Immunological and cardiometabolic risk factors in the prediction of type 2 diabetes and coronary events: MONICA/KORA Augsburg case-cohort study.
Immunological and cardiometabolic risk factors in the prediction of type 2 diabetes and coronary events: MONICA/KORA Augsburg case-cohort study.
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免疫和心脏代谢危险因素在预测2型糖尿病和冠状动脉事件中的作用：Monica/KORA Augsburg病例队列研究。
DOI：
10.1371/journal.pone.0019852
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Thorand B
中科院分区：
文献类型：
--
作者：
Herder C;Baumert J;Zierer A;Roden M;Meisinger C;Karakas M;Chambless L;Rathmann W;Peters A;Koenig W;Thorand B
本研究在基于人群的 MONICA/KORA 奥格斯堡队列中的一项前瞻性病例队列研究中，比较了炎症相关生物标志物与已确定的心脏代谢危险因素在预测 2 型糖尿病和冠状动脉事件发生方面的作用。对 2 型糖尿病的分析基于 436 名患有糖尿病的个体和 1410 名未患糖尿病的个体。冠状动脉事件分析基于 314 名患有冠状动脉事件的个体和 1659 名未发生冠状动脉事件的个体。平均随访时间接近 11 年。计算不同模型的接受者操作特征曲线下面积（AUC）、赤池信息标准（ΔAIC）的变化、综合辨别改进（IDI）和净重分类指数（NRI）。由年龄、性别和调查组成的基本模型预测 2 型糖尿病，AUC 为 0.690。添加 13 种炎症相关生物标志物（CRP、IL-6、IL-18、MIF、MCP-1/CCL2、IL-8/CXCL8、IP-10/CXCL10、脂联素、瘦素、RANTES/CCL5、TGF-β1、sE-选择素、sICAM-1；均在非空腹血清中测量）使 AUC 增加至 0.801，而添加心脏代谢危险因素（BMI、收缩压、总胆固醇/HDL-胆固醇比率、吸烟、饮酒、体力活动、父母糖尿病）使 AUC 增加至 0.803（与基本模型相比，ΔAUC [95% CI] 分别为 0.111 [0.092–0.149] 和 0.113 [0.093–0.149]）。所有炎症相关生物标志物和心脏代谢风险因素的组合使 AUC 进一步增加至 0.847（与心脏代谢风险模型相比，ΔAUC [95% CI] 0.044 [0.028–0.066]）。冠状动脉事件的相应 AUC 为 0.807、0.825（与基本模型相比，ΔAUC [95% CI] 0.018 [0.013–0.038]）、0.845（与基本模型相比，ΔAUC [95% CI] 0.038 [0.028–0.059]）和 0.851（ΔAUC [95%与心脏代谢风险模型相比，CI] 0.006 [0.003–0.021]。将多种炎症相关生物标志物纳入基本模型和包含心脏代谢危险因素的模型显着改善了 2 型糖尿病和冠状动脉事件的预测，尽管后者终点的改善不太明显。
This study compares inflammation-related biomarkers with established cardiometabolic risk factors in the prediction of incident type 2 diabetes and incident coronary events in a prospective case-cohort study within the population-based MONICA/KORA Augsburg cohort. Analyses for type 2 diabetes are based on 436 individuals with and 1410 individuals without incident diabetes. Analyses for coronary events are based on 314 individuals with and 1659 individuals without incident coronary events. Mean follow-up times were almost 11 years. Areas under the receiver-operating characteristic curve (AUC), changes in Akaike's information criterion (ΔAIC), integrated discrimination improvement (IDI) and net reclassification index (NRI) were calculated for different models. A basic model consisting of age, sex and survey predicted type 2 diabetes with an AUC of 0.690. Addition of 13 inflammation-related biomarkers (CRP, IL-6, IL-18, MIF, MCP-1/CCL2, IL-8/CXCL8, IP-10/CXCL10, adiponectin, leptin, RANTES/CCL5, TGF-β1, sE-selectin, sICAM-1; all measured in nonfasting serum) increased the AUC to 0.801, whereas addition of cardiometabolic risk factors (BMI, systolic blood pressure, ratio total/HDL-cholesterol, smoking, alcohol, physical activity, parental diabetes) increased the AUC to 0.803 (ΔAUC [95% CI] 0.111 [0.092–0.149] and 0.113 [0.093–0.149], respectively, compared to the basic model). The combination of all inflammation-related biomarkers and cardiometabolic risk factors yielded a further increase in AUC to 0.847 (ΔAUC [95% CI] 0.044 [0.028–0.066] compared to the cardiometabolic risk model). Corresponding AUCs for incident coronary events were 0.807, 0.825 (ΔAUC [95% CI] 0.018 [0.013–0.038] compared to the basic model), 0.845 (ΔAUC [95% CI] 0.038 [0.028–0.059] compared to the basic model) and 0.851 (ΔAUC [95% CI] 0.006 [0.003–0.021] compared to the cardiometabolic risk model), respectively. Inclusion of multiple inflammation-related biomarkers into a basic model and into a model including cardiometabolic risk factors significantly improved the prediction of type 2 diabetes and coronary events, although the improvement was less pronounced for the latter endpoint.
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影响因子：
7.7
作者：
Duncan, BB;Schmidt, MI;Heiss, G
通讯作者：
Heiss, G
影响因子：
5
作者：
Mihaescu, Raluca;van Zitteren, Moniek;Janssens, A. Cecile J. W.
通讯作者：
Janssens, A. Cecile J. W.
影响因子：
16.2
作者：
Kolberg JA;Jørgensen T;Gerwien RW;Hamren S;McKenna MP;Moler E;Rowe MW;Urdea MS;Xu XM;Hansen T;Pedersen O;Borch-Johnsen K
通讯作者：
Borch-Johnsen K
影响因子：
5
作者：
Pepe, MS;Janes, H;Newcomb, P
通讯作者：
Newcomb, P
影响因子：
39.2
作者：
Goldfine AB;Fonseca V;Jablonski KA;Pyle L;Staten MA;Shoelson SE;TINSAL-T2D (Targeting Inflammation Using Salsalate in Type 2 Diabetes) Study Team
通讯作者：
TINSAL-T2D (Targeting Inflammation Using Salsalate in Type 2 Diabetes) Study Team