ABSTRACT
Introducción y objetivos: Algunas medidas antropométricas muestran mayor capacidad que otras para discriminar la presencia de factores de riesgo cardiovascular. Este trabajo estima la magnitud de la asociación de diversos indicadores antropométricos de obesidad con hipertensión, dislipemia y prediabetes (glucemia basal o glucohemoglobina alteradas). Métodos: Análisis transversal de la información recogida en 2.022 sujetos del estudio PREDAPS (etapa basal). Se definió obesidad general como índice de masa corporal ≥ 30 kg/m2 y obesidad abdominal con 2 criterios: a) perímetro de cintura (PC) ≥ 102 cm en varones/PC ≥ 88 cm en mujeres, y b) índice cintura/estatura (ICE) ≥ 0,55. La magnitud de la asociación se estimó mediante regresión logística. Resultados: La hipertensión arterial mostró la asociación más alta con la obesidad general en mujeres (OR = 3,01; IC95%, 2,24-4,04) y con la obesidad abdominal según el criterio del ICE en varones (OR = 3,65; IC95%, 2,66-5,01). La hipertrigliceridemia y los valores bajos de colesterol unido a lipoproteínas de alta densidad mostraron la asociación más alta con obesidad abdominal según el criterio del ICE en mujeres (OR = 2,49; IC95%, 1,68-3,67 y OR = 2,70; IC95%, 1,89-3,86) y la obesidad general en varones (OR = 2,06; IC95%, 1,56-2,73 y OR = 1,68; IC95%, 1,21-2,33). La prediabetes mostró la asociación más alta con obesidad abdominal según el criterio del ICE en mujeres (OR = 2,48; IC95%, 1,85-3,33) y con obesidad abdominal según el criterio del PC en varones (OR = 2,33; IC95%, 1,75-3,08). Conclusiones: Los indicadores de obesidad abdominal mostraron la mayor asociación con la presencia de prediabetes. La relación de los indicadores antropométricos con hipertensión y con dislipemia mostró resultados heterogéneos (AU)
Introduction and objectives: Some anthropometric measurements show a greater capacity than others to identify the presence of cardiovascular risk factors. This study estimated the magnitude of the association of different anthropometric indicators of obesity with hypertension, dyslipidemia, and prediabetes (altered fasting plasma glucose and/or glycosylated hemoglobin). Methods: Cross-sectional analysis of information collected from 2022 participants in the PREDAPS study (baseline phase). General obesity was defined as body mass index ≥ 30 kg/m2 and abdominal obesity was defined with 2 criteria: a) waist circumference (WC) ≥ 102 cm in men/WC ≥ 88 cm in women, and b) waist-height ratio (WHtR) ≥ 0.55. The magnitude of the association was estimated by logistic regression. Results: Hypertension showed the strongest association with general obesity in women (OR, 3.01; 95%CI, 2.24-4.04) and with abdominal obesity based on the WHtR criterion in men (OR, 3.65; 95%CI, 2.66-5.01). Hypertriglyceridemia and low levels of high-density lipoprotein cholesterol showed the strongest association with abdominal obesity based on the WHtR criterion in women (OR, 2.49; 95%CI, 1.68-3.67 and OR, 2.70; 95%CI, 1.89-3.86) and with general obesity in men (OR, 2.06; 95%CI, 1.56-2.73 and OR, 1.68; 95%CI, 1.21-2.33). Prediabetes showed the strongest association with abdominal obesity based on the WHtR criterion in women (OR, 2.48; 95%CI, 1.85-3.33) and with abdominal obesity based on the WC criterion in men (OR, 2.33; 95%CI, 1.75-3.08). Conclusions: Abdominal obesity indicators showed the strongest association with the presence of prediabetes. The association of anthropometric indicators with hypertension and dyslipidemia showed heterogeneous results (AU)
Subject(s)
Humans , Male , Female , Adult , Middle Aged , Aged , Hypertension/epidemiology , Hypertension/prevention & control , Obesity/complications , Hyperlipidemias/complications , Prediabetic State/diagnosis , Obesity, Abdominal/complications , Hyperlipidemias/prevention & control , Prediabetic State/prevention & control , Anthropometry/methods , Waist-Height Ratio , Logistic Models , Blood Glucose/metabolismABSTRACT
BACKGROUND: Kidney disease is associated with an increased total mortality and cardiovascular morbimortality in the general population and in patients with Type 2 diabetes. The aim of this study is to determine the prevalence of kidney disease and different types of renal disease in patients with type 2 diabetes (T2DM). METHODS: Cross-sectional study in a random sample of 2,642 T2DM patients cared for in primary care during 2007. Studied variables: demographic and clinical characteristics, pharmacological treatments and T2DM complications (diabetic foot, retinopathy, coronary heart disease and stroke). Variables of renal function were defined as follows: 1) Microalbuminuria: albumin excretion rate & 30 mg/g or 3.5 mg/mmol, 2) Macroalbuminuria: albumin excretion rate & 300 mg/g or 35 mg/mmol, 3) Kidney disease (KD): glomerular filtration rate according to Modification of Diet in Renal Disease < 60 ml/min/1.73 m2 and/or the presence of albuminuria, 4) Renal impairment (RI): glomerular filtration rate < 60 ml/min/1.73 m2, 5) Nonalbuminuric RI: glomerular filtration rate < 60 ml/min/1.73 m2 without albuminuria and, 5) Diabetic nephropathy (DN): macroalbuminuria or microalbuminuria plus diabetic retinopathy. RESULTS: The prevalence of different types of renal disease in patients was: 34.1% KD, 22.9% RI, 19.5% albuminuria and 16.4% diabetic nephropathy (DN). The prevalence of albuminuria without RI (13.5%) and nonalbuminuric RI (14.7%) was similar. After adjusting per age, BMI, cholesterol, blood pressure and macrovascular disease, RI was significantly associated with the female gender (OR 2.20; CI 95% 1.86-2.59), microvascular disease (OR 2.14; CI 95% 1.8-2.54) and insulin treatment (OR 1.82; CI 95% 1.39-2.38), and inversely associated with HbA1c (OR 0.85 for every 1% increase; CI 95% 0.80-0.91). Albuminuria without RI was inversely associated with the female gender (OR 0.27; CI 95% 0.21-0.35), duration of diabetes (OR 0.94 per year; CI 95% 0.91-0.97) and directly associated with HbA1c (OR 1.19 for every 1% increase; CI 95% 1.09-1.3). CONCLUSIONS: One-third of the sample population in this study has KD. The presence or absence of albuminuria identifies two subgroups with different characteristics related to gender, the duration of diabetes and metabolic status of the patient. It is important to determine both albuminuria and GFR estimation to diagnose KD.
