CONTEXT Improved strategies to identify persons at high risk of type 2 diabetes are important to target costly preventive efforts to those who will benefit most. OBJECTIVE This work aimed to assess whether novel biomarkers improve the prediction of type 2 diabetes beyond noninvasive standard clinical risk factors alone or in combination with glycated hemoglobin A1c (HbA1c). METHODS We used a population-based case-cohort study for discovery (689 incident cases and 1850 noncases) and an independent cohort study (262 incident cases, 2549 noncases) for validation. An L1-penalized (lasso) Cox model was used to select the most predictive set among 47 serum biomarkers from multiple etiological pathways. All variables available from the noninvasive German Diabetes Risk Score (GDRSadapted) were forced into the models. The C index and the category-free net reclassification index (cfNRI) were used to evaluate the predictive performance of the selected biomarkers beyond the GDRSadapted model (plus HbA1c). RESULTS Interleukin-1 receptor antagonist, insulin-like growth factor binding protein 2, soluble E-selectin, decorin, adiponectin, and high-density lipoprotein cholesterol were selected as the most relevant biomarkers. The simultaneous addition of these 6 biomarkers significantly improved the predictive performance both in the discovery (C index [95% CI], 0.053 [0.039-0.066]; cfNRI [95% CI], 67.4% [57.3%-79.5%]) and the validation study (0.034 [0.019-0.053]; 48.4% [35.6%-60.8%]). Significant improvements by these biomarkers were also seen on top of the GDRSadapted model plus HbA1c in both studies. CONCLUSION The addition of 6 biomarkers significantly improved the prediction of type 2 diabetes when added to a noninvasive clinical model or to a clinical model plus HbA1c.

中文翻译：

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一组 6 个生物标志物显着提高了 MONICA/KORA 研究人群中 2 型糖尿病的预测。

背景 改进识别 2 型糖尿病高危人群的策略对于将代价高昂的预防工作瞄准最受益的人群非常重要。目的 本研究旨在评估新的生物标志物是否能改善对 2 型糖尿病的预测，超越单独的无创标准临床风险因素或与糖化血红蛋白 A1c (HbA1c) 相结合。方法 我们使用基于人群的病例队列研究（689 例事件病例和 1850 例非病例）和独立队列研究（262 例病例，2549 例非病例）进行验证。使用 L1 惩罚（套索）Cox 模型从多种病因途径的 47 种血清生物标志物中选择最具预测性的组。来自无创德国糖尿病风险评分 (GDRSadapted) 的所有变量都被强加到模型中。C 指数和无类别净重分类指数 (cfNRI) 用于评估所选生物标志物在 GDR 适应模型（加上 HbA1c）之外的预测性能。结果 白细胞介素 1 受体拮抗剂、胰岛素样生长因子结合蛋白 2、可溶性 E-选择蛋白、核心蛋白聚糖、脂联素和高密度脂蛋白胆固醇被选为最相关的生物标志物。同时添加这 6 种生物标志物显着提高了发现中的预测性能（C 指数 [95% CI]，0.053 [0.039-0.066]；cfNRI [95% CI]，67.4% [57.3%-79.5%]）和验证研究（0.034 [0.019-0.053]；48.4% [35.6%-60.8%]）。在两项研究中，这些生物标志物的显着改善也出现在 GDR 适应模型和 HbA1c 之上。