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Impact of dietary carbohydrate type and protein–carbohydrate interaction on metabolic health

Nature Metabolism volume 3, pages 810–828 (2021)Cite this article

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Abstract

Reduced protein intake, through dilution with carbohydrate, extends lifespan and improves mid-life metabolic health in animal models. However, with transition to industrialised food systems, reduced dietary protein is associated with poor health outcomes in humans. Here we systematically interrogate the impact of carbohydrate quality in diets with varying carbohydrate and protein content. Studying 700 male mice on 33 isocaloric diets, we find that the type of carbohydrate and its digestibility profoundly shape the behavioural and physiological responses to protein dilution, modulate nutrient processing in the liver and alter the gut microbiota. Low (10%)-protein, high (70%)-carbohydrate diets promote the healthiest metabolic outcomes when carbohydrate comprises resistant starch (RS), yet the worst outcomes were with a 50:50 mixture of monosaccharides fructose and glucose. Our findings could explain the disparity between healthy, high-carbohydrate diets and the obesogenic impact of protein dilution by glucose–fructose mixtures associated with highly processed diets.

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Fig. 1: The effects of fructose, glucose and protein intake on metabolic characteristics of mice.
Fig. 2: The effects of fructose, glucose and protein intake on liver metabolism.
Fig. 3: The effect of substituting HFCS with sucrose on metabolic phenotype.
Fig. 4: The effects of sucrose, starch and protein intake on metabolic characteristics of mice.
Fig. 5: The effects of sucrose, starch and protein intake on glucose homoeostasis.
Fig. 6: The effects of sucrose, starch and protein intake on liver metabolism.
Fig. 7: The effect of substituting NS with RS on metabolic phenotype.

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Data availability

The data that support the plots within this article and other findings of this study are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

Standard scripts used for data analysis in R and ImageJ software are available from the corresponding authors upon reasonable request. Custom R scripts used for data analysis were also uploaded to GitHub and are available at https://github.com/AlistairMcNairSenior/GFN_SugarMouse/tree/main. Correspondence and requests for codes and their details can be addressed to S.J.S. and J.A.W.

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Acknowledgements

J.A.W. was supported by a Peter Doherty Biomedical Research Fellowship from the National Health and Medical Research Council of Australia (GNT1125343). A.M.S. was supported by a discovery early career researcher award from the Australian Research Council (DE180101520). A.W.S.L. was supported by a top-up scholarship from the Centre for Advanced Food Enginomics, The University of Sydney. This work was supported by a program grant from the National Health and Medical Research Council (GNT1149976) awarded to S.J.S., D.G.L.C. and D.R. (and their colleagues J. George, J. Gunton and H. Durrant-Whyte), a project grant from Diabetes Australia (Y17G-WALJ) awarded to J.A.W. and funding from the Ageing and Alzheimers Institute, Concord Repatriation General Hospital, NSW, Australia. We thank M. Kuligowski, E. Feng, A. Guttentag, B. Nguyen, K.M. Perera, J. Hwang, G. Pinget, L. Sweeting, H. Feibleman, D. Ni and Y. Todorova for their technical support; P. Teixeira for administrative support; F. Held for helping with data analysis; the Laboratory Animal Services at the University of Sydney for animal care and support; and N. Sunn at the Sydney imaging facility, W. Potts from the Specialty Feeds company and D. Kouzios from Concord Hospital for their technical input. Finally, a special thank you to the McKnight bequest of the Sydney Medical School Foundation.

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Authors and Affiliations

  1. Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia

    Jibran A. Wali, Annabelle J. Milner, Alison W. S. Luk, Tamara J. Pulpitel, Tim Dodgson, Harrison J. W. Facey, Devin Wahl, Melkam A. Kebede, Alistair M. Senior, Amanda E. Brandon, Belinda Yau, Yen Chin Koay, Rosilene Ribeiro, Samantha M. Solon-Biet, Kim S. Bell-Anderson, John F. O’Sullivan, Laurence Macia, Gregory J. Cooney, Victoria C. Cogger, Andrew Holmes, David Raubenheimer, David G. Le Couteur & Stephen J. Simpson

  2. Faculty of Science, School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia

    Jibran A. Wali, Alison W. S. Luk, Tamara J. Pulpitel, Tim Dodgson, Melkam A. Kebede, Belinda Yau, Rosilene Ribeiro, Kim S. Bell-Anderson, Andrew Holmes, David Raubenheimer & Stephen J. Simpson

