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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Variations in the metabolome in response to disease activity of rheumatoid arthritis
|
|---|---|
| Published in |
BMC Musculoskeletal Disorders, August 2016
|
| DOI | 10.1186/s12891-016-1214-5 |
| Pubmed ID | |
| Authors |
Zuzana Tatar, Carole Migne, Melanie Petera, Philippe Gaudin, Thierry Lequerre, Hubert Marotte, Jacques Tebib, Estelle Pujos Guillot, Martin Soubrier |
| Abstract |
Anti-Tumor Necrosis Factor (TNF) therapies are able to control rheumatoid arthritis (RA) disease activity and limit structural damage. Yet no predictive factor of response to anti-TNF has been identified. Metabolomic profile is known to vary in response to different inflammatory rheumatisms so determining it could substantially improve diagnosis and, consequently, prognosis. The aim of this study was to use mass spectrometry to determine whether there is variation in the metabolome in patients treated with anti-TNF and whether any particular metabolomic profile can serve as a predictor of therapeutic response. Blood samples were analyzed in 140 patients with active RA before initiation of anti-TNF treatment and after 6 months of Anti-TNF treatment (100 good responders and 40 non-responders). Plasma was deproteinized, extracted and analyzed by reverse-phase chromatography-QToF mass spectrometry. Extracted and normalized ions were tested by univariate and ANOVA analysis followed by partial least-squares regression-discriminant analysis (PLS-DA). Orthogonal Signal Correction (OSC) was also used to filter data from unwanted non-related effects. Disease activity scores (DAS 28) obtained at 6 months were correlated with metabolome variation findings to identify a metabolite that is predictive of therapeutic response to anti-TNF. After 6 months of anti-TNF therapy, 100 patients rated as good responders and 40 patients as non-responders according to EULAR criteria. Metabolomic investigations suggested two different metabolic fingerprints splitting the good-responders group and the non-responders group, without differences in anti-TNF therapies. Univariate analysis revealed 24 significant ions in positive mode (p < 0.05) and 31 significant ions in negative mode (p < 0.05). Once intersected with PLS results, only 35 ions remained. Carbohydrate derivates emerged as strong candidate determinants of therapeutic response. This is the first study describing metabolic profiling in response to anti-TNF treatments using plasma samples. The study highlighted two different metabolic profiles splitting good responders from non-responders. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 25% |
| Germany | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 39 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 23% |
| Researcher | 5 | 13% |
| Other | 3 | 8% |
| Student > Doctoral Student | 3 | 8% |
| Professor | 3 | 8% |
| Other | 7 | 18% |
| Unknown | 9 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 17 | 44% |
| Agricultural and Biological Sciences | 3 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 5% |
| Immunology and Microbiology | 2 | 5% |
| Environmental Science | 1 | 3% |
| Other | 4 | 10% |
| Unknown | 10 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 February 2017.
All research outputs
#6,053,255
of 23,881,329 outputs
Outputs from BMC Musculoskeletal Disorders
#1,117
of 4,185 outputs
Outputs of similar age
#93,512
of 347,201 outputs
Outputs of similar age from BMC Musculoskeletal Disorders
#26
of 94 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 4,185 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 347,201 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 94 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.