Chronic inflammation is postulated to be involved in the development of end-stage renal disease in diabetes, but which specific circulating inflammatory proteins contribute to this risk remain unknown. To study this, we examined 194 circulating inflammatory proteins in subjects from three independent cohorts with type 1 and type 2 diabetes. In each cohort, we identified an extremely robust kidney risk inflammatory signature (KRIS), consisting of 17 proteins enriched in tumor necrosis factor-receptor superfamily members, that was associated with a 10-year risk of end-stage renal disease. All these proteins had a systemic, non-kidney source. Our prospective study findings provide strong evidence that KRIS proteins contribute to the inflammatory process underlying end-stage renal disease development in both types of diabetes. These proteins point to new therapeutic targets and new prognostic tests to identify subjects at risk of end-stage renal disease, as well as biomarkers to measure responses to treatment of diabetic kidney disease.

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Global proteomic profiling coming from the prospective study followed for ESRD risk are provided in the Supplementary Information of this article. The datasets that support the findings of this study are available in an anonymous manner from the corresponding authors upon request.

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This study was supported by grants: from the National Institutes of Health to A.S.K. (DK41526 and DP3DK112177), K.S. (DK 087635 and DK108220) and the Joslin Diabetes Center (P30 DK036836); from the Novo Nordisk Foundation to A.S.K. (NNF OC0013659); and from the JDRF to M.A.N. (5-CDA-2015-89-A-B). The study was also supported by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases. P.F. is supported by the Romeo ed Enrica Invernizzi Foundation. We would like to acknowledge E. Mills and N. Rashidi from Olink Proteomics Inc. for their assistance with protein measurements.

Author information


  1. Research Division, Joslin Diabetes Center, Boston, MA, USA

    • Monika A. Niewczas
    • , Jan Skupien
    • , Adam Smiles
    • , Zaipul I. Md Dom
    • , Andrew Schlafly
    • , Eiichiro Satake
    • , Christopher A. Simeone
    • , Hetal Shah
    • , Jennifer K. Sun
    • , Alessandro Doria
    •  & Andrzej S. Krolewski
  2. Department of Medicine, Harvard Medical School, Boston, MA, USA

    • Monika A. Niewczas
    • , Zaipul I. Md Dom
    • , Eiichiro Satake
    • , Hetal Shah
    • , Jennifer K. Sun
    • , Alessandro Doria
    •  & Andrzej S. Krolewski
  3. Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA, USA

    • Meda E. Pavkov
  4. Department of Metabolic Diseases, Jagiellonian University Medical College, Krakow, Poland

    • Jan Skupien
  5. Diabetes and Complications Department, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA

    • Jonathan M. Wilson
    •  & Kevin L. Duffin
  6. Renal Electrolyte and Hypertension Division, Department of Medicine, Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    • Jihwan Park
    • , Chengxiang Qiu
    •  & Katalin Susztak
  7. Nephrology/Internal Medicine and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    • Viji Nair
    •  & Matthias Kretzler
  8. Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA

    • Pierre-Jean Saulnier
    • , Helen C. Looker
    •  & Robert G. Nelson
  9. CHU Poitiers, University of Poitiers, Inserm, Clinical Investigation Center CIC1402, Poitiers, France

    • Pierre-Jean Saulnier
  10. Nephrology Division, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    • Paolo Fiorina
  11. Romeo ed Enrica Invernizzi Pediatric Center, Department of Biomedical and Clinical Science L. Sacco, University of Milan, Milan, Italy

    • Paolo Fiorina
  12. Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA

    • Carl F. Ware


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M.A.N. contributed to the design of the study, supervised proteomics data collection, conducted the data analysis, interpreted the results and wrote the manuscript. J.S, A. Smiles, A. Schlafly, E.S. and C.A.S. were involved in data collection and data management of the Joslin Kidney Study, performed preliminary data analyses and reviewed the manuscript. A.D., Z.I.M.D, H.S. and J.K.S. designed the Joslin study on retinopathy, contributed to eye data collection and analysis, and edited and reviewed the manuscript. R.G.N., M.E.P., P.-J.S. and H.C.L. were responsible for design and implementation of the Pima Indian Study, contributed to the proteomic data collection in the Pima Indian Study, performed preliminary data analysis, and reviewed and edited the manuscript. M.K., V.N. and R.G.N. designed the expression study in Pima Indians, provided data analysis and reviewed the manuscript. J.P., C.Q. and K.S. designed the expression study (1KGP) used in the present study, performed the data analyses, and interpreted the results and edited the manuscript. P.F. and C.F.W. were involved in the interpretation of the results of the study and edited the manuscript. K.L.D. and J.M.W. provided the samples from the baricitinib study, facilitated measurements on the Olink platform, reviewed the analysis, and reviewed and edited the manuscript. A.S.K designed the whole study, supervised all aspects of the study implementation, planned and contributed the data analysis, interpreted the data, and contributed to writing the manuscript. M.A.N. and A.S.K. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Competing interests

A.S.K. and M.A.N. are co-inventors of the TNF-R1 and TNF-R2 patent for predicting risk of ESRD. This patent was licensed by the Joslin Diabetes Center to EKF Diagnostics. The other authors of this report declare no competing conflicts of interest. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Corresponding authors

Correspondence to Monika A. Niewczas or Andrzej S. Krolewski.

