Comprehensive analysis of the relationships between somatometric, biochemical and clinical indicators of the condition of patients with chronic kidney diseases

Objective: To identify potential predictors of CKD based on the analysis of relationships between somatometric (including bioimpedance), biochemical and clinical parameters of patients with chronic kidney disease (CKD).

Material and methods. The values of 58 parameters describing the condition of 357 participants were collected: 128 patients with CKD and 229 participants in the control group (without kidney pathology). Demographic, anthropometric, anamnestic data (a total of 19 diagnoses according to the International Classification of Diseases, 10th revision), bioimpedance values, results of general and biochemical blood tests (a total of 19 parameters), and diet parameters according to the CINDI questionnaire were studied. New mathematical approaches were used to establish intervals of informative values of numerical parameters, find metric concentrations in the space of biomedical research parameters and construct metric maps.

Results. In the CPP group, there was a predominance of older patients (mean age 54.1±13.1 years) compared to the control group (48.78±9.75 years), as well as overweight people (82.18±19 versus 74.7±17.45 kg). Patients with CPP had impaired adipose tissue metabolism, decreased active and reactive resistance of bioimpedance, high systolic blood pressure, and multiple organ pathology.

Conclusion. The analysis of the cluster of interactions of indicators allowed us to formulate promising areas for further research: it is necessary to study in more detail the informativeness and "strength" of CPP predictors, conduct a comprehensive assessment of the effectiveness of therapy, identify differences between subgroups of patients with different nosologies and stages of CPP, evaluate the effectiveness of various approaches to therapy, as well as the role of physical activity and micronutrient supply. 

1. Bоhm A., Heitmann B.L. The use of bioelectrical impedance analysis for body composition in epidemiological studies. Eur J Clin Nutr. 2013; 67 (1): 79–85. https://doi.org/10.1038/ejcn.2012.168.

2. Zybalova T.S. Bioimpedance analysis in clinical practice. In: Trisvetova Е.L. (Ed.) Internal diseases today: collection of scientific papers dedicated to the 100th anniversary of the Belarusian State Medical University. Minsk: Kovcheg; 2021: 79–90 (in Russ.).

3. Zharnov A., Bashun N., Zharnova O., et al. Comparison of indexes of physical development biological objects measured by anthropometric and bio-impedance methods. J Hygien Engin Design. 2019; 27 (9): 120–4.

4. Carney E.F. The impact of chronic kidney disease on global health. Nat Rev Nephrol. 2020; 16 (5): 251. https://doi.org/10.1038/s41581-020-0268-7.

5. Zharnova V.V., Slizevich T.N., Chekel A.V., et al. Application of the method of vector analysis of bioimpedance for the assessment of the status of hydration in patients with diffuse liver changes. News of Medical and Biological Sciences. 2019; 19(4): 48–54 (in Russ.).

6. Gupta A., Sontakke T., Acharya S., Kumar S. A comprehensive review of biomarkers for chronic kidney disease in older individuals: current perspectives and future directions. Cureus. 2024; 16 (9): e70262. https://doi.org/10.7759/cureus.70262.

7. Sintra E., Jenny S., Wallengren O., et al. Bioimpedance analysis in patients with chronic kidney disease. J Ren Care. 2023; 49 (3): 147–57. https://doi.org/10.1111/jorc.12474.

8. Jaffrin M.Y. Body composition determination by bioimpedance: an update. Curr Opin Clin Nutr Metab Care. 2009; 12 (5): 482–6. https://doi.org/10.1097/MCO.0b013e32832da22c.

9. Torshin I.Y. Optimal dictionaries of the final information on the basis of the solvability criterion and their applications in bioinformatics. Pattern Recognit Image Anal. 2013; 23 (2): 319–27. https://doi.org/10.1134/S1054661813020156.

10. Zhuravlev Yu.I. Selected scientific works. 1st ed. Moscow: Magistr; 1998: 416 pp. (in Russ.).

11. Zhuravlev Yu.I., Rudakov K.V., Torshin I.Yu. Algebraic criteria of local solvability and regularity as a tool for studying the morphology of amino acid sequences. Proceedings of MIPT. Series 3. Physicochemical Biology. 2011; 3 (4): 67–76 (in Russ.).

12. Gromova O.A., Kalacheva A.G., Torshin I.Yu., et al. Magnesium deficiency is a reliable risk factor for comorbid conditions: results of a large-scale screening of magnesium status in the regions of Russia. Farmateka. 2013; 1 (6): 116–29.

13. Kerimkulova N.V., Nikiforova N.V., Vladimirova I.S., et al. The impact of undifferentiated connective tissue dysplasia on pregnancy and childbirth outcomes. Comprehensive examination of pregnant women with connective tissue dysplasia using data mining methods. Zemsky Vrach. 2013; 2 (19): 34–38 (in Russ.).

14. Kolmogorov A.N. Probability theory and mathematical statistics. Vol. 2. Moscow: Nauka; 2005: 581 pp. (in Russ.).

15. Smirnov N.V. Approximation of distribution laws of random variables by empirical data. Uspekhi matematicheskikh nauk. 1944; 10: 179–206 (in Russ.).