GeroScience

Antagonistic pleiotropy of the IGF-1 signaling cascade is well recognized, as it promotes growth and development at younger ages and delays aging later in life. The goal of this study is to test in a mouse longevity experiment whether orally delivered small-molecule IGF1R inhibitors have promise as an anti-aging therapy. C57BL/6 mice (25 male and 25 female mice per treatment) were treated with selective IGF1R inhibitors, picropodophyllin (PPP) or 5-[3-(phenylmethoxy)phenyl]-7-[trans-3-(1-pyrrolidinylmethyl)cyclobutyl]-7H-pyrrolo[3-d]pyrimidin-4-amine (NVP-ADW742), via powdered diets starting at 13 months of age, and physiological and behavioral parameters, as well as survival, were assessed. Both compounds protected both sexes from short-term memory decline; reduced systolic blood pressure in males and pulse rate in both sexes; rescued declining glucose tolerance in males; and abolished grey hair development, reduced frailty, and protected against decline in grip strength in female mice. There were no sex differences in survival curves within groups. No significant differences between groups were observed in the Kaplan-Meier analysis. However, the survival curve in the NVP-ADW742 group was "squarer" than in controls, indicating a 93-day longer healthspan (p = 0.02). PPP treatment was associated with toxicity (GI bleeding). Additional analysis of the drug likeness of NVP-ADW742 demonstrated potential cardiotoxicity and brain bioaccumulation. To conclude, small-molecule IGF1R inhibitors hold promise as a therapy that may improve human health span and lifespan; however, both molecules tested in this study have side effects that may outweigh their anti-aging effects. Statements and DeclarationsYB is an employee of ReGENE LLC. GB received compensation from ReGENE LLC as a consultant. CJE received compensation from and is a member of ReGENE LLC. AS received compensation from and is a member of ReGENE LLC. TS declares no conflict of interest.

Background and ObjectivesMethylation profile scores (MPSs) index biological aging and aging-related disease in adults and are cross-sectionally associated with social determinants of health in childhood. MPSs thus provide an opportunity to trace how aging-related biology responds to environmental changes in early life. Information regarding the stability of MPSs in early life is currently lacking. MethodWe use longitudinal data from children and adolescents ages 8-18 (N = 428, M age = 12.15 years) from the Texas Twin Project. Participants contributed two waves of salivary DNA-methylation data (mean lag = 3.94 years), which were used to construct four MPSs reflecting multi-system physiological decline and mortality risk (PhenoAgeAccel and GrimAgeAccel), pace of biological aging (DunedinPACE), and cognitive function (Epigenetic-g). Furthermore, we exploit variation among participants in whether they were exposed to the COVID-19 pandemic during the course of study participation, in order to test how a historical period characterized by environmental disruption might affect childrens aging-related MPSs. ResultsAll MPSs showed moderate longitudinal stability (test-retest rs = 0.42, 0.44, 0.46, 0.51 for PhenoAgeAccel, GrimAgeAccel, and Epigenetic-g, and DunedinPACE, respectively). No differences in the stability of MPSs were apparent between those whose second assessment took place after the onset of the COVID-19 pandemic vs. those for whom both assessments took place prior to the pandemic. ConclusionsAging-related DNA-methylation patterns are less stable in childhood than has been previously observed in adulthood. Further developmental research on the methylome is necessary to understand which environmental perturbations in childhood impact trajectories of biological aging and when children are most sensitive to those impacts. Article SummaryMethylation profiles of biological aging are less stable in childhood than has been previously observed in adulthood. Whats Known on This SubjectMethylation profile scores (MPSs) index biological aging in adults and are cross-sectionally associated with social determinants of health in childhood. Aging-related MPSs in adulthood show very high test-retest stability but data on longitudinal stability of MPSs in childhood is sparse. What This Study AddsChildrens methylation profiles of biological aging are moderately stable across an approximately four-year period. Methylation profiles are less stable in childhood than in adulthood, suggesting that aging-related biology in childhood might be more responsive to environmental changes than in adulthood.

