Abstract
Background
People living with HIV (PLWHIV) are at a higher risk of developing coronary artery disease (CAD). We aimed to assess the factors associated with CAD among PLWHIV in Colombia.
Methods
We conducted a retrospective cohort study based on adults newly diagnosed with HIV, reported to the Colombian HIV/AIDS registry from 2018 to 2021. Baseline demographic and clinical characteristics were compared by age (<50 and ≥ 50 years). Our main outcome was the presence of CAD. Logistic regression models were used to assess the association between traditional and HIV-related factors with CAD. These associations were also evaluated in stratified models by age. Effect measures were odds ratios (OR) and their 95% confidence intervals.
Results
Among 36,483 PLWHIV, the frequency of CAD was 0.53% (n = 196). There was a high prevalence of impaired fasting glucose/diabetes mellitus (12.62%), overweight/obesity (27.79%), elevated LDL-c (86.69%), and hypertriglyceridemia (72.76%). Factors associated with CAD included male gender (OR: 2.01, 95% CI: 1.12–3.58), age ≥50 years (OR: 4.96, 95% CI: 3.29–7.45), lipoatrophy or lipodystrophy (OR 5.12, 95% CI: 1.12–23.33), AIDS-defining conditions (OR: 1.83, 95% CI: 1.07–3.12), obesity (OR: 2.95, 95% CI: 1.69–5.10), diabetes mellitus (OR: 2.50, 95% CI: 1.25–4.97), and renal impairment (OR: 3.15, 95% CI: 1.83–5.42).
Conclusions
Traditional CAD risk factors are common in PLWHIV. There were traditional and disease-specific factors associated with increased odds of CAD. These findings may aid clinicians and decision-makers in reducing the impact of CAD in PLWHIV.
Keywords: HIV, Heart disease risk factors, Coronary artery disease, Colombia
Highlights
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This study is based on a large nationwide registry of PLWHIV in Colombia.
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Traditional risk factors (e.g., diabetes, obesity, dyslipidemia) were common.
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AIDS-defining conditions and lipodystrophy were associated with CAD.
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Those with obesity, diabetes mellitus, or kidney disease were more likely to have CAD.
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Identifying those at risk will aid surveillance and prevention of CAD in PLWHIV.
1. Introduction
People living with HIV (PLWHIV) have improved their life expectancy due to effective antiretroviral therapy (ART), shifting to a chronic course with an increased risk of non-communicable conditions such as cardiovascular diseases (CVDs) [1,2]. PLWHIV are at a higher risk of developing coronary artery disease (CAD) [2,3]. A meta-analysis including 80 studies and 793,635 subjects estimated a crude incidence of CVD of 61.8 (95% CI, 45.8–83.4) per 10,000 person-years among PLWHIV. Additionally, these authors identified a 2-fold increase in the risk of developing CVD when compared to people without HIV [4]. Furthermore, there has been an increased burden due to CVDs among PLWHIV throughout the last decades [4,5]. Despite falling overall mortality rates, an analysis of a nationwide database in the United States found a significant increase in CVD-related mortality from 1999 to 2013 [5].
Multiple factors may increase the risk of CAD and CVD in this population. These can be classified as HIV-related and ART-related factors in addition to the traditional CVD risk factors [3]. Within the first group, lower CD4 cell counts were described as a predictive factor in a Brazilian cohort study [6]. Additionally, a combination of ART including abacavir (e.g., abacavir-lamivudine-darunavir) has been associated with a higher risk of acute myocardial infarction [7]. Moreover, known atherogenic risk factors, including increased low-density lipoprotein cholesterol (LDL-c), diabetes mellitus (DM), and obesity are also highly prevalent in PLWHIV [8,9].
Recognizing the predictors associated with an even higher risk of CAD is crucial. This will be useful for the early identification of PLWHIV which requires clinicians and healthcare systems to develop tailored preventive and surveillance strategies to reduce CAD-related morbidity and mortality. Thus, our study sought to assess the factors associated with CAD among incident cases of HIV/AIDS in a large, nationwide, and real-world database in Colombia.
