The relationship of facial vertical development with skeletal maturation and dental age

Authors: Ceyla Demirer, Gülşilay Sayar

Abstract

Purpose:

This study aimed to determine whether there is a correlation between three different types of vertical facial development, chronological age, skeletal maturation, and dental maturation.

Materials and

Lateral cephalometric, panoramic, and hand-wrist radiographs of 150 orthodontic patients (75 males and 75 females; mean ages 13.54 and 13.74 years, respectively) were analyzed. Skeletal maturation was determined using the Greulich-Pyle atlas method via hand and wrist bones. Dental maturation was assessed using the Demirjian Index on the left mandibular canines, first premolars, second premolars, and second molars. The SN-GoMe angle was evaluated on lateral cephalometric radiographs.

Results:

A statistically significant difference in median skeletal age between genders was found in the hyperdivergent group (p = 0.024), with females showing more advanced skeletal age than males. A significant positive correlation between chronological age and skeletal age was observed in both the hypodivergent (p < 0.001) and hyperdivergent (p < 0.001) groups. In the normodivergent group, a very strong correlation between chronological and skeletal age was also found (p < 0.001). Additionally, significant gender-based differences were noted in the development of the canine (p = 0.003) and first premolar (p = 0.048) teeth in the hyperdivergent group.

Conclusion:

Maturational stages may differ between males and females of the same chronological age attending orthodontic clinics, largely due to gender-based developmental differences. Chronological and skeletal ages are compatible; however, vertical facial parameters should also be considered in growth and development assessments.

Introduction

The terms “growth” and “development” may cause conceptual difficulties, as they appear closely related. Although the two are similar, they do not convey the same meaning. Growth should not be understood merely as change; rather, it refers to an increase in size or number. Development, on the other hand, is often described as an increase in the level of complexity or specialization. As a result, anatomical changes are referred to as growth, whereas physiological and behavioral changes are termed development (1).

By identifying stages of growth and maturation, the detection of skeletal imbalances and dentofacial problems in growing patients becomes a critical component of orthodontic treatment planning. These assessments are typically based on chronological age, skeletal and dental maturation evaluation, measurements of height and weight, and identification of prepubertal maturation characteristics (2).

In dentofacial orthopedic management, the timing of treatment plays a crucial role in achieving successful orthodontic outcomes. The advantages offered by a child’s growth and development—particularly during the pubertal growth spurt—can be harnessed by evaluating the maturation of hand and wrist bones or cervical vertebrae (3).

Post-treatment evaluation of remaining growth is essential in predicting relapse (4). In cases with vertical skeletal anomalies, even when the desired treatment outcomes are initially achieved, both the difficulty of treatment and the likelihood of relapse pose challenges for orthodontists. Therefore, it is important to evaluate patients with consideration of vertical skeletal dimensions (5).

In the existing literature, studies that examine the relationship between chronological age, skeletal and dental maturation, and vertical facial dimensions in combination are limited. Therefore, the aim of this study was to determine whether dental and skeletal maturation levels differ among patients with varying vertical facial patterns, in relation to chronological age. The null hypothesis was that there is no significant difference in dental and skeletal maturation relative to chronological age among hypodivergent, normodivergent, and hyperdivergent cases.

Materials and methods

Ethical approval

For the present study, ethical approval was obtained from the Istanbul Medipol University Non-Interventional Clinical Research Ethics Committee, with the decision numbered 10840098-772.02-E.36556.

Sample size estimation

Power analysis was performed using the GPower 3.1.9.2* program (6) to measure the difference in mandibular plane angle, based on a previous study (7). It was determined that a sample of 50 individuals per group would achieve 80% power, with an effect size of 0.26 and a 5% Type I error margin.

Study sample

The dentofacial skeletal patterns of 150 individuals included in this study were analyzed in the vertical direction. Grouping was based on vertical skeletal anomalies, and the sample consisted of an equal number of male (n = 75) and female (n = 75) individuals. This retrospective study utilized hand-wrist, panoramic, and cephalometric radiographs of patients who applied for orthodontic treatment at the Istanbul Medipol University Faculty of Dentistry, Department of Orthodontics, between 2012 and 2022. The radiology archive of the department was reviewed, and, according to predefined inclusion criteria, the radiographs of 150 individuals aged 7–18 years were included.

Imaging devices

The panoramic, lateral cephalometric, and hand-wrist radiographs used in this study were obtained using the Kodak CARESTREAM 9000C (Kodak 9000C, Carestream Health, Inc., New York, USA) radiography device and cephalostat, located in the radiology department of the Istanbul Medipol University Faculty of Dentistry.