  3. The University of Sydney, ANZAC Research Institute, Sydney, New South Wales, Australia

    Jibran A. Wali, Devin Wahl, Glen P. Lockwood, Victoria C. Cogger & David G. Le Couteur

  4. Mater Research Institute, The University of Queensland, Translational Research Institute, Brisbane, Queensland, Australia

    Mitchell A. Sullivan & Josephine M. Forbes

  5. Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, New South Wales, Australia

    Amanda E. Brandon, Laurence Macia & Gregory J. Cooney

  6. Heart Research Institute, The University of Sydney, Sydney, New South Wales, Australia

    Yen Chin Koay & John F. O’Sullivan

  7. Department of Cardiology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia

    John F. O’Sullivan

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  1. Jibran A. Wali
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Contributions

S.J.S., D.R., D.G.L.C. and J.A.W. conceived the study. J.A.W., S.J.S. and D.G.L.C. wrote the paper. S.M.S.-B., K.S.B.-A., J.F.O.’S., L.M., J.M.F., G.J.C., V.C.C., A.H. and D.R. reviewed the paper and provided intellectual input. J.A.W., A.J.M., A.W.S.L., T.J.P., T.D. and H.J.W.F. conducted mouse studies. D.W., M.K., M.A.S., A.E.B., B.Y., G.P.L., Y.C.K., S.M.S.-B., A.W.S.L. and L.M. participated in experimental work. J.A.W., A.W.S.L., A.M.S. and R.R. were involved in data analysis.

Corresponding authors

Correspondence to Jibran A. Wali or Stephen J. Simpson.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Metabolism thanks Richard Johnson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Christoph Schmitt.

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

Extended data

Extended Data Fig. 1 Related to Fig. 1. (See Supplementary Table 10 for statistics).

(a) Faecal glucose and fructose content and their sum (picomoles/mg of freeze-dried faeces) at 18-19 weeks. Mice (n = 6 mice/diet) were fed diets with indicated compositions. Animal groups without a common letter were significantly different when data was analysed by one-way ANOVA. (P = Protein, C = Carbohydrate, F = Fat). (b, c) Plots showing the effect of dietary fructose (kJ/g of food) on energy intake (kJ/mouse/day) at 5-6 weeks (b) and fructose intake (kJ/mouse/day) on body weights (grams) of mice at 13 weeks (c) shown at low (10% energy; 1.43 kJ/g), medium (20%; 2.86 kJ/g) and high (30%; 4.29 kJ/g) dietary protein content. As fructose increases along the x-axis, glucose in diet/eaten decreases. For the diets containing a 50:50 fructose:glucose, each monosaccharide provided 3.5, 3.0 and 2.5 kJ/g for 10%, 20% and 30% protein diets respectively. Each symbol (o) represents average energy intake/mouse/cage (n = 4 mice/cage) (b) or an individual mouse (n = 294 mice) (c). The fitted lines are derived from data analysed by GAM, fitting an interaction between a smooth term for fructose content/intake (in one carbohydrate dimension) and protein content as a three-level categorical factor and the dotted lines represent s.e.m. for fitted values. (d) Response surfaces showing the relationship between the intake of fructose, glucose and protein derived energy (kJ/mouse/day) and body weight (grams) at 6 weeks. (e, f) Plots showing the effect of fructose intake (kJ/mouse/day) on lean mass (grams) (e) and fat mass (grams) (f) of mice at 13 weeks. The relationship between lean and fat mass and fructose intake is shown at low (10% energy), medium (20%) and high (30%) dietary protein content. Each symbol (o) represents an individual mouse (n = 294 and 293 mice for e and f respectively). The fitted lines are derived from GAM and the dashed lines represent s.e.m. for fitted values. (g, h) Response surfaces showing the relationship between the intake of fructose, glucose and protein derived energy (kJ/mouse/day) and lean mass (grams) (f) and fat mass (grams) (g) of mice at 13 weeks.

Extended Data Fig. 2 Related to Fig. 1. (See Supplementary Table 11 for statistics).

