Association between Dietary Pattern and Periodontitis-A Cross-Sectional Study.
Ersin AltunCarolin WaltherKatrin BorofElina Larissa PetersenBerit LieskeDimitros KasapoudisNavid JalilvandThomas BeiklerBettina JagemannBirgit-Christiane ZyriaxGhazal AarabiPublished in: Nutrients (2021)
The aim of the study was to investigate the relationship between specific known dietary patterns and the prevalence of periodontal disease in a northern population-based cohort study. We evaluated data from 6209 participants of the Hamburg City Health Study (HCHS). The HCHS is a prospective cohort study and is registered at ClinicalTrial.gov (NCT03934957). Dietary intake was assessed with the food frequency questionnaire (FFQ2). Periodontal examination included probing depth, gingival recession, plaque index, and bleeding on probing. Descriptive analyses were stratified by periodontitis severity. Ordinal logistic regression models were used to determine the association. Ordinal regression analyses revealed a significant association between higher adherence to the DASH diet/Mediterranean diet and lower odds to be affected by periodontal diseases in an unadjusted model (OR: 0.92; 95% CI: 0.87, 0.97; p < 0.001/OR: 0.93; 95% CI: 0.91, 0.96; p < 0.001) and an adjusted model (age, sex, diabetes) (OR: 0.94; 95% CI: 0.89, 1.00; p < 0.0365/OR: 0.97; 95% CI: 0.94, 1.00; p < 0.0359). The current cross-sectional study identified a significant association between higher adherence to the DASH and Mediterranean diets and lower odds to be affected by periodontal diseases (irrespective of disease severity). Future randomized controlled trials are needed to evaluate to which extent macro- and micronutrition can affect periodontitis initiation/progression.
Keyphrases
- randomized controlled trial
- type diabetes
- weight loss
- public health
- cardiovascular disease
- risk factors
- cross sectional
- atrial fibrillation
- clinical trial
- mental health
- coronary artery disease
- molecular dynamics simulations
- single molecule
- skeletal muscle
- machine learning
- single cell
- insulin resistance
- deep learning
- electronic health record
- climate change