Characterization and risk stratification of coronary artery disease in people living with HIV: a global systematic review

Abstract

Background:

Coronary artery disease (CAD) is a leading cause of mortality among people living with HIV (PLWH). Risk stratification remains inconsistent due to geographic disparities, ART-related metabolic effects, and overreliance on strength of association. This review synthesizes global evidence to classify CAD risk factors in PLWH, aiming to improve predictive models and preventative strategies.

Methods:

Following the PRISMA 2020 guidelines, a systematic review was conducted across six databases: PubMed, Scopus, Web of Science, Medline, CINAHL, and African Journals (SABINET). Two independent reviewers screened studies and extracted data. Narrative synthesis and meta-analysis were conducted. Risk factors were classified using Rw, causality index (CI), and public health priority (PHP).

Findings:

Twenty-two studies involving 103,370 participants were included. First-class risk factors (CI: 7–10) included hypertension (OR: 4.9; p < 0.05; Rw: 4.5), advanced age (≥50 years) (OR: 4.96, p < 0.05, Rw: 3.58), dyslipidemia (OR: 2.15, p < 0.04, Rw: 2.15), and overweight/obesity (OR: 1.81, p < 0.05, Rw: 1.36). Second-class risk factors (CI: 5–6) included family history of CVD (OR: 3.25, p < 0.05; Rw: 2. 24). Third-class risk factors (CI ≤4) included diabetes (OR: 2.64, p < 0.05, Rw: 1.32), antiretroviral therapy exposure (OR: 1.68, p < 0.05, Rw: 0.63), and homosexuality (OR: 1.82, p < 0.05, Rw: 0.62). Critical thresholds (cumulative Rw: 14.8 and 8.0) were set at 75th and 50th percentiles of cumulative Rw. At GTT value of 0.50, the parsimonious global clinical prediction model for HIV-related CAD included age, hypertension, dyslipidemia, family history of CVD, diabetes, and overweight/obesity (Rw: 15.5, GTT: 4.05). For primary prevention, the optimal model comprised hypertension, dyslipidemia, and obesity (Rw: 8.01, GTT: 2.07). Advanced age and hypertension were “necessary causes” of CAD among PLWH.

Conclusion:

Association strength alone cannot determine CAD risk. Cumulative risk indexing and responsiveness provide a robust framework. Prevention should prioritize hypertension and dyslipidemia management, with interventions for obesity, smoking, and virological failure. Age and hypertension should prompt cardiovascular screening. Standardized risk definitions, accounting for the role of protective factors and integrating evidence with domain knowledge are vital for improved CAD risk stratification and prediction in PLWH. Routine cardiovascular screening in HIV care remains essential.

Systematic Review Registration:

https://www.crd.york.ac.uk/PROSPERO/view/CRD42024524494, PROSPERO CRD42024524494.

1 Introduction

Antiretroviral therapy (ART) has transformed human immunodeficiency virus (HIV) from a fatal disease to a chronic condition, markedly improving life expectancy (1). This shift has increased the burden of non-communicable diseases, including cardiovascular disease (CVD), among people living with HIV (PLWH) (2). Coronary artery disease (CAD) is a leading cause of morbidity and mortality in PLWH, with higher incidence rates than in HIV-negative individuals (3). Despite extensive research, gaps persist in our understanding of CAD-specific risk factors, their causal interactions, and geographic variability.

CAD in PLWH arises from a complex interplay between traditional cardiovascular risk factors and HIV-specific mechanisms. Traditional risk factors, such as hypertension, diabetes, dyslipidemia, smoking, obesity, and physical inactivity, contribute to atherosclerosis and cardiovascular complications in both HIV-positive and HIV-negative populations. However, PLWH also experience chronic systemic inflammation, immune activation, ART-induced metabolic disturbances (e.g., dyslipidemia and insulin resistance), and direct viral effects on vascular endothelial function (4, 5). While some studies emphasize immune dysregulation in CAD pathogenesis, others highlight ART-related metabolic disturbances, particularly prolonged exposure (6, 7). These divergent findings indicate the need for a comprehensive synthesis of the traditional and HIV-specific risk factors across diverse populations.

Most research has been conducted in high-income settings, despite low- and middle-income countries (LMICs) having the highest HIV burden (8, 9). Geographic disparities in ART regimens, healthcare access, and socio-economic conditions influence CAD risk profiles (10, 11). In high-income countries, research often centers on ART-induced metabolic changes, whereas studies from LMICs highlight untreated hypertension, lifestyle-related factors, and limited access to health care (12, 13). Additionally, methodological inconsistencies, such as variation in cohort characteristics, statistical modeling, and CAD endpoint definitions, complicate direct comparisons across studies (14, 15). Addressing these limitations requires a global, systematic synthesis using structured epidemiological frameworks to enhance CAD risk stratification in PLWH.

This review applied four complementary epidemiological frameworks to provide a structured, theory-informed evaluation of CAD risk factors in PLWH. The epidemiological triangle contextualizes disease as an interaction between the agent (HIV and ART), host (genetics, comorbidities), and environment (socioeconomic determinants, healthcare access) (16). Bradford Hill's criteria assess the strength and consistency of causal associations (17), while Rothman's causal pie model identifies necessary, component, and sufficient causes also known as primary risk factors, contributory risk factors and causal path, respectively (18). Nweke's cumulative risk index refines traditional risk stratification approaches by incorporating predictive consistency, irreversibility, and temporality (19).

While each model has limitations, such as the subjectivity of Bradford Hill's criteria (17) and Rothman's limited incorporation of social determinants, integrating them offers a robust framework for stratifying CAD risk in PLWH.

Through the systematic classification of CAD risk factors based on predictive strength and causal relevance, this review aims to improve risk prediction and guide resource allocation, particularly in LMICs. A structured risk stratification approach enables clinicians and public health professionals to identify causal pathways and optimize preventive strategies. To guide this review, we posed the following research question: What are the key cardiovascular, HIV-related, intrinsic, and extrinsic risk factors for CAD in PLWH, and how can epidemiological models be applied to classify and stratify these risks for improved prediction and prevention?

2 Methods

2.1 Study design

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure methodological rigor, transparency, and reproducibility (20). The review was registered with PROSPERO (CRD42024524494).

2.2 Conceptual and theoretical underpinning

To address the complexity of CAD risk among PLWH, we applied four theoretical frameworks: epidemiological triangle, Bradford Hill's criteria, Rothman's causal pie model, and Nweke's cumulative risk index. These models were selected for their strengths in capturing multifactorial disease causation and addressing the limitations of individual theories. The specific applications and limitations are detailed in Supplementary File 1.

2.3 PECOT criteria

We used the Population, Exposure, Comparison, Outcome, and Timeframe (PECOT) framework to define our inclusion criteria.

Population (P): PLWH of any age, sex, or geographic location, with or without CAD.

2.4 Inclusion and exclusion criteria

2.4.1 Inclusion criteria

Population: PLWH with or without CAD; all ages, sexes, and geographical locations.

Study design: Cohort studies (prospective and retrospective), case-control studies, meta-analyses and systematic reviews that provided pooled risk estimates, and nested cross-sectional studies.

Exposure and risk factors: Studies assessing traditional cardiovascular risk factors (hypertension, diabetes, dyslipidemia, obesity, smoking, and physical inactivity). Studies examining HIV-related factors (ART use, HIV duration, immune function [CD4 count, viral load], and inflammation. Studies analyzing sociodemographic and environmental influences (income, education, healthcare access, and air pollution).

Outcomes: Studies reporting clinically confirmed CAD (acute myocardial infarction, angina, ischemic heart disease, or atherosclerosis). Studies that include risk estimates (odds ratio, hazard ratio, or relative risk) or data are sufficient to compute risk estimates.

Timeframe: Studies published up to January 2024.

Language: Irrespective of the language. Articles published in languages other than English were translated using Google Translate.

2.4.2 Exclusion criteria

Study design: Simple cross-sectional studies, qualitative studies, editorials, case reports, conference abstracts, and commentaries.

Population: Studies that combined HIV-positive and HIV-negative populations and did not separately report CAD risk for PLWH.

