Abstract
Background
Dyslipidemia and poor immune reconstruction are mutually causal in HIV/AIDS patients. However, the predictive value of dyslipidemia for poor immune reconstruction remains a subject of debate. This study aims to assess the predictive value of specific blood lipid levels for poor immune reconstitution and changes in lipid profiles in HIV/AIDS patients.
Methods
The lipids level of 429 HIV/AIDS patients were measured, and patients were treated with antiretroviral therapy (ART) and followed up. The cohort was stratified into two groups based on immune function. Logistic regression analyses and receiver-operating characteristic (ROC) curves were employed to confirm the independent prognostic factors influencing immune reconstitution outcome. Sensitivity analyses were conducted across different subgroups.
Results
At the conclusion of the follow-up period, the rate of overall immunological non-responders (INR) was 21.0%. The incidence of baseline dyslipidemia was higher in the INR group, with high-density lipoprotein cholesterol (HDL-C) being the primary contributor. Total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) levels increased significantly after 24 months of viral suppression (P < 0.05), with a greater percentage increase in TC observed in the INR group. Logistic regression analysis revealed that HDL-C was a significant predictor of INR (OR = 0.247, 95% CI = 0.088–0.696, P = 0.008). Sensitivity analyses confirmed the robustness of the associations between HDL-C and poor immune reconstitution across various subgroups.
Conclusion
Baseline HDL-C level can serve as a prognostic marker for poor immune reconstitution in HIV/AIDS patients, and INR are more likely to exhibit elevated TC levels. Early lipid profile assessment could inform clinical management strategies, thereby improving better immune outcomes in this vulnerable population.
Clinical trial number
Not applicable.
Keywords: Dyslipidemia, HDL-C, Immune reconstitution, HIV, AIDS
Introduction
Acquired immunodeficiency syndrome (AIDS), which is caused by human immunodeficiency virus (HIV) infection, remains a significant global challenge. According to the World Health Organization, approximately 39.9 million people worldwide were living with HIV by the end of 2023 (https://crossroads.unaids.org/). Although the incidence of HIV is gradually declining, the overall number of individuals living with HIV continues to rise. The advent of standardized antiretroviral therapy (ART) has transformed HIV/AIDS from an “incurable disease” to a manageable and treatable chronic condition. Despite this progress, ART fails to eliminate the reservoir of latent HIV-1, which primarily resides in resting CD4 T cells. The goal of ART is to suppress HIV replication and promote immune reconstitution. Nevertheless, a subset of patients, could not restore their immune function when effective viral suppression is achieved, and are classified as immunological non-responders (INR) [1]. The incidence of INR ranges from 10% to 40% [2], and these patients face significantly elevated risks of opportunistic infections, malignancies, non-AIDS-related complications, and mortality [3]. As the life expectancy of HIV/AIDS patients increases, the issue of INR is gaining prominence. Therefore, it is necessary to understand the mechanisms underlying immune reconstitution and develop effective research strategies.
To date, no treatment has successfully restored CD4 T cells in INR. Over the years, various therapeutic drugs, such as immunosuppressants [4], (5R)-5-hydroxytriptolide [5] and recombinant human interleukin 7 [6], have been utilized to address poor immune reconstitution. However, they have not been validated by large-scale prospective controlled studies. This underscores the importance of early HIV/AIDS patient evaluation, because a thorough initial examination can facilitate the early detection of issues and guide the development of tailored treatment plans. Studies have demonstrated that specific markers [7, 8], including CD4 T cell count, age, gender, body mass index, and total bilirubin, can help to predict immune recovery. However, none of these markers individually fully explains the poor immune reconstitution observed in these patients. A recent study suggested that immunological mechanisms—such as elevated frequencies of interleukin-17 A (IL-17 A)–producing CD4 T cells and IL-17 A–producing regulatory T (Treg) cells before ART—may contribute to immunodiscordant responses, underscoring the complex interplay among immune cell subsets in poor CD4 T cell recovery [9]. Therefore, identifying additional biomarkers and elucidating immunological pathways is essential for enhancing the prediction, assessment, and management of INR.
Dyslipidemia and poor immune reconstitution are interrelated in HIV/AIDS patients. Dyslipidemia is characterized by low high-density lipoprotein cholesterol (HDL-C), elevated low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglyceride (TG). Dyslipidemia affects the choice of ART, lowers the quality of life of HIV/AIDS patients and increases the risk of cardiovascular disease (CVD) [10]. Epidemiological data indicate that HIV/AIDS patients, particularly the elderly, are more susceptible to dyslipidemia compared to the healthy population [11]. The development of dyslipidemia in HIV/AIDS patients involves complex interactions among traditional risk factors, ART, and the direct impact of HIV infection [12]. HIV-encoded proteins modulate the expression of regulatory genes and alter cell membrane proteins, thereby leading to dyslipidemia [12]. ART regimens, such as protease inhibitors (PIs), nucleoside reverse transcriptase inhibitors (NRTIs), and non-nucleoside reverse transcriptase inhibitors (NNRTIs), lead to adipose tissue redistribution and lipodystrophy, and ultimately exacerbate dyslipidemia [13]. Immune function and dyslipidemia are mutually causal. HIV-1-infected nonprogressors can delay disease progression without ART for extended periods through the regulation of cholesterol metabolism [14]. Managing dyslipidemia in HIV/AIDS patients has thus become crucial in comprehensive HIV care.
While the association between dyslipidemia and immune reconstruction is well-documented, there is a notable lack of data on the role of dyslipidemia as a biomarker in predicting incomplete immune reconstruction. Predicting poor immune reconstruction in HIV/AIDS patients remains a great clinical challenge. The idea of using dyslipidemia as a comprehensive index for diagnosis and monitoring is highly promising, as this could facilitate early intervention. Early dyslipidemia detection is of beneficial in clinical practice and can potentially prevent or mitigate immunodeficiency in HIV/AIDS patients. To address this challenge, we conducted a retrospective cohort study to evaluate the predictive value of specific blood lipid levels in forecasting poor immune reconstruction and changes in lipid profiles in HIV/AIDS patients.
Materials and methods
Subjects and study design
This study was a retrospective cohort analysis of HIV/AIDS patients who received HIV care at Ningbo Hospital of Integrated Traditional Chinese and Western Medicine from November 2018 to August 2024. Patients were confirmed to have HIV infection by the Centers for Disease Control of Ningbo or other cities. The exclusion criteria were as follows: (1) active inflammatory, tumor or autoimmune diseases; (2) active syphilis, viral hepatitis, tuberculosis and other active infectious diseases; (3) pregnant or lactating women, or women planning pregnancy during the study period; (4) use of lipid-lowering agents; (5) smoking; (6) Severe liver and kidney insufficiency. Hypertension and type 2 diabetes were not considered exclusion criteria. Ultimately, 429 participants met the inclusion and exclusion criteria (Fig. 1). All subjects received a standard treatment regimen containing two NRTIs with a third agent, such as NNRTI, PI, or integrase strand transfer inhibitor (INSTI).
Fig. 1.
Flow chart of sample size
Study variables
Baseline data for the enrolled patients, including demographic, clinical, and laboratory characteristics, were collected. The baseline was established as the date on which the included HIV/AIDS patients first initiated ART at this institution. Relevant demographic and clinical data were extracted from an electronic database specifically designed for this study. Each participant provided detailed medical history information, including drug use, disease history, smoking status, age, gender, and transmission category. Participants were classified into three groups based on transmission categories: men who have sex with men (MSM), heterosexuals, and those who were unwilling to disclose their status. After an overnight fasting of at least eight hours, blood samples were collected and processed via centrifugation. Laboratory characteristics included hemoglobin (HGB), platelets (PLT), white blood cell (WBC), TC, TG, LDL-C, HDL-C, plasma HIV RNA, CD3 T cell count, CD4 T cell count, as well as CD8 T cell count. Serum lipid levels were assessed through enzymatic methods (Sekisui Medical, Tokyo, Japan) on an automated analyzer (Hitachi Labospect 008; Hitachi-Hitec, Tokyo, Japan). The reagents were sourced from Ningbo Medical System Biotechnology Co., Ltd. (Ningbo, Zhejiang, China). Dyslipidemia was diagnosed according to the Chinese expert consensus on integrated lipid management in HIV/AIDS [15]: TC ≥ 5.20 mmol/L, HDL-C < 1.00 mmol/L, LDL-C ≥ 3.4 mmol/L, or TG ≥ 1.70 mmol/L.
Outcome
Patients were evaluated every six months for up to two years. The primary endpoint of this study was the incidence of poor immune reconstitution in HIV/AIDS patients. Poor immune reconstitution was defined as a CD4 T cell count below 350 cells/µL after 24 months of ART with a viral load of fewer than 50 copies/mL after ≥ 24 months of viral suppression [16]. Patients with poor immune reconstitution were defined as INRs, while those with CD4 T cell count above 350 cells/µL were defined as immune responders (IRs). Based on immune reconstitution status, patients were divided into two groups.
Statistical analysis
The Kolmogorov-Smirnov test was employed to assess the characteristics of data distribution. We performed descriptive analysis of patient characteristics using counts and frequencies (%) for categorical variables, and medians (IQR, 25th-75th) for continuous variables. The Chi-square and Fisher’s exact test was applied to compare categorical data. The Mann-Whitney U test was conducted to detect differences in continuous variables between the two groups. Comparisons before and after ART were conducted through the paired rank-sum test. Correlations between baseline lipid parameters and the percentage increase in CD4 T cell count after 24 months of ART were assessed using Pearson partial correlation analysis, controlling for age and baseline CD4 T cell count. Logistic regression analyses were performed based on significant discriminators of immune reconstitution identified through the Mann-Whitney U test (P < 0.05) and the Chi-squared test. Finally, the receiver-operating characteristic (ROC) curves demonstrated HDL-C as a marker for INR detection, with adjustments for confounders (age and gender). Youden index was calculated to get the optimal threshold (Youden index = sensitivity + specificity − 1). Sensitivity analyses were performed to assess robustness, including subgroup analyses stratified by gender, age, history of hypertension, type 2 diabetes, transmission category and ART regimen. Statistical analyses were performed with the help of IBM SPSS Statistics (Version 24.0) and GraphPad Prism 9.
Power analysis: Based on the incidence of dyslipidemia and the number of subjects in the two groups, the power of the sample size was over 90%, as calculated by G*Power 3.0 (Heinrich-Heine).
Results
Baseline characteristics of the subjects
Table 1 summarizes the clinical characteristics of the 429 subjects. After the follow-up period, the overall INR rate was 21.0%. As anticipated, the INR group exhibited more lipid abnormalities, such as a history of dyslipidemia and lower HDL-C levels (P < 0.05), in comparison to the IR group. A notable difference was found in the transmission category between the INR and IR groups. In the INR group, age was significantly higher (P < 0.001), while HGB, PLT, WBC, CD3 T cell count, CD4 T cell count and CD4/CD8 ratio were lower (P < 0.05). No significant differences were observed between the two groups in other variables, including gender, history of hypertension and type 2 diabetes, ART regimen, fasting glucose, TC, TG, LDL-C, CD8 T cell count, and HIV viral load (P > 0.05).
Table 1.
