Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-27T23:36:35.347Z Has data issue: false hasContentIssue false
  • English
  • Français

Determining the glycaemic responses of foods: conventional and emerging approaches

Published online by Cambridge University Press:  01 February 2021

S R Priyadarshini ,
J A Moses Open the ORCID record for J A Moses [Opens in a new window]  and
C Anandharamakrishnan Open the ORCID record for C Anandharamakrishnan [Opens in a new window]
S R Priyadarshini
Affiliation:
Computational Modeling and Nanoscale Processing Unit, Indian Institute of Food Processing Technology (IIFPT), Ministry of Food Processing Industries, Govt. of India Thanjavur - 613005, Tamil Nadu, India
J A Moses
Affiliation:
Computational Modeling and Nanoscale Processing Unit, Indian Institute of Food Processing Technology (IIFPT), Ministry of Food Processing Industries, Govt. of India Thanjavur - 613005, Tamil Nadu, India
C Anandharamakrishnan
Affiliation:
Computational Modeling and Nanoscale Processing Unit, Indian Institute of Food Processing Technology (IIFPT), Ministry of Food Processing Industries, Govt. of India Thanjavur - 613005, Tamil Nadu, India
Get access Rights & Permissions [Opens in a new window]

Abstract

A low-glycaemic diet is crucial for those with diabetes and cardiovascular diseases. Information on the glycaemic index (GI) of different ingredients can help in designing novel food products for such target groups. This is because of the intricate dependency of material source, composition, food structure and processing conditions, among other factors, on the glycaemic responses. Different approaches have been used to predict the GI of foods, and certain discrepancies exist because of factors such as inter-individual variation among human subjects. Besides other aspects, it is important to understand the mechanism of food digestion because an approach to predict GI must essentially mimic the complex processes in the human gastrointestinal tract. The focus of this work is to review the advances in various approaches for predicting the glycaemic responses to foods. This has been carried out by detailing conventional approaches, their merits and limitations, and the need to focus on emerging approaches. Given that no single approach can be generalised to all applications, the review emphasises the scope of deriving insights for improvements in methodologies. Reviewing the conventional and emerging approaches for the determination of GI in foods, this detailed work is intended to serve as a state-of-the-art resource for nutritionists who work on developing low-GI foods.

Keywords

Glycaemic indexLow GI foodsDiabetesFood digestionGlycaemic responses
Type
Review Article
Information
Nutrition Research Reviews , Volume 35 , Issue 1 , June 2022 , pp. 1 - 27
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Nutrition Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Masood, W, Annamaraju, P & Uppaluri, K (2020) Ketogenic Diet. StatPearls. Treasure Isl. StatPearls Publ. https://www.ncbi.nlm.nih.gov/books/NBK499830/ (accessed 2020 March 29).Google Scholar
Feinman, RD, Pogozelski, WK, Astrup, A, et al. (2015) Dietary carbohydrate restriction as the first approach in diabetes management: critical review and evidence base. Nutrition 31, 1–13. http://www.sciencedirect.com/science/article/pii/S0899900714003323 CrossRefGoogle ScholarPubMed
Saeedi, P, Petersohn, I, Salpea, P, et al. (2019) Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 157, 107843. https://linkinghub.elsevier.com/retrieve/pii/S0168822719312306 CrossRefGoogle ScholarPubMed
Melish, JS (2019) Beyond Carbohydrate Counting (CC). Poster Present Clin Diabetes/Therapeutics 974-P, 68. https://doi.org/10.2337/db19-974-P Google Scholar
Jenkins, DJ, Wolever, TM, Taylor, RH, et al. (1981) Glycemic index of foods: a physiological basis for carbohydrate exchange. Am J Clin Nutr 34, 362–366. https://academic.oup.com/ajcn/article/34/3/362/4692881 CrossRefGoogle ScholarPubMed
Livesey, G, Taylor, R, Livesey, HF, et al. (2019) Dietary glycemic index and load and the risk of type 2 diabetes: a systematic review and updated meta-analyses of prospective cohort studies. Nutrients 11, 1280.CrossRefGoogle ScholarPubMed
Vega-López, S, Venn, B & Slavin, J (2018) Relevance of the glycemic index and glycemic load for body weight, diabetes, and cardiovascular disease. Nutrients 10, 1361. http://www.mdpi.com/2072-6643/10/10/1361 CrossRefGoogle ScholarPubMed
Augustin, LSA, Kendall, CWC, Jenkins, DJA, et al. (2015) Glycemic index, glycemic load and glycemic response: an International Scientific Consensus Summit from the International Carbohydrate Quality Consortium (ICQC). Nutr Metab Cardiovasc Dis 25, 795–815. http://www.sciencedirect.com/science/article/pii/S0939475315001271 CrossRefGoogle Scholar
Marinangeli, CPF, Castellano, J, Torrance, P, et al. (2019) Positioning the value of dietary carbohydrate, carbohydrate quality, glycemic index, and gi labelling to the canadian consumer for improving dietary patterns. Nutrients 11, 457.CrossRefGoogle Scholar
Brouwer-Brolsma, EM, Berendsen, AAM, Sluik, D, et al. (2019) The glycaemic index-food-frequency questionnaire: development and validation of a food frequency questionnaire designed to estimate the dietary intake of glycaemic index and glycaemic load: An effort by the PREVIEW Consortium. Nutrients 11: 13.CrossRefGoogle Scholar
Wolever, T, Meynier, A, Jenkins, AL, et al. (2019) Glycemic index and insulinemic index of foods: an interlaboratory study using the ISO 2010 method. Nutrients 11, 2218.CrossRefGoogle ScholarPubMed
