Forensic Epigenetic Approaches In Human Biological Samples From Medico-Legal Investigations: A Systematic Review Of Public Health Applications For Age, Sex, And Lifestyle Estimation

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Udom, Ginika R. Akaegbusi, Dilinna A. Nwokike-Okoye, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9519723/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Forensic epigenetics has emerged as an advancement of traditional forensic genetics, facilitating the extraction of physiologically and socially significant characteristics from human biological materials beyond identity profiling. DNA methylation-based methods especially hold potential for determining age, sex, and lifestyle-related traits in medico-legal investigations, with increasing relevance to public health intelligence. This systematic review aimed to consolidate evidence on forensic epigenetic approaches applied to human biological specimens, with emphasis on age, sex, and lifestyle estimation. Methods: A comprehensive search of PubMed/MEDLINE, Scopus, Web of Science, Embase, and Google Scholar was conducted using predefined search strategies. The Population–Exposure–Outcome framework guided study selection, while methodological quality was assessed using the Critical Appraisal Skills Programme checklist. Due to heterogeneity among included studies, a narrative synthesis approach was employed. Results: Five studies met the inclusion criteria. DNA methylation-based age estimation was the most reliable and consistently validated application, particularly using age-associated CpG markers such as ELOVL2, although performance varied by tissue type. Epigenetic approaches to sex estimation and biological differentiation showed promise, particularly in complex cases such as monozygotic twin discrimination, but remain less developed than age prediction models. Lifestyle, environmental, and biological factors were found to influence epigenetic signatures, highlighting their dynamic nature. Tissue specificity significantly affected analytical accuracy, with blood and semen yielding more stable signals than touch DNA. Conclusion: DNA methylation-based forensic epigenetic approaches provide valuable tools for age estimation and supplementary biological inference in medico-legal contexts. Despite ongoing methodological and interpretative challenges, these approaches hold significant potential for advancing forensic investigations and informing public health applications. Forensic epigenetics DNA methylation Medico-legal investigations Age estimation Sex estimation Lifestyle factors Tissue specificity Public health applications Figures Figure 1 INTRODUCTION Human biological samples obtained from medico-legal investigations include materials such as blood, saliva, semen, hair, bone, teeth, skin cells, and other tissues collected from crime scenes, disaster sites, unidentified human remains, and individuals involved in legal proceedings. These samples are often limited in quantity, environmentally degraded, or aged, posing significant challenges to conventional DNA profiling techniques. Beyond individual identification, medico-legal investigations increasingly require contextual biological information—such as age, sex, and lifestyle characteristics—to support investigative leads, victim identification, and broader public health insights. Forensic epigenetics has emerged as a valuable extension of traditional forensic genetics, enabling the extraction of additional biological information from human samples. Epigenetics refers to heritable yet reversible modifications that regulate gene expression without altering the DNA sequence, with DNA methylation being the most extensively studied mechanism in forensic research. Unlike genetic markers, epigenetic signatures are dynamic and influenced by age, environmental exposures, and behavioural factors. Advances in high-throughput sequencing and methylation-specific technologies have enhanced the ability to analyse epigenetic markers in low-quality or limited forensic samples, thereby expanding the scope of biological evidence beyond identity determination. In particular, DNA methylation-based approaches have demonstrated strong potential for estimating age, sex, and lifestyle-related traits. Age prediction models have shown high accuracy across various tissue types, making them valuable in identifying unknown individuals. Epigenetic markers for sex estimation provide complementary approaches when genetic markers are inconclusive, while lifestyle-associated markers—linked to smoking, alcohol consumption, diet, physical activity, and environmental exposures—offer insights into behavioural and social determinants of health. These applications are increasingly relevant in medico-legal and humanitarian contexts, including missing person investigations, disaster victim identification, and epidemiological surveillance. Despite these advancements, the application of forensic epigenetics in public health contexts remains limited. Existing studies exhibit considerable variability in design, biological matrices, marker selection, analytical platforms, and population representation, which constrains comparability and practical implementation. Furthermore, there is limited integrated evidence on how epigenetic inferences from medico-legal samples can contribute to population-level health insights, including exposure patterns and health disparities. Ethical, legal, and interpretative challenges also hinder broader application, particularly in low- and middle-income settings. To date, no comprehensive systematic review has synthesised available evidence on forensic epigenetic methodologies applied to human biological samples, with a specific focus on their utility for age, sex, and lifestyle estimation in public health contexts. Therefore, this study aims to systematically evaluate and integrate existing evidence on forensic epigenetic approaches in medico-legal investigations and assess their relevance for public health applications. METHODS Study design This study is a systematic review conducted to synthesise evidence on forensic epigenetic methodologies applied to human biological samples in medico-legal investigations, with a focus on age, sex, and lifestyle estimation for public health applications. The review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Search strategy A comprehensive literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, Embase, and Google Scholar. Both Medical Subject Headings (MeSH) and free-text terms were used. Key search terms included “forensic epigenetics,” “DNA methylation,” “medico-legal,” “forensic samples,” “age estimation,” “sex determination,” and “lifestyle factors.” Boolean operators (AND, OR) were applied as appropriate. An example PubMed search strategy was: (“forensic epigenetics” OR “DNA methylation” OR “epigenetic markers”) AND (“medico-legal” OR “forensic samples”) AND (“age estimation” OR “sex determination” OR “lifestyle” OR “smoking” OR “alcohol”). Reference lists of included studies were screened to identify additional eligible studies. Eligibility criteria Study selection was guided by the Population–Exposure–Outcome framework. The criteria for inclusion and exclusion are as shown in Table 1 . Table 1 Table 1 Inclusion and Exclusion Criteria Criteria Inclusion criteria Exclusion criteria Population Human biological samples from medico-legal or forensic investigations (e.g. blood, saliva, semen, bone, teeth) Animal studies; non-forensic or purely clinical samples Exposure Forensic epigenetic approaches, including DNA methylation or other epigenomic markers Studies focusing solely on genetic (non-epigenetic) markers Outcomes Age, sex, and/or lifestyle or exposure estimation (e.g. smoking, alcohol, environmental exposures) Studies without relevant estimation outcomes Study design Observational studies, experimental laboratory studies, validation studies Editorials, commentaries, opinion pieces Timeframe Studies published from 2015 to 2025 Studies published before 2015 Language English-language publications Non-English publications without translation Publication type Peer-reviewed journal articles Conference abstracts only, theses, grey literature (unless methodologically robust) Study selection All records were imported into Zotero reference management software and duplicates were removed. Titles and abstracts were screened against the eligibility criteria, followed by full-text assessment of potentially eligible studies. Reasons for exclusion at the full-text stage were documented. Discrepancies were resolved through discussion. The selection process was summarised using a PRISMA flow