(a - c) Response surfaces showing the relationship between the intake of fructose, glucose and protein derived energy (kJ/mouse/day) and gonadal (visceral) fat pad weight (mg/g body weight) (a), inguinal (subcutaneous) fat pad weight (mg/g of body weight) (b) and ratio of visceral and subcutaneous fat (c) of mice at 18-19 weeks. The ratio of subcutaneous and visceral fat is derived from absolute weights (mg) of inguinal (subcutaneous) and gonadal (visceral) fat pads. (d - f) Response surfaces showing the relationship between the intake of fructose, glucose and protein derived energy (kJ/mouse/day) and interscapular (brown) fat pad weight (mg/g body weight) (d), physical activity (beam breaks) (e) and average respiratory quotient over 24 hours (ratio of carbon dioxide produced and oxygen consumed) (f) at 12-14 weeks.

Extended Data Fig. 3 Related to Fig. 1. (See Supplementary Table 12 for statistics).

(a - d) Plots showing the effect of fructose intake (kJ/mouse/day) on insulin tolerance (AUC) (a) of mice at 15-16 weeks and fasting blood glucose concentration (mmol/l) (b), fasting blood insulin concentration (ng/ml) (c) and their product (mmol/l x ng/ml) (d) at 14 weeks. The relationship between metabolic parameters and fructose intake is shown at low (10% energy; 1.43 kJ/g), medium (20%; 2.86 kJ/g) and high (30%; 4.29 kJ/g) dietary protein content. As the fructose intake increases along the x-axis, the amount of glucose eaten decreases. Each symbol (o) represents an individual mouse (n = 292, 293, 288 and 288 mice for a, b, c and d respectively). The fitted lines are derived from data analysed by GAM, fitting an interaction between a smooth term for dietary fructose intake (in one carbohydrate dimension) and protein content as a three-level categorical factor and the dotted lines represent s.e.m. for fitted values. (e) Response surfaces showing the relationship between the intake of fructose, glucose and protein derived energy (kJ/mouse/day) and product of fasting blood glucose and fasting blood insulin concentration (mmol/l x ng/ml) at 6 weeks. (f) Relationship between fat mass (grams) and insulin sensitivity (fasting blood glucose (mmol/l) x fasting insulin (ng/ml)) measured at 13-14 weeks (n = 288 mice). R2 and P value (P = 2.37E-31) for linear regression of data are shown. (g) Plot showing the effect of fructose intake (kJ/mouse/day) on glucose tolerance (AUC) of mice at 14 weeks. The relationship between glucose tolerance and fructose intake is shown at low (10%), medium (20%) and high (30%) dietary protein content. Each symbol (o) represents an individual mouse (n = 293 mice). The fitted lines are derived from GAM and the dashed lines represent s.e.m. for fitted values. (h, i) Response surfaces showing the relationship between the intake of fructose, glucose and protein derived energy (kJ/mouse/day) and total AUC calculated from the glucose tolerance test (h) at 6 weeks and peak blood insulin concentrations (ng/ml) (i) (15 minutes after glucose administration) measured at 14 weeks.

Extended Data Fig. 4 Related to Fig. 2 and Fig. 3. (See Supplementary Table 13 for statistics).

(a - c) Response surfaces showing the relationship between the intake of fructose, glucose and protein derived energy (kJ/mouse/day) and expression of fructose metabolism gene KHK (isoform C) (a), de novo lipogenesis pathway gene SCD1 (b) and cholesterol synthesis pathway gene HMGCR (c). Mice were culled at 18-19 weeks and liver tissue was collected for RNA isolation. A ‘pooled’ sample was prepared by combining RNA from mice across all the diets. The gene expression data is expressed as fold change relative to the pooled sample. (d) Response surfaces showing the relationship between the intake of fructose, glucose and protein derived energy (kJ/mouse/day) and fasting plasma urea (mmol/) concentrations at 18-19 weeks. (e) Discriminant predicted microbial metabolic pathways in mice fed 100% glucose vs 100% fructose vs 50% glucose/50% fructose vs 100% sucrose diets identified by linear discriminant analysis effect size (LEfSe) (n = 6 mice/diet). No pathways were statistically significant for the 100% glucose diet. Mice were maintained on experimental diets for 18-19 weeks before collection of caecal samples.

Extended Data Fig. 5 Related to Fig. 4. (See Supplementary Table 14 for statistics).

(a) Plot showing the effect of dietary sucrose content (kJ/g) on energy intake (kJ/mouse/day) at 5-6 weeks shown at 5% (0.72 kJ/g), 10% (1.43 kJ/g) and 15% (2.15 kJ/g) protein. As the sucrose increases along the x-axis, the starch in diet decreases. Each symbol (o) represents the average energy intake/mouse/cage (n = 4 mice/cage). The fitted lines are derived from data analysed by GAM, fitting an interaction between a smooth term for dietary sucrose content (in one carbohydrate dimension) and protein content as a three-level categorical factor and the dotted lines represent s.e.m. for fitted values. (b, f - h, j - m) Response surfaces showing the relationship between the intake of sucrose, starch and protein derived energy (kJ/mouse/day) and water intake (ml/mouse/day) (b) and fat mass (grams) (f) at 13 weeks, body weight (grams) (g) at 6 weeks, change in lean mass (h) (grams) between 5-6 and 12-14 weeks, inguinal (subcutaneous) fat pad weight (mg/g) (j), the ratio of visceral and subcutaneous fat (k) and the expression (relative to the pooled sample) of IL18 (l) and CD68 (m) genes in skeletal muscle tissue of mice at 18-19 weeks. (c - e) Plots showing the effect of sucrose intake (kJ/mouse/day) at 5%, 10% and 15% protein on body weights (grams) (c), fat mass (grams) (d) and lean mass (grams) (e) at 13 weeks. Each symbol (o) represents an individual mouse (n = 298 mice for c, d and e). The fitted lines are derived from GAM and the dashed lines represent s.e.m. for fitted values. (i) Protein intake (kJ/mouse/day) on diets containing 5%, 10% or 15% protein (data for diets with different sucrose-starch ratios pooled for each % protein) at 12-14 weeks. Each dot represents average protein intake/mouse/cage (n = 25/group). Groups without a common letter represent statistically significant differences when analysed by ANOVA. ***P < 0.001 for the comparison between the indicated diets by two-sided t-test (P = 3.55E-34, 6.09E-18 and 9.01E-35 for 5% vs 10%, 10% vs 15% and 5% vs 15% comparison). Mean ± s.e.m..

Extended Data Fig. 6 Related to Fig. 4 and Fig. 5. (See Supplementary Table 15 for statistics).

(a, b, e, g, i) Response surfaces showing the relationship between the intake of sucrose, starch and protein derived energy (kJ/mouse/day) and physical activity (beam breaks) (a) and average respiratory quotient over 24 hours (ratio of carbon dioxide produced and oxygen consumed) (b) at 12-14 weeks, product of fasting blood glucose and fasting blood insulin concentration (mmol/l x ng/ml) (e) and total AUC calculated from the glucose tolerance test (g) performed at 6 weeks, and glucose stimulated insulin secretion (GSIS; pg insulin/ng of islet DNA) (i) from the pancreatic islets isolated from mice at the time of euthanasia at 18-19 weeks. (c, d, f) Plots showing the effect of sucrose intake (kJ/mouse/day) on insulin tolerance (AUC) (c) of mice at 15-16 weeks and product of fasting blood glucose and fasting blood insulin concentration (mmol/l x ng/ml) (d) and glucose tolerance (AUC) (f) of mice at 14 weeks. The relationship between metabolic parameter and sucrose intake is shown at 5% (0.72 kJ/g), 10% (1.43 kJ/g) and 15% (2.15 kJ/g) protein content. As the sucrose intake increases along the x-axis, the amount of starch eaten decreases. Each symbol (o) represents an individual mouse (n = 228, 219 and 220 mice for c, d and f). The fitted lines are derived from data analysed by GAM, fitting an interaction between a smooth term for dietary sucrose content (in one carbohydrate dimension) and protein content as a three-level categorical factor and the dotted lines represent s.e.m. for fitted values. (h) Relationship between fat mass (grams) and insulin tolerance (AUC) measured at 14-16 weeks (n = 228 mice). R2 and P value (5.23E-22) for linear regression of data are shown.

Extended Data Fig. 7 Related to Fig. 5 and Fig. 6. (See Supplementary Table 16 for statistics).

