Research priorities for measuring biologic age: summary and future directions from the Research Centers Collaborative Network Workshop

Brinkley, Tina E.; Justice, Jamie N.; Basu, Shubhashrita; Bauer, Scott R.; Loh, Kah Poh; Mukli, Péter; Ng, Ted Kheng Siang; Turney, Indira C.; Ferrucci, Luigi; Cummings, Steven R.; Kritchevsky, Stephen B.

doi:10.1007/s11357-022-00661-w

Cited by 9 publications

(7 citation statements)

References 75 publications

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“…Multidisciplinary cooperation with health professionals and society as a whole is needed to tackle the problem of ageing 315 . At present, most social programmes and policies for older people are developed based on chronological age as a criterion for eligibility.…”

Section: Application Of Ageing Biomarkersmentioning

confidence: 99%

“…Multidisciplinary cooperation with health professionals and society as a whole is needed to tackle the problem of ageing. 315 At present, most social programmes and policies for older people are developed based on chronological age as a criterion for eligibility. However, due to the heterogeneity of ageing, there is still a large number of people who do not meet the criteria yet suffer from accelerated biological ageing and impaired health status.…”

Section: Incorporate Social Policymentioning

confidence: 99%

“…Of note, there has been no standard way of defining biological age (see details in Part 2). 315 Generally, various objective and subjective assessments have served as ground truth of biological age, including objective metrics covering chronological age in the healthy population and age-related phenotypic data (e.g., mortality risk), as well as subjective metrics such as perception of ageing. The prediction of the ageing process may have certain characteristics.Chronological age in a healthy population is considered as the most common way of defining biological age.…”

Section: Ground Truth Of Biological Agementioning

confidence: 99%

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Biomarkers of ageing: Current state‐of‐art, challenges, and opportunities

Chen

Wang

Zhang

et al. 2023

MedComm – Future Medicine

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Given the unprecedented phenomenon of population ageing, studies have increasing captured the heterogeneity within the ageing process. In this context, the concept of "biological age" has been introduced as an integrated measure reflecting the individualized ageing pace. Identifying reliable and robust biomarkers of age is critical for the accurate risk stratification of individuals and exploration into antiageing interventions. Numerous potential biomarkers of ageing have been proposed, spanning from molecular changes and imaging characteristics to clinical phenotypes. In this review, we will start off with a discussion of the development of ageing biomarkers, then we will provide a comprehensive summary of currently identified ageing biomarkers in humans, discuss the rationale behind each biomarker and highlight their accuracy and clinical value with a contemporary perspective. Additionally, we MedComm -Future Medicine.

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Section: Application Of Ageing Biomarkersmentioning

confidence: 99%

Section: Incorporate Social Policymentioning

confidence: 99%

Section: Ground Truth Of Biological Agementioning

confidence: 99%

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Biomarkers of ageing: Current state‐of‐art, challenges, and opportunities

Chen

Wang

Zhang

et al. 2023

MedComm – Future Medicine

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“…This established change in BMD as a valid surrogate marker for the effect of treatment on risk of fracture Vol:. (1234567890) [17]. Epigenetic age has become the most popular approach; derived from patterns of DNA methylation, commonly used epigenetic clocks are highly correlated with calendar age.…”

Section: Implications For the Use Of "Biological Age" In Clinical Trialsmentioning

confidence: 99%

Endpoints for geroscience clinical trials: health outcomes, biomarkers, and biologic age

Cummings

Kritchevsky

2022

GeroScience

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Treatments that target fundamental processes of aging are expected to delay several aging-related conditions simultaneously. Testing the efficacy of these treatments for potential anti-aging benefits will require clinical trials with endpoints that reflect the potential benefits of slowing processes of aging. There are several potential types of endpoints to capture the benefits of slowing a process of aging, and a consensus is needed to standardize and compare the results of these trials and to guide the analysis of observational data to support trial planning. Using biomarkers instead of clinical outcomes would substantially reduce the size and the duration of clinical trials. This requires validation of surrogate markers showing that treatment induced change in the marker reliably predicts the magnitude of change in the clinical outcome. The surrogate marker must also reflect the biological mechanism for the effect of treatment on the clinical outcome. “Biological age” is a superficially attractive marker for such trials. However, it is essential to establish that treatment induced change in biological age reliably predict the magnitude of benefits in the clinical outcome. Reaching consensus on clinical outcomes for geroscience trials and then validating potential surrogate biomarkers requires time, effort, and coordination that will be worthwhile to develop surrogate outcomes that can be trusted to efficiently test the value of many anti-aging treatments under development.

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“…However, there is considerable variability in health outcomes among individuals with the same chronological age, suggesting a heterogeneous rate of ageing [ 2 ]. Based on this, the concept of biological age has emerged as a measurement that reflects physiological and functional decline with ageing [ 3 , 4 ]. Accurate quantification of biological age is crucial for risk stratification of individuals with accelerated ageing and exploring anti-ageing interventions, reducing the burden on public health.…”

Section: Introductionmentioning

confidence: 99%

Deep neural network-estimated age using optical coherence tomography predicts mortality

Chen,

Zhang,

Peng

et al. 2023

GeroScience

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The concept of biological age has emerged as a measurement that reflects physiological and functional decline with ageing. Here we aimed to develop a deep neural network (DNN) model that predicts biological age from optical coherence tomography (OCT). A total of 84,753 high-quality OCT images from 53,159 individuals in the UK Biobank were included, among which 12,631 3D-OCT images from 8,541 participants without any reported medical conditions at baseline were used to develop an age prediction model. For the remaining 44,618 participants, OCT age gap, the difference between the OCT-predicted age and chronological age, was calculated for each participant. Cox regression models assessed the association between OCT age gap and mortality. The DNN model predicted age with a mean absolute error of 3.27 years and showed a strong correlation of 0.85 with chronological age. After a median follow-up of 11.0 years (IQR 10.9–11.1 years), 2,429 deaths (5.44%) were recorded. For each 5-year increase in OCT age gap, there was an 8% increased mortality risk (hazard ratio [HR] = 1.08, CI:1.02–1.13, P = 0.004). Compared with an OCT age gap within ± 4 years, OCT age gap less than minus 4 years was associated with a 16% decreased mortality risk (HR = 0.84, CI: 0.75–0.94, P = 0.002) and OCT age gap more than 4 years showed an 18% increased risk of death incidence (HR = 1.18, CI: 1.02–1.37, P = 0.026). OCT imaging could serve as an ageing biomarker to predict biological age with high accuracy and the OCT age gap, defined as the difference between the OCT-predicted age and chronological age, can be used as a marker of the risk of mortality.

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Research priorities for measuring biologic age: summary and future directions from the Research Centers Collaborative Network Workshop

Cited by 9 publications

References 75 publications

Biomarkers of ageing: Current state‐of‐art, challenges, and opportunities

Biomarkers of ageing: Current state‐of‐art, challenges, and opportunities

Endpoints for geroscience clinical trials: health outcomes, biomarkers, and biologic age

Deep neural network-estimated age using optical coherence tomography predicts mortality

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