconsistent, especially in Asian adults and children with atopic status. Meta-analysis was used to analyze combined data.The +2044G/A and +1923C/T polymorphisms were investigated using data from 18 and 11 studies, respectively. The results suggested that there was an association between asthma and the IL-13 +2044G/A polymorphisms: odds ratio (OR) 1.34, 95% confidence interval (CI) 1.03-1.75 for AA versus GG + GA and +1923C/T; OR 1.50, 95% CI 1.26-1.78 for TT versus CC; and OR 1.15, 95% CI 1.10-1.21 for TC versus CC. The subgroup meta-analysis demonstrated that IL-13 +2044G/A polymorphisms are associated with asthma: OR 1.47, 95% CI 1.06-2.04 for AA versus GG + GA and +1923C/T; OR 1.70, 95% CI 1.26-2.30 for TT versus CC; and OR 1.27, 95% CI 1.03-1.56 for TC versus CC. In particular, IL-13 +2044G/A polymorphisms are specifically associated with Asian ethnicity in both adults and children with atopic status. However, the 1923C/T polymorphisms were not significantly associated with age group or atopic status within the Asian subgroups. Further investigation using larger samples and meta-analysis is required. No publication bias was detected.This meta-analysis indicates that the IL13 +2044G/A and +1923C/T polymorphisms are risk factors for asthma, especially among Asians.
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Affiliation(s)
- Quanhui Mei
- Department of Intensive Care Unit, The First People's Hospital of Changde City, Changde, Hunan
| | - Jingjing Qu
- Department of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital, Affiliated Cancer Hospital of Xiangya School of Medicine, Changsha, China
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8
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Bowden JA, Heckert A, Ulmer CZ, Jones CM, Koelmel JP, Abdullah L, Ahonen L, Alnouti Y, Armando AM, Asara JM, Bamba T, Barr JR, Bergquist J, Borchers CH, Brandsma J, Breitkopf SB, Cajka T, Cazenave-Gassiot A, Checa A, Cinel MA, Colas RA, Cremers S, Dennis EA, Evans JE, Fauland A, Fiehn O, Gardner MS, Garrett TJ, Gotlinger KH, Han J, Huang Y, Neo AH, Hyötyläinen T, Izumi Y, Jiang H, Jiang H, Jiang J, Kachman M, Kiyonami R, Klavins K, Klose C, Köfeler HC, Kolmert J, Koal T, Koster G, Kuklenyik Z, Kurland IJ, Leadley M, Lin K, Maddipati KR, McDougall D, Meikle PJ, Mellett NA, Monnin C, Moseley MA, Nandakumar R, Oresic M, Patterson R, Peake D, Pierce JS, Post M, Postle AD, Pugh R, Qiu Y, Quehenberger O, Ramrup P, Rees J, Rembiesa B, Reynaud D, Roth MR, Sales S, Schuhmann K, Schwartzman ML, Serhan CN, Shevchenko A, Somerville SE, St John-Williams L, Surma MA, Takeda H, Thakare R, Thompson JW, Torta F, Triebl A, Trötzmüller M, Ubhayasekera SJK, Vuckovic D, Weir JM, Welti R, Wenk MR, Wheelock CE, Yao L, Yuan M, Zhao XH, Zhou S. Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in Frozen Human Plasma. J Lipid Res 2017; 58:2275-2288. [PMID: 28986437 PMCID: PMC5711491 DOI: 10.1194/jlr.m079012] [Citation(s) in RCA: 278] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2017] [Revised: 10/02/2017] [Indexed: 12/22/2022] Open
Abstract
As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950-Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each laboratory using a different lipidomics workflow. A total of 1,527 unique lipids were measured across all laboratories and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra- and interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.
