Prevalence and predictors of metabolic syndrome among people living with human immunodeficiency virus (PLWHIV)

Dula Dessalegn Bosho; Lemessa Dube; Teshale Ayele Mega; Dawit Abera Adare; Mikyas Gashaw Tesfaye; Tesfahun Chanie Eshetie

doi:10.1186/s13098-018-0312-y

Prevalence and predictors of metabolic syndrome among people living with human immunodeficiency virus (PLWHIV)

Diabetol Metab Syndr. 2018 Feb 21:10:10. doi: 10.1186/s13098-018-0312-y. eCollection 2018.

Authors

Dula Dessalegn Bosho¹, Lemessa Dube², Teshale Ayele Mega¹, Dawit Abera Adare³, Mikyas Gashaw Tesfaye³, Tesfahun Chanie Eshetie¹

Affiliations

¹ 1Department of Pharmacy, Faculty of Health Sciences, Institute of Health, Jimma University, Jimma, Ethiopia.
² 2Department of Epidemiology, Public Health Faculty, Institute of Health, Jimma University, Jimma, Ethiopia.
³ 3Department of Medical Laboratory, Jimma University Medical Centre, Institute of Health, Jimma University, Jimma, Ethiopia.

Abstract

Background: Use of combination antiretroviral therapy (cART) has led to significant reductions in morbidity and mortality. However, there is a growing concern about metabolic syndromes (MS), among patients receiving cART. Despite this fact, there is limited evidence for the prevalence of the MS among HIV-infected persons receiving cART in developing countries, particularly Ethiopia.

Objective: To determine the prevalence and predictors of MS among people living with HIV/AIDS in Jimma health centre, Jimma Zone south west Ethiopia.

Methods: A cross-sectional study was conducted on people living with HIV/AIDS (PLWHA) in Jimma health centre that fulfilled the inclusion criteria. Data on demographic and anthropometric characteristics were collected using World health organization (WHO) stepwise approach. Fasting blood glucose and lipid profile was measured. The Third Report of National Cholesterol Education Program-adult treatment panel III (NCEP-ATP III)-2001, the International Diabetes Federation (IDF)-2005 and the Joint interim statement-2009 (JIS) criteria were used to define MS. Data were analyzed using statistical software package (SPSS) version 20.0. Logistic regression analysis was done to identify predictors of MS and predictors with p value < 0.05 were used to declare statistical significance.

Results: Of 268 HIV-infected participants included in the analysis, 211 (78.7%) were women. The mean age of the participants was 39.32 ± 10.626 years. Using the NCEP-ATP III criteria, the prevalence of MS was found to be 23.5% (63 patients). While it was 20.5% (55 patients) and 27.6% (74 patients) with IDF and JIS criteria respectively. Enrollment in formal education resulted in 75% increment in the odds of MS (AOR = 0.25, 95% CI [0.072-0.879]). The odds of MS in patients with body mass index > 25 kg/m² was elevated to 13.4 times (AOR = 13.39, 95% CI [3.943-45.525]) and exposure to D-drugs was attributed to 59% increment in the odds of MS (AOR = 1.59, 95% CI [0.58-4.56]), although the finding lacks statistical significance.

Conclusions: Metabolic syndromes was relatively common to the study population. Hence, promoting health education and monitoring patient's clinical and laboratory parameters at every visit and taking appropriate measure is ideal.

Keywords: Ethiopia; HIV/AIDS; Metabolic syndrome.