Severity of COPD and its relationship with IL-10.

BACKGROUND: The present study was designed to compare inflammatory and metabolic responses according to severity of airflow among patients with COPD and to verify the relationship between pulmonary function, body composition, metabolic and inflammatory profile. METHODS: Fifty-one patients with mild to very severe COPD were recruited and divided according lung function in Mild-moderate (GOLD 1-2) n= 21; Severe (GOLD 3) n=25 and Very severe (GOLD 4) n=5. Patients were submitted to assessments of lung function (spirometry), functional exercise capacity (6-min walk test), body composition (Octopolar bioelectrical impedance), metabolic profile (glucose, triglycerides, total cholesterol, HDL-cholesterol and albumin (colorimetric assay)) and inflammatory profile (cytokines: IL-6, IL-10, TNF-α and IL-15 (ELISA)). RESULTS: We found that patients in GOLD 3 group had lower levels of IL-10, triglycerides, visceral fat area, and higher IL-6 and IL-6/IL-10 ratio when compared to GOLD 1-2 patients. Additionally, GOLD 1-2 group presented negative correlation between TNF-α and HDL cholesterol (p= .01) and positive correlation between IL-15 and FEV1/FVC (p=.01), while GOLD 3 group showed positive correlation between IL-6 and IL-10 (p< .01), IL-6 and total cholesterol (p<.01) and negative correlation between IL-10 and HDL-cholesterol (p=.01). CONCLUSION: Our findings suggest that patients with severe COPD can exhibit compromised "inflammatory status", characterized by higher IL6, IL-6/IL-10 ratio and lower IL-10 concentration. Furthermore, IL-10 seems to be an interesting cytokine to be investigated in this kind of patients.

Non-invasive method to analyse the risk of developing diabetic foot.

Foot complications (diabetic foot) are among the most serious and costly complications of diabetes mellitus. Amputation of all or part of a lower extremity is usually preceded by a foot ulcer. To prevent diabetic foot, an automatic non-invasive method to identify patients with diabetes who have a high risk of developing diabetic foot is proposed. To design the proposed method, information concerning social scope and self-care of 153 diabetic patients was presented to the K-means clustering algorithm, which divided the data into two groups: high risk and low risk of developing diabetic foot. In the operational stage, the Euclidian distance from the information vector to the centroids of each group of risk is used as criterion for classification. Both real and simulated data were used to evaluate the method in which promising results were achieved with accuracy of 0.97 ± 0.06 for simulated data and 0.68 ± 0.16 considering the classification of specialists as the gold standard for real data. The method requires a simple computational processing and can be useful for basic health units to triage diabetic patients helping the health-care team to reduce the number of cases of diabetic foot.