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Affiliation(s)
- John A Bowden
- Marine Biochemical Sciences Group, Chemical Sciences Division, Hollings Marine Laboratory, National Institute of Standards and Technology, Charleston, SC
| | - Alan Heckert
- Statistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD
| | - Candice Z Ulmer
- Marine Biochemical Sciences Group, Chemical Sciences Division, Hollings Marine Laboratory, National Institute of Standards and Technology, Charleston, SC
| | - Christina M Jones
- Marine Biochemical Sciences Group, Chemical Sciences Division, Hollings Marine Laboratory, National Institute of Standards and Technology, Charleston, SC
| | - Jeremy P Koelmel
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | | | - Linda Ahonen
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Yazen Alnouti
- Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, NE
| | - Aaron M Armando
- Departments of Chemistry and Biochemistry and Pharmacology, School of Medicine, University of California, San Diego, La Jolla, CA
| | - John M Asara
- Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Takeshi Bamba
- Division of Metabolomics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - John R Barr
- Division of Laboratory Sciences, Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA
| | - Jonas Bergquist
- Department of Chemistry-BMC, Analytical Chemistry, Uppsala University, Uppsala, Sweden
| | - Christoph H Borchers
- University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, Victoria, British Columbia, Canada
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada
- Gerald Bronfman Department of Oncology McGill University, Montreal, Quebec, Canada
- Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
| | - Joost Brandsma
- Faculty of Medicine, Academic Unit of Clinical and Experimental Sciences, Southampton General Hospital, University of Southampton, Southampton, United Kingdom
| | - Susanne B Breitkopf
- Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, MA
| | - Tomas Cajka
- National Institutes of Health West Coast Metabolomics Center, University of California Davis Genome Center, Davis, CA
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore and Singapore Lipidomic Incubator (SLING), Life Sciences Institute, Singapore
| | - Antonio Checa
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Michelle A Cinel
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Romain A Colas
- Department of Anesthesiology, Perioperative and Pain Medicine, Center for Experimental Therapeutics and Reperfusion Injury, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Serge Cremers
- Biomarker Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY
| | - Edward A Dennis
- Departments of Chemistry and Biochemistry and Pharmacology, School of Medicine, University of California, San Diego, La Jolla, CA
| | | | - Alexander Fauland
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Oliver Fiehn
- National Institutes of Health West Coast Metabolomics Center, University of California Davis Genome Center, Davis, CA
- Biochemistry Department, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Michael S Gardner
- Division of Laboratory Sciences, Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA
| | - Timothy J Garrett
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | - Katherine H Gotlinger
- Department of Pharmacology, New York Medical College School of Medicine, Valhalla, NY
| | - Jun Han
- University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, Victoria, British Columbia, Canada
| | | | - Aveline Huipeng Neo
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore and Singapore Lipidomic Incubator (SLING), Life Sciences Institute, Singapore
| | | | - Yoshihiro Izumi
- Division of Metabolomics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Hongfeng Jiang
- Biomarker Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY
| | - Houli Jiang
- Department of Pharmacology, New York Medical College School of Medicine, Valhalla, NY
| | - Jiang Jiang
- Departments of Chemistry and Biochemistry and Pharmacology, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Maureen Kachman
- Metabolomics Core, BRCF, University of Michigan, Ann Arbor, MI
| | | | | | | | - Harald C Köfeler
- Core Facility for Mass Spectrometry, Medical University of Graz, Graz, Austria
| | - Johan Kolmert
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | | | - Grielof Koster
- Faculty of Medicine, Academic Unit of Clinical and Experimental Sciences, Southampton General Hospital, University of Southampton, Southampton, United Kingdom
| | - Zsuzsanna Kuklenyik
- Division of Laboratory Sciences, Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA
| | - Irwin J Kurland
- Stable Isotope and Metabolomics Core Facility, Diabetes Research Center, Albert Einstein College of Medicine, Bronx, NY
| | - Michael Leadley
- Analytical Facility of Bioactive Molecules, The Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Karen Lin
- University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, Victoria, British Columbia, Canada
| | - Krishna Rao Maddipati
- Lipidomics Core Facility and Department of Pathology, Wayne State University, Detroit, MI
| | - Danielle McDougall
