IRAK4 gene was selected as a candidate gene for this investigation based on its central function in innate and acquired immunity. The election of a specific IRAK4 polymorphism was performed according to the host response against the oral flora.
Study subjects
A retrospective study review was initially conducted to identify and characterize computed tomography (CT) imaging findings of a Portuguese Caucasian population. Institutional Review Board approval was obtained, and appropriate measures were taken to safeguard patient privacy.
The CT scans were performed according to the following indications: implant planning, study of impacted maxillary teeth, suspected lesion/cyst on the maxillary sinus, ankylosis of the anterior teeth, suspected foreign body in the maxillary sinus, endodontic lesions, oroantral fistula, orthodontic planning, local trauma and dental or bone fractures.
The criteria for patient selection were based on etiological factors of the maxillary sinusitis of odontogenic origin and the pathologic changes present in the maxillary sinus [21–23]. The CT’s observed were from the period between 1993 and 2013. Two observers with over 20 years experience in the field of oral surgery identified the eventual changes present on CT.
In the analysed maxillary sinus, only the adjacent teeth to the maxillary sinus were evaluated [22–28] and the following signs were identified—opacity, presence of fluid, mucosal thickening, cortical bone loss [21, 29] and potential etiologic factors to develop sinusitis of odontogenic origin—teeth protruding into the maxillary sinus (maxillary sinus communication), dental caries, periodontal disease, apical periodontitis, endodontic treatment, iatrogenic signs, dental implants, cysts, foreign bodies, ectopic teeth, oroantral fistula and inclusion of teeth associated with maxillary sinus.
These completed a total of 504 patients, corresponding to 1008 analysed maxillary sinus.
From this base, 153 patients were randomly selected, according to the value of 13 % for the maxillary sinusitis prevalence, with 95 % CI and estimated error of 5.3 % to perform a genetic test.
According to the classification of Maillet et al. (2011), the patients were divided and included into four groups:
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Group IA:
Presence of imagiological findings suggesting sinusitis of odontogenic origin—density of soft tissue mass or air-fluid within the sinus and presence of one of the following criteria: decayed tooth, tooth restoration faulty and extraction site with mucosal thickening. Forty-six (46) patients were identified in this group.
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Group IB:
The sinus was considered healthy in the absence of mucosal thickening or uniform mucosal thickening less than 2 mm. Also, adjacent teeth should evidence signs of carious lesions, decayed tooth, exposed pulp, restoration, extraction and have imaging of apical periodontitis (presence of potential etiologic odontogenic factors). Thirty-four (34) patients were identified as having at least one of this potential etiologic odontogenic factors.
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Group IIA:
Presence of soft tissue mass within the sinus—sinusitis of non-odontogenic origin, having fulfilled the following criteria: healthy teeth, coronal restoration and/or endodontic good quality, absence of periapical lesion, tooth extracted intact or healed alveoli and thickening of the mucosa not limited to any tooth. Thirty-five (35) patients were identified with this condition.
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Group IIB:
Healthy patients—absence of mucosal thickening or uniform mucosal thickening less than 2 mm defective, having fulfilled the following criteria: healthy teeth, coronal restoration and/or endodontic good quality, absence of periapical lesion, tooth extracted intact or healed alveoli and thickening of the mucosa not limited to any tooth (absence of potential etiologic odontogenic factors). Thirty-eight (38) patients were considered in this group.
The genetic test was performed in the same appointment where the baseline demographics and medical data were recorded: familiar history of sinusitis, past maxillary sinus surgery, sinusitis or rhinitis diagnosed by an otolaryngologist, actual drug prescription, asthma or diabetes, smoking habit and presence of symptoms according to the Rosenfeld (2007) criteria—headache, nasal obstruction (congestion), ear pain, mucopurulent drainage, decreased sense of smell and dental pain. Intra-oral examination was carried out concerning facial pain on percussion or palpation, facial edema and erythema.
Inclusion criteria
All adult Caucasian patients (>18 years) who had at least one CT scan performed were included in this study. The patients with immune-compromised conditions, patients with CT without sufficient slices or poor quality image to analyze the maxillary sinus and patients with CT that did not allow analyzing the maxillary teeth were excluded.
Selection of polymorphism in the human IRAK4 gene
The IRAK4 gene analysis was initially performed by sequencing all gene (Fig. 1). The latter was done by polymerase chain reaction (PCR) amplification and sequencing of the entire coding region, including the adjacent intronic regions of the IRAK4 gene. In this first analysis, pathogenic mutations were not detected in the IRAK4 gene. However, the variant c.1882G>A (p.Ala428Thr) was found in heterozygosity (a single nucleotide polymorphism—SNP). This SNP was the same reported to be related with the host susceptibility for development of Gram-positive bacteria infection [13].
After this SNP identification, the following samples were studied directly target to the c.1282G>A (p.Ala428Thr) variant region (reference sequence: NM_001114182.2, being the A of the ATG start position 1). The primers selected to detect the SNP on the IRAK4 gene are present in Table 1. Furthermore, the D (down) was considered the forward, the U (up) the reverse and the 12/13 were together in just a fragment (because it was smaller and likely to be amplified with a single primer pair region).
Table 1 Primers used to detect the SNP on the IRAK4 human gene [Ref Seq: NM_016123]
The genotypic analysis was performed in a blinded fashion, without clinical information. Patients’ genotypes were determined by real-time PCR and the DNA sequencing resorted to the Sanger method.
Statistical analysis
Using the software IBM® SPSS® Statistics 22.0, the sample was characterized, an analytical study of association was conducted and data modelling was performed using the binary logistic regression, for dependent variable “diagnosis” corresponding to two categories, one for healthy individuals and the other for individuals with sinusitis of odontogenic origin. With this, the odds ratio due to the presence or absence of certain risk factor in this sample was evaluated.
After a univariate selection of variables, the construction of a model proceeded. In the construction of the logistic regression model, the forward stepwise technique was used to optimize variable selection, in order to select a set of variables that could contribute to the outcome and that this contribution revealed statistically significant. Therefore, in the previous univariate analysis, we took into account the p values obtained in the Wald statistic, and these variables enter as candidates to integrate the model.
The objective was to achieve a model that contained all variables that were important with regard to pre-established criteria of pE and pR values (p values of entry and removal of the variable in the model) chosen so that they become statistically and clinically significant. In other words, a variable could not present a p value <0.05 but may be selected to include the model. Sometimes, a variable cannot have a significant effect individually, but in a particular set potentiate its effect on the outcome variable. That was performed taking into account the importance of the selected variable in the recent literature outlines and in the clinical setting.
Categorical variables were coded according to the technique referred by Hosmer and Lemeshow [30, 31].
Receiver operating characteristic (ROC) analysis is a useful tool for evaluating the performance of diagnostic tests and more generally for evaluating the accuracy of a statistical model (e.g. logistic regression, linear discriminant analysis) that classifies subjects into one of two categories, diseased or non-diseased [19, 31]. Using a graphical tool, ROC curve, and an accuracy measure such as the area under ROC curve, is in our days used for displaying the accuracy of a predictive model to estimate expected outcomes. So, to evaluate the accuracy of our model, we use ROC curve and its measure area under curve (AUC).