Poor Oral Hygiene and High Levels of Inflammatory Cytokines in Saliva Predict...

[Full title: Poor Oral Hygiene and High Levels of Inflammatory Cytokines in Saliva Predict the Risk of Overweight and Obesity]

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International Journal of Environmental Research and Public Health Article Poor Oral Hygiene and High Levels of Inflammatory Cytokines in Saliva Predict the Risk of Overweight and Obesity Kacper Nijakowski 1, * , † , Anna Lehmann 1, † , Rafał Rutkowski 2 , Katarzyna Korybalska 2 , Janusz Witowski 2 and Anna Surdacka 1 1 Department of Conservative Dentistry and Endodontics, Poznan University of Medical Sciences, 61-701 Pozna ´n, Poland; annalehmann@ump.edu.pl (A.L.); annasurd@ump.edu.pl (A.S.) 2 Department of Pathophysiology, Poznan University of Medical Sciences, 61-701 Pozna ´n, Poland; rrutkowski@ump.edu.pl (R.R.); koryb@ump.edu.pl (K.K.); jwitow@ump.edu.pl (J.W.) * Correspondence: kacpernijakowski@ump.edu.pl † These authors contributed equally to this work Received: 27 July 2020; Accepted: 28 August 2020; Published: 30 August 2020 Abstract: The study aimed to determine if oral hygiene influences not only oral health but also potentially metabolic disorders such as overweight or obesity. Participants were 94 patients: 40 with increased body mass and 54 with normal body mass. The methods included dental examination, a questionnaire concerning hygienic habits and an assessment of selected salivary inflammatory markers. The new parameter named “cleaning index” (describing the interaction between average time of tooth brushing in minutes and its frequency per day) significantly correlated with Body Mass Index (R Spearman = 0.300). The multivariate regression model incorporating cleaning index, approximal plaque index, receptor 1 for tumor necrosis factor-alpha (TNF α -R 1) and interleukin-15 (IL-15) had a high power to predict overweight or obesity (AUC = 0.894). Patients with poor oral hygiene (approximal plaque index > 40%) were more than eight times more likely to su ff er from obesity than patients with good oral hygiene. Cleaning index higher than 4 decreased the odds by about 85%. Oral hygiene habits, adjusted by salivary concentrations of selected inflammatory markers may allow predicting e ff ectively overweight or obesity risk. Early proper dental prophylaxis and treatment could lead to the better prevention of metabolic disorders Keywords: obesity; oral health; oral hygiene 1. Introduction Obesity is a chronic disease with a strong tendency to family history. It a ff ects not only adults but also children and adolescents. In recent years, according to the World Health Organization, obesity has reached pandemic proportions [ 1 ]. The most common causes of the disorder are an excessive intake of food, mainly highly processed carbohydrates, and lack of physical activity Substances secreted by adipose tissue are present not only in blood but also in saliva [ 2 – 6 ]. Many scientific reports indicate that in addition to the systemic e ff ect, obesity also has a negative impact on the oral cavity. The relationship between overweight and tooth decay and periodontitis is being discussed among researchers [ 7 – 9 ]. More and more scientific reports point to changes in the microflora of the oral cavity in the course of obesity. The research by Goodson et al. [ 10 ] seems very interesting, who are trying to answer the question of whether oral cavity bacteria are capable of causing obesity. Bacterial DNA was isolated and identified from saliva samples. It turned out that in the saliva of 98.4% of obese women, the presence of Selenomonas noxia was detected. The authors put forward a hypothesis about the induction of inflammatory processes leading to obesity with these bacteria Int. J. Environ. Res. Public Health 2020 , 17 , 6310; doi:10.3390 / ijerph 17176310 www.mdpi.com / journal / ijerph

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Int. J. Environ. Res. Public Health 2020 , 17 , 6310 2 of 10 Poor dietary habits or ubiquitous carbohydrates added to most food products are, in a way, the common denominator of 21 st-century diseases. Both obesity and oral diseases are multifactorial and multidimensional. Many overlapping factors are needed for their development. It is obvious that obese patients have an increased and more frequent supply of food, often of little nutritional value and rich in simple sugars and fats. Many researchers also describe the deterioration of oral hygiene and reduced saliva flow in this group. All of these parameters are factors contributing