Previously published evidence of the association between overweight/obesity and periodontitis has largely been based upon cross‐sectional epidemiological data collection and/or re‐analysis of existing health survey databases (Chaffee & Weston 2010 , Suvan et al. 2011 ). These studies have been valuable in generating the hypothesis of association, however fall short of using robust and consistent methods to diagnose periodontitis (pragmatic screening and heterogeneous case definitions) which may lead to biased results. The aim of this study was to ascertain the odds of having periodontitis if an individual is overweight or obese. The null hypothesis was of no association between overweight/obesity and diagnosis of periodontitis (case definition proposed for studies of risk factors for periodontitis (Tonetti & Claffey 2005 ) in an age‐matched sample of adults).

Periodontitis is a chronic inflammatory disease driven by bacterial pathogens and is one of the most common oral infections worldwide (WHO 2004 ). The host response to periodontal pathogens represents a crucial determinant of the individual's susceptibility to periodontitis. Several pro‐inflammatory molecules and processes implicated in the pathogenesis of periodontitis, including cytokines (e.g. Interleukin‐IL‐6), chemokines and T‐cell function, could be altered by obesity (Falagas & Kompoti 2006 ). An altered (delayed and sustained) inflammatory state, such as that found both in animal experimental conditions (Amar et al. 2007 ) and in obese individuals, could predispose individuals to increased periodontal tissue destruction (Boesing et al. 2009 ). These findings are also corroborated by several lines of clinical evidence suggesting that obesity could be a novel risk factor for periodontitis (Suvan et al. 2011 ). Indeed, obese individuals present with an increased prevalence of periodontitis, with raised gingival inflammatory responses and possibly altered periodontal microbial compositions (Boesing et al. 2009 ).

Obesity is a highly prevalent condition worldwide especially in developed countries (WHO 2006 ). It is a metabolic condition occurring due to an energy imbalance (intake > consumption), which subsequently leads to an increase in adipose tissue deposits (Bray 2007 ). As adipocytes exert a number of endocrine functions, increased adiposity is associated with a state of low‐grade inflammation and insulin resistance (Bray 2004 , Gimeno & Klaman 2005 ).

In order to explore the relevance of different BMI (kg/m 2 ) cut‐off points from those used in the WHO definitions and the categories adopted here, a receiver operating characteristic curve (ROC) was generated of all possible BMI values above which it is likely an individual has a chance of having periodontitis. This was achieved by determining a unique pair of values for sensitivity and specificity for each cut‐off point of BMI, and then plotting the sensitivity (the chance of a true positive) against one minus the specificity (the chance of a false positive), thus comparing the probabilities of a positive test result (i.e. when the BMI value suggests periodontitis is present) in people with and without periodontitis. The area under the ROC curve gives a measure of the discrimination between those that due and do not have the diagnosis of periodontitis. Interpretation of the area under the ROC is performed based on the notion that a perfect test for discriminating between presence or absence of the disease would have an area under the curve of 1 and a non‐discriminating curve (i.e. one which performs no better than chance) would have an area equal to 0.5.

Multivariable conditional logistic regression, accounting for the age matching, was used to estimate the odds ratio for diagnosis of periodontitis when either BMI as a continuous variable (kg/m 2 ) or BMI expressed as a categorical variable based upon WHO categories (BMI normal 18.5–24.99 kg/m 2 , overweight BMI 25–29.99 kg/m 2 , obese BMI ≥30 kg/m 2 ) were the explanatory variables of interest (WHO 2006 ). The regression models comprised gender, smoking status (never smoker, smoker, former smoker), ethnicity (White, Asian including Indian Asian, Other including African, Afro‐Caribbean and any other) and full‐mouth dental plaque scores as covariates. The variable of primary interest was BMI (continuous or categorical) with other variables included to adjust for potential confounding effects.

Following completion of descriptive statistics, statistical significance was set at a p ‐value of <0.05. Continuous, normally distributed variables were reported as means and standard deviations (SD). Categorical variables were summarized as percentages. Comparisons of all continuous and categorical data between groups (cases and controls) were analysed with paired t ‐tests and conditional logistic regression, respectively, to check for statistically significant differences between the groups. As the next step, univariable conditional logistic regression analyses were carried out to investigate the relationship of BMI and gender, ethnicity, smoking status and full‐mouth plaque score with periodontal diagnosis (being a case or control). Those factors found to be statistically significant at the 10% level were then included in the final multivariable models as potential predictors of being a case.

