Abstract
Background
Protein leverage (PL) is the phenomenon of consuming food until absolute intake of protein approaches a ‘target value’, such that total energy intake (TEI) varies passively with the ratio of protein: non-protein energy (fat + carbohydrate) in the diet. The PL hypothesis (PLH) suggests that the dilution of protein in energy-dense foods, particularly those rich in carbohydrates and fats, combines with protein leverage to contribute to the global obesity epidemic. Evidence for PL has been reported in younger adults, children and adolescents. This study aimed to test for PL and the protein leverage hypothesis (PLH) in a cohort of older adults.
Methods
We conducted a retrospective analysis of dietary intake in a cohort of 1699 community-dwelling older adults aged 67–84 years from the NuAge cohort. We computed TEI and the energy contribution (EC) from each macronutrient. The strength of leverage of macronutrients was assessed through power functions (\({TEI}=\mu * {{EC}}^{L}\)). Body mass index (BMI) was calculated, and mixture models were fitted to predict TEI and BMI from macronutrients’ ECs.
Results
In this cohort of older adults, 53% of individuals had obesity and 1.5% had severe cases. The mean TEI was 7673 kJ and macronutrients’ ECs were 50.4%, 33.2% and 16.4%, respectively for carbohydrates, fat, and protein. There was a strong negative association (L = −0.37; p < 0.001) between the protein EC and TEI. Each percent of energy intake from protein reduced TEI by 77 kJ on average, ceteris paribus. However, BMI was unassociated with TEI in this cohort.
Conclusions
Findings indicate clear evidence for PL on TEI, but not on BMI, likely because of aging, body composition, sarcopenia, or protein wasting.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
All data supporting the conclusions of these analyses are presented in the manuscript or the supplementary material. Details of additional data can be obtained from the study authors upon reasonable request.
Code availability
The developed R project, containing all computer codes used for generating and analyzing the results presented in this article, is available upon request to ensure transparency and reproducibility. Interested parties may contact the corresponding author to obtain access to the codes. We are committed to facilitating open and collaborative research practices and will provide the codes promptly, along with any necessary documentation and version details.
References
Bourre JM. Effects of nutrients (in food) on the structure and function of the nervous system: update on dietary requirements for brain. Part 2: macronutrients. J Nutr Health Aging. 2009;10:386–99.
Hawkesford M, Horst W, Kichey T, Lambers H, Schjoerring J, Møller IS, et al. Functions of macronutrients. In: Marschner P (ed). Marschner’s Mineral Nutrition of Higher Plants. 3rd ed. Academic Press, London, UK, 2012. pp 135–89.
Burton BT, Foster WR. Human Nutrition. 4th ed. McGraw-Hill Book Company, New York, 1988.
Simpson SJ, Raubenheimer D. Obesity: the protein leverage hypothesis. Obes Rev. 2005;6:133–42.
Després JP, Lemieux I. Abdominal obesity and metabolic syndrome. Nature. 2006;444:881–7.
Simpson SJ, Batley R, Raubenheimer D. Geometric analysis of macronutrient intake in humans: the power of protein? Appetite. 2003;41:123–40.
Raubenheimer D, Simpson SJ. Protein leverage: theoretical foundations and ten points of clarification. Obesity. 2019;27:1225–38.
Simpson SJ, Raubenheimer D. The nature of nutrition: a unifying framework. Aust J Zool. 2012;59:350–68.
Allaway D, de Alvaro CH, Hewson-Hughes A, Staunton R, Morris P, Alexander J. Impact of dietary macronutrient profile on feline body weight is not consistent with the protein leverage hypothesis. Br J Nutr. 2018;120:1310–8.
Sørensen A, Mayntz D, Raubenheimer D, Simpson SJ. Protein‐leverage in mice: the geometry of macronutrient balancing and consequences for fat deposition. Obesity. 2008;16:566–71.
Saner C, Tassoni D, Harcourt BE, Kao KT, Alexander EJ, McCallum Z, et al. Evidence for protein leverage in children and adolescents with obesity. Obesity. 2020;28:822–9.
Gosby AK, Conigrave AD, Lau NS, Iglesias MA, Hall RM, Jebb SA, et al. Testing protein leverage in lean humans: a randomised controlled experimental study. PLoS One. 2011;6:e25929.
Martens EA, Lemmens SG, Westerterp-Plantenga MS. Protein leverage affects energy intake of high-protein diets in humans. Am J Clin Nutr. 2013;97:86–93.
Campbell CP, Raubenheimer D, Badaloo AV, Gluckman PD, Martinez C, Gosby A, et al. Developmental contributions to macronutrient selection: a randomized controlled trial in adult survivors of malnutrition. Evol Med Public Health. 2016;2016:158–69.
