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

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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 hackathonBringing Deep Longevity out of stealth, Young.ai companion appDeep Longevity introduction including company aimsDescription of Young.AI appBiomarkers of aging as the accelerant of market for aging interventionsIntroduction to aging clocks: Horvath, HannumTaking a novel and superior technological approach to aging clocks, using deep neural networks, instead of shallow machine learningLimitations of shallow machine learning modelsAbility of neural networks to capture highly non linear dependencies and what that matters for biological age determinationInvesting in anticipated payoff from deep learning over the long-term, even if machine learning may be good enough in many cases nowBiological age prediction with Aging.aiTwo approaches to designing aging clocksMachine learned PhenoAge biological age scoreIntroducing mortality, with the GrimAge scoreLongevity clinics and life insurance as marketBiological age scoring as onboarding tool for life insurance marketsTraining datasetsCommon blood analytes used in PhenoAge vs Aging.aiOptimized blood analyte levels for a given individual to get youngerOrthodox medicine uses blood analyte levels that are not specific to the individual and not optimized ranges; designed to detect only late-stage pathologiesCheapness of regular blood analytesEmerging market is likely to age score bodily subsystems rather than provide an overall singular biological age scoreGoal is to find the fastest ticking clock in your bodyBiological age test using a selfieProviding a library of biological age scores, from free to expensive, so users can upgrade, find out more about themselvesBelief that proteomic and transcriptomic clocks will outperform epigenetic clocks in terms of being actionable with interventionsEpigenetics and agingAcceleration of the aging rate may show up "late" in terms of being able to intervene, on the epigenomeYouthful blood plasma exchanges and age quantificationTranscriptomic, proteomic, and glycomic clocksAnticipated 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)'Biohorology and Biomarkers of Aging: Current State-of-the-Art, Challenges and Opportunities' (Paper)'Deep Aging Clocks: The Emergence of AI-Based Biomarkers of Aging and Longevity' (Paper)'Deep Integrated Biomarkers of Aging' (Paper)'Deep Biomarkers of Aging and Longevity: from Research to Applications' (Paper)