| Title |
Genetic determinants of serum lipid levels in Chinese subjects: a population-based study in Shanghai, China
|
|---|---|
| Published in |
European Journal of Epidemiology, November 2009
|
| DOI | 10.1007/s10654-009-9402-3 |
| Pubmed ID | |
| Authors |
Gabriella Andreotti, Idan Menashe, Jinbo Chen, Shih-Chen Chang, Asif Rashid, Yu-Tang Gao, Tian-Quan Han, Lori C. Sakoda, Stephen Chanock, Philip S. Rosenberg, Ann W. Hsing |
| Abstract |
We examined the associations between 21 single nucleotide polymorphisms (SNPs) of eight lipid metabolism genes and lipid levels in a Chinese population. This study was conducted as part of a population-based study in China with 799 randomly selected healthy residents who provided fasting blood and an in-person interview. Associations between variants and mean lipid levels were examined using a test of trend and least squares mean test in a general linear model. Four SNPs were associated with lipid levels: LDLR rs1003723 was associated with total cholesterol (P-trend = 0.002) and LDL (P-trend = 0.01), LDLR rs6413503 was associated with total cholesterol (P-trend = 0.05), APOB rs1367117 was associated with apoB (P-trend = 0.02), and ABCB11 rs49550 was associated with total cholesterol (P-trend = 0.01), triglycerides (P-trend = 0.01), and apoA (P-trend = 0.01). We found statistically significant effects on lipid levels for LDLR rs6413503 among those with high dairy intake, LPL rs263 among those with high allium vegetable intake, and APOE rs440446 among those with high red meat intake. We identified new associations between SNPs and lipid levels in Chinese previously found in Caucasians. These findings provide insight into the role of lipid metabolism genes, as well as the mechanisms by which these genes may be linked with disease. |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 5 | 19% |
| Student > Ph. D. Student | 5 | 19% |
| Student > Master | 4 | 15% |
| Student > Doctoral Student | 2 | 8% |
| Researcher | 2 | 8% |
| Other | 2 | 8% |
| Unknown | 6 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 27% |
| Medicine and Dentistry | 6 | 23% |
| Biochemistry, Genetics and Molecular Biology | 2 | 8% |
| Business, Management and Accounting | 1 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 7 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 07 August 2010.
All research outputs
#7,453,827
of 22,787,797 outputs
Outputs from European Journal of Epidemiology
#770
of 1,617 outputs
Outputs of similar age
#34,125
of 94,466 outputs
Outputs of similar age from European Journal of Epidemiology
#5
of 9 outputs
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