The aim of our study was to evaluate the craniofacial characteristics of children with mild hypodontia using conventional and principal component (PC) analysis.
We used radiographic images of 124 children (8-12 years old) with up to 4 missing teeth (55 boys, 69 girls) and of 676 reference children (365 boys, 311 girls) from the Rotterdam Generation R Study and the Nijmegen Growth Study in The Netherlands. Fifteen cephalometric measurements of children with hypodontia were compared with those of the reference children. Moreover, cephalometric parameters were combined into standardized PC scores using PC analysis, and the components were compared between the 2 groups.
PC analysis showed common dental characteristics for all types of hypodontia: a significant increase of the interincisal angle, and decreases of the maxillary and mandibular incisor angles. Other findings were consistent when both methods were applied: (1) anterior hypodontia was significantly associated with the high-angle (hyperdivergent) craniofacial pattern, (2) the tendency toward a Class III malocclusion was identified in maxillary hypodontia, and (3) we observed a significant reduction of lower posterior facial height in children with posterior and mandibular hypodontia.
Our findings suggest that children with mild hypodontia have distinctive skeletal and dental features.
Anterior hypodontia was associated with bimaxillary retrognathism.
Maxillary hypodontia was associated with a tendency toward Class III malocclusion.
Posterior and mandibular hypodontia was associated with reduced vertical parameters.
Principal component analysis showed that all types of hypodontia had changes in dental parameters.
Hypodontia is the most prevalent developmental tooth disorder in which a person has at least 1 missing tooth, excluding the third molars. The population prevalence of hypodontia varies between 3.9% and 6.9%, and a slightly higher prevalence was reported in females than in males. Hypodontia mostly occurs in its mildest form, with the highest percentage of just 1 tooth (49%) and lower percentages for 2 (35%), 3 (7%), or 4 missing teeth (6%). Previous studies have reported that isolated hypodontia could affect the dental morphology of adjacent teeth and the relationship between jaws. More severe cases of hypodontia (6 or more missing teeth) are rare and often combined with specific syndromic disorders. In this study, we examined children with mild hypodontia, with up to 4 missing teeth; they are referred to as children with hypodontia.
Cephalometric studies performed on people with hypodontia showed that they have a distinctive facial morphology, including the following characteristics: maxillary retrognathism, mandibular retrognathism, or prognathism, increased overjet, increased overbite, reduction in vertical jaw relationship, higher interincisal angle, and a tendency toward a Class III malocclusion. Contradictory results of previous studies could be attributed to varying sizes and genetic backgrounds of the samples, and different methods for quantifying hypodontia and measuring the morphology of the dentofacial complex.
Most of the previous similar studies divided patients into groups, depending on the number of missing teeth. Other studies compared craniofacial measurements between hypodontia and reference subjects by dividing them into groups depending on the location of the missing teeth or by combining both criteria.
On the other hand, the most common method for measuring linear and angular parameters of the facial profile is conventional 2-dimensional cephalometric analysis. Although there are numerous parameters available from different types of analyses, investigators often select linear, angular, and other derived cephalometric parameters that reflect the best morphology of the facial profile region that they are investigating. These led to inclusion of 9 up to 65 parameters in previous similar studies that examined the craniofacial characteristics in subjects with hypodontia. Consequently, including more parameters in the analysis increases the number of statistical comparisons, which, if not considered, may present a potential issue of discovering false significant results, also known as a multiple comparisons problem. Therefore, increasing the significance threshold or reducing the number of comparisons by unifying correlated parameters may be a solution to overcome this issue.
Several cephalometric studies have suggested a principal component (PC) analysis to reduce the number of parameters in their analyses. This statistical technique uses the correlation between a set of variables to create a set of new variables, named PCs. Application of a PC analysis might be a suitable adjunct method next to conventional analysis for craniofacial studies with many cephalometric parameters and for revealing hidden underlying structures and making stronger conclusions than by using each parameter independently.
The aim of this study was to determine the cephalometric characteristics of children with mild hypodontia using conventional cephalometric analysis and PC analysis.
Material and methods
We used radiographic images of 124 children (8-12 years old) with up to 4 missing teeth (55 boys, 69 girls) that we compared with the images of 676 reference children (365 boys, 311 girls) from the Nijmegen Growth Study and the Generation R Study in Rotterdam in The Netherlands ( Table I ).
|No (n = 676)||Yes (n = 124)|
|Sex (n, %)|
|Boys||365 (56)||55 (46)|
|Girls||311 (44)||69 (54)|
|Age (y; mean, SD)||9.67 (0.35)||9.77 (0.24)|
|Study population (n, %)|
|Nijmegen||203 (30)||7 (6)|
|Generation R||473 (70)||117 (94)|
|Tooth agenesis (n, %)|
|Incisive and canine region||–||39 (31)|
|Premolar and molar region||–||85 (69)|
|Both jaws||–||9 (7)|
|Frequency (n, %)|
|1 agenetic tooth||–||68 (55)|
|2 agenetic teeth||–||46 (37)|
|3 or more agenetic teeth||–||10 (8)|
The Nijmegen Growth Study is a mixed longitudinal population-based cohort study conducted from 1971 to 1976 at the Radboud University Medical Center in Nijmegen, The Netherlands. This study included 3 cohorts: children were enrolled at 4, 7, and 9 years of age and followed until they were 9, 12, and 14 years of age. We used only 1 radiographic image per child taken between 8 and 12 years. If a child had more than 1 radiographic image available, we selected the one taken at the age that was closer to the mean age of the Generation R Study sample. In total, 7 children with hypodontia and 203 reference children were included from the Nijmegen Growth Study, with an average age of 9.39 ± 0.32 years. Before the inclusion of the participating children in the Nijmegen Growth Study, signed consents were obtained from the parents.
