International Journal of
Reproductive Biomedicine
Thu, Apr 25, 2024
[Archive]
Volume 14, Issue 1 (1-2016)
IJRM 2016, 14(1): 1-8 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Ghazi N, Arjmand M, Akbari Z, Owsat Mellati A, Saheb-Kashaf H, Zamani Z. 1H NMR- based metabolomics approaches as non- invasive tools for diagnosis of endometriosis. IJRM 2016; 14 (1) :1-8
URL: http://ijrm.ir/article-1-693-en.html
URL: http://ijrm.ir/article-1-693-en.html
Negar Ghazi1 
, Mohammad Arjmand * 2, Ziba Akbari1 , Ali Owsat Mellati1 , Hamid Saheb-Kashaf3 , Zahra Zamani4
, Mohammad Arjmand * 2, Ziba Akbari1 , Ali Owsat Mellati1 , Hamid Saheb-Kashaf3 , Zahra Zamani4
1- Department of Biochemistry, Zanjan Medical University, Zanjan, Iran
2- Department of Biochemistry, Pasteur Institute of Iran, Tehran, Iran , arjmand1@yahoo.com
3- Navid Medical Center, Tehran, Iran
4- Department of Biochemistry, Pasteur Institute of Iran, Tehran, Iran
2- Department of Biochemistry, Pasteur Institute of Iran, Tehran, Iran , arjmand1@yahoo.com
3- Navid Medical Center, Tehran, Iran
4- Department of Biochemistry, Pasteur Institute of Iran, Tehran, Iran
Abstract: (2907 Views)
Background: So far, non-invasive diagnostic approaches such as ultrasound, magnetic resonance imaging, or blood tests do not have sufficient diagnostic power for endometriosis disease. Lack of a non-invasive diagnostic test contributes to the long delay between onset of symptoms and diagnosis of endometriosis.
Objective: The present study focuses on the identification of predictive biomarkers in serum by pattern recognition techniques and uses partial least square discriminant analysis, multi-layer feed forward artificial neural networks (ANNs) and quadratic discriminant analysis (QDA) modeling tools for the early diagnosis of endometriosis in a minimally invasive manner by 1H- NMR based metabolomics.
Materials and Methods: This prospective cohort study was done in Pasteur Institute, Iran in June 2013. Serum samples of 31 infertile women with endometriosis (stage II and III) who confirmed by diagnostic laparoscopy and 15 normal women were collected and analyzed by nuclear magnetic resonance spectroscopy. The model was built by using partial least square discriminant analysis, QDA, and ANNs to determine classifier metabolites for early prediction risk of disease.
Results: The levels of 2- methoxyestron, 2-methoxy estradiol, dehydroepiandrostion androstendione, aldosterone, and deoxy corticosterone were enhanced significantly in infertile group. While cholesterol and primary bile acids levels were decreased. QDA model showed significant difference between two study groups. Positive and negative predict value levels obtained about 71% and 78%, respectively. ANNs provided also criteria for detection of endometriosis.
Conclusion: The QDA and ANNs modeling can be used as computational tools in noninvasive diagnose of endometriosis. However, the model designed by QDA methods is more efficient compared to ANNs in diagnosis of endometriosis patients.
Objective: The present study focuses on the identification of predictive biomarkers in serum by pattern recognition techniques and uses partial least square discriminant analysis, multi-layer feed forward artificial neural networks (ANNs) and quadratic discriminant analysis (QDA) modeling tools for the early diagnosis of endometriosis in a minimally invasive manner by 1H- NMR based metabolomics.
Materials and Methods: This prospective cohort study was done in Pasteur Institute, Iran in June 2013. Serum samples of 31 infertile women with endometriosis (stage II and III) who confirmed by diagnostic laparoscopy and 15 normal women were collected and analyzed by nuclear magnetic resonance spectroscopy. The model was built by using partial least square discriminant analysis, QDA, and ANNs to determine classifier metabolites for early prediction risk of disease.
Results: The levels of 2- methoxyestron, 2-methoxy estradiol, dehydroepiandrostion androstendione, aldosterone, and deoxy corticosterone were enhanced significantly in infertile group. While cholesterol and primary bile acids levels were decreased. QDA model showed significant difference between two study groups. Positive and negative predict value levels obtained about 71% and 78%, respectively. ANNs provided also criteria for detection of endometriosis.
Conclusion: The QDA and ANNs modeling can be used as computational tools in noninvasive diagnose of endometriosis. However, the model designed by QDA methods is more efficient compared to ANNs in diagnosis of endometriosis patients.
Type of Study: Original Article |
References
1. Adamson G, Kennedy S, Hummelshoj L. Creating solutions in endometriosis: global collaboration through the World Endometriosis Research Foundation. J Endometriosis 2010; 2: 3-6.
