BACKGROUND: We evaluated to what extent the presence of risk factors and their interactions increased the likelihood of microalbuminuria (MAU) among individuals with type 2 diabetes. METHODS: Fifty-five Italian diabetes outpatient clinics enrolled a sample of patients with type 2 diabetes, without urinary infections and overt diabetic nephropathy. A morning spot urine sample was collected to centrally determine the urinary albumin/creatinine ratio (ACR). A tree-based regression technique (RECPAM) and multivariate analyses were performed to investigate interaction between correlates of MAU. RESULTS: Of the 1841 patients recruited, 228 (12.4%) were excluded due to the presence of urinary infections and 56 (3.5%) for the presence of macroalbuminuria. Overall, the prevalence of MAU (ACR = 30-299 mg/g) was of 19.1%. The RECPAM algorithm led to the identification of seven classes showing a marked difference in the likelihood of MAU. Non-smoker patients with HbA1c <7% and waist circumference </=102 cm showed the lowest prevalence of MAU (7.5%), and represented the reference class. Patients with retinopathy, waist circumference >98 cm and HbA1c >8% showed the highest likelihood of MAU (odds ratio = 13.7; 95% confidence intervals 6.8-27.6). In the other classes identified, the risk of MAU ranged between 3 and 5. Age, systolic blood pressure, HDL cholesterol levels and diabetes treatment represented additional, global correlates of MAU. CONCLUSIONS: The likelihood of MAU is strongly related to the interaction between diabetes severity, smoking habits and several components of the metabolic syndrome. In particular, abdominal obesity, elevated blood pressure levels and low HDL cholesterol levels substantially increase the risk of MAU. It is of primary importance to monitor MAU in high-risk individuals and aggressively intervene on modifiable risk factors.
BACKGROUND: We evaluated to what extent the presence of risk factors and their interactions increased the likelihood of microalbuminuria (MAU) among individuals with type 2 diabetes. METHODS: Fifty-five Italian diabetesoutpatient clinics enrolled a sample of patients with type 2 diabetes, without urinary infections and overt diabetic nephropathy. A morning spot urine sample was collected to centrally determine the urinary albumin/creatinine ratio (ACR). A tree-based regression technique (RECPAM) and multivariate analyses were performed to investigate interaction between correlates of MAU. RESULTS: Of the 1841 patients recruited, 228 (12.4%) were excluded due to the presence of urinary infections and 56 (3.5%) for the presence of macroalbuminuria. Overall, the prevalence of MAU (ACR = 30-299 mg/g) was of 19.1%. The RECPAM algorithm led to the identification of seven classes showing a marked difference in the likelihood of MAU. Non-smoker patients with HbA1c <7% and waist circumference </=102 cm showed the lowest prevalence of MAU (7.5%), and represented the reference class. Patients with retinopathy, waist circumference >98 cm and HbA1c >8% showed the highest likelihood of MAU (odds ratio = 13.7; 95% confidence intervals 6.8-27.6). In the other classes identified, the risk of MAU ranged between 3 and 5. Age, systolic blood pressure, HDL cholesterol levels and diabetes treatment represented additional, global correlates of MAU. CONCLUSIONS: The likelihood of MAU is strongly related to the interaction between diabetes severity, smoking habits and several components of the metabolic syndrome. In particular, abdominal obesity, elevated blood pressure levels and low HDL cholesterol levels substantially increase the risk of MAU. It is of primary importance to monitor MAU in high-risk individuals and aggressively intervene on modifiable risk factors.
Authors: Iciar Martín-Timón; Cristina Sevillano-Collantes; Amparo Segura-Galindo; Francisco Javier Del Cañizo-Gómez Journal: World J Diabetes Date: 2014-08-15
Authors: V M Cambuli; M Galdiero; M Mastinu; F Pigliaru; R S Auriemma; A Ciresi; R Pivonello; M Amato; C Giordano; S Mariotti; A Colao; M G Baroni Journal: J Endocrinol Invest Date: 2011-04-28 Impact factor: 4.256
Authors: Peter S Kim; Christian Woods; Lauren Dutcher; Patrick Georgoff; Alice Rosenberg; Jo Ann M Mican; Jeffrey B Kopp; Margo A Smith; Colleen Hadigan Journal: PLoS One Date: 2011-09-13 Impact factor: 3.240