慢性肾脏疾病（CKD）是糖尿病患者常见的合并症，并成为进一步危及生命的疾病如心血管疾病的主要风险因素。早期预测CKD进展是一项重要的临床目标，但由于其多方面的性质而难以实现。我们验证了一个已建立的蛋白质生物标志物组合，用于预测中度晚期慢性肾脏病和糖尿病患者的估计肾小球滤过率（eGFR）轨迹。我们的目标是确定哪些生物标志物与基线eGFR相关，或对未来eGFR轨迹的预测至关重要。我们将Bayesian线性混合模型用于临床预测因子（n = 12）和蛋白质生物标志物的收缩先验（n = 19）来对来自德国全国慢性肾脏疾病研究的糖尿病患者（n = 838）的eGFR轨迹进行建模。我们使用基线eGFR来更新模型的预测值，从而评估预测因子的重要性并提高了重复交叉验证计算的预测准确性。将临床和蛋白预测因子组合的模型比仅包含临床的模型具有更高的预测性能，在更新基线eGFR之前和之后的 [公式请参见原文] 分别为0.44（95%可信区间0.37-0.50）和0.59（95%可信区间0.51-0.65）。只有少数预测因子就足以获得与主模型相当的性能，如肿瘤坏死因子受体1和高级糖基化终末产物受体与基线eGFR相关，而肾损伤分子1和尿白蛋白/肌酐比例则预测未来的eGFR下降。与仅包含临床预测因子相比，蛋白质生物标志物只能适度提高预测准确性。不同的蛋白质标志物在预测纵向eGFR轨迹方面具有不同的作用，可能反映了它们在疾病途径中的作用。©2023.作者。

Chronic kidney disease (CKD) is a common comorbidity in people with diabetes mellitus, and a key risk factor for further life-threatening conditions such as cardiovascular disease. The early prediction of progression of CKD therefore is an important clinical goal, but remains difficult due to the multifaceted nature of the condition. We validated a set of established protein biomarkers for the prediction of trajectories of estimated glomerular filtration rate (eGFR) in people with moderately advanced chronic kidney disease and diabetes mellitus. Our aim was to discern which biomarkers associate with baseline eGFR or are important for the prediction of the future eGFR trajectory.We used Bayesian linear mixed models with weakly informative and shrinkage priors for clinical predictors (n = 12) and protein biomarkers (n = 19) to model eGFR trajectories in a retrospective cohort study of people with diabetes mellitus (n = 838) from the nationwide German Chronic Kidney Disease study. We used baseline eGFR to update the models' predictions, thereby assessing the importance of the predictors and improving predictive accuracy computed using repeated cross-validation.The model combining clinical and protein predictors had higher predictive performance than a clinical only model, with an [Formula: see text] of 0.44 (95% credible interval 0.37-0.50) before, and 0.59 (95% credible interval 0.51-0.65) after updating by baseline eGFR, respectively. Only few predictors were sufficient to obtain comparable performance to the main model, with markers such as Tumor Necrosis Factor Receptor 1 and Receptor for Advanced Glycation Endproducts being associated with baseline eGFR, while Kidney Injury Molecule 1 and urine albumin-creatinine-ratio were predictive for future eGFR decline.Protein biomarkers only modestly improve predictive accuracy compared to clinical predictors alone. The different protein markers serve different roles for the prediction of longitudinal eGFR trajectories potentially reflecting their role in the disease pathway.© 2023. The Author(s).