2. Giudice LC, Kao LC. Endometriosis. Lancet 2004; 364: 1789-1799. [DOI:10.1016/S0140-6736(04)17403-5]
3. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, et al. HMDB 3.0-The Human Metabolome Database in 2013. Nucleic Acids Res 2013; 41: D801-807. [DOI:10.1093/nar/gks1065]
4. Weljie AM, Newton J, Mercier P, Carlson E, Slupsky CM. Targeted profiling: quantitative analysis of 1H NMR metabolomics data. Anal Chem 2006; 78: 4430-4442. [DOI:10.1021/ac060209g]
5. Kell DB. Metabolomic biomarkers: search, discovery and validation. Expert Rev Mol Diagn 2007; 7: 329-333. [DOI:10.1586/14737159.7.4.329]
6. Amato F, López A, Pe-a-Méndez EM, Vaňhara P, Hampl A, Havel J. Artificial neural networks in medical diagnosis. J Applied Biomed 2013; 11: 47-58. [DOI:10.2478/v10136-012-0031-x]
7. Al-Shayea QK. Artificial neural networks in medical diagnosis. Int J Computer Sci Issues 2011; 8: 150-154.
8. Beckonert O, Keun HC, Ebbels TM, Bundy J, Holmes E, Lindon JC, et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc 2007; 2: 2692-2703. [DOI:10.1038/nprot.2007.376]
9. "American Society for Reproductive Medicine. Revised American Society for Reproductive Medicine classification of endometriosis: 1996. Fertil Steril 1997; 67: 817-821. [DOI:10.1016/S0015-0282(97)81391-X]
10. Arjmand M, Akbari Z, Taghizadeh N, Shahbazzadeh D, Zamani Z. NMR-based metabonomics survey in rats envenomed by Hemiscorpius lepturus venom. Toxicon 2014; 94C: 16-22.
11. Park Y, Kim SB, Wang B, Blanco RA, Le NA, Wu S, et al. Individual variation in macronutrient regulation measured by proton magnetic resonance spectroscopy of human plasma. Am J Physiol Reg Int Comp Physiol 2009; 297: R202-209. [DOI:10.1152/ajpregu.90757.2008]
12. Nicholson JK, Foxall PJ, Spraul M, Farrant RD, Lindon JC. 750 MHz 1H and 1H-13C NMR spectroscopy of human blood plasma. Anal Chem 1995; 67: 793-811. [DOI:10.1021/ac00101a004]
13. Nothnick WB. The emerging use of aromatase inhibitors for endometriosis treatment. Reprod Biol Endocrinol 2011; 9: 87. [DOI:10.1186/1477-7827-9-87]
14. Ferrero S, Remorgida V, Maganza C, Venturini PL, Salvatore S, Papaleo E, et al. Aromatase and endometriosis: estrogens play a role. Ann N Y Acad Sci 2014; 1317: 17-23. [DOI:10.1111/nyas.12411]
15. Hashim HA. Potential role of aromatase inhibitors in the treatment of endometriosis. Int J womens Health 2014; 6: 671. [DOI:10.2147/IJWH.S34684]
16. Huhtinen K, Desai R, Stahle M, Salminen A, Handelsman DJ, Perheentupa A, et al. Endometrial and endometriotic concentrations of estrone and estradiol are determined by local metabolism rather than circulating levels. J Clin Endocrinol Metab 2012; 97: 4228-4235. [DOI:10.1210/jc.2012-1154]
17. Machado-Linde F, Pelegrin P, Sanchez-Ferrer ML, Leon J, Cascales P, Parrilla JJ. 2-methoxyestradiol in the pathophysiology of endometriosis: focus on angiogenesis and therapeutic potential. Reprod Sci 2012; 19: 1018-1029. [DOI:10.1177/1933719112446080]
18. Miller KK, Al-Rayyan N, Ivanova MM, Mattingly KA, Ripp SL, Klinge CM, et al. DHEA metabolites activate estrogen receptors alpha and beta. Steroids 2013; 78: 15-25. [DOI:10.1016/j.steroids.2012.10.002]
19. Jana SK, Dutta M, Joshi M, Srivastava S, Chakravarty B, Chaudhury K. 1H NMR based targeted metabolite profiling for understanding the complex relationship connecting oxidative stress with endometriosis. BioMed Res Int 2013; 2013: 329058-329167. [DOI:10.1155/2013/329058]
20. Turgut A, Ozler A, Goruk NY, Tunc SY, Evliyaoglu O, Gul T. Copper, ceruloplasmin and oxidative stress in patients with advanced-stage endometriosis. Eur Rev Med Pharmacol Sci 2013; 17: 1472- 1478.
21. Melo AS, Rosa-e-Silva JC, Rosa-e-Silva AC, Poli-Neto OB, Ferriani RA, Vieira CS. Unfavorable lipid profile in women with endometriosis. Fertil Steril 2010; 93: 2433-2436. [DOI:10.1016/j.fertnstert.2009.08.043]
22. Michnovicz JJ, Bradlow HL. Altered estrogen metabolism and excretion in humans following consumption of indole-3-carbinol. Nut Cancer 1991; 16: 59-66. [DOI:10.1080/01635589109514141]
23. Siristatidis CS, Chrelias C, Pouliakis A, Katsimanis E, Kassanos D. Artificial neural networks in gynaecological diseases: Current and potential future applications. Med Sci Monit 2010; 16: RA231-RA236.
24. Lapeer R, Dalton K, Prager R, Forsström J, Selbmann H, Derom R. Application of neural networks to the ranking of perinatal variables influencing birthweight. Scand J Clin Lab Invest 1995; 55: 83-93. [DOI:10.3109/00365519509088454]
25. Atkov OY, Gorokhova SG, Sboev AG, Generozov 8 International Journal of Reproductive BioMedicine Vol. 14. No. 1. pp: 1-8, January 2016.
26. EV, Muraseyeva EV, Moroshkina SY, et al. Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters. J Cardiol 2012; 59: 190-194. [DOI:10.1016/j.jjcc.2011.11.005]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
