Skip to main content

Advertisement

Springer Nature Link
Log in

Association of urinary KIM-1, L-FABP, NAG and NGAL with incident end-stage renal disease and mortality in American Indians with type 2 diabetes mellitus

  • Article
  • Published:
Diabetologia Aims and scope Submit manuscript

Abstract

Aims/hypothesis

Kidney injury molecule 1 (KIM-1), liver fatty acid-binding protein (L-FABP), N-acetyl-β-d-glucosaminidase (NAG) and neutrophil gelatinase-associated lipocalin (NGAL) are urinary biomarkers of renal tubular injury. We examined their association with incident end-stage renal disease (ESRD) and all-cause mortality in American Indians with type 2 diabetes.

Methods

Biomarker concentrations were measured in baseline urine samples in 260 Pima Indians who were followed for a median of 14 years. HRs were reported per SD of creatinine (Cr)-normalised log-transformed KIM-1, NAG and NGAL, and for three categories of L-FABP.

Results

During follow-up, 74 participants developed ESRD and 101 died. Median concentrations of KIM-1/Cr, NAG/Cr and NGAL/Cr and the proportion of detectable L-FABP were highest in those with macroalbuminuria (p < 0.001 for KIM-1/Cr, NAG/Cr and L-FABP; p = 0.006 for NGAL/Cr). After multivariable adjustment, NGAL/Cr was positively associated with ESRD (HR 1.59, 95% CI 1.20, 2.11) and mortality (HR 1.39, 95% CI 1.06, 1.82); L-FABP/Cr was inversely associated with ESRD (HR [for highest vs lowest tertile] 0.40, 95% CI 0.19, 0.83). Addition of NGAL/Cr to models that included albuminuria and glomerular filtration rate increased the c-statistic for predicting ESRD from 0.828 to 0.833 (p = 0.001) and for death from 0.710 to 0.722 (p = 0.018). Addition of L-FABP/Cr increased the c-statistic for ESRD from 0.828 to 0.832 (p = 0.042).

Conclusions/interpretation

In Pima Indians with type 2 diabetes, urinary concentrations of NGAL and L-FABP are associated with important health outcomes, but they are unlikely to add to risk prediction with standard markers in a clinically meaningful way given the small increase in the c-statistic.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Canada)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

ACE:

Angiotensin-converting enzyme

Alb/Cr:

Urinary albumin-to-creatinine ratio

CKD:

Chronic kidney disease

Cr:

Creatinine

ESRD:

End-stage renal disease

GFR:

Glomerular filtration rate

IDI:

Integrated discrimination improvement

IQR:

Interquartile range

KIM-1:

Kidney injury molecule 1

L-FABP:

Liver fatty acid-binding protein

LOD:

Limit of detection

NAG:

N-acetyl-β-d-glucosaminidase

NGAL:

Neutrophil gelatinase-associated lipocalin

TRIBE-AKI:

Translation Research Investigating Biomarker Endpoints for Acute Kidney Injury

References

  1. U.S. Renal Data System (2013) USRDS 2013 Annual data report: atlas of chronic kidney disease and end-stage renal disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA

  2. Araki S, Haneda M, Sugimoto T et al (2005) Factors associated with frequent remission of microalbuminuria in patients with type 2 diabetes. Diabetes 54:2983–2987

    Article  CAS  PubMed  Google Scholar 

  3. Perkins BA, Ficociello LH, Silva KH, Finkelstein DM, Warram JH, Krolewski AS (2003) Regression of microalbuminuria in type 1 diabetes. N Engl J Med 348:2285–2293

    Article  CAS  PubMed  Google Scholar 

  4. Pavkov ME, Knowler WC, Hanson RL, Bennett PH, Nelson RG (2008) Predictive power of sequential measures of albuminuria for progression to ESRD or death in Pima Indians with type 2 diabetes. Am J Kidney Dis 51:759–766

    Article  PubMed Central  PubMed  Google Scholar 

  5. Gaede P, Tarnow L, Vedel P, Parving HH, Pedersen O (2004) Remission to normoalbuminuria during multifactorial treatment preserves kidney function in patients with type 2 diabetes and microalbuminuria. Nephrol Dial Transplant 19:2784–2788

    Article  PubMed  Google Scholar 

  6. Fioretto P, Mauer M, Brocco E et al (1996) Patterns of renal injury in NIDDM patients with microalbuminuria. Diabetologia 39:1569–1576

    Article  CAS  PubMed  Google Scholar 

  7. Nosadini R, Velussi M, Brocco E et al (2000) Course of renal function in type 2 diabetic patients with abnormalities of albumin excretion rate. Diabetes 49:476–484