to a higher incidence of dental caries [ 11 – 13 ]. Our study aimed to assess if oral hygiene care influences not only on oral health status, but also potentially on metabolic disorders such as overweight or obesity 2. Materials and Methods The study group comprised 94 patients (aged 20–54 years, including 57 women) from Department of Internal Diseases, Metabolic Diseases and Dietetics ( n = 40, with increased body mass) and Department of Conservative Dentistry and Periodontology ( n = 54, with normal body mass) at the Poznan University of Medical Sciences. None of the patients su ff ered from coexisting systemic diseases All patients were measured and weighted to determine values of Body Mass Index (BMI). Table 1 presents the distribution of BMI values for this group according to intervals established by the World Health Organization (WHO) Table 1. Distribution of Body Mass Index in patients Category BMI, kg / m 2 n Underweight < 18.5 6 Normal [18.5–25) 48 Overweight [25–30) 11 Obese class I [30–35) 13 Obese class II [35–40) 9 Obese class III > 40 7 The patients were asked to answer a questionnaire regarding oral hygiene habits, especially the average time of single tooth brushing (in minutes) and its frequency per day. Based on these two parameters, cleaning index was calculated as their interaction—it was equal to the product of them and thus represented the total number of minutes spent on brushing during the day. Moreover, a clinical examination was performed to assess selected parameters of dental (decayed, missing or filled teeth, DMF-T; decayed, missing or filled surfaces, DMF-S—according to WHO criteria), oral hygiene (approximal plaque index, API; plaque index, PlI) and periodontal (gingival index, GI; sulcus bleeding index, SBI; periodontal probing depth, PPD) status, according to routine methods using mirror, dental probe and periodontal probe WHO-621 (Hu-Friedy Mfg. Co., US) in artificial light All patients were examined by the same investigator (A.L.). Included patients were free from active caries and periodontitis After examination, unstimulated whole mixed saliva was collected for 20 min using the Falcon tubes (volume 50 mL), at the same time of day between 11 a.m. and 1 p.m. Patients were instructed to refrain from eating and drinking at least 2 h before the collection, and to avoid swallowing during it. Subjects were seated comfortably with their eyes open, head tilted slightly forward and their saliva was dripping from the lower lip to the tube located in the container with ice. Then, saliva samples were centrifuged for 10 min with 2000 rpm using Centrifuge MPW-223 e (MPW Med. Instruments, Poland), aliquoted into tubes, and stored at − 80 ◦ C until assayed. Salivary concentrations of 7 selected markers associated with inflammation (receptor 1 and receptor 2 for tumor necrosis factor-alpha, TNF α -R 1 and TNF α -R 2; pentraxin 3, PTX-3; interleukin 15, IL-15; monocyte chemoattractant protein-1, MCP-1; soluble intercellular adhesion molecule-1, sICAM-1; soluble CD 40 Ligand, sCD 40 L) were measured with high-sensitivity ELISA immunoassays (DuoSet Immunoassay Development Kit—R&D Systems, Minneapolis, MN, USA) according to instructions.

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Int. J. Environ. Res. Public Health 2020 , 17 , 6310 3 of 10 A statistical analysis was performed using software STATISTICA 13.3 (StatSoft, Cracow, Poland) Due to the lack of normal distribution (assessed by the Shapiro–Wilk test p < 0.05), the data were presented as medians and analyzed with nonparametric statistics using the Mann–Whitney test to compare two unpaired groups. The association between selected parameters was evaluated with the Spearman rank correlation and logistic regression. The significance level was defined as α = 0.05 for all analyses The study was approved by the Poznan University of Medical Sciences Bioethics Committee (No. 189 / 14). All patients gave their written informed consent. Every procedure performed in studies involving human participants was in accordance with the 1964 Helsinki declaration and its later amendments, or comparable ethical standards 3. Results 3.1. Basic Statistics The new parameter, named “cleaning index” (describing the interaction between average time of tooth brushing in minutes and its frequency per day) showed significant correlations with other selected parameters evaluated in the study. The index values were observed to decrease with increasing age (R Spearman = − 0.418, p -value < 0.001) and increasing BMI (R Spearman = − 0.300, p -value = 0.003), and less significantly