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

Clinical Trials & Research
  • 1.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 2.

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

    CAS 
    PubMed 

    Google Scholar
     

  • 3.

    Solon-Biet, S. M. et al. Defining the nutritional and metabolic context of FGF21 using the geometric framework. Cell Metab. 24, 555–565 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • 4.

    Simpson, S. J., Le Couteur, D. G. & Raubenheimer, D. Putting the balance back in diet. Cell 161, 18–23 (2015).

    CAS 
    PubMed 

    Google Scholar
     

  • 5.

    Stanhope, K. L. Sugar consumption, metabolic disease and obesity: the state of the controversy. Crit. Rev. Clin. Lab. Sci. 53, 52–67 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • 6.

    Te Morenga, L., Mallard, S. & Mann, J. Dietary sugars and body weight: systematic review and meta-analyses of randomised controlled trials and cohort studies. BMJ 346, e7492 (2012).

  • 7.

    Wali, J. A., Raubenheimer, D., Senior, A. M., Le Couteur, D. G. & Simpson, S. J. Cardio–metabolic consequences of dietary carbohydrates: reconciling contradictions using nutritional geometry. Cardiovasc. Res. 117, 386–401 (2020).

  • 8.

    Raubenheimer, D. & Simpson, S. J. Protein leverage: theoretical foundations and ten points of clarification. Obesity 27, 1225–1238 (2019).

    CAS 
    PubMed 

    Google Scholar
     

  • 9.

    Senior, A. M. et al. Dietary macronutrient content, age-specific mortality and lifespan. Proc. Biol. Sci. 286, 20190393 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 10.

    Tappy, L. & Le, K. A. Metabolic effects of fructose and the worldwide increase in obesity. Physiol. Rev. 90, 23–46 (2010).

    CAS 
    PubMed 

    Google Scholar
     

  • 11.

    Bray, G. A., Nielsen, S. J. & Popkin, B. M. Consumption of high-fructose corn syrup in beverages may play a role in the epidemic of obesity. Am. J. Clin. Nutr. 79, 537–543 (2004).

    CAS 
    PubMed 

    Google Scholar
     

  • 12.

    Elia, M. & Cummings, J. H. Physiological aspects of energy metabolism and gastrointestinal effects of carbohydrates. Eur. J. Clin. Nutr. 61, S40–S74 (2007).

    CAS 
    PubMed 

    Google Scholar
     

  • 13.

    Rendeiro, C. et al. Fructose decreases physical activity and increases body fat without affecting hippocampal neurogenesis and learning relative to an isocaloric glucose diet. Sci. Rep. 5, 9589 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 14.

    Schultz, A., Barbosa-da-Silva, S., Aguila, M. B. & Mandarim-de-Lacerda, C. A. Differences and similarities in hepatic lipogenesis, gluconeogenesis and oxidative imbalance in mice fed diets rich in fructose or sucrose. Food Funct. 6, 1684–1691 (2015).

    CAS 
    PubMed 

    Google Scholar
     

  • 15.

    Tillman, E. J., Morgan, D. A., Rahmouni, K. & Swoap, S. J. Three months of high-fructose feeding fails to induce excessive weight gain or leptin resistance in mice. PLoS ONE 9, e107206 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 16.

    Lustig, R. H. et al. Isocaloric fructose restriction and metabolic improvement in children with obesity and metabolic syndrome. Obesity 24, 453–460 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • 17.

    Choo, V. L. et al. Food sources of fructose-containing sugars and glycaemic control: systematic review and meta-analysis of controlled intervention studies. BMJ 363, k4644 (2018).

  • 18.

    Lustig, R. H. Sickeningly sweet: does sugar cause type 2 diabetes? Yes. Can. J. Diabetes 40, 282–286 (2016).

    PubMed 

    Google Scholar
     

  • 19.

    Rippe, J. M. & Marcos, A. Controversies about sugars consumption: state of the science. Eur. J. Nutr. 55, 11–16 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 20.

    Vos, M. B., Kimmons, J. E., Gillespie, C., Welsh, J. & Blanck, H. M. Dietary fructose consumption among US children and adults: the Third National Health and Nutrition Examination Survey. Medscape J. Med. 10, 160 (2008).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 21.

    Goran, M. I., Ulijaszek, S. J. & Ventura, E. E. High fructose corn syrup and diabetes prevalence: a global perspective. Glob. Public Health 8, 55–64 (2013).

    PubMed 

    Google Scholar
     

  • 22.

    Gross, L. S., Li, L., Ford, E. S. & Liu, S. Increased consumption of refined carbohydrates and the epidemic of type 2 diabetes in the United States: an ecologic assessment. Am. J. Clin. Nutr. 79, 774–779 (2004).

