Improved age determination of blood and teeth samples using a selected set of DNA methylation markers

TrACES of time: Towards estimating time-of-day of bloodstain deposition by targeted RNA sequencing

In forensic molecular biology, the main task consists of identifying individuals who contributed to biological traces recovered from (potential) crime scenes. However, to support evidence-based reconstruction of the course of activities having taken place at the scene, contextualising information regarding how and when a biological trace was deposited is oftentimes required. Here we present the development of a forensic molecular biological analysis procedure for the prediction of the time-of-day at which a bloodstain has been deposited by targeted quantification of selected mRNA markers. Time-of-day candidate prediction markers with diurnally rhythmic expression have previously been identified by whole transcriptome sequencing. Here, we build on our previous findings by establishing a targeted cDNA sequencing protocol on an Ion S5 massively parallel sequencing device for the targeted gene expression quantification of 69 time-of-day candidate prediction markers. Based on expression measurements of these markers in 408 blood samples (from 51 individuals deposited at eight time points over a day), we establish and compare different statistical methods to predict time of deposition. The most suitable model employing penalised regression achieved a root mean squared error of 3 h and 44 min with 78 % of predictions being correct within ± 4 h (evaluated by five-fold cross-validation), showing pronounced inter-individual differences. While the prediction accuracies of the method in its current state limit its use in the evaluative stage of a criminal trial, the method may nonetheless provide valuable information in the investigative phase. Our study provides the first prediction model for time-of-day of bloodstain deposition based on targeted RNA sequencing and thus represents an important step towards forensic trace deposition timing. It thereby relevantly contributes to the growing knowledge on Transcriptomic Analyses for the Contextualisation of Evidential Stains (TrACES).

A SNaPshot Assay for Epigenetic Age Prediction of Costal Cartilage

Estimating age at death narrows the pool of potential donors in mass disasters and criminal investigations. In this study, we developed a capillary electrophoresis-based SNaPshot assay for age prediction of costal cartilage and used it to analyze DNA methylation at 11 CpG sites across six genes in 136 samples from deceased Koreans aged 28-84 years. To develop the predictive model, DNA methylation levels at these sites from a training set of 83 samples were analyzed using multivariate linear regression in five ways. We then compared the performance parameters calculated from the training set and a test set of 53 samples. Considering experimental simplicity, we selected a model that incorporates four CpGs (MIR29B2CHG_C2, FHL2_C4, TRIM59_C3, and KLF14_C3) as the optimal age prediction model, demonstrating high performance with a mean absolute error of 4.60 years and a root mean square error of 5.41 years in the test set. Subsequently, we developed a multiplex SNaPshot system covering CpGs included in the optimal model, requiring a minimum of 4 ng of bisulfite-converted DNA for reliable prediction and demonstrating multi-tissue applicability, particularly in blood and buccal swabs. We believe this tool will support forensic investigations, including the identification of victims and missing persons.

Reproducibility and validation of a targeted and flexible epigenetic clock for forensic applications

DNA methylation variants have been widely used as biomarkers of ageing and several mathematical models have been developed to estimate the biological age. More recently, DNA technology has triggered efforts toward the simplification of the array-based epigenetic clocks and targeted approaches, based on the assessment of a small number of CpG sites have been developed. Among the markers included in these clocks, ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 resulted to be the most strongly validated markers. We tested the reproducibility and validation of a previously developed targeted epigenetic clock purposely optimized for the measurement of chronological age in blood samples. The clock includes DNAm biomarkers strongly correlated with chronological age whose DNA methylation levels were measured by using a multiplex methylation SNaPshot assay. We found that epigenetic age, calculated using the developed clock, was highly correlated with age (r = 0.97) in a total of 201 blood samples covering a full spectrum of human ages. For 74 of these, methylation profiles of the whole genome were obtained through the Infinium Methylation EPIC v2.0 Kit which also allowed to estimate the most frequently used clocks of Horvath. These results show the potential of our efficient and affordable test for simultaneously measuring DNA methylation levels at multiple target CpG sites to assess chronological age. We observed a strong correlation between the prediction models for the analyzed CpG sites measured using the SNaPshot method and those obtained with the Illumina EPIC array, especially with the Horvath2 clock, which was specifically developed for DNA from skin and blood cells.

Advancing Forensic Human Chronological Age Estimation: Biochemical, Genetic, and Epigenetic Approaches from the Last 15 Years: A Systematic Review

Forensic age estimation is crucial for identifying unknown individuals and narrowing suspect pools in criminal investigations. Over the past 15 years, significant progress has been made in using biochemical, genetic, and epigenetic markers to estimate chronological age.

METHODS

From research on PubMed a total of 155 studies, related to advancements in age prediction techniques, were selected following PRISMA guidelines. Studies considered eligible dealt with radiocarbon dating, aspartic acid racemization, mitochondrial DNA analysis, signal joint T-cell receptor excision circles, RNA analysis, telomeres, and DNA methylation in the last 15 years and were summarized in a table.

RESULTS

Despite these advancements, challenges persist, including variability in prediction accuracy, sample degradation, and the lack of standardization and reproducibility. DNA methylation emerged as the most promising approach capable of high accuracy across diverse populations and age ranges. Multimodal methods integrating several biomarkers show promise in improving reliability and addressing these limitations.

CONCLUSION

While significant progress has been made, further standardization, validation, and technological integration are needed to enhance forensic age estimation. These efforts are essential for meeting the growing demands of forensic science while addressing ethical and legal considerations.

Dental color measurement to estimate age in adults: a systematic review and meta-analysis

Age estimation is a major challenge in anthropology and forensic odontology laboratories, as well as in judicial settings, as one of the tools used in human identification. The aim of this study was to evaluate the usefulness of age estimation methods based on the accurate measurement of tooth color changes. A systematic review was carried out following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and in compliance with Cochrane criteria recommendations (PROSPERO registration number CRD 42022343371). An electronic search was performed in the following databases: Pubmed, Web of Science, Medline, Current Contents Connect, SciELO, KCI-Korean Journal Database, Derwent Innovations Index and Russian Citation Index. The search strategy yielded a total of 18 articles. A randomized meta-analysis model of the results for the CIE L*a*b* color variables stratified by age (less than 30 years, 30-60 years, 60 years and older) was performed with 9 of the 18 studies included in this systematic review. According to our results, sex and location of color measurement are the most influential factors in color estimation. All studies were carried out in healthy anterior teeth by spectrophotometry as the most commonly used method for color measurement, with CIE L*a*b* being the most commonly analyzed parameters. Studies based on age as a dependent variable showed R2 values between 0.28 and 0.56, being higher in ex vivo teeth. Studies based on age as an independent variable showed R2 values ranging from 0.10 to 0.48. The random model showed high heterogeneity for the L*, a* and b* parameters in all age groups, which is explained by discrepancies in age range and non-standardized conditions for color measurement. This systematic review highlights the need to protocolize age estimation studies that measure tooth color, in order to apply this method in different forensic settings.

Multiplexing and massive parallel sequencing of targeted DNA methylation to predict chronological age

Estimation of chronological age is particularly informative in forensic contexts. Assessment of DNA methylation status allows for the prediction of age, though the accuracy may vary across models. In this study, we started with a carefully designed discovery cohort with more elderly subjects than other age categories, to diminish the effect of epigenetic drifting. We applied multiplexing and massive parallel sequencing of targeted DNA methylation, which let us to construct a model comprising 25 CpG sites with substantially improved accuracy (MAE = 2.279, R = 0.920). This model is further validated by an independent cohort (MAE = 2.204, 82.7% success (±5 years)). Remarkably, in a multi-center test using trace blood samples from forensic caseworks, the correct predictions (±5 years) are 91.7%. The nature of our analytical pipeline can easily be scaled up with low cost. Taken together, we propose a new age-prediction model featuring accuracy, sensitivity, high-throughput, and low cost. This model can be readily applied in both classic and newly emergent forensic contexts that require age estimation.

