Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2023 Jan 10.
Published in final edited form as: AIDS Care. 2017 Jan 24;29(9):1107–1111. doi: 10.1080/09540121.2017.1281880

Association of CD4+ T cell subpopulations and psychological stress measures in women living with HIV

Kristina E Rehm 1, Deborah Konkle-Parker 2
PMCID: PMC9830587  NIHMSID: NIHMS981740  PMID: 28114801

Abstract

Psychological stress is a known immunomodulator. In individuals with HIV, depression, the most common manifestation of increased psychological stress, can affect immune function with lower CD4+ T cell counts correlating with higher levels of depression. It is unknown how other forms of psychological stress can impact immune markers in people living with HIV. We conducted a cross-sectional study to determine how CD4+ T cell subpopulations correlated with different forms of psychological stress. We recruited 50 HIV-positive women as part of the Women’s Interagency HIV Study. We assessed perceived stress, worry, acute anxiety, trait anxiety, and depression through self-report questionnaires and CD4+ T cell subpopulations using flow cytometry. Our sample was 96% African American with a mean ±SD age and body mass index (BMI) of 42 ±8.8 years and 36.6 ±11.5 kg/m2, respectively. The mean ±SD scores on the psychological measures were as follows: Perceived Stress Scale (PSS), 16.5 ±6.4; Penn State Worry Questionnaire (PSWQ), 47.7 ±13.8; State-Trait Anxiety Inventory – State (STAIS), 39.1 ±12.3; State-Trait Anxiety Inventory – Trait (STAIT), 40.2 ±11.4; Center for Epidemiological Studies Depression Scale (CES-D), 15.6 ±11.4. The mean +SD values for the immune parameters were as follows: regulatory T cells (Treg), 1.25% ±0.7; T helper 1 (Th1), 14.9% ±6.1; T helper 2 (Th2), 3.8% ±2; Th1/Th2 ratio, 4.6 ±3; and CD4+ T cell count (cells/mm3), 493 ±251. Treg levels positively correlated with PSS, STAIS, and STAIT. CD4+ T cell count negatively correlated with PSS, PSWQ, STAIS, STAIT, and CES-D. These data suggest that immune function may be impacted by various forms of psychological stress in HIV-positive women. Interventions that target stress reduction may be useful in improving immune parameters and quality of life.

Keywords: stress, worry, anxiety, depression, T cells

Introduction

Psychological stress is known to modulate immunity (Pedersen, Zachariae, & Bovbjerg, 2009; Uchakin et al., 2011; Marshall et al., 1998). The mechanisms underlying this increased risk likely involve the action of stress hormones on components of the immune system (Hall et al., 2012). We and others have shown that stress hormones decrease pro-inflammatory (anti-viral) immunity while increasing anti-inflammatory immunity (Salicru, Sams, & Marshall, 2007). Clinically, this manifests as an increase in risk for infectious diseases and an increase in the activity of inflammatory-based diseases (Hodgson et al., 2012; Ashcraft & Bonneau, 2008; Trueba, Ryan, Vogel, & Ritz, 2016).

Stress is a particular concern in people living with HIV (PLWH) with depression being the most common psychiatric condition reported among this population (Lowther, Selman, Harding, & Higginson, 2014). Depression in PLWH is associated with medication non-adherence and risky sexual behaviors (Blashill et al., 2013; Kacanek et al., 2015). Depression may also affect immune function, with higher levels of depression associated with lower CD4+ T cell counts (Kaharuza et al., 2006). There is very little data on how other forms of psychological stress (stress perception, worry, and anxiety) may affect HIV progression biomarkers. In addition, it is not known how stress can affect CD4+ T cell subpopulations in PLWH.

In this study we investigated the association of CD4+ T cell subpopulations with psychological stress in a group of women that were part of the Women’s Interagency HIV Study (WIHS) Mississippi site cohort.

Materials and Methods

Participants

We recruited 50 HIV-positive women that were part of the WIHS Study (Bacon et al., 2005). Participants were recruited after obtaining written informed consent according to a University of Mississippi Medical Center Institutional Review Board approved protocol.

Blood Collection, PBMC Isolation, and Cryopreservation

Venous blood was collected into heparinized tubes for peripheral blood mononuclear cell (PBMC) isolation. PBMC were isolated using a Ficoll-Hypaque gradient as previously described (Rehm, Elci, Hahn, & Marshall, 2013). Cell counts were obtained using a Scepter Handheld Automated Cell Counter (Millipore). After counting, PBMC were washed and resuspended in cryopreservation media of RPMI containing 20% FBS and 10% DMSO at a concentration of 6–10 × 106 cells/ml. One milliliter aliquots were placed into cryopreservation vials, transferred to a Nalgene Cryo Freezing container, and placed at −80° C. After 24–48 hours, vials were transferred to a storage box and stored at −80°C until they were batch analyzed by flow cytometry.

