This was a cross-sectional, single center study conducted between March 2009 and October 2013 in consecutive patients with type 1 diabetes who regularly attended a tertiary care diabetes outpatient unit at Pedro Ernesto University Hospital. These patients were matched to healthy control subjects by age, gender and body mass index (BMI). Age was matched by age group with a range of five years because of our difficulty in finding older healthy control subjects. Control subjects were recruited among the patients’ spouses and relatives, university students and hospital employees. The study was approved by the local research ethics committee of Pedro Ernesto University Hospital, Rio de Janeiro State Univerity, Rio de Janeiro, Brazil. Participants were subjected to clinical and laboratory evaluation.
Initially, written informed consent was obtained from all participants. Subjects were submitted to an interview for demographic and clinical information and underwent physical examination. On the morning of the interview, subjects brought in the first 10- hour overnight urine sample, and fasting and post-prandial blood samples were collected. Subjects were then conducted to the Echo laboratory acclimatized room to perform the IMT measurement.
The inclusion criteria included individuals older than 10 years, patients with type 1 diabetes for more than five years, patients who continuously used insulin since diagnosis and healthy control subjects. The exclusion criteria included type 1 diabetes patients and healthy control subjects who were unable to tolerate dorsal decumbency for long periods, a previous history of invasive procedure in the carotid artery, chronic usage of glucocorticoids, asthma or chronic obstructive pulmonary disease, kidney disease, liver failure, thyroid disease and control subjects with previous histories of high cholesterol levels or using cholesterol-lowering drugs, hypertension or using pressure-lowering drugs and cardiovascular disease.
We followed the American Diabetes Association (ADA) statement for the definition of childhood and adolescence. . BMI was calculated by dividing the weight in kilograms by the squared height in meters. Overweigh was defined as a BMI ≥ 25 for adults or ≥ 85th percentile for children, and obesity was defined as a BMI ≥ 30 for adults or ≥ 95th percentile for children and adolescents .
Blood pressure (BP) was calculated using the mean of the three measurements. Adult subjects were classified as having hypertension when the mean was higher than 140 mmHg for systolic blood pressure (SBP) and/or 90 mmHg for diastolic blood pressure (DBP) [36, 37]. Children and adolescents were considered to have hypertension if SBP or DBP was ≥ 95th percentile for age, sex and height .
Glucose control was assessed by fasting glucose (FG) and HbA1c (high performance liquid chromatography (HPLC), Bio-Rad Kit, hemoglobin testing system equipment from Bio-Rad Lab., Irvine, USA. Reference values = 4.0 to 6.0%).
Uric acid, serum creatinine, triglycerides, High density cholesterol (HDL) and total cholesterol levels were measured by enzymatic techniques (Cobas Mira; Roche, Bohemia, NY, USA). Low density cholesterol (LDL) was calculated using the Friedewald equation, except when the triglyceride levels were higher than 400 mg/dL . Creatinine clearance was calculated using the Cockcroft-Gault equation. Urinary albumin excretion rate (UAER) was estimated by solid-phase competitive chemiluminescent enzyme immunoassay (sensitivity of 0.5 mcg ⁄mL; Immulite 1000 Systems; DPC Medlab, Los Angeles, CA, USA) with intra- and inter-assay variation coefficients of 4.4 and 6.1%, respectively. Serum CRP was measured using a highly sensitive immunonephelometry assay (Behring Nephelometer; Behring, Marburg, Germany) with a detection limit of 0.01 mg/dl and intra- and inter-assay variation coefficients of 1 and 5.3%, respectively.
The IMT image was digitally recorded using a commercially available system (Envisor CHD, Philips, Bothell, WA, USA) equipped with a linear L12-13Hz transducer. The IMT was measured using the semi-automated edge-detection software package Q-LAB Advanced Ultrasound Quantification Software version 7.1, Philips. This software measured the IMT in millimeters with a three decimal places precision. Measurements were obtained according the American Society of Echocardiography recommendations .
A single examiner performed all IMT images in a quiet, dark, acclimatized room, after the patient rested for at least five minutes. A bilateral transversal scanning from the common carotid artery and its visible ramifications was performed to look for apparent plaques. The distal one centimeter length of the common carotid IMT image was stored in a digital media after positioning the transducer longitudinal to the carotid vessel. The images were taken in three different angles, posterior, lateral and anterior, for each right and left common carotid artery. The IMT was measured off-line using the semi-automated edge-detection software by two independent skilled examiners who were blinded for the condition of the subject analyzed.
Statistical analysis was performed using the SPSS software version 21 (IBM Corp., USA). To detect a 0.021 mm difference in the IMT between type 1 diabetes and control subjects with a statistical power of 80%, we would need 120 individuals in each group. To determine the agreement between both examiners, we calculated the intra-class coefficient (95% CI) and performed the Bland-Altman box-plot. Data are presented in median and interquartile range (IQR). The non-parametric data were analyzed using the Mann–Whitney U test or the unpaired t test when applicable. The bivariate analysis was performed using gamma regression model between the IMT and the variables analyzed. Those variables with a p value < 0.1 in the bivariate model were included in the multivariate gamma regression model, which was performed in backwards. To avoid multicollinearity, when two or more variables were a measure of the same risk factor, we chose the most significant one. We performed the multivariate gamma model analysis in backwards because there were a lot of variables to be analyzed.