URINE PROTEOME ANALYSIS MAY ALLOW NON-INVASIVE DIFFERENTIAL DIAGNOSIS OF DIABETIC NEPHROPATHY
- Massimo Papale, PhD1,
- Salvatore Di Paolo, MD2,
- Riccardo Magistroni, MD3,
- Olga Lamacchia, MD4,
- Angela De Mattia, MD5,
- Maria Teresa Rocchetti, PhD1,
- Luciana Furci, MD3,
- Sonia Pasquali, MD7,
- Salvatore De Cosmo, MD8,
- Mauro Cignarelli, MD4 and
- Loreto Gesualdo, MD (l.gesualdo{at}unifg.it)9
- 1. Core facility of Proteomics and Mass Spectrometry, Dept. of BioAgroMed, Faculty of Medicine, University of Foggia
- 2. Division of Nephrology and Dialysis, Hospital “Dimiccoli” , ASL BAT, Barletta, Italy
- 3. Division of Nephrology and Dialysis, Dept. of Medicine and Medical Specialities, University of Modena and Reggio Emilia, Modena. Italy
- 4. Division of Endocrinology, Dept. of Medical Sciences, University of Foggia
- 5. Division of Nephrology and Dialysis, Dept. of Biomedical Sciences, Faculty of Medicine, University of Foggia
- 6. Division of Nephrology and Dialysis, Dept. of Biomedical Sciences, Faculty of Medicine, University of Foggia
- 7. Division of Nephrology and Dialysis, Sant'Orsola Hospital, Bologna, Italy
- 8. Unit of Endocrinology, Scientific Institute “Casa Sollievo della Sofferenza” San Giovanni Rotondo, Italy
- 9. Division of Nephrology, Dept. of Biomedical Sciences and Bioagromed, Faculty of Medicine, University of Foggia, Foggia, Italy
Abstract
Objective: Chronic renal insufficiency and/or proteinuria in type 2 diabetes may stem from chronic renal diseases (CKD) other than classic diabetic nephropathy (DN) in over one third of cases. We interrogated urine proteomic profiles generated by SELDI-TOF/MS with the aim to isolate a set of biomarkers able to reliably identify biopsy-proven DN and to establish a stringent correlation with the different patterns of renal injury.
Research design and methods: Ten μg urine proteins from 190 subjects [20 healthy subjects (HS), 20 normoalbuminuric (NAD) and 18 microalbuminuric (MICRO) diabetic patients, and 132 patients with biopsy-proven nephropathy (65 DN, 10 diabetics with non-diabetic CKD (nd-CKD) and 57 non-diabetic patients with CKD)] were run by CM10 ProteinChip array and analysed by supervised learning methods (CART analysis).
Results: The classification model correctly identified 75% NAD, 87.5% MICRO and 87.5% DN when applied to a blinded testing set. Most importantly, it was able to reliably differentiate DN from nd-CKD in both diabetic and non-diabetic patients. Among the best predictors of the classification model, we identified and validated 2 proteins, ubiquitin and ß2-microglobulin.
Conclusions: Our data suggest the presence of a specific urine proteomic signature able to reliably identify type 2 diabetic patients with diabetic glomerulosclerosis.
Footnotes
- Copyright © American Diabetes Association