    CAS 
    PubMed 

    Google Scholar
     

  • 23.

    Light, H. R., Tsanzi, E., Gigliotti, J., Morgan, K. & Tou, J. C. The type of caloric sweetener added to water influences weight gain, fat mass, and reproduction in growing Sprague–Dawley female rats. Exp. Biol. Med. 234, 651–661 (2009).

    CAS 

    Google Scholar
     

  • 24.

    Bocarsly, M. E., Powell, E. S., Avena, N. M. & Hoebel, B. G. High-fructose corn syrup causes characteristics of obesity in rats: increased body weight, body fat and triglyceride levels. Pharmacol. Biochem. Behav. 97, 101–106 (2010).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 25.

    Forshee, R. A. et al. A critical examination of the evidence relating high fructose corn syrup and weight gain. Crit. Rev. Food Sci. Nutr. 47, 561–582 (2007).

    CAS 
    PubMed 

    Google Scholar
     

  • 26.

    Bravo, S., Lowndes, J., Sinnett, S., Yu, Z. & Rippe, J. Consumption of sucrose and high-fructose corn syrup does not increase liver fat or ectopic fat deposition in muscles. Appl. Physiol. Nutr. Metab. 38, 681–688 (2013).

    CAS 
    PubMed 

    Google Scholar
     

  • 27.

    Angelopoulos, T. J., Lowndes, J., Sinnett, S. & Rippe, J. M. Fructose containing sugars at normal levels of consumption do not effect adversely components of the metabolic syndrome and risk factors for cardiovascular disease. Nutrients 8, 179 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 28.

    Stanhope, K. L. et al. Twenty-four-hour endocrine and metabolic profiles following consumption of high-fructose corn syrup-, sucrose-, fructose-, and glucose-sweetened beverages with meals. Am. J. Clin. Nutr. 87, 1194–1203 (2008).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 29.

    Raubenheimer, D. & Simpson, S. J. Nutritional ecology and human health. Annu. Rev. Nutr. 36, 603–626 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • 30.

    Ludwig, D. S., Willett, W. C., Volek, J. S. & Neuhouser, M. L. Dietary fat: from foe to friend? Science 362, 764–770 (2018).

    CAS 
    PubMed 

    Google Scholar
     

  • 31.

    Bindels, L. B., Walter, J. & Ramer-Tait, A. E. Resistant starches for the management of metabolic diseases. Curr. Opin. Clin. Nutr. Metab. Care 18, 559–565 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 32.

    Reeves, P. G., Nielsen, F. H. & Fahey, G. C. Jr. AIN-93 purified diets for laboratory rodents: final report of the American Institute of Nutrition ad hoc writing committee on the reformulation of the AIN-76A rodent diet. J. Nutr. 123, 1939–1951 (1993).

    CAS 
    PubMed 

    Google Scholar
     

  • 33.

    Truswell, A. S., Seach, J. M. & Thorburn, A. W. Incomplete absorption of pure fructose in healthy subjects and the facilitating effect of glucose. Am. J. Clin. Nutr. 48, 1424–1430 (1988).

    CAS 
    PubMed 

    Google Scholar
     

  • 34.

    Fisher, F. M. & Maratos-Flier, E. Understanding the physiology of FGF21. Annu. Rev. Physiol. 78, 223–241 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • 35.

    Rafecas, I., Esteve, M., Fernández-López, J.-A., Remesar, X. & Alemany, M. Methodological evaluation of indirect calorimetry data in lean and obese rats. Clin. Exp. Pharmacol. Physiol. 20, 731–742 (1993).

    CAS 
    PubMed 

    Google Scholar
     

  • 36.

    Kroemer, G., Lopez-Otin, C., Madeo, F. & de Cabo, R. Carbotoxicity—noxious effects of carbohydrates. Cell 175, 605–614 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 37.

    Softic, S. et al. Divergent effects of glucose and fructose on hepatic lipogenesis and insulin signaling. J. Clin. Invest. 127, 4059–4074 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 38.

    Sato, M. et al. Low protein diets posttranscriptionally repress apolipoprotein B expression in rat liver. J. Nutr. Biochem. 7, 381–385 (1996).

    CAS 

    Google Scholar
     

  • 39.

    Treviño-Villarreal, J. H. et al. Dietary protein restriction reduces circulating VLDL triglyceride levels via CREBH–APOA5-dependent and -independent mechanisms. JCI Insight 3, e99470 (2018).

  • 40.