Baseline characteristics between INR and IR groups
| Overall (n = 429) | INR group(n = 90) | IR group (n = 339) | P-Value | |
|---|---|---|---|---|
| Gender (n, %) | NS | |||
| Male | 380(88.6) | 84(93.3) | 296(87.3) | |
| Female | 46(11.4) | 6(6.7) | 43(12.7) | |
| Age (years, IQR) | 34(27–46) | 39(30–53) | 33(27–44) | < 0.001 |
| Dyslipidemia (n, %) | 0.004 | |||
| Yes | 274(63.2) | 69(76.7) | 205(59.6) | |
| No | 155(36.8) | 21(23.3) | 134(40.4) | |
| Hypertension (n, %) | NS | |||
| Yes | 21(4.9) | 4(4.4) | 17(5.0) | |
| No | 408(95.1) | 86(95.6) | 322(95.0) | |
| Type 2 diabetes (n, %) | NS | |||
| Yes | 9(2.1) | 2(2.2) | 7(2.1) | |
| No | 420(97.9) | 88(97.8) | 332(97.9) | |
| Transmission category (n, %) | 0.047 | |||
| Unknown | 26(6.1) | 2(2.2) | 24(7.1) | |
| MSM | 226(52.7) | 42(46.7) | 184(54.3) | |
| Heterosexual | 177(41.3) | 46(51.1) | 131(38.6) | |
| ART regimen (n, %) | NS | |||
| NNRTI-based | 319(74.4) | 63(70.0) | 256(75.5) | |
| PI/r-based | 15(3.5) | 3(3.3) | 12(3.5) | |
| INSTI-based | 95(22.1) | 24(26.7) | 71(20.9) | |
| Fasting glucose (mmol/L, IQR) | 5.3(4.9-6.0) | 5.4(5.1–6.3) | 5.3(4.9–5.9) | NS |
| TC (mmol/L, IQR) | 4.2(3.6–4.8) | 4.1(3.4–4.7) | 4.3(3.7–4.9) | NS |
| TG (mmol/L, IQR) | 1.4(0.9–2.1) | 1.4(1.0-2.1) | 1.3(0.9–2.1) | NS |
| HDL-C (mmol/L, IQR) | 1.1(0.9–1.3) | 1.0(0.8–1.1) | 1.1(0.9–1.3) | < 0.001 |
| LDL-C (mmol/L, IQR) | 2.6(2.1–3.1) | 2.5(2.1–3.3) | 2.7(2.1–3.1) | NS |
| HGB (109/L, IQR) | 151(141–159) | 149(135–155) | 152(142–160) | 0.012 |
| PLT (109/L, IQR) | 220.0(184.5-253.5) | 207.5(171.5-240.5) | 224.0(187.0-257.0) | 0.011 |
| WBC (109/L, IQR) | 5.2(4.2–6.1) | 4.9(3.8–5.7) | 5.2(4.3–6.2) | 0.003 |
| CD3 T cell count (cells/uL, IQR) | 1041(804–1354) | 831(670–1110) | 1071(858–1407) | < 0.001 |
| CD4 T cell count (cells/uL, IQR) | 342(231–464) | 181(103–308) | 373(269–494) | < 0.001 |
| CD8 T cell count (cells/uL, IQR) | 619(466–845) | 570(397–817) | 625(473–859) | NS |
| CD4/CD8 ratio (IQR) | 0.54(0.34–0.79) | 0.33(0.17–0.60) | 0.59(0.39–0.85) | < 0.001 |
| HIV viral load (log copies/mL, IQR) | 1.0 (1-4.4) | 1.0 (1.0-4.6) | 1.0 (1.0-4.3) | NS |
Abbreviations: INR, immunological non-responders; IR, immunological responders; MSM, men who have sex with men; ART, antiretroviral therapy; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor; INSTI, integrase strand transfer inhibitor; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; HGB, hemoglobin; PLT, platelets; WBC, white blood cell
Different types of baseline lipids between INR and IR
To further unravel the association between baseline dyslipidemia and immune reconstruction after 24 months of viral suppression, we subdivided the baseline lipid profiles into TC, TG, HDL-C, and LDL-C. In the INR group, the baseline percentages of dyslipidemia for TC, TG, HDL-C, and LDL-C were 13.3%, 37.8%, 60.0%, and 13.3%, respectively, while those of dyslipidemia in the IR group were 16.8%, 36.0%, 36.6%, and 12.7%, respectively (Fig. 2). The INR group had a higher proportion of low baseline HDL-C than the IR group (P < 0.05), while differences in baseline TC, TG, and LDL-C were not statistically significant (P > 0.05).
Fig. 2.
Different types of lipid profiles between INR and IR groups. Abbreviations: TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; INR, immunological non-responder; IR, immune responder
Longitudinal changes in lipids and immune reconstruction
After the follow-up period, several lipid parameters showed unfavorable changes (Fig. 3). Compared with baseline values, TC, TG, and LDL-C levels increased significantly after 24-month viral suppression (all P < 0.05), while HDL-C levels remained relatively stable (P > 0.05), indicating overall lipid deterioration during long-term ART. To further assess whether these changes differed between groups, the magnitude of lipid changes from baseline to 24 months was compared (Table 2). The percentage increase in TC was greater in the INR group than in the IR group. Although TG, HDL-C, and LDL-C also exhibited increase in varying degrees, the between-group differences in their changes were not statistically significant (P > 0.05).
Fig. 3.
Relationship between evolution of dyslipidemia and immune reconstitution after 24 months of viral suppression. The levels of TC (A), TG (B), HDL-C (C), and LDL-C (D). Abbreviations: TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; INR, immunological non-responder; IR, immune responder
Table 2.
