Tag: Aging Research

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First Evidence of a Pan-tissue Decline in Stemness During Human Aging

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In this new study, researchers provide the first evidence of a pan-tissue decrease of stemness during human aging.

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Aging is still shrouded in proverbial darkness. But, some researchers hypothesize that aging may be linked to stem cell exhaustion. Stemness, the ability of a cell to differentiate into various cell types, is an essential characteristic defining the functionality of stem cells. It has been observed that stem cells seem to diminish with age, although the precise role of stem cells in human aging remains to be elucidated. 

“Among the biological pathways associated with aging, we can highlight stem cell exhaustion, which argues that during normal aging, the decrease in the number or activity of these cells contributes to physiological dysfunction in aged tissues [4].”

In a new study, researchers Gabriel Arantes dos Santos, Gustavo Daniel Vega Magdaleno and João Pedro de Magalhães from the Universidade de Sao Paulo, University of Birmingham and the University of Liverpool applied a machine learning method to detect stemness signatures from transcriptome data of healthy human tissues. Their research paper was published on April 4, 2024, and chosen as the cover of Aging’s Volume 16, Issue 7, entitled, “Evidence of a pan-tissue decline in stemness during human aging.”

The Study

In this study, the researchers delve into the intricate relationship between aging and stemness, offering vital insights into this complex interplay. The researchers conducted an in-depth analysis of healthy human tissue samples, assigning “stemness scores” to track the stemness levels across different age groups.

“In this context, detecting stemness-associated expression signatures is a promising strategy for studying stem cell biology.”

This research is the first to provide evidence of a pan-tissue decline in stemness during human aging. It is an important step forward in understanding the cellular mechanisms involved in the aging process and their potential implications for human health.

Methodology & Data Sources

The researchers used the RNA-Seq-based gene expression data from human tissues, downloaded from the Genomics of Ageing and Rejuvenation Lab’s Genomics of Ageing (GTEx) portal. This comprehensive dataset included over 17,000 healthy human tissue samples, spanning an age range of 20 to 79 years.

A machine learning methodology, originally developed by Malta et al., was applied to the GTEx transcriptome data to assign stemness scores to all samples. This advanced machine learning model was trained on stem cell classes and their differentiated progenitors, enabling the researchers to detect stemness signatures from the transcriptome data of healthy human tissues.

Key Findings

The analysis revealed a significant negative correlation between the subject’s age and stemness score in approximately 60% of the studied tissues. Interestingly, the only exception was the uterus, which exhibited increased stemness with age. This finding is particularly noteworthy, as it provides the first evidence of a pan-tissue decline in stemness during human aging. It supports the hypothesis that stem cell deterioration may contribute to the aging process.

The researchers also observed interesting correlations between stemness and other cellular processes. They found that stemness was positively correlated with cell proliferation. However, this relationship was not universal, with some tissues showing exceptions.

In contrast, when they examined the association between stemness and cellular senescence, a negative correlation was observed across the board. This finding suggests that although senescent cells and stem cells are not technically opposite states, they behave in opposite ways at the transcriptomic level within a living organism.

Implications & Future Directions

The findings of this study have far-reaching implications for our understanding of the aging process and its cellular underpinnings. By providing the first evidence of a pan-tissue decline in stemness during human aging, the study adds significant weight to the notion that stem cell deterioration may contribute to human aging.

However, many questions remain. For instance, it is not yet clear whether the loss of stemness contributes to aging or is a consequence of it. Moreover, it is uncertain whether the decline in stemness is due to a direct reduction in the stem cell pool or refers to intrinsic changes in different cells within the tissue.

Further research is needed to address these questions, and more robust studies are required to draw more assertive conclusions. It is also crucial to determine which factors drive these changes and which patterns and genes are associated with this process. This will be pivotal in advancing our understanding of stemness aging and its potential implications for human health.

“In conclusion, we provide the first evidence of a pan-tissue decrease of stemness during human aging and report an association between stemness and cell proliferation and senescence. This study also assigned a stemness score to more than 17,000 human samples, and these data can be useful for the scientific community for further studies.”

Click here to read the full research paper published in Aging.

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Aging is an open-access, traditional, peer-reviewed journal that publishes high-impact papers in all fields of aging research. All papers are available to readers (at no cost and free of subscription barriers) in bi-monthly issues at Aging-US.com.

Click here to subscribe to Aging publication updates.

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How Menopause Changes Brain Structure and Connectivity

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In this study, researchers use neuroimaging to see how menopause alters brain structure and connectivity in postmenopausal women.

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Menopause marks the beginning of the next biological chapter in a woman’s life. Characterized by the natural ebb of reproductive hormones (particularly estrogen), menopause ushers in a new season of aging. This hormonal shift not only signifies a transition in fertility but also influences systemic health. The menopause-associated decline in estrogen has been associated with various health issues, including alterations in brain structure and function. However, the mechanics of this phenomenon are still poorly understood. A greater understanding of how menopause alters the brain could aid in the early detection, and possible prevention, of neurodegenerative disease.

In a new study, researchers Gwang-Won Kim, Kwangsung Park, Yun-Hyeon Kim, and Gwang-Woo Jeong from Chonnam National University used neuroimaging to shed light on how menopause alters brain morphology and functional connectivity in postmenopausal women. On March 23, 2024, their research paper was published as the cover of Aging’s Volume 16, Issue 6, entitled, “Altered brain morphology and functional connectivity in postmenopausal women: automatic segmentation of whole-brain and thalamic subnuclei and resting-state fMRI.” 

“To the best of our knowledge, no comparative neuroimaging study on alterations in the brain volume and functional connectivity, especially focusing on the thalamic subnuclei in premenopausal vs. postmenopausal women has been reported.”

The Study

The decline in estrogen levels during menopause has been linked to an elevated risk of neurodegenerative diseases, notably Alzheimer’s disease (AD). Estrogen plays a pivotal role in modulating neurotransmitter systems, neurotrophins, and brain cytoarchitecture, and there is evidence that these interactions also affect mood, memory, and cognition. The biological mechanisms underlying the increased AD risk in postmenopausal women are not fully understood.

In this study, 21 premenopausal women and 21 postmenopausal women were subjected to magnetic resonance imaging (MRI). The researchers utilized T1-weighted MRI and resting-state functional MRI data to assess differences in brain volume and seed-based functional connectivity. For statistical analysis, they employed multivariate analysis of variance, factoring in age and whole brain volume as covariates, to compare the surface areas and subcortical volumes between the two groups.

Results

Postmenopausal women showed significantly smaller cortical surface, especially in the left medial orbitofrontal cortex (mOFC), right superior temporal cortex (STC), and right lateral orbitofrontal cortex, compared to premenopausal women. These findings suggest that diminished brain volume may be linked to menopause-related symptoms caused by lower sex hormone levels.

