Biomarkers of Aging:
– Biomarkers predict functional capacity better than chronological age.
– Biomarkers give the true biological age.
– Validated biomarkers allow testing interventions to extend lifespan.
– Maximum lifespan is impractical for long-lived species like humans.
– Biomarkers should assay biological aging processes, cause minimal trauma, and be reproducibly measurable.
– Ageotype as an assemblage of biomarker data for an organism.
– Efforts continue to find and validate biomarkers of aging.
– CD4 and CD8 memory T cells can predict lifespan in mice.
– Epigenetic clock can accurately predict human chronological age.
Histone Biology:
– Loss of histones linked to cell division.
– Histone loss leads to gene transcription amplification.
– Reduced histone synthesis in human cells due to telomere shortening.
– Core histone loss may be a general mark of aging.
– Histone variants like H3.3 and macroH2A regulate chromatin dynamics.
– H3.3 is incorporated into the genome independently of replication.
– MacroH2A implicated in transcriptional silencing.
– Histone modifications such as increased histone acetylation and methylation linked to lifespan regulation in organisms.
– UTX-1 and H3K27me3 play critical roles in aging regulation.
Mechanisms of Aging Biomarkers:
– DNA methylation, loss of histones, and histone modification are potential biomarkers of aging.
– These mechanisms are used to identify physical parameters of biological aging.
– Changes in the physical biomarker should correlate with the age of the species.
– Biomarkers of aging research can lead to extending life spans and predicting genetic diseases.
– DNA methylation examines the structure of DNA to determine age and transcriptional activity.
Applications of Aging Biomarkers:
– Biomarkers of aging help determine biological age, mortality, and morbidity.
– DNA methylation can identify the biological age of a person.
– Biomarkers are used in clinical trials to measure age-related decline.
– The Biomarkers of Aging Consortium explores longevity interventions.
– Research on aging biomarkers is crucial for understanding human aging processes.
Related Concepts and Longevity Interventions:
– Epigenetic clock and hallmarks of aging are relevant concepts.
– Biomarkers in medicine and senescence are linked to aging research.
– Understanding DNA methylation age in human tissues is essential.
– Deep neural networks are applied to develop biomarkers of aging.
– Population-specific biomarkers of human aging are studied extensively.
– Biomarkers of aging aid in identifying and evaluating longevity interventions.
– Single-cell transcriptomics reveal insights into aging and longevity.
– Senescent T cells can predict the development of hyperglycemia.
– Hair cycle and pigmentation changes are associated with aging.
– Stress-associated methylation accumulates during postnatal development and aging.
Biomarkers of aging are biomarkers that could predict functional capacity at some later age better than chronological age. Stated another way, biomarkers of aging would give the true "biological age", which may be different from the chronological age.
Validated biomarkers of aging would allow for testing interventions to extend lifespan, because changes in the biomarkers would be observable throughout the lifespan of the organism. Although maximum lifespan would be a means of validating biomarkers of aging, it would not be a practical means for long-lived species such as humans because longitudinal studies would take far too much time. Ideally, biomarkers of aging should assay the biological process of aging and not a predisposition to disease, should cause a minimal amount of trauma to assay in the organism, and should be reproducibly measurable during a short interval compared to the lifespan of the organism. An assemblage of biomarker data for an organism could be termed its "ageotype".
Although graying of hair increases with age, hair graying cannot be called a biomarker of ageing. Similarly, skin wrinkles and other common changes seen with aging are not better indicators of future functionality than chronological age. Biogerontologists have continued efforts to find and validate biomarkers of aging, but success thus far has been limited. Levels of CD4 and CD8 memory T cells and naive T cells have been used to give good predictions of the expected lifespan of middle-aged mice.
Advances in big data analysis allowed for the new types of "aging clocks" to be developed. The epigenetic clock is a promising biomarker of aging and can accurately predict human chronological age. Basic blood biochemistry and cell counts can also be used to accurately predict the chronological age. Further studies of the hematological clock on the large datasets from South Korean, Canadian, and Eastern European populations demonstrated that biomarkers of aging may be population-specific and predictive of mortality. It is also possible to predict the human chronological age using the transcriptomic clock.