Chronic Kidney Disease: Non-invasive Diagnosis of Chronic Renal Failure by Monochrome Nanoparticle Analysis


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Abstract

The relevance of research in the development of methods for non-invasive laboratory diagnostics of chronic kidney disease and concomitant chronic renal failure is due to the high frequency of their occurrence, long-term asymptomatic course of the disease and the high cost of treatment for this category of patients: the cost of their treatment makes up a significant part of the national health budgets of developed countries with a comparatively small proportion of these patients from the total number of all patients. The aim of this work was to assess the capabilities of saliva spectroscopy by the method of monochrome analysis of nanoparticles to study the characteristic features of its subfractional composition in patients with chronic kidney disease with the development of chronic renal failure. to do this, it is necessary to solve a number of problems: to develop a diagnostic algorithm for monochrome analysis of nanoparticles to determine the severity and pathophysiological orientation of homeostatic changes in patients with various forms of chronic kidney disease using samples from oropharyngeal swabs. material and methods. studies were carried out at the center for European and oriental medicine from 2019 to 2021 (39 patients with verified diagnoses of chronic kidney disease were examined), during which it was found that the most typical saliva spectra of these patients were characterized by a multimodal distribution of nanoparticles saliva in size and contribution to light scattering on large particles larger than iooo nm, which was statistically significant (p <0.001) when conducting a comparative analysis with saliva spectra of practically healthy individuals and patients with general somatic inflammatory kidney diseases without the development of chronic renal failure. the indicator of the diagnostic sensitivity of the method in relation to chronic kidney disease with chronic renal failure was 92%. Conclusions. the use of laser spectroscopy of saliva is scientifically substantiated for the non-invasive detection of chronic kidney diseases with the development of chronic renal failure, when, with a timely diagnosis, therapeutic measures will be most effective.

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About the authors

E. G Choi

Center of European and Oriental Medicine

Email: drchoiworld@gmail.com
MD, Professor of MMU, oncologist, pediatrician, Chief Physician

