scholarly journals Aging Clocks

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 818-819
Author(s):  
Morgan Levine
Keyword(s):  
Biological Sciences ◽  
At Risk ◽  
Risk Factor ◽  
Disease Onset ◽  
Biological Age ◽  
Chronological Age ◽  
Biological Aging ◽  
Biomarkers Of Aging ◽  
Underlying Mechanisms

Abstract While chronological age is arguably the strongest risk factor for death, disease, and disability, same-aged individuals remain heterogeneous in their susceptibilities to these various outcomes. One explanation is that chronological age is an imperfect proxy of the degree of biological aging an individual has undergone. Thus, defining measurable estimates of ‘biological age’ (in contrast to chronological age) has become a major initiative in Geroscience research. Such biomarkers of aging, or ‘aging clocks’ will 1) help identify underlying mechanisms of aging, 2) enable identification of at-risk individuals prior to disease onset, and 3) provide outcomes to assess efficacy of interventions. In this session, I will describe the various aging clocks, how they were developed, and what they track. I will also describe how aging clocks can facilitate research both within and outside of the biological sciences.

scholarly journals COVID-19 severity is predicted by earlier evidence of accelerated aging

2020 ◽  
Author(s):  
Chia-Ling Kuo ◽  
Luke C. Pilling ◽  
Janice L Atkins ◽  
Jane AH Masoli ◽  
João Delgado ◽  
...  
Keyword(s):  
Clinical Chemistry ◽  
Accelerated Aging ◽  
Biological Age ◽  
Chronological Age ◽  
Biological Aging ◽  
Severe Illness ◽  
Mortality Data ◽  
Age Related ◽  
Underlying Mechanisms ◽  
The Uk

AbstractWith no known treatments or vaccine, COVID-19 presents a major threat, particularly to older adults, who account for the majority of severe illness and deaths. The age-related susceptibility is partly explained by increased comorbidities including dementia and type II diabetes [1]. While it is unclear why these diseases predispose risk, we hypothesize that increased biological age, rather than chronological age, may be driving disease-related trends in COVID-19 severity with age. To test this hypothesis, we applied our previously validated biological age measure (PhenoAge) [2] composed of chronological age and nine clinical chemistry biomarkers to data of 347,751 participants from a large community cohort in the United Kingdom (UK Biobank), recruited between 2006 and 2010. Other data included disease diagnoses (to 2017), mortality data (to 2020), and the UK national COVID-19 test results (to May 31, 2020) [3]. Accelerated aging 10-14 years prior to the start of the COVID-19 pandemic was associated with test positivity (OR=1.15 per 5-year acceleration, 95% CI: 1.08 to 1.21, p=3.2×10−6) and all-cause mortality with test-confirmed COVID-19 (OR=1.25, per 5-year acceleration, 95% CI: 1.09 to 1.44, p=0.002) after adjustment for demographics including current chronological age and pre-existing diseases or conditions. The corresponding areas under the curves were 0.669 and 0.803, respectively. Biological aging, as captured by PhenoAge, is a better predictor of COVID-19 severity than chronological age, and may inform risk stratification initiatives, while also elucidating possible underlying mechanisms, particularly those related to inflammaging.

scholarly journals Biological age in UK Biobank: biomarker composition and prediction of mortality, coronary heart disease and hospital admissions

2019 ◽  
Author(s):  
Mei Sum Chan ◽  
Matthew Arnold ◽  
Alison Offer ◽  
Imen Hammami ◽  
Marion Mafham ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Risk Factor ◽  
Heart Disease ◽  
Principal Components ◽  
Hospital Admissions ◽  
Principal Component ◽  
Age Effects ◽  
Biological Age ◽  
Chronological Age ◽  
Uk Biobank

