BackgroundDiabetes mellitus (DM) is a prevalent chronic disease marked by significant metabolic dysfunctions. Understanding its molecular mechanisms is vital for early diagnosis and treatment strategies.MethodsWe used datasets GSE7014, GSE25724, and GSE156248 from the GEO database to build a diagnostic model for DM using Random Forest (RF) and LASSO regression models. GSE20966 served as a validation cohort. DM patients were classified into two subtypes for functional enrichment analysis. Expression levels of key diagnostic genes were validated using quantitative real‐time PCR (qRT‐PCR) on Peripheral Blood Mononuclear Cells (PBMCs) from DM patients and healthy controls, focusing on CXCL12 and PPP1R12B with GAPDH as the internal control.ResultsAfter de‐batching the datasets, we identified 131 differentially expressed genes (DEGs) between DM and control groups, with 70 up‐regulated and 61 down‐regulated. Enrichment analysis revealed significant down‐regulation in the IL‐12 signaling pathway, JAK signaling post‐IL‐12 stimulation, and the ferroptosis pathway in DM. Five genes (CXCL12, MXRA5, UCHL1, PPP1R12B, and C7) were identified as having diagnostic value. The diagnostic model showed high accuracy in both the training and validation cohorts. The gene set also enabled the subclassification of DM patients into groups with distinct functional traits. qRT‐PCR results confirmed the bioinformatics findings, particularly the up‐regulation of CXCL12 and PPP1R12B in DM patients.ConclusionOur study pinpointed seven energy metabolism‐related genes differentially expressed in DM and controls, with five holding diagnostic value. Our model accurately diagnosed DM and facilitated patient subclassification, offering new insights into DM pathogenesis.

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基于机器学习算法的糖尿病代谢相关诊断模型的构建与评估

背景糖尿病（DM）是一种普遍存在的慢性疾病，其特征是显着的代谢功能障碍。了解其分子机制对于早期诊断和治疗策略至关重要。方法我们使用 GEO 数据库中的数据集 GSE7014、GSE25724 和 GSE156248，使用随机森林 (RF) 和 LASSO 回归模型构建 DM 诊断模型。 GSE20966 作为验证队列。糖尿病患者被分为两个亚型进行功能丰富分析。使用定量实时 PCR (qRT-PCR) 对来自 DM 患者和健康对照的外周血单核细胞 (PBMC) 验证关键诊断基因的表达水平，重点关注 CXCL12 和 PPP1R12B，以 GAPDH 作为内参。结果分批后通过数据集，我们确定了 DM 组和对照组之间的 131 个差异表达基因 (DEG)，其中 70 个上调，61 个下调。富集分析显示，DM 中 IL-12 信号通路、IL-12 刺激后的 JAK 信号通路以及铁死亡通路显着下调。五个基因（CXCL12、MXRA5、UCHL1、PPP1R12B 和 C7）被确定为具有诊断价值。该诊断模型在训练和验证队列中都显示出很高的准确性。该基因集还能够将糖尿病患者细分为具有不同功能特征的组。 qRT-PCR 结果证实了生物信息学研究结果，特别是 DM 患者中 CXCL12 和 PPP1R12B 的上调。 结论我们的研究确定了 7 个在 DM 和对照组中差异表达的能量代谢相关基因，其中 5 个具有诊断价值。我们的模型准确诊断了 DM 并促进了患者的亚分类，为 DM 发病机制提供了新的见解。