Plasma biomarkers improve prediction of diabetic kidney disease in adults with type 1 diabetes over a 12-year follow-up: CACTI study.

Background: The objective of the study was to determine whether plasma biomarkers of kidney injury improve the prediction of diabetic kidney disease (DKD) in adults with type 1 diabetes (T1D) over a period of 12 years.

Methods: Participants (n = 527, 53% females) in the Coronary Artery Calcification in T1D (CACTI) Study were examined during 2002-04, at a mean (± standard deviation) age of 39.6 ± 9.0 years with 24.8 years as the median duration of diabetes. Urine albumin-to-creatinine (ACR) and estimated glomerular filtration rate (eGFR) by CKD-EPI (chronic kidney disease epidemiology collaboration) creatinine were measured at the baseline and after mean follow-up of 12.1 ± 1.5 years. Albuminuria was defined as ACR ≥30 mg/g and impaired GFR as eGFR <60 mL/min/1.73 m2. Kidney injury biomarkers (Meso Scale Diagnostics) were measured on stored baseline plasma samples. A principal component analysis (PCA) identified two components: (i) kidney injury molecule-1, calbindin, osteoactivin, trefoil factor 3 and vascular endothelial growth factor; and (ii) β-2 microglobulin, cystatin C, neutrophil gelatinase-associated lipocalin and osteopontin that were used in the multivariable regression analyses.

Results: Component 2 of the PCA was associated with increase in log modulus ACR [β ± standard error (SE): 0.16 ± 0.07, P = 0.02] and eGFR (β ± SE: -2.56 ± 0.97, P = 0.009) over a period of 12 years after adjusting for traditional risk factors (age, sex, HbA1c, low-density lipoprotein cholesterol and systolic blood pressure and baseline eGFR/baseline ACR). Only Component 2 of the PCA was associated with incident-impaired GFR (odds ratio 2.08, 95% confidence interval 1.18-3.67, P = 0.01), adjusting for traditional risk factors. The addition of Component 2 to traditional risk factors significantly improved C-statistics and net-reclassification improvement for incident-impaired GFR (ΔAUC: 0.02 ± 0.01, P = 0.049, and 29% non-events correctly reclassified, P < 0.0001).

Conclusions: Plasma kidney injury biomarkers can help predict development of DKD in T1D.