BACKGROUND: Experimental studies have shown that adiponectin has antiproteinuric and nephroprotective effects. The purpose of the study was to assess the value of plasma adiponectin as a predictor of proteinuria in type 2 diabetes (T2D) patients. METHODS: In this one-year prospective follow-up study, we included T2D patients with positive visual test for microalbuminuria (Micral) and negative visual test for proteinuria. Exclusion criteria were: glomerular filtration ratio (GFR) < 30 ml/min, acute infection/inflammation, uncontrolled hypertension, and atherosclerotic complications. The main outcome measure was the change in urinary albumin/creatinine ratio (UACR) after 1 year follow-up (Δ UACR). RESULTS: Fifty-six patients (66% males) completed the study. Their initial mean UACR was 81.58 ± 26.42 mg/g and mean GFR was 81.15 ± 3.96 ml/min. At baseline, simple regression disclosed significant correlations between UACR and plasma adiponectin (r = 0.54, P = 0.00002) and GFR (r = -0.28, P = 0.03); in multiple regression analysis, plasma adiponectin remained the only predictor of UACR (P = 0.00007). Baseline plasma adiponectin was significantly correlated to body mass index (r = -0.28, P = 0.04), waist circumference (r = -0.27, P = 0.05), HDL cholesterol (r = 0.35, P = 0.01), and LDL cholesterol (r = 0.27, P = 0.04). Baseline plasma adiponectin significantly correlated in simple (r = -0.38, P = 0.004) and multiple regression (P = 0.04) to Δ UACR. When patients were divided according to Δ UACR in nonprogressors (Δ UACR < 0) and progressors (Δ UACR > 0), logistic regression showed that baseline GFR (OR = 1.04, CI95%: 1.00-1.09, P = 0.04) and plasma adiponectin (OR = 1.16, CI95%: 1.02-1.32, P = 0.02) were the only factors that predicted whether the patient would be a progressor or not. CONCLUSION: In T2D patients, lower plasma adiponectin levels seem to be predictive of increased UACR.
BACKGROUND: Experimental studies have shown that adiponectin has antiproteinuric and nephroprotective effects. The purpose of the study was to assess the value of plasma adiponectin as a predictor of proteinuria in type 2 diabetes (T2D) patients. METHODS: In this one-year prospective follow-up study, we included T2D patients with positive visual test for microalbuminuria (Micral) and negative visual test for proteinuria. Exclusion criteria were: glomerular filtration ratio (GFR) < 30 ml/min, acute infection/inflammation, uncontrolled hypertension, and atherosclerotic complications. The main outcome measure was the change in urinary albumin/creatinine ratio (UACR) after 1 year follow-up (Δ UACR). RESULTS: Fifty-six patients (66% males) completed the study. Their initial mean UACR was 81.58 ± 26.42 mg/g and mean GFR was 81.15 ± 3.96 ml/min. At baseline, simple regression disclosed significant correlations between UACR and plasma adiponectin (r = 0.54, P = 0.00002) and GFR (r = -0.28, P = 0.03); in multiple regression analysis, plasma adiponectin remained the only predictor of UACR (P = 0.00007). Baseline plasma adiponectin was significantly correlated to body mass index (r = -0.28, P = 0.04), waist circumference (r = -0.27, P = 0.05), HDL cholesterol (r = 0.35, P = 0.01), and LDL cholesterol (r = 0.27, P = 0.04). Baseline plasma adiponectin significantly correlated in simple (r = -0.38, P = 0.004) and multiple regression (P = 0.04) to Δ UACR. When patients were divided according to Δ UACR in nonprogressors (Δ UACR < 0) and progressors (Δ UACR > 0), logistic regression showed that baseline GFR (OR = 1.04, CI95%: 1.00-1.09, P = 0.04) and plasma adiponectin (OR = 1.16, CI95%: 1.02-1.32, P = 0.02) were the only factors that predicted whether the patient would be a progressor or not. CONCLUSION: In T2D patients, lower plasma adiponectin levels seem to be predictive of increased UACR.
Authors: Y Arita; S Kihara; N Ouchi; M Takahashi; K Maeda; J Miyagawa; K Hotta; I Shimomura; T Nakamura; K Miyaoka; H Kuriyama; M Nishida; S Yamashita; K Okubo; K Matsubara; M Muraguchi; Y Ohmoto; T Funahashi; Y Matsuzawa Journal: Biochem Biophys Res Commun Date: 1999-04-02 Impact factor: 3.575
Authors: G D Norata; I Baragetti; S Raselli; A Stucchi; K Garlaschelli; S Vettoretti; G Piloni; G Buccianti; A L Catapano Journal: Nutr Metab Cardiovasc Dis Date: 2009-04-09 Impact factor: 4.222
Authors: Kieren J Mather; Qing Pan; William C Knowler; Tohru Funahashi; George A Bray; Richard Arakaki; Bonita Falkner; Kumar Sharma; Barry J Goldstein Journal: PLoS One Date: 2015-08-27 Impact factor: 3.240