Background The initiation and progression of diabetic nephropathy (DN) is complex. Quantification of mRNA expression in urinary sediment has emerged as a novel strategy for studying renal diseases. Considering the numerous molecules involved in DN development, a high-throughput platform with parallel detection of multiple mRNAs is needed. In this study, we constructed a self-assembling mRNA array to analyze urinary mRNAs in DN patients with aims to reveal its potential in searching novel biomarkers. Methods mRNA array containing 88 genes were fabricated and its performance was evaluated. A pilot study with 9 subjects including 6 DN patients and 3 normal controls were studied with the array. DN patients were assigned into two groups according to their estimate glomerular rate (eGFR): DNI group (eGFR>60 ml/min/1.73 m2, n = 3) and DNII group (eGFR<60 ml/min/1.73 m2, n = 3). Urinary cell pellet was collected from each study participant. Relative abundance of these target mRNAs from urinary pellet was quantified with the array. Results The array we fabricated displayed high sensitivity and specificity. Moreover, the Cts of Positive PCR Controls in our experiments were 24±0.5 which indicated high repeatability of the array. A total of 29 mRNAs were significantly increased in DN patients compared with controls (p<0.05). Among these genes, α-actinin4, CDH2, ACE, FAT1, synaptopodin, COL4α, twist, NOTCH3 mRNA expression were 15-fold higher than those in normal controls. In contrast, urinary TIMP-1 mRNA was significantly decreased in DN patients (p<0.05). It was shown that CTGF, MCP-1, PAI-1, ACE, CDH1, CDH2 mRNA varied significantly among the 3 study groups, and their mRNA levels increased with DN progression (p<0.05). Conclusion Our pilot study demonstrated that mRNA array might serve as a high-throughput and sensitive tool for detecting mRNA expression in urinary sediment. Thus, this primary study indicated that mRNA array probably could be a useful tool for searching new biomarkers for DN.
Abstract The aim was to investigate the association between mixed exposure to phthalates and serum thyroid function among US adolescents. The study used 2007–2008 survey data from the National Health and Nutrition Examination Survey (NHANES). Data on urinary phthalates metabolites and serum thyroid function indicators were collected. The weighted multivariable linear regression models and Bayesian kernel machine regression (BKMR) analyses were used to analyze the relationship between phthalates metabolites and thyroid function. A total of 356 adolescents aged 12–19 years were included in the analysis. Linear regression models showed that mono-(carboxyisoctyl) phthalate (MCOP) was positively correlated with total triiodothyronine (TT3) (β = 0.045, 95% confidence interval [CI] 0.022, 0.068) and thyroid stimulating hormone (TSH) (β = 0.1461, 95% CI 0.059, 0.232), while mono-(carboxyisononyl) phthalate (MCNP) was negatively correlated with TSH (β = − 0.119, 95% CI − 0.196, − 0.042). BKMR analyses showed phthalate metabolites mixtures have significantly positive overall effect on TT3. Exposure to phthalate mixtures might be positively correlated with increased TT3 serum level in US adolescents. The study provided evidence for the association between mixed phthalates exposure and thyroid health in adolescent population.
Abstract Renal fibrosis is a common pathological pathway of progressive chronic kidney disease (CKD). However, kidney function parameters are suboptimal for detecting early fibrosis, and therefore, novel biomarkers are urgently needed. We designed a 2-stage study and constructed a targeted microarray to detect urinary mRNAs of CKD patients with renal biopsy and healthy participants. We analysed the microarray data by an iterative random forest method to select candidate biomarkers and produce a more accurate classifier of renal fibrosis. Seventy-six and 49 participants were enrolled into stage I and stage II studies, respectively. By the iterative random forest method, we identified a four-mRNA signature in urinary sediment, including TGFβ1, MMP9, TIMP2, and vimentin, as important features of tubulointerstitial fibrosis (TIF). All four mRNAs significantly correlated with TIF scores and discriminated TIF with high sensitivity, which was further validated in the stage-II study. The combined classifiers showed excellent sensitivity and outperformed serum creatinine and estimated glomerular filtration rate measurements in diagnosing TIF. Another four mRNAs significantly correlated with glomerulosclerosis. These findings showed that urinary mRNAs can serve as sensitive biomarkers of renal fibrosis, and the random forest classifier containing urinary mRNAs showed favourable performance in diagnosing early renal fibrosis.
Background Adverse outcome of chronic kidney disease, such as end stage renal disease, is a significant burden on personal health and healthcare costs. Urinary tubular injury markers, such as NGAL, KIM-1 and NAG, could provide useful prognostic value for the early identification of high-risk patients. However, discrepancies between recent large prospective studies have resulted in controversy regarding the potential clinical value of these markers. Therefore, we conducted the first meta-analysis to provide a more persuasive argument to this debate. Methods In the current meta-analysis, based on ten prospective studies involving 29366 participants, we evaluated the role of urinary tubular injury markers (NGAL, KIM-1 and NAG) in predicting clinical outcomes including CKD stage 3, end stage renal disease and mortality. The prognostic values of these biomarkers were estimated using relative risks and 95% confidence interval in adjusted models. All risk estimates were normalized to those of 1 standard deviation increase in log-scale concentrations to minimize heterogeneity. Fixed-effects models were adopted to combine risk estimates. The quality of the research and between-study heterogeneity were evaluated. The level of research evidence was identified according to the GRADE profiler. Results uNGAL was identified as an independent risk predictor of ESRD (pooled adjusted relative risk: 1.40[1.21 to 1.61], p<0.001) and of overall mortality (pooled adjusted relative risk: 1.10[1.03 to 1.18], p = 0.001) in patients with chronic kidney disease. A borderline significance of uKIM-1 in predicting CKD stage 3 independently in the community-based population was observed (pooled adjusted relative risk: 1.13[1.00 to 1.27], p = 0.057). Only the prognostic value of uNGAL for ESRD was supported by a grade B level of evidence. Conclusion The concentration of uNGAL can be used in practice as an independent predictor of end stage renal disease among patients with chronic kidney disease, but it may be not useful in predicting disease progression to CKD stage 3 among community-based population.
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