Age-related decline in mobility and cognition are associated with cellular senescence and NAD+ depletion in dogs and people. A combination of a novel NAD+ precursor and senolytic, LY-D6/2 was examined in this randomized controlled trial. Seventy dogs were enrolled and allocated into placebo, low or full dose groups. Primary outcomes were change in cognitive impairment measured with the owner-reported Canine Cognitive Dysfunction Rating (CCDR) scale and change in activity measured with physical activity monitors. Fifty-nine dogs completed evaluations at the three-month primary endpoint, and 51 reached the six-month secondary endpoint. There was a significant difference in CCDR score across treatment groups from baseline to the primary endpoint (p=0.02) with the largest decrease in the full dose group. There were no significant differences between groups in changes in measured activity. However, the proportion of dogs that improved in frailty and owner-reported activity levels and happiness was higher in the full dose group than other groups. Adverse events occurred equally across groups. All groups showed improvement in cognition, frailty, and activity suggesting placebo effect and benefits of trial participation. We conclude that LY-D6/2 significantly improves owner-assessed cognitive function and may have broader effects on frailty, activity and happiness as reported by owners.

Despite being principally prescribed to treat type 2 diabetes, biguanides, especially metformin and phenformin, have been shown to extend lifespan and healthspan in preclinical models, and to reduce the impact of aging-associated diseases such as cancer. While there have been conflicting results in studies involving rodents and humans, consistent evidence from laboratories worldwide, including our own, indicates metformin and phenformins ability to significantly extend lifespan in C. elegans. However, the pro-longevity effect of metformin can vary depending on environmental conditions. Specifically, the choice of agar from different manufacturers or batches influences metformins ability to extend lifespan in C. elegans. We traced ability of certain agar batches to interfere with metformin-prompted lifespan extension to the presence of a factor that acts directly in the worm, independently of the bacterial food source, that prevents longevity promoting effects downstream of longevity effectors skn-1 and AMPK. In contrast, phenformin prompts robust lifespan extension in the face of environmental changes and exhibits broad positive effects in aging across genetically diverse Caenorhabditis species where the impact of metformin is highly variable. Thus metformin effects in aging are impacted by heretofore unappreciated environmental factors. Phenformin may represent a more robust agent with which to understand the longevity promoting mechanisms downstream of biguanides.

Rapamycin, also known as sirolimus, has demonstrated great potential for application in longevity medicine. However, the bioavailability of generic and compounded rapamycin at longevity doses in normative aging individuals remains unknown. We conducted a retrospective, real-world study determining the 24-hour blood rapamycin levels to establish the relative bioavailability, dose-to-blood level linearity and inter-individual heterogeneity in a normative aging cohort. Participants received either compounded rapamycin (n = 23, dosages 5, 10, or 15 mg) or generic rapamycin (n= 44, dosages 2, 3, 6, or 8 mg) once per week, and were asked to obtain a sirolimus level blood draw 24 hours after dose self-administration. Similar blood rapamycin levels and a linear dose-to-blood level relationship were observed for both formulations, although a higher bioavailability per milligram of rapamycin was noted for the generic formulation (compounded averaged 0.287 (28.7%) bioavailability relative to generic rapamycin in (ng/mL) / mg rapamycin). While substantial inter-individual heterogeneity in blood rapamycin levels was observed for both formulations, repeat tests for individuals demonstrated high test-retest reliability. As we detected no significant association between bioavailability and measures of body mass index (BMI), sex, age, or length of time taking rapamycin, we suggest that individualized dosing and routine monitoring of blood rapamycin levels should be applied to ensure optimal longevity efficacy. Finally, we contextualize our data with a brief review of the literature on the currently available knowledge of rapamycins bioavailability in normative aging populations, and provide implications for the clinical use of rapamycin in longevity medicine moving forward.

BackgroundAn individuals biological age is a measurement of health status and provides a mechanistic understanding of aging. Age clocks estimate a biological age of an individual based on their various features. Existing clocks have key limitations caused by the undesirable tradeoff between accuracy (i.e., predictive performance for chronological age or mortality, often achieved by complex, black-box models) and interpretability (i.e., the contributions of features to biological age). Here, we present ENABL (ExplaiNAble BioLogical) Age, a computational framework that combines machine learning (ML) models with explainable AI (XAI) methods to accurately estimate biological age with individualized explanations. MethodsTo construct ENABL Age clock, we first need to predict an age-related outcome of interest (e.g., all-cause or cause-specific mortality), and then rescale the predictions nonlinearly to estimate biological age. We trained and evaluated the ENABL Age clock using the UK Biobank (501,366 samples with 825 features) and NHANES 1999-2014 (47,084 samples with 158 features) datasets. To explain the ENABL Age clock, we extended existing XAI methods so we could linearly decompose any individuals ENABL Age into contributing risk factors. To make ENABL Age clock broadly accessible, we developed two versions: (1) ENABL Age-L, which is based on popular blood tests, and (2) ENABL Age-Q, which is based on questionnaire features. Finally, when we created ENABL Age clocks based on predictions of different age-related outcomes, we validated that each one captures sensible, yet disparate aging mechanisms by performing GWAS association analyses. FindingsOur results indicate that ENABL Age clocks successfully separate healthy from unhealthy aging individuals and are stronger predictors of mortality than existing age clocks. We externally validated our results by training ENABL Age clocks on UK Biobank data and testing on NHANES data. The individualized explanations that reveal the contribution of specific features to ENABL Age provide insights into the important features for biological age. Association analysis with risk factors and agingrelated morbidities, and genome-wide association study (GWAS) results on ENABL Age clocks trained on different mortality causes show that each one captures sensible aging mechanisms. InterpretationWe developed and validated a new ML and XAI-based approach to calculate and interpret biological age based on multiple aging mechanisms. Our results show strong mortality prediction power, interpretability, and flexibility. ENABL Age takes a consequential step towards accurate interpretable biological age prediction built with complex, high-performance ML models. Research in context Evidence before this studyBiological age plays an important role to understanding the mechanisms underlying aging. We search PubMed for original articles published in all languages with the terms "biological age" published until June 22, 2022. Most prior studies focus on the first generation of biological age clocks that are designed to predict chronological age. These clocks have weak and variable associations with mortality risk and other aging outcomes. Only a few studies present the second-generation of biological age clocks, which are built directly with aging outcomes. However, these studies use linear models and do not provide individualized explanations. Moreover, previous biological age clocks cannot specify what aging process they capture. Unlike our study, none of the previous studies have combined a complex machine learning (ML) model and an explainable artificial intelligence (XAI) method, which allows us to build biological ages that are both accurate and interpretable. Added value of this studyIn this study, we present ENABL Age, a new approach to estimate and understand biological age that combines complex ML models and XAI method. The ENABL Age approach is designed to measure secondgeneration biological age clocks by directly predicting age-related outcomes. Our results indicate that ENABL Age accurately reflects individual health status. We also introduce two variants of ENABL Age clocks: (1) ENABL Age-L, which takes popular blood tests as inputs (usable by medical professionals), and (2) ENABL Age-Q, which takes questionnaire features as inputs (usable by non-professional healthcare consumers). We extend existing XAI methods to calculate the contributions of input features to ENABL Age estimate in units of years, which makes our biological age clocks more human-interpretable. Our association analysis and GWAS results show that ENABL Age clocks trained on different age-related outcomes can capture different aging mechanisms. Implications of all the available evidenceWe develop and validate a new ML and XAI-based approach to measure and interpret biological age based on multiple aging mechanisms. Our results demonstrate that ENABL age has strong mortality prediction power, is interpretable, and is flexible. ENABL Age takes a consequential step towards applying XAI to interpret biological age models. Its flexibility allows for many future extensions to omics data, even multi-omic data, and multi-task learning.

Cellular stress is a fundamental component of age-associated disease. Cells encounter various forms of stress - oxidative stress, protein misfolding, DNA damage, etc. - and respond by activating specific, well-defined stress response pathways. As we age, the burden of stress and resulting damage increases while our cells ability to deal with the consequences becomes diminished due to dysregulation of cellular stress response pathways. Many interventions that extend lifespan activate one or more stress response pathways or allow cells to maintain normal stress response later in life. The nematode Caenorhabditis elegans is a commonly used model for both aging and stress response research. As such, stress response experiments are regularly conducted as part of studies focused on mechanisms of aging in C. elegans. However, experimental design across experiments in the field are highly variable, including stressor dose, age at exposure, culture type (liquid vs. solid), bacterial strain used as a food source, and environmental temperature. These differences can result in different experimental outcomes, making comparison of results between studies challenging. Here we evaluate several experimental variables that are variable in the published literature and find that each can meaningfully alter experimental outcomes for multiple stressors. Our goal is to raise awareness of the issue of experimental variability within the field and suggest a standardized experimental design to serve as a set of guidelines for future experiments. By adopting these guidelines as a starting point, and explicitly noting differences in specific experiments, we aim to promote rigor and reproducibility, ultimately fostering more interpretable and translatable outcomes in geroscience research.

Myeloid skewing is a central and therefore often cited hallmark of hematopoietic aging. Myeloid skewing refers to an elevated myeloid-to-lymphoid cell ratio in aged compared to young mice. Interestingly, whether the extent of myeloid skewing might be in itself a quantitative biological marker of aging has not been addressed yet, nor whether this parameter has also relevance for the extent of aging in humans. Aged mice with high level of myeloid skewing (>50% myeloid cells in blood) showed accelerated hematopoietic aging compared to mice with a low level of myeloid skewing (<30% of myeloid cells in blood), as well as an increased level of inflammatory cytokines and elevated levels of diseases. Hematopoietic stem cells (HSCs) from mice with high myeloid skewing showed an impaired repopulation capacity. Epigenetic clock analyses demonstrated that mice with a high level of myeloid skewing present with a biological age that is older than their chronological age. In humans, a high degree of myeloid skewing was associated with elevated levels of inflammatory markers, reduced mobility, a greater burden of comorbidities, and an increased mortality hazard ratio. The data support that, besides overall myeloid skewing being a central hallmark of aging in mice, the extent of the frequency of myeloid cells in blood might serve as a biological marker of aging and disease in both mice and humans. Key PointsThe extent of myeloid skewing in aged mice correlates to an increased hematological and epigenetic age and increased disease burden. The extent of myeloid skewing in older adults is associated with an increased hazard ratio of mortality and correlates with higher frailty and inflammatory markers.

Young blood plasma is known to confer beneficial effects on various organs in mice. However, it was not known whether young plasma rejuvenates cells and tissues at the epigenetic level; whether it alters the epigenetic clock, which is a highly-accurate molecular biomarker of aging. To address this question, we developed and validated six different epigenetic clocks for rat tissues that are based on DNA methylation values derived from n=593 tissue samples. As indicated by their respective names, the rat pan-tissue clock can be applied to DNA methylation profiles from all rat tissues, while the rat brain-, liver-, and blood clocks apply to the corresponding tissue types. We also developed two epigenetic clocks that apply to both human and rat tissues by adding n=850 human tissue samples to the training data. We employed these six clocks to investigate the rejuvenation effects of a plasma fraction treatment in different rat tissues. The treatment more than halved the epigenetic ages of blood, heart, and liver tissue. A less pronounced, but statistically significant, rejuvenation effect could be observed in the hypothalamus. The treatment was accompanied by progressive improvement in the function of these organs as ascertained through numerous biochemical/physiological biomarkers and behavioral responses to assess cognitive functions. Cellular senescence, which is not associated with epigenetic aging, was also considerably reduced in vital organs. Overall, this study demonstrates that a plasma-derived treatment markedly reverses aging according to epigenetic clocks and benchmark biomarkers of aging.

DNA methylation data have been successfully used to develop highly accurate estimators of age ("epigenetic clocks") in many mammalian species. With a view of extending such epigenetic clocks to all primate species, we analyzed DNA methylation profiles of 2400 tissues derived from 37 primate species including 11 haplorhine species (baboons, marmosets, vervets, rhesus macaque, chimpanzees, gorillas, orangutan, humans) and 26 strepsirrhine species (suborders Lemuriformes and Lorisiformes). From these we present here, pan-primate epigenetic clocks which are highly accurate for all primates including humans (age correlation R=0.98). We also carried out in-depth analysis of baboon DNA methylation profiles and generated five epigenetic clocks for baboons (Olive-yellow baboon hybrid), one of which, the pan-tissue epigenetic clock, was trained on seven tissue types (fetal cerebral cortex, adult cerebral cortex, cerebellum, adipose, heart, liver, and skeletal muscle) with ages ranging from late fetal life to 22.8 years of age. To facilitate translation of findings in baboons to humans, we further constructed two dual-species, human-baboon clocks. We also identified and present here, epigenetic predictors of sex that apply to all primate species. Low overlap can be observed between age- and sex-related CpGs. Overall, this study advances our understanding of conserved age- and sex-related epigenetic changes in primates, and provides biomarkers to study the aging of all primate species with the facility to readily translate any findings between primate species.

Dietary restriction (DR) mitigates loss of proteostasis associated with aging that underlies neurodegenerative conditions including Alzheimers disease and related dementias. Previously, we observed increased translational efficiency of certain FMRFamide-like neuropeptide (flp) genes and the neuroprotective growth factor progranulin gene prgn-1 under dietary restriction in C. elegans. Here, we tested the effects of flp-5, flp-14, flp-15 and pgrn-1 on lifespan and proteostasis under both standard and dietary restriction conditions. We also tested and distinguished function based on their expression in either neuronal or non-neuronal tissue. Lowering the expression of pgrn-1 and flp genes selectively in neural tissue showed no difference in survival under normal feeding conditions nor under DR in two out of three experiments performed. Reduced expression of flp-14 in non-neuronal tissue showed decreased lifespan that was not specific to DR. With respect to proteostasis, a genetic model of DR from mutation of the eat-2 gene that showed increased thermotolerance compared to fully fed wild type animals demonstrated no change in thermotolerance in response to knockdown of pgrn-1 or flp genes. Finally, we tested effects on motility in a neural-specific model of proteotoxicity and found that neuronal knockdown of pgrn-1 and flp genes improved motility in early life regardless of diet. However, knocking these genes down in non-neuronal tissue had variable results. RNAi targeting flp-14 increased motility by day seven of adulthood regardless of diet. Interestingly, non-neuronal RNAi of pgrn-1 decreased motility under standard feeding conditions while DR increased motility for this gene knockdown by day seven (early mid-life). Results show that pgrn-1, flp-5, flp-14, and flp-15 do not have major roles in diet-related changes in longevity or whole-body proteostasis. However, reduced expression of these genes in neurons increases motility early in life in a neural-specific model of proteotoxicity, whereas knockdown of non-neuronal expression mostly increases motility in mid-life under the same conditions.

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.

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.

Aims/HypothesisReactive oxygen species modulator 1 (ROMO1) is a highly conserved inner mitochondrial membrane protein that senses reactive oxygen species and regulates mitochondrial dynamics. ROMO1 is required for mitochondrial fusion in vitro, and silencing ROMO1 increases sensitivity to cell death stimuli. The physiological role of ROMO1 remains unclear. MethodsTo determine the role of ROMO1 in vivo, we used gene targeting in mice to ablate ROMO1 in the whole mouse and to conditionally knock out ROMO1 in the pancreatic beta cell. Mitochondrial functional analyses were performed on isolated mouse and human islets lacking ROMO1. ResultsWe show that ROMO1 is essential for embryonic development, as ROMO1-null mice die before embryonic day 8.5, earlier than GTPases OPA1 or MFN1/2 that catalyze mitochondrial inner and outer membrane fusion. Knockout of ROMO1 in adult pancreatic beta cells results in impaired glucose homeostasis in young male mice due to an insulin secretion defect. Isolated islets from male, but not female, mice showed impaired glucose-stimulated insulin secretion. While mitochondria from female mice were morphologically normal, mitochondria in Romo1 adult beta cell knockout (RABKO) cells from male mice were swollen and fragmented, with a reduction in mtDNA content. Knockout of ROMO1 did not affect basal respiration in males or females, but deletion of ROMO1 in both sexes in mice and isolated human islets reduced spare respiratory capacity (SRC), which involved the specific loss of respiratory activity at Complex II/SDH. Aging of female ROMO1 KO mice resulted in loss of spare respiratory capacity and glucose intolerance. Conclusions/InterpretationOur data demonstrate that ROMO1 is a key regulator of mitochondrial bioenergetics and SRC and is required for effective nutrient coupling to insulin secretion in the beta cell. These observations point to a critical role for spare respiratory capacity in the maintenance of euglycemia and to the potential for targeting ROMO1-complex II to promote glucose coupling in settings of insulin insufficiency. Research in ContextWhat is already known about this subject? O_LIROMO1 is required for mitochondrial fusion C_LIO_LIGlucose coupling to insulin secretion is accomplished in part via generation of NADH during the oxidation of glycolytic metabolites in the TCA cycle C_LIO_LISpare respiratory capacity is lost in aging C_LI What is the key question? O_LIWhat is the physiological role of ROMO1 in the whole animal and the pancreatic beta cell? C_LI What are the new findings? O_LIROMO1 is essential for mouse development C_LIO_LIROMO1 is required to maintain spare respiratory capacity (SRC) and to promote insulin secretion in the beta cells of mice and humans C_LIO_LIAblation of the Romo1 gene in the pancreatic beta cell leads to glucose coupling defects and glucose intolerance in young males and aged females C_LIO_LIAging highlights the importance of SRC in the beta cell for maintaining euglycemia C_LI How might this impact clinical practice in the foreseeable future? O_LIAging is a significant risk factor for T2D. Human males, but not females, experience a loss of insulin secretion with age; designing strategies that enhance ROMO1 and complex II activity to promote SRC may help to reverse these effects. C_LI

Chronic infection with human cytomegalovirus (CMV) may contribute to poor vaccine efficacy in older adults. We assessed effects of CMV serostatus on antibody quantity and quality, as well as cellular memory recall responses, after 2 and 3 SARS-CoV-2 mRNA vaccine doses, in older adults in assisted living facilities. CMV serostatus did not affect anti-Spike and anti-RBD IgG antibody levels, nor neutralization capacity against wildtype or beta variants of SARS-CoV-2 several months after vaccination. CMV seropositivity altered T cell expression of senescence-associated markers and increased TEMRA cell numbers, as has been previously reported; however, this did not impact Spike-specific CD4+ T cell memory recall responses. CMV seropositive individuals did not have a higher incidence of COVID-19, though prior infection influenced humoral immunity. Therefore, CMV seropositivity may alter T cell composition but does not impede the durability of humoral protection or cellular memory responses after SARS-CoV-2 mRNA vaccination in older adults. Key PointsCMV seropositive older adults have more EMRA and terminally differentiated T cells CMV seropositivity does not prevent antibody maintenance after SARS-CoV-2 vaccination CMV seropositivity does not impede SARS-CoV-2 vaccine T cell memory recall responses

BackgroundThe remarkable regenerative abilities observed in planarians and cnidarians are closely linked to the active proliferation of adult stem cells and the precise differentiation of their progeny, both of which typically deteriorate during aging in low regenerative animals. While regeneration-specific genes conserved in highly regenerative organisms may confer regenerative abilities and long-term maintenance of tissue homeostasis, it remains unclear whether introducing these regenerative genes into low regenerative animals can improve their regeneration and aging processes. ResultsHere we ectopically express high regenerative species-specific JmjC domain-encoding genes (HRJDs) in Drosophila, a widely used low regenerative model organism. Surprisingly, HRJD expression impedes tissue regeneration in the developing wing disc but extends organismal lifespan when expressed in the intestinal stem cell lineages of the adult midgut under non-regenerative conditions. Notably, HRJDs enhance the proliferative activity of intestinal stem cells while maintaining their differentiation fidelity, ameliorating age-related decline in gut barrier functions. ConclusionsThese findings together suggest that the introduction of highly regenerative species-specific genes can improve stem cell functions and promote a healthy lifespan when expressed in aging animals.

Immune responses are highly variable from one individual to another, with this variability determined by factors such as age, sex, genetics and environment, which also affect physical and mental well-being. Systems immunology studies have been applied to human cohort studies to better understand these effects, but most are performed in Western populations of mostly European ancestry, neglecting the vast majority of human global diversity. To overcome this limitation, we established the Healthy Human Global Project - Hong Kong (HHGP-HK) cohort to allow the study of immune variability in a healthy Asian population. Taking inspiration from the established French Milieu Interieur study we adapted inclusion and exclusion criteria to identify healthy donors, and to facilitate cross comparison between the two cohorts we collected similar demographic, medical and lifestyle information. To study the effects of age and sex, donors were stratified for both ranging from 20-79 years. We report here significant age and sex effects on multiple clinical laboratory measures, many of which were consistently observed in the Milieu Interieur cohort. C-reactive protein and liver enzymes were two exceptions perhaps reflecting different environmental effects between the two cohorts. We applied biological aging, physiological, and mental health scores to our cohort which highlights the need to develop and adapt such scores for application to populations of non-European descent. We also included wearable fitness trackers from which we observed significant age and sex differences for sleep, physical activity, and heart rate variability. This unique cohort will enable the better understanding of factors that determine immune variability within Asian populations.

Quantifying biological aging is crucial for understanding functional decline before the onset of morbidity. While many accelerated aging and frailty measures based on clinical data exist for humans and several for rodent models of aging, there are few options for non-human primates (NHPs). NHP clinical data has several unique features including a lack of clinically delineated normative values for features and variability in data collection over long lifespans. There are also wide discrepancies in the number of available clinical measures and number of animals across data sets. To address these challenges, we developed and validated "Aging Resilience" (AR) metrics using longitudinal, routine clinical data from two distinct non-human primate cohorts: 4,328 baboons and 281 rhesus macaques. We trained five computational models--including Linear Mixed-Effects Models, Random Forest, and Recurrent Neural Networks (RNN)--to predict chronological age, subsequently deriving AR metrics that represent the velocity (Rate of Aging) and cumulative burden (Normalized Cumulative Aging) of physiological deviation. While linear models achieved high precision in predicting chronological age (test R2 up to 0.99), they correlated poorly with actual lifespan. In contrast, AR metrics derived from non-linear models (RNN and Random Forest) displayed strong predictive validity for mortality (Pearsons r > 0.8). These findings highlight a critical paradox: models that best predict chronological age do not necessarily capture the biological resilience determining healthspan. This study establishes a scalable framework for monitoring biological aging in translational models using standard veterinary records.

A growing body of research suggests that changes in both structural and functional connectivity in the aging brain contribute to declines in cognitive functions such as response inhibition. In recent years, transcranial alternating current stimulation (tACS) has garnered substantial research interest as a potential tool for the modulation of functional connectivity. Here, we report the findings from a double-blind crossover study that investigated the effects of dual-site beta tACS over the right inferior frontal gyrus (rIFG) and pre-supplementary motor area (preSMA) on functional connectivity measured with electroencephalography and response inhibition (stop-signal task performance) of healthy young (n = 18, aged 18-34 years) and older (n =15, aged 61-79 years) adults. Two tACS conditions were administered in separate sessions: in-phase tACS, where electrical currents delivered to the rIFG and preSMA had a 0{degrees} phase difference, and anti-phase tACS, where currents had a 180{degrees} phase difference. Stop-signal task performance was assessed before and after tACS. We found significant improvements in response inhibition that were not due to the phase of the tACS applied. There were also no significant changes in rIFG-preSMA phase connectivity in either age group from in- or anti-phase tACS. Furthermore, we did not observe significant differences in rIFG-preSMA phase connectivity between successful and unsuccessful inhibition, which suggests that rIFG-preSMA phase-coupling might not underlie effective response inhibition. The results offer insight into the neurophysiology of response inhibition and contribute to the future development of non-pharmacological interventions aimed at alleviating age-related declines in cognitive function.

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.