Dataset Information

Clinical characteristics and disease predictors of a large Chinese cohort of patients with autosomal dominant polycystic kidney disease.

ABSTRACT:

Objective

Autosomal dominant polycystic kidney disease (ADPKD) is a relentlessly progressing form of chronic kidney disease for which there is no cure. The aim of this study was to characterize Chinese patients with ADPKD and to identify the factors which predict cyst growth and renal functional deterioration.

Methods

To analyze disease predicting factors we performed a prospective longitudinal observational study in a cohort of 541 Chinese patients with ADPKD and an eGFR ? 30 ml/min/1.73 m(2). Patients were followed clinically and radiologically with sequential abdominal magnetic resonance imaging (MRI). Clinical characteristics and laboratory data were related to changes in estimated glomerular filtration rate (eGFR) and total kidney volume (TKV). A linear regression model was developed to analyze the factors which determine eGFR and TKV changes.

Results

The age range of this unselected cohort ranged from 4 to 77 years. Median follow-up time was 14.3 ± 10.6 months. Although inter-individual differences in eGFR and TKV were large, there was a consistent link between these two parameters. Baseline log10-transformed TKV and urinary protein/creatinine ratio were identified as the major predictors for a faster eGFR decline and were associated with a higher TKV growth rate. Interestingly, a lower thrombocyte count correlated significantly with lower eGFR (r = 0.222) and higher TKV (r = 0.134).

Conclusions

This large cohort of Chinese patients with ADPKD provides unique epidemiological data for comparison with other cohorts of different ethnicity. In Chinese patients we identified a lower thrombocyte count as a significant predictor of disease progression. These results are important for the design of future clinical trials to retard polycystic kidney disease progression.

SUBMITTER: Chen D

PROVIDER: S-EPMC3961326 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Clinical characteristics and disease predictors of a large Chinese cohort of patients with autosomal dominant polycystic kidney disease.

Chen Dongping D Ma Yiyi Y Wang Xueqi X Yu Shengqiang S Li Lin L Dai Bing B Mao Zhiguo Z Sun Lijun L Xu Chenggang C Rong Shu S Tang Mengjun M Zhao Hongbo H Liu Hongchao H Serra Andreas L AL Graf Nicole N Liu Shiyuan S Wüthrich Rudolf P RP Mei Changlin C

PloS one 20140320 3

<h4>Objective</h4>Autosomal dominant polycystic kidney disease (ADPKD) is a relentlessly progressing form of chronic kidney disease for which there is no cure. The aim of this study was to characterize Chinese patients with ADPKD and to identify the factors which predict cyst growth and renal functional deterioration.<h4>Methods</h4>To analyze disease predicting factors we performed a prospective longitudinal observational study in a cohort of 541 Chinese patients with ADPKD and an eGFR ≥ 30 ml/ ...[more]

PMID: 24651850

Similar Datasets

Project description:BACKGROUND:Markers currently used to predict the likelihood of rapid disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD) are expensive and time consuming to assess and often have limited sensitivity. New, easy-to-measure markers are therefore needed that alone or in combination with conventional risk markers can predict the rate of disease progression. In the present study, we investigated the ability of tubular damage and inflammation markers to predict kidney function decline. METHODS:At baseline, albumin, immunoglobulin G, kidney injury molecule 1, ?2 microglobulin (?2MG), heart-type fatty acid-binding protein, neutrophil gelatinase-associated lipocalin, and monocyte chemotactic protein-1 -(MCP-1) were measured in 24-h urine samples of patients participating in a study investigating the therapeutic efficacy of lanreotide in ADPKD. Individual change in estimated glomerular filtration rate (eGFR) during follow-up was calculated using mixed-model analysis taking into account 13 -eGFRs (chronic kidney disease EPIdemiology) per patient. Logistic regression analysis was used to select urinary biomarkers that had the best association with rapidly progressive disease. The predictive value of these selected urinary biomarkers was compared to other risk scores using C-statistics. RESULTS:Included were 302 patients of whom 53.3% were female, with an average age of 48 ± 7 years, eGFR of 52 ± 12 mL/min/1.73 m2, and a height-adjusted total kidney volume (htTKV) of 1,082 (736-1,669) mL/m. At baseline, all urinary damage and inflammation markers were associated with baseline eGFR, also after adjustment for age, sex and baseline htTKV. For longitudinal analyses only patients randomized to standard care were considered (n = 152). A stepwise backward analysis revealed that ?2MG and MCP-1 showed the strongest association with rapidly progressive disease. A urinary biomarker score was created by summing the ranking of tertiles of ?2MG and MCP-1 excretion. The predictive value of this urinary biomarker score was higher compared to that of the Mayo htTKV classification (area under the curve [AUC] 0.73 [0.64-0.82] vs. 0.61 [0.51-0.71], p = 0.04) and comparable to that of the predicting renal outcomes in -ADPKD score (AUC 0.73 [0.64-0.82] vs. 0.65 [0.55-0.75], p = 0.18). In a second independent cohort with better kidney function, similar results were found for the urinary biomarker score. CONCLUSION:Measurement of urinary ?2MG and MCP-1 excretion allows selection of ADPKD patients with rapidly progressive disease, with a predictive value comparable to or even higher than that of TKV or PKD mutation. Easy and inexpensive to measure urinary markers therefore hold promise to help predict prognosis in ADPKD.

Dataset Information

Clinical characteristics and disease predictors of a large Chinese cohort of patients with autosomal dominant polycystic kidney disease.

Objective

Methods

Results

Conclusions

Publications

Clinical characteristics and disease predictors of a large Chinese cohort of patients with autosomal dominant polycystic kidney disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets