Prediction of Periodontal Inflammation via Metabolic Profiling of Saliva
Prediction of Periodontal Inflammation via Metabolic Profiling of Saliva
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DOI：
10.1177/0022034516661142
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发表时间：
2016-11-01
影响因子：
7.6
通讯作者：
Amano, A.
中科院分区：
文献类型：
--
作者：
Kuboniwa, M.;Sakanaka, A.;Amano, A.
牙周病的特征在于龈下区域的慢性炎症，其中大量炎症相关代谢物可能由组织分解、血管通透性增加和微生物代谢产生，然后最终显示稳定流入唾液。因此，长期牙周炎是疾病活动的一个关键特征。虽然唾液代谢组学在牙周病诊断中的潜在应用引起了人们的关注，但很少有人使用它来研究牙周炎症检测。在这项初步研究中，作者探讨了使用唾液代谢物来反映牙周炎症的严重程度，最近提出的参数牙周炎表面积（比萨）用于量化牙周炎负担的个体患者的高准确性。比萨测定后，全唾液样本收集从19名受试者之前和之后的龈上菌斑和结石（清创术）与超声波洁治器，以评估唾液代谢谱的程序的影响。采用气相色谱-飞行时间质谱联用技术对唾液进行代谢谱分析，然后采用正交投影潜在结构多元回归分析（OPLS）研究比萨与唾液代谢谱之间的关系。鉴定了63种代谢产物。OPLS分析表明，清创后唾液提供了一个更精细的模型预测的比萨比清创前的样品，这表明清创可以提高检测的代谢物从龈下区域在唾液中洗脱，从而更准确地反映牙周炎的病理生理。基于通过OPLS获得的投影值中的变量重要性，8种代谢物被鉴定为牙周炎的潜在指标，其中尸胺、5-氧代脯氨酸和组氨酸的组合对于牙周炎的诊断产生了令人满意的准确性（曲线下面积= 0.881）。作者的研究结果确定了潜在的生物标志物，可能有助于反映牙周炎的严重程度，作为监测牙周炎患者疾病活动的一部分。
Periodontal disease is characterized by chronic inflammation in subgingival areas, where a vast array of inflammation-associated metabolites are likely produced from tissue breakdown, increased vascular permeability, and microbial metabolism and then eventually show a steady flow into saliva. Thus, prolonged periodontal inflammation is a key feature of disease activity. Although salivary metabolomics has drawn attention for its potential use in diagnosis of periodontal disease, few authors have used that to investigate periodontal inflammation detection. In this pilot study, the authors explored the use of salivary metabolites to reflect periodontal inflammation severity with a recently proposed parameterperiodontal inflamed surface area (PISA)used to quantify the periodontal inflammatory burden of individual patients with high accuracy. Following PISA determination, whole saliva samples were collected from 19 subjects before and after removal of supragingival plaque and calculus (debridement) with an ultrasonic scaler to assess the influence of the procedure on salivary metabolic profiles. Metabolic profiling of saliva was performed with gas chromatography coupled to time-of-flight mass spectrometry, followed by multivariate regression analysis with orthogonal projections to latent structures (OPLS) to investigate the relationship between PISA and salivary metabolic profiles. Sixty-three metabolites were identified. OPLS analysis showed that postdebridement saliva provided a more refined model for prediction of PISA than did predebridement samples, which indicated that debridement may improve detection of metabolites eluted from subgingival areas in saliva, thus more accurately reflecting the pathophysiology of periodontitis. Based on the variable importance in the projection values obtained via OPLS, 8 metabolites were identified as potential indicators of periodontal inflammation, of which the combination of cadaverine, 5-oxoproline, and histidine yielded satisfactory accuracy (area under the curve = 0.881) for diagnosis of periodontitis. The authors' findings identified potential biomarkers that may be useful for reflecting the severity of periodontal inflammation as part of monitoring disease activity in periodontitis patients.