2. Methods
2.1. Study design and population
A retrospective cohort study based on data obtained from the Colombian Registry of HIV/AIDS was conducted. The information, structure, and management of the registry have been published elsewhere [10]. In short, this is an administrative registry managed by the High-Cost Diseases Fund (CAC, in Spanish) since 2012 by a resolution of the Colombian Ministry of Health and Social Protection [11]. In this registry, all health insurers and providers throughout Colombia are required to report the data of PLWHIV and those at risk (e.g., pregnant women, and people with active tuberculosis [TB]) within the framework of the health system [10,11]. Importantly, the registry has a national scope as 97–99% of Colombian citizens are affiliated with the health system [12].
The data collected are extracted from medical records by the insurers and providers and subsequently, uploaded to a health interoperability platform. These data include demographic, clinical (e.g., AIDS-defining conditions), ART-related, prevention interventions (e.g., prophylaxis, STD prevention), and administrative variables. Moreover, the registry undergoes a strict data-monitoring process to validate the reported data with the clinical records and correct them in case of inconsistencies. Lastly, confidentiality is assured by anonymization of the records from each subject with a unique identifier number and by limiting access to the database to authorized people only [10].
2.2. Eligibility criteria
We included adults (≥18 years old) that were reported to the registry as incident cases of HIV (at any stage) from February 1st, 2018, to January 31st, 2021. Those with unclear HIV diagnosis (ruled out infection, under evaluation, or without HIV testing) and pregnant women were excluded from the analysis.
2.3. Study variables and definitions
Demographic variables included age, sex, self-reported ethnicity, health insurance, and region of residence. We classified our variables into three main categories: HIV-related, ART-related, and traditional CVD risk factors. From the former, we assessed the following, viral load, and CDC 2014 clinical stage at diagnosis [13]. Also, we evaluated the presence of hepatitis C, hepatitis B, TB coinfection (including treatment), AIDS-defining infections or malignancies, lipoatrophy/lipodystrophy, and initial ART divided by class (nucleoside analog reverse transcriptase inhibitors [NRTIs], non-nucleoside analog reverse transcriptase inhibitors [NNRTIs], protease inhibitors [PIs], integrase inhibitors, fusion inhibitors, or coreceptor antagonist).
Regarding CVD risk factors, data on fasting plasma glucose (FPG), LDL-c, triglycerides (TG), and weight/height were obtained from the registry. FPG levels were classified as normal (<100 mg/dL), impaired fasting glucose (IFG; 100–125 mg/dL), and DM (≥126 mg/dL) [14]. LDL-c levels were divided into normal (<130 mg/dL) and abnormal or elevated (≥130 mg/dL), based on the Colombian Association of Infectious Diseases (ACIN, its acronym in Spanish) consensus on CVD risk where 130–159 mg/dL is considered “borderline high” [15]. Hypertriglyceridemia was defined as TG levels ≥175 mg/dL [16]. Body mass index (BMI) was calculated based on weight and height and classified according to the World Health Organization (WHO) as underweight (<18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), and obesity (≥30 kg/m2) [17].
The Framingham Risk Score (FRS) adjusted to the Colombian population (i.e., FRS times a factor of 0.75) [15,18] was calculated by their treating physician and its result (in percentage) was reported to the registry. We classified the CVD risk into three categories, low (<10%), intermediate (10–20%), and high-risk (≥20%) [19]. Lastly, other laboratories available were hemoglobin (gr/dL), alanine aminotransferase (ALT; UI/L), and glomerular filtration rate (GFR) based on creatinine levels using the CKD-EPI formula (mL/min/1.73 m2) [20].
2.4. Outcome
The main outcome was CAD, reported to the registry as its presence or absence at the cohort entry, or its development during the follow-up, according to the clinical records. This information is commonly sent to the registry as a diagnosis of “coronary artery disease”, “cardiovascular disease”, “ischemic cardiomyopathy”, “previous infarction”, or “myocardial infarction”. It is then grouped as “coronary artery disease” and validated/audited by the registry team.