AbstractBackgroundAge is the strongest risk factor for most chronic diseases, and yet individuals may age at different rates biologically. A biological age formed from biomarkers may be a stronger risk factor than chronological age and understanding what factors contribute to it could provide insight into new opportunities for disease prevention.Methods and findingsAmong 480,019 UK Biobank participants aged 40-70 recruited in 2006-2010 and followed up for 6-12 years via linked death registry and secondary care records, a subpopulation of 141,254 (29.4%) non-smoking adults in good health and with no medication use or disease history at baseline were identified. Independent components of 72 biomarkers measured at baseline were characterised by principal component analysis. The Klemera Doubal method (KDM), which derived a weighted sum of biomarker principal components based on the strengths of their linear associations with chronological age, was used to derive sex-specific biological ages in this healthy subpopulation. The proportions of the overall biological and chronological age effects on mortality, coronary heart disease and age-related non-fatal hospital admissions (based on a hospital frailty index) that were explained by biological age were assessed using log-likelihoods of proportional hazards models.Reduced lung function, reduced kidney function, slower reaction time, lower insulin-like-growth factor 1, lower hand grip strength and higher blood pressure were key contributors to biological age (explaining the highest percentages of its variance) in both men and women, while lower albumin, higher sex hormone-binding globulin and lower muscle mass in men, and higher liver enzymes, blood lipids and HbA1c in women were also important. Across both sexes, a 51-principal component biological age explained 66%, 80% and 63% of the age effects on mortality, coronary heart disease and hospital admissions, respectively. Restricting the biological age to the 12-13 key biomarkers corresponding to the 10 most importantly contributing principal components resulted in little change in these proportions for women, but a reduction to 53%, 63% and 50%, respectively, for men.ConclusionsThis study identified that markers of impaired function in a range of organs account for a substantial proportion of the apparent effect of age on disease and hospital admissions. It supports a broader, multi-system approach to research and prevention of diseases of ageing.

scholarly journals COMPARABILITY OF BIOLOGICAL AGING MEASURES IN THE NATIONAL HEALTH AND NUTRITION EXAMINATION STUDY, 1999-2002

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S479-S479
Author(s):  
Waylon J Hastings ◽  
Daniel Belsky ◽  
Idan Shalev
Keyword(s):  
Risk Factors ◽  
Telomere Length ◽  
Cellular Level ◽  
Biological Age ◽  
Effect Sizes ◽  
Chronological Age ◽  
Biological Aging ◽  
Patient Level ◽  
Age Related ◽  
Level Information

Abstract Biological processes of aging are thought to be modifiable causes of many chronic diseases. Measures of biological aging could provide sensitive endpoints for studies of risk factors hypothesized to shorten healthy lifespan and/or interventions that extend it. However, uncertainty remains about how to measure biological aging and if proposed measures assess the same thing. We tested four proposed measures of biological aging with available data from NHANES 1999-2002: Klemera-Doubal method (KDM) Biological Age, homeostatic dysregulation, Levine Method (LM) Biological Age, and leukocyte telomere length. All measures of biological aging were correlated with chronological age. KDM Biological Age, homeostatic dysregulation, and LM Biological Age were all significantly associated with each other, but were each not associated with telomere length. NHANES participants with older biological ages performed worse on tests of physical, cognitive, perceptual, and subjective functions known to decline with advancing chronological age and thought to mediate age-related disability. Further, NHANES participants with higher levels of exposure to life-course risk factors were measured as having older biological ages. In both sets of analyses, effect-sizes tended to be larger for KDM Biological Age, homeostatic dysregulation, and LM Biological Age as compared to telomere length. Composite measures combining cellular- and patient-level information tended to have the largest effect-sizes. The cellular-level aging biomarker telomere length may measure different aspects of the aging process relative to the patient-level physiological measures. Studies aiming to test if risk factors accelerate aging or if interventions may slow aging should not treat proposed measures of biological aging as interchangeable.

scholarly journals Exploring Epigenetic Age in Response to Intensive Relaxing Training: A Pilot Study to Slow Down Biological Age

2019 ◽  
Vol 16 (17) ◽  
pp. 3074 ◽  
Author(s):  
Pavanello ◽  
Campisi ◽  
Tona ◽  
Lin ◽  
Iliceto
Keyword(s):  
Myocardial Infarction ◽  
Age Estimation ◽  
Healthy Subjects ◽  
Molecular Mechanisms ◽  
Biological Age ◽  
Chronological Age ◽  
Biological Aging ◽  
Epigenetic Clock ◽  
Age Related ◽  
Epigenetic Age

DNA methylation (DNAm) is an emerging estimator of biological aging, i.e., the often-defined “epigenetic clock”, with a unique accuracy for chronological age estimation (DNAmAge). In this pilot longitudinal study, we examine the hypothesis that intensive relaxing training of 60 days in patients after myocardial infarction and in healthy subjects may influence leucocyte DNAmAge by turning back the epigenetic clock. Moreover, we compare DNAmAge with another mechanism of biological age, leucocyte telomere length (LTL) and telomerase. DNAmAge is reduced after training in healthy subjects (p = 0.053), but not in patients. LTL is preserved after intervention in healthy subjects, while it continues to decrease in patients (p = 0.051). The conventional negative correlation between LTL and chronological age becomes positive after training in both patients (p < 0.01) and healthy subjects (p < 0.05). In our subjects, DNAmAge is not associated with LTL. Our findings would suggest that intensive relaxing practices influence different aging molecular mechanisms, i.e., DNAmAge and LTL, with a rejuvenating effect. Our study reveals that DNAmAge may represent an accurate tool to measure the effectiveness of lifestyle-based interventions in the prevention of age-related diseases.

scholarly journals Cancer Treatment-Induced Accelerated Aging in Cancer Survivors: Biology and Assessment

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 427
Author(s):  
Shuo Wang ◽  
Anna Prizment ◽  
Bharat Thyagarajan ◽  
Anne Blaes
Keyword(s):  
Quality Of Life ◽  
Cancer Survivors ◽  
Cancer Treatment ◽  
Accelerated Aging ◽  
Biological Age ◽  
Chronological Age ◽  
Specific Information ◽  
Biomarkers Of Aging ◽  
Risk Of Cancer

Rapid improvements in cancer survival led to the realization that many modalities used to treat or control cancer may cause accelerated aging in cancer survivors. Clinically, “accelerated aging” phenotypes in cancer survivors include secondary cancers, frailty, chronic organ dysfunction, and cognitive impairment, all of which can impact long-term health and quality of life in cancer survivors. The treatment-induced accelerated aging in cancer survivors could be explained by telomere attrition, cellular senescence, stem cell exhaustion, DNA damage, and epigenetic alterations. Several aging clocks and biomarkers of aging have been proposed to be potentially useful in estimating biological age, which can provide specific information about how old an individual is biologically independent of chronological age. Measuring biological age in cancer survivors may be important for two reasons. First, it can better predict the risk of cancer treatment-related comorbidities than chronological age. Second, biological age may provide additional value in evaluating the effects of treatments and personalizing cancer therapies to maximize efficacy of treatment. A deeper understanding of treatment-induced accelerated aging in individuals with cancer may lead to novel strategies that reduce the accelerated aging and improve the quality of life in cancer survivors.

Potential biomarkers of ageing

2008 ◽  
Vol 389 (3) ◽  
pp. 257-265 ◽  
Author(s):  
Andreas Simm ◽  
Norbert Nass ◽  
Babett Bartling ◽  
Britt Hofmann ◽  
Rolf-Edgar Silber ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Risk Factor ◽  
Stress Resistance ◽  
Stress Protein ◽  
Biological Age ◽  
Protein Glycation ◽  
Chronological Age ◽  
Degenerative Diseases ◽  
Ageing Rate ◽  
Potential Biomarkers

AbstractLife span in individual humans is very heterogeneous. Thus, the ageing rate, measured as the decline of functional capacity and stress resistance, is different in every individual. There have been attempts made to analyse this individual age, the so-called biological age, in comparison to chronological age. Biomarkers of ageing should help to characterise this biological age and, as age is a major risk factor in many degenerative diseases, could be subsequently used to identify individuals at high risk of developing age-associated diseases or disabilities. Markers based on oxidative stress, protein glycation, inflammation, cellular senescence and hormonal deregulation are discussed.

scholarly journals Biological age is a universal marker of aging, stress, and frailty

2019 ◽  
Author(s):  
Timothy V. Pyrkov ◽  
Peter O. Fedichev
Keyword(s):  
Large Scale ◽  
Systematic Investigation ◽  
Biological Age ◽  
Biological Aging ◽  
Physical And Mental Health ◽  
Mortality And Morbidity ◽  
Markers Of Inflammation ◽  
Biomarkers Of Aging ◽  
Age Related

SummaryWe carried out a systematic investigation of supervised learning techniques for biological age modeling. The biological aging acceleration is associated with the remaining health- and life-span. Artificial Deep Neural Networks (DNN) could be used to reduce the error of chronological age predictors, though often at the expense of the ability to distinguish health conditions. Mortality and morbidity hazards models based on survival follow-up data showed the best performance. Alternatively, logistic regression trained to identify chronic diseases was shown to be a good approximation of hazards models when data on survival follow-up times were unavailable. In all models, the biological aging acceleration was associated with disease burden in persons with diagnosed chronic age-related conditions. For healthy individuals, the same quantity was associated with molecular markers of inflammation (such as C-reactive protein), smoking, current physical, and mental health (including sleeping troubles, feeling tired or little interest in doing things). The biological age thus emerged as a universal biomarker of age, frailty and stress for applications involving large scale studies of the effects of longevity drugs on risks of diseases and quality of life.To be published as Chapter 2 in “Biomarkers of aging”, ed. A. Moskalev, Springer, 2019.

Epigenetic Age and the Risk of Incident Atrial Fibrillation

Circulation ◽  
2021 ◽  
Author(s):  
Jason D. Roberts ◽  
Eric Vittinghoff ◽  
Ake T. Lu ◽  
Alvaro Alonso ◽  
Biqi Wang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Mendelian Randomization ◽  
Meta Analysis ◽  
Population Based ◽  
Chronological Age ◽  
Biological Aging ◽  
Cox Models ◽  
Epigenetic Age ◽  
Underlying Mechanisms ◽  
Pai 1

Background: The most prominent risk factor for atrial fibrillation (AF) is chronological age, however underlying mechanisms are unexplained. Algorithms using epigenetic modifications to the human genome effectively predict chronological age. Chronological and epigenetic predicted ages may diverge, a phenomenon termed epigenetic age acceleration (EAA), which may reflect accelerated biological aging. We sought to evaluate for associations between epigenetic age measures and incident AF. Methods: Measures for 4 epigenetic clocks (Horvath, Hannum, DNAm PhenoAge, and DNAm GrimAge) and an epigenetic predictor of PAI-1 levels (DNAm PAI-1) were determined for study participants from 3 population-based cohort studies. Cox models evaluated for associations with incident AF and results were combined via random-effects meta-analysis. Two-sample summary-level Mendelian randomization analyses evaluated for associations between genetic instruments of the EAA measures and AF. Results: Among 5,600 individuals (mean age: 65.5 years; 60.1% female; 50.7% black), there were 905 incident AF cases during a mean follow-up of 12.9 years. Unadjusted analyses revealed all 4 epigenetic clocks and the DNAm PAI-1 predictor were associated with statistically significant higher hazards of incident AF, though the magnitudes of their point estimates were smaller relative to the associations observed for chronological age. The pooled EAA estimates for each epigenetic measure, with the exception of Horvath EAA, were associated with incident AF in models adjusted for chronological age, race, sex, and smoking variables. Following multivariable adjustment for additional known AF risk factors that could also potentially function as mediators, pooled EAA measures for 2 clocks remained statistically significant. Five year increases in EAA measures for DNAm GrimAge and DNAm PhenoAge were associated with 19% (adjusted hazard ratio [HR]: 1.19; 95% confidence intervals [CI]: 1.09-1.31; p<0.01) and 15% (adjusted HR: 1.15; 95% CI: 1.05-1.25; p<0.01) higher hazards of incident AF, respectively. Mendelian randomization analyses for the 5 EAA measures did not reveal statistically significant associations with AF. Conclusions: Our study identified adjusted associations between EAA measures and incident AF, suggesting biological aging plays an important role independent of chronological age, though a potential underlying causal relationship remains unclear. These aging processes may be modifiable and not constrained by the immutable factor of time.

scholarly journals Association of Facial Aging with DNA Methylation and Epigenetic Age Predictions

2018 ◽  
Author(s):  
Riccardo E Marioni ◽  
Daniel W Belsky ◽  
Ian J Deary ◽  
Wolfgang Wagner
Keyword(s):  
Dna Methylation ◽  
Healthy Aging ◽  
Later Life ◽  
Biological Age ◽  
Chronological Age ◽  
Biological Aging ◽  
Facial Aging ◽  
Epigenetic Age ◽  
All Cause Mortality ◽  
Facial Images

AbstractEvaluation of biological age, as opposed to chronological age, is of high relevance for interventions to increase healthy aging. Highly reproducible age-associated DNA methylation (DNAm) changes can be integrated into algorithms for epigenetic age predictions. These predictors have mostly been trained to correlate with chronological age, but they are also indicative for biological aging. For example accelerated epigenetic age of blood is associated with higher risk of all-cause mortality in later life. The perceived age of facial images (face-age) is also associated with all-cause mortality and other aging-associated traits. In this study, we therefore tested the hypothesis that an epigenetic predictor for biological age might be trained on face-age as surrogate for biological age, rather than on chronological age. Our data demonstrate that facial aging and DNAm changes in blood provide two independent measures for biological aging.

scholarly journals Navigating Black Aging: The Biological Consequences of Stress and Depression

2021 ◽  
Author(s):  
Sarah N Forrester ◽  
Keith E Whitfield ◽  
Catarina I Kiefe ◽  
Roland J Thorpe
Keyword(s):  
At Risk ◽  
Depressive Symptoms ◽  
Social Inequality ◽  
Racial Discrimination ◽  
Accelerated Aging ◽  
The Body ◽  
Biological Age ◽  
Biological Aging ◽  
Long Term Effects ◽  
Physiological Dysregulation

Abstract Objectives Black persons in the US are more likely to suffer from social inequality. Chronic stress caused by social inequality and racial discrimination results in weathering of the body that causes physiological dysregulation and biological age being higher than chronological age (accelerated aging). Depression has been linked to both racial discrimination and accelerated aging and accelerated aging has been demonstrated to be higher in Black than White persons, on average. However, we know little about accelerated aging across the life course in Black Americans. Methods We used mixed effects growth models to measure biological age acceleration, measured with cardiometabolic markers, over a 20-year period in Black participants of the Coronary Artery Risk Development in Young Adults Study (CARDIA) who were aged 27 - 42 years at analytic baseline. We included an interaction between depressive symptoms and time to determine whether risk of depression was associated with a faster rate of biological aging. Results We found that the rate of biological aging increased over a 20-year span and that those at risk for depression had a faster rate of biological aging than those not at risk. We also found that various social factors were associated with biological age acceleration over time. Discussion Given the known association between perceived racial discrimination and depressive symptoms, we provide a novel instance of the long-term effects of social inequality. Specifically, biological age acceleration, a marker of physiological dysregulation, is associated with time among Black persons and more strongly associated among those with depressive symptoms.

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