head JofIMAB
Journal of IMAB - Annual Proceeding (Scientific Papers)
Publisher: Peytchinski Publishing Ltd.
ISSN: 1312-773X (Online)
Issue: 2022, vol. 28, issue4
Subject Area: Medicine
DOI: 10.5272/jimab.2022284.4742
Published online: 19 December 2022

Original article
J of IMAB. 2022 Oct-Dec;28(4):4742-4748
Radka Tomova1ORCID logo Corresponding Autoremail, Svetla Asenova1ORCID logo, Rodina Nestorova2ORCID logo, Bisera Atanasova3ORCID logo, Liliya Atanasova4ORCID logo, Mariana Nikolova5ORCID logo, Miglena Slavova6ORCID logo,
1) Department of Chemistry and Biochemistry, Faculty of Pharmacy, Medical University of Pleven, Bulgaria.
2) Rheumatology centre "Saint Irina" Sofia, Bulgaria.
3) Department of Clinical Chemistry, Faculty of Medicine, Medical University of Sofia, Bulgaria.
4) Department of Physics and Biophysics, Faculty of Medicine, Medical University of Sofia, Bulgaria.
5) Department of Mental Health, Social work and Integrative Medicine, Middlesex University, London, United Kingdom.
6) IEES, Bulgarian Academy of Science, Sofia, Bulgaria.

The aim of the present study is to reveal hidden relationships between bone density, biogenic elements content, and the level of oxidative stress of female patients with osteoporosis and osteopenia. Additionally, specific links between the patients are sought in order to interpret different similarity patterns of objects (patients) helping to a better understanding of the significance of the clinical variables for each identified similarity pattern.
Material and Methods: The input dataset consisting of 59 objects (patients) and 11 experimentally determined variables (clinical parameters) was subject to intelligent data analysis including cluster analysis (hierarchical and non-hierarchical mode) and factor analysis.
Results and Discussion: In the hierarchical dendrogram for clustering of 11 variables are formed 3 major clusters. We could assume that three factors (impact) are linked to the structure of the data set: descriptors responsible for osteoporosis diagnosis; descriptors (essential elements) related to osteoporosis status; descriptors related to the "overall health status impact”. The patients are clustered in 3 clusters corresponding to 3 different levels of health status (improving, worsening and intermediate) by K-means clustering. The specific descriptors are defined for each identified cluster.
Factor analysis shows that 3 latent factors explain nearly 70 % of the total variance of the system ˗ each of them with respective clinical meaning. A relationship is proven between T-score, diagnosis, and antioxidant activity by a 3D plot of factor loadings.
Conclusion: The multivariate statistical data interpretation for patients with osteoporosis problems reveals hidden relationships between specific similarity clusters among all patients or between the clinical parameters experimentally measured. It helps to better distinguish the variations between the specific groups and to determine the indicators for the variability. All this helps for more individual approachesto medical treatment.

Keywords: osteoporosis, calcium, oxidative stress, multivariate statistics,

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Please cite this article as: Tomova R, Asenova S, Nestorova R, Atanasova B, Atanasova L, Nikolova M, Slavova M. Multivariate statistical analysis for assessment of the relationships between bone density, biogenic elements content, and the level of oxidative stress in osteoporotic women. J of IMAB. 2022 Oct-Dec;28(4):4742-4748. DOI: 10.5272/jimab.2022284.4742

Corresponding AutorCorrespondence to: Radka Tomova, Faculty of Pharmacy, Medical University of Pleven; 1, Kliment Ohridski Str., Pleven 5800, Bulgaria; E-mail: rtomova@mail.bg

