Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging

Lehallier, Benoit; Shokhirev, Maxim N.; Wyss‐Coray, Tony; Johnson, Adiv A.

doi:10.1111/acel.13256

Cited by 73 publications

(78 citation statements)

References 129 publications

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“…Unsurprisingly, DNAme-based clocks built in ORCADES were able to estimate age in both Scottish (Generation Scotland) and Estonian Biobanks (EBB), as the Hannum and Horvath epigenetic clocks have been used successfully in numerous populations. We showed for the first time that clocks built from Olink PEA-based proteomics replicate (in EBB and Croatia-Vis), while clocks built using the SOMAlogic 20 proteomics platform have been shown to replicate across populations previously. Our UPLC IgG glycomics clock also replicated in an independent population, mirroring the applicability of published GlycanAge measures 17 .…”

Section: Discussionmentioning

confidence: 89%

“…The extremely high correlations with chronAge reported, such as the r = 0.97 of the Mega-omics OCA, highlight an issue that has been discussed in prior work: that if enough biomarkers were included in the model it would be possible to perfectly estimate chronAge and, by definition, fail to detect (distinct) BA. Lehallier et al 20 showed that correlation between OCA and chronAge increases with the number of proteins included in the model. Further, it is possible to explain 100% of the variance in chronAge using DNAme data in large samples 28 .…”

Section: Discussionmentioning

confidence: 99%

“…The notion of BA raises fundamental questions. Is there one BA for a person, or a set of BAs, perhaps relating to different bodily systems 20,27 . Are measured (chronAge trained) OCAs tracking a single BA, with differences arising due to their focus and accuracy, or are they tracking different underlying BAs?…”

Section: Introductionmentioning

confidence: 99%

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

Macdonald-Dunlop

Taba

Klarić

et al. 2021

Preprint

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Biological age (BA), a measure of functional capacity and prognostic of health outcomes that discriminates between individuals of the same chronological age (chronAge), has been estimated using a variety of biomarkers. Previous comparative studies have mainly used epigenetic models (clocks), we use ~1000 participants to create eleven omics ageing clocks, with correlations of 0.45-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 94% of the variance in chronAge being shared among clocks. The difference between BA and chronAge – omics clock age acceleration (OCAA) – often associates with health measures. One year’s OCAA typically has the same effect on risk factors/10-year disease incidence as 0.46/0.45 years of chronAge. Epigenetic and IgG glycomics clocks appeared to track generalised ageing while others capture specific risks. We conclude BA is measurable and prognostic and that future work should prioritise health outcomes over chronAge.

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Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

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

Macdonald-Dunlop

Taba

Klarić

et al. 2021

Preprint

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“…Overexpressing Pck1 in mice extends life span and enhances exercise capacity (Hakimi et al, 2007). We recently found that many proteins which change their expression level with age in plasma can accurately predict human age and are also direct regulators of life span and/or age-related disease (Lehallier et al, 2020). Thus, this attribute appears to be shared between genes and proteins.…”

Section: Discussionmentioning

confidence: 99%

Modeling the human aging transcriptome across tissues, health status, and sex

Shokhirev

Johnson²

2020

Aging Cell

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Aging in humans is an incredibly complex biological process that leads to increased susceptibility to various diseases. Understanding which genes are associated with healthy aging can provide valuable insights into aging mechanisms and possible avenues for therapeutics to prolong healthy life. However, modeling this complex biological process requires an enormous collection of high‐quality data along with cutting‐edge computational methods. Here, we have compiled a large meta‐analysis of gene expression data from RNA‐Seq experiments available from the Sequence Read Archive. We began by reprocessing more than 6000 raw samples—including mapping, filtering, normalization, and batch correction—to generate 3060 high‐quality samples spanning a large age range and multiple different tissues. We then used standard differential expression analyses and machine learning approaches to model and predict aging across the dataset, achieving an R2 value of 0.96 and a root‐mean‐square error of 3.22 years. These models allow us to explore aging across health status, sex, and tissue and provide novel insights into possible aging processes. We also explore how preprocessing parameters affect predictions and highlight the reproducibility limits of these machine learning models. Finally, we develop an online tool for predicting the ages of human transcriptomic samples given raw gene expression counts. Together, this study provides valuable resources and insights into the transcriptomics of human aging.

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“…Tanaka et al characterized the plasma proteomic signature in healthy aging humans and found that GDF15 had the strongest positive association with age (82). GDF15 was found to be one of the proteins in the SASP repertoire in many other studies, indicating the possibility for GDF15 to modulate and/or predict senescence (41,(83)(84)(85)(86)(87)(88). Over expression of human GDF15 in female mice extended lifespan in a study by Wang et al (89).…”

Section: Role Of Gdf15 In Senescencementioning

confidence: 99%

Role of Growth Differentiation Factor 15 in Lung Disease and Senescence: Potential Role Across the Lifespan

et al. 2020

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Growth Differentiation Factor 15 (GDF15) is a divergent member of transforming growth factor-beta (TGF-β) superfamily and is ubiquitously expressed, under normal physiological conditions. GDF15 expression increases during many pathological states and serves a marker of cellular stress. GDF15 has multiple and even paradoxical roles within a pathological condition, as its effects can be dose- and time-dependent and vary based on the targeted tissues and downstream pathways. GDF15 has emerged as one of the most recognized proteins as part of the senescence associated secretory phenotype. Cellular senescence plays a major role in many lung diseases across the life-span from bronchopulmonary dysplasia in the premature neonate to COPD and idiopathic pulmonary fibrosis in aged adults. GDF15 levels have been reported to be as a useful biomarker in chronic obstructive pulmonary disease, lung fibrosis and pulmonary arterial hypertension and predict disease severity, decline in lung function and mortality. Glial-cell-line-derived neurotrophic factor family receptor alpha-like (GFRAL) in the brain stem has been identified as the only validated GDF15 receptor and mediates GDF15-mediated anorexia and wasting. The mechanisms and pathways by which GDF15 exerts its pulmonary effects are being elucidated. GDF15 may also have an impact on the lung based on the changes in circulating levels or through the central action of GDF15 activating peripheral metabolic changes. This review focuses on the role of GDF15 in different lung diseases across the lifespan and its role in cellular senescence.

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Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 73 publications

References 129 publications

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

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

Modeling the human aging transcriptome across tissues, health status, and sex

Role of Growth Differentiation Factor 15 in Lung Disease and Senescence: Potential Role Across the Lifespan

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