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Article

Cardiovascular Risk in HIV Patients: Ageing Analysis of the Involved Genes

by
Fabiola Boccuto
1,†,
Salvatore De Rosa
1,†,
Pierangelo Veltri
2,
Daniele Torella
3 and
Pietro Hiram Guzzi
1,*
1
Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, viale Europa, 88100 Catanzaro, Italy
2
Department of Informatics, Modelling and Systems DIMES, University of Calabria, Via P. Bucci, 87036 Rende, Italy
3
Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, viale Europa, 88100 Catanzaro, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2024, 14(17), 7526; https://doi.org/10.3390/app14177526
Submission received: 5 June 2024 / Revised: 19 August 2024 / Accepted: 22 August 2024 / Published: 26 August 2024
(This article belongs to the Section Applied Biosciences and Bioengineering)
Browse Figures
Versions Notes

Abstract

:
Acquired immunodeficiency syndrome (AIDS) has transitioned from a progressive, fatal disease to a chronic, manageable disease thanks to better defining of antiretroviral therapy, contributing to increased life expectancy. In parallel, a growing number of subjects without clinical signs of disease but living with chronic HIV infection (also indicated as PLWHs, i.e., People Living With HIV) are experiencing early cardiovascular disease, and the risk increases with age. However, a progressive increase in the prevalence of multiple comorbidity diseases has been reported as these patients age, including cardiovascular disease (CVD). Cardiovascular mortality can be related to viral infection, a progressive reduction in response to antiretroviral therapy, chronic inflammation, and lifestyle. Cardiovascular ageing represents a relevant issue in the management of HIV-infected patients. Although the exact pathophysiological mechanism that leads PLWHs to develop cardiovascular disease is not entirely understood, there is substantial evidence that they accumulate age-related conditions earlier than the general population. Furthermore, since the proportion of PLWHs growing older than 50 years has progressively increased, this results in a complex interaction between disease-related pathophysiology and the exposition of a growing burden of cardiovascular risk factors. We performed a study to relate the effect ageing gas on genes associated with HIV and cardiovascular diseases. We performed a systematic review of the genes most frequently associated with ageing in HIV-infected subjects, followed by a bioinformatic analysis to explore the biological impact of the ageing-related genes.

1. Introduction

Cardiovascular diseases represent the world’s leading cause of death [1]. Older age is a significant risk factor for cardiovascular disease, which increases with exposure to hypertension, diabetes, hypercholesterolemia, and smoking [2]. In addition, the intrinsic ageing of the heart makes it more prone to stress, with an increase in cardiovascular mortality and morbidity in older adults. Intrinsic cardiac ageing is defined as the slowly progressive structural changes and functional declines with age, without significant cardiovascular risks [3]. At the same time, the progressive deterioration of immune functions with age (immunosenescence) increases older adults’ susceptibility to infection and their risk of severe outcomes in the case of disease. Moreover, ageing may be the cause of infection, but infection can also be the cause of ageing [4]. Mechanisms may include enhanced inflammation, pathogen-dependent tissue destruction, or accelerated cellular ageing through increased turnover. However, the connection between cardiovascular ageing and infections and how the two phenomena can influence each other is still unknown.
The human immunodeficiency virus (HIV) is a lipid-enveloped retrovirus containing two copies of a single-stranded RNA genome [5]. HIV binds to the CD4 molecule and CCR5 (a chemokine co-receptor), infecting the T-helper lymphocyte. Upon integration into the host genome, the HIV provirus is formed, leading to transcription and viral mRNA production, which result in the assembly of structural proteins within the host cell. Viral release from host cells can unleash millions of HIV particles that have the potential to infect other cells [6]. Within two to four weeks, HIV enters the body, and the individual may exhibit symptoms of primary infection. A prolonged chronic HIV infection follows this initial phase. The ultimate stage of HIV disease manifests as acquired immunodeficiency syndrome (AIDS), characterised by opportunistic infections and tumours, which are typically fatal in the absence of treatment [7,8]. The longer life expectancy associated with HIV has led to a heightened prevalence of age-related illnesses, including morbidity and mortality linked to cardiovascular diseases [9].
In this paper, we focus on the possible interactions between HIV and the development of cardiovascular diseases with advancing age, considering the increase in life expectancy of these patients thanks to modern therapies. We focus on genes of interest for cardiovascular pathology and their association with HIV, and we also study variations in gene expression during patient life. Indeed, we use a publicly available database, GTEx [10], containing information about gene expression variation during patient ageing in given tissues.
The risk of cardiovascular disease (CVD) mortality is significantly elevated among HIV patients across age groups from 25 to 64 years compared to the general population [11]. It has been observed that the risk of CVD mortality is markedly reduced in individuals living with HIV who achieve viral suppression compared to those without complete suppression. The development of cardiovascular diseases is connected to gene dysregulation exacerbated by the infection and the associated inflammatory environment [12]. The upregulation or downregulation of some Rho-associated kinases and T-cell components is common in People Living With HIV (PLWHs), with their expression patterns varying based on the specific tissue, while also being influenced by factors like sex, race, and age. Their involvement is at the basis of inflammatory and fibrosis processes in cardiovascular diseases.
The introduction of Highly Active Antiretroviral Therapy (HAART) has significantly reduced AIDS-related mortality, as demonstrated by Hammer et al. [13]. However, non-HIV-related mortality, such as that attributable to CVD, has become increasingly crucial for the estimated 33.3 million People Living With HIV (PLWHs) [14]. Data from the New York City HIV Surveillance Registry for 2001 to 2012 showed that the proportion of CVD deaths among all deaths increased in the HIV population from 6 per cent to 15 per cent and decreased in the general population [15].
Thus, a growing number of subjects living with chronic HIV infection are experiencing early cardiovascular disease. Our goal is to offer a comprehensive quantitative synthesis of the genetic pathways implicated in HIV and their association with CVD, taking into account age- and sex-related disparities as well [16]. Figure 1 reports the workflow representing the performed analysis to gather genes involved in HIV infection and to study their role with respect to CVD.
Despite evidence of the earlier onset of CVD in the population living with HIV, it is not well known how HIV infection increases the risk of cardiovascular diseases. Smoking remains one of the most significant contributors to the development of CVD among PLWHs [17]. At the same time, HIV infection has been recognized as a prothrombotic condition in which a hypercoagulable state places patients at increased risk for deep vein thrombosis and other ischemic CVD events. Activated platelets favour proinflammatory and thrombogenic effects. However, no genes have been identified as the cause of the increased risk of developing CVD in PLWHs.
This paper aims to study gene expression variation during age in patients with HIV, focusing on genes associated with cardiovascular diseases. We use the GTEx database to identify genes of interest, and we review the genes associated with CVD, whose expression varies with age in HIV-infected subjects. We also performed a bioinformatic analysis to explore the biological impact of ageing-related genes.

2. Methods

We took multiple actions to relate gene analyses with HIV-related data. First, we performed a systematic review of all studies reporting the association of specific genes and related pathways with HIV to select those most frequently reported as dysregulated in HIV patients. Then, we performed multiple systematic meta-analyses, one for each of the selected genes, to assess their potential impact on patient prognosis. In particular, we focused on genes selected to highlight a possible correlation between the infection and CVD pathogenesis. Finally, we performed a bioinformatic analysis, intersecting the results of our meta-analysis with open-access genomics and RNA-omics data.

