Literature DB >> 26209743

Prognostic clinical and molecular biomarkers of renal disease in type 2 diabetes.

Michelle J Pena1, Dick de Zeeuw1, Harald Mischak2, Joachim Jankowski3, Rainer Oberbauer4, Wolfgang Woloszczuk5, Jacqueline Benner5, Guido Dallmann6, Bernd Mayer7, Gert Mayer8, Peter Rossing9, Hiddo J Lambers Heerspink1.   

Abstract

Diabetic kidney disease occurs in ∼ 25-40% of patients with type 2 diabetes. Given the high risk of progressive renal function loss and end-stage renal disease, early identification of patients with a renal risk is important. Novel biomarkers may aid in improving renal risk stratification. In this review, we first focus on the classical panel of albuminuria and estimated glomerular filtration rate as the primary clinical predictors of renal disease and then move our attention to novel biomarkers, primarily concentrating on assay-based multiple/panel biomarkers, proteomics biomarkers and metabolomics biomarkers. We focus on multiple biomarker panels since the molecular processes of renal disease progression in type 2 diabetes are heterogeneous, rendering it unlikely that a single biomarker significantly adds to clinical risk prediction. A limited number of prospective studies of multiple biomarkers address the predictive performance of novel biomarker panels in addition to the classical panel in type 2 diabetes. However, the prospective studies conducted so far have small sample sizes, are insufficiently powered and lack external validation. Adequately sized validation studies of multiple biomarker panels are thus required. There is also a paucity of studies that assess the effect of treatments on novel biomarker panels and determine whether initial treatment-induced changes in novel biomarkers predict changes in long-term renal outcomes. Such studies can not only improve our healthcare but also our understanding of the mechanisms of actions of existing and novel drugs and may yield biomarkers that can be used to monitor drug response. We conclude that this will be an area to focus research on in the future.
© The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Entities:  

Keywords:  CKD; biomarker panels; metabolomics; novel biomarkers; proteomics

Mesh:

Substances:

Year:  2015        PMID: 26209743     DOI: 10.1093/ndt/gfv252

Source DB:  PubMed          Journal:  Nephrol Dial Transplant        ISSN: 0931-0509            Impact factor:   5.992


  15 in total

1.  Segmental Sclerosis and Extracapillary Hypercellularity Predict Diabetic ESRD.

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

Review 2.  Key elements of metabolomics in the study of biomarkers of diabetes.

Authors:  Jerzy Adamski
Journal:  Diabetologia       Date:  2016-10-06       Impact factor: 10.122

Review 3.  The nephrologist of tomorrow: towards a kidney-omic future.

Authors:  Mina H Hanna; Alessandra Dalla Gassa; Gert Mayer; Gianluigi Zaza; Patrick D Brophy; Loreto Gesualdo; Francesco Pesce
Journal:  Pediatr Nephrol       Date:  2016-03-09       Impact factor: 3.714

4.  Use of Proteomics To Investigate Kidney Function Decline over 5 Years.

Authors:  Axel C Carlsson; Erik Ingelsson; Johan Sundström; Juan Jesus Carrero; Stefan Gustafsson; Tobias Feldreich; Markus Stenemo; Anders Larsson; Lars Lind; Johan Ärnlöv
Journal:  Clin J Am Soc Nephrol       Date:  2017-07-21       Impact factor: 8.237

5.  Metabolomic Markers of Kidney Function Decline in Patients With Diabetes: Evidence From the Chronic Renal Insufficiency Cohort (CRIC) Study.

Authors:  Brian Kwan; Tobias Fuhrer; Jing Zhang; Manjula Darshi; Benjamin Van Espen; Daniel Montemayor; Ian H de Boer; Mirela Dobre; Chi-Yuan Hsu; Tanika N Kelly; Dominic S Raj; Panduranga S Rao; Santosh L Saraf; Julia Scialla; Sushrut S Waikar; Kumar Sharma; Loki Natarajan
Journal:  Am J Kidney Dis       Date:  2020-05-05       Impact factor: 8.860

6.  Untargeted serum metabolomics and tryptophan metabolism profiling in type 2 diabetic patients with diabetic glomerulopathy.

Authors:  Fanliang Zhang; Ruixue Guo; Wen Cui; Li Wang; Jing Xiao; Jin Shang; Zhanzheng Zhao
Journal:  Ren Fail       Date:  2021-12       Impact factor: 2.606

Review 7.  Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease.

Authors:  Michelle J Pena; Harald Mischak; Hiddo J L Heerspink
Journal:  Diabetologia       Date:  2016-06-25       Impact factor: 10.122

8.  Proteomic Biomarkers Panel: New Insights in Chronic Kidney Disease.

Authors:  Simona Mihai; Elena Codrici; Ionela Daniela Popescu; Ana-Maria Enciu; Elena Rusu; Diana Zilisteanu; Radu Albulescu; Gabriela Anton; Cristiana Tanase
Journal:  Dis Markers       Date:  2016-09-07       Impact factor: 3.434

9.  Novel Urinary Biomarkers For Improved Prediction Of Progressive Egfr Loss In Early Chronic Kidney Disease Stages And In High Risk Individuals Without Chronic Kidney Disease.

Authors:  María E Rodríguez-Ortiz; Claudia Pontillo; Mariano Rodríguez; Petra Zürbig; Harald Mischak; Alberto Ortiz
Journal:  Sci Rep       Date:  2018-10-29       Impact factor: 4.379

10.  Can targeted metabolomics predict depression recovery? Results from the CO-MED trial.

Authors:  Andrew H Czysz; Charles South; Bharathi S Gadad; Erland Arning; Abigail Soyombo; Teodoro Bottiglieri; Madhukar H Trivedi
Journal:  Transl Psychiatry       Date:  2019-01-16       Impact factor: 6.222

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