Metabolic Syndrome Performs Better Than the Individual Factors in Predicting Renal Graft Outcome

• Published in: Transplantation proceedings Vol. 45; no. 10; pp. 3517 - 3519
• Main Authors: Mohsin, N, Mourad, G, Faure, M, Szawarc, I, Bringer, J
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-12-2013
• Subjects: Adiposity; Biomarkers - blood; Blood Glucose - metabolism; Blood Pressure; Glomerular Filtration Rate; Graft Survival; Humans; Kidney - diagnostic imaging; Kidney - physiopathology; Kidney - surgery; Kidney Transplantation - adverse effects; Linear Models; Lipids - blood; Longitudinal Studies; Metabolic Syndrome - blood; Metabolic Syndrome - diagnosis; Metabolic Syndrome - physiopathology; Multivariate Analysis; Predictive Value of Tests; Radionuclide Imaging; Risk Factors; Surgery; Time Factors; Treatment Outcome
• ISSN: 0041-1345; 1873-2623
• DOI: 10.1016/j.transproceed.2013.09.013

Abstract Aim Individual components of the metabolic syndrome (MS), especially obesity and hypertension, have a deleterious effect on renal graft outcome. Whether MS is better than its individual components in predicting the decline of renal function is unknown. We studied the presence of MS and its individual components at 12 months post-transplantation according to the Adult Treatment Panel III classification and their influence on measured graft function. Methods A cohort of 322 patients who underwent transplantation between 1996 and 2003 and who agreed to have their glomerular filtration rate (GFR) measured by urinary clearance of technetium 99m (Tc*-DTPA) (measured GFR [mGFR]) at 3, 12, 48, 60, and 96 months after transplantation were included. The patients were followed up until patient death, graft loss, or December 2009 (mean follow-up: 3 ± 2.8 years). The linear mixed effect model for longitudinal repeated measures was applied. To compare MS versus its components we used the Akaike information Criterion (AIC) to determine the best model according to the Anderson and Burnham method. Results Univariate and multivariate analyses models using MS were more efficient than those using the individual components, which consisted of waist circumference, low high-density lipoprotein–cholesterol, hypertriglyceridemia, hyperglycemia, and systolic and diastolic blood pressure. The AIC was the lowest with MS models indicating better prediction on graft function than the individual components. Conclusion MS is a better predictor of mGFR decline than its individual components. It is a valid and precious tool to assess outcomes.