|Year : 2016 | Volume
| Issue : 2 | Page : 23-30
Diagnostic value of panoramic indices to predict osteoporosis and osteopenia in postmenopausal women
Somayeh Nemati1, Zahra Dalili Kajan1, Bardia Vadiati Saberi2, Zohre Arzin3, Mohammad Hashem Erfani4
1 Department of Dentomaxillofacial Radiology, Guilan University of Medical Sciences, Rasht, Iran
2 Department of Periodontology, Guilan University of Medical Sciences, Rasht, Iran
3 Dentist, Guilan University of Medical Sciences, Rasht, Iran
4 Specialist in Physical Medicine and Rehabilitation, Guilan University of Medical Sciences, Rasht, Iran
|Date of Web Publication||10-Jun-2016|
Zahra Dalili Kajan
Department of Radiology, Oro-Maxillofacial Developmental Disease Research Center, Faculty of Dentistry, The End of Professor Samii Boulevard, Guilan University of Medical Sciences Complex, Rasht
Source of Support: None, Conflict of Interest: None
Background: Osteoporosis is characterized by the progressive loss of bone density, which leads to severe fractures. This study conducted to evaluate the diagnostic value of the mandibular radiomorphometric indices of panoramic radiographs to predict the status of bone mineral density (BMD) in postmenopausal women. Materials and Methods: Panoramic radiographs of ninety postmenopausal women (thirty osteoporotic, thirty osteopenic, and thirty controls) with the mean age of 54.47 years were evaluated. Bone densitometry was performed by using the dual-energy X-ray absorptiometry method. Variables such as the mandibular cortical width (MCW), panoramic mandibular index (PMI), gonial angle, mandibular cortical index (MCI), and the number of teeth lost were assessed. Results: The average of MCW in normal, osteopenic, and osteoporotic groups were 4.56, 3.97, and 3.39 mm, respectively. The PMI in normal, osteopenic, and osteoporotic groups was calculated as 0.33, 0.28, and 0.24, respectively. The MCW, PMI, and the number of teeth lost in the three groups showed statistically significant differences (P = 0.0001). The MCI had a significant difference in the three groups (P = 0.0001). The optimal cutoff points for MCW and PMI to predict low BMD were 4.13 mm and 0.29, respectively. Conclusions: The MCW, PMI, and MCI have a high diagnostic value to predict low BMD. By increasing the age and the postmenopausal duration, MCW and PMI decrease, whereas C3 form of MCI and the number of teeth lost showed increase. About 1 mm decrease in MCW increased the odds of reduced BMD (osteoporosis, osteopenia) up to 3.22-fold.
Keywords: Mandible, metabolic bone disease, panoramic radiography, postmenopausal osteoporosis
|How to cite this article:|
Nemati S, Kajan ZD, Saberi BV, Arzin Z, Erfani MH. Diagnostic value of panoramic indices to predict osteoporosis and osteopenia in postmenopausal women. J Oral Maxillofac Radiol 2016;4:23-30
|How to cite this URL:|
Nemati S, Kajan ZD, Saberi BV, Arzin Z, Erfani MH. Diagnostic value of panoramic indices to predict osteoporosis and osteopenia in postmenopausal women. J Oral Maxillofac Radiol [serial online] 2016 [cited 2022 Jun 29];4:23-30. Available from: https://www.joomr.org/text.asp?2016/4/2/23/183820
| Introduction|| |
Osteoporosis is a systemic disease characterized by reduced bone density and increased risk of bone fracture.  It is the most common metabolic bone disorder in the adults, especially in postmenopausal women.  Individuals, especially postmenopausal women, can be diagnosed by panoramic radiography indices in dental clinics.  The age-related osteopenia has been an important research topic in the recent studies. In both men and women, the bone mineral density (BMD) begins to decrease after 35-year-old and continues to do so at variable intensities in both genders. The important point in women is that the postmenopausal females undergo a significant reduction of bone mass because of hormonal imbalance.  The main clinical complications are vertebral and hip fractures.  The positive family history and the estrogen deficiency in women represent supplementary risk factors or low bone mass.  The most common method to diagnose osteoporosis is the dual energy X-ray absorptiometry (DXA) method.  The DXA represent an accurate method to evaluate bone mass.  Hip or lumbar vertebra DXA is defined as the gold standard for the diagnosis of osteoporosis.  Osteoporosis is defined by the World Health Organization (WHO) as having a BMD ≤2.5 times the standard deviation in young adult women by DXA method.  Nowadays, a large number of patients visit the dentists for oral care or treatment. Furthermore, panoramic radiographs are used for diagnosis of multiple oral conditions.  Osteoporosis has several manifestations in the jaw bone and these alterations can be detected by the dentists.  However, there are different results in the outcomes of the studies concerning the correlation between the mandibular radiomorphometric indices and the results of bone densitometry. , A recent review literature concluded that majority of the researches have focused on highlighting the role of panoramic radiographic indices in the detection of osteoporosis. Very limited number of studies has been designed to establish the cutoff point threshold values for the various radiomorphometric indices. There is scarcity in literature with respect to evaluation and comparisons of the influence of age, gender, and dental status on all the oral radiographic parameters in different ethnic populations and races.  Another recent systematic review has demonstrated that mandibular cortical width (MCW), panoramic mandibular index (PMI), and mandibular cortical index (MCI) are overall useful tools that potentially could be used by dentists to screen for low BMD. However, their limitations mainly related to the experience/agreement between different observers and the different image quality and magnification of the panoramic radiographs.  Therefore, the aim of the present study was to evaluate the correlation between the mandibular radiomorphometric indices and BMD in postmenopausal women and also to determine the optimal cutoff point for MCW, PMI, and gonial angle (GA) indices to predict low bone mass density. In addition, to evaluate the influence of the age, postmenopausal duration, and the number of teeth lost in mentioned parameters with details.
| Materials and Methods|| |
In this comparative analytical study, a total of ninety volunteer postmenopausal women (thirty osteoporotic, thirty osteopenic, and thirty healthy controls), with a mean age of 54.47 ± 6.68 years in the range of 39-82 years, were selected. These patients were referred to the Department of Dentomaxillofacial Radiology Clinic of Dental School, Guilan University of Medical Sciences in Rasht, Iran, for a dental checkup. They participated voluntarily in this study and had the study criteria required for enrollment. All of them signed an informed consent letter before participation in this study. We originally obtained the approval of the Ethics Committee of the Research Foundation of Guilan University of Medical Sciences in Rasht, Iran, before conducting this investigation (Ethics approval number: 3920141903), to ensure our agreement with the recommendations of the Declaration of Helsinki and Tokyo for human subjects. The participants were postmenopausal women who had no menstruation for at least 1 year prior to the study. Hip and lumbar spine (L1-L4) BMD was measured by using the DXA method with the Discovery DXA® densitometer device (Hologic Inc., Bedford, MA, USA). The BMD values were classified based on the T-score levels into three groups such as normal (>−1), osteopenic (−2.5-−1), and osteoporotic (<−2.5) according to the WHO criteria.
The exclusion criteria were a medical history positive for: 
- Metabolic bone disease (hyperparathyroidism, hyperthyroidism, osteomalacia, osteogenesis imperfecta, renal osteodystrophia, and Paget disease).
- Hormone therapy (estrogen or pharmaceutical supplements) or antiosteoporotic drugs, and other medications, such as corticosteroid drugs that influence the bone metabolism.
- Cancer with bone metastasis.
- Destructive lesions in the jaw bones (such as malignant tumors or osteomyelitis).
- Renal insufficiency or blood origin malignancies.
- Tobacco or alcohol consumption.
