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Table of Contents
REVIEW ARTICLE
Year : 2021  |  Volume : 5  |  Issue : 1  |  Page : 1-11

Biological age estimation using DNA methylation analysis: A systematic review


Department of Oral Biology, Division of Forensic Odontology, Faculty of Dentistry, Universitas Indonesia, Jakarta, Indonesia

Date of Submission26-Jun-2020
Date of Decision08-Jul-2020
Date of Acceptance12-Jan-2021
Date of Web Publication16-Feb-2021

Correspondence Address:
Nurtami Soedarsono
Department of Oral Biology, Division of Forensic Odontology, Faculty of Dentistry, Universitas Indonesia, Jakarta
Indonesia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/SDJ.SDJ_27_20

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  Abstract 


Age estimation is a fundamental part in forensic, criminal, legal, and anthropological investigations. The biomolecular analysis is considered to have a good capability in estimating age because it can describe a person's biological age. According to previous studies, DNA methylation has the best effectiveness for estimating biological age, compared to other biomolecular analysis. Although DNA methylation is influenced by a number of factors such as heredity, environment, lifestyle and systemic diseases, DNA methylation still has accuracy that accountable to estimate age. A literature review search of PubMed, ScienceDirect, Scopus, and EBSCO was conducted to get all studies that published before February 2020. The review was then performed on 22 papers that selected based on inclusion and exclusion criteria. The purpose of this reviewed paper was to identify all gene markers that have been used to estimate age using DNA methylation analysis; and to find out the subject, age range, tissue taken for DNA methylation analysis, and the effectiveness of the analysis.

Keywords: Age estimation, biological age, biomolecular analysis, DNA methylation, forensic science, medicolegal


How to cite this article:
Hanafi MG, Soedarsono N, Auerkari EI. Biological age estimation using DNA methylation analysis: A systematic review. Sci Dent J 2021;5:1-11

How to cite this URL:
Hanafi MG, Soedarsono N, Auerkari EI. Biological age estimation using DNA methylation analysis: A systematic review. Sci Dent J [serial online] 2021 [cited 2021 Mar 2];5:1-11. Available from: https://www.scidentj.com/text.asp?2021/5/1/1/309541




  Background Top


Age estimation is a fundamental part in forensic, criminal, legal, and anthropological investigations.[1] Age estimation usually applied in a living person or a corpse, in individual or mass disaster. In a living person, the certainty of age is required in the case of custody, marriage, determining the age of athletes in a championship, health or education insurance, ownership of identity cards and important letters, and determining the age for legal application.[2] In a case of individually unnatural death, age estimation is often needed when the body is not identifiable. Estimating age will make it easier for forensic practitioners to reveal the identity of the body. Individuals who die in a mass disaster such as acts of terrorism with bombs, mass killings, plane crashes, shipwrecks, and natural disasters such as earthquakes, tsunamis, volcanic eruptions, and so forth. These situations necessitate an accountable age estimation method.[3]

Age estimation is also an important component in Indonesian law because there are cases of age falsification for the production of important documents and events such as Driver License, ID Card, and registration in a championship. Various laws and regulations enforced in Indonesia mentioning the importance of age identification in various age ranges. Identification is important so that there is no violation in carrying out the mandate of the Act that has been enacted. [Table 1] lists the laws relating to age in Indonesia.
Table 1: Rationale for the importance of identifying the age of young adults in legal aspects in Indonesia

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There are several methods for estimating age, namely the anthropological, radiological, histological, and biomolecular methods. Anthropological methods, for example, by assessing the degree of tooth attrition, analysis of the symphysis pubis, and analysis of ribs' costochondral surface. Radiological methods for example by assessing M3 tooth eruption, and apical closure of teeth. The histological method for example by assessing periodontal tissue to assess enamel thickness.[4] On the other hand, the example of the biomolecular method is by analyzing DNA methylation, sjTRECs, mitochondrial DNA deletions, and shortening of telomeres. The biomolecular analysis is considered as the best age estimation for determining a person's age,[5] because it is free from radiation exposure, it does not depend on the subjectivity of the researcher. Thus, it can be used both on the living person or dead body.[5],[6]

Age estimation based on the biomolecular approach is reposed on the fact that as a person ages, there will be alterations in biomolecules of said person.[7],[8] The best biomolecular approach in estimating age is to do DNA methylation analysis.[6],[7],[9] Recent advances in the epigenomic field have enabled the identification of several DNA methylation loci which can be useful for analyzing age estimation.[6],[10] In addition, hypermethylation or hypomethylation has a linear correlation with increasing age at some CpG, so it can be used for age prediction.[5],[11]