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | | | - Cian Monnin
- Department of Chemistry and Biochemistry, Concordia University, Montréal, Québec, Canada
| | - M Arthur Moseley
- Proteomics and Metabolomics Shared Resource, Levine Science Research Center, Duke University School of Medicine, Durham, NC
| | - Renu Nandakumar
- Biomarker Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY
| | - Matej Oresic
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | - Rainey Patterson
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | | | - Jason S Pierce
- Department of Biochemistry and Molecular Biology Medical University of South Carolina, Charleston, SC
| | - Martin Post
- Analytical Facility of Bioactive Molecules, The Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Anthony D Postle
- Faculty of Medicine, Academic Unit of Clinical and Experimental Sciences, Southampton General Hospital, University of Southampton, Southampton, United Kingdom
| | - Rebecca Pugh
- Chemical Sciences Division, Environmental Specimen Bank Group, Hollings Marine Laboratory, National Institute of Standards and Technology, Charleston, SC
| | - Yunping Qiu
- Stable Isotope and Metabolomics Core Facility, Diabetes Research Center, Albert Einstein College of Medicine, Bronx, NY
| | - Oswald Quehenberger
- Departments of Medicine and Pharmacology, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Parsram Ramrup
- Department of Chemistry and Biochemistry, Concordia University, Montréal, Québec, Canada
| | - Jon Rees
- Division of Laboratory Sciences, Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA
| | - Barbara Rembiesa
- Department of Biochemistry and Molecular Biology Medical University of South Carolina, Charleston, SC
| | - Denis Reynaud
- Analytical Facility of Bioactive Molecules, The Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Mary R Roth
- Division of Biology, Kansas Lipidomics Research Center, Kansas State University, Manhattan, KS
| | - Susanne Sales
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Kai Schuhmann
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | | | - Charles N Serhan
- Department of Anesthesiology, Perioperative and Pain Medicine, Center for Experimental Therapeutics and Reperfusion Injury, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Andrej Shevchenko
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Stephen E Somerville
- Hollings Marine Laboratory, Medical University of South Carolina, Charleston, SC
| | - Lisa St John-Williams
- Proteomics and Metabolomics Shared Resource, Levine Science Research Center, Duke University School of Medicine, Durham, NC
| | | | - Hiroaki Takeda
- Division of Metabolomics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Rhishikesh Thakare
- Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, NE
| | - J Will Thompson
- Proteomics and Metabolomics Shared Resource, Levine Science Research Center, Duke University School of Medicine, Durham, NC
| | - Federico Torta
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore and Singapore Lipidomic Incubator (SLING), Life Sciences Institute, Singapore
| | - Alexander Triebl
- Core Facility for Mass Spectrometry, Medical University of Graz, Graz, Austria
| | - Martin Trötzmüller
- Core Facility for Mass Spectrometry, Medical University of Graz, Graz, Austria
| | | | - Dajana Vuckovic
- Department of Chemistry and Biochemistry, Concordia University, Montréal, Québec, Canada
| | - Jacquelyn M Weir
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Ruth Welti
- Division of Biology, Kansas Lipidomics Research Center, Kansas State University, Manhattan, KS
| | - Markus R Wenk
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore and Singapore Lipidomic Incubator (SLING), Life Sciences Institute, Singapore
| | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Libin Yao
- Division of Biology, Kansas Lipidomics Research Center, Kansas State University, Manhattan, KS
| | - Min Yuan
- Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, MA
| | - Xueqing Heather Zhao
- Stable Isotope and Metabolomics Core Facility, Diabetes Research Center, Albert Einstein College of Medicine, Bronx, NY
| | - Senlin Zhou
- Lipidomics Core Facility and Department of Pathology, Wayne State University, Detroit, MI
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Burr T, Williams B, Croft S, White M, Hanson K. Meta-Analysis Options for Inconsistent Nuclear Measurements. NUCL SCI ENG 2017. [DOI: 10.13182/nse11-112] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Tom Burr
- Los Alamos National Laboratory Los Alamos, New Mexico 87545
| | - Brian Williams
- Los Alamos National Laboratory Los Alamos, New Mexico 87545
| | - Stephen Croft
- Los Alamos National Laboratory Los Alamos, New Mexico 87545
| | - Morgan White
- Los Alamos National Laboratory Los Alamos, New Mexico 87545
| | - Ken Hanson
- Los Alamos National Laboratory Los Alamos, New Mexico 87545