Matthan, NR, Ausman, LM, Meng, H, et al. (2016) Estimating the reliability of glycemic index values and potential sources of methodological and biological variability. Am J Clin Nutr. 104, 1004–1013. https://academic.oup.com/ajcn/article/104/4/1004/4557132 CrossRefGoogle ScholarPubMed
The Glycemic Index Foundation (2018) Glycemic Index What is GI. Glycemic Index Found. https://www.gisymbol.com/infographics/what-is-gi-infographic/ Google Scholar
Clar, C, Al-Khudairy, L, Loveman, E, et al. (2017) Low glycaemic index diets for the prevention of cardiovascular disease. Cochrane database Syst Rev 7, CD004467–CD004467. https://pubmed.ncbi.nlm.nih.gov/28759107 Google ScholarPubMed
Turati, F, Galeone, C, Augustin, LSA, et al. (2019) Glycemic index, glycemic load and cancer risk: an updated meta-analysis. Nutrients 11, 2342.CrossRefGoogle ScholarPubMed
Zafar, MI, Mills, KE, Zheng, J, et al. (2019) Low-glycemic index diets as an intervention for diabetes: a systematic review and meta-analysis. Am J Clin Nutr 110, 891–902.CrossRefGoogle ScholarPubMed
Livesey, G & Livesey, H (2019) Coronary heart disease and dietary carbohydrate, glycemic index, and glycemic load: dose-response meta-analyses of prospective cohort studies. Mayo Clin Proc Innov Qual Outcomes 3, 52–69. https://linkinghub.elsevier.com/retrieve/pii/S2542454819300025 CrossRefGoogle ScholarPubMed
Eleazu, CO (2016) The concept of low glycemic index and glycemic load foods as panacea for type 2 diabetes mellitus; prospects, challenges and solutions. Afr Health Sci 16, 468–479.CrossRefGoogle ScholarPubMed
Evans, CEL, Greenwood, DC, Threapleton, DE, et al. (2017) Glycemic index, glycemic load, and blood pressure: a systematic review and meta-analysis of randomized controlled trials. Am J Clin Nutr 105, 1176–1190.CrossRefGoogle ScholarPubMed
Unwin, DJ, Tobin, SD, Murray, SW, et al. (2019) Substantial and sustained improvements in blood pressure, weight and lipid profiles from a carbohydrate restricted diet: an observational study of insulin resistant patients in primary care. Int J Environ Res Public Health 16, 2680.CrossRefGoogle ScholarPubMed
Pavithran, N, Kumar, H, Menon, AS, et al. (2020) The effect of a low GI diet on truncal fat mass and glycated hemoglobin in South Indians with type 2 diabetes—A single centre randomized prospective study. Nutrients 12, 179.CrossRefGoogle ScholarPubMed
Mathews, MJ, Liebenberg, L & Mathews, EH (2015) How do high glycemic load diets influence coronary heart disease? Nutr Metab (Lond) 12, 6. http://www.nutritionandmetabolism.com/content/12/1/6 CrossRefGoogle ScholarPubMed
Luz, ABS, dos Santos Figueredo, JB, Salviano, BDPD, et al. (2018) Adipocytes and intestinal epithelium dysfunctions linking obesity to inflammation induced by high glycemic index pellet-diet in Wistar rats. Biosci Rep 38.CrossRefGoogle ScholarPubMed
Trouwborst, I, Bowser, SM, Goossens, GH, et al. (2018) Ectopic fat accumulation in distinct insulin resistant phenotypes; targets for personalized nutritional interventions. Front Nutr 5, 77.CrossRefGoogle ScholarPubMed
Kaur, B, Quek Yu Chin, R, Camps, S, et al. (2016) The impact of a low glycaemic index (GI) diet on simultaneous measurements of blood glucose and fat oxidation: a whole body calorimetric study. J Clin Transl Endocrinol 4, 45–52. http://www.sciencedirect.com/science/article/pii/S2214623716300060 Google ScholarPubMed
Papakonstantinou, E, Orfanakos, N, Farajian, P, et al. (2017) Short-term effects of a low glycemic index carob-containing snack on energy intake, satiety, and glycemic response in normal-weight, healthy adults: results from two randomized trials. Nutrition 42, 12–19.CrossRefGoogle ScholarPubMed
Gbenga-Fabusiwa, FJ, Oladele, EP, Oboh, G, et al. (2019) Glycemic response in diabetic subjects to biscuits produced from blends of pigeon pea and wheat flour. Plant Foods Hum Nutr 74, 553–559.CrossRefGoogle ScholarPubMed
Lin, AH-M (2018) Structure and digestion of common complementary food starches. J Pediatr Gastroenterol Nutr 66, S35–S38.CrossRefGoogle ScholarPubMed
Akhtar, S, Layla, A, Sestili, P, et al. (2019) Glycemic and insulinemic responses of vegetables and beans powders supplemented chapattis in healthy humans: a randomized, crossover trial. Biomed Res Int 2019.CrossRefGoogle ScholarPubMed
Turco, I, Bacchetti, T, Morresi, C, et al. (2019) Polyphenols and the glycaemic index of legume pasta. Food Funct 10, 5931–5938.CrossRefGoogle ScholarPubMed
Adefegha, SA, Olasehinde, TA & Oboh, G (2018) Pasting alters glycemic index, antioxidant activities, and starch-hydrolyzing enzyme inhibitory properties of whole wheat flour. Food Sci Nutr 6, 1591–600.CrossRefGoogle ScholarPubMed
Ritudomphol, O & Luangsakul, N (2018) Optimization of processing condition of instant rice to lower the glycemic index. J Food Sci 84, 101–110. http://doi.wiley.com/10.1111/1750-3841.14406 Google ScholarPubMed
Shafaeizadeh, S, Muhardi, L, Henry, CJ, et al. (2018) Macronutrient composition and food form affect glucose and insulin responses in humans. Nutrients 10, 188.CrossRefGoogle ScholarPubMed
Liu, Y, Ye, F, Zhang, S, et al. (2020) Characteristics of myoelectrical activities along the small intestine and their responses to test meals of different glycemic index in rats. Am J Physiol Integr Comp Physiol 318, R997–R1003.CrossRefGoogle ScholarPubMed
Yeboah, ES, Agbenohervi, JK & Sampson, GO (2019) Glycemic index of five ghanaian corn and cassava staples. J Food Nutr Res 7, 624–631.CrossRefGoogle Scholar