DNA methylation not only changes with age but has the possibility to change in the pattern of DNA methylation aberration that are influenced by various factors.[6] The influencing factors are heredity,[12] certain disorders or diseases[6] like breast cancer, colon cancer,[13] leukemia, glioblastoma,[14] treatment history, lifestyle of the patient,[8] and the environment.[12] However, this method of analysis using DNA methylation is still reliable in relation to estimating a person's age.[6] Regarding the resistance to the factors that influence DNA methylation, a variety of biochemical materials and DNA markers have been studied so far to assess the level of resistance of these markers can be used as a tool for age estimation.[8]

The purpose of this reviewed paper is to identify a variety of DNA methylation markers that are considered to have a correlation with a person's age both in healthy individuals and those with systemic abnormalities, along with age ranges and how effective the analysis is used for age estimation.


  Methods Top


This study was conducted from January to March 2020 using Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines. Based on these guidelines, there are several steps in this systematic review as follows:

Eligibility criteria

Inclusion criteria

The inclusion criteria used in this literature review are: (i) studies on age estimation using DNA methylation analysis; (ii) various paper which discuss race and age range in various tribes and regions of the world; (iii) research that uses healthy persons as well as those with systemic diseases; (iv) research published before February 2020; (v) research article using English; (vi) research examining various tissues taken for analysis of DNA methylation; and (vii) case-control, cohort, cross-sectional studies.

Exclusion criteria

Exclusion criteria in this literature review are: (i) case reports and editorial articles; (ii) animal studies; (iii) articles that do not contain complete information; (iv) paper in the form of systematic review and meta-analysis; (v) summary or report on the results of scientific meetings.

Information sources

Subsequently, the research questions are formulated using Participants, Intervention, Comparison, Outcomes, Study Design to find suitable literature. Participants in this systematic review are estimated age in a living person or corpses, participants in good health or with systemic disease, participants in various races, and age ranges. Intervention is biomolecular analysis, epigenetic modification, and DNA methylation. There is no comparison in this review. Outcomes are the accuracy of biological age estimation to chronological age (Mean Absolute Error [MAE], Median Average Deviation [MAD]). The study design that included in this systematic review is cross-sectional, cohort, or case–control studies.

Online libraries that used as database in this review are MEDLINE, ScienceDirect, Elsevier (SCOPUS), and IEEE Xplore.

Study selection

The study selection was conducted sequentially as follows:

  1. Search literature using keywords: (“age estimation” or “age determination” or “living person” or “corpse” or “healthy person” or “systemic disease”) and (“DNA methylation analysis” or “Biomolecular analysis” or “epigenetics modification”) and (“accuracy” or “mean average error” or “mean average deviation” or “biological age” or “chronological age”) in the MEDLINE, ScienceDirect, Elsevier (SCOPUS) and IEEE Xplore
  2. Screening of title, abstract, and keywords were conducted based on eligibility criteria
  3. Eliminating the articles using the eligibility criteria
  4. Scanning the references of the articles to find related studies.


Data collection and data items

Extraction data were carried out to collect the data from the references. The contents of the data extraction form are the type of articles, journal name, year, topic, title, gene markers used, type and number of samples, country, age range, tissue taken, DNA sequencing techniques, research results, and accuracy.


  Age Estimation Using DNA Methylation Analysis Top


DNA methylation appears as an excellent marker to solve problems in the field of forensic genetics.[15] Which in addition to estimate age, DNA methylation analysis can also be used to identify two twin individuals, the origin of human body fluids, to environmental exposure that occurs in an individual or even the population.[16] The use of DNA methylation analysis to estimate age began to be developed since the last few years.[7],[17] The tissue that is usually used to extract DNA is blood, saliva, and buccal epithelial cells.[17] However, in certain conditions that do not allow the taking of blood tissue, saliva or buccal epithelial cells, other tissues such as soft tissue (brain and muscle), hard tissue (teeth and bones),[17] epidermis, as well as other bodily fluids such as semen, urine, and vaginal fluids can be used.[15] Even if all of this tissue is not available, DNA samples can be taken from blood stains, toothbrushes, or cigarette butts that have been used by suspect's body.[18] This is what makes DNA methylation analysis a promising marker for age estimation or even overall identification.

