Skip to main content
Log in

A Mathematical Model of the Disruption of Glucose Homeostasis in Cancer Patients

  • Original Article
  • Published:
Bulletin of Mathematical Biology Aims and scope Submit manuscript

Abstract

In this paper, we investigate the disruption of the glucose homeostasis at the whole-body level by the presence of cancer disease. Of particular interest are the potentially different responses of patients with or without hyperglycemia (including diabetes mellitus) to the cancer challenge, and how tumor growth, in turn, responds to hyperglycemia and its medical management. We propose a mathematical model that describes the competition between cancer cells and glucose-dependent healthy cells for a shared glucose resource. We also include the metabolic reprogramming of healthy cells by cancer-cell-initiated mechanism to reflect the interplay between the two cell populations. We parametrize this model and carry out numerical simulations of various scenarios, with growth of tumor mass and loss of healthy body mass as endpoints. We report sets of cancer characteristics that show plausible disease histories. We investigate parameters that change cancer cells’ aggressiveness, and we exhibit differing responses in diabetic and non-diabetic, in the absence or presence of glycemic control. Our model predictions are in line with observations of weight loss in cancer patients and the increased growth (or earlier onset) of tumor in diabetic individuals. The model will also aid future studies on countermeasures such as the reduction of circulating glucose in cancer patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • American Diabetes Association (2019) Standards of medical care in diabetes. Clin Diabetes 37:11–34

    Article  Google Scholar 

  • Atlan P, Bayar MA, Lanoy E, Besse B, Planchard D, Ramon J, Raynard B, Antoun S (2017) Factors which modulate the rates of skeletal muscle mass loss in non-small cell lung cancer patients: a pilot study. Support Care Cancer 25:3365–3373

    Article  Google Scholar 

  • Baglia ML, Cui Y, Zheng T, Yang G, Li H, You M, Xu L, Murff H, Gao Y-T, Zheng W, Xiang Y-B, Shu X-O (2019) Diabetes medication use in association with survival among patients of breast, colorectal, lung, or gastric cancer. Cancer Res Treat 51:538–546

    Article  Google Scholar 

  • Ben Sahra I, Le Marchand-Brustel Y, Tanti JF, Bost F (2010) Metformin in cancer therapy: a new perspective for an old antidiabetic drug? Mol Cancer Ther 9:1092–1099

    Article  Google Scholar 

  • Berg JM, Tymoczko JL, Stryer L (2002) Biochemistry, 5th edn. W. H. Freeman, New York

    Google Scholar 

  • Campbell JM, Bellman SM, Stephenson MD, Lisy K (2017) Metformin reduces all-cause mortality and diseases of ageing independent of its effect on diabetes control: A systematic review and meta-analysis. Ageing Res Rev 40:31–44

    Article  Google Scholar 

  • Cao M, Isaac R, Yan W, Ruan X, Jiang L, Wan Y, Wang J, Wang E, Caron C, Neben S, Drygin D, Pizzo DP, Wu X, Liu X, Chin AR, Fong MY, Gao Z, Guo K, Fadare O, Schwab RB, Yuan Y, Yost SE, Mortimer J, Zhong W, Ying W, Bui JD, Sears DD, Olefsky JM, Wang SE (2022) Cancer-cell-secreted extracellular vesicles impair systemic glucose homeostasis by suppressing insulin secretion. Nat Cell Biol 24:954–967

    Article  Google Scholar 

  • Cowart SL, Stachura ME (2002) Glycosuria. In: Walker HK, Hall WD, Hurst JW (eds) Clinical methods: the history, physical, and laboratory examinations, 3rd edn. Butterworths, Boston (Chap. 139)

    Google Scholar 

  • Dahan M, Hequet D, Bonneau C, Paoletti X, Rouzier R (2021) Has tumor doubling time in breast cancer changed over the past 80 years? A systematic review. Cancer Med 10:5203–5217

    Article  Google Scholar 

  • Ferranini E (2011) Learning from glycosuria. Diabetes 60:695–696

    Article  Google Scholar 

  • Fong MY, Zhou W, Liu L, Alontaga AY, Chandra M, Ashby J, Chow A, O’Connor STF, Li S, Chin AR, Somlo G, Palomares M, Li Z, Tremblay JR, Tsuyada A, Sun G, Reid MA, Wu X, Swiderski P, Ren X, Shi Y, Kong M, Zhong W, Chen Y, Wang SE (2015) Breast-cancer-secreted miR-122 reprograms glucose metabolism in premetastatic niche to promote metastasis. Nat Cell Biol 17:183–194

    Article  Google Scholar 

  • Ghaffari P, Mardinoglu A, Nielsen J (2015) Cancer metabolism: a modeling perspective. Front Physiol 6:382

    Article  Google Scholar 

  • Giovannucci E, Harlan DM, Archer MC, Bergenstal RM, Gapstur SM, Habel LA, Pollak M, Regensteiner JG, Yee D (2010) Diabetes and cancer: a consensus report. Diabetes Care 33:1674–1685

    Article  Google Scholar 

  • Gray A, Threlkeld RJ (2019) Nutritional recommendations for individuals with diabetes. www.ncbi.nlm.nih.gov/books/NBK279012/

  • Hay N (2016) Reprogramming glucose metabolism in cancer: can it be exploited for cancer therapy? Nat Rev Cancer 16:635–649

    Article  Google Scholar 

  • Hu Z, Curtis C (2020) Looking backward in time to define the chronology of metastasis. Nat Commun 11:3213

    Article  Google Scholar 

  • Jiralerspong S, Goodwin PJ (2016) Obesity and breast cancer prognosis: evidence, challenges, and opportunities. J Clin Oncol 34:4203–4216

    Article  Google Scholar 

  • Kakehi E, Kotani K, Nakamura T, Takeshima T, Kajii E (2018) Non-diabetic glucose levels and cancer mortality: a literature review. Current Diabetes Rev 14:434–445

    Article  Google Scholar 

  • Keys A, Henschel A, Taylor HL (1947) The size and function of the human heart at rest in semi-starvation and in subsequent rehabilitation. Am J Physiol 150:153–169

    Article  Google Scholar 

  • Liberti MV, Locasale JW (2016) The Warburg effect: how does it benefit cancer cells? Trends Biochem Sci 41:211–218

    Article  Google Scholar 

  • Luo J, Chen YJ, Chang LJ (2012) Fasting blood glucose level and prognosis in non-small cell lung cancer (NSCLC) patients. Lung Cancer 76:242–247

    Article  Google Scholar 

  • Macheda ML, Rogers S, Best JD (2005) Molecular and cellular regulation of glucose transporter (GLUT) proteins in cancer. J Cell Physiol 202:654–662

    Article  Google Scholar 

  • Maddalena F, Lettini G, Gallicchio R, Sisinni L, Simeon V, Nardelli A, Venetucci AA, Storto G, Landriscina M (2015) Evaluation of glucose uptake in normal and cancer cell lines by positron emission tomography. Mol Imaging 14(9)

  • Orgel E, Mittelman SD (2013) The links between insulin resistance, diabetes, and cancer. Curr Diab Rep 13:213–222

    Article  Google Scholar 

  • Palumbo P, Ditlevsen S, Bertuzzi A, De Gaetano A (2013) Mathematical modeling of the glucose-insulin system: a review. Math Biosci 244:69–81

    Article  MathSciNet  MATH  Google Scholar 

  • Schwartsburd P (2019) Cancer-induced reprogramming of host glucose metabolism: “Vicious cycle’’ supporting cancer progression. Front Oncol 9:218

    Article  Google Scholar 

  • Slavin J, Carlson J (2014) Carbohydrates. Adv Nutr 5:760–761

    Article  Google Scholar 

  • Usuda K, Saito Y, Sagawa M, Sato M, Kanma K, Takahashi S, Endo C, Chen Y, Sakurada A, Fujimura S (1994) Tumor doubling time and prognostic assessment of patients with primary lung cancer. Cancer 74:2239–2244

    Article  Google Scholar 

  • Wasserman DH (2009) Four grams of glucose. Am J Physiol Endocrinol Metab 296:11–21

    Article  Google Scholar 

Download references

Acknowledgements

Jonathan Doria and Chinh Nguyen received awards from the Support for Undergraduate Research Fellows (SURF) program at the University of Wisconsin—Milwaukee. Noah Salentine was supported by the Ronald E. McNair Postbaccalaureate Achievement Program at the University of Wisconsin—Milwaukee. Shizhen Emily Wang was partially supported by the National Institutes of Health grants R01CA218140 and R01CA266486. The funding bodies had no influence on study design, collection, analysis and interpretation of results. We thank Dr. Wei Ying (University of California San Diego) and two unknown readers for valuable comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peter Hinow.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest. Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Additional information

Dedicated to Professor Glenn Webb in honor of his 80th birthday.

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salentine, N., Doria, J., Nguyen, C. et al. A Mathematical Model of the Disruption of Glucose Homeostasis in Cancer Patients. Bull Math Biol 85, 58 (2023). https://doi.org/10.1007/s11538-023-01146-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11538-023-01146-3

Keywords

Navigation