Background: Epidemiological studies and feeding trials with supplements suggest that fibre intake is associated with a reduction in cardiovascular risk. However, the effects of changes in dietary fibre on risk factor levels have not been evaluated in free-living individuals. Thus, the effects of changes in dietary fibre intake on cardiovascular risk factors were assessed over 3 months in free-living high-risk subjects. Methods: 772 high-risk subjects (age 69¡5 years) were assigned to a low-fat diet or two Mediterranean-style diets. All participants received behavioural and nutritional education, including recommendations for increasing the consumption of vegetables, fruits, and legumes. Changes in food and nutrient intake, body weight, blood pressure, lipid profiles, glucose control and inflammatory markers were evaluated. Results: Most participants increased consumption of vegetable products, but the increase in dietary fibre exhibited wide between-subject variability (6-65 g/day). Body weight, waist circumference, and mean systolic and diastolic blood pressure decreased across quintiles of fibre intake (p,0.005; all). Reductions in fasting glucose and total cholesterol levels, and increments in HDL cholesterol were highest among participants in the upper 20% of fibre intake (p = 0.04 and 0.02 respectively). Plasma concentrations of C-reactive protein, but not those of inflammatory cytokines, decreased in parallel with increasing dietary fibre (p = 0.04). Significant reductions in LDL cholesterol were observed only among participants with the greatest increases in soluble fibre intake (p = 0.04). Conclusions: Increasing dietary fibre intake with natural foods is associated with reductions in classical and novel cardiovascular risk factors in a high-risk cohort.

Purpose:To compare the results obtained by two screening techniques for diabetic retinopathy.Methods:Patients were assessed in two groups, according to whether the retinal images were analyzed by the general practitioner (Group 1) or by the ophthalmologist (Group 2) in a two-year prospective study using telemedicine.Results:The number of patients referred to the nonmydriatic fundus camera unit was higher in Group 1 than in Group 2 (63.80% versus 17.63%). Greater patient adherence was observed in Group 1 than in Group 2 when patients came to retinography (98.25% versus 87.52%). There were no significant differences in other technique variables. The prevalence of diabetic retinopathy was similar in both groups (8.98% in Group 1 and 9.16% in Group 2), but the prevalence of severe proliferative diabetic retinopathy was higher in Group 2 (1.69% [severe] and 0.45% [proliferative]) than in Group 1 (1.01% and 0.11%, respectively). Diabetic macular edema was more prevalent in Group 2 (2.03%).Conclusions:The inclusion of general practitioners in the screening method seems to be important. A great number of patients with diabetes mellitus were screened, and a higher percentage of patients with diabetic retinopathy or macular edema were detected.

ObjectiveThe aim of present study was to evaluate our clinical decision support system (CDSS) for predicting risk of diabetic retinopathy (DR). We selected randomly a real population of patients with type 2 diabetes (T2DM) who were attending our screening programme.Methods and analysisThe sample size was 602 patients with T2DM randomly selected from those who attended the DR screening programme. The algorithm developed uses nine risk factors: current age, sex, body mass index (BMI), duration and treatment of diabetes mellitus (DM), arterial hypertension, Glicated hemoglobine (HbA1c), urine–albumin ratio and glomerular filtration.ResultsThe mean current age of 67.03±10.91, and 272 were male (53.2%), and DM duration was 10.12±6.4 years, 222 had DR (35.8%). The CDSS was employed for 1 year. The prediction algorithm that the CDSS uses included nine risk factors: current age, sex, BMI, DM duration and treatment, arterial hypertension, HbA1c, urine–albumin ratio and glomerular filtration. The area under the curve (AUC) for predicting the presence of any DR achieved a value of 0.9884, the sensitivity of 98.21%, specificity of 99.21%, positive predictive value of 98.65%, negative predictive value of 98.95%, α error of 0.0079 and β error of 0.0179.ConclusionOur CDSS for predicting DR was successful when applied to a real population.