An Economic Prevention Plan for Alzheimer's Disease Based on Blood Biomarkers

Document Type : Research Article

Authors

Department of Industrial Engineering & Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Iran

Abstract

This work presents a practical and cost-effective dynamic plan for preventing Alzheimer's disease. The plan involves periodic monitoring of an individual's blood biomarkers, personal characteristics, and budget constraints. The primary goal is to provide a feasible and realistic plan for each individual, with the highest likelihood of being followed. A Markov decision process model is proposed and solved using two algorithms: policy iteration and value iteration. In contrast to cerebrospinal fluid biomarkers, this plan relies on blood-based biomarkers, specifically Tau181 and APOE4, which are more cost-efficient and accessible for periodic testing. The interventions or actions within the model encompass choices between light or intense physical activity and adopting a less or more stringent diet. The decision model seeks to maximize the individual's quality of life while considering associated expenses. The proposed plan is tested on an modified dataset derived from clinical records, and it reveals insightful findings. Notably, our experimental study indicates that younger individuals at risk of the disease are more inclined to invest in preventive measures than those over 65. However, this trend does not apply to individuals lacking the APOE4 gene and those with higher tau181 concentration. The proposed plan can assist physicians in making appropriate recommendations.

Keywords

Main Subjects

References

  1. Ahlskog, J. E., Geda, Y. E., Graff-Radford, N. R., & Petersen, R. C. (2011). Physical exercise as a preventive or disease-modifying treatment of dementia and brain aging. In Mayo clinic proceedings, 86(9), 876-884.
  2. Alzheimer’s Association. (2017). Alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 13(4), 325373.
  3. Bartochowski, Z., Conway, J., Wallach, Y., Chakkamparambil, B., Alakkassery, S., & Grossberg, G. T. (2020). Dietary interventions to prevent or delay alzheimer’s disease: what the evidence shows. Current Nutrition Reports, 1-16.
  4. Basu, R. (2013). Willingness-to-pay to prevent Alzheimer’s disease: a contingent valuation approach. International journal of health care finance and economics, 13(3-4), 233-245.
  5. Brookmeyer, R., Johnson, E., Ziegler-Graham, K., & Arrighi, H. M. (2007). Forecasting the global burden of Alzheimer’s disease. Alzheimer’s & dementia, 3(3), 186191.
  6. Canter, R. G., Penney, J., & Tsai, L. H. (2016). The road to restoring neural circuits for the treatment of Alzheimer’s disease. Nature, 539(7628), 187-196.
  7. Castro, D. M., Dillon, C., Machnicki, G., & Allegri, R. F. (2010). The economic cost of Alzheimer’s disease: Family or public-health burden? Dementia & Neuropsychologia, 4(4), 262-267.
  8. Crous-Bou, M., Minguillón, C., Gramunt, N., & Molinuevo, J. L. (2017). Alzheimer’s disease prevention: from risk factors to early intervention. Alzheimer’s research & therapy, 9(1), 1-9.
  9. Denton, B. T. (2018). Optimization of sequential decision making for chronic diseases: From data to decisions. In Recent Advances in Optimization and Modeling of Contemporary Problems (pp. 316-348). INFORMS.
  10. Donegan, K., Fox, N., Black, N., Livingston, G., Banerjee, S., & Burns, A. (2017). Trends in diagnosis and treatment for people with dementia in the UK from 2005 to 2015: a longitudinal retrospective cohort study. The Lancet Public Health, 2(3), e149-e156.
  11. Fan, L., Mao, C., Hu, X., Zhang, S., Yang, Z., Hu, Z., ... & Xu, Y. (2020). New insights into the pathogenesis of Alzheimer’s disease. Frontiers in neurology, 10, 1312.
  12. Fillit, H. M. (2002). The role of hormone replacement therapy in the prevention of Alzheimer disease. Archives of Internal Medicine, 162(17), 1934-1942.
  13. Grassi, M., Perna, G., Caldirola, D., Schruers, K., Duara, R., & Loewenstein, D. A. (2018). A clinicallytranslatable machine learning algorithm for the prediction of Alzheimer’s disease conversion in individuals with mild and premild cognitive impairment. Journal of Alzheimer’s Disease, 61(4), 1555-1573.
  14. Holland, D., Brewer, J. B., Hagler, D. J., FennemaNotestine, C., Dale, A. M., & Alzheimer’s Disease Neuroimaging Initiative. (2009). Subregional neuroanatomical change as a biomarker for Alzheimer’s disease. Proceedings of the National Academy of Sciences, 106(49), 20954-20959.
  15. Karikari, T. K., Pascoal, T. A., Ashton, N. J., Janelidze, S., Benedet, A. L., Rodriguez, J. L., ... & Blennow, K. (2020). Blood phosphorylated tau 181 as a biomarker for Alzheimer’s disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. The Lancet Neurology, 19(5), 422433.
