The serum uric acid concentration is not causally linked to diabetic nephropathy in type 1 diabetes

Kidney International

Volumn 91, Issue 5, 2017, Pages 1178-1185

  Ahola, Aila J ^a,b,c   Sandholm, Niina ^a,b,c   Forsblom, Carol ^a,b,c   Harjutsalo, Valma ^a,b,c,d   Dahlström, Emma ^a,b,c   Groop, Per Henrik ^a,b,c,e  

^a FOLKHÄLSAN INSTITUTE OF GENETICS   (Finland)

^b HELSINKI UNIVERSITY CENTRAL HOSPITAL   (Finland)

^c UNIVERSITY OF HELSINKI   (Finland)

^d NATIONAL PUBLIC HEALTH INSTITUTE   (Finland)

^e BAKER IDI HEART AND DIABETES INSTITUTE   (Australia)

Author keywords

diabetic nephropathy; Mendelian randomization; serum uric acid; type 1 diabetes

Indexed keywords

ALBUMIN; URIC ACID; BIOLOGICAL MARKER; GLUCOSE TRANSPORTER; SLC2A9 PROTEIN, HUMAN;

ADULT; ARTICLE; CONTROLLED STUDY; DIABETIC NEPHROPATHY; ESTIMATED GLOMERULAR FILTRATION RATE; FEMALE; FOLLOW UP; GENOME-WIDE ASSOCIATION STUDY; HUMAN; INSULIN DEPENDENT DIABETES MELLITUS; MAJOR CLINICAL STUDY; MALE; MENDELIAN RANDOMIZATION ANALYSIS; SINGLE NUCLEOTIDE POLYMORPHISM; URIC ACID BLOOD LEVEL; ALBUMINURIA; BLOOD; COMPLICATION; CROSS-SECTIONAL STUDY; DIABETES MELLITUS, TYPE 1; DIABETIC NEPHROPATHIES; DISEASE COURSE; GENETICS; GLOMERULUS FILTRATION RATE; MIDDLE AGED; RISK FACTOR; URINE;

ADULT; ALBUMINURIA; BIOMARKERS; CROSS-SECTIONAL STUDIES; DIABETES MELLITUS, TYPE 1; DIABETIC NEPHROPATHIES; DISEASE PROGRESSION; FEMALE; FOLLOW-UP STUDIES; GLOMERULAR FILTRATION RATE; GLUCOSE TRANSPORT PROTEINS, FACILITATIVE; HUMANS; MALE; MENDELIAN RANDOMIZATION ANALYSIS; MIDDLE AGED; POLYMORPHISM, SINGLE NUCLEOTIDE; RISK FACTORS; URIC ACID;

EID: 85013058647     PISSN: 00852538     EISSN: 15231755     Source Type: Journal    
DOI: 10.1016/j.kint.2016.11.025     Document Type: Article

References (24)

1. 1 Hovind, P., Tarnow, L., Rossing, K., et al. Decreasing incidence of severe diabetic microangiopathy in type 1 diabetes. Diabetes Care 26 (2003), 1258–1264.
2. 2 Hovind, P., Rossing, P., Tarnow, L., et al. Progression of diabetic nephropathy. Kidney Int 59 (2001), 702–709.
3. 3 Groop, P.H., Thomas, M.C., Moran, J.L., et al. The presence and severity of chronic kidney disease predicts all-cause mortality in type 1 diabetes. Diabetes 58 (2009), 1651–1658.
4. 4 Jalal, D.I., Rivard, C.J., Johnson, R.J., et al. Serum uric acid levels predict the development of albuminuria over 6 years in patients with type 1 diabetes: findings from the Coronary Artery Calcification in Type 1 Diabetes study. Nephrol Dial Transplant 25 (2010), 1865–1869.
5. 5 Hovind, P., Rossing, P., Tarnow, L., et al. Serum uric acid as a predictor for development of diabetic nephropathy in type 1 diabetes: an inception cohort study. Diabetes 58 (2009), 1668–1671.
6. 6 Ficociello, L.H., Rosolowsky, E.T., Niewczas, M.A., et al. High-normal serum uric acid increases risk of early progressive renal function loss in type 1 diabetes: results of a 6-year follow-up. Diabetes Care 33 (2010), 1337–1343.
7. 7 Köttgen, A., Albrecht, E., Teumer, A., et al. Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. Nat Genet 45 (2013), 145–154.
8. 8 Kolz, M., Johnson, T., Sanna, S., et al. Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations. PLoS Genet, 5, 2009, e1000504.
9. 9 Testa, A., Mallamaci, F., Spoto, B., et al. Association of a polymorphism in a gene encoding a urate transporter with CKD progression. Clin J Am Soc Nephrol 9 (2014), 1059–1065.
10. 10 Hughes, K., Flynn, T., de Zoysa, et al. Mendelian randomization analysis associates increased serum urate, due to genetic variation in uric acid transporters, with improved renal function. Kidney Int 85 (2014), 344–351.
11. 11 Kleber, M.E., Delgado, G., Grammer, T.B., et al. Uric acid and cardiovascular events: a Mendelian randomization study. JASN 26 (2015), 2831–2838.
12. 12 Palmer, T.M., Bordestgaard, B.G., Benn, M., et al. Association of plasma uric acid with ischaemic heart disease and blood pressure: mendelian randomisation analysis of two large cohorts. BMJ, 347, 2013, f4262.
13. 13 Staiger, D., Stock, J., Instrumental variables regression with weak instruments. Econometrica 65 (1997), 557–586.
14. 14 Palmer, T.M., Sterne, J.A., Harbord, R.M., et al. Instrumental variable estimation of causal risk ratios and causal odds ratios in Mendelian randomization analyses. Am J Epidemiol 173 (2011), 1392–1403.
15. 15 Fox, J., Simultaneous equation models and two-stage least squares. Sociological Method 10 (1979), 130–150.
16. 16 Krolewski, A.S., Niewczas, M.A., Skupien, J., et al. Early progressive renal decline precedes the onset of microalbuminuria and its progression to macroalbuminuria. Diabetes Care 37 (2014), 226–234.
17. 17 Miao, Y., Ottenbros, S.A., Laverman, G.D., et al. Effect of a reduction in uric acid on renal outcomes during losartan treatment: a post hoc analysis of the reduction of endpoints in non-insulin-dependent diabetes mellitus with the Angiotensin II Antagonist Losartan Trial. Hypertension 58 (2011), 2–7.
18. 18 Lawlor, D.A., Harbord, R.M., Sterne, J.A., et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27 (2008), 1133–1163.
19. 19 DeBosch, B.J., Kluth, O., Fujiwara, H., et al. Early-onset metabolic syndrome in mice lacking the intestinal uric acid transporter SLC2A9. Nat Commun, 5, 2014, 4642.
20. 20 Fioretto, P., Mauer, M., Histopathology of diabetic nephropathy. Semin Nephrol 27 (2007), 195–207.
21. 21 Thorn, L.M., Forsblom, C., Fagerudd, J., et al. Metabolic syndrome in type 1 diabetes: association with diabetic nephropathy and glycemic control (the FinnDiane study). Diabetes Care 28 (2005), 2019–2024.
22. 22 Levey, A.S., Stevens, L.A., Schmid, C.H., et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 150 (2009), 604–612.
23. 23 Sandholm, N., Salem, R.M., McKnight, A.J., et al. New susceptibility loci associated with kidney disease in type 1 diabetes. PLoS Genet, 8, 2012, e1002921.
24. 24 Chang, C.C., Chow, C.C., Tellier, L.C., et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience, 4, 2015 7-015-0047-8 (eCollection).