| Literature DB >> 25784779 |
Chia-Ter Chao1, Yen-Ching Chen2, Chih-Kang Chiang3, Jenq-Wen Huang4, Fu-Chang Hu5, Cheng-Chung Fang6, Chen-Chih Chang4, Chung-Jen Yen7.
Abstract
BACKGROUND: PPAR-γ single nucleotide polymorphisms (SNPs) reportedly play an important role in determining metabolic risk among diverse population. Whether PPAR-γ SNPs affect the clinical courses in ESRD patients is unknown.Entities:
Mesh:
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Year: 2015 PMID: 25784779 PMCID: PMC4345048 DOI: 10.1155/2015/763459
Source DB: PubMed Journal: Dis Markers ISSN: 0278-0240 Impact factor: 3.434
Minor allele frequency and Hardy-Weinberg equilibrium of two PPAR-γ SNPs.
| SNP name | rs # | Nucleotide change | Location | Controls | Cases | ||
|---|---|---|---|---|---|---|---|
| MAF (%) | HWE | MAF (%) | HWE | ||||
| Pro12Ala | rs1801282 | C → G | Exon B | 3.07 | 0.752 | 4.87 | 0.006 |
| C161T | rs3856806 | C → T | Exon 6 | 24.23 | 0.688 | 26.15 | 0.520 |
MAF: minor allele frequency; HWE: Hardy-Weinberg equilibrium.
Characteristics of the study population by case and control status.
| Variables | Controls | Cases |
|
|---|---|---|---|
| Number of subjects | 782 | 698 | |
| Age (years) | 60.0 ± 19.2 | 58.8 ± 14.6 | <0.001 |
| Male | 412 (52.2%) | 334 (44.8%) | 0.068 |
| DM | 83 (10.6%) | 263 (37.7%) | <0.001 |
| Serum creatinine (mg/dL) | 1.0 ± 0.3 | 11.2 ± 2.5 | <0.001 |
| Serum albumin (g/dL) | 4.4 ± 0.2 | 3.9 ± 0.5 | <0.001 |
| PPAR- | 0.037 | ||
| CC | 735 (94.0%) | 635 (91.1%) | |
| CG | 46 (5.9%) | 58 (8.2%) | |
| GG | 1 (0.1%) | 5 (0.7%) | |
| PPAR- | 0.482 | ||
| CC | 451 (57.7%) | 384 (55.0%) | |
| CT | 283 (36.2%) | 263 (37.7%) | |
| TT | 48 (6.1%) | 51 (7.3%) | |
| Death | 39 (5.0%) | 217 (31.1%) | <0.001 |
*Continuous variables were tested by Mann-Whitney U tests, whereas categorical variables were tested by Fisher's exact test.
Genotype distributions of the study population by cases with DM, cases without DM, and control status.
| Variables | Controls | Cases_non-DM | Cases_DM |
|
|---|---|---|---|---|
| Number of subjects | 782 | 512 | 186 | |
| PPAR- | 0.027 | |||
| CC | 735 (94.0%) | 471 (92.0%) | 164 (88.2%) | |
| CG | 46 (5.9%) | 37 (7.2%) | 21 (11.3%) | |
| GG | 1 (0.1%) | 4 (0.8%) | 1 (0.5%) | |
| PPAR- | 0.673 | |||
| CC | 451 (57.7%) | 278 (54.3%) | 106 (57.0%) | |
| CT | 283 (36.2%) | 198 (38.7%) | 65 (34.9%) | |
| TT | 48 (6.1%) | 36 (7.0%) | 15 (8.1%) |
*Continuous variables were tested by Mann-Whitney U tests, whereas categorical variables were tested by chi-square test or Fisher's exact test.
The predictors of ESRD identified by fitting multiple logistic regression model weighted by the inverse of survival probability using the stepwise variable selection method.
| Covariate* | Regression coefficient | Standard error |
|
| Odds ratio | 95% confidence interval of odds ratio | ||
|---|---|---|---|---|---|---|---|---|
| Intercept | 0.075 | 0.192 | 0.390 | 0.697 | ||||
| Age (years) | −0.008 | 0.003 | −2.666 | 0.008 | 0.992 | 0.985 | — | 0.998 |
| DM | 1.811 | 0.141 | 12.846 | <0.001 | 6.225 | 4.724 | — | 8.203 |
| Non-DM × PPAR- | 13.939 | 0.511 | 27.266 | <0.001 | 1.1 × 106 | 4 × 105 | — | 3 × 106 |
Multiple weighted logistic regression model: n = 1480; the estimated area under the receiver operating characteristic (ROC) curve = 0.689.
DM: diabetes mellitus; ESRD: end-stage renal disease.
*The symbol “×” indicates the interaction between two covariates and it can be literally interpreted as “and” in this case.
Univariate analysis of risk factors for mortality in dialysis cases.
| Variables | Alive | Dead |
|
|---|---|---|---|
| Age (years) | 54.7 ± 13.4 | 67.8 ± 13.1 | <0.001 |
| Male | 224 (46.5%) | 110 (50.7%) | 0.327 |
| DM | 141 (29.3%) | 122 (56.2%) | <0.001 |
| Serum albumin (g/dL) | 4.0 ± 0.4 | 3.7 ± 0.5 | <0.001 |
| Serum hemoglobin (g/dL) | 10.2 ± 1.5 | 9.8 ± 1.7 | 0.001 |
| PPAR- | 0.417 | ||
| CC | 435 (90.5%) | 200 (92.2%) | |
| CG | 41 (8.5%) | 17 (7.8%) | |
| GG | 5 (1.0%) | 0 (0%) | |
| PPAR- | 0.332 | ||
| CC | 257 (53.3%) | 127 (58.6%) | |
| CT | 185 (38.5%) | 78 (35.9%) | |
| TT | 39 (8.1%) | 12 (5.5%) |
*Continuous variables were tested by Mann-Whitney U tests, whereas categorical variables were tested by Fisher's exact test.
The predictors of time to death in dialysis cases by fitting multiple Cox's proportional hazards model** weighted by the inverse of survival probability using the stepwise variable selection method.
| Covariate* | Regression coefficient | Standard error |
|
| Hazard ratio | Lower | Upper |
|---|---|---|---|---|---|---|---|
| Age (years) | 0.065 | 0.006 | 10.707 | <0.001 | 1.068 | 1.055 | 1.080 |
| PPAR- | −14.61 | 0.695 | −21.021 | <0.001 | <0.01 | <0.01 | <0.01 |
| DM × PPAR- | 1.050 | 0.156 | 6.728 | <0.001 | 2.858 | 2.105 | 3.858 |
| DM × PPAR- | 0.655 | 0.178 | 3.675 | <0.001 | 1.926 | 1.358 | 2.731 |
*The symbol “×” indicates the interaction between two covariates and it can be literally interpreted as “and” in this case.
**Weighted Cox's proportional hazards model: n = 698, number of events = 217, and concordance = 0.756 (se = 0.021).