In addition to DNA methylation, techniques for estimating age by using biomolecular analysis are mitochondrial and DNA deletions (mtDNA deletions), telomere shortening, circular excision of T-cell receptors (sjTRECs), advanced glycation end products (AGEs), and racemization of aspartic acid.[7] However, according to Zubakov et al. in 2016, the best biomolecular method was through DNA methylation analysis, because it has the lowest Mean Absolute Deviation (MAD) compared to other biomolecular methods. The diagnostic limitations of each method, apart from DNA methylation, are: MtDNA deletions are only effective for age range <20 years and >70 years; telomere shortening and sjTRECs has low accuracy rate, >10 years; AGEs are only effective for ages >45 years; aspartic acid racemization is only effective if dentin is taken as the sample.[7],[19]

Several studies about DNA methylation analysis have been carried out in the various region of the world with a variety of populations and age ranges, they have proven the ability of DNA methylation as a promising marker for age estimation. Researchers have performed DNA methylation analysis in healthy individuals and those with systemic diseases by taking from various body tissues, and by various analytical techniques. A summary of research on DNA methylation that has been carried out is presented in [Table 2].
Table 2: Summary of age estimation research through DNA methylation analysis

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DNA methylation analysis on living person and dead body

Age estimation is often carried out both on a living person and on a corpse. In living person, certainty of age are often required in the case of custody, marriage, determination of the age of athletes in a championship, insurance both health or education, ownership of identity cards and important letters, and determining the age for legal application.[2] While in the corpse, age estimation are often used to lead forensic practitioners in the identification process, both in individual death cases or mass disasters. In addition to living person, research by taking samples of the body is also needed so that this can lead to the conclusion whether a sample taken at the corpse is as good as that taken on a living person or not, and also to also determinate which tissue or organ samples are most effective to be taken from the body to estimate the age using DNA methylation analysis.

Sampling of these bodies has been carried out by these researchers, namely Beckaert et al. in 2015, and Correia Dias et al. in 2019. The two studies showed that the samples taken from the corpses are not much different from those taken in living person. First, the samples taken in living person tissue or corpses both indicate that the ELOVL2 marker is the best gene marker. Second, MAE in the range between 3 and 6 years are not much different from living person who also range between 0.3 and 5 years.

Correlation between systemic disease and DNA methylation

Healthy individuals usually have high age estimation accuracy. On the other hand, there are several effects in people with systemic diseases that affect DNA methylation, resulting in hypermethylation or hypomethylation. This can cause inaccuracies in age estimation using DNA methylation analysis. Research on DNA methylation in individuals with systemic abnormalities was conducted by Wagner et al. in 2015 and Spolnicka et al. in 2018.

Spolnicka et al. estimated the age by analyzing DNA methylation profiles on five markers from five different genes (ELOVL2, C1orf132, KLF14, FHL2, and TRIM59) used for age prediction in three groups of individuals with medical conditions that had been diagnosed positive.[6] These medical diseases consist of Early Onset alzheimer Disease (EOAD), Late Onset alzheimer Disease (LOAD), and Grave's Disease (GD). From the results of this study, it was found that in the LOAD group, all genes did not lose their ability as the means of age estimation. While in the EOAD group, there was an aberration and a decrease in the accuracy to estimate the age in the KLF14 and TRIM59 genes. In the GD group, there was hypermethylation in the TRIM59 gene and hypomethylation in the FHL2 gene. Moreover, from the results of this study, the ELOVL2 and C1orf132 genes have a high resistance to estimating chronological age, as attested by the unchanging accuracy of age prediction for all types of systemic diseases. In the case of the three systemic diseases mentioned above, there is a decrease in the accuracy of the estimated age when compared with healthy persons. This is evidenced by the MAE value that is quite large, i.e., ±4.4 years on GD, ±6.1 years on LOAD, and ±7.1 years on EOAD.[6]

Wagner et al. conducted research whether DNA methylation that occurs can initiate the occurrence of acute myeloid leukemia (AML). The gene markers observed by Wagner et al. is the DNMT3A gene. Based on the results, this study concluded that the hypermethylation that occurs in the DNMT3A gene can initiate AML. The study also revealed that changes in other age-related genes, whether hypermethylation or hypomethylation, were also likely to initiate AML in a patient.[20]

Biological age often looks younger or older than chronological age, and there is a hypothesis that if there is an abnormality in an age-related gene, the risk of abnormality increases. This argument is strengthened by Lars Lind et al. in 2016 when they conducted a study to see the correlation between biological age and cases of cardiovascular disease (CVD). From this study, it was found that an increase in biological age is directly proportional to an increase in CVD rates in patients who have risk factors for the disease, such as smokers, people with high blood pressure, high body mass index, diabetes, and cholesterol.[21]

DNA markers for age estimation using DNA methylation analysis

The accuracy of DNA methylation analysis for estimated age varies depending on the DNA marker chosen, the condition of the individual under study (in a healthy condition or experiencing systemic disease), age, to the type of tissue taken. From several studies that have been shown, it can be noted that there are several age-related genes, namely ELOVL2, C1orf132, EDARADD, PDE4C, TRIM59, KCNAB3, to NPTX2. There are also genes that in some studies mentioned are related to age, but others mention the opposite, namely the FHL2 gene.