    Article  CAS  PubMed  Google Scholar 

  8. Bolignano D, Lacquaniti A, Coppolino G et al (2009) Neutrophil gelatinase-associated lipocalin (NGAL) and progression of chronic kidney disease. Clin J Am Soc Nephrol 4:337–344

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Nielsen SE, Sugaya T, Hovind P, Baba T, Parving HH, Rossing P (2010) Urinary liver-type fatty acid-binding protein predicts progression to nephropathy in type 1 diabetic patients. Diabetes Care 33:1320–1324

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Nielsen SE, Andersen S, Zdunek D, Hess G, Parving HH, Rossing P (2011) Tubular markers do not predict the decline in glomerular filtration rate in type 1 diabetic patients with overt nephropathy. Kidney Int 79:1113–1118

    Article  CAS  PubMed  Google Scholar 

  11. von Eynatten M, Baumann M, Heemann U et al (2010) Urinary L-FABP and anaemia: distinct roles of urinary markers in type 2 diabetes. Eur J Clin Invest 40:95–102

    Article  Google Scholar 

  12. Nielsen SE, Hansen HP, Jensen BR, Parving HH, Rossing P (2011) Urinary neutrophil gelatinase-associated lipocalin and progression of diabetic nephropathy in type 1 diabetic patients in a four-year follow-up study. Nephron Clin Pract 118:130–135

    Article  Google Scholar 

  13. Vaidya VS, Niewczas MA, Ficociello LH et al (2011) Regression of microalbuminuria in type 1 diabetes is associated with lower levels of urinary tubular injury biomarkers, kidney injury molecule-1, and N-acetyl-β-d-glucosaminidase. Kidney Int 79:464–470

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Nielsen SE, Reinhard H, Zdunek D et al (2012) Tubular markers are associated with decline in kidney function in proteinuric type 2 diabetic patients. Diabetes Res Clin Pract 97:71–76

    Article  CAS  PubMed  Google Scholar 

  15. Kim SS, Song SH, Kim IJ et al (2012) Clinical implication of urinary tubular markers in the early stage of nephropathy with type 2 diabetic patients. Diabetes Res Clin Pract 97:251–257

    Article  CAS  PubMed  Google Scholar 

  16. Fu WJ, Li BL, Wang SB et al (2012) Changes of the tubular markers in type 2 diabetes mellitus with glomerular hyperfiltration. Diabetes Res Clin Pract 95:105–109

    Article  CAS  PubMed  Google Scholar 

  17. Conway BR, Manoharan D, Manoharan D et al (2012) Measuring urinary tubular biomarkers in type 2 diabetes does not add prognostic value beyond established risk factors. Kidney Int 82:812–818

    Article  CAS  PubMed  Google Scholar 

  18. Liu KD, Yang W, Anderson AH et al (2013) Urine neutrophil gelatinase-associated lipocalin levels do not improve risk prediction of progressive chronic kidney disease. Kidney Int 83:909–914

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Araki S, Haneda M, Koya D et al (2013) Predictive effects of urinary liver-type fatty acid-binding protein for deteriorating renal function and incidence of cardiovascular disease in type 2 diabetic patients without advanced nephropathy. Diabetes Care 36:1248–1253

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. O’Seaghdha CM, Hwang SJ, Larson MG, Meigs JB, Vasan RS, Fox CS (2013) Analysis of a urinary biomarker panel for incident kidney disease and clinical outcomes. J Am Soc Nephrol 24:1880–1888

    Article  PubMed Central  PubMed  Google Scholar 

  21. Chou KM, Lee CC, Chen CH, Sun CY (2013) Clinical value of NGAL, L-FABP and albuminuria in predicting GFR decline in type 2 diabetes mellitus patients. PLoS One 8:e54863

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Panduru NM, Forsblom C, Saraheimo M et al (2013) Urinary liver-type fatty acid-binding protein and progression of diabetic nephropathy in type 1 diabetes. Diabetes Care 36:2077–2083

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Kamijo-Ikemori A, Sugaya T, Yasuda T et al (2011) Clinical significance of urinary liver-type fatty acid-binding protein in diabetic nephropathy of type 2 diabetic patients. Diabetes Care 34:691–696

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Nelson RG, Bennett PH, Beck GJ et al (1996) Development and progression of renal disease in Pima Indians with non-insulin-dependent diabetes mellitus. N Engl J Med 335:1636–1642

    Article  CAS  PubMed  Google Scholar 

  25. Weil EJ, Fufaa G, Jones LI et al (2013) Effect of losartan on prevention and progression of early diabetic nephropathy in American Indians with type 2 diabetes. Diabetes 62:3224–3231

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  26. Myers BD, Nelson RG, Tan M et al (1995) Progression of overt nephropathy in non-insulin-dependent diabetes. Kidney Int 47:1781–1789

    Article  CAS  PubMed  Google Scholar 

  27. Pencina MJ, D’Agostino RB (2004) Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med 23:2109–2123

    Article  PubMed  Google Scholar 

  28. Demler OV, Pencina MJ, D’Agostino RB Sr (2012) Misuse of DeLong test to compare AUCs for nested models. Stat Med 31:2577–2587

    Article  PubMed Central  PubMed  Google Scholar 

  29. Pepe MS, Kerr KF, Longton G, Wang Z (2013) Testing for improvement in prediction model performance. Stat Med 32:1467–1482

    Article  PubMed Central  PubMed  Google Scholar 

  30. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS (2008) Comments on integrated discrimination and net reclassification improvements: practical advice. Stat Med 27:207–212

    Article  Google Scholar 

  31. Pencina MJ, D’Agostino RB, Pencina KM, Cecile A, Janssens JW, Greenland P (2012) Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol 176:473–481

    Article  PubMed Central  PubMed  Google Scholar 

  32. Thraikill KM, Moreau CS, Cockrell GE (2010) Disease and gender-specific dysregulation of NGAL and MMP-9 in type 1 diabetes mellitus. Endocrine 37:336–343

    Article  Google Scholar 

  33. Meunier-Durmort C, Poirier H, Niot I, Forest C, Besnard P (1996) Up-regulation of the expression of the gene for liver fatty acid-binding protein by long-chain fatty acids. Biochem J 319:483–487

    CAS  PubMed Central  PubMed  Google Scholar 

  34. Kamijo A, Sugaya T, Hikawa A et al (2004) Urinary excretion of fatty-acid binding protein reflects stress overload on the proximal tubules. Am J Pathol 165:1243–1255

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  35. Coca SG, Garg AX, Thiessen-Philbrook H et al (2014) Urinary biomarkers of AKI and mortality 3 years after cardiac surgery. J Am Soc Nephrol 25:1063–1071

    Article  CAS  PubMed  Google Scholar 

  36. Tramontia G, Kanwar Y (2011) Tubular biomarkers to assess progression of diabetic nephropathy. Kidney Int 79:1042–1044

    Article  Google Scholar 

  37. Xu S, Venge P (2000) Lipocalins as biochemical markers of disease. Biochim Biophys Acta 1482:298–307

    Article  CAS  PubMed  Google Scholar 

  38. Zoja C, Benigni A, Remuzzi G (2004) Cellular responses to protein overload: key event in renal disease progression. Curr Opin Nephrol Hypertens 13:31–37

    Article  CAS  PubMed  Google Scholar 

  39. Sharma SG, Bomback AS, Radhakrishnan J et al (2013) The modern spectrum of renal biopsy findings in patients with diabetes. Clin J Am Soc Nephrol 8:1718–1724

    Article  PubMed Central  PubMed  Google Scholar 

  40. Helmersson-Karlqvist J, Larsson A, Carlsson AC et al (2013) Urinary neutrophil gelatinase-associated lipocalin (NGAL) is associated with mortality in a community-based cohort of older Swedish men. Atherosclerosis 227:408–413

    Article  CAS  PubMed  Google Scholar 

  41. Yoshikawa K, Adachi H, Hirai Y et al (2013) High serum N-acetyl-ß-d-glucosaminidase activity is a predictor of 28-year mortality in a population of community-dwelling Japanese-the Tanushimaru study. J Am Geriatr Soc 61:467–468

    Article  PubMed  Google Scholar 

  42. Jungbauer CG, Birner C, Jung B et al (2011) Kidney injury molecule-1 and N-acetyl-β-d-glucosaminidase in chronic heart failure: a possible biomarker of cardiorenal syndrome. Eur J Heart Fail 13:1104–1110

    Article  CAS  PubMed  Google Scholar 

  43. Sarnak MJ, Katz R, Newman A et al (2014) Association of urinary injury biomarkers with mortality and cardiovascular events. J Am Soc Nephrol 25:1545–1553

    Article  CAS  PubMed  Google Scholar 

  44. Han WK, Wagener G, Zhu Y, Wang S, Lee HT (2009) Urinary biomarkers in the early detection of acute kidney injury after cardiac surgery. Clin J Am Soc Nephrol 4:873–882

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  45. Nielsen SE, Schjoedt KJ, Astrup AS et al (2010) Neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule 1 (KIM1) in patients with diabetic nephropathy: a cross-sectional study and the effects of lisinopril. Diabet Med 27:1144–1150

    Article  CAS  PubMed  Google Scholar 

  46. Nielsen SE, Rossing K, Hess G et al (2012) The effect of RAAS blockade on markers of renal tubular damage in diabetic nephropathy: u-NGAL, u-KIM1 and u-LFABP. Scand J Clin Lab Invest 72:137–142

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Abbott Laboratories for supporting the measurement of urinary NGAL and CMIC for providing control materials for our studies. Abbott Laboratories and CMIC had no role in study design, data collection, data analysis, data interpretation or writing of the report. We also thank E. Cotter at University College Dublin, Dublin, Ireland for performing the urinary NGAL assays and M. J. Pencina at Duke University, Durham, NC, USA for his suggestions on how to assess the incremental value of markers added to risk prediction models. Parts of this study were presented in abstract form at the American Society of Nephrology annual meeting and scientific exposition in Atlanta, Georgia, 7–10 November 2013.

Funding

This work was supported by the Chronic Kidney Disease Biomarker Consortium funded by NIDDK U01DK85649, U01DK085673, U01DK085660, U01DK085688, U01DK085651 and U01DK085689, and by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases.

Duality of interest

JVB appears as co-inventor on KIM-1 patents, which have been licensed by Partners Healthcare to a number of companies. He has received royalty income from Partners Healthcare. KDL had reagents donated for previous biomarker studies by Abbott and CMIC. All other authors declare that there is no duality of interest associated with their contribution to this manuscript.

Contribution statement

RGN, CyH, KDL, GDF, EJW, JVB and SSW designed the study with input into the study protocol from all authors. TEM, VS and JVB performed the biomarker assays. GDF, EJW, RGN, RLH, XZ and DX performed the statistical analyses. GDF, EJW, RGN and KDL drafted the manuscript and all authors assisted with revising it critically for important intellectual content. All authors contributed to the interpretation of the data and approved the final version. RGN is the guarantor of this work.

Author information

Authors and Affiliations

  1. Diabetes Epidemiology and Clinical Research Section, Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 1550 East Indian School Road, Phoenix, AZ, 85014-4972, USA

    Gudeta D. Fufaa, E. Jennifer Weil, Robert G. Nelson & Robert L. Hanson

  2. Renal Division and Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Joseph V. Bonventre, Venkata Sabbisetti & Sushrut S. Waikar

  3. Translational Core Laboratory (TCL), Smilow Center for Translational Research (SCTR), University of Pennsylvania, Philadelphia, PA, USA

    Theodore E. Mifflin

  4. Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Xiaoming Zhang, Dawei Xie & Harold I. Feldman

  5. Division of Nephrology, Department of Medicine, University of California-San Francisco, San Francisco, CA, USA

    Chi-yuan Hsu & Kathleen D. Liu

  6. Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Harold I. Feldman

  7. Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Harold I. Feldman

  8. Department of Epidemiology and Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins Medical Institutions, Baltimore, MD, USA

    Josef Coresh

  9. Department of Medicine, Johns Hopkins Hospital, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, MD, USA

    Josef Coresh

  10. Department of Medicine, Boston University School of Medicine, Boston, MA, USA

    Ramachandran S. Vasan

  11. Division of Kidney Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA

    Paul L. Kimmel

Authors
  1. Gudeta D. Fufaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Jennifer Weil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robert G. Nelson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robert L. Hanson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Joseph V. Bonventre
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Venkata Sabbisetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sushrut S. Waikar
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Theodore E. Mifflin
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xiaoming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Dawei Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Chi-yuan Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Harold I. Feldman
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Josef Coresh
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Ramachandran S. Vasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Paul L. Kimmel
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Kathleen D. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

for the Chronic Kidney Disease Biomarkers Consortium Investigators

Corresponding author

Correspondence to Robert G. Nelson.

Additional information

Gudeta D. Fufaa and E. Jennifer Weil contributed equally to this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fufaa, G.D., Weil, E.J., Nelson, R.G. et al. Association of urinary KIM-1, L-FABP, NAG and NGAL with incident end-stage renal disease and mortality in American Indians with type 2 diabetes mellitus. Diabetologia 58, 188–198 (2015). https://doi.org/10.1007/s00125-014-3389-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00125-014-3389-3

Keywords

Search

Navigation