Flow Cytometry

Cryopreserved cells were quick-thawed, washed in 10% cRPMI cell culture media, and then resuspended in 1 ml Hank’s Balanced Saline Solution (HBSS) before being counted using the Sceptor Handheld Automated Cell Counter (Millipore). Cells were also stained with trypan blue to determine viability. Viability was determined to be ~85% (data not shown). T cell populations were analyzed by flow cytometry according to previously described methods (Rehm, Elci, Hahn, & Marshall, 2013). Data (at least 150,000 events) were collected and analyzed using the Beckman Coulter Cytomics FC500 flow cytometer. Cell populations were defined as follows, Regulatory T cells (Treg): CD4+CD25hiFoxP3+ (these cells control the responses on Th1 and Th2 cells); T helper 1 cells (Th1): CD3+CD8−IFNγ+IL4− (anti-inflammatory T cells); T helper 2 cells (Th2): CD3+CD8−IFNγ−IL4+ (pro-inflammatory T cells). CD4+ T cells coordinate the adaptive immune response. CD4+ T cell count (cells/mm3) and was measured in the University of Mississippi Medical Center’s Clinical Flow Cytometry Laboratory using the Beckman Coulter (Miami, FL) Cyto-Stat tetraCHROME kit which includes the following antibodies: CD45-FITC, CD4-PE, CD8-ECD, and CD3-PC5. Cells were stained using the manufacturer’s instructions. Appropriate isotype controls were used to define positive and negative populations. Data were collected using a Beckman Coulter Navios Flow Cytometer and analyzed using CellQuest software.

Measurement of Psychological Stress

To measure levels of perceived stress, worry, anxiety, and depression, participants completed a battery of questionnaires. The Perceived Stress Scale (PSS; range 0–40) was used to measure perception of acute stress over the last month (Cohen, Kamarck, & Mermelstein, 1983). The Penn State Worry Questionnaire (PSWQ; range 16–80) was used to measure tendencies to experience excessive and uncontrollable worry (Meyer, Miller, Metzger, & Borkovec, 1990). The State Trait Anxiety Inventory (STAI) was used to assess the severity of acute (STAIS; range 0–80) (i.e., at this moment) and trait (STAIT; range 0–80) anxiety (Julian, 2011). Finally, the Center for Epidemiological Studies Depression Scale (CES-D; range 0–60) (Smarr & Keefer, 2011) was used to measure depression. All measures have extensive support for their validity and reliability (Roemer, 2001).

Statistical Analyses

Descriptive statistics were computed as mean (standard deviation) and frequencies (percentages) for continuous and categorical variables, respectively (Table 1). The relationship between outcomes of biomarkers and predictors of stress measurements (PSS, PSWQ, STAIS, STAIT, and CES-D) was evaluated using simple linear regression (Table 2). Slopes (β) and p values are reported (Table 2). Normality was tested by visual inspection of q-q plots. Regression analyses were performed after log transformations were applied to skewed data (Treg, Th1, Th2, and Th1/Th2 ratio). A p-value of <0.05 was deemed statistically significant. Statistical analysis was performed using SPSS v23 (Armonk, NY).

Table 1.

Participant Characteristics

Age, yrs 42 (8.8) 26–58
Race (% AA) 96% (48)
Gender (% Female) 100% (50)
BMI, kg/m2 36.6 (11.5) 19.5–64.6
Psychological Stress Measures
Perceived Stress Scale (PSS) 16.5 (6.4) 5–33
Penn State Worry Questionnaire (PSWQ) 47.7 (13.8) 16–77
State Trait Anxiety Inventory - State (STAIS) 39.1 (12.3) 20–65
State Trait Anxiety Inventory - Trait (STAIT) 40.2 (12.7) 21–65
Center for Epidemiological Studies Depression Scale (CES-D) 15.6 (11.4) 0–42
Immune Biomarkers
Treg: CD4+CD25hiFoxP3+ (%) 1.25 (0.7) 0.43–3.6
Th1: CD3+CD8−IFNg+IL4− (%) 14.9 (6.1) 7–30.1
Th2: CD3+CD8−IFNg−IL4+ (%) 3.8 (2) 1.1–10.8
Th1/Th2 Ratio 4.6 (3) 1.7–16.9
CD4 Count (cells/mm3) 493 (251) 94–994
Open in a new tab

