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Animal models of obesity and diabetes mellitus for dietary bioactive component development: A systematic review

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  • Corresponding author: baojunxu@bnbu.edu.cn
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    1. Diet-induced rodent models dominate bioactive research.

      Outcome reporting is skewed toward basic metabolic markers.

      Mechanistic depth varies and is often limited.

      Sex bias and design variability affect reproducibility.

      A framework is proposed to guide animal model selection.

  • Animal models are widely used to investigate the metabolic effects of dietary bioactive compounds in obesity and type 2 diabetes mellitus (T2DM). However, unlike conventional drugs, dietary bioactives often exert pleiotropic and context-dependent effects that are strongly influenced by disease severity, dietary background, and experimental conditions. Consequently, model selection and methodological variability may affect the reproducibility and translational relevance of preclinical findings. This systematic review evaluated animal models used in dietary bioactive research, identified methodological trends and limitations, and proposed a framework to guide future model selection. A systematic search of PubMed, Scopus, Web of Science, ScienceDirect, SpringerLink, and Wiley Online Library was conducted for studies published within the last five years. Data were extracted on species, strain, model type, dietary induction, intervention characteristics, outcome measures, and mechanistic depth. A total of 194 studies were included. Diet-induced obesity models were the most frequently used (67.2%), followed by genetic, hybrid, and chemically induced models. Rodents, particularly mice and rats, predominated, while alternative models such as zebrafish and invertebrates were used infrequently. Outcome reporting focused mainly on body weight, glucose metabolism, and lipid profiles, whereas mechanistic investigations varied considerably. Substantial methodological heterogeneity was observed in diet composition, dosing strategies, study duration, and reporting quality, alongside a marked bias toward male animals. Overall, current preclinical research is characterized by model dominance, methodological variability, and inconsistent mechanistic validation. The proposed framework provides a structured approach to improve study design and enhance the translational relevance of dietary bioactive research in metabolic disease.
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  • [1] World Health Organization. (2022). Https://www.Who.Int/News-Room/Fact Sheets/Detail/Obesity-and-Overweight.

    View in Article Google Scholar

    [2] Genitsaridi I., Salpea P., Salim A., et al. (2026). 11th edition of the IDF diabetes atlas: Global, regional, and national diabetes prevalence estimates for 2024 and projections for 2050. Lancet Diabetes Endocrinol. 14:149−156. DOI:10.1016/S2213-8587(25)00299-2

    View in Article CrossRef Google Scholar

    [3] Donath M.Y. and Drucker D.J. (2025). Obesity, diabetes, and inflammation: Pathophysiology and clinical implications. Immunity 58:2373−2382. DOI:10.1016/j.immuni.2025.09.011

    View in Article CrossRef Google Scholar

    [4] Aguerd O., Elhrech H., El Fessikh M., et al. (2025). Dietary bioactive compounds for type 2 diabetes: A comprehensive review of molecular interactions and mechanistic insights. J. Funct. Foods 126:106705. DOI:10.1016/j.jff.2025.106705

    View in Article CrossRef Google Scholar

    [5] Sayed S.M.U.F., Moshawih S., Goh H.P., et al. (2023). Natural products as novel anti-obesity agents: Insights into mechanisms of action and potential for therapeutic management. Front. Pharmacol. 14:1182937. DOI:10.3389/fphar.2023.1182937

    View in Article CrossRef Google Scholar

    [6] Jia H., Ren F. and Liu H. (2024). Evaluation of bioaccessibility and bioavailability of dietary bioactives and their application in food systems. Food Biosci. 62:105428. DOI:10.1016/j.fbio.2024.105428

    View in Article CrossRef Google Scholar

    [7] Athmuri D.N. and Shiekh P.A. (2023). Experimental diabetic animal models to study diabetes and diabetic complications. MethodsX 11:102474. DOI:10.1016/j.mex.2023.102474

    View in Article CrossRef Google Scholar

    [8] de Moura e Dias M., dos Reis S.A., da Conceição L.L., et al. (2021). Diet-induced obesity in animal models: Points to consider and influence on metabolic markers. Diabetol. Metab. Syndr. 13:47. DOI:10.1186/s13098-021-00647-2

