| Literature DB >> 29636065 |
Changming Zhang1, Shaoshan Liang1, Shuiqin Cheng1, Wei Li2, Xia Wang1, Chunxia Zheng1, Caihong Zeng1, Shaolin Shi1, Lu Xie2, Ke Zen3, Zhihong Liu4.
Abstract
BACKGROUND: Urinary miRNAs may potentially serve as noninvasive biomarkers in various kidney diseases to reflect disease activity, severity and progression, especially those correlated with the pathogenesis of kidney diseases. This study demonstrates that urinary miR-196a, a kidney-enriched miRNA, can predict progression of chronic kidney disease (CKD).Entities:
Keywords: Biomarker; CKD progression; FSGS; Urinary MIR-196A
Mesh:
Substances:
Year: 2018 PMID: 29636065 PMCID: PMC5894160 DOI: 10.1186/s12967-018-1470-2
Source DB: PubMed Journal: J Transl Med ISSN: 1479-5876 Impact factor: 5.531
Fig. 1Schematic depiction of the research approach. FSGS focal segmental glomerulosclerosis, IF/TA interstitial fibrosis and tubular atrophy
Fig. 2Correlations of urinary or plasma miR-196a levels with the disease activity. a Urinary miR-196a levels in different groups. ROC curve analysis indicates that urinary miR-196a can distinguish FSGS-A patients from normal controls (b) and FSGS-CR patients (c) with high sensitivity and specificity. d Plasma miR-196a levels in different groups. ROC curve analysis indicates that plasma miR-196a could not distinguish FSGS-A patients from normal controls (e) and FSGS-CR patients (f). Note that no significant correlation was seen between plasma miR-196a level and the disease activity. Bars indicate median with inter-quartile range. NC normal controls, FSGS-A FSGS patients with nephrotic-range proteinuria, FSGS-CR FSGS in complete remission, AUC area under the receiver operating characteristic curve, ROC receiver operating characteristic curve
Baseline characteristics for patients with FSGS according to tertiles of urinary miR-196a
| Characteristic | Total (n = 231) | Patients according to tertiles of urinary miR-196a | |||
|---|---|---|---|---|---|
| Tertile 1 (n = 78) | Tertile 2 (n = 77) | Tertile 3 (n = 76) | |||
| Age (years) | 26.0 (20.0, 41.0) | 24.5 (19.0, 39.0) | 28.0 (21.0, 42.0) | 25.5 (20.0, 42.8) | 0.554 |
| Male, % (n) | 68.4 (158) | 67.9 (53) | 66.2 (51) | 71.1 (54) | 0.810 |
| Hypertension, % (n) | 17.3 (40) | 10.3 (8) | 28.6 (22) | 13.2 (10) | 0.005 |
| eGFR (mL/min/1.73 m2) | 104.07 (62.61, 123.73) | 111.57 (82.77, 130.05) | 99.75 (57.85, 123.26) | 86.23 (55.43, 115.24) | 0.005 |
| Proteinuria (g/24 h) | 6.93 (3.97, 9.94) | 5.49 (2.87, 8.14) | 7.80 (4.29, 10.42) | 7.15 (5.07, 10.85) | 0.003 |
| Albumin (g/L) | 21.20 (18.70, 26.90) | 22.55 (19.18, 30.03) | 20.30 (18.08, 27.20) | 20.65 (18.20, 23.98) | 0.064 |
| Serum creatinine (mg/dL) | 0.91 (0.69, 1.42) | 0.82 (0.64, 1.01) | 0.95 (0.67, 1.54) | 1.03 (0.75, 1.66) | 0.005 |
| Total cholesterol (mmol/L) | 9.52 (7.48, 12.28) | 8.74 (6.86, 11.95) | 9.52 (7.67, 12.25) | 10.04 (7.94, 13.04) | 0.056 |
| Triglyceride (mmol/L) | 2.81 (2.02, 3.75) | 2.47 (1.77, 3.41) | 2.96 (2.25, 4.00) | 3.00 (2.17, 4.05) | 0.013 |
| Global glomerulosclerosis (%) | 0.00 (0.00, 6.06) | 0.00 (0.00, 6.67) | 0.00 (0.00, 5.44) | 0.00 (0.00, 4.89) | 0.304 |
| Segmental glomerulosclerosis (%) | 12.50 (5.88, 22.22) | 9.88 (5.26, 19.78) | 12.50 (5.88, 23.30) | 14.29 (6.32, 25.00) | 0.436 |
| Interstitial fibrosis (%) | 4.45 (0.00, 13.39) | 2.98 (0.00, 9.86) | 4.45 (0.00, 8.95) | 7.12 (2.31, 21.21) | 0.004 |
| Tubular atrophy (%) | 1.01 (0.00, 7.01) | 0.76 (0.00, 6.16) | 0.82 (0.00, 5.00) | 1.70 (0.00, 20.25) | 0.038 |
