Early prediction of orthodontic gingival enlargement using S100A4: a biomarker-based risk stratification model.

IF 2.4 3区 医学 Q2 DENTISTRY, ORAL SURGERY & MEDICINE
Víctor Simancas-Escorcia, Jaime Plazas-Román, Antonio Díaz-Caballero, Adel Martínez-Martínez, Carlos M Ardila
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引用次数: 0

Abstract

Orthodontic-induced gingival enlargement (OIGE) affects approximately 15-30% of patients undergoing orthodontic treatment and remains largely unpredictable, often relying on subjective clinical assessments made after irreversible tissue changes have occurred. S100A4 is a well-characterized marker of activated fibroblasts involved in pathological tissue remodeling. This was a cross-sectional precision biomarker study that analyzed gingival tissue samples from three groups: healthy controls (n = 60), orthodontic patients without gingival enlargement (n = 31), and patients with clinically diagnosed OIGE (n = 61). Immunohistochemical analysis quantified S100A4-positive fibroblasts, type I collagen synthesis, and microvascular density. Advanced statistical analyses included multivariate logistic regression, machine learning-based validation, causal mediation analysis, and survival modeling for risk stratification. The density of S100A4-positive fibroblasts was significantly higher in OIGE patients (245.8 ± 38.7 cells/mm2) compared to orthodontic controls (165.3 ± 29.4 cells/mm2) and healthy individuals (98.2 ± 18.5 cells/mm2) (p < 0.001; η2 = 0.891). Multivariate analysis confirmed S100A4 as an independent predictor of OIGE (OR = 1.028 per cell/mm2; 95%CI 1.021-1.035; p < 0.001). Machine learning validation demonstrated high predictive accuracy (AUC = 0.946). Survival analysis identified distinct risk strata: individuals with S100A4 densities > 180 cells/mm2 had a 78% probability of developing OIGE within 24 months, compared to 12% for those with < 130 cells/mm2. S100A4 demonstrates 95% predictive accuracy for OIGE, supporting its role in personalized risk stratification and early preventive interventions during a defined therapeutic window. This study presents the first validated precision biomarker in orthodontics with the potential to prevent an estimated 180,000-360,000 OIGE cases globally each year.

使用S100A4早期预测正畸牙龈肿大:基于生物标志物的风险分层模型。
正畸诱导的牙龈肿大(OIGE)影响了大约15-30%接受正畸治疗的患者,并且在很大程度上是不可预测的,通常依赖于在发生不可逆的组织改变后进行的主观临床评估。S100A4是参与病理组织重塑的活化成纤维细胞的特征标记物。这是一项横断面精确生物标志物研究,分析了来自三组的牙龈组织样本:健康对照组(n = 60)、没有牙龈肿大的正畸患者(n = 31)和临床诊断为OIGE的患者(n = 61)。免疫组织化学分析定量s100a4阳性成纤维细胞、I型胶原合成和微血管密度。高级统计分析包括多元逻辑回归、基于机器学习的验证、因果中介分析和风险分层的生存模型。OIGE患者的s100a4阳性成纤维细胞密度(245.8±38.7 cells/mm2)明显高于正畸对照组(165.3±29.4 cells/mm2)和健康人(98.2±18.5 cells/mm2) (p = 0.891)。多变量分析证实S100A4是OIGE的独立预测因子(OR = 1.028 / cells/mm2; 95%CI 1.021-1.035; p 180 cells/mm2), 24个月内发生OIGE的概率为78%,而2个月内发生OIGE的概率为12%。S100A4对OIGE的预测准确率为95%,支持其在个性化风险分层和确定治疗窗口期早期预防干预中的作用。这项研究提出了正畸学中第一个经过验证的精确生物标志物,有可能预防全球每年约18万至36万例OIGE病例。
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来源期刊
Odontology
Odontology 医学-牙科与口腔外科
CiteScore
5.30
自引率
4.00%
发文量
91
审稿时长
>12 weeks
期刊介绍: The Journal Odontology covers all disciplines involved in the fields of dentistry and craniofacial research, including molecular studies related to oral health and disease. Peer-reviewed articles cover topics ranging from research on human dental pulp, to comparisons of analgesics in surgery, to analysis of biofilm properties of dental plaque.
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