University Medical Center Hamburg-Eppendorf, Endocrinology and Metabolism of Ageing, Hamburg, Germany.
PLoS One. 2013 Sep 30;8(9):e75807. doi: 10.1371/journal.pone.0075807. eCollection 2013.
To investigate the association of risk alleles for type 2 diabetes with prediabetes accounting for age, anthropometry, inflammatory markers and lifestyle habits.
Cross-sectional study of 129 men and 157 women of medium-sized companies in northern Germany in the Delay of Impaired Glucose Tolerance by a Healthy Lifestyle Trial (DELIGHT).
Besides established risk factors, 41 single nucleotide polymorphisms (SNPs) that have previously been found to be associated with type 2 diabetes were analyzed. As a nonparametric test a random forest approach was used that allows processing of a large number of predictors. Variables with the highest impact were entered into a multivariate logistic regression model to estimate their association with prediabetes.
Individuals with prediabetes were characterized by a slightly, but significantly higher number of type 2 diabetes risk alleles (42.5±4.1 vs. 41.3±4.1, p = 0.013). After adjustment for age and waist circumference 6 SNPs with the highest impact in the random forest analysis were associated with risk for prediabetes in a logistic regression model. At least 5 of these SNPs were positively related to prediabetic status (odds ratio for prediabetes 1.57 per allele (Cl 1.21-2.10, p = 0.001)).
This explorative analysis of data of DELIGHT demonstrates that at least 6 out of 41 genetic variants characteristic of individuals with type 2 diabetes may also be associated with prediabetes. Accumulation of these risk alleles may markedly increase the risk for prediabetes. However, prospective studies are required to corroborate these findings and to demonstrate the predictive value of these genetic variants for the risk to develop prediabetes.
研究 2 型糖尿病风险等位基因与年龄、人体测量学、炎症标志物和生活方式习惯相关的前驱糖尿病的关联性。
在德国北部中等规模公司的延迟糖耐量受损健康生活方式试验(DELIGHT)中,对 129 名男性和 157 名女性进行了横断面研究。
除了已确立的危险因素外,还分析了之前发现与 2 型糖尿病相关的 41 个单核苷酸多态性(SNP)。作为一种非参数检验,使用随机森林方法,该方法允许处理大量预测因子。具有最高影响的变量被输入多元逻辑回归模型,以估计它们与前驱糖尿病的关联性。
前驱糖尿病患者的 2 型糖尿病风险等位基因数量略高,但具有统计学意义(42.5±4.1 对 41.3±4.1,p=0.013)。在校正年龄和腰围后,随机森林分析中具有最高影响的 6 个 SNP 与逻辑回归模型中的前驱糖尿病风险相关。至少有 5 个这些 SNP 与前驱糖尿病状态呈正相关(每个等位基因的前驱糖尿病比值比 1.57(Cl 1.21-2.10,p=0.001))。
对 DELIGHT 数据的探索性分析表明,至少 6 个与 2 型糖尿病患者特征相关的 41 个遗传变异中的 6 个也可能与前驱糖尿病相关。这些风险等位基因的积累可能会显著增加前驱糖尿病的风险。然而,需要前瞻性研究来证实这些发现,并证明这些遗传变异对发展前驱糖尿病的风险的预测价值。