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Background: The Zambia National Health Strategic Plan (ZNHSP) 2011–2017 underscores the need for community-level risk factor stratification to bolster insufficient incidence and prevalence data. This study evaluates the prevalence and incidence of hypertension and diabetes mellitus in HIV-infected patients on Highly Active Anti-Retroviral Therapy (HAART) and assesses associations with ARV use.

Methodology: A retrospective cohort analysis was conducted using the SMART CARE database in Chongwe district, involving 2070 HIV-infected persons on HAART. Statistical analysis included Chi-square, Kruskal-Wallis tests, and logistic regression.

Results: Among participants, 33.8% exhibited hypertension, with an incidence case fatality rate of 85.7 per 1000 person-years (PYFU). Hypertension was notably higher in men (64%) than women (49%) and prevalent in the 18–45 age group (57.2%). The incidence of type II diabetes was 37.4 per 1000 PYFU. Logistic regression highlighted significant predictors for both conditions, including ART combination, age, and smoking status.

Conclusion: High prevalence and incidence rates of hypertension and type II diabetes were observed among HIV-positive individuals on HAART. Certain HAART combinations, particularly twoNRTIs plus a PI or INSTI, correlated with increased hypertension risk, while combinations including NNRTI or INSTI were linked to higher diabetes incidence.

Introduction

Globally, 37,900,000 people are living with HIV, with a 0.8% adult prevalence. Approximately 770,000 individuals die annually from HIV-related illnesses. Among those aware of their HIV status, 78% are receiving treatment. Most people with HIV, around 68%, live in sub-Saharan Africa. In Zambia, 1,200,000 people are HIV-positive, with an 11.3% adult prevalence and about 17,000 AIDS-related deaths each year [1].

Diabetes mellitus is a significant global health challenge, having more than tripled in adult cases over the past two decades, particularly in low- and middle-income countries [2], [3]. By 2019, there were 463,000,000 people aged 20 to 79 years living with diabetes, and another 374,000,000 with impaired glucose tolerance. Diabetes and its complications led to an estimated 4,200,000 deaths, mostly in lower-income areas. In Zambia, the estimated diabetes cases were about 222,000 in 2017, with projections indicating an incidence rate of 4%–5% by 2019 [2].

The Global Status Report on Non-Communicable Diseases (NCDs) underscores the severe impact of NCDs on impoverished populations, especially in Sub-Saharan Africa, where poverty aggravates health challenges like diabetes, heart disease, and cancer. The lack of healthcare access, education, and preventive measures worsens the situation, necessitating urgent health policy interventions [4].

Mental disorders add significantly to the NCD burden in these regions, where social stigma and insufficient mental health services complicate treatment and management [5], [6]. Developing cost-effective strategies for mental health is challenging, like other NCDs [7], [8]. Mental disorders disrupt daily activities and are linked with high rates of substance use, worsening health outcomes [9], [10]. People with mental health issues often suffer from other NCDs, such as obesity, hypertension, and diabetes, which further complicates their health challenges [11].

A 2016 systematic analysis by Mills and colleagues revealed significant global disparities in hypertension prevalence and control across 90 countries. The study found that in 2010, approximately 1,390,000,000 people, or 31.1% of the global population, had hypertension, with a higher prevalence in low- and middle-income countries. The same research indicated less than 20% prevalence in Zambia in 2010, though more recent data from 2019 showed an increase to 25.9% in some regions [12], [13].

In Zambia’s Chongwe District, the second largest in Lusaka Province with a population of 182,174, significant numbers of HIV/AIDS and NCD cases were recorded in 2018. The local health information system noted 3899 new hypertension cases and 887 new diabetes cases, highlighting the district’s growing healthcare challenges [14].

Since the mid-1990s, widespread antiretroviral (ARV) therapy has significantly reduced HIV morbidity and mortality, transforming it from a fatal disease to a manageable chronic condition [15]. However, HIV and ARVs are associated with an increased risk of chronic comorbidities like Type II diabetes mellitus (T2DM). Studies indicate that T2DM is up to four times more prevalent in those with HIV than in those without, due to both immune response to HIV and metabolic effects of ARVs [16]–[18].

Cardiovascular diseases, bone density reductions, and certain cancers are also seen at higher rates in the HIV-positive population [16]. Managing these comorbid conditions requires integrated healthcare approaches that cover both HIV treatment and other health issues.

Risk factors for T2DM in the general population include age, obesity, smoking, and physical inactivity. For individuals with HIV, additional specific risk factors such as the duration of HIV infection and exposure to certain ARVs increase their susceptibility to diabetes [19], [20].

Nutritional interventions have been shown to be pivotal in managing metabolic risks associated with HIV treatment. Clinical treatment guidelines for people living with HIV (PLHIV) include strategies for both the prevention and management of cardiometabolic risk factors [21]. However, these guidelines predominantly emphasize pharmacological treatments [22].