Fogel, DB (2018) Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review. Contemp Clin Trials Commun 11, 156–164. https://linkinghub.elsevier.com/retrieve/pii/S2451865418300693 CrossRefGoogle ScholarPubMed
Doke, SK & Dhawale, SC (2015) Alternatives to animal testing: a review. Saudi Pharm J 23, 223–229. http://www.sciencedirect.com/science/article/pii/S1319016413001096 CrossRefGoogle ScholarPubMed
Lamond, AR, Janssen, AEM, Mackie, A, et al. (2019) An engineering perspective on human digestion. Interdiscip Approaches Food Dig, 255–273.CrossRefGoogle Scholar
Hantzidiamantis, PJ & Lappin, SL (2019) Physiology, Glucose. Treasure Island (FL): StatPearls Publishing. http://europepmc.org/books/NBK545201 Google Scholar
Sethupathy, P, Moses, JA & Anandharamakrishnan, C (2020) Food oral processing and tribology: instrumental approaches and emerging applications. Food Rev Int, 1–34. https://www.tandfonline.com/doi/full/10.1080/87559129.2019.1710749 Google Scholar
Ahuja, NK & Chan, WW (2016) Assessing upper esophageal sphincter function in clinical practice: a primer. Curr Gastroenterol Rep 18, 7.CrossRefGoogle ScholarPubMed
Laguna, L, Barrowclough, RA, Chen, J, et al. (2016) New approach to food difficulty perception: food structure, food oral processing and individual’s physical strength. J Texture Stud 47, 413–422. http://doi.wiley.com/10.1111/jtxs.12190 CrossRefGoogle Scholar
Gamero, A, Nguyen, QC, Varela, P, et al. (2019) Potential impact of oat ingredient type on oral fragmentation of biscuits and oro-digestibility of starch—an in vitro approach. Foods 8, 148.CrossRefGoogle ScholarPubMed
Kumari, SK & Mathana, JM (2019) Blood sugar level indication through chewing and swallowing from acoustic MEMS sensor and deep learning algorithm for diabetic management. J Med Syst 43, 1. http://link.springer.com/10.1007/s10916-018-1115-2 CrossRefGoogle Scholar
Argyrakopoulou, G, Simati, S, Dimitriadis, G, et al. (2020) How important is eating rate in the physiological response to food intake, control of body weight, and glycemia? Nutrients 12, 1734.CrossRefGoogle ScholarPubMed
Bridges, J, Smythe, J & Reddrick, R (2017) Impact of salivary enzyme activity on the oral perception of starch containing foods. J Texture Stud 48, 288–293.CrossRefGoogle ScholarPubMed
Tan, VMH, Ooi, DSQ, Kapur, J, et al. (2016) The role of digestive factors in determining glycemic response in a multiethnic Asian population. Eur J Nutr 55, 1573–1581.CrossRefGoogle Scholar
Hollis, JH (2018) The effect of mastication on food intake, satiety and body weight. Physiol Behav 193, 242–245.CrossRefGoogle ScholarPubMed
Grimble, G (2017) The physiology of nutrient digestion and absorption. Hum Nutr.Google Scholar
Boland, M (2016) Human digestion–a processing perspective. J Sci Food Agric 96, 2275–2283.CrossRefGoogle Scholar
Gopirajah, R, Keshav, R, Wadhwa, R, et al. (2016) Glycemic response to fibre rich foods and their relationship with gastric emptying and motor functions: an MRI study. Food Funct.CrossRefGoogle ScholarPubMed
Gopirajah, R & Anandharamakrishnan, C (2014) Methods integrating physical mechanisms underlying the food digestion and release of nutrients in human stomach. J Nutr Nutr Epidemiol. http://www.zealscienza.com/methods-integrating-physical-mechanisms-underlying-the-food-digestion-and-release-of-nutrients-in-human-stomach/%5Cninternal-pdf://887/methods-integrating-physical-mechanisms-underlying-the-food-digestion-and-release-of-nutrient Google Scholar
Goyal, RK, Guo, Y & Mashimo, H (2019) Advances in the physiology of gastric emptying. Neurogastroenterol Motil 31, e13546. https://onlinelibrary.wiley.com/doi/abs/10.1111/nmo.13546 CrossRefGoogle ScholarPubMed
Kong, F & Singh, RP (2008) Disintegration of solid foods in human stomach. J Food Sci 73, R67–R80. http://doi.wiley.com/10.1111/j.1750-3841.2008.00766.x CrossRefGoogle ScholarPubMed
Pletsch, EA & Hamaker, BR (2018) Brown rice compared to white rice slows gastric emptying in humans. Eur J Clin Nutr 72, 367–373. http://www.nature.com/articles/s41430-017-0003-z CrossRefGoogle Scholar
Bawden, S, Stephenson, M, Falcone, Y, et al. (2017) Increased liver fat and glycogen stores after consumption of high versus low glycaemic index food: a randomized crossover study. Diabetes, Obes Metab 19, 70–77.CrossRefGoogle ScholarPubMed
Mackie, AR, Bajka, BH, Rigby, NM, et al. (2017) Oatmeal particle size alters glycemic index but not as a function of gastric emptying rate. Am J Physiol Liver Physiol 313, G239–G246. https://www.physiology.org/doi/10.1152/ajpgi.00005.2017 Google Scholar
Date, K, Satoh, A, Iida, K, et al. (2015) Pancreatic $α$-amylase controls glucose assimilation by duodenal retrieval through N-glycan-specific binding, endocytosis, and degradation. J Biol Chem 290, 17439–17450.CrossRefGoogle ScholarPubMed
Janiak, MC (2016) Digestive enzymes of human and nonhuman primates. Evol Anthropol Issues, News, Rev 25, 253–266. http://doi.wiley.com/10.1002/evan.21498 CrossRefGoogle ScholarPubMed
Sinnott, MD, Cleary, PW & Harrison, SM (2017) Peristaltic transport of a particulate suspension in the small intestine. Appl Math Model 44, 143–159. http://www.sciencedirect.com/science/article/pii/B9780702033674000086 CrossRefGoogle Scholar
Omer, A & Quigley, EMM (2018) Carbohydrate maldigestion and malabsorption. Clin Gastroenterol Hepatol 16, 1197–1199.CrossRefGoogle ScholarPubMed
Patching, SG (2017) Glucose transporters at the blood-brain barrier: function, regulation and gateways for drug delivery. Mol Neurobiol 54, 1046–1077. http://link.springer.com/10.1007/s12035-015-9672-6 CrossRefGoogle ScholarPubMed