Epigenetic Clock: DNA Methylation as a Marker of Biological Age and Age-Associated Diseases

Age is one of the key criteria of human health used in practical medicine to predict the risk of common chronic diseases. However, biological age, which reflects the state of an individual organism, functional capabilities, social well-being, and risk of premature death from various causes, often does not coincide with chronological age. To determine biological age of a particular individuals and the rate of their aging, specific panels of DNA methylation markers called "epigenetic clock" (EC) were proposed. This review summarizes the data about the main types of ECs developed to date and their key characteristics. We described the results of works studying individual aging rates in common age-associated diseases and outlined main directions, development of which could expand application of ECs in fundamental and practical medicine. There is no doubt that revealing complex mechanisms underlying interaction between the rate of epigenetic aging and the risk of age-associated diseases could play a key role for prediction and early diagnosis, as well as for the development of preventive measures that could delay onset of the disease.

A Ge.F.I. Collaborative Study: Evaluating Reproducibility and Accuracy of a DNA-Methylation-Based Age-Predictive Assay for Routine Implementation in Forensic Casework

The increasing interest in DNA methylation (DNAm) analysis within the forensic scientific community prompted a collaborative project by Ge.F.I. (Genetisti Forensi Italiani). The study evaluated a standardized bisulfite conversion-based Single Base Extension (SBE) protocol for the analysis of the methylation levels at five age-predictive loci (ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59). The study encompassed three phases: (1) setting up and validating the protocol to ensure consistency and reproducibility; (2) comparing fresh peripheral blood with blood spots; and (3) evaluating sources of intra- and inter-laboratory variability. Samples from 22 Italian volunteers were analyzed by 6 laboratories in replicates for a total of 528 records. From phase I emerged that the choice of genetic sequencer significantly contributed to inter-laboratory data variation, resulting in separate regression analyses performed for each laboratory. In phase II, blood spots were found to be a reliable source for DNAm analysis, despite exhibiting increased experimental variation compared to fresh peripheral blood. In phase III, a strong correlation between the individual's predicted and true ages was observed across different laboratories. Analysis of variance (ANOVA) of the residuals indicated that one-third of the total variance could be attributed to laboratory-specific factors, whereas two-thirds could be attributed to inter-individual biological differences. The leave-one-out cross-validation (LOO-CV) method yielded an overall mean absolute deviation (MAD) value of 4.41 years, with an average 95% confidence interval of 5.24 years. Stepwise regression analysis proved that a restricted model (ELOVL2, C1orf132/MIR29B2C, and TRIM59) produced results virtually indistinguishable from the five-loci model. Additionally, the analysis of samples in replicates greatly improved the fit of the regression model, balancing the slight effects of intra-laboratory variability. In conclusion, the bisulfite conversion-based SBE protocol, combined with replicate analysis and in-lab calibration of a regression-prediction model, proves to be a reliable and easily implementable method for age prediction in forensic laboratories.

Age estimation of burnt human remains through DNA methylation analysis

The identification of human fire victims is a challenging task in forensic medicine. The heat-induced alterations of biological tissues can make the conventional anthropological analyses difficult. Even if the DNA profile of the victim is achieved, it is possible that no match can be found in a forensic DNA database, thus hindering positive identification. In such cases, any information useful to nail down a possible identity should be collected, such as DNA methylation analysis which could provide useful investigative leads. In the present study, five age-related epigenetic markers (ELOVL2, FHL2, KLF14, C1orf132, and TRIM59) were initially analysed in blood samples of 72 living Italian individuals of known age, using a Single Base Extension (SBE) assay. An age prediction model was built by multiple linear regression including all the markers (Mean Absolute Error, MAE: 3.15 years). This model was tested on 29 blood samples collected during autopsies from burnt human remains, already identified through DNA analysis, providing a MAE of 6.92 years. The model allowed a correct prediction in 79.3% of the cases (95% prediction interval), while six cases were associated with inaccurate predictions (min-max prediction error: 9.8-37.3 years). Among the different sample variables considered to explain these results, only the DNA degradation index was a relevant factor affecting the reliability of the predictions. In conclusion, the SBE typing of blood from burnt remains proved to be a reliable tool to estimate chronological age of most of the samples, also in consideration of its cost-effectiveness and the availability of CE sequencers in every forensic genetics laboratory.

Epigenetic Age Estimation by Detecting DNA Methylation Status in Buccal Swabs

The analysis of DNA methylation (DNAm) levels at specific CpG sites represents one of the most promising molecular techniques for estimating an individual's age. To date, a considerable number of studies have reported the development of age prediction models on the basis of DNAm in body fluids, with only a few utilizing buccal swabs. The objective of this study was to identify age-dependent methylation CpG sites in three different genes (HOXC4, TRIM59, and ELOVL2) in buccal swab samples from the Chinese Han population. A total of 461 buccal swabs, with an age range of 0.4-80.8 years, were divided into a training set (n = 325) and a validation set (n = 136). Samples were analyzed by pyrosequencing in order to identify age-related genes with correlation coefficient. A random forest regression model was ultimately proposed, including eight CpGs in three genes, with a mean absolute error (MAE) of 2.119 years. The model performs independent validation set with an MAE of 4.391 years. Our findings illustrate that buccal swabs present a suitable alternative to biological traces for age prediction based on DNAm pattern using pyrosequencing and random forest regression, offering the additional advantage of being collected noninvasively.

Epigenetic changes in patients with post-acute COVID-19 symptoms (PACS) and long-COVID: A systematic review

BACKGROUND

Up to 30% of people infected with SARS-CoV-2 report disabling symptoms 2 years after the infection. Over 100 persistent symptoms have been associated with Post-Acute COVID-19 Symptoms (PACS) and/or long-COVID, showing a significant clinical heterogeneity. To develop effective, patient-targeted treatment, a better understanding of underlying mechanisms is needed. Epigenetics has helped elucidating the pathophysiology of several health conditions and it might help unravelling inter-individual differences in patients with PACS and long-COVID. As accumulating research is exploring epigenetic mechanisms in PACS and long-COVID, we systematically summarized the available literature on the topic.

METHODS

We interrogated five databases (Medline, Embase, Web of Science, Scopus and medXriv/bioXriv) and followed PRISMA and SWiM guidelines to report our results.

RESULTS

Eight studies were included in our review. Six studies explored DNA methylation in PACS and/or long-COVID, while two studies explored miRNA expression in long-COVID associated with lung complications. Sample sizes were mostly small and study quality was low or fair. The main limitation of the included studies was a poor characterization of the patient population that made a homogeneous synthesis of the literature challenging. However, studies on DNA methylation showed that mechanisms related to the immune and the autonomic nervous system, and cell metabolism might be implicated in the pathophysiology of PACS and long-COVID.

CONCLUSION

Epigenetic changes might help elucidating PACS and long-COVID underlying mechanisms, aid subgrouping, and point towards tailored treatments. Preliminary evidence is promising but scarce. Biological and epigenetic research on long-COVID will benefit millions of people suffering from long-COVID and has the potential to be transferable and benefit other conditions as well, such as Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). We urge future research to employ longitudinal designs and provide a better characterization of included patients.

Exploring nanopore direct sequencing performance of forensic STRs, SNPs, InDels, and DNA methylation markers in a single assay

INTRODUCTION

The field of forensic DNA analysis has undergone rapid advancements in recent decades. The integration of massively parallel sequencing (MPS) has notably expanded the forensic toolkit, moving beyond identity matching to predicting phenotypic traits and biogeographical ancestry. This shift is of particular significance in cases where conventional DNA profiling fails to identify a single suspect. Supplementing forensic analyses with estimated biological age may be valuable but involves a complex and time-consuming DNA methylation analysis. This study explores and validates the performance of a comprehensive forensic third-generation sequencing assay utilizing Oxford Nanopore Technologies (ONT) in an adaptive and direct sequencing approach. We incorporated the most widely used forensic markers, i.e., STRs, SNPs, InDels, mitochondrial DNA (mtDNA), and two methylation-based clock classifiers, thereby combining forensic genetic and epigenetic analysis in one single workflow.

