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Cited by 248

The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.

Protein and amino acid restriction, aging and disease: from yeast to humans

Open access
Mirzaei, Hamed, Suarez, Jorge A., Longo, Valter D.·2014·Trends in Endocrinology & Metabolism

Abstract

Many of the effects of dietary restriction (DR) on longevity and health span in model organisms have been linked to reduced protein and amino acid (AA) intake and the stimulation of specific nutrient signaling pathways. Studies in yeast have shown that addition of serine, threonine, and valine in media promotes cellular sensitization and aging by activating different but connected pathways. Protein or essential AA restriction extends both lifespan and healthspan in rodent models. In humans, protein restriction (PR) has been associated with reduced cancer, diabetes, and overall mortality. Thus, interventions aimed at lowering the intake of proteins or specific AAs can be beneficial and have the potential to be widely adopted and effective in optimizing healthspan.

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Crossref · 10.1016/j.tem.2014.07.002

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Metadata

Authors
Mirzaei, H., Suarez, J. A., & Longo, V. D.
Venue
Trends in Endocrinology & Metabolism
Publisher
Elsevier BV
Published
2014-11
Volume
25
Issue
11
Pages
558-566
ISSN
1043-2760 (ISSN-L)
OpenAlex
W2084013192
Cited by
248
References
20
FWCI
2.27
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Protein and amino acid restriction, aging and disease: from yeast to humans. https://app.cochat.ai/papers/10.1016/j.tem.2014.07.002
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Mirzaei, H., Suarez, J. A., & Longo, V. D. (2014). Protein and amino acid restriction, aging and disease: from yeast to humans. Trends in Endocrinology & Metabolism. https://doi.org/10.1016/j.tem.2014.07.002

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.