Prevalence and factors associated with metabolic syndrome among ART Naïve people living with HIV in Accra, Ghana: a multicenter cross-sectional study

Abstract

Background

To inform strategies aimed at reducing Metabolic Syndrome (MetS) among People Living with HIV (PLWH), it is important to understand the contribution of pre-Antiretroviral Therapy (ART) health. We estimated the prevalence and factors associated with MetS among ART naïve PLWH.

Methods

A multi-centre cross-sectional study was conducted among adult ART naïve PLWH. MetS was defined as presence of any three sub-components; central obesity, raised blood pressure, impaired fasting glucose, reduced high-density lipoprotein cholesterol and raised triglycerides. Modified World Health Organization (WHO) Steps questionnaire was used to collect information on demographics, behavioral, and physical measurements. Fasting blood samples were taken for blood sugar, high density lipoprotein cholesterol (HDLc) and triglyceride measurements. MetS prevalence was estimated and logistic regression used to determine associated factors. Adjusted odds ratios (aOR) and 95% confidence intervals (95%CI) were reported.

Results

Of 347 ART naïve PLWH with median age 38 years (IQR:19–67), MetS prevalence was at 15.3% (95% CI: 11.7–19.5). Abnormal HDLc was the most prevalent MetS sub-component 64.8% (95% CI: 59.6–69.9). Each year increase in age of participants increased odds of Mets by 4% (aOR = 1.04, 95% CI: 1.01–1.07). Being overweight/obese increased the odds of having MetS by 3.2 times compared to being of healthy weight (aOR = 3.2, 95% CI: 1.6–6.3).

Conclusion

We found that about one in seven ART Naïve PLWH in Accra, Ghana, met the diagnostic criteria for MetS. The contributory factors were consistent with known risk factors for cardiometabolic illnesses. We recommend routine screening of PLWH for MetS sub-components.

Introduction

Metabolic Syndrome (MetS) refers to clustering of three or more of five interconnected metabolic risk factors including raised blood pressure, impaired fasting blood glucose, abnormal waist circumference (central obesity), and dyslipidemia (low High-density lipoproteins and or high triglycerides) [1]. MetS increases the risk of development of cardiovascular diseases and type 2 diabetes by more than 5-fold and 3-fold respectively, increases all-cause mortality and is now considered an epidemic among PLWH [2, 3].

In Sub-Saharan Africa (SSA), where almost two-thirds of HIV burden is concentrated (65% of 39 million PLWH globally), previous research has estimated MetS prevalence among ART naïve PLWH aged 18 and above to range from 6.7 to 40.8% using various definitions [4–6]. In Ghana, HIV prevalence was estimated at 1.5% in 2023 with MetS prevalence among ART naïve PLWH aged eighteen and above estimated to range between 20.1 and 23.6% from two single center studies conducted in 2013 and 2019 using the joint definition criteria [7–9]. Key factors playing a role in the high occurrence of MetS among PLWH have included aging, inflammation related to the HIV virus, high viral loads, lifestyle factors, genetic predispositions, cultural and environmental factors [7, 9].

Previous studies on MetS among PLWH’s in SSA and Ghana were concentrated among ART experienced PLWH with paucity of studies assessing MetS among ART naïve PLWH [7] Additional context specific data are however essential for understanding of pre-ART health status and for early detection of MetS and MetS components among PLWH. This multi-centre study estimated the prevalence and factors associated with MetS among ART naïve PLWH form four urban ART centers in Accra, Ghana.

Methods

Study design and setting

We conducted a cross-sectional study analysing baseline data collected from March 2023 to June 2024 for an ongoing prospective cohort study (The MeTS-HIV cohort) recruiting newly diagnosed ART naive PLWH. The cohort was assembled across four HIV high burden facilities namely the Greater Accra Regional Hospital, the Korle-Bu teaching hospital, Tema General Hospital and the Ga West Municipal Hospital who all operate out-patient ART clinics for PLWH. Further details on the cohort can be found in our previously published protocol [10]. Study participants included newly diagnosed ART naïve PLWH aged ≥ 18 years from the study sites. PLWH who were bedridden, pregnant women and individuals diagnosed with cancer and on corticoid treatment were excluded.

Sample size and sampling

The sample size was calculated using the standard sample size for estimating a proportion in a single population [n = Z² p (1-p)/D2] with 5% margin of error and a 1.96 z-value for a 95% confidence interval (CI) [11]. Estimated prevalence of MetS among ART naïve PLWH of 23.6% from a previous Ghanaian study was utilized [7]. Additionally, we added a 15% non-response rate, and this gave a minimum sample size of 326 participants. However, since this was a baseline for an ongoing prospective study, the data for all 347 participants was analysed.

