DNA methylation of the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C,and TRIM59 genes for age prediction from blood,saliva, and buccal swab samples

Many studies have reported age-associated DNA methylation changes and age-predictive models in various tissues and body fluids. Although age-associated DNA methylation changes can be tissue-specific, a multi-tissue age predictor that is applicable to various tissues and body fluids with considerable prediction accuracy might be valuable. In this study, DNA methylation at 5 CpG sites from the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes were investigated in 448 samples from blood, saliva, and buccal swabs. A multiplex methylation SNaPshot assay was developed to measure DNA methylation simultaneously at the 5 CpG sites. Among the 5 CpG sites, 3 CpG sites in the ELOVL2, KLF14 and TRIM59 genes demonstrated strong correlation between DNA methylation and age in all 3 sample types. Age prediction models built separately for each sample type using the DNA methylation values at the 5 CpG sites showed high prediction accuracy with a Mean Absolute Deviation from the chronological age (MAD) of 3.478 years in blood, 3.552 years in saliva and 4.293 years in buccal swab samples. A tissue-combined model constructed with 300 training samples including 100 samples from each blood, saliva and buccal swab samples demonstrated a very strong correlation between predicted and chronological ages (r = 0.937) and a high prediction accuracy with a MAD of 3.844 years in the 148 independent test set samples of 50 blood, 50 saliva and 48 buccal swab samples. Although more validation might be needed, the tissue-combined model’s prediction accuracies in each sample type were very much similar to those obtained from each tissue-specific model. The multiplex methylation SNaPshot assay and the age prediction models in our study would be useful in forensic analysis, which frequently involves DNA from blood, saliva, and buccal swab samples.     

A common epigenetic clock from childhood to old age

Forensic age estimation is a DNA intelligence tool that forms an important part of Forensic DNA Phenotyping. Criminal cases with no suspects or with unsuccessful matches in searches on DNA databases; human identification analyses in mass disasters; anthropological studies or legal disputes; all benefit from age estimation to gain investigative leads. Several age prediction models have been developed to date based on DNA methylation. Although different DNA methylation technologies as well as diverse statistical methods have been proposed, most of them are based on blood samples and mainly restricted to adult age ranges. In the current study, we present an extended age prediction model based on 895 evenly distributed Spanish DNA blood samples from 2 to 104 years old. DNA methylation levels were detected using Agena Bioscience EpiTYPER® technology for a total of seven CpG sites located at seven genomic regions: ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, MIR29B2CHG and chr16:85395429 (GRCh38). The accuracy of the age prediction system was tested by comparing three statistical methods: quantile regression (QR), quantile regression neural network (QRNN) and quantile regression support vector machine (QRSVM). The most accurate predictions were obtained when using QRNN or QRSVM (mean absolute prediction error, MAE of ± 3.36 and ± 3.41, respectively). Validation of the models with an independent Spanish testing set (N = 152) provided similar accuracies for both methods (MAE: ± 3.32 and ± 3.45, respectively). The main advantage of using quantile regression statistical tools lies in obtaining age-dependent prediction intervals, fitting the error to the estimated age. An additional analysis of dimensionality reduction shows a direct correlation of increased error and a reduction of correct classifications as the training sample size is reduced. Results indicated that a minimum sample size of six samples per year-of-age covered by the training set is recommended to efficiently capture the most inter-individual variability..     