A significant randomized controlled study demonstrated that dietary strategies could effectively reduce key cardiometabolic risk factors in PLHIV undergoing ARV therapy, impacting hypertension, blood glucose levels, and lipid profiles positively [17], [23], [24].

Despite advancements in ARVs, traditional T2DM risk factors like hypertension and dyslipidemia are escalating among HIV patients as they age, highlighting the need for early prevention and targeted strategies [25].

Highly Active Antiretroviral Therapy (HAART), involving a combination of three or more antiretroviral drugs, is fundamental in managing HIV. Over 25 medications are available, classified into six distinct classes, providing a robust framework for treatment aimed at maintaining viral suppression and improving patient outcomes [26], [27].

This study examines the prevalence and incidence rates of hypertension and diabetes among HIV-infected individuals on HAART in the Chongwe district. It also explores potential correlations between HAART usage and the emergence of these conditions, aiming to contribute to understanding the metabolic and cardiovascular side effects of long-term HAART. This research will inform clinical practice and policymaking to enhance health outcomes for this vulnerable population.

Method

Study Design

This retrospective cohort study utilized data from the SMART CARE electronic database in Chongwe district to assess the incidence rates of hypertension and Type II diabetes mellitus among individuals infected with HIV and undergoing HAART treatment.

Study Setting

The study was conducted in the Chongwe district using the SMART CARE electronic database, which provided a reliable sampling frame for the ART program.

Study Population

The study included HIV-positive individuals aged 18 and older who were receiving ART. Participants were selected from six high-volume facilities in Chongwe district, ensuring they had been on ART for at least one year.

Eligibility Criteria: Inclusion Criteria

Eligibility was determined based on the inclusion of case files in the SMART CARE database, participant age (18 years or older), and a minimum of one year on ART or HAART from January 2006 to April 2021.

Eligibility Criteria: Exclusion Criteria

Participants were excluded if they were pre-hypertensive or diabetic before starting ART, had chronic conditions (e.g., kidney disease, liver conditions, heart conditions, thyroid disorders), were engaged in polypharmacy, were terminally ill at the start of HAART, or were lost to follow-up, transferred out, or deceased during the study. This was to minimize confounding factors.

Sample Size and Sampling Techniques

The SMART CARE database contained 50,517 case files from 2006 to 2021. After applying inclusion and exclusion criteria, 8404 individuals were eligible. Stratified random sampling was employed across six high-volume healthcare facilities to ensure a representative sample. The sample size for each facility was calculated using Fisher’s formula, considering a 36% prevalence of sub-clinical cardiovascular disease, a 95% confidence level, and a 5% margin of error, leading to a final sample size of 2070 [28]. Random sampling was then applied within each stratum to ensure that the sample was proportionally representative of the different sites, as depicted in Fig. 1.

Fig. 1. Flow chart of the screening and selection process for the case files.

Data Collection Procedure and Tools

Data were extracted manually from SMART CARE patient files due to system limitations, covering a 15-year period. Information was entered into Excel for cleaning before analysis with SPSS version 20.

Study Variables: Dependent (Outcome) Variables

Outcome variables included categorized blood pressure readings and blood glucose levels collected 12 months post-ART initiation. Definitions for hypertension and diabetes were based on established medical criteria.

Study Variables: Independent (Explanatory) Variables

Independent variables included HIV status, ARV use, age (both categorical and continuous), duration on ART, and body mass index (BMI), categorized from underweight to obese.

Data Analysis

Descriptive statistics estimated the incidence of hypertension and diabetes. The incidence rate was calculated per total person-years of follow-up [29]. Chi-square and Kruskal-Wallis tests examined associations and differences among categorical and continuous variables, respectively. Logistic regression analyzed determinants of hypertension and diabetes, considering traditional risk factors, and adjusting for confounders.

Control for Confounders and Extraneous Variables

The study controlled for age, sex, BMI, and duration of ART to mitigate confounder effects, incorporated into regression models to adjust during analysis.

Ethical Considerations

The study protocol was approved by the University of Zambia Biomedical Research Ethics Committee and the National Health Research Authority. It used secondary data, minimizing participant risk and providing indirect benefits through contributions to national public health strategies.

Results

Sample Pool Description

The study analyzed 2070 participant records from the SMART CARE electronic database, spanning from 2006 to April 2021. These records represent HIV-positive individuals undergoing HAART in Chongwe district, collected from six primary health facilities. The distribution of participants across these facilities is summarized in Table I, indicating a strategic focus on high-volume sites for robust data representation.

