Abstract
Background:
Hyperuricemia is associated with an elevated risk of cardiovascular diseases (CVD) among people with HIV (PLWH). However, there is a paucity of studies examining the factors associated with hyperuricemia among PLWH in sub-Saharan Africa.
Objective:
This study aimed to determine the prevalence and factors associated with hyperuricemia among PLWH at a tertiary hospital in Uganda.
Design:
We conducted a cross-sectional study among PLWH receiving antiretroviral therapy (ART) at the HIV clinic at Kiruddu National Referral Hospital in Kampala, Uganda.
Methods:
Data were collected using a structured questionnaire, anthropometric and blood pressure measurements, and analysis of fasting blood glucose, blood lipids, glycated hemoglobin, and serum uric acid of blood samples from participants. Modified Poisson regression with robust standard errors was used to assess factors associated with hyperuricemia. Statistical significance was set at p < 0.05 for all analyses.
Results:
Among 390 PLWH, the mean (SD) age was 41.4 (12.3) years, and 209 (53.6%) were female. A total of 360 (92.3%) were on dolutegravir-based ART regimens, and 94.7% (306/323) were virally suppressed (viral load < 1000 copies/mL). The prevalence of hyperuricemia was 21.3% (83/390). Current alcohol use (adjusted prevalence ratio (aPR) = 2.07, 95% CI: 1.26, 3.41, p = 0.004) and increased respiratory rate (aPR = 1.09, 95% CI: 1.02, 1.16, p = 0.015) were independently associated with hyperuricemia. Lower oxygen saturation, duration on ART, and increased diastolic blood pressure, triglycerides, weight, BMI, and circumferences (waist, hip, neck, and mid-upper arm) were associated with hyperuricemia at bivariable analysis but lost significance after adjusting for confounders.
Conclusion:
One in five PLWH had hyperuricemia in this study. Alcohol use was identified as a potential modifiable risk factor for hyperuricemia. While alcohol cessation programs are needed to mitigate the risk of hyperuricemia, studies should explore the effect of hyperuricemia on lung function among PLWH.
Keywords: alcohol use, antiretroviral therapy, cardiovascular diseases, hyperuricemia, people living with HIV, sub-saharan Africa, Uganda
Background
Hyperuricemia, characterized by elevated serum uric acid levels, has been associated with cardiovascular diseases (CVD), a leading cause of morbidity and mortality in people with HIV (PLWH).1–4 By promoting reactive oxygen stress and disrupting inflammatory signaling pathways, hyperuricemia impairs the function of the endothelium and vascular smooth muscle cells, contributing to hypertension, coronary heart disease, and atrial fibrillation.5–7 A systematic review highlighted the potential of hyperuricemia as an independent risk factor for CVDs through its association with hypertension, glucose intolerance, renal insufficiency, and adiposity, particularly in high-risk subgroups. 8 Moreover, hyperuricemia is an independent predictor of cardiovascular mortality. 9
Hyperuricemia was first described in 1993 as one of the most common rheumatic manifestations of HIV. 10 Over the decades, there has been little progress in terms of research aimed at understanding hyperuricemia in this vulnerable population. This situation is particularly concerning in sub-Saharan Africa (SSA), where, until only recently, studies have begun to shed light on the burden of hyperuricemia among PLWH. Two studies conducted in this region reported highly divergent prevalence rates of hyperuricemia in PLWH, ranging from 13% to 46.5%, underscoring the need for urgent and robust research in this area.1,11 Since the development of ART, hyperuricemia has been reported with the nucleoside reverse transcriptase inhibitors, didanosine and stavudine, and the protease inhibitor, ritonavir. 12 The burden of hyperuricemia and its associations are understudied in the era of universal use of dolutegravir-based regimens.
While previous studies have confirmed an association between hyperuricemia and CVD in PLWH, 13 the specific factors contributing to hyperuricemia in this population remain elusive among African populations. Gout is the major manifestation of hyperuricemia, and in a study done in 2012, the factors that were associated with its manifestation among PLWH included male sex, ethnicity (being of black African origin), high body mass index (BMI), diagnosis of and treatment for hypertension, and treatment for hypertriglyceridemia. 12 Exposure to NNRTIs was found to be a protective factor. 12
The burden of comorbidities in PLWH is substantial, with associated increased healthcare costs and poorer patient outcomes compared to the general population, 14 which results in poor patient outcomes. Addressing hyperuricemia is crucial for improving overall health and reducing CVD risk in PLWH, thereby lowering the comorbidity burden in this population. Understanding the prevalence and factors associated with hyperuricemia will enable early prediction of cardiovascular risks in PLWH, thereby ensuring early screening and intervention for the prevention of CVD.