    View in Article CrossRef Google Scholar

    [9] Hooijmans C.R., Rovers M.M., De Vries R.B.M., et al. (2014). SYRCLE’s risk of bias tool for animal studies. BMC Med. Res. Methodol. 14:43. DOI:10.1186/1471-2288-14-43

    View in Article CrossRef Google Scholar

    [10] Kleinert M., Clemmensen C., Hofmann S.M., et al. (2018). Animal models of obesity and diabetes mellitus. Nat. Rev. Endocrinol. 14:140−162. DOI:10.1038/nrendo.2017.161

    View in Article CrossRef Google Scholar

    [11] Speakman J., Hambly C., Mitchell S., et al. (2008). The contribution of animal models to the study of obesity. Lab. Anim. 42:413−432. DOI:10.1258/la.2007.006067

    View in Article CrossRef Google Scholar

    [12] Bastías-Pérez M., Serra D. and Herrero L. (2020). Dietary options for rodents in the study of obesity. Nutrients 12:3234. DOI:10.3390/nu12113234

    View in Article CrossRef Google Scholar

    [13] Hariri N. and Thibault L. (2010). High-fat diet-induced obesity in animal models. Nutr. Res. Rev. 23:270−299. DOI:10.1017/S0954422410000168

    View in Article CrossRef Google Scholar

    [14] Speakman J.R. (2019). Use of high-fat diets to study rodent obesity as a model of human obesity. Int. J. Obes. 43:1491−1492. DOI:10.1038/s41366-019-0363-7

    View in Article CrossRef Google Scholar

    [15] Stott N.L. and Marino J.S. (2020). High fat rodent models of type 2 diabetes: From rodent to human. Nutrients 12:3650. DOI:10.3390/nu12123650

    View in Article CrossRef Google Scholar

    [16] Buettner R., Parhofer K.G., Woenckhaus M., et al. (2006). Defining high-fat-diet rat models: Metabolic and molecular effects of different fat types. J. Mol. Endocrinol. 36:485−501. DOI:10.1677/JME.1.01909

    View in Article CrossRef Google Scholar

    [17] Saleh N.E.H., Ibrahim M.Y., Saad A.H., et al. (2024). The impact of consuming different types of high-caloric fat diet on the metabolic status, liver, and aortic integrity in rats. Sci. Rep. 14:68299. DOI:10.1038/s41598-024-68299-6

    View in Article CrossRef Google Scholar

    [18] Ivić V., Zjalić M., Blažetić S., et al. (2023). Elderly rats fed with a high-fat high-sucrose diet developed sex-dependent metabolic syndrome regardless of long-term metformin and liraglutide treatment. Front. Endocrinol. 14:1181064. DOI:10.3389/fendo.2023.1181064

    View in Article CrossRef Google Scholar

    [19] Softic S., Gupta M.K., Wang G.X., et al. (2017). Divergent effects of glucose and fructose on hepatic lipogenesis and insulin signaling. J. Clin. Invest. 127:4059−4074. DOI:10.1172/JCI94585

    View in Article CrossRef Google Scholar

    [20] Fisher S.L., Campbell G.J., Senior A. and Bell-Anderson K. (2024). The effect of high-sugar feeding on rodent metabolic phenotype: A systematic review and meta-analysis. NPJ Metab. Health Dis. 2:43. DOI:10.1038/s44324-024-00043-0

    View in Article CrossRef Google Scholar

    [21] Preguiça I., Alves A., Nunes S., et al. (2020). Diet-induced rodent models of obesity-related metabolic disorders—A guide to a translational perspective. Obes. Rev. 21:e13081. DOI:10.1111/obr.13081

    View in Article CrossRef Google Scholar

    [22] Jin Y., Kozan D., Young E.D., et al. (2024). A high-cholesterol zebrafish diet promotes hypercholesterolemia and fasting-associated liver steatosis. J. Lipid Res. 65:100637. DOI:10.1016/j.jlr.2024.100637