2.1. Study Selection

We are interested in studying the correlation between infections (HIV in particular) and cardiovascular diseases concerning ageing. For this aim, we searched for relevant studies in a publicly available publication database (www.scopus.org, accessed in 28 February 2024), using keywords and corresponding Medical Subject Heading (MeSH) terms related to cardiovascular diseases, HIV and known genes responsible for CVD. We used keywords and MeSH for meta-analysis. We performed queries by using two selection phases (i.e., meta-analysis). First, we used conjunction (i.e., AND operator) of keywords such as cardiovascular, ageing, and infection combined with at least (i.e., disjunctive OR operator) one of the following keywords omics, sequencing, genomics, proteomics, metabolomics to perform a primary meta-analysis. We then used gene keywords and MeSH terms to perform further data selection (i.e., second meta-analysis) using a second set of filtering criteria using MeSCH and genes, such as (TLR4) AND (HIV), (CXCR4) AND (HIV), (mTOR) AND (HIV) and so on. We chose to also look for (NCF4) AND (HIV), (NCF2) AND (HIV), (IRS1) AND (HIV) for a first meta-analysis. Also, we selected TLR4, CXCR4, mTOR, NCF4, NCF2, and IRS1 combined with the keywords “cardiovascular”, and “disease”. Figure 1 illustrates the workflow used to extract interesting genes. We selected the genes by analyzing the expression of the population in the GTEx database. We considered the genes presenting an increasing or decreasing pattern of expression, i.e., those genes whose average expression value increases (or decreases) with age. The rationale was to analyse genes whose basal expression changes with age and thus may be related to phenotypic changes. Also, we analyzed the literature to find possible differences in selected genes in HIV patients with cardiovascular disease. While selecting from the Scopus database, we also checked reference lists of eligible studies and screened scientific abstracts and relevant websites.
Two independent screenings were performed by two clinical cardiologists to search for records and identify eligible trials. For the first meta-analysis, we considered a dataset from randomised or observational studies focused on the association between cardiovascular ageing and infections. Moreover, we selected papers based on whether the information about the genes was sufficiently detailed to allow for their correct identification. Some of the selected studies have been filtered out for different reasons, such as studies being unrelated to the research question, the absence of a control arm, the clinical outcome not being reported, and the absence of original articles and editorial comments. In addition to these criteria, studies needed reported data on prognosis to be selected for the second-level meta-analyses. Figure 2 depicts the PRISMA workflow for meta-analysis.

2.2. Data Synthesis and Analysis

The primary analysis was focused on the association between cardiovascular ageing and HIV. For this purpose, the first study was performed using data extracted from the original primary publications. We based our analyses on the dis-regulated gene pathways in patients with HIV, drew up a list of all the genes involved, and selected only the genes most expressed in aortic and coronary tissues: TLR4, CXCR4, mTOR, NCF2, NCF4, and IRS1. We used the VoyAGEr application [18] to search expression gene levels in the general population of TLR4, CXCR4, mTOR, NCF2, NCF4, and IRS1, in particular on the aorta and coronary tissues, with related age and sex differences, as depicted in Table 1.
Using the genes/pathways selected from the meta-analysis described above, we performed additional meta-analyses to identify their role in HIV and CVD. Finally, we compared the variability of these genes in the general population, in patients with HIV, and in patients with cardiovascular diseases to understand how the development of cardiac pathologies in PLWHs correlates with the expression of these genes.

2.3. Bioinformatic Data Analysis

We used the GTEx data portal [10], a widely recognized resource for accessing whole-genome sequencing and RNA-seq data gathered from several patients. GTEx includes pertinent details about each specimen, including the origin tissue, sex, and age, which are categorized into six distinct groups. We used the latest GTEx V8 database version, containing 17,382 samples from 54 different tissues across 948 donors (accessible at https://gtexportal.org/home/tissueSummaryPage), accessed on 28 February 2024. This version is hosted online and features a user-friendly query interface and data visualization tools. These resources are extensively utilized in numerous studies related to ageing [19].
GTEx provides information about the age and sex of the patient, categorised into six different classes, 20–29, 30–39, 40–49, 50–59, 60–69, and 70–79, decreasing with age for each sex. To identify the interesting genes, we started by querying the GTEx data portal to gather the expression values of the samples and the metadata related to age and tissue. Samples were grouped using tissues, and each tissue expression was analysed. Also, median gene expression values were calculated with respect to age classes: 20–29, 30–39, 40–49, 50–59, 60–69, 70–79 years. We then selected those genes whose average values of expression are monotonically increasing or decreasing in that age interval. For each gene, we calculated the significance of the difference in the expression between the intervals using an ANOVA test. A Bonferroni-corrected p-value of less than 0.05 was considered significant.

3. Results

A total of 108 scientific articles were identified through a search in SCOPUS database to evaluate the connection between cardiovascular ageing and infections. Using a PRISMA-based screening strategy depicted in Figure 2, we identified 11 studies, seven related to HIV [20,21,22,23,24,25], 3 related to COVID-19 [26,27,28] and one related to Micobactherium Avium [29]. We focused our analysis only on HIV-related studies, drawing up a list of all the genes involved and selecting only the genes most expressed in aortic and coronary tissues: TLR4, CXCR4, mTOR, NCF2, NCF4, and IRS1. Our aim was to clarify the variability of these genes in the general population, in PLWHs, and in people with cardiovascular diseases in order to provide a comprehensive quantitative synthesis of genetic pathways involved in HIV and their correlation with CVD, also considering age-related differences, as depicted in Figure 3.

3.1. Increasing and Decreasing Genes in General Population

We focus on the six identified genes of interest for CVD, and we study their level expression variations with respect to age intervals.
The TLR4 levels both in aortic and coronary tissues tend to decrease starting from the age of 50, but the highest rate of genetic alterations occurs at 25 years in coronary tissue and at 55 years in aortic tissue (see Figure 4 and Figure 5).
The CXCR4 levels in aortic tissue continuously rise throughout life, and the highest rate of genetic alterations occurs around 40 years old. Instead, in coronary tissue, gene levels continuously increase up to the age of 60 and then decrease exponentially. In this case, the highest rate of genetic alterations occurs during a young age (30 years) (see Figure A1 and Figure A2 in Appendix A).
The mTOR levels both in aortic and coronary tissues tend to decrease starting from the age of 50, and at the same time the highest rate of genetic alterations occurs in this phase (highest point after 60 years for coronary tissue and at 55 years for aortic tissue) (see Figure A3 and Figure A4 in Appendix A).
The IRS1 levels in both aortic and coronary tissues tend to decrease from age 50. At the same time, the highest rate of genetic alterations occurs in this phase (highest point at 50 years for coronary tissue and 60 years for aortic tissue) (see Figure A5 and Figure A6 of the Appendix A).
NCF2 levels in aortic tissue do not vary throughout life, and the highest rate of genetic alterations occurs around 35 years old. Instead, in coronary tissue, gene levels tend to decrease starting from an age of 60 years. In this case, the highest rate of genetic alterations occurs at 50 years (see Figure A7 and Figure A8 of the Appendix A).
NCF4 levels in aortic tissue do not vary throughout life and the highest rate of genetic alterations occurs around 35 years old. Instead, in coronary tissue, gene levels tend to decrease starting from the age of 60 years. In this case, the highest rate of genetic alterations occurs at 60 years (see Figure A9 and Figure A10 of the Appendix A).