The aim of this study was to determine the burden and factors associated with hyperuricemia among PLWH at a tertiary hospital in Uganda.
Materials and methods
Study design and setting
The current study analyzed data from a previous study that compared cardiometabolic profiles of PLWH with and without prior active TB. This primary study was conducted at Kiruddu National Referral Hospital (KNRH) and is described elsewhere. 15 The study site, KNRH is public general hospital specializing in Internal Medicine, Burns and Plastic surgery and is located in the capital city of the country, Kampala. This boasts of an active HIV clinic with over 25,000 patients under active follow-up.
Study population
All participants with serum uric acid measurement in the primary study were included in this analysis.
Eligibility criteria
All participants in the original study, from which the secondary data were obtained, had to have provided informed consent for inclusion. They were required to be at least 18 years old and receiving ART KNRH. For data to be eligible for this secondary analysis, it had to be complete, including uric acid measurements.
Sample size estimation and sampling
Considering a prevalence of hyperuricemia of 38.4% among PLWH in Ethiopia, 1 a 95% confidence interval, and a possible 7% rate of data missingness, we estimated that a sample size of 389 would be adequate using the OpenEpi calculator. 16 We, therefore, conducted a census of all 390 participants in the primary study. All PLWH in the primary study were randomly selected from the HIV care database at KNRH as described in the primary study.
Study measurements
These were carried out in the primary study by trained research assistants who administered pretested questionnaires to gather information on sociodemographic characteristics, medical history, and cigarette smoking and alcohol use. Sociodemographic and clinical history of the participants was collected using interviewer-administered questionnaires. Sociodemographics included age, sex, tribe, residence, educational level, marital and employment status. Smoking and family history of cardiovascular disease in first-degree relatives constituted the clinical history. Age was recorded as a continuous variable, whereas sex was categorized as “male” or “female.” We categorized tribes into three groups: “Baganda” and “Banyakitara,” which are the major tribes in Uganda, collectively accounting for over 35% of the population, 17 and “Others,” representing all other tribes. All the other categorizations of the demographic characteristics are presented in the results section of this paper. Alcohol use was assessed using the CAGE questionnaire, which consisted of four questions about drinking habits. The CAGE questionnaire is a screening tool used to assess alcohol use disorder with a high specificity of 74% and sensitivity of 91%, and has been widely used within Ugandan populations.18,19 This has four questions which assess the need to reduce drinking, annoyance with criticism of drinking, guilt feelings about drinking, and the use of alcohol as a morning eye-opener among people suspected of alcohol use disorder. Each positive answer to any of the questions is scored 1, and a total score of 2 or above is indicative of hazardous alcohol use.
Data relating to ART regimens, defaulting, CD4 counts, viral load suppression status, and previous opportunistic infections were obtained from the treatment database of the hospital. The study participants underwent a general physical examination, which included measuring vital signs such as respiratory rate, pulse rate, and axillary temperature using a digital clinical thermometer. Participants’ anthropometric measurements, including weight and height, were undertaken using a weighing scale (Seca 760® Seca GmbH, Hamburg, Germany) and a stadiometer (Seca 213® Seca GmbH, Hamburg, Germany), respectively. The mid-upper-arm, neck, and hip circumference were measured using a tape measure. BMI was calculated by dividing the participant’s weight in kilograms (kg) by the square of their height in meters (m2). Blood pressure (BP) was recorded as the average of two office BP readings, taken 20 min apart, using a calibrated digital blood pressure machine (Omron®, Hem 7120, Omron Healthcare Co., Kyoto, Japan). Blood samples were collected for laboratory tests by a trained and experienced phlebotomist. A point-of-care glucometer (Accu-Chek®, Roche Diabetes Care Inc., Indianapolis, IN, USA) was used to measure fasting blood glucose (FBG), whereas blood lipids (triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), and serum uric acid were analyzed using the Cobas® 6000 analyzer series (Roche Diagnostics, Indianapolis, IN, USA).
Hypertension was diagnosed as a systolic BP ⩾ 140 mmHg and/or diastolic BP ⩾ 90 mmHg. Dyslipidemia criteria included a total cholesterol >5.0 mmol/L, LDL-c > 4.14 mmol/L, triglycerides ⩾ 1.7 mmol/L, and/or low HDL-c (<1.03 mmol/L for men and <1.29 mmol/L for women). FBG and HbA1c were deemed elevated (hyperglycemia) at levels ⩾5.6 mmol/L and ⩾5.7%, respectively. DM was defined by an FBG ⩾ 7.0 mmol/L and/or HbA1c ⩾ 6.5%. High BMI was defined as ⩾25 kg/m2, while obesity was classified as a BMI ⩾ 30 kg/m². Central obesity was identified by a waist circumference ⩾102 cm for males or ⩾88 cm for females and/or a waist-to-hip ratio ⩾0.90 for males or ⩾0.85 for females. Data were collected between October 2023–March 2024.