Overall, the origin of osteoporosis in the studied group was the postmenopausal status. As a control group, a normal group (having T-score >−1) was selected. Subject's age and the duration of passing the postmenopausal period were recorded at the time of the densitometry session. Dental panoramic radiographs were obtained at maximum 2 weeks after BMD assessment. Indirect digital panoramic radiographs with photostimulable phosphor plate sensor were taken for each subject by using a panoramic machine (Planmeca EC Proline, Helsinki, Finland), scanned and read out by a computed radiography unit (Konica Minolta Inc., Tokyo, Japan). The patient's head was positioned in a standard manner, as much as possible to achieve the best film quality. The exposure factors (mA, kVp, time) were variable and adjusted based on the age and size of the subjects. All radiographs had been printed in true size after applying 100% zooming power. The number of teeth was counted and recorded while the third molar teeth were not expected. The qualitative diagnosis of MCI was carried out by two oral radiologists, independently, who were blinded to the densitometry results. For evaluation of inter- and intra-observer agreement (about the qualitative index), the observation was performed 2 weeks after the initial session, randomly, on 15 radiographs. The same oral and maxillofacial radiologists, in agreement to each other, defined the location of mental foramen and the other landmarks in a room with ambient light and on the view box. Quantitative mandibular radiomorphometric indices were measured on both sides of the mandible by one of the oral radiologists. In the present study, the software of the digital system was not reliable sufficiently for linear and angular measurements. Hence, the measurements were performed manually by using electronic digital caliper (Nippon, Japan), with 0.01 precision.
Herein, the mandibular radiomorphometric indices, which were evaluated in the present study, were quantitative indices, such as PMI, MCW, GA, and qualitative index of MCI. Morphological classification of the lower mandibular cortex was carried out by evaluating the MCI in the distal part of the mental foramen, bilaterally, and it was classified into C1, C2, and C3 groups according to Klemetti et al.  method:
- C1: The endosteal margin of mandibular cortex is smooth and clear on both sides.
- C2: It has semilunar defects.
- C3: The endosteal margin of the mandibular cortex has clearly porosity.
The MCW index was measured bilaterally, at the level of mental formant, by digital caliper. A line was drawn parallel to the longitudinal axis of the mandible and tangential to the lower border of the mandible. Then, another line was drawn, parallel to the first one, from the inner surface of the endosteum. The distance between these two lines, at the level of mental foramen, was considered as MCW.  The PMI was calculated by dividing the MCW value by the distance between the center of the mental foramen and the lower cortex of the mandible.  The limited studies have been done about the correlation between the GA and BMD, and the results are different. , Therefore, in the present study, GA was measured by tracing a line on panoramic radiographs, tangential to the most inferior points at the GA, and the lower border of the mandibular body, and the other line, that was drawn tangentially on the posterior borders of the ramus and the condyle.  The intersection of these two lines, considered as the mandibular angle, was measured bilaterally.
The SPSS version 21 statistics program (SPSS Inc., Chicago, IL, USA) was used for statistical analysis. Data regarding bone densitometry and radiographic measurements were analyzed by descriptive statistics (frequency, percentage, mean, and standard deviations).
Chi-square test was used for the evaluation of the relationship between mandibular qualitative index of MCI and BMD. One-way analysis of variance (ANOVA) was applied to compare quantitative indices. To assess sensitivity and specificity of MCI, C1 was considered as normal and C2 and C3 were considered as eroded form. In the same way, BMD categorized as normal and low bone mass (osteopenia and osteoporosis). Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cutoff point of quantitative indices (MCW, PMI, and GA) for identification of hip and lumbar spine osteoporosis and osteopenia. Pearson's correlation coefficient was used for evaluation of the correlation between the age and duration of postmenopausal period in addition to the number of teeth lost with MCW, PMI, and GA. The effect of decreased MCW, PMI, and GA indices on the likelihood of occurrence of osteoporosis or osteopenia was assessed with the backward logistic regression (LR) analysis by considering the effect of age and postmenopausal duration. Kappa test was used for the evaluation of intra- and inter-observer agreement of MCI.
| Results|| |
In this comparative analytical study, ninety cases were included. The demographic details of the subjects are reported in [Table 1].
|Table 1: Details of the patients according to the bone mineral density of the hip and lumbar spine|
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The kappa values were calculated at 0.78 for interobserver agreement of MCI (P = 0.0001) and 0.83 for intraobserver agreement (P = 0.0001).
According to [Table 1], MCW and PMI showed statistically significant differences among the three groups (P = 0.0001) based on ANOVA analysis, and no statistically significant difference was found for GA between the groups (P = 0.37).
[Table 2] shows the frequency distribution of MCI (C1, C2, and C3) among the three groups, on both sides, the frequency of C1 in normal group was statistically greater than the osteopenic and osteoporotic groups (P = 0.0001).
|Table 2: Frequency of mandibular cortical index in normal, osteopenic, and osteoporotic patients in the right and left sides of the mandible|
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The ROC curve was used to determine the optimal cutoff point for MCW and PMI to identify low BMD in postmenopausal women. The cutoff point for MCW was 4.13 mm, which had the highest sensitivity (81.7%) and specificity (71.36%) to predict reduced BMD. The area under curve (AUC) was 82.6% (P = 0.0001, and 95% confidence interval [CI]: 0.887-0.765) [Figure 1]. In addition, based on ROC analysis, the optimal cutoff point for PMI to identify reduced BMD was calculated at 0.29, which had the highest sensitivity and specificity to predict reduced BMD. The AUC of ROC curve of PMI was 80.2% (sensitivity = 71.7, specificity = 70.5, P = 0.0001, and 95% CI: 0.739-0.864) [Figure 2].
|Figure 1: Receiver operating characteristic curve showing the diagnostic validity of mandibular cortical width in identifying low bone mineral density|
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|Figure 2: Receiver operating characteristic curve showing the diagnostic validity of panoramic mandibular index in identifying low bone mineral density|
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The AUC of ROC curve of GA for identifying reduced BMD was 0.573, which was not statistically significant (P = 0.11). Therefore, the cutoff point for GA was not determined. For determination of sensitivity and specificity of MCI, C2 and C3 groups were considered as one group. The comparisons were done according to the comparison of C1 group with C2 and C3 groups. [Table 3] shows that 63.11% of C2 and C3 groups have reduced BMD (osteopenia/osteoporosis), while 75% of C1 groups were normal groups. A positive predictive value of 83.7% means that 83.7% reduced BMD was seen in C2 and C3 groups. Therefore, C2 and C3, in comparison with C1, have higher likelihood of reduced BMD.
|Table 3: Specificity, sensitivity, positive predictive value, and negative predictive value of mandibular cortical index in identifying low bone mineral density of C2 and C3 groups|
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The correlation of the age, postmenopausal duration, and the number of lost teeth with MCI was evaluated by ANOVA test. The subjects in the C1 category presented lower mean age and postmenopausal duration in comparison with C2 and C3. Moreover, the patients in the C3 group have higher teeth loss in comparison with the patients in C1 and C2 groups [Table 4].
|Table 4: Correlation between the variables of age, postmenopausal duration, and the number of teeth lost with mandibular cortical index among the three groups|
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The correlation coefficient of the variables of age, postmenopausal duration, and the number of teeth lost with MCW, PMI, and GA was assessed by Pearson's correlation coefficient analysis. There was the negatively significant correlation between the MCW and PMI with the number of teeth lost, age, and postmenopausal duration. However, GA did not show any significant correlation [Table 5].
|Table 5: Correlation between the age, postmenopausal duration, and the number of teeth lost with mandibular cortical width, panoramic mandibular index, and gonial angle|
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Backward LR analysis was carried between the MCW, PMI, and reduced BMD. The results in [Table 6] showed that a 1 mm decrease in MCW increased the odds of reduced BMD (osteoporosis, osteopenia) up to 3.22-fold. In addition, a 0.01 decrease in PMI increased the odds of reduced BMD to up to 1.12-fold.
|Table 6: Association between mandibular cortical width, panoramic mandibular index, as well as postmenopausal duration and bone mineral density, adjusted for age and postmenopausal duration|
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| Discussion|| |
In osteoporotic patients, the inferior cortex of the mandible undergoes osteoporotic changes and its manifestations could be detected on panoramic radiography.  In the present study, the MCW of normal, osteopenic, and osteoporotic patients were 4.56 mm, 3.39 mm, and 3.97 mm, respectively, and there was a statistically significant difference among the three groups, which is consistent with the results of previous studies. ,,, Nevertheless, there are some other studies that show differences between these categories of patients.  Inferior mandibular cortex is a one of the most important landmarks in the diagnosis of osteoporosis.  The value of MCW in normal and osteoporotic groups was reported as 3.7 mm and 2.8 mm, respectively, in Hastar et al.  study. Another study also demonstrated significant differences between the MCW of normal (4.1 ± 0.9 mm) and of reduced BMD (3.2 ± 0.9 mm) groups.  In Jagelaviciene et al.  study, there was a significant correlation (r = 0.336) between MCW and the density of heel bone. The PMI showed a statistically significant difference among the three groups in our study. These results are in agreement with previous studies, , while they disagree with the other reports. , In Jagelaviciene et al.  study, there was a significant correlation (r = 0.397) between PMI and heel bone density. Recently, another study showed a positive correlation between PMI and quantitative ultrasound BMD scan of heel bone of both males and females.  Ηakur et al.  found the negatively significant correlation between the BMD of vertebrae and the mandibular angle in osteoporotic men. There are limited studies about the correlations of GA and BMD results in the females. , Shakeel et al. found a positive correlation for mandibular angle (0.5249) in female patients more than 35 years of age. However, in the case of female patients <35 years of age, it had a negative correlation (−0.5589).  We could not find a positive correlation for GA among the three groups. However, some other reports suggested that a reduced BMD can alter the mandibular angle. ,
In our study, the MCI had a statistically significant difference among three groups. We evaluated the MCI (C1, C2, and C3) in the left and right sides of the mandible, separately, which is not similar to the previous studies, who only presented their results generally. Hastar et al.  found that C1 was observed in normal group and C2 was detected in reduced BMD group. In Johari Khatoonabad et al.  study also, C2 and C3 were detected in osteoporotic and osteopenic patients, significantly. In addition, in their study, the mean age, duration of postmenopausal period, and MCW of C1, C2, and C3 groups had statistically significant differences. Consequently, with the increase in age and postmenopausal duration, the decrease of MCW, C2, or C3 category showed a significant correlation.  Hashimoto et al.  proved that patients with severe erosion of the mandibular cortex were osteopenic or osteoporotic. Valerio et al.  stated that MCI can be used to identify the postmenopausal females with low bone densities. In other studies, , MCI showed no significant difference in normal and osteoporotic groups. Several factors, such as differences in race, gender, sample size, experience of the observers in the diagnosis of MCI, image quality and also, the candidate bone for DXA examination, could be responsible for the presence of variations among the studies.
In the present study, there were statistically significant differences in the number of teeth lost among the groups. This finding has confirmed that the mean of teeth lost in osteoporotic patients was greater than for normal and osteopenic groups. However, in Johari Khatoonabad et al.  study, no statistically significant difference was noted in the number of teeth lost among the three groups. One of the remarkable points of the present study is the determination of cutoff point for quantitative indices by using ROC curve. The optimal cutoff point of MCW based on ROC curve was determined at 4.13 mm, which was similar to some other studies. , The results of our investigation revealed that MCW has a high accuracy in the prediction of low BMD. This was reported as 3 mm in Devlin and Horner  and Hastar et al.  studies. The percentage of AUC for MCW was 78% in Ishii et al.  and 73.3% in Devlin and Horner  studies. The optimal cutoff point of PMI to identify the low BMD was 0.29. This value is in agreement with Benson et al.  report. However, PMI did not show statistically significant difference among the groups in Johari Khatoonabad et al.  study. Khojastehpour et al.  also found a statistically significant difference between the average of PMI in normal, osteopenic, and osteoporotic postmenopausal women. However, they did not calculate any cutoff point for PMI. Recently, Shakeel et al. found a positive correlation between the heel densitometry and PMI, but they did not calculate any cutoff point for this index. 
We found a statistically significant correlation between MCI and variables such as age, postmenopausal duration, and the number of teeth lost. The patients that were having C1 form of inferior mandibular cortex had lower mean age and duration of postmenopausal period when compared to C2 or C3 categories. Moreover, the number of teeth lost was increased in patients having C3 form of the inferior mandibular cortex, compared with C1 and C2. These findings were in agreement with the results of the study of Hastar et al.  Gulsahi et al.  demonstrated that the dental status is one of the most important effective factors in MCI. Furthermore, they stated that dental status only influences the MCW and not the PMI. A correlation between the MCW and the variables of age, postmenopausal duration, and the number of teeth lost was detected in our study. These results are similar to findings of other studies. , In Hastar et al.  study, edentulous patients had lower MCW. Johari Khatoonabad et al.  after carrying out a linear regression analysis between the MCW, age and duration of postmenopausal period, demonstrated that there was a 0.035 mm decrease in MCW with each 1-year increase in the postmenopausal period duration.
We found a significant correlation between the PMI and factors such as age, duration of postmenopausal period, and the number of teeth lost. These results are similar to other studies. , Drozdzowska et al.  did not present any correlation between PMI and the variable of age. In the present study, a 1 mm decrease in MCW increased the odds of reduced BMD (osteoporosis, osteopenia) up to 3.22-fold when age and postmenopausal duration were taken into account. In addition, a 0.01 decrease in PMI increased the odds of reduced BMD up to 1.12-fold when age and postmenopausal duration were taken into account. Vlasiadis et al.  demonstrated that 1 mm decrease in MCW resulted in 47% increase in the likelihood of osteopenia or osteoporosis. This value was 40% in Johari Khatoonabad et al.  study. Overall, our study has remarkable findings in comparison with the previous studies. One of which is the determination of the optimal cutoff point for MCW and PMI, which limited previous studies measured those. In addition, based on the current research, dental status, age, and postmenopausal duration influence the MCI, PMI, and MCW significantly. These interesting findings have not been considered in the previous studies. The effect of age and postmenopausal duration was assessed in very limited studies, thus we focused on this relation.
Limitations of the present study could be a limited number of the sample size and the availability of the patients having all of the inclusive criteria.
| Conclusions|| |
The MCW, PMI, and MCI have high diagnostic value to predict low BMD. By increasing the age and the postmenopausal duration, MCW and PMI decrease, whereas the clear porosity of endosteal margin of the mandibular cortex (C3 form of MCI) and the number of teeth lost showed increase. A 1 mm decrease in MCW increased the odds of reduced BMD (osteoporosis, osteopenia) up to 3.22-fold. Therefore, dentists and dentomaxillofacial radiologists could play an important role in the early diagnosis of low BMD and help to refer the high-risk patients for precise evaluation.
We would like to thank the Vice Chancellor of Research Foundation of Guilan University of Medical Sciences, Rasht, Iran, for financial support. In addition, we wish to express our gratitude to Dr. Fazelpour and Dr. Jafarian as the managers of the bone densitometry clinic, and all the technicians, for their invaluable contributions.
Financial support and sponsorship
Vice Chancellor of Research Foundation of Guilan University of Medical Sciences, Rasht, Iran.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]
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| ||Ersen Bilgili |
| ||European Annals of Dental Sciences. 2021; : 13 |
|[Pubmed] | [DOI]|