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 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 Schizophrenia is characterized by age- and sex-specific effects on epigenetic aging

2019 ◽  
Author(s):  
Anil P.S. Ori ◽  
Loes M. Olde Loohuis ◽  
Jerry Guintivano ◽  
Eilis Hannon ◽  
Emma Dempster ◽  
...  
Keyword(s):  
Accelerated Aging ◽  
Disease Status ◽  
Risk Scores ◽  
Chronological Age ◽  
Biological Aging ◽  
Disease Trajectory ◽  
Clinical Biomarkers ◽  
Age Related ◽  
Epigenetic Aging ◽  
Increased Risk

AbstractSchizophrenia (SCZ) is a severe mental illness that is associated with an increased prevalence of age-related disability and morbidity compared to the general population. An accelerated aging process has therefore been hypothesized as a component of the SCZ disease trajectory. Here, we investigated differential aging using three DNA methylation (DNAm) clocks (i.e. Hannum, Horvath, Levine) in a multi-cohort SCZ whole blood sample consisting of 1,100 SCZ cases and 1,200 controls. It is known that all three DNAm clocks are highly predictive of chronological age and capture different features of biological aging. We found that blood-based DNAm aging is significantly altered in SCZ with age- and sexspecific effects that differ between clocks and map to distinct chronological age windows. Most notably, the predicted phenotypic age (Levine clock) in female cases, starting at age 36 and beyond, is 3.21 years older compared to matching control subjects (95% CI: 1.92-4.50, P=1.3e-06) explaining 7.7% of the variance in disease status. Female cases with high SCZ polygenic risk scores present the highest age acceleration in this age group with +7.03 years (95% CI: 3.87-10.18, P=1.7E-05). Since increased phenotypic age is associated with increased risk of all-cause mortality, our findings suggests that specific and identifiable patient groups are at increased mortality risk as measured by the Levine clock. These results provide new biological insights into the aging landscape of SCZ with age- and sexspecific effects and warrant further investigations into the potential of DNAm clocks as clinical biomarkers that may help with disease management in schizophrenia.

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 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 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 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.

scholarly journals Role of sleep quality in the acceleration of biological aging and its potential for preventive interaction on air pollution insults: findings from the UK Biobank cohort

2021 ◽  
Author(s):  
Xu Gao ◽  
Ninghao Huang ◽  
Tao Huang
Keyword(s):  
Air Pollution ◽  
Sleep Quality ◽  
Accelerated Aging ◽  
Biological Age ◽  
Risk Scores ◽  
Biological Aging ◽  
Uk Biobank ◽  
Unit Increase ◽  
Sleep Behaviors ◽  
The Uk

Background: Sleep has been associated with aging and relevant health outcomes, but their causal relationship remains inconclusive. Methods: In this study, we investigated the associations of sleep behaviors with biological ages (BAs) among 363,886 middle and elderly-aged adults from UK Biobank. Sleep index (0 [worst]-6 [best]) of each participant was retrieved from six sleep behaviors: snoring, chronotype, daytime sleepiness, sleep duration, insomnia, and difficulties in getting up. Two BAs, the KDM-biological age and PhenoAge, were estimated by corresponding algorithms based on clinical traits, and their discrepancies with chronological age were defined as the age accelerations (AAs). Results: We first observed negative associations between the sleep index and the two AAs, and demonstrated that the change of AAs could be the consequence of sleep quality using Mendelian randomization with genetic risk scores of sleep index and BAs. Particularly, one unit increase in sleep index was associated with 0.105- and 0.125-year decreases in KDM-biological age acceleration and PhenoAge acceleration, respectively. Furthermore, we observed significant independent and joint effects of sleep and air pollution (i.e. PM2.5 and NO2), another key driver of aging, on BAs. Sleep quality also showed modifying effect on the associations of elevated PM2.5 and NO2 levels with accelerated aging. For instance, an interquartile range increase in PM2.5 level was associated with 0.011-, 0.047-, and 0.078-year increase in PhenoAge acceleration among people with high (5-6), medium (3-4), and low (0-2) sleep index, respectively. Conclusions: Our findings elucidate that better sleep quality could lessen accelerated biological aging resulting from exogenous exposures including air pollution.