Röder, PV, Wu, B, Liu, Y, et al. (2016) Pancreatic regulation of glucose homeostasis. Exp Mol Med 48, e219–e219. http://www.nature.com/articles/emm20166 CrossRefGoogle ScholarPubMed
Navale, AM & Paranjape, AN (2016) Glucose transporters: physiological and pathological roles. Biophys Rev 8, 5–9. http://link.springer.com/10.1007/s12551-015-0186-2 CrossRefGoogle ScholarPubMed
Pfeiffer, AFH & Keyhani-Nejad, F (2018) High glycemic index metabolic damage – a pivotal role of GIP and GLP-1. Trends Endocrinol Metab 29, 289–299. https://linkinghub.elsevier.com/retrieve/pii/S1043276018300468 CrossRefGoogle Scholar
Nounmusig, J, Kongkachuichai, R, Sirichakwal, PP, et al. (2018) The effect of low and high glycemic index based rice varieties in test meals on postprandial blood glucose, insulin and incretin hormones response in prediabetic subjects. Int Food Res J 25, 835–841.Google Scholar
Eelderink, C, Noort, MWJ, Sozer, N, et al. (2017) Difference in postprandial GLP-1 response despite similar glucose kinetics after consumption of wheat breads with different particle size in healthy men. Eur J Nutr 56, 1063–1076.CrossRefGoogle ScholarPubMed
Hira, T, Pinyo, J & Hara, H (2020) What Is GLP-1 really doing in obesity? Trends Endocrinol Metab 31, 71–80.CrossRefGoogle ScholarPubMed
Vella, A & Camilleri, M (2017) The gastrointestinal tract as an integrator of mechanical and hormonal response to nutrient ingestion. Diabetes 66, 2729–2737.CrossRefGoogle ScholarPubMed
Kamoi, K, Inoue, K, Kontai, Y, et al. (2014) Effect of DPP-4 inhibitor s on energy and content of dietary intake in Japanese patients with type 2 diabetes mellitus. J Hum Nutr Food Sci 2, 1029–1035.Google Scholar
Zhang, Y, Wu, P, Jeantet, R, et al. (2020) How motility can enhance mass transfer and absorption in the duodenum: taking the structure of the villi into account. Chem Eng Sci 213, 115406.CrossRefGoogle Scholar
Avberšek Lužnik, I, Lušnic Polak, M, Demšar, L, et al. (2019) Does type of bread ingested for breakfast contribute to lowering of glycaemic index? J Nutr Intermed Metab CrossRefGoogle Scholar
FAO (2016) Module II : scientific guidelines for the preparation of veterinary drug residue monographs, working papers and related summary documents for Joint FAO/WHO Expert Committee on Food Additives (JECFA) drafting experts and reviewers assigned by FAO. http://www.fao.org/3/a-bl003e.pdf Google Scholar
Heinemann, L & Stuhr, A (2018) Self-measurement of blood glucose and continuous glucose monitoring – is there only one future? Eur Endocrinol Touch Medical Media 14, 24. http://www.touchendocrinology.com/articles/self-measurement-blood-glucose-and-continuous-glucose-monitoring-there-only-one-future Google ScholarPubMed
Zhang, JXJ & Hoshino, K (2014) Chapter 4 - Electrical transducers: electrochemical sensors and semiconductor molecular sensors. In [JXJ Zhang and KBT-MS Hoshino, editors]. Oxford: William Andrew Publishing, pp. 169–232. http://www.sciencedirect.com/science/article/pii/B9781455776313000041 Google Scholar
Brouns, F, Bjorck, I, Frayn, KN, et al. (2005) Glycaemic index methodology. Nutr Res Rev 18, 145–171.CrossRefGoogle ScholarPubMed
Wolever, TMS (2004) Effect of blood sampling schedule and method of calculating the area under the curve on validity and precision of glycaemic index values. Br J Nutr 91, 295–300.CrossRefGoogle ScholarPubMed
Dodd, H, Williams, S, Brown, R, et al. (2011) Calculating meal glycemic index by using measured and published food values compared with directly measured meal glycemic index. Am J Clin Nutr 94, 992–996.CrossRefGoogle ScholarPubMed
Wolever, TMS & Bhaskaran, K (2012) Use of glycemic index to estimate mixed-meal glycemic response. Am J Clin Nutr 95, 256–257.CrossRefGoogle ScholarPubMed
Lin Lee, JJ, Chan, B, Chun, C, et al. (2020) A preparation of β-glucans and anthocyanins (LoGiCarbTM) lowers the in vitro digestibility and in vivo glycemic index of white rice. RSC Adv 10, 5129–5133. http://xlink.rsc.org/?DOI=C9RA08147J CrossRefGoogle Scholar
Mandal, UK, Chatterjee, B & Senjoti, FG (2016) Gastro-retentive drug delivery systems and their in vivo success: a recent update. Asian J Pharm Sci 11, 575–584.CrossRefGoogle Scholar
Peyser, TA, Balo, AK, Buckingham, BA, et al. (2017) Glycemic variability percentage: a novel method for assessing glycemic variability from continuous glucose monitor data. Diabetes Technol Ther 20, 6–16. https://doi.org/10.1089/dia.2017.0187 CrossRefGoogle ScholarPubMed
Haldar, S, Egli, L, De Castro, CA, et al. (2020) High or low glycemic index (GI) meals at dinner results in greater postprandial glycemia compared with breakfast: a randomized controlled trial. BMJ Open Diabetes Res Care 8, e001099.CrossRefGoogle ScholarPubMed
Kownacka, AE, Vegelyte, D, Joosse, M, et al. (2018) Clinical evidence for use of a noninvasive biosensor for tear glucose as an alternative to painful finger-prick for diabetes management utilizing a biopolymer coating. Biomacromolecules 19, 4504–4511.CrossRefGoogle ScholarPubMed
Lu, W & Bao, Y (2018) Operation standards for continuous glucose monitoring. Contin Glucose Monit, 27–33.CrossRefGoogle Scholar
Campbell, GJ, Belobrajdic, DP & Bell-Anderson, KS (2018) Determining the glycaemic index of standard and high-sugar rodent diets in C57BL/6 mice. Nutrients 10, 856.CrossRefGoogle ScholarPubMed
Van Norman, GA (2019) Limitations of animal studies for predicting toxicity in clinical trials: is it time to rethink our current approach? JACC Basic Transl Sci 4, 845–854.CrossRefGoogle ScholarPubMed