Extended data

  1. Extended Data Fig. 1 Circulating KRIS proteins are enriched in TNF-RSF members.

    a, Distribution of the inflammatory classes among a proteomic platform (SOMAscan) array; and b, within the KRIS in the Joslin cohorts (n = 363). A two-sided Fisher’s exact test detected enrichment in TNF-RSF members (P = 0.007, the class marked in dark red), but not in other inflammatory classes (shades of gray). Results extracted from the multivariate screen – volcano plot (Fig. 1a). ILEU, interleukins; ILEUR, interleukin receptors; CHK, chemokines; CHKR, chemokine receptors; CPL, complement proteins; IFN, interferons; TNFL, TNF superfamily ligands; TNF-RSF, TNF-receptor superfamily members, VR, varia or other inflammatory proteins.

  2. Extended Data Fig. 2 Circulating KRIS proteins and progressive renal function decline rate over time in three cohorts.

    Spearman’s rank correlation coefficients (r) between baseline concentration of KRIS proteins and renal function decline rate (eGFR loss – Joslin; GFR loss – Pima) over 8–11 years of follow-up in the Joslin cohorts (n = 363) and the Pima cohort (n = 162). Red bars are a graphic representation of the effect size. Corresponding two-sided P values have been transformed into their base 10 logarithms.

  3. Extended Data Fig. 3 Orthogonal relationships among circulating KRIS proteins.

    a, Hierarchical cluster analysis in the Joslin cohort with T1D, n = 219. b, Joslin cohort with T2D, n = 144. c, Spearman’s rank correlation matrix in the two Joslin cohorts, n = 363. a, b, Distances are shown (Ward’s method). TNF-RSF members are marked in red. c, Spearman’s correlation matrix represents relationships among proteins in the two cohorts in the analysis adjusted for the cohort indicator. Coefficients (r) are presented. Color intensity corresponds to the effect size (r).

  4. Extended Data Fig. 4 KRIS proteins in the urine and development of ESRD.

    Aptamer-based, urinary creatinine-adjusted KRIS protein profiles and development of ESRD in nested case–control studies selected from a, Joslin cohort with T1D (n = 60) and b, Joslin cohort with T2D (n = 52). Effect size (fold-change) is presented. Urinary proteins significant for corresponding thresholds used in multivariate screening for plasma proteomics study (two-sided) are marked with dark red bars.

  5. Extended Data Fig. 5 Circulating KRIS proteins and renal mRNA expression of KRIS-encoding genes.

    Plasma KRIS proteins and Affymetrix-based mRNA expression of the corresponding KRIS genes in kidney tissue derived from the Pima Indian Study (n = 56). Spearman’s rank correlation coefficients (r) are presented between glomerular and tubular gene expressions and circulating KRIS protein levels. The nominally significant correlations (two-sided P values) are marked with an asterisk. Shades of red, white and blue correspond to the magnitude of the effect size (r).

  6. Extended Data Fig. 6 Renal mRNA expression of KRIS-encoding genes and histology of the diabetic kidney tissue.

    Affymetrix-based mRNA expression data in kidney tissue specimens from subjects with T2D (1KGP). Glomerular (n = 23) and tubular (n = 37) expressions of candidate genes are correlated with histological indices of the diabetic kidney: glomerular sclerosis (Glom scl), tubulointerstitial fibrosis (Tub-int fibrosis) and lymphocytic infiltrate in the tubulointerstitium (Tub-int l-infiltr). Spearman’s rank correlation coefficients (r) are presented. The asterisk marks significant correlations (threshold for α = 0.05). Light-red fields mark proteins for which at least two coefficients were significant and positive.

  7. Extended Data Fig. 7 Circulating KRIS proteins and prevalent diabetic eye complications.

    Circulating KRIS proteins and prevalent PDR in the Joslin Kidney Study subjects with T1D (n = 180). OR and 95% CIs are adjusted for HbA1c, ACR and eGFR in the logistic analysis. Effect of KRIS proteins is per one tertile change; the two-sided P value was examined.

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