Longevity Papers

Sickle Cell Disease (SCD) is a blood disorder affecting millions worldwide. Emerging evidence reveals that SCD pathophysiology increases risk of myeloid malignancies and hematopoietic stem cell (HSC) dysfunction, possibly due to pathological stress on bone marrow. To investigate this further, we interrogated mice and individuals with SCD and observed extended cell cycle times, oxidative stress, DNA damage, senescence, and dysregulation of molecular programs associated with these processes in bone marrow hematopoietic stem and progenitor cells (HSPCs). Human SCD HSPCs displayed poor hematopoietic potential ex vivo. SCD mice displayed a dramatic loss of transplantable bone marrow HSPCs, which was reversed upon treatment of SCD mice with the senolytic agent, ABT-263 (navitoclax). Thus, senolytics restore bone marrow function during SCD in mice and represent a novel strategy to improve bone marrow health in individuals with SCD and improve the safety of potentially curative gene therapies that utilize autologous HSPCs from individuals with SCD.

We derive and test a CT-based biological age model for predicting longevity, using an automated pipeline of explainable AI algorithms that quantifies skeletal muscle, abdominal fat, aortic calcification, bone density, and solid abdominal organs. We apply these AI tools to abdominal CT scans from 123,281 adults (mean age, 53.6 years; 47% women; median follow-up, 5.3 years). The final weighted CT biomarker selection was based on the index of prediction accuracy. The CT model significantly outperforms standard demographic data for predicting longevity (IPA = 29.2 vs. 21.7; 10-year AUC = 0.880 vs. 0.779; p < 0.001). Age- and sex-corrected survival hazard ratio for the highest-vs-lowest risk quartile was 8.73 (95% CI,8.14-9.36) for the CT biological age model, and increased to 24.79 after excluding cancer diagnoses within 5 years of CT. Muscle density, aortic plaque burden, visceral fat density, and bone density contributed the most. Here we show a personalized phenotypic CT biological age model that can be opportunistically-derived, regardless of clinical indication, to better inform risk assessment.

Neurons maintain their morphology over prolonged periods of adult life with limited regeneration after injury. C. elegans DIP-2 is a conserved regulator of lipid metabolism that affects axon maintenance and regeneration after injury. Here, we investigated genetic interactions of dip-2 with mutants in genes involved in lipid biosynthesis and identified roles of phospholipids in axon regrowth and maintenance. CEPT-2 and EPT-1 are enzymes catalyzing the final steps in the de novo phospholipid synthesis (Kennedy) pathway. Loss of function mutants of cept-2 or ept-1 show reduced axon regrowth and failure to maintain axon morphology. We demonstrate that CEPT-2 is cell-autonomously required to prevent age-related axonal defects. Interestingly, loss of function in dip-2 led to suppression of the axon regrowth phenotype observed in either cept-2 or ept-2 mutants, suggesting that DIP-2 acts to counterbalance phospholipid synthesis. Our findings reveal the genetic regulation of lipid metabolism to be critical for axon maintenance under injury and during aging.

Healthy aging is a common goal for humanity and society, and one key to achieving it is the rejuvenation of senescent resident stem cells and empowerment of aging organ regeneration. However, the mechanistic understandings of stem cell senescence and the potential strategies to counteract it remain elusive. Here, we reveal that the aging bone microenvironment impairs the Golgi apparatus thus diminishing mesenchymal stem cell (MSC) function and regeneration. Interestingly, replenishment of cell aggregates-derived extracellular vesicles (CA-EVs) rescues Golgi dysfunction and empowers senescent MSCs through the Golgi regulatory protein Syntaxin 5. Importantly, in vivo administration of CA-EVs significantly enhanced the bone defect repair rate and improved bone mass in aging mice, suggesting their therapeutic value for treating age-related osteoporosis and promoting bone regeneration. Collectively, our findings provide insights into Golgi regulation in stem cell senescence and bone aging, which further highlight CA-EVs as a potential rejuvenative approach for aging bone regeneration.

