Dynamic model formulation of glucose and lipid lowering by blue-green algae extract (spirulina platensis)
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Abstract
Metabolic diseases such as diabetes mellitus and hyperlipidemia are the leading causes of global morbidity, with their prevalence steadily increasing every year. Spirulina platensis, as one of the natural ingredients rich in bioactive compounds, has been empirically proven to have antidiabetic and antihyperlipidemic effects. However, until now, there is no dynamic mathematical model that can model the effect of Spirulina on blood glucose and lipid levels over time. This study aims to develop a dynamic mathematical model based on a system of nonlinear differential equations that models the effect of Spirulina on the decrease in glucose and lipid levels in the body. The model was compiled using the principles of pharmacokinetics-pharmacodynamics and Michaelis-Menten kinetics, then simulated for 72 hours with a daily dose scenario. The simulation results showed that the administration of Spirulina periodically was able to reduce blood glucose levels from 160 mg/dL to 157.79 mg/dL, and lipid levels from 220 mg/dL to 193.85 mg/dL. Spirulina exhibits significant pharmacodynamic effects with faster glucose depreciation than lipids, as well as concentrations of active substances in the body that follow a daily pharmacokinetic pattern of elimination. This model is able to predict the metabolic dynamics of the body against dose and time variations, and can be the basis for the development of personalized therapies based on individual physiological parameters. This research also fills the gap in the quantitative approach in the study of Spirulina, which has been dominated by descriptive experimental studies.
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How to Cite
Pase, M., Ainun, K., Zuidah, Z., & Kristina , K. (2025). Dynamic model formulation of glucose and lipid lowering by blue-green algae extract (spirulina platensis). International Journal of Basic and Applied Science, 13(4), 191–201. https://doi.org/10.35335/ijobas.v13i4.659
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G. Roglic, “WHO Global report on diabetes: A summary,” Int. J. Noncommunicable Dis., vol. 1, no. 1, pp. 3–8, 2016.
M. F. Van Stee, A. A. de Graaf, and A. K. Groen, “Actions of metformin and statins on lipid and glucose metabolism and possible benefit of combination therapy,” Cardiovasc. Diabetol., vol. 17, pp. 1–22, 2018.
L. B. A. Rojas and M. B. Gomes, “Metformin: an old but still the best treatment for type 2 diabetes,” Diabetol. Metab. Syndr., vol. 5, pp. 1–15, 2013.
M. P. Spínola, A. R. Mendes, and J. A. M. Prates, “Chemical composition, bioactivities, and applications of Spirulina (Limnospira platensis) in food, feed, and medicine,” Foods, vol. 13, no. 22, p. 3656, 2024, doi: https://doi.org/10.3390/foods13223656.
M. A. El-Sakhawy et al., “The mechanism of action of Spirulina as antidiabetic: a narrative review,” Ital. J. Med., vol. 17, no. 2, pp. 1639–1649, 2023, doi: https://doi.org/10.4081/itjm.2023.1639.
B. P. Singh, R. E. Aluko, S. Hati, and D. Solanki, “Bioactive peptides in the management of lifestyle-related diseases: Current trends and future perspectives,” Crit. Rev. Food Sci. Nutr., vol. 62, no. 17, pp. 4593–4606, 2022, [Online]. Available: https://www.tandfonline.com/doi/abs/10.1080/10408398.2021.1877109
V. Sorrenti, D. A. Castagna, S. Fortinguerra, A. Buriani, G. Scapagnini, and D. C. Willcox, “Spirulina microalgae and brain health: A scoping review of experimental and clinical evidence,” Mar. Drugs, vol. 19, no. 6, p. 293, 2021, doi: https://doi.org/10.3390/md19060293.
H. Huang, D. Liao, R. Pu, and Y. Cui, “Quantifying the effects of spirulina supplementation on plasma lipid and glucose concentrations, body weight, and blood pressure,” Diabetes, Metab. Syndr. Obes. targets Ther., vol. 11, no. 1, pp. 729–742, 2018, [Online]. Available: https://www.tandfonline.com/journals/dmso20
Z. Yusof, Y. W. Tong, K. Selvarajoo, S. K. Parakh, and S. C. Foo, “Overcoming Challenges in Microalgal Bioprocessing through Data-driven and Computational Approaches,” Curr. Opin. Food Sci., vol. 61, no. 2, p. 101253, 2024, doi: https://doi.org/10.1016/j.cofs.2024.101253.
H. A. A. Rostami, A. Marjani, M. Mojerloo, B. Rahimi, and M. Marjani, “Effect of spirulina on lipid Profile, glucose and malondialdehyde levels in type 2 diabetic patients,” Brazilian J. Pharm. Sci., vol. 58, no. 1, p. e191140, 2022, doi: https://doi.org/10.1590/s2175-97902022e191140.
U. V Mani, U. M. Iyer, S. A. Dhruv, I. U. Mani, and K. S. Sharma, “Therapeutic utility of Spirulina,” in Spirulina in human nutrition and health, CRC press, 2007, pp. 85–114.
