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Beheshti Univ Dent J 2005; 22(Special Issue): 100-106
The association between occlusal features
and craniofacial structure
Downloaded from jds.sbmu.ac.ir at 10:54 +0430 on Friday May 5th 2017
Safavi S.M.R. DDS, MS1, Tahmasbi S. DDS 2, Shoai SH. DDS2
1
Assistant Prof., Dept. of Orthodontic, Dental School, Shahid Beheshti University of Medical Sciences,
Tehran-Iran; 2Dentist.
ABSTRACT
Purpose: To investigate the relationship between craniofacial skeletal structure and
occlusion.
Materials & Methods: Data were obtained from lateral cephalometric radiographs and study
casts of 60 patients with various malocclusions, with the mean age of 13.3±1.5 years. Thirteen
angular and 14 linear skeletal measures as well as 5 occlusal measures including; overjet,
overbite, molar relation, maxillary and mandibular crowding were applied. Relations were
examined by use of univariate and multivariate statistical methods.
Results: Simple correlation analysis between individual skeletal measures and individual
occlusal characteristics showed that the strongest linear correlation exists between overbite
and Pal-GoMe angle (r=-0.451, P-value≤0.01). Stepwise multiple regression was carried out
for quantitative occlusal variables. The prediction formula for overjet had the highest
multiple R and R2 values (multiple R=0.654, R2= 0.428), which shows that multiple skeletal
features explained 43% of the variance in overjet.
Conclusion: Individually and in combination, skeletal measures were poorly associated with
individual features of occlusion, so that variations in skeletal structure account for at most
43% of the variations in occlusion.
_______________________________________________________________________________
Keywords: Occlusion, Craniofacial structure, Association.
INTRODUCTION
O
cclusion varies from optimal to severe
malocclusion, with continuous variation
between the two and reflect bone
growth, dental development and neuromuscular maturation. The concept that
definitive
relationships exist between
occlusion and craniofacial morphology is
well known to orthodontists.(1-3) It has been
argued that orthodontic diagnosis might be
facilitated if the relationship between
occlusion and craniofacial structure were
better understood.(1) The orthodontist needs
to know how the major functional
components of the face (cranial base, jaws,
teeth) are related to each other.(2) Numerous
studies have examined the relationship
between craniofacial morphology and a
single characteristic of occlusion such
as openbite,(4-7) deep bite,(8,9) mandibular
and
Angle
anterior
crowding(10-12)
classification.(13-16) Others have examined the
correlation between craniofacial structure
and multiple features of occlusion.(16,17)
The results of these studies often are
contradictory because of several problems
inherent in examining the association
between craniofacial structure and occlusion.
Not only do most samples rarely include a
typical assortment of occlusal variation, but
the Angle classification schemes alone do not
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Safavi et al.
best represent that variation.(1) In addition, a
malocclusion is the combination of several
individual skeletal problems of which none
may be remarkable in and of itself.(18,19)
The purpose of this study was to examine
the relationship between craniofacial skeletal
structure and occlusion on a sample of
occlusal variation with several angular and
linear skeletal measures using univariate and
multivariate statistical methods.
MATERIALS & METHODS
In this analytical study, the data were
obtained
from
pretreatment
lateral
cephalometric radiographs and dental casts of
children in early permanent dentition selected
from the archive of Shahid Beheshti
University Orthodontic department. There
were 37 girls and 23 boys with ages ranged
from 11 to 16 years.
All subjects met the following criteria:
1. Presence of no primary tooth.
2. Presence of all permanent teeth except for
the third molars. The second molars were
in various stages of eruption.
3. No premature loss of primary teeth in the
orthodontic records.
4. No tempromandibular joint abnormality,
mandibular
deviation
or
facial
asymmetry.
The lateral cephalograms were traced by
one investigator (ST). Landmarks were
identified as described by Riolo and
associates.(20) 13 angular and 14 linear
measures were selected to assess: cranial
base flexure and length, maxillary horizontal
and vertical positions and length, mandibular
horizontal and vertical positions and length,
anteroposterior and vertical maxillarymandibular discrepancies.
All occlusal measurements were taken
directly from the dental casts. Overbite,
overjet and molar relationship were identified
and measured as reported by Solow.(21)
Maxillary and mandibular crowdings were
measured from one first molar to the other
directly on dental casts.
The following statistical analysis were
performed:
1. Pearson’s product-moment coefficient of
correlation to assess the association,
among skeletal measures, and between
individual occlusal characteristics and
skeletal measures.
2. Stepwise multiple regression to determine
whether
combinations
of skeletal
measures explained more variance in any
single occlusal characteristic than did
single measures. In this procedure a
skeletal variable was entered and retained
in the regression model at each step if it
explained a significant amount of
variance in occlusal variable at the
P=0.05 significance level. Addition of
skeletal variables to the model was
stopped when no other variable met the
0.05 significance level criterion and R
reached to the maximum level.
RESULTS
The study sample consisted of 37 girls and
23 boys with the mean age of 13.3±1.5 years.
The distribution of malocclusion in the
sample was approximately similar to that in
the Iranian population. Table 1 shows the
mean, range and standard deviation of
overjet, overbite, maxillary crowding and
mandibular crowding and indicates that wide
range of occlusal characteristics have been
selected in order to strengthen external
validity. Figure 1 shows the distribution of
cases according to Angle classification.
Table 1. Mean, range and standard deviation of
occlusal variables(mm).
Overjet
Overbite
Max. Crowding
Mand.
Crowding
Mean
SD
Maximum
Minimum
3.41
2.13
4.03
2.5
2.42
4.17
10
+8
+13
0
-4
-6
3.11
3.48
+13
-4
The correlation among skeletal variables
revealed several strong (r≥0.75, P≤0.05)
associations, most of them reflecting
redundancy or topographic relationship.
The significant linear correlation between
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individual occlusal features and individual
skeletal measures ranged from 0.275to -0.451
(Table2).
Fig 1. Histogram showing distribution of
malocclusion in the sample.
Overjet showed significant correlation
with several skeletal measures while the
highest of these correlation were between
overjet and A-N-B (r=0.377, P≤0.05) (Fig.2).
The strongest correlation between overbite
and skeletal structure was found between
overbite and Pal-GoMe (r=-0.451, P≤ 0.05)
(Fig.3).
with other skeletal measures were not
statistically significant.
Fig 3. Linear correlation between overbite and
Pa-GoMe angle (r=-0.451).
The only significant correlation of
mandibular crowding was found with ANS–
Me/N-Me (r=-0.292, P≤0.05).
Multiple R and R2 values were calculated
with stepwise multiple regression to assess
whether linear combinations of skeletal
measures explained more variance in any
single quantitative occlusal variable than did
single
measures.
Unstandardized and
standardized regression coefficients were
also determined.
Fig 2. Linear correlation between overjet and
A-N-B angle (r=0.377).
Maxillary crowding showed significant
correlation with N-Se (r=-0.302, P≤0.05)
(Fig.4) and ramus length, but it’s correlation
Fig 4. Linear correlation between maxillary
crowding and N-Se (r=0.302).
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Safavi et al.
Table 2. Linear correlation between individual occlusal characteristics
and individual skeletal measures.
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Occlusal
Skeletal
S-N-B
S-N-Pog
A-N-Per
B-N-Per
Pog-N.Per
N-Se
Mandible
Man. Dis.
Maxila
Max. Dis
Ramus
A.L.F.H
ANS-Me/N-Me
N-S-Ar
S-Ar-Go
Ar-Go-Me
Sum- Post
Pn-Pal
Pal-GoMe
SN-MeGo
N-S-Gn
P.F.H
A.F.H
Jarabak I.
S-N-A
S-N-B
Mand.
Crowding
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
0.292
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
From occlusal variables, the highest
multiple R and R2 were related to overjet
regression
model
(multiple R=0.654,
2
P≤0.05),
which
shows
R =0.428,
combination of 5 skeletal measures explained
43 percent of the variance in overjet.
Overbite prediction formula showed that only
29 percent of the variance in overbite were
explained by multiple skeletal measures.
The multiple R values of maxillary
crowding and mandibular crowding were less
than 0.5.
DISCUSSION
This study investigated the relationship
between
features
of
occlusion and
craniofacial structure. The simple correlation
analysis of overbite with skeletal measures
showed that increase of overbite is
accompanied with decrease of A.F.H (NMe), A.L.F.H (ANS-Me), sum of posterior
angles, SN-MeGo and Jarabak Index
Max.
Crowding
NS
NS
NS
NS
NS
-0.302
NS
NS
NS
NS
-0.284
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Overbite
Overjet
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
-0.365
-0.303
NS
NS
NS
-0.300
NS
-0.451
-0.354
NS
NS
-0.327
0.354
NS
NS
0.377
-0.282
NS
-0.321
-0.339
0.275
NS
-0.348
NS
NS
NS
NS
NS
0.377
-0.282
NS
-0.321
NS
NS
NS
NS
NS
NS
NS
NS
-0.303
(P.F.H/A.F.H×100). Similar findings have
been reported by Bjork(1947),(22) Schud
(1964),(23) Solow(1966),(21) Parlow(1972),(24)
Keeling(1989),(17)
Tsang(1997),(7) and
(25)
Meyers(1992).
However our results
showed that individual features of occlusion
were poorly related to individual skeletal
measures; the highest correlation (r=-0.451)
explained only 20 percent (R2=0.20) of the
variance between overbite and Pal-GoMe
(basal angle). These values were lower than
comparable values reported by Beckmann
et al(1998)(9) (R2=0.31) but were nearly
similar to the findings of Solow(1966),(21)
Keeling(1989),(17) and Meyers(1992).(25)
In multiple regression, when A-N-B
entered the overbite prediction formula,
raised r from 0.451 to multiple R=0.537;
adding 9 per cent to explained variance. It
was interesting that A-N-B, which is an
anteroposterior skeletal measure, showed a
considerable correlation with overbite, which
is a vertical occlusal feature.
In anteroposterior relations, simple
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Beheshti Univ Dent J 2005; 22(Special Issue): 100-106
correlation analysis showed that increase of
overjet is associated with decrease of S-N-B,
S-N-Pog and mandibular length and increase
of N-Se. The strongest correlation was found
between overjet and A-N-B angle (r=0.377)
which indicated that only 14 per cent of the
variance in overjet could be explained by AN-B angle. These values are higher than
comparable values reported by Solow,(21)
Bjork(1947)(22) and Keeling et al(1989)(17)
(R2=0.09) but are considerably lower than
values reported by Meyers (R2=0.42).(25) The
higher values of shared variance reported by
Meyers, may indicate that Dipaolo’s
quadrilateral measures, which are consistent
with Enlow’s counterpart principle and are
used in Meyers’ study, better express the
relations
between
variations
in
anteroposterior skeletal
structure and
variations in sagittal occlusion than other
measures. Another reason for this difference,
may be the variation in study sample.
The multiple regression between overjet
and five skeletal measures, increased
multiple R as high as 0.654. This indicated
that up to 43 percent of the variance in
overjet could be accounted for by the
variance in skeletal measures. The interesting
point was the relation of vertical skeletal
measures such as Pn-Pal, SN-MeGo and
Jarabak index, with overjet, a sagittal
occlusal characteristic.
Although, the skeletal measures used in
our study are different from Dipaolo’s
quadrilateral measures used in Meyers’
study, the results of multiple regression
analysis were similar (multiple R2=0.43).
Mandibular
crowding
showed
a
significant correlation with ANS-Me/N-Me
but it’s correlations with other skeletal
measures such as Jarabak index and SNMeGo were not significant. Similar findings
have been reported by Meyers(1992)(25) and
Eslamian(2003),(26)
but
Sakuda(1976)
reported a significant correlation between
mandibular
crowding
and
Sn-MeGo
(mandibular plane angle) and mandibular
body length.(11)
Finally, it seems that, the reason of this
low correlation between occlusion and
skeletal structure, is the influence of other
factors.
With the explosion of discoveries in
developmental biology and genomics in the
past decade, the direct relevance has affected
the understanding of development, growth
and adaptation of craniofacial skeletal tissue.
It is essential to identify and understanding
the role of key intrinsic and extrinsic factors
that influence gene expression and cell
growth during the development of the
craniofacial complex.(27,28,29)
CONCLUSION
1. Individually, occlusal characteristics were
poorly
associated
with individual
measures of craniofacial morphology (14
per cent of the variance in overjet and 20
per cent of the variance in overbite could
be explained by a single skeletal
measure).
2. A linear combination of skeletal measures
explained more variance in an occlusal
variable (43 percent of the variance in
overjet and 24 percent of the variance in
overbite).
Acknowlegdement
We are grateful to Hamid Farhadi for his
assistance to complete the manuscript and his
documentation.
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