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Article

Proinflammatory Biomarkers and Clinical Factors Associated with Long-Term Mortality in People with HIV

by
Agnieszka Lembas
1,2,
Andrzej Załęski
1,2,*,
Tomasz Mikuła
1,2,
Joanna Kozłowska
1,2 and
Alicja Wiercińska-Drapało
1,2
1
Department of Infectious and Tropical Diseases and Hepatology, Medical University of Warsaw, 02-091 Warsaw, Poland
2
Hospital for Infectious Diseases in Warsaw, 01-201 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Viruses 2025, 17(2), 243; https://doi.org/10.3390/v17020243
Submission received: 6 January 2025 / Revised: 7 February 2025 / Accepted: 9 February 2025 / Published: 11 February 2025
(This article belongs to the Section Human Virology and Viral Diseases)
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Abstract

:
People with HIV (PWH) receiving antiretroviral therapy (ART), despite a similar life expectancy, have a higher incidence of comorbidities than the general population. This study assessed the influence of proinflammatory biomarkers and clinical factors on mortality of PWH. We included PWH hospitalized from 2009 to 2014 who continued ART until 2023. The baseline lipid profile, CD4+ cell count, platelets, CRP, PCT, TNF-α, VCAM-1, and HCV and HBV coinfection were evaluated. Multivariable logistic regression was used to evaluate factors associated with mortality. Among 72 PWH, 19 were lost to a follow-up and 13 died before 2023. The mean follow-up was 12.07 years, while the mean time to death was 4.32 years. The main causes of death were cancer (n = 7) and drug-related death (n = 4). In the multivariate analysis, HCV coinfection, CRP ≥ 5 mg/L, PCT ≥ 0.05 ng/mL, and VCAM-1 ≥ 922 ng/mL were associated with higher odds of death. Although people who died had lower total cholesterol and triglyceride concentrations, these parameters were not associated with mortality. Determining HCV coinfections and CRP, PCT, and VCAM-1 levels may help identify PWH at increased risk of death for intensified monitoring. Care should also be taken of PWH with normal lipid parameters.

1. Introduction

Untreated human immunodeficiency virus (HIV) infection causes the progressive and continuous impairment of the immune system, resulting in the development of opportunistic infections and neoplasms, which are the clinical manifestations of acquired immunodeficiency syndrome (AIDS). Since the identification of HIV in 1983, approximately 40.1 million people have died of AIDS [1,2,3]. At the beginning of the global HIV pandemic, no antiretroviral therapy (ART) was available, which made AIDS-related diseases the main cause of death among people with HIV (PWH) [4]. Current studies suggest that PWH receiving effective antiretroviral therapy have similar life expectancies as the general population, albeit with a higher incidence of comorbidities [5].
The mechanisms by which the risk of comorbidities is increased in PWH are complex and multifactorial and include accelerated aging, lifestyle, ART toxicity, and other viral coinfections [6,7,8]. The chronic stimulation of inflammation by HIV itself plays an important role in the increased inflammatory status. Several molecules promote inflammation in HIV infection, including the C-reactive protein (CRP), whose concentrations are higher in PWH in comparison to the general population, and interleukin-6, whose high circulating levels are associated with HIV replication [9,10]. HIV also contributes to the production of interleukin-1β via the transformation of pro-interleukin-1β into bioactive interleukin-1β, which is associated with the progression to AIDS [11]. Replicating HIV induces a higher tumor necrosis factor alpha (TNF-α) serum concentration, and the overexpression of transforming growth factor beta (TGF-β) in PWH promotes viral replication and plays an important role in the progression of HIV infection [12,13]. Other proinflammatory factors in HIV are the adhesion molecules vascular cell adhesion molecule 1 (VCAM-1) and intercellular adhesion molecule 1 (ICAM-1), which mediate inflammation and promote leukocyte migration, whose expression is stimulated by the HIV-Tat-1 protein [14]. Other mechanisms currently being examined include CD4+ lymphocyte depletion, cellular reservoirs of HIV, increased intestinal permeability and gut microbiome impairment, and altered cholesterol and glucose metabolism. Another important proinflammatory mechanism involves cell activation biomarkers, including CD38 and HLA-DR. CD38 is a glycoprotein that plays an important role in regulating the differentiation and activation state of the cell. During early HIV infection, expression of the CD38 molecule is significantly increased. Higher expression levels of CD38 on CD4+ T lymphocytes are associated with more rapid development of AIDS [15]. The expression of HLA-DR on CD4+ T lymphocytes correlates with the level of total HIV DNA contributing to HIV persistence. HLA-DR+ cells are crucial in HIV infections because of their ability to release the virus when stimulated. The proportion of intact viral DNA sequences in the proliferative HLA-DR+ subset is high, which suggests they are critical in maintaining HIV infections [16].
PWH are also more prone to develop comorbidities because of unhealthy behaviors such as smoking, alcohol intake, and stimulant or other drug usage [17]. In people with HIV, people who inject drugs (PWID) may be especially vulnerable to concomitant diseases and have a higher risk of premature death for multiple reasons. For example, because of stigmatization, PWID may have limited access to HIV testing and antiretroviral therapy [18]. Moreover, PWID often have unstable lives and may be less likely to adhere to ART, which may result in higher proinflammatory molecule concentrations and a higher risk of comorbidities and mortality [19].
Effective ART leads to a decrease in the concentrations of the majority of proinflammatory molecules, although most remain higher than in the general population [20,21]. The prevalence of smoking, alcohol intake, and drug use in PWH remains higher than in the general population, increasing the risk of comorbidities, like neoplasms and cardiovascular disease (CVD), and premature death. This situation is particularly illustrated by the phenomenon that, in the general population, mortality rates from CVD are decreasing, whereas among PWH, they are increasing, mainly because of heart failure and stroke [22,23]. Despite more chronic conditions, PWH have a less pronounced increase in the risk of death than those with fewer conditions, compared with the general population [24]. This might be explained by the fact that PWH have multiple medical appointments due to HIV infection and, therefore, undergo regular and general health assessments and early treatment of comorbidities.
This study assessed the influence of proinflammatory biomarkers and clinical factors on long-term all-cause mortality in PWH treated with ART. An additional aim of the study was to evaluate factors impacting the mortality of PWH who inject drugs and HIV/HCV-coinfected individuals.

2. Materials and Methods

2.1. Patients

The study included the population of adult PWH hospitalized between 2009 and 2014. The inclusion criteria were HIV infection, regardless of the stage of the disease and time of treatment, continuing care in the HIV treatment center until 2023, and age 18 or older. The exclusion criteria were lipid-lowering therapy and signs of acute infection at the point of blood collection, including fever, cough, dyspnea, diarrhea, vomiting, dysuria, and skin infection. Patients receiving lipid-lowering agents were excluded in order to assess the differences in lipid parameters in between-group comparisons. Patients with infection were excluded to ensure that the elevation in proinflammatory biomarkers did not result from acute infection and may be assessed as a risk factor for long-term mortality.

2.2. Assessments

This was an observational, retrospective study. All patients underwent physical examination and laboratory testing. Each included patient was asked about the time of HIV infection diagnosis, the time of introduction of ART, concomitant disorders, and administered medications. Information about age, gender, education level, socio-economic status, and intravenous drug use was collected. Fasting blood samples were collected for total cholesterol, HDL cholesterol, LDL cholesterol, triglyceride level, CD4+ cell count, platelets, C-reactive protein, procalcitonin (PCT), TNF-α, and VCAM-1. The VCAM-1 and TNF-α serum levels were evaluated by enzyme-linked immunosorbent assay (Human sVCAM-1/CD106 Quantikine ELISA and Human TNF-alpha Quantikine ELISA, R&D Systems, respectively). All patients were assessed for hepatitis C virus (HCV) and hepatitis B virus (HBV) coinfection. HCV coinfection was confirmed by obtaining a positive HCV RNA, and HBV coinfection was confirmed by obtaining a positive HBs antigen or HBV DNA.

2.3. Long-Term Evaluation

All included patients were analyzed in 2023 in terms of death from any cause. Among patients who died, the time and cause of death were analyzed. The laboratory results and demographic data obtained in 2009–2014 were evaluated as long-term risk factors for death. The applied schemes of ART were not included in the analysis because of multiple switches in regimens during the observation period.

2.4. Statistical Analysis

The Shapiro–Wilk test was performed to verify the normality of the distributions in the analyzed variables. The two-sample Student t-test or Mann–Whitney U test was used to evaluate the difference in the mean values among quantitative variables, and χ2 or Fisher exact tests were performed for categorical variables. ROC curves and the Youden index were used to choose an appropriate cutoff point for VCAM-1 and TNF-α concentrations. Univariate and multivariable logistic regression analysis was used to evaluate the factors associated with odds of death during the observation period. Multivariable logistic regression was adjusted by age (<35 years old vs. ≥35 years old) and gender (male vs. female). The Kaplan–Meier estimator was used to estimate patients’ survival. The p-value was set at 0.05. All statistical analyses were performed using Python 3.7 software and Statistica 13.1 program (StatSoft Poland, Kraków, Poland).

