Chang-Yien Chan1, Lourdes Paula Resontoc1, Md Abdul Qader1, Yiong-Huak Chan2, Isaac Desheng Liu1, Perry Yew-Weng Lau1, Mya Than1, Wee-Song Yeo1, Alwin Hwai-Liang Loh3, Puay-Hoon Tan3, Changli Wei4, Jochen Reiser4, Subhra K Biswas5, Kar-Hui Ng1, Hui-Kim Yap6. 1. Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Level 12 NUHS Tower Block, 1E Kent Ridge Road, Singapore, 119228, Singapore. 2. Biostatistics Unit, National University of Singapore, Singapore, Singapore. 3. Department of Anatomic Pathology, Singapore General Hospital, Singapore, Singapore. 4. Department of Medicine, Rush University Medical Center, Chicago, IL, USA. 5. Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Singapore, Singapore. 6. Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Level 12 NUHS Tower Block, 1E Kent Ridge Road, Singapore, 119228, Singapore. paeyaphk@nus.edu.sg.
Abstract
BACKGROUND: A lack of consensus exists as to the timing of kidney biopsy in children with steroid-dependent nephrotic syndrome (SDNS) where minimal change disease (MCD) predominates. This study aimed at examining the applicability of a biomarker-assisted risk score model to select SDNS patients at high risk of focal segmental glomerulosclerosis (FSGS) for biopsy. METHODS: Fifty-five patients with SDNS and biopsy-proven MCD (n = 40) or FSGS (n = 15) were studied. A risk score model was developed with variables consisting of age, sex, eGFR, suPAR levels and percentage of CD8+ memory T cells. Following multivariate regression analysis, total risk score was calculated as sum of the products of odds ratios and corresponding variables. Predictive cut-off point was determined using receiver operator characteristics (ROC) curve analysis. RESULTS: Plasma suPAR levels in FSGS patients were significantly higher, while percentage of CD45RO+CD8+CD3+ was significantly lower than in MCD patients and controls. ROC analysis suggests the risk score model with threshold score of 16.7 (AUC 0.84, 95% CI 0.72-0.96) was a good predictor of FSGS on biopsy. The 100% PPV cut-off was >24.0, while the 100% NPV was <13.3. CONCLUSION: A suPAR and CD8+ memory T cell percentage-based risk score model was developed to stratify SDNS patients for biopsy and for predicting FSGS.
BACKGROUND: A lack of consensus exists as to the timing of kidney biopsy in children with steroid-dependent nephrotic syndrome (SDNS) where minimal change disease (MCD) predominates. This study aimed at examining the applicability of a biomarker-assisted risk score model to select SDNS patients at high risk of focal segmental glomerulosclerosis (FSGS) for biopsy. METHODS: Fifty-five patients with SDNS and biopsy-proven MCD (n = 40) or FSGS (n = 15) were studied. A risk score model was developed with variables consisting of age, sex, eGFR, suPAR levels and percentage of CD8+ memory T cells. Following multivariate regression analysis, total risk score was calculated as sum of the products of odds ratios and corresponding variables. Predictive cut-off point was determined using receiver operator characteristics (ROC) curve analysis. RESULTS: Plasma suPAR levels in FSGS patients were significantly higher, while percentage of CD45RO+CD8+CD3+ was significantly lower than in MCD patients and controls. ROC analysis suggests the risk score model with threshold score of 16.7 (AUC 0.84, 95% CI 0.72-0.96) was a good predictor of FSGS on biopsy. The 100% PPV cut-off was >24.0, while the 100% NPV was <13.3. CONCLUSION: A suPAR and CD8+ memory T cell percentage-based risk score model was developed to stratify SDNS patients for biopsy and for predicting FSGS.
Authors: Salim S Hayek; Kwi Hye Koh; Morgan E Grams; Changli Wei; Yi-An Ko; Jing Li; Beata Samelko; Hyun Lee; Ranadheer R Dande; Ha Won Lee; Eunsil Hahm; Vasil Peev; Melissa Tracy; Nicholas J Tardi; Vineet Gupta; Mehmet M Altintas; Garrett Garborcauskas; Nikolina Stojanovic; Cheryl A Winkler; Michael S Lipkowitz; Adrienne Tin; Lesley A Inker; Andrew S Levey; Martin Zeier; Barry I Freedman; Jeffrey B Kopp; Karl Skorecki; Josef Coresh; Arshed A Quyyumi; Sanja Sever; Jochen Reiser Journal: Nat Med Date: 2017-06-26 Impact factor: 53.440
Authors: C F Sier; N Sidenius; A Mariani; G Aletti; V Agape; A Ferrari; G Casetta; R W Stephens; N Brünner; F Blasi Journal: Lab Invest Date: 1999-06 Impact factor: 5.662