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Herschtal A, Foroudi F, Kron T, Mengersen K. A Comparison of Bayesian Models of Heteroscedasticity in Nested Normal Data. COMMUN STAT-SIMUL C 2016. [DOI: 10.1080/03610918.2014.936467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Liang RF, Zheng LL. The efficacy and safety of panitumumab in the treatment of patients with metastatic colorectal cancer: a meta-analysis from five randomized controlled trials. DRUG DESIGN DEVELOPMENT AND THERAPY 2015; 9:4471-8. [PMID: 26300630 PMCID: PMC4535553 DOI: 10.2147/dddt.s85178] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Background The efficacy of adding panitumumab to chemotherapy remains controversial in the treatment of metastatic colorectal cancer (mCRC). Thus, we conducted this meta-analysis to assess the efficacy and safety of this combination regimen in patients with mCRC. Methods The PubMed, Embase, and Web of Science databases were comprehensively searched. Eligible studies included randomized controlled trials (RCTs) that estimated the efficacy of panitumumab with or without chemotherapy in the treatment of patients with mCRC. Hazard ratio (HR), risk ratio (RR), and 95% confidence intervals (CIs) were calculated, and heterogeneity was tested using I2 statistics. Results Four studies involving a total of 3,066 patients were included in this meta-analysis. The addition of panitumumab to chemotherapy significantly improved progression-free survival (PFS) (HR =0.84, 95% CI =0.78–0.91, P=0.000) and the objective response rate (ORR) (RR =2.18, 95% CI =1.13–4.22, P=0.021) compared to chemotherapy alone, but no effect was noted on overall survival (OS) (HR =0.97, 95% CI =0.89–1.05, P=0.402). Subgroup analysis based on KRAS gene status revealed that the combined therapy significantly improved PFS (HR =0.71, 95% CI =0.57–0.88, P=0.002) and ORR (RR =2.43, 95% CI =1.21–4.90, P=0.013) in patients with wild-type KRAS tumors. Irinotecan-based chemotherapy plus panitumumab significantly prolonged PFS in patients with mCRC (HR =0.84, 95% CI =0.76–0.94, P=0.002). The combined treatment also increased the incidence of grade 3/4 adverse events. Conclusion This meta-analysis indicates that the combination of panitumumab and chemotherapy effectively improved PFS and ORR, but it did not prolong OS. However, as the number of studies in the meta-analysis was limited, more large-scale, better-designed RCTs are needed to assess the combination of panitumumab and chemotherapy.
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Affiliation(s)
- Ruo-feng Liang
- General Department, University Hospital, Affiliated to Zhejiang Science-Technology University, Hangzhou, People's Republic of China
| | - Lei-lei Zheng
- Department of Psychiatry, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, People's Republic of China
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Bellio R, Guolo A. Integrated Likelihood Inference in Small Sample Meta-analysis for Continuous Outcomes. Scand Stat Theory Appl 2015. [DOI: 10.1111/sjos.12172] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Xu L, Yang F, Chen R, Yu S. A Parametric Bootstrap Test for Two-Way ANOVA Model Without Interaction Under Heteroscedasticity. COMMUN STAT-SIMUL C 2015. [DOI: 10.1080/03610918.2013.818689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Ni S, Fu Z, Zhao J, Liu H. Inhaled corticosteroids (ICS) and risk of mycobacterium in patients with chronic respiratory diseases: a meta-analysis. J Thorac Dis 2014; 6:971-8. [PMID: 25093095 DOI: 10.3978/j.issn.2072-1439.2014.07.03] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2013] [Accepted: 06/10/2014] [Indexed: 11/14/2022]
Abstract
BACKGROUND Studies have indicated that therapy with inhaled corticosteroids (ICS) can be associated with a higher risk of pneumonia. However, it is not known whether ICS increases the risk of mycobacterium. Most of these published studies were small, and the conclusions were inconsistent. METHODS A meta-analysis was conducted into whether ICS increases the risk of mycobacterium in patients with chronic respiratory diseases. PubMed, OVID, EMBASE and Cochrane Library databases were searched. RESULTS Five studies involving 4,851 cases and 28,477 controls were considered in the meta-analysis. From the pooled analyses, there was significant association between ICS and risk of mycobacterium in all patients with chronic respiratory diseases [risk ratio (RR) =1.81; 95% confidence interval (CI), 1.23-2.68; P=0.003]. Among patients with chronic respiratory diseases, the relationship between ICS and risk of tuberculosis (TB) was also significant (RR =1.34; 95% CI, 1.15-1.55; P=0.0001). And meta-analysis of four studies in patients with chronic obstructive pulmonary disease (COPD) (RR =1.42; 95% CI, 1.18-1.72; P=0.0003) or two studies in patients who have prior pulmonary TB (RR =1.61; 95% CI, 1.35-1.92; P<0.00001) or three studies in patients with high-dose ICS (RR =1.60; 95% CI, 1.28-1.99; P<0.0001) showed a relationship between ICS and risk of mycobacterium. CONCLUSIONS Significant relationship has been shown between ICS use and risk of mycobacterium in all patients with chronic respiratory diseases. ICS use also increases the risk of TB among the patients with chronic respiratory diseases. Use of ICS increases the risk of mycobacterium in patients with COPD or patients with prior pulmonary TB or patients inhaling high-dose corticosteroids. Further research is required to establish the potential adverse effect of ICS as a therapy for chronic respiratory diseases.
