The relation between insulin resistance and lung function: a cross sectional study.

BACKGROUND: Impaired lung function and insulin resistance have been associated and thereby have also been indicated to be powerful predictors of cardiovascular mortality. Therefore, the co-existence of insulin resistance and impaired lung function accompanied with cardiovascular risk factors should induce cardiovascular mortality even in patients without known respiratory disease in a cumulative pattern. It could be useful to determine the lung function of patients with insulin resistance in order to decrease cardiovascular mortality by means of taking measures that minimize the risk of decline in lung function. However, no prior studies have been done on association between insulin resistance and lung function in adults in Turkey. We aimed to determine if insulin resistance plays a detrimental role in lung function in outpatients admitted to internal medicine clinics in adults from Turkey. METHODS: A total of 171 outpatients (mean ± SD) age: 43.1 ± 11.9) years) admitted to internal medicine clinics were included in this single-center cross-sectional study, and were divided into patients with (n = 63, mean ± SD) age: 43.2 ± 12.5) years, 83.5 % female) or without (n = 108, mean ± SD) age: 43.0 ± 11.6) years, 93.5 % female) insulin resistance. All patients were non-smokers. Data on gender, age, anthropometrics, blood pressure, blood biochemistry, metabolic syndrome (MetS), and lung function tests were collected in each patient. Correlates of insulin resistance were determined via logistic regression analysis. RESULTS: Insulin resistance was present in 36.8 % of patients. Logistic regression analysis revealed an increase in the likelihood of having insulin resistance of 1.07 times with every 1-point increase in waist circumference, 1.01 times with every 1-point increase in triglycerides, 0.93 times with every 1-point decrease in HDL (high density lipoprotein) cholesterol, and 0.86 times with every 1-point decrease in percentage of FEV1/FVC pre (FEV1%pre: Forced expiratory volume in the first second of expiration for predicted values; FVC%pre.: Forced vital capacity for predicted values). CONCLUSIONS: Insulin resistance should also be considered amongst the contributing factors for decline in lung function.

Levels of F2 isoprostane in Behcet's disease: Correlation with cardiometabolic risk factors.

OBJECTIVE: Behcet's disease (BD) is a chronic inflammatory disease and recent findings suggest a role of oxidative stress in the pathogenesis of BD. Free radical-induced oxidative stress is also involved in the pathogenesis of cardiovascular and other rheumatic diseases. Oxidative stress may be detected in vivo by measuring F2 isoprostanes. Here, we measured plasma levels of F2 isoprostane in patients with BD and evaluated the correlation of F2 isoprostane with cardiometabolic risk factors. METHODS: Forty-three patients with BD in remission and 37 age- and sex-matched controls were recruited for the study. Blood samples were obtained to determine F2 isoprostane, C-reactive protein levels, erythrocyte sedimentation rate, and other biochemical parameters. Homeostasis model assessment insulin resistance and body mass index were calculated. Systolic blood pressure, diastolic blood pressure, and waist circumference were measured. RESULTS: Plasma F2 isoprostane, fasting plasma glucose, triglyceride, and C-reactive protein levels were significantly higher in patients with BD compared with healthy controls, whereas high-density lipoprotein cholesterol levels were significantly lower in patients with BD. F2 isoprostane levels did not correlate with cardiometabolic risk factors, C-reactive protein levels, or erythrocyte sedimentation rate. CONCLUSION: High levels of F2 isoprostane in patients with BD indicate oxidative stress. Antioxidant therapeutic approaches could potentially affect the course of this disease.

Application of alternative anthropometric measurements to predict metabolic syndrome.