Martinez-Cordero C, Kuzawa CW, Sloboda DM, Stewart J, Simpson SJ, Raubenheimer D. Testing the Protein Leverage Hypothesis in a free-living human population. Appetite. 2012;59:312–5.
Gosby AK, Conigrave AD, Raubenheimer D, Simpson SJ. Protein leverage and energy intake. Obes Rev. 2014;15:183–91.
United Nations, Department of Economic and Social Affairs, Population Division (eds). World population ageing 2019: Highlights (ST/ESA/SER.A/430). United Nations, New York, 2019.
Pontzer H, Yamada Y, Sagayama H, Ainslie PN, Andersen LF, Anderson LJ, et al. Daily energy expenditure through the human life course. Science. 2021;373:808–12.
Senior AM, Solon-Biet SM, Cogger VC, Le Couteur DG, Nakagawa S, Raubenheimer D, et al. Dietary macronutrient content, age-specific mortality and lifespan. Proc R Soc Lond B Biol Sci. 2019;286:20190393.
Austin GL, Ogden LG, Hill JO. Trends in carbohydrate, fat, and protein intakes and association with energy intake in normal-weight, overweight, and obese individuals: 1971–2006. Am J Clin Nutr. 2011;93:836–43.
Gaudreau P, Morais JA, Shatenstein B, Gray-Donald K, Khalil A, Dionne I, et al. Nutrition as a determinant of successful aging: description of the Quebec longitudinal study Nuage and results from cross-sectional pilot studies. Rejuvenation Res. 2007;10:377–86.
Moshfegh AJ, Borrud L, Perloff B, LaComb R. Improved method for the 24-hour dietary recall for use in national surveys. FASEB J. 1999;13:A603.
Gray-Donald K, Arnaud-McKenzie DS, Gaudreau P, Morais JA, Shatenstein B, Payette H. Protein intake protects against weight loss in healthy community-dwelling older adults. J Nutr. 2014;144:321–6.
Huang HH, Cohen AA, Gaudreau P, Auray-Blais C, Allard D, Boutin M, et al. Vitamin B-12 intake from dairy but not meat is associated with decreased risk of low vitamin B-12 status and deficiency in older adults from Quebec, Canada. J Nutr. 2022. https://doi.org/10.1093/jn/nxac143.
Resnick HE, Valsania P, Halter JB, Lin X. Relation of weight gain and weight loss on subsequent diabetes risk in overweight adults. J Epidemiol Community Health. 2000;54:596–602.
Wannamethee SG, Shaper AG, Walker M. Weight change, body weight and mortality: the impact of smoking and ill health. Int J Epidemiol. 2001;30:777–86.
Washburn RA, McAuley E, Katula J, Mihalko SL, Boileau RA. The physical activity scale for the elderly (PASE): evidence for validity. J Clin Epidemiol. 1999;52:643–51.
R Core Team (eds). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2022.
Scheffé H. Experiments with mixtures. J R Stat Soc Series B Stat Methodol. 1958;20:344–60.
Lawson J, Willden C. Mixture experiments in R using mixexp. J Stat Softw. 2016;72:1–20.
Akaike H. Information theory and an extension of the maximum likelihood principle. In: Parzen E, Tanabe K, Kitagawa G (eds). Selected Papers of Hirotugu Akaike. Springer, New York, 1998. pp 199–213.
Raubenheimer D. Toward a quantitative nutritional ecology: the right‐angled mixture triangle. Ecol Monogr. 2011;81:407–27.
Greenway F. Physiological adaptations to weight loss and factors favouring weight regain. Int J Obes. 2015;39:1188–96.
Leibel RL, Rosenbaum M, Hirsch J. Changes in energy expenditure resulting from altered body weight. N Engl J Med. 1995;332:621–8.
DeLany JP, Kelley DE, Hames KC, Jakicic JM, Goodpaster BH. Effect of physical activity on weight loss, energy expenditure, and energy intake during diet induced weight loss. Obesity. 2014;22:363–70.
Zhao Z, Zhen S, Yan Y, Liu N, Ding D, Kong J. Association of dietary patterns with general and central obesity among Chinese adults: a longitudinal population-based study. BMC Public Health. 2023;23:1588.
Roman G, Rusu A, Graur M, Creteanu G, Morosanu M, Radulian G, et al. Dietary patterns and their association with obesity: a cross-sectional study. Acta Endocrinol. 2019;15:86–95.
Gutiérez-Pliego LE, Camarillo-Romero EDS, Montenegro-Morales LP, Garduño-García JDJ. Dietary patterns associated with body mass index (BMI) and lifestyle in Mexican adolescents. BMC Public Health. 2016;16:850.
Batsis JA, Villareal DT. Sarcopenic obesity in older adults: aetiology, epidemiology and treatment strategies. Nat Rev Endocrinol. 2018;14:513–37.
Hengeveld LM, Boer JM, Gaudreau P, Heymans MW, Jagger C, Mendonça N, et al. Prevalence of protein intake below recommended in community‐dwelling older adults: a meta‐analysis across cohorts from the PROMISS consortium. J Cachexia Sarcopenia Muscle. 2020;11:1212–22.
Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48:601.
Tessier A, Wing SS, Rahme E, Morais JA, Chevalier S. Physical function‐derived cut‐points for the diagnosis of sarcopenia and dynapenia from the Canadian longitudinal study on aging. J Cachexia Sarcopenia Muscle. 2019;10:985–99.
Saner C, Senior AM, Zhang H, Eloranta AM, Magnussen CG, Sabin MA, et al. Evidence for protein leverage in a general population sample of children and adolescents. Eur J Clin Nutr. 2023;77:652–9.
Bosy-Westphal A, Hägele FA, Müller MJ. What is the impact of energy expenditure on energy intake? Nutrients. 2021;13:3508.
Palmer AK, Jensen MD. Metabolic changes in aging humans: current evidence and therapeutic strategies. J Clin Investig. 2022;132:e158451.
Bartke A, Brannan S, Hascup E, Hascup K, Darcy J. Energy metabolism and aging. Worl J Mens Health. 2021;39:222–32.
Mashili FL, Austin RL, Deshmukh AS, Fritz T, Caidahl K, Bergdahl K, et al. Direct effects of FGF21 on glucose uptake in human skeletal muscle: implications for type 2 diabetes and obesity. Diabetes Metab Res Rev. 2011;27:286–97.
Chavez AO, Molina-Carrion M, Abdul-Ghani MA, Folli F, DeFronzo RA, Tripathy D. Circulating fibroblast growth factor-21 is elevated in impaired glucose tolerance and type 2 diabetes and correlates with muscle and hepatic insulin resistance. Diabetes Care. 2009;32:1542–6.
Nakanishi K, Ishibashi C, Ide S, Yamamoto R, Nishida M, Nagatomo I, et al. Serum FGF21 levels are altered with various factors including lifestyle behaviors. Sci Rep. 2021;11:22632.
Søberg S, Sandholt CH, Jespersen NZ, Toft U, Madsen AL, von Holstein-Rathlou S, et al. FGF21 is a sugar-induced hormone associated with sweet intake and preference in humans. Cell Metab. 2017;25:1045–53.
Coelho-Júnior HJ, Milano-Teixeira L, Rodrigues B, Bacurau R, Marzetti E, Uchida M. Relative protein intake and physical function in older adults: a systematic review and meta-analysis of observational studies. Nutrients. 2018;10:1330.
Dawadi H, Al-Bayyari N, Tayyem R, Shi Z. Protein intake among patients with insulin-treated diabetes is linked to poor glycemic control: findings of NHANES data. Diabetes Metab Syndr Obes. 2022;15:767–75.
Beaudry KM, Devries MC. Nutritional strategies to combat type 2 diabetes in aging adults: the importance of protein. Front Nutr. 2019;6:138.
Beasley JM, Wylie-Rosett J. The role of dietary proteins among persons with diabetes. Curr Atheroscler Rep. 2013;15:1–11. https://doi.org/10.1007/s11883-013-0348-2.
Kelava A, Nagengast B, Brandt H. A nonlinear structural equation mixture modeling approach for nonnormally distributed latent predictor variables. Struct Equ Modeling. 2014;21:468–81.
Parent SÉ. Why we should use balances and machine learning to diagnose ionomes. Authorea Prepr. 2020;1:1–13. https://doi.org/10.1080/07351699409533991.
Funding
The NuAge Study was supported by a research grant from the Canadian Institutes of Health Research (CIHR; MOP-62842). The NuAge Database and Biobank are supported by the Fonds de recherche du Québec (FRQ; 2020-VICO-279753), the Quebec Network for Research on Aging, a thematic network funded by the Fonds de Recherche du Québec - Santé (FRQS) and by the Merck-Frosst Chair funded by La Fondation de l’Université de Sherbrooke. NP is a Junior 1 Research Scholar of the FRQS. AAC is a Senior Research Scholar of the FRQS. PG is a fellow of the Canadian Academy of Health Sciences.
Author information
Authors and Affiliations
Contributions
SHH conducted data analysis data and wrote the manuscript. AAC designed the study and reviewed the manuscript. NP, VT gave access to NuAge data and reviewed the manuscript. VL, PG, SJS and DR reviewed the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Honfo, S.H., Senior, A.M., Legault, V. et al. Evidence for protein leverage on total energy intake, but not body mass index, in a large cohort of older adults. Int J Obes 48, 654–661 (2024). https://doi.org/10.1038/s41366-023-01455-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41366-023-01455-6