Coelho, LP, Kultima, JR, Costea, PI, et al. (2018) Similarity of the dog and human gut microbiomes in gene content and response to diet. Microbiome 6, 1–11.CrossRefGoogle Scholar
Falsafi, SR, Maghsoudlou, Y, Aalami, M, et al. (2019) Physicochemical and morphological properties of resistant starch type 4 prepared under ultrasound and conventional conditions and their in-vitro and in-vivo digestibilities. Ultrason 53, 110–119.Google ScholarPubMed
Small, L, Brandon, AE, Turner, N, et al. (2018) Modeling insulin resistance in rodents by alterations in diet: what have high-fat and high-calorie diets revealed? Am J Physiol Metab 314, E251–E265.Google ScholarPubMed
Pacini, G, Omar, B & Ahrén, B (2013) Methods and models for metabolic assessment in mice. J Diabetes Res 2013, 1–8. http://www.hindawi.com/journals/jdr/2013/986906/ CrossRefGoogle ScholarPubMed
Nielsen, KL, Hartvigsen, ML, Hedemann, MS, et al. (2014) Similar metabolic responses in pigs and humans to breads with different contents and compositions of dietary fibers: a metabolomics study. Am J Clin Nutr 99, 941–949. https://academic.oup.com/ajcn/article/99/4/941/4637875 CrossRefGoogle ScholarPubMed
King, AJ (2012) The use of animal models in diabetes research. Br J Pharmacol. 166, 877–894. http://doi.wiley.com/10.1111/j.1476-5381.2012.01911.x CrossRefGoogle ScholarPubMed
Renner, S, Blutke, A, Clauss, S, et al. (2020) Porcine models for studying complications and organ crosstalk in diabetes mellitus. Cell Tissue Res. 380, 341–378. http://link.springer.com/10.1007/s00441-019-03158-9 CrossRefGoogle ScholarPubMed
Kawai, T, Ito, T, Ohwada, K, et al. (2006) Hereditary postprandial hypertriglyceridemic rabbit exhibits insulin resistance and central obesity. Arterioscler Thromb Vasc Biol 26, 2752–2757. https://www.ahajournals.org/doi/10.1161/01.ATV.0000245808.12493.40 CrossRefGoogle ScholarPubMed
Wang, J, Wan, R, Mo, Y, et al. (2010) Creating a long-term diabetic rabbit model. Exp Diabetes Res. 2010.CrossRefGoogle ScholarPubMed
Khan, FR & Alhewairini, SS (2018) Zebrafish (Danio rerio) as a model organism. Curr Trends Cancer Manag.Google Scholar
Liguori, GR, Jeronimus, BF, de Aquinas Liguori, TT, et al. (2017) Ethical issues in the use of animal models for tissue engineering: reflections on legal aspects, moral theory, three rs strategies, and harm--benefit analysis. Tissue Eng Part C Methods 23, 850–862.CrossRefGoogle ScholarPubMed
Radenković, M, Stojanović, M & Prostran, M (2016) Experimental diabetes induced by alloxan and streptozotocin: the current state of the art. J Pharmacol Toxicol Methods 78, 13–31.CrossRefGoogle ScholarPubMed
Al-Awar, A, Kupai, K, Veszelka, M, et al. (2016) Experimental diabetes mellitus in different animal models. J Diabetes Res 2016.CrossRefGoogle ScholarPubMed
Dusinska, M, Rundén-Pran, E, Schnekenburger, J, et al. (2017) Chapter 3 - toxicity tests: in vitro and in vivo. In [B Fadeel, A Pietroiusti, editors], Shvedova AABT-AE of EN (Second edition). Academic Press, pp. 51–82.Google Scholar
Pearce, SC, Coia, HG, Karl, JP, et al. (2018) Intestinal in vitro and ex vivo models to study host-microbiome interactions and acute stressors. Front Physiol 9, 1584. https://www.frontiersin.org/article/10.3389/fphys.2018.01584/full CrossRefGoogle ScholarPubMed
Parthasarathi, S, Bhushani, JA & Anandharamakrishnan, C (2018) Engineered small intestinal system as an alternative to in-situ intestinal permeability model. J Food Eng 222, 110–114. https://doi.org/10.1016/j.jfoodeng.2017.11.019 CrossRefGoogle Scholar
Mills, JAN, France, J, Ellis, JL, et al. (2017) A mechanistic model of small intestinal starch digestion and glucose uptake in the cow. J Dairy Sci 100, 4650–4670.CrossRefGoogle ScholarPubMed
Dixit, P, Jain, DK & Dumbwani, J (2012) Standardization of an ex vivo method for determination of intestinal permeability of drugs using everted rat intestine apparatus. J Pharmacol Toxicol Methods 65, 13–17.CrossRefGoogle Scholar
Chukwuma, CI & Islam, MS (2015) Effects of xylitol on carbohydrate digesting enzymes activity, intestinal glucose absorption and muscle glucose uptake: a multi-mode study. Food Funct 6, 955–962.CrossRefGoogle ScholarPubMed
Chukwuma, CI & Islam, MS (2017) Sorbitol increases muscle glucose uptake ex vivo and inhibits intestinal glucose absorption ex vivo and in normal and type 2 diabetic rats. Appl Physiol Nutr Metab 42, 377–383.CrossRefGoogle ScholarPubMed
Chukwuma, CI, Matsabisa, MG, Erukainure, OL, et al. (2019) D-mannitol modulates glucose uptake ex vivo; suppresses intestinal glucose absorption in normal and type 2 diabetic rats. Food Biosci 29, 30–36.CrossRefGoogle Scholar
Arnold, YE, Thorens, J, Bernard, S, et al. (2019) Drug transport across porcine intestine using an Ussing chamber system: regional differences and the effect of P-glycoprotein and CYP3A4 activity on drug absorption. Pharmaceutics 11, 139.CrossRefGoogle ScholarPubMed
Luo, Z, Liu, Y, Zhao, B, et al. (2013) Ex vivo and in situ approaches used to study intestinal absorption. J Pharmacol Toxicol Methods 68, 208–216. https://linkinghub.elsevier.com/retrieve/pii/S1056871913002736 CrossRefGoogle ScholarPubMed
Thomson, A, Smart, K, Somerville, MS, et al. (2019) The Ussing chamber system for measuring intestinal permeability in health and disease. BMC Gastroenterol 19, 98. https://bmcgastroenterol.biomedcentral.com/articles/10.1186/s12876-019-1002-4 CrossRefGoogle ScholarPubMed
Ripken, D & Hendriks, HFJ (2015) Porcine Ex Vivo Intestinal Segment Model BT - The Impact of Food Bioactives on Health: in vitro and ex vivo models. In [K Verhoeckx, P Cotter, I López-Expósito, et al., editors]. Cham: Springer International Publishing, pp. 255–262.Google Scholar
