Added value of soluble tumor necrosis factor-α receptor 1 as a biomarker of ESRD risk in patients with type 1 diabetes

Diabetes Care

Volumn 37, Issue 8, 2014, Pages 2334-2342

  Forsblom, Carol ^a,b   Moran, John ^c   Harjutsalo, Valma ^a,b,d   Loughman, Tony ^e   Wadén, Johan ^a,b   Tolonen, Nina ^a,b   Thorn, Lena ^a,b   Saraheimo, Markku ^a,b   Gordin, Daniel ^a,b   Groop, Per Henrik ^a,b,f   Thomas, Merlin C ^a,f  

^a FOLKHÄLSAN INSTITUTE OF GENETICS   (Finland)

^b HELSINKI UNIVERSITY CENTRAL HOSPITAL   (Finland)

^c QUEEN ELIZABETH HOSPITAL   (Australia)

^d NATIONAL PUBLIC HEALTH INSTITUTE   (Finland)

^e EKF Diagnostics   (United Kingdom)

^f BAKER IDI HEART AND DIABETES INSTITUTE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; HEMOGLOBIN A1C; INSULIN; SOLUBLE TUMOR NECROSIS FACTOR ALPHA RECEPTOR 1; TUMOR NECROSIS FACTOR ALPHA RECEPTOR; UNCLASSIFIED DRUG; TUMOR NECROSIS FACTOR ALPHA;

ADULT; ARTICLE; COHORT ANALYSIS; DEATH; DIABETIC NEPHROPATHY; DIABETIC PATIENT; DIAGNOSTIC VALUE; DISEASE DURATION; END STAGE RENAL DISEASE; FEMALE; FOLLOW UP; GLOMERULUS FILTRATION RATE; HUMAN; INCIDENCE; INSULIN DEPENDENT DIABETES MELLITUS; INTEGRATED DISCRIMINATION IMPROVEMENT; MAJOR CLINICAL STUDY; MALE; NET RECLASSIFICATION INDEX; PREDICTION; PROPORTIONAL HAZARDS MODEL; PROSPECTIVE STUDY; RECEIVER OPERATING CHARACTERISTIC; RISK ASSESSMENT; STATISTICAL ANALYSIS; STATISTICAL CONCEPTS; SURVIVAL; AGED; BLOOD; COMPLICATION; DIABETES MELLITUS, TYPE 1; DIABETIC NEPHROPATHIES; KIDNEY FAILURE, CHRONIC; MIDDLE AGED; PREDICTIVE VALUE; PROGNOSIS; RISK FACTOR;

ADULT; AGED; BIOLOGICAL MARKERS; COHORT STUDIES; DIABETES MELLITUS, TYPE 1; DIABETIC NEPHROPATHIES; FEMALE; HUMANS; INCIDENCE; KIDNEY FAILURE, CHRONIC; MALE; MIDDLE AGED; PREDICTIVE VALUE OF TESTS; PROGNOSIS; RISK FACTORS; TUMOR NECROSIS FACTOR-ALPHA;

EID: 84904977699     PISSN: 01495992     EISSN: 19355548     Source Type: Journal    
DOI: 10.2337/dc14-0225     Document Type: Article

References (37)