Study outcomes
The primary outcome of this study was the prevalence of hyperuricemia and its associated factors. Hyperuricemia was defined as serum uric acid levels >420 µmol/L for males and >360 µmol/L for females.
Data analysis
The data collected was checked for accuracy and completeness. After cleaning, this was entered into Stata version 18.0 (StataCorp LLC, College Station, TX, USA) for data analysis. Participants’ sociodemographic characteristics were presented in simple descriptive statistics as frequencies and percentages. The prevalence of hyperuricemia was calculated as the percentage of individuals with hyperuricemia within the study population.
To determine the factors associated with hyperuricemia, univariate analysis was initially conducted to compute the frequencies and percentages of the different variables. Bivariate analysis examined categorical variables using Pearson’s chi-square test or Fisher’s exact test, and continuous variables using the independent t-test or Mann–Whitney U test, as appropriate. Multivariable Modified Poisson regression models with robust standard errors were finally constructed for the factors associated with hyperuricemia. For all the data analyses, statistical significance was set at p < 0.05. In constructing the multivariable model, we included all factors with p < 0.05 at bivariable analysis in the final model and controlled for sex.
Ethics approval and consent to participate
All study procedures were conducted in accordance with the Declaration of Helsinki. The study was approved by the Mildmay Uganda Research Ethics Committee (MUREC-2023-240), and the Uganda National Council of Science and Technology (HS2991ES) prior to participant recruitment. Study participants provided written informed consent before study procedures were performed.
Results
Of 396 participants in the original study, six did not have a uric acid measurement and were therefore excluded from the current study.
Among 390 PLWH included in this analysis, the mean (standard deviation) age was 41.4 (12.3) years, and 209 (53.6%) were female. A total of 360 (92.3%) were on dolutegravir-based ART regimens, and 94.7% (306/323) were virally suppressed (viral load < 1000 copies/mL). A total of 232 (59.8%) had a history of alcohol use, while 77 (20.0%) had a history of smoking. Further, 182 (46.7%) participants had hyperglycemia, and 348 (89.2%) had dyslipidemia. Detailed participant characteristics are presented in Table 1.
Table 1.
Sociodemographic and clinical characteristics of the study participants (n = 390).
| Characteristic | n | %/SD/IQR |
|---|---|---|
| Age, mean (standard deviation, SD) | 41.4 | 12.3 |
| Tribe | ||
| Baganda | 249 | 64.2 |
| Banyakitara | 81 | 20.9 |
| Others | 58 | 14.9 |
| Urban residence | 274 | 70.3 |
| Married | 173 | 44.4 |
| Education level | ||
| Below secondary | 202 | 51.8 |
| Secondary+ | 188 | 48.2 |
| Unemployed | 66 | 16.9 |
| Currently on cotrimoxazole prophylaxis | 98 | 25.2 |
| Drugs in the antiretroviral therapy (ART) regimen | ||
| Tenofovir | 374 | 95.9 |
| Lamivudine | 386 | 99.0 |
| Abacavir | 6 | 1.5 |
| Zidovudine | 6 | 1.5 |
| Efavirenz | 20 | 5.1 |
| Dolutegravir | 360 | 92.3 |
