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 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 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 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 Cardiovascular-related deaths at the beginning of the COVID-19 outbreak: a prospective analysis based on the UK Biobank

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046931
Author(s):  
Junren Wang ◽  
Jianwei Zhu ◽  
Huazhen Yang ◽  
Yao Hu ◽  
Yajing Sun ◽  
...  
Keyword(s):  
Primary Diagnosis ◽  
Secondary Outcome ◽  
Hospital Inpatient ◽  
Reference Period ◽  
Stress Reactions ◽  
Uk Biobank ◽  
The Uk ◽  
The Impact ◽  
Distinct Increase ◽  
Related Mortality

ObjectiveTo assess the impact of the COVID-19 outbreak on cardiovascular disease (CVD) related mortality and hospitalisation.DesignCommunity-based prospective cohort study.SettingThe UK Biobank.Participants421 372 UK Biobank participants who were registered in England and alive as of 1 January 2020.Primary and secondary outcome measuresThe primary outcome of interest was CVD-related death, which was defined as death with CVD as a cause in the death register. We retrieved information on hospitalisations with CVD as the primary diagnosis from the UK Biobank hospital inpatient data. The study period was 1 January 2020 to June 30 2020, and we used the same calendar period of the three preceding years as the reference period. In order to control for seasonal variations and ageing of the study population, standardised mortality/incidence ratios (SMRs/SIRs) with 95% CIs were used to estimate the relative risk of CVD outcomes during the study period, compared with the reference period.ResultsWe observed a distinct increase in CVD-related deaths in March and April 2020, compared with the corresponding months of the three preceding years. The observed number of CVD-related deaths (n=218) was almost double in April, compared with the expected number (n=120) (SMR=1.82, 95% CI 1.58 to 2.07). In addition, we observed a significant decline in CVD-related hospitalisations from March onwards, with the lowest SIR observed in April (0.45, 95% CI 0.41 to 0.49).ConclusionsThere was a distinct increase in the number of CVD-related deaths in the UK Biobank population at the beginning of the COVID-19 outbreak. The shortage of medical resources for hospital care and stress reactions to the pandemic might have partially contributed to the excess CVD-related mortality, underscoring the need of sufficient healthcare resources and improved instructions to the public about seeking healthcare in a timely way.

scholarly journals Lifetime depression and age-related changes in body composition, cardiovascular function, grip strength and lung function: sex-specific analyses in the UK Biobank

BJPsych Open ◽  
2021 ◽  
Vol 7 (S1) ◽  
pp. S44-S44
Author(s):  
Julian Mutz ◽  
Cathryn M Lewis
Keyword(s):  
Body Composition ◽  
Lung Function ◽  
Cardiovascular Function ◽  
Healthy Controls ◽  
Physiological Measures ◽  
Uk Biobank ◽  
Mineral Density ◽  
Age Related ◽  
The Uk ◽  
Age Related Changes

AimsIndividuals with mental disorders, on average, die prematurely, have higher levels of physical comorbidities and may experience accelerated ageing. In individuals with lifetime depression and healthy controls, we examined associations between age and multiple physiological measures.MethodThe UK Biobank study recruited >500,000 participants, aged 37–73 years, between 2006–2010. Generalised additive models were used to examine associations between age and grip strength, cardiovascular function, body composition, lung function and bone mineral density. Analyses were conducted separately in males and females with depression compared to healthy controls.ResultAnalytical samples included up to 342,393 adults (mean age = 55.87 years; 52.61% females). We found statistically significant differences between individuals with depression and healthy controls for most physiological measures, with standardised mean differences between -0.145 and 0.156. There was some evidence that age-related changes in body composition, cardiovascular function, lung function and heel bone mineral density followed different trajectories in individuals with depression. These differences did not uniformly narrow or widen with age. For example, BMI in female cases was 1.1 kg/m2 higher at age 40 and this difference narrowed to 0.4 kg/m2 at age 70. In males, systolic blood pressure was 1 mmHg lower in cases at age 45 and this difference widened to 2.5 mmHg at age 65.ConclusionIndividuals with depression differed from healthy controls across a broad range of physiological measures. Differences in ageing trajectories differed by sex and were not uniform across physiological measures, with evidence of both age-related narrowing and widening of case-control differences.

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.

Lifestyle factors and genetic variants on two biological age measures: evidence from 94,443 Taiwan Biobank participants

2021 ◽  
Author(s):  
Wan-Yu Lin
Keyword(s):  
Genetic Variants ◽  
Genome Wide Association Study ◽  
Lifestyle Factors ◽  
Biological Age ◽  
Biological Aging ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide ◽  
A Genome ◽  
The Uk

Abstract Background Biological age (BA) can be estimated by phenotypes and is useful for predicting lifespan and healthspan. Levine et al. proposed a PhenoAge and a BioAge to measure BA. Although there have been studies investigating the genetic predisposition to BA acceleration in Europeans, little has been known regarding this topic in Asians. Methods I here estimated PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 94,443 Taiwan Biobank (TWB) participants, wherein 25,460 TWB1 subjects formed a discovery cohort and 68,983 TWB2 individuals constructed a replication cohort. Lifestyle factors and genetic variants associated with PhenoAgeAccel and BioAgeAccel were investigated through regression analysis and a genome-wide association study (GWAS). Results A unit (kg/m 2) increase of BMI was associated with a 0.177-year PhenoAgeAccel (95% C.I. = 0.163~0.191, p = 6.0×) and 0.171-year BioAgeAccel (95% C.I. = 0.165~0.177, p = 0). Smokers on average had a 1.134-year PhenoAgeAccel (95% C.I. = 0.966~1.303, p = 1.3×) compared with non-smokers. Drinkers on average had a 0.640-year PhenoAgeAccel (95% C.I. = 0.433~0.847, p = 1.3×) and 0.193-year BioAgeAccel (95% C.I. = 0.107~0.279, p = 1.1×) relative to non-drinkers. A total of 11 and 4 single-nucleotide polymorphisms (SNPs) were associated with PhenoAgeAccel and BioAgeAccel (p<5× in both TWB1 and TWB2), respectively. Conclusions A PhenoAgeAccel-associated SNP (rs1260326 in GCKR) and two BioAgeAccel-associated SNPs (rs7412 in APOE; rs16998073 near FGF5) were consistent with the finding from the UK Biobank. The lifestyle analysis shows that prevention from obesity, cigarette smoking, and alcohol consumption is associated with a slower rate of biological aging.

Associations of ophthalmic and systemic conditions with incident dementia in the UK Biobank

2021 ◽  
pp. bjophthalmol-2021-319508
Author(s):  
Xianwen Shang ◽  
Zhuoting Zhu ◽  
Yu Huang ◽  
Xueli Zhang ◽  
Wei Wang ◽  
...  
Keyword(s):  
Heart Disease ◽  
Age Related Macular Degeneration ◽  
Hospital Inpatient ◽  
Uk Biobank ◽  
Age Related ◽  
Incident Dementia ◽  
Increased Risk ◽  
The Uk ◽  
Systemic Condition

AimsTo examine independent and interactive associations of ophthalmic and systemic conditions with incident dementia.MethodsOur analysis included 12 364 adults aged 55–73 years from the UK Biobank cohort. Participants were assessed between 2006 and 2010 at baseline and were followed up until the early of 2021. Incident dementia was ascertained using hospital inpatient, death records and self-reported data.ResultsOver 1 263 513 person-years of follow-up, 2304 cases of incident dementia were documented. The multivariable-adjusted HRs (95% CI) for dementia associated with age-related macular degeneration (AMD), cataract, diabetes-related eye disease (DRED) and glaucoma at baseline were 1.26 (1.05 to 1.52), 1.11 (1.00 to 1.24), 1.61 (1.30 to 2.00) and (1.07 (0.92 to 1.25), respectively. Diabetes, heart disease, stroke and depression at baseline were all associated with an increased risk of dementia. Of the combination of AMD and a systemic condition, AMD-diabetes was associated with the highest risk for incident dementia (HR (95% CI): 2.73 (1.79 to 4.17)). Individuals with cataract and a systemic condition were 1.19–2.29 times more likely to develop dementia compared with those without cataract and systemic conditions. The corresponding number for DRED and a systemic condition was 1.50–3.24. Diabetes, hypertension, heart disease, depression and stroke newly identified during follow-up mediated the association between cataract and incident dementia as well as the association between DRED and incident dementia.ConclusionsAMD, cataract and DRED but not glaucoma are associated with an increased risk of dementia. Individuals with both ophthalmic and systemic conditions are at higher risk of dementia compared with those with an ophthalmic or systemic condition only.

scholarly journals Improving educator’s knowledge and confidence to teach infection prevention and antimicrobial resistance

2020 ◽  
pp. 001789692094959
Author(s):  
Catherine Hayes ◽  
Charlotte Eley ◽  
Carla Brown ◽  
Rowshonara Syeda ◽  
Neville Q Verlander ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Infection Prevention ◽  
Action Plan ◽  
Service Evaluation ◽  
Infection Prevention And Control ◽  
Post Intervention ◽  
Logistic Regression Models ◽  
Train The Trainer ◽  
The Uk

Objective: e-Bug is a teaching resource that addresses the UK 5-year National Action Plan on antimicrobial resistance (AMR) that pledges to work with educators and local authorities to ensure young people understand infection prevention and control (IPC) and AMR. This study aimed to evaluate the effectiveness and acceptability of the e-Bug face-to-face train-the-trainer intervention with school and community educators. Design: Service evaluation of an educational intervention via surveys. Setting: Workshops were organised by Public Health England (PHE) and collaborators in seven regions of the UK during 2018–2019. Method: Pre- and post-intervention surveys measured satisfaction with training, knowledge of IPC and AMR, and confidence to teach others. Statistical analyses included multilevel and ordinal logistic regression models to measure change in educator knowledge and confidence. Results: In all, 262 educators participated: primary (46%), secondary (17%), college (2%), healthcare (29%) and community (7%). Educators had high pre-intervention knowledge of topics, with significant improvement ( p < .05) in confidence to teach all topics and some significant IPC knowledge improvement, post-intervention. There was strong evidence for a difference in confidence change between educator types, with primary educators improving the most. Ninety-five percent of educators rated the train-the-trainer workshop positively, valued the interactive workshops and felt confident to use the resources. Conclusion: Confident and knowledgeable educators, achieved via e-Bug train-the-trainer workshops, will enhance education of IPC and AMR topics in schools and communities, and therefore support the UK 5-year AMR action plan. The intervention will be monitored with long-term follow-up surveys to explore how training has been disseminated and to evaluate long-term benefits.

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