Over the human lifespan, our cells encounter numerous stressors that can trigger an intrinsic defense mechanism called cellular senescence. Cellular senescence is characterized by irreversible growth arrest and can act as a safeguard against cancer. However, when senescent cells accumulate in various tissues as we age, it can contribute to tissue degeneration and chronic diseases. 

The senescence-associated secretory phenotype (SASP), a hallmark of senescent cells, plays a critical role by secreting inflammatory factors, proteases, and growth factors, disrupting tissue balance and fueling pathological conditions. Consequently, selectively eliminating senescent cells has emerged as a promising therapeutic strategy, potentially restoring tissue function and mitigating age-related disorders.

Full blog - https://aging-us.org/2024/07/links-between-exercise-senescence-and-lung-health/

Research Paper DOI - https://doi.org/10.18632/aging.205976

Corresponding author - Masataka Sugimoto - msugimot@tmig.or.jp

Video short - https://www.youtube.com/watch?v=Bzgb2PEiV9c

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Keywords - aging, senescence, exercise, PEDF, myokine, COPD

About Aging-US

The mission of the journal is to understand the mechanisms surrounding aging and age-related diseases, including cancer as the main cause of death in the modern aged population.

The journal aims to promote 1) treatment of age-related diseases by slowing down aging, 2) validation of anti-aging drugs by treating age-related diseases, and 3) prevention of cancer by inhibiting aging. (Cancer and COVID-19 are age-related diseases.)

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Aging (Aging-US)

Links Between Exercise, Senescence, and Lung Health

BUFFALO, NY — December 8, 2025 — A new #research paper was #published in Volume 17, Issue 11 of Aging-US on September 12, 2025, titled “Infusion of blood from young and old mice modulates amyloid pathology.”

This study was led by co-first authors Matias Pizarro from Universidad Adolfo Ibáñez and Ruben Gomez-Gutierrez from The University of Texas Health Science Center at Houston, alongside corresponding authors Claudia Duran-Aniotz from Universidad Adolfo Ibáñez and Rodrigo Morales from The University of Texas Health Science Center at Houston and Universidad Bernardo O’Higgins. The goal was to investigate how blood from young and old mice influences Alzheimer’s-related changes in a transgenic mouse model. The findings indicate that age-dependent circulating factors can either worsen or mitigate brain changes associated with dementia, highlighting blood and its components as potential therapeutic targets.

Alzheimer’s disease is a progressive neurodegenerative disorder characterized by misfolded amyloid proteins, inflammation, and gradual cognitive decline, with aging as its main risk factor. In this work, whole blood from young adult or very old wild-type mice was repeatedly infused into Tg2576 mice, a well-established model of amyloid accumulation and memory impairment. Over several months, recipient mice received 30 weekly blood infusions, followed by behavioral testing and detailed neuropathological analyses.

“Tg2576 mice express the human APP harboring the Swedish mutation.”

Mice that received blood from old donors performed worse in both short- and long-term spatial memory tasks than mice infused with young blood, suggesting that aged blood contains factors that impair cognition. When the team examined brain tissue, they found more cortical amyloid deposits detected by a specific antibody in mice treated with old blood, while overall amyloid levels measured biochemically did not change, suggesting differences in plaque type or compactness rather than total amount. The expression of amyloid precursor protein in the brain was also higher after old-blood infusion, which may partly explain the shift in amyloid pathology.​

Despite these changes in plaques and memory, classical markers of astrocyte activation, a sign of brain inflammation, did not differ between groups, pointing to more subtle molecular shifts. A broad proteomic analysis of brain samples revealed dysregulation of proteins involved in synapse formation, calcium signaling, and the endocannabinoid system, pathways important for neuronal communication and plasticity. Among them, the calcium channel–related protein CACNA2D2 and the signaling protein BRAF were increased in mice that received old blood, confirming that aged blood circulation can reshape key signaling networks linked to neuronal function and degeneration.

Overall, this study supports the idea that blood is not just a passive carrier but a powerful modulator of brain health during aging and disease. While young blood has been associated in previous work with improved synaptic function and reduced amyloid and tau changes, this study emphasizes the harmful impact of old blood, particularly on cortical amyloid patterns and memory. The identification of CACNA2D2 and BRAF as potential mediators of these effects suggests new avenues for targeting blood-borne factors or downstream brain pathways to slow or modify Alzheimer’s-related decline.