3.2. Increasing and Decreasing Genes in Patients with HIV

An integrated Scopus and PubMed search revealed clinical data for only TLR4, mTOR, CXCR4, IRS1, and NCF4.
A total of 266 TLR4 articles were identified through a search in SCOPUS and 310 through a search in PUBMED. Using a PRIMSA-based screening strategy (see Figure A11 of the Appendix A), we identified 38 studies. A growing body of research utilizing various cell types from individuals with HIV suggests that TLR4 plays a crucial role in controlling the progression of viral infections [30]. Elevated levels of TLR4 expression and the production of inflammatory cytokines have been observed in HIV-infected individuals, with some studies noting higher expression levels in patients not undergoing HAART treatment [31,32]. Several studies have highlighted a link between specific TLR4 genetic variations and HIV susceptibility [33]. Carriers of variants A+896G (rs4986790) and C+1196T (rs4986791) have shown reduced inflammatory responses to inhaled LPS compared to non-carriers [34]. Additionally, SNPs in TLR4 such as 1063A/G and 1363C/T have been linked to changes in CD4 cell counts, viral load, and disease progression in HIV infection [35]. The presence of the TLR4 Asp299Gly heterozygous genotype has been found to be higher in HIV-1 infected individuals [36] and independently associated with cardiovascular diseases in HIV-infected patients, potentially due to its proinflammatory effects contributing to atherosclerosis [37]. Furthermore, HIV entry into host cells requires interaction with the CD4 receptor on the cell membrane and relies on the activation of the co-receptor CXCR4 [38], which is notably increased in patients with advanced HIV disease [39]. A total of 3825 articles on CXCR4 were identified in SCOPUS, and 3970 in PUBMED. Using a PRISMA-based screening approach (refer to Appendix A Figure A12), 62 relevant studies were identified. In this case, no correlation emerged between the disease and specific polymorphisms, but natural ligands for CXCR4, such as peptidic compounds T22 (an 18-mer), T134 (a 14-mer), ALX40-4C (a 9-mer), and CGP 64222 (also a 9-mer), have been identified as CXCR4 antagonists with anti-HIV properties [40,41,42].
Another new technology consists of a small-molecule inhibitor, ALX40-4C, that inhibits HIV-1 envelope (Env)-mediated membrane fusion and viral entry directly at the level of the coreceptor use [43]. In the future, HIV entry/fusion inhibitors will become important new antiviral agents to combat AIDS. Mutations in the CXCR4 gene are generally rare and have not been implicated in HIV-1/AIDS pathogenesis. Comprehensive mutation analysis of the CXCR4 gene confirm a high degree of genetic conservation within the coding region of this ancient population [44].
mTOR also plays a crucial role since one hundred fifty-one mTOR articles were identified through a search in SCOPUS and 187 through a search in PUBMED. Using a PRIMSA-based screening strategy (see Figure A13 of the Appendix A), we identified 19 studies. CD4+ and CD8+ T cells from PLWHs play a role in cell adhesion, apoptosis, and migration processes involved in atherosclerosis, and the upregulated mTOR pathway mediates these atherogenic processes [21,45]. The HIV-1 viral life cycle depends on mTOR because it drives signalling and metabolic pathways required for viral entry, replication, and latency. In HIV-1 pathogenesis, mTOR alters host cell metabolism to create an optimal environment for viral replication [45]. Preclinical evidence indicates that selective inhibitors of mTOR, such as rapamycin, could represent a novel therapeutic approach for the treatment of these pathologies [46].
In PLWH monocytes, NCF genes are involved in superoxide production and are a positive regulator of P13K signalling [21]. From our integrated search on Pubmed and Scopus, only one article was selected (see Figure A14 of the Appendix A), where microarray data analysis of datasets involving HIV cases was performed to scrutinize the differentially expressed genes. NCF4 might have the potential to be exploited as a possible drug target and biomarker in the diagnosis, prognosis, and treatment of HIV and its comorbidities [47].
Finally, blood sugar metabolism abnormalities are linked to HIV-associated neurocognitive disorders (HANDs) [48]. In untreated HIV patients, there is a significant presence of insulin resistance. In our meta-analysis, a total of five articles related to IRS1 were found in SCOPUS, while 20 articles were identified in PUBMED. By employing a PRISMA-based screening approach (refer to Appendix A Figure A15), nine relevant studies were identified. What we observed from this study is the connection between elevation in inflammatory cytokines and mitochondrial dysfunction, inflammasome, and serine kinase activation (PKR, JNK, and IKKbeta) that leads to serine phosphorylation of IRS1/2, ultimately leading to a reduction in insulin signalling [49]. Moreover, the use of antiretroviral therapies such as indinavir, lopinavir, and nelfinavir has been linked to the development of insulin resistance [50,51,52].

3.3. Increasing and Decreasing Genes in Patients with CVD

The integrated Scopus and Pubmed search found clinical data for TLR4, mTOR, CXCR4, and IRS1. It was of great interest to require information about baseline characteristics of patients with cardiovascular disease and the de-regulation of a specific gene expression.
A total of 884 TLR4 articles were identified through a search in SCOPUS and 141 through a search in PUBMED. Using a PRISMA-based screening strategy, we identified 22 studies. (see Appendix Figure A16 of the Appendix A). The people included were both male and female, of any age eighteen years or older, and with many diseases such as diabetes mellitus, hypertension, dyslipidemia, obesity, bicuspid aortic valve, abdominal aortic aneurysm, peripheral artery disease, a history of acute myocardial infarction, or unstable angina. Six studies have highlighted the correlation between cardiovascular diseases and specific TLR4 polymorphisms [53,54,55,56,57,58]: rs1927914 TC, TC/CC genotypes and TLR4 rs1927914 TC genotype were associated with aortic aneurysm; rs10759932 polymorphism was associated with a reduced risk of AAD. The C/T genotype of the rs4986791 polymorphism was significantly associated with severe non-coronary atherosclerosis. The frequency of SNP896A/G in the TLR4 gene was not significantly different between AMI patients and controls. Finally, 299Gly carriers (with a story of CCS) had a lower risk of cardiovascular events during follow-up with pravastatin. Instead, sixteen studies demonstrated that the specific cardiovascular disease (aortic aneurysm, acute or chronic coronary syndrome, etc.) was associated with higher TLR4 blood levels [59,60,61,62,63,64,65,66,67,68,69].
Concerning CXCR4, 128 articles were identified through a search in SCOPUS and 83 through a search in PUBMED. Using a PRISMA-based screening strategy (as reported in Figure A17 of the Appendix A), we identified one study. The people included were both male and female, of any age eighteen years or older, and with many diseases such as diabetes mellitus, hypertension, dyslipidemia, atrial fibrillation, the story of acute myocardial infarction, or unstable angina. In this study, the platelet surface expression of CXCR4 was measured in 284 patients with symptomatic CAD at the time of percutaneous coronary intervention (PCI). The primary combined endpoint was defined as all-cause death and myocardial infarction (MI) during a 12-month follow-up. There were differences in CXCR4 values in patients who developed a combined endpoint compared with event-free patients and in patients who subsequently died. In fact, lower platelet CXCR4 levels were independently and significantly associated with all-cause mortality (hazard ratio 0.24, 95 percent CI 0.07–0.87) and the primary combined end point of all-cause death and MI (hazard ratio 0.30, 95 percent CI 0.13–0.72) [70].
A total of 129 scientific articles on mTOR were identified through a search in Scopus and 361 through a search in PUBMED. Using a PRIMSA-based screening strategy (see Figure A18 of the Appendix A), we identified two studies. The people included were both male and female, of any age eighteen years or older, and with many diseases such as diabetes mellitus, hyper- or hypothyroidism, severe infection, malignancy, CVD, stroke/TIA, rheumatoid arthritis, and acute myocardial infarction. One study demonstrated that mTOR inhibition using everolimus at the time of an acute STEMI reduces LV infarct size following successful PCI [71]. In the other study, patients with rheumatoid arthritis showed the hypoexpression of Raptor, a positive regulator of mTOR activity, resulting in decreased LDL levels [72].
A total of 1994 scientific articles were identified about IRS1 through a search in SCOPUS and 32 through a search in PUBMED (see Figure A19 of the Appendix A). Using a PRISMA-based screening strategy, we identified the studies [72,73,74,75]. People included were both male and female, of any age from eighteen years, and with many diseases such as diabetes mellitus, obesity, hypertension, dyslipidemia, previous acute myocardial infarction, multivessel CAD, prior stroke/TIA, and chronic renal insufficiency. Five studies have highlighted the correlation between cardiovascular diseases and specific IRS1 polymorphisms: the C allele of the IRS1 gene (rs2943640) in both homozygous and heterozygous states may indicate an increased risk of dyslipidemia in type 2 diabetic patients with comorbidities [76]. Arg/Arg and Gly/Arg polymorphism of the IRS-1 gene is associated with such components of metabolic syndrome as hypertriglyceridemia and fasting hyperglycemia. In another study, type 2 DM patients who are carriers of the C allele of the rs956115 marker of the IRS-1 gene have a hyperreactive platelet phenotype and increased risk of MACE, while hypertensive patients with the GA genotype Gly972Arg polymorphism of the IRS-1 gene are predisposed to insulin resistance and disorders of lipid metabolism. Finally, the Arg972 variant in insulin receptor substrate-1 is associated with an atherogenic profile in type 2 diabetic patients.

4. Discussion

Antiretroviral therapy (ART) significantly improved life expectancy among PLWHs with great modification in mortality and morbidity patterns [77]. CVD events increase with age, but it is unclear why they occur most frequently in PLWHs compared to general populations of similar age [78]. We focus on linking ageing, cardiovascular diseases and HIV, aiming to identify and study the roles of genes involved in these pathologies. We based our analyses on the genes’ dis-regulated pathways in patients with HIV, selecting the genes that are simultaneously the most expressed in aortic and coronary tissue. We compared the gene expression of TLR4, CXCR4, mTOR, NCF2, NCF4, and IRS1 in the general population, in patients with HIV, and in patients with cardiovascular disease, obtaining interesting results. The findings presented in the analysis highlight the intricate relationship between genetic pathways involved in HIV infection and cardiovascular diseases. The dysregulation of genes such as TLR4, CXCR4, mTOR, NCF2, NCF4, and IRS1 in individuals living with HIV not only impacts viral pathogenesis but also contributes to the development of cardiovascular complications.
The upregulation of TLR4, a key regulator of proinflammatory cytokines, in HIV patients, particularly in those not on antiretroviral therapy, underscores the role of inflammation in HIV pathogenesis and its potential link to cardiovascular diseases. Similarly, the increased activation of CXCR4 in advanced HIV disease indicates its involvement in viral entry and immune dysregulation, which can also impact cardiovascular health.
Furthermore, the dysregulation of mTOR, NCF2, NCF4, and IRS1 in HIV-infected individuals sheds light on the metabolic disturbances and insulin resistance observed in these patients, which are known risk factors for cardiovascular diseases. The interplay between these genetic pathways not only affects the progression of HIV-related complications but also influences the development of cardiovascular events in this population.
The identification of specific polymorphisms and gene expressions associated with cardiovascular diseases in individuals living with HIV provides valuable insights into potential genetic markers for risk stratification and targeted interventions. Understanding these genetic pathways’ molecular mechanisms can pave the way for personalized approaches to managing cardiovascular risks in PLWHs.
Overall, the integration of genetic studies in the context of HIV infection and cardiovascular diseases offers a comprehensive understanding of the intricate interplay between viral pathogenesis, immune responses, metabolic dysregulation, and cardiovascular complications. Finally, bioinformatics analysis tools allowed us to define a pipeline for studying comorbidities in chronic diseases by focusing on genes and ages, but most of all related to cardiovascular conditions concerning infections and other interesting conditions such as long-term COVID-19.

Limitations

Even in its complexity, such a study represents an important connection between infections and CVD. However, it relies on publicly available databases, and it should be validated by a set of in vivo experiments. Moreover, a significant limitation is related to the lack of information related to critical cardiovascular risk factors such as smoking and obesity, which are known to influence the development of cardiovascular diseases. Having such clinical information may enrich the results and may allow the study of relations among genes, ageing, HIV infection, and CVD in major detail. Thus, the data availability for future studies would provide a more comprehensive understanding of the interactions between HIV, gene expression, and cardiovascular risk factors. The study could benefit from a subsequent analysis to draw causal inferences regarding the relationship between gene expression changes and the development of cardiovascular diseases in People Living With HIV. Longitudinal studies are needed to confirm these findings and explore the temporal dynamics of gene expression changes concerning cardiovascular outcomes.

5. Conclusions

We have identified possible correlations between the genetic pathways involved in HIV and the development of cardiovascular diseases. Further research in this area is essential to elucidate the causal relationships between these genetic pathways and disease outcomes, ultimately guiding the development of novel therapeutic strategies for improving the health outcomes of individuals living with HIV. We also highlighted the importance of ageing in studies relating genes to pathologies and age-related conditions. This may be considered an interesting direction for further studies.

Author Contributions

P.H.G. defined the problem that has been studied in depth by F.B. and S.D.R. P.V. and D.T. supported writing and analysis of obtained results. PhG directed the entire study, and he is responsible for F.B.’s PhD project. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Next Generation EU—Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ’Innovation Ecosystems’, building ’Territorial R&D Leaders’ (Directorial Decree n. 2021/3277)—project Tech4You—Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them. PV was partially supported by project SERICS (PE00000014) under the MUR385 National Recovery and Resilience Plan funded by the European Union: NextGen- erationEU.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data we used are available online. Code availability: under requirements, the authors furnished the pipeline and support even if it can be extracted from the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

AIDSAcquired Immunodeficiency Syndrome
HIVHuman Immunodeficiency Virus
PLWHsPeople Living With HIV
CVDCardiovascular Diseases
PWHPeople With HIV
HAARTHighly Active Antiretroviral Therapy
CCSChronic Coronary Syndrome
AMIAcute Myocardial Infarction
CADCoronary Artery Disease
TIATransient Ischemic Attack
DMDiabetes Mellitus
MACEMajor Adverse Cardiovascular Events
ARTAnti Retroviral Therapy

Appendix A

This section contains a set of supplementary figures related to the analysis of the paper.
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Figure A1. CXCR4 levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
Figure A1. CXCR4 levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
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Figure A2. CXCR4 levels in coronary tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
Figure A2. CXCR4 levels in coronary tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
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Figure A3. mTOR levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
Figure A3. mTOR levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
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Figure A4. mTOR levels in coronary tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
Figure A4. mTOR levels in coronary tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
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Figure A5. IRS1 levels in coronary tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
Figure A5. IRS1 levels in coronary tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
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Figure A6. IRS1 levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
Figure A6. IRS1 levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
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Figure A7. NCF2 levels in carotid tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
Figure A7. NCF2 levels in carotid tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
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Figure A8. NCF2 levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
Figure A8. NCF2 levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
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Applsci 14 07526 g0a9
Figure A9. NCF4 levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
Figure A9. NCF4 levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
Applsci 14 07526 g0a9
Applsci 14 07526 g0a10
Figure A10. NCF4 levels in coronary tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
Figure A10. NCF4 levels in coronary tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
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Figure A11. PRISMA-based screening strategy to identify the possible connection between TLR4 and HIV.
Figure A11. PRISMA-based screening strategy to identify the possible connection between TLR4 and HIV.
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Figure A12. PRISMA-based screening strategy to identify the possible connection between CXCR4 and HIV.
Figure A12. PRISMA-based screening strategy to identify the possible connection between CXCR4 and HIV.
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Figure A13. PRISMA-based screening strategy to identify the possible connection between mTOR and HIV.
Figure A13. PRISMA-based screening strategy to identify the possible connection between mTOR and HIV.
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Figure A14. PRISMA-based screening strategy to identify the possible connection between NCF4 and HIV.
Figure A14. PRISMA-based screening strategy to identify the possible connection between NCF4 and HIV.
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Figure A15. PRISMA-based screening strategy to identify the possible connection between IRS1 and HIV.
Figure A15. PRISMA-based screening strategy to identify the possible connection between IRS1 and HIV.
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Figure A16. PRISMA-based screening strategy to identify the possible connection between TLR4 and CVD. * indicates exclusion criteria.
Figure A16. PRISMA-based screening strategy to identify the possible connection between TLR4 and CVD. * indicates exclusion criteria.
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Figure A17. PRISMA-based screening strategy to identify the possible connection between CXCR4 and CVD. * indicates exclusion criteria.
Figure A17. PRISMA-based screening strategy to identify the possible connection between CXCR4 and CVD. * indicates exclusion criteria.
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Figure A18. PRISMA-based screening strategy to identify the possible connection between mTOR and CVD. * indicates exclusion criteria.
Figure A18. PRISMA-based screening strategy to identify the possible connection between mTOR and CVD. * indicates exclusion criteria.
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Figure A19. PRISMA-based screening strategy to identify the possible connection between IRS1 and CVD. * indicates exclusion criteria.
Figure A19. PRISMA-based screening strategy to identify the possible connection between IRS1 and CVD. * indicates exclusion criteria.
Applsci 14 07526 g0a19

References

  1. Bykowska-Derda, A.; Spychala, M.; Czlapka-Matyasik, M.; Sojka, M.; Bykowski, J.; Ptak, M. The Relationship between Mortality from Cardiovascular Diseases and Total Drinking Water Hardness: Systematic Review with Meta-Analysis. Foods 2023, 12, 3255. [Google Scholar] [CrossRef]
  2. Sniderman, A.D.; Furberg, C.D. Age as a modifiable risk factor for cardiovascular disease. Lancet 2008, 371, 1547–1549. [Google Scholar] [CrossRef] [PubMed]
  3. Obas, V.; Vasan, R.S. The aging heart. Clin. Sci. 2018, 132, 1367–1382. [Google Scholar] [CrossRef]
  4. High, K.P. Infection as a cause of age-related morbidity and mortality. Ageing Res. Rev. 2004, 3, 1–14. [Google Scholar] [CrossRef] [PubMed]
  5. Hardy, W.D. The human immunodeficiency virus. Med. Clin. 1996, 80, 1239–1261. [Google Scholar] [CrossRef]
  6. Hanson, H.M.; Willkomm, N.A.; Yang, H.; Mansky, L.M. Human retrovirus genomic RNA packaging. Viruses 2022, 14, 1094. [Google Scholar] [CrossRef]
  7. Schuman, J.S.; Orellana, J.; Friedman, A.H.; Teich, S.A. Acquired immunodeficiency syndrome (AIDS). Surv. Ophthalmol. 1987, 31, 384–410. [Google Scholar] [CrossRef] [PubMed]
  8. Tradigo, G.; De Rosa, S.; Vizza, P.; Fragomeni, G.; Guzzi, P.H.; Indolfi, C.; Veltri, P. Calculation of intracoronary pressure-based indexes with jlabchart. Appl. Sci. 2022, 12, 3448. [Google Scholar] [CrossRef]
  9. Stott, D.J.; Lowe, G.D. Cardiovascular Disease and Health in the Older Patient: Expanded from ‘Pathy’s Principles and Practice of Geriatric Medicine’; John Wiley & Sons: Hoboken, NJ, USA, 2012. [Google Scholar]
  10. Lonsdale, J.; Thomas, J.; Salvatore, M.; Phillips, R.; Lo, E.; Shad, S.; Hasz, R.; Walters, G.; Garcia, F.; Young, N.; et al. The genotype-tissue expression (GTEx) project. Nat. Genet. 2013, 45, 580–585. [Google Scholar] [CrossRef]
  11. Petoumenos, K.; Reiss, P.; Ryom, L.; Rickenbach, M.; Sabin, C.; El-Sadr, W.; d’Arminio Monforte, A.; Phillips, A.N.; De Wit, S.; Kirk, O.; et al. Increased risk of cardiovascular disease (CVD) with age in HIV-positive men: A comparison of the D:A:D CVD risk equation and general population CVD risk equations. HIV Med. 2014, 15, 595–603. [Google Scholar] [CrossRef]
  12. Guaraldi, G.; Malagoli, A.; Calcagno, A.; Mussi, C.; Celesia, B.; Carli, F.; Piconi, S.; De Socio, G.; Cattelan, A.; Orofino, G.; et al. The increasing burden and complexity of multi-morbidity and polypharmacy in geriatric HIV patients: A cross sectional study of people aged 65–74 years and more than 75 years. BMC Geriatr. 2018, 18, 99. [Google Scholar] [CrossRef] [PubMed]
  13. Hammer, S.M.; Saag, M.S.; Schechter, M.; Montaner, J.S.; Schooley, R.T.; Jacobsen, D.M.; Thompson, M.A.; Carpenter, C.C.; Fischl, M.A.; Gazzard, B.G.; et al. Treatment for adult HIV infection: 2006 recommendations of the International AIDS Society—USA panel. JAMA 2006, 296, 827–843. [Google Scholar] [CrossRef] [PubMed]
  14. Cockerham, L.; Scherzer, R.; Zolopa, A.; Rimland, D.; Lewis, C.E.; Bacchetti, P.; Grunfeld, C.; Shlipak, M.; Tien, P.C. Association of HIV infection, demographic and cardiovascular risk factors with all-cause mortality in the recent HAART era. JAIDS J. Acquir. Immune Defic. Syndr. 2010, 53, 102–106. [Google Scholar] [CrossRef] [PubMed]
  15. Hanna, D.B.; Ramaswamy, C.; Kaplan, R.C.; Kizer, J.R.; Anastos, K.; Daskalakis, D.; Zimmerman, R.; Braunstein, S.L. Trends in cardiovascular disease mortality among persons with HIV in New York City, 2001–2012. Clin. Infect. Dis. 2016, 63, 1122–1129. [Google Scholar] [CrossRef] [PubMed]
  16. Aberg, J.A. Aging and HIV infection: Focus on cardiovascular disease risk. Top. Antivir. Med. 2020, 27, 102. [Google Scholar] [PubMed]
  17. Petoumenos, K.; Worm, S.; Reiss, P.; De Wit, S.; d’Arminio Monforte, A.; Sabin, C.; Friis-Møller, N.; Weber, R.; Mercie, P.; Pradier, C.; et al. Rates of cardiovascular disease following smoking cessation in patients with HIV infection: Results from the D:A:D study. HIV Med. 2011, 12, 412–421. [Google Scholar] [CrossRef]
  18. Schneider, A.L.; Martins-Silva, R.; Kaizeler, A.; Saraiva-Agostinho, N.; Barbosa-Morais, N.L. voyAGEr, a free web interface for the analysis of age-related gene expression alterations in human tissues. eLife 2024, 12, RP88623. [Google Scholar] [CrossRef]
  19. Guzz, P.H.; Lomoio, U.; Veltri, P. GTExVisualizer: A web platform for supporting ageing studies. Bioinformatics 2023, 39, btad303. [Google Scholar] [CrossRef]
  20. Vos, W.A.; Groenendijk, A.L.; Blaauw, M.J.; Van Eekeren, L.E.; Navas, A.; Cleophas, M.C.; Vadaq, N.; Matzaraki, V.; Dos Santos, J.C.; Meeder, E.M.; et al. The 2000HIV study: Design, multi-omics methods and participant characteristics. Front. Immunol. 2022, 13, 982746. [Google Scholar] [CrossRef]
  21. Palshikar, M.G.; Palli, R.; Tyrell, A.; Maggirwar, S.; Schifitto, G.; Singh, M.V.; Thakar, J. Executable models of immune signaling pathways in HIV-associated atherosclerosis. npj Syst. Biol. Appl. 2022, 8, 35. [Google Scholar] [CrossRef]
  22. Kundu, S.; Freiberg, M.S.; Tracy, R.P.; So-Armah, K.A.; Koethe, J.R.; Duncan, M.S.; Tindle, H.A.; Beckman, J.A.; Feinstein, M.J.; McDonnell, W.J.; et al. Circulating T cells and cardiovascular risk in people with and without HIV infection. J. Am. Coll. Cardiol. 2022, 80, 1633–1644. [Google Scholar] [CrossRef] [PubMed]
  23. Tsukamoto, T. HIV accelerates clonal hematopoiesis and cardiovascular aging. AIDS 2022, 36, 1599–1601. [Google Scholar] [CrossRef] [PubMed]
  24. Villanueva-Millán, M.J.; Pérez-Matute, P.; Recio-Fernández, E.; Lezana Rosales, J.M.; Oteo, J.A. Characterization of gut microbiota composition in HIV-infected patients with metabolic syndrome. J. Physiol. Biochem. 2019, 75, 299–309. [Google Scholar] [CrossRef]
  25. Babu, H.; Ambikan, A.T.; Gabriel, E.E.; Svensson Akusjärvi, S.; Palaniappan, A.N.; Cheedarla, N.; Hanna, L.E.; Neogi, U. Systemic inflammation and the increased risk of inflamm-aging and age-associated diseases in people living with HIV on long term suppressive antiretroviral therapy. Front. Immunol. 2019, 10, 474418. [Google Scholar] [CrossRef] [PubMed]
  26. de Almeida Chuffa, L.G.; Freire, P.P.; dos Santos Souza, J.; de Mello, M.C.; de Oliveira Neto, M.; Carvalho, R.F. Aging whole blood transcriptome reveals candidate genes for SARS-CoV-2-related vascular and immune alterations. J. Mol. Med. 2022, 100, 285–301. [Google Scholar] [CrossRef]
  27. Guan, S.P.; Seet, R.C.S.; Kennedy, B.K. Does eNOS derived nitric oxide protect the young from severe COVID-19 complications? Ageing Res. Rev. 2020, 64, 101201. [Google Scholar] [CrossRef]
  28. Nicin, L.; Abplanalp, W.T.; Mellentin, H.; Kattih, B.; Tombor, L.; John, D.; Schmitto, J.D.; Heineke, J.; Emrich, F.; Arsalan, M.; et al. Cell type-specific expression of the putative SARS-CoV-2 receptor ACE2 in human hearts. Eur. Heart J. 2020, 41, 1804–1806. [Google Scholar] [CrossRef]
  29. Headley, C.A.; Gerberick, A.; Mehta, S.; Wu, Q.; Yu, L.; Fadda, P.; Khan, M.; Ganesan, L.P.; Turner, J.; Rajaram, M.V. Nontuberculous mycobacterium M. avium infection predisposes aged mice to cardiac abnormalities and inflammation. Aging Cell 2019, 18, e12926. [Google Scholar] [CrossRef]
  30. Ben Haij, N.; Planès, R.; Leghmari, K.; Serrero, M.; Delobel, P.; Izopet, J.; BenMohamed, L.; Bahraoui, E. HIV-1 Tat protein induces production of proinflammatory cytokines by human dendritic cells and monocytes/macrophages through engagement of TLR4-MD2-CD14 complex and activation of NF-κB pathway. PLoS ONE 2015, 10, e0129425. [Google Scholar] [CrossRef]
  31. Hernández, J.C.; Stevenson, M.; Latz, E.; Urcuqui-Inchima, S. HIV type 1 infection up-regulates TLR2 and TLR4 expression and function in vivo and in vitro. AIDS Res. Hum. Retroviruses 2012, 28, 1313–1328. [Google Scholar] [CrossRef]
  32. Hernández, J.C.; Arteaga, J.; Paul, S.; Kumar, A.; Latz, E.; Urcuqui-Inchima, S. Up-regulation of TLR2 and TLR4 in dendritic cells in response to HIV type 1 and coinfection with opportunistic pathogens. AIDS Res. Hum. Retroviruses 2011, 27, 1099–1109. [Google Scholar] [CrossRef] [PubMed]
  33. Koval, T.I.; Syzova, L.M.; Dubynska, H.M.; Pryimenko, N.O.; Rudenko, S.S.; Marchenko, O.H.; Shlykova, O. TLR4 and TLR7 genetic polymorphism in patients with HIV/HCV-coinfection: Prevalence and gender-related features. Wiad. Lek. 2018, 71, 1566–1570. [Google Scholar] [PubMed]
  34. Yong, Y.K.; Shankar, E.M.; Solomon, A.; Spelman, T.; Fairley, C.K.; Elliott, J.H.; Hoy, J.; Cameron, P.U.; Kamarulzaman, A.; Lewin, S.R. Polymorphisms in the CD14 and TLR4 genes independently predict CD4+ T-cell recovery in HIV-infected individuals on antiretroviral therapy. AIDS 2016, 30, 2159–2168. [Google Scholar] [CrossRef]
  35. Said, E.; Al-Yafei, F.; Zadjali, F.; Al-Balushi, M.; Hasson, S.; Al-Mahroqi, S.; Koh, C.; Al-Naamani, K.; Al-Busaidi, J.; Idris, M.; et al. Frequency of TLR4 (1063A/G and 1363C/T) polymorphisms in healthy and HIV-infected Omani individuals and their relationship to viral load and T cell count. Genet. Mol. Res. 2016, 15, 10–4238. [Google Scholar] [CrossRef] [PubMed]
  36. Vidyant, S.; Chatterjee, A.; Dhole, T. A single-nucleotide polymorphism in TLR4 is linked with the risk of HIV-1 infection. Br. J. Biomed. Sci. 2019, 76, 59–63. [Google Scholar] [CrossRef]
  37. Tarancon-Diez, L.; De Pablo-Bernal, R.S.; Jiménez, J.L.; Álvarez-Ríos, A.I.; Genebat, M.; Rosado-Sánchez, I.; Muñoz-Fernández, M.Á.; Ruiz-Mateos, E.; Leal, M. Role of toll-like receptor 4 Asp299Gly polymorphism in the development of cardiovascular diseases in HIV-infected patients. AIDS 2018, 32, 1035–1041. [Google Scholar] [CrossRef]
  38. Grande, F.; Occhiuzzi, M.A.; Rizzuti, B.; Ioele, G.; De Luca, M.; Tucci, P.; Svicher, V.; Aquaro, S.; Garofalo, A. CCR5/CXCR4 dual antagonism for the improvement of HIV infection therapy. Molecules 2019, 24, 550. [Google Scholar] [CrossRef]
  39. Ostrowski, M.A.; Justement, S.J.; Catanzaro, A.; Hallahan, C.A.; Ehler, L.A.; Mizell, S.B.; Kumar, P.N.; Mican, J.A.; Chun, T.W.; Fauci, A.S. Expression of chemokine receptors CXCR4 and CCR5 in HIV-1-infected and uninfected individuals. J. Immunol. 1998, 161, 3195–3201. [Google Scholar] [CrossRef]
  40. De Clercq, E.; Schols, D. Inhibition of HIV infection by CXCR4 and CCR5 chemokine receptor antagonists. Antivir. Chem. Chemother. 2001, 12, 19–31. [Google Scholar]
  41. Choi, W.T.; Kumar, S.; Madani, N.; Han, X.; Tian, S.; Dong, C.Z.; Liu, D.; Duggineni, S.; Yuan, J.; Sodroski, J.G.; et al. A novel synthetic bivalent ligand to probe chemokine receptor CXCR4 dimerization and inhibit HIV-1 entry. Biochemistry 2012, 51, 7078–7086. [Google Scholar] [CrossRef]
  42. Xu, Y.; Duggineni, S.; Espitia, S.; Richman, D.D.; An, J.; Huang, Z. A synthetic bivalent ligand of CXCR4 inhibits HIV infection. Biochem. Biophys. Res. Commun. 2013, 435, 646–650. [Google Scholar] [CrossRef]
  43. Doranz, B.J.; Grovit-Ferbas, K.; Sharron, M.P.; Mao, S.H.; Goetz, M.B.; Daar, E.S.; Doms, R.W.; O’Brien, W.A. A small-molecule inhibitor directed against the chemokine receptor CXCR4 prevents its use as an HIV-1 coreceptor. J. Exp. Med. 1997, 186, 1395–1400. [Google Scholar] [CrossRef]
  44. Petersen, D.C.; Glashoff, R.H.; Shrestha, S.; Bergeron, J.; Laten, A.; Gold, B.; van Rensburg, E.J.; Dean, M.; Hayes, V.M. Risk for HIV-1 infection associated with a common CXCL12 (SDF1) polymorphism and CXCR4 variation in an African population. JAIDS J. Acquir. Immune Defic. Syndr. 2005, 40, 521–526. [Google Scholar] [CrossRef] [PubMed]
  45. Crater, J.M.; Nixon, D.F.; Furler O’Brien, R.L. HIV-1 replication and latency are balanced by mTOR-driven cell metabolism. Front. Cell. Infect. Microbiol. 2022, 12, 1068436. [Google Scholar] [CrossRef]
  46. Nicoletti, F.; Fagone, P.; Meroni, P.; McCubrey, J.; Bendtzen, K. mTOR as a multifunctional therapeutic target in HIV infection. Drug Discov. Today 2011, 16, 715–721. [Google Scholar] [CrossRef] [PubMed]
  47. Jadeja, K.; Verma, A.; Rathi, B. An Integrative Bioinformatics Analysis for Identifying Hub Genes in Human Immunodeficiency Virus. Curr. Trends Biotechnol. Pharm. 2021, 15, 67–79. [Google Scholar]
  48. Gerena, Y.; Skolasky, R.L.; Velez, J.M.; Toro-Nieves, D.; Mayo, R.; Nath, A.; Wojna, V. Soluble and cell-associated insulin receptor dysfunction correlates with severity of HAND in HIV-infected women. PLoS ONE 2012, 7, e37358. [Google Scholar] [CrossRef]
  49. Pedro, M.N.; Rocha, G.Z.; Guadagnini, D.; Santos, A.; Magro, D.O.; Assalin, H.B.; Oliveira, A.G.; Pedro, R.d.J.; Saad, M.J. Insulin resistance in HIV-patients: Causes and consequences. Front. Endocrinol. 2018, 9, 514. [Google Scholar] [CrossRef]
  50. Djedaini, M.; Peraldi, P.; Drici, M.D.; Darini, C.; Saint-Marc, P.; Dani, C.; Ladoux, A. Lopinavir co-induces insulin resistance and ER stress in human adipocytes. Biochem. Biophys. Res. Commun. 2009, 386, 96–100. [Google Scholar] [CrossRef] [PubMed]
  51. Ismail, W.I.W.; King, J.A.; Anwar, K.; Pillay, T.S. Indinavir and nelfinavir inhibit proximal insulin receptor signaling and salicylate abrogates inhibition: Potential role of the NFkappa B pathway. J. Cell. Biochem. 2013, 114, 1729–1737. [Google Scholar] [CrossRef]
  52. Mondal, D.; Liu, K.; Hamblin, M.; Lasky, J.A.; Agrawal, K.C. Nelfinavir suppresses insulin signaling and nitric oxide production by human aortic endothelial cells: Protective effects of thiazolidinediones. Ochsner J. 2013, 13, 76–90. [Google Scholar]
  53. Boekholdt, S.M.; Agema, W.R.; Peters, R.J.; Zwinderman, A.H.; van der Wall, E.E.; Reitsma, P.H.; Kastelein, J.J.; Jukema, J.W. Variants of toll-like receptor 4 modify the efficacy of statin therapy and the risk of cardiovascular events. Circulation 2003, 107, 2416–2421. [Google Scholar] [CrossRef]
  54. Li, T.; Jing, J.; Dong, N.; Liu, X.; Ma, C.; Yang, J. TLR4 rs1927914 polymorphism contributes to serum TLR4 levels in patients with aortic aneurysm. Exp. Mol. Pathol. 2021, 119, 104609. [Google Scholar] [CrossRef] [PubMed]
