Sugar-sweetened beverage consumption and periodontitis among adults: A population-based cross-sectional study

J Clin Periodontol. 2024 Jun;51(6):712-721. doi: 10.1111/jcpe.13961. Epub 2024 Mar 7.

Abstract

Aim: Investigating the association between sugar-sweetened beverages (SSBs) and periodontitis and whether the awareness of diabetes modifies this relationship.

Materials and methods: Cross-sectional analysis was conducted using the National Health and Nutrition Examination Survey (NHANES III) data involving US adults aged 30-50. Periodontitis was classified according to the Centers for Disease Control and Prevention and American Academy of Periodontology (CDC-AAP), and SSB consumption as dichotomous (<5 or ≥5, <7 or ≥7 and <14 or ≥14 times/week), ordinal and continuous variables. Confounders included family income poverty ratio, education, race/ethnicity, sex, age, food energy intake, smoking and alcohol. Odds ratios (ORs) were obtained by logistic regressions using inverse probability weighting. Effect modification analysis was performed considering self-reported diabetes.

Results: Among 4473 cases analysed, 198 self-reported diabetes. SSBs were associated with periodontitis when individuals consumed ≥5 (OR 1.64; 95% confidence interval [CI] = 1.30-2.06), ≥7 (OR 1.92; 95% CI = 1.50-2.46) and ≥14 (OR 2.19; 95% CI = 1.50-3.18) times/week. The combined effect of consuming SSBs (≥5 and ≥14 times/week) and self-reported diabetes had less impact than the cumulative effect.

Conclusions: SSB consumption was associated with higher odds of periodontitis, and the estimates were reduced among those with awareness of diabetes.

Keywords: diabetes mellitus; non‐communicable diseases; periodontitis; sugar‐sweetened beverages.

MeSH terms

  • Adult
  • Cross-Sectional Studies
  • Diabetes Mellitus / epidemiology
  • Female
  • Humans
  • Male
  • Middle Aged
  • Nutrition Surveys*
  • Periodontitis* / epidemiology
  • Risk Factors
  • Sugar-Sweetened Beverages* / adverse effects
  • Sugar-Sweetened Beverages* / statistics & numerical data
  • United States / epidemiology