2.5. Ethical Approval

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Medical University of Warsaw, Poland (AKBE/128/2021).

3. Results

3.1. Patients

The study included 72 patients assessed in 2009–2014. Among them, 40 (55.56%) patients were alive in 2023, 13 (18.06%) patients had died, and 19 (26.39%) patients were lost at a follow-up. The reasons for the loss of a follow-up included moving to another treatment center (n = 10) and discontinuing care (n = 9). The mean follow-up was 12.07 years (min 9.22 years, max 13.78 years). In the analyzed cohort, 39 (54.17%) of the population were PWID. All PWID in the study had primary or secondary education and did not have regular employment. At baseline, one person was receiving anticoagulants because of deep vein thrombosis, two patients were receiving hypotensive drugs, and one patient was receiving oral antihyperglycemic treatment. Significant differences in analyzed parameters in the people who died and those who lived are presented in Table 1.
We observed significant differences in mean values of the duration of HIV infection, the presence of HCV coinfection, and serum concentrations of CRP, PCT, total cholesterol, triglycerides, and VCAM-1 among patients who lived and patients who died. All people diagnosed with HCV who stayed in care received HCV treatment with direct-acting antiviral agents with sustained virologic response at post-treatment week 24. Four patients died before the treatment was implemented.

3.2. Death

Among patients who died, the mean time to death was 4.32 years (min. 0.34, max. 11.12 years). The causes of death were cancer (n = 7), drug-related deaths (n = 4), COVID-19 (n = 1), and septic shock (n = 1). Among patients who died of cancer, the causes of death were Hodgkin’s lymphoma (n = 2), esophageal cancer (n = 2), Kaposi sarcoma (n = 1), cervical cancer (n = 1), and bladder cancer (n = 1). There were no cancer diagnoses in patients who lived. In the group of people who died because of drug-related deaths, two individuals died of mental illness and two because of unintentional overdosing. The Kaplan–Meier curve is presented in Figure 1.

3.3. Factors Associated with the Risk of Death

We next analyzed whether clinical and immunological biomarkers were associated with the risk of death among people with HIV. We assessed the cutoff point of 922 ng/mL for VCAM-1 and 43.45 pg/mL for TNF-α using the Youden index. The remaining parameters were assessed according to normal limits. The ROC curves for VCAM-1 and TNF-α are presented in Figure 2.
The analyzed clinical factors associated with the risk of death are shown in Table 2.
The univariate analysis showed that age ≥35 years old, HCV coinfection, CRP ≥ 5 mg/L, PCT ≥ 0.05 ng/mL, VCAM-1 ≥ 922 ng/mL, and HDL cholesterol ≤ 1 mmol/L were associated with higher odds of death. In the multivariable analysis, only HCV coinfection, CRP ≥ 5 mg/L, PCT ≥ 0.05 ng/mL, and VCAM-1 ≥ 922 ng/mL were significant risk factors for death.

3.4. Factors Associated with Mortality of PWID

The majority (54.17%) of the study population declared intravenous drug use. In this population, 11 patients died. The causes of death were cancer (n = 5), drug-related deaths (n = 4), COVID-19 (n = 1), and septic shock (n = 1). We analyzed factors associated with the risk of death among PWH who inject drugs. The results are presented in Table 3.
Among PWH who inject drugs, only HCV coinfection and CRP ≥ 5 mg/L were significant risk factors for death.

3.5. Factors Associated with Cancer Incidence

Cancer was the primary cause of death in the study population. There were no cases of cancer in patients who lived. We evaluated factors associated with the risk of death due to cancer among PWH. The results are presented in Table 4.
In our analysis, PCT ≥ 0.05 ng/mL and VCAM-1 ≥ 922 ng/mL were independent risk factors for cancer incidence among PWH.

3.6. Differences in Analyzed Parameters in PWH with HCV Coinfection

Since HCV coinfection was a significant risk factor for death in PWH and also in PWH who inject drugs, we evaluated the differences in mean analyzed parameters between HIV/HCV-coinfected individuals who died and those who lived. In the analyzed subpopulation, there were eight people who died and ten people who lived. The results are presented in Table 5.
Individuals with HIV/HCV coinfection who died had significantly higher concentrations of VCAM-1 and significantly lower concentrations of HDL cholesterol.

4. Discussion

4.1. Cause of Death Among PWH

The study was a long-term observation of all-cause mortality of PWH receiving ART. From the initial study population of 72 patients, we observed 74%. Among them, 25% died during the observation period. The main cause of death was cancer and drug-related deaths. According to current research, the most prevalent causes of death of PWH include AIDS, non-AIDS malignancy, and cardiovascular reasons. Although AIDS is still the main cause of death in untreated PWH, the rates of death due to AIDS are declining [25]. In our cohort, there were no deaths due to AIDS; however, our study included only patients who were receiving ART and regularly attended follow-up. The majority of deaths were due to cancer, which corresponds with current research [25]. In our analysis, cancer incidence in PWH was related to increased levels of PCT and VCAM-1. Our findings correspond with current research, and procalcitonin is being evaluated as a prognostic marker of cancer patients [26]. Increased PCT concentrations may be an indication of non-small cell lung cancer and may be useful in the follow-up of medullary thyroid cancer [27,28]. VCAM-1 may be linked to the development of cancer [29], and elevated concentrations of VCAM-1 have been found in the peripheral blood of patients with cancer [30].
The second most prevalent cause of death in our population was drug-related death; 30% of deaths were related to intravenous drug use (mental illness in people who inject drugs (15%) and drug abuse (15%)). One study showed that PWH who inject drugs have higher mortality rates than PWH who do not inject drugs, and in this population, drug-related deaths accounted for 45.5% of all deaths [31]. In our cohort, PWID accounted for over 50% of all analyzed patients. In this population, HCV coinfection and CRP ≥ 5 mg/L were independent risk factors for mortality. HCV coinfection is prevalent in PWID since it is estimated that 52.3% of all PWID are HCV-antibody positive [32]. Its incidence enhances the mortality of PWID since HCV infection is a known risk factor for mortality and cancer, especially in HIV/HCV-coinfected individuals [33]. CRP levels of PWID with no acute infection have not been extensively studied; however, it is possible that PWID experience increased levels of proinflammatory biomarkers resulting from the detectable HIV viral load that is prevalent in this population [34].

4.2. Possible Mechanisms of Premature Death Among PWH

4.2.1. Proinflammatory Biomarkers

One possible explanation of mortality in PWH is proinflammatory mechanisms. It was observed that PWH have higher levels of proinflammatory biomarkers than the general population. Studies show elevated CRP, D-Dimer, interleukins 1β, 6, 11, and 27, TNF-α, TGF-β, cell adhesion molecules, and others in PWH compared to healthy controls [35,36,37,38]. Even when receiving ART and with a suppressed HIV viral load, the levels of these biomarkers are still elevated compared to healthy controls [35,36,37].
A chronic increase in CRP levels, one of the most prevalent markers of inflammation, is also a well-known risk factor for CVD and a predictor of all-cause mortality [38,39]. There are reports suggesting that higher levels of CRP may be related not only to CVD and all-cause mortality but also to higher odds of death due to cancer, which is a very common cause of death in PWH [40]. In PWH, increased levels of CRP are also associated with a higher HIV viral load, microbial translocation, immune activation, and mortality [9]. Based on the former research, we evaluated whether the elevation of CRP concentration may also be a long-term mortality predictor in PWH. We found that CRP ≥ 5 mg/L in PWH with no signs of acute infection was associated with higher odds of death. Our findings correspond with research suggesting that higher baseline CRP in PWH is associated with higher all-cause mortality [41]. Studies also indicate that even people with CD4+ cell count > 500 cells/µL experience intensified proinflammatory processes, resulting in an increase in CRP concentrations and mortality. People with HIV with CRP > 3 mg/L had 2.7-fold higher odds of death during the 5-year observation period than those with CRP < 1 mg/L [42]. In our analyses, CRP correlated with higher odds of death in the entire PWH cohort, as well as in the subgroup of PWH who inject drugs. It was observed that PWID experience higher levels of CRP, which also might contribute to increased vulnerability to death [43].
Procalcitonin, together with CRP, is a marker for systemic inflammation and an important diagnostic marker of sepsis that is used for the diagnosis and prediction of the severity of infection [44]. PCT may also be produced because of noninfectious causes, such as surgery, shock, burn injury, trauma, chronic kidney disease, and liver cirrhosis [44,45]. There are reports that suggest that an increased PCT level is a predictive marker of mortality in trauma and also an independent risk factor associated with poor prognosis in cancer patients [26,46]. There are few studies evaluating the predictive value of PCT in HIV infection. The studies assessing the impact of PCT concentrations on long-term mortality in patients with no acute infection are also limited. In a study by Zhang et al., elevated PCT levels were identified as an independent risk factor for mortality of HIV/Talaromyces marneffei coinfection [47]. Osawa et al. showed that increased PCT levels may predict mortality in tuberculosis patients with no HIV infection [48]. Our research corresponds with these findings since we observed significantly higher odds of death among patients with PCT serum concentrations of ≥0.5 ng/dL. To our knowledge, our study is the first to evaluate PCT concentrations as a long-term mortality risk in PWH with no acute infection.
VCAM-1 is a cell adhesion molecule [49]. Chronic overexpression of VCAM-1, which was observed in PWH, promotes proatherogenic mechanisms and increases cardiovascular risk [50]. We observed that VCAM-1 ≥ 922 ng/mL was associated with higher mortality rates. Our findings correspond with other research, which suggests that VCAM-1 may be a biomarker for predicting the risk of HIV-1 disease progression, morbidity, and mortality [51]. In a study by Affi et al., VCAM-1 baseline concentrations ≥ 1458 ng/mL were associated with higher mortality rates of PWH during 30 months of observation [52]. Interestingly, in PWH, VCAM-1 levels before ART implementation were significantly associated with mortality, independently of whether ART was started immediately or deferred [35]. VCAM-1 is also currently being evaluated as a biomarker of mortality in other populations. For example, elevated VCAM-1 increases the risks of sepsis, septic shock, multiple-organ dysfunction syndrome, and death [53].