Englyst, HN, Veenstra, J & Hudson, GJ (1996) Measurement of rapidly available glucose (RAG) in plant foods: a potential in vitro predictor of the glycaemic response. Br J Nutr 75, 327. http://www.journals.cambridge.org/abstract_S0007114596000372 CrossRefGoogle ScholarPubMed
Wolever, TM, Jenkins, DJ, Jenkins, AL, et al. (1991) The glycemic index: methodology and clinical implications. Am J Clin Nutr 54, 846–854. https://academic.oup.com/ajcn/article/54/5/846/4694343 CrossRefGoogle ScholarPubMed
Jenkins, DJ, Wolever, TM, Taylor, RH, et al. (1982) Slow release dietary carbohydrate improves second meal tolerance. Am J Clin Nutr. 35, 1339–1346. https://academic.oup.com/ajcn/article/35/6/1339/4693290 CrossRefGoogle ScholarPubMed
Alam, MA, Al-jenoobi, FI & Al-mohizea, AM (2012) Everted gut sac model as a tool in pharmaceutical research: limitations and applications. J Pharm Pharmacol, 326–336.Google Scholar
Maddula, K & Juluru, A (2016) Intestinal absorption models. Res Rev J 4, 1–12.Google Scholar
Ravnic, DJ, Leberfinger, AN & Ozbolat, IT (2017) Bioprinting and cellular therapies for type 1 diabetes. Trends Biotechnol. 35, 1025–1034. https://linkinghub.elsevier.com/retrieve/pii/S0167779917301853 CrossRefGoogle ScholarPubMed
Yi, B, Shim, KY, Ha, SK, et al. (2017) Three-dimensional in vitro gut model on a villi-shaped collagen scaffold. BioChip J 11, 219–231.CrossRefGoogle Scholar
Shim, K-Y, Lee, D, Han, J, et al. (2017) Microfluidic gut-on-a-chip with three-dimensional villi structure. Biomed Microdevices 19, 37. http://link.springer.com/10.1007/s10544-017-0179-y CrossRefGoogle ScholarPubMed
Kasendra, M, Tovaglieri, A, Sontheimer-Phelps, A, et al. (2018) Development of a primary human Small Intestine-on-a-Chip using biopsy-derived organoids. Sci Rep. 8, 2871. http://www.nature.com/articles/s41598-018-21201-7 CrossRefGoogle ScholarPubMed
Lee, SH & Sung, JH (2020) Chapter 9 - Gut-on-a-chip microphysiological systems for the recapitulation of the gut microenvironment. In [J Hoeng, D Bovard and MCBT-O Peitsch, editors]. Academic Press, pp. 295–310. http://www.sciencedirect.com/science/article/pii/B9780128172025000103 Google Scholar
Southgate, DAT (1969) Determination of carbohydrates in foods. I.—available carbohydrate. J Sci Food Agric 20, 326–330. http://doi.wiley.com/10.1002/jsfa.2740200602 CrossRefGoogle ScholarPubMed
Englyst, H, Wiggins, HS & Cummings, JH (1982) Determination of the non-starch polysaccharides in plant foods by gas-liquid chromatography of constituent sugars as alditol acetates. Analyst 107, 307–318.CrossRefGoogle ScholarPubMed
Jenkins, DJA, Wolever, TMS, Thorne, MJ, et al. (1984) The relationship between glycemic response, digestibility, and factors influencing the dietary habits of diabetics. Am J Clin Nutr 40, 1175–1191. https://academic.oup.com/ajcn/article/40/6/1175/4691488 CrossRefGoogle ScholarPubMed
Berry, CS (1986) Resistant starch: formation and measurement of starch that survives exhaustive digestion with amylolytic enzymes during the determination of dietary fibre. J Cereal Sci 4, 301–314.CrossRefGoogle Scholar
Granfeldt, Y & Björck, I (1991) Glycemic response to starch in pasta: a study of mechanisms of limited enzyme availability. J Cereal Sci 14, 47–61. https://linkinghub.elsevier.com/retrieve/pii/S0733521009800179 CrossRefGoogle Scholar
Englyst, HN, Kingman, SM & Cummings, JH (1992) Classification and measurement of nutritionally important starch fractions. Eur J Clin Nutr 46, S33–S50.Google ScholarPubMed
Muir, JG & O’Dea, K (1993) Validation of an in vitro assay for predicting the amount of starch that escapes digestion in the small intestine of humans. Am J Clin Nutr 57, 540–546. https://academic.oup.com/ajcn/article/57/4/540/4715725 CrossRefGoogle Scholar
Brighenti, F, Pellegrini, N, Casiraghi, MC, et al. (1995) In vitro studies to predict physiological effects of dietary fibre. Eur J Clin NutrGoogle Scholar
Goñi, I, Garcia-Alonso, A & Saura-Calixto, F (1997) A starch hydrolysis procedure to estimate glycemic index. Nutr Res 17, 427–437. https://linkinghub.elsevier.com/retrieve/pii/S0271531797000109 CrossRefGoogle Scholar
Englyst, KN, Englyst, HN, Hudson, GJ, et al. (1999) Rapidly available glucose in foods: an in vitro measurement that reflects the glycemic response. Am J Clin Nutr 69, 448–454. https://academic.oup.com/ajcn/article/69/3/448/4694167 CrossRefGoogle Scholar
Guraya, HS, James, C & Champagne, ET (2001) Effect of cooling, and freezing on the digestibility of debranched rice starch and physical properties of the resulting material. Starch-Stärke 53, 64–74.3.0.CO;2-R>CrossRefGoogle Scholar
Woolnough, JW, Monro, JA, Brennan, CS, et al. (2008) Simulating human carbohydrate digestion in vitro: a review of methods and the need for standardisation. Int J food Sci Technol 43, 2245–2256.CrossRefGoogle Scholar
Dartois, A, Singh, J, Kaur, L, et al. (2010) Influence of guar gum on the in vitro starch digestibility—rheological and microstructural characteristics. Food Biophys 5, 149–160.CrossRefGoogle Scholar
Naumann, S, Schweiggert-Weisz, U, Bader-Mittermaier, S, et al. (2018) Differentiation of adsorptive and viscous effects of dietary fibres on bile acid release by means of in vitro digestion and dialysis. Int J Mol Sci 19, 2193. http://www.mdpi.com/1422-0067/19/8/2193 CrossRefGoogle ScholarPubMed
