For this study, newly admitted consecutive patients (≥18 years of age) at the Academic Centre for Dentistry Amsterdam (ACTA) were recruited while attending for the first time the outpatient clinic. There was no (recent) dental or medical information available about these patients beforehand. The study was approved by the medical ethical committee of the Vrije Universiteit Medical Centre (2014.585 [A2016.155]). All patients with at least one natural tooth were eligible; edentulous patients, with or without full dentures, were not considered (regardless of dental implant support). First, the SROH questionnaire was conducted, followed by the oral rinse sampling. Next, a complete clinical periodontal examination was performed by one of two calibrated periodontists (SB and WJT). Finally, demographic data, such as age, sex and smoking status were derived from the electronic health records.
Self-reported oral health questionnaire (SROH)
The original SROH questionnaire consisted of eight items [14]. The questions, their Dutch translations and the used abbreviations can be found in Additional file 1. The questionnaire was conducted by a non-dentist (author MJLV), mimicking the setting where non-dental medical professionals conduct the questionnaire. All questions were closed-ended; five items had ‘yes’ or ‘no’ as answer possibilities (Q1 and Q3 – Q6). In one item (Q2), the patient was asked to rate the health of his/her teeth and gums on a five-point scale. Question 7 and 8 inquired how often the patient used interdental hygiene products (Q7) or mouthwash/oral rinse products (Q8), expressed as days per week. Finally, each item had an additional answer possibility of ‘don’t know’, and the patient was also allowed to refuse to answer if desired.
Oral rinse sampling
To collect the oral rinses, a sampling and processing method was used as described previously [18]. The following minor adjustments were made to make the method more suitable for the primary care setting. Instead of the rather time-consuming protocol of three times 30 s rinsing with 20 ml phosphate buffered saline (PBS) – which also tastes rather bitter – the patient was instructed to thoroughly rinse once with a more user-friendly 10 ml saline solution (0.9% sodium chloride) for 30 s, as used before for the characterization of oral neutrophils [19]. In order to create realistic circumstances comparable to daily practice in a non-dental setting, there were no pre-defined requirements for the participants regarding the condition of the oral cavity (e.g. tooth brushing or time after having a meal). The patients were instructed to swallow once before starting the 30 s oral rinse protocol. After expectorating in a medicine cup, the sample was transferred back into a coded 50 ml Falcon tube and stored on ice for a maximum of three hours. Later, the samples were resuspended by vortexing for several seconds, and filtered, to remove any food residue and epithelial cells. Filters with a pore size of 70 μm (EASY Strainer™, Greiner Bio-One GmbH, Frickenhausen, Germany), 30 μm and 10 μm (both Merck Millipore™ Ltd., Cork, Ireland) were used in that specific order. Next, the filtered samples were centrifuged at 500 g at 4 °C for 10 min. The supernatant was aliquoted and stored at − 80 °C until further use.
Periodontal examination
Clinical periodontal examination was performed by one of two calibrated periodontist (SB or WJT). The examination consisted of measurements of both positive and negative gingival recessions, probing pocket depth (measured from the margin of the gingiva to the depth of the pocket) and bleeding on probing (presence/absence) at six sites per tooth. The Centers for Disease Control and Prevention-American Academy of Periodontology (CDC-AAP) case definition for periodontitis was used: Severe periodontitis: the presence of 2 or more interproximal sites with ≥6 mm attachment loss (not on the same tooth) and 1 or more interproximal site(s) with ≥5 mm probing pocket depth; Moderate periodontitis: 2 or more interproximal sites with ≥4 mm clinical attachment loss (not on the same tooth) or 2 or more interproximal sites with probing pocket depth ≥ 5 mm, also not on the same tooth); Mild or no periodontitis: neither “severe” nor “moderate” periodontitis. Total periodontitis represents all patients with moderate or severe periodontitis [20].
Biomarker analysis
Albumin
Albumin levels were analyzed using Enzyme-Linked Immuno Sorbent Assay (ELISA). To do so, 96-wells microplates were coated overnight at 4 °C with 100 μl polyclonal rabbit anti-(human albumin) antibody (DAKO, Denmark, Glostrup) in 100 μl coating buffer (Na2CO3; pH 9.6). Next, the oral rinse samples were diluted 1:100 in PBS supplemented with 0.1% Tween-20 (PBS-T), and added to the microplates in duplicate. As standard, human serum albumin solution was used. Both the standard and the samples were serially diluted twofold in PBS-T and incubated for 1 h at 37 °C. Next, horseradish peroxidase-conjugated rabbit anti-(human albumin) (GeneTex, Irvine, CA, USA) was added, followed by one hour of incubation at 37 °C. Finally, O-Phenylenediamine dihydrochloride solution was used as substrate, and the reaction was stopped by adding 50 μl of 4 N H2SO4 solution. The final albumin concentration (μg/ml) was measured using a Multiskan™ FC Microplate Photometer (Thermo Scientific Multiskan FC, Rockford, IL, USA).
Chitinase activity
Chitinase activity was measured by adding 90 μl oral rinse sample to 10 μl substrate solution of 4-Methylumbelliferyl β-D-N,N′,N′′-triacetylchitotrioside (Sigma-Aldrich Chemie B.V., Zwijndrecht, Netherlands) in black 96-wells microplates (Greiner Bio-One GmbH, Frickenhausen, Germany) [21]. All microplates contained a reference sample that consisted of pooled oral rinses, collected from 10 healthy dental students and staff members. All samples were analyzed in duplicate. Chitinase activity, expressed as increase in fluorescence, was measured for 1 h at 37 °C using a microplate reader (BMG FLUOstar Galaxy, MTX Lab Systems, Vienna, VA, USA). The chitinase activity for the reference sample was set as 1 arbitrary unit (AU)/ml; all oral rinse samples were quantified relative to the reference sample.
Protease activity
Protease activity in the oral rinses was determined using black 96-wells microplates (Greiner Bio-One GmbH, Frickenhausen, Germany). In each well, 80 μl oral rinse sample was added, together with 20 μl of PEK-054 ([FITC]-NleKKKKVLPIQLNAATDK-[KDbc]), a substrate previously used to assess total protease activity [22]. A trypsin solution from bovine pancreas (500 U start concentration) was used as standard, which was serially diluted in two-fold steps. The samples and standards were analyzed in duplicate. Protease activity, expressed as increase in fluorescence, was measured continuously at 37 °C using the BMG FLUOstar Galaxy microplate reader (MTX Lab Systems), and terminated when no further increase was observed (after approximately 1 h).
MMP-8
The MMP-8 concentrations in the oral rinse samples were analyzed using a commercially available pre-coated human MMP-8 ELISA kit (Boster Biological Technology, Pleasanton, CA, USA) according to the instruction manual provided by the manufacturer.
Statistical analysis
IBM SPSS statistics version 25 was used for statistical analysis. Similar to the original study [14], items from the SROH questionnaire with more than 2 outcome possibilities were dichotomized, and all responses where coded with either 0 or 1. Further, as described previously, missing and refused items, as well as the response ‘don’t know’, were excluded from analysis [14]. Age was also recoded into two groups: younger than 40 years old vs. 40 years or older.
Next, Shapiro-Wilk tests were used to assess whether the continuous variables (clinical measurements and biomarkers) followed a normal distribution. Descriptive statistics were applied to provide insight in patient characteristics. Next, univariate associations between the potential predictors and periodontitis were assessed. One-way ANOVAs or Kruskal-Wallis tests were used to analyze continuous data (clinical measurements and biomarkers) across all periodontitis groups (no/mild, moderate and severe periodontitis). Student’s t-test or Mann-Whitney U tests were used to test for differences between total periodontitis and no/mild periodontitis, and between severe periodontitis and no severe periodontitis. Chi-square tests were used to analyze categorical data (SROH and demographics).
To prevent overfitting of the prediction models, only candidate predictors that showed a univariate association with total and/or severe periodontitis were selected for the prediction modelling, with a cut-off p-value of 0.20 [23]. Each of the predictors selected for modelling was tested for multi-collinearity through assessment of the variance inflation factor (VIF) and tolerance, by running linear regression analysis. VIF values higher than 5 and/or tolerance values lower than 0.1 indicated serious suspicion of multicollinearity, as suggested in literature [24, 25].
Clinical screening tools were developed for two specific disease outcomes: total periodontitis (moderate and severe combined) and severe periodontitis. This consisted of five steps:
Step 1: modeling
Prediction models were developed by performing binary logistic regression analysis. For both disease outcomes (total periodontitis or severe periodontitis), three models were created and assessed. Model 1 included all candidate predictors in the analysis. The biomarkers were omitted in the analysis for model 2, while model 3 only included the SROH questionnaire. Total and severe periodontitis were entered as dependent variables, while candidate predictors – determined with the univariate analysis – were entered as covariates. Stepwise backward elimination by likelihood ratio removed the predicting items with the highest p-value from the models step by step, until all of the remaining items had a statistically significant contribution. The predicted probability was saved as a new variable.
Step 2: discrimination
The discriminative performance – or accuracy – of the created models could be described as the ability to distinguish between subjects with and without the disease outcome, in this case (severe) periodontitis. The accuracy was expressed as the area under the receiver operating characteristic (ROC) curve (AUROCC), also known as c-statistic [26]. This was derived by plotting the predicted probability (derived during step 1) against the actual disease state in an ROC curve. An AUROCC of 1.0 indicates that the model perfectly discriminates between diseased and non-diseased subjects; an AUROCC of 0.5 means the model doesn’t discriminate better than ‘random’ (i.e. flipping a coin) [26]. For further analysis, the optimal predicted probability cut-off value had to be identified. This predicted probability was defined as the value with the highest sum of sensitivity and specificity across the ROC curve.
Step 3: calibration
Agreement between the predicted probability and observed probability (the prevalence of total or severe periodontitis) was assessed by calculating the Hosmer-Lemeshow goodness of fit statistic. A p > 0.10 indicated that the model fitted the data [27].
Step 4: clinical values
A new binary variable was computed, which represented the predicted disease outcome by classifying each individual as either diseased or non-diseased, based on the cut-off score derived in step 2. By creating a two-by-two contingency table with the predicted disease outcome versus the actual disease state, several clinical values were calculated (sensitivity, specificity, positive and negative predictive value).
Step 5: scoring method
An individual sumscore was composed by expressing the prediction model with the following formula:
$$ Y={B}_1\ast {X}_1+{B}_2\ast {X}_2\dots {B}_n\ast {X}_n $$
In this formula, the sumscore (Y) was composed by the predictors (X) – that remained after the binary logistic regression analysis – which all had their own weight, expressed as a constant value (B [regression coefficient]). For the binary predictors, a reference outcome was set a priori by coding a negative outcome as 1, and a positive outcome as 0. For the biomarkers, no reference outcome was required. The sum of responses to all predictors – multiplied with their individual weight (B) – resulted in an individual sumscore. The sumscore that corresponded with the predicted probability cut-off value determined in step 2 was identified. This score represented the threshold value from which the model classified an individual as (severe) periodontitis patient.
The algorithm for total periodontitis using SROH and demographics (model 2) will be applied to develop a freely accessible, web-based screening tool.