4.2.2. HCV Coinfection

Our analysis showed that concomitant HCV coinfection is associated with higher mortality rates in comparison to HIV mono-infection. The elevation in levels of plasma inflammation and microbial translocation biomarkers, especially soluble CD14 and interleukin-6 in people with HIV/HCV coinfection compared to PWH, have also been previously observed [54]. HCV-related liver diseases remain a significant cause of morbidity, in-hospital treatment, and mortality of PWH, as HIV/HCV-coinfected individuals experience an accelerated progression of chronic liver disease and higher mortality [55,56]. Moreover, HIV/HCV-coinfected individuals have a higher risk of all-cause and non-liver-related deaths and non-liver-related cancers than HCV mono-infected patients [33]. Morbidity and mortality in this population remain high due to metabolic disorders associated with the occurrence of cardiovascular events [57]. In our analysis, people with HIV/HCV coinfection who died had significantly higher concentrations of VCAM-1 than those who lived. The elevation of VCAM-1 concentrations in HCV coinfection is common and seems to decrease with HCV eradication [58]. Limited studies also suggest that VCAM-1 may predict liver-related events and death in patients with HIV/HCV coinfection [59].

4.2.3. CVD and Lipid Profile

Since CVD is one of the main causes of death in PWH, we evaluated the lipid profiles among the study group and observed that in people who died, the concentration of total cholesterol and triglycerides was significantly lower compared to those who lived. However, in multivariable analysis, lipid parameters were not associated with the odds of death. It has been well documented that a significant portion of PWH have impaired lipid profiles that get worse during ART [60]. Hypothetically, our results might be explained by the fact that 62% of people who died were coinfected with HCV, and chronic HCV infection is associated with low levels of lipid parameters, especially VLDL and LDL. Patients with HCV infection, despite a seemingly beneficial lipid profile, still have accelerated development of atherosclerosis, leading to an increased cardiovascular risk [61].

4.2.4. Other Evaluated Risk Factors for Premature Death in PWH

In our study, age, gender, HBV coinfection, CD4+ cell count, platelet count, and TNF-α were not correlated with the odds of death. According to current research, HIV/HBV coinfection is associated with increased mortality [62]. Our study group may have included too few patients with HBV coinfection to observe this relationship. Our analysis included only incidental CD4+ cell count measurements, and studies show that the CD4+ lymphocyte nadir is a more important parameter to predict mortality following noncommunicable diseases in PWH [63]. To our knowledge, even though platelets are involved in inflammatory reactions, there are no direct reports of their impact on mortality [64]. However, there are suggestions that platelets containing HIV may contribute to a worse immunological response to ART [65]. Moreover, platelet activation, which is often increased in PWH, may stimulate neutrophil–platelet–endothelium interactions and, therefore, increase the risk of CVD. This may indirectly be associated with morbidity and mortality of PWH [66]. Interestingly, in our cohort, the levels of TNF-α were lower in people who died compared to those who lived. Some studies show that the levels of TNF-α in PWH remain high even with long-term ART, but its impact on mortality is currently unknown [67]. Other research suggests that HIV infection does not have any effect on TNF-α levels, regardless of treatment status [68].
In the future, we hope to evaluate additional biomarkers, such as D-dimer, interleukins (including 1β, 6, 11, and 27), and TGF-β as PWH mortality risk factors. D-dimer was the most predictive biomarker of overall mortality in PWH out of a panel of inflammatory and coagulation biomarkers [69]. Moreover, among HIV/HCV-coinfected patients initiating ART, D-dimer was also associated with an increased risk of death [70]. Elevated concentrations of interleukins, especially interleukin-6, have been linked with higher mortality rates in men with HIV with undetectable HIV viral loads [71]. TGF-β expression is an important cancer prognostic factor, and HIV-1 Tat protein triggers TGF-β production; therefore, investigating its association with PWH mortality is a promising approach [72,73].

4.3. Significance and Limitations of the Study

Our study is significant because of its long follow-up (12 years) and the analysis of multiple biomarkers related to the mortality of PWH. Among analyzed biomarkers, we evaluated PCT in a long-term assessment of PWH, excluding acute patients, which, to our knowledge, has not previously been evaluated as a factor associated with long-term mortality in PWH. However, our study has several limitations. Firstly, the study cohort was relatively small in reliably evaluating the association between the analyzed biomarkers and PWH mortality, which was exacerbated by the fact that we followed up with only 74% of the initial study population. The loss of 26% of patients to follow-up can lead to selection bias, and the variety of analyzed biomarkers in a small study population may pose a risk of type II errors. Secondly, the measurement of analyzed biomarkers was performed only once. Although we included only patients with no signs of acute infection, using single measurements may impact the results. Another limitation is the lack of analysis of ART regimens as a potential risk factor for death, which was impossible to perform because of multiple switches in a limited group of patients. Nevertheless, we believe our observations are an overture to further research using larger populations.

5. Conclusions

Long-term mortality in PWH may be connected to high CRP, PCT, and VCAM-1 concentrations, as well as HCV coinfection. Determining CRP, PCT, and VCAM-1 levels may help identify PWH at increased long-term risk of death for intensified monitoring. PWH should be regularly assessed for HCV coinfection. Care should also be taken for PWH with normal lipid parameters since a beneficial lipid profile is not a sufficient factor to reduce the odds of death.

Author Contributions

Conceptualization, A.L. and T.M.; methodology, A.L.; software, A.L.; validation, A.Z., T.M. and J.K.; formal analysis, A.L.; investigation, A.L.; resources, T.M. and J.K.; data curation, A.L.; writing—original draft preparation, A.L.; writing—review and editing, A.Z., T.M., and A.W.-D.; visualization, A.L.; supervision, A.W.-D.; project administration, A.L. and T.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Medical University of Warsaw, Poland (AK-BE/128/2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
PWHPeople with HIV
ARTAntiretroviral therapy
HIVHuman immunodeficiency virus
AIDSAcquired immunodeficiency syndrome
CVDCardiovascular disease
CRPC-reactive protein
PCTProcalcitonin
TNF-αTumor necrosis factor alpha
VCAM-1Vascular cell adhesion molecule 1
ICAM-1Intercellular adhesion molecule 1
HCVHepatitis C virus
HBVHepatitis B virus
PLTPlatelets
PWIDPeople who inject drugs
TGF-βTransforming growth factor beta