Percentage change of different types of lipids between INR and IR groups
| Overall (n = 429) | INR (n = 90) | IR (n = 339) | P-Value | |
|---|---|---|---|---|
| Increased TC percentage (%) | 13.0(0.0-29.1) | 27.4(8.4–41.1) | 11.0(-0.8-26.7) | < 0.001 |
| Increased TG percentage (%) | 30.3(-8.5-94.4) | 36.7(0.3-103.4) | 26.8(-11.8-93.9) | NS |
| Increased HDL-C percentage (%) | -5.4(-17.5-9.8) | -3.0(-17.4-11.0) | -5.9(-17.7-9.8) | NS |
| Increased LDL-C percentage (%) | 8.1(-14.5-37.6) | 16.8(-3.4-33.2) | 3.8(-16.6-38.2) | 0.056 |
Abbreviation: INR, immunological non-responders; IR, immunological responders; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol
Correlation between baseline lipid profile and immune recovery
To further examine the relationship between the increase in CD4 T cell count over 24 months and baseline lipid profiles, Pearson partial correlation analysis was performed (Fig. 4). Considering the potential confounding effects of age and baseline CD4 T cell count on immune recovery, these variables were adjusted for in the analysis. Pearson partial correlation analysis revealed a significant positive association between baseline HDL-C levels and the percentage increase in CD4 T cell count after 24 months of ART (r = 0.262, P = 0.001; Fig. 4C). No significant correlations were observed for TC (Fig. 4A), TG (Fig. 4B), or LDL-C levels (Fig. 4D; all P > 0.05).
Fig. 4.
Scatter plots showing Spearman correlations between baseline lipid parameters and the percentage increase in CD4 T cell count after 24 months of ART. Panels A–D correspond to TC, TG, HDL-C, and LDL-C, respectively. Abbreviations: TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol
Risk factor analysis of poor immune reconstruction
Multivariate logistic regression analyses were performed on HIV/AIDS patients (Table 3). In Model 1, HDL-C (OR = 0.170, 95% CI = 0.067–0.434, P < 0.001) was identified as an independent factor linked to poor immune reconstruction. In Model 2, after further adjustments for age and sex, the OR for HDL-C was 0.172 (95% CI = 0.067–0.443, P < 0.001). In Model 3, even after adjustments for potential confounders such as sex, age, transmission category, HGB, PLT, WBC, CD3 T cell count, CD4 T cell count and CD4/CD8 ratio, the correlation between HDL-C and poor immune reconstruction remained strong, with an OR of 0.247 (95% CI = 0.088–0.696, P = 0.008).
Table 3.
Multivariate logistic regression analyses of poor immune reconstruction in HIV/AIDS patients
| Variable | OR (95%CI) | P | Adjusted R square | |
|---|---|---|---|---|
| Model 1 | HDL | 0.170(0.067–0.434) | < 0.001 | 0.056 |
| Model 2 | HDL | 0.172(0.067–0.443) | < 0.001 | 0.123 |
| Age | 1.039(1.021–1.057) | < 0.001 | ||
| Model 3 | HDL | 0.247(0.088–0.696) | 0.008 | 0.382 |
| Gender | ||||
| Male | Ref. | |||
| Female | 0.301(0.094–0.967) | 0.044 | ||
| Age | 1.030(1.006–1.055) | 0.013 | ||
| CD4 T cell count | 0.992(0.987–0.996) | < 0.001 |
Notes: Model 1: no adjustment. Model 2: adjusted for age and gender. Model 3: adjusted for age, gender, transmission category, HGB, PLT, WBC, CD3 T cell count, CD4 T cell count and CD4/CD8 ratio
Abbreviations: HDL-C, high-density lipoprotein cholesterol
ROC curves of HDL-C
As presented in Fig. 5, ROC curves were constructed to evaluate the predictive value of HDL-C for the incidence of INR. The unadjusted AUC was 0.643 (95% CI 0.579–0.707, P < 0.001), when the HDL-C levels was set to 1.015, the maximum Youden index was 0.294 (sensitivity, 61.7%; specificity, 67.8%). While the AUC adjusted for gender and age was 0.693 (95% CI 0.634–0.752, P < 0.001).
Fig. 5.
ROC curves of HDL-C for INR diagnosis
Sensitivity analysis
Sensitivity analyses were performed to assess the robustness of the association between HDL-C levels and immune reconstitution (Fig. 6). Stratified analyses by gender, age, hypertension, type 2 diabetes, transmission category, and ART regimen demonstrated that the association between higher HDL-C levels and improved immune recovery was consistent across most subgroups. However, among patients with hypertension or diabetes, as well as among women, participants with an unknown transmission category, and those receiving a PI/r-based regimen, the predictive value of HDL-C for immune reconstitution was not statistically significant (all P > 0.05), likely due to the limited sample sizes within these subgroups. In all other subgroups, higher HDL-C levels remained significantly positively associated with immune recovery (P < 0.05), further supporting the robustness of the overall findings.
Fig. 6.
Sensitivity analysis showing the robust association between HDL-C and INR in HIV/AIDS patients. Abbreviations: MSM, men who have sex with men; ART, antiretroviral therapy; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor; INSTI, integrase strand transfer inhibitor
Discussion
In our study, the incidence of INR among HIV/AIDS patients was 21.0%. Despite the availability of safe and effective combination ART, achieving immune reconstitution is a challenge. 429 HIV/AIDS patients were enrolled in a retrospective cohort to explore the association between baseline lipid levels and immune reconstitution outcomes through logistic regression analyses and ROC curves. HDL-C level proved to be a reliable predictor of poor immune reconstitution in HIV/AIDS patients, and independent of gender, age, and baseline CD4 T cell count. This association remained robust across sensitivity analyses. HDL-C served as a stable marker in both the INR and IR groups, and exhibited no significant changes before and after ART.
