Exploring the Relationship Among Dental Caries, Nutritional Habits and Peri-implantitis

Caries,Dental, Mucositis, Nutrition, Peri-Implantitis

Aim: A study was made of the prevalence, co-occurrence and association among caries, nutritional habits and peri-implant disease, with an analysis of the influence of other patient and implant factors upon peri-implant disease. Material and methods: The included subjects underwent a clinical examination and were asked to complete a questionnaire. Demographic data and potential lifestyle/behavioral variables were collected. Clinical and radiographic assessment allowed calculation of the decayed, missing and filled teeth (DMFT) index and peri-implant diagnosis. Uni- and multivariate logistic regression analyses were applied to identify predictors of peri-implant disease.

2.1. Study design The present cross-sectional study was conducted after approval from the local Ethics Committee (Ref. PER-ECL-PER-2017-08) and in accordance with the ethical principles outlined in the Declaration of Helsinki. It is reported according the Strengthening the Reporting of Observational Studies Epidemiology (STROBE) statement recommendations (von Elm et al., 2009). Selected subjects were informed about the aims of the research, and written consent was obtained before starting the study. 2.2. Study population Patients visiting the Postgraduate Periodontology Clinic of the Faculty of Dentistry of the Universitat Internacional de Catalunya (Barcelona, Spain) from January 2018 to December 2019 were consecutively enrolled in the study by one of the researchers (JV), if they met the criteria addressed later on in the form. 2.3. Data collection Data collection comprised a patient interview and clinical and radiographic assessment. Initially, a previously trained examiner (MP) interviewed the patients and collected the following data: * Age (years). * Gender (female/male). * Smoking habit: smoker, non-smoker or ex-smoker. In the case of smokers, the total amount of cigarettes per day was categorized as \< 10 or more than 10 cigarettes per day. * Systemic diseases: presence or absence. * Diabetes mellitus: presence or absence. In the case of diabetic patients, glycemic control was assessed on the basis of a previous blood test. * Body mass index (BMI): recorded as weight (kg)/ height (m)2. * Dietary habits: assessed by the Mediterranean Diet Score (MDS) questionnaire (Martínez-González et al., 2012) and classified as low adherence (score ≤ 5), medium adherence (score 6-9) or high adherence (score ≥10). * Regular sugar consumption: yes or no. Sugar consumers were also asked about their level of sugar intake (low, medium, high). * Nutrient or vitamin deficiencies: presence or absence. * Oral dryness: patient perception of dry mouth (presence or absence). * Educational level (EL): primary and secondary or professional and university. * Oral hygiene measures: frequency of teeth brushing and interproximal hygiene. * Supportive periodontal treatment (SPT): regular (≥ 2 times/year) or irregular (\< 2 times/year). * Cause of tooth loss: caries, mobility, caries and mobility, and trauma/fracture. Any doubts coming from the questionnaire were solved by the examiner. A previously calibrated examiner (LG) conducted the intraoral examination (with a Cohen inter-agreement kappa index \> 85%). The exploration was conducted to assess the following parameters: * Periodontal indexes: full mouth plaque score (FMPS) (O'Leary et al. 1972) and bleeding score (FMBS) (Ainamo & Bay 1975). * History of periodontitis: assessed radiographically by the presence or absence of bone loss. * Number of decayed, missing and filled teeth (DMFT) assessed by visual inspection and radiographic assessment following the ICDAS (Pitts & Ekstrand, 2013). All tooth surfaces were examined, but the observations were recorded per tooth. * Probing pocket depth (PPD) (in mm), bleeding on probing (BoP) (yes/no), suppuration (SUP) (yes/no), keratinized mucosa (KM) (in mm) were all recorded at 6 sites per implant using a PCP UNC 15 probe (Hu-Friedy ®). * Radiographic bone level (in mm) at mesial and distal to the implant site using the parallel cone technique. * Implant position (anterior maxilla, anterior mandible, posterior maxilla, posterior mandible). * Interproximal untreated caries or fillings adjacent to implants: yes/no. If these conditions were present, their location was recorded (mesial, distal or both). Patients presenting with caries or periodontal or peri-implant disease were referred to the corresponding clinical department within the Universitat Internacional de Catalunya for further evaluation and management. 2.4. Outcome measures The main outcome measure of the study was the prevalence of dental caries and peri-implant disease. All other variables obtained from the questionnaire and clinical examination were regarded as secondary outcome measures. 2.5. Sample size calculation A logit regression model used to associate the outcome diagnosis at the patient level and each exposure variable reached a statistical power of 82.5% in detecting odds ratio (OR) = 2.5 as being significant in the recruited sample (n= 169), assuming a confidence level of 95%. At the implant level, the power was 96.2% under the same previous conditions. Due to the multi-level design, the power had to be corrected. In this regard, assuming a moderate intra-subject correlation (ρ = 0.5), a power of 87.7% was estimated. 2.6. Statistical analysis A descriptive analysis was carried out, with the calculation of absolute and relative frequencies (categorical variables) and the mean and standard deviation (SD) (continuous variables). At patient level, simple binary logistic regression models were estimated to study the association between the patient diagnosis (H versus M, and H versus PI) and each of the exposure variables. At implant level, simple binary logistic regression models were estimated using generalized estimating equations (GEEs). The models estimated odds ratio (OR) from the Wald chi-squared statistic. The GEE approach addressed intra-subject dependency between observations due to the multiplicity of implants per patient. Relevant exposure variables (p\<0.10) were incorporated into a multiple logistic regression model at patient and implant level to obtain adjusted ORs. The SPPS version 21.0 statistical package (SPSS Inc., Chicago, IL, USA) was used throughout. The level of significance was 5% (α = 0.05).

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