Biogerontology

Human life expectancy has increased dramatically over the past two centuries, marking a significant public health achievement. While some projections predict a future where median lifespans reach 100 years, others contend that further longevity will depend on breakthroughs targeting the biological processes of aging. Recent studies in mice have demonstrated that telomerase activation, achieved via gene therapy and transgenic approaches, can extend both median and maximum lifespans substantially without an accompanying increase in cancer risk. We analysed survival data from three such studies using the Gompertz mortality model and show that these interventions reduce the slope parameter, indicative of a slower aging rate, rather than merely lowering baseline mortality. This observation challenges traditional models that assume independent, additive damage accumulation, suggesting instead that aging is driven by a limited number of interdependent processes with significant cross-talk. Mathematical modelling indicates that only three to five processes with substantial cross-talk may account for the observed deceleration. Extrapolation using Swedish survivorship data further implies that a reduction in the aging rate, similar to that seen in mice, could elevate the median human lifespan from 85 to over 100 years. These findings provide a compelling framework for developing targeted anti-aging interventions and a new perspective on the modifiability of the aging process.

Biological-age models quantify the physiological aging process by relating biomarker profiles (e.g., blood biochemistry, DNA methylation) to all-cause mortality risk. These models outperform chronological age in predicting disease and mortality, making them useful metrics for preventative health. However, in existing biological-age models, biomarker contributions do not align with the non-linear associations biomarkers exhibit with long-term mortality risk, nor do they account for normative trajectories that occur in healthy aging, limiting their utility in a clinical setting. To address these limitations, we developed a biological-age framework (NiaAge) where biomarker contributions are derived directly from non-linear associations with long-term mortality risk and aligned with normative trajectories observed in healthy aging. As a result, biomarker contributions to NiaAge are consistent with known biomarker risk profiles and normative reference ranges. We trained NiaAge in the 1999-2000 cohort of the US National Health and Nutrition Examination Survey (NHANES; N=2028) on 59 biomarkers spanning multiple physiological domains (e.g., hematology, metabolism, inflammation), then evaluated it in the 2001-2002 cohort (N=2346). NiaAge predicted long-term mortality, physical-health, and cognitive-health significantly better than chronological age. It also outperformed several DNA-methylation age clocks on mortality and physical/cognitive health-span metrics, while performing comparably to leading physiological age clocks. These results position NiaAge as a valuable tool for preventative health.

It is hypothesized that there are inter-individual differences in biological aging; however, differences in aging among (heart images vs. electrophysiology) and across (e.g., brain vs heart) physiological dimensions have not been systematically evaluated and compared. We analyzed 676,787 samples from 502,211 UK Biobank participants aged 37-82 years with deep learning approaches to build a total of 331 chronological age predictors on different data modalities such as videos (e.g. heart magnetic resonance imaging [MRI]), images (e.g. brain, liver and pancreas MRIs), time-series (e.g. electrocardiograms [ECGs], wrist accelerometer data) and scalar data (e.g. blood biomarkers) to characterize the multiple dimensions of aging. We combined these age predictors into 11 main aging dimensions, 31 subdimensions and 84 sub-subdimensions ensemble models based on specific organ systems. Heart dimension features predict chronological age with a testing root mean squared error (RMSE) and standard error of 2.83{+/-}0.04 years and musculoskeletal dimension features predict age with a RMSE of 2.65{+/-}0.04 years. We defined "accelerated" agers as participants whose predicted age was greater than their chronological age and computed the correlation between these different definitions of accelerated aging. We found that most aging dimensions are modestly correlated (average correlation=.139{+/-}.090) but that dimensions that are biologically related tend to be more positively correlated. For example, we found that heart anatomical (from MRI) accelerated aging and heart electrical (from ECG) accelerated aging are correlated (average Pearson of .249{+/-}.005). Overall, most dimensions of aging are complex traits with both genetic and non-genetic correlates. We identified 9,697 SNPs in 3,318 genes associated with accelerated aging and found an average GWAS-based heritability for accelerated aging of 26.1{+/-}7.42% (e.g. heart aging: 35.2{+/-}1.6%). We used GWAS summary statistics to estimate genetic correlation between aging dimensions and we found that most aging dimensions are genetically not correlated (average correlation=.104{+/-}.149). However, on the other hand, specific dimensions were genetically correlated, such as heart anatomical and electrical accelerated aging (Pearson rho .508{+/-}.089 correlated [r_g]). Finally, we identified biomarkers, clinical phenotypes, diseases, family history, environmental variables and socioeconomic variables associated with accelerated aging in each aging dimension and computed the correlation between the different aging dimensions in terms of these associations. We found that environmental and socioeconomic variables are similarly associated with accelerated aging across aging dimensions (average correlations of respectively .639{+/-}.180 and .607{+/-}.309). Dimensions are weakly correlated with each other, highlighting the multidimensionality of the aging process. Our results can be interactively explored on the following website: https://www.multidimensionality-of-aging.net/

