Department of Endocrinology, The Second Affiliated Hospital of Bengbu Medical University, 233040, Bengbu, Anhui, China.
Department of General Medicine, The Second Affiliated Hospital of Bengbu Medical University, 233040, Bengbu, Anhui, China.
BMC Endocr Disord. 2024 Mar 4;24(1):28. doi: 10.1186/s12902-024-01557-w.
This study aimed to examine the diagnostic predictive value of long non-coding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1(MALAT1) and NOD-like receptor protein 3(NLRP3) expression in patients with type 2 diabetes mellitus(T2DM) and lower extremity atherosclerosis disease (LEAD).
A total of 162 T2DM patients were divided into T2DM with LEAD group (T2DM + LEAD group) and T2DM alone group (T2DM group). The lncRNA MALAT1 and NLRP3 expression levels were measured in peripheral blood, and their correlation was examined. Least absolute shrinkage and selection operator (LASSO) regression model was used to screen for the best predictors of LEAD, and multivariate logistic regression was used to establish a predictive model and construct the nomogram. The effectiveness of the nomogram was assessed using the receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve, and decision curve analysis (DCA).
The levels of the lncRNA MALAT1 and NLRP3 in the T2DM + LEAD group were significantly greater than those in the T2DM group (P <0.001), and the level of the lncRNA MALAT1 was positively correlated with that of NLRP3 (r = 0.453, P<0.001). The results of the LASSO combined with the logistic regression analysis showed that age, smoking, systolic blood pressure (SBP), NLRP3, and MALAT1 were the influencing factors of T2DM with LEAD(P<0.05). ROC curve analysis comparison: The discriminatory ability of the model (AUC = 0.898), MALAT1 (AUC = 0.804), and NLRP3 (AUC = 0.794) was greater than that of the other indicators, and the predictive value of the model was the greatest. Calibration curve: The nomogram model was consistent in predicting the occurrence of LEAD in patients with T2DM (Cindex = 0.898). Decision curve: The net benefit rates obtained from using the predictive models for clinical intervention decision-making were greater than those obtained from using the individual factors within the model.
MALAT1 and NLRP3 expression increased significantly in T2DM patients with LEAD, while revealing the correlation between MALAT1 and NLRP3. The lncRNA MALAT1 was found as a potential biomarker for T2DM with LEAD.
本研究旨在探讨长链非编码 RNA（lncRNA）肺腺癌转移相关 lncRNA1（MALAT1）和核苷酸结合寡聚结构域样受体蛋白 3（NLRP3）表达在 2 型糖尿病（T2DM）合并下肢动脉粥样硬化（LEAD）患者中的诊断预测价值。
将 162 例 T2DM 患者分为 T2DM 合并 LEAD 组（T2DM+LEAD 组）和 T2DM 组。检测外周血中 lncRNA MALAT1 和 NLRP3 的表达水平，并分析其相关性。采用最小绝对收缩和选择算子（LASSO）回归模型筛选 LEAD 的最佳预测因子，采用多变量逻辑回归建立预测模型并构建列线图。采用受试者工作特征（ROC）曲线、曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估列线图的有效性。
T2DM+LEAD 组 lncRNA MALAT1 和 NLRP3 水平明显高于 T2DM 组（P<0.001），lncRNA MALAT1 水平与 NLRP3 水平呈正相关（r=0.453，P<0.001）。LASSO 结合 logistic 回归分析结果显示，年龄、吸烟、收缩压（SBP）、NLRP3 和 MALAT1 是 T2DM 合并 LEAD 的影响因素（P<0.05）。ROC 曲线分析比较：模型（AUC=0.898）、MALAT1（AUC=0.804）和 NLRP3（AUC=0.794）的鉴别能力大于其他指标，模型预测价值最大。校准曲线：列线图模型在预测 T2DM 患者 LEAD 发生情况方面具有一致性（Cindex=0.898）。决策曲线：通过预测模型进行临床干预决策的净获益率大于模型中个体因素的净获益率。
T2DM 合并 LEAD 患者 MALAT1 和 NLRP3 表达显著升高，同时揭示了 MALAT1 与 NLRP3 之间的相关性。lncRNA MALAT1 可能是 T2DM 合并 LEAD 的潜在生物标志物。