Outcomes: Studies reporting only the prevalence or frequency of CAD risk factors did not include a measure of association (e.g., odds ratio or hazard ratio). Studies that assessed general CVD outcomes but presented no specific report on CAD.

Bias and data quality: Studies with a high risk of bias [assessed using the Joanna Briggs Institute (JBI) risk-of-bias tool]. Studies with incomplete or non-reproducible data.

2.5 Outcome definitions and measurements

2.5.1 Primary outcomes

The primary outcomes included the risk factors associated with CAD in PLWH. The risk factors were categorized into four domains.

HIV-specific risk factors: HIV duration, ART type and duration of ART, and CD4 count and viral load.

Cardiovascular risk factors: hypertension (systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg), diabetes mellitus (fasting glucose ≥126 mg/dl or HbA1c ≥ 6.5%), dyslipidemia (LDL cholesterol ≥130 mg/dl or total cholesterol ≥200 mg/dl), obesity [body mass index (BMI) ≥ 30 kg/m²], smoking [current or past tobacco use (self-reported or biochemical validation)], and a history of cardiovascular events (e.g., acute myocardial infarction and ischemic heart disease). CAD was diagnosed on the basis of electrocardiographic abnormalities, imaging confirmed atherosclerosis, and clinically confirmed ischemic heart disease.

Sociodemographic factors: age, sex, genetic predispositions, level of education, income and employment status, healthcare accessibility, and environmental factors (e.g., diet and air pollution).

2.5.2 Secondary outcomes

From the above data, we calculated the temporality of exposure-outcome relationships (assessed whether CAD risk factors preceded disease onset (21, 22). Consistency of associations across studies (evaluated using Nweke's cumulative risk index) (21–24). CI was estimated based on Bradford Hill's criteria, Rothman's causal pie model, and an emerging hypothesis (22). Nature/stage of factors in terms of bio-behavioral status (22). We also assessed PHP (22).

2.6 Search strategy

We searched multiple electronic databases to ensure broad coverage of the relevant studies. The databases searched included PubMed/MEDLINE, SCOPUS, EMBASE, Cochrane Library, Web of Science, Cumulative Index for Nursing and Allied Health Literature (CINAHL), and African Journals Online (AJOL)/SABINET.

The reference lists of the included studies and relevant systematic reviews were manually searched for additional articles. Gray literature sources, including conference proceedings and preprint repositories, were considered to minimize publication bias.

This search strategy was developed in collaboration with an experienced information specialist. The search terms were based on Medical Subject Headings (MeSH) and free-text keywords. The search strategy was initially tested in PubMed and adapted for each database using appropriate Boolean operators (AND, OR, NOT) and truncation symbols, where applicable.

2.7 Study selection

Search results were exported to EndNote 20 for deduplication and reference management. Two independent reviewers screened the titles and abstracts by considering the predefined inclusion and exclusion criteria. If the studies were selected for full-text screening, full texts were retrieved and assessed for inclusion. Two independent reviewers conducted the selection process and resolved any disagreements through discussion or consultation with a third reviewer.

Reasons for exclusion were systematically documented. The selection process was represented using the PRISMA flow diagram (Figure 1).

Figure 1

2.8 Data extraction

Data were extracted using a standardized data extraction template to capture study characteristics, exposure variables, and outcome measures. Two reviewers independently extracted the data, and discrepancies were resolved through discussion or third-party arbitration.

2.9 Quality control and data validation

Quality control measures were applied throughout the extraction process to minimize bias and enhance data reliability. The extracted data were crosschecked to ensure consistency and accuracy. When multiple studies reported overlapping populations, they were carefully evaluated to ensure nonredundant data extraction. This structured data extraction approach ensures data integrity, comparability, and reproducibility in accordance with PRISMA recommendations.

2.10 Risk of bias assessment

The methodological quality of the included studies was assessed using the JBI risk-of-bias assessment tool for case-control and cohort studies (25). This tool systematically evaluates the study design, sampling, measurement, and analytical approaches to determine the potential risk of bias in each study. Two independent reviewers conducted assessments to ensure reliability and minimize subjectivity. Discrepancies were resolved by discussion or consultation with a third reviewer. Based on the JBI criteria, each study was classified as having a low, moderate, or high risk of bias (Supplementary File 3).

Studies with a high risk of bias were interpreted with caution and considered for sensitivity analysis. Studies with critical methodological flaws that could not be adjusted for were excluded from the final analysis.

2.11 Data analysis

We present the study characteristics using an evidence table (Table 1). The strength of association between risk each factor and CAD in PLWH was quantified using odds ratios (ORs), relative risks (RRs), and hazard ratios (HRs). These effect sizes were extracted from the included studies to assess the magnitude of the CAD risk associated with different exposures. Where possible, 95% confidence intervals (CIs) were used to estimate the precision of each measure. To ensure consistent interpretation of the risk estimates, OR/RR/HR >1.0 indicated a positive association between exposure and CAD risk; OR/RR/HR <1.0 suggested a protective effect, and OR/RR/HR = 1.0, implying no association between exposure and CAD.

To evaluate the clinical and epidemiological relevance of the findings, the thresholds established by Chen et al. (26) were applied, where OR/RR/HR ≈ 1.0–1.5 indicated a minimal or small association, OR/RR/HR ≈ 1.5–3.0 indicated a moderate association, OR/RR/HR ≥3.0–4.0 indicated a strong association, and OR/RR/HR ≥10 indicated a very strong association.

To enhance comparability, risk estimates were transformed into standardized effect sizes where possible. Additionally, Nweke's CI was employed to integrate both statistical significance (strength of association) and predictive consistency, refining the ranking of CAD risk factors in PLWH (19). The summarized effect sizes and their confidence intervals are presented in Table 2 and categorized by risk factor type.

To assess the certainty of the synthesized findings, the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system was applied (27–30). The quality of evidence was graded using the JBI risk-of-bias tool, considering inconsistency (variability in reported effect sizes), indirectness (applicability of study findings to real-world settings), imprecision (wide confidence intervals affecting reliability), and publication bias (selective reporting or overrepresentation of positive findings). Studies that met all the five criteria were classified as high-certainty evidence, whereas those with multiple concerns were downgraded to moderate, low, or very low certainty.

Narrative synthesis was conducted using a structured stepwise approach designed to uncover and evaluate the relationships between CAD and its associated risk factors in PLWH. This approach was chosen because of the heterogeneity in study methodologies, exposure definitions, and outcome measures across the included studies. Where quantitative synthesis was feasible, the effect sizes were standardized, and the overall associations were quantified.

A combination of descriptive statistics, data visualization, predictive modeling, and advanced statistical tests was applied to achieve the research objectives. Bar charts were used to illustrate the composition of the risk classes, which showed the dominance of first- and third-class factors. Risk estimates (HR, OR, and HR) were standardized to ORs for comparison using 1/OR for OR <1 to standardize the effect direction and the confidence intervals were adjusted as follows [1/OR²] (21–24, 31). The aggregated ORs and heterogeneity (I²) were estimated using a random-effects meta-analysis model. The critical risk threshold (CRT) was identified at the 75th percentile of the cumulative Rw. Meta-analyses were conducted using R version 4.4.3 and ChatGPT-Python (version 3.x) interface, with a significance level of α = 0.05. We examined the impacts of variability in the mode of analysis (adjusted vs. unadjusted), and effect size type (OR, HR, and RR) on the distribution of significance. Furthermore, we undertook epidemiological synthesis and computed geotemporal trends per risk, risk responsiveness, predictive consistency (Rw), CI, and PHP per factor. Details of the epidemiological synthesis are provided in Supplementary File 2.

3 Results

3.1 Study selection

A total of 17,074 articles were retrieved, and 210 duplicates were removed. The remaining 16,864 articles were screened by title and abstract; 16,817 ineligible articles were excluded. Full-text screening was performed in 47 studies, of which 22 met the inclusion criteria (Figure 1). Fifteen risk factors, reported in at least two studies, were included in the narrative synthesis. Eleven risk factors, reported in three or more studies, were eligible for meta-analysis.

3.2 Study characteristics

The 22 studies (11 cohort, seven case-control, and four nested cross-sectional studies) included 103,370 participants. The studies were conducted across six continents: Europe (n = 7), North America (n = 6), Australia (n = 4), South America (n = 2), Asia (n = 1), and Africa (n = 1). One study (32) included participants from Europe, North America, and Australia. Five studies (33–37) were conducted in the United States, four studies (38–41) were conducted in Australia, and three studies (42–44) were conducted in Switzerland (Table 1).

3.3 Risk of bias

Fourteen studies (64%) were rated as having a low risk of bias; eight (36%) as moderate. All the studies recruited comparable exposed and unexposed groups. However, variations in the measurement tools, follow-up duration, and reporting completeness were noted (Supplementary File 3).

3.4 Risk factor associations

3.4.1 Age, education, and race/ethnicity

Eleven studies examined the association between age and CAD. Of these, eight studies reported significant associations between age and CAD. Five were cohort studies (32, 36, 42, 50, 52), one was a case–control study (43), and two were nested cross-sectional studies (48, 53). Two studies reported non-significant associations between education and CAD (41, 42). No significant associations were reported between race/ethnicity and CAD (33, 48) (Table 2).

3.4.2 Sex and sexual orientation

Of nine studies, five reported that sex was significantly associated with CAD (33, 36, 42, 48, 51). Three studies examined sexual orientation and CAD; one (36) showed a significant association.

3.4.3 Smoking, alcohol, intravenous drug use, and cocaine

Among 12 studies on smoking, three reported significant associations with CAD (42, 43, 48). Of the three studies on alcohol, no significant associations were reported (36, 48). Of the two cohorts examining intravenous drug use, one reported that drug use was associated with a higher risk of CAD (36). One of the two studies reported that cocaine use was associated with an increased risk of CAD (40).

3.4.4 CD4 count and viral load

Ten studies examined CD4 count, and six (32, 41, 45, 46, 47, 53) reported significant associations with CAD. Of the ten studies examining viral load, five reported significant associations with CAD (32, 38, 40, 45, 52). In two cohorts (32, 52), a higher viral load was reported to be protective against CAD (Table 2).

3.4.5 Duration of HIV and history of AIDS-defining illness

Among the five studies on HIV duration and CAD, three (40, 41, 53) reported significant associations. Of five the studies on AIDS-defining illnesses, three (33, 52, 53) reported significant associations (Table 2).

3.4.6 BMI, hypertension, and diabetes

Of eight studies on BMI, four reported significant associations with CAD (33, 38, 48, 52). Eleven studies examined the association between hypertension and CAD, but only one study did not find a significant association (43). Eight studies examined diabetes, of which three reported associations with CAD (46, 48, 52) (Table 2).

3.4.7 Dyslipidemia and lipid profile

Three studies (42, 43, 50) reported that dyslipidemia was significantly associated with CAD. Of the five studies on LDL, three (36, 41, 47) reported significant associations. Five studies examined associations with total cholesterol, and four reported significant associations (36, 44, 45, 47). Four studies reported significant associations between triglycerides and CAD (36, 40, 41, 47). Among the five studies on HDL, two (44, 45) reported significant associations (Table 2).

3.4.8 Family history of CVD

Among the seven studies investigating family history, four (32, 38, 41, 45) reported significant associations with CAD (Table 2).

3.4.9 Use of antiplatelet and lipid-lowering medications

Three studies (32, 40, 41) reported significant associations between antiplatelet use and CAD. Two studies (32, 53) reported that the use of lipid-lowering drugs was associated with an increased risk of CAD (Table 2).

3.4.10 Exposure to ART

Of the nine studies on protease inhibitors, four (32, 36, 38, 42) reported significant associations. Seven studies investigated the use of abacavir, of which five (38, 39, 42, 43, 45) reported significance. One of the two studies on NRTIs (36) and both studies on NNRTIs (32, 36) reported significant associations. Two of four studies on the duration of protease inhibitor use (32, 43) reported a significant association with increased risk of CAD (Table 2).

3.5 Meta-analysis of factors associated with CAD in PLWH

Sixteen factors met the eligibility criteria for the meta-analysis. Eleven factors [hypertension, advanced age (≥50 years), family history of CVD, dyslipidemia, diabetes, history of AIDS-defining illness, smoking, overweight/obesity, viral load, sexual orientation, and use of ART] were significantly associated with an increased risk of CAD (p < 0.05). Variations in the type of effect size (OR, HR, and RR) did not significantly affect the distribution of significance (p > 0.05). Likewise, for each of the factors, variability in the mode of analysis (adjusted vs. non-adjusted) did not impact the distribution of significance (p > 0.05). Among the significant factors, ORs ranged from 1.42 (95% CI: 1.06–8.85) for viral load to 4.96 (95% CI: 3.30–7.47) for advanced age. Of the 16 factors, meta-syntheses were associated with substantial heterogeneity (I² ≥ 50%) (Table 3).

3.6 Epidemiologic synthesis

3.6.1 Risk responsiveness and geotemporal trend

Hypertension (Ri = 0.91) and dyslipidemia (Ri = 1.00) showed the highest risk responsiveness. Age (Ri = 0.73), overweight/obesity (Ri = 0.75), and family history of CVD (Ri = 0.71) showed good risk responsiveness.

Five factors (age, hypertension, smoking, history of AIDS-defining illness, and viral load) had a broad geographical coverage (4/6 continents). Sexual orientation, obesity, diabetes, and dyslipidemia showed an average coverage (3/6 continents). Nine risk factors were consistently reported across all three periods (2000–2010, 2011–2020, and ≥2021).

Age (GTT = 1.00) and hypertension (GTT = 0.83) showed the strongest geotemporal trends. Smoking (GTT = 0.67), history of AIDS-defining illness (GTT = 0.67), viral load (GTT = 0.67), diabetes (GTT = 0.5), and a family history of CVD (GTT = 0.5) showed average or above average geotemporal coverage (Table 3).

3.6.2 Risk factor stratification, critical risk threshold and modelling CAD in PLWH

The Rw was used to classify risk factors. Hypertension (Rw = 4.5), age (Rw = 3.58), family history of CVD (Rw = 2.24), and dyslipidemia (Rw = 2.15) belonged to the high-risk group. Diabetes (Rw = 1.32) and obesity (Rw = 1.36) belonged to the middle risk group, whereas the rest belonged to the lower-risk group (Table 4, Supplementary File 2–Figure S1).

The critical risk threshold was 14.8 (75th percentile of cumulative Rw). Two “necessary causes” of CAD in PLWH emerged: advanced age (Ri = 0.73; GTT = 0.87) and hypertension (Ri = 0.91, GTT = 0.87). Component causes included any of the remaining factors, prioritized by Rw, GTT, and intervention feasibility. We assumed that HIV-related CAD is imminent in individuals whose risk factor combination equals the CRT. In addition, factors with a minimum GTT of 0.5 were preferred to improve the global utility of the model. A GTT of 0.5 signifies fair geographical (3/6 continents) and temporal coverage (2/3 periods). Clinically, the most parsimonic prediction model for HIV-related CAD includes the following factors: age, hypertension, dyslipidemia, family history of CVD, diabetes, and overweight/obesity. The primary prevention model included hypertension, dyslipidemia, and overweight/obesity (Supplementary File 2).

3.6.3 Necessary vs. synergistic “causes”

In determining the path/model for HIV-related CAD, we asked the question “in how many ways can these eleven factors combine to produce the critical risk threshold (Rw ≥ 14.8)?” Following the principle of permutation and combination, there are over 130 ways in which these eleven risk factors could interact to produce an Rw of 14.8 (Supplementary File 2). There are over 130 ways in which these 11 factors can combine to meet the CRT, and in all these combinations, the two irreplaceable factors were age and hypertension.

3.6.4 Causality index and public health priority

We further classified the 11 risk factors using CI and PHP. Similar to Rw, first-class factors included dyslipidemia, hypertension, advanced age, and obesity. In terms of PHP, hypertension, dyslipidemia, and overweight/obesity were critical contributors to HIV-related CAD, followed by diabetes, smoking, and viral load.

A mass prevention threshold of 8.0 was set, integrating both Rw and PHP to identify actionable causal paths for primary prevention. Full model details are provided in Supplementary File 2.

