A systematic review of the agreement between chronological age and skeletal age based on the Greulich and Pyle atlas.

OBJECTIVES: This systematic review examines the agreement between assessed skeletal age by the Greulich and Pyle atlas (GP skeletal age) and chronological age. METHODS: We searched electronic databases until January 2017 for studies reporting GP skeletal age and confirmed chronological age in healthy individuals aged 10-25 years. Results are presented as forest plots and meta-analyses (random-effects models). RESULTS: In separate meta-analyses for each age group and sex (14-18 years for girls, 14-19 years for boys), the pooled mean differences between GP skeletal age and chronological age varied from -0.52 years to 0.47 years. In individual studies, age group and sex-specific mean differences between GP skeletal age and chronological age rarely exceeded 1 year, but between-study heterogeneities were large in most age groups. Few studies examined mean chronological age and distribution for each GP skeletal age. One study of good methodological quality indicates that 95% prediction intervals for chronological age from given GP skeletal ages are typically around 4 years. CONCLUSIONS: There is still good correlation between GP skeletal age and mean chronological age in modern populations. However, the individual variation of development within a population and heterogeneities between studies are substantial. KEY POINTS: • The GP atlas still corresponds well with mean chronological age in modern populations. • The substantial variation within a population must be considered. • The heterogeneity between studies is relatively large and of unknown origin.

Age assessment by Demirjian's development stages of the third molar: a systematic review.

OBJECTIVES: Radiographic evaluation of the wisdom teeth (third molar) formation is a widely used age assessment method for adolescents and young adults. This systematic review examines evidence on the agreement between Demirjian's development stages of the third molar and chronological age. METHODS: We searched four databases up until May 2016 for studies reporting Demirjian's stages of third molar and confirmed chronological age of healthy individuals aged 10-25 years. Heterogeneity test of the included studies was performed. RESULTS: We included 21 studies from all continents except Australia, all published after 2005. The mean chronological age for Demirjian's stages varied considerably between studies. The results from most studies were affected by age mimicry bias. Only a few of the studies based their results on an unbiased age structure, which we argue as important to provide an adequate description of the method's ability to estimate age. CONCLUSION: Observed study variation in the timing of Demirjian's development stages for third molars has often been interpreted as differences between populations and ethnicities. However, we consider age mimicry to be a dominant bias in these studies. Hence, the scientific evidence is insufficient to conclude whether such differences exist. KEY POINTS: • There is significant heterogeneity between studies evaluating age assessment by Demirjian's third molar development. • Most of the studies were subject to the selection bias age mimicry which can be a source of heterogeneity. • Presence of age mimicry bias makes it impossible to compare and combine results. These biased studies should not be applied as reference studies for age assessment.

Advancing estimation of chronological age by utilizing available evidence based on two radiographical methods.

This paper describes a strategy for estimating chronological age of individuals based on age indicators of X-ray of the hand and the third molar tooth. The great majority of studies in the field provide group-wise data of different formats, which makes them difficult to compare and utilize in a model. In this paper, we have provided a framework to utilize different types of data formats to build a common model for estimating chronological age. We used transition analysis to describe the relationship between the age indicators and chronological age. Further, likelihood ratio weight of evidence and posterior distribution of chronological age were used to model the distribution of chronological age given the observed age indicators. Being able to utilize such a large amount of data, with different data formats, from different studies, as presented in this paper improves previous age estimation methods.

BioAlder: a tool for assessing chronological age based on two radiological methods.

We have created the tool BioAlder as an age prediction model based on the systems Greulich and Pyle (hand) and the Demirjian's grading of the third molar tooth. The model compiles information from studies representing a total of 17,151 individuals from several parts of the world. The model offers a solution where issues as group-wise data format and age mimicry bias are bypassed. The model also provides a solution for combining the two grading systems, hand and tooth, to one combined age prediction result assuming independency. We have tested our model of age prediction and the independency assumption on a separate data set from Lebanon with 254 young individuals. The prediction intervals of BioAlder covered most of the data points; however, we observed some outliers. Our analyses indicate at least a weak dependency between the two methods.

A new blood based epigenetic age predictor for adolescents and young adults.

Children have special rights for protection compared to adults in our society. However, more than 1/4 of children globally have no documentation of their date of birth. Hence, there is a pressing need to develop biological methods for chronological age prediction, robust to differences in genetics, psychosocial events and physical living conditions. At present, DNA methylation is the most promising biological biomarker applied for age assessment. The human genome contains around 28 million DNA methylation sites, many of which change with age. Several epigenetic clocks accurately predict chronological age using methylation levels at age associated GpG-sites. However, variation in DNA methylation increases with age, and there is no epigenetic clock specifically designed for adolescents and young adults. Here we present a novel age Predictor for Adolescents and Young Adults (PAYA), using 267 CpG methylation sites to assess the chronological age of adolescents and young adults. We compared different preprocessing approaches and investigated the effect on prediction performance of the epigenetic clock. We evaluated performance using an independent validation data set consisting of 18-year-old individuals, where we obtained a median absolute deviation of just below 0.7 years. This tool may be helpful in age assessment of adolescents and young adults. However, there is a need to investigate the robustness of the age predictor across geographical and disease populations as well as environmental effects.