A catalogue of omics biological ageing clocks reveals substantial commonality and associations with disease risk

Macdonald-Dunlop, Erin; Taba, Nele; Klarić, Lucija; Frkatović, Azra; Walker, Rosie M.; Hayward, Caroline; Esko, Tõnu; Haley, Chris; Fischer, Krista; Wilson, James F.; Joshi, Peter K.

doi:10.18632/aging.203847

Cited by 26 publications

(33 citation statements)

References 74 publications

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“…It is hypothesized that biological age, influenced by different molecular hallmarks such as telomere shortening, genomic instability and cellular senescence, gives rise to age-related disease risk. Therefore, biological age is a much more potent prognostic tool for health outcomes than chronological age, and more importantly, it can be reversed ( Jurić et al, 2020 ; Greto et al, 2021 ; Macdonald-Dunlop et al, 2022 ). Since this notion has been proposed, different predictors of biological age, termed aging clocks, were constructed using various methods ( Horvath et al, 2020 ; Macdonald-Dunlop et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, biological age is a much more potent prognostic tool for health outcomes than chronological age, and more importantly, it can be reversed ( Jurić et al, 2020 ; Greto et al, 2021 ; Macdonald-Dunlop et al, 2022 ). Since this notion has been proposed, different predictors of biological age, termed aging clocks, were constructed using various methods ( Horvath et al, 2020 ; Macdonald-Dunlop et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Heritability of the glycan clock of biological age

Mijakovac

Frkatović²,

Ivok³

et al. 2022

Front. Cell Dev. Biol.

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Immunoglobulin G is posttranslationally modified by the addition of complex N-glycans affecting its function and mediating inflammation at multiple levels. IgG glycome composition changes with age and health in a predictive pattern, presumably due to inflammaging. As a result, a novel biological aging biomarker, glycan clock of age, was developed. Glycan clock of age is the first of biological aging clocks for which multiple studies showed a possibility of clock reversal even with simple lifestyle interventions. However, none of the previous studies determined to which extent the glycan clock can be turned, and how much is fixed by genetic predisposition. To determine the contribution of genetic and environmental factors to phenotypic variation of the glycan clock, we performed heritability analysis on two TwinsUK female cohorts. IgG glycans from monozygotic and dizygotic twin pairs were analyzed by UHPLC and glycan age was calculated using the glycan clock. In order to determine additive genetic, shared, and unique environmental contributions, a classical twin design was applied. Heritability of the glycan clock was calculated for participants of one cross-sectional and one longitudinal cohort with three time points to assess the reliability of measurements. Heritability estimate for the glycan clock was 39% on average, suggesting a moderate contribution of additive genetic factors (A) to glycan clock variation. Remarkably, heritability estimates remained approximately the same in all time points of the longitudinal study, even though IgG glycome composition changed substantially. Most environmental contributions came from shared environmental factors (C), with unique environmental factors (E) having a minor role. Interestingly, heritability estimates nearly doubled, to an average of 71%, when we included age as a covariant. This intervention also inflated the estimates of unique environmental factors contributing to glycan clock variation. A complex interplay between genetic and environmental factors defines alternative IgG glycosylation during aging and, consequently, dictates the glycan clock’s ticking. Apparently, environmental factors (including lifestyle choices) have a strong impact on the biological age measured with the glycan clock, which additionally clarifies why this aging clock is one of the most potent biomarkers of biological aging.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heritability of the glycan clock of biological age

Mijakovac

Frkatović²,

Ivok³

et al. 2022

Front. Cell Dev. Biol.

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“…Recent work by Macdonald-Dunlop et al performed a thorough comparison of 15 different omics-based clocks. The authors found that, while some clocks correlated well with specific disease risk factors (e.g., systolic blood pressure and cortisol), others were more prognostic of age-related disease incidence and appeared to better reflect the generalized effects of aging (Macdonald-Dunlop et al, 2022). Moreover, there is an interesting relationship between accuracy and usability.…”

Section: Outstanding Questions and Limitations In The Fieldmentioning

confidence: 99%

Human age reversal: Fact or fiction?

et al. 2022

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Although chronological age correlates with various age‐related diseases and conditions, it does not adequately reflect an individual's functional capacity, well‐being, or mortality risk. In contrast, biological age provides information about overall health and indicates how rapidly or slowly a person is aging. Estimates of biological age are thought to be provided by aging clocks, which are computational models (e.g., elastic net) that use a set of inputs (e.g., DNA methylation sites) to make a prediction. In the past decade, aging clock studies have shown that several age‐related diseases, social variables, and mental health conditions associate with an increase in predicted biological age relative to chronological age. This phenomenon of age acceleration is linked to a higher risk of premature mortality. More recent research has demonstrated that predicted biological age is sensitive to specific interventions. Human trials have reported that caloric restriction, a plant‐based diet, lifestyle changes involving exercise, a drug regime including metformin, and vitamin D3 supplementation are all capable of slowing down or reversing an aging clock. Non‐interventional studies have connected high‐quality sleep, physical activity, a healthy diet, and other factors to age deceleration. Specific molecules have been associated with the reduction or reversal of predicted biological age, such as the antihypertensive drug doxazosin or the metabolite alpha‐ketoglutarate. Although rigorous clinical trials are needed to validate these initial findings, existing data suggest that aging clocks are malleable in humans. Additional research is warranted to better understand these computational models and the clinical significance of lowering or reversing their outputs.

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“…The benefits of reducing the number of features down to single digit or low double digit sites also improves accuracy. Macdonald-Dunlop and colleagues showed that for -omics based aging clocks, those with lower model complexity (built from fewer principal components) had greater accuracy [ 18 ]. While fewer CpG sites may leave the lone few features more vulnerable to confounding effects, the clocks of several hundred CpGs suffer from too much noise rather than signal for age predictions.…”

Section: Introductionmentioning

confidence: 99%

Novel feature selection methods for construction of accurate epigenetic clocks

Mueller

English

et al. 2022

PLoS Comput Biol

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Epigenetic clocks allow us to accurately predict the age and future health of individuals based on the methylation status of specific CpG sites in the genome and are a powerful tool to measure the effectiveness of longevity interventions. There is a growing need for methods to efficiently construct epigenetic clocks. The most common approach is to create clocks using elastic net regression modelling of all measured CpG sites, without first identifying specific features or CpGs of interest. The addition of feature selection approaches provides the opportunity to optimise the identification of predictive CpG sites. Here, we apply novel feature selection methods and combinatorial approaches including newly adapted neural network, genetic algorithms, and ‘chained’ combinations. Human whole blood methylation data of ~470,000 CpGs was used to develop clocks that predict age with R2 correlation scores of greater than 0.73, the most predictive of which uses 35 CpG sites for a R2 correlation score of 0.87. The five most frequent sites across all clocks were modelled to build a clock with a R2 correlation score of 0.83. These two clocks are validated on two external datasets where they maintain excellent predictive accuracy. When compared with three published epigenetic clocks (Hannum, Horvath, Weidner) also applied to these validation datasets, our clocks outperformed all three models. We identified gene regulatory regions associated with selected CpG as possible targets for future aging studies. Thus, our feature selection algorithms build accurate, generalizable clocks with a low number of CpG sites, providing important tools for the field.

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A catalogue of omics biological ageing clocks reveals substantial commonality and associations with disease risk

Cited by 26 publications

References 74 publications

Heritability of the glycan clock of biological age

Heritability of the glycan clock of biological age

Human age reversal: Fact or fiction?

Novel feature selection methods for construction of accurate epigenetic clocks

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