| Lopinavir | 1 | 0.3 |
| Ritonavir | 4 | 1.0 |
| Atazanavir | 8 | 2.1 |
| Other | 3 | 0.8 |
| Years on ART (mean and SD) | 6.4 | 6.2 |
| History of ART default | 40 | 10.4 |
| Current opportunistic infection | 22 | 5.6 |
| Current clinical stage of HIV | 174 | |
| Stage I | 38 | 44.6 |
| Stage II | 178 | 9.7 |
| Stage III/IV | 45.6 | |
| Comorbidities | ||
| Diabetes | 24 | 6.2 |
| Hypertension | 64 | 16.4 |
| Dyslipidemia | 1 | 0.3 |
| Renal disease | 1 | 0.3 |
| Asthma | 8 | 2.1 |
| Allergies | 15 | 3.8 |
| Alcohol use | ||
| Never used alcohol | 156 | 40.2 |
| Formerly used alcohol (>6 months) | 115 | 29.6 |
| Currently uses alcohol (<6 months) | 117 | 30.2 |
| Smoking | ||
| Never smoked | 309 | 80.1 |
| Formerly smoked (>6 months) | 50 | 13.0 |
| Currently smokes (<6 months) | 27 | 7.0 |
| Family history of cardiovascular disease in first-degree relative | 175 | 44.9 |
| Body mass index | ||
| Under weight | 29 | 7.5 |
| Normal | 209 | 53.7 |
| Overweight | 87 | 22.4 |
| Obese | 64 | 16.5 |
| Waist circumference (cm), mean (SD) | 84.0 | 12.7 |
| Central obesity | 191 | 49.1 |
| Neck circumference (cm), mean (SD) | 34.0 | 3.1 |
| Temperature (°C), mean (SD) | 35.9 | 0.7 |
| Pulse rate (beats per minute), mean (SD) | 76.4 | 12.9 |
| Abnormalities on chest auscultation | 32 | 8.2 |
| SPO2 (%), mean (SD) | 96.5 | 2.8 |
| Respiratory rate (breaths/min), median (interquartile range, IQR) | 19.0 | 17.0–20.0 |
| Systolic blood pressure, mmHg, median (IQR) | 121.5 | 111.5–135.0 |
| Diastolic blood pressure, mmHg, median (IQR) | 80.5 | 74.0–88.5 |
| Hypertension | 125 | 32.1 |
| Fasting blood glucose (FBG) (mmol/L), median (IQR) | 5.3 | 4.7–6.0 |
| Elevated FBG | 146 | 38.4 |
| Glycated hemoglobin (HbA1c) (%), median (IQR) | 4.6 | 4.1–5.3 |
| Elevated HBA1c | 60 | 15.4 |
| Diabetes mellitus | 52 | 13.3 |
| Total cholesterol (mmol/L), median (IQR) | 4.4 | 3.8–5.2 |
| LDL-c (mmol/L), median (IQR) | 2.9 | 2.3–3.5 |
| HDL-c (mmol/L), median (IQR) | 0.9 | 0.7–1.2 |
| Triglycerides (mmol/L), median (IQR) | 1.3 | 1.0–1.7 |
| Dyslipidemia | 348 | 89.2 |
| Serum uric acid (mmol/L), median (IQR) | 303.0 | 245.5–375.5 |
HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol.
Prevalence of and factors associated with hyperuricemia among PLWH
The prevalence of hyperuricemia in the study population was 21.3%. At bivariable analysis, PLWH with hyperuricemia had a longer duration on ART (7.8 years vs 6.0 years; p = 0.018), higher waist circumference (89.4 cm vs 82.6 cm; p < 0.001), higher triglycerides levels (1.5 mmol/L vs 1.3 mmol/L; p < 0.001), higher neck circumference (34.9 cm vs 33.8 cm; p = 0.004), and higher diastolic BP (82.0 mmHg vs 79.5 mmHg; p = 0.029) (Table 2). In addition, a higher proportion of central obesity (61.4% vs 45.8%; p = 0.011) and alcohol use within the last 6 months (47.6% vs 25.5%; p < 0.001) was noted among those with hyperuricemia. The mean respiratory rate was significantly higher in the hyperuricemia group (19.2 ± 2.6 breaths/min) compared to the non-hyperuricemia group (18.4 ± 2.4 breaths/min; p = 0.011).
Table 2.
Bivariable analysis of factors associated with hyperuricemia in PLWH (n = 390).