2.5. Statistical analysis
Descriptive statistics were used for baseline demographic and clinical characteristics. According to the data distribution, continuous variables are presented as medians and interquartile ranges (IQR). Categorical variables are described with frequencies and percentages. Baseline characteristics were compared across age (<50 years and ≥50 years) using X2 and Mann-Whitney U tests for qualitative and quantitative variables, respectively. To avoid the effect of group sizes on hypothesis tests, we also estimated absolute standardized differences (ASD), and values ≥ 10% were considered significant [21].
To identify the characteristics associated with CAD, we used a logistic regression model. Variables retrieved in the final model were selected following a combination of the statistical criterion and their clinical relevance. We also repeated the final model by age (<50 years and ≥50 years) as a sensitivity analysis to evaluate a potential effect modification. Effect measures were odds ratios (OR) and their 95% confidence intervals. A complete-case approach was used. Hosmer and Lemeshow's test evaluated the goodness of fit in this model. Statistical significance was set at a p-value ≤0.05. R version 4.2.0 [22] and RStudio [23] were used for statistical analyses.
2.6. Ethics declaration
This study was approved by Fundación del Caribe para la Investigación Biomédica's Ethics Committee (Record 0250 - May 24, 2022). We followed the Colombian Health Ministry (Resolution 8430 of 1993) and the Declaration of Helsinki research standards. The need for informed consent was waived considering the retrospective design which is based on secondary data sources (administrative registry). The Ministry of Health, as custodian of the information, authorizes the use of the information for research purposes on relevant topics for decision-making in the country.
3. Results
3.1. Baseline characteristics
We analyzed 36,483 incident cases with CAD being reported in 196 cases (0.53%). Overall, most were <50 years old (87.95%), male (86.01%), had third-payer insurance (58.46%), and lived between the Central region (28.43%) and Bogotá, D.C (22.98%). Viral load at diagnosis was unsuppressed (>1000 copies/mL) in 92.97% and slightly higher in people over 50 years. Stage 3 at diagnosis was seen in 40.91% of patients and was higher (67.47%) in ≥50 years old. The majority started ART after being diagnosed (91.13%) and the most common ART was NRTIs in 99.28%.
Globally, 18.66% had AIDS-defining conditions and a low percentage (<1%) had lipoatrophy/lipodystrophy; both were higher in subjects ≥50 years. The prevalence of comorbidities such as cirrhosis, chronic kidney disease (CKD), and cancer was less than 1%. Regarding the cardiometabolic profile, IFG/DM, overweight/obesity, and intermediate and high cardiovascular risk were more common in patients ≥50 years. On the contrary, a worse lipid profile was more frequent in younger patients.
Table 1 shows the characterization by age group. Significant differences were found in gender, health insurance, and region of residence. Clinical stage at diagnosis, AIDS-defining conditions, cardiovascular risk as well as LDL-c, TG, FPG, BMI, and GFR values were the main clinical variables with significant differences across groups.
Table 1.
Baseline characteristics of the population studied by age groups in Colombia, 2018–2021.