References

  1. Barre-Sinoussi, F.F.; Chermann, J.C.; Rey, F.; Nugeyre, M.T.; Chamaret, S.; Gruest, J.; Dauguet, C.; Axler-Blin, C.; Vezinet-Brun, F.; Rouzioux, C.; et al. Isolation of a T-lymphotropic retrovirus from a patient at risk for acquired immune deficiency syndrome (AIDS). Science 1983, 220, 868–871. [Google Scholar] [CrossRef] [PubMed]
  2. Gallo, R.C.; Salahuddin, S.Z.; Popovic, M.; Shearer, G.M.; Kaplan, M.; Haynes, B.F.; Palker, T.J.; Redfield, R.; Oleske, J.; Safai, B.; et al. Frequent detection and isolation of cytopathic retroviruses (HTLV-III) from patients with AIDS and at risk for AIDS. Science 1984, 224, 500–503. [Google Scholar] [CrossRef] [PubMed]
  3. Fact Sheet—Latest Statistics on the Status of the AIDS Epidemic. UNAIDS. 2021. Available online: https://www.unaids.org/en/resources/fact-sheet (accessed on 30 December 2024).
  4. Smith, C.; Sabin, C.A.; Lundgren, J.D.; Thiebaut, R.; Weber, R.; Law, M.; Monforte, A.; Kirk, O.; Friis-Moller, N.; Phillips, A.; et al. Factors associated with specific causes of death amongst HIV-positive individuals in the D:A:D study. AIDS 2010, 24, 1537–1548. [Google Scholar] [PubMed]
  5. Marcus, J.L.; Leyden, W.A.; Alexeeff, S.E.; Anderson, A.N.; Hechter, R.C.; Hu, H.; Lam, J.O.; Towner, W.J.; Yuan, Q.; Horberg, M.A.; et al. Comparison of Overall and Comorbidity-Free Life Expectancy Between Insured Adults With and Without HIV Infection, 2000–2016. JAMA Netw. Open 2020, 3, e207954. [Google Scholar] [CrossRef]
  6. Singh, S.; Giron, L.B.; Shaikh, M.W.; Shankaran, S.; Engen, P.A.; Bogin, Z.R.; Bambi, S.A.; Goldman, A.R.; Azevedo, J.L.L.C.; Orgaz, L.; et al. Distinct intestinal microbial signatures linked to accelerated systemic and intestinal biological aging. Microbiome 2024, 12, 31. [Google Scholar] [CrossRef]
  7. Hinton, A.O., Jr.; N’Jai, A.U.; Vue, Z.; Wanjalla, C. Connection between HIV and mitochondria in cardiovascular disease and implications for treatments. Circul. Res. 2024, 134, 1581–1606. [Google Scholar] [CrossRef]
  8. De Francesco, D.; Wit, F.W.; Burkle, A.; Oehlke, S.; Kootstra, N.A.; Winston, A.; Franceschi, C.; Garagnani, P.; Pirazzini, C.; Libert, C.; et al. Do people living with HIV experience greater age advancement than their HIV-negative counterparts? AIDS 2019, 33, 259–268. [Google Scholar] [CrossRef]
  9. Martínez-Ayala, P.; Alanis-Sánchez, G.A.; Álvarez-Zavala, M.; Sánchez-Reyes, K.; Ruiz-Herrera, V.V.; Cabrera-Silva, R.I.; González-Hernández, L.A.; Ramos-Becerra, C.; Cardona Muñoz, E.; Andrade-Villanueva, J.F. Effect of antiretroviral therapy on decreasing arterial stiffness, metabolic profile, vascular and systemic inflammatory cytokines in treatment-naïve HIV: A one-year prospective study. PLoS ONE 2023, 18, e0282728. [Google Scholar] [CrossRef]
  10. Borges, Á.H.; O’Connor, J.L.; Phillips, A.N.; Rönsholt, F.F.; Pett, S.; Vjecha, M.J.; French, M.A.; Lundgren, J.D.; INSIGHT SMART and ESPRIT Study Groups and the SILCAAT Scientific Committee. Factors Associated with plasma IL-6 levels during HIV Infection. J. Infect. Dis. 2015, 212, 585–595. [Google Scholar] [CrossRef]
  11. Guo, H.; Gao, J.; Taxman, D.J.; Ting, J.P.; Su, L. HIV-1 Infection induces interleukin-1β production via TLR8 protein-dependent and NLRP3 inflammasome mechanisms in human monocytes. J. Biol. Chem. 2014, 289, 21716–21726. [Google Scholar] [CrossRef]
  12. Collora, J.A.; Liu, R.; Pinto-Santini, D.; Ravindra, N.; Ganoza, C.; Lama, J.R.; Alfaro, R.; Chiarella, J.; Spudich, S.; Mounzer, K.; et al. Single-cell multiomics reveals persistence of HIV-1 in expanded cytotoxic T cell clones. Immunity 2022, 55, 1013–1031.e7. [Google Scholar] [CrossRef] [PubMed]
  13. Chinnapaiyan, S.; Dutta, R.K.; Nair, M.; Chand, H.S.; Rahman, I.; Unwalla, H.J. TGF-β1 increases viral burden and promotes HIV-1 latency in primary differentiated human bronchial epithelial cells. Sci. Rep. 2019, 9, 12552. [Google Scholar] [CrossRef] [PubMed]
  14. Pu, H.; Tian, J.; Flora, G.; Lee, Y.W.; Nath, A.; Hennig, B.; Toborek, M. HIV-1 Tat protein upregulates inflamma tory mediators and induces monocyte invasion into the brain. Mol. Cell. Neurosci. 2003, 24, 224–237. [Google Scholar] [CrossRef] [PubMed]
  15. Lu, L.; Wang, J.; Yang, Q.; Xie, X.; Huang, Y. The role of CD38 in HIV infection. AIDS Res. Ther. 2021, 18, 11. [Google Scholar] [CrossRef]
  16. Horsburgh, B.A.; Lee, E.; Hiener, B.; Eden, J.S.; Schlub, T.E.; von Stockenstrom, S.; Odevall, L.; Milush, J.M.; Liegler, T.; Sinclair, E.; et al. High levels of genetically intact HIV in HLA-DR+ memory T cells indicates their value for reservoir studies. AIDS 2020, 4, 659–668. [Google Scholar] [CrossRef]
  17. Achhra, A.C.; Lyass, A.; Borowsky, L.; Bogorodskaya, M.; Plutzky, J.; Massaro, J.M.; D’Agostino, R.B., Sr.; Triant, V.A. Assessing Cardiovascular Risk in People Living with HIV: Current Tools and Limitations. Curr. HIV/AIDS Rep. 2021, 18, 271–279. [Google Scholar] [CrossRef]
  18. Larney, S.; Peacock, A.; Leung, J.; Colledge, S.; Hickman, M.; Vickerman, P.; Grebely, J.; Dumchev, K.V.; Griffiths, P.; Hines, L.; et al. Global, regional, and country-level coverage of interventions to prevent and manage HIV and hepatitis C among people who inject drugs: A systematic review. Lancet Glob Heal. 2017, 5, e1208–e1220. [Google Scholar] [CrossRef]
  19. Bangsberg, D.R.; Hecht, F.M.; Clague, H.; Charlebois, E.D.; Ciccarone, D.; Chesney, M.; Moss, A. Provider assessment of adherence to HIV antiretroviral therapy. J. Acquir. Immune Defic. Syndr. 2001, 26, 435–442. [Google Scholar] [CrossRef]
  20. Freiberg, M.S.; Chang, C.H.; Skanderson, M.; Patterson, O.V.; DuVall, S.L.; Brandt, C.A.; So-Armah, K.A.; Vasan, R.S.; Oursler, K.A.; Gottdiener, J.; et al. Association Between HIV Infection and the Risk of Heart Failure With Reduced Ejection Fraction and Preserved Ejection Fraction in the Antiretroviral Therapy Era: Results From the Veterans Aging Cohort Study. JAMA Cardiol. 2017, 2, 536–546. [Google Scholar] [CrossRef]
  21. Perkins, M.V.; Joseph, S.B.; Dittmer, D.P.; Mackman, N. Cardiovascular Disease and Thrombosis in HIV Infection. Arterioscler. Thromb. Vasc. Biol. 2023, 43, 175–191. [Google Scholar] [CrossRef]
  22. Feinstein, M.J.; Bahiru, E.; Achenbach, C.; Longenecker, C.T.; Hsue, P.; So-Armah, K.; Freiberg, M.S.; Lloyd-Jones, D. Patterns of Cardiovascular Mortality for HIV-Infected Adults in the United States: 1999 to 2013. Am. J. Cardiol. 2016, 117, 214–220. [Google Scholar] [CrossRef] [PubMed]