3.7 Certainty of evidence

The certainty of evidence was moderate to high for 10 of the 11 significant variables, suggesting that further research is unlikely to alter the conclusions. Evidence supporting viral load as a risk factor for HIV-related CAD was rated low. Three factors were upgraded owing to large effect sizes. Heterogeneity and imprecision were the primary reasons for downgrading (Table 5).

3.8 Comparison of classification systems

Figure 2

4 Discussion

This systematic review and meta-analysis synthesized evidence on the risk factors for CAD among PLWH, integrating epidemiological models to enhance risk classification and prediction. The findings identified both traditional and HIV-specific factors with varying levels of predictive strength, causal relevance, and PHP. Advanced age and hypertension emerged as consistent and necessary causes, whereas dyslipidemia, diabetes, obesity, smoking, and viral load contributed as component or synergistic factors. By applying structured metrics—such as CI, risk responsiveness, and geotemporal coverage—this study offers a refined framework for understanding CAD risk in PLWH, with implications for clinical decision-making, public health policy, and future research.

4.1 Advanced age and hypertension

Advanced age and hypertension are well–known risk factors for CAD among PLWH, with high CI values and geotemporal coverage. While previous models included these factors in CVD prediction among PLWH (58), this is the first review to isolate their specific association with CAD in this population.

Contrary to prior hypotheses suggesting a higher CAD risk at younger ages (<55 years) (55), our findings affirm that advanced age (≥50 years) significantly increases CAD risk in PLWH. The biological mechanisms, vascular aging, endothelial dysfunction, and arterial stiffness, are well documented (56). Similarly, the pathophysiological role of hypertension in atherosclerosis and its universal inclusion in CVD risk models underscores its importance (54).

Both age and hypertension received high weights in prior models (54) owing to strong geotemporal coverage, predictive consistency, and low cost of assessment. The certainty of evidence for both is strong, indicating that future research is unlikely to change their classification. Althoug age is critical for risk prediction, its non-modifiable nature limits its relevance in primary prevention.

4.2 Family history of CVD, dyslipidemia, and diabetes mellitus

A family history of CVD, dyslipidemia, and diabetes also emerged as first-class CAD risk factors. PLWH with a family history of CVD had a threefold increased risk of developing CAD, consistent with general population studies (57–60). Bachmann et al. reported a 50% increase in lifetime mortality among patients with a history of premature CVD (55).

Dyslipidemia in PLWH was associated with a twofold increased risk of CAD, aligning with previous findings in ART-treated patients (61–63). Diabetes showed a threefold increase in CAD risk, corroborating the findings of Gupta et al (64). and supporting type 2 diabetes as a CAD risk equivalent (65). Mechanistically, chronic hyperglycemia, insulin resistance, and oxidative stress drive endothelial dysfunction and atherosclerosis (66).

Despite its predictive value, family history is non-modifiable and often excluded from clinical tools (50, 67–69). Dyslipidemia and diabetes are included more consistently; however, the moderate-to-high cost of dyslipidemia testing limits its use in LMICs. Our analysis suggests that, in the absence of age and hypertension, dyslipidemia or diabetes alone may be insufficient to indicate CAD risk in PLWH.

4.3 Obesity

Obesity ranked fifth the among risk factors, with obese PLWH experiencing approximately double the risk of CAD. This finding mirrors the general-population meta-analysis of 21 cohorts (70). Obesity contributes to CAD through increased blood volume, cardiac output, heart rate, and sodium retention, all promoting hypertension (71). Visceral and pericardial fat deposition correlates with plaque formation and vulnerability (72), whereas elevated fibrinogen, PAI-1, and coagulation factors increase thrombotic risk (73). The multifactorial effects of obesity reinforce hypertension as a “necessary cause” of HIV-related CAD. The moderate certainty of this evidence suggests that future findings may refine, but not reverse this association.

4.4 Smoking

Current or past smoking doubled HIV-related CAD risk, consistent with previous studies (74, 75). Smoking promotes vascular injury via oxidative stress, cytokine release, and endothelial dysfunction (76–78). Carbon monoxide and renal dysfunction further increase cardiovascular strain (79, 80). Smoking is relatively inexpensive to assess, supporting its inclusion in most CAD prediction models (54). The moderate certainty of evidence implies potential refinement, but not invalidation, of smoking risk status.

4.5 Viral load

Viral load has emerged as a third-class CAD risk factor, a novel finding for PLWH. Viral load has a low predictive value, explaining its omission from key models (e.g., D:A:D, SMART) (54). Mechanistically, viral proteins (Tat, gp120) trigger monocyte activation and endothelial apoptosis (81, 82). Early ART and sustained suppression mitigate these effects (83, 84). A high viral load likely acts as a potentiating factor, and is not a direct cause. Given its central role in HIV management, viral load qualifies as a PHP, despite a lower PHP score. The moderate certainty of evidence suggests that this association may evolve with further research.

4.6 History of AIDS-defining illness

A history of AIDS-defining illness doubled the CAD risk, consistent with the findings of the MACS cohort study (33). Chronic inflammation, activated T cells, and upregulated procoagulants drive atherosclerosis and thrombosis (85–87). Despite its biological plausibility, its non-modifiable nature and low predictive weight limit its value as a primary prevention target.

4.7 ART exposure

ART use was associated with a 1.7-fold increased in CAD risk, albeit inconsistently, —aligning with earlier data on subclinical atherosclerosis (88). Protease inhibitors disrupt lipid metabolism, impair glucose uptake, and induce mitochondrial toxicity (89–92). As a third-class factor, ART exposure gains predictive value only when combined with first-class risk factors, explaining its exclusion from some existing models (50, 54).

4.8 Sexual orientation (homosexual)

Homosexual orientation was associated with a twofold increase in CAD risk among PLWH, reflecting broader disparities in sexual minority populations (93). Though not universally applicable, regional prevalence and behavioral profiles may enhance its contextual relevance (94–97). This association is unlikely to be overturned, although its Rw may vary by setting.

4.8.1 Antiplatelet and lipid lowering medications

One of the most important, although, accidental finding in this review is the fact that lipid lowering and antiplatelet medication constituted protective factors. Individuals on antiplatelet and lipid lowering medications were 36— and 18 —times, respectively, less likely to have CAD compare to their counterparts on no such medications. The efficacy of antiplatelet medication for secondary prevention of CAD is well-established (98). Low-dose aspirin is recommended for secondary prevention in patients with-established CAD, including those with a history of a heart attack, stroke, or angina. It works by inhibiting platelet aggregation, which helps prevent blood clots from forming in narrowed arteries. This can reduce the risk of future heart attack, stroke, and other cardiovascular events. Aspirin is often used in combination with other antiplatelet medications such as clopidogrel (98). Although effective, long-term aspirin use can increase the risk of bleeding, particularly in the stomach and intestines. Although its use in secondary prevention of CAD is justifiable, its benefits for primary prevention are a subject of debate. Similarly, the use of lipid-lowering medications (e.g., statins) for individuals at risk of CAD or established CAD is common practice (99). The goal of this practice is to significantly lower low-density lipoprotein cholesterol levels, thereby increasing the risk or progression of CAD (100). Therefore, there is a need to account for the role of protective factors, such as the use of antiplatelet and lipid-lowering medications, when modelling HV-related CAD, as this will enhance model performance by reducing the false positives.

4.9 Clinical relevance

This review confirms that Rw and CI are useful for stratifying CAD risk among PLWH (101). A CI ≥ 7 indicates high PHP risk factors that require targeted screening. PLWH older than 50 years with hypertension constitute a core surveillance group. This is especially true in the presence of additional factors such as a family history of CVD and/or dyslipidemia. Emphasizing modifiable behaviors risk factors such as hypertension, dyslipidemia, diabetes, obesity, smoking, and viral load aligns with existing prevention frameworks (102, 103).

