A population-based cross-sectional study of the association between facial morphology and cardiometabolic risk factors in adolescence

The association between dental and facial symmetry in adolescents

INTRODUCTION

Facial aesthetics have become one of the most important objectives of orthodontic treatment. The correction of dental arches should be performed in accordance with the face. This study explored the association between occlusal and facial asymmetries in adolescents, particularly emphasizing a Class II subdivision.

METHODS

Eighty-one adolescents (43 males, 38 females) with a median age of 15.9 (interquartile range, 15.17-16.33) years were enrolled. Of these patients, 30 had a Class II subdivision (right side, n = 12; left side, n = 18). Three-dimensional facial scans were analyzed using surface- and landmark-based methods. Chin asymmetry was determined using the chin volume asymmetry score. Three-dimensional intraoral scans were analyzed to assess occlusal asymmetry.

RESULTS

The surface matching scores were 59.0% ± 11.3% for the whole face and 39.0% ± 19.2% for the chin. Chin volume was larger on the right side than on the left side in most patients (n = 51, 63%), and it was associated with a dental midline shift to the corresponding subdivision side. A correlation between dental and facial asymmetries was noted. In addition, the dental midline shifted to the left in patients with a Class II subdivision, regardless of the side, and to the right in those with a symmetrical Class II subdivision. However, several patients did not possess asymmetrical occlusal traits sufficient for statistical analysis.

CONCLUSIONS

Dental asymmetry was weak but significantly correlated with facial asymmetry.

An exploration of adolescent facial shape changes with age via multilevel partial least squares regression

BACKGROUND AND OBJECTIVES

Multilevel statistical models represent the existence of hierarchies or clustering within populations of subjects (or shapes in this work). This is a distinct advantage over single-level methods that do not. Multilevel partial-least squares regression (mPLSR) is used here to study facial shape changes with age during adolescence in Welsh and Finnish samples comprising males and females.

METHODS

3D facial images were obtained for Welsh and Finnish male and female subjects at multiple ages from 12 to 17 years old. 1000 3D points were defined regularly for each shape by using "meshmonk" software. A three-level model was used here, including level 1 (sex/ethnicity); level 2, all "subject" variations excluding sex, ethnicity, and age; and level 3, age. The mathematical formalism of mPLSR is given in an Appendix.

RESULTS

Differences in facial shape between the ages of 12 and 17 predicted by mPLSR agree well with previous results of multilevel principal components analysis (mPCA); buccal fat is reduced with increasing age and features such as the nose, brow, and chin become larger and more distinct. Differences due to ethnicity and sex are also observed. Plausible simulated faces are predicted from the model for different ages, sexes and ethnicities. Our models provide good representations of the shape data by consideration of appropriate measures of model fit (RMSE and R2).

CONCLUSIONS

Repeat measures in our dataset for the same subject at different ages can only be modelled indirectly at the lowest level of the model at discrete ages via mPCA. By contrast, mPLSR models age explicitly as a continuous covariate, which is a strong advantage of mPLSR over mPCA. These investigations demonstrate that multivariate multilevel methods such as mPLSR can be used to describe such age-related changes for dense 3D point data. mPLSR might be of much use in future for the prediction of facial shapes for missing persons at specific ages or for simulating shapes for syndromes that affect facial shape in new subject populations.

Multilevel principal components analysis of three-dimensional facial growth in adolescents

BACKGROUND AND OBJECTIVES

The study of age-related facial shape changes across different populations and sexes requires new multivariate tools to disentangle different sources of variations present in 3D facial images. Here we wish to use a multivariate technique called multilevel principal components analysis (mPCA) to study three-dimensional facial growth in adolescents.

METHODS

These facial shapes were captured for Welsh and Finnish subjects (both male and female) at multiple ages from 12 to 17 years old (i.e., repeated-measures data). 1000 "dense" 3D points were defined regularly for each shape by using a deformable template via "meshmonk" software. A three-level model was used here, namely: level 1 (sex/ethnicity); level 2, all "subject" variations excluding sex, ethnicity, and age; and level 3, age. The technicalities underpinning the mPCA method are presented in Appendices.

RESULTS

Eigenvalues via mPCA predicted that: level 1 (ethnicity/sex) contained 7.9% of variation; level 2 contained 71.5%; and level 3 (age) contained 20.6%. The results for the eigenvalues via mPCA followed a similar pattern to those results of single-level PCA. Results for modes of variation made sense, where effects due to ethnicity, sex, and age were reflected in modes at appropriate levels of the model. Standardised scores at level 1 via mPCA showed much stronger differentiation between sex and ethnicity groups than results of single-level PCA. Results for standardised scores from both single-level PCA and mPCA at level 3 indicated that females had different average "trajectories" with respect to these scores than males, which suggests that facial shape matures in different ways for males and females. No strong evidence of differences in growth patterns between Finnish and Welsh subjects was observed.