In addition to structural changes, the functional connectivity between the brain regions also showed changes. The study found significantly decreased functional connectivity between the left mOFC and the right thalamus in postmenopausal women — reinforcing the hypothesis that the left orbitofrontal-bilateral thalamus connectivity is associated with cognitive impairment. Although postmenopausal women did not show volume atrophy in the right thalamus, the volume of the right pulvinar anterior (PuA), a significant thalamic subnuclei, was significantly decreased. Decreased PuA volume in postmenopausal women is closely related to decreases in female sex hormone levels following menopause.

Expectedly, the study found a significant difference in age and sex hormone levels between premenopausal and postmenopausal women. Postmenopausal women had lower total estrogen and estradiol (E2) levels and higher follicle-stimulating hormone (FSH) and luteinizing hormone (LH) levels than premenopausal women. Estrogen levels were positively correlated with the surface area of the left mOFC, right STC, and right lOFC, as well as the volume of the right PuA.

“Concerning the close connection between the estrogen level and STC volume, our findings support a potential role of decreases in sex hormones following menopause due to the correspondent brain structural atrophy. However, further study is needed to elucidate the specific cognitive and emotional implications in connection with these structural changes.”

Conclusions & Future Directions

Postmenopausal women showed significantly lower left mOFC, right lOFC, and right STC surface areas, reduced right PuA volume, and decreased left mOFC-right thalamus functional connectivity compared to premenopausal women. These findings provide novel insight into the structural and functional changes in the brain associated with menopause. However, further research is needed to validate these findings in a larger cohort and to understand the potential cognitive implications of these changes.

“Our findings provide novel insight into the structural and functional changes in the brain associated with menopause.”

Click here to read the full research paper published in Aging.

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Aging is an open-access, traditional, peer-reviewed journal that publishes high-impact papers in all fields of aging research. All papers are available to readers (at no cost and free of subscription barriers) in bi-monthly issues at Aging-US.com.

Click here to subscribe to Aging publication updates.

For media inquiries, please contact media@impactjournals.com.

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Predicting Brain Age With Machine Learning and Transcriptome Profiling

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In this study, researchers investigated age-associated gene expression changes in the prefrontal cortex of male and female brains and used machine learning to develop age prediction models.

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The human brain is a complex organ, and its aging process is influenced by a plethora of factors, both genetic and environmental. Aging-related changes in the brain can lead to cognitive decline and susceptibility to neurodegenerative diseases. Therefore, understanding the molecular mechanisms underlying these changes is crucial for developing therapeutic strategies to delay or prevent age-related cognitive decline.

Over the past few years, a myriad of scientific studies have been conducted to understand the intricate relationship between our genes and the aging process. In a new study, researchers Joseph A. Zarrella and Amy Tsurumi from Harvard T.H. Chan School of Public Health, Massachusetts General Hospital, Harvard Medical School, and Shriner’s Hospitals for Children-Boston explored the concept of genome brain age prediction, a groundbreaking area of study that employs advanced bioinformatics tools to analyze changes in gene expression associated with aging. On February 28, 2024, their research paper was published and chosen as the cover paper for Aging’s Volume 16, Issue 5, entitled, “Genome-wide transcriptome profiling and development of age prediction models in the human brain.”

“[…] we aimed to profile transcriptome changes in the aging PFC [prefrontal cortex] overall and compare females and males, and develop prediction models for age.”

Transcriptome Profiling in the Prefrontal Cortex

The prefrontal cortex (PFC) plays a significant role in the aging process. It is responsible for a host of cognitive functions, including decision-making and planning. Throughout the aging process, significant transcriptome alterations occur in the PFC compared to other regions of the brain. These alterations can influence cognitive decline and susceptibility to neurodegenerative diseases.

Delving deeper into the complexities of aging, researchers have turned to transcriptome profiling as a powerful tool to uncover the molecular changes occurring within the prefrontal cortex. Transcriptome profiling allows scientists to measure the expression levels of all genes in a cell or tissue. By analyzing the transcriptome of the PFC, researchers can identify genes that are differentially expressed during the aging process. These genes can serve as potential biomarkers for age prediction.

The Study

In their groundbreaking research, Zarrella and Tsurumi aimed to develop prediction models for age based on the expression levels of specific panels of transcripts in the PFC. They leveraged advanced machine learning algorithms, including the least absolute shrinkage and selection operator (Lasso), Elastic Net (EN), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), to develop accurate prediction models for chronological age.

The researchers used postmortem PFC transcriptome datasets obtained from the Gene Expression Omnibus (GEO) repository, ranging in age from 21 to 105 years. They identified differentially regulated transcripts in old and elderly samples compared to young samples and assessed the genes associated with age using ontology, pathway, and network analyses.

Machine learning algorithms were used to develop accurate prediction models for chronological age based on the expression levels of specific transcripts. The study found that specific gene expression changes in the PFC are highly correlated with age. Some transcripts showed female and male-specific differences, indicating that sex may play a role in the aging process at the molecular level.

Key Findings & Implications

The study identified several key genes whose expression levels change significantly with age. These genes include Carbonic Anhydrase 4 (CA4), Calbindin 1 (CALB1), Neuropilin and Tolloid Like 2 (NETO2), and Olfactomedin1 (OLFM1), among others. Many of these genes have been previously implicated in aging or aging-related diseases, validating the results of this study.

The researchers also developed four highly accurate age prediction models using different machine learning algorithms. These models were validated in a test set and an external validation set, demonstrating their potential application in predicting chronological age based on gene expression levels.

“Our results support the notions that specific gene expression changes in the PFC are highly correlated with age, that some transcripts show female and male-specific differences, and that machine learning algorithms are useful tools for developing prediction models for age based on transcriptome information.”

Conclusions & Future Directions

This study sheds light on the complex relationship between gene expression changes and the aging process in the human brain. The findings underscore the potential of using transcriptome profiling and machine learning algorithms for age prediction. The identified genes could serve as potential biomarkers for age prediction and may offer new insights into the molecular mechanisms underlying the aging process.

However, further validation of these models in larger populations and molecular studies to elucidate the potential mechanisms by which the identified transcripts may be related to aging phenotypes would be beneficial. Additionally, more inclusive studies investigating the interplay between genetic markers and factors such as sex, lifestyle, and environmental exposures are warranted.

In conclusion, this study provides a promising foundation for future research on genome brain age prediction. It also underscores the potential of transcriptome profiling and machine learning for exploring the complex interplay between our genes and the aging process. This approach could pave the way for personalized medicine strategies aimed at preventing or delaying age-related cognitive decline and neurodegenerative diseases.

Click here to read the full research paper published in Aging.

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Aging is an open-access, traditional, peer-reviewed journal that publishes high-impact papers in all fields of aging research. All papers are available to readers (at no cost and free of subscription barriers) in bi-monthly issues at Aging-US.com.

Click here to subscribe to Aging publication updates.

For media inquiries, please contact media@impactjournals.com.

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Overcoming Missing Data in the Swedish National Study on Aging

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In this new study, researchers compared three multiple imputation strategies for overcoming the missing discrete variable of gait speed in the Swedish National Study on Aging and Care (SNAC).

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Missing data in aging studies, especially in the assessment of gait speed (the time it takes individuals to cover a set distance), presents a significant challenge. The elderly are more prone to health and functional issues, which often interfere with data collection efforts. Given that gait speed is a key indicator of functional status and overall health in older individuals, ensuring its availability and accurate measurement is essential for the integrity of aging research.

