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The impact of diabetes on periodontal diseases
Abstract
The susceptibility and severity of periodontal diseases is made more severe by diabetes, with the impact on the disease process inversely proportional to the level of glycemic control. Although type 1 diabetes mellitus and type 2 diabetes mellitus have different etiologies, and their impact on bone is not identical, they share many of the same complications. Studies in animals and humans agree that both forms of diabetes increase inflammatory events in periodontal tissue, impair new bone formation, and increase expression of RANKL in response to bacterial challenge. High levels of glucose, reactive oxygen species, and advanced glycation end‐products are found in the periodontium of diabetic individuals and lead to increased activation of nuclear factor‐kappa B and expression of inflammatory cytokines such as tumor necrosis factor and interleukin‐1. Studies in animals, moreover, suggest that there are multiple cell types in periodontal tissues that are affected by diabetes, including leukocytes, vascular cells, mesenchymal stem cells, periodontal ligament fibroblasts, osteoblasts, and osteocytes. The etiology of periodontal disease involves the host response to bacterial challenge that is affected by diabetes, which increases the expression of RANKL and reduces coupled bone formation. In addition, the inflammatory response also modifies the oral microbiota to render it more pathogenic, as demonstrated by increased inflammation and bone loss in animals where bacteria are transferred from diabetic donors to germ‐free hosts compared with transfer from normoglycemic donors. This approach has the advantage of not relying upon limited knowledge of the specific bacterial taxa to determine pathogenicity, and examines the overall impact of the microbiota rather than the presumed pathogenicity of a few bacterial groups. Thus, animal studies have provided new insights into pathogenic mechanisms that identify cause‐and‐effect relationships that are difficult to perform in human studies.
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Self-citation
Cited by
Li, Y., Yang, L., Hou, Y., Zhang, Z., Chen, M., Wang, M., Liu, J., Wang, J., Zhao, Z., Xie, C., et al. (2022) · Bioactive Materials · doi:10.1016/j.bioactmat.2022.03.021 · Cited by 280
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Metadata
- Authors
- Graves, D. T., Ding, Z., & Yang, Y.
- Venue
- Periodontology 2000
- Publisher
- Wiley
- Published
- 2019-12-18
- Volume
- 82
- Issue
- 1
- Pages
- 214-224
- ISSN
- 0906-6713 (ISSN-L)
- 1600-0757
- OpenAlex
- W2994797447
- Cited by
- 398
- References
- 131
- FWCI
- 25.78
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.