Facility n %
Chongwe District Hospital 354 17.1
Chainda RHC 304 14.7
Chalimbana RHC 265 12.8
Chongwe RHC 364 17.6
Kasisi RHC 425 20.5
Ngwerere RHC 358 17.3
Total 2070 100
Table I. Research Facility Sample Pool

Socio-Demographic Characteristics

Participants’ ages ranged from 18 to 68 years, with a mean age of 40.4 years. The breakdown by gender was nearly equal, with 1,014 males (49.0%) and 1056 females (51.0%). The demographic profile showed 60% were married, 17.7% single, 9.1% divorced, and 5.2% widowed, with 7.9% not disclosing their marital status. Educational backgrounds varied: 46.3% had primary education, 21.8% secondary, 6.5% tertiary, and 25.4% had no formal education. Residency was distributed as 44.6% urban, 33.4% rural, and 22.0% peri urban.

Clinical Characteristics of the Study Population

The nutritional assessment at HAART initiation indicated that 13.9% were underweight, 60.3% normal weight, 14.8% overweight, and 11.0% obese. Regarding family health history, 18.7% reported hypertension, and 14.2% diabetes, with the majority having no family history of these conditions (81% for hypertension, 85.8% for diabetes). The average duration on HAART was 5.7 years, with varied ART combinations. The most common was EFV + FTC + TDF (Efavirenz + Emtricitabine + Tenofovir) (35.5%), followed by 3TC + DTG + TDF (Lamivudine + Dolutegravir + Tenofovir) (23.2%).

Prevalence of Hypertension and Type II Diabetes Mellitus in the Study Population

Hypertension prevalence was 33.8%, and Type II diabetes was 14.9%. These conditions were particularly prevalent in patients on certain ART combinations, with some combinations showing a 100% hypertension prevalence. In contrast, the prevalence for diabetes varied significantly among the different regimens.

The highest prevalence of hypertension among different HAART combinations was observed in patients on 3TC (Lamivudine, NRTI) + ABC (Abacavir, NRTI) + LPV/r (Lopinavir, PI), 3TC (Lamivudine, NRTI) + EFV (Efavirenz, NNRTI) + TDF (Tenofovir, NRTI), and DTG (Dolutegravir, INSTI) + FTC (Emtricitabine, NRTI) + TAF (Tenofovir, NRTI), all recording a 100% prevalence rate with respective participant counts of 86, 19, and 23. The lowest prevalence was found in the regimen combining 3TC (Lamivudine, NRTI) + DTG (Dolutegravir, INSTI) + TDF (Tenofovir, NRTI) at 51.7%.

For Type II diabetes, the highest prevalence was observed in participants on the HAART regimen of EFV (Efavirenz, NNRTI) + FTC (Emtricitabine, NRTI) + TDF (Tenofovir, NRTI) at 31%. The lowest prevalence was recorded in those on 3TC (Lamivudine, NRTI) + EFV (Efavirenz, NNRTI) + TDF (Tenofovir, NRTI) at 5.3%.

Incidence Rates of Hypertension and Type II Diabetes Mellitus in the Study Population

The incidence rate of hypertension among 2070 case files observed over a fifteen-year period (13,519 person-years of follow-up) was 85.7 cases per 1000 enrolled ART clients annually, according to SMART CARE records from 2006 to 2021. Meanwhile, the incidence rate of T2DM among HIV-infected persons on HAART was 37.4 cases per 1,000 registered ART clients per year.

Distribution of Different Risk Factors to NCDs

This study investigated the effects of ART combinations on cardiovascular and metabolic health among HIV-positive patients. They found significant variances in systolic and diastolic blood pressures across different ART groups, highlighting a potential influence of medication on cardiovascular health. Notably, both systolic and diastolic pressures showed substantial differences across four main ART combinations, indicating significant impacts (p < 0.001 for both).

Contrastingly, the study observed no significant differences in blood sugar levels across ART groups (p = 0.592), suggesting that these regimens may not uniformly affect glycemic control. However, analyses of median blood pressures confirmed significant disparities across ART combinations, reinforcing the notion of ART’s influence on cardiovascular parameters.

Furthermore, significant differences in the incidence of hypertension and Type II diabetes mellitus were noted among the ART regimens. A Pearson Chi-square test showed considerable variation in hypertension incidence, with 56% of patients developing this condition, particularly among those on specific ART combinations. Diabetes incidence also varied, with the highest observed in patients on the EFV + FTC + TDF combination (31.0%) and the lowest in those on the 3TC + EFV + TDF regimen (5.3%).

These findings highlight the need for personalized approaches in ART management, emphasizing careful consideration of potential adverse effects on cardiovascular and metabolic health.

Predictors of Hypertension and Type II Diabetes Mellitus Outcomes

Logistic regression analyses to identify the predictors of hypertension and T2DM among HIV-positive patients on HAART were conducted. It was discovered that both conditions are influenced by multiple factors, emphasizing the complex interplay of genetics, lifestyle, and medication in disease development.

For T2DM, significant predictors included the type of ART, age, and smoking habits. Specifically, patients on the EFV + FTC + TDF + FDC regimen were four times more likely to develop diabetes compared to those on the 3TC + ABC + LPV regimen. Conversely, the risk was lower for those on the 3TC + EFV + TDF regimen. Age also played a role, with those in the 46–55-year age group showing a reduced risk compared to younger adults. Furthermore, smokers had a 26.7% higher risk of developing diabetes.

In terms of hypertension, the analysis revealed that the type of ART regimen, biological sex, family history, and smoking status were significant contributors. Males and smokers had higher odds of developing hypertension, as did individuals with a family history of the condition.

Despite the robust predictive power regarding case classification, the models explained a modest proportion of the outcome variability for both T2DM and hypertension. This suggests that additional unmeasured factors such as dietary patterns, lipid profiles, and individual genetic variances may also significantly impact disease development in this population.

Discussion

Prevalence of Hypertension

The prevalence of hypertension in this study was 33.8% by diagnosis and 56% when assessed by cutoff readings, with men showing a significantly higher prevalence (64%) than women (49%) [χ^2 = 49.238, df = 1; p < 0.001]. This gender disparity could be attributed to the protective effects of female sex hormones, which influence renal hemodynamics and sodium re-absorption [30]. The prevalence also varied significantly across age groups, with the highest (57.2%) in the 18–45-year bracket [χ^2 = 11.194, df = 2; p = 0.004], suggesting an association between hypertension and younger adults, similar to findings in Cameroon and Uganda [31]–[33]. Key predictors of hypertension in this cohort included ART regimen, family history, and smoking.

Prevalence of Type II Diabetes Mellitus

This study reported a Type II diabetes mellitus prevalence of 14.9% by diagnosis and 24.4% by cutoff readings, higher than in similar settings [34], [35]. Factors such as study duration, methodological approaches, and HAART regimen duration likely influenced these disparities, with longer exposure periods possibly increasing diabetes risks [36]. Demographically, no significant gender differences were observed, but age was a determinant, with a peak prevalence of 27.5% in individuals over 55, aligning with observations in a London study [36].

Incidence Rate of Hypertension

The incidence rate of hypertension among the cohort was 85.7 cases per 1000 person-years, which is lower than rates reported in Cameroon, Uganda, Tanzania, and by a European and Australian study [37]–[40]. Contrarily, a South African survey suggested that ART might reduce hypertension risk, highlighting the varied effects of ART on hypertension globally [41].

Incidence Rate of Type II Diabetes Mellitus

The incidence of Type II diabetes mellitus in this study was notably higher at 37.4 cases per 1000 person-years compared to other studies, which reported lower rates [42], [43]. This difference could be attributed to the larger sample size and longer follow-up in this study, as well as differences in ART regimens and cultural practices.

HAART Combinations Associated with Hypertension

This finding aligns with research by Pangmekeh et al., which associated Tenofovir and Lamivudine use with hypertension, suggesting a role for these NRTIs in its development [15]. Furthermore, a meta-analysis by Fahme and their colleagues indicated that Protease Inhibitors may induce hypertension through inflammatory pathways and increased ROS production [44]. Specifically, Ritonavir-boosted Lopinavir has been linked to activating the adipocyte renin-angiotensin system, contributing to hypertension, and Efavirenz to renal dysfunction and hypertension [45], [46].

However, Byonanebye with their colleagues reported no direct link between PIs and hypertension, though they observed a higher hypertension prevalence with INSTIs, possibly due to weight gain and oxidative stress [40]. Contrasting these findings, other studies, including a cohort in Cameroon and the D.A.D study, found no consistent HAART-hypertension relationship when accounting for confounders [37], [47].

HAART Combinations Associated with Type II Diabetes Mellitus

Contrary to some studies that found no link between Type II diabetes and specific ART regimens, this research identified significant associations, particularly with regimens combining two NRTIs with an NNRTI or INSTI [48]. This finding is supported by studies linking PI/NRTI treatments to increased diabetes risks, possibly due to mitochondrial toxicity affecting insulin metabolism and lipid profiles [49]–[52]. However, a systematic review highlighted the lack of consistent associations, possibly due to study heterogeneity and methodological limitations [48].

Conclusion

This study has demonstrated that HIV-positive clients on HAART exhibit a high prevalence and incidence of hypertension and Type II diabetes mellitus. Notably, the male gender is more susceptible to developing hypertension compared to females. Additionally, aging is strongly correlated with an increased risk of developing both hypertension and Type II diabetes mellitus. Specific HAART combinations, particularly those involving two NRTI classes combined with either a PI or an INSTI, were linked to a higher incidence of hypertension. Conversely, regimens combining two NRTIs with an NNRTI or an INSTI, as well as triple therapy involving NRTI, NNRTI, and INSTI, showed a higher association with Type II diabetes mellitus among treated clients.

Strengths

The robustness of this study is attributed to its use of routinely collected clinical data from diverse cohorts within a comprehensive national database. This database facilitated multiple readings per case, ensuring that reported prevalences and incidences reflect true diagnoses. The substantial number of case files reviewed significantly enhanced the statistical power of the findings. Moreover, the lengthy study period of 15 years, accounting for 13,519 person-years of follow-up, further bolstered the statistical validity.

Recommendations

Given the observed high incidence of hypertension among individuals on HAART, it is imperative to consider them for lifestyle modifications, nutritional counseling, and enhanced blood pressure monitoring. Additionally, there is a pressing need for targeted screening and preventive programs, including NCDs and cardiovascular risk assessments, to be integrated into the HAART initiation process for HIV-positive clients, given the not only prevalent hypertension but also the significant incidence of Type II diabetes mellitus observed.

Further research should focus on individual drug comparisons, especially NRTIs, PIs, and particularly INSTIs, as they are becoming the preferred treatment options. These studies should be conducted in larger cohorts within the Zambian context. Moreover, there is a necessity to explore additional factors influencing diabetes outcomes, such as dietary patterns, lipid profiles, and individual variances in pathophysiological changes due to disease progression, which were not examined in this study.

Study Limitations

This study offers significant insights but also has limitations affecting its validity and generalizability. Using secondary data from the SMART CARE database may introduce biases from data entry errors and gaps, potentially skewing results. The absence of randomization, characteristic of retrospective studies, may lead to selection bias, and the sample may not represent the broader HIV-positive population. Additionally, uncontrolled confounding variables such as genetic factors, socioeconomic status, and lifestyle choices could obscure the true effects of HAART on hypertension and diabetes. The retrospective design limits causal inferences, and changes in healthcare protocols over the study’s 15-year span may affect data consistency. The manual data handling and variable follow-up times could further compromise data integrity.

