Antiretroviral treatment has modified the HIV progression, lengthened survival, and improved quality of life among PLWHA [34, 35]. However this overwhelming benefit is not without limitations. In recent time, PLWHA are at increased risk of cardiometabolic (CMet) derangements [36–39].
The prevalence data of CMetS among HIV-infected persons varies from country to country. In our study the prevalence using the NCEP-2005 definition was 28.5% (82/288); and it was 43.5% (126/288) using the IDF definition. A compiled report of studies showed that prevalence of CMetS in HIV infected population was 20.6% using NCEP-2005, and 31.3% using the IDF-2005 [40]. By considering this report as a reference, the chi-square goodness of fit test (2X2) result indicated that the prevalence in our study was significantly higher by using both the NCEP-2005 (X2 = 121.94, df = 1, p < 0.001), and the IDF-2005 models (X2 = 32.99, df = 1, p < 0.001) [40]. However, in comparison to individual study reports, our prevalence falls within the range of previously done studies of [41–44], and in other aspect, it is a bit higher than the Polish [45], SHIVA (France) [46], Australian [47], South Korean [48], the Ethiopian [8, 44, 49], and the global meta analysis [50] studies; while it is lower than those reported from Nigeria [51], and China [52]. This variability could be due to the difference in study design, sample size, population genetics, study duration, duration with HIV and ART, treatment regimen or switch of therapy, and socio-demographic differences.
Several studies reported that sex, age, weight, BMI, sedentary lifestyle, central obesity, and cigarette smoking had an impact on the prevalence, pathogenesis, and progression of CMetS 2,18−21. The European AIDS Clinical Society (EACS) guidelines has strongly addressed that the risk of contracting CMetS is age related [49]. In our study, male gender is less likely to be associated with CMetS (OR = .086, C.I. 0.025–0.292, p < 0.001) using the NCEP-2005 model; and this is in agreement with the studies reported from Latin America [53, 54] and Ethiopia [55]. However, a study from South Africa reported that the prevalence was higher in males [56].
Individuals with longer duration on ART had an increased odds of CMetS (NCEP-2005: OR = 1.024, C.I. 1.005–1.043, p = 0.014) and (IDF-2005: OR = 1.251, C.I. 1.061–1.472, p = 0.007). This result is similar to the study reported from Malawi where the duration of ART > 3 years was associated with CMetS. Moreover, a Kenyan study reported that a step-up increment in the duration of ART is significantly related to an increased in CMetS [57]. The long-term exposure to ART could transform into considerable persistent metabolic risk [58–60]. However, the increase in age alone is not a determinant for the prevalence of CMetS. As per the result of our study, age increment was less likely to be associated with CMetS (OR = .786, C.I. 0.633–0.977, p = 0.03). This could be due to the fact that CMetS is more prevalent in age between 45 to 65 years [61] and the prevalence of CMetS could then decline thereafter [54]. Age 65 and above have fewer CMetS episodes than their younger counterparts for a number of reasons [62], one could be people who developed CMetS earlier than 60 could die prematurely before reaching 65 and above, the other is it could be due to the fact that diet has an impact on CMetS where elders are usual undernourished [63]. A life expectancy of many nations in the world is also under 65 and can affect the outcome [64]. Moreover, the incidence of CMetS is independent of patient age, and it is rather higher in patients who have other risk factors such as smoking [37].
The age at which ART initiated yet had an impact on the outcomes of CMetS (NCEP-2005: OR = 1.27, C.I. 1.031–1.564, p = 0.025), indicates individuals who started ART treatment at older age are more likely to have CMetS than their younger counterparts. Contrary to this finding, the CMet to childhood association was symmetrical in non-HIV infected population [65].
Our study further identified that, an increase in waist-grid (central adiposity) was associated with the prevalence of CMetS using both the NCEP-2005 (OR = 1.21, C.I. 1.029–1.418, p = 0.021) and the IDF-2005 models (OR = 1.730 C.I. 1.454–2.058, p < 0.001). This finding is well-documented in a number of other studies [39, 48, 66–69]. Additionally, central adeposity can also contribute to CMetS due to its positive correlation with the DBP as described elsewhere [70, 71].
Individuals with increased TG were more likely to have CMetS using both the NCEP-2005 (OR = 1.027, C.I. 0.015–0.039, p < 0.001) and the IDF-2005 models (OR = 1.015 C.I. 0.004–0.025, p < 0.001); similarly individuals with increased LDL were more likely to have CMetS using the NCEP-2005 (OR = 1.075, C.I. 0.020–0.134, p = 0.007) as well as the IDF-2005 models (OR = 1.064, C.I. 0.012–0.119, p = 0.016). These result are in harmony with a number of other publications [37, 69, 72, 73]. Studies also indicated that if lipid abnormalities are not treated as aggressively as individuals living without HIV, and this can pose severe management crisis and premature fatalities in PLWHA [74].
Concerning the pattern of ART medication, 48.6% of the participants in our study were on 2NRTIs + 1INSTI regimen, 33.3% on 2NRTIs + 1NNRTI, 17.4% on 2NRTIs + 1PI, and 0.7% on 1NRTI + 1NNRTI + 1INSTI + 1PI regimen. This regimen pattern was similar to a number of other studies [30, 47, 53]. Besides, the role of ART in the development and progression of CMetS has been elucidated in several other studies, but no significant result was obtained in our study [68, 75]. On the other hand, individuals who had changed their baseline ART regimen had less likely to have CMetS (IDF-2005: OR = 0.926, C.I. 0.876–0.980, p = 0.007). This could be the newer ART medications have relatively fewer CMet effect than the orders [76], though this is still a point of debate among the scientific community. Several studies reported that ART regimens such as PIs and NNRTIs are considered as a significant contributor to the development and progression of CMetS in HIV infected population [77–81].
PLWHA without comorbidity were less likely to have CMetS (NCEP-2005: OR = 0.086, C.I. 0.025–0.292, p < 0.001). Among the comorbidities notably, Type 2 diabetes mellitus (T2DM), heart failure (HF), dyslipidemia and high BP were known to have a direct impact on the emergence of CMetS [82, 83].