1 hr 30 min

Deep Biomarkers of Aging and Longevity – EP20: Polina Mamoshina (Deep Longevity‪)‬ Quantified Health, Wellness & Aging

    • Health & Fitness

In this twentieth episode, Polina Mamoshina introduces recently launched Deep Longevity, and its app (young.ai).



Read the transcript



Biomarkers of aging are introduced. She explains that they have taken a superior approach by using deep learning instead of machine learning. Aging clocks in general are covered. Finally, she shares her view that transcriptomic and proteonomic clocks are the likely future.

Topics we discussed in this episode



Personal background: Moscow State University, Oxford University, Insilico Medicine hackathon

Bringing Deep Longevity out of stealth, Young.ai companion app

Deep Longevity introduction including company aims

Description of Young.AI app

Biomarkers of aging as the accelerant of market for aging interventions

Introduction to aging clocks: Horvath, Hannum

Taking a novel and superior technological approach to aging clocks, using deep neural networks, instead of shallow machine learning

Limitations of shallow machine learning models

Ability of neural networks to capture highly non linear dependencies and what that matters for biological age determination

Investing in anticipated payoff from deep learning over the long-term, even if machine learning may be good enough in many cases now

Biological age prediction with Aging.ai

Two approaches to designing aging clocks

Machine learned PhenoAge biological age score

Introducing mortality, with the GrimAge score

Longevity clinics and life insurance as market

Biological age scoring as onboarding tool for life insurance markets

Training datasets

Common blood analytes used in PhenoAge vs Aging.ai

Optimized blood analyte levels for a given individual to get younger

Orthodox medicine uses blood analyte levels that are not specific to the individual and not optimized ranges; designed to detect only late-stage pathologies

Cheapness of regular blood analytes

Emerging market is likely to age score bodily subsystems rather than provide an overall singular biological age score

Goal is to find the fastest ticking clock in your body

Biological age test using a selfie

Providing a library of biological age scores, from free to expensive, so users can upgrade, find out more about themselves

Belief that proteomic and transcriptomic clocks will outperform epigenetic clocks in terms of being actionable with interventions

Epigenetics and aging

Acceleration of the aging rate may show up "late" in terms of being able to intervene, on the epigenome

Youthful blood plasma exchanges and age quantification

Transcriptomic, proteomic, and glycomic clocks

Anticipated rise of longevity clinics



Show links



Deep Longevity (Company Website)

Insilico Medicine (Company Website)

Human Longevity, Inc. (Company Website)

Regent Pacific Group (Company Website)

Young.AI (App from Deep Longevity)

Aging.AI (Biological Age Prediction)

'DNA Methylation Age of Human Tissues and Cell Types' (Paper)

'Assessment of Epigenetic Clocks as Biomarkers of Aging in Basic and Population Research' (Paper)

Steve Horvath (WikiPedia Entry)

'Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates' (Paper)

Gregory Hannum (LinkedIn)

Morgan Levine (LinkedIn)

'An epigenetic biomarker of aging for lifespan and healthspan' (Paper)

Elysium Health (Company Website)

'DNA Methylation GrimAge Strongly Predicts Lifespan and Healthspan' (Paper)

FOXO BioScience (Company Website)

NHANES III (CDC)

AgeoTypes (Stanford Article)

GlycanAge (Company Website)

GENOS (Company Website)

In this twentieth episode, Polina Mamoshina introduces recently launched Deep Longevity, and its app (young.ai).



Read the transcript



Biomarkers of aging are introduced. She explains that they have taken a superior approach by using deep learning instead of machine learning. Aging clocks in general are covered. Finally, she shares her view that transcriptomic and proteonomic clocks are the likely future.

Topics we discussed in this episode



Personal background: Moscow State University, Oxford University, Insilico Medicine hackathon

Bringing Deep Longevity out of stealth, Young.ai companion app

Deep Longevity introduction including company aims

Description of Young.AI app

Biomarkers of aging as the accelerant of market for aging interventions

Introduction to aging clocks: Horvath, Hannum

Taking a novel and superior technological approach to aging clocks, using deep neural networks, instead of shallow machine learning

Limitations of shallow machine learning models

Ability of neural networks to capture highly non linear dependencies and what that matters for biological age determination

Investing in anticipated payoff from deep learning over the long-term, even if machine learning may be good enough in many cases now

Biological age prediction with Aging.ai

Two approaches to designing aging clocks

Machine learned PhenoAge biological age score

Introducing mortality, with the GrimAge score

Longevity clinics and life insurance as market

Biological age scoring as onboarding tool for life insurance markets

Training datasets

Common blood analytes used in PhenoAge vs Aging.ai

Optimized blood analyte levels for a given individual to get younger

Orthodox medicine uses blood analyte levels that are not specific to the individual and not optimized ranges; designed to detect only late-stage pathologies

Cheapness of regular blood analytes

Emerging market is likely to age score bodily subsystems rather than provide an overall singular biological age score

Goal is to find the fastest ticking clock in your body

Biological age test using a selfie

Providing a library of biological age scores, from free to expensive, so users can upgrade, find out more about themselves

Belief that proteomic and transcriptomic clocks will outperform epigenetic clocks in terms of being actionable with interventions

Epigenetics and aging

Acceleration of the aging rate may show up "late" in terms of being able to intervene, on the epigenome

Youthful blood plasma exchanges and age quantification

Transcriptomic, proteomic, and glycomic clocks

Anticipated rise of longevity clinics



Show links



Deep Longevity (Company Website)

Insilico Medicine (Company Website)

Human Longevity, Inc. (Company Website)

Regent Pacific Group (Company Website)

Young.AI (App from Deep Longevity)

Aging.AI (Biological Age Prediction)

'DNA Methylation Age of Human Tissues and Cell Types' (Paper)

'Assessment of Epigenetic Clocks as Biomarkers of Aging in Basic and Population Research' (Paper)

Steve Horvath (WikiPedia Entry)

'Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates' (Paper)

Gregory Hannum (LinkedIn)

Morgan Levine (LinkedIn)

'An epigenetic biomarker of aging for lifespan and healthspan' (Paper)

Elysium Health (Company Website)

'DNA Methylation GrimAge Strongly Predicts Lifespan and Healthspan' (Paper)

FOXO BioScience (Company Website)

NHANES III (CDC)

AgeoTypes (Stanford Article)

GlycanAge (Company Website)

GENOS (Company Website)

1 hr 30 min

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