Abstract
Introduction:
Age estimation using regressive alterations such as root dentin translucency, PDL attachment, and attrition are easy and reliable way of predicting the age. However, extensive and population-specific formula has not been generated. This attempt was to assess the correlation of these alterations with age and to generate a Linear regressive formula, specific to this population.
Methods and Material:
Three alterations were assessed such as dental attrition, root dentin translucency, and periodontal attachment level from the extracted teeth. Dental attrition was measured using Johanson's and Li and Ji criteria. PDL attachment level and root dentin translucency was measured using the Johanson method and the Lamendin method.
Statistical Analysis:
SPSS software (Version 27), Pearson correlation test, and Linear regressive analysis were used.
Results:
Our results showed all three factors/parameters such as attrition, periodontal ligament, and translucency having a very good correlation with age and correlation coefficient r value ranging from 0.6 to 0.8. All the parameters were having statistically significant correlation with P value <0.005. Among them, root dentin translucency with Johanson G method showed excellent correlation with r = 0.83 followed by PDL attachment by Johanson G method with r = 0.702.
Conclusions:
Regressive changes such as Dentin translucency, PDL attachment and attrition on Coastal Karnataka showed a very good correlation with age. Among them, Dentin translucency by Johanson G method had the best correlation with the a standard error of estimate (SEE). Results of our study indicates that all these parameters [Translucency, PDL attachment, and attrition] can be utilized in age estimation.
Keywords: Dental age estimation, Johanson, Lamendin and Li and Ji
INTRODUCTION
Identity refers to the characteristics with which a person can be recognized. Age is one of the important factors that gives identity and uniqueness to an individual.[1] Age estimation techniques have been introduced by many anthropologist, archaeologist, and forensic scientists, but an important turning point was in the era of forensic odontology.[1] For proper identification of the dead, age estimation becomes mandatory particularly if there are no e-mortem details available and can also be useful in connecting crimes.[2]
Estimation of age through dental parameters is a valuable technique in human identification.[3] Schour and Massler gave a first attempt on identifying the age of an extracted teeth. There are various methods of age estimation followed by Schour and Massler, all of which are broadly classified under morphologic, radiographic, and biochemical methods.[4] The main drawback of estimating the age using histologic and biochemical methods is the destruction of teeth where teeth preservation is mandatory. In order to overcome this, age estimation methods were modified using the whole tooth structure. Based on the above context, Gustafson gave six retrogressive changes such as dental attrition, periodontal ligament attachment, root dentin translucency, secondary dentin deposition, cementum apposition, and root resorption and root apex destruction for age estimation without the need for destroying the teeth.[5] Later, multiple methods were developed by Dalitz, Johanson, Burns and Maples and Lamendin.[6]
With the view of the above background, the present study was attempted to correlate the chronological age with the estimated age using tooth attrition, dentin translucency, and periodontal ligament attachment level using Johanson's modification of Gustafson, Lamendin, and Li and Ji methods. This attempt was to generate a population-specific formula for Coastal Karnataka population.
MATERIALS AND METHODS
Teeth were collected from 80 individuals with the age group of 20 to 60 years, from the coastal area of Karnataka. Maxillary and mandibular incisors, canines, and single-rooted premolars that are extracted because of periodontal diseases or orthodontic treatment were included. Molars and other teeth that are extracted due to caries and fractured teeth were not included in the study. Institutional ethical clearance was obtained (Protocol No: YEC2/979).
Dental attrition, root dentin translucency, and periodontal attachment level were evaluated in this study. Johanson[7] and Li and Ji[8] criteria were used for determining dental attrition. Johanson method was initially used on ground sections and Li and Ji method was used for only molars. In this study, both approaches were modified for unsectioned non-molars. Johanson considered the dental attrition, root translucency, and PDL attachment level and suggested seven grades with the regression formula through which age was calculated.[7]
Johanson method[7] and Lamendin[9] method was used to determine the level of PDL attachment and root dentin translucency. Lamendin et al. is a quantitative measurement, which is measured with the help of vernier calliper. In method, PDL attachment level describes different grades of gingival recession in terms of distance in millimetre from the CEJ that was measured with the help of vernier calliper [Figure 1]. Root translucency by Lamendin[9] describes the maximum root translucency length from the apex, on the labial aspect that was also measured manually by vernier calliper [Figure 2]. To assess the potential inter-observer variations, two observers were included and the mean value of both the observers was considered. With the help of the statistical software tool, the extracted value was subjected to linear regression analysis with age as dependent variable and the dental changes as independent variables. In addition to looking at the dental changes individually, a stepwise regression procedure was also used to evaluate which combination of the assessed dental variables is best correlated to age and contributes the most to age estimation.
Figure 1.
PDL attachment level by Lamendin H method
Figure 2.
Root translucency by Lamendin H method
RESULTS
Pearson's correlation coefficient r value, along with the P value and the standard error of estimate (SEE—Difference between the estimated age and actual age) was statistically analyzed. The obtained data are compiled in the Table 1.
Table 1.
Depicting the Pearsons correlation r value, P value and standard error of estimate (SEE)
| Variable | Method | Pearson's correlation R value | P | SEE in years |
|---|---|---|---|---|
| Attrition | Johanson | 0.648 | <0.0001 | 7.969 |
| Attrition | Li and Ji | 0.608 | <0.0001 | 8.313 |
| Root translucency | Johanson | 0.830 | <0.0001 | 5.838 |
| Root translucency | Lamendin | 0.693 | <0.0001 | 7.545 |
| PDL loss | Johanson | 0.702 | <0.0001 | 7.454 |
| PDL loss | Lamendin | 0.646 | <0.0001 | 7.994 |
| SEE | Regression value generated for this population | 0.830 | <0.0001 | 5.838 |
Our results showed all three factors/parameters such as attrition, periodontal ligament, and translucency with all the age estimation methods that showed a very good correlation with age having correlation coefficient r value ranging from 0.6 to 0.8 and SEE of 5.8 to 8.3 years. All the parameters were having statistically significant correlation with P value < 0.005. Among them root dentin translucency with Johanson method showed excellent correlation with r = 0.83 followed by PDL attachment with Johanson method showing a r value of 0.702. When all the parameters were considered together, r value was 0.83 and SEE being 5.8 years for this specific population.