    Schlein, C. et al. FGF21 lowers plasma triglycerides by accelerating lipoprotein catabolism in white and brown adipose tissues. Cell Metab. 23, 441–453 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • 41.

    Kim, K. H. et al. Autophagy deficiency leads to protection from obesity and insulin resistance by inducing Fgf21 as a mitokine. Nat. Med. 19, 83–92 (2013).

    CAS 
    PubMed 

    Google Scholar
     

  • 42.

    Kovatcheva-Datchary, P. et al. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab. 22, 971–982 (2015).

    CAS 
    PubMed 

    Google Scholar
     

  • 43.

    Parker, K., Salas, M. & Nwosu, V. C. High fructose corn syrup: production, uses and public health concerns. Biotechnol. Mol. Biol. Rev. 5, 71–78 (2010).

    CAS 

    Google Scholar
     

  • 44.

    Gonzalez, J. T., Fuchs, C. J., Betts, J. A. & van Loon, L. J. Glucose plus fructose ingestion for post-exercise recovery—greater than the sum of its parts? Nutrients 9, 344 (2017).

  • 45.

    Tan, H. E. et al. The gut–brain axis mediates sugar preference. Nature 580, 511–516 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 46.

    Stice, E., Burger, K. S. & Yokum, S. Relative ability of fat and sugar tastes to activate reward, gustatory, and somatosensory regions. Am. J. Clin. Nutr. 98, 1377–1384 (2013).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 47.

    Akhavan, T. & Anderson, G. H. Effects of glucose-to-fructose ratios in solutions on subjective satiety, food intake, and satiety hormones in young men. Am. J. Clin. Nutr. 86, 1354–1363 (2007).

    CAS 
    PubMed 

    Google Scholar
     

  • 48.

    Rodin, J. Effects of pure sugar vs. mixed starch fructose loads on food intake. Appetite 17, 213–219 (1991).

    CAS 
    PubMed 

    Google Scholar
     

  • 49.

    Theytaz, F. et al. Metabolic fate of fructose ingested with and without glucose in a mixed meal. Nutrients 6, 2632–2649 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 50.

    Hudgins, L. C., Parker, T. S., Levine, D. M. & Hellerstein, M. K. A dual sugar challenge test for lipogenic sensitivity to dietary fructose. J. Clin. Endocrinol. Metab. 96, 861–868 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 51.

    van de Wouw, M., Schellekens, H., Dinan, T. G. & Cryan, J. F. Microbiota–gut–brain axis: modulator of host metabolism and appetite. J. Nutr. 147, 727–745 (2017).

    PubMed 

    Google Scholar
     

  • 52.

    Million, M. et al. Comparative meta-analysis of the effect of Lactobacillus species on weight gain in humans and animals. Microb. Pathog. 53, 100–108 (2012).

    PubMed 

    Google Scholar
     

  • 53.

    Armougom, F., Henry, M., Vialettes, B., Raccah, D. & Raoult, D. Monitoring bacterial community of human gut microbiota reveals an increase in Lactobacillus in obese patients and methanogens in anorexic patients. PLoS ONE 4, e7125 (2009).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 54.

    Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).

    CAS 
    PubMed 

    Google Scholar
     

  • 55.

    Everard, A. et al. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc. Natl Acad. Sci. USA 110, 9066–9071 (2013).

    CAS 
    PubMed 

    Google Scholar
     

  • 56.

    Dao, M. C. et al. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut 65, 426–436 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • 57.

    Togo, J., Hu, S., Li, M., Niu, C. & Speakman, J. R. Impact of dietary sucrose on adiposity and glucose homeostasis in C57BL/6J mice depends on mode of ingestion: liquid or solid. Mol. Metab. 27, 22–32 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 58.

    DiMeglio, D. P. & Mattes, R. D. Liquid versus solid carbohydrate: effects on food intake and body weight. Int. J. Obes. Relat. Metab. Disord. 24, 794–800 (2000).

    CAS 
    PubMed 

    Google Scholar
     

  • 59.

    Jang, C. et al. The small intestine converts dietary fructose into glucose and organic acids. Cell Metab. 27, 351–361 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 60.

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

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 61.

    Koay, Y. C. et al. Ingestion of resistant starch by mice markedly increases microbiome-derived metabolites. FASEB J. 33, 8033–8042 (2019).

    CAS 
    PubMed 

    Google Scholar
     

  • 62.

    Dodd, D. et al. A gut bacterial pathway metabolizes aromatic amino acids into nine circulating metabolites. Nature 551, 648–652 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 63.

    Solon-Biet, S. M. et al. Dietary protein to carbohydrate ratio and caloric restriction: comparing metabolic outcomes in mice. Cell Rep. 11, 1529–1534 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 64.

