Yao Huiyu, Cao Zixin, Huang Liangfu, Pan Haojie, Xu Xiaomin, Sun Fucai, Ding Xi, Wu Wan
Department of Stomatology, The First Affiliated Hospital of Wenzhou Medical University, Nanbaixiang Ouhai District, Wenzhou, Zhejiang, 325000, People's Republic of China.
BMC Oral Health. 2025 May 10;25(1):703. doi: 10.1186/s12903-025-06095-y.
Oral mucosal lesions are widespread globally, have a high prevalence in clinical practice, and significantly impact patients' quality of life. However, their pathogenesis remains unclear. Recent evidences suggested that hematological parameters may play a role in their development. Our study investigated the differences in humoral immune indexes, serum vitamin B levels, and micronutrients among patients with oral mucosal lesions and healthy controls. Additionally, it evaluated a Random Forest machine learning model for classifying various oral mucosal diseases based on peripheral blood biomarkers.
We recruited 237 patients with recurrent aphthous ulcers (RAU), 35 with oral lichen planus (OLP), 67 with atrophic glossitis (AG), 35 with burning mouth syndrome (BMS), and 82 healthy controls. Clinical data were analyzed by SPSS 24 software. Serum levels of immunoglobulins (IgG, IgA, IgM), complements (C3, C4), vitamin B (VB1, VB2, VB3, VB5), serum zinc (Serum Zn), serum iron (Serum Fe), unsaturated iron-binding capacity (UIBC), total iron-binding capacity (TIBC), and iron saturation (Iron Sat) were measured and compared among groups. A Random Forest model was applied to analyze a dataset comprising 319 samples with eight key biomarkers.
Significant differences were observed between the oral mucosal diseases groups and controls in the serum levels of VB2, VB3, VB5, zinc, iron, TIBC, and Iron Sat. Specifically, serum levels of VB2 and VB3 were significantly higher in patients compared to controls (*p < 0.05), while levels of VB5, Serum Zn, Serum Fe, TIBC, and Iron Sat were significantly lower (*p < 0.05). No significant differences were found for C3, C4, IgG, IgM, IgA, VB1, and UIBC. The optimized Random Forest model demonstrated high performance, and effectively classified different disease groups, though some overlap between groups was noted. Feature importance analysis, based on the Mean Decrease Accuracy and Gini Index, identified VB2, VB3, Serum Fe, TIBC, and Serum Zn as key biomarkers, indicating their potential in distinguishing oral mucosal diseases.
Our study identified significant associations between the contents of VB2, VB3, VB5, Serum Fe, Serum Zn, and other micronutrients and oral mucosal lesions. It suggested that regulating these micronutrient levels could be essential for preventing and curing such lesions. The Random Forest model demonstrated high accuracy (94.68%) in classifying disease groups, emphasizing the potential of machine learning to enhance diagnostic precision in oral mucosal diseases. Future research should focus on validating these findings in larger cohorts and exploring alternative machine-learning algorithms to improve diagnostic accuracy further.
口腔黏膜病变在全球范围内广泛存在，在临床实践中患病率较高，对患者的生活质量有显著影响。然而，其发病机制仍不清楚。最近的证据表明，血液学参数可能在其发展中起作用。我们的研究调查了口腔黏膜病变患者和健康对照者在体液免疫指标、血清维生素B水平和微量营养素方面的差异。此外，还评估了一种基于外周血生物标志物对各种口腔黏膜疾病进行分类的随机森林机器学习模型。
我们招募了237例复发性阿弗他溃疡（RAU）患者、35例口腔扁平苔藓（OLP）患者、67例萎缩性舌炎（AG）患者、35例灼口综合征（BMS）患者和82例健康对照者。临床数据采用SPSS 24软件进行分析。测量并比较了各组血清免疫球蛋白（IgG、IgA、IgM）、补体（C3、C4）、维生素B（VB1、VB2、VB3、VB5）、血清锌（Serum Zn）、血清铁（Serum Fe）、不饱和铁结合能力（UIBC）、总铁结合能力（TIBC）和铁饱和度（Iron Sat）水平。应用随机森林模型分析了一个包含319个样本和8个关键生物标志物的数据集。
口腔黏膜疾病组与对照组在VB2、VB3、VB5、锌、铁、TIBC和铁饱和度的血清水平上存在显著差异。具体而言，患者血清VB2和VB3水平显著高于对照组（*p < 0.05），而VB5、血清锌、血清铁、TIBC和铁饱和度水平显著低于对照组（*p < 0.05）。C3、C4、IgG、IgM、IgA、VB1和UIBC未发现显著差异。优化后的随机森林模型表现出高性能，能够有效区分不同疾病组，尽管组间存在一些重叠。基于平均减少准确率和基尼指数的特征重要性分析确定VB2、VB3、血清铁、TIBC和血清锌为关键生物标志物，表明它们在区分口腔黏膜疾病方面的潜力。
我们的研究发现VB2、VB3、VB5、血清铁、血清锌和其他微量营养素含量与口腔黏膜病变之间存在显著关联。这表明调节这些微量营养素水平可能对预防和治疗此类病变至关重要。随机森林模型在疾病组分类中显示出较高的准确率（94.68%），强调了机器学习在提高口腔黏膜疾病诊断准确性方面的潜力。未来的研究应集中在更大的队列中验证这些发现，并探索替代的机器学习算法以进一步提高诊断准确性。
