Abstract：
OBJECTIVE: Metabolic risk factors and plasma biomarkers for diabetes have previously been shown to change prior to a clinical diabetes diagnosis. However, these markers only cover a small subset of molecular biomarkers linked to the disease. In this study, we aimed to profile a more comprehensive set of molecular biomarkers and explore their temporal association with incident diabetes.
METHODS: We performed a targeted analysis of 54 proteins and 171 metabolites and lipoprotein particles measured in three sequential samples spanning up to 11 years of follow-up in 324 individuals with incident diabetes and 359 individuals without diabetes in the Danish Blood Donor Study (DBDS) matched for sex and birth year distribution. We used linear mixed-effects models to identify temporal changes before a diabetes diagnosis, either for any incident diabetes diagnosis or for type 1 and type 2 diabetes mellitus diagnoses specifically. We further performed linear and non-linear feature selection, adding 28 polygenic risk scores to the biomarker pool. We tested the time-to-event prediction gain of the biomarkers with the highest variable importance, compared with selected clinical covariates and plasma glucose.
RESULTS: We identified two proteins and 16 metabolites and lipoprotein particles whose levels changed temporally before diabetes diagnosis and for which the estimated marginal means were significant after FDR adjustment. Sixteen of these have not previously been described. Additionally, 75 biomarkers were consistently higher or lower in the years before a diabetes diagnosis. We identified a single temporal biomarker for type 1 diabetes, IL-17A/F, a cytokine that is associated with multiple other autoimmune diseases. Inclusion of 12 biomarkers improved the 10-year prediction of a diabetes diagnosis (i.e. the area under the receiver operating curve increased from 0.79 to 0.84), compared with clinical information and plasma glucose alone.
CONCLUSIONS: Systemic molecular changes manifest in plasma several years before a diabetes diagnosis. A particular subset of biomarkers shows distinct, time-dependent patterns, offering potential as predictive markers for diabetes onset. Notably, these biomarkers show shared and distinct patterns between type 1 diabetes and type 2 diabetes. After independent replication, our findings may be used to develop new clinical prediction models.
METHODS: We performed a targeted analysis of 54 proteins and 171 metabolites and lipoprotein particles measured in three sequential samples spanning up to 11 years of follow-up in 324 individuals with incident diabetes and 359 individuals without diabetes in the Danish Blood Donor Study (DBDS) matched for sex and birth year distribution. We used linear mixed-effects models to identify temporal changes before a diabetes diagnosis, either for any incident diabetes diagnosis or for type 1 and type 2 diabetes mellitus diagnoses specifically. We further performed linear and non-linear feature selection, adding 28 polygenic risk scores to the biomarker pool. We tested the time-to-event prediction gain of the biomarkers with the highest variable importance, compared with selected clinical covariates and plasma glucose.
RESULTS: We identified two proteins and 16 metabolites and lipoprotein particles whose levels changed temporally before diabetes diagnosis and for which the estimated marginal means were significant after FDR adjustment. Sixteen of these have not previously been described. Additionally, 75 biomarkers were consistently higher or lower in the years before a diabetes diagnosis. We identified a single temporal biomarker for type 1 diabetes, IL-17A/F, a cytokine that is associated with multiple other autoimmune diseases. Inclusion of 12 biomarkers improved the 10-year prediction of a diabetes diagnosis (i.e. the area under the receiver operating curve increased from 0.79 to 0.84), compared with clinical information and plasma glucose alone.
CONCLUSIONS: Systemic molecular changes manifest in plasma several years before a diabetes diagnosis. A particular subset of biomarkers shows distinct, time-dependent patterns, offering potential as predictive markers for diabetes onset. Notably, these biomarkers show shared and distinct patterns between type 1 diabetes and type 2 diabetes. After independent replication, our findings may be used to develop new clinical prediction models.
摘要：
目的：糖尿病的代谢危险因素和血浆生物标志物在临床糖尿病诊断之前已经显示出变化。然而,这些标记仅覆盖了与疾病相关的一小部分分子生物标志物.在这项研究中,我们旨在分析一组更全面的分子生物标志物,并探讨它们与糖尿病发病的时间关联.
方法：我们在丹麦献血者研究（DBDS）中对性别和出生年份分布相匹配的324例糖尿病患者和359例非糖尿病患者进行了长达11年随访的三个连续样本中测量的54种蛋白质和171种代谢物和脂蛋白颗粒的靶向分析。我们使用线性混合效应模型来识别糖尿病诊断前的时间变化，对于任何意外糖尿病诊断或特别是1型和2型糖尿病诊断。我们进一步进行了线性和非线性特征选择，在生物标志物池中增加28项多基因风险评分。我们测试了具有最高变量重要性的生物标志物的事件时间预测增益，与选定的临床协变量和血浆葡萄糖进行比较。
结果：我们确定了2种蛋白质和16种代谢物和脂蛋白颗粒，其水平在糖尿病诊断前发生了时间变化，并且在FDR调整后估计的边缘均值具有统计学意义。其中16个以前没有描述过。此外,在糖尿病诊断之前的几年中，有75种生物标志物始终较高或较低。我们确定了1型糖尿病的单一时间生物标志物，IL-17A/F,与多种其他自身免疫性疾病相关的细胞因子。纳入12种生物标志物改善了糖尿病诊断的10年预测（即受试者工作曲线下的面积从0.79增加到0.84）。与单独的临床信息和血浆葡萄糖进行比较。
结论：在糖尿病诊断前几年，血浆中出现了系统性分子变化。一个特定的生物标志物子集显示出不同的，时间依赖的模式，提供作为糖尿病发病的预测标志物的潜力。值得注意的是,这些生物标志物在1型糖尿病和2型糖尿病之间显示出共同和不同的模式.独立复制后，我们的发现可用于开发新的临床预测模型.
方法：我们在丹麦献血者研究（DBDS）中对性别和出生年份分布相匹配的324例糖尿病患者和359例非糖尿病患者进行了长达11年随访的三个连续样本中测量的54种蛋白质和171种代谢物和脂蛋白颗粒的靶向分析。我们使用线性混合效应模型来识别糖尿病诊断前的时间变化，对于任何意外糖尿病诊断或特别是1型和2型糖尿病诊断。我们进一步进行了线性和非线性特征选择，在生物标志物池中增加28项多基因风险评分。我们测试了具有最高变量重要性的生物标志物的事件时间预测增益，与选定的临床协变量和血浆葡萄糖进行比较。
结果：我们确定了2种蛋白质和16种代谢物和脂蛋白颗粒，其水平在糖尿病诊断前发生了时间变化，并且在FDR调整后估计的边缘均值具有统计学意义。其中16个以前没有描述过。此外,在糖尿病诊断之前的几年中，有75种生物标志物始终较高或较低。我们确定了1型糖尿病的单一时间生物标志物，IL-17A/F,与多种其他自身免疫性疾病相关的细胞因子。纳入12种生物标志物改善了糖尿病诊断的10年预测（即受试者工作曲线下的面积从0.79增加到0.84）。与单独的临床信息和血浆葡萄糖进行比较。
结论：在糖尿病诊断前几年，血浆中出现了系统性分子变化。一个特定的生物标志物子集显示出不同的，时间依赖的模式，提供作为糖尿病发病的预测标志物的潜力。值得注意的是,这些生物标志物在1型糖尿病和2型糖尿病之间显示出共同和不同的模式.独立复制后，我们的发现可用于开发新的临床预测模型.