4.10 Public health implications

A PHP threshold ≥8 supports community level interventions targeting high-CI modifiable risks (104, 105). Mass media, educational initiatives, and counseling have demonstrated efficacy in promoting behavioral change, particularly in resource-limited settings. In low- and middle-income countries (LMICs), where health systems frequently encounter infrastructural and workforce challenges, cost-effective, population-based strategies are increasingly vital. For instance, mass media campaigns can enhance awareness of cardiovascular risk factors, while programs based in schools and workplaces can encourage healthy lifestyles and facilitate early detection. Surveillance should monitor established and emerging risks such as alcohol use and ART-induced metabolic syndromes (106). Incorporating CAD risk surveillance into existing primary healthcare systems and national health surveys in LMICs could improve early detection and prevention on a large scale. Enhancing data systems and training community health workers to screen for risk factors can mitigate access to care disparities. Ultimately, translating these findings into national guidelines and policies could guide targeted resource allocation and reduce the long-term burden of coronary artery disease among vulnerable populations.

4.11 Policy implications

A CI: ≥7 threshold offers policymakers a quantitative tool for resource allocation and guideline development. Emphasizing behavioral determinants in planning, such as smoking cessation and viral-load monitoring, can curb CAD morbidity and mortality in HIV populations (107).

4.12 Recommendations

4.13 Limitations

Despite its methodological rigor, this study has some limitations. Internal metrics Rw, CI, and PHP require external validation. Inconsistent definitions of CAD may have affected the outcomes of the studies included in this review. Moreover, the strict criteria were impractical because of the limited number of eligible studies. Most factors lack representation from multiple continents, thus impacting their global applicability. Subjectivity in assessing the critical risk thresholds may affect the robustness of the model. However, a modest effort was made to account for this disparity by computing the geotemporal trends. Nevertheless, the validated framework may be applied differently to LMICs. In addition, the inability to account for protective factors such as the use of lipid-lowering and antiplatelet medication represents a limitation for which a methodological innovation is warranted. These findings should be validated in independent cohort studies.

5 Conclusion

This review identified advanced age and hypertension as primary risk factors for CAD in PLWH. Additional significant factors include dyslipidemia, obesity, diabetes, smoking, viral load, and ART exposure. The most parsimonic clinical prediction model included age, hypertension, dyslipidemia, family history of CVD, diabetes, and obesity as risk factors for HIV-related CAD. The most efficient models for primary prevention include hypertension, dyslipidemia, and obesity. These findings highlight the importance of risk stratification by integrating biological plausibility, PHP, and causality into risk assessments. The use of antiplatelet or lipid-lowering medications is protective against CAD in PLWH. Therefore, a methodological innovation that accounts for the use of antiplatelet and lipid-lowering medications in prediction models is essential to reduce false positives. Behavioral and socioeconomic factors were poorly sampled in eligible studies. Future primary studies should be guided by relevant epidemiological models to ensure exhaustive sampling of the exposure variables. Future studies should employ stricter assessment criteria to improve homogeneity and external validity.

References

• 1.

  World Health Organization. Global Health Sector Strategies on, Respectively, HIV, Viral Hepatitis and Sexually Transmitted Infections for the Period 2022–2030. Geneva: World Health Organization (2022).

  • Google Scholar

• 2.

  American Health Association. As HIV patients live longer, heart disease might be their next challenge. (2019).

  • Google Scholar

• 3.

  Triant VA Lee H Hadigan C Grinspoon SK . Increased acute myocardial infarction rates and cardiovascular risk factors among patients with human immunodeficiency virus disease. J Clin Endocrinol Metab. (2007) 92(7):2506–12. 10.1210/jc.2006-2190

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4.

  Friis-Møller N Thiebaut R Reiss P El-Sadr W Weber R D’Arminio Monforte A et al Predicting the risk of coronary heart disease (CHD) in HIV-infected patients: the D: a: d CHD risk equation. 14th Conference on Retroviruses and Opportunistic Infections (2007). Vol. 27.

  • Google Scholar

• 5.

  Shah AS Stelzle D Lee KK Beck EJ Alam S Clifford S et al Global burden of atherosclerotic cardiovascular disease in people living with HIV: systematic review and meta-analysis. Circulation. (2018) 138(11):1100–12. 10.1161/CIRCULATIONAHA.117.033369

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6.

  Hsue PY Deeks SG Hunt PW . Immunologic basis of cardiovascular disease in HIV-infected adults. J Infect Dis. (2012) 205(Suppl 3):S375–82. 10.1093/infdis/jis200

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7.

  Freiberg MS Chang CC Kuller LH Skanderson M Lowy E Kraemer KL et al HIV Infection and the risk of acute myocardial infarction. JAMA Intern Med. (2013) 173(8):614–22. 10.1001/jamainternmed.2013.3728

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 8.

  Bekker LG Alleyne G Baral S Cepeda J Daskalakis D Dowdy D et al Advancing global health and strengthening the HIV response in the era of the sustainable development goals: the international AIDS society-lancet commission. Lancet. (2018) 392(10144):312–58. 10.1016/S0140-6736(18)31070-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 9.

  Nou E Lo J Grinspoon SK . Inflammation, immune activation, and cardiovascular disease in HIV. AIDS. (2016) 30(10):1495–509. 10.1097/QAD.0000000000001109

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 10.

  Drain PK Dorward J Bender A Lillis L Marinucci F Sacks J et al Point-of-care HIV viral load testing: an essential tool for a sustainable global HIV/AIDS response. Clin Microbiol Rev. (2019) 32(3):e00097–18. 10.1128/CMR.00097-18

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11.

  Mdodo R Frazier EL Dube SR Mattson CL Sutton MY Brooks JT et al Cigarette smoking prevalence among adults with HIV compared with the general adult population in the United States: cross-sectional surveys. Ann Intern Med. (2015) 162:335–44. 10.7326/M14-0954

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12.

  Feinstein MJ Bogorodskaya M Bloomfield GS Vedanthan R Siedner MJ Kwan GF et al Cardiovascular complications of HIV in endemic countries. Curr Cardiol Rep. (2016) 18(11):113. 10.1007/s11886-016-0794-x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13.

  Triant VA Lyass A Hurley LB Borowsky LH Ehrbar RQ He W et al Cardiovascular risk estimation is suboptimal in people with HIV. J Am Heart Assoc. (2024) 13(10):e029228. 10.1161/JAHA.123.029228

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 14.

  Vachiat A McCutcheon K Tsabedze N Zachariah D Manga P . HIV And ischemic heart disease. J Am Coll Cardiol. (2017) 69(1):73–82. 10.1016/j.jacc.2016.09.979

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15.

  Mills EJ Bärnighausen T Negin J . HIV And aging—preparing for the challenges ahead. N Engl J Med. (2012) 366(14):1270–3. 10.1056/NEJMp1113643

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 16.

  Murray A Huang MJ Hardnett F Sutton MY . Strengthening HIV knowledge and awareness among undergraduate students at historically black colleges and universities. J Health Disparities Res Pract. (2014) 7(4):4. Available online at: https://oasis.library.unlv.edu/jhdrp/vol7/iss4/4

  • Google Scholar

• 17.

  Hill AB . The environment and disease: association or causation?.

  • Google Scholar

• 18.

  Rothman KJ . Causes. Am J Epidemiol. (1976) 104(6):587–92. 10.1093/oxfordjournals.aje.a112335

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 19.

  Nweke M Ukwuoma M Adiuku-Brown AC Ugwu P Nseka E . Characterization and stratification of the correlates of postpartum depression in sub-Saharan Africa: a systematic review with meta-analysis. Womens Health (Lond). (2022) 18:17455057221118773. 10.1177/17455057221118773

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 20.

  Moher D Liberati A Tetzlaff J Altman DG , PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. (2009) 339:b2535. 10.1136/bmj.b2535

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21.

  Nweke M van Vuuren M Bester K Maritz A van Vuuren L Vilakazi Y et al Association between socio-demographic and injury factors, and physical activity behaviour in people with spinal cord injury: a theory-informed systematic review and meta-analysis. BMC Sports Sci Med Rehabil. (2025) 17(1):179. 10.1186/s13102-024-01021-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 22.

  Nweke M Mshunqane N . Characterization and stratification of risk factors of stroke in people living with HIV: a theory-informed systematic review. BMC Cardiovasc Disord. (2025) 25(1):405. 10.1186/s12872-025-04833-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 23.