1. Groop PH, Thomas MC, Moran J.L., et al.; FinnDiane Study Group. The presence and severity of chronic kidney disease predicts allcause mortality in type 1 diabetes. Diabetes 2009; 58: 1651-1658
2. Miyazawa I, Araki S, Obata T., et al. Associationbetween serum soluble TNFα receptors and renal dysfunction in type 2 diabetic patients without proteinuria. Diabetes Res Clin Pract 2011; 92: 174-180
3. Lin J, Hu FB, Mantzoros C, Curhan GC Lipid and inflammatory biomarkers and kidney function decline in type 2 diabetes. Diabetologia 2010; 53: 263-267
4. Lin J, Hu FB, Rimm E.B., Rifai N, Curhan GC. The association of serum lipids and inflammatory biomarkers with renal function in men with type II diabetes mellitus. Kidney Int 2006; 69: 336-342
5. Ward R, McLeish KR Soluble TNF alpha receptors are increased in chronic renal insufficiency and hemodialysis and inhibit neutrophil priming by TNF alpha. Artif Organs 1996; 20: 390-395
6. Halwachs G, Tiran A, Reisinger E.C., et al. Serum levels of the soluble receptor for tumor necrosis factor in patients with renal disease. Clin Investig 1994; 72: 473-476
7. Brockhaus M, Bar-Khayim Y, Gurwicz S., Frensdorff A, Haran N. Plasma tumor necrosis factor soluble receptors in chronic renal failure. Kidney Int 1992; 42: 663-667
8. Gohda T, Niewczas MA, Ficociello L.H., et al. Circulating TNF receptors 1and 2 predictstage 3 CKD in type 1 diabetes. J Am Soc Nephrol 2012; 23: 516-524
9. Niewczas MA, Gohda T, Skupien J., et al. Circulating TNF receptors 1 and 2 predict ESRD in type 2 diabetes. J Am Soc Nephrol 2012; 23: 507-515
10. Lopes-Virella M.F., Baker NL, Hunt K.J., Cleary PA, Klein R, Virella G; DCCT/EDIC Research Group. Baseline markers of inflammation are associated with progression to macroalbuminuria in type 1 diabetic subjects. Diabetes Care 2013; 36: 2317-2323
11. Andersen PK, Geskus RB, de Witte T, Putter H. Competing risks in epidemiology: possibilities and pitfalls. Int J Epidemiol 2012; 41: 861-870
12. Pintilie M. Testing a covariate. In Competing Risks: A Practical Perspective. Pintilie M, Ed. Chichester, U.K., John Wiley & Sons Ltd., 2006, p. 71-85
13. Pepe MS, Janes H, Longton G., Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol 2004; 159: 882-890
14. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007; 115: 928-935
15. Moons KG, de Groot JA, Linnet K, Reitsma JB, Bossuyt PM Quantifying the added value of a diagnostic test or marker. Clin Chem 2012; 58: 1408-1417
16. Steyerberg EW, Pencina MJ, Lingsma H.F., Kattan MW, Vickers AJ, Van Calster B. Assessing the incremental value of diagnostic and prognostic markers: a review and illustration. Eur J Clin Invest 2012; 42: 216-228
17. Uno H, Tian L, Cai T., Kohane IS, Wei LJ. A unified inference procedure for a class of measures to assess improvement in risk prediction systems with survival data. Stat Med 2013; 32: 2430-2442
18. Thorn LM, Forsblom C, Fagerudd J., et al.; FinnDiane Study Group. Metabolic syndrome in type 1 diabetes: association with diabetic nephropathy and glycemic control (the FinnDiane study). Diabetes Care 2005; 28: 2019-2024
19. Mäkinen V.P., Forsblom C, Thorn L.M., et al.; FinnDiane Study Group. Metabolic phenotypes, vascular complications, and premature deaths in a population of 4,197 patients with type 1 diabetes. Diabetes 2008; 57: 2480-2487
20. Fine J, Gray RJ A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999; 94: 496-509
21. Pintilie M. Competing Risks: A Practical Perspective. Chichester, U.K., John Wiley & Sons Ltd., 2006
22. Kuha J. AIC and BIC. Comparisons of assumptions and performance. Sociol Methods Res 2005; 33: 188-229
23. Royston P, Sauerbrei W. Multivariable Model-Building: A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables. Chichester, West Sussex, U.K., John Wiley & Sons Ltd., 2008
24. Royston P. Explained variation for survival models. Stata J 2006; 6: 83-96
25. Ihaka R, Gentleman RR A language for data analysis and graphics. J Comput Graph Statist 1996; 5: 299-314
26. Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics 2005; 61: 92-105
27. Heagerty P.J., Saha-Chaudhri P. Risksetroc: Riskset roc curve estimation from censored survival data [Internet], 2013. Available from http://cran.r-project.org/web/packages/risksetROC/risksetROC.pdf. Accessed 26 July 2013
28. Harrell FE J.r., Lee KL, Mark DB Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996; 15: 361-387
29. Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 2011; 30: 11-21
30. Mühlenbruch K., Heraclides A, Steyerberg E.W., Joost HG, Boeing H, Schulze MB. Assessing improvement in disease prediction using net reclassification improvement: impact of risk cut-offs and number of risk categories. Eur J Epidemiol 2013; 28: 25-33
31. Pencina MJ, D'Agostino RB, Pencina KM, Janssens A.C., Greenland P. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol 2012; 176: 473-481
32. Kerr KF, McClelland RL, Brown E.R., Lumley T. Evaluating the incremental value of new biomarkers with integrated discrimination improvement. Am J Epidemiol 2011; 174: 364-374
33. Fernández-Real J.M., Vendrell J, García I, Ricart W., Valle's M. Structural damage in diabetic nephropathy is associated with TNF-α system activity. Acta Diabetol 2012; 49: 301-305
34. Iwata M, Silva Enciso JE, Greenberg BH. Selective and specific regulation of ectodomain shedding of angiotensin-converting enzyme 2 by tumor necrosis factor alpha-converting enzyme. Am J Physiol Cell Physiol 2009; 297: C1318-C1329
35. Foucher Y, Combescure C, Ashton-Chess J, Giral M. Prognostic markers: data misinterpretation often leads to overoptimistic conclusions. Am J Transplant 2012; 12: 1060-1061
36. Cook NR, Paynter NP Performance of reclassification statistics in comparing risk prediction models. Biom J 2011; 53: 237-258
37. Saraheimo M, Teppo AM, Forsblom C, Fagerudd J., Groop PH. Diabetic nephropathy is associated with low-grade inflammation in type 1 diabetic patients. Diabetologia 2003; 46: 1402-1407