diagram. Data extraction Data were extracted using a standardised form. Extracted variables included author, year, country, study aim, study design, sample size, forensic context, biological sample type, epigenetic markers analysed, laboratory and analytical methods, and key findings related to age, sex, and lifestyle estimation. Quality assessment Methodological quality of included studies was assessed using the Critical Appraisal Skills Programme checklist. Studies were categorised as high, moderate, or low quality based on methodological rigour and risk of bias. Studies with substantial methodological limitations were excluded. Data synthesis Due to heterogeneity in study design, biological samples, epigenetic markers, and outcome measures, meta-analysis was not performed. A narrative synthesis approach was used to summarise and interpret findings across included studies. RESULTS Overview of search process A total of 955 records were identified from PubMed/MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and the Cochrane Library. After removal of duplicates and title/abstract screening, 331 full-text articles were assessed for eligibility. Five studies were included in the final synthesis. The study selection process is illustrated in the PRISMA flow diagram (Fig. 1 ). Characteristics of included studies The five included studies were conducted in China, Japan, Indonesia, the United States, and Serbia, and are summarised in Table 2 . The studies comprised cross-sectional and experimental validation designs focusing on DNA methylation-based forensic epigenetic applications. Sample sizes ranged from 150 to 332 participants or samples, and included blood, saliva, semen, buccal cells, and touch DNA. All studies investigated CpG-based DNA methylation markers associated with age estimation, sex differentiation, or monozygotic twin discrimination. Table 2 Table 2 Characteristics of Included Studies Author(s) and year of publication Study aim Country and setting Sample size and participant characteristics Study design and methodology Exposure variables Outcome measures Study findings Xu et al ., ( 26 ) To assess whether LINE-1 DNA methylation can discriminate monozygotic twins in forensic contexts China; forensic medicine laboratories in Hebei, Shanxi, and Yunnan provinces 176 twin pairs (119 monozygotic, 57 dizygotic); males and females aged 0–74 years; blood and buccal cell samples Cross-sectional forensic epigenetic study; bisulfite conversion and pyrosequencing of three LINE-1 CpG sites; comparative twin-pair analysis LINE-1 global DNA methylation levels; tissue type; age; sex Ability to discriminate MZ twins; methylation differences across tissues; influence of demographic factors LINE-1 methylation showed tissue-specific patterns and modest twin-to-twin variability. Approximately 12.6% of MZ twin pairs could be differentiated. Findings support LINE-1 methylation as a complementary tool when STR profiling fails Hamano et al ., ( 27 ) To develop and validate a practical forensic age-prediction model for saliva and cigarette butt samples Japan; forensic medicine departments and police forensic laboratories 263 saliva samples from healthy donors aged 1–73 years; 16 cigarette butt samples; training set (n = 197), test set (n = 50) Experimental validation study; methylation-sensitive high-resolution melting (MS-HRM); support vector regression modelling DNA methylation scores of ELOVL2 and EDARADD ; sample type; smoking status Chronological age prediction accuracy (MAD, R²) Saliva-based model achieved MAD = 5.96 years (training) and 6.25 years (test). Application to cigarette butts yielded MAD = 7.65 years. Smoking had no significant impact, demonstrating forensic applicability to trace evidence Suryani et al ., ( 17 ) To compare and validate tissue-specific DNA methylation models for forensic age estimation Indonesia; forensic and biomedical research centres in Palembang, South Sumatra 150 healthy male volunteers aged 18–65 years; semen, saliva, and high-yield touch DNA samples Laboratory-based validation study; bisulfite conversion and targeted pyrosequencing of five CpG markers; multiple linear regression with cross-validation DNA methylation levels at ELOVL2 , FHL2 , TRIM59 , KCNQ1DN , C1orf132 ; tissue type Age-prediction accuracy (MAD, R², prediction intervals) Semen and saliva models showed high accuracy (MAD = 3.19 and 3.55 years). Touch DNA model was less precise (MAD = 5.49 years) but informative. Highlights importance of tissue-specific epigenetic models Anaya et al . ( 1 ) To evaluate DNA methylation-based age prediction models using decedent blood samples for forensic casework United States; academic and forensic science laboratories 264 decedent blood samples; adult age range; varying DNA quality and degradation indices Observational forensic epigenetic study; bisulfite conversion and pyrosequencing of five age-associated CpG markers; training and test set modelling CpG-specific DNA methylation levels; DNA degradation index; age group Accuracy of age estimation; deviation between predicted and chronological age The age-prediction model produced useful forensic estimates. Accuracy declined with increasing age, particularly > 50 years. DNA degradation did not consistently predict methylation failure, supporting applicability in post-mortem samples Pierre Louis , ( 28 ) To examine the effects of age and sex on the discriminatory power of cg18562578 DNA methylation for monozygotic twin differentiation Serbia (sample collection) and USA (laboratory analysis at John Jay College of Criminal Justice) 332 monozygotic twins from the Twin Identification Epigenetic Study (TIDES); males and females across multiple age groups; saliva samples Cross-sectional forensic epigenetic study; bisulfite conversion followed by nested qPCR; relative quantification analysis DNA methylation level at cg18562578; age; sex MZ twin discrimination rate; association between methylation, age, and sex cg18562578 showed strong discriminatory potential for MZ twins. Age significantly influenced methylation variability, while sex had a smaller effect, underscoring the need to account for demographic factors in forensic epigenetic analyses Critical appraisal of included studies Methodological quality assessment using the Critical Appraisal Skills Programme checklist indicated that all five studies were of moderate to high quality. All studies had clearly defined objectives, appropriate forensic epigenetic methodologies, and well-described analytical approaches. However, confounding variables were inconsistently addressed across studies, and ethical considerations were not consistently reported. Overall results from the included studies were considered reliable for narrative synthesis (Table 3 ). Table 3 Table 3 Critical Appraisal of the Included Studies CASP appraisal questions Xu et al ., ( 26 ) Hamano et al ., ( 27 ) Suryani et al ., ( 17 ) Anaya et al ., ( 1 ) Pierre Louis, ( 28 ) 1. Did the study address a clearly focused issue? Yes Yes Yes Yes Yes 2. Was the research design appropriate to the aims of the study? Yes Yes Yes Yes Yes 3. Was the recruitment strategy appropriate to the aims? Yes Yes Yes Yes Yes 4. Were the study samples adequately described? Yes Yes Yes Yes Yes 5. Was the data collection method clearly described? Yes Yes Yes Yes Yes 6. Were the exposure variables measured accurately? Yes Yes Yes Yes Yes 7. Were the outcome measures clearly defined and reliable? Yes Yes Yes Yes Yes 8. Were confounding factors identified? Can’t tell Yes Yes Yes Yes 9. Were confounding factors adequately accounted for in the analysis? No Yes Yes Can’t tell Yes 10. Was the data analysis sufficiently rigorous? Yes Yes Yes Yes Yes 11. Are the results clearly presented? Yes Yes Yes Yes Yes 12. Are the findings precise and credible? Yes Yes Yes Yes Yes 13. Are the findings applicable to forensic practice? Yes Yes Yes Yes Yes 14. Were ethical issues considered and addressed? Can’t tell Yes Yes Can’t tell Yes 15. Do the conclusions follow logically from the results? Yes Yes Yes Yes Yes FINDINGS OF INCLUDED STUDIES DNA methylation for forensic age estimation All included studies reported DNA methylation as a reliable marker for age estimation. CpG sites including ELOVL2, FHL2, TRIM59, C1orf132 , and KLF14 were most frequently used. Mean absolute deviation (MAD) values ranged from approximately 3.19 to 7.65 years depending on tissue type and sample quality. Accuracy was highest in semen and saliva samples and lowest in touch DNA. Reduced predictive performance was observed in older individuals, particularly those above 50 years of age. Epigenetic sex estimation and biological differentiation Sex-associated differences in DNA methylation were reported in LINE-1 regions, with females generally showing lower methylation levels than males in blood samples. However, this pattern was tissue-dependent. Methylation variability at cg18562578 also demonstrated utility in distinguishing monozygotic twins, with both age and sex influencing epigenetic variation. Sex was also identified as a confounding factor in some age prediction models, although it was not consistently controlled across all studies. Influence of lifestyle, environmental, and biological factors Lifestyle factors such as smoking and alcohol consumption showed limited but measurable influence on DNA methylation patterns. One study reported no significant impact of smoking on age prediction accuracy in saliva and cigarette butt samples. However, other studies suggested that biological ageing is influenced by environmental and lifestyle exposures, particularly in older populations, where prediction accuracy declined. Ethnicity and residential background were not significantly associated with methylation variation in the included datasets. Tissue specificity and sample type effects All studies demonstrated that DNA methylation patterns are tissue-specific. Blood and semen samples produced the highest accuracy in age prediction, followed by saliva, while touch DNA showed the lowest performance due to cellular heterogeneity and low DNA yield. Differences in methylation patterns were also observed between blood and buccal samples from the same individuals, confirming the importance of tissue-specific calibration in forensic epigenetic models. Public health relevance of forensic epigenetics Collectively, the findings indicate that DNA methylation markers provide not only forensic identification tools but also population-level biological information relevant to public health. Age estimation models showed applicability across diverse populations, while methylation variability reflected cumulative biological and environmental influences associated with ageing and exposure history. DISCUSSION This systematic review synthesised evidence on forensic epigenetic methodologies applied to human biological specimens in medico-legal contexts, with emphasis on age, sex, and lifestyle-related inferences relevant to public health. Overall, the findings demonstrate that DNA methylation-based approaches provide reliable biological information, while also highlighting key methodological and translational limitations. Consistent with established forensic epigenetics literature, DNA methylation-based age estimation remains the most developed application. Included studies reported high predictive accuracy, particularly using well-characterised CpG markers such as ELOVL2 ( 17 , 26 , 27 ). These findings align with earlier foundational studies, including Bocklandt et al. and Zbieć-Piekarska et al., which demonstrated strong associations between DNA methylation and chronological age. The present review extends these findings by confirming applicability across multiple tissue types and forensic samples, including degraded materials such as touch DNA and cigarette butts. However, consistent with Vidaki and Kayser, reduced accuracy in older age groups suggests a biological plateau in epigenetic ageing, supporting the need for age-stratified or non-linear predictive models. For sex estimation and biological differentiation, epigenetic markers currently play a complementary rather than primary forensic role. Sex-associated differences in LINE-1 methylation reported by Xu et al. support biological plausibility linked to X-chromosome inactivation, consistent with previous population-based findings. However, variability across tissues limits the robustness of epigenetic sex inference compared with genetic markers. Studies on monozygotic twins further indicate that epigenetic variation can assist in biological differentiation beyond DNA sequence information, in line with prior epigenome-wide twin studies. Nevertheless, inter-individual variability and partial marker overlap limit standalone forensic applicability. Lifestyle, environmental, and biological influences were identified as important modifiers of epigenetic signatures. Smoking, environmental exposure, and biological ageing processes were shown to contribute to methylation variability without completely undermining predictive performance. These findings are consistent with epidemiological studies such as Breitling et al. and Horvath, which demonstrated associations between methylation patterns, smoking exposure, and epigenetic ageing. However, forensic studies have not consistently incorporated lifestyle variables, highlighting an important translational gap between forensic epigenetics and public health research. Tissue specificity emerged as a major determinant of forensic epigenetic performance. Blood and semen consistently produced the most stable and accurate methylation signals, while saliva and buccal cells showed moderate performance and touch DNA demonstrated the highest variability. These findings align with pan-tissue epigenetic studies demonstrating variability in CpG stability across biological matrices. Tissue-specific modelling, as demonstrated in some included studies, improves predictive performance and addresses limitations of single-tissue approaches. Collectively, these findings have implications for forensic practice and policy. DNA methylation-based models may support medico-legal investigations and disaster victim identification; however, their probabilistic nature requires cautious interpretation and clear communication of uncertainty. Future research should prioritise large-scale, multi-ethnic, sex-balanced, and longitudinal datasets that integrate environmental and lifestyle variables to improve model generalisability and forensic applicability. LIMITATIONS This review has several limitations. First, the use of narrative synthesis was necessary due to substantial heterogeneity in study design, biological samples, epigenetic markers, and analytical approaches, which precluded meta-analysis and pooled effect estimation. Second, variability in reporting across included studies limited direct comparability, particularly regarding confounder adjustment, validation procedures, and error estimation. Third, restricting inclusion to English-language, peer-reviewed publications may have introduced language and publication bias, potentially excluding relevant studies published in other languages or in grey literature. Fourth, although the Critical Appraisal Skills Programme checklist provides a structured assessment of methodological quality, it may not fully capture laboratory-specific sources of bias such as batch effects, bisulfite conversion efficiency, and platform variability. Finally, differences in population characteristics and tissue-specific modelling approaches limit the generalisability of findings across forensic contexts. CONCLUSION This systematic review demonstrates that DNA methylation-based approaches are the most developed application of forensic epigenetics, particularly for age estimation, with consistent performance across multiple biological matrices. Epigenetic markers also show potential for sex estimation and biological differentiation, although these remain supplementary rather than standalone forensic tools. In addition, epigenetic signatures reflect lifestyle, environmental, and biological influences, supporting their relevance as indicators of cumulative life course exposure. Tissue specificity significantly affects analytical performance and must be considered in forensic interpretation. Overall, forensic epigenetics offers a promising bridge between medico-legal investigation and public health intelligence by enabling probabilistic inference of biological characteristics beyond identification. However, its application requires careful interpretation, alongside the development of validated, tissue-specific, and population-representative models to support reliable and ethical use in forensic practice. LIST OF ABBREVIATIONS CASP: Critical Appraisal Skills Programme CpG: Cytosine-phosphate-Guanine DNA: Deoxyribonucleic Acid MAD: Mean Absolute Deviation MeSH: Medical Subject Headings PEO: Population–Exposure–Outcome PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses Declarations Ethics approval and consent to participate This study is a systematic review of previously published literature on forensic epigenetic approaches in medico-legal investigations. It does not involve direct interaction with human participants or the use of identifiable personal data. Therefore, ethical approval was not required. Consent for publication Not applicable Funding No funding was received for this study. Availability of data and material All data analysed during this study are included in this published article. Competing interests The authors declare that they have no competing interests Funding No funding was received for this study. Authors' contributions ULU conceived and designed the study, conducted the literature search, performed data extraction and analysis, and drafted the manuscript. AGR contributed to study design, data interpretation, and critically revised the manuscript. NDA participated in data extraction, quality assessment, and manuscript review. AJ contributed to literature screening, data synthesis, and manuscript editing. IBO contributed to data interpretation and critically revised the manuscript for important intellectual content. All authors read and approved of the final manuscript. Acknowledgements Not applicable References Anaya Y, Yew P, Roberts KA, Hardy WR (2021) DNA methylation of decedent blood samples to estimate the chronological age of human remains. Int J Legal Med 135(6):2163–2173 Silva DS, Antunes J, Balamurugan K, Duncan G, Alho CS, McCord B (2016) Developmental validation studies of epigenetic DNA methylation markers for the detection of blood, semen and saliva samples. 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Sci Rep 7(1):10444 Pierre Louis C (2021) Effect of Age and Sex on the Discrimination of Monozygotic Twins using cg18562578 DNA Methylation in Saliva Zbieć-Piekarska R, Spólnicka M, Kupiec T, Makowska Ż, Spas A, Parys-Proszek A et al (2015) Examination of DNA methylation status of the ELOVL2 marker may be useful for human age prediction in forensic science. Forensic Sci Int Genet 14:161–167 Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F, Ballestar ML et al (2005) Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci 102(30):10604–10609 Breitling LP, Yang R, Korn B, Burwinkel B, Brenner H (2011) Tobacco-smoking-related differential DNA methylation: 27K discovery and replication. Am J Hum Genet 88(4):450–457 Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14(10):3156 Additional Declarations The authors declare no competing interests. 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Obijiofor","email":"","orcid":"","institution":"NIL","correspondingAuthor":false,"prefix":"","firstName":"Izuchukwu","middleName":"B.","lastName":"Obijiofor","suffix":""}],"badges":[],"createdAt":"2026-04-24 17:31:57","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9519723/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9519723/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107965888,"identity":"db1a97f1-2695-4795-b837-eb0a1556e018","added_by":"auto","created_at":"2026-04-28 05:41:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100888,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA Flow Diagram (21), Demonstrating the Study Selection Process\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9519723/v1/cb662c7ebf2f4215100a0782.jpg"},{"id":107965960,"identity":"f1ee7a80-0b44-489f-a6c0-c65303d24aac","added_by":"auto","created_at":"2026-04-28 05:42:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":416145,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9519723/v1/d461a0a1-0963-4627-89e6-a8deac2b229b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eForensic Epigenetic Approaches In Human Biological Samples From Medico-Legal Investigations: A Systematic Review Of Public Health Applications For Age, Sex, And Lifestyle Estimation\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eHuman biological samples obtained from medico-legal investigations include materials such as blood, saliva, semen, hair, bone, teeth, skin cells, and other tissues collected from crime scenes, disaster sites, unidentified human remains, and individuals involved in legal proceedings. These samples are often limited in quantity, environmentally degraded, or aged, posing significant challenges to conventional DNA profiling techniques. Beyond individual identification, medico-legal investigations increasingly require contextual biological information\u0026mdash;such as age, sex, and lifestyle characteristics\u0026mdash;to support investigative leads, victim identification, and broader public health insights.\u003c/p\u003e \u003cp\u003eForensic epigenetics has emerged as a valuable extension of traditional forensic genetics, enabling the extraction of additional biological information from human samples. Epigenetics refers to heritable yet reversible modifications that regulate gene expression without altering the DNA sequence, with DNA methylation being the most extensively studied mechanism in forensic research. Unlike genetic markers, epigenetic signatures are dynamic and influenced by age, environmental exposures, and behavioural factors. Advances in high-throughput sequencing and methylation-specific technologies have enhanced the ability to analyse epigenetic markers in low-quality or limited forensic samples, thereby expanding the scope of biological evidence beyond identity determination.\u003c/p\u003e \u003cp\u003eIn particular, DNA methylation-based approaches have demonstrated strong potential for estimating age, sex, and lifestyle-related traits. Age prediction models have shown high accuracy across various tissue types, making them valuable in identifying unknown individuals. Epigenetic markers for sex estimation provide complementary approaches when genetic markers are inconclusive, while lifestyle-associated markers\u0026mdash;linked to smoking, alcohol consumption, diet, physical activity, and environmental exposures\u0026mdash;offer insights into behavioural and social determinants of health. These applications are increasingly relevant in medico-legal and humanitarian contexts, including missing person investigations, disaster victim identification, and epidemiological surveillance.\u003c/p\u003e \u003cp\u003eDespite these advancements, the application of forensic epigenetics in public health contexts remains limited. Existing studies exhibit considerable variability in design, biological matrices, marker selection, analytical platforms, and population representation, which constrains comparability and practical implementation. Furthermore, there is limited integrated evidence on how epigenetic inferences from medico-legal samples can contribute to population-level health insights, including exposure patterns and health disparities. Ethical, legal, and interpretative challenges also hinder broader application, particularly in low- and middle-income settings.\u003c/p\u003e \u003cp\u003eTo date, no comprehensive systematic review has synthesised available evidence on forensic epigenetic methodologies applied to human biological samples, with a specific focus on their utility for age, sex, and lifestyle estimation in public health contexts. Therefore, this study aims to systematically evaluate and integrate existing evidence on forensic epigenetic approaches in medico-legal investigations and assess their relevance for public health applications.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis study is a systematic review conducted to synthesise evidence on forensic epigenetic methodologies applied to human biological samples in medico-legal investigations, with a focus on age, sex, and lifestyle estimation for public health applications. The review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSearch strategy\u003c/h3\u003e\n\u003cp\u003eA comprehensive literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, Embase, and Google Scholar. Both Medical Subject Headings (MeSH) and free-text terms were used. Key search terms included \u0026ldquo;forensic epigenetics,\u0026rdquo; \u0026ldquo;DNA methylation,\u0026rdquo; \u0026ldquo;medico-legal,\u0026rdquo; \u0026ldquo;forensic samples,\u0026rdquo; \u0026ldquo;age estimation,\u0026rdquo; \u0026ldquo;sex determination,\u0026rdquo; and \u0026ldquo;lifestyle factors.\u0026rdquo; Boolean operators (AND, OR) were applied as appropriate.\u003c/p\u003e \u003cp\u003eAn example PubMed search strategy was:\u003c/p\u003e \u003cp\u003e(\u0026ldquo;forensic epigenetics\u0026rdquo; OR \u0026ldquo;DNA methylation\u0026rdquo; OR \u0026ldquo;epigenetic markers\u0026rdquo;) AND (\u0026ldquo;medico-legal\u0026rdquo; OR \u0026ldquo;forensic samples\u0026rdquo;) AND (\u0026ldquo;age estimation\u0026rdquo; OR \u0026ldquo;sex determination\u0026rdquo; OR \u0026ldquo;lifestyle\u0026rdquo; OR \u0026ldquo;smoking\u0026rdquo; OR \u0026ldquo;alcohol\u0026rdquo;).\u003c/p\u003e \u003cp\u003eReference lists of included studies were screened to identify additional eligible studies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEligibility criteria\u003c/b\u003eStudy selection was guided by the Population\u0026ndash;Exposure\u0026ndash;Outcome framework. The criteria for inclusion and exclusion are as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInclusion and Exclusion Criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInclusion criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExclusion criteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePopulation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman biological samples from medico-legal or forensic investigations (e.g. blood, saliva, semen, bone, teeth)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnimal studies; non-forensic or purely clinical samples\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForensic epigenetic approaches, including DNA methylation or other epigenomic markers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudies focusing solely on genetic (non-epigenetic) markers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, sex, and/or lifestyle or exposure estimation (e.g. smoking, alcohol, environmental exposures)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudies without relevant estimation outcomes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStudy design\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservational studies, experimental laboratory studies, validation studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEditorials, commentaries, opinion pieces\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTimeframe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudies published from 2015 to 2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudies published before 2015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLanguage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish-language publications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-English publications without translation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePublication type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeer-reviewed journal articles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConference abstracts only, theses, grey literature (unless methodologically robust)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy selection\u003c/b\u003eAll records were imported into Zotero reference management software and duplicates were removed. Titles and abstracts were screened against the eligibility criteria, followed by full-text assessment of potentially eligible studies. Reasons for exclusion at the full-text stage were documented. Discrepancies were resolved through discussion. The selection process was summarised using a PRISMA flow diagram.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData extraction\u003c/b\u003eData were extracted using a standardised form. Extracted variables included author, year, country, study aim, study design, sample size, forensic context, biological sample type, epigenetic markers analysed, laboratory and analytical methods, and key findings related to age, sex, and lifestyle estimation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuality assessment\u003c/b\u003eMethodological quality of included studies was assessed using the Critical Appraisal Skills Programme checklist. Studies were categorised as high, moderate, or low quality based on methodological rigour and risk of bias. Studies with substantial methodological limitations were excluded.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData synthesis\u003c/b\u003eDue to heterogeneity in study design, biological samples, epigenetic markers, and outcome measures, meta-analysis was not performed. A narrative synthesis approach was used to summarise and interpret findings across included studies.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOverview of search process\u003c/h2\u003e \u003cp\u003eA total of 955 records were identified from PubMed/MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and the Cochrane Library. After removal of duplicates and title/abstract screening, 331 full-text articles were assessed for eligibility. Five studies were included in the final synthesis. The study selection process is illustrated in the PRISMA flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCharacteristics of included studies\u003c/h3\u003e\n\u003cp\u003eThe five included studies were conducted in China, Japan, Indonesia, the United States, and Serbia, and are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The studies comprised cross-sectional and experimental validation designs focusing on DNA methylation-based forensic epigenetic applications. Sample sizes ranged from 150 to 332 participants or samples, and included blood, saliva, semen, buccal cells, and touch DNA. All studies investigated CpG-based DNA methylation markers associated with age estimation, sex differentiation, or monozygotic twin discrimination.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of Included Studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor(s) and year of publication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy aim\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry and setting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size and participant characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStudy design and methodology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExposure variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOutcome measures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStudy findings\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXu\u003c/b\u003e \u003cb\u003eet al\u003c/b\u003e., (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo assess whether LINE-1 DNA methylation can discriminate monozygotic twins in forensic contexts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina; forensic medicine laboratories in Hebei, Shanxi, and Yunnan provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176 twin pairs (119 monozygotic, 57 dizygotic); males and females aged 0\u0026ndash;74 years; blood and buccal cell samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCross-sectional forensic epigenetic study; bisulfite conversion and pyrosequencing of three LINE-1 CpG sites; comparative twin-pair analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLINE-1 global DNA methylation levels; tissue type; age; sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAbility to discriminate MZ twins; methylation differences across tissues; influence of demographic factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLINE-1 methylation showed tissue-specific patterns and modest twin-to-twin variability. Approximately 12.6% of MZ twin pairs could be differentiated. Findings support LINE-1 methylation as a complementary tool when STR profiling fails\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHamano\u003c/b\u003e \u003cb\u003eet al\u003c/b\u003e., (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo develop and validate a practical forensic age-prediction model for saliva and cigarette butt samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan; forensic medicine departments and police forensic laboratories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e263 saliva samples from healthy donors aged 1\u0026ndash;73 years; 16 cigarette butt samples; training set (n\u0026thinsp;=\u0026thinsp;197), test set (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExperimental validation study; methylation-sensitive high-resolution melting (MS-HRM); support vector regression modelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDNA methylation scores of \u003cem\u003eELOVL2\u003c/em\u003e and \u003cem\u003eEDARADD\u003c/em\u003e; sample type; smoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChronological age prediction accuracy (MAD, R\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSaliva-based model achieved MAD\u0026thinsp;=\u0026thinsp;5.96 years (training) and 6.25 years (test). Application to cigarette butts yielded MAD\u0026thinsp;=\u0026thinsp;7.65 years. Smoking had no significant impact, demonstrating forensic applicability to trace evidence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSuryani\u003c/b\u003e \u003cb\u003eet al\u003c/b\u003e., (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo compare and validate tissue-specific DNA methylation models for forensic age estimation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndonesia; forensic and biomedical research centres in Palembang, South Sumatra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150 healthy male volunteers aged 18\u0026ndash;65 years; semen, saliva, and high-yield touch DNA samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLaboratory-based validation study; bisulfite conversion and targeted pyrosequencing of five CpG markers; multiple linear regression with cross-validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDNA methylation levels at \u003cem\u003eELOVL2\u003c/em\u003e, \u003cem\u003eFHL2\u003c/em\u003e, \u003cem\u003eTRIM59\u003c/em\u003e, \u003cem\u003eKCNQ1DN\u003c/em\u003e, \u003cem\u003eC1orf132\u003c/em\u003e; tissue type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAge-prediction accuracy (MAD, R\u0026sup2;, prediction intervals)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSemen and saliva models showed high accuracy (MAD\u0026thinsp;=\u0026thinsp;3.19 and 3.55 years). Touch DNA model was less precise (MAD\u0026thinsp;=\u0026thinsp;5.49 years) but informative. Highlights importance of tissue-specific epigenetic models\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnaya\u003c/b\u003e \u003cb\u003eet al\u003c/b\u003e. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo evaluate DNA methylation-based age prediction models using decedent blood samples for forensic casework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States; academic and forensic science laboratories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e264 decedent blood samples; adult age range; varying DNA quality and degradation indices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eObservational forensic epigenetic study; bisulfite conversion and pyrosequencing of five age-associated CpG markers; training and test set modelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCpG-specific DNA methylation levels; DNA degradation index; age group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAccuracy of age estimation; deviation between predicted and chronological age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eThe age-prediction model produced useful forensic estimates. Accuracy declined with increasing age, particularly\u0026thinsp;\u0026gt;\u0026thinsp;50 years. DNA degradation did not consistently predict methylation failure, supporting applicability in post-mortem samples\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePierre Louis\u003c/b\u003e, (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo examine the effects of age and sex on the discriminatory power of cg18562578 DNA methylation for monozygotic twin differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSerbia (sample collection) and USA (laboratory analysis at John Jay College of Criminal Justice)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e332 monozygotic twins from the Twin Identification Epigenetic Study (TIDES); males and females across multiple age groups; saliva samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCross-sectional forensic epigenetic study; bisulfite conversion followed by nested qPCR; relative quantification analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDNA methylation level at cg18562578; age; sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMZ twin discrimination rate; association between methylation, age, and sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecg18562578 showed strong discriminatory potential for MZ twins. Age significantly influenced methylation variability, while sex had a smaller effect, underscoring the need to account for demographic factors in forensic epigenetic analyses\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCritical appraisal of included studies\u003c/h2\u003e \u003cp\u003eMethodological quality assessment using the Critical Appraisal Skills Programme checklist indicated that all five studies were of moderate to high quality. All studies had clearly defined objectives, appropriate forensic epigenetic methodologies, and well-described analytical approaches. However, confounding variables were inconsistently addressed across studies, and ethical considerations were not consistently reported. Overall results from the included studies were considered reliable for narrative synthesis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCritical Appraisal of the Included Studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASP appraisal questions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXu \u003cem\u003eet al\u003c/em\u003e., (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHamano \u003cem\u003eet al\u003c/em\u003e., (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSuryani \u003cem\u003eet al\u003c/em\u003e., (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnaya \u003cem\u003eet al\u003c/em\u003e., (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePierre Louis, (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1. Did the study address a clearly focused issue?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2. Was the research design appropriate to the aims of the study?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3. Was the recruitment strategy appropriate to the aims?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4. Were the study samples adequately described?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5. Was the data collection method clearly described?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6. Were the exposure variables measured accurately?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7. Were the outcome measures clearly defined and reliable?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8. Were confounding factors identified?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCan\u0026rsquo;t tell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9. Were confounding factors adequately accounted for in the analysis?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCan\u0026rsquo;t tell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10. Was the data analysis sufficiently rigorous?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11. Are the results clearly presented?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12. Are the findings precise and credible?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13. Are the findings applicable to forensic practice?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14. Were ethical issues considered and addressed?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCan\u0026rsquo;t tell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCan\u0026rsquo;t tell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15. Do the conclusions follow logically from the results?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"FINDINGS OF INCLUDED STUDIES","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDNA methylation for forensic age estimation\u003c/h2\u003e \u003cp\u003eAll included studies reported DNA methylation as a reliable marker for age estimation. CpG sites including \u003cem\u003eELOVL2, FHL2, TRIM59, C1orf132\u003c/em\u003e, and \u003cem\u003eKLF14\u003c/em\u003e were most frequently used. Mean absolute deviation (MAD) values ranged from approximately 3.19 to 7.65 years depending on tissue type and sample quality. Accuracy was highest in semen and saliva samples and lowest in touch DNA. Reduced predictive performance was observed in older individuals, particularly those above 50 years of age.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEpigenetic sex estimation and biological differentiation\u003c/h2\u003e \u003cp\u003eSex-associated differences in DNA methylation were reported in LINE-1 regions, with females generally showing lower methylation levels than males in blood samples. However, this pattern was tissue-dependent. Methylation variability at cg18562578 also demonstrated utility in distinguishing monozygotic twins, with both age and sex influencing epigenetic variation. Sex was also identified as a confounding factor in some age prediction models, although it was not consistently controlled across all studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of lifestyle, environmental, and biological factors\u003c/h2\u003e \u003cp\u003eLifestyle factors such as smoking and alcohol consumption showed limited but measurable influence on DNA methylation patterns. One study reported no significant impact of smoking on age prediction accuracy in saliva and cigarette butt samples. However, other studies suggested that biological ageing is influenced by environmental and lifestyle exposures, particularly in older populations, where prediction accuracy declined. Ethnicity and residential background were not significantly associated with methylation variation in the included datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTissue specificity and sample type effects\u003c/h2\u003e \u003cp\u003eAll studies demonstrated that DNA methylation patterns are tissue-specific. Blood and semen samples produced the highest accuracy in age prediction, followed by saliva, while touch DNA showed the lowest performance due to cellular heterogeneity and low DNA yield. Differences in methylation patterns were also observed between blood and buccal samples from the same individuals, confirming the importance of tissue-specific calibration in forensic epigenetic models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePublic health relevance of forensic epigenetics\u003c/h2\u003e \u003cp\u003eCollectively, the findings indicate that DNA methylation markers provide not only forensic identification tools but also population-level biological information relevant to public health. Age estimation models showed applicability across diverse populations, while methylation variability reflected cumulative biological and environmental influences associated with ageing and exposure history.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis systematic review synthesised evidence on forensic epigenetic methodologies applied to human biological specimens in medico-legal contexts, with emphasis on age, sex, and lifestyle-related inferences relevant to public health. Overall, the findings demonstrate that DNA methylation-based approaches provide reliable biological information, while also highlighting key methodological and translational limitations.\u003c/p\u003e \u003cp\u003eConsistent with established forensic epigenetics literature, DNA methylation-based age estimation remains the most developed application. Included studies reported high predictive accuracy, particularly using well-characterised CpG markers such as \u003cem\u003eELOVL2\u003c/em\u003e (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). These findings align with earlier foundational studies, including Bocklandt et al. and Zbieć-Piekarska et al., which demonstrated strong associations between DNA methylation and chronological age. The present review extends these findings by confirming applicability across multiple tissue types and forensic samples, including degraded materials such as touch DNA and cigarette butts. However, consistent with Vidaki and Kayser, reduced accuracy in older age groups suggests a biological plateau in epigenetic ageing, supporting the need for age-stratified or non-linear predictive models.\u003c/p\u003e \u003cp\u003eFor sex estimation and biological differentiation, epigenetic markers currently play a complementary rather than primary forensic role. Sex-associated differences in LINE-1 methylation reported by Xu et al. support biological plausibility linked to X-chromosome inactivation, consistent with previous population-based findings. However, variability across tissues limits the robustness of epigenetic sex inference compared with genetic markers. Studies on monozygotic twins further indicate that epigenetic variation can assist in biological differentiation beyond DNA sequence information, in line with prior epigenome-wide twin studies. Nevertheless, inter-individual variability and partial marker overlap limit standalone forensic applicability.\u003c/p\u003e \u003cp\u003eLifestyle, environmental, and biological influences were identified as important modifiers of epigenetic signatures. Smoking, environmental exposure, and biological ageing processes were shown to contribute to methylation variability without completely undermining predictive performance. These findings are consistent with epidemiological studies such as Breitling et al. and Horvath, which demonstrated associations between methylation patterns, smoking exposure, and epigenetic ageing. However, forensic studies have not consistently incorporated lifestyle variables, highlighting an important translational gap between forensic epigenetics and public health research.\u003c/p\u003e \u003cp\u003eTissue specificity emerged as a major determinant of forensic epigenetic performance. Blood and semen consistently produced the most stable and accurate methylation signals, while saliva and buccal cells showed moderate performance and touch DNA demonstrated the highest variability. These findings align with pan-tissue epigenetic studies demonstrating variability in CpG stability across biological matrices. Tissue-specific modelling, as demonstrated in some included studies, improves predictive performance and addresses limitations of single-tissue approaches.\u003c/p\u003e \u003cp\u003eCollectively, these findings have implications for forensic practice and policy. DNA methylation-based models may support medico-legal investigations and disaster victim identification; however, their probabilistic nature requires cautious interpretation and clear communication of uncertainty. Future research should prioritise large-scale, multi-ethnic, sex-balanced, and longitudinal datasets that integrate environmental and lifestyle variables to improve model generalisability and forensic applicability.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS\u003c/h2\u003e \u003cp\u003eThis review has several limitations. First, the use of narrative synthesis was necessary due to substantial heterogeneity in study design, biological samples, epigenetic markers, and analytical approaches, which precluded meta-analysis and pooled effect estimation. Second, variability in reporting across included studies limited direct comparability, particularly regarding confounder adjustment, validation procedures, and error estimation. Third, restricting inclusion to English-language, peer-reviewed publications may have introduced language and publication bias, potentially excluding relevant studies published in other languages or in grey literature. Fourth, although the Critical Appraisal Skills Programme checklist provides a structured assessment of methodological quality, it may not fully capture laboratory-specific sources of bias such as batch effects, bisulfite conversion efficiency, and platform variability. Finally, differences in population characteristics and tissue-specific modelling approaches limit the generalisability of findings across forensic contexts.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis systematic review demonstrates that DNA methylation-based approaches are the most developed application of forensic epigenetics, particularly for age estimation, with consistent performance across multiple biological matrices. Epigenetic markers also show potential for sex estimation and biological differentiation, although these remain supplementary rather than standalone forensic tools. In addition, epigenetic signatures reflect lifestyle, environmental, and biological influences, supporting their relevance as indicators of cumulative life course exposure. Tissue specificity significantly affects analytical performance and must be considered in forensic interpretation.\u003c/p\u003e \u003cp\u003eOverall, forensic epigenetics offers a promising bridge between medico-legal investigation and public health intelligence by enabling probabilistic inference of biological characteristics beyond identification. However, its application requires careful interpretation, alongside the development of validated, tissue-specific, and population-representative models to support reliable and ethical use in forensic practice.\u003c/p\u003e"},{"header":"LIST OF ABBREVIATIONS ","content":"\u003cp\u003eCASP: Critical Appraisal Skills Programme\u003cbr\u003e\u0026nbsp;CpG: Cytosine-phosphate-Guanine\u003cbr\u003e\u0026nbsp;DNA: Deoxyribonucleic Acid\u003cbr\u003e\u0026nbsp;MAD: Mean Absolute Deviation\u003cbr\u003e\u0026nbsp;MeSH: Medical Subject Headings\u003cbr\u003e\u0026nbsp;PEO: Population\u0026ndash;Exposure\u0026ndash;Outcome\u003cbr\u003e\u0026nbsp;PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a systematic review of previously published literature on forensic epigenetic approaches in medico-legal investigations. It does not involve direct interaction with human participants or the use of identifiable personal data. Therefore, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eULU conceived and designed the study, conducted the literature search, performed data extraction and analysis, and drafted the manuscript. AGR contributed to study design, data interpretation, and critically revised the manuscript. NDA participated in data extraction, quality assessment, and manuscript review. AJ contributed to literature screening, data synthesis, and manuscript editing. IBO contributed to data interpretation and critically revised the manuscript for important intellectual content. All authors read and approved of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnaya Y, Yew P, Roberts KA, Hardy WR (2021) DNA methylation of decedent blood samples to estimate the chronological age of human remains. Int J Legal Med 135(6):2163\u0026ndash;2173\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva DS, Antunes J, Balamurugan K, Duncan G, Alho CS, McCord B (2016) Developmental validation studies of epigenetic DNA methylation markers for the detection of blood, semen and saliva samples. Forensic Sci Int Genet 23:55\u0026ndash;63\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDias HC, Cunha E, Real FC, Manco L (2020) Age prediction in living: Forensic epigenetic age estimation based on blood samples. Leg Med 47:101763\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eButler JM (2011) Advanced topics in forensic DNA typing: methodology. Academic\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKayser M, Forensic DNA, Phenotyping (2015) Predicting human appearance from crime scene material for investigative purposes. Forensic Sci Int Genet 18:33\u0026ndash;48\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastagnola MJ, Medina-Paz F, Zapico SC (2024) Uncovering forensic evidence: a path to age estimation through DNA methylation. Int J Mol Sci 25(9):4917\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBastos FB (2021) Epigenetic Tools in Age Estimations\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBocklandt S, Lin W, Sehl ME, S\u0026aacute;nchez FJ, Sinsheimer JS, Horvath S et al (2011) Epigenetic predictor of age. PLoS ONE 6(6):e14821\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerra MC, Dallabona C, Cecchi R (2024) Epigenetic analyses in forensic medicine: future and challenges. Int J Legal Med 138(3):701\u0026ndash;719\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidaki A, Kayser M (2017) From forensic epigenetics to forensic epigenomics: broadening DNA investigative intelligence. Genome Biol 18(1):238\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiuliani C, Cilli E, Bacalini MG, Pirazzini C, Sazzini M, Gruppioni G et al (2016) Inferring chronological age from DNA methylation patterns of human teeth. Am J Phys Anthropol 159(4):585\u0026ndash;595\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasnain SE (2019) Forensic epigenetic analysis: the path ahead. Med Princ Pract 28(4):301\u0026ndash;308\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlobet MO, Johansson \u0026Aring;, Gyllensten U, Allen M, Enroth S (2023) Forensic prediction of sex, age, height, body mass index, hip-to-waist ratio, smoking status and lipid lowering drugs using epigenetic markers and plasma proteins. Forensic Sci Int Genet 65:102871\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung SE, Shin KJ, Lee HY (2017) DNA methylation-based age prediction from various tissues and body fluids. 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Int J Surg 88:105906\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCritical Appraisal Skills Programme (2018) CASP randomised controlled trial checklist\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong HA, French DP, Brooks JM (2020) Optimising the value of the critical appraisal skills programme (CASP) tool for quality appraisal in qualitative evidence synthesis. Res Methods Med Health Sci 1(1):31\u0026ndash;42\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePopay J, Roberts H, Sowden A, Petticrew M, Arai L, Rodgers M et al (2006) Guidance on the conduct of narrative synthesis in systematic reviews. Prod ESRC Methods Programme Version 1(1):b92\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLisy K, Porritt K (2016) Narrative synthesis: considerations and challenges. JBI Evid Implement 14(4):201\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu J, Fu G, Yan L, Craig JM, Zhang X, Fu L et al (2015) LINE-1 DNA methylation: A potential forensic marker for discriminating monozygotic twins. Forensic Sci Int Genet 19:136\u0026ndash;145\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamano Y, Manabe S, Morimoto C, Fujimoto S, Tamaki K (2017) Forensic age prediction for saliva samples using methylation-sensitive high resolution melting: exploratory application for cigarette butts. Sci Rep 7(1):10444\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePierre Louis C (2021) Effect of Age and Sex on the Discrimination of Monozygotic Twins using cg18562578 DNA Methylation in Saliva\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZbieć-Piekarska R, Sp\u0026oacute;lnicka M, Kupiec T, Makowska Ż, Spas A, Parys-Proszek A et al (2015) Examination of DNA methylation status of the ELOVL2 marker may be useful for human age prediction in forensic science. Forensic Sci Int Genet 14:161\u0026ndash;167\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraga MF, Ballestar E, Paz MF, Ropero S, Setien F, Ballestar ML et al (2005) Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci 102(30):10604\u0026ndash;10609\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBreitling LP, Yang R, Korn B, Burwinkel B, Brenner H (2011) Tobacco-smoking-related differential DNA methylation: 27K discovery and replication. Am J Hum Genet 88(4):450\u0026ndash;457\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14(10):3156\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Forensic epigenetics, DNA methylation, Medico-legal investigations, Age estimation, Sex estimation, Lifestyle factors, Tissue specificity, Public health applications","lastPublishedDoi":"10.21203/rs.3.rs-9519723/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9519723/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eForensic epigenetics has emerged as an advancement of traditional forensic genetics, facilitating the extraction of physiologically and socially significant characteristics from human biological materials beyond identity profiling. DNA methylation-based methods especially hold potential for determining age, sex, and lifestyle-related traits in medico-legal investigations, with increasing relevance to public health intelligence. This systematic review aimed to consolidate evidence on forensic epigenetic approaches applied to human biological specimens, with emphasis on age, sex, and lifestyle estimation.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA comprehensive search of PubMed/MEDLINE, Scopus, Web of Science, Embase, and Google Scholar was conducted using predefined search strategies. The Population\u0026ndash;Exposure\u0026ndash;Outcome framework guided study selection, while methodological quality was assessed using the Critical Appraisal Skills Programme checklist. Due to heterogeneity among included studies, a narrative synthesis approach was employed.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eFive studies met the inclusion criteria. DNA methylation-based age estimation was the most reliable and consistently validated application, particularly using age-associated CpG markers such as ELOVL2, although performance varied by tissue type. Epigenetic approaches to sex estimation and biological differentiation showed promise, particularly in complex cases such as monozygotic twin discrimination, but remain less developed than age prediction models. Lifestyle, environmental, and biological factors were found to influence epigenetic signatures, highlighting their dynamic nature. Tissue specificity significantly affected analytical accuracy, with blood and semen yielding more stable signals than touch DNA.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eDNA methylation-based forensic epigenetic approaches provide valuable tools for age estimation and supplementary biological inference in medico-legal contexts. Despite ongoing methodological and interpretative challenges, these approaches hold significant potential for advancing forensic investigations and informing public health applications.\u003c/p\u003e","manuscriptTitle":"Forensic Epigenetic Approaches In Human Biological Samples From Medico-Legal Investigations: A Systematic Review Of Public Health Applications For Age, Sex, And Lifestyle Estimation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 05:40:55","doi":"10.21203/rs.3.rs-9519723/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b096d81-0ef9-472a-9069-e11b2f12f41c","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T05:40:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 05:40:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9519723","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9519723","identity":"rs-9519723","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}