Age estimation using biomolecular approach can also be combined with other methods, for example, the radiograph method. It aims to improve the accuracy of age estimation analysis. Shi Lei, et al. in 2017 examined age-related marker DDO, PRPH 2, DHX8, ITGA2B, and unknown gene with Ilumina ID 22398226. This study combined DNA methylation analysis with skeletal age (SA) analysis and dental age (DA) analysis. Blood samples and carpal bone radiographs and teeth were taken from Chinese children aged 6–15 years. The results of this study are the analysis of age estimation based on DNA methylation combined with the analysis of SA and DA has a significantly increased level of accuracy. In boys, the MAE recorded was only ±0.47 years, the female counterparts had an even better accuracy of ±0.33 years.[27]

In addition to ELOVL2, there are also several other genes that are also age-related and are considered to have good capabilities as well for DNA methylation analysis. Correia Dias et al. in 2019 carried out age-related gene analysis, observed in DNA methylation. The genes are ELOVL2, FHL2, EDARADD, PDE4C, and C1orf132. The sample in this study was blood taken from the corpses with an age range of 24–86 years. From this study, it was concluded that the stabilization of these gene markers for DNA methylation analysis sequentially was ELOVL2, C1orf132, PDE4C, EDARADD and the last was FHL2.[33] ELOVL2 is also a stable gene, whether it is taken from healthy individuals, with systemic abnormalities, in living person or dead body.[8]

Tissue samples taken for age estimation using DNA methylation analysis

Some studies take blood samples from corpses, some take blood samples from living persons.[33],[35] Results of these studies found that both in dead and living person, DNA methylation analysis can still be done for age estimation. Various tissues were taken to compare age-related DNA markers, ranging from bloodstains, peripheral blood, complete blood, non-blood, teeth (cementum, dentin, pulp), brain, muscles, and buccal epithelial cells.[14] They also took samples from other body fluids such as saliva and semen.[28] According to some of these studies, almost all tissues have a good ability to provide the DNA needed for DNA methylation analysis, but some studies said that cement does not occur in DNA methylation.[13]

Blood samples are the most common samples taken for DNA methylation analysis. As Freire-Aradas et al. done in 2018, where they carried out an estimated age analysis using DNA methylation in the ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, C1orf132, and chr16: 85395429 genes in blood samples.[27] These samples were taken from donors with an age range of 19–101 years, and peripheral blood samples were taken from donors with an age range of 18–104 years. In addition, peripheral blood samples were also taken from twin donors with an age range of 42–69 years. From this study, it was found that the 7 markers contained a Median Absolute Error (MAE) of ±3.07 years and an average age prediction error of 6.3%.[26]

Blood samples are the best tissue samples for DNA methylation analysis. However, by looking at previous studies, other tissue samples also have good capabilities for carrying out DNA methylation analysis, teeth being one example.[24] Giuliani et al. in 2016 conducted an anthropological study of age estimation using DNA methylation analysis. Tissue samples in the form of cementum, dentin, and pulp were taken from modern human teeth with an age range of 17–77 years. The genes seen from the study are ELOVL2, FHL2, and PENK2, all of which are age-related genes. This study found that ELOVL2, FHL2, and PENK2 taken from dental samples were excellent markers for age estimation in anthropological applications.[31] In addition, Xu Yan et al. in 2019 conducted research on gene markers SALL4, MBP, C17orf76, B3GALT6, NOC2 L, SNN, NPTX2, SLC22A18, TMEM106A, LEP, SCAP, C16orf30, and FLJ25410 in non-blood tissue, such as liver, spleen, lung, and other tissues. Samples from nonblood tissue were taken in normal Chinese individuals aged 0–90 years. Based on the results of this study, the level of accuracy to estimate the age of nonvessel tissue samples are excellent. The regulation of DNA methylation on human aging can be understood by the means of this study, and will accurately observe the process of individual aging.[25]

In the oral cavity, samples can also be taken on buccal epithelial tissue in addition to dental samples. The technique of taking this tissue is by using a swab.[36] This research was conducted by Mayer et al. in 2016. They took samples by swabing the patient's buccal mucosa. The reason for sampling a patient's buccal epithelial cell is that DNA can be cultured relatively easily, and the method is noninvasive. However, there are many mixed buccal cells and leukocytes with different epigenetic arrangements in the results of the buccal mucosal swab. Therefore, we need a special gene marker that is specific to DNA methylation. In this study, PDE4C, ASPA, CD6, SERPINB5, and ITGA2B genes were selected as specific markers. The results of this study are the determination of these markers can make age estimates using buccal swab samples more effective and accurate.[5]

In addition to being taken directly from human tissue, DNA markers must also have capabilities if sampling has to be taken in less ideal conditions, such as bloodstains, cigarette butts, or toothbrushes used by someone. Peng-Fuduan, et al. in 2019 conducted a study to look at the estimated age marker capabilities of TRIM59, RASSF5, C1orf132, chr10: 22334463/65, PDE4C, CCDC102B, and ELOVL2 in bloodstain samples. These samples were taken in two periods, the first period was in Han Chinese men with an age range of 18–59 years. The second period is also for Han men in China but with a different age range, which is 21–66 years. The result is, all markers seen in this study have excellent capabilities for estimating age even though samples were only obtained from bloodstains. The Median Average Deviation (MAD) range in this study ranged from 2.94 to 3.55 years.[18] This effectiveness is not much different from observations made on blood samples or other body tissues. This is also supported by Fleckhaus et al. in 2020, where they conducted a study to look at the capabilities of age-related genes as a marker for DNA methylation analysis to estimate age in very small sample sizes. The genes are ELOVL2, FHL2, CCDC102B, C1orf132, KLF14, EDARADD, PDE4C, and SST. As a result, all markers except FHL2 can be well sequenced. This shows that almost all of the markers above are good markers for age estimation by DNA methylation analysis.[34]

The capability of age-related gene markers was also conveyed by Zbieć-Piekarska, et al. in 2015, where they mentioned that the ELOVL2 gene contained in blood-stain samples was still able to be analyzed using DNA methylation, even after 4 weeks in storage.[8]

Researched race

The capabilities of an age-related gene must be tested in a variety of populations to see the ability of a gene in estimating age. For this purpose, various races have been studied, ranging from Caucasian races in Europe, Sweden, Croatia, Han tribes in China, Mongoloid races in Indonesia, to the Korean population.

Research on the Chinese population has been done by at least two researchers. The study was conducted by Feng Lei et al. in 2018 and Peng-Fuduan et al. in 2019. They conducted a study to look at the capabilities of the TRIM59, RASSF5, Clorf132, CSNK1D, ELOVL2, PDE4C, chr17: 21452808 genes to estimate age by analyzing DNA methylation. Blood samples were taken from adult men in China, and the result is that all the marker genes mentioned above have the ability and capability to be used as markers for age estimation by DNA methylation analysis. This is evidenced by the low MAD rate, which is ± 2.89 years.[30] Subsequent research was carried out by Peng-Fuduan et al. where they conducted a study to look at the estimated age marker capabilities of TRIM59, RASSF5, C1orf132, chr10: 22334463/65, PDE4C, CCDC102B, and ELOVL2 in Han samples in China. The sample in this study was taken from the subject's bloodstain and was taken in two periods, the first period was in Han Chinese men with an age range of 18–59 years. The second period is also for Han men in China but with a different age range of 21–66 years. The result, all markers seen in this study have excellent capabilities for estimating age even though samples are only obtained from blood stains. The median average deviation in this study ranged from 2.94 to 3.55 years.[18]

In addition to the Chinese population, research on DNA methylation analysis for the estimated age has been done on people of European descent. This research was conducted by Fleckhaus et al. in 2020 who took venous blood samples in 10 people of European descent. The genes observed in this study were ELOVL2, FHL2, CDC102B, C1orf132, KLF14, EDARADD, PDE4C, and SST. Although MAE/RMSE/MAD was not presented in this study, this study concluded that besides the FHL2 gene, all markers showed a significant correlation to changes in a person's biological age.[34]

Age estimation studies using DNA methylation analysis have also been conducted in Korean populations. This research was conducted by Park et al. in 2016. The study was conducted on 765 Koreans with an age range of 11–90 years and the samples taken were blood. The observed gene markers were ELOVL2, ZNF423, and CCDC102B. Based on this study, the MAD observed was ±3.156 years.[29] This shows that DNA methylation analysis for age estimation can also be done in the Korean population because the effectiveness of biological age forecasts for chronological age is not too far away.

The results of studies in various populations reveal that DNA methylation analysis is a reliable method for estimating age in various populations around the world.

Researched age range

Age range varies, but still within the range of 0–104 years. From these various types of populations and age ranges, the average effectiveness of age estimates is in the range of +0.33–7.01 years. The average study that has a good effectiveness value (MAE/MAD is low) is research conducted on healthy individuals. On the contrary, individuals who have systemic abnormalities, MAE/MAD will increase and range in numbers from 4 to 7 years. The importance of knowing this age range is to increase the effectiveness of a study. For example, a study will only look at the age range of 21–30 years, so reports on age-related genes in the 21–30 years group are very useful for determining what gene markers can be observed in that study. By knowing the gene markers in the age range of 21–30 years, a study will deduct its research to observe the particular gene in respective of the age target range, without the need for additional genes. However, various reported researches have a very far age range. For example, 0–90 years,[25] 2–75 years,[8] 11–90 years,[29] 19–101 years,[32] or 24–86 years.[33]

One study that used a short age range is Freire-Aradas, et al. in 2018. They conducted research to look at the estimated age marker capabilities of SDS, PGLYRP2, HKR1, TOM1 L1, KCNAB3, PRKG2, EDARADD, FLJ46365, ITGA2B, ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, mir29bch916:85 chr. Blood samples were taken from adolescents with an age range of 2–18 years. This study aimed to see which genes are best used as gene markers for DNA methylation analysis for age estimation. Moreover, the results of the study are KCNAB3 is the best gene marker that can be used to estimate age in the age range of 2–18 years.[26]


  Conclusion Top


Biological age estimation with biomolecular approaches have received great attention in the last few years. Correlating the level of DNA methylation as a method for looking at a person's biological age with chronological age has been a key step in increasing the prediction accuracy of these tests and accelerating their development for forensic purposes. Research that has discussed in detail the CpG methylation level underlies the development of DNA methylation analysis for the benefit of overall age estimation.

From a variety of age-related DNA markers that have been widely studied, ELOVL2 still scores the best capability compared to others. Followed by other age-related DNA markers such as TRIM59, EDARADD, PDE4C, NPTX2, ITGA2B, to KCNAB3. There are conflicting opinions regarding the capability of the FHL2 gene marker, some researchers say that FHL2 is a good marker, yet several others say that FHL2 is not very effective for age estimation using DNA methylation analysis. Therefore, further research is needed on this FHL2 to determine the effectiveness of this gene marker to estimate age.

In this review, DNA methylation analysis has also been carried out on various types of races, and the results of the effectiveness of the estimated age are relatively the same in one race with another. DNA methylation analysis has been carried out in various age ranges, which in general are at the age of 0–104 years. The effectiveness is not too much different from the mean Mean Average Deviation (MAD), Mean Average Error (MAE), or Root Square Mean Error (RMSE) ranging from +0.33 to 7.01 years. Usually, in high MAD/MAE/RMSE there are systemic abnormalities that accompany, such as EOAD, LOAD, GD, CVD or AML. However, research is needed on the gene markers that have the most relationship with certain age groups for the development of DNA methylation analysis utilization vis-à-vis age estimation.

The availability of age-related gene markers is also considered good in a variety of tissues. As with blood tissue; nonblood such as heart, lungs, liver, bile; other body fluids such as saliva and semen; teeth and parts such as cementum, dentin and pulp; or even samples containing very little DNA substance such as blood stains, cigarette butts or toothbrushes.

Through the results of this literature review, it can be concluded that methylation analysis can be a promising alternative for estimating age, because of its ability to estimate age in both living persons and corpses, in various races, age ranges, and analyzed tissues. However, the limitation of this literature review is that not all studies convey the accuracy of age estimates through DNA methylation analysis. Therefore, shall further research on age estimation through DNA methylation analysis happened, researchers should conduct an analysis regarding the accuracy of biological age estimate to chronological age. This information will be very useful to determine whether DNA methylation in a particular age range can be implemented properly or not.

