Hu Hongying, Leung Wai Keung
State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Department of Oral Medical Imaging, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China.
Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
Int J Mol Sci. 2023 Sep 27;24(19):14599. doi: 10.3390/ijms241914599.
Periodontitis is one of the primary causes of tooth loss, and is also related to various systemic diseases. Early detection of this condition is crucial when it comes to preventing further oral damage and the associated health complications. This study offers a systematic review of the literature published up to April 2023, and aims to clearly explain the role of proteomics in identifying salivary biomarkers for periodontitis. Comprehensive searches were conducted on PubMed and Web of Science to shortlist pertinent studies. The inclusion criterion was those that reported on mass spectrometry-driven proteomic analyses of saliva samples from periodontitis cohorts, while those on gingivitis or other oral diseases were excluded. An assessment for risk of bias was carried out using the Newcastle-Ottawa Scale and Quality Assessment of Diagnostic Accuracy Studies or the NIH quality assessment tool, and a meta-analysis was performed for replicable candidate biomarkers, i.e., consistently reported candidate biomarkers (in specific saliva samples, and periodontitis subgroups, reported in ≥2 independent cohorts/reports) were identified. A Gene Ontology enrichment analysis was conducted using the Database for Annotation, Visualization, and Integrated Discovery bioinformatics resources, which consistently expressed candidate biomarkers, to explore the predominant pathway wherein salivary biomarkers consistently manifested. Of the 15 studies included, 13 were case-control studies targeting diagnostic biomarkers for periodontitis participants (periodontally healthy/diseased, = 342/432), while two focused on biomarkers responsive to periodontal treatment ( = 26 participants). The case-control studies were considered to have a low risk of bias, while the periodontitis treatment studies were deemed fair. Summary estimate and confidence/credible interval, etc. determination for the identified putative salivary biomarkers could not be ascertained due to the low number of studies in each case. The results from the included case-control studies identified nine consistently expressed candidate biomarkers (from nine studies with 230/297 periodontally healthy/diseased participants): (i) those that were upregulated: alpha-amylase, serum albumin, complement C3, neutrophil defensin, profilin-1, and S100-P; and (ii) those that were downregulated: carbonic anhydrase 6, immunoglobulin J chain, and lactoferrin. All putative biomarkers exhibited consistent regulation patterns. The implications of the current putative marker proteins identified were reviewed, with a focus on their potential roles in periodontitis diagnosis and pathogenesis, and as putative therapeutic targets. Although in its early stages, mass spectrometry-based salivary periodontal disease biomarker proteomics detection appeared promising. More mass spectrometry-based proteomics studies, with or without the aid of already available clinical biochemical approaches, are warranted to aid the discovery, identification, and validation of periodontal health/disease indicator molecule(s). Protocol registration number: CRD42023447722; supported by RD-02-202410 and GRF17119917.
牙周炎是牙齿缺失的主要原因之一，还与多种全身性疾病有关。尽早发现这种情况对于预防进一步的口腔损害及相关健康并发症至关重要。本研究对截至2023年4月发表的文献进行了系统综述，旨在清晰阐释蛋白质组学在识别牙周炎唾液生物标志物中的作用。在PubMed和Web of Science上进行了全面检索，以筛选相关研究。纳入标准为那些报告了对牙周炎队列唾液样本进行质谱驱动的蛋白质组学分析的研究，而关于牙龈炎或其他口腔疾病的研究则被排除。使用纽卡斯尔-渥太华量表以及诊断准确性研究的质量评估或美国国立卫生研究院质量评估工具对偏倚风险进行了评估，并对可重复的候选生物标志物进行了荟萃分析，即识别出始终被报告的候选生物标志物（在特定唾液样本和牙周炎亚组中，在≥2个独立队列/报告中被报告）。使用注释、可视化和综合发现生物信息学资源数据库对始终表达候选生物标志物的基因进行了本体富集分析，以探索唾液生物标志物始终表现的主要途径。在纳入的15项研究中，13项为针对牙周炎参与者（牙周健康/患病，n = 342/432）诊断生物标志物的病例对照研究，而两项研究聚焦于对牙周治疗有反应的生物标志物（n = 26名参与者）。病例对照研究被认为偏倚风险较低，而牙周炎治疗研究被认为质量一般。由于每个案例中的研究数量较少，无法确定已识别的假定唾液生物标志物的汇总估计值和置信/可信区间等。纳入的病例对照研究结果识别出9种始终表达的候选生物标志物（来自9项研究，涉及230/297名牙周健康/患病参与者）：（i）上调的标志物：α-淀粉酶、血清白蛋白、补体C3、中性粒细胞防御素、肌动蛋白结合蛋白-1和S100-P；以及（ii）下调的标志物：碳酸酐酶6、免疫球蛋白J链和乳铁蛋白。所有假定的生物标志物均表现出一致的调节模式。对当前识别出的假定标志物蛋白的意义进行了综述，重点关注它们在牙周炎诊断和发病机制中的潜在作用以及作为假定治疗靶点的可能性。尽管基于质谱的唾液牙周疾病生物标志物蛋白质组学检测尚处于早期阶段，但看起来很有前景。需要更多基于质谱的蛋白质组学研究，无论是否借助现有的临床生化方法，以助力牙周健康/疾病指示分子的发现、识别和验证。方案注册号：CRD42023447722；由RD - 02 - 202410和GRF17119917资助。