G. V Halade and A. R. Juvekar, “Effect of Spirulina Platensis Pretreatment on Isoproterenol Induced Hyperlipidemia in Rats,” PharmacologyOnLine, vol. 2, no. 2, p. 243, 2006, [Online]. Available: https://pharmacologyonline.silae.it/front/archives_2006_2
E. H. Lee, J.-E. Park, Y.-J. Choi, K.-B. Huh, and W.-Y. Kim, “A randomized study to establish the effects of spirulina in type 2 diabetes mellitus patients,” Nutr. Res. Pract., vol. 2, no. 4, pp. 295–300, 2008.
S. R. Karizi et al., “A randomized, double‐blind placebo‐controlled add‐on trial to assess the efficacy, safety, and anti‐atherogenic effect of Spirulina platensis in patients with inadequately controlled type 2 diabetes mellitus,” Phyther. Res., vol. 37, no. 4, pp. 1435–1448, 2023, doi: https://doi.org/10.1002/ptr.7674.
Z. Hamedifard, A. Milajerdi, Ž. Reiner, M. Taghizadeh, F. Kolahdooz, and Z. Asemi, “The effects of spirulina on glycemic control and serum lipoproteins in patients with metabolic syndrome and related disorders: A systematic review and meta‐analysis of randomized controlled trials,” Phyther. Res., vol. 33, no. 10, pp. 2609–2621, 2019, doi: https://doi.org/10.1002/ptr.6441.
M. S. Islam, K. M. A. Kabir, J. Tanimoto, and B. B. Saha, “Study on Spirulina platensis growth employing non-linear analysis of biomass kinetic models,” Heliyon, vol. 7, no. 10, pp. 1–23, 2021, doi: https://www.cell.com/heliyon/fulltext/S2405-8440(21)02288-X.
F. Ghanbari, A. Amerizadeh, P. Behshood, S. Moradi, and S. Asgary, “Effect of microalgae Arthrospira on biomarkers of glycemic control and glucose metabolism: A systematic review and meta-analysis,” Curr. Probl. Cardiol., vol. 47, no. 10, p. 100942, 2022, doi: https://doi.org/10.1016/j.cpcardiol.2021.100942.
N. Aowtrakool, A. Sopitthummakhun, T. Laomettachit, and M. Ruengjitchatchawalya, “A dynamic model for predicting biomass and phycocyanin yields in Arthrospira (Spirulina) platensis: A guidance for effective batch cultivation,” Algal Res., vol. 83, no. 2, p. 103709, 2024, doi: https://doi.org/10.1016/j.algal.2024.103709.
B. Candas and J. Radziuk, “An adaptive plasma glucose controller based on a nonlinear insulin/glucose model,” IEEE Trans. Biomed. Eng., vol. 41, no. 2, pp. 116–124, 2002, doi: https://doi.org/10.1109/10.284922.
C. Peetla, A. Stine, and V. Labhasetwar, “Biophysical interactions with model lipid membranes: applications in drug discovery and drug delivery,” Mol. Pharm., vol. 6, no. 5, pp. 1264–1276, 2009, doi: https://pubs.acs.org/doi/abs/10.1021/mp9000662.
C. Baroukh, “Metabolic modeling under non-balanced growth. Application to microalgae for biofuels production,” 2014, Université Montpellier 2 (Sciences et Techniques).
M. S. Mohamed, J. S. Tan, S. Kadkhodaei, R. Mohamad, M. N. Mokhtar, and A. B. Ariff, “Kinetics and modeling of microalga Tetraselmis sp. FTC 209 growth with respect to its adaptation toward different trophic conditions,” Biochem. Eng. J., vol. 88, no. 15, pp. 30–41, 2014, doi: https://doi.org/10.1016/j.bej.2014.04.002.
J. A. Kopec et al., “Validation of population-based disease simulation models: a review of concepts and methods,” BMC Public Health, vol. 10, no. 710, pp. 1–13, 2010, [Online]. Available: https://link.springer.com/article/10.1186/1471-2458-10-710
J. P. C. Kleijnen, “Statistical validation of simulation models,” Eur. J. Oper. Res., vol. 87, no. 1, pp. 21–34, 1995, [Online]. Available: https://doi.org/10.1016/0377-2217(95)00132-A
W. Wang and Y. Lu, “Analysis of the mean absolute error (MAE) and the root mean square error (RMSE) in assessing rounding model,” in IOP conference series: materials science and engineering, IOP Publishing, 2018, p. 12049. doi: 10.1088/1757-899X/324/1/012049.
C. J. Willmott and K. Matsuura, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance,” Clim. Res., vol. 30, no. 1, pp. 79–82, 2005, doi: https://doi.org/10.3354/cr030079.
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” Peerj Comput. Sci., vol. 7, no. 2, p. e623, 2021, doi: https://peerj.com/articles/cs-623/.
F. Pelletier et al., “Endocrine and growth abnormalities in 4H leukodystrophy caused by variants in POLR3A, POLR3B, and POLR1C,” J. Clin. Endocrinol. Metab., vol. 106, no. 2, pp. e660–e674, 2021, doi: https://doi.org/10.1210/clinem/dgaa700.
J. C. Felger et al., “What does plasma CRP tell us about peripheral and central inflammation in depression?,” Mol. Psychiatry, vol. 25, no. 6, pp. 1301–1311, 2020, doi: https://www.nature.com/articles/s41380-018-0096-3.
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