  Nweke M Oyirinnaya P Nwoha P Mitha SB Mshunqane N Govender N et al Development of a high-performing, cost-effective and inclusive afrocentric predictive model for stroke: a meta-analysis approach. BMC Neurol. (2025) 25(1):282. 10.1186/s12883-025-04229-x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 24.

  Nwagha T Nweke M . Stratification of risk factors of lung cancer-associated venous thromboembolism and determining the critical point for preemptive intervention: a systematic review with meta-analysis. Clin Med Insights Oncol. (2023) 17:11795549231175221. 10.1177/11795549231175221

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 25.

  Moola S Munn Z Tufanaru C Aromataris E Sears K Sfetcu R et al Chapter 7. Systematic reviews of etiology and risk. In: AromatarisEMunnZ, editors. Joanna Briggs Institute Reviewer’s Manual. Adelaide: The Joanna Briggs institute (2017). p. 211–46. Available online at:https://reviewersmanual.joannabriggs.org/ (Accessed July 31, 2025).

  • Google Scholar

• 26.

  Chen H Cohen P Chen S . How big is a big odds ratio? Interpreting the magnitudes of odds ratios in epidemiological studies. Emerg Infect Dis. (2010) 16(6):e24. 10.1080/03610911003650383

  • CrossRef
  • Google Scholar

• 27.

  Guyatt GH Oxman AD Kunz R Woodcock J Brozek J Helfand M et al GRADE Guidelines: 7. Rating the quality of evidence—inconsistency. GRADE guidelines. J Clin Epidemiol. (2011) 64(12):1294–302. 10.1016/j.jclinepi.2011.03.017

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 28.

  Guyatt GH Oxman AD Kunz R Woodcock J Brozek J Helfand M et al GRADE Guidelines: 8. Rating the quality of evidence—indirectness. GRADE guidelines. J Clin Epidemiol. (2011) 64(12):1303–10. 10.1016/j.jclinepi.2011.04.014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29.

  Guyatt GH Oxman AD Kunz R Brozek J Alonso-Coello P Rind D et al RADE Guidelines 6. GRADE guidelines 6. GRADE guidelines 6. Rating the quality of evidence—imprecision. J Clin Epidemiol. (2011) 64(12):1283–93. 10.1016/j.jclinepi.2011.01.012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 30.

  Guyatt GH Oxman AD Montori V Vist G Kunz R Brozek J et al GRADE Guidelines: 5. Rating the quality of evidence—publication bias. J Clin Epidemiol. (2011) 64(12):1277–82. 10.1016/j.jclinepi.2011.01.011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 31.

  Ibeneme SC Odoh E Martins N Ibeneme GC . Developing an HIV-specific falls risk prediction model with a novel clinical index: a systematic review and meta-analysis method. BMC Infect Dis. (2024) 24(1):1402. 10.1186/s12879-024-10141-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 32.

  Worm SW De Wit S Weber R Sabin CA Reiss P El-Sadr W et al Diabetes mellitus, preexisting coronary heart disease, and the risk of subsequent coronary heart disease events in patients infected with human immunodeficiency virus: the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) Study. Circulation. (2009) 119(6):805–11. 10.1161/CIRCULATIONAHA.108.790857

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 33.

  Kaplan RC Kingsley LA Sharrett AR Li X Lazar J Tien PC et al Ten-year predicted coronary heart disease risk in HIV-infected men and women. Clin Infect Dis. (2007) 45(8):1074–81. 10.1086/521935

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34.

  Dale SK Weber KM Cohen MH Brody LR . Abuse, nocturnal stress hormones, and coronary heart disease risk among women with HIV. AIDS Care. (2017) 29(5):598–602. 10.1080/09540121.2016.1241378

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 35.

  Freiberg MS Chang CC Skanderson M McGinnis K Kuller LH Kraemer KL et al The risk of incident coronary heart disease among veterans with and without HIV and hepatitis C. Circ Cardiovasc Qual Outcomes. (2011) 4(4):425–32. 10.1161/CIRCOUTCOMES.110.957415

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 36.

  Lai S Fishman EK Lai H Moore R Cofrancesco JJ Pannu H et al Long-term cocaine use and antiretroviral therapy are associated with silent coronary artery disease in African Americans with HIV infection who have no cardiovascular symptoms. Clin Infect Dis. (2008) 46(4):600–10. 10.1086/526782

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37.

  Hadigan C Meigs JB Wilson PW D’Agostino RB Davis B Basgoz N et al Prediction of coronary heart disease risk in HIV-infected patients with fat redistribution. Clin Infect Dis. (2003) 36(7):909–16. 10.1086/368185

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 38.

  Berquist VL Hearps AC Ford P Jaworowski A Leishman SJ Hoy JF et al Porphyromonas gingivalis antibody levels and diagnosis of coronary artery disease in HIV-positive individuals. J Periodont Res. (2017) 52(5):930–5. 10.1111/jre.12460

  • CrossRef
  • Google Scholar

• 39.

  Mushin AS Trevillyan JM Lee SJ Hearps AC Hoy JF . Factors associated with the development of coronary artery disease in people with HIV. Sex Health. (2023) 20(5):470–4. 10.1071/SH23043

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 40.

  Trevillyan JM Gardiner EE Andrews RK Maisa A Hearps AC Al-Tamimi M et al Decreased levels of platelet-derived soluble glycoprotein VI detected prior to the first diagnosis of coronary artery disease in HIV-positive individuals. Platelets. (2017) 28(3):301–4. 10.1080/09537104.2016.1237627

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 41.

  Trevillyan JM Cheng AC Hoy JF . Abacavir Exposure and cardiovascular risk factors in HIV-positive patients with coronary heart disease: a retrospective case–control study. Sex Health. (2013) 10(2):97–101. 10.1071/SH12081

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42.

  Chammartin F Darling K Abela IA Battegay M Furrer H Calmy A et al Swiss HIV cohort study. CD4:d8 ratio and CD8 cell count and their prognostic relevance for coronary heart disease events and stroke in antiretroviral-treated individuals: the Swiss HIV cohort study. J Acquir Immune Defic Syndr. (2022) 91(5):508–15. 10.1097/QAI.0000000000003094

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 43.

  Engel T Raffenberg M Schoepf IC Kootstra NA Reiss P Thorball CW et al Swiss HIV cohort study. Telomere length, traditional risk factors, factors related to human immunodeficiency virus (HIV) and coronary artery disease events in Swiss persons living with HIV. Clin Infect Dis. (2021) 73(7):e2070-6–e2076. 10.1093/cid/ciaa1034

  • CrossRef
  • Google Scholar

• 44.

  May M Sterne JA Shipley M Brunner E d’Agostino R Whincup P et al A coronary heart disease risk model for predicting the effect of potent antiretroviral therapy in HIV-1 infected men. Int J Epidemiol. (2007) 36(6):1309–18. 10.1093/ije/dym135

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 45.

  Bucher HC Richter W Glass TR Magenta L Wang Q Cavassini M et al Small dense lipoproteins, apolipoprotein B, and risk of coronary events in HIV-infected patients on antiretroviral therapy: the Swiss HIV cohort study. J Acquir Immune Defic Syndr. (2012) 60(2):135–42. 10.1097/QAI.0b013e31824476e1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 46.

  Egaña-Gorroño L Martínez E Escribà T Calvo M Gatell JM Arnedo M . Association study of lipoprotein(a) genetic markers, traditional risk factors, and coronary heart disease in HIV-1-infected patients. Front Immunol. (2012) 3:367. 10.3389/fimmu.2012.00367

  • CrossRef
  • Google Scholar

• 47.

  Escaut L Monsuez JJ Chironi G Merad M Teicher E Smadja D et al Coronary artery disease in HIV-infected patients. Intensive Care Med. (2003) 29(6):969–73. 10.1007/s00134-003-1740-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 48.

  Fuchs SC Alencastro PR Ikeda ML Barcellos NT Wolff FH Brandão AB et al Risk of coronary heart disease among HIV-infected patients: a multicenter study in Brazil. Sci World J. (2013) 2013:163418. 10.1155/2013/163418

  • CrossRef
  • Google Scholar

• 49.