Acknowledgments

The authors would like to thank A. Winoto Suhartono (Forensic Odontology Division, University of Indonesia) and Djaja Surya Atmadja (Forensic Medicine Department, University of Indonesia) for discussion about the substance of this review.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Freire-Aradas A, Phillips C, Lareu MV. Forensic individual age estimation with DNA: From initial approaches to methylation tests. Forensic Sci Rev 2017;29:121-44.  Back to cited text no. 1
    
2.
Firdaus. Prakiraan Usia 8-25 Tahun Berdasarkan Tahap Kalsifikasi Gigi Molar Tiga (Aplikasi Metode Demirjian) Pada Suatu Populasi Di Indonesia [Tesis]. Jakarta: Universitas Indonesia; 2014. p. 37-61.  Back to cited text no. 2
    
3.
Gusti LP. Uji Estimasi Usia 8-17 Tahun Menggunakan Metode Tooth Coronal Index Khoman Dibandingkan Dengan Metode Nolla. Jakarta: Universitas Indonesia; 2018.  Back to cited text no. 3
    
4.
Putri AS, Nehemia B, Soedarsono N. Prakiraan usia individu melalui pemeriksaan gigi untuk kepentingan forensik kedokteran gigi (Age estimation through dental examination in forensic denstistry). Journal of Indonesian Dental Association 2013;62:55-63.  Back to cited text no. 4
    
5.
Eipel M, Mayer F, Arent T, Ferreira MR, Birkhofer C, Gerstenmaier U, et al. Epigenetic age predictions based on buccal swabs are more precise in combination with cell type-specific DNA methylation signatures. Aging (Albany NY) 2016;8:1034-48.  Back to cited text no. 5
    
6.
Spólnicka M, Pośpiech E, Pepłońska B, Zbieć-Piekarska R, Makowska Ż, Pięta A, et al. DNA methylation in ELOVL2 and C1orf132 correctly predicted chronological age of individuals from three disease groups. Int J Legal Med 2018;132:1-1.  Back to cited text no. 6
    
7.
Hartomo BT, Soedarsono N, Adrianto AW, Auerkari EI. Review of biomolecular methods for age estimation in application of forensic odontology. AIP Conf Proc 2019;2193:40002.  Back to cited text no. 7
    
8.
Zbieć-Piekarska R, Spólnicka M, Kupiec T, Makowska Ż, Spas A, Parys-Proszek A, et al. Examination of DNA methylation status of the ELOVL2 marker may be useful for human age prediction in forensic science. Forensic Sci Int Genet 2015;14:161-7.  Back to cited text no. 8
    
9.
Dhingra R, Kwee LC, Diaz-Sanchez D, Devlin RB, Cascio W, Hauser ER, et al. Evaluating DNA methylation age on the Illumina MethylationEPIC Bead Chip. PLoS One 2019;14:e0207834.  Back to cited text no. 9
    
10.
McEwen LM, Jones MJ, Lin DT, Edgar RD, Husquin LT, MacIsaac JL, et al. Systematic evaluation of DNA methylation age estimation with common preprocessing methods and the Infinium MethylationEPIC BeadChip array. Clin Epigenetics 2018;10:123.  Back to cited text no. 10
    
11.
Shabani M, Borry P, Smeers I, Bekaert B. Forensic epigenetic age estimation and beyond: Ethical and legal considerations. Trends Genet 2018;34:489-91.  Back to cited text no. 11
    
12.
Xu C, Qu H, Wang G, Xie B, Shi Y, Yang Y, et al. A novel strategy for forensic age prediction by DNA methylation and support vector regression model. Sci Rep 2015;5:17788.  Back to cited text no. 12
    
13.
Youn A, Wang S. The MiAge calculator: A DNA methylation-based mitotic age calculator of human tissue types. Epigenetics 2018;13:192-206.  Back to cited text no. 13
    
14.
Horvath S. DNA methylation age of human tissues and cell types. Genome Biol 2013;14:R115.  Back to cited text no. 14
    
15.
Lee HY, Lee SD, Shin KJ. Forensic DNA methylation profiling from evidence material for investigative leads. BMB Rep 2016;49:359-69.  Back to cited text no. 15
    
16.
Richards R, Patel J, Stevenson K, Harbison S. Evaluation of massively parallel sequencing for forensic DNA methylation profiling. Electrophoresis 2018;39:2798-805.  Back to cited text no. 16
    
17.
Naue J, Sänger T, Hoefsloot HC, Lutz-Bonengel S, Kloosterman AD, Verschure PJ. Proof of concept study of age-dependent DNA methylation markers across different tissues by massive parallel sequencing. Forensic Sci Int Genet 2018;36:152-9.  Back to cited text no. 17
    
18.
Peng F, Feng L, Chen J, Wang L, Li P, Ji A, et al. Validation of methylation-based forensic age estimation in time-series bloodstains on FTA cards and gauze at room temperature conditions. Forensic Sci Int Genet 2019;40:168-74.  Back to cited text no. 18
    
19.
Zubakov D, Liu F, Kokmeijer I, Choi Y, van Meurs JBJ, van IJcken WFJ, et al. Human age estimation from blood using mRNA, DNA methylation, DNA rearrangement, and telomere length. Forensic Sci Int Genet 2016;24:33-43.  Back to cited text no. 19
    
20.
Wagner W, Weidner CI, Lin Q. Do age-associated DNA methylation changes increase the risk of malignant transformation? Bioessays 2015;37:20-4.  Back to cited text no. 20
    
21.
Lind L, Ingelsson E, Sundström J, Siegbahn A, Lampa E. Methylation-based estimated biological age and cardiovascular disease. Eur J Clin Invest. 2018;48. doi: 10.1111/eci.12872. Epub 2017 Dec 27. PMID: 29231988.  Back to cited text no. 21
    
22.
Gu T, Gu HF, Hilding A, Sjöholm LK, Ostenson CG, Ekström TJ, et al. Increased DNA methylation levels of the insulin-like growth factor binding protein 1 gene are associated with type 2 diabetes in Swedish men. Clin Epigenetics 2013;5:21.  Back to cited text no. 22
    
23.
Heyn H, Moran S, Esteller M. Aberrant DNA methylation profiles in the premature aging disorders Hutchinson-Gilford Progeria and Werner syndrome. Epigenetics 2013;8:28-33.  Back to cited text no. 23
    
24.
Bekaert B, Kamalandua A, Zapico SC, Van de Voorde W, Decorte R. Improved age determination of blood and teeth samples using a selected set of DNA methylation markers. Epigenetics 2015;10:922-30.  Back to cited text no. 24
    
25.
Xu Y, Li X, Yang Y, Li C, Shao X. Human age prediction based on DNA methylation of non-blood tissues. Comput Methods Programs Biomed 2019;171:11-8.  Back to cited text no. 25
    
26.
Freire-Aradas A, Phillips C, Girón-Santamaría L, Mosquera-Miguel A, Gómez-Tato A, Casares de Cal MÁ et al. Tracking age-correlated DNA methylation markers in the young. Forensic Sci Int Genet 2018;36:50-9.  Back to cited text no. 26
    
27.
Shi L, Jiang F, Ouyang F, Zhang J, Wang Z, Shen X. DNA methylation markers in combination with skeletal and dental ages to improve age estimation in children. Forensic Sci Int Genet 2018;33:1-9.  Back to cited text no. 27
    
28.
Aliferi A, Ballard D, Gallidabino MD, Thurtle H, Barron L, Syndercombe Court D. DNA methylation-based age prediction using massively parallel sequencing data and multiple machine learning models. Forensic Sci Int Genet 2018;37:215-26.  Back to cited text no. 28
    
29.
Park JL, Kim JH, Seo E, Bae DH, Kim SY, Lee HC, et al. Identification and evaluation of age-correlated DNA methylation markers for forensic use. Forensic Sci Int Genet 2016;23:64-70.  Back to cited text no. 29
    
30.
Feng L, Peng F, Li S, Jiang L, Sun H, Ji A, et al. Systematic feature selection improves accuracy of methylation-based forensic age estimation in Han Chinese males. Forensic Sci Int Genet 2018;35:38-45.  Back to cited text no. 30
    
31.
Giuliani C, Cilli E, Bacalini MG, Pirazzini C, Sazzini M, Gruppioni G, et al. Inferring chronological age from DNA methylation patterns of human teeth. Am J Phys Anthropol 2016;159:585-95.  Back to cited text no. 31
    
32.
Freire-Aradas A, Phillips C, Mosquera-Miguel A, Girón-Santamaría L, Gómez-Tato A, Casares de Cal M, et al. Development of a methylation marker set for forensic age estimation using analysis of public methylation data and the Agena Bioscience EpiTYPER system. Forensic Sci Int Genet 2016;24:65-74.  Back to cited text no. 32
    
33.
Correia Dias H, Cordeiro C, Corte Real F, Cunha E, Manco L. Age estimation based on DNA methylation using blood samples from deceased individuals. J Forensic Sci 2020;65:465-70.  Back to cited text no. 33
    
34.
Fleckhaus J, Schneider PM. Novel multiplex strategy for DNA methylation-based age prediction from small amounts of DNA via pyrosequencing. Forensic Sci Int Genet 2020;44:102189.  Back to cited text no. 34
    
35.
Alisch RS, Barwick BG, Chopra P, Myrick LK, Satten GA, Conneely KN, et al. Age-associated DNA methylation in pediatric populations. Genome Res 2012;22:623-32.  Back to cited text no. 35
    
36.
Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet 2018;19:371-84.  Back to cited text no. 36
    



 
 
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