References

  1. Zhang Q.L., Rothenbacher D. Prevalence of chronic kidney disease in population-based studies: systematic review. BMC. Publ. Health. 2008;8: 110-7. https://doi.org/10.1186/1471-2458-8-117.
  2. Levey A.S., Atkins R., Coresh J., et al. Chronic kidney disease as a global public health problem: approaches and initiatives - a position statement from Kidney Disease Improving Global Outcomes. Kidney Int. 2007;72(3):247-59. https://doi.org/10.1038/sj.ki.5002343.
  3. Schieppati A., Remuzzi G. Chronic renal diseases as a public health problem: epidemiology, social, and economic implications. Kidney Int. Suppl. 2005;(98):S7-10. https://doi.org/10.1111/j.1523-1755.2005.09801.x
  4. Bommer J. Prevalence and socio-economic aspects of chronic kidney disease. Nephrol. Dial. Transplant. 2002;11:8-12. https://doi.org/10.1093/ndt/17.suppl_11.8
  5. Go A.S., Chertow G.M., Fan D., et al. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N. Engl. J. Med. 2004;(13):1296-305. https://doi.org/10.1056/NEJMoa041031
  6. Berthoux F., Jones E., Gellert R., et al. Epidemiological data of treated end-stage renal failure in the European Union (EU) during the year 1995: report of the European Renal Association Registry and the National Registries. Nephrol. Dial. Transplant. 1999;14(10):2332-42. https://doi.org/10.1093/ndt/14.10.2332
  7. Lopez-Novoa J.M., Rodriguez-Pena A.B., Ortiz A., et al. Etiopathology of chronic tubular, glomerular and renovascular nephropathies: clinical implications. J. Transl. Med. 2011(20);9:13. https://doi.org/10.1186/1479-5876-9-13
  8. Stengel B., Tarver-Carr M.E., Powe N.R., et al. Lifestyle factors, obesity and the risk of chronic kidney disease. Epidemiol. 2003;14(4):479-87. https://doi.org/10.1097/01.EDE.0000071413.55296.c4
  9. Vassalotti J.A., Li S., Chen S.C., Collins A.J. Screening populations at increased risk of CKD: the Kidney Early Evaluation Program (KEEP) and the public health problem. Am. J. Kidney Dis. 2009;(53):S107-14. https://doi.org/10.1053/j.ajkd.2008.07.049
  10. Go A.S., Chertow G.M., Fan D., et al. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N. Engl. J. Med. 2004;(13):1296-305. https://doi.org/10.1056/NEJMoa041031
  11. Rule A.D., Larson T.S., Bergstralh E.J., et al. Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease. Ann. Intern. Med. 2004;141(12):929-37. https://doi.org/W.7320/0003-0019-141-12-200412010-00009
  12. McClellan W.M., Flanders W.D. Risk factors for progressive chronic kidney disease. J. Am. Soc. Nephrol. 2003;14:S65-70. https://doi.org/10.1097/01.asn.0000070147.10399.9e
  13. Ma Y.C., Zuo L., Chen J.H., et al. Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J. Am. Soc. Nephrol. 2006;17(10):2937-44. https://doi.org/10.1681/ASN.2006040368
  14. Levey A.S., Stevens L.A., Schmid C.H., et al. CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 2009;150(9):604-12. https://doi.org/10.7326/0003-4819-150-9-200905050-00006
  15. Levey A.S., Bosch J.P., Lewis J.B., et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann. Intern. Med. 1999; 130(6):461-70. https://doi.or7/10.7326/0003-4819-130-6-199903160-00002
  16. Jha V., Garcia-Garcia G., Iseki K., et al. Chronic kidney disease: global dimension and perspectives. Lancet. 2013;382(9888):260- 72. https://doi.org/10.1016/S0140-6736(13)60687-X
  17. Mandal A.K., Mount D.B. The molecular physiology of uric acid homeostasis. Ann. Rev. Physiol. 2015;77:323-45. https://doi.org/10.1146/annurev-physiol-021113-170343.
  18. Stuveling E.M., Bakker S.J., Hillege H.L., et al. Biochemical risk markers: a novel area for better prediction of renal risk? Nephrol. Dial. Transplant. 2005;20(3):497-508. https://doi.org/10.1093/ndt/gfh680
  19. Wyss M., Kaddurah-Daouk R. Creatine and creatinine metabolism. Physiol Rev. 2000;80(3):1107-213. https://doi.org/10.1152/physrev.2000.80.3.1107.
  20. Gulari E., Chu B., Gulari E., Tsunashima Y. Photon correlation spectroscopy of particle distributions. J. Chem. Phys. 1979;70:3965-72. https://doi.org/10.1063/1.437950
  21. Величко Е.Н., Непомнящая Э.К., Соколов А.В., Кудряшова Т.Ю. Лазерный корреляционный спектрометр для оценки размеров и динамики изменения размеров структур в биологических жидкостях. Оптика и спектроскопия. Журнал технической физики. 2020;129(7):950. https://doi.org/10.21883/OS.2020.07.49567.63-20
