Can markers of biological age predict dependency in old age?

Jylhävä, Juulia; Jiang, Miao; Foebel, Andrea D.; Pedersen, Nancy L.; Hägg, Sara

doi:10.1007/s10522-019-09795-5

Cited by 19 publications

(14 citation statements)

References 32 publications

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“…Telomeres are found at the ends of chromosomes and shorten during cell division, meaning that telomere length decreases over time ( Lai, Wright, & Shay, 2018 ). Another popular aging biomarker is DNA methylation, often referred to as the “epigenetic clock”, a form of epigenetic modification that accumulates over time and can therefore be correlated with chronological age ( Chen et al, 2016 ; Jylhävä, Jiang, Foebel, Pedersen, & Hägg, 2019 ). Another notable approach to biological aging is the search for composite biomarkers, combining several factors, such as cholesterol and blood pressure, to produce weighted operationalizations of biological age ( Levine, 2013 ; Levine & Crimmins, 2014a ).…”

Section: Biological Agementioning

confidence: 99%

Anti-aging technoscience & the biologization of cumulative inequality: Affinities in the biopolitics of successful aging

Fletcher

2020

Journal of Aging Studies

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This paper charts the emergence of under-remarked affinities between contemporary anti-aging technoscience and some social scientific work on biological aging. Both have recently sought to develop increasingly sophisticated operationalizations of age, aging and agedness as biological phenomena, in response to traditional notions of normal and chronological aging. Rather than being an interesting coincidence, these affinities indicate the influence of a biopolitics of successful aging on government, industry and social science. This biopolitics construes aging as a personal project that is mastered through specific forms of entrepreneurial individual action, especially consumption practices. Social scientists must remain alert to this biopolitics and its influence on their own work, because the individualization of cumulative inequalities provides intellectual and moral justifications for anti-aging interventions that exploit those inequalities.

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Section: Biological Agementioning

confidence: 99%

Anti-aging technoscience & the biologization of cumulative inequality: Affinities in the biopolitics of successful aging

Fletcher

2020

Journal of Aging Studies

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“…Understanding the biological mechanisms involved in ageing will be a critical step towards preventing, slowing or reversing age-associated phenotypes. Because of substantial inter-individual variation in age-associated phenotypes, there is considerable interest in identifying robust biomarkers of ‘biological’ age, a quantitative phenotype that is thought to better capture an individual’s risk of age-related outcomes than actual chronological age ( Jylhävä et al , 2019 ). Several data modalities have been used to generate estimates of biological age; these include measures of physical fitness (e.g.…”

Section: Introductionmentioning

confidence: 99%

Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex

Shireby

Davies

Francis

et al. 2020

Brain

104

125

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Human DNA methylation data have been used to develop biomarkers of ageing, referred to as 'epigenetic clocks', which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into 'training' and 'testing' samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

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“…Understanding the biological mechanisms involved in ageing will be a critical step towards preventing, slowing or reversing age-associated phenotypes. Due to the substantial inter-individual variation in age-associated phenotypes, there is considerable interest in identifying robust biomarkers of 'biological' age, a quantitative phenotype that is thought to better capture an individuals' risk of age-related outcomes than actual chronological age (Jylhävä et al, 2019). Several data modalities have been used to generate estimates of biological age; these include measures of physical fitness (e.g.…”

Section: Introductionmentioning

confidence: 99%

Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

Shireby

Davies

Francis

et al. 2020

Preprint

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Human DNA-methylation data have been used to develop biomarkers of ageingreferred to 'epigenetic clocks' -that have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks are highly accurate in blood but are less precise when used in older samples or on brain tissue. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life-course (n = 1,397, ages = 1 to 104 years). This dataset was split into 'training' and 'testing' samples (training: n = 1,047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel human cortex dataset (n = 1,221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1,175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically out-performed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts. mortem 6 samples were split into a "training" dataset (used to determine the DNAm sites included in the model and their weighted coefficients) and a "testing" dataset (used to profile the performance of the proposed model). To reduce the effects of experimental batch in our model, we maximised the number of different datasets included in the training data by combining the ten cohorts and randomly assigning individuals within them to either the training or testing dataset in a 3:1 ratio ( Table 1). In total, our training dataset (age range = 1-108 years, median = 57 years; see Supplementary Fig. S1) comprised DNAm data from 1,047 cortex samples (derived from 832 donors) and our testing dataset (age range = 1-108 years, median = 56 years; see Supplementary Fig. S1) comprised DNAm data from 350 cortex samples (derived from 323 donors). Individuals with a diagnosis of Alzheimer's disease and other major neurological phenotypes were excluded from our analysis given the ...

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Can markers of biological age predict dependency in old age?

Cited by 19 publications

References 32 publications

Anti-aging technoscience & the biologization of cumulative inequality: Affinities in the biopolitics of successful aging

Anti-aging technoscience & the biologization of cumulative inequality: Affinities in the biopolitics of successful aging

Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex

Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

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