Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
Diabetes. 2019 Feb;68(2):281-290. doi: 10.2337/db18-0892. Epub 2018 Nov 8.
Numerous studies have investigated individual biomarkers in relation to risk of type 2 diabetes. However, few have considered the interconnectivity of these biomarkers in the etiology of diabetes as well as the potential changes in the biomarker correlation network during diabetes development. We conducted a secondary analysis of 27 plasma biomarkers representing glucose metabolism, inflammation, adipokines, endothelial dysfunction, IGF axis, and iron store plus age and BMI at blood collection from an existing case-control study nested in the Nurses' Health Study (NHS), including 1,303 incident diabetes case subjects and 1,627 healthy women. A correlation network was constructed based on pairwise Spearman correlations of the above factors that were statistically different between case and noncase subjects using permutation tests ( < 0.0005). We further evaluated the network structure separately among diabetes case subjects diagnosed <5, 5-10, and >10 years after blood collection versus noncase subjects. Although pairwise biomarker correlations tended to have similar directions comparing diabetes case subjects to noncase subjects, most correlations were stronger in noncase than in case subjects, with the largest differences observed for the insulin/HbA and leptin/adiponectin correlations. Leptin and soluble leptin receptor were two hubs of the network, with large numbers of different correlations with other biomarkers in case versus noncase subjects. When examining the correlation network by timing of diabetes onset, there were more perturbations in the network for case subjects diagnosed >10 years versus <5 years after blood collection, with consistent differential correlations of insulin and HbA C-peptide was the most highly connected node in the early-stage network, whereas leptin was the hub for mid- or late-stage networks. Our results suggest that perturbations of the diabetes-related biomarker network may occur decades prior to clinical recognition. In addition to the persistent dysregulation between insulin and HbA, our results highlight the central role of the leptin system in diabetes development.
许多研究已经调查了与 2 型糖尿病风险相关的个体生物标志物。然而,很少有研究考虑这些生物标志物在糖尿病发病机制中的相互关联性,以及在糖尿病发展过程中生物标志物相关网络的潜在变化。我们对现有的护士健康研究(NHS)嵌套病例对照研究中的 27 种血浆生物标志物进行了二次分析,这些生物标志物代表了葡萄糖代谢、炎症、脂肪因子、内皮功能障碍、IGF 轴和铁储存以及采血时的年龄和 BMI,包括 1303 例新诊断的糖尿病病例和 1627 例健康女性。使用置换检验(<0.0005),基于病例和非病例受试者之间统计学上有差异的上述因素的两两 Spearman 相关,构建了一个相关网络。我们进一步分别评估了病例受试者在采血后 <5 年、5-10 年和>10 年诊断为糖尿病与非病例受试者的网络结构。尽管病例与非病例受试者相比,生物标志物的两两相关性趋于具有相似的方向,但在非病例受试者中,大多数相关性更强,胰岛素/HbA 和瘦素/脂联素相关性的差异最大。瘦素和可溶性瘦素受体是网络的两个枢纽,与病例和非病例受试者中其他生物标志物的相关性数量较多。当按糖尿病发病时间检查相关网络时,与采血后 <5 年诊断为糖尿病的病例相比,网络的扰动更多,胰岛素和 HbA C-肽的差异相关性一致,它们是早期网络中连接度最高的节点,而瘦素是中晚期网络的枢纽。我们的研究结果表明,与糖尿病相关的生物标志物网络的扰动可能在临床诊断前几十年就已经发生。除了胰岛素和 HbA 之间的持续失调外,我们的研究结果还强调了瘦素系统在糖尿病发展中的核心作用。