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Affiliation(s)
- Songshi Ni
- Department of Respiratory Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Zhenxue Fu
- Department of Respiratory Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Jing Zhao
- Department of Respiratory Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Hua Liu
- Department of Respiratory Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
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Li QT, Kang W, Wang M, Yang J, Zuo Y, Zhang W, Su DK. Association between esophageal cancer risk and EPHX1 polymorphisms: A meta-analysis. World J Gastroenterol 2014; 20:5124-5130. [PMID: 24803829 PMCID: PMC4009551 DOI: 10.3748/wjg.v20.i17.5124] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2013] [Revised: 11/18/2013] [Accepted: 02/20/2014] [Indexed: 02/06/2023] Open
Abstract
AIM: To summarize the relationship between p.Tyr113His and p.His139Arg polymorphisms in microsomal epoxide hydrolase (EPHX1) and risk for esophageal cancer (EC).
METHODS: The MEDLINE/PubMed and EMBASE databases were searched for studies of the association between EPHX1 polymorphisms and EC risk that were published from the database inception date to April 2013. A total of seven case-control studies, including seven on p.Tyr113His (cases, n = 1118; controls, n = 1823) and six on p.His139Arg (cases, n = 861; controls, n = 1571), were included in the meta-analysis. After data extraction by two investigators working independently, the meta-analyses were carried out with STATA 11.0 software. Pooled odds ratios and 95%CI were calculated using a fixed-effects model or a random-effects model, as appropriate.
RESULTS: The pooled EPHX1 p.Tyr113His polymorphism data showed no significant association with EC in any of the genetic models (OR = 1.00, 95%CI: 0.70-1.48 for Tyr/His vs Tyr/Tyr; OR = 1.10, 95%CI: 0.77-1.57 for His/His vs Tyr/Tyr; OR = 1.06, 95%CI: 0.75-1.49 for a dominant model; OR = 1.09, 95%CI: 0.89-1.34 for a recessive model). Similar results were obtained from the p.His139Arg polymorphism analysis (Arg/His vs His/His: OR = 1.02, 95%CI: 0.84-1.23; Arg/Arg vs His/His: OR = 0.96, 95%CI: 0.60-1.54; OR = 1.03, 95%CI: 0.78-1.37 for the dominant model; OR = 0.97, 95%CI: 0.61-1.56 for the recessive model). Subgroup analyses for ethnicity, subtype of EC, and source of controls (population-based or hospital-based) showed trends that were consistent with the pooled analysis (reported above), with no significant associations found.
CONCLUSION: This meta-analysis suggests that the p.Tyr113His and p.His139Arg polymorphisms in EPHX1 may not be associated with EC development.
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Kang Z, Li Y, He X, Jiu T, Wei J, Tian F, Gu C. Quantitative assessment of the association between miR-196a2 rs11614913 polymorphism and cancer risk: evidence based on 45,816 subjects. Tumour Biol 2014; 35:6271-82. [DOI: 10.1007/s13277-014-1822-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2014] [Accepted: 03/05/2014] [Indexed: 12/30/2022] Open
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Zimmerman BE, Pibida L, King LE, Bergeron DE, Cessna JT, Mille MM. Calibration of Traceable Solid Mock (131)I Phantoms Used in an International SPECT Image Quantification Comparison. JOURNAL OF RESEARCH OF THE NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY 2013; 118:359-74. [PMID: 26401437 PMCID: PMC4487311 DOI: 10.6028/jres.118.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/24/2013] [Indexed: 06/05/2023]
Abstract
The International Atomic Energy Agency (IAEA) has organized an international comparison to assess Single Photon Emission Computed Tomography (SPECT) image quantification capabilities in 12 countries. Iodine-131 was chosen as the radionuclide for the comparison because of its wide use around the world, but for logistical reasons solid (133)Ba sources were used as a long-lived surrogate for (131)I. For this study, we designed a set of solid cylindrical sources so that each site could have a set of phantoms (having nominal volumes of 2 mL, 4 mL, 6 mL, and 23 mL) with traceable activity calibrations so that the results could be properly compared. We also developed a technique using two different detection methods for individually calibrating the sources for (133)Ba activity based on a National standard. This methodology allows for the activity calibration of each (133)Ba source with a standard uncertainty on the activity of 1.4 % for the high-level 2-, 4-, and 6-mL sources and 1.7 % for the lower-level 23 mL cylinders. This level of uncertainty allows for these sources to be used for the intended comparison exercise, as well as in other SPECT image quantification studies.
