scholarly journals Chronic Exposure to Youthful Circulation Leads to Epigenetic Reprogramming and Lifespan Extension

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 682-682
Author(s):  
Bohan Zhang ◽  
David Lee ◽  
Alexander Tyshkovskiy ◽  
Akshay Bareja ◽  
Csaba Kerepesi ◽  
...  
Keyword(s):  
Chronic Exposure ◽  
Aging Process ◽  
Biological Age ◽  
Biological Aging ◽  
Lifespan Extension ◽  
Molecular Signatures ◽  
Signature Analysis ◽  
Rna Seq ◽  
Aging Research

Abstract Heterochronic parabiosis is a powerful rejuvenation model in aging research. Due to limitations in the duration of blood sharing and/or physical attachment, it is currently unclear if parabiosis retards the molecular signatures of aging or affects healthspan/lifespan in the mouse. Here, we describe a long-term heterochronic parabiosis model, which appears to slow down the aging process. We observed a “deceleration” of biological age based on molecular aging biomarkers estimated with DNA methylation clock and RNA-seq signature analysis. The slowing of biological aging was accompanied by systemic amelioration of aging phenotypes. Consistent with these findings, we found that aged mice, which underwent heterochronic parabiosis, had an increased healthspan and lifespan. Overall, our study re-introduces a prolonged parabiosis and detachment model as a novel rejuvenation therapy, suggesting that a systemic reset of biological age in old organisms can be achieved through the exposure to young environment.

scholarly journals Predictors of Biological Age: The Implications for Wellness and Aging Research

2021 ◽  
Vol 7 ◽  
pp. 233372142110464
Author(s):  
Trevor Lohman ◽  
Gurinder Bains ◽  
Lee Berk ◽  
Everett Lohman
Keyword(s):  
Dna Methylation ◽  
Lifestyle Modification ◽  
Biological Age ◽  
Current Evidence ◽  
Biological Aging ◽  
Clinical Value ◽  
Aging Research ◽  
Mortality And Morbidity ◽  
Morbidity Risk ◽  
Pharmaceutical Interventions

As healthspan and lifespan research breakthroughs have become more commonplace, the need for valid, practical markers of biological age is becoming increasingly paramount. The accessibility and affordability of biological age predictors that can reveal information about mortality and morbidity risk, as well as remaining years of life, has profound clinical and research implications. In this review, we examine 5 groups of aging biomarkers capable of providing accurate biological age estimations. The unique capabilities of these biomarkers have far reaching implications for the testing of both pharmaceutical and non-pharmaceutical interventions designed to slow or reverse biological aging. Additionally, the enhanced validity and availability of these tools may have increasingly relevant clinical value. The authors of this review explore those implications, with an emphasis on lifestyle modification research, and provide an overview of the current evidence regarding 5 biological age predictor categories: Telomere length, composite biomarkers, DNA methylation “epigenetic clocks,” transcriptional predictors of biological age, and functional age predictors.

scholarly journals Deep learning for biological age estimation

2020 ◽  
Author(s):  
Syed Ashiqur Rahman ◽  
Peter Giacobbi ◽  
Lee Pyles ◽  
Charles Mullett ◽  
Gianfranco Doretto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Age Estimation ◽  
Current Data ◽  
Biological Age ◽  
Machine Learning Techniques ◽  
Biological Aging ◽  
Feature Engineering ◽  
Complex Data ◽  
Aging Research

Abstract Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data mining approaches are still popular in aging research, they typically need feature engineering or feature extraction for robust performance. Explicit feature engineering represents a major challenge, as it requires significant domain knowledge. The latest advances in deep learning provide a paradigm shift in eliciting meaningful knowledge from complex data without performing explicit feature engineering. In this article, we review the recent literature on applying deep learning in biological age estimation. We consider the current data modalities that have been used to study aging and the deep learning architectures that have been applied. We identify four broad classes of measures to quantify the performance of algorithms for biological age estimation and based on these evaluate the current approaches. The paper concludes with a brief discussion on possible future directions in biological aging research using deep learning. This study has significant potentials for improving our understanding of the health status of individuals, for instance, based on their physical activities, blood samples and body shapes. Thus, the results of the study could have implications in different health care settings, from palliative care to public health.

scholarly journals Multi-omic rejuvenation and lifespan extension upon exposure to youthful circulation

2021 ◽  
Author(s):  
Bohan Zhang ◽  
David E Lee ◽  
Alexandre Trapp ◽  
Alexander Tyshkovskiy ◽  
Ake T Lu ◽  
...  
Keyword(s):  
Physiological Parameters ◽  
Biological Age ◽  
Intermediate Phenotype ◽  
Lifespan Extension ◽  
Mouse Tissues ◽  
Clock Models

