Longevity Papers

Given the well-established structural and functional changes in the aging brain, it is widely assumed that cognitive aging is primarily driven by robust group-level differences between young and older adults. However, our individual-level EEG functional connectivity analysis challenges this notion. We investigated the impact of cognitive training on functional brain connectivity using task-switching paradigms in 39 younger (18-25 years) and 40 older (60-75 years) women. Participants were randomly assigned to either a training group, which completed eight individualized one-hour cognitive training sessions, or a no-contact control group. EEG was recorded at both pre- and post-training sessions across three task-switching paradigms (trained and near-transfer versions). Unique functional connectivity of different sources of variation was examined by calculating how much variance was shared across stable traits (e.g., individual, age, and common factors), or dynamic states (e.g., task and training effects). Our results revealed that age accounted for only a modest proportion of variance, whereas self-similarity was a dominant factor - particularly in older adults. Similarly, group-level training effects were small but strongly modulated by individual neural profiles, suggesting person-specific trajectories. Participants recruited distinct neural networks across tasks, and even within the same task engaged unique, individual-specific network configurations, reflecting personalised brain adaptations to cognitive demands. Importantly, older adults displayed a shift from common to individual network patterns, consistent with increased neural specialization and compensatory mechanisms. These findings underscore the importance of moving beyond group-level contrasts toward models that capture the complexity of individual brain dynamics in cognitive aging and training responsiveness.

Sex differences in ageing and lifespan are widespread across taxa, yet their evolutionary causes remain debated. A leading hypothesis suggests these differences are adaptive and driven by sex-specific life-history trade-offs, but formal theoretical support is lacking. To address this, we developed a mathematical model to investigate how such trade-offs shape lifespan evolution in a monogamous mating system. In the model, individuals evolve to optimise a trade-off between reproduction and survival -- mediated by mating opportunities in males and offspring production in females. By systematically varying trade-off strengths, we show that either sex can evolve greater longevity, but male-biased longevity evolves under a broader set of conditions -- consistent with patterns in monogamous species. This asymmetry arises because female longevity is more constrained: the trade-off between offspring production and survival directly affects the fertility of both sexes. In contrast, the male trade-off for mating opportunities has a weaker indirect effect on female fertility, allowing selection to more readily favour longer male lifespans. We also show that extrinsic density-dependent mortality can disproportionately affect the intrinsically longer-living sex, and obscure the magnitude of this evolved difference. Together, our results provide new theoretical insights into the adaptive bases of sex-biased longevity and highlight the importance of life-history trade-offs in shaping lifespan evolution.

The hormone insulin-like growth factor 1 (IGF-1) is a key player in the insulin/IGF-1 signaling (IIS) pathway. Extensive biogerontological research demonstrates that this evolutionarily conserved nutrient-sensing pathway plays a causal role in the regulation of growth, reproduction and longevity under laboratory conditions. However, its potential role as a mediator of adaptive life-history variation in highly variable natural environments remains unclear. We measured IGF-1 concentrations in blood samples from approximately four-month-old wild Soay sheep lambs (n=669), collected over nine summers. We tested whether IGF-1 (i) was positively correlated with proxies of resource availability, (ii) was associated with morphological traits measured concurrently, and (iii) predicted subsequent fitness-related traits. Plasma IGF-1 concentrations were higher in males compared to females, and positively correlated with measures of resource availability in both sexes. IGF-1 was lower in years of high population density when per capita food availability was reduced; in twin lambs who have fewer available resources compared to singletons; and in lambs born to young and old mothers, who have poor maternal provisioning compared to mothers of intermediate age. Higher IGF-1 levels in summer were correlated with higher body mass, faster post-natal somatic growth and increased skeletal size, measured at the same time. These associations were independent of our proxies of resource availability. Lambs with higher summer IGF-1 were more likely to survive their first winter and reproduce the following spring. The association between IGF-1 and reproduction was independent of our resource availability proxies, whereas the association with first-winter survival was not. The association between summer IGF-1 and reproduction was mediated by positive associations with summer body mass. Our study reveals population-level phenotypic plasticity in circulating IGF-1, also finding IGF-1 to be positively associated with key morphological traits and positively predict fitness traits in early life. These findings highlight IGF-1 as a candidate physiological mechanism underpinning plastic responses to variation in food availability and influencing life-history traits in a wild mammal.

The regenerative response of retinal cells to injury and aging depends on the epigenomic plasticity that enables the dedifferentiation and neuronal differentiation capacities of Muller glial cells (MG). In mammals, this regenerative ability is extremely limited, and disruptions in epigenetic mechanisms, particularly those involving DNA methylation and demethylation, may underlie this restricted potential. To explore this possibility, we aimed to develop DNA methylation-targeting molecular tools to enhance the dedifferentiation and neurogenic capacity of primary MG cultures derived from mouse retina. Using CRISPR/dCas9-based gene regulation technology, we selectively and transiently inhibited Dnmt3a, a de novo DNA methyltransferase previously implicated in maintaining transcriptional repression. Our results show that Dnmt3a knockdown leads to sustained upregulation of pluripotency-associated genes, including Ascl1, Lin28, and Nestin, as measured by RT-qPCR and immunofluorescence. This epigenetic modulation also promoted increased cell proliferation and migration, both hallmarks of a regenerative response. Furthermore, Dnmt3a knockdown, either alone or in combination with neurogenic stimuli, induced MG to acquire neuronal-like morphologies and express the early neuronal marker {beta}III-tubulin. These findings suggest that Dnmt3a acts as a repressive regulator of MG plasticity, likely serving as an epigenetic barrier that counteracts injury-induced demethylation events. Overall, our study identifies Dnmt3a as a critical modulator of MG fate and highlights the potential of its targeted downregulation to facilitate reprogramming. By prolonging the transient progenitor-like state of MG, DNMT3a inhibition may serve as a complementary approach to unlock the neurogenic and regenerative potential of the mammalian retina, offering promising avenues for future therapeutic strategies.

Although tea polyphenols have antiaging potential, the molecular interplay between black tea components and cellular longevity remains unclear. This study has pioneered a dual approach combining

As the aging population continues to grow, falls among older adults have become a significant public health concern worldwide. Data-driven approaches for effective fall risk prediction, which integrate standard functional tests with 3D skeleton data from depth sensors, are gaining increasing attention. However, the complex physiological and functional interactions among skeletal keypoints during ambulation pose challenges for multidimensional feature extraction in most predictive models. In this study, we developed a novel approach based on preprocessed 3D skeleton data, named Multivariate SpatialTemporal Gated Transformer (MSTG-Transformer). This approach consists of three main stages. First, gait cycle sequences are constructed to sophisticatedly depict the movement patterns of subjects, amplifying the distinctions between groups. Then, spatial and topological features are extracted via convolutional modules, and a dual-stream encoder block is employed to encode the features of 3D skeleton data across both time steps and time channels. Finally, a voting scheme is used to determine fall risk by integrating the classification results of individual gait cycle segments. Validation experiments on a real-world dataset demonstrate that our proposed approach outperforms classical methods, achieving a superior prediction accuracy of 0.9510 ± 0.0240. Additionally, our study highlights the crucial role of potential interactions between skeletal keypoints in accurately predicting fall risk.

Signaling pathways and transcriptional regulation during cellular senescence have been investigated; however, energy metabolism is one of the understudied areas. Senescent cells secrete pro-inflammatory cytokines and release proteins and RNAs via exosomes that contribute to organismal aging. Although senescent fibroblasts in solid organs are in a low oxygen environment, these fibroblasts have more active glucose metabolism and consume more oxygen than proliferating ones. A critical gap in our knowledge is how senescent fibroblasts facilitate glucose metabolism and organismal aging by creating a distinct microenvironment. Our high throughput profiling of mRNAs and proteins from Human Diploid Fibroblasts (HDFs) revealed that the expression of pyruvate metabolic enzymes is inhibited by the anti-senescent RNA-binding protein (RBP) AUF1 (AU-binding Factor 1). Our studies revealed that AUF1 promotes the decay of mRNAs encoding two enzymes: PGAM1 (phosphoglycerate mutase 1), a glycolytic enzyme involved in the pyruvate synthetic pathway, and PDP2 (Pyruvate Dehydrogenase Phosphatase 2), which activates Pyruvate Dehydrogenase. We also demonstrated that AUF1 is phosphorylated by a Serine/Threonine kinase, MST1 (Mammalian Ste20-like kinase 1; encoded by STK4), resulting in the inactivation of AUF1, which leads to target mRNA stabilization and senescence. Overexpression of PGAM1 and PDP2 predicts an acceleration of pyruvate production and subsequent citrate metabolism, leading to cellular senescence and aging. Thus, our studies revealed regulatory mechanisms of glycolysis-driven cellular senescence by AUF1-mediated decay of

Age-related cognitive decline occurs, in part, due to diminishing white matter integrity. Higher cardiorespiratory fitness (CRF) is associated with better cognitive performance, but the neurobiological mechanisms underlying this association remain uncertain. Previous magnetic resonance imaging (MRI) studies have suggested that CRF-related changes in white matter microstructure might prevent or slow age-related cognitive decline, but have been limited by small sample sizes and methodological limitations. Specifically, most prior studies used tensor-based diffusion-weighted MRI metrics, which are insensitive to complex white matter architectures, including crossing fibers.

Accurate and convenient assessment of individual aging is crucial for identifying health risks and preventing aging-related diseases. Nonetheless, current aging proxies often face challenges such as methodological limitations, weak associations with adverse outcomes and limited generalizability. Here we propose a framework that leverages large language models (LLMs) to estimate individual overall and organ-specific aging using only health examination reports. We validated this approach across six population-based cohorts, encompassing over 10 million participants and demonstrated effectiveness and reliability. Our results showed that the LLM-predicted overall age achieved a concordance index (C-index) of 0.757 (95% CI 0.752-0.761) for all-cause mortality, significantly outperforming other aging proxies such as telomere length, frailty index, eight epigenetic ages and four machine-learning models predictions. The overall age gap was strongly associated with multiple aging-related phenotypes and health outcomes, showing a hazard ratio of 1.055 (95% CI 1.050-1.060) for all-cause mortality. For organ-specific aging, LLM-predicted ages and age gaps also demonstrated superior performance in predicting corresponding organ-specific diseases compared to machine-learning models. Additionally, we examined the dynamic aging assessment capability of LLMs and applied age gaps to identify proteomic biomarkers associated with accelerated aging and to develop risk prediction models of 270 diseases. Interpretability analyses were also conducted to explore the decision-making process of LLMs. In conclusion, our LLM-based aging assessment framework offers a precise, reliable and cost-effective approach for estimating overall and organ-specific aging. It has potential for personalized aging assessment and health management in large-scale general populations.

Gut microbiota (GM) is implicated in aging biology, yet its dual regulatory role in the distinct yet interconnected processes of lifespan extension and aging remains poorly understood. This study employed genetic approaches to identify GM taxa exerting causal effects on longevity and aging and assess the mediation role of cerebrospinal fluid (CSF) proteins. We leveraged summary statistics of the GM taxa (207 taxa, 7738 participants from the Dutch Microbiome Project), the CSF proteins (7008 aptamers, 3506 participants), and the longevity and aging phenotypes (UK Biobank, Longevity Genomics, and Edinburgh DataShare) from the largest genome-wide association studies so far. We performed bidirectional Mendelian randomization (MR) to explore causal relationships between GM and longevity and aging and two mediation analyses, the two-step MR and the multivariable MR (MVMR), to discover potential mediating proteins. Nine taxa exhibited causal associations with longevity-related phenotypes, such as the Bacteroides vulgatus and Blautia. Three taxa demonstrated causal associations with aging-related phenotypes, such as Streptococcus. Among them, Streptococcus was causally associated with aging (β = 0.3310, P = 0.0019) and lifespan-shortening (β =  - 0.0247, P = 0.0184), while Bacteroides vulgatus associated with aging (β = 0.3884, P = 0.0204) and lifespan-promoting (β = 0.0354, P = 0.0198). Mediation MR analysis found that CSF protein CD28 mediated the causal effects of Streptococcus on longevity (mediation proportion 11.7%, P = 0.0394). These findings establish genetic links between GM, CSF proteins, and longevity and aging-related outcomes, advancing gut-brain axis understanding while offering insights for future mechanistic exploration and clinical investigation.

Ageing significantly impacts lung function and increases susceptibility to chronic lung diseases. The lung is a complex organ with multiple cell types that undergo cellular age-related perturbations or hallmarks. As knowledge of ageing mechanisms has progressed, we have a better understanding how intracellular adaptations impact cellular crosstalk and integrate to increase the susceptibility to age-related diseases in the lung. Herein, we discuss the prospects of exhaustion of lung progenitor cells, disrupted lung cell plasticity, perturbation in fibroblasts, impaired adaptive immune responses and alterations in lung microenvironment in the promotion of ageing and age-related lung diseases. Additionally, the ageing process trajectory of the lung depends on a combination of biological, genetic, metabolic, biomechanical and sociobehavioural factors that range from protective phenotypes to accelerated ageing phenotypes. We propose the concept of AgEnOmics, which expands the temporal dimension of lung ageing by distinguishing between chronological ageing and accelerated lung ageing phenotypes. Based on this concept, we define biomarkers of biological ageing that will help to define accelerated ageing and early interventions in biological ageing-related lung diseases.

For most animals, intrinsic senescence-induced mortality increases with age, while deaths from extrinsic threats, such as predation or accidents, decline during development as individuals grow and mature. Age-dependent modulation of extrinsic mortality is known to influence the evolution of aging, yet how the timing of mortality shapes evolutionary forces remains poorly understood. To address this, we developed two complementary mathematical models that integrate survival benefits arising during development with the progressive increase in mortality associated with senescence. Agent-based simulations and deterministic analyses revealed a strong and consistent influence of the timing of developmental survival benefits on their evolutionary impact: early-life survival benefits reduced the selection for slower aging, while late-acting benefits enhanced it. This difference arises because early-life benefits more strongly accelerate population growth than late-life benefits, diminishing the relative evolutionary advantage of increased longevity. Our results underscore the importance of mortality timing in the evolution of aging and provide a theoretical framework for connecting developmental trajectories to aging dynamics.

A plethora of senolytic compounds and technologies have been identified. However, the efficacy of these treatments in eliminating senescent cells and the suppression of chronic inflammation remains to be substantiated in vivo. Here, we employ single-cell RNA sequencing and find the selective elimination of highly inflammatory fibroblasts, endothelial cells in the lung, and proximal tubule cells in the kidney from the GLS1 inhibitor BPTES-treated aged mice. The eliminated fibroblasts share typical phenotypes with in vitro human senescent cells. These cells predominantly express Dpp4 (CD26) and Cadm3, showing activated IFN signaling and lysosomal membrane damage. Cells eliminated by BPTES in lungs exhibit transcriptomes similar to those eliminated in p16-DTR mice, where DTR expression is limited in p16-expressing cells. BPTES treatment results in the T cell population shift from cytotoxic to a protective state, suggesting the suppression of age-related chronic inflammation. CellChat analysis revealed that multiple cytokine signals are transmitted from inflammatory fibroblasts and proximal tubular cells to immune cells in lungs and kidneys. These results provide evidence that GLS1 inhibitor functions as a bona fide senolytic drug to eliminate inflammatory cells, including a subset of senescent cells, and suppresses age-related chronic inflammation.

This study is the first to investigate the anti-aging effects of the newly synthesized compound, 3,4,5-tri-feruloylquinic acid (TFQA), in vivo using the senescence-accelerated mouse prone 8 (SAMP8) model, through integrated whole-transcriptomic and biochemical analyses. Oral administration of TFQA (1 mg/kg body weight) for 37 days led to significant cognitive improvements, as demonstrated by enhanced performance in the Morris water maze (MWM) test, including reduced escape latency and increased time spent in the target zone, target crossings, distance traveled, and swimming speed. Gene Ontology (GO) analysis of hippocampal microarray data revealed that TFQA upregulated genes associated with neurotransmitter and synaptic functions, while downregulating inflammatory pathways. Protein-protein interaction (PPI) analysis indicated that neurotrophin signaling was a critical upstream regulator in TFQA-treated mice. Further biochemical validation showed elevated levels of neurotransmitters (serotonin, noradrenaline, and dopamine) in the brain. Additionally, mRNA expression of tumor necrosis factor (Tnf), an inflammatory cytokine, was significantly reduced in the brain. Examination of the neurotrophins involved in neurotrophin signaling revealed that TFQA most prominently regulated brain-derived neurotrophic factor (Bdnf) co-expressed genes, which was validated by increased Bdnf expression in both the brain and serum of SAMP8 mice. Bioluminescence imaging demonstrated that a single oral dose of TFQA significantly increased BDNF expression in the brains of BDNF-IRES-AkaLuc mice. Binding studies showed that TFQA has a strong affinity for the tyrosine receptor kinase B (TrkB) receptor. In summary, TFQA holds potential as a neuroprotective agent, mitigating learning and memory deficits in aging mice via neurotrophin signaling, particularly through BDNF upregulation.

Telomere homeostasis is pivotal in various biological processes including ontogeny, reproduction, physiological aging, and the onset of numerous diseases such as tumors. In human stem cells and approximately 85% of tumor cells, telomerase formed by TERT and TERC RNA complex is responsible for elongating telomeres. However, the intricate and precise regulatory mechanisms governing telomerase remain largely elusive.

Evolutionary theories of aging indicate trade-offs between reproduction and longevity. Epigenetic clocks, such as PhenoAge, GrimAge, and DunedinPoAm, were designed to reflect biological age and be used as surrogates for mortality and healthspan. The current study investigated the connection between reproductive profiles, epigenetic aging and mortality among post-menopausal women (50-85 years) with data from the National Health and Nutrition Examination Survey across the United States (N = 770). Using latent profile analysis, we identified four distinct reproductive profiles: high gravidity but average parity (Class 1); high gravidity and parity (Class 2); premature menopause (Class 3); an average profile (Class 4). Women of Class 3 had an accelerated pace of aging as indicated by DunedinPoAm, but not an older epigenetic age as measured by PhenoAge or GrimAge. The association was significant among women who had ever used female hormones (β = 0.521; 95%CI 0.014-1.027). Women of Class 1 or 2 did not exhibit accelerated epigenetic aging. Women of Class 3 had higher mortality (HR = 1.40, 95%CI 1.08-1.81), and 36.3% of the effect was mediated through accelerated DunedinPoAm. Findings suggest that women with reproductive profiles characterized by premature menopause may have altered epigenetic aging trajectories. Pace of aging may be more sensitive to the impact of reproductive profile variations than the status of biological age as indicated by PhenoAge or GrimAge. Clinically monitoring the pace of biological aging among women with premature menopause and an appropriate application of hormone replacement therapy may minimize the negative consequence of accelerated biological aging and reduce premature mortality.

Aging is commonly associated with cognitive decline, particularly in memory, and is linked to neuronal hyperexcitability and disrupted sleep-related oscillations in key brain regions such as the hippocampus and prefrontal cortex. Melatonin has been proposed as a potential therapeutic agent to counteract age-related cognitive impairments. In this study, 24-month-old male Wistar rats were treated with melatonin (10 mg/kg, intraperitoneally) for 30 days. Local field potentials were recorded from the hippocampus and prefrontal cortex to assess neuronal activity. Memory performance was evaluated using the novel object recognition test, and qPCR measured expression levels of inflammatory and amyloidogenesis markers. Melatonin treatment significantly reduced neuronal hyperexcitability, enhanced delta and theta oscillations, and increased sleep spindle amplitude, which were associated with improved memory performance. Additionally, melatonin attenuated the expression of pro-inflammatory markers without affecting the expression of amyloidogenesis-related genes. These findings suggest that melatonin may enhance cognitive function in aging by modulating neuronal excitability, sleep oscillations, and neuroinflammatory processes.

Aging is associated with alterations in immune cell function, contributing to age-related diseases and frailty. As peripheral blood mononuclear cells (PBMCs) are key drivers of the immune response, we investigated their transcriptome using RNA-sequencing before and immediately after a single bout of high-intensity knee-extension exercise in young (Young; n=7, 23 ± 4 years) and older individuals (Old; n=8, 65 ± 7 years). We used bioinformatics analyses to identify the biological processes and pathways that may be altered with age and in response to acute exercise. At baseline, 665 genes differed between Young and Old with notable differences in pathways involved in DNA Damage/Telomere Stress Induced Senescence, NAD Signaling Pathway, and Oxidative Stress Induced Senescence. After the exercise bout, 53 genes were differentially expressed in Young, while 1,026 genes changed in Old. In Young, the enriched processes and predicted pathways were linked to natural killer cells, while in Old these pathways were associated with cell signaling immune responses. Lastly, 26 genes exhibited similar responses to exercise between groups, enriching the biological process of natural killer cell-mediated immunity regulation. Our findings indicate that PBMC gene expression and the response to acute exercise are altered with aging, where exercise induced more pronounced PBMC transcriptomic adaptations in the Old. Additionally, while aging is associated with increased expression of genes linked to cellular dysfunction and suppressed immune function, acute exercise attenuated these age-related differences by downregulating the genes related to those pathways. Finally, acute exercise activated similar immune related pathways in both age groups.

As populations age, identifying the neurobiological basis of cognitive resilience is critical for delaying or preventing Alzheimers disease (AD). While most older adults experience memory decline, a subset known as superagers (SA) maintains youthful memory into late life, offering a unique window into protective mechanisms against neurodegeneration. Here, we identified a functional connectivity (FC) signature, termed Alzheimer\'s-resilient connectome (ARC), that robustly differentiates SA from age-matched patients with AD. Using resting-state fMRI in a discovery cohort (N = 290), we identified ARC derived from machine learning classifiers that distinguished SA from AD with high accuracy (AUC = 0.85), and validated the replicability of the ARC in an independent replication cohort (N = 143). ARC involved prefrontal, temporal and insular networks and was strongly associated with brain age. When applied to cognitively unimpaired (CU) adults (discovery cohort: N = 818 and replication cohort: N = 497), ARC-based subtyping revealed SA-like and AD-like subgroups with similar baseline cognitive performance but markedly divergent longitudinal trajectories. SA-like CU individuals showed slower cognitive decline, reduced amyloid-{beta} accumulation, and lower risk of conversion to mild cognitive impairment and AD, reinforcing the ARC signature as a potential early indicator of resilience. Genome-wide association analysis identified CLYBL and FRMD6 as novel genetic modulators associated with these divergent aging phenotypes. Together, our findings position ARC as a sensitive and generalizable biomarker of resilience, enabling early risk stratification and precision prevention for AD.

Fatty acids are involved in disease risk and aging processes. In the US National Health and Nutrition Examination Survey (1999-2002), we tested for associations of total, saturated (SFA), monounsaturated (MUFA), polyunsaturated (PUFA), and subtypes of dietary fatty acids with DNA methylation-based aging biomarkers, adjusting for age, BMI, total energy intake, and sociodemographic and behavioral factors (N=2,260). Higher SFA and MUFA were associated with greater GrimAge2, an aging biomarker of mortality; PUFA was associated with lower Horvath1, Hannum, and PhenoAge (p<0.05). Omega-3 and the PUFA:SFA ratio were negatively associated with Horvath1, Hannum, Vidal-Bralo, and PhenoAge. Notably, a one-unit increase in PUFA:SFA was associated with 1.05 years lower PhenoAge (95% CI=-1.87, -0.22). There were consistent trends of positive associations of SFA subtypes and negative associations of PUFA subtypes with epigenetic aging; associations of MUFA subtypes varied. Future studies, including randomized controlled trials, are needed to investigate causality and downstream clinical outcomes.

Age-related differences in white matter structure are increasingly understood as nonlinear, regionally specific, and behaviorally relevant. Using whole-brain fixel-based analysis (FBA), we examined how recognition memory speed relates to micro- and macrostructural white matter properties across a sample spanning young to middle adulthood. Slower response times were associated with higher fiber density (FD) in left frontoparietal tracts, nonlinear increases in fiber cross-section (log(FC)) in the anterior corpus callosum, and elevated combined fiber density and cross-section (FDC) in posterior callosal and cingulum pathways. These associations were most pronounced among individuals in the later decades of this age range, suggesting that white matter morphology reflects both extended maturation and emerging age-related decline. In a separate, hypothesis-driven analysis, we applied deterministic tractography to reconstruct the fornix and extracted mean fractional anisotropy (FA) along its length. Greater fornix FA and younger age together explained 34% of the variance in retrieval speed. These findings highlight regionally distinct structural contributions to memory performance and support lifespan models emphasizing individual variability and neuroplasticity in white matter development. This integrative approach underscores the value of combining whole-brain and tract-specific analyses to advance our understanding of white matter contributions to cognitive aging.

Observational studies have shown that physical activity and sedentary behavior are associated with aging. However, whether these associations underlie causal effects remains unknown. Thus, this study aimed to assess the genetic correlation and causal relationships between genetically predicted physical activity, sedentary behavior, and aging.