The incidence of dyslipidemia in HIV/AIDS patients has been as high as 51% [17]. However, specific types of dyslipidemia were less detailed. In our study, the incidence of dyslipidemia was 63.2%, with HDL-C being the most common abnormality (60.0%). Despite the high prevalence, treatment rate is relatively low. In the future, clinical interventions should prioritize the systematic monitoring and treatment of dyslipidemia to lower cardiovascular risks and improve health outcomes in this vulnerable population.
In this study, we examined the relationship between baseline lipid levels and immune reconstitution in HIV/AIDS patients undergoing ART. Partial correlation analysis revealed that higher baseline HDL-C levels were associated with improved immune recovery. To further validate our findings, sensitivity analyses were performed, stratified by gender, age, history of hypertension, type 2 diabetes, transmission category, and ART regimen. The association between higher HDL-C levels and improved immune recovery remained significant in most subgroups, suggesting that this relationship was broadly consistent across diverse patient characteristics. However, among patients with hypertension or diabetes, and among those receiving PI/r-based regimens or with an unknown transmission category, the predictive value of HDL-C for immune reconstitution was not statistically significant. These findings may partly reflect the limited sample sizes in these subgroups, which reduced statistical power, or the effects of medications commonly used for these comorbidities (e.g., statins or metformin) that can influence lipid metabolism. The lack of significance among women may also reflect biological or hormonal factors that modulate HDL-C function and immune responses, warranting further investigation. Collectively, these results underscore the complex interplay between metabolic and immune processes and highlight the need to consider comorbidities and treatment regimens in future studies investigating lipid-related mechanisms of immune recovery in HIV/AIDS patients.
Several retrospective studies have delved into the correlation between lipid levels and immune reconstitution. There is no correlation between baseline TC or TG and immune reconstruction, which is consistent with the findings of most studies [7, 18]. However, the results of HDL-C and LDL-C were somewhat controversial. Two studies supported a positive correlation between HDL-C levels and CD4 T cell count [19, 20]. However, these studies did not include HIV/AIDS patients with viral suppression, and viral load could interfere with HDL-C and CD4 T cell count. Moreover, the cross-sectional design of these studies precludes the establishment of a causal relationship between HDL-C and CD4 T cell count. Two cohort studies have reported an association between baseline lipid levels and immune reconstitution, suggesting their potential predictive value for immune outcomes. Rodríguez-Gallego et al. [21] supported this association, reporting that patients with higher baseline HDL-C levels exhibited better immunological recovery after ART. In contrast, Wang et al. [22] found that LDL-C, rather than HDL-C, was a predictor of INR. Discrepancies among previous studies may be attributable to several reasons. First, smoking status may be an important factor. Smoking can adversely affect lipids, including HDL-C and LDL-C, and CVD morbidity [23]. In the study by Wang et al., the inclusion of smokers may interfere with the results. Second, the disease characteristics of the study populations varied among the studies. In Rodríguez-Gallego et al.’s study, 17 of 64 individuals (27%) failed to arrive IR, whereas in Wang et al.’s study, more than half of the HIV/AIDS patients (56.4%) experienced poor immune reconstitution. In our study, the exclusion criteria and patient structure are more comparable to those in Rodríguez-Gallego et al.’s research. Therefore, a similar conclusion was drawn. It is noteworthy that our study had a larger sample size and included a broader range of ART regimens, such as INSTIs, which further validates the predictive value of HDL-C for poor immune reconstitution.
Following ART, dyslipidemia in HIV/AIDS patients was characterized by elevated levels of TC, TG, and LDL-C, whereas HDL levels remained largely constant [24, 25]. This pattern was observed in both groups within our study. As shown in Fig. 3, TC, TG, and LDL-C levels increased significantly compared with baseline, reflecting a general deterioration of lipid metabolism during long-term ART. However, when comparing the magnitude of lipid changes between the INR and IR groups (Table 2), only TC showed a statistically significant difference, with a greater increase observed in the INR group. TG and LDL-C levels also increased in both groups but did not differ significantly between them. This discrepancy may be attributable to individual variability and the limited sample size, which may have masked moderate group-specific differences. From a physiological perspective, TC represents the combined cholesterol content across all lipoprotein fractions and is therefore more sensitive than TG or LDL-C to global disturbances in lipid metabolism. Consequently, TC serves as a more comprehensive marker of chronic metabolic alterations induced by ART and immune activation. The greater increase in TC observed in INR patients may reflect ongoing immune activation and metabolic dysregulation despite sustained viral suppression. Mechanistically, the lipid alterations observed during ART are complex. Insights from randomized controlled trials [26–28] indicate that adjunctive statin therapy does not improve immune recovery, suggesting that lipid abnormalities may result from, rather than cause, impaired immune function. Clinically, INR appear to be at a higher risk for dyslipidemia and CVD [29, 30], primarily due to chronic low-grade inflammation driven by the HIV-1 DNA reservoir. These findings proved that early lipid monitoring in patients with poor immune reconstitution is crucial. It remains uncertain whether the threshold for statin use should be lower in INR patients, given their higher risk of dyslipidemia and greater burden of subclinical atherosclerosis. CVD risk calculators designed for the general population likely underestimate this risk in HIV-infected patients. Throughout ART, HDL-C remained stable, which suggests its potential as a predictive indicator for poor immune reconstitution.