  16. Kepka, A., Ochocinska, A., Borzym-Kluczyk, M., Skorupa, E., Stasiewicz-Jarocka, B., Chojnowska, S., & Waszkiewicz, N. (2020). Preventive role of L-Carnitine and balanced diet in Alzheimer’s disease. Nutrients, 12(7), 1987.
  17. Viña, J. Sanz-Ros, Alzheimer’s disease: only prevention makes sense. European journal of clinical investigation, 48(10) (2018), e13005.
  18. Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., & Feng, D. (2014). Early diagnosis of Alzheimer’s disease with deep learning. In 2014 IEEE 11th international symposium on biomedical imaging (ISBI), 1015-1018. IEEE.
  19. Moscoso, A., Grothe, M. J., Ashton, N. J., Karikari, T. K., Rodriguez, J. L., Snellman, A., ... & Alzheimer’s Disease Neuroimaging Initiative. (2021). Time course of phosphorylated-tau181 in blood across the Alzheimer’s disease spectrum. Brain, 144(1), 325-339.
  20. Murad, A. L. (2019). 15 simple diet tweaks that could cut your Alzheimer’s risk. https://www.mayoclinic.org/.
  21. Prieto, L., & Sacristán, J. A. (2003). Problems and solutions in calculating quality-adjusted life years (QALYs). Health and quality of life outcomes, 1, 1-8.
  22. Nakamura, A. (2018). plasma biomarker for Alzheimer’s disease: are we ready now for clinical practice and drug trials? The Journal of Prevention of Alzheimer’s Disease, 5, 158-159.
  23. Ning, K., Chen, B., Sun, F., Hobel, Z., Zhao, L., Matloff, W., ... & Alzheimer’s Disease Neuroimaging Initiative. (2018). Classifying Alzheimer’s disease with brain imaging and genetic data using a neural network framework. Neurobiology of aging, 68, 151-158.
  24. Rapp, T., Andrieu, S., Chartier, F., Deberdt, W., Reed, C., Belger, M., & Vellas, B. (2018). Resource use and cost of alzheimer’s disease in France: 18-month results from the GERAS observational study. Value in Health, 21(3), 295-303.
  25. Rive, B., Grishchenko, M., Guilhaume–Goulant, C., Katona, C., Livingston, G., Lamure, M.,... & Francois, C. (2010). Cost effectiveness of memantine in Alzheimer’s disease in the UK. Journal of medical economics, 13(2), 371-380.
  26. Safieh, M., Korczyn, A. D., & Michaelson, D. M. (2019). ApoE4: an emerging therapeutic target for Alzheimer’s disease. BMC medicine, 17(1), 1-17.
  27. Shen, X. N., Li, J. Q., Wang, H. F., Li, H. Q., Huang, Y. Y., Yang, Y. X., ... & Alzheimer’s Disease Neuroimaging Initiative. (2020). Plasma amyloid, tau, and neurodegeneration biomarker profiles predict Alzheimer’s disease pathology and clinical progression in older adults without dementia. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 12(1), e12104.
  28. Stone, R. I. (2001). Alzheimer’s disease and related dementias: important policy issues. Aging & mental health, 5(1), 146-148.
  29. Toledo, J. B., Zetterberg, H., Van Harten, A. C., Glodzik, L., Martinez-Lage, P., Bocchio-Chiavetto, L., ... & Trojanowski, J. Q. (2015). Alzheimer’s disease cerebrospinal fluid biomarker in cognitively normal subjects. Brain, 138(9), 2701-2715.
  30. Toombs, J., & Zetterberg, H. (2020). In the blood: Biomarkers for amyloid pathology and neurodegeneration in Alzheimer’s disease. Brain Communications, 2(1), fcaa054.
  31. N. Steimle, B.T. Denton, Markov decision processes for screening and treatment of chronic diseases. Markov Decision Processes in Practice, (2017) 189-222.
  32. Udeh-Momoh, C., Zheng, B., Sandebring-Matton, A., Novak, G., Kivipelto, M., Jönsson, L., & Middleton, L. (2022). Blood Derived Amyloid Biomarkers for Alzheimer’s Disease Prevention. The Journal of Prevention of Alzheimer’s Disease, 9(1), 12-21.
  33. Vozikis, A., Goulionis, J. E., & Benos, V. K. (2012). The partially observable Markov decision processes in healthcare: an application to patients with ischemic heart disease (IHD). Operational Research, 12, 3-14.
  34. Yu, J. T., Xu, W., Tan, C. C., Andrieu, S., Suckling, J., Evangelou, E., ... & Vellas, B. (2020). Evidence-based prevention of Alzheimer’s disease: systematic review and meta-analysis of 243 observational prospective studies and 153 randomised controlled trials. Journal of Neurology, Neurosurgery & Psychiatry, 91(11), 12011209.
  35. Zencir, M., Kuzu, N., Beşer, N. G., Ergin, A., Çatak, B., & Şahiner, T. (2005). Cost of Alzheimer’s disease in a developing country setting. International Journal of Geriatric Psychiatry: A journal of the psychiatry of late life and allied sciences, 20(7), 616-622.
  36. Zhu, C. W., Scarmeas, N., Torgan, R., Albert, M., Brandt, J., Blacker, D., ... & Stern, Y. (2006). Clinical characteristics and longitudinal changes of informal cost of Alzheimer’s disease in the community. Journal of the American Geriatrics Society, 54(10), 1596-1602.
AUT Journal of Modeling and Simulation
Volume 55, Issue 2
December 2023
Pages 215-226

Files

Share

How to cite

Statistics