Less-invasive age estimation using hair based on DNA methylation in brown bears

Less-invasive age estimation using hair based on DNA methylation in brown bears | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Less-invasive age estimation using hair based on DNA methylation in brown bears Shiori Nakamura, Jumpei Yamazaki, Naoya Matsumoto, Kyogo Hagino, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7042508/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Information on age is essential for exploring the life history, conservation, and management of wildlife. Recently, DNA methylation levels-based methods using blood or skin have been established as alternatives to the traditional tooth-based method in bear species. However, the collection of these tissues is limited to captured or dead individuals. In the present study, we established the first hair-based age estimation model based on DNA methylation levels in brown bears, aiming for future application to less-invasively obtained hair of wild individuals. We performed bisulfite pyrosequencing and measured the methylation levels of hair root DNA. The methylation levels of cytosine-phosphate-guanine sites adjacent to the genes VGF , KCNK12 , and ELOVL2 were found to be correlated with age. The best age estimation model used three cytosine-phosphate-guanine sites adjacent to two genes, VGF and KCNK12 , with a mean absolute error of 3.2 years and median absolute error of 2.2 years after leave-one-out cross-validation. Our method is innovative because of the simplicity of sampling and the lack of requirement to capture bears. If this method can be widely applied to hair samples obtained in the field, the age structure of wild populations can be understood, contributing to ecological research, conservation, and management of bear species. Biological sciences/Biological techniques Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Genetics Biological sciences/Molecular biology Biological sciences/Zoology epigenetic clock age estimation brown bear DNA methylation wildlife management aging Figures Figure 1 Figure 2 Figure 3 Introduction Age information is essential for wildlife research to examine the life history of animals (including sexual maturity, reproductive success, disease dynamics, and risk of mortality) and contributes to wildlife conservation and management by providing estimates of age structure and population dynamics. Although the simplest way to obtain age information is to ascertain the year of birth by direct observation or camera-trap-based surveys 1 – 3 , these methods require a long-term effort and considerable human resources; therefore, the target species and fields available for investigation are limited. Various methods have been developed to determine the age of wild animals. The most common method, especially for terrestrial and marine mammals, is based on counting the cementum annuli or growth layer groups of the teeth that form annually 4 , 5 . Other methods include tooth eruption patterns, wear, skeletal features, physical characteristics, otoliths, and scales 6 – 8 . However, most of them are only applicable to dead or immobilized animals and are often difficult to implement in live animals. Methods based on samples that can be obtained noninvasively, such as hair and feces, have also been attempted. For example, the measurement of steroid hormone levels in hair can distinguish the age classes of brown bears ( Ursus arctos ) 9 , and fecal near-infrared reflectance spectroscopy can be applied to discriminate the age classes of giant pandas ( Ailuropoda melanoleuca ) 10 . Over the past decade, another age estimation method based on DNA methylation levels was developed (i.e., the epigenetic clock 11 ). DNA methylation is a type of epigenetic modification. In vertebrates, it predominantly occurs at cytosine-phosphate-guanine (CpG) sites, forming 5-methylcytosine by transferring a methyl group to the C5 position of cytosine 12 . DNA methylation regulates gene expression, and its function has been suggested to vary with genomic context 13 , 14 . DNA methylation levels can change with age 15 , which has enabled its use as an indicator for age estimation, first demonstrated in humans ( Homo sapiens ) 16 , 17 . Epigenetic clocks are now available in various animals 18 and have already been used to examine population structure 19 . DNA methylation is not static among tissues; the number of age-related differentially methylated positions and rate of change with age differ among tissues 20 . DNA methylation also varies among animal species, and its association with phylogenetic relations and lifespan has attracted attention from the perspective of comparative epigenomics 21 – 23 . Consequently, various species- and tissue-specific epigenetic clocks have been developed (skin of humpback whales [ Megaptera novaeangliae ] 24 ; blood of mice [ Mus musculus ] 25 ; blood of chimpanzees [ Pan troglodytes ] 26 ; blood of long-lived seabirds [ Ardenna tenuirostris ] 27 ; blood of Asian elephants [ Elephas maximus ] 28 ). By contrast, some epigenetic clocks are shared among closely related species 29 – 33 and different tissues (mice) 34 . Indeed, epigenetic clocks have been established using samples of 59 tissue types across 185 mammalian species, analyzed by mammalian methylation microarrays (HorvathMammalMethylChip40 35 ) using hundreds of CpG methylation levels 36 . Such genome-wide deep sequencing approaches provide robust and large epigenetic age predictors; however, highly accurate age estimation can also be achieved with a limited number of CpGs using site-specific analysis 37 . Among the eight bear species worldwide, six (e.g., polar bears [ Ursus maritimus ]) are listed as vulnerable on the International Union for Conservation of Nature Red List 38 owing to population declines caused by habitat reduction, poaching, and other factors. While attempts are being made to restore the population, human–bear conflicts can hinder the acceptance of conservation initiatives 39 . Addressing these challenges requires the implementation of population management strategies grounded in a comprehensive understanding of population dynamics, and the age at harvest has already been incorporated into population dynamics models 40 , 41 . If it were possible to estimate the age of living bears, this would contribute significantly to the improvement of population dynamics models. In bear species, the traditional age estimation method uses the cementum annuli of teeth 42 – 44 . However, this method has several disadvantages: 1) it requires pulling teeth and is invasive to live animals; 2) the observer requires sufficient experience to ensure accurate and precise age estimation 45 ; and 3) accuracy declines in older bears 46 . Recently, age estimation methods based on DNA methylation levels have been developed for bear species, using blood, skin, and fibroblast samples 33 , 47 , 48 . A critical limitation of these methods is the requirement to obtain samples from either captured or dead individuals, which complicates their application in wildlife studies. Reports on epigenetic clocks using noninvasive or less-invasive samples, including human 49 and cattle ( Bos taurus ) 50 hair, as well as Indo-Pacific bottlenose dolphin ( Tursiops aduncus ) 3 and mouse 51 feces, are limited. In the present study, we aimed to establish an epigenetic clock using hair from brown bears. In brown bear research, hair samples that can be obtained noninvasively have been used for various purposes, including identifying species, estimating population size, determining genetic diversity, and detecting diet items 52 , 53 . In particular, the technique of using bait or scent lures to attract brown bears and collecting hairs snagged in barbed wire placed around them (hereafter simply referred to as "hair-traps") is commonly used 2 , 54 , 55 . This study aimed to establish an age estimation method based on DNA methylation in brown bears using hair. DNA was extracted from hair roots collected from both captive and wild brown bears of known age, and the DNA methylation levels of 39 CpGs in 12 distinct genomic locations were determined by pyrosequencing. Subsequently, we sought to identify the CpG sites in hair-derived DNA that exhibited a strong correlation between DNA methylation levels and age. Finally, we attempted to establish an epigenetic clock that could be implemented with fewer CpG sites (i.e., with a limited amount of DNA) with the aim of application in DNA scarce sources, such as hairs obtained from hair-traps. Materials and Methods Ethical Statement All procedures involved in sample collection from animals were conducted in accordance with the Guidelines for Animal Care and Use, Hokkaido University, and were approved by the Animal Care and Use Committee of the Graduate School of Veterinary Medicine, Hokkaido University (Permit Number: 1152, 15009, 17005, 18-0083, 19-0021, 20-0146, and 23-0014). In addition, all methods were carried out in compliance with ARRIVE guidelines. Study area, animals, and hair sampling Captive bears Hair samples were obtained from 35 brown bears (18 males and 17 females) maintained at Noboribetsu Bear Park (Noboribetsu, Hokkaido, Japan). Their ages ranged from 1 to 33 years. Detailed information (bear ID, sex, birth year, sampling date, and age at sampling) is provided in Supplementary Table S_M1. Age at sampling was determined based on the assumption that all bears were born on February 1, on the grounds that the bears were born between mid-January and early February 56-58 . All hair samples were plucked under anesthesia, and the location was the upper back, where wild bears were thought to leave hair when they rubbed their backs against trees. The bears were anesthetized by intramuscular administration of xylazine HCl (1 mg/kg; Selactar; Bayer) and a 1:1 mixture of zolazepam HCl and tiletamine HCl (2.0–4.0 mg/kg; Zoletil 100; Virbac), administered remotely using a blow dart. After sampling was completed, atipamezole HCl (1 mg/kg; Atipame; Kyoritsu Co., Ltd.) was injected intramuscularly to facilitate recovery from anesthesia. The collected samples were stored at −30 °C until genomic DNA extraction. Please refer to Nakamura et al. (2023) 47 for details regarding the housing conditions at this facility. Wild bears Hair samples were obtained from 12 female brown bears living in the Rusha area (44°11′N, 145°11′E) of the Shiretoko Peninsula, eastern Hokkaido, Japan. Their ages ranged from 4 to 27 years. In this area, long-term continuous bear monitoring surveys have been conducted since 1997, enabling age determination using visual and DNA-based identification 1 . Detailed information (bear ID, sex, birth year, sampling date, and age at sampling) is provided in Supplementary Table S_M1. Age at sampling was determined based on the assumption that all bears were born on February 1, similar to captive bears. The bears were anesthetized by intramuscular administration of medetomidine HCl (75 μg/kg; Dorbene Vet; Kyoritsu Co., Ltd.) and a 1:1 mixture of zolazepam HCl and tiletamine HCl (5.5 mg/kg; Zoletil 100; Virbac), with dosages calculated based on estimated body weight. The drugs were administered remotely using an air rifle. After hair samples were plucked from the upper back, atipamezole HCl (375 μg/kg; Atipame; Kyoritsu Co., Ltd.) was injected intramuscularly to facilitate recovery from anesthesia. The collected samples were stored at −30 °C until genomic DNA extraction. Genomic DNA extraction and bisulfite conversion Genomic DNA was extracted from 10 hair roots using a DNA Extractor FM Kit (FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan). Extraction was performed according to the manufacturer’s protocol, and the DNA was finally eluted in 50 µL of AE Buffer. The extracted DNA was stored at −30 °C until bisulfite conversion. Twenty microliters of DNA were subjected to bisulfite conversion using the EZ DNA Methylation-Gold Kit (Zymo Research, Irvine, CA, USA), according to the manufacturer's instructions. After final elution in 10 µL of Elution Buffer supplied with the kit, 30 µL of TE Buffer was added (i.e., 40 µL in total). Jimbo et al. (2020) 59 reported that guard hair bulbs change seasonally in morphology and are classified into three types (white sphere type, black hook type and white hook type). Bear guard hair grows year-round, except during hibernation, and is shed once a year in the summer. Newly grown and growing hairs are considered to be of the black hook type (around June to August), then change to the white hook type, continue to grow (around September to October), and then change to the white sphere type (from around November to shedding in the following summer) after growth stops until the hair sheds. Hair of the white sphere type is present, except for a brief period in autumn, around September and October. Most of the samples we obtained were of the white sphere type, and we did not obtain sufficient numbers for the other two types. Therefore, we only used hair bulbs of the white sphere type and excluded the other two types from the analysis. Target genomic locations, polymerase chain reaction (PCR), and pyrosequencing Target genomic locations were adjacent to 12 genes, GSE1 , VGF , SLC12A5 , SCGN , KCNK12 , OTUD7A , BCL6B , POU4F2 , ELOVL2 , RALYL , KISS1R , and CAPS2 as in our previous study on the epigenetic clock in blood samples from brown bears 47 . DNA methylation levels of CpG sites adjacent to these genes have been reported to change with age in humans 60-63 , dogs 64,65 , and cats 66 . The DNA methylation levels of the target CpGs were analyzed using PCR followed by pyrosequencing, in accordance with our previous reports 33,47,65 . PCR was conducted in two steps using TaKaRa EpiTaq HS (Takara Bio Inc., Shiga, Japan). Each PCR product was electrophoresed on a 2% agarose gel to confirm that the target was successfully amplified. Pyrosequencing was performed using PyroMark Q48 software (Qiagen, Venlo, Netherlands) with PyroMark Q48 Advanced Reagents (Qiagen), according to the manufacturer’s instructions. The mean methylation levels obtained from duplicate reactions of the two-step PCR and pyrosequencing were used to establish age estimation models. Details, including primer sequences and amplification conditions, are available in our previous study 47 . Initially, as a screening step, each target CpG site was analyzed across nine samples to identify the CpG sites whose DNA methylation levels were strongly correlated with age. These samples were selected from captive bears, and the number of individuals was balanced according to age and sex. From 12 candidate genomic locations, those with R² values greater than 0.8 and a range of methylation level changes greater than 20% were selected for further analysis. Finally, we analyzed the DNA methylation levels of the selected genomic locations for the remaining hair samples and calculated Pearson’s product-moment correlation coefficients and p -values. Age estimation model and model validation Building age estimation models was performed similarly to the method described in our previous studies using blood samples from bears 33,47 . We used only one sample per animal to avoid overfitting the model to methylation changes in specific individuals sampled multiple times. Based on the DNA methylation levels obtained, three age estimation models were generated using R ver. 4.2.2 67 . Single regression, elastic net regression, and support vector regression (SVR) models were built using the R command “lm” and R packages “glmnet” and “e1071,” respectively. Elastic net regression models, a type of penalized regression, have frequently been used for age estimation 11,29,36,68 , whereas SVR models have been reported to achieve high estimation accuracy 33,47,66,69 . Unlike elastic net regression, SVR does not perform feature selection, which necessitates careful consideration of predictor combinations. We constructed SVR models using feature sets in which the less important features identified by the elastic net regression were sequentially removed. In addition, SVR models were constructed using CpGs from each genomic location. Age and DNA methylation levels were standardized before integration into the models. Leave-one-out cross-validation (LOOCV) was performed to validate all models. To evaluate whether age, sex, or growth environment (i.e., captive or wild condition) affected the deviation of the age estimation model, linear regression and generalized linear regression were generated with Δage (predicted age − chronological age) and |Δage| (absolute difference between predicted age and chronological age) as dependent variables and the three factors and their interactions as explanatory variables. Model construction and selection were performed using the R command “lm” and R packages “MuMIn” and “lme4.” Results Selection of age-related genomic locations After screening nine samples, three genomic locations that met the requirements were selected from 12 candidate genomic locations, namely those adjacent to VGF , KCNK12 , and ELOVL2 . All the results are shown in Supplementary Figures S_R1_1–11. Correlation between DNA methylation level and chronological age For each genomic location, multiple CpG sites showed significant correlations between DNA methylation levels and age. The three CpGs adjacent to VGF , KCNK12 , and ELOVL2 are graphically presented in Supplementary Figures S_R2_1, 2, and 3, and the corresponding correlation coefficients and p -values for each CpG are provided in Supplementary Table S_R1. The CpGs that exhibited the strongest correlations for each genomic location are shown in Figure 1. Age estimation model We constructed 15 age estimation models (three single regression models, one elastic net regression model, and 11 SVR models) based on the DNA methylation levels of CpGs adjacent to VGF (VGF-1, -2, and -3), KCNK12 (KCNK12-1, -2, and -3), and ELOVL2 (ELOVL2-1, -2, and -3). Their performance can be confirmed from the mean absolute error (MAE), median absolute error (MedAE), and root mean square error values for each model in Table 1. Among the single regression models, the model using the methylation level of KCNK12-3 showed the best performance; the MAE value after LOOCV was 3.978 years (Figure 2a). The formula for age estimation was as follows: predicted age = (−1.3183e-11 + 0.80980 × standardized methylation level of KCNK12-3) × 8.2771 (standard deviation of training data) + 12.149 (mean of training data). In the elastic net regression model, the MAE after LOOCV was 3.627 years (Figure 2b). The formula for age estimation was as follows: predicted age = (1.2403e-11 + 0.13467 × standardized methylation level of VGF-1 + 0.097753 × standardized methylation level of VGF-2 + 0.16030 × standardized methylation level of VGF-3 + 0.12121 × standardized methylation level of KCNK12-1 + 0.13121 × standardized methylation level of KCNK12-2 + 0.15645 × standardized methylation level of KCNK12-3 + 0.026854 × standardized methylation level of ELOVL2-2 + 0.13107 × standardized methylation level of ELOVL2-3) × 8.2771 + 12.149 Among the SVR models, the model using the methylation levels of VGF-1, VGF-3, and KCNK12-3 showed the best performance; the MAE after LOOCV was 3.192 (Figure 2c). The parameter details for the elastic net regression and SVR models are listed in Supplementary Table S_R2. Linear regression analysis showed that one explanatory variable significantly affected Δage in the best model (i.e., the SVR model using VGF-1, -3, and KCNK12-3). When Δage was used as the dependent variable, the optimal regression model included age and growth environment as explanatory variables (adjusted R² = 0.215) (Table 2 and Figure 3). Among these variables, age had a significant negative effect on Δage. Generalized linear regression analysis revealed that none of the explanatory variables (i.e., age, sex, and growth environment [i.e., captive or wild condition], or their interactions) significantly affected |Δage| in the best model (Table 2). The R script used to build the models, estimate age, and evaluate the factors influencing model deviation is available in the Supplementary File. Discussion In this study, we identified three genomic locations where DNA methylation levels correlated with age using hair-derived DNA and established age estimation models for captive and wild brown bears. To the best of our knowledge, this is the third report of age estimation using hair-derived DNA and first involving wild animals 49,50 . One advantage of this method is the simplicity of sampling: hair is much easier to collect than teeth or blood. Collecting blood from live wild bears requires anesthesia and appropriate sampling techniques. In dead individuals, the most reliable approach involves removing the sternum or ribs to access the thoracic cavity and collect blood from the heart or major vessels; however, this procedure is labor-intensive and often results in coagulation. By contrast, hair can be collected from hunted or opportunistically discovered carcasses, as it requires no special equipment, such as needles or syringes, and can be performed by non-specialists. Our results show that the values of MAE (3.192) and MedAE (2.188) are satisfactory given the lifespan of brown bears, which is typically 20–30 years 70 , with a maximum of 40 years (AnAge: The animal aging and longevity database 71 ). Moreover, the MAE and MedAE values were not substantially inferior to those reported in previous studies using less invasive samples. For instance, in the previous study using human hair DNA, the MedAE was 4.15 years 49 . In cattle tail hair, with a lifespan of approximately 20 years (AnAge: The animal aging and longevity database 71 ), MAE values were 1.4 years for individuals younger than 3 years, 1.5 years for those aged 3–10 years, and 9 years for individuals over 10 years 50 . For human nails, the MAE values were 5.48 years for combined fingernails and toenails and 5.61 years for toenails alone 72,73 . Additionally, there was no difference in the prediction error of our model between wild and captive bears, although the number of analyzed samples was limited. This finding suggests that our method can be applied to wild populations. Until now, hair collected via hair-traps has primarily been used to identify individuals and determine sex for population estimation in wild bear studies 2,74 . The current method adds valuable biological information, i.e., age, enabling the estimation of sex and age structure within a population. This advancement will significantly improve our understanding of population dynamics and support the development of more scientifically informed management and conservation strategies. Furthermore, it may broaden the scope of ecological research using hair, such as monitoring age-related dietary shifts through stable isotope analysis. Therefore, this method holds potential for advancing the field of wildlife ecology. However, there is room for improving accuracy. The lower accuracy of hair-based age estimation in brown bears, compared to blood-based methods (MAE = 1.30, MedAE = 1.00) 47 , may be attributed to suboptimal selection of genomic targets. The same genomic regions used in previous blood DNA analyses were applied here. Compared to blood-based studies, data on epigenetic age markers in hair are scarce, and methylation changes associated with aging vary across species, making target selection challenging. Age-related methylation changes in the same genomic region often differ among tissues. Of the four genomic locations that showed strong correlations between methylation levels and age in bear blood ( SLC12A5 , POU4F2 , VGF , and SCGN ), only one ( VGF ) was included in the hair-based model, and its methylation levels differed between blood- and hair-derived DNA (Supplementary Figures S_R3_1–11). Among genes identified as epigenetic age markers for hair in humans ( LAG3 , SCGN , ELOVL2 , KLF14 , C1orf132 , SLC12A5 , GRIA2 , and PDE4C ) 49 and cattle ( LHX1 , ISL1 , NKX6-1 , SIM1 , and TLX3 ; Shown here are only the five most significant markers among 43) 50 , only ELOVL2 was found in brown bear hair. To improve the accuracy of hair-based age estimation models, it is necessary to employ genome-wide approaches to identify suitable target regions in hair DNA. Linear regression analysis of Δage suggested that the ages of young bears tended to be overestimated, whereas the ages of older bears were underestimated. However, generalized linear regression analysis of |Δage| indicated that the magnitude of prediction error was not significantly associated with age. Many previous studies have reported that age estimation models based on DNA methylation often underestimate the ages of older individuals 30,32,68,75 . One possible explanation is that epigenetic aging follows a logarithmic trajectory across the lifespan; that is, DNA methylation changes occur rapidly in early life but decelerate with age 76 . Alternatively, given that the offspring of long-lived individuals appear to age more slowly than expected in human studies 77,78 , it is possible that individuals who survive to older ages possess biological mechanisms that retard age-related methylation changes. If the relation between DNA methylation and age at the CpG sites used in our model is nonlinear, this may explain why SVR, which accommodates nonlinear patterns, outperformed elastic net regression, which assumes linearity. Nonetheless, the SVR model only partially mitigated the underestimation of older individuals (Figure 2). Teschendorff and Horvath (2025) 79 have suggested that nonlinear methods, such as Gaussian processes, probabilistic kernel density-based approaches, or deep learning could better capture complex and previously unrecognized aging trajectories—particularly when integrated with explainable artificial intelligence tools—thereby improving biological age prediction and advancing our understanding of epigenetic aging. In contrast to prior studies that reported increasing prediction error with age 80,81 , our findings did not show such a trend, and reports of age overestimation in younger individuals remain limited. Future investigations are needed to elucidate the factors contributing to errors in DNA methylation-based age estimation. The genes located adjacent to the CpG sites that demonstrated strong age-related methylation changes in this study were VGF (VGF Nerve Growth Factor Inducible), KCNK12 (Potassium Two Pore Domain Channel Subfamily K Member 12), and ELOVL2 (ELOVL fatty acid elongase 2). Correlations between blood DNA methylation levels and age have been previously reported for VGF in humans, dogs, and brown bears 47,61,64 ; for KCNK12 in humans and dogs 60,64 ; and for ELOVL2 in humans, mice, and cats 62,66,82 . ELOVL2 is recognized as a robust epigenetic age marker in humans 20 . These three genomic locations are not tissue-specific, suggesting that hair roots may share common age-related epigenetic markers with other tissues. Although age-associated CpG sites do not necessarily influence gene expression, previous studies have reported that genes linked to these CpG sites are related to lifespan and age-related diseases. Notably, VGF has been associated with Alzheimer's disease, whereas ELOVL2 has been linked to visual function 83 ; deletion of ELOVL2 results in early-onset hair loss in mice 84 . As the epigenetics of hair remains an emerging research area 85,86 , further studies are required to clarify the potential relations between gene expression and the age-related methylation changes identified in this study. The limitations and future challenges of this study are as follows. First, as previously noted, the selection of target genomic regions lacked sufficient rationale. Comprehensive genome-wide analyses may address this limitation. Second, our analysis was restricted to hair bulbs of the white sphere type, owing to the limited availability of the other types (i.e., white hook and black hook). Including additional hair types in future analyses would improve model applicability. Third, while we used 10 follicles per sample, further validation is necessary to determine whether accuracy is maintained when fewer follicles are used. Previous studies have not clearly defined the number of hairs required for DNA extraction, with reported values ranging from five to 30 49,50 . Fourth, it is essential to investigate whether hair storage conditions affect DNA methylation levels. This is particularly relevant for samples stored at varying temperatures over extended periods or collected from hair-traps left in the field for several weeks. Fifth, the body hair collected in field settings may not originate from the upper back, which was the source used in this study. Nevertheless, no significant differences in DNA methylation have been reported among various body hair sites—such as the calf, pubic area, armpit, and scalp—in humans 49 . The aforementioned issues are required to be addressed before our method can be applied to field-collected hair samples, such as those obtained using hair traps. In conclusion, we developed the first hair-based age estimation model for brown bears, achieving an MAE of 3.192 years following LOOCV. In bear research, DNA obtained from hair-traps has traditionally been used to identify individuals and estimate population size. If our method can be applied to past or future hair-trap samples, it may enable the reconstruction of population age structures in wild bears. Although our hair-based model is less accurate than blood-based models and remains limited in estimating the age of single individuals, it offers a simpler, less invasive sample collection method and shows promise in determining population-level age distributions. Moreover, DNA methylation-based age estimation using blood in brown bears has proven applicable to other bear species 33 . Our hair-based method therefore holds substantial potential for cross-species application. While further accuracy improvements are necessary, age estimation using hair is expected to contribute meaningfully to bear conservation and management. Declarations Funding This study was supported by funding from the Japan Society for the Promotion of Science (JSPS) (https://www.jsps.go.jp/english/e-grants/index.html) KAKENHI grant numbers JP19K06833, JP23K05312, JP25H01002, JP24KJ0304, and JP22K14910, Grant for Basic Science Research Projects from The Sumitomo Foundation (grant no. 200561), a grants-in-aid of The Inui Memorial Trust for Research on Animal Science, Japan, JST SPRING (grant no. JPMJSP2119), and World-leading Innovative and Smart Education (WISE) Program from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) (grant no. 1801). Acknowledgements We would like to express our sincere thanks to all staff at Noboribetsu Bear Park for providing bear hair and sample information. We wish to thank Hatsusaburo Ose and all the members of the Shiretoko Fishery Productive Association for their kind support. We are deeply grateful to all the members of the Shiretoko Nature Foundation for their generous support. We also thank everyone involved in sample collection. Finally, we thank Editage (www.editage.jp) for English language editing. Author information Authors and Affiliations Faculty of Veterinary Medicine, Hokkaido University, Hokkaido, 060-0818, Japan Shiori Nakamura, Jumpei Yamazaki, Mina J, Yojiro Yanagawa, Toshio Tsubota & Michito Shimozuru One Health Research Center, Hokkaido University, Hokkaido, 060-0818, Japan Jumpei Yamazaki & Michito Shimozuru Azabu University, Kanagawa, 252-5201, Japan Naoya Matsumoto Noboribetsu Bear Park, Hokkaido, 059-0551, Japan Naoya Matsumoto, Kyogo Hagino & Hideyuki Sakamoto Shiretoko Nature Foundation, Hokkaido, 099-4356, Japan Masami Yamanaka & Masanao Nakanishi Hokkaido Research Organization, Hokkaido, 060-0819, Japan Mina Jimbo Kyoto City Zoo, Kyoto, 606-8333, Japan Hideyuki Ito Wildlife Research Center, Kyoto University, Kyoto, 606-8203, Japan Hideyuki Ito Contributions S.N. designed the study, performed laboratory work, and constructed each age estimation model. S.N., N.M., K.H., H.S., M.Y., M.N., M.J., Y.Y., and M.S. were involved in sample collection. J.Y. and H.I. supported with the technical aspects of the experiment. S.N. and M.S. wrote the article with inputs from J.Y., H.I., and T.T. All authors reviewed the article. Corresponding author Correspondence to Michito Shimozuru. Data availability The data obtained from pyrosequencing analyses are available in Dryad at: doi: 10.5061/dryad.h44j0zpzc. Ethics declarations Competing interests The authors declare no competing interests. References Shimozuru, M. et al. Reproductive parameters and cub survival of brown bears in the Rusha area of the Shiretoko Peninsula, Hokkaido, Japan. PLOS ONE . 12 , e0176251. https://doi.org/10.1371/journal.pone.0176251 (2017). Shimozuru, M. et al. Estimation of breeding population size using DNA-based pedigree reconstruction in brown bears. Ecol. Evol. 12 https://doi.org/10.1002/ece3.9246 (2022). Yagi, G. et al. Non-invasive age estimation based on faecal DNA using methylation-sensitive high-resolution melting for Indo-Pacific bottlenose dolphins. Mol. Ecol. Resour. 24 https://doi.org/10.1111/1755-0998.13906 (2024). Thomas, D. C. Metachromatic staining of dental cementum for mammalian age determination. J. Wildl. Manag. 41 , 207–210. https://doi.org/10.2307/3800596 (1977). Laws, R. M. A new method of age determination for mammals. Nature 169 , 972–973. https://doi.org/10.1038/169972b0 (1952). Lundervold, M., Langvatn, R. & Milner-Gulland, E. J. A comparison of age estimation methods for the saiga antelope Saiga tatarica. Wildl. Biology . 9 , 219–227. https://doi.org/https://doi.org/10.2981/wlb.2003.054 (2003). Zhang, Y., Bi, J., Ning, Y. & Feng, J. Methodology Advances in Vertebrate Age Estimation. Animals 14 , 343. https://doi.org/10.3390/ani14020343 (2024). Kimura, D. K., Mandapat, R. R. & Oxford, S. L. Method, validity, and variability in the age determination of yellowtail rockfish ( Sebastes flavidus ), using otoliths. J. Fish. Res. Board Can. 36 , 377–383. https://doi.org/10.1139/f79-057 (1979). Cattet, M. et al. Can concentrations of steroid hormones in brown bear hair reveal age class? Conservation Physiology 6 (2018). https://doi.org/10.1093/conphys/coy001 Wiedower, E. E. et al. Fecal Near Infrared Spectroscopy to Discriminate Physiological Status in Giant Pandas. PLoS ONE . 7 , e38908. https://doi.org/10.1371/journal.pone.0038908 (2012). Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14 , R115. https://doi.org/10.1186/gb-2013-14-10-r115 (2013). Bogdanović, O. & Veenstra, G. J. C. DNA methylation and methyl-CpG binding proteins: developmental requirements and function. Chromosoma 118 , 549–565. https://doi.org/10.1007/s00412-009-0221-9 (2009). Jones, P. A. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 13 , 484–492. https://doi.org/10.1038/nrg3230 (2012). Moore, L. D., Le, T. & Fan, G. D. N. A. Methylation and Its Basic Function. Neuropsychopharmacology 38 , 23–38. https://doi.org/10.1038/npp.2012.112 (2013). Jones, M. J., Goodman, S. J. & Kobor, M. S. DNA methylation and healthy human aging. Aging Cell. 14 , 924–932. https://doi.org/10.1111/acel.12349 (2015). Bocklandt, S. et al. Epigenetic Predictor of Age. PLoS ONE . 6 , e14821. https://doi.org/10.1371/journal.pone.0014821 (2011). Koch, C. M. & Wagner, W. Epigenetic-aging-signature to determine age in different tissues. Aging 3 , 1018–1027. https://doi.org/10.18632/aging.100395 (2011). Le Clercq, L. S., Kotzé, A., Grobler, J. P. & Dalton, D. L. Biological clocks as age estimation markers in animals: a systematic review and meta-analysis. Biol. Rev. 98 , 1972–2011. https://doi.org/10.1111/brv.12992 (2023). Riekkola, L. et al. Application of a multi-disciplinary approach to reveal population structure and Southern Ocean feeding grounds of humpback whales. Ecol. Ind. 89 , 455–465. https://doi.org/https://doi.org/10.1016/j.ecolind.2018.02.030 (2018). Slieker, R. C., Relton, C. L., Gaunt, T. R., Slagboom, P. E. & Heijmans, B. T. Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception. Epigenetics Chromatin . 11 https://doi.org/10.1186/s13072-018-0191-3 (2018). Lowe, R. et al. Ageing-associated DNA methylation dynamics are a molecular readout of lifespan variation among mammalian species. Genome Biol. 19 https://doi.org/10.1186/s13059-018-1397-1 (2018). Klughammer, J. et al. Comparative analysis of genome-scale, base-resolution DNA methylation profiles across 580 animal species. Nat. Commun. 14 , 232. https://doi.org/10.1038/s41467-022-34828-y (2023). Haghani, A. et al. DNA methylation networks underlying mammalian traits. Science 381 , eabq5693. https://doi.org/10.1126/science.abq5693 (2023). Polanowski, A. M., Robbins, J., Chandler, D. & Jarman, S. N. Epigenetic estimation of age in humpback whales. Mol. Ecol. Resour. 14 , 976–987. https://doi.org/10.1111/1755-0998.12247 (2014). Petkovich, D. A. et al. Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metabol. 25 , 954–960e956. https://doi.org/10.1016/j.cmet.2017.03.016 (2017). Ito, H., Udono, T., Hirata, S. & Inoue-Murayama, M. Estimation of chimpanzee age based on DNA methylation. Sci. Rep. 8 , 9998. https://doi.org/10.1038/s41598-018-28318-9 (2018). De Paoli-Iseppi, R. et al. Age estimation in a long-lived seabird ( Ardenna tenuirostris ) using DNA methylation-based biomarkers. Mol. Ecol. Resour. 19 , 411–425. https://doi.org/10.1111/1755-0998.12981 (2019). Arai, K., Qi, H. & Inoue-Murayama, M. Age estimation of captive Asian elephants (Elephas maximus) based on DNA methylation: An exploratory analysis using methylation-sensitive high-resolution melting (MS-HRM). PLOS ONE . 18 , e0294994. https://doi.org/10.1371/journal.pone.0294994 (2023). Thompson, M. J., Vonholdt, B., Horvath, S. & Pellegrini, M. An epigenetic aging clock for dogs and wolves. Aging 9 , 1055–1068. https://doi.org/10.18632/aging.101211 (2017). Prado, N. A. et al. Epigenetic clock and methylation studies in elephants. Aging Cell. 20 https://doi.org/10.1111/acel.13414 (2021). Mayne, B. et al. Age prediction of green turtles with an epigenetic clock. Mol. Ecol. Resour. 22 , 2275–2284. https://doi.org/10.1111/1755-0998.13621 (2022). Qi, H., Lim, Q. L., Kinoshita, K., Nakajima, N. & Inoue-Murayama, M. 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 model. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13928 (2024). Shimozuru, M. et al. Epigenetic Clock in Bears: A Simple Cost-Effective Blood DNA Methylation-Based Age Estimation Method Applicable to Multiple Bear Species. Ecol. Evol. 15 https://doi.org/10.1002/ece3.71424 (2025). Stubbs, T. M. et al. Multi-tissue DNA methylation age predictor in mouse. Genome Biol. 18 , 1–14. https://doi.org/10.1186/s13059-017-1203-5 (2017). Arneson, A. et al. A mammalian methylation array for profiling methylation levels at conserved sequences. Nat. Commun. 13 https://doi.org/10.1038/s41467-022-28355-z (2022). Lu, A. T. et al. Universal DNA methylation age across mammalian tissues. Nat. Aging . 3 , 1144–1166. https://doi.org/10.1038/s43587-023-00462-6 (2023). Han, Y. et al. Epigenetic age-predictor for mice based on three CpG sites. eLife 7, e37462 (2018). https://doi.org/10.7554/eLife.37462 IUCN. The IUCN Red List of Threatened Species. Version 2025-1. https://www.iucnredlist.org (accessed 26 Apr 2025). Can, Ö. E., D'Cruze, N., Garshelis, D. L., Beecham, J. & Macdonald, D. W. Resolving human-bear conflict: A global survey of countries, experts, and key factors. Conserv. Lett. 7 , 501–513. https://doi.org/10.1111/conl.12117 (2014). Hatter, I. W., Mowat, G. & Mclellan, B. N. Statistical population reconstruction to evaluate grizzly bear trends in British Columbia. Can. Ursus . 29 , 1. https://doi.org/10.2192/ursus-d-18-00001.1 (2018). Mano, T., Matsuda, H., Natsume, S. & Tsuruga, H. Harvest-based demographic estimation of a brown bear population on the Oshima Peninsula, Hokkaido. Ursus 2024 (2025). https://doi.org/10.2192/ursus-d-23-00016.1 Mundy, K. R. D. & Fuller, W. A. Age Determination in the Grizzly Bear. J. Wildl. Manag. 28 , 863–866 (1964). Marks, S., Erickson, A. W. & A. & Age determination in the black bear. J. Wildl. Manag. 30 , 389–410 (1966). Stoneberg, R., Jonkel, C. J. & P. & Age determination of black bears by cementum layers. J. Wildl. Manag. 30 , 411–414 (1966). Mclaughlin, C. R. et al. Precision and accuracy of estimating age of Maine black bears by cementum annuli. Bears: Their Biology Manage. 8 , 415–419 (1990). Costello, C. M. et al. Reliability of the cementum annuli technique for estimating age of black bears in New Mexico. Wildl. Soc. Bull. 32 , 169–176. https://doi.org/10.2193/0091-7648(2004)32[169:rotcat]2.0.co;2 (2004). Nakamura, S. et al. Age estimation based on blood DNA methylation levels in brown bears. Mol. Ecol. Resour. 23 , 1211–1225. https://doi.org/10.1111/1755-0998.13788 (2023). Czajka, N., Northrup, J. M., Jones, M. J. & Shafer, A. B. A. Epigenetic clocks, sex markers and age-class diagnostics in three harvested large mammals. Mol. Ecol. Resour. 24 https://doi.org/10.1111/1755-0998.13956 (2024). Hao, T. et al. Predicting human age by detecting DNA methylation status in hair. ELECTROPHORESIS 42 , 1255–1261. https://doi.org/10.1002/elps.202000349 (2021). Hayes, B. J. et al. An Epigenetic Aging Clock for Cattle Using Portable Sequencing Technology. Front. Genet. 12 https://doi.org/10.3389/fgene.2021.760450 (2021). Hanski, E. et al. Epigenetic age estimation of wild mice using faecal samples. Mol. Ecol. 33 https://doi.org/10.1111/mec.17330 (2024). Waits, L. P. & Paetkau, D. Noninvasive Genetic Sampling Tools for Wildlife Biologists: A Review of Applications and Recommendations for Accurate Data Collection. J. Wildl. Manag. 69 , 1419–1433 (2005). Jimbo, M. et al. Diet selection and asocial learning: Natal habitat influence on lifelong foraging strategies in solitary large mammals. Ecosphere 13 https://doi.org/10.1002/ecs2.4105 (2022). Mowat, G. & Strobeck, C. Estimating Population Size of Grizzly Bears Using Hair Capture, DNA Profiling, and Mark-Recapture Analysis. J. Wildl. Manage. 64 , 183–193. https://doi.org/10.2307/3802989 (2000). Sato, Y. et al. Evaluation of the Effectiveness of Scented Wooden Posts for DNA Hair Snagging of Brown Bears. Mammal Study . 45 , 213. https://doi.org/10.3106/ms2018-0045 (2020). Tsubota, T., Takahashi, Y. & Kanagawa, H. Changes in Serum Progesterone Levels and Growth of Fetuses in Hokkaido Brown Bears. Bears: Their Biology Manage. 7 , 355–358. https://doi.org/10.2307/3872643 (1987). Tsubota, T., Maeda, N. & Kanagawa, H. Parturition and Postnatal Development in the Captive Hokkaido Brown Bear Ursus arctos yesoensis . J. Mammalogical Soc. Japan . 19 , 75–82. https://doi.org/10.11238/jmammsocjapan.19.75 (1994). Friebe, A. et al. Factors Affecting Date of Implantation, Parturition, and Den Entry Estimated from Activity and Body Temperature in Free-Ranging Brown Bears. PLoS ONE . 9 , e101410. https://doi.org/10.1371/journal.pone.0101410 (2014). Jimbo, M. et al. Hair Growth in Brown Bears and Its Application to Ecological Studies on Wild Bears. Mammal Study . 45 https://doi.org/10.3106/ms2020-0021 (2020). Day, K. et al. Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape. Genome Biol. 14 , R102. https://doi.org/10.1186/gb-2013-14-9-r102 (2013). Florath, I., Butterbach, K., Muller, H., Bewerunge-Hudler, M. & Brenner, H. Cross-sectional and longitudinal changes in DNA methylation with age: an epigenome-wide analysis revealing over 60 novel age-associated CpG sites. Hum. Mol. Genet. 23 , 1186–1201. https://doi.org/10.1093/hmg/ddt531 (2014). Bekaert, B., Kamalandua, A., Zapico, S. C., Van De Voorde, W. & Decorte, R. Improved age determination of blood and teeth samples using a selected set of DNA methylation markers. Epigenetics 10 , 922–930. https://doi.org/10.1080/15592294.2015.1080413 (2015). Lowe, R. et al. Ageing-associated DNA methylation dynamics are a molecular readout of lifespan variation among mammalian species. Genome Biol. 19 , 22. https://doi.org/10.1186/s13059-018-1397-1 (2018). Ito, G., Yoshimura, K. & Momoi, Y. Analysis of DNA methylation of potential age-related methylation sites in canine peripheral blood leukocytes. J. Vet. Med. Sci. 79 , 745–750. https://doi.org/10.1292/jvms.16-0341 (2017). Yamazaki, J. et al. Obese status is associated with accelerated DNA methylation change in peripheral blood of senior dogs. Res. Vet. Sci. 139 , 193–199. https://doi.org/10.1016/j.rvsc.2021.07.024 (2021). Qi, H. et al. Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease. Sci. Rep. 11 https://doi.org/10.1038/s41598-021-99424-4 (2021). R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2022). https://www.R-project.org/ Raj, K. et al. Epigenetic clock and methylation studies in cats. GeroScience 43, 2363–2378 (2021). https://doi.org/10.1007/s11357-021-00445-8 Xu, C. et al. A novel strategy for forensic age prediction by DNA methylation and support vector regression model. Sci. Rep. 5 , 17788. https://doi.org/10.1038/srep17788 (2015). Interagency Grizzly Bear Committee. Grizzly Bear Compendium (The National Wildlife Federation, 1987). de Magalhães, J. P. et al. Human Ageing Genomic Resources: updates on key databases in ageing research. Nucleic Acids Res. 52 , D900–d908. https://doi.org/10.1093/nar/gkad927 (2024). Fokias, K., Dierckx, L., Van de Voorde, W. & Bekaert, B. Age determination through DNA methylation patterns in fingernails and toenails. Forensic Sci. International: Genet. 64 , 102846. https://doi.org/https://doi.org/10.1016/j.fsigen.2023.102846 (2023). Fokias, K., Dierckx, L., Van de Voorde, W. & Bekaert, B. Improving the age estimation model for toenails. Forensic Sci. International: Genet. 66 , 102911. https://doi.org/https://doi.org/10.1016/j.fsigen.2023.102911 (2023). Ciucci, P. et al. Estimating abundance of the remnant Apennine brown bear population using multiple noninvasive genetic data sources. J. Mammal. 96 , 206–220. https://doi.org/10.1093/jmammal/gyu029 (2015). El Khoury, L. Y. et al. Systematic underestimation of the epigenetic clock and age acceleration in older subjects. Genome Biol. 20 https://doi.org/10.1186/s13059-019-1810-4 (2019). Snir, S., Farrell, C. & Pellegrini, M. Human epigenetic ageing is logarithmic with time across the entire lifespan. Epigenetics 14 , 912–926. https://doi.org/10.1080/15592294.2019.1623634 (2019). Gentilini, D. et al. Role of epigenetics in human aging and longevity: genome-wide DNA methylation profile in centenarians and centenarians’ offspring. AGE 35, 1961–1973 (2013). https://doi.org/10.1007/s11357-012-9463-1 Horvath, S. et al. Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring. Aging 7 , 1159–1170. https://doi.org/10.18632/aging.100861 (2015). Teschendorff, A. E. & Horvath, S. Epigenetic ageing clocks: statistical methods and emerging computational challenges. Nat. Rev. Genet. 26 , 350–368. https://doi.org/10.1038/s41576-024-00807-w (2025). Zbieć-Piekarska, R. et al. Development of a forensically useful age prediction method based on DNA methylation analysis. Forensic Sci. International: Genet. 17 , 173–179. org/10.1016/j.fsigen.2015.05.001 (2015). https://doi.org/https://doi. Hamano, Y. et al. Forensic age prediction for dead or living samples by use of methylation-sensitive high resolution melting. Leg. Med. 21 , 5–10. https://doi.org/10.1016/j.legalmed.2016.05.001 (2016). Spiers, H. et al. Age-associated changes in DNA methylation across multiple tissues in an inbred mouse model. Mech. Ageing Dev. 154 , 20–23. 10.1016/j.mad.2016.02.001 (2016). https://doi.org/https://doi.org/ Johnson, A. A., Torosin, N. S., Shokhirev, M. N. & Cuellar, T. L. A set of common buccal CpGs that predict epigenetic age and associate with lifespan-regulating genes. iScience 25 , 105304. https://doi.org/10.1016/j.isci.2022.105304 (2022). Li, X. et al. Lipid metabolism dysfunction induced by age-dependent DNA methylation accelerates aging. Signal. Transduct. Target. Therapy . 7 https://doi.org/10.1038/s41392-022-00964-6 (2022). Li, J. et al. Progressive Alopecia Reveals Decreasing Stem Cell Activation Probability during Aging of Mice with Epidermal Deletion of DNA Methyltransferase 1. J. Invest. Dermatology . 132 , 2681–2690. https://doi.org/10.1038/jid.2012.206 (2012). Wang, S. et al. Integrative Analysis of Methylome and Transcriptome Reveals the Regulatory Mechanisms of Hair Follicle Morphogenesis in Cashmere Goat. Cells 9 , 969. https://doi.org/10.3390/cells9040969 (2020). Tables Table 1. Values of mean absolute error (MAE), median absolute error (MedAE), and root mean square error (RMSE) for each age estimation model. model CpG sites MAE MedAE RMSE Single regression VGF-3 4.138 3.127 5.353 KCNK12-3 3.978 3.270 5.010 ELOVL2-3 5.525 4.359 7.278 Elastic net regression all CpGs 3.627 3.375 4.630 Support vector regression all CpGs 3.398 2.916 4.409 VGF-1, -2, -3, KCNK12-1, -2, -3, ELOVL2 -2, -3 3.342 2.914 4.329 VGF-1, -2, -3, KCNK12-1, -2, -3, ELOVL2 -3 3.255 2.511 4.138 VGF-1, -3, KCNK12-1, -2, -3, ELOVL2 -3 3.359 2.647 4.241 VGF-1, -3, KCNK12-2, -3, ELOVL2 -3 3.367 2.718 4.246 VGF-1, -3, KCNK12-2, -3 3.634 3.015 4.533 VGF-1, -3, KCNK12-3 3.192 2.188 4.238 VGF-3, KCNK12-3 3.513 2.766 4.468 VGF-1, -2, -3 3.462 2.439 5.020 KCNK12-1, -2, -3 3.845 3.214 4.909 ELOVL2-1, -2, -3 5.048 3.864 7.260 All values were rounded to the fourth decimal place. Each bold value denotes the minimum value for each regression model/ Table 2. Coefficients and p-values from the linear regression analysis of Δage and generalized linear regression analysis of |Δage| in the best-performing model (SVR model using three CpGs: VGF-1, VGF-3, and KCNK12-3). Estimate p -value Δage (Intercept) 0.07009 0.33846 Age -0.21261 0.00169 Growth environment (wild) -0.2037000 0.1647 |Δage| (Intercept) -1.041 – Note: Bold values denote statistical significance (p<0.05). Additional Declarations No competing interests reported. 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03:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7042508/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7042508/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-27455-2","type":"published","date":"2025-12-29T15:57:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88247172,"identity":"40f667c0-8c01-44b2-b663-3c4400b6fcf8","added_by":"auto","created_at":"2025-08-04 12:43:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108354,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of chronological age (year) versus DNA methylation level (%) at the cytosine-phosphate-guanine (CpG) sites that showed the strongest correlation ateach selected genomic location. Each plot represents a CpG site adjacent to \u003cem\u003eVGF\u003c/em\u003e(a), \u003cem\u003eKCNK12\u003c/em\u003e (b), or \u003cem\u003eELOVL2\u003c/em\u003e (c).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7042508/v1/7c21cbd035b029c1b3237ee7.jpg"},{"id":88246073,"identity":"e9fa6c7e-5ee6-4280-9164-2ea88b28f57a","added_by":"auto","created_at":"2025-08-04 12:35:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83676,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of predicted age (year) versus chronological age (year) for the age estimation models after leave-one-out cross-validation (LOOCV). The solid line represents predicted age = chronologicalage. Thedistance between the solid and dotted lines indicatesthe mean absolute error (MAE) of each model after LOOCV. Panels show the single regression model (a), elastic net regression model (b), and best support vector regression (SVR) model among all models (c).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7042508/v1/0edbd7eb4d756d7a1df82a72.jpg"},{"id":88246075,"identity":"51521731-4aca-4f23-9b95-1a0a7f64dbcf","added_by":"auto","created_at":"2025-08-04 12:35:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91155,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plotof Δage (year) versuschronological age (year) in the best-performingmodel.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7042508/v1/0e44354b142a498be0586025.jpg"},{"id":99545545,"identity":"8a9a4cb4-b262-4982-8314-1e464feb57bb","added_by":"auto","created_at":"2026-01-05 16:08:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1460797,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7042508/v1/735ed1bb-d70f-4cf8-9149-e447cc40fbfa.pdf"},{"id":88247451,"identity":"3f4114f9-6fea-4419-b475-82e7535630af","added_by":"auto","created_at":"2025-08-04 12:51:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2774452,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytablesandfigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7042508/v1/757195db181d32396a355bdc.pdf"},{"id":88246080,"identity":"4f7961ba-d80e-45de-9d47-86d1429a73af","added_by":"auto","created_at":"2025-08-04 12:35:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1671859,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileRscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7042508/v1/da0c77929e1c6450718ed22a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Less-invasive age estimation using hair based on DNA methylation in brown bears","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAge information is essential for wildlife research to examine the life history of animals (including sexual maturity, reproductive success, disease dynamics, and risk of mortality) and contributes to wildlife conservation and management by providing estimates of age structure and population dynamics. Although the simplest way to obtain age information is to ascertain the year of birth by direct observation or camera-trap-based surveys\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, these methods require a long-term effort and considerable human resources; therefore, the target species and fields available for investigation are limited. Various methods have been developed to determine the age of wild animals. The most common method, especially for terrestrial and marine mammals, is based on counting the cementum annuli or growth layer groups of the teeth that form annually\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Other methods include tooth eruption patterns, wear, skeletal features, physical characteristics, otoliths, and scales\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, most of them are only applicable to dead or immobilized animals and are often difficult to implement in live animals. Methods based on samples that can be obtained noninvasively, such as hair and feces, have also been attempted. For example, the measurement of steroid hormone levels in hair can distinguish the age classes of brown bears (\u003cem\u003eUrsus arctos\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and fecal near-infrared reflectance spectroscopy can be applied to discriminate the age classes of giant pandas (\u003cem\u003eAiluropoda melanoleuca\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOver the past decade, another age estimation method based on DNA methylation levels was developed (i.e., the epigenetic clock\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e). DNA methylation is a type of epigenetic modification. In vertebrates, it predominantly occurs at cytosine-phosphate-guanine (CpG) sites, forming 5-methylcytosine by transferring a methyl group to the C5 position of cytosine\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. DNA methylation regulates gene expression, and its function has been suggested to vary with genomic context\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. DNA methylation levels can change with age\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, which has enabled its use as an indicator for age estimation, first demonstrated in humans (\u003cem\u003eHomo sapiens\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Epigenetic clocks are now available in various animals\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and have already been used to examine population structure\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. DNA methylation is not static among tissues; the number of age-related differentially methylated positions and rate of change with age differ among tissues\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. DNA methylation also varies among animal species, and its association with phylogenetic relations and lifespan has attracted attention from the perspective of comparative epigenomics\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Consequently, various species- and tissue-specific epigenetic clocks have been developed (skin of humpback whales [\u003cem\u003eMegaptera novaeangliae\u003c/em\u003e]\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e; blood of mice [\u003cem\u003eMus musculus\u003c/em\u003e]\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e; blood of chimpanzees [\u003cem\u003ePan troglodytes\u003c/em\u003e]\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e; blood of long-lived seabirds [\u003cem\u003eArdenna tenuirostris\u003c/em\u003e]\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e; blood of Asian elephants [\u003cem\u003eElephas maximus\u003c/em\u003e]\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e). By contrast, some epigenetic clocks are shared among closely related species\u003csup\u003e\u003cspan additionalcitationids=\"CR30 CR31 CR32\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and different tissues (mice)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Indeed, epigenetic clocks have been established using samples of 59 tissue types across 185 mammalian species, analyzed by mammalian methylation microarrays (HorvathMammalMethylChip40\u003csup\u003e35\u003c/sup\u003e) using hundreds of CpG methylation levels\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Such genome-wide deep sequencing approaches provide robust and large epigenetic age predictors; however, highly accurate age estimation can also be achieved with a limited number of CpGs using site-specific analysis\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAmong the eight bear species worldwide, six (e.g., polar bears [\u003cem\u003eUrsus maritimus\u003c/em\u003e]) are listed as vulnerable on the International Union for Conservation of Nature Red List\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e owing to population declines caused by habitat reduction, poaching, and other factors. While attempts are being made to restore the population, human\u0026ndash;bear conflicts can hinder the acceptance of conservation initiatives\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Addressing these challenges requires the implementation of population management strategies grounded in a comprehensive understanding of population dynamics, and the age at harvest has already been incorporated into population dynamics models\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. If it were possible to estimate the age of living bears, this would contribute significantly to the improvement of population dynamics models.\u003c/p\u003e\u003cp\u003eIn bear species, the traditional age estimation method uses the cementum annuli of teeth\u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. However, this method has several disadvantages: 1) it requires pulling teeth and is invasive to live animals; 2) the observer requires sufficient experience to ensure accurate and precise age estimation\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e; and 3) accuracy declines in older bears\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Recently, age estimation methods based on DNA methylation levels have been developed for bear species, using blood, skin, and fibroblast samples\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. A critical limitation of these methods is the requirement to obtain samples from either captured or dead individuals, which complicates their application in wildlife studies. Reports on epigenetic clocks using noninvasive or less-invasive samples, including human\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and cattle (\u003cem\u003eBos taurus\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e hair, as well as Indo-Pacific bottlenose dolphin (\u003cem\u003eTursiops aduncus\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e and mouse\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e feces, are limited. In the present study, we aimed to establish an epigenetic clock using hair from brown bears. In brown bear research, hair samples that can be obtained noninvasively have been used for various purposes, including identifying species, estimating population size, determining genetic diversity, and detecting diet items\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. In particular, the technique of using bait or scent lures to attract brown bears and collecting hairs snagged in barbed wire placed around them (hereafter simply referred to as \"hair-traps\") is commonly used\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study aimed to establish an age estimation method based on DNA methylation in brown bears using hair. DNA was extracted from hair roots collected from both captive and wild brown bears of known age, and the DNA methylation levels of 39 CpGs in 12 distinct genomic locations were determined by pyrosequencing. Subsequently, we sought to identify the CpG sites in hair-derived DNA that exhibited a strong correlation between DNA methylation levels and age. Finally, we attempted to establish an epigenetic clock that could be implemented with fewer CpG sites (i.e., with a limited amount of DNA) with the aim of application in DNA scarce sources, such as hairs obtained from hair-traps.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures involved in sample collection from animals were conducted in accordance with the Guidelines for Animal Care and Use, Hokkaido University, and were approved by the Animal Care and Use Committee of the Graduate School of Veterinary Medicine, Hokkaido University (Permit Number: 1152, 15009, 17005, 18-0083, 19-0021, 20-0146, and 23-0014). In addition, all methods were carried out in compliance with ARRIVE guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy area, animals, and hair sampling\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCaptive bears\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHair samples were obtained from 35 brown bears\u0026nbsp;(18 males and 17 females)\u0026nbsp;maintained at Noboribetsu Bear Park\u0026nbsp;(Noboribetsu, Hokkaido, Japan).\u0026nbsp;Their ages ranged from\u0026nbsp;1\u0026nbsp;to 33 years. Detailed\u0026nbsp;information\u0026nbsp;(bear ID, sex, birth year, sampling date, and age at sampling)\u0026nbsp;is\u0026nbsp;provided\u0026nbsp;in\u0026nbsp;Supplementary Table S_M1.\u0026nbsp;Age\u0026nbsp;at sampling\u0026nbsp;was\u0026nbsp;determined based on the assumption that all bears were born on February 1,\u0026nbsp;on the\u0026nbsp;grounds\u0026nbsp;that\u0026nbsp;the bears\u0026nbsp;were born between mid-January and early February\u003csup\u003e56-58\u003c/sup\u003e. All hair samples were plucked under anesthesia, and the location\u0026nbsp;was\u0026nbsp;the upper back, where wild bears\u0026nbsp;were\u0026nbsp;thought to leave\u0026nbsp;hair\u0026nbsp;when they\u0026nbsp;rubbed\u0026nbsp;their backs against trees.\u0026nbsp;The bears were anesthetized by intramuscular administration of xylazine HCl (1\u0026thinsp;mg/kg; Selactar; Bayer) and a 1:1 mixture of zolazepam HCl and tiletamine HCl (2.0\u0026ndash;4.0\u0026thinsp;mg/kg; Zoletil 100; Virbac), administered remotely using a blow dart. After sampling was completed, atipamezole HCl (1\u0026thinsp;mg/kg; Atipame; Kyoritsu Co., Ltd.) was injected intramuscularly to\u0026nbsp;facilitate recovery from anesthesia.\u0026nbsp;The collected samples were stored at \u0026minus;30\u0026nbsp;\u0026deg;C\u0026nbsp;until genomic DNA extraction. Please refer to Nakamura et al. (2023)\u003csup\u003e47\u003c/sup\u003e for details regarding the housing conditions at this facility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWild bears\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHair samples were obtained from 12 female brown bears living in the Rusha area (44\u0026deg;11\u0026prime;N, 145\u0026deg;11\u0026prime;E) of the Shiretoko Peninsula, eastern Hokkaido, Japan. Their ages ranged from 4 to 27 years. In this area, long-term continuous bear monitoring surveys have been conducted since 1997, enabling age determination using visual and DNA-based identification\u003csup\u003e1\u003c/sup\u003e. Detailed information (bear ID, sex, birth year, sampling date, and age at sampling) is provided in\u0026nbsp;Supplementary Table S_M1.\u0026nbsp;Age\u0026nbsp;at sampling\u0026nbsp;was\u0026nbsp;determined based on the assumption that all bears were born on February 1, similar to captive bears.\u0026nbsp;The bears were anesthetized by intramuscular administration of medetomidine HCl (75\u0026thinsp;\u0026mu;g/kg; Dorbene Vet; Kyoritsu Co., Ltd.) and a 1:1 mixture of zolazepam HCl and tiletamine HCl (5.5\u0026thinsp;mg/kg; Zoletil 100; Virbac), with dosages calculated based on estimated body weight. The drugs were administered remotely using an air rifle.\u0026nbsp;After\u0026nbsp;hair samples\u0026nbsp;were\u0026nbsp;plucked from the upper back, atipamezole HCl (375\u0026thinsp;\u0026mu;g/kg; Atipame; Kyoritsu Co., Ltd.) was injected intramuscularly to facilitate recovery from anesthesia. The collected samples were stored at \u0026minus;30\u0026nbsp;\u0026deg;C\u0026nbsp;until genomic DNA extraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic DNA extraction and bisulfite conversion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from 10 hair roots using a DNA Extractor FM Kit\u0026nbsp;(FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan).\u0026nbsp;Extraction was performed according to the manufacturer\u0026rsquo;s protocol, and the DNA was finally eluted in 50 \u0026micro;L of AE Buffer.\u0026nbsp;The extracted DNA\u0026nbsp;was\u0026nbsp;stored at \u0026minus;30\u0026nbsp;\u0026deg;C\u0026nbsp;until bisulfite conversion.\u0026nbsp;Twenty\u0026nbsp;microliters\u0026nbsp;of DNA\u0026nbsp;were\u0026nbsp;subjected to bisulfite conversion using the EZ DNA Methylation-Gold Kit\u0026nbsp;(Zymo Research, Irvine, CA, USA),\u0026nbsp;according to the manufacturer\u0026apos;s instructions. After final elution in 10 \u0026micro;L of Elution Buffer supplied with the kit, 30 \u0026micro;L of TE Buffer was added (i.e., 40 \u0026micro;L in total).\u003c/p\u003e\n\u003cp\u003eJimbo et al. (2020)\u003csup\u003e59\u003c/sup\u003e reported that guard hair bulbs change seasonally in morphology and are classified into three types (white sphere type, black hook type and white hook type). Bear guard hair grows year-round, except during hibernation, and is shed once a year in the summer. Newly grown and growing hairs are considered to be of the black hook type (around June to August), then change to the white hook type, continue to grow (around September to October), and then change to the white sphere type (from around November to shedding in the following summer) after growth stops until the hair sheds. Hair of the white sphere type is present, except for a brief period in autumn, around September and October. Most of the samples we obtained were of the white sphere type, and we did not obtain sufficient numbers for the other two types. Therefore, we only used hair bulbs of the white sphere type and excluded the other two types from the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTarget genomic locations,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003epolymerase\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;chain reaction (PCR), and pyrosequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTarget genomic locations were adjacent to 12 genes,\u0026nbsp;\u003cem\u003eGSE1\u003c/em\u003e, \u003cem\u003eVGF\u003c/em\u003e, \u003cem\u003eSLC12A5\u003c/em\u003e, \u003cem\u003eSCGN\u003c/em\u003e, \u003cem\u003eKCNK12\u003c/em\u003e, \u003cem\u003eOTUD7A\u003c/em\u003e, \u003cem\u003eBCL6B\u003c/em\u003e, \u003cem\u003ePOU4F2\u003c/em\u003e, \u003cem\u003eELOVL2\u003c/em\u003e, \u003cem\u003eRALYL\u003c/em\u003e, \u003cem\u003eKISS1R\u003c/em\u003e, and \u003cem\u003eCAPS2\u003c/em\u003e as in our previous study on\u0026nbsp;the\u0026nbsp;epigenetic clock in blood samples from brown bears\u003csup\u003e47\u003c/sup\u003e. DNA methylation levels of CpG sites adjacent to these genes\u0026nbsp;have been\u0026nbsp;reported to change with age in humans\u003csup\u003e60-63\u003c/sup\u003e, dogs\u003csup\u003e64,65\u003c/sup\u003e, and cats\u003csup\u003e66\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe DNA methylation levels of the target CpGs were analyzed using PCR followed by pyrosequencing, in accordance with our previous reports\u003csup\u003e33,47,65\u003c/sup\u003e. PCR was conducted in two steps using TaKaRa EpiTaq HS (Takara Bio Inc., Shiga, Japan). Each PCR product was electrophoresed on a 2% agarose gel to confirm that the target was successfully amplified. Pyrosequencing was performed using PyroMark Q48 software (Qiagen, Venlo, Netherlands) with PyroMark Q48 Advanced Reagents (Qiagen), according to the manufacturer\u0026rsquo;s instructions. The mean methylation levels obtained from duplicate reactions of the two-step PCR and pyrosequencing were used to establish age estimation models. Details, including primer sequences and amplification conditions, are available in our previous study\u003csup\u003e47\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eInitially, as a screening step, each target CpG site was analyzed across nine samples to identify the CpG sites whose DNA methylation levels were strongly correlated with age. These samples were selected from captive bears, and the number of individuals was balanced according to age and sex. From 12 candidate genomic locations, those with R\u0026sup2; values greater than 0.8 and a range of methylation level changes greater than 20% were selected for further analysis. Finally, we analyzed the DNA methylation levels of the selected genomic locations for the remaining hair samples and calculated Pearson\u0026rsquo;s product-moment correlation coefficients and \u003cem\u003ep\u003c/em\u003e-values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge estimation model and model validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBuilding age estimation models was performed similarly to the method described in our previous studies using blood samples from bears\u003csup\u003e33,47\u003c/sup\u003e. We used only one sample per animal to avoid overfitting the model to methylation changes in specific individuals sampled multiple times.\u003c/p\u003e\n\u003cp\u003eBased on the DNA methylation levels obtained, three age estimation models were generated using R ver. 4.2.2\u003csup\u003e67\u003c/sup\u003e. Single regression, elastic net regression, and support vector regression (SVR) models were built using the R command \u0026ldquo;lm\u0026rdquo; and R packages \u0026ldquo;glmnet\u0026rdquo; and \u0026ldquo;e1071,\u0026rdquo; respectively. Elastic net regression models, a type of penalized regression, have frequently been used for age estimation\u003csup\u003e11,29,36,68\u003c/sup\u003e, whereas SVR models have been reported to achieve high estimation accuracy\u003csup\u003e33,47,66,69\u003c/sup\u003e. Unlike elastic net regression, SVR does not perform feature selection, which necessitates careful consideration of predictor combinations. We constructed SVR models using feature sets in which the less important features identified by the elastic net regression were sequentially removed. In addition, SVR models were constructed using CpGs from each genomic location. Age and DNA methylation levels were standardized before integration into the models. Leave-one-out cross-validation (LOOCV) was performed to validate all models.\u003c/p\u003e\n\u003cp\u003eTo evaluate whether age, sex, or growth environment (i.e., captive or wild condition) affected the deviation of the age estimation model, linear regression and generalized linear regression were generated with \u0026Delta;age (predicted age \u0026minus; chronological age) and |\u0026Delta;age| (absolute difference between predicted age and chronological age) as dependent variables and the three factors and their interactions as explanatory variables. Model construction and selection were performed using the R command \u0026ldquo;lm\u0026rdquo; and R packages \u0026ldquo;MuMIn\u0026rdquo; and \u0026ldquo;lme4.\u0026rdquo;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSelection of age-related genomic locations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter screening nine samples, three genomic locations that met the requirements were selected from 12 candidate genomic locations, namely those adjacent to \u003cem\u003eVGF\u003c/em\u003e, \u003cem\u003eKCNK12\u003c/em\u003e, and \u003cem\u003eELOVL2\u003c/em\u003e. All the results are shown in\u0026nbsp;Supplementary Figures S_R1_1–11.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between DNA methylation level and chronological age\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each genomic location, multiple CpG sites showed significant correlations between DNA methylation levels and age. The three CpGs adjacent to \u003cem\u003eVGF\u003c/em\u003e, \u003cem\u003eKCNK12\u003c/em\u003e, and \u003cem\u003eELOVL2\u0026nbsp;\u003c/em\u003eare graphically presented in\u0026nbsp;Supplementary Figures S_R2_1, 2, and 3, and the corresponding correlation coefficients and \u003cem\u003ep\u003c/em\u003e-values for each CpG are provided in\u0026nbsp;Supplementary Table S_R1. The CpGs\u0026nbsp;that exhibited\u0026nbsp;the strongest correlations for each genomic location are shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge estimation model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe constructed 15 age estimation models (three single regression models, one elastic net regression model, and 11 SVR models) based on the DNA methylation levels of CpGs adjacent to \u003cem\u003eVGF\u003c/em\u003e (VGF-1, -2, and -3), \u003cem\u003eKCNK12\u003c/em\u003e (KCNK12-1, -2, and -3), and \u003cem\u003eELOVL2\u003c/em\u003e (ELOVL2-1, -2, and -3). Their performance can be confirmed from the mean absolute error (MAE), median absolute error (MedAE), and root mean square error values for each model in Table 1.\u003c/p\u003e\n\u003cp\u003eAmong the single regression models, the model using the methylation level of KCNK12-3 showed the best performance; the MAE value after LOOCV was 3.978 years (Figure 2a). The formula for age estimation was as follows:\u003c/p\u003e\n\u003cp\u003epredicted age = (−1.3183e-11 + 0.80980 × standardized methylation level of KCNK12-3) × 8.2771 (standard deviation of training data) + 12.149 (mean of training data).\u003c/p\u003e\n\u003cp\u003eIn the elastic net regression model, the MAE after LOOCV was 3.627 years (Figure 2b). The formula for age estimation was as follows:\u003c/p\u003e\n\u003cp\u003epredicted age = (1.2403e-11 + 0.13467 × standardized methylation level of VGF-1 + 0.097753 × standardized methylation level of VGF-2 + 0.16030 × standardized methylation level of VGF-3 + 0.12121 × standardized methylation level of KCNK12-1 + 0.13121 × standardized methylation level of KCNK12-2 + 0.15645 × standardized methylation level of KCNK12-3 + 0.026854 × standardized methylation level of ELOVL2-2 + 0.13107 × standardized methylation level of ELOVL2-3) × 8.2771 + 12.149\u003c/p\u003e\n\u003cp\u003eAmong the SVR models, the model using the methylation levels of VGF-1, VGF-3, and KCNK12-3 showed the best performance; the MAE after LOOCV was 3.192 (Figure 2c). The parameter details for the elastic net regression and SVR models are listed in Supplementary Table S_R2.\u003c/p\u003e\n\u003cp\u003eLinear regression analysis showed that one explanatory variable significantly affected Δage in the best model (i.e., the SVR model using VGF-1, -3, and KCNK12-3). When Δage was used as the dependent variable, the optimal regression model included age and growth environment as explanatory variables (adjusted R² = 0.215) (Table 2 and Figure 3). Among these variables, age had a significant negative effect on Δage. Generalized linear regression analysis revealed that none of the explanatory variables (i.e., age, sex, and growth environment [i.e., captive or wild condition], or their interactions) significantly affected |Δage| in the best model (Table 2). The R script used to build the models, estimate age, and evaluate the factors influencing model deviation is available in the Supplementary File.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we identified three genomic locations where DNA methylation levels correlated with age using hair-derived DNA and established age estimation models for captive and wild brown bears. To the best of our knowledge, this is the third report of age estimation using hair-derived DNA and first involving wild animals\u003csup\u003e49,50\u003c/sup\u003e. One advantage of this method is the simplicity of sampling: hair is much easier to collect than teeth or blood. Collecting blood from live wild bears requires anesthesia and appropriate sampling techniques. In dead individuals, the most reliable approach involves removing the sternum or ribs to access the thoracic cavity and collect blood from the heart or major vessels; however, this procedure is labor-intensive and often results in coagulation. By contrast, hair can be collected from hunted or opportunistically discovered carcasses, as it requires no special equipment, such as needles or syringes, and can be performed by non-specialists. Our results show that the values of MAE (3.192) and MedAE (2.188) are satisfactory given the lifespan of brown bears, which is typically 20–30 years\u003csup\u003e70\u003c/sup\u003e, with a maximum of 40 years\u0026nbsp;(AnAge: The animal aging and longevity database\u003csup\u003e71\u003c/sup\u003e).\u0026nbsp;Moreover, the\u0026nbsp;MAE and MedAE values\u0026nbsp;were\u0026nbsp;not substantially inferior to those\u0026nbsp;reported\u0026nbsp;in previous\u0026nbsp;studies\u0026nbsp;using less invasive samples. For\u0026nbsp;instance, in the previous study\u0026nbsp;using human\u0026nbsp;hair DNA, the MedAE was 4.15 years\u003csup\u003e49\u003c/sup\u003e.\u0026nbsp;In cattle\u0026nbsp;tail hair, with a lifespan\u0026nbsp;of\u0026nbsp;approximately\u0026nbsp;20 years\u0026nbsp;(AnAge: The animal aging and longevity database\u003csup\u003e71\u003c/sup\u003e),\u0026nbsp;MAE\u0026nbsp;values were 1.4 years for\u0026nbsp;individuals younger\u0026nbsp;than 3 years, 1.5 years for\u0026nbsp;those\u0026nbsp;aged 3–10 years, and 9 years for\u0026nbsp;individuals over\u0026nbsp;10 years\u003csup\u003e50\u003c/sup\u003e. For\u0026nbsp;human\u0026nbsp;nails, the\u0026nbsp;MAE\u0026nbsp;values were 5.48\u0026nbsp;years\u0026nbsp;for combined fingernails and toenails and 5.61\u0026nbsp;years\u0026nbsp;for toenails alone\u003csup\u003e72,73\u003c/sup\u003e.\u0026nbsp;Additionally, there was no difference in the\u0026nbsp;prediction\u0026nbsp;error of our model between wild and captive bears, although the number of\u0026nbsp;analyzed\u0026nbsp;samples was limited. This finding suggests\u0026nbsp;that our method can be applied to wild\u0026nbsp;populations. Until now,\u0026nbsp;hair\u0026nbsp;collected\u0026nbsp;via\u0026nbsp;hair-traps\u0026nbsp;has primarily\u0026nbsp;been used to identify individuals\u0026nbsp;and determine sex\u0026nbsp;for population\u0026nbsp;estimation\u0026nbsp;in\u0026nbsp;wild\u0026nbsp;bear studies\u003csup\u003e2,74\u003c/sup\u003e. The current method\u0026nbsp;adds valuable\u0026nbsp;biological information, i.e.,\u0026nbsp;age,\u0026nbsp;enabling the estimation of\u0026nbsp;sex and age structure\u0026nbsp;within a\u0026nbsp;population. This\u0026nbsp;advancement will\u0026nbsp;significantly improve\u0026nbsp;our\u0026nbsp;understanding of population dynamics and\u0026nbsp;support the development of\u0026nbsp;more scientifically\u0026nbsp;informed\u0026nbsp;management and conservation\u0026nbsp;strategies. Furthermore, it may\u0026nbsp;broaden\u0026nbsp;the\u0026nbsp;scope of\u0026nbsp;ecological\u0026nbsp;research\u0026nbsp;using hair, such as\u0026nbsp;monitoring\u0026nbsp;age-related dietary\u0026nbsp;shifts through\u0026nbsp;stable isotope analysis. Therefore,\u0026nbsp;this\u0026nbsp;method\u0026nbsp;holds\u0026nbsp;potential\u0026nbsp;for advancing the field of\u0026nbsp;wildlife\u0026nbsp;ecology.\u003c/p\u003e\n\u003cp\u003eHowever, there is room for improving accuracy. The lower accuracy of hair-based age estimation in brown bears, compared to blood-based methods (MAE = 1.30, MedAE = 1.00)\u003csup\u003e47\u003c/sup\u003e, may be attributed to suboptimal selection of genomic targets. The same genomic regions used in previous blood DNA analyses were applied here. Compared to blood-based studies, data on epigenetic age markers in hair are scarce, and methylation changes associated with aging vary across species, making target selection challenging. Age-related methylation changes in the same genomic region often differ among tissues. Of the four genomic locations that showed strong correlations between methylation levels and age in bear blood\u0026nbsp;(\u003cem\u003eSLC12A5\u003c/em\u003e, \u003cem\u003ePOU4F2\u003c/em\u003e, \u003cem\u003eVGF\u003c/em\u003e, and \u003cem\u003eSCGN\u003c/em\u003e), only one (\u003cem\u003eVGF\u003c/em\u003e) was\u0026nbsp;included\u0026nbsp;in\u0026nbsp;the hair-based\u0026nbsp;model, and\u0026nbsp;its\u0026nbsp;methylation levels\u0026nbsp;differed\u0026nbsp;between blood-\u0026nbsp;and hair-derived\u0026nbsp;DNA\u0026nbsp;(Supplementary Figures S_R3_1–11).\u0026nbsp;Among\u0026nbsp;genes\u0026nbsp;identified\u0026nbsp;as epigenetic age markers for\u0026nbsp;hair\u0026nbsp;in humans\u0026nbsp;(\u003cem\u003eLAG3\u003c/em\u003e, \u003cem\u003eSCGN\u003c/em\u003e, \u003cem\u003eELOVL2\u003c/em\u003e, \u003cem\u003eKLF14\u003c/em\u003e, \u003cem\u003eC1orf132\u003c/em\u003e, \u003cem\u003eSLC12A5\u003c/em\u003e, \u003cem\u003eGRIA2\u003c/em\u003e, and \u003cem\u003ePDE4C\u003c/em\u003e)\u003csup\u003e49\u003c/sup\u003e and cattle\u0026nbsp;(\u003cem\u003eLHX1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;ISL1\u003c/em\u003e, \u003cem\u003eNKX6-1\u003c/em\u003e, \u003cem\u003eSIM1\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;TLX3\u003c/em\u003e; Shown here are only the five most significant markers among 43)\u003csup\u003e50\u003c/sup\u003e, only \u003cem\u003eELOVL2\u003c/em\u003e was\u0026nbsp;found\u0026nbsp;in brown bear\u0026nbsp;hair. To improve the accuracy of hair-based age estimation\u0026nbsp;models, it is\u0026nbsp;necessary\u0026nbsp;to\u0026nbsp;employ\u0026nbsp;genome-wide approaches to\u0026nbsp;identify\u0026nbsp;suitable target regions\u0026nbsp;in\u0026nbsp;hair DNA.\u003c/p\u003e\n\u003cp\u003eLinear regression analysis of Δage suggested that the ages of young bears tended to be overestimated, whereas the ages of older bears were underestimated. However, generalized linear regression analysis of |Δage| indicated that the magnitude of prediction error was not significantly associated with age. Many previous studies have reported that age estimation models based on DNA methylation often underestimate the ages of older individuals\u003csup\u003e30,32,68,75\u003c/sup\u003e. One possible explanation is that epigenetic aging follows a logarithmic trajectory across the lifespan; that is, DNA methylation changes occur rapidly in early life but decelerate with age\u003csup\u003e76\u003c/sup\u003e. Alternatively, given that the offspring of long-lived individuals appear to age more slowly than expected in human studies\u003csup\u003e77,78\u003c/sup\u003e, it is possible that individuals who survive to older ages possess biological mechanisms that retard age-related methylation changes. If the relation between DNA methylation and age at the CpG sites used in our model is nonlinear, this may explain why SVR, which accommodates nonlinear patterns, outperformed elastic net regression, which assumes linearity. Nonetheless, the SVR model only partially mitigated the underestimation of older individuals (Figure 2). Teschendorff and Horvath (2025)\u003csup\u003e79\u003c/sup\u003e have suggested that nonlinear methods, such as Gaussian processes, probabilistic kernel density-based approaches, or deep learning could better capture complex and previously unrecognized aging trajectories—particularly when integrated with explainable artificial intelligence tools—thereby improving biological age prediction and advancing our understanding of epigenetic aging. In contrast to prior studies that reported increasing prediction error with age\u003csup\u003e80,81\u003c/sup\u003e, our findings did not show such a trend, and reports of age overestimation in younger individuals remain limited. Future investigations are needed to elucidate the factors contributing to errors in DNA methylation-based age estimation.\u003c/p\u003e\n\u003cp\u003eThe genes located adjacent to the CpG sites that demonstrated strong age-related methylation changes in this study were \u003cem\u003eVGF\u003c/em\u003e (VGF Nerve Growth Factor Inducible), \u003cem\u003eKCNK12\u003c/em\u003e (Potassium Two Pore Domain Channel Subfamily K Member 12), and\u003cem\u003e\u0026nbsp;ELOVL2\u003c/em\u003e(ELOVL fatty acid elongase 2).\u0026nbsp;Correlations\u0026nbsp;between blood DNA methylation levels and age have been\u0026nbsp;previously\u0026nbsp;reported\u0026nbsp;for \u003cem\u003eVGF\u003c/em\u003e in humans, dogs,\u0026nbsp;and brown bears\u003csup\u003e47,61,64\u003c/sup\u003e;\u0026nbsp;for\u0026nbsp;\u003cem\u003eKCNK12\u003c/em\u003e in humans and dogs\u003csup\u003e60,64\u003c/sup\u003e; and\u0026nbsp;for\u0026nbsp;\u003cem\u003eELOVL2\u003c/em\u003e in humans, mice,\u0026nbsp;and cats\u003csup\u003e62,66,82\u003c/sup\u003e.\u0026nbsp;\u003cem\u003eELOVL2\u003c/em\u003e is\u0026nbsp;recognized as\u0026nbsp;a robust epigenetic age marker in humans\u003csup\u003e20\u003c/sup\u003e. These three\u0026nbsp;genomic locations\u0026nbsp;are not tissue-specific,\u0026nbsp;suggesting that\u0026nbsp;hair roots may share\u0026nbsp;common age-related\u0026nbsp;epigenetic markers\u0026nbsp;with\u0026nbsp;other tissues.\u0026nbsp;Although age-associated CpG sites do\u0026nbsp;not necessarily\u0026nbsp;influence gene\u0026nbsp;expression, previous studies have\u0026nbsp;reported that genes\u0026nbsp;linked to\u0026nbsp;these CpG sites are\u0026nbsp;related to\u0026nbsp;lifespan and age-related diseases. Notably,\u0026nbsp;\u003cem\u003eVGF\u003c/em\u003e has been associated with\u0026nbsp;Alzheimer's disease, whereas\u0026nbsp;\u003cem\u003eELOVL2\u003c/em\u003e has been linked to visual function\u003csup\u003e83\u003c/sup\u003e;\u0026nbsp;deletion of \u003cem\u003eELOVL2\u003c/em\u003e results in\u0026nbsp;early-onset\u0026nbsp;hair loss in mice\u003csup\u003e84\u003c/sup\u003e.\u0026nbsp;As\u0026nbsp;the epigenetics of hair\u0026nbsp;remains an emerging research area\u003csup\u003e85,86\u003c/sup\u003e,\u0026nbsp;further\u0026nbsp;studies are required to clarify\u0026nbsp;the potential\u0026nbsp;relations\u0026nbsp;between gene\u0026nbsp;expression\u0026nbsp;and\u0026nbsp;the\u0026nbsp;age-related methylation changes identified\u0026nbsp;in this study.\u003c/p\u003e\n\u003cp\u003eThe limitations and future challenges of this study are as follows. First, as previously noted, the selection of target genomic regions lacked sufficient rationale. Comprehensive genome-wide analyses may address this limitation. Second, our analysis was restricted to hair bulbs of the white sphere type, owing to the limited availability of the other types (i.e., white hook and black hook). Including additional hair types in future analyses would improve model applicability. Third, while we used 10 follicles per sample, further validation is necessary to determine whether accuracy is maintained when fewer follicles are used. Previous studies have not clearly defined the number of hairs required for DNA extraction, with reported values ranging from five to 30\u003csup\u003e49,50\u003c/sup\u003e. Fourth, it is essential to investigate whether hair storage conditions affect DNA methylation levels. This is particularly relevant for samples stored at varying temperatures over extended periods or collected from hair-traps left in the field for several weeks. Fifth, the body hair collected in field settings may not originate from the upper back, which was the source used in this study. Nevertheless, no significant differences in DNA methylation have been reported among various body hair sites—such as the calf, pubic area, armpit, and scalp—in humans\u003csup\u003e49\u003c/sup\u003e. The aforementioned issues are required to be addressed before our method can be applied to field-collected hair samples, such as those obtained using hair traps.\u003c/p\u003e\n\u003cp\u003eIn conclusion, we developed the first hair-based age estimation model for brown bears, achieving an MAE of 3.192 years following LOOCV. In bear research, DNA obtained from hair-traps has traditionally been used to identify individuals and estimate population size. If our method can be applied to past or future hair-trap samples, it may enable the reconstruction of population age structures in wild bears. Although our hair-based model is less accurate than blood-based models and remains limited in estimating the age of single individuals, it offers a simpler, less invasive sample collection method and shows promise in determining population-level age distributions. Moreover, DNA methylation-based age estimation using blood in brown bears has proven applicable to other bear species\u003csup\u003e33\u003c/sup\u003e. Our hair-based method therefore holds substantial potential for cross-species application. While further accuracy improvements are necessary, age estimation using hair is expected to contribute meaningfully to bear conservation and management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by funding from the Japan Society for the Promotion of Science (JSPS) (https://www.jsps.go.jp/english/e-grants/index.html) KAKENHI grant numbers JP19K06833, JP23K05312, JP25H01002, JP24KJ0304, and JP22K14910, Grant for Basic Science Research Projects from The Sumitomo Foundation (grant no. 200561), a grants-in-aid of The Inui Memorial Trust for Research on Animal Science, Japan, JST SPRING (grant no. JPMJSP2119), and World-leading Innovative and Smart Education (WISE) Program from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) (grant no. 1801).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere thanks to all staff at Noboribetsu Bear Park for providing bear hair and sample information. We wish to thank Hatsusaburo Ose and all the members of the Shiretoko Fishery Productive Association for their kind support. We are deeply grateful to all the members of the Shiretoko Nature Foundation for their generous support. We also thank everyone involved in sample collection. Finally, we thank Editage (www.editage.jp) for English language editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFaculty of Veterinary Medicine, Hokkaido University, Hokkaido, 060-0818, Japan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShiori Nakamura, Jumpei Yamazaki, Mina J, Yojiro Yanagawa, Toshio Tsubota \u0026amp; Michito Shimozuru\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOne Health Research Center, Hokkaido University, Hokkaido, 060-0818, Japan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJumpei Yamazaki \u0026amp; Michito Shimozuru\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAzabu University, Kanagawa, 252-5201, Japan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNaoya Matsumoto\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNoboribetsu Bear Park, Hokkaido, 059-0551, Japan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNaoya Matsumoto, Kyogo Hagino \u0026amp; Hideyuki Sakamoto\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShiretoko Nature Foundation, Hokkaido, 099-4356, Japan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMasami Yamanaka \u0026amp; Masanao Nakanishi\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHokkaido Research Organization, Hokkaido, 060-0819, Japan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMina Jimbo\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKyoto City Zoo, Kyoto, 606-8333, Japan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHideyuki Ito\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWildlife Research Center, Kyoto University, Kyoto, 606-8203, Japan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHideyuki Ito\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003eS.N. designed the study, performed laboratory work, and constructed each age estimation model. S.N., N.M., K.H., H.S., M.Y., M.N., M.J., Y.Y., and M.S. were involved in sample collection. J.Y. and H.I. supported with the technical aspects of the experiment. S.N. and M.S. wrote the article with inputs from J.Y., H.I., and T.T. All authors reviewed the article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorresponding author\u003c/p\u003e\n\u003cp\u003eCorrespondence to Michito Shimozuru.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data obtained from pyrosequencing analyses are available in Dryad at: doi:\u0026nbsp;10.5061/dryad.h44j0zpzc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShimozuru, M. et al. Reproductive parameters and cub survival of brown bears in the Rusha area of the Shiretoko Peninsula, Hokkaido, Japan. \u003cem\u003ePLOS ONE\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, e0176251. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0176251\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0176251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShimozuru, M. et al. Estimation of breeding population size using DNA-based pedigree reconstruction in brown bears. \u003cem\u003eEcol. Evol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.9246\u003c/span\u003e\u003cspan address=\"10.1002/ece3.9246\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYagi, G. et al. Non-invasive age estimation based on faecal DNA using methylation-sensitive high-resolution melting for Indo-Pacific bottlenose dolphins. \u003cem\u003eMol. Ecol. Resour.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1755-0998.13906\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.13906\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThomas, D. C. Metachromatic staining of dental cementum for mammalian age determination. \u003cem\u003eJ. Wildl. Manag.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 207\u0026ndash;210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3800596\u003c/span\u003e\u003cspan address=\"10.2307/3800596\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1977).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaws, R. M. A new method of age determination for mammals. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e169\u003c/b\u003e, 972\u0026ndash;973. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/169972b0\u003c/span\u003e\u003cspan address=\"10.1038/169972b0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1952).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLundervold, M., Langvatn, R. \u0026amp; Milner-Gulland, E. J. A comparison of age estimation methods for the saiga antelope Saiga tatarica. \u003cem\u003eWildl. Biology\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, 219\u0026ndash;227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.2981/wlb.2003.054\u003c/span\u003e\u003cspan address=\"10.2981/wlb.2003.054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, Y., Bi, J., Ning, Y. \u0026amp; Feng, J. Methodology Advances in Vertebrate Age Estimation. \u003cem\u003eAnimals\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ani14020343\u003c/span\u003e\u003cspan address=\"10.3390/ani14020343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKimura, D. K., Mandapat, R. R. \u0026amp; Oxford, S. L. Method, validity, and variability in the age determination of yellowtail rockfish (\u003cem\u003eSebastes flavidus\u003c/em\u003e), using otoliths. \u003cem\u003eJ. Fish. Res. Board Can.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 377\u0026ndash;383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1139/f79-057\u003c/span\u003e\u003cspan address=\"10.1139/f79-057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1979).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCattet, M. et al. Can concentrations of steroid hormones in brown bear hair reveal age class? \u003cem\u003eConservation Physiology\u003c/em\u003e 6 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/conphys/coy001\u003c/span\u003e\u003cspan address=\"10.1093/conphys/coy001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWiedower, E. E. et al. Fecal Near Infrared Spectroscopy to Discriminate Physiological Status in Giant Pandas. \u003cem\u003ePLoS ONE\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, e38908. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0038908\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0038908\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHorvath, S. DNA methylation age of human tissues and cell types. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, R115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/gb-2013-14-10-r115\u003c/span\u003e\u003cspan address=\"10.1186/gb-2013-14-10-r115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBogdanović, O. \u0026amp; Veenstra, G. J. C. DNA methylation and methyl-CpG binding proteins: developmental requirements and function. \u003cem\u003eChromosoma\u003c/em\u003e \u003cb\u003e118\u003c/b\u003e, 549\u0026ndash;565. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00412-009-0221-9\u003c/span\u003e\u003cspan address=\"10.1007/s00412-009-0221-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones, P. A. Functions of DNA methylation: islands, start sites, gene bodies and beyond. \u003cem\u003eNat. Rev. Genet.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 484\u0026ndash;492. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrg3230\u003c/span\u003e\u003cspan address=\"10.1038/nrg3230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoore, L. D., Le, T. \u0026amp; Fan, G. D. N. A. Methylation and Its Basic Function. \u003cem\u003eNeuropsychopharmacology\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, 23\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/npp.2012.112\u003c/span\u003e\u003cspan address=\"10.1038/npp.2012.112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones, M. J., Goodman, S. J. \u0026amp; Kobor, M. S. DNA methylation and healthy human aging. \u003cem\u003eAging Cell.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 924\u0026ndash;932. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/acel.12349\u003c/span\u003e\u003cspan address=\"10.1111/acel.12349\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBocklandt, S. et al. Epigenetic Predictor of Age. \u003cem\u003ePLoS ONE\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e, e14821. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0014821\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0014821\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoch, C. M. \u0026amp; Wagner, W. Epigenetic-aging-signature to determine age in different tissues. \u003cem\u003eAging\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 1018\u0026ndash;1027. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/aging.100395\u003c/span\u003e\u003cspan address=\"10.18632/aging.100395\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLe Clercq, L. S., Kotz\u0026eacute;, A., Grobler, J. P. \u0026amp; Dalton, D. L. Biological clocks as age estimation markers in animals: a systematic review and meta-analysis. \u003cem\u003eBiol. Rev.\u003c/em\u003e \u003cb\u003e98\u003c/b\u003e, 1972\u0026ndash;2011. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/brv.12992\u003c/span\u003e\u003cspan address=\"10.1111/brv.12992\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiekkola, L. et al. Application of a multi-disciplinary approach to reveal population structure and Southern Ocean feeding grounds of humpback whales. \u003cem\u003eEcol. Ind.\u003c/em\u003e \u003cb\u003e89\u003c/b\u003e, 455\u0026ndash;465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.ecolind.2018.02.030\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2018.02.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSlieker, R. C., Relton, C. L., Gaunt, T. R., Slagboom, P. E. \u0026amp; Heijmans, B. T. Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception. \u003cem\u003eEpigenetics Chromatin\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13072-018-0191-3\u003c/span\u003e\u003cspan address=\"10.1186/s13072-018-0191-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLowe, R. et al. Ageing-associated DNA methylation dynamics are a molecular readout of lifespan variation among mammalian species. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-018-1397-1\u003c/span\u003e\u003cspan address=\"10.1186/s13059-018-1397-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlughammer, J. et al. Comparative analysis of genome-scale, base-resolution DNA methylation profiles across 580 animal species. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-022-34828-y\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-34828-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaghani, A. et al. DNA methylation networks underlying mammalian traits. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e381\u003c/b\u003e, eabq5693. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.abq5693\u003c/span\u003e\u003cspan address=\"10.1126/science.abq5693\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePolanowski, A. M., Robbins, J., Chandler, D. \u0026amp; Jarman, S. N. Epigenetic estimation of age in humpback whales. \u003cem\u003eMol. Ecol. Resour.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 976\u0026ndash;987. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1755-0998.12247\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.12247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePetkovich, D. A. et al. Using DNA methylation profiling to evaluate biological age and longevity interventions. \u003cem\u003eCell Metabol.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 954\u0026ndash;960e956. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cmet.2017.03.016\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2017.03.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIto, H., Udono, T., Hirata, S. \u0026amp; Inoue-Murayama, M. Estimation of chimpanzee age based on DNA methylation. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 9998. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-018-28318-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-28318-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Paoli-Iseppi, R. et al. Age estimation in a long-lived seabird (\u003cem\u003eArdenna tenuirostris\u003c/em\u003e) using DNA methylation-based biomarkers. \u003cem\u003eMol. Ecol. Resour.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 411\u0026ndash;425. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1755-0998.12981\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.12981\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArai, K., Qi, H. \u0026amp; Inoue-Murayama, M. Age estimation of captive Asian elephants (Elephas maximus) based on DNA methylation: An exploratory analysis using methylation-sensitive high-resolution melting (MS-HRM). \u003cem\u003ePLOS ONE\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, e0294994. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0294994\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0294994\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThompson, M. J., Vonholdt, B., Horvath, S. \u0026amp; Pellegrini, M. An epigenetic aging clock for dogs and wolves. \u003cem\u003eAging\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 1055\u0026ndash;1068. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/aging.101211\u003c/span\u003e\u003cspan address=\"10.18632/aging.101211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrado, N. A. et al. Epigenetic clock and methylation studies in elephants. \u003cem\u003eAging Cell.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/acel.13414\u003c/span\u003e\u003cspan address=\"10.1111/acel.13414\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMayne, B. et al. Age prediction of green turtles with an epigenetic clock. \u003cem\u003eMol. Ecol. Resour.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 2275\u0026ndash;2284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1755-0998.13621\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.13621\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQi, H., Lim, Q. L., Kinoshita, K., Nakajima, N. \u0026amp; Inoue-Murayama, M. A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (\u003cem\u003ePrionailurus bengalensis euptilurus\u003c/em\u003e) and \u003cem\u003ePanthera\u003c/em\u003e species, using targeted bisulphite sequencing and machine learning model. \u003cem\u003eMol. Ecol. Resour.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1755-0998.13928\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.13928\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShimozuru, M. et al. Epigenetic Clock in Bears: A Simple Cost-Effective Blood DNA Methylation-Based Age Estimation Method Applicable to Multiple Bear Species. \u003cem\u003eEcol. Evol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.71424\u003c/span\u003e\u003cspan address=\"10.1002/ece3.71424\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStubbs, T. M. et al. Multi-tissue DNA methylation age predictor in mouse. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-017-1203-5\u003c/span\u003e\u003cspan address=\"10.1186/s13059-017-1203-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArneson, A. et al. A mammalian methylation array for profiling methylation levels at conserved sequences. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-022-28355-z\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-28355-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu, A. T. et al. Universal DNA methylation age across mammalian tissues. \u003cem\u003eNat. Aging\u003c/em\u003e. \u003cb\u003e3\u003c/b\u003e, 1144\u0026ndash;1166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43587-023-00462-6\u003c/span\u003e\u003cspan address=\"10.1038/s43587-023-00462-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan, Y. et al. Epigenetic age-predictor for mice based on three CpG sites. \u003cem\u003eeLife\u003c/em\u003e 7, e37462 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7554/eLife.37462\u003c/span\u003e\u003cspan address=\"10.7554/eLife.37462\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIUCN. The IUCN Red List of Threatened Species. Version 2025-1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iucnredlist.org\u003c/span\u003e\u003cspan address=\"https://www.iucnredlist.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 26 Apr 2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCan, \u0026Ouml;. E., D'Cruze, N., Garshelis, D. L., Beecham, J. \u0026amp; Macdonald, D. W. Resolving human-bear conflict: A global survey of countries, experts, and key factors. \u003cem\u003eConserv. Lett.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 501\u0026ndash;513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/conl.12117\u003c/span\u003e\u003cspan address=\"10.1111/conl.12117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHatter, I. W., Mowat, G. \u0026amp; Mclellan, B. N. Statistical population reconstruction to evaluate grizzly bear trends in British Columbia. \u003cem\u003eCan. Ursus\u003c/em\u003e. \u003cb\u003e29\u003c/b\u003e, 1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2192/ursus-d-18-00001.1\u003c/span\u003e\u003cspan address=\"10.2192/ursus-d-18-00001.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMano, T., Matsuda, H., Natsume, S. \u0026amp; Tsuruga, H. Harvest-based demographic estimation of a brown bear population on the Oshima Peninsula, Hokkaido. \u003cem\u003eUrsus\u003c/em\u003e 2024 (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2192/ursus-d-23-00016.1\u003c/span\u003e\u003cspan address=\"10.2192/ursus-d-23-00016.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMundy, K. R. D. \u0026amp; Fuller, W. A. Age Determination in the Grizzly Bear. \u003cem\u003eJ. Wildl. Manag.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 863\u0026ndash;866 (1964).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarks, S., Erickson, A. W. \u0026amp; A. \u0026amp; Age determination in the black bear. \u003cem\u003eJ. Wildl. Manag.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 389\u0026ndash;410 (1966).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStoneberg, R., Jonkel, C. J. \u0026amp; P. \u0026amp; Age determination of black bears by cementum layers. \u003cem\u003eJ. Wildl. Manag.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 411\u0026ndash;414 (1966).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMclaughlin, C. R. et al. Precision and accuracy of estimating age of Maine black bears by cementum annuli. \u003cem\u003eBears: Their Biology Manage.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 415\u0026ndash;419 (1990).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCostello, C. M. et al. Reliability of the cementum annuli technique for estimating age of black bears in New Mexico. \u003cem\u003eWildl. Soc. Bull.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 169\u0026ndash;176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2193/0091-7648(2004)32[169:rotcat]2.0.co;2\u003c/span\u003e\u003cspan address=\"10.2193/0091-7648(2004)32[169:rotcat]2.0.co;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNakamura, S. et al. Age estimation based on blood DNA methylation levels in brown bears. \u003cem\u003eMol. Ecol. Resour.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 1211\u0026ndash;1225. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1755-0998.13788\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.13788\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCzajka, N., Northrup, J. M., Jones, M. J. \u0026amp; Shafer, A. B. A. Epigenetic clocks, sex markers and age-class diagnostics in three harvested large mammals. \u003cem\u003eMol. Ecol. Resour.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1755-0998.13956\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.13956\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHao, T. et al. Predicting human age by detecting DNA methylation status in hair. \u003cem\u003eELECTROPHORESIS\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e, 1255\u0026ndash;1261. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/elps.202000349\u003c/span\u003e\u003cspan address=\"10.1002/elps.202000349\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHayes, B. J. et al. An Epigenetic Aging Clock for Cattle Using Portable Sequencing Technology. \u003cem\u003eFront. Genet.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2021.760450\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2021.760450\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHanski, E. et al. Epigenetic age estimation of wild mice using faecal samples. \u003cem\u003eMol. Ecol.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mec.17330\u003c/span\u003e\u003cspan address=\"10.1111/mec.17330\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWaits, L. P. \u0026amp; Paetkau, D. Noninvasive Genetic Sampling Tools for Wildlife Biologists: A Review of Applications and Recommendations for Accurate Data Collection. \u003cem\u003eJ. Wildl. Manag.\u003c/em\u003e \u003cb\u003e69\u003c/b\u003e, 1419\u0026ndash;1433 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJimbo, M. et al. Diet selection and asocial learning: Natal habitat influence on lifelong foraging strategies in solitary large mammals. \u003cem\u003eEcosphere\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ecs2.4105\u003c/span\u003e\u003cspan address=\"10.1002/ecs2.4105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMowat, G. \u0026amp; Strobeck, C. Estimating Population Size of Grizzly Bears Using Hair Capture, DNA Profiling, and Mark-Recapture Analysis. \u003cem\u003eJ. Wildl. Manage.\u003c/em\u003e \u003cb\u003e64\u003c/b\u003e, 183\u0026ndash;193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3802989\u003c/span\u003e\u003cspan address=\"10.2307/3802989\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSato, Y. et al. Evaluation of the Effectiveness of Scented Wooden Posts for DNA Hair Snagging of Brown Bears. \u003cem\u003eMammal Study\u003c/em\u003e. \u003cb\u003e45\u003c/b\u003e, 213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3106/ms2018-0045\u003c/span\u003e\u003cspan address=\"10.3106/ms2018-0045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsubota, T., Takahashi, Y. \u0026amp; Kanagawa, H. Changes in Serum Progesterone Levels and Growth of Fetuses in Hokkaido Brown Bears. \u003cem\u003eBears: Their Biology Manage.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 355\u0026ndash;358. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3872643\u003c/span\u003e\u003cspan address=\"10.2307/3872643\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1987).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsubota, T., Maeda, N. \u0026amp; Kanagawa, H. Parturition and Postnatal Development in the Captive Hokkaido Brown Bear \u003cem\u003eUrsus arctos yesoensis\u003c/em\u003e. \u003cem\u003eJ. Mammalogical Soc. Japan\u003c/em\u003e. \u003cb\u003e19\u003c/b\u003e, 75\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11238/jmammsocjapan.19.75\u003c/span\u003e\u003cspan address=\"10.11238/jmammsocjapan.19.75\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1994).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFriebe, A. et al. Factors Affecting Date of Implantation, Parturition, and Den Entry Estimated from Activity and Body Temperature in Free-Ranging Brown Bears. \u003cem\u003ePLoS ONE\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, e101410. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0101410\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0101410\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJimbo, M. et al. Hair Growth in Brown Bears and Its Application to Ecological Studies on Wild Bears. \u003cem\u003eMammal Study\u003c/em\u003e. \u003cb\u003e45\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3106/ms2020-0021\u003c/span\u003e\u003cspan address=\"10.3106/ms2020-0021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDay, K. et al. Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, R102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/gb-2013-14-9-r102\u003c/span\u003e\u003cspan address=\"10.1186/gb-2013-14-9-r102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFlorath, I., Butterbach, K., Muller, H., Bewerunge-Hudler, M. \u0026amp; Brenner, H. Cross-sectional and longitudinal changes in DNA methylation with age: an epigenome-wide analysis revealing over 60 novel age-associated CpG sites. \u003cem\u003eHum. Mol. Genet.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 1186\u0026ndash;1201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/hmg/ddt531\u003c/span\u003e\u003cspan address=\"10.1093/hmg/ddt531\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBekaert, B., Kamalandua, A., Zapico, S. C., Van De Voorde, W. \u0026amp; Decorte, R. Improved age determination of blood and teeth samples using a selected set of DNA methylation markers. \u003cem\u003eEpigenetics\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 922\u0026ndash;930. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15592294.2015.1080413\u003c/span\u003e\u003cspan address=\"10.1080/15592294.2015.1080413\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLowe, R. et al. Ageing-associated DNA methylation dynamics are a molecular readout of lifespan variation among mammalian species. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-018-1397-1\u003c/span\u003e\u003cspan address=\"10.1186/s13059-018-1397-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIto, G., Yoshimura, K. \u0026amp; Momoi, Y. Analysis of DNA methylation of potential age-related methylation sites in canine peripheral blood leukocytes. \u003cem\u003eJ. Vet. Med. Sci.\u003c/em\u003e \u003cb\u003e79\u003c/b\u003e, 745\u0026ndash;750. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1292/jvms.16-0341\u003c/span\u003e\u003cspan address=\"10.1292/jvms.16-0341\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYamazaki, J. et al. Obese status is associated with accelerated DNA methylation change in peripheral blood of senior dogs. \u003cem\u003eRes. Vet. Sci.\u003c/em\u003e \u003cb\u003e139\u003c/b\u003e, 193\u0026ndash;199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rvsc.2021.07.024\u003c/span\u003e\u003cspan address=\"10.1016/j.rvsc.2021.07.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQi, H. et al. Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-99424-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-99424-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaj, K. et al. Epigenetic clock and methylation studies in cats. \u003cem\u003eGeroScience\u003c/em\u003e 43, 2363\u0026ndash;2378 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11357-021-00445-8\u003c/span\u003e\u003cspan address=\"10.1007/s11357-021-00445-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu, C. et al. A novel strategy for forensic age prediction by DNA methylation and support vector regression model. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 17788. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep17788\u003c/span\u003e\u003cspan address=\"10.1038/srep17788\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInteragency Grizzly Bear Committee. \u003cem\u003eGrizzly Bear Compendium\u003c/em\u003e (The National Wildlife Federation, 1987).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Magalh\u0026atilde;es, J. P. et al. Human Ageing Genomic Resources: updates on key databases in ageing research. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e, D900\u0026ndash;d908. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkad927\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkad927\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFokias, K., Dierckx, L., Van de Voorde, W. \u0026amp; Bekaert, B. Age determination through DNA methylation patterns in fingernails and toenails. \u003cem\u003eForensic Sci. International: Genet.\u003c/em\u003e \u003cb\u003e64\u003c/b\u003e, 102846. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.fsigen.2023.102846\u003c/span\u003e\u003cspan address=\"10.1016/j.fsigen.2023.102846\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFokias, K., Dierckx, L., Van de Voorde, W. \u0026amp; Bekaert, B. Improving the age estimation model for toenails. \u003cem\u003eForensic Sci. International: Genet.\u003c/em\u003e \u003cb\u003e66\u003c/b\u003e, 102911. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.fsigen.2023.102911\u003c/span\u003e\u003cspan address=\"10.1016/j.fsigen.2023.102911\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCiucci, P. et al. Estimating abundance of the remnant Apennine brown bear population using multiple noninvasive genetic data sources. \u003cem\u003eJ. Mammal.\u003c/em\u003e \u003cb\u003e96\u003c/b\u003e, 206\u0026ndash;220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jmammal/gyu029\u003c/span\u003e\u003cspan address=\"10.1093/jmammal/gyu029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEl Khoury, L. Y. et al. Systematic underestimation of the epigenetic clock and age acceleration in older subjects. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-019-1810-4\u003c/span\u003e\u003cspan address=\"10.1186/s13059-019-1810-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSnir, S., Farrell, C. \u0026amp; Pellegrini, M. Human epigenetic ageing is logarithmic with time across the entire lifespan. \u003cem\u003eEpigenetics\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 912\u0026ndash;926. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15592294.2019.1623634\u003c/span\u003e\u003cspan address=\"10.1080/15592294.2019.1623634\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGentilini, D. et al. Role of epigenetics in human aging and longevity: genome-wide DNA methylation profile in centenarians and centenarians\u0026rsquo; offspring. \u003cem\u003eAGE\u003c/em\u003e 35, 1961\u0026ndash;1973 (2013). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11357-012-9463-1\u003c/span\u003e\u003cspan address=\"10.1007/s11357-012-9463-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHorvath, S. et al. Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring. \u003cem\u003eAging\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 1159\u0026ndash;1170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/aging.100861\u003c/span\u003e\u003cspan address=\"10.18632/aging.100861\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeschendorff, A. E. \u0026amp; Horvath, S. Epigenetic ageing clocks: statistical methods and emerging computational challenges. \u003cem\u003eNat. Rev. Genet.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e, 350\u0026ndash;368. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41576-024-00807-w\u003c/span\u003e\u003cspan address=\"10.1038/s41576-024-00807-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZbieć-Piekarska, R. et al. Development of a forensically useful age prediction method based on DNA methylation analysis. \u003cem\u003eForensic Sci. International: Genet.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 173\u0026ndash;179. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eorg/10.1016/j.fsigen.2015.05.001\u003c/span\u003e\u003cspan address=\"10.1016/j.fsigen.2015.05.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.\u003c/span\u003e\u003cspan address=\"https://doi.org/https://doi.\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamano, Y. et al. Forensic age prediction for dead or living samples by use of methylation-sensitive high resolution melting. \u003cem\u003eLeg. Med.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 5\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.legalmed.2016.05.001\u003c/span\u003e\u003cspan address=\"10.1016/j.legalmed.2016.05.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpiers, H. et al. Age-associated changes in DNA methylation across multiple tissues in an inbred mouse model. \u003cem\u003eMech. Ageing Dev.\u003c/em\u003e \u003cb\u003e154\u003c/b\u003e, 20\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.mad.2016.02.001\u003c/span\u003e\u003cspan address=\"10.1016/j.mad.2016.02.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/\u003c/span\u003e\u003cspan address=\"https://doi.org/https://doi.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson, A. A., Torosin, N. S., Shokhirev, M. N. \u0026amp; Cuellar, T. L. A set of common buccal CpGs that predict epigenetic age and associate with lifespan-regulating genes. \u003cem\u003eiScience\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 105304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.isci.2022.105304\u003c/span\u003e\u003cspan address=\"10.1016/j.isci.2022.105304\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, X. et al. Lipid metabolism dysfunction induced by age-dependent DNA methylation accelerates aging. \u003cem\u003eSignal. Transduct. Target. Therapy\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41392-022-00964-6\u003c/span\u003e\u003cspan address=\"10.1038/s41392-022-00964-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, J. et al. Progressive Alopecia Reveals Decreasing Stem Cell Activation Probability during Aging of Mice with Epidermal Deletion of DNA Methyltransferase 1. \u003cem\u003eJ. Invest. Dermatology\u003c/em\u003e. \u003cb\u003e132\u003c/b\u003e, 2681\u0026ndash;2690. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/jid.2012.206\u003c/span\u003e\u003cspan address=\"10.1038/jid.2012.206\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, S. et al. Integrative Analysis of Methylome and Transcriptome Reveals the Regulatory Mechanisms of Hair Follicle Morphogenesis in Cashmere Goat. \u003cem\u003eCells\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 969. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cells9040969\u003c/span\u003e\u003cspan address=\"10.3390/cells9040969\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Values of mean absolute error (MAE), median absolute error (MedAE), and root mean square error (RMSE) for each age estimation model.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003emodel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCpG sites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eMAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eMedAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eSingle regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eVGF-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e4.138\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.127\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5.353\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eKCNK12-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.978\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e3.270\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.010\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eELOVL2-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.525\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e4.359\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e7.278\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eElastic net regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eall CpGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.627\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.375\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.630\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eSupport vector regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eall CpGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.398\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.916\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.409\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eVGF-1, -2, -3,\u003c/p\u003e\n \u003cp\u003eKCNK12-1, -2, -3,\u003c/p\u003e\n \u003cp\u003eELOVL2 -2, -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.342\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.914\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.329\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eVGF-1, -2, -3,\u003c/p\u003e\n \u003cp\u003eKCNK12-1, -2, -3,\u003c/p\u003e\n \u003cp\u003eELOVL2 -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.255\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.511\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.138\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eVGF-1, -3,\u003c/p\u003e\n \u003cp\u003eKCNK12-1, -2, -3,\u003c/p\u003e\n \u003cp\u003eELOVL2 -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.359\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.647\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.241\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eVGF-1, -3,\u003c/p\u003e\n \u003cp\u003eKCNK12-2, -3,\u003c/p\u003e\n \u003cp\u003eELOVL2 -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.367\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.718\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.246\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eVGF-1, -3,\u003c/p\u003e\n \u003cp\u003eKCNK12-2, -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.634\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e3.015\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.533\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eVGF-1, -3,\u003c/p\u003e\n \u003cp\u003eKCNK12-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.192\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.188\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.238\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eVGF-3,\u003c/p\u003e\n \u003cp\u003eKCNK12-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.513\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.766\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.468\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eVGF-1, -2, -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.462\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.439\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5.020\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eKCNK12-1, -2, -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.845\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e3.214\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.909\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eELOVL2-1, -2, -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.048\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e3.864\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e7.260\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAll values were rounded to the fourth decimal place.\u003c/p\u003e\n\u003cp\u003eEach bold value denotes the minimum value for each regression model/\u003c/p\u003e\n\u003cp\u003eTable 2. Coefficients and p-values from the linear regression analysis of \u0026Delta;age and generalized linear regression analysis of |\u0026Delta;age| in the best-performing model (SVR model using three CpGs: VGF-1, VGF-3, and KCNK12-3).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.07009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.33846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e-0.21261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00169\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eGrowth environment (wild)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e-0.2037000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.1647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e|\u0026Delta;age|\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e-1.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Bold values denote statistical significance (p\u0026lt;0.05).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"epigenetic clock, age estimation, brown bear, DNA methylation, wildlife management, aging","lastPublishedDoi":"10.21203/rs.3.rs-7042508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7042508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInformation on age is essential for exploring the life history, conservation, and management of wildlife. Recently, DNA methylation levels-based methods using blood or skin have been established as alternatives to the traditional tooth-based method in bear species. However, the collection of these tissues is limited to captured or dead individuals. In the present study, we established the first hair-based age estimation model based on DNA methylation levels in brown bears, aiming for future application to less-invasively obtained hair of wild individuals. We performed bisulfite pyrosequencing and measured the methylation levels of hair root DNA. The methylation levels of cytosine-phosphate-guanine sites adjacent to the genes \u003cem\u003eVGF\u003c/em\u003e, \u003cem\u003eKCNK12\u003c/em\u003e, and \u003cem\u003eELOVL2\u003c/em\u003e were found to be correlated with age. The best age estimation model used three cytosine-phosphate-guanine sites adjacent to two genes, \u003cem\u003eVGF\u003c/em\u003e and \u003cem\u003eKCNK12\u003c/em\u003e, with a mean absolute error of 3.2 years and median absolute error of 2.2 years after leave-one-out cross-validation. Our method is innovative because of the simplicity of sampling and the lack of requirement to capture bears. If this method can be widely applied to hair samples obtained in the field, the age structure of wild populations can be understood, contributing to ecological research, conservation, and management of bear species.\u003c/p\u003e","manuscriptTitle":"Less-invasive age estimation using hair based on DNA methylation in brown bears","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 12:35:13","doi":"10.21203/rs.3.rs-7042508/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-19T09:35:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-19T07:33:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100548416987162707811569179056482622983","date":"2025-09-17T13:12:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109307824149303784072456016243180606465","date":"2025-09-17T12:53:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75053717622540100459797969751926454124","date":"2025-09-17T06:43:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174440135776716284417098549021893583509","date":"2025-09-15T07:42:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-19T18:18:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112581341614522072624985048108444571400","date":"2025-07-30T15:24:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T14:47:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T14:35:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-25T13:35:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-21T10:59:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-21T10:40:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6de42415-5d72-481a-bc0b-dc6bc74f5db6","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52529503,"name":"Biological sciences/Biological techniques"},{"id":52529504,"name":"Biological sciences/Ecology"},{"id":52529505,"name":"Earth and environmental sciences/Ecology"},{"id":52529506,"name":"Biological sciences/Genetics"},{"id":52529507,"name":"Biological sciences/Molecular biology"},{"id":52529508,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2026-01-05T16:06:46+00:00","versionOfRecord":{"articleIdentity":"rs-7042508","link":"https://doi.org/10.1038/s41598-025-27455-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-29 15:57:35","publishedOnDateReadable":"December 29th, 2025"},"versionCreatedAt":"2025-08-04 12:35:13","video":"","vorDoi":"10.1038/s41598-025-27455-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-27455-2","workflowStages":[]},"version":"v1","identity":"rs-7042508","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7042508","identity":"rs-7042508","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}