    View in Article CrossRef Google Scholar

    [23] Musselman L.P., Fink J.L., Narzinski K., et al. (2011). A high-sugar diet produces obesity and insulin resistance in wild-type Drosophila . Dis. Model. Mech. 4:842−849. DOI:10.1242/dmm.007948

    View in Article CrossRef Google Scholar

    [24] Nakamura T., Fujiwara K., Saitou M., et al. (2021). Non-human primates as a model for human development. Stem Cell Rep. 16:1093−1103. DOI:10.1016/j.stemcr.2021.03.021

    View in Article CrossRef Google Scholar

    [25] Rashmi P., Urmila A., Likhit A., et al. (2023). Rodent models for diabetes. 3 Biotech 13, 89. DOI:10.1007/s13205-023-03488-0.

    View in Article Google Scholar

    [26] Liu R., Wang J., Zhao Y., et al. (2024). Study on the mechanism of modified Gegen Qinlian decoction in regulating the intestinal flora-bile acid-TGR5 axis for the treatment of type 2 diabetes mellitus based on macro genome sequencing and targeted metabonomics integration. Phytomedicine 132:155329. DOI:10.1016/j.phymed.2023.155329

    View in Article CrossRef Google Scholar

    [27] Wang X., Zhang L., Qin L., et al. (2022). Physicochemical properties of the soluble dietary fiber from Laminaria japonica and its role in the regulation of type 2 diabetes mice . Nutrients 14:329. DOI:10.3390/nu14020329

    View in Article CrossRef Google Scholar

    [28] Zhang X., Wang H., Xie C., et al. (2022). Shenqi compound ameliorates type 2 diabetes mellitus by modulating the gut microbiota and metabolites. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 1194:123189. DOI:10.1016/j.jchromb.2022.123189

    View in Article CrossRef Google Scholar

    [29] King A.J. (2012). The use of animal models in diabetes research. Br. J. Pharmacol. 166:877−894. DOI:10.1111/j.1476-5381.2012.01911.x

    View in Article CrossRef Google Scholar

    [30] Pandey U.B. and Nichols C.D. (2011). Human disease models in Drosophila melanogaster and the role of the fly in therapeutic drug discovery . Pharmacol. Rev. 63:411−436. DOI:10.1124/pr.110.003293

    View in Article CrossRef Google Scholar

    [31] Sharchil C., Vijay A., Ramachandran V., et al. (2022). Zebrafish: A model to study and understand the diabetic nephropathy and other microvascular complications of type 2 diabetes mellitus. Vet. Sci. 9:312. DOI:10.3390/vetsci9070312

    View in Article CrossRef Google Scholar

    [32] Martins T., Castro-Ribeiro C., Lemos S., et al. (2022). Murine models of obesity. Obesities 2:127−147. DOI:10.3390/obesities2020012

    View in Article CrossRef Google Scholar

    [33] du Sert N.P., Hurst V., Ahluwalia A., et al. (2020). The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol. 18:e3000410. DOI:10.1371/journal.pbio.3000410.

    View in Article Google Scholar

    [34] Maric I., Krieger J.P., van der Velden P., et al. (2022). Sex and species differences in the development of diet-induced obesity and metabolic disturbances in rodents. Front. Nutr. 9:828522. DOI:10.3389/fnut.2022.828522

    View in Article CrossRef Google Scholar

    [35] Mauvais-Jarvis F., Manson J.A.E., Stevenson J.C., et al. (2017). Menopausal hormone therapy and type 2 diabetes prevention: Evidence, mechanisms, and clinical implications. Endocr. Rev. 38:173−188. DOI:10.1210/er.2016-1146

    View in Article CrossRef Google Scholar

    [36] Escobar-Morreale H.F. (2018). Polycystic ovary syndrome: definition, aetiology, diagnosis and treatment. Nat. Rev. Endocrinol. 14:270−284. DOI:10.1038/nrendo.2018.24

    View in Article CrossRef Google Scholar

    [37] Williamson G. (2017). The role of polyphenols in modern nutrition. Nutr. Bull. 42:226−235. DOI:10.1111/nbu.12278

    View in Article CrossRef Google Scholar

    [38] Scalbert A., Johnson I.T. and Saltmarsh M. (2005). Polyphenols: Antioxidants and beyond. Am. J. Clin. Nutr. 81:215S−217S. DOI:10.1093/ajcn/81.1.215S

    View in Article CrossRef Google Scholar

    [39] Aranaz P., Clavel-Millan M., Gil-Cardoso K., et al. (2025). Preclinical research in obesity-associated metabolic diseases using in vitro, multicellular, and non-mammalian models . J. Physiol. Biochem. 81:1225−1255. DOI:10.1007/s13105-025-01130-6

    View in Article CrossRef Google Scholar

    [40] Mauvais-Jarvis F. (2015). Sex differences in metabolic homeostasis, diabetes, and obesity. Biol. Sex Differ. 6:14. DOI:10.1186/s13293-015-0033-y

    View in Article CrossRef Google Scholar

    [41] Hotamisligil G.S. (2017). Foundations of immunometabolism and implications for metabolic health and disease. Immunity 47:406−420. DOI:10.1016/j.immuni.2017.08.009

    View in Article CrossRef Google Scholar

    [42] Fan Y., and Pedersen O. (2021). Gut microbiota in human metabolic health and disease. Nat Rev Microbiol. 19:55−71. DOI:10.1038/s41579-020-0433-9

    View in Article CrossRef Google Scholar

    [43] Lutz T.A. (2023). Mammalian models of diabetes mellitus, with a focus on type 2 diabetes mellitus. Nat. Rev. Endocrinol. 19:350−360. DOI:10.1038/s41574-023-00818-3

    View in Article CrossRef Google Scholar

    [44] Pellizzon M.A. and Ricci M.R. (2018). Effects of rodent diet choice and fiber type on data interpretation of gut microbiome and metabolic disease research. Curr. Protoc. Toxicol. 77:e55. DOI:10.1002/cptx.55

    View in Article CrossRef Google Scholar

    [45] Bart van der Worp H., Howells D.W., Sena E.S., et al. (2010). Can animal models of disease reliably inform human studies. PLoS Med. 7:e1000245. DOI:10.1371/journal.pmed.1000245

    View in Article CrossRef Google Scholar

    [46] Kottaisamy C.P.D., Raj D.S., Prasanth Kumar V., et al. (2021). Experimental animal models for diabetes and its related complications—a review. Lab. Anim. Res. 37:23. DOI:10.1186/s42826-021-00101-4

    View in Article CrossRef Google Scholar

    [47] Suriano F., Vieira-Silva S., Falony G., et al. (2021). Novel insights into the genetically obese (ob/ob) and diabetic (db/db) mice: Two sides of the same coin. Microbiome 9:147. DOI:10.1186/s40168-021-01097-8

    View in Article CrossRef Google Scholar

    [48] Godschall E.N., Gungul T.B., Sajonia I.R., et al. (2026) A brain reward circuit inhibited by next-generation weight loss drugs. Nature DOI: 10.1038/s41586-026-10444-4.

    View in Article Google Scholar

    [49] Fatehullah A., Tan S.H. and Barker N. (2016). Organoids as an in vitro model of human development and disease. Nat. Cell Biol. 18:246−254. DOI:10.1038/ncb3312

    View in Article CrossRef Google Scholar

    [50] Song X., Chen X., Chen Q., et al. (2026). Organoids in drug development: From predictive models to regulatory integration. Drug Discov. Today 31:104608. DOI:10.1016/j.drudis.2026.104608

    View in Article CrossRef Google Scholar

  • Cite this article:

    Randeni N., Luo J. and Xu B. (2026). Animal models of obesity and diabetes mellitus for dietary bioactive component development: A systematic review. The Innovation Nutrition 1:100029. https://doi.org/10.59717/j.xinn-nutri.2026.100029
    Randeni N., Luo J. and Xu B. (2026). Animal models of obesity and diabetes mellitus for dietary bioactive component development: A systematic review. The Innovation Nutrition 1:100029. https://doi.org/10.59717/j.xinn-nutri.2026.100029

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