| Characteristic | PLWH without hyperuricemia (n = 307) | PLWH with hyperuricemia (n = 83) | p Value | ||
|---|---|---|---|---|---|
| N | %/SD/IQR | ||||
| Age | 41.2 | 12.3 | 42.1 () | 12.3 | 0.56 |
| Sex | |||||
| Male | 136 | 4.3 | 45 (%) | 54.2 | 0.065 |
| Female | 171 | 55.7 | 38 (%) | 45.8 | |
| Tribe | |||||
| Baganda | 202 | 66.2 | 47 | 56.6 | 0.065 |
| Banyakitara | 56 | 18.4 | 25 | 30.1 | |
| Others | 47 | 15.4 | 11 | 13.3 | |
| Residence | |||||
| Urban | 221 | 72.0 | 53 | 63.9 | 0.15 |
| Rural | 86 | 28.0 | 30 | 36.1 | |
| Duration at current residence | 7.0 | 3.0–18.0 | 8.0 | 4.0–21.0 | 0.28 |
| Marital status | |||||
| Not married | 177 | 57.7 | 40 | 48.2 | 0.12 |
| Married | 130 | 42.3 | 43 | 51.8 | |
| Education level | |||||
| Below secondary | 157 | 51.1 | 45 | 54.2 | 0.62 |
| Secondary+ | 150 | 48.9 | 38 | 45.8 | |
| Employment | |||||
| Unemployed | 51 | 16.6 | 15 | 18.1 | 0.75 |
| Employed | 256 | 83.4 | 68 | 81.9 | |
| Currently on cotrimoxazole prophylaxis | |||||
| No | 224 (%) | 73.2 | 67 | 80.7 | 0.16 |
| Yes | 82 (%) | 26.8 | 16 | 19.3 | |
| ART regimen | |||||
| Tenofovir | 297 | 96.7 | 77 | 92.8 | 0.12 |
| Lamivudine | 305 | 99.3 | 81 | 97.6 | 0.20 |
| Abacavir | 3 | 1.0 | 3 | 3.6 | 0.11 |
| Zidovudine | 5 | 1.6 | 1 | 1.2 | 1.00 |
| Efavirenz | 17 | 5.5 | 3 | 3.6 | 0.59 |
| Dolutegravir | 282 | 91.9 | 78 | 94.0 | 0.65 |
| Lopinavir | 1 | 0.3 | 0 | 0.0 | 1.00 |
| Ritonavir | 3 | 1.0 | 1 | 1.2 | 1.00 |
| Atazanavir | 4 | 1.3 | 4 | 4.8 | 0.067 |
| Other | 2 | 0.7 | 1 | 1.2 | 0.51 |
| Duration on ART (Years), mean (SD) | 6.0 | 6.0 | 7.8 | 6.8 | 0.018 |
| History of ART default | |||||
| No | 270 | 89.110.9 | 76 | 91.6 | 0.52 |
| Yes | 33 | 7 | 8.4 | ||
| Viral load suppression | |||||
| Unsuppressed | 11 | 4.3 | 6 | 8.8 | 0.14 |
| Suppressed | 244 | 95.7 | 62 | 91.2 | |
| Current opportunistic infection | |||||
| No | 288 | 93.8 | 80 | 96.4 | 0.37 |
| Yes | 19 | 6.2 | 3 | 3.6 | |
| Current clinical stage of HIV | |||||
| Stage I | 137 | 44.6 | 37 | 44.6 | 0.45 |
| Stage II | 27 | 8.8 | 11 | 13.3 | |
| Stage III/IV | 143 | 46.6 | 35 | 42.2 | |
| Comorbidities | |||||
| Diabetes | 21 | 6.8 | 3 | 3.6 | 0.44 |
| Hypertension | 49 | 16.0 | 15 | 18.1 | 0.62 |
| Dyslipidemia | 0 | 0.0 | 1 | 1.2 | 0.21 |
| Renal disease | 1 | 0.3 | 0 | 0.0 | 1.00 |
| Asthma | 7 | 2.3 | 1 | 1.2 | 1.00 |
| Allergies | 12 | 3.9 | 3 | 3.6 | 1.00 |
| Alcohol use | |||||
| Never used alcohol | 133 | 43.5 | 23 | 28.0 | <0.001 |
| Formerly used alcohol (>6 months) | 95 | 31.0 | 20 | 24.4 | |
| Currently uses alcohol (<6 months) | 78 | 25.5 | 39 | 47.6 | |
| CAGE score if formerly used alcohol | |||||
| 0–1 | 64 | 82.1 | 32 | 82.1 | 1.00 |
| 2–4 | 14 | 17.9 | 7 | 17.9 | |
| CAGE score if currently uses alcohol | |||||
| 0–1 | 292 | 95.4 | 75 | 91.5 | 0.16 |
| 2–4 | 14 | 4.6 | 7 | 8.5 | |
| Smoking | |||||
| Never smoked | 247 | 81.0 | 62 | 76.5 | 0.50 |
| Formerly smoked (>6 months) | 39 | 12.8 | 11 | 13.6 | |
| Currently smokes (<6 months) | 19 | 6.2 | 8 | 9.9 | |
| Family history of cardiovascular disease in first-degree relative | |||||
| No | 164 | 53.4 | 51 | 61.4 | 0.19 |
| Yes | 143 | 46.6 | 32 | 38.6 | |
| Body mass index | |||||
| Underweight | 27 | 8.8 | 2 | 2.4 | 0.007 |
| Normal | 173 | 56.5 | 36 | 43.4 | |
| Overweight | 61 | 19.9 | 26 19 | 31.3 | |
| Obese | 45 | 14.7 | 22.9 | ||
| Waist circumference (cm), mean (SD) | 82.6 | 11.8 | 89.4 | 14.4 | <0.001 |
| Central obesity | |||||
| No | 166 | 54.2 | 32 | 38.6 | 0.011 |
| Yes | 140 | 45.8 | 51 | 61.4 | |
| Neck circumference (cm), mean (SD) | 33.8 | 3.0 | 34.9 | 3.2 | 0.004 |
| Conjunctival pallor | |||||
| No | 265 | 86.3 | 73 | 88.0 | 0.70 |
| Yes | 42 | 13.7 | 10 | 12.0 | |
| Temperature (°C) | 35.9 | 0.7 | 36.0 | 0.6 | 0.082 |
| Pulse rate (beats per minute), mean (SD) | 76.0 | 13.2 | 77.8 | 11.6 | 0.25 |
| Abnormalities on chest auscultation | |||||
| No | 285 | 92.8 | 73 | 88.0 | 0.15 |
| Yes | 22 | 7.2 | 10 | 12.0 | |
| SPO2 (%), mean (SD) | 96.6 | 2.9 | 96.2 | 2.2 | 0.18 |
| Digital clubbing | |||||
| No | 299 | 97.4 | 82 | 98.8 | 0.69 |
| Yes | 8 | 2.6 | 1 | 1.2 | |
| Respiratory rate (breaths/min), mean | 18.4 | 2.6 | 19.2 | 2.4 | 0.011 |
| Systolic blood pressure, mmHg, median (IQR) | 120.0 | 111.0–135.0) | 123.5 | 115.5–134.5 | 0.13 |
| Diastolic blood pressure, mmHg, median (IQR) | 79.5 | 73.0–88.5 | 82.0 | 77.0–90.0 | 0.029 |
| Hypertension | |||||
| No | 212 | 69.1 | 53 | 63.9 | 0.37 |
| Yes | 95 | 30.9 | 30 | 36.1 | |
| Fasting blood glucose (FBG) (mmol/L), median (IQR) | 5.3 | 4.7–6.0 | 5.2 | 4.7–5.7 | 0.55 |
| Elevated FBG | |||||
| Normal | 179 | 59.9 | 55 | 67.9 | 0.19 |
| Elevated FBG | 120 | 40.1 | 26 | 32.1 | |
| HBA1c (%) | 4.5 | 4.1–5.2 | 4.7 | 4.2–5.3 | 0.19 |
| Elevated HBA1c | |||||
| Normal | 260 | 85.0 | 69 | 83.1 | 0.68 |
| Elevated HBA1c | 46 | 15.0 | 14 | 16.9 | |
| Hyperglycemia | |||||
| Normal | 162 | 52.8 | 46 | 55.4 | 0.67 |
| Hyperglycemia | 145 | 47.2 | 37 | 44.6 | |
| Diabetes mellitus | |||||
| No | 264 | 86.0 | 74 | 89.2 | 0.45 |
| Yes | 43 | 14.0 | 9 | 10.8 | |
| Total Cholesterol (mmol/L) | 4.4 | 3.8–5.2 | 4.4 | 3.9–5.2 | 0.73 |
| LDL-c (mmol/L) | 3.0 | 2.3–3.6 | 2.8 | 2.1–3.4 | 0.068 |
| HDL-c (mmol/L) | 0.9 | 0.7–1.2 | 0.9 () | 0.7–1.1 | 0.24 |
| Triglycerides (mmol/L) | 1.3 | 1.0–1.6 | 1.5 | 1.2–2.0 | <0.001 |
| Dyslipidemia | |||||
| No | 32 | 10.4 | 10 | 12.0 | 0.67 |
| Yes | 275 | 89.6 | 73 | 88.0 | |
HDL-c, high-density lipoprotein cholesterol; IQR, interquartile range; LDL-c, low-density lipoprotein cholesterol; SD, standard deviation.
Current alcohol use (adjusted prevalence ratio (aPR) = 2.07, 95% CI: 1.26, 3.41, p = 0.004) and an increase in the respiratory rate (aPR = 1.09, 95% CI: 1.02, 1.16, p = 0.015) were independently associated with hyperuricemia at multivariable analysis (Table 3).
Table 3.
Multivariate analysis of the factors associated with hyperuricemia among people living with HIV (n = 390).
| Characteristic | Crude Prevalence Ratio (cPR) | 95% CI | p Value | aPR | 95% CI | p Value |
|---|---|---|---|---|---|---|
| Sex | ||||||
| Male | 1.00 | Ref | ||||
| Female | 0.73 | (0.50, 1.07) | 0.110 | 0.70 | (0.40, 1.20) | 0.195 |
| Tribe | ||||||
| Baganda | 1.00 | Ref | ||||
| Banyakitara | 1.64 | (1.08, 2.48) | 0.020 | 1.51 | (0.99, 2.32) | 0.059 |
| Others | 1.00 | (0.56, 1.82) | 0.987 | 1.06 | (0.59, 1.89) | 0.849 |
| Alcohol use | ||||||
| Never used alcohol | 1.00 | Ref | ||||
| Formerly used alcohol (>6 months) | 1.18 | (0.68, 2.04) | 0.56 | 1.07 | (0.62, 1.83) | 0.813 |
| Currently uses alcohol (<6 months) | 2.26 | (1.43, 3.57) | <0.001 | 2.07 | (1.26, 3.41) | 0.004 |
| Duration on ART | 1.03 | (1.01, 1.06) | 0.011 | 1.02 | (1.00, 1.05) | 0.113 |
| Weight | 1.02 | (1.01, 1.03) | <0.001 | 0.99 | (0.95, 1.03) | 0.576 |
| Body mass index | 1.06 | (1.03, 1.09) | <0.001 | 1.03 | (0.95, 1.10) | 0.511 |
| Waist circumference | 1.03 | (1.02, 1.04) | <0.001 | 1.00 | (0.96, 1.05) | 0.888 |
| Hip circumference | 1.01 | (1.00, 1.02) | 0.036 | 1.02 | (0.98, 1.06) | 0.356 |
| Mid-upper arm circumference | 1.05 | (1.01, 1.10) | 0.007 | 1.01 | (0.92, 1.12) | 0.769 |
| Neck circumference | 1.09 | (1.03, 1.16) | 0.004 | 1.01 | (0.93, 1.10) | 0.765 |
| Respiratory rate | 1.10 | (1.03, 1.18) | 0.007 | 1.09 | (1.02, 1.16) | 0.015 |
| Diastolic blood pressure | 1.01 | (1.00, 1.03) | 0.036 | 1.00 | (0.98, 1.02) | 0.799 |
| Triglycerides (mmol/L) | 1.22 | (1.09, 1.37) | 0.001 | 1.11 | (0.97, 1.27) | 0.126 |
| Waist-hip ratio | 9.93 | (3.60, 27.43) | <0.001 | 8.61 | (0.14, 519.03) | 0.303 |
Bolded p-values indicate a statistically significant result.
aPR, adjusted prevalence ratio; ART, antiretroviral therapy.
Discussion
We aimed to assess the prevalence of hyperuricemia among PLWH and identify the factors associated with elevated uric acid levels. Our findings suggest a prevalence rate of 21.3% of hyperuricemia among PLWH. Such a high prevalence of hyperuricemia implies that one in five PLWH is at risk of complications of elevated uric acid levels—including endothelial dysfunction, type 2 diabetes mellitus, chronic kidney disease, coronary heart disease, ischemic stroke, gout, and heart failure.20,21 Prospective studies among PLWH in SSA are needed to establish causality between hyperuricemia and these disease states and to determine whether uric acid-lowering therapies are protective. Given the high prevalence of hyperuricemia among PLWH, routine screening for uric acid levels and these potential complications should be incorporated into standard HIV care. The prevalence in our study is similar to that reported by Pirro et al. (25%) among PLWH on ART in Italy. 21 However, this rate is lower than the 38.4% prevalence reported among PLWH in Ethiopia 1 and higher than the 13% prevalence observed among PLWH with opportunistic infections in Zimbabwe. 11 These discrepancies could be attributed to differences in the cut-offs used for defining hyperuricemia. The Ethiopian study used a significantly lower cut-off in women (>291 µmol/L) compared to ours (>360 µmol/L), while the Zimbabwean study did not report the cut-off.
Hyperuricemia in HIV has historically been attributed to ART.1,22 Our study, however, finds that current alcohol use is a potential risk factor for hyperuricemia in PLWH. This is consistent with studies among HIV negative individuals.23,24 Ethanol contributes to hyperuricemia through multiple mechanisms. First, ethanol enhances the degradation of adenine nucleotides, increasing the production of uric acid precursors such as hypoxanthine and xanthine. 25 This process is further amplified by the metabolism of acetate, a byproduct of ethanol, which accelerates the turnover of the adenine nucleotide pool.26,27 In addition, ethanol metabolism leads to increased production of lactic acid, which inhibits the renal excretion of uric acid, resulting in elevated serum uric acid levels. 28 Furthermore, the oxidation of ethanol to acetaldehyde generates NADH, shifting the metabolic balance toward lactate production from pyruvate. 28 This shift disrupts renal uric acid transport, further exacerbating hyperuricemia.
