[Smoking, obesity and body fat distribution are independently associated with insulin resistance and other cardiovascular risk factors]. / El tabaquismo, la obesidad y la distribución de la grasa corporal se asocian de manera independiente con la resistencia a la insulina y con otros factores de riesgo cardiovascular.

BACKGROUND AND AIM: In recent years, smoking has been shown to increase insulin resistance. However, other studies have failed to demonstrate this association after correcting for confounding factors such as age, gender, body mass index (BMI) or waist to hip ratio (WHR). This study was conducted to elucidate the relative contributions of smoking, obesity and body fat distribution on insulin resistance and other cardiovascular risk factors. PATIENTS AND METHODS: Cases and controls matched for age, gender and degree of obesity. Evaluations included anthropometrical and biochemical assessments with body mass index (BMI), waist to hip ratio (WHR), lipid profiles and insulin resistance determined by HOMA (Homeostasis Model Assessment). RESULTS AND CONCLUSION: A total of 126 patients (52 smokers and 74 non-smokers) participated in the study. Of all the patients, 22 (17.5%) were lean, 49 (38.9%) overweight and 55 (43.7%) obese. Multivariate stepwise linear regression showed an association of WHR (beta = 0.414, p < 0.001), BMI (beta = 0.211, p = 0.012), the number of smoked cigarettes per day (beta = 0.200, p = 0.011) and serum triglycerides levels (beta = 0.241, p = 0.007) on insulin resistance (R = 0.628, F = 13.841, p < 0.001). An independent effect of smoking on triglycerides levels was also shown. Therefore, smoking, obesity and body fat distribution are independently associated with insulin resistance and lipid profile.

El tabaquismo, la obesidad y la distribución de la grasa corporal se asocian de manera independiente con la resistencia a la insulina y con otros factores de riesgo cardiovascular / Smoking, obesity and body fat distribution are independently associated with insulin resistance and other cardiovascular risk factors

Fundamento y objetivos. En los últimos años se ha asociado el tabaquismo con la resistencia a la insulina. Sin embargo, algunos estudios han mostrado que dicho efecto desaparece cuando se tienen en cuenta factores de confusión tales como la edad, el género, el índice de masa corporal (IMC) o el índice cintura-cadera (ICC). El objetivo de este estudio fue el de investigar la influencia relativa del tabaquismo, la obesidad y la distribución de la grasa corporal en la resistencia a la insulina y en otros factores de riesgo cardiovascular. Pacientes y método. Casos y controles pareados por edad, género y grado de obesidad. Evaluación antropométrica y bioquímica, determinando el IMC, ICC, perfil lipídico y grado de resistencia a la insulina valorada por el método HOMA (Homeostasis Model Assessment). Resultados y conclusiones. Participaron un total de 126 pacientes, 52 fumadores y 74 no fumadores. De ellos, 22 (17,5%) tenían normopeso, 49 (38,9%) sobrepeso y 55 (43,7%) obesidad. En el análisis multivariante mediante regresión lineal se demostró una asociación de la resistencia a la insulina con el ICC (β = 0,414, p < 0,001), el IMC (β = 0,211, p = 0,012), el número de cigarrillos consumidos por día (β = 0,200, p = 0,011) y las concentraciones séricas de triglicéridos (β = 0,241, p = 0,007) (R = 0,628, F = 13,841, p < 0,001). También existió una asociación independiente de las concentraciones séricas de triglicéridos con el número de cigarrillos. Por tanto, el tabaquismo, la obesidad y la distribución de la grasa corporal se asocian de manera independiente con la resistencia a la insulina y el perfil lipídico (AU)

Background and aim. In recent years, smoking has been shown to increase insulin resistance. However, other studies have failed to demonstrate this association after correcting for confounding factors such as age, gender, body mass index (BMI) or waist to hip ratio (WHR). This study was conducted to elucidate the relative contributions of smoking, obesity and body fat distribution on insulin resistance and other cardiovascular risk factors. Patients and methods. Cases and controls matched for age, gender and degree of obesity. Evaluations included anthropometrical and biochemical assessments with body mass index (BMI), waist to hip ratio (WHR), lipid profiles and insulin resistance determined by HOMA (Homeostasis Model Assessment). Results and conclusion. A total of 126 patients (52 smokers and 74 non-smokers) participated in the study. Of all the patients, 22 (17.5%) were lean, 49 (38.9%) overweight and 55 (43.7%) obese. Multivariate stepwise linear regression showed an association of WHR (β = 0.414, p < 0.001), BMI (β = 0.211, p = 0.012), the number of smoked cigarettes per day (β = 0.200, p = 0.011) and serum triglycerides levels (β = 0.241, p = 0.007) on insulin resistance (R = 0.628, F = 13.841, p < 0.001). An independent effect of smoking on triglycerides levels was also shown. Therefore, smoking, obesity and body fat distribution are independently associated with insulin resistance and lipid profile (AU)

Gammagrafía pulmonar de perfusión / Lung perfusion scintigraphy

No disponible

Captación tiroidea del yodo / Thyroid uptake of iodine

No disponible

[A comparative analysis of monoclonal assays for the determination of PSA: radioimmunometric assay (ELSA) vs microparticle immunoenzyme assay (MEIA)]. / Análisis comparativo de dos ensayos monoclonales para la determinación de PSA: ensayo radioinmunométrico (ELSA) vs ensayo inmunoenzimático de micropartículas (MEIA).

Comparative analysis of PSA values measured by MEIA and ELSA techniques in a group of 70 unselected patients. A good correlation was observed between PSA levels determined by ELSA-PSA immunoradiometric techniques and those obtained by MEIA-PSA (r = 0.93, p < 0.00001). However, ELSA-PSA values have been 1.73 +/- 0.1 times higher than those by MEIA-PSA. A mean-paired comparison indicates that PSA mean levels (0.48 +/- 0.07 and 0.29 +/- 0.05 for ELSA and MEIA, respectively) are significantly different and define two groups of nonhomogeneous values (p < 0.0001). The same results are obtained when patients with PSA values higher and lower than 4 ng/ml are analyzed separately. For patients with PSA lower than 1 ng/ml, the difference between mean ELSA-PSA and MEIA-PSA values disappears; 0.74 +/- 0.08 vs 0.62 +/- 0.05, respectively (p > 0.1). In this group, the results from both assays are statistically consistent. When considering the group of patients with PSA < 1 ng/ml, no difference between both techniques becomes apparent, which seems to indicate the absence of differences in sensitivity between both techniques when considering low levels of serum PSA. Nevertheless, it is clear that the results from these techniques can not overlap and are not comparable and so, to all practical effects, it is recommended that follow-up of any particular patient is made always with the same technique and even at the same laboratory.