SPSS 20.0 (IBM Corp 2011 ) statistical software package and Stata 10.0 (StataCorp) were used for data analyses. Data for all participants were combined in SPSS 20.0 and the database was checked for accuracy and consistency. The data were then imported into Stata 10.0.

Periodontal health or disease status was based upon recording of whole mouth probing pocket depths and clinical recession. Dental plaque levels and gingival bleeding on probing scores were assessed dichotomously as plaque or bleeding being present or absent (O'Leary et al. 1972 , Ainamo & Bay 1975 ). All clinical parameters were assessed at six sites per tooth for all teeth present in the mouth.

At the study visit, following informed consent, the study examiner obtained the participant's medical history update, oral examination, a lifestyle questionnaire (exercise, diet, oral hygiene practice, etc.), demographic data (age, gender, smoking history and ethnicity) and body weight and height for calculation of BMI. Height and weight were measured by a trained dental nurse using a wall‐mounted height measure and mechanical scales (BMI was calculated as kg/m 2 during data processing).

Data collection methods were the same for all study participants. All visits were carried out in the same setting and measurements were performed by one of the three calibrated study examiners. Prior to the initiation of the study, an examiner repeatability exercise was undertaken to confirm adequate intra‐examiner repeatability using 10 non‐study patients suffering from moderate to severe periodontitis (duplicate measurements with another patient or waiting a time period of 30 min between). Following this, a calibration exercise was carried out on 10 patients per examiner to confirm inter‐examiner agreement (measured by one examiner then alternate examiner on the same day). The data were analysed to confirm intra‐examiner repeatability and inter‐examiner calibration at a pre‐determined success criteria level of agreement within ±2 mm for CAL in a minimum 98% of sites measured. All examiners reached levels above the 98% agreement threshold for repeatability and calibration.

Sample Size Tables for Clinical Studies software were used to calculate the sample size for this study (Machin et al. 2008 ). A sample of 274 individuals (137 participants per group) would have 90% power to detect a clinically relevant odds ratio of 2.0 for presence of periodontitis in obese compared to normal weight individuals, assuming a prevalence of obesity in the general population of 23.8% and using a 0.05 two‐sided significance level (DOH UK 2006 ).

This subset study sample was selected from the overall based upon the European Federation of Periodontology (EFP) case definition of periodontitis for epidemiological studies of risk factors. A “periodontitis case” was defined when “proximal attachment loss of ≥3 mm in ≥2 non‐adjacent teeth” was identified (Tonetti & Claffey 2005 ). Based on presence/absence of this criterion a case or a control population were identified. Furthermore, as ageing represents an important risk factor for periodontal tissue loss (Grossi et al. 1994 ), 143 age‐matched pairs (one to one based on year of birth) were identified for analysis.

The overall clinical study sample consisted of 471 individuals recruited from the UCLH NHS Trust (UCLH Eastman Dental Hospital) new patient clinics between June 2006 and December 2010. Periodontitis cases were recruited from referrals to the Unit of Periodontology whilst periodontally healthy controls from other units within UCLH Eastman Dental Hospital. All participants (cases and controls) were ≥18 years old, had a minimum of 20 natural teeth and were otherwise systemically healthy (no active infectious diseases such as hepatitis, HIV or TB, or uncontrolled metabolic diseases including diabetes, kidney, liver or cardiovascular diseases assessed by the examining clinician and medical history). Exclusion criteria comprised pregnancy, chronic use of non‐steroidal anti‐inflammatory drugs; antibiotics use within 3 months of study visit and past periodontal therapy. Cases were included if they presented with the presence of one or more sites with PPD ≥5 mm or radiographic evidence of bone loss ≥20% of the root length. Controls were included if they had no evidence of periodontitis (absence of sites with PPD ≥5 mm).

This study was designed as a case–control analysis of age‐matched participants enrolled in a larger clinical study recruiting cases and controls based on the presence or absence of periodontitis as confirmed by full‐mouth periodontal examination. The exposure of body mass index (BMI) was calculated during data analysis based upon height and weight (kg/m 2) measured by the clinician during the study visit. The protocol was reviewed and approved by the Joint UCL/UCLH Committees on the Ethics of Human Research (Ref 05/Q0502/84). All participants gave written informed consent prior to participation. This report conforms to the STROBE guidelines for the strengthening of reporting of observational studies (von Elm et al. 2008 ).

