The 2 major forms of periodontitis, chronic (CP) and aggressive (AgP), do not display sufficiently distinct histopathological characteristics or microbiological/immunological features. We used molecular profiling to explore biological differences between CP and AgP and subsequently carried out supervised classification using machine-learning algorithms including an internal validation. We used whole-genome gene expression profiles from 310 ‘healthy’ or ‘diseased’ gingival tissue biopsies from 120 systemically healthy non-smokers, 65 with CP and 55 with AgP, each contributing with Ã¢â€°Â¥ 2 ‘diseased’ gingival papillae (n = 241; with bleeding-on-probing, probing depth Ã¢â€°Â¥ 4 mm, and clinical attachment loss Ã¢â€°Â¥ 3 mm), and, when available, a ‘healthy’ papilla (n = 69; no bleeding-on-probing, probing depth Ã¢â€°Â¤ 4 mm, and clinical attachment loss Ã¢â€°Â¤ 4 mm). Our analyses revealed limited differences between the gingival tissue transcriptional profiles of AgP and CP, with genes related to immune responses, apoptosis, and signal transduction overexpressed in AgP, and genes related to epithelial integrity and metabolism overexpressed in CP. Different classifying algorithms discriminated CP from AgP with an area under the curve ranging from 0.63 to 0.99. The small differences in gene expression and the highly variable classifier performance suggest limited dissimilarities between established AgP and CP lesions. Future analyses may facilitate the development of a novel, ‘intrinsic’ classification of periodontitis based on molecular profiling.
Bibliographical noteFunding Information:
This research was supported by the National Institutes of Health (NIH grants DE-015649 , DE-021820 , UL1-TR000040 ) and Colgate-Palmolive, NJ, USA. MK was also supported by the German Research Foundation (KFO208, TP6 & TP9), the German Society for Periodontology (DGParo), the German Society for Oral and Maxillofacial Sciences (DGZMK), and the Neue Gruppe; RTD by NIH K99 DE-018739 ; and PP by the Michael Smith Foundation for Health Research.
- gene expression
- machine learning
- microarray analysis