| Title |
Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review
|
|---|---|
| Published in |
Metabolomics, November 2014
|
| DOI | 10.1007/s11306-014-0746-7 |
| Pubmed ID | |
| Authors |
Abdul-Hamid Emwas, Claudio Luchinat, Paola Turano, Leonardo Tenori, Raja Roy, Reza M. Salek, Danielle Ryan, Jasmeen S. Merzaban, Rima Kaddurah-Daouk, Ana Carolina Zeri, G. A. Nagana Gowda, Daniel Raftery, Yulan Wang, Lorraine Brennan, David S. Wishart |
| Abstract |
The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition. |
X Demographics
The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 13% |
| United Kingdom | 2 | 13% |
| Canada | 1 | 7% |
| United States | 1 | 7% |
| Germany | 1 | 7% |
| Spain | 1 | 7% |
| Unknown | 7 | 47% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 67% |
| Scientists | 3 | 20% |
| Science communicators (journalists, bloggers, editors) | 2 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 346 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | <1% |
| Italy | 2 | <1% |
| Portugal | 1 | <1% |
| Colombia | 1 | <1% |
| Brazil | 1 | <1% |
| South Africa | 1 | <1% |
| Germany | 1 | <1% |
| Czechia | 1 | <1% |
| Slovakia | 1 | <1% |
| Other | 2 | <1% |
| Unknown | 333 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 69 | 20% |
| Student > Ph. D. Student | 65 | 19% |
| Student > Master | 35 | 10% |
| Student > Bachelor | 30 | 9% |
| Student > Doctoral Student | 29 | 8% |
| Other | 51 | 15% |
| Unknown | 67 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 62 | 18% |
| Agricultural and Biological Sciences | 58 | 17% |
| Biochemistry, Genetics and Molecular Biology | 54 | 16% |
| Medicine and Dentistry | 30 | 9% |
| Pharmacology, Toxicology and Pharmaceutical Science | 12 | 3% |
| Other | 51 | 15% |
| Unknown | 79 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 April 2024.
All research outputs
#2,236,309
of 24,288,381 outputs
Outputs from Metabolomics
#91
of 1,344 outputs
Outputs of similar age
#31,167
of 371,113 outputs
Outputs of similar age from Metabolomics
#2
of 30 outputs
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