:: Volume 29, Issue 6 (1-2022) ::
Journal of Ilam University of Medical Sciences 2022, 29(6): 69-80 Back to browse issues page
Application of Bivariate Capula Additive Regression in Determining Factors Affecting ALT and AST Liver Enzymes
Farhad Mohammadi1 , Morteza Sedahi * 2, Soleiman Kheiri1 , Ali Ahmadi1 , Mehdi Omidi3
1- Dept of Epidemiology and Biostatistics, Faculty of Health, Modeling in Health Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran
2- Dept of Epidemiology and Biostatistics, Faculty of Health, Modeling in Health Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran , sedehi56@gmail.com
3- Dept of Mathematics, Faculty of Sciences, Ilam University, Ilam, Iran
Abstract:   (1335 Views)
Introduction: Nonparametric regression can usually be used when the distribution of the dependent variable does not follow the property of normality. In this study, due to the nature of the variables, a bivariate Capula regression model was used to identify the factors affecting the liver enzymes (ALT and AST) and the relationship between these enzymes. This type of regression is suitable when the response variables have a relatively high degree of skewness and interdependence.
Material & Methods: In this cross-sectional study, a sample of 2000 participants in the Shahrekord cohort study were randomly selected. To achieve the Capula regression model, the inverse Gaussian margin distribution and the Gumble joint function were selected according to the Akaike criterion. Gamlss, Copula, and Ggrm statistical packages were used in the R software.
(Ethic code: 3316)
Findings: According to the findings, some variables were identified as effective factors on the concentration of ALT and AST enzymes through marginal distribution parameters and Capula function. Blood urea, triglyceride, GGT, ALP, and BMI had a nonlinear and significant effect on the mean concentration of the ALT enzyme. The BMI, GGT, ALP, LDL, and HDL (nonlinearly), as well as blood urea (linearly), had a significant effect on the mean concentration of AST enzyme. Finally, the variables of BMI, triglycerides, GGT, and ALP affect the relationship between the concentration levels of the liver enzymes (ALT and AST).
Discussion & Conclusion: Using this model, in addition to identifying the effective factors, it is possible to distinguish between linear and nonlinear relationships between independent and dependent variables.
Keywords: ALT and AST liver enzymes, Copula function, Copula regression
Full-Text [PDF 587 kb]   (524 Downloads)    
Type of Study: Research | Subject: biostatistics
Received: 2021/03/3 | Accepted: 2021/09/29 | Published: 2022/02/4
1. Dasgupta A. Alcohol and Its Biomarkers: Elsevier; 2015.
2. Field HA, Ober EA, Roeser T, Stainier DY. Formation of the digestive system in zebrafish liver morphogenesis. Deve Biol2003;253:279-90. doi.10.1016/S0012-1606(02)0017-9
3. Hilmi I, Horton CN, Planinsic RM, Sakai T, Nicolau R, Damian D, et al. The impact of postreperfusion syndrome on short term patient and liver allograft outcome in patients undergoing orthotopic liver transplantation. Liver Trans 2008;14:504-8. doi.10.1002/lt.21381
4. Ko SH, Baeg MK, Han KD, Ko SH, Ahn YB. Increased liver markers are associated with higher risk of type 2 diabetes. World J Gastroenterol 2015;21:7478. doi.10.3748/wjg.v21.i24.7478
5. Bral M, Aboelnazar N, Hatami S, Thiesen A, Bigam DL, Freed DH, et al. Clearance of transaminases during normothermic ex situ liver perfusion. Plos One 2019;14:215619. doi.10.1371/journal.pone.0215619
6. Kharchenko N, Synyts V, Kovtun T. Comparative analysis of the effects of alcoholism and opium addiction on liver function. Fiziolohichnyi 2001;47:81-6.
7. Espasandín J, Cadarso C, Kneib T, Marra G, Klein N, Radice R, et al. Assessing the relationship between markers of glycemic control through flexible copula regression models. Stat Med 2019;38:5161-81. doi.10.1002/Sim.8358
8. Mccullagh P, Nelder J. Generalized linear models. 2th ed. Chapman Hall London Publication. 1989;P.214-29.
9. Chen Y, Hanson T. Copula regression models for discrete and mixed bivariate responses. J Stat Theor Pract 2017;11:515-30.