| Characteristica | Total |
≥50 years |
<50 years |
ASDb(%) |
|---|---|---|---|---|
| n = 36,483 | n = 4395 | n = 32,088 | ||
| Sex, male | 31,379 (86.01) | 3270 (74.40) | 28,109 (87.60) | 34.1 |
| Self-reported ethnicity | 6.7 | |||
| Afro descendant | 1975 (5.41) | 299 (6.80) | 1676 (5.22) | |
| Other | 34,508 (94.59) | 4096 (93.20) | 30,412 (94.78) | |
| Health insurance | 28.1 | |||
| Third payer | 21,328 (58.46) | 2034 (46.28) | 19,294 (60.13) | |
| State | 13,570 (37.20) | 2134 (48.56) | 11,436 (35.64) | |
| Other | 1585 (4.34) | 227 (5.16) | 1358 (4.23) | |
| Region of residence | 24.0 | |||
| Central | 10,373 (28.43) | 1359 (30.92) | 9014 (28.09) | |
| Bogotá D.C. | 8384 (22.98) | 671 (15.27) | 7713 (24.04) | |
| Caribbean | 7099 (19.46) | 1002 (22.80) | 6097 (19.00) | |
| Pacific | 5578 (15.29) | 800 (18.20) | 4778 (14.89) | |
| Eastern | 4470 (12.25) | 507 (11.54) | 3963 (12.35) | |
| Amazonian | 579 (1.59) | 56 (1.27) | 523 (1.63) | |
| Viral load at diagnosisd | 2.7 | |||
| Suppressed (<1000 copies/mL) | 2298 (7.03) | 251 (6.43) | 2047 (7.11) | |
| Unsuppressed (>1000 copies/mL) | 30,390 (92.97) | 3650 (93.57) | 26,740 (92.89) | |
| Clinical stage at diagnosisd | 65.0 | |||
| Stage 1 | 6352 (19.65) | 322 (8.09) | 6030 (21.27) | |
| Stage 2 | 12,749 (39.44) | 972 (24.43) | 11,777 (41.55) | |
| Stage 3 | 13,223 (40.91) | 2684 (67.47) | 10,539 (37.18) | |
| ART at diagnosis | 32,187 (91.13) | 3920 (92.08) | 28,267 (91.00) | 3.9 |
| Current ART groupsd | ||||
| NRTIs | 32,035 (99.28) | 3878 (98.90) | 28,157 (99.34) | 4.6 |
| NNRTIS | 20,033 (62.09) | 2118 (54.02) | 17,915 (63.20) | 18.7 |
| PIs | 8949 (27.74) | 1080 (27.54) | 7869 (27.76) | 0.5 |
| Fusion inhibitors | 3 (0.01) | 0 (0.00) | 3 (0.01) | 1.5 |
| Integrase inhibitors | 3192 (9.89) | 712 (18.16) | 2480 (8.75) | 27.8 |
| CCR5 antagonist | 2 (0.01) | 0 (0.00) | 2 (0.01) | 1.2 |
| HIV-related conditionsc | ||||
| Lipoatrophy or lipodystrophy | 74 (0.20) | 17 (0.39) | 57 (0.18) | 4.0 |
| Coinfection with chronic HBV or HCV | 695 (1.91) | 91 (2.07) | 604 (1.88) | 1.4 |
| Previous TB | 1423 (3.90) | 313 (7.12) | 1110 (3.46) | 16.4 |
| Anti-TB treatment, | 1352 (5.10) | 305 (9.60) | 1047 (4.49) | 20.1 |
| AIDS-defining conditions | 6758 (18.66) | 1551 (35.66) | 5207 (16.34) | 45.2 |
| Comorbiditiesc | ||||
| Cirrhosis | 14 (0.04) | 8 (0.18) | 6 (0.02) | 5.2 |
| Chronic kidney disease | 165 (0.45) | 82 (1.87) | 83 (0.26) | 15.7 |
| Cancer | 180 (0.49) | 49 (1.12) | 131 (0.41) | 8.1 |
| Fasting plasma glucosed | 39.8 | |||
| Normal (<100 mg/dL) | 27,350 (87.38) | 2738 (74.28) | 24,612 (89.13) | |
| Impaired fasting glucose (100–125 mg/dL) | 3371 (10.77) | 754 (20.46) | 2617 (9.48) | |
| Diabetes (≥126 mg/dL) | 580 (1.85) | 194 (5.26) | 386 (1.40) | |
| Hypertrigliceridemiad | 23,772 (72.76) | 2454 (63.51) | 21,318 (74.00) | 22.8 |
| Abnormal LDL-cd | 24,849 (86.69) | 2802 (81.17) | 22,047 (87.45) | 17.3 |
| Cardiovascular riske | 62.0 | |||
| Low (<10%) | 12,456 (96.06) | 2049 (82.22) | 10,407 (99.35) | |
| Intermediate (10–20%) | 310 (2.39) | 266 (10.67) | 44 (0.42) | |
| High (>20%) | 201 (1.55) | 177 (7.10) | 24 (0.23) | |
| Body mass indexc | 16.5 | |||
| Underweight | 3022 (8.54) | 437 (10.32) | 2585 (8.30) | |
| Normal | 22,538 (63.67) | 2398 (56.65) | 20,140 (64.63) | |
| Overweight | 8038 (22.71) | 1128 (26.65) | 6910 (22.17) | |
| Obesity | 1798 (5.08) | 270 (6.38) | 1528 (4.90) | |
| Hemoglobin (gr/dL)c | 15 (13.30–16.00) | 13 (11.90–15.00) | 15 (13.60–16.00) | 66.7 |
| ALT(IU/L)c | 26 (18.00–40.00) | 25 (18.00–38.00) | 26 (18.00–41.00) | 6.4 |
| Glomerular Filtration Rate (ml/min/1.73m)c | 109 (93.46–121.00) | 86 (72.07–99.00) | 113 (97.76–122.00) | 122.0 |
Abbreviations: ALT: alanine transaminase, ART: antiretroviral therapy, HBV: hepatitis B virus, HCV: hepatitis C virus, LDL-c: low-density lipoprotein cholesterol, NNRTIs: non-nucleoside analog reverse transcriptase inhibitors, NRTIs: nucleoside analog reverse transcriptase inhibitors, PIs: protease inhibitors, TB: tuberculosis.
