Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Lifelong restriction of dietary branched-chain amino acids has sex-specific benefits for frailty and life span in mice

Nature Aging volume 1, pages 73–86 (2021)Cite this article

Subjects

Abstract

Protein-restricted diets promote health and longevity in many species. While the precise components of a protein-restricted diet that mediate the beneficial effects to longevity have not been defined, we recently showed that many metabolic effects of protein restriction can be attributed to reduced dietary levels of the branched-chain amino acids (BCAAs) leucine, isoleucine and valine. Here, we demonstrate that restricting dietary BCAAs increases the survival of two different progeroid mouse models, delays frailty and promotes the metabolic health of wild-type C57BL/6J mice when started in midlife, and leads to a 30% increase in life span and a reduction in frailty in male, but not female, wild-type mice when they undergo lifelong feeding. Our results demonstrate that restricting dietary BCAAs can increase health span and longevity in mice and suggest that reducing dietary BCAAs may hold potential as a translatable intervention to promote healthy aging.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Branched-chain amino acid restriction extends the life span of progeroid mice.
Fig. 2: A low BCAA diet promotes the metabolic health of aged mice.
Fig. 3: A low BCAA diet promotes healthy aging.
Fig. 4: A low BCAA diet does not extend the life span of middle-aged mice.
Fig. 5: Lifelong consumption of a low BCAA diet promotes health span and extends male life span.
Fig. 6: Transcriptional profiling of skeletal muscle identifies male-specific changes in longevity-associated signaling pathways.

Similar content being viewed by others

Data availability

RNA-sequencing data have been deposited with the Gene Expression Omnibus and are accessible through accession number GSE155064. The data that support the plots and other findings of this study are available from the corresponding author upon reasonable request. Full scans of western blot images are provided as source data.

References

  1. Weindruch, R., Walford, R. L., Fligiel, S. & Guthrie, D. The retardation of aging in mice by dietary restriction: longevity, cancer, immunity and lifetime energy intake. J. Nutr. 116, 641–654 (1986).

    Article  CAS  PubMed  Google Scholar 

  2. Cummings, N. E. & Lamming, D. W. Regulation of metabolic health and aging by nutrient-sensitive signaling pathways. Mol. Cell. Endocrinol. 455, 13–22 (2017).

    Article  CAS  PubMed  Google Scholar 

  3. Solon-Biet, S. M. et al. Macronutrients and caloric intake in health and longevity. J. Endocrinol. 226, R17–R28 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Green, C. L. & Lamming, D. W. Regulation of metabolic health by essential dietary amino acids. Mech. Ageing Dev. 177, 186–200 (2019).

    Article  CAS  PubMed  Google Scholar 

  5. Speakman, J. R., Mitchell, S. E. & Mazidi, M. Calories or protein? The effect of dietary restriction on life span in rodents is explained by calories alone. Exp. Gerontol. 86, 28–38 (2016).

    Article  CAS  PubMed  Google Scholar 

  6. Gannon, M. C., Nuttall, F. Q., Saeed, A., Jordan, K. & Hoover, H. An increase in dietary protein improves the blood glucose response in persons with type 2 diabetes. Am. J. Clin. Nutr. 78, 734–741 (2003).

    Article  CAS  PubMed  Google Scholar 

  7. Dong, J. Y., Zhang, Z. L., Wang, P. Y. & Qin, L. Q. Effects of high-protein diets on body weight, glycaemic control, blood lipids and blood pressure in type 2 diabetes: meta-analysis of randomised controlled trials. Br. J. Nutr. 110, 781–789 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. Seino, Y., Seino, S., Ikeda, M., Matsukura, S. & Imura, H. Beneficial effects of high protein diet in treatment of mild diabetes. Hum. Nutr. Appl. Nutr. 37 A, 226–230 (1983).

    CAS  PubMed  Google Scholar 

  9. Coelho-Junior H. J., Rodrigues B., Uchida M., Marzetti E. Low protein intake is associated with frailty in older adults: a systematic review and meta-analysis of observational studies. Nutrients 10, 1334 (2018).

  10. Coelho-Júnior H. J., et al. Protein-related dietary parameters and frailty status in older community-dwellers across different frailty instruments. Nutrients 12, 508 (2020).

  11. Levine, M. E. et al. Low protein intake is associated with a major reduction in IGF-1, cancer and overall mortality in the 65 and younger but not older population. Cell Metab. 19, 407–417 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sluijs, I. et al. Dietary intake of total, animal and vegetable protein and risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-NL study. Diabetes Care 33, 43–48 (2010).

    Article  CAS  PubMed  Google Scholar 

  13. Lagiou, P. et al. Low carbohydrate–high protein diet and mortality in a cohort of Swedish women. J. Intern. Med. 261, 366–374 (2007).