DNA methylation-based age prediction from saliva: High age predictability by combination of 7 CpG markers

DNA methylation is currently one of the most promising age-predictive biomarkers. Many studies have reported DNA methylation-based age predictive models, but most of these are based on DNA methylation patterns from blood. Only a few studies have examined age-predictive DNA patterns in saliva, which is one of the most frequently-encountered body fluids at crime scenes. In this study, we generated genome-wide DNA methylation profiles of saliva from 54 individuals and identified CpG markers that showed a high correlation between methylation and age. Because the age-associated marker candidates from saliva differed from those of blood, we investigated DNA methylation patterns of 6 age-associated CpG marker candidates (cg00481951, cg19671120, cg14361627, cg08928145, cg12757011, and cg07547549 of the SST, CNGA3, KLF14, TSSK6, TBR1, and SLC12A5 genes, respectively) in addition to a cell type-specific CpG marker (cg18384097 of the PTPN7 gene) in an independent set of saliva samples obtained from 226 individuals aged 18 to 65 years. Multiplex methylation SNaPshot reactions were used to generate the data. We then generated a linear regression model with age information and the methylation profile from the 113 training samples. The model exhibited a 94.5% correlation between predicted and chronological age with a mean absolute deviation (MAD) from chronological age of 3.13 years. In subsequent validation using 113 test samples, we also observed a high correlation between predicted and chronological age (Spearman’s rho = 0.952, MAD from chronological age = 3.15 years). The model composed of 7 selected CpG sites enabled age prediction in saliva with high accuracy, which will be useful in saliva analysis for investigative leads.     

Some tips on age estimation using DNA methylation in saliva samples as an index across the Japanese and Indonesian ethnicities

Age estimation of unidentified bodies is of marked importance in forensic medicine. In previous studies, the analysis of DNA methylation in body fluids led to the identification of several age-related CpG sites in genes such as EDARADD and FHL2. However, limited information is available on whether interethnic differences may affect the age prediction results. In the present study, we examined the effect of ethnicity on the age prediction method based on methylation scores, which were determined via methylation-sensitive high-resolution melting. We found that there was a significant difference in methylation scores between Japanese and Indonesian participants of early 20 s group, and that the nationality coefficient was significant for age estimation when applying the existing method for the analysis of the methylation status of EDARADD and FHL2. This suggests that when using certain biochemical indicators as a predictor of age, the effects of ethnicity on DNA methylation should be considered to improve the accuracy of the estimation.     

Age-related DNA methylation analysis for forensic age estimation using post-mortem blood samples from Japanese individuals

As one of external visible characteristics (EVCs) in forensic phenotyping, age estimation is essential to providing additional information about a sample donor. With the development of epigenetics, age-related DNA methylation may be used as a reliable predictor of age estimation. With the aim of building a feasible age estimation model for Japanese individuals, 53 CpG sites distributed between 11 candidate genes were selected from previous studies. The DNA methylation level of each target CpG site was identified and measured on a massive parallel platform (synthesis by sequencing, Illumina, California, United States) from 60 forensic blood samples during the initial training phase. Multiple linear regression and quantile regression analyses were later performed to build linear and quantile age estimation models, respectively. Four CpG sites on four genes— ASPA, ELOVL2, ITGA2B, and PDE4C —, were found to be highly correlated with chronological age in DNA samples from Japanese individuals (|R| > 0.75). Subsequently, an independent validation dataset (n = 30) was used to verify and evaluate the performance of the two models. Comparison of mean absolute deviation (MAD) with other indicators showed that both models provide accurate age predictions (MAD: linear = 6.493 years; quantile = 6.243 years). The quantile model, however, can provide the changeable prediction intervals that grow wider with increasing age, and this tendency is consistent with the natural aging process in humans. Hence, the quantile model is recommended in this study.     

Application of droplet digital PCR method for DNA methylation-based age prediction from saliva

