Introduction

Cardiometabolic syndrome (CMetS) is an umbrella term used to broadly describe a cluster of diseases, including diabetes, obesity, hypertension, dyslipidemia, and other heart, kidney, prothrombotic, and inflammatory abnormalities [1,2].

CMetS has recently emerged as a major health concern, particularly for patients with chronic illnesses like HIV [3–6]. CMetS is most common in those over 40 years of age, having comorbidities, a sedentary lifestyle, obesity, physical and cognitive limitations, substance use, hereditary vulnerability, low socioeconomic status, consumption of genetically modified foods, and a poor quality of life [7–12]. It is also becoming an acknowledged component in childhood and adolescent overweight and obesity [13,14].

Millions of people worldwide are affected by HIV/AIDS and other chronic diseases, and CMetS is increasingly becoming a major concern that necessitates prevention, routine monitoring, and proper treatment [15,16]. In the sub-Saharan African region (SSA), where two-thirds of the world’s HIV-positive people live, HIV has established itself as a cause of chronic illness and high mortality [17]. Chronic diseases and their repercussions are therefore expected to be on the rise throughout Africa, putting a strain on the limited resources available for healthcare delivery systems [18–21].

Though much-anticipated vaccines to eradicate HIV have yet to appear [22,23], existing combination antiretroviral therapy (cART), which is designed to slow disease progression and prolong survival, is facing significant challenges from non-adherence, virus resistance, drug–drug interactions, side effects, switching medication, pregnancy-related factors, and the presence of overlapping chronic comorbidities in the form of CMetS [24–31].

Even though CMetS is well known to be a concern to both HIV+ and HIV-negative people [1,32–34], few studies comparing the burden in both groups are available in the literature. Moreover, the studies focused on specific disease derangements such as carotid artery intima-media thickening [35], blood pressure [36], arterial wave reflection [37], anthropometric alterations [38], and the male [39] or female gender [39]; rather than making a comprehensive comparison. Thus, one could say that the burden of CMetS has not been thoroughly examined.

Even though SSA is considered the world epicenter of HIV/AIDS, there are currently few studies concentrating on CMetS and comparisons between HIV+ and HIV-negative patients [36,40,41]. Ethiopia, as one of those countries, lacks such studies, although investigations on CMetS are thought to be both necessary and urgent to develop effective prevention and control strategies [42].

Moreover, if findings emanating from such studies are effectively translated into clinical practice, there will be an overall improvement in healthcare service delivery as well as faster patient recovery and fewer hospitalizations [43,44]. The objective of this study was therefore to see how common CMetS is and determine its prevalence, incidence, biomarkers, and related variables in HIV+ and HIV-negative patients.

Methods Study design, period, and setting

A hospital-based comparative cohort study was conducted from 25 January 2019 to 25 February 2021 among patients visiting the HIV and adult ambulatory clinics of Zewditu Memorial Hospital (ZMH), Addis Ababa, Ethiopia. This hospital has been a pioneer in establishing and launching ART care services in Ethiopia since 2003 [45]. It also provides other clinical services and palliative care for the general population, in addition to HIV counseling and testing, sexually transmitted infection services, and post-exposure prophylaxis services. As a general hospital, there are also all-round services offered through the different clinics, departments, and wards. Currently, ZMH provides service to over 1163 HIV+ and more than 3000 HIV-negative patients every month.

Population and sample size determination

Patients visiting ZMH for HIV and other chronic conditions formed the source population. The study population consisted of eligible patients who satisfy the inclusion criteria. All patients age 18 years and above, with a minimum of three completed appointments, willing to participate in the study and provide written consent were included in the study. Severely ill patients, and pregnant and breastfeeding patients during the study period were excluded.

The following sample size estimation formula for independent cohort studies was used to calculate the sample size for the study [46].

n=[Z1−α/2(1+1/m)p∗(1−p)+Z1−βp0∗(1−p0/m)p1(1−p1)]2(p0−p1)2.

Given a two-sided significance threshold (1-alpha) of 95 percent, a power (1-beta, percent chance of detecting) of 80 percent, a ratio of Unexposed/Exposed = 1, and a percentage of Exposed with Outcome of 11.3% [41], a sample size of 590 was calculated. Adding a 5% contingency, the sample size increased to 620, with 320 exposed and 300 unexposed participants. A systematic sampling technique was used to recruit study participants.

Data collection

Detailed information about the participants was obtained through laboratory tests, clinical examination and measurements, patient interviews, and chart review. 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) [47]. The questionnaire includes information related to sociodemographic characteristics [age, gender, waist circumferences (WCs), height, weight, BMI, religion, civil status, address, educational level, occupation, and monthly income]; substance use (tobacco use, alcohol consumption, coffee use, and use of the khat plant); and clinical measurements (blood pressure, blood sugar, lipid profile, and use of any medications).

Study procedure

All participants recruited in this study were categorized as (1) HIV+: those registered at follow-up care of ART clinic, and (2) HIV-negative: those registered at follow-up care of adult ambulatory clinics. All patients who had CMetS at baseline (point prevalence) or later at any time (incidence or period prevalence) were considered study participants. There are five commonly used definitions for the determination of CMetS [5,48,49]. However, we used two of the tools considering their applicability and feasibility: The National Cholesterol Education Adult Treatment Program III (NCEP-ATP III) – 2005 or NCEP or NCEP – 2005, and The International Diabetes Federation (IDF) – 2005 or simply IDF.

The following biomarkers were considered during calculating CMetS using the NCEP tool: WC in inch (>40 inches in male and >35 inches in female); lipid-1 [triglycerides (TGs) >150 mg/dL or >1.7 mmol] or use of any lipid-lowering drug/s; lipid-2 [high-density lipoprotein (HDL-C) <40 mg/dL or <1.034 mmol in male, and <50 mg/dL or <1.293 mmol in female] or use of any lipid-lowering drug/s; fasting blood glucose (FBS) >100 mg/dL or >5.56 mmol or use of any blood glucose-lowering medications; SBP >130 mmHg, and DBP >85 mmHg or use of any blood pressure-lowering medications (Annex 1, Supplemental Digital Content 1, https://links.lww.com/CAEN/A36).

The biomarkers used in the case of the IDF tool were similar to the NCEP except in two conditions (S1 Fig. 1): (1) WC was measured in cm and the cutoff values were lower than the NCEP (>94 cm in males or >80 cm in females) and (2) WC was considered as an absolute criterion for calculating CMetS by IDF, whereas there were no criteria set for the NCEP as per the guidelines.

Patients were reexamined at the 8th and 18th months after baseline data collection. The incidence and prevalence of CMetS were assessed using the five clinical definitions needed to determine CMetS according to the tools. These were hypertension, SBP > 130 mmHg and DBP > 85 mmHg or hypertension treatment; hyperglycemia, pre-prandial serum glucose >100 mg/dL, and/or diabetes treatment; dyslipidemia-1, serum TG >150 mg/dL, and/or lipid-lowering treatment; dyslipidemia-2, serum HDL-C <50 mg/dL in female or <40 mg/dL in male, and/or lipid-lowering treatment; and central obesity, using NCEP: (WC >35 inches in women or >40 inches in men) or using IDF (WC > 80 cm in women or >94 cm in men).

The NCEP-ATP III – 2005 confirms CMetS if any three of the five criteria are fulfilled. On the other hand, the IDF – 2005 confirms CMetS, if three of the five are fulfilled, one of the three scores must be the WC [5,50–52].

We used the NIH protocol for measuring WC instead of the WHO STEPS protocol due to the convenience of measuring [53]. BP was measured by Omron HEM 7203 (Omron Healthcare Co. Ltd., Kyoto, Japan). The devices were regularly calibrated for proper validation. A Mercury sphygmomanometer was also used for evaluating the accuracy of the devices. An appropriate BP arm cuff of the correct size was used before measurements were taken. Participants were allowed to sit and relax without talking for 5 min before BP measurement, and legs were uncrossed and the arm was supported at heart level during measurements. Three BP recordings were obtained from the right arm with an interval of 5 min and the mean was used for analysis [54,55]. Lipid profiles and glucose were analyzed using SIEMENS (Siemens Healthcare GmbH Henkestr, Erlangen, Germany) (Dimension EXL 200 Integrated Chemistry System), Omnia Health, North Road Chaoyang, Beijing, China (CS-T240 Auto-Chemistry Analyzer), and LipidPlus, Ellicott, Maryland, USA. Operational definitions used in the present study are included in the supporting information (Annex 2, Supplemental Digital Content 2, https://links.lww.com/CAEN/A37).

Data analysis

Data were coded, double-entered, and analyzed using IBM statistics software version 25 for Windows. All categorical variables were coded as 0 or 2 (for females, no responses, and HIV-negative) and 1 (for males, yes responses, and HIV-positive). The dependent variables were coded as dichotomous measurements and were coded as ‘0 or 2’ for ‘No-CMetS’ and ‘1’ for ‘CMetS’).

Descriptive statistics were used to present sociodemographic information, incidence, and prevalence data. Data were expressed as mean (±SD). The weighted odds ratios in a 2 × 2 contingency table were determined using the Mantel-Haenszel test. Logistic regression analysis was employed to determine the association of predictors with the outcome variables. Independent variables having a P value <0.20 in the bivariate logistic regression were entered into a multivariate logistic regression to control the effect of confounders.

Friedman analysis of variance (ANOVA) was used to compare the mean ranks between the related repeated measurements and results were presented in chi-square statistic (χ2) value and the significance level (‘Asymp. Sig’.) was set at P < 0.05. Since the Friedman test identifies only the presence of an overall difference among the repeated measurements, a post-hoc test using Wilcoxon signed-rank was conducted for all statistically significant results. The Bonferroni adjustment less than 0.05/3 = 0.017 was then used to report significant values of the post-hoc analysis. Moreover, Cochran’s Q test was used to determine the statistical difference of CMetS (burden of CMetS) at the three-time points (baseline, the 8th, and 18th month). Significant values were tested by McNemar’s test, and results were reported by considering the Bonferroni adjustments. Except for the post-hoc analysis, in all parts of the analyses, a 95% CI and P value of <0.05 were considered statistically significant. For post-hoc analysis, the Bonferroni adjustment (less than P value divided by the degree of freedom) was considered significant.

Results Enrolment

Of the 620 randomly selected participants for screening and baseline data, a total of 320 HIV+ and 300 HIV-negative patients were recruited. Thirty-two individuals from the HIV+ and 78 from the HIV-negative group refused to continue after consent was obtained. Baseline data were, therefore, complete for 288 HIV+ and 222 HIV-negative individuals. A total of 10 patients were missing from the first follow-up appointment at the 8th-month data collection period due to refusal (7 individuals) and clinical illnesses (3 individuals). Data were complete for 284 HIV+ and 216 HIV-negative patients at the 8th month of appointment. All the ‘lost to follow-up’ cases were from the HIV-negative group and the final 490 participants comprising 281 (55.1%) HIV+ and 209 (41%) HIV-negative participants completed the final 18th-month follow-up (Fig. 1).

Fig. 1:

Flowchart for screening, enrolment, and follow-up of patients for cardiometabolic syndrome study at Zewditu Memorial Hospital in Addis Ababa, Ethiopia. ART, antiretroviral therapy; CMetS, cardiometabolic syndrome; IDF, International Diabetes Federation; NCEP, National Cholesterol Education Program.

Sociodemographic characteristics

Most participants in the HIV+ group were relatively younger (<45 years old, mean 43.5 ± 11.3) and high schoolers (grades 9–12); whereas those in the HIV-negative group were relatively older (>45 years old, mean 50.7 ± 14.3) and college-educated. The majority of the participants came from Addis Ababa’s Kirkos sub-city, where the study site is located. Substance use (tobacco smoking and alcohol consumption) was found to be more prevalent among HIV-negative than the HIV+ group. Chi-square analysis found significant variations in age, family history, traditional medicine (TM) use, educational status, monthly income, and coffee use between HIV+ and HIV-negative groups (Table 1).

Table 1 - Sociodemographic characteristics of HIV-positive and HIV-negative patients on follow-up care at Zewditu Memorial Hospital, Addis Ababa, Ethiopia, 2021 Characteristics Baseline (n = 510) χ2 value P value 8th month (n = 500) χ2 value P value 18th month (n = 490) χ 2 value P value HIV+ HIV-negative HIV+ HIV-negative HIV+ HIV-negative Number, n (%) 288 (56.5) 222 (43.5) 284 (56.8) 216 (43.2) 281 (57.3) 209 (42.7) Age (mean ± SD) 43.51 (11.27) 50.74 (14.31) 44.57 (11.26) 51.47 (14.30) 43.54 (11.31) 52.84 (14.03) Age  >45 131 (45.5) 147 (66.2) 20.898 <0.001 129 (45.4) 141 (65.3) 18.680 <0.001 132 (47.0) 142 (67.9) 20.534 <0.001  <45 157 (54.5) 75 (33.8) 155 (54.6) 75 (34.7) 149 (53.0) 67 (32.1) Gender  Male 126 (43.8) 87 (39.2) 0.893 0.343 126 (44.4) 83 (38.4) 1.544 0.214 124 (44.1) 79 (37.8) 1.726 0.189  Female 162 (56.3) 135 (60.8) 158 (55.6) 133 (61.6) 157 (55.9) 130 (62.2) Address  Kirkos sub-city 113 (39.6) 77 (34.8) 1.030 0.310 111 (39.5) 75 (34.9) 0.920 0.337 110 (39.6) 71 (34.1) 1.280 0.258  Else‡ 172 (60.4) 144(65.2) 170 (60.5) 140 (65.1) 168 (60.4) 137 (65.9) Civil status  Never married 53 (18.4) 36 (16.2%) 6.577 0.087 53 (18.7) 34 (15.7) 5.732 0.125 52 (18.5) 31 (14.8) 6.745 0.080  Married 130 (45.1) 125 (56.3) 126 (44.4) 119 (55.1) 124 (44.1) 117 (56.0)  Divorced 62 (21.5) 36 (16.2) 62 (21.8) 38 (17.6) 62 (22.1)  Widowed/r 43 (14.9) 25 (11.3) 43 (15.1) 25 (11.6) 43 (15.3) 25 (12.0) Edu  No-formal education 35 (12.2) 40 (18.0) 93.441 <0.001 34 (12.0) 38 (17.6) 89.732 <0.001 34 (12.1) 37 (17.7) 88.383 <0.001  Primary 65 (22.6) 18 (8.1) 63 (22.2) 18 (8.3) 62 (22.1) 18 (8.6)  Secondary 27 (9.4) 17 (7.7) 27 (9.5) 17 (7.9) 26 (9.3) 17 (8.1)  High school 96 (33.3) 36 (16.2) 94 (33.1) 34 (15.7) 93 (33.1) 31 (14.8)  College (diploma) 47 (16.3) 111 (50.0) 48 (16.9) 109 (50.5) 48 (17.1) 106 (50.7)  University (first degree and above) 18 (6.3) 0 (0.0) 18 (6.3) 0 (0.0) 18 (6.4) 0 (0.0) Family history  Yes 47 (16.3) 63 (28.5) 10.256 0.001 47 (16.5) 61 (28.4) 47 (16.7) 59 (28.4) 8.864 0.003  No 241(83.7) 158 (71.5) 237 (83.5) 154 (71.6) 9.400 0.002 234 (83.3) 149 (71.6) Income  >50 USD 133 (46.2) 139 (62.6) 12.948 <0.001 132 (46.5) 136 (27.2) 12.750 <0.001 130 (46.3) 130 (62.2) 11.592 0.001  <50 USD 155 (53.8) 83 (37.4) 152 (53.5) 80 (37.0) 151 (53.7) 79 (37.8) TM  Yes 2 (0.7) 52 (23.5) 66.367 <0.001 2 (0.7) 51 (23.7) 65.883 <0.001 2 (0.7) 51 (24.5) 67.662 <0.001  No 286 (99.3) 169 (76.5) 282 (99.3) 164 (76.3) 279 (99.3) 157 (75.5) Tobacco-smoking  Yes 15 (5.2) 23 (10.4) 4.169 0.041 15 (5.3) 22 (10.2) 3.678 0.055 15 (5.3) 21 (10.1)  No 273 (94.8) 198 (89.6) 269 (94.7) 193 (89.8) 266 (94.7) 187 (89.9) 3.301 0.069 Alcohol-drinking  Yes 25 (8.7) 55 (24.9) 23.584 <0.001 24 (8.5) 51 (23.7) 21.162 <0.001 24 (8.5) 47 (22.6) 17.909 <0.001  No 263 (91.3) 166 (75.1) 260 (91.5) 164 (76.3) 257 (91.5) 161 (77.4) Coffee-drinking  Yes 135 (46.9) 190 (86.0) 81.129 <0.001 135 (47.5) 184 (85.6) 75.169 <0.001 133 (47.3) 177 (85.1) 71.841 <0.001  No 153 (53.1) 31 (14.0) 149 (52.5) 31 (14.4) 148 (52.7) 31 (14.9) Khat-chewinga  Yes 14(4.9%) 14 (6.3) 0.277 0.598 14 (4.9) 14 (6.5) 0.318 0.573 14 (5.0) 14 (6.7) 0.392 0.531  No 274 (95.1) 207 (93.7) 270 (95.1) 201 (93.5) 267 (95.0) 194 (93.3)

aKhat, plant/substance chewed in East-Africa and the Middle East as a stimulant or benefit for ‘recreational values’; Pearson Chi-Square was used and Continuity Correction was computed for a 2 × 2 table; Primary, 1st–6th grades; Secondary Junior, 7th–8th grades; High school, 9th–12th grades; TM, traditional medicine.

Edu, education; TM, traditional medicine.

Clinical characteristics Prevalence and incidence of cardiometabolic syndrome

CMetS was found to have a greater point prevalence, period prevalence, and incidence estimation in HIV-negative than HIV+ patients using NCEP and IDF tools. Furthermore, the prevalence estimates obtained by IDF were typically higher than that of NCEP (Fig. 2).

Fig. 2:

Prevalence and incidence of CMetS as computed by NCEP and IDF tools per 1000 of the population of the respective study groups at the Zewditu Memorial Hospital in Addis Ababa, Ethiopia, 2021. CMetS, cardiometabolic syndrome; IDF, International Diabetes Federation; NCEP, The National Cholesterol Education Program.

HIV status and biomarkers

Table 2 presents biomarker measurements within the follow-up period. The majority of the biomarkers had mean values within the reference range. The mean values of SBP, TG, and HDL (male) were; however, above the reference range, with SBP and TG tending to be higher in HIV-negative than HIV+ patients in all the follow-up periods. HDL was higher at baseline in HIV-negative patients but became higher in HIV+ patients in the 8th and 18th follow-up periods (Table 2). Even though the mean WC remained within acceptable limits, it was slightly greater in the HIV+ group (significantly higher in males) as compared to the HIV-negative group.

Table 2 - Clinical characteristics among HIV-positive and HIV-negative patients undergoing follow-up care at Zewditu Memorial Hospital in Addis Ababa, Ethiopia, in 2021 Characteristics Baseline (n = 510) Pearson’s R P value In the 8th month (n = 500) Pearson’s R P value In the 18th month (n = 490) Pearson’s R P value Reference values HIV+ (n = 288) HIV-negative (n = 222) HIV+ (n = 284) HIV-negative (n = 216) HIV+ (n = 281) HIV-negative (n = 209) Mean (±SD) Mean (±SD) Mean (±SD) Mean (±SD) Mean (±SD) Mean (±SD) Mean WC (inch)  Female