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The aim of the study was to compare endocrine parameters such as leptin, visfatin, insulin resistance, exercise capacity and body composition change, the pulmonary functions test (PFT) and arterial blood gases (ABG) parameters of chronic obstructive pulmonary disease (COPD) patients and in healthy controls.Fifty-five patients with COPD and without malnutrition and 25 healthy controls were included in our study. The serum leptin, visfatin, tumor necrosis factor alpha (TNF-alpha) and insulin resistance, body fat-free mass (FFM) and fat mass (FM) were measured in the groups. Additionally, body mass index (BMI) was calculated and the 6-minute walk test (6MWT), PFT and ABG analyses were performed in all of the cases.No difference in BMI between the COPD group and controls was determined. Serum leptin and visfatin levels, FFM and 6MWT distance were significantly lower in the patients with COPD (p0.001, p = 0.001, p = 0.032, p0.001, respectively). A correlation was found between serum leptin levels and BMI (r = 0.333, p = 0.027), and with FM (r = 0.365, p = 0.029). Serum visfatin level was correlated with the percentage of forced expiratory volume in the first second in the patients with COPD (r = 0.371, p = 0.013). HOMA-IR (Homeostasis model assessment of insulin resistance) and serum TNF-alpha levels in the patients with COPD were found to be significantly higher than controls (p = 0.001, p0.001).These results may be earlier signs for further diseases that can emerge in the advanced stages in patients with COPD. Evaluating the patients not only with the pulmonary function and also systemically, contributes to minimizing the mortality and morbidity.

Predictive models play a pivotal role in the provision of accurate and useful probabilistic assessments of clinical outcomes in chronic diseases. This study was aimed to develop a dedicated prognostic index for quantifying progression risk in chronic obstructive pulmonary disease (COPD). Data were collected prospectively from 75 COPD patients during a three years period. A predictive model of progression risk of COPD was developed using Bayesian logistic regression analysis by Markov chain Monte Carlo method. One-year cycles were used for the disease progression in this model. Primary end points for progression were impairment in basal dyspne index (BDI) score, FEV(1) decline, and exacerbation frequency in last three years. Time-varying covariates age, smoking, body mass index (BMI), severity of disease according to GOLD, PaO2, PaCO(2), IC, RV/TLC, DLCO were used under the study. The mean age was 57.1 + or - 8.1. BDI were strongly correlated with exacerbation frequency (p= 0.001) but not with FEV(1) decline. BMI was found to be a predictor factor for impairment in BDI (p= 0.03). The following independent risk factors were significant to predict exacerbation frequency: GOLD staging (OR for GOLD I vs. II and III = 2.3 and 4.0), hypoxemia (OR for mild vs moderate and severe = 2.1 and 5.1) and hyperinflation (OR= 1.6). PaO2 (p= 0.026), IC (p= 0.02) and RV/TLC (p= 0.03) were found to be predictive factors for FEV(1) decline. The model estimated BDI, lung function and exacerbation frequency at the last time point by testing initial data of three years with 95% reliability (p0.001). Accordingly, this model was evaluated as confident of 95% for assessing the future status of COPD patients. Using Bayesian predictive models, it was possible to develop a risk-stratification index that accurately predicted progression of COPD. This model can provide decision-making about future in COPD patients with high reliability looking clinical data of beginning.