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ORIGINAL ARTICLE
Year : 2020  |  Volume : 4  |  Issue : 2  |  Page : 295-299

Role of matrix metalloproteinase-2, matrix metalloproteinase-9, and tissue inhibitors of metalloproteinase-1 in recurrent depression


1 Department of Psychiatry, Faculty of Medicine for Girls, Al-Azhar University, Cairo, Egypt
2 Department of Medical Biochemistry, National Research Centre, Cairo, Egypt
3 Department of Pharmacology, National Research Centre, Cairo, Egypt
4 Department of Clinical Pathology, Faculty of Medicine for Girls, Al-Azhar University, Cairo, Egypt

Correspondence Address:
MD Rania A Hamed
Department of Psychiatry, Faculty of Medicine for Girls, Al-Azhar University, 66 Ahmed El Zomor Street, Nasr City, Cairo
Egypt
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/sjamf.sjamf_51_20

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Objectives Matrix metalloproteinases (MMPs) play a major role in inflammatory processes, and their effect in depression has been examined. This study evaluates MMP-2, MMP-9, and tissue inhibitors of metalloproteinase-1 (TIMP-1) in patients with major depressive disorder and in a control group of healthy individuals, as well as correlation between their levels and the risk of recurrence of depression. Patients and methods The study included 50 patients with depression (22 in their first episode and 28 had recurrent episodes), of both sexes, aged between 18 and 65 years, diagnosed with major depressive disorder, from psychiatric outpatient clinic in Al-Zahraa Hospital, Cairo, Egypt, as well as 50 matched healthy individuals as a control group. Serum MMP-2, MMP-9, and TIMP-1 levels were determined. Results MMP-2 and MMP-9 showed a statistically significant difference in depressed patients compared with the control group. Moreover, recurrent depression group was significantly different from first episode depression group regarding MMP-2 and MMP-9 with higher levels, whereas TIMP-1 was higher in the control group. Conclusion Changes in the serum levels of MMPs and TIMP in patients with depression are common and can be used to predict recurrence.


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