    Wu, Y. et al. Very-low-protein diets lead to reduced food intake and weight loss, linked to inhibition of hypothalamic mTOR signaling, in mice. Cell Metab. https://doi.org/10.1016/j.cmet.2021.01.017 (2021).

  • 65.

    Pezeshki, A., Zapata, R. C., Singh, A., Yee, N. J. & Chelikani, P. K. Low protein diets produce divergent effects on energy balance. Sci. Rep. 6, 25145 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 66.

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

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 67.

    Lasker, D. A., Evans, E. M. & Layman, D. K. Moderate carbohydrate, moderate protein weight loss diet reduces cardiovascular disease risk compared to high carbohydrate, low protein diet in obese adults: a randomized clinical trial. Nutr. Metab. 5, 30 (2008).


    Google Scholar
     

  • 68.

    Bueno, N. B., de Melo, I. S. V., de Oliveira, S. L. & da Rocha Ataide, T. Very-low-carbohydrate ketogenic diet v. low-fat diet for long-term weight loss: a meta-analysis of randomised controlled trials. Br. J. Nutr. 110, 1178–1187 (2013).

    CAS 
    PubMed 

    Google Scholar
     

  • 69.

    Astrup, A., Grunwald, G., Melanson, E., Saris, W. & Hill, J. The role of low-fat diets in body weight control: a meta-analysis of ad libitum dietary intervention studies. Int. J. Obes. Relat. Metab. Disord. 24, 1545–1552 (2000).


    Google Scholar
     

  • 70.

    Hall, K. D. et al. Effect of a plant-based, low-fat diet versus an animal-based, ketogenic diet on ad libitum energy intake. Nat. Med. 27, 344–353 (2021).

  • 71.

    Nilsson, L. M. et al. Low-carbohydrate, high-protein score and mortality in a northern Swedish population-based cohort. Eur. J. Clin. Nutr. 66, 694–700 (2012).

    PubMed 

    Google Scholar
     

  • 72.

    Trichopoulou, A., Psaltopoulou, T., Orfanos, P., Hsieh, C. & Trichopoulos, D. Low-carbohydrate–high-protein diet and long-term survival in a general population cohort. Eur. J. Clin. Nutr. 61, 575–581 (2007).


    Google Scholar
     

  • 73.

    Dehghan, M. et al. Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): a prospective cohort study. Lancet 390, 2050–2062 (2017).

    CAS 
    PubMed 

    Google Scholar
     

  • 74.

    Ma, C., Mirth, C. K., Hall, M. D. & Piper, M. D. W. Amino acid quality modifies the quantitative availability of protein for reproduction in Drosophila melanogaster. J. Insect Physiol. https://doi.org/10.1016/j.jinsphys.2020.104050 (2020).

  • 75.

    Solon-Biet, S. M. et al. Macronutrient balance, reproductive function, and lifespan in aging mice. Proc. Natl Acad. Sci. USA 112, 3481–3486 (2015).

    CAS 
    PubMed 

    Google Scholar
     

  • 76.

    Alexander, J., Chang, G. Q., Dourmashkin, J. T. & Leibowitz, S. F. Distinct phenotypes of obesity-prone AKR/J, DBA2J and C57BL/6J mice compared to control strains. Int. J. Obes. 30, 50–59 (2006).

    CAS 

    Google Scholar
     

  • 77.

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

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 78.

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

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 79.

    Hastie, T. & Tibshirani, R. Generalized additive models for medical research. Stat. Methods Med. Res. 4, 187–196 (1995).

    CAS 
    PubMed 

    Google Scholar
     

  • 80.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).

  • 81.

    Livesey, G. A perspective on food energy standards for nutrition labelling. Br. J. Nutr. 85, 271–287 (2001).

    CAS 
    PubMed 

    Google Scholar
     

  • 82.

    Kieffer, D. A. et al. Mice fed a high-fat diet supplemented with resistant starch display marked shifts in the liver metabolome concurrent with altered gut bacteria. J. Nutr. 146, 2476–2490 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 83.

    Johnston, K. L., Thomas, E. L., Bell, J. D., Frost, G. S. & Robertson, M. D. Resistant starch improves insulin sensitivity in metabolic syndrome. Diabet. Med. 27, 391–397 (2010).

    CAS 
    PubMed 

    Google Scholar
     

  • 84.

    Keenan, M. J. et al. Role of resistant starch in improving gut health, adiposity, and insulin resistance. Adv. Nutr. 6, 198–205 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 85.

