Genetic associations for two biological age measures point to distinct aging phenotypes

Kuo, Chia‐Ling; Pilling, Luke C.; Liu, Zuyun; Atkins, Janice L.; Levine, Morgan E.

doi:10.1101/2020.07.10.20150797

Cited by 11 publications

(21 citation statements)

References 52 publications

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“…Given that PhenoAge and PhenoAccelAge were estimated with assessments at admission, the impact of pre-admission PhenoAge values and how its associated change may be related to reduced physiological reserve, diminished intrinsic capacity or frailty remains to be further studied [31][32][33]. Lastly, we only used PhenoAge and PhenoAgeAccel to estimate adaptive responses potentially linked to aging; however, it is well known that different biological age estimations may illustrate distinct points of view of the aging process [34]. Prospective studies assessing aging measures before, during and after the infection are necessary to further elucidate the impact of premature aging on the clinical course of COVID-19 patients; additionally, other parameters should be taken into account, such as imaging features, immunophenotyping and histopathological findings.…”

Section: Discussionmentioning

confidence: 99%

Adaptive metabolic and inflammatory responses identified using accelerated aging metrics are linked to adverse outcomes in severe SARS-CoV-2 infection

Márquez‐Salinas

Antonio-Villa

et al. 2020

Preprint

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INTRODUCTION: Chronological age (CA) is a predictor of adverse COVID-19 outcomes; however, CA alone has not shown to be the better predictor of adverse outcomes in COVID-19 as it does not capture individual responses to SARS-CoV-2 infection. Here, we evaluated the influence of aging metrics PhenoAge and PhenoAccelAge on the adaptive responses to SARS-CoV-2 infection in hospitalized patients. METHODS: We assessed cases admitted to a COVID-19 reference center in Mexico City. PhenoAge and PhenoAccelAge were estimated using laboratory values at admission. Cox proportional hazards models were fitted to estimate risk for COVID-19 lethality and adverse outcomes (ICU admission, intubation, or death), and k-means clustering was performed to explore reproducible patterns of adaptive response to SARS-CoV-2 infection using PhenoAge components. RESULTS: We included 1069 subjects of whom 401 presented critical illness and 204 died. PhenoAge was a better predictor of adverse outcomes and lethality compared to CA and SpO2 and its predictive capacity was sustained for all age groups. Patients with responses associated PhenoAccelAge >0 had higher risk of death and critical illness compared to those who had values according to CA (log-rank p<0.001). Using unsupervised clustering we identified four adaptive responses to SARS-CoV-2 infection: 1) Inflammaging associated with CA, 2) adaptive metabolic dysfunction associated with cardio-metabolic comorbidities, 3) adaptive unfavorable hematological response, and 4) response associated with favorable outcomes. CONCLUSIONS: Adaptive responses related to accelerated aging metrics are linked to adverse COVID-19 outcomes and have unique and distinguishable features. PhenoAge is a better predictor of adverse outcomes compared to CA.

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Section: Discussionmentioning

confidence: 99%

Adaptive metabolic and inflammatory responses identified using accelerated aging metrics are linked to adverse outcomes in severe SARS-CoV-2 infection

Márquez‐Salinas

Antonio-Villa

et al. 2020

Preprint

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“…A total of 583 single‐nucleotide polymorphisms (SNPs) based on the up‐to‐date largest GWAS of the six sleep behaviors in ~1.3 million participants from 23andMe and UK Biobank cohorts were selected for the GRS of six sleep behaviors and sleep index (reported p ‐value < 5 × 10 −8 ) (Jansen et al, 2019). For the GRS of AAs, we used the latest and the only GWAS on both BAs (Kuo et al, 2021), which, respectively, reported 16 and 29 SNPs that were robustly associated with KDM‐biological age and PhenoAge (reported p ‐value <5 × 10 −8 ). Because the SNPs were evaluated in pruned genetic data in previous studies, no further loci pruning was conducted in our study.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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Sleep has been associated with aging and relevant health outcomes, but the causal relationship remains inconclusive. In this study, we investigated the associations of sleep behaviors with biological ages (BAs) among 363,886 middle and elderly adults from UK Biobank. Sleep index (0 [worst]–6 [best]) of each participant was retrieved from the following 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 residual discrepancies with chronological age were defined as the age accelerations (AAs). 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, a one‐unit increase in sleep index was associated with 0.104‐ and 0.119‐year decreases in KDM‐biological AA and PhenoAge acceleration, respectively. Air pollution is another key driver of aging. We further observed significant independent and joint effects of sleep and air pollution (PM2.5 and NO2) on AAs. Sleep quality also showed a modifying effect on the associations of elevated PM2.5 and NO2 levels with accelerated AAs. For instance, an interquartile range increase in PM2.5 level was associated with 0.009‐, 0.044‐, and 0.074‐year increase in PhenoAge acceleration among people with high (5–6), medium (3–4), and low (0–2) sleep index, respectively. Our findings elucidate that better sleep quality could lessen accelerated biological aging resulting from air pollution.

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“…The association with COVID-19 severity that these diseases share with PhenoAge may reflect the role of underlying immune-related pathways. In our recent genome-wide association study (GWAS) on accelerated PhenoAgeAccel, we observed enrichment for biological processes involved in immune system, cell function, and carbohydrate homeostasis [ 17 ]. A methylation clock (DNAmPhenoAge) trained using PhenoAge as a surrogate for biological age, instead of chronological age, has been shown to be associated with activation of pro-inflammatory, interferon, DNAm damage repair, transcriptional/translational signaling, and various markers of immunosenescence: a decline of naïve T cells and shortened leukocyte telomere length [ 2 ].…”

Section: Discussionmentioning

confidence: 99%

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

Kuo

Pilling

Atkins

et al. 2020

Preprint

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With 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. 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) 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). 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=3.2x10-6) 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.

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Genetic associations for two biological age measures point to distinct aging phenotypes

Cited by 11 publications

References 52 publications

Adaptive metabolic and inflammatory responses identified using accelerated aging metrics are linked to adverse outcomes in severe SARS-CoV-2 infection

Adaptive metabolic and inflammatory responses identified using accelerated aging metrics are linked to adverse outcomes in severe SARS-CoV-2 infection

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

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

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