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Cynthia Kenyon on Muck Rack

Cynthia Kenyon

As seen in: Nature, TED, PNAS, Science Magazine, BioRxiv, Cell Press, Pediatrics, medRxiv, eLife, Inkl and
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Articles

A mathematical model that predicts human biological age from physiological traits identifies environmental and genetic factors that influence aging

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By Sergiy Libert, Alex Chekholko, Cynthia Kenyon
| eLife
eLife Assessment This important study developed a mathematical model to predict biological age by leveraging physiological traits across multiple organ systems. The results presented are convincing, utilizing comprehensive data-driven approaches. However, additional external validation could further strengthen its generalizability. The model provides a way to identify environmental and genetic factors impacting aging and lifespan, revealing new factors potentially affecting aging.
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Impact of the training, support and access model (TSAM) on patient health outcomes in Rwanda: controlled longitudinal study

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By Celestin Hategeka, Larry Lynd, Cynthia Kenyon, Anaclet Ngabonzima
| medRxiv
Drs. Hategeka, Lynd, Kenyon, Ngabonzima, Luginaah and Cechetto were involved in the development and / or implementation of TSAM in Rwanda. Dr. Hategeka received support through a Vanier Canada Graduate Scholarship and a Banting Postdoctoral Fellowship from the Canadian Institutes of Health Research. Dr. Cechetto was awarded the grant from Global Affairs Canada that supported the development and implementation of TSAM in Rwanda.
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A mathematical model that predicts human biological age from physiological traits identifies environmental and genetic factors that influence aging

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By Sergiy Libert, Cynthia Kenyon, Alex Chekholko
| BioRxiv
Abstract Why people age at different rates is a fundamental, unsolved problem in biology. We created a model that predicts an individual`s age from physiological traits that change with age in the large UK Biobank dataset, such as blood pressure, lung function, strength and stimulus-reaction time.
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