    Allison, D. B., Paultre, F., Maggio, C., Mezzitis, N. & Pi-Sunyer, F. X. The use of areas under curves in diabetes research. Diabetes Care 18, 245–250 (1995).

    CAS 
    PubMed 

    Google Scholar
     

  • 86.

    Gong, H. et al. Evaluation of candidate reference genes for RT–qPCR studies in three metabolism related tissues of mice after caloric restriction. Sci. Rep. 6, 38513 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 87.

    Yamamoto, H. et al. Characterization of genetically engineered mouse hepatoma cells with inducible liver functions by overexpression of liver-enriched transcription factors. J. Biosci. Bioeng. 125, 131–139 (2018).

    CAS 
    PubMed 

    Google Scholar
     

  • 88.

    Asghar, Z. A. et al. Maternal fructose drives placental uric acid production leading to adverse fetal outcomes. Sci. Rep. 6, 25091 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 89.

    Simbulan, R. K. et al. Adult male mice conceived by in vitro fertilization exhibit increased glucocorticoid receptor expression in fat tissue. J. Dev. Orig. Health Dis. 7, 73–82 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • 90.

    Yang, S. et al. Impaired adipogenesis in adipose tissue associated with hepatic lipid deposition induced by chronic inflammation in mice with chew diet. Life Sci. 137, 7–13 (2015).

    CAS 
    PubMed 

    Google Scholar
     

  • 91.

    Koya-Miyata, S. et al. Propolis prevents diet-induced hyperlipidemia and mitigates weight gain in diet-induced obesity in mice. Biol. Pharm. Bull. 32, 2022–2028 (2009).

    CAS 
    PubMed 

    Google Scholar
     

  • 92.

    Marek, G. et al. Adiponectin resistance and proinflammatory changes in the visceral adipose tissue induced by fructose consumption via ketohexokinase-dependent pathway. Diabetes 64, 508–518 (2015).

    CAS 
    PubMed 

    Google Scholar
     

  • 93.

    Nelson, M. E. et al. Inhibition of hepatic lipogenesis enhances liver tumorigenesis by increasing antioxidant defence and promoting cell survival. Nat. Commun. 8, 14689 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 94.

    Schwab, A. et al. Polyol pathway links glucose metabolism to the aggressiveness of cancer cells. Cancer Res. 78, 1604–1618 (2018).

    CAS 
    PubMed 

    Google Scholar
     

  • 95.

    Andres-Hernando, A., Johnson, R. J. & Lanaspa, M. A. Endogenous fructose production: what do we know and how relevant is it? Curr. Opin. Clin. Nutr. Metab. Care 22, 289–294 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 96.

    Lowry, O. A Flexible System of Enzymatic Analysis (Elsevier, 2012).

  • 97.

    Sullivan, M. A. et al. Molecular insights into glycogen α-particle formation. Biomacromolecules 13, 3805–3813 (2012).

    CAS 
    PubMed 

    Google Scholar
     

  • 98.

    Burchfield, J. G. et al. High dietary fat and sucrose results in an extensive and time-dependent deterioration in health of multiple physiological systems in mice. J. Biol. Chem. 293, 5731–5745 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 99.

    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108, 4516–4522 (2011).

    CAS 
    PubMed 

    Google Scholar
     

  • 100.

    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 101.

    Glockner, F. O. et al. 25 years of serving the community with ribosomal RNA gene reference databases and tools. J. Biotechnol. 261, 169–176 (2017).

    PubMed 

    Google Scholar
     

  • 102.

    Bodenhofer, U., Bonatesta, E., Horejs-Kainrath, C. & Hochreiter, S. msa: an R package for multiple sequence alignment. Bioinformatics 31, 3997–3999 (2015).

    CAS 
    PubMed 

    Google Scholar
     

  • 103.

    Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).

    CAS 
    PubMed 

    Google Scholar
     

  • 104.

    Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).

    CAS 

    Google Scholar
     

  • 105.

    McIver, L. J. et al. bioBakery: a meta’omic analysis environment. Bioinformatics 34, 1235–1237 (2018).

    CAS 
    PubMed 

    Google Scholar
     

  • 106.

    Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 107.

    Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. & Neufeld, J. D. PANDAseq: paired-end assembler for Illumina sequences. BMC Bioinformatics 13, 31 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 108.

    Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 109.

    Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 110.

    Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011).

    PubMed 

    Google Scholar
     

  • 111.

    McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 112.

    van den Boogaart, K., Tolosana, R. & Bren, M. compositions: compositional data analysis. R package version 1.40-1. (R Foundation for Statistical Computing, 2014).

  • 113.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  • 114.

    Oksanen, J. et al. vegan: community ecology package. R package version 1 (2019).

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