Lundgaard Agnete T, Westergaard David, Röder Timo, Burgdorf Kristoffer S, Larsen Margit H, Schwinn Michael, Thørner Lise W, Sørensen Erik, Nielsen Kaspar R, Hjalgrim Henrik, Erikstrup Christian, Kjerulff Bertram D, Hindhede Lotte, Hansen Thomas F, Nyegaard Mette, Birney Ewan, Stefansson Hreinn, Stefánsson Kári, Pedersen Ole B V, Ostrowski Sisse R, Rossing Peter, Ullum Henrik, Mortensen Laust H, Vistisen Dorte, Banasik Karina, Brunak Søren
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Methods and Analysis, Statistics Denmark, Copenhagen, Denmark.
Diabetologia. 2024 Oct;67(10):2289-2303. doi: 10.1007/s00125-024-06231-3. Epub 2024 Jul 30.
AIMS/HYPOTHESIS: 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.
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.
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/INTERPRETATION: 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.
目的/假设：先前的研究表明，糖尿病的代谢风险因素和血浆生物标志物在临床诊断糖尿病之前就已发生变化。然而，这些标志物仅涵盖了与该疾病相关的一小部分分子生物标志物。在本研究中，我们旨在分析一套更全面的分子生物标志物，并探讨它们与新发糖尿病的时间关联。
在丹麦献血者研究（DBDS）中，我们对324例新发糖尿病患者和359例非糖尿病患者的三个连续样本中测量的54种蛋白质、171种代谢物和脂蛋白颗粒进行了靶向分析，这些样本的随访时间长达11年，且在性别和出生年份分布上相匹配。我们使用线性混合效应模型来确定糖尿病诊断前的时间变化，无论是针对任何新发糖尿病诊断，还是针对1型和2型糖尿病的诊断。我们进一步进行了线性和非线性特征选择，并将28个多基因风险评分添加到生物标志物库中。我们测试了具有最高变量重要性的生物标志物与选定的临床协变量和血浆葡萄糖相比，在事件发生时间预测方面的增益。
我们鉴定出两种蛋白质、16种代谢物和脂蛋白颗粒，其水平在糖尿病诊断前随时间发生变化，并且在错误发现率（FDR）调整后，估计的边际均值具有显著性。其中16种此前未曾被描述过。此外，75种生物标志物在糖尿病诊断前的几年中始终较高或较低。我们鉴定出一种1型糖尿病的时间生物标志物IL-17A/F，它是一种与多种其他自身免疫性疾病相关的细胞因子。与仅使用临床信息和血浆葡萄糖相比，纳入12种生物标志物可改善糖尿病诊断的10年预测（即受试者工作特征曲线下面积从0.79增加到0.84）。
结论/解读：在糖尿病诊断前数年，血浆中就会出现全身性分子变化。一部分特定的生物标志物呈现出独特的、随时间变化的模式，具有作为糖尿病发病预测标志物的潜力。值得注意的是，这些生物标志物在1型糖尿病和2型糖尿病之间呈现出共同和独特的模式。经过独立验证后，我们的发现可用于开发新的临床预测模型。
