Literature DB >> 11063391

Periodontal disease progression.

M S Reddy1, N C Geurs, R L Jeffcoat, H Proskin, M K Jeffcoat.   

Abstract

BACKGROUND: The objective of this investigation is to use noninvasive, state-of-the-art, diagnostic techniques to measure periodontal disease progression and model periodontal disease activity over time. In this investigation, digital subtraction radiography and an electronic controlled force periodontal probe capable of attachment level measurement were used to measure bone loss and attachment loss, respectively. The use of these nearly continuous measures of attachment and bone loss allowed detection of small amounts of disease activity and provided data to be used in modeling of the disease process over time.
METHODS: Forty-four patients were studied for 18 months. Examinations used clinical attachment level measures at 1-month intervals and quantitative radiology at 6-month intervals. The sites were analyzed by regression for statistically significant changes. These data were used to determine sites of periodontal disease activity for testing various models of periodontal disease progression.
RESULTS: Overall 22.8% of sites lost attachment, 5.4% gained, and 71.7% demonstrated no statistically significant change. The mean time to lose 1 mm of attachment was 8.4 +/- 0.6 months. In the first model tested a step-wise discriminant analysis was used to determine whether or not baseline measurements of plaque (PI), gingival inflammation (GI), attachment loss, and probing depth (PD) could be used to derive a satisfactory model for disease progression. Although the overall model was statistically significant with PI, PD, and GI contributing to the model (Wilks' lambda = 0.859, F = 5.71, P <0.0012), its predictive power was relatively weak. A considerably stronger significant model resulted when the rate of attachment loss over the first 6 months, baseline PI, and baseline GI were included (Wilks' lambda = 0.712, F = 14.17, P<0.00001). A significant model also resulted when bone loss during the first 6 months and baseline probing depth were included (Wilks' lambda = 0.438, F = 61.48, P<0.00001). When the last model was applied to each site, the sensitivity in predicting disease progression was 80.0% and the specificity in ruling out progressive disease was 93.9%.
CONCLUSIONS: This study indicates that clinically significant progression of attachment loss in posterior tooth sites occurs as a frequent event in adult periodontitis. The modeling data also suggest that short-term (6 month) measures of periodontal disease progression greatly improve the ability to model attachment loss over a longer period in untreated periodontitis patients.

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Mesh:

Year:  2000        PMID: 11063391     DOI: 10.1902/jop.2000.71.10.1583

Source DB:  PubMed          Journal:  J Periodontol        ISSN: 0022-3492            Impact factor:   6.993


  9 in total

1.  Inflammation and genetic risk indicators for early periodontitis in adults.

Authors:  Philip Stashenko; Thomas Van Dyke; Patrice Tully; Ralph Kent; Stephen Sonis; Anne C R Tanner
Journal:  J Periodontol       Date:  2010-11-08       Impact factor: 6.993

2.  Clinical and other risk indicators for early periodontitis in adults.

Authors:  Anne C R Tanner; Ralph Kent; Thomas Van Dyke; Steven T Sonis; Lora A Murray
Journal:  J Periodontol       Date:  2005-04       Impact factor: 6.993

3.  Changes in salivary matrix metalloproteinase-3, -8, and -9 concentrations after 6 weeks of non-surgical periodontal therapy.

Authors:  Han-Na Kim
Journal:  BMC Oral Health       Date:  2022-05-13       Impact factor: 3.747

Review 4.  Macrophage immunomodulation in chronic osteolytic diseases-the case of periodontitis.

Authors:  Corneliu Sima; Ana Viniegra; Michael Glogauer
Journal:  J Leukoc Biol       Date:  2018-11-19       Impact factor: 4.962

5.  Salivary biomarkers in the diagnosis of periodontal diseases.

Authors:  Jeffrey J Kim; Christine J Kim; Paulo M Camargo
Journal:  J Calif Dent Assoc       Date:  2013-02

6.  What is the influence of tonsillectomy on the level of periodontal pathogens on the tongue dorsum and in periodontal pockets.

Authors:  V N Diener; A Gay; M B Soyka; T Attin; P R Schmidlin; P Sahrmann
Journal:  BMC Oral Health       Date:  2018-04-06       Impact factor: 2.757

7.  A decision support system based on support vector machine for diagnosis of periodontal disease.

Authors:  Maryam Farhadian; Parisa Shokouhi; Parviz Torkzaban
Journal:  BMC Res Notes       Date:  2020-07-13

8.  Electronic Cigarette Use Promotes a Unique Periodontal Microbiome.

Authors:  Scott C Thomas; Fangxi Xu; Smruti Pushalkar; Xin Li; Deepak Saxena; Ziyan Lin; Nirali Thakor; Mridula Vardhan; Zia Flaminio; Alireza Khodadadi-Jamayran; Rebeca Vasconcelos; Adenike Akapo; Erica Queiroz; Maria Bederoff; Malvin N Janal; Yuqi Guo; Deanna Aguallo; Terry Gordon; Patricia M Corby; Angela R Kamer
Journal:  mBio       Date:  2022-02-22       Impact factor: 7.867

9.  Comparative Effects of E-Cigarette Aerosol on Periodontium of Periodontitis Patients.

Authors:  Fangxi Xu; Eman Aboseria; Malvin N Janal; Smruti Pushalkar; Maria V Bederoff; Rebeca Vasconcelos; Sakshi Sapru; Bidisha Paul; Erica Queiroz; Shreya Makwana; Julia Solarewicz; Yuqi Guo; Deanna Aguallo; Claudia Gomez; Donna Shelly; Yindalon Aphinyanaphongs; Terry Gordon; Patricia M Corby; Angela R Kamer; Xin Li; Deepak Saxena
Journal:  Front Oral Health       Date:  2021-09-07
  9 in total

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