In vivo partial reprogramming alters age-associated molecular changes during physiological aging in mice

Browder, Kristen C.; Reddy, Pradeep; Yamamoto, Masaaki; Haghani, Amin; Guillen, Isabel; Sahu, Sanjeeb Kumar; Wang, Chao; Luque, Yosu; Prieto, Javier; Shi, Lei; Shojima, Kensaku; Hishida, Tomoaki; Lai, Zijuan; Li, Qingling; Choudhury, Feroza Kaneez; Wong, Weng Ruh; Liang, Yuxin; Sangaraju, Dewakar; Sandoval, Wendy; Esteban, Concepción Rodrı́guez; Núñez-Delicado, Estrella; García, Pedro Guillén; Pawlak, Michał; Heiden, Jason A. Vander; Horvath, Steve; Jasper, Heinrich; Belmonte, Juan Carlos Izpisúa

doi:10.1038/s43587-022-00183-2

Cited by 147 publications

(158 citation statements)

References 42 publications

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“…4 The trained HAI is also the world’s first clinically validated model of human aging that could substantially speed up the discovery of preventive medicine for all age-related diseases. 11 This study demonstrates that disruptive breakthroughs necessitate the smallest team size to attain the largest HAI for optimal knowledge extraction from high-dimensional, low-quality data spaces, thus establishing the first prospective proof of the previous discovery that small teams disrupt 12 . The global adoption of how to train a HAI may provide a new path to mass producing scientific and technological breakthroughs via exponential knowledge extraction and better data label designs for training better-performing AI.…”

mentioning

confidence: 52%

“…The trained ABNN is also the world's first clinically validated model of human aging (Figure 8 and Figure 9). Its application to the development of cellular rejuvenation programming could substantially speed up the discovery of preventive medicines for age-related diseases 11 .…”

Section: Discussionmentioning

confidence: 99%

“…Its application to the development of cellular rejuvenation programming could substantially speed up the discovery of preventive medicines for age-related diseases. 11…”

Section: Discussionmentioning

confidence: 99%

“…Its application to the development of cellular rejuvenation programming could substantially speed up the discovery of preventive medicines for age-related diseases. 11 The ABNN also leaves room for improvement as 17.2% (27/157) of predictions were either false positives (18/157) or false negatives (9/157) in the PROTOCOLS validation. As such, further work is planned to investigate why the ABNN performs better for harder diseases (Figure 6d).…”

Section: Discussionmentioning

confidence: 99%

“…This result confirms that exponentially extracted knowledge alone may be sufficient to accurately predict clinical success, effecting a complete reversal of Eroom’s law 4 . The trained HAI is also the world’s first clinically validated model of human aging that could substantially speed up the discovery of preventive medicine for all age-related diseases 11 . This study demonstrates that disruptive breakthroughs necessitate the smallest team size to attain the largest HAI for optimal knowledge extraction from high-dimensional, low-quality data spaces, thus establishing the first prospective proof of the previous discovery that small teams disrupt 12 .…”

mentioning

confidence: 99%

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Prospectively validated augmented intelligence for disease-agnostic predictions of clinical success for novel therapeutics

Lovetrue¹,

Lovetrue²

2022

Preprint

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Despite numerous superhuman achievements in complex challenges, standalone AI does not free life science from the long-term bottleneck of linearly extracting new knowledge from exponentially growing new data, severely limiting the success rate of drug discovery. Inspired by the state-of-the-art AI training methods, we trained a human-centric hybrid augmented intelligence (HAI) to learn a foundation model6 that extracts all-encompassing knowledge of human physiology and diseases. To evaluate the quality of HAI's extracted knowledge, we designed the public, prospective prediction of pivotal ongoing clinical trial outcomes at large scale (PROTOCOLS) challenge to benchmark HAI's real-world performance of predicting drug clinical efficacy without access to human data. HAI achieved a 10.5-fold improvement from the baseline with 99% confidence in the PROTOCOLS validation, readily increasing the average clinical success rate of investigational new drugs from 7.9% to 90% for almost any human diseases. The validated HAI confirms that exponentially extracted knowledge alone is sufficient for accurately predicting drug clinical efficacy, effecting a total reversal of Eroom's aw. HAI is also the world's first clinically validated model of human aging that could substantially speed up the discovery of preventive medicine for all age-related diseases. Our results demonstrate that disruptive breakthroughs necessitate the smallest team size to attain the largest HAI for optimal knowledge extraction from high-dimensional low-quality data space, thus establishing the first prospective proof of the previous discovery that small teams disrupt. The global adoption of training HAI provides a well-beaten economic path to mass-produce scientific and technological breakthroughs via exponential knowledge extraction and better designs of data labels for training better performing AI.

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mentioning

confidence: 52%

Section: Discussionmentioning

confidence: 99%

“…Its application to the development of cellular rejuvenation programming could substantially speed up the discovery of preventive medicines for age-related diseases. 11…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Prospectively validated augmented intelligence for disease-agnostic predictions of clinical success for novel therapeutics

Lovetrue¹,

Lovetrue²

2022

Preprint

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Promoting longevity with less cancer: The 2022 International Conference on Aging and Cancer

Cui,

Wang,

Jiang

et al. 2023

Aging and Cancer

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Aging and cancer are increasingly becoming big challenges for public health worldwide due to increased human life expectancy. Meanwhile, aging is one of the major risk factors for cancer. In December 2019, the first International Conference on Aging and Cancer was held in Haikou, Hainan province (island), China, preluding the establishment of the International Center for Aging and Cancer (ICAC) at Hainan, an institute dedicated to the research at the intersection of aging and cancer. Since then, the ICAC has hosted the annual conference each December in Hainan. The 2022 ICAC conference, with the theme of “promoting longevity with less cancer,” invited 17 internationally renowned scientists to share their new research and insights. Topics included DNA methylation in rejuvenation, development, and cellular senescence; lifespan regulation and longevity manipulation; metabolism and aging; cellular senescence and diseases; and novel therapeutics for cancer and antiaging/anticancer drug discovery. The forum highlighted the interconnectedness of aging and senescence with cancer evolution and risk. Although there is hope for preventing diseases like cancer by modulating systems that also control lifespan, attention has to be paid to the conflicting needs and competing demands in human biology.

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Epigenetic aging in older breast cancer survivors and noncancer controls: preliminary findings from the Thinking and Living with Cancer Study

Rentscher

Bethea

Zhai

et al. 2023

Cancer

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BackgroundCancer and its treatments may accelerate aging in survivors; however, research has not examined epigenetic markers of aging in longer term breast cancer survivors. This study examined whether older breast cancer survivors showed greater epigenetic aging than noncancer controls and whether epigenetic aging related to functional outcomes.MethodsNonmetastatic breast cancer survivors (n = 89) enrolled prior to systemic therapy and frequency‐matched controls (n = 101) ages 62 to 84 years provided two blood samples to derive epigenetic aging measures (Horvath, Extrinsic Epigenetic Age [EEA], PhenoAge, GrimAge, Dunedin Pace of Aging) and completed cognitive (Functional Assessment of Cancer Therapy‐Cognitive Function) and physical (Medical Outcomes Study Short Form‐12) function assessments at approximately 24 to 36 and 60 months after enrollment. Mixed‐effects models tested survivor‐control differences in epigenetic aging, adjusting for age and comorbidities; models for functional outcomes also adjusted for racial group, site, and cognitive reserve.ResultsSurvivors were 1.04 to 2.22 years biologically older than controls on Horvath, EEA, GrimAge, and DunedinPACE measures (p = .001–.04) at approximately 24 to 36 months after enrollment. Survivors exposed to chemotherapy were 1.97 to 2.71 years older (p = .001–.04), and among this group, an older EEA related to worse self‐reported cognition (p = .047) relative to controls. An older epigenetic age related to worse physical function in all women (p < .001–.01). Survivors and controls showed similar epigenetic aging over time, but Black survivors showed accelerated aging over time relative to non‐Hispanic White survivors.ConclusionOlder breast cancer survivors, particularly those exposed to chemotherapy, showed greater epigenetic aging than controls that may relate to worse outcomes. If replicated, measurement of biological aging could complement geriatric assessments to guide cancer care for older women.

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In vivo partial reprogramming alters age-associated molecular changes during physiological aging in mice

Cited by 147 publications

References 42 publications

Prospectively validated augmented intelligence for disease-agnostic predictions of clinical success for novel therapeutics

Prospectively validated augmented intelligence for disease-agnostic predictions of clinical success for novel therapeutics

Promoting longevity with less cancer: The 2022 International Conference on Aging and Cancer

Epigenetic aging in older breast cancer survivors and noncancer controls: preliminary findings from the Thinking and Living with Cancer Study

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