Santhi Rajkumar, P, Suriyamoorthy, P, Moses, JA, et al. (2020) Mass transfer approach to in-vitro glycemic index of different biscuit compositions. J Food Process Eng, e13559. https://doi.org/10.1111/jfpe.13559 Google Scholar
Sams, L, Paume, J, Giallo, J, et al. (2016) Relevant pH and lipase for in vitro models of gastric digestion. Food Funct 7, 30–45. http://xlink.rsc.org/?DOI=C5FO00930H CrossRefGoogle ScholarPubMed
Bornhorst, G & Singh, R (2012) Bolus formation and disintegration during digestion of food carbohydrates. Compr Rev Food Sci Food Saf.CrossRefGoogle Scholar
Sun, L, Ranawana, DV, Tan, WJK, et al. (2015) The impact of eating methods on eating rate and glycemic response in healthy adults. Physiol Behav 139, 505–510. https://linkinghub.elsevier.com/retrieve/pii/S0031938414006179 CrossRefGoogle ScholarPubMed
Gao, J, Tan, EYN, Low, SHL, et al. (2020) From bolus to digesta: how structural disintegration affects starch hydrolysis during oral-gastro-intestinal digestion of bread. J Food Eng, 110161.Google Scholar
Wessel, MD, Jurs, PC, Tolan, JW, et al. (1998) Prediction of human intestinal absorption of drug compounds from molecular structure. J Chem Inf Comput Sci 38, 726–735.CrossRefGoogle ScholarPubMed
Ogston, AG, Preston, BN & Wells, JD (1973) On the transport of compact particles through solutions of chain-polymers. Proc R Soc London A Math Phys Sci. 333, 297–316. https://royalsocietypublishing.org/doi/10.1098/rspa.1973.0064 Google Scholar
Sinko, PJ, Leesman, GD & Amidon, GL (1991) Predicting fraction dose absorbed in humans using a macroscopic mass balance approach. Pharm Res 8, 979–988.CrossRefGoogle ScholarPubMed
Oh, D-H & Marshall, DL (1993) Antimicrobial activity of ethanol, glycerol monolaurate or lactic acid against Listeria monocytogenes. Int J Food Microbiol 20, 239–246. https://linkinghub.elsevier.com/retrieve/pii/016816059390168G CrossRefGoogle ScholarPubMed
Yu, LX, Crison, JR & Amidon, GL (1996) Compartmental transit and dispersion model analysis of small intestinal transit flow in humans. Int J Pharm 140, 111–118. https://linkinghub.elsevier.com/retrieve/pii/0378517396045929 CrossRefGoogle Scholar
Du, P, Paskaranandavadivel, N, Angeli, TR, et al. (2016) The virtual intestine: in silico modeling of small intestinal electrophysiology and motility and the applications. Wiley Interdiscip Rev Syst Biol Med 8, 69–85. https://www.ncbi.nlm.nih.gov/pubmed/26562482 CrossRefGoogle ScholarPubMed
Fullard, LA, Lammers, WJ & Ferrua, MJ (2015) Advective mixing due to longitudinal and segmental contractions in the ileum of the rabbit. J Food Eng 160, 1–10. https://linkinghub.elsevier.com/retrieve/pii/S0260877415001168 CrossRefGoogle Scholar
Moxon, TE, Gouseti, O & Bakalis, S (2016) In silico modelling of mass transfer & absorption in the human gut. J Food Eng 176, 110–120. https://linkinghub.elsevier.com/retrieve/pii/S0260877415300200 CrossRefGoogle ScholarPubMed
Burton, PS, Goodwin, JT, Vidmar, TJ, et al. (2002) Predicting drug absorption: how nature made it a difficult problem. J Pharmacol Exp Ther 303, 889–895.CrossRefGoogle ScholarPubMed
Rozendaal, YJ, Maas, AH, van Pul, C, et al. (2018) Model-based analysis of postprandial glycemic response dynamics for different types of food. Clin Nutr Exp 19, 32–45. https://linkinghub.elsevier.com/retrieve/pii/S2352939317300374 CrossRefGoogle Scholar
Flint, A, Møller, BK, Raben, A, et al. (2004) The use of glycaemic index tables to predict glycaemic index of composite breakfast meals. Br J Nutr 91, 979–989. https://www.cambridge.org/core/product/identifier/S0007114504001199/type/journal_article CrossRefGoogle ScholarPubMed
Gyuk, P, Vassányi, I & Kósa, I (2019) Blood glucose level prediction for diabetics based on nutrition and insulin administration logs using personalized mathematical models. J Healthc Eng 2019, 1–12. https://www.hindawi.com/journals/jhe/2019/8605206/ CrossRefGoogle ScholarPubMed
Plis, K, Bunescu, R, Marling, C, et al. (2014) A machine learning approach to predicting blood glucose levels for diabetes management. In Work Twenty-Eighth AAAI Conf Artif Intell.Google Scholar
Huizinga, JD, Parsons, SP, Chen, J-H, et al. (2015) Motor patterns of the small intestine explained by phase-amplitude coupling of two pacemaker activities: the critical importance of propagation velocity. Am J Physiol Physiol. 309, C403–C414. https://www.physiology.org/doi/10.1152/ajpcell.00414.2014 CrossRefGoogle ScholarPubMed
Lentle, RG, De Loubens, C, Hulls, C, et al. (2012) A comparison of the organization of longitudinal and circular contractions during pendular and segmental activity in the duodenum of the rat and guinea pig. Neurogastroenterol Motil 24, 686–e298. http://doi.wiley.com/10.1111/j.1365-2982.2012.01923.x CrossRefGoogle ScholarPubMed
Ferdowsian, HR & Beck, N (2011) Ethical and scientific considerations regarding animal testing and research. PLoS One 6, e24059–e24059.CrossRefGoogle ScholarPubMed
Lefèbvre, PJ (2012) Glucagon III. Springer Science & Business Media.Google Scholar
Mojsov, S (2000) Glucagon-like Peptide-1 (GLP-1) and the control of glucose metabolism in mammals and teleost fish. Am Zool 40, 246–258. https://academic.oup.com/icb/article-lookup/doi/10.1093/icb/40.2.246 Google Scholar
Muttakin, S, Moxon, TE & Gouseti, O (2019) In vivo, in vitro, and in silico studies of the gi tract. Interdiscip Approaches Food Dig, 29–67. http://link.springer.com/10.1007/978-3-030-03901-1_3 CrossRefGoogle Scholar
Nunes, R, Silva, C & Chaves, L (2016) 4.2 - Tissue-based in vitro and ex vivo models for intestinal permeability studies. In [Sarmento BBT-C and M for DPS, editor]. Woodhead Publishing, pp. 203–236. http://www.sciencedirect.com/science/article/pii/B9780081000946000134 Google Scholar