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Affiliation(s)
- BE Zimmerman
- National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - L Pibida
- National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - LE King
- National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - DE Bergeron
- National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - JT Cessna
- National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - MM Mille
- National Institute of Standards and Technology, Gaithersburg, MD 20899
- Nuclear Engineering and Engineering Physics Program, Rensselaer Polytechnic Institute, Troy, NY 12180
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Hu Z, Hu X, Long J, Su L, Wei B. XRCC1 polymorphisms and differentiated thyroid carcinoma risk: a meta-analysis. Gene 2013; 528:67-73. [PMID: 23872202 DOI: 10.1016/j.gene.2013.07.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2013] [Revised: 06/14/2013] [Accepted: 07/02/2013] [Indexed: 11/27/2022]
Abstract
The objective of this study is to quantitatively derive a more precise estimation of the association between X-ray repair cross-complementing group 1 (XRCC1) gene polymorphisms and differentiated thyroid carcinoma risk. A comprehensive literature search of three databases was conducted. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated with fixed-effect models and random-effect models when appropriate. Overall, no association of the XRCC1 Arg399Gln, Arg280His, and Arg194Trp polymorphisms with differentiated thyroid carcinoma risk was found. In subgroup analyses, a decreased differentiated thyroid carcinoma risk was observed among Caucasians (Gln vs. Arg, OR=0.86, 95% CI=0.77-0.96, P=0.343 for heterogeneity; Gln/Arg vs. Arg/Arg, OR=0.84, 95% CI=0.71-0.98, P=0.229 for heterogeneity; Gln/Gln vs. Arg/Arg, OR=0.77, 95% CI=0.60-0.99, P=0.477 for heterogeneity; dominant genetic model, OR=0.82, 95% CI=0.71-0.95, P=0.272 for heterogeneity), not among Asians. No publication bias was observed. Our results suggest that XRCC1 Arg399Gln polymorphism is not associated with differentiated thyroid carcinoma risk, while a decreased risk is observed among Caucasian population.
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Affiliation(s)
- Zhen Hu
- School of Public Health, Guangxi Medical University, 22 Shuangyong Road, Nanning, People's Republic of China
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Results of an international comparison for the activity measurement of 177Lu. Appl Radiat Isot 2012; 70:1825-30. [DOI: 10.1016/j.apradiso.2012.02.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2011] [Accepted: 02/16/2012] [Indexed: 11/24/2022]
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Arendacká B. A note on fiducial generalized pivots for in one-way heteroscedastic ANOVA with random effects. STATISTICS-ABINGDON 2012. [DOI: 10.1080/02331888.2010.540669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Barbora Arendacká
- a Institute of Measurement Science, Slovak Academy of Sciences , Dúbravská cesta 9, 841 04 , Bratislava , Slovakia
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Sharma G, Mathew T. Higher order inference for the consensus mean in inter-laboratory studies. Biom J 2010; 53:128-36. [DOI: 10.1002/bimj.201000032] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2010] [Revised: 09/07/2010] [Accepted: 09/17/2010] [Indexed: 11/08/2022]
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Mathew T, Nahtman T, von Rosen D, Sinha BK. Non-negative estimation of variance components in heteroscedastic one-way random-effects ANOVA models. STATISTICS-ABINGDON 2010. [DOI: 10.1080/02331880903237106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Searching a robust reference value from intercomparison exercise data: Seaweed radionuclide Standard Reference Material (SRM4359). J Radioanal Nucl Chem 2008. [DOI: 10.1007/s10967-008-0507-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Jung I, Kumar Sen P. Robust testing for random effects in unbalanced heteroscedastic one-way models. J Nonparametr Stat 2008. [DOI: 10.1080/10485250802018477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Krishnamoorthy K, Lu F, Mathew T. A parametric bootstrap approach for ANOVA with unequal variances: Fixed and random models. Comput Stat Data Anal 2007. [DOI: 10.1016/j.csda.2006.09.039] [Citation(s) in RCA: 107] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Toman B. Bayesian Approaches to Calculating a Reference Value in Key Comparison Experiments. Technometrics 2007. [DOI: 10.1198/004017006000000273] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Adaptive estimation of error density in nonparametric regression with small sample size. J Stat Plan Inference 2007. [DOI: 10.1016/j.jspi.2006.01.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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