The recent studies reported that DNA methylation markers show changes with age, and expected that the DNA methylation markers can be effectively used for estimation of age in forensic genetics. In this study, we applied droplet digital PCR (ddPCR) method to investigate the DNA methylation pattern in the CpG sites, and we constructed an age prediction model based on the ddPCR method. The ddPCR is capable of highly sensitive quantitation of nucleic acid and detection of sequence variations in gene by separating the sample into large number of partitions and clonally amplifying nucleic acids in each partition. We extracted DNA from saliva samples collected from several age groups. The DNA was bisulfite converted and subjected to ddPCR using specifically designed primers and probes. The methylation ratio of each sample was calculated and correlation between the methylation ratio and the chronological age was analyzed. In the results, methylated DNA ratio at the 4 CpG sites (cg14361627, cg14361627, cg08928145 and cg07547549) showed strong correlation with chronological age. Percent-methylation values at 4 CpG markers and chronological ages of the 76 individuals were analyzed by multiple regression analysis, and we constructed an age prediction model. We observed a strong correlation (Spearman’s rho = 0.922) between predicted and chronological ages of 76 individuals with a MAD from chronological age of 3.3 years. Collectively, the result in this study showed the potential applicability of ddPCR to predict age from saliva.     

Age prediction in living: Forensic epigenetic age estimation based on blood samples

DNA methylation analysis in a variety of genes has brought promising results in age estimation. The main aim of this study was to evaluate DNA methylation levels from four age-correlated genes, ELOVL2, FHL2, EDARADD and PDE4C, in blood samples of healthy Portuguese individuals. Fifty-three samples were analyzed through the bisulfite polymerase chain reaction (PCR) sequencing method for CpG dinucleotide methylation status. Linear regression models were used to analyze relationships between methylation levels and chronological age. The highest age-associated CpG in each locus was chosen to build a multi-locus age prediction model (APM), allowing to obtain a Mean Absolute Deviation (MAD) between chronological and predicted ages of 5.35 years, explaining 94.1% of age variation. Validation approaches demonstrated the accuracy and reproducibility of the proposed multi-locus APM. Testing the APM in 51 blood samples from deceased individuals a MAD of 9.72 years was obtained. Potential differences in methylation status between samples from living and deceased individuals could exist since the highest age-correlated CpGs were different in some genes between both groups. In conclusion, our study using the bisulfite PCR sequencing method is in accordance with the high age prediction accuracy of DNA methylation levels in four previously reported age-associated genes. DNA methylation pattern differences between blood samples from living and deceased individuals should be taken into account in forensic contexts.     

Improving the age estimation model for toenails

In a previous study, we have provided the first proof that chronological age can be estimated through DNA methylation (DNAm) patterns in fingernails and toenails. DNAm data of 15 CpGs located in 4 genetic markers (ASPA, EDARADD, ELOVL2 and PDE4C) were evaluated, of which variable selection yielded age prediction models with a mean absolute deviation (MAD) ranging from 7.68 to 9.36 years, depending on the sampling location. Three additional age-associated markers (KLF14, MIR29B2CHG and TRIM59) were assessed in the current study with the goal of increasing the prediction accuracy of the model initially constructed for toenails. This new and improved age estimation assay yielded an MAD of 4.82 and 5.61 years for the training and test set, respectively. The feasibility of the application for post-mortem cases was also demonstrated through testing a limited set of samples collected from deceased individuals.     

Development of a methylation marker set for forensic age estimation using analysis of public methylation data and the Agena Bioscience EpiTYPER system

Individual age estimation has the potential to provide key information that could enhance and extend DNA intelligence tools. Following predictive tests for externally visible characteristics developed in recent years, prediction of age could guide police investigations and improve the assessment of age-related phenotype expression patterns such as hair colour changes and early onset of male pattern baldness. DNA methylation at CpG positions has emerged as the most promising DNA tests to ascertain the individual age of the donor of a biological contact trace. Although different methodologies are available to detect DNA methylation, EpiTYPER technology (Agena Bioscience, formerly Sequenom) provides useful characteristics that can be applied as a discovery tool in localized regions of the genome. In our study, a total of twenty-two candidate genomic regions, selected from the assessment of publically available data from the Illumina HumanMethylation 450 BeadChip, had a total of 177 CpG sites with informative methylation patterns that were subsequently investigated in detail. From the methylation analyses made, a novel age prediction model based on a multivariate quantile regression analysis was built using the seven highest age-correlated loci of ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, C1orf132 and chr16:85395429. The detected methylation levels in these loci provide a median absolute age prediction error of ±3.07 years and a percentage of prediction error relative to the age of 6.3%. We report the predictive performance of the developed model using cross validation of a carefully age-graded training set of 725 European individuals and a test set of 52 monozygotic twin pairs. The multivariate quantile regression age predictor, using the CpG sites selected in this study, has been placed in the open-access Snipper forensic classification website.     