NAD+ homeostasis is an important determinant of lifespan and may be a key mechanism of caloric restriction (CR) expansion of lifespan. Ketone bodies such as beta-hydroxybutyrate (BHB) that regulate NAD+ abundance and NAD+ precursors such nicotinamide mononucleotide (NMN), as are known to extend life in experimental animals and ameliorate age-related conditions in humans. We tested the hypothesis that chronic BHB and NMN exposure separately or in combination can extend lifespan in a model organism Daphnia, a freshwater zooplankton crustacean with the magnitude similar to that of the CR treatment. We also measured fecundity, lipofuscin accumulation, and lipid investments into offspring in Daphnia fed the full diet, full diet with BHB, NMN, and combined treatments, and fed the CR diet (25% of the full diet). We also conducted an RNAseq experiment comparing the two diets and the two exposure treatments. We show that BHB exposure, but not NMN exposure reduces early life mortality in Daphnia fed the full diet to levels similar to those observed under CR without compromising fecundity. We also observed that in a combined exposure cohort, NMN nearly eliminates the beneficial effect of BHB. None of the treatments affected lipofuscin accumulation, but the NMN and the combined treatment mimicked the effect of CR on neonate size in older females. We show that BHB-treated Daphnia change expression of a variety of genes, including genes with known longevity extending effects, but differential expression of few genes is consistent with the effects of CR and their functionality is not clear.

Chronological age overlooks the heterogeneity in aging. In response, a wide range of molecular aging biomarkers has been developed to better capture an individual"s aging rate. Yet, a comprehensive comparison of modeling choices in the development of these biomarkers is lacking. In this study, we trained aging biomarkers on the Rockwood frailty index (FI) and all-cause mortality using UK Biobank Olink proteomics and metabolomics (1H-NMR) data (n=40,696). We systematically established the impact of model choice, target outcome, and molecular data source on several age-related outcomes. From this, we developed ProteinFrailty (ProtFI), an elastic net model using a minimal set of proteins to predict FI. ProtFI outperformed established aging biomarkers in relation to diverse outcomes, including incident cardiovascular disease, handgrip strength, and self-rated health, both in internal validation and two Dutch external cohorts (n=995, n=500). Our findings show that an efficient frailty-trained proteomic biomarker robustly predicts age-related decline.

Gait is a scalable biomarker of functional, physical, and brain health, but most studies rely on gait speed alone. Here, we developed and validated gait age clocks that estimate age from multidimensional gait features and quantify deviations as gait age gaps, with gaps >0 (<0) for accelerated (delayed) aging. We included data from 5,681 participants, including healthy controls and clinical groups (Parkinson's disease, neurodegenerative diseases, stroke, diabetes, fallers, and frailty). Normative models trained in healthy controls showed robust age prediction (r=0.851, p<0.001), and full gait models outperformed gait speed alone ({Delta}R2=0.175). Gaps captured accelerated aging across neurological and physical conditions, tracked Parkinson's disease severity, and were associated with frailty, physical performance, white matter hyperintensities, and geriatric depression. Gait age gaps are also related to brain aging, risk/protective lifestyle factors, and mortality risk. These findings support gait age gaps as an interpretable biomarker for aging, risk stratification, and clinical monitoring.

Cellular hallmarks of aging have been discovered and characterized in a number of model species for studying aging biology - such as humans, mice, fruit flies, and nematodes. Whether these canonical age-related changes to cellular physiology are present across diverse species that have variable rates of demographic aging remains less studied. Here, we tested whether several ubiquitous cellular hallmarks of aging - mitochondrial function, reactive oxygen species generation, and inducible DNA damage - change with age and in a sex-dependent manner in a species with indeterminate growth and reproduction (painted turtles, Chrysemys picta). A further feature of their biology that recommends them for an ecological model of vertebrate aging is their female-biased longevity, despite an absence of genotypic sex determination. Thus lifespan and aging may be reliable features of sex-specific life-histories. We measured aspects of mitochondrial health (cellular basal, maximal, and spare oxygen consumption rates), cellular levels of reactive oxygen species, and aspects of DNA damage and repair from exposure to UVB. We used these measures across several physiological axes as proxies for age-related physiological dysfunction. We further assessed our measures across several populations of painted turtles. We found that sex explained the largest proportion of variation, with males differing from females in mitochondrial function, reactive oxygen species production, and inducible DNA damage. In several cases, age significantly interacted with sex, but the effect size was small relative to sex alone. Thus, we found that sex, rather than age or size, was a consistent predictor of cellular aging physiological in this species with where females live longer and age slower.

Epigenetic clocks are powerful tools for estimating both chronological and biological age, enabling the integration of age information into population monitoring, demographic modelling, and research on the ecophysiology and evolution of ageing. Most epigenetic clocks so far have been developed for mammals: here, we present the Bird Epigenetic Ageing Clock (BEAC) for estimating chronological age in avian species. BEAC was established based on genome-wide enzymatic methylation sequencing data of known-age king penguins (Aptenodytes patagonicus), and validated in nine other bird species. The BEAC collects age-informative signals into a bisulfite amplicon sequencing panel of 24 primer pairs, providing a highly accurate and cost-effective alternative to sequencing-intensive approaches. It achieved strong predictive performance in independent king penguin training (R{superscript 2}=0.88; MAE=1.7 years, n=78) and testing data (R{superscript 2}=0.79; MAE=2.3 years, n=41), with negligible batch effects, high longitudinal consistency, and resilience to reduced sample size or missing loci. Importantly, cross-species validation across 180 samples showed that BEAC reliably captures age-associated methylation signals in nine additional bird species across seven clades, demonstrating that a single set of loci can be predictive of ageing across multiple different bird species. BEAC offers a flexible, empirically validated tool and a transferable framework for developing epigenetic clocks in avian species, providing a highly valuable resource for eco-evolutionary studies of ageing in wild species.

Caloric restriction (CR) is the most studied pro-longevity intervention; however, a complete understanding of its underlying mechanisms remains elusive, and new research directions may emerge from the identification of novel CR-related genes and CR-related genetic features. This work used a Machine Learning (ML) approach to classify ageing-related genes as CR-related or NotCR-related using 9 different types of predictive features: PathDIP pathways, two types of features based on KEGG pathways, two types of Protein-Protein Interactions (PPI) features, Gene Ontology (GO) terms, Genotype-Tissue Expression (GTEx) expression features, Gene-Friends co-expression features and protein sequence descriptors. Our findings suggested that features biased towards curated knowledge (i.e. GO terms and biological pathways), had the greatest predictive power, while unbiased features (mainly gene expression and co-expression data) have the least predictive power. Moreover, a combination of all the feature types diminished the predictive power compared to predictions based on curated knowledge. Feature importance analysis on the two most predictive classifiers mostly corroborated existing knowledge and supported recent findings linking CR to the Nuclear Factor Erythroid 2-Related Factor 2 (NRF2) signalling pathway and G protein-coupled receptors (GPCR). We then used the two strongest combinations of feature type and ML algorithm to predict CR-relatedness among ageing-related genes currently lacking CR-related annotations in the data, resulting in a set of promising candidate CR-related genes (GOT2, GOT1, TSC1, CTH, GCLM, IRS2 and SESN2) whose predicted CR-relatedness remain to be validated in future wet-lab experiments.

We present the first radiographic ageing framework for common dolphins (Delphinus delphis), based on ossification and epiphyseal fusion patterns in the pectoral flipper, demonstrating higher reliability for chronological age estimation than currently available epigenetic approaches for this species. Using individuals of known dental age, we calibrated two modelling approaches to predict dental age from radiographic bone scores: 1) a univariate polynomial regression using a total bone score (sum of 16 scores across all assessed flipper bones), and 2) a multivariate canonical analysis of principal coordinates (CAP) incorporating 16 individual bone-score variables. Both approaches successfully predicted dental age from skeletal ossification patterns. For an age range of 0 to 24 years, polynomial regression demonstrated high predictive accuracy with median absolute errors (MAEs) of 1.25 years in females (Spearmans {rho} = 0.93, R{superscript 2} = 0.90) and 1.08 years in males ({rho} = 0.95, R{superscript 2} = 0.86). The CAP model yielded MAEs of 1.35 years in females ({rho} = 0.90, R{superscript 2} = 0.85) and 1.80 years in males ({rho} = 0.94, R{superscript 2} = 0.84). Notably, both radiographic bone ageing models achieved equal or lower median absolute errors and higher coefficients of determination than a recently developed epigenetic clock for common dolphins derived from the same population (MAE = 1.80, Pearsons correlation (r) = 0.91, R{superscript 2} = 0.82). When applying the bone ageing models to individuals of unknown dental age, both models produced age estimates consistent with expected life-history stages (foetus, neonate, juvenile, subadult, adult), although accuracy declined in dolphins above 20 years, likely as a consequence of subtle age-related variation in skeletal changes in this species. Radiographic ageing provides an accurate non-invasive tool for demographic assessment to support conservation management of common dolphins.

Proteome imbalance can lead to protein misfolding and aggregation which is associated with pathologies. Protein aggregation can also be an active, organized process and can be exploited by cells as a survival strategy. In adverse conditions, it is beneficial to deposit the proteins in a condensate rather degrading and resynthesizing. Membraneless organelles (MLOs) are biological condensates formed through liquid-liquid phase separation (LLPS), involving cellular components such as nucleic acids and proteins. LLPS is a regulated process, which when perturbed, can undergo a transition from a physiological liquid condensate to pathological solid-like protein aggregates. To understand how the MLO-associated proteins (MLO-APs) behave during aging, we performed a comparative meta-analysis with age related proteome of C. elegans. We found that the MLO-APs are highly abundant throughout the lifespan. Interestingly, they are aggregating more in long-lived mutant worms compared to the age matched wildtype worms. GO term analysis revealed that the cell cycle and embryonic development are among the top enriched processes in addition to RNP components in insoluble proteome. Considering antagonistic pleotropic nature of these developmental genes and post mitotic status of C. elegans, we assume that these proteins phase transit during post development. As the organism ages, these MLO-APs either mature to become more insoluble or dissolve in uncontrolled manner. However, in the long-lived daf-2 mutant worms, the MLOs may attain protective states due to extended availability and association of molecular chaperones.

Clustering effects, such as those introduced by housing animals in shared cages, are often overlooked in preclinical lifespan studies, despite their potential to distort variance estimates and inflate Type I error rates, leading to misleading conclusions. This methodological oversight reduces statistical rigor and may undermine the reliability of findings. To address this gap, the current study examines the impact of accounting for clustering and nesting effects on lifespan analyses by comparing the results of statistical models which both account for and ignore these effects. Using 2019 data from the Interventions Testing Program (ITP), a large-scale initiative evaluating the effects of compounds on lifespan in UM-HET3 mice as a case study, we illustrate how different modeling approaches influence statistical estimates and conclusions. Clustering and nesting effects were addressed using linear mixed effects, and Cox frailty models, both of which explicitly account for cage-level dependencies and different levels of data nesting. Comparisons were made between unadjusted lifespan analyses and those incorporating clustering and nesting adjustments. The results of this case study indicate that properly adjusting for clustering and nesting effects can change the conclusions drawn from statistical significance tests as compared to unadjusted model approaches, and so it remains best practice to properly account for clustering and nesting to reduce the potential for inflated Type I error rates. These findings highlight the importance of accounting for clustering and nesting in preclinical research to ensure valid and robust statistical inference. By demonstrating the practical application of clustering adjustments, this work underscores the broader implications for improving reproducibility and rigor in lifespan studies and other experimental designs.

IntroductionAn urgent need to delay the onset of aging-associated diseases has arisen due to increasing human lifespan. A dramatic surge in the number of identified potential molecular targets that could promote successful aging, has led to the challenge of prioritizing these targets for further research and drug development. In our previous work, we prioritized genes associated with aging processes based on their similarity to known aging-related genes and dysfunction marker genes in C. elegans. The goal of this study was to demonstrate the ability of our computational platform to identify molecular drivers of neuronal aging using specialized causal inference techniques. S6K was highly ranked in the previous study and here the nearby neighbors in its protein interaction network were selected to explore ALaSCAs (Adaptable Large-Scale Causal Analysis) ability to identify possible drivers of Alzheimers disease. MethodsUtilizing head and brain proteome data, two of ALaSCAs capabilities were used to understand how protein changes over the lifespan of Drosophila melanogaster affect a feature of neuronal aging, namely climbing ability: O_LIPearson correlation analysis was used to assess the relationship between the changes in abundance of specific proteins associated (through protein-protein interactions) with S6K and climbing ability. C_LIO_LIPearlian causal inference, required to achieve formal causal analysis, was used to determine which pathway, associated with proteins linked to S6K, has the largest effect on climbing ability and therefore to what degree these specific proteins are driving neuronal aging. C_LI Results and discussionBased on the correlation results, the proteins associated with fz, a gene encoding for the fz family of receptors that are involved in Wnt signaling, display an increase in abundance as climbing ability declines over time. When viewed together with the fz proteins strong negative causal value, it seems that their increased abundance over the lifespan of Drosophila is an important driver of the observed decrease in climbing ability. Additionally, expression of the genes FZD1 and FZD7 (fz orthologs) is altered in the hippocampus early on in Alzheimers disease human samples and in an amyloid precursor protein mouse model. ConclusionWe have demonstrated the potential of the ALaSCA platform to identify and provide evidence behind molecular mechanisms. This capability enables identification of possible drivers of Alzheimers disease - as the human orthologs of the proteins identified here, through its Pearlian causal inference capability, have been linked to Alzheimers disease progression.

Life history theory suggests that aging is one of the costs of reproduction. Accordingly, a higher reproductive allocation is expected to increase the deterioration of both the somatic and the germinal lines through enhanced telomere attrition. In most species, males reproductive allocation mainly regards traits that increase mating and fertilization success, i.e. sexually selected traits. In the current study, we tested the hypothesis that a higher investment in sexually selected traits is associated with a reduced telomere length in the guppy (Poecilia reticulata), an ectotherm species characterized by strong pre- and postcopulatory sexual selection. We first measured telomere length in both the soma and the sperm over the course of guppys lifespan to see if there was any variation in telomere length associated with age. Secondly, we investigated whether a greater expression of pre- and postcopulatory sexually selected traits is linked to shorter telomere length in both the somatic and the sperm germinal lines, and in young and old males. We found that telomeres lengthened with age in the somatic tissue, but there was no age-dependent variation in telomere length in the sperm cells. Telomere length in guppies was significantly and negatively correlated with sperm production in both tissues and life stages considered in this study. Our findings indicate that telomere erosion in male guppies is more strongly associated with their reproductive investment (sperm production) rather than their age, suggesting a trade-off between reproduction and maintenance is occurring at each stage of males life in this species.

The gut microbiome is known to be a driver of age-related health decline. Various studies have shone light on the role of the gut microbiome as a marker as well as modulator of aging processes. However, the mechanisms by which the microbiome affects aging are still unclear. We have developed a Microbiome Metabolite Aging (MMA) fusion network by building upon a metabolic interaction network of gut microbiota to develop associations with the hallmarks of aging. The MMA, consisting of 238 metabolite-aging hallmark interactions serves as a tool to investigate the mammalian (and in particular human) gut microbiome as an effector of aging at a systems-level. The network further identifies 249 microbes that unequivocally affect the hallmarks of aging. The results highlight how the underlying biology of microbial metabolite mediated interactions, in conjunction with the topological properties at a network level, differentially regulate the aging hallmarks. This detailed microbial and metabolite association to the hallmarks of aging provides a foundation which is envisaged to be instrumental in advancing our knowledge of the physiology of aging, and for the development of novel therapeutic tools.

Accurate measurement of the biological markers of the aging process could provide an "aging clock" measuring predicted longevity and allow for the quantification of the effects of specific lifestyle choices on healthy aging. Using modern machine learning techniques, we demonstrate that chronological age can be predicted accurately from (a) the expression level of human genes in capillary blood, and (b) the expression level of microbial genes in stool samples. The latter uses the largest existing metatranscriptomic dataset, stool samples from 90,303 individuals, and is the highest-performing gut microbiome-based aging model reported to date. Our analysis suggests associations between biological age and lifestyle/health factors, e.g., people on a paleo diet or with IBS tend to be biologically older, and people on a vegetarian diet tend to be biologically younger. We delineate the key pathways of systems-level biological decline based on the age-specific features of our model; targeting these mechanisms can aid in development of new anti-aging therapeutic strategies.

While a large number of biological age predictors have been built from blood samples, a blood count-based biological age predictor is lacking, and the genetic and environmental factors associated with blood-measured accelerated aging remain elusive. In the following, we leveraged 31 blood count biomarkers measured from 489,079 blood samples, 28 blood biochemistry biomarkers measured from 245,147 blood samples, and four urine biochemistry biomarkers measured from 158,381 samples to build three distinct biological age predictors by training machine learning models to predict age. Blood biochemistry significantly outperformed blood count and urine biochemistry in terms of age prediction (RMSE: 5.92+-0.02 vs. 7.60+-0.02 years and 7.72+-0.04 years). We performed genome wide association studies [GWASs], and found accelerated blood biochemistry, blood count and urine biochemistry aging to be respectively 26.2+-0.3%, 18.1+-0.2% and 10.5{+/-}0.5% GWAS-heritable. We identified 1,081 single nucleotide polymorphisms [SNPs] associated with accelerated blood biochemistry aging, 2,636 SNPs associated with accelerated blood cells aging and 24 SNPs associated with accelerated urine biochemistry aging. Similarly, we identified biomarkers, clinical phenotypes, diseases, environmental and socioeconomic factors associated with accelerated blood biochemistry, blood cells and urine biochemistry aging.

BackgroundAge is the most important risk factor for the majority of human diseases. Addressing the impact of age-related diseases has become a priority in healthcare practice, leading to the exploration of innovative approaches, including the development of predictors to estimate biological age (so-called "ageing clocks"). These predictors offer promising insights into the ageing process and age-related diseases. This study aims to showcase the significance of ageing clocks within a unique, deeply phenotyped longitudinal cohort. By utilising omics-based approaches alongside gold-standard clinical risk predictors, we elucidate the potential of these novel predictors in revolutionising personalised healthcare and better understanding the ageing process. MethodsWe analysed data from the IAM Frontier longitudinal study that collected extensive data from 30 healthy individuals over the timespan of 13 months: DNA methylation data, clinical biochemistry, proteomics and metabolomics measurements as well as data from physical health examinations. For each individual, biological age (BA) and health traits predictions were computed from 29 epigenetic clocks, 4 clinical-biochemistry clocks, 2 proteomics clocks, and 3 metabolomics clocks. FindingsWithin the BA prediction framework, comprehensive analyses can discover deviations in biological ageing. Our study shows that the within-person BA predictions at different time points are more similar to each other than the between-person predictions at the same time point, indicating that the ageing process is different between individuals but relatively stable within individuals. Individual-based analyses show interesting findings for three study participants, including observed hematological problems, that further supported and complemented by the current gold standard clinical laboratory profiles. InterpretationOur analyses indicate that BA predictions can serve as instruments for explaining many biological phenomena and should be considered crucial biomarkers that can complement routine medical tests. With omics becoming routinely measured in regular clinical settings, omics-based BA predictions can be added to the lab results to give a supplementary outlook assisting decision-making in doctors assessments. Funding-

Metals and per- and polyfluoroalkyl substances (PFAS) represent a significant environmental concern, yet their association with epigenetic age acceleration (EAA) remain largely understudied in marine mammals. Here, associations between EAA in common dolphins (Delphinus delphis) and life history (sex and sexual maturity), trace metals, and PFAS were investigated. EAA was calculated as the residual in the regression of epigenetic age vs chronological age, hence providing a direct measure of the deviation of the epigenetic age of an organism (positive or negative) by comparison with expectation, given their actual chronological age. Sixteen trace elements were quantified in hepatic and renal tissues (n = 53). In addition, 28 PFAS were quantified in hepatic tissue (n = 58). Associations between EAA and explanatory variables were assessed using regression-based and multivariate modelling approaches (linear models and canonical analysis of principal coordinates). No effect of sex was observed, although sexual maturity did significantly increase EAA. Exposure to metals was significantly associated with EAA, explaining 55.4% of the variation, with hepatic metals (Se, Zn, Cu, Al, Mn) driving this relationship. Although EAA was not significantly related to the total PFAS exposure overall, a subset of PFAS variables (PFBA, PFDA, PFHxS-B, PFNA) showed significant association with EAA after adjusting for sex and sexual maturity. Together, these subsets of metal and PFAS variables, in addition to the selenium-to-mercury (Se:Hg) molar ratio, explained 66.7% of the variation in EAA. Our results identify sexual maturity and specific contaminant mixtures as key potential drivers of EAA in common dolphins, highlighting the possible use of EAA as a biomarker of environmental and physiological stress in marine mammals.

Epigenetic modifications, including histone post-translational modifications, are central drivers of age-associated structural and functional changes in the genome, influencing gene expression and cellular resilience. Our objective was to determine the effects of inhibiting histone deacetylases (HDACs) and the histone methyltransferase SETDB1 on lifespan, reproduction, and stress response in the rotifer Brachionus manjavacas, a model organism for aging studies. We exposed rotifers to three pharmaceutical compounds, including the HDAC inhibitors {beta}-hydroxybutyrate and sodium butyrate and the SETDB1 inhibitor mithramycin A. We quantified changes in the global histone modification levels by immunoblotting, and measured lifespan, reproduction, and heat stress resistance in the drug-treated rotifers relative to a control. Global histone acetylation levels increase with {beta}-hydroxybutyrate and sodium butyrate treatments. Histone 3 K9 trimethylation (H3K9me3) levels were reduced by treatment with mithramycin A. {beta}-hydroxybutyrate significantly extended lifespan without significantly modifying heat stress resistance. In contrast, mithramycin A increased lifespan and enhanced heat stress tolerance, demonstrating a dual protective effect. Sodium butyrate specifically improved heat stress resistance without affecting overall lifespan. Importantly, none of the three treatments had a significant impact on lifetime reproduction. These findings provide insights into the role of histone modifications in aging and suggest potential interventions targeting epigenetic marks to promote longevity and resilience.