  55. Li, T.; Liu, X.; Ning, H.; Li, X.; Yang, J.; Ma, C. Association of Toll-like receptor 4 gene polymorphisms with acute aortic dissection in a Chinese Han population. BioMed Res. Int. 2020, 2020. [Google Scholar] [CrossRef] [PubMed]
  56. Li, T.; Jing, J.; Sun, L.; Jiang, B.; Xin, S.; Yang, J.; Yuan, Y. TLR4 and MMP2 polymorphisms and their associations with cardiovascular risk factors in susceptibility to aortic aneurysmal diseases. Biosci. Rep. 2019, 39, BSR20181591. [Google Scholar] [CrossRef]
  57. Kutikhin, A.G.; Ponasenko, A.V.; Khutornaya, M.V.; Yuzhalin, A.E.; Zhidkova, I.I.; Salakhov, R.R.; Golovkin, A.S.; Barbarash, O.L.; Barbarash, L.S. Association of TLR and TREM-1 gene polymorphisms with atherosclerosis severity in a Russian population. Meta Gene 2016, 9, 76–89. [Google Scholar] [CrossRef]
  58. Džumhur, A.; Zibar, L.; Wagner, J.; Šimundić, T.; Dembić, Z.; Barbić, J. Association studies of gene polymorphisms in toll-like receptors 2 and 4 in Croatian patients with acute myocardial infarction. Scand. J. Immunol. 2012, 75, 517–523. [Google Scholar] [CrossRef] [PubMed]
  59. Chen, X.; Tao, T.; Wang, H.; Zhao, H.; Lu, L.; Wu, F. Arterial thrombosis is accompanied by elevated mitogen-activated protein kinase (MAPK) and cyclooxygenase-2 (COX-2) expression via toll-like receptor 4 (TLR-4) activation by S100A8/A9. Med. Sci. Monit. Int. Med. J. Exp. Clin. Res. 2018, 24, 7673. [Google Scholar] [CrossRef] [PubMed]
  60. Balistreri, C.R.; Marullo, A.G.; Madonna, M.; Cavarretta, E.; Allegra, A.; Cesarini, V.; Iaccarino, A.; Schiavon, S.; Peruzzi, M.; Greco, E.; et al. Deregulation of TLR4 signaling pathway characterizes Bicuspid Aortic valve syndrome. Sci. Rep. 2019, 9, 11028. [Google Scholar] [CrossRef]
  61. Liang, S.; Aiqun, M.; Jiwu, L.; Ping, Z. TLR3 and TLR4 as potential clinical biomarkers for in-stent restenosis in drug-eluting stents patients. Immunol. Res. 2016, 64, 424–430. [Google Scholar] [CrossRef]
  62. Satoh, S.; Yada, R.; Inoue, H.; Omura, S.; Ejima, E.; Mori, T.; Takenaka, K.; Kawamura, N.; Numaguchi, K.; Mori, E.; et al. Toll-like receptor-4 is upregulated in plaque debris of patients with acute coronary syndrome more than Toll-like receptor-2. Heart Vessel. 2016, 31, 1–5. [Google Scholar] [CrossRef]
  63. Benhamou, Y.; Bellien, J.; Armengol, G.; Brakenhielm, E.; Adriouch, S.; Iacob, M.; Remy-Jouet, I.; Le Cam-Duchez, V.; Monteil, C.; Renet, S.; et al. Role of Toll-like receptors 2 and 4 in mediating endothelial dysfunction and arterial remodeling in primary arterial antiphospholipid syndrome. Arthritis Rheumatol. 2014, 66, 3210–3220. [Google Scholar] [CrossRef]
  64. Katoh, S.; Honda, S.; Watanabe, T.; Suzuki, S.; Ishino, M.; Kitahara, T.; Funayama, A.; Netsu, S.; Sasaki, T.; Shishido, T.; et al. Atrial endothelial impairment through Toll-like receptor 4 signaling causes atrial thrombogenesis. Heart Vessel. 2014, 29, 263–272. [Google Scholar] [CrossRef]
  65. Wyss, C.A.; Neidhart, M.; Altwegg, L.; Spanaus, K.S.; Yonekawa, K.; Wischnewsky, M.B.; Corti, R.; Kucher, N.; Roffi, M.; Eberli, F.R.; et al. Cellular actors, Toll-like receptors, and local cytokine profile in acute coronary syndromes. Eur. Heart J. 2010, 31, 1457–1469. [Google Scholar] [CrossRef] [PubMed]
  66. Ammirati, E.; Cianflone, D.; Vecchio, V.; Banfi, M.; Vermi, A.C.; De Metrio, M.; Grigore, L.; Pellegatta, F.; Pirillo, A.; Garlaschelli, K.; et al. Effector memory T cells are associated with atherosclerosis in humans and animal models. J. Am. Heart Assoc. 2012, 1, e000125. [Google Scholar] [CrossRef] [PubMed]
  67. Kashiwagi, M.; Imanishi, T.; Ozaki, Y.; Satogami, K.; Masuno, T.; Wada, T.; Nakatani, Y.; Ishibashi, K.; Komukai, K.; Tanimoto, T.; et al. Differential expression of Toll-like receptor 4 and human monocyte subsets in acute myocardial infarction. Atherosclerosis 2012, 221, 249–253. [Google Scholar] [CrossRef] [PubMed]
  68. Sheu, J.J.; Chang, L.T.; Chiang, C.H.; Youssef, A.A.; Wu, C.J.; Lee, F.Y.; Yip, H.K. Prognostic value of activated toll-like receptor-4 in monocytes following acute myocardial infarction. Int. Heart J. 2008, 49, 1–11. [Google Scholar] [CrossRef]
  69. Satoh, M.; Shimoda, Y.; Maesawa, C.; Akatsu, T.; Ishikawa, Y.; Minami, Y.; Hiramori, K.; Nakamura, M. Activated toll-like receptor 4 in monocytes is associated with heart failure after acute myocardial infarction. Int. J. Cardiol. 2006, 109, 226–234. [Google Scholar] [CrossRef]
  70. Rath, D.; Chatterjee, M.; Borst, O.; Müller, K.; Langer, H.; Mack, A.; Schwab, M.; Winter, S.; Gawaz, M.; Geisler, T. Platelet surface expression of stromal cell–derived factor-1 receptors CXCR4 and CXCR7 is associated with clinical outcomes in patients with coronary artery disease. J. Thromb. Haemost. 2015, 13, 719–728. [Google Scholar] [CrossRef]
  71. Klingenberg, R.; Stähli, B.E.; Heg, D.; Denegri, A.; Manka, R.; Kapos, I.; von Eckardstein, A.; Carballo, D.; Hamm, C.W.; Vietheer, J.; et al. Controlled-Level EVERolimus in Acute Coronary Syndrome (CLEVER-ACS)-A phase II, randomized, double-blind, multi-center, placebo-controlled trial. Am. Heart J. 2022, 247, 33–41. [Google Scholar] [CrossRef]
  72. Samimi, Z.; Izadpanah, A.; Feizollahi, P.; Roghani, S.A.; Assar, S.; Zafari, P.; Taghadosi, M. The association between the plasma sugar and lipid profile with the gene expression of the regulatory protein of mTOR (Raptor) in patients with rheumatoid arthritis. Immunol. Investig. 2021, 50, 597–608. [Google Scholar] [CrossRef]
  73. Marushchak, M.; Hevko, U.; Krynytska, I. Insulin receptor substrate 1 gene variations and lipid profile characteristics in the type 2 diabetic patients with comorbid obesity and chronic pancreatitis. Endocr. Regul. 2022, 56, 1–9. [Google Scholar] [CrossRef]
  74. Dziwura, J.; Pełka-Lalik, B.; Widecka, K. The effect of dietary salt load on insulin resistance in hypertensive patients with the Gly972Arg polymorphism of the insulin receptor substrate 1 (IRS-1) gene. Arter. Hypertens. 2007, 11, 12–20. [Google Scholar]
  75. Marini, M.A.; Frontoni, S.; Mineo, D.; Bracaglia, D.; Cardellini, M.; De Nicolais, P.; Baroni, A.; D’Alfonso, R.; Perna, M.; Lauro, D.; et al. The Arg972 variant in insulin receptor substrate-1 is associated with an atherogenic profile in offspring of type 2 diabetic patients. J. Clin. Endocrinol. Metab. 2003, 88, 3368–3371. [Google Scholar] [CrossRef]
  76. Succurro, E.; Cicone, F.; Papa, A.; Miceli, S.; Vizza, P.; Fiorentino, T.V.; Perticone, M.; Sciacqua, A.; Guzzi, P.H.; Veltri, P.; et al. Impaired insulin-stimulated myocardial glucose metabolic rate is associated with reduced estimated myocardial energetic efficiency in subjects with different degrees of glucose tolerance. Cardiovasc. Diabetol. 2023, 22, 4. [Google Scholar] [CrossRef]
  77. HIV-Causal Collaboration. The effect of combined antiretroviral therapy on the overall mortality of HIV-infected individuals. AIDS 2010, 24, 123–137. [Google Scholar] [CrossRef]
  78. Triant, V.A.; Lee, H.; Hadigan, C.; Grinspoon, S.K. Increased acute myocardial infarction rates and cardiovascular risk factors among patients with human immunodeficiency virus disease. J. Clin. Endocrinol. Metab. 2007, 92, 2506–2512. [Google Scholar] [CrossRef]
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Figure 1. The protocol applied for the proposed study. We made a first meta-analysis based on studies evaluating the link between cardiovascular ageing and infections, choosing only HIV-related articles. A list of human genes involved in HIV infection has been drawn up, and only major genes expressed in the aorta and coronary tissue (TLR4, CXCR4, mTOR, NCF2, NCF4, and IRS1) have been selected to highlight a possible correlation between the infection and CVD pathogenesis. An analysis was carried out on three levels: we analyzed gene expression in the general population, and we performed a meta-analysis to evaluate the connection between gene expression and HIV and a meta-analysis to evaluate the connection between gene expression and cardiovascular diseases.
Figure 1. The protocol applied for the proposed study. We made a first meta-analysis based on studies evaluating the link between cardiovascular ageing and infections, choosing only HIV-related articles. A list of human genes involved in HIV infection has been drawn up, and only major genes expressed in the aorta and coronary tissue (TLR4, CXCR4, mTOR, NCF2, NCF4, and IRS1) have been selected to highlight a possible correlation between the infection and CVD pathogenesis. An analysis was carried out on three levels: we analyzed gene expression in the general population, and we performed a meta-analysis to evaluate the connection between gene expression and HIV and a meta-analysis to evaluate the connection between gene expression and cardiovascular diseases.
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Figure 2. PRISMA-based screening strategy: one hundred eight articles were identified through a search in SCOPUS to evaluate the connection between cardiovascular ageing and infections. Using a PRISMA-based screening strategy, we identified 11 studies, seven related to HIV, 3 related to COVID-19, and 1 related to Mycobacterium avium. We focused our analysis only on HIV-related studies. Each sentence with * describe an exclusion criteria.
Figure 2. PRISMA-based screening strategy: one hundred eight articles were identified through a search in SCOPUS to evaluate the connection between cardiovascular ageing and infections. Using a PRISMA-based screening strategy, we identified 11 studies, seven related to HIV, 3 related to COVID-19, and 1 related to Mycobacterium avium. We focused our analysis only on HIV-related studies. Each sentence with * describe an exclusion criteria.
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Figure 3. The variability of the genes of interest in the general population, patients with HIV, and patients with cardiovascular diseases. We have also clarified the association between specific polymorphisms of the genes of our interest and the development of HIV and cardiovascular diseases. The main polymorphisms associated with HIV include A+896G (rs4986790) and C+1196T (rs4986791). Compared to non-carriers, carriers of A+896G/C+1196T showed blunted inflammatory responses to inhaled LPS. At the same time, single nucleotide polymorphisms (SNPs) in TLRs such as TLR4 1063A/G and 1363C/T are associated with changes in CD4 count, viral load (VL), and disease progression during HIV infection. The main polymorphisms associated with cardiovascular diseases are rs1927914, rs10759932, rs4986791, 299Gly for TLR4 and rs2943640, Arg/Arg, Gly/Arg, rs956115, Gly972Arg, Arg972 for IRS1. NA: No clinical data are available for NCF2 and NCF4. Due to the lack of data, it is not possible to specify whether the considered data concern viral or aviremic patients.
Figure 3. The variability of the genes of interest in the general population, patients with HIV, and patients with cardiovascular diseases. We have also clarified the association between specific polymorphisms of the genes of our interest and the development of HIV and cardiovascular diseases. The main polymorphisms associated with HIV include A+896G (rs4986790) and C+1196T (rs4986791). Compared to non-carriers, carriers of A+896G/C+1196T showed blunted inflammatory responses to inhaled LPS. At the same time, single nucleotide polymorphisms (SNPs) in TLRs such as TLR4 1063A/G and 1363C/T are associated with changes in CD4 count, viral load (VL), and disease progression during HIV infection. The main polymorphisms associated with cardiovascular diseases are rs1927914, rs10759932, rs4986791, 299Gly for TLR4 and rs2943640, Arg/Arg, Gly/Arg, rs956115, Gly972Arg, Arg972 for IRS1. NA: No clinical data are available for NCF2 and NCF4. Due to the lack of data, it is not possible to specify whether the considered data concern viral or aviremic patients.
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Figure 4. The TLR4 levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
Figure 4. The TLR4 levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
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Figure 5. The TLR4 levels in coronary tissue levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
Figure 5. The TLR4 levels in coronary tissue levels in aortic tissue by age. The term GE stands for gene expression and represents the gene expression of a gene evaluated by applying a linear model to the GTEx data [18]. The reported p-values represent a sensible change in the expression of the genes at a precise age interval represented in the x interval. Reported p-values represent expression alterations over age; p-values are for the moderated t-statistics of differential gene expression associated with the effect of age.
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Table 1. A list of genes gathered from the GTEx database whose expression levels are monotonically increasing or decreasing with age grouped by tissue.
Table 1. A list of genes gathered from the GTEx database whose expression levels are monotonically increasing or decreasing with age grouped by tissue.
Tissue GenesIncreasedDecreased
Artery AortaNCF2 TLR4 NCF4CXCR4 NARF
Artery CoronaryIRS1MTOR NARF
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MDPI and ACS Style

Boccuto, F.; De Rosa, S.; Veltri, P.; Torella, D.; Guzzi, P.H. Cardiovascular Risk in HIV Patients: Ageing Analysis of the Involved Genes. Appl. Sci. 2024, 14, 7526. https://doi.org/10.3390/app14177526

AMA Style

Boccuto F, De Rosa S, Veltri P, Torella D, Guzzi PH. Cardiovascular Risk in HIV Patients: Ageing Analysis of the Involved Genes. Applied Sciences. 2024; 14(17):7526. https://doi.org/10.3390/app14177526

Chicago/Turabian Style

Boccuto, Fabiola, Salvatore De Rosa, Pierangelo Veltri, Daniele Torella, and Pietro Hiram Guzzi. 2024. "Cardiovascular Risk in HIV Patients: Ageing Analysis of the Involved Genes" Applied Sciences 14, no. 17: 7526. https://doi.org/10.3390/app14177526

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