1. Petranova C, Sheytanov J, Sheytanov I. [Osteoporosis.] [in Bulgarian]. Steno publishing house, Varna, 2016: 5.
2. Shao B, Fu X, Yu Y. Regulatory effects of miRNA-181a on FasL expression in bone marrow mesenchymal stem cells and its effect on CD4+T lymphocyte apoptosis in estrogen deficiency-induced osteoporosis. Molec Med Reports. 2018 Jul;18(1):920-930. [PubMed]
3. Mao SS, Li D, Syed YS, Gao Y, Luo Y, Flores F, et al. Thoracic Quantitative Computed Tomography (QCT) Can Sensitively Monitor Bone Mineral Metabolism: Comparison of Thoracic QCT vs Lumbar QCT and Dual-energy X-ray Absorptiometry in Detection of Age-relative Change in Bone Mineral Density. Acad. Radiol. 2017 Dec;24(12):1582-1587. [PubMed]
4. Boianov M. Clinical X-ray Densitometry and Quantitative Bone Ultrasound. Central Medical Library. MU-Sofia. 2006:31-34
5. Recommendations for good practice in Osteoporosis, Bulgarian Society of Endocrinology. Bulgarian Society of Rheumatology. Ministry of Health. Sofia. 2013;6:12-17,27-28.
6. Camacho PM,Petak SM, Binkley N, Diab DL, Eldeiry LS, Farooki A et al. American Association of Clinical Endocrinologists and American College of Endocrinology Clinical Practice Guidelines for the Diagnosis and Treatment of Post-menopausal Osteoporosis – 2016. Endocrine Practice. 2016 May;22(4):9-10. [Crossref]
7. Pepa GD, Brandi ML. Мicroelements for bone boost: The last but not the least.Clin Cases Miner Bone Metab. 2016 Sep-Dec;13(3):181-185. [Crossref]
8. Mahdavi-Roshan M, Ebrahimi M, Ebrahimi A. Copper, magnesium, zinc and calcium status in osteopenic and osteoporotic post-menopausal women. Clin Cases Miner Bone Metab. 2015 Jan-Apr;12(1):18-21. [Crossref]
9. RondanelliM, Faliva MA, Infantino V, GasparriC, Iannello G, Perna S, et al.Copperas Dietary Supplement for Bone Metabolism: A Review. Nutrients. 2021; 13(7):2246. [Crossref]
10. Zheng J, Mao X, Ling J, He Quan J. Low serum levels of zinc, copper, and iron as risk factors for osteoporosis: A meta-analysis. Biol Trace Element Res. 2014Jul;160(1):15-23.[Crossref]
11. Okyay E, Ertugrul C, Acar B, Sisman AR, Onvural B, OzaksoyD. Comparative evaluation of serum levels of main minerals and post-menopausal osteoporosis. Maturitas. 2013Dec;76(4):320-5. [Crossref]
12. Wang L, Yang G, Zhang Y, Zhang  Y, Wang  W, TianjiaoSu, et al. Correlation between bone mineral density and serum trace element contents of elderly males in Beijing urban area. IJCEM. 2015Oct;8(10):19250-19257.
13. Alghadir AH, Gabr SA, Al-Eisa ES, Alghadir MH. Correlation between bone mineral density and serum trace elements in response to supervised aerobic training in older adults. Clin. Interv. Aging. 2016Feb 29;11:265-73. [Crossref]
14. Romqn F, Urra C, Porras O, Pino AM, Rosen CJ, Rodrнguez JP.Real-Time H2O2 Measurements in Bone Marrow Mesenchymal Stem Cells (MSCs) Show Increased Antioxidant Capacity in Cells From Osteoporotic Women. J Cell Biochem. 2017Mar;118(3):585-593. [Crossref]
15. Mazzanti L, Battino M, Nanetti L, Raffaelli F, Alidori A, Sforza G,et al. Effect of 1-year dietary supplementation dellevitaminized olive oil del markers of bone turnover and oxidative stress in healthy post-menopausal women. Endocrine. 2015 Jan;50(2):326-334. [Crossref]
16. Rondanelli M, Peroni G, Fossari F, Vecchio M, Faliva MA, Naso M, et al. Evidence of a Positive Link between Consumption and Supplementation of Ascorbic Acid and Bone Mineral Density. Nutrients. 2021Mar 21;13(3):1012. [Crossref]
17. Domazetovic V, Marcucci G, Iantomasi T, Brandi ML, Vincenzini MT. Oxidative stress in bone remodeling: role of antioxidants. Clin Cases Miner Bone Metab. 2017 May-Aug;14(2):209-216. [PubMed]
18. Simeonov V. Classification: Encyclopedia of environmetrics. J< Wiley& Sons, New York 2002.
19. Georgieva-Nikolova R, Kamenov Z, Slavova M, Nikolov M,Simeonov V. Multivariate statistical interpretation of clinical data of prolactinoma patients. Ecol Chem. & Eng. 2013;20(4-5):585-594. [Crossref]
20. Andreeva-Gateva P, Konsulova P, Orbetzova M, Georgieva-Nikolova R,Tafradjiiska-Hadjiolova  R, Angelova V, et al. Differentiation of obese patients at moderate or higher Findrisc score based on their atherogenic index. Postgrad Med. 2016Nov;128(8):790-796. [Crossref]
21. Litwic A E, Westbury LD, Robinson DE, Ward KA, Cooper C, Dennison EM. Bone Phenotype Assessed by HRpQCT and Associations with Fracture Risk in the GLOW Study. Calcified Tissue Int. 2018Jan;102(1):14-22. [Crossref]
22. Deloumeau A, Moltу A, Roux C, Briot K. Determinants of short term fracture risk in patients with a recent history of low-trauma non-vertebral fracture. Bone. 2017Dec;105:287-291.[Crossref]
23. Toyoda H, Takahashi S, Hoshino M, Takayama K, Iseki K, Sasaoka R, et al. Characterizing the course of back pain after osteoporotic vertebral fracture: a hierarchical cluster analysis of a prospective cohort study.Arch Osteoporos. 2017 Sep 23;12(1):82. [PubMed]
24. Re R, Pellegrini N, Proteggente A, Pannala A, Yang M, Rice-Evans C. Antioxidant activity applying an improved ABTS radical cation decolorization assay. Free Radic Biol Med. 1999 May;26(9-10):1231-7. [Crossref]
25. Massart DL, Kaufman L. The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis. John Wiley & Sons, New York, 1983.
26. Massart DL, Vandeginste BGM, Deming SN, Michotte Y, Kaufman L. Chemometrics: A textbook. Elsevier, Amsterdam. 1988. [Crossref]

Received: 10 October 2022
Published online: 19 December 2022

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