Our study also revealed a significant association between an increase in the respiratory rate and hyperuricemia. Moreover, PLWH hyperuricemia had a lower mean oxygen saturation than PLWH without hyperuricemia. The implication of this finding is not apparent, but it suggests a possible effect of hyperuricemia on the lung function of PLWH. In a 2016 Korean National Health and Nutrition Examination Survey, hyperuricemia was negatively correlated with lung function.29,30 Similar findings have been recently reported in the National Health and Nutrition Examination Survey in the US population. 31 The mechanisms by which hyperuricemia affects lung function are postulated to be related to oxidative stress in the lung epithelium from superoxides formed during the formation of uric acid. 32 Another possible mechanism is reverse causation, whereby hypoxia caused by chronic lung diseases results in increased breakdown of purines to uric acid. 33 A more detailed evaluation of lung function among PLWH with and without hyperuricemia is needed.
Other associations from our study were duration on ART and an increase in the diastolic BP, triglycerides, weight, and anthropometric measurements, but the association was attenuated after adjusting for confounders. Nonetheless, interventions focused on addressing modifiable risk factors, such as alcohol consumption, weight loss, and BP control, are essential to reduce the burden of hyperuricemia and other CVD risk factors in PLWH. Interestingly, we did not find an association between specific ART drugs with hyperuricemia, although there was a trend with atazanavir (p = 0.067). Although previous studies have shown an association between dolutegravir-based therapy and hyperuricemia, we did not find any association with dolutegravir.34,35 It might be because our study was not powered for the comparison between ART regimens since >90% of PLWH in our study were on dolutegravir. Similar to our findings, Nicholson et al, also found no association between any ART class drug with developing hyperuricemia among PLWH. 36
The limitations of this study include it being a cross-sectional evaluation, which limits the ability to establish causal relationships and the potential for residual confounding due to unmeasured factors. Future longitudinal studies are needed to confirm the identified associations and elucidate the underlying mechanisms.
Conclusion
In conclusion, we found that one in five PLWH has hyperuricemia. Hyperuricemia was associated with alcohol use and an increase in the respiratory rate. Screening for hyperuricemia should be considered for PLWH, especially those with alcohol use, to prevent gout and CVD. Promoting healthy lifestyle choices, including reducing alcohol consumption and maintaining a healthy weight, can help mitigate the risk of hyperuricemia. Future studies need to assess the association of hyperuricemia with lung function among PLWH to determine whether hyperuricemia is a potential modifiable risk factor for lung function decline in PLWH.
Acknowledgments
None.
Footnotes
ORCID iDs: Jeremiah Mutinye Kwesiga 
https://orcid.org/0000-0001-5507-1664
Joseph Baruch Baluku
https://orcid.org/0000-0002-5852-9674
Contributor Information
Jeremiah Mutinye Kwesiga, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit P.O. Box 49, Plot 51-59 Nakiwogo Road, Entebbe, Wakiso District, Uganda.
Reagan Nkonge, St Catherine’s Hospital, Kampala, Uganda.
Brenda Namanda, Makerere Lung Institute, Kampala, Uganda.
Martin Nabwana, Makerere University—John Hopkins Research Collaboration, Kampala, Uganda.
Joseph Baruch Baluku, Makerere Lung Institute, Kampala, Uganda; Division of Pulmonology, Kiruddu National Referral Hospital, Kampala, Uganda.
Declarations
Ethics approval and consent to participate: The study was approved by the Mildmay Uganda Research Ethics Committee (MUREC-2023-240), and the Uganda National Council of Science and Technology (HS2991ES) prior to participant recruitment. Study participants provided written informed consent before study procedures were performed.
Consent for publication: Not applicable.
Author contributions: Jeremiah Mutinye Kwesiga: Conceptualization; Investigation; Validation; Writing – original draft; Writing – review & editing.
Reagan Nkonge: Investigation; Project administration; Writing – original draft; Writing – review & editing.
Brenda Namanda: Investigation; Writing – original draft; Writing – review & editing.
Martin Nabwana: Data curation; Formal analysis; Writing – review & editing.
Joseph Baruch Baluku: Conceptualization; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Writing – review & editing.
Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the Fogarty International Center of the National Institutes of Health under grant number D43TW009345 awarded to the Northern Pacific Global Health Fellows Program. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Competing interests: The authors declare that there is no conflict of interest.
Availability of data and materials: All underlying research data and materials related to this study can be provided upon reasonable request from the corresponding author.
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