  23. Alonso, A.; Barnes, A.E.; Guest, J.L.; Shah, A.; Shao, I.Y.; Marconi, V. HIV Infection and Incidence of Cardiovascular Diseases: An Analysis of a Large Healthcare Database. J. Am. Hear. Assoc. 2019, 8, e012241. [Google Scholar] [CrossRef] [PubMed]
  24. Yu, X.; Westra, J.R.; Giordano, T.P.; Berenson, A.B.; Baillargeon, J.G.; Kuo, Y.F. Assessing comorbidities and survival in HIV-infected and uninfected matched Medicare enrollees. AIDS 2021, 35, 1667–1675. [Google Scholar] [CrossRef]
  25. Trickey, A.; McGinnis, K.; Gill, M.J.; Abgrall, S.; Berenguer, J.; Wyen, C.; Hessamfar, M.; Reiss, P.; Kusejko, K.; Silverberg, M.J.; et al. Longitudinal trends in causes of death among adults with HIV on antiretroviral therapy in Europe and North America from 1996 to 2020: A collaboration of cohort studies. Lancet HIV 2024, 11, e176–e185. [Google Scholar] [CrossRef]
  26. Zhou, Q.; Lu, X.; Qian, L.; Yu, C.; Xie, J.; Kong, D. Procalcitonin, C-reactive protein, and white blood cell count levels in end-stage cancer patients: A retrospective study on inflammatory markers and their prognostic value. Medicine 2024, 103, e40792. [Google Scholar] [CrossRef] [PubMed]
  27. Soeroso, N.N.; Tanjung, M.F.; Afiani, D.; Pradana, A.; Tarigan, S.P.; Wahyuni, A.S. Procalcitonin Level in Non-Small Cell Lung Cancer Patients among Indonesian Population. Open Access Maced. J. Med. Sci. 2018, 6, 2123–2127. [Google Scholar] [CrossRef]
  28. Censi, S.; Manso, J.; Benvenuti, T.; Piva, I.; Iacobone, M.; Mondin, A.; Torresan, F.; Basso, D.; Crivellari, G.; Zovato, S.; et al. The role of procalcitonin in the follow-up of medullary thyroid cancer. Eur. Thyroid J. 2023, 12, e220161. [Google Scholar] [CrossRef]
  29. Kong, D.H.; Kim, Y.K.; Kim, M.R.; Jang, J.H.; Lee, S. Emerging Roles of Vascular Cell Adhesion Molecule-1 (VCAM-1) in Immunological Disorders and Cancer. Int. J. Mol. Sci. 2018, 19, 1057. [Google Scholar] [CrossRef]
  30. VanHeyst, K.A.; Choi, S.H.; Kingsley, D.T.; Huang, A.Y. Ectopic Tumor VCAM-1 Expression in Cancer Metastasis and Therapy Resistance. Cells 2022, 11, 3922. [Google Scholar] [CrossRef]
  31. Metcalfe, R.; Fraser, R.; Trayner, K.M.A.; Glancy, M.; Yeung, A.; Sills, L.; Ritchie, T.; Priyadarshi, S.; Peters, S.E.; McAuley, A.; et al. Rising mortality among people who inject drugs living with HIV in Scotland, UK: A 20-year retrospective cohort study. HIV Med. 2024, 18, 265–274. [Google Scholar] [CrossRef]
  32. Degenhardt, L.; Peacock, A.; College, S.; Leung, J.; Grebely, J.; Vickerman, P.; Stone, J.; Cunningham, E.B.; Trickey, A.; Dumchev, K.; et al. Global prevalence of injecting drug use and sociodemographic characteristics and prevalence of HIV, HBV, and HCV in people who inject drugs: A multistage systematic review. Lancet Glob. Health 2017, 5, e1192–e1207. [Google Scholar] [CrossRef]
  33. Chalouni, M.; Pol, S.; Sogni, P.; Fontaine, H.; Lacombe, K.; Marc-Lacombe, J.; Esterle, L.; Dorival, C.; Bourlière, M.; Bani-Sadr, F.; et al. ANRS CO13 HEPAVIH and ANRS CO22 HEPATHER cohort study groups. Increased mortality in HIV/HCV-coinfected compared to HCV-monoinfected patients in the DAA era due to non-liver-related death. J. Hepatol. 2021, 74, 37–47. [Google Scholar] [CrossRef] [PubMed]
  34. Sazonova, Y.; Kulchynska, R.; Azarskova, M.; Liulchuk, M.; Salyuk, T.; Doan, I.; Barzilay, E. Population-level prevalence of detectable HIV viremia in people who inject drugs (PWID) in Ukraine: Implications for HIV treatment and case finding interventions. PLoS ONE 2023, 18, e0290661. [Google Scholar] [CrossRef] [PubMed]
  35. Affi, R.; Gabillard, D.; Kouame, G.M.; Ntakpe, J.B.; Moh, R.; Badje, A.; Danel, C.; Inwoley, A.; Eholié, S.P.; Anglaret, X.; et al. Plasma sVCAM-1, antiretroviral therapy and mortality in HIV-1-infected west African adults. HIV Med. 2022, 23, 717–726. [Google Scholar] [CrossRef] [PubMed]
  36. Okay, G.; Koc, M.M.; Guler, E.M.; Yabaci, A.; Kocyigit, A.; Akkoyunlu, Y. The effect of antiretroviral therapy on IL-6, IL-1β, TNF-α, IFN-γ levels and their relationship with HIV-RNA and CD4 + T cells in HIV patients. Curr. HIV Res. 2020, 18, 354–361. [Google Scholar] [CrossRef] [PubMed]
  37. Teasdale, C.A.; Hernandez, C.; Zerbe, A.; Chege, D.; Hawken, M.; El-Sadr, W.M. Changes in D-dimer after initiation of antiretroviral therapy in adults living with HIV in Kenya. BMC Inf. Dis. 2020, 20, 508. [Google Scholar] [CrossRef]
  38. Sharif, S.; Van der Graaf, Y.; Cramer, M.J.; Kapelle, L.J.; de Borst, G.J.; Visseren, F.L.J.; Westerink, J.; SMART study group. Low-grade inflammation as a risk factor for cardiovascular events and all-cause mortality in patients with type 2 Diabetes. Cardiovasc. Diabetol. 2021, 20, 220. [Google Scholar] [CrossRef]
  39. Reddy, K.S.S.; Varadaraj, P.; Nallusamy, G.; SenthilNathan, S. Correlation Between Hemoglobin A1c (HbA1c) and High-Sensitivity C-Reactive Protein (hs-CRP) in Myocardial Infarction Patients and Their Six-Month Mortality Follow-Up. Cureus 2024, 16, e67070. [Google Scholar] [CrossRef]
  40. Li, Y.; Zhong, X.; Cheng, G.; Zhao, C.; Zhang, L.; Hong, Y.; Wan, Q.; He, R.; Wang, Z. Hs-CRP and all-cause, cardiovascular, and cancer mortality risk: A meta-analysis. Atherosclerosis 2017, 259, 75–82. [Google Scholar] [CrossRef]
  41. Riitho, V.; Connon, R.; Gwela, A.; Namusanje, J.; Nhema, R.; Siika, A.; Bwakura-Dangarembizi, M.; Musiime, V.; Berkley, J.A.; Szubert, A.J.; et al. Biomarkers of mortality in adults and adolescents with advanced HIV in sub-Saharan Africa. Nat. Commun. 2024, 15, 5492. [Google Scholar] [CrossRef]
  42. Tien, P.C.; Choi, A.I.; Zolopa, A.R.; Benson, C.; Tracy, R.; Scherzer, R.; Bacchetti, P.; Shlipak, M.; Grunfeld, C. Inflammation and mortality in HIV-infected adults: Analysis of the FRAM study cohort. J. Acquir. Immune Defic. Syndr. 2010, 55, 316–322. [Google Scholar] [CrossRef]
  43. Salter, M.L.; Lau, B.; Mehta, S.H.; Go, V.F.; Leng, S.; Kirk, G.D. Correlates of elevated interleukin-6 and C-reactive protein in persons with or at high risk for HCV and HIV infections. J. Acquir. Immune Defic. Syndr. 2013, 64, 488–495. [Google Scholar] [CrossRef] [PubMed]
  44. Parrey, A.H.; Koka, M.; Kasana, B.; Ismail, M. Procalcitonin and qSOFA as a marker of mortality in sepsis. Rev. Recent Clin. Trials 2024, 19, 196–203. [Google Scholar] [CrossRef] [PubMed]
  45. Sato, S.; Sato, S.; Tsuzura, H.; Ikeda, Y.; Hayashida, S.; Takahashi, S.; Amano, N.; Murata, A.; Shimada, Y.; Iijima, K.; et al. Elevated serum procalcitonin levels and their association with the prognosis of patients with liver cirrhosis. Eur. J. Gastroenterol. Hepatol. 2020, 32, 1222–1228. [Google Scholar] [CrossRef] [PubMed]
  46. Özel, A.; Yuce, S.; Ilbegi, E.N. Advancing Prognostic Prediction in Pediatric Trauma: The Role of Inflammatory Markers. Klin. Padiatr. 2024. [Google Scholar] [CrossRef]
  47. Osawa, T.; Watanabe, M.; Morimoto, K.; Okumura, M.; Yoshiyama, T.; Ogata, H.; Goto, H.; Kudoh, S.; Ohta, K.; Sasaki, Y. Serum Procalcitonin Levels Predict Mortality Risk in Patients With Pulmonary Tuberculosis: A Single-Center Prospective Observational Study. J. Infect. Dis. 2020, 222, 1651–1654. [Google Scholar] [CrossRef]
  48. Zhang, Y.; Gu, K.; Du, W.; Xu, A. Risk factors and prediction model for mortality in HIV/Talaromyces marneffei co-infection: A retrospective cohort study. Heliyon 2024, 10, e32560. [Google Scholar] [CrossRef]
  49. VCAM1 Vascular Cell Adhesion Molecule 1. Available online: https://www.ncbi.nlm.nih.gov/gene/7412 (accessed on 20 December 2024).
  50. Andalibi, M.S.; Fields, J.A.; Iudicello, J.E.; Diaz, M.M.; Tang, B.; Letendre, S.L.; Ellis, R.J. Elevated Biomarkers of Inflammation and Vascular Dysfunction Are Associated with Distal Sensory Polyneuropathy in People with HIV. Int. J. Mol. Sci. 2024, 25, 4245. [Google Scholar] [CrossRef]
  51. Graham, S.M.; Rajwans, N.; Jaoko, W.; Estambale, B.B.; McClelland, R.S.; Overbaugh, J.; Liles, W.C. Endothelial activation biomarkers increase after HIV-1 acquisition: Plasma vascular cell adhesion molecule-1 predicts disease progression. AIDS 2013, 27, 1803–1813. [Google Scholar] [CrossRef]
  52. Affi, R.; Gabillard, D.; Dunyach-Remy, C.; Ntakpe, J.B.; Moh, R.; Badje, A.; Kouame, G.M.; Karcher, S.; Le Carrou, J.; Danel, C.; et al. Association of Plasma Soluble Vascular Cell Adhesion Molecule-1 and sCD14 With Mortality in HIV-1-Infected West African Adults With High CD4 Counts. J. Acquir. Immune Defic. Syndr. 2021, 86, 138–145. [Google Scholar] [CrossRef]
  53. He, J.; Duan, M.; Zhuang, H. ICAM1 and VCAM1 are associated with outcome in patients with sepsis: A systematic review and meta-analysis. Heliyon 2024, 10, e40003. [Google Scholar] [CrossRef]
  54. Juno, J.A.; Phetsouphanh, C.; Klenerman, P.; Kent, S.J. Perturbation of mucosal-associated invariant T cells and iNKT cells in HIV infection. Curr. Opin. HIV AIDS 2019, 14, 77–84. [Google Scholar] [CrossRef] [PubMed]
  55. Kronfli, N.; Bhatnagar, S.R.; Hull, M.W.; Moodie, E.E.M.; Cox, J.; Walmsley, S.; Gill, J.; Cooper, C.; Martel-Laferrière, V.; Pick, N.; et al. Canadian Co-Infection Cohort Investigators. Trends in cause-specific mortality in HIV-hepatitis C coinfection following hepatitis C treatment scale-up. AIDS 2019, 33, 1013–1022. [Google Scholar] [CrossRef]
  56. Rosenthal, E.; Roussillon, C.; Salmon-Céron, D.; Georget, A.; Hénard, S.; Huleux, T.; Gueit, I.; Mortier, E.; Costagliola, D.; Morlat, P.; et al. Liver-related deaths in HIV-infected patients between 1995 and 2010 in France: The Mortavic 2010 study in collaboration with the Agence Nationale de Recherche sur le SIDA (ANRS) EN 20 Mortalité 2010 survey. HIV Med. 2015, 16, 230–239. [Google Scholar] [CrossRef] [PubMed]
  57. Platt, L.; Easterbrook, P.; Gower, E.; McDonald, B.; Sabin, K.; McGowan, C.; Yanny, I.; Razavi, H.; Vickerman, P. Prevalence and burden of HCV co-infection in people living with HIV: A global systematic review and meta-analysis. Lancet Infect. Dis. 2016, 16, 797–808. [Google Scholar] [CrossRef] [PubMed]
  58. Mikuła, T.; Suchacz, M.; Sapuła, M.; Wiercińska-Drapało, A. Significance of Vascular Cell Adhesion Molecule-1 and Tumor Necrosis Factor-Alpha in HIV-Infected Patients. J. Clin. Med. 2022, 11, 514. [Google Scholar] [CrossRef] [PubMed]
  59. Aldámiz-Echevarría, T.; Berenguer, J.; Miralles, P.; Jiménez-Sousa, M.A.; Carrero, A.; Pineda-Tenor, D.; Díez, C.; Tejerina, F.; Pérez-Latorre, L.; Bellón, J.M.; et al. Soluble Adhesion Molecules in Patients Coinfected with HIV and HCV: A Predictor of Outcome. PLoS ONE 2016, 11, e0148537. [Google Scholar] [CrossRef]
  60. Schulte-Hermann, K.; Schalk, H.; Haider, B.; Hutterer, J.; Gmeinhart, B.; Pichler, K.; Brath, H.; Dorner, T.E. Impaired lipid profile and insulin resistance in a cohort of Austrian HIV patients. J. Infect. Chemother. 2016, 22, 248–253. [Google Scholar] [CrossRef]
  61. Huang, H.; Kang, R.; Zhao, Z. Is hepatitis C associated with atherosclerotic burden? A systematic review and meta-analysis. PLoS ONE 2014, 9, e106376. [Google Scholar] [CrossRef]
  62. Nikolopoulos, G.K.; Paraskevis, D.; Psichogiou, M.; Hatzakis, A. HBV-DNA levels predict overall mortality in HIV/HBV coinfected individuals. J. Med. Virol. 2016, 88, 466–473. [Google Scholar] [CrossRef]
  63. Castilho, J.L.; Turner, M.; Shepherd, B.E.; Koethe, J.R.; Furukawa, S.S.; Bofill, C.E.; Raffanti, S.; Sterling, T.R. CD4/CD8 Ratio and CD4 Nadir Predict Mortality Following Noncommunicable Disease Diagnosis in Adults Living with HIV. AIDS Res. Hum. Retroviruses 2019, 35, 960–967. [Google Scholar] [CrossRef] [PubMed]
  64. Scherlinger, M.; Richez, C.; Tsokos, G.C.; Boilard, E.; Blanco, P. The role of platelets in immune-mediated inflammatory diseases. Nat. Rev. Immunol. 2023, 23, 495–510. [Google Scholar] [CrossRef] [PubMed]
  65. Zhu, A.; Real, F.; Zhu, J.; Greffe, S.; de Truchis, P.; Rouveix, E.; Bomsel, M.; Capron, C. HIV-Sheltering Platelets From Immunological Non-Responders Induce a Dysfunctional Glycolytic CD4+ T-Cell Profile. Front. Immunol. 2022, 12, 781923. [Google Scholar] [CrossRef] [PubMed]
  66. Madzime, M.; Rossouw, T.M.; Theron, A.J.; Anderson, R.; Steel, H.C. Interactions of HIV and Antiretroviral Therapy With Neutrophils and Platelets. Front. Immunol. 2021, 12, 634386. [Google Scholar] [CrossRef]
  67. De Pablo-Bernal, R.S.; Ruiz-Mateos, E.; Rosado, I.; Dominguez-Molina, B.; Alvarez-Ríos, A.I.; Carrillo-Vico, A.; De La Rosa, R.; Delgado, J.; Muñoz-Fernández, M.A.; Leal, M.; et al. TNF-α levels in HIV-infected patients after long-term suppressive cART persist as high as in elderly, HIV-uninfected subjects. J. Antimicrob. Chemother. 2014, 69, 3041–3046. [Google Scholar] [CrossRef]
  68. Aikpitanyi-Iduitua, G.A.; Ibeh, I.N.; Idemudia, N.L.; Aikpitanyi-Iduitua, R.O.; Omoregie, R. Interferon gamma, interleukin 6 and tissue necrosis factor alpha levels among asymptomatic HIV patients in Benin City, Nigeria. Hum. Antibodies 2022, 30, 177–182. [Google Scholar] [CrossRef]
  69. Kuller, L.H.; Tracy, R.; Belloso, W.; De Wit, S.; Drummond, F.; Lane, H.C.; Ledergerber, B.; Lundgren, J.; Neuhaus, J.; Nixon, D.; et al. INSIGHT SMART Study Group. Inflammatory and coagulation biomarkers and mortality in patients with HIV infection. PLoS Med. 2008, 5, e203. [Google Scholar] [CrossRef]
  70. Andrade, B.B.; Hullsiek, K.H.; Boulware, D.R.; Rupert, A.; French, M.A.; Ruxrungtham, K.; Montes, M.L.; Price, H.; Barreiro, P.; Audsley, J.; et al. Biomarkers of inflammation and coagulation are associated with mortality and hepatitis flares in persons coinfected with HIV and hepatitis viruses. J. Infect. Dis. 2013, 207, 1379–1388. [Google Scholar] [CrossRef]
  71. Wada, N.I.; Bream, J.H.; Martínez-Maza, O.; Macatangay, B.; Galvin, S.R.; Margolick, J.B.; Jacobson, L.P. Inflammatory Biomarkers and Mortality Risk Among HIV-Suppressed Men: A Multisite Prospective Cohort Study. Clin. Infect. Dis. 2016, 63, 984–990. [Google Scholar] [CrossRef]
  72. Li, J.; Shen, C.; Wang, X.; Lai, Y.; Zhou, K.; Li, P.; Liu, L.; Che, G. Prognostic value of TGF-β in lung cancer: Systematic review and meta-analysis. BMC Cancer 2019, 19, 691. [Google Scholar] [CrossRef]
  73. Poggi, A.; Zocchi, M.R. HIV-1 Tat triggers TGF-beta production and NK cell apoptosis that is prevented by pertussis toxin B. Clin. Dev. Immunol. 2006, 13, 369–372. [Google Scholar]
Viruses 17 00243 g001
Figure 1. The Kaplan–Meier curve for study population survival.
Figure 1. The Kaplan–Meier curve for study population survival.
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Viruses 17 00243 g002
Figure 2. The ROC curves for VCAM-1 and TNF-α. (a) ROC curve for VCAM-1. (b) ROC curve for TNF-α.
Figure 2. The ROC curves for VCAM-1 and TNF-α. (a) ROC curve for VCAM-1. (b) ROC curve for TNF-α.
Viruses 17 00243 g002
Table 1. Baseline data of all analyzed patients.
Table 1. Baseline data of all analyzed patients.
VariableAll Patients (n = 72)Patients Who Lived (n = 40)Patients Who Died (n = 13)p
Age (years) (mean (SD))38.68 (11.04)37.73 (11.42)40.85 (11.52)0.153
Male gender (n (%))56 (77.78)31 (77.50)11 (84.62)0.711
Female gender (n (%))16 (22.22)9 (22.50)2 (15.38)0.711
Intravenous drug use (n (%))39 (54.17)18 (45.00)11 (84.62)0.013
HCV coinfection (n (%))26 (36.11)10 (25.00)8 (61.54)0.022
HBV coinfection (n (%))13 (18.06)6 (15.00)4 (30.77)0.237
Duration of HIV infection (years) (mean (SD))4.48 (5.21)2.80 (3.11)7.54 (6.80)0.001
Duration of HIV treatment (weeks) (mean (SD))93.50 (142.28)76.28 (126.44)116.61 (181.23)0.355
PLT (G/L) (mean (SD))217.82 (99.59)225.88 (111.34)182.46 (90.98)0.209
CRP (mg/L) (mean (SD))22.10 (44.61)10.75 (15.15)39.17 (50.42)0.003
PCT (ng/mL) (mean (SD))0.72 (3.98)0.05 (0.01)0.36 (0.60)0.002
VCAM-1 (ng/mL) (mean (SD))912.00 (68.52)729.10 (556.28)1574.54 (770.91)<0.001
TNF-α (pg/mL) (mean (SD))64.55 (64.99)67.96 (79.82)48.71 (24.96)0.113
CD4+ cell count (cells/μL) (mean (SD))294.47 (210.60)280.95 (204.23)191.31 (111.84)0.085
Total cholesterol (mmol/L) (mean (SD))4.18 (1.20)4.27 (0.95)3.41 (1.28)0.011
LDL cholesterol (mmol/L) (mean (SD))2.43 (1.00)1.58 (0.83)1.69 (0.76)0.316
HDL cholesterol (mmol/L) (mean (SD))1.15 (0.41)2.30 (0.82)1.95 (0.93)0.120
Triglycerides (mmol/L) (mean (SD))1.70 (0.86)1.18 (0.35)0.82 (0.28)0.002
Table 2. Factors associated with the risk of death in people with HIV by univariate and multivariable logistic regression analysis. The analysis compared patients who died with those who lived who stayed in care for the whole observation period.
Table 2. Factors associated with the risk of death in people with HIV by univariate and multivariable logistic regression analysis. The analysis compared patients who died with those who lived who stayed in care for the whole observation period.
ParameterOR (95% CI)paOR * (95% CI)p
Age
<35 years (n = 23)Ref.
≥35 years (n = 30)6.08 (1.19–31.01)0.0304.01 (0.75–21.49)0.104
Gender
Male (n = 42)Ref.
Female (n = 11)2.86 (0.32–25.80)0.3502.84 (0.31–26.76)0.362
Intravenous drug use
No (n = 24)Ref.
Yes (n = 29)6.72 (1.32–34.32)0.0096.59 (1.22–35.66)0.029
HCV infection
No (n = 35)Ref.
Yes (n = 18)4.80 (1.27–18.09)0.0215.64 (1.17–27.22)0.031
HBV infection
No (n = 43)Ref.
Yes (n = 10)2.52 (0.58–10.88)0.2162.46 (0.51–11.85)0.262
CD4+ cell count
≥200 cells/μL (n = 35)Ref.
<200 cells/μL (n = 17)1.46 (0.40–5.38)0.5711.70 (0.37–7.91)0.498
PLT
≥140 G/L (n = 40)Ref.
<140 G/L (n = 13)1.53 (0.38–6.16)0.5492.63 (0.52–13.29)0.242
CRP
<5 mg/L (n = 28)Ref.
≥5 mg/L (n = 25)5.56 (1.32–23.46)0.02020.65 (1.88–227.24)0.013
PCT
<0.05 ng/mL (n = 44)Ref.
≥0.05 ng/mL (n = 9)22.17 (3.69–133.02)<0.00116.60 (2.43–113.20)0.004
VCAM-1
<922 ng/mL (n = 33)Ref.
≥922 ng/mL (n = 20)48.00 (5.41–425.56)<0.00133.49 (3.54–316.98)0.002
TNF-α
<43.45 pg/mL (n = 22)Ref.
≥43.45 pg/mL (n = 31)0.34 (0.09–1.23)0.0990.30 (0.07–1.35)0.116
Total cholesterol
<4.91 mmol/L (n = 44)Ref.
≥4.91 mmol/L (n = 9)0.33 (0.04–2.96)0.3240.24 (0.00–5.64)0.997
HDL cholesterol
>1 mmol/L (n = 34)Ref.
≤1 mmol/L (n = 19)4.22 (1.13–15.72)0.0322.67 (0.62–11.56)0.188
LDL cholesterol
<2.6 mmol/L (n = 40)Ref.
≥2.6 mmol/L (n = 13)0.19 (0.02–1.67)0.1350.17 (0.00–3.78)0.998
Triglyceride
<1.7 mmol/L (n = 38)Ref.
≥1.7 mmol/L (n = 15)1.88 (0.50–7.07)0.3531.60 (0.35–7.28)0.543
* aOR—adjusted odds ratio. The odds ratio was adjusted in the multivariate logistic model by age (<35 years old vs. ≥35 years old) and gender (male vs. female).
Table 3. Factors associated with the risk of death in PWH who inject drugs by univariate and multivariable logistic regression analysis. The analysis compared patients who died and those who lived who stayed in care for the whole observation period.
Table 3. Factors associated with the risk of death in PWH who inject drugs by univariate and multivariable logistic regression analysis. The analysis compared patients who died and those who lived who stayed in care for the whole observation period.
ParameterOR (95% CI)paOR * (95% CI)p
Age
<35 years (n = 11)Ref.
≥35 years (n = 18)4.50 (0.75–26.93)0.1004.93 (0.73–33.21)0.764
Gender
Male (n = 23)Ref.
Female (n = 7)1.50 (0.43–5.21)0.5231.72 (0.08–6.18)0.362
HCV infection
No (n = 15)Ref.
Yes (n = 14)5.33 (1.03–27.76)0.0476.18 (1.03–36.89)0.046
HBV infection
No (n = 22)Ref.
Yes (n = 7)1.31 (0.23–7.41)0.7581.01 (0.14–7.46)0.996
CD4+ cell count
≥200 cells/μL (n = 22)Ref.
<200 cells/μL (n = 7)2.86 (0.50–16.36)0.2382.84 (0.37–21.57)0.314
PLT
≥140 G/L (n = 24)Ref.
<140 G/L (n = 5)3.00 (0.41–21.74)0.2772.44 (0.27–21.97)0.426
CRP
<5 mg/L (n = 13)Ref.
≥5 mg/L (n = 16)7.07 (1.17–42.85)0.0339.42 (1.30–68.01)0.026
PCT
<0.05 ng/mL (n = 6)Ref.
≥0.05 ng/mL (n = 23)7.06 (0.00–250.09)0.99725.09 (0.00–286.89)0.997
VCAM-1
<922 ng/mL (n = 14)Ref.
≥922 ng/mL (n = 15)4.50 (0.75–26.93)0.0993.28 (0.36–35.78)0.994
TNF-α
<43.45 pg/mL (n = 19)Ref.
≥43.45 pg/mL (n = 10)0.47 (0.14–1.56)0.2180.29 (0.05–1.74)0.117
Total cholesterol
<4.91 mmol/L (n = 25)Ref.
≥4.91 mmol/L (n = 4)0.50 (0.05–5.51)0.5710.26 (0.02–3.17)0.288
HDL cholesterol
>1 mmol/L (n = 17)Ref.
≤1 mmol/L (n = 12)4.55 (0.92–22.63)0.0643.28 (0.60–17.98)0.172
LDL cholesterol
<2.6 mmol/L (n = 21)Ref.
≥2.6 mmol/L (n = 8)0.16 (0.02–1.51)0.1090.06 (0.01–0.69)0.052
Triglyceride
<1.7 mmol/L (n = 21)Ref.
≥1.7 mmol/L (n = 8)2.00 (0.38–10.48)0.4121.89 (0.30–11.94)0.497
* aOR—adjusted odds ratio. The odds ratio was adjusted in the multivariate logistic model by age (<35 years old vs. ≥35 years old) and gender (male vs. female).
Table 4. Factors associated with the risk of cancer incidence in PWH—univariate and multivariable logistic regression analysis. The analysis compared patients who died and those who lived who stayed in care for the whole observation period.
Table 4. Factors associated with the risk of cancer incidence in PWH—univariate and multivariable logistic regression analysis. The analysis compared patients who died and those who lived who stayed in care for the whole observation period.
ParameterOR (95% CI)paOR * (95% CI)p
Age
<35 years (n = 22)Ref.
≥35 years (n = 25)6.63 (0.73–60.22)0.0937.86 (0.80–77.16)0.077
Gender
Male (n = 36)Ref.
Female (n = 11)0.73 (0.12–4.39)0.7260.45 (0.06–3.29)0.434
Intravenous drug use
No (n = 24)Ref.
Yes (n = 23)3.06 (0.53–17.66)0.2123.00 (0.48–18.73)0.239
HCV infection
No (n = 33)Ref.
Yes (n = 14)4.00 (0.76–21.02)0.1026.89 (1.00–46.69)0.051
HBV infection
No (n = 39)Ref.
Yes (n = 8)2.27 (0.35–14.49)0.3871.37 (0.18–10.27)0.759
CD4+ cell count
≥200 cells/μL (n = 31)Ref.
<200 cells/μL (n = 16)4.67 (0.75–29.01)0.0984.39 (0.65–29.53)0.128
PLT
≥140 G/L (n = 34)Ref.
<140 G/L (n = 13)4.59 (0.86–24.42)0.0745.15 (0.76–34.84)0.093
CRP
<5 mg/L (n = 27)Ref.
≥5 mg/L (n = 20)4.17 (0.72–24.23)0.1124.41 (0.70–27.84)0.115
PCT
<0.05 ng/mL (n = 41)Ref.
≥0.05 ng/mL (n = 6)25.33 (3.21–199.69)0.00218.41 (1.84–183.98)0.013
VCAM-1
<922 ng/mL (n = 33)Ref.
≥922 ng/mL (n = 14)24.00 (2.52–228.70)0.00619.09 (1.92–189.82)0.012
TNF-α
<43.45 pg/mL (n = 20)Ref.
≥43.45 pg/mL (n = 27)0.09 (0.01–0.82)0.0330.11 (0.01–1.08)0.058
Total cholesterol
<4.91 mmol/L (n = 38)Ref.
≥4.91 mmol/L (n = 9)0.67 (0.07–6.35)0.7249.18 (0.90–93.99)0.465
HDL cholesterol
>1 mmol/L (n = 32)Ref.
≤1 mmol/L (n = 15)3.52 (0.68–18.30)0.1352.97 (0.51–17.17)0.225
LDL cholesterol
<2.6 mmol/L (n = 34)Ref.
≥2.6 mmol/L (n = 13)0.39 (0.04–3.59)0.4050.22 (0.02–2.26)0.205
Triglyceride
<1.7 mmol/L (n = 34)Ref.
≥1.7 mmol/L (n = 13)2.25 (0.43–11.83)0.3382.32 (0.35–15.30)0.380
* aOR—adjusted odds ratio. The odds ratio was adjusted in the multivariate logistic model by age (<35 years old vs. ≥35 years old) and gender (male vs. female).
Table 5. Differences in analyzed parameters in HIV/HCV-coinfected individuals depending on mortality. The analysis compared patients who died and those who lived who stayed in care for the whole observation period.
Table 5. Differences in analyzed parameters in HIV/HCV-coinfected individuals depending on mortality. The analysis compared patients who died and those who lived who stayed in care for the whole observation period.
VariableAll HIV/HCV-Coinfected Patients (n = 18)Patients Who Lived (n = 10)Patients Who Died (n = 8)p
Age (years) (mean (SD))35.94 (6.66)33.80 (6.81)38.63 (5.78)0.130
Male gender (n (%))14 (77.78)8 (80.00)6 (75.00)0.800
Female gender (n (%))4 (22.22)2 (20.00)2 (25.00)0.800
Intravenous drug use (n (%))14 (77.78)6 (60.00)8 (100.00)0.196
HBV coinfection (n (%))3 (16.67)1 (10.00)2 (25.00)0.466
Duration of HIV infection (years) (mean (SD))5.42 (6.53)2.65 (3.73)8.88 (7.81)0.322
Duration of HIV treatment (weeks) (mean (SD))86.39 (168.59)82.50 (176.06)91.25 (170.67)0.427
PLT (G/L) (mean (SD))215.17 (128.37)236.20 (156.64)188.88 (83.96)0.454
CRP (mg/L) (mean (SD))18.83 (23.32)14.30 (23.36)24.50 (24.66)0.252
PCT (ng/mL) (mean (SD))0.11 (0.20)0.05 (0.00)0.17 (0.30)0.056
VCAM-1 (ng/mL) (mean (SD))1234.67 (925.33)797.60 (786.00)1781.00 (819.19)0.002
TNF-α (pg/mL) (mean (SD))54.87 (23.46)56.49 (21.11)52.84 (27.49)0.374
CD4+ cell count (cells/μL) (mean (SD))253.89 (203.67)313.00 (258.00)180.00 (62.66)0.504
Total cholesterol (mmol/L) (mean (SD))3.88 (1.02)4.07 (0.82)3.65 (1.24)0.395
LDL cholesterol (mmol/L) (mean (SD))2.14 (0.74)2.06 (0.59)2.23 (0.93)0.924
HDL cholesterol (mmol/L) (mean (SD))1.04 (0.36)1.25 (0.28)0.78 (0.27)0.002
Triglycerides (mmol/L) (mean (SD))1.72 (0.89)1.65 (0.91)1.80 (0.91)0.964
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Lembas, A.; Załęski, A.; Mikuła, T.; Kozłowska, J.; Wiercińska-Drapało, A. Proinflammatory Biomarkers and Clinical Factors Associated with Long-Term Mortality in People with HIV. Viruses 2025, 17, 243. https://doi.org/10.3390/v17020243

AMA Style

Lembas A, Załęski A, Mikuła T, Kozłowska J, Wiercińska-Drapało A. Proinflammatory Biomarkers and Clinical Factors Associated with Long-Term Mortality in People with HIV. Viruses. 2025; 17(2):243. https://doi.org/10.3390/v17020243

Chicago/Turabian Style

Lembas, Agnieszka, Andrzej Załęski, Tomasz Mikuła, Joanna Kozłowska, and Alicja Wiercińska-Drapało. 2025. "Proinflammatory Biomarkers and Clinical Factors Associated with Long-Term Mortality in People with HIV" Viruses 17, no. 2: 243. https://doi.org/10.3390/v17020243

APA Style

Lembas, A., Załęski, A., Mikuła, T., Kozłowska, J., & Wiercińska-Drapało, A. (2025). Proinflammatory Biomarkers and Clinical Factors Associated with Long-Term Mortality in People with HIV. Viruses, 17(2), 243. https://doi.org/10.3390/v17020243

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