The ROC analyses had an area under the curve of 0.62, suggesting that it was reasonable to use this approach to discriminate between those individuals with and without periodontitis (Fig. 1 ). The cutpoint of 24.32 (related to BMI) produced the optimal sensitivity of 61.54% and specificity of 61.54% and 61.89% were correctly assigned overall with a likelihood ratio (LR) of 1.6 (meaning the disease is 1.6 times more likely in an individual with the BMI ≥ 24.32).

Multivariable conditional logistic regression with BMI considered in three categories demonstrated that the overweight category and the obese category were associated with increased odds of diagnosis of periodontitis when compared to the normal category (Table 3 ). The estimated odds ratio for having periodontitis if an individual was overweight was 2.56 (95% CI 1.210–5.400, p = 0.014). The odds ratio for a diagnosis of periodontitis for obesity was: 3.11 (95% CI 1.05–6.48, p = 0.015). As in the continuous analysis (Table 2 ) gender, ethnicity and smoking were statistically significant predictors of periodontitis diagnosis in this categorical analysis.

Multivariable conditional logistic regression with BMI as a continuous variable (kg/m 2 ) demonstrated that BMI had a dose–response association with an increased risk of being a case compared to a control, with an odds ratio of 1.12 per increase in 1 kg/m 2 (95% CI 1.045–1.197, p = 0.001), after adjusting for the other covariates in the model (Table 2 ). Gender, ethnicity and smoking were also statistically significant predictors of periodontitis diagnosis, whilst dental plaque scores were not.

The study sample consisted of 286 individuals (143 case/control pairs) ranging in age from 25 to 66 years. Statistically significant differences in gender, ethnicity and distribution of former smokers were noted (Table 1 ). Furthermore, cases tended to exhibit greater values of BMI and there were more overweight/obese individuals than controls. All periodontal clinical variables recorded in the sample resulted in statistically significantly higher average values for cases compared to controls (Table 1 ). Univariable analysis showed all covariates to be statistically significant at the 10% level. Therefore, all were included in the multivariable conditional regression models (Tables 2 and 3 ).

Discussion

The main finding of this analysis was a positive strong association between body mass index (continuous and categorical definitions) and diagnosis of periodontitis in an age‐matched case–control study, and this association was independent of gender, ethnicity, smoking status and dental plaque levels.

These results were obtained in a sample representative of the UK population BMI distribution with a prevalence (49.0%) of overweight/obese (BMI ≥ 25) that is similar to that for the English population (50%) at the time of study recruitment. It was slightly less representative, however, with regard to prevalence of overweight and obese categories separately, with 32.0% of the sample classed as overweight and 17.0% classed as obese compared to estimates for the English population of 26.2% and 23.8% for overweight and obese, respectively (DOH UK 2006).

Previously published research has suggested a positive association between BMI or overweight/obesity and prevalence or presence of periodontitis, based upon cross‐sectional surveys and case–control analyses (Suvan et al. 2011). One epidemiologic database analysis considered the prospective onset of periodontitis and demonstrated an association with periodontitis prevalence (Jimenez et al. 2012). However, there is a lack published reports of an association between BMI and periodontitis based on a sample that was recruited according to presence or absence of periodontitis based upon full‐mouth clinical periodontal assessment.

Published systematic reviews have identified two other case–control studies of the association between periodontitis and obesity (Buhlin et al. 2003, Alabdulkarim et al. 2005). Buhlin and co‐workers reported an association between overweight/obesity and periodontitis in a sample of 50 periodontitis cases and 46 periodontally healthy controls. They reported an odds ratio of 4.54 (95% CI 1.588–13.00) which is higher than that found in this study (OR 2.76 95% CI 1.487–5.127) (Buhlin et al. 2003). This could be due to a difference in sample size or differences in covariates included in analyses. A study of 200 obese compared to 200 non‐obese participants demonstrated an odds ratio of 1.86 (95% CI 0.99, 3.51) (Alabdulkarim et al. 2005), which is lower than the odds ratio of 3.11 (95% CI 1.05–6.48) for the same comparison in this study. These differences could be due to the retrospective nature of the Alabdulkarim study and the use of radiographs as the sole means of periodontal assessment. Differences might also be due to other unknown confounding factors.

