Artificial intelligence for aging and longevity research: Recent advances and perspectives

Zhavoronkov, Alex; Mamoshina, Polina; Vanhaelen, Quentin; Scheibye‐Knudsen, Morten; Moskalev, Alexey; Aliper, Alex

doi:10.1016/j.arr.2018.11.003

Cited by 158 publications

(101 citation statements)

References 148 publications

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“…Emerging technologies for deep-learning analysis (Zhavoronkov et al, 2019) may improve methylation measurement of Pace of Aging. Alternatively, integration of methylation data with additional molecular datasets (Hasin et al, 2017;Zierer et al, 2015) may be needed to achieve precise measurement of Pace of Aging from a single time-point blood sample.…”

Section: Discussionmentioning

confidence: 99%

Quantification of the pace of biological aging in humans through a blood test: The DunedinPoAm DNA methylation algorithm

Belsky

Caspi

Arseneault

et al. 2020

Preprint

143

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ABSTRACTBiological aging is the gradual, progressive decline in system integrity that occurs with advancing chronological age, causing morbidity and disability. Measurements of the pace of aging are needed to serve as surrogate endpoints in trials of therapies designed to prevent disease by slowing biological aging. We report a blood DNA-methylation measure that is sensitive to variation in the pace of biological aging among individuals born the same year. We first modeled longitudinal change in 18 biomarkers tracking organ-system integrity across 12 years of follow-up in the Dunedin birth cohort. Rates of change in each biomarker were composited to form a measure of aging-related decline, termed Pace of Aging. Elastic-net regression was used to develop a DNA-methylation predictor of Pace of Aging, called DunedinPoAm for Dunedin (P)ace (o)f (A)ging (m)ethylation. Validation analyses showed DunedinPoAm was associated with functional decline in the Dunedin Study and more advanced biological age in the Understanding Society Study, predicted chronic disease and mortality in the Normative Aging Study, was accelerated by early-life adversity in the E-risk Study, and DunedinPoAm prediction was disrupted by caloric restriction in the CALERIE trial. DunedinPoAm generally outperformed epigenetic clocks. Findings provide proof-of-principle for DunedinPoAm as a single-time-point measure of a person’s pace of biological aging.

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Section: Discussionmentioning

confidence: 99%

Quantification of the pace of biological aging in humans through a blood test: The DunedinPoAm DNA methylation algorithm

Belsky

Caspi

Arseneault

et al. 2020

Preprint

143

View full text Add to dashboard Cite

show abstract

“…There is a range of machine learning analyses in use in molecular epidemiology. For example, lasso regression analysis of GWAS data is being used to develop prediction algorithms [69], although these have not yet been applied to studies of aging-related phenotypes; elastic-net regression analysis of epigenomic data is being used to develop novel measurements of the aging rate [24]; and neural-net analysis of a range of data types, including transcriptomic data, is being used to develop aging biomarkers and identify drug targets [70]. As machine learning approaches continue to mature, studies will be needed to compare methods and define best practices for implementation.…”

Section: New Developments In Molecular Epidemiology Of Agingmentioning

confidence: 99%

Developments in molecular epidemiology of aging

Hägg

Belsky

Cohen

2019

Emerging Topics in Life Sciences

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The field of molecular epidemiology of aging involves the application of molecular methods to measure aging processes and their genetic determinants in human cohorts. Over the last decade, the field has undergone rapid progress with a dramatic increase in the number of papers published. The aim of this review is to give an overview of the research field, with a specific focus on new developments, opportunities, and challenges.Aging occurs at multiple hierarchical levels. There is increasing consensus that agingrelated changes at the molecular level cause declines in physiological integrity, functional capacity, and ultimately lifespan. Molecular epidemiology studies seek to quantify this process. Telomere length, composite scores integrating clinical biomarkers, and omics clocks are among the most well-studied metrics in molecular epidemiology studies.New developments in the field include bigger data and hypothesis-free analysis together with new modes of collaborations in interdisciplinary teams and open access norms around data sharing. Key challenges facing the field are the lack of a gold standard by which to evaluate molecular measures of aging, inconsistency in which metrics of aging are measured and analyzed across studies, and a need for more longitudinal data necessary to observe change over time.

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“…Dr. Alex Zhavoronkov from Insilico Medicine provided an overview of the recent advances in the applications of DL to the development of aging biomarkers, target identification, generation of synthetic human data using the generative adversarial networks (GANs) using age as a generation condition (Zhavoronkov et al, 2019). He demonstrated the application of the deep neural networks for the prediction of chronological age of patients using the basic anonymized clinical test data available for public testing using the aging.ai system.…”

Section: A Few Examples On Application Of Ai Approaches In Bio-medicinementioning

confidence: 99%

Artificial Intelligence Based Approaches to Identify Molecular Determinants of Exceptional Health and Life Span-An Interdisciplinary Workshop at the National Institute on Aging

Moore

Raghavachari

2019

Front. Artif. Intell.

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Artificial intelligence (AI) has emerged as a powerful approach for integrated analysis of the rapidly growing volume of multi-omics data, including many research and clinical tasks such as prediction of disease risk and identification of potential therapeutic targets. However, the potential for AI to facilitate the identification of factors contributing to human exceptional health and life span and their translation into novel interventions for enhancing health and life span has not yet been realized. As researchers on aging acquire large scale data both in human cohorts and model organisms, emerging opportunities exist for the application of AI approaches to untangle the complex physiologic process(es) that modulate health and life span. It is expected that efficient and novel data mining tools that could unravel molecular mechanisms and causal pathways associated with exceptional health and life span could accelerate the discovery of novel therapeutics for healthy aging. Keeping this in mind, the National Institute on Aging (NIA) convened an interdisciplinary workshop titled "Contributions of Artificial Intelligence to Research on Determinants and Modulation of Health Span and Life Span" in August 2018. The workshop involved experts in the fields of aging, comparative biology, cardiology, cancer, and computational science/AI who brainstormed ideas on how AI can be leveraged for the analyses of large-scale data sets from human epidemiological studies and animal/model organisms to close the current knowledge gaps in processes that drive exceptional life and health span. This report summarizes the discussions and recommendations from the workshop on future application of AI approaches to advance our understanding of human health and life span.

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Artificial intelligence for aging and longevity research: Recent advances and perspectives

Cited by 158 publications

References 148 publications

Quantification of the pace of biological aging in humans through a blood test: The DunedinPoAm DNA methylation algorithm

Quantification of the pace of biological aging in humans through a blood test: The DunedinPoAm DNA methylation algorithm

Developments in molecular epidemiology of aging

Artificial Intelligence Based Approaches to Identify Molecular Determinants of Exceptional Health and Life Span-An Interdisciplinary Workshop at the National Institute on Aging

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