Association between periodontitis in pregnancy and preterm or low birth weight: Review of the literature.

The aim of this paper is to descibe the pathogenic mechanisms that could explain the relationship between periodontitis and adverse pregnancy outcomes, and to review the evidence from systematic reviews and interventional studies, regarding the association between the clinical indicators of periodontitis and the incidence of low birth weight or preterm births. Preterm birth and low birth weight are world wide leading perinatal problems and have evident public health implications, due to the fact that their incidence doesn't decrease in spite of the many attempts at their prevention. Both intrauterus infections and bacterial vaginosis of the mother are well known risk factors, but distant infections, even subclinicals, may also produce preterm births.

Periodontal disease definition may determine the association between periodontitis and pregnancy outcomes.

Aim: The aim of this secondary analysis is to explore whether the application of different definition criteria of periodontitis, used in other similar studies, has an influence on the significance of the association between periodontitis and prematurity or low birth weight.

Material And Methods: Fourteen periodontitis definitions and more than 50 periodontal disease continuous measurements, found in 23 published studies, were applied to a cohort study that included 1296 pregnant women. The associations with adverse pregnancy outcomes were analysed using logistic regression analysis.

Periodontal disease as a risk factor for adverse pregnancy outcomes: a prospective cohort study.

Aim: The aim of this study was to determine the association between periodontitis and the incidence of preterm birth (PB), low birth weight (LBW) and preterm low birth weight (PLBW) MATERIAL AND METHODS: One thousand and ninety-six women were enrolled. Periodontal data, pregnancy outcome variables and information on other factors that may influence adverse pregnancy outcomes were collected. Data were analysed using a logistic regression model.