Thirty-One Novel Biomarkers as Predictors for Clinically Incident Diabetes

BackgroundThe prevalence of diabetes is increasing in all industrialized countries and its prevention has become a public health priority. However, the predictors of diabetes risk are insufficiently understood. We evaluated, whether 31 novel biomarkers could help to predict the risk of incident diabetes.
Methods and FindingsThe biomarkers were evaluated primarily in the FINRISK97 cohort (n = 7,827; 417 cases of clinically incident diabetes during the follow-up). The findings were replicated in the Health 2000 cohort (n = 4,977; 179 cases of clinically incident diabetes during the follow-up). We used Cox proportional hazards models to calculate the relative risk of diabetes, after adjusting for the classic risk factors, separately for each biomarker. Next, we assessed the discriminatory ability of single biomarkers using receiver operating characteristic curves and C-statistics, integrated discrimination improvement (IDI) and net reclassification improvement (NRI). Finally, we derived a biomarker score in the FINRISK97 cohort and validated it in the Health 2000 cohort. A score consisting of adiponectin, apolipoprotein B, C-reactive protein and ferritin almost doubled the relative risk of diabetes in the validation cohort (HR per one standard deviation increase 1.88, p = 2.8 e-5). It also improved discrimination of the model (IDI = 0.0149, p<0.0001) and reclassification of diabetes risk (NRI = 11.8%, p = 0.006). Gender-specific analyses suggested that the best score differed between men and women. Among men, the best results were obtained with the score of four biomarkers: adiponectin, apolipoprotein B, ferritin and interleukin-1 receptor antagonist, which gave an NRI of 25.4% (p<0.0001). Among women, the best score included adiponectin, apolipoprotein B, C-reactive protein and insulin. It gave an NRI of 13.6% (p = 0.041).
ConclusionsWe identified novel biomarkers that were associated with the risk of clinically incident diabetes over and above the classic risk factors. This gives new insights into the pathogenesis of diabetes and may help with targeting prevention and treatment.

背景：糖尿病患病率在所有工业化国家均呈上升趋势，其预防已成为公共卫生领域的优先工作。然而，当前对糖尿病风险预测因子的认识仍存在不足。本研究旨在评估31种新型生物标志物是否有助于预测新发糖尿病风险。

方法与结果：本研究首先在FINRISK97队列（n=7827；随访期间共发生417例临床确诊新发糖尿病病例）中评估上述生物标志物，随后在Health 2000队列（n=4977；随访期间共发生179例临床确诊新发糖尿病病例）中对研究结果进行了重复验证。我们针对每种生物标志物，在校正经典糖尿病风险因素后，采用Cox比例风险模型（Cox proportional hazards models）计算糖尿病相对风险。随后，我们通过受试者工作特征曲线（Receiver Operating Characteristic curves, ROC曲线）、C统计量（C-statistics）、综合判别改善度（Integrated Discrimination Improvement, IDI）以及净重新分类改善度（Net Reclassification Improvement, NRI）评估单个生物标志物的判别能力。最后，我们在FINRISK97队列中构建了生物标志物评分模型，并在Health 2000队列中对其进行验证。由脂联素（adiponectin）、载脂蛋白B（apolipoprotein B）、C反应蛋白（C-reactive protein）及铁蛋白（ferritin）组成的评分模型，在验证队列中使糖尿病相对风险提升近一倍（每1个标准差升高对应的风险比（Hazard Ratio, HR）为1.88，p=2.8×10^-5）。该模型同时提升了判别能力（IDI=0.0149，p<0.0001）与糖尿病风险的重新分类效能（NRI=11.8%，p=0.006）。性别分层分析结果显示，最优生物标志物评分模型存在性别差异。在男性群体中，由脂联素、载脂蛋白B、铁蛋白及白细胞介素-1受体拮抗剂（interleukin-1 receptor antagonist）组成的评分模型效果最佳，其NRI达25.4%（p<0.0001）。在女性群体中，最优评分模型包含脂联素、载脂蛋白B、C反应蛋白及胰岛素，其NRI为13.6%（p=0.041）。

结论：本研究筛选出了独立于经典糖尿病风险因素、与临床新发糖尿病风险相关的新型生物标志物。该发现为糖尿病发病机制提供了新的研究视角，或可助力糖尿病的精准预防与个体化治疗。