Dyslipidemia can impair immune reconstruction, thus further exacerbating adverse lipid outcome. Traditionally, HDL-C has been recognized for its role in lowering lipid levels by transporting cholesterol to the liver. However, beyond its metabolic role, HDL-C has emerged as an important regulator of immune activity and cellular function. Low HDL-C levels have been linked to poorer clinical outcomes in many diseases, including HIV [19, 20], COVID-19 [31], and sepsis [32]. Mechanistically, HDL-C may influence immune reconstitution by modulating viral reservoirs and immune cell regulation. The persistence of latent HIV reservoirs is the main barrier to eradicate HIV infection [33]. Even under ART, HIV can facilitate trans-infection of CD4 T cells mediated by antigen-presenting cells (APCs) [34]. Cholesterol metabolism, regulated by ATP-binding cassette A1 and ATP-binding cassette G1, is crucial in APC-mediated HIV-1 trans-infection. Higher HDL-C levels may attenuate viral reservoir infectivity by reducing APC-mediated HIV-1 trans-infection [35]. Thus, HDL-C appears to support immune recovery partly by restricting viral persistence through its regulation of cholesterol metabolism. In addition to its effects on viral reservoirs, HDL-C modulates immune homeostasis through T cell regulation. Treg cells have been shown to play an important immunoregulatory role in HIV/AIDS patients [36]. Notably, after 30-day normocaloric, low-cholesterol diet, the proportion of Treg cells decreased in patients with chronic hepatitis C [37], suggesting a link between cholesterol metabolism and Treg maintenance. HDL contributes to CD4 T cell survival and expansion by modulating their activation, proliferation, and function. Moreover, HDL promotes Treg cell homeostasis by enhancing their survival [38]. Recent evidence further suggests that dysregulation of Treg and Th17 subsets may play a crucial role in impaired immune recovery among INR. Rosado-Sánchez et al. [9] reported increased frequencies of Th17 cells, IL-17 A–producing Treg cells, and distinct IL-17 A–containing cytokine combinations preceding poor CD4 T cell recovery in patients initiating ART. Collectively, these findings suggest that HDL-C supports immune reconstitution not only by limiting viral reservoir activity but also by maintaining Treg and Th17 balance, thereby reducing immune dysregulation in HIV/AIDS patients.
In our study, HDL-C—but not other lipid parameters—showed predictive value for immune reconstitution, which may be attributed to its unique biological characteristics. HDL-C differs from other lipid fractions, such as TG and LDL-C, in both composition and biological function. Unlike LDL, which primarily delivers cholesterol to peripheral tissues, HDL mediates reverse cholesterol transport by carrying excess cholesterol from peripheral cells back to the liver for excretion [39]. HDL particles are structurally diverse, consisting of a complex mixture of lipids and apolipoproteins—most notably APOA1 and APOE—that confer anti-inflammatory, antioxidant, and immunomodulatory properties [39]. In particular, APOA1 and APOE play critical roles in maintaining CD4 T cell homeostasis. APOA1 deficiency leads to increased systemic T cell activation and chronic inflammation [40], whereas APOE deficiency reduces Treg frequency and suppressive capacity [41]. These findings indicate that HDL acts not only as a cholesterol transporter but also as an active regulator of adaptive immunity. This mechanistic insight complements our findings on HDL-C, suggesting that metabolic pathways may influence Treg homeostasis and, consequently, overall immune reconstitution.
This study has several limitations. First, due to constrained laboratory conditions, we were unable to measure HIV-1 DNA. Therefore, the impact of latent HIV reservoirs on immune reconstruction via lipid metabolism remains unclear. Second, all participants received a standard ART regimen comprising two NRTIs and a third agent—either a NNRTI, a PI/r, or an INSTI. Consequently, the effects of other antiretroviral drug classes or lipid metabolism in treatment-naïve HIV/AIDS patients could not be assessed. Third, as the study cohort comprised solely of Chinese participants, the findings should be interpreted with caution when extrapolated to other populations due to potential genetic diversity and variability. Additionally, other factors potentially related to immune reconstruction, such as body mass index or hepatitis C virus infection, were not explored. Further prospective studies involving diverse populations and varied ART regimens are warranted to conclusively validate the predictive value of HDL-C for poor immune reconstruction and to elucidate the underlying mechanisms.
Conclusion
Our study demonstrates that baseline HDL-C can serve as a prognostic marker for poor immune reconstruction in HIV/AIDS patients, who tend to present with elevated levels of TC after ART. This study aims to deepen our understanding of the potential role of dyslipidemia as an early indicator of compromised immune reconstruction, thereby informing and improving clinical management strategies for this vulnerable population.
Acknowledgements
Thanks to Hangzhou Xixi Hospital for enlightening our research team. We would also like to express our sincere gratitude to Dr. Shan Zhang for her contributions to the coordination, and initial correspondence of this study.
Author contributions
YJ and QC contributed equally to this work and are co–first authors. YJ and TX led the writing and editing of the manuscript. YJ and YG contributed to the study design and acquisition of funding. QC was responsible for data collection, statistical analysis, and interpretation of the findings. YG supervised the overall project and served as the corresponding author. All authors contributed to the interpretation of the results, critically reviewed the manuscript, and approved the final version for submission.
Funding
This work was supported by the Medical Health Science and Technology Project of Zhejiang Province (grant numbers: 2024KY1608).
Data availability
The data presented in this study are available on request to the corresponding author. The data are not publicly available due to the privacy protection for HIV/AIDS patients.
Declarations
Ethical approval and consent to participate
This study was reviewed and approved by the Institutional Review Board of Ningbo Hospital of Integrated Traditional Chinese and Western Medicine with the approval number: 2023-050. All experiments involving human participants and/or human tissue samples were conducted in accordance with relevant ethical guidelines and regulations. Data confidentiality was strictly maintained through the complete exclusion of personal identifiers. Furthermore, neither raw nor derived data were disseminated to any third party.
Consent for publication
The participant in this study was briefed about the survey and consented to share his past medical records and disease history. The requirement for informed consent was waived due to the retrospective study design.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yong Jin and Qi Chen contributed equally to this work and share first authorship.
