Comparison of Circulating Biomarkers in Predicting Diabetic Kidney Disease Progression With Autoantibodies to Erythropoietin Receptor

Megumi Oshima; Akinori Hara; Tadashi Toyama; Min Jun; Carol Pollock; Meg Jardine; Stephen Harrap; Neil Poulter; Mark E Cooper; Mark Woodward; John Chalmers; Vlado Perkovic; Muh Geot Wong; Takashi Wada

doi:10.1016/j.ekir.2020.10.039

Comparison of Circulating Biomarkers in Predicting Diabetic Kidney Disease Progression With Autoantibodies to Erythropoietin Receptor

Kidney Int Rep. 2020 Nov 10;6(2):284-295. doi: 10.1016/j.ekir.2020.10.039. eCollection 2021 Feb.

Authors

Megumi Oshima^{1

2

3}, Akinori Hara², Tadashi Toyama², Min Jun¹, Carol Pollock³, Meg Jardine^{1

4}, Stephen Harrap⁵, Neil Poulter⁶, Mark E Cooper⁷, Mark Woodward^{1

8

9}, John Chalmers¹, Vlado Perkovic¹, Muh Geot Wong^{1

3}, Takashi Wada²

Affiliations

¹ Department of Renal and Metabolic, The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia.
² Department of Nephrology and Laboratory Medicine, Kanazawa University, Kanazawa, Japan.
³ Renal Department, Kolling Institute of Medical Research, Sydney Medical School, University of Sydney, Royal North Shore Hospital, Sydney, New South Wales, Australia.
⁴ Nephrology Unit, Concord Repatriation General Hospital, Sydney, New South Wales, Australia.
⁵ Department of Physiology, Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia.
⁶ International Center for Circulatory Health, Imperial College, London, UK.
⁷ Departiment of Diabetes, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia.
⁸ The George Institute for Global Health, University of Oxford, Oxford, UK.
⁹ Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA.

Abstract

Introduction: Several circulating markers, including autoantibodies to erythropoietin receptor (anti-EPOR antibodies), have been identified as useful biomarkers in predicting diabetic kidney disease progression. However, a direct comparison of their utility is lacking. We aimed to validate and to compare the prognostic value of anti-EPOR antibodies with that of other known biomarkers, using the ADVANCE trial and its long-term follow-up, ADVANCE-ON, cohorts.

Methods: In this nested case-control study from the ADVANCE trial cohort, we included 165 case participants who had the composite kidney outcome (renal replacement therapy, renal death, or doubling of serum creatinine to ≥200 μmol/l) and 330 matched controls. We compared the associations of baseline plasma levels of anti-EPOR antibodies, tumor necrosis factor receptor (TNFR)-1 and -2, and bone morphogenetic protein (BMP)-7 with kidney outcomes.

Results: Cases had higher baseline plasma levels of anti-EPOR antibodies than controls (median 1.7 vs. 0.6 enzyme-linked immunosorbent assay unit, P < 0.001). Higher levels of anti-EPOR antibodies were associated with an increased risk of kidney outcome (odds ratio 2.16 [95% confidence interval 1.51, 3.08], per 1 SD of log-transformed levels) after adjusting for conventional markers. Elevated circulating TNFR1 and TNFR2 levels, and lower BMP-7 levels at baseline, were associated with poor kidney outcome (odds ratios 2.06 [1.29, 3.30], 1.66 [1.13, 2.43], and 0.45 [0.32, 0.65], respectively). The addition of anti-EPOR antibodies into the model improved the prediction of kidney outcome, regardless of other biomarkers.

Conclusion: Anti-EPOR antibodies provide a promising biomarker, as with TNFR1, TNFR2, and BMP-7, in predicting kidney disease progression in people with type 2 diabetes mellitus.

Keywords: biomarker; end-stage kidney disease; type 2 diabetes.