Age-related long-term disability is attracting increasing attention due to the growing ageing population worldwide. However, the current understanding of the senescent spinal cord remains insufficient. Bulk RNA sequencing reveals that 526 genes are upregulated and 300 genes are downregulated in senescent spinal cords. Pathway enrichment analysis of differentially expressed genes shows that senescence in spinal cords is related to phagosome function, neuroinflammation, ferroptosis, and necroptosis. Prediction of upstream transcription factors and interactome analysis identify Spi1 as a transcription factor that potentially plays a core role in senescent spinal cords. Spatial transcriptomics illustrates the spatial distribution of the transcriptomic landscape in both young and senescent spinal cords and identifies distinct neuronal and glial subtypes. The ferroptosis-associated gene Fth1 is upregulated in aged spinal cords. Flow cytometry reveals increased accumulation of free Fe

Current deep-learning-based image-analysis solutions exhibit limitations in holistically capturing spatiotemporal cellular changes, particularly during aging. We present scCamAge, an advanced context-aware multimodal prediction engine that co-leverages image-based cellular spatiotemporal features at single-cell resolution alongside cellular morphometrics and aging-associated bioactivities such as genomic instability, mitochondrial dysfunction, vacuolar dynamics, reactive oxygen species levels, and epigenetic and proteasomal dysfunctions. scCamAge employed heterogeneous datasets comprising ∼1 million single yeast cells and was validated using pro-longevity drugs, genetic mutants, and stress-induced models. scCamAge also predicted a pro-longevity response in yeast cells under iterative thermal stress, confirmed using integrative omics analyses. Interestingly, scCamAge, trained solely on yeast images, without additional learning, surpasses generic models in predicting chemical and replication-induced senescence in human fibroblasts, indicating evolutionary conservation of aging-related morphometrics. Finally, we enhanced the generalizability of scCamAge by retraining it on human fibroblast senescence datasets, which improved its ability to predict senescent cells.

Failures of the lysosome-autophagy system are a hallmark of aging and many disease states. As a consequence, interventions that enhance lysosome function are of keen interest in the context of drug development. Throughout the biomedical literature, evolutionary biologists have discovered that challenges faced by humans in clinical settings have been resolved by non-model organisms adapting to wild environments. Here, we used a primary cell culture approach to survey lysosomal characteristics in selected species of the genus Mus. We found that cells from M. musculus, mice adapted to human environments, had weak lysosomal acidification and high expression and activity of the lysosomal enzyme {beta}-galactosidase, a classic marker of cellular senescence. Cells of wild relatives, especially the Mediterranean mouse M. spretus, had more robustly performing lysosomes and dampened {beta}-galactosidase levels. We propose that classic laboratory models of lysosome function and senescence may reflect characters that diverge from the phenotypes of wild mice. The M. spretus phenotype may ultimately provide a blueprint for interventions that ameliorate lysosome breakdown in stress and disease.

Multi-organ biological aging clocks derived from clinical phenotypes and neuroimaging have emerged as valuable tools for studying human aging and disease1,2,3,4. Plasma proteomics provides an additional molecular dimension to enrich these clocks5. Here, we used 2448 plasma proteins from 43,498 participants in the UK Biobank to develop 11 multi-organ proteome-based biological age gaps (ProtBAG). We compared them to 9 multi-organ phenotype-based biological age gaps (PhenoBAG1) regarding genetics, causal associations with 525 disease endpoints (DE) from FinnGen and PGC, and their clinical promise to predict 14 disease categories and mortality. We highlighted critical clinical and methodological considerations for generating ProtBAG, including the need for age bias correction6 and addressing protein organ specificity to enhance model performance and generalizability. Genetic analyses revealed overlap between ProtBAGs and PhenoBAGs, including shared loci, genetic correlations, and colocalization signals. A three-layer causal network linked ProtBAG, PhenoBAG, and DE, exemplified by the pathway of obesity[->]renal PhenoBAG[->]renal ProtBAG to holistically understand human aging and disease. Combining features across multiple organs improved predictions for disease categories and mortality. These findings provide a framework for integrating multi-omics and multi-organ biological aging clocks in biomedicine. All results are publicly disseminated at https://labs-laboratory.com/medicine/.

Sleep is a fundamental biological process with profound implications for physical and mental health, yet our understanding of its complex patterns and their relationships to a broad spectrum of diseases remains limited. While polysomnography (PSG), the gold standard for sleep analysis, captures rich multimodal physiological data, analyzing these measurements has been challenging due to limited flexibility across recording environments, poor generalizability across cohorts, and difficulty in leveraging information from multiple signals simultaneously. To address this gap, we curated over 585,000 hours of high-quality sleep recordings from approximately 65,000 participants across multiple cohorts and developed SleepFM, a multimodal sleep foundation model trained with a novel contrastive learning approach, designed to accommodate any PSG montage. SleepFM produces informative sleep embeddings that enable predictions of future diseases. We systematically demonstrate that SleepFM embeddings can predict 130 future diseases, as modeled by Phecodes, with C-Index and AUROC of at least 0.75 on held-out participants (Bonferroni-corrected p < 0.01). This includes accurate predictions for death (C-Index: 0.84 [95% CI: 0.81-0.87]), heart failure (C-Index: 0.80 [95% CI: 0.77-0.83]), chronic kidney disease (C-Index: 0.79 [95% CI: 0.77-0.81]), dementia (C-Index: 0.85 [95% CI: 0.82-0.87]), stroke (C-Index: 0.78 [95% CI: 0.76-0.81]), atrial fibrillation (C-Index: 0.78 [95% CI: 0.75-0.81]), and myocardial infarction (C-Index: 0.81 [95% CI: 0.78-0.84]). The model's generalizability was further validated through strong performance on the Sleep Heart Health Study (SHHS), a dataset unseen during pre-training. Additionally, SleepFM demonstrates strong performance on traditional sleep analysis tasks, achieving competitive results in both sleep staging (mean F1 scores: 0.70-0.78) and sleep apnea diagnosis (AUROC: 0.90-0.94). Beyond these standard applications, our analysis reveals that specific sleep stages and physiological signals carry distinct predictive power for different diseases. This work demonstrates how foundation models can leverage sleep polysomnography data to uncover the extensive relationship between sleep physiology and future disease risk.

Blood perfusion delivers oxygen and nutrients to all cells, making it a fundamental feature of brain organization. How cerebral blood perfusion maps onto micro-, meso- and macro-scale brain structure and function is therefore a key question in neuroscience. Here we analyze pseudo-continuous arterial spin labeling (ASL) data from 1,305 healthy individuals in the HCP Lifespan studies (5-100 years) to reconstruct a high-resolution normative cerebral blood perfusion map. At the cellular and molecular level, cerebral blood perfusion co-localizes with granular layer IV, biological pathways for maintenance of cellular relaxation potential and mitochondrial organization, and with neurotransmitter and neuropeptide receptors involved in vasomodulation. At the regional level, blood perfusion aligns with cortical arealization and is greatest in regions with high metabolic demand and resting-state functional hubs. Looking across individuals, blood perfusion is dynamic throughout the lifespan, follows micro-architectural changes in development, and maps onto individual differences in physiological changes in aging. In addition, we find that cortical atrophy in multiple neurodegenerative diseases (late-onset Alzheimer\'s disease, TDP-43C, and dementia with Lewy bodies) is most pronounced in regions with lower perfusion, highlighting the utility of perfusion topography as an indicator of transdiagnostic vulnerability. Finally, we show that ASL-derived perfusion can be used to delineate arterial territories in a data-driven manner, providing insights into how the vascular system is linked to human brain function. Collectively, this work highlights how cerebral blood perfusion is central to, and interlinked with, multiple structural and functional systems in the brain.

Giant tortoises exhibit exceptional longevity, often exceeding the human lifespan. To understand the genomic and epigenomic basis of their longevity, we analyzed the DNA sequence and methylome of Jonathan, an Aldabra giant tortoise (Aldabrachelys gigantea), estimated to be 192 years old. Relative to other giant tortoises (Aldabrachelys gigantea and Chelonoidis abingdonii), we found Jonathan has gene variants in pathways associated with aging, including DNA repair and telomere regulation. Consistent with his advanced age, Jonathan has significant age-related changes in DNA methylation and methylation entropy, compared with a 5-year-old Aldabra individual. Notably, we found that low entropy regions in Jonathan\'s methylome were enriched for genes involved in the electron transport chain. This suggests that high-fidelity transcription of these genes may be crucial for extreme longevity. With this data, we propose a model for aging, that links efficient mitochondrial energy production with nuclear maintenance of low methylation entropy.

There is a growing interest in developing drugs with a general geroprotective effect, aimed at slowing down aging. Several compounds have been shown to increase the lifespan and reduce the incidence of age-related diseases in model organisms. Translating these results is challenging, due to the long lifespan of humans. To address this, we propose using a battery of medical imaging protocols that allow for assessments of age-related processes known to precede disease onset. These protocols, based on magnetic resonance imaging, positron emission-, computed-, and optical coherence tomography, are already in use in drug development and are available at most modern hospitals. Here, we outline how an informed use of these techniques allows for detecting changes in the accumulation of age-related pathologies in a diverse set of physiological systems. This in vivo imaging battery enables efficient screening of candidate geroprotective compounds in early phase clinical trials, within reasonable trial durations.

Brown adipose tissue (BAT) is the primary site for non-shivering thermogenesis in the body and plays a crucial role in maintaining core body temperature. However, its function gradually declines with age. To mitigate the age-related decline in BAT thermogenic capacity, we treated progeroid mice with metformin to investigate the potential mechanisms by which metformin can slow the reduction in BAT thermogenic function. We found that progeroid mice, after receiving metformin treatment, showed significant improvement in the senescent state of brown adipocytes through the activation of SIRT1, and effectively reduced mitochondrial oxidative stress. Additionally, metformin slowed the age-related decline in UCP1 expression levels in brown adipose tissue, thereby maintaining the thermogenic capacity of the progeroid mice. Moreover, metformin reduced inflammatory responses around senescent cells, further improving the overall senescent state of the tissue. These findings suggest that metformin can slow down the aging process in brown adipose tissue by targeting SIRT1, thereby enhancing its thermogenic capacity.

Age-related vestibular dysfunction (ARVD) is a prevalent, debilitating condition in the elderly. The etiology and molecular mechanisms are poorly understood. We focused on mechanosensitive hair cells (HCs) as they are particularly vulnerable to aging. Using single-cell RNA-seq transcriptomes of young and old mouse vestibular HCs, we show that aging HCs display both universal molecular blueprints, such as genomic instability, mitochondrial dysfunction, and impaired proteostasis, and cell type-specific aging signatures associated with deterioration of hair bundles and mechanotransduction. These signatures are also observed in aged human vestibular HCs, suggesting shared mechanisms. Importantly, morphological and functional analysis revealed that bundle degeneration and vestibular functional decline precede HC loss, highlighting the deterioration of mechanotransduction as a key contributor to ARVD. Furthermore, molecular and cellular changes associated with aging signatures are less pronounced in vestibular HCs than in cochlear HCs, underscoring the different pace of aging between the two mammalian inner ear sensory epithelia.

Epigenetic clocks use DNA methylation (DNAm) levels to predict an individual's biological age. However, relationships between lifestyle/biomarkers and epigenetic age acceleration (EAA) in Asian populations remain unknown. We here explored associations between lifestyle factors, physiological conditions, and epigenetic markers, including HannumEAA, IEAA, PhenoEAA, GrimEAA, DunedinPACE, DNAm-based smoking pack-years (DNAmPACKYRS), and DNAm plasminogen activator inhibitor 1 level (DNAmPAI1). A total of 2474 Taiwan Biobank (TWB) individuals aged between 30 and 70 provided physical health examinations, lifestyle questionnaire surveys, and blood and urine samples. Partial correlation analysis (while adjusting for chronological age, smoking, and drinking status) demonstrated that 29 factors were significantly correlated with at least one epigenetic marker (Pearson's correlation coefficient |r|> 0.15). Subsequently, by exploring the model with the smallest Akaike information criterion (AIC), we identified the best model for each epigenetic marker. As a DNAm-based marker demonstrated to predict healthspan and lifespan with greater accuracy, GrimEAA was also found to be better explained by lifestyle factors and physiological conditions. Totally 15 factors explained 44.7% variability in GrimEAA, including sex, body mass index (BMI), waist-hip ratio (WHR), smoking, hemoglobin A1c (HbA1c), high-density lipoprotein cholesterol (HDL-C), creatinine, uric acid, gamma-glutamyl transferase (GGT), hemoglobin, and five cell-type proportions. In summary, smoking, elevated HbA1c, BMI, WHR, GGT, and uric acid were associated with more than one kind of EAA. At the same time, higher HDL-C and hemoglobin were related to epigenetic age deceleration (EAD). These findings offer valuable insights into biological aging.

Aging is associated with intestinal dysbiosis, a condition characterized by diminished microbial biodiversity and inflammation. This leads to increased vulnerability to extraintestinal manifestations such as autoimmune, metabolic, and neurodegenerative conditions thereby accelerating mortality. As such, modulation of the gut microbiome is a promising way to extend healthspan. In this study, we explore the effects of fecal microbiota transplant (FMT) from long-living Ames dwarf donors to their normal littermates, and vice versa, on the recipient gut microbiota and liver transcriptome. Importantly, our previous studies highlight differences between the microbiome of Ames dwarf mice relative to their normal siblings, potentially contributing to their extended lifespan and remarkable healthspan. Our findings demonstrate that FMT from Ames dwarf mice to normal mice significantly alters the recipient's gut microbiota, potentially reprogramming bacterial functions related to healthy aging, and changes the liver transcriptome, indicating improved metabolic health. Particularly, the microbiome of Ames dwarf mice, characterized by a higher abundance of beneficial bacterial families such as Peptococcaceae, Oscillospiraceae, and Lachnospiraceae, appears to play a crucial role in modulating these effects. Alongside, our mRNA sequencing and RT-PCR validation reveals that FMT may contribute to the significant downregulation of p21, Elovl3, and Insig2, genes involved with cellular senescence and liver metabolic pathways. Our data suggest a regulatory axis exists between the gut and liver, highlighting the potential of microbiome-targeted therapies in promoting healthy aging. Future research should focus on functional validation of altered microbial communities and explore the underlying biomolecular pathways that confer geroprotection.

Telomeres shorten with each cell division, serving as biomarkers of aging, with human tissues exhibiting short telomeres and restricted telomerase expression. In contrast, mice have longer telomeres and widespread telomerase activity, limiting their relevance as models for human telomere biology. To address this, we engineer a mouse strain with a humanized mTert gene (hmTert), replacing specific non-coding sequences with human counterparts. The hmTert gene, which is repressed in adult tissues except the gonads and thymus, closely mimics human TERT regulation. This modification rescues telomere dysfunction in mTert-knockout mice. Successive intercrosses of Tert

Human social connections are complex ecosystems formed of structural, functional and quality components. Deficits in social connections are associated with adverse age-related health outcomes, but we know little about the ageing-related mechanistic processes underlying this. Using data from 7,047 adults aged 50+ in the English Longitudinal Study of Ageing, we explored associations between diverse aspects of social deficits and both perceived and physiological age acceleration, which provide complementary psycho-behavioural and biological mechanistic explanations. We created and validated a novel physiological ageing index using clinical indicators pertaining to the cardiovascular, respiratory, haematologic, metaboloic and cognitive systems using principal component analysis. Doubly-robust estimations using inverse-probability-weighted regression adjustment estimators showed that living alone, low social integration and high social isolation were risk factors for physiological age acceleration, with those who lived alone on average 1.9 years older than those who lived with others (95% CI 0.9-3.0 years older; 32% greater age acceleration than people who live with others). However, social deficits were not related to accelerations in perceived age. Analyses were robust to multiple sensitivity analyses and maintained four years later. These findings provide important mechanistic insight that helps to explain the relationship between social deficits and age-related morbitidy and mortality outcomes.

Mitochondria are a diverse family of organelles that specialize to accomplish complimentary functions. All mitochondria share general features, but not all mitochondria are created equal.Here we develop a quantitative pipeline to define the degree of molecular specialization among different mitochondrial phenotypes - or mitotypes. By distilling hundreds of validated mitochondrial genes/proteins into 149 biologically interpretable MitoPathway scores (MitoCarta 3.0) the simple mitotyping pipeline allows investigators to quantify and interpret mitochondrial diversity and plasticity from transcriptomics or proteomics data across a variety of natural and experimental contexts. We show that mouse and human multi-organ mitotypes segregate along two main axes of mitochondrial specialization, contrasting anabolic (liver) and catabolic (brain) tissues. In cultured primary human fibroblasts exhibiting robust time-dependent and treatment-induced metabolic plasticity, we demonstrate how the mitotype of a given cell type recalibrates i) over time in parallel with hallmarks of aging, and ii) in response to genetic, pharmacological, and metabolic perturbations. Investigators can now use MitotypeExplorer.org and the associated code to visualize, quantify and interpret the multivariate space of mitochondrial biology.

The success of clinical trials of longevity drugs relies heavily on identifying integrative health and aging biomarkers, such as biological age. Epigenetic aging clocks predict the biological age of an individual using their DNA methylation profiles, commonly retrieved from blood samples. However, there is no standardized methodology to validate and compare epigenetic clock models as yet. We propose ComputAgeBench, a unifying framework that comprises such a methodology and a dataset for comprehensive benchmarking of different clinically relevant aging clocks. Our methodology exploits the core idea that reliable aging clocks must be able to distinguish between healthy individuals and those with aging-accelerating conditions. Specifically, we collected and harmonized 66 public datasets of blood DNA methylation, covering 19 such conditions across different ages, and tested 13 published clock models. Additionally, we compiled 46 separate datasets to facilitate the training of new aging clocks. We believe our work will bring the fields of aging biology and machine learning closer together for the research on reliable biomarkers of health and aging.

Muscle atrophy occurs during natural aging and under disease conditions. Muscle cell apoptosis is considered one of the main causes of muscle atrophy, while several recent studies argued that muscle cells do not die during muscle atrophy. Here, sensor zebrafish are generated to visualize muscle cell apoptosis and the engulfment of dead muscle cells by macrophages. Using these sensor zebrafish, starvation, and natural aging-induced muscle atrophy models are established. The data showed that the diameters of muscle cells decreased in both models; however, muscle cell apoptosis is not found in the process of muscle atrophy. In starvation-induced muscle atrophy, it also showed that the number of nuclei in muscle cells remained constant, and there is no increase in the number of macrophages in muscle tissues, both of which further confirmed that muscle cells do not die. In both models, transcriptional analysis showed that the apoptosis pathway is down-regulated, and autophagy and protein degradation pathways are up-regulated. All these data indicated that although there is a great reduction of muscle mass during starvation or aging-induced muscle atrophy, muscle cells do not die by apoptosis. These findings provide new insights into muscle atrophy and can benefit the treatments for muscle atrophy-related diseases.

Recent studies have shown that disruptions in the nicotinamide adenine dinucleotide (NAD

The molecular mechanisms of aging are not fully understood. Here, we used label-free Stimulated Raman scattering (SRS) microscopy to investigate changes in proteins and lipids throughout the lifespan of C. elegans. We observed a dramatic buildup of proteins within the body cavity or pseudocoelom of aged adults that was blunted by interventions that extend lifespan: caloric restriction (CR) and the reduced insulin/insulin-like growth factor signaling (IIS) pathway. Using a combination of microscopy, proteomic analysis, and validation with mutant strains, we identified vitellogenins as the key molecular components of the protein buildup in the pseudocoelom. Vitellogenins shuttle nutrients from intestine to embryos and are homologous to human apolipoprotein B, the causal driver of cardiovascular disease. We then showed that CR and knockdown of vitellogenins both extend lifespan by >60%, but their combination has no additional effect on lifespan, suggesting that CR extends the lifespan of C. elegans in part by inhibiting vitellogenesis. The extensive dataset of more than 12,000 images stitched into over 350 whole-animal SRS images of C. elegans at different ages and subjected to different longevity intervention will be a valuable resource for researchers interested in aging.

The repurposing of existing biosafety datasets offers unique opportunities in biomedical research. Here, we demonstrate how pesticide toxicity data, which include long-term survival studies in mammalian models, can be harnessed to uncover potential drug candidates or drug targets that can improve survival. We show that these substances frequently affect mitochondrial bioenergetics and that they can improve animal healthspan.