with increasing WHR (R Spearman = − 0.191, p -value = 0.065). Moreover, the higher the cleaning index was, the lower the values were achieved by dental parameters, such as DMF-T, DMF-S and PPD. In turn, a significant increase in API values was observed with increasing BMI (R Spearman = 0.324, p -value = 0.002) and increasing WHR (R Spearman = 0.232, p -value = 0.028), and in plaque index values only with increasing BMI (R Spearman = 0.342, p -value < 0.001). The parameters plaque index and API correlated strongly positively with each other as well as with the gingival index Tables 2 and 3 present the medians of basic clinical and biochemical parameters determined in the study groups and results of comparison as p -values for the Mann–Whitney test Table 2. Medians of assessed dental parameters in patients Parameter All BMI < 25 BMI ≥ 25 p -Value DMF-T 9.5 8.5 11.0 0.002 * DMF-S 13.0 11.0 19.5 < 0.001 * Approximal plaque index, % 60.0 40.0 80.0 < 0.001 * Plaque index 0.4 0.3 0.7 0.003 * Gingival index 0.3 0.1 0.5 0.013 * Sulcus bleeding index, % 7.7 1.9 15.4 < 0.001 * Periodontal probing depth, mm 0.9 0.8 1.1 0.029 * Brushing frequency / day 2.0 2.0 2.0 0.004 * Brushing time, min 2.0 3.0 2.0 0.010 * Cleaning index 6.0 6.0 4.0 < 0.001 * * Significant di ff erence for p -value < 0.05 according to the Mann–Whitney test Table 3. Medians of assessed salivary concentrations of inflammatory cytokines Cytokine, pg / mL All BMI < 25 BMI ≥ 25 p -Value TNF α -R 1 154.6 98.4 230.6 < 0.001 * TNF α -R 2 53.0 42.6 71.1 0.026 * PTX-3 314.2 246.5 386.8 0.005 * IL-15 9.9 9.0 12.0 0.012 * MCP-1 39.3 20.0 62.6 0.007 * sICAM-1 418.1 325.8 596.9 0.002 * sCD 40 L 4.6 4.7 3.1 0.003 * * Significant di ff erence for p -value < 0.05 according to the Mann–Whitney test.

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Int. J. Environ. Res. Public Health 2020 , 17 , 6310 4 of 10 3.2. Logistic Regression Models In order to confirm the influence of oral health on metabolic disorders such as obesity, the univariate logistic regression for assessed dental parameters was demonstrated on forest plot with odd ratios (Figure 1 ). Binominal categories for these indices were created based on the technique weight of evidence (in relation to the dependent variable—categorical BMI). The most potent risk factor for being in the obese group was the cleaning index equal to or less than 4, directly related to oral hygiene habits Int. J. Environ. Res. Public Health 2020 , 17 , x 4 of 11 3.2. Logistic Regression Models In order to confirm the influence of oral health on metabolic disorders such as obesity, the univariate logistic regression for assessed dental parameters was demonstrated on forest plot with odd ratios (Figure 1). Binominal categories for these indices were created based on the technique weight of evidence (in relation to the dependent variable—categorical BMI). The most potent risk factor for being in the obese group was the cleaning index equal to or less than 4, directly related to oral hygiene habits Figure 1. Forest plot of assessed dental parameters in patients with poor oral hygiene concerning odds ratios for obesity. To better explain the association between oral health and obesity, two logistic regression models were constructed, which take into account predictors with the highest predictive values of d-Somers coefficient, significantly improving their quality. Both models include the approximal plaque index (as a categorical variable), TNF-R 1 and IL-15 concentrations. Still, they differ in parameters of oral hygiene habits regarding cleaning index or brushing frequency per day (as categorical variables). Tables 4 and 5 show the parameters of the predictors incorporated in the models. The calculated odds ratios indicate that the higher API values increase the chance of obesity prevalence to the control group with normal weight—even about eight times. Moreover, higher cleaning index reduces the odds by 85% and only brushing frequency per day by 58%. Table 4. Parameters of predictors incorporated into the multivariate logistic regression model, including cleaning index. β SE Wald Stat. p -Value Odds Ratio Confidence OR − 95% Confidence OR 95% intercept − 3.537 1.054 11.269 <0.001 0.029 0.004 0.229 cleaning index > 4 − 1.913 0.601 10.128 0.001 0.148 0.045 0.480 API > 40% 2.087 0.678 9.471 0.002 8.061 2.134 30.457 TNF-R 1, pg/mL 0.008 0.002 11.402 <0.001 1.008 1.003 1.012 IL-15, pg/mL 0.136 0.054 6.350 0.012 1.145 1.031 1.273 bold variables with significant difference for p -value < 0.05. Figure 1. Forest plot of assessed dental parameters