Reevaluation of immune activation in the era of cART and an aging HIV-infected population

Abstract

Biological aging is associated with immune activation (IA) and declining immunity due to systemic inflammation. It is widely accepted that HIV infection causes persistent IA and premature immune senescence despite effective antiretroviral therapy and virologic suppression; however, the effects of combined HIV infection and aging are not well defined. Here, we assessed the relationship between markers of IA and inflammation during biological aging in HIV-infected and -uninfected populations. Antibody response to seasonal influenza vaccination was implemented as a measure of immune competence and relationships between IA, inflammation, and antibody responses were explored using statistical modeling appropriate for integrating high-dimensional data sets. Our results show that markers of IA, such as coexpression of HLA antigen D related (HLA-DR) and CD38 on CD4+ T cells, exhibit strong associations with HIV infection but not with biological age. Certain variables that showed a strong relationship with aging, such as declining naive and CD38+ CD4 and CD8+ T cells, did so regardless of HIV infection. Interestingly, the variable of biological age was not identified in a predictive model as significantly impacting vaccine responses in either group, while distinct IA and inflammatory variables were closely associated with vaccine response in HIV-infected and -uninfected populations. These findings shed light on the most relevant and persistent immune defects during virological suppression with antiretroviral therapy.

Figure 1. Absolute CD4⁺ and CD8⁺ T cell counts by age group.

(A) CD4⁺ T cell counts, (B) CD8⁺ T cell counts, and (C) CD4⁺/CD8⁺ ratio were calculated per μl of whole blood in each age group for HIV-negative (HC) and HIV⁺ groups. Data are shown as box-and-whisker plots and 2-tailed Student’s t test was used to measure statistical differences between HIV⁺ and HC. **P < 0.01, ****P < 0.0001. Pearson correlation analysis was performed between each measure and biological age (years) and results are designated by blue- or red-font P values for HC and HIV⁺ groups, respectively. ns, not significant.

Figure 2. CD4⁺ T cell compartment subset distribution during aging and HIV infection.

(A) Gating scheme for analysis of CD4⁺ T cell subsets by multiparameter flow cytometry. (B–G) x-y plots are shown plotting biological age (years) and individual CD4⁺ T cell subset frequencies for each study participant: (B) naive, (C) central memory (TCM), (D) effector memory (TEM), (E) effector (TEff), (F) transitional memory (TTM), and (G) peripheral T follicular helper (pTfh) cells. P values displayed indicate results from linear regression analysis for HIV-negative (blue, HC) and HIV⁺ (red, HIV) study participants. ns, not significant.

Figure 3. Immune activation markers show higher expression in HIV⁺ groups.

Surface expression of (A) CD38, (B) HLA-DR, (C) PD-1, (D) ICOS, and (E) Ki-67 was measured using multiparameter flow cytometry and expressed as percentage positive in total CD4⁺ T cells by HIV status and age group. Data are shown as box-and-whisker plots and 2-tailed Student’s t test was used to measure statistical differences between HIV⁺ and HIV-negative (HC) for each age group. *P < 0.05, **P < 0.01, ***P < 0.01, ****P < 0.0001. Pearson correlation analysis of each marker with age within HIV⁺ and HC are shown in Table 2.

Figure 4. Coexpression of immune activation markers on CD4⁺ T cells at higher frequencies in HIV⁺ individuals compared with HIV negative (HC) regardless of age.

(A) The relationship between frequencies of CD38⁺HLA-DR⁺ CD4⁺ T cells (left) and CD8⁺ T cells (right) with age (years) is shown. P values indicate results of 2-tailed Student’s t test to measure statistical differences between HIV⁺ and HC. (B) Ring graphs represent combination gate (Boolean) analysis using the 5 immune activation markers (CD38, HLA-DR, PD-1, ICOS, Ki-67) on CD4⁺ T cells. Each ring shows relative frequency of cells expressing 0, 1, 2, 3, 4, or 5 markers simultaneously for each group. Multiple t tests were performed to compare relative frequencies from one ring (i.e., study group) to another. *Indicates significant FDR < 1% when compared with HIV⁺ group. ^#Indicates significant FDR < 1% when compared with HIV young group.

Figure 5. The effect of HIV on prevaccination plasma biomarkers in study participants by age group.

Heatmap showing fold-change difference between HIV⁺ participants and HIV negative (HC) for different age groups. Colored boxes represent significant (P < 0.05) differences between HIV⁺ and HC using 2-tailed Student’s t test. Blue color represents lower concentration in HIV⁺ relative to HC, and red color represents higher concentration of the indicated soluble protein in HIV⁺ relative to HC.

Figure 6. HIV⁺ individuals have lower response rate and magnitude against trivalent inactivated influenza vaccine compared with HIV negative (HC).

(A) Bar graph showing the distribution of absolute responders and nonresponders by age group in HIV⁺ and HC study participants. χ² analysis was used to determine differences in responder status between the groups. *P < 0.05, **P < 0.01, ***P < 0.01. (B) Distribution of response scores in HC (left) and HIV⁺ (right) groups. χ² analysis was performed to compare score distributions between HC and HIV⁺. *P < 0.05.

Figure 7. Discrimination of influenza vaccine responders and nonresponders using LASSO-identified variables.

Partial least squares discriminant analysis (PLSDA) plots are shown for (A) HIV-negative (HC) (n = 57) and (B) HIV⁺ (n = 64) absolute responders (Δ) and nonresponders (○) based on the variables identified by LASSO in Table 4.