  22. Stetefeld J., McKenna S.A., Patel T.R. Dynamic light scattering: a practical guide and applications in biomedical sciences. Biophys. Rev. 2016;8:409-27. https://doi.org/10.1007/s12551-016-0218-6
  23. Lebedev A.D., Ivanova M.A., Lomakin A.V., Noskin V.A. Heterodyne quasi elastic light-scattering instrument for biomedical diagnostics. Appl. Opt. 1997;36(30):7518-22. https://doi.org/10.1364/ao.36.007518
  24. Ломакин А.В. Изучение внутренней динамики макромолекул методом лазерной корреляционной спектроскопии. УФН. Сов. физ. Усп. 1987;30:914-916. [Lomakin A.V. Study of the internal dynamics of macromolecules by the method of laser correlation spectroscopy Sov. Phys. Usp. 1987;30:914-916 (In Russ.)]. https://doi.org/10.3367/ UFNr.0153.198710j.0360
  25. Kotov O.I., Liokumovich L.B., Markov S.I., et al. Remote interferometer with polarizing beam splitting. Tech. Phys. Lett. 2000;26:415-17. https://doi.org/10.1134/1.1262863
  26. Максимова Е.А., Бурейко С.Ф., Левин С.Б., Державец Л.М. Метод двумерной корреляционной спектроскопии для улучшения аппроксимации одномерных спектров. Химическая физика. 2015.9;4:558-560. [Maksimova E.A., Bureiko S.F., Levin S.B., Derzhavets L.M. Russian Journal of Physical Chemistry. 2015.9;4:558-560. (In Russ)]]. https://doi.org/10.7868/S0207401X15080130
  27. Liokumovich L.B., Kostromitin A.O., Ushakov N.A., Kudryashov A.V. Method for Measuring Laser Frequency Noise. J. Appl. Spectrosc. 2020;86:1106-12. https://doi.org/10.1007/s10812-020-00947-x.
  28. Xu Renliang. Light scattering: A review of particle characterization applications. Particuology. 2014. 18. https://doi.org/10.1016/j.partic.2014.05.002
  29. Südhof T. The molecular machinery of neurotransmitter release (Nobel lecture). Angew. Chem. Int. Ed. Engl. 2014;53(47): 126-717. https://doi.org/10.1002/anie.201406359
  30. Mogridge J. Using light scattering to determine the stoichiometry of protein complexes. Methods Mol. Biol. 2004;261:113-8. https://doi.org/10.1385/1-59259-762-9:113
  31. Gast K., Fiedler C. Dynamic and static light scattering of intrinsically disordered proteins. Methods Mol. Biol. 2012;896:137-61. https://doi.org/10.1007/978l-4614-4604-3_9
  32. Малек А.В., Самсонов Р.В., Кьези А. Перспективы разработки методов диагностики и мониторинга онкологических заболеваний на основе анализа экзосом, секретируемых опухолевыми клетками. Рос. биотерапевт. журн. 2015;14(4):9-18. https://doi.org/10.17650/1726-9784-2015-14-4-9-18
  33. Südhof T. The molecular machinery of neurotransmitter release (Nobel lecture). Angew Chem. Int. Ed. Engl. 2014;53(47):126-717. https://doi.org/10.1002/anie.201406359
  34. Николаев А.И., Антонова И.Н., Донская О.С., Владимирова Л.Г. Алгоритм анализа ЛК-спектров для неинвазивной диагностики заболеваний по образцам ротоглоточного смыва. Мед. алфавит. 2909;4(35):23-_ [Nikolaev A.I., Antonova I.N., Donskaya O.S., Vladimirova L.G. LC-spectra analysis algorithmfor non-invasive diagnostics by oropharyngeal washout samples. Med. Alphab. 2019;4(35):23-7 (In Russ.)]. https://doi.org/10.33667/2078-5631-2019-4-35(410)-23-27
  35. Liokumovich L., Muravyov K., Skliarov P., Ushakov N. Signal detection algorithms for interferometric sensors with harmonic phase modulation: miscalibration of modulation parameters. Appl. Optics. 2018;57:7127-34. https://doi.org/10.1364/AO.57.007127
  36. Stetefeld J., McKenna S.A., Patel T.R. Dynamic light scattering: a practical guide and applications in biomedical sciences. Biophys. Rev. 2016;8:409-27. https://doi.org/10.1007/s12551-016-0218-6
  37. Носкин В.А. Лазерная корреляционная спектроскопия квазиупругого рассеяния. Сов. физ. Усп. 1987;30(10):913. [Noskin V. A. Laser correlation spectroscopy of quasi elastic scattering. Soviet Physics Uspekhi. 1987;30(10):913]. https://doi.org/10.1070/PU1987v030n10ABEH002972
  38. Chayen N., Dieckmann M., Dierks K., Fromme P. Ann N.Y. Size and shape determination of proteins in solution by a noninvasive depolarized dynamic light scattering instrument. Acad. Sci. 2004;1027:20-7. https://doi.org/10.1196/annals.1324.003
  39. Nepomniashchaia E.K., Velichko E.N., Aksenov E.T. Inverse problem of laser correlation spectroscopy for analysis of polydisperse solutions of nanoparticles. J. Phys.: Conference Series. 2016;769:012025. https://doi.org/10.1088/1742-6596/769/1/012025
  40. Xu R. Light scattering: A review of particle characterization applications. Particuol. 2015;18:11-21. https://doi.org/10.1016/j.partic.2014.05.002

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