We also have generated our own regression formula for age estimation [Table 2].
Table 2.
Linear regression formulae generated for Coastal Karnataka population
| Variable | Method | Formula |
|---|---|---|
| Attrition | Johanson | AGE=31.757+7.350×Attrition |
| Attrition | Li and Ji | AGE=31.622+3.022×Attrition |
| Translucency | Johanson | AGE=27.997+8.226×translucency |
| Translucency | Lamendin | AGE=31.051 + 1.680×translucency |
| PDL loss | Johanson | AGE=31.056+6.656×PDL loss |
| PDL | Lamendin | AGE=32.335+1.775 × PDL |
| Stepwise regression | Regression formula generated for this population | AGE=27.997+8.226×translucency (Johanson) |
DISCUSSION
Correlation of individual dental parameters with previous studies has been summarized as follows
1. Root dentin translucency
One of the important regressive changes related to the age estimation is the root dentin translucency. Johanson found that the translucent zone in the apical area of teeth corresponds to the age of the teeth. An Indian study by Ajmal et al.[10] showed that the root dentin translucency showed lower correlation to age with r = 0.09 which is followed by PDL (r = 0.40) and attrition (r = 0.16). An Iranian study also showed similar results with translucency showing lowest correlation to age (r = 0.34) followed by attrition (r = 0.39) and PDL (r = 0.38).[11] Studies by Johanson[7] and Solheim[12] showed an increase in correlation of age with root dentin translucency having r value of 0.86 and r value of 0.71 for the latter. According to the study by Solheim, translucency measured on unsectioned teeth showed strongest correlation than sectioned teeth.[12] An Indian study by Acharya AB et al.[13] showed that translucency has best correlation to age by evaluating Gustafson's six parameters. This proves that some prominent studies showed an increase in correlation of age with root translucency. The increase in translucency is seen progressing with age and is not affected by periodontal conditions. Thus, from our study, when compared to previous study on Indian population,[13] showed that translucency strongly indicated an increased correlation to age r = 0.830 and SEE = 5.838, which was far better than results of the previous study with r value of 0.05–0.09.[13]
2. PDL Attachment
PDL attachment level has shown to have a direct effect on age among other factors, this is because of the gingival recession that is a constant feature in ageing.[13,14] Followed by root dentin translucency, our study showed PDL attachment level with increased correlation to age having r value of 0.702 and SEE value of 7.454. It was measured using Johanson[7] and Lamendin et al.,[9] with both the methods showing negligible difference in the r value [Table 1]. The Pearson correlation r in our study was from 0.646 to 0.702, which is higher and statistically significant when compared to the previous study on Indian population by Acharya et al.[13] with r value from 0.38 to 0.40. A similar research by Johanson[7] showed the correlation value of r = 0.49. In a study by Solheim,[12] he found that PDL attachment has one of the least correlations to age when compared to translucency with the r value of 0.32. However, he mentioned that PDL attachment level is an important regressive change that has to be included in age estimation method along with other factors.[12]
3. Attrition
In this study, the lowest Pearson's correlation to age is for attrition (r = 0.60 to 0.64) followed by PDL attachment. But it has a correlation coefficient which is higher than the Johanson study, thus making it statistically significant. Among all the eleven methods of identifying the level of attrition, Johanson method was found to be the most satisfied method for attrition.[7] According to Solheim, different teeth have different rates of attrition. He suggested the use of different formulas for each individual tooth type. They also found that the anterior maxillary canines showed a lower correlation to age.[12] We have measured the level among different tooth types (incisors, canines, and single-rooted premolars) with this same method. This may be one of the reasons for our low correlation value. When compared to an Indian study,[13] the Pearson's correlation value for attrition was r = 0.16 to 0.21, which is lower when compared to our study which had a correlation value r of 0.60–0.64. These results indicate a low correlation of attrition and age. However, r value here for attrition is relatively higher compared to the previous research (even when compared to other 2 parameters of PDL and translucency of previous samples). From these data, we were able to analyze that attrition had a very low correlation value when compared to root translucency and PDL attachment. Another reason for the low correlation value is that attrition does not happen only as a process of ageing, but it can also be attributed by the parafunctional habits. Grading of the teeth have to be done cautiously with individual teeth and not altogether. Thus, attrition cannot be used as a sole indicator for identification of age.
CONCLUSION
One of the most important relevant areas in forensic investigations is age estimation. There are various methods with its own applications and accuracy. However, age estimation using teeth will have a more accurate representation of age. Regressive changes such as dentin translucency, PDL attachment, and attrition on Coastal Karnataka showed a very good correlation (r = 0.83, SEE = 5.8) with age. Among them, Dentin translucency by Johanson method had the best correlation with the least SEE. Results of our study indicates that all these parameters (translucency, PDL attachment, and attrition) can be utilized in age estimation. However, more research on different population groups with higher sample may provide better and accurate regressive formulae.
Key messages
Age estimation using regressive changes of the teeth is the easiest way of assessing the age of the dead individuals. However, population-specific formula is yet to be generated, in different regions of our country. This attempt was to generate a population-specific formula in the coastal Karnataka region.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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