Abdalsada Habiba Khdair, Abdulsaheb Yusra Sebri, Zolghadri Samaneh, Al-Hakeim Hussein Kadhem, Stanek Agata
Department of Clinical Laboratory Sciences, College of Pharmacy, Al-Muthanna University, Al-Muthanna 66001, Iraq.
Clinical Pharmacy Department, College of Pharmacy, Missan University, Missan 62001, Iraq.
Biomedicines. 2024 Sep 4;12(9):2015. doi: 10.3390/biomedicines12092015.
The search for new parameters for the prediction of type 2 diabetes mellitus (T2DM) or its harmful consequences remains an important field of study. Depending on the low-grade inflammatory nature of diabetes, we investigated three proteins in T2DM patients: 1-aminocyclopropane-1-carboxylate synthase (ACCS), granulocyte-colony-stimulating factor (G-CSF), and Sma Mothers Against Decapentaplegic homolog-4 (SMAD4). In brief, sixty T2DM and thirty healthy controls had their serum levels of ACCS, G-CSF, SMAD4, and insulin tested using the ELISA method. The insulin resistance (IR) parameter (HOMA2IR), beta-cell function percentage (HOMA2%B), and insulin sensitivity (HOMA2%S) were all determined by the Homeostasis Model Assessment-2 (HOMA2) calculator. The predictability of these protein levels was investigated by neural network (NN) analysis and was associated with measures of IR. Based on the results, ACCS, G-CSF, and SMAD4 increased significantly in the T2DM group compared with the controls. Their levels depend on IR status and inflammation. The multivariate GLM indicated the independence of the levels of these proteins on the covariates or drugs taken. The receiver operating characteristic area under the curve (AUC) for the prediction of T2DM using NN analysis is 0.902, with a sensitivity of 71.4% and a specificity of 93.8%. The network predicts T2DM well with predicted pseudoprobabilities over 0.5. The model's predictive capability (normalized importance) revealed that ACCS is the best model (100%) for the prediction of T2DM, followed by G-CSF (75.5%) and SMAD4 (69.6%). It can be concluded that ACCS, G-CSF, and SMAD4 are important proteins in T2DM prediction, and their increase is associated with the presence of inflammation.
寻找预测2型糖尿病（T2DM）或其有害后果的新参数仍然是一个重要的研究领域。鉴于糖尿病的低度炎症性质，我们研究了T2DM患者体内的三种蛋白质：1-氨基环丙烷-1-羧酸合酶（ACCS）、粒细胞集落刺激因子（G-CSF）和抗五聚体瘫痪同源蛋白4（SMAD4）。简而言之，采用酶联免疫吸附测定（ELISA）法检测了60例T2DM患者和30例健康对照者血清中的ACCS、G-CSF、SMAD4和胰岛素水平。胰岛素抵抗（IR）参数（HOMA2IR）、β细胞功能百分比（HOMA2%B）和胰岛素敏感性（HOMA2%S）均通过稳态模型评估-2（HOMA2）计算器进行测定。通过神经网络（NN）分析研究了这些蛋白质水平的可预测性，并将其与IR指标相关联。结果显示，与对照组相比，T2DM组的ACCS、G-CSF和SMAD4显著升高。它们的水平取决于IR状态和炎症。多变量广义线性模型表明这些蛋白质水平不受协变量或所服用药物的影响。使用NN分析预测T2DM的受试者工作特征曲线下面积（AUC）为0.902，敏感性为71.4%，特异性为93.8%。该网络能很好地预测T2DM，预测伪概率超过0.5。模型的预测能力（标准化重要性）显示，ACCS是预测T2DM的最佳模型（100%），其次是G-CSF（75.5%）和SMAD4（69.6%）。可以得出结论，ACCS、G-CSF和SMAD4是预测T2DM的重要蛋白质，它们的升高与炎症的存在有关。
