Yin Mengsha, Dong Wenke, Ren Linan, Han Mingyue, Wang Guixia, Wang Yao, Gang Xiaokun
Department of Endocrinology and Metabolism, The First Affiliated Hospital of Jilin University, Changchun, China.
Department of Medical Imaging Technology, Changzhi Medical College, Changzhi, China.
Front Endocrinol (Lausanne). 2025 May 5;16:1499866. doi: 10.3389/fendo.2025.1499866. eCollection 2025.
Diabetic retinopathy (DR) and diabetic kidney disease (DKD) are the most common microvascular complications associated with type 2 diabetes mellitus (T2DM). However, the occurrence of DR and DKD is not parallel. The aim of our study is to identify the risk factors for combining DKD in T2DM patients with pre-existing DR and construct a nomogram predictive model to identify high-risk patients with DR combined with DKD. We retrospectively reviewed 683 T2DM patients with DR from March 2017 to March 2023. The patients were divided into the DR group and the DR combined with DKD group. The hold-out method was used to randomly divide all subjects into a training set (70%) and a validation set (30%). Using multivariate logistic regression, we identified eight independent risk factors: fibrinogen (FIB), albumin (ALB), atherogenic index of plasma (AIP), low-density lipoprotein cholesterol (LDL-C), body mass index (BMI), classification of DR, gender, and history of hypertension. These factors were used to construct the nomogram prediction model. The model's discriminative ability was assessed using receiver operating characteristic (ROC) curve analysis, yielding an area under the curve (AUC) of 0.780 (95% CI: 0.736-0.823) in the training set and 0.739 (95% CI: 0.668-0.809) in the validation set. Calibration curves and decision curve analysis (DCA) further demonstrated the model's clinical utility. Additionally, to explore potential genetic predisposition, single nucleotide polymorphism (SNP) genotyping analysis was conducted on a subset of 50 randomly selected patients (25 from each group). The results suggested that the rs6591190 and rs12146493 loci of the AP5B1 gene might be associated with an increased susceptibility to DKD in patients with DR, warranting further investigation. In summary, our nomogram represents a valuable tool for identifying T2DM patients with DR who are at high risk for developing DKD.
糖尿病视网膜病变（DR）和糖尿病肾病（DKD）是2型糖尿病（T2DM）最常见的微血管并发症。然而，DR和DKD的发生并非平行。本研究的目的是确定已患有DR的T2DM患者合并DKD的危险因素，并构建列线图预测模型以识别DR合并DKD的高危患者。我们回顾性分析了2017年3月至2023年3月期间683例患有DR的T2DM患者。将患者分为DR组和DR合并DKD组。采用留出法将所有受试者随机分为训练集（70%）和验证集（30%）。通过多因素logistic回归，我们确定了8个独立危险因素：纤维蛋白原（FIB）、白蛋白（ALB）、血浆致动脉粥样硬化指数（AIP）、低密度脂蛋白胆固醇（LDL-C）、体重指数（BMI）、DR分级、性别和高血压病史。这些因素用于构建列线图预测模型。使用受试者工作特征（ROC）曲线分析评估模型的判别能力，训练集中曲线下面积（AUC）为0.780（95%CI：0.736 - 0.823），验证集中为0.739（95%CI：0.668 - 0.809）。校准曲线和决策曲线分析（DCA）进一步证明了该模型的临床实用性。此外，为了探索潜在的遗传易感性，对随机选择的50例患者（每组25例）的子集进行了单核苷酸多态性（SNP）基因分型分析。结果表明，AP5B1基因的rs6591190和rs12146493位点可能与DR患者发生DKD的易感性增加有关，值得进一步研究。总之，我们的列线图是识别有发生DKD高风险的DR的T2DM患者的有价值工具。