Across the four recruiting sites, newly diagnosed PLWH who were above 18 years old were enrolled consecutively into the study until the sample size was reached. Given the era of treat all policy in Ghana (All diagnosed must receive ART at diagnosis), the study utilized counsellors as trained data collectors. Recruitment into the study was done by counsellors on day of diagnosis with consenting and study questionnaire administered immediately post initial diagnosis counselling.

Data collection, anthropometric and biochemical measurements

Trained research assistants at each site interviewed consenting participants using a structured questionnaire modified from the WHO STEPS survey.

Height of participants was measured using a Seca 285 standing stadiometer on the wall at the study sites with participants standing upright to the nearest 0.1 cm. Weight was measured using a portable Seca^® 763 electronic scale with participants in light clothing and bare feet to the nearest 0.5 kg. Waist circumference (WC) was measured in the mid-axillary line at the midpoint between lower margin of the last palpable rib and the top of the iliac crest at the end of normal expiration using an inelastic tape measure to the nearest 0.1 cm.

Right arm systolic and diastolic blood pressure were measured using Omron M3 comfort automatic upper arm blood pressure monitor by trained nurses and research assistants. Before measurement, participants relaxed for 5 min. Three measurements were taken ten minutes apart with participants feet on the floor and arms supported at heart level. The average of the three readings was calculated.

After an 8-hour fast by participants, 5 ml of venous blood was collected antecubital fossa for biochemical analysis. Blood samples were left undisturbed for 30 min, spun using a centrifuge and blood serum separated for storage. The frozen serum was transported from the four study sites to a central laboratory with cold chain maintained (between temperature of 2 °C and 8 °C). Analysis of high-density lipoprotein cholesterol [HDLc], low-density lipoprotein cholesterol [LDLc], total cholesterol and triglycerides was conducted on the samples at one designated central laboratory.

Data management and analysis

Data collected were downloaded in Microsoft Excel format from Kobo collect, cleaned, and imported to STATA version 17 for statistical analysis.

BMI was calculated using the standard formula of weight(kg)/height (m²) and categorized as underweight (< 18.5), healthy (18.5–24.9), overweight (25–30) and obese (> 30). WHO clinical stages were determined following the criteria from WHO. The criteria groups HIV patients according to the symptoms with asymptomatic cases classified as stage one, mildly symptomatic as stage two, moderately symptomatic and severely symptomatic as stages three and four respectively [12]. Exercise levels were categorized as none, low intensity, moderate intensity and high intensity respectively based on participants responses to the level of exercise involved in their work and leisure tasks. For alcohol consumption and smoking, previous drinkers/smokers were ever drinkers/smokers that have not consumed alcohol/smoked in the past 12 months.

Categorical variables were presented using frequencies and percentages. Median and 25th–75th percentiles were reported for continuous variables based on their distribution.

The primary outcome of Metabolic Syndrome (MetS) was defined using the Joint consensus definition that combines the International Diabetes Federation (IDF) and the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) definitions as having any three of the following [1]: (i) elevated waist circumference of > 80 cm in women and > 94 cm in men (adjusted for the African population), (ii) Triglycerides 150 mg/dl (3.88mmol/l) or greater or on treatment for dyslipidemia, (iii) HDLc < 40 mg/dl (1.03 mmol/l) in men and < 50 mg/dl (1.29 mmol/l) in women or on treatment for dyslipidemia, (iv) BP 130/85 mmHg or greater or on treatment for hypertension, (v) fasting glucose 100 mg/dl (5.6 mmol/l) or greater or on treatment for diabetes [1].

Prevalence of MetS was calculated as the number of participants with three or more MetS components divided by the total number of participants screened. To determine factors associated with prevalence of MetS among ART naïve PLWH, logistic regression was conducted to model the log-odds of MetS.

Unadjusted odds ratios (ORs) and adjusted odds ratios (aORs) with surrounding 95% Confidence Intervals (95%CI) were used to summarize strengths of associations of interest. Variables with significance (p value < 0.05) at bivariate level were fitted into the multivariable logistic model. We used both the magnitude of the effect size (OR ≥ 1.5) and coverage of 95% CI (including the majority of effect sizes in one direction) to report on clinically and statistically meaningful findings.

Results

Sample characteristics

A total of 347 study participants with median age of 38 (25th, 75th percentiles: 29–45) were recruited into the study from four major hospitals in the Greater Accra Region. Majority of participants were female 61.1% (212/347), 50.7% (176/347) were in WHO HIV clinical stage one and 27.9% (94/347) were current drinkers (consumed alcohol in the past year). Age, Body Mass Index (BMI) and number of MetS components at baseline were significantly different across the MetS and non-MetS group (Table 1).

Table 1.