Shown are mean (SD) min-max for continuous variables and % (n) for categorical variables

Table 2:

Correlation between stress measures and immune biomarkers

PSS PSWQ STAIS STAIT CES-D
Treg, % # 0.014** 0.004 0.006* 0.005* 0.004
Th1, % # 0.005 0.002 0.002 0.003 0.00002
Th2, % # 0.001 0.001 −0.001 −0.001 −0.003
Th1/Th2 # 0.003 0.001 0.003 0.004 0.003
CD4 count, cells/mm 3 −15.6** −7.8** −8.7** −8.8** −7.8*
Open in a new tab

Shown are regression coefficients (β)

*

p<0.05;

**

p<0.01

#

log transformed

Results

Participant Characteristics

The characteristics of the participants are shown in Table 1. Our sample was 100% female and 96% African American with a mean (SD) age of 42 (8.8) years (range 26–58) and BMI of 36.6 (11.5) kg/m2 (range 19.5–64.6). The mean (SD) min-max scores on the psychological measures were as follows: PSS 16.5 (6.4) 5–33; PSWQ 47.7 (13.8) 16–77; STAIS 39.1 (12.3) 20–65; STAIT 40.2 (12.7) 21–65; and CES-D 15.6 (11.4) 0–42. The mean (SD) min-max of the immune and HIV biomarkers measured were as follows: Treg (CD4+CD25hiFoxP3+), 1.3% (0.7) 0.43–3.6%; Th1 (CD3+CD8−IFNγ+IL4−), 14.9% (6.1) 7–30.1%; Th2 (CD3+CD8−IFNγ−IL4+), 3.8% (2) 1.1–10.8%; Th1/Th2 ratio, 4.6 (3) 1.7–16.9; and CD4+ count, 493 cells/mm3 (251) 94–994.

Correlation between stress measures and immune biomarkers

Levels of many of the peripheral blood immune biomarkers were associated with the stress measures. The percentage of Treg cells was significantly and positively associated with scores on the PSS (β=0.014; p=0.003), STAIS (β=0.006; p=0.010), and STAIT (β=0.005; p=0.030). HIV-relevant clinical biomarkers were also associated with the stress measures. CD4+ T cell count was significantly and negatively associated with scores on all the stress measures: PSS (β=−15.6; p=0.004), PSWQ (β=−7.8; p=0.002), STAIS (β=−8.7; p=0.002), STAIT (β=−8.8; p=0.001), and CES-D (β=−7.8; p=0.011).

Discussion

This cross-sectional study examined the relationship between CD4+ T cell subpopulations and various forms of psychological stress (stress perception, worry, state anxiety, trait anxiety, and depression) in 50 women living with HIV. Our sample was predominantly African-American with an average BMI in the obese range. Our results show that low CD4+ counts were associated with higher levels of all stress measures. In addition, we found that that Treg levels were positively correlated with stress and anxiety. To our knowledge this is the first paper to show some of these relationships, particularly in women who are typically understudied in the HIV literature.

Psychological stress has been linked to immune dysfunction and negative health outcomes in both infectious and inflammatory-based diseases (Pedersen, Zachariae, & Bovbjerg, 2009; Uchakin et al., 2011; Marshall et al., 1998; Hodgson et al., 2012; Ashcraft & Bonneau, 2008; Trueba, Ryan, Vogel, & Ritz, 2016). Treg cells keep the immune system in check by controlling unwanted responses of Th1 and Th2 cells (Lehner, 2008). Stress-induced dysregulation of these regulatory mechanisms may result in inappropriate activation of effector mechanisms that can increase infectious disease susceptibility and inflammatory-based disease activity (Hodgson et al., 2012; Ashcraft & Bonneau, 2008; Trueba, Ryan, Vogel, & Ritz, 2016). Our results suggest that in PLWH Treg populations can be affected by stress which may lead to ineffective control of immune responses. CD4+ T cells are important in coordinating the overall adaptive immune response and a reduction in these as a result of higher stress levels can impact the overall progression of HIV.

Our study has several limitations. First, our study design was cross-sectional. Longitudinal changes to immune measures associated with the various forms of psychological stress would be of interest. Second, our sample was small, predominantly African American, and included only one site of the WIHS study, which make the generalizability of the findings difficult. Third, we did not recruit a group of HIV-negative women so we do not know if these relationships are specific to HIV or if this population (in terms of gender, age, and race) is just prone to stress that would impact immunity in general. Future studies should address these limitations.