  Longenecker CT Bogorodskaya M Margevicius S Nazzinda R Bittencourt MS Erem G et al Sex modifies the association between HIV and coronary artery disease among older adults in Uganda. J Int AIDS Soc. (2022) 25(1):e25868. 10.1002/jia2.25868

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 50.

  Lui G Leung HS Lee J Wong CK Li X Ho M et al An efficient approach to estimate the risk of coronary artery disease for people living with HIV using machine-learning-based retinal image analysis. PLoS One. (2023) 18(2):e0281701. 10.1371/journal.pone.0281701

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51.

  Pullinger CR Aouizerat BE Gay C Coggins T Movsesyan I Davis H et al Metabolic abnormalities and coronary heart disease risk in human immunodeficiency virus–infected adults. Metab Syndr Relat Disord. (2010) 8(3):279–86. 10.1089/met.2009.0094

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 52.

  Urina-Jassir M Patiño-Aldana AF Herrera-Parra LJ Hernández Vargas JA Trujillo-Cáceres SJ Valbuena-García AM et al Factors associated with coronary artery disease among people living with human immunodeficiency virus: results from the Colombian HIV/AIDS registry. Int J Cardiol CardioVasc Risk Prev. (2023) 18:200205. 10.1016/j.ijcrp.2023.200205

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53.

  Trøseid M Molinaro A Gelpi M Vestad B Kofoed KF Fuchs A et al Gut microbiota alterations and circulating imidazole propionate levels are associated with obstructive coronary artery disease in people with HIV. J Infect Dis. (2024) 229(3):898–907. 10.1093/infdis/jiad604

  • CrossRef
  • Google Scholar

• 54.

  Yu J Liu X Zhu Z Yang Z He J Zhang L et al Prediction models for cardiovascular disease risk among people living with HIV: a systematic review and meta-analysis. Front Cardiovasc Med. (2023) 10:1138234. 10.3389/fcvm.2023.1138234

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 55.

  Bachmann JM Willis BL Ayers CR Khera A Berry JD . Association between family history and coronary heart disease death across long-term follow-up in men: the cooper center longitudinal study. Circulation. (2012) 125(25):3092–8. 10.1161/CIRCULATIONAHA.111.065490

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 56.

  Kemp CD Conte JV . The pathophysiology of heart failure. Cardiovasc Pathol. (2012) 21(5):365–71. 10.1016/j.carpath.2011.11.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 57.

  Valerio L Peters RJ Zwinderman AH Pinto-Sietsma SJ . Association of family history with cardiovascular disease in hypertensive individuals in a multiethnic population. J Am Heart Assoc. (2016) 5(12):e004260. 10.1161/JAHA.116.004260

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 58.

  Lloyd-Jones DM Nam BH D’Agostino RB Sr Levy D Murabito JM Wang TJ et al Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. JAMA. (2004) 291(18):2204–11. 10.1001/jama.291.18.2204

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 59.

  Marenberg ME Risch N Berkman LF Floderus B de Faire U . Genetic susceptibility to death from coronary heart disease in a study of twins. N Engl J Med. (1994) 330(15):1041–6. 10.1056/NEJM199404143301503

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 60.

  Andresdottir MB Sigurdsson G Sigvaldason H Gudnason V , Reykjavik Cohort Study. Fifteen percent of myocardial infarctions and coronary revascularizations explained by family history unrelated to conventional risk factors: the Reykjavik cohort study. Eur Heart J. (2002) 23(21):1655–63. 10.1053/euhj.2002.3235

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 61.

  Di Lenarda F Balestrucci A Terzi R Lopes P Ciliberti G Marchetti D et al Coronary artery disease, family history, and screening perspectives: an up-to-date review. J Clin Med. (2024) 13(19):5833. 10.3390/jcm13195833

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 62.

  Grand M Bia D Diaz A . Cardiovascular risk assessment in people living with HIV: a systematic review and meta-analysis of real-life data. Curr HIV Res. (2020) 18(1):5–18. 10.2174/1570162X17666191212091618

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 63.

  Dubé MP Stein JH Aberg JA Fichtenbaum CJ Gerber JG Tashima KT et al Guidelines for the evaluation and management of dyslipidemia in human immunodeficiency virus (HIV)-infected adults receiving antiretroviral therapy: recommendations of the HIV medical association of the infectious disease society of America and the adult AIDS clinical trials group. Clin Infect Dis. (2003) 37(5):613–27. 10.1086/378131

  • CrossRef
  • Google Scholar

• 64.

  Gupta N Elnour AA Sadeq A Gupta R . Diabetes and the heart: coronary artery disease. E-J Cardiol Pract. (2022) 22(10):112–20. Available online at: https://www.escardio.org/Journals/E-Journal-of-Cardiology-Practice/Volume-22/diabetes-and-the-heart-coronary-artery-disease#

  • Google Scholar

• 65.

  National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final report. Circulation. (2002) 106(25):3143–421. 10.1161/circ.106.25.3143

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 66.

  Poznyak A Grechko AV Poggio P Myasoedova VA Alfieri V Orekhov AN . The diabetes mellitus-atherosclerosis connection: the role of lipid and glucose metabolism and chronic inflammation. Int J Mol Sci. (2020) 21(5):1835. 10.3390/ijms21051835

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 67.

  Genders TS Steyerberg EW Hunink MG Nieman K Galema TW Mollet NR et al Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. Br Med J. (2012) 344:e3485. 10.1136/bmj.e3485

  • CrossRef
  • Google Scholar

• 68.

  Muhammad LJ Al-Shourbaji I Haruna AA Mohammed IA Ahmad A Jibrin MB . Machine learning predictive models for coronary artery disease. SN Comput Sci. (2021) 2(5):350. 10.1007/s42979-021-00731-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 69.

  Wang C Zhao Y Jin B Gan X Liang B Xiang Y et al Development and validation of a predictive model for coronary artery disease using machine learning. Front Cardiovasc Med. (2021) 8:614204. 10.3389/fcvm.2021.614204

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 70.

  Bogers RP Bemelmans WJ Hoogenveen RT Boshuizen HC Woodward M Knekt P et al Association of overweight with increased risk of coronary heart disease partly independent of blood pressure and cholesterol levels: a meta-analysis of 21 cohort studies including more than 300 000 persons. Arch Intern Med. (2007) 167(16):1720–8. 10.1001/archinte.167.16.1720

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 71.

  Jung UJ Choi MS . Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. Int J Mol Sci. (2014) 15(4):6184–223. 10.3390/ijms15046184

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 72.

  Ohashi N Yamamoto H Horiguchi J Kitagawa T Kunita E Utsunomiya H et al Association between visceral adipose tissue area and coronary plaque morphology assessed by CT angiography. JACC Cardiovasc Imaging. (2010) 3(9):908–17. 10.1016/j.jcmg.2010.06.014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 73.

  Volpe M Gallo G . Obesity and cardiovascular disease: an executive document on pathophysiological and clinical links promoted by the Italian society of cardiovascular prevention (SIPREC). Front Cardiovasc Med. (2023) 10:1136340. 10.3389/fcvm.2023.1136340

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 74.

  Steverson A Pawlowski AE Schneider D Nannapaneni P Sanders J Achenbach CJ et al Associations of HIV-related factors with adjudicated heart failure (HF) in an electronic cohort of HIV-infected (HIV+) persons. J Am Coll Cardiol. (2017) 69(11):740. 10.1016/S0735-1097(17)34129-3

  • CrossRef
  • Google Scholar

• 75.

  Yen YF Ko MC Yen MY Hu BS Wang TH Chuang PH et al Human immunodeficiency virus increases the risk of incident heart failure. J Acquir Immune Defic Syndr. (2019) 80(3):255–63. 10.1097/QAI.0000000000001917

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 76.

  Messner B Bernhard D . Smoking and cardiovascular disease: mechanisms of endothelial dysfunction and early atherogenesis. Arterioscler Thromb Vasc Biol. (2014) 34(3):509–15. 10.1161/ATVBAHA.113.300156

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 77.