scholarly journals Diabetes mellitus correlates with increased biological age as indicated by clinical biomarkers

GeroScience ◽  
2021 ◽  
Author(s):  
Nadine Bahour ◽  
Briana Cortez ◽  
Hui Pan ◽  
Hetal Shah ◽  
Alessandro Doria ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Cellular Level ◽  
Biological Age ◽  
Chronological Age ◽  
Mortality Data ◽  
Diabetes Mortality ◽  
Clinical Biomarkers ◽  
Gender And Age

AbstractChronological age (CA) is determined by time of birth, whereas biological age (BA) is based on changes on a cellular level and strongly correlates with morbidity, mortality, and longevity. Type 2 diabetes (T2D) associates with increased morbidity and mortality; thus, we hypothesized that BA would be increased and calculated it from biomarkers collected at routine clinical visits. Deidentified data was obtained from three cohorts of patients (20–80 years old)—T2D, type 1 diabetes (T1D), and prediabetes—and compared to gender- and age-matched non-diabetics. Eight clinical biomarkers that correlated with CA in people without diabetes were used to calculate BA using the Klemera and Doubal method 1 (KDM1) and multiple linear regression (MLR). The phenotypic age (PhAge) formula was used with its predetermined biomarkers. BA of people with T2D was, on average, 12.02 years higher than people without diabetes (p < 0.0001), while BA in T1D was 16.32 years higher (p < 0.0001). Results were corroborated using MLR and PhAge. The biomarkers with the strongest correlation to increased BA in T2D using KDM were A1c (R2 = 0.23, p < 0.0001) and systolic blood pressure (R2 = 0.21, p < 0.0001). BMI had a positive correlation to BA in non-diabetes subjects but disappeared in those with diabetes. Mortality data using the ACCORD trial was used to validate our results and showed a significant correlation between higher BA and decreased survival. In conclusion, BA is increased in people with diabetes, irrespective of pathophysiology, and to a lesser extent in prediabetes.

scholarly journals Biological Age Prediction From Wearable Device Movement Data Identifies Nutritional and Pharmacological Interventions for Healthy Aging

2021 ◽  
Vol 2 ◽  
Author(s):  
Rebecca L. McIntyre ◽  
Mizanur Rahman ◽  
Siva A. Vanapalli ◽  
Riekelt H. Houtkooper ◽  
Georges E. Janssens
Keyword(s):  
Healthy Aging ◽  
Machine Learning Algorithms ◽  
Biological Age ◽  
Wearable Device ◽  
Biological Aging ◽  
Accelerometer Data ◽  
Pharmacological Interventions ◽  
C Elegans ◽  
Movement Data ◽  
Age Related

Intervening in aging processes is hypothesized to extend healthy years of life and treat age-related disease, thereby providing great benefit to society. However, the ability to measure the biological aging process in individuals, which is necessary to test for efficacy of these interventions, remains largely inaccessible to the general public. Here we used NHANES physical activity accelerometer data from a wearable device and machine-learning algorithms to derive biological age predictions for individuals based on their movement patterns. We found that accelerated biological aging from our “MoveAge” predictor is associated with higher all-cause mortality. We further searched for nutritional or pharmacological compounds that associate with decelerated aging according to our model. A number of nutritional components peak in their association to decelerated aging later in life, including fiber, magnesium, and vitamin E. We additionally identified one FDA-approved drug associated with decelerated biological aging: the alpha-blocker doxazosin. We show that doxazosin extends healthspan and lifespan in C. elegans. Our work demonstrates how a biological aging score based on relative mobility can be accessible to the wider public and can potentially be used to identify and determine efficacy of geroprotective interventions.

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