Values are absolute values (percentages) for categorical variables. In the case of numeric variables, they correspond to the median (IQRs).
Absolute standardized difference (ASD). Values higher than 10% were considered significant.
Less than 10% missing values.
10–20% missing values.
More than 20% missing values.
3.2. Multivariable analysis to identify factors associated with CAD
A total of 22,125 (60.64%) patients had complete information and were included in multivariable analysis. The prevalence of CAD was 0.55%. Being male, age ≥50 years, lipoatrophy or lipodystrophy, the presence of AIDS-defining conditions, obesity, DM, and renal impairment (GFR ≤60 ml/min/1.73 m2) were significantly associated with higher odds of CAD. Unexpectedly, patients with a viral load >1000 copies/mL at diagnosis had a significantly lower chance of CAD (Table 2).
Table 2.
Multivariable-adjusted logistic model of coronary artery disease in Colombian adults newly diagnosed with HIV, 2018–2022.
| Characteristic | OR | 95% CI | p-value |
|---|---|---|---|
| Male versus female | 2.01 | 1.12–3.58 | 0.018 |
| Age ≥50 years versus <50 | 4.96 | 3.29–7.45 | <0.001 |
| Health insurance | |||
| State versus third-payer | 0.91 | 0.61–1.32 | 0.607 |
| Other versus third-payer | 0.65 | 0.19–2.08 | 0.465 |
| Viral load at diagnosis unsuppressed (>1000 copies/mL) versus suppressed (<1000 copies/mL) | 0.49 | 0.28–0.86 | 0.014 |
| Clinical stage at diagnosis | |||
| Stage 2 versus 1 | 0.88 | 0.51–1.50 | 0.645 |
| Stage 3 versus 1 | 0.69 | 0.36–1.28 | 0.242 |
| HIV-related conditions | |||
| Lipoatrophy or lipodystrophy | 5.12 | 1.12–23.33 | 0.035 |
| AIDS-defining conditions | 1.83 | 1.07–3.12 | 0.026 |
| Obesity (BMI ≥30 kg/m2) | 2.95 | 1.69–5.10 | <0.001 |
| Diabetes Mellitus | 2.50 | 1.25–4.97 | 0.009 |
| Hypertrigliceridemia | 1.32 | 0.87–1.98 | 0.189 |
| Abnormal LDL-c, No | 0.79 | 0.45–1.36 | 0.397 |
| GFR ≤60 ml/min/1.73m2versus >60 ml/min/1.73m2 | 3.15 | 1.83–5.42 | <0.001 |
Abbreviations: BMI: body mass index, CI: confidence interval, GFR: glomerular filtration rate, OR: odds ratio, LDL-c: low-density lipoprotein cholesterol.
In stratified models, among people aged ≥50 years, being male or having DM and renal impairment were significantly associated with increased odds of CAD, whereas in individuals <50 years, those with AIDS-defining conditions were more likely to develop CAD (Table S1).
4. Discussion
In this retrospective cohort study from the largest database of PLWHIV in Colombia, we identified a high prevalence of traditional CVD risk factors. Age ≥50 years, male gender, comorbidities such as obesity, DM and renal dysfunction, and AIDS-defining conditions were associated with higher odds of CAD in newly diagnosed cases.
As described by other authors, regardless of the definitions and criteria used, we identified a high frequency of CAD risk factors in PLWHIV [8,24,25]. The Copenhagen Comorbidity Study described that PLWHIV had a higher risk of abdominal obesity, elevated LDL-c, and hypertriglyceridemia [8]. Consistent with this, we identified a high frequency of elevated LDL-c (87%), hypertriglyceridemia (73%), and overweight/obesity (28%). To an extent, these findings are aligned with those described by Cahn et al. in a cohort study among Latin American individuals where 80% out of 4010 PLWHIV had dyslipidemia [25]. Identifying the cardio-metabolic risk profile of PLWHIV in Colombia will raise awareness and support the lifestyle and pharmacological strategies needed to reduce the burden of disease associated with these risk factors, including CAD.
Moreover, the prevalence of these risk factors varied by age group. For instance, we identified that hypertriglyceridemia and elevated LDL-c were more frequent in those under 50 years. In the mentioned study by Cahn et al., where a high dyslipidemia rate was reported, most subjects were in the 28–47-year age group [25]. In contrast, other authors have described increased rates of dyslipidemia in older people [26]. These findings should alarm clinicians and public health institutions to tailor strategies specifically to young populations for the prevention of premature cardiovascular morbidity and mortality.
Regarding the outcome, we identified a relatively low frequency of CAD in incident cases of HIV. Others have described CVD prevalence as ranging from 1.7% to 8.4% [6,27,28]. In the HIV-Brazil Cohort Study, 1.7% of their population developed a cardiovascular event at a rate of 3.5 per 1000 person-years [6]. On the other hand, Delabays et al. utilizing data from the Swiss HIV Cohort Study, informed that 8.4% of 6373 PLWHIV developed atherosclerotic cardiovascular disease (ASCVD) [28]. Our results may differ due to methodological aspects such as a lack of data on other ASCVD events (e.g., stroke or cardiovascular death) and by having a relatively short follow-up period. Our research intends to be a starting point for new studies among Colombian and Latin American populations with prolonged follow-up periods and strict cardiovascular events definitions.
Our main aim was to identify accessible predictors or factors associated with CAD in PLWHIV to be used by clinicians in their daily practice. In that regard, demographical, traditional, and HIV-related factors were associated with a higher chance of having CAD in this population. As expected, and as described by other authors [6,28,29], age (≥50 years) and being a male increased the odds of CAD. Regarding traditional risk factors, the three variables that reached statistical significance were obesity, DM, and renal dysfunction, which are well-known CAD risk factors [[30], [31], [32]]. This is a worrisome finding considering the high frequency of these conditions among PLWHIV in this study and reported by other authors [8,33] which places a considerable number of PLWHIV at an even higher risk of developing CAD. In our analysis, we did not identify a significant effect of lipid profile abnormalities on CAD. However, other authors have described that, as in the population without HIV, dyslipidemia increases the risk of CAD [6,29]. Considering this, clinicians must actively screen, prevent, and treat these risk factors (DM, dyslipidemia, obesity, and chronic kidney disease) to lower the risk of developing CAD and further complications in this population.
Prior research has identified that HIV as a sole entity increases the risk of CVD [4], and some studies point out specific HIV-related variables associated with even higher risk [6,29,34]. In this cohort, AIDS-defining conditions were associated with a higher likelihood of CAD. Similarly, Mesquita et al. described an association between severe infections (bacterial, viral, fungal, or parasites) and CVD among PLWHIV in a hospital setting [35]. This may represent a clinical surrogate of inflammation, which has been one of the pathophysiological hypotheses related to developing CAD in this population [2,36]. Furthermore, an additional clinical factor associated with CAD was lipoatrophy/lipodystrophy, which is considered an HIV-related CVD risk-enhancing factor by recent guidelines [2].