10. Masarotto G, Varin C. Gaussian copula regression in R. J Stat Soft2017;77:1-26.
11. Sun J, Frees EW, Rosenberg MA. Heavy tailed longitudinal data modeling using copulas. Ins Mathe Econom 2008;42:817-30. doi.10.1016/j.insmatheco.2007.09.009
12. Marra G, Radice R. GJRM generalised joint regression modelling. 1 th ed. Sunders Publication.2017;P.173-92.
13. Khaledifar A, Hashemzadeh M, Solati K, Poustchi H, Bollati V, Ahmadi A, et al. The protocol of a population based prospective cohort study in southwest of Iran to analyze common non-communicable diseases Shahrekord cohort study. BMC Publ Health 2018;18:1-10.
14. Ning C. Dependence structure between the equity market and the foreign exchange market a copula approach. J Int Mon Fin2010;29:743-59. doi.10.1016/j.jimonfin.2009.12.002
15. Chavezdemoulin V, Vatter T. Generalized additive models for conditional copulas. J Mult Ana 2015;141:147-67. doi.10.1016/j.jmva.2015.07.003
16. Stasinopoulos MD, Rigby RA, Heller GZ, Voudouris V, De Bastiani F. Flexible regression and smoothing using. Gamlss CRC Publication. 2017;P.231-55.
17. Robinson D, Whitehead T. Effect of body mass and other factors on serum liver enzyme levels in men attending for well population screening. Ann Clin Biochem 1989;26:393-400. doi:10.1177/000456328902600503
18. Tohidi M, Harati H, Hadaegh F, Mehrabi Y, Azizi F. Association of liver enzymes with incident type 2 diabetes Tehran lipid and glucose study. Iranian J Diabete Metab2007;7:167-76.
19. Stranges S, Trevisan M, Dorn JM, Dmochowski J, Donahue RP. Body fat distribution liver enzymes and risk of hypertension evidence from the Western New York study. Hypertension 2005;46:1186-93. doi.10.1161/01.HYP.0000185688.81320.4d
20. Javaid A, Hasan R, Zohra A, Hussain Z. A comparative study of the antihypertensive agents on serum liver enzymes ALT, AST and ALP of hypertensive and cardiac patients. J Bas Appl Sci2012;8:468-72.
21. Afsharnezhad S, Masoomi A, Mohammadi M, Lotfi M, Shohodi Far S. Evaluation the level of Serum Liver enzymes in Crystal Addicts. Modares Journal of Biotechnology. 2010;1(1):0-.
22. Hussein RR, Soliman RH, Ali AMA, Tawfeik MH, Abdelrahim ME. Effect of antiepileptic drugs on liver enzymes. Benisuef Uni J Bas Appl Sci 2013;2:9-14. doi.10.1016/j.bjbas.2013.09.002
23. Jain R, Varghese ST. Diagnostic usefulness of liver function tests in alcohol and opioid dependent patients. Add Dis Their Treat 2005;4:117-20. doi.10.1097/01.adt.0000155724.37245.9c
24. Tomizawa M, Kawanabe Y, Shinozaki F, Sato S, Motoyoshi Y, Sugiyama T, et al. Elevated levels of alanine transaminase and triglycerides within normal limits are associated with fatty liver. Exp Therape Med2014;8:759-62. doi.10.3892/etm.2014.1798
25. Deb S, Puthanveetil P, Sakharkar P. A Population based cross-sectional study of the association between liver enzymes and lipid levels. Int J Hepatol2018;2018:39-43.
26. Jiang ZG, Mukamal K, Tapper E, Robson SC, Tsugawa Y. Low LDL-C and high HDL-C levels are associated with elevated serum transaminases amongst adults in the United States a cross sectional study. Plos One2014;9:85366. doi.10.1371/journal.pone.0085366

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