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Biomarkers and prevalence of cardiometabolic syndrome among people living with HIV/AIDS, Addis Ababa, Ethiopia: a hospital- based, observational study


 Background

Although marked improvements in life expectancy have been observed with the rapid expansion of Antiretroviral Therapy (ART), Cardiometabolic Syndrome (CMetS) is becoming a serious challenge for People Living with HIV/AIDS (PLWHA). The present study aimed in determining biomarkers and prevalence of CMetS in PLWHA.
Methods

A hospital-based, observational study was carried out between January 2019 & February 2020 among HIV infected adults (n = 288). Binary logistic regression was used to estimate odds ratio (OR) and corresponding 95% confidence interval (CI) for the association between the outcomes against the predictor variables.
Results

The current study revealed that the prevalence of CMetS was 28.5% (82/288) using the National Cholesterol Education Program (NCEP)-2005 definition; and it was 43.5% (126/288) using the International Diabetes Federation (IDF)-2005. Male gender was less likely to be associated with CMetS (OR = .086, C.I. 0.025–0.292, p < 0.001) using the NCEP-2005 definition. Individuals with longer duration on ART have an increased odds of CMetS using both the NCEP-2005 (OR = 1.024, C.I. 1.005–1.043, p = 0.014) and the IDF-2005 (OR = 1.251, C.I. 1.061–1.472, p = 0.007) definitions. The age at which ART initiated yet have an impact on the outcomes of CMetS (NCEP-2005: OR = 1.27, C.I. 1.031–1.564, p = 0.025), indicating that individuals who started ART treatment at older age are more likely to have CMetS than their younger counterparts. The study further verified that, individuals with increased waist-grid (central adiposity) were more likely to have CMetS using both the NCEP-2005 (OR = 1.21, C.I. 1.029–1.418, p = 0.021) and the IDF-2005 (OR = 1.730 C.I. 1.454–2.058, p < 0.001) definitions. PLWHA with increased in DBP (OR = 1.164, C.I.1.080–1.254, p < 0.001), Triglyceride (OR = 1.027, C.I. 0.015–0.039, p < 0.001), and low density lipoproteins (OR = 1.075, C.I. 0.020–0.134, p = 0.007) were more likely to have CMetS using the NCEP-2005 definition. PLWHA without comorbidity were less likely to have CMetS (NCEP-2005: OR = 0.086, C.I. 0.025–0.292, p < 0.001).
Conclusions

The prevalence of CMetS in the study area was high. Risk factors associated with CMetS were waist circumference, gender, duration on ART; ART initiated age, waist-grid, and comorbidity. Biomarkers that were more likely contributed to the prevalence of CMetS include triglyceride, low density lipoproteins, and systolic blood pressure.

Biomarkers or biological markers are objective indications of medical state that can be measured accurately and reproducibly [21].
Biomarkers provide a dynamic and powerful approach to understanding the spectrum of disease detection, progression, and monitoring [22]. Historically, biomarker were coined only to biological uid, tissues, and chemicals that has been related and used to evaluate the disease conditions [23]. Nowadays, the term biomarker can also be used in various aspects outside biological samples as long as it is helpful in diagnosis, measuring, and monitoring disease conditions [21,24]. HIV guidelines globally are focusing on HIV treatment and disease monitoring, giving less attention to emerging problems like CMetS, makes the standard of ART care imperfect [25][26][27]. Hence, the present study is aimed at determining the prevalence and biomarkers of CMetS among HIV infected adults on follow-up care.

Study design, period and setting
This was a hospital-based, observational study conducted during the period of 25/01/2019 to 25/02/2020 in HIV infected adults on follow-up care at Zewditu Memorial Hospital (ZMH), Addis Ababa, Ethiopia. The study was part of a large cohort study aimed at reporting the prevalence data. ZMH is the rst hospital inaugurated as well as initiated ART service in Ethiopia, July 2003 [28,29].

Study population
The study population was all PLWHA attending the ART follow-up care at ZMH. The target population was PLWHA with age ≥ 18 years and willing to participate in the study.

Sample Size 286
Where, N (7674) is the total HIV infected population registered for follow-up care and P is the prevalence for CMetS in HIV-infected population obtained from published articles [30]. Considering 10% contingency (lost to follow-up and defaulters), the nal sample size of the study became 314. Sampling procedure and enrollment A systematic random sampling technique was used to recruit study participants. The sample interval (K) was calculated using the formula N/n (7674/314≅24). The rst participant was selected using a lottery method.

Data collection
Participants' information was collected in the form of interview and by tracking participants' charts. The questionnaire for a face-to-face interview was adapted from the structured questionnaire used by the WHO Stepwise approach to non-communicable disease risk factor surveillance (STEPS-2014) [31]. The questionnaire was composed of information related to socio-demographic characteristics and clinical characteristics. Data were collected by two trained data collectors who administered the questionnaire, performed anthropometric measurements, measured BP, and took blood samples for biomedical measurements. Anthropometric measurements including weight (in Kg), Height (in meter), a derived Body Mass Index (BMI = weight in kg/ height in m 2 ), and Waist Grid Circumference/Abdominal Circumference (in inch) were taken.