OBJECTIVE: The association between rarely used anthropometric measurements (e.g., mid-upper arm, forearm, and calf circumference) and metabolic syndrome has not been proven. The aim of this study was to assess whether mid-upper arm, forearm, calf, and waist circumferences, as well as waist/height ratio and waist-to-hip ratio, were associated with metabolic syndrome. METHODS: We enrolled 387 subjects (340 women, 47 men) who were admitted to the obesity outpatient department of Istanbul Medeniyet University Goztepe Training and Research Hospital between September 2010 and December 2010. The following measurements were recorded: waist circumference, hip circumference, waist/height ratio, waist-to-hip ratio, mid-upper arm circumference, forearm circumference, calf circumference, and body composition. Fasting blood samples were collected to measure plasma glucose, lipids, uric acid, insulin, and HbA1c. RESULTS: The odds ratios for visceral fat (measured via bioelectric impedance), hip circumference, forearm circumference, and waist circumference/hip circumference were 2.19 (95% CI, 1.30-3.71), 1.89 (95% CI, 1.07-3.35), 2.47 (95% CI, 1.24-4.95), and 2.11(95% CI, 1.26-3.53), respectively. The bioelectric impedance-measured body fat percentage correlated with waist circumference only in subjects without metabolic syndrome; the body fat percentage was negatively correlated with waist circumference/hip circumference in the metabolic syndrome group. All measurements except for forearm circumference were equally well correlated with the bioelectric impedance-measured body fat percentages in both groups. Hip circumference was moderately correlated with bioelectric impedance-measured visceral fat in subjects without metabolic syndrome. Muscle mass (measured via bioelectric impedance) was weakly correlated with waist and forearm circumference in subjects with metabolic syndrome and with calf circumference in subjects without metabolic syndrome. CONCLUSION: Waist circumference was not linked to metabolic syndrome in obese and overweight subjects; however, forearm circumference, an unconventional but simple and appropriate anthropometric index, was associated with metabolic syndrome and bioelectric impedance-measured visceral fat, hip circumference, and waist-to-hip ratio.

Application of alternative anthropometric measurements to predict metabolic syndrome

OBJECTIVE: The association between rarely used anthropometric measurements (e.g., mid-upper arm, forearm, and calf circumference) and metabolic syndrome has not been proven. The aim of this study was to assess whether mid-upper arm, forearm, calf, and waist circumferences, as well as waist/height ratio and waist-to-hip ratio, were associated with metabolic syndrome. METHODS: We enrolled 387 subjects (340 women, 47 men) who were admitted to the obesity outpatient department of Istanbul Medeniyet University Goztepe Training and Research Hospital between September 2010 and December 2010. The following measurements were recorded: waist circumference, hip circumference, waist/height ratio, waist-to-hip ratio, mid-upper arm circumference, forearm circumference, calf circumference, and body composition. Fasting blood samples were collected to measure plasma glucose, lipids, uric acid, insulin, and HbA1c. RESULTS: The odds ratios for visceral fat (measured via bioelectric impedance), hip circumference, forearm circumference, and waist circumference/hip circumference were 2.19 (95% CI, 1.30-3.71), 1.89 (95% CI, 1.07-3.35), 2.47 (95% CI, 1.24-4.95), and 2.11(95% CI, 1.26-3.53), respectively. The bioelectric impedance-measured body fat percentage correlated with waist circumference only in subjects without metabolic syndrome; the body fat percentage was negatively correlated with waist circumference/hip circumference in the metabolic syndrome group. All measurements except for forearm circumference were equally well correlated with the bioelectric impedance-measured body fat percentages in both groups. Hip circumference was moderately correlated with bioelectric impedance-measured visceral fat in subjects without metabolic syndrome. Muscle mass (measured via bioelectric impedance) was weakly correlated with waist and forearm circumference in subjects with metabolic syndrome and with calf circumference in subjects without metabolic syndrome. ...

Secondary metabolic syndrome: the frequency of factors which may underlie the parameters of metabolic syndrome.

BACKGROUND AND OBJECTIVES: Each of the metabolic syndrome (MetS) components (central obesity, hypertriglyceridemia, hypertension, low high-density lipoprotein cholesterol, and insulin resistance) may arise from an underlying disease or factors such as hormonal or pharmacological factors. These components arising secondary to a reason other than life style disturbances cause secondary MetS. The present study aimed to present, for the first time, the factors affecting secondary MetS. DESIGN AND SETTINGS: An observational study at Medeniyet University Goztepe Training and Research Hospital, Istanbul, from June 2010 to February 2011. PATIENTS AND METHODS: The underlying causes in 902 MetS patients with a mean age of 53.5 (12.9) years, of whom 79% were female, were investigated. A detailed evaluation was made, which comprised a history for drugs, diseases and habits that may manifest MetS parameters, physical examination, and laboratory analysis. RESULTS: In 10.6% of the patients, hypothyroidism was determined as the main factor affecting secondary MetS, and in 4.1% the use of corticosteroid was determined as the main factor. Other factors underlie affecting secondary MetS are as follows: the use of thiazide diuretics (22.8%), beta-blockers (12.5%), antiphysichotics (2.1%), insulins (12.8%), insulin secretagog oral hypoglycemics (13.8%), thiazolidinediones (4.9%), oral contraceptives (0.8%), and alcohol intake (2.2%). CONCLUSION: Hypothyroidism and corticosteroid treatment are the leading causes of secondary MetS. While evaluating the patients, it is a prerequisite to determine the high frequency of other factors that may affect the presence and the degree of MetS parameters.