  McEvoy JW Nasir K DeFilippis AP Lima JA Bluemke DA Hundley WG et al Relationship of cigarette smoking with inflammation and subclinical vascular disease: the multi-ethnic study of atherosclerosis. Arterioscler Thromb Vasc Biol. (2015) 35(4):1002–10. 10.1161/ATVBAHA.114.304960

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 78.

  Morris PB Ference BA Jahangir E Feldman DN Ryan JJ Bahrami H et al Cardiovascular effects of exposure to cigarette smoke and electronic cigarettes: clinical perspectives from the prevention of cardiovascular disease section leadership council and early career councils of the American College of Cardiology. J Am Coll Cardiol. (2015) 66(12):1378–91. 10.1016/j.jacc.2015.07.037

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 79.

  Kamimura D Cain LR Mentz RJ White WB Blaha MJ DeFilippis AP et al Cigarette smoking and incident coronary artery disease: insights from the Jackson heart study. Circulation. (2018) 137(24):2572–82. 10.1161/CIRCULATIONAHA.117.031912

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 80.

  Hall ME Wang W Okhomina V Agarwal M Hall JE Dreisbach AW et al Cigarette smoking and chronic kidney disease in African Americans in the Jackson heart study. J Am Heart Assoc. (2016) 5(6):e003280. 10.1161/JAHA.116.003280

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81.

  Mu H Chai H Lin PH Yao Q Chen C . Current update on HIV-associated vascular disease and endothelial dysfunction. World J Surg. (2007) 31(4):632–43. 10.1007/s00268-006-0730-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 82.

  Dau B Holodniy M . The relationship between HIV infection and cardiovascular disease. Curr Cardiol Rev. (2008) 4(3):203–18. 10.2174/157340308785160589

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 83.

  Phillips AN Carr A Neuhaus J Visnegarwala F Prineas R Burman WJ et al Interruption of antiretroviral therapy and risk of cardiovascular disease in persons with HIV-1 infection: exploratory analyses from the SMART trial. Antivir Ther. (2008) 13(2):177–87. 10.1177/135965350801300215

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 84.

  Baker JV Sharma S Achhra AC Bernardino JI Bogner JR Duprez D et al Changes in cardiovascular disease risk factors with immediate versus deferred antiretroviral therapy initiation among HIV-positive participants in the START (strategic timing of antiretroviral treatment) trial. J Am Heart Assoc. (2017) 6(5):e004987. 10.1161/JAHA.116.004987

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 85.

  Obeagu EI . Influence of cytokines on the recovery trajectory of HIV patients on antiretroviral therapy: a review. Medicine (Baltimore). (2025) 104(1):e41222. 10.1097/MD.0000000000041222

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 86.

  Starikova EA Mammedova JT Rubinstein AA Sokolov AV Kudryavtsev IV . Activation of the coagulation cascade as a universal danger sign. Curr Issues Mol Biol. (2025) 47(2):108. 10.3390/cimb47020108

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 87.

  Funderburg NT Mayne E Sieg SF Asaad R Jiang W Kalinowska M et al Increased tissue factor expression on circulating monocytes in chronic HIV infection: relationship to in vivo coagulation and immune activation. Blood. (2010) 115(2):161–7. 10.1182/blood-2009-03-210179

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 88.

  Flint OP Noor MA Hruz PW Hylemon PB Yarasheski K Kotler DP et al The role of protease inhibitors in the pathogenesis of HIV-associated lipodystrophy: cellular mechanisms and clinical implications. Toxicol Pathol. (2009) 37(1):65–77. 10.1177/0192623308327119

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 89.

  den Boer MA Berbée JF Reiss P van der Valk M Voshol PJ Kuipers F et al Ritonavir impairs lipoprotein lipase-mediated lipolysis and decreases uptake of fatty acids in adipose tissue. Arterioscler Thromb Vasc Biol. (2006) 26(1):124–9. 10.1161/01.ATV.0000194073.87647.10

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 90.

  Tran H Robinson S Mikhailenko I Strickland DK . Modulation of the LDL receptor and LRP levels by HIV protease inhibitors. J Lipid Res. (2003) 44(10):1859–69. 10.1194/jlr.M200487-JLR200

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 91.

  Anuurad E Bremer A Berglund L . HIV Protease inhibitors and obesity. Curr Opin Endocrinol Diabetes Obes. (2010) 17(5):478–85. 10.1097/MED.0b013e32833dde87

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 92.

  Hresko RC Hruz PW . HIV Protease inhibitors act as competitive inhibitors of the cytoplasmic glucose binding site of GLUTs with differing affinities for GLUT1 and GLUT4. PLoS One. (2011) 6(9):e25237. 10.1371/journal.pone.0025237

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 93.

  Sherman J Dyar C McDaniel J Funderburg NT Rose KM Gorr M et al Sexual minorities are at elevated risk of cardiovascular disease from a younger age than heterosexuals. J Behav Med. (2022) 45(4):571–9. 10.1007/s10865-021-00269-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 94.

  Stacey L . An updated data portrait of heterosexual, gay/lesbian, bisexual, and other sexual minorities in the United States. Soc Curr. (2024) 11(5):383–400. 10.1177/23294965241260057

  • CrossRef
  • Google Scholar

• 95.

  van Kampen SC Lee WE Fornasiero M Husk K . The proportion of the population of England that self-identifies as lesbian, gay or bisexual: producing modelled estimates based on national social surveys. BMC Res Notes. (2017) 10(1):594. 10.1186/s13104-017-2921-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 96.

  Bonomo JA Luo K Ramallo JA . LGBTQ+ cardiovascular health equity: a brief review. Front Cardiovasc Med. (2024) 11:1350603. 10.3389/fcvm.2024.1350603

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 97.

  Caceres BA Streed CG Jr Corliss HL Lloyd-Jones DM Matthews PA Mukherjee M et al Assessing and addressing cardiovascular health in LGBTQ adults: a scientific statement from the American Heart Association. Circulation. (2020) 142(19):e321–32. 10.1161/CIR.0000000000000914

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 98.

  Maqsood MH Levine GN Kleiman ND Hasdai D Uretsky BF Birnbaum Y . Do we still need aspirin in coronary artery disease?J Clin Med. (2023) 12(24):7534. 10.3390/jcm12247534

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 99.

  Cholesterol Treatment Trialists’ (CTT) Collaboration. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet. (2010) 376(9753):1670–81. 10.1016/S0140-6736(10)61350-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 100.

  Wilson PW Polonsky TS Miedema MD Khera A Kosinski AS Kuvin JT . Systematic review for the 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines. J Am Coll Cardiol. (2019) 73(24):3210–27. 10.1161/CIR.0000000000000626

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 101.

  Ridker PM Danielson E Fonseca FA Genest J Gotto AM Jr Kastelein JJ et al Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med. (2008) 359(21):2195–207. 10.1056/NEJMoa0807646

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 102.

  World Health Organization (WHO). Global status Report on Noncommunicable Diseases 2014. Geneva: World Health Organization (2014).

  • Google Scholar

• 103.

  Antiretroviral Therapy Cohort Collaboration. Life expectancy of individuals on combination antiretroviral therapy in high-income countries: a collaborative analysis of 14 cohort studies. Lancet. (2008) 372(9635):293–9. 10.1016/S0140-6736(08)61113-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 104.

  Fowler G . The strategy of preventive medicine. Br J Gen Pract. (1992) 42(364):495. Available online at: https://pmc.ncbi.nlm.nih.gov/articles/PMC1372287/

  • Google Scholar

• 105.

  GBD. DALYs and HALE collaborators. Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition. Lancet. (2015) 386(10009):2145–91. 10.1016/S0140-6736(15)61340-X

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 106.

  Friis-Møller N Weber R Reiss P Thiébaut R Kirk O d’Arminio Monforte A et al Cardiovascular disease risk factors in HIV patients—association with antiretroviral therapy. Results from the DAD study. AIDS. (2003) 17(8):1179–93. 10.1097/01.aids.0000060358.78202.c1

  • CrossRef
  • Google Scholar

• 107.

  Naar-King S Parsons JT Johnson AM . Motivational interviewing targeting risk reduction for people with HIV: a systematic review. Curr HIV/AIDS Rep. (2012) 9(4):335–43. 10.1007/s11904-012-0132-x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar