ArticleInternational journal of legal medicine2021
DNA methylation of decedent blood samples to estimate the chronological age of human remains.
Article in International journal of legal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Advancing Forensic Human Chronological Age Estimation: Biochemical, Genetic, and Epigenetic Approaches from the Last 15 Years: A Systematic Review.International journal of molecular sciences · 2025Pooled it
- Development of a Model for Predicting Chronological Age Based on DNA Methylation Data from Buccal Swabs Using Ion Torrent S5 Technology.International journal of molecular sciences · 2026Article
- Improved epigenetic age prediction models by combining sex chromosome and autosomal markers.Epigenetics & chromatin · 2025Article
- Age estimation of burnt human remains through DNA methylation analysis.International journal of legal medicine · 2025Article
- Uncovering Forensic Evidence: A Path to Age Estimation through DNA Methylation.International journal of molecular sciences · 2024Review
- Longitudinal changes and variation in human DNA methylation analysed with the Illumina MethylationEPIC BeadChip assay and their implications on forensic age prediction.Scientific reports · 2023Article
- Prediction of chronological age and its applications in forensic casework: methods, current practices, and future perspectives.Forensic sciences research · 2023Review
- Chronological Age Prediction: Developmental Evaluation of DNA Methylation-Based Machine Learning Models.Frontiers in bioengineering and biotechnology · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronological age estimation may offer valuable investigative leads in human identification cases. Bisulfite pyrosequencing analysis of single CpG sites on five genes (KLF14, ELOVL2, C1orf132, TRIM59, and FHL2) was performed on 264 postmortem blood samples from individuals aged 3 months to 93 years. The goals were to develop age prediction models based on the correlation between the methylation profile and chronological age and to assess the accuracy of the prediction. Linear regression between methylation levels and age at each CpG site revealed that the five markers show a statistically significant correlation with age. The methylation data from a training set of 160 postmortem blood samples were used to develop an age prediction model with a correlation coefficient of 0.65, explaining 73.1% of age variation, with a mean absolute deviation from the chronological age of 7.60 years. The accuracy of the model was evaluated with a test set of 72 samples producing a mean absolute deviation of 7.42 years. The training and test sets were also categorized by specific age groups to assess accuracy and deviation from chronological age. The data for both sets revealed a lower prediction potential as an individual increases in age, particularly for the age categories above 50 years.
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Registered trials
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