METHODS AND RESULTS

In our investigation, DNA from six anonymous individuals was sequenced using the ONT standard adaptive direct sequencing approach, reaching a mean percentage of on-target reads ranging from 6.6 % to 7.7 % per sample. ONT data was compared to standard MPS data and Illumina EPIC DNA methylation profiles. Basecalling employed recommended ONT software packages. TREAT was used for ONT-based analysis of autosomal and Y-chromosome STRs, achieving 90-92 % correct calls depending on allelic read depth thresholds. InDel analyses for two lower-quality samples proved challenging due to inadequate read depth, while the remaining four samples significantly contributed to the observed percentage markers (60.9 %) and correct calls (97.8 %). SNP analysis achieved a 98 % call rate, with only two mismatches and two missed alleles. ONT-generated DNA methylation data demonstrated Pearson's correlation coefficients with EPIC data ranging from 0.67 to 0.97 for Horvath's clock. Additional age-associated markers exhibited Pearson's correlation coefficients with chronological age between 0.14 (ELOVL2) and 0.96 (FHL2) at read depths of <30 and <20, respectively. Despite excluding mtDNA from our targeted sequencing approach, adaptive proof-reading fragments covered the complete mtDNA with an average read depth of 21-72, showing 100 % concordance with reference data.

DISCUSSION

Our exploratory study using ONT adaptive sequencing for conventional forensic and age associated DNA methylation markers showed high sequencing accuracy for a significant number of markers, showcasing ONT as a promising (epi)genetic forensic method. Future studies must address three critical aspects: determining clear quantity and quality measures and detection thresholds for accuracy, optimizing input DNA quantity for forensic casework expectations, and addressing ethical considerations associated with phenotype and ancestry analysis to prevent ethnic biases.

Exploring legal age estimation using DNA methylation

Minors (subjects under the legal age, established at this study at 18 years) benefit from a series of legal rights created to protect them and guarantee their welfare. However, throughout the world there are many minors who have no way to prove they are underaged, leading to a great interest in predicting legal age with the highest possible accuracy. Current methods, mainly involving X-ray analysis, are highly invasive, so new methods to predict legal age are being studied, such as DNA methylation. To further such studies, we created two age prediction models based on five epigenetic markers: cg21572722 (ELOVL2), cg02228185 (ASPA), cg06639320 (FHL2), cg19283806 (CCDC102B) and cg07082267 (no associated gene), that were analysed in blood samples to determine possible limitations regarding DNA methylation as an effective tool for legal age estimation. A wide age range prediction model was created using a broad set of samples (14-94 years) yielding a mean absolute error (MAE) of ±4.32 years. A second model, the constrained age prediction model, was created using a reduced range of samples (14-25 years) yielding an MAE of ±1.54 years. Both models, in addition to Horvath's Skin & Blood epigenetic clock, were evaluated using a test set comprising 732 pairs of 18-year-old twins (N=426 monozygotic (MZ) and N=306 dizygotic (DZ) pairs), representing a relevant age of study. Through analysis of the two former age prediction models, we found that constraining the age of the samples forming the training set around the desired age of study significantly reduced the prediction error (from MAE: ±4.07 and ±4.27 years for MZ and DZ twins, respectively; to ±1.31 and ±1.3 years). However, despite low prediction errors, DNA methylation models are still prone to classify same-aged individuals in different categories (minors or adults), despite each sample belonging to the same twin pair. Additional evaluation of Horvath's Skin & Blood model (391 CpGs) led to similar results in terms of age prediction errors than if using only five epigenetic markers (MAE: ±1.87 and ±1.99 years for MZ and DZ twins, respectively).

Epigenetic age prediction using costal cartilage for the investigation of disaster victims and missing persons

The DNA intelligence tool, DNA methylation-based age prediction, can help identify disaster victims and suspects in criminal investigations. In this study, we developed a costal cartilage-based age prediction tool that uses massive parallel sequencing (MPS) of age-associated DNA methylation markers. Costal cartilage samples were obtained from 85 deceased Koreans, aged between 26 and 89 years. An MPS library was prepared using two rounds of multiplex polymerase chain reaction of nine genes (TMEM51, MIR29B2CHG, EDARADD, FHL2, TRIM59, ELOVL2, KLF14, ASPA, and PDE4C). The DNA methylation status of 45 CpG sites was determined and used to train an age prediction model via stepwise regression analysis. Nine CpGs in MIR29B2CHG, FHL2, TRIM59, ELOVL2, KLF14, and ASPA were selected for regression model construction. A leave-one-out cross-validation analysis revealed the high performance of the age prediction model, with a mean absolute error (MAE) and root mean square error of 4.97 and 6.43 years, respectively. Additionally, our model showed good performance with a MAE of 6.06 years in the analysis of data of 181 costal cartilage samples collected from Europeans. Our model effectively estimates the age of deceased individuals using costal cartilage samples; therefore, it can be a valuable forensic tool for disaster victim and missing person investigation.

A systematic review of phenotypic and epigenetic clocks used for aging and mortality quantification in humans

Aging is the leading driver of disease in humans and has profound impacts on mortality. Biological clocks are used to measure the aging process in the hopes of identifying possible interventions. Biological clocks may be categorized as phenotypic or epigenetic, where phenotypic clocks use easily measurable clinical biomarkers and epigenetic clocks use cellular methylation data. In recent years, methylation clocks have attained phenomenal performance when predicting chronological age and have been linked to various age-related diseases. Additionally, phenotypic clocks have been proven to be able to predict mortality better than chronological age, providing intracellular insights into the aging process. This review aimed to systematically survey all proposed epigenetic and phenotypic clocks to date, excluding mitotic clocks (i.e., cancer risk clocks) and those that were modeled using non-human samples. We reported the predictive performance of 33 clocks and outlined the statistical or machine learning techniques used. We also reported the most influential clinical measurements used in the included phenotypic clocks. Our findings provide a systematic reporting of the last decade of biological clock research and indicate possible avenues for future research.

Estimation of human age using machine learning on panoramic radiographs for Brazilian patients

This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution.

DNA methylation in cardiovascular disease and heart failure: novel prediction models?

BACKGROUND

Cardiovascular diseases (CVD) affect over half a billion people worldwide and are the leading cause of global deaths. In particular, due to population aging and worldwide spreading of risk factors, the prevalence of heart failure (HF) is also increasing. HF accounts for approximately 36% of all CVD-related deaths and stands as the foremost cause of hospitalization. Patients affected by CVD or HF experience a substantial decrease in health-related quality of life compared to healthy subjects or affected by other diffused chronic diseases.

MAIN BODY

For both CVD and HF, prediction models have been developed, which utilize patient data, routine laboratory and further diagnostic tests. While some of these scores are currently used in clinical practice, there still is a need for innovative approaches to optimize CVD and HF prediction and to reduce the impact of these conditions on the global population. Epigenetic biomarkers, particularly DNA methylation (DNAm) changes, offer valuable insight for predicting risk, disease diagnosis and prognosis, and for monitoring treatment. The present work reviews current information relating DNAm, CVD and HF and discusses the use of DNAm in improving clinical risk prediction of CVD and HF as well as that of DNAm age as a proxy for cardiac aging.

CONCLUSION

DNAm biomarkers offer a valuable contribution to improving the accuracy of CV risk models. Many CpG sites have been adopted to develop specific prediction scores for CVD and HF with similar or enhanced performance on the top of existing risk measures. In the near future, integrating data from DNA methylome and other sources and advancements in new machine learning algorithms will help develop more precise and personalized risk prediction methods for CVD and HF.

