A quantitative bias analysis to assess the impact of unmeasured confounding on associations between diabetes and periodontitis.

AIM: To investigate unmeasured confounding in bidirectional associations between periodontitis and diabetes using quantitative bias analysis. METHODS: Subsamples from the Veterans Affairs Dental Longitudinal Study were selected. Adjusted for known confounders, we used Cox proportional hazards models to estimate associations between pre-existing clinical periodontitis and incident Type II Diabetes (n = 672), and between pre-existing diabetes and incident severe periodontitis (n = 521), respectively. Hypothetical confounders were simulated into the dataset using Bernoulli trials based on pre-specified distributions of confounders within categories of each exposure and outcome. We calculated corrected hazard ratios (HR) over 10,000 bootstrapped samples. RESULTS: In models using periodontitis as the exposure and incident diabetes as the outcome, adjusted HR = 1.21 (95% CI: 0.64-2.30). Further adjustment for simulated confounders positively associated with periodontitis and diabetes greatly attenuated the association or explained it away entirely (HR = 1). In models using diabetes as the exposure and incident periodontitis as the outcome, adjusted HR = 1.35 (95% CI: 0.79-2.32). After further adjustment for simulated confounders, the lower bound of the simulation interval never reached the null value (HR >/= 1.03). CONCLUSIONS: Presence of unmeasured confounding does not explain observed associations between pre-existing diabetes and incident periodontitis. However, presence of weak unmeasured confounding eliminated observed associations between pre-existing periodontitis and incident diabetes. These results clarify the bidirectional periodontitis-diabetes association.