Data analysis
Data was coded, double-entered, and analyzed using IBM SPSS Statistics 25. Descriptive and inferential statistics were used to present data. Binary logistic regression was used to estimate odds ratios (ORs) and corresponding 95% con dence intervals (CIs) for the association between the outcomes against the predictor variables. The mean of the repeated measures were used in cases of Pulse Rate (PR), Systolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP); whereas, for the other variables, the baseline data was used to determine the effect on the prevalence of CMetS. A 95% C.I was used and the level of signi cance for statistical analysis was set at less than 0.05.
Height and weight were measured by Type ZT-160 body-weight balance. Biomedical measurements such as PR (per minute), SBP (mmHg), DBP (mmHg), and tests such as FBG (mg/dl), and Fasting Blood Cholesterol (mg/dl) were taken routinely during appointments (every six months). BP and PR were measured by Omron HEM 7203, a fully automatic digital blood pressure monitor (Omron Healthcare Co. Ltd., Kyoto, Japan). The devices were regularly calibrated for proper validation. Mercury sphygmomanometer was used for evaluating the accuracy of the devices. Three BP recordings were obtained from the right arm of the patients with an interval of 5 min and the mean of the three readings was used for the nal result. All measurements were taken at baseline and then repeated after 6 months [32,33].
An absolute CD4 count (AbsCD4) and per cent CD4 (%CD4) were measured by BD FACSPresto™ and BD FACSCalibur™. Viral load was measured using the Abbott RealTime HIV-1 assay. Lipid pro les were measured using SIEMENS (Dimension EXL 200 Integrated Chemistry System) and the Omina Health (CS-T240 Auto-Chemistry Analyzer). The instruments analyze integrated clinical chemistry and immunoassay.

Operational de nition
Biomarkers: are measurable indicators of some biological state or condition. Includes waist-grid, BMI, BP, Lipid pro le, CD4 and VL measurements, and blood sugar measurements.
Risk factor or determinant: is a variable associated with an increased risk of disease or infection. Examples include: Age, Race/ethnicity, Gender, Some medical conditions, Use of certain medications, Poverty and crowding, certain occupations, Pregnancy.

Results
In this observational study, although 314 patients were initially enrolled, the nal sample size used for analysis was (n = 288) HIV infected persons. Twenty-six patients were out of the analysis for various reasons: two were discontinued from follow-up due to change of addresses, four were due to critical illnesses (one due to HBS, three due to high BP), and the rest 10 were defaulter for unknown reasons (Fig. 1).
The demographic data illustrated that a slight preponderance of female (162, 56.2%), and nearly 1 in 2 were married, half 62 (21.5%) were divorced, 1 in 3 were completed secondary high school of grade 9-12th education, and 1 in 4 were involved in small self-employed with employee business. Almost, 10 % of the population was jobless and 4.5 % were students. Majority 271 (94.1%) were from Addis Ababa (see Table 1).  Table 2). Half (139, 48.3%) were on 1j (TDF + 3TC + DTG) regimen, (Fig. 2). The overall mean age of the population was 43.51 with standard deviation (SD) of ± 11.273. That is about, 40.7 ± 10.5 for women, and 47.1 ± 11.4 for men. The overall mean age at the time of HIV con rmation was 32.7 ± 11.1, where, 30.2 ± 10.4 were women, and 35.9 ± 11.4 were men. The details of the characteristics of subjects are shown in Table 3. years and the oldest age among participant for HIV con rmation was 63 for women and 65 for men, and for ART initiation, 67 for women and 65 for men ( Table 3).
In order to determine the impact of biomarkers, risk factors, related sociodemographic variables on the prevalence of CMetS, two separate  Table 4). The strongest predictors were waist circumference, DBP and ART start age, which had an odds  Similarly, coded and hypothesized variables were also checked for their impact on the CMetS prevalence using the IDF-2005 de nition.
The full model containing all predictors had a statistically signi cant association, X 2 (23; 288) = 202.268, representing that the model was able to distinguish b/n those with and without CMetS. The total model explained b/n 50.5% (Cox and Snell R square) and 67.6% (Nagelkereke R Square) of the variance in CMetS and correctly classi ed 85.1% of the cases. Only ve of the predictor variables (Waist Circumference, TG, LDL-C, duration with ART, and Frequency of ART change) had a statistically signi cant contribution to the model (see Table 5). The strongest predictors were waist circumference and duration with ART. Indicating that for each additional inch in waist circumference, the odds of CMetS is increases by a factor of 1.73 (73%) and in each year increment in duration with ART, the odds of CMetS increases by 6.7%. Discussion Antiretroviral treatment has modi ed the HIV progression, lengthened survival, and improved quality of life among PLWHA [34,35].
The prevalence data of CMetS among HIV-infected persons varies from country to country. In our study the prevalence using the NCEP-2005 de nition was 28.5% (82/288); and it was 43.5% (126/288) using the IDF de nition. 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 t test (2X2) result indicated that the prevalence in our study was signi cantly higher by using both the NCEP-2005 (X 2 = 121.94, df = 1, p < 0.001), and the IDF-2005 models (X 2 = 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][42][43][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.

Conclusions
The prevalence of CMetS using the NCEP-2005 de nition was 28.5% (82/288); and it was 43.5% (126/288) using the IDF de nition. Risk factors associated with CMetS were waist circumference, gender, duration on ART; ART initiated age, waist-grid, and comorbidity. Biomarkers more likely contributed to the prevalence of CMetS were triglyceride, low density lipoproteins, and systolic blood pressure.

Consent of publication
Not applicable.

Availability of data and materials
Not applicable.

Competing interests
The authors declare that they have no competing interests.

Funding information
The original funding source for conducting the research was sponsored by DAAD. Part of the research was also sponsored by the EDCTP. Pattern of current ART regimen

Supplementary Files
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