in patients with poor oral hygiene concerning odds ratios for obesity To better explain the association between oral health and obesity, two logistic regression models were constructed, which take into account predictors with the highest predictive values of d-Somers coe ffi cient, significantly improving their quality. Both models include the approximal plaque index (as a categorical variable), TNF-R 1 and IL-15 concentrations. Still, they di ff er in parameters of oral hygiene habits regarding cleaning index or brushing frequency per day (as categorical variables) Tables 4 and 5 show the parameters of the predictors incorporated in the models. The calculated odds ratios indicate that the higher API values increase the chance of obesity prevalence to the control group with normal weight—even about eight times. Moreover, higher cleaning index reduces the odds by 85% and only brushing frequency per day by 58% Table 4. Parameters of predictors incorporated into the multivariate logistic regression model, including cleaning index β SE WaldStat. p -Value Odds Ratio Confidence OR − 95% Confidence OR 95% intercept − 3.537 1.054 11.269 < 0.001 0.029 0.004 0.229 cleaning index > 4 − 1.913 0.601 10.128 0.001 0.148 0.045 0.480 API > 40% 2.087 0.678 9.471 0.002 8.061 2.134 30.457 TNF-R 1, pg / mL 0.008 0.002 11.402 < 0.001 1.008 1.003 1.012 IL-15, pg / mL 0.136 0.054 6.350 0.012 1.145 1.031 1.273 Bold variables with significant di ff erence for p -value < 0.05.

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Int. J. Environ. Res. Public Health 2020 , 17 , 6310 5 of 10 Table 5. Parameters of predictors incorporated into the multivariate logistic regression model, including brushing frequency per day β SE Wald Stat. p -Value Odds Ratio Confidence OR − 95% Confidence OR 95% intercept − 4.165 1.048 15.788 < 0.001 0.016 0.002 0.121 cleaning index > 4 − 0.858 0.609 1.983 0.159 0.424 0.128 1.400 API > 40% 2.012 0.627 10.296 0.001 7.476 2.188 25.550 TNF-R 1, pg / ml 0.007 0.002 11.350 < 0.001 1.007 1.003 1.011 IL-15, pg / ml 0.145 0.051 8.244 0.004 1.157 1.047 1.277 Bold variables with significant di ff erence for p -value < 0.05 The Hosmer–Lemeshow test was used to assess the goodness of fit— p -values higher than 0.05 indicate that the models are well fitted (0.77 vs. 0.25, respectively). The models also have a relatively low Akaike Information Criterion—the regression model incorporating the cleaning index has lower AIC (84.6 vs. 93.9). In addition, the models were validated by the v-fold cross method, and the ROC curves (Figures 2 and 3 ) for the training (blue solid lines) and testing (blue dotted lines) sample were obtained. The high values of the area under the curve for the training curves (AUC = 0.894 vs. 0.864) and the small di ff erences in areas between the training and testing curves ( < 0.025) confirm the excellent quality of the models—again, the logistic regression model including cleaning index is better Int. J. Environ. Res. Public Health 2020 , 17 , x 5 of 11 Table 5. Parameters of predictors incorporated into the multivariate logistic regression model, including brushing frequency per day. β SE Wald Stat. p -Value Odds Ratio Confidence OR − 95% Confidence OR 95% intercept − 4.165 1.048 15.788 <0.001 0.016 0.002 0.121 cleaning index > 4 − 0.858 0.609 1.983 0.159 0.424 0.128 1.400 API > 40% 2.012 0.627 10.296 0.001 7.476 2.188 25.550 TNF-R 1, pg/ml 0.007 0.002 11.350 <0.001 1.007 1.003 1.011 IL-15, pg/ml 0.145 0.051 8.244 0.004 1.157 1.047 1.277 bold variables with significant difference for p -value < 0.05. The Hosmer–Lemeshow test was used to assess the goodness of fit— p -values higher than 0.05 indicate that the models are well fitted (0.77 vs. 0.25, respectively). The models also have a relatively low Akaike Information Criterion—the regression model incorporating the cleaning index has lower AIC (84.6 vs. 93.9). In addition, the models were validated by the v-fold cross method, and the ROC curves (Figures 2 and 3) for the training (blue solid lines) and testing (blue dotted lines) sample were obtained. The high values of the area under the curve for the training curves (AUC = 0.894 vs. 0.864) and the small differences in areas between the training and testing curves (<0.025) confirm the excellent quality of the models—again, the logistic regression model including cleaning index is better. Figure 2. V-fold cross-validation—learning ROC curves for the logistic regression model including cleaning index. Figure 2. V-fold cross-validation—learning ROC curves for the logistic regression model including cleaning index.

