Biogerontology

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.

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.

Traditional epigenetic aging clocks are limited because they do not incorporate clinical information and functional tests, and rely on DNA samples and methylation profiling infrastructure which are not easily accessible. To address these limitations, we built a new framework, FusionAge, with which we trained 26 aging clocks using interpretable nonlinear models, including deep neural networks (DNNs). Our results show that multimodal clocks built with DNNs significantly outperform clocks derived from single modalities or traditional linear models. FusionAge-derived biological age is more strongly associated with incident disease and mortality compared to chronological age in UK Biobank individuals. We validated these findings in the National Health and Nutrition Examination Survey, confirming that cardiorespiratory fitness is a major, consistent driver of biological age. Finally, we applied FusionAge to demonstrate its utility in detecting biological age changes in astronauts following spaceflight. Together, we demonstrate a powerful, portable framework for assessing biological age that captures the complex, multifactorial nature of human aging.

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.

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.

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.

Abstract Background: Biological systems exhibit dynamic patterns over multiple temporal scales -from minutes to months- that are poorly captured by conventional cross-sectional or low-frequency longitudinal studies. These patterns, including circadian and ultradian rhythms, may be critical determinants of health, resilience, and disease risk in aging. Existing longitudinal studies in older adults lack high-frequency, multimodal measurements that integrate molecular, physiological, and digital health data streams. Objectives: The TIME Study aims to: (i) Characterize temporal patterns in molecular, physiological, and digital health measures in healthy older adults; (ii) determine how these patterns vary across biological domains and relate to each other; and (iii) assess how physiological systems respond to defined perturbations (oral glucose tolerance and maximal exercise). Methods: TIME is a single-site, observational, longitudinal study enrolling up to 150 adults aged [≥] 55 years. Over an 11-week main phase, participants complete seven weekly low-frequency visits, two perturbation challenge visits, and two, two-day high-frequency sampling epochs. Biospecimens, clinical measures, cognitive and physical performance tests, and continuous digital health data are collected. Follow-up visits occur at 6 and 12 months. Expected Impact: By integrating multimodal, temporally resolved data, TIME will provide a foundational dataset for understanding the role of biological rhythms in aging and inform future precision health strategies.

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.

Healthspan, the period of life where organisms are without frailty and/or disease, is a major focus of biogerontological research. To understand late-life decline and increased mortality risk, short-lived organisms such as nematode worms are commonly used. Pristionchus nematodes are established models for evolutionary developmental genetics research and show promise as systems for comparative and experimental study of ageing. To support this, we developed phenotypic ageing profiles for the evo-devo model Pristionchus pacificus and its little-studied congener Pristionchus fissidentatus. We find that various life history traits differ between P. pacificus and P. fissidentatus (lifespan, brood size, and reproductive period), demonstrating their utility for studying divergent ageing trajectories. Further, several traits are consistently impacted by age, including intestinal barrier function, body size, and locomotory ability. Additionally, in P. pacificus, rupture avoidance, cuticle integrity, and feeding rate decline with age, indicating dysregulation across many tissue types. Several age-linked patterns resemble those documented for Caenorhabditis elegans despite considerable evolutionary distance, suggesting conserved senescent processes across the Rhabditida family of nematodes. This work highlights similarities and differences in the impact of ageing in two Pristionchus nematodes and supports their development as models for evolutionary genetic study of senescence.

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.

Birds provide a unique model for ageing research, as they exhibit higher mass-adjusted metabolic rates and blood glucose levels than other vertebrate groups, yet demonstrate greater longevity and slower senescence compared to mammals of similar body size. This challenges the "pace of life syndrome" hypothesis, which predicts that high metabolic rates and elevated glucose should correlate with shorter lifespans. While the effects of glucose, glycation, and advanced glycation end-products (AGEs) on ageing are well-documented in humans and the conventional models used in biomedical research, their impact on avian physiology and ageing remains poorly understood. Some evidence suggests that birds possess adaptations mitigating the potential detrimental effects of glucose levels, which are much higher than those of all other vertebrate groups. However, previous studies indicate that elevated glucose predicts reduced lifespan, and protein glycation--varying with age--can influence survival and some fitness-related traits. This implies that glycation or AGE accumulation may have relevant effects on avian longevity. In this study, we experimentally investigated how one year of dietary supplementation with glucose or methylglyoxal affects survival and ageing markers (metabolic rate, flying performance, and beak coloration) in captive zebra finches (Taeniopygia guttata). Our results reveal a significant increase in mortality exclusively in glucose-supplemented birds. Although glucose treatment elevated albumin glycation rate and AGE formation--the latter also observed with methylglyoxal supplementation--these variables did not directly explain the increased mortality in glucose-treated birds, which was absent in methylglyoxal-treated individuals despite similar AGE accumulation. Additionally, we observed some effects on the assessed senescence markers, with an age-related constraint on seasonal metabolic adjustment, and a treatment-influenced age decline in secondary sexual traits expression. These findings support the use of these markers as proxies for senescence in zebra finches. We also discuss alternative mechanisms, independent of the glycation cascade, which may contribute to mortality. A seasonal decline in flight performance, particularly during peak mortality periods, suggests a broader deterioration of health. Thus, although we demonstrate glucose supplementation to be more deleterious than methylglyoxal, the underlying mechanisms for the observed increase in mortality induced by the treatment remain unresolved.

Optimal sleep plays a vital role in promoting healthy aging and enhancing longevity. This study proposes a Sleep Chart to assess the relationship between self-reported sleep duration and 23 biological aging clocks across 17 organ systems or tissues and 3 omics data types (imaging1, proteomics2, and metabolomics3). First, a systemic, U-shaped pattern shows that both short (<6 hours) and long (>8 hours) sleep duration are linked to elevated biological age gaps (BAGs) across 9 brain and body systems and 3 omics types. The lowest BAGs are achieved between 6.4 and 7.8 hours of sleep duration, and vary by organ and sex in the UK Biobank (ages 37-84 years). Furthermore, short and long sleep duration, compared to a normal sleep duration ([6-8] hours), are consistently linked to increased risk of systemic diseases beyond the brain and all-cause mortality, with evidence from genetic correlations and time to incident disease predictions, such as migraine, depression, and diabetes. Finally, short and long sleep duration are associated with late-life depression via distinct pathways: long sleep may contribute indirectly through biological aging processes, while short sleep shows a more direct link. Although our Mendelian randomization does not show strong causal effects from disease to sleep disturbances, it does not fully rule out the possibility that sleep disturbances may, in part, reflect underlying disease burden. Our findings suggest that the U-shaped relationship is likely driven by modifiable sleep disturbances rather than genetic predisposition, highlighting the potential of sleep optimization to support healthy aging, lower disease risk, and extend longevity. An interactive web portal is available to explore the Sleep Chart at: https://labs-laboratory.com/sleepchart.

BACKGROUNDSleep disturbances and altered circadian rhythms are well-documented in both physiological and biological studies of dementia. The exact causal relationship remains unclear. Several other long-term health conditions may also influence sleep patterns. Examining sleep patterns in relation to chronological ageing and the variations and progression of dementia, considering factors such as age, sex, and disease stage, can offer new insights for developing early screening and risk identification measures. Remote sleep monitoring enables routine assessment of cognitive decline symptoms in high-risk groups, aiding early risk identification. Integrating predictive models with routine sleep data holds promise for shaping more proactive approaches to assessing and caring for older adults and people living with dementia (PLWD). METHODSWe developed a machine learning pipeline to estimate Sleep Age Index (SAI) from longitudinal remote sleep monitoring data collected using under-the-mattress sleep sensors and use it to identify dementia risk. The study utilised a dataset of nocturnal activity and physiology data (n=1,672; 18,369 person-samples) collected from individuals in the general population and a cohort of PLWD. Dementia risk scores were stratified into high, medium and low-risk categories to support clinical monitoring and decision-making. RESULTSOur study indicates that sleep patterns in dementia do not follow typical ageing processes. For individuals with dementia, our pre-trained machine learning model predicted age in a negative direction. Further investigation revealed distinct patterns associated with these predictions, including irregular times to bed and rise, lower variation in deep sleep duration, and elevated breathing and heart rates. While age predictions were largely similar for female and male participants, the patterns observed in the female group were more consistent. Chronological age was predicted from sleep data with a mean absolute error of 5.52 (95% CI: 5.37 - 5.67) on held-out test data. A sensitivity of 75.7% (95% CI: 71.4% - 79.9%) and specificity of 74.7% (95% CI: 69.2% - 80.0%) was achieved post-stratification on unseen data in dementia versus control. To evaluate the real-world clinical applicability of the model, we conducted a pilot study in a population with a higher risk of cognitive decline (n = 50). Pilot data analysis indicated a slight positive bias between model predictions and the clinical experts judgement (mean difference 0.98 units, limits of agreement from -0.83 to 2.78 units, reported to 3 s.f.). This analysis also revealed that the traffic light system, designed to indicate the risk of cognitive decline, can serve as a complementary source of information to enhance decision support. This is especially evaluated for its applicability in improving clinical decision-making for high-risk groups, where there is often insufficient data available regarding an individuals cognitive status. CONCLUSIONSThe study offers new insights into sleep and dementia, highlighting how age and sex differences manifest differently in typical ageing compared to dementia. We demonstrate the utility of leveraging sleep monitoring data and predictive analysis in identifying individuals who may benefit from further clinical evaluation and early disease-modifying interventions.

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.

Understanding the biological mechanisms linking genetic variants to disease risk is essential for advancing precision health. We have developed a causal mediation analysis framework, the C-MAPLE (Causal Mediation Analysis of Pathways Linking Exposures) method, to identify disease-causing pathways for which the effect of genetic variants is mediated through metabolites to impact age-related diseases. Unlike Mendelian randomization, our approach is robust to horizontal pleiotropy, and models multiple mediators and interactions between genetic variants and metabolites simultaneously. To ensure robust model selection, we incorporate least absolute shrinkage and selection operator (LASSO) with stability selection, which can effectively select relevant mediators even in the presence of unmeasured confounding. We also introduce a dynamic adjustment to the number of bootstrap trials to reduce computational burden during uncertainty estimation. Applying this novel framework to the Canadian Longitudinal Study on Aging, we identified 190 potential causal links involving 108 genetic variants, 176 metabolites, and 6 age-related diseases. Our method and findings highlight the utility of causal mediation analysis in uncovering metabolite-mediated genetic mechanisms. This method, combined with large-scale population data sets, has the potential to revolutionize the identification of targets for downstream clinical research, and the development of personalized disease prevention, interventions, and therapeutics.

BackgroundCircadian rest-activity rhythmicity, a manifestation of circadian rhythms, characterizes 24-hour activity patterns. Growing evidence links disruption of circadian rhythms in late life to adverse outcomes, including functional and cognitive declines. Yet, most studies have been cross-sectional or restricted to single monitoring period, lacking longitudinal assessment. Consequently, it remains unknown whether deterioration or improvement in circadian rest-activity rhythmicity reflects late-life vulnerability in aging populations. MethodsWe analyzed data from the National Health and Aging Trends Study (NHATS), a nationally representative U.S. cohort of adults aged [&ge;]70 years. In Cycles 11 and 12, participants completed 7-day wearable monitoring and assessments of functional, cognitive, and neuropsychiatric outcomes. Circadian biomarkers were derived using cosinor and non-parametric methods (amplitude, MESOR, acrophase, pseudo-F, RA, IS, IV, M10, L5). Participants were classified into four transition groups (Optimal, Improved, Deteriorated, Adverse). Survey-weighted regression and Cox models adjusted for demographics and comorbidities estimated associations, with multiple testing correction. ResultsAt baseline, weaker rhythm intensity (low amplitude, MESOR, M10, RA) and poor stability (high fragmentation, low regularity) were associated with greater ADL disability, lower SPPB, weaker grip strength, and poorer recall. Longitudinal analyses revealed a graded hierarchy of risk. Participants with deteriorating rhythms represented the most dynamic risk state: declining amplitude was linked to reduced SPPB ({beta}= -0.71, 95% CI: - 0.98 to -0.44) and diminished immediate recall ({beta}= -0.60, -0.91 to -0.29). Declining regularity was also associated with impaired function (SPPB: {beta}= -0.52, -0.82 to -0.21) and cognition (immediate recall: {beta}= -0.43, -0.68 to -0.18; delayed recall: {beta}= -0.45, -0.70 to - 0.20). By contrast, rhythmicity improvement aligned with stabilization. For neuropsychiatric outcomes, high fragmentation was linked to probable dementia, while poor regularity was tied to anxiety/depression. ConclusionCircadian rest-activity rhythmicity is a robust and dynamic determinant of late-life outcomes. Persistent weakness or deterioration in intensity and regularity was linked to accelerated decline in function and cognition, whereas improvement aligned with stabilization. These findings position circadian rhythmicity as both an early biomarker of vulnerability and a potential modifiable target. Digital phenotyping via wearables offers a scalable, noninvasive framework for early risk detection, personalized intervention, and resilience promotion in aging populations.