In a new study, researchers Robert Thiesmeier, Ahmad Abbadi, Debora Rizzuto, Amaia Calderón-Larrañaga, Scott M. Hofer, and Nicola Orsini from Karolinska Institutet, Stockholm University, Stockholm Gerontology Research Center, and Oregon Health and Science University address the systematic challenge of missing gait speed data in aging research and explore the application of multiple imputation (MI), a statistical technique that has emerged as a constructive approach to handle such gaps in data. The team critically examined the implementation strategies, methodologies, and the impact that these missing variables could have on the outcomes of aging studies, thereby offering a framework to manage and interpret incomplete datasets in aging research. On February 14, 2024, their research paper was published in Aging’s Volume 16, Issue 4, entitled, “Multiple imputation of systematically missing data on gait speed in the Swedish National Study on Aging and Care.”

“[…] this study aims to investigate and assess the performance of different MI strategies specifically targeting the systematically missing discrete variable of gait speed in the SNAC [Swedish National Study on Aging and Care] IPDMA [individual participant data meta-analyses] with only four large cohort studies.”

Setting the Context

Before delving into the specifics of the study, it’s crucial to comprehend the broader context. Aging, as a biological process, presents numerous challenges, particularly in healthcare. Addressing these challenges requires comprehensive data to inform clinical diagnosis and prognosis. The Swedish National Study on Aging and Care (SNAC) is one such initiative that aims to provide a holistic view of aging and elderly data.

SNAC was launched in 2001 as an ongoing longitudinal cohort study based on samples of the Swedish elderly population. The study comprises four sites: Kungsholmen, Skåne, Nordanstig, and Blekinge. Each site collects data on health determinants, disease outcomes, functional capacity, and social conditions. SNAC’s diverse data collection has facilitated the development of an innovative Health Assessment Tool integrating indicators of both clinical and functional health in a population aged 60+ years.

SNAC, like any extensive study, faces the issue of missing data. One variable, gait speed, is systematically absent in one study site, Blekinge. This absence poses a significant challenge for researchers. They must decide between using complete data from only three studies, risking information loss and potential bias in combined estimates, or employing multiple imputation (MI) methods to estimate missing values based on observed data.

What is Multiple Imputation?

Gait speed, or the speed at which a person walks, is a simple but powerful indicator of health and functional status in older adults. It can predict the risk of mortality, disability, cognitive decline, and institutionalization. However, measuring gait speed is not always feasible in large-scale epidemiological studies, especially when participants are frail, have mobility limitations, or live in remote areas. This can result in missing data on gait speed, which can bias the estimates of its association with health outcomes and reduce the statistical power of the analyses.

One way to handle missing data on gait speed is to use multiple imputation, a statistical technique that replaces each missing value with a set of plausible values that reflect the uncertainty about the true value. Multiple imputation can reduce bias and increase precision compared to excluding cases with missing data or using a single imputation method. However, there are different ways to perform multiple imputation, and some may be more suitable than others depending on the type and pattern of missing data.

The Study

In the current study, the researchers compared three multiple imputation strategies for dealing with systematically missing data on gait speed in the SNAC. The SNAC consists of four prospective cohort studies that measured gait speed at baseline and follow-up, except for one study that did not measure gait speed at all. The authors simulated 1000 individual participant data meta-analyses (IPDMA) based on the characteristics of the SNAC and evaluated the performance of three multiple imputation strategies: fully conditional specification (FCS), multivariate normal (MVN), and conditional quantile imputation (CQI).

The FCS method imputes each variable separately by using regression models that depend on the other variables in the dataset. The MVN method assumes that the data follow a multivariate normal distribution and imputes all variables simultaneously by using an expectation-maximization algorithm. The CQI method imputes discrete variables by using quantile regression models that preserve the distribution of the original data.

The authors analyzed the imputed datasets with a two-stage common-effect multivariable logistic model that estimated the effect of three levels of gait speed (<0.8 m/s, 0.8-1.2 m/s, >1.2 m/s) on 5-years mortality. They found that all three imputation methods performed relatively well in terms of bias and coverage of the confidence intervals. However, the CQI method showed the smallest bias and the best coverage for both low and high levels of gait speed. The FCS and MVN methods tended to overestimate the effect of low gait speed and underestimate the effect of high gait speed on mortality.

Conclusions

The authors concluded that multiple imputation can be a useful tool for dealing with systematically missing data on gait speed in IPDMA based on the SNAC. They recommended the CQI method as the preferred approach for imputing discrete variables such as gait speed, as it preserves the original distribution and avoids unrealistic values. They also highlighted the importance of reporting the details of the multiple imputation procedure and checking the plausibility of the imputed values.

This study provides valuable insights for researchers who face similar challenges with missing data on gait speed or other discrete variables in aging research. By using appropriate multiple imputation methods, they can improve the validity and reliability of their results and avoid losing valuable information.

Click here to read the full research paper published in Aging.

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Aging is an open-access, traditional, peer-reviewed journal that has published high-impact papers in all fields of aging research since 2009. All papers are available to readers (at no cost and free of subscription barriers) in bi-monthly issues at Aging-US.com.

Click here to subscribe to Aging publication updates.

For media inquiries, please contact media@impactjournals.com.

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Aging’s Top 10 Papers in 2023 (Crossref Data)

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Crossref is a non-profit organization that logs and updates citations for scientific publications. Each month, Crossref identifies a list of the most popular Aging (Aging-US) papers based on the number of times a DOI is successfully resolved. 

Below are Crossref’s Top 10 Aging DOIs in 2023.

#10: Old-age-induced obesity reversed by a methionine-deficient diet or oral administration of recombinant methioninase-producing Escherichia coli in C57BL/6 mice

DOI: https://doi.org/10.18632/aging.204783

Authors: Yutaro Kubota, Qinghong Han, Jose Reynoso, Yusuke Aoki, Noriyuki Masaki, Koya Obara, Kazuyuki Hamada, Michael Bouvet, Takuya Tsunoda, and Robert M. Hoffman

Institutions: AntiCancer Inc., University of California San Diego and Showa University School of Medicine 

Quote: “This is the first report that showed the efficacy of methionine restriction to reverse old-age-induced obesity.”

#9: Metformin use history and genome-wide DNA methylation profile: potential molecular mechanism for aging and longevity

DOI: https://doi.org/10.18632/aging.204498 

Authors: Pedro S. Marra, Takehiko Yamanashi, Kaitlyn J. Crutchley, Nadia E. Wahba, Zoe-Ella M. Anderson, Manisha Modukuri, Gloria Chang, Tammy Tran, Masaaki Iwata, Hyunkeun Ryan Cho, and Gen Shinozaki

Institutions: Stanford University School of Medicine, University of Iowa, Tottori University Faculty of Medicine, University of Nebraska Medical Center College of Medicine, and Oregon Health and Science University School of Medicine 

Quote: “In this study, we compared genome-wide DNA methylation rates among metformin users and nonusers […]”

#8: Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis

DOI: https://doi.org/10.18632/aging.204787 

Authors: Jérôme Salignon, Omid R. Faridani, Tasso Miliotis, Georges E. Janssens, Ping Chen, Bader Zarrouki, Rickard Sandberg, Pia Davidsson, and Christian G. Riedel

Institutions: Karolinska Institutet, University of New South Wales, Garvan Institute of Medical Research, and AstraZeneca

Quote: “[…] we see our work as an indication that combining different molecular data types could be a general strategy to improve future aging clocks.”

