Fernández-Bergés Daniel, Consuegra-Sánchez Luciano, Peñafiel Judith, Cabrera de León Antonio, Vila Joan, Félix-Redondo Francisco Javier, Segura-Fragoso Antonio, Lapetra José, Guembe María Jesús, Vega Tomás, Fitó Montse, Elosua Roberto, Díaz Oscar, Marrugat Jaume
Unidad de Investigación Cardiovascular GRIMEX, Programa de Investigación Cardiovascular (PERICLES), Villanueva de la Serena, Badajoz, Spain; Hospital Don Benito-Villanueva, Gerencia de Área de Salud Don Benito-Villanueva, Don Benito, Badajoz, Spain.
Unidad de Investigación Cardiovascular GRIMEX, Programa de Investigación Cardiovascular (PERICLES), Villanueva de la Serena, Badajoz, Spain; Servicio de Cardiología, Hospital Universitario de Santa Lucía, Cartagena, Murcia, Spain.
Rev Esp Cardiol (Engl Ed). 2014 Aug;67(8):624-31. doi: 10.1016/j.rec.2013.10.019. Epub 2014 Apr 3.
There is a paucity of data regarding the differences in the biomarker profiles of patients with obesity, metabolic syndrome, and diabetes mellitus as compared to a healthy, normal weight population. We aimed to study the biomarker profile of the metabolic risk continuum defined by the transition from normal weight to obesity, metabolic syndrome, and diabetes mellitus.
We performed a pooled analysis of data from 7 cross-sectional Spanish population-based surveys. An extensive panel comprising 20 biomarkers related to carbohydrate metabolism, lipids, inflammation, coagulation, oxidation, hemodynamics, and myocardial damage was analyzed. We employed age- and sex-adjusted multinomial logistic regression models for the identification of those biomarkers associated with the metabolic risk continuum phenotypes: obesity, metabolic syndrome, and diabetes mellitus.
A total of 2851 subjects were included for analyses. The mean age was 57.4 (8.8) years, 1269 were men (44.5%), and 464 participants were obese, 443 had metabolic syndrome, 473 had diabetes mellitus, and 1471 had a normal weight (healthy individuals). High-sensitivity C-reactive protein, apolipoprotein B100, leptin, and insulin were positively associated with at least one of the phenotypes of interest. Apolipoprotein A1 and adiponectin were negatively associated.
There are differences between the population with normal weight and that having metabolic syndrome or diabetes with respect to certain biomarkers related to the metabolic, inflammatory, and lipid profiles. The results of this study support the relevance of these mechanisms in the metabolic risk continuum. When metabolic syndrome and diabetes mellitus are compared, these differences are less marked.
与健康的正常体重人群相比，关于肥胖、代谢综合征和糖尿病患者生物标志物谱差异的数据较为匮乏。我们旨在研究由正常体重向肥胖、代谢综合征和糖尿病转变所定义的代谢风险连续体的生物标志物谱。
我们对来自7项基于西班牙人群的横断面调查数据进行了汇总分析。分析了一个包含20种与碳水化合物代谢、脂质、炎症、凝血、氧化、血流动力学和心肌损伤相关的生物标志物的综合指标。我们采用年龄和性别调整的多项逻辑回归模型来识别与代谢风险连续体表型（肥胖、代谢综合征和糖尿病）相关的生物标志物。
共有2851名受试者纳入分析。平均年龄为57.4（8.8）岁，男性1269名（44.5%），464名参与者肥胖，443名患有代谢综合征，473名患有糖尿病，1471名体重正常（健康个体）。高敏C反应蛋白、载脂蛋白B100、瘦素和胰岛素与至少一种感兴趣的表型呈正相关。载脂蛋白A1和脂联素呈负相关。
正常体重人群与患有代谢综合征或糖尿病的人群在某些与代谢、炎症和脂质谱相关的生物标志物方面存在差异。本研究结果支持这些机制在代谢风险连续体中的相关性。当比较代谢综合征和糖尿病时，这些差异不太明显。