DOI - https://doi.org/10.18632/aging.206319

Corresponding authors - Claudia Duran-Aniotz - Claudia.Duran@uai.cl, and Rodrigo Morales - Rodrigo.MoralesLoyola@uth.tmc.edu

Abstract video - https://www.youtube.com/watch?v=zsBDSAipH3w

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Aging-US

Young and Old Mice Blood Differently Shapes Alzheimer’s-Related Brain Changes

The paper featured on the cover of this issue of Aging-US, published on October 30, 2025, entitled “SAMP-Score: a morphology-based machine learning classification method for screening pro-senescence compounds in p16-positive cancer cells,” represents an important methodological and conceptual advance at the interface of senescence biology, imaging and drug discovery.

In this study, led by first author Ryan Wallis and corresponding author Cleo L. Bishop (Queen Mary University of London), the authors introduce SAMP-Score, a machine-learning–based framework designed to identify bona fide senescence induction in cancer cells where canonical markers fail. This is a timely and much-needed contribution to the field.

Therapy-induced senescence has emerged as a powerful strategy to restrain tumor growth, yet its reliable detection in cancer cells remains a major bottleneckIn these contexts, cells often already display features associated with cellular aging, rendering conventional senescence markers ambiguous or misleading. Distinguishing true senescence from toxicity, stress responses or baseline “aged” phenotypes is therefore a critical unmet need.

Rather than relying on predefined molecular readouts, the authors take a different approach and train a machine-learning model to recognize senescence-associated morphological profiles (SAMPs) which are subtle but reproducible changes in cellular architecture captured through high-content microscopy. By learning directly from image-based phenotypes, SAMP-Score is able to identify senescence with a level of precision that is difficult to achieve using marker-based strategies alone.

The strength of the platform demonstrated through a large-scale screen of over 10,000 novel chemical entities in p16-positive basal-like breast cancer cells. From this screen, the compound QM5928 emerged as a robust inducer of senescence across multiple cancer models, notably without inducing cytotoxicity. Importantly, QM5928 retains activity in cellular contexts that are resistant to CDK4/6 inhibition, including palbociclib-refractory, p16-high tumors.

Mechanistically, the authors show that QM5928 promotes nuclear relocalization of p16, consistent with a functional engagement of cell-cycle arrest pathways. These nuanced phenotypic changes would likely have gone undetected without the resolution and discrimination provided by SAMP-Score, underscoring the platform’s ability to separate true senescence from confounding cellular states.

This work exemplifies how machine learning and quantitative imaging can be harnessed to solve long-standing problems in senescence research, moving the field beyond binary marker expression toward phenotype-driven classification. Beyond its immediate relevance for cancer therapy, SAMP-Score offers a broadly applicable framework for senescence-based screening efforts across biological contexts.

DOI - https://doi.org/10.18632/aging.206333

Corresponding author - Cleo L. Bishop - c.l.bishop@qmul.ac.uk

Abstract video - https://www.youtube.com/watch?v=qXI_KI3EgHE

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Aging-US Editors' Choice

Treating aggressive cancers that do not respond to standard therapies remains one of the most significant challenges in oncology. Among these are basal-like breast cancers (BLBC), which lack hormone receptors and HER2 amplification. This makes them unsuitable for many existing targeted treatments. As a result, therapeutic options are limited, and patient outcomes are often poor.

One emerging strategy is to induce senescence, a state in which cancer cells permanently stop dividing but remain metabolically active. This approach aims to slow or stop tumor growth without killing the cells directly. Although promising, the clinical application of senescence-based therapies has been limited by several challenges.

Senescence is typically identified using biomarkers such as p16, p21, and beta-galactosidase activity. However, these markers are often already present in aggressive cancers like BLBC (Sen‑Mark+ tumors), making it difficult to determine whether a treatment is truly inducing senescence or merely reflecting the tumor’s existing biology. Moreover, conventional screening methods may mistake reduced cell growth for senescence, cell death, or temporary growth arrest, leading to inaccurate assessments. This is especially problematic in large-scale drug screening, where thousands of compounds must be evaluated quickly and reliably.

To overcome these issues, researchers from Queen Mary University of London and the University of Dundee have developed a new machine learning–based method to improve the detection of senescence in cancer cells. Their findings were recently published in Aging-US.

The Study: Developing the SAMP-Score

The study, titled “SAMP-Score: a morphology-based machine learning classification method for screening pro-senescence compounds in p16-positive cancer cells,” was led by Ryan Wallis and corresponding author Cleo L. Bishop from Queen Mary University of London. This paper was featured on the cover of Aging-US Volume 17, Issue 11, and highlighted as our Editors’ Choice.

Full blog - https://aging-us.org/2025/12/using-machine-learning-to-identify-senescence-inducing-drugs-for-resistant-cancers/

Paper DOI - https://doi.org/10.18632/aging.206333

Corresponding author - Cleo L. Bishop - c.l.bishop@qmul.ac.uk

Abstract video - https://www.youtube.com/watch?v=qXI_KI3EgHE

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Keywords - aging, SAMP-Score, senescence, senescent marker positive cancer cells, Sen-Mark+, machine learning, pro-senescence, high-throughput compound screening

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Using Machine Learning to Identify Senescence-Inducing Drugs for Resistant Cancers

BUFFALO, NY — December 12, 2025 — A new #research paper was #published in Volume 17, Issue 11 of Aging-US on November 14, 2025, titled “Methylglyoxal-induced glycation stress promotes aortic stiffening: putative mechanistic roles of oxidative stress and cellular senescence.”

The study was led by first authors Parminder Singh of the Buck Institute for Research on Aging and Ravinandan Venkatasubramanian of the University of Colorado Boulder, with senior contributions from corresponding authors Pankaj Kapahi (Buck Institute for Research on Aging) and Zachary S. Clayton (University of Colorado Boulder and University of Colorado Anschutz Medical Campus). The researchers investigated how methylglyoxal (MGO), a toxic byproduct that builds up in blood vessels with age or metabolic dysfunction like diabetes, contributes to artery stiffening. Their findings are especially important to aging and diabetes-related cardiovascular risk.

Aortic stiffening, which reduces the flexibility of the body’s largest artery, is a key predictor of cardiovascular disease in older adults. The research team used young and aged mice to study how MGO affects vascular health. In young mice, chronic exposure to MGO increased aortic stiffness by 21%. However, when treated with Gly-Low, a supplement containing natural compounds such as nicotinamide and alpha-lipoic acid, this stiffening was completely prevented. Gly-Low also reduced the buildup of MGO and its harmful byproducts, particularly MGH-1, in both blood and tissue.

“Aortic stiffness was assessed in vivo via pulse wave velocity (PWV) and ex vivo through elastic modulus.”

The research showed that MGO’s damage goes beyond structural changes. It also caused the endothelial cells that line blood vessels to enter senescence, a state in which cells stop dividing and begin releasing inflammatory signals. This led to lower levels of nitric oxide, a molecule essential for blood vessel relaxation. In human vascular cells in lab culture, Gly-Low reversed these aging-like changes and restored nitric oxide production.

In older mice, which naturally develop stiffer arteries, Gly-Low treatment during four months significantly reduced stiffness and lowered MGO and MGH-1 levels. This suggests that Gly-Low may help slow or even reverse vascular aging by reducing glycation stress.

The study also identified the glyoxalase-1 pathway as a critical mechanism. This is a natural detox system that helps clear harmful molecules like MGO. Gly-Low appeared to boost this pathway. When the pathway was chemically blocked, Gly-Low’s protective effects disappeared, confirming its role in the process.

Overall, the findings highlight glycation stress as a modifiable contributor to vascular aging. The results suggest that natural compound-based therapies, like Gly-Low, may offer a potential strategy to protect arteries from age- and diabetes-related damage.

DOI - https://doi.org/10.18632/aging.206335

Corresponding authors: Pankaj Kapahi - pkapahi@buckinstitute.org; Zachary S. Clayton - Zachary.Clayton@cuanschutz.edu

Abstract video: https://www.youtube.com/watch?v=i_rtq8eIb8c

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Glycation Stress Promotes Arterial Stiffening and Is Reversed by a Natural Compound in Aging Mice

Cancer treatment is moving toward a more precision-based approach, where therapies are guided not just by the tumor’s location but also by its genetic features. Mutations in cancer cells can point to specific drugs that may be more effective for certain patients. However, detecting these mutations often requires broad and detailed analysis. This is where comprehensive genomic profiling becomes especially important.

One of the main challenges in cancer care is that many existing genetic tools focus on only a limited number of mutations. As a result, some treatment opportunities may be missed. Certain mutations are also difficult to detect because they occur at low levels or exist in complex forms, such as gene fusions. Without advanced screening methods, these changes may go unnoticed.

To address these challenges, researchers from Exact Sciences Corporation conducted a large-scale study using a broad genomic screening approach. The findings were recently published in the journal Oncotarget.

Full blog - https://www.oncotarget.org/2025/12/15/comprehensive-genomic-profiling-in-cancer-insights-from-over-10000-tumors/

Paper DOI - https://doi.org/10.18632/oncotarget.28757

Correspondence to - Jean-Paul De La O - jdelao@exactsciences.com

Abstract video - https://www.youtube.com/watch?v=awiRhDfiMTE

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Keywords - cancer, solid tumors, comprehensive genomic profiling, matched therapy, gene fusions, limit of detection

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Oncotarget

Comprehensive Genomic Profiling in Cancer: Insights from Over 10,000 Tumors

BUFFALO, NY — December 16, 2025 — A new #research paper was #published in Aging-US on December 10, 2025, titled “Theobromine is associated with slower epigenetic ageing.”

In this study, led by Ramy Saad from King’s College London and Great Ormond Street Hospital for Children NHS Foundation Trust, alongside Jordana T. Bell from King’s College London, researchers found that higher levels of theobromine, a natural compound found in cocoa, are associated with slower biological aging in humans. The findings suggest that theobromine may support healthy aging.

Epigenetic aging refers to biological changes that affect how genes function over time. It is measured using blood-based markers such as DNA methylation and telomere length, which together provide a more accurate picture of aging than chronological age.

In this work, researchers analyzed data from two large European studies. In 509 women from the TwinsUK cohort, they found that higher blood levels of theobromine were associated with slower aging, especially based on GrimAge, an epigenetic clock that predicts the risk of age-related disease and early death. The results were confirmed in 1,160 men and women from the German KORA study.

“We initially tested for the association between six metabolites found in coffee and cocoa, and epigenetic measures of ageing in blood samples from 509 healthy females from the TwinsUK cohort (median age = 59.8, IQR = 12.81, BMI = 25.35).“

Importantly, theobromine’s effects were independent of related compounds such as caffeine. Even after adjusting for these other substances and different lifestyle factors, the association with slower aging remained strong. The study also associated higher theobromine levels with longer telomeres, another marker of healthy aging.

While theobromine is commonly found in cocoa and chocolate, the study does not suggest increasing chocolate intake. However, it highlights the potential of everyday dietary components such as theobromine to influence aging. These findings support growing evidence that certain plant-based compounds may play a role in promoting long-term health. By identifying a connection between theobromine and slower biological aging, the study opens new directions for research into nutritional strategies for healthy aging.

DOI - https://doi.org/10.18632/aging.206344

Corresponding authors - Ramy Saad - ramy.saad@kcl.ac.uk, and Jordana T. Bell - jordana.bell@kcl.ac.uk

Abstract video - https://www.youtube.com/watch?v=S0P1USM8L6E

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Keywords - aging, theobromine, epigenetic aging, DNA methylation, metabolomics, nutrition

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Theobromine From Cocoa Linked to Slower Biological Aging

BUFFALO, NY - December 17, 2025 – A new #research paper was #published in Oncotarget (Volume 16) on December 15, 2025, titled “Machine learning-based survival prediction in colorectal cancer combining clinical and biological features.”

In this study, led by Lucas M. Vieira from the University of Brasília and the University of California San Diego, researchers used machine learning to predict survival in patients with colorectal cancer. They built a model by combining biological markers with clinical data. This approach could help improve prognosis and guide treatment strategies for one of the world’s most common and deadly cancers.

The team analyzed data from over 500 patients, using clinical details such as age, chemotherapy status, and cancer stage, along with molecular features like gene expression and microRNAs. Their goal was to improve how clinicians identify high-risk patients and make outcome predictions more precise. Researchers evaluated three different patient data scenarios using different machine learning techniques. The best-performing was an adaptive boosting model, which achieved 89.58% accuracy. This approach showed that integrating clinical and biological data led to significantly better predictions than using either data type alone. 

Among the biological markers, the gene E2F8 was consistently influential in all patient groups and is known to play a role in tumor growth. Other important markers included WDR77 and hsa-miR-495-3p, which are also associated with cancer development. Key clinical predictors included cancer stage, patient age, lymph node involvement, and whether chemotherapy was administered.

“The proposed method combines biological and clinical features to predict patient survival, using as input data from patients from the United States, available in the TCGA database.”

Unlike earlier models that relied on either clinical or molecular data alone, this study demonstrates the added value of combining both. Ensemble methods, which merge multiple learning algorithms, provided more stable and consistent results across all patient groups tested.

These research findings could lead to new tools that help clinicians better predict how a patient's disease might progress or respond to treatment. The study also highlights the importance of collecting complete clinical information, such as lifestyle factors, which were missing from the dataset but could enhance future predictions.

Overall, the study demonstrated how machine learning can support more accurate and personalized survival predictions in colorectal cancer. It also points to potential future research on markers like E2F8, which may be useful for monitoring or targeted therapy.

DOI - https://doi.org/10.18632/oncotarget.28783

Correspondence to - Lucas M. Vieira - lvieira@health.ucsd.edu

Abstract video - https://www.youtube.com/watch?v=cy7UL5ZUKuI

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Keywords - cancer, colorectal cancer, machine learning, feature selection, non-coding RNAs, genes

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Colorectal Cancer Survival Predicted by AI Using Clinical and Molecular Features

BUFFALO, NY— December 17, 2025 — We are pleased to announce that we have officially joined ResearchGate, the professional network for scientists and researchers. This collaboration enhances the visibility, accessibility, and impact of research published in Aging-US among the global scientific community.

By integrating ResearchGate, Aging-US offers authors and readers an additional channel to discover, share, and discuss cutting-edge findings in aging research. The journal’s presence on the platform includes a dedicated profile, article listings, author profiles, and metrics that help track readership and engagement.

As the field of aging research continues to grow rapidly, it is essential that high-quality studies are easy to find, access, and share. Joining ResearchGate allows Aging-US authors to connect their work with a wider network of peers, fostering collaboration, advancing understanding of the biology of aging, and helping translate discoveries into better health outcomes.

ResearchGate hosts millions of researchers worldwide and provides tools for sharing publications, asking and answering research questions, and discovering new collaborators across institutions and disciplines. Aging-US’s participation on the platform reinforces its commitment to open scientific dialogue and timely dissemination of rigorously reviewed aging research.

Authors publishing in Aging-US can now:

-Link their publications directly to their ResearchGate profiles.

-Track reads, recommendations, and citations through the platform’s analytics.

-Engage with other scientists interested in aging, geroscience, and translational research.

Readers and researchers can follow Aging-US on ResearchGate to stay updated on newly published articles, special issues, and calls for papers.

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Aging-US Now on ResearchGate, Enhancing Visibility for Authors and Readers

BUFFALO, NY — December 19, 2025 — A new #research paper was #published in Volume 17, Issue 11 of Aging-US on November 18, 2025, titled “Epigenetic age predicts depressive symptoms during the COVID-19 pandemic in the Canadian Longitudinal Study on Aging: importance of biological sex.”

This study, led by Cindy K. Barha of the University of Calgary and the University of British Columbia, along with Teresa Liu-Ambrose of the University of British Columbia, found that older women with a younger biological age measured years before the COVID-19 pandemic experienced a greater increase in depressive symptoms during the early lockdown period. These findings could help shape future mental health strategies, particularly for women with high emotional or caregiving demands.

Epigenetic age is a biological marker that reflects how the body is aging and may differ from a person’s actual age. Using long-term data from the Canadian Longitudinal Study on Aging (CLSA), the researchers investigated whether epigenetic age could predict changes in mental health during a major public health crisis. The study included over 600 adults, with an average baseline age of 63, and used two widely accepted epigenetic clocks, the DNAmAge and the Hannum Age, to estimate biological age. Depressive symptoms were tracked at four time points between 2012 and 2020, including during the height of the pandemic.

“The mean participant chronological age at study entry was 63±10 years (46% female).”

The analysis showed that in women, a younger biological age predicted a greater rise in depression during the early phase of the COVID-19 pandemic. This was not observed in men or in individuals with older biological ages.

The study challenges the common belief that a younger biological age always signals better mental or physical resilience. The researchers suggest that women with younger biological profiles may have been more socially or professionally active before the pandemic. When lockdowns disrupted daily routines and social connections, these individuals may have experienced more emotional distress.

Additional factors, such as reduced physical activity, loss of routine, and decreased social interaction, known to affect both mental health and biological aging, may have had a stronger emotional effect on this group. The findings highlight the importance of considering biological sex when studying how aging affects mental well-being during stressful events.

Although the study has some limitations, including the time gap between biological age measurement and the pandemic, it gives valuable insights into how biological and social factors interact during periods of crisis. Future research could use epigenetic clocks to better identify individuals at greater risk of mental health challenges during large-scale public health emergencies.

Overall, this study adds to the growing field of social epigenetics and suggests that biological age may support more targeted public health planning, especially for older adults.

DOI - https://doi.org/10.18632/aging.206337

Corresponding author - Teresa Liu-Ambrose - teresa.ambrose@ubc.ca

Abstract video - https://www.youtube.com/watch?v=DVm78jKsdkY

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Younger Biological Age May Increase Depression Risk in Older Women During COVID-19

BUFFALO, NY — December 23, 2025 — A new #research paper was #published in Volume 17, Issue 11 of Aging-US on November 25, 2025, titled “A natural language processing–driven map of the aging research landscape.”

In this study, Jose Perez-Maletzki from Universidad Europea de Valencia and Universitat de València, together with Jorge Sanz-Ros from Stanford University School of Medicine, used artificial intelligence (AI) to analyze a century of global aging research, revealing shifts in focus and highlighting underexplored areas.

The team analyzed over 460,000 scientific abstracts published between 1925 and 2023 to identify key themes, trends, and research gaps in the study of aging. Their goal was to provide a comprehensive, unbiased view of how the field has evolved and where future research could have the greatest impact.

The study found that aging research has moved from basic cellular studies and animal models to a growing focus on clinical topics, particularly age-related diseases such as Alzheimer’s and dementia. Using natural language processing and machine learning, the researchers grouped publications into thematic clusters and tracked how interest in each topic changed over time.

“By integrating Latent Dirichlet Allocation (LDA), term frequency-inverse document frequency (TF-IDF) analysis, dimensionality reduction and clustering, we delineate a comprehensive thematic landscape of aging research.”

One key finding was the growing separation between basic biological studies and clinical research. While both areas have grown significantly, they often progress independently with limited overlap. Clinical studies tend to focus on geriatrics, healthcare, and neurodegenerative diseases, while basic science emphasizes cellular mechanisms such as oxidative stress, telomere shortening, mitochondrial dysfunction, and senescence. The authors note that this lack of integration limits the translation of laboratory discoveries into medical applications.

The study also showed that some emerging topics, such as autophagy, RNA biology, and nutrient sensing, are expanding rapidly but remain separated from clinical applications. In contrast, long-established links, such as those between cancer and aging, remain strong. The analysis also highlighted that potentially important associations, such as those between mitochondrial dysfunction and senescence or epigenetics and autophagy, are rarely studied and may be new research opportunities.

This AI-driven analysis offers a new way to guide future research by identifying how different areas of aging science are interconnected or isolated. It also highlights how research priorities may be shaped by policy or funding trends, as seen in the heavy focus on Alzheimer’s disease. 

As the global population continues to age, understanding how biological processes relate to clinical outcomes is critical. This study not only offers a historical map of aging science but also serves as a tool to support more connected, interdisciplinary, and effective future research.

DOI - https://doi.org/10.18632/aging.206340

Corresponding author - Jorge Sanz-Ros - jsanzros@stanford.edu

Abstract video - https://www.youtube.com/watch?v=O4dJUGQ2ZcU

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AI Tracks Nearly 100 Years of Aging Research, Revealing Key Trends and Gaps

Aging-US is dedicated to advancing our understanding of the biological mechanisms that drive aging and the development of age-related diseases. Our mission is to serve as a platform for high-quality research that uncovers the cellular, molecular, and systemic processes underlying aging, and translates these insights into strategies to extend healthspan and delay the onset of chronic disease.

Read about the Aging (Aging-US) Scientific Integrity Process: https://aging-us.com/scientific-integrity

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