Criteria for age estimation in living individuals

This paper presents updated recommendations of the Study Group on Forensic Age Diagnostics for age estimations in living individuals in criminal proceedings. In order to increase the diagnostic accuracy and to improve the identification of age-relevant developmental disorders, a physical examination, an X-ray examination of the left hand, as well as a dental examination including the determination of the dental status and an X-ray of the dentition should be performed in each case. If the skeletal development of the hand is completed, an additional radiological examination of the clavicles should be carried out. Minimum requirements for reference studies are defined and recommendable studies are listed. Instructions for the examination and the preparation of expert reports are presented. The committee of the study group organizes annual proficiency tests for quality assurance.     

Epigenetic age signatures in the forensically relevant body fluid of semen: a preliminary study

To date, DNA methylation has been regarded as the most promising age-predictive biomarker. In support of this, several researchers have reported age predictive models based on the use of blood or even across a broad spectrum of tissues. However, there have been no publications that report epigenetic age signatures from semen, one of the most forensically relevant body fluids. In genome-wide DNA methylation profiles of 36 body fluids including blood, saliva, and semen, the previous age predictive models showed considerable prediction accuracy in blood and saliva but not in semen. Therefore, we selected CpG sites, whose methylation levels are strongly correlated with age in 12 semen profiles obtained from individuals of different ages, and investigated DNA methylation changes at these CpGs in 68 additional semen samples obtained from individuals aged 20 to 73 years using methylation SNaPshot reaction. Among the selected age-related CpG candidates, outstanding age correlation was obtained at cg06304190 in the TTC7B gene. Interestingly, the region around the TTC7B gene has been reported to show age-related DNA methylation alteration in the sperm methylome of 2 samples collected from individuals at certain time intervals. The age-predictive linear regression model trained with 3 CpGs (cg06304190 in the TTC7B gene, cg06979108 in the NOX4 gene and cg12837463) showed a high correlation between the predicted age and the chronological age, with an average absolute difference of approximately 5 years. These selected epigenetic age signatures are expected to be useful for considerably accurate age estimation in the forensically relevant body fluid of semen. However, because the findings were limited by small sample size, it will be necessary to further evaluate the age correlation of the selected CpGs and to encourage further investigation.     

Human age estimation from blood using mRNA,DNA methylation,DNA rearrangement,and telomere length

Establishing the age of unknown persons, or persons with unknown age, can provide important leads in police investigations, disaster victim identification, fraud cases, and in other legal affairs. Previous methods mostly relied on morphological features available from teeth or skeletal parts. The development of molecular methods for age estimation allowing to use human specimens that possess no morphological age information, such as bloodstains, is extremely valuable as this type of samples is commonly found at crime scenes. Recently, we introduced a DNA-based approach for human age estimation from blood based on the quantification of T-cell specific DNA rearrangements (sjTRECs), which achieves accurate assignment of blood DNA samples to one of four 20-year-interval age categories. Aiming at improving the accuracy of molecular age estimation from blood, we investigated different types of biomarkers. We started out by systematic genome-wide surveys for new age-informative mRNA and DNA methylation markers in blood from the same young and old individuals using microarray technologies. The obtained candidate markers were validated in independent samples covering a wide age range using alternative technologies together with previously proposed DNA methylation, sjTREC, and telomere length markers. Cross-validated multiple regression analysis was applied for estimating and validating the age predictive power of various sets of biomarkers within and across different marker types. We found that DNA methylation markers outperformed mRNA, sjTREC, and telomere length in age predictive power. The best performing model included 8 DNA methylation markers derived from 3 CpG islands reaching a high level of accuracy (cross-validated R² = 0.88, SE ± 6.97 years, mean absolute deviation 5.07 years). However, our data also suggest that mRNA markers can provide independent age information: a model using a combined set of 5 DNA methylation markers and one mRNA marker could provide similarly high accuracy (cross-validated R² = 0.86, SE ± 7.62 years, mean absolute deviation 4.60 years). Overall, our study provides new and confirms previously suggested molecular biomarkers for age estimation from blood. Moreover, our comparative study design revealed that DNA methylation markers are superior for this purpose over other types of molecular biomarkers tested. While the new and some previous findings are highly promising, before molecular age estimation can eventually meet forensic practice, the proposed biomarkers should be tested further in larger sets of blood samples from both healthy and unhealthy individuals, and markers and genotyping methods shall be validated to meet forensic standards.     

Epigenome-wide screening of CpG markers to develop a multiplex methylation SNaPshot assay for age prediction

Age prediction can provide important information about the contributors of biological evidence left at crime scenes. DNA methylation has been regarded as the most promising age-predictive biomarker. Measuring the methylation level at the genome-wide scale is an important step to screen specific markers for forensic age prediction. In present study, we screened out five age-related CpG sites from the public EPIC BeadChip data and evaluated them in a training set (115 blood) by multiplex methylation SNaPshot assay. Through full subset regression, the five markers were narrowed down to three, namely cg10501210 (C1orf132), cg16867657 (ELOVL2), and cg13108341 (DNAH9), of which the last one was a newly discovered age-related CpG site. An age prediction model was built based on these three markers, explaining 86.8% of the variation of age with a mean absolute deviation (MAD) of 4.038 years. Then, the multiplex methylation SNaPshot assay was adjusted according to the age prediction model. Considering that bloodstains are one of the most common biological samples in practical cases, three validation sets composed of 30 blood, 30 fresh bloodstains and 30 aged bloodstains were used for evaluation of the age prediction model. The MAD of each set was estimated as 4.734, 4.490, and 5.431 years, respectively, suggesting that our age prediction model was applicable for age prediction for blood and bloodstains in Chinese Han population of 11–71 age. In general, this study describes a workflow of screening CpG markers from public chip data and presents a 3-CpG markers model for forensic age prediction.     

Age estimation in children by measurement of open apices in teeth

This paper concerns a method for estimating the age of children based on their teeth. The sample consisted of 455 Italian white children (213 boys, 242 girls) aged between 5 and 15 years. The purpose of the present investigation was to present a method for assessing chronological age based on the relationship between age and measurement of the open apices in teeth. Pearson’s correlation coefficients between age and these variables showed that the correlations between age and the open apices in teeth were significant and negative. Furthermore, gender and the number of teeth with the apical end of the root canals completely closed (N ₀) showed a significant correlation with chronological age. With the aid of a stepwise multiple regression model, a linear relationship between open apices, N ₀, and age was shown. Statistical analysis indicated that these morphological variables explain 83.6% of the variations in estimated chronological age. The median of residual errors between the actual and estimated ages was −0.035 years [interquartile range (IQR)=1.18 years].     

Computed tomographic analysis of medial clavicular epiphyseal fusion for age estimation in Indian population

Forensic age estimation is a crucial aspect of the identification process. While epiphyseal fusion of long bones has been studied for age estimation since a long time, over the past few years, the role of medial clavicular epiphyseal fusion in age estimation is being explored. The medial clavicular epiphyseal fusion can be used to estimate age in young adults, and can also determine whether medicolegally significant ages of 16 and 18 years have been attained by an individual. The present study aimed at generating regression models to estimate age by evaluating the medial clavicular epiphyseal fusion in Indian population using Schmeling et al. and Kellinghaus et al. method, and to assess whether an individual’s age is over medicolegally significant thresholds of 16 and 18 years. Degree of ossification of the medial clavicular epiphysis was studied in CT images of 350 individuals aged 10.01–35.47 years. Significant statistical correlation (P < 0.001) was observed between the degree of fusion and the chronological age of the participants, with Spearman’s correlation (ρ) = 0.918 in females, and ρ = 0.905 in males. Regression models were generated using degree of ossification of medial end of clavicle of 350 individuals (147 females and 203 males) and these models were applied on a test set of 50 individuals (25 females and 25 males). Mean absolute error of 1.50 for females, 1.14 for males, and 1.32 for the total test set was observed when the variance between the chronological ages and estimated ages was calculated.     

Development of a forensically useful age prediction method based on DNA methylation analysis

Forensic DNA phenotyping needs to be supplemented with age prediction to become a relevant source of information on human appearance. Recent progress in analysis of the human methylome has enabled selection of multiple candidate loci showing linear correlation with chronological age. Practical application in forensic science depends on successful validation of these potential age predictors. In this study, eight DNA methylation candidate loci were analysed using convenient and reliable pyrosequencing technology. A total number of 41 CpG sites was investigated in 420 samples collected from men and women aged from 2 to 75 years. The study confirmed correlation of all the investigated markers with human age. The five most significantly correlated CpG sites in ELOVL2 on 6p24.2, C1orf132 on 1q32.2, TRIM59 on 3q25.33, KLF14 on 7q32.3 and FHL2 on 2q12.2 were chosen to build a prediction model. This restriction allowed the technical analysis to be simplified without lowering the prediction accuracy significantly. Model parameters for a discovery set of 300 samples were R² = 0.94 and the standard error of the estimate = 4.5 years. An independent set of 120 samples was used to test the model performance. Mean absolute deviation for this testing set was 3.9 years. The number of correct predictions ±5 years achieved a very high level of 86.7% in the age category 2–19 and gradually decreased to 50% in the age category 60–75. The prediction model was deterministic for individuals belonging to these two extreme age categories. The developed method was implemented in a freely available online age prediction calculator.     

Combining current knowledge on DNA methylation-based age estimation towards the development of a superior forensic DNA intelligence tool

The estimation of chronological age from biological fluids has been an important quest for forensic scientists worldwide, with recent approaches exploiting the variability of DNA methylation patterns with age in order to develop the next generation of forensic ‘DNA intelligence’ tools for this application. Drawing from the conclusions of previous work utilising massively parallel sequencing (MPS) for this analysis, this work introduces a DNA methylation-based age estimation method for blood that exhibits the best combination of prediction accuracy and sensitivity reported to date. Statistical evaluation of markers from 51 studies using microarray data from over 4000 individuals, followed by validation using in-house generated MPS data, revealed a final set of 11 markers with the greatest potential for accurate age estimation from minimal DNA material. Utilising an algorithm based on support vector machines, the proposed model achieved an average error (MAE) of 3.3 years, with this level of accuracy retained down to 5 ng of starting DNA input (~ 1 ng PCR input). The accuracy of the model was retained (MAE = 3.8 years) in a separate test set of 88 samples of Spanish origin, while predictions for donors of greater forensic interest (< 55 years of age) displayed even higher accuracy (MAE = 2.6 years). Finally, no sex-related bias was observed for this model, while there were also no signs of variation observed between control and disease-associated populations for schizophrenia, rheumatoid arthritis, frontal temporal dementia and progressive supranuclear palsy in microarray data relating to the 11 markers.     

A collaborative exercise on DNA methylation-based age prediction and body fluid typing

DNA methylation has become one of the most useful biomarkers for age prediction and body fluid identification in the forensic field. Therefore, several assays have been developed to detect age-associated and body fluid-specific DNA methylation changes. Among the many methods developed, SNaPshot-based assays should be particularly useful in forensic laboratories, as they permit multiplex analysis and use the same capillary electrophoresis instrumentation as STR analysis. However, technical validation of any developed assays is crucial for their proper integration into routine forensic workflow. In the present collaborative exercise, two SNaPshot multiplex assays for age prediction and a SNaPshot multiplex for body fluid identification were tested in twelve laboratories. The experimental set-up of the exercise was designed to reflect the entire workflow of SNaPshot-based methylation analysis and involved four increasingly complex tasks designed to detect potential factors influencing methylation measurements. The results of body fluid identification from each laboratory provided sufficient information to determine appropriate age prediction methods in subsequent analysis. In age prediction, systematic measurement differences resulting from the type of genetic analyzer used were identified as the biggest cause of DNA methylation variation between laboratories. Also, the use of a buffer that ensures a high ratio of specific to non-specific primer binding resulted in changes in DNA methylation measurement, especially when using degenerate primers in the PCR reaction. In addition, high input volumes of bisulfite-converted DNA often caused PCR failure, presumably due to carry-over of PCR inhibitors from the bisulfite conversion reaction. The proficiency of the analysts and experimental conditions for efficient SNaPshot reactions were also important for consistent DNA methylation measurement. Several bisulfite conversion kits were used for this study, but differences resulting from the use of any specific kit were not clearly discerned. Even when different experimental settings were used in each laboratory, a positive outcome of the study was a mean absolute age prediction error amongst participant’s data of only 2.7 years for semen, 5.0 years for blood and 3.8 years for saliva.     

Assessment of dental age estimation methods applied to Brazilian children: a systematic review and meta-analysis

Objectives:To review the scientific literature of studies on dental age estimation methods applied to Brazilian children.Methods:A systematic literature review was designed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and registered in PROSPERO (CRD42020136170). Six scientific databases were used as primary search sources (PubMed, Scopus, LILACS, SciELO, Embase and Web of Science) and two databases (Open Grey and Open Thesis) were searched to partially select the “grey literature.” Only cross-sectional studies were included. The risk of bias was assessed by means of Joanna Briggs Institute Critical Appraisal Tools for Systematic Reviews. The standardized mean difference (SMD) between the estimated dental and chronological ages was meta-analysed via random effects model.Results:The search resulted in 2,527 studies, from which 13 met the eligibility criteria. Out of the eligible studies, 76.92% had low risk of bias and high methodological quality. Ten studies provided proper information to be included in the meta-analysis.The methods and their SMD between estimated and chronological ages were: Willems’=0.05, Lilequist and Lundberg’s = −0.11, Nolla’s = 0.22, Mornstad’s = 0.27, Cameriere’s = −0.31, Demirjian’s = 0.74 and Haavikko’s = −0.87.Conclusion:Although originally trained in populations worldwide, most of the international methods for radiographical dental age estimation had optimal performance in Brazilian children.     

The chronological age estimation of third molar mineralization of Han population in southwestern China

The purpose of the study was to estimate the chronology of third molar mineralization in Han population of southwestern China and find its unique characteristics so that it would provide a reference in several legal cases like forensic age estimation. The study used Demirjian's staging method to study 2192 orthopantomograms of 984 male and 1208 female subjects aged between 8 and 25 years. The statistical data was analyzed by Student's t test and ANOVA.The conclusions of the study are: (1) The chronological mineralization age of third molars of Han population in Southwestern China is similar to the Turkish and the Japanese, was earlier than the Austrian and Han of South China, but later than the Spanish. (2) The mineralization timing of the third molars between two sides in maxilla or mandible has no significant differences in the same gender group. (3) There is no significant difference in mineralization of third molars between male and female, except for tooth 48 in Demirjian's stage E. (4) The mineralization of third molar in maxilla is earlier than mandible.