Longevity Papers

The causal link between the aging microenvironment and T cell aging remains elusive. Here, we demonstrate that adenosine within aging tissues actively reprograms CD8+ T cells into a pro-aging Granzyme K+ (Gzmk+) population. Mechanistically, senescent cells create an adenosine-rich niche via p16-dependent CD39 upregulation, triggering A2aR signaling to induce Gzmk+ T cell differentiation. Once released, Gzmk promotes systemic inflammaging through PAR1 and complement activation. Crucially, targeting this axis-either via genetic Gzmk ablation or pharmacological A2aR blockade-reverses multi-organ aging phenotypes and significantly extends healthy lifespan in mice. Human analysis reveals age-dependent Gzmk+ T cell accumulation in multi organs, while coffee intake (an A2aR antagonist) inversely correlates with plasma Gzmk levels. Our findings uncover how metabolic niche changes drive T cell aging and establish the adenosine-Gzmk axis as a pivotal therapeutic target for combating age-related diseases.

Senescent cells contribute to degenerative processes in multiple tissues, including the retina. In the retinal pigment epithelium (RPE), their accumulation is closely associated with retinal aging and disease progression. Eliminating senescent RPE cells has shown therapeutic potential, but conventional senolytics often lack the specificity required to spare non-senescent cells, raising safety concerns. To overcome this, we performed integrated transcriptomic analyses of male mouse-derived RPE cells under natural aging and chemically induced senescence conditions. These analyses identified Bst2 as a membrane-localized marker selectively upregulated in senescent RPE cells, with minimal expression in young controls. Based on this discovery, we developed a modular, antibody-pluggable drug delivery platform-B-Z-PON-comprising mesoporous silica nanoparticles functionalized with a recombinant Fc-binding domain and conjugated with anti-Bst2 antibodies. This nanocarrier selectively accumulates in Bst2-expressing senescent RPE cells, enabling targeted drug delivery and sparing healthy retinal cells. In vivo administration of ABT-263-loaded B-Z-PON in aged and senescence-induced retinal degeneration models resulted in the selective ablation of senescent cells, restoration of RPE function, and improved visual outcomes. Together, our study integrates senescence-specific marker discovery with precision nanomedicine, establishing a versatile platform for targeted senotherapy. These findings offer a promising therapeutic approach for retinal aging disorders, such as age-related macular degeneration.

Global growth hormone receptor knockout (GHR

Longitudinal brain analysis is essential for understanding healthy aging and identifying pathological deviations. Longitudinal registration of sequential brain MRI underpins such analyses. However, existing methods are limited by reliance on densely sampled time series, a trade-off between accuracy and temporal smoothness, and an inability to prospectively forecast future brain states. To overcome these challenges, we introduce TimeFlow, a learning-based framework for longitudinal brain MRI registration. TimeFlow uses a U-Net backbone with temporal conditioning to model neuroanatomy as a continuous function of age. Given only two scans from an individual, TimeFlow estimates accurate and temporally coherent deformation fields, enabling non-linear extrapolation to predict future brain states. This is achieved by our proposed inter-/extrapolation consistency constraints applied to both the deformation fields and deformed images. Remarkably, these constraints preserve temporal consistency and continuity without requiring explicit smoothness regularizers or densely sampled sequential data. Extensive experiments demonstrate that TimeFlow outperforms state-of-the-art methods in terms of both future timepoint forecasting and registration accuracy. Moreover, TimeFlow supports novel biological brain aging analyses by differentiating neurode-generative trajectories from normal aging without requiring segmentation, thereby eliminating the need for labor-intensive annotations and mitigating segmentation inconsistency. TimeFlow offers an accurate, data-efficient, and annotation-free framework for longitudinal analysis of brain aging and chronic diseases, capable of forecasting brain changes beyond the observed study period.

While aging manifests differently across organs and individuals, existing approaches to measure it lack the spatial resolution to capture this complexity. Here, we develop an approach that applies multi-modal imaging, segmentation algorithms, and deep-learning to assess organ-specific aging across 39 anatomical regions in a total of 134K individuals in the UK Biobank. Our analysis reveals significant organ aging heterogeneity across and within individuals and a remarkable prevalence of organ-specific extreme aging. We validate that our imaging measures capture pathophysiologically meaningful aging through correlation with organ-specific biomarkers, revealing biologically coherent patterns. We find that accelerated organ aging is robustly predictive of corresponding organ disease. We identify the cerebrum as one of the strongest predictors of organismal aging. We investigate organ aging patterns underlying disease risk and find that each disease is linked to aging of highly distinct subsets of organs. Exploring lifestyle factors and interventions reveals a range of divergent organ-specific effects. Our work establishes a powerful paradigm for noninvasively evaluating human aging at anatomical resolution and population scale.

Network geometry offers a principled lens for understanding the structure of biomedical knowledge. We apply exact Ollivier-Ricci curvature (ORC) -- a discrete analogue of Riemannian curvature computed via optimal transport -- to medical ontologies, disease comorbidity networks, biological interaction networks, and brain functional connectivity graphs. Three main results emerge. First, within a single database (the Human Phenotype Ontology), the formal IS-A taxonomy is hyperbolic (mean ORC = -0.112, tree-like), while the disease co-occurrence network is spherical (mean ORC = +0.430, clique-rich) -- a six-order-of-magnitude gap in the density parameter that the curvature phase transition framework predicts without free parameters. Second, age-stratified disease comorbidity networks from 8.9 million Austrian hospital patients reveal a geometric aging trajectory: mean ORC increases monotonically from +0.018 (age 20-30) to +0.119 (age 80+), driven by rising clustering and density that encode the accumulation of multimorbidity. Third, sedenion (R16) Mandelbrot orbit features -- exploiting the zero-divisor structure of the Cayley-Dickson tower -- discriminate ASD-like from ADHD-like brain network topology (AUROC = 0.990, sedenion-only), providing complementary geometric information to ORC. Canonical biological networks (C. elegans neural, E. coli gene regulatory, protein-protein interaction) are uniformly spherical, suggesting that evolved biological networks universally favour redundant, triangle-rich connectivity. All core mathematical claims are machine-verified in Lean 4 (0 sorry in 7 core modules). These results establish ORC as a quantitative geometric biomarker for biomedical network analysis and demonstrate that the same phase transition framework governing semantic networks extends to clinical and biological domains.

A current impediment to bringing anti-aging therapies to market is the lack of accepted clinical endpoints that fit within reasonable trial time horizons and budgets. Recent theoretical models predict that sparse sampling of interconnected physiological subsystems can capture the essential dynamics of aging, suggesting that sparse biomarker panels could serve as surrogate endpoints for geroscience clinical trials. Here, we test this prediction using NHANES 1999-2018 data linked to the National Death Index. To overcome variable dropout caused by between-subsystem collinearity, we developed a two-stage dimensionality reduction architecture: Generalized Additive Models first compress each multi-variable subsystem into a single non-linear mortality risk score, which is then integrated via Levine's biological age algorithm. The resulting biological age estimates outperformed chronological age in predicting mortality and all fourteen age-related diseases examined, and detected the effects of diet, sleep, and physical activity on biological aging. Sex-stratified analysis revealed that the mortality sex gap penetrates to every physiological subsystem measured, with males and females requiring different biomarker panels - consistent with sex-specific differences in physiological network topology. Critically, male biological age was substantially more sensitive to both mortality prediction and lifestyle interventions than female biological age, a robustness-sensitivity trade-off predicted by network resilience theory. These findings carry direct implications for trial design: older males currently offer the most favourable signal-to-noise ratio for proof-of-concept geroscience trials using standard pathology tests, while the development of validated female-specific biomarker panels - capable of resolving the more distributed aging signal imposed by greater female physiological robustness - should be treated as an urgent and independent research priority.