Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, China.
Front Endocrinol (Lausanne). 2023 Jul 28;14:1227260. doi: 10.3389/fendo.2023.1227260. eCollection 2023.
Our previous cross-sectional study has demonstrated the independently non-linear relationship between fasting C-peptide with renal dysfunction odds in patients with type 2 diabetes (T2D) in China. This longitudinal observational study aims to explore the role of serum C-peptide in risk prediction of new-onset renal dysfunction, then construct a predictive model based on serum C-peptide and other clinical parameters.
The patients with T2D and normal renal function at baseline were recruited in this study. The LASSO algorithm was performed to filter potential predictors from the baseline variables. Logistic regression (LR) was performed to construct the predictive model for new-onset renal dysfunction risk. Power analysis was performed to assess the statistical power of the model.
During a 2-year follow-up period, 21.08% (35/166) of subjects with T2D and normal renal function at baseline progressed to renal dysfunction. Six predictors were determined using LASSO regression, including baseline albumin-to-creatinine ratio, glycated hemoglobin, hypertension, retinol-binding protein-to-creatinine ratio, quartiles of fasting C-peptide, and quartiles of fasting C-peptide to 2h postprandial C-peptide ratio. These 6 predictors were incorporated to develop model for renal dysfunction risk prediction using LR. Finally, the LR model achieved a high efficiency, with an AUC of 0.83 (0.76 - 0.91), an accuracy of 75.80%, a sensitivity of 88.60%, and a specificity of 70.80%. According to the power analysis, the statistical power of the LR model was found to be 0.81, which was at a relatively high level. Finally, a nomogram was developed to make the model more available for individualized prediction in clinical practice.
Our results indicated that the baseline level of serum C-peptide had the potential role in the risk prediction of new-onset renal dysfunction. The LR model demonstrated high efficiency and had the potential to guide individualized risk assessments for renal dysfunction in clinical practice.
我们之前的横断面研究表明，在中国 2 型糖尿病（T2D）患者中，空腹 C 肽与肾功能障碍几率之间存在独立的非线性关系。本纵向观察性研究旨在探讨血清 C 肽在预测新发生肾功能障碍风险中的作用，然后基于血清 C 肽和其他临床参数构建预测模型。
本研究纳入了基线时肾功能正常的 T2D 患者。使用 LASSO 算法从基线变量中筛选潜在的预测因子。使用逻辑回归（LR）构建新发生肾功能障碍风险的预测模型。进行功效分析以评估模型的统计功效。
在 2 年的随访期间，基线时肾功能正常的 166 例 T2D 患者中有 21.08%（35/166）进展为肾功能障碍。LASSO 回归确定了 6 个预测因子，包括基线白蛋白与肌酐比值、糖化血红蛋白、高血压、视黄醇结合蛋白与肌酐比值、空腹 C 肽四分位数和空腹 C 肽与餐后 2 小时 C 肽比值四分位数。这些 6 个预测因子被纳入使用 LR 开发的肾功能障碍风险预测模型中。最终，LR 模型具有较高的效率，AUC 为 0.83（0.76-0.91），准确性为 75.80%，敏感性为 88.60%，特异性为 70.80%。根据功效分析，LR 模型的统计功效为 0.81，处于较高水平。最后，开发了一个列线图，使模型在临床实践中更便于个体化预测。
我们的研究结果表明，血清 C 肽的基线水平可能在预测新发生肾功能障碍的风险中发挥作用。LR 模型具有较高的效率，有可能指导临床实践中对肾功能障碍的个体化风险评估。
