A conversation with Maureen Meister about her new book

Arts and Crafts Architecture across America

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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Aging-US

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

Socio-cultural anthropologist Todne Thomas, Associate Professor of Divinity and Religious Studies at Yale Divinity School, discusses researching a 2015 arson of a predominantly Black church in Tennessee, what it says about humanity, and why in-depth study of American religious life is critically important.

https://divinity.yale.edu/news/2025-12-12-what-a-2015-church-burning-reveals-about-race-religion-and-life-in-america-with

What a 2015 Church Burning Reveals About Race, Religion, and Life in America with Prof Todne Thomas

YJBM Science News is back with Part 1 of our very first Special Episode! Join Hanna, Majo, and Roxanna as they sit down with Dr. Diyendo Massilani, a researcher and professor at Yale investigating evolutionary genetics. We’ll hear from Dr. Massilani about the challenges and ethical considerations of working with ancient DNA, as well as the incredible doors it has opened to what we can interrogate and what we know about extinct human populations. Keep an eye out in the new year for Part 2, which will dive into the groundbreaking work of Dr. Massilani’s former mentor and 2022 Nobel prize winner Dr. Svante Pääbo.

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https://x.com/Diyendo

Related links:
https://news.yale.edu/2024/08/26/seeking-ethical-approach-ancient-dna-analysis

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Science News Special Episode - Ancient DNA and Evolutionary Genetics: An Interview with Dr. Diyendo Massilani

In this tender and powerful episode, Leader’s Way host Brandon Nappi interviews Nelba Márquez-Greene, a devoted advocate for survivors of gun violence and someone who has been named one of the “Ten Women Changing the World,” according to People Magazine’s October 2019 issue. A Community Scholar at the Yale School of Public Health, Nelba also hosts the “Shared Humanity” podcast and video series, which focuses on the humans behind the headlines of gun violence in a moment in history where all too often we focus on everything but our shared humanity. Márquez-Greene seeks ways to partner with community organizations, clergy, and those looking to strengthen their response to the trauma of gun violence and injustice in our communities.  

 
Nelba would like to dedicate this episode to all clergy who have ever prayed for her and her family and all people who are in winter seasons of their souls. 


Host: Brandon Nappi 

Guest: Nelba Márquez-Greene 

 
Instagram: @theleadersway.podcast 

 
berkeleydivinity.yale.edu/podcast 


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76: Walking with Survivors of Gun Violence with Nelba Márquez-Greene

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

The cover for Issue 42 of Oncotarget features Figure 4, "Generation of parametric and texture maps from radiofrequency data," recently published in "Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer" by Dasgupta, et al. which reported that to investigate quantitative ultrasound based higher-order texture derivatives in predicting the response to neoadjuvant chemotherapy in patients with locally advanced breast cancer.

Three machine learning algorithms based on linear discriminant, k-nearest-neighbors, and support vector machine were used for developing radiomic models of response prediction.

The most helpful features in separating the two response groups were QUS-Tex1-Tex2 features.

The 5-year recurrence-free survival calculated for KNN predicted responders and non-responders using QUS-Tex1-Tex2 model was comparable to RFS for the actual response groups.

The Oncotarget authors report the first study demonstrating QUS texture-derivative methods in predicting NAC responses in LABC, which leads to better results compared to using texture features alone.

Dr. Gregory J. Czarnota from The Sunnybrook Health Sciences Centre, The University of Toronto, The Sunnybrook Research Institute, as well as York University said, "Breast cancer is the second most common cancer globally in terms of incidence, comprising 11.6% of all new cancers and is the 5th leading cause of mortality attributed to 6.6% of all cancer deaths."

Imaging modalities like ultrasonography, mammography, magnetic resonance imaging, and computed tomography are commonly used in response monitoring of NAC, which primarily considers long-term size related changes of the disease.

Radiofrequency data from QUS provides valuable information compared to conventional B-mode ultrasound imaging, where there is a loss of crucial details involved with instrument-based signal processing. The analysis of the power spectra from ultrasound RF data can be used to determine quantitative parameters, which include average scatterer diameter, average acoustic concentration, mid-band fit, spectral slope, spectral 0-MHz intercept.

The spatial distribution of features within QUS parametric images can be further studied using grey-level co-occurrence matrix analyses, which represent the angular relationship and distance between neighboring pixels.

In the study here, higher-order imaging features in the form of QUS texture-derivatives have been determined from pretreatment QUS data for patients with LABC undergoing NAC to predict treatment response.

The Czarnota Research Team concluded in their Oncotarget Research paper that QUS is a simple, easily accessible imaging modality, with similar scanning techniques akin to the B-mode US, which is widely used in clinical practice.

With appropriate data processing, it is possible to use the information normally processed to generate B-mode images to obtain additional information from tumors. These are related to tumor acoustic properties, which can be linked with biological features and clinical behavior. In this research, we established the role of higher-order imaging analysis (radiomics) of QUS in predicting treatment response to NAC involving 100 patients with LABC.

The work provides a framework for using QUS-radiomics in clinical practice to choose appropriate chemotherapy regimens or other treatment modalities like upfront surgery (in predicted chemo-resistant tumors), leading the way towards personalized oncology.

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DOI - https://doi.org/10.18632/oncotarget.27742

Full text - https://www.oncotarget.com/article/27742/text/

Correspondence to - Gregory J. Czarnota - gregory.czarnota@sunnybrook.ca

Keywords - radiomics, breast cancer, texture derivatives, quantitative ultrasound, neoadjuvant chemotherapy

Oncotarget - Generation Of Parametric And Texture Maps From Radiofrequency Data

Oncotarget is a primarily oncology-focused, peer-reviewed, open access journal. Papers are published continuously within yearly volumes in their final and complete form and then quickly released to Pubmed.

Oncotarget is now indexed by MEDLINE, PubMed and PMC/PubMed.

Read about the Oncotarget Scientific Integrity Process: https://www.oncotarget.com/scientific_integrity/

Oncotarget - Generation Of Parametric And Texture Maps From Radiofrequency Data

Oncotarget Podcast