CONCLUSIONS

mPCA results agree with existing research relating to the general process of facial changes in adolescents with respect to age quoted in the literature. They support previous evidence that suggests that males demonstrate larger changes and for a longer period of time compared to females, especially in the lower third of the face. These calculations are therefore an excellent initial test that multivariate multilevel methods such as mPCA can be used to describe such age-related changes for "dense" 3D point data.

What's in a Smile? Initial Analyses of Dynamic Changes in Facial Shape and Appearance

Single-level principal component analysis (PCA) and multi-level PCA (mPCA) methods are applied here to a set of (2D frontal) facial images from a group of 80 Finnish subjects (34 male; 46 female) with two different facial expressions (smiling and neutral) per subject. Inspection of eigenvalues gives insight into the importance of different factors affecting shapes, including: biological sex, facial expression (neutral versus smiling), and all other variations. Biological sex and facial expression are shown to be reflected in those components at appropriate levels of the mPCA model. Dynamic 3D shape data for all phases of a smile made up a second dataset sampled from 60 adult British subjects (31 male; 29 female). Modes of variation reflected the act of smiling at the correct level of the mPCA model. Seven phases of the dynamic smiles are identified: rest pre-smile, onset 1 (acceleration), onset 2 (deceleration), apex, offset 1 (acceleration), offset 2 (deceleration), and rest post-smile. A clear cycle is observed in standardized scores at an appropriate level for mPCA and in single-level PCA. mPCA can be used to study static shapes and images, as well as dynamic changes in shape. It gave us much insight into the question “what’s in a smile?”.

Facial Genetics: A Brief Overview

Historically, craniofacial genetic research has understandably focused on identifying the causes of craniofacial anomalies and it has only been within the last 10 years, that there has been a drive to detail the biological basis of normal-range facial variation. This initiative has been facilitated by the availability of low-cost hi-resolution three-dimensional systems which have the ability to capture the facial details of thousands of individuals quickly and accurately. Simultaneous advances in genotyping technology have enabled the exploration of genetic influences on facial phenotypes, both in the present day and across human history. There are several important reasons for exploring the genetics of normal-range variation in facial morphology.     - Disentangling the environmental factors and relative parental biological contributions to heritable traits can help to answer the age-old question "why we look the way that we do?"     - Understanding the etiology of craniofacial anomalies; e.g., unaffected family members of individuals with non-syndromic cleft lip/palate (nsCL/P) have been shown to differ in terms of normal-range facial variation to the general population suggesting an etiological link between facial morphology and nsCL/P.     - Many factors such as ancestry, sex, eye/hair color as well as distinctive facial features (such as, shape of the chin, cheeks, eyes, forehead, lips, and nose) can be identified or estimated using an individual's genetic data, with potential applications in healthcare and forensics.     - Improved understanding of historical selection and adaptation relating to facial phenotypes, for example, skin pigmentation and geographical latitude.     - Highlighting what is known about shared facial traits, medical conditions and genes.

Face morphology: Can it tell us something about body weight and fat?

This paper proposes a method for an automatic extraction of geometric features, related to weight parameters, from 3D facial data acquired with low-cost depth scanners. The novelty of the method relies both on the processing of the 3D facial data and on the definition of the geometric features which are conceptually simple, robust against noise and pose estimation errors, computationally efficient, invariant with respect to rotation, translation, and scale changes. Experimental results show that these measurements are highly correlated with weight, BMI, and neck circumference, and well correlated with waist and hip circumference, which are markers of central obesity. Therefore the proposed method strongly supports the development of interactive, non obtrusive systems able to provide a support for the detection of weight-related problems.

Diagnosis of Class III malocclusion in 7- to 8-year-old children--a 3D evaluation

OBJECTIVES

The aim of this study was to characterize facial and jaw morphology of children with Class III malocclusion in early mixed dentition.

METHODS

This study was conducted on 7- to 8-year-old Caucasian children, 48 children with Class III malocclusion and 91 children with normal occlusion. Surface images of faces and study casts were obtained using laser scanning. Two average facial templates were constructed for the males and females in the control group. The facial images were superimposed on the corresponding average templates. Facial parameters, palatal volumes, and gingival surface areas were measured and group differences were quantified. The analysis of variance was used for statistical evaluation of the measured parameters.

RESULTS

The results revealed shorter lower face height (P < 0.001), concave facial profile (P < 0.001), retruded maxilla (P < 0.001), protruded mandible (P < 0.001), retrusive mid-face restricted area (P < 0.001), reduced gingival surface area of the maxilla (P = 0.013), and reduced maxilla/mandible gingival surface area ratio (P < 0.001) in the Class III group compared to the control group. There were no differences between the groups in upper face height, restricted areas of the upper and lower face, palatal volume, and gingival surface area of the mandible (P > 0.05).

LIMITATIONS

Regardless of the fact that the prevalence of Class III malocclusion is rather small, the sample size could be larger.

CONCLUSIONS

Class III subjects show clinically relevant facial and jaws characteristics in pre-pubertal growth period. A comprehensive diagnosis should include transverse dimension analysis.