The Generation R Study is a population-based cohort study from fetal life to adulthood, established in Rotterdam, The Netherlands, at the Erasmus University Medical Centre. From the fourth data collection phase, we used data from 117 children with hypodontia and 473 healthy children with a mean age of 9.67 ± 0.40 years. The study was approved by the medical ethics committee of the Erasmus Medical Centre (MEC-2012-165) in Rotterdam. At the start of each phase, mothers and their partners received written and oral information about the study and were asked for their written informed consent.
Hypodontia in children was assessed from their dental panoramic radiographs. One dentist (B.D.) determined the number and position of the missing teeth for each subject. In total, 124 children had hypodontia ( Table I ). Of those, 39 children had anterior hypodontia (tooth agenesis of incisors and canines), and 85 children had posterior hypodontia (tooth agenesis of premolars and molars). Also, we classified hypodontia based on the jaw in which the tooth was missing. Our sample consisted of 29 subjects with maxillary hypodontia, 86 subjects with mandibular hypodontia, and 9 subjects who had missing teeth in both jaws. No child had more than 4 missing teeth or a combination of anterior and posterior hypodontia.
We selected 14 landmarks on cephalograms ( Table II ); from these cephalometric points, we measured 12 angular parameters, 1 distance, and 2 derived proportions. The mean values of the cephalometric parameters for boys and girls are given in Tables III and IV , respectively. Lines Mx (palatal plane) and Mn (mandibular plane) were obtained by connecting points ANS and PNS, and Go and Me, respectively. Line Ui passes through the axis of the maxillary central incisors by connecting points Is and Rs, and line Li goes through the axis of the mandibular central incisors by connecting points Ii and Ri. Before each measurement, the image was recalibrated depending on the magnification of the cephalometric radiograph. We included the most frequently used cephalometric parameters from previous studies that investigated cephalometric differences between hypodontia and reference groups. In both the Nijmegen Growth Study and the Generation R Study, cephalometric landmarks were digitized from which linear and angular cephalometric parameters were calculated. Cephalometric points in the Generation R Study were digitized by a dentist (S.V.) using Viewbox software (version 4.0; dHAL Software, Kifissia, Greece).
|1||S||Sella||Centre of sella turcica|
|2||N||Nasion||Most anterior limit of the frontonasal suture|
|3||Ar||Articulare||Point where the posterior outline of the condyle passes over the posterior and lower margin of the cranial base|
|4||Go||Gonion||Midpoint of the angle of the mandible|
|5||Me||Menton||Most inferior point on the symphysis of the mandible|
|6||Pg||Pogonion||Most anterior point on the symphysis of the mandible|
|7||B||B-point||Deepest point on the contour of the mandible|
|8||A||A-point||Deepest point on the contour of the premaxilla|
|9||ANS||Anterior nasal spine||Tip of the anterior nasal spine|
|10||PNS||Posterior nasal spine||Most posterior point in the sagittal plane on the bony hard palate|
|11||Is||Incision superius||Incisal tip of the most anterior maxillary central incisor|
|12||Rs||Upper incisor apex||Root apex of the most prominent upper incisor|
|13||Ii||Incision inferius||Incisal tip of the most anterior medial mandibular central incisor|
|14||Ri||Lower incisor apex||Root apex of the most prominent lower incisor|
|Cephalometric measurement||Hypodontia||Difference||P value|
|Cephalometric measurement||Hypodontia||Difference||P value|
We applied a PC analysis to combine correlated cephalometric parameters into a new set of uncorrelated PCs, each representing a distinct craniofacial pattern. A detailed description of the PC procedure used in this study is provided in the Supplemental material . Briefly, we explored first the intercorrelation among cephalometric parameters using Pearson correlation ( Table S1 ). Secondly, a standardized score was created for each PC with a mean value of 0 and a standard deviation of 1. If a cephalometric parameter had a loading greater than 0.5 for a component, we interpreted the increase or decrease of that component as an increase or a decrease of that cephalometric parameter. If a cephalometric parameter had a loading lower than −0.5, an inverse principle was applied. Cephalometric parameters that are included in 1 PC are bolded in Table S2 .
Interobserver agreement for the cephalometric measurements in the Generation R sample was tested using intraclass correlation on a subsample of 20 randomly selected cephalograms scored by 2 independent observers (S.V. and B.D.). Values of the correlation coefficient ranged from 0.699 to 0.972. The reliability of the measurements done in the Nijmegen Growth Study was published earlier, and it was considered highly reliable. We added study sample as a covariate in the linear regression analysis to adjust for the potential measurement differences between the Nijmegen Growth Study and the Generation R Study.
The differences of individual cephalometric parameters and craniofacial patterns (PCs) between the hypodontia and reference groups were compared using multiple linear regression models. All linear regression models were adjusted for age, sex, study sample, and ethnicity of the children. We tested for statistical interaction by adding the interaction term in the linear regression model.
The Markov Chain Monte Carlo imputation method was used to reduce potential bias associated with missing data. The numbers of missing values for each variable are provided in Table S3 . As a result, 5 imputed data sets were generated, and a pooled effect estimate was calculated. The analysis was performed with the statistical software SPSS for Windows (version 21.0; IBM, Armonk, NY). We used the following thresholds of the P values: 0.05, 0.01, and the Bonferroni-adjusted threshold that takes into account the number of statistical comparisons and the average cross-correlation coefficient of outcome variables given the alpha level of 0.05.