#7: Characterization of the HDAC/PI3K inhibitor CUDC-907 as a novel senolytic

DOI: https://doi.org/10.18632/aging.204616 

Authors: Fares Al-Mansour, Abdullah Alraddadi, Buwei He, Anes Saleh, Marta Poblocka, Wael Alzahrani, Shaun Cowley, and Salvador Macip

Institutions: University of Leicester, Najran University and Universitat Oberta de Catalunya

Quote: “The mechanisms of induction of senescent cell death by CUDC-907 remain to be fully elucidated.”

#6: Potential reversal of biological age in women following an 8-week methylation-supportive diet and lifestyle program: a case series

DOI: https://doi.org/10.18632/aging.204602 

Authors: Kara N. Fitzgerald, Tish Campbell, Suzanne Makarem, and Romilly Hodges

Institutions: Institute for Functional Medicine, Virginia Commonwealth University and the American Nutrition Association

Quote: “[…] these data suggest that a methylation-supportive diet and lifestyle intervention may favorably influence biological age in both sexes during middle age and older.”

#5: Leukocyte telomere length, T cell composition and DNA methylation age

DOI: https://doi.org/10.18632/aging.101293 

Authors: Brian H. Chen, Cara L. Carty, Masayuki Kimura, Jeremy D. Kark, Wei Chen, Shengxu Li, Tao Zhang, Charles Kooperberg, Daniel Levy, Themistocles Assimes, Devin Absher, Steve Horvath, Alexander P. Reiner, and Abraham Aviv

Institutions: National Institute on Aging, National Heart, Lung and Blood Institute, George Washington University, Children’s National Medical Center, Rutgers State University of New Jersey, Hebrew University-Hadassah School of Public Health and Community Medicine, Tulane University, Fred Hutchinson Cancer Research Center, Stanford University School of Medicine, HudsonAlpha Institute for Biotechnology, University of California LA, and University of Washington

Quote: “The two key observations of this study are: (a) LTL is inversely correlated with EEAA; and (b) the LTL-EEAA correlation largely reflects the proportions of imputed naïve and memory CD8+ T cell populations in the leukocytes from which DNA was extracted.”

#4: DNA methylation GrimAge strongly predicts lifespan and healthspan

DOI: https://doi.org/10.18632/aging.101684 

Authors: Ake T. Lu, Austin Quach, James G. Wilson, Alex P. Reiner, Abraham Aviv, Kenneth Raj, Lifang Hou, Andrea A. Baccarelli, Yun Li, James D. Stewart, Eric A. Whitsel, Themistocles L. Assimes, Luigi Ferrucci, and Steve Horvath

Institutions: University of California LA, University of Mississippi Medical Center, Fred Hutchinson Cancer Research Center, Rutgers State University of New Jersey, Public Health England, Northwestern University Feinberg School of Medicine, Columbia University Mailman School of Public Health, University of North Carolina, Chapel Hill, Stanford University School of Medicine, VA Palo Alto Health Care System, and National Institutes of Health 

Quote: “We coin this DNAm-based biomarker of mortality “DNAm GrimAge” because high values are grim news, with regards to mortality/morbidity risk. Our comprehensive studies demonstrate that DNAm GrimAge stands out when it comes to associations with age-related conditions, clinical biomarkers, and computed tomography data.”

#3: Deep biomarkers of aging and longevity: from research to applications

DOI: https://doi.org/10.18632/aging.102475 

Authors: Alex Zhavoronkov, Ricky Li, Candice Ma, and Polina Mamoshina

Institutions: Insilico Medicine, The Buck Institute for Research on Aging, The Biogerontology Research Foundation, Sinovation Ventures, Sinovation AI Institute, and Deep Longevity, Ltd

Quote: “Here we present the current state of development of the deep aging clocks in the context of the pharmaceutical research and development and clinical applications.”

#2: An epigenetic biomarker of aging for lifespan and healthspan

DOI: https://doi.org/10.18632/aging.101414 

Authors: Morgan E. Levine, Ake T. Lu, Austin Quach, Brian H. Chen, Themistocles L. Assimes, Stefania Bandinelli, Lifang Hou, Andrea A. Baccarelli, James D. Stewart, Yun Li, Eric A. Whitsel, James G Wilson, Alex P Reiner, Abraham Aviv, Kurt Lohman, Yongmei Liu, Luigi Ferrucci, and Steve Horvath

Institutions: University of California LA, National Institute on Aging, Stanford University School of Medicine, Azienda Toscana Centro, Northwestern University Feinberg School of Medicine, Columbia University Mailman School of Public Health, University of North Carolina, Chapel Hill, University of Mississippi Medical Center, Fred Hutchinson Cancer Research Center, Rutgers State University of New Jersey, and Wake Forest School of Medicine

Quote: “Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging.”

#1: Chemically induced reprogramming to reverse cellular aging

DOI: https://doi.org/10.18632/aging.204896

Authors: Jae-Hyun Yang, Christopher A. Petty, Thomas Dixon-McDougall, Maria Vina Lopez, Alexander Tyshkovskiy, Sun Maybury-Lewis, Xiao Tian, Nabilah Ibrahim, Zhili Chen, Patrick T. Griffin, Matthew Arnold, Jien Li, Oswaldo A. Martinez, Alexander Behn, Ryan Rogers-Hammond, Suzanne Angeli, Vadim N. Gladyshev, and David A. Sinclair

Institutions: Harvard Medical School, University of Maine and Massachusetts Institute of Technology (MIT) 

Quote: “We identify six chemical cocktails, which, in less than a week and without compromising cellular identity, restore a youthful genome-wide transcript profile and reverse transcriptomic age. Thus, rejuvenation by age reversal can be achieved, not only by genetic, but also chemical means.”

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Click here to read the latest papers published by Aging.

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Aging is an open-access, traditional, peer-reviewed journal that has published high-impact papers in all fields of aging research since 2009. All papers are available to readers (at no cost and free of subscription barriers) in bi-monthly issues at Aging-US.com.

Click here to subscribe to Aging publication updates.

For media inquiries, please contact media@impactjournals.com.

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Senescence-Related TME Genes as Key Prognostic Predictors in HNSCC

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In a new study, researchers aimed to investigate the prognostic significance of senescence-related TME genes in head and neck squamous cell carcinoma (HNSCC) and their potential implications for immunotherapy response. 