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Int. J. Environ. Res. Public Health 2020 , 17 , 6310 6 of 10 Int. J. Environ. Res. Public Health 2020 , 17 , x 6 of 11 Figure 3. V-fold cross-validation—learning ROC curves for the logistic regression model including brushing frequency per day. 3.3. Comparative Analysis In addition, among selected salivary inflammatory markers, a significant decrease in concentration in the obese patients brushing more than twice a day was observed only for MCP-1 and IL-15 (Figures 4 and 5). Similar relationships were not found related to higher cleaning index and longer brushing time, suggesting that frequency is more effective than time. In the control group, there were no significant differences in cytokine concentrations depending on oral hygiene habits. Figure 3. V-fold cross-validation—learning ROC curves for the logistic regression model including brushing frequency per day 3.3. Comparative Analysis In addition, among selected salivary inflammatory markers, a significant decrease in concentration in the obese patients brushing more than twice a day was observed only for MCP-1 and IL-15 (Figures 4 and 5 ). Similar relationships were not found related to higher cleaning index and longer brushing time, suggesting that frequency is more e ff ective than time. In the control group, there were no significant di ff erences in cytokine concentrations depending on oral hygiene habits Int. J. Environ. Res. Public Health 2020 , 17 , x 7 of 11 Figure 4. Box plot for salivary monocyte chemoattractant protein 1 (MCP-1) concentrations in the obese patients related to brushing frequency per day ( p -value < 0.05 according to the Mann–Whitney test). Figure 5. Box plot for salivary interleukin 15 (IL-15) concentrations in the obese patients related to brushing frequency per day ( p -value < 0.05 according to the Mann–Whitney test). Figure 4. Box plot for salivary monocyte chemoattractant protein 1 (MCP-1) concentrations in the obese patients related to brushing frequency per day ( p -value < 0.05 according to the Mann–Whitney test).

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Int. J. Environ. Res. Public Health 2020 , 17 , 6310 7 of 10 Int. J. Environ. Res. Public Health 2020 , 17 , x 7 of 11 Figure 4. Box plot for salivary monocyte chemoattractant protein 1 (MCP-1) concentrations in the obese patients related to brushing frequency per day ( p -value < 0.05 according to the Mann–Whitney test). Figure 5. Box plot for salivary interleukin 15 (IL-15) concentrations in the obese patients related to brushing frequency per day ( p -value < 0.05 according to the Mann–Whitney test). Figure 5. Box plot for salivary interleukin 15 (IL-15) concentrations in the obese patients related to brushing frequency per day ( p -value < 0.05 according to the Mann–Whitney test) 4. Discussion The secretory activity of the adipose tissue and its e ff ect on individual cells, tissues, and organs have been of interest to researchers lately. An excess of adipose tissue, particularly of the visceral type, has been demonstrated to promote and modify the course of periodontitis [ 8 , 9 ]. It results in a higher incidence of dental caries and susceptibility to periodontal diseases. Moreover, the reduced amount of secreted saliva in individuals with obesity was shown to cause changes in the microflora of the oral cavity [ 14 ]. Apart from the deteriorated oral health of patients with obesity, a lower volume of secreted saliva was also observed in this group [ 15 ]. Saliva plays essential roles in cleaning the oral cavity by removing food remnants and bacteria. Higher salivary flow rate increases oral pH, promotes enamel remineralisation, bu ff er capacity, and reduces caries. Therefore, a decrease of the saliva flow significantly restricts its protective properties. Previously published studies have shown that, in individuals with obesity, there is moderate persistent underlying inflammation in the parotid glands. It was coupled with inflammatory mediators secreted by adipose tissue and acting along the HPA axis, which could be contributing to the reduced activity of salivary glands [ 16 ]. Mod é er et al. [ 17 ] proposed a hypothesis on the relationship between obesity, reduced salivary flow, and increased incidence of dental caries in young individuals, which later manifests as one of the negative sequels of obesity in the oral cavity in adulthood. As already mentioned above, the development of dental caries in individuals with obesity is considerably a ff ected by the composition and amount of secreted saliva. Its reduced secretion corresponds to the increased accumulation of dental plaque and deterioration of oral cavity hygiene. This study has shown that, in people with high BMIs ( > 25 kg / m 2 ), more severe dental caries was noted. Additionally, sociodemographic factors such as patient’s age may also a ff ect oral health and saliva secretion. As a result of the aging processes and decreased saliva flow rate, the oral cavity becomes more susceptible to caries, periodontal diseases and mucosal problems. However, in our study, age was not determined as a confounder significantly influencing on the amount of saliva or the condition of the oral cavity Nevertheless, not only tooth decay is a negative consequence of obesity. Chronic gingivitis or periodontitis is one of the most prevalent diseases of an inflammatory-destructive background and,

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Int. J. Environ. Res. Public Health 2020 , 17 , 6310 8 of 10 similarly to obesity, it is classified as a social disease [ 18 ]. We found benign gingivitis in both the study and control groups, as shown by the GI and SBI. A previous study by Range et al. [ 19 ] showed moderate inflammatory conditions in the gingivae of people with obesity and benign gingivitis in patients with normal body weight. They reported a GI index of 1.95 in the study group and 0.51 in the control group. The critical variable which promotes the development of gingivitis involves the presence of bacteria and their interaction with multiple markers of the host-induced inflammatory process. Cells of adipose tissue produce several cytokines and hormones which increase the risk or alter the course of periodontitis [ 20 ]. The measurement of the PPD is also used in evaluating periodontal conditions. The median PPD values were 1.1 mm and 0.8 mm in the study and control group, respectively. We found a statistically significant di ff erence between these values Our study demonstrated and confirmed an apparent relationship between augmented body weight and poor oral hygiene, or the manifestation of gingivitis, or an increased number of teeth a ff ected by dental caries. It has been observed that patients with obesity increase their amount and frequency of food consumed with no appropriate breaks, leading to the continuous accumulation of biofilms, thereby necessitating proper and frequent dental hygienic procedures. Nevertheless, numerous studies indicate that most individuals with obesity have insu ffi cient oral care and inappropriate dental follow-ups Both Hujoel et al. [ 21 ] and Konopka et al. [ 22 ] pointed out the lack of flossing of interdental spaces in patients with obesity. While the presence of dental deposits did not in any way correlate with the intensity of caries and the number of teeth, it generated the occurrence of extensive gingivitis. Furthermore, obesity is typically related to unhealthy lifestyle behaviors, and oral health habits (particularly frequency of tooth brushing) is considering as an indicator of general health-related behaviors. In addition to many somatic diseases that a ff ect a significant deterioration in the quality of life of obese patients, there is also a psychological problem. Obese people often su ff er from depression caused by social rejection and stigma. These mentioned factors, together with decreased physical fitness and dexterity, may be reasons why they pay less attention to regular oral hygiene. The importance of proper hygienic and dietary habits should be emphasized during developing health promotion programs, aimed at preventing obesity already among children and young people [ 23 ]. Overweight and obese patients have a greater chance of being a ff ected by gingivitis due to a combination of metabolic and inflammatory profiles and a neglected attitude towards oral hygiene [ 24 , 25 ]. In earlier studies, we speculated that changes in the salivary cytokine concentrations may have clinical implications for distinguishing obesity-linked comorbidities on oral health [ 26 ]. Porcelli et al. [ 27 ] reported a positive impact of the oral health preventive program in patients who underwent gastroplasties, contributing to their quality of life. Our study demonstrated statistically significant di ff erences between the indices describing hygienic conditions of the oral cavity in individuals with overweight or obesity and those with normal body weights. The analysis of PlI and API indices showed satisfactory hygiene in both groups. Nevertheless, the median API indicated that the study group had an overall more inadequate oral hygiene when compared with the control group (80% vs. 40%) and the median PlI was 0.7 and 0.3, respectively. Poor oral hygiene may be strongly associated with higher odds of predisposition to overweight or obesity, as suggested by our regression models incorporating the cleaning index and approximal plaque index. Constructed models allow predicting overweight or obesity risk connected with inappropriate oral hygiene habits 5. Conclusions Our results demonstrated that it is possible to confirm the hypothesis that adipose tissue metabolites interact with the clinical conditions of the oral cavity, strongly related to individual hygienic habits. Perhaps it will allow for the implementation of systemic solutions that would enable appropriate steps to be taken to increase dental care for overweight and obese patients, both in terms of prevention and treatment.

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Int. J. Environ. Res. Public Health 2020 , 17 , 6310 9 of 10 Author Contributions: Conceptualization, J.W., A.S., A.L. and K.N.; methodology, J.W. and A.S.; formal analysis, K.N.; investigation, A.L. and R.R.; resources, A.L. and R.R.; writing—original draft preparation, K.N. and A.L.; writing—review and editing, K.N., A.S., K.K. and J.W.; visualization, K.N.; supervision, A.S., K.K. and J.W. All authors have read and agreed to the published version of the manuscript Funding: This research received no external funding Conflicts of Interest: The authors declare no conflict of interest References 1 World Health Organization Obesity: Preventing and Managing the Global Epidemic ; Report of a WHO Consultation, World Health Organization Technical Report Series 894; World Health Organization: Geneva, Switzerland, 2000; pp. 1–253 2 Du ffl es, L.F.; Hermont, A.P.; Abreu, L.G.; Pordeus, I.A.; Silva, T.A. Association between obesity and adipokines levels in saliva and gingival crevicular fluid: A systematic review and meta-analysis J. Evid Based Med 2019 , 12 , 313–324. [ CrossRef ] 3 Goodson, J.M.; Kantarci, A.; Hartman, M.-L.; Denis, G.V.; Stephens, D.; Hasturk, H.; Yaskell, T.; Vargas, J.; Wang, X.; Cugini, M.; et al. Metabolic Disease Risk in Children by Salivary Biomarker Analysis PLoS ONE 2014 , 9 , e 98799. [ CrossRef ] 4 Mamali, I.; Roupas, N.D.; Armeni, A.K.; Theodoropoulou, A.; Markou, K.B.; Georgopoulos, N.A. Measurement of salivary resistin, visfatin and adiponectin levels Peptides 2012 , 33 , 120–124. [ CrossRef ] [ PubMed ] 5 Nigro, E.; Piombino, P.; Scudiero, O.; Monaco, M.L.; Schettino, P.; Chambery, A.; Daniele, A. Evaluation of salivary adiponectin profile in obese patients Peptides 2015 , 63 , 150–155. [ CrossRef ] [ PubMed ] 6 Thanakun, S.; Watanabe, H.; Thaweboon, S.; Izumi, Y. Comparison of salivary and plasma adiponectin and leptin in patients with metabolic syndrome Diabetol. Metab. Syndr 2014 , 6 , 19. [ CrossRef ] 7 Kim, J.S.; Kim, S.Y.; Byon, M.J.; Lee, J.H.; Jeong, S.H.; Kim, J.B. Association between Periodontitis and Metabolic Syndrome in a Korean Nationally Representative Sample of Adults Aged 35–79 Years Int. J Environ. Res. Public Health 2019 , 16 , 2930. [ CrossRef ] [ PubMed ] 8 De Moura-Grec, P.G.; Marsicano, J.A.; de Carvalho, C.A.P.; Sales-Peres, S.H.D.C. Obesity and periodontitis: Systematic review and meta-analysis Cien. Saude Colet 2014 , 19 , 1763–1772. [ CrossRef ] [ PubMed ] 9 Suvan, J.E.; Finer, N.; D’Aiuto, F. Periodontal complications with obesity Periodontol. 2000 2018 , 78 , 98–128 [ CrossRef ] 10 Goodson, J.M.; Groppo, D.; Halem, S.; Carpino, E. Is Obesity an Oral Bacterial Disease? J. Dent. Res 2009 , 88 , 519–523. [ CrossRef ] 11 Andreeva, V.A.; Egnell, M.; Galan, P.; Feron, G.; Hercberg, S.; Julia, C. Association of the Dietary Index Underpinning the Nutri-Score Label with Oral Health: Preliminary Evidence from a Large, Population-Based Sample Nutrients 2019 , 11 , 1998. [ CrossRef ] 12 Garcia, R.I.; Kleinman, D.; Holt, K.; Battrell, A.; Casamassimo, P.; Grover, J.; Tinano ff , N. Healthy Futures: Engaging the oral health community in childhood obesity prevention—Conference summary and recommendations J. Public Health Dent 2017 , 77 , S 136–S 140. [ CrossRef ] [ PubMed ] 13 Kantovitz, K.R.; Pascon, F.M.; Rontani, R.M.P.; Gavi ã o, M.B.D. Obesity and dental caries—A systematic review Oral Health Prev. Dent 2006 , 4 , 137–144. [ PubMed ] 14 Mervish, N.A.; Hu, J.; Hagan, L.A.; Arora, M.; Frau, C.; Choi, J.; Attaie, A.; Ahmed, M.; Teitelbaum, S.L.; Wol ff , M.S. Associations of the Oral Microbiota with Obesity and Menarche in Inner City Girls J. Child. Obes 2019 , 4 , 2. [ CrossRef ] [ PubMed ] 15 Lehmann, A.P.; Nijakowski, K.; Swora-Cwynar, E.; Łuczak, J.; Czepulis, N.; Surdacka, A. Characteristics of salivary inflammation profile in obesity Pol. Arch. Intern. Med 2020 , 130 , 297–303. [ CrossRef ] [ PubMed ] 16 Amar, S.; Zhou, Q.; Shaik-Dasthagirisaheb, Y.; Leeman, S.E. Diet-induced obesity in mice causes changes in immune responses and bone loss manifested by bacterial challenge Proc. Natl. Acad. Sci. USA 2007 , 104 , 20466–20471. [ CrossRef ] 17 Mod é er, T.; Blomberg, C.C.; Wondimu, B.; Julihn, A.; Marcus, C. Association Between Obesity, Flow Rate of Whole Saliva, and Dental Caries in Adolescents Obesity 2010 , 18 , 2367–2373. [ CrossRef ] 18 Roa, I.; Del Sol, M. Obesity, salivary glands and oral pathology Colomb. Med 2018 , 49 , 280–287. [ CrossRef ]

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Int. J. Environ. Res. Public Health 2020 , 17 , 6310 10 of 10 19 Rang é , H.; L é ger, T.; Huchon, C.; Ciangura, C.; Diallo, D.; Poitou, C.; Meilhac, O.; Bouchard, P.; Chaussain, C Salivary proteome modifications associated with periodontitis in obese patients J. Clin. Periodontol 2012 , 39 , 799–806. [ CrossRef ] 20 Akram, Z.; Abduljabbar, T.; Abu Hassan, M.I.; Javed, F.; Vohra, F. Cytokine Profile in Chronic Periodontitis Patients with and without Obesity: A Systematic Review and Meta-Analysis Dis. Markers 2016 , 2016 , 1–12 [ CrossRef ] 21 Hujoel, P.; Kressin, N.R.; Cunha-Cruz, J. Spurious associations in oral epidemiological research: The case of dental flossing and obesity J. Clin. Periodontol 2006 , 33 , 520–523. [ CrossRef ] 22 Konopka, T.; Matuszewska, A.; Chrz˛eszczyk, D.; Zawada, D. Body Mass Index and selected periodontal clinical parameters Dent. Med. Probl 2011 , 48 , 189–197 23 Park, J.B.; Nam, G.E.; Han, K.; Ko, Y.; Park, Y.G. Obesity in relation to oral health behaviors: An analysis of the Korea National Health and Nutrition Examination Survey 2008–2010 Exp. Ther. Med 2016 , 12 , 3093–3100 [ CrossRef ] [ PubMed ] 24 Franchini, R.; Petri, A.; Migliario, M.; Rimondini, L. Poor oral hygiene and gingivitis are associated with obesity and overweight status in paediatric subjects J. Clin. Periodontol 2011 , 38 , 1021–1028. [ CrossRef ] 25 Ferraz, E.G.; Silva, L.R.; Sarmento, V.A.; Campos, E.D.J.; De Oliveira, T.F.L.; Magalh ã es, J.C.; Paraguass ú , G.M.; Sorte, N.C.B. Association between childhood obesity and oral hygiene status Nutr. Hosp 2014 , 30 , 253–259 [ PubMed ] 26 Lehmann-Kalata, A.; Miechowicz, I.; Korybalska, K.; Swora-Cwynar, E.; Czepulis, N.; Łuczak, J.; Orzechowska, Z.; Grzymisławski, M.; Surdacka, A.; Witowski, J. Salivary fingerprint of simple obesity Cytokine 2018 , 110 , 174–180. [ CrossRef ] 27 Porcelli, I.C.D.S.; Corsi, N.M.; Fracasso, M.D.L.C.; Pascotto, R.C.; Cardelli, A.A.M.; Poli-Frederico, R.C.; Nasser, D.; Maciel, S.M. Oral Health Promotion in Patients with Morbid Obesity After Gastroplasty: A Randomized Clinical Trial Arq. Bras. Cir. Dig 2019 , 32 , e 1437. [ CrossRef ] [ PubMed ] © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http: // creativecommons.org / licenses / by / 4.0 / ).

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