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Mendeley readers
Attention Score in Context
| Title |
Meta-Analysis of 28,141 Individuals Identifies Common Variants within Five New Loci That Influence Uric Acid Concentrations
|
|---|---|
| Published in |
PLoS Genetics, June 2009
|
| DOI | 10.1371/journal.pgen.1000504 |
| Pubmed ID | |
| Authors |
Melanie Kolz, Toby Johnson, Serena Sanna, Alexander Teumer, Veronique Vitart, Markus Perola, Massimo Mangino, Eva Albrecht, Chris Wallace, Martin Farrall, Åsa Johansson, Dale R. Nyholt, Yurii Aulchenko, Jacques S. Beckmann, Sven Bergmann, Murielle Bochud, Morris Brown, Harry Campbell, John Connell, Anna Dominiczak, Georg Homuth, Claudia Lamina, Mark I. McCarthy, Thomas Meitinger, Vincent Mooser, Patricia Munroe, Matthias Nauck, John Peden, Holger Prokisch, Perttu Salo, Veikko Salomaa, Nilesh J. Samani, David Schlessinger, Manuela Uda, Uwe Völker, Gérard Waeber, Dawn Waterworth, Rui Wang-Sattler, Alan F. Wright, Jerzy Adamski, John B. Whitfield, Ulf Gyllensten, James F. Wilson, Igor Rudan, Peter Pramstaller, Hugh Watkins, Angela Doering, H.-Erich Wichmann, Tim D. Spector, Leena Peltonen, Henry Völzke, Ramaiah Nagaraja, Peter Vollenweider, Mark Caulfield, Thomas Illig, Christian Gieger |
| Abstract |
Elevated serum uric acid levels cause gout and are a risk factor for cardiovascular disease and diabetes. To investigate the polygenetic basis of serum uric acid levels, we conducted a meta-analysis of genome-wide association scans from 14 studies totalling 28,141 participants of European descent, resulting in identification of 954 SNPs distributed across nine loci that exceeded the threshold of genome-wide significance, five of which are novel. Overall, the common variants associated with serum uric acid levels fall in the following nine regions: SLC2A9 (p = 5.2x10(-201)), ABCG2 (p = 3.1x10(-26)), SLC17A1 (p = 3.0x10(-14)), SLC22A11 (p = 6.7x10(-14)), SLC22A12 (p = 2.0x10(-9)), SLC16A9 (p = 1.1x10(-8)), GCKR (p = 1.4x10(-9)), LRRC16A (p = 8.5x10(-9)), and near PDZK1 (p = 2.7x10(-9)). Identified variants were analyzed for gender differences. We found that the minor allele for rs734553 in SLC2A9 has greater influence in lowering uric acid levels in women and the minor allele of rs2231142 in ABCG2 elevates uric acid levels more strongly in men compared to women. To further characterize the identified variants, we analyzed their association with a panel of metabolites. rs12356193 within SLC16A9 was associated with DL-carnitine (p = 4.0x10(-26)) and propionyl-L-carnitine (p = 5.0x10(-8)) concentrations, which in turn were associated with serum UA levels (p = 1.4x10(-57) and p = 8.1x10(-54), respectively), forming a triangle between SNP, metabolites, and UA levels. Taken together, these associations highlight additional pathways that are important in the regulation of serum uric acid levels and point toward novel potential targets for pharmacological intervention to prevent or treat hyperuricemia. In addition, these findings strongly support the hypothesis that transport proteins are key in regulating serum uric acid levels. |
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 268 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 2% |
| United Kingdom | 3 | 1% |
| Switzerland | 2 | <1% |
| Japan | 2 | <1% |
| Australia | 1 | <1% |
| Unknown | 255 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 57 | 21% |
| Student > Ph. D. Student | 45 | 17% |
| Student > Master | 29 | 11% |
| Student > Bachelor | 23 | 9% |
| Professor | 22 | 8% |
| Other | 56 | 21% |
| Unknown | 36 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 69 | 26% |
| Agricultural and Biological Sciences | 61 | 23% |
| Biochemistry, Genetics and Molecular Biology | 42 | 16% |
| Pharmacology, Toxicology and Pharmaceutical Science | 15 | 6% |
| Computer Science | 5 | 2% |
| Other | 29 | 11% |
| Unknown | 47 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 05 April 2020.
All research outputs
#4,792,785
of 25,837,817 outputs
Outputs from PLoS Genetics
#3,624
of 9,017 outputs
Outputs of similar age
#20,643
of 127,436 outputs
Outputs of similar age from PLoS Genetics
#27
of 68 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,017 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.9. This one has gotten more attention than average, scoring higher than 59% 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 127,436 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 68 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 60% of its contemporaries.