Frequency of cardiovascular risk factors and metabolic syndrome in patients with chronic kidney disease.

OBJECTIVE: Metabolic syndrome is a clustering of cardio-metabolic risk factors. Cardiovascular disease is the main cause of morbidity and mortality in end-stage renal disease. The aim of this study was to elucidate the frequency of traditional and novel cardiovascular and metabolic syndrome risk factors in patients with chronic kidney disease. Identification of these risk factors will allow for precautions to be taken earlier to prevent cardiovascular diseases and metabolic syndrome in chronic kidney disease patients. METHODS: A total of 214 patients (91 females, 123 males, mean age 56.1 ± 14.4 years) with chronic kidney disease who were followed in the Nephrology Department of Istanbul Goztepe Training and Research Hospital were included in the study. Anthropometric and biochemical measurements for cardiovascular risk factors and metabolic syndrome parameters were recorded. Glomerular filtration rates (GFR) were estimated using the Cockroft Gault formula. Metabolic syndrome was defined according to International Diabetes Federation criteria. RESULTS: Thirty-seven percent of patients with chronic renal failure were found to have three or more major cardiovascular risk factors. Seventy percent of patients were found to have metabolic syndrome. The mean numbers of major cardiovascular risk factors and metabolic syndrome parameters in patients with different GFR stages were: 1.8 ± 1.0, 2.6 ± 1.2 (GFR <15 mL/min per 1.73 m(2), n = 102); 2.4 ± 1.0, 3.0 ± 1.0 (GFR 15-29 mL/min per 1.73 m(2), n = 51 ); 2.5 ± 1.1, 3.3 ± 1.0 (GFR 30-59 mL/min per 1.73 m(2), n = 39); 2.4 ± 1.1, 3.5 ± 0.7 (GFR 60-89 mL/min per 1.73 m(2), n = 22), respectively (P = .001). CONCLUSION: Although the frequency of cardiovascular risk factors and metabolic syndrome were high in patients with chronic kidney disease, they were negatively correlated with the stage of renal failure.

Frequency of abdominal obesity and metabolic syndrome in healthcare workers and their awareness levels about these entities.

OBJECTIVES: We investigated the frequency of metabolic syndrome (MetS) and abdominal obesity and evaluated the level of awareness about these two conditions in healthcare workers. STUDY DESIGN: A total of 723 healthcare workers (372 physicians, 247 nurses, 104 other healthcare staff; mean age 32.8+/-8.2 years) from four centers were included. Demographic, anthropometric, and biochemical data were recorded, lifestyle features were inquired, and the levels of awareness about abdominal obesity and MetS were surveyed. The criteria recommended by the Adult Treatment Panel (ATP) III were used for the diagnosis of abdominal obesity and MetS. The presence of MetS was evaluated in 178 subjects. Univariate and multivariate analyses were performed to evaluate the association between lifestyle features and abdominal obesity. RESULTS: The frequency of abdominal obesity was 13.8% and it was significantly higher in males than in females (19.1% vs 10.6%; p=0.002). Metabolic syndrome was diagnosed in 14 participants (7.9%), and there was no significant difference between men and women in this respect (p>0.05). The cut-off values for abdominal obesity and at least three criteria of MetS were correctly listed by 47 participants (6.5%) and 240 participants (33.2%), respectively, with physicians showing significantly higher awareness levels (p=0.001). In multivariate analysis, age =or>40 years and male gender were significantly associated with abdominal obesity. CONCLUSION: Our results demonstrate that only a minority of healthcare workers are cognizant of MetS as a clinical syndrome and the definition of abdominal obesity.