Sociodemographic, clinical and behavioral characteristics of study participants, Accra, Ghana

Characteristic	Frequency (n = 347) n (%)	MetS status n (%)		P-value
		Present(n = 53)	Absent (n = 294)
Sex
Male	135 (38.9)	19 (14.1)	116 (39.5)	0.62
Female	212 (61.1)	34 (64.2)	178 (60.6)
Age Median (25th–75th percentiles) 38 (29–45)
< 25	42 (12.1)	3 (5.7)	39 (13.3)	0.02*
25–49	253 (72.9)	36 (67.9)	217 (73.8)
> 50	52 (14.9)	14 (26.4)	38 (12.9)
BMI (kg/m2)
Underweight: <18.5	80 (23.1)	18 (34.6)	143 (25.5)	< 0.00*
Healthy: 18.5–24.9	166 (47.8)	6 (11.5)	74 (49.3)
Overweight: 25–30	53 (15.3)	9 (17.3)	44 (15.2)
Obese: >30	48 (13.8)	19 (36.5)	29 (10.0)
WHO HIV Clinical stage at enrolment
Stage 1	176 (50.7)	29 (54.7)	147 (50.0)	0.46
Stage 2	83 (23.9)	12 (22.6)	71 (24.2)
Stage 3	75 (21.6)	12 (22.6)	63 (21.4)
Stage 4	13 (3.8)	0 (0.0)	13 (4.4)
Alcohol consumption
Never consumed	220 (63.4)	30 (56.6)	190 (64.6)	0.12
Previous drinker	33 (9.5)	9 (16.9)	24 (8.2)
Current drinker	94 (27.1)	14 (26.4)	80 (27.2)
Smoking
Never smoked	306 (88.2)	49 (92.5)	257 (87.4)	0.49
Previous smoker	27 (7.8)	2 (3.8)	25 (8.5)
Current smoker	14 (4.0)	2 (3.8)	12 (4.1)
Exercise habits
No exercise	55 (15.9)	7 (13.2)	48 (16.3)	0.52
Low Intensity	143 (57.1)	19 (35.9)	124 (42.2)
Moderate Intensity	68 (19.6)	14 (26.4)	54 (18.4)
High Intensity	81 (23.3)	13 (24.5)	68 (23.1)
Number of MetS Sub-Components at baseline
None	33 (9.5)	–	33 (11.2)	< 0.00*
One	121 (34.9)	–	121 (41.2)
Two	140 (40.4)	–	140 (47.6)
Three	46 (13.3)	46 (86.8)	0 (0.0)
Four	7 (2.0)	7 (13.2)	0 (0.0)

BMI = Body Mass Index; HIV = Human Immunodeficiency Virus; MetS = Metabolic Syndrome; WHO = World Health Organization

*Statistically significant

Prevalence of MetS and sub-components

Overall, prevalence of MetS using the joint criteria definition was at 15.3% (95% CI: 11.7–19.5), with abnormal HDLc (64.8% 95% CI: 59.6–69.9) and abnormal blood glucose (61.9%, 95% CI: 56.6–67.1) as the most prevalent MetS sub-components and abnormal triglyceride as the least prevalent MetS sub component (1.15%, 95% CI: 0.3–2.9) (Fig. 1).

Fig. 1.

Prevalence (%) of MetS and MetS Sub-Components with 95% confidence interval

Factors associated with MetS

In adjusted analyses, Each year increase in age of the participants increased odds of Mets by 4% (aOR = 1.04, 95% CI: 1.01–1.07). Additionally, participants who were overweight/obese had 3.2 times higher odds of having MetS compared to those of healthy weight (aOR = 3.2, 95% CI: 1.6–6.3). (Table 2).

Table 2.

Factors associated with metabolic syndrome among ART Naïve PLWH, Accra, Ghana, 2023

Variable		MetS Status		Unadjusted OR (95% CI)	Adjusted OR (95% CI)
		Present n (%)	Absent n (%)
Age (years) Median (25th–75th percentiles)
38 (29–45) 1		53/347 (15.3)	294/347 (84.7)	1.04 (1.2–7.2)	1.04 (1.01–1.07)
BMI (kg/m2)
Healthy (18.5–24.9)		18/161 (11.2)	143/161 (88.8)	Reference	Reference
Underweight (< 18.5)		6/80 (7.5)	74/80 (92.5)	0.6 (0.2–17)	0.6 (0.2–1.7)
Overweight/Obese (≥ 25)		28/101 (27.7)	73/53 (72.3)	3.0 (1.6–5.9)	3.2 (1.6–6.3)
Alcohol consumption
Never consumed		30/220 (13.6)	190/220 (86.4)	Reference	Reference
Previous consumer		9/33 (27.3)	24/33 (72.7)	2.4 (1.0–5.6)	2.2 (0.9–5.4)
Current consumer		14/94 (14.9)	80/94 (85.1)	1.1 (0.6–2.2)	1.1 (0.5–2.2)

BMI = Body Mass Index; OR = Odds Ratio; MetS = Metabolic Syndrome

Discussion

We sought to determine the prevalence and factors associated with MetS among ART naïve newly diagnosed PLWH across four ART centers in Accra, Ghana. Using the Joint MetS criteria, about one in seven newly diagnosed ART naïve PLWH had MetS (15.8%, 95%CI 95% CI: 11.7–19.5). Older age and being overweight or obese were associated with MetS

The MetS prevalence found in our study is on par with the globally estimated prevalence of 18.5% among ART naïve adults eighteen years and above [13]. Our prevalence estimate is not statistically different than the 23.6% (95%CI 16.7, 30.1) estimated among ART naïve PLWH by a single-site cross-sectional study conducted in a peri-urban hospital in Ghana and the 20.1% (95%CI 13.8, 27.8) estimated from another single-site cross-sectional study in Akwatia using the joint definition [7, 9]. The slight differences may be due to the variations in the total sample size and the settings of the studies (peri-urban and rural compared to predominantly urban). Compared to other Sub-Saharan African studies, our estimated prevalence is similar to 15.2% estimated among ART naïve PLWH in Western Kenya, higher than the 6.7% from Nigeria and lower than the 40.8% from Ethiopia [4–6]. The differences may be due to population differences, differences in recruitment approaches and differences in the definitions of MetS used

Age remains a strong pre-disposing factor to MetS development among this cohort of ART naïve PLWH’s. Every year increase in participants age increased odds of Mets by 4%. This finding is concordant with findings from previous studies among both ART naïve and ART experienced PLWH’s from various countries [4, 14]. The risk of developing all five MetS sub-components increases substantially with age, hence with increasing life expectancy of PLWH, MetS prevalence is on the increase as well [15].

Being overweight/obese significantly increased the odds of MetS in line with previous evidence linking overweight and obesity to all five MetS sub-components [16, 17]. This finding agrees with a previous studies conducted in Kenya where presence of obesity increased the odds of developing MetS [18, 19]. Consequently, strategies for management and treatment of obesity including exercise, dietary restrictions, medications and surgery are beneficial for reduction in progression of MetS [20].

This study is limited by a number of factors. Firstly, as this was a baseline for an ongoing prospective cohort study that focused on newly diagnosed PLWH, data on some key variables such as viral load, and CD4 T cell count was not collected at this stage. Additionally, smoking habits, exercise frequency and diet were through self-report leading to possibility of information bias. To reduce this bias, interviews were conducted in private settings to ensure the comfort of respondents. Additionally, because waist circumference is an indirect measure of obesity, the association between obesity and MetS reported in this study reflects the total effect of obesity on MetS including the indirect effects of waist circumference.

Conclusion

We found that about one in seven newly diagnosed ART Naïve PLWH in Accra, Ghana met the criteria for MetS. Being overweight/obese, and older age were associated with higher prevalence of MetS. We recommend routine screening of newly diagnosed PLWH’s for MetS sub-components as part of their counselling and clinical care sessions

Abbreviations

ART

Anti-retroviral therapy

BMI

Body mass index

CI

Confidence interval

HDL-c

High density lipoprotein-cholesterol

IDF

International diabetes federation

LDL-c

Low density lipoprotein-cholesterol

MetS

Metabolic syndrome

NCEP

National cholesterol education panel

OR

Odds ratio

PLWH

People living with HIV

SSA

Sub-Saharan Africa

STEPS

Stepwise approach to NCD risk factor surveillance

WHO

World Health Education

WC

Waist circumference

Author contributions

MAO, KT, ML, EP and EK conceptualized and designed the research study. MAO, VG, EK provided support for data collection. MAO, KA, VS analyzed and interpreted the data. MAO wrote the original draft of this manuscript. All authors revised the original draft, then reviewed and approved the final content of this manuscript.

Funding

Research reported in this publication is being supported by the Fogarty International Center and the National Institute of Alcohol Abuse and Alcoholism of the National Institutes of Health under Award Number D43 TW011526. The lead and last authors are also providing additional support for this study. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Data availability

The datasets generated and analysed during this study are available from the authors on reasonable request.

Declarations

Ethics approval and consent to participate

Ethical approval for this study was granted by Ghana Health Service Ethical Review Committee and the Korle Bu Teaching Hospital Institutional Review Board (KBTH IRB) with approval numbers GHSERC: 019/11/22 and KBTH-IRB 000137/2022 respectively. Permission was received from the regional health directorates and facility heads. All participants gave informed consent and could willingly drop out of the study at any point. Data were handled with confidentiality, with only research team having access to the data and all personal identifiers removed from the data.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.