Pediatric and Public Health, Adams School of Dentistry and Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC.
Oral and Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC.
J Periodontol. 2020 Oct;91 Suppl 1:S50-S55. doi: 10.1002/JPER.20-0096. Epub 2020 Jun 25.
Medicine and dentistry need to treat the individual not the "average patient." This personalized or precision approach to health care involves correctly diagnosing and properly classifying people to effectively customize prevention, diagnosis, and treatment. This is not a trivial undertaking. Achieving precision health requires making sense of big data, both at the population level and at the molecular level. The latter can include genetic, epigenetic, transcriptomic, proteomic, metabolomic data, and microbiome data. This biological information can augment established clinical measurements and supplement data on socioeconomic status, lifestyle, behaviors, and environmental conditions. Here, the central thesis is that, with sufficient data and appropriate methods, it is possible to segregate symptom-based and phenotypically based categories of patients into clinically and biologically similar groups. These groups are likely to have different clinical trajectories and benefit from different treatments. Additionally, such groups are optimal for investigations seeking to unveil the genomic basis of periodontal disease susceptibility. Analysis of these complex data to produce actionable and replicable health and disease categories requires appropriately sophisticated bioinformatics approaches and thorough validation in diverse patient samples and populations. Successful research programs will need to consider both population-level and well-controlled deep phenotyping approaches. Biologically informed stratification of periodontal disease is both feasible and desirable. Ultimately, this approach can accelerate the development of precision health through improvements in research and clinical applications.
医学和牙科需要治疗个体，而不是“平均患者”。这种个性化或精准医疗方法涉及正确诊断和适当分类，以有效地定制预防、诊断和治疗方案。这不是一件微不足道的事情。实现精准医疗需要从人群水平和分子水平两个方面理解大数据。后者可以包括遗传、表观遗传、转录组学、蛋白质组学、代谢组学数据和微生物组数据。这些生物信息可以补充既定的临床测量结果，并补充有关社会经济地位、生活方式、行为和环境条件的数据。在这里，核心论点是，只要有足够的数据和适当的方法，就有可能将基于症状和表型的患者类别划分为具有临床和生物学相似性的群组。这些群组可能具有不同的临床轨迹，并且受益于不同的治疗方法。此外，这些群组非常适合探索牙周病易感性的基因组基础的研究。为了产生可操作和可复制的健康和疾病类别，需要对这些复杂数据进行适当复杂的生物信息学分析，并在不同的患者样本和人群中进行彻底验证。成功的研究计划将需要考虑人群水平和精心控制的深度表型方法。基于生物学的牙周病分层既可行又可取。最终，这种方法可以通过改进研究和临床应用来加速精准医疗的发展。