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Head and neck squamous cell carcinoma (HNSCC) is a prevalent and heterogeneous form of cancer that affects thousands of individuals worldwide. The prognosis for HNSCC patients can vary greatly, depending on factors such as tumor stage and site. The tumor microenvironment (TME) plays a crucial role in tumorigenesis and disease progression, with cellular senescence being a key component. Senescent cells, characterized by cell-cycle arrest, have been shown to have both tumor-suppressive and tumor-promoting effects. However, the prognostic significance of senescence-related TME genes in HNSCC remains poorly understood.

In a new study, researchers Young Chan Lee, Yonghyun Nam, Minjeong Kim, Su Il Kim, Jung-Woo Lee, Young-Gyu Eun, and Dokyoon Kim from Kyung Hee University, Kyung Hee University Hospital at Gangdong, and the University of Pennsylvania aimed to investigate the prognostic significance of senescence-related TME genes in HNSCC and their potential implications for immunotherapy response. They utilized data from The Cancer Genome Atlas (TCGA) to identify two distinct subtypes of HNSCC based on the expression of senescence-related TME genes. The team then constructed a risk model consisting of senescence-related TME core genes (STCGs) and validated its prognostic capability in independent cohorts. Their research paper was chosen as an Aging cover paper and published in Volume 16, Issue 2, entitled, “Prognostic significance of senescence-related tumor microenvironment genes in head and neck squamous cell carcinoma.”

“To the best of our knowledge, this is the first study to offer a comprehensive evaluation of the senescence related TME status by integrating senescence related TME genes through a gene-gene network and clustering. Furthermore, we have introduced a novel risk model that utilizes a selected gene set to predict prognosis and confirmed the expression of STCGs in immune cells at single-cell levels.”

The Study

Identification of Prognostic Senescence-Related TME Genes

To identify prognostic senescence-related TME genes, the researchers screened a total of 7,878 genes in the TCGA-HNSCC dataset. They identified 288 genes that belonged to TME-related genes, tumor-associated senescence (TAS) genes, and immune-related genes. From these genes, they selected 91 prognostic senescence-related TME genes (PSTGs) based on differential expression analysis and Cox regression analysis.

Senescence-Related TME Subtypes and Characterization

Using consensus clustering analysis, the researchers classified the HNSCC samples into two distinct subtypes based on the expression of PSTGs: subtype 1 and subtype 2. The two subtypes exhibited significant differences in clinical and molecular characteristics. Subtype 2 had a higher prevalence of HPV-positive and oropharyngeal cancer cases, while subtype 1 was characterized by a higher proportion of advanced tumor stage and overall stage.

Further analysis revealed distinct differences between the subtypes in terms of genetic alterations, methylation patterns, enriched pathways, and immune infiltration. Subtype 1 had a higher mutation rate in the TP53 gene and exhibited hypomethylation in several CpG sites compared to subtype 2. Additionally, subtype 2 showed higher immune scores, stromal scores, and ESTIMATE scores, indicating a more favorable immune microenvironment.

The two subtypes also displayed differences in survival outcomes. Kaplan-Meier survival analysis showed that subtype 2 had a more favorable overall survival compared to subtype 1. This difference was enhanced in the HPV-positive cohort, suggesting that the senescence-related TME subtypes may have implications for prognosis in specific patient subgroups.

Risk Scoring Based on Senescence-Related TME Status

Using the 91 PSTGs, the researchers constructed a risk scoring model based on the LASSO Cox regression algorithm. They identified 21 STCGs that were associated with either increased risk or protection. The risk scores based on the expression levels of these genes were calculated for each patient, and the patients were classified into high- and low-risk groups.

The prognostic performance of the risk scoring model was tested in independent cohorts, including the TCGA-HNSCC test set, the GSE41613 cohort, and the KHUMC cohort. The high-risk group showed significantly lower overall survival compared to the low-risk group in the TCGA-HNSCC test set and the GSE41613 cohort. Although not statistically significant, the low-risk group demonstrated a trend towards higher overall survival in the KHUMC cohort.

Immunotherapy Response Prediction and Single-Cell Analysis

The team also investigated the immunotherapy response prediction based on the risk model and the expression of STCGs. They found that the low-risk group had higher immunophenoscores and a significantly higher proportion of responders to immunotherapy compared to the high-risk group.

To further evaluate the senescence-related TME characteristics at the single-cell level, the researchers analyzed single-cell transcriptome data from HNSCC tissue. They found that STCGs were enriched in fibroblast, mono/macrophage, and T cell populations, suggesting that these cell types contribute to the senescent features of HNSCC.

Conclusion

In conclusion, the study sheds light on the prognostic significance of senescence-related TME genes in HNSCC. Their findings highlight the heterogeneity of HNSCC and the importance of the senescence-related TME in prognosis and immunotherapy response. The risk scoring model based on STCGs provides a potential prognostic biomarker for HNSCC patients, and the single-cell analysis further elucidates the association between STCGs and specific cell populations within the TME. These findings contribute to a deeper understanding of the complex interplay between senescence and the TME in HNSCC and have implications for precision medicine and personalized treatment approaches. Further research and validation are needed to fully understand the clinical implications of senescence-related TME genes in HNSCC. However, this study provides valuable insights into the role of cellular senescence in tumor progression and the potential for targeting senescence-related pathways in the development of novel therapeutic strategies for HNSCC patients.

“In conclusion, this study comprehensively investigated the prognostic and immunological features of senescence related TME genes in HNSC. By leveraging these senescence related TME genes, we successfully developed a risk model to predict HNSC prognosis and immunotherapy response, which was robustly validated using external transcriptome datasets. These findings provided evidence for the role of senescence in the TME and highlighted the potential of senescence-related biomarkers as promising therapeutic targets.”

Click here to read the full research paper published in Aging.

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Aging is an open-access, traditional, peer-reviewed journal that has published high-impact papers in all fields of aging research since 2009. All papers are available to readers (at no cost and free of subscription barriers) in bi-monthly issues at Aging-US.com.

Click here to subscribe to Aging publication updates.

For media inquiries, please contact media@impactjournals.com.

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Efficacy and Safety of EGFR-TKIs for Elderly Patients With NSCLC

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In a new study, researchers investigated the effectiveness and safety of EGFR-tyrosine kinase inhibitors in elderly patients with EGFR-mutated advanced non-small-cell lung cancer (NSCLC).

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Lung cancer is a significant global health issue, being the second most commonly diagnosed cancer and the leading cause of cancer-related death worldwide. Non-small-cell lung cancer (NSCLC) represents the majority of lung cancer cases and is often diagnosed at an advanced stage. Epidermal growth factor receptor (EGFR) mutations are more common in Asian NSCLC populations than in Western populations. 

Activating EGFR mutations, such as exon 19 deletions and L858R, are predictive of response to tyrosine kinase inhibitors (TKIs) and have revolutionized the treatment landscape for patients with EGFR-mutated NSCLC. However, most clinical trials tend to lack data for the elderly population, even though a significant proportion of lung cancer patients are aged 65 years and older. This underrepresentation of elderly patients in clinical trials limits our understanding of the effectiveness and safety of EGFR-TKIs in this specific population.

In this new study, researchers Ling-Jen Hung, Ping-Chih Hsu, Cheng-Ta Yang, Chih-Hsi Scott Kuo, John Wen-Cheng Chang, Chen-Yang Huang, Ching-Fu Chang, and Chiao-En Wu from Chang Gung University and Taoyuan General Hospital conducted a multi-institute retrospective study to investigate the effectiveness and safety of afatinib, gefitinib, and erlotinib for treatment-naïve elderly patients with EGFR-mutated advanced NSCLC. On January 8, 2024, their research paper was published in Aging’s Volume 16, Issue 1, entitled, “Effectiveness and safety of afatinib, gefitinib, and erlotinib for treatment-naïve elderly patients with epidermal growth factor receptor-mutated advanced non-small-cell lung cancer: a multi-institute retrospective study.”

“[…] comparisons of the effectiveness and safety of these EGFR-TKIs approved for patients aged ≥65 years are limited. The available real-world evidence for EGFR-TKI treatment of elderly patients is also limited. Therefore, this study aimed to describe the effectiveness and safety of afatinib, gefitinib, and erlotinib for treatment-naïve elderly patients (aged ≥65 years) with EGFR-mutated advanced NSCLC.”

The Study

In this study, 1,343 treatment-naïve patients with EGFR-mutated advanced NSCLC were enrolled from multiple hospitals in Taiwan. The patients were divided into four age groups: <65 years, 65-74 years, 75-84 years, and ≥85 years. Patient characteristics, including sex, smoking history, performance status, tumor involvement, EGFR mutation type, metastatic sites, and choice of EGFR-TKI, were compared among the age groups.

The researchers found that afatinib was more effective than gefitinib and erlotinib in elderly patients aged ≥65 years, as evidenced by longer median progression-free survival (PFS) and overall survival (OS). The median PFS for afatinib was 14.7 months, compared to 9.9 months for gefitinib and 10.8 months for erlotinib (p = 0.003). Similarly, the median OS for afatinib was 22.2 months, compared to 17.7 months for gefitinib and 18.5 months for erlotinib (p = 0.026).

Further analysis by age subgroup revealed that the significant differences in PFS and OS were primarily driven by patients aged 65-74 years. In this age group, afatinib demonstrated superior efficacy compared to gefitinib and erlotinib, with a median PFS of 14.7 months and median OS of 22.2 months (p = 0.032 for PFS, p = 0.018 for OS). While afatinib showed greater effectiveness, it was also associated with more adverse events (AEs) compared to gefitinib and erlotinib. The study reported a higher incidence of grade ≥3 AEs, including skin toxicities, paronychia, mucositis, and diarrhea, in patients receiving afatinib. Notably, patients receiving afatinib also required more dose reductions or discontinuation due to AEs.

Various factors were identified as independent prognostic factors of PFS and OS in elderly patients with EGFR-mutated advanced NSCLC. A performance status score of 2-4, stage IV disease, liver, bone, pleural, adrenal, and pericardial metastasis, and treatment with gefitinib were associated with worse PFS and OS.

Conclusion

This large retrospective study provides valuable real-world evidence on the effectiveness and safety of EGFR-TKIs in elderly patients with EGFR-mutated advanced NSCLC. The findings suggest that afatinib is more effective as a first-line treatment than gefitinib or erlotinib for elderly patients, particularly those aged 65-74 years. However, it is important to consider the increased risk of adverse events associated with afatinib in this population. These results highlight the need for individualized treatment decisions for elderly patients with NSCLC. Clinicians should carefully consider the patient’s age, performance status, and comorbidities when selecting an appropriate EGFR-TKI. Additionally, close monitoring of AEs and appropriate management strategies are crucial to ensure optimal treatment outcomes in this population.

“In conclusion, this study demonstrated the effectiveness and safety of EGFR-TKIs for elderly patients with EGFR-mutated advanced NSCLC, a population that has often been underrepresented in clinical trials and real-world evidence. For elderly patients with EGFR-mutated advanced NSCLC, clinicians were more likely to prefer gefitinib or erlotinib to afatinib as a therapy, in contrast to the treatment regimen for younger patients. Nevertheless, afatinib still emerged as the primary choice for first-line treatment for older patients compared to other EGFR-TKIs, as it is more effective than gefitinib or erlotinib in elderly patients with EGFR-mutated advanced NSCLC.”

Click here to read the full research paper published in Aging.

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Aging is an open-access, traditional, peer-reviewed journal that has published high-impact papers in all fields of aging research since 2009. All papers are available to readers (at no cost and free of subscription barriers) in bi-monthly issues at Aging-US.com.

Click here to subscribe to Aging publication updates.

For media inquiries, please contact media@impactjournals.com.

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Rooted in Chinese Medicine: Zicao’s Anti-Cancer Effects on Lung Cancer

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In this new study, researchers investigated a plant used in traditional Chinese medicine and its anti-cancer effects in non-small cell lung cancer (NSCLC).

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Traditional Chinese medicine has long been explored for its potential in treating various diseases, including cancer. Lithospermum erythrorhizon, or purple gromwell, is a mysterious plant native to East Asia, and its dried root is often referred to as Zicao. Acetylshikonin, a compound derived from Zicao, has shown promise in exhibiting a variety of anti-cancer properties. While the effects of acetylshikonin on lung cancer are not yet fully understood, recent research has shed light on its potential as a therapeutic agent. 

In a new study, researchers Shih-Sen Lin, Tsung-Ming Chang, Augusta I-Chin Wei, Chiang-Wen Lee, Zih-Chan Lin, Yao-Chang Chiang, Miao-Ching Chi, and Ju-Fang Liu from Shin Kong Wu Ho-Su Memorial Hospital, Chang Gung Memorial Hospital, Chang Gung University of Science and Technology, Ming Chi University of Technology, Taipei Medical University, and China Medical University aimed to explore the mechanisms underlying acetylshikonin-induced cell death in non-small cell lung cancer (NSCLC). On December 19, 2023, their research paper was published in Aging’s Volume 15, Issue 24, entitled, “Acetylshikonin induces necroptosis via the RIPK1/RIPK3-dependent pathway in lung cancer.”

“This study explored the mechanisms underlying acetylshikonin-induced cell death in non-small cell lung cancer (NSCLC).”

Acetylshikonin and Cell Viability Reduction

In this study, researchers investigated the effects of acetylshikonin on the viability of NSCLC cells. The researchers treated H1299 and A549 cells with varying concentrations of acetylshikonin and assessed cell viability using a cell counting kit-8 (CCK-8) assay. The results showed that acetylshikonin significantly reduced cell viability in a dose-dependent manner. The IC50 values for H1299 and A549 cells were determined to be 2.34 μM and 3.26 μM, respectively. These findings suggest that acetylshikonin has the potential to effectively reduce the viability of lung cancer cells without causing significant damage to normal cells.

Cell Death Induction by Acetylshikonin

To further investigate the effects of acetylshikonin on NSCLC cells, the team examined the morphological changes associated with cell death. They observed that acetylshikonin treatment led to chromatin condensation, cell shrinkage, and the formation of cell debris, indicating cell death. Additionally, Annexin V/propidium iodide (PI) staining demonstrated an increase in the population of cells positive for Annexin V and PI, suggesting the induction of both apoptotic and necrotic cell death. Further analysis revealed that acetylshikonin increased membrane permeability, as evidenced by the uptake of PI by the cells. These findings indicate that acetylshikonin promotes cell death in NSCLC cells, potentially through necrotic pathways.

Acetylshikonin and Cell Cycle Arrest

In addition to its effects on cell viability and cell death, acetylshikonin was found to induce cell cycle arrest in NSCLC cells. The researchers examined the cell cycle progression of H1299 and A549 cells treated with acetylshikonin. Flow cytometry analysis revealed an increase in the proportion of cells in the subG1 and G2/M phases, indicating DNA fragmentation and cell cycle arrest in the G2/M phase. Western blot analysis further confirmed these findings by showing a decrease in the expression of cell cycle regulatory proteins, CDK1 and cyclin B1, in acetylshikonin-treated cells. These results suggest that acetylshikonin exerts its anti-cancer effects by inducing cell cycle arrest, thereby inhibiting cancer cell proliferation.

Oxidative Stress and Mitochondrial Dysfunction

The team also investigated the involvement of oxidative stress and mitochondrial dysfunction in acetylshikonin-induced cell death. Acetylshikonin treatment was found to increase intracellular reactive oxygen species (ROS) levels in NSCLC cells. This increase in ROS was associated with a decrease in mitochondrial membrane potential (MMP), indicating mitochondrial dysfunction. These findings suggest that acetylshikonin induces oxidative stress and disrupts mitochondrial function in NSCLC cells, potentially contributing to cell death.

Lipid Peroxidation and GPX4 Expression

The researchers explored the role of lipid peroxidation and the expression of glutathione peroxidase 4 (GPX4) in acetylshikonin-induced cell death. They observed that acetylshikonin treatment caused lipid peroxidation, as evidenced by the quenching of red fluorescence in BODIPY™ 581/591 C11-stained cells. This lipid peroxidation was associated with a decrease in GPX4 expression. GPX4 is an enzyme involved in maintaining cellular homeostasis and protecting against oxidative stress. The downregulation of GPX4 in NSCLC cells treated with acetylshikonin suggests a potential mechanism for inducing cell death.

Necroptosis Pathway Activation by Acetylshikonin

The team further investigated the mechanism by which acetylshikonin induces cell death in NSCLC cells. They found that acetylshikonin promoted the phosphorylation of receptor-interacting serine/threonine-protein kinase 1 (RIPK1), RIPK3, and mixed lineage kinase domain-like kinase (MLKL). These proteins are key players in the necroptosis signaling pathway. Immunofluorescence staining showed an increase in MLKL phosphorylation in acetylshikonin-treated cells, while Western blot analysis confirmed the activation of RIPK1, RIPK3, and MLKL. Importantly, pretreatment with RIPK1 inhibitors reversed the phosphorylation of MLKL and significantly attenuated cell death induced by acetylshikonin, suggesting that the activation of the RIPK1/RIPK3/MLKL cascade is involved in the necroptotic cell death pathway triggered by acetylshikonin.

Conclusion

In conclusion, acetylshikonin exhibits promising anti-cancer effects in NSCLC cells. It reduces cell viability, induces cell death, and promotes cell cycle arrest in the G2/M phase. Acetylshikonin also increases membrane permeability and activates the necroptosis signaling pathway through the phosphorylation of RIPK1, RIPK3, and MLKL. Furthermore, acetylshikonin induces oxidative stress, disrupts mitochondrial function, and promotes lipid peroxidation. These findings suggest that acetylshikonin holds potential as a therapeutic agent for the treatment of lung cancer. Further research is warranted to explore the clinical applications of acetylshikonin and its potential synergistic effects with existing lung cancer therapies.

“We determined that even low doses of acetylshikonin reduced the viability of lung cancer cells without significantly affecting normal cells. When used to treat lung cancer, acetylshikonin was shown to promote cell death and arrest cell cycle progression in the G2/M phase.”

Click here to read the full research paper published in Aging.

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Aging is an open-access, traditional, peer-reviewed journal that has published high-impact papers in all fields of aging research since 2009. All papers are available to readers (at no cost and free of subscription barriers) in bi-monthly issues at Aging-US.com.

Click here to subscribe to Aging publication updates.

For media inquiries, please contact media@impactjournals.com.

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Understanding the Mechanisms of Brain Aging and Longevity in Neurons

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In a new editorial, researchers discuss interconnected mechanisms of neuronal functionality and available tools to investigate neuronal aging and longevity. 

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Neurons, the building blocks of the nervous system, play a vital role in our body’s function and longevity. Unlike other cells, neurons do not undergo replicative aging. However, they are still susceptible to various sources of damage throughout life, leading to neuronal death. Understanding the mechanisms behind aging and neuronal death is crucial for uncovering the secrets of brain longevity and developing potential interventions to promote healthy aging.

In a new editorial, researchers Fang Fang, Robert Usselman and Renee Reijo Pera from University of Science and Technology of China, Florida Institute of Technology and McLaughlin Research Institute discussed new interconnected mechanisms of neuronal functionality and available tools to investigate neuronal aging and longevity. On December 13, 2023, their editorial was published in Aging’s Volume 15, Issue 23, entitled, “Aging and neuronal death.”

Neuronal Durability, Differentiation & Maintenance

Neurons, born during embryonic development, must function in the body for the entire lifespan of the organism. They are incredibly durable cells, but they are not immune to damage. Neurons require a significant amount of oxygen and glucose to carry out their activities, making them vulnerable to ischemia. Ischemia occurs when the blood supply to a particular tissue is restricted, leading to oxygen and nutrient deprivation. 

Neurons can accumulate damage over time, which may result in cell death linked to reactive oxygen species (ROS). Neurons may also die due to ion overload and swelling caused by the malfunction of voltage-gated ion channels on their membranes. High concentrations of neurotransmitters and the accumulation of misfolded proteins are also implicated in neuronal death, observed in various neurodegenerative diseases.

To gain insights into the factors that promote neuron differentiation and maintenance, researchers have developed innovative screening methods. For example, Cui and colleagues described a high-throughput screening method using a luciferase reporter construct inserted downstream of the endogenous tyrosine hydroxylase (TH) gene. They differentiated neurons from human pluripotent stem cells and monitored their activity over time. This approach allows for the modeling of cell survival and demise, providing valuable information about the factors that influence neuronal longevity.

The Role of ROS in Survival & Death

Reactive oxygen species (ROS) are molecules produced during normal cellular metabolism. They play a crucial role in various biological processes but can also lead to oxidative stress when their levels exceed normal functional levels. Recent research has shed light on the distinction between global and local ROS balances and imbalances in cell phenotyping and mitochondrial energy management.

While global ROS homeostasis is essential for overall cellular health, ROS signaling pathways are driven locally by cellular microdomain-specific ROS production and degradation. Neurons have developed mechanisms to control ROS production and combat oxidative stress. For example, they express neurotrophic proteins that enhance mitochondrial activity, promoting the overall health of neurons.

“A sustained disruption of ROS balance can result in desirable enhanced cell signaling or undesirable oxidative stress, which can either improve function or diminish performance, respectively.”

Mechanisms for Longevity

Neurons have evolutionarily developed intricate mechanisms to maintain their longevity. They possess a distinct transcriptome signature that represses genes related to neural excitation and synaptic function. By preventing neurons from experiencing ion overload, this mechanism contributes to their long-term survival.

These brain cells have also developed specific DNA repair mechanisms to correct errors induced by active transcription. Neurons can turn off pro-apoptotic genes through alternative splicing, avoiding apoptosis and promoting long-term survival. These interconnected mechanisms work together to reduce the accumulation of aging-related damage in neurons. Understanding the fundamental mechanisms that enable the longevity of neurons is crucial for developing interventions that promote healthy brain aging. Researchers can use novel tools, including cell-based models, imaging techniques and animal studies, to investigate these mechanisms.

Conclusions

Neurons, although durable cells, are susceptible to various forms of damage that can lead to their demise. By studying the interplay between ROS, neuronal excitation, DNA repair, and apoptosis, researchers aim to uncover the secrets of brain longevity and develop strategies to mitigate the effects of aging on neurons. By understanding these mechanisms, researchers aim to develop interventions that promote healthy brain aging and enhance our overall understanding of brain health.

“Together, these findings suggest that neurons have evolved a set of intrinsically interconnected mechanisms to reduce long-term accumulations of aging-related damages. Disruption in these mechanisms may tip the neuron homeostasis off-balance and drive the neurons into the path of degeneration. We have a plethora of tools to probe the fundamental mechanisms with hopes of translation to clinical applications.”

Click here to read the full editorial published in Aging.

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Aging is an open-access, traditional, peer-reviewed journal that has published high-impact papers in all fields of aging research since 2009. All papers are available to readers (at no cost and free of subscription barriers) in bi-monthly issues at Aging-US.com.

Click here to subscribe to Aging publication updates.

For media inquiries, please contact media@impactjournals.com.

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What Makes Children of Older Fathers at Increased Risk of Autism?

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In this new study, researchers investigated the relationship between paternal age, the BEGAIN gene and autism.

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in communication and social interaction, as well as repetitive behaviors. It has been observed that children born to older fathers have an increased risk of developing ASD and other neurodevelopmental disorders. This phenomenon suggests that paternal age may have an impact on the risk of ASD in offspring.

Recent research has focused on understanding the potential mechanisms underlying the association between paternal age and ASD. One area of interest is the epigenome, specifically DNA methylation, which refers to the addition or removal of methyl groups to DNA molecules. DNA methylation can affect gene expression and play a role in various biological processes.

In a new study, researchers Ramya Potabattula, Andreas Prell, Marcus Dittrich, Caroline Nava, Christel Depienne, Yosra Bejaoui, Nady El Hajj, Thomas Hahn, Martin Schorsch, and Thomas Haaf from Julius Maximilians University, Groupe Hospitalier Pitié-Salpêtrière, University Hospital Essen, Hamad Bin Khalifa University, and Fertility Center in Wiesbaden, Germany, explored the relationship between paternal age, DNA methylation of the BEGAIN gene, and the risk of ASD. The BEGAIN gene encodes a protein involved in protein-protein interactions at synapses, which are crucial for proper brain function. On November 28, 2023, their research paper was published in Aging’s Volume 15, Issue 22, entitled, “Effects of paternal and chronological age on BEGAIN methylation and its possible role in autism.”

“So far, only 40 genes with sperm ageDMRs [age-associated differentially methylated regions] have been replicated in at least three independent genome-wide methylation screens [19], which makes them primary candidates for mediating paternal age effects on the next generation. Here, we focused on one of these top candidates, the BEGAIN promoter region.”

The Study

The study focused on examining the impact of paternal age on BEGAIN methylation. Various techniques were employed to investigate this relationship. Sperm samples from normozoospermic individuals attending a fertility center were analyzed. The researchers aimed to understand how paternal age influences BEGAIN methylation, specifically observing its trends in sperm.

To extend their exploration of transgenerational effects, fetal cord blood samples were also examined. The team aimed to discern whether paternal age influenced BEGAIN methylation differently in male and female offspring. The research team employed meticulous analyses to understand the sex-specific patterns associated with paternal age and BEGAIN methylation.

They also delved into the effects of chronological age on BEGAIN methylation. Peripheral blood samples from individuals of different ages were analyzed to investigate the relationship between chronological age and BEGAIN methylation. The study aimed to discern whether BEGAIN methylation undergoes changes with age in a sex-specific manner.

“It is tempting to speculate that transmission of paternal age-associated sperm methylation changes into the next generation modulates BEGAIN regulation and susceptibility to neurodevelopmental disorders.”

The Results

The research yielded significant findings. A negative correlation between paternal age and BEGAIN methylation was identified, suggesting a decrease in BEGAIN methylation in sperm as paternal age increases. The sex-specific impact of paternal age on BEGAIN methylation was observed, with a significant negative correlation in male offspring but not in female offspring.

Regarding chronological age, a significant negative correlation with BEGAIN methylation was found in males but not in females, indicating a potential sex-specific age-related change in BEGAIN methylation.

The study also explored the association between BEGAIN methylation and Autism Spectrum Disorder (ASD). Individuals with ASD were found to have significantly lower levels of BEGAIN methylation compared to age- and sex-matched controls, suggesting a potential involvement of BEGAIN methylation in the development of ASD.

Furthermore, the researchers identified a genetic variant, SNP rs7141087, associated with BEGAIN methylation. The CC genotype of this SNP was linked to lower levels of BEGAIN methylation compared to the TT genotype, potentially contributing to observed differences in BEGAIN methylation between individuals with ASD and controls.

“Individuals with CC genotype of SNP rs7141087 which show a 6% lower methylation than the TT genotype are significantly more frequent in our ASD group than in controls. This could be due to an association of the C allele with autism.”

Conclusions & Future Research

In conclusion, this research provides valuable insights into the effects of paternal and chronological age on BEGAIN methylation and its potential role in ASD. The findings suggest that paternal age and chronological age can influence BEGAIN methylation, and these changes may be associated with an increased risk of ASD. Further research is needed to fully understand the mechanisms underlying these associations and their implications for the development of ASD.

“The male-specific hypomethylation of the BEGAIN promoter in blood, and by extrapolation other somatic tissues is exaggerated in males suffering from autism. Moreover, our results also show a paternal age effect on BEGAIN methylation in sperm and the male offspring (FCB). […] However, the functional implications of small age-associated methylation changes in BEGAIN in a multifactorial disease model remain to be elucidated.”

Click here to read the full study published in Aging.

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Aging is an open-access, traditional, peer-reviewed journal that has published high-impact papers in all fields of aging research since 2009. All papers are available to readers (at no cost and free of subscription barriers) in bi-monthly issues at Aging-US.com.

Click here to subscribe to Aging publication updates.

For media inquiries, please contact media@impactjournals.com.

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