References

  1. UNAIDS. UNAIDS data 2019. UNAIDS Joint United Nations Programme on HIV/AIDS. 2019. Available from: https://www.unaids.org/sites/default/files/media_asset/2019-UNAIDS-data_en.pdf. [accessed 30.12.2022].
     Google Scholar
  2. International Diabetes Federation. IDF Diabetes ATlas. 9th ed. Brussels, Belgium: International Diabetes Federation; 2022. https://www.diabetesatlas.org/upload/resources/material/20200302_133351_IDFATLAS9e-final-web.pdf. [accessed 20.12.2022].
     Google Scholar
  3. World Health OrganizationRegionalOffice for Africa, 2018.WHO Country Cooperation Strategy 2017–2021. Zambia: CCS Zambia; 2017. https://www.afro.who.int/sites/default/files/2017-10/WHO%20ZAMBIA%20CCS%202017_2021%20printed.pdf. [accessed 21.12.2022].
     Google Scholar
  4. World Health Organization. Global Status Report on Noncommunicable Diseases 2014. Geneva: World Health Organization; 2014. https://iris.who.int/bitstream/handle/10665/148114/9789241564854_eng.pdf?sequence=1. [accessed 21.12.2022].
     Google Scholar
  5. Lungu G, Tsarkov A, Petlovanyi P, Phiri C, Musonda NC, Hamakala D, et al. Health-seeking behaviors and associated factors in individuals with substance use disorders at Chainama Hills college hospital, Lusaka, Zambia. World J Adv Res Rev. 2023;17(3):480–99. doi: 10.30574/wjarr.2023.17.3.0424.
     Google Scholar
  6. Moonga VJ, Tsarkov A, Petlovanyi P. A descriptive study on the factors influencing readmission of mentally Ill adults at Chainama Hills College Hospital, Lusaka, Zambia. Eur J Med Health Sci. 2023;5(3):51–9. doi: 10.24018/ejmed.2023.5.3.1721.
     Google Scholar
  7. Tsarkov A, Petlovanyi P, Paul R, Prashar L. Modern approach to the treatment of Parkinson’s disease: the role of pramipexole in the correction of motor and non-motor disorders. Br JMed Health Res (BJMHR). 2017;4(2):63–71.
     Google Scholar
  8. Tsarkov A, Petlovanyi P. Use of pramipexole in neuropsychiatry. World J Adv Res Rev. 2020;7(2):82–8. doi: 10.30574/wjarr.2020.7.2.0283.
     Google Scholar
  9. Tsarkov A, Msoni P, Petlovanyi P. Induced delusional disorder: a case report. Br J Med Health Res. 2018;5(6):12–22.
     Google Scholar
  10. Kumar JS, Paul R, Tsarkov A, Zyambo C. The Prevalence of Alcohol Use among Pregnant Women Attending Antenatal Clinic at Mother and New Born Hospital-University Teaching Hospital, Lusaka, Zambia, vol. 9. Zambia: EC Psychology and Psychiatry; 2020. pp. 87–111.
     Google Scholar
  11. Pandu MH, Tsarkov A, Petlovanyi P, Paul R. Optimization of early diagnosis of glucose metabolism impairment for patients receiving antipsychotic medications at the outpatient psychiatric clinic of the University Teaching Hospital, Lusaka, Zambia. Eur J Med Health Sci. 2022;4(4):75–83. doi: 10.24018/ejmed.2022.4.4.1410.
     Google Scholar
  12. Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, et al. Global disparities of hypertension prevalence and control: a systematic analysis of population-based studies from 90 countries. Circulation. 2016;134(6):441–50. doi: 10.1161/CIRCULATIONAHA. 115.018912.
     Google Scholar
  13. Goma FM, Mwewa B, Tembo GK, Kachamba M, Syatalimi C, Simweemba C, et al.Maymeasurement month 2017: blood pressure screening results from Zambia—sub-Saharan Africa. Eur Heart J Suppl. 2019;21(D):D130–2. doi: 10.1093/eurheartj/suz077.
     Google Scholar
  14. Central Statistical Office. Zambia in Figures 2018. 2018. Available from: https://acazambia.org/wp-content/uploads/2019/09/Zambiain-Figure-2018.pdf. [accessed 24.12.2022].
     Google Scholar
  15. Pangmekeh PJ, Awolu MM, Gustave S, Gladys T, Cumber SN. Association between highly active antiretroviral therapy (HAART) and hypertension in persons living with HIV/AIDS at the Bamenda regional hospital, Cameroon. Pan AfrMed J. 2019;33(1):1–11. doi: 10.11604/pamj.2019.33.87.15574.
     Google Scholar
  16. FeinsteinMJ, Hsue PY, Benjamin LA, Bloomfield GS, Currier JS, Freiberg MS, et al. Characteristics, prevention, and management of cardiovascular disease in people living with HIV: a scientific statement from the American Heart Association. Circulation. 2019;140(2):e98–124. doi: 10.1161/CIR.0000000000000695.
     Google Scholar
  17. Kalaluka PK, Tsarkov A, Petlovanyi P, Kunda R, Himalowa S, Bwembya P, et al. Dietary patterns and metabolic syndrome risk in adults living with HIV: a cross-sectional study in Lusaka district, Zambia. Eur J Med Health Sci. 2024;6(1):17–24. doi: 10.24018/ejmed.2024.6.1.2032.
     Google Scholar
  18. Monroe AK, Glesby MJ, Brown TT. Diagnosing and managing diabetes in HIV-infected patients: current concepts. Clin Infect Dis. 2015;60(3):453–62. doi: 10.1093/cid/ciu779.