Inclusion criteria

The following inclusion criteria were applied in selecting study chronological age between 7–18 years, absence of systemic disease, no history of trauma or congenital/acquired anomalies in the face, neck, hand, or wrist, no previous orthodontic treatment, mo more than one congenitally missing tooth (excluding third molars), radiographic interval less than three months between panoramic, lateral cephalometric, and hand-wrist radiographs, absence of cleft lip and palate, no anatomical deformation visible on any of the radiographs, radiographs with clearly visible bone structures

Determination of age and maturation

Chronological age was calculated by subtracting the date of birth (in days, months, and years) from the date of the panoramic radiograph. Vertical skeletal measurement was assessed using the SN-GoMe angle. Skeletal age was evaluated from hand-wrist radiographs using the Greulich-Pyle atlas. Additionally, the developmental indicators described by Björk (8) and Grave and Brown (9) were applied during hand-wrist analysis. Dental age was assessed using the Demirjian method (1973) (10) on panoramic X-rays. The teeth evaluated included the second molars, second premolars, first premolars, and canines on the left side of the mandible. Lateral cephalometric radiographs taken for routine diagnostic purposes were classified according to the SN-GoMe angle. Based on the norms stated by Gazilerli (9), three skeletal vertical pattern groups were SN-GoMe = 31 ± 5° (skeletally normal), SN-GoMe < 26° (skeletally deep bite), SN-GoMe > 36° (skeletally open bite, Figure 1, Figure 2), Figure 3 .

Statistical analysis

The collected data from all groups were imported into the IBM Statistical Package for the Social Sciences (SPSS) for Windows, version 23 (IBM Corp., Armonk, N.Y., USA). The appropriateness of the data for normal distribution was assessed using the Shapiro–Wilk test. For variables that did not follow a normal distribution, the Mann–Whitney U test was used for comparisons. The Fisher–Freeman–Halton test was applied to analyze categorical data. The Pearson correlation coefficient was used to examine the relationship between continuous variables that followed a normal distribution, while the Spearman’s rho correlation coefficient was employed for variables that did not meet normality assumptions. To assess agreement between the initial measurements and those repeated after 30 days, Kappa statistics and the intraclass correlation coefficient (ICC) were calculated. The analysis results were reported as frequency (percentage) for categorical variables, and as mean ± standard deviation and median (minimum – maximum) for quantitative variables. The level of statistical significance was set at p < 0.05.

Results

There was no statistically significant difference in the median SN-GoMe angle values between genders across the developmental types. However, a statistically significant difference was observed in the median skeletal age between genders within the hyperdivergent group (p = 0.024). In this group, the median skeletal age was 12.5 years for males and 15 years for females (Table 1). In the hyperdivergent type, there was a statistically significant correlation between gender and dental age for mandibular canine and first premolar (Table 2).

When the distribution of dental age stages of the mandibular canine in the hyperdivergent group was analyzed by gender, 37% of males were at stage F, 14.8% at stage G, and 48.1% at stage H. Among females, 4.3% were at stage F, 4.3% at stage G, and 91.3% at stage H. This indicates a statistically significant association between gender and mandibular canine dental age in the hyperdivergent group (p = 0.003) (Table 2).

Regarding the mandibular first premolar in the hyperdivergent group, 22.2% of males were at stage F, 14.8% at stage G, and 63% at stage H. Among females, none were at stage F, 21.7% were at stage G, and 78.3% were at stage H.

In males, a statistically significant and positive correlation was found between chronological age and skeletal age in the hypodivergent group (r = 0.734; p = 0.001). A strong positive correlation was also observed in the normodivergent group (r = 0.933; p < 0.001), as well as in the hyperdivergent group (r = 0.710; p < 0.001).

In females, chronological age was also significantly and positively correlated with skeletal age in all vertical developmental types. In the hypodivergent group, the correlation was r = 0.780 (p < 0.001); in the normodivergent group, r = 0.823 (p < 0.001); and in the hyperdivergent group, r = 0.896 (p < 0.001) (Table 3).

The method error for SN-GoMe angle measurement was calculated as 1.095 using Dahlberg’s formula, and the intraclass correlation coefficient (ICC) for this measurement was 0.984. Kappa statistics and ICC values for the dental stages were as second molars, 0.930; first premolars, 0.889; second premolars, 0.962; and canines, 0.737.

Discussion

Understanding the development of the face and jaw in children is highly important for orthodontists. This is particularly critical when evaluating growing children with dentofacial orthopedic issues. It is essential to assess their maturational changes before initiating treatment for skeletal problems. A comprehensive study examining methods of maturation assessment alongside vertical facial dimensions has not been previously identified in the literature, and thus, this study was conducted for that purpose (11, 12, 13, 14).

If the growth and development process is well understood, effective treatment strategies can be implemented by guiding these processes (15). Therefore, orthodontists must be familiar with normal growth and development patterns, possess the ability to evaluate a patient’s growth, and predict the timing, magnitude, and direction of growth and development (16, 17).

Due to individual variability, maturational age cannot be accurately determined based solely on chronological age. Instead, biological age, defined using developmental indicators such as dental age, skeletal age, height and weight, and secondary sex characteristics, is a more reliable marker. Biological age is crucial in evaluating developmental status, identifying deviations from normal patterns, selecting treatment types, determining optimal timing for intervention, and estimating prognosis (18, 19).

Darendeliler and Bundak (20) reported that discrepancies between chronological and skeletal age may vary across age ±6 months in children aged two years and younger, ±1 year between ages two and four, and ±2 years from age four to puberty. Therefore, chronological age alone is not considered a dependable method for evaluating growth and development.

Among other indicators, height at the time of the child's fastest growth (i.e., peak height velocity) has been regarded as a reliable marker of overall skeletal growth rate. However, its utility in predicting future growth rate and remaining growth potential is limited (4). Additionally, body weight gain and pubertal markers have been shown to lack practicality and reliability in determining the onset of growth spurts (21, 23).

Previous studies have demonstrated that growth and development stages differ between genders (23, 24). Kimura (23) compared the skeletal maturity of Japanese and British children aged 0–18 years and found that chronological and skeletal ages were similar in both genders until the age of 8. Büken et al. (25), in a study involving 492 girls (aged 11–18 years) and 251 boys (aged 11–19 years), evaluated the applicability of the Greulich–Pyle (G–P) method and found that girls generally matured faster than boys. Consequently, skeletal age was higher in girls. Our findings are in line with this, as females in the hyperdivergent group showed higher skeletal age than males in the same group.

This finding supports the conclusion of Büken et al. (25) that gender differences play a role in maturation timing. The skeletal age of girls in the hyperdivergent group being higher than that of boys reinforces the presence of gender-based developmental variation.

In previous research evaluating the correlation between chronological age, skeletal maturation, dental maturation, and ANB angle, a strong relationship was observed between chronological age and both hand-wrist and cervical vertebral maturation (CVM) in both genders. Furthermore, in males, second premolar maturation was most strongly correlated with chronological age, while in females, the correlation between second premolar development and hand-wrist assessment was the highest (26).

Dental maturity, or dental age, is a widely used indicator of biological maturity in growing children. It can be assessed based on the stages of tooth eruption or calcification (27). Tooth eruption, however, is known to be less reliable due to its susceptibility to external factors such as local anatomical conditions, systemic diseases, and nutritional status (28, 29). Since both chronological age and tooth eruption have been shown to have weak correlations with skeletal maturation during the growth period, they are not considered reliable indicators for treatment timing (2). Demirjian (10) recommended the use of calcification stages instead of eruption patterns to determine dental maturity. Several studies have since confirmed a strong correlation between dental and skeletal maturation (30, 31).

Janson et al. (32) reported that individuals with long-face (vertical) growth patterns exhibited more advanced dental maturation than those with short-face patterns, with an average difference in dental age of approximately six months. Similarly, in a study comparing hyperdivergent and hypodivergent cases, hyperdivergent individuals of both sexes were found to have higher dental ages (33). Neves et al. (34) also observed that individuals with vertical growth patterns showed earlier tooth maturation compared to those with horizontal patterns.

In the present study, the strong correlation between hyperdivergent growth patterns and advanced dental maturation is consistent with these earlier findings. However, the correlation was specifically observed in the mandibular canine and first premolar teeth.

This study has several limitations. First, the sample was composed of patients from a single institution, and therefore the data may not be generalizable to other populations. Additionally, factors such as increasing racial admixture, globalization, changes in dietary habits, evolving cultural structures, and socioeconomic disparities can affect growth and development patterns, potentially limiting the broader applicability of the findings. Future studies with larger and more diverse populations across different countries are warranted. The development of an AI-based evaluation system for dental and skeletal age may also support broader epidemiological research and enhance future clinical interventions. Accurate, standardized, and timely systems are essential for effective orthodontic diagnosis and planning.

Conclusion

Based on the findings of this study, a significant difference in skeletal age between genders was observed in the hyperdivergent group, with females showing higher skeletal age than males. Across all vertical developmental types, a strong positive correlation was found between chronological age and skeletal maturation. Additionally, in the hyperdivergent group, there was a statistically significant association between gender and the developmental stages of the left mandibular canine and first premolar teeth. These findings indicate that males and females of the same chronological age may be at different developmental stages in terms of dental maturity. While there was general agreement between skeletal age and chronological age, vertical growth patterns should also be considered in clinical evaluation and treatment planning.