(a - c) Response surfaces showing the relationship between the intake of sucrose, starch and protein derived energy (kJ/mouse/day) and liver glycogen content (µmol glycogen/g of tissue) in fasting (6 hours) mice and expression of gluconeogenesis pathway genes PEPCK (b) and G6Pase (c) in the liver at 18-19 weeks. A ‘pooled’ sample was prepared by combining RNA from mice across all the diets. The gene expression data is expressed as fold change relative to the pooled sample. (d) Response surfaces showing the relationship between the intake of sucrose, starch and protein derived energy (kJ/mouse/day) and lipid droplet area (% droplet area). Formalin-fixed sections of liver tissue isolated from mice maintained on experimental diets for 18-19 weeks were stained with Haematoxylin and Eosin. For each section, lipid droplet area was calculated as a percentage of total tissue section area by scanning the slides on a slide scanner (Olympus) and analysing the images with an automated image-J script. (e, f) Response surfaces showing the relationship between the intake of sucrose, starch and protein derived energy (kJ/mouse/day) and expression of glycerol synthesis pathway gene GPAT3 (e) and gene encoding the transcription factor PGC1-α (f) in the liver at 18-19 weeks. A ‘pooled’ sample was prepared by combining RNA from mice across all the diets. The gene expression data is expressed as fold change relative to the pooled sample. (g - i) Response surfaces showing the relationship between the intake of sucrose, starch and protein derived energy (kJ/mouse/day) and fasting (6 hours) plasma cholesterol (mmol/l) (g), protein (g/L) (h) and urea (mmol/) (i) concentrations at 18-19 weeks. (j, k) Response surfaces showing the relationship between the intake of sucrose, starch and protein derived energy (kJ/mouse/day) and systolic (mmHg) (j) and diastolic (mmHg) (k) blood pressure of the mice measured at 15 weeks.

Extended Data Fig. 8 Related to Fig. 7. (See Supplementary Table 17 for statistics).

(a - c) Impact of carbohydrate composition (n = 18 mice/carbohydrate composition) (a), mouse cohort (n = 15 mice/cohort 1, 32 mice/cohort 2, 43 mice/cohort 3) (b), macronutrient composition (n = 30 mice/macronutrient composition) (c) on gut bacterial communities visualised by principal coordinate analysis ordination of weighted UniFrac distances. (d - f) Impact of macronutrient composition on gut bacterial communities in mice in cohort 1 (n = 5 mice/macronutrient composition) (d), cohort 2 (n = 10-12 mice/macronutrient composition) (e), cohort 3 (n = 13-15 mice/macronutrient composition) (f) visualised by principal coordinate analysis ordination of weighted UniFrac distances. Ellipses represent 95% confidence intervals. Mice were maintained on experimental diets for 18-19 weeks before collection of caecal samples. P = Protein, F = Fat, C = Carbohydrate. Dietary carbohydrates consisted of five different starch-sucrose ratios (20/80, 35/65, 50/50, 65/35, 80/20).

Extended Data Fig. 9 Related to Fig. 7. (See Supplementary Table 18 for statistics).

(a - f) Response surfaces showing the relationship between the intake of sucrose, starch and protein derived energy (kJ/mouse/day) and Lachnoclostridium (a), Lachnospiraceae_UCG-004 (b), Lactobacillus (c), Oscillibacter (d), Roseburia (e), Tyzzerella (f) abundance at genus level (clr transformed) (n = 90). Mice were maintained on experimental diets for 18-19 weeks before collection of caecal samples.

Extended Data Fig. 10 Related to Fig. 7.

(a, b) Discriminant gut bacteria taxa at genus level in mice fed 65% RS vs all NS diets (a), 20% RS vs all NS diets (b) identified by linear discriminant analysis effect size (LEfSe) (n = 6 mice/RS diet). NS diets consisted of all starch-sucrose ratios (20/80, 35/65, 50/50, 65/35, 80/20) analysed together (n = 30). Mice were maintained on experimental diets (10% Protein, 20% Fat and 70% Carbohydrate) for 18-19 weeks before collection of caecal samples. (c, d) Discriminant predicted microbial metabolic pathways in mice fed 65% RS vs all NS diets (c), 20% RS vs all NS diets (d) identified by linear discriminant analysis effect size (LEfSe) (n = 6 mice/RS diet). NS diets consisted of all starch-sucrose ratios (20/80, 35/65, 50/50, 65/35, 80/20) analysed together (n = 30). Mice were maintained on experimental diets (10% Protein, 20% Fat and 70% Carbohydrate) for 18-19 weeks before collection of caecal samples.

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Unprocessed western blot for Fig. 5g.

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Wali, J.A., Milner, A.J., Luk, A.W.S. et al. Impact of dietary carbohydrate type and protein–carbohydrate interaction on metabolic health. Nat Metab 3, 810–828 (2021). https://doi.org/10.1038/s42255-021-00393-9

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