| Title |
A scheme for a flexible classification of dietary and health biomarkers
|
|---|---|
| Published in |
Genes & Nutrition, December 2017
|
| DOI | 10.1186/s12263-017-0587-x |
| Pubmed ID | |
| Authors |
Qian Gao, Giulia Praticò, Augustin Scalbert, Guy Vergères, Marjukka Kolehmainen, Claudine Manach, Lorraine Brennan, Lydia A. Afman, David S. Wishart, Cristina Andres-Lacueva, Mar Garcia-Aloy, Hans Verhagen, Edith J. M. Feskens, Lars O. Dragsted |
| Abstract |
Biomarkers are an efficient means to examine intakes or exposures and their biological effects and to assess system susceptibility. Aided by novel profiling technologies, the biomarker research field is undergoing rapid development and new putative biomarkers are continuously emerging in the scientific literature. However, the existing concepts for classification of biomarkers in the dietary and health area may be ambiguous, leading to uncertainty about their application. In order to better understand the potential of biomarkers and to communicate their use and application, it is imperative to have a solid scheme for biomarker classification that will provide a well-defined ontology for the field. In this manuscript, we provide an improved scheme for biomarker classification based on their intended use rather than the technology or outcomes (six subclasses are suggested: food compound intake biomarkers (FCIBs), food or food component intake biomarkers (FIBs), dietary pattern biomarkers (DPBs), food compound status biomarkers (FCSBs), effect biomarkers, physiological or health state biomarkers). The application of this scheme is described in detail for the dietary and health area and is compared with previous biomarker classification for this field of research. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 143 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 35 | 24% |
| Student > Ph. D. Student | 21 | 15% |
| Student > Master | 19 | 13% |
| Student > Bachelor | 9 | 6% |
| Student > Doctoral Student | 7 | 5% |
| Other | 17 | 12% |
| Unknown | 35 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 27 | 19% |
| Biochemistry, Genetics and Molecular Biology | 18 | 13% |
| Medicine and Dentistry | 15 | 10% |
| Nursing and Health Professions | 12 | 8% |
| Chemistry | 11 | 8% |
| Other | 18 | 13% |
| Unknown | 42 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 17 March 2022.
All research outputs
#3,218,732
of 23,365,820 outputs
Outputs from Genes & Nutrition
#65
of 393 outputs
Outputs of similar age
#72,313
of 441,236 outputs
Outputs of similar age from Genes & Nutrition
#3
of 7 outputs
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