Assessing Risk Factors for Periodontitis Using Multivariable Regression Analysis

  • J. A. Lobo PereiraEmail author
  • Maria Cristina Ferreira
  • Teresa A. Oliveira
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 136)


Risk is associated with all areas of Life, and studies designed to decrease it play a key role, particularly in what concerns Individual Health. Considering Epidemiological Research, the identification of Risk Factors is crucial to select prevention actions in order to improve Public Health Systems. The aim of this work is to identify the main Risk Factors for periodontal disease, using Multivariate Statistical Methods, since according to the literature these are the most important tools to assess associations and interactions between different putative risk factors and a given health condition. An application of Generalized Linear Models (GLM) with probit link function was performed to assess the impact of socio-demographic, biochemical and behavioural factors on periodontal status. We analysed data collected from a sample of 79 individuals with chronic periodontal disease, attending the clinic of Porto Dentistry School. We found a significant association between extensive periodontitis and decreased levels of high density lipoproteins (HDL). We believe public health efforts on prevention, including education of the population at risk, are highly recommended in order to decrease early causes of the illness.


Periodontitis Risk factors Multivariate regression 



Research partially sponsored by national founds through the Fundação Nacional para a Ciência e Tecnologia, Portugal - FCT under the projects:

(PEst-OE/MAT/UI0006/2011 and PEst-OE/MAT/UI0006/2014).


  1. 1.
    Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics. Foundations and Basic Theory. Springer Series in Statistics, Perspectives in Statistics, vol. 1, pp. 610–624. Springer, New York (1992)Google Scholar
  2. 2.
    Burnham, K.P., Anderson, D.R.: Kullback-Leibler information as a basis for strong inference in ecological studies. Wildl. Res. 28, 111–119 (2001)CrossRefGoogle Scholar
  3. 3.
    Burnham, K.P., Anderson, D.R.: Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd edn. Springer, New York (2002)Google Scholar
  4. 4.
    Chaffee, B.W., Weston, S.J.: Association between chronic periodontal disease and obesity: a systematic review and meta-analysis. J Periodontol. 81(12), 1708–1724 (2010). doi: 10.1902/jop.2010.100321 CrossRefGoogle Scholar
  5. 5.
    Fox, J.: Companion to Applied Regression R Foundation for Statistical Computing. Vienna (2007)Google Scholar
  6. 6.
    Genco, R.J., Borgnakke, W.S.: Risk factors for periodontal disease. Periodontology 2000 62(1), 59–94 (2013)CrossRefGoogle Scholar
  7. 7.
    Griffiths, R., Barbour, S.: Lipoproteins and lipoprotein metabolism in periodontal disease. Clin. Lipidol. 5(3), 397–411 (2010)CrossRefGoogle Scholar
  8. 8.
    Guisan, A., Edwards, T., Hastie, C.: Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol. Model. 157, 89–100 (2002)CrossRefGoogle Scholar
  9. 9.
    Hilbe, J.: Generalized linear models. Am. Stat. Assoc. 48, 255–265 (1994)Google Scholar
  10. 10.
    Hoffmann, J.P.: Generalized Linear Models: An Applied Approach. Pearson, Boston (2004)Google Scholar
  11. 11.
    Kinane, D.F., Marshall, G.J.: Periodontal manifestations of systemic disease. Aust. Dent. J. 46(1), 2–12 (2001)CrossRefGoogle Scholar
  12. 12.
    Kornman, K.S.: Mapping the pathogenesis of periodontitis: a new look. J Period. 79(Suppl.), 1560–1568 (2008)CrossRefGoogle Scholar
  13. 13.
    Lobo Pereira, J.A., Ferreira, M.C., Oliveira, T.A.: Assessing risk factors for periodontitis using regression. In: Proceedings of ICNAAM 2013, Rhodes Island - Greece, 21–27 September 2013 (in Press)Google Scholar
  14. 14.
    Nelder, J., Wedderburn, R.: Generalized linear models. J. R. Stat. Soc. A. 135, 370–384 (1972)CrossRefGoogle Scholar
  15. 15.
    Page, R.C., Kornman, K.S.: The pathogenesis of human periodontitis: an introduction. Periodontology 2000(14), 9–11 (1997)CrossRefGoogle Scholar
  16. 16.
    RTeam: R Development Core Team. R: a language and environmental for statistical computing. Version 2.8.0. Vol. R Foundation for Statistical Computing. Vienna (2008)Google Scholar
  17. 17.
    Streckfus, C.F., Parsell, D.E., Streckfus, J.E., Pennington, W., Johnson, R.B.: Relationship between oral alveolar bone loss and aging among African-American and Caucasian individuals. Gerontology 45, 110–114 (1999)CrossRefGoogle Scholar
  18. 18.
    Vacek, J.S., Gher, M.E., Assad, D.A., Richardson, A.C., Giambarresi, L.I.: The dimensions of the human dentogingival junction. Int J Periodontics Restor. Dent. 14(2), 154–165 (1994)Google Scholar
  19. 19.
    Venables, W.N., Ripley, B.D.: Modern Applied Statistics with S, 4th edn. Springer, New York (2002)CrossRefGoogle Scholar
  20. 20.
    Ojima, M., Hanioka, T.: Destructive effects of smoking on molecular and genetic factors of periodontal disease. Tob. Induc. Dis. 8(4), (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • J. A. Lobo Pereira
    • 1
    Email author
  • Maria Cristina Ferreira
    • 2
  • Teresa A. Oliveira
    • 3
  1. 1.Department of PeriodontologyFMUP and Universidade AbertaLisbonPortugal
  2. 2.Master of MEMCUniversidade AbertaLisbonPortugal
  3. 3.Universidade Aberta and CEAULLisbonPortugal

Personalised recommendations