Monro, JA & Mishra, S (2010) Glycemic impact as a property of foods is accurately measured by an available carbohydrate method that mimics the glycemic response. J Nutr 140, 1328–1334. https://academic.oup.com/jn/article/140/7/1328/4688980 CrossRefGoogle ScholarPubMed
Dupont, D, Alric, M, Blanquet-Diot, S, et al. (2019) Can dynamic in vitro digestion systems mimic the physiological reality? Crit Rev Food Sci Nutr 59, 1546–1562.CrossRefGoogle ScholarPubMed
Li, Z, Zhu, L, Zhang, W, et al. (2020) New dynamic digestion model reactor that mimics gastrointestinal function. Biochem Eng J 154, 107431. https://linkinghub.elsevier.com/retrieve/pii/S1369703X19303705 CrossRefGoogle Scholar
Wu, P, Bhattarai, RR, Dhital, S, et al. (2017) In vitro digestion of pectin-and mango-enriched diets using a dynamic rat stomach-duodenum model. J Food Eng 202, 65–78.CrossRefGoogle Scholar
Priyadarshini, S, Elumalai, A, Moses, JA, et al. (2020) Predicting human glucose response curve using an engineered small intestine model in combination with mathematical modeling. J Food Eng, 110395. https://doi.org/10.1016/j.jfoodeng.2020.110395 Google Scholar
Tharakan, A, Norton, IT, Fryer, PJ, et al. (2010) Mass transfer and nutrient absorption in a simulated model of small intestine. J Food Sci 75, E339–E346. http://doi.wiley.com/10.1111/j.1750-3841.2010.01659.x CrossRefGoogle Scholar
Barroso, E, Cueva, C, Peláez, C, et al. (2015) The computer-controlled multicompartmental dynamic model of the gastrointestinal system simgi bt-the impact of food bioactives on health: in vitro and ex vivo models. In [K Verhoeckx, P Cotter, I López-Expósito, et al., editors]. Cham: Springer International Publishing, pp. 319–327. https://doi.org/10.1007/978-3-319-16104-4_28 CrossRefGoogle Scholar
Ménard, O, Picque, D & Dupont, D (2015) The DIDGI®system. Impact Food Bioact. Heal. Cham: Springer, pp. 73–81.Google Scholar
Wright, ND, Kong, F, Williams, BS, et al. (2016) A human duodenum model (HDM) to study transport and digestion of intestinal contents. J Food Eng 171, 129–136.CrossRefGoogle Scholar
Passannanti, F, Nigro, F, Gallo, M, et al. (2017) In vitro dynamic model simulating the digestive tract of 6-month-old infants. PLoS One 12, e0189807. https://dx.plos.org/10.1371/journal.pone.0189807 CrossRefGoogle ScholarPubMed
Minekus, M (2015) The TNO Gastro-Intestinal Model (TIM). Impact Food Bioact. Heal. Cham: Springer International Publishing, pp. 37–46. http://link.springer.com/10.1007/978-3-319-16104-4_5 Google Scholar
Gopirajah, R & Anandharamakrishnan, C (2014) Methods integrating physical mechanisms underlying the food digestion and release of nutrients in human stomach. J Nutr Nutr Epidemiol 1, 1–13.Google Scholar
Qing, S, Zhang, Q, Li, W, et al. (2019) Effects of different satiety levels on the fate of soymilk protein in gastrointestinal digestion and antigenicity assessed by an in vitro dynamic gastrointestinal model. Food Funct 10, 7855–7864. http://xlink.rsc.org/?DOI=C9FO01965K CrossRefGoogle ScholarPubMed
Bellmann, S, Minekus, M, Sanders, P, et al. (2018) Human glycemic response curves after intake of carbohydrate foods are accurately predicted by combining in vitro gastrointestinal digestion with in silico kinetic modeling. Clin Nutr Exp 17, 8–22. https://doi.org/10.1016/j.yclnex.2017.10.003 CrossRefGoogle Scholar
Gabbia, D, Dall’Acqua, S, Di Gangi, I, et al. (2017) The phytocomplex from fucus vesiculosus and ascophyllum nodosum controls postprandial plasma glucose levels: an in vitro and in vivo study in a mouse model of nash. Mar Drugs 15, 41. http://www.mdpi.com/1660-3397/15/2/41 CrossRefGoogle Scholar
Hasselwander, O, DiCosimo, R, You, Z, et al. (2017) Development of dietary soluble fibres by enzymatic synthesis and assessment of their digestibility in in vitro, animal and randomised clinical trial models. Int J Food Sci Nutr 68, 849–864. https://www.tandfonline.com/doi/full/10.1080/09637486.2017.1295027 CrossRefGoogle ScholarPubMed
Zheng, Y, Wang, Q, Huang, J, et al. (2019) Hypoglycemic effect of dietary fibers from bamboo shoot shell: an in vitro and in vivo study. Food Chem Toxicol 127, 120–126.CrossRefGoogle ScholarPubMed
Marques A de, CR, Schiavon, FPM, Travassos, PB, et al. (2016) Evaluation of the impact of orally administered carbohydrates on postprandial blood glucose levels in different pre-clinical models. Brazilian J Pharm Sci 52, 761–769. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1984-82502016000400761&lng=en&tlng=en CrossRefGoogle Scholar
Larsen, MO, Rolin, B, Wilken, M, et al. (2003) Measurements of insulin secretory capacity and glucose tolerance to predict pancreatic -cell mass in vivo in the nicotinamide/streptozotocin gottingen minipig, a model of moderate insulin deficiency and diabetes. Diabetes 52, 118–123. http://diabetes.diabetesjournals.org/cgi/doi/10.2337/diabetes.52.1.118 CrossRefGoogle Scholar
Elo, B, Villano, CM, Govorko, D, et al. (2007) Larval zebrafish as a model for glucose metabolism: expression of phosphoenolpyruvate carboxykinase as a marker for exposure to anti-diabetic compounds. J Mol Endocrinol 38, 433–440.CrossRefGoogle ScholarPubMed
Eames, SC, Philipson, LH, Prince, VE, et al. (2010) Blood sugar measurement in zebrafish reveals dynamics of glucose homeostasis. Zebrafish 7, 205–213.CrossRefGoogle ScholarPubMed
Okazaki, F, Zang, L, Nakayama, H, et al. (2019) Microbiome alteration in type 2 diabetes mellitus model of zebrafish. Sci Rep 9, 1–10.CrossRefGoogle ScholarPubMed