In conclusion, these findings show an important relationship between immune measures and psychological stress in women with HIV. These data could have implications in the development of stress management techniques that may improve overall immune profiles. These stress reduction techniques, such as physical activity and mindfulness-based stress reduction, may delay progression to AIDS. This is an area of research that should be investigated further. Physicians should partner with local health organizations to provide stress management strategies to those most at risk.

Acknowledgements:

Data in this manuscript were collected by the Women’s Interagency HIV Study (WIHS). The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH). WIHS (Principal Investigators): UAB-MS WIHS (Michael Saag, Mirjam-Colette Kempf, and Deborah Konkle-Parker), U01-AI-103401; Atlanta WIHS (Ighovwerha Ofotokun and Gina Wingood), U01-AI-103408; Bronx WIHS (Kathryn Anastos), U01-AI-035004; Brooklyn WIHS (Howard Minkoff and Deborah Gustafson), U01-AI-031834; Chicago WIHS (Mardge Cohen and Audrey French), U01-AI-034993; Metropolitan Washington WIHS (Mary Young and Seble Kassaye), U01-AI-034994; Miami WIHS (Margaret Fischl and Lisa Metsch), U01-AI-103397; UNC WIHS (Adaora Adimora), U01-AI-103390; Connie Wofsy Women’s HIV Study, Northern California (Ruth Greenblatt, Bradley Aouizerat, and Phyllis Tien), U01-AI-034989; WIHS Data Management and Analysis Center (Stephen Gange and Elizabeth Golub), U01-AI-042590; Southern California WIHS (Joel Milam), U01-HD-032632 (WIHS I - WIHS IV). The WIHS is funded primarily by the National Institute of Allergy and Infectious Diseases (NIAID), with additional co-funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), the National Cancer Institute (NCI), the National Institute on Drug Abuse (NIDA), and the National Institute on Mental Health (NIMH). Targeted supplemental funding for specific projects is also provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute on Alcohol Abuse and Alcoholism (NIAAA), the National Institute on Deafness and other Communication Disorders (NIDCD), and the NIH Office of Research on Women’s Health. WIHS data collection is also supported by UL1-TR000004 (UCSF CTSA) and UL1-TR000454 (Atlanta CTSA). The authors wish to thank Venetra McKinney and Courtney Harris for their assistance with the study. We would also like to thank Dr. Gailen Marshall for use of laboratory space and flow cytometer. This work was supported by the NIH National Institute for Allergy and Infectious Disease under grant U01AI103401-02.

Footnotes

Research was conducted at University of Mississippi Medical Center

Contributor Information

Kristina E. Rehm, Laboratory of Behavioral Immunology, Division of Clinical Immunology and Allergy, University of Mississippi Medical Center, 2500 North State St Suite N416, Jackson, MS, 39216

Deborah Konkle-Parker, Division of Infectious Diseases, University of Mississippi Medical Center, Division of Infectious Diseases, 2500 N State St, Jackson, MS, 39216.

