Risk factors of chronic periodontitis on healing response: a multilevel modelling analysis

Chronic periodontitis is a multifactorial polygenetic disease with an increasing number of associated factors that have been identified over recent decades. Longitudinal epidemiologic studies have demonstrated that the risk factors were related to the progression of the disease [1, 2]. Plaque is an initial and necessary factor for the development of periodontal disease, but it alone does not necessitate periodontitis. The combined effects of different risk factors associated with periodontitis will eventually lead to periodontitis. Currently, some known risk factors associated with periodontitis include plaque, bleeding on probing (BOP), tooth position, age, gender, socio-economic position, smoking, stress-experience, obesity, systemic disease, and more [3‐6].^. In the past 30 years, one of the great advances in periodontology was the discovery of a huge difference in peoples’ susceptibility to periodontitis, which was mainly demonstrated in the occurrence, development, and response to treatment. Risk factor assessment and control have been paid increasing amounts of attention due to the belief that these are important parts of periodontal diagnosis and treatment [7]. Recently, researchers have proposed periodontal risk assessments (PRA) or periodontal risk calculators (PRC) [8, 9]. The basic idea is to put various periodontal risk factors together for analysis to help doctors more accurately analyse the progression and prognosis of diseases and then plan customized treatment for each individual.

A traditional multivariate regression model was used to find risk factors associated with chronic periodontitis. However, the approach requirement of standard statistical procedures demands individual independence. If this is not done, the analysis will give incorrect or potentially misleading results. Another drawback is that in averaging lower level data to a higher level, the data is artificially simplified; consequently, the statistical power will appear biased. Multilevel modelling (MLM) data analysis has widely been used in recent years, regarding thorough hierarchical structuring of the data, decomposing the error terms into different levels, and providing a new analytic method and framework for solving this problem [10].

In much of periodontal research, statistical methods assume that the site observations are independent and use the mean values of sites to represent the full mouth. Periodontal data with an inherently hierarchical structure poses difficulties for analysis, as sites are clustered around a tooth, and teeth are clustered within the patient. The statistical approach of MLM may solve this problem. MLM has been widely used in education research, management, economics, sociology, psychology, and other fields but less for medical research [11]. In 1992, Albandar [12] stated that the application of multilevel modelling analysis on periodontal research may have important significance. Application of multilevel modelling analysis may provide a more accurate explanation of the natural hierarchical structure of the progression and healing responses after periodontal treatment [13].

MLM can provide a novel approach for periodontal disease research [14, 15]. The aim of the present study was to investigate, by means of multilevel analysis, risk factors that may affect moderate to severe periodontitis prognosis in Northern Chinese who have undergone non-surgical periodontal treatment.

In this study, 54 patients were investigated. A summary of each level and variable is described in Table 2. The mean age was 44.7 ± 1 years old, ranging from 35 to 59, and the sample was equally distributed in terms of gender (55.6% females). Fourteen patients were smokers among the males, and there were no female smokers. The mean BMI was 23.2 ± 0.5 kg/m². There were 5 patients who socio-economic position in terms of income monthly was more than 599.60 USD, 28 patients with 299.76–599.60 USD, 13 patients with 149.88–299.76 USD, 6 patients with 74.94–149.88, and 2 patients with less than 74.94 USD. There were 5 patients with a post-graduate education level, 38 patients with a graduate level, and 11 patients with senior school or below. 57.4% (31/54) of the patients had stress-experience. The mean number of missing teeth was 1.5 ± 0.2. The sample included a total of 1431 teeth, included 641 anterior teeth, 413 premolars, and 377 M. There were 1131 teeth with mobility of 0, 202 teeth with mobility of 1, 80 teeth with mobility of 2, and 18 teeth with mobility of 3. The mean percentage sites with plaque at baseline were79.3% ± 1.6%. The mean percentage sites with BOP at baseline were 66.5% ± 2.0%. The mean percentage sites with PD ≥ 5 mm at baseline were 28.3% ± 1.8%. Sites with positive plaque index totaled 4547, while 3784 sites were positive in BOP index, making a total of 5724 sites at baseline.

In response to non-surgical periodontal treatment, the full-mouth mean PD and CAL showed significant reductions at 3, 6, and 12 months compared with the baseline. The mean PD was 3.83 ± 0.17 mm at baseline, 2.04 ± 0.01 mm at 3 months, 2.01 ± 0.01 mm at 6 months, 1.19 ± 0.01 mm at 12 months. The mean CAL was 3.70 ± 0.03 mm at baseline, 3.04 ± 0.02 mm at 3 months, 2.98 ± 0.02 mm at 6 months, 2.89 ± 0.02 mm at 12 months.

The present study used variance component models to correctly specify the periodontal data structure and to compare the risk factors of moderate to severe periodontitis. The results of this study demonstrated that variances at each level were statistically significant, implying that ignoring any level may result in an erroneous finding. The variance components showed that the teeth variable was less similar than the subjects variable, and the sites variable was greater than both, implying that the reduction of PD and CAL is more substantial among different sites. Thus, the subject, tooth, and site levels should be considered when investigating the correlation structure of periodontal data.

A previous study [17] used a “periodontal pocket close” situation as evaluation criteria for the efficacy of periodontal treatment. However, “periodontal pocket close” did not fully reflect the effects of basic treatment, especially for patients with moderate to severe periodontitis. Therefore, in this study we used not only PD reduction but also CAL reduction as indicators to evaluate the efficacy of non-surgical periodontal treatment for patients with severe chronic periodontitis. The results of the present study demonstrated that non-surgical periodontal treatment was significant for moderate to severe periodontitis. Site-level had the most effect on PD and CAL reductions. The result was the same as that in Tu YK’s study [18].

Subject-level showed that males have a higher prevalence of clinical attachment loss. Studies by Hujoel et al. [19] have shown that male attachment loss is ≥3 mm, ≥4 mm, and ≥5 mm higher than 23%, 44%, and 55% that of females, respectively; that males consistently had more attachment loss than females at each age is not surprising due to males’ poor oral health awareness, self-care, and dental visiting practices, which may be related to hormones and oral hygiene habits [20]. Recent studies have shown that even after excluding smoking and oral hygiene, males still have higher morbidity, that relatively severe periodontitis may be related to differences in sex-specific immune function of the X chromosome differences, and that hormone secretion may lead to differences in the immune response of gender [21]. Male and female innate immunity and acquired immunity are quite different: the male body contains more IL-1β and TNF-α than the female body, but for these cytokines, infection and damage are closely linked. Smoking showed a negative impact on CAL reduction, which corroborates data reported in reviews on the effect of smoking on the outcome of periodontal treatment [22]. Smoking seriously affects periodontal health and inhibits osteoblast growth due to aggravated bone resorption. The random intercept models revealed an interaction between smoking and CAL reduction, yielding negative effect of smoking compared with non-smokers [23].Non-surgical periodontal therapy in patients with the poor treatment effect of smoking may inhibit fibroblast cell growth and attachment, affect the repair of periodontal tissue, affect periodontal tissue microcirculation, and impede periodontal tissue metabolism. Similar findings have previously been reported showing that smoking was a significant factor in determining the short-term clinical outcome of non-surgical periodontal treatment [24]. Stress-experience factors included stress, depression, anxiety, and tension. The higher the stress-experience, the higher the level of gingival sulcus fluid IL-6, which may affect immune function [25]. Pressure increases glucocorticoids to suppress the immune response of T cells, which makes it easy to accumulate plaque and affect the effectiveness of the treatment.

The tooth-level covariates continued to show a linear relationship for the percentage of sites with PD ≥ 5 mm at baseline and mobility. The main indicator of the degree of periodontal inflammation is PD. The more percentage of sites with PD ≥ 5 mm at baseline represents a more mobility-described infection and more severe inflammation, which means that then on-surgical periodontal treatment had room for improvement. During the period of observation, the premolars showed less overall change in CAL reduction than the anterior but more than the molars. Molars (M) are considered to have a more doubtful prognosis [26]. The presence of furcation defects, the divergence of roots, the dimensions of the furcation entrances, the concavities on the root surfaces, the cervical enamel projections, and even the more posterior position of the arches are considered factors that influence the establishment and progression of the periodontal destruction of molars. Due to the presence of molar root bifurcation, inflammation is not easy to control, and cleaning the rearward position is difficult. Because of the existence of premolar root surface concaves, infection is not easy to control.

Among the site-level covariates, there were some new discoveries. BOP at baseline and surface of tooth showed positive predictions for PD reduction and CAL reduction with non-surgical periodontal treatment [27]. As we all know, plaque is the initial factor for periodontitis. However, the present study showed that the plaque at baseline and the percentage of sites with plaque at baseline had no significant effect on the prognosis of non-surgical periodontal treatment. Tomasi C et al. [17], in a multilevel approach of factors influencing the outcome of non-surgical periodontal treatment, showed that the aggregated variable of percentage of sites with plaque on the subject-level was not a significant factor, but the presence of plaque at site-level was identified as significant. The main difference is the reason for the research object selected: the Chinese people have relatively poor oral hygiene practices overall, with percentage of sites with plaque at baseline of more than 95%. Therefore, this study did not show an effect of the presence of plaque at the site-level. China is still a developing country; economic development is especially lacking in Northern China, where the socio-economic position and education level is low. The cost of treatment is expensive and not reimbursed, leading to poor awareness of dental initiative, poor oral hygiene, and poor attendance rates in accordance with the development of the treatment plan. Therefore, few patients meet the requirements of the treatment plan.

Taken together, MLM was appropriately applied in the analysis of periodontal data. By means of the relevant data analysis, the risk factors that affected the prognosis of periodontitis could successfully be investigated. Accordingly, the effect of periodontal therapy could be enhanced along with proper control of the relevant risk factors. Although the one-factor evaluation was far from adequate for predicting disease prognosis, multi-factor analysis was overwhelmingly successful in evaluating the prognosis of individual periodontitis [24, 28]. For the reason that various risk factors showed different effects on disease and the same individual was affected by multiple risk factors at the same time, evaluation of the effect on disease prognosis from multiple risk factors on the same individual was particularly important in guiding our clinical work.

The study found that non-surgical periodontal treatment with regular follow-up visits had a remarkable curative effect for moderate to severe chronic periodontitis in terms of PD and CAL reductions. Periodontal data have been confirmed to have an inherently hierarchical structure. Use of MLM allowed determination of the impact of the patient, the tooth, and the tooth site factors on healing responses to non-surgical periodontal therapy in chronic periodontitis. Through MLM analysis found all three levels had a substantial influence on the reduction of PD and CAL. Site-level had the largest effect on PD and CAL reductions.