Profiling age and body fluid DNA methylation markers using nanopore adaptive sampling

DNA methylation plays essential roles in regulating physiological processes, from tissue and organ development to gene expression and aging processes and has emerged as a widely used biomarker for the identification of body fluids and age prediction. Currently, methylation markers are targeted independently at specific CpG sites as part of a multiplexed assay rather than through a unified assay. Methylation detection is also dependent on divergent methodologies, ranging from enzyme digestion and affinity enrichment to bisulfite treatment, alongside various technologies for high-throughput profiling, including microarray and sequencing. In this pilot study, we test the simultaneous identification of age-associated and body fluid-specific methylation markers using a single technology, nanopore adaptive sampling. This innovative approach enables the profiling of multiple CpG marker sites across entire gene regions from a single sample without the need for specialized DNA preparation or additional biochemical treatments. Our study demonstrates that adaptive sampling achieves sufficient coverage in regions of interest to accurately determine the methylation status, shows a robust consistency with whole-genome bisulfite sequencing data, and corroborates known CpG markers of age and body fluids. Our work also resulted in the identification of new sites strongly correlated with age, suggesting new possible age methylation markers. This study lays the groundwork for the systematic development of nanopore-based methodologies in both age prediction and body fluid identification, highlighting the feasibility and potential of nanopore adaptive sampling while acknowledging the need for further validation and expansion in future research.

The impact of COVID-19 on "biological aging"

The global impact of the SARS-CoV-2 pandemic has been unprecedented, posing a significant public health challenge. Chronological age has been identified as a key determinant for severe outcomes associated with SARS-CoV-2 infection. Epigenetic age acceleration has previously been observed in various diseases including human immunodeficiency virus (HIV), Cytomegalovirus (CMV), cardiovascular diseases, and cancer. However, a comprehensive review of this topic is still missing in the field. In this review, we explore and summarize the research work focusing on biological aging markers, i.e., epigenetic age and telomere attrition in COVID-19 patients. From the reviewed articles, we identified a consistent pattern of epigenetic age dysregulation and shortened telomere length, revealing the impact of COVID-19 on epigenetic aging and telomere attrition.

Methylation-based markers for the estimation of age in African cheetah, Acinonyx jubatus

Age is a key demographic in conservation where age classes show differences in important population metrics such as morbidity and mortality. Several traits, including reproductive potential, also show senescence with ageing. Thus, the ability to estimate age of individuals in a population is critical in understanding the current structure as well as their future fitness. Many methods exist to determine age in wildlife, with most using morphological features that show inherent variability with age. These methods require significant expertise and become less accurate in adult age classes, often the most critical groups to model. Molecular methods have been applied to measuring key population attributes, and more recently epigenetic attributes such as methylation have been explored as biomarkers for age. There are, however, several factors such as permits, sample sovereignty, and costs that may preclude the use of extant methods in a conservation context. This study explored the utility of measuring age-related changes in methylation in candidate genes using mass array technology. Novel methods are described for using gene orthologues to identify and assay regions for differential methylation. To illustrate the potential application, African cheetah was used as a case study. Correlation analyses identified six methylation sites with an age relationship, used to develop a model with sufficient predictive power for most conservation contexts. This model was more accurate than previous attempts using PCR and performed similarly to candidate gene studies in other mammal species. Mass array presents an accurate and cost-effective method for age estimation in wildlife of conservation concern.

Uncovering Forensic Evidence: A Path to Age Estimation through DNA Methylation

Age estimation is a critical aspect of reconstructing a biological profile in forensic sciences. Diverse biochemical processes have been studied in their correlation with age, and the results have driven DNA methylation to the forefront as a promising biomarker. DNA methylation, an epigenetic modification, has been extensively studied in recent years for developing age estimation models in criminalistics and forensic anthropology. Epigenetic clocks, which analyze DNA sites undergoing hypermethylation or hypomethylation as individuals age, have paved the way for improved prediction models. A wide range of biomarkers and methods for DNA methylation analysis have been proposed, achieving different accuracies across samples and cell types. This review extensively explores literature from the past 5 years, showing scientific efforts toward the ultimate goal: applying age prediction models to assist in human identification.

A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus) and Panthera species, using targeted bisulphite sequencing and machine learning models

Individual age can be used to design more efficient and suitable management plans in both in situ and ex situ conservation programmes for targeted wildlife species. DNA methylation is a promising marker of epigenetic ageing that can accurately estimate age from small amounts of biological material, which can be collected in a minimally invasive manner. In this study, we sequenced five targeted genetic regions and used 8-23 selected CpG sites to build age estimation models using machine learning methods at only about $3-7 per sample. Blood samples of seven Felidae species were used, ranging from small to big, and domestic to endangered species: domestic cats (Felis catus, 139 samples), Tsushima leopard cats (Prionailurus bengalensis euptilurus, 84 samples) and five Panthera species (96 samples). The models achieved satisfactory accuracy, with the mean absolute error of the most accurate models recorded at 1.966, 1.348 and 1.552 years in domestic cats, Tsushima leopard cats and Panthera spp. respectively. We developed the models in domestic cats and Tsushima leopard cats, which were applicable to individuals regardless of health conditions; therefore, these models are applicable to samples collected from individuals with diverse characteristics, which is often the case in conservation. We also showed the possibility of developing universal age estimation models for the five Panthera spp. using only two of the five genetic regions. We do not recommend building a common age estimation model for all the target species using our markers, because of the degraded performance of models that included all species.

Heterogeneity of increased biological age in type 2 diabetes correlates with differential tissue DNA methylation, biological variables, and pharmacological treatments

Biological age (BA) closely depicts age-related changes at a cellular level. Type 2 diabetes mellitus (T2D) accelerates BA when calculated using clinical biomarkers, but there is a large spread in the magnitude of individuals' age acceleration in T2D suggesting additional factors contributing to BA. Additionally, it is unknown whether BA can be changed with treatment. We hypothesized that potential determinants of the heterogeneous BA distribution in T2D could be due to differential tissue aging as reflected at the DNA methylation (DNAm) level, or biological variables and their respective therapeutic treatments. Publicly available DNAm samples were obtained to calculate BA using the DNAm phenotypic age (DNAmPhenoAge) algorithm. DNAmPhenoAge showed age acceleration in T2D samples of whole blood, pancreatic islets, and liver, but not in adipose tissue or skeletal muscle. Analysis of genes associated with differentially methylated CpG sites found a significant correlation between eight individual CpG methylation sites and gene expression. Clinical biomarkers from participants in the NHANES 2017-2018 and ACCORD cohorts were used to calculate BA using the Klemera and Doubal (KDM) method. Cardiovascular and glycemic biomarkers associated with increased BA while intensive blood pressure and glycemic management reduced BA to CA levels, demonstrating that accelerated BA can be restored in the setting of T2D.

Collaborative exercise: analysis of age estimation using a QIAGEN protocol and the PyroMark Q48 platform

Human age estimation from trace samples may give important leads early in a police investigation by contributing to the description of the perpetrator. Several molecular biomarkers are available for the estimation of chronological age, and currently, DNA methylation patterns are the most promising. In this study, a QIAGEN age protocol for age estimation was tested by five forensic genetic laboratories. The assay comprised bisulfite treatment of the extracted DNA, amplification of five CpG loci (in the genes of ELOVL2, C1orf132, TRIM59, KLF14, and FHL2), and sequencing of the amplicons using the PyroMark Q48 platform. Blood samples from 49 individuals with ages ranging from 18 to 64 years as well as negative and methylation controls were analyzed. An existing age estimation model was applied to display a mean absolute deviation of 3.62 years within the reference data set.

Key points

Age determination as an intelligence tool during investigations can be a powerful tool in forensic genetics.In this study, five laboratories ran 49 samples and obtained a mean absolute deviation of 3.62 years.Five markers were analyzed on a PyroMark Q48 platform.

Predicting age from blood by droplet digital PCR using a set of three DNA methylation markers

Evaluation of DNA methylation (DNAm) patterns is a promising tool for age estimation. The duplex droplet digital PCR (ddPCR) method has been recently investigated for DNAm evaluation, revealing to be a potential methodology for DNAm evaluation and molecular age estimation. In this study, we evaluated DNAm levels of CpGs located at the three age-associated genes ELOVL2, FHL2 and PDE4C using ddPCR to develop an age prediction model. Blood-derived DNA samples from 58 healthy individuals (42 women and 16 men; aged 1-93 years old) were submitted to bisulfite conversion followed by ddPCR using dual-labeled probes targeting methylated and unmethylated DNA sequences. Simple linear regression statistics revealed a strong correlation between DNAm levels and chronological age for FHL2 (R = 0.948; P = 1.472 × 10-29) and PDE4C (R = 0.819; P = 3.917 × 10-15), addressing only one CpG for each gene. For the ELOVL2 gene, evaluating five CpG sites in simultaneous, revealed a strong age correlation (R = 0.887; P = 2.099 × 10-20) in a simple linear regression statistics and very strong age correlation (R = 0.926; P = 2.202 × 10-25) when using quadratic regression statistics. The multivariable regression analysis, using methylation information captured on ELOVL2 (squared), FHL2 and PDE4C genes, revealed a very strong age correlation (R = 0.970; P = 5.356 ×10-33), explaining 93.7 % of age variance, displaying a mean absolute deviation (MAD) between chronological and predicted age of 4.657 years (RMSE = 6.044). We postulate that the ddPCR method should be further investigated for DNAm-based age prediction, because it is a relatively simple and an accurate method that can be routinely used in forensic laboratories for testing a few numbers of markers.

DNA methylation-based age estimation and quantification of the degradation levels of bisulfite-converted DNA

DNA methylation modifications are known to influence epigenetic phenomena and have been a focus of forensic science research for some time. Degraded DNA after bisulfite treatment is widely used in DNA methylation analysis. In this study, we analyzed methylation levels at 12 CpG sites of four selected genomic regions by pyrosequencing after bisulfite treatment. DNA was extracted from buccal swab samples collected from 102 Japanese individuals who were 21-77 years old. We also developed a simple method to quantify the degradation levels of bisulfite-converted DNA by real-time PCR, and evaluated the effect of DNA degradation on age estimation. We found that the methylation levels and chronological ages were highly correlated in the four selected regions, and the mean absolute deviation (MAD) between chronological and estimated ages was low at 3.88 years. These results indicated that pyrosequencing analysis at the 12 CpGs was useful for age estimation in the Japanese population. To develop a sensitive quantification method, we analyzed the amplification efficiency of short and long fragments from 10 regions by real-time PCR. The amplification efficiency was highest for CCDC102B, and the degradation levels of bisulfite-converted DNA for the 102 samples were categorized as moderately or heavily degraded. For the younger age groups (20-49 years), the MADs were lower for moderately degraded DNA than they were for heavily degraded DNA. This finding indicates that degradation levels affected the accuracy of age estimation in most of the samples; the exception was the samples from the 50-77 years age group.

Development and comparison of forensic interval age prediction models by statistical and machine learning methods based on the methylation rates of ELOVL2 in blood DNA

Age estimation can be useful information for narrowing down candidates of unidentified donors in criminal investigations. Various age estimation models based on DNA methylation biomarkers have been developed for forensic usage in the past decade. However, many of these models using ordinary least squares regression cannot generate an appropriate estimation due to the deterioration in prediction accuracy caused by an increased prediction error in older age groups. In the present study, to address this problem, we developed age estimation models that set an appropriate prediction interval for all age groups by two approaches: a statistical method using quantile regression (QR) and a machine learning method using an artificial neural network (ANN). Methylation datasets (n = 1280, age 0-91 years) of the promoter for the gene encoding ELOVL fatty acid elongase 2 were used to develop the QR and ANN models. By validation using several test datasets, both models were shown to enlarge prediction intervals in accordance with aging and have a high level of correct prediction (>90 %) for older age groups. The QR and ANN models also generated a point age prediction with high accuracy. The ANN model enabled a prediction with a mean absolute error (MAE) of 5.3 years and root mean square error (RMSE) of 7.3 years for the test dataset (n = 549), which were comparable to those of the QR model (MAE = 5.6 years, RMSE = 7.8 years). Their applicability to casework was also confirmed using bloodstain samples stored for various periods of time (1-14 years), indicating the stability of the models for aged bloodstain samples. From these results, it was considered that the proposed models can provide more useful and effective age estimation in forensic settings.

Longitudinal changes and variation in human DNA methylation analysed with the Illumina MethylationEPIC BeadChip assay and their implications on forensic age prediction

DNA methylation, a pivotal epigenetic modification, plays a crucial role in regulating gene expression and is known to undergo dynamic changes with age. The present study investigated epigenome-wide methylation profiles in 64 individuals over two time points, 15 years apart, using the Illumina EPIC850k arrays. A mixed-effects model identified 2821 age-associated differentially methylated CpG positions (aDMPs) with a median rate of change of 0.18% per year, consistent with a 10-15% change during a human lifespan. Significant variation in the baseline DNA methylation levels between individuals of similar ages as well as inconsistent direction of change with time across individuals were observed for all the aDMPs. Twenty-three of the 2821 aDMPs were previously incorporated into forensic age prediction models. These markers displayed larger changes in DNA methylation with age compared to all the aDMPs and less variation among individuals. Nevertheless, the forensic aDMPs also showed inter-individual variations in the direction of DNA methylation changes. Only cg16867657 in ELOVL2 exhibited a uniform direction of the age-related change among the investigated individuals, which supports the current knowledge that CpG sites in ELOVL2 are the best markers for age prediction.

ELOVL2-methylation and renal and cardiovascular event in patients with chronic kidney disease

BACKGROUND

Methylation of the Elongation Of Very Long Chain Fatty Acids-Like 2 (ELOVL2) gene promoter may predict premature ageing and cardiovascular risk.

METHODS

We studied the cross-sectional associations between blood ELOVL2-methylation and cardiovascular risk factors in 350 patients with chronic kidney disease (CKD) stage G2-G4 aged between 22 and 90 years. In a follow-up study for a mean of 3.9 years, we investigated the association between baseline ELOVL2-methylation and renal or cardiovascular events including death.

RESULTS

ELOVL2-methylation at seven CpG cites increased with age (the correlation coefficients between 0.67 and 0.87, p < 0.001). The ELOVL2-CpGs methylation was lower in patients with CKD stage G2 versus those in stage G3a, G3b and G4, but the differences were explained by age. ELOVL2-CpGs methylation showed no correlations with cardiovascular risk factors after adjusting for age. During the follow-up, 64 patients showed deterioration in renal function or died and 77 showed cardiovascular events or died. The hazard ratio and 95% confidence intervals for renal or cardiovascular events according to baseline ELOVL2-CpGs methylation were not significant after adjustment for covariates.

CONCLUSIONS

ELOVL2-hypermethylation showed a strong association with age, but was not independently associated with cardiovascular risk factors or with future renal or cardiovascular events in patients with CKD. ELOVL2 gene methylation is not likely to be itself a cause for ageing or illnesses, but it could be rather influenced by other upstream processes that deserve investigation.

Decelerated Epigenetic Aging in Long Livers

Epigenetic aging is a hot topic in the field of aging research. The present study estimated epigenetic age in long-lived individuals, who are currently actively being studied worldwide as an example of successful aging due to their longevity. We used Bekaert's blood-based age prediction model to estimate the epigenetic age of 50 conditionally "healthy" and 45 frail long-livers over 90 years old. Frailty assessment in long-livers was conducted using the Frailty Index. The control group was composed of 32 healthy individuals aged 20-60 years. The DNA methylation status of 4 CpG sites (ASPA CpG1, PDE4C CpG1, ELOVL2 CpG6, and EDARADD CpG1) included in the epigenetic clock was assessed through pyrosequencing. According to the model calculations, the epigenetic age of long-livers was significantly lower than their chronological age (on average by 21 years) compared with data from the group of people aged 20 to 60 years. This suggests a slowing of epigenetic and potentially biological aging in long livers. At the same time, the obtained results showed no statistically significant differences in delta age (difference between the predicted and chronological age) between "healthy" long livers and long livers with frailty. We also failed to detect sex differences in epigenetic age either in the group of long livers or in the control group. It is possible that the predictive power of epigenetic clocks based on a small number of CpG sites is insufficient to detect such differences. Nevertheless, this study underscores the need for further research on the epigenetic status of centenarians to gain a deeper understanding of the factors contributing to delayed aging in this population.

Improved age estimation from semen using sperm-specific age-related CpG markers

Accurate age estimation from semen has the potential to greatly narrow the pool of unidentified suspects in sexual assault investigations. However, previous efforts utilizing semen age-related CpG (AR-CpG) markers have shown lower accuracy compared to blood AR-CpG-based methods. This discrepancy may be attributed to DNA methylation (DNAm) interferences from "round cells" such as leukocytes and immature sperm cells in semen. This study aimed to develop age calculators based on sperm-specific AR-CpG markers and to achieve performance-improved age estimates from sperm DNA. Through an analysis of publicly available MethylationEPIC microarray data from 90 sperm samples of healthy males aged 22-51 years, we identified 31 sperm-specific AR-CpG markers with absolute Pearson's R values > 0.5 and Benjamini-Hochberg adjusted p values < 0.013. The top 19 AR-CpG markers with the largest absolute R values and beta ranges > 0.10, along with 3 reported semen AR-CpG markers (cg06304190, cg06979108, and cg12837463), were integrated into two methylation SNaPshot panels (Ⅰ and Ⅱ), each containing 11 markers. The 21 qualified AR-CpG markers showed absolute R values ≥ 0.427 in an independent validation cohort of 253 sperm DNA samples (22-67 years), with cg21843517 exhibiting the strongest age correlation (R = 0.853). The optimal models, constructed using sperm DNAm data of the training set (n = 214, 22-67 years) and markers from panel Ⅰ (n = 11), panel Ⅱ (n = 10), or both panels, achieved mean absolute errors (MAEs) of 2.526-4.746, 3.890-5.715, and > 9.800 years on the test sets of sperm (n = 39, 23-64 years), semen (same donors as the sperm test set), and whole blood (n = 40, 22-65 years), respectively. The simplified models incorporating 3, 5, 9, or 14 AR-CpG markers (MAE = 2.918-4.139 years for sperm) still outperformed the Lee et al. original model (MAE = 6.444 years for semen) and the reconstructed panel Lee model (MAE = 6.011 years for sperm). The final models, utilizing all sperm DNAm data (n = 253) and markers from panel Ⅰ, panel Ⅱ, or both panels, yielded mean MAEs of 2.587, 2.766, and 2.200 years, respectively, on the 50 test sets generated by 5 repeats of 10-fold cross-validations. Additionally, multiple markers in both panels demonstrated the ability to discern sperm or semen from blood with 100% accuracy. In summary, our study substantiates the potential of sperm-specific AR-CpG markers for precise age estimation from sperm DNA, providing an improved toolset for forensic investigations.

Development of an epigenetic age predictor for costal cartilage with a simultaneous somatic tissue differentiation system

Age prediction from DNA has been a topic of interest in recent years due to the promising results obtained when using epigenetic markers. Since DNA methylation gradually changes across the individual's lifetime, prediction models have been developed accordingly for age estimation. The tissue-dependence for this biomarker usually necessitates the development of tissue-specific age prediction models, in this way, multiple models for age inference have been constructed for the most commonly encountered forensic tissues (blood, oral mucosa, semen). The analysis of skeletal remains has also been attempted and prediction models for bone have now been reported. Recently, the VISAGE Enhanced Tool was developed for the simultaneous DNA methylation analysis of 8 age-correlated loci using targeted high-throughput sequencing. It has been shown that this method is compatible with epigenetic age estimation models for blood, buccal cells, and bone. Since when dealing with decomposed cadavers or postmortem samples, cartilage samples are also an important biological source, an age prediction model for cartilage has been generated in the present study based on methylation data collected using the VISAGE Enhanced Tool. In this way, we have developed a forensic cartilage age prediction model using a training set composed of 109 samples (19-74 age range) based on DNA methylation levels from three CpGs in FHL2, TRIM59 and KLF14, using multivariate quantile regression which provides a mean absolute error (MAE) of ± 4.41 years. An independent testing set composed of 72 samples (19-75 age range) was also analyzed and provided an MAE of ± 4.26 years. In addition, we demonstrate that the 8 VISAGE markers, comprising EDARADD, TRIM59, ELOVL2, MIR29B2CHG, PDE4C, ASPA, FHL2 and KLF14, can be used as tissue prediction markers which provide reliable blood, buccal cells, bone, and cartilage differentiation using a developed multinomial logistic regression model. A training set composed of 392 samples (n = 87 blood, n = 86 buccal cells, n = 110 bone and n = 109 cartilage) was used for building the model (correct classifications: 98.72%, sensitivity: 0.988, specificity: 0.996) and validation was performed using a testing set composed of 192 samples (n = 38 blood, n = 36 buccal cells, n = 46 bone and n = 72 cartilage) showing similar predictive success to the training set (correct classifications: 97.4%, sensitivity: 0.968, specificity: 0.991). By developing both a new cartilage age model and a tissue differentiation model, our study significantly expands the use of the VISAGE Enhanced Tool while increasing the amount of DNA methylation-based information obtained from a single sample and a single forensic laboratory analysis. Both models have been placed in the open-access Snipper forensic classification website.

Getting the chronological age out of DNA: using insights of age-dependent DNA methylation for forensic DNA applications

BACKGROUND

DNA analysis for forensic investigations has a long tradition with important developments and optimizations since its first application. Traditionally, short tandem repeats analysis has been the most powerful method for the identification of individuals. However, in addition, epigenetic changes, i.e., DNA methylation, came into focus of forensic DNA research. Chronological age prediction is one promising application to allow for narrowing the pool of possible individuals who caused a trace, as well as to support the identification of unknown bodies and for age verification of living individuals.

OBJECTIVE

This review aims to provide an overview of the current knowledge, possibilities, and (current) limitations about DNA methylation-based chronological age prediction with emphasis on forensic application.

METHODS

The development, implementation and application of age prediction tools requires a deep understanding about the biological background, the analysis methods, the age-dependent DNA methylation markers, as well as the mathematical models for age prediction and their evaluation. Furthermore, additional influences can have an impact. Therefore, the literature was evaluated in respect to these diverse topics.

CONCLUSION

The numerous research efforts in recent years have led to a rapid change in our understanding of the application of DNA methylation for chronological age prediction, which is now on the way to implementation and validation. Knowledge of the various aspects leads to a better understanding and allows a more informed interpretation of DNAm quantification results, as well as the obtained results by the age prediction tools.

Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm

Chronological age prediction from DNA methylation sheds light on human aging, health, and lifespan. Current clocks are mostly based on linear models and rely upon hundreds of sites across the genome. Here, we present GP-age, an epigenetic non-linear cohort-based clock for blood, based upon 11,910 methylomes. Using 30 CpG sites alone, GP-age outperforms state-of-the-art models, with a median accuracy of ∼2 years on held-out blood samples, for both array and sequencing-based data. We show that aging-related changes occur at multiple neighboring CpGs, with implications for using fragment-level analysis of sequencing data in aging research. By training three independent clocks, we show enrichment of donors with consistent deviation between predicted and actual age, suggesting individual rates of biological aging. Overall, we provide a compact yet accurate alternative to array-based clocks for blood, with applications in longitudinal aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer.

Mitochondrial epigenetics in aging and cardiovascular diseases

Mitochondria are cellular organelles which generate adenosine triphosphate (ATP) molecules for the maintenance of cellular energy through the oxidative phosphorylation. They also regulate a variety of cellular processes including apoptosis and metabolism. Of interest, the inner part of mitochondria-the mitochondrial matrix-contains a circular molecule of DNA (mtDNA) characterised by its own transcriptional machinery. As with genomic DNA, mtDNA may also undergo nucleotide mutations that have been shown to be responsible for mitochondrial dysfunction. During physiological aging, the mitochondrial membrane potential declines and associates with enhanced mitophagy to avoid the accumulation of damaged organelles. Moreover, if the dysfunctional mitochondria are not properly cleared, this could lead to cellular dysfunction and subsequent development of several comorbidities such as cardiovascular diseases (CVDs), diabetes, respiratory and cardiovascular diseases as well as inflammatory disorders and psychiatric diseases. As reported for genomic DNA, mtDNA is also amenable to chemical modifications, namely DNA methylation. Changes in mtDNA methylation have shown to be associated with altered transcriptional programs and mitochondrial dysfunction during aging. In addition, other epigenetic signals have been observed in mitochondria, in particular the interaction between mtDNA methylation and non-coding RNAs. Mitoepigenetic modifications are also involved in the pathogenesis of CVDs where oxygen chain disruption, mitochondrial fission, and ROS formation alter cardiac energy metabolism leading to hypertrophy, hypertension, heart failure and ischemia/reperfusion injury. In the present review, we summarize current evidence on the growing importance of epigenetic changes as modulator of mitochondrial function in aging. A better understanding of the mitochondrial epigenetic landscape may pave the way for personalized therapies to prevent age-related diseases.

Centenarian clocks: epigenetic clocks for validating claims of exceptional longevity

Claims surrounding exceptional longevity are sometimes disputed or dismissed for lack of credible evidence. Here, we present three DNA methylation-based age estimators (epigenetic clocks) for verifying age claims of centenarians. The three centenarian clocks were developed based on n = 7039 blood and saliva samples from individuals older than 40, including n = 184 samples from centenarians, 122 samples from semi-supercentenarians (aged 105 +), and 25 samples from supercentenarians (aged 110 +). The oldest individual was 115 years old. Our most accurate centenarian clock resulted from applying a neural network model to a training set composed of individuals older than 40. An epigenome-wide association study of age in different age groups revealed that age effects in young individuals (age < 40) are correlated (r = 0.55) with age effects in old individuals (age > 90). We present a chromatin state analysis of age effects in centenarians. The centenarian clocks are expected to be useful for validating claims surrounding exceptional old age.

Prediction of chronological age and its applications in forensic casework: methods, current practices, and future perspectives

Estimating an individual's age can be relevant in several areas primarily related to the clinical and forensic fields. In the latter, estimation of an individual's chronological age from biological material left by the perpetrator at a crime scene may provide helpful information for police investigation. Estimation of age is also beneficial in immigration cases, where age can affect the person's protection status under the law, or in disaster victim identification to narrow the list of potential missing persons. In the last decade, research has focused on establishing new approaches for age prediction in the forensic field. From the first forensic age estimations based on morphological inspections of macroscopic changes in bone and teeth, the focus has shifted to molecular methods for age estimation. These methods allow the use of samples from human biological material that does not contain morphological age features and can, in theory, be investigated in traces containing only small amounts of biological material. Molecular methods involving DNA analyses are the primary choice and estimation of DNA methylation levels at specific sites in the genome is the most promising tool. This review aims to provide an overview of the status of forensic age prediction using molecular methods, with particular focus in DNA methylation. The frequent challenges that impact forensic age prediction model development will be addressed, together with the importance of validation efforts within the forensic community.

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

BACKGROUND

The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine-guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the challenge of high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, our aim was to test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We utilised both nested cross-validation and two independent test sets for the comparisons.

RESULTS

We found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. This approach achieved a correlation between estimated and actual TL of 0.295 (83.4% CI [0.201, 0.384]) on the EXTEND test data set. Contrastingly, the baseline model of elastic net regression with no prior feature reduction stage performed less well in general-suggesting a prior feature-selection stage may have important utility. A previously developed TL estimator, DNAmTL, achieved a correlation of 0.216 (83.4% CI [0.118, 0.310]) on the EXTEND data. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities.

CONCLUSIONS

The variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.

Recent advances in Forensic DNA Phenotyping of appearance, ancestry and age

Forensic DNA Phenotyping (FDP) comprises the prediction of a person's externally visible characteristics regarding appearance, biogeographic ancestry and age from DNA of crime scene samples, to provide investigative leads to help find unknown perpetrators that cannot be identified with forensic STR-profiling. In recent years, FDP has advanced considerably in all of its three components, which we summarize in this review article. Appearance prediction from DNA has broadened beyond eye, hair and skin color to additionally comprise other traits such as eyebrow color, freckles, hair structure, hair loss in men, and tall stature. Biogeographic ancestry inference from DNA has progressed from continental ancestry to sub-continental ancestry detection and the resolving of co-ancestry patterns in genetically admixed individuals. Age estimation from DNA has widened beyond blood to more somatic tissues such as saliva and bones as well as new markers and tools for semen. Technological progress has allowed forensically suitable DNA technology with largely increased multiplex capacity for the simultaneous analysis of hundreds of DNA predictors with targeted massively parallel sequencing (MPS). Forensically validated MPS-based FDP tools for predicting from crime scene DNA i) several appearance traits, ii) multi-regional ancestry, iii) several appearance traits together with multi-regional ancestry, and iv) age from different tissue types, are already available. Despite recent advances that will likely increase the impact of FDP in criminal casework in the near future, moving reliable appearance, ancestry and age prediction from crime scene DNA to the level of detail and accuracy police investigators may desire, requires further intensified scientific research together with technical developments and forensic validations as well as the necessary funding.

Distinguishable DNA methylation defines a cardiac-specific epigenetic clock

BACKGROUND

The present study investigates whether epigenetic differences emerge in the heart of patients undergoing cardiac surgery for an aortic valvular replacement (AVR) or coronary artery bypass graft (CABG). An algorithm is also established to determine how the pathophysiological condition might influence the human biological cardiac age.

RESULTS

Blood samples and cardiac auricles were collected from patients who underwent cardiac procedures: 94 AVR and 289 CABG. The CpGs from three independent blood-derived biological clocks were selected to design a new blood- and the first cardiac-specific clocks. Specifically, 31 CpGs from six age-related genes, ELOVL2, EDARADD, ITGA2B, ASPA, PDE4C, and FHL2, were used to construct the tissue-tailored clocks. The best-fitting variables were combined to define new cardiac- and blood-tailored clocks validated through neural network analysis and elastic regression. In addition, telomere length (TL) was measured by qPCR. These new methods revealed a similarity between chronological and biological age in the blood and heart; the average TL was significantly higher in the heart than in the blood. In addition, the cardiac clock discriminated well between AVR and CABG and was sensitive to cardiovascular risk factors such as obesity and smoking. Moreover, the cardiac-specific clock identified an AVR patient's subgroup whose accelerated bioage correlated with the altered ventricular parameters, including left ventricular diastolic and systolic volume.

CONCLUSION

This study reports on applying a method to evaluate the cardiac biological age revealing epigenetic features that separate subgroups of AVR and CABG.

Refining epigenetic prediction of chronological and biological age

BACKGROUND

Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture.

METHODS

First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women's Health Initiative study).

RESULTS

Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10-52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10-60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations.

CONCLUSIONS

The integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age.

DNA methylation-based age estimation for adults and minors: considering sex-specific differences and non-linear correlations

DNA methylation patterns change during human lifetime; thus, they can be used to estimate an individual's age. It is known, however, that correlation between DNA methylation and aging might not be linear and that the sex might influence the methylation status. In this study, we conducted a comparative evaluation of linear and several non-linear regressions, as well as sex-specific versus unisex models. Buccal swab samples from 230 donors aged 1 to 88 years were analyzed using a minisequencing multiplex array. Samples were divided into a training set (n = 161) and a validation set (n = 69). The training set was used for a sequential replacement regression and a simultaneous 10-fold cross-validation. The resulting model was improved by including a cut-off of 20 years, dividing the younger individuals with non-linear from the older individuals with linear dependence between age and methylation status. Sex-specific models were developed and improved prediction accuracy in females but not in males, which might be explained by a small sample set. We finally established a non-linear, unisex model combining the markers EDARADD, KLF14, ELOVL2, FHL2, C1orf132, and TRIM59. While age- and sex-adjustments did not generally improve the performance of our model, we discuss how other models and large cohorts might benefit from such adjustments. Our model showed a cross-validated MAD and RMSE of 4.680 and 6.436 years in the training set and of 4.695 and 6.602 years in the validation set, respectively. We briefly explain how to apply the model for age prediction.