| Patients with nephrotic syndrome, % (n) | 80.1 (185) | 66.7 (52) | 84.4 (65) | 89.5 (68) | 0.001 |
| Patients progression to ESRD, % (n) | 20.3 (47) | 7.7 (6) | 15.6 (12) | 38.2 (29) | < 0.001 |
Data are shown as median (25th, 75th percentiles) for continuous variables and percent (n) for categorical variables
Conversion factors: serum creatinine in mg/dL to μmol/L, ×88.4
FSGS focal segmental glomerulosclerosis, eGFR estimated glomerular filtration rate
Fig. 3Correlations of urinary miR-196a levels with clinical outcome. a Urinary miR-196a concentrations were significantly higher in patients who would develop ESRD as compared with those who would not. b In patients with nephrotic-range proteinuria, urinary miR-196a levels were also significantly higher in patients who would develop ESRD as compared with those who would not. Urinary miR-196a was significantly correlated with proteinuria (c) and eGFR (d). Bars indicate median with inter-quartile range. ESRD end-stage renal disease, non-ESRD not progression to ESRD, NS nephrotic-range proteinuria, eGFR estimated glomerular filtration rate
Fig. 4Correlations of urinary and intrarenal miR-196a with interstitial fibrosis and tubular atrophy. Urinary miR-196a was significantly correlated with interstitial fibrosis (a) and tubular atrophy (b). Intrarenal miR-196a was significantly correlated with interstitial fibrosis (c) and tubular atrophy (d)
Fig. 5Correlation of urinary miR-196a and risk of ESRD. a Kaplan–Meier curves of renal survival rate stratified by urinary miR-196a tertiles. There were significant differences of the renal survival rate among the different miR-196a tertiles analyzed by the log-rank test. b Association between tertiles of urinary miR-196a with risk of progression to ESRD. HRs with 95% CI was plotted for tertiles of urinary miR-196a. c The additional value of urinary miR-196a in predicting ESRD as assessed by the time-dependent ROC curves. The AUC of model2 (dash line; addition of urinary miR-196a to proteinuria and eGFR, adjusting for age, sex) is superior over time when compared with the AUC of model1 (solid line; including only proteinuria and eGFR, adjusting for age, sex). The overall survival probability is indicated by the dotted line. ESRD end-stage renal disease, HR hazard ratio, 95% CI 95% confidence interval, ROC receiver operating characteristic curve, AUC area under the ROC curve, eGFR estimated glomerular filtration rate
Cox analysis between various parameters and renal outcome (n = 231)
| Parameters | HR (95% CI) | p value |
|---|---|---|
| Univariate Cox analysis | ||
| Age | 0.998 (0.976–1.020) | 0.851 |
| Sex | 1.165 (0.644–2.107) | 0.613 |
| eGFR | 0.992 (0.985–1.000) | 0.053 |
| Proteinuria | 1.065 (1.005–1.129) | 0.035 |
| Urinary miR-196a | 2.512 (1.613–3.912) | < 0.0001 |
| Multivariate Cox analysis | ||
| Urinary miR-196a | 2.616 (1.592–4.301) | < 0.001 |
The multivariate Cox analysis was adjusted for age, sex, eGFR and proteinuria
HR hazard ratio, 95% CI 95% confidence interval, eGFR estimated glomerular filtration rate
Evaluation of various prediction models
| Models | AIC | C-statistics | |
|---|---|---|---|
| Model1: eGFR + proteinuria | 461 | 0.61 (0.52–0.70) | – |
| Model2: eGFR + proteinuria + urinary miR-196a | 448 | 0.68 (0.60–0.76) | 0.001 |
Both models were adjusted for age and sex
AIC akaike information criterion, eGFR estimated glomerular filtration rate