Heterochronic parabiosis (HPB) is known for its functional rejuvenation effects across several mouse tissues. However, its impact on the biological age of organisms and their long-term health remains unknown. Here, we performed extended (3-month) HPB, followed by a 2-month detachment period of anastomosed pairs. Old detached mice exhibited improved physiological parameters and lived longer than control isochronic mice. HPB drastically reduced the biological age of blood and liver based on epigenetic analyses across several clock models on two independent platforms; remarkably, this rejuvenation effect persisted even after 2 months of detachment. Transcriptomic and epigenomic profiles of anastomosed mice showed an intermediate phenotype between old and young, suggesting a comprehensive multi-omic rejuvenation effect. In addition, old HPB mice showed transcriptome changes opposite to aging, but akin to several lifespan-extending interventions. Altogether, we reveal that long-term HPB can decrease the biological age of mice, in part through long-lasting epigenetic and transcriptome remodeling, culminating in the extension of lifespan and healthspan.

scholarly journals Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 678-678
Author(s):  
Chia-Ling Kuo ◽  
Luke Pilling ◽  
Janice Atkins ◽  
Jane Masoli ◽  
João Delgado ◽  
...  
Keyword(s):  
Biological Age ◽  
Biological Aging ◽  
Assessment Centers ◽  
Uk Biobank ◽  
Logistic Regression Models ◽  
Age Related ◽  
Inpatient Settings ◽  
The Uk ◽  
Related Mortality

Abstract Age and disease prevalence are the two biggest risk factors for COVID-19 symptom severity and death. We therefore hypothesized that increased biological age, beyond chronological age, may be driving disease-related trends in COVID-19 severity. Using the UK Biobank England data, we tested whether a biological age estimate (PhenoAge) measured more than a decade prior to the COVID-19 pandemic was predictive of two COVID-19 severity outcomes (inpatient test positivity and COVID-19 related mortality with inpatient test-confirmed COVID-19). Logistic regression models were used with adjustment for age at the pandemic, sex, ethnicity, baseline assessment centers, and pre-existing diseases/conditions. 613 participants tested positive at inpatient settings between March 16 and April 27, 2020, 154 of whom succumbed to COVID-19. PhenoAge was associated with increased risks of inpatient test positivity and COVID-19 related mortality (ORMortality=1.63 per 5 years, 95% CI: 1.43-1.86, p=4.7x10E-13) adjusting for demographics including age at the pandemic. Further adjustment for pre-existing disease s/conditions at baseline (OR_M=1.50, 95% CI: 1.30-1.73 per 5 years, p=3.1x10E-8) and at the early pandemic (OR_M=1.21, 95% CI: 1.04-1.40 per 5 years, p=0.011) decreased the association. PhenoAge measured in 2006-2010 was associated with COVID-19 severity outcomes more than 10 years later. These associations were partly accounted for by prevalent chronic diseases proximate to COVID-19 infection. Overall, our results suggest that aging biomarkers, like PhenoAge may capture long-term vulnerability to diseases like COVID-19, even before the accumulation of age-related comorbid conditions.

scholarly journals HeALTH: An Automated Platform for Long-term Longitudinal Studies of Whole Organisms under Precise Environmental Control

2019 ◽  
Author(s):  
Kim N. Le ◽  
Mei Zhan ◽  
Yongmin Cho ◽  
Jason Wan ◽  
Dhaval S. Patel ◽  
...  
Keyword(s):  
Aging Process ◽  
Large Scale ◽  
Environmental Control ◽  
Model Organism ◽  
Model Systems ◽  
Aging Research ◽  
C Elegans ◽  
Environmental Perturbations ◽  
Automated Platform

ABSTRACTHealth and longevity in all organisms are strongly influenced by the environment. To fully understand how environmental factors interact with genetic and stochastic factors to modulate the aging process, it is crucial to precisely control environmental conditions for long-term studies. In the commonly used model organism Caenorhabditis elegans, existing assays for healthspan and lifespan have inherent limitations, making it difficult to perform large-scale, longitudinal aging studies under precise environmental control. To address this constraint, we developed the Health and Lifespan Testing Hub (HeALTH), an automated, microfluidic-based system for robust, long-term, longitudinal behavioral monitoring. Our system provides spatiotemporal environmental control. We demonstrate health and lifespan studies under a variety of genetic and environmental perturbations while observing how individuality plays a role in the aging process. This system is generalizable beyond aging research for C. elegans, particularly for short- or long-term behavioral assays, and is also possible to be adapted for other model systems.

scholarly journals Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants

2021 ◽  
Author(s):  
Chia-Ling Kuo ◽  
Luke C Pilling ◽  
Janice L Atkins ◽  
Jane A H Masoli ◽  
João Delgado ◽  
...  
Keyword(s):  
Biological Age ◽  
Biological Aging ◽  
Assessment Centers ◽  
Uk Biobank ◽  
Logistic Regression Models ◽  
Age Related ◽  
Inpatient Settings ◽  
The Uk ◽  
Related Mortality

Abstract Background Age and disease prevalence are the two biggest risk factors for COVID-19 symptom severity and death. We therefore hypothesized that increased biological age, beyond chronological age, may be driving disease-related trends in COVID-19 severity. Methods Using the UK Biobank England data, we tested whether a biological age estimate (PhenoAge) measured more than a decade prior to the COVID-19 pandemic was predictive of two COVID-19 severity outcomes (inpatient test positivity and COVID-19 related mortality with inpatient test-confirmed COVID-19). Logistic regression models were used with adjustment for age at the pandemic, sex, ethnicity, baseline assessment centers, and pre-existing diseases/conditions. Results 613 participants tested positive at inpatient settings between March 16 and April 27, 2020, 154 of whom succumbed to COVID-19. PhenoAge was associated with increased risks of inpatient test positivity and COVID-19 related mortality (ORMortality=1.63 per 5 years, 95% CI: 1.43-1.86, p=4.7×10 -13) adjusting for demographics including age at the pandemic. Further adjustment for pre-existing disease s/conditions at baseline (ORM=1.50, 95% CI: 1.30-1.73 per 5 years, p=3.1×10 -8) and at the early pandemic (ORM=1.21, 95% CI: 1.04-1.40 per 5 years, p=0.011) decreased the association. Conclusions PhenoAge measured in 2006-2010 was associated with COVID-19 severity outcomes more than 10 years later. These associations were partly accounted for by prevalent chronic diseases proximate to COVID-19 infection. Overall, our results suggest that aging biomarkers, like PhenoAge may capture long-term vulnerability to diseases like COVID-19, even before the accumulation of age-related comorbid conditions.

scholarly journals New Computational Approaches to Aging Research

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 736-736
Author(s):  
Morgan E Levine
Keyword(s):  
Aging Process ◽  
Molecular Level ◽  
Biological Aging ◽  
Systems Dynamics ◽  
Learning Approaches ◽  
Biological Organization ◽  
Aging Research ◽  
Biomarkers Of Aging ◽  
Age Related ◽  
Novel Biomarkers

Abstract Aging is associated with numerous changes at all levels of biological organization. Harnessing this information to develop measures that accurately and reliably quantify the biological aging process will require systems biology approaches. This talk will illustrate how epigenetic data can be integrated with cellular, physiological, proteomic, and clinical data to model age-related changes that propagate up the levels—finally manifesting as age-related disease or death. I will also describe how network modeling and machine learning approaches (linear and non-linear) can be used to identify causal features in aging from which to generate novel biomarkers. Given the complexity of the biological aging process, modeling of systems dynamics over time will both lead to the development of better biomarkers of aging, and also inform our conceptualization of how alterations at the molecular level propagate up levels of organization to eventually influence morbidity and mortality risk.

scholarly journals Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 332-333
Author(s):  
Chia-Ling Kuo ◽  
Luke Pilling ◽  
Janice Atkins ◽  
Jane Masoli ◽  
Joao Delgado ◽  
...  
Keyword(s):  
Biological Age ◽  
Biological Aging ◽  
Assessment Centers ◽  
Uk Biobank ◽  
Logistic Regression Models ◽  
Age Related ◽  
Inpatient Settings ◽  
The Uk ◽  
Related Mortality

Abstract Age and disease prevalence are the two biggest risk factors for COVID-19 symptom severity and death. We therefore hypothesized that increased biological age, beyond chronological age, may be driving disease-related trends in COVID-19 severity. Using the UK Biobank England data, we tested whether a biological age estimate (PhenoAge) measured more than a decade prior to the COVID-19 pandemic was predictive of two COVID-19 severity outcomes (inpatient test positivity and COVID-19 related mortality with inpatient test-confirmed COVID-19). Logistic regression models were used with adjustment for age at the pandemic, sex, ethnicity, baseline assessment centers, and pre-existing diseases/conditions. 613 participants tested positive at inpatient settings between March 16 and April 27, 2020, 154 of whom succumbed to COVID-19. PhenoAge was associated with increased risks of inpatient test positivity and COVID-19 related mortality (ORMortality=1.63 per 5 years, 95% CI: 1.43-1.86, p=4.7x10E-13) adjusting for demographics including age at the pandemic. Further adjustment for pre-existing disease s/conditions at baseline (OR_M=1.50, 95% CI: 1.30-1.73 per 5 years, p=3.1x10E-8) and at the early pandemic (OR_M=1.21, 95% CI: 1.04-1.40 per 5 years, p=0.011) decreased the association. PhenoAge measured in 2006-2010 was associated with COVID-19 severity outcomes more than 10 years later. These associations were partly accounted for by prevalent chronic diseases proximate to COVID-19 infection. Overall, our results suggest that aging biomarkers, like PhenoAge may capture long-term vulnerability to diseases like COVID-19, even before the accumulation of age-related comorbid conditions.

Biological age and rates of aging of patients with vibration disease and bilateral sensorineural hearing loss

2020 ◽  
pp. 624-624
Author(s):  
S. I. Ereniev ◽  
O. V. Plotnikova
Keyword(s):  
Hearing Loss ◽  
Sensorineural Hearing Loss ◽  
Sensorineural Hearing ◽  
Biological Age ◽  
Biological Aging ◽  
Vibration Disease ◽  
Bilateral Sensorineural Hearing Loss

Biological age and rates of aging of patients with vibration disease and bilateral sensorineural hearing loss were studied. The biological age of patients exceeded the calendar age by an average of 7.36±0.36 years and the proper biological age by 10.79±0.72 years. The rate of biological aging of the examined patients was 1.14±0.08 times higher than the rate of aging of their healthy peers.

scholarly journals Acute and chronic exposure to air pollution in relation with incidence, prevalence, severity and mortality of COVID-19: a rapid systematic review

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Patrick D. M. C. Katoto ◽  
Amanda S. Brand ◽  
Buket Bakan ◽  
Paul Musa Obadia ◽  
Carsi Kuhangana ◽  
...  
Keyword(s):  
Air Pollution ◽  
Chronic Exposure ◽  
Ambient Air ◽  
Short Term ◽  
Incidence And Mortality ◽  
Acute And Chronic Exposure ◽  
Anthropogenic Pollutant ◽  
Incident Cases ◽  
Incidence Prevalence

Abstract Background Air pollution is one of the world’s leading mortality risk factors contributing to seven million deaths annually. COVID-19 pandemic has claimed about one million deaths in less than a year. However, it is unclear whether exposure to acute and chronic air pollution influences the COVID-19 epidemiologic curve. Methods We searched for relevant studies listed in six electronic databases between December 2019 and September 2020. We applied no language or publication status limits. Studies presented as original articles, studies that assessed risk, incidence, prevalence, or lethality of COVID-19 in relation with exposure to either short-term or long-term exposure to ambient air pollution were included. All patients regardless of age, sex and location diagnosed as having COVID-19 of any severity were taken into consideration. We synthesised results using harvest plots based on effect direction. Results Included studies were cross-sectional (n = 10), retrospective cohorts (n = 9), ecological (n = 6 of which two were time-series) and hypothesis (n = 1). Of these studies, 52 and 48% assessed the effect of short-term and long-term pollutant exposure, respectively and one evaluated both. Pollutants mostly studied were PM2.5 (64%), NO2 (50%), PM10 (43%) and O3 (29%) for acute effects and PM2.5 (85%), NO2 (39%) and O3 (23%) then PM10 (15%) for chronic effects. Most assessed COVID-19 outcomes were incidence and mortality rate. Acutely, pollutants independently associated with COVID-19 incidence and mortality were first PM2.5 then PM10, NO2 and O3 (only for incident cases). Chronically, similar relationships were found for PM2.5 and NO2. High overall risk of bias judgments (86 and 39% in short-term and long-term exposure studies, respectively) was predominantly due to a failure to adjust aggregated data for important confounders, and to a lesser extent because of a lack of comparative analysis. Conclusion The body of evidence indicates that both acute and chronic exposure to air pollution can affect COVID-19 epidemiology. The evidence is unclear for acute exposure due to a higher level of bias in existing studies as compared to moderate evidence with chronic exposure. Public health interventions that help minimize anthropogenic pollutant source and socio-economic injustice/disparities may reduce the planetary threat posed by both COVID-19 and air pollution pandemics.

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