Age determination through DNA methylation patterns in fingernails and toenails

Over the past decade, age prediction based on DNA methylation has become a vastly investigated topic; many age prediction models have been developed based on different DNAm markers and using various tissues. However, the potential of using nails to this end has not yet been explored. Their inherent resistance to decay and ease of sampling would offer an advantage in cases where post-mortem degradation poses challenges concerning sample collection and DNA-extraction. In the current study, clippings from both fingernails and toenails were collected from 108 living test subjects (age range: 0-96 years). The methylation status of 15 CpGs located in 4 previously established age-related markers (ASPA, EDARADD, PDE4C, ELOVL2) was investigated through pyrosequencing of bisulphite converted DNA. Significant dissimilarities in methylation levels were observed between all four limbs, hence both limb-specific age prediction models and prediction models combining multiple sampling locations were developed. When applied to their respective test sets, these models yielded a mean absolute deviation between predicted and chronological age ranging from 5.48 to 9.36 years when using ordinary least squares regression. In addition, the assay was tested on methylation data derived from 5 nail samples collected from deceased individuals, demonstrating its feasibility for application in post-mortem cases. In conclusion, this study provides the first proof that chronological age can be assessed through DNA methylation patterns in nails.

DNA methylation-based age prediction with bloodstains using pyrosequencing and random forest regression

The use of DNA methylation to predict chronological age has shown promising potential for obtaining additional information in forensic investigations. To date, several studies have reported age prediction models based on DNA methylation in body fluids with high DNA content. However, it is often difficult to apply these existing methods in practice due to the low amount of DNA present in stains of body fluids that are part of a trace material. In this study, we present a sensitive and rapid test for age prediction with bloodstains based on pyrosequencing and random forest regression. This assay requires only 0.1 ng of genomic DNA and the entire procedure can be completed within 10 h, making it practical for forensic investigations that require a short turnaround time. We examined the methylation levels of 46 CpG sites from six genes using bloodstain samples from 128 males and 113 females aged 10-79 years. A random forest regression model was then used to construct an age prediction model for males and females separately. The final age prediction models were developed with seven CpG sites (three for males and four for females) based on the performance of the random forest regression. The mean absolute deviation was less than 3 years for each model. Our results demonstrate that DNA methylation-based age prediction using pyrosequencing and random forest regression has potential applications in forensics to accurately predict the biological age of a bloodstain donor.

An ELOVL2-Based Epigenetic Clock for Forensic Age Prediction: A Systematic Review

The prediction of chronological age from methylation-based biomarkers represents one of the most promising applications in the field of forensic sciences. Age-prediction models developed so far are not easily applicable for forensic caseworkers. Among the several attempts to pursue this objective, the formulation of single-locus models might represent a good strategy. The present work aimed to develop an accurate single-locus model for age prediction exploiting ELOVL2, a gene for which epigenetic alterations are most highly correlated with age. We carried out a systematic review of different published pyrosequencing datasets in which methylation of the ELOVL2 promoter was analysed to formulate age prediction models. Nine of these, with available datasets involving 2298 participants, were selected. We found that irrespective of which model was adopted, a very strong relationship between ELOVL2 methylation levels and age exists. In particular, the model giving the best age-prediction accuracy was the gradient boosting regressor with a prediction error of about 5.5 years. The findings reported here strongly support the use of ELOVL2 for the formulation of a single-locus epigenetic model, but the inclusion of additional, non-redundant markers is a fundamental requirement to apply a molecular model to forensic applications with more robust results.

Age prediction by methylation analysis of small amounts of DNA using locked nucleic acids

Age prediction based on methylation analysis has been reported in many populations, with 10 ng or more of DNA usually required for each determination. In this study, we designed thermostable locked nucleic acid (LNA) primers by replacing a small number of DNA bases in standard DNA primers with LNAs. We evaluated these primer sets by single-base extension analysis using 10, 5, or 2 ng of DNA that would be less than template DNA used in standard methylation testing, and determined sensitivity and accuracy. We analyzed EDARADD, SST, and KLF14 genes, targeting one CpG site in each gene. Melting temperature values of most LNA primers were 4°C higher than those of DNA primers. The intensities of signals from the EDARADD and SST genes were significantly improved by the LNA primers, by 3.3 times and 1.4 times, respectively, compared with the DNA primers using 2 ng of DNA. Coefficient of variation (CV) analysis was used to assess the accuracy of the determined methylation levels. CVs were increased using small amounts of DNA, but lower CVs were detected using LNA primers. We also showed high accuracy of age prediction for 51 individuals using LNA primers. The lowest mean absolute deviation was obtained using 10 ng of DNA and was 3.88 years with the LNA primers. Thermostable PCR primers were simply designed, and the LNAs improved the sensitivity and accuracy of methylation analysis for 10 ng or less of DNA.

Pyrosequencing: Current forensic methodology and future applications-a review

The recent development of small, single-amplicon-based benchtop systems for pyrosequencing has opened up a host of novel procedures for applications in forensic science. Pyrosequencing is a sequencing by synthesis technique, based on chemiluminescent inorganic pyrophosphate detection. This review explains the pyrosequencing workflow and illustrates the step-by-step chemistry, followed by a description of the assay design and factors to keep in mind for an exemplary assay. Existing and potential forensic applications are highlighted using this technology. Current applications include identifying species, identifying bodily fluids, and determining smoking status. We also review progress in potential applications for the future, including research on distinguishing monozygotic twins, detecting alcohol and drug abuse, and other phenotypic characteristics such as diet and body mass index. Overall, the versatility of the pyrosequencing technologies renders it a useful tool in forensic genomics.

A Blood-Based Molecular Clock for Biological Age Estimation

In the last decade, extensive efforts have been made to identify biomarkers of biological age. DNA methylation levels of ELOVL fatty acid elongase 2 (ELOVL2) and the signal joint T-cell receptor rearrangement excision circles (sjTRECs) represent the most promising candidates. Although these two non-redundant biomarkers echo important biological aspects of the ageing process in humans, a well-validated molecular clock exploiting these powerful candidates has not yet been formulated. The present study aimed to develop a more accurate molecular clock in a sample of 194 Italian individuals by re-analyzing the previously obtained EVOLV2 methylation data together with the amount of sjTRECs in the same blood samples. The proposed model showed a high prediction accuracy both in younger individuals with an error of about 2.5 years and in older subjects where a relatively low error was observed if compared with those reported in previously published studies. In conclusion, an easy, cost-effective and reliable model to measure the individual rate and the quality of aging in human population has been proposed. Further studies are required to validate the model and to extend its use in an applicative context.

Centenarians consistently present a younger epigenetic age than their chronological age with four epigenetic clocks based on a small number of CpG sites

Aging is a progressive time-dependent biological process affecting differentially individuals, who can sometimes present exceptional longevity. Epigenetic alterations are one of the hallmarks of aging, which comprise the epigenetic drift and clock at DNA methylation level. In the present study, we estimated the DNA methylation-based age (DNAmage) using four epigenetic clocks based on a small number of CpGs in French centenarians and semi-supercentenarians (CSSC, n=214) as well as nonagenarians' and centenarians' offspring (NCO, n=143) compared to individuals from the French general population (CG, n=149). DNA methylation analysis of the nine CpGs included in the epigenetic clocks showed high correlation with chronological age (-0.66>R>0.54) and also the presence of an epigenetic drift for four CpGs that was only visible in CSSC. DNAmage analysis showed that CSSC and to a lesser extend NCO present a younger DNAmage than their chronological age (15-28.5 years for CSSC, 4.4-11.5 years for NCO and 4.2-8.2 years for CG), which were strongly significant in CSSC compared to CG (p-values<2.2e-16). These differences suggest that epigenetic aging and potentially biological aging are slowed in exceptionally long-lived individuals and that epigenetic clocks based on a small number of CpGs are sufficient to reveal alterations of the global epigenetic clock.