     Google Scholar
  19. American Diabetes Association. Standards of medical care in diabetes—2014. Diabetes Care. 2014;37(1):S14–80. doi: 10.2337/dc14-S014.
     Google Scholar
  20. Hadigan C, Kattakuzhy S. Diabetes mellitus type 2 and abnormal glucose metabolism in the setting of human immunodeficiency virus. Endocrinol Metab Clin. 2014;43(3):685–96. doi: 10.1016/j.ecl.2014.05.003.
     Google Scholar
  21. Althoff KN, McGinnis KA, Wyatt CM, Freiberg MS, Gilbert C, Oursler KK, et al. Comparison of risk and age at diagnosis of myocardial infarction, end-stage renal disease, and non-AIDSdefining cancer in HIV-infected versus uninfected adults. Clin Infect Dis. 2015;60(4):627–38. doi: 10.1093/cid/ciu869.
     Google Scholar
  22. Atun R, Davies JI, Gale EA, Bärnighausen T, Beran D, Kengne AP, et al. Diabetes in sub-Saharan Africa: from clinical care to health policy. Lancet Diabetes Endocrinol. 2017;5(8):622–67. doi: 10.1016/S2213-8587(17)30181-X.
     Google Scholar
  23. Remais JV, Zeng G, Li G, Tian L, Engelgau MM. Convergence of non-communicable and infectious diseases in low-and middle-income countries. Int J Epidemiol. 2013;42(1):221–7. doi: 10.1093/ije/dys135.
     Google Scholar
  24. Aparecida Silveira E, Falco MO, Santos AS, Noll M, de Oliveira C. Nutritional intervention reduces dyslipidemia, fasting glucose and blood pressure in people living with hiv/aids in antiretroviral therapy: a randomized clinical trial comparing two nutritional interventions. Nutrients. 2020;12(10):2970. doi: 10.3390/nu12102970.
     Google Scholar
  25. Guaraldi G, Orlando G, Zona S, Menozzi M, Carli F, Garlassi E, et al. Premature age-related comorbidities among HIV-infected persons compared with the general population. Clin Infect Dis. 2011;53(11):1120–6. doi: 10.1093/cid/cir627.
     Google Scholar
  26. Günthard HF, Saag MS, Benson CA, Del Rio C, Eron JJ, Gallant JE, et al. Antiretroviral drugs for treatment and prevention of HIV infection in adults: 2016 recommendations of the International Antiviral Society-USA panel. JAMA. 2016;316(2):191–210. doi: 10.1001/jama.2016.8900.
     Google Scholar
  27. Saag MS, Benson CA, Gandhi RT, Hoy JF, Landovitz RJ, Mugavero MJ, et al. Antiretroviral drugs for treatment and prevention of HIV infection in adults: 2018 recommendations of the International Antiviral Society-USA Panel. JAMA. 2018;320(4):379–96. doi: 10.1001/jama.2018.8431.
     Google Scholar
  28. Kabwe L, Lakhi S, Kalinichenko S, Mulenga L. Prevalence of subclinical cardiovascular disease in healthy HIV infected patients at the University Teaching Hospital in Lusaka, Zambia. Med J Zambia. 2016;43(1):12–23.
     Google Scholar
  29. Ford G. Prevalence vs. Incidence: what is the difference. Students 4 best evidence. 2020. Available from: https://s4be.cochrane.org/blog/2020/11/06/prevalence-vs-incidence-what-is-the-difference/.
     Google Scholar
  30. Hejazi N, Huang MS, Lin KG, Choong LC. Hypertension among HIV-infected adults receiving highly active antiretroviral therapy (HAART) in Malaysia. Glob J Health Sci. 2014;6(2):58–71. doi: 10.5539/gjhs.v6n2p58.
     Google Scholar
  31. Dimala CA, Atashili J, Mbuagbaw JC, Wilfred A, Monekosso GL. Prevalence of hypertension in HIV/AIDS patients on highly active antiretroviral therapy (HAART) compared with HAARTnaïve patients at the Limbe Regional Hospital, Cameroon. PloS One. 2016;11(2):e0148100. doi: 10.1371/journal.pone.0148100.
     Google Scholar
  32. Korem M, Wallach T, Bursztyn M, Maayan S, Olshtain-Pops K. High prevalence of hypertension in Ethiopian and non-Ethiopian HIV-infected adults. Int J Hypertens. 2018;2018:8637101. doi: 10.1155/2018/8637101.
     Google Scholar
  33. Lubega G, Mayanja B, Lutaakome J, Abaasa A, Thomson R, Lindan C. Prevalence and factors associated with hypertension among people living with HIV/AIDS on antiretroviral therapy in Uganda. Pan Afr Med J. 2021;38(216):1–10. doi: 10.11604/pamj.2021.38.216.28034.
     Google Scholar
  34. Divala OH, Amberbir A, Ismail Z, Beyene T, Garone D, Pfaff C, et al. The burden of hypertension, diabetes mellitus, and cardiovascular risk factors among adult Malawians in HIV care: consequences for integrated services. BMC Public Health. 2016;16:1–11. doi: 10.1186/s12889-016-3916-x.
     Google Scholar
  35. Duguma F, Gebisa W, Mamo A, Tamiru D, Woyesa S. Diabetes mellitus and associated factors among adult HIV patients on highly active anti-retroviral treatment. HIV/AIDS-Res Palliat Care. 2020;12:657–65. doi: 10.2147/HIV.S279732.
     Google Scholar
  36. Duncan AD, Goff LM, Peters BS. Type 2 diabetes prevalence and its risk factors in HIV: a cross-sectional study. PloS One. 2018;13(3):e0194199. doi: 10.1371/journal.pone.0194199.
     Google Scholar
  37. Yoah TA, Nicholas T, Nkem NE, Nji KE, Akonwei NS, Peter NF, et al. Incidence and associated risk factors of hypertension among HIV patients on antiretroviral therapy in fako division: a 5-years retrospective cohort. Int Arch Public Health Commun Med. 2021;5(67):1–10. doi: 10.23937/2643-4512/1710067.
     Google Scholar
  38. Okello S, Kanyesigye M, Muyindike WR, Annex BH, Hunt PW, Haneuse S, et al. Incidence and predictors of hypertension in adults with HIV-initiating antiretroviral therapy in south-western Uganda. J Hypertens. 2015;33(10):2039–45. doi: 10.1097/HJH.0000000000000657.
     Google Scholar
  39. Rodríguez-Arbolí E, Mwamelo K, Kalinjuma AV, Furrer H, Hatz C, Tanner M, et al. Incidence and risk factors for hypertension among HIV patients in rural Tanzania-A prospective cohort study. PloS One. 2017;12(3):e0172089. doi: 10.1371/journal. pone.0172089.
     Google Scholar
  40. Byonanebye DM, Polizzotto MN, Neesgaard B, Sarcletti M, Matulionyte R, Braun DL, et al. Incidence of hypertension in people with HIV who are treated with integrase inhibitors versus other antiretroviral regimens in the RESPOND cohort consortium. HIV Med. 2022;23(8):895–910. doi: 10.1111/hiv.13273.
     Google Scholar
  41. Malaza A, Mossong J, Bärnighausen T, Viljoen J, Newell ML. Population-based CD4 counts in a rural area in South Africa with high HIV prevalence and high antiretroviral treatment coverage. PloS One. 2013;8(7):e70126. doi: 10.1371/journal.pone.0070126.
     Google Scholar
  42. De Wit S, Sabin CA, Weber R, Worm SW, Reiss P, Cazanave C, et al. Incidence and risk factors for new-onset diabetes in HIV-infected patients: the data collection on adverse events of anti-HIV drugs (D: A: D) study. Diabetes Care. 2008;31(6):1224–9. doi: 10.2337/dc07-2013.
     Google Scholar
  43. Nansseu JR, Bigna JJ, Kaze AD, Noubiap JJ. Incidence and risk factors for prediabetes and diabetes mellitus among HIV-infected adults on antiretroviral therapy: a systematic review and meta-analysis. Epidemiology. 2018;29(3):431–41. doi: 10.1097/EDE.0000000000000815.
     Google Scholar
  44. Fahme SA, Bloomfield GS, Peck R. Hypertension in HIV-infected adults: novel pathophysiologic mechanisms. Hypertension. 2018;72(1):44–55. doi: 10.1161/HYPERTENSIONAHA. 118.10893.
     Google Scholar
  45. Fan H, Guo F, Hsieh E, Chen WT, Lv W, Han Y, et al. Incidence of hypertension among persons living with HIV in China: a multicenter cohort study. BMC Public Health. 2020;20:834. doi: 10.1186/s12889-020-08586-9.
     Google Scholar
  46. Magande PN, Chirundu D, Gombe NT, Mungati M, Tshimanga M. Determinants of uncontrolled hypertension among clients on anti-retroviral therapy in Kadoma City, Zimbabwe, 2016. Clin Hypertens. 2017;23(14):1–7. doi: 10.1186/s40885-017-0070-4.
     Google Scholar
  47. Hatleberg CI, Ryom L, d’Arminio Monforte A, Fontas E, Reiss P, Kirk O, et al. Association between exposure to antiretroviral drugs and the incidence of hypertension in HIV-positive persons: the data collection on adverse events of anti-HIV drugs (D: A: D) study. HIV Med. 2018;19(9):605–18. doi: 10.1111/hiv.12639.
     Google Scholar
  48. Prioreschi A, Munthali RJ, Soepnel L, Goldstein JA, Micklesfield LK, Aronoff DM, et al. Incidence and prevalence of type 2 diabetes mellitus with HIV infection in Africa: a systematic review and meta-analysis. BMJ Open. 2017;7(3):e013953. doi: 10.1136/bmjopen-2016-013953.
     Google Scholar
  49. Ledergerber B, Furrer H, Rickenbach M, Lehmann R, Elzi L, Hirschel B, et al. Factors associated with the incidence of type 2 diabetes mellitus in HIV-infected participants in the Swiss HIV cohort study. Clin Infect Dis. 2007;45(1):111–9. doi: 10.1086/518619.
     Google Scholar
  50. Brown TT, Li X, Cole SR, Kingsley LA, Palella FJ, Riddler SA, et al. Cumulative exposure to nucleoside analogue reverse transcriptase inhibitors is associated with insulin resistance markers in the Multicenter AIDS Cohort Study. AIDS. 2005;19(13):1375–83. doi: 10.1097/01.aids.0000181011.62385.91.
     Google Scholar
  51. Eggleton JS, Nagalli S. Highly active antiretroviral therapy (HAART). StatPearls. 2023. Available from: https://www.ncbi.nlm.nih.gov/books/NBK554533/.
     Google Scholar
  52. Kakuda TN. Pharmacology of nucleoside and nucleotide reverse transcriptase inhibitor-induced mitochondrial toxicity. Clin Ther. 2000;22(6):685–708. doi: 10.1016/S0149-2918(00)90004-3.
     Google Scholar