References
- 1.Bono V, Augello M, Tincati C, Marchetti G. Failure of CD4 + T-cell recovery upon Virally-Effective cART: an enduring gap in the Understanding of HIV + Immunological non-Responders. New Microbiol. 2022;45(3):155–72. [PubMed] [Google Scholar]
- 2.Yang X, Su B, Zhang X, Liu Y, Wu H, Zhang T. Incomplete immune reconstitution in HIV/AIDS patients on antiretroviral therapy: challenges of immunological non-responders. J Leukoc Biol. 2020;107(4):597–612. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Mocroft A, Reiss P, Gasiorowski J, Ledergerber B, Kowalska J, Chiesi A, et al. Serious fatal and nonfatal non-AIDS-defining illnesses in Europe. J Acquir Immune Defic Syndr. 2010;55(2):262–70. [DOI] [PubMed] [Google Scholar]
- 4.Bandera A, Colella E, Rizzardini G, Gori A, Clerici M. Strategies to limit immune-activation in HIV patients. Expert Rev Anti Infect Ther. 2017;15(1):43–54. [DOI] [PubMed] [Google Scholar]
- 5.Cao W, Liu X, Han Y, Song X, Lu L, Li X, et al. (5R)-5-hydroxytriptolide for HIV immunological non-responders receiving ART: a randomized, double-blinded, placebo-controlled phase II study. Lancet Reg Health West Pac. 2023;34:100724. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Thiébaut R, Jarne A, Routy JP, Sereti I, Fischl M, Ive P, et al. Repeated cycles of Recombinant human Interleukin 7 in HIV-Infected patients with low CD4 T-Cell reconstitution on antiretroviral therapy: results of 2 phase II multicenter studies. Clin Infect Dis. 2016;62(9):1178–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Zhang W, Yan J, Luo H, Wang X, Ruan L. Incomplete immune reconstitution and its predictors in people living with HIV in Wuhan, China. BMC Public Health. 2023;23(1):1808. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Zhu J, Huang H, Wang M, Zhang Y, Mo J, Tian W, et al. High baseline body mass index predicts recovery of CD4 + T lymphocytes for HIV/AIDS patients receiving long-term antiviral therapy. PLoS ONE. 2022;17(12):e0279731. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Rosado-Sánchez I, Herrero-Fernández I, Tarancon-Diez L, Moreno S, Iribarren JA, Dalmau D, et al. Increased frequencies of Th17 cells and IL17a-producing regulatory T-cells preceding the immunodiscordant response to antiretroviral treatment. J Infect. 2018;76(1):86–92. [DOI] [PubMed] [Google Scholar]
- 10.Zaid D, Greenman Y. Human immunodeficiency virus infection and the endocrine system. Endocrinol Metab (Seoul). 2019;34(2):95–105. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Gelpi M, Afzal S, Lundgren J, Ronit A, Roen A, Mocroft A, et al. Higher risk of abdominal Obesity, elevated Low-Density lipoprotein Cholesterol, and Hypertriglyceridemia, but not of Hypertension, in people living with human immunodeficiency virus (HIV): results from the Copenhagen comorbidity in HIV infection study. Clin Infect Dis. 2018;67(4):579–86. [DOI] [PubMed] [Google Scholar]
- 12.Papantoniou E, Arvanitakis K, Markakis K, Papadakos SP, Tsachouridou O, Popovic DS, et al. Pathophysiology and clinical management of dyslipidemia in people living with HIV: sailing through rough seas. Life (Basel). 2024;14(4). [DOI] [PMC free article] [PubMed]
- 13.Kalra DK, Vorla M, Michos ED, Agarwala A, Virani S, Duell PB, et al. Dyslipidemia in human immunodeficiency virus disease: JACC review topic of the week. J Am Coll Cardiol. 2023;82(2):171–81. [DOI] [PubMed] [Google Scholar]
- 14.Rappocciolo G, Jais M, Piazza P, Reinhart TA, Berendam SJ, Garcia-Exposito L, et al. Alterations in cholesterol metabolism restrict HIV-1 trans infection in nonprogressors. mBio. 2014;5(3):e01031–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Association APGSoTDaPoCM. Chinese expert consensus on integrated lipid management in HIV/AIDS. Chin J Intern Med. 2023;62(6):661–72. [DOI] [PubMed] [Google Scholar]
- 16.Rb-Silva R, Goios A, Kelly C, Teixeira P, João C, Horta A, et al. Definition of immunological nonresponse to antiretroviral therapy: A systematic review. J Acquir Immune Defic Syndr. 2019;82(5):452–61. [DOI] [PubMed] [Google Scholar]
- 17.Guo F, Hsieh E, Lv W, Han Y, Xie J, Li Y, et al. Cardiovascular disease risk among Chinese antiretroviral-naïve adults with advanced HIV disease. BMC Infect Dis. 2017;17(1):287. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Hou X, Wang D, Zuo J, Li J, Wang T, Guo C, et al. Development and validation of a prognostic nomogram for HIV/AIDS patients who underwent antiretroviral therapy: data from a China population-based cohort. EBioMedicine. 2019;48:414–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.El-Sadr WM, Mullin CM, Carr A, Gibert C, Rappoport C, Visnegarwala F, et al. Effects of HIV disease on lipid, glucose and insulin levels: results from a large antiretroviral-naive cohort. HIV Med. 2005;6(2):114–21. [DOI] [PubMed] [Google Scholar]
- 20.Rose H, Woolley I, Hoy J, Dart A, Bryant B, Mijch A, et al. HIV infection and high-density lipoprotein: the effect of the disease vs the effect of treatment. Metabolism. 2006;55(1):90–5. [DOI] [PubMed] [Google Scholar]
- 21.Rodríguez-Gallego E, Gómez J, Pacheco YM, Peraire J, Viladés C, Beltrán-Debón R, et al. A baseline metabolomic signature is associated with immunological CD4 + T-cell recovery after 36 months of antiretroviral therapy in HIV-infected patients. Aids. 2018;32(5):565–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Wang Y, Liu S, Zhang W, Zheng L, Li E, Zhu M, et al. Development and evaluation of a nomogram for predicting the outcome of immune reconstitution among HIV/AIDS patients receiving antiretroviral therapy in China. Adv Biol (Weinh). 2024;8(2):e2300378. [DOI] [PubMed] [Google Scholar]
- 23.Qigang N, Afra A, Ramírez-Coronel AA, Turki Jalil A, Mohammadi MJ, Gatea MA, et al. The effect of polycyclic aromatic hydrocarbon biomarkers on cardiovascular diseases. Rev Environ Health. 2023. [DOI] [PubMed]
- 24.Riddler SA, Smit E, Cole SR, Li R, Chmiel JS, Dobs A, et al. Impact of HIV infection and HAART on serum lipids in men. JAMA. 2003;289(22):2978–82. [DOI] [PubMed] [Google Scholar]
- 25.Sun LQ, Liu JY, He Y, Zhou Y, Xu LM, Zhang LK, et al. Evolution of blood lipids and risk factors of dyslipidemia among people living with human immunodeficiency virus who had received first-line antiretroviral regimens for 3 years in Shenzhen. Chin Med J (Engl). 2020;133(23):2808–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Hürlimann D, Chenevard R, Ruschitzka F, Flepp M, Enseleit F, Béchir M, et al. Effects of Statins on endothelial function and lipid profile in HIV infected persons receiving protease inhibitor-containing anti-retroviral combination therapy: a randomised double blind crossover trial. Heart. 2006;92(1):110–2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Bonnet F, Aurillac-Lavignolle V, Breilh D, Thiébaut R, Peuchant E, Bernard N, et al. Pravastatin in HIV-infected patients treated with protease inhibitors: a placebo-controlled randomized study. HIV Clin Trials. 2007;8(1):53–60. [DOI] [PubMed] [Google Scholar]
- 28.Mallon PW, Miller J, Kovacic JC, Kent-Hughes J, Norris R, Samaras K, et al. Effect of Pravastatin on body composition and markers of cardiovascular disease in HIV-infected men–a randomized, placebo-controlled study. Aids. 2006;20(7):1003–10. [DOI] [PubMed] [Google Scholar]
- 29.Nazari I, Feinstein MJ. Evolving mechanisms and presentations of cardiovascular disease in people with HIV: implications for management. Clin Microbiol Rev. 2024;37(1):e0009822. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Oliveira DPR, Macêdo APA, Dos Santos Gonçalves M, Couto RD. Dyslipidemia secondary to the use of HAART in adults: A systematic review. Curr Drug Res Rev. 2022;14(3):239–46. [DOI] [PubMed] [Google Scholar]
- 31.Parra S, Saballs M, DiNubile M, Feliu M, Iftimie S, Revuelta L, et al. Low HDL-c levels at admission are associated with greater severity and worse clinical outcomes in patients with COVID-19 disease. Atheroscler Plus. 2023;52:1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Cirstea M, Walley KR, Russell JA, Brunham LR, Genga KR, Boyd JH. Decreased high-density lipoprotein cholesterol level is an early prognostic marker for organ dysfunction and death in patients with suspected sepsis. J Crit Care. 2017;38:289–94. [DOI] [PubMed] [Google Scholar]
- 33.Pedro KD, Henderson AJ, Agosto LM. Mechanisms of HIV-1 cell-to-cell transmission and the establishment of the latent reservoir. Virus Res. 2019;265:115–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Bracq L, Xie M, Benichou S, Bouchet J. Mechanisms for Cell-to-Cell transmission of HIV-1. Front Immunol. 2018;9:260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Okpaise D, Sluis-Cremer N, Rappocciolo G, Rinaldo CR. Cholesterol metabolism in antigen-presenting cells and HIV-1 trans-infection of CD4(+) T cells. Viruses (Basel). 2023;15(12). [DOI] [PMC free article] [PubMed]
- 36.Georgia AN, Claudine NE, Carole SN, Loveline NN, Abel L, Flaurent TT, et al. Regulatory T cells modulate monocyte functions in immunocompetent antiretroviral therapy Naive HIV-1 infected people. BMC Immunol. 2024;25(1):68. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Maggio R, Viscomi C, Andreozzi P, D’Ettorre G, Viscogliosi G, Barbaro B, et al. Normocaloric low cholesterol diet modulates Th17/Treg balance in patients with chronic hepatitis C virus infection. PLoS ONE. 2014;9(12):e112346. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Atehortua L, Davidson WS, Chougnet CA. Interactions between HDL and CD4 + T cells: A novel Understanding of HDL Anti-Inflammatory properties. Arterioscler Thromb Vasc Biol. 2024;44(6):1191–201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Kontush A, Lindahl M, Lhomme M, Calabresi L, Chapman MJ, Davidson WS. Structure of HDL: particle subclasses and molecular components. Handb Exp Pharmacol. 2015;224:3–51. [DOI] [PubMed] [Google Scholar]
- 40.Ouweneel AB, Reiche ME, Snip OSC, Wever R, van der Wel EJ, Schaftenaar FH, et al. Apolipoprotein A1 deficiency in mice primes bone marrow stem cells for T cell lymphopoiesis. J Cell Sci. 2022;135(5). [DOI] [PMC free article] [PubMed]
- 41.Xie JJ, Wang J, Tang TT, Chen J, Gao XL, Yuan J, et al. The Th17/Treg functional imbalance during atherogenesis in ApoE(-/-) mice. Cytokine. 2010;49(2):185–93. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data presented in this study are available on request to the corresponding author. The data are not publicly available due to the privacy protection for HIV/AIDS patients.