Unexpectedly, an unsuppressed viral load at diagnosis was associated with lower odds of CAD. Delabays et al. reported that patients with ASCVD had lower viral load than those without an ASCVD [28]. This differs from other studies that have found no association between viral load and CAD/CVD [6,29]. More recently, a study utilizing longitudinal coronary computed tomography angiography identified that individuals with inadequate viral suppression were at a higher risk of progression of coronary artery stenosis [37]. In this analysis, the authors utilized multiple viral loads over time to assess viral suppression, while in our study we used a single measurement taken at diagnosis [37]. Overall, our findings suggest that identifying patients with advanced stages of HIV (AIDS-defining conditions) to provide closer follow-up and aiming for HIV/AIDS control/suppression is crucial for reducing the impact of CAD.
4.1. Strengths and limitations
Our study analyzed a large sample from a well-established nationwide administrative registry that allows us to perform statistical analysis with sufficient power and improves the generalizability of our results. In addition, all records were under a standardized data-monitoring process to ensure data quality and completeness.
This study is not without limitations. First, the registry includes the information reported by the providers and health insurers which could lead to possible underreporting or errors in the reporting of the study variables, including our outcome (CAD). However, as mentioned before, a strict data monitoring and auditing process aims to reduce this to the minimum. Second, even though the model had an adequate fit, the low frequency of CAD may affect the distribution of predictors across outcome groups. Moreover, patients with advanced disease at diagnosis might have had a shorter follow-up time, preventing the development or detection of CAD. Third, despite the clear evidence of lifestyle habits on the development of CAD, we could not evaluate the effect of smoking, diet, or physical activity as these are not collected in the registry. Further studies evaluating the longitudinal effect of these key factors are needed. Fourth, information on other known risk factors (e.g., hypertension, abdominal obesity) which could have an impact on CAD was lacking in the registry. Fifth, non-HIV-related-pharmacotherapy is not included in the registry, thus the effect of possible preventive medications such as lipid-lowering therapy was not assessed. Studies evaluating this relationship are also welcomed.
5. Conclusions
There is a high frequency of cardiovascular risk factors among incident PLWHIV in Colombia. CAD poses a major risk for morbidity in this population. We identified both traditional and non-traditional risk factors that are associated with CAD including age, gender, comorbidities such as obesity, DM, and renal dysfunction as well as lipoatrophy/lipodystrophy and AIDS-defining conditions. These results may serve as a starting point in developing strategies and interventions targeting those at a higher risk to develop CAD among PLWHIV and highlights the importance of early identification of comorbidities and risk classification in this population. Further studies with longer follow-ups that include other atherosclerotic cardiovascular outcomes are needed to further clarify the prevalence and incidence of ASCVD in PLWHIV.
Credit author statement
Manuel Urina-Jassir: Conceptualization, Methodology, Visualization, Writing – original draft, Writing – review & editing. Andrés Felipe Patiño-Aldana: Conceptualization, Methodology, Formal analysis, Visualization, Writing – review & editing. Lina Johana Herrera-Parra: Conceptualization, Methodology, Formal analysis, Visualization, Validation, Writing – review & editing. Juliana Alexandra Hernández Vargas: Conceptualization, Methodology, Formal analysis, Visualization, Writing – review & editing. Silvia Juliana Trujillo-Cáceres: Conceptualization, Methodology, Formal analysis, Visualization, Writing – review & editing. Ana María Valbuena-García: Conceptualization, Methodology, Formal analysis, Visualization, Writing – review & editing, Supervision. Lizbeth Acuña-Merchán: Conceptualization, Methodology, Formal analysis, Visualization, Writing – review & editing, Supervision. Daniela Urina-Jassir: Conceptualization, Methodology, Visualization, Writing – original draft, Writing – review & editing. Miguel Urina-Triana: Conceptualization, Methodology, Visualization, Writing – review & editing, Supervision.
Competing interests
The authors report no relationships that could be construed as a conflict of interest.
Funding/grant support
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Acknowledgments
An abstract version of this project will be presented at the American College of Cardiology (ACC) Latin America 2023 Conference in Costa Rica in August 2023.
Handling Editor: D Levy
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijcrp.2023.200205.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
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