    Article  CAS  PubMed  Google Scholar 

  14. Vergnaud, A. C. et al. Macronutrient composition of the diet and prospective weight change in participants of the EPIC-PANACEA study. PLoS ONE 8, e57300 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Fontana, L. et al. Decreased consumption of branched-chain amino acids improves metabolic health. Cell Rep. 16, 520–530 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Laeger, T. et al. FGF21 is an endocrine signal of protein restriction. J. Clin. Invest. 124, 3913–3922 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Maida, A. et al. A liver stress-endocrine nexus promotes metabolic integrity during dietary protein dilution. J. Clin. Invest. 126, 3263–3278 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Cummings, N. E. et al. Restoration of metabolic health by decreased consumption of branched-chain amino acids. J. Physiol. 596, 623–645 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Lee, K. P. et al. Lifespan and reproduction in Drosophila: new insights from nutritional geometry. Proc. Natl Acad. Sci. USA 105, 2498–2503 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Solon-Biet, S. M. et al. The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging and longevity in ad libitum-fed mice. Cell Metab. 19, 418–430 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Mair, W., Piper, M. D. & Partridge, L. Calories do not explain extension of life span by dietary restriction in Drosophila. PLoS Biol. 3, e223 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Ross, M. H. Length of life and nutrition in the rat. J. Nutr. 75, 197–210 (1961).

    Article  CAS  PubMed  Google Scholar 

  23. Lee, B. C. et al. Methionine restriction extends lifespan of Drosophila melanogaster under conditions of low amino-acid status. Nat. Commun. 5, 3592 (2014).

    Article  PubMed  CAS  Google Scholar 

  24. Grandison, R. C., Piper, M. D. & Partridge, L. Amino-acid imbalance explains extension of lifespan by dietary restriction in Drosophila. Nature 462, 1061–1064 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Juricic, P., Gronke, S. & Partridge, L. Branched-chain amino acids have equivalent effects to other essential amino acids on lifespan and aging-related traits in Drosophila. J. Gerontol. A Biol. Sci. Med. Sci. 75, 24–31 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Yoshida, S. et al. Role of dietary amino acid balance in diet restriction-mediated lifespan extension, renoprotection and muscle weakness in aged mice. Aging Cell 17, e12796 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Miller, R. A. et al. Methionine-deficient diet extends mouse lifespan, slows immune and lens aging, alters glucose, T4, IGF-I and insulin levels, and increases hepatocyte MIF levels and stress resistance. Aging Cell 4, 119–125 (2005).

    Article  CAS  PubMed  Google Scholar 

  28. Orentreich, N., Matias, J. R., DeFelice, A. & Zimmerman, J. A. Low methionine ingestion by rats extends life span. J. Nutr. 123, 269–274 (1993).

    CAS  PubMed  Google Scholar 

  29. Brown-Borg, H. M. et al. Growth hormone signaling is necessary for lifespan extension by dietary methionine. Aging Cell 13, 1019–1027 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ooka, H., Segall, P. E. & Timiras, P. S. Histology and survival in age-delayed low-tryptophan-fed rats. Mech. Ageing. Dev. 43, 79–98 (1988).

    Article  CAS  PubMed  Google Scholar 

  31. De Marte, M. L. & Enesco, H. E. Influence of low tryptophan diet on survival and organ growth in mice. Mech. Ageing. Dev. 36, 161–171 (1986).

    Article  PubMed  Google Scholar 

  32. Newgard, C. B. et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 9, 311–326 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Batch, B. C. et al. Branched chain amino acids are novel biomarkers for discrimination of metabolic wellness. Metabolism 62, 961–969 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Le Couteur, D. G. et al. Branched chain amino acids, cardiometabolic risk factors and outcomes in older men: the Concord Health and Ageing in Men Project. J. Gerontol. A Biol. Sci. Med. Sci. 75, 1805–1810 (2019).

    Article  Google Scholar 

  35. Karusheva, Y. et al. Short-term dietary reduction of branched-chain amino acids reduces meal-induced insulin secretion and modifies microbiome composition in type 2 diabetes: a randomized controlled crossover trial. Am. J. Clin. Nutr. 110, 1098–1107 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  36. White, P. J. et al. Branched-chain amino acid restriction in Zucker-fatty rats improves muscle insulin sensitivity by enhancing efficiency of fatty acid oxidation and acyl-glycine export. Mol. Metab. 5, 538–551 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. D’Antona, G. et al. Branched-chain amino acid supplementation promotes survival and supports cardiac and skeletal muscle mitochondrial biogenesis in middle-aged mice. Cell Metab. 12, 362–372 (2010).

    Article  PubMed  CAS  Google Scholar 

  38. Solon-Biet, S. M. et al. Branched-chain amino acids impact health and lifespan indirectly via amino acid balance and appetite control. Nat. Metab. 1, 532–545 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Wolfson, R. L. et al. Sestrin2 is a leucine sensor for the mTORC1 pathway. Science 351, 43–48 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Kennedy, B. K. & Lamming, D. W. The Mechanistic Target Of Rapamycin: The Grand ConducTOR of Metabolism and Aging. Cell Metab. 23, 990–1003 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wu, J. J. et al. Increased mammalian lifespan and a segmental and tissue-specific slowing of aging after genetic reduction of mTOR expression. Cell Rep. 4, 913–920 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Selman, C. et al. Ribosomal protein S6 kinase 1 signaling regulates mammalian life span. Science 326, 140–144 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Harrison, D. E. et al. Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature 460, 392–395 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Vellai, T. et al. Genetics: influence of TOR kinase on lifespan in C. elegans. Nature 426, 620 (2003).

    Article  CAS  PubMed  Google Scholar 

  45. Kapahi, P. et al. Regulation of lifespan in Drosophila by modulation of genes in the TOR signaling pathway. Curr. Biol. 14, 885–890 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Arriola Apelo, S. I. & Lamming, D. W. Rapamycin: an InhibiTOR of aging emerges from the soil of Easter Island. J. Gerontol. A Biol. Sci. Med. Sci. 71, 841–849 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Ramos, F. J. et al. Rapamycin reverses elevated mTORC1 signaling in lamin A/C-deficient mice, rescues cardiac and skeletal muscle function and extends survival. Sci. Transl. Med. 4, 144ra103 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Wang, C., Li, Q., Redden, D. T., Weindruch, R. & Allison, D. B. Statistical methods for testing effects on “maximum lifespan”. Mech. Ageing Dev. 125, 629–632 (2004).

    Article  PubMed  Google Scholar 

  49. Zaghini, A. et al. Long-term breeding of the LmnaG609G progeric mouse: characterization of homozygous and heterozygous models. Exp. Gerontol. 130, 110784 (2020).

    Article  CAS  PubMed  Google Scholar 

  50. Osorio, F. G. et al. Splicing-directed therapy in a new mouse model of human accelerated aging. Sci. Transl. Med. 3, 106ra107 (2011).

    Article  PubMed  CAS  Google Scholar 

  51. Whitehead, J. C. et al. A clinical frailty index in aging mice: comparisons with frailty index data in humans. J. Gerontol. A Biol. Sci. Med. Sci. 69, 621–632 (2014).

    Article  PubMed  Google Scholar 

  52. Kane, A. E. et al. Impact of longevity interventions on a validated mouse clinical frailty index. J. Gerontol. A Biol. Sci. Med. Sci. 71, 333–339 (2016).

    Article  CAS  PubMed  Google Scholar 

  53. Yousefzadeh M. J., et al. Circulating levels of monocyte chemoattractant protein-1 as a potential measure of biological age in mice and frailty in humans. Aging Cell 17, e12706 (2018).

  54. Mitchell, S. E. et al. The effects of graded levels of calorie restriction: impact of short term calorie and protein restriction on circulating hormone levels, glucose homeostasis and oxidative stress in male C57BL/6 mice. Oncotarget 6, 23213–23237 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Chaumontet, C. et al. Low-protein and methionine, high-starch diets increase energy intake and expenditure, increase FGF21, decrease IGF-1 and have little effect on adiposity in mice. Am. J. Physiol. Regul. Integr. Comp. Physiol. 316, R486–R501 (2019).

    Article  CAS  PubMed  Google Scholar 

  56. Miller, R. A. et al. Rapamycin-mediated lifespan increase in mice is dose and sex dependent and metabolically distinct from dietary restriction. Aging Cell 13, 468–477 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Mao, K. et al. Late-life targeting of the IGF-1 receptor improves healthspan and lifespan in female mice. Nat. Commun. 9, 2394 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Mitchell, S. J. et al. Effects of sex, strain and energy intake on hallmarks of aging in mice. Cell Metab. 23, 1093–1112 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Hahn, O. et al. A nutritional memory effect counteracts benefits of dietary restriction in old mice. Nat. Metab. 1, 1059–1073 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Yuan, R. et al. Genetic coregulation of age of female sexual maturation and lifespan through circulating IGF1 among inbred mouse strains. Proc. Natl Acad.Sci. USA 109, 8224–8229 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Turturro, A. et al. Growth curves and survival characteristics of the animals used in the Biomarkers of Aging Program. J. Gerontol. A Biol. Sci. Med. Sci. 54, B492–B501 (1999).

    Article  CAS  PubMed  Google Scholar 

  62. Hu, W. et al. Relationship between branched-chain amino acids, metabolic syndrome and cardiovascular risk profile in a Chinese population: a cross-sectional study. Int. J. Endocrinol. 2016, 8173905 (2016).

    PubMed  PubMed Central  Google Scholar 

  63. Du, X. et al. Relationships between circulating branched chain amino acid concentrations and risk of adverse cardiovascular events in patients with STEMI treated with PCI. Sci. Rep. 8, 15809 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Wang, W. et al. Defective branche-chain amino acid catabolism contributes to cardiac dysfunction and remodeling following myocardial infarction. Am. J. Physiol. Heart Circ. Physiol. 311, H1160–H1169 (2016).

    Article  PubMed  Google Scholar 

  65. Sun, H. et al. Catabolic defect of branched-chain amino acids promotes heart failure. Circulation 133, 2038–2049 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Chantranupong, L. et al. The CASTOR proteins are arginine sensors for the mTORC1 pathway. Cell 165, 153–164 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Chantranupong, L. et al. The sestrins interact with GATOR2 to negatively regulate the amino-acid-sensing pathway upstream of mTORC1. Cell Rep. 9, 1–8 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Barcena, C. et al. Methionine restriction extends lifespan in progeroid mice and alters lipid and bile acid metabolism. Cell Rep. 24, 2392–2403 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Vermeij, W. P. et al. Restricted diet delays accelerated ageing and genomic stress in DNA-repair-deficient mice. Nature 537, 427–431 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Frock, R. L. et al. Lamin A/C and emerin are critical for skeletal muscle satellite cell differentiation. Genes Dev. 20, 486–500 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Arriola Apelo, S. I. et al. Ovariectomy uncouples lifespan from metabolic health and reveals a sex-hormone-dependent role of hepatic mTORC2 in aging. eLife 9, e56177 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Chellappa, K. et al. Hypothalamic mTORC2 is essential for metabolic health and longevity. Aging Cell 18, e13014 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Yu, D. et al. Calorie-restriction-induced insulin sensitivity is mediated by adipose mTORC2 and not required for lifespan extension. Cell Rep. 29, 236–248 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Sharples, A. P. et al. Longevity and skeletal muscle mass: the role of IGF signalling, the sirtuins, dietary restriction and protein intake. Aging Cell 14, 511–523 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Ericksen, R. E. et al. Loss of BCAA catabolism during carcinogenesis enhances mTORC1 activity and promotes tumor development and progression. Cell Metab. 29, 1151–1165 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Ananieva, E. A. & Wilkinson, A. C. Branched-chain amino acid metabolism in cancer. Curr. Opin. Clin. Nutr. Metab. Care 21, 64–70 (2018).

    Article  CAS  PubMed  Google Scholar 

  77. Jensen, K., McClure, C., Priest, N. K. & Hunt, J. Sex-specific effects of protein and carbohydrate intake on reproduction but not lifespan in Drosophila melanogaster. Aging Cell 14, 605–615 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Larson, K. R. et al. Sex differences in the hormonal and metabolic response to dietary protein dilution. Endocrinology 158, 3477–3487 (2017).

    Article  CAS  PubMed  Google Scholar 

  79. Miller, D. S. & Payne, P. R. Longevity and protein intake. Exp. Gerontol. 3, 231–234 (1968).

    Article  CAS  PubMed  Google Scholar 

  80. Liao, C. Y., Rikke, B. A., Johnson, T. E., Diaz, V. & Nelson, J. F. Genetic variation in the murine lifespan response to dietary restriction: from life extension to life shortening. Aging Cell 9, 92–95 (2010).

    Article  CAS  PubMed  Google Scholar 

  81. Barrington, W. T. et al. Improving metabolic health through precision dietetics in mice. Genetics 208, 399–417 (2018).

    Article  CAS  PubMed  Google Scholar 

  82. Sittig, L. J. et al. Genetic background limits generalizability of genotype–phenotype relationships. Neuron 91, 1253–1259 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Baar, E. L., Carbajal, K. A., Ong, I. M. & Lamming, D. W. Sex- and tissue-specific changes in mTOR signaling with age in C57BL/6J mice. Aging Cell 15, 155–166 (2016).

    Article  CAS  PubMed  Google Scholar 

  84. Joseph, G. A. et al. Partial inhibition of mTORC1 in aged rats counteracts the decline in muscle mass and reverses molecular signaling associated with sarcopenia. Mol. Cell. Biol. 39, e00141 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Zhang, Y. K. et al. Enoyl-CoA hydratase-1 regulates mTOR signaling and apoptosis by sensing nutrients. Nat. Commun. 8, 464 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Zhou, M. et al. Targeting BCAA catabolism to treat obesity-associated insulin resistance. Diabetes 68, 1730–1746 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Stephenson E. J., et al. Skeletal muscle mTORC1 activation increases energy expenditure and reduces longevity in mice. Preprint at bioRxiv https://doi.org/10.1101/720540720540 (2019).

  88. Dai, D. F. et al. Altered proteome turnover and remodeling by short-term caloric restriction or rapamycin rejuvenate the aging heart. Aging Cell 13, 529–539 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Flynn, J. M. et al. Late-life rapamycin treatment reverses age-related heart dysfunction. Aging Cell 12, 851–862 (2013).

    Article  CAS  PubMed  Google Scholar 

  90. Zhang, Y. et al. The starvation hormone, fibroblast growth factor-21, extends lifespan in mice. eLife 1, e00065 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  91. Sullivan, T. et al. Loss of A-type lamin expression compromises nuclear envelope integrity leading to muscular dystrophy. J. Cell Biol. 147, 913–920 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Arriola Apelo, S. I. et al. Alternative rapamycin treatment regimens mitigate the impact of rapamycin on glucose homeostasis and the immune system. Aging Cell 15, 28–38 (2016).

    Article  CAS  PubMed  Google Scholar 

  93. Mitnitski, A. B., Mogilner, A. J. & Rockwood, K. Accumulation of deficits as a proxy measure of aging. ScientificWorldJournal 1, 323–336 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Fried, L. P. et al. Frailty in older adults: evidence for a phenotype. J. Gerontol. A Biol. Sci. Med. Sci. 56, M146–M156 (2001).

    Article  CAS  PubMed  Google Scholar 

  95. Harris, S. P. et al. Hypertrophic cardiomyopathy in cardiac myosin binding protein-C knockout mice. Circ. Res. 90, 594–601 (2002).

    Article  CAS  PubMed  Google Scholar 

  96. Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. 46, D754–D761 (2018).

    Article  CAS  PubMed  Google Scholar 

  97. R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2017).

  98. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  101. Liang, H. et al. Genetic mouse models of extended lifespan. Exp. Gerontol. 38, 1353–1364 (2003).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank all members of the Lamming laboratory for their valuable insights and comments and C. Alexander and J. Baur for critical reading of the manuscript. We thank T. Herfel (Envigo) for assistance with the formulation of the AA-defined diets. We thank C. López-Otin and B. Kennedy for providing the LmnaG609 mutant mice and Y. Hsu and M. O’Leary for their assistance with initial genotyping of the LmnaG609 mice. We thank C. Green for assistance with analysis of RNA-sequencing data and generating panels in Fig. 6 and Extended Data Fig. 4. We apologize for any papers not cited. This work was supported in part by grants from the NIH (AG041765, AG050135, AG051974, AG056771 and AG062328 to D.W.L.), by a Glenn Foundation Award for Research in the Biological Mechanisms of Aging (to D.W.L.) and by funding from the University of Wisconsin-Madison School of Medicine and Public Health and Department of Medicine (to D.W.L). This work was supported by a grant from the Progeria Research Foundation (to D.W.L). This research was conducted in part while D.W.L. was an AFAR Research Grant recipient from the American Federation for Aging Research. N.E.R. was supported in part by a training grant from the UW Institute on Aging (NIA T32 AG000213). H.H.P. was supported in part by a NIA F31 predoctoral fellowship (NIA F31 AG066311). V.F. was supported in part by a Research Supplement to Promote Diversity in Health‐Related Research (R01 AG056771-01A1S1). D.Y. was supported in part by a fellowship from the American Heart Association (17PRE33410983). The UW Carbone Cancer Center (UWCCC) Experimental Pathology Laboratory is supported by UWCCC support grant P30 CA014520 from the NIH National Cancer Institute. The Lamming laboratory is supported in part by the U.S. Department of Veterans Affairs (I01-BX004031), and this work was supported using facilities and resources from the William S. Middleton Memorial Veterans Hospital. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work does not represent the views of the Department of Veterans Affairs or the United States Government.

Author information

Authors and Affiliations

  1. Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA

    Nicole E. Richardson, Elizabeth N. Konon, Haley S. Schuster, Alexis T. Mitchell, Colin Boyle, Allison C. Rodgers, Megan Finke, Lexington R. Haider, Deyang Yu, Victoria Flores, Heidi H. Pak, Soha Ahmad, Sareyah Ahmed, Abigail Radcliff, Jessica Wu, Elizabeth M. Williams, Lovina Abdi, Dawn S. Sherman, Timothy A. Hacker & Dudley W. Lamming

  2. William S. Middleton Memorial Veterans Hospital, Madison, WI, USA

    Nicole E. Richardson, Elizabeth N. Konon, Haley S. Schuster, Alexis T. Mitchell, Colin Boyle, Megan Finke, Lexington R. Haider, Deyang Yu, Victoria Flores, Heidi H. Pak, Soha Ahmad, Sareyah Ahmed, Abigail Radcliff, Jessica Wu, Elizabeth M. Williams, Lovina Abdi, Dawn S. Sherman & Dudley W. Lamming

  3. Endocrinology and Reproductive Physiology Graduate Training Program, University of Wisconsin-Madison, Madison, WI, USA

    Nicole E. Richardson & Dudley W. Lamming

  4. Interdisciplinary Graduate Program in Nutritional Sciences, University of Wisconsin-Madison, Madison, WI, USA

    Victoria Flores, Heidi H. Pak & Dudley W. Lamming

  5. University of Wisconsin Carbone Cancer Center, Madison, WI, USA

    Dudley W. Lamming

Authors
  1. Nicole E. Richardson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elizabeth N. Konon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haley S. Schuster
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alexis T. Mitchell
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Colin Boyle
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Allison C. Rodgers
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Megan Finke
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lexington R. Haider
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Deyang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Victoria Flores
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Heidi H. Pak
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Soha Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Sareyah Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Abigail Radcliff
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jessica Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Elizabeth M. Williams
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Lovina Abdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Dawn S. Sherman
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Timothy A. Hacker
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Dudley W. Lamming
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Experiments were performed in the Lamming and Hacker laboratories. N.E.R., T.H. and D.W.L. conceived and designed the experiments. All authors participated in performing the experiments. N.E.R., T.H. and D.W.L. analysed the data and prepared the manuscript.

Corresponding author

Correspondence to Dudley W. Lamming.

Ethics declarations

Competing interests

D.W.L. has received funding from, and is a scientific advisory board member of, Aeovian Pharmaceuticals, which seeks to develop new, selective mTOR inhibitors for the treatment of various diseases. The University of Wisconsin-Madison has applied for a patent for the use of BCAA-restricted diets to promote metabolic health, for which N.E.R. and D.W.L. are inventors.

Additional information

Peer review information Nature Aging thanks Matt Kaeberlein and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended Data

Extended Data Fig. 1 A Low BCAA diet promotes the metabolic health of aged wild-type mice.

a-g, Female and h-n, Male C57BL/6 J.Nia mice were fed the indicated diets beginning at 16 months of age. a, The lean mass of a subset of female mice was tracked (n varies by month; maximum N = 10 biologically independent animals for both groups; * p < 0.05 (p value by month of age: 19 mo. = 0.0091, 21.5 mo. <0.0001, 24.5 mo. = 0.0004). b, Food consumption over time calculated as total kcal/d (maximum N = 3 independent cages for both groups). c, Energy expenditure (Heat) was assessed using metabolic chambers at 20 months of age (N; Control = 20, Low BCAA = 17 biologically independent animals). d, Respiratory exchange ratio and e, ambulatory movement was assessed using metabolic chambers at 20 and 25 months of age (maximum N; Control = 20, Low BCAA = 17 biologically independent animals; * p < 0.05 (p value for d by month of age: 20 mo. light=0.0056, dark=0.0105, 25 mo. dark=0.0012). f, Area under the curve (AUC) corresponding to the glucose tolerance test in Fig. 2g as well as repeat tests performed at 19 and 24 months of age (maximum N = 20 biologically independent animals for both groups; * p < 0.05, # p < 0.1 (p value by month of age: 17 mo. = 0.0034, 19 mo. = 0.0714, 24 mo. = 0.0016). g, Insulin tolerance test and corresponding area under the curve after four weeks of diet feeding (N; Control = 20, Low BCAA = 15 biologically independent animals). h, The lean mass of a subset of male mice was tracked (n varies by month; maximum N = 20 biologically independent animals for both groups, * p = 0.0066). i, Food consumption over time calculated as total kcal/d (maximum N = 3 independent cages for both groups). j, Energy expenditure (Heat) was assessed using metabolic chambers at 25 months of age (N = 13 biologically independent animals for both groups). k, Respiratory exchange ratio and (l) ambulatory movement was assessed using metabolic chambers at 20 and 25 months of age (maximum N = 14 biologically independent animals for both groups). m, AUC corresponding to glucose tolerance test in Fig. 2m as well as repeat tests performed at 19 and 24 months of age (maximum N = 20 biologically independent animals for both groups; * p < 0.05 (p-value by month of age: 17 mo. = 0.0006, 19 mo. =0.0240, 24 mo. = 0.0026). n, Insulin tolerance test and corresponding area under the curve after four weeks of diet feeding (N; Control = 10, Low BCAA = 9 biologically independent animals; * p = 0.0192). a-b,d-f,h-i,k-m, Statistics for the overall effects of diet, age, and the interaction represent the p value from a mixed-effects model (restricted maximum likelihood [REML]) or two-way repeated measures ANOVA, multiple comparisons by two-sided Sidak’s post-test. c,j, Energy expenditure data was analysed by linear regression of energy expenditure by body weight (ANCOVA). g,n, AUC comparisons were made by two-sided t-test, * p < 0.05. Data are represented as mean ± SEM.

Extended Data Fig. 2 Effects of a lifelong Low BCAA diet on the health span of female mice.

a, Schematic showing timeline of measurements taken from male and female mice fed Control or Low BCAA diets, relevant to Fig. 5 and Extended Data Figs. 2 and 3. b-n, Wild-type female mice were placed on either Control or Low BCAA diets at weaning. b-c, The b, lean mass and c, fat mass of a subset of mice was tracked (n varies by month, maximum N; Control = 15, Low BCAA = 12 biologically independent animals). b, * p < 0.05 (p-values for by month of age: 2.75 mo. = 0.0004. 5 mo. < 0.0001, 8.5 mo. = 0.0003, 15 mo. = 0.0341). c, * p < 0.05, # p < 0.1 (p-values by month of age: 2 mo. = 0.0102, 8.5 mo. = 0.0695, 15 mo. = 0.0652). d-e, Food consumption was calculated per gram of body weight d, and by animal e, (N; Control = 4, Low BCAA = 7 biologically independent animals). f-h, Respiratory exchange ratio (f), ambulatory movement (g), and energy expenditure (heat) (h) were assessed using metabolic chambers at 5 months of age (N; Control = 6, Low BCAA = 7 biologically independent animals). i, Glucose tolerance test at 2 months of age (N; Control = 18, Low BCAA = 16 biologically independent animals; * p < 0.05 (p-value by time: 0 m = 0.0005, 15 m = 0.0120, 60 m = 0.0267), and corresponding area under the curve (AUC), also from tests performed at 3.5 and 12 months of age (maximum N; Control = 24, Low BCAA = 18 biologically independent animals; * p = 0.0063). j, Insulin tolerance test at 2.5 months of age (N; Control = 13, Low BCAA = 12 biologically independent animals), and corresponding area under the curve, also from a test at 4 months of age (N; Control = 3, Low BCAA = 4 biologically independent animals). k, Rotarod performance (n varies by month, maximum N; Control = 12, Low BCAA = 8 biologically independent animals) and l, grip strength (n varies by month, maximum N; Control = 15, Low BCAA = 12 biologically independent animals) was assessed longitudinally. m-n, Levels of m, insulin and n, fibroblast growth factor 21 (FGF21) were measured in serum by ELISA (16 months of age; N = 4 biologically independent animals per group). b-n, Statistics for the overall effects of diet, age, and the interaction represent the p value from a mixed-effects model (restricted maximum likelihood [REML]), two-way repeated measures ANOVA, or a two-tailed, unpaired t-test in m-n; multiple comparisons by two sided Sidak’s post-test. Data are represented as mean ± SEM.

Extended Data Fig. 3 Effects of a lifelong Low BCAA diet on the healthspan of male mice.

a-m, Wild-type male mice were placed on either Control or Low BCAA diets at weaning. a-b, The a, lean mass and b, fat mass of a subset of mice was tracked (n varies by month, maximum N; Control = 11, Low BCAA = 8 biologically independent animals; * p < 0.05 (p-values for a by month of age: 2 mo. = 0.0028, 2.75 mo. = 0.0194, 5 mo. = 0.0011, 8.5 mo. = 0.0246; for b * p = 0.0095). c-d, Food consumption (N; Control = 4, Low BCAA = 6 biologically independent animals), was calculated c, per gram of body weight and d, by animal. e, Respiratory exchange ratio, f, ambulatory movement, and g, energy expenditure (heat) were assessed using metabolic chambers at 5 months of age (e-g; N = 6 biologically independent animals for both groups). h, Glucose tolerance test at 2 months of age (N; Control = 14, Low BCAA = 8 biologically independent animals), and corresponding area under the curve (AUC), also from tests performed at 3.5 and 12 months of age (maximum N; Control = 23, Low BCAA = 12 biologically independent animals; * p < 0.05 (p-values by month of age: 3.5 mo. = 0.0379, 12 mo. = 0.0054). i, Insulin tolerance test at 2.5 months of age (N; Control = 15, Low BCAA = 13 biologically independent animals), and corresponding area under the curve, also from a test at 4 months of age (maximum N; Control = 15, Low BCAA = 13 biologically independent animals). j, Rotarod performance (n varies by month, maximum N; Control = 15, Low BCAA = 12 biologically independent animals) and k, grip strength (n varies by month, maximum N; Control = 12, Low BCAA = 8 biologically independent animals) were assessed longitudinally. Levels of l, insulin and m, fibroblast growth factor 21 (FGF21) were measured in serum by ELISA (16 months of age; N; Control = 5, Low BCAA = 4 biologically independent animals per group). a-m, Statistics for the overall effects of diet, age, and the interaction represent the p value from a mixed-effects model (restricted maximum likelihood [REML]), two-way repeated measures ANOVA, or a two-tailed, unpaired t-test in l-m; multiple comparisons by two-sided Sidak’s post-test. Data are represented as mean ± SEM.

Extended Data Fig. 4 Transcriptional profiling of skeletal muscle.

Transcriptional profiling was performed on mRNA from the skeletal muscle of male and female mice that consumed either Control or Low BCAA diets from weaning until 16 months of age (N = 6 biologically independent animals for all groups; Supplementary Tables 13 and 14). a, Workflow from raw RNA sequencing reads through data analysis and data representation. b-c, Principal component analysis for b, Control and c, Low BCAA fed groups. d, Heatmaps of differentially expressed genes by mouse from significant KEGG over-representation analysis (ORA) pathways of interest identified in Supplementary Tables 13b and 14b. DEGs were identified using an empirical Bayes moderated linear model. *Two-sided P values adjusted with the Benjamini–Hochberg procedure ORA was performed on DEGs (designated by an adjusted P value of 0.3 for female and 0.2 for male contrasts) using a one-sided hypergeometric test, and P values were adjusted using the Benjamini–Hochberg procedure.

Extended Data Fig. 5 A Low BCAA diet reduces mTORC1 activity in male, but not female, muscle.

a-b, mTORC1 activity determined by Western blotting and quantification of muscle tissue lysates from male and female mice. Young (12 months females; 15 months males) and aged (22 months females; 25 months males) mice were fed either a Control or Low BCAA diets from 6.5 months of age for young and 16 months of age for aged mice, then sacrificed following an overnight fast followed by 4 hours of refeeding. a, Male and b, Female muscle. Quantification was by ImageJ (N = 3 biologically independent animals for all groups). a-b, * p < 0.05 (p-values for (a); pS6/S6, Young CTL vs. Young LBC = 0.0025; Young CTL vs. Aged CTL = 0.0327; pS6K1/S6K1, Aged CTL vs. Aged LBC = 0.0254). Statistics for the overall effects of diet, age and the interaction represent the p value from a two-way repeated measures ANOVA, multiple comparisons by two-sided Sidak’s post-test. Full scans of the cropped western blots shown here are provided as Source Data files. CTL = Control, LBC = Low BCAA. Data are represented as mean ± SEM.

Extended Data Fig. 6 A Low BCAA diet reduces mTORC1 signaling in the liver of male mice.

a-b, mTORC1 activity determined by Western blotting and quantification of liver tissue lysates from male and female mice. Young (12 months females; 15 months males) and aged (22 months females; 25 months males) mice were fed either a Control or Low BCAA diets from 6.5 months of age for young and 16 months of age for aged mice, then sacrificed following an overnight fast followed by 4 hours of refeeding. a, Male and b, female liver. Quantification was by ImageJ (N = 3 biologically independent animals for all groups). a, * p < 0.05 (pS6/S6, Young CTL vs. Young LBC = 0.0028, Young CTL vs. Aged CTL = 0.021; pS6K1/S6K1, Young CTL vs. Young LBC = 0.0086, Young LBC vs. Aged LBC = 0.0309; pULK1/ULK1, Aged CTL vs. Aged LBC = 0.0432, Young LBC vs. Aged LBC = 0.003). b, * p = 0.0047, # = 0.056. Statistics for the overall effects of diet, age and the interaction represent the p value from a two-way repeated measures ANOVA, multiple comparisons by two-sided Sidak’s post-test. Full scans of the cropped western blots shown here are provided as Source Data files. CTL = Control, LBC = Low BCAA. Data are represented as mean ± SEM.

Supplementary information

Source data

Source Data Fig. 5

Full scans of muscle western blots.

Source Data Fig. 6

Full scans of liver western blots.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Richardson, N.E., Konon, E.N., Schuster, H.S. et al. Lifelong restriction of dietary branched-chain amino acids has sex-specific benefits for frailty and life span in mice. Nat Aging 1, 73–86 (2021). https://doi.org/10.1038/s43587-020-00006-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43587-020-00006-2

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing