Journal of Clinical Periodontology

Discovering medical conditions associated with periodontitis using linked electronic health records


  • Conflict of interest and sources of funding statement

    The authors declare that they have no conflict of interests. This study was supported by grants R01LM009886, R01LM010815 and R01 LM006910 from the National Library of Medicine, grant UL1 TR000040 from the National Center for Research Resources and an AHRQ grant R01 HS019853.


Mary Regina Boland Department of Biomedical Informatics, Columbia University, 622 W 168th Street, VC-5, New York, NY 10032, USA




To use linked electronic medical and dental records to discover associations between periodontitis and medical conditions independent of a priori hypotheses.

Materials and Methods

This case-control study included 2475 patients who underwent dental treatment at the College of Dental Medicine at Columbia University and medical treatment at NewYork-Presbyterian Hospital. Our cases are patients who received periodontal treatment and our controls are patients who received dental maintenance but no periodontal treatment. Chi-square analysis was performed for medical treatment codes and logistic regression was used to adjust for confounders.


Our method replicated several important periodontitis associations in a largely Hispanic population, including diabetes mellitus type I (OR = 1.6, 95% CI 1.30–1.99, p < 0.001) and type II (OR = 1.4, 95% CI 1.22–1.67, p < 0.001), hypertension (OR = 1.2, 95% CI 1.10–1.37, p < 0.001), hypercholesterolaemia (OR = 1.2, 95% CI 1.07–1.38, p = 0.004), hyperlipidaemia (OR = 1.2, 95% CI 1.06–1.43, p = 0.008) and conditions pertaining to pregnancy and childbirth (OR = 2.9, 95% CI: 1.32–7.21, p = 0.014). We also found a previously unreported association with benign prostatic hyperplasia (OR = 1.5, 95% CI 1.05–2.10, p = 0.026) after adjusting for age, gender, ethnicity, hypertension, diabetes, obesity, lipid and circulatory system conditions, alcohol and tobacco abuse.


This study contributes a high-throughput method for associating periodontitis with systemic diseases using linked electronic records.