BACKGROUND: The effect of clinical and pathological parameters on the estimated glomerular filtration rate (eGFR) decline has not been investigated in patients with type 2 diabetes and overt proteinuric biopsy-proven diabetic nephropathy. METHODS: Among 198 patients with type 2 diabetes who underwent renal biopsy and were confirmed to have pure diabetic nephropathy according to the recent classification, 128 patients with overt proteinuria were enrolled. Receiver operating characteristic, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analyses were performed using models adjusted for various clinical and pathological covariates to determine the best predictors of rapid eGFR decline [defined as >14.9%/year (median eGFR decline)]. RESULTS: A model that incorporated proteinuria showed the largest area under the curve (AUC) among clinical models, which suggested that proteinuria was the best clinical predictor. Although a model incorporating interstitial fibrosis and tubular atrophy (IFTA) score did not display a significantly larger AUC than the model with proteinuria (0.843 vs 0.812, respectively, p = 0.47), a model with both IFTA score and proteinuria had a significantly larger AUC than the model with proteinuria alone (0.875 vs 0.812, respectively, p = 0.014). Similarly, the addition of IFTA score resulted in a significantly greater net reclassification improvement and integrated discrimination improvement than the model with proteinuria alone [NRI: 0.78 (95% CI: 0.43-1.13; p < 0.001), IDI: 0.13 (95% CI: 0.07-0.19; p < 0.001)]. CONCLUSIONS: Our results suggest that not only proteinuria but also tubulointerstitial lesions should be assessed to predict rapid eGFR decline in patients with type 2 diabetes who have overt proteinuria and biopsy-proven diabetic nephropathy.
BACKGROUND: The effect of clinical and pathological parameters on the estimated glomerular filtration rate (eGFR) decline has not been investigated in patients with type 2 diabetes and overt proteinuric biopsy-proven diabetic nephropathy. METHODS: Among 198 patients with type 2 diabetes who underwent renal biopsy and were confirmed to have pure diabetic nephropathy according to the recent classification, 128 patients with overt proteinuria were enrolled. Receiver operating characteristic, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analyses were performed using models adjusted for various clinical and pathological covariates to determine the best predictors of rapid eGFR decline [defined as >14.9%/year (median eGFR decline)]. RESULTS: A model that incorporated proteinuria showed the largest area under the curve (AUC) among clinical models, which suggested that proteinuria was the best clinical predictor. Although a model incorporating interstitial fibrosis and tubular atrophy (IFTA) score did not display a significantly larger AUC than the model with proteinuria (0.843 vs 0.812, respectively, p = 0.47), a model with both IFTA score and proteinuria had a significantly larger AUC than the model with proteinuria alone (0.875 vs 0.812, respectively, p = 0.014). Similarly, the addition of IFTA score resulted in a significantly greater net reclassification improvement and integrated discrimination improvement than the model with proteinuria alone [NRI: 0.78 (95% CI: 0.43-1.13; p < 0.001), IDI: 0.13 (95% CI: 0.07-0.19; p < 0.001)]. CONCLUSIONS: Our results suggest that not only proteinuria but also tubulointerstitial lesions should be assessed to predict rapid eGFR decline in patients with type 2 diabetes who have overt proteinuria and biopsy-proven diabetic nephropathy.
Authors: Amy K Mottl; Adil Gasim; Fernanda Payan Schober; Yichun Hu; Askia K Dunnon; Susan L Hogan; J Charles Jennette Journal: J Am Soc Nephrol Date: 2017-11-27 Impact factor: 10.121
Authors: Laura Barisoni; Jonathan P Troost; Cynthia Nast; Serena Bagnasco; Carmen Avila-Casado; Jeffrey Hodgin; Matthew Palmer; Avi Rosenberg; Adil Gasim; Chrysta Liensziewski; Lino Merlino; Hui-Ping Chien; Anthony Chang; Shane M Meehan; Joseph Gaut; Peter Song; Lawrence Holzman; Debbie Gibson; Matthias Kretzler; Brenda W Gillespie; Stephen M Hewitt Journal: Mod Pathol Date: 2016-04-22 Impact factor: 7.842