The study reported here infers a dose–response association between a common measure of overweight/obesity (BMI) and diagnosis of periodontitis. Both continuous and different categorical definitions of overweight/obesity were strongly associated with diagnosis of periodontitis. Odds ratios were higher than those resulting from a systematic review meta‐analysis published in 2011 (Suvan et al. 2011), which showed odds ratios (compared to BMI < 25 Kg/m2) of 1.27 (95% CI 1.06–1.51), 1.81 (95% CI 1.42–2.30) and 2.13 (95% CI 1.40–3.26) for overweight, obese and overweight/obese combined categories respectively. This study found odds ratios of 2.56 (95% CI 1.21–5.40), 3.11 (95% CI 1.05–6.48) and 2.76 (95% CI 1.48–5.12) for the overweight, obese and overweight/obese combined categories, respectively.

When considering the odds ratios of the associated risk of smoking for presence of periodontitis in this study, OR 2.46 (95% CI 1.09–5.56), this is similar to that for overweight (OR 2.56, 95% CI 1.21–5.40), but that for obese is higher (OR 3.11, 95% CI 1.05–6.48) (Table 3).

Exploration by use of the ROC curve of whether a different cut‐off point of BMI (than those currently defined by WHO for overweight (BMI 25–29.99 kg/m2) and obesity (BMI ≥ 30 kg/m2) would be a better predictor of increased risk of periodontitis found that the optimal combination of sensitivity and specificity coincided with the current combined categorization of overweight and obese (BMI ≥25) compared to normal weight individuals (BMI 18.5–24.99 kg/m2). This cutpoint of BMI which resulted in an estimated OR of 2.46, although less than the OR for of 3.11 for obese offers insight of the importance of considering overweight individuals when considering oral health status and investigating the possible mechanisms involved in the association.

Limitations of this study are associated with the bias that may occur in the case–control study design. These include the fact that the participants were identified and selected based on whether or not they had the disease (periodontitis in this case) and the exposure (BMI, overweight, obesity) was then investigated within the cases and controls. It is recognized that there may be potential for selection bias due to the recruitment of all individuals from those referred to a dental hospital. Interpretation of results is limited by the lack of knowledge of the onset of periodontitis or the start time of the exposure. This is a common limitation for the case–control study designs but is heightened in this study, because the complexity of the exposure is increased due to the apparent dose–response associated with various levels of overweight and obesity. In addition, variation in the exposure over time increases the complexity of quantifying the exposure. Overweight and obesity are not exposures that have a fixed start date but, rather, weight gain normally occurs gradually. This analysis was based on BMI and BMI categories without analysis of body fat percentage or some other measure of body fat. Increase in fat mass with decrease in lean tissue mass over time with ageing is a normal physiological change in the body (Kotani et al. 1994). Although consistent and statistically significant, the increased odds or magnitude of effect of BMI on the risk of having periodontitis cannot prove a causal relationship between the BMI and the presence of periodontitis. Results from studies demonstrating a temporal sequence of exposure followed by disease are required before firm conclusions can be drawn about causality. Recently published evidence from two longitudinal studies of 40 years reported an association of obesity as well as weight gain with periodontal disease progression (Gorman et al. 2012a,b), but further prospective studies are needed to confirm a temporal association between overweight/obesity and periodontitis.

Finally, the results of this study may have been influenced by the relatively small sample size and imbalances between the groups for gender and ethnicity. Strengths of the study include age matching of the sample, which minimized bias that may have been introduced by the complex clinical association of increasing disease and exposure associated with increasing age. Using a widely accepted case definition of periodontitis following full‐mouth detailed periodontal assessment adds to the external validity of these findings together with the adjustment for various covariates including dental plaque.

The statistical significance of the positive association between BMI by unit, overweight and obesity (WHO category >25 kg/m²) and the presence of periodontitis is consistent with the hypothesis of an altered inflammatory response associated with obesity as a chronic inflammatory disease. This dysregulated inflammatory response has been proposed to be at the patient level or limited to the site level, that is, the host response to the plaque biofilm within individuals with a BMI at the level of obesity (Genco & Borgnakke 2013).

The validity of BMI as a measurement tool for the study of the association of body composition or obesity with disease or health risks is controversial (Vazquez et al. 2007, Jimenez 2013). In this project, information secured through other measures of body composition including analyses of body fat measurements together with BMI might have provided further insight into the nature of the association, including the association with the severity of disease in the cases. Future studies of the relationship between periodontitis and overweight/obesity should include measures of body fat and preferably, visceral fat in addition to BMI, to continue to evaluate the validity of each body composition measurement for assessing associated health risks.