» Articles » PMID: 35073279

A Catalogue of Omics Biological Ageing Clocks Reveals Substantial Commonality and Associations with Disease Risk

Overview
Specialty Geriatrics
Date 2022 Jan 24
PMID 35073279
Authors
Affiliations
Soon will be listed here.
Abstract

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 compare fifteen omics ageing clocks, with correlations of 0.21-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 95% 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 0.09/0.25 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.

Citing Articles

Deep learning and generative artificial intelligence in aging research and healthy longevity medicine.

Wilczok D Aging (Albany NY). 2025; 17(1):251-275.

PMID: 39836094 PMC: 11810058. DOI: 10.18632/aging.206190.


Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites.

Faquih T, Faquih T, van Hylckama Vlieg A, Surendran P, Butterworth A, Li-Gao R J Gerontol A Biol Sci Med Sci. 2025; 80(3).

PMID: 39821408 PMC: 11809259. DOI: 10.1093/gerona/glae280.


Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning algorithms.

Mutz J, Iniesta R, Lewis C Sci Adv. 2024; 10(51):eadp3743.

PMID: 39693428 PMC: 11654675. DOI: 10.1126/sciadv.adp3743.


Longevity biotechnology: bridging AI, biomarkers, geroscience and clinical applications for healthy longevity.

Lyu Y, Fu Q, Wilczok D, Ying K, King A, Antebi A Aging (Albany NY). 2024; 16(20):12955-12976.

PMID: 39418098 PMC: 11552646. DOI: 10.18632/aging.206135.


The importance of IgG glycosylation-What did we learn after analyzing over 100,000 individuals.

Kristic J, Lauc G Immunol Rev. 2024; 328(1):143-170.

PMID: 39364834 PMC: 11659926. DOI: 10.1111/imr.13407.


References
1.
Jansen R, Han L, Verhoeven J, Aberg K, van den Oord E, Milaneschi Y . An integrative study of five biological clocks in somatic and mental health. Elife. 2021; 10. PMC: 7872513. DOI: 10.7554/eLife.59479. View

2.
Alsaleh H, Haddrill P . Identifying blood-specific age-related DNA methylation markers on the Illumina MethylationEPIC® BeadChip. Forensic Sci Int. 2019; 303:109944. DOI: 10.1016/j.forsciint.2019.109944. View

3.
Levine M, Lu A, Bennett D, Horvath S . Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer's disease related cognitive functioning. Aging (Albany NY). 2015; 7(12):1198-211. PMC: 4712342. DOI: 10.18632/aging.100864. View

4.
Pucic M, Knezevic A, Vidic J, Adamczyk B, Novokmet M, Polasek O . High throughput isolation and glycosylation analysis of IgG-variability and heritability of the IgG glycome in three isolated human populations. Mol Cell Proteomics. 2011; 10(10):M111.010090. PMC: 3205872. DOI: 10.1074/mcp.M111.010090. View

5.
Chen W, Qian W, Wu G, Chen W, Xian B, Chen X . Three-dimensional human facial morphologies as robust aging markers. Cell Res. 2015; 25(5):574-87. PMC: 4423077. DOI: 10.1038/cr.2015.36. View