References

  1. Ashcraft KA, & Bonneau RH (2008). Psychological stress exacerbates primary vaginal herpes simplex virus type 1 (HSV-1) infection by impairing both innate and adaptive immune responses. Brain, Behavior and Immunity, 22(8), 1231–40. doi: 10.1016/j.bbi.2008.06.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bacon MC, von Wyl V, Alden C, Sharp G, Robison E, Hessol N, … Young MA (2005). The Women’s Interagency HIV Study: an observational cohort brings clinical sciences to the bench. Clinical and Diagnostic Laboratory Immunology, 12(9), 1013–1019. doi: 10.1128/CDLI.12.9.1013-1019.2005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Blashill AJ, Mayer KH, Crane H, Magidson JF, Grasso C, Mathews WC, … Safren SA (2013). Physical activity and health outcomes among HIV-infected men who have sex with men: a longitudinal mediational analysis. Annals of Behavioral Medicine, 46, 149–156. doi: 10.1007/s12160-013-9489-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Cohen S, Kamarck T, & Mermelstein R (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24, 385–396. [PubMed] [Google Scholar]
  5. Hall JM, Cruser D, Podawiltz A, Mummert DI, Jones H, & Mummert ME (2012). Psychological Stress and the Cutaneous Immune Response: Roles of the HPA Axis and the Sympathetic Nervous System in Atopic Dermatitis and Psoriasis. Dermatology Research and Practice, 2012, 403908. doi: 10.1155/2012/403908 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Hodgson PD, Aich P, Stookey J, Popowych Y, Potter A, Babiuk L, & Griebel PJ (2012). Stress significantly increases mortality following a secondary bacterial respiratory infection. Veterinary Research, 43(1), 21. doi: 10.1186/1297-9716-43-21 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Julian LJ (2011). Measures of anxiety: State-Trait Anxiety Inventory (STAI), Beck Anxiety Inventory (BAI), and Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Arthritis Care and Research, 63 Suppl 11, S467–472. doi: 10.1002/acr.20561 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Kacanek D, Angelidou K, Williams PL, Chernoff M, Gadow KD,, Nachman S; International Maternal Pediatric Adolescent AIDS Clinical Trials Group (IMPAACT) P1055 Study Team. (2015). Psychiatric symptoms and antiretroviral nonadherence in US youth with perinatal HIV: a longitudinal study. AIDS, 29(10), 1227–1237. doi: 10.1097/QAD.0000000000000697 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Kaharuza FM, Bunnell R, Moss S, Purcell DW, Bikaako-Kajura W, Wamai N,… Mermin J (2006). Depression and CD4+ cell count among persons with HIV infection in Uganda. AIDS and Behavior, 10(4 Suppl), S105–11. [DOI] [PubMed] [Google Scholar]
  10. Lehner T (2008). Special regulatory T cell review: The resurgence of the concept of contrasuppression in immunoregulation. Immunology, 123(1), 40–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Lowther K, Selman L, Harding R, & Higginson IJ (2014). Experience of persistent psychological symptoms and perceived stigma among people with HIV on antiretroviral therapy (ART): a systematic review. International Journal of Nursing Studies, 51(8), 1171–1189. doi: 10.1016/j.ijnurstu.2014.01.015 [DOI] [PubMed] [Google Scholar]
  12. Marshall GD, Agarwal SK, Lloyd C, Cohen L, Henninger EM, & Morris GJ (1998). Cytokine dysregulation associated with exam stress in healthy medical students. Brain, Behavior, & Immunity, 12(4): 297–307. [DOI] [PubMed] [Google Scholar]
  13. Meyer TJ, Miller ML, Metzger RL, & Borkovec TD (1990). Development and validation of the penn state worry questionnaire. Behavior Research and Therapy, 28, 487–495. [DOI] [PubMed] [Google Scholar]
  14. Pedersen AF, Zachariae R, & Bovbjerg DH (2009). Psychological stress and antibody response to influenza vaccination: a meta-analysis. Brain, Behavior, & Immunity, 23 (4): 427–33. doi: 10.1016/j.bbi.2009.01.004 [DOI] [PubMed] [Google Scholar]
  15. Rehm KE, Elci OU, Hahn K, Marshall GD (2013). The impact of self-reported psychological stress levels on changes to peripheral blood immune biomarkers in recreational marathon runners during training and recovery. Neuroimmunomodulation, 20, 164–176. doi: 10.1159/000346795 [DOI] [PubMed] [Google Scholar]
  16. Roemer L (2001). Measures for anxiety and related constructs. In: Antony MM, Orsillo SM, Roemer L, editors. Practitioner’s guide to empirically based measures of anxiety (pp. 49–83). New York: Guilford Publications. [Google Scholar]
  17. Salicrú AN, Sams CF, & Marshall GD (2007). Cooperative effects of corticosteroids and catecholamines upon immune deviation of the type-1/type-2 cytokine balance in favor of type-2 expression in human peripheral blood mononuclear cells. Brain, Behavior, and Immunity, 21(7), 913–20. [DOI] [PubMed] [Google Scholar]
  18. Smarr KL, & Keefer AL (2011). Measures of depression and depressive symptoms: Beck Depression Inventory-II (BDI-II), Center for Epidemiologic Studies Depression Scale (CES-D), Geriatric Depression Scale (GDS), Hospital Anxiety and Depression Scale (HADS), and Patient Health Questionnaire-9 (PHQ-9). Arthritis Care and Research (Hoboken), 63 Suppl 11, S454–466. doi: 10.1002/acr.20556 [DOI] [PubMed] [Google Scholar]
  19. Trueba A, Ryan MW, Vogel PD, & Ritz T (2016). Effects of academic exam stress on nasal leukotriene B4 and vascular endothelial growth factor in asthma and health. Biological Psychology, 118, 44–51. doi: 10.1016/j.biopsycho.2016.04.009. [DOI] [PubMed] [Google Scholar]
  20. Uchakin PN, Parish DC, Dane FC, Uchakina ON, Scheetz AP, Agarwal NK, & Smith BE (2011). Fatigue in medical residents leads to reactivation of herpes virus latency. Interdisciplinary Perspectives on Infectious Disease, 2011, 571340. doi: 10.1155/2011/571340 [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES