Identification of inflammatory response patterns in experimental gingivitis studies

Gustavo Nascimento; Bo Danielsen; Vibeke Baelum; Rodrigo Lopez

doi:10.1111/eos.12588

Identification of inflammatory response patterns in experimental gingivitis studies

Gustavo Nascimento, Bo Danielsen, Vibeke Baelum, Rodrigo Lopez

Department of Odontology

13 Citations (Scopus)

Abstract

We used novel analytical approaches to identify inflammatory response patterns to plaque accumulation in experimental gingivitis studies in humans. Data from two experimental gingivitis studies [Dataset I (n = 40) and Dataset II (n = 42)], which differed in design and recording methods, were used. Both studies comprised a three‐phase program as follows: pre‐induction period (oral hygiene as usual for Dataset I; professional tooth cleaning for Dataset II); induction period (plaque accumulation); and resolution period (oral hygiene as usual). Clinical recordings of plaque and gingival inflammation were made on days 0, 4, 9, and 14 for Dataset I and on days −14, 0, 7, 21, and 35 for Dataset II. Group‐based‐trajectory and growth curve modeling were used for data analysis. In Dataset I, gingival response to plaque accumulation was found to be lagged in time. Different group‐based response patterns for gingival inflammation were not identified. However, in Dataset II, ‘fast’ and ‘slow’ gingival inflammation responders were identified. ‘Slow’ responders had lagged inflammation responses, whereas ‘fast’ responders seemed to respond immediately to plaque. The findings show that analytical approaches which consider the data structure allow investigation of the dynamics of the relationship between plaque accumulation and gingival inflammation and facilitate the identification of differential patterns of gingival inflammation development.

Original language	English
Journal	European Journal of Oral Sciences Online
Volume	127
Issue number	1
Pages (from-to)	33-39
ISSN	1600-0722
DOIs	https://doi.org/10.1111/eos.12588
Publication status	Published - 1 Feb 2019

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@article{efb57319efeb4fd882a3d10254247c8d,

title = "Identification of inflammatory response patterns in experimental gingivitis studies",

abstract = "We used novel analytical approaches to identify inflammatory response patterns to plaque accumulation in experimental gingivitis studies in humans. Data from two experimental gingivitis studies [Dataset I (n = 40) and Dataset II (n = 42)], which differed in design and recording methods, were used. Both studies comprised a three‐phase program as follows: pre‐induction period (oral hygiene as usual for Dataset I; professional tooth cleaning for Dataset II); induction period (plaque accumulation); and resolution period (oral hygiene as usual). Clinical recordings of plaque and gingival inflammation were made on days 0, 4, 9, and 14 for Dataset I and on days −14, 0, 7, 21, and 35 for Dataset II. Group‐based‐trajectory and growth curve modeling were used for data analysis. In Dataset I, gingival response to plaque accumulation was found to be lagged in time. Different group‐based response patterns for gingival inflammation were not identified. However, in Dataset II, {\textquoteleft}fast{\textquoteright} and {\textquoteleft}slow{\textquoteright} gingival inflammation responders were identified. {\textquoteleft}Slow{\textquoteright} responders had lagged inflammation responses, whereas {\textquoteleft}fast{\textquoteright} responders seemed to respond immediately to plaque. The findings show that analytical approaches which consider the data structure allow investigation of the dynamics of the relationship between plaque accumulation and gingival inflammation and facilitate the identification of differential patterns of gingival inflammation development. ",

author = "Gustavo Nascimento and Bo Danielsen and Vibeke Baelum and Rodrigo Lopez",

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AU - Nascimento, Gustavo

AU - Danielsen, Bo

AU - Baelum, Vibeke

AU - Lopez, Rodrigo

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N2 - We used novel analytical approaches to identify inflammatory response patterns to plaque accumulation in experimental gingivitis studies in humans. Data from two experimental gingivitis studies [Dataset I (n = 40) and Dataset II (n = 42)], which differed in design and recording methods, were used. Both studies comprised a three‐phase program as follows: pre‐induction period (oral hygiene as usual for Dataset I; professional tooth cleaning for Dataset II); induction period (plaque accumulation); and resolution period (oral hygiene as usual). Clinical recordings of plaque and gingival inflammation were made on days 0, 4, 9, and 14 for Dataset I and on days −14, 0, 7, 21, and 35 for Dataset II. Group‐based‐trajectory and growth curve modeling were used for data analysis. In Dataset I, gingival response to plaque accumulation was found to be lagged in time. Different group‐based response patterns for gingival inflammation were not identified. However, in Dataset II, ‘fast’ and ‘slow’ gingival inflammation responders were identified. ‘Slow’ responders had lagged inflammation responses, whereas ‘fast’ responders seemed to respond immediately to plaque. The findings show that analytical approaches which consider the data structure allow investigation of the dynamics of the relationship between plaque accumulation and gingival inflammation and facilitate the identification of differential patterns of gingival inflammation development.

AB - We used novel analytical approaches to identify inflammatory response patterns to plaque accumulation in experimental gingivitis studies in humans. Data from two experimental gingivitis studies [Dataset I (n = 40) and Dataset II (n = 42)], which differed in design and recording methods, were used. Both studies comprised a three‐phase program as follows: pre‐induction period (oral hygiene as usual for Dataset I; professional tooth cleaning for Dataset II); induction period (plaque accumulation); and resolution period (oral hygiene as usual). Clinical recordings of plaque and gingival inflammation were made on days 0, 4, 9, and 14 for Dataset I and on days −14, 0, 7, 21, and 35 for Dataset II. Group‐based‐trajectory and growth curve modeling were used for data analysis. In Dataset I, gingival response to plaque accumulation was found to be lagged in time. Different group‐based response patterns for gingival inflammation were not identified. However, in Dataset II, ‘fast’ and ‘slow’ gingival inflammation responders were identified. ‘Slow’ responders had lagged inflammation responses, whereas ‘fast’ responders seemed to respond immediately to plaque. The findings show that analytical approaches which consider the data